\\\" sign at the beginning of the description line\\n\",\n \"- Make the sequence lowercase\\n\",\n \"- Write the sequence data\\n\",\n \"\\n\",\n \"NOTE: Please get into the good habit of commenting your code and describing what you are going to do or are doing. There must be at least one comment in your code.\\n\",\n \"\\n\",\n \"NOTE: Next week we will see how to break the sequence into 80-mers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Write your code here (if you wish)\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you would like to create a file with your source doe paste it in the cell below and run. __Please remember__ to add your name to the file.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%%writefile FASTA_formatter.py\\n\",\n \"\\n\",\n \"#Paste Code here\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%%python3 FASTA_formatter.py\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python [conda root]\",\n \"language\": \"python\",\n \"name\": \"conda-root-py\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":46186,"cells":{"repo_name":{"kind":"string","value":"cougarTech2228/Scouting-2016"},"path":{"kind":"string","value":"notebooks/robocop.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"33175"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# import libraries\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import matplotlib\\n\",\n \"matplotlib.style.use('ggplot')\\n\",\n \"import random as rng\\n\",\n \"import numpy as np\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"# take a url of the csv or can read the csv locally into a pandas data frame\\n\",\n \"data = pd.read_csv(\\\"robodummy.csv\\\", index_col=0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"9 defenses \\n\",\n \"\\n\",\n \"1. Low Bar\\n\",\n \"2. ALLIANCE selected\\n\",\n \"3. Audience selected\\n\",\n \"4. ALLIANCE selected\\n\",\n \"5. ALLIANCE selected\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Data structure choices include:\\n\",\n \" - Pandas dataframes\\n\",\n \" - Numpy Arrays\\n\",\n \" - Object oriented\\n\",\n \" - Dictionary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Object oriented approach, would have to feed csv data into objects\\n\",\n \"\\n\",\n \"# maybe get rid of this and just use library analysis tools\\n\",\n \"class Robot(object):\\n\",\n \"\\n\",\n \"\\tdef __init__(self, name, alliance, auto_points, points):\\n\",\n \"\\n\",\n \"\\t\\tself.name = name\\n\",\n \"\\t\\tself.alliance = alliance\\n\",\n \"\\t\\tself.auto_points = auto_points\\n\",\n \"\\t\\tself.points = points\\n\",\n \"\\n\",\n \"\\tdef points_per_sec(self):\\n\",\n \"\\t\\treturn self.points / 150\\n\",\n \"\\n\",\n \"\\tdef auto_points_per_sec(self):\\n\",\n \"\\t\\treturn self.auto_points / 15\\n\",\n \"\\n\",\n \"\\tdef get_name(self):\\n\",\n \"\\t\\treturn self.name\\n\",\n \"\\n\",\n \"\\tdef get_alliance(self):\\n\",\n \"\\t\\treturn self.alliance\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0mdata\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mpd\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mread_csv\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"robodummy.csv\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 2\\u001b[0m \\u001b[0mfig\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0maxs\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mplt\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0msubplots\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m 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2556\\u001b[0;31m **kwds)\\n\\u001b[0m\\u001b[1;32m 2557\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 2558\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/dinbecevic/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc\\u001b[0m in \\u001b[0;36m_plot\\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\\u001b[0m\\n\\u001b[1;32m 2382\\u001b[0m \\u001b[0mplot_obj\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mklass\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdata\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0msubplots\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0msubplots\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0max\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0max\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkind\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mkind\\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[1;32m 2383\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 2384\\u001b[0;31m \\u001b[0mplot_obj\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mgenerate\\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 2385\\u001b[0m \\u001b[0mplot_obj\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdraw\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 2386\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mplot_obj\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mresult\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/dinbecevic/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc\\u001b[0m in \\u001b[0;36mgenerate\\u001b[0;34m(self)\\u001b[0m\\n\\u001b[1;32m 985\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_compute_plot_data\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 986\\u001b[0m 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\\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1557\\u001b[0m \\u001b[0mlabel\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mNone\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1558\\u001b[0;31m scatter = ax.scatter(data[x].values, data[y].values, c=c_values,\\n\\u001b[0m\\u001b[1;32m 1559\\u001b[0m label=label, cmap=cmap, **self.kwds)\\n\\u001b[1;32m 1560\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mcb\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/dinbecevic/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\\u001b[0m in \\u001b[0;36m__getitem__\\u001b[0;34m(self, key)\\u001b[0m\\n\\u001b[1;32m 1967\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_getitem_multilevel\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mkey\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1968\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1969\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_getitem_column\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mkey\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 1970\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1971\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0m_getitem_column\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkey\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/dinbecevic/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\\u001b[0m in \\u001b[0;36m_getitem_column\\u001b[0;34m(self, key)\\u001b[0m\\n\\u001b[1;32m 1974\\u001b[0m \\u001b[0;31m# get column\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1975\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcolumns\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mis_unique\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1976\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_get_item_cache\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mkey\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 1977\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1978\\u001b[0m \\u001b[0;31m# duplicate columns & possible reduce dimensionality\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/dinbecevic/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc\\u001b[0m in \\u001b[0;36m_get_item_cache\\u001b[0;34m(self, item)\\u001b[0m\\n\\u001b[1;32m 1089\\u001b[0m \\u001b[0mres\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mcache\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mitem\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1090\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mres\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1091\\u001b[0;31m \\u001b[0mvalues\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_data\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mitem\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 1092\\u001b[0m \\u001b[0mres\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_box_item_values\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mitem\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mvalues\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1093\\u001b[0m \\u001b[0mcache\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mitem\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mres\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/dinbecevic/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc\\u001b[0m in \\u001b[0;36mget\\u001b[0;34m(self, item, fastpath)\\u001b[0m\\n\\u001b[1;32m 3209\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 3210\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0misnull\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mitem\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 3211\\u001b[0;31m \\u001b[0mloc\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mitems\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget_loc\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mitem\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 3212\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 3213\\u001b[0m \\u001b[0mindexer\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0marange\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mlen\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mitems\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0misnull\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mitems\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/dinbecevic/anaconda/lib/python2.7/site-packages/pandas/core/index.pyc\\u001b[0m in \\u001b[0;36mget_loc\\u001b[0;34m(self, key, method, tolerance)\\u001b[0m\\n\\u001b[1;32m 1757\\u001b[0m 'backfill or nearest lookups')\\n\\u001b[1;32m 1758\\u001b[0m \\u001b[0mkey\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0m_values_from_object\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mkey\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1759\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_engine\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget_loc\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mkey\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 1760\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1761\\u001b[0m indexer = self.get_indexer([key], method=method,\\n\",\n \"\\u001b[0;32mpandas/index.pyx\\u001b[0m in \\u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3979)\\u001b[0;34m()\\u001b[0m\\n\",\n \"\\u001b[0;32mpandas/index.pyx\\u001b[0m in \\u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3843)\\u001b[0;34m()\\u001b[0m\\n\",\n \"\\u001b[0;32mpandas/hashtable.pyx\\u001b[0m in \\u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12265)\\u001b[0;34m()\\u001b[0m\\n\",\n \"\\u001b[0;32mpandas/hashtable.pyx\\u001b[0m in \\u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12216)\\u001b[0;34m()\\u001b[0m\\n\",\n \"\\u001b[0;31mKeyError\\u001b[0m: 'x'\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"data = pd.read_csv(\\\"robodummy.csv\\\")\\n\",\n \"fig, axs = plt.subplots(1, 4, sharey = True)\\n\",\n \"data.plot(kind='scatter', x = 'x', y = 'y', ax = axs[0], figsize = (16, 8))\\n\",\n \"data.plot(kind='scatter', x = 'x', y = 'y', ax = axs[1])\\n\",\n \"data.plot(kind='scatter', x = 'x', y = 'y', ax = axs[2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 4],\\n\",\n \" [5, 6],\\n\",\n \" [3, 9]])\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = np.array(([1, 4], [6, 5], [9, 3]))\\n\",\n \"np.sort(a)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":46187,"cells":{"repo_name":{"kind":"string","value":"Teaonly/easyLearning.js"},"path":{"kind":"string","value":"heatmap/heatmap.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"213476"},"content":{"kind":"string","value":"{\n \"metadata\": {\n \"language\": \"lua\",\n \"name\": \"\",\n \"signature\": \"sha256:639cbe855630b29ca41ce6e5988722152e1dd3c72de45b15f10baaa1a97e903e\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n {\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# \\u5377\\u79ef\\u7f51\\u7edc\\u7684\\u70ed\\u56fe\\u751f\\u6210\\n\",\n \"\\n\",\n \"\\u9996\\u5148\\u67e5\\u770b\\u5206\\u7c7b\\u8f93\\u51fa\\uff0c\\u53ea\\u67e5\\u770b\\u8fd9\\u4e2a\\u8f93\\u51fa\\u503c\\u3002\\u6ed1\\u52a8\\u4e00\\u4e2a\\u5c0f\\u7684\\u56fe\\u50cf\\uff0c\\u4fee\\u6539\\u56fe\\u50cf\\u8f93\\u5165\\u4e3a0\\uff0c\\u89c2\\u5bdf\\u5206\\u7c7b\\u8f93\\u51fa\\u7684\\u503c\\uff0c\\u7531\\u8fd9\\u4e2a\\u503c\\u6765\\u7ed8\\u5236\\u6240\\u8c13\\u70ed\\u56fe\\u3002\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"require('nn')\\n\",\n \"require('image')\\n\",\n \"require('loadcaffe')\\n\",\n \"require('cunn')\\n\",\n \"\\n\",\n \"torch.setdefaulttensortype('torch.FloatTensor')\\n\",\n \"\\n\",\n \"\\n\",\n \"-- \\u51c6\\u5907\\u8f93\\u5165\\u56fe\\u50cf\\n\",\n \"--imgFile = './dog.png'\\n\",\n \"imgFile = './lion.png'\\n\",\n \"ximg = image.loadPNG(imgFile, 3)\\n\",\n \"itorch.image(ximg)\\n\",\n \"ximg = ximg:float() * 256\\n\",\n \"ximg[1] = ximg[1] - 103.939\\n\",\n \"ximg[2] = ximg[2] - 116.779\\n\",\n \"ximg[3] = ximg[3] - 123.68\\n\",\n \"\\n\",\n \"\\n\",\n \"print(ximg:size())\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {\n \"png\": {\n \"height\": 224,\n \"width\": 224\n }\n },\n \"output_type\": \"display_data\",\n \"png\": 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\",\n \"text\": [\n \"Console does not support images\"\n ]\n },\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 1,\n \"text\": [\n \" 3\\n\",\n \" 224\\n\",\n \" 224\\n\",\n \"[torch.LongStorage of size 3]\\n\",\n \"\\n\"\n ]\n }\n ],\n \"prompt_number\": 1\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"-- \\u8f7d\\u5165\\u5206\\u7c7b\\u6587\\u672c\\n\",\n \"classText = torch.load('synset.t7','ascii')\\n\",\n \"-- \\u52a0\\u8f7d\\u6a21\\u578b\\u6587\\u4ef6\\n\",\n \"cnn = loadcaffe.load('vgg19/VGG_ILSVRC_19_layers_deploy.prototxt', 'vgg19/VGG_ILSVRC_19_layers.caffemodel', 'nn')\\n\",\n \"cnn:evaluate()\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 2,\n \"text\": [\n \"Successfully loaded vgg19/VGG_ILSVRC_19_layers.caffemodel\\n\"\n ]\n },\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 2,\n \"text\": [\n \"conv1_1: 64 3 3 3\\n\",\n \"conv1_2: 64 64 3 3\\n\",\n \"conv2_1: 128 64 3 3\\n\",\n \"conv2_2: 128 128 3 3\\n\"\n ]\n },\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 2,\n \"text\": [\n \"conv3_1: 256 128 3 3\\n\",\n \"conv3_2: 256 256 3 3\\n\",\n \"conv3_3: 256 256 3 3\\n\"\n ]\n },\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 2,\n \"text\": [\n \"conv3_4: 256 256 3 3\\n\"\n ]\n },\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 2,\n \"text\": [\n \"conv4_1: 512 256 3 3\\n\"\n ]\n },\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 2,\n \"text\": [\n \"conv4_2: 512 512 3 3\\n\"\n ]\n },\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 2,\n \"text\": [\n \"conv4_3: 512 512 3 3\\n\"\n ]\n },\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 2,\n \"text\": [\n \"conv4_4: 512 512 3 3\\n\"\n ]\n },\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 2,\n \"text\": [\n \"conv5_1: 512 512 3 3\\n\"\n ]\n },\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 2,\n \"text\": [\n \"conv5_2: 512 512 3 3\\n\"\n ]\n },\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 2,\n \"text\": [\n \"conv5_3: 512 512 3 3\\n\"\n ]\n },\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 2,\n \"text\": [\n \"conv5_4: 512 512 3 3\\n\"\n ]\n },\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 2,\n \"text\": [\n \"fc6: 1 1 25088 4096\\n\"\n ]\n },\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 2,\n \"text\": [\n \"fc7: 1 1 4096 4096\\n\"\n ]\n },\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 2,\n \"text\": [\n \"fc8: 1 1 4096 1000\\n\"\n ]\n }\n ],\n \"prompt_number\": 2\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"-- \\u76f4\\u63a5\\u5f97\\u5230\\u5206\\u7c7b\\u7ed3\\u679c\\n\",\n \"local score, obj = cnn:forward(ximg):max(1)\\n\",\n \"targetObj = obj[1]\\n\",\n \"print(score[1] .. \\\" : \\\" .. classText[obj[1]])\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 3,\n \"text\": [\n \"0.2681924700737 : lion, king of beasts, Panthera leo\\t\\n\"\n ]\n }\n ],\n \"prompt_number\": 3\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"local K = 32 --\\u75288x8\\u7684\\u53bbmask\\u539f\\u56fe\\n\",\n \"local S = 3\\n\",\n \"local W,H = 224,224\\n\",\n \"heatMap = torch.zeros(math.floor((W-K+1)/S+1), math.floor((H-K+1)/S+1) )\\n\",\n \"local xx,yy = 1,1\\n\",\n \"\\n\",\n \"cnn:cuda()\\n\",\n \"ximg = ximg:cuda()\\n\",\n \"\\n\",\n \"for y = 1, H-K+1, S do\\n\",\n \" xx = 1\\n\",\n \" for x = 1, W-K+1, S do\\n\",\n \" local xximg = ximg:clone()\\n\",\n \" xximg[{{},{y,y+K-1},{x,x+K-1} }] = -128\\n\",\n \" local scores = cnn:forward(xximg)\\n\",\n \" \\n\",\n \" heatMap[yy][xx] = scores[targetObj]\\n\",\n \" \\n\",\n \" xx = xx + 1\\n\",\n \" end\\n\",\n \" yy = yy + 1\\n\",\n \"end\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 4\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"heatBMP = heatMap:clone()\\n\",\n \"\\n\",\n \"local maxv = heatBMP:max()\\n\",\n \"local minv = heatBMP:min()\\n\",\n \"heatBMP = heatBMP - minv\\n\",\n \"heatBMP = heatBMP / (maxv - minv)\\n\",\n \"heatBMP = heatBMP * (-1) + 1\\n\",\n \"\\n\",\n \"heatBMP = image.scale(heatBMP, 224, 224)\\n\",\n \"local showImage = image.loadPNG(imgFile, 3)\\n\",\n \"showImage = image.rgb2yuv(showImage)\\n\",\n \"showImage[1] = (heatBMP * 4 + showImage[1]) / 5\\n\",\n \"showImage = image.yuv2rgb(showImage)\\n\",\n \"\\n\",\n \"itorch.image(heatBMP)\\n\",\n \"itorch.image(showImage)\\n\",\n \"\\n\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {\n \"png\": {\n \"height\": 224,\n \"width\": 224\n }\n },\n \"output_type\": \"display_data\",\n \"png\": 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\",\n 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\",\n \"text\": [\n \"Console does not support images\"\n ]\n }\n ],\n \"prompt_number\": 6\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": []\n }\n ],\n \"metadata\": {}\n }\n ]\n}"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":46188,"cells":{"repo_name":{"kind":"string","value":"avijaira/udacity"},"path":{"kind":"string","value":"self-driving-car/P1/P1.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"269154"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Self-Driving Car Engineer Nanodegree\\n\",\n \"\\n\",\n \"\\n\",\n \"## Project: **Finding Lane Lines on the Road** \\n\",\n \"***\\n\",\n \"In this project, you will use the tools you learned about in the lesson to identify lane lines on the road. You can develop your pipeline on a series of individual images, and later apply the result to a video stream (really just a series of images). Check out the video clip \\\"raw-lines-example.mp4\\\" (also contained in this repository) to see what the output should look like after using the helper functions below. \\n\",\n \"\\n\",\n \"Once you have a result that looks roughly like \\\"raw-lines-example.mp4\\\", you'll need to get creative and try to average and/or extrapolate the line segments you've detected to map out the full extent of the lane lines. You can see an example of the result you're going for in the video \\\"P1_example.mp4\\\". Ultimately, you would like to draw just one line for the left side of the lane, and one for the right.\\n\",\n \"\\n\",\n \"In addition to implementing code, there is a brief writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a [write up template](https://github.com/udacity/CarND-LaneLines-P1/blob/master/writeup_template.md) that can be used to guide the writing process. Completing both the code in the Ipython notebook and the writeup template will cover all of the [rubric points](https://review.udacity.com/#!/rubrics/322/view) for this project.\\n\",\n \"\\n\",\n \"---\\n\",\n \"Let's have a look at our first image called 'test_images/solidWhiteRight.jpg'. Run the 2 cells below (hit Shift-Enter or the \\\"play\\\" button above) to display the image.\\n\",\n \"\\n\",\n \"**Note: If, at any point, you encounter frozen display windows or other confounding issues, you can always start again with a clean slate by going to the \\\"Kernel\\\" menu above and selecting \\\"Restart & Clear Output\\\".**\\n\",\n \"\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**The tools you have are color selection, region of interest selection, grayscaling, Gaussian smoothing, Canny Edge Detection and Hough Tranform line detection. You are also free to explore and try other techniques that were not presented in the lesson. Your goal is piece together a pipeline to detect the line segments in the image, then average/extrapolate them and draw them onto the image for display (as below). Once you have a working pipeline, try it out on the video stream below.**\\n\",\n \"\\n\",\n \"---\\n\",\n \"\\n\",\n \"\\n\",\n \" ![\\\"Combined](\\\"examples/line-segments-example.jpg\\\")
\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"\\n\",\n \" ![\\\"Combined](\\\"examples/laneLines_thirdPass.jpg\\\")
\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Run the cell below to import some packages. If you get an `import error` for a package you've already installed, try changing your kernel (select the Kernel menu above --> Change Kernel). Still have problems? Try relaunching Jupyter Notebook from the terminal prompt. Also, consult the forums for more troubleshooting tips.** \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Import Packages\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#importing some useful packages\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import matplotlib.image as mpimg\\n\",\n \"import numpy as np\\n\",\n \"import cv2\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Read in an Image\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"This image is: with dimensions: (540, 960, 3)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Cougar Tech Webster PIttsford Rush Henrietta\\n\",\n \"Auto Points 10.00 15.0 5.0 20.00\\n\",\n \"Points 35.00 45.0 15.0 55.00\\n\",\n \"Low Bar 1.00 1.0 1.0 1.00\\n\",\n \"Portcullis 1.00 0.5 1.0 0.50\\n\",\n \"Cheval de Frise 1.00 1.0 1.0 0.75\\n\",\n \"Moat 0.25 1.0 1.0 0.30\\n\",\n \"Ramparts 0.50 1.0 0.3 0.50\\n\",\n \"Drawbridge 0.50 1.0 0.5 0.30\\n\",\n \"Sally Port 1.00 0.3 0.5 0.90\\n\",\n \"Rock Wall 0.75 0.7 0.9 0.50\\n\",\n \"Rough Terrain 0.30 0.5 0.9 1.00\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(45.0, 0.23333333333333334, 0.66666666666666663)\\n\"\n ]\n }\n ],\n \"source\": [\n \"def analyze(dataframe, team):\\n\",\n \" total_points = dataframe[team]['Points'] + dataframe[team]['Auto Points']\\n\",\n \" cumulative_success_rate = 4\\n\",\n \" pps = dataframe[team]['Points'] / 150\\n\",\n \" auto_pps = dataframe[team]['Auto Points'] / 15\\n\",\n \" \\n\",\n \" return(total_points, pps, auto_pps)\\n\",\n \"\\n\",\n \"stuff = analyze(data, 'Cougar Tech')\\n\",\n \"print stuff\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Analysis Functions:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"KeyError\",\n \"evalue\": \"'x'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mKeyError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m| \\n\",\n \" | Cougar Tech | \\n\",\n \"Webster | \\n\",\n \"PIttsford | \\n\",\n \"Rush Henrietta | \\n\",\n \"
|---|---|---|---|---|
| Auto Points | \\n\",\n \"10.00 | \\n\",\n \"15.0 | \\n\",\n \"5.0 | \\n\",\n \"20.00 | \\n\",\n \"
| Points | \\n\",\n \"35.00 | \\n\",\n \"45.0 | \\n\",\n \"15.0 | \\n\",\n \"55.00 | \\n\",\n \"
| Low Bar | \\n\",\n \"1.00 | \\n\",\n \"1.0 | \\n\",\n \"1.0 | \\n\",\n \"1.00 | \\n\",\n \"
| Portcullis | \\n\",\n \"1.00 | \\n\",\n \"0.5 | \\n\",\n \"1.0 | \\n\",\n \"0.50 | \\n\",\n \"
| Cheval de Frise | \\n\",\n \"1.00 | \\n\",\n \"1.0 | \\n\",\n \"1.0 | \\n\",\n \"0.75 | \\n\",\n \"
| Moat | \\n\",\n \"0.25 | \\n\",\n \"1.0 | \\n\",\n \"1.0 | \\n\",\n \"0.30 | \\n\",\n \"
| Ramparts | \\n\",\n \"0.50 | \\n\",\n \"1.0 | \\n\",\n \"0.3 | \\n\",\n \"0.50 | \\n\",\n \"
| Drawbridge | \\n\",\n \"0.50 | \\n\",\n \"1.0 | \\n\",\n \"0.5 | \\n\",\n \"0.30 | \\n\",\n \"
| Sally Port | \\n\",\n \"1.00 | \\n\",\n \"0.3 | \\n\",\n \"0.5 | \\n\",\n \"0.90 | \\n\",\n \"
| Rock Wall | \\n\",\n \"0.75 | \\n\",\n \"0.7 | \\n\",\n \"0.9 | \\n\",\n \"0.50 | \\n\",\n \"
| Rough Terrain | \\n\",\n \"0.30 | \\n\",\n \"0.5 | \\n\",\n \"0.9 | \\n\",\n \"1.00 | \\n\",\n \"
\\n\",\n \" Your output should look something like this (above) after detecting line segments using the helper functions below
\\n\",\n \"\\n\",\n \" Your goal is to connect/average/extrapolate line segments to get output like this
\\n\",\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#reading in an image\\n\",\n \"image = mpimg.imread('test_images/solidWhiteRight.jpg')\\n\",\n \"\\n\",\n \"#printing out some stats and plotting\\n\",\n \"print('This image is:', type(image), 'with dimensions:', image.shape)\\n\",\n \"plt.imshow(image) # if you wanted to show a single color channel image called 'gray', for example, call as plt.imshow(gray, cmap='gray')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Ideas for Lane Detection Pipeline\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Some OpenCV functions (beyond those introduced in the lesson) that might be useful for this project are:**\\n\",\n \"\\n\",\n \"`cv2.inRange()` for color selection \\n\",\n \"`cv2.fillPoly()` for regions selection \\n\",\n \"`cv2.line()` to draw lines on an image given endpoints \\n\",\n \"`cv2.addWeighted()` to coadd / overlay two images \\n\",\n \"`cv2.cvtColor()` to grayscale or change color \\n\",\n \"`cv2.imwrite()` to output images to file \\n\",\n \"`cv2.bitwise_and()` to apply a mask to an image\\n\",\n \"\\n\",\n \"**Check out the OpenCV documentation to learn about these and discover even more awesome functionality!**\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Helper Functions\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Below are some helper functions to help get you started. They should look familiar from the lesson!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import math\\n\",\n \"\\n\",\n \"\\n\",\n \"def grayscale(img):\\n\",\n \" \\\"\\\"\\\"Applies the Grayscale transform\\n\",\n \" This will return an image with only one color channel\\n\",\n \" but NOTE: to see the returned image as grayscale\\n\",\n \" (assuming your grayscaled image is called 'gray')\\n\",\n \" you should call plt.imshow(gray, cmap='gray')\\\"\\\"\\\"\\n\",\n \" return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\\n\",\n \" # Or use BGR2GRAY if you read an image with cv2.imread()\\n\",\n \" # return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\\n\",\n \" \\n\",\n \"\\n\",\n \"def canny(img, low_threshold, high_threshold):\\n\",\n \" \\\"\\\"\\\"Applies the Canny transform\\\"\\\"\\\"\\n\",\n \" return cv2.Canny(img, low_threshold, high_threshold)\\n\",\n \"\\n\",\n \"\\n\",\n \"def gaussian_blur(img, kernel_size):\\n\",\n \" \\\"\\\"\\\"Applies a Gaussian Noise kernel\\\"\\\"\\\"\\n\",\n \" return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)\\n\",\n \"\\n\",\n \"\\n\",\n \"def region_of_interest(img, vertices):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Applies an image mask.\\n\",\n \" \\n\",\n \" Only keeps the region of the image defined by the polygon\\n\",\n \" formed from `vertices`. The rest of the image is set to black.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" #defining a blank mask to start with\\n\",\n \" mask = np.zeros_like(img) \\n\",\n \" \\n\",\n \" #defining a 3 channel or 1 channel color to fill the mask with depending on the input image\\n\",\n \" if len(img.shape) > 2:\\n\",\n \" channel_count = img.shape[2] # i.e. 3 or 4 depending on your image\\n\",\n \" ignore_mask_color = (255,) * channel_count\\n\",\n \" else:\\n\",\n \" ignore_mask_color = 255\\n\",\n \" \\n\",\n \" #filling pixels inside the polygon defined by \\\"vertices\\\" with the fill color\\n\",\n \" cv2.fillPoly(mask, vertices, ignore_mask_color)\\n\",\n \" \\n\",\n \" #returning the image only where mask pixels are nonzero\\n\",\n \" masked_image = cv2.bitwise_and(img, mask)\\n\",\n \" return masked_image\\n\",\n \"\\n\",\n \"\\n\",\n \"def draw_lines(img, lines, color=[255, 0, 0], thickness=2):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" NOTE: this is the function you might want to use as a starting point once you want to \\n\",\n \" average/extrapolate the line segments you detect to map out the full\\n\",\n \" extent of the lane (going from the result shown in raw-lines-example.mp4\\n\",\n \" to that shown in P1_example.mp4). \\n\",\n \" \\n\",\n \" Think about things like separating line segments by their \\n\",\n \" slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left\\n\",\n \" line vs. the right line. Then, you can average the position of each of \\n\",\n \" the lines and extrapolate to the top and bottom of the lane.\\n\",\n \" \\n\",\n \" This function draws `lines` with `color` and `thickness`. \\n\",\n \" Lines are drawn on the image inplace (mutates the image).\\n\",\n \" If you want to make the lines semi-transparent, think about combining\\n\",\n \" this function with the weighted_img() function below\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # Right/Left Slope\\n\",\n \" lslope = []\\n\",\n \" rslope = []\\n\",\n \" \\n\",\n \" # Right/Left Centers\\n\",\n \" lcenter = []\\n\",\n \" rcenter = []\\n\",\n \"\\n\",\n \" for line in lines:\\n\",\n \" for x1, y1, x2, y2 in line:\\n\",\n \" slope = (y2 - y1) / (x2 - x1)\\n\",\n \" center = [(x1 + x2) / 2, (y1 + y2) / 2]\\n\",\n \" if slope > 0.5 and slope < 1.0: # Right Lane\\n\",\n \" rslope.append(slope)\\n\",\n \" rcenter.append(center)\\n\",\n \" if slope < -0.5 and slope > -1.0: # Left Lane\\n\",\n \" lslope.append(slope)\\n\",\n \" lcenter.append(center)\\n\",\n \"\\n\",\n \" lslope_avg = np.sum(lslope) / len(lslope)\\n\",\n \" rslope_avg = np.sum(rslope) / len(rslope)\\n\",\n \" lcenter_avg = np.divide(np.sum(lcenter, axis=0), len(lcenter))\\n\",\n \" rcenter_avg = np.divide(np.sum(rcenter, axis=0), len(rcenter))\\n\",\n \" \\n\",\n \" ly1 = int(img.shape[0])\\n\",\n \" lx1 = int((ly1 - lcenter_avg[1]) / lslope_avg + lcenter_avg[0])\\n\",\n \" ly2 = int(img.shape[0] * 0.6)\\n\",\n \" lx2 = int((ly2 - lcenter_avg[1]) / lslope_avg + lcenter_avg[0])\\n\",\n \"\\n\",\n \" ry1 = int(img.shape[0])\\n\",\n \" rx1 = int((ry1 - rcenter_avg[1]) / rslope_avg + rcenter_avg[0])\\n\",\n \" ry2 = int(img.shape[0] * 0.6)\\n\",\n \" rx2 = int((ry2 - rcenter_avg[1]) / rslope_avg + rcenter_avg[0])\\n\",\n \" \\n\",\n \" cv2.line(img, (lx1, ly1), (lx2, ly2), color, thickness)\\n\",\n \" cv2.line(img, (rx1, ry1), (rx2, ry2), color, thickness)\\n\",\n \" \\n\",\n \" \\n\",\n \"def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" `img` should be the output of a Canny transform.\\n\",\n \" \\n\",\n \" Returns an image with hough lines drawn.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]),\\n\",\n \" minLineLength=min_line_len,\\n\",\n \" maxLineGap=max_line_gap)\\n\",\n \" line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)\\n\",\n \" draw_lines(line_img, lines, thickness=10)\\n\",\n \" return line_img\\n\",\n \"\\n\",\n \"\\n\",\n \"# Python 3 has support for cool math symbols.\\n\",\n \"def weighted_img(img, initial_img, α=0.8, β=1., γ=0.):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" `img` is the output of the hough_lines(), An image with lines drawn on it.\\n\",\n \" Should be a blank image (all black) with lines drawn on it.\\n\",\n \" \\n\",\n \" `initial_img` should be the image before any processing.\\n\",\n \" \\n\",\n \" The result image is computed as follows:\\n\",\n \" \\n\",\n \" initial_img * α + img * β + γ\\n\",\n \" NOTE: initial_img and img must be the same shape!\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" return cv2.addWeighted(initial_img, α, img, β, γ)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Test Images\\n\",\n \"\\n\",\n \"Build your pipeline to work on the images in the directory \\\"test_images\\\" \\n\",\n \"**You should make sure your pipeline works well on these images before you try the videos.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['solidYellowCurve.jpg',\\n\",\n \" 'solidYellowLeft.jpg',\\n\",\n \" 'solidYellowCurve2.jpg',\\n\",\n \" 'solidWhiteRight.jpg',\\n\",\n \" 'whiteCarLaneSwitch.jpg',\\n\",\n \" 'solidWhiteCurve.jpg']\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import os\\n\",\n \"os.listdir(\\\"test_images/\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Build a Lane Finding Pipeline\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Build the pipeline and run your solution on all test_images. Make copies into the `test_images_output` directory, and you can use the images in your writeup report.\\n\",\n \"\\n\",\n \"Try tuning the various parameters, especially the low and high Canny thresholds as well as the Hough lines parameters.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# TODO: Build your pipeline that will draw lane lines on the test_images\\n\",\n \"# then save them to the test_images_output directory.\\n\",\n \"low_threshold = 50 # Canny edge detection\\n\",\n \"high_threshold = 150 # Canny edge detection\\n\",\n \"kernel_size = 5 # Gaussian blurring\\n\",\n \"rho = 2 # Hough Tranform, distance resolution in pixels of the Hough grid\\n\",\n \"theta = np.pi / 180 # Hough Tranform, angular resolution in radians of Hough grid\\n\",\n \"threshold = 15 # Hough Tranform, minimum number of votes (intersections in Hough grid cell)\\n\",\n \"min_line_len = 40 # Hough Tranform, minimum number of pixels making up a line\\n\",\n \"max_line_gap = 20 # Hough Tranform, maximum gap in pixels between connectable line segments\\n\",\n \"\\n\",\n \"\\n\",\n \"original_images = os.listdir('test_images/')\\n\",\n \"for image in original_images:\\n\",\n \" img = mpimg.imread('test_images/' + image)\\n\",\n \" vertices = np.array([[(0, img.shape[0]), (450, 320),\\n\",\n \" (510, 320), (img.shape[1], img.shape[0])]],\\n\",\n \" dtype=np.int32) # Image mask polygon\\n\",\n \" gray_img = grayscale(img) # Greyed out image\\n\",\n \" edge_img = canny(gray_img, low_threshold, high_threshold) # Canny edges\\n\",\n \" mask_img = region_of_interest(edge_img, vertices) # Region of interest\\n\",\n \" line_img = hough_lines(mask_img, rho, theta, threshold,\\n\",\n \" min_line_len, max_line_gap)\\n\",\n \" lane_line_img = weighted_img(img, line_img)\\n\",\n \" #cv2.imwrite('test_images_output/' + image, lane_line_img)\\n\",\n \" mpimg.imsave('test_images_output/' + image, lane_line_img)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"This image is: with dimensions: (540, 960, 3)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#reading in an image\\n\",\n \"image = mpimg.imread('test_images_output/whiteCarLaneSwitch.jpg')\\n\",\n \"\\n\",\n \"#printing out some stats and plotting\\n\",\n \"print('This image is:', type(image), 'with dimensions:', image.shape)\\n\",\n \"plt.imshow(image)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Test on Videos\\n\",\n \"\\n\",\n \"You know what's cooler than drawing lanes over images? Drawing lanes over video!\\n\",\n \"\\n\",\n \"We can test our solution on two provided videos:\\n\",\n \"\\n\",\n \"`solidWhiteRight.mp4`\\n\",\n \"\\n\",\n \"`solidYellowLeft.mp4`\\n\",\n \"\\n\",\n \"**Note: if you get an import error when you run the next cell, try changing your kernel (select the Kernel menu above --> Change Kernel). Still have problems? Try relaunching Jupyter Notebook from the terminal prompt. Also, consult the forums for more troubleshooting tips.**\\n\",\n \"\\n\",\n \"**If you get an error that looks like this:**\\n\",\n \"```\\n\",\n \"NeedDownloadError: Need ffmpeg exe. \\n\",\n \"You can download it by calling: \\n\",\n \"imageio.plugins.ffmpeg.download()\\n\",\n \"```\\n\",\n \"**Follow the instructions in the error message and check out [this forum post](https://discussions.udacity.com/t/project-error-of-test-on-videos/274082) for more troubleshooting tips across operating systems.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Import everything needed to edit/save/watch video clips\\n\",\n \"from moviepy.editor import VideoFileClip\\n\",\n \"from IPython.display import HTML\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def process_image(image):\\n\",\n \" # NOTE: The output you return should be a color image (3 channel) for processing video below\\n\",\n \" # TODO: put your pipeline here,\\n\",\n \" # you should return the final output (image where lines are drawn on lanes)\\n\",\n \" \\n\",\n \" vertices = np.array([[(0, image.shape[0]), (450, 320),\\n\",\n \" (510, 320), (image.shape[1], image.shape[0])]],\\n\",\n \" dtype=np.int32) # Image mask polygon\\n\",\n \" gray_img = grayscale(image) # Greyed out image\\n\",\n \" edge_img = canny(gray_img, low_threshold, high_threshold) # Canny edges\\n\",\n \" mask_img = region_of_interest(edge_img, vertices) # Region of interest\\n\",\n \" line_img = hough_lines(mask_img, rho, theta, threshold,\\n\",\n \" min_line_len, max_line_gap)\\n\",\n \" lane_line_img = weighted_img(image, line_img)\\n\",\n \" \\n\",\n \" return lane_line_img\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's try the one with the solid white lane on the right first ...\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[MoviePy] >>>> Building video test_videos_output/solidWhiteRight.mp4\\n\",\n \"[MoviePy] Writing video test_videos_output/solidWhiteRight.mp4\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|█████████▉| 221/222 [00:02<00:00, 101.67it/s]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[MoviePy] Done.\\n\",\n \"[MoviePy] >>>> Video ready: test_videos_output/solidWhiteRight.mp4 \\n\",\n \"\\n\",\n \"CPU times: user 1.68 s, sys: 202 ms, total: 1.89 s\\n\",\n \"Wall time: 2.58 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"white_output = 'test_videos_output/solidWhiteRight.mp4'\\n\",\n \"## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video\\n\",\n \"## To do so add .subclip(start_second,end_second) to the end of the line below\\n\",\n \"## Where start_second and end_second are integer values representing the start and end of the subclip\\n\",\n \"## You may also uncomment the following line for a subclip of the first 5 seconds\\n\",\n \"##clip1 = VideoFileClip(\\\"test_videos/solidWhiteRight.mp4\\\").subclip(0,5)\\n\",\n \"clip1 = VideoFileClip(\\\"test_videos/solidWhiteRight.mp4\\\")\\n\",\n \"white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!\\n\",\n \"%time white_clip.write_videofile(white_output, audio=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Play the video inline, or if you prefer find the video in your filesystem (should be in the same directory) and play it in your video player of choice.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"HTML(\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"\\\"\\\"\\\".format(white_output))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Improve the draw_lines() function\\n\",\n \"\\n\",\n \"**At this point, if you were successful with making the pipeline and tuning parameters, you probably have the Hough line segments drawn onto the road, but what about identifying the full extent of the lane and marking it clearly as in the example video (P1_example.mp4)? Think about defining a line to run the full length of the visible lane based on the line segments you identified with the Hough Transform. As mentioned previously, try to average and/or extrapolate the line segments you've detected to map out the full extent of the lane lines. You can see an example of the result you're going for in the video \\\"P1_example.mp4\\\".**\\n\",\n \"\\n\",\n \"**Go back and modify your draw_lines function accordingly and try re-running your pipeline. The new output should draw a single, solid line over the left lane line and a single, solid line over the right lane line. The lines should start from the bottom of the image and extend out to the top of the region of interest.**\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now for the one with the solid yellow lane on the left. This one's more tricky!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[MoviePy] >>>> Building video test_videos_output/solidYellowLeft.mp4\\n\",\n \"[MoviePy] Writing video test_videos_output/solidYellowLeft.mp4\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|█████████▉| 681/682 [00:07<00:00, 85.35it/s]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[MoviePy] Done.\\n\",\n \"[MoviePy] >>>> Video ready: test_videos_output/solidYellowLeft.mp4 \\n\",\n \"\\n\",\n \"CPU times: user 5.91 s, sys: 627 ms, total: 6.54 s\\n\",\n \"Wall time: 8.44 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"yellow_output = 'test_videos_output/solidYellowLeft.mp4'\\n\",\n \"## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video\\n\",\n \"## To do so add .subclip(start_second,end_second) to the end of the line below\\n\",\n \"## Where start_second and end_second are integer values representing the start and end of the subclip\\n\",\n \"## You may also uncomment the following line for a subclip of the first 5 seconds\\n\",\n \"##clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4').subclip(0,5)\\n\",\n \"clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4')\\n\",\n \"yellow_clip = clip2.fl_image(process_image)\\n\",\n \"%time yellow_clip.write_videofile(yellow_output, audio=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"HTML(\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"\\\"\\\"\\\".format(yellow_output))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Writeup and Submission\\n\",\n \"\\n\",\n \"If you're satisfied with your video outputs, it's time to make the report writeup in a pdf or markdown file. Once you have this Ipython notebook ready along with the writeup, it's time to submit for review! Here is a [link](https://github.com/udacity/CarND-LaneLines-P1/blob/master/writeup_template.md) to the writeup template file.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"## Optional Challenge\\n\",\n \"\\n\",\n \"Try your lane finding pipeline on the video below. Does it still work? Can you figure out a way to make it more robust? If you're up for the challenge, modify your pipeline so it works with this video and submit it along with the rest of your project!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[MoviePy] >>>> Building video test_videos_output/challenge.mp4\\n\",\n \"[MoviePy] Writing video test_videos_output/challenge.mp4\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|██████████| 251/251 [00:06<00:00, 38.21it/s]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[MoviePy] Done.\\n\",\n \"[MoviePy] >>>> Video ready: test_videos_output/challenge.mp4 \\n\",\n \"\\n\",\n \"CPU times: user 6.31 s, sys: 487 ms, total: 6.79 s\\n\",\n \"Wall time: 7.65 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"challenge_output = 'test_videos_output/challenge.mp4'\\n\",\n \"## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video\\n\",\n \"## To do so add .subclip(start_second,end_second) to the end of the line below\\n\",\n \"## Where start_second and end_second are integer values representing the start and end of the subclip\\n\",\n \"## You may also uncomment the following line for a subclip of the first 5 seconds\\n\",\n \"##clip3 = VideoFileClip('test_videos/challenge.mp4').subclip(0,5)\\n\",\n \"clip3 = VideoFileClip('test_videos/challenge.mp4')\\n\",\n \"challenge_clip = clip3.fl_image(process_image)\\n\",\n \"%time challenge_clip.write_videofile(challenge_output, audio=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"HTML(\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"\\\"\\\"\\\".format(challenge_output))\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"},"license":{"kind":"string","value":"gpl-3.0"}}},{"rowIdx":46189,"cells":{"repo_name":{"kind":"string","value":"atulsingh0/MachineLearning"},"path":{"kind":"string","value":"scikit-learn/Numpy_02.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"37504"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0, 1, 2, 3, 4])\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = np.arange(5)\\n\",\n \"a\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0 2 4 6 8]\\n\",\n \"[3 4 5 6 7]\\n\",\n \"[-2 -1 0 1 2]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(a*2)\\n\",\n \"print(a+3)\\n\",\n \"print(a-2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 1., 1., 1., 1., 1.])\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"b = np.ones(5)\\n\",\n \"b\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[-2. -1. 0. 1. 2.]\\n\",\n \"[ 1. 2. 3. 4. 5.]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(a-b*2)\\n\",\n \"print(a+b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The slowest run took 6.27 times longer than the fastest. This could mean that an intermediate result is being cached.\\n\",\n \"100000 loops, best of 3: 11.4 µs per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"# these operations are faster in numpy than pure python\\n\",\n \"\\n\",\n \"a = np.arange(10000)\\n\",\n \"%timeit a+2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1000 loops, best of 3: 1.03 ms per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"# in native python\\n\",\n \"a = range(10000)\\n\",\n \"%timeit [i+2 for i in a]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Array Multiplication\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 1-D array\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([2, 8, 3, 4, 2])\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = np.random.randint(1, 10, 5)\\n\",\n \"a\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([6, 3, 9, 3, 4])\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"b =np.random.randint(1, 10, 5)\\n\",\n \"b \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([12, 24, 27, 12, 8])\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a * b # elementwise operations\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 2-D array\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[9, 5, 5],\\n\",\n \" [7, 2, 4]])\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = np.random.randint(1, 10, (2,3))\\n\",\n \"a\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[2, 1, 5],\\n\",\n \" [5, 2, 7]])\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"b = np.random.randint(1, 10, (2,3))\\n\",\n \"b\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[18, 5, 25],\\n\",\n \" [35, 4, 28]])\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a * b # element wise multiplication\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# a.dot(b) this will throw an error as Matix multiplication is not possible here\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[48, 90],\\n\",\n \" [36, 67]])\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a.dot(b.T) # b.T will transpose the 2,3 matrix to 3,2 matrix\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Comparison\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1 3 7 6 5] [1 2 7 8 5]\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = np.array([1, 3, 7, 6, 5])\\n\",\n \"b = np.array([1, 2, 7, 8, 5])\\n\",\n \"\\n\",\n \"print(a,b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ True, False, True, False, True], dtype=bool)\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Comparison\\n\",\n \"a == b\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([False, True, False, False, False], dtype=bool)\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a > b\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Arraywise Comparison\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"a = np.array([1,2,3,4])\\n\",\n \"b = np.array([3,4, 9, 8])\\n\",\n \"c = np.array([1,2,3,4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.array_equal(a, b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.array_equal(a, c)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Logical Operations\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ True True False False] [ True False True False]\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = np.array([1,1,0,0], dtype='bool')\\n\",\n \"b = np.array([1,0,1,0], dtype='bool')\\n\",\n \"\\n\",\n \"print(a, b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ True, True, True, False], dtype=bool)\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.logical_or(a,b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ True, False, False, False], dtype=bool)\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.logical_and(a,b)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Adv mathematics func\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 1. , 0.5, 0. , 3. ])\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = np.array([1, 0.5, 0, 3])\\n\",\n \"a\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 0.84147098, 0.47942554, 0. , 0.14112001])\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.sin(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\tools\\\\Anaconda3\\\\lib\\\\site-packages\\\\ipykernel\\\\__main__.py:1: RuntimeWarning: divide by zero encountered in log\\n\",\n \" if __name__ == '__main__':\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 0. , -0.69314718, -inf, 1.09861229])\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.log(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 2.71828183, 1.64872127, 1. , 20.08553692])\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.exp(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 0., 1., 1.],\\n\",\n \" [ 0., 0., 1.],\\n\",\n \" [ 0., 0., 0.]])\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = np.triu(np.ones((3, 3)), 1)\\n\",\n \"a\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 0., 0., 0.],\\n\",\n \" [ 1., 0., 0.],\\n\",\n \" [ 1., 1., 0.]])\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a.T # transposition of array is just a view and stored in memory\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Counting Sums\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"10\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = np.array([1,2,3,4])\\n\",\n \"x.sum()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 2, 3],\\n\",\n \" [3, 4, 5]])\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = np.array([[1,2,3],[3,4,5]])\\n\",\n \"x\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([4, 6, 8])\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x.sum(axis=0) # column sum\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 6, 12])\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x.sum(axis=1) # row sum\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Other functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x.min()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1, 3])\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x.min(axis=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([3, 4, 5])\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x.max(axis=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x.argmin() # index of min element\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"5\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x.argmax() # index of max element\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Logical Operations\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.all([True, False, True])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.any([True, False, True])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 42,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = np.zeros((10,10))\\n\",\n \"np.any(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.all(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 44,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.all(a==0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 45,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.any(a!=0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1 2 3 1] \\n\",\n \"\\n\",\n \" [[1 2 3]\\n\",\n \" [5 6 1]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = np.array([1, 2, 3, 1])\\n\",\n \"y = np.array([[1, 2, 3], [5, 6, 1]])\\n\",\n \"\\n\",\n \"print(x, \\\"\\\\n\\\\n\\\",y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1.75\"\n ]\n },\n \"execution_count\": 47,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x.mean()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1.5\"\n ]\n },\n \"execution_count\": 48,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.median(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 2., 5.])\"\n ]\n },\n \"execution_count\": 49,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.median(y, axis=-1) # last axis\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.82915619758884995\"\n ]\n },\n \"execution_count\": 50,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x.std()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### broadcasing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 1., 1., 1., 1.],\\n\",\n \" [ 1., 1., 1., 1.],\\n\",\n \" [ 1., 1., 1., 1.]])\"\n ]\n },\n \"execution_count\": 51,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = np.ones((3,4))\\n\",\n \"a\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 1., 1., 1., 1.],\\n\",\n \" [ 1., 1., 1., 1.],\\n\",\n \" [ 1., 1., 0., 0.]])\"\n ]\n },\n \"execution_count\": 52,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a[2:,2:]=0\\n\",\n \"a\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(4,)\"\n ]\n },\n \"execution_count\": 53,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = np.arange(0, 40, 10)\\n\",\n \"a.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### newaxis\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 0],\\n\",\n \" [10],\\n\",\n \" [20],\\n\",\n \" [30]])\"\n ]\n },\n \"execution_count\": 54,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = a[:, np.newaxis] # newaxis is a nice feature supported by numpy\\n\",\n \"a\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(4, 1)\"\n ]\n },\n \"execution_count\": 55,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"mileposts = np.array([0, 198, 303, 736, 871, 1175, 1475, 1544,1913, 2448])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 0, 198, 303, 736, 871, 1175, 1475, 1544, 1913, 2448],\\n\",\n \" [ 198, 0, 105, 538, 673, 977, 1277, 1346, 1715, 2250],\\n\",\n \" [ 303, 105, 0, 433, 568, 872, 1172, 1241, 1610, 2145],\\n\",\n \" [ 736, 538, 433, 0, 135, 439, 739, 808, 1177, 1712],\\n\",\n \" [ 871, 673, 568, 135, 0, 304, 604, 673, 1042, 1577],\\n\",\n \" [1175, 977, 872, 439, 304, 0, 300, 369, 738, 1273],\\n\",\n \" [1475, 1277, 1172, 739, 604, 300, 0, 69, 438, 973],\\n\",\n \" [1544, 1346, 1241, 808, 673, 369, 69, 0, 369, 904],\\n\",\n \" [1913, 1715, 1610, 1177, 1042, 738, 438, 369, 0, 535],\\n\",\n \" [2448, 2250, 2145, 1712, 1577, 1273, 973, 904, 535, 0]])\"\n ]\n },\n \"execution_count\": 57,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"distance = np.abs(mileposts - mileposts[:, np.newaxis])\\n\",\n \"distance\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0 1 2 3 4] \\n\",\n \"\\n\",\n \" [[0]\\n\",\n \" [1]\\n\",\n \" [2]\\n\",\n \" [3]\\n\",\n \" [4]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x, y = np.arange(5), np.arange(5)[:, np.newaxis]\\n\",\n \"\\n\",\n \"print(x, \\\"\\\\n\\\\n\\\", y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 0. , 1. , 2. , 3. , 4. ],\\n\",\n \" [ 1. , 1.41421356, 2.23606798, 3.16227766, 4.12310563],\\n\",\n \" [ 2. , 2.23606798, 2.82842712, 3.60555128, 4.47213595],\\n\",\n \" [ 3. , 3.16227766, 3.60555128, 4.24264069, 5. ],\\n\",\n \" [ 4. , 4.12310563, 4.47213595, 5. , 5.65685425]])\"\n ]\n },\n \"execution_count\": 59,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"distance = np.sqrt(x ** 2 + y ** 2)\\n\",\n \"distance\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"the numpy.ogrid function allows to directly create vectors x and y of the previous example, with two\\n\",\n \"\\\"significant dimensions\\\":\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0]\\n\",\n \" [1]\\n\",\n \" [2]\\n\",\n \" [3]\\n\",\n \" [4]] \\n\",\n \"\\n\",\n \" [[0 1 2 3 4]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x, y = np.ogrid[0:5, 0:5]\\n\",\n \"\\n\",\n \"print(x, \\\"\\\\n\\\\n\\\", y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(5, 1) (1, 5)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(x.shape, y.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 0. , 1. , 2. , 3. , 4. ],\\n\",\n \" [ 1. , 1.41421356, 2.23606798, 3.16227766, 4.12310563],\\n\",\n \" [ 2. , 2.23606798, 2.82842712, 3.60555128, 4.47213595],\\n\",\n \" [ 3. , 3.16227766, 3.60555128, 4.24264069, 5. ],\\n\",\n \" [ 4. , 4.12310563, 4.47213595, 5. , 5.65685425]])\"\n ]\n },\n \"execution_count\": 62,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"distance = np.sqrt(x ** 2 + y ** 2)\\n\",\n \"distance\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"np.mgrid\\n\",\n \"directly providesmatrices full of indices for cases where we can’t (or don’t want to) benefit frombroadcasting\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0 0 0 0]\\n\",\n \" [1 1 1 1]\\n\",\n \" [2 2 2 2]\\n\",\n \" [3 3 3 3]] \\n\",\n \"\\n\",\n \" [[0 1 2 3]\\n\",\n \" [0 1 2 3]\\n\",\n \" [0 1 2 3]\\n\",\n \" [0 1 2 3]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x, y = np.mgrid[0:4, 0:4]\\n\",\n \"print(x, \\\"\\\\n\\\\n\\\", y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Flattening the array\\n\",\n \"Higher dimensions: last dimensions ravel out “first”.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 2, 3],\\n\",\n \" [4, 5, 6]])\"\n ]\n },\n \"execution_count\": 64,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = np.array([[1, 2, 3], [4, 5, 6]])\\n\",\n \"a\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1, 2, 3, 4, 5, 6])\"\n ]\n },\n \"execution_count\": 65,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a.ravel()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1, 4, 2, 5, 3, 6])\"\n ]\n },\n \"execution_count\": 66,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a.T.ravel()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Reshaping\\n\",\n \"reshaping may return view or copy, so use caustiously\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(2, 3)\"\n ]\n },\n \"execution_count\": 67,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1, 2, 3, 4, 5, 6])\"\n ]\n },\n \"execution_count\": 68,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a.ravel()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 2],\\n\",\n \" [3, 4],\\n\",\n \" [5, 6]])\"\n ]\n },\n \"execution_count\": 69,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a.ravel().reshape(3,2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 70,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 2, 3],\\n\",\n \" [4, 5, 6]])\"\n ]\n },\n \"execution_count\": 70,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a.reshape((2, -1)) # # unspecified (-1) value is inferred\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 71,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 2],\\n\",\n \" [3, 4],\\n\",\n \" [5, 6]])\"\n ]\n },\n \"execution_count\": 71,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a.reshape((3, -1))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Adding a dimention\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 72,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0, 1, 2, 3])\"\n ]\n },\n \"execution_count\": 72,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = np.arange(4)\\n\",\n \"a\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 73,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0],\\n\",\n \" [1],\\n\",\n \" [2],\\n\",\n \" [3]])\"\n ]\n },\n \"execution_count\": 73,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a[:, np.newaxis]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 74,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0, 1, 2, 3]])\"\n ]\n },\n \"execution_count\": 74,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a[np.newaxis,:]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Dimension shuffling\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 75,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[[ 0, 1],\\n\",\n \" [ 2, 3],\\n\",\n \" [ 4, 5]],\\n\",\n \"\\n\",\n \" [[ 6, 7],\\n\",\n \" [ 8, 9],\\n\",\n \" [10, 11]],\\n\",\n \"\\n\",\n \" [[12, 13],\\n\",\n \" [14, 15],\\n\",\n \" [16, 17]],\\n\",\n \"\\n\",\n \" [[18, 19],\\n\",\n \" [20, 21],\\n\",\n \" [22, 23]]])\"\n ]\n },\n \"execution_count\": 75,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = np.arange(4*3*2).reshape(4, 3, 2)\\n\",\n \"a\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 76,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(4, 3, 2)\"\n ]\n },\n \"execution_count\": 76,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 77,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2\"\n ]\n },\n \"execution_count\": 77,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a[0,1,0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 78,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[[ 0, 1, 2, 3],\\n\",\n \" [ 4, 5, 6, 7],\\n\",\n \" [ 8, 9, 10, 11]],\\n\",\n \"\\n\",\n \" [[12, 13, 14, 15],\\n\",\n \" [16, 17, 18, 19],\\n\",\n \" [20, 21, 22, 23]]])\"\n ]\n },\n \"execution_count\": 78,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a.ravel().reshape(2,3,4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Resizing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 83,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0, 1, 2, 3, 0, 0, 0, 0])\"\n ]\n },\n \"execution_count\": 83,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"o = np.arange(4)\\n\",\n \"o.resize((8,))\\n\",\n \"o\\n\",\n \"\\n\",\n \"# It will throw an error \\n\",\n \"# ValueError: cannot resize an array that references or is referenced\\n\",\n \"# by another array in this way. Use the resize function\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Sorting data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[4, 3, 5],\\n\",\n \" [1, 2, 1]])\"\n ]\n },\n \"execution_count\": 84,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = np.array([[4, 3, 5], [1, 2, 1]])\\n\",\n \"a\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[3, 4, 5],\\n\",\n \" [1, 1, 2]])\"\n ]\n },\n \"execution_count\": 87,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"b = np.sort(a, axis=1) # Sorts each row separately!\\n\",\n \"b\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 89,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[3, 4, 5],\\n\",\n \" [1, 1, 2]])\"\n ]\n },\n \"execution_count\": 89,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a.sort(axis=1) # inplace sort\\n\",\n \"a\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 90,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([2, 3, 1, 0], dtype=int64)\"\n ]\n },\n \"execution_count\": 90,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Sorting with fancy indexing:\\n\",\n \"a = np.array([4, 3, 1, 2])\\n\",\n \"j = np.argsort(a)\\n\",\n \"j\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 91,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1, 2, 3, 4])\"\n ]\n },\n \"execution_count\": 91,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a[j]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 93,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0 2\\n\",\n \"4 1\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Finding minima and maxima\\n\",\n \"j_max = np.argmax(a)\\n\",\n \"j_min = np.argmin(a)\\n\",\n \"\\n\",\n \"print(j_max, j_min) # indexes\\n\",\n \"print(a[j_max], a[j_min])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python [conda root]\",\n \"language\": \"python\",\n \"name\": \"conda-root-py\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"},"license":{"kind":"string","value":"gpl-3.0"}}},{"rowIdx":46190,"cells":{"repo_name":{"kind":"string","value":"google/or-tools"},"path":{"kind":"string","value":"examples/notebook/contrib/secret_santa.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"6890"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"google\",\n \"metadata\": {},\n \"source\": [\n \"##### Copyright 2021 Google LLC.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"apache\",\n \"metadata\": {},\n \"source\": [\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n\",\n \"you may not use this file except in compliance with the License.\\n\",\n \"You may obtain a copy of the License at\\n\",\n \"\\n\",\n \" http://www.apache.org/licenses/LICENSE-2.0\\n\",\n \"\\n\",\n \"Unless required by applicable law or agreed to in writing, software\\n\",\n \"distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n\",\n \"See the License for the specific language governing permissions and\\n\",\n \"limitations under the License.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"basename\",\n \"metadata\": {},\n \"source\": [\n \"# secret_santa\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"link\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"doc\",\n \"metadata\": {},\n \"source\": [\n \"First, you must install [ortools](https://pypi.org/project/ortools/) package in this colab.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"install\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"!pip install ortools\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"code\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Copyright 2010 Hakan Kjellerstrand hakank@gmail.com\\n\",\n \"#\\n\",\n \"# Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n\",\n \"# you may not use this file except in compliance with the License.\\n\",\n \"# You may obtain a copy of the License at\\n\",\n \"#\\n\",\n \"# http://www.apache.org/licenses/LICENSE-2.0\\n\",\n \"#\\n\",\n \"# Unless required by applicable law or agreed to in writing, software\\n\",\n \"# distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n\",\n \"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n\",\n \"# See the License for the specific language governing permissions and\\n\",\n \"# limitations under the License.\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" Secret Santa problem in Google CP Solver.\\n\",\n \"\\n\",\n \" From Ruby Quiz Secret Santa\\n\",\n \" http://www.rubyquiz.com/quiz2.html\\n\",\n \" '''\\n\",\n \" Honoring a long standing tradition started by my wife's dad, my friends\\n\",\n \" all play a Secret Santa game around Christmas time. We draw names and\\n\",\n \" spend a week sneaking that person gifts and clues to our identity. On the\\n\",\n \" last night of the game, we get together, have dinner, share stories, and,\\n\",\n \" most importantly, try to guess who our Secret Santa was. It's a crazily\\n\",\n \" fun way to enjoy each other's company during the holidays.\\n\",\n \"\\n\",\n \" To choose Santas, we use to draw names out of a hat. This system was\\n\",\n \" tedious, prone to many 'Wait, I got myself...' problems. This year, we\\n\",\n \" made a change to the rules that further complicated picking and we knew\\n\",\n \" the hat draw would not stand up to the challenge. Naturally, to solve\\n\",\n \" this problem, I scripted the process. Since that turned out to be more\\n\",\n \" interesting than I had expected, I decided to share.\\n\",\n \"\\n\",\n \" This weeks Ruby Quiz is to implement a Secret Santa selection script.\\n\",\n \"\\n\",\n \" Your script will be fed a list of names on STDIN.\\n\",\n \" ...\\n\",\n \" Your script should then choose a Secret Santa for every name in the list.\\n\",\n \" Obviously, a person cannot be their own Secret Santa. In addition, my friends\\n\",\n \" no longer allow people in the same family to be Santas for each other and your\\n\",\n \" script should take this into account.\\n\",\n \" '''\\n\",\n \"\\n\",\n \" Comment: This model skips the file input and mail parts. We\\n\",\n \" assume that the friends are identified with a number from 1..n,\\n\",\n \" and the families is identified with a number 1..num_families.\\n\",\n \"\\n\",\n \" Compare with the following model:\\n\",\n \" * MiniZinc: http://www.hakank.org/minizinc/secret_santa.mzn\\n\",\n \"\\n\",\n \"\\n\",\n \" This model gives 4089600 solutions and the following statistics:\\n\",\n \" - failures: 31264\\n\",\n \" - branches: 8241726\\n\",\n \" - WallTime: 23735 ms (note: without any printing of the solutions)\\n\",\n \"\\n\",\n \" This model was created by Hakan Kjellerstrand (hakank@gmail.com)\\n\",\n \" Also see my other Google CP Solver models:\\n\",\n \" http://www.hakank.org/google_or_tools/\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"import sys\\n\",\n \"from ortools.constraint_solver import pywrapcp\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# Create the solver.\\n\",\n \"solver = pywrapcp.Solver('Secret Santa problem')\\n\",\n \"\\n\",\n \"#\\n\",\n \"# data\\n\",\n \"#\\n\",\n \"family = [1, 1, 1, 1, 2, 3, 3, 3, 3, 3, 4, 4]\\n\",\n \"num_families = max(family)\\n\",\n \"n = len(family)\\n\",\n \"\\n\",\n \"#\\n\",\n \"# declare variables\\n\",\n \"#\\n\",\n \"x = [solver.IntVar(0, n - 1, 'x[%i]' % i) for i in range(n)]\\n\",\n \"\\n\",\n \"#\\n\",\n \"# constraints\\n\",\n \"#\\n\",\n \"solver.Add(solver.AllDifferent(x))\\n\",\n \"\\n\",\n \"# Can't be one own's Secret Santa\\n\",\n \"# Ensure that there are no fix-point in the array\\n\",\n \"for i in range(n):\\n\",\n \" solver.Add(x[i] != i)\\n\",\n \"\\n\",\n \"# No Secret Santa to a person in the same family\\n\",\n \"for i in range(n):\\n\",\n \" solver.Add(family[i] != solver.Element(family, x[i]))\\n\",\n \"\\n\",\n \"#\\n\",\n \"# solution and search\\n\",\n \"#\\n\",\n \"db = solver.Phase(x, solver.INT_VAR_SIMPLE, solver.INT_VALUE_SIMPLE)\\n\",\n \"\\n\",\n \"solver.NewSearch(db)\\n\",\n \"num_solutions = 0\\n\",\n \"while solver.NextSolution():\\n\",\n \" num_solutions += 1\\n\",\n \" print('x:', [x[i].Value() for i in range(n)])\\n\",\n \" print()\\n\",\n \"\\n\",\n \"print('num_solutions:', num_solutions)\\n\",\n \"print('failures:', solver.Failures())\\n\",\n \"print('branches:', solver.Branches())\\n\",\n \"print('WallTime:', solver.WallTime(), 'ms')\\n\",\n \"\\n\"\n ]\n }\n ],\n \"metadata\": {},\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"},"license":{"kind":"string","value":"apache-2.0"}}},{"rowIdx":46191,"cells":{"repo_name":{"kind":"string","value":"mohanprasath/Course-Work"},"path":{"kind":"string","value":"coursera/data_visualization_with_python/DV0101EN-3-5-1-Generating-Maps-in-Python-py-v2.0.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"46559"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"![](\\\"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\\\")
\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Exploring Datasets with *pandas* and Matplotlib\\n\",\n \"\\n\",\n \"Toolkits: This lab heavily relies on [*pandas*](http://pandas.pydata.org/) and [**Numpy**](http://www.numpy.org/) for data wrangling, analysis, and visualization. The primary plotting library we will explore in this lab is [**Folium**](https://github.com/python-visualization/folium/).\\n\",\n \"\\n\",\n \"Datasets: \\n\",\n \"\\n\",\n \"1. San Francisco Police Department Incidents for the year 2016 - [Police Department Incidents](https://data.sfgov.org/Public-Safety/Police-Department-Incidents-Previous-Year-2016-/ritf-b9ki) from San Francisco public data portal. Incidents derived from San Francisco Police Department (SFPD) Crime Incident Reporting system. Updated daily, showing data for the entire year of 2016. Address and location has been anonymized by moving to mid-block or to an intersection. \\n\",\n \"\\n\",\n \"2. Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml) from United Nation's website. The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. For this lesson, we will focus on the Canadian Immigration data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Downloading and Prepping Data \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Import Primary Modules:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np # useful for many scientific computing in Python\\n\",\n \"import pandas as pd # primary data structure library\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Introduction to Folium \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Folium is a powerful Python library that helps you create several types of Leaflet maps. The fact that the Folium results are interactive makes this library very useful for dashboard building.\\n\",\n \"\\n\",\n \"From the official Folium documentation page:\\n\",\n \"\\n\",\n \"> Folium builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the Leaflet.js library. Manipulate your data in Python, then visualize it in on a Leaflet map via Folium.\\n\",\n \"\\n\",\n \"> Folium makes it easy to visualize data that's been manipulated in Python on an interactive Leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing Vincent/Vega visualizations as markers on the map.\\n\",\n \"\\n\",\n \"> The library has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys. Folium supports both GeoJSON and TopoJSON overlays, as well as the binding of data to those overlays to create choropleth maps with color-brewer color schemes.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"#### Let's install **Folium**\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Folium** is not available by default. So, we first need to install it before we are able to import it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"!conda install -c conda-forge folium=0.5.0 --yes\\n\",\n \"import folium\\n\",\n \"\\n\",\n \"print('Folium installed and imported!')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Generating the world map is straigtforward in **Folium**. You simply create a **Folium** *Map* object and then you display it. What is attactive about **Folium** maps is that they are interactive, so you can zoom into any region of interest despite the initial zoom level. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# define the world map\\n\",\n \"world_map = folium.Map()\\n\",\n \"\\n\",\n \"# display world map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Go ahead. Try zooming in and out of the rendered map above.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"You can customize this default definition of the world map by specifying the centre of your map and the intial zoom level. \\n\",\n \"\\n\",\n \"All locations on a map are defined by their respective *Latitude* and *Longitude* values. So you can create a map and pass in a center of *Latitude* and *Longitude* values of **[0, 0]**. \\n\",\n \"\\n\",\n \"For a defined center, you can also define the intial zoom level into that location when the map is rendered. **The higher the zoom level the more the map is zoomed into the center**.\\n\",\n \"\\n\",\n \"Let's create a map centered around Canada and play with the zoom level to see how it affects the rendered map.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# define the world map centered around Canada with a low zoom level\\n\",\n \"world_map = folium.Map(location=[56.130, -106.35], zoom_start=4)\\n\",\n \"\\n\",\n \"# display world map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's create the map again with a higher zoom level\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# define the world map centered around Canada with a higher zoom level\\n\",\n \"world_map = folium.Map(location=[56.130, -106.35], zoom_start=8)\\n\",\n \"\\n\",\n \"# display world map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"As you can see, the higher the zoom level the more the map is zoomed into the given center.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Question**: Create a map of Mexico with a zoom level of 4.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"### type your answer here\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Double-click __here__ for the solution.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Another cool feature of **Folium** is that you can generate different map styles.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"### A. Stamen Toner Maps\\n\",\n \"\\n\",\n \"These are high-contrast B+W (black and white) maps. They are perfect for data mashups and exploring river meanders and coastal zones. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's create a Stamen Toner map of canada with a zoom level of 4.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# create a Stamen Toner map of the world centered around Canada\\n\",\n \"world_map = folium.Map(location=[56.130, -106.35], zoom_start=4, tiles='Stamen Toner')\\n\",\n \"\\n\",\n \"# display map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Feel free to zoom in and out to see how this style compares to the default one.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"### B. Stamen Terrain Maps\\n\",\n \"\\n\",\n \"These are maps that feature hill shading and natural vegetation colors. They showcase advanced labeling and linework generalization of dual-carriageway roads.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's create a Stamen Terrain map of Canada with zoom level 4.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# create a Stamen Toner map of the world centered around Canada\\n\",\n \"world_map = folium.Map(location=[56.130, -106.35], zoom_start=4, tiles='Stamen Terrain')\\n\",\n \"\\n\",\n \"# display map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Feel free to zoom in and out to see how this style compares to Stamen Toner and the default style.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"### C. Mapbox Bright Maps\\n\",\n \"\\n\",\n \"These are maps that quite similar to the default style, except that the borders are not visible with a low zoom level. Furthermore, unlike the default style where country names are displayed in each country's native language, *Mapbox Bright* style displays all country names in English.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's create a world map with this style.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# create a world map with a Mapbox Bright style.\\n\",\n \"world_map = folium.Map(tiles='Mapbox Bright')\\n\",\n \"\\n\",\n \"# display the map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Zoom in and notice how the borders start showing as you zoom in, and the displayed country names are in English.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Question**: Create a map of Mexico to visualize its hill shading and natural vegetation. Use a zoom level of 6.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"### type your answer here\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Double-click __here__ for the solution.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Maps with Markers \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's download and import the data on police department incidents using *pandas* `read_csv()` method.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Download the dataset and read it into a *pandas* dataframe:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"df_incidents = pd.read_csv('https://ibm.box.com/shared/static/nmcltjmocdi8sd5tk93uembzdec8zyaq.csv')\\n\",\n \"\\n\",\n \"print('Dataset downloaded and read into a pandas dataframe!')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's take a look at the first five items in our dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"df_incidents.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"So each row consists of 13 features:\\n\",\n \"> 1. **IncidntNum**: Incident Number\\n\",\n \"> 2. **Category**: Category of crime or incident\\n\",\n \"> 3. **Descript**: Description of the crime or incident\\n\",\n \"> 4. **DayOfWeek**: The day of week on which the incident occurred\\n\",\n \"> 5. **Date**: The Date on which the incident occurred\\n\",\n \"> 6. **Time**: The time of day on which the incident occurred\\n\",\n \"> 7. **PdDistrict**: The police department district\\n\",\n \"> 8. **Resolution**: The resolution of the crime in terms whether the perpetrator was arrested or not\\n\",\n \"> 9. **Address**: The closest address to where the incident took place\\n\",\n \"> 10. **X**: The longitude value of the crime location \\n\",\n \"> 11. **Y**: The latitude value of the crime location\\n\",\n \"> 12. **Location**: A tuple of the latitude and the longitude values\\n\",\n \"> 13. **PdId**: The police department ID\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's find out how many entries there are in our dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"df_incidents.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"So the dataframe consists of 150,500 crimes, which took place in the year 2016. In order to reduce computational cost, let's just work with the first 100 incidents in this dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# get the first 100 crimes in the df_incidents dataframe\\n\",\n \"limit = 100\\n\",\n \"df_incidents = df_incidents.iloc[0:limit, :]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's confirm that our dataframe now consists only of 100 crimes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"df_incidents.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Now that we reduced the data a little bit, let's visualize where these crimes took place in the city of San Francisco. We will use the default style and we will initialize the zoom level to 12. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# San Francisco latitude and longitude values\\n\",\n \"latitude = 37.77\\n\",\n \"longitude = -122.42\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# create map and display it\\n\",\n \"sanfran_map = folium.Map(location=[latitude, longitude], zoom_start=12)\\n\",\n \"\\n\",\n \"# display the map of San Francisco\\n\",\n \"sanfran_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Now let's superimpose the locations of the crimes onto the map. The way to do that in **Folium** is to create a *feature group* with its own features and style and then add it to the sanfran_map.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# instantiate a feature group for the incidents in the dataframe\\n\",\n \"incidents = folium.map.FeatureGroup()\\n\",\n \"\\n\",\n \"# loop through the 100 crimes and add each to the incidents feature group\\n\",\n \"for lat, lng, in zip(df_incidents.Y, df_incidents.X):\\n\",\n \" incidents.add_child(\\n\",\n \" folium.features.CircleMarker(\\n\",\n \" [lat, lng],\\n\",\n \" radius=5, # define how big you want the circle markers to be\\n\",\n \" color='yellow',\\n\",\n \" fill=True,\\n\",\n \" fill_color='blue',\\n\",\n \" fill_opacity=0.6\\n\",\n \" )\\n\",\n \" )\\n\",\n \"\\n\",\n \"# add incidents to map\\n\",\n \"sanfran_map.add_child(incidents)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"You can also add some pop-up text that would get displayed when you hover over a marker. Let's make each marker display the category of the crime when hovered over.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# instantiate a feature group for the incidents in the dataframe\\n\",\n \"incidents = folium.map.FeatureGroup()\\n\",\n \"\\n\",\n \"# loop through the 100 crimes and add each to the incidents feature group\\n\",\n \"for lat, lng, in zip(df_incidents.Y, df_incidents.X):\\n\",\n \" incidents.add_child(\\n\",\n \" folium.features.CircleMarker(\\n\",\n \" [lat, lng],\\n\",\n \" radius=5, # define how big you want the circle markers to be\\n\",\n \" color='yellow',\\n\",\n \" fill=True,\\n\",\n \" fill_color='blue',\\n\",\n \" fill_opacity=0.6\\n\",\n \" )\\n\",\n \" )\\n\",\n \"\\n\",\n \"# add pop-up text to each marker on the map\\n\",\n \"latitudes = list(df_incidents.Y)\\n\",\n \"longitudes = list(df_incidents.X)\\n\",\n \"labels = list(df_incidents.Category)\\n\",\n \"\\n\",\n \"for lat, lng, label in zip(latitudes, longitudes, labels):\\n\",\n \" folium.Marker([lat, lng], popup=label).add_to(sanfran_map) \\n\",\n \" \\n\",\n \"# add incidents to map\\n\",\n \"sanfran_map.add_child(incidents)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Isn't this really cool? Now you are able to know what crime category occurred at each marker.\\n\",\n \"\\n\",\n \"If you find the map to be so congested will all these markers, there are two remedies to this problem. The simpler solution is to remove these location markers and just add the text to the circle markers themselves as follows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# create map and display it\\n\",\n \"sanfran_map = folium.Map(location=[latitude, longitude], zoom_start=12)\\n\",\n \"\\n\",\n \"# loop through the 100 crimes and add each to the map\\n\",\n \"for lat, lng, label in zip(df_incidents.Y, df_incidents.X, df_incidents.Category):\\n\",\n \" folium.features.CircleMarker(\\n\",\n \" [lat, lng],\\n\",\n \" radius=5, # define how big you want the circle markers to be\\n\",\n \" color='yellow',\\n\",\n \" fill=True,\\n\",\n \" popup=label,\\n\",\n \" fill_color='blue',\\n\",\n \" fill_opacity=0.6\\n\",\n \" ).add_to(sanfran_map)\\n\",\n \"\\n\",\n \"# show map\\n\",\n \"sanfran_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"The other proper remedy is to group the markers into different clusters. Each cluster is then represented by the number of crimes in each neighborhood. These clusters can be thought of as pockets of San Francisco which you can then analyze separately.\\n\",\n \"\\n\",\n \"To implement this, we start off by instantiating a *MarkerCluster* object and adding all the data points in the dataframe to this object.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from folium import plugins\\n\",\n \"\\n\",\n \"# let's start again with a clean copy of the map of San Francisco\\n\",\n \"sanfran_map = folium.Map(location = [latitude, longitude], zoom_start = 12)\\n\",\n \"\\n\",\n \"# instantiate a mark cluster object for the incidents in the dataframe\\n\",\n \"incidents = plugins.MarkerCluster().add_to(sanfran_map)\\n\",\n \"\\n\",\n \"# loop through the dataframe and add each data point to the mark cluster\\n\",\n \"for lat, lng, label, in zip(df_incidents.Y, df_incidents.X, df_incidents.Category):\\n\",\n \" folium.Marker(\\n\",\n \" location=[lat, lng],\\n\",\n \" icon=None,\\n\",\n \" popup=label,\\n\",\n \" ).add_to(incidents)\\n\",\n \"\\n\",\n \"# display map\\n\",\n \"sanfran_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Notice how when you zoom out all the way, all markers are grouped into one cluster, *the global cluster*, of 100 markers or crimes, which is the total number of crimes in our dataframe. Once you start zooming in, the *global cluster* will start breaking up into smaller clusters. Zooming in all the way will result in individual markers.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Choropleth Maps \\n\",\n \"\\n\",\n \"A `Choropleth` map is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map, such as population density or per-capita income. The choropleth map provides an easy way to visualize how a measurement varies across a geographic area or it shows the level of variability within a region. Below is a `Choropleth` map of the US depicting the population by square mile per state.\\n\",\n \"\\n\",\n \"![](\\\"https://ibm.box.com/shared/static/2kzaknzdf6crt3n5rx6haskg3wiaklxl.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Now, let's create our own `Choropleth` map of the world depicting immigration from various countries to Canada.\\n\",\n \"\\n\",\n \"Let's first download and import our primary Canadian immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\\n\",\n \"```\\n\",\n \"!conda install -c anaconda xlrd --yes\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Download the dataset and read it into a *pandas* dataframe:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"df_can = pd.read_excel('https://ibm.box.com/shared/static/lw190pt9zpy5bd1ptyg2aw15awomz9pu.xlsx',\\n\",\n \" sheet_name='Canada by Citizenship',\\n\",\n \" skiprows=range(20),\\n\",\n \" skipfooter=2)\\n\",\n \"\\n\",\n \"print('Data downloaded and read into a dataframe!')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's take a look at the first five items in our dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"df_can.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's find out how many entries there are in our dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# print the dimensions of the dataframe\\n\",\n \"print(df_can.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to *Introduction to Matplotlib and Line Plots* and *Area Plots, Histograms, and Bar Plots* notebooks for a detailed description of this preprocessing.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# clean up the dataset to remove unnecessary columns (eg. REG) \\n\",\n \"df_can.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\\n\",\n \"\\n\",\n \"# let's rename the columns so that they make sense\\n\",\n \"df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\\n\",\n \"\\n\",\n \"# for sake of consistency, let's also make all column labels of type string\\n\",\n \"df_can.columns = list(map(str, df_can.columns))\\n\",\n \"\\n\",\n \"# add total column\\n\",\n \"df_can['Total'] = df_can.sum(axis=1)\\n\",\n \"\\n\",\n \"# years that we will be using in this lesson - useful for plotting later on\\n\",\n \"years = list(map(str, range(1980, 2014)))\\n\",\n \"print ('data dimensions:', df_can.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's take a look at the first five items of our cleaned dataframe.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"df_can.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"In order to create a `Choropleth` map, we need a GeoJSON file that defines the areas/boundaries of the state, county, or country that we are interested in. In our case, since we are endeavoring to create a world map, we want a GeoJSON that defines the boundaries of all world countries. For your convenience, we will be providing you with this file, so let's go ahead and download it. Let's name it **world_countries.json**.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# download countries geojson file\\n\",\n \"!wget --quiet https://ibm.box.com/shared/static/cto2qv7nx6yq19logfcissyy4euo8lho.json -O world_countries.json\\n\",\n \" \\n\",\n \"print('GeoJSON file downloaded!')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Now that we have the GeoJSON file, let's create a world map, centered around **[0, 0]** *latitude* and *longitude* values, with an intial zoom level of 2, and using *Mapbox Bright* style.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"world_geo = r'world_countries.json' # geojson file\\n\",\n \"\\n\",\n \"# create a plain world map\\n\",\n \"world_map = folium.Map(location=[0, 0], zoom_start=2, tiles='Mapbox Bright')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And now to create a `Choropleth` map, we will use the *choropleth* method with the following main parameters:\\n\",\n \"\\n\",\n \"1. geo_data, which is the GeoJSON file.\\n\",\n \"2. data, which is the dataframe containing the data.\\n\",\n \"3. columns, which represents the columns in the dataframe that will be used to create the `Choropleth` map.\\n\",\n \"4. key_on, which is the key or variable in the GeoJSON file that contains the name of the variable of interest. To determine that, you will need to open the GeoJSON file using any text editor and note the name of the key or variable that contains the name of the countries, since the countries are our variable of interest. In this case, **name** is the key in the GeoJSON file that contains the name of the countries. Note that this key is case_sensitive, so you need to pass exactly as it exists in the GeoJSON file.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# generate choropleth map using the total immigration of each country to Canada from 1980 to 2013\\n\",\n \"world_map.choropleth(\\n\",\n \" geo_data=world_geo,\\n\",\n \" data=df_can,\\n\",\n \" columns=['Country', 'Total'],\\n\",\n \" key_on='feature.properties.name',\\n\",\n \" fill_color='YlOrRd', \\n\",\n \" fill_opacity=0.7, \\n\",\n \" line_opacity=0.2,\\n\",\n \" legend_name='Immigration to Canada'\\n\",\n \")\\n\",\n \"\\n\",\n \"# display map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"As per our `Choropleth` map legend, the darker the color of a country and the closer the color to red, the higher the number of immigrants from that country. Accordingly, the highest immigration over the course of 33 years (from 1980 to 2013) was from China, India, and the Philippines, followed by Poland, Pakistan, and interestingly, the US.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Notice how the legend is displaying a negative boundary or threshold. Let's fix that by defining our own thresholds and starting with 0 instead of -6,918!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"world_geo = r'world_countries.json'\\n\",\n \"\\n\",\n \"# create a numpy array of length 6 and has linear spacing from the minium total immigration to the maximum total immigration\\n\",\n \"threshold_scale = np.linspace(df_can['Total'].min(),\\n\",\n \" df_can['Total'].max(),\\n\",\n \" 6, dtype=int)\\n\",\n \"threshold_scale = threshold_scale.tolist() # change the numpy array to a list\\n\",\n \"threshold_scale[-1] = threshold_scale[-1] + 1 # make sure that the last value of the list is greater than the maximum immigration\\n\",\n \"\\n\",\n \"# let Folium determine the scale.\\n\",\n \"world_map = folium.Map(location=[0, 0], zoom_start=2, tiles='Mapbox Bright')\\n\",\n \"world_map.choropleth(\\n\",\n \" geo_data=world_geo,\\n\",\n \" data=df_can,\\n\",\n \" columns=['Country', 'Total'],\\n\",\n \" key_on='feature.properties.name',\\n\",\n \" threshold_scale=threshold_scale,\\n\",\n \" fill_color='YlOrRd', \\n\",\n \" fill_opacity=0.7, \\n\",\n \" line_opacity=0.2,\\n\",\n \" legend_name='Immigration to Canada',\\n\",\n \" reset=True\\n\",\n \")\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Much better now! Feel free to play around with the data and perhaps create `Choropleth` maps for individuals years, or perhaps decades, and see how they compare with the entire period from 1980 to 2013.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"### Thank you for completing this lab!\\n\",\n \"\\n\",\n \"This notebook was created by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab interesting and educational. Feel free to contact me if you have any questions!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"This notebook is part of a course on **Coursera** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DV0101EN_Coursera_Week3_LAB2).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"Copyright © 2018 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/).\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n },\n \"widgets\": {\n \"state\": {},\n \"version\": \"1.1.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"gpl-3.0"}}},{"rowIdx":46192,"cells":{"repo_name":{"kind":"string","value":"crdguez/mat4ac"},"path":{"kind":"string","value":"notebooks/matematicas con python/.ipynb_checkpoints/ttm1819-checkpoint.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"22932"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n }\n },\n \"source\": [\n \"### Instrucciones\\n\",\n \"\\n\",\n \"requires a copy of reveal.js (version 3.x)\\n\",\n \"\\n\",\n \"```\\n\",\n \"jupyter nbconvert --to slides --SlidesExporter.reveal_theme=sky ttm1819.ipynb\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[NbConvertApp] Converting notebook ttm1819.ipynb to slides\\n\",\n \"[NbConvertApp] Writing 283046 bytes to ttm1819.slides.html\\n\"\n ]\n }\n ],\n \"source\": [\n \"!jupyter nbconvert --to slides --SlidesExporter.reveal_theme=white --SlidesExporter.reveal_scroll=True ttm1819.ipynb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"# Taller de Talento Matemático \\n\",\n \"\\n\",\n \"![](\\\"files/img/ttm_logo.jpg\\\")
\\n\",\n \"\\n\",\n \"## Programando la Geometría con BlocksCAD\\n\",\n \"\\n\",\n \"![](\\\"files/img/attribution-share-alike-creative-commons-license.png\\\")
\\n\",\n \"\\n\",\n \"\\n\",\n \"**Página Web:** [https://mat3d.github.io/](https://mat3d.github.io/)\\n\",\n \"\\n\",\n \"**Documentación:** [https://github.com/mat3d](https://github.com/mat3d)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Introducción\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"* Saludo y presentación\\n\",\n \" - **Pablo Beltrán Pellicer** *[@pbeltranp](https://twitter.com/pbeltranp)*\\n\",\n \" - **Carlos Rodríguez Jaso** *[@es3a10](https://twitter.com/es3a10)*\\n\",\n \"* Objetivo de la sesión:\\n\",\n \" - Iniciarse en las posibilidades de [BlocksCAD](https://www.blockscad3d.com/) desde un punto de vista matemático\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"# Introducción a [BlocksCAD](https://www.blockscad3d.com/)\\n\",\n \"\\n\",\n \"**BlocksCAD** es un programa para modelar objetos en 3D sin necesidad de tener elevados conocimientos de programación.\\n\",\n \"\\n\",\n \"La forma más sencilla de trabajar es de manera online a través de su web: [https://www.blockscad3d.com/](https://www.blockscad3d.com/)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"## El entorno\\n\",\n \"\\n\",\n \"El entorno de trabajo de BlocksCAD lo podemos dividir en tres partes:\\n\",\n \"\\n\",\n \"![](\\\"files/img/zonas_trabajo.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"## Aprendiendo lo básico\\n\",\n \"\\n\",\n \"### Formas 3D: \\n\",\n \"\\n\",\n \"Son los objetos primitivos que podemos utilizar:\\n\",\n \"\\n\",\n \"#### Cubos: Mi primer objeto en 3D\\n\",\n \"\\n\",\n \"Dentro del bloque *Formas 3d*, arrastramos el bloque \\n\",\n \"![](\\\"./img/bloque_cubo.png\\\")
y lo llevamos a la área de programa. Al renderizar (botón **Hacer**) ya tenemos nuestro primer objeto con BlocksCAD.\\n\",\n \"\\n\",\n \"![](\\\"./img/cube10x10x10c.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Cubos: Mi primer objeto en 3D\\n\",\n \"\\n\",\n \"Observa qué pasa cuando modificas los parámetros:\\n\",\n \"\\n\",\n \"![](\\\"./img/cubo_bs.gif\\\")
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Formas 3D: Esferas\\n\",\n \"\\n\",\n \"Dentro del bloque *Formas 3D* podemos arrastrar el bloque ![](\\\"./img/bloque_esfera.png\\\")
\\n\",\n \"\\n\",\n \"Al renderizarlo obtendremos una esfera de *10mm* de radio:\\n\",\n \"\\n\",\n \"![](\\\"./img/esfera10.png\\\")
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Formas 3D: Cilindros y mucho más\\n\",\n \"\\n\",\n \"Dentro del bloque *Formas 3D*, tenemos el bloque ![](\\\"./img/bloque_cilindro.png\\\")
\\n\",\n \"\\n\",\n \"Explicación de algunos parámetros:\\n\",\n \"\\n\",\n \"- **radio1**, es el radio de la base inferior de la figura a modelar\\n\",\n \"- **candado**, por defecto aparece cerrado, y esto hace que el parámetro **radio2** herede el valor de **radio1**\\n\",\n \"- **radio2**, radio de la base superior de la figura. Cuando coincida con **radio1** tendremos un cilindro, y cuando no, tendremos un **tronco de cono** o un **cono** si ponemos que el radio es cero\\n\",\n \"\\n\",\n \"**Ejemplos:**\\n\",\n \"\\n\",\n \"| Bloque | Renderizado |\\n\",\n \"| :---------------------------------------: | :---------------------------------------------: |\\n\",\n \"| ![](\\\"./img/bloque11.png\\\")
| ![](\\\"./img/cilindro5_5_10c.png\\\")
|\\n\",\n \"| ![](\\\"./img/bloque12.png\\\")
| ![](\\\"./img/tronco10_5_20nc.png\\\")
|\\n\",\n \"| ![](\\\"./img/bloque13.png\\\")
| ![](\\\"./img/cono10_20nc.png\\\")
|\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"BlocksCAD interpreta la base del cilindro como un polígono regular de **\\\"muchos\\\"** lados. Podemos generar un **prisma** de base regular modificando ese \\\"muchos\\\" con el bloque ![](\\\"./img/bloque_aristas.png\\\")
que aparece en *Transformaciones*. Mira este ejemplo que compara un cilindro con un prisma de base triangular:\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"![](\\\"./img/cilindro_nlados.png\\\")
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Transformaciones\\n\",\n \"\\n\",\n \"#### Traslaciones \\n\",\n \"Dentro del bloque *Formas 3D*, arrastramos el bloque ![](\\\"./img/bloque_traslacion.png\\\")
\\n\",\n \"En este caso, los parámetros **X**, **Y** y **Z**, son las coordenadas del vector de traslación.\\n\",\n \"\\n\",\n \"#### Rotaciones\\n\",\n \"Bloque ![](\\\"./img/bloque_rotacion.png\\\")
\\n\",\n \"Los parámetros **X**, **Y** y **Z**, son los grados a rotar en los diferentes ejes.\\n\",\n \"\\n\",\n \"**Ejemplos:**\\n\",\n \"\\n\",\n \"| Ejemplo | Bloque | Renderizado |\\n\",\n \"| :---------------------------------------------------------: | :----------------------------- | :-------------------------------: |\\n\",\n \"| Prisma de 10x20x30 centrado y trasladado 30 en la dirección X, 30 en la Y y 40 en la Z | ![](\\\"./img/bloque21.png\\\")
| ![](\\\"./img/traslacion1.png\\\")
|\\n\",\n \"| Ejercicio anterior rotado 45º en el eje X | ![](\\\"./img/bloque22.png\\\")
| ![](\\\"./img/rotacion1.png\\\")
|\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Ops de Conjuntos: Operaciones lógicas \\n\",\n \"\\n\",\n \"#### Unión: Pegando objetos\\n\",\n \"\\n\",\n \"Para unir dos o más objetos tenemos que seleccionar el bloque ![](\\\"./img/bloque_union.png\\\")
\\n\",\n \"\\n\",\n \"#### Diferencia: Haciendo agujeros\\n\",\n \"\\n\",\n \"Con la diferencia lo que hacemos es hacer \\\"agujeros\\\". La forma de trabajar con el bloque es similar a la unión.\\n\",\n \"\\n\",\n \"**Ejemplos:**\\n\",\n \"\\n\",\n \"| Bloque | Renderizado |\\n\",\n \"| :----------------------------: | :-----------------------------: |\\n\",\n \"| ![](\\\"./img/bloque31.png\\\")
| ![](\\\"./img/ejemplo31.png\\\")
|\\n\",\n \"| ![](\\\"./img/bloque32.png\\\")
| ![](\\\"./img/ejemplo32.png\\\")
|\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"# ¡Ya conoces lo básico para poder trabajar!\\n\",\n \"\\n\",\n \"![](\\\"./img/18614-NRQ2AH.jpg\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"# Desafío 1: \\n\",\n \"\\n\",\n \"Entrénate con BlocksCASD intentando modelar la siguiente figura. No tiene que ser exactamente igual pero sí parecida:\\n\",\n \"\\n\",\n \"| Vista 1 | Vista 2 |\\n\",\n \"| :--------------------------: | :--------------------------: |\\n\",\n \"| ![](\\\"./img/des1_1.png\\\")
| ![](\\\"./img/des1_2.png\\\")
|\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"Tienes una posible solución en https://www.blockscad3d.com/community/projects/82576\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"# Desafío 2: Estrella Mudéjar\\n\",\n \"\\n\",\n \"| Actividad |\\n\",\n \"| -------------------------------------------- |\\n\",\n \"| Estrella mudéjar sencilla |\\n\",\n \"| \\\"Estrella mudéjar\\\" sobre un hexágono regular |\\n\",\n \"| \\\"Estrella mudéjar\\\" paramétrica sencilla |\\n\",\n \"| \\\"Estrella mudéjar\\\" paramétrica avanzada |\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Estrella mudéjar sencilla\\n\",\n \"\\n\",\n \"#### Modelo\\n\",\n \"Para empezar, vamos a realizar **una sencilla estrella mudejar** con dos prismas cuadrados de dimensiones 10x10x5 (eje_x, eje_y, eje_z o para entendernos, ancho por largo por alto):\\n\",\n \"\\n\",\n \"![](\\\"files/img/r_est1.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Cuestiones iniciales\\n\",\n \"\\n\",\n \"* ¿Existe alguna **relación** entre el **lado de un cuadrado y su apotema**?\\n\",\n \"* ¿Crees que para otros polígonos regulares va a seguir existiendo una **relación entre el lado y la apotema**?\\n\",\n \"* ¿Sabes algo de **trigonometría**? En caso afirmativo, las preguntas anteriores deberían resultarte sencillas. En caso contrario, **no te preocupes** la parte teórica la facilitaremos para que puedas hacer el modelado si te ves desbordado.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Respuestas a las preguntas\\n\",\n \"\\n\",\n \"* La **apotema** es la mitad del lado. ![](\\\"./img/apotema_cuadrado.png\\\")
\\n\",\n \"\\n\",\n \"* Sí, siempre va a haber relación. La respuesta está en la **semejanza de triángulos rectángulos** y **la trigonometría**. Esto nos permitirá calcular bien el radio, la apotema o el lado del polígono regular siempre que nos den uno de ellos. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Modelado en BlocksCAD\\n\",\n \"\\n\",\n \"| Paso | Código | Renderizado |\\n\",\n \"| ---------------------------------| --------------------------------------------------| ------------------------------------------ |\\n\",\n \"| Insertamos un cubo de 10x10x5 centrado en origen | ![](\\\"./img/cubo.png\\\")
| ![](\\\"./img/r_cubo.png\\\")
|\\n\",\n \"| Insertamos otro igual pero girado 45º sobre el plano XY (girar el eje Z) | ![](\\\"./img/cubo45.png\\\")
| ![](\\\"./img/r_cubo45.png\\\")
|\\n\",\n \"| Juntamos los dos objetos para formar un único objeto | ![](\\\"./img/est1.png\\\")
| ![](\\\"./img/r_est1.png\\\")
|\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"Tienes la solución en https://www.blockscad3d.com/community/projects/266387\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## \\\"Estrella mudéjar\\\" sobre un hexágono regular\\n\",\n \"\\n\",\n \"#### Modelo\\n\",\n \"Ahora se propone realizar una especie de estrella mudéjar modificada a partir de un hexágono regular de radio y altura que quieras\\n\",\n \"\\n\",\n \"![](\\\"./img/est_hex.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Cuestiones previas\\n\",\n \"\\n\",\n \"* ¿Hacia qué objeto tiende un polígono regular cuando aumentamos el **número de lados** al polígono?\\n\",\n \"* ¿Cuánto tiene que girar el prisma hexagonal superpuesto para generar las puntas en mitad de las aristas del prisma original?¿Puedes dar una **fórmula general** que vaya en función del número de lados y que por tanto sirva para el cuadrado o el hexágono?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Consideraciones a las cuestiones\\n\",\n \"\\n\",\n \"* Al aumentar el número de lados, el **polígono** se va acercando a un **círculo** como se puede ver en la siguiente animación:\\n\",\n \"\\n\",\n \" ![](\\\"./img/lim_circulo.gif\\\")
\\n\",\n \"\\n\",\n \"* **BlocksCAD** interpreta los cilindros de esta manera, como un **prisma** de base regular **con un número de lados elevado**. Podemos convertir un cilindrode BlocksCAD en un prisma modificando el número de lados:\\n\",\n \"\\n\",\n \"\\n\",\n \"* La **rotación** que hay que hacer es la mitad del ángulo central: $\\\\alpha=\\\\frac{180}{nlados}$\\n\",\n \"\\n\",\n \"* **Importante:** Al usar el bloque cilindro modificado por el número de aristas, el parámetro que le damos es el radio del polígono regular y no la longitud de la arista (o lado del polígono regular)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Modelado en BlocksCAD\\n\",\n \"\\n\",\n \"* Se propone como ejercicio su modelado. \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"Tienes una posible solución en https://www.blockscad3d.com/community/projects/272240\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## \\\"Estrella mudéjar\\\" paramétrica sencilla\\n\",\n \"#### Modelo\\n\",\n \"Se pide modificar la estrella anterior para que acepte los siguientes parámetros o variables:\\n\",\n \"* **Número de lados** del polígono regular que genera la estrella\\n\",\n \"* **Longitud** del radio del polígono\\n\",\n \"* Longitud del radio del polígono que genera el **hueco**\\n\",\n \"\\n\",\n \"**Ejemplo de modelo:** Estrella de David generada con triángulos de radios 10 y 8, exterior e interior respectivamente. \\n\",\n \"\\n\",\n \"![](\\\"./img/est_david.png\\\")
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Modelado en BlocksCAD\\n\",\n \"\\n\",\n \"* Se propone como ejercicio su modelado. \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"Tienes una posible solución en https://www.blockscad3d.com/community/projects/278006\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## \\\"Estrella mudéjar\\\" paramétrica avanzada\\n\",\n \"\\n\",\n \"#### Modelo\\n\",\n \"Vamos a añadir un poco de complejidad al programa. En las estrellas anteriores todas las puntas son iguales. Ahora en lugar de que la mitad de las puntas las genere el prisma poligonal girado se pide que sean los generados por un prisma cuadrado con la diagonal superpuesta sobre la arista (el lado del cuadrado que sea 1/3 de la arista).\\n\",\n \"\\n\",\n \"**Ejemplo:** Para 8 lados tiene que quedar algo así\\n\",\n \"![](\\\"./img/estpar.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Explicación del Modelo\\n\",\n \"Como una imagen vale más que mil palabras:\\n\",\n \"\\n\",\n \"![](\\\"./img/hex_cuad.png\\\")
\\n\",\n \"\\n\",\n \"Se pide por tanto modelar una estrella que tenga como parámetros:\\n\",\n \"\\n\",\n \"* **Número de lados**\\n\",\n \"* Longitud del **lado**\\n\",\n \"* **Grosor** de la estrella: Altura y anchura del contorno. Opcional: Si se quiere se pueden hacer dos parámetros.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Cuestiones previas\\n\",\n \"\\n\",\n \"* Dado un polígono regular: ¿Existe alguna **fórmula general** que **relacione** la longitud del **lado** con el **radio**?¿Y alguna relación entre la **apotema** y el **lado**?\\n\",\n \"* El cuadrado que genera las puntas, **¿Cuánto hay que trasladarlo?**\\n\",\n \"* ¿Cuántas **rotaciones** hay que hacer del cuadrado para generar todas las puntas?¿Qué ángulo entre ellos?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Respuestas a las cuestiones previas\\n\",\n \" ![](\\\"./img/semi_angulo_central.gif\\\")
\\n\",\n \"\\n\",\n \" * El **triángulo** formado por el centro del polígono, el punto medio de un lado y un vértice adyacente es **siempre rectángulo**. El ángulo correspondiente al centro del polígono es **la mitad del ángulo central**: $$\\\\alpha=\\\\dfrac{\\\\frac{360}{n_{lados}}}{2}$$\\n\",\n \"\\n\",\n \" * **Fijado el número de lados** del polígono regular, aunque varíe el tamaño del lado, los triángulos que salgan serán semejantes. **Al ser semejantes los lados son proporcionales**, o dicho de otra forma, **la razón entre lados se mantiene constante y dependen exclusivamente del ángulo** que se apoya en el centro del polígono: Son las **razones trigonométricas**. La principales son:\\n\",\n \"\\n\",\n \"| Razón trigonométrica | Aplicación en el polígono regular |\\n\",\n \"| ------------------------------------------- | --------------------------------------- |\\n\",\n \"| **seno** = lado opuesto / hipotenusa | $\\\\sin{\\\\alpha}=\\\\frac{semilado}{radio}$ |\\n\",\n \"| **coseno** = lado contiguo / hipotenusa | $\\\\cos{\\\\alpha}=\\\\frac{apotema}{radio}$ |\\n\",\n \"| **tangente** = lado opuesto / lado contiguo | $\\\\tan{\\\\alpha}=\\\\frac{semilado}{apotema}$ |\\n\",\n \" \\n\",\n \"\\n\",\n \"* El cuadrado hay que trasladarlo **la apotema del polígono**\\n\",\n \"* Habrá que hacer **tantas puntas como lados** y habrá que **rotar la mitad del ángulo central**: $$\\\\alpha=\\\\frac{180}{nlados}$$\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Ejemplo de aplicación:** Determina el radio y la apotema para un **pentágono regular de lado 6**:\\n\",\n \"\\n\",\n \"![](\\\"./img/pent.png\\\")
\\n\",\n \"\\n\",\n \"\\n\",\n \"* **radio = (6/2)/seno(180/5)**\\n\",\n \"* **apotema = (6/2)/tan(180/5)**\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Modelado en BlocksCAD\\n\",\n \"\\n\",\n \"* Se propone como ejercicio su modelado. \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"Tienes una posible solución en https://www.blockscad3d.com/community/projects/267149\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## ¡Gracias por vuestra presencia y a seguir modelando con BlocksCAD!\\n\",\n \"\\n\",\n \"Podéis seguir trabajando en vuestra casa y difundir vuestros progresos en:\\n\",\n \"\\n\",\n \"\\n\",\n \"[#ttmestrellamudejar](https://twitter.com/search?f=tweets&q=%23ttmestrellamudejar&src=typd)\\n\",\n \"\\n\",\n \"\\n\",\n \"**Otros enlaces que pueden ser interesantes:**\\n\",\n \"\\n\",\n \"* [Tutoriales de BlocksCAD](https://www.youtube.com/channel/UCovK2cRIjoaZNzRwpQP2sFg/playlists) oficiales\\n\",\n \"* [Proyecto RepRap](https://reprap.org/wiki/RepRap) sobre impresoras 3D libres\\n\",\n \"* [Proyecto Clone Wars](https://www.reprap.org/wiki/Proyecto_Clone_Wars) , subproyecto de RepRap en castellano\"\n ]\n }\n ],\n \"metadata\": {\n \"celltoolbar\": \"Slideshow\",\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"gpl-3.0"}}},{"rowIdx":46193,"cells":{"repo_name":{"kind":"string","value":"maojrs/riemann_book"},"path":{"kind":"string","value":"FV_compare.ipynb"},"copies":{"kind":"string","value":"3"},"size":{"kind":"string","value":"22022"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Finite volume discretizations with approximate Riemann solvers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the previous few chapters we introduced several approximate Riemann solvers for different nonlinear conservation laws and showed some comparisons of the approximate solution to a single Riemann problem with the true solution, where there often appear to be significant differences. However, their intended use is as a building block for finite volume methods such as Godunov's method or high-resolution extensions. \\n\",\n \"\\n\",\n \"How do these solvers impact the solution accuracy when used within a finite volume discretization? To investigate, we will use them within [PyClaw](http://www.clawpack.org/pyclaw/) to solve several test problems.\\n\",\n \"\\n\",\n \"In particular, for both the shallow water equations and the Euler equations, we will apply the numerical method to solve dam break and shock tube problems, which are themselves Riemann problems and so we can compute the exact solution, but once discretized require the solution of a different set of Riemann problems at each cell interface every time step. \\n\",\n \"\\n\",\n \"For the Euler equations we will also consider the Woodward-Colella blast wave problem. The initial data consists of two Riemann problems, with resulting shock waves of differing strengths. These shock waves later interact with each other.\\n\",\n \" \\n\",\n \"In this chapter we include extensive sections of code in the notebook. This is meant to more easily allow the reader to use these as templates for setting up other problems in PyClaw. For the code in this chapter we use approximate Riemann solvers from PyClaw that can be found in these files:\\n\",\n \"\\n\",\n \"- [euler_1D_py.py,](https://github.com/clawpack/riemann/blob/FA16/riemann/euler_1D_py.py)\\n\",\n \"- [shallow_roe_with_efix_1D.](https://github.com/clawpack/riemann/blob/FA16/riemann/shallow_1D_py.py)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"hide\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"hide\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"%config InlineBackend.figure_format = 'svg'\\n\",\n \"import numpy as np\\n\",\n \"from exact_solvers import euler\\n\",\n \"from clawpack import riemann\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from ipywidgets import interact\\n\",\n \"from ipywidgets import widgets\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Shallow water equations\\n\",\n \"\\n\",\n \"We compare results obtained with using the high-resolution wave propagation method implemented in PyClaw (with limiters to avoid nonphysical oscillations). We compare the results obtained when combined with two different approximate Riemann solvers: the Roe solver and HLLE.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from exact_solvers import shallow_water as sw\\n\",\n \"from clawpack import riemann\\n\",\n \"from clawpack.riemann.shallow_roe_with_efix_1D_constants import depth, momentum, num_eqn\\n\",\n \"\\n\",\n \"def setup(riemann_solver='roe',N=20,IC='dam-break'):\\n\",\n \"\\n\",\n \" from clawpack import pyclaw\\n\",\n \"\\n\",\n \" if riemann_solver.lower() == 'roe':\\n\",\n \" rs = riemann.shallow_roe_with_efix_1D\\n\",\n \" elif riemann_solver.lower() == 'hlle':\\n\",\n \" rs = riemann.shallow_hlle_1D\\n\",\n \" \\n\",\n \" solver = pyclaw.ClawSolver1D(rs)\\n\",\n \" \\n\",\n \" solver.bc_lower[0] = pyclaw.BC.extrap\\n\",\n \" solver.bc_upper[0] = pyclaw.BC.extrap\\n\",\n \"\\n\",\n \" xlower = -5.0\\n\",\n \" xupper = 5.0\\n\",\n \" x = pyclaw.Dimension(xlower,xupper,N,name='x')\\n\",\n \" domain = pyclaw.Domain(x)\\n\",\n \" state = pyclaw.State(domain,num_eqn)\\n\",\n \"\\n\",\n \" # Gravitational constant\\n\",\n \" state.problem_data['grav'] = 1.0\\n\",\n \" state.problem_data['dry_tolerance'] = 1e-3\\n\",\n \" state.problem_data['sea_level'] = 0.0\\n\",\n \" \\n\",\n \" xc = state.grid.x.centers\\n\",\n \"\\n\",\n \" x0=0.\\n\",\n \"\\n\",\n \" hl = 10.\\n\",\n \" ul = 0.\\n\",\n \" hr = 0.5\\n\",\n \" ur = 0.\\n\",\n \" state.q[depth,:] = hl * (xc <= x0) + hr * (xc > x0)\\n\",\n \" state.q[momentum,:] = hl*ul * (xc <= x0) + hr*ur * (xc > x0)\\n\",\n \"\\n\",\n \" claw = pyclaw.Controller()\\n\",\n \" claw.keep_copy = True\\n\",\n \" claw.output_format = None\\n\",\n \" claw.tfinal = 1.0\\n\",\n \" claw.solution = pyclaw.Solution(state,domain)\\n\",\n \" claw.solver = solver\\n\",\n \"\\n\",\n \" return claw\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"N = 40 # number of grid cells to use\\n\",\n \"q_l = [10,0]\\n\",\n \"q_r = [0.5,0]\\n\",\n \"\\n\",\n \"# Roe solution\\n\",\n \"roe_sw = setup(riemann_solver='roe',N=N)\\n\",\n \"roe_sw.verbosity = 0\\n\",\n \"status = roe_sw.run()\\n\",\n \"xc_sw = roe_sw.grid.x.centers\\n\",\n \"\\n\",\n \"# HLLE solution\\n\",\n \"hlle_sw = setup(riemann_solver='hlle',N=N)\\n\",\n \"hlle_sw.verbosity = 0\\n\",\n \"status = hlle_sw.run()\\n\",\n \"\\n\",\n \"# Exact solution\\n\",\n \"xc_exact_sw = np.linspace(-5,5,2000)\\n\",\n \"states_sw, speeds_sw, reval_sw, wave_types_sw = \\\\\\n\",\n \" sw.exact_riemann_solution(q_l, q_r)\\n\",\n \"def plot_frame(i):\\n\",\n \" t = roe_sw.frames[i].t+1.e-13\\n\",\n \" fig, ax = plt.subplots(2,1, sharex=True, figsize=(8,5))\\n\",\n \" variablenames = [\\\"Depth\\\", \\\"Momentum\\\"]\\n\",\n \" variables = [depth, momentum]\\n\",\n \" ylims = [[-1,12], [-5,12]]\\n\",\n \" plt.subplots_adjust(hspace=0)\\n\",\n \" ax[0].title.set_text('Solutions at t={:.2f}'.format(t))\\n\",\n \" ax[0].set_xlim((-5,5))\\n\",\n \" ax[1].set(xlabel = 'x')\\n\",\n \" for j, variable in enumerate(variables):\\n\",\n \" ax[j].set_ylim(ylims[j])\\n\",\n \" ax[j].plot(xc_exact_sw,reval_sw(xc_exact_sw/(t+1.e-16))[j],'-k',lw=1)\\n\",\n \" ax[j].plot(xc_sw, hlle_sw.frames[i].q[variable,:],'-ob',lw=2,markersize=4)\\n\",\n \" ax[j].plot(xc_sw, roe_sw.frames[i].q[variable,:],'-or',lw=0.5,markersize=3)\\n\",\n \" ax[j].legend(['Exact','HLLE','Roe'],loc='best')\\n\",\n \" ax[j].set(ylabel=variablenames[j])\\n\",\n \" plt.show()\\n\",\n \" \\n\",\n \"interact(plot_frame, i=widgets.IntSlider(min=0,max=10,value=5));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Clearly, both solvers give very similar results for this problem. They converge to the same solution, as you can verify by increasing the value of $N$ above in the live notebook. You can also try varying the left and right states.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Euler equations\\n\",\n \"\\n\",\n \"We perform a similar test for the Euler equations with shock tube initial data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Sod shock tube problem\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We first consider the classic shocktube problem proposed by Sod and already discussed in [Euler](Euler.ipynb). This is a particular Riemann problem in which the initial velocity is zero on both sides of a discontinuity in pressure and/or density of a gas, and so the exact Riemann solver for the Euler equations would provide the exact solution for all time, consisting of a right-going shock, a left-going rarefaction, and an intermediate contact discontinuity.\\n\",\n \"\\n\",\n \"In the numerical experiments done in this notebook, we use this initial data for a more general finite volume method that could be used to approximate the solution for any initial data. In the first time step there is a single cell interface with nontrivial Riemann data, but as the solution evolves on the grid the Riemann problems that arise in subsequent time steps are very different from the single problem we started with. Depending on the accuracy of the numerical method, the resolution of the grid, and the choice of approximate Riemann solver to use at each grid cell every time step, the numerical solution may deviate significantly from the exact solution to the original shocktube problem. This makes a good initial test problem for numerical methods because the exact solution can be computed for comparison purposes, and because it clearly shows whether the method introduces oscillations around discontinuities and/or smears them out. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from clawpack.riemann.euler_with_efix_1D_constants \\\\\\n\",\n \" import density, momentum, energy, num_eqn\\n\",\n \"\\n\",\n \"def shocktube(q_l, q_r, N=50, riemann_solver='HLL', \\n\",\n \" solver_type='classic'):\\n\",\n \"\\n\",\n \" from clawpack import pyclaw\\n\",\n \" from clawpack import riemann\\n\",\n \"\\n\",\n \" if riemann_solver == 'Roe':\\n\",\n \" rs = riemann.euler_1D_py.euler_roe_1D\\n\",\n \" elif riemann_solver == 'HLL':\\n\",\n \" rs = riemann.euler_1D_py.euler_hll_1D\\n\",\n \"\\n\",\n \" if solver_type == 'classic':\\n\",\n \" solver = pyclaw.ClawSolver1D(rs) \\n\",\n \" solver.limiters = pyclaw.limiters.tvd.MC\\n\",\n \" else:\\n\",\n \" solver = pyclaw.SharpClawSolver1D(rs)\\n\",\n \"\\n\",\n \" solver.kernel_language = 'Python'\\n\",\n \" \\n\",\n \" solver.bc_lower[0]=pyclaw.BC.extrap\\n\",\n \" solver.bc_upper[0]=pyclaw.BC.extrap\\n\",\n \"\\n\",\n \" x = pyclaw.Dimension(-1.0,1.0,N,name='x')\\n\",\n \" domain = pyclaw.Domain([x])\\n\",\n \" state = pyclaw.State(domain,num_eqn)\\n\",\n \"\\n\",\n \" gamma = 1.4\\n\",\n \" state.problem_data['gamma']= gamma\\n\",\n \" state.problem_data['gamma1']= gamma-1.\\n\",\n \"\\n\",\n \" state.problem_data['efix'] = False\\n\",\n \"\\n\",\n \" xc = state.grid.p_centers[0]\\n\",\n \" \\n\",\n \" velocity = (xc<=0)*q_l[1] + (xc>0)*q_r[1]\\n\",\n \" pressure = (xc<=0)*q_l[2] + (xc>0)*q_r[2]\\n\",\n \"\\n\",\n \" state.q[density ,:] = (xc<=0)*q_l[0] + (xc>0)*q_r[0]\\n\",\n \" state.q[momentum,:] = velocity * state.q[density,:]\\n\",\n \" state.q[energy ,:] = pressure/(gamma - 1.) + \\\\\\n\",\n \" 0.5 * state.q[density,:] * velocity**2\\n\",\n \"\\n\",\n \" claw = pyclaw.Controller()\\n\",\n \" claw.tfinal = 0.5\\n\",\n \" claw.solution = pyclaw.Solution(state,domain)\\n\",\n \" claw.solver = solver\\n\",\n \" claw.num_output_times = 10\\n\",\n \" claw.keep_copy = True\\n\",\n \" claw.verbosity=0\\n\",\n \"\\n\",\n \" return claw\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"N = 80 # number of grid cells to use\\n\",\n \"\\n\",\n \"prim_l = [1.,0.,1.]\\n\",\n \"prim_r = [1./8,0.,1./10]\\n\",\n \"q_l = euler.conservative_to_primitive(*prim_l)\\n\",\n \"q_r = euler.conservative_to_primitive(*prim_r)\\n\",\n \"\\n\",\n \"# Roe-based solution\\n\",\n \"roe_st = shocktube(q_l,q_r,N=N,riemann_solver='Roe')\\n\",\n \"roe_st.run()\\n\",\n \"xc_st = roe_st.solution.state.grid.p_centers[0]\\n\",\n \"\\n\",\n \"# HLL-based solution\\n\",\n \"hll_st = shocktube(q_l,q_r,N=N,riemann_solver='HLL')\\n\",\n \"hll_st.run()\\n\",\n \"\\n\",\n \"# Exact solution\\n\",\n \"xc_exact_st = np.linspace(-1,1,2000)\\n\",\n \"states_st, speeds_st, reval_st, wave_types_st = euler.exact_riemann_solution(prim_l, prim_r)\\n\",\n \"\\n\",\n \"def plot_frame(i):\\n\",\n \" t = roe_st.frames[i].t\\n\",\n \" fig, ax = plt.subplots(3,1, sharex=True, figsize=(8,6))\\n\",\n \" variablenames = [\\\"Density\\\", \\\"Momentum\\\", \\\"Energy\\\"]\\n\",\n \" variables = [density, momentum, energy]\\n\",\n \" ylims = [[0,1.1], [-0.05,0.35], [0,1.1]]\\n\",\n \" plt.subplots_adjust(hspace=0)\\n\",\n \" ax[0].title.set_text('Solutions at t={:.2f}'.format(t))\\n\",\n \" ax[0].set_xlim((-1,1))\\n\",\n \" ax[2].set(xlabel = 'x')\\n\",\n \" for j, variable in enumerate(variables):\\n\",\n \" ax[j].set_ylim(ylims[j])\\n\",\n \" ax[j].plot(xc_exact_st,reval_st(xc_exact_st/(t+1.e-16))[j],'-k',lw=1)\\n\",\n \" ax[j].plot(xc_st,hll_st.frames[i].q[variable,:],'-ob',lw=2,markersize=4)\\n\",\n \" ax[j].plot(xc_st,roe_st.frames[i].q[variable,:],'-or',lw=0.5,markersize=3)\\n\",\n \" ax[j].legend(['Exact','HLL','Roe'],loc='best')\\n\",\n \" ax[j].set(ylabel=variablenames[j])\\n\",\n \" plt.show()\\n\",\n \" \\n\",\n \"interact(plot_frame, i=widgets.IntSlider(min=0, max=10, value=5, description='Frame'));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As you might expect, the HLL solver smears the middle wave (contact discontinuity) significantly more than the Roe solver does. Perhaps surprisingly, it captures the shock just as accurately as the Roe solver does. In the live notebook you can refine the grid and observe the convergence.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### High-order WENO + Runge-Kutta\\n\",\n \"\\n\",\n \"Next we look at the difference between the HLL and Roe solution when these solvers are employed within a higher-order method of lines discretization using fifth-order WENO and a 4th-order Runge-Kutta scheme.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"N = 80 # number of grid cells to use\\n\",\n \"\\n\",\n \"prim_l = [1.,0.,1.]\\n\",\n \"prim_r = [1./8,0.,1./10]\\n\",\n \"q_l = euler.conservative_to_primitive(*prim_l)\\n\",\n \"q_r = euler.conservative_to_primitive(*prim_r)\\n\",\n \"\\n\",\n \"roe_weno = shocktube(q_l,q_r,N=N,riemann_solver='Roe',solver_type='sharpclaw')\\n\",\n \"roe_weno.run()\\n\",\n \"hll_weno = shocktube(q_l,q_r,N=N,riemann_solver='HLL',solver_type='sharpclaw')\\n\",\n \"hll_weno.run()\\n\",\n \"\\n\",\n \"xc_weno = roe_weno.solution.state.grid.p_centers[0]\\n\",\n \"\\n\",\n \"# Exact solution\\n\",\n \"xc_exact_weno = np.linspace(-1,1,2000)\\n\",\n \"states_weno, speeds_weno, reval_weno, wave_types_weno = euler.exact_riemann_solution(prim_l, prim_r)\\n\",\n \"\\n\",\n \"def plot_frame(i):\\n\",\n \" t = roe_weno.frames[i].t\\n\",\n \" fig, ax = plt.subplots(3,1, sharex=True, figsize=(8,6))\\n\",\n \" variablenames = [\\\"Density\\\", \\\"Momentum\\\", \\\"Energy\\\"]\\n\",\n \" variables = [density, momentum, energy]\\n\",\n \" ylims = [[0,1.1], [-0.05,0.35], [0,1.1]]\\n\",\n \" plt.subplots_adjust(hspace=0)\\n\",\n \" ax[0].title.set_text('Solutions at t={:.2f}'.format(t))\\n\",\n \" ax[0].set_xlim((-1,1))\\n\",\n \" ax[2].set(xlabel = 'x')\\n\",\n \" for j, variable in enumerate(variables):\\n\",\n \" ax[j].set_ylim(ylims[j])\\n\",\n \" ax[j].plot(xc_exact_weno,reval_weno(xc_exact_weno/(t+1.e-16))[j],'-k',lw=1)\\n\",\n \" ax[j].plot(xc_weno,hll_weno.frames[i].q[variable,:],'-ob',lw=2,markersize=4)\\n\",\n \" ax[j].plot(xc_weno,roe_weno.frames[i].q[variable,:],'-or',lw=0.5,markersize=3)\\n\",\n \" ax[j].legend(['Exact','HLL','Roe'],loc='best')\\n\",\n \" ax[j].set(ylabel=variablenames[j])\\n\",\n \" plt.show()\\n\",\n \" \\n\",\n \"interact(plot_frame, i=widgets.IntSlider(min=0, max=10, value=5, description='Frame'));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With higher-order discretizations, the difference in solutions due to using different Riemann solvers is less significant. This is partly because these high-order schemes use more accurate values as inputs to the Riemann problem, so that in smooth regions the jump between most cells is very small.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Woodward-Colella blast wave\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, we consider the Woodward-Colella blast wave problem, which is discussed for example in (LeVeque 2002). Here the initial velocity is zero and the density is one everywhere. The pressure is\\n\",\n \"\\\\begin{align}\\n\",\n \" p_0(x) = \\\\begin{cases} 1000 & 0 \\\\le x \\\\le 0.1 \\\\\\\\\\n\",\n \" 0.01 & 0.1 \\\\le x \\\\le 0.9 \\\\\\\\\\n\",\n \" 100 & 0.9 \\\\le x \\\\le 1\\n\",\n \" \\\\end{cases}\\n\",\n \"\\\\end{align}\\n\",\n \"The boundaries at $x=0$ and $x=1$ are solid walls. The solution involves a Riemann problem at $x=0.1$ and another at $x=0.9$. Later, the waves resulting from these Riemann problems interact with each other.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from clawpack.riemann.euler_with_efix_1D_constants \\\\\\n\",\n \" import density, momentum, energy, num_eqn\\n\",\n \"\\n\",\n \"def blastwave(N=400, riemann_solver='HLL', solver_type='classic'):\\n\",\n \"\\n\",\n \" from clawpack import pyclaw\\n\",\n \" from clawpack import riemann\\n\",\n \"\\n\",\n \" if riemann_solver == 'Roe':\\n\",\n \" kernel_language = 'Fortran'\\n\",\n \" rs = riemann.euler_with_efix_1D\\n\",\n \" elif riemann_solver == 'HLL':\\n\",\n \" kernel_language = 'Python'\\n\",\n \" rs = riemann.euler_1D_py.euler_hll_1D\\n\",\n \"\\n\",\n \" if solver_type == 'classic':\\n\",\n \" solver = pyclaw.ClawSolver1D(rs)\\n\",\n \" solver.limiters = pyclaw.limiters.tvd.MC\\n\",\n \" else:\\n\",\n \" solver = pyclaw.SharpClawSolver1D(rs)\\n\",\n \"\\n\",\n \" solver.kernel_language = kernel_language\\n\",\n \" \\n\",\n \" solver.bc_lower[0]=pyclaw.BC.wall\\n\",\n \" solver.bc_upper[0]=pyclaw.BC.wall\\n\",\n \"\\n\",\n \" x = pyclaw.Dimension(0.0,1.0,N,name='x')\\n\",\n \" domain = pyclaw.Domain([x])\\n\",\n \" state = pyclaw.State(domain,num_eqn)\\n\",\n \"\\n\",\n \" gamma = 1.4\\n\",\n \" state.problem_data['gamma']= gamma\\n\",\n \" state.problem_data['gamma1']= gamma-1.\\n\",\n \"\\n\",\n \" state.problem_data['efix'] = False\\n\",\n \"\\n\",\n \" xc = state.grid.p_centers[0]\\n\",\n \"\\n\",\n \" pressure = (xc<0.1)*1.e3 + (0.1<=xc)*(xc<0.9)*1.e-2 + (0.9<=xc)*1.e2\\n\",\n \" \\n\",\n \" state.q[density ,:] = 1.\\n\",\n \" state.q[momentum,:] = 0.\\n\",\n \" state.q[energy ,:] = pressure / (gamma - 1.)\\n\",\n \"\\n\",\n \" claw = pyclaw.Controller()\\n\",\n \" claw.tfinal = 0.038\\n\",\n \" claw.solution = pyclaw.Solution(state,domain)\\n\",\n \" claw.solver = solver\\n\",\n \" claw.num_output_times = 30\\n\",\n \" claw.keep_copy = True\\n\",\n \" claw.verbosity=0\\n\",\n \"\\n\",\n \" return claw\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"N = 400 # number of grid cells to use\\n\",\n \"\\n\",\n \"roe_bw = blastwave(N=N, riemann_solver='Roe')\\n\",\n \"roe_bw.run()\\n\",\n \"hll_bw = blastwave(N=N, riemann_solver='HLL')\\n\",\n \"hll_bw.run()\\n\",\n \"fine_bw = blastwave(N=4000,riemann_solver='Roe')\\n\",\n \"fine_bw.run();\\n\",\n \"xc_bw = roe_bw.solution.state.grid.p_centers[0]\\n\",\n \"xc_fine_bw = fine_bw.solution.state.grid.p_centers[0]\\n\",\n \"\\n\",\n \"def plot_frame(i):\\n\",\n \" t = roe_bw.frames[i].t\\n\",\n \" fig, ax = plt.subplots(3,1, sharex=True, figsize=(8,6))\\n\",\n \" variablenames = [\\\"Density\\\", \\\"Momentum\\\", \\\"Energy\\\"]\\n\",\n \" variables = [density, momentum, energy]\\n\",\n \" ylims = [[-1,10], [-50,120], [-300,3000]]\\n\",\n \" plt.subplots_adjust(hspace=0)\\n\",\n \" ax[0].title.set_text('Solutions at t={:.3f}'.format(t))\\n\",\n \" ax[0].set_xlim((0,1))\\n\",\n \" ax[2].set(xlabel = 'x')\\n\",\n \" for j, variable in enumerate(variables):\\n\",\n \" ax[j].set_ylim(ylims[j])\\n\",\n \" ax[j].plot(xc_fine_bw,fine_bw.frames[i].q[variable,:],'-k',lw=1)\\n\",\n \" ax[j].plot(xc_bw,hll_bw.frames[i].q[variable,:],'-b',lw=2)\\n\",\n \" ax[j].plot(xc_bw,roe_bw.frames[i].q[variable,:],'--r',lw=2)\\n\",\n \" ax[j].legend(['Fine','HLL','Roe'],loc='best')\\n\",\n \" ax[j].set(ylabel=variablenames[j])\\n\",\n \" plt.show()\\n\",\n \" \\n\",\n \"interact(plot_frame, i=widgets.IntSlider(min=0, max=30, value=15, description='Frame'));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here no exact solution is available, so we compare with a solution computed on a finer grid.\\n\",\n \"Again the solutions are fairly similar, though the HLL solution is a bit more smeared.\\n\",\n \"\\n\",\n \"One should not conclude from these tests that, for instance, the Roe solver is always *better* than the HLL solver. Many factors besides accuracy should be considered, including cost and robustness. As we have seen the HLL solver is more robust in the presence of near-vacuum states.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.5\"\n },\n \"toc\": {\n \"base_numbering\": 1,\n \"nav_menu\": {},\n \"number_sections\": true,\n \"sideBar\": true,\n \"skip_h1_title\": false,\n \"title_cell\": \"Table of Contents\",\n \"title_sidebar\": \"Contents\",\n \"toc_cell\": false,\n \"toc_position\": {},\n \"toc_section_display\": \"block\",\n \"toc_window_display\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"},"license":{"kind":"string","value":"bsd-3-clause"}}},{"rowIdx":46194,"cells":{"repo_name":{"kind":"string","value":"DSSG2017/florence"},"path":{"kind":"string","value":"dev/notebooks/EDA-FC-plots-MM.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"48783"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"#### Notebook to generate FirenzeCard analysis\\n\",\n \"#### Timeseries, and summary statistics\\n\",\n \"import sys\\n\",\n \"sys.path.append('../src/')\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings('ignore')\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline \\n\",\n \"import psycopg2\\n\",\n \"from features.firenzecard import *\\n\",\n \"from IPython.core.debugger import Tracer\\n\",\n \"\\n\",\n \"from scipy.stats import norm\\n\",\n \"from sklearn.neighbors import KernelDensity\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def frequency(dataframe,columnname):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" :param dataframe: a pandas dataframe\\n\",\n \" :param columnname: a single column, with discrete (including integer) values\\n\",\n \" :return: a frequency table, suitable for plotting the empirical PMF, empirical CDF, or empirical CCDF\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" out = dataframe[columnname].value_counts().to_frame()\\n\",\n \" out.columns = ['frequency']\\n\",\n \" out.index.name = columnname\\n\",\n \" out.reset_index(inplace=True)\\n\",\n \" out.sort_values('frequency',inplace=True,ascending=False)\\n\",\n \" out['cumulative'] = out['frequency'].cumsum()/out['frequency'].sum()\\n\",\n \" out['ccdf'] = 1 - out['cumulative']\\n\",\n \" return out\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Index([u'user_id', u'museum_id', u'entrances_per_card_per_museum',\\n\",\n \" u'museum_name', u'longitude', u'latitude', u'short_name', u'string',\\n\",\n \" u'entry_time', u'adults_first_use', u'adults_reuse', u'total_adults',\\n\",\n \" u'minors', u'time', u'date', u'hour', u'day_of_week', u'total_people',\\n\",\n \" u'time_since_previous_museum', u'total_duration_card_use',\\n\",\n \" u'entry_is_adult', u'is_card_with_minors', u'is_in_museum_1',\\n\",\n \" u'is_in_museum_2', u'is_in_museum_3', u'is_in_museum_4',\\n\",\n \" u'is_in_museum_5', u'is_in_museum_6', u'is_in_museum_7',\\n\",\n \" u'is_in_museum_8', u'is_in_museum_9', u'is_in_museum_10',\\n\",\n \" u'is_in_museum_11', u'is_in_museum_12', u'is_in_museum_13',\\n\",\n \" u'is_in_museum_14', u'is_in_museum_15', u'is_in_museum_16',\\n\",\n \" u'is_in_museum_17', u'is_in_museum_18', u'is_in_museum_19',\\n\",\n \" u'is_in_museum_20', u'is_in_museum_21', u'is_in_museum_22',\\n\",\n \" u'is_in_museum_23', u'is_in_museum_24', u'is_in_museum_25',\\n\",\n \" u'is_in_museum_26', u'is_in_museum_27', u'is_in_museum_28',\\n\",\n \" u'is_in_museum_29', u'is_in_museum_30', u'is_in_museum_31',\\n\",\n \" u'is_in_museum_32', u'is_in_museum_33', u'is_in_museum_34',\\n\",\n \" u'is_in_museum_35', u'is_in_museum_36', u'is_in_museum_37',\\n\",\n \" u'is_in_museum_38', u'is_in_museum_39'],\\n\",\n \" dtype='object')\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('../src/output/firenzedata_feature_extracted.csv')\\n\",\n \"df.columns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"df1 = df.groupby(['user_id','entry_time']).agg({'time_since_previous_museum':'max', 'total_adults':'sum', 'total_people':'sum', 'museum_id':'max'})\\n\",\n \"# df1 = df.groupby(['user_id','entry_time','museum_id']).agg({'time_until_next_museum':['min','max'], 'total_adults':['sum'], 'total_people':['sum']})\\n\",\n \"df1.dropna(how=\\\"any\\\",inplace=True)\\n\",\n \"df1 = df1[df1['time_since_previous_museum']>0]\\n\",\n \"df1.reset_index(inplace=True)\\n\",\n \"# df1[df1.time_until_next_museum['min']!=df1.time_until_next_museum['max']] # All have minors\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# time_until_next_museum\\n\",\n \"trace1 = go.Histogram(x=x1, \\n\",\n \" xbins=dict(start=np.min(x1), \\n\",\n \" size=0.25, end=np.max(x1)), \\n\",\n \" marker=dict(color='#CC171D'), \\n\",\n \" name='Firenze Cards'\\n\",\n \" )\\n\",\n \"trace2 = go.Histogram(x=x2, \\n\",\n \" xbins=dict(start=np.min(x2), \\n\",\n \" size=0.25, end=np.max(x2)), \\n\",\n \" marker=dict(color='#1789CC'), \\n\",\n \" name='Total People on Visit'\\n\",\n \" )\\n\",\n \"\\n\",\n \"layout = go.Layout(\\n\",\n \" title=\\\"Time Between Museum Visits\\\",\\n\",\n \" titlefont=dict(size=28),\\n\",\n \" barmode='stack',\\n\",\n \" legend=dict(\\n\",\n \" x=0.8,\\n\",\n \" y=0.9,\\n\",\n \" traceorder='normal',\\n\",\n \" font=dict(\\n\",\n \" family='sans-serif',\\n\",\n \" size=16,\\n\",\n \" color='#000'\\n\",\n \" ),\\n\",\n \" bgcolor='#FFFFFF',\\n\",\n \" bordercolor='#E2E2E2',\\n\",\n \" borderwidth=2\\n\",\n \" ),\\n\",\n \" width=1200,\\n\",\n \" height=800,\\n\",\n \" xaxis=dict(\\n\",\n \" title='Time Gap in Hours (15 minute bins)',\\n\",\n \" titlefont=dict(size=20),\\n\",\n \" nticks=32,\\n\",\n \" ticks='outside',\\n\",\n \" tickfont=dict(size=16)\\n\",\n \" ),\\n\",\n \" yaxis=dict(\\n\",\n \" title='Number of People-Visits with Given Time Gap',\\n\",\n \" titlefont=dict(size=20),\\n\",\n \" ticks='outside',\\n\",\n \" tickfont=dict(size=16)\\n\",\n \" )\\n\",\n \")\\n\",\n \"fig = go.Figure(data=go.Data([trace1,trace2]), layout=layout)\\n\",\n \"py.iplot(fig, filename='TUNME', sharing='private', auto_open=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 185,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ]\n },\n \"execution_count\": 201,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Frequency plot of number of unique museums visited per card\\n\",\n \"x = df3['museums_visited']\\n\",\n \"trace1 = go.Histogram(x=x, xbins=dict(start=np.min(x)-.25, size=.5, end=np.max(x)+.25),\\n\",\n \" marker=dict(color='#CC171D'),\\n\",\n \" name = 'Museums visited')\\n\",\n \"\\n\",\n \"# trace2 = go.Histogram(x=adult, xbins=dict(start=np.min(adult), size=1, end=np.max(adult)),\\n\",\n \"# marker=dict(color='rgb(0, 100, 0)'),\\n\",\n \"# name = 'Adults')\\n\",\n \"\\n\",\n \"layout = go.Layout(\\n\",\n \" title=\\\"Number of Museums Visited per Card\\\",\\n\",\n \" titlefont=dict(size=28),\\n\",\n \"# legend=dict(\\n\",\n \"# traceorder='normal',\\n\",\n \"# font=dict(\\n\",\n \"# family='sans-serif',\\n\",\n \"# size=12,\\n\",\n \"# color='#000'\\n\",\n \"# ),\\n\",\n \"# bgcolor='#CC171D',\\n\",\n \"# bordercolor='#FFFFFF',\\n\",\n \"# borderwidth=0\\n\",\n \"# ),\\n\",\n \" width=1200,\\n\",\n \" height=800,\\n\",\n \" xaxis=dict(\\n\",\n \" title='Number of Museums Visited',\\n\",\n \" titlefont=dict(size=20),\\n\",\n \" range=[0.75,32.25],\\n\",\n \" nticks=32,\\n\",\n \" ticks='outside',\\n\",\n \" tickfont=dict(size=16)\\n\",\n \" ), \\n\",\n \" yaxis=dict(\\n\",\n \" title='Number of Cards',\\n\",\n \" titlefont=dict(size=20),\\n\",\n \" ticks='outside',\\n\",\n \" tickfont=dict(size=16)\\n\",\n \" )\\n\",\n \")\\n\",\n \"fig = go.Figure(data=go.Data([trace1]), layout=layout)\\n\",\n \"py.iplot(fig, filename='MPC_2', sharing='private', auto_open=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 204,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"user_id\\n\",\n \"1459702 29.016667\\n\",\n \"1473903 52.183333\\n\",\n \"1473904 52.183333\\n\",\n \"1473905 25.166667\\n\",\n \"1473906 48.466667\\n\",\n \"Name: total_duration_card_use, dtype: float64\"\n ]\n },\n \"execution_count\": 204,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = df.groupby('user_id')['total_duration_card_use'].max()/pd.Timedelta('1 hour')\\n\",\n \"x.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 101,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"KernelDensity(algorithm='auto', atol=0, bandwidth=0.75, breadth_first=True,\\n\",\n \" kernel='gaussian', leaf_size=40, metric='euclidean',\\n\",\n \" metric_params=None, rtol=0)\"\n ]\n },\n \"execution_count\": 101,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# kde = KernelDensity(kernel='gaussian', bandwidth=0.75).fit(x.as_matrix()c\\n\",\n \"# kde\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 208,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 208,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"trace = go.Histogram(x=x[x>0],\\n\",\n \" xbins=dict(start=np.min(x), size=1.0/4.0,\\n\",\n \" end=np.max(x)),\\n\",\n \" marker=dict(color='#CC171D'))\\n\",\n \"\\n\",\n \"layout = go.Layout(\\n\",\n \" title=\\\"Duration of Card Usage\\\",\\n\",\n \" titlefont=dict(size=28),\\n\",\n \" width=1200,\\n\",\n \" height=800,\\n\",\n \" xaxis=dict(\\n\",\n \" title='Hours Between First and Last Use of Card (bins of 15 minutes)',\\n\",\n \" titlefont=dict(size=20),\\n\",\n \" range=[0,72],\\n\",\n \" nticks=32,\\n\",\n \" ticks='outside',\\n\",\n \" tickfont=dict(size=16)\\n\",\n \" ),\\n\",\n \" yaxis=dict(\\n\",\n \" title='Number of Cards',\\n\",\n \" titlefont=dict(size=20),\\n\",\n \" ticks='outside',\\n\",\n \" tickfont=dict(size=16)\\n\",\n \" )\\n\",\n \")\\n\",\n \"fig = go.Figure(data=go.Data([trace]), layout=layout)\\n\",\n \"py.iplot(fig, filename='DOU_2', sharing='private', auto_open=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 196,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# df['time_until_next_museum'] = df['time_until_next_museum'].apply(\\n\",\n \"# lambda x: pd.Timedelta(x) / pd.Timedelta('1 hour'))\\n\",\n \"# trace = go.Histogram(x=df['time_until_next_museum'], xbins=dict(start=np.min(x), size=0.25, end=np.max(x)),\\n\",\n \"# marker=dict(color='rgb(0, 0, 100)'))\\n\",\n \"# layout = go.Layout(\\n\",\n \"# title=\\\"\\\"\\n\",\n \"# )\\n\",\n \"# fig = go.Figure(data=go.Data([trace]), layout=layout)\\n\",\n \"# py.iplot(fig, filename='TUNM', sharing='private', auto_open=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Histogram of Monthly total museum entry data - from florence card \\n\",\n \"#https://plot.ly/~qiweihan/110\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 194,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The sql extension is already loaded. To reload it, use:\\n\",\n \" %reload_ext sql\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Histogram of Monthly total museum entry data - from National Museums\\n\",\n \"# Comparison of PROPORTION of Firenze card entries with museum totals (pie chart?)\\n\",\n \"%load_ext sql\\n\",\n \"#TODO: connect with dbutils\\n\",\n \"conn_str = \\\"\\\"\\n\",\n \"conn = psycopg2.connect(conn_str)\\n\",\n \"c = conn.cursor()\\n\",\n \"\\n\",\n \"test = get_national_museums(conn, export_to_csv=True, export_path='../src/output/')\\n\",\n \"test = test[(test['visit_month'] == 'June') | (test['visit_month'] == 'July') | \\n\",\n \" (test['visit_month'] == 'August') | (test['visit_month'] == 'September')]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 195,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 195,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"trace = Bar(\\n\",\n \" x=test['place'],\\n\",\n \" y=test['total_visitors'],\\n\",\n \" )\\n\",\n \"\\n\",\n \"fig = go.Figure(data=go.Data([trace]))\\n\",\n \"fig['layout'].update(height=800, width=900, title='Stacked subplots')\\n\",\n \"\\n\",\n \"py.iplot(fig, filename='State Museum Entries', sharing='private')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# # Which museums are most popular? number of entries per museum, per date\\n\",\n \"# trace = Bar(\\n\",\n \"# x=df[],\\n\",\n \"# y=df[],\\n\",\n \"# )\\n\",\n \"\\n\",\n \"# fig = go.Figure(data=go.Data([trace]))\\n\",\n \"# fig['layout'].update(height=800, width=900, title='Stacked subplots')\\n\",\n \"\\n\",\n \"# py.iplot(fig, filename='', sharing='private')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Timeline of usage(per avg hour, calendar day, calendar month, weekday) - segment per museum\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# # Daytype of activation of card\\n\",\n \"# # get day of week for first use for every user\\n\",\n \"# trace = go.Histogram(x=df[''], xbins=dict(start=np.min(x), size=0.25, end=np.max(x)),\\n\",\n \"# marker=dict(color='rgb(0, 0, 100)'))\\n\",\n \"# layout = go.Layout(\\n\",\n \"# title=\\\"\\\"\\n\",\n \"# )\\n\",\n \"# fig = go.Figure(data=go.Data([trace]), layout=layout)\\n\",\n \"# py.iplot(fig, filename='daytype of activation', sharing='private', auto_open=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Frequency plot of number of unique museums visited per card\\n\",\n \"trace = go.Bar(x=['Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Satuday'], \\n\",\n \" y=[4919,6439,9959,8150,8150,7601,5648],\\n\",\n \" marker=dict(color='#CC171D'))\\n\",\n \"\\n\",\n \"layout = go.Layout(\\n\",\n \" title=\\\"Day of Firenze Card Activation\\\",\\n\",\n \" titlefont=dict(size=28),\\n\",\n \" width=1200,\\n\",\n \" height=800,\\n\",\n \" xaxis=dict(\\n\",\n \" title='Day of the Week',\\n\",\n \" titlefont=dict(size=20),\\n\",\n \" nticks=7,\\n\",\n \" ticks='outside',\\n\",\n \" tickfont=dict(size=16)\\n\",\n \" ),\\n\",\n \" yaxis=dict(\\n\",\n \" title='Number of Cards Activated',\\n\",\n \" titlefont=dict(size=20),\\n\",\n \" ticks='outside',\\n\",\n \" tickfont=dict(size=16)\\n\",\n \" )\\n\",\n \")\\n\",\n \"fig = go.Figure(data=go.Data([trace]), layout=layout)\\n\",\n \"py.iplot(fig, filename='daytype of activation', sharing='private', auto_open=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":46195,"cells":{"repo_name":{"kind":"string","value":"mattmcd/PyBayes"},"path":{"kind":"string","value":"scripts/indexed_expressions_20211107.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"18999"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import sympy as sp\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"outputs\": [],\n \"source\": [\n \"x, y = [sp.IndexedBase(e) for e in ['x', 'y']]\\n\",\n \"m = sp.symbols('m', integer=True)\\n\",\n \"a, b = sp.symbols('a b', real=True)\\n\",\n \"i = sp.Idx('i', m)\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"outputs\": [],\n \"source\": [\n \"loss = (y[i] - (a*x[i] + b))**2\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"(-a*x[i] - b + y[i])**2\",\n \"text/latex\": \"$\\\\displaystyle \\\\left(- a {x}_{Idx\\\\left(i, \\\\left( 0, \\\\ m - 1\\\\right)\\\\right)} - b + {y}_{Idx\\\\left(i, \\\\left( 0, \\\\ m - 1\\\\right)\\\\right)}\\\\right)^{2}$\"\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"loss\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"Having defined the loss function using indexed variables we might hope that the\\n\",\n \"implicit summation of repeated indexes might fall through to derivative\\n\",\n \"but it looks like this isn't the case.\\n\",\n \"\\n\",\n \"Below we see taking derivative wrt to fit parameters is only applied\\n\",\n \"to each point rather than the whole sum, which is incorrect.\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"[(-b + y[i])/x[i]]\"\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sp.solve(loss.diff(a), a)\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"[-a*x[i] + y[i]]\"\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sp.solve(loss.diff(b), b)\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"Try adding explicit summation around the loss expression. This gives the\\n\",\n \"correct set of equations for derivatives but a solution can't be found.\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"Sum(-2*(-a*x[i] - b + y[i])*x[i], (i, 0, m - 1))\",\n \"text/latex\": \"$\\\\displaystyle \\\\sum_{Idx\\\\left(i, \\\\left( 0, \\\\ m - 1\\\\right)\\\\right)=0}^{m - 1} - 2 \\\\left(- a {x}_{Idx\\\\left(i, \\\\left( 0, \\\\ m - 1\\\\right)\\\\right)} - b + {y}_{Idx\\\\left(i, \\\\left( 0, \\\\ m - 1\\\\right)\\\\right)}\\\\right) {x}_{Idx\\\\left(i, \\\\left( 0, \\\\ m - 1\\\\right)\\\\right)}$\"\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sp.diff(sp.Sum(loss, i),a)\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"Sum(2*a*x[i] + 2*b - 2*y[i], (i, 0, m - 1))\",\n \"text/latex\": \"$\\\\displaystyle \\\\sum_{Idx\\\\left(i, \\\\left( 0, \\\\ m - 1\\\\right)\\\\right)=0}^{m - 1} \\\\left(2 a {x}_{Idx\\\\left(i, \\\\left( 0, \\\\ m - 1\\\\right)\\\\right)} + 2 b - 2 {y}_{Idx\\\\left(i, \\\\left( 0, \\\\ m - 1\\\\right)\\\\right)}\\\\right)$\"\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sp.diff(sp.Sum(loss, i), b)\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"[]\"\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sp.solve([sp.diff(sp.Sum(loss, i),a), sp.diff(sp.Sum(loss, i),b)], [a, b])\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"{a: -b/x[i] + y[i]/x[i]}\"\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sp.solve([loss.expand().diff(a), loss.expand().diff(b)], [a,b])\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"MatrixSymbol seems to be the trick\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"outputs\": [],\n \"source\": [\n \"x_2 = sp.MatrixSymbol('x', m, 1)\\n\",\n \"y_2 = sp.MatrixSymbol('y', m, 1)\\n\",\n \"a_2 = sp.MatrixSymbol('a', 1, 1)\\n\",\n \"b_2 = b*sp.OneMatrix(m, 1)\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"(-b)*1 - x*a + y\",\n \"text/latex\": \"$\\\\displaystyle - b \\\\mathbb{1} - x a + y$\"\n },\n \"execution_count\": 87,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"err = y_2 - (x_2*a_2 + b_2)\\n\",\n \"err\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 91,\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"((-b)*1 - a.T*x.T + y.T)*((-b)*1 - x*a + y)\",\n \"text/latex\": \"$\\\\displaystyle \\\\left(- b \\\\mathbb{1} - a^{T} x^{T} + y^{T}\\\\right) \\\\left(- b \\\\mathbb{1} - x a + y\\\\right)$\"\n },\n \"execution_count\": 91,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"objective = (err.T * err)\\n\",\n \"objective\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 93,\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"-2*x.T*((-b)*1 - x*a + y)\",\n \"text/latex\": \"$\\\\displaystyle - 2 x^{T} \\\\left(- b \\\\mathbb{1} - x a + y\\\\right)$\"\n },\n \"execution_count\": 93,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"objective.diff(a_2)\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 94,\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"-((-b)*1 - a.T*x.T + y.T)*1 - 1*((-b)*1 - x*a + y)\",\n \"text/latex\": \"$\\\\displaystyle - \\\\left(- b \\\\mathbb{1} - a^{T} x^{T} + y^{T}\\\\right) \\\\mathbb{1} - \\\\mathbb{1} \\\\left(- b \\\\mathbb{1} - x a + y\\\\right)$\"\n },\n \"execution_count\": 94,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"objective.diff(b)\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"Functions of Matrices e.g. generator of rotations\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"t = sp.symbols('t', real=True)\\n\",\n \"g = sp.Matrix([[0, -t], [t, 0]])\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"cannot determine truth value of Relational\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001B[0;31m---------------------------------------------------------------------------\\u001B[0m\",\n \"\\u001B[0;31mTypeError\\u001B[0m Traceback (most recent call last)\",\n \"\\u001B[0;32m\\u001B[0m in \\u001B[0;36m\\u001B[0;34m\\u001B[0m\\n\\u001B[0;32m----> 1\\u001B[0;31m \\u001B[0msp\\u001B[0m\\u001B[0;34m.\\u001B[0m\\u001B[0meye\\u001B[0m\\u001B[0;34m(\\u001B[0m\\u001B[0mm\\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 2\\u001B[0m \\u001B[0mg\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\",\n \"\\u001B[0;32m~/Work/Projects/PyBayes/venv/lib/python3.8/site-packages/sympy/matrices/dense.py\\u001B[0m in \\u001B[0;36meye\\u001B[0;34m(*args, **kwargs)\\u001B[0m\\n\\u001B[1;32m 916\\u001B[0m \\\"\\\"\\\"\\n\\u001B[1;32m 917\\u001B[0m \\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[0;32m--> 918\\u001B[0;31m \\u001B[0;32mreturn\\u001B[0m \\u001B[0mMatrix\\u001B[0m\\u001B[0;34m.\\u001B[0m\\u001B[0meye\\u001B[0m\\u001B[0;34m(\\u001B[0m\\u001B[0;34m*\\u001B[0m\\u001B[0margs\\u001B[0m\\u001B[0;34m,\\u001B[0m \\u001B[0;34m**\\u001B[0m\\u001B[0mkwargs\\u001B[0m\\u001B[0;34m)\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[0m\\u001B[1;32m 919\\u001B[0m \\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[1;32m 920\\u001B[0m \\u001B[0;34m\\u001B[0m\\u001B[0m\\n\",\n \"\\u001B[0;32m~/Work/Projects/PyBayes/venv/lib/python3.8/site-packages/sympy/matrices/common.py\\u001B[0m in \\u001B[0;36meye\\u001B[0;34m(kls, rows, cols, **kwargs)\\u001B[0m\\n\\u001B[1;32m 975\\u001B[0m \\u001B[0;32mif\\u001B[0m \\u001B[0mcols\\u001B[0m \\u001B[0;32mis\\u001B[0m \\u001B[0;32mNone\\u001B[0m\\u001B[0;34m:\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[1;32m 976\\u001B[0m \\u001B[0mcols\\u001B[0m \\u001B[0;34m=\\u001B[0m \\u001B[0mrows\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[0;32m--> 977\\u001B[0;31m \\u001B[0;32mif\\u001B[0m \\u001B[0mrows\\u001B[0m \\u001B[0;34m<\\u001B[0m \\u001B[0;36m0\\u001B[0m \\u001B[0;32mor\\u001B[0m \\u001B[0mcols\\u001B[0m \\u001B[0;34m<\\u001B[0m \\u001B[0;36m0\\u001B[0m\\u001B[0;34m:\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[0m\\u001B[1;32m 978\\u001B[0m raise ValueError(\\\"Cannot create a {} x {} matrix. \\\"\\n\\u001B[1;32m 979\\u001B[0m \\\"Both dimensions must be positive\\\".format(rows, cols))\\n\",\n \"\\u001B[0;32m~/Work/Projects/PyBayes/venv/lib/python3.8/site-packages/sympy/core/relational.py\\u001B[0m in \\u001B[0;36m__bool__\\u001B[0;34m(self)\\u001B[0m\\n\\u001B[1;32m 396\\u001B[0m \\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[1;32m 397\\u001B[0m \\u001B[0;32mdef\\u001B[0m \\u001B[0m__bool__\\u001B[0m\\u001B[0;34m(\\u001B[0m\\u001B[0mself\\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--> 398\\u001B[0;31m \\u001B[0;32mraise\\u001B[0m \\u001B[0mTypeError\\u001B[0m\\u001B[0;34m(\\u001B[0m\\u001B[0;34m\\\"cannot determine truth value of Relational\\\"\\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 399\\u001B[0m \\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[1;32m 400\\u001B[0m \\u001B[0;32mdef\\u001B[0m \\u001B[0m_eval_as_set\\u001B[0m\\u001B[0;34m(\\u001B[0m\\u001B[0mself\\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\",\n \"\\u001B[0;31mTypeError\\u001B[0m: cannot determine truth value of Relational\"\n ]\n }\n ],\n \"source\": [\n \"g\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"Matrix([\\n[cos(t), -sin(t)],\\n[sin(t), cos(t)]])\",\n \"text/latex\": \"$\\\\displaystyle \\\\left[\\\\begin{matrix}\\\\cos{\\\\left(t \\\\right)} & - \\\\sin{\\\\left(t \\\\right)}\\\\\\\\\\\\sin{\\\\left(t \\\\right)} & \\\\cos{\\\\left(t \\\\right)}\\\\end{matrix}\\\\right]$\"\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sp.exp(g)\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"m is not an integer\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001B[0;31m---------------------------------------------------------------------------\\u001B[0m\",\n \"\\u001B[0;31mTypeError\\u001B[0m Traceback (most recent call last)\",\n \"\\u001B[0;32m~/Work/Projects/PyBayes/venv/lib/python3.8/site-packages/sympy/core/compatibility.py\\u001B[0m in \\u001B[0;36mas_int\\u001B[0;34m(n, strict)\\u001B[0m\\n\\u001B[1;32m 300\\u001B[0m \\u001B[0;32mraise\\u001B[0m \\u001B[0mTypeError\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[0;32m--> 301\\u001B[0;31m \\u001B[0;32mreturn\\u001B[0m \\u001B[0moperator\\u001B[0m\\u001B[0;34m.\\u001B[0m\\u001B[0mindex\\u001B[0m\\u001B[0;34m(\\u001B[0m\\u001B[0mn\\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 302\\u001B[0m \\u001B[0;32mexcept\\u001B[0m \\u001B[0mTypeError\\u001B[0m\\u001B[0;34m:\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\",\n \"\\u001B[0;31mTypeError\\u001B[0m: 'Symbol' object cannot be interpreted as an integer\",\n \"\\nDuring handling of the above exception, another exception occurred:\\n\",\n \"\\u001B[0;31mValueError\\u001B[0m Traceback (most recent call last)\",\n \"\\u001B[0;32m\\u001B[0m in \\u001B[0;36m\\u001B[0;34m\\u001B[0m\\n\\u001B[0;32m----> 1\\u001B[0;31m \\u001B[0ma\\u001B[0m\\u001B[0;34m*\\u001B[0m\\u001B[0msp\\u001B[0m\\u001B[0;34m.\\u001B[0m\\u001B[0mones\\u001B[0m\\u001B[0;34m(\\u001B[0m\\u001B[0mm\\u001B[0m\\u001B[0;34m,\\u001B[0m\\u001B[0;36m1\\u001B[0m\\u001B[0;34m)\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[0m\\u001B[1;32m 2\\u001B[0m \\u001B[0;34m\\u001B[0m\\u001B[0m\\n\",\n \"\\u001B[0;32m~/Work/Projects/PyBayes/venv/lib/python3.8/site-packages/sympy/matrices/dense.py\\u001B[0m in \\u001B[0;36mones\\u001B[0;34m(*args, **kwargs)\\u001B[0m\\n\\u001B[1;32m 1129\\u001B[0m \\u001B[0mkwargs\\u001B[0m\\u001B[0;34m[\\u001B[0m\\u001B[0;34m'cols'\\u001B[0m\\u001B[0;34m]\\u001B[0m \\u001B[0;34m=\\u001B[0m \\u001B[0mkwargs\\u001B[0m\\u001B[0;34m.\\u001B[0m\\u001B[0mpop\\u001B[0m\\u001B[0;34m(\\u001B[0m\\u001B[0;34m'c'\\u001B[0m\\u001B[0;34m)\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[1;32m 1130\\u001B[0m \\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[0;32m-> 1131\\u001B[0;31m \\u001B[0;32mreturn\\u001B[0m \\u001B[0mMatrix\\u001B[0m\\u001B[0;34m.\\u001B[0m\\u001B[0mones\\u001B[0m\\u001B[0;34m(\\u001B[0m\\u001B[0;34m*\\u001B[0m\\u001B[0margs\\u001B[0m\\u001B[0;34m,\\u001B[0m \\u001B[0;34m**\\u001B[0m\\u001B[0mkwargs\\u001B[0m\\u001B[0;34m)\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[0m\\u001B[1;32m 1132\\u001B[0m \\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[1;32m 1133\\u001B[0m \\u001B[0;34m\\u001B[0m\\u001B[0m\\n\",\n \"\\u001B[0;32m~/Work/Projects/PyBayes/venv/lib/python3.8/site-packages/sympy/matrices/common.py\\u001B[0m in \\u001B[0;36mones\\u001B[0;34m(kls, rows, cols, **kwargs)\\u001B[0m\\n\\u001B[1;32m 1155\\u001B[0m \\u001B[0mcols\\u001B[0m \\u001B[0;34m=\\u001B[0m \\u001B[0mrows\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[1;32m 1156\\u001B[0m \\u001B[0mklass\\u001B[0m \\u001B[0;34m=\\u001B[0m \\u001B[0mkwargs\\u001B[0m\\u001B[0;34m.\\u001B[0m\\u001B[0mget\\u001B[0m\\u001B[0;34m(\\u001B[0m\\u001B[0;34m'cls'\\u001B[0m\\u001B[0;34m,\\u001B[0m \\u001B[0mkls\\u001B[0m\\u001B[0;34m)\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[0;32m-> 1157\\u001B[0;31m \\u001B[0mrows\\u001B[0m\\u001B[0;34m,\\u001B[0m \\u001B[0mcols\\u001B[0m \\u001B[0;34m=\\u001B[0m \\u001B[0mas_int\\u001B[0m\\u001B[0;34m(\\u001B[0m\\u001B[0mrows\\u001B[0m\\u001B[0;34m)\\u001B[0m\\u001B[0;34m,\\u001B[0m \\u001B[0mas_int\\u001B[0m\\u001B[0;34m(\\u001B[0m\\u001B[0mcols\\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 1158\\u001B[0m \\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[1;32m 1159\\u001B[0m \\u001B[0;32mreturn\\u001B[0m \\u001B[0mklass\\u001B[0m\\u001B[0;34m.\\u001B[0m\\u001B[0m_eval_ones\\u001B[0m\\u001B[0;34m(\\u001B[0m\\u001B[0mrows\\u001B[0m\\u001B[0;34m,\\u001B[0m \\u001B[0mcols\\u001B[0m\\u001B[0;34m)\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\",\n \"\\u001B[0;32m~/Work/Projects/PyBayes/venv/lib/python3.8/site-packages/sympy/core/compatibility.py\\u001B[0m in \\u001B[0;36mas_int\\u001B[0;34m(n, strict)\\u001B[0m\\n\\u001B[1;32m 301\\u001B[0m \\u001B[0;32mreturn\\u001B[0m \\u001B[0moperator\\u001B[0m\\u001B[0;34m.\\u001B[0m\\u001B[0mindex\\u001B[0m\\u001B[0;34m(\\u001B[0m\\u001B[0mn\\u001B[0m\\u001B[0;34m)\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[1;32m 302\\u001B[0m \\u001B[0;32mexcept\\u001B[0m \\u001B[0mTypeError\\u001B[0m\\u001B[0;34m:\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[0;32m--> 303\\u001B[0;31m \\u001B[0;32mraise\\u001B[0m \\u001B[0mValueError\\u001B[0m\\u001B[0;34m(\\u001B[0m\\u001B[0;34m'%s is not an integer'\\u001B[0m \\u001B[0;34m%\\u001B[0m \\u001B[0;34m(\\u001B[0m\\u001B[0mn\\u001B[0m\\u001B[0;34m,\\u001B[0m\\u001B[0;34m)\\u001B[0m\\u001B[0;34m)\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[0m\\u001B[1;32m 304\\u001B[0m \\u001B[0;32melse\\u001B[0m\\u001B[0;34m:\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\\u001B[1;32m 305\\u001B[0m \\u001B[0;32mtry\\u001B[0m\\u001B[0;34m:\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0;34m\\u001B[0m\\u001B[0m\\n\",\n \"\\u001B[0;31mValueError\\u001B[0m: m is not an integer\"\n ]\n }\n ],\n \"source\": [],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"},"license":{"kind":"string","value":"apache-2.0"}}},{"rowIdx":46196,"cells":{"repo_name":{"kind":"string","value":"Patri-meteocat/Meteocat_ANL_collaboration"},"path":{"kind":"string","value":"notebooks/.ipynb_checkpoints/maconvolution_fn-checkpoint.ipynb"},"copies":{"kind":"string","value":"2"},"size":{"kind":"string","value":"2047"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import pylab as plb\\n\",\n \"import numpy as np\\n\",\n \"import scipy as sp\\n\",\n \"import numpy.ma as ma\\n\",\n \"\\n\",\n \"from pylab import *\\n\",\n \"from scipy import ndimage\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def maconvolve(inp, weights, norm=False, output=None, mode='reflect', cval=0.0, origin=0):\\n\",\n \" \\n\",\n \" k = weights\\n\",\n \" data = inp.data\\n\",\n \" msk = inp.mask\\n\",\n \" \\n\",\n \" # Invert the mask and create ones-and-zeros array\\n\",\n \" mask_arr = np.logical_not(msk).astype(int)\\n\",\n \" # Mask data for convolution\\n\",\n \" data_msk = data*mask_arr\\n\",\n \" # Convolve masked data with kernel\\n\",\n \" data_conv = ndimage.convolve(data_msk, k, mode=mode, output=output, cval=cval, origin=origin)\\n\",\n \" \\n\",\n \" if norm:\\n\",\n \" # normalisation kernel\\n\",\n \" k_norm = np.ones(shape(k))\\n\",\n \" # Convolve mask with normalisation kernel\\n\",\n \" mask_conv = ndimage.convolve(mask_arr, k_norm, mode=mode, output=output, cval=cval, origin=origin)\\n\",\n \" # Normalisation factor (depending on number of non-masked values)\\n\",\n \" w = 1./mask_conv\\n\",\n \" data_conv = data_conv*w\\n\",\n \" \\n\",\n \" data_out = ma.masked_array(data_conv, msk)\\n\",\n \" return data_out\\n\",\n \" \"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"bsd-2-clause"}}},{"rowIdx":46197,"cells":{"repo_name":{"kind":"string","value":"KshitijT/fundamentals_of_interferometry"},"path":{"kind":"string","value":"7_Observing_Systems/7_3_analogue.ipynb"},"copies":{"kind":"string","value":"2"},"size":{"kind":"string","value":"60705"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"***\\n\",\n \"\\n\",\n \"* [Outline](../0_Introduction/0_introduction.ipynb)\\n\",\n \"* [Glossary](../0_Introduction/1_glossary.ipynb)\\n\",\n \"* [7. Observing Systems](0_introduction.ipynb) \\n\",\n \" * Previous: [7.2 The Radio Interferometer Measurement Equation (RIME)](7_2_rime.ipynb) \\n\",\n \" * Next: [7.4 Digital Correlators](7_4_digital.ipynb)\\n\",\n \"\\n\",\n \"***\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Import standard modules:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"from IPython.display import HTML \\n\",\n \"HTML('../style/course.css') #apply general CSS\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Import section specific modules:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from IPython.display import Image\\n\",\n \"import scipy.signal\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"HTML('../style/code_toggle.html')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"***\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 7.3 Analogue Electronics (G- and B-Jones)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the first sections of this chapter, we introduced Jones notation and the Radio Interferometric Measurement Equation (RIME). These ideas have been presented abstractly, as a way to lay out the mathematics of how we describe observing systems. For the remainder of the chapter, we will explore the details of different instrumental effects, their corresponding Jones matrices, and how these effects alter the sky signal. Our ultimate goal - recovering the original sky signal - requires knowledge of the observation system. How well we understand an observing system affects how accurate we can recover the true sky signal. We will discuss the process of calibration in the next chapter.\\n\",\n \"\\n\",\n \"In [$\\\\S$ 7.2.3 ➞](./7_2_rime.ipynb) we introduced the concepts of direction-dependent and direction-independent effects (DDE's and DIE's). Historically, direction-independent effects were the primary concern during observations. This was mainly due to simplicity, hardware and array limitations, and because DIE's are the dominant effects on the sky signal. These effects occur upon conversion of the sky's electric field to a voltage - i.e. at the feed - and propagates through the telescope and array electronics. Typically, all the DIE's are wrapped up into a single 'gain' Jones matrix called the $\\\\mathbf{G}$-Jones. This term is made up of numerous effect, primarily due to the analogue electronic components such as filters, amplifiers, mixers, cables, etc. This will be discussed in this section.\\n\",\n \"\\n\",\n \"Often, there is a second DIE Jones matrix, the *bandpass* or $\\\\mathbf{B}$-Jones which is related to the $\\\\mathbf{G}$-Jones. As we have discussed in the [$\\\\S$ 7.1 ➞](.s/7_1_jones_notation.ipynb) a Jones matrix is a function of time and frequency. But, it is often a good approximation to think of the analogue electronics effects as separable into a frequency-variable, time-stable function (the bandpass) and a time-variable, frequency-stable function (gain), such that the total, time- and frequency-dependent gain matrix $\\\\mathbf{G}'$ is\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\mathbf{G}'(t, \\\\nu) \\\\approx \\\\mathbf{G}(t) \\\\cdot \\\\mathbf{B}(\\\\nu) \\n\",\n \"$$\\n\",\n \"\\n\",\n \"Confusingly, in the literature and software, $\\\\mathbf{G}$ can be either a time-dependent or time- and frequency-dependent term, depending the author's personal definition. Keep this in mind! The most important point here is that the $\\\\mathbf{G}$ term has the biggest impact on the observed signal, out of all the Jones terms. This is why we will start with it.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 7.3.1 An Idealized Source\\n\",\n \"\\n\",\n \"Let us ignore interferometry for now, and consider a single radio telescope. For a simple sky containing only a single source, what would we expect to observe? The source should have some intrinsic flux $I$ (measured in Janskys) and some spectral index $\\\\alpha$ of the form\\n\",\n \"\\n\",\n \"$$\\n\",\n \"I(\\\\nu) = I_0 \\\\left( \\\\frac{\\\\nu}{\\\\nu_0} \\\\right)^{-\\\\alpha}\\n\",\n \"$$\\n\",\n \"\\n\",\n \"where $I_0$ is the source's flux at some reference frequency $\\\\nu_0$. We think of most radio sources as being stable over long lifetimes. The source flux will thus not change noticably over short periods of time. Though the signal we are measuring is a time-domain signal, we will look at the frequency spectrum throughout this sectionL frequency is what we are ultimately interested in for now. If we were to plot the source's flux as a function of time and observing frequency in a *waterfall plot*, it would look like the figure below:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Setup an ideal source coherency spectrum\\n\",\n \"nchans = 512 # number of frequency channels\\n\",\n \"freq0 = 1.1e9 # start frequency (Hz) (centre of first channel)\\n\",\n \"freq1 = 1.5e9 # stop frequency (Hz) (centre of last channel)\\n\",\n \"freqs = np.linspace(freq0, freq1, nchans)\\n\",\n \"ntime = 500\\n\",\n \"\\n\",\n \"I0 = 1. # flux at reference frequency (Jy)\\n\",\n \"nu0 = 1.421e9 # reference frequency (Jy)\\n\",\n \"alpha = -0.25 # source spectral index\\n\",\n \"Inu = lambda nu: I0 * (nu/nu0)**alpha # Stokes I spectrum\\n\",\n \"\\n\",\n \"# Stokes Parameters\\n\",\n \"I = Inu(freqs)\\n\",\n \"Q = np.zeros_like(I)\\n\",\n \"U = np.zeros_like(I)\\n\",\n \"V = np.zeros_like(I)\\n\",\n \"\\n\",\n \"# Compute the polarisation values\\n\",\n \"brightness = np.empty(shape=(nchans, 2, 2), dtype=np.complex128)\\n\",\n \"brightness[:,0,0] = I + Q\\n\",\n \"brightness[:,0,1] = U + V*1j\\n\",\n \"brightness[:,1,0] = U - V*1j\\n\",\n \"brightness[:,1,1] = I - Q\\n\",\n \"\\n\",\n \"brightness = np.tile(brightness[np.newaxis], [ntime, 1, 1, 1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"fig, axes = plt.subplots(figsize=(8,8))\\n\",\n \"\\n\",\n \"ax1 = plt.subplot2grid((5,5), (0,0), colspan=4, rowspan=4)\\n\",\n \"plt.imshow(np.abs(brightness[:,:,0,0]+brightness[:,:,1,1]), aspect='auto')\\n\",\n \"ax1.get_xaxis().set_visible(False)\\n\",\n \"ax1.get_yaxis().set_visible(False)\\n\",\n \"\\n\",\n \"ax2 = plt.subplot2grid((5,5), (4,0), colspan=4, rowspan=1)\\n\",\n \"plt.plot(freqs/1e9, brightness[10,:,0,0]+brightness[10,:,1,1])\\n\",\n \"plt.ylabel('flux (Jy)')\\n\",\n \"plt.xlabel('Frequency (GHz)')\\n\",\n \"\\n\",\n \"ax3 = plt.subplot2grid((5,5), (0,4), colspan=1, rowspan=4)\\n\",\n \"plt.plot(brightness[:,10,0,0]+brightness[:,10,1,1], np.arange(brightness.shape[0]))\\n\",\n \"ax3.get_xaxis().set_visible(False)\\n\",\n \"ax3.invert_yaxis()\\n\",\n \"ax3.yaxis.tick_right()\\n\",\n \"plt.ylabel('Time (s)')\\n\",\n \"ax3.yaxis.set_label_position('right')\\n\",\n \"\\n\",\n \"plt.suptitle('Idealized Source Spectrum')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The bottom plot shows the spectrum for a single moment in time, and the plot on the right shows the flux at a single frequency. The source is stable in time and frequency. This is what we would like to detect. Of course, things are not so simple in practice...any system we build will introduce noise into the measurement. Furthermore, as we have hinted at, our measurement systems alters the original signal! Instead of this nice, ideal measurement we then see something like the waterfall plot at the end of this section. We will step through the various stages of an example analogue front-end to understand why the observed spectrum differs from the original source spectrum.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 7.3.2 System Noise and Sensitivity\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"All measurement systems have noise. In fact, anything which is not at absolute zero (i.e. everything) has some energy, which is radiated as heat; another term for heat is noise. We are interested in measuring some signal. If our system introduces too much noise into the measurement, then we can not recover the original signal. When we discuss the *sensitivity* of a telescope, we are primarily interested in how much noise the system adds during measurement. This *system noise* is, among other things, a result of electronic components, the telescope's engineering design (antenna feed, dish design, etc.), and sky signals. Newer electronic components tend to have lower noise characteristics than older systems. Telescopes are thus often upgraded with newer systems.\\n\",\n \"\\n\",\n \"#### Noise as Temperature\\n\",\n \"\\n\",\n \"It is very common to discuss noise in terms of temperature. Noise is a consequence of heat, and heat is measured as a temperature. From an electronics perspective, we can consider a signal as a voltage in an electrical circuit. The signal is then a Johnson-Nyquist noise source, and the power dissapated by that noise source is:\\n\",\n \"\\n\",\n \"$$P = k_B T \\\\Delta \\\\nu$$\\n\",\n \"\\n\",\n \"Where $P$ is the power of the voltage signal, $k_B$ is the Boltzmann constant, $T$ is temperature in Kelvin, and $\\\\Delta \\\\nu$ is the bandwidth of the signal in Hertz. The noise power of a radio telescope (or a thereof) is thus often described in the literature in terms of a temperature\\n\",\n \"\\n\",\n \"$$T = \\\\frac{P}{k_B \\\\Delta \\\\nu}$$\\n\",\n \"\\n\",\n \"#### System Temperature\\n\",\n \"\\n\",\n \"If we want to determine a telescope's sensitivity, we must first understand its noise characteristics. We can then decide if it is possible to detect the signal we are after. We start by assuming that there is some temperature which defines our telescope's noise characteristics. As you might expect, this is the *system temperature*, and is made up of multiple components\\n\",\n \"\\n\",\n \"$$ T_{\\\\textrm{sys}} = T_{\\\\textrm{sky}} + T_{\\\\textrm{atmosphere}} + T_{\\\\textrm{spillover}} + T_{\\\\textrm{rx}} + \\\\ldots$$\\n\",\n \"\\n\",\n \"where $T_{\\\\textrm{sky}}$ is the stochastic signal from the sky. In the L-band (1.4 GHz) the sky temperature is approximately 10 K, 2.73 K of which are from the Cosmic Microwave Background (CMB). The sky temperature varies depending on the observing frequency,: at low frequency - say 100 MHz -, for example, the sky is close to 1000 K . This is primarily due to synchrotron radiation. We can approximate a 'room temperature' on Earth as 300 K. If the sky noise is significantly 'hotter' than room temperature, then there is clearly little advantage to building instruments with low noise characteristics.\\n\",\n \"\\n\",\n \"Depending on the observing freqency, a component of noise is also due to the atmosphere and ionosphere: $T_{\\\\textrm{atmosphere}}$. Though this noise is \\\"natural\\\", we nevertheless treat it as part of the observing system. If our telescope was placed on the moon, then there would be essentially no atmospheric noise. Further details of various propagation effects are given in [$\\\\S$ 7.7 ➞](7_7_propagation_effects.ipynb). In the L-band this is a small noise effect ($\\\\sim 1$ K); the phase rotation effects discussed later in the chapter are a much more serious effect.\\n\",\n \"\\n\",\n \"The Earth is a 300 K black-body radiation source, i.e. a very hot noise source. We would like to block the noise from the earth (known as *ground noise*) before it enters our measurement system. This can be done in two ways: cooling the electronics, and/or isolating the system. One way the ground noise enters the system is from the primary beam's illumination pattern. We will discuss primary beams later [$\\\\S$ 7.5 ➞](7_5_primary_beam.ipynb). For now, what matters is that the telescope optics produce a 'beam', which is sensitive to all locations on the sky. The optics are designed such that the beam is most sensitive to the direction towards which the dish is pointed, but there is always some sensitivity in other directions. Thus, no matter where we point the telescope, it is always sensitive - to some extent - to signal from the ground. If the telescope is pointed low on the horizon, an even more sensitive section of the beam will be pointed at the ground. This is called *spillover noise* $T_{\\\\textrm{spillover}}$, and is a pointing direction-dependent noise term. If the integrated spillover is on the order of a few precent, this can result in noise of the order of 10's of Kelvin.\\n\",\n \"\\n\",\n \"The final term of note in the system temperature is the receiver noise: $T_{\\\\textrm{rx}}$. This is the noise introduced into the measurement by the analogue electronics used to convert the weak sky signal into a digital signal which can be recorded. A KAT-7 or MeerKAT telescope produce approximately 20 K in receiver noise. We will discuss the analogue receiver system further in the rest of this section.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Radiometer Equation\\n\",\n \"\\n\",\n \"For a single radio telescope, the measurement of the average power over some bandwidth for a direction in the sky is done with a *radiometer*. Given that the noise signal we are measuring is approximately Gaussian, the error of the measured signal can be described by the *ideal radiometer equation*\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\sigma_{T} = \\\\frac{T_{\\\\textrm{sys}}}{\\\\sqrt{\\\\Delta\\\\nu \\\\tau}}\\n\",\n \"$$\\n\",\n \"\\n\",\n \"where $\\\\sigma_{T}$ is the standard deviation of the measured noise temperature about the true noise temperature. The radiometer equation describes the necessary bandwidth ($\\\\Delta \\\\nu$) and integration time ($\\\\tau$) required to reach a desired noise level ($\\\\sigma_{T}$) for an ideal system with a given system temperature ($T_{\\\\textrm{sys}}$) when observing a broadband source.\\n\",\n \"\\n\",\n \"From the radiometer equation we can see that a linear decrease in system temperature will lead to a linear reduction in $\\\\sigma_{T}$. A linear increase in the bandwidth or intergration time only leads to square-root improvement in $\\\\sigma_{T}$. For example, If there are two systems, with the same bandwidth $\\\\Delta \\\\nu$, in which one has a system temperature twice that of the other, i.e. $T_{\\\\textrm{sys,0}} = 2 T_{\\\\textrm{sys,1}}$, then $T_{\\\\textrm{sys,0}}$ will take 4 times longer to reach the same sensitivity as $T_{\\\\textrm{sys,1}}$.\\n\",\n \"\\n\",\n \"The radiometer equation above holds for an ideal system, but real-life analogue systems are unstable and introduce 'gain fluctuations'. These fluctuations are due to a number of effects: the physical temperature of the electronics, cross-coupling leakage, RF shielding, etc. The system stability over an observation will depend on how well these effects are managed. These gain fluctuations depend on both time and frequency.\\n\",\n \"\\n\",\n \"#### System Equivalent Flux Density (SEFD)\\n\",\n \"\\n\",\n \"The overall sensitivity of a radio telescope is described by the *system equivalent flux density* (SEFD) which is a ratio of the noisiness of the instrument ($T_{\\\\textrm{sys}}$) to the effective signal *gain* $G_{eff}$. Here, \\\"gain\\\" is a catch-all term for all the various amplifications and attenuations of the sky signal we are interested in. Formally the SEFD is defined as\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\textrm{SEFD} = \\\\frac{T_{\\\\textrm{sys}}}{G_{eff}} = \\\\frac{2 k_B \\\\eta T_{\\\\textrm{sys}}}{A_{eff}}\\n\",\n \"$$\\n\",\n \"\\n\",\n \"where $k_B$ is the Boltzman constant, $\\\\eta$ is an generic efficiency factor (always *greater* than 1 - not a typo!) used to represent the signal loss in various system temperature components of the telescope (e.g. dish surface inefficiencies, feed under-illumination, quantization, etc...), and $A_{eff}$ is the effective collecting area of the telescope. For a dish telescope $A_{eff}$ can be approximated, to first order, as the geometric surface area of the dish.\\n\",\n \"\\n\",\n \"A decrease in the SEFD is equivalent to an improvement in the sensitivity of the telescope.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 7.3.3 Analogue Receiver Front-End\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To understand the details of the system's temperature and gain, we need to start with the *analogue receiver front-end*, also known as the analogue chain. This is the set of analogue electronic components which are used to condition the weak sky signal in order to do digital computations (like correlation). Ideally, we prefer a digital signal for a number of reasons. Digital signals are predictable, and any computation is deterministic. It is easier to perform operations such as Fourier transforms and correlations on digital signals than to analogue signals. An advantage of analogue signals is that they are continuous: they are free of issues due to Nyquist sampling or aliasing. Analogue components, however, are affected by feedback, cross-talk, temperature, and power supplies. Worst of all, analogue components introduce noise into the signal! In general, analogue components produce variable response in time and frequency, whereas a digital system has a stable response in time and frequency.\\n\",\n \"\\n\",\n \"We would like to digitize our sky signal as soon as possible. Unfortunately, the sky signal is very weak, and needs to be amplified and conditioned before it can be converted to digital. The *analogue-to-digital converter* (ADC) transforms an analogue signal to a digital signal, but if the signal is too weak then the ADC cannot sample the analogue signal. Below is a block diagram of an example analogue front-end.\\n\",\n \"\\n\",\n \"*Amplification* is the effect of increasing the strength of a signal absolutely or relative to another signal. In electronics an amplifier might be used for this, but other forms of amplification are possible, e.g. building a larger dish to collect more signal. The opposite effect is called *attenuation*. All Jones matrices can be thought of as performing an amplification or attenuation on the input signal to produce the output signal. This amplification (attenuation can also be seen as an amplification less than 1) can be complex or real-valued, and a function of time, frequency, or direction. Some Jones matrices will have unity gain, e.g. a rotation transform. Signal *conditioning* is the application of different filters to select out a limited signal of interest.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**Figure 7.3.1**: Block diagram example of an analogue receiver front-end for a radio telescope. A current is induced in the receptor by the electric field of the sky source. The resistance of the components creates a voltage which is propotional to the amplitude of the electric field. This voltage is amplified by a low noise amplifier (LNA) in a cryostat at 20 K. A second stage amplifier further amplifies the signal in a 70 K intermediate cryostat. The signal is bandpass filtered to select the band of interest. A bandstop filter is used to filter out frequencies with radio frequency interference (RFI). A local oscillator (LO) is mixed with the signal to down mix the original radio frequency (RF) to an intermediate frequency (IF) which is then digitized by an analogue-to-digital converter (ADC). \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The electric field of the sky signal induces a current on the *receptor* (also known as a *feed*). We will discuss feeds in [$\\\\S$ 7.4 ➞](7_6_polarization.ipynb). This is a very weak signal: we need to use an amplifier to increase the strength of the signal. The process of amplification introduces noise, which we would like to minimize. As technology improves, the noise introduced by amplifiers has decreased. The common method to reduce the noise of an amplifier is by cooling it using a *cryostat*. This is a chamber placed under vacuum, which can be cooled down to very low temepratures (20 K if using liquid helium, 70 K for liquid nitrogen). Remember - noise and temperature are the same, so the lower the temperature, the lower the noise. This special amplifier is called a *low noise amplifier* (LNA), and is usually the main component of the system noise. When building a telescope system, we focus on making sure the LNA has the best possible noise performance (i.e. the LNA is a significant cost to the analogue front end).\\n\",\n \"\\n\",\n \"The idea of the LNA is that we have a weak signal which needs to be amplified while adding the smallest possible amount of noise into the signal. Once the signal has been amplified with the LNA, we can use normal amplifiers and apply filters, because the signal to noise ratio is now sufficiently large that the added noise from these components will not affect the original signal very much. This can be seen in the receiver temperature equation.\\n\",\n \"\\n\",\n \"$$ T_{\\\\textrm{rx}} = T_{\\\\textrm{feed}} + \\\\frac{T_{\\\\textrm{passive}}}{G_{\\\\textrm{feed}}} + \\\\frac{T_{\\\\textrm{LNA}}}{G_{\\\\textrm{feed}} G_{\\\\textrm{passive}}} + \\\\frac{T_{\\\\textrm{amp}}}{G_{\\\\textrm{feed}} G_{\\\\textrm{passive}} G_{\\\\textrm{LNA}}} + \\\\ldots$$\\n\",\n \"\\n\",\n \"where the $T$ terms are the additional noise temperatures introduced by each component, and $G$ is the 'gain' term - which can be less than 1 (attenuation) or greater than 1 (amplification). The gain from the feed and passive components will be less than 1, and are similar to an efficiency term in when discussing the primary beam. These will cause the temperature from these components to increase in the system temperature etienne: what does this mean, what \\\"these\\\"? Efficiencies?. By placing the LNA as soon as possible, we see that the weak sky signal is amplified and noisier components - such as filters and second stage amplifiers - can be introduced without drowning the sky signal in their noise.\\n\",\n \"\\n\",\n \"From the receiver temperature equation above, we can see that each additional temperature term includes the gain of the LNA in the denominator. Thus, the more gain is introduced by the LNA, the smaller each additional noise term. There are some noise terms which come before the LNA stage. There is always a feed temperature $T_{\\\\textrm{feed}}$ (any physical feed will have some resistance - else, there would be no current!). There are passive components such as cables, orthomode transducers (OMTs), waveguides etc. which all add a noise component $T_{\\\\textrm{passive}}$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Decibel Units\\n\",\n \"\\n\",\n \"As a quick aside, it is common to discuss electronic and system gain using logarithmic decibel units. This is worth a quick review. We usually describe a signal in linear units. It is often more intuitive, however, to use logarithmic units when discussing values which cover many orders of magnitude, such as in electronics. The standard unit to use is the *decibel*, which is defined (for a power $P$) as:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"P_{dB} = 10 \\\\log_{10} \\\\left ( \\\\frac{P}{P_0} \\\\right )\\n\",\n \"$$\\n\",\n \"\\n\",\n \"where $P_0$ is the reference power, typically set to unity. Important values to remember are: 3 dB is a factor of 2 increase, 10 dB is a factor of 10, -3 dB is a factor of 0.5. Every factor of 10 increase in decibels is an order of magnitude increase. Note that if the signal is not a power (e.g. it is a voltage or electric field) the definition of decibel is\\n\",\n \"\\n\",\n \"$$\\n\",\n \"P_{dB} = 20 \\\\log_{10} \\\\left ( \\\\frac{V}{V_0} \\\\right )\\n\",\n \"$$\\n\",\n \"\\n\",\n \"as we need to compute the power of the voltage in the process of converting to decibels. This is useful for describing the response of Jones matrices in decibels.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Returning to the LNA in our analogue front-end example, we typically amplify the sky power signal by 20 to 30 decibels with the LNA. That is, the LNA amplifies the sky signal by a factor of 100 to 1000. We can then use other components without worrying about introducing significant noise relative to the signal strength.\\n\",\n \"\\n\",\n \"Below is an example of the LNA response in time and frequency. This is a simple model, where the LNA bandpass response is stable in time but the overall system gain varies in time. The bandpass has a frequency-dependent response that ranges between 30 dB and 20 dB. There is a'ripple' across the band due to the LNA design. The LNA peaks around 1.2 GHz and drops off at higher frequencies. The overall gain varies by a few precent over time, which can be due to a number of effects. The heating and cooling of the cryostat is never perfect, so there is variation in the gain. Depending on the observing source, we should expect the gain to change: a bright source will introduce more noise than a dimmer source. Feedback and cross-talk between analogue components will always exist even with good isolation. This is thus a fairly typical LNA response, such as one could expect to see in real life.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"#LNA bandpass\\n\",\n \"#xrange: 0,1\\n\",\n \"#yrange: 20-30 dB\\n\",\n \"#a made up LNA response spectrum (in dB)\\n\",\n \"nfreqVals = np.array([-0.5, 0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45,\\n\",\n \" 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0, 1.05])\\n\",\n \"lnaVals = np.array([27, 27, 28.1, 28, 27.5, 29, 30.1, 30, 28.5, 28.5, 30,\\n\",\n \" 27, 27, 25.5, 23, 24, 25, 22.5, 21, 21, 22, 19.5, 19.5])\\n\",\n \"\\n\",\n \"#add a little noise for randomness\\n\",\n \"lnaStd = 0.5 #standard deviation of noise in dB\\n\",\n \"noiseVals = np.random.normal(loc=0.0, scale=lnaStd, size=lnaVals.shape)\\n\",\n \"\\n\",\n \"nfreqs = np.linspace(0, 1, num=nchans) #normalized frequencies\\n\",\n \"coeffs = np.polyfit(nfreqVals, lnaVals + noiseVals, deg=15)\\n\",\n \"lnaGain = 10.**(np.polyval(coeffs, nfreqs)/10) #convert to linear\\n\",\n \"\\n\",\n \"#Gain variation\\n\",\n \"#xrange: 0,1\\n\",\n \"#yrange: 1 + std\\n\",\n \"tVals = np.linspace(-0.5, 1.5, num=60)\\n\",\n \"gainStd = 0.10\\n\",\n \"gVals = np.random.normal(loc=1.0, scale=gainStd, size=tVals.shape)\\n\",\n \"\\n\",\n \"ts = np.linspace(0, 1, num=ntime)\\n\",\n \"gCoeffs = np.polyfit(tVals, gVals, deg=20)\\n\",\n \"gain = np.polyval(gCoeffs, ts)\\n\",\n \"\\n\",\n \"gainSpectrum = np.outer(gain, 10. * np.log10(lnaGain))\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(8,8))\\n\",\n \"\\n\",\n \"ax1 = plt.subplot2grid((5,5), (0,0), colspan=4, rowspan=4)\\n\",\n \"plt.imshow(np.abs(gainSpectrum), aspect='auto')\\n\",\n \"ax1.get_xaxis().set_visible(False)\\n\",\n \"ax1.get_yaxis().set_visible(False)\\n\",\n \"\\n\",\n \"ax2 = plt.subplot2grid((5,5), (4,0), colspan=4, rowspan=1)\\n\",\n \"plt.plot(freqs/1e9, gainSpectrum[100, :])\\n\",\n \"plt.ylabel('Gain (dB)')\\n\",\n \"plt.xlabel('Frequency (GHz)')\\n\",\n \"\\n\",\n \"ax3 = plt.subplot2grid((5,5), (0,4), colspan=1, rowspan=4)\\n\",\n \"plt.plot(gainSpectrum[:, 100], np.arange(gainSpectrum.shape[0]))\\n\",\n \"ax3.get_xaxis().set_visible(False)\\n\",\n \"ax3.invert_yaxis()\\n\",\n \"ax3.yaxis.tick_right()\\n\",\n \"plt.ylabel('Time (s)')\\n\",\n \"ax3.yaxis.set_label_position('right')\\n\",\n \"\\n\",\n \"plt.suptitle('LNA Response')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Filters\\n\",\n \"\\n\",\n \"Applying *signal filters* to the observed signal is necessary for a number of reasons. We are limited to a finite observing bandwdith, because our antenna feed is only sensitive to a limited frequency range. The LNA is only well-characterized for a limited bandwidth, human-made radio interference limits the usable observing band, the ADC only Nyquist samples up to a specific bandwidth before aliasing occurs, etc. All these factors define the usable bandwidth of the instrument. There will be a *start frequency* $\\\\nu_0$ - which is the lowest frequency the system records data at - and a *stop frequency* $\\\\nu_f$ - which is the highest frequency. The range within these frequencies is the *bandwidth* $\\\\Delta \\\\nu = \\\\nu_f - \\\\nu_0$. We can further define two types of bandwidth: the *analogue bandwidth* is the bandwidth of the system based on the analogue filters, and the *digital bandwidth* is defined by half the sampling rate of the ADC (i.e. the digital bandwidth is set by the Nyquist rate). \\n\",\n \"\\n\",\n \"The two building blocks of signal filtering are the *high-pass filter*, which supresses low-frequency signals while leaving high-frequency signals untouched, and the *low-pass filter* which does the opposite. Filter design is an active field of study, and a lifetime can be spent on the topic - we will not go into any depth, but merely present the basics needed to understand a radio telescope analogue front end.\\n\",\n \"\\n\",\n \"A filter works by applying a weighted average in the time domain using some number of samples (called *taps*). The weights within the filter are defined by a window function. A simple window function is a boxcar or truncated Gaussian, but there are many types depending on the desired response. When the time domain signal is transformed into the frequency domain, the window function's Fourier transform is called the *window response function*. The window response of a boxcar is the sinc function, and the response of a Gaussian filter is another Gaussian.\\n\",\n \"\\n\",\n \"To set the band of the analogue system we use a *bandpass filter* which consists of a low-pass and high-pass filter to created a limted band response. An example of a bandpass filter using a low-pass and high-pass Hann window with 33 taps is shown below. These are *finite-impulse response filters* (FIRs) which are typically used for bandpass filters. Another type of filter is the *infinite-impulse response filter* (IIRs) which we will not discuss further.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Modified from: https://scipy.github.io/old-wiki/pages/Cookbook/FIRFilter.html\\n\",\n \"# The Nyquist rate of the signal.\\n\",\n \"sample_rate = 800.0 # ADC sampling rate (MHz)\\n\",\n \"nyq_rate = sample_rate / 2.0 # Digital Bandwidth\\n\",\n \"\\n\",\n \"# Low-Pass Filter\\n\",\n \"# The cutoff frequency of the filter.\\n\",\n \"lpf_cutoff_hz = 360. # MHz\\n\",\n \"# Use firwin with a Hann window to create a low-pass FIR filter.\\n\",\n \"lpf_taps = scipy.signal.firwin(33, lpf_cutoff_hz/nyq_rate, window='hann')\\n\",\n \"\\n\",\n \"# High-Pass Filter\\n\",\n \"# The cutoff frequency of the filter.\\n\",\n \"hpf_cutoff_hz = 40. # MHz\\n\",\n \"# Use firwin with a Hann window to create a high-pass FIR filter.\\n\",\n \"hpf_taps = scipy.signal.firwin(33, hpf_cutoff_hz/nyq_rate, window='hann', pass_zero=False)\\n\",\n \"\\n\",\n \"lpf_w, lpf_h = scipy.signal.freqz(lpf_taps, worN=512)\\n\",\n \"hpf_w, hpf_h = scipy.signal.freqz(hpf_taps, worN=512)\\n\",\n \"lpfAmp = np.absolute(lpf_h)\\n\",\n \"hpfAmp = np.absolute(hpf_h)\\n\",\n \"bandpassAmp = lpfAmp*hpfAmp\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(16,6))\\n\",\n \"\\n\",\n \"plt.subplot(1, 2, 1)\\n\",\n \"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, bandpassAmp, linewidth=2)\\n\",\n \"plt.xlabel('Frequency (MHz)')\\n\",\n \"plt.ylabel('Gain')\\n\",\n \"plt.title('Bandpass Filter Response')\\n\",\n \"plt.ylim(-0.05, 1.05)\\n\",\n \"plt.grid(True)\\n\",\n \"\\n\",\n \"plt.subplot(1, 2, 2)\\n\",\n \"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, 10.*np.log10(bandpassAmp), linewidth=2)\\n\",\n \"plt.xlabel('Frequency (MHz)')\\n\",\n \"plt.ylabel('Gain (dB)')\\n\",\n \"plt.title('Bandpass Filter Response')\\n\",\n \"plt.ylim(-10, 0.2)\\n\",\n \"plt.grid(True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Designing the bandpass filter depends on a number of factors, including desired bandwidth, out-of-band suppression, and financial cost. Filter design is an engineering challenge, set by the project specifications. We will not attempt to cover the topic; see [Understanding Digital Signal Processing ⤴](https://books.google.co.za/books?id=UBU7Y2tpwWUC) for an introduction. As far as we are concerned, there are a few important characteristics. *Usable band*: this is the amount of band which can be used for observation, in the example above this is approximately 1175 MHz to 1425 MHz. *Roll-off*: this is the rate at which the filter drops in response. There is a transtion region in our example from 1100 MHz to 1175 MHz between the bandpass and bandstop regions. *Bandpass ripple*: we see that the bandpass is not perfectly flat;there is variation, especially when the filter begins to roll-off. We would like a nearly flat response across the band. The filter is continuous in frequency, so even though we would like to put a hard stop to the filter at 1100 MHz and 1500 MHz, the response is non-zero. We will be sampling the analogue signal at some Nyquist rate, which will create aliasing effects. We need to have a low enough filter response at the start and stop frequencies that signals from out of the band do not alias into the band. The cost of supressing aliasing is to reduce the overall usable band, or require sharper filters.\\n\",\n \"\\n\",\n \"The opposite of a bandpass filter is a *bandstop filter* which supresses signals within a limited frequency range. These are useful in radio astronomy to supress *radio frequency interference* (RFI) which is strong human-made radio signals. We discuss RFI in [$\\\\S$ 7.8 ➞](7_8_rfi.ipynb); for now, we simply need to kow that RFI can be orders of magnitude stronger than astronomical signals. Signals such as those of mobile telephones at 800 MHz and FM radio aroun 100 MHz (among many others) are present in most populated regions of the Earth.\\n\",\n \"\\n\",\n \"We can partially seperate out the RFI from astronomical signals when the observed signal is channelized, but before that, RFI can cause issues with the analogue front-end due to the dynamic range of the electronic components. Any analogue electronic component is designed with a limited input amplitude range, kown as the device's *dynamic range*. Within this dynamic range, the device has a *linear response*, i.e. the output can be directly mapped to the input based on a linear scaling relation. Input signals out of the dynamic range produce a non-linear response, along with feedback which can affect other components. We would like to build analogue electronics which are optimized to detect astronomical signals, and so we filter out strong RFI signals before these components are used. In the case of strong, persistent, narrow-band RFI (such as FM radio and mobile telephones) a solution is to use a bandstop filter (or *notch filter*) to supress signals within a limited band around the RFI. Below is a simple spectrum which has three strong, narrow-band RFI source.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Spectrum for strong, narrow-band RFI sources\\n\",\n \"rfiSpectrum = np.ones_like(freqs)\\n\",\n \"# Add RFI sources\\n\",\n \"rscale = 20.\\n\",\n \"idx = np.abs(freqs - 1.33e9).argmin()\\n\",\n \"rfiSpectrum[idx] += 1. * rscale\\n\",\n \"idx = np.abs(freqs - 1.34e9).argmin()\\n\",\n \"rfiSpectrum[idx] += 0.3 * rscale\\n\",\n \"idx = np.abs(freqs - 1.335e9).argmin()\\n\",\n \"rfiSpectrum[idx] += 0.5 * rscale\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(8,6))\\n\",\n \"\\n\",\n \"plt.plot(freqs/1e9, rfiSpectrum-1)\\n\",\n \"plt.title('Narrow Band RFI')\\n\",\n \"plt.xlabel('Frequency (GHz)')\\n\",\n \"plt.ylabel('Flux (Jy)')\\n\",\n \"plt.grid(True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If we do nothing to supress the RFI the resulting observed spectrum dynamic range will be dominated by the RFI as shown below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"fig, axes = plt.subplots(figsize=(8,6))\\n\",\n \"\\n\",\n \"plt.plot(freqs, bandpassAmp * rfiSpectrum, linewidth=2)\\n\",\n \"plt.xlabel('Frequency (GHz)')\\n\",\n \"plt.ylabel('Gain')\\n\",\n \"plt.title('Bandpass with RFI')\\n\",\n \"plt.grid(True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To mitigate this, we can design a notch filter to supress 40 MHz of band around these sources. In the figure below, the filter is shown in linear and decibel scales. We can see the notch filter will supress signals up to -30 dB in amplitude.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Bandstop/Notch Filter\\n\",\n \"# The cutoff frequency of the filter.\\n\",\n \"bsf_cutoff_hz = np.array([215., 255.])\\n\",\n \"# Use firwin with a Hann window to create a low-pass FIR filter.\\n\",\n \"bsf_taps = scipy.signal.firwin(73, bsf_cutoff_hz/nyq_rate, window='hann')\\n\",\n \"\\n\",\n \"bsf_w, bsf_h = scipy.signal.freqz(bsf_taps, worN=512)\\n\",\n \"bsfAmp = np.absolute(bsf_h)\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(16,6))\\n\",\n \"\\n\",\n \"plt.subplot(1, 2, 1)\\n\",\n \"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, bsfAmp, linewidth=2)\\n\",\n \"plt.xlabel('Frequency (MHz)')\\n\",\n \"plt.ylabel('Gain')\\n\",\n \"plt.title('Notch Filter Response')\\n\",\n \"plt.ylim(-0.05, 1.05)\\n\",\n \"plt.grid(True)\\n\",\n \"\\n\",\n \"plt.subplot(1, 2, 2)\\n\",\n \"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, 10.*np.log10(bsfAmp), linewidth=2)\\n\",\n \"plt.xlabel('Frequency (MHz)')\\n\",\n \"plt.ylabel('Gain (dB)')\\n\",\n \"plt.title('Notch Filter Response')\\n\",\n \"plt.ylim(-35, 1.)\\n\",\n \"plt.grid(True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can then combine the bandstop filter with the bandpass filter, shown below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"fig, axes = plt.subplots(figsize=(16,6))\\n\",\n \"\\n\",\n \"plt.subplot(1, 2, 1)\\n\",\n \"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, bandpassAmp*bsfAmp, linewidth=2)\\n\",\n \"plt.xlabel('Frequency (MHz)')\\n\",\n \"plt.ylabel('Gain')\\n\",\n \"plt.title('Bandpass Filter w/ Notch Filter Response')\\n\",\n \"plt.ylim(-0.05, 1.05)\\n\",\n \"plt.grid(True)\\n\",\n \"\\n\",\n \"plt.subplot(1, 2, 2)\\n\",\n \"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, 10.*np.log10(bandpassAmp*bsfAmp), linewidth=2)\\n\",\n \"plt.xlabel('Frequency (MHz)')\\n\",\n \"plt.ylabel('Gain (dB)')\\n\",\n \"plt.title('Bandpass Filter w/ Notch Filter Response')\\n\",\n \"plt.ylim(-10, 0.2)\\n\",\n \"plt.grid(True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The resulting observed RFI signal (shown below) is significantly suppressed by the notch filter, at the cost of losing usable bandwidth. This trade-off that is often necessary.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"fig, axes = plt.subplots(figsize=(8,6))\\n\",\n \"\\n\",\n \"plt.plot(freqs, bandpassAmp * bsfAmp * rfiSpectrum, linewidth=2)\\n\",\n \"plt.xlabel('Frequency (GHz)')\\n\",\n \"plt.ylabel('Gain')\\n\",\n \"plt.title('Bandpass with Notch Filter for RFI')\\n\",\n \"plt.grid(True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Hetrodyne Mixing\\n\",\n \"\\n\",\n \"After the filters in our block diagram analogue front-end example, there is a mixer with a local oscillator. This is know as a *hetrodyne detection*, which is the process of 'mixing' down the original radio frequencies (RF) $\\\\nu_{\\\\textrm{RF}}$ to an intermediate frequency (IF) $\\\\nu_{\\\\textrm{IF}}$ using a local oscillator (LO) $\\\\nu_{\\\\textrm{LO}}$ such that the signal can be sampled digitally. A *local oscillator* is a pure, single-frequency tone. Mixing of the RF and LO results in $\\\\nu_{\\\\textrm{IF}} = \\\\nu_{\\\\textrm{RF}} - \\\\nu_{\\\\textrm{LO}}$. Consider two pure sine waves $\\\\sin(2 \\\\pi \\\\nu_{\\\\textrm{RF}})$, $\\\\sin(2 \\\\pi \\\\nu_{\\\\textrm{LO}})$, mixing is the process of multiplying the signals together, using trigonomic identities this results in\\n\",\n \"\\n\",\n \"$$\\\\sin(2 \\\\pi \\\\nu_{\\\\textrm{RF}}) \\\\cdot \\\\sin(2 \\\\pi \\\\nu_{\\\\textrm{LO}}) = \\\\frac{1}{2} \\\\cos(2 \\\\pi (\\\\nu_{\\\\textrm{RF}} - \\\\nu_{\\\\textrm{LO}})t) + \\\\frac{1}{2} \\\\cos(2 \\\\pi (\\\\nu_{\\\\textrm{RF}} + \\\\nu_{\\\\textrm{LO}})t) $$\\n\",\n \"\\n\",\n \"Because we have used a bandpass filter, any signal above the stop frequency will be suppressed, so the $\\\\cos(2 \\\\pi (\\\\nu_{\\\\textrm{RF}} + \\\\nu_{\\\\textrm{LO}})t)$ term can be dropped, resulting in a mixer output of $\\\\frac{1}{2} \\\\cos(2 \\\\pi (\\\\nu_{\\\\textrm{RF}} - \\\\nu_{\\\\textrm{LO}})t)$. The mixer has shifted the original signal at frequency $\\\\nu_{\\\\textrm{RF}}$ down to $\\\\nu_{\\\\textrm{IF}} = \\\\nu_{\\\\textrm{RF}} - \\\\nu_{\\\\textrm{LO}}$.\\n\",\n \"\\n\",\n \"The process of hetrodyne mixing is very useful. From an engineering perspective, it is much easier to build electronics at lower frequencies than higher frequencies. Using a mixer a high frequency signal is mixed down to lower frequencies, and cheaper electronics can be used. Without heterodyne mixers, the alternative is to to use *direct RF sampling*. This is common is very low frequency radio astronomy (< 200 MHz). As technology improves, direct RF sampling will see more use at higher frequencies, but for now at least, most radio telescopes uses hetrodyne mixing.\\n\",\n \"\\n\",\n \"With nearly all the analogue components in place (we will come to the digitization process soon) we are ready to construct the observed spectrum of our idealized source (where we started the section) while including the effects due to the various analogue electronics. As we can see in the figure below, it is quite different from the idealized spectrum we started with! The bottom figure shows the time-dependent bandpass change, and the figure to the right shows the frequency-dependent gain change.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Observed Source Spectrum\\n\",\n \"# noise spectrum\\n\",\n \"noiseSpectrum = np.random.normal(loc=0., scale=2., size=gainSpectrum.shape)\\n\",\n \"\\n\",\n \"# bandpass spectrum\\n\",\n \"bpSpectrum = np.repeat(bandpassAmp * bsfAmp * rfiSpectrum, ntime).reshape(nchans, ntime)\\n\",\n \"\\n\",\n \"# observed spectrum\\n\",\n \"obsSpectrum = bpSpectrum.T * ( (gainSpectrum * np.abs(brightness[:,:,0,0]+brightness[:,:,1,1])) + noiseSpectrum)\\n\",\n \"\\n\",\n \"# local oscillator frequencies\\n\",\n \"lo = 1.1e9\\n\",\n \"ifFreqs = freqs - lo\\n\",\n \"\\n\",\n \"# save spectrum for use in the next section\\n\",\n \"# np.savez('data/analogue_spectrum.npz', obsSpectrum, ifFreqs)\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(8,8))\\n\",\n \"\\n\",\n \"ax1 = plt.subplot2grid((5,5), (0,0), colspan=4, rowspan=4)\\n\",\n \"plt.imshow(np.abs(obsSpectrum), aspect='auto')\\n\",\n \"ax1.get_xaxis().set_visible(False)\\n\",\n \"ax1.get_yaxis().set_visible(False)\\n\",\n \"\\n\",\n \"ax2 = plt.subplot2grid((5,5), (4,0), colspan=4, rowspan=1)\\n\",\n \"plt.plot(ifFreqs/1e9, obsSpectrum[50, :])\\n\",\n \"plt.plot(ifFreqs/1e9, obsSpectrum[250, :])\\n\",\n \"plt.plot(ifFreqs/1e9, obsSpectrum[450, :])\\n\",\n \"plt.xlim(ifFreqs[0]/1e9, ifFreqs[-1]/1e9)\\n\",\n \"plt.ylabel('Flux')\\n\",\n \"plt.xlabel('Frequency (GHz)')\\n\",\n \"\\n\",\n \"ax3 = plt.subplot2grid((5,5), (0,4), colspan=1, rowspan=4)\\n\",\n \"plt.plot(obsSpectrum[:, 130], np.arange(obsSpectrum.shape[0]))\\n\",\n \"plt.plot(obsSpectrum[:, 260], np.arange(obsSpectrum.shape[0]))\\n\",\n \"plt.plot(obsSpectrum[:, 390], np.arange(obsSpectrum.shape[0]))\\n\",\n \"ax3.get_xaxis().set_visible(False)\\n\",\n \"ax3.invert_yaxis()\\n\",\n \"ax3.yaxis.tick_right()\\n\",\n \"plt.ylabel('Time (s)')\\n\",\n \"ax3.yaxis.set_label_position('right')\\n\",\n \"\\n\",\n \"plt.suptitle('Observed Spectrum')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"System noise has hidden the smooth source structure. The LNA response has scaled the flux values and added a bit of ripple to the source. The bandpass filter has supressed the source response at the edges of the band. The notch filter has supressed the RFI signal and part of the source signal. The signals range from 0 MHz (DC) to 400 MHz due to the hetrodyne mixer.\\n\",\n \"\\n\",\n \"We can see that the analogue front-end transforms the original astronomical signal into the observed signal: this is what the $\\\\mathbf{G}$-Jones describes. It is generally a time- and frequency-dependent Jones matrix. The time and frequency dependence is due to the variation in response from the analogue electronic components used to filter, condition, and amplify the input voltage in order to digitally sample the signal. The generic $\\\\mathbf{G}$-Jones is often broken into two components: a time-stable bandpass $\\\\mathbf{B}$-Jones matrix which represents the filter responses and the general LNA response, and a frequency-stable $\\\\mathbf{G}$-Jones matrix which accounts for the time-dependent variation of the overall analogue system.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 7.3.4 Digitisation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After signal conditioning in the analogue front-end, we are ready to capture the signal into a digital form so that we can perform the correlation. The term digitisation is worth reflecting upon: it is the process of taking a time-continuous, analogue signal, and converting it to a time-discrete voltage signal (which are 'digits' described by integers). The last analogue component is the *analogue to digital converter (ADC)*. A simple form of analogue-to-digital conversion is a cascading resistor ladder, which measures discrete steps in an analogue signal. Each output of the ladder triggers a bit value of either 0 or 1 - a digital signal is thus created. See Chapter 13 of [The Art of Electronics ⤴](http://artofelectronics.net/).\\n\",\n \"\\n\",\n \"The dynamic range of an ADC is determined by the number of bits an input analogue signal can be digitized into. For an $n$-bit ADC there are $2^n$ possible digital values, by convention this range is $[-2^{n-1}, 2^{n-1}-1]$. Just as in other electronic components, an ADC has input range in which the resulting digitisation produces linear results. An astronomical signal needs to be amplified such that the analogue signal is within this linear range. The signal is then digitized: an example of a 4-bit (256 values) ADC is shown in the figure below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"adcBits = 4 # number of bits in the ADC\\n\",\n \"adcMin = -1 * 2**(adcBits-1) # minimum ADC value\\n\",\n \"adcMax = 2**(adcBits-1) - 1 # maximumx ADC value\\n\",\n \"\\n\",\n \"nsamp = 256 # number of time samples\\n\",\n \"phi0 = .1*np.pi # starting phase\\n\",\n \"dphi = 6.*np.pi # length\\n\",\n \"phis = np.linspace(0, dphi, nsamp)\\n\",\n \"amp = 6.5 # signal amplitude\\n\",\n \"\\n\",\n \"analogueSignal = amp*np.sin(phis+phi0)\\n\",\n \"digitalSignal = analogueSignal.astype(int)\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(16,8))\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 1)\\n\",\n \"plt.plot(analogueSignal, c='k', label='analogue', linewidth=3, alpha=0.3)\\n\",\n \"plt.plot(digitalSignal, '.', label='digital', linewidth=3)\\n\",\n \"plt.ylim(adcMin-1, adcMax+1)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.axhline(adcMin, ls='--', c='k')\\n\",\n \"plt.axhline(adcMax, ls='--', c='k')\\n\",\n \"plt.title('Signal Digitization (4-bit)')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.legend()\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 2)\\n\",\n \"plt.plot(analogueSignal-digitalSignal)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.ylim(-1.2, 1.2)\\n\",\n \"plt.title('Digitization Error')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.xlabel('Time Sample')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The digitization results in time-discrete samples. The amplitude of each sample is the closest step in the ADC to the analogue amplitude. This results in *digitization error* as the conversion to digital results in information loss. To reduce the digitisation error, the maximum number of steps in the ADC should be used. This requires knowledge of the input signal amplitude. If the signal is too weak, then only a few bits will be used in digitisation and the dynamic range is not being used optimally; this is shown in the figure below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"adcBits = 4 # number of bits in the ADC\\n\",\n \"adcMin = -1 * 2**(adcBits-1) # minimum ADC value\\n\",\n \"adcMax = 2**(adcBits-1) - 1 # maximumx ADC value\\n\",\n \"\\n\",\n \"nsamp = 256 # number of time samples\\n\",\n \"phi0 = .1*np.pi # starting phase\\n\",\n \"dphi = 6.*np.pi # length\\n\",\n \"phis = np.linspace(0, dphi, nsamp)\\n\",\n \"amp = 3. # signal amplitude\\n\",\n \"\\n\",\n \"analogueSignal = amp*np.sin(phis+phi0)\\n\",\n \"digitalSignal = analogueSignal.astype(int)\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(16,8))\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 1)\\n\",\n \"plt.plot(analogueSignal, c='k', label='analogue', linewidth=3, alpha=0.3)\\n\",\n \"plt.plot(digitalSignal, '.', label='digital', linewidth=3)\\n\",\n \"plt.ylim(adcMin-1, adcMax+1)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.axhline(adcMin, ls='--', c='k')\\n\",\n \"plt.axhline(adcMax, ls='--', c='k')\\n\",\n \"plt.title('Signal Digitization (4-bit)')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.legend()\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 2)\\n\",\n \"plt.plot(analogueSignal-digitalSignal)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.ylim(-1.2, 1.2)\\n\",\n \"plt.title('Digitization Error')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.xlabel('Time Sample')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Another solution to reduce digitization error is to use an ADC with more bits, such as the 6-bit ADC figure below. As the number of bits increases, digitisation will eventually approach the true analogue signal amplitude.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"adcBits = 6 # number of bits in the ADC\\n\",\n \"adcMin = -1 * 2**(adcBits-1) # minimum ADC value\\n\",\n \"adcMax = 2**(adcBits-1) - 1 # maximumx ADC value\\n\",\n \"\\n\",\n \"nsamp = 256 # number of time samples\\n\",\n \"phi0 = .1*np.pi # starting phase\\n\",\n \"dphi = 6.*np.pi # length\\n\",\n \"phis = np.linspace(0, dphi, nsamp)\\n\",\n \"amp = 28 # signal amplitude\\n\",\n \"\\n\",\n \"analogueSignal = amp*np.sin(phis+phi0)\\n\",\n \"digitalSignal = analogueSignal.astype(int)\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(16,8))\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 1)\\n\",\n \"plt.plot(analogueSignal, c='k', label='analogue', linewidth=3, alpha=0.3)\\n\",\n \"plt.plot(digitalSignal, '.', label='digital', linewidth=3)\\n\",\n \"plt.ylim(adcMin-1, adcMax+1)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.axhline(adcMin, ls='--', c='k')\\n\",\n \"plt.axhline(adcMax, ls='--', c='k')\\n\",\n \"plt.title('Signal Digitization (6-bit)')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.legend()\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 2)\\n\",\n \"plt.plot(analogueSignal-digitalSignal)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.ylim(-1.2, 1.2)\\n\",\n \"plt.title('Digitization Error')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.xlabel('Time Sample')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A problem in radio astronomy is that we wish to maximize the dynamic range of the ADC for a weak astronomical signal, but observe in an environment with strong RFI which dominate the time-domain telescope signal. We need to sample the full dynamic range of the RFI signal. If the maximum/minimum ADC value is smaller than the RFI signal then the RFI will *saturate* the ADC, causing the output of the ADC to sit at the highest or lowest value and no sky signal to be captured: this is shown in the figure below. This results in *spectral leakage*, where a signal which is located at a specific frequency 'leaks' into nearby frequencies due to instrumental effects. A strong RFI source is usually a strong sinusoidal wave. When we take the Fourier transform of that signal, a strong $\\\\delta$-function is produced in the resulting spectrum, isolated to a single frequency channel. If the ADC is saturated, then the RFI sinusoid will be clipped and turn into a square wave, the Fourier transform of which is similar to the Fourier transform of a boxcar function: a sinc function. A sinc function has longer extended wings - these will leak into the other channels. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"adcBits = 4 # number of bits in the ADC\\n\",\n \"adcMin = -1 * 2**(adcBits-1) # minimum ADC value\\n\",\n \"adcMax = 2**(adcBits-1) - 1 # maximumx ADC value\\n\",\n \"\\n\",\n \"amp = 2. # signal amplitude\\n\",\n \"rfiAmp = 8.5 # RFI signal amplitude\\n\",\n \"rfiPhi0 = 1.35*np.pi\\n\",\n \"rfiFreq = 3.37 # frequecy relative to signal frequency\\n\",\n \"\\n\",\n \"analogueSignal = rfiAmp*np.sin(rfiFreq*(phis+rfiPhi0)) + amp*np.sin(phis+phi0)\\n\",\n \"digitalSignal = analogueSignal.astype(int).clip(adcMin, adcMax)\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(16,8))\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 1)\\n\",\n \"plt.plot(amp*np.sin(phis+phi0), c='r', label='signal', linewidth=3, alpha=0.3)\\n\",\n \"plt.plot(analogueSignal, c='k', label='RFI + signal', linewidth=3, alpha=0.3)\\n\",\n \"plt.plot(digitalSignal, '.', label='digital', linewidth=3)\\n\",\n \"plt.ylim(adcMin-4, adcMax+4)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.axhline(adcMin, ls='--', c='k')\\n\",\n \"plt.axhline(adcMax, ls='--', c='k')\\n\",\n \"plt.title('Signal + RFI Digitization (4-bit)')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.legend()\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 2)\\n\",\n \"plt.plot(analogueSignal-digitalSignal)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.title('Digitization Error')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.xlabel('Time Sample')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Not only does saturation cause spectral leakage, but there is significant digitisation error. The naive solution is to attenuate the in-out signal such that the RFI does not cause the ADC to saturate. If we turn off the RFI, however, we can see in the figure below that the sky signal is not well-sampled by the ADC. We are not using the full dynamic range of the ADC. Again, the solution is to use an ADC with more bits to balance RFI from saturating and sampling the sky signal to a sufficient level.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"analogueSignal = amp*np.sin(phis+phi0)\\n\",\n \"digitalSignal = analogueSignal.astype(int).clip(adcMin, adcMax)\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(16,8))\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 1)\\n\",\n \"plt.plot(analogueSignal, c='r', label='signal', linewidth=3, alpha=0.3)\\n\",\n \"plt.plot(digitalSignal, '.', label='digital', linewidth=3)\\n\",\n \"plt.ylim(adcMin-4, adcMax+4)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.axhline(adcMin, ls='--', c='k')\\n\",\n \"plt.axhline(adcMax, ls='--', c='k')\\n\",\n \"plt.title('Signal Digitization (4-bit, Undersampled)')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.legend()\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 2)\\n\",\n \"plt.plot(analogueSignal-digitalSignal)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.title('Digitization Error')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.xlabel('Time Sample')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A typical, modern ADC used for radio astronomy will have 8-12 bits. This is necessary for the strong -RFI environment many telescopes operate in. There is an additional issue for *wide-band system*, which are telescopes which cover significant fractional bandwidth. Most astronomical radio sources are continuum sources, i.e. the spectrum of the source is smooth and continuous in frequency. One way to improve the sensitivity of the telescope, as per the radiometer equation, is to increase the observing bandwidth. This is a fine idea, but means that there is necessarily more power being observed by the telescope, and thus requires an ADC with increased dynamic range compared to a narrow-band system.\\n\",\n \"\\n\",\n \"In the past, all the electronic components of a radio telescope were analogue. As we have seen, analogue components add noise and distort signals. What we really want is to digitise the observed signal. This has the advantage of allowing us to perfectly control the signal, at the cost of turning a continuous signal into a discrete one. Analogue components are still required to amplify and filter the signal, but modern radio telescope design aims to convert the signal to digital as soon as possible. Once we have a digital signal we are free to do what we want with the signal - in our case, correlate it.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"***\\n\",\n \"\\n\",\n \"* [Outline](../0_Introduction/0_introduction.ipynb)\\n\",\n \"* [Glossary](../0_Introduction/1_glossary.ipynb)\\n\",\n \"* [7. Observing Systems](0_introduction.ipynb) \\n\",\n \" * Previous: [7.2 The Radio Interferometer Measurement Equation (RIME)](7_2_rime.ipynb) \\n\",\n \" * Next: [7.4 Digital Correlators](7_4_digital.ipynb)\\n\",\n \"\\n\",\n \"***\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"![](\\\"https://raw.githubusercontent.com/google/or-tools/master/tools/colab_32px.png\\\"/) Run in Google Colab\\n\",\n \" | \\n\",\n \"\\n\",\n \"![](\\\"https://raw.githubusercontent.com/google/or-tools/master/tools/github_32px.png\\\"/) View source on GitHub\\n\",\n \" | \\n\",\n \"
\\n\",\n \"\\n\",\n \"Generating Maps with Python
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"## Introduction\\n\",\n \"\\n\",\n \"In this lab, we will learn how to create maps for different objectives. To do that, we will part ways with Matplotlib and work with another Python visualization library, namely **Folium**. What is nice about **Folium** is that it was developed for the sole purpose of visualizing geospatial data. While other libraries are available to visualize geospatial data, such as **plotly**, they might have a cap on how many API calls you can make within a defined time frame. **Folium**, on the other hand, is completely free.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"## Table of Contents\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"1. [Exploring Datasets with *p*andas](#0)
\\n\",\n \"2. [Downloading and Prepping Data](#2)
\\n\",\n \"3. [Introduction to Folium](#4)
\\n\",\n \"4. [Map with Markers](#6)
\\n\",\n \"5. [Choropleth Maps](#8)
\\n\",\n \"
\\n\",\n \"\\n\",\n \"2. [Downloading and Prepping Data](#2)
\\n\",\n \"3. [Introduction to Folium](#4)
\\n\",\n \"4. [Map with Markers](#6)
\\n\",\n \"5. [Choropleth Maps](#8)
\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Exploring Datasets with *pandas* and Matplotlib\\n\",\n \"\\n\",\n \"Toolkits: This lab heavily relies on [*pandas*](http://pandas.pydata.org/) and [**Numpy**](http://www.numpy.org/) for data wrangling, analysis, and visualization. The primary plotting library we will explore in this lab is [**Folium**](https://github.com/python-visualization/folium/).\\n\",\n \"\\n\",\n \"Datasets: \\n\",\n \"\\n\",\n \"1. San Francisco Police Department Incidents for the year 2016 - [Police Department Incidents](https://data.sfgov.org/Public-Safety/Police-Department-Incidents-Previous-Year-2016-/ritf-b9ki) from San Francisco public data portal. Incidents derived from San Francisco Police Department (SFPD) Crime Incident Reporting system. Updated daily, showing data for the entire year of 2016. Address and location has been anonymized by moving to mid-block or to an intersection. \\n\",\n \"\\n\",\n \"2. Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml) from United Nation's website. The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. For this lesson, we will focus on the Canadian Immigration data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Downloading and Prepping Data \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Import Primary Modules:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np # useful for many scientific computing in Python\\n\",\n \"import pandas as pd # primary data structure library\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Introduction to Folium \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Folium is a powerful Python library that helps you create several types of Leaflet maps. The fact that the Folium results are interactive makes this library very useful for dashboard building.\\n\",\n \"\\n\",\n \"From the official Folium documentation page:\\n\",\n \"\\n\",\n \"> Folium builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the Leaflet.js library. Manipulate your data in Python, then visualize it in on a Leaflet map via Folium.\\n\",\n \"\\n\",\n \"> Folium makes it easy to visualize data that's been manipulated in Python on an interactive Leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing Vincent/Vega visualizations as markers on the map.\\n\",\n \"\\n\",\n \"> The library has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys. Folium supports both GeoJSON and TopoJSON overlays, as well as the binding of data to those overlays to create choropleth maps with color-brewer color schemes.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"#### Let's install **Folium**\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Folium** is not available by default. So, we first need to install it before we are able to import it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"!conda install -c conda-forge folium=0.5.0 --yes\\n\",\n \"import folium\\n\",\n \"\\n\",\n \"print('Folium installed and imported!')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Generating the world map is straigtforward in **Folium**. You simply create a **Folium** *Map* object and then you display it. What is attactive about **Folium** maps is that they are interactive, so you can zoom into any region of interest despite the initial zoom level. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# define the world map\\n\",\n \"world_map = folium.Map()\\n\",\n \"\\n\",\n \"# display world map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Go ahead. Try zooming in and out of the rendered map above.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"You can customize this default definition of the world map by specifying the centre of your map and the intial zoom level. \\n\",\n \"\\n\",\n \"All locations on a map are defined by their respective *Latitude* and *Longitude* values. So you can create a map and pass in a center of *Latitude* and *Longitude* values of **[0, 0]**. \\n\",\n \"\\n\",\n \"For a defined center, you can also define the intial zoom level into that location when the map is rendered. **The higher the zoom level the more the map is zoomed into the center**.\\n\",\n \"\\n\",\n \"Let's create a map centered around Canada and play with the zoom level to see how it affects the rendered map.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# define the world map centered around Canada with a low zoom level\\n\",\n \"world_map = folium.Map(location=[56.130, -106.35], zoom_start=4)\\n\",\n \"\\n\",\n \"# display world map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's create the map again with a higher zoom level\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# define the world map centered around Canada with a higher zoom level\\n\",\n \"world_map = folium.Map(location=[56.130, -106.35], zoom_start=8)\\n\",\n \"\\n\",\n \"# display world map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"As you can see, the higher the zoom level the more the map is zoomed into the given center.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Question**: Create a map of Mexico with a zoom level of 4.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"### type your answer here\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Double-click __here__ for the solution.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Another cool feature of **Folium** is that you can generate different map styles.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"### A. Stamen Toner Maps\\n\",\n \"\\n\",\n \"These are high-contrast B+W (black and white) maps. They are perfect for data mashups and exploring river meanders and coastal zones. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's create a Stamen Toner map of canada with a zoom level of 4.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# create a Stamen Toner map of the world centered around Canada\\n\",\n \"world_map = folium.Map(location=[56.130, -106.35], zoom_start=4, tiles='Stamen Toner')\\n\",\n \"\\n\",\n \"# display map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Feel free to zoom in and out to see how this style compares to the default one.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"### B. Stamen Terrain Maps\\n\",\n \"\\n\",\n \"These are maps that feature hill shading and natural vegetation colors. They showcase advanced labeling and linework generalization of dual-carriageway roads.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's create a Stamen Terrain map of Canada with zoom level 4.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# create a Stamen Toner map of the world centered around Canada\\n\",\n \"world_map = folium.Map(location=[56.130, -106.35], zoom_start=4, tiles='Stamen Terrain')\\n\",\n \"\\n\",\n \"# display map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Feel free to zoom in and out to see how this style compares to Stamen Toner and the default style.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"### C. Mapbox Bright Maps\\n\",\n \"\\n\",\n \"These are maps that quite similar to the default style, except that the borders are not visible with a low zoom level. Furthermore, unlike the default style where country names are displayed in each country's native language, *Mapbox Bright* style displays all country names in English.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's create a world map with this style.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# create a world map with a Mapbox Bright style.\\n\",\n \"world_map = folium.Map(tiles='Mapbox Bright')\\n\",\n \"\\n\",\n \"# display the map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Zoom in and notice how the borders start showing as you zoom in, and the displayed country names are in English.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Question**: Create a map of Mexico to visualize its hill shading and natural vegetation. Use a zoom level of 6.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"### type your answer here\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Double-click __here__ for the solution.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Maps with Markers \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's download and import the data on police department incidents using *pandas* `read_csv()` method.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Download the dataset and read it into a *pandas* dataframe:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"df_incidents = pd.read_csv('https://ibm.box.com/shared/static/nmcltjmocdi8sd5tk93uembzdec8zyaq.csv')\\n\",\n \"\\n\",\n \"print('Dataset downloaded and read into a pandas dataframe!')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's take a look at the first five items in our dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"df_incidents.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"So each row consists of 13 features:\\n\",\n \"> 1. **IncidntNum**: Incident Number\\n\",\n \"> 2. **Category**: Category of crime or incident\\n\",\n \"> 3. **Descript**: Description of the crime or incident\\n\",\n \"> 4. **DayOfWeek**: The day of week on which the incident occurred\\n\",\n \"> 5. **Date**: The Date on which the incident occurred\\n\",\n \"> 6. **Time**: The time of day on which the incident occurred\\n\",\n \"> 7. **PdDistrict**: The police department district\\n\",\n \"> 8. **Resolution**: The resolution of the crime in terms whether the perpetrator was arrested or not\\n\",\n \"> 9. **Address**: The closest address to where the incident took place\\n\",\n \"> 10. **X**: The longitude value of the crime location \\n\",\n \"> 11. **Y**: The latitude value of the crime location\\n\",\n \"> 12. **Location**: A tuple of the latitude and the longitude values\\n\",\n \"> 13. **PdId**: The police department ID\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's find out how many entries there are in our dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"df_incidents.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"So the dataframe consists of 150,500 crimes, which took place in the year 2016. In order to reduce computational cost, let's just work with the first 100 incidents in this dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# get the first 100 crimes in the df_incidents dataframe\\n\",\n \"limit = 100\\n\",\n \"df_incidents = df_incidents.iloc[0:limit, :]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's confirm that our dataframe now consists only of 100 crimes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"df_incidents.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Now that we reduced the data a little bit, let's visualize where these crimes took place in the city of San Francisco. We will use the default style and we will initialize the zoom level to 12. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# San Francisco latitude and longitude values\\n\",\n \"latitude = 37.77\\n\",\n \"longitude = -122.42\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# create map and display it\\n\",\n \"sanfran_map = folium.Map(location=[latitude, longitude], zoom_start=12)\\n\",\n \"\\n\",\n \"# display the map of San Francisco\\n\",\n \"sanfran_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Now let's superimpose the locations of the crimes onto the map. The way to do that in **Folium** is to create a *feature group* with its own features and style and then add it to the sanfran_map.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# instantiate a feature group for the incidents in the dataframe\\n\",\n \"incidents = folium.map.FeatureGroup()\\n\",\n \"\\n\",\n \"# loop through the 100 crimes and add each to the incidents feature group\\n\",\n \"for lat, lng, in zip(df_incidents.Y, df_incidents.X):\\n\",\n \" incidents.add_child(\\n\",\n \" folium.features.CircleMarker(\\n\",\n \" [lat, lng],\\n\",\n \" radius=5, # define how big you want the circle markers to be\\n\",\n \" color='yellow',\\n\",\n \" fill=True,\\n\",\n \" fill_color='blue',\\n\",\n \" fill_opacity=0.6\\n\",\n \" )\\n\",\n \" )\\n\",\n \"\\n\",\n \"# add incidents to map\\n\",\n \"sanfran_map.add_child(incidents)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"You can also add some pop-up text that would get displayed when you hover over a marker. Let's make each marker display the category of the crime when hovered over.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# instantiate a feature group for the incidents in the dataframe\\n\",\n \"incidents = folium.map.FeatureGroup()\\n\",\n \"\\n\",\n \"# loop through the 100 crimes and add each to the incidents feature group\\n\",\n \"for lat, lng, in zip(df_incidents.Y, df_incidents.X):\\n\",\n \" incidents.add_child(\\n\",\n \" folium.features.CircleMarker(\\n\",\n \" [lat, lng],\\n\",\n \" radius=5, # define how big you want the circle markers to be\\n\",\n \" color='yellow',\\n\",\n \" fill=True,\\n\",\n \" fill_color='blue',\\n\",\n \" fill_opacity=0.6\\n\",\n \" )\\n\",\n \" )\\n\",\n \"\\n\",\n \"# add pop-up text to each marker on the map\\n\",\n \"latitudes = list(df_incidents.Y)\\n\",\n \"longitudes = list(df_incidents.X)\\n\",\n \"labels = list(df_incidents.Category)\\n\",\n \"\\n\",\n \"for lat, lng, label in zip(latitudes, longitudes, labels):\\n\",\n \" folium.Marker([lat, lng], popup=label).add_to(sanfran_map) \\n\",\n \" \\n\",\n \"# add incidents to map\\n\",\n \"sanfran_map.add_child(incidents)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Isn't this really cool? Now you are able to know what crime category occurred at each marker.\\n\",\n \"\\n\",\n \"If you find the map to be so congested will all these markers, there are two remedies to this problem. The simpler solution is to remove these location markers and just add the text to the circle markers themselves as follows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# create map and display it\\n\",\n \"sanfran_map = folium.Map(location=[latitude, longitude], zoom_start=12)\\n\",\n \"\\n\",\n \"# loop through the 100 crimes and add each to the map\\n\",\n \"for lat, lng, label in zip(df_incidents.Y, df_incidents.X, df_incidents.Category):\\n\",\n \" folium.features.CircleMarker(\\n\",\n \" [lat, lng],\\n\",\n \" radius=5, # define how big you want the circle markers to be\\n\",\n \" color='yellow',\\n\",\n \" fill=True,\\n\",\n \" popup=label,\\n\",\n \" fill_color='blue',\\n\",\n \" fill_opacity=0.6\\n\",\n \" ).add_to(sanfran_map)\\n\",\n \"\\n\",\n \"# show map\\n\",\n \"sanfran_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"The other proper remedy is to group the markers into different clusters. Each cluster is then represented by the number of crimes in each neighborhood. These clusters can be thought of as pockets of San Francisco which you can then analyze separately.\\n\",\n \"\\n\",\n \"To implement this, we start off by instantiating a *MarkerCluster* object and adding all the data points in the dataframe to this object.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from folium import plugins\\n\",\n \"\\n\",\n \"# let's start again with a clean copy of the map of San Francisco\\n\",\n \"sanfran_map = folium.Map(location = [latitude, longitude], zoom_start = 12)\\n\",\n \"\\n\",\n \"# instantiate a mark cluster object for the incidents in the dataframe\\n\",\n \"incidents = plugins.MarkerCluster().add_to(sanfran_map)\\n\",\n \"\\n\",\n \"# loop through the dataframe and add each data point to the mark cluster\\n\",\n \"for lat, lng, label, in zip(df_incidents.Y, df_incidents.X, df_incidents.Category):\\n\",\n \" folium.Marker(\\n\",\n \" location=[lat, lng],\\n\",\n \" icon=None,\\n\",\n \" popup=label,\\n\",\n \" ).add_to(incidents)\\n\",\n \"\\n\",\n \"# display map\\n\",\n \"sanfran_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Notice how when you zoom out all the way, all markers are grouped into one cluster, *the global cluster*, of 100 markers or crimes, which is the total number of crimes in our dataframe. Once you start zooming in, the *global cluster* will start breaking up into smaller clusters. Zooming in all the way will result in individual markers.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Choropleth Maps \\n\",\n \"\\n\",\n \"A `Choropleth` map is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map, such as population density or per-capita income. The choropleth map provides an easy way to visualize how a measurement varies across a geographic area or it shows the level of variability within a region. Below is a `Choropleth` map of the US depicting the population by square mile per state.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Now, let's create our own `Choropleth` map of the world depicting immigration from various countries to Canada.\\n\",\n \"\\n\",\n \"Let's first download and import our primary Canadian immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\\n\",\n \"```\\n\",\n \"!conda install -c anaconda xlrd --yes\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Download the dataset and read it into a *pandas* dataframe:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"df_can = pd.read_excel('https://ibm.box.com/shared/static/lw190pt9zpy5bd1ptyg2aw15awomz9pu.xlsx',\\n\",\n \" sheet_name='Canada by Citizenship',\\n\",\n \" skiprows=range(20),\\n\",\n \" skipfooter=2)\\n\",\n \"\\n\",\n \"print('Data downloaded and read into a dataframe!')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's take a look at the first five items in our dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"df_can.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's find out how many entries there are in our dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# print the dimensions of the dataframe\\n\",\n \"print(df_can.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to *Introduction to Matplotlib and Line Plots* and *Area Plots, Histograms, and Bar Plots* notebooks for a detailed description of this preprocessing.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# clean up the dataset to remove unnecessary columns (eg. REG) \\n\",\n \"df_can.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\\n\",\n \"\\n\",\n \"# let's rename the columns so that they make sense\\n\",\n \"df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\\n\",\n \"\\n\",\n \"# for sake of consistency, let's also make all column labels of type string\\n\",\n \"df_can.columns = list(map(str, df_can.columns))\\n\",\n \"\\n\",\n \"# add total column\\n\",\n \"df_can['Total'] = df_can.sum(axis=1)\\n\",\n \"\\n\",\n \"# years that we will be using in this lesson - useful for plotting later on\\n\",\n \"years = list(map(str, range(1980, 2014)))\\n\",\n \"print ('data dimensions:', df_can.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's take a look at the first five items of our cleaned dataframe.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"df_can.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"In order to create a `Choropleth` map, we need a GeoJSON file that defines the areas/boundaries of the state, county, or country that we are interested in. In our case, since we are endeavoring to create a world map, we want a GeoJSON that defines the boundaries of all world countries. For your convenience, we will be providing you with this file, so let's go ahead and download it. Let's name it **world_countries.json**.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# download countries geojson file\\n\",\n \"!wget --quiet https://ibm.box.com/shared/static/cto2qv7nx6yq19logfcissyy4euo8lho.json -O world_countries.json\\n\",\n \" \\n\",\n \"print('GeoJSON file downloaded!')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Now that we have the GeoJSON file, let's create a world map, centered around **[0, 0]** *latitude* and *longitude* values, with an intial zoom level of 2, and using *Mapbox Bright* style.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"world_geo = r'world_countries.json' # geojson file\\n\",\n \"\\n\",\n \"# create a plain world map\\n\",\n \"world_map = folium.Map(location=[0, 0], zoom_start=2, tiles='Mapbox Bright')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And now to create a `Choropleth` map, we will use the *choropleth* method with the following main parameters:\\n\",\n \"\\n\",\n \"1. geo_data, which is the GeoJSON file.\\n\",\n \"2. data, which is the dataframe containing the data.\\n\",\n \"3. columns, which represents the columns in the dataframe that will be used to create the `Choropleth` map.\\n\",\n \"4. key_on, which is the key or variable in the GeoJSON file that contains the name of the variable of interest. To determine that, you will need to open the GeoJSON file using any text editor and note the name of the key or variable that contains the name of the countries, since the countries are our variable of interest. In this case, **name** is the key in the GeoJSON file that contains the name of the countries. Note that this key is case_sensitive, so you need to pass exactly as it exists in the GeoJSON file.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# generate choropleth map using the total immigration of each country to Canada from 1980 to 2013\\n\",\n \"world_map.choropleth(\\n\",\n \" geo_data=world_geo,\\n\",\n \" data=df_can,\\n\",\n \" columns=['Country', 'Total'],\\n\",\n \" key_on='feature.properties.name',\\n\",\n \" fill_color='YlOrRd', \\n\",\n \" fill_opacity=0.7, \\n\",\n \" line_opacity=0.2,\\n\",\n \" legend_name='Immigration to Canada'\\n\",\n \")\\n\",\n \"\\n\",\n \"# display map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"As per our `Choropleth` map legend, the darker the color of a country and the closer the color to red, the higher the number of immigrants from that country. Accordingly, the highest immigration over the course of 33 years (from 1980 to 2013) was from China, India, and the Philippines, followed by Poland, Pakistan, and interestingly, the US.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Notice how the legend is displaying a negative boundary or threshold. Let's fix that by defining our own thresholds and starting with 0 instead of -6,918!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"button\": false,\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"world_geo = r'world_countries.json'\\n\",\n \"\\n\",\n \"# create a numpy array of length 6 and has linear spacing from the minium total immigration to the maximum total immigration\\n\",\n \"threshold_scale = np.linspace(df_can['Total'].min(),\\n\",\n \" df_can['Total'].max(),\\n\",\n \" 6, dtype=int)\\n\",\n \"threshold_scale = threshold_scale.tolist() # change the numpy array to a list\\n\",\n \"threshold_scale[-1] = threshold_scale[-1] + 1 # make sure that the last value of the list is greater than the maximum immigration\\n\",\n \"\\n\",\n \"# let Folium determine the scale.\\n\",\n \"world_map = folium.Map(location=[0, 0], zoom_start=2, tiles='Mapbox Bright')\\n\",\n \"world_map.choropleth(\\n\",\n \" geo_data=world_geo,\\n\",\n \" data=df_can,\\n\",\n \" columns=['Country', 'Total'],\\n\",\n \" key_on='feature.properties.name',\\n\",\n \" threshold_scale=threshold_scale,\\n\",\n \" fill_color='YlOrRd', \\n\",\n \" fill_opacity=0.7, \\n\",\n \" line_opacity=0.2,\\n\",\n \" legend_name='Immigration to Canada',\\n\",\n \" reset=True\\n\",\n \")\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Much better now! Feel free to play around with the data and perhaps create `Choropleth` maps for individuals years, or perhaps decades, and see how they compare with the entire period from 1980 to 2013.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"### Thank you for completing this lab!\\n\",\n \"\\n\",\n \"This notebook was created by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab interesting and educational. Feel free to contact me if you have any questions!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"This notebook is part of a course on **Coursera** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DV0101EN_Coursera_Week3_LAB2).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"deletable\": true,\n \"editable\": true,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"Copyright © 2018 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/).\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n },\n \"widgets\": {\n \"state\": {},\n \"version\": \"1.1.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"gpl-3.0"}}},{"rowIdx":46192,"cells":{"repo_name":{"kind":"string","value":"crdguez/mat4ac"},"path":{"kind":"string","value":"notebooks/matematicas con python/.ipynb_checkpoints/ttm1819-checkpoint.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"22932"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n }\n },\n \"source\": [\n \"### Instrucciones\\n\",\n \"\\n\",\n \"requires a copy of reveal.js (version 3.x)\\n\",\n \"\\n\",\n \"```\\n\",\n \"jupyter nbconvert --to slides --SlidesExporter.reveal_theme=sky ttm1819.ipynb\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[NbConvertApp] Converting notebook ttm1819.ipynb to slides\\n\",\n \"[NbConvertApp] Writing 283046 bytes to ttm1819.slides.html\\n\"\n ]\n }\n ],\n \"source\": [\n \"!jupyter nbconvert --to slides --SlidesExporter.reveal_theme=white --SlidesExporter.reveal_scroll=True ttm1819.ipynb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"# Taller de Talento Matemático \\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"## Programando la Geometría con BlocksCAD\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"**Página Web:** [https://mat3d.github.io/](https://mat3d.github.io/)\\n\",\n \"\\n\",\n \"**Documentación:** [https://github.com/mat3d](https://github.com/mat3d)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Introducción\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"* Saludo y presentación\\n\",\n \" - **Pablo Beltrán Pellicer** *[@pbeltranp](https://twitter.com/pbeltranp)*\\n\",\n \" - **Carlos Rodríguez Jaso** *[@es3a10](https://twitter.com/es3a10)*\\n\",\n \"* Objetivo de la sesión:\\n\",\n \" - Iniciarse en las posibilidades de [BlocksCAD](https://www.blockscad3d.com/) desde un punto de vista matemático\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"# Introducción a [BlocksCAD](https://www.blockscad3d.com/)\\n\",\n \"\\n\",\n \"**BlocksCAD** es un programa para modelar objetos en 3D sin necesidad de tener elevados conocimientos de programación.\\n\",\n \"\\n\",\n \"La forma más sencilla de trabajar es de manera online a través de su web: [https://www.blockscad3d.com/](https://www.blockscad3d.com/)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"## El entorno\\n\",\n \"\\n\",\n \"El entorno de trabajo de BlocksCAD lo podemos dividir en tres partes:\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"## Aprendiendo lo básico\\n\",\n \"\\n\",\n \"### Formas 3D: \\n\",\n \"\\n\",\n \"Son los objetos primitivos que podemos utilizar:\\n\",\n \"\\n\",\n \"#### Cubos: Mi primer objeto en 3D\\n\",\n \"\\n\",\n \"Dentro del bloque *Formas 3d*, arrastramos el bloque \\n\",\n \" y lo llevamos a la área de programa. Al renderizar (botón **Hacer**) ya tenemos nuestro primer objeto con BlocksCAD.\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Cubos: Mi primer objeto en 3D\\n\",\n \"\\n\",\n \"Observa qué pasa cuando modificas los parámetros:\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Formas 3D: Esferas\\n\",\n \"\\n\",\n \"Dentro del bloque *Formas 3D* podemos arrastrar el bloque \\n\",\n \"\\n\",\n \"Al renderizarlo obtendremos una esfera de *10mm* de radio:\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Formas 3D: Cilindros y mucho más\\n\",\n \"\\n\",\n \"Dentro del bloque *Formas 3D*, tenemos el bloque \\n\",\n \"\\n\",\n \"Explicación de algunos parámetros:\\n\",\n \"\\n\",\n \"- **radio1**, es el radio de la base inferior de la figura a modelar\\n\",\n \"- **candado**, por defecto aparece cerrado, y esto hace que el parámetro **radio2** herede el valor de **radio1**\\n\",\n \"- **radio2**, radio de la base superior de la figura. Cuando coincida con **radio1** tendremos un cilindro, y cuando no, tendremos un **tronco de cono** o un **cono** si ponemos que el radio es cero\\n\",\n \"\\n\",\n \"**Ejemplos:**\\n\",\n \"\\n\",\n \"| Bloque | Renderizado |\\n\",\n \"| :---------------------------------------: | :---------------------------------------------: |\\n\",\n \"| | |\\n\",\n \"| | |\\n\",\n \"| | |\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"BlocksCAD interpreta la base del cilindro como un polígono regular de **\\\"muchos\\\"** lados. Podemos generar un **prisma** de base regular modificando ese \\\"muchos\\\" con el bloque que aparece en *Transformaciones*. Mira este ejemplo que compara un cilindro con un prisma de base triangular:\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Transformaciones\\n\",\n \"\\n\",\n \"#### Traslaciones \\n\",\n \"Dentro del bloque *Formas 3D*, arrastramos el bloque \\n\",\n \"En este caso, los parámetros **X**, **Y** y **Z**, son las coordenadas del vector de traslación.\\n\",\n \"\\n\",\n \"#### Rotaciones\\n\",\n \"Bloque \\n\",\n \"Los parámetros **X**, **Y** y **Z**, son los grados a rotar en los diferentes ejes.\\n\",\n \"\\n\",\n \"**Ejemplos:**\\n\",\n \"\\n\",\n \"| Ejemplo | Bloque | Renderizado |\\n\",\n \"| :---------------------------------------------------------: | :----------------------------- | :-------------------------------: |\\n\",\n \"| Prisma de 10x20x30 centrado y trasladado 30 en la dirección X, 30 en la Y y 40 en la Z | | |\\n\",\n \"| Ejercicio anterior rotado 45º en el eje X | | |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Ops de Conjuntos: Operaciones lógicas \\n\",\n \"\\n\",\n \"#### Unión: Pegando objetos\\n\",\n \"\\n\",\n \"Para unir dos o más objetos tenemos que seleccionar el bloque \\n\",\n \"\\n\",\n \"#### Diferencia: Haciendo agujeros\\n\",\n \"\\n\",\n \"Con la diferencia lo que hacemos es hacer \\\"agujeros\\\". La forma de trabajar con el bloque es similar a la unión.\\n\",\n \"\\n\",\n \"**Ejemplos:**\\n\",\n \"\\n\",\n \"| Bloque | Renderizado |\\n\",\n \"| :----------------------------: | :-----------------------------: |\\n\",\n \"| | |\\n\",\n \"| | |\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"# ¡Ya conoces lo básico para poder trabajar!\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"# Desafío 1: \\n\",\n \"\\n\",\n \"Entrénate con BlocksCASD intentando modelar la siguiente figura. No tiene que ser exactamente igual pero sí parecida:\\n\",\n \"\\n\",\n \"| Vista 1 | Vista 2 |\\n\",\n \"| :--------------------------: | :--------------------------: |\\n\",\n \"| | |\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"Tienes una posible solución en https://www.blockscad3d.com/community/projects/82576\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"# Desafío 2: Estrella Mudéjar\\n\",\n \"\\n\",\n \"| Actividad |\\n\",\n \"| -------------------------------------------- |\\n\",\n \"| Estrella mudéjar sencilla |\\n\",\n \"| \\\"Estrella mudéjar\\\" sobre un hexágono regular |\\n\",\n \"| \\\"Estrella mudéjar\\\" paramétrica sencilla |\\n\",\n \"| \\\"Estrella mudéjar\\\" paramétrica avanzada |\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Estrella mudéjar sencilla\\n\",\n \"\\n\",\n \"#### Modelo\\n\",\n \"Para empezar, vamos a realizar **una sencilla estrella mudejar** con dos prismas cuadrados de dimensiones 10x10x5 (eje_x, eje_y, eje_z o para entendernos, ancho por largo por alto):\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Cuestiones iniciales\\n\",\n \"\\n\",\n \"* ¿Existe alguna **relación** entre el **lado de un cuadrado y su apotema**?\\n\",\n \"* ¿Crees que para otros polígonos regulares va a seguir existiendo una **relación entre el lado y la apotema**?\\n\",\n \"* ¿Sabes algo de **trigonometría**? En caso afirmativo, las preguntas anteriores deberían resultarte sencillas. En caso contrario, **no te preocupes** la parte teórica la facilitaremos para que puedas hacer el modelado si te ves desbordado.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Respuestas a las preguntas\\n\",\n \"\\n\",\n \"* La **apotema** es la mitad del lado. \\n\",\n \"\\n\",\n \"* Sí, siempre va a haber relación. La respuesta está en la **semejanza de triángulos rectángulos** y **la trigonometría**. Esto nos permitirá calcular bien el radio, la apotema o el lado del polígono regular siempre que nos den uno de ellos. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Modelado en BlocksCAD\\n\",\n \"\\n\",\n \"| Paso | Código | Renderizado |\\n\",\n \"| ---------------------------------| --------------------------------------------------| ------------------------------------------ |\\n\",\n \"| Insertamos un cubo de 10x10x5 centrado en origen | | |\\n\",\n \"| Insertamos otro igual pero girado 45º sobre el plano XY (girar el eje Z) | | |\\n\",\n \"| Juntamos los dos objetos para formar un único objeto | | |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"Tienes la solución en https://www.blockscad3d.com/community/projects/266387\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## \\\"Estrella mudéjar\\\" sobre un hexágono regular\\n\",\n \"\\n\",\n \"#### Modelo\\n\",\n \"Ahora se propone realizar una especie de estrella mudéjar modificada a partir de un hexágono regular de radio y altura que quieras\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Cuestiones previas\\n\",\n \"\\n\",\n \"* ¿Hacia qué objeto tiende un polígono regular cuando aumentamos el **número de lados** al polígono?\\n\",\n \"* ¿Cuánto tiene que girar el prisma hexagonal superpuesto para generar las puntas en mitad de las aristas del prisma original?¿Puedes dar una **fórmula general** que vaya en función del número de lados y que por tanto sirva para el cuadrado o el hexágono?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Consideraciones a las cuestiones\\n\",\n \"\\n\",\n \"* Al aumentar el número de lados, el **polígono** se va acercando a un **círculo** como se puede ver en la siguiente animación:\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"* **BlocksCAD** interpreta los cilindros de esta manera, como un **prisma** de base regular **con un número de lados elevado**. Podemos convertir un cilindrode BlocksCAD en un prisma modificando el número de lados:\\n\",\n \"\\n\",\n \"\\n\",\n \"* La **rotación** que hay que hacer es la mitad del ángulo central: $\\\\alpha=\\\\frac{180}{nlados}$\\n\",\n \"\\n\",\n \"* **Importante:** Al usar el bloque cilindro modificado por el número de aristas, el parámetro que le damos es el radio del polígono regular y no la longitud de la arista (o lado del polígono regular)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Modelado en BlocksCAD\\n\",\n \"\\n\",\n \"* Se propone como ejercicio su modelado. \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"Tienes una posible solución en https://www.blockscad3d.com/community/projects/272240\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## \\\"Estrella mudéjar\\\" paramétrica sencilla\\n\",\n \"#### Modelo\\n\",\n \"Se pide modificar la estrella anterior para que acepte los siguientes parámetros o variables:\\n\",\n \"* **Número de lados** del polígono regular que genera la estrella\\n\",\n \"* **Longitud** del radio del polígono\\n\",\n \"* Longitud del radio del polígono que genera el **hueco**\\n\",\n \"\\n\",\n \"**Ejemplo de modelo:** Estrella de David generada con triángulos de radios 10 y 8, exterior e interior respectivamente. \\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Modelado en BlocksCAD\\n\",\n \"\\n\",\n \"* Se propone como ejercicio su modelado. \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"Tienes una posible solución en https://www.blockscad3d.com/community/projects/278006\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## \\\"Estrella mudéjar\\\" paramétrica avanzada\\n\",\n \"\\n\",\n \"#### Modelo\\n\",\n \"Vamos a añadir un poco de complejidad al programa. En las estrellas anteriores todas las puntas son iguales. Ahora en lugar de que la mitad de las puntas las genere el prisma poligonal girado se pide que sean los generados por un prisma cuadrado con la diagonal superpuesta sobre la arista (el lado del cuadrado que sea 1/3 de la arista).\\n\",\n \"\\n\",\n \"**Ejemplo:** Para 8 lados tiene que quedar algo así\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Explicación del Modelo\\n\",\n \"Como una imagen vale más que mil palabras:\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Se pide por tanto modelar una estrella que tenga como parámetros:\\n\",\n \"\\n\",\n \"* **Número de lados**\\n\",\n \"* Longitud del **lado**\\n\",\n \"* **Grosor** de la estrella: Altura y anchura del contorno. Opcional: Si se quiere se pueden hacer dos parámetros.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Cuestiones previas\\n\",\n \"\\n\",\n \"* Dado un polígono regular: ¿Existe alguna **fórmula general** que **relacione** la longitud del **lado** con el **radio**?¿Y alguna relación entre la **apotema** y el **lado**?\\n\",\n \"* El cuadrado que genera las puntas, **¿Cuánto hay que trasladarlo?**\\n\",\n \"* ¿Cuántas **rotaciones** hay que hacer del cuadrado para generar todas las puntas?¿Qué ángulo entre ellos?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Respuestas a las cuestiones previas\\n\",\n \" \\n\",\n \"\\n\",\n \" * El **triángulo** formado por el centro del polígono, el punto medio de un lado y un vértice adyacente es **siempre rectángulo**. El ángulo correspondiente al centro del polígono es **la mitad del ángulo central**: $$\\\\alpha=\\\\dfrac{\\\\frac{360}{n_{lados}}}{2}$$\\n\",\n \"\\n\",\n \" * **Fijado el número de lados** del polígono regular, aunque varíe el tamaño del lado, los triángulos que salgan serán semejantes. **Al ser semejantes los lados son proporcionales**, o dicho de otra forma, **la razón entre lados se mantiene constante y dependen exclusivamente del ángulo** que se apoya en el centro del polígono: Son las **razones trigonométricas**. La principales son:\\n\",\n \"\\n\",\n \"| Razón trigonométrica | Aplicación en el polígono regular |\\n\",\n \"| ------------------------------------------- | --------------------------------------- |\\n\",\n \"| **seno** = lado opuesto / hipotenusa | $\\\\sin{\\\\alpha}=\\\\frac{semilado}{radio}$ |\\n\",\n \"| **coseno** = lado contiguo / hipotenusa | $\\\\cos{\\\\alpha}=\\\\frac{apotema}{radio}$ |\\n\",\n \"| **tangente** = lado opuesto / lado contiguo | $\\\\tan{\\\\alpha}=\\\\frac{semilado}{apotema}$ |\\n\",\n \" \\n\",\n \"\\n\",\n \"* El cuadrado hay que trasladarlo **la apotema del polígono**\\n\",\n \"* Habrá que hacer **tantas puntas como lados** y habrá que **rotar la mitad del ángulo central**: $$\\\\alpha=\\\\frac{180}{nlados}$$\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Ejemplo de aplicación:** Determina el radio y la apotema para un **pentágono regular de lado 6**:\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"* **radio = (6/2)/seno(180/5)**\\n\",\n \"* **apotema = (6/2)/tan(180/5)**\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Modelado en BlocksCAD\\n\",\n \"\\n\",\n \"* Se propone como ejercicio su modelado. \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"Tienes una posible solución en https://www.blockscad3d.com/community/projects/267149\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## ¡Gracias por vuestra presencia y a seguir modelando con BlocksCAD!\\n\",\n \"\\n\",\n \"Podéis seguir trabajando en vuestra casa y difundir vuestros progresos en:\\n\",\n \"\\n\",\n \"\\n\",\n \"[#ttmestrellamudejar](https://twitter.com/search?f=tweets&q=%23ttmestrellamudejar&src=typd)\\n\",\n \"\\n\",\n \"\\n\",\n \"**Otros enlaces que pueden ser interesantes:**\\n\",\n \"\\n\",\n \"* [Tutoriales de BlocksCAD](https://www.youtube.com/channel/UCovK2cRIjoaZNzRwpQP2sFg/playlists) oficiales\\n\",\n \"* [Proyecto RepRap](https://reprap.org/wiki/RepRap) sobre impresoras 3D libres\\n\",\n \"* [Proyecto Clone Wars](https://www.reprap.org/wiki/Proyecto_Clone_Wars) , subproyecto de RepRap en castellano\"\n ]\n }\n ],\n \"metadata\": {\n \"celltoolbar\": \"Slideshow\",\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"gpl-3.0"}}},{"rowIdx":46193,"cells":{"repo_name":{"kind":"string","value":"maojrs/riemann_book"},"path":{"kind":"string","value":"FV_compare.ipynb"},"copies":{"kind":"string","value":"3"},"size":{"kind":"string","value":"22022"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Finite volume discretizations with approximate Riemann solvers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the previous few chapters we introduced several approximate Riemann solvers for different nonlinear conservation laws and showed some comparisons of the approximate solution to a single Riemann problem with the true solution, where there often appear to be significant differences. However, their intended use is as a building block for finite volume methods such as Godunov's method or high-resolution extensions. \\n\",\n \"\\n\",\n \"How do these solvers impact the solution accuracy when used within a finite volume discretization? To investigate, we will use them within [PyClaw](http://www.clawpack.org/pyclaw/) to solve several test problems.\\n\",\n \"\\n\",\n \"In particular, for both the shallow water equations and the Euler equations, we will apply the numerical method to solve dam break and shock tube problems, which are themselves Riemann problems and so we can compute the exact solution, but once discretized require the solution of a different set of Riemann problems at each cell interface every time step. \\n\",\n \"\\n\",\n \"For the Euler equations we will also consider the Woodward-Colella blast wave problem. The initial data consists of two Riemann problems, with resulting shock waves of differing strengths. These shock waves later interact with each other.\\n\",\n \" \\n\",\n \"In this chapter we include extensive sections of code in the notebook. This is meant to more easily allow the reader to use these as templates for setting up other problems in PyClaw. For the code in this chapter we use approximate Riemann solvers from PyClaw that can be found in these files:\\n\",\n \"\\n\",\n \"- [euler_1D_py.py,](https://github.com/clawpack/riemann/blob/FA16/riemann/euler_1D_py.py)\\n\",\n \"- [shallow_roe_with_efix_1D.](https://github.com/clawpack/riemann/blob/FA16/riemann/shallow_1D_py.py)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"hide\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"hide\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"%config InlineBackend.figure_format = 'svg'\\n\",\n \"import numpy as np\\n\",\n \"from exact_solvers import euler\\n\",\n \"from clawpack import riemann\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from ipywidgets import interact\\n\",\n \"from ipywidgets import widgets\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Shallow water equations\\n\",\n \"\\n\",\n \"We compare results obtained with using the high-resolution wave propagation method implemented in PyClaw (with limiters to avoid nonphysical oscillations). We compare the results obtained when combined with two different approximate Riemann solvers: the Roe solver and HLLE.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from exact_solvers import shallow_water as sw\\n\",\n \"from clawpack import riemann\\n\",\n \"from clawpack.riemann.shallow_roe_with_efix_1D_constants import depth, momentum, num_eqn\\n\",\n \"\\n\",\n \"def setup(riemann_solver='roe',N=20,IC='dam-break'):\\n\",\n \"\\n\",\n \" from clawpack import pyclaw\\n\",\n \"\\n\",\n \" if riemann_solver.lower() == 'roe':\\n\",\n \" rs = riemann.shallow_roe_with_efix_1D\\n\",\n \" elif riemann_solver.lower() == 'hlle':\\n\",\n \" rs = riemann.shallow_hlle_1D\\n\",\n \" \\n\",\n \" solver = pyclaw.ClawSolver1D(rs)\\n\",\n \" \\n\",\n \" solver.bc_lower[0] = pyclaw.BC.extrap\\n\",\n \" solver.bc_upper[0] = pyclaw.BC.extrap\\n\",\n \"\\n\",\n \" xlower = -5.0\\n\",\n \" xupper = 5.0\\n\",\n \" x = pyclaw.Dimension(xlower,xupper,N,name='x')\\n\",\n \" domain = pyclaw.Domain(x)\\n\",\n \" state = pyclaw.State(domain,num_eqn)\\n\",\n \"\\n\",\n \" # Gravitational constant\\n\",\n \" state.problem_data['grav'] = 1.0\\n\",\n \" state.problem_data['dry_tolerance'] = 1e-3\\n\",\n \" state.problem_data['sea_level'] = 0.0\\n\",\n \" \\n\",\n \" xc = state.grid.x.centers\\n\",\n \"\\n\",\n \" x0=0.\\n\",\n \"\\n\",\n \" hl = 10.\\n\",\n \" ul = 0.\\n\",\n \" hr = 0.5\\n\",\n \" ur = 0.\\n\",\n \" state.q[depth,:] = hl * (xc <= x0) + hr * (xc > x0)\\n\",\n \" state.q[momentum,:] = hl*ul * (xc <= x0) + hr*ur * (xc > x0)\\n\",\n \"\\n\",\n \" claw = pyclaw.Controller()\\n\",\n \" claw.keep_copy = True\\n\",\n \" claw.output_format = None\\n\",\n \" claw.tfinal = 1.0\\n\",\n \" claw.solution = pyclaw.Solution(state,domain)\\n\",\n \" claw.solver = solver\\n\",\n \"\\n\",\n \" return claw\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"N = 40 # number of grid cells to use\\n\",\n \"q_l = [10,0]\\n\",\n \"q_r = [0.5,0]\\n\",\n \"\\n\",\n \"# Roe solution\\n\",\n \"roe_sw = setup(riemann_solver='roe',N=N)\\n\",\n \"roe_sw.verbosity = 0\\n\",\n \"status = roe_sw.run()\\n\",\n \"xc_sw = roe_sw.grid.x.centers\\n\",\n \"\\n\",\n \"# HLLE solution\\n\",\n \"hlle_sw = setup(riemann_solver='hlle',N=N)\\n\",\n \"hlle_sw.verbosity = 0\\n\",\n \"status = hlle_sw.run()\\n\",\n \"\\n\",\n \"# Exact solution\\n\",\n \"xc_exact_sw = np.linspace(-5,5,2000)\\n\",\n \"states_sw, speeds_sw, reval_sw, wave_types_sw = \\\\\\n\",\n \" sw.exact_riemann_solution(q_l, q_r)\\n\",\n \"def plot_frame(i):\\n\",\n \" t = roe_sw.frames[i].t+1.e-13\\n\",\n \" fig, ax = plt.subplots(2,1, sharex=True, figsize=(8,5))\\n\",\n \" variablenames = [\\\"Depth\\\", \\\"Momentum\\\"]\\n\",\n \" variables = [depth, momentum]\\n\",\n \" ylims = [[-1,12], [-5,12]]\\n\",\n \" plt.subplots_adjust(hspace=0)\\n\",\n \" ax[0].title.set_text('Solutions at t={:.2f}'.format(t))\\n\",\n \" ax[0].set_xlim((-5,5))\\n\",\n \" ax[1].set(xlabel = 'x')\\n\",\n \" for j, variable in enumerate(variables):\\n\",\n \" ax[j].set_ylim(ylims[j])\\n\",\n \" ax[j].plot(xc_exact_sw,reval_sw(xc_exact_sw/(t+1.e-16))[j],'-k',lw=1)\\n\",\n \" ax[j].plot(xc_sw, hlle_sw.frames[i].q[variable,:],'-ob',lw=2,markersize=4)\\n\",\n \" ax[j].plot(xc_sw, roe_sw.frames[i].q[variable,:],'-or',lw=0.5,markersize=3)\\n\",\n \" ax[j].legend(['Exact','HLLE','Roe'],loc='best')\\n\",\n \" ax[j].set(ylabel=variablenames[j])\\n\",\n \" plt.show()\\n\",\n \" \\n\",\n \"interact(plot_frame, i=widgets.IntSlider(min=0,max=10,value=5));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Clearly, both solvers give very similar results for this problem. They converge to the same solution, as you can verify by increasing the value of $N$ above in the live notebook. You can also try varying the left and right states.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Euler equations\\n\",\n \"\\n\",\n \"We perform a similar test for the Euler equations with shock tube initial data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Sod shock tube problem\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We first consider the classic shocktube problem proposed by Sod and already discussed in [Euler](Euler.ipynb). This is a particular Riemann problem in which the initial velocity is zero on both sides of a discontinuity in pressure and/or density of a gas, and so the exact Riemann solver for the Euler equations would provide the exact solution for all time, consisting of a right-going shock, a left-going rarefaction, and an intermediate contact discontinuity.\\n\",\n \"\\n\",\n \"In the numerical experiments done in this notebook, we use this initial data for a more general finite volume method that could be used to approximate the solution for any initial data. In the first time step there is a single cell interface with nontrivial Riemann data, but as the solution evolves on the grid the Riemann problems that arise in subsequent time steps are very different from the single problem we started with. Depending on the accuracy of the numerical method, the resolution of the grid, and the choice of approximate Riemann solver to use at each grid cell every time step, the numerical solution may deviate significantly from the exact solution to the original shocktube problem. This makes a good initial test problem for numerical methods because the exact solution can be computed for comparison purposes, and because it clearly shows whether the method introduces oscillations around discontinuities and/or smears them out. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from clawpack.riemann.euler_with_efix_1D_constants \\\\\\n\",\n \" import density, momentum, energy, num_eqn\\n\",\n \"\\n\",\n \"def shocktube(q_l, q_r, N=50, riemann_solver='HLL', \\n\",\n \" solver_type='classic'):\\n\",\n \"\\n\",\n \" from clawpack import pyclaw\\n\",\n \" from clawpack import riemann\\n\",\n \"\\n\",\n \" if riemann_solver == 'Roe':\\n\",\n \" rs = riemann.euler_1D_py.euler_roe_1D\\n\",\n \" elif riemann_solver == 'HLL':\\n\",\n \" rs = riemann.euler_1D_py.euler_hll_1D\\n\",\n \"\\n\",\n \" if solver_type == 'classic':\\n\",\n \" solver = pyclaw.ClawSolver1D(rs) \\n\",\n \" solver.limiters = pyclaw.limiters.tvd.MC\\n\",\n \" else:\\n\",\n \" solver = pyclaw.SharpClawSolver1D(rs)\\n\",\n \"\\n\",\n \" solver.kernel_language = 'Python'\\n\",\n \" \\n\",\n \" solver.bc_lower[0]=pyclaw.BC.extrap\\n\",\n \" solver.bc_upper[0]=pyclaw.BC.extrap\\n\",\n \"\\n\",\n \" x = pyclaw.Dimension(-1.0,1.0,N,name='x')\\n\",\n \" domain = pyclaw.Domain([x])\\n\",\n \" state = pyclaw.State(domain,num_eqn)\\n\",\n \"\\n\",\n \" gamma = 1.4\\n\",\n \" state.problem_data['gamma']= gamma\\n\",\n \" state.problem_data['gamma1']= gamma-1.\\n\",\n \"\\n\",\n \" state.problem_data['efix'] = False\\n\",\n \"\\n\",\n \" xc = state.grid.p_centers[0]\\n\",\n \" \\n\",\n \" velocity = (xc<=0)*q_l[1] + (xc>0)*q_r[1]\\n\",\n \" pressure = (xc<=0)*q_l[2] + (xc>0)*q_r[2]\\n\",\n \"\\n\",\n \" state.q[density ,:] = (xc<=0)*q_l[0] + (xc>0)*q_r[0]\\n\",\n \" state.q[momentum,:] = velocity * state.q[density,:]\\n\",\n \" state.q[energy ,:] = pressure/(gamma - 1.) + \\\\\\n\",\n \" 0.5 * state.q[density,:] * velocity**2\\n\",\n \"\\n\",\n \" claw = pyclaw.Controller()\\n\",\n \" claw.tfinal = 0.5\\n\",\n \" claw.solution = pyclaw.Solution(state,domain)\\n\",\n \" claw.solver = solver\\n\",\n \" claw.num_output_times = 10\\n\",\n \" claw.keep_copy = True\\n\",\n \" claw.verbosity=0\\n\",\n \"\\n\",\n \" return claw\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"N = 80 # number of grid cells to use\\n\",\n \"\\n\",\n \"prim_l = [1.,0.,1.]\\n\",\n \"prim_r = [1./8,0.,1./10]\\n\",\n \"q_l = euler.conservative_to_primitive(*prim_l)\\n\",\n \"q_r = euler.conservative_to_primitive(*prim_r)\\n\",\n \"\\n\",\n \"# Roe-based solution\\n\",\n \"roe_st = shocktube(q_l,q_r,N=N,riemann_solver='Roe')\\n\",\n \"roe_st.run()\\n\",\n \"xc_st = roe_st.solution.state.grid.p_centers[0]\\n\",\n \"\\n\",\n \"# HLL-based solution\\n\",\n \"hll_st = shocktube(q_l,q_r,N=N,riemann_solver='HLL')\\n\",\n \"hll_st.run()\\n\",\n \"\\n\",\n \"# Exact solution\\n\",\n \"xc_exact_st = np.linspace(-1,1,2000)\\n\",\n \"states_st, speeds_st, reval_st, wave_types_st = euler.exact_riemann_solution(prim_l, prim_r)\\n\",\n \"\\n\",\n \"def plot_frame(i):\\n\",\n \" t = roe_st.frames[i].t\\n\",\n \" fig, ax = plt.subplots(3,1, sharex=True, figsize=(8,6))\\n\",\n \" variablenames = [\\\"Density\\\", \\\"Momentum\\\", \\\"Energy\\\"]\\n\",\n \" variables = [density, momentum, energy]\\n\",\n \" ylims = [[0,1.1], [-0.05,0.35], [0,1.1]]\\n\",\n \" plt.subplots_adjust(hspace=0)\\n\",\n \" ax[0].title.set_text('Solutions at t={:.2f}'.format(t))\\n\",\n \" ax[0].set_xlim((-1,1))\\n\",\n \" ax[2].set(xlabel = 'x')\\n\",\n \" for j, variable in enumerate(variables):\\n\",\n \" ax[j].set_ylim(ylims[j])\\n\",\n \" ax[j].plot(xc_exact_st,reval_st(xc_exact_st/(t+1.e-16))[j],'-k',lw=1)\\n\",\n \" ax[j].plot(xc_st,hll_st.frames[i].q[variable,:],'-ob',lw=2,markersize=4)\\n\",\n \" ax[j].plot(xc_st,roe_st.frames[i].q[variable,:],'-or',lw=0.5,markersize=3)\\n\",\n \" ax[j].legend(['Exact','HLL','Roe'],loc='best')\\n\",\n \" ax[j].set(ylabel=variablenames[j])\\n\",\n \" plt.show()\\n\",\n \" \\n\",\n \"interact(plot_frame, i=widgets.IntSlider(min=0, max=10, value=5, description='Frame'));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As you might expect, the HLL solver smears the middle wave (contact discontinuity) significantly more than the Roe solver does. Perhaps surprisingly, it captures the shock just as accurately as the Roe solver does. In the live notebook you can refine the grid and observe the convergence.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### High-order WENO + Runge-Kutta\\n\",\n \"\\n\",\n \"Next we look at the difference between the HLL and Roe solution when these solvers are employed within a higher-order method of lines discretization using fifth-order WENO and a 4th-order Runge-Kutta scheme.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"N = 80 # number of grid cells to use\\n\",\n \"\\n\",\n \"prim_l = [1.,0.,1.]\\n\",\n \"prim_r = [1./8,0.,1./10]\\n\",\n \"q_l = euler.conservative_to_primitive(*prim_l)\\n\",\n \"q_r = euler.conservative_to_primitive(*prim_r)\\n\",\n \"\\n\",\n \"roe_weno = shocktube(q_l,q_r,N=N,riemann_solver='Roe',solver_type='sharpclaw')\\n\",\n \"roe_weno.run()\\n\",\n \"hll_weno = shocktube(q_l,q_r,N=N,riemann_solver='HLL',solver_type='sharpclaw')\\n\",\n \"hll_weno.run()\\n\",\n \"\\n\",\n \"xc_weno = roe_weno.solution.state.grid.p_centers[0]\\n\",\n \"\\n\",\n \"# Exact solution\\n\",\n \"xc_exact_weno = np.linspace(-1,1,2000)\\n\",\n \"states_weno, speeds_weno, reval_weno, wave_types_weno = euler.exact_riemann_solution(prim_l, prim_r)\\n\",\n \"\\n\",\n \"def plot_frame(i):\\n\",\n \" t = roe_weno.frames[i].t\\n\",\n \" fig, ax = plt.subplots(3,1, sharex=True, figsize=(8,6))\\n\",\n \" variablenames = [\\\"Density\\\", \\\"Momentum\\\", \\\"Energy\\\"]\\n\",\n \" variables = [density, momentum, energy]\\n\",\n \" ylims = [[0,1.1], [-0.05,0.35], [0,1.1]]\\n\",\n \" plt.subplots_adjust(hspace=0)\\n\",\n \" ax[0].title.set_text('Solutions at t={:.2f}'.format(t))\\n\",\n \" ax[0].set_xlim((-1,1))\\n\",\n \" ax[2].set(xlabel = 'x')\\n\",\n \" for j, variable in enumerate(variables):\\n\",\n \" ax[j].set_ylim(ylims[j])\\n\",\n \" ax[j].plot(xc_exact_weno,reval_weno(xc_exact_weno/(t+1.e-16))[j],'-k',lw=1)\\n\",\n \" ax[j].plot(xc_weno,hll_weno.frames[i].q[variable,:],'-ob',lw=2,markersize=4)\\n\",\n \" ax[j].plot(xc_weno,roe_weno.frames[i].q[variable,:],'-or',lw=0.5,markersize=3)\\n\",\n \" ax[j].legend(['Exact','HLL','Roe'],loc='best')\\n\",\n \" ax[j].set(ylabel=variablenames[j])\\n\",\n \" plt.show()\\n\",\n \" \\n\",\n \"interact(plot_frame, i=widgets.IntSlider(min=0, max=10, value=5, description='Frame'));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With higher-order discretizations, the difference in solutions due to using different Riemann solvers is less significant. This is partly because these high-order schemes use more accurate values as inputs to the Riemann problem, so that in smooth regions the jump between most cells is very small.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Woodward-Colella blast wave\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, we consider the Woodward-Colella blast wave problem, which is discussed for example in (LeVeque 2002). Here the initial velocity is zero and the density is one everywhere. The pressure is\\n\",\n \"\\\\begin{align}\\n\",\n \" p_0(x) = \\\\begin{cases} 1000 & 0 \\\\le x \\\\le 0.1 \\\\\\\\\\n\",\n \" 0.01 & 0.1 \\\\le x \\\\le 0.9 \\\\\\\\\\n\",\n \" 100 & 0.9 \\\\le x \\\\le 1\\n\",\n \" \\\\end{cases}\\n\",\n \"\\\\end{align}\\n\",\n \"The boundaries at $x=0$ and $x=1$ are solid walls. The solution involves a Riemann problem at $x=0.1$ and another at $x=0.9$. Later, the waves resulting from these Riemann problems interact with each other.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from clawpack.riemann.euler_with_efix_1D_constants \\\\\\n\",\n \" import density, momentum, energy, num_eqn\\n\",\n \"\\n\",\n \"def blastwave(N=400, riemann_solver='HLL', solver_type='classic'):\\n\",\n \"\\n\",\n \" from clawpack import pyclaw\\n\",\n \" from clawpack import riemann\\n\",\n \"\\n\",\n \" if riemann_solver == 'Roe':\\n\",\n \" kernel_language = 'Fortran'\\n\",\n \" rs = riemann.euler_with_efix_1D\\n\",\n \" elif riemann_solver == 'HLL':\\n\",\n \" kernel_language = 'Python'\\n\",\n \" rs = riemann.euler_1D_py.euler_hll_1D\\n\",\n \"\\n\",\n \" if solver_type == 'classic':\\n\",\n \" solver = pyclaw.ClawSolver1D(rs)\\n\",\n \" solver.limiters = pyclaw.limiters.tvd.MC\\n\",\n \" else:\\n\",\n \" solver = pyclaw.SharpClawSolver1D(rs)\\n\",\n \"\\n\",\n \" solver.kernel_language = kernel_language\\n\",\n \" \\n\",\n \" solver.bc_lower[0]=pyclaw.BC.wall\\n\",\n \" solver.bc_upper[0]=pyclaw.BC.wall\\n\",\n \"\\n\",\n \" x = pyclaw.Dimension(0.0,1.0,N,name='x')\\n\",\n \" domain = pyclaw.Domain([x])\\n\",\n \" state = pyclaw.State(domain,num_eqn)\\n\",\n \"\\n\",\n \" gamma = 1.4\\n\",\n \" state.problem_data['gamma']= gamma\\n\",\n \" state.problem_data['gamma1']= gamma-1.\\n\",\n \"\\n\",\n \" state.problem_data['efix'] = False\\n\",\n \"\\n\",\n \" xc = state.grid.p_centers[0]\\n\",\n \"\\n\",\n \" pressure = (xc<0.1)*1.e3 + (0.1<=xc)*(xc<0.9)*1.e-2 + (0.9<=xc)*1.e2\\n\",\n \" \\n\",\n \" state.q[density ,:] = 1.\\n\",\n \" state.q[momentum,:] = 0.\\n\",\n \" state.q[energy ,:] = pressure / (gamma - 1.)\\n\",\n \"\\n\",\n \" claw = pyclaw.Controller()\\n\",\n \" claw.tfinal = 0.038\\n\",\n \" claw.solution = pyclaw.Solution(state,domain)\\n\",\n \" claw.solver = solver\\n\",\n \" claw.num_output_times = 30\\n\",\n \" claw.keep_copy = True\\n\",\n \" claw.verbosity=0\\n\",\n \"\\n\",\n \" return claw\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"N = 400 # number of grid cells to use\\n\",\n \"\\n\",\n \"roe_bw = blastwave(N=N, riemann_solver='Roe')\\n\",\n \"roe_bw.run()\\n\",\n \"hll_bw = blastwave(N=N, riemann_solver='HLL')\\n\",\n \"hll_bw.run()\\n\",\n \"fine_bw = blastwave(N=4000,riemann_solver='Roe')\\n\",\n \"fine_bw.run();\\n\",\n \"xc_bw = roe_bw.solution.state.grid.p_centers[0]\\n\",\n \"xc_fine_bw = fine_bw.solution.state.grid.p_centers[0]\\n\",\n \"\\n\",\n \"def plot_frame(i):\\n\",\n \" t = roe_bw.frames[i].t\\n\",\n \" fig, ax = plt.subplots(3,1, sharex=True, figsize=(8,6))\\n\",\n \" variablenames = [\\\"Density\\\", \\\"Momentum\\\", \\\"Energy\\\"]\\n\",\n \" variables = [density, momentum, energy]\\n\",\n \" ylims = [[-1,10], [-50,120], [-300,3000]]\\n\",\n \" plt.subplots_adjust(hspace=0)\\n\",\n \" ax[0].title.set_text('Solutions at t={:.3f}'.format(t))\\n\",\n \" ax[0].set_xlim((0,1))\\n\",\n \" ax[2].set(xlabel = 'x')\\n\",\n \" for j, variable in enumerate(variables):\\n\",\n \" ax[j].set_ylim(ylims[j])\\n\",\n \" ax[j].plot(xc_fine_bw,fine_bw.frames[i].q[variable,:],'-k',lw=1)\\n\",\n \" ax[j].plot(xc_bw,hll_bw.frames[i].q[variable,:],'-b',lw=2)\\n\",\n \" ax[j].plot(xc_bw,roe_bw.frames[i].q[variable,:],'--r',lw=2)\\n\",\n \" ax[j].legend(['Fine','HLL','Roe'],loc='best')\\n\",\n \" ax[j].set(ylabel=variablenames[j])\\n\",\n \" plt.show()\\n\",\n \" \\n\",\n \"interact(plot_frame, i=widgets.IntSlider(min=0, max=30, value=15, description='Frame'));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here no exact solution is available, so we compare with a solution computed on a finer grid.\\n\",\n \"Again the solutions are fairly similar, though the HLL solution is a bit more smeared.\\n\",\n \"\\n\",\n \"One should not conclude from these tests that, for instance, the Roe solver is always *better* than the HLL solver. Many factors besides accuracy should be considered, including cost and robustness. As we have seen the HLL solver is more robust in the presence of near-vacuum states.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.5\"\n },\n \"toc\": {\n \"base_numbering\": 1,\n \"nav_menu\": {},\n \"number_sections\": true,\n \"sideBar\": true,\n \"skip_h1_title\": false,\n \"title_cell\": \"Table of Contents\",\n \"title_sidebar\": \"Contents\",\n \"toc_cell\": false,\n \"toc_position\": {},\n \"toc_section_display\": \"block\",\n \"toc_window_display\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"},"license":{"kind":"string","value":"bsd-3-clause"}}},{"rowIdx":46194,"cells":{"repo_name":{"kind":"string","value":"DSSG2017/florence"},"path":{"kind":"string","value":"dev/notebooks/EDA-FC-plots-MM.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"48783"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"#### Notebook to generate FirenzeCard analysis\\n\",\n \"#### Timeseries, and summary statistics\\n\",\n \"import sys\\n\",\n \"sys.path.append('../src/')\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings('ignore')\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline \\n\",\n \"import psycopg2\\n\",\n \"from features.firenzecard import *\\n\",\n \"from IPython.core.debugger import Tracer\\n\",\n \"\\n\",\n \"from scipy.stats import norm\\n\",\n \"from sklearn.neighbors import KernelDensity\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def frequency(dataframe,columnname):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" :param dataframe: a pandas dataframe\\n\",\n \" :param columnname: a single column, with discrete (including integer) values\\n\",\n \" :return: a frequency table, suitable for plotting the empirical PMF, empirical CDF, or empirical CCDF\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" out = dataframe[columnname].value_counts().to_frame()\\n\",\n \" out.columns = ['frequency']\\n\",\n \" out.index.name = columnname\\n\",\n \" out.reset_index(inplace=True)\\n\",\n \" out.sort_values('frequency',inplace=True,ascending=False)\\n\",\n \" out['cumulative'] = out['frequency'].cumsum()/out['frequency'].sum()\\n\",\n \" out['ccdf'] = 1 - out['cumulative']\\n\",\n \" return out\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Index([u'user_id', u'museum_id', u'entrances_per_card_per_museum',\\n\",\n \" u'museum_name', u'longitude', u'latitude', u'short_name', u'string',\\n\",\n \" u'entry_time', u'adults_first_use', u'adults_reuse', u'total_adults',\\n\",\n \" u'minors', u'time', u'date', u'hour', u'day_of_week', u'total_people',\\n\",\n \" u'time_since_previous_museum', u'total_duration_card_use',\\n\",\n \" u'entry_is_adult', u'is_card_with_minors', u'is_in_museum_1',\\n\",\n \" u'is_in_museum_2', u'is_in_museum_3', u'is_in_museum_4',\\n\",\n \" u'is_in_museum_5', u'is_in_museum_6', u'is_in_museum_7',\\n\",\n \" u'is_in_museum_8', u'is_in_museum_9', u'is_in_museum_10',\\n\",\n \" u'is_in_museum_11', u'is_in_museum_12', u'is_in_museum_13',\\n\",\n \" u'is_in_museum_14', u'is_in_museum_15', u'is_in_museum_16',\\n\",\n \" u'is_in_museum_17', u'is_in_museum_18', u'is_in_museum_19',\\n\",\n \" u'is_in_museum_20', u'is_in_museum_21', u'is_in_museum_22',\\n\",\n \" u'is_in_museum_23', u'is_in_museum_24', u'is_in_museum_25',\\n\",\n \" u'is_in_museum_26', u'is_in_museum_27', u'is_in_museum_28',\\n\",\n \" u'is_in_museum_29', u'is_in_museum_30', u'is_in_museum_31',\\n\",\n \" u'is_in_museum_32', u'is_in_museum_33', u'is_in_museum_34',\\n\",\n \" u'is_in_museum_35', u'is_in_museum_36', u'is_in_museum_37',\\n\",\n \" u'is_in_museum_38', u'is_in_museum_39'],\\n\",\n \" dtype='object')\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('../src/output/firenzedata_feature_extracted.csv')\\n\",\n \"df.columns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"df1 = df.groupby(['user_id','entry_time']).agg({'time_since_previous_museum':'max', 'total_adults':'sum', 'total_people':'sum', 'museum_id':'max'})\\n\",\n \"# df1 = df.groupby(['user_id','entry_time','museum_id']).agg({'time_until_next_museum':['min','max'], 'total_adults':['sum'], 'total_people':['sum']})\\n\",\n \"df1.dropna(how=\\\"any\\\",inplace=True)\\n\",\n \"df1 = df1[df1['time_since_previous_museum']>0]\\n\",\n \"df1.reset_index(inplace=True)\\n\",\n \"# df1[df1.time_until_next_museum['min']!=df1.time_until_next_museum['max']] # All have minors\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" user_id entry_time total_people time_since_previous_museum \\\\\\n\",\n \"0 1459702 2016-06-22 14:26:00 1 4.36667 \\n\",\n \"1 1459702 2016-06-22 15:49:00 1 1.38333 \\n\",\n \"2 1459702 2016-06-23 09:43:00 1 17.9 \\n\",\n \"3 1459702 2016-06-23 11:14:00 1 1.51667 \\n\",\n \"4 1459702 2016-06-23 12:57:00 1 1.71667 \\n\",\n \"\\n\",\n \" total_adults museum_id \\n\",\n \"0 1 15 \\n\",\n \"1 1 11 \\n\",\n \"2 1 3 \\n\",\n \"3 1 29 \\n\",\n \"4 1 23 \"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df1.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"| \\n\",\n \" | user_id | \\n\",\n \"entry_time | \\n\",\n \"total_people | \\n\",\n \"time_since_previous_museum | \\n\",\n \"total_adults | \\n\",\n \"museum_id | \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" user_id entry_time total_people time_since_previous_museum \\\\\\n\",\n \"26 1473906 2016-07-24 11:58:00 2 1.16667 \\n\",\n \"76 2017453 2016-06-17 20:04:00 2 0.783333 \\n\",\n \"189 2017468 2016-06-16 12:00:00 3 1.23333 \\n\",\n \"190 2017468 2016-06-16 12:56:00 3 0.933333 \\n\",\n \"191 2017468 2016-06-17 12:34:00 3 23.6333 \\n\",\n \"192 2017468 2016-06-18 10:45:00 3 22.1833 \\n\",\n \"193 2017468 2016-06-18 13:17:00 3 2.53333 \\n\",\n \"194 2017468 2016-06-18 14:58:00 3 1.68333 \\n\",\n \"195 2017468 2016-06-18 15:56:00 3 0.966667 \\n\",\n \"198 2017470 2016-06-16 12:07:00 3 2.43333 \\n\",\n \"199 2017470 2016-06-16 14:19:00 3 2.2 \\n\",\n \"260 2017487 2016-06-14 14:04:00 3 1.83333 \\n\",\n \"261 2017487 2016-06-14 21:35:00 3 7.51667 \\n\",\n \"262 2017487 2016-06-15 11:26:00 3 13.85 \\n\",\n \"263 2017487 2016-06-15 13:13:00 3 1.78333 \\n\",\n \"264 2017487 2016-06-15 16:10:00 3 2.95 \\n\",\n \"270 2017489 2016-06-15 13:46:00 2 25.7167 \\n\",\n \"317 2017800 2016-06-20 14:22:00 2 21.5 \\n\",\n \"433 2017818 2016-06-24 10:58:00 3 22.6667 \\n\",\n \"453 2017821 2016-06-22 14:33:00 2 2.23333 \\n\",\n \"\\n\",\n \" total_adults museum_id \\n\",\n \"26 1 25 \\n\",\n \"76 1 41 \\n\",\n \"189 1 37 \\n\",\n \"190 1 11 \\n\",\n \"191 1 38 \\n\",\n \"192 1 10 \\n\",\n \"193 1 23 \\n\",\n \"194 1 41 \\n\",\n \"195 1 29 \\n\",\n \"198 1 3 \\n\",\n \"199 1 11 \\n\",\n \"260 1 32 \\n\",\n \"261 1 23 \\n\",\n \"262 1 10 \\n\",\n \"263 1 3 \\n\",\n \"264 1 11 \\n\",\n \"270 1 29 \\n\",\n \"317 1 3 \\n\",\n \"433 1 38 \\n\",\n \"453 1 29 \"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df1[df1.total_people>1].head(20)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# df[(df['user_id']==2017470)].sort_values('entry_time')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 4.36667\\n\",\n \"1 1.38333\\n\",\n \"2 17.9\\n\",\n \"3 1.51667\\n\",\n \"4 1.71667\\n\",\n \"Name: time_since_previous_museum, dtype: object\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x1 = df1.loc[np.repeat(df1.index.values,df1['total_people'])]['time_since_previous_museum']\\n\",\n \"x1.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"x2 = df1['time_since_previous_museum']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(309330, 6)\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df1.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(340810,)\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x1.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(309330,)\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x2.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"| \\n\",\n \" | user_id | \\n\",\n \"entry_time | \\n\",\n \"total_people | \\n\",\n \"time_since_previous_museum | \\n\",\n \"total_adults | \\n\",\n \"museum_id | \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" user_id entry_time museum_id total_people total_adults\\n\",\n \"0 1459702 2016-06-22 10:04:00 10 1 1\\n\",\n \"1 1459702 2016-06-22 14:26:00 15 1 1\\n\",\n \"2 1459702 2016-06-22 15:49:00 11 1 1\\n\",\n \"3 1459702 2016-06-23 09:43:00 3 1 1\\n\",\n \"4 1459702 2016-06-23 11:14:00 29 1 1\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df2 = df.groupby(['user_id','entry_time','museum_id']).sum()[['total_people','total_adults']].reset_index()\\n\",\n \"df2.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"| \\n\",\n \" | user_id | \\n\",\n \"entry_time | \\n\",\n \"museum_id | \\n\",\n \"total_people | \\n\",\n \"total_adults | \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" museums_visited total_entries adult_entries\\n\",\n \"user_id \\n\",\n \"1459702 8 8 8\\n\",\n \"1473903 6 6 6\\n\",\n \"1473904 6 6 6\\n\",\n \"1473905 5 5 5\\n\",\n \"1473906 11 12 11\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df3 = df2[['user_id','museum_id']].groupby('user_id').count().join(df2[['user_id','total_people','total_adults']].groupby('user_id').sum())\\n\",\n \"df3.columns = ['museums_visited','total_entries','adult_entries']\\n\",\n \"df3.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 201,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"| \\n\",\n \" | museums_visited | \\n\",\n \"total_entries | \\n\",\n \"adult_entries | \\n\",\n \"
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\\n\",\n \"\\n\",\n \"**Figure 7.3.1**: Block diagram example of an analogue receiver front-end for a radio telescope. A current is induced in the receptor by the electric field of the sky source. The resistance of the components creates a voltage which is propotional to the amplitude of the electric field. This voltage is amplified by a low noise amplifier (LNA) in a cryostat at 20 K. A second stage amplifier further amplifies the signal in a 70 K intermediate cryostat. The signal is bandpass filtered to select the band of interest. A bandstop filter is used to filter out frequencies with radio frequency interference (RFI). A local oscillator (LO) is mixed with the signal to down mix the original radio frequency (RF) to an intermediate frequency (IF) which is then digitized by an analogue-to-digital converter (ADC). \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The electric field of the sky signal induces a current on the *receptor* (also known as a *feed*). We will discuss feeds in [$\\\\S$ 7.4 ➞](7_6_polarization.ipynb). This is a very weak signal: we need to use an amplifier to increase the strength of the signal. The process of amplification introduces noise, which we would like to minimize. As technology improves, the noise introduced by amplifiers has decreased. The common method to reduce the noise of an amplifier is by cooling it using a *cryostat*. This is a chamber placed under vacuum, which can be cooled down to very low temepratures (20 K if using liquid helium, 70 K for liquid nitrogen). Remember - noise and temperature are the same, so the lower the temperature, the lower the noise. This special amplifier is called a *low noise amplifier* (LNA), and is usually the main component of the system noise. When building a telescope system, we focus on making sure the LNA has the best possible noise performance (i.e. the LNA is a significant cost to the analogue front end).\\n\",\n \"\\n\",\n \"The idea of the LNA is that we have a weak signal which needs to be amplified while adding the smallest possible amount of noise into the signal. Once the signal has been amplified with the LNA, we can use normal amplifiers and apply filters, because the signal to noise ratio is now sufficiently large that the added noise from these components will not affect the original signal very much. This can be seen in the receiver temperature equation.\\n\",\n \"\\n\",\n \"$$ T_{\\\\textrm{rx}} = T_{\\\\textrm{feed}} + \\\\frac{T_{\\\\textrm{passive}}}{G_{\\\\textrm{feed}}} + \\\\frac{T_{\\\\textrm{LNA}}}{G_{\\\\textrm{feed}} G_{\\\\textrm{passive}}} + \\\\frac{T_{\\\\textrm{amp}}}{G_{\\\\textrm{feed}} G_{\\\\textrm{passive}} G_{\\\\textrm{LNA}}} + \\\\ldots$$\\n\",\n \"\\n\",\n \"where the $T$ terms are the additional noise temperatures introduced by each component, and $G$ is the 'gain' term - which can be less than 1 (attenuation) or greater than 1 (amplification). The gain from the feed and passive components will be less than 1, and are similar to an efficiency term in when discussing the primary beam. These will cause the temperature from these components to increase in the system temperature etienne: what does this mean, what \\\"these\\\"? Efficiencies?. By placing the LNA as soon as possible, we see that the weak sky signal is amplified and noisier components - such as filters and second stage amplifiers - can be introduced without drowning the sky signal in their noise.\\n\",\n \"\\n\",\n \"From the receiver temperature equation above, we can see that each additional temperature term includes the gain of the LNA in the denominator. Thus, the more gain is introduced by the LNA, the smaller each additional noise term. There are some noise terms which come before the LNA stage. There is always a feed temperature $T_{\\\\textrm{feed}}$ (any physical feed will have some resistance - else, there would be no current!). There are passive components such as cables, orthomode transducers (OMTs), waveguides etc. which all add a noise component $T_{\\\\textrm{passive}}$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Decibel Units\\n\",\n \"\\n\",\n \"As a quick aside, it is common to discuss electronic and system gain using logarithmic decibel units. This is worth a quick review. We usually describe a signal in linear units. It is often more intuitive, however, to use logarithmic units when discussing values which cover many orders of magnitude, such as in electronics. The standard unit to use is the *decibel*, which is defined (for a power $P$) as:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"P_{dB} = 10 \\\\log_{10} \\\\left ( \\\\frac{P}{P_0} \\\\right )\\n\",\n \"$$\\n\",\n \"\\n\",\n \"where $P_0$ is the reference power, typically set to unity. Important values to remember are: 3 dB is a factor of 2 increase, 10 dB is a factor of 10, -3 dB is a factor of 0.5. Every factor of 10 increase in decibels is an order of magnitude increase. Note that if the signal is not a power (e.g. it is a voltage or electric field) the definition of decibel is\\n\",\n \"\\n\",\n \"$$\\n\",\n \"P_{dB} = 20 \\\\log_{10} \\\\left ( \\\\frac{V}{V_0} \\\\right )\\n\",\n \"$$\\n\",\n \"\\n\",\n \"as we need to compute the power of the voltage in the process of converting to decibels. This is useful for describing the response of Jones matrices in decibels.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Returning to the LNA in our analogue front-end example, we typically amplify the sky power signal by 20 to 30 decibels with the LNA. That is, the LNA amplifies the sky signal by a factor of 100 to 1000. We can then use other components without worrying about introducing significant noise relative to the signal strength.\\n\",\n \"\\n\",\n \"Below is an example of the LNA response in time and frequency. This is a simple model, where the LNA bandpass response is stable in time but the overall system gain varies in time. The bandpass has a frequency-dependent response that ranges between 30 dB and 20 dB. There is a'ripple' across the band due to the LNA design. The LNA peaks around 1.2 GHz and drops off at higher frequencies. The overall gain varies by a few precent over time, which can be due to a number of effects. The heating and cooling of the cryostat is never perfect, so there is variation in the gain. Depending on the observing source, we should expect the gain to change: a bright source will introduce more noise than a dimmer source. Feedback and cross-talk between analogue components will always exist even with good isolation. This is thus a fairly typical LNA response, such as one could expect to see in real life.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"#LNA bandpass\\n\",\n \"#xrange: 0,1\\n\",\n \"#yrange: 20-30 dB\\n\",\n \"#a made up LNA response spectrum (in dB)\\n\",\n \"nfreqVals = np.array([-0.5, 0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45,\\n\",\n \" 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0, 1.05])\\n\",\n \"lnaVals = np.array([27, 27, 28.1, 28, 27.5, 29, 30.1, 30, 28.5, 28.5, 30,\\n\",\n \" 27, 27, 25.5, 23, 24, 25, 22.5, 21, 21, 22, 19.5, 19.5])\\n\",\n \"\\n\",\n \"#add a little noise for randomness\\n\",\n \"lnaStd = 0.5 #standard deviation of noise in dB\\n\",\n \"noiseVals = np.random.normal(loc=0.0, scale=lnaStd, size=lnaVals.shape)\\n\",\n \"\\n\",\n \"nfreqs = np.linspace(0, 1, num=nchans) #normalized frequencies\\n\",\n \"coeffs = np.polyfit(nfreqVals, lnaVals + noiseVals, deg=15)\\n\",\n \"lnaGain = 10.**(np.polyval(coeffs, nfreqs)/10) #convert to linear\\n\",\n \"\\n\",\n \"#Gain variation\\n\",\n \"#xrange: 0,1\\n\",\n \"#yrange: 1 + std\\n\",\n \"tVals = np.linspace(-0.5, 1.5, num=60)\\n\",\n \"gainStd = 0.10\\n\",\n \"gVals = np.random.normal(loc=1.0, scale=gainStd, size=tVals.shape)\\n\",\n \"\\n\",\n \"ts = np.linspace(0, 1, num=ntime)\\n\",\n \"gCoeffs = np.polyfit(tVals, gVals, deg=20)\\n\",\n \"gain = np.polyval(gCoeffs, ts)\\n\",\n \"\\n\",\n \"gainSpectrum = np.outer(gain, 10. * np.log10(lnaGain))\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(8,8))\\n\",\n \"\\n\",\n \"ax1 = plt.subplot2grid((5,5), (0,0), colspan=4, rowspan=4)\\n\",\n \"plt.imshow(np.abs(gainSpectrum), aspect='auto')\\n\",\n \"ax1.get_xaxis().set_visible(False)\\n\",\n \"ax1.get_yaxis().set_visible(False)\\n\",\n \"\\n\",\n \"ax2 = plt.subplot2grid((5,5), (4,0), colspan=4, rowspan=1)\\n\",\n \"plt.plot(freqs/1e9, gainSpectrum[100, :])\\n\",\n \"plt.ylabel('Gain (dB)')\\n\",\n \"plt.xlabel('Frequency (GHz)')\\n\",\n \"\\n\",\n \"ax3 = plt.subplot2grid((5,5), (0,4), colspan=1, rowspan=4)\\n\",\n \"plt.plot(gainSpectrum[:, 100], np.arange(gainSpectrum.shape[0]))\\n\",\n \"ax3.get_xaxis().set_visible(False)\\n\",\n \"ax3.invert_yaxis()\\n\",\n \"ax3.yaxis.tick_right()\\n\",\n \"plt.ylabel('Time (s)')\\n\",\n \"ax3.yaxis.set_label_position('right')\\n\",\n \"\\n\",\n \"plt.suptitle('LNA Response')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Filters\\n\",\n \"\\n\",\n \"Applying *signal filters* to the observed signal is necessary for a number of reasons. We are limited to a finite observing bandwdith, because our antenna feed is only sensitive to a limited frequency range. The LNA is only well-characterized for a limited bandwidth, human-made radio interference limits the usable observing band, the ADC only Nyquist samples up to a specific bandwidth before aliasing occurs, etc. All these factors define the usable bandwidth of the instrument. There will be a *start frequency* $\\\\nu_0$ - which is the lowest frequency the system records data at - and a *stop frequency* $\\\\nu_f$ - which is the highest frequency. The range within these frequencies is the *bandwidth* $\\\\Delta \\\\nu = \\\\nu_f - \\\\nu_0$. We can further define two types of bandwidth: the *analogue bandwidth* is the bandwidth of the system based on the analogue filters, and the *digital bandwidth* is defined by half the sampling rate of the ADC (i.e. the digital bandwidth is set by the Nyquist rate). \\n\",\n \"\\n\",\n \"The two building blocks of signal filtering are the *high-pass filter*, which supresses low-frequency signals while leaving high-frequency signals untouched, and the *low-pass filter* which does the opposite. Filter design is an active field of study, and a lifetime can be spent on the topic - we will not go into any depth, but merely present the basics needed to understand a radio telescope analogue front end.\\n\",\n \"\\n\",\n \"A filter works by applying a weighted average in the time domain using some number of samples (called *taps*). The weights within the filter are defined by a window function. A simple window function is a boxcar or truncated Gaussian, but there are many types depending on the desired response. When the time domain signal is transformed into the frequency domain, the window function's Fourier transform is called the *window response function*. The window response of a boxcar is the sinc function, and the response of a Gaussian filter is another Gaussian.\\n\",\n \"\\n\",\n \"To set the band of the analogue system we use a *bandpass filter* which consists of a low-pass and high-pass filter to created a limted band response. An example of a bandpass filter using a low-pass and high-pass Hann window with 33 taps is shown below. These are *finite-impulse response filters* (FIRs) which are typically used for bandpass filters. Another type of filter is the *infinite-impulse response filter* (IIRs) which we will not discuss further.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Modified from: https://scipy.github.io/old-wiki/pages/Cookbook/FIRFilter.html\\n\",\n \"# The Nyquist rate of the signal.\\n\",\n \"sample_rate = 800.0 # ADC sampling rate (MHz)\\n\",\n \"nyq_rate = sample_rate / 2.0 # Digital Bandwidth\\n\",\n \"\\n\",\n \"# Low-Pass Filter\\n\",\n \"# The cutoff frequency of the filter.\\n\",\n \"lpf_cutoff_hz = 360. # MHz\\n\",\n \"# Use firwin with a Hann window to create a low-pass FIR filter.\\n\",\n \"lpf_taps = scipy.signal.firwin(33, lpf_cutoff_hz/nyq_rate, window='hann')\\n\",\n \"\\n\",\n \"# High-Pass Filter\\n\",\n \"# The cutoff frequency of the filter.\\n\",\n \"hpf_cutoff_hz = 40. # MHz\\n\",\n \"# Use firwin with a Hann window to create a high-pass FIR filter.\\n\",\n \"hpf_taps = scipy.signal.firwin(33, hpf_cutoff_hz/nyq_rate, window='hann', pass_zero=False)\\n\",\n \"\\n\",\n \"lpf_w, lpf_h = scipy.signal.freqz(lpf_taps, worN=512)\\n\",\n \"hpf_w, hpf_h = scipy.signal.freqz(hpf_taps, worN=512)\\n\",\n \"lpfAmp = np.absolute(lpf_h)\\n\",\n \"hpfAmp = np.absolute(hpf_h)\\n\",\n \"bandpassAmp = lpfAmp*hpfAmp\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(16,6))\\n\",\n \"\\n\",\n \"plt.subplot(1, 2, 1)\\n\",\n \"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, bandpassAmp, linewidth=2)\\n\",\n \"plt.xlabel('Frequency (MHz)')\\n\",\n \"plt.ylabel('Gain')\\n\",\n \"plt.title('Bandpass Filter Response')\\n\",\n \"plt.ylim(-0.05, 1.05)\\n\",\n \"plt.grid(True)\\n\",\n \"\\n\",\n \"plt.subplot(1, 2, 2)\\n\",\n \"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, 10.*np.log10(bandpassAmp), linewidth=2)\\n\",\n \"plt.xlabel('Frequency (MHz)')\\n\",\n \"plt.ylabel('Gain (dB)')\\n\",\n \"plt.title('Bandpass Filter Response')\\n\",\n \"plt.ylim(-10, 0.2)\\n\",\n \"plt.grid(True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Designing the bandpass filter depends on a number of factors, including desired bandwidth, out-of-band suppression, and financial cost. Filter design is an engineering challenge, set by the project specifications. We will not attempt to cover the topic; see [Understanding Digital Signal Processing ⤴](https://books.google.co.za/books?id=UBU7Y2tpwWUC) for an introduction. As far as we are concerned, there are a few important characteristics. *Usable band*: this is the amount of band which can be used for observation, in the example above this is approximately 1175 MHz to 1425 MHz. *Roll-off*: this is the rate at which the filter drops in response. There is a transtion region in our example from 1100 MHz to 1175 MHz between the bandpass and bandstop regions. *Bandpass ripple*: we see that the bandpass is not perfectly flat;there is variation, especially when the filter begins to roll-off. We would like a nearly flat response across the band. The filter is continuous in frequency, so even though we would like to put a hard stop to the filter at 1100 MHz and 1500 MHz, the response is non-zero. We will be sampling the analogue signal at some Nyquist rate, which will create aliasing effects. We need to have a low enough filter response at the start and stop frequencies that signals from out of the band do not alias into the band. The cost of supressing aliasing is to reduce the overall usable band, or require sharper filters.\\n\",\n \"\\n\",\n \"The opposite of a bandpass filter is a *bandstop filter* which supresses signals within a limited frequency range. These are useful in radio astronomy to supress *radio frequency interference* (RFI) which is strong human-made radio signals. We discuss RFI in [$\\\\S$ 7.8 ➞](7_8_rfi.ipynb); for now, we simply need to kow that RFI can be orders of magnitude stronger than astronomical signals. Signals such as those of mobile telephones at 800 MHz and FM radio aroun 100 MHz (among many others) are present in most populated regions of the Earth.\\n\",\n \"\\n\",\n \"We can partially seperate out the RFI from astronomical signals when the observed signal is channelized, but before that, RFI can cause issues with the analogue front-end due to the dynamic range of the electronic components. Any analogue electronic component is designed with a limited input amplitude range, kown as the device's *dynamic range*. Within this dynamic range, the device has a *linear response*, i.e. the output can be directly mapped to the input based on a linear scaling relation. Input signals out of the dynamic range produce a non-linear response, along with feedback which can affect other components. We would like to build analogue electronics which are optimized to detect astronomical signals, and so we filter out strong RFI signals before these components are used. In the case of strong, persistent, narrow-band RFI (such as FM radio and mobile telephones) a solution is to use a bandstop filter (or *notch filter*) to supress signals within a limited band around the RFI. Below is a simple spectrum which has three strong, narrow-band RFI source.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Spectrum for strong, narrow-band RFI sources\\n\",\n \"rfiSpectrum = np.ones_like(freqs)\\n\",\n \"# Add RFI sources\\n\",\n \"rscale = 20.\\n\",\n \"idx = np.abs(freqs - 1.33e9).argmin()\\n\",\n \"rfiSpectrum[idx] += 1. * rscale\\n\",\n \"idx = np.abs(freqs - 1.34e9).argmin()\\n\",\n \"rfiSpectrum[idx] += 0.3 * rscale\\n\",\n \"idx = np.abs(freqs - 1.335e9).argmin()\\n\",\n \"rfiSpectrum[idx] += 0.5 * rscale\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(8,6))\\n\",\n \"\\n\",\n \"plt.plot(freqs/1e9, rfiSpectrum-1)\\n\",\n \"plt.title('Narrow Band RFI')\\n\",\n \"plt.xlabel('Frequency (GHz)')\\n\",\n \"plt.ylabel('Flux (Jy)')\\n\",\n \"plt.grid(True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If we do nothing to supress the RFI the resulting observed spectrum dynamic range will be dominated by the RFI as shown below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"fig, axes = plt.subplots(figsize=(8,6))\\n\",\n \"\\n\",\n \"plt.plot(freqs, bandpassAmp * rfiSpectrum, linewidth=2)\\n\",\n \"plt.xlabel('Frequency (GHz)')\\n\",\n \"plt.ylabel('Gain')\\n\",\n \"plt.title('Bandpass with RFI')\\n\",\n \"plt.grid(True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To mitigate this, we can design a notch filter to supress 40 MHz of band around these sources. In the figure below, the filter is shown in linear and decibel scales. We can see the notch filter will supress signals up to -30 dB in amplitude.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Bandstop/Notch Filter\\n\",\n \"# The cutoff frequency of the filter.\\n\",\n \"bsf_cutoff_hz = np.array([215., 255.])\\n\",\n \"# Use firwin with a Hann window to create a low-pass FIR filter.\\n\",\n \"bsf_taps = scipy.signal.firwin(73, bsf_cutoff_hz/nyq_rate, window='hann')\\n\",\n \"\\n\",\n \"bsf_w, bsf_h = scipy.signal.freqz(bsf_taps, worN=512)\\n\",\n \"bsfAmp = np.absolute(bsf_h)\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(16,6))\\n\",\n \"\\n\",\n \"plt.subplot(1, 2, 1)\\n\",\n \"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, bsfAmp, linewidth=2)\\n\",\n \"plt.xlabel('Frequency (MHz)')\\n\",\n \"plt.ylabel('Gain')\\n\",\n \"plt.title('Notch Filter Response')\\n\",\n \"plt.ylim(-0.05, 1.05)\\n\",\n \"plt.grid(True)\\n\",\n \"\\n\",\n \"plt.subplot(1, 2, 2)\\n\",\n \"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, 10.*np.log10(bsfAmp), linewidth=2)\\n\",\n \"plt.xlabel('Frequency (MHz)')\\n\",\n \"plt.ylabel('Gain (dB)')\\n\",\n \"plt.title('Notch Filter Response')\\n\",\n \"plt.ylim(-35, 1.)\\n\",\n \"plt.grid(True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can then combine the bandstop filter with the bandpass filter, shown below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"fig, axes = plt.subplots(figsize=(16,6))\\n\",\n \"\\n\",\n \"plt.subplot(1, 2, 1)\\n\",\n \"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, bandpassAmp*bsfAmp, linewidth=2)\\n\",\n \"plt.xlabel('Frequency (MHz)')\\n\",\n \"plt.ylabel('Gain')\\n\",\n \"plt.title('Bandpass Filter w/ Notch Filter Response')\\n\",\n \"plt.ylim(-0.05, 1.05)\\n\",\n \"plt.grid(True)\\n\",\n \"\\n\",\n \"plt.subplot(1, 2, 2)\\n\",\n \"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, 10.*np.log10(bandpassAmp*bsfAmp), linewidth=2)\\n\",\n \"plt.xlabel('Frequency (MHz)')\\n\",\n \"plt.ylabel('Gain (dB)')\\n\",\n \"plt.title('Bandpass Filter w/ Notch Filter Response')\\n\",\n \"plt.ylim(-10, 0.2)\\n\",\n \"plt.grid(True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The resulting observed RFI signal (shown below) is significantly suppressed by the notch filter, at the cost of losing usable bandwidth. This trade-off that is often necessary.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"fig, axes = plt.subplots(figsize=(8,6))\\n\",\n \"\\n\",\n \"plt.plot(freqs, bandpassAmp * bsfAmp * rfiSpectrum, linewidth=2)\\n\",\n \"plt.xlabel('Frequency (GHz)')\\n\",\n \"plt.ylabel('Gain')\\n\",\n \"plt.title('Bandpass with Notch Filter for RFI')\\n\",\n \"plt.grid(True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Hetrodyne Mixing\\n\",\n \"\\n\",\n \"After the filters in our block diagram analogue front-end example, there is a mixer with a local oscillator. This is know as a *hetrodyne detection*, which is the process of 'mixing' down the original radio frequencies (RF) $\\\\nu_{\\\\textrm{RF}}$ to an intermediate frequency (IF) $\\\\nu_{\\\\textrm{IF}}$ using a local oscillator (LO) $\\\\nu_{\\\\textrm{LO}}$ such that the signal can be sampled digitally. A *local oscillator* is a pure, single-frequency tone. Mixing of the RF and LO results in $\\\\nu_{\\\\textrm{IF}} = \\\\nu_{\\\\textrm{RF}} - \\\\nu_{\\\\textrm{LO}}$. Consider two pure sine waves $\\\\sin(2 \\\\pi \\\\nu_{\\\\textrm{RF}})$, $\\\\sin(2 \\\\pi \\\\nu_{\\\\textrm{LO}})$, mixing is the process of multiplying the signals together, using trigonomic identities this results in\\n\",\n \"\\n\",\n \"$$\\\\sin(2 \\\\pi \\\\nu_{\\\\textrm{RF}}) \\\\cdot \\\\sin(2 \\\\pi \\\\nu_{\\\\textrm{LO}}) = \\\\frac{1}{2} \\\\cos(2 \\\\pi (\\\\nu_{\\\\textrm{RF}} - \\\\nu_{\\\\textrm{LO}})t) + \\\\frac{1}{2} \\\\cos(2 \\\\pi (\\\\nu_{\\\\textrm{RF}} + \\\\nu_{\\\\textrm{LO}})t) $$\\n\",\n \"\\n\",\n \"Because we have used a bandpass filter, any signal above the stop frequency will be suppressed, so the $\\\\cos(2 \\\\pi (\\\\nu_{\\\\textrm{RF}} + \\\\nu_{\\\\textrm{LO}})t)$ term can be dropped, resulting in a mixer output of $\\\\frac{1}{2} \\\\cos(2 \\\\pi (\\\\nu_{\\\\textrm{RF}} - \\\\nu_{\\\\textrm{LO}})t)$. The mixer has shifted the original signal at frequency $\\\\nu_{\\\\textrm{RF}}$ down to $\\\\nu_{\\\\textrm{IF}} = \\\\nu_{\\\\textrm{RF}} - \\\\nu_{\\\\textrm{LO}}$.\\n\",\n \"\\n\",\n \"The process of hetrodyne mixing is very useful. From an engineering perspective, it is much easier to build electronics at lower frequencies than higher frequencies. Using a mixer a high frequency signal is mixed down to lower frequencies, and cheaper electronics can be used. Without heterodyne mixers, the alternative is to to use *direct RF sampling*. This is common is very low frequency radio astronomy (< 200 MHz). As technology improves, direct RF sampling will see more use at higher frequencies, but for now at least, most radio telescopes uses hetrodyne mixing.\\n\",\n \"\\n\",\n \"With nearly all the analogue components in place (we will come to the digitization process soon) we are ready to construct the observed spectrum of our idealized source (where we started the section) while including the effects due to the various analogue electronics. As we can see in the figure below, it is quite different from the idealized spectrum we started with! The bottom figure shows the time-dependent bandpass change, and the figure to the right shows the frequency-dependent gain change.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Observed Source Spectrum\\n\",\n \"# noise spectrum\\n\",\n \"noiseSpectrum = np.random.normal(loc=0., scale=2., size=gainSpectrum.shape)\\n\",\n \"\\n\",\n \"# bandpass spectrum\\n\",\n \"bpSpectrum = np.repeat(bandpassAmp * bsfAmp * rfiSpectrum, ntime).reshape(nchans, ntime)\\n\",\n \"\\n\",\n \"# observed spectrum\\n\",\n \"obsSpectrum = bpSpectrum.T * ( (gainSpectrum * np.abs(brightness[:,:,0,0]+brightness[:,:,1,1])) + noiseSpectrum)\\n\",\n \"\\n\",\n \"# local oscillator frequencies\\n\",\n \"lo = 1.1e9\\n\",\n \"ifFreqs = freqs - lo\\n\",\n \"\\n\",\n \"# save spectrum for use in the next section\\n\",\n \"# np.savez('data/analogue_spectrum.npz', obsSpectrum, ifFreqs)\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(8,8))\\n\",\n \"\\n\",\n \"ax1 = plt.subplot2grid((5,5), (0,0), colspan=4, rowspan=4)\\n\",\n \"plt.imshow(np.abs(obsSpectrum), aspect='auto')\\n\",\n \"ax1.get_xaxis().set_visible(False)\\n\",\n \"ax1.get_yaxis().set_visible(False)\\n\",\n \"\\n\",\n \"ax2 = plt.subplot2grid((5,5), (4,0), colspan=4, rowspan=1)\\n\",\n \"plt.plot(ifFreqs/1e9, obsSpectrum[50, :])\\n\",\n \"plt.plot(ifFreqs/1e9, obsSpectrum[250, :])\\n\",\n \"plt.plot(ifFreqs/1e9, obsSpectrum[450, :])\\n\",\n \"plt.xlim(ifFreqs[0]/1e9, ifFreqs[-1]/1e9)\\n\",\n \"plt.ylabel('Flux')\\n\",\n \"plt.xlabel('Frequency (GHz)')\\n\",\n \"\\n\",\n \"ax3 = plt.subplot2grid((5,5), (0,4), colspan=1, rowspan=4)\\n\",\n \"plt.plot(obsSpectrum[:, 130], np.arange(obsSpectrum.shape[0]))\\n\",\n \"plt.plot(obsSpectrum[:, 260], np.arange(obsSpectrum.shape[0]))\\n\",\n \"plt.plot(obsSpectrum[:, 390], np.arange(obsSpectrum.shape[0]))\\n\",\n \"ax3.get_xaxis().set_visible(False)\\n\",\n \"ax3.invert_yaxis()\\n\",\n \"ax3.yaxis.tick_right()\\n\",\n \"plt.ylabel('Time (s)')\\n\",\n \"ax3.yaxis.set_label_position('right')\\n\",\n \"\\n\",\n \"plt.suptitle('Observed Spectrum')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"System noise has hidden the smooth source structure. The LNA response has scaled the flux values and added a bit of ripple to the source. The bandpass filter has supressed the source response at the edges of the band. The notch filter has supressed the RFI signal and part of the source signal. The signals range from 0 MHz (DC) to 400 MHz due to the hetrodyne mixer.\\n\",\n \"\\n\",\n \"We can see that the analogue front-end transforms the original astronomical signal into the observed signal: this is what the $\\\\mathbf{G}$-Jones describes. It is generally a time- and frequency-dependent Jones matrix. The time and frequency dependence is due to the variation in response from the analogue electronic components used to filter, condition, and amplify the input voltage in order to digitally sample the signal. The generic $\\\\mathbf{G}$-Jones is often broken into two components: a time-stable bandpass $\\\\mathbf{B}$-Jones matrix which represents the filter responses and the general LNA response, and a frequency-stable $\\\\mathbf{G}$-Jones matrix which accounts for the time-dependent variation of the overall analogue system.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 7.3.4 Digitisation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After signal conditioning in the analogue front-end, we are ready to capture the signal into a digital form so that we can perform the correlation. The term digitisation is worth reflecting upon: it is the process of taking a time-continuous, analogue signal, and converting it to a time-discrete voltage signal (which are 'digits' described by integers). The last analogue component is the *analogue to digital converter (ADC)*. A simple form of analogue-to-digital conversion is a cascading resistor ladder, which measures discrete steps in an analogue signal. Each output of the ladder triggers a bit value of either 0 or 1 - a digital signal is thus created. See Chapter 13 of [The Art of Electronics ⤴](http://artofelectronics.net/).\\n\",\n \"\\n\",\n \"The dynamic range of an ADC is determined by the number of bits an input analogue signal can be digitized into. For an $n$-bit ADC there are $2^n$ possible digital values, by convention this range is $[-2^{n-1}, 2^{n-1}-1]$. Just as in other electronic components, an ADC has input range in which the resulting digitisation produces linear results. An astronomical signal needs to be amplified such that the analogue signal is within this linear range. The signal is then digitized: an example of a 4-bit (256 values) ADC is shown in the figure below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"adcBits = 4 # number of bits in the ADC\\n\",\n \"adcMin = -1 * 2**(adcBits-1) # minimum ADC value\\n\",\n \"adcMax = 2**(adcBits-1) - 1 # maximumx ADC value\\n\",\n \"\\n\",\n \"nsamp = 256 # number of time samples\\n\",\n \"phi0 = .1*np.pi # starting phase\\n\",\n \"dphi = 6.*np.pi # length\\n\",\n \"phis = np.linspace(0, dphi, nsamp)\\n\",\n \"amp = 6.5 # signal amplitude\\n\",\n \"\\n\",\n \"analogueSignal = amp*np.sin(phis+phi0)\\n\",\n \"digitalSignal = analogueSignal.astype(int)\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(16,8))\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 1)\\n\",\n \"plt.plot(analogueSignal, c='k', label='analogue', linewidth=3, alpha=0.3)\\n\",\n \"plt.plot(digitalSignal, '.', label='digital', linewidth=3)\\n\",\n \"plt.ylim(adcMin-1, adcMax+1)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.axhline(adcMin, ls='--', c='k')\\n\",\n \"plt.axhline(adcMax, ls='--', c='k')\\n\",\n \"plt.title('Signal Digitization (4-bit)')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.legend()\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 2)\\n\",\n \"plt.plot(analogueSignal-digitalSignal)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.ylim(-1.2, 1.2)\\n\",\n \"plt.title('Digitization Error')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.xlabel('Time Sample')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The digitization results in time-discrete samples. The amplitude of each sample is the closest step in the ADC to the analogue amplitude. This results in *digitization error* as the conversion to digital results in information loss. To reduce the digitisation error, the maximum number of steps in the ADC should be used. This requires knowledge of the input signal amplitude. If the signal is too weak, then only a few bits will be used in digitisation and the dynamic range is not being used optimally; this is shown in the figure below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"adcBits = 4 # number of bits in the ADC\\n\",\n \"adcMin = -1 * 2**(adcBits-1) # minimum ADC value\\n\",\n \"adcMax = 2**(adcBits-1) - 1 # maximumx ADC value\\n\",\n \"\\n\",\n \"nsamp = 256 # number of time samples\\n\",\n \"phi0 = .1*np.pi # starting phase\\n\",\n \"dphi = 6.*np.pi # length\\n\",\n \"phis = np.linspace(0, dphi, nsamp)\\n\",\n \"amp = 3. # signal amplitude\\n\",\n \"\\n\",\n \"analogueSignal = amp*np.sin(phis+phi0)\\n\",\n \"digitalSignal = analogueSignal.astype(int)\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(16,8))\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 1)\\n\",\n \"plt.plot(analogueSignal, c='k', label='analogue', linewidth=3, alpha=0.3)\\n\",\n \"plt.plot(digitalSignal, '.', label='digital', linewidth=3)\\n\",\n \"plt.ylim(adcMin-1, adcMax+1)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.axhline(adcMin, ls='--', c='k')\\n\",\n \"plt.axhline(adcMax, ls='--', c='k')\\n\",\n \"plt.title('Signal Digitization (4-bit)')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.legend()\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 2)\\n\",\n \"plt.plot(analogueSignal-digitalSignal)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.ylim(-1.2, 1.2)\\n\",\n \"plt.title('Digitization Error')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.xlabel('Time Sample')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Another solution to reduce digitization error is to use an ADC with more bits, such as the 6-bit ADC figure below. As the number of bits increases, digitisation will eventually approach the true analogue signal amplitude.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"adcBits = 6 # number of bits in the ADC\\n\",\n \"adcMin = -1 * 2**(adcBits-1) # minimum ADC value\\n\",\n \"adcMax = 2**(adcBits-1) - 1 # maximumx ADC value\\n\",\n \"\\n\",\n \"nsamp = 256 # number of time samples\\n\",\n \"phi0 = .1*np.pi # starting phase\\n\",\n \"dphi = 6.*np.pi # length\\n\",\n \"phis = np.linspace(0, dphi, nsamp)\\n\",\n \"amp = 28 # signal amplitude\\n\",\n \"\\n\",\n \"analogueSignal = amp*np.sin(phis+phi0)\\n\",\n \"digitalSignal = analogueSignal.astype(int)\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(16,8))\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 1)\\n\",\n \"plt.plot(analogueSignal, c='k', label='analogue', linewidth=3, alpha=0.3)\\n\",\n \"plt.plot(digitalSignal, '.', label='digital', linewidth=3)\\n\",\n \"plt.ylim(adcMin-1, adcMax+1)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.axhline(adcMin, ls='--', c='k')\\n\",\n \"plt.axhline(adcMax, ls='--', c='k')\\n\",\n \"plt.title('Signal Digitization (6-bit)')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.legend()\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 2)\\n\",\n \"plt.plot(analogueSignal-digitalSignal)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.ylim(-1.2, 1.2)\\n\",\n \"plt.title('Digitization Error')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.xlabel('Time Sample')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A problem in radio astronomy is that we wish to maximize the dynamic range of the ADC for a weak astronomical signal, but observe in an environment with strong RFI which dominate the time-domain telescope signal. We need to sample the full dynamic range of the RFI signal. If the maximum/minimum ADC value is smaller than the RFI signal then the RFI will *saturate* the ADC, causing the output of the ADC to sit at the highest or lowest value and no sky signal to be captured: this is shown in the figure below. This results in *spectral leakage*, where a signal which is located at a specific frequency 'leaks' into nearby frequencies due to instrumental effects. A strong RFI source is usually a strong sinusoidal wave. When we take the Fourier transform of that signal, a strong $\\\\delta$-function is produced in the resulting spectrum, isolated to a single frequency channel. If the ADC is saturated, then the RFI sinusoid will be clipped and turn into a square wave, the Fourier transform of which is similar to the Fourier transform of a boxcar function: a sinc function. A sinc function has longer extended wings - these will leak into the other channels. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"adcBits = 4 # number of bits in the ADC\\n\",\n \"adcMin = -1 * 2**(adcBits-1) # minimum ADC value\\n\",\n \"adcMax = 2**(adcBits-1) - 1 # maximumx ADC value\\n\",\n \"\\n\",\n \"amp = 2. # signal amplitude\\n\",\n \"rfiAmp = 8.5 # RFI signal amplitude\\n\",\n \"rfiPhi0 = 1.35*np.pi\\n\",\n \"rfiFreq = 3.37 # frequecy relative to signal frequency\\n\",\n \"\\n\",\n \"analogueSignal = rfiAmp*np.sin(rfiFreq*(phis+rfiPhi0)) + amp*np.sin(phis+phi0)\\n\",\n \"digitalSignal = analogueSignal.astype(int).clip(adcMin, adcMax)\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(16,8))\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 1)\\n\",\n \"plt.plot(amp*np.sin(phis+phi0), c='r', label='signal', linewidth=3, alpha=0.3)\\n\",\n \"plt.plot(analogueSignal, c='k', label='RFI + signal', linewidth=3, alpha=0.3)\\n\",\n \"plt.plot(digitalSignal, '.', label='digital', linewidth=3)\\n\",\n \"plt.ylim(adcMin-4, adcMax+4)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.axhline(adcMin, ls='--', c='k')\\n\",\n \"plt.axhline(adcMax, ls='--', c='k')\\n\",\n \"plt.title('Signal + RFI Digitization (4-bit)')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.legend()\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 2)\\n\",\n \"plt.plot(analogueSignal-digitalSignal)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.title('Digitization Error')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.xlabel('Time Sample')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Not only does saturation cause spectral leakage, but there is significant digitisation error. The naive solution is to attenuate the in-out signal such that the RFI does not cause the ADC to saturate. If we turn off the RFI, however, we can see in the figure below that the sky signal is not well-sampled by the ADC. We are not using the full dynamic range of the ADC. Again, the solution is to use an ADC with more bits to balance RFI from saturating and sampling the sky signal to a sufficient level.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"analogueSignal = amp*np.sin(phis+phi0)\\n\",\n \"digitalSignal = analogueSignal.astype(int).clip(adcMin, adcMax)\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(figsize=(16,8))\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 1)\\n\",\n \"plt.plot(analogueSignal, c='r', label='signal', linewidth=3, alpha=0.3)\\n\",\n \"plt.plot(digitalSignal, '.', label='digital', linewidth=3)\\n\",\n \"plt.ylim(adcMin-4, adcMax+4)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.axhline(adcMin, ls='--', c='k')\\n\",\n \"plt.axhline(adcMax, ls='--', c='k')\\n\",\n \"plt.title('Signal Digitization (4-bit, Undersampled)')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.legend()\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 2)\\n\",\n \"plt.plot(analogueSignal-digitalSignal)\\n\",\n \"plt.xlim(0, nsamp-1)\\n\",\n \"plt.title('Digitization Error')\\n\",\n \"plt.ylabel('Amplitude')\\n\",\n \"plt.xlabel('Time Sample')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A typical, modern ADC used for radio astronomy will have 8-12 bits. This is necessary for the strong -RFI environment many telescopes operate in. There is an additional issue for *wide-band system*, which are telescopes which cover significant fractional bandwidth. Most astronomical radio sources are continuum sources, i.e. the spectrum of the source is smooth and continuous in frequency. One way to improve the sensitivity of the telescope, as per the radiometer equation, is to increase the observing bandwidth. This is a fine idea, but means that there is necessarily more power being observed by the telescope, and thus requires an ADC with increased dynamic range compared to a narrow-band system.\\n\",\n \"\\n\",\n \"In the past, all the electronic components of a radio telescope were analogue. As we have seen, analogue components add noise and distort signals. What we really want is to digitise the observed signal. This has the advantage of allowing us to perfectly control the signal, at the cost of turning a continuous signal into a discrete one. Analogue components are still required to amplify and filter the signal, but modern radio telescope design aims to convert the signal to digital as soon as possible. Once we have a digital signal we are free to do what we want with the signal - in our case, correlate it.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"***\\n\",\n \"\\n\",\n \"* [Outline](../0_Introduction/0_introduction.ipynb)\\n\",\n \"* [Glossary](../0_Introduction/1_glossary.ipynb)\\n\",\n \"* [7. Observing Systems](0_introduction.ipynb) \\n\",\n \" * Previous: [7.2 The Radio Interferometer Measurement Equation (RIME)](7_2_rime.ipynb) \\n\",\n \" * Next: [7.4 Digital Correlators](7_4_digital.ipynb)\\n\",\n \"\\n\",\n \"***\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Future Additions:
\\n\",\n \"\\n\",\n \"* images: receiver/feed, balun, wave guide, OMT, cryostat, cables, lna\\n\",\n \"* sampling rate in ADC section\\n\",\n \"* expand radiometer equation discussion\\n\",\n \"* expand SEFD discussion, example of observing time for KAT-7, MeerKAT, VLA\\n\",\n \"* meerkat or kat-7 analogue receiver chain block diagram\\n\",\n \"* full simulation of time-domain voltage signal to auto-correlation spectrum: complex LNA response with phase slope\\n\",\n \"* example: KAT-7, MeerKAT waterfall response\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"gpl-2.0"}}},{"rowIdx":46198,"cells":{"repo_name":{"kind":"string","value":"pcmagic/stokes_flow"},"path":{"kind":"string","value":"head_Force/do_calculate_table_loc/phase_map_v4_ecoB01B05_w0.015.ipynb"},"copies":{"kind":"string","value":"2"},"size":{"kind":"string","value":"2480578"},"content":{"kind":"null"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":46199,"cells":{"repo_name":{"kind":"string","value":"d-k-b/udacity-deep-learning"},"path":{"kind":"string","value":"semi-supervised/semi-supervised_learning_2.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"28335"},"content":{"kind":"string","value":"{\r\n \"cells\": [\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"metadata\": {},\r\n \"source\": [\r\n \"In this notebook, we'll learn how to use GANs to do semi-supervised learning.\\n\",\r\n \"\\n\",\r\n \"In supervised learning, we have a training set of inputs $x$ and class labels $y$. We train a model that takes $x$ as input and gives $y$ as output.\\n\",\r\n \"\\n\",\r\n \"In semi-supervised learning, our goal is still to train a model that takes $x$ as input and generates $y$ as output. However, not all of our training examples have a label $y$. We need to develop an algorithm that is able to get better at classification by studying both labeled $(x, y)$ pairs and unlabeled $x$ examples.\\n\",\r\n \"\\n\",\r\n \"To do this for the SVHN dataset, we'll turn the GAN discriminator into an 11 class discriminator. It will recognize the 10 different classes of real SVHN digits, as well as an 11th class of fake images that come from the generator. The discriminator will get to train on real labeled images, real unlabeled images, and fake images. By drawing on three sources of data instead of just one, it will generalize to the test set much better than a traditional classifier trained on only one source of data.\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"%matplotlib inline\\n\",\r\n \"\\n\",\r\n \"import pickle as pkl\\n\",\r\n \"import time\\n\",\r\n \"\\n\",\r\n \"import matplotlib.pyplot as plt\\n\",\r\n \"import numpy as np\\n\",\r\n \"from scipy.io import loadmat\\n\",\r\n \"import tensorflow as tf\\n\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"!mkdir data\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"from urllib.request import urlretrieve\\n\",\r\n \"from os.path import isfile, isdir\\n\",\r\n \"from tqdm import tqdm\\n\",\r\n \"\\n\",\r\n \"data_dir = 'data/'\\n\",\r\n \"\\n\",\r\n \"if not isdir(data_dir):\\n\",\r\n \" raise Exception(\\\"Data directory doesn't exist!\\\")\\n\",\r\n \"\\n\",\r\n \"class DLProgress(tqdm):\\n\",\r\n \" last_block = 0\\n\",\r\n \"\\n\",\r\n \" def hook(self, block_num=1, block_size=1, total_size=None):\\n\",\r\n \" self.total = total_size\\n\",\r\n \" self.update((block_num - self.last_block) * block_size)\\n\",\r\n \" self.last_block = block_num\\n\",\r\n \"\\n\",\r\n \"if not isfile(data_dir + \\\"train_32x32.mat\\\"):\\n\",\r\n \" with DLProgress(unit='B', unit_scale=True, miniters=1, desc='SVHN Training Set') as pbar:\\n\",\r\n \" urlretrieve(\\n\",\r\n \" 'http://ufldl.stanford.edu/housenumbers/train_32x32.mat',\\n\",\r\n \" data_dir + 'train_32x32.mat',\\n\",\r\n \" pbar.hook)\\n\",\r\n \"\\n\",\r\n \"if not isfile(data_dir + \\\"test_32x32.mat\\\"):\\n\",\r\n \" with DLProgress(unit='B', unit_scale=True, miniters=1, desc='SVHN Training Set') as pbar:\\n\",\r\n \" urlretrieve(\\n\",\r\n \" 'http://ufldl.stanford.edu/housenumbers/test_32x32.mat',\\n\",\r\n \" data_dir + 'test_32x32.mat',\\n\",\r\n \" pbar.hook)\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"trainset = loadmat(data_dir + 'train_32x32.mat')\\n\",\r\n \"testset = loadmat(data_dir + 'test_32x32.mat')\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"idx = np.random.randint(0, trainset['X'].shape[3], size=36)\\n\",\r\n \"fig, axes = plt.subplots(6, 6, sharex=True, sharey=True, figsize=(5,5),)\\n\",\r\n \"for ii, ax in zip(idx, axes.flatten()):\\n\",\r\n \" ax.imshow(trainset['X'][:,:,:,ii], aspect='equal')\\n\",\r\n \" ax.xaxis.set_visible(False)\\n\",\r\n \" ax.yaxis.set_visible(False)\\n\",\r\n \"plt.subplots_adjust(wspace=0, hspace=0)\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"def scale(x, feature_range=(-1, 1)):\\n\",\r\n \" # scale to (0, 1)\\n\",\r\n \" x = ((x - x.min())/(255 - x.min()))\\n\",\r\n \" \\n\",\r\n \" # scale to feature_range\\n\",\r\n \" min, max = feature_range\\n\",\r\n \" x = x * (max - min) + min\\n\",\r\n \" return x\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"class Dataset:\\n\",\r\n \" def __init__(self, train, test, val_frac=0.5, shuffle=True, scale_func=None):\\n\",\r\n \" split_idx = int(len(test['y'])*(1 - val_frac))\\n\",\r\n \" self.test_x, self.valid_x = test['X'][:,:,:,:split_idx], test['X'][:,:,:,split_idx:]\\n\",\r\n \" self.test_y, self.valid_y = test['y'][:split_idx], test['y'][split_idx:]\\n\",\r\n \" self.train_x, self.train_y = train['X'], train['y']\\n\",\r\n \" # The SVHN dataset comes with lots of labels, but for the purpose of this exercise,\\n\",\r\n \" # we will pretend that there are only 1000.\\n\",\r\n \" # We use this mask to say which labels we will allow ourselves to use.\\n\",\r\n \" self.label_mask = np.zeros_like(self.train_y)\\n\",\r\n \" self.label_mask[0:1000] = 1\\n\",\r\n \" \\n\",\r\n \" self.train_x = np.rollaxis(self.train_x, 3)\\n\",\r\n \" self.valid_x = np.rollaxis(self.valid_x, 3)\\n\",\r\n \" self.test_x = np.rollaxis(self.test_x, 3)\\n\",\r\n \" \\n\",\r\n \" if scale_func is None:\\n\",\r\n \" self.scaler = scale\\n\",\r\n \" else:\\n\",\r\n \" self.scaler = scale_func\\n\",\r\n \" self.train_x = self.scaler(self.train_x)\\n\",\r\n \" self.valid_x = self.scaler(self.valid_x)\\n\",\r\n \" self.test_x = self.scaler(self.test_x)\\n\",\r\n \" self.shuffle = shuffle\\n\",\r\n \" \\n\",\r\n \" def batches(self, batch_size, which_set=\\\"train\\\"):\\n\",\r\n \" x_name = which_set + \\\"_x\\\"\\n\",\r\n \" y_name = which_set + \\\"_y\\\"\\n\",\r\n \" \\n\",\r\n \" num_examples = len(getattr(dataset, y_name))\\n\",\r\n \" if self.shuffle:\\n\",\r\n \" idx = np.arange(num_examples)\\n\",\r\n \" np.random.shuffle(idx)\\n\",\r\n \" setattr(dataset, x_name, getattr(dataset, x_name)[idx])\\n\",\r\n \" setattr(dataset, y_name, getattr(dataset, y_name)[idx])\\n\",\r\n \" if which_set == \\\"train\\\":\\n\",\r\n \" dataset.label_mask = dataset.label_mask[idx]\\n\",\r\n \" \\n\",\r\n \" dataset_x = getattr(dataset, x_name)\\n\",\r\n \" dataset_y = getattr(dataset, y_name)\\n\",\r\n \" for ii in range(0, num_examples, batch_size):\\n\",\r\n \" x = dataset_x[ii:ii+batch_size]\\n\",\r\n \" y = dataset_y[ii:ii+batch_size]\\n\",\r\n \" \\n\",\r\n \" if which_set == \\\"train\\\":\\n\",\r\n \" # When we use the data for training, we need to include\\n\",\r\n \" # the label mask, so we can pretend we don't have access\\n\",\r\n \" # to some of the labels, as an exercise of our semi-supervised\\n\",\r\n \" # learning ability\\n\",\r\n \" yield x, y, self.label_mask[ii:ii+batch_size]\\n\",\r\n \" else:\\n\",\r\n \" yield x, y\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"def model_inputs(real_dim, z_dim):\\n\",\r\n \" inputs_real = tf.placeholder(tf.float32, (None, *real_dim), name='input_real')\\n\",\r\n \" inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')\\n\",\r\n \" y = tf.placeholder(tf.int32, (None), name='y')\\n\",\r\n \" label_mask = tf.placeholder(tf.int32, (None), name='label_mask')\\n\",\r\n \" \\n\",\r\n \" return inputs_real, inputs_z, y, label_mask\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"def generator(z, output_dim, reuse=False, alpha=0.2, training=True, size_mult=128):\\n\",\r\n \" with tf.variable_scope('generator', reuse=reuse):\\n\",\r\n \" # First fully connected layer\\n\",\r\n \" x1 = tf.layers.dense(z, 4 * 4 * size_mult * 4)\\n\",\r\n \" # Reshape it to start the convolutional stack\\n\",\r\n \" x1 = tf.reshape(x1, (-1, 4, 4, size_mult * 4))\\n\",\r\n \" x1 = tf.layers.batch_normalization(x1, training=training)\\n\",\r\n \" x1 = tf.maximum(alpha * x1, x1)\\n\",\r\n \" \\n\",\r\n \" x2 = tf.layers.conv2d_transpose(x1, size_mult * 2, 5, strides=2, padding='same')\\n\",\r\n \" x2 = tf.layers.batch_normalization(x2, training=training)\\n\",\r\n \" x2 = tf.maximum(alpha * x2, x2)\\n\",\r\n \" \\n\",\r\n \" x3 = tf.layers.conv2d_transpose(x2, size_mult, 5, strides=2, padding='same')\\n\",\r\n \" x3 = tf.layers.batch_normalization(x3, training=training)\\n\",\r\n \" x3 = tf.maximum(alpha * x3, x3)\\n\",\r\n \" \\n\",\r\n \" # Output layer\\n\",\r\n \" logits = tf.layers.conv2d_transpose(x3, output_dim, 5, strides=2, padding='same')\\n\",\r\n \" \\n\",\r\n \" out = tf.tanh(logits)\\n\",\r\n \" \\n\",\r\n \" return out\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"def discriminator(x, reuse=False, alpha=0.2, drop_rate=0., num_classes=10, size_mult=64):\\n\",\r\n \" with tf.variable_scope('discriminator', reuse=reuse):\\n\",\r\n \" x = tf.layers.dropout(x, rate=drop_rate/2.5)\\n\",\r\n \" \\n\",\r\n \" # Input layer is 32x32x3\\n\",\r\n \" x1 = tf.layers.conv2d(x, size_mult, 3, strides=2, padding='same')\\n\",\r\n \" relu1 = tf.maximum(alpha * x1, x1)\\n\",\r\n \" relu1 = tf.layers.dropout(relu1, rate=drop_rate)\\n\",\r\n \" \\n\",\r\n \" x2 = tf.layers.conv2d(relu1, size_mult, 3, strides=2, padding='same')\\n\",\r\n \" bn2 = tf.layers.batch_normalization(x2, training=True)\\n\",\r\n \" relu2 = tf.maximum(alpha * x2, x2)\\n\",\r\n \" \\n\",\r\n \" \\n\",\r\n \" x3 = tf.layers.conv2d(relu2, size_mult, 3, strides=2, padding='same')\\n\",\r\n \" bn3 = tf.layers.batch_normalization(x3, training=True)\\n\",\r\n \" relu3 = tf.maximum(alpha * bn3, bn3)\\n\",\r\n \" relu3 = tf.layers.dropout(relu3, rate=drop_rate)\\n\",\r\n \" \\n\",\r\n \" x4 = tf.layers.conv2d(relu3, 2 * size_mult, 3, strides=1, padding='same')\\n\",\r\n \" bn4 = tf.layers.batch_normalization(x4, training=True)\\n\",\r\n \" relu4 = tf.maximum(alpha * bn4, bn4)\\n\",\r\n \" \\n\",\r\n \" x5 = tf.layers.conv2d(relu4, 2 * size_mult, 3, strides=1, padding='same')\\n\",\r\n \" bn5 = tf.layers.batch_normalization(x5, training=True)\\n\",\r\n \" relu5 = tf.maximum(alpha * bn5, bn5)\\n\",\r\n \" \\n\",\r\n \" x6 = tf.layers.conv2d(relu5, 2 * size_mult, 3, strides=2, padding='same')\\n\",\r\n \" bn6 = tf.layers.batch_normalization(x6, training=True)\\n\",\r\n \" relu6 = tf.maximum(alpha * bn6, bn6)\\n\",\r\n \" relu6 = tf.layers.dropout(relu6, rate=drop_rate)\\n\",\r\n \" \\n\",\r\n \" x7 = tf.layers.conv2d(relu5, 2 * size_mult, 3, strides=1, padding='valid')\\n\",\r\n \" # Don't use bn on this layer, because bn would set the mean of each feature\\n\",\r\n \" # to the bn mu parameter.\\n\",\r\n \" # This layer is used for the feature matching loss, which only works if\\n\",\r\n \" # the means can be different when the discriminator is run on the data than\\n\",\r\n \" # when the discriminator is run on the generator samples.\\n\",\r\n \" relu7 = tf.maximum(alpha * x7, x7)\\n\",\r\n \" \\n\",\r\n \" # Flatten it by global average pooling\\n\",\r\n \" features = raise NotImplementedError()\\n\",\r\n \" \\n\",\r\n \" # Set class_logits to be the inputs to a softmax distribution over the different classes\\n\",\r\n \" raise NotImplementedError()\\n\",\r\n \" \\n\",\r\n \" \\n\",\r\n \" # Set gan_logits such that P(input is real | input) = sigmoid(gan_logits).\\n\",\r\n \" # Keep in mind that class_logits gives you the probability distribution over all the real\\n\",\r\n \" # classes and the fake class. You need to work out how to transform this multiclass softmax\\n\",\r\n \" # distribution into a binary real-vs-fake decision that can be described with a sigmoid.\\n\",\r\n \" # Numerical stability is very important.\\n\",\r\n \" # You'll probably need to use this numerical stability trick:\\n\",\r\n \" # log sum_i exp a_i = m + log sum_i exp(a_i - m).\\n\",\r\n \" # This is numerically stable when m = max_i a_i.\\n\",\r\n \" # (It helps to think about what goes wrong when...\\n\",\r\n \" # 1. One value of a_i is very large\\n\",\r\n \" # 2. All the values of a_i are very negative\\n\",\r\n \" # This trick and this value of m fix both those cases, but the naive implementation and\\n\",\r\n \" # other values of m encounter various problems)\\n\",\r\n \" \\n\",\r\n \" raise NotImplementedError()\\n\",\r\n \" \\n\",\r\n \" return out, class_logits, gan_logits, features\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"def model_loss(input_real, input_z, output_dim, y, num_classes, label_mask, alpha=0.2, drop_rate=0.):\\n\",\r\n \" \\\"\\\"\\\"\\n\",\r\n \" Get the loss for the discriminator and generator\\n\",\r\n \" :param input_real: Images from the real dataset\\n\",\r\n \" :param input_z: Z input\\n\",\r\n \" :param output_dim: The number of channels in the output image\\n\",\r\n \" :param y: Integer class labels\\n\",\r\n \" :param num_classes: The number of classes\\n\",\r\n \" :param alpha: The slope of the left half of leaky ReLU activation\\n\",\r\n \" :param drop_rate: The probability of dropping a hidden unit\\n\",\r\n \" :return: A tuple of (discriminator loss, generator loss)\\n\",\r\n \" \\\"\\\"\\\"\\n\",\r\n \" \\n\",\r\n \" \\n\",\r\n \" # These numbers multiply the size of each layer of the generator and the discriminator,\\n\",\r\n \" # respectively. You can reduce them to run your code faster for debugging purposes.\\n\",\r\n \" g_size_mult = 32\\n\",\r\n \" d_size_mult = 64\\n\",\r\n \" \\n\",\r\n \" # Here we run the generator and the discriminator\\n\",\r\n \" g_model = generator(input_z, output_dim, alpha=alpha, size_mult=g_size_mult)\\n\",\r\n \" d_on_data = discriminator(input_real, alpha=alpha, drop_rate=drop_rate, size_mult=d_size_mult)\\n\",\r\n \" d_model_real, class_logits_on_data, gan_logits_on_data, data_features = d_on_data\\n\",\r\n \" d_on_samples = discriminator(g_model, reuse=True, alpha=alpha, drop_rate=drop_rate, size_mult=d_size_mult)\\n\",\r\n \" d_model_fake, class_logits_on_samples, gan_logits_on_samples, sample_features = d_on_samples\\n\",\r\n \" \\n\",\r\n \" \\n\",\r\n \" # Here we compute `d_loss`, the loss for the discriminator.\\n\",\r\n \" # This should combine two different losses:\\n\",\r\n \" # 1. The loss for the GAN problem, where we minimize the cross-entropy for the binary\\n\",\r\n \" # real-vs-fake classification problem.\\n\",\r\n \" # 2. The loss for the SVHN digit classification problem, where we minimize the cross-entropy\\n\",\r\n \" # for the multi-class softmax. For this one we use the labels. Don't forget to ignore\\n\",\r\n \" # use `label_mask` to ignore the examples that we are pretending are unlabeled for the\\n\",\r\n \" # semi-supervised learning problem.\\n\",\r\n \" raise NotImplementedError()\\n\",\r\n \" \\n\",\r\n \" # Here we set `g_loss` to the \\\"feature matching\\\" loss invented by Tim Salimans at OpenAI.\\n\",\r\n \" # This loss consists of minimizing the absolute difference between the expected features\\n\",\r\n \" # on the data and the expected features on the generated samples.\\n\",\r\n \" # This loss works better for semi-supervised learning than the tradition GAN losses.\\n\",\r\n \" raise NotImplementedError()\\n\",\r\n \"\\n\",\r\n \" pred_class = tf.cast(tf.argmax(class_logits_on_data, 1), tf.int32)\\n\",\r\n \" eq = tf.equal(tf.squeeze(y), pred_class)\\n\",\r\n \" correct = tf.reduce_sum(tf.to_float(eq))\\n\",\r\n \" masked_correct = tf.reduce_sum(label_mask * tf.to_float(eq))\\n\",\r\n \" \\n\",\r\n \" return d_loss, g_loss, correct, masked_correct, g_model\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"def model_opt(d_loss, g_loss, learning_rate, beta1):\\n\",\r\n \" \\\"\\\"\\\"\\n\",\r\n \" Get optimization operations\\n\",\r\n \" :param d_loss: Discriminator loss Tensor\\n\",\r\n \" :param g_loss: Generator loss Tensor\\n\",\r\n \" :param learning_rate: Learning Rate Placeholder\\n\",\r\n \" :param beta1: The exponential decay rate for the 1st moment in the optimizer\\n\",\r\n \" :return: A tuple of (discriminator training operation, generator training operation)\\n\",\r\n \" \\\"\\\"\\\"\\n\",\r\n \" # Get weights and biases to update. Get them separately for the discriminator and the generator\\n\",\r\n \" raise NotImplementedError()\\n\",\r\n \"\\n\",\r\n \" # Minimize both players' costs simultaneously\\n\",\r\n \" raise NotImplementedError()\\n\",\r\n \" shrink_lr = tf.assign(learning_rate, learning_rate * 0.9)\\n\",\r\n \" \\n\",\r\n \" return d_train_opt, g_train_opt, shrink_lr\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"class GAN:\\n\",\r\n \" \\\"\\\"\\\"\\n\",\r\n \" A GAN model.\\n\",\r\n \" :param real_size: The shape of the real data.\\n\",\r\n \" :param z_size: The number of entries in the z code vector.\\n\",\r\n \" :param learnin_rate: The learning rate to use for Adam.\\n\",\r\n \" :param num_classes: The number of classes to recognize.\\n\",\r\n \" :param alpha: The slope of the left half of the leaky ReLU activation\\n\",\r\n \" :param beta1: The beta1 parameter for Adam.\\n\",\r\n \" \\\"\\\"\\\"\\n\",\r\n \" def __init__(self, real_size, z_size, learning_rate, num_classes=10, alpha=0.2, beta1=0.5):\\n\",\r\n \" tf.reset_default_graph()\\n\",\r\n \" \\n\",\r\n \" self.learning_rate = tf.Variable(learning_rate, trainable=False)\\n\",\r\n \" inputs = model_inputs(real_size, z_size)\\n\",\r\n \" self.input_real, self.input_z, self.y, self.label_mask = inputs\\n\",\r\n \" self.drop_rate = tf.placeholder_with_default(.5, (), \\\"drop_rate\\\")\\n\",\r\n \" \\n\",\r\n \" loss_results = model_loss(self.input_real, self.input_z,\\n\",\r\n \" real_size[2], self.y, num_classes,\\n\",\r\n \" label_mask=self.label_mask,\\n\",\r\n \" alpha=0.2,\\n\",\r\n \" drop_rate=self.drop_rate)\\n\",\r\n \" self.d_loss, self.g_loss, self.correct, self.masked_correct, self.samples = loss_results\\n\",\r\n \" \\n\",\r\n \" self.d_opt, self.g_opt, self.shrink_lr = model_opt(self.d_loss, self.g_loss, self.learning_rate, beta1)\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"def view_samples(epoch, samples, nrows, ncols, figsize=(5,5)):\\n\",\r\n \" fig, axes = plt.subplots(figsize=figsize, nrows=nrows, ncols=ncols, \\n\",\r\n \" sharey=True, sharex=True)\\n\",\r\n \" for ax, img in zip(axes.flatten(), samples[epoch]):\\n\",\r\n \" ax.axis('off')\\n\",\r\n \" img = ((img - img.min())*255 / (img.max() - img.min())).astype(np.uint8)\\n\",\r\n \" ax.set_adjustable('box-forced')\\n\",\r\n \" im = ax.imshow(img)\\n\",\r\n \" \\n\",\r\n \" plt.subplots_adjust(wspace=0, hspace=0)\\n\",\r\n \" return fig, axes\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"def train(net, dataset, epochs, batch_size, figsize=(5,5)):\\n\",\r\n \" \\n\",\r\n \" saver = tf.train.Saver()\\n\",\r\n \" sample_z = np.random.normal(0, 1, size=(50, z_size))\\n\",\r\n \"\\n\",\r\n \" samples, train_accuracies, test_accuracies = [], [], []\\n\",\r\n \" steps = 0\\n\",\r\n \"\\n\",\r\n \" with tf.Session() as sess:\\n\",\r\n \" sess.run(tf.global_variables_initializer())\\n\",\r\n \" for e in range(epochs):\\n\",\r\n \" print(\\\"Epoch\\\",e)\\n\",\r\n \" \\n\",\r\n \" t1e = time.time()\\n\",\r\n \" num_examples = 0\\n\",\r\n \" num_correct = 0\\n\",\r\n \" for x, y, label_mask in dataset.batches(batch_size):\\n\",\r\n \" assert 'int' in str(y.dtype)\\n\",\r\n \" steps += 1\\n\",\r\n \" num_examples += label_mask.sum()\\n\",\r\n \"\\n\",\r\n \" # Sample random noise for G\\n\",\r\n \" batch_z = np.random.normal(0, 1, size=(batch_size, z_size))\\n\",\r\n \"\\n\",\r\n \" # Run optimizers\\n\",\r\n \" t1 = time.time()\\n\",\r\n \" _, _, correct = sess.run([net.d_opt, net.g_opt, net.masked_correct],\\n\",\r\n \" feed_dict={net.input_real: x, net.input_z: batch_z,\\n\",\r\n \" net.y : y, net.label_mask : label_mask})\\n\",\r\n \" t2 = time.time()\\n\",\r\n \" num_correct += correct\\n\",\r\n \"\\n\",\r\n \" sess.run([net.shrink_lr])\\n\",\r\n \" \\n\",\r\n \" \\n\",\r\n \" train_accuracy = num_correct / float(num_examples)\\n\",\r\n \" \\n\",\r\n \" print(\\\"\\\\t\\\\tClassifier train accuracy: \\\", train_accuracy)\\n\",\r\n \" \\n\",\r\n \" num_examples = 0\\n\",\r\n \" num_correct = 0\\n\",\r\n \" for x, y in dataset.batches(batch_size, which_set=\\\"test\\\"):\\n\",\r\n \" assert 'int' in str(y.dtype)\\n\",\r\n \" num_examples += x.shape[0]\\n\",\r\n \"\\n\",\r\n \" correct, = sess.run([net.correct], feed_dict={net.input_real: x,\\n\",\r\n \" net.y : y,\\n\",\r\n \" net.drop_rate: 0.})\\n\",\r\n \" num_correct += correct\\n\",\r\n \" \\n\",\r\n \" test_accuracy = num_correct / float(num_examples)\\n\",\r\n \" print(\\\"\\\\t\\\\tClassifier test accuracy\\\", test_accuracy)\\n\",\r\n \" print(\\\"\\\\t\\\\tStep time: \\\", t2 - t1)\\n\",\r\n \" t2e = time.time()\\n\",\r\n \" print(\\\"\\\\t\\\\tEpoch time: \\\", t2e - t1e)\\n\",\r\n \" \\n\",\r\n \" \\n\",\r\n \" gen_samples = sess.run(\\n\",\r\n \" net.samples,\\n\",\r\n \" feed_dict={net.input_z: sample_z})\\n\",\r\n \" samples.append(gen_samples)\\n\",\r\n \" _ = view_samples(-1, samples, 5, 10, figsize=figsize)\\n\",\r\n \" plt.show()\\n\",\r\n \" \\n\",\r\n \" \\n\",\r\n \" # Save history of accuracies to view after training\\n\",\r\n \" train_accuracies.append(train_accuracy)\\n\",\r\n \" test_accuracies.append(test_accuracy)\\n\",\r\n \" \\n\",\r\n \"\\n\",\r\n \" saver.save(sess, './checkpoints/generator.ckpt')\\n\",\r\n \"\\n\",\r\n \" with open('samples.pkl', 'wb') as f:\\n\",\r\n \" pkl.dump(samples, f)\\n\",\r\n \" \\n\",\r\n \" return train_accuracies, test_accuracies, samples\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"!mkdir checkpoints\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"real_size = (32,32,3)\\n\",\r\n \"z_size = 100\\n\",\r\n \"learning_rate = 0.0003\\n\",\r\n \"\\n\",\r\n \"net = GAN(real_size, z_size, learning_rate)\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true,\r\n \"scrolled\": false\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"dataset = Dataset(trainset, testset)\\n\",\r\n \"\\n\",\r\n \"batch_size = 128\\n\",\r\n \"epochs = 25\\n\",\r\n \"train_accuracies, test_accuracies, samples = train(net,\\n\",\r\n \" dataset,\\n\",\r\n \" epochs,\\n\",\r\n \" batch_size,\\n\",\r\n \" figsize=(10,5))\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"fig, ax = plt.subplots()\\n\",\r\n \"plt.plot(train_accuracies, label='Train', alpha=0.5)\\n\",\r\n \"plt.plot(test_accuracies, label='Test', alpha=0.5)\\n\",\r\n \"plt.title(\\\"Accuracy\\\")\\n\",\r\n \"plt.legend()\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"metadata\": {},\r\n \"source\": [\r\n \"When you run the fully implemented semi-supervised GAN, you should usually find that the test accuracy peaks at 69-71%. It should definitely stay above 68% fairly consistently throughout the last several epochs of training.\\n\",\r\n \"\\n\",\r\n \"This is a little bit better than a [NIPS 2014 paper](https://arxiv.org/pdf/1406.5298.pdf) that got 64% accuracy on 1000-label SVHN with variational methods. However, we still have lost something by not using all the labels. If you re-run with all the labels included, you should obtain over 80% accuracy using this architecture (and other architectures that take longer to run can do much better).\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"_ = view_samples(-1, samples, 5, 10, figsize=(10,5))\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"!mkdir images\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": [\r\n \"for ii in range(len(samples)):\\n\",\r\n \" fig, ax = view_samples(ii, samples, 5, 10, figsize=(10,5))\\n\",\r\n \" fig.savefig('images/samples_{:03d}.png'.format(ii))\\n\",\r\n \" plt.close()\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"source\": [\r\n \"Congratulations! You now know how to train a semi-supervised GAN. This exercise is stripped down to make it run faster and to make it simpler to implement. In the original work by Tim Salimans at OpenAI, a GAN using [more tricks and more runtime](https://arxiv.org/pdf/1606.03498.pdf) reaches over 94% accuracy using only 1,000 labeled examples.\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"execution_count\": null,\r\n \"metadata\": {\r\n \"collapsed\": true\r\n },\r\n \"outputs\": [],\r\n \"source\": []\r\n }\r\n ],\r\n \"metadata\": {\r\n \"kernelspec\": {\r\n \"display_name\": \"Python 3\",\r\n \"language\": \"python\",\r\n \"name\": \"python3\"\r\n },\r\n \"language_info\": {\r\n \"codemirror_mode\": {\r\n \"name\": \"ipython\",\r\n \"version\": 3\r\n },\r\n \"file_extension\": \".py\",\r\n \"mimetype\": \"text/x-python\",\r\n \"name\": \"python\",\r\n \"nbconvert_exporter\": \"python\",\r\n \"pygments_lexer\": \"ipython3\",\r\n \"version\": \"3.6.1\"\r\n }\r\n },\r\n \"nbformat\": 4,\r\n \"nbformat_minor\": 2\r\n}\r\n"},"license":{"kind":"string","value":"mit"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":461,"numItemsPerPage":100,"numTotalItems":47214,"offset":46100,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTczNjMxMzYzMCwic3ViIjoiL2RhdGFzZXRzL2NvZGVwYXJyb3QvZ2l0aHViLWp1cHl0ZXIiLCJleHAiOjE3MzYzMTcyMzAsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.QmqF_YHmiz6Dg73yfEjD5OyFhwu_ZLRMAt0CugC0AtWKkxVNSkvRQ_Ri4W9G-P7fVrDOplIJPcLFQq6SVtfpBw","displayUrls":true},"discussionsStats":{"closed":2,"open":0,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false}">
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blab/antibody-response-pulse | bcell-array/code/VBMG_vaccination_1st-BgrowV.ipynb | 2 | 256062 | {
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Antibody Response Pulse\n",
"https://github.com/blab/antibody-response-pulse/\n",
"\n",
"### B-cells evolution --- cross-reactive antibody response after influenza virus infection or vaccination\n",
"### Adaptive immune response for sequential vaccination"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
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{
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Y2UvAA8CEekFJ8wLvN7NTO/BnB+Af6WNwJtK6nVWBfzVx/w9ga0mzd+B3EAw9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wAAAC\n+ElEQVQh3HPXcxFxo6R/B/6fpL8BjwGv0f2G93KkevJoIS/PSjoN+CzwAGk4VF0R8YykHYALJO0S\nEf9sJQP5Rm0isHtERKsX0Gs9LDvocfmNctmNpWvtCtfVwS4/Mxtb5O8qq5C0IWkdoyMi4rTC+0cD\nXyetATQ9hyE/MiJWzp+vCTwQEa/0It82m9Ki59OBtSLiL73OjzXPZWeDwnXVzKx/eVim1fLGGkR5\nfaEjgIsqwzRJUcqKAVSOcMNu9ElaWtKuVW/vAtzvG67+5rKzQeG6amY2WNy4s6K7gadIkdWQtCBw\nHvA8cHgh3TjS2kFIOhC4eFRzaRWnAz+RtBCApLVJQVc+0dNcWTNcdjYoXFfNzAaI59zZGyLiJUn7\nAN+UtDmwCGle3b4RMbOQ9GvACZJOAm6JiFYiBFp5fgG8GfhcXi5hFWCXiLilt9myJrjsbFC4rpqZ\nDRDPuTMzMzMzMxsCHpZpZmZmZmY2BNy4MzMzMzMzGwJu3JmZmZmZmQ0BN+7MzMzMzMyGgBt3ZmZm\nZmZmQ8CNOzMzMzMzsyHgxp2ZmZmZmdkQcOPOzMzMzMxsCLhxZ2ZmZmZmNgTcuDMzMzMzMxsCbtyZ\nmZmZmZkNATfuzMzMzMzMhoAbd2ZmZmZmZkPAjTszMzMzM7Mh4MadmZmZmZnZEHDjzszMzMzMbAi4\ncWdmZmZmZjYE3LgzMzMzMzMbAm7cmZmZmZmZDQE37szMzMzMzIaAG3dmZmZmZmZDwI07MzMzMzOz\nIeDGnZmZmZmZ2RBw487MzMzMzGwIuHFnZmZmZmY2BNy4MzMzMzMzGwJu3JmZmZmZmQ0BN+7MzMzM\nzMyGgBt3ZmZmZmZmQ8CNOzMzMzMzsyHgxp2ZmZmZmdkQcOPOzMzMzMxsCLhxZ2ZmZmZmNgTcuDMz\nMzMzMxsCbtyZmZmZmZkNATfuzMzMzMzMhoAbd2ZmZmZmZkPAjTszMzMzM7Mh4MadmZmZmZnZEHDj\nzszMzMzMbAi4cWdmZmZmZjYE/j8riq8PXIktSgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10aa8fdd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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zuZDwOS8ifM5VwAB3/yCtbJ33VLFFP+p8T7gv0oe/x8vVdm+dH20b6O4jY8ccQ1i5YSph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\nIJzkm5mZmZmZmQ2I/wcgnsvCTZPwRAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10abed550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"'''\n",
"author: Alvason Zhenhua Li\n",
"date: 04/09/2015\n",
"'''\n",
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"from matplotlib.ticker import FuncFormatter\n",
"\n",
"AlvaFontSize = 23\n",
"AlvaFigSize = (9, 6)\n",
"numberingFig = 0\n",
"\n",
"# plotting\n",
"dir_path = '/Users/al/Desktop/GitHub/antibody-response-pulse/bcell-array/figure'\n",
"file_name = 'Vaccine-Bcell-IgM-IgG'\n",
"#figure_name = '-equation'\n",
"#file_suffix = '.png'\n",
"#save_figure = os.path.join(dir_path, file_name + figure_name + file_suffix)\n",
"\n",
"numberingFig = numberingFig + 1\n",
"plt.figure(numberingFig, figsize=(12, 5))\n",
"plt.axis('off')\n",
"plt.title(r'$ Vaccine-Bcell-IgM-IgG \\ equations \\ (antibody-response \\ for \\ sequential-vaccination) $'\n",
" , fontsize = AlvaFontSize)\n",
"plt.text(0, 7.0/9, r'$ \\frac{\\partial V_n(t)}{\\partial t} = \\\n",
" +\\xi_{v}V_{n}(t)(1 - \\frac{V_n(t)}{V_{max}}) - \\phi_{m} M_{n}(t) V_{n}(t) - \\phi_{g} G_{n}(t) V_{n}(t) $'\n",
" , fontsize = 1.2*AlvaFontSize)\n",
"plt.text(0, 5.0/9, r'$ \\frac{\\partial B_n(t)}{\\partial t} = \\\n",
" +\\xi_{b}V_{n}(t)(1 - \\frac{V_n(t)}{V_{max}}) + (\\beta_{m} + \\beta_{g}) V_{n}(t) B_{n}(t) - \\mu_{b} B_{n}(t) $'\n",
" , fontsize = 1.2*AlvaFontSize)\n",
"plt.text(0, 3.0/9,r'$ \\frac{\\partial M_n(t)}{\\partial t} = \\\n",
" +\\xi_{m} B_{n}(t) - \\phi_{m} M_{n}(t) V_{n}(t) - \\mu_{m} M_{n}(t) $'\n",
" , fontsize = 1.2*AlvaFontSize)\n",
"plt.text(0, 1.0/9,r'$ \\frac{\\partial G_n(t)}{\\partial t} = \\\n",
" +\\xi_{g} B_{n}(t) - \\phi_{g} G_{n}(t) V_{n}(t) - \\mu_{g} G_{n}(t) $' \n",
" , fontsize = 1.2*AlvaFontSize)\n",
"\n",
"#plt.savefig(save_figure, dpi = 100)\n",
"plt.show()\n",
"\n",
"\n",
"\n",
"# define the V-B-M-G partial differential equations\n",
"def dVdt_array(VBMGxt = [], *args):\n",
" # naming\n",
" V = VBMGxt[0]\n",
" B = VBMGxt[1]\n",
" M = VBMGxt[2]\n",
" G = VBMGxt[3]\n",
" x_totalPoint = VBMGxt.shape[1]\n",
" # there are n dSdt\n",
" dV_dt_array = np.zeros(x_totalPoint)\n",
" # each dSdt with the same equation form\n",
" for xn in range(x_totalPoint):\n",
" dV_dt_array[xn] = +inRateV*V[xn]*(1 - V[xn]/maxV) - killRateVm*M[xn]*V[xn] - killRateVg*G[xn]*V[xn]\n",
" return(dV_dt_array)\n",
"\n",
"def dBdt_array(VBMGxt = [], *args):\n",
" # naming\n",
" V = VBMGxt[0]\n",
" B = VBMGxt[1]\n",
" M = VBMGxt[2]\n",
" G = VBMGxt[3]\n",
" x_totalPoint = VBMGxt.shape[1]\n",
" # there are n dSdt\n",
" dB_dt_array = np.zeros(x_totalPoint)\n",
" # each dSdt with the same equation form\n",
" for xn in range(x_totalPoint):\n",
" dB_dt_array[xn] = +inRateB*V[xn]*(1 - V[xn]/maxV) + (actRateBm + actRateBg)*B[xn]*V[xn] - outRateB*B[xn]\n",
" return(dB_dt_array)\n",
"\n",
"def dMdt_array(VBMGxt = [], *args):\n",
" # naming\n",
" V = VBMGxt[0]\n",
" B = VBMGxt[1]\n",
" M = VBMGxt[2]\n",
" G = VBMGxt[3]\n",
" x_totalPoint = VBMGxt.shape[1]\n",
" # there are n dSdt\n",
" dM_dt_array = np.zeros(x_totalPoint)\n",
" # each dSdt with the same equation form\n",
" for xn in range(x_totalPoint):\n",
" dM_dt_array[xn] = +inRateM*B[xn] - consumeRateM*M[xn]*V[xn] - outRateM*M[xn]\n",
" return(dM_dt_array)\n",
"\n",
"def dGdt_array(VBMGxt = [], *args):\n",
" # naming\n",
" V = VBMGxt[0]\n",
" B = VBMGxt[1]\n",
" M = VBMGxt[2]\n",
" G = VBMGxt[3]\n",
" x_totalPoint = VBMGxt.shape[1]\n",
" # there are n dSdt\n",
" dG_dt_array = np.zeros(x_totalPoint)\n",
" # each dSdt with the same equation form\n",
" for xn in range(x_totalPoint):\n",
" dG_dt_array[xn] = +inRateG*B[xn] - consumeRateG*G[xn]*V[xn] - outRateG*G[xn]\n",
" return(dG_dt_array)\n",
"\n",
"# define RK4 for an array (3, n) of coupled differential equations\n",
"def AlvaRungeKutta4ArrayXT(pde_array, startingOut_Value, minX_In, maxX_In, totalGPoint_X, minT_In, maxT_In, totalGPoint_T):\n",
" # primary size of pde equations\n",
" outWay = pde_array.shape[0]\n",
" # initialize the whole memory-space for output and input\n",
" inWay = 1; # one layer is enough for storing \"x\" and \"t\" (only two list of variable)\n",
" # define the first part of array as output memory-space\n",
" gridOutIn_array = np.zeros([outWay + inWay, totalGPoint_X, totalGPoint_T])\n",
" # loading starting output values\n",
" for i in range(outWay):\n",
" gridOutIn_array[i, :, :] = startingOut_Value[i, :, :]\n",
" # griding input X value \n",
" gridingInput_X = np.linspace(minX_In, maxX_In, num = totalGPoint_X, retstep = True)\n",
" # loading input values to (define the final array as input memory-space)\n",
" gridOutIn_array[-inWay, :, 0] = gridingInput_X[0]\n",
" # step-size (increment of input X)\n",
" dx = gridingInput_X[1]\n",
" # griding input T value \n",
" gridingInput_T = np.linspace(minT_In, maxT_In, num = totalGPoint_T, retstep = True)\n",
" # loading input values to (define the final array as input memory-space)\n",
" gridOutIn_array[-inWay, 0, :] = gridingInput_T[0]\n",
" # step-size (increment of input T)\n",
" dt = gridingInput_T[1]\n",
" # starting\n",
" # initialize the memory-space for local try-step \n",
" dydt1_array = np.zeros([outWay, totalGPoint_X])\n",
" dydt2_array = np.zeros([outWay, totalGPoint_X])\n",
" dydt3_array = np.zeros([outWay, totalGPoint_X])\n",
" dydt4_array = np.zeros([outWay, totalGPoint_X])\n",
" # initialize the memory-space for keeping current value\n",
" currentOut_Value = np.zeros([outWay, totalGPoint_X])\n",
" for tn in range(totalGPoint_T - 1):\n",
" # cut off --- setting virus = 0 if virus < 1\n",
" if gridOutIn_array[0, 0, tn] < 1.0:\n",
" gridOutIn_array[0, 0, tn] = 0.0\n",
" # bottom line --- setting bcell = 1 if bcell < 1\n",
" if gridOutIn_array[1, 0, tn] < 1.0:\n",
" gridOutIn_array[1, 0, tn] = 1.0\n",
" # keep initial value at the moment of tn\n",
" currentOut_Value[:, :] = np.copy(gridOutIn_array[:-inWay, :, tn])\n",
" currentIn_T_Value = np.copy(gridOutIn_array[-inWay, 0, tn])\n",
" # first try-step\n",
" for i in range(outWay):\n",
" for xn in range(totalGPoint_X):\n",
" dydt1_array[i, xn] = pde_array[i](gridOutIn_array[:, :, tn])[xn] # computing ratio \n",
" gridOutIn_array[:-inWay, :, tn] = currentOut_Value[:, :] + dydt1_array[:, :]*dt/2 # update output\n",
" gridOutIn_array[-inWay, 0, tn] = currentIn_T_Value + dt/2 # update input\n",
" # second half try-step\n",
" for i in range(outWay):\n",
" for xn in range(totalGPoint_X):\n",
" dydt2_array[i, xn] = pde_array[i](gridOutIn_array[:, :, tn])[xn] # computing ratio \n",
" gridOutIn_array[:-inWay, :, tn] = currentOut_Value[:, :] + dydt2_array[:, :]*dt/2 # update output\n",
" gridOutIn_array[-inWay, 0, tn] = currentIn_T_Value + dt/2 # update input\n",
" # third half try-step\n",
" for i in range(outWay):\n",
" for xn in range(totalGPoint_X):\n",
" dydt3_array[i, xn] = pde_array[i](gridOutIn_array[:, :, tn])[xn] # computing ratio \n",
" gridOutIn_array[:-inWay, :, tn] = currentOut_Value[:, :] + dydt3_array[:, :]*dt # update output\n",
" gridOutIn_array[-inWay, 0, tn] = currentIn_T_Value + dt # update input\n",
" # fourth try-step\n",
" for i in range(outWay):\n",
" for xn in range(totalGPoint_X):\n",
" dydt4_array[i, xn] = pde_array[i](gridOutIn_array[:, :, tn])[xn] # computing ratio \n",
" # solid step (update the next output) by accumulate all the try-steps with proper adjustment\n",
" gridOutIn_array[:-inWay, :, tn + 1] = currentOut_Value[:, :] + dt*(dydt1_array[:, :]/6 \n",
" + dydt2_array[:, :]/3 \n",
" + dydt3_array[:, :]/3 \n",
" + dydt4_array[:, :]/6)\n",
" # restore to initial value\n",
" gridOutIn_array[:-inWay, :, tn] = np.copy(currentOut_Value[:, :])\n",
" gridOutIn_array[-inWay, 0, tn] = np.copy(currentIn_T_Value)\n",
" # end of loop\n",
" return (gridOutIn_array[:-inWay, :])\n",
"# -----------------------------------------\n",
"\n",
"# setting parameter\n",
"timeUnit = 'day'\n",
"if timeUnit == 'hour':\n",
" hour = float(1); day = float(24); \n",
"elif timeUnit == 'day':\n",
" day = float(1); hour = float(1)/24; \n",
"\n",
"# Experimental lab data from (Quantifying the Early Immune Response and Adaptive Immune) paper\n",
"gT_lab = np.array([0, 7, 14, 28])*day\n",
"gFM1_lab = np.array([2**(5 + 1.0/3), 2**7, 2**(8 + 1.0/6), 2**(8 - 1.0/2)])\n",
"error_FM1 = gFM1_lab**(4.0/5)\n",
"bar_width = 2\n",
"\n",
"###\n",
"maxV = float(16) # max vaccine/micro-liter\n",
"inRateV = 0.2/hour # in-rate of virus\n",
"killRateVm = 0.001/hour # kill-rate of virus by antibody-IgM\n",
"killRateVg = killRateVm # kill-rate of virus by antibody-IgG\n",
"\n",
"inRateB = 0.06/hour # in-rate of B-cell\n",
"outRateB = inRateB/8 # out-rate of B-cell\n",
"actRateBm = killRateVm # activation rate of naive B-cell\n",
"actRateBg = killRateVg # activation rate of memory B-cell\n",
"\n",
"inRateM = 0.16/hour # in-rate of antibody-IgM from naive B-cell\n",
"outRateM = inRateM/1 # out-rate of antibody-IgM from naive B-cell\n",
"consumeRateM = killRateVm # consume-rate of antibody-IgM by cleaning virus\n",
"\n",
"inRateG = inRateM/6 # in-rate of antibody-IgG from memory B-cell\n",
"outRateG = outRateM/60 # out-rate of antibody-IgG from memory B-cell\n",
"consumeRateG = killRateVg # consume-rate of antibody-IgG by cleaning virus\n",
"\n",
"# time boundary and griding condition\n",
"minT = float(0)\n",
"maxT = float(80*day)\n",
"totalGPoint_T = int(2*10**3 + 1)\n",
"gridT = np.linspace(minT, maxT, totalGPoint_T)\n",
"spacingT = np.linspace(minT, maxT, num = totalGPoint_T, retstep = True)\n",
"gridT = spacingT[0]\n",
"dt = spacingT[1]\n",
"\n",
"# space boundary and griding condition\n",
"minX = float(0)\n",
"maxX = float(1)\n",
"totalGPoint_X = int(1 + 1)\n",
"gridX = np.linspace(minX, maxX, totalGPoint_X)\n",
"gridingX = np.linspace(minX, maxX, num = totalGPoint_X, retstep = True)\n",
"gridX = gridingX[0]\n",
"dx = gridingX[1]\n",
"gridV_array = np.zeros([totalGPoint_X, totalGPoint_T])\n",
"gridB_array = np.zeros([totalGPoint_X, totalGPoint_T])\n",
"gridM_array = np.zeros([totalGPoint_X, totalGPoint_T])\n",
"gridG_array = np.zeros([totalGPoint_X, totalGPoint_T])\n",
"# initial output condition\n",
"gridV_array[0, 0] = float(16)\n",
"gridB_array[0, 0] = float(0)\n",
"gridM_array[0, 0] = float(0)\n",
"gridG_array[0, 0] = float(0)\n",
"# Runge Kutta numerical solution\n",
"pde_array = np.array([dVdt_array, dBdt_array, dMdt_array, dGdt_array])\n",
"startingOut_Value = np.array([gridV_array, gridB_array, gridM_array, gridG_array])\n",
"gridOut_array = AlvaRungeKutta4ArrayXT(pde_array, startingOut_Value, minX, maxX, totalGPoint_X, minT, maxT, totalGPoint_T)\n",
"# plotting\n",
"gridV = gridOut_array[0] \n",
"gridB = gridOut_array[1] \n",
"gridM = gridOut_array[2]\n",
"gridG = gridOut_array[3]\n",
"\n",
"figure_name = '-first-vaccination'\n",
"file_suffix = '.png'\n",
"save_figure = os.path.join(dir_path, file_name + figure_name + file_suffix)\n",
"numberingFig = numberingFig + 1\n",
"ymin = -40\n",
"ymax = 400\n",
"for i in range(1):\n",
" plt.figure(numberingFig, figsize = AlvaFigSize)\n",
" plt.plot(gridT, gridV[i], color = 'red', label = r'$ V_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)\n",
" plt.plot(gridT, gridB[i], color = 'purple', label = r'$ B_{%i}(t) $'%(i), linewidth = 5.0, alpha = 0.5\n",
" , linestyle = '-.')\n",
" plt.plot(gridT, gridM[i], color = 'blue', label = r'$ IgM_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)\n",
" plt.plot(gridT, gridG[i], color = 'green', label = r'$ IgG_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)\n",
" plt.plot(gridT, gridM[i] + gridG[i], color = 'black', linewidth = 5.0, alpha = 0.5, linestyle = 'dashed'\n",
" , label = r'$ IgM_{%i}(t) + IgG_{%i}(t) $'%(i, i))\n",
" plt.bar(gT_lab - bar_width/2, gFM1_lab, bar_width, alpha = 0.3, color = 'black', yerr = error_FM1\n",
" , error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ (FM1-vaccine) $')\n",
" plt.grid(True, which = 'both')\n",
" plt.title(r'$ Antibody \\ for \\ First-Vaccination $', fontsize = AlvaFontSize)\n",
" plt.xlabel(r'$time \\ (%s)$'%(timeUnit), fontsize = AlvaFontSize)\n",
" plt.ylabel(r'$ Serum \\ antibody \\ (pg/ml) $', fontsize = AlvaFontSize)\n",
" plt.xticks(fontsize = AlvaFontSize*0.7)\n",
" plt.yticks(fontsize = AlvaFontSize*0.7) \n",
" plt.text(maxT*16.0/10, ymax*8.0/10, r'$ V_{max} = %f $'%(maxV), fontsize = AlvaFontSize)\n",
" plt.text(maxT*16.0/10, ymax*7.0/10, r'$ \\mu_{v} = %f $'%(inRateV), fontsize = AlvaFontSize)\n",
" plt.text(maxT*16.0/10, ymax*6.0/10, r'$ \\phi_{m} = %f $'%(killRateVm), fontsize = AlvaFontSize)\n",
" plt.text(maxT*16.0/10, ymax*5.0/10, r'$ \\phi_{g} = %f $'%(killRateVg), fontsize = AlvaFontSize)\n",
" plt.text(maxT*16.0/10, ymax*4.0/10, r'$ \\mu_{b} = %f $'%(inRateB), fontsize = AlvaFontSize)\n",
" plt.text(maxT*16.0/10, ymax*3.0/10, r'$ \\xi_{m} = %f $'%(inRateM), fontsize = AlvaFontSize)\n",
" plt.text(maxT*16.0/10, ymax*2.0/10, r'$ \\xi_{g} = %f $'%(inRateG), fontsize = AlvaFontSize)\n",
" plt.text(maxT*16.0/10, ymax*1.0/10, r'$ \\mu_{g} = %f $'%(outRateG), fontsize = AlvaFontSize)\n",
" plt.ylim(ymin, ymax)\n",
" plt.gca().xaxis.set_major_locator(plt.MultipleLocator(7))\n",
" plt.legend(loc = (1, 0), fontsize = AlvaFontSize)\n",
" plt.savefig(save_figure, dpi = 100, bbox_inches='tight')\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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qsVeT3DuZBUsXtHXIbSYyPJKRSSMZmTSSS0ZdQnZhNp/nfs7hrYfr5cstzeW9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"text/plain": [
"<matplotlib.figure.Figure at 0x10ab9b150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"numberingFig = numberingFig + 1\n",
"ymin = 2**0\n",
"ymax = 2**9\n",
"for i in range(1):\n",
" plt.figure(numberingFig, figsize = AlvaFigSize)\n",
" plt.plot(gridT, gridV[i], color = 'red', label = r'$ V_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)\n",
" plt.plot(gridT, gridB[i], color = 'purple', label = r'$ B_{%i}(t) $'%(i), linewidth = 5.0, alpha = 0.5\n",
" , linestyle = '-.')\n",
" plt.plot(gridT, gridM[i], color = 'blue', label = r'$ IgM_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)\n",
" plt.plot(gridT, gridG[i], color = 'green', label = r'$ IgG_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)\n",
" plt.plot(gridT, gridM[i] + gridG[i], color = 'black', linewidth = 5.0, alpha = 0.5, linestyle = 'dashed'\n",
" , label = r'$ IgM_{%i}(t) + IgG_{%i}(t) $'%(i, i))\n",
" plt.bar(gT_lab - bar_width/2, gFM1_lab, bar_width, alpha = 0.3, color = 'black', yerr = error_FM1\n",
" , error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ (FM1-vaccine) $')\n",
" plt.grid(True, which = 'both')\n",
" plt.title(r'$ Antibody \\ for \\ First-Vaccination $', fontsize = AlvaFontSize)\n",
" plt.xlabel(r'$time \\ (%s)$'%(timeUnit), fontsize = AlvaFontSize)\n",
" plt.ylabel(r'$ Serum \\ antibody \\ (pg/ml) $', fontsize = AlvaFontSize)\n",
" plt.xticks(fontsize = AlvaFontSize*0.7)\n",
" plt.yticks(fontsize = AlvaFontSize*0.7) \n",
" plt.ylim(ymin, ymax)\n",
" plt.yscale('log', basey = 2)\n",
" # gca()---GetCurrentAxis and Format the ticklabel to be 2**x\n",
" plt.gca().yaxis.set_major_formatter(FuncFormatter(lambda x, pos: int(2**(np.log(x)/np.log(2)))))\n",
" plt.gca().xaxis.set_major_locator(plt.MultipleLocator(7))\n",
" plt.legend(loc = (1, 0), fontsize = AlvaFontSize)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
udaypandit/black_scholes | Lessons.and.Assignments/Potential.Flow.Airfoil/Potential Flow over 2D NACA Airfoil.ipynb | 2 | 970878 | {
"metadata": {
"name": "",
"signature": "sha256:41f8773b78bec42aa16303c957d907319e0c182b6a15dd869f765cf06a0bb479"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Content under Creative Commons Attribution license CC-BY 4.0, code under MIT license (c)2014 P. Y. Chuang."
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Potential Flow Over an Airfoil with Finite Difference Method"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Potential-flow theory has been used to study the flow over 2D airfoils for a long time. Using complex analysis, we can obtain analytical solutions for many potential flows, especially the flow over various 2D airfoils. When computers became available, people could also solve potential flows numerically using panel methods, finite-difference methods, boundary-element methods, etc.\n",
"\n",
"Though there are many unrealistic assumptions in potential flow, the theory provides a good beginning for further analyses. For example, some CFD codes can solve potential flow first, in order to provide a good initial guess of viscous flow solver. \n",
"\n",
"In 2D potential flow, we can solve for either the potential function or stream function through their governing equtaions: the Laplace equation. The governing equation for stream function is\n",
"$$\n",
"\\nabla^2\\Psi=\\frac{\\partial^2 \\Psi}{\\partial x^2}+\\frac{\\partial^2 \\Psi}{\\partial y^2}=0\n",
"$$\n",
"where $\\Psi$ is the stream function. If the velocity vector $\\vec{V}=u\\vec{i}+v\\vec{j}$ represents the flow velocity, the relationship between flow velocity and the stream function is:\n",
"$$\n",
"u=\\frac{\\partial \\Psi}{\\partial y}\\\\\n",
"v=-\\frac{\\partial \\Psi}{\\partial x}\n",
"$$\n",
"\n",
"In this notebook, we show how to solve potential flow over 2D airfoils using the finite difference method."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"NACA 4-digit Airfoil Series"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We use NACA 4-digit airfoils for our example in this notebook. For an introduction to NACA 4-digit airfoils, refer to [reference [1]](http://en.wikipedia.org/wiki/NACA_airfoil).\n",
"\n",
"Let's first define a Python function that can be used to generate the thickness profile of a NACA airfoil, given by\uff1a\n",
"\n",
"$$\n",
"y_t = 5t\\left[ 0.2969\\sqrt{\\frac{x}{c}} -0.126\\left(\\frac{x}{c}\\right) -0.3516\\left(\\frac{x}{c}\\right)^2 +0.2843\\left(\\frac{x}{c}\\right)^3 -0.1036\\left(\\frac{x}{c}\\right)^4 \\right]\n",
"$$\n",
"\n",
"where $y_t$, $t$, $x$, and $c$ represent the thickness, maximum thickness, $x$-coordinate and chord length, respectively. \uff08Note: the last coefficient is modified from $-0.1015$ to $-0.1036$ in order to \"close\" the trailing edge. If we used $-0.1015$, there would be a thickness at the trailing edge, just like airfoils in the real world. But in the computational world, we prefer to use zero-thickness trailing edges.)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def AFyt(x, t, c):\n",
" '''\n",
" input: \n",
" x: x coordinate of center line, float\n",
" t: maximum thickness, in fraction of chord length, float\n",
" c: chord lrngth, float\n",
" output:\n",
" half thickness of airfoil at corresponding x coordinate\n",
" '''\n",
" return 5. * t * (0.2969 * ((x/c)**0.5) - \n",
" 0.126 * (x/c) - \n",
" 0.3516 * ((x/c)**2) + \n",
" 0.2843 * ((x/c)**3) - \n",
" 0.1036 * ((x/c)**4))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that $y_t$ is in fact just the ***half thickness*** of the airfoil, so the upper and lower profiles must be generated separately. The lower part takes the negetive values of those corresponding to the upper part. Using this function, we can generate the profiles for airfoils with the first and the second digits being zero (i.e., symmetrical airfoils). Here we show the profile of the NACA-0014 airfoil as an example:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.linspace(0., 1., 101)\n",
"plt.plot(x, AFyt(x, 0.14, 1.0), 'k-', lw=2) # upper surface\n",
"plt.plot(x, - AFyt(x, 0.14, 1.0), 'k-', lw=2) # lower surface\n",
"plt.axis('equal'); plt.ylim((-0.5, 0.5))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"(-0.5, 0.5)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10624bf50>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For cambered 4-digit arifoils (i.e., the first and the second digit are not zero), we need to define Python functions for center lines (mean camber lines) and the angles, $\\theta$, between center lines and the horizontal line.\n",
"\n",
"$$\n",
"y_c = \\left\\{\\begin{array}{lr} \n",
"m\\frac{x}{p^2}\\left(2p-\\frac{x}{c}\\right)\\text{,} && 0\\le x\\le p\\times c\\\\\n",
"m\\frac{c-x}{(1-p)^2}\\left(1+\\frac{x}{c}-2p\\right)\\text{,} && p\\times c \\le x \\le c \n",
"\\end{array}\\right.\n",
"$$\n",
"\n",
"and\n",
"\n",
"$$\n",
"\\theta=\\tan^{-1}{\\frac{dy_c}{dx}}\n",
"$$\n",
"\n",
"where\n",
"\n",
"$$\n",
"\\frac{dy_c}{dx}=\\left\\{\\begin{array}{lr}\n",
"\\frac{2m}{p^2}\\left(p-\\frac{x}{c}\\right)\\text{,} && 0\\le x\\le p\\times c\\\\\n",
"\\frac{2m}{(1-p)^2}\\left(p-\\frac{x}{c}\\right)\\text{,} && p\\times c \\le x \\le c\n",
"\\end{array}\\right.\n",
"$$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def AFyc(x, m, p, c):\n",
" '''\n",
" input:\n",
" x: x coordinate of center line, float\n",
" m: the maximum camber (100 m is the first of the four digits), float\n",
" p: location of maximum camber (10 p is the second digit), float\n",
" c: chord lrngth, float\n",
" output:\n",
" y coordinate of center line at corresponding x coordinate\n",
" '''\n",
" if (x >= 0.0) and (x <= p*c):\n",
" return m * x * (2. * p - (x/c)) / (p**2.)\n",
" elif (x > p*c) and (x <= c):\n",
" return m * (c - x) * (1. + (x/c) - 2. * p) / ((1. - p)**2)\n",
" else:\n",
" raise ValueError"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def AFth(x, m, p, c):\n",
" '''\n",
" input:\n",
" x: x coordinate of center line, float\n",
" m: the maximum camber (100 m is the first of the four digits), float\n",
" p: location of maximum camber (10 p is the second digit), float\n",
" c: chord lrngth, float\n",
" output:\n",
" angle between center and horizontal line at corresponding x coordinate\n",
" '''\n",
" if (x >= 0.0) and (x <= p*c):\n",
" return np.arctan(2.0 * m * (p - (x/c)) / (p**2))\n",
" elif (x > p*c) and (x <= c):\n",
" return np.arctan(2.0 * m * (p - (x/c)) / ((1. - p)**2))\n",
" else:\n",
" raise ValueError"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, the relationship between the coordinates on the upper and lower surfaces of cambered airfoils ($x_U$, $y_U$, $x_L$, and $y_L$), the center line coordinate ($x$ and $y_c$) and the thickness ($y_t$) are\n",
"$$\n",
"\\begin{array}{ll}\n",
"x_U=x-y_t\\sin{\\theta}\\text{,} && y_U=y_c+y_t\\cos{\\theta}\\\\\n",
"x_L=x+y_t\\sin{\\theta}\\text{,} && y_L=y_c-y_t\\cos{\\theta}\n",
"\\end{array}\n",
"$$\n",
"\n",
"We wrote a function that can generate both symmetrical and cambered NACA 4-digit airfoils and use a sign to represent upper or lower profile:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def AF(x, t, sign, m, p, c):\n",
" '''\n",
" input:\n",
" x: x coordinate of center line, float\n",
" t: maximum thickness, in fraction of chord length, float\n",
" sign: indicate upper (1) or lower (-1) surface of airfoil\n",
" m: the maximum camber (100 m is the first of the four digits), float\n",
" p: location of maximum camber (10 p is the second digit), float\n",
" c: chord lrngth, float\n",
" output:\n",
" x, y coordinates on airfoil surface at corresponding \n",
" center line x coordinate\n",
" '''\n",
" if (m == 0.) or (p == 0):\n",
" return x, sign * AFyt(x, t, c)\n",
" else:\n",
" return np.array([x[i] - \n",
" sign * AFyt(x[i], t, c) * np.sin(AFth(x[i], m, p, c)) \n",
" for i in range(np.size(x))]), \\\n",
" np.array([AFyc(x[i], m, p, c) + \n",
" sign * AFyt(x[i], t, c) * np.cos(AFth(x[i], m, p, c))\n",
" for i in range(np.size(x))])\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following shows the profile of a NACA 2412 airfoil as an example."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.linspace(0., 1., 101)\n",
"xU, yU = AF(x, 0.12, 1, 0.02, 0.4, 1.0)\n",
"xL, yL = AF(x, 0.12, -1, 0.02, 0.4, 1.0)\n",
"plt.plot(xU, yU, 'k-', lw=2)\n",
"plt.plot(xL, yL, 'k-', lw=2)\n",
"plt.axis('equal'); plt.ylim((-0.5, 0.5))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"(-0.5, 0.5)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10624b810>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Discretization in Space of NACA 2412 Airfoil"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Physical Domain and Computational Doamin"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we introduce the concepts of physical and computational domains, following chapter 9 of Hoffmann & Chiang, 2000 [2]. \n",
"\n",
"With the finite difference method, rectangular grids work best. But it is difficult to discretize the domain into rectangular grids when there are objects (like airfoils) within it. \n",
"\n",
"In such a case, the domain can be discretized into a non-Cartesian structured mesh. Such a mesh may contain arbitrary quadrilateral grids, instead of rectangular grids. The approach is to map our real domain into another space that is discretized into rectangular grids and solve our problem in this space. The original domain is called *physical domain*, and the space in which we solve our problems is called *computational domain*. The following figure shows the concepts of physical and computational domains.\n",
"\n",
"\n",
"\n",
"###### Image credit: Hoffmann & Chiang, 2000 (Fig. 9-23a and b).\n",
"\n",
"\n",
"We define the coordinates in the computational domain to be $\\xi$ and $\\eta$ (called computational coordinates) and those in the physical doamin to be $x$ and $y$ (physical coordinates). Physical coordinates are functions of computational coordinates, and vice versa. This means that all calculations performed in the physical domain can also be performed in the computational domain through the relationship between $x$, $y$ and $\\xi$, $\\eta$, after their relationship has been determined. \n",
"\n",
"If we fix the computational coordinates of grid points in a way that all grids in computational domain are rectangular grids with $\\Delta\\xi=\\Delta\\eta=1$, what we need to do next is to determine the physical coordinates of these computational grid points. Let $i$ and $j$ be the indices of grid points in the computational domain. What we want are the values $x_{i,j}, y_{i,j}$ representing the $x, y$ components in physical space of the grid points $(i,j)$ in the computational domain.\n",
"\n",
"In this notebook, we use an O-type mesh on the physical domain, that is, the outer boundary of the physical domain is a circle, as shown in the figure below. \n",
"\n",
"\n",
"\n",
"###### Image credit: Hoffmann & Chiang, 2000 (Fig. 9-28).\n",
"\n",
"We divide the annular area between the arifoil and the outer boundary by a line $\\overline{AC}$:\n",
"\n",
"\n",
"\n",
"###### Image credit: Hoffmann & Chiang, 2000 (Fig. 9-29).\n",
"\n",
"Then we \"expand\" the divided annular area to a rectangular domain, which will be our computational domain:\n",
"\n",
"\n",
"\n",
"###### Image credit: Hoffmann & Chiang, 2000 (Fig. 9-30).\n",
"\n",
"From the name of boundaries ($B_1$~$B_4$), we can understand the relationship between our physical domain and the computational domain."
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Grid Generation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The grids on our computational domain are simply rectangular grids. The lengths of boundaries and the size of the grids in the computational domain are not important, because they only make a difference on the transformation relationships between two domains. So let's fix the size of grids in our computational domain to 1, i.e., $\\Delta\\xi=\\Delta\\eta=1$. Also, the numbers of nodes on the $\\xi$ and $\\eta$ directions are $N_{\\xi}=51$ and $N_{\\eta}=21$. The grids on the computational domain are defined in code as follows:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Nxi = 51\n",
"Neta = 21\n",
"eta, xi = np.meshgrid(np.linspace(0, Neta-1, Neta), np.linspace(0, Nxi-1, Nxi))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def plotMesh(x, y):\n",
" for i in range(Nxi):\n",
" plt.plot(x[i, :], y[i, :], 'k.-', lw=2)\n",
" for i in range(Neta):\n",
" plt.plot(x[:, i], y[:, i], 'k.-', lw=2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plotMesh(xi, eta)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEACAYAAACuzv3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADVpJREFUeJzt3X+o3XUdx/HnckbNSWbKNmpx90eBhTAJhNLQwmRBmH9V\n/lGjP6K/Mipy1h/Vf4pQ+UcQUU5EbBGIpgTmLP+QIMva8BfTfuxG1toEp/Vfkrc/vt91z865nn3O\nved77+e1z/MBY+d8z72vz5s3472zz/1+zkCSJEmSJEmSJEmSJEmSJGmudgKPAs8ATwM39tcvBA4C\nzwMPAxdsSHWSpDPaDuzuH28FngMuAW4Dbuqv7wNuXf/SJEmrcT9wDXAE2NZf294/lyRVbgH4K3A+\ncHLk+qax55KkCm0Ffg9c3z8fH9wvrW85kqRTNhd8zbnAvcDddFsrAMfptlT+CewATqzwfUvzKFCS\nGrRpli9+Q0HYHcCzwO0j1x8A9vaP97I84E+zZcsWFhcXWVpa+v+veb9mnr21F/YiKa9krXm7EngN\nOAwc6n/tobv98BGm3364tLi4uDSO7p363F4zL2et2vOSa7cXOXkla60wT6ea6e37jPq6xhbctOnU\ni3N5zbyctWrPS67dXuTklazFnLdWJEmVc5BLUjgHuSSFc5BLUjgHuSSFc5BLUrhBbz8cMFuSzmbe\nfihJLRl0kHtcuP685Nrthb1IzCtZqyYeFw7IS67dXgyXl1x77Xkla00frZM8ot94XnLt9mK4vOTa\na88rWQv3yCWpLQ5ySQrnIJekcA5ySQrnIJekcA5ySQrnEX1Jqo+3H0pSSzyi33hecu32wl4k5pWs\nVROPCwfkJdduL4bLS6699ryStaaP1kke0W88L7l2ezFcXnLtteeVrIV75JLUFge5JIVzkEtSOAe5\nJIVzkEtSOAe5JIXziL4k1cfbDyWpJR7RbzwvuXZ7YS8S80rWqonHhQPykmu3F8PlJddee17JWtNH\n6ySP6Deel1y7vRguL7n22vNK1sI9cklqi4NcksI5yCUpnINcksI5yCUpnINcksJ5RF+S6uPth5LU\nEo/oN56XXLu9sBeJeSVr1cTjwgF5ybXbi+HykmuvPa9kremjdZJH9BvPS67dXgyXl1x77Xkla+Ee\nuSS1xUEuSeEc5JIUrmSQ7weOA0+NXPsW8AJwqP+1Z+6VSZKKlAzyO5kc1EvAd4DL+l8PzbkuSVKh\nkkH+GHByhetD3vEiSSq0eQ3f+wXgM8ATwFeAl8e/YORWmgnzfs28nLVqz1vPtWrPW8+1Wss702uz\nWO0PO78P7AJ2A8eAb8+lGknSzFY7yE+wfALpR8DlK32Rx4Xrz0uu3V7Yi8S8krWGssDpd63sGHn8\nJeDHK3yPx4UD8pJrtxfD5SXXXnteyVorzNOpSjZoDgBXARfR3Yb4TeBqum2VJeAo8Pn+tVF9XWML\nVn58trW85NrtxXB5ybXXnleyFjPeTFLyw84bVri2f5ZFJEnD8WSnJIVzkEtSOAe5JIVzkEtSOAe5\nJIVzkEtSuCE/+Grmm9olSYD/1ZsktWXQQe7nPtSfl1y7vbAXiXkla9XEz30IyEuu3V4Ml5dce+15\nJWtNH62TBt0jX1rhb5jaPwehtbzk2u3FcHnJtdeeV7IW7pFLUlsc5JIUzkEuSeEc5JIUzkEuSeEc\n5JIUziP6klQfbz+UpJZ4RL/xvOTa7YW9SMwrWasmHhcOyEuu3V4Ml5dce+15JWtNH62TPKLfeF5y\n7fZiuLzk2mvPK1kL98glqS0OckkK5yCXpHAOckkK5yCXpHAOckkK5xF9SaqPtx9KUks8ot94XnLt\n9sJeJOaVrFUTjwsH5CXXbi+Gy0uuvfa8krWmj9ZJHtFvPC+5dnsxXF5y7bXnlayFe+SS1BYHuSSF\nc5BLUjgHuSSFc5BLUjgHuSSF84i+JNXH2w8lqSUe0W88L7l2e2EvEvNK1qqJx4UD8pJrtxfD5SXX\nXnteyVrTR+skj+g3npdcu70YLi+59trzStbCPXJJaouDXJLCOcglKVzJIN8PHAeeGrl2IXAQeB54\nGLhg/qVJkkqUDPI7gT1j126mG+TvBn7ZP5ckbYCSQf4YcHLs2nXAXf3ju4Dr51mUJKlc6S0uC8CD\nwKX985PAW0cyXhp5fsrM90JKkoANuP1wVTewS5LmY7WD/DiwvX+8Azix0hd5XLj+vOTa7YW9SMwr\nWWsoC5x+18ptwL7+8c3ArSt8j8eFA/KSa7cXw+Ul1157XslaK8zTqUr2YQ4AVwEX0b0T/wbwM+Cn\nwDuBReATwMtj39fXNbZg5cdnW8tLrt1eDJeXXHvteSVrMeMe+eaCr7nhda5fM8tCkqRheLJTksI5\nyCUpnINcksI5yCUpnINcksIN+j8EDZgtSWcz/4cgSWrJoIPc48L15yXXbi/sRWJeyVo18bhwQF5y\n7fZiuLzk2mvPK1lr+midNOge+dIKf8PUfny2tbzk2u3FcHnJtdeeV7IW7pFLUlsc5JIUzkEuSeEc\n5JIUzkEuSeEc5JIUzkEuSeH8rBVJqo/3kUtSS/yslcbzkmu3F/YiMa9krZr4uQ8Becm124vh8pJr\nrz2vZK3po3WSn7XSeF5y7fZiuLzk2mvPK1kL98glqS0OckkK5yCXpHAOckkK5yCXpHAOckkK5xF9\nSaqPtx9KUks8ot94XnLt9sJeJOaVrFUTjwsH5CXXbi+Gy0uuvfa8krWmj9ZJHtFvPC+5dnsxXF5y\n7bXnlayFe+SS1BYHuSSFc5BLUjgHuSSFc5BLUjgHuSSF84i+JNXH2w8lqSUe0W88L7l2e2EvEvNK\n1qqJx4UD8pJrtxfD5SXXXnteyVrTR+skj+g3npdcu70YLi+59trzStbCPXJJaouDXJLCOcglKdzm\nNX7/IvAv4L/Aq8Dlay1IkjSbtQ7yJeBq4KW1lyJJWo15bK0MeeeLJOkM1jqE/wK8Qre18gPghyOv\nzXwvpCQJmHE2r3Vr5QrgGHAxcBA4Ajy2xkxJ0gzWurVyrP/9ReA+xn7Y6XHh+vOSa7cX9iIxr2St\n9bQFOL9/fB7wa+Dakdc9LhyQl1y7vRguL7n22vNK1mJGa9kj30X3Lhy6LZp7gFtGXu/rGluw8uOz\nreUl124vhstLrr32vJK1WMc98qPA7jV8vyRpDjzZKUnhHOSSFM5BLknhHOSSFM5BLknhBv0fggbM\nlqSzmf9DkCS1ZNBB7nHh+vOSa7cX9iIxr2StmnhcOCAvuXZ7MVxecu2155WsNX20Thp0j3xphb9h\naj8+21pecu32Yri85NprzytZC/fIJaktDnJJCucgl6RwDnJJCucgl6RwDnJJCucRfUmqj7cfSlJL\nPKLfeF5y7fbCXiTmlaxVE48LB+Ql124vhstLrr32vJK1po/WSR7RbzwvuXZ7MVxecu2155WshXvk\nktQWB7kkhXOQS1I4B7kkhXOQS1I4B7kkhfOIviTVx9sPJaklHtFvPC+5dnthLxLzStaqiceFA/KS\na7cXw+Ul1157Xsla00frJI/oN56XXLu9GC4vufba80rWwj1ySWqLg1ySwjnIJSmcg1ySwjnIJSmc\ng1ySwjnIJSmcn7UiSfXxPnJJaomftdJ4XnLt9sJeJOaVrFUTP/chIC+5dnsxXF5y7bXnlaw1fbRO\n8rNWGs9Lrt1eDJeXXHvteSVr4R65JLXFQS5J4dYyyPcAR4A/AvvmU44kaVarHeTnAN+jG+bvAW4A\nLplXUZKkcqsd5JcDfwIWgVeBnwAfn1NNkqQZrHaQvx3428jzF/prkqR1tnmV31d0n+PIrTSDv2Ze\nzlq1563nWrXnredareWd6bVZrPYd+d+BnSPPd9K9K5ckhdgM/BlYAN4IHMYfdkpSnI8Cz9H90PNr\nG1yLJEmSpFEtHxbaDxwHnhq5diFwEHgeeBi4YAPq2gg7gUeBZ4CngRv76y32403A43TbkM8Ct/TX\nW+zFKecAh4AH++et9mIReJKuF7/tr214L86h225ZAM6lvf3zDwKXcfogvw24qX+8D7h1vYvaINuB\n3f3jrXRbcZfQbj+29L9vBn4DXEm7vQD4MnAP8ED/vNVeHKUb3KM2vBfvBx4aeX5z/6slC5w+yI8A\n2/rH2/vnLbofuAb7sQX4HfBe2u3FO4BHgA+x/I681V4cBd42dm2mXgzxoVkeFpq0jW67hf73bVO+\n9my1QPcvlcdptx9voPsX6nGWt5xa7cV3ga8Cr41ca7UXS3R/qT0BfK6/NlMvVnsg6ExF6fWt6oPj\nw20F7gW+CPx77LWW+vEa3VbTW4Bf0L0bHdVKLz4GnKDbE776db6mlV4AXAEcAy6m2xcff/d9xl4M\n8Y7cw0KTjtP98whgB90f4lacSzfE76bbWoG2+wHwCvBz4H202YsPANfRbSkcAD5M9+ejxV5AN8QB\nXgTuo/ssq5l6McQgfwJ4F8uHhT7J8g8zWvUAsLd/vJflgXa22wTcQXeXxu0j11vsx0Us33nwZuAj\ndO9IW+zF1+ne4O0CPgX8Cvg0bfZiC3B+//g84Fq6n69V0YuWDwsdAP4B/IfuZwWfpfuJ9CO0d1vV\nlXTbCYfphtYhultTW+zHpcAf6HrxJN3+MLTZi1FXsfxGr8Ve7KL7M3GY7hbdU/OyxV5IkiRJkiRJ\nkiRJkiRJkiRJkiRJbfgf7h7rIzXkyIkAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x106441cd0>"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next step is to determine the physical coordinates of the grid points, $x_{i,j}$ and $y_{i,j}$, in the computational domain. In the following two subsections, we introduce two grid generation methods: the algebraic method and elliptic method."
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Algebraic Grid Generation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The idea of algebraic grid generation is to interpolate the physical coordinates of interior grid points between two known boundaries, i.e., the airfoil and the outer boundary in our case.\n",
"\n",
"We know that the number of grid points on the airfoil and outer boundary should be the same with $N_{\\xi}$, while on the dividing line, $\\overline{AC}$, they should equal $N_{\\eta}$. We also know the physical coordinates of grid points on $\\eta=0$ and $\\eta=N_{\\eta}$, because they are located on the airfoil surface and the outer boundary and can be determined as follows: \n",
"\n",
"$$\n",
"\\begin{array}{ll}\n",
"x_{i, j=0} = x_{airfoil}\\text{,} && y_{i, j=0} = y_{airfoil}\\\\\n",
"x_{i, j=-1} = x_{outer\\ boundary}\\text{,} && y_{i, j=-1} = y_{outer\\ boundary}\n",
"\\end{array}\n",
"$$\n",
"\n",
"For convenience, we use Python indexing, where $j=-1$ represents the last grid points in the $\\eta$ direction.\n",
"\n",
"The $x$ and $y$ coordinates for interior points (i.e., $0\\le i\\le -1$ and $1\\le j \\le -2$, using Python indexing) can be obtained through interpolation. This is called algebraic grid-generation.\n",
"\n",
"We now generate algebraic grids for a NACA 2412 airfoil. The ordering of grid points on the airfoil is clockwise and starts from the trailing edge. The center of the circular outer boundary is located at $0.5c$."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rBC = 5.0 # the radius of outer boundary\n",
"m, p, t, c = 0.02, 0.4, 0.12, 1.0 # parameters of NACA 2412 airfoil\n",
"\n",
"# Initialize x[i, j] and y[i, j]\n",
"x = np.empty((Nxi, Neta))\n",
"y = np.empty((Nxi, Neta))\n",
"\n",
"# Generate grid points on airfoil surface\n",
"Nxc = (Nxi-1)/2 + 1\n",
"xc = np.linspace(0., 1., Nxc)\n",
"xU, yU = AF(xc, 0.12, 1, 0.02, 0.4, 1.0)\n",
"xL, yL = AF(xc, 0.12, -1, 0.02, 0.4, 1.0)\n",
"\n",
"# Set x_{i, j=0} and y_{i, j=0}\n",
"x[:Nxc, 0] = xL[-1::-1].copy()\n",
"x[Nxc:, 0] = xU[1:].copy()\n",
"\n",
"y[:Nxc, 0] = yL[-1::-1].copy()\n",
"y[Nxc:, 0] = yU[1:].copy()\n",
"\n",
"# Generate grid points on circular outer boundary\n",
"# and set x_{i, j=-1}, y_{i, j=-1}\n",
"dr = 2. * np.pi / (Nxi -1)\n",
"th = - np.array([i * dr for i in range(Nxi)])\n",
"x[:, -1] = rBC * np.cos(th) + 0.5 * c\n",
"y[:, -1] = rBC * np.sin(th)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we interpolate the coordinates of interior grid points."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in range(Nxi):\n",
" x[i, 1:-1] = np.linspace(x[i, 0], x[i, -1], Neta)[1:-1]\n",
" y[i, 1:-1] = np.linspace(y[i, 0], y[i, -1], Neta)[1:-1]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see what the mesh looks like."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure(figsize=(8, 8), dpi=100)\n",
"plotMesh(x, y)\n",
"plt.axis('equal')\n",
"plt.xlim((-4.5, 5.5)); plt.ylim((-5, 5))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"(-5, 5)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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gcEhISMC9e/fg7++PVatWMRpQVa1QoULo1q0b1q5di4cPHxqkCXK0oZpoGBUR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1YtyjiUzvAmJyeblNdsmeEFMPzmCEZhr5yvtV6wOUOk93vvvWfyoAuCAFeuXIGJEydKhOVVKhV0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dj628XVFREZ0+fZpmzJhhc2jBsGHDaOvWrWYRFh/s3MNsS/Lz84Xxk1W/MspSmjegoKCAYmJi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\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+czRs2NDo9m6HDh3iMdSCBQvy+sC8r2E3ni7DDChLKFRjx66urpg9e7bBJRTMqIwZMwae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\n",
"text": [
"<matplotlib.figure.Figure at 0x1066bbf50>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looking closer to the vicinity of the airfoil:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure(figsize=(8, 8), dpi=100)\n",
"plotMesh(x, y)\n",
"plt.axis('equal')\n",
"plt.xlim((-0.5, 1.5)); plt.ylim((-1, 1))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"(-1, 1)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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ptVpUqlQJRAQvLy9hdowFI6/ZtWuXanNgdQmm8lLLhfr166tW2f748WMQEcqUKaO4bUBP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m3X32Igi9/fbbTtKs0dHROH78OH755RcMHToUX331FcqVK8e/d0aHyWRC0aJF8dZbb+HTTz9F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\n",
"text": [
"<matplotlib.figure.Figure at 0x10666f9d0>"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Though the mesh quality wasn't actually examined here, it does not looks good near the airfoil. That's why we need another grid-generation method."
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Elliptic Grid Generation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the elliptic grid-generation method, we use elliptic PDEs to constrain the relationship between physical and computational coordinates. The elliptic PDEs used in this grid generation method are:\n",
"\n",
"$$\n",
"\\frac{\\partial^2 \\xi}{\\partial x^2}+\\frac{\\partial^2 \\xi}{\\partial y^2}=0\\\\\n",
"$$\n",
"$$\n",
"\\frac{\\partial^2 \\eta}{\\partial x^2}+\\frac{\\partial^2 \\eta}{\\partial y^2}=0\\\\\n",
"$$\n",
"\n",
"However, if we solve these PDEs (if we could), what we would obtain are the computational coordinates, i.e., $\\xi=\\xi(x, y)$ and $\\eta=\\eta(x, y)$, which are not what we want, because we already fixed the computational coordinates (they're simply on a rectangular grid with $\\Delta\\xi=\\Delta\\eta=1$).\n",
"\n",
"Therefore, the actual PDEs that we are going to solve, which are derived from the above PDEs (see reference [2]), are\n",
"\n",
"$$\n",
"a\\frac{\\partial^2 x}{\\partial \\xi^2}-2b\\frac{\\partial^2 x}{\\partial \\xi \\partial \\eta}+c\\frac{\\partial^2 x}{\\partial \\eta^2}=0\n",
"$$\n",
"\n",
"$$\n",
"a\\frac{\\partial^2 y}{\\partial \\xi^2}-2b\\frac{\\partial^2 y}{\\partial \\xi \\partial \\eta}+c\\frac{\\partial^2 y}{\\partial \\eta^2}=0\n",
"$$\n",
"\n",
"where\n",
"\n",
"$$\n",
"a=\\left(\\frac{\\partial x}{\\partial \\eta}\\right)^2+\\left(\\frac{\\partial y}{\\partial \\eta}\\right)^2\\\\\n",
"b=\\left(\\frac{\\partial x}{\\partial \\xi}\\right)\\left(\\frac{\\partial x}{\\partial \\eta}\\right)+\\left(\\frac{\\partial y}{\\partial \\xi}\\right)\\left(\\frac{\\partial y}{\\partial \\eta}\\right)\\\\\n",
"c=\\left(\\frac{\\partial x}{\\partial \\xi}\\right)^2+\\left(\\frac{\\partial y}{\\partial \\eta}\\right)^2\n",
"$$\n",
"\n",
"We can see that, in order to solve the PDEs, we need two boundary conditions on each direction, This means that we need $x_{i,j=0}$, $x_{i,j=-1}$, $x_{i=0,j}$, $x_{i=-1,j}$, $y_{i,j=0}$, $y_{i,j=-1}$, $y_{i=0,j}$, and $y_{i=-1,j}$.\n",
"\n",
"Luckly, $x_{i,j=0}$, $x_{i,j=-1}$, $y_{i,j=0}$, and $y_{i,j=-1}$ are known and are the physical coordinates of nodes on the airfoils and outer boundary, as described in the previous subsection.\n",
"\n",
"From the illustration above, we also know that $x_{i=0,j}$ and $x_{i=-1,j}$ are actually the same, because they both represent the physical coordinates of nodes on the dividing line $\\overline{AC}$. Similarly, $y_{i=0,j}$ and $y_{i=-1,j}$ are the same, too. This implies that ***periodic boundary conditions*** can be applied on $x_{i=0,j}$, $x_{i=-1,j}$, $y_{i=0,j}$, and $y_{i=-1,j}$.\n",
"\n",
"These PDEs are non-linear but we linearize the solution by letting the coefficients lag behind by one iteration. We'll use the basic Jacobi iterative method with the results obtained from the algebraic grid-generation method as initial guess. \n",
"\n",
"We can use central differences on these PDEs, and with $\\Delta\\xi=\\Delta\\eta=1$, the discretized equations are very simple. Since the forms of these two PDEs are the same, their discretized PDEs are also the same. The discretized equation of $x$ are\n",
"\n",
"$$\n",
"a(x_{i+1, j}-2x_{i,j}+x_{i-1, j}) - \\frac{1}{2}b(x_{i+1, j+1}-x_{i+1, j-1}+x_{i-1,j-1}-x_{i-1,j+1})+c(x_{i, j+1}-2x_{i,j}+x_{i, j-1})=0\n",
"$$\n",
"\n",
"or\n",
"\n",
"$$\n",
"x_{i,j}=\\frac{1}{2}\\left\\{\n",
"a(x_{i+1, j}+x_{i-1, j}) - \\frac{1}{2}b(x_{i+1, j+1}-x_{i+1, j-1}+x_{i-1,j-1}-x_{i-1,j+1})+c(x_{i, j+1}+x_{i, j-1})\n",
"\\right\\} / (a+c)\n",
"$$\n",
"\n",
"where\n",
"\n",
"$$\n",
"a=(\\frac{x_{i,j+1}-x_{i,j-1}}{2})^2+(\\frac{y_{i,j+1}-y_{i,j-1}}{2})^2\\\\\n",
"b=(\\frac{x_{i+1,j}-x_{i-1,j}}{2})(\\frac{x_{i,j+1}-x_{i,j-1}}{2})+(\\frac{y_{i+1,j}-y_{i-1,j}}{2})(\\frac{y_{i,j+1}-y_{i,j-1}}{2})\\\\\n",
"c=(\\frac{x_{i+1,j}-x_{i-1,j}}{2})^2+(\\frac{y_{i+1,j}-y_{i-1,j}}{2})^2\n",
"$$\n",
"\n",
"Replace $x$ with $y$ when we want to solve $y$.\n",
"\n",
"We can now solve these equations. The criterion for stopping the iteration is that the maximum difference between a current result and the result of the previous iteration should be less than $10^{-6}$."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def Solve_a_b_c(x, y):\n",
" '''\n",
" input:\n",
" x: the x coordinate of x_{i=i-1~i+1, j=j-1~j+1}, at least 3x3 array\n",
" y: the y coordinate of y_{i=i-1~i+1, j=j-1~j+1}, at least 3x3 array\n",
" output:\n",
" a, b, c: at least 1x1 float\n",
" '''\n",
" a = 0.25 * (((x[1:-1, 2:] - x[1:-1, :-2])**2) + \n",
" ((y[1:-1, 2:] - y[1:-1, :-2])**2))\n",
" b = 0.25 * ((x[2:, 1:-1] - x[:-2, 1:-1]) * \n",
" (x[1:-1, 2:] - x[1:-1, :-2]) + \n",
" (y[2:, 1:-1] - y[:-2, 1:-1]) * \n",
" (y[1:-1, 2:] - y[1:-1, :-2]))\n",
" c = 0.25 * (((x[2:, 1:-1] - x[:-2, 1:-1])**2) + \n",
" ((y[2:, 1:-1] - y[:-2, 1:-1])**2))\n",
" return a, b, c"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def SolveEq(a, b, c, U):\n",
" '''\n",
" input:\n",
" a, b, c: as described in the content\n",
" U: the result of the last iteration\n",
" output:\n",
" return the result of current iteration\n",
" '''\n",
" return 0.5 * (\n",
" a * (U[2:, 1:-1] + U[:-2, 1:-1]) + \n",
" c * (U[1:-1, 2:] + U[1:-1, :-2]) -\n",
" b * 0.5 * (U[2:, 2:] - U[2:, :-2] + U[:-2, :-2] - U[:-2, 2:])\n",
" ) / (a + c)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"iters=0\n",
"while True:\n",
" \n",
" # count the number of iterations\n",
" iters += 1\n",
" \n",
" # backup the last result\n",
" xn = x.copy()\n",
" yn = y.copy()\n",
" \n",
" # solve periodic BC first\n",
" tempx = np.append([x[-2, :].copy()], x[0:2, :].copy(), 0) \n",
" tempy = np.append([y[-2, :].copy()], y[0:2, :].copy(), 0)\n",
" a, b, c = Solve_a_b_c(tempx, tempy)\n",
" x[0, 1:-1] = SolveEq(a, b, c, tempx)\n",
" y[0, 1:-1] = SolveEq(a, b, c, tempy)\n",
"\n",
" x[-1, 1:-1] = x[0, 1:-1].copy()\n",
" y[-1, 1:-1] = y[0, 1:-1].copy()\n",
" \n",
" # solve interior\n",
" a, b, c = Solve_a_b_c(x, y)\n",
" x[1:-1, 1:-1] = SolveEq(a, b, c, x)\n",
" y[1:-1, 1:-1] = SolveEq(a, b, c, y)\n",
" \n",
" # calculate difference between current and the last result\n",
" errx = np.abs(x - xn)\n",
" erry = np.abs(y - yn)\n",
" \n",
" # adjudge whether the iteration should stop\n",
" if (errx.max() <= 1e-6) and (erry.max() <= 1e-6):\n",
" break"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see what the mesh looks like."
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"plt.figure(figsize=(8, 8), dpi=100)\n",
"plotMesh(x, y)\n",
"plt.axis('equal')\n",
"plt.xlim((-4.5, 5.5)); plt.ylim((-5, 5))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"(-5, 5)"
]
},
{
"metadata": {},
"output_type": "display_data",
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WqampNGHCBH7Kq1u3Lj148ECY/cDAQFKpVGRlZUXXrl0TZjc7REVFcXED3cUg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YGHzyySf8XHq9Hrt37wbwH1kGEfvHx8fLKBQtR+nSpSXh4pkzZ3KP4ptvvsG+fft4ScnHH3+M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GBYt24dTJs2DXr37g2NGjUyEmthbwqFAj777DPYsmULPHr0SDCJrXnz5kCItFo8G1TqsFmz\nZrIoPTEMA8HBwUbEL09PTxmu1jQ0Gg2yxJs1ayZoQqLVarGP3t7eHoKCgkRdw4sXL/C38fHxEW3U\nYQm0F9rd3Z0jwvNPBXkddMWD1kHkqHOywabsDxo0SFJaKj8/H9OFFSpUgMuXL/Par7S0FD7++GMg\nRN8eY2um4jfffGM0UDdu3BhOnDgheVCU076PgvrOzp8/X9JxEhISULdZyFa+fHlo2LAh9OzZE3x9\nfWH16tUYLHv16gVXrlyBsLAwuHz5MoSGhsKlS5fg4sWLcPHiRbhw4QL88ccfcP78eQgJCYFz587B\n2bNn4cyZM3D69Gno378/EKIXNJk9ezYMGDAAmjVrhlkXS5urqyu8++678Omnn2ItVqlUQnx8vKT7\nRJ+PUaNGSToORWFhIaa5z507J+lYV65cwVQy/b70v+fNmyfL9ZrDixcvUAZx6NChvN6VoqIidAty\ndnaGs2fPSrqGnJwcFCmpUqWKaGa5KVBPZJVKZbOMQVmDvA664sEwDNb1fHx8ZDFtDwoKwj7JsWPH\nyrLaKy4uhn79+uEEgU+tjj27ZLcm2QKPHz/GmiZ5OfNmGxJ07NhRtMweAMhm30eh0+mQ7CXHZIuS\n88jLFGp8fDykpKTAlStXYN++fbB69Wrw9fWFnj17QsOGDS2uKG29OTo6QoMGDaBHjx4wefJk8PPz\ng0OHDkF4eDhkZmZyBv19+/YBIXqVKKmIjo4GQvTpeLlqo9QG86OPPhK1f0xMDE5QCNG7a/3www8Q\nGxuLrTVOTk42l068e/cuMr7/97//WfxsXl4ePm8VK1aUrf2RPbl3c3OTxU/76tWrOBbKTar8K0Fe\nB11pKC4uxlluy5YtJakJ7d69G+tgX375pazpXK1Wiy0/dnZ2FmtZbEcRW88uGYbB3slPP/0Um9yL\niopg7dq1nNXVZ599Jji9FBkZiYOfVPs+irCwMCCEQK1atSSvwhmGwe/o6enJi53LMAxkZmZCREQE\nHD58GDZs2AAzZ840Sm3a2dlB69atoU2bNtC2bVto164dtG/fHtq3bw8ffPABdOjQATp27Agffvgh\ndOrUCTp37gxdunQxOo67uztcuXIFnj59Kuj7ZmVlYaCWo6f07bffBkKI5JUZRUZGBgar27dv894v\nLS0Npk6diit5JycnWLBggVGL3uDBg4EQ8727ciIoKAgI0afxzQmSsMtNVatWlXVFCqBfQVMDFBcX\nF7h48aLoY7Fbo6QYgPwdQV4HXelIS0tDkszw4cNFvWBbt25FwsnChQtt8pIyDAMzZ87EFJgpYf0b\nN27gqnPTpk2yX4MhqFmAu7u7yfamrKwsmDt3Ll6TWq2GKVOm8O4THjZsGBBC4IsvvpDtmuk9lENO\n8MmTJ/j9pf7mlPlNiDRJPJrSk3ocAECJRDncphYuXAiEEPD19ZV8LArqYDV48GCrny0oKICVK1ci\n10KpVMK4cePMMnefPn2KvbtyE7ZMYenSpUCIvoxkWFt9+vQpEitr1qxps9qoRqPByYaTkxOcPn1a\n8DHy8/NRV75Tp042Vdj7K0BeB115cOfOHZw1r169WtC+bONoa+khqWAYBtNqhHCNqlNTU7HH9/PP\nP7f57PzFixeYhtu5c6fFzyYnJ8PYsWOxXla+fHlYunSpxRptQkICqFQqUKlUsknEMQyDBCi+9XFL\noKzqzp07Sz4W7X12c3OT9H2PHj2KK1Sp961Pnz5ACIG9e/dKOg7AK2ZwtWrVZMsCPXny5P/tnXl4\njFf7x7+TPbGGxBqSlGrVFktVCbHvVC19UX5VVaWi1L60VC2vWqvCS1FtqaolhCJUScQuiCzWyILI\ngqyyzzz374/xHDNJJpLMeSbE+VzXc1UzmXPOTGae+yz3/f2ShYUFmZmZGTxGUavVtHnzZvbdwLPV\na1Ey7Dds2MDGrHTWrUajYdvdDRo0YOWFkZGRXEsIX4RarabPPvuMHRXt27evyM/VaDQse75evXqv\nhFVfcYEIuvzYt28f2+Ipiq9t3hq5devWmWCUWtauXcv6nT17NmVkZLDSnfbt25tE1HzUqFHszLao\nAT4kJIR5xgLaxI1169YVOBuWSypGjBjBbczyjb9mzZpcbvzy6sQY71kZWSHr77//Nqodf39/AkDt\n2rUzekzz588nQGs2biy6Ex6e8n9yFu+ECRPy9ff333+z+lBAmyVcnCMXjUbDpFNNsU2alpZGTZs2\nJQDUs2dPCgkJYfkRPMVyXoRGo6GvvvqKHVMVddIlfx8qVqzIZXfkZQQi6PJl0aJFbCVW2ExYkiS2\ntVWSGjke6J4hy+d4zs7OJvlinjhxggCtl60hDebC8Pf3Z5MEQKt8tHv3bha8de37goODuY17zpw5\nXG+gchYpDy9kuSzK2BW4fA5urPkC0fOJaEmTlfIif2d4KhKFhISw7VD5s3/p0iVWjoNn5/c7duwo\n0UQrJCSE+e6eO3eO27gNERERwWrI5e9A+/btTV7fKkkS+76oVKoX+kTLJW9mZmZ0+PBhE43S9EAE\nXb5IksTONFxcXAoMYGq1mj7//HOW7LJnz55SGKkWHx8fPaPqSpUqKe7WkZmZydRsvv/++xK3I0kS\n7dmzh7UFaJPZ/P396dtvvyXAsH1fSfuTJyfGlpnIyCs3QwIbxUH2AQ4NDTWqnaioKAL4eJNGRkYS\nYLy3rkxAQAABWsMPnscfcnmcp6ennkSpvb09rVq1qljmHgUh16E3bdpU8TNKSZLYKhPPVpqluWpc\nvHgxG4shK1Bdu9CVK1eaeISmBSLo8icjI4MVjHfo0EFvqzYnJ4eGDx/OzsyOHDlSiiMlOnLkSD7F\nIAcHB0X7lANiw4YNuWxj5+Tk0Pr161m2I55NZoCi2fcVlbCwMAJAVatW5ZKNm5iYyFYjPErDZO1n\nY00ikpOTCdBmoBqLJEksmYhHSZ1arWZ/5+KKvRTGpk2b9L4DVlZWNGPGDG6C/unp6UwFS8m8jUeP\nHukdv8hXtWrVFOuzKKxZs4aNJa/DU2xsLDMi+fTTTxXPJSltIIKuMsTExLDEizFjxpAkSZSZmckS\nS8qXL09+fn6lOsabN2+yG6JunSygzcw1dnZfEGFhYSwgBgQEcG07LS2NFixYQNbW1ux1mJmZ0dy5\nc7lsmX///fcEaOuneSBvsbdu3ZpLe/LfsKQykjIajYZNxHhMBjw8PAgAty1D2ZHJ2Lrr5ORkWr9+\nPcuU1b1q1KjBZay6+Pr6skmWsWIhBfHvv/+ye07lypVZljWgLREyxqqRB5s3b2afq9mzZ7N7olzG\nVFy70FcViKCrHLrlN8uWLdMrHjdG7IEHSUlJbKt04MCBFBERQbVr16Zp06axc97mzZtzlXXTaDRM\nuebzzz/n1q4u2dnZTLhC97K0tKTBgweTr69viQOJm5sbl0QlmZUrVxIA+uKLL4xuKzMzk71OHisF\neTLGY6U3adIkAkBLliwxui2i58GrJGfOkiTRmTNnaNSoUWRnZ8c+H1WqVGGTQQCKldTIW9c9e/bk\ntqLLycmhOXPmsIDWrl07ioqKoqioKKpduza1bNmSAK2JSGkHtR07drD7i6enJ40YMYIArT58Se1C\nXzUggq6y7NixQ+/mr1KpSn1LWa1WM+nBpk2b5iu7OXfuHJOWs7Ozo82bN3O5Qfz8889s1q2UD+fW\nrVvZChfQJmp16tRJT5qvbt269N1331F0dHSR27179y7bcuV14xo5ciQBfCzmYmNjuW4jymfNPFZk\n8t/ko48+4jAy7cRKnhQUNQnv8ePHtHr1ar1MZECbOf/HH39QZmYmRUREsICwbds2LmPNS2xsLLMy\n5KHZHhERoWcYMn/+/HxHH3FxcWz79rPPPiv17dv9+/czpSn5MiToURaBCLrKI7vIyBePBBVjmDZt\nGju7NSRRl5yczM6eAX3P25Kge7P5888/S9xOYeja961YsYKpWxERPXjwgBYtWsQmE3g2AerRowft\n3r37hWfLy5YtIwA0bNgwbuOVHVqM8a2VuXHjBgHa+kweyGUnxVFqMsTVq1cJ0GbI80JeIRVWEy9J\nEp08eZKGDx+ud+RQrVo1mjFjRoHOW1u2bCFAq/utVHDauHEj28IuilWnIXbs2MHczurUqVNo/kJg\nYCDbdfvpp59K3CcPJEli5YIvyz3RlEAEXeXJzc1lM2gAtGrVqlIbi6w2ZGFhUaQzZV3P27p165b4\nHFbO6Oa5rZaX/fv3sxuQoQxRjUZDx48fp2HDhunNth0cHGjKlCkGs4jlcydemeYZGRlkbm5OZmZm\nRp/BEml3J8DxfLhDhw4EgE6ePGl0W7reuk+fPjV+cPRc9rCg1xsfH08//PCDXla7PMHas2dPoROs\nrKwsdi6q1OpLo9Ew6diSqGulpaXpBa2BAwcWSURC3nUrTfMASZJo+vTpegHXWNWzVw2IoGsaIiMj\n9YTqTSmEIXP+/Hk24y/OluadO3fo3XffLXQLqzCOHDmiaAIJkfbLLAdGQ2UJeXn8+DGtWbOGyePJ\nV9u2bemXX35h2+73799n4+cVNGTD+YYNG3Jp7/DhwwTwq4eVM2CLoyZUGM2aNSMA3OpU09PTWYnJ\nvXv3SKPR0NGjR2nw4MF6JXC1a9emb7/9tlimA/KuhoeHB5exFkRoaCir3S2O6UBgYCCbTNjY2NCG\nDRuKNYnVNTJR6rtoCLVaTWPHjmWT/rVr1+rtRr0uQARd07J06VJ2Q+CVWFIUYmJiWIJRSWbX2dnZ\nNGvWrHzJGi/i6dOnTH932bJlJRl6kZBVlKpUqVLswChJEl28eJHGjh2rNzEqX748ff755zR16lQC\nQB9++CG38crn28OHD+fSnryK+c9//sOlPfm8+ddff+XS3ieffMLt/FpGds7q06cP+4zh2cSwX79+\ndODAgRKVdqWkpLBtWx5b/4aQhSOaNGnywtpdjUZDK1asYMleTZo0KVE9tlqtZq5bTZo04WZ1+SJ0\nNZltbGy4JSO+ikAEXdPzv//9jwWvmTNnKp7YkJGRwVaqHTt2NKo4//jx4yx4V6pU6YXJIPJWUrNm\nzRQVBZBvJPPnzzeqnbS0NPrll1+YdJ/uZWlpSevWreNSfiSbE+hqXxuDbMrOIxOaiGjixInF2jV4\nEbL3tLHje/LkCXl7e5Onp6ee/SOerWoXLlxo0ICgOMgrwoEDBxrdliEyMjJY7W5hZ9NxcXHUo0cP\n9jo9PT2NcjNLTk5mlQsffvghVzezgsjIyGDiIxUqVCj1UsnSBiLolg5//PEHO+cdN26cYh98SZJY\n0omLiwsXe7u8BfijR48ucHV59epVMjc3J5VKpWiJlBL2fURE169f13Pu0b0aN25Mnp6etHfv3hL1\nKUs28rKpk1V/Zs6cyaU92dHnu+++49KeXJP83nvvFet5qampdOjQIZo6dSo1b948n5AL8gRdXjx8\n+JCsrKxIpVJxLZvLy9GjR9lntyDDBV9fX6pWrRoBWlEWHx8fLv3q1ujz+hsXREpKCqvTrlKlCl26\ndEmxvl4VIIJu6XHgwAF2xjp8+HBFVoLy+VS5cuW4+mdKkkReXl5s/A0aNNAzdFer1Wx1zdNWryBk\n+76vvvqKe9vyNjCerXTbtWuXT0gE0JZeffXVV7Rv374XJrWo1WpWI/r48WMu45R3FIrrcGWIFStW\nEAD6+uuvubT35MkTFlwKq5POyMig48eP05w5c6hNmzZ6CYiAVinKw8ODFixYQAEBAWy71crKivvZ\noCzVOmbMGK7t5kWuEujRowfb9crOzmbHGgCoU6dOXFbwuhw+fJhNYry9vbm2TaSdnMvKfDVr1jRa\nnrSsABF0S5cTJ06wc8S+fftSRkYGt7YPHTqk6JeKSCvmLtc+Wlpa0sqVK0mj0dBPP/3EVh95zb15\nooR9ny5yPbOuXV5WVhadOnWKFixYQJ06ddIrRwG0mbJubm40efJk8vHxyVdqdfPmTQK0Wda8kAPE\nhg0buLQnyyJ++umnXNojIuY5fePGDfaz7OxsCggIoAULFpCHh0e++k1zc3Nq06YNzZkzh44fP57v\n+7FkyRIWlHhz69YtUqlUZGVlxUXC0hBxcXGsnG7nzp10+/ZtJmhhbm5Oixcv5qIMVhC6k3KexiAx\nMTH0zjvvEAB64403DNomvo5ABN3S58KFC2Rvb89uHqmpqUa3eePGDZYMYoypQFHIyMigL7/8kt0o\nPTw82EqOV/arIWT7vpEjR3JvOykpiSwtLcnMzKzQLeTMzEzy8/Oj+fPnFxg4VCoVtWjRgqZMmUIH\nDx5ktaD9+vXjNtYhQ4awmzYPdu/ezf1Ms3///gSAFi1aREuXLqXu3bvrqULJ71Xz5s1p6tSpdOjQ\noRdO2OLi4lhgVGJyJydr8dq2N4S8o1KxYkXmy+3i4qK4K5EkSWynyNXVlcvOy927d1k9/DvvvGO0\nFnhZAyLovhyEhIQwwfp3333XqA9/YmIiKysYPHiwyRRo9u/fT1WqVNFbpSglp0ekb99XFEPx4iLX\nNBd3FZWRkUEnTpygb7/9ltq3b68nL6h7mZmZ0eeff05r166lgwcPUnBwcIkDR9euXQkA+fr6luj5\nefnnn38IAHXu3LnYz01LS6Pg4GDy8fGhNWvW0OTJk+mDDz7QM6TQvd555x3y9PQkb2/vEpmWy9Ki\nSoiunD9/ngVDJXdsLly4wCbJeLZdzvM4qDAyMjLYyrpz585GHXOFhoayRMtWrVpxOz4pS0AE3ZeH\n8PBwVvrQuHHjEm1p5ebmUvfu3QnQZgzzqistKps3b863Pajrc8sT2a2ob9++3Nsmeu516+XlZVQ7\n6enpdPz4cZo7dy4TRSjssre3Jzc3NxowYABNmjSJVq9eTd7e3nTlyhV68uRJge+lfHbGK2Ht4sWL\nBGhN2/OSmZlJN27coCNHjtD69etp+vTpNGTIEGrVqhXzcS3KVaVKFS4i/HJmNC+ZybzIiUBKlLxF\nREToWQnqXqZUabp//z6bFJU0N+LSpUvs7+/h4aHoJOVVBgaCropjcDXEs/4FusTExKBbt264ceMG\n3njjDRw/fhyurrv7DIoAACAASURBVK5Ffv7UqVOxatUqODo64tKlS3B2dlZwtPqkpqbinXfeQUxM\nDABApVJB/hu3adMGy5YtQ/v27bn09fTpU9StWxdJSUkICAiAu7s7l3Z123d0dERWVhYePHiA2rVr\nc2mXiGBmZgYAsLS0xOTJk5GSkoLo6GhER0cjKioKWVlZhbZRvnx5uLi4wNnZmf135cqViI+Px6+/\n/gonJycQEXvvdf9r6N95fxYTE4Px48fD0dERY8eORWRkJCIjIxEVFYXY2NhCx2dtbQ0XFxe4urqy\n/7q6usLS0hIffvghAMDOzg7Xr1/n8vmMioqCq6srypUrh8ePH8PGxsboNnU5cuQIevfujZo1ayIy\nMhLW1tZGt5mYmIjFixfDy8sLOTk5sLa2xqRJk/DTTz8hKysLKpUKt2/fRv369Tm8gqJx9uxZdOzY\nEbm5udiyZQtGjx5d5Of6+/ujX79+SEtLQ58+fbB7927Y2toqONpXF5VKBZgmxuajNCYZrwQJCQnU\nokULAkC1atUqssG5LCxvaWnJ1Ue2qMi1nU2bNqXatWvTnTt3aP369azkAQB98MEHXAy15dVNu3bt\nOIw8P7t27SJA68zCkwcPHrCzy4JUkiRJovj4eLpw4QLt2rWLli1bRhMmTKC+fftS48aN9cQ7Suuy\nsLCgN954gzp37kyfffYZLVq0iLZv305nzpyhhw8fGix/kySJjf/ixYtc31f5+3LgwAGu7RJpxy2r\nlm3ZssWotjIzM2n58uUscUqlUtHIkSNZkt6NGzdYxjavTPTiIO9UWVpa0pkzZ4r0nL///ptl9A8d\nOlTRevyyAMT28stJcnIytW/fngBtfV5gYGChv3/27FmWwPPzzz+baJTPuXDhAqlUKjI3N89nLp6a\nmkrz589nCSJmZmY0duzYEmeEZmdnM2EEJW6yRM+1olesWMG13YMHD5bonFhGkiR68uQJXblyhby9\nvWn16tXMOk++rK2tqXPnztSlSxfq2rUrde3albp160bdunWj7t27U48ePahHjx7Us2dP6tWrF/Xq\n1Yt69+5NvXv3pj59+lDfvn2Z2Ih82dvbk5+fH0VFRZVI5UlG1nTmdfYss2jRIgJAo0aN4tquzLZt\n2wjQmjaUpKZeo9HQ9u3bmXsTAOratWuBhhK6tbumlmokej55rl69Ot2/f7/Q3/3zzz+Z7ObYsWMV\ny7IuS0AE3ZeX9PR06tWrFwFaJRd/f/8Cf+/+/fssCcvT09PEo9RKvMnautOnTzf4e7GxsTRu3Dg2\nk7ezs6N58+YVO1tbXtE3atRIEVGRzMxMtiLjfdNbuHAhAfzqX4m0iUvPvshcxeOVaFO+ofNexV2/\nfp2dExszKTBETk4OK3nav39/sZ7777//spU4oJVf9PX1LTTPQa7dVdIgxBA5OTnUqVMnArTJUIbK\nGDdu3MhKEqdPn17qloGvChBB9+UmOzubPvroIwK0mqWHDx/We1w387BTp06lsrUj1/q5uLgUKXHr\nxo0bLEkJ0FqtrVu3rkhj12g09PbbbxMA+u2333gMPx8HDhwgANS8eXPubcslKL///ju3Nu/du8d2\nEHjWKjs4OBDAV39YLpcaOnQotzZlZGlDpRx0fvzxR3bkUJQAExISwibNgLZmfevWrUVaDerW7vLw\n3S0ujx8/ZmU/I0aMyPd6ly9fzl7X4sWLRcAtBhBB9+VHrVbTmDFj2Hma/CWUJIllPvKqsSsuERER\nrGznyJEjxXru6dOnmRwiAHrzzTdp7969hX6Bi2LfZyyybdqiRYu4ty3fyHiWOAUHB7OVP0/kIFbU\nnIKicPnyZQJAb7/9Nrc2ZWbPnk0AaMKECdzbJtKad8glcYXlTDx48IBGjx5NZmZmbJdqyZIlxbZw\nlGt3jfXdLSnBwcHsSEjWCJckiebOncu+s8Zm9r+OQATdVwNJkmjKlCks+WLTpk3Msah8+fKK1KkW\nZUyyYlNJVy6SJNHevXv1vE/ff/99On36dIG/W1z7vuKSk5PDhEp4JHzpkpSUxM5ceU4YTp06RQD/\npLL33nuPABTLeu5FZGZmMks73uVssl1irVq1FNMynzdvHgEFl6mlpKTQ3Llz2STUwsKCJk6cWGKD\nDGN9d3mwd+9etoty6NAhJkZjbm7OdbfmdQIi6L46SJLEzgR1r+KeMfHizz//JABUuXJliouLM6qt\nnJwcWrdunV6m84ABA/QkA42x7ysqx44dI4Cfz60ufn5+7JyMJz4+PgRoLe54Itd75z3SMBY5E5i3\nbZ4kSVSnTh0C+Pn25qUgQZacnBzy8vIiR0dH9tkdPHgwF2GYkvru8mT+/PksoxnQCncorTRXloGB\noGumZLQVlAyVSoVvvvkGixcvZj8zNzcvVh0vL5KSkjBp0iQAwLJly1C9enWj2rO0tMSXX36J8PBw\nzJs3D3Z2dti/fz8aN26M8ePHIy4uDkuXLgUATJw4EeXKlTP6NRSEt7c3AGDQoEHc27569SoAoHnz\n5lzbTU5OBgDY29tzbbdy5coAgJSUFK7turm5AQCuXbvGtV2VSoWBAwcCeP535I2joyOrX12+fDm8\nvb3RqFEjeHp64tGjR2jXrh3OnTuH3bt3c6mxbdSoEaZPnw4iwhdffIHc3Fyj2ywuH3/8Mezt7Vnf\nNjY23D/DAtNQ2hOOVxpdaUFzc/Mi19TxQhbYd3d3V2Qr7+HDh/TFF1+wTGd5dcHbvk8XtVrNVHkK\nKuUwFtnIfd26dVzbXbNmjSKZ6/LfmKfxPBHRypUrFdsylXcT6tevr1hyz+3bt/PtNjVo0ID27dun\nSJ/p6enMd/eHH37g3n5hbN++vcDacFOqZZU1IFa6ryaVKlUCoJ3dazQadOjQAYsXL4ZGo1G874CA\nAGzatAmWlpb4+eefmcIST2rWrIkNGzYgJCQEH3zwATIzMwEAmZmZ+OSTT/DPP/9AkiSufZ49exbx\n8fFwdXVlqzGeKLXSTUpKAvDqrHSbNWsGAAgKCuLaLgC4u7vD0dER4eHhCA0N5dp2dHQ05s2bh06d\nOun9vHLlyggNDcWAAQNktSGu2NnZYf369QCA7777DpGRkdz7yEtaWho++eQTjBgxAk+fPsWQIUNQ\ntWpVNp7Tp08rPgZB8ZkKQAJQxcDjpT3heKWJiooiJycnun37NvNSBUAdO3Z8YUG7MWRlZVHDhg0J\nAH377beK9aNLREREvpk2npUoLVy4kJvP6OTJkwkATZ06lUt7umRlZSmWQCSPe+XKlVzblQUnZs2a\nxbXdR48eEaC1k1NCTEHO9Odhzp6Tk0N79+6lHj16sJpUPEtmlP9tKmMCuVJB6drdS5cuUf369dnO\n0qZNm0iSJHbPUcJC83UCCiVS1QHgCyBSBF3TcPToUbY1WqVKFcWSq77//nu2nZaZmalIH3mZMGEC\nu8HZ2trSlClT9JR9zMzMqG/fvuTj41NiYQRJkpj4gRIJK0qWysglTsZKFObFy8tLsW1gJycnAkC3\nbt3i3vbhw4cJ0MqRlpQ7d+7QzJkz9ZyRrK2tafjw4XTy5EmKjIxkPsqLFy/mOHrDxMbGKlq7q9Fo\naMWKFezoqmnTptwz+AXKBd3dAJqKoGta4uPjWQkPnp3x8QyMt27dYjeaEydOcGu3MOLj45mua/Xq\n1dksW61W09GjR2nw4MF659s1a9akOXPmFNs0W3bVUarcRNa0VUIU4oMPPiAA5O3tzbVdWfpw2LBh\nXNslIurbt69iwSMrK4vZ5IWHhxfreX/++SdTY5KvRo0a0Y8//pivDv748eMEaMVdDKk28Wbjxo2s\ndjc5OZlbu3FxcdSjRw/2midOnGiySfXrhhJB9wMAq5/9WwRdE6PRaGjlypUsEDVp0oSLuIEkSexm\npJS+bUF88803BusiZeLj42n58uXUoEEDvZtlly5daOfOnZSVlfXCfmbNmkWAcsIK8mpdiUQY2XqO\n90RI1onu3bs313aJnv9dZ8+ezb1toudbsbKoQ2Fcv36dvv76az1bQltbWxo1ahSdOXPG4FauJElM\n3nHDhg28X0KBaDQaatu2LQGgL7/8kkubvr6+rFSvatWqiumZC7SUNOj+AyCkgKs/gPMAKuoE3aqG\ngu78+fPZdfLkydJ+L8oUgYGBeucyGzduNOocSNY7dnBwMJnyVWpqKttOK0gsIy+SJNGpU6do5MiR\nbHUs30i+/vprg5MPSZKYOIdSK3j5Rnns2DHubcu617wzrmXRjbZt23Jtl4hoz549igV0IqLdu3cT\nYNglKj09nX777bd8/sZubm60fv36Iq8i//rrL5YtbSqx/5CQEJYfYEw9cnZ2Nk2bNk0vH4RXfoTg\nOSdPntSLdSUNuoZoDCD+WbCNBJALIApAtYKCrkBZUlNTWZkKnhXsJyYmFrudhIQEJn9nShUaubSk\nJEpLSUlJ5OXlxQKSfLVt25a2bt2ql8wkyyg6ODgoIpav0WiYnF5J1YkKQz6LLsgq0BiUkpckIgoP\nD2fb+UqQlpbGJl4xMTHs50FBQTRhwgSqVKkS+0yUL1+exo4dS5cuXSr2xDQ3N5eV8+zevZv3yzCI\nvDPTtGnTEqmb3b59m2m2m5ub0+LFi4VDkIngHXTzIraXXwK2b99OFSpUIABUt27dIq0adRk5ciTb\nrjWVsLmufd/BgwdL3I4kSXTp0iUaO3asXr1hxYoVady4cXT58mU2+/zss884voLn3Lp1iwCt4L0S\nyOeXvPV5o6OjFRu3RqNhf4/4+Hju7RM9P+teuXIlbdq0iVq3bq03AWvdujVt2rSJ0tLSjOpn3bp1\nBGiVxkz1/UhPT2c63suWLSvWc3///Xf23js7O5ea0tXrChQOuhEQQfelIDw8nN59912W7btgwYIi\nzWz/+ecfArQORzxk7YrKL7/8wlZZvBKb0tLSaMuWLXomC8Dz8o9y5coVOwGrKOzcufOF59IlRa1W\ns9fAOwEsJSWFvS9KIG/tKrHl/uDBAxo7dqze3xkAVapUiTw9PbmW+WRkZDAJSFMlGBIRHTlyhB0f\nFWWXIyUlhUaMGMHei48++qhUjBRed6Bw0C2M0n7trx3Z2dk0c+ZM9qXr0KFDoTW9GRkZVK9ePYIJ\nyyKItKsg2eFGqe3skJAQmjx5Mjszhk4AHjBgAK1Zs4aCg4O5BDJ5K/Cbb77hMHJ9EhMTCdDqX/NG\no9GwCYkS2+5ycllxV2oFkZCQQLt27aJx48blS6iTL3t7+2I7/RQVuZSuZ8+eirRviKFDh7Kz8cJW\n2RcvXmTfZTs7O9qyZYuw4yslIILu68exY8eY6X2VKlUMipfPmTOHrTazs7NNNr59+/axrXCl/YEL\nMpDQvRwcHGjw4MG0bt06unHjRoluVHIpxt69e7mP/+7duwRohUKUQD77VCJ5btOmTQSAhg8fXuzn\nJiUlkY+PD02aNIkZKOhe5cuXp969ezN7PSsrK0VFHZ48eUJ2dnYEgIKCghTrJy+xsbHsb7Rr1658\nj2s0Glq2bBlZWFgQAGrWrJmeiYjA9EAE3deT+Ph4PYPt8ePH69UayhmSAEyq6yxJErOUW7NmjeL9\nyVvuVatWpaioKIqIiKAtW7bQiBEj2Jmy7lWjRg0aNmwY/fzzz3Tnzp0XBmFJklg5RkREBPfxy6Ib\nbm5u3NsmIiZCosS2u2zF984777zwd58+fUq+vr40Y8YMevfdd1kwlS8bGxvq0qULLV68mM6dO8cm\na/KkqmvXrtzHn5dJkyYRAPr4448V70uXDRs2sM+mbtZ1bGwsdevWjb1HkyZNErW3LwEQQff1RaPR\n0OrVq1lNb+PGjSk0NFSvFnDcuHEmHZMsWF+1alXF7Ptk5EQhOzu7ArcdJUmi27dv08aNG2no0KF6\n6kTy5eTkRP/3f/9HW7duLXAl9fDhQ3aWqMR23r///ksAqFOnTtzbJiJq2rQpAaDLly9zbzsjI4PM\nzc3J3Nw8n7hEZmYmnTx5kr799ltyd3fXE0ABtF617u7uNG/ePPLz8zMYTGJiYlhQNjZh6kVERUWx\n18M7k7wwNBoNy1OQ68wPHz7MzpkdHByMSkYU8AUi6AouX77MzsFsbGxo+PDhbOZs6kQLWVGLh27u\ni/jxxx8JAA0aNKhIvy9JEoWFhZGXlxcNGjRIT0xBvlxdXWn06NG0bds2evDgAR06dIgAkIeHhyKv\nQa53/fDDDxVpv0OHDoomCDVq1IgArf/t2bNnadGiRdSlSxe9Oms8S/579913aebMmeTr61usCZkc\nkExR0iMnKn311VeK96VLcHAw25mShUEAUOfOnfVKpgSlD0TQFRBpM3s//fRTvRtduXLlTCpuHhQU\nxFaephDgaN++PQGgHTt2lOj5Go2Grl27Rj/++CP1799fr/YTOsECz1Zma9eupfPnz1NcXBy3Va8s\nLzl69Ggu7eWlX79+BPCTmExNTaVr166Rj48P/fjjjyxhrqCradOmNHnyZDpw4IBRk7/ly5ezYKQ0\n165dM+lnWJePPvpI7/2bMWOGqL19CYEIugJd5Exb+bKzszPZTFmeoU+aNEnxvmJjY0mlUpGVlRWl\npKRwaVOtVlNgYCAtX76cevfuXaAPqXzZ2trS22+/TT179qRx48bR0qVLaefOnXTu3DmKjY0tclBe\nsWIFAaApU6ZweQ15kWu0t27dWqTfT09Pp7CwMDp06BB5eXnRtGnTaNCgQdSyZUsmsFLYVa5cOdq1\naxdXERFZiKNixYpFkgQ1FjlXYsGCBYr3RaQN9Lq6yfIlPG9fTmAg6FooEmYFLz3//e9/sWHDBiQn\nJwMAMjIy8Oabb2Lq1KmYPn06KlSooEi/ERER+Ouvv2BhYYEpU6Yo0ocuPj4+ICJ069YNFStWfPET\nioC5uTlatmyJli1bYtq0acjNzYW1tTWICObm5ujWrRsSEhIQFRWFxMRE3Lx5Ezdv3iywLRsbGzg7\nO8PFxUXvkn9WvXp1mJmZMS9d2fuWN7Jvs/x5yM7Oxr179xAZGYnIyEhERUXp/TchIaHQ9mxsbODi\n4gJXV1e4uLggNzcXmzdvBqD1aQ0LC4OzszPX11CvXj00bdoUwcHBOHHiBHr16sW1/bzMmDEDR44c\nwdq1azFt2jTY2dkp0s+DBw8wb948/PrrryAiVKxYETk5OcjKyhKet68g/J2Y8/Ms6AteNqKjo+Hu\n7o7ff/8dXl5e8Pb2BgBUq1YNCxYswJgxY2BhwXdeNmHCBKxfvx7/93//h99++41r2wXRvXt3/PPP\nP/jll1/w6aefKtJHSkoKC4Z37txB/fr12WOpqamIjo5GVFSU3iX/7MmTJ4W2bW1tDWdnZ0RERECt\nVsPCwgKDBg1CuXLl9H5P9zuW9/tWlMeCgoIQEhLCzNlf9J21tLSEs7MzC6p5/1u9enU9o/eEhARU\nr14dgHbi5erqWmj7JWXBggX47rvvMGbMGGzatEmRPmSICG3atMHFixfh5eWFCRMmcG0/NTUVP/zw\nA1avXo3MzExYWlriyy+/xDfffIP09HS4u7vj9OnT3CcvAj48+/zni7Ei6AoYZ86cwbRp03D+/HkA\nwFtvvYUffvgB/fv317uBlpSEhAQ4OzsjKysLoaGhaNSokdFtFkZiYiKqV68OIkJ8fDyqVjXkyWEc\np06dgoeHB1q2bInAwMBiPTctLa3QoPz48WNFxlwUDAVUFxcX1KpVC2ZmZsVqr1atWoiNjUV4eDjq\n1aunyJiDg4PRrFkzODo6IjY2Fubm5or0I7N3714MHjwYrq6uuH37NpdJam5uLn7++WcsWLAAjx49\nAgAMGTIE//3vfxV73wT8MRR0xfaygNGuXTucPXsWe/fuxaxZs3Dr1i0MGDAA7du3x/Lly/Hee+8Z\n1f5PP/2ErKws9OvXT/GACwAHDx6EWq1Gly5dFAu4gHaVCABubm7Ffm6FChXQuHFjNG7cuMDHnz59\niujoaDRr1gwajQYWFhZYuHAhHBwc8k2EdP+/uI/5+flh69atAABbW1uEhIRwv8G7ubkhNjYWQUFB\nigWPJk2aoF69erh79y7OnDmDDh06KNKPzIABA/Dmm2/izp072LNnD4YOHVritogI+/btw6xZs3Dn\nzh0A2u/kihUr0KZNG15DFrwGmP4EW2A02dnZtGbNGr1ymY8++qjE4gnFte/jgZyRu379ekX7GTVq\nFAEgLy8vxfqQ/VyVKofZtWsXKyVTKpN99uzZBCgjk6mLbGNnikQ9oueG882bNy9xtvrZs2dZzTwA\natCgAe3bt09IOL7CQGQvC0pCcnIyzZo1i9VTWlpa0uTJk4tdJiFn37q7uys0Un1SU1PJ2tqaVCoV\nPXz4UNG+ZFtBJRW9GjZsSAAoNDRUkfaPHTvG6j2VQvakVcIQQpezZ88SoJUXNUXQyszMZIIqxTV1\nuH37Ng0aNIgFW0dHR1q3bp3isqgC5YEIugJjuHfvHn3yySdMGL9SpUr0ww8/FEluLisri2rVqkWA\ncfZ9xUF2/CmJR29xyMrKYkbjSioh1axZkwAoZj5+4cIFAkAtWrRQpH0iops3bxIAqlOnjmJ9EGnr\nquX3KzAwUNG+ZJYsWUJA0WUoHz16RBMnTmRCF7a2tvTNN99wK2sTlD4i6Aq4cPXqVT2d17p169K2\nbdsKdenZsmULAVr5Sd62dIYYMmQIAaBVq1Yp2s+VK1fYdqCS2NraEgDF3HNkL+B69eop0j6Rtr5Z\nNgtQWlBi/PjxBIDmzp2raD8yiYmJrF67sECfkZFBS5YsYd7IKpWKRo8erdhkSlB6QARdAU98fX31\nXF+aN29Ox48fz/d7prDvy0tGRgaVK1eOACiutCVPKP7zn/8o1kdWVhbb2ldquzQuLo4ArX6vkrRp\n04YA0L///qtoP7I/dMOGDRXtR5cpU6YY/Cyo1Wr69ddfycnJiX1nevXqRcHBwSYbn8C0QARdAW/U\najVt3bpVz6WnV69eFBISwn7H29ubrYhNdU61f/9+AkAtW7ZUvC9PT08CQEuXLlWsDzkgVqtWTbE+\nMjMzmYylkueg48aNIwC0cuVKxfogIsrJySF7e3sCYDKLu/v375OFhQWZmZlReHg4+/nRo0fZuT8K\nmaAKyhYwEHSLV2gnEOhgbm6OUaNG4fbt21i8eDEqVKiAI0eOoFmzZhgzZgwePHiApUuXAgCmTp0K\nS0tLk4xr7969AIBBgwYp3tfVq1cBAM2bN1esD6XVqACtgpS1tTXUajUyMzMV66dZs2YAgGvXrinW\nB6AV7+jXrx8AYN++fYr2JePk5ISPP/4YkiRh1apVuHbtGnr06IEePXrg2rVrqFu3LrZt24bAwEB0\n6dLFJGMSvJ6U9oRDYCISEhLI09OTJYdYW1sTALK3t1fcvk8mOzublSbdunVL0b40Gg07x4uPj1es\nn3PnzhEAat26tWJ9EBHzA1ZSg1t+LU2bNlWsDxl5x6NVq1aK9yUTGhpKAMjc3LxESYeCsgPESleg\nNI6Ojli7di3CwsIwaNAgZGdnA9Cu1AYPHoyjR49CkiRFx3Dy5EkkJyejUaNGaNCggaJ93b17F0+f\nPkWtWrVQrVo1xfqR9ZDt7e0V6wN4vpJOSUlRrI8mTZpApVLh+vXr7POhFN27d4ednR0CAwNx7949\nRfvKzMzE9u3b8eWXXwIANBoNiAijR4/G3bt3MWPGDNjY2Cg6BsGrgQi6Au40aNAAe/bs0bvJ+Pr6\nomfPnnjjjTewcOFCxMTEKNJ3WdtaBkyzvQzkNz1QgnLlyuHNN9+EWq3G9evXFesH0CpryaYH+/fv\nV6SP0NBQTJo0CbVr18bIkSNx6tQpvcePHTumqBqa4NVDBF2BYpQvXx6A9uY3depUuLi4IDo6GvPm\nzUPdunXRv39/JtXIA41Gw26upgi6xsg/Fgc5CCoddE2x0gWev19Kn+sCwMCBAwGAmXnwID09HVu3\nbsX777+PJk2a4KeffkJSUhJatWqFjRs3siArHIAEBSGCrkAxAgMD4eTkhBs3bmDFihW4e/cujh07\nhsGDB8PMzAwHDx5E//794eLignnz5iE6Otqo/k6fPo1Hjx6hXr16aNKkCadXYRhTr3SV3l6WV7qm\nCrrypEVJ+vTpA0tLSwQEBDDzgJJy9epVjB8/HrVq1cLo0aNx/vx5VKxYEePHj8eVK1dw6dIljB07\nFpcvX4aTkxOuX78uHIAE+RBBV6AYzs7OuH//PrvxmJmZoVu3bti9ezdiYmKwbNkyvPnmm4iJicHC\nhQvh6uqKXr16wdvbG7m5ucXuT3drmYcr0oswVdA19UpXye1lwLRBt1KlSujSpQskScKBAweK/fzU\n1FRs3LgRrVq1QosWLbBhwwakpqbi/fffx9atW/Hw4UOsX79e7zOQ93MvEOgigq6gVKhWrRqmT5+O\nW7du4eTJkxg+fDgsLS3h6+uLQYMGoU6dOpg9ezbu3r1bpPYkSWJbiKbYWo6NjUV8fDwqVqyomDes\njKmCrqlWunLZUFBQ0At9e3lQ3C1mIsKFCxcwZswY1KpVC+PGjcPly5dhb2+Pr776CiEhITh79ixG\njRqVz9dYIHgRIugKShWVSoWOHTvijz/+wMOHD7F69Wo0bNgQ8fHxWLp0KerXr48uXbpg586dhWa7\nXrp0CTExMXByckKrVq0UH7fuea7Sq2pTbS+baqVbs2ZNODo6IiUlxegjhaIg+0EfP34cqampBn8v\nOTkZXl5ecHNzQ5s2bbBlyxakp6ejQ4cO2L59O2JiYrBmzRqDNowCQVEQQVfw0lC1alVMnjwZYWFh\nOH36ND755BPY2trixIkTGDZsGGrXro2pU6fi5s2b+Z4rby0PHDiw2ObqJcFUW8tA2VvpqlQqkyZT\nVa9eHe7u7sjJycHhw4f1HiMi9lmrWbMmJk6ciODgYDg4OGDatGm4efMm/P398fHHH8PW1lbxsQrK\nPiLoCl46VCoV2rVrh19//RUPHz6El5cXmjVrhidPnmDVqlVo2LAhOnTogG3btiEzMxNEZNKtZaBs\nBl1TrXQB0VxEPAAACLVJREFU057rAvm3mJ88eYLVq1ejUaNGaN++PX7//XdkZWWha9eu+Ouvv/Dg\nwQMsX74cb731lknGJxDwpFRVQQRlA0mS6OLFizRmzBhmZgCAKleuTMOGDWNepGq12iTjqV+/PgGg\noKAgxfuqV68eAaDbt28r2o+Pjw8BoD59+ijaDxHR9u3bCQANGDBA8b6IiCIjIwkA2djY0JAhQ8jK\nyop9hmrUqEGzZ8/W00sWCIwFBhSplE/x1AZdE3QjeF1IS0vDn3/+iU2bNiEwMFDvsYEDB6J///7o\n3Lkz6tSpo0j/qampqFSpEqysrPD06VPFNaUdHBzw5MkTJCQkwNHRUbF+/P390bFjR7i7uyMgIECx\nfgAgLCwMjRs3houLCyIjIxXpg4hw48YNnDhxAidOnMinwdyrVy98/vnn6Nu3r8l0wQWvD89yPfLF\nWBF0Ba80QUFBaNmyZYHykvXr10enTp3QuXNndOrUCdWrV+fSZ0BAADp06IAWLVrg8uXLXNo0BBHB\n0tISGo0GOTk5igaHa9euwc3NDY0bN0ZISIhi/QCAWq1G+fLlkZ2djaSkJC5b50SEu3fv4uTJkzhx\n4gROnjyJ+Pj4An+3Ro0aiI2NNbpPgcAQhoKuhemHIhDww83NDVWqVMHjx49hY2OD6dOnIygoCP7+\n/ggPD0d4eDg2bdoEAGjUqBELwh4eHqhSpUqJ+jTlee7Tp0+h0WhQrlw5xVdjpkqkAgALCws0adIE\ngYGBCA4ORocOHUrUzv3791mQPXHiBO7fv6/3eM2aNdnffPr06UhKSoKdnR3Onz/P42UIBMVGBF3B\nK09gYCDc3d1x+vRpJkigVqtx5coVdkMOCAhAWFgYwsLC4OXlBZVKhebNm7Mbcvv27VGhQoUi9Wcq\n+UfAdElUun2YIpEK0NbrBgYGIigoqMhBNz4+Hn5+fizIhoeH6z1etWpVdOzYEZ07d0bnzp3x1ltv\nsZKurl275vucCASmRgRdwSuPrACki4WFBVq3bo3WrVtj5syZyMnJwYULF1gQPnfuHK5cuYIrV65g\n5cqVMDc3R+vWrVkQbtu2rcESEVOudE1VowuATTrS0tKg0Whgbm6uaH9FyWBOSkqCv78/C7JhYWF6\nj1eoUAEeHh4syDZp0sRgyVhBnxOBwNSIM13Ba0lGRgbOnj3LgvClS5eg0WjY41ZWVmjbti0Lwq1b\nt4aVlRVycnJQvnx5qNVqpKamMlMHpTh16hQ8PDxMktwEaLeYU1NTkZiYqHigP336NNq3b693Np6W\nlobTp0+zIHv16lU91SpbW1u4u7uzINuiRQtYWIi1g+DlQ5zpCgQ62NnZoWvXrujatSsAbUZyQEAA\nC8JBQUHw8/ODn58f5s+fDzs7O7i7u+Ptt99Gbm4u6tevr3jABUxn6ycjB92UlBTFg65sShEcHIzZ\ns2fD398fFy9ezDf5adOmDQuyrVu3hrW1taLjEgiURARdgQBAxYoV0adPH/Tp0weAVjzB39+fBeHr\n16/j2LFjOHbsGAAgPDwcZmZmaNy4Md566y24uLjA1dUVLi4u7LKzszN6XKYysJepXLky7t+/zy2Z\nKjk5GVFRUYiKikJkZGS+fwPa8/elS5cCAMzNzfHee++xINu2bVsu76NA8LLwSgddPz8/dOzYsbSH\nUaZ5Xd/jqlWrYuDAgUzJKC4uDn5+fvjf//7HjMqJCCEhIQbLa6pVq5YvGMv/dnZ2ho2NDYDC32NT\nJlIBxTeyT0tL0wumeYNrcZKyqlatioiICFSsWLFEYy+M1/VzbGrE+/xiRNAVFIp4j7XUqFEDQ4cO\nxdChQ+Ho6IjHjx/D1tYW27dvR1ZWVr6AEx0djYSEBCQkJODixYsFtlmzZk24uLggLS2N+QrLgblO\nnTqwtrY2+fZyXiP79PT0QleqiYmJhbZnZ2dX4KRD/vdbb72Fx48fw87ODpcvX1Yk4ALic2wqxPv8\nYl7poCsQlAYFlSjlRZIkPHz40GDAunfvHmJjY5lAQ2hoqN7zVSoVatWqhbi4OADAkiVLEBwcrPg5\nsq+vLwDgww8/ROXKlV8YVG1sbAwGVBcXFzg4OBTqwlSU91IgKEuIoCsQFJOilJ6YmZnByckJTk5O\ncHd3z/e4Wq3Gw4cPERkZidWrV6N58+Z6wfn+/fuIiYlhv6/RaODj48P9tRhCkiQkJibCysoKzs7O\nBler1atXN8raUJTxCF43TFEy5AfAwwT9CAQCgUDwsuAPoGNpD0IgEAgEAoFAIBAIBAKBQCAQCAQC\ngUAgEAgErx9TAUgASubVJiiM5QBuALgGwBtApdIdTpmjJ4CbAO4AmFnKYymL1AFwEkAYgFAAX5Xu\ncMo05gCuAjhY2gMRKEsdAL4AIiGCrhJ0AyDbtix9dgn4YA4gHIALAEsAQQAaluaAyiA1AMgejOUB\n3IJ4j5ViCoA/ABwo7YG8zBTsgfVqsQrAjNIeRBnmH2h3EQDgAgCnUhxLWaM1tEE3CkAugJ0APijN\nAZVB4qCdzADAU2h3bWqV3nDKLE4AegPYDNOUor6yvOpB9wMADwAEl/ZAXhNGAzhc2oMoQ9QGoKsM\n8eDZzwTK4AKgObSTRwFfVgOYjucTdIEBXgVFqn+g3SLKy1wAswF01/mZmGGVDEPv8Rw8P5+ZCyAH\nwA5TDeo1QBhNm47yAPYAmATtilfAj74AEqA9z+1YukMRKEljAPHQnuVGQrs9FwWgWimOqawyCsAZ\nADalPI6yRhto8xFkZkMkUymBJYCjACaX9kDKKEug3bGJBBALIB3A76U6IoFJEIlUytAT2sxPh9Ie\nSBnEAsBdaLc9rSASqZRABW0AWF3aA3lN8IDIXn5tiIAIukpwB0A0tFtHVwGsL93hlDl6QZtRGw7t\nSlfAF3dozxmD8Pwz3LNUR1S28YDIXhYIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAg\nEAgEAoFAIBAIBAKBQCAQCASc+X/uibSEF+DRqgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x106601b10>"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The grids near the airfoil look more clustered. This is good. And how about the grids in the vicinity of the airfoil?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure(figsize=(8, 8), dpi=100)\n",
"plotMesh(x, y)\n",
"plt.axis('equal')\n",
"plt.xlim((-0.5, 1.5)); plt.ylim((-1, 1))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"(-1, 1)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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YYOnSpexiSkxM5PzhRo0aqa7TnR7i4+NRvnx5ECmb13zv3j189913yVTM6tat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\nSwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1066a1690>"
]
}
],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It looks better than what it looks like in algebraic grid generation method. We can use it to solvo our potential flow now! "
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Potential Flow over NACA 2412"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Governing Equation in the Computational Domain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We know the governing equation of the stream function $\\Psi$ in the physical domain is the Laplace equation, as described in the introduction section. To solve the stream function in the computational domain, we need a new governing equation. According to the derivation in reference [3], we obtain the governing equation for stream function in the computational domain as follows:\n",
"\n",
"$$\n",
"a\\frac{\\partial^2 \\Psi}{\\partial \\xi^2}-2b\\frac{\\partial^2 \\Psi}{\\partial \\xi \\partial \\eta}+c\\frac{\\partial^2 \\Psi}{\\partial \\eta^2}=0\n",
"$$\n",
"\n",
"where\n",
"\n",
"$$\n",
"a=\\left(\\frac{\\partial x}{\\partial \\eta}\\right)^2+\\left(\\frac{\\partial y}{\\partial \\eta}\\right)^2\\\\\n",
"b=\\left(\\frac{\\partial x}{\\partial \\xi}\\right)\\left(\\frac{\\partial x}{\\partial \\eta}\\right)+\\left(\\frac{\\partial y}{\\partial \\xi}\\right)\\left(\\frac{\\partial y}{\\partial \\eta}\\right)\\\\\n",
"c=\\left(\\frac{\\partial x}{\\partial \\xi}\\right)^2+\\left(\\frac{\\partial y}{\\partial \\eta}\\right)^2\n",
"$$\n",
"\n",
"The form of the governing equation is the same as we solved in the elliptic grid-generation method! This means we can use the functions we wrote previously for that purpose."
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Boundary Conditions in the Computational Domain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Though the form of the governing equation is the same as the PDEs for the elliptic grid-generation method, the boundary conditions are not. We now examine the boundary conditions. \n",
"\n",
"On the outer boundary, we expect the flow velocity to be equal to the free-stream velocity, that is:\n",
"\n",
"$$\n",
"\\left\\{\n",
"\\begin{array}{l}\n",
"u=\\frac{\\partial \\Psi}{\\partial y}=V_\\infty\\cos(AOA)\\\\\n",
"v=-\\frac{\\partial \\Psi}{\\partial x}=V_\\infty\\sin(AOA)\n",
"\\end{array}\\right.,\\ on\\ outer\\ boundary\n",
"$$\n",
"\n",
"where $AOA$ represents angle of attact, and $V_\\infty$ is the free stream velocity.\n",
"\n",
"Since $V_\\infty$ and $AOA$ are constant, we can simply integrate the above boundary conditions for the stream function on the outer boundary\n",
"\n",
"$$\n",
"\\Psi=-xV_\\infty\\sin(AOA)+yV_\\infty\\cos(AOA)+C_1,\\ on\\ outer\\ boundary\n",
"$$\n",
"\n",
"In potential flow, the values of $\\Psi$ are not important; what matters are the derivatives of the stream function. Since the constant $C_1$ in the above equation will disappear in the derivatives, we can simply make $C_1=0$ here. Now we can apply Dirichlet boundary condition on the outer boundary:\n",
"\n",
"$$\n",
"\\Psi_{i,j=-1}=-x_{i,j=-1}V_\\infty\\sin(AOA)+y_{i,j=-1}V_\\infty\\cos(AOA)\n",
"$$\n",
"\n",
"Next, we examine the boundary conditions for nodes on the airfoil. For walls in potential flows, the no-slip boundary condition does not apply, given the lack of viscosity. The boundary condition for walls in potential flows is that the flow can not penetrate the wall (no-thru BC). This implies that the wall itself is a streamline.\n",
"\n",
"$$\n",
"\\Psi=C_2,\\ on\\ the\\ airfoil\n",
"$$\n",
"\n",
"or\n",
"\n",
"$$\n",
"\\Psi_{i,j=0}=C_2\n",
"$$\n",
"\n",
"Since we have determined the values of $\\Psi$ on the outer boundary, we cannot determine the value of $\\Psi$ on the airfoil, $C_2$ (because we don't know which streamline on the outer boundary corresponds to the streamline on the airfoil surface). Fortunately, we will resolve this problem later after we introduce the Kutta condition.\n",
"\n",
"Finally, the boundary condition on the dividing line $\\overline{AC}$ is again given from periodic boundary condition.\n",
"\n",
"$$\n",
"\\Psi_{i=0,j}=\\Psi_{i=-1,j}\n",
"$$"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"The Kutta Condition"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In potential flow, due to the lack of viscosity, the flow under an airfoil will flow back to the upper surface after it has passed the trailing edge, even at a low angle of attact and low free stream velocity. This is not physical. The Kutta condition ensures this situation won't happen: it makes the flow smoothly pass by the trailing edge. More details can be found in any textbook covering inviscid flow or potential flow. Here we only introduce the implementation of the Kutta condition in our code.\n",
"\n",
"According to reference [3], for the airfoils with finite-angle trailing edge, like the NACA 2412, the Kutta condition results in the value of $\\Psi$ on the node next to the trailing edge being the same as that on the trailing edge, in order to provide a smooth streamline near the trailing edge. In other words,\n",
"\n",
"$$\n",
"\\Psi_{i=0, j=1}=\\Psi_{i=0, j=0}\n",
"$$\n",
"\n",
"Recall the boundary condition on the airfoil surface: $\\Psi_{i,j=0}=C_2$. We can determine $C_2$ and $\\Psi_{i,j=0}$ now. In each iteration when solving the governing equation, let\n",
"\n",
"$$\n",
"\\Psi_{i,j=0}=C_2=\\Psi_{i=0,j=1}\n",
"$$\n",
"\n",
"The problem of the boundary condition on the airfoil surface is then resolved. We can start to solve for the stream functions!"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Solve It Now!!!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let the angle of attack be $15^\\circ$ and free stream velocity be $70\\ m/s$. Other parameters are the same as we used in previous sections."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# set angle of attact\n",
"AOA = 15. / 180. * np.pi\n",
"\n",
"# set free stream velocity\n",
"Vinf = 70.\n",
"\n",
"# initialize stram functions\n",
"stream = np.zeros((Nxi, Neta))\n",
"\n",
"# set up the BCs on the outer boundary\n",
"stream[:, -1] = - x[:, -1] * Vinf * np.sin(AOA) + y[:, -1] * Vinf * np.cos(AOA)\n",
"\n",
"# solve the PDE by iterative method\n",
"iters = 0\n",
"while True:\n",
" \n",
" # count the number of current interation\n",
" iters += 1\n",
" \n",
" # backup the last result\n",
" stream_n = stream.copy()\n",
" \n",
" # apply periodic BC on dividing line\n",
" temp = np.append([stream[-2, :].copy()], stream[:2, :].copy(), 0)\n",
" tempx = np.append([x[-2, :].copy()], x[:2, :].copy(), 0) \n",
" tempy = np.append([y[-2, :].copy()], y[:2, :].copy(), 0)\n",
" a, b, c = Solve_a_b_c(tempx, tempy)\n",
" stream[0, 1:-1] = SolveEq(a, b, c, temp)\n",
" stream[-1, :] = stream[0, :].copy()\n",
" \n",
" # apply Kutta condition \n",
" # and set the value of stream function on the airfoil surface\n",
" stream[:, 0] = stream[0, 1]\n",
"\n",
" # solve interior\n",
" a, b, c = Solve_a_b_c(x, y)\n",
" stream[1:-1, 1:-1] = SolveEq(a, b, c, stream)\n",
" \n",
" # calculate difference between current and the last result\n",
" err = np.abs(stream - stream_n)\n",
" \n",
" # adjudge whether the iteration should stop\n",
" if (err.max() <= 1e-6):\n",
" break"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Though the values of stream function are not important, conventionally, we make the streamline on the airfoil surface equal to zero."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"stream = stream - stream[0, 0]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, the streamlines are shown in the following figure."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# set the contour lines with negative values to be solid lines\n",
"import matplotlib as mpl\n",
"mpl.rcParams['contour.negative_linestyle'] = 'solid'\n",
"\n",
"# contour\n",
"plt.figure(figsize=(10, 8), dpi=100)\n",
"cs = plt.contour(x, y, stream, 100, colors='k')\n",
"plt.clabel(cs)\n",
"plt.plot(x[:, 0], y[:, 0], 'k-', lw=1) # plot the airfoil\n",
"plt.xlim((-2., 3.))\n",
"plt.ylim((-1.5, 1.5))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
"(-1.5, 1.5)"
]
},
{
"metadata": {},
"output_type": "display_data",
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Zs2ZRU1OTvXr1oru7O48fP86SkhJZy2sWqqqqeO7cOS5atIh9+vRhp06dOGvWLO7bt69Z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\nV0bsv0Qk2bT8HxHZENGvDfR/8vDw8PDw8PDIJI5EdJ+IKonoMREd/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"text": [
"<matplotlib.figure.Figure at 0x1066d9950>"
]
}
],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We successfully solved the potential flow over a NACA airfoil. We can start to try other parameters now!"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Reference"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[1] *NACA airfoil*, Wikipedia, http://en.wikipedia.org/wiki/NACA_airfoil\n",
"\n",
"[2] K.A. Hoffmann, and S.T. Chiang, *Computational fluid dynamics, Vol. 1*, Wichita, KS: Engineering Education System (2000).\n",
"\n",
"[3] F. Mohebbi and M. Sellier. *On the Kutta Condition in Potential Flow over Airfoil*, Journal of Aerodynamics (2014)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.core.display import HTML\n",
"css_file = '../../styles/numericalmoocstyle.css'\n",
"HTML(open(css_file, \"r\").read())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<link href='http://fonts.googleapis.com/css?family=Alegreya+Sans:100,300,400,500,700,800,900,100italic,300italic,400italic,500italic,700italic,800italic,900italic' rel='stylesheet' type='text/css'>\n",
"<link href='http://fonts.googleapis.com/css?family=Arvo:400,700,400italic' rel='stylesheet' type='text/css'>\n",
"<link href='http://fonts.googleapis.com/css?family=PT+Mono' rel='stylesheet' type='text/css'>\n",
"<link href='http://fonts.googleapis.com/css?family=Shadows+Into+Light' rel='stylesheet' type='text/css'>\n",
"<link href='http://fonts.googleapis.com/css?family=Nixie+One' rel='stylesheet' type='text/css'>\n",
"<style>\n",
"\n",
"@font-face {\n",
" font-family: \"Computer Modern\";\n",
" src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
"}\n",
"\n",
"#notebook_panel { /* main background */\n",
" background: rgb(245,245,245);\n",
"}\n",
"\n",
"div.cell { /* set cell width */\n",
" width: 750px;\n",
"}\n",
"\n",
"div #notebook { /* centre the content */\n",
" background: #fff; /* white background for content */\n",
" width: 1000px;\n",
" margin: auto;\n",
" padding-left: 0em;\n",
"}\n",
"\n",
"#notebook li { /* More space between bullet points */\n",
"margin-top:0.8em;\n",
"}\n",
"\n",
"/* draw border around running cells */\n",
"div.cell.border-box-sizing.code_cell.running { \n",
" border: 1px solid #111;\n",
"}\n",
"\n",
"/* Put a solid color box around each cell and its output, visually linking them*/\n",
"div.cell.code_cell {\n",
" background-color: rgb(256,256,256); \n",
" border-radius: 0px; \n",
" padding: 0.5em;\n",
" margin-left:1em;\n",
" margin-top: 1em;\n",
"}\n",
"\n",
"div.text_cell_render{\n",
" font-family: 'Alegreya Sans' sans-serif;\n",
" line-height: 140%;\n",
" font-size: 125%;\n",
" font-weight: 400;\n",
" width:600px;\n",
" margin-left:auto;\n",
" margin-right:auto;\n",
"}\n",
"\n",
"\n",
"/* Formatting for header cells */\n",
".text_cell_render h1 {\n",
" font-family: 'Nixie One', serif;\n",
" font-style:regular;\n",
" font-weight: 400; \n",
" font-size: 45pt;\n",
" line-height: 100%;\n",
" color: rgb(0,51,102);\n",
" margin-bottom: 0.5em;\n",
" margin-top: 0.5em;\n",
" display: block;\n",
"}\t\n",
".text_cell_render h2 {\n",
" font-family: 'Nixie One', serif;\n",
" font-weight: 400;\n",
" font-size: 30pt;\n",
" line-height: 100%;\n",
" color: rgb(0,51,102);\n",
" margin-bottom: 0.1em;\n",
" margin-top: 0.3em;\n",
" display: block;\n",
"}\t\n",
"\n",
".text_cell_render h3 {\n",
" font-family: 'Nixie One', serif;\n",
" margin-top:16px;\n",
"\tfont-size: 22pt;\n",
" font-weight: 600;\n",
" margin-bottom: 3px;\n",
" font-style: regular;\n",
" color: rgb(102,102,0);\n",
"}\n",
"\n",
".text_cell_render h4 { /*Use this for captions*/\n",
" font-family: 'Nixie One', serif;\n",
" font-size: 14pt;\n",
" text-align: center;\n",
" margin-top: 0em;\n",
" margin-bottom: 2em;\n",
" font-style: regular;\n",
"}\n",
"\n",
".text_cell_render h5 { /*Use this for small titles*/\n",
" font-family: 'Nixie One', sans-serif;\n",
" font-weight: 400;\n",
" font-size: 16pt;\n",
" color: rgb(163,0,0);\n",
" font-style: italic;\n",
" margin-bottom: .1em;\n",
" margin-top: 0.8em;\n",
" display: block;\n",
"}\n",
"\n",
".text_cell_render h6 { /*use this for copyright note*/\n",
" font-family: 'PT Mono', sans-serif;\n",
" font-weight: 300;\n",
" font-size: 9pt;\n",
" line-height: 100%;\n",
" color: grey;\n",
" margin-bottom: 1px;\n",
" margin-top: 1px;\n",
"}\n",
"\n",
".CodeMirror{\n",
" font-family: \"PT Mono\";\n",
" font-size: 90%;\n",
"}\n",
"\n",
"</style>\n",
"<script>\n",
" MathJax.Hub.Config({\n",
" TeX: {\n",
" extensions: [\"AMSmath.js\"],\n",
" equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
" },\n",
" tex2jax: {\n",
" inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
" },\n",
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
" \"HTML-CSS\": {\n",
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
" }\n",
" });\n",
"</script>\n"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"<IPython.core.display.HTML at 0x106400190>"
]
}
],
"prompt_number": 23
}
],
"metadata": {}
}
]
} | mit |
shariqiqbal2810/WGAN-GP-PyTorch | evaluate.ipynb | 1 | 195481 | {
"cells": [
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import sys\n",
"from scipy.io import loadmat\n",
"sys.path.append(\"code/\")\n",
"from utils import Generator, Discriminator, enable_gradients, disable_gradients\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load data and set constants"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_dict = loadmat(\"/home/shariqbal/data/train_32x32.mat\")\n",
"real_imgs = np.transpose(data_dict['X'], (3, 0, 1, 2))"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model_name = 'run1'\n",
"batch_size = 64\n",
"noise_dim = 128"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"disc_loss = np.load(\"models/%s/disc_loss.npy\" % model_name)\n",
"gen_loss = np.load(\"models/%s/gen_loss.npy\" % model_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot Loss Functions"
]
},
{
"cell_type": "code",
"execution_count": 290,
"metadata": {},
"outputs": [
{
"data": {
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t2oQVK1YAALZs2YLbt287PDBX9uupNJSzvgURERFVk2QidvToUXz44YcICQnB9OnT8f33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\nAOCQ/wExyOiNly9hYgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf6f620fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with sns.axes_style(\"darkgrid\"):\n",
" plt.figure(figsize=(10,8))\n",
" plt.ylabel(\"Negative Discriminator Loss\")\n",
" plt.xlabel(\"Iterations\")\n",
" plt.plot(-disc_loss);"
]
},
{
"cell_type": "code",
"execution_count": 291,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Fb25J1/6Ags1etlv2OSVJou02Rvs6ra6ptevCtI7D232uEP/5ST4hcWdmb+OSLEbgLKIH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\nR8X+Ob6H6BDIS6Lv61wmYw8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RLszuiI8McZlk3dyvFe65ur1XCb0z/5nYC/+9uY/L7Rqf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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf6f484cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with sns.axes_style(\"darkgrid\"):\n",
" plt.figure(figsize=(10,8))\n",
" plt.ylabel(\"Generator Loss\")\n",
" plt.xlabel(\"Iterations\")\n",
" plt.plot(gen_loss);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load model and evaluate generated images"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {},
"outputs": [],
"source": [
"gen_net = Generator()\n",
"gen_net.load_state_dict(torch.load(\"models/%s/gen_net.pt\" % model_name))\n",
"gen_net = gen_net.cuda()"
]
},
{
"cell_type": "code",
"execution_count": 268,
"metadata": {},
"outputs": [],
"source": [
"gen_noise = torch.FloatTensor(batch_size, noise_dim).cuda()\n",
"gen_noise.normal_()\n",
"disable_gradients(gen_net)\n",
"gen_data_cuda = gen_net(gen_noise)\n",
"gen_data = np.transpose(gen_data_cuda.cpu().data.numpy(), axes=[0,3,2,1])\n",
"gen_imgs = (((gen_data + 1) / 2) * 255).astype(\"uint8\")"
]
},
{
"cell_type": "code",
"execution_count": 279,
"metadata": {},
"outputs": [
{
"data": {
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XNxnalk8fe7RTQoIB1/Zcv7Ic8/Brxjk+g+nHdYoxm8dBWmJAv5K46jkvRucdEYtI0WTo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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf6f1feac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gen_indices = np.random.randint(len(gen_imgs), size=9)\n",
"real_indices = np.random.randint(len(real_imgs), size=9)\n",
"\n",
"plt.figure(figsize=(16,6))\n",
"plt.subplot(2, 1, 1)\n",
"plt.axis('off')\n",
"plt.title(\"Generated Images\")\n",
"plt.imshow(np.hstack(gen_imgs[gen_indices]))\n",
"plt.subplot(2, 1, 2)\n",
"plt.title(\"Real Images\")\n",
"plt.imshow(np.hstack(real_imgs[real_indices]));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generate interpolations between two points in random space"
]
},
{
"cell_type": "code",
"execution_count": 218,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def interpolate_vectors(vec1, vec2, num):\n",
" \"\"\"\n",
" Interpolate points between two vectors\n",
" \"\"\"\n",
" return np.array([alpha * vec1 + (1 - alpha) * vec2 for alpha in np.linspace(0, 1, num)])"
]
},
{
"cell_type": "code",
"execution_count": 264,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gen_interp_np = interpolate_vectors(np.random.normal(size=noise_dim),\n",
" np.random.normal(size=noise_dim), 10).astype(\"float32\")\n",
"gen_interp = torch.FloatTensor(gen_interp_np).cuda()\n",
"\n",
"disable_gradients(gen_net)\n",
"gen_interp_cuda = gen_net(gen_interp)\n",
"gen_interp = np.transpose(gen_interp_cuda.cpu().data.numpy(), axes=[0,3,2,1])\n",
"gen_interp_imgs = (((gen_interp + 1) / 2) * 255).astype(\"uint8\")"
]
},
{
"cell_type": "code",
"execution_count": 281,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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tA2SqJAlODlMAAoFYtKiIc6f76XqHMo84RqN1sqVlGXGJmXuxPxeHO7i5Ar/i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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf6f2ccbe0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(16,6))\n",
"plt.axis('off')\n",
"plt.title(\"Image Interpolation\")\n",
"plt.imshow(np.hstack(gen_interp_imgs));"
]
},
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| mit |
AnaniSkywalker/UDACITY_Machine_Learning | titanic_survival_exploration/titanic_survival_exploration.ipynb | 1 | 78135 | {
"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",
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" <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",
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" <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",
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" <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",
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" <td>373450</td>\n",
" <td>8.0500</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
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"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 "
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},
"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": 8,
"metadata": {
"collapsed": false
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{
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" 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 "
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],
"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": 10,
"metadata": {
"collapsed": false
},
"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": 11,
"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": 13,
"metadata": {
"collapsed": false
},
"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": [
"***\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": {
"collapsed": false
},
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{
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P1BPwLd34RwPXZqbHpZckaYCrZxT99RHxIPAy8LGIGAa80tiyJElST3Tags/M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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118ea42b0>"
]
},
"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": 25,
"metadata": {
"collapsed": false
},
"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",
" predictions.append(1 if passenger['Sex'] == 'female' else 0)\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": 26,
"metadata": {
"collapsed": false
},
"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": [
"***\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": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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RJyLujIiFEfGprghOkiR1Xj3N+Adn5ivASOBp4B+BrzYzKEmS1Dj1JPuWpv7D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\n16MvnYiIfsANwL9m5iu1y3p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"text/plain": [
"<matplotlib.figure.Figure at 0x11c3dc4e0>"
]
},
"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": 28,
"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",
" predicting_data = 0\n",
" predicting_data = 1 if passenger['Sex'] == 'female' else predicting_data\n",
" predicting_data = 1 if passenger['Sex'] == 'male' and passenger['Age'] < 10 else predicting_data\n",
" predictions.append(predicting_data)\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": 30,
"metadata": {
"collapsed": false
},
"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": [
"**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": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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78hFxAcXe+7rANyLi9D6rSpIk9Vhne/J7AaPKG9O8C7iHoqW9JEkaADo7J//3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\npIr6/1kQnFo+81nbAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c3c4f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"vs.survival_stats(data, outcomes, 'Age', [\"Sex == 'male'\", \"Age < 18\"])"
]
},
{
"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": 38,
"metadata": {
"collapsed": false
},
"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",
" predicting_data = 0\n",
" predicting_data = 1 if passenger['Sex'] == 'female' else predicting_data\n",
" predicting_data = 1 if passenger['Sex'] == 'male' and passenger['Age'] < 10 else predicting_data\n",
" predictions.append(predicting_data)\n",
" \n",
" # Return our predictions\n",
" return pd.Series(predictions)\n",
"\n",
"# Make the predictions\n",
"predictions = predictions_3(data)"
]
},
{
"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": 39,
"metadata": {
"collapsed": false
},
"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": [
"**Answer**: *Replace this text with your answer to the question above.*"
]
},
{
"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": [
"For example you want to see if a student will be accepted to certain schools based on the grades, and test scores, in which case you would have the target variables default with acceptable levels of 'True' and 'False' should their scores meet certain standards.\n",
"So in theory the feature sufficient for predicting this would be did the student's grade score meet requirements on the scale, did the student's 'test' scores meet the requirements.\n",
"So in conclusion, to finalize a decision, it comes to did the grades and test score excell on the grid?"
]
},
{
"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."
]
}
],
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"kernelspec": {
"display_name": "Python 3",
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| mit |
IanHawke/RiemannPython | Lesson_04_Shallow_Water.ipynb | 1 | 93906 | {
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" margin-top: 1px;\n",
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"div.cell.code_cell { /* Group area containing both code and output */\n",
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"border-radius: 10px; /* rounded borders */\n",
"padding: 1em;\n",
"}\n",
"\n",
"</style>\n",
"\n",
"<script type=\"text/x-mathjax-config\">\n",
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" Macros: {N : \"\\\\mathbb{N}\"}\n",
" },\n",
" tex2jax: {\n",
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" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
" },\n",
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
" \"HTML-CSS\": {\n",
" styles: {'.MathJax_Display': {margin: \"40em 40em 40em 40em\",line-height : '200%', border: '1px solid ##CC0000' } },\n",
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"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.core.display import HTML\n",
"def css_styling():\n",
" styles = open(\"./styles/custom.css\", \"r\").read()\n",
" return HTML(styles)\n",
"css_styling()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Shallow Water Equations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a simple example of solving the Riemann problem for a nonlinear system we look at the *shallow water* equations. These are a simplification of the Navier-Stokes equations, reduced here to one spatial dimension $x$, which determine the height $h(x, t)$ of the water with respect to some reference location, and its velocity $u(x, t)$. In the simplest case (where the bed of the channel is flat, and the gravitational constant is renormalised to $1$) these can be written in the conservation law form\n",
"$$\n",
" \\partial_t \\begin{pmatrix} h \\\\ h u \\end{pmatrix} + \\partial_x \\begin{pmatrix} hu \\\\ h u^2 + \\tfrac{1}{2} h^2 \\end{pmatrix} = {\\bf 0}.\n",
"$$\n",
"The *conserved variables* ${\\bf q} = (q_1, q_2)^T = (h, h u)^T$ are effectively the total mass and momentum of the fluid."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Quasilinear form"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As seen in the [theory lesson](Lesson_Theory.ipynb), to construct the solution we need the eigenvalues and eigenvectors of the Jacobian matrix. We can construct them directly, by first noting that, in terms of the conserved variables, \n",
"$$\n",
" {\\bf f} = \\begin{pmatrix} f_1 \\\\ f_2 \\end{pmatrix} = \\begin{pmatrix} q_2 \\\\ \\frac{q_2^2}{q_1} + \\frac{q_1^2}{2} \\end{pmatrix}.\n",
"$$\n",
"Therefore the Jacobian is\n",
"$$ \\frac{\\partial {\\bf f}}{\\partial {\\bf q}} = \\begin{pmatrix} 0 & 1 \\\\ -u^2 + h & 2 u \\end{pmatrix}.\n",
"$$\n",
"The eigenvalues and eigenvectors follow immediately as\n",
"$$\n",
"\\begin{align}\n",
" \\lambda_{1} & = u - \\sqrt{h}, & \\lambda_{2} & = u + \\sqrt{h}, \\\\\n",
" {\\bf r}_{1} & = \\begin{pmatrix} 1 \\\\ u - \\sqrt{h} \\end{pmatrix}, & {\\bf r}_{2} & = \\begin{pmatrix} 1 \\\\ u + \\sqrt{h} \\end{pmatrix} .\n",
"\\end{align}\n",
"$$\n",
"Here we have followed the standard convention $\\lambda_1 \\le \\lambda_2 \\le \\dots \\le \\lambda_N$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An alternative approach that may be considerably easier to apply for more complex problems is to write down a different quasilinear form of the equation, which in this case is in terms of the *primitive variables* ${\\bf w} = (h, u)^T$,\n",
"$$\n",
" \\partial_t \\begin{pmatrix} h \\\\ u \\end{pmatrix} + \\begin{pmatrix} u & h \\\\ 1 & u \\end{pmatrix} \\partial_x \\begin{pmatrix} h \\\\ u \\end{pmatrix} = {\\bf 0}.\n",
"$$\n",
"The general form here would be written \n",
"$$\n",
" \\partial_t {\\bf w} + B({\\bf w}) \\partial_x {\\bf w} = {\\bf 0}.\n",
"$$\n",
"It is straightforward to check that \n",
"$$\n",
" B = \\left( \\frac{\\partial {\\bf q}}{\\partial {\\bf w}} \\right)^{-1} \\frac{\\partial {\\bf f}}{\\partial {\\bf w}} = \\left( \\frac{\\partial {\\bf q}}{\\partial {\\bf w}} \\right)^{-1} \\frac{\\partial {\\bf f}}{\\partial {\\bf q}} \\left( \\frac{\\partial {\\bf q}}{\\partial {\\bf w}} \\right).\n",
"$$\n",
"Thus $B$ is *similar* to the Jacobian, so must have the same eigenvalues, which is straightforward to check. We also have that\n",
"$$\n",
"\\begin{align}\n",
" B \\left\\{ \\left( \\frac{\\partial {\\bf q}}{\\partial {\\bf w}} \\right)^{-1} {\\bf r} \\right\\} = \\lambda \\left\\{ \\left( \\frac{\\partial {\\bf q}}{\\partial {\\bf w}} \\right)^{-1} {\\bf r} \\right\\},\n",
"\\end{align}\n",
"$$\n",
"showing that the eigenvectors of the Jacobian can be straightforwardly found from the eigenvectors of $B$, which for the shallow water case are\n",
"$$\n",
"\\begin{align}\n",
" {\\bf \\hat{r}}_1 &= \\begin{pmatrix} -\\sqrt{h} \\\\ 1 \\end{pmatrix} & {\\bf \\hat{r}}_2 &= \\begin{pmatrix} \\sqrt{h} \\\\ 1 \\end{pmatrix}.\n",
"\\end{align}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rarefaction waves"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The solution across a continuous rarefaction wave is given by the solution of the ordinary differential equation\n",
"$$\n",
" \\partial_{\\xi} {\\bf q} = \\frac{{\\bf r}}{{\\bf r} \\cdot \\partial_{{\\bf q}} \\lambda}\n",
"$$\n",
"where $\\lambda, {\\bf r}$ are the eigenvalues and eigenvectors of the Jacobian matrix. Note that we can change variables to get the (physically equivalent) relation differential equation\n",
"$$\n",
" \\partial_{\\xi} {\\bf w} = \\frac{{\\bf r}}{{\\bf r} \\cdot \\partial_{{\\bf w}} \\lambda}\n",
"$$\n",
"where now the eigenvectors are those of the appropriate matrix for the quasilinear form for ${\\bf w}$. Where ${\\bf w}$ are the primitive variables as above, the matrix is $B$ and the eigenvectors given by ${\\bf \\hat{r}}$ as above."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the shallow water equations we will solve this equation for the primitive variables for the first wave only - symmetry gives the other wave straightforwardly. Starting from\n",
"$$\n",
" \\lambda_1 = u - \\sqrt{h}, \\qquad {\\bf \\hat{r}}_1 = \\begin{pmatrix} -\\sqrt{h} \\\\ 1 \\end{pmatrix}\n",
"$$\n",
"we have\n",
"$$\n",
" \\partial_{{\\bf w}} \\lambda_1 = \\begin{pmatrix} -\\frac{1}{2 \\sqrt{h}} \\\\ 1 \\end{pmatrix}\n",
"$$\n",
"and hence\n",
"$$\n",
" {\\bf \\hat{r}}_1 \\cdot \\partial_{{\\bf w}} \\lambda_1 = \\frac{3}{2}\n",
"$$\n",
"from which we have\n",
"$$\n",
" \\partial_{\\xi} \\begin{pmatrix} h \\\\ u \\end{pmatrix} = \\frac{2}{3} \\begin{pmatrix} -\\sqrt{h} \\\\ 1 \\end{pmatrix}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is straightforwardly integrated to get\n",
"$$\n",
" \\begin{pmatrix} h \\\\ u \\end{pmatrix} = \\begin{pmatrix} \\left( c_1 - \\frac{\\xi}{3} \\right)^2 \\\\ \\frac{2}{3} \\xi + c_2 \\end{pmatrix}. \n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To fix the integration constants $c_{1,2}$ we need to say which state the solution is starting from. As we are looking at the left wave, we expect it to start from the left state ${\\bf w}_l = (h_l, u_l)^T$. The left state will connect to the rarefaction wave when the characteristic speeds match, i.e. when $\\xi = \\xi_l = \\lambda_1 = u_l - \\sqrt{h_l}$. Therefore we have\n",
"$$\n",
" \\begin{pmatrix} h_l \\\\ u_l \\end{pmatrix} = \\begin{pmatrix} \\left( c_1 - \\frac{\\xi_l}{3} \\right)^2 \\\\ \\frac{2}{3} \\xi_l + c_2 \\end{pmatrix},\n",
"$$\n",
"from which we determine\n",
"$$\n",
" c_1 = \\frac{1}{3} \\xi_l + \\sqrt{h_l}, \\qquad c_2 = u_l - \\frac{2}{3} \\xi_l.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This gives the final solution\n",
"$$\n",
" \\begin{pmatrix} h \\\\ u \\end{pmatrix} = \\begin{pmatrix} \\left( \\frac{\\xi_l - \\xi}{3} + \\sqrt{h_l} \\right)^2 \\\\ \\frac{2}{3} (\\xi - \\xi_l) + u_l \\end{pmatrix}. \n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rarefaction examples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us look at all points that can be connected to a certain state by a rarefaction. We do this in the *phase plane*, which is the $(h, u)$ plane. The \"known state\" will be given by a marker, and all states along the rarefaction curve given by the line, sometimes known as an integral curve."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"hl = np.linspace(0.1, 10.1)\n",
"ul = np.linspace(-1.0, 1.0)\n",
"HL, UL = np.meshgrid(hl, ul)\n",
"XIL = UL - np.sqrt(HL)\n",
"xi_min = np.min(XIL)\n",
"xi_max = np.max(XIL)\n",
"h_min = np.min(hl)\n",
"h_max = np.max(hl)\n",
"u_min = np.min(ul)\n",
"u_max = np.max(ul)\n",
"xi = np.linspace(xi_min, xi_max)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def plot_sw_rarefaction(hl, ul):\n",
" \"Plot the rarefaction curve through the state (hl, ul)\"\n",
" \n",
" xil = ul - np.sqrt(hl)\n",
" h = ((xil - xi) / 3.0 + np.sqrt(hl))**2\n",
" u = 2.0 * (xi - xil) / 3.0 + ul\n",
" \n",
" fig = plt.figure(figsize=(12,8))\n",
" ax = fig.add_subplot(111)\n",
" ax.plot(hl, ul, 'rx', markersize = 16, markeredgewidth = 3)\n",
" ax.plot(h, u, 'k--', linewidth = 2)\n",
" ax.set_xlabel(r\"$h$\")\n",
" ax.set_ylabel(r\"$u$\")\n",
" dh = h_max - h_min\n",
" du = u_max - u_min\n",
" ax.set_xbound(h_min - 0.1 * dh, h_max + 0.1 * dh)\n",
" ax.set_ybound(u_min - 0.1 * du, u_max + 0.1 * du)\n",
" fig.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
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"interactive(children=(FloatSlider(value=1.0, description='hl', max=10.0, min=0.1), FloatSlider(value=0.0, description='ul', max=1.0, min=-1.0), Output()), _dom_classes=('widget-interact',))"
]
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"metadata": {},
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],
"source": [
"from ipywidgets import interactive, FloatSlider\n",
"\n",
"interactive(plot_sw_rarefaction, \n",
" hl = FloatSlider(min = 0.1, max = 10.0, value = 1.0), \n",
" ul = FloatSlider(min = -1.0, max = 1.0, value = 0.0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is a problem with this: we haven't checked if the states on the curve can be *physically* connected to this point. That is, we haven't checked how the characteristic speed changes along the curve.\n",
"\n",
"Here it is obvious: we know that the characteristics must spread across the rarefaction, so $\\lambda$ must increase, and as $\\xi = \\lambda$ we must have the characteristic coordinate increasing."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def plot_sw_rarefaction_physical(hl, ul):\n",
" \"Plot the rarefaction curve through the state (hl, ul)\"\n",
" \n",
" xil = ul - np.sqrt(hl)\n",
" xi_physical = np.linspace(xil, xi_max)\n",
" xi_unphysical = np.linspace(xi_min, xil)\n",
" h_physical = ((xil - xi_physical) / 3.0 + np.sqrt(hl))**2\n",
" u_physical = 2.0 * (xi_physical - xil) / 3.0 + ul\n",
" h_unphysical = ((xil - xi_unphysical) / 3.0 + np.sqrt(hl))**2\n",
" u_unphysical = 2.0 * (xi_unphysical - xil) / 3.0 + ul\n",
" \n",
" \n",
" fig = plt.figure(figsize=(12,8))\n",
" ax = fig.add_subplot(111)\n",
" ax.plot(hl, ul, 'rx', markersize = 16, markeredgewidth = 3)\n",
" ax.plot(h_physical, u_physical, 'k-', linewidth = 2, label=\"Physical\")\n",
" ax.plot(h_unphysical, u_unphysical, 'k--', linewidth = 2, label=\"Unphysical\")\n",
" ax.set_xlabel(r\"$h$\")\n",
" ax.set_ylabel(r\"$u$\")\n",
" dh = h_max - h_min\n",
" du = u_max - u_min\n",
" ax.set_xbound(h_min - 0.1 * dh, h_max + 0.1 * dh)\n",
" ax.set_ybound(u_min - 0.1 * du, u_max + 0.1 * du)\n",
" ax.legend()\n",
" fig.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
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" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
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" it may mean that your frontend doesn't currently support widgets.\n",
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],
"text/plain": [
"interactive(children=(FloatSlider(value=1.0, description='hl', max=10.0, min=0.1), FloatSlider(value=0.0, description='ul', max=1.0, min=-1.0), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"interactive(plot_sw_rarefaction_physical, \n",
" hl = FloatSlider(min = 0.1, max = 10.0, value = 1.0), \n",
" ul = FloatSlider(min = -1.0, max = 1.0, value = 0.0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that along the physical part of the rarefaction curve the height $h$ decreases."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instead of writing the solution in terms of the similarity coordinate $\\xi$ we can instead write the solution in terms of any other single parameter. It is useful to write it in terms of the height, which can be done simply by re-arranging the equations giving $u$ and $h$ in terms of $\\xi$. So, a state with height $h_m$ to the right of the state $(h_l, u_l)$ can be connected across a rarefaction if\n",
"$$\n",
" u_m = u_l + 2 \\left( \\sqrt{h_l} - \\sqrt{h_m} \\right).\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this form we will look at the characteristic curves and the behaviour in state space to cross-check."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def plot_sw_rarefaction_physical_characteristics(hl, ul, hm):\n",
" \"Plot the rarefaction curve through the state (hl, ul) finishing at (hm, um)\"\n",
" \n",
" um = ul + 2.0 * (np.sqrt(hl) - np.sqrt(hm))\n",
" \n",
" h_maximum = np.max([h_max, hl, hm])\n",
" h_minimum = np.min([h_min, hl, hm])\n",
" u_maximum = np.max([u_max, ul, um])\n",
" u_minimum = np.min([u_min, ul, um])\n",
" dh = h_maximum - h_minimum\n",
" du = u_maximum - u_minimum\n",
" xi_min = u_minimum - np.sqrt(h_maximum)\n",
" xi_max = u_maximum - np.sqrt(h_minimum)\n",
" \n",
" xil = ul - np.sqrt(hl)\n",
" xim = um - np.sqrt(hm)\n",
" xi_physical = np.linspace(xil, xi_max)\n",
" xi_unphysical = np.linspace(xi_min, xil)\n",
" h_physical = ((xil - xi_physical) / 3.0 + np.sqrt(hl))**2\n",
" u_physical = 2.0 * (xi_physical - xil) / 3.0 + ul\n",
" h_unphysical = ((xil - xi_unphysical) / 3.0 + np.sqrt(hl))**2\n",
" u_unphysical = 2.0 * (xi_unphysical - xil) / 3.0 + ul\n",
" \n",
" \n",
" fig = plt.figure(figsize=(12,8))\n",
" ax1 = fig.add_subplot(121)\n",
" ax1.plot(hl, ul, 'rx', markersize = 16, markeredgewidth = 3, label=r\"$(h_l, u_l)$\")\n",
" ax1.plot(hm, um, 'b+', markersize = 16, markeredgewidth = 3, label=r\"$(h_m, u_m)$\")\n",
" ax1.plot(h_physical, u_physical, 'k-', linewidth = 2, label=\"Physical\")\n",
" ax1.plot(h_unphysical, u_unphysical, 'k--', linewidth = 2, label=\"Unphysical\")\n",
" ax1.set_xlabel(r\"$h$\")\n",
" ax1.set_ylabel(r\"$u$\")\n",
" ax1.set_xbound(h_minimum - 0.1 * dh, h_maximum + 0.1 * dh)\n",
" ax1.set_ybound(u_minimum - 0.1 * du, u_maximum + 0.1 * du)\n",
" ax1.legend()\n",
" \n",
" ax2 = fig.add_subplot(122)\n",
" left_edge = np.min([-1.0, -1.0 - xil])\n",
" right_edge = np.max([1.0, 1.0 - xim])\n",
" x_start_points_l = np.linspace(left_edge, 0.0, 20)\n",
" x_start_points_r = np.linspace(0.0, right_edge, 20)\n",
" x_end_points_l = x_start_points_l + xil\n",
" x_end_points_r = x_start_points_r + xim\n",
" \n",
" for xs, xe in zip(x_start_points_l, x_end_points_l):\n",
" ax2.plot([xs, xe], [0.0, 1.0], 'b-')\n",
" for xs, xe in zip(x_start_points_r, x_end_points_r):\n",
" ax2.plot([xs, xe], [0.0, 1.0], 'g-')\n",
" \n",
" # Rarefaction wave\n",
" if (xim > xil):\n",
" xi = np.linspace(xil, xim, 11)\n",
" x_end_rarefaction = xi\n",
" for xe in x_end_rarefaction:\n",
" ax2.plot([0.0, xe], [0.0, 1.0], 'r--')\n",
" else:\n",
" x_fill = [x_end_points_l[-1], x_start_points_l[-1], x_end_points_r[0]]\n",
" t_fill = [1.0, 0.0, 1.0]\n",
" ax2.fill_between(x_fill, t_fill, 1.0, facecolor = 'red', alpha = 0.5)\n",
" \n",
" ax2.set_xbound(-1.0, 1.0)\n",
" ax2.set_ybound(0.0, 1.0)\n",
" ax2.set_xlabel(r\"$x$\")\n",
" ax2.set_ylabel(r\"$t$\")\n",
" fig.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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"application/vnd.jupyter.widget-view+json": {
"model_id": "f7624fe13abb44eabcf433104b4b4f2f",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"interactive(children=(FloatSlider(value=1.0, description='hl', max=10.0, min=0.1), FloatSlider(value=0.0, description='ul', max=1.0, min=-1.0), FloatSlider(value=0.5, description='hm', max=10.0, min=0.1), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"interactive(plot_sw_rarefaction_physical_characteristics, \n",
" hl = FloatSlider(min = 0.1, max = 10.0, value = 1.0), \n",
" ul = FloatSlider(min = -1.0, max = 1.0, value = 0.0), \n",
" hm = FloatSlider(min = 0.1, max = 10.0, value = 0.5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We clearly see that only if $h_m < h_l$ do the characteristics spread as they should for a rarefaction. This is, in fact, already given by results above: we showed that $\\partial_{\\xi} h \\propto -\\sqrt{h}$. As the height $h$ is positive, this means that as $\\xi$ increase across the rarefaction, the height must decrease."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## All rarefaction solution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above exercise assumed we knew the left state and found all right states connecting it by a rarefaction. Now we assume we know both left *and* right states, and assume they connect to a central state, *both* along rarefactions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we need to find which states will connect to the right state across a rarefaction."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Repeat the above calculations for states connecting to a known right state. That is, show that, given the right state $(h_r, u_r)$, the left state that connects to it across a rarefaction satisfies\n",
"$$\n",
" \\begin{pmatrix} h \\\\ u \\end{pmatrix} = \\begin{pmatrix} \\left( -\\frac{\\xi_r - \\xi}{3} + \\sqrt{h_r} \\right)^2 \\\\ \\frac{2}{3} (\\xi - \\xi_r) + u_r \\end{pmatrix}. \n",
"$$\n",
"or equivalently, given $h_m$, that\n",
"$$\n",
" u_m = u_r - 2 \\left( \\sqrt{h_r} - \\sqrt{h_m} \\right).\n",
"$$\n",
"Also check that $h$ decreases across the rarefaction, so for a physical solution $h_m < h_r$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we can plot the curve of all states that can be connected to $(h_l, u_l)$ across a left rarefaction, and the curve of all states that can be connected to $(h_r, u_r)$ across a right rarefaction. *If* they intersect along the *physical* part of the curve, then we have the solution to the Riemann problem. Clearly this only occurs if $h_m < h_l$ *and* $h_m < h_r$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case (and note that this is a special case!) we can solve it analytically. We note that, using our *assumption* that both curves are rarefactions, we have that\n",
"$$\n",
"\\begin{align}\n",
" u_m & = u_l + 2 \\left( \\sqrt{h_l} - \\sqrt{h_m} \\right) \\\\\n",
" & = u_r - 2 \\left( \\sqrt{h_r} - \\sqrt{h_m} \\right)\n",
"\\end{align}\n",
"$$\n",
"Therefore we have\n",
"$$\n",
" h_m = \\frac{1}{16} \\left( u_l - u_r + 2 \\left( \\sqrt{h_l} + \\sqrt{h_r} \\right) \\right)^2.\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def plot_sw_all_rarefaction(hl, ul, hr, ur):\n",
" \"Plot the all rarefaction solution curve for states (hl, ul) and (hr, ur)\"\n",
" \n",
" hm = (ul - ur + 2.0 * (np.sqrt(hl) + np.sqrt(hr)))**2 / 16.0\n",
" um = ul + 2.0 * (np.sqrt(hl) - np.sqrt(hm))\n",
" \n",
" h_maximum = np.max([h_max, hl, hr, hm])\n",
" h_minimum = np.min([h_min, hl, hr, hm])\n",
" u_maximum = np.max([u_max, ul, ur, um])\n",
" u_minimum = np.min([u_min, ul, ur, um])\n",
" dh = h_maximum - h_minimum\n",
" du = u_maximum - u_minimum\n",
" xil_min = u_minimum - np.sqrt(h_maximum)\n",
" xil_max = u_maximum - np.sqrt(h_minimum)\n",
" xir_min = u_minimum + np.sqrt(h_minimum)\n",
" xir_max = u_maximum + np.sqrt(h_maximum)\n",
" \n",
" xil = ul - np.sqrt(hl)\n",
" xilm = um - np.sqrt(hm)\n",
" xil_physical = np.linspace(xil, xil_max)\n",
" xil_unphysical = np.linspace(xil_min, xil)\n",
" hl_physical = ((xil - xil_physical) / 3.0 + np.sqrt(hl))**2\n",
" ul_physical = 2.0 * (xil_physical - xil) / 3.0 + ul\n",
" hl_unphysical = ((xil - xil_unphysical) / 3.0 + np.sqrt(hl))**2\n",
" ul_unphysical = 2.0 * (xil_unphysical - xil) / 3.0 + ul\n",
" \n",
" xir = ur + np.sqrt(hr)\n",
" xirm = um + np.sqrt(hm)\n",
" xir_unphysical = np.linspace(xir, xir_max)\n",
" xir_physical = np.linspace(xir_min, xir)\n",
" hr_physical = (-(xir - xir_physical) / 3.0 + np.sqrt(hr))**2\n",
" ur_physical = 2.0 * (xir_physical - xir) / 3.0 + ur\n",
" hr_unphysical = (-(xir - xir_unphysical) / 3.0 + np.sqrt(hr))**2\n",
" ur_unphysical = 2.0 * (xir_unphysical - xir) / 3.0 + ur\n",
" \n",
" fig = plt.figure(figsize=(12,8))\n",
" ax1 = fig.add_subplot(111)\n",
" if (hm < np.min([hl, hr])):\n",
" ax1.plot(hm, um, 'b+', markersize = 16, markeredgewidth = 3, \n",
" label=r\"$(h_m, u_m)$, physical solution\")\n",
" else:\n",
" ax1.plot(hm, um, 'b+', markersize = 16, markeredgewidth = 3, \n",
" label=r\"$(h_m, u_m)$, not physical solution\")\n",
" ax1.plot(hl, ul, 'rx', markersize = 16, markeredgewidth = 3, label=r\"$(h_l, u_l)$\")\n",
" ax1.plot(hr, ur, 'go', markersize = 16, markeredgewidth = 3, label=r\"$(h_r, u_r)$\")\n",
" ax1.plot(hl_physical, ul_physical, 'k-', linewidth = 2, label=\"Physical (left)\")\n",
" ax1.plot(hl_unphysical, ul_unphysical, 'k--', linewidth = 2, label=\"Unphysical (left)\")\n",
" ax1.plot(hr_physical, ur_physical, 'c-', linewidth = 2, label=\"Physical (right)\")\n",
" ax1.plot(hr_unphysical, ur_unphysical, 'c--', linewidth = 2, label=\"Unphysical (right)\")\n",
" ax1.set_xlabel(r\"$h$\")\n",
" ax1.set_ylabel(r\"$u$\")\n",
" ax1.set_xbound(h_minimum - 0.1 * dh, h_maximum + 0.1 * dh)\n",
" ax1.set_ybound(u_minimum - 0.1 * du, u_maximum + 0.1 * du)\n",
" ax1.legend()\n",
" \n",
" fig.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "36712c27d2a84211a9e2d7bccb645b21",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"interactive(children=(FloatSlider(value=1.0, description='hl', max=10.0, min=0.1), FloatSlider(value=-0.5, description='ul', max=1.0, min=-1.0), FloatSlider(value=1.0, description='hr', max=10.0, min=0.1), FloatSlider(value=0.5, description='ur', max=1.0, min=-1.0), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"interactive(plot_sw_all_rarefaction, \n",
" hl = FloatSlider(min = 0.1, max = 10.0, value = 1.0), \n",
" ul = FloatSlider(min = -1.0, max = 1.0, value = -0.5), \n",
" hr = FloatSlider(min = 0.1, max = 10.0, value = 1.0), \n",
" ur = FloatSlider(min = -1.0, max = 1.0, value = 0.5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given the central state and the relation along rarefaction curves, we can then construct the characteristics and the solution in terms of the similarity coordinate (which, given a time $t$, gives the solution as a function of $x$)."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"def plot_sw_all_rarefaction_solution(hl, ul, hr, ur):\n",
" \"Plot the all rarefaction solution curve for states (hl, ul) and (hr, ur)\"\n",
" \n",
" hm = (ul - ur + 2.0 * (np.sqrt(hl) + np.sqrt(hr)))**2 / 16.0\n",
" um = ul + 2.0 * (np.sqrt(hl) - np.sqrt(hm))\n",
" \n",
" xi1l = ul - np.sqrt(hl)\n",
" xi1m = um - np.sqrt(hm)\n",
" xi1r = ur - np.sqrt(hr)\n",
" hl_raref = np.linspace(hl, hm, 20)\n",
" ul_raref = ul + 2.0 * (np.sqrt(hl) - np.sqrt(hl_raref))\n",
" xil_raref = ul_raref - np.sqrt(hl_raref)\n",
" \n",
" xi2r = ur + np.sqrt(hr)\n",
" xi2m = um + np.sqrt(hm)\n",
" xi2l = ul + np.sqrt(hl)\n",
" hr_raref = np.linspace(hm, hr)\n",
" ur_raref = ur - 2.0 * (np.sqrt(hr) - np.sqrt(hr_raref))\n",
" xir_raref = ur_raref + np.sqrt(hr_raref)\n",
" \n",
" xi_min = np.min([-1.0, xi1l, xi1m, xi2r, xi2m])\n",
" xi_max = np.max([1.0, xi1l, xi1m, xi2r, xi2m])\n",
" d_xi = xi_max - xi_min\n",
" h_max = np.max([hl, hr, hm])\n",
" h_min = np.min([hl, hr, hm])\n",
" d_h = h_max - h_min\n",
" u_max = np.max([ul, ur, um])\n",
" u_min = np.min([ul, ur, um])\n",
" d_u = u_max - u_min\n",
" \n",
" xi = np.array([xi_min - 0.1 * d_xi, xi1l])\n",
" h = np.array([hl, hl])\n",
" u = np.array([ul, ul])\n",
" xi = np.append(xi, xil_raref)\n",
" h = np.append(h, hl_raref)\n",
" u = np.append(u, ul_raref)\n",
" xi = np.append(xi, [xi1m, xi2m])\n",
" h = np.append(h, [hm, hm])\n",
" u = np.append(u, [um, um])\n",
" xi = np.append(xi, xir_raref)\n",
" h = np.append(h, hr_raref)\n",
" u = np.append(u, ur_raref)\n",
" xi = np.append(xi, [xi2r, xi_max + 0.1 * d_xi])\n",
" h = np.append(h, [hr, hr])\n",
" u = np.append(u, [ur, ur])\n",
" \n",
" fig = plt.figure(figsize=(12,8))\n",
" ax1 = fig.add_subplot(221)\n",
" if (hm < np.min([hl, hr])):\n",
" ax1.plot(xi, h, 'b-', label = \"Physical solution\")\n",
" else:\n",
" ax1.plot(xi, h, 'r--', label = \"Unphysical solution\")\n",
" ax1.set_ybound(h_min - 0.1 * d_h, h_max + 0.1 * d_h)\n",
" ax1.set_xlabel(r\"$\\xi$\")\n",
" ax1.set_ylabel(r\"$h$\")\n",
" ax1.legend()\n",
" ax2 = fig.add_subplot(222)\n",
" if (hm < np.min([hl, hr])):\n",
" ax2.plot(xi, u, 'b-', label = \"Physical solution\")\n",
" else:\n",
" ax2.plot(xi, u, 'r--', label = \"Unphysical solution\")\n",
" ax2.set_ybound(u_min - 0.1 * d_u, u_max + 0.1 * d_u)\n",
" ax2.set_xlabel(r\"$\\xi$\")\n",
" ax2.set_ylabel(r\"$u$\")\n",
" ax2.legend()\n",
" \n",
" ax3 = fig.add_subplot(223)\n",
" left_end = np.min([-1.0, 1.1*xi1l])\n",
" right_end = np.max([1.0, 1.1*xi2r])\n",
" left_edge = left_end - xi1l\n",
" right_edge = right_end - xi1r\n",
" x1_start_points_l = np.linspace(np.min([left_edge, left_end]), 0.0, 20)\n",
" x1_start_points_r = np.linspace(0.0, np.max([right_edge, right_end]), 20)\n",
" x1_end_points_l = x1_start_points_l + xi1l\n",
" t1_end_points_r = np.ones_like(x1_start_points_r)\n",
" \n",
" # Look for intersections\n",
" t1_end_points_r = np.minimum(t1_end_points_r, x1_start_points_r / (xi2r - xi1r))\n",
" x1_end_points_r = x1_start_points_r + xi1r * t1_end_points_r\n",
" # Note: here we are cheating, and using the characteristic speed of the middle state, \n",
" # ignoring howo it varies across the rarefaction\n",
" x1_final_points_r = x1_end_points_r + (1.0 - t1_end_points_r) * xi1m\n",
" \n",
" for xs, xe in zip(x1_start_points_l, x1_end_points_l):\n",
" ax3.plot([xs, xe], [0.0, 1.0], 'b-')\n",
" for xs, xe, te in zip(x1_start_points_r, x1_end_points_r, t1_end_points_r):\n",
" ax3.plot([xs, xe], [0.0, te], 'g-')\n",
" for xs, xe, ts in zip(x1_end_points_r, x1_final_points_r, t1_end_points_r):\n",
" ax3.plot([xs, xe], [ts, 1.0], 'g-')\n",
" \n",
" # Highlight the edges of both rarefactions\n",
" ax3.plot([0.0, xi1l], [0.0, 1.0], 'r-', linewidth=2)\n",
" ax3.plot([0.0, xi1m], [0.0, 1.0], 'r-', linewidth=2)\n",
" ax3.plot([0.0, xi2m], [0.0, 1.0], 'r-', linewidth=2)\n",
" ax3.plot([0.0, xi2r], [0.0, 1.0], 'r-', linewidth=2)\n",
" \n",
" # Rarefaction wave\n",
" if (xi1l < xi1m):\n",
" xi = np.linspace(xi1l, xi1m, 11)\n",
" x_end_rarefaction = xi\n",
" for xe in x_end_rarefaction:\n",
" ax3.plot([0.0, xe], [0.0, 1.0], 'r--')\n",
" else:\n",
" x_fill = [xi1l, 0.0, xi1m]\n",
" t_fill = [1.0, 0.0, 1.0]\n",
" ax3.fill_between(x_fill, t_fill, 1.0, facecolor = 'red', alpha = 0.5)\n",
" \n",
" ax3.set_xlabel(r\"$x$\")\n",
" ax3.set_ylabel(r\"$t$\")\n",
" ax3.set_title(\"1-characteristics\")\n",
" ax3.set_xbound(left_end, right_end)\n",
" \n",
" ax4 = fig.add_subplot(224)\n",
" left_end = np.min([-1.0, 1.1*xi1l])\n",
" right_end = np.max([1.0, 1.1*xi2r])\n",
" left_edge = left_end - xi2l\n",
" right_edge = right_end - xi2r\n",
" x2_start_points_l = np.linspace(np.min([left_edge, left_end]), 0.0, 20)\n",
" x2_start_points_r = np.linspace(0.0, np.max([right_edge, right_end]), 20)\n",
" x2_end_points_r = x2_start_points_r + xi2r\n",
" t2_end_points_l = np.ones_like(x2_start_points_l)\n",
" \n",
" # Look for intersections\n",
" t2_end_points_l = np.minimum(t2_end_points_l, x2_start_points_l / (xi1l - xi2r))\n",
" x2_end_points_l = x2_start_points_l + xi2r * t2_end_points_l\n",
" # Note: here we are cheating, and using the characteristic speed of the middle state, \n",
" # ignoring howo it varies across the rarefaction\n",
" x2_final_points_l = x2_end_points_l + (1.0 - t2_end_points_l) * xi2m\n",
" \n",
" for xs, xe in zip(x2_start_points_r, x2_end_points_r):\n",
" ax4.plot([xs, xe], [0.0, 1.0], 'g-')\n",
" for xs, xe, te in zip(x2_start_points_l, x2_end_points_l, t2_end_points_l):\n",
" ax4.plot([xs, xe], [0.0, te], 'b-')\n",
" for xs, xe, ts in zip(x2_end_points_l, x2_final_points_l, t2_end_points_l):\n",
" ax4.plot([xs, xe], [ts, 1.0], 'b-')\n",
" \n",
" # Highlight the edges of both rarefactions\n",
" ax4.plot([0.0, xi1l], [0.0, 1.0], 'r-', linewidth=2)\n",
" ax4.plot([0.0, xi1m], [0.0, 1.0], 'r-', linewidth=2)\n",
" ax4.plot([0.0, xi2m], [0.0, 1.0], 'r-', linewidth=2)\n",
" ax4.plot([0.0, xi2r], [0.0, 1.0], 'r-', linewidth=2)\n",
" \n",
" # Rarefaction wave\n",
" if (xi2r > xi2m):\n",
" xi = np.linspace(xi2m, xi2r, 11)\n",
" x_end_rarefaction = xi\n",
" for xe in x_end_rarefaction:\n",
" ax4.plot([0.0, xe], [0.0, 1.0], 'r--')\n",
" else:\n",
" x_fill = [xi2m, 0.0, xi2r]\n",
" t_fill = [1.0, 0.0, 1.0]\n",
" ax4.fill_between(x_fill, t_fill, 1.0, facecolor = 'red', alpha = 0.5)\n",
" \n",
" ax4.set_xlabel(r\"$x$\")\n",
" ax4.set_ylabel(r\"$t$\")\n",
" ax4.set_title(\"2-characteristics\")\n",
" ax4.set_xbound(left_end, right_end)\n",
" \n",
" fig.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
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"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"interactive(children=(FloatSlider(value=1.0, description='hl', max=10.0, min=0.1), FloatSlider(value=-0.5, description='ul', max=1.0, min=-1.0), FloatSlider(value=1.0, description='hr', max=10.0, min=0.1), FloatSlider(value=0.5, description='ur', max=1.0, min=-1.0), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"interactive(plot_sw_all_rarefaction_solution, \n",
" hl = FloatSlider(min = 0.1, max = 10.0, value = 1.0), \n",
" ul = FloatSlider(min = -1.0, max = 1.0, value = -0.5), \n",
" hr = FloatSlider(min = 0.1, max = 10.0, value = 1.0), \n",
" ur = FloatSlider(min = -1.0, max = 1.0, value = 0.5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Shocks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We note that the [general theory](Lesson_Theory.ipynb) tells us that across a shock the Rankine-Hugoniot conditions\n",
"$$\n",
" V_s \\left[ {\\bf q} \\right] = \\left[ {\\bf f}({\\bf q}) \\right]\n",
"$$\n",
"must be satisfied."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the shallow water equations we will start, as with the rarefaction case, by assuming we know the left state ${\\bf q}_l = (h_l, u_l)$, and work out which states ${\\bf q}_m$ can be connected to it across a shock. \n",
"\n",
"Note here that the procedure is *identical* for the right state as the direction does not matter. However, there will be multiple solutions, and checking which is physically correct does require checking whether the left or the right state is known"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Writing out the conditions in full we see that\n",
"$$\n",
"\\begin{align}\n",
" V_s \\left( h_m - h_l \\right) & = h_m u_m - h_l u_l \\\\\n",
" V_s \\left( h_m u_m - h_l u_l \\right) & = h_m u_m^2 + \\tfrac{1}{2} h_m^2 - h_l u_l^2 - \\tfrac{1}{2} h_l^2\n",
"\\end{align}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Eliminating the shock speed $V_s$ gives, using the second equation,\n",
"$$\n",
" u_m^2 - (2 u_l) u_m + \\left[ u_l^2 - \\tfrac{1}{2} \\left( h_l - h_m \\right) \\left( \\frac{h_l}{h_m} - \\frac{h_m}{h_l} \\right) \\right] = 0.\n",
"$$\n",
"This has the solutions (assuming that $h_m$ is known!)\n",
"$$\n",
" u_m = u_l \\pm \\sqrt{\\tfrac{1}{2} \\left( h_l - h_m \\right) \\left( \\frac{h_l}{h_m} - \\frac{h_m}{h_l} \\right)}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can again use the Rankine-Hugoniot relations to find the shock speed.\n",
"$$\n",
" V_s = u_l \\pm \\frac{h_m}{h_m - h_l} \\sqrt{\\tfrac{1}{2} \\left( h_l - h_m \\right) \\left( \\frac{h_l}{h_m} - \\frac{h_m}{h_l} \\right)}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We should at this point find which sign is appropriate. Comparing the shock speeds against the characteristic speed will show that\n",
"\n",
"* we need $h_m > h_l$ for the wave to be a shock, and\n",
"* we take the negative sign if connected to a left state, and the positive if connected to a right state.\n",
"\n",
"However, we can see this by plotting the *Hugoniot locus*: the curve of all states that can be connected to $(h_l, u_l)$ across a shock."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"def plot_sw_shock_physical(hl, ul):\n",
" \"Plot the shock curve through the state (hl, ul)\"\n",
" \n",
" h = np.linspace(h_min, h_max, 500)\n",
" u_negative = ul - np.sqrt(0.5 * (hl - h) * (hl / h - h / hl))\n",
" u_positive = ul + np.sqrt(0.5 * (hl - h) * (hl / h - h / hl))\n",
" \n",
" vs_negative = ul - h / (h - hl) * np.sqrt(0.5 * (hl - h) * (hl / h - h / hl))\n",
" vs_positive = ul + h / (h - hl) * np.sqrt(0.5 * (hl - h) * (hl / h - h / hl))\n",
" \n",
" xi1_negative = u_negative - np.sqrt(h) \n",
" xi1_positive = u_positive - np.sqrt(h)\n",
" xi2_negative = u_negative + np.sqrt(h) \n",
" xi2_positive = u_positive + np.sqrt(h)\n",
" \n",
" xi1_l = ul - np.sqrt(hl)\n",
" xi2_l = ul + np.sqrt(hl)\n",
" \n",
" h1_physical = h[np.logical_and(xi1_negative <= vs_negative, xi1_l >= vs_negative)]\n",
" u1_physical = u_negative[np.logical_and(xi1_negative <= vs_negative, xi1_l >= vs_negative)]\n",
" h2_physical = h[np.logical_and(xi2_positive >= vs_positive, xi2_l <= vs_positive)]\n",
" u2_physical = u_positive[np.logical_and(xi2_positive >= vs_positive, xi2_l <= vs_positive)]\n",
" h1_unphysical = h[np.logical_or(xi1_negative >= vs_negative, xi1_l <= vs_negative)]\n",
" u1_unphysical = u_negative[np.logical_or(xi1_negative >= vs_negative, xi1_l <= vs_negative)]\n",
" h2_unphysical = h[np.logical_or(xi2_positive <= vs_positive, xi2_l >= vs_positive)]\n",
" u2_unphysical = u_positive[np.logical_or(xi2_positive <= vs_positive, xi2_l >= vs_positive)]\n",
" \n",
" fig = plt.figure(figsize=(12,8))\n",
" ax = fig.add_subplot(111)\n",
" ax.plot(hl, ul, 'rx', markersize = 16, markeredgewidth = 3)\n",
" ax.plot(h1_physical, u1_physical, 'b-', linewidth = 2, \n",
" label=\"Physical, 1-shock\")\n",
" ax.plot(h1_unphysical, u1_unphysical, 'b--', linewidth = 2, \n",
" label=\"Unphysical, 1-shock\")\n",
" ax.plot(h2_physical, u2_physical, 'g-', linewidth = 2, \n",
" label=\"Physical, 2-shock\")\n",
" ax.plot(h2_unphysical, u2_unphysical, 'g--', linewidth = 2, \n",
" label=\"Unphysical, 2-shock\")\n",
" ax.plot(h[::5], u_negative[::5], 'co', markersize = 12, markeredgewidth = 2, alpha = 0.3,\n",
" label=\"Negative branch\")\n",
" ax.plot(h[::5], u_positive[::5], 'ro', markersize = 12, markeredgewidth = 2, alpha = 0.3,\n",
" label=\"Positive branch\")\n",
" ax.set_xlabel(r\"$h$\")\n",
" ax.set_ylabel(r\"$u$\")\n",
" dh = h_max - h_min\n",
" du = u_max - u_min\n",
" ax.set_xbound(h_min, h_max)\n",
" ax.set_ybound(u_min, u_max)\n",
" ax.legend()\n",
" fig.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
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"application/vnd.jupyter.widget-view+json": {
"model_id": "9d5b8698f1f74b47abc98feff828adff",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"interactive(children=(FloatSlider(value=1.0, description='hl', max=10.0, min=0.1), FloatSlider(value=0.0, description='ul', max=1.0, min=-1.0), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"interactive(plot_sw_shock_physical, \n",
" hl = FloatSlider(min = 0.1, max = 10.0, value = 1.0), \n",
" ul = FloatSlider(min = -1.0, max = 1.0, value = 0.0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see from these results, as claimed above, that\n",
"\n",
"* we need $h_m > h_l$ (or $h_m > h_r$) for the wave to be a shock, and\n",
"* we take the negative sign if connected to a left state, and the positive if connected to a right state."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## All shock solution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we assumed the solution contained two rarefactions it was possible to write the full solution in closed form. If we assume the solution contains two shocks then it is not possible to do this. However, it is straightforward to find the solution numerically. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We assume the left state ${\\bf w}_l = (h_l, u_l)$ and the right state ${\\bf w}_r = (h_r, u_r)$ are known, and that they both connect to the central state ${\\bf w}_m = (h_m, u_m)$ through shocks. We know that\n",
"$$\n",
"\\begin{align}\n",
" u_m & = u_l - \\sqrt{\\tfrac{1}{2} \\left( h_l - h_m \\right) \\left( \\frac{h_l}{h_m} - \\frac{h_m}{h_l} \\right)}, \\\\\n",
" u_m & = u_r + \\sqrt{\\tfrac{1}{2} \\left( h_r - h_m \\right) \\left( \\frac{h_r}{h_m} - \\frac{h_m}{h_r} \\right)}.\n",
"\\end{align}\n",
"$$\n",
"We schematically write these equations as\n",
"$$\n",
"\\begin{align}\n",
" u_m & = \\phi_l \\left( h_m; {\\bf w}_l \\right), \\\\\n",
" u_m & = \\phi_r \\left( h_m; {\\bf w}_r \\right),\n",
"\\end{align}\n",
"$$\n",
"to indicate that the velocity in the central state, $u_m$, can be written as a function of the single unknown $h_m$ and known data.\n",
"\n",
"We immediately see that $h_m$ is a root of the nonlinear equation\n",
"$$\n",
" \\phi \\left( h_m; {\\bf w}_l, {\\bf w}_r \\right) = \\phi_l \\left( h_m; {\\bf w}_l \\right) - \\phi_r \\left( h_m; {\\bf w}_r \\right) = 0.\n",
"$$\n",
"\n",
"Finding the roots of scalar nonlinear equations is a standard problem in numerical methods, with methods such as bisection, Newton-Raphson and more being well-known. `scipy` provides a number of standard algorithms - here we will use the recommended `brentq` method.\n",
"\n",
"Note that as soon as we have numerically determined $h_m$ then either formula above gives $u_m$, and the shock speeds follow."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def plot_sw_all_shock(hl, ul, hr, ur):\n",
" \"Plot the all shock solution curve for states (hl, ul) and (hr, ur)\"\n",
" \n",
" from scipy.optimize import brentq\n",
" \n",
" def phi(hstar):\n",
" \"Function defining the root\"\n",
" \n",
" phi_l = ul - np.sqrt(0.5 * (hl - hstar) * (hl / hstar - hstar / hl))\n",
" phi_r = ur + np.sqrt(0.5 * (hr - hstar) * (hr / hstar - hstar / hr))\n",
" \n",
" return phi_l - phi_r\n",
" \n",
" # There is a solution only in the physical case. \n",
" physical_solution = True\n",
" try:\n",
" hm = brentq(phi, np.max([hl, hr]), 10.0 * h_max)\n",
" except ValueError:\n",
" physical_solution = False\n",
" hm = hl\n",
" um = ul - np.sqrt(0.5 * (hl - hm) * (hl / hm - hm / hl))\n",
" \n",
" h = np.linspace(h_min, h_max, 500)\n",
" u_negative = ul - np.sqrt(0.5 * (hl - h) * (hl / h - h / hl))\n",
" u_positive = ur + np.sqrt(0.5 * (hr - h) * (hr / h - h / hr))\n",
" \n",
" h_maximum = np.max([h_max, hl, hr, hm])\n",
" h_minimum = np.min([h_min, hl, hr, hm])\n",
" u_maximum = np.max([u_max, ul, ur, um])\n",
" u_minimum = np.min([u_min, ul, ur, um])\n",
" dh = h_maximum - h_minimum\n",
" du = u_maximum - u_minimum\n",
" xil_min = u_minimum - np.sqrt(h_maximum)\n",
" xil_max = u_maximum - np.sqrt(h_minimum)\n",
" xir_min = u_minimum + np.sqrt(h_minimum)\n",
" xir_max = u_maximum + np.sqrt(h_maximum)\n",
" \n",
" vs_negative = ul - h / (h - hl) * np.sqrt(0.5 * (hl - h) * (hl / h - h / hl))\n",
" vs_positive = ur + h / (h - hr) * np.sqrt(0.5 * (hr - h) * (hr / h - h / hr))\n",
" \n",
" xi1_negative = u_negative - np.sqrt(h) \n",
" xi1_positive = u_positive - np.sqrt(h)\n",
" xi2_negative = u_negative + np.sqrt(h) \n",
" xi2_positive = u_positive + np.sqrt(h)\n",
" \n",
" xi1_l = ul - np.sqrt(hl)\n",
" xi2_r = ur + np.sqrt(hr)\n",
" \n",
" h1_physical = h[np.logical_and(xi1_negative <= vs_negative, xi1_l >= vs_negative)]\n",
" u1_physical = u_negative[np.logical_and(xi1_negative <= vs_negative, xi1_l >= vs_negative)]\n",
" h2_physical = h[np.logical_and(xi2_positive >= vs_positive, xi2_r <= vs_positive)]\n",
" u2_physical = u_positive[np.logical_and(xi2_positive >= vs_positive, xi2_r <= vs_positive)]\n",
" h1_unphysical = h[np.logical_or(xi1_negative >= vs_negative, xi1_l <= vs_negative)]\n",
" u1_unphysical = u_negative[np.logical_or(xi1_negative >= vs_negative, xi1_l <= vs_negative)]\n",
" h2_unphysical = h[np.logical_or(xi2_positive <= vs_positive, xi2_r >= vs_positive)]\n",
" u2_unphysical = u_positive[np.logical_or(xi2_positive <= vs_positive, xi2_r >= vs_positive)]\n",
" \n",
" fig = plt.figure(figsize=(12,8))\n",
" ax = fig.add_subplot(111)\n",
" ax.plot(hl, ul, 'rx', markersize = 16, markeredgewidth = 3, label = r\"${\\bf w}_l$\")\n",
" ax.plot(hr, ur, 'r+', markersize = 16, markeredgewidth = 3, label = r\"${\\bf w}_r$\")\n",
" if physical_solution:\n",
" ax.plot(hm, um, 'ro', markersize = 16, markeredgewidth = 3, label = r\"${\\bf w}_m$\")\n",
" ax.plot(h1_physical, u1_physical, 'b-', linewidth = 2, \n",
" label=\"Physical, 1-shock\")\n",
" ax.plot(h1_unphysical, u1_unphysical, 'b--', linewidth = 2, \n",
" label=\"Unphysical, 1-shock\")\n",
" ax.plot(h2_physical, u2_physical, 'g-', linewidth = 2, \n",
" label=\"Physical, 2-shock\")\n",
" ax.plot(h2_unphysical, u2_unphysical, 'g--', linewidth = 2, \n",
" label=\"Unphysical, 2-shock\")\n",
" ax.set_xlabel(r\"$h$\")\n",
" ax.set_ylabel(r\"$u$\")\n",
" ax.set_xbound(h_minimum - 0.1 * dh, h_maximum + 0.1 * dh)\n",
" ax.set_ybound(u_minimum - 0.1 * du, u_maximum + 0.1 * du)\n",
" ax.legend()\n",
" \n",
" fig.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
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"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"interactive(children=(FloatSlider(value=1.0, description='hl', max=10.0, min=0.1), FloatSlider(value=0.2, description='ul', max=1.0, min=-1.0), FloatSlider(value=1.0, description='hr', max=10.0, min=0.1), FloatSlider(value=-0.2, description='ur', max=1.0, min=-1.0), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"interactive(plot_sw_all_shock, \n",
" hl = FloatSlider(min = 0.1, max = 10.0, value = 1.0), \n",
" ul = FloatSlider(min = -1.0, max = 1.0, value = 0.2), \n",
" hr = FloatSlider(min = 0.1, max = 10.0, value = 1.0), \n",
" ur = FloatSlider(min = -1.0, max = 1.0, value = -0.2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we can plot the solution in physical space."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def plot_sw_all_shock_solution(hl, ul, hr, ur):\n",
" \"Plot the all shock solution for states (hl, ul) and (hr, ur)\"\n",
" \n",
" from scipy.optimize import brentq\n",
" \n",
" def phi(hstar):\n",
" \"Function defining the root\"\n",
" \n",
" phi_l = ul - np.sqrt(0.5 * (hl - hstar) * (hl / hstar - hstar / hl))\n",
" phi_r = ur + np.sqrt(0.5 * (hr - hstar) * (hr / hstar - hstar / hr))\n",
" \n",
" return phi_l - phi_r\n",
" \n",
" # There is a solution only in the physical case. \n",
" physical_solution = True\n",
" try:\n",
" hm = brentq(phi, np.max([hl, hr]), 10.0 * h_max)\n",
" except ValueError:\n",
" physical_solution = False\n",
" hm = hl\n",
" um = ul - np.sqrt(0.5 * (hl - hm) * (hl / hm - hm / hl))\n",
" \n",
" xi1l = ul - np.sqrt(hl)\n",
" xi1m = um - np.sqrt(hm)\n",
" xi1r = ur - np.sqrt(hr)\n",
" if physical_solution:\n",
" vsl = ul - hm / (hm - hl) * np.sqrt(0.5 * (hl - hm) * (hl / hm - hm / hl))\n",
" else:\n",
" vsl = xi1l\n",
" \n",
" xi2r = ur + np.sqrt(hr)\n",
" xi2m = um + np.sqrt(hm)\n",
" xi2l = ul + np.sqrt(hl)\n",
" if physical_solution:\n",
" vsr = ur + hm / (hm - hr) * np.sqrt(0.5 * (hr - hm) * (hr / hm - hm / hr))\n",
" else:\n",
" vsr = xi2r\n",
" \n",
" xi_min = np.min([-1.0, xi1l, xi1m, xi2r, xi2m])\n",
" xi_max = np.max([1.0, xi1l, xi1m, xi2r, xi2m])\n",
" d_xi = xi_max - xi_min\n",
" h_maximum = np.max([hl, hr, hm])\n",
" h_minimum = np.min([hl, hr, hm])\n",
" d_h = h_maximum - h_minimum\n",
" u_maximum = np.max([ul, ur, um])\n",
" u_minimum = np.min([ul, ur, um])\n",
" d_u = u_maximum - u_minimum\n",
" \n",
" xi = np.array([xi_min - 0.1 * d_xi, vsl, vsl, vsr, vsr, xi_max + 0.1 * d_xi])\n",
" h = np.array([hl, hl, hm, hm, hr, hr])\n",
" u = np.array([ul, ul, um, um, ur, ur])\n",
" \n",
" fig = plt.figure(figsize=(12,8))\n",
" ax1 = fig.add_subplot(221)\n",
" if (hm > np.max([hl, hr])):\n",
" ax1.plot(xi, h, 'b-', label = \"Physical solution\")\n",
" else:\n",
" ax1.plot(xi, h, 'r--', label = \"Unphysical solution\")\n",
" ax1.set_ybound(h_minimum - 0.1 * d_h, h_maximum + 0.1 * d_h)\n",
" ax1.set_xlabel(r\"$\\xi$\")\n",
" ax1.set_ylabel(r\"$h$\")\n",
" ax1.legend()\n",
" ax2 = fig.add_subplot(222)\n",
" if (hm > np.max([hl, hr])):\n",
" ax2.plot(xi, u, 'b-', label = \"Physical solution\")\n",
" else:\n",
" ax2.plot(xi, u, 'r--', label = \"Unphysical solution\")\n",
" ax2.set_ybound(u_minimum - 0.1 * d_u, u_maximum + 0.1 * d_u)\n",
" ax2.set_xlabel(r\"$\\xi$\")\n",
" ax2.set_ylabel(r\"$u$\")\n",
" ax2.legend()\n",
" \n",
" ax3 = fig.add_subplot(223)\n",
" left_end = np.min([-1.0, 1.1*xi1l])\n",
" right_end = np.max([1.0, 1.1*xi2r])\n",
" left_edge = left_end - xi1l\n",
" right_edge = right_end - xi1r\n",
" x1_start_points_l = np.linspace(np.min([left_edge, left_end]), 0.0, 20)\n",
" x1_start_points_r = np.linspace(0.0, np.max([right_edge, right_end]), 20)\n",
" t1_end_points_l = np.ones_like(x1_start_points_l)\n",
" t1_end_points_r = np.ones_like(x1_start_points_r)\n",
" \n",
" # Look for intersections\n",
" t1_end_points_l = np.minimum(t1_end_points_l, x1_start_points_l / (vsl - xi1l))\n",
" x1_end_points_l = x1_start_points_l + xi1l * t1_end_points_l\n",
" t1_end_points_r = np.minimum(t1_end_points_r, x1_start_points_r / (vsr - xi1r))\n",
" x1_end_points_r = x1_start_points_r + xi1r * t1_end_points_r\n",
" # Note: here we are cheating, and using the characteristic speed of the middle state, \n",
" # ignoring how it varies across the rarefaction\n",
" t1_final_points_r = np.ones_like(x1_start_points_r)\n",
" t1_final_points_r = np.minimum(t1_final_points_r, \n",
" (x1_end_points_r - t1_end_points_r * xi1m) / (vsl - xi1m))\n",
" x1_final_points_r = x1_end_points_r + (t1_final_points_r - t1_end_points_r) * xi1m\n",
" \n",
" for xs, xe, te in zip(x1_start_points_l, x1_end_points_l, t1_end_points_l):\n",
" ax3.plot([xs, xe], [0.0, te], 'b-')\n",
" for xs, xe, te in zip(x1_start_points_r, x1_end_points_r, t1_end_points_r):\n",
" ax3.plot([xs, xe], [0.0, te], 'g-')\n",
" for xs, xe, ts, te in zip(x1_end_points_r, x1_final_points_r, t1_end_points_r, \n",
" t1_final_points_r):\n",
" ax3.plot([xs, xe], [ts, te], 'g-')\n",
" \n",
" # Highlight the shocks\n",
" ax3.plot([0.0, vsl], [0.0, 1.0], 'r-', linewidth=2)\n",
" ax3.plot([0.0, vsr], [0.0, 1.0], 'r-', linewidth=2)\n",
" \n",
" # Unphysical shock\n",
" if not physical_solution:\n",
" x_fill = []\n",
" if xi1l < xi1m:\n",
" x_fill = [xi1l, 0.0, xi1m]\n",
" elif xi1l < vsl:\n",
" x_fill = [xi1l, 0.0, vsl]\n",
" elif vsl < xi1m:\n",
" x_fill = [vsl, 0.0, xi1m]\n",
" if len(x_fill) > 0:\n",
" t_fill = [1.0, 0.0, 1.0]\n",
" ax3.fill_between(x_fill, t_fill, 1.0, facecolor = 'red', alpha = 0.5)\n",
" \n",
" x_fill = []\n",
" if xi2r > xi2m:\n",
" x_fill = [xi2m, 0.0, xi2r]\n",
" elif xi2m < vsr:\n",
" x_fill = [xi2m, 0.0, vsr]\n",
" elif vsr < xi2r:\n",
" x_fill = [vsr, 0.0, xi2r]\n",
" if len(x_fill) > 0:\n",
" t_fill = [1.0, 0.0, 1.0]\n",
" ax3.fill_between(x_fill, t_fill, 1.0, facecolor = 'red', alpha = 0.5)\n",
" \n",
" ax3.set_xlabel(r\"$x$\")\n",
" ax3.set_ylabel(r\"$t$\")\n",
" ax3.set_title(\"1-characteristics\")\n",
" ax3.set_xbound(left_end, right_end)\n",
" \n",
" ax4 = fig.add_subplot(224)\n",
" left_end = np.min([-1.0, 1.1*xi1l])\n",
" right_end = np.max([1.0, 1.1*xi2r])\n",
" left_edge = left_end - xi2l\n",
" right_edge = right_end - xi2r\n",
" x2_start_points_l = np.linspace(np.min([left_edge, left_end]), 0.0, 20)\n",
" x2_start_points_r = np.linspace(0.0, np.max([right_edge, right_end]), 20)\n",
" x2_end_points_r = x2_start_points_r + xi2r\n",
" t2_end_points_l = np.ones_like(x2_start_points_l)\n",
" t2_end_points_r = np.ones_like(x2_start_points_r)\n",
" \n",
" # Look for intersections\n",
" t2_end_points_r = np.minimum(t2_end_points_r, x2_start_points_r / (vsr - xi2r))\n",
" x2_end_points_r = x2_start_points_r + xi2r * t2_end_points_r\n",
" t2_end_points_l = np.minimum(t2_end_points_l, x2_start_points_l / (vsl - xi2l))\n",
" x2_end_points_l = x2_start_points_l + xi2l * t2_end_points_l\n",
" # Note: here we are cheating, and using the characteristic speed of the middle state, \n",
" # ignoring how it varies across the rarefaction\n",
" t2_final_points_l = np.ones_like(x2_start_points_l)\n",
" t2_final_points_l = np.minimum(t2_final_points_l, \n",
" (x2_end_points_l - t2_end_points_l * xi2m) / (vsr - xi2m))\n",
" x2_final_points_l = x2_end_points_l + (t2_final_points_l - t2_end_points_l) * xi2m\n",
" \n",
" for xs, xe, te in zip(x2_start_points_r, x2_end_points_r, t2_end_points_r):\n",
" ax4.plot([xs, xe], [0.0, te], 'b-')\n",
" for xs, xe, te in zip(x2_start_points_l, x2_end_points_l, t2_end_points_l):\n",
" ax4.plot([xs, xe], [0.0, te], 'g-')\n",
" for xs, xe, ts, te in zip(x2_end_points_l, x2_final_points_l, t2_end_points_l, \n",
" t2_final_points_l):\n",
" ax4.plot([xs, xe], [ts, te], 'g-')\n",
" \n",
" # Highlight the shocks\n",
" ax4.plot([0.0, vsl], [0.0, 1.0], 'r-', linewidth=2)\n",
" ax4.plot([0.0, vsr], [0.0, 1.0], 'r-', linewidth=2)\n",
" \n",
" # Unphysical shock\n",
" if not physical_solution:\n",
" x_fill = []\n",
" if xi1l < xi1m:\n",
" x_fill = [xi1l, 0.0, xi1m]\n",
" elif xi1l < vsl:\n",
" x_fill = [xi1l, 0.0, vsl]\n",
" elif vsl < xi1m:\n",
" x_fill = [vsl, 0.0, xi1m]\n",
" if len(x_fill) > 0:\n",
" t_fill = [1.0, 0.0, 1.0]\n",
" ax4.fill_between(x_fill, t_fill, 1.0, facecolor = 'red', alpha = 0.5)\n",
" \n",
" x_fill = []\n",
" if xi2r > xi2m:\n",
" x_fill = [xi2m, 0.0, xi2r]\n",
" elif xi2m < vsr:\n",
" x_fill = [xi2m, 0.0, vsr]\n",
" elif vsr < xi2r:\n",
" x_fill = [vsr, 0.0, xi2r]\n",
" if len(x_fill) > 0:\n",
" t_fill = [1.0, 0.0, 1.0]\n",
" ax4.fill_between(x_fill, t_fill, 1.0, facecolor = 'red', alpha = 0.5)\n",
" \n",
" ax4.set_xlabel(r\"$x$\")\n",
" ax4.set_ylabel(r\"$t$\")\n",
" ax4.set_title(\"2-characteristics\")\n",
" ax4.set_xbound(left_end, right_end)\n",
" \n",
" fig.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
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},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"interactive(children=(FloatSlider(value=1.0, description='hl', max=10.0, min=0.1), FloatSlider(value=0.2, description='ul', max=1.0, min=-1.0), FloatSlider(value=1.0, description='hr', max=10.0, min=0.1), FloatSlider(value=-0.2, description='ur', max=1.0, min=-1.0), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"interactive(plot_sw_all_shock_solution, \n",
" hl = FloatSlider(min = 0.1, max = 10.0, value = 1.0), \n",
" ul = FloatSlider(min = -1.0, max = 1.0, value = 0.2), \n",
" hr = FloatSlider(min = 0.1, max = 10.0, value = 1.0), \n",
" ur = FloatSlider(min = -1.0, max = 1.0, value = -0.2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Full solution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The all shock solution illustrates how the full solution can be obtained. We know that \n",
"\n",
"1. the central state ${\\bf w}_m$ will be connected to the known states ${\\bf w}_{l, r}$ across waves that are either shocks or rarefactions,\n",
"2. if $h_m > h_{l, r}$ then the wave will be a shock, otherwise it will be a rarefaction, and\n",
"3. given $h_m$ and the known data, we can compute $u_m$ for either a shock or a rarefaction.\n",
"\n",
"So, using the results above, we can find the full solution to the Riemann problem by solving the nonlinear algebraic root-finding problem\n",
"$$\n",
" \\Phi \\left( h_m ; {\\bf w}_l, {\\bf w}_r \\right) = 0,\n",
"$$\n",
"where\n",
"$$\n",
" \\Phi \\left( h_m ; {\\bf w}_l, {\\bf w}_r \\right) = \\Phi_l \\left( h_m ; {\\bf w}_l \\right) - \\Phi_r \\left( h_m ; {\\bf w}_r \\right),\n",
"$$\n",
"and\n",
"$$\n",
"\\begin{align}\n",
" \\Phi_l & = u_m \\left( h_m ; {\\bf w}_l \\right) & \\Phi_r & = u_m \\left( h_m ; {\\bf w}_r \\right) \\\\\n",
" & = \\begin{cases} u_l + 2 \\left( \\sqrt{h_l} - \\sqrt{h_m} \\right) & h_l > h_m \\\\ u_l - \\sqrt{\\tfrac{1}{2} \\left( h_l - h_m \\right) \\left( \\frac{h_l}{h_m} - \\frac{h_m}{h_l} \\right)} & h_l < h_m \\end{cases} & & = \\begin{cases} u_r - 2 \\left( \\sqrt{h_r} - \\sqrt{h_m} \\right) & h_r > h_m \\\\ u_r + \\sqrt{\\tfrac{1}{2} \\left( h_r - h_m \\right) \\left( \\frac{h_r}{h_m} - \\frac{h_m}{h_r} \\right)} & h_r < h_m \\end{cases}.\n",
"\\end{align}\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"def plot_sw_Riemann_curves(hl, ul, hr, ur):\n",
" \"Plot the solution curves for states (hl, ul) and (hr, ur)\"\n",
" \n",
" from scipy.optimize import brentq\n",
" \n",
" def phi(hstar):\n",
" \"Function defining the root\"\n",
" \n",
" if hl < hstar:\n",
" phi_l = ul - np.sqrt(0.5 * (hl - hstar) * (hl / hstar - hstar / hl))\n",
" else:\n",
" phi_l = ul + 2.0 * (np.sqrt(hl) - np.sqrt(hstar))\n",
" if hr < hstar:\n",
" phi_r = ur + np.sqrt(0.5 * (hr - hstar) * (hr / hstar - hstar / hr))\n",
" else:\n",
" phi_r = ur - 2.0 * (np.sqrt(hr) - np.sqrt(hstar))\n",
" \n",
" return phi_l - phi_r\n",
" \n",
" hm = brentq(phi, 0.1 * h_min, 10.0 * h_max)\n",
" if hl < hm:\n",
" um = ul - np.sqrt(0.5 * (hl - hm) * (hl / hm - hm / hl))\n",
" else:\n",
" um = ul + 2.0 * (np.sqrt(hl) - np.sqrt(hm))\n",
" \n",
" h_maximum = np.max([h_max, hl, hr, hm])\n",
" h_minimum = np.min([h_min, hl, hr, hm])\n",
" u_maximum = np.max([u_max, ul, ur, um])\n",
" u_minimum = np.min([u_min, ul, ur, um])\n",
" dh = h_maximum - h_minimum\n",
" du = u_maximum - u_minimum\n",
" \n",
" # Now plot the rarefaction and shock curves as appropriate\n",
" # Here we only plot the physical pieces.\n",
" \n",
" h1_shock = np.linspace(hl, h_max)\n",
" u1_shock = ul - np.sqrt(0.5 * (hl - h1_shock) * (hl / h1_shock - h1_shock / hl))\n",
" h2_shock = np.linspace(hr, h_max)\n",
" u2_shock = ur + np.sqrt(0.5 * (hr - h2_shock) * (hr / h2_shock - h2_shock / hr))\n",
" \n",
" h1_rarefaction = np.linspace(h_min, hl)\n",
" u1_rarefaction = ul + 2.0 * (np.sqrt(hl) - np.sqrt(h1_rarefaction))\n",
" h2_rarefaction = np.linspace(h_min, hr)\n",
" u2_rarefaction = ur - 2.0 * (np.sqrt(hr) - np.sqrt(h2_rarefaction))\n",
" \n",
" fig = plt.figure(figsize=(12,8))\n",
" ax = fig.add_subplot(111)\n",
" ax.plot(hl, ul, 'rx', markersize = 16, markeredgewidth = 3, label = r\"${\\bf w}_l$\")\n",
" ax.plot(hr, ur, 'r+', markersize = 16, markeredgewidth = 3, label = r\"${\\bf w}_r$\")\n",
" ax.plot(hm, um, 'ro', markersize = 16, markeredgewidth = 3, label = r\"${\\bf w}_m$\")\n",
" ax.plot(h1_shock, u1_shock, 'b-', linewidth = 2, \n",
" label=\"1-shock\")\n",
" ax.plot(h1_rarefaction, u1_rarefaction, 'b-.', linewidth = 2, \n",
" label=\"1-rarefaction\")\n",
" ax.plot(h2_shock, u2_shock, 'g-', linewidth = 2, \n",
" label=\"2-shock\")\n",
" ax.plot(h2_rarefaction, u2_rarefaction, 'g-.', linewidth = 2, \n",
" label=\"2-rarefaction\")\n",
" ax.set_xlabel(r\"$h$\")\n",
" ax.set_ylabel(r\"$u$\")\n",
" ax.set_xbound(h_minimum - 0.1 * dh, h_maximum + 0.1 * dh)\n",
" ax.set_ybound(u_minimum - 0.1 * du, u_maximum + 0.1 * du)\n",
" ax.legend()\n",
" \n",
" fig.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1c183cd1cc1043fcb9148ee6e2f78523",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"interactive(children=(FloatSlider(value=1.0, description='hl', max=10.0, min=0.1), FloatSlider(value=0.2, description='ul', max=1.0, min=-1.0), FloatSlider(value=1.0, description='hr', max=10.0, min=0.1), FloatSlider(value=-0.2, description='ur', max=1.0, min=-1.0), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"interactive(plot_sw_Riemann_curves, \n",
" hl = FloatSlider(min = 0.1, max = 10.0, value = 1.0), \n",
" ul = FloatSlider(min = -1.0, max = 1.0, value = 0.2), \n",
" hr = FloatSlider(min = 0.1, max = 10.0, value = 1.0), \n",
" ur = FloatSlider(min = -1.0, max = 1.0, value = -0.2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we can plot the solution in physical space."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"def plot_sw_Riemann_solution(hl, ul, hr, ur):\n",
" \"Plot the Riemann problem solution for states (hl, ul) and (hr, ur)\"\n",
" \n",
" from scipy.optimize import brentq\n",
" \n",
" def phi(hstar):\n",
" \"Function defining the root\"\n",
" \n",
" if hl < hstar:\n",
" phi_l = ul - np.sqrt(0.5 * (hl - hstar) * (hl / hstar - hstar / hl))\n",
" else:\n",
" phi_l = ul + 2.0 * (np.sqrt(hl) - np.sqrt(hstar))\n",
" if hr < hstar:\n",
" phi_r = ur + np.sqrt(0.5 * (hr - hstar) * (hr / hstar - hstar / hr))\n",
" else:\n",
" phi_r = ur - 2.0 * (np.sqrt(hr) - np.sqrt(hstar))\n",
" \n",
" return phi_l - phi_r\n",
" \n",
" left_raref = False\n",
" left_shock = False\n",
" right_raref = False\n",
" right_shock = False\n",
" \n",
" hm = brentq(phi, 0.1 * h_min, 10.0 * h_max)\n",
" if hl < hm:\n",
" um = ul - np.sqrt(0.5 * (hl - hm) * (hl / hm - hm / hl))\n",
" else:\n",
" um = ul + 2.0 * (np.sqrt(hl) - np.sqrt(hm))\n",
" \n",
" h_maximum = np.max([h_max, hl, hr, hm])\n",
" h_minimum = np.min([h_min, hl, hr, hm])\n",
" u_maximum = np.max([u_max, ul, ur, um])\n",
" u_minimum = np.min([u_min, ul, ur, um])\n",
" dh = h_maximum - h_minimum\n",
" du = u_maximum - u_minimum\n",
" \n",
" xi1l = ul - np.sqrt(hl)\n",
" xi1m = um - np.sqrt(hm)\n",
" xi1r = ur - np.sqrt(hr)\n",
" if hm > hl:\n",
" left_shock = True\n",
" vsl = ul - hm / (hm - hl) * np.sqrt(0.5 * (hl - hm) * (hl / hm - hm / hl))\n",
" else:\n",
" left_raref = True\n",
" hl_raref = np.linspace(hl, hm, 20)\n",
" ul_raref = ul + 2.0 * (np.sqrt(hl) - np.sqrt(hl_raref))\n",
" xil_raref = ul_raref - np.sqrt(hl_raref)\n",
" \n",
" xi2r = ur + np.sqrt(hr)\n",
" xi2m = um + np.sqrt(hm)\n",
" xi2l = ul + np.sqrt(hl)\n",
" if hm > hr:\n",
" right_shock = True\n",
" vsr = ur + hm / (hm - hr) * np.sqrt(0.5 * (hr - hm) * (hr / hm - hm / hr))\n",
" else:\n",
" right_raref = True\n",
" hr_raref = np.linspace(hm, hr)\n",
" ur_raref = ur - 2.0 * (np.sqrt(hr) - np.sqrt(hr_raref))\n",
" xir_raref = ur_raref + np.sqrt(hr_raref)\n",
" \n",
" xi_min = np.min([-1.0, xi1l, xi1m, xi2r, xi2m])\n",
" xi_max = np.max([1.0, xi1l, xi1m, xi2r, xi2m])\n",
" d_xi = xi_max - xi_min\n",
" h_maximum = np.max([hl, hr, hm])\n",
" h_minimum = np.min([hl, hr, hm])\n",
" d_h = h_maximum - h_minimum\n",
" u_maximum = np.max([ul, ur, um])\n",
" u_minimum = np.min([ul, ur, um])\n",
" d_u = u_maximum - u_minimum\n",
" \n",
" xi = np.array([xi_min - 0.1 * d_xi])\n",
" h = np.array([hl])\n",
" u = np.array([ul])\n",
" if left_shock:\n",
" xi = np.append(xi, [vsl, vsl])\n",
" h = np.append(h, [hl, hm])\n",
" u = np.append(u, [ul, um])\n",
" else:\n",
" xi = np.append(xi, xil_raref)\n",
" h = np.append(h, hl_raref)\n",
" u = np.append(u, ul_raref)\n",
" if right_shock:\n",
" xi = np.append(xi, [vsr, vsr])\n",
" h = np.append(h, [hm, hr])\n",
" u = np.append(u, [um, ur])\n",
" else:\n",
" xi = np.append(xi, xir_raref)\n",
" h = np.append(h, hr_raref)\n",
" u = np.append(u, ur_raref)\n",
" xi = np.append(xi, [xi_max + 0.1 * d_xi])\n",
" h = np.append(h, [hr])\n",
" u = np.append(u, [ur])\n",
" \n",
" fig = plt.figure(figsize=(12,8))\n",
" ax1 = fig.add_subplot(221)\n",
" ax1.plot(xi, h, 'b-', label = \"True solution\")\n",
" ax1.set_ybound(h_minimum - 0.1 * d_h, h_maximum + 0.1 * d_h)\n",
" ax1.set_xlabel(r\"$\\xi$\")\n",
" ax1.set_ylabel(r\"$h$\")\n",
" ax1.legend()\n",
" ax2 = fig.add_subplot(222)\n",
" ax2.plot(xi, u, 'b-', label = \"True solution\")\n",
" ax2.set_ybound(u_minimum - 0.1 * d_u, u_maximum + 0.1 * d_u)\n",
" ax2.set_xlabel(r\"$\\xi$\")\n",
" ax2.set_ylabel(r\"$u$\")\n",
" ax2.legend()\n",
" \n",
" ax3 = fig.add_subplot(223)\n",
" left_end = np.min([-1.0, 1.1*xi1l])\n",
" right_end = np.max([1.0, 1.1*xi2r])\n",
" left_edge = left_end - xi1l\n",
" right_edge = right_end - xi1r\n",
" x1_start_points_l = np.linspace(np.min([left_edge, left_end]), 0.0, 20)\n",
" x1_start_points_r = np.linspace(0.0, np.max([right_edge, right_end]), 20)\n",
" t1_end_points_l = np.ones_like(x1_start_points_l)\n",
" t1_end_points_r = np.ones_like(x1_start_points_r)\n",
" \n",
" # Look for intersections\n",
" if left_shock:\n",
" t1_end_points_l = np.minimum(t1_end_points_l, x1_start_points_l / (vsl - xi1l))\n",
" x1_end_points_l = x1_start_points_l + xi1l * t1_end_points_l\n",
" if right_shock:\n",
" t1_end_points_r = np.minimum(t1_end_points_r, x1_start_points_r / (vsr - xi1r))\n",
" else:\n",
" t1_end_points_r = np.minimum(t1_end_points_r, x1_start_points_r / (xi2r - xi1r))\n",
" x1_end_points_r = x1_start_points_r + xi1r * t1_end_points_r\n",
" # Note: here we are cheating, and using the characteristic speed of the middle state, \n",
" # ignoring how it varies across the rarefaction\n",
" t1_final_points_r = np.ones_like(x1_start_points_r)\n",
" if left_shock:\n",
" t1_final_points_r = np.minimum(t1_final_points_r, \n",
" (x1_end_points_r - t1_end_points_r * xi1m) / \n",
" (vsl - xi1m))\n",
" x1_final_points_r = x1_end_points_r + (t1_final_points_r - t1_end_points_r) * xi1m\n",
" \n",
" for xs, xe, te in zip(x1_start_points_l, x1_end_points_l, t1_end_points_l):\n",
" ax3.plot([xs, xe], [0.0, te], 'b-')\n",
" for xs, xe, te in zip(x1_start_points_r, x1_end_points_r, t1_end_points_r):\n",
" ax3.plot([xs, xe], [0.0, te], 'g-')\n",
" for xs, xe, ts, te in zip(x1_end_points_r, x1_final_points_r, t1_end_points_r, \n",
" t1_final_points_r):\n",
" ax3.plot([xs, xe], [ts, te], 'g-')\n",
" \n",
" # Highlight the waves\n",
" if left_shock:\n",
" ax3.plot([0.0, vsl], [0.0, 1.0], 'r-', linewidth=2)\n",
" else:\n",
" ax3.plot([0.0, xi1l], [0.0, 1.0], 'r-', linewidth=2)\n",
" ax3.plot([0.0, xi1m], [0.0, 1.0], 'r-', linewidth=2)\n",
" xi = np.linspace(xi1l, xi1m, 11)\n",
" x_end_rarefaction = xi\n",
" for xe in x_end_rarefaction:\n",
" ax3.plot([0.0, xe], [0.0, 1.0], 'r--')\n",
" if right_shock:\n",
" ax3.plot([0.0, vsr], [0.0, 1.0], 'r-', linewidth=2)\n",
" else:\n",
" ax3.plot([0.0, xi2m], [0.0, 1.0], 'r-', linewidth=2)\n",
" ax3.plot([0.0, xi2r], [0.0, 1.0], 'r-', linewidth=2)\n",
" \n",
" ax3.set_xlabel(r\"$x$\")\n",
" ax3.set_ylabel(r\"$t$\")\n",
" ax3.set_title(\"1-characteristics\")\n",
" ax3.set_xbound(left_end, right_end)\n",
" \n",
" ax4 = fig.add_subplot(224)\n",
" left_end = np.min([-1.0, 1.1*xi1l])\n",
" right_end = np.max([1.0, 1.1*xi2r])\n",
" left_edge = left_end - xi2l\n",
" right_edge = right_end - xi2r\n",
" x2_start_points_l = np.linspace(np.min([left_edge, left_end]), 0.0, 20)\n",
" x2_start_points_r = np.linspace(0.0, np.max([right_edge, right_end]), 20)\n",
" x2_end_points_r = x2_start_points_r + xi2r\n",
" t2_end_points_l = np.ones_like(x2_start_points_l)\n",
" t2_end_points_r = np.ones_like(x2_start_points_r)\n",
" \n",
" # Look for intersections\n",
" if right_shock:\n",
" t2_end_points_r = np.minimum(t2_end_points_r, x2_start_points_r / (vsr - xi2r))\n",
" x2_end_points_r = x2_start_points_r + xi2r * t2_end_points_r\n",
" if left_shock:\n",
" t2_end_points_l = np.minimum(t2_end_points_l, x2_start_points_l / (vsl - xi2l))\n",
" else:\n",
" t2_end_points_l = np.minimum(t2_end_points_l, x2_start_points_l / (xi1l - xi2l))\n",
" x2_end_points_l = x2_start_points_l + xi2l * t2_end_points_l\n",
" # Note: here we are cheating, and using the characteristic speed of the middle state, \n",
" # ignoring how it varies across the rarefaction\n",
" t2_final_points_l = np.ones_like(x2_start_points_l)\n",
" if right_shock:\n",
" t2_final_points_l = np.minimum(t2_final_points_l, \n",
" (x2_end_points_l - t2_end_points_l * xi2m) / \n",
" (vsr - xi2m))\n",
" x2_final_points_l = x2_end_points_l + (t2_final_points_l - t2_end_points_l) * xi2m\n",
" \n",
" for xs, xe, te in zip(x2_start_points_r, x2_end_points_r, t2_end_points_r):\n",
" ax4.plot([xs, xe], [0.0, te], 'b-')\n",
" for xs, xe, te in zip(x2_start_points_l, x2_end_points_l, t2_end_points_l):\n",
" ax4.plot([xs, xe], [0.0, te], 'g-')\n",
" for xs, xe, ts, te in zip(x2_end_points_l, x2_final_points_l, t2_end_points_l, \n",
" t2_final_points_l):\n",
" ax4.plot([xs, xe], [ts, te], 'g-')\n",
" \n",
" # Highlight the waves\n",
" if left_shock:\n",
" ax4.plot([0.0, vsl], [0.0, 1.0], 'r-', linewidth=2)\n",
" else:\n",
" ax4.plot([0.0, xi1l], [0.0, 1.0], 'r-', linewidth=2)\n",
" ax4.plot([0.0, xi1m], [0.0, 1.0], 'r-', linewidth=2)\n",
" if right_shock:\n",
" ax4.plot([0.0, vsr], [0.0, 1.0], 'r-', linewidth=2)\n",
" else:\n",
" ax4.plot([0.0, xi2m], [0.0, 1.0], 'r-', linewidth=2)\n",
" ax4.plot([0.0, xi2r], [0.0, 1.0], 'r-', linewidth=2)\n",
" xi = np.linspace(xi2m, xi2r, 11)\n",
" x_end_rarefaction = xi\n",
" for xe in x_end_rarefaction:\n",
" ax4.plot([0.0, xe], [0.0, 1.0], 'r--')\n",
" \n",
" ax4.set_xlabel(r\"$x$\")\n",
" ax4.set_ylabel(r\"$t$\")\n",
" ax4.set_title(\"2-characteristics\")\n",
" ax4.set_xbound(left_end, right_end)\n",
" \n",
" fig.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
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"model_id": "29ed701b74d14c70a6acb95f4dd2e7a3",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
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"text/plain": [
"interactive(children=(FloatSlider(value=1.0, description='hl', max=10.0, min=0.1), FloatSlider(value=0.2, description='ul', max=1.0, min=-1.0), FloatSlider(value=1.0, description='hr', max=10.0, min=0.1), FloatSlider(value=-0.2, description='ur', max=1.0, min=-1.0), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"interactive(plot_sw_Riemann_solution, \n",
" hl = FloatSlider(min = 0.1, max = 10.0, value = 1.0), \n",
" ul = FloatSlider(min = -1.0, max = 1.0, value = 0.2), \n",
" hr = FloatSlider(min = 0.1, max = 10.0, value = 1.0), \n",
" ur = FloatSlider(min = -1.0, max = 1.0, value = -0.2))"
]
}
],
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| mit |
freininghaus/adventofcode | 2020/day04-haskell.ipynb | 1 | 9539 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Day 4: Passport Processing\n",
"https://adventofcode.com/2020/day/4"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"inputLines = lines <$> readFile \"input/day04.txt\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"testInput = [ \"ecl:gry pid:860033327 eyr:2020 hcl:#fffffd\"\n",
" , \"byr:1937 iyr:2017 cid:147 hgt:183cm\"\n",
" , \"\"\n",
" , \"iyr:2013 ecl:amb cid:350 eyr:2023 pid:028048884\"\n",
" , \"hcl:#cfa07d byr:1929\"\n",
" , \"\"\n",
" , \"hcl:#ae17e1 iyr:2013\"\n",
" , \"eyr:2024\"\n",
" , \"ecl:brn pid:760753108 byr:1931\"\n",
" , \"hgt:179cm\"\n",
" , \"\"\n",
" , \"hcl:#cfa07d eyr:2025 pid:166559648\"\n",
" , \"iyr:2011 ecl:brn hgt:59in\"]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import Data.List.Split (splitOn) -- install with 'stack install split'\n",
"import Data.List (intercalate)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Split input on blank lines to get the individual passports:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"passports = splitOn \"\\n\\n\" . intercalate \"\\n\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[\"ecl:gry pid:860033327 eyr:2020 hcl:#fffffd\\nbyr:1937 iyr:2017 cid:147 hgt:183cm\",\"iyr:2013 ecl:amb cid:350 eyr:2023 pid:028048884\\nhcl:#cfa07d byr:1929\",\"hcl:#ae17e1 iyr:2013\\neyr:2024\\necl:brn pid:760753108 byr:1931\\nhgt:179cm\",\"hcl:#cfa07d eyr:2025 pid:166559648\\niyr:2011 ecl:brn hgt:59in\"]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"passports testInput"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Represent a passport as a map:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import qualified Data.Map as Map"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"parsePassport :: String -> Map.Map String String\n",
"parsePassport = Map.fromList\n",
" . map ((\\ [k, v] -> (k, v)) . splitOn \":\")\n",
" . words"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 1\n",
"All fields except `cid` are required:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"required = [\"byr\", \"iyr\", \"eyr\", \"hgt\", \"hcl\", \"ecl\", \"pid\"]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"checkPassport passport = all ((/= Nothing). (`Map.lookup` passport)) required"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"countValidPassports1 = length . filter id . map (checkPassport . parsePassport)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Verify given result for test input:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"countValidPassports1 . passports $ testInput"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solution, part 1"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"228"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"countValidPassports1 . passports <$> inputLines"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 2\n",
"For all fields that contain years, we have to compare the value with the minimal and maximal accepted year."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"intValidator :: Int -> Int -> String -> Bool\n",
"intValidator minValue maxValue value = minValue <= number && number <= maxValue\n",
" where\n",
" number = read value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hair color, eye color, and passport ID are best validated with regular expressions."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import Text.Regex.PCRE -- install with 'stack install regex-pcre'"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"regexValidator :: String -> String -> Bool\n",
"regexValidator pattern value = (value =~ pattern)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To validate the height, we parse the value and the unit wit a regular expression. If this is successful, we compare the height with the min and max value for the respective unit."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"heightValidator :: String -> Bool\n",
"heightValidator value = \n",
" case heightAndUnit of \n",
" Nothing -> False\n",
" Just (height, unit) -> minHeight unit <= height && height <= maxHeight unit\n",
" where\n",
" heightAndUnit = do\n",
" [[_, heightValue, unit]] <- value =~~ \"^([0-9]+)(cm|in)$\"\n",
" return (read heightValue, unit) \n",
"\n",
" minHeight \"cm\" = 150\n",
" minHeight \"in\" = 59\n",
"\n",
" maxHeight \"cm\" = 193\n",
" maxHeight \"in\" = 76"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assign the correct validator to each passport field."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"validators :: [(String, String -> Bool)]\n",
"validators = [\n",
" (\"byr\", intValidator 1920 2002),\n",
" (\"iyr\", intValidator 2010 2020),\n",
" (\"eyr\", intValidator 2020 2030),\n",
" (\"hgt\", heightValidator),\n",
" (\"hcl\", regexValidator \"^#[0-9a-f]{6}$\"),\n",
" (\"ecl\", regexValidator \"^(amb|blu|brn|gry|grn|hzl|oth)$\"),\n",
" (\"pid\", regexValidator \"^[0-9]{9}$\")]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A passport is valid if all fields are valid."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"validatePassport passport = all \n",
" ((== Just True) . \n",
" (\\ (field, validator) -> validator <$> field `Map.lookup` passport)) validators"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"countValidPassports2 = length . filter id . map (validatePassport . parsePassport)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solution, part 2"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"175"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"countValidPassports2 . passports <$> inputLines"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Appendix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`validatePassport` can be written without a lambda."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"validatePassport' passport = all \n",
" ((== Just True) . \n",
" uncurry (flip (<$>) . (`Map.lookup` passport))) validators"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"countValidPassports2' = length . filter id . map (validatePassport . parsePassport)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"175"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"countValidPassports2' . passports <$> inputLines"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Haskell",
"language": "haskell",
"name": "haskell"
},
"language_info": {
"codemirror_mode": "ihaskell",
"file_extension": ".hs",
"mimetype": "text/x-haskell",
"name": "haskell",
"pygments_lexer": "Haskell",
"version": "8.8.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
chengsoonong/crowdastro | notebooks/13_source_catalogue.ipynb | 1 | 195435 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Source Catalogue\n",
"\n",
"Can we use astronomical source catalogues to search for potential hosts, rather than trying to find potential hosts ourselves? This has some advantages:\n",
"\n",
"- We can ignore the potential hosts problem entirely - someone else has already solved it for us!\n",
"- We would be in line with other astrophysics research.\n",
"- Astronomers who make catalogues are probably better than me at finding potential hosts.\n",
"- We can probably get astronomical features associated with each potential host, which may be useful for the classification task.\n",
"\n",
"I think we could use the AllWISE Source Catalog. It seems to cover all of the regions we care about. We need to worry about querying the server too much. In future, we might just be able to download the whole catalogue for the regions we care about, but for now I'll just make sure that all queries to the server are issued manually. This will ensure that I won't hammer the server with requests."
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import collections\n",
"import io\n",
"from pprint import pprint\n",
"import sqlite3\n",
"import sys\n",
"import warnings\n",
"\n",
"import astropy.io.votable\n",
"import astropy.wcs\n",
"import matplotlib.pyplot\n",
"import numpy\n",
"import requests\n",
"import requests_cache\n",
"\n",
"%matplotlib inline\n",
"\n",
"sys.path.insert(1, '..')\n",
"import crowdastro.data\n",
"import crowdastro.labels\n",
"import crowdastro.rgz_analysis.consensus\n",
"import crowdastro.show\n",
"\n",
"warnings.simplefilter('ignore', UserWarning) # astropy always raises warnings on Windows.\n",
"\n",
"requests_cache.install_cache(cache_name='gator_cache', backend='sqlite', expire_after=None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Querying for potential hosts\n",
"\n",
"I'll grab a subject and try and find potential hosts using the AllWISE Source Catalog."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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1DsNSVlUQ/V9jeftYt43tVSl1CbjT6cydo8uGumJjjZn9WPqvoGlDI2UcOh7K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QiCJZqnB7I0voAEey5tFFIfnvEQw+WUXulM3v+UF6LJdTqEAVDr6Ti0qcsNkIHHm/64ExfZIEPn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\nQWNapmVaLlT+P1hp3OnBUhIZAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1da367d1940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"subject = crowdastro.data.db.radio_subjects.find_one({'metadata.survey': 'atlas', 'state': 'complete',\n",
" 'zooniverse_id': 'ARG0003r18'})\n",
"crowdastro.show.subject(subject)\n",
"matplotlib.pyplot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first question is: Which patch of sky do we query? I know that the image patch is $2 \\times 2\\ \\mbox{arcmin}$, but I also need to know what point it's centred on. Maybe we can get this from the subject itself, or the corresponding FITS file."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[53.538672, -28.405543]\n"
]
}
],
"source": [
"pprint(subject['coords'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That looks right to me. I think this is RA and DEC, but I don't think I need to care as long as they're in the right order — the query server is pretty flexible with formats.\n",
"\n",
"The format of a query is:\n",
"http://irsa.ipac.caltech.edu/cgi-bin/Gator/nph-query?[keyword1=value1]&[keyword2=value2]&...[keywordn=valuen]\n",
"This should be pretty easy with the `requests` module.\n",
"\n",
"A query requires\n",
"\n",
"- `catalog` — Julie said to use different catalogues for different subjects. For CDFS, we need `chandra_cat_f05`; for ELAIS S1, we need `elaiss1_cat_f05`. More catalogues are available [here](http://irsa.ipac.caltech.edu/applications/Gator/GatorAid/irsa/catlist.html).\n",
"- `spatial` — the type of spatial query, in our case `box`.\n",
"- `objstr` — centre coordinate.\n",
"- `size` — since we're using box. This is the width of the box in arcseconds (so 120 arcseconds).\n",
"- `outfmt` — the format of the output, for which I will use XML VOTable (`3`) which can be opened with `astropy.io.votable`."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"query = {\n",
" 'catalog': 'chandra_cat_f05',\n",
" 'spatial': 'box',\n",
" 'objstr': '{} {}'.format(*subject['coords']),\n",
" 'size': '120',\n",
" 'outfmt': '3',\n",
"}\n",
"url = 'http://irsa.ipac.caltech.edu/cgi-bin/Gator/nph-query'\n",
"\n",
"r = requests.get(url, params=query)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: W22: None:5:0: W22: The DEFINITIONS element is deprecated in VOTable 1.1. Ignoring [astropy.io.votable.tree]\n",
"WARNING:astropy:W22: None:5:0: W22: The DEFINITIONS element is deprecated in VOTable 1.1. Ignoring\n",
"WARNING: W27: None:6:0: W27: COOSYS deprecated in VOTable 1.2 [astropy.io.votable.tree]\n",
"WARNING:astropy:W27: None:6:0: W27: COOSYS deprecated in VOTable 1.2\n",
"WARNING: W06: None:21:0: W06: Invalid UCD 'ID_MAIN': Unknown word 'ID_MAIN' [astropy.io.votable.tree]\n",
"WARNING:astropy:W06: None:21:0: W06: Invalid UCD 'ID_MAIN': Unknown word 'ID_MAIN'\n",
"WARNING: W06: None:23:0: W06: Invalid UCD 'POS_EQ_RA_MAIN': Unknown word 'POS_EQ_RA_MAIN' [astropy.io.votable.tree]\n",
"WARNING:astropy:W06: None:23:0: W06: Invalid UCD 'POS_EQ_RA_MAIN': Unknown word 'POS_EQ_RA_MAIN'\n",
"WARNING: W06: None:24:0: W06: Invalid UCD 'POS_EQ_DEC_MAIN': Unknown word 'POS_EQ_DEC_MAIN' [astropy.io.votable.tree]\n",
"WARNING:astropy:W06: None:24:0: W06: Invalid UCD 'POS_EQ_DEC_MAIN': Unknown word 'POS_EQ_DEC_MAIN'\n",
"WARNING: W50: None:30:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:30:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:31:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:31:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:32:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:32:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:33:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:33:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:37:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:37:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:38:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:38:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:39:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:39:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:40:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:40:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:44:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:44:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:45:0: W50: Invalid unit string 'ujy' (suppressing further warnings of this type...) [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:45:0: W50: Invalid unit string 'ujy' (suppressing further warnings of this type...)\n"
]
}
],
"source": [
"votable = astropy.io.votable.parse_single_table(io.BytesIO(r.content), pedantic=False)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Pull out the RAs and DECs\n",
"ras = votable.array['ra']\n",
"decs = votable.array['dec']\n",
"\n",
"# We need to convert to pixels. We can do this with astropy.wcs.\n",
"fits = crowdastro.data.get_ir_fits(subject)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wcs = astropy.wcs.WCS(fits.header)\n",
"xs, ys = wcs.all_world2pix(ras, decs, 0)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Let's also find the consensus hosts to compare.\n",
"consensus_xs = []\n",
"consensus_ys = []\n",
"consensus = crowdastro.rgz_analysis.consensus.consensus(subject['zooniverse_id'])\n",
"for answer in consensus['answer'].values():\n",
" consensus_xs.append(answer['ir_peak'][0] * 201 / 500 )\n",
" consensus_ys.append(201 - answer['ir_peak'][1] * 201 / 500)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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xAoZprZWTwkx2wsAsxz8+jz64mYPHgaBBpeS5FFJn9yGpthmMdwI6Jdxy5BUk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\nUxddQ6ElQtPCWpCct00fnBP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l2QkGT1JfCiP7E90uX+Px9JLYqlWrtHr1ar3w1lv1mU2b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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1da377514a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot them!\n",
"crowdastro.show.ir(subject)\n",
"matplotlib.pyplot.scatter(xs, ys, c='r', marker='+')\n",
"matplotlib.pyplot.scatter(consensus_xs, consensus_ys, c='cyan', marker='x')\n",
"matplotlib.pyplot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"This seems pretty good! We can even get the fluxes for these and have a look at them."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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B2NjYoE9Hi8wQq90MsZKkUlx00cWcdtp6li+f4IEHptm0aSOnnPKGQZ+WFpEh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\nxEqSJKl3ngFQ7EbtEQVzAwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1da3e76ba58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"matplotlib.pyplot.figure(figsize=(10, 10))\n",
"matplotlib.rcParams.update({'font.size': 22})\n",
"xs = votable.array['flux_ap2_36']\n",
"ys = votable.array['flux_ap2_58']\n",
"matplotlib.pyplot.scatter(xs, ys)\n",
"matplotlib.pyplot.xlabel('$S_{3.6 \\mathrm{μm}}$')\n",
"matplotlib.pyplot.ylabel('$S_{5.8 \\mathrm{μm}}$')\n",
"matplotlib.pyplot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, I want to apply this over the whole ATLAS dataset. I've frozen the ATLAS consensuses in a database, but we'll also need the relevant catalogues - I can't figure out how to download the whole catalogues, so I'll only run this over a hundred or so subjects so I don't hammer the server too hard.\n",
"\n",
"I'll try and see what the fluxes are for objects people click on."
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fluxes = []\n",
"all_fluxes = []\n",
"\n",
"conn = sqlite3.connect('../crowdastro-data/processed.db')\n",
"\n",
"def append_fluxes(subject):\n",
" if subject['metadata']['source'].startswith('C'):\n",
" # CDFS\n",
" catalog = 'chandra_cat_f05'\n",
" else:\n",
" # ELAIS-S1\n",
" catalog = 'elaiss1_cat_f05'\n",
" \n",
" query = {\n",
" 'catalog': catalog,\n",
" 'spatial': 'box',\n",
" 'objstr': '{} {}'.format(*subject['coords']),\n",
" 'size': '120',\n",
" 'outfmt': '3',\n",
" }\n",
" url = 'http://irsa.ipac.caltech.edu/cgi-bin/Gator/nph-query'\n",
"\n",
" r = requests.get(url, params=query)\n",
" votable = astropy.io.votable.parse_single_table(io.BytesIO(r.content), pedantic=False)\n",
" \n",
" ras = votable.array['ra']\n",
" decs = votable.array['dec']\n",
"\n",
" # Convert to px.\n",
" fits = crowdastro.data.get_ir_fits(subject)\n",
" wcs = astropy.wcs.WCS(fits.header)\n",
" xs, ys = wcs.all_world2pix(ras, decs, 0)\n",
" \n",
" consensus_xs = []\n",
" consensus_ys = []\n",
" consensus = crowdastro.labels.get_subject_consensus(subject, conn, 'classifications')\n",
" for x, y in consensus.values():\n",
" consensus_xs.append(x)\n",
" consensus_ys.append(y)\n",
" \n",
" for cx, cy in zip(consensus_xs, consensus_ys):\n",
" if cx is None or cy is None:\n",
" continue\n",
"\n",
" closest = None\n",
" min_distance = float('inf')\n",
" \n",
" for i, x, y in zip(range(len(xs)), xs, ys):\n",
" dist = numpy.hypot(x - cx, y - cy)\n",
" if dist < min_distance:\n",
" closest = (x, y)\n",
" min_distance = dist\n",
" \n",
" flux_36 = votable.array['flux_ap2_36'][i]\n",
" flux_58 = votable.array['flux_ap2_58'][i]\n",
" fluxes.append((flux_36, flux_58))\n",
" \n",
" for flux_36, flux_58 in zip(votable.array['flux_ap2_36'], votable.array['flux_ap2_58']):\n",
" all_fluxes.append((flux_36, flux_58))"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: W22: None:5:0: W22: The DEFINITIONS element is deprecated in VOTable 1.1. Ignoring [astropy.io.votable.tree]\n",
"WARNING:astropy:W22: None:5:0: W22: The DEFINITIONS element is deprecated in VOTable 1.1. Ignoring\n",
"WARNING: W27: None:6:0: W27: COOSYS deprecated in VOTable 1.2 [astropy.io.votable.tree]\n",
"WARNING:astropy:W27: None:6:0: W27: COOSYS deprecated in VOTable 1.2\n",
"WARNING: W06: None:21:0: W06: Invalid UCD 'ID_MAIN': Unknown word 'ID_MAIN' [astropy.io.votable.tree]\n",
"WARNING:astropy:W06: None:21:0: W06: Invalid UCD 'ID_MAIN': Unknown word 'ID_MAIN'\n",
"WARNING: W06: None:23:0: W06: Invalid UCD 'POS_EQ_RA_MAIN': Unknown word 'POS_EQ_RA_MAIN' [astropy.io.votable.tree]\n",
"WARNING:astropy:W06: None:23:0: W06: Invalid UCD 'POS_EQ_RA_MAIN': Unknown word 'POS_EQ_RA_MAIN'\n",
"WARNING: W06: None:24:0: W06: Invalid UCD 'POS_EQ_DEC_MAIN': Unknown word 'POS_EQ_DEC_MAIN' [astropy.io.votable.tree]\n",
"WARNING:astropy:W06: None:24:0: W06: Invalid UCD 'POS_EQ_DEC_MAIN': Unknown word 'POS_EQ_DEC_MAIN'\n",
"WARNING: W50: None:30:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:30:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:31:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:31:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:32:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:32:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:33:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:33:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:37:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:37:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:38:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:38:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:39:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:39:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:40:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:40:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:44:0: W50: Invalid unit string 'ujy' [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:44:0: W50: Invalid unit string 'ujy'\n",
"WARNING: W50: None:45:0: W50: Invalid unit string 'ujy' (suppressing further warnings of this type...) [astropy.io.votable.tree]\n",
"WARNING:astropy:W50: None:45:0: W50: Invalid unit string 'ujy' (suppressing further warnings of this type...)\n"
]
}
],
"source": [
"for subject in crowdastro.data.get_all_subjects(atlas=True).limit(100):\n",
" append_fluxes(subject)"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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SSYfkqpM2VYAf7tixGFx/PbzzDuzfH2z/u7+D6dNh/vygxo5y+lIACvxSeH6x8OSAHW7t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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1da40073d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"all_fluxes = numpy.array(all_fluxes)\n",
"fluxes = numpy.array(fluxes)\n",
"\n",
"matplotlib.pyplot.loglog(all_fluxes[:, 0], all_fluxes[:, 1], c='r', marker='+', linestyle='None')\n",
"matplotlib.pyplot.loglog(fluxes[:, 0], fluxes[:, 1], c='g', marker='*', linestyle='None')\n",
"matplotlib.pyplot.show()"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 104.39, 56.32],\n",
" [ 104.39, 56.32],\n",
" [ 46.27, nan],\n",
" [ 46.27, nan],\n",
" [ 19.77, nan],\n",
" [ 19.77, nan],\n",
" [ 76.16, nan],\n",
" [ 20.11, nan],\n",
" [ 34.79, nan],\n",
" [ 34.79, nan],\n",
" [ 159.98, 138.37],\n",
" [ 159.98, 138.37],\n",
" [ 29.76, nan],\n",
" [ 62.07, nan],\n",
" [ 10.92, nan],\n",
" [ 10.92, nan],\n",
" [ 10.92, nan],\n",
" [ 10.92, nan],\n",
" [ 23.8 , nan],\n",
" [ 17.36, nan],\n",
" [ 22.08, nan],\n",
" [ 22.08, nan],\n",
" [ 124.19, 50.43],\n",
" [ 124.19, 50.43],\n",
" [ 87.5 , -99. ],\n",
" [ 87.5 , -99. ],\n",
" [ 17.71, nan],\n",
" [ 17.71, nan],\n",
" [ 17.71, nan],\n",
" [ 17.71, nan],\n",
" [ 14.28, nan],\n",
" [ 41.18, nan],\n",
" [ 11.9 , nan],\n",
" [ 26.54, -99. ],\n",
" [ 26.54, -99. ],\n",
" [ 44.91, nan],\n",
" [ 66.69, -99. ],\n",
" [ 66.69, -99. ],\n",
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" [ 24.14, nan],\n",
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" [ 11.25, 25.98],\n",
" [ 11.25, 25.98],\n",
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" [ 22.84, nan],\n",
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" [ 45.59, nan],\n",
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" [ 142.03, 79.15],\n",
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]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fluxes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are a lot of NaNs, which is a bit concerning. Either way, the source catalogue seems to work."
]
},
{
"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",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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| mit |
unnati-xyz/ensemble-package | .ipynb_checkpoints/Base Models-checkpoint.ipynb | 1 | 25418 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using Theano backend.\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import datasets, linear_model, preprocessing\n",
"from sklearn.preprocessing import Imputer, PolynomialFeatures\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.cross_validation import KFold\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.cross_validation import StratifiedKFold, KFold\n",
"from keras.layers import Dense, Activation, LSTM\n",
"from keras.models import Sequential\n",
"from keras.regularizers import l2, activity_l2\n",
"from sklearn.ensemble import GradientBoostingClassifier\n",
"import xgboost as xgb\n",
"from sklearn.metrics import roc_auc_score\n",
"from sklearn.cross_validation import train_test_split\n",
"from joblib import Parallel, delayed"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Reading the data, into a Data Frame.\n",
"Data = pd.read_csv('/home/prajwal/Desktop/bank-additional/bank-additional-full.csv',delimiter=';',header=0)\n",
"\n",
"#Encoding the data, encoding the string values into numerical values.\n",
"encode = preprocessing.LabelEncoder()\n",
"\n",
"#Selcting the columns of string data type\n",
"names = Data.select_dtypes(include=['object'])\n",
"\n",
"#Function that encodes the string values to numerical values.\n",
"def enc(data,column):\n",
" data[column] = encode.fit_transform(data[column])\n",
" return data\n",
"for column in names:\n",
" Data = enc(Data,column)\n",
" \n",
"#Splitting the data into training and testing datasets\n",
"Data, test = train_test_split(Data, test_size = 0.05,stratify=Data['y'])\n",
"\n",
"#Initializing two data frames that will be used as training data for the stacked model.\n",
"stack_X = pd.DataFrame() #The data frame will contain the predictions of the base models.\n",
"stack_Y = pd.DataFrame() #The data frame will contain the calss labels of the base models.\n",
"\n",
"#Initializing two data frames that will be used as training data for the blending model.\n",
"blend_X = pd.DataFrame() #The data frames will contain the predictions and raw features of the base models.\n",
"raw_features_X = pd.DataFrame() #The data frames will contain the raw features of the data, which will be concatenated with the predictions."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#This function is used to convert the predictions of the base models into a DataFrame.\n",
"def build_data_frame(data):\n",
" data_frame = pd.DataFrame(data).T\n",
" return data_frame"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Defining the parameters for the XGBoost (Gradient Boosting) Algorithm.\n",
"def param_set():\n",
" #Gradient Boosting (XGBoost)\n",
" param = {}\n",
" #Setting Parameters for the Booster\n",
" param['booster'] = 'gbtree'\n",
" param['objective'] = 'binary:logistic'\n",
" param[\"eval_metric\"] = \"auc\"\n",
" param['eta'] = 0.3\n",
" param['gamma'] = 0\n",
" param['max_depth'] = 6\n",
" param['min_child_weight'] = 1\n",
" param['max_delta_step'] = 0\n",
" param['subsample'] = 1\n",
" param['colsample_bytree'] = 1\n",
" param['silent'] = 1\n",
" param['seed'] = 0\n",
" param['base_score'] = 0.5\n",
" param['lambda_bias'] = 1\n",
" return param"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#This function is used to train the base and stacking models. Returns all the models to be used for further computations.\n",
"def train_models(train_X,train_Y,model):\n",
" \n",
" param = param_set()\n",
" #Trains only the base models.\n",
" if(model=='base'):\n",
" \n",
" #Gradient Boosting\n",
" dtrain = xgb.DMatrix(train_X,label=train_Y)\n",
" gradient_boosting = xgb.train(param, dtrain)\n",
" \n",
" #Multi Layer Perceptron\n",
" multi_layer_perceptron = Sequential()\n",
" multi_layer_perceptron.add(Dense(output_dim = 64, input_dim = 20, init = 'uniform', activation = 'sigmoid'))\n",
" multi_layer_perceptron.add(Dense(output_dim = 1, input_dim = 64,activation = 'sigmoid',))\n",
" multi_layer_perceptron.compile(optimizer = 'rmsprop',loss = 'binary_crossentropy',metrics = ['accuracy'])\n",
" multi_layer_perceptron.fit(train_X.as_matrix(), train_Y.as_matrix(), nb_epoch = 5, batch_size = 128)\n",
" \n",
" #Decision Tree\n",
" decision_tree = DecisionTreeClassifier(max_depth = 6)\n",
" decision_tree.fit(train_X,train_Y)\n",
" \n",
" #Random Forest (Deafult=10 Trees)\n",
" random_forest = RandomForestClassifier()\n",
" random_forest.fit(train_X,train_Y)\n",
" \n",
" #Scaling the data\n",
" train_X = preprocessing.StandardScaler().fit_transform(train_X) \n",
" \n",
" #Linear Regression\n",
" linear_regression = linear_model.LinearRegression()\n",
" linear_regression.fit(train_X,train_Y)\n",
" \n",
" #Logistic Regression (L1)\n",
" logistic_regression_L1 = linear_model.LogisticRegression(penalty = 'l1')\n",
" logistic_regression_L1.fit(train_X,train_Y)\n",
" \n",
" #Logistic Regression (L2)\n",
" logistic_regression_L2 = linear_model.LogisticRegression(penalty = 'l2')\n",
" logistic_regression_L2.fit(train_X,train_Y)\n",
" \n",
" #Returns a dictionary containing the model names and their respective models.\n",
" return {'XGBoost':gradient_boosting,'Multi Layer Perceptron':multi_layer_perceptron,'Decision Tree':decision_tree,\n",
" 'Random Forest':random_forest,'Linear Regression':linear_regression,'L1':logistic_regression_L1,\n",
" 'L2':logistic_regression_L2}\n",
" \n",
" #Trains the stacking model (Gradient Boosting - XGBoost)\n",
" elif(model == 'stack'):\n",
" \n",
" dtrain = xgb.DMatrix(train_X,label = train_Y)\n",
" stack = xgb.train(param, dtrain)\n",
" return {'Stack':stack}\n",
" \n",
" #Trains the blending model (Gradient Boosting - XGBoost)\n",
" else:\n",
" \n",
" dtrain = xgb.DMatrix(train_X,label = train_Y)\n",
" blend = xgb.train(param, dtrain)\n",
" return {'Blend':blend}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Function calculates area under the curve and predictions on the given data, for the model specified.\n",
"def cross_validation(model_name,model,cross_val_X,cross_val_Y):\n",
" \n",
" if(model_name == 'Gradient Boosting' or model_name == 'Linear Regression'):\n",
" \n",
" predict = model.predict(cross_val_X)\n",
" \n",
" elif(model_name == 'Multi Layer Perceptron'):\n",
" \n",
" predict = model.predict_on_batch(cross_val_X)\n",
" else:\n",
" \n",
" predict = model.predict_proba(cross_val_X)[:,1]\n",
" \n",
" auc = roc_auc_score(cross_val_Y,predict)\n",
" \n",
" return[auc,predict]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Initialzing the variables that will be used to calculate the area under the curve. (cross Validation Data)\n",
"metric_linear_regression=list()\n",
"avg_linear_regeression=0\n",
"metric_logistic_regression_L2=list()\n",
"avg_logistic_regression_L2=0\n",
"metric_logistic_regression_L1=list()\n",
"avg_logistic_regression_L1=0\n",
"metric_decision_tree=list()\n",
"avg_decision_tree=0\n",
"metric_random_forest=list()\n",
"avg_random_forest=0\n",
"metric_multi_layer_perceptron=list()\n",
"avg_multi_layer_perceptron=0\n",
"metric_gradient_boosting=list()\n",
"avg_gradient_boosting=0"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Cross Validation using Stratified K Fold\n",
"kf = StratifiedKFold(Data['y'], n_folds=5, shuffle=True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"31302/31302 [==============================] - 0s - loss: 0.3660 - acc: 0.8812 \n",
"Epoch 2/5\n",
"31302/31302 [==============================] - 0s - loss: 0.3424 - acc: 0.8874 \n",
"Epoch 3/5\n",
"31302/31302 [==============================] - 0s - loss: 0.3392 - acc: 0.8874 \n",
"Epoch 4/5\n",
"31302/31302 [==============================] - 0s - loss: 0.3362 - acc: 0.8874 \n",
"Epoch 5/5\n",
"31302/31302 [==============================] - 0s - loss: 0.3282 - acc: 0.8874 \n",
"Epoch 1/5\n",
"31302/31302 [==============================] - 0s - loss: 0.3841 - acc: 0.8358 \n",
"Epoch 2/5\n",
"31302/31302 [==============================] - 0s - loss: 0.2690 - acc: 0.8875 \n",
"Epoch 3/5\n",
"31302/31302 [==============================] - 0s - loss: 0.2569 - acc: 0.8978 \n",
"Epoch 4/5\n",
"31302/31302 [==============================] - 0s - loss: 0.2540 - acc: 0.9012 \n",
"Epoch 5/5\n",
"31302/31302 [==============================] - 0s - loss: 0.2483 - acc: 0.9025 \n",
"Epoch 1/5\n",
"31302/31302 [==============================] - 0s - loss: 0.3304 - acc: 0.8816 \n",
"Epoch 2/5\n",
"31302/31302 [==============================] - 0s - loss: 0.2661 - acc: 0.8886 \n",
"Epoch 3/5\n",
"31302/31302 [==============================] - 0s - loss: 0.2581 - acc: 0.8984 \n",
"Epoch 4/5\n",
"31302/31302 [==============================] - 0s - loss: 0.2559 - acc: 0.9003 \n",
"Epoch 5/5\n",
"31302/31302 [==============================] - 0s - loss: 0.2495 - acc: 0.9047 \n",
"Epoch 1/5\n",
"31303/31303 [==============================] - 0s - loss: 0.3044 - acc: 0.8873 \n",
"Epoch 2/5\n",
"31303/31303 [==============================] - 0s - loss: 0.2786 - acc: 0.8874 \n",
"Epoch 3/5\n",
"31303/31303 [==============================] - 0s - loss: 0.2667 - acc: 0.8876 \n",
"Epoch 4/5\n",
"31303/31303 [==============================] - 0s - loss: 0.2609 - acc: 0.8912 \n",
"Epoch 5/5\n",
"31303/31303 [==============================] - 0s - loss: 0.2554 - acc: 0.8952 \n",
"Epoch 1/5\n",
"31303/31303 [==============================] - 0s - loss: 0.3272 - acc: 0.8845 \n",
"Epoch 2/5\n",
"31303/31303 [==============================] - 0s - loss: 0.2764 - acc: 0.8874 \n",
"Epoch 3/5\n",
"31303/31303 [==============================] - 0s - loss: 0.2645 - acc: 0.8914 \n",
"Epoch 4/5\n",
"31303/31303 [==============================] - 0s - loss: 0.2598 - acc: 0.8920 \n",
"Epoch 5/5\n",
"31303/31303 [==============================] - 0s - loss: 0.2562 - acc: 0.8929 \n"
]
}
],
"source": [
"#Training the base models, and calculating AUC on the cross validation data.\n",
"for train_index, cross_val_index in kf:\n",
" \n",
" #Selecting the data (Traing Data & Cross Validation Data)\n",
" train, cross_val = Data.iloc[train_index], Data.iloc[cross_val_index]\n",
" train_Y=train['y']\n",
" train_X=train.drop(['y'],axis=1)\n",
" cross_val_Y=cross_val['y']\n",
" cross_val_X=cross_val.drop(['y'],axis=1)\n",
" scale=preprocessing.StandardScaler()\n",
" \n",
" #Training the base models, the resulting model names and models are stored in the variable model in the from of a dictionary.\n",
" model=train_models(train_X,train_Y,'base')\n",
" \n",
" #Gradient Boosting (XGBoost)\n",
" #The AUC error (Cross Validation Data)\n",
" [auc,predict_gradient_boosting]=cross_validation('Gradient Boosting',model['XGBoost'],xgb.DMatrix(cross_val_X,label=cross_val_Y),cross_val_Y)\n",
" metric_gradient_boosting.append(auc)\n",
"\n",
" #Multi Layer Perceptron\n",
" #The AUC (Cross Validation Data)\n",
" predict_mlp=list()\n",
" [auc,predict_multi_layer_perceptron]=cross_validation('Multi Layer Perceptron',model['Multi Layer Perceptron'],cross_val_X,cross_val_Y)\n",
" metric_multi_layer_perceptron.append(auc)\n",
" #predict_multi_layer_perceptron returns a list of lists containing the predictions, this cannot be converted to a dataframe.\n",
" #This inner lists are converted to floats and then used to convert it to a dataframe.\n",
" for i in predict_multi_layer_perceptron:\n",
" predict_mlp.append(float(i))\n",
" \n",
" #Decision Tree)\n",
" #The AUC (Cross Validation Data)\n",
" [auc,predict_decision_tree]=cross_validation('Decision Tree',model['Decision Tree'],cross_val_X,cross_val_Y)\n",
" metric_decision_tree.append(auc)\n",
" \n",
" #Random Forest (Deafult=10 Trees)\n",
" #The AUC (Cross Validation Data)\n",
" [auc,predict_random_forest]=cross_validation('Random Forest',model['Random Forest'],cross_val_X,cross_val_Y)\n",
" metric_random_forest.append(auc)\n",
" \n",
" #Scaling the cross validation data.\n",
" cross_val_X=scale.fit_transform(cross_val_X)\n",
" \n",
" #Linear Regression\n",
" #The AUC (Cross Validation Data)\n",
" [auc,predict_linear_regression]=cross_validation('Linear Regression',model['Linear Regression'],cross_val_X,cross_val_Y)\n",
" metric_linear_regression.append(auc)\n",
" \n",
" #Logistic Regression (Default=l2)\n",
" #The AUC (Cross Validation Data)\n",
" [auc,predict_logistic_regression_L2]=cross_validation('L2',model['L2'],cross_val_X,cross_val_Y)\n",
" metric_logistic_regression_L2.append(auc)\n",
" \n",
" #Logistic Regression-L1\n",
" #The AUC (Cross Validation Data)\n",
" [auc,predict_logistic_regression_L1]=cross_validation('L1',model['L1'],cross_val_X,cross_val_Y)\n",
" metric_logistic_regression_L1.append(auc)\n",
" \n",
" #Building a list that contains all the predictions of the base models.\n",
" predict_list=[predict_gradient_boosting,predict_decision_tree,predict_random_forest, \n",
" predict_linear_regression,predict_logistic_regression_L2,\n",
" predict_logistic_regression_L1,predict_mlp]\n",
" \n",
" #Rescaling the cross validation data back to its original values.\n",
" cross_val_X=scale.inverse_transform(cross_val_X)\n",
" \n",
" #Converting the above list of predictions into a dataframe, which will be used to train the stacking model.\n",
" stack_Y=stack_Y.append(cross_val_Y.tolist())\n",
" stack_X=stack_X.append(build_data_frame(predict_list))\n",
" \n",
" #Building a list that contains all the raw features used as cross validation data for the base models.\n",
" raw_features_X=raw_features_X.append(cross_val_X.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Calculating the average AUC across all the AUC computed on the cross validation folds.\n",
"avg_linear_regression=np.mean(metric_linear_regression)\n",
"avg_logistic_regression_L2=np.mean(metric_logistic_regression_L2)\n",
"avg_logistic_regression_L1=np.mean(metric_logistic_regression_L1)\n",
"avg_decision_tree=np.mean(metric_decision_tree)\n",
"avg_random_forest=np.mean(metric_random_forest)\n",
"avg_multi_layer_perceptron=np.mean(metric_multi_layer_perceptron)\n",
"avg_gradient_boosting=np.mean(metric_gradient_boosting)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" AUC (Linear Regression)\n",
" 0.927633509729\n",
" AUC (Logistic Regression - L2)\n",
" 0.929307749504\n",
" AUC (Logistic Regression - L1)\n",
" 0.929319089088\n",
" AUC (Decision Tree)\n",
" 0.925024685812\n",
" AUC (Random Forest)\n",
" 0.917223061441\n",
" AUC (Multi Layer Perceptron)\n",
" 0.879044122467\n",
" AUC (Gradient Boosting - XGBoost)\n",
" 0.948086039187\n"
]
}
],
"source": [
"#Printing the AUC for the base models.\n",
"print (' AUC (Linear Regression)\\n',avg_linear_regression)\n",
"print (' AUC (Logistic Regression - L2)\\n',avg_logistic_regression_L2)\n",
"print (' AUC (Logistic Regression - L1)\\n',avg_logistic_regression_L1)\n",
"print (' AUC (Decision Tree)\\n',avg_decision_tree)\n",
"print (' AUC (Random Forest)\\n',avg_random_forest)\n",
"print (' AUC (Multi Layer Perceptron)\\n',avg_multi_layer_perceptron)\n",
"print (' AUC (Gradient Boosting - XGBoost)\\n',avg_gradient_boosting)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Training the stacking model(XGBoost-Gradient Boosting)\n",
"model_stack=train_models(stack_X,stack_Y,'stack')\n",
"\n",
"#Converting the above list of predictions and raw features (Concatenate) into a dataframe, which will be used to train the blending model.\n",
"blend_X=pd.concat([raw_features_X, stack_X], axis=1,ignore_index=True)\n",
"\n",
"#Training the blending model(XGBoost-Gradient Boosting)\n",
"model_blend=train_models(blend_X,stack_Y,'blend')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Initialzing the variables that will be used to calculate the area under the curve. (Test Data)\n",
"metric_logistic_regression_L2=list()\n",
"metric_logistic_regression_L1=list()\n",
"metric_decision_tree=list()\n",
"metric_random_forest=list()\n",
"metric_multi_layer_perceptron=list()\n",
"metric_gradient_boosting=list()\n",
"metric_stack=list()\n",
"metric_blend=list()\n",
"blend_X = pd.DataFrame()\n",
"raw_features_X = pd.DataFrame()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Calculating AUC for all the models (Base Models & Stack Model) on the test data.\n",
"\n",
"#Selecting the test data\n",
"test_Y=test['y']\n",
"test_X=test.drop(['y'],axis=1)\n",
"scale=preprocessing.StandardScaler()\n",
" \n",
"#Gradient Boosting (XGBoost)\n",
"#The AUC error (Test Data)\n",
"[auc,predict_XGB]=cross_validation('Gradient Boosting',model['XGBoost'],xgb.DMatrix(test_X,label=test_Y),test_Y)\n",
"metric_gradient_boosting=(auc)\n",
"\n",
" \n",
"#Multi Layer Perceptron\n",
"#The AUC (Test Data)\n",
"predict_mlp=list()\n",
"[auc,predict_multi_layer_perceptron]=cross_validation('Multi Layer Perceptron',model['Multi Layer Perceptron'],test_X,test_Y)\n",
"metric_multi_layer_perceptron=(auc)\n",
"\n",
"#predict_multi_layer_perceptron returns a list of lists containing the predictions, this cannot be converted to a dataframe.\n",
"#This inner lists are converted to floats and then used to convert it to a dataframe.\n",
"for i in predict_multi_layer_perceptron:\n",
" predict_mlp.append(float(i))\n",
"\n",
"\n",
"#Decision Tree)\n",
"#The AUC (Test Data)\n",
"[auc,predict_decision_tree]=cross_validation('Decision Tree',model['Decision Tree'],test_X,test_Y)\n",
"metric_decision_tree=(auc)\n",
" \n",
" \n",
"#Random Forest (Deafult=10 Trees)\n",
"#The AUC (Test Data)\n",
"[auc,predict_random_forest]=cross_validation('Random Forest',model['Random Forest'],test_X,test_Y)\n",
"metric_random_forest=(auc)\n",
" \n",
"test_X=scale.fit_transform(test_X)\n",
"#Linear Regression\n",
"#The AUC (Test Data)\n",
"[auc,predict_linear_regression]=cross_validation('Linear Regression',model['Linear Regression'],test_X,test_Y)\n",
"metric_linear_regression=(auc)\n",
" \n",
"#Logistic Regression (Default=l2)\n",
"#The AUC (Test Data)\n",
"[auc,predict_logistic_regression_L2]=cross_validation('L2',model['L2'],test_X,test_Y)\n",
"metric_logistic_regression_L2=(auc)\n",
"\n",
"#Logistic Regression-L1\n",
"#The AUC (Test Data)\n",
"[auc,predict_logistic_regression_L1]=cross_validation('L1',model['L1'],test_X,test_Y)\n",
"metric_logistic_regression_L1=(auc)\n",
"\n",
"#Building a list that contains all the predictions of the base models.\n",
"predict_list=[predict_XGB,predict_decision_tree,predict_random_forest, \n",
" predict_linear_regression,predict_logistic_regression_L2,\n",
" predict_logistic_regression_L1,predict_mlp]\n",
"\n",
"#Rescaling the test data back to its original values.\n",
"test_X=scale.inverse_transform(test_X)\n",
" \n",
"#Stacking (XGBoost - Gradient Boosting)\n",
"dstack_X=build_data_frame(predict_list) #Converting the list of predictions into a dataframe.\n",
"[auc,predict_stack]=cross_validation('Gradient Boosting',model_stack['Stack'],xgb.DMatrix(dstack_X,label=test_Y),test_Y)\n",
"metric_stack=(auc) \n",
"\n",
"#Blending (XGBoost - Gradient Boosting)\n",
"raw_features_X=raw_features_X.append(test_X.tolist())\n",
"blend_X=pd.concat([raw_features_X, dstack_X], axis=1,ignore_index=True)#Converting the above list of predictions and raw features (Concatenate) into a dataframe\n",
"[auc,predict_blend]=cross_validation('Gradient Boosting',model_blend['Blend'],xgb.DMatrix(blend_X,label=test_Y),test_Y)\n",
"metric_blend=(auc) "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" AUC (Linear Regression)\n",
" 0.932326643024\n",
" AUC (Logistic Regression - L2)\n",
" 0.934594997359\n",
" AUC (Logistic Regression - L1)\n",
" 0.934630366709\n",
" AUC (Decision Tree)\n",
" 0.928261525692\n",
" AUC (Random Forest)\n",
" 0.914071342338\n",
" AUC (Multi Layer Perceptron)\n",
" 0.881355872255\n",
" AUC (Gradient Boosting - XGBoost)\n",
" 0.950255366709\n",
" AUC (Stacking)\n",
" 0.949767269675\n",
" AUC (Blending)\n",
" 0.951025239568\n"
]
}
],
"source": [
"print (' AUC (Linear Regression)\\n',metric_linear_regression)\n",
"print (' AUC (Logistic Regression - L2)\\n',metric_logistic_regression_L2)\n",
"print (' AUC (Logistic Regression - L1)\\n',metric_logistic_regression_L1)\n",
"print (' AUC (Decision Tree)\\n',metric_decision_tree)\n",
"print (' AUC (Random Forest)\\n',metric_random_forest)\n",
"print (' AUC (Multi Layer Perceptron)\\n',metric_multi_layer_perceptron)\n",
"print (' AUC (Gradient Boosting - XGBoost)\\n',metric_gradient_boosting)\n",
"print (' AUC (Stacking)\\n',metric_stack)\n",
"print (' AUC (Blending)\\n',metric_blend)"
]
}
],
"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 |
MLWave/kepler-mapper | docs/generated/gallery/plot_digits.ipynb | 1 | 3765 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\nDigits Dataset\n================\n\nThis digits example shows two ways of customizing the tooltips options in the HTML visualization. It generates the visualization with tooltips set as the y-label, or number of the image. The second generated result uses the actual image in the tooltips. \n\n`Visualization with y-label tooltip <../../_static/digits_ylabel_tooltips.html>`_\n\n`Visualization with custom tooltips <../../_static/digits_custom_tooltips.html>`_\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import io\nimport sys\nimport base64\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport sklearn\nfrom sklearn import datasets\nimport kmapper as km\n\ntry:\n from scipy.misc import imsave, toimage\nexcept ImportError as e:\n print(\"imsave requires you to install pillow. Run `pip install pillow` and then try again.\")\n sys.exit()\n\n\n# Load digits dat\ndata, labels = datasets.load_digits().data, datasets.load_digits().target\n\n# Create images for a custom tooltip array\ntooltip_s = []\nfor image_data in data:\n output = io.BytesIO()\n img = toimage(image_data.reshape((8, 8))) # Data was a flat row of 64 \"pixels\".\n img.save(output, format=\"PNG\")\n contents = output.getvalue()\n img_encoded = base64.b64encode(contents)\n img_tag = \"\"\"<img src=\"data:image/png;base64,{}\">\"\"\".format(img_encoded.decode('utf-8'))\n tooltip_s.append(img_tag)\n output.close()\n\ntooltip_s = np.array(tooltip_s) # need to make sure to feed it as a NumPy array, not a list\n\n# Initialize to use t-SNE with 2 components (reduces data to 2 dimensions). Also note high overlap_percentage.\nmapper = km.KeplerMapper(verbose=2)\n\n# Fit and transform data\nprojected_data = mapper.fit_transform(data,\n projection=sklearn.manifold.TSNE())\n\n# Create the graph (we cluster on the projected data and suffer projection loss)\ngraph = mapper.map(projected_data,\n clusterer=sklearn.cluster.DBSCAN(eps=0.3, min_samples=15),\n cover=km.Cover(35, 0.4))\n\n# Create the visualizations (increased the graph_gravity for a tighter graph-look.)\nprint(\"Output graph examples to html\" )\n# Tooltips with image data for every cluster member\nmapper.visualize(graph,\n title=\"Handwritten digits Mapper\",\n path_html=\"output/digits_custom_tooltips.html\",\n color_function=labels,\n custom_tooltips=tooltip_s)\n# Tooltips with the target y-labels for every cluster member\nmapper.visualize(graph,\n title=\"Handwritten digits Mapper\",\n path_html=\"output/digits_ylabel_tooltips.html\",\n custom_tooltips=labels)\n\n# Matplotlib examples\nkm.draw_matplotlib(graph, layout=\"spring\")\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": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | mit |
faizankshaikh/Project | trials/trial_shift.ipynb | 1 | 69761 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using gpu device 0: GeForce GTX TITAN X (CNMeM is disabled)\n"
]
}
],
"source": [
"# import modules\n",
"%matplotlib inline\n",
"\n",
"import os\n",
"import random\n",
"import pylab\n",
"import pandas as pd\n",
"import numpy as np\n",
"import cPickle as pkl\n",
"from skimage import transform\n",
"from lasagne import layers, updates\n",
"from scipy.misc import imread, imresize\n",
"from lasagne.nonlinearities import softmax\n",
"from nolearn.lasagne import NeuralNet, BatchIterator\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"\n",
"project_root = 'workspace/.project/project'\n",
"script_root = os.path.join(os.path.expanduser('~'), project_root, 'scripts')\n",
"model_root = os.path.join(os.path.expanduser('~'), project_root, 'models')\n",
"data_root = os.path.join(os.path.expanduser('~'), project_root, 'datasets')\n",
"chars74k_root = os.path.join(data_root, 'English')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def shiftup(dataset):\n",
" \n",
" return shifted_dataset"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# load train_test set\n",
"#TODO see why they get distorted\n",
"data = pd.read_csv(os.path.join(script_root, 'LISTFILE.txt'), sep = ' ', header = None)\n",
"data_x = np.zeros((data.shape[0], 1, 32, 32))\n",
"data_y = np.ones((data.shape[0], )).astype('int32')\n",
"\n",
"mms = MinMaxScaler()\n",
"\n",
"for idx, path in enumerate(data[0]):\n",
" img = imread(os.path.join(chars74k_root, path))\n",
" img = imresize(img, (32, 32))\n",
" if len(img.shape) == 3:\n",
" data_x[idx, ...] = mms.fit_transform(img.dot([0.299, 0.587, 0.144]))\n",
" else:\n",
" data_x[idx, ...] = mms.fit_transform(img.astype(float))\n",
" \n",
"data_x = data_x.astype('float32')\n",
"train1_x, test1_x, train1_y, test1_y = train_test_split(data_x, data_y, test_size = 0.2)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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74oDvAB/Hv9KHLyr8Z4wbx4iF+lhXNrGHr2R7PRiRYdRRhfysveqcjqPIor+O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\nA7lbWYnBmg6DAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f40f77854d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"i = random.randrange(0, train1_x.shape[0])\n",
"img = np.reshape(train1_x[i, ...], ( train1_x.shape[2], train1_x.shape[3] ))\n",
"pylab.imshow(img)\n",
"pylab.gray()\n",
"pylab.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(6164, 1, 32, 32)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train1_x.shape"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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zlpkCv5QSBwcHdtoqB990+EnNSh3KanV+t+FGxPibdJG3Pt8enQJRXIAeAiCq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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f40faca6b10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"i = random.randrange(0, train1_x.shape[0])\n",
"img = np.reshape(train1_x[i, ...], ( train1_x.shape[2], train1_x.shape[3] ))\n",
"\n",
"tform = transform.SimilarityTransform(translation = (4, 0))\n",
"new_img = transform.warp(img, tform)\n",
"\n",
"pylab.imshow(new_img)\n",
"pylab.gray()\n",
"pylab.show()"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(32, 32)"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train1_x[i, 0, ...].shape"
]
},
{
"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 |
kmunve/APS | aps/notebooks/regional_precipitation_distribution.ipynb | 1 | 489407 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Regional distribution of precipitation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"D:\\Dev\\APS\\aps\n"
]
}
],
"source": [
"import sys\n",
"import os\n",
"aps_path = os.path.dirname(os.path.abspath(\".\"))\n",
"sys.path.append(aps_path)\n",
"print(aps_path)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from netCDF4 import Dataset\n",
"import numpy as np\n",
"from numba import jit\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.patches import Rectangle\n",
"from aps_io.get_arome import nc_load"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Defining functions"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"@jit\n",
"def regional_precip(precip):\n",
" pf = precip.flatten()\n",
" pfc = np.clip(pf, 0, 1000)\n",
"\n",
" box_bot = np.nanpercentile(pfc, 25.0)\n",
" box_top = np.nanpercentile(pfc, 75.0)\n",
" box_center = np.nanpercentile(pfc, 50.0)\n",
" flier_low = np.nanpercentile(pfc, 0.0) #np.min(pfc)\n",
" flier_high = np.nanpercentile(pfc, 100.0) #np.max(pfc)\n",
" pf_mean = np.nanmean(pfc)\n",
"\n",
" return flier_low, box_bot, box_center, box_top, flier_high"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#@jit\n",
"def regional_precip_max(precip, window_x = 20, window_y = 20, step_x = 5, step_y = 5):\n",
"\n",
" highest_med_bot = 0\n",
" highest_med_top = 0\n",
" highest_med_center = 0\n",
" highest_med_low = 0\n",
" highest_med_high = 0\n",
" \n",
" highest_max_bot = 0\n",
" highest_max_top = 0\n",
" highest_max_center = 0\n",
" highest_max_low = 0\n",
" highest_max_high = 0\n",
" \n",
" for i in range(0, precip.shape[0]-window_y, step_y):\n",
" for j in range(0, precip.shape[1]-window_x, step_x):\n",
"\n",
" Pwin = precip[i:i+window_y, j:j+window_x]\n",
"\n",
" pf = Pwin.flatten() # make 1-D\n",
" pfc = np.clip(pf, 0, 1000) # remove unrealistic values; should probaby be set to NaN\n",
"\n",
" box_bot = np.nanpercentile(pfc, 25.0)\n",
" box_top = np.nanpercentile(pfc, 75.0)\n",
" box_center = np.nanpercentile(pfc, 50.0)\n",
" flier_low = np.nanpercentile(pfc, 0.0) #np.min(pfc) \n",
" flier_high = np.nanpercentile(pfc, 100.0) #np.max(pfc)\n",
" #pf_mean = np.nanmean(pfc)\n",
"\n",
" if box_center > highest_med_center:\n",
" highest_med_bot = box_bot\n",
" highest_med_top = box_top\n",
" highest_med_center = box_center\n",
" highest_med_low = flier_low\n",
" highest_med_high = flier_high\n",
" #print(flier_high, box_top) # there is an issue with the box_top value !!!\n",
" med_i = i\n",
" med_j = j\n",
" if flier_high > highest_max_high:\n",
" highest_max_high = flier_high\n",
" max_i = i\n",
" max_j = j\n",
" #print(flier_low, box_bot, box_center, box_top, flier_high, pf_mean)\n",
" \n",
" return highest_med_low, highest_med_bot, highest_med_center, highest_med_top, highest_med_high, med_i, med_j"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load region-mask"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"vr = Dataset(r\"../data/terrain_parameters/VarslingsOmr_2017.nc\", \"r\")\n",
"\n",
"regions = vr.variables[\"VarslingsOmr_2017\"][:]\n",
"\n",
"region_mask = np.where(regions==3012) # Sør-Troms\n",
"# get the lower left and upper right corner of a rectangle around the region\n",
"y_min, y_max, x_min, x_max = min(region_mask[0].flatten()), max(region_mask[0].flatten()), min(region_mask[1].flatten()), max(region_mask[1].flatten())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Read precipitation data from met_obs_grid netcdf file and clip to a subregion."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nc = Dataset(\"../data/met_obs_grid/rr_2016_12_12.nc\", \"r\")\n",
"\n",
"time_var = nc.variables['time']\n",
"precip_var = nc.variables['precipitation_amount']"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x1, x2 = x_min, x_max # possible to add a buffer of step_x\n",
"y1, y2 = y_min, y_max # possible to add a buffer of step_y\n",
"\n",
"precip = precip_var[0, y1:y2, x1:x2]"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"region_mask = regions[y1:y2, x1:x2] # redefine region_mask, now clipped to area of interest\n",
"\n",
"precip = np.ma.masked_where(region_mask!=3012, precip)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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AyjJEjQE1RRlnKti+mb1brIeMNGvirIPs1q6fPAsVT2Qfyq9b4vmNGUksT0iYQiRt+SZD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\nWHLAAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2a000f65f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(precip, aspect='equal')\n",
"plt.colorbar()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'numpy.ma.core.MaskedArray'>\n",
"0.0 13.5 21.6000003815 26.5 43.9000015259\n"
]
},
{
"data": {
"image/png": 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3uOthZqUcKIaYL5Fat7jrMYQcIKzbnFGYWSlnFEPOVz0mp8lXNCajVhmFpH0krZD0S0lr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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2a0011e6748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(type(precip))\n",
"flier_low, box_bot, box_center, box_top, flier_high = regional_precip(precip)\n",
"print(flier_low, box_bot, box_center, box_top, flier_high)\n",
"\n",
"precip_high = np.greater(precip, box_top) # mark region of precip over threshold\n",
"plt.imshow(precip_high, aspect='equal')\n",
"plt.title(\"Område(r) med mer enn {0:.1f} mm nedbør\".format(box_top))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"window_x = 20; window_y = 20;"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"step_x = 5; step_y = 5;"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'numpy.ma.core.MaskedArray'>\n",
"30.2999992371 35.2999992371 37.9500007629 41.2000007629 43.7000007629 115 60\n"
]
}
],
"source": [
"print(type(precip))\n",
"highest_med_low, highest_med_bot, highest_med_center, highest_med_top, highest_med_high, med_i, med_j = regional_precip_max(precip) \n",
"print(highest_med_low, highest_med_bot, highest_med_center, highest_med_top, highest_med_high, med_i, med_j)\n",
"#print(highest_max, max_i, max_j)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x2a0016dcf60>"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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V+p8dZme2zMdRNGZ+i9EI6mgTa4r9qZOH0Skj4nkYzqHDNYstioicATwcuBI4MQoSgFsI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\nOI4zFRcWjuM4zlT+f4aglJ/Qyb6eAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2a003ae0160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(precip, aspect='equal')\n",
"plt.gca().add_patch(Rectangle((med_j, med_i), window_x, window_y, hatch='/', fill=False, edgecolor='purple', linewidth=2))\n",
"#plt.gca().add_patch(Rectangle((max_j, max_i), window_x, window_y, hatch='\\\\', fill=False, edgecolor='lightgrey', linewidth=2))\n",
"# the max will be unchanged in a neighboring window with a potentially higher median and not update its position - it can be removed. \n",
"plt.title(\"Kvadraten representer et områd på {0} km$^2$ med mest nedbør\".format(window_x*window_y))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"### 2nd example"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nc = Dataset(\"../data/met_obs_grid/rr_2017_01_07.nc\", \"r\")\n",
"\n",
"time_var = nc.variables['time']\n",
"precip_var = nc.variables['precipitation_amount']"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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rWK0yusJWCnapgl2qYJZqmINDmIND2OUB7MoS7MoSeDRwWXq1q95GWiWFhdbO060q5/l2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\naoAbY8dxnBrwf4KsfXJ9mUtNAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2a001e9e6d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Svartisen\n",
"region_mask = np.where(regions==3017)\n",
"# get the lower left and upper right corner of a rectangle around the region\n",
"y_min, y_max, x_min, x_max = min(region_mask[0].flatten()), max(region_mask[0].flatten()), min(region_mask[1].flatten()), max(region_mask[1].flatten())\n",
"\n",
"x1, x2 = x_min, x_max # possible to add a buffer of step_x\n",
"y1, y2 = y_min, y_max # possible to add a buffer of step_y\n",
"\n",
"precip = precip_var[0, y1:y2, x1:x2]\n",
"\n",
"region_mask = regions[y1:y2, x1:x2] # redefine region_mask, now clipped to area of interest\n",
"\n",
"precip = np.ma.masked_where(region_mask!=3017, precip)\n",
"\n",
"plt.imshow(precip, aspect='equal')\n",
"plt.colorbar()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0 0.0 13.1999998093 17.3999996185 32.4000015259\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x2a003597898>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ddwauSp//XcDxRZU/mj9fNmdmpdYLzWEzs65xEDSzUnMQNLNScxA0s1JzEDSz\nUnMQNLNScxA0s1L7/108xSLhP0hoAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2a003342f28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"flier_low, box_bot, box_center, box_top, flier_high = regional_precip(precip)\n",
"print(flier_low, box_bot, box_center, box_top, flier_high)\n",
"\n",
"precip_high = np.greater(precip, box_top) # mark region of precip over threshold\n",
"plt.imshow(precip_high, aspect='equal')\n",
"plt.title(\"Område(r) med mer enn {0:.1f} mm nedbør\".format(box_top))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"23.7000007629 27.8999996185 29.6000003815 30.7000007629 32.4000015259 90 50\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x2a0033496a0>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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7eAiNiAaFAmC//iKu03ago4SdgssCVIXrBRUsqS3fF1gE86GeGFeMx1AoDiDWvLBg5v8A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\nxjKPyuLiOZbv1z6f8v3and1m7mIWjuM4zs4zj5aF4ziOs5O4snAcx3H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"text/plain": [
"<matplotlib.figure.Figure at 0x2a001e14c18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"highest_med_low, highest_med_bot, highest_med_center, highest_med_top, highest_med_high, med_i, med_j = regional_precip_max(precip) \n",
"print(highest_med_low, highest_med_bot, highest_med_center, highest_med_top, highest_med_high, med_i, med_j)\n",
"\n",
"plt.imshow(precip, aspect='equal')\n",
"plt.gca().add_patch(Rectangle((med_j, med_i), window_x, window_y, hatch='/', fill=False, edgecolor='purple', linewidth=2))\n",
"#plt.gca().add_patch(Rectangle((max_j, max_i), window_x, window_y, hatch='\\\\', fill=False, edgecolor='red', linewidth=2))\n",
"# the max will be unchanged in a neighboring window with a potentially higher median and not update its position - it can be removed. \n",
"plt.title(\"Kvadraten representer et områd på {0} km$^2$ med mest nedbør\".format(window_x*window_y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3rd example"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nc = Dataset(\"../data/met_obs_grid/rr_2016_12_31.nc\", \"r\")\n",
"\n",
"time_var = nc.variables['time']\n",
"precip_var = nc.variables['precipitation_amount']"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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YgMYL5TmL5+RjOnavsJPEar3NyvaHpncDABrb5Y5oBeiK+ph2+M9eBAA4+fPC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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2a003691f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Hardanger\n",
"region_mask = np.where(regions==3034)\n",
"# get the lower left and upper right corner of a rectangle around the region\n",
"y_min, y_max, x_min, x_max = min(region_mask[0].flatten()), max(region_mask[0].flatten()), min(region_mask[1].flatten()), max(region_mask[1].flatten())\n",
"\n",
"x1, x2 = x_min, x_max # possible to add a buffer of step_x\n",
"y1, y2 = y_min, y_max # possible to add a buffer of step_y\n",
"\n",
"precip = precip_var[0, y1:y2, x1:x2]\n",
"\n",
"region_mask = regions[y1:y2, x1:x2] # redefine region_mask, now clipped to area of interest\n",
"\n",
"precip = np.ma.masked_where(region_mask!=3034, precip)\n",
"\n",
"plt.imshow(precip, aspect='equal')\n",
"plt.colorbar()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.10000000149 23.3999996185 47.7999992371 68.5 142.0\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x2a0036e7cf8>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x2a0012f0438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"flier_low, box_bot, box_center, box_top, flier_high = regional_precip(precip)\n",
"print(flier_low, box_bot, box_center, box_top, flier_high)\n",
"\n",
"precip_high = np.greater(precip, box_top) # mark region of precip over threshold\n",
"plt.imshow(precip_high, aspect='equal')\n",
"plt.title(\"Område(r) med mer enn {0:.1f} mm nedbør\".format(box_top))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"83.9000015259 101.099998474 106.699996948 114.850002289 142.0 75 0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n",
"C:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:703: RuntimeWarning: Mean of empty slice\n",
" warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x2a0038cf978>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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NES0iACgwyxDJDbH4i+NMLZwVz0tuL0wjR5GpTnojlc+QMNnMIyH0PYQIGUUkUUq8PR8k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\n9D45SwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2a07fcce5f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"highest_med_low, highest_med_bot, highest_med_center, highest_med_top, highest_med_high, med_i, med_j = regional_precip_max(precip) \n",
"print(highest_med_low, highest_med_bot, highest_med_center, highest_med_top, highest_med_high, med_i, med_j)\n",
"\n",
"plt.imshow(precip, aspect='equal')\n",
"plt.gca().add_patch(Rectangle((med_j, med_i), window_x, window_y, hatch='/', fill=False, edgecolor='purple', linewidth=2))\n",
"#plt.gca().add_patch(Rectangle((max_j, max_i), window_x, window_y, hatch='\\\\', fill=False, edgecolor='red', linewidth=2))\n",
"# the max will be unchanged in a neighboring window with a potentially higher median and not update its position - it can be removed. \n",
"plt.title(\"Kvadraten representer området på {0} km$^2$ med mest nedbør\".format(window_x*window_y))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Live example"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"region_id = 3017\n",
"year = 2017; month = 1; day = 27\n",
"\n",
"region_mask = np.where(regions==region_id)\n",
"# get the lower left and upper right corner of a rectangle around the region\n",
"y_min, y_max, x_min, x_max = min(region_mask[0].flatten()), max(region_mask[0].flatten()), min(region_mask[1].flatten()), max(region_mask[1].flatten())\n",
"\n",
"x1, x2 = x_min, x_max # possible to add a buffer of step_x\n",
"y1, y2 = y_min, y_max # possible to add a buffer of step_y"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from aps_io.bil import BILdata"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ nan nan nan ..., 0. 0. 0.]\n",
" [ nan nan nan ..., 0. 0. 0.]\n",
" [ nan nan nan ..., 0. 0. 0.]\n",
" ..., \n",
" [ nan nan nan ..., nan nan nan]\n",
" [ nan nan nan ..., nan nan nan]\n",
" [ nan nan nan ..., nan nan nan]]\n"
]
}
],
"source": [
"### Using BIL file format\n",
"bd = BILdata(r\"Y:\\snowsim\\fsw\\{yyyy}\\fsw_{yyyy}_{mm:02}_{dd:02}.bil\".format(yyyy=year, mm=month, dd=day), \"uint8\")\n",
"\n",
"bd.read()\n",
"\n",
"a = 1\n",
"tmp = bd.data[y1:y2, x1:x2]\n",
"#tmp[tmp>250] = np.nan # removes the \"barmark\" flag, which anyway seems unnecessary for the new snow product\n",
"\n",
"precip = np.float32(tmp)\n",
"precip[precip>250.] = np.nan\n",
"print(precip)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"### Using netCDF file format\n",
"nc = Dataset(r\"Y:\\metdata\\met_obs_v2.0\\rr\\{yyyy}\\rr_{yyyy}_{mm:02}_{dd:02}.nc\".format(yyyy=year, mm=month, dd=day), \"r\")\n",
"\n",
"time_var = nc.variables['time']\n",
"precip_var = nc.variables['precipitation_amount']\n",
"\n",
"precip = precip_var[0, y1:y2, x1:x2]"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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aqHH48Am++93/62o31JPm5mi7tMqBRtT5lgN0oISrCngHaIXEPCi4Ekyp0FIx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\np9UKra3gC1seSSawBv05vYgW4t1AI6Edlm3o2ONJwAx0foH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"text/plain": [
"<matplotlib.figure.Figure at 0x5e84048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"region_mask = regions[y1:y2, x1:x2] # redefine region_mask, now clipped to area of interest\n",
"\n",
"precip = np.ma.masked_where(region_mask!=region_id, precip)\n",
"\n",
"plt.imshow(precip, aspect='equal')\n",
"plt.colorbar()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.0, 0.0, 0.0, 0.0, 158.0]\n"
]
},
{
"data": {
"image/png": 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xXo+jhGDV21QtLOKv3E8bqQgBRiOE6s2tJ55YzuOPf3r6jdeZyH3GHyFcDzwP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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x46f71d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"flier_low, box_bot, box_center, box_top, flier_high = regional_precip(precip)\n",
"print(\"[{0}, {1}, {2}, {3}, {4}]\".format(flier_low, box_bot, box_center, box_top, flier_high))\n",
"\n",
"precip_high = np.greater(precip, box_top) # mark region of precip over threshold\n",
"plt.imshow(precip_high, aspect='equal')\n",
"plt.title(\"Område(r) med mer enn {0:.1f} mm nedbør\".format(box_top))\n",
"plt.savefig(r\"D:\\Dev\\APs\\viz\\resources\\precip_thresh.png\")"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.0, 0.0, 39.0, 63.0, 158.0] - (70, 90)\n"
]
},
{
"data": {
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rDBpIoOkfcdtzEEKkA3ullFN6bD8KrJRS1ggh8oEyKeXMEOfrnoNmQHA4HLh6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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x896ef60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"highest_med_low, highest_med_bot, highest_med_center, highest_med_top, highest_med_high, med_i, med_j = regional_precip_max(precip) \n",
"print(\"[{0}, {1}, {2}, {3}, {4}] - ({5}, {6})\".format(highest_med_low, highest_med_bot, highest_med_center, highest_med_top, highest_med_high, med_i, med_j))\n",
"\n",
"plt.imshow(precip, aspect='equal')\n",
"plt.gca().add_patch(Rectangle((med_j, med_i), window_x, window_y, hatch='/', fill=False, edgecolor='purple', linewidth=2))\n",
"#plt.gca().add_patch(Rectangle((max_j, max_i), window_x, window_y, hatch='\\\\', fill=False, edgecolor='red', linewidth=2))\n",
"# the max will be unchanged in a neighboring window with a potentially higher median and not update its position - it can be removed. \n",
"plt.title(\"Kvadraten representer området på {0} km$^2$ med mest nedbør\".format(window_x*window_y))\n",
"plt.savefig(r\"D:\\Dev\\APs\\viz\\resources\\precip_high.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**TODO:** need to remove the zeros that come from clipping the rain. Maybe I need to make my own conversion from rain to snow. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Clustering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Spectral clustering"
]
},
{
"cell_type": "code",
"execution_count": 258,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Automatically created module for IPython interactive environment\n"
]
}
],
"source": [
"print(__doc__)\n",
"\n",
"# Authors: Emmanuelle Gouillart <[email protected]>\n",
"# Gael Varoquaux <[email protected]>\n",
"# License: BSD 3 clause\n",
"\n",
"from sklearn.feature_extraction import image\n",
"from sklearn.cluster import spectral_clustering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate sample data"
]
},
{
"cell_type": "code",
"execution_count": 259,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x11cfc550>"
]
},
"execution_count": 259,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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P6Vvp11smuF77odh9Yq5Px1aD892EnjBDiB2zmnpd/joubynjWzynOwDg7D7/8Do6z+pu\nK5hqwNkuT88V1R6y/wWzZYAKDMeMS0Mzhic1bCzDZ2LZpq6x+9g5YDhx4EJbOHuyRBY/kkjOQKkc\ncG9WALTieaYZrKGIW7VktGCtG60MVUIq5QRJPXUbJuiegnUAsHYrCSMK8lkKKqXdMPlwpeMrVYEg\nk57Kfk7IL6svYLpNLcVgr5O4Uai15yZJaq50AUk/tC5ZY9aFulu3t4odj/pXJv2Skh0LfpXPI2XX\npurGyZxVeYzpzkNMd1p6sFo3pqAaDyJNeM1VXofn79RSPJiDELDqnRdMCovAWGYWCSlvErxSL8Om\n83JkE4JN42mJJhsdpstSLpPrLN0m85WVZeZpfauY8bVoRg/iWjMuYl8pQ3mcDznvJh1bpbnMz0W1\n9wMASnYxXWktoqV62+/0Hn/o5/2lujcAAG7dJxZbyWRhNaIZc9h3QKs2jcemfyfdeABWjow0BCz9\nDfD+JQwWsPaOmi+EKpv44Ciha9lCzrd1CvowVdsjE5gTpfMiiSQSI5Vj8fv5Ip0Yi6GWT9+O+bJJ\nm+0pkcfYyeQGrrz1E/wZAHDnvj/44+SJqf/dc7kAgF/ukgM1re/TeVfgPQDAzOnSaSX1Tr4Z7zIk\nlpEs7ujCGF/TVR3NHB3iC1RTaXpdtGw2dVe2mBemH+VwW+QPh8Y85wqek5a+Xt3/DlUKHXNPeTW5\n1eP4vgXZ6asAAKv70lu5X46TVXd9qNME7wIAZrqe8kEvT4JSSXueKblJTMndKOdITRcspGSpma/L\n4rw4Xl/j6zaEKoqJfA6hU4edim4/uYNSUkdSkTfSEpo+AR2y6FXcRSunRKzLvNvTI3geADB7FGnG\nioFsgRe1yNlx36nlNsVRSQM5nkNSoJRVR5S3bvUBfFoW52tTvZPPpcIUaNO2LOi5SU7e7RXf/Wl+\ndzKKtIcAn5nEbF+zX76C3iC9r0ceFbr3k8eHhzqlRfSE97qoSCeSSCKJlUqx+ImHDqL8Mb6tbGGC\nxr76luRb7q73fhuqPLteepppCWNYzmkILvpmT2zF0tY5PJfpBPPG6BwAwE1lEt+Wrxad9M6e01p8\nLQMt7Q+3N24LhMSPxgPEqmnZ67nsAbd2lC9Qaf2oYBUfQ6ije6fSKlgSTH7cOWbRJZpjuJqEJVrU\nl9j3bzSFhb19H7Mez7OoprtYu4QpLGbZaay6YhZEoO9tMxYA8LujvwxVflBd4seig5fK/qtl/xZd\nGXdP8nF36hB6Ta/RC+D9TJ5ovJ4t9HpI4GrxROw1AEDhTLmO1n1Y5NNf5jDzOVNyu5tU6C40YEvE\nE2mb/maoU7CbvedKqJNGj2iK9y4aP0rsYzfdzXiykM1zKclHcYB4rAbwHqxSgHWODf7S6SH2XHyB\nBTSEN1Im+gKxa6qKy/hSf8kKaV++koXGw1I8Q70VPjMpN/rjNK8q7kANHMJLLjey+JFEEomX6Icf\nSSRnoFQaV7/tncJIWF/WIvzuLHLWjywQ971FT3EDbapu7276xbPosRDYuPNVnzOafhNzMbmySe/K\n9tGjGoQ6YcpDP1L+tgk90oYQqHH1YnVMhdvgIY8DACYvvFc+UDdQ05UfGs9KK7V0f3X/DGiV1rWQ\nXwkIpq5dYo4BdYokLMnOYrrR0X2/v6U/kKaTdMwKmNrUGnWyn5XjaONTDVMA4B3HlmOzxD1OuZ3p\ns1Vsgd3cjGsiQyp1bxWku910lunI0GwcP7iMvvE2E8pwXtp1Fl/6z2Uyhp8k/zlUeQyjAABXjdkW\nsw9839LQBS98joSufPrCFxsdDStzuaXbffcN0kJ98s/u9bqcL60MVWD5hbI7QhW9NyFguwonC4ec\nuTw2RZrV3qQ02Vwz/SE+u9v5oJpmolrlqd1+QgBwir8nlycL2Wjz9GygXwTuRRJJJEYqx+LPCbwl\nyrffcrGH1QRuUk9OQW19Qd6E2T1p7RYyXWXSglm3sZKsgGmWIn7hsyWxbZgBbzGK/Ee1usXVek8g\n7dGCcQr4KOEjR/8vA0pJ9ZVbpWddBABIPCR11+U7U2LPDfiW0HpcHXOuV8nrJgsR563hwh6a8snx\nOo0figWtqrBvQM3afjwH9sl4sEKsbY8+BAS3+r5u2pmma10x418wDbesgGk0W9+vGB69HgXu3nnR\nEG/yuZ3GG8bUawxwt5AkIXopwUYxUsWDPMElfTi9CL0cteLW4mtRIG/bkl4yjo5DfYVbmK7jPSjn\nShCJ6glM8KpVn5UL07r5sgUywMSOxuuZIvc0cSCB5S28x7bRvD4rGbLJbETLP9V061EPjc9B4mU8\n3qwUr5MUt1Vp7tO6rdOFOr726nbA6sjiRxJJJEYqx+LvDHz9vO3AkyubxAZ8u2nKz1BaQ1JHXmx9\neeO2niSybQJpqUzfJeexU800z5tMypXj/KS6xGn7Wf9e0+T83nzlZ/LHDtksvK8DAKDrsmV+QPyO\nbFfk9+al6Cpbtgfcw9yyVsjtZwfVxmaRC6bLQkufw60prgljOaYrByZPBeDJTQDw7JuS9kxvzxix\ntcSICYvNuYrkXMmN5YB/JzHlxALT8SiuL1wY488jRdZiBtpINh4/udC7Y1WI45wYLudo/Lzct4+P\nXRLqlO2U+xTsE+NUeD1j9JtNHlWdCDotGM3tUq9STi9gOy1iM70XtmeiWvTnue3MrWI1vzG6wtjF\ngH6SO9ZuO6H3AwBpsSSmzEdpzTsZa57HLbsR9Ugn0ch6WkqJllOgbXv2Q1zvQaiHWgqIMv6oeIBH\nltSK2cdKw2abscM1iyx+JJFE4qXS+urXuk7i5wPTfblg9p2M23dJ3J5dX/6/yyzOcyVppmEBSFws\nJAeQTfqYuMUgh/prS0oVy36kRKD2oITvPEuxVGvyaez2wFgfVJ17TGLn6s1PyAdqQTT2NKsclTeU\nbaIebw23npGM3LG/AwDMXCNUYiVpaF98ANjbnQfN4AeKOeT444RFMNeIZW78FmP+vr5IJ3WGkEKu\n5EW/0V8IJVXG+RLZm2q/AgCYP52MlFWyUUv9YVldf31KlFKLz5LitPpm7L1l7HnPE6e4hK5Rvh97\nkHxKowS8Yv7WMH123HaGVymn1UxcxA9YZa2LVQLwy7xqrK8enHoAtn+d4ggM6d11fJ5MEVjS0Fha\nL/KZrbDPp4p6U8R1tDMv4Lv7lBXxAVdCkCW8DaQnNVzO0WW2FA+th8/uHHiTFzgHwLQoxo8kkkiM\nRD/8SCI5A6VyXP3HAuB2phxWm6os5XBkcKsA13CTq1snSlWyWYvOxRti2mLr35qyI+iUMs9zmA9O\no++lOJa6dH69ghDY2dkrHQBQv7RYrsECdnTfw7DgN7Gf75vo0y91riiN0S18k6DVFgNaaeaSoJlb\nLN1d0tKLQpW9V9PVJ+B2699mAgBemm+6/RAA1M42Jx7jhVqQUEMEhkZdBrEzTEF3r6OpRuWj66Ii\nuXxOJnvPcehTjwIAJnYSck2YthoZqqDWewzxptIFZdS0racPGdRV/fkr9N81xZlsxq4LsCtIrK75\nUaPziGy0C1GCAndDjA71AwJ3Tqn+bbi1YYE2yNHQgffYtTS/GQWr9ZHV+bNpTwKHTR8lAWcSb4pZ\nZSuhiLUV5WfzeARujxhPXatFGaZ0eZb3r6+/f0mTGXqsqAXcGLn6kUQSiZFKsfhJRz7FkS2Sekhr\nc/LyQ2GKSN+2tvqJ1mPoq2Jd1DqsLTDLNGlXFa3Yo3EPfmledtLVGTtniDU/hzTV9NmmKwrJIPcN\newAA0BPS/aTRp3tClbEXCaVzzAih7upbOKB1+kuKJ51cXCbHTmC132EatPfLfVVdq9e5cIV6IAS0\n3GfmvuRwS0uYPFjM+3Gz0EcILtFSJKdJF5qygaYVjJ5DrZTOmyUoxVssrV9Xyq2x5phDL26BeHHt\nhgjqddzcwIJRRKcIbAUneE8eMcfZj5jP1ONqMLrY60gXchydIbaq6pcCriZaAFDHrp6CpsgMBya8\n5g7cviqbkj9a90Ik9ZUyAMDLN3UCAHRMknXTz23oddxG3qd8fqDAnyWYvUqC2XhOgs6tbRGvAGk8\nrdvQu1VaZ7GKcZL8BpRSDAA7LpWegWkfFWKv+1Fk8SOJJBIvlRPjrwuQ3lJec58c9BUTIXlF69+1\nyCP3FEUe+iYkgSdmsct1ksIa1eZBAMD/xv8FADRYaSwG3/4b7hVzd9UkvvrNGoNhaigz7v+3epXt\nA+RLrcNvMFzOUfKkWIzUx8u8ssaNmuJjSqqwu8/11FvEeF8zhvRMMMgfxi3jPaLB0KXDc2q/Feqs\nfFMmJrs9U6QPSIo0ZoHH+LSgbm2HoXku9jt1GJQEY70xeg61Okscr73gWsJTrt+iu/LBHQxMleRj\nOFEagxd3l3udPob3/yKjoxkrnR+13JcanZWxnxW/xONZr06b1dzHbWHsPjC3T9N5c/vJ3GqRTp2P\nPWVXCVvu50zvMibXjkEAsOwZpqL1tuscmFRy09tkgqtxYUxdAv1s04lXu0afmEb8Rj0100fw1oeI\n/xT0iTrwRBJJJLFSeV12ufR16zq+1HJtJ4lRUhaTfLKatNAV5gAat7PIp20bUhmXGmYD46p/sBut\nxtQxZAyN8V9j/NhQLHWwxqs4IvRzJ8ob/mr2yk8dasyAIsSKJtMqHU2Wd2j1v5zwunGIf+gBfGTG\nRbC7oplsE7T5kK0XIjmo96Lf879C9nhju3FXNPbWa9dMhJ2DIm61d78mFzr6gLRhHSmcUjrwnt6k\nno5jPP+ZycqEPQllo4VC1kqpbCy9GgDg1GIblnA4L0rKoRVeN9lgIYsEC9l+g3hcjR4R3OXAfZ5c\nVZVuYcoYuZ49Y8Vdydxo1rNSti0tfMlqemrTeI9Nzz0l7oT3XHEJQ9LS7IK7Wam7spn+nC/dXQSh\ngv+VLkS4wKZF/ulZNe1J5H8+kf9WBixgcVXoMcRnEoAQE+jabDYWujsiix9JJJF4+Zc/fOdcmnNu\npXNuu3PuPefcf/HzC5xzy5xzu5xzS51zJ8OikUQSyWkp/9LVd86lAkgNgmCLcy4JwCaIw9QXwMEg\nCB53zo0AcEEQBCNPsb802/zsAgCxtfbqmh2CfLfn13QrB55i3Xd1SxVcWuVVPlgulV71JlFJPWBL\n7tDqK63fJjU+JIJYaRz3//b+z4AAnVMXWrkTdHs/v8Lrnvsu/+jPfbkSlzMLdIRurgJZ6l5akClX\nNjsvkjCl4cwi+WCd0dExMyRKyBdCSMuafr5XT2Ivg3x+wDRqvTZbQ53C3T+WP3QF4xx+wS479Z43\nuvfQFSfgmpzNqsgPfQoxOJ/HUaBUr8/W0TMkKm0grmzKp/RhP/UqxY0FqFsGaajZ/1OyvWxaUO+J\nzqW6wK8bHZ1XVlWG4YqCl6/iZCmP+78FJhV07Csb9yd5dmv19LHagb6M53L4AUG5pCYedMyoXgQA\n2DaKtRUK/FnOv45DiW8j5VxpdT2b7dBR8fWPrKoFdPkW4F4QBCVBEGzh30cgZQ1pkB//TKrNhI8c\nI4kkktNcvhG455zLgNjaxgA+DoLgAvNdaRAEKafYJ8DeL9CtjvRX/tCwRbS33oFf842Yyy/MGzap\ngbwVz6kuS1YdKBDdKg28OT9+mACPkjnI7Tl6hX+vVe9O0I28m7CO24I5iklpiqcsbguEqb6juXLs\ns8vluAlMJa27yQBSW2gd1UK8p1+Y42kaaVLc1noi/WK/azJxLQBga29zIJ0z4lhJS2TeRlQfH6o8\n4MhpVlKOGgrTmlDBoaZtCDL9lCCTgoZFXrXeEC5SOkGuOVyAYq8nMQUX0ODQAP6huXBvLzYmv+M2\nulF6zeqkDDDjqs2tWm8l4Nj04vi47zT4NA14wtSX6qr3pdVwJlOn+y1/V+bgEsiiIg1eMWlibYlA\n78Rdxd+TBe70b+1CpX6xXWZLr0MJQAS4q8zxz/kJ0nlT0uRkpa0EDG+3wZcLbjkuKb+/HzoPFRcm\nf6XF/9qr5dLNnwdgSBAER5xz8W+Mr36DTBiH988XtPgfOdcgKafpV6pGEkkk/578bdV7+HylWKDj\n5dX+qe7XsvjOubMg76I3giCYxM92AMgJgmA/cYC3giBoeIp9g8xgG/aMkvg9caTpV6b9xJRAso0v\nJ9MxZcIyd9QPAAAgAElEQVQTstjDsCulbh2LxbQFH5plstVK2jQLgIqp/u8EJWwof0j//7LZQa2a\ndnthrfbml/1lNV0vHx5oKV5GrQIBD3a2jaUCA0D6LLGA5TfJ/xPV8g8z59QuPRq3aVxrrHAFvZIE\nxn0H2sq5W5kgXxecSMkQSxouwtnFOGG8vvRu7FvgZDKaBn65rs27xLpl1Y+lmSYPFVeibjUfT27e\nSm+A+Em7NlyYtG+XUGf3jB8AADJHyP6BlOXjsZShoc4dpEb/sFTmy5Ehvb25X0yi0Svy4b6b5Hrq\nbONzZHEOLbhSI8m4O7yfQBiTb7iBRK7edAHiad8AoCuoKw7Dcx2+1ac0z39P0pwlTcS9+MF0cm+t\nV5fEVOhj3I9Ob4th3hV552EOUj1QxaSs56A4jqb4NiaerENTnjdsBPLc4986nfcHAO/rj56yCN45\n7wNgYfxOkUQSyekpXwfVbwPgT5AINeC/+wC8A+AlAJcAKAZwaxAEh06xf4CNAe5tJss1PT5pTPhd\n0yFi2t8vE2t1/Et5Xf20pqeiLtvN+snb+eLi8k9rh/ngvNVHjKVpTUJU19A5A779ta4lQUuATTcc\n1V8+RCzZtdfR9bCeBFHgw0SQPyknIWgL4z77amSsmU8uRy4tffGTpnvsHUR2td5GL8tSiTUge4lb\nTkmzem+HKiHhg0POfkqou7p8FwAUP0w3gmh3tzEyCefgi1Bn1ov9ESOKGfRj/J5hxn6AnsMmHpdl\npp3e8G7U6wO7yR/aI0/LYO28a9aEcxB6ONcZHV5e6ZNE/ieJ1TswxBN4apSJmVQClxZkNZjqY/L7\nBkgB1iPrH5JzcegJ8l+sfLJ1qNvuEcFSNI6fO0w8me6PmBY89MIWvyTA0s/W8AJNfit5hUziFdUE\n5Fndg9kVC4ezCCphFctzt5HOfpkHvFrUEfCjCU/6+zfJLLoQXricXFbhOmx1rf/9GD8IgjXwj2W8\nXPMVn0cSSSSnsUTMvUgiOQOlUrj6WcHa8P9bX2x1spICWboIwS3+q9RuAuocKpM80xePsK+1Ocxy\nrjd+7Rrxc3e2ERfv4uM+ZXT+agFYFrcVl6xLK+bfLJdd+eNax902bguEXYL2PUeQqYAgE8GhDYM8\n+0eBo4ApKKcpJENMCYEoBaYU8zJNcQ7cSyDxSiI+GsqYVOSN4/8IAFi4ia1qtGv4Ea8T9j3QGm8F\ntHK9Sq3RkncbQLL/Aggyuc3R3037sVemO5s+SFx+TXeFRCEAQXPxNDe3EYC06TNETBfBi+K9cgk4\nnCkg2Pm5fqGIxc/zvs3mfdN6BwsnEysrmUj+/RbmYS2nVPfTugDe/woNE02m7tNk3uMRHKCSs0x6\nd+4gcf/V/f7RWCKClj+vYLWmZTWNZ1dhvkZc+sw6u2Hlc3gQu2SrgJ1pWVzq7QXGoKvMDhpsDwRw\nTVSdF0kkkRipnOq8XgE6PC+g/7L1fjGCLi3ZM2ySmLcQQJrlAaTMAVyg4EpJBwYD5QV2uJ9JqbxL\ny6Cpume49Z28Q1JP+MZXzCTfq1SQVqpkHH2zr871vIPsT9nthISUsIU2a/YPvHYKsEmJRYqobIQX\nTbeqt6GpGZOmqiD1N8FUEgKIsZquHu9jBnVzCBLlnx/qaOeesm00OfmyyXzaLGe1VeY5NSvW0yp/\nkmlBu3xTDrdaTamemrm+4EUaHAXw1BJeaY6jXonOOwHUih94lQRScxePp+UfIcqrx5t7s17uzdEm\nYs+KqmUAABrt9B2UwjboStVVMFbJVbYH43FuFZBUT8tWV6pTSe/LteYycPX9DXz/oIDXN9SUGzb/\n2l4x+wBA2ydYdfqClDomdolbkgvwnoPeA/UYTCef9PoEXLc2AJpEFj+SSCIxUikWf2jwCCY+zE6s\ndgELLUDQrrqfkZAw0OgwjaNvxFVDpf9ZTCENqbaFg9jFdhZfhTY2V9G3o75t7RLK/PtAd3mlnndU\nTFGi7bKrlorWduFELrPVe9nJY8/nNlc2JW0Yey7yQWIFx5igll7PdR+8KPFHY1U1Jjd4Fbec93Ew\nvZ+9CbG6ADKH0XuayWKojLhxAqg1Q1yZ48clkaPLbOnyW2HvPQDZr7Dbz8MS07cezV5wo3w/xH+M\nZI8E5apoEYxd1opU3QPNiWV8IvO+4WKDl8wiIKFWNzl2XwDhfMztwLTb65J2W3K9fxA6zuVA6A0W\nt2SXnvWmS4+Kegfqwag3YGm9PE5JAxJ4pjLItjG+Zv9yuNXOx7ZTNC+1YTPxWnbcTU/G5M3qdZW0\ntRa1HbiSJ7/d67SmJ/RuWROU16gZWfxIIonES+XE+POCcPHLGtU9x+cPkEUDO8wXIkp2N1qQ/h4V\n1jdnixnypl4/Jkc+sJZa4zMC/iFnxSLHjOUeuFQ8j4cGStfekCoLeCqtWlu1qBaFVzRYu7Lom51g\n7p57feVF5hayX9RiaIxvxr6hA6mjH9Oi5fILi08QRzg6hF1+CqUwaF9jH/89wyZ9j+4WohRK5EVf\nq40pD52p5aH0CobTK7DdXhX5V6vE7jrdsiSdMX93z1A1XJZssFjNxImMS+f5cQUHaXA0TtaY2uIc\narW1dFe9JpsA0nugc0enYmZz3xCxT/5LsTp6HIv8vxf32YPcKtZiMQxdXkuzMbpcWk2vsq8Bkf+G\ncu0ZO2Sn4qmGc53BrZLG6AEk7fVexuXVpZblnQfonej8m+ZBYfaLj0qVLlxDoYtpZ6QZm8YAJkYx\nfiSRRGKkciz+hydCC5TY2BTp7KRlkIpd1HuCZZ67fa64Q10x28ucZANO0II4uzqK0lvjV0OxPddZ\nkarFNA2WScI239BCc9XyKNVX3+yWt6gWixjBJjJcwyWZbYmrvuG1WYOea4dXKeeYE/U7tSomBn52\nWB/uLrXEmdNoBgxe4q6U+5h6pyDYJa9Jzjelo+lGXCIXFNwmueGXVwtecv3RN0KdRI3FNYPAVHE5\neQUxeAfz2us6SFluq41y/37U3A/sg9lNYo5XTuuZ+HGogp0NeE82yj3Z0Jxe0OO+fWwFS3RXJYsp\nbEaXIWWNqeFWD09xAMVjLJdBkXr11OZyq8VS1ktUbEVz/OrtWQq3XiohEHe33IehjR4NVSa+QHyL\nl9Pj0T/I4fb5vnxIo+upnpDef+P1tG0pONeWYzKn2vDkkUb3hDr3zX9K/sgA0Dyy+JFEEomR6Icf\nSSRnoFSKq58ZbAvpnAXXmrbYLNvOHCJppkPHBZkonVPH69D1SXlSXNaD6yRlV9zGVInNJUiiKTq6\nq/v6eZCp5lEJMXZVFz8tK5vlYR3gRYE+BXqYMtqXe1JjobDqrdXjrAykx7nyQVPd1VmoyqWvyYHz\nWSD+qzHP+gMpEEmCy2GCTufbxRs1fCC99762rDDr+1Co4u5nh6GJ4tl1fVr80gXPGHdSSTTafUjd\nVBM2ldON1d4BO2+iG/4Ruaymqu5oW4KNo+Xch8cLWHjOEU+1TaB7u70N22LfIaHIwZdCFdTUlKNG\ngUyjlZt0pd63T9mK59qP6Av38DqhW8w5rSBombDJq8xqJ9WCvT6aH3s9xEAL+rUIdeu6dwAAadqd\nR8OwB+GFc/pcd1F6jDzmPZMaeR19rrT5VAM+LLZZxo1xC5koOSffHCaD4OmRc+WDxXLgpF5mAZpV\n8rtIaHX4n3bgiSx+JJGcgVI54F5+EL6NW9dfGX6nSy19SfTs+WP/GwBwW7UXQx3ty6e03uBmp194\nYTbjQGMSQCYQzTFpuHXjCUD1p4VWAMiSfBRYIZC1px4XZMg3OZV44o9aHAUY7fEUxNN91MKamv3F\nq0lB7S3zcjifBSrrvNUM04C85vd5Ll9FD/SQfqgoLJDrDAo4T7W9TsDrcgpW6VhtalRBJfYvKLyE\npKibaYIsqJoZu09AgNLZHncESkNPQq26sfhh8YvOj1rYuV5l6xB6aq+IiV58k8ybLjkF+P4JRxfR\nE5kinsgfhni34OdXEaGLXy5dr2tEqIoKOmYJSqK5PW4fIASSS58X61uz/xcxnwPwc8pOxaEHZ7pX\nNB3NHoevCXiZ2nnPSddXvLVB7PE0dWfPpXO3CsCsCNyLJJJIjFSOxV8VhIUctR4yhJJiqcKolf5X\n+f9UIZjkD7gt1JnKVqtrd/8UABC8xXfVbHhRK8K33c5hjEs/NTWWWrijL3/GlSW5vmYz9SpSaR/g\nBxozmhTP6n5CpdzCXNHgRdMBAOWM9RKzvS4rWr1lZAppc67p4fc63QK1eurJmJSfxvb7BgnWEOIL\nN/se966R3MegvrzgA9JLnUkLFtwg8WvbZRK7FneQeLD2UR8jJubzD02NqtekXoepd1neTy726qNy\nkkQdu6W0KiFIU2Ac18vNO4UquuTWpWzh22jbnthzAn5elICj52hjdDT25i3dupRewmADTOTGXY96\neSydfvllP66btzHNqfdCPSSDPaj1d9eyn35Xeb4P3OQZWMlzWBx1IcldGutPhhfNXK7iluXZd7b0\nStOnDo7ZP709C3Ku9jlk7fZT9mQqcH9k8SOJJBIjlVaWq1TIXzzrA9zfF0hglZItiH1YELLKl5Iq\nqjnhVem2+6s7JPDa8EdTwDGGr8u4FU9Kx3s+brVjEitVbytxXwWR7QRbYqnWTVdnITpcbkgrVWm1\nlUhybW+aVO0Fb8o6N0wkEWUlx6dEEkNeCeN/jWeV7GNW71Ei0uoB4m1kt998sg6n43OC+OVH2Juu\nt5kUJa9okw+1ZIaCqhmNlbmSnWh369pYXVs8pEVN7PpbowbLkC0OQIu8sgOPN0mOt9432UVFINel\nFj/fiTkf3dLrhHF1rmwOq2d11JRnz66IOWfxpSzA2WYKcNRj0cVJOdYRN+UBAMb3zfO6StXVxU/V\no+jtVZT44xbydzSQ873NcME55pB+S2JXw56bQ5VPjslkluXRK2AfvbRhZpnz+fVijpM2iA05XvOM\noqzO7I58RStgW2TxI4kkEiPRDz+SSM5AqbTqvLAFsK1BVjf3s9jvsgctD1X+DnH/t84XpG1HtwwA\nwCshcgaMKuAqCFrrPYhbu0y7YiR0A3VRhPOHmLSZ9r8j9rb9XkHlqoY+su/jd/6Mith9dGmnh+FF\niSNafq/RieWD080upwt9qDpr9mubxm4KaDEEqciXbdEhv6JivVuZblP3mO5ouSnc+nt1pjufEZf8\n2UFSA3DX6JleiWNU8kyijlXxyMe9apg64n1cdzFTpqM96BgSpNQb1TDKhgysdzi8iPdkmsxtYPoO\nOg2h1OXnfGt3IgBI8OuDimh4cpH57DC3+vjky+Ygx1NztNHlXK68nmHKMwx7TLWfq8/0XT+69tqN\nyPQtCJ9vbaetvwG7hFZ5rG5yL/Hnq57ln73SD0lsU8BW6w1spaOGk/MAfBm5+pFEEomRyrH4/QL/\n1rvffxd2G/m1gDsdnmBfvq2+L198LXvQgC8wS1rQenKtqmOq79khfUKVu9rPjPkuBGxs3btaZH2T\nErxa3tzn6K7tzNctLcb7tFaXs968opk/XIIu4aW16LR+s4Z1C3V6jSZ19KxYHfisZ/jdyu5ieQ6R\nudHMvOrTPyGApV4O17Q4PNSAXwX0UnQO6P2UG8uaqAQWWs/D62iFB3FfQ5ENOxYPJymL8799kF/6\nSpcUy7xDLJiCsldt9JV3oRcRd/9ilrfWakFN36knY8lCSv1VXE09JUu4US9Ts7gkDQWk46446O91\nTg251wnqHRBMbd/Lr6W9srfwztOeF6Dt8+NCpy0dZ2jnStzRBTrV+3zMq4SkHl5Dwz78bSw060wq\n4Uf7KcyTe1NrmH9YzuV8F7/ZIOqyG0kkkcRKpfXV3/om2TA2xuffjZ/YAMBbsr27/VLaVZLkDda7\n9v8FAMx85i4AQNDEvMjUGqjF1hjMhMkhZVStCC1awZO+KKPtb4TYEtbNq1dga9DVems/NjUQ+7k9\nbHRpaRY2YF++T1j5Mtzo6JjV82Dtf3EPU4SUTWueK5t1/SSWTnA+lm6mqSa9PqWX2pSfZoY0fadx\npSXKaEqTFnXhTRz7HTL23xri1K/UcjEl+vKTQn65eaOv7w/PqVv1zgyd+mjDWPtTfdGJmOMC8AQe\nekTlJNMkms7Dh9vTO/mEayhcQo9kqKeJhx6DGknFYXT+7IKmz3FLA18jV4hmNap5rm24fJj2TFTP\n1ixr1a4nFxP9KavS8viF9VqLuGWn4vQhcsD9ZR6gKN8W1+lY04P2+dT5/QxAbmTxI4kkEiOVE+Pv\nDHyZoe0hpm9JvggTLhNzWbvm/lBFY8TCNWLlFIUNGpsXGQkRpRtIWtlCU3bUq2j8OPNi6dHW5wap\nEjmwyDdZW0DY9Y5jsqRL9b+I5Vn+I3+YazVeVFqvXgN5SQX7jAexjR5EfD98s5jnvg7s2XYrOaj0\nVvaMNb372nPStEOQdvax1cJKJyU1ec9EFhht9BP+h+YSoP98I822zo/JfmhHoLB8eTRNNS3J5Evv\nDHUHrxe6chhLa2hvUWZF89Wz0T54n55Ch/hBSXdmNkYbl03nnddczv/PMz3yenF+jnYXe3b8LHGf\nzp9oMjfq7WhFrHoiPHdgyEyOmQ13NX8j+gzbxS4VD7qdB9rJizEQRuhR8VlJHCj3+vLk90OVzbXo\nOuYidmu8gvFP3Q0AGDHzafmAvfu6zvVu2G7yebcVNwEyzo4sfiSRROIl+uFHEskZKP9ymez/iGyE\nJzZ0MZ/rQgMEQirOEo7+L2uOCVXC5oEKCtK1umLIO6HO8iHSjjt1griGe4aJm3vcXN5SInaDC8Q9\nLV5EHvetnsfdvxp9aAX16H1da7v06CHpkhffwONcIsex69HjIo6RRJDDVxB8esm7nnUy6eIrKYNA\nUmqSiYkIou28mFWHl7Dq0LqTrO5bPJGA1m8IaJm6gB813yV/5MumgmSjhLu8TiLd/6xJ4rpWLKQO\nj9MEhjGjBBnlmBCAU+ITAPyohpDjEzRlqOlA2wNAU3P07FM/4R9m0ZRy3oNEpt8S6d32+sAchym6\nQ9UEJA7n1pCqHujB9urDpRnm+0zjXc5wx5n0ri6HlTiSnW8+k4UssNd4z9pDIFtc/MRU6pabOEzd\ndSVHDZbvNvczpZz53C7gdmPsPgAw4sWnY3R7vMWmnet/HuoMaynMobPTj8FXApwskcWPJJIzUCoH\n3JsSIOEWLuLYy1Te8YWXOpotoQtoKSyVMZHWsVyspXbw+cyUlH2QT1aGVrhpbzTT5aX0QQJ/gwn8\nKb3zOa+jwNjRyfI+rPqlgHv/SDQkmB0cj6bJmP7Zdy9Bujt8MXrpH3nOR+Scv71PTOuvXjc999Rq\nksSyfT97040whe+6iCSNSFEyu+Js2XuSTiF7EtZryO/e9CpqnV6+nmm3T5l2syBoPrc/4zU05zVM\nKI/9HvCWVMkwtJbbL/UWP1ywUtOfCuTZFJQavrgqOFfon83gL2JlDzxM2vHrtme2yL7reQ928h6Q\nxFRyn+m5sJHehIKyuoT5AF101HtjXdOl7/vCSXRTtF3kYnjhZ1UulEm8urYsDnOJcbVm3UOWV/xy\n8CvMb++yItmuujR2HIf8s9c4S9Le27pfBQBIn8t6/LG+Hr/FGGE0vbO9LdA4SudFEkkkRirF4jcM\nNuFvJOeUbPLWoFYzYVEcOijfzakprJNn8MtQZ+XuzvLHcL64tI7bLAaZMpwdeK9n0QoLVPb18nFW\nncdpBeIpm7bTjZI7lsX93zRwUYulpJPqfUk2URzALH0c0ALOSxFgI4ftVWp96q3V0WQ5zrlH5TjH\nmGZKNH35dKy6KKgWLjVdbwavi36QJqxei5XqwzhWLZBRo2QW5igcIOdQMtVVk7bF6tiegkqw0WyS\nLoFlFrIMWJTjWOxzuDdxjiYmxZbLLXGK1R2Epnr1Pp8HbVFHXLR3fioD6PSWmPM33rw51Mlsz0VB\ni03+FUDShZ5wU7f6bgDA1jUklBFbaTeCJJv5BoTipdcaw646u3hDJ3oV7S+xgqtb7nF/AwA0NT+r\n4VzN5Q7HAF7TsXbhUKUZK6kql1ubGh1Mdtg48ZrTR9PiX2tWcVFvokYAXFYlsviRRBKJl69t8Z1z\nVSDvn71BENzgnLsAwIsA0iGEw1uDICg7xX4BZgWetrjLvFE1ROWLsOHTgkP+LWwfCpTsZsxzv7y4\nkqYJen7kMU9p1bdk4v1i1b/oyvjfdnChcQzyOS4t7vjLyTqhVdM+eKYQpIKlqB8nkyAzkOi7Uj2t\npdbjqBfAt/q+S4wnssg2qEPYFffZlr7A6HesM35vGclBSgCaZfZTy8CkwtFJ9Eg+OuF1eB0FA9h7\n74fMOtilvRVhV2IKw9uCi7lPe59N0fnZOZHZhisl23B4o8FEtFuw9jx8N3ZfANj+EnGNZwQPyGMx\ny9id5tlUKqxmMnROzTINYeydL5vkyew/t9GARmGBTGyhS3g8Y81D68lMTmp9Gd/l8MSb0CPd4mLH\nYBYiTWgQS0zb21/54+ZcRbLJepUddIql2islzTOdLq4qaZNtayTGh6yohVEP+Ub/mlWadU///9ii\nmUMAc8XASAArgiCoD2AlgFHf4FiRRBLJ9yhf64fvnEuDdJOzZQVdAWgHh5mIJTJGEkkkp7F8LVff\nOTcXkrxJBjCMrv7fgiC4wOiUBkFw0lpTzrkAAwNAm8UYT79TFgGa6QRo6G5pnT4A7PgZ65G1jl9B\nJpvyU/cvV3z+rnWkO+UUeGZK7VKJQhzd+X1tZKg1jnngp3qBuMXhCrZaP22bKyqIpgVfcVVjMfX9\n+p2OWUMP29xSiSx0QQPWw1f5wi+XUYVdWI7vJDFdK/AMkKhc9s+5Quy5dN83P+nbxWQQjUuZVR4z\n5tUX+Zrv7Fky9xVM5+1Ophv/iLjxBfedoppRU5uKxZmVocKQQYFAnRPTAyCcMw27mGp19/jFJDCO\nLrnedxK6NPQDgCMjJfzr8rTkdWuzZHL60sGhTgioaQGoPjsaStzuVdOuYzPLNeKad2gjbKb2IRsN\n2MSJf+luCc3SnuY+S82SunrsHM77Avr4NiNJtz31Laa2t2bGjhdAn0GSBp55E59rWa0LSY3NHLRi\nCLy44p9y9f8lc8851xnA/iAItjjncv6J6le/QTbkASSNoXYOcNU/O0wkkUTy78iXf1oD7KeVeer4\nP9X9lxbfOfcIpLvalwDOAXAepOFzcwA5QRDsd86lAngrCIKGp9g/wJLAv/kX+O9SpkgarnQwu5Uo\nUGP7lSmwspZvwoV8E5q3ZVpPvmWn8y2rAYl5ewdT5MW3nMSNa1/jF57bEVrm0kHyRl7v5A3d6V6v\ncmA8CSStOADW7h9+kGmqB02aSkup1crpuU5hqdWruG6pTFBMFyJat4PdZF37lOG0HO3McRQo0gUx\neScW5nq+8U/wZxl7AceuVtjSZwkcHs7k9Tws11PyICvmNhr8Vp8tBcw07WmPp6CnknPUOzBU1LCq\nT6v8+Kzc1iM/VJm3XwrVT8wR9LHeEOlFcK5ZSGzrVUzRzeM9mEUvwYJoN/J5/1Keh5TL+Azm8Bk0\nzkGH28TCK1ls83xhGj3SzaxHP56Uck5p44eEZNPE5EhnrSGBRwFY9QCuCVWQ3IpA5PDU2O8sLVvB\ncO11qKzuJV6l4XPisV2O9zHf9f73wb0gCO4LguCHQRBkQn5KK4Mg6A1hledSrQ+Ahf/qWJFEEsnp\nId+IwOOcawsf46dASLGXACiGpPMOnWKfAIsDJLQ6HP+VXziDb7VHxsib9L7+T4U6YUpmirwJ7xoi\nKyE8u/1XoU56IzHjV0PokrNmxlEkAbRoSSpj8U8AAMG4s+ULk/Lb048purlyzuXduWjGVBNoaQ0O\nsywV7MeWoF6GCe223sCa9gIxhUdbMcX2iU+xuU84/2ogbqQ1/8yYKXopwRfy8i7MJS13qqHsKvbA\nePlAB/FM/g5fsF6DTdt0+a92K9k11i5ASgdh7kUCxnR/nPkptVbjja7ScJUIpDG6pQBrStOeA8C+\n7ialWciUphKndFmqvf7ZzOxGcs50Lj+tHp8hH4UpNJK8OmyItdgAsPke8oM1hTmPWz65o57wqbFH\nO9FNmUIPokg8iHZtPWd35SaCVrS+idks0sk/eWn10LtTYtAAs5zcC3LjQu91vTw7MfF7EeN3LVjj\nY5k8zhd0lRUZ8KvBV6fzvlF1XhAEBWA2OAiCUsQ4K5FEEsn/FKmcIp0tvstucrZ5O9GKh8ilvoZ8\nyz3Ue0hiuQv4Sn7nGZoQ03lFEf+sj4X8cAtf4w/MfNLr6Gm1IISWousg371kwVSuP8VYc89kegCf\n+jEXXkRrq33stYc/rZ52fwEMeYZUVvcL/n+dfwmn94ztrXZLsnTdnbXPpxIG1/k/AICnbxbI/+hs\neg5LDTlHswxKwFHPw6IuvK7CfryGEbwG03deKbaff8JzPCjn2DeeBTDLDOFI0XjFFZTo9Buvovd0\nSQO5bx0LCmI+BxASbrY+Rw/pBrF6zj6btpwbQEo/ic0/P3JO+Fn53pSY44V97Fb5/TIP0HPYfbl8\nsELuRZUbxU25rvbSUPeNscw2ZcimSkfROZHqFytI+IzFZx+K95rSXMZVtaoH1w68xonSbrvMcKU9\napbH2lQvVmcoPb9DxvNTr4veZebbvJY3G4UqXdpLRmPx/O7ALVGRTiSRRGIk+uFHEskZKJXj6ucF\nyBwTB84AqHenuPFFByWH9NOabwEAlo0yqSytny+hy6NpwTS/CmxKqqBMpa0kJZOw5BRAYj6BRA0j\nGCrUam8Alul0yRQfyeDW1l/3kvOm1ykCABQX15XPFwjwk9DLnzujpuTLCn8tjUJTn4hLSQJI7yqu\nfk3GQpv3EQGaY1w8pqCCcXxPa3NM0wzn6JbYasERf8wDAPwUb4U6HfuLm13BCCjsvGM7DGm2Tt1K\nBcy4Ym9Mqk5Tcno5CpjZZcS0NwJTmC8/yF4AI0wLbg01mOrb3ETik2YzPUM8vQ8r0WYKYtuuD2s/\nxkWqPCsAACAASURBVJoYQMM/usvjN0hzyjFlY0OV8skMB1jdmfYqwTS2dG9c15fDbdtFTrxGepqG\nmxeqoMpEuv/rxP2/tauQWTX9CAAnmjM00MpSTSnmGS9cwUq97Vqucr9/ztvVEeJQZzK4hk39nXxx\njf8NZ9UV9tPWNa2A7MjVjySSSIxUjsV/zLTXHu4JLglJQr5oWVPeUqv3XS1f7DTWTgkMWn8/MO5z\nwNOBM7jV7iqmZj/cT9mW+iK1RBIFGYtk0/R5+eBjw8O9iaZv2n4xhScW822uqRrTWSbxMQHCaiTH\nZjlDOiYAXMiBFMk1p7WhBRpv8oIcc/ALvrwVE7LUWJ0yHYdabtvaW5eC0u5DTAE+169XqNI/hyV/\nuXHnUPByiAEvswku6oKWmj41rbN3DiHldzb7BGqqz5JqOGfPrhbaa9+jYjXPmeefzbQ+sWmu0CPx\n64aGn/V4iL3oHmYvui+9SvJIMd/1qwmV9J27BHRMepJVn7181WfCNAJ3ufQW6Sl1q+/LIhcdlB7c\nFaupo16BBZ95/q49BUheeJWUPKZv2BmqFI9vEHs9Cn7aRydPNq3/Kkju2glkcOUYHT3vKgADI4sf\nSSSRGKkUi984eAfbtrNPWCP/ljt4VIgVP6kuVNKaZITMvtt3DQ2tuFro+Pgb8FY2Trf1037ppLWj\n2sXsX2sIu6rM9NY8jCO3y9u3TyMWRRSb4uoVpIHGW3gNNa0l02WU1AqoB+LDv7DGu2QmvQDGk11H\n+DTjwt3kHm+Tl/de9huoc51JrSkNmFnA2Y0FJ+nxsSFUMvX43+zk+2PtxGNSfqW95AI+J105TbNb\nSr21Pfx0SWkW9ISeyKtGZ4hsAmZhnfYrsEtX67zQyjWbIUSszUtNF9rGvKm6HLXOoeGrpHeW+1ef\nhSHLZhIr8u0dPL2V56r1NJ+D7XwOrAeoqTVa0cad2fPupqtClT6v8BlZQ8CEnmXme9tDnT1TiWvp\ns5HBrfFE1LLX6ibjqcovS1403qGOzVB0AcQu16XYx6QuwNDI4kcSSSRGKifG7xiEJYS2iylG0npm\n8P/6RrN9xjQ2z+NWF5y0lpUEHo3Jrq8pFTgLXzRrOpP00HU546wX5LsOPb1FXPYmLYSiuBqzWrKJ\nUnOHct6W8IWqsZUtMKLBSp5C2vEKmidTYJR8I78j1TI5g/9fZ0wZswqdnpAy5td/xGW2V5lzafEL\ne9vpUlDHDA6QqF2I1cJrI1/Tez+8B+rRkG/zxq0cg+lcHFp4Je5wupVSDACXlQq449SqK3vWNBFW\n0tHhzvI8JI+TclzNBAHAnt6xVN0qU4imr/BkmrDsdZ52VGYBziqzZDWJW6nD6GntkzRFQqKcs2KO\n6QKtuJQ6qdqRZ6hX0aKepBxiBEqrtTr6HKnjqBCBtfj6jA3m7+MIfxs5XqXKNl7zZF6zdsAoMsfh\n/PTp8yxmul9GFj+SSCLxEv3wI4nkDJRKcfV7BNMx+x4B7Bo+ZbrrjJXOL1lj2GBwAuuprQvUK5az\nnJB6CnLOUHHPEibKd0qcqQbfwWVbMdGvxeJCpQ8ikDfflPBpPUGuuNsXVZMOLoUTsryOpgNZsZWV\nLgusb53PsVuyj7p2+hkBqS7N5oYqi7d3jzlu6hC6oGMNqKNAVlHsGIJD3otbMoNc+KvE7965gWm0\nT4pDnVkXS4jQa5vUA8S0FldR912xRQUA6brqgh8A0GiqjHXmAK5AfAfjANtdR118hhPb/8jGmpOM\nr09KvPsln0UN4/LMcRS8VBdawVSTsu00gh2dtssBuzUSn/pdViMCQNH+DABAVXY1CitEteLtdh+K\n3pouqybris0zpxLAswAggcPENFblaUt3Ayim5DPkeFJCjhYj5B4dM/nYrdPl+Um8RY5zTbKgqFXN\nj0G7/ex9mClNdfUT/W94S936AIAmd38ATI7AvUgiicRI5YB7OwPcWl9IGS/d5dtGJ08kkHWNAFkJ\nC0iYMBVX2EmQg+SJHsuFnLHeFNKfyzfyx8ekWP7GakKymbnb941OvFAWOggruBSwaRKcpHNJspin\nwrtp6W3HHO39p5wXHmfiqwMAADPQN1TdupRegJIwinCyqIVhm2cFneyCn9XI0VULcWCspJ6Gjnk0\n1HlqjZhm7SVYZyNTfbZSjst0FTRgq+wR7JnX1+goQPcuK/jmCsJV2P0Uy3Zx/fgQWNTae5Oq2349\nLfwsua7DPdjZ5zVvWd0MuQdZr9Dzc+cCABI+ywh1tPpNQdR6jYTuXdjXeGN6T+i5Nb6N6bef+fSb\negx3tWdfhzXS1yGrDc+tC20AvpKTLbcTc2VOWyb7FuMFo9g2Sqngmro1mciUVrT4H9aJHafpEBWC\nqXrb6cmkDN0XqpT2k/1TZ8T15TNgeNM7hXS2eXt2tIRWJJFEEiuVY/EXBMjqyjfqa/6NWq8z39qT\n+NbuImNJSv0s1Dkyj+kRxkxVWjGlceTcUKdDXclhhb3R+svrNuExjweERToaE2roZJuC61tb43jt\nAZhqUpCMDbFYAlHFA8omitdSJde3nzmxV9IuulTYgX00hUdMLlLHoW9tNaims0xSPlNFs2Qufj9A\nWDq/eM2sVcXjBG/JC371RMFPstubxZK1e86x2H12tk0PVRrMIibAFN3m5yT313QjAQHbXYe1+2GK\nTi2ZpQmzz+DWe1lr/4yACFcM8lZzW2taZM0CxtOrYZaqXiceTZfrBCdZevC6UKdiOO+xpo55fb0a\n+ZVRa0C8uvfwY7nMc+QmD/5C3KHJo0yDRSXK6HhYDFOv7n+HKoVXZsXoNB1Ai3uXMfn0GNsNILnm\nntiuPQCQPXs5AGD1a7Lke5fOcn1Fhqm2rYDzpHgEj9uimV/x5Z1L6Xb1AjAusviRRBKJkUoryw1j\nHktw0RiH8U1mMyFsaDdYAJhVnCt/aKxPY5ne1hQ4aGlsiehon7Ijq8wyWxo7qTNxYdznABKb06rM\nIg6gNOFbjMXXJZfiLTN1h671cfd+roc176DA8hXrxCL16PyHUGf2tZLtyFwu137wmJjPY+Vnhzph\nV55N0kuwcTOJXc8OTTeweaxMcNJwufYN1cU6NJjmUf2QqKMU23zZlE72Hkg+A/5fZXMpbwWeubQX\nmvnDgTBC8cMyz+lr5Nx72njy0aWlYtaUSHTOHD5vk+FFEyvqfek98hWpSMgl/pMX67m13uBp2Wcz\ni1PwMF01JdysMufSYxZxm8Etn4PMloY0VCCkoZRsxuhTJMYeOsjf44m7xb1oUfdPAICLWKG0+EWf\n2hh8G72JvvfGjL3FDm+p3z0omYfQa+Gc6IKwgI/xMY4XwfvW+NENoU64vNZeALdHFj+SSCIxUjkW\nf1bg37CW/prBLa1nlV6M36cZGqainWrgaZzuGvDbUOXZXey4q2W19C6a1vfdcXcfkwC+rItYo1uX\nM8uw0GcZwhiTsWXaCJaCLjQlshqX6eKK+XGfm9i8ykAbEAMnaMW7pvtODgt/QBhdvZ9cbj3M4dcW\naEs0l4Ub6bcZr6c7TQRz/g1vk9j+/fXGRCvCr2i8UoftMuB6SJ13xUSU5mubbOTH6Wp++xmjQxjC\ndeVzNpTe0xy/sKY+B01vk/ul3YBXrvFNNkJkXOm3Sp02NIzwHnAOWrcXb6BlWIcMTLyCSzxqO0Y+\nT4lNxH3pl+xXiZs8Xyy0Fs4c6M5CHkvH5bObehvvzXQi7ZaLwueqRWd2el7DODzNe5LKBzlIwES9\nlz3rfeOaEHPSwjDlhxg4Qc+b3K8EZYk/iCx+JJFE4iX64UcSyRkoleLqNww2YcdUSS/VG7A1/K5w\nDVMhuqwWu/S3uM6kJ6bTLWI6L60b3e9R3v3Of/Q2AMBTkAU5to5lytCm6gj09JotqZ1w0Y1sc/1P\n0itSl1XDEpN9a9GH7tpUjkvdS+7TopsZ+9Wic+vbDCte48KKnU1b5Qd4HerVqvtta8hXyabeMKY/\nd0sqCquNF0fwCwSHWj/BLi0vmnW2GIY8/6j4wr2Wkbo7xJxrNmKkpInk46pyvaxaH5vSQtJ5C58n\nuacz/d7rvUq4kAaX5lrej4uU1DGLlAzgVkMQZg7d5/7eJNxIcG91LPgVs/BkkWyGdRMEecZxASoP\nfeYn88QhhpEawuj90+F8CC98Zpp2Xc1LkUq+kks9bTl5J9O5swhodiTwNtA8NAok67F1scscv1jG\nOdWlG9UXR4W8dmQngWm7YEj886jHHW50tPJvNaIOPJFEEkmsVA64NzkI31IpuSY90SVusUylthoA\n8I+jxWzf8SLdguYy3oZ1fYtZTaEUOH1LEtAyIEzrt8QCaortGbaj6bRrlVdSgE2tie5v+6dprz+O\no1ZdyZEduIfAj7VACvDoekOaSppjdPLijkurkPKYn6eaVQXpKzyPHpISW2xqdAoPTnJQp/osWJlw\ns9dRgpLp2AIA97e5L/x73K/JyuE9SWgilvYffyUTx+CuKECMlDOD9Ul1n87LnECTqo2OFDvzp/Qd\nexSHJFj18iWdQpWbF0lX3vY3iPIN3OktU7C+8E0BSpNaiSWtWV06OhU/YxDAfG41rThLDGK3MWIq\n51/t+w/q/U/IkTmoepbc0PJ+Znks1uOHKb9tfKZNKjIETEkIumu2ANPTDvrOTucmCe28bBrnTsFd\nu5CI3ne9fwSYk9Z5z0GLjy6qth+Frklk8SOJJBIvlWLx+wS/w6vHJYArHWm6oWi56VlxW0NlvPc6\n6Yn+1EGJ3ysOnQcAGFr3sVBn2lF5cx6ZRouvHoTpf47beZ1cvurWnhJ3f2Iaxa+eL3RJjam7Pi0B\n7weoH+rsmClYRdjrTd++qadY7FJpuHo9pFi27mx6AY6XGFxLNb9k61s7rpKHGVPG91yzOABTRlpu\nfAnZOqtnXut17o/b6vGKvEpWT1KrNwlOErxHCnCuXPeVx3zQWf11dtmlZzOzu5TnXg7fD/+quXRl\nNMWnTZFs4dN+bv/ErcIStoc/ZXUTGcfVL0j6y3pPqa9KSu1B5i1/+WY+gNhl2y6uJqDDjt0s1f2M\nBpG3LS3L4C9TBX9JvJ3ErpFi6Ws969diOH5c7lfpOj7XGm/7TGuYok1+TMbRpJrMYV9dWw2+uKtg\nFx/eybH7AgBqyDPctK5wovUe205T7W4jLXjfNUDaOZHFjySSSLxUisXPC+5F3i5WiJzlz9e2rrRw\nLfipvOXueiu2VBKAf+NpocSpiBsaKtEYZY2m1errC4KazhBkNnxLsudeTO8+PZdaI56rSg1PxLmo\ntpinkh60whn8QhFbu36wGkddDeVJnizH6BRxqzEdX/gN25zcsKTtGDH1n+AHAIDC/r4ktd1zfNP3\nZlCo5I77QxU0HcYCkhcYRGeTQDLFk2mCDDEQmwewOCefELui/WbVnX3DWAK8htxd7eVvCTxquRQb\n0C69x4wOkf/S+9grbyPny/ZcEcgCBZNZUtybRT7Gc3Ct+GwptqLPymMm4B7Ke6D3SclH+bLJetp3\n9ggLyhrwuIdONp7pzdjQRfviK1ZjcKF6z0o25q9HxYU5slE80+y2y0OdizkJ7/KCCqfKve0z4NlQ\nZ6aTRiD3BuIFTz0m6ZCYpbF5/qbdVmOzuzqy+JFEEomX6IcfSSRnoFROOm9igC5DpL74z8d/En53\nRVVZsL2ggP4tgZrfPZsb6vzygXz5Q8k4BOyyHjUu2UK6ZM3FpUtkaiTsfwag3at0hR+mK6wuvnX1\n1WNSF70fXeFZhleu49Cab3U16TomNfeplbPZsvn4lwIAnVft7wCAvbUM918543T1Q5LOdNNZRsOA\n+HSnTcvdKGNtmC5zuuPXEh5UGe7DlCa1JQW6+ad09ZmKKrvRVwKeX8BrVtxJi8zovu9ZalJ1/QWs\n2kSXupFNZVLmVWefv7kkC2kK0JYxKP8/vpbA8t2JuZUOkxv2MaTbUtZGU2gga0nC1eQz3YXXcpZf\nq75hHQEed6whSKuPkS5kWWI844y4BVJfpDu/yoxLuf55cZWd+V7lzg0S70yfwAlXrDHXHEejEX0e\nFRjOMDqXiVJWHXlAPz4uc1B6lu8tiS2X+uM0iAg8kUQSiZHKWzRzZB4AIDPwdcra8fTEAiI/TE/F\nVJ0t5VtWab1Ksiny57izD9+oD/CNqmlCW+HG/bIPCKDy5/3ieZzYYhgp+pZVi6rW/Umvol19KlaY\nhReAWOKOilpojqPpowTXJphyKr3UXNl0ayNEklf23xSq3Fb7RQDA0uPSbUY9CO36AyAk56TcKESS\n66uK+Zs1qb8fO5fwvrymWL2tdzFlt84YBb+itIhmHrUu36xREnbpGUIgcJEAgWNv8F1sxtzKRn9c\nvmtzW9G9osy3+E3QpbiUa0TLDcOlCamouqCH9ve7zugQqKt6gdyM5rXlhr6zq63XGRyrq/ct/Wm5\nEb+FB5b/DHlGph0T9Lhsisz3vUP8JD3+5hj5g15iYj9a/r2G5HMkbqvpWLMgZuZzXEZ+E6vx1Csw\n6cr05wkkvsnfRB6/8K0l0akniVtrbv72y2Q755Kdc3Odczucc9udcy2dcxc455Y553Y555Y655K/\nzrEiiSSS71++lsV3zuUDKAiCYIZz7ixIcuY+AAeDIHjcOTcCwAVBEIw8xb4BNgY+lrJURiXY6Bsr\npMMaHdISm66ltZyZfZKO9trX7rx90iXovMSsDTXxqBCAjqwgyUdjKZN2qXKZBJ5hLLw+O3ZcgPcG\n9K09TuavW90XAADzHzZmSj0OJdrotfczc57DF/Je+Sw9kAUfi0f5fGWHR2WZr2W/5hJfNPTpw4xn\nNJb6GXHnXhCqoMfbXD76Nen6EyTx3LZHnlrdL+O2mn6bYHTVKdG0mdaufGJ01KEiWaiCBUEJI4wO\na/0DLvjpNMZ/2ascuEhuVK2+Yja3zmAPvy0mxteUI7sGu4OcZ1vowrlL7SZkn2foAnQbK25G2hh/\nPC1MKv415zaHX1jsgTjL79vI4F9ATwC+rh4APjkuabxzqwr2dOioPBA/qO4n6lxIkc6ush8BAMq3\nicegy6YDwN6tcs2ZWeIdfA553u36EcUTONYu+HYxvnPufABXB0EwAwCCIPgyCIIyAF0BzKTaTMTW\nwkUSSSSnsfxLi++cywLwewDvA8iCRMJDAewLguACo1caBEHKKfYPMC8ICSUtZpiy1U0SeyVexrgo\nh7sb0kmItGuPu2yazb0GjlePQYk8U+K29phE4bs8JVkG2xstLM3M5VZZwWaV7DAmp5VLe49lwjOJ\n1FtrEFdKHFqQAsM+UmvErMCERr8EAAwr+J3X0fiWyY+ibtKtN6PgU6+ji5EOjesJaLvF0Ova9rT0\nKGzUW6xewfMtQpW2j5MYo7RZNTiyOBGW3+fxiWtnccJ4OQeai1U+ZLjE9Qo4ECX3MH5/bor3jPq3\n5wVqKK6G0MSuociSCSgZK5Fl6poy/50mS5RAxKKf627wbs+yUfSalNbNeUtIEotbsdFgN4r5KDlL\n59J0/w2LaFSnJO7/gDeJGtPXiNsCQCLv34e8f/osmjb/3a5jIdHdMndV7mfHqlkep7p/mFQ/jZv/\nCHDLt4vxzwLQFMAzQRA0hSRiRgKIf2N8tyhhJJFE8h+Ts/61CvYC+DgIAn3/zYf88Pc752oHQbDf\nOZcK4NOvPMKLecAH8ufhVTVwfs6VX6kaSSSR/Jvy4SoU5L0tf7+f909Vvy64VwCgfxAEHzjnxgDQ\n1SxKgyAY/y/BvVaBd5dN5Z0CUVWuoctyP10W2zxQPXq6SeECFktMKks9Z4KFDUcLz/3Dg5eFKhUd\nxYVruoEg4XyeZCK8qNvGZa2SLpSTHulX62QduuiNn+AyTf2lrXGL53wo899lVwAAypszhNHQwVRu\npY2Oa+ipbqRFTFZxq/x7HfMsr5L+HFM9GkboOcw06Zg/HSMVjrUmML90hdHhMJZfKvPzk2PS6rz6\nIlbiWXBP7ymBv5JB4n6fe/zzUOX8PXRhdfENXV7LVufp/RbuEdZdL+SlyxN9t6aN5a0BAO2WrZUP\nNKi0FXzLZLMkN3YBUXe/ecbp9SdOZHhZwgOpG27c+PDejOWk6Jza0E/vhaaQ9fnuZVBsLvgagsTx\n6T1z7LbPCmpc0J2xiAW6dbm1jRIOdOhG0LdTV6+jv8BcAEVf7ep/HYsPAP8F4AXnXAKAPRDctCqA\nl5xzPwdQDODWr3msSCKJ5HuWSls0MwRGDM1Ulw/+adW3AADztwto0amRz+NoSu73PSQP1Hg2LexN\nZiFEtWpK7lGg6zNDtdW3tb51tZX2jf7NXKuORCsHXmS7GPUkkvwcpWQI8lTai/XX+urkC7p1T19r\nrzX1GWQbFdxFpRw/rMSOYnmqkd77d/YbOK/G30OdsslygUkDhQ6sVODSBb63QeNunJeHZV6yR3NJ\nprt8PX7Rs2Ju06eSVnyqmm+m1ML0K2vkKx6QbYLvPo2DTMnVfJAfaK+9H3odBeN2DuCy3TdzgQ8D\nWoXzTI/k5Qel887Nr7/hddghu4L8mlUExmoEfs3qqz6Vm1tBstEXSXL/j1X1y1H/HJLSXPwCQV11\nChVktUtgq7dJL6DpCPEWz4KnAOs93lucIR8ovdt4Wl3uFCB5FyRVV3iPeDRanw8AZSO5gz7D6s0N\nN4u5sCV5uxGLY8axbPcNoUoV0tVPbKsOXBNRdiOJJBIjlWLxmwZvY/PdDOR82B0uO1zeReKsTm+T\nbtjd9IljR5MWLSVe27hfgp4TJYZqq/EZjXfD6yTG33F101Cl29tMhUwQryJ7GC1icY4/ThHf1rR2\nWSPkj91H64YqR2iRtdNOlVTBJ85OlEC3fIHPaCbeyOvTBT5vpwkx8e38p8VMTmPg+CHkXIV3mSId\ntUbssad98CpuMaknrS/PkM0vek4CAPz+Gd9CN/hEXv7BMPm/Yybs8A+9Z/RRVTlA1lTm8TRFRuji\n8INe9/znaY3I2Tx8A5fAPuit1IaLxIRe9Ru6WhqTW1xhLrdtuFUY+QajozRe4gClLyfyvz8OVdq+\nwlSkGnjl0BhCkbucz7umy9Tr0f6FtmiLktWe/R26000xy1s37MZnbd/lAIC0OuKhngOPcxQu5L3k\n85lyS1x/PgAdshivq/WeSENtPSMljTGOz27EZ3i3aQJxP/cbjG9P2Y0kkkj+/5LKifFXBMhuz7fT\neNMDLr77rKK7H5oxadcT9hvDXvl/lQZmOeo5tP4at+vW9hvvErut1YjLIhWYgJSWJnUYl0O6hxxU\nWyzCt27r0exb/2thuvR5QjqlvI3/Far+FIJdTL9aaKFZb9NyFJjXuGIPjFk73MY3/wSP1GYN435L\nZT9dd+Cdh33xScpIsSKXVRXmiHYTzjDVTKtGSOy8ejyX0H6EXX5slYUulaWdctSBUa/jQaPLOpvD\nhHXPHxinC3har9KC1dupaXRIelo3SCzjRWzCl7nSx8Al7UjYyaebop6DWSlcUX2N0w8vEw9kf9Xa\noYrG+FuOykDCTrz3xNFyAY+6q5eqcfdGo5MbpxOP8gNALgGKBi1lS7yrxQBDZtvKe6lEMz7DSU/6\nMu8juj7AYLmu7Ff5m9plflMcT+raPShxdSOLH0kkkXiJfviRRHIGSuW4+l0C70KZSre0GSRI3E0E\nSV3qInMAJcpwDfBtL5Ao09O4SY5uktbNE5xLnbsn1DlyVEC5IzcKGSdhDgEysw596zpCVnn/mAA1\nZYuZYrEpHg1HlP6tYJDWTduW3jncjozddn3Fr1O1cFKPmP1rrWUIUvyDUCeRqb3yxfS71eW0RBJS\n4rOHiPunIGHJTL/cU3B+bDXe0YflvV/9vRP+OHp/1F1XoExBPrvEls6FuvPK67/I6EziVvFaertY\naXQUYNMwQCMY26Ja3W7WDIQhhznXznqSMvyMcUR2NkMZU7OhFXu68vDBg5JfrthCoNRWj2qkoa49\n03rtCheHKisLGDtqbUQOD7DEoIQMA1q0lWe2CSf39/PN2mVaJaqgo445z6sk95MBnVNNgMMwFLWh\nlQHPMS0C9yKJJBIjlWPxi/4BZDMNNNh/13qEvPY/Yg6q5Bm+wUzVkhJi1q5nuViRbDrd5kk+Z5Mz\n+up+SYWcSBWwLzPYHurseVM6m6S1p5cxnSbMviFVCDYNbi/dY6aVedNaPo5WVz+iJaqSRtrxFLvG\nlEiXMawEfIakEdN5JfQKlOCkVs+mlYr0QNwqjdN0GGp9HefpKpmnWhvi1nQHEGjtP9uygV2xQyAP\n8EtbKXaqFRhaBLfD6KplVqfiy1Po6GeavlPC03teJazD53f79sscn3fck5jOLpcUYWJ8JeZvQhWU\nL5Lt27Se1y7lFwa8/ElLudgPeeMPbOf8qNeR5nU7jWZ6ufhn8sFePsNm3pOuEfDtSBJdo/zzY8cH\n+HtLD6IsX7zM5HW+jj69TVx3HVrxWsP84h0HpnOs6oGqpbdLvPHvxJxSlNeoGVn8SCKJxEvlWPwl\ngY+JrbVTy5XBrcZJt/hAq22dVQCAgt3SXK1DXXmtL3vBp7sa9pRY7mx2Itnam+kym1Jh7Jo8XF67\nGdWKRPcmk1rTt73WajPG7DbMV8MsOiheRcU4ebOnPSUeRHsGgDOX3uWPRwudWhEXTw41xBsdo1KZ\n1dJP9iodZsSRO3Q5b7ugItNAae/So9lFj8bErEEF91NLoVbYpsRo4QPGmE6hlKrc2qIY7cpDQ1Ry\nCVNu00yNPCm7YczKtU4rTBu8BMUBGL+H3XUmmO46SiXWIiE99yAznhxuFesh6afcxPjnNNHFMmWT\n1JgWewkLsXLN8fQ4SpzR4xsL2+5Odm8eJTcj/VFa7r6+58IvZsgF7icgsXACcR3bE1LxBD0X8YSu\njQwe1JD75fGDbAtIUCbLA5T96HKsdh0iix9JJJF4qRyLnx8goYug6NohFgBOjGM8rG++DG5tSap6\nCqqjFtKSKPTlSqsZLlmcX+dkHT2OxvaNPbUSC0itVWSV5xrV0rNW/og7AADF+zhYpeHqmFeZwc+U\niAAAIABJREFUcSkNlNY4aY5Yl6TqPnYt2ScmNLPObvy/9r4+vqrySnftaGoq0SAfQ6rxd2IwhUi8\nQWBCGOIkAhUpodGLFShwCRcpMuAFB0RatYaCFRQKXGHAQaZxDOVDMkIJClZqMkL5qCj0BoHyYVKx\nDSLU1OAEUjj3j/U8+13nhF6nvzrR3+Vd/2w45z17v3vvN+9a61nPWktE5PQ5RaTDpA0RyVqoFg0r\n+NQ8qJEN2+6JLZhePQz4nOQoY/VEJ+jmX9dVtVvkb0EOsbBEfMNKWgeWPkvBOziNx9MeBJqzfZ0+\naXMQEQP6uUTh55nzEGOgFUBtvtUNOT5Nn0fGIahG1P3/1TyTpPOemnX1N6nlcRjNTgsW7wnHXHO/\nmjSNNfoMsnqDcju+R+w8RVpUsR3RX8k/1xsO8CvITDr4OH6P555WYGrlVcD6yoOGRhuv7Jd+FY6p\nuQ5JZyQAEQuxadVYu8lFsFJQP7JPsQuRDEH78CdOz5LmDile43vx4sWJ/8P34uUylNYx9btHZeg7\nyI7b4YjvKb1QTScJ9gxMqoRSx8O/pZM2f6jZCFMoG/M95CyYzMGx3Ug7t1GzOcymEpFRL60QEZG2\n8gcRcc0S3t5oyv3QvCJmQkvaNNSIRBW8OX1WTfLGDQCF6ELYLr4w/3P/A9z60SguuuRMOOTeFG0t\nVX5M4z9ZnRX9OtjHZRaGYBLAvOwpIDPtMDUJMNfJ02JDkE3rXbZgtArPjE0xCHqZZMizI0DqmQYT\nnSZ+XGUeEZFv/FB9ovofqGlN8CpnsTNz2WzjlrMa40tCfw2xZVnpavBnwLPOHjEuw4cXY+Z8/EmY\n/r8zJZ1Q5WfFEl1j43fomjvby50nuQy59HjX48ahGctKxJmd9yRpK3RCzLQ7slVzCZLzDX9+Ot4/\nOyK/rmZ81hjT7fieWDeAoHFiqWsH3DwZgO98nCcPLqR1PdIxLxRvPfENvBQTgqS7mzKnXhqSvuZN\nfS9evDhpHY0/IOq0qAGbMqagbdBotA3qKi1leFxvcmpfk3mX2RONJquR90xAyuze3EmZL33iId0t\ncxc66u+7Z5Wq27gcuzjDeoZmzIo5bHhAQlLKLtU811/lgJ9PRGnCIbjD6jUWmATFl9mGF+eq+uuz\n0AE2FxBL2zNILYZRr6r1sunckHBMw3RYTXHNF9kaSkSk9ltIvWPLKtJxbZlUPLtfzUAe/St684e+\niQo6O+rcWD4X3g8xUPvcaVWQR8S8els6Oy7//mwJrI55hkq8NHbML1agBl/ZTjcGRJ36exBWnIqw\noqmgfntfRSDbIq5cuRJfFmKdNRoFWYsjrLnIOH2W3wxvQmRZBUoCIRSdMwWZlH1MmBj3mjIcFu7r\n+q6yBxtwb6tab7QmGudgDZpy7Xc+g7DuXoSySRO32aMglJX1HSYlwTqv8b148eKkVTR+brRK9nwP\nsRpb1Rb+S/5TyCvejLxi24wA9N0ZY7RR4buiWrnyQbeNZz4b21o6eTh2zbamOi41DEN19JutD8XQ\nSVzr5EGdXw6HsDXSngq9n5Qi7OIIvyU+Zvy26eq3JS/BfBZhPrXmmjP1GolIxGleD1/P+G2jBquG\nX9+gLaeb5sLasAQeEoCgBe58AtphqSM6fTgJ1XVfgVqg4upvzgOt+eN+SkQaJtqw84bdaul8v/fj\n4dAfPT1bRER2zdDnnpePqrg2qYmV1JnIg+o/0smMocan5mIo0bbSpjUBWm4zwoKsqycicm2J0nr3\nvwgCUDVAg03uNMF1+rxZvyCsgsN1YGs40EKrxRFrJnmL8fH5TtNj7yG50Y0Jx3bH2C34m6s1yphJ\nSHH5/EWTXgqHVN6ja37cy3G4hCUCMX+oLCpyc4LX+F68eHHSOj7+81GRMnxgyoOFCSkk6ZAEYxNU\n4KK2m4wd+pDu0MnpZtfdpztpdl+g3dVAu63lwOsTucfOypp5l5KLNW1ifyPiqLT0pakheC1bL75U\nDx03IWEmADMlL9ONKcSR4DTquXUcaJIzjt2o/6D/WauHxHxjXZTDUgAlOvcJRBJudNzYD99Xjd+2\nQdXLuynQjEcMNZZCpiir2rDztcE7Dn0/gkuqWXbrOc28aXPE+ObUmtDQZ6fBf19gxsBvP5KtZk7m\naDjMpgX2kVH4bge+w2tbdueYcMzEH6KVI2/HvgtIz2nacKILOrysflwbiKbNBlK+1ryb4Xi+RNzZ\nEm22Iefs1fETe/5YRESeOzlBRAw5TcRhV5P1fMmNug7CBq4ikl8MqzeA1ctUbvu3QH+/EMebUdtw\nsqkmXYJjpYiU+bRcL168GPF/+F68XIbSOqZ+TVTSuql59B35afjd0wee0H8wI4n8+Tnu9wSKwmo9\nden6wa7EFmMSU9WU+rv2Wkmn+vG73Bhm/hFAgtmUlOfINE3bAZp1j+VUx5j6c2IBv1c/QBcJEDcs\nKFfaXztOlD6oxPTsZ9UVOSeu6s+R8QhB0hwcrtfOucGVVdlfoaGhEEisBQppWmiFJZ/bYu7363wG\nverqFjCvPPowro/syOoZplvuW+C1oxhm3U1qjv4eiFveYtfWKjRDmWwI87vOtByLvKcuWeVNmgRw\nE/yUbq+46kjh86VpTkKRrfazDkfm/jMKe5MZQ9dgM46wyK+Y5XpVhfUS+BHdS6yHPgUujLrzISQu\nEERF6DVprlszA1I00aDqbKGIiHyKUuoXnzemPqsy8TwA4O58Z2M45LW1CsKmDcM6HxvXUk0kdDPD\nGgCj8JxtpSIC1ANE5DFv6nvx4sVIq2j8rOheObhUaYsTJ/04/G7ZYZAfCFoA5Jv4qBkzAmNYuQcK\nLTnPgHtL4gg31BxlZiLcORlqonVh6Y61OGIHTZp6RuKlaQusgrzYBob3DVVgad1KBzbljgPAtlIB\ntvvG6Zi3TCfE47NAXiLoyXnarCz8O7cY57sHgF2hG5I5RTUx2yqRoLJzWz83COBUdB6UAKvEWK1J\nRYVXUDcfmXyL8bwNGYaaefuuuHLdhnSCDmiOLERDZpoZw3oACCuemaSmxFXnXIWaNm8BDCTZCCHA\nD5503F+GHMPaAZpIKcFP3BrP6otsvAOg0ZbhC66dWjMvWjS0FvGOSns+Eg4pXYU0Q4bUinCt142i\npTWXFkcS6mDy6SfjYgT1aAWVmPmEbd9wRNiZ1rCIyMnTGidtXn6t1/hevHiJlVbR+KnRY3L+gm75\nbAopItL8ujqHWcPi8s0fNsknJTiW4piuh8xnnK9Ze1pVVvMWOJtl+MJqHu6g9PGpUbe7ITmzdQsl\n1bYX1Mq/y+3hmPqtGTHzkCv/DAFHRHIm6Plqz+nghudxUdseeSbJHDrZjIiGmY7v7+bGUEtCQXSc\ngPDgfldPLysHmmy/arJ5OQ/q8YLrXH5mF8gqyLOJ3hlXg0+kpaZhQSESS0zTzLDlFfCA7bAA8leY\nMZhiFEZOEE+gEgmtjGW91VqaOPqFmHnGCB8v52lqOIb1AtG/Ing/2nJMFY5cG6E2xtHgS8m74Esf\nUqsnrSf870Em5If1lDkSJLJVcZiN/d08/I7trqscTlU8RgGNjYc1gyq3S8umKe6a+vu0SK2ety49\n/GpMRF/QC2snigz3Gt+LFy9GWkXjJzd+KI3b4YcbDcuGgMWDdbcjcrxnm9nl6F/RJ6ffVWvOkxx7\nbJcHsk+VqcBDf5bIajrGTv0gHHLmAR0/YoVWWlk9SMkdxa86eJlNGo/vhUam5gByO/QZB7VvblDE\nv6kUfii0wNAJbkzFk1AZtAJgFHScYgg81Oxs112mm3jWbJP6WQGflYkbxDlMW3Le8+SBmhu7ZKyy\ncqL3G6WgHaXkzLf0QbebDDODPrmNcNBv5+sCnZYaN+Y7BheYu3LajIlL4PnjNDTfzHLNN/94EJ+t\n1s/qRym3+CthuR6Rdk/rXG+ZsVdERA7uwDOpMtfic8Z9RW6oFRGRuix9OclvOezoT6gW1dQW749r\n0KRpE3d5D2bTqQrcjLU2idukx83BNsTkd3zHW/SdjJj0L+GQ1Vt1PYZVqPM0XTjpY4cVNBVirlNF\npMRrfC9evBhpnTj+1KikzNUYdJerDoff7Tmg6iC1G5pUjob/bGm91DAEwmkB2NRPRAMSKpHaiiaa\nxVOcpmbN/S6d9Pr0hcOkBhGHlpI+a31xCim7c/WQ1RO+9VY934sDXd7x6BthBlDrluJo4+90walF\nYLVkrnAYxpEDObHzopjzJMzVe+/VSXGJr4OSWh6MdIPWo6ZgnD8b1tsXkbenaepul3P6nNr8Dmg6\n6+D/yFyfPjWssJ/fqU75N35mzDpqf2p1xuMNVsA23dfuUm2+vS+iBE84i4YcgXdRZfcW1uOz6wBI\nfxDBmqYlaJJYhq5AQZgstbRmH1TO9eOL8QKqzPnwjkM6Nt/jCTOGCUbEIy5xzbBwxtrY9OzkDc66\nKGyjF35flJ7NVOyQfi7iUtphJGYsRFr7NwwehOhEYskffc09L168xIr/w/fi5TKUVjH1+0S3yeEL\nWur4vGlS2bhGAb+CccrZrd6hdkpRX5ODvBmMEVhikTfQsOCAiZewSQfNLFKAbV44abiHYJfCRMx6\n1ABkKLE8boXa86salAHStMuRRPIHxvYk79cFDRW2AjW0rgMBxVrOB8+63llfffoqRZTNQBhCXLDq\nsXBMPIEkuyeyEI85ItB3O/9vERH5563aiDGs5LLd1CSIJ4AgzNVvipv0tldQ1YevgPdA4M6W4kbu\n/q4pyMf/OkJav3GsqMzXdPKr71RK6i2iNRRzxpqMQNzGzyepvUzy0d+Wm1RAvL7y+7UmwaixWqvQ\nNvgIzuvzTZgOl28LJmtor5kvYo474D7h2aYOg7s5yDUZpfne8VGETzeqvzKo2NCghyCGSTMcoGry\nzYZgtgvvoAwf4Lkn3uyyK69OVqCuYXlqzLWTu7rztG9zGresVZ7YHJR1KETErbX7m0XSv/LXmfpB\nEDwUBEFNEAS/DoJgVRAEXwmC4LogCF4LguBwEARbgyBI+ewzefHi5csgn6nxgyC4XjQI1zUajZ4P\ngmCtaODlFhE5HY1Gnw6C4BERuS4ajc68xO+jGdGaUJOtq3CU1jDkAdAkIRs79XqjVrDTZ0wDkHGP\nAhmRl11mQt0Q1f5pm+LaR9nKJHHATEF/WBl3mEQeknpoTFDh2MaahXrIGYjaarPyYs4bU0uuVmIF\nYZj8/j8PP2JFn4NDADYSwFtj3st2bNqsW4Ap95jgQLS396uKSExD++/nwXQx+dwjpiBMuU3DQkX9\n0czzNsPDJZiKe44uxrVZtsCVFHSAHe+ZEch/M2NA2Y1CEwawJKonmMSg+5AYRJIQmLcHsp327bb7\neMy5g/+uzyelu0M8G8rxArmuaAHakGYcOEtwbtsYrbrMBhkiIgv6qNWVuhPWwO2Yj23NxudLsJJh\nVDuGBhWfE9elTa6hkcTzsVahYfWyIUtoQQxAz7FFhoHF95cvIt3/+nDeFSLSJgiCK0XkqyLygYgU\niwgoVvKCxPa/8eLFy5dY/lM+fhAE/0tEnhSRT0XktWg0OjoIgj9Eo9HrzJgz0Wi03SV+q00zGRqz\nVXHw2ZjZy0RE5MWT/0NERC6eMBqf/jr829Te2H03Om2QU6za9+9E03GXbdXEnsyBJiS2Tf2glHyk\ntm5R7WDbD7F++i+Wqr+ePUl9afpSIiL11bgucIVIsW7bneSkiIjsOfb3bu6TdbPt+Cp8xD6qInN3\nusq+e7aC4UItBU2bnNYy1FO5GJo5HV9YPIEag+nG01EN2FCkQ4ootQgtGmP0hOep1UNxsYZENyz9\njsRLUwmuxVr5VJbVZhBfJcKB25cjVLfAhOoG40iLAYrsj1PckGvRMiu4Guu1Cl/YtNUN+O4olBy1\np63zSNIMabPz8Uym4rcbjIIsxDG+z4Il3nA9c32TXGPxJUtaE3GWpQ0X4xopJVifd2OQqQnJFPKr\nk/9DRETO5IOgZtPYaRUWichdf17jX3mpD60EQdBWVLtHRLukvxQEwUgRid8x/vwO8mKpCBOt2haK\ndC78rMt68eLlL5V9VSK7q/Tff/h/D/3P+Pj3isjAaDQ6Hv8fLbrn9RORwmg0ejIIglQReSMajWZd\n4vdROWRq7hkSSuZPYjvghOm1rgaF9Fit2+XbWwFzUssZv43ps013xxocg95wziaJETUB1QpU44Zh\n7gfw09ptAOV3MnbU6e4ZJaeqg3ZrG1VhOxcg7ZU7vQGiQ9JRqAVUy+RGfhkOYbXekGpL8lKVOQ9p\nxtQU9A2NJklcor79pPZagH5R8D394qibe6SzknK6iB7ZVLKzMcOuFtUmld+Osy54LaOBov8KZcIG\nmNTuC9yYsBU3HnszavedaOuG3ESIoQRH+PjBATf3xOnALpDYxWeRmG3qDlbhO1oBhS3nHDqkjATB\nwsqdjaSYhw1dHO8ipRBaeH1qzLVFxOE4JPBgWWV1M9GibcBveM+MlFirgFYE3y2xiDVuSOIAPIM8\n3Cc1ve2WTctxi4is/+t8/N+KSF4QBElBEASiWdPvigZ4SjBmjIhsvPTPvXjx8mWTzzT1o9HoniAI\n1ovIOyLSjOM/i8g1IrIuCIL/KSJ1InLff+VEvXjx8vlJ63D1U6MhSHdfzgvhd+ueQ2iPIAyBJQvG\nwCxiKenmSpg5dstqih0bmkBtLzGGJlUVjoYnE5peNK3ngmhe2tuNiS8BjtDMi301frNWnOtQuUBt\n2DAUuUBDkckPGHLH0ThyB0OHhste8A5Cj3sVhUtMx7PY4HL/OY+OjwBIHK9AYgzn/3EQPRDmyn1E\nzdvD57qEYxomx4FKfBesG7DGWI40S+P46bbvO0tvn9iYGXt/G8QJn3t3dYVGRcpERKR8yHg3hrkM\nNNFpzluweI6+5GTURmgksFlva1RD+Hx5n0kA+4abWo4MydG9BPEmY+iBcMjx3fpOB/VWt/LVHSD0\n2LVH95YhOt6vdQunxo7NHtqyPmNYd+IQ3jvXtKltcF9vVIJaNUZklM/O8+LFi5FWy8c/16TJ26yA\nKyJSPR5xJGpRaIPsLk5jMEvp/bMKziW30d38v4XpYiK/BoL04UmtN3ZxKlAmE6bqOAaacHNsvnRq\nsav2Wr85I+Y7aiCpNFqgyOWI2+9KJ2kdNlYREhF5fCLUFFsoky5swy9lsdVYMsaoNrlN3gmHVGxG\nOhZpxx+11GA9chR9+81Z1d6j22ghu2Ud/9ENAjGp4E21IA7L10XENZcQcVZFCCoBoEy5GwBXvkO2\nCn4FS2Q/fsPQq9FAbAG19bSWwG2uh7YymppVY0lJrVuLiVriFDT0qGVa3qe8rkQ/qHfvJtJbTZAb\nwRoKW7IZzZrzCIhXB2D6IUQ3cZwWGVw23jwvtsPqBeLMmpZVbSPPgEK+VefMkFvT8wZoJqmHzweW\nbY8phoAF8HrdwCG4XTVfX5voWqAVL0OVnmr0OWczF8ugIei9SEQOeY3vxYsXI58J7n0e0vh6R6dF\nh5ovELaLrMCuuVR3zZoqk4NMn7wW5+qlflv9FpNMwbBZKY6gRqYOdNqc1WdDrQm5LnQaReqZW81a\n/qxplmxqrFXGkmDov5duRkzLkjIYBpyjGjp1GchHlW7u0yL6uwW7FGzoNEaJQD87/a1wzGODNfF9\n3mllRDcf1fNFhjrVwzBc42OqlZZtV81VcGpLOKY6UGugN5Lkq1eppq7vap4lSgjkPAPN+Jxlq4gL\nM4lI9cP6+6xnUJOgEmGru5wV+al8NebnfbopYerTbleHn+2/Va/RWAONekIfbsYNx8Ixxw+pL/3T\nkyASncB7MCv4ZIP26aqrhMWQji9MfQeGMm/tphZj+UzFEZYJNL31zdPVGmMlHlogTCoTEaleAGsH\n+MY1A3UBNInR+PF9AzC9t4e4ooKpm3Rt3PckOnzW6oF/GyIiG69RTV/1iWJOhWv0HbVLdzzqM0cR\ngt4lsZTgOPEa34uXy1BaB9V/IHpJAkiLCjf0ffcZrXwz/Fn+nl9Z5L8URyDFKdPhj24wTAsiszhd\n8iL4bfNN2ip24j7DVCux2m5M1V8ivXSzEQHIeB/I/Qedw6HtUtVnPXMCHSPnqpaynViaTkAz0A9l\ngpCt8kKjhMg2/bj73RDO+XfIU62bpSdq95ipKfh6XEtoPMNBA02aaaCodFoUCU8dM2Ov/ZY44bMg\nwk5MxSLtuB/633X7u7YY03Go4i+kPdfs1eed2tPgL2thlTDyw+dVZNbKcn0pmbM1knEeRQHrVpoU\nbmjm5Ml4/9Px/vksLdJOQaWjxDWIpnzkoilhWvZDuhAKFgL3WOoAppxJsJ42w3qCwZDzrCsf9O7p\nW2Iu2Vyk1yjY6ayL3Q2a2HRTSq2IuKpPMd2gNmA99RKRbO/je/HixYj/w/fi5TKU1jH106LOHDSl\niQncJRQhD78cYbiPzRia/zDF+q2AaXVTUTgkYRd+vwS/r9VDxouOaPHxBUVtzmxRc3fG4FkiIvL0\n3ifCMRk9dfz7pzV02Hw3G3S4Z5TRWSvIHL9GwabcT8Dxvk453u0+MqY1gRbmHuBI3rmISNv2erOn\n7kCYkeG9+00IESSjFwvUth69ShG4ESNN6eVqlF6m+8SjqUnQ7gGdW/oVmv729mKAS+Z5pz0BE//b\nMPHT8QUf983GtN6lpnXSAISw4LaweKqISP1zMNHL8AFDUDYiyc+Yr05yj3ELs4tRdegw3C66Ctku\nvJod0QdcsxSuwiSdxzXySTimA+p677xHcyxyX8b7W6rvL2mUMZtH6f1kbIIbNxpFLQ3pK6Et1t4o\nrL30uHsSkcQO+r5vb//vIiLyi8fxMN0SDtfsncOU+c5M0Y1jR7gxIBCN6omQ5k3jW1yLwHRBwRap\nDgZ5U9+LFy9OWkXjR6IHw1239mx6+F1jJYAVaCU21HxXHNDxy9NaGaW5HNqXJaUtMIgW0cVPgOAw\nArvkXKcN8iNVIuIy0k6tUg3bbrjT0F+5QpszMPR3Yi+0nsNXwnBP5iQFkBhGI6gWEoREpGKwJqgP\n3Y1OEQTGbBCVIZdCHEl+sVFHWgwAtrLGIHw2r4cbg1AT6aTHF0M7FZn3+5Fu/qz1dlWS3m/j6wbg\nZDgLoSxpUssjbOm0ymWvdRz5W1xa1S8bdLLmgYhr6Z2UCqtgLqyCp4xVcECtgoQOqj3bdlAT5Ey5\naYjCvh4T1ARc2vAP+nFXEzajdViGYyGOw90QWgoJedDUk6Gp1+N+1xtLiyAqLSICf6PcukpI0nzz\ni/k4DyxakpJEzDonTZjhxbJwiEw9+JSIiCx6WLMqs5+BhbPfAMtc87SI0vWwaVL/cMiQB7Vt99Bn\ny6UiGO01vhcvXpy0WkON5DmXCJ+FrYBw5M5qw0HYHft00XDVzo3Ifzd5ymE4jzsiSCgx+c7QpKwo\ns+2snrhxspkPd2KE/jJXq1Y/dtJxRy8+pjt7SDp6Mo5eapte4P5yx8RpSzsv7t7UBvQfbWUZnpPk\nGdxnv76uBM9vYMn8G3pX585SgkrBE85cIf2ZFYIpGV1M0slYWAp4F31e1udOKnLMb5nYsgVaEn47\n6dEiIqcOqAWU3Q0a7A7VYInrHc5xfXsloJDA1WeSXvO90GEWqT+GunKLVIGlPQssYqwjVyXOR7ht\nH6xD4humaeaMU8B2HldsZ9HsCSIi8o8n1dq8t9P6cOy63UgiY+iYloPBRCJjsA6ewzrgGnRTb9mQ\nI2zUaejfCPWG9FuuixJzHjZb6Ra7Lr/T6afhkP8D+vr+tXm+aaYXL15ipXU0/nyXlttui0G989SH\nC5M9UFc/BvElsluKI3dPS62kJiS6WauH7Cku2admHnwl0nuxe/br7bTmvgu3iYjI7Vco+rpx8YgW\n52Ga6cdn9dg4Ry2GcU/pJFZmOfWScxDEjY15MffS7qh7Bu2vULV0pA64xhLs/JYoU6qHSAG0y9o4\n7WLuR/LjEnlMiynWc7sACmrjelg71rog/gDu06CnlNxDTWIR8lxQf184oL20U24GcSrPEKf4/jDX\njEnOuqAcr4CVkY4PeqHoXo2pHktLCEh4uxJUSSI5SkSk1vjnImF7svydrqoxSUIVicjAqdJDSi/M\nvdLMnT49NDzbwDVsN2Pmxo4hLddSbUPSEkhZGYOBw8wyra9KVPuHbdJvj7W8RMRZslzv8W3lRCTj\nUZx7YjeR5V7je/HixYj/w/fi5TKU1jH1y6IOwLNhKoBWHQciV/6wAkFFXUwLredQiZGAyKXCefP1\npPk3vCkiBoAy2Ukswb1/lU4kMlK/tKZrzZNwB9L1kFSEENQcEzKKb9dFvjqBmrnG3JyJz+IyyTJ6\nO3OXpufOJxW0zH8ULbr2GhAN15zYHznjG5FJdvfPwiE/ir4Rcz8P7gdaaMDGooH6XM+jqgtDkDU7\nTMiInHW6TTDVcz7R58dcdxGRygq8m1p8QNDS9oZnjgXNUT6vbLfu0jqr/3ZiB4C6+LwFEfe+cZ6k\n4fpubkl5Nxzy9mEQkgjSApRLyD8bjrmn08siIvKOqFvHqki8VlK+I/CcRw2Ji7UI1cFdJclJROTE\nCMwZwB/ba736gWvMIWlJMb/vMxBANcKfIiLJeQC/56r7lTgVQOUWU2UpHUe6uaU42vBwWFFIPpeG\nGl68ePn/SFpF42dEa+T4bdhZl7vvMnujgeFi1IKDxmArLRGRH3T6oYiIlH5vXuzvS8xFSH1kuAQA\nS7s/GSCxHk0xkMEVapCpJqQCcCirIJYgkzLVEFLuSo29Jsg1pAcfP+bIRyM6axcI5qQTLIypLEML\nohZzngnQ6m5HXsnahPks1vnkTtHw4Ltn3bVYvYZNRVaPBoU33V1q0Gxoo42oC4dn8N2Ri8MxDPmt\nrFCQMvkuaCKSfGy1F1pxfO7AZhmOExF5p0FfBht7hOBV3dfDMUmokTc2pUxERDaJVqE5sdvUQYhv\nhEqrwoZ+aYXRKuQ7qjJjCnEcpes+t7MCubR++PxERNY9rOG8sAknKeG2AQnXIy2a51tlzGXvAAAL\nEElEQVQ2RnWhWnxXgu9sJSZIu3x9/yHAvNFQdmtx5H3GAbEx/z4kPpznxYuXWGkdH78mKtIWzn2N\nidXFUVHDcIXdCVm1BDt8blS1XXskW4iYyqapei9FnfVElXe4ZpBD31A1wMo2zWvUdxo0yeWiU9u9\nthJ1zqCNR0wzyTAroUnhTpZ20Vp772CiG4eYHbpED/2Gasjwlw1KP6bvKGK0CDUX8Aqpdc8pKRtY\nw3bFGpILVQuHlWFEpGk+cAhqo3y81/Vmwyehhc+ZIcMaA7xMx3XjQkUTFwJf2G1q0nXANUD9lXL9\nbUKJs9guluL+6HvSwrH1Bnh5aKvs/iD7jDfYAzEHanqSakrMeUwjFhFxlpW5VvY4nHulnrvjuDja\n8VLnd+dOAvFqZUHsPI3VypAhE6ZWfw3rw9K8y/RQtBBNSmdhXRr8pc8y+P2rcP18WKIfG8yISUy0\ntKjdZ37qxpxQXZ5w5QW5mJrsNb4XL16ctIrG7xitk1MvoGHkGNMwchZ2Umo77OpFOQ7V3wdNSg3/\n/gVNmf34I8fgudhLtcqI97HrXsK/7TgbkYPrkERDyq+pCBsi82sSY39v2yzDX5u8WjtFvnhOG302\nTMX2O9NgBkdxHiLbRKttK21qQPw8oy+wguDGcEhGVJH0MF14KhOWzE7/PGrYUSMCpc4oMHRcEmVg\nYTFZJ0yAEnGIMf1H8psu1egxHUc+nwG6lmJqwFUBq+gKdbk9Kfb8IpI5FFjPNTmx5zXvps9OaERE\nP0KsIfs9N6hcCT85IxHBWQvVaMhQKXNAwrmX74u/xdHQeyPd4qoG8b1ZjY/nE1Z9WqLnzZ1m1vlm\nXecJvYAVLIIVVOZOk3xUrbivtdFnd6Qaz8KkVacUxbXyokXTwfwNz0ej1mW/lVNBxGt8L168OPF/\n+F68XIbSOuBeuSm2acNBMK8S5qsJdH0nNXPIgxcRubGNmrkT5DkREZm6So9SaACpNTAfaTYT5Jnr\nhozptkxERF6Ypbxymt1ZL7mupiyuSRLMwWNK8gj7rYu4DC0CZDBhc27Qi+7fbcpRl+BYqgcWxNy5\n1gFI/OxvQOSpOneHiIg07DMxGppyh2KttqGDy8N/V3wb3HOGuwDy2VyEN0//vYiINM+HaR924XVr\nIKmD9lfukKKuVViTAJfqt9Cdb6RoVti4scgOw62TXCMi0jZFX0r9Rs25b9EKTURkgD7Dohu0tHRl\noPOUo4aHvwX3nq6HHoN1QbGgpojLINx/TMtP0+U4M9OFRlOf0ToA5y/o784siquSZEpxh+5Inj6f\nOzsrYeq1CtfkIsyFKNQDgdfGEy7rs0cXdHz+AIjpTKxXk6U54hG4qbvVTR3RW/9va1OEOR98BZyz\nqeSTOBnPt/ZakV4+nOfFixcjraLxEz9qkOb7dYdPe9nQHQ9Dm1B5x5ePFnHADMNU3JENoJGQrCDX\njZ3UOrgV7bV2i2t2eSpLQb2UfQBIUBnmkhlusE4i4wDubHbc0cQ87KgdtPJOUVSJFpWbEaIxYEx4\nvlIcGaY0TSlCgIbAETWIPQ/mmPMUQKut2PlthiL/Hd9c0p4HGW4Z/QEgMgOs3ACSaEnVoze01B35\nsfMz8t1hSvz56dnvxHweVpwRCd9lu0IlplxxhWplW6lIipCb/wCsAL6Te90QApKsN7dVtCXXqb3m\nPLDiUobjHbfFO3Z9K8Ls0DO7oOlp7ZSpYpwxe1Y49ukszdn/7kG9z09FAdTyQaaZJ7XuW1jEZdDm\nRgsTrK58CGuEa7jW3B/XCtZ71rNqiR497dheYcUktvIiENn043BMJKpU4brbuors8xrfixcvRlql\nhVbzqGvDXffk6U7m6tht4b8lL4d/VO9UPimVeypcrTcREcl3G1n332uDybfX6kXq9qmGttVsaVWc\na0LbYZAncgtahl0417oKaHrTsrr5T2h0EFVq5xWIn7W7C5rkdedPFgxEnYG5MFeIc5hGGAy/DcoB\nnTb5HhFxVoyII/nUnkvXaw3AtVJNTbpSHDHlhFGKm8zp5ErCfv+hhSIicryDavqMN1XzXyXnwjF/\nE/lQRByZKWkDyENsOX00LuddHJGoqS1IRDPdd1lPgW58O+oDkpBlaaZ3QdOz5RhacQ8tdhgGk2rK\nF6i2Zeuy63u6ggO39kRbrBegkWtwvnK3VsIwcJUech/V9ZU8W3Gdp4/9wM0L98EaDOsqUJFnibGS\nl+PcxJlw6JPjaMuVS6HpSeqhdWYtW2p8QDXh8zKWVjPXD39PvKmXI1V9RdAWfY7EVvGNE6/xvXi5\nDKV1UP250ZAC2qO/aQ28GaqVPj6JPOnu9z2GwddchbFE0ze4MfEttDLfASFkSI4bswR+bCk0FnfL\nWnMeoKwz+qIu22bU3LfNB7Ez50xQTXPsrLbM6t9Gy9jYltNPT8TvubMX4ugUmUN2cXuhb11tHFNq\nR2ADI54FAhyMddNqVGeeTTPDa9qel6y0Q01Ae8/UL+y4EESntfCdSSudqkSZlCbXBLPheZ1Y2iTU\nv1upmM3Qce4GXz+nDu2Aq/TiRwFqhG2qRVpEIoh7DH3WRC2WQhXWxI6xJJiQWJOOBYW25Gn9Da50\nDXAl+sew5pKOqmVzc4pr1FmzSmm9CQNAvGF6rq2KwzXL+VCr25qQXN9VOGI9ZQwz5KrvAW/ha8d9\nzXjJYA53YD3RCsC1Oz5qahzuxnsbJSJHvY/vxYsXI/4P34uXy1Bax9R/4Q2R9wr1A8Nzjvwe4bIP\n0vUD9H1P6m7aGDWSg45MNIJLxpRKmg8Aal9c51kbqkM4aNBIBdEI2Ow84Mg0zCBMTDovF7e/KT2L\n1a7fs80Ai1V6GDMbhKBVSghiY44zDxjADaZrmNN+VM3wjBxj4rHxRSE+IFblqjxLSiXCU/PVtE59\nVEkofU3ueMWtaSJtC10WW7oeIjmm6ONiRf6SSvC87tfnlbjcgaBd2mv/+JAMU60meUJXNXd7dXLE\n9/dEufGndsC8jC8jLeIKp9biaPPFj1aJ3FzozFsQUyKzsS6+bUrwMLQHMzvhbp1P7067wyFh6XUS\nXJgRaLj6bEbyu3Oaf9/QXScUOYhrVptrcq3SK6kVkferRK4rdGNYnmE2QnYLAOSluyE09ZPvjs2q\nJElKROTENnVBcvsr2PzrBi1uGlNh6Dnj/om4UKINe9Kt6CAieV+0qb+nqlUu83lKdMf2zx70ZZKG\nqi96Bn+5HKv6omfwl8uJqi96Bp+LtEo478YOx+R9khZMGKduN3ZXEFtS1qtmC0NuIiLPQ2WQ6stQ\nxkxnqTStV80VNt98CyDMcENMeUAthVfTkLtPzdrdDeG5mxu/KnL+StmzEZre1pDDlDdd0Lx+5nqH\nmt5Qkv9pcImIiPxDYpl+AEDpeLIpq0xaMXdvak0TEmtACGryo5oRuGTeDBERqbjXlJ/uPkuk2NwX\n9q26RU6DRX6iWm0NkM0+hXrR5vWOPluThxx4aNaygmGYjrZ42nPYWT9jusDqqQENmuChBUOpNfkM\nSSiqEtWgVeLy5a2lIBJTXafjMICO1Wpd3NJJNeHOrcZi463yGszZNyGx9xrSRURkeooyneYU/khE\nXO3Dul1G4zPsat//QXG1CsSBvK+c/GbMtSPTjKW1QM85q42Cc9MW/JOIiJyodLUc095QAHJPndZs\nkO26Xt9ubAnyRopjLSKSo0REzizXdZg2+EhMyYN48T6+Fy+XobSOj+/Fi5cvRP6cj/9f/ofvxYuX\nL594U9+Ll8tQ/B++Fy+Xofg/fC9eLkPxf/hevFyG4v/wvXi5DOX/AiTaYgnxFolKAAAAAElFTkSu\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12c31908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"l = 100\n",
"x, y = np.indices((l, l))\n",
"\n",
"center1 = (28, 24)\n",
"center2 = (40, 50)\n",
"center3 = (67, 58)\n",
"center4 = (24, 70)\n",
"\n",
"radius1, radius2, radius3, radius4 = 16, 14, 15, 14\n",
"\n",
"circle1 = (x - center1[0]) ** 2 + (y - center1[1]) ** 2 < radius1 ** 2\n",
"circle2 = (x - center2[0]) ** 2 + (y - center2[1]) ** 2 < radius2 ** 2\n",
"circle3 = (x - center3[0]) ** 2 + (y - center3[1]) ** 2 < radius3 ** 2\n",
"circle4 = (x - center4[0]) ** 2 + (y - center4[1]) ** 2 < radius4 ** 2\n",
"\n",
"img = circle1 + circle2 + circle3 + circle4\n",
"\n",
"# We use a mask that limits to the foreground: the problem that we are\n",
"# interested in here is not separating the objects from the background,\n",
"# but separating them one from the other.\n",
"mask = img.astype(bool)\n",
"\n",
"img = img.astype(float)\n",
"img += 1 + 0.2 * np.random.randn(*img.shape)\n",
"\n",
"# Convert the image into a graph with the value of the gradient on the\n",
"# edges.\n",
"graph = image.img_to_graph(img, mask=mask)\n",
"\n",
"# Take a decreasing function of the gradient: we take it weakly\n",
"# dependent from the gradient the segmentation is close to a voronoi\n",
"graph.data = np.exp(-graph.data / graph.data.std())\n",
"\n",
"plt.matshow(img)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute clustering with Spectral clustering"
]
},
{
"cell_type": "code",
"execution_count": 260,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#graph = np.double(precip)"
]
},
{
"cell_type": "code",
"execution_count": 261,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x11d13208>"
]
},
"execution_count": 261,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x11e5bf28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Force the solver to be arpack, since amg is numerically\n",
"# unstable on this example\n",
"labels = spectral_clustering(graph, n_clusters=2, eigen_solver='arpack')\n",
"label_im = -np.ones(mask.shape)\n",
"label_im[mask] = labels\n",
"\n",
"plt.matshow(label_im)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Affinity propagation"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Automatically created module for IPython interactive environment\n"
]
}
],
"source": [
"print(__doc__)\n",
"\n",
"from sklearn.cluster import AffinityPropagation\n",
"from sklearn import metrics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate sample data"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.datasets.samples_generator import make_blobs"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"centers = [[1, 1], [-1, -1], [1, -1]]\n",
"precip, labels_true = make_blobs(n_samples=300, centers=centers, cluster_std=0.5,\n",
" random_state=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute Affinity propagation"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Estimated number of clusters: 3\n",
"Silhouette Coefficient: 0.753\n"
]
}
],
"source": [
"af = AffinityPropagation(preference=-50.).fit(precip)\n",
"cluster_centers_indices = af.cluster_centers_indices_\n",
"labels = af.labels_\n",
"\n",
"n_clusters_ = len(cluster_centers_indices)\n",
"\n",
"print('Estimated number of clusters: %d' % n_clusters_)\n",
"#print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels_true, labels))\n",
"#print(\"Completeness: %0.3f\" % metrics.completeness_score(labels_true, labels))\n",
"#print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels_true, labels))\n",
"#print(\"Adjusted Rand Index: %0.3f\"\n",
"# % metrics.adjusted_rand_score(labels_true, labels))\n",
"#print(\"Adjusted Mutual Information: %0.3f\"\n",
"# % metrics.adjusted_mutual_info_score(labels_true, labels))\n",
"print(\"Silhouette Coefficient: %0.3f\"\n",
" % metrics.silhouette_score(precip, labels, metric='sqeuclidean'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot results"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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JqocH3TG5uWyjLT3Aa1WrciDy8uIgdfw4cPEiB6Jeveg+qljRPsFq++z4sv2+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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x79e72e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#import matplotlib.pyplot as plt\n",
"from itertools import cycle\n",
"\n",
"plt.close('all')\n",
"plt.figure(1)\n",
"plt.clf()\n",
"\n",
"colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')\n",
"for k, col in zip(range(n_clusters_), colors):\n",
" class_members = labels == k\n",
" cluster_center = precip[cluster_centers_indices[k]]\n",
" plt.plot(precip[class_members, 0], precip[class_members, 1], col + '.')\n",
" plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=14)\n",
" for x in precip[class_members]:\n",
" plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)\n",
"\n",
"plt.title('Estimated number of clusters: %d' % n_clusters_)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
queenqueen/bioinf-python | notebooks/00_Intro/R_magic.ipynb | 1 | 82628 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The cell below will get the data file, you only need to run it once "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(you do not need to do this if you have done it in the Interfacing_R notebook)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2015-05-13 14:53:59-- ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/sequence.index\n",
" => ‘sequence.index’\n",
"Resolving ftp.1000genomes.ebi.ac.uk (ftp.1000genomes.ebi.ac.uk)... 193.62.192.8\n",
"Connecting to ftp.1000genomes.ebi.ac.uk (ftp.1000genomes.ebi.ac.uk)|193.62.192.8|:21... connected.\n",
"Logging in as anonymous ... Logged in!\n",
"==> SYST ... done. ==> PWD ... done.\n",
"==> TYPE I ... done. ==> CWD (1) /vol1/ftp ... done.\n",
"==> SIZE sequence.index ... 67069489\n",
"==> PASV ... done. ==> RETR sequence.index ... done.\n",
"Length: 67069489 (64M) (unauthoritative)\n",
"\n",
"sequence.index 100%[=====================>] 63.96M 1.30MB/s in 24s \n",
"\n",
"2015-05-13 14:54:25 (2.64 MB/s) - ‘sequence.index’ saved [67069489]\n",
"\n"
]
}
],
"source": [
"!rm sequence.index 2>/dev/null\n",
"!wget ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/sequence.index"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import rpy2.robjects as robjects\n",
"import rpy2.robjects.lib.ggplot2 as ggplot2\n",
"\n",
"%load_ext rpy2.ipython"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"seq_data = %R read.delim('sequence.index', header=TRUE, stringsAsFactors=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" [1] \"FASTQ_FILE\" \"MD5\" \"RUN_ID\" \n",
" [4] \"STUDY_ID\" \"STUDY_NAME\" \"CENTER_NAME\" \n",
" [7] \"SUBMISSION_ID\" \"SUBMISSION_DATE\" \"SAMPLE_ID\" \n",
"[10] \"SAMPLE_NAME\" \"POPULATION\" \"EXPERIMENT_ID\" \n",
"[13] \"INSTRUMENT_PLATFORM\" \"INSTRUMENT_MODEL\" \"LIBRARY_NAME\" \n",
"[16] \"RUN_NAME\" \"RUN_BLOCK_NAME\" \"INSERT_SIZE\" \n",
"[19] \"LIBRARY_LAYOUT\" \"PAIRED_FASTQ\" \"WITHDRAWN\" \n",
"[22] \"WITHDRAWN_DATE\" \"COMMENT\" \"READ_COUNT\" \n",
"[25] \"BASE_COUNT\" \"ANALYSIS_GROUP\" \n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<StrVector - Python:0x7f5328b8dfc8 / R:0xac17730>\n",
"[str, str, str, ..., str, str, str]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"as_integer = robjects.r('as.integer')\n",
"match = robjects.r.match\n",
"my_col = match('READ_COUNT', seq_data.colnames)[0] # Vector returned\n",
"seq_data[my_col - 1] = as_integer(seq_data[my_col - 1])\n",
"my_col = match('BASE_COUNT', seq_data.colnames)[0] # Vector returned\n",
"seq_data[my_col - 1] = as_integer(seq_data[my_col - 1])\n",
"%R -i seq_data\n",
"%R print(colnames(seq_data))\n",
"%R seq_data$CENTER_NAME <- toupper(seq_data$CENTER_NAME)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%R\n",
"seq_data <- seq_data[seq_data$WITHDRAWN==0, ]\n",
"seq_data <- seq_data[, c('STUDY_ID', 'STUDY_NAME', 'CENTER_NAME', 'SAMPLE_ID', 'SAMPLE_NAME', 'POPULATION', 'INSTRUMENT_PLATFORM', 'LIBRARY_LAYOUT', 'PAIRED_FASTQ', 'READ_COUNT', 'BASE_COUNT', 'ANALYSIS_GROUP')]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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gqFnFnsKy+g2yaY150ZheY9aKxqqOm3hwAMAb251N20uywUW1bz0V6XxWOnfbm8VbZSM1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\nQmCC\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R\n",
"bar <- ggplot(seq_data) + aes(factor(CENTER_NAME)) + geom_bar() + theme(axis.text.x = element_text(angle = 90, hjust = 1))\n",
"print(bar)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%R\n",
"seq_data$POPULATION <- as.factor(seq_data$POPULATION)\n",
"yri_ceu <- seq_data[seq_data$POPULATION %in% c(\"YRI\", \"CEU\") & seq_data$BASE_COUNT < 2E9 & seq_data$READ_COUNT < 3E7, ]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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+/v7R0dFr1qxpaGjYv38/wzBZWVn0OAzDJCYm0o2UVqv94osv5s6du2HDBrE+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\nENAAADL1/wHDWLlJFn5xzQAAAABJRU5ErkJggg==\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R\n",
"scatter <- ggplot(yri_ceu, aes(x=BASE_COUNT, y=READ_COUNT, col=factor(ANALYSIS_GROUP), shape=POPULATION)) + geom_point()\n",
"print(scatter)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Loading required package: grid\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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t24x3Llq0yPGRAMZBDRoAdlGpVMY7GVn1CjAOEjQAALAUdLMDAACWggQNAAAs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\nAICBAgINAABgoIBAAwAAGCj/D/LmpuzkFIwUAAAAAElFTkSuQmCC\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R\n",
"library(gridExtra)\n",
"g <- arrangeGrob(bar, scatter, ncol=1)\n",
"g"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Saving 6.67 x 6.67 in image\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#%R ggsave('fig.pdf', g, dpi=600)\n",
"%R ggsave('fig.png', g, dpi=600)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
tboch/VO-access-GaiaDR1 | notebooks/magnitude-distribution.ipynb | 1 | 22964 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"def query_TAP(tap_endpoint, adql_query, table_to_upload=None):\n",
" \"\"\"\n",
" Query a TAP service (designated by its tap_endpoint)\n",
" with a given ADQL query\n",
" \n",
" Query is performed synchronously\n",
" \n",
" Return an AstroPy Table object\n",
" \"\"\"\n",
" import requests\n",
" from astropy.table import Table\n",
" from astropy.io.votable import parse_single_table\n",
" import os\n",
" import tempfile\n",
" import warnings\n",
" warnings.simplefilter(\"ignore\")\n",
" \n",
" r = requests.post(tap_endpoint + '/sync', data={'query': adql_query, 'request': 'doQuery', 'lang': 'adql', 'format': 'votable', 'phase': 'run'})\n",
" \n",
" tmp_vot = tempfile.NamedTemporaryFile(delete = False)\n",
" with open(tmp_vot.name, 'w') as h:\n",
" for line in r.iter_lines():\n",
" if line:\n",
" h.write(line.decode(r.encoding)+'\\n')\n",
"\n",
" table = parse_single_table(tmp_vot.name).to_table()\n",
"\n",
" # finally delete temp files\n",
" os.unlink(tmp_vot.name)\n",
"\n",
" return table\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"endpoint = 'http://tapvizier.u-strasbg.fr/TAPVizieR/tap'\n",
"adql = \"\"\"SELECT phot_g_mean_mag\n",
" FROM \"I/337/tgas\"\n",
" \"\"\"\n",
"\n",
"result = query_TAP(endpoint, adql)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"phot_g_mean_mag [1]\n",
" mag \n",
"-------------------\n",
" 11.585\n",
" 11.791\n",
" 11.331\n",
" 10.272\n",
" 11.474\n",
" 11.398\n",
" 9.935\n",
" 10.528\n",
" 11.17\n",
" 11.498\n",
" ...\n",
" 12.003\n",
" 10.732\n",
" 10.801\n",
" 9.682\n",
" 11.223\n",
" 11.943\n",
" 11.689\n",
" 12.169\n",
" 11.776\n",
" 9.597\n",
"Length = 2057050 rows\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"histvals, binvals, patches = plt.hist(result['phot_g_mean_mag'], bins=50, facecolor='g', alpha=0.75)\n",
"\n",
"plt.xlabel('magnitude')\n",
"plt.ylabel('count')\n",
"plt.title('G mag histogram')\n",
"plt.grid(True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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hwoVt2qOjo3Hw4ME27d7e3ti0aZPVdWVkZCAjI+OGGenWpZ+kh9FktHqbsdII\nNdp2qRGR7TpUcP761786OgfdgCzfeGTKmflSptVxGgA4/uJxJyeyToa9G0Cu7e7uZMhoqw6N4VRW\nVuKRRx5B37590bdvXzz22GOorKx0dDYiIupCOlRwZsyYgQkTJuDHH3/Ejz/+iPHjx1+3a4zs79rD\nT90Vc9oXz8OxLxlyypDRVh0qOGfPnsWMGTPg6ekJT09PZGRk4OzZs47ORkREXUiHCk7v3r3x7rvv\noqmpCU1NTXj33XfRu3dvR2ejFmTp12VO++IYjn3JkFOGjLbqUMFZt24dNm3aBLVajX79+uHDDz9U\nzjglIiLqiA4VnMWLF6OgoABnz57FmTNnsG7dOixZssTR2agFWfp1mdO+OIZjXzLklCGjrTpUcL75\n5hv4+f26a+/v748DBw44LBQREXU9HSo4FosFZvOv37Sqq6vR2NjosFDUliz9usxpXxzDsS8ZcsqQ\n0VYdOvHzhRdewIgRIzB58mQAV69b9vvf/96hwYiIqGvp0B5OWloaNm/ejICAAAQEBGDz5s1ITU11\ndDZqQZZ+Xea0L47h2JcMOWXIaKsO7eEAVy+uqdPpHJmFiIi6sE7/PAG5hiz9usxpXxzDsS8ZcsqQ\n0VYsOERE5BQsOJKQpV+XOe2LYzj2JUNOGTLaigWHiIicggVHErL06zKnfXEMx75kyClDRlux4BAR\nkVOw4EhCln5d5rQvjuHYlww5ZchoKxYcIiJyChYcScjSr8uc9sUxHPuSIacMGW3FgkNERE7BgiMJ\nWfp13TGnfpIe2lhtq78B4QNgrDS6OtoNcQzHvmTIKUNGW3X4WmpEsjKajFDPUbdqM1eYcTrvtIsS\nEd2auIcjCVn6dWXJKcvYiCw5ZdnuMuSUIaOtWHCIiMgpWHAkIUu/riw5ZRkbkSWnLNtdhpwyZLQV\nCw4RETmFQwvOzJkzERAQgMjISKXNbDYjMTER4eHh0Ov1qKmpUW7LycmBRqOBVqvFJ598orSXlZUh\nMjISYWFhmDdvntJeX1+PlJQUaDQajBgxAkbjr0cdFRQUICwsDOHh4Vi/fr0jH6ZTyNKvK0tOWcZG\nWuY0Go1tjrbTxmqhn6R3YcKrZNnuMuSUIaOtHFpwZsyYgaKiolZtubm5GD16NI4cOYKEhATk5OQA\nAA4dOoRNmzahoqIC27Ztw+zZsyGEAABkZmYiLy8PR48exdGjR5V15uXlwd/fH8eOHcO8efMwf/58\nAFeL2tK/ShtrAAAWs0lEQVSlS7Fv3z7s3bsX2dnZrQobkYwaLY1Qz1G3+TOa3P/wbiLAwQXn/vvv\nh59f62+ShYWFSE9PBwCkp6djy5YtAICtW7ciJSUFnp6eCA0NhUajQWlpKaqqqnDx4kXExsYCANLS\n0pRlWq4rKSkJO3fuBAAUFRUhMTERPj4+8PX1RWJiIrZv3+7Ih+pwsvTrypJTlrERWXLKst1lyClD\nRls5fQznzJkzCAgIAACo1WqcOXMGAGAymRAcHKzMFxgYCJPJBJPJhKCgIKU9KCgIJpOpzTLdunWD\nj48Pqqur210XERG5jstP/FSpVHZbV3MXXGdlZGQgNDQUAODr64uoqCilH7X52wanOzbd3OYueUpK\nSnC59rKSrXmPoXlspL3p9ua31FtgrjDf9PzXm7bUW9qs83rzt3x87vB8u/N0c5u75GlvumVWd8gz\natQolJSUID8/HwCUz8vOUglbP6U76NSpUxg/fjy++eYbAIBWq0VJSQkCAgJQVVWF+Ph4VFRUIDc3\nFyqVCllZWQCAsWPHIjs7GyEhIco8AGAwGPD5559jzZo1yjzDhg1DU1MT+vXrhzNnzsBgMKCkpARr\n164FAMyaNQvx8fGYMmVK2ydApbK5UJEctLHaNlcaAIA9L+7ByD+MtLpMe7fZq92e66paWYWKfRVW\n74PIUWz57HR4l5oQolWoCRMmKFWyoKAAEydOVNoNBgPq6+tx4sQJfPfdd4iLi4NarYaPjw9KS0sh\nhMD69etbLVNQUAAA+OCDD5CQkAAA0Ov1KC4uRk1NDcxmM4qLi6HXu/5Inptx7TcfdyVLTlnGRmTJ\nKct2lyGnDBlt5dAutWnTpqGkpATnz5/HgAEDkJ2djQULFmDy5MlYt24dQkJCsGnTJgCATqdDcnIy\ndDodvLy8sHr1aqW7bdWqVcjIyEBdXR3GjRuHsWPHArh62HVqaio0Gg169+4Ng8EAAPDz88OiRYsQ\nExMDlUqFJUuWwNfX15EPlYiIbsChBWfDhg1W2z/99FOr7QsXLsTChQvbtEdHR+PgwYNt2r29vZWC\nda2MjAxkZGR0PKyba9kH7c5kySnjeTjuTJbtLkNOGTLailcaICIip2DBkYQs/bqy5JRlbESWnLJs\ndxlyypDRViw4RETkFCw4kpClX1eWnLKMjciSU5btLkNOGTLaigWHiIicggVHErL067oyp36S3urV\nlI2VbS9uKcvYiCw5+fq0Hxky2srll7YhshejyWj1igLHXzzugjREdC3u4UhCln5dWXLKMjYiS05Z\ntrsMOWXIaCsWHCIicgoWHEnI0q8rS05ZxkZkySnLdpchpwwZbcWCQ0RETsGCIwlZ+nVlySnL2Igs\nOWXZ7jLklCGjrVhwiIjIKVhwJCFLv64sOWUZG5ElpyzbXYacMmS0FQsOERE5BQuOJGTp15Ulpyxj\nI7LklGW7y5BThoy2YsEhIiKnYMGRhCz9urLklGVsRJacsmx3GXLKkNFWLDhEROQULDiSkKVfV5ac\nsoyNyJJTlu0uQ04ZMtqKV4sm6egn6WE0tf3JAWOlEWq0vVo0EbkH7uFIQpZ+XWfkbP4Zgmv/Ghsb\nO7wOWcZGZMnJ16f9yJDRViw4RETkFCw4kpClX1eWnLKMjciSU5btLkNOGTLaigWHiIicggVHErL0\n68qSU5axEVlyyrLdZcgpQ0ZbseAQEZFTsOBIQpZ+XVlyyjI2IktOWba7DDllyGgrlxWc0NBQ3Hvv\nvRgyZAji4uIAAGazGYmJiQgPD4der0dNTY0yf05ODjQaDbRaLT755BOlvaysDJGRkQgLC8O8efOU\n9vr6eqSkpECj0WDEiBEwGtuet0FERM7jsoLj4eGBkpISHDhwAKWlpQCA3NxcjB49GkeOHEFCQgJy\ncnIAAIcOHcKmTZtQUVGBbdu2Yfbs2RBCAAAyMzORl5eHo0eP4ujRoygqKgIA5OXlwd/fH8eOHcO8\nefMwf/581zxQO5GlX1eWnLKMjciSU5btLkNOGTLaymUFRwgBi8XSqq2wsBDp6ekAgPT0dGzZsgUA\nsHXrVqSkpMDT0xOhoaHQaDQoLS1FVVUVLl68iNjYWABAWlqaskzLdSUlJWHHjh3OemhERGSFywqO\nSqXCmDFjEBsbi7/85S8AgNOnTyMgIAAAoFarcebMGQCAyWRCcHCwsmxgYCBMJhNMJhOCgoKU9qCg\nIJhMpjbLdOvWDb6+vqiurnbKY3MEWfp1Zckpy9iILDll2e4y5JQho61cdi213bt3o1+/fjh79qwy\nbqNSqVrNc+30zWjugiM5tHe9NIDXTCOSlcsKTr9+/QAAd955JyZNmoTS0lIEBAQoezlVVVXo27cv\ngKt7ND/88IOybGVlJQIDA9ttb7lM//790dTUhNraWvj7+1vNkpGRgdDQUACAr68voqKilG8Zzf2p\nrp5ubnOXPO1N/+lPf7LL89d8vbTmMYzmb/rmCjPq19Qrz4m126+dttRb2sx/o+Xbu91Sb4G5wnzT\n83c07w9FP6DHgB7Xnf9y7WVlfr4+rz9tr9enI6fLy8uVA6DcIU/zdElJCfLz8wFA+bzsLJVwwVf/\nn3/+GRaLBT169MDly5eRmJiIJUuWYMeOHfD390dWVhZee+01mM1m5Obm4tChQ5g+fTr27t0Lk8mE\nMWPG4NixY1CpVBg+fDjeeustxMbG4t/+7d/w7LPPYuzYsVi9ejX++c9/YvXq1TAYDNiyZQsMBkPb\nJ0ClkmLvp6SkRIpdbXvl1MZqoZ5jfS9mz4t7MPIPI2+q3VxhRkVehdX57XUf12vv6DIti1V781et\nrELFvgqr9+Est9rr05FkyAjY9tnpkj2c06dP45FHHoFKpUJjYyOmT5+OxMRExMTEIDk5GevWrUNI\nSAg2bdoEANDpdEhOToZOp4OXlxdWr16tdLetWrUKGRkZqKurw7hx4zB27FgAwMyZM5GamgqNRoPe\nvXtbLTYykeEFCMiTU5axEVlyyrLdZcgpQ0ZbuaTgDBw4EOXl5W3a/f398emnn1pdZuHChVi4cGGb\n9ujoaBw8eLBNu7e3t1KwiIjI9XilAUm07Ct3Z7LklOX8FllyyrLdZcgpQ0ZbseAQEZFTsOBIQpZ+\nXVlyyjI2IktOWba7DDllyGgrFhwiInIKFhxJyNKvK0tOWcZGZMkpy3aXIacMGW3lshM/iYD2ryjA\nqwkQdT0sOJKQpV+3szmbryhwreMvHrdTIutkGRvpSE6j0QhtrLZN+4DAASjaUuSIWG101denK8iQ\n0VYsOESSa7Q0Wi3axpX8DShyLxzDkYQs/bqy5JRlbESWnLJsdxlyypDRViw4RETkFCw4kpClX1eW\nnF1pDMcdyLLdZcgpQ0ZbseAQEZFTsOBIQpZ+3fZy6ifpoY3VtvkzVrpmYFuWsRFZcsr++nQnMmS0\nFY9SI6dw1eHPROQ+uIcjCVn6dWXJKcvYiCw5ZdnuMuSUIaOtWHCIiMgpWHAkIUu/riw5ZRkbkSWn\nLNtdhpwyZLQVCw4RETkFDxqQhCz9ujl/ykHmS5lt2t3tYpyyjI3IklOW16cMOWXIaCsWHLIrHo1G\nRO1hl5okZOnXvVx72dUROkSWsRFZcsry+pQhpwwZbcWCQ0RETsEuNUm4U79uez+aBgDna8/jt/it\nkxN1nixjI7LkdKfX5/XIkFOGjLZiwaFOa2+cBuBYDRG1j11qkpClX9dSb3F1hA6RZWxElpyyvD5l\nyClDRltxD4fa1V7Xmbsd4kxEcmDBkYQr+nVtOcTZ4zY5dpplGRuRJacs4w4y5JQho61YcIh7MkTk\nFHJ8Hb0J27dvxz333IOwsDC89tprro5jM0f26zbvyVz719jY2Ol1cQzHvm4mp9FotPobRNpYLfST\n9HZMKc+4gww5Zchoqy69h2OxWDBnzhzs2LED/fv3R2xsLCZOnIh77rnH1dE6rby8/KZ3tZ2xJ2Np\nlKPgXDJecnWEDrlkvGRzt1qjpbHdowmNK+37w3f2eH06gww5Zchoqy5dcEpLS6HRaBASEgIASElJ\nQWFhoZQF58KFCx2a73rnyBgrjYjLjWvTbtdDmYX9VuVIjT93fu/NFWTJ2dHXp6vJkFOGjLbq0gXH\nZDIhODhYmQ4KCkJpaakLE9nP9fZWrBUVgOfIEJFrdemCYw8bN27E2rVrrd6Wl5eHu+66yy73014B\nOXvmLO7seyd+PP4jNv5jo9LulL0VG4gmOXZx6s7VuTpChzgqZ/P4zrUGBA5A0ZaiTq/v5MmTdkjl\neDLklCGjrVRCCDk+IWzw1Vdf4ZVXXsH27dsBALm5uVCpVMjKylLmUalUropHRCS1zpaPLl1wmpqa\nEB4ejh07dqBfv36Ii4vD+++/D6227Tc7IiJyrC7dpdatWzesXLkSiYmJsFgsmDlzJosNEZGLdOk9\nHCIich9d/sTPG7FYLBg6dCgmTJjg6ijtqqmpweTJk6HVajFo0CDs3bvX1ZGsysnJwaBBgxAZGYnp\n06ejvr7e1ZEAADNnzkRAQAAiIyOVNrPZjMTERISHh0Ov16OmpsaFCa+ylnP+/PnQarWIiorCY489\nhtraWhcmtJ6x2RtvvAEPDw9UV1e7IFlr7eV8++23odVqERERgQULFrgo3a+s5dy3bx/i4uIwZMgQ\nxMXFYf/+/S5MeFVlZSUSEhIwaNAgRERE4K233gJgw/tI3OJWrFghpk+fLsaPH+/qKO1KT08X69at\nE0II0dDQIGpqalycqK2TJ0+KgQMHiitXrgghhEhOThYFBQUuTnXVl19+KQ4cOCAiIiKUtvnz54vX\nXntNCCFEbm6uyMrKclU8hbWcxcXFoqmpSQghRFZWlliwYIGr4gkhrGcUQogffvhB6PV6ERoaKs6f\nP++idL+ylvOzzz4TY8aMEQ0NDUIIIc6ePeuqeAprOUeNGiWKioqEEEJ8/PHHYtSoUa6Kp/jpp5/E\ngQMHhBBCXLx4UYSFhYmKiopOv49u6T2cyspKfPzxx3jyySddHaVdtbW1+PLLLzFjxgwAgKenJ3r1\n6uXiVG316tULt912Gy5fvozGxkb8/PPP6N+/v6tjAQDuv/9++Pm1Plu/sLAQ6enpAID09HRs2bLF\nFdFasZZz9OjR8PC4+jYdPnw4KisrXRFNYS0jADz33HN4/fXXXZDIOms516xZgwULFsDT8+rQdZ8+\nfVwRrRVrOfv166fsKVy4cAGBgYGuiNaKWq1GVFQUAKBHjx7QarWorKzs9Pvoli44zW8Sdz40+sSJ\nE+jTpw9mzJiBoUOH4t///d/xyy+/uDpWG35+fnjhhRcwYMAABAYGwtfXF6NHj3Z1rHadOXMGAQEB\nAK6+mc6cOePiRDe2bt06PPTQQ66O0cbWrVsRHByMiIgIV0e5rqNHj+KLL77A8OHDER8f7xZdVdbk\n5ubi+eefx4ABAzB//nzk5OS4OlIrJ0+eRHl5OYYPH47Tp0936n10yxacf/zjHwgICEBUVBSEEJ0+\nntxZGhsbUVZWhqeffhplZWXo3r07cnNzXR2rjePHj+OPf/wjTp06hR9//BGXLl3Chg0bXB2rw9z5\nSwcAvPrqq/Dy8sK0adNcHaWVX375BcuWLUN2drbS5s7vJbPZjK+++grLly9HcnKyqyNZNXPmTLz9\n9tswGo344x//iCeeeMLVkRSXLl1CUlIS3nzzTfTo0aPN++ZG76NbtuDs3r0bW7duxV133YWpU6fi\ns88+Q1pamqtjtREUFITg4GDExMQAAJKSklBWVubiVG3t378f9913H/z9/dGtWzc8+uij2LNnj6tj\ntSsgIACnT58GAFRVVaFv374uTtS+/Px8fPzxx25ZwL///nucPHkS9957LwYOHIjKykpER0e75R5j\ncHAwHn30UQBAbGwsPDw8cP78eRenamvv3r2YNGkSgKvvd3e5HFdjYyOSkpKQmpqKiRMnAuj8++iW\nLTjLli2D0WjE8ePHYTAYkJCQgPXr17s6VhsBAQEIDg7G0aNHAQA7duyATqdzcaq2wsPD8dVXX6Gu\nrg5CCOzYscOtznm6di92woQJyM/PBwAUFBQobyBXuzbn9u3b8frrr2Pr1q3w9vZ2YbJftcw4ePBg\nVFVV4fjx4zhx4gSCgoJw4MABtyjg1z6XkyZNws6dOwFc7V5raGhA7969XRVPcW1OjUaDzz//HMDV\n93tYWJirorXyxBNPQKfTYe7cuUpbp99HDjigQTolJSVufZRaeXm5iImJEffee6945JFHxIULF1wd\nyarly5cLnU4nIiIiRFpamqivr3d1JCGEEFOnThX9+vUTt912mwgODhbr1q0T1dXV4sEHHxRhYWFi\nzJgxwmw2uzqm1Zx33323GDBggBgyZIgYMmSIyMzMdLuMLQ0cONAtjlKzlrOhoUE8/vjjYvDgwSI6\nOlqUlJS4OqbVnPv37xdxcXEiKipKDB8+XJSVlbk6pti1a5fw8PAQ9957r4iKihJDhgwR27ZtE+fP\nn+/U+4gnfhIRkVPcsl1qRETkXCw4RETkFCw4RETkFCw4RETkFCw4RETkFCw4RETkFCw4RBJ45513\n8O677wK4eoJdVVVVp9cxcOBAt/jpALp1delf/CTqKn73u98p/+fn52Pw4MFQq9WdWoe7Xy+Ouj7u\n4RDZ4NSpU9BqtZgxYwbCw8Mxffp0FBcX47777kN4eDj279+Pffv2YeTIkYiOjsb999+PY8eOAbh6\nwcspU6Zg8ODBePTRRzF8+HDl+ng9e/bEyy+/jKioKIwcORJnz54FAGRnZ+ONN97ARx99hP379+Px\nxx/H0KFDUVdX12rP5euvv0Z8fDwAoLq6Gnq9HhEREXjqqadaXT7lvffew7BhwzB06FBkZma67QU3\nqWthwSGy0ffff4+XXnoJR44cwZEjR2AwGLB79268/vrrePXVV6HVarFr1y58/fXXyM7OxsKFCwEA\nq1evhr+/P/75z3/iP//zP1tdjPXy5csYOXIkysvL8cADD+DPf/6zcptKpcJjjz2GmJgYbNiwAWVl\nZbj99tvbvWJvdnY2HnjgARw8eBCPPPIIjEYjAODw4cPYuHEj9uzZg7KyMnh4eOC9995z9NNFxC41\nIlsNHDhQuZDqoEGDlN//iYiIwKlTp3DhwgWkpaXh2LFjUKlUaGxsBADs2rUL8+bNU5Zr+Tsy3t7e\nGDduHAAgOjoan376qdX7brlH0t7eyRdffIG//e1vAIBx48YpP/S1Y8cOlJWVITY2FkII1NXVKb9p\nQuRILDhENmp59WYPDw9l2sPDAw0NDVi0aBESEhKwefNmnDp1Sunquh4vLy/l/27duilF6no8PT1h\nsVgAAHV1de3O11yYhBBIT0/Hq6++esN1E9kTu9SIbHSjcY/a2lrl54H/+te/Ku333XcfNm7cCAA4\ndOgQDh482OF1AlfHeWpra5XpgQMH4uuvvwYAfPTRR0r7v/zLvyhdZdu2bcOFCxcAAA8++CA+/PBD\nZXzIbDYr3W1EjsSCQ2SjlmMn1sZR5s+fjwULFiA6OlrZAwGA2bNn49y5cxg8eDAWL16MwYMHw8fH\nx+p6rMnIyMCsWbMwdOhQXLlyBYsXL8azzz6LuLg4eHr+2mmxZMkSfPHFF4iIiMCWLVswYMAAAIBW\nq8V//dd/ITExEffeey8SExNtOsyaqLP48wRETmaxWNDQ0ABvb28cP34cY8aMwZEjR1oVC6KuiK9w\nIif7+eefER8fj4aGBgDAmjVrWGzolsA9HCIicgqO4RARkVOw4BARkVOw4BARkVOw4BARkVOw4BAR\nkVOw4BARkVP8P7JOYsjCGzi7AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f887d2a6400>"
]
}
],
"prompt_number": 11
}
],
"metadata": {}
}
]
} | mit |
schymans/Schymanski_leaf-scale_2016 | leaf_chamber_eqs.ipynb | 1 | 240214 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Equations to derive leaf energy balance components from wind tunnel measurements and compare against leaf model</h1>\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%capture storage\n",
"# The above redirects all output of the below commands to the variable 'storage' instead of displaying it.\n",
"# It can be viewed by typing: 'storage()'\n",
"# Setting up worksheet and importing equations from other worksheets\n",
"load('temp/Worksheet_setup.sage')\n",
"load_session('temp/leaf_enbalance_eqs.sobj')\n",
"fun_loadvars(dict_vars) # any variables defined using var2() are saved with their attributes in dict_vars. Here we re-define them based on dict_vars from the other worksheet."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# %load -s fun_SS 'temp/leaf_enbalance_eqs.sage'\n",
"def fun_SS(vdict1):\n",
" '''\n",
" Steady-state T_l, R_ll, H_l and E_l under forced conditions.\n",
" Parameters are given in a dictionary (vdict) with the following entries:\n",
" a_s, a_sh, L_l, P_a, P_wa, R_s, Re_c, T_a, g_sw, v_w\n",
" ''' \n",
" vdict = vdict1.copy()\n",
"\n",
" # Nusselt number\n",
" vdict[nu_a] = eq_nua.rhs().subs(vdict)\n",
" vdict[Re] = eq_Re.rhs().subs(vdict)\n",
" vdict[Nu] = eq_Nu_forced_all.rhs().subs(vdict)\n",
" \n",
" # h_c\n",
" vdict[k_a] = eq_ka.rhs().subs(vdict)\n",
" vdict[h_c] = eq_hc.rhs().subs(vdict)\n",
" \n",
" # gbw\n",
" vdict[D_va] = eq_Dva.rhs().subs(vdict)\n",
" vdict[alpha_a] = eq_alphaa.rhs().subs(vdict)\n",
" vdict[rho_a] = eq_rhoa.rhs().subs(vdict)\n",
" vdict[Le] = eq_Le.rhs().subs(vdict)\n",
" vdict[g_bw] = eq_gbw_hc.rhs().subs(vdict) \n",
" \n",
" # Hl, Rll\n",
" vdict[R_ll] = eq_Rll.rhs().subs(vdict)\n",
" vdict[H_l] = eq_Hl.rhs().subs(vdict) \n",
"\n",
" # El\n",
" vdict[g_tw] = eq_gtw.rhs().subs(vdict)\n",
" vdict[C_wa] = eq_Cwl.rhs()(P_wl = P_wa, T_l = T_a).subs(vdict)\n",
" vdict[P_wl] = eq_Pwl.rhs().subs(vdict)\n",
" vdict[C_wl] = eq_Cwl.rhs().subs(vdict)\n",
" vdict[E_lmol] = eq_Elmol.rhs().subs(vdict)\n",
" vdict[E_l] = eq_El.rhs().subs(eq_Elmol).subs(vdict) \n",
"\n",
" # Tl\n",
" try:\n",
" vdict[T_l] = find_root((eq_Rs_enbal - R_s).rhs().subs(vdict), 273, 373)\n",
" except: \n",
" print 'too many unknowns for finding T_l: ' + str((eq_Rs_enbal - R_s).rhs().subs(vdict).args())\n",
" \n",
" # Re-inserting T_l\n",
" Tlss = vdict[T_l]\n",
" for name1 in [C_wl, P_wl, R_ll, H_l, E_l, E_lmol]:\n",
" vdict[name1] = vdict[name1].subs(T_l = Tlss)\n",
" \n",
" # Test for steady state\n",
" if n((E_l + H_l + R_ll - R_s).subs(vdict))>1.:\n",
" return 'error in energy balance: El + Hl + Rll - R_s = ' + str(n((E_l + H_l + R_ll - R_s).subs(vdict))) \n",
" return vdict\n",
"\n",
"\n",
"# In[27]:\n",
"\n",
"# Test\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"C_wa 0.647207041733317\n",
"C_wl 1.12479267904924\n",
"D_va 0.0000248765000000000\n",
"E_l 142.258640360008\n",
"E_lmol 0.00322581950929723\n",
"H_l -112.722448184652\n",
"L_l 0.0300000000000000\n",
"Le 0.888469037042992\n",
"M_N2 0.0280000000000000\n",
"M_O2 0.0320000000000000\n",
"M_w 0.0180000000000000\n",
"Nu 26.1863624980041\n",
"P_a 101325 \n",
"P_wa 1606.28367076831\n",
"P_wl 2768.40610422238\n",
"Pr 0.710000000000000\n",
"R_ll -29.5361921753575\n",
"R_mol 8.31447200000000\n",
"R_s 0.000000000000000\n",
"Re 1927.40122068744\n",
"Re_c 3000 \n",
"T_a 298.500000000000\n",
"T_l 296.021082253 \n",
"T_w 298.500000000000\n",
"a_s 1.00000000000000\n",
"a_sh 2 \n",
"alpha_a 0.0000221020000000000\n",
"c_pa 1010 \n",
"epsilon_l 1 \n",
"g 9.81000000000000\n",
"g_bw 0.0208112438130012\n",
"g_sw 0.0100000000000000\n",
"g_tw 0.00675443157676732\n",
"h_c 22.7362219510171\n",
"k_a 0.0260474000000000\n",
"lambda_E 2.45000000000000e6\n",
"nu_a 0.0000155650000000000\n",
"rho_a 1.17040820486688\n",
"sigm 5.67000000000000e-8\n",
"v_w 1 \n"
]
}
],
"source": [
"# Test\n",
"vdict = cdict.copy()\n",
"vdict[a_s] = 1.0 # one sided stomata\n",
"vdict[g_sw] = 0.01 \n",
"vdict[T_a] = 273 + 25.5\n",
"vdict[T_w] = vdict[T_a] # Wall temperature equal to air temperature\n",
"vdict[P_a] = 101325\n",
"rha = 0.5\n",
"vdict[P_wa] = rha*eq_Pwl.rhs()(T_l = T_a).subs(vdict)\n",
"vdict[L_l] = 0.03\n",
"#vdict[L_A] = vdict[L_l]^2\n",
"vdict[Re_c] = 3000\n",
"vdict[R_s] = 0.\n",
"#vdict[Q_in] = 0\n",
"vdict[v_w] = 1\n",
"\n",
"dict_print(fun_SS(vdict))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Gas and energy exchange in a leaf chamber\n",
"Calculations based on leaf_capacitance_steady_state1. However, following the LI-6400XT user manual (Eq. 17-3), we replace the air temperature by wall temperature in the calculation of the net longwave balance of the leaf, as wall temperature can be measured in the chamber. Following the same equation, we also add the leaf thermal emissivity of 0.95 (P. 17-3). \n",
"**Note that in order to measure sensible heat flux from the leaf, wall temperature must be equal to air temperature!**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Volume = 0.310000000000000 l\n",
"min flow rate for flushing = 1.55000000000000e-11 m3/s\n",
"min flow rate for flushing = 0.930000000000000 l/min\n",
"flow rate for 1 m/s direct wind = 0.00150000000000000 m3/s\n",
"flow rate for 1 m/s direct wind = 90.0000000000000 l/m\n",
"flow rate for 5 m/s direct wind = 0.00750000000000000 m3/s\n",
"flow rate for 5 m/s direct wind = 450.000000000000 l/m\n"
]
}
],
"source": [
"width = 0.05\n",
"height = 0.03\n",
"volume = 310/(100^3)\n",
"print 'Volume = ' + str((volume*1000).n()) + ' l'\n",
"\n",
"print 'min flow rate for flushing = ' + str((volume*3/100^3/60).n()) + ' m3/s'\n",
"print 'min flow rate for flushing = ' + str((volume*3*1000).n()) + ' l/min'\n",
"print 'flow rate for 1 m/s direct wind = ' + str(width*height*1) + ' m3/s'\n",
"print 'flow rate for 1 m/s direct wind = ' + str(width*height*1*1000*60) + ' l/m'\n",
"print 'flow rate for 5 m/s direct wind = ' + str(width*height*5) + ' m3/s'\n",
"print 'flow rate for 5 m/s direct wind = ' + str(width*height*5*1000*60) + ' l/m'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Volume = 0.400000000000000 l\n",
"min flow rate for flushing = 2.00000000000000e-11 m3/s\n",
"min flow rate for flushing = 1.20000000000000 l/min\n",
"flow rate for 1 m/s direct wind = 0.00150000000000000 m3/s\n",
"flow rate for 1 m/s direct wind = 90.0000000000000 l/m\n"
]
}
],
"source": [
"width = 0.05\n",
"height = 0.03\n",
"volume = 400/(100^3)\n",
"print 'Volume = ' + str((volume*1000).n()) + ' l'\n",
"\n",
"print 'min flow rate for flushing = ' + str((volume*3/100^3/60).n()) + ' m3/s'\n",
"print 'min flow rate for flushing = ' + str((volume*3*1000).n()) + ' l/min'\n",
"print 'flow rate for 1 m/s direct wind = ' + str(width*height*1) + ' m3/s'\n",
"print 'flow rate for 1 m/s direct wind = ' + str(width*height*1*1000*60) + ' l/m'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2>Chamber insulation material</h2>"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dT_i"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"var2('c_pi', 'Heat capacity of insulation material', joule/kilogram/kelvin, latexname='c_{pi}')\n",
"var2('lambda_i', 'Heat conductivity of insulation material', joule/second/meter/kelvin)\n",
"var2('rho_i', 'Density of insulation material', kilogram/meter^3)\n",
"var2('L_i', 'Thickness of insulation material', meter)\n",
"var2('A_i', 'Conducting area of insulation material', meter^2)\n",
"var2('Q_i', 'Heat conduction through insulation material', joule/second)\n",
"var2('dT_i', 'Temperature increment of insulation material', kelvin)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[c_pi is real]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assumptions(c_pi)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}Q_{i} = \\frac{A_{i} \\mathit{dT}_{i} \\lambda_{i}}{L_{i}}</script></html>"
],
"text/plain": [
"Q_i == A_i*dT_i*lambda_i/L_i"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"kilogram*meter^2/second^3 == kilogram*meter^2/second^3"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eq_Qi = Q_i == dT_i*lambda_i*A_i/L_i\n",
"units_check(eq_Qi)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}L_{i} = \\frac{A_{i} \\mathit{dT}_{i} \\lambda_{i}}{Q_{i}}</script></html>"
],
"text/plain": [
"L_i == A_i*dT_i*lambda_i/Q_i"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"meter == meter"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eq_Li = solve(eq_Qi, L_i)[0]\n",
"units_check(eq_Li)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}L_{i} {c_{pi}} \\mathit{dT}_{i} \\rho_{i}</script></html>"
],
"text/plain": [
"L_i*c_pi*dT_i*rho_i"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"kilogram/second^2"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The amount of heat absorbed by the insulation material per unit area to increase the wall temperature by the same amount as dT_i for given heat flux Q_i\n",
"units_check(c_pi*rho_i*dT_i*L_i)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"A_i*c_pi*dT_i^2*lambda_i*rho_i/Q_i"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(c_pi*rho_i*dT_i*L_i).subs(eq_Li)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"277.200000000000"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# From http://www.sager.ch/default.aspx?navid=25, PIR\n",
"vdict = {}\n",
"vdict[lambda_i] = 0.022\n",
"vdict[c_pi] = 1400\n",
"vdict[rho_i] = 30\n",
"(c_pi*rho_i*dT_i*L_i).subs(eq_Li).subs(vdict)(A_i = 0.3, dT_i = 0.1, Q_i = 0.01)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"239.400000000000"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# From http://www.sager.ch/default.aspx?navid=25, Sagex 15\n",
"vdict = {}\n",
"vdict[lambda_i] = 0.038\n",
"vdict[c_pi] = 1400\n",
"vdict[rho_i] = 15\n",
"(c_pi*rho_i*dT_i*L_i).subs(eq_Li).subs(vdict)(A_i = 0.3, dT_i = 0.1, Q_i = 0.01)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}A_{i} L_{i} \\mathit{dT}_{i} \\lambda_{i}</script></html>"
],
"text/plain": [
"A_i*L_i*dT_i*lambda_i"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"kilogram*meter^4/second^3"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"units_check(lambda_i*A_i*dT_i*L_i)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"L_i == 0.0342000000000000"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Assuming a 30x10x5 cm chamber, how thick would the insulation have to be in order to lose \n",
"# less than 0.01 W heat for 0.1 K dT_i?\n",
"eq_Li(A_i = 0.3*0.1*2 + 0.3*0.05*2, dT_i = 0.1, Q_i = 0.01).subs(vdict)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"415.800000000000"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# From http://www.sager.ch/default.aspx?navid=25, Sagex 30\n",
"vdict = {}\n",
"vdict[lambda_i] = 0.033\n",
"vdict[c_pi] = 1400\n",
"vdict[rho_i] = 30\n",
"(c_pi*rho_i*dT_i*L_i).subs(eq_Li).subs(vdict)(A_i = 0.3, dT_i = 0.1, Q_i = 0.01)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Leaf radiation balance"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"S_s"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Additional variables\n",
"var2('alpha_l', 'Leaf albedo, fraction of shortwave radiation reflected by the leaf', watt/watt)\n",
"var2('R_d', 'Downwelling global radiation', joule/second/meter^2)\n",
"var2('R_la', 'Longwave radiation absorbed by leaf', joule/second/meter^2, latexname='R_{la}')\n",
"var2('R_ld', 'Downwards emitted/reflected global radiation from leaf', joule/second/meter^2, latexname='R_{ld}')\n",
"var2('R_lu', 'Upwards emitted/reflected global radiation from leaf', joule/second/meter^2, latexname='R_{lu}')\n",
"var2('R_u', 'Upwelling global radiation', joule/second/meter^2)\n",
"var2('S_a', 'Radiation sensor above leaf reading', joule/second/meter^2)\n",
"var2('S_b', 'Radiation sensor below leaf reading', joule/second/meter^2)\n",
"var2('S_s', 'Radiation sensor beside leaf reading', joule/second/meter^2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Measuring radiative exchange\n",
"\n",
"<p>The leaf is exposed to downwelling radiation ($R_d$) originating from shortwave irradiance entering through the glass window plus the longwave irradiance transmitted througha and emitted by the glass window, plus the upwelling radiation ($R_u$) emitted by the bottom glass window.</p>\n",
"<p>The leaf itself reflects some of the radiation in both direction and emits its own black body longwave radiation. The sum of refelcted and emitted radiation away from the leaf is denoted as $R_{lu}$ and $R_{ld}$ for upward and downwards respectively. We have three net radiation sensors in place, one above the leaf measuring $S_a$, one below the leaf measureing $S_b$ and one at the same level beside the leaf measureing $S_s$. These sensor measure:</p>\n",
"<p><img style=\"float: right;\" src=\"figures/Leaf_radbalance.png\" alt=\"\" width=\"400\" height=\"300\" /></p>\n",
"<p>$S_a = R_d - R_{lu}$</p>\n",
"<p>$S_b = R_{ld} - R_u$</p>\n",
"<p>$S_s = R_d - R_u$</p>\n",
"<p>This leaves us with 3 equations with 4 unknows, so we either have to assume that $R_{lu} = R_{ld}$, assuming that both sides of the leaf have the same temperature or $R_u = 0$ to solve the algebraic problem. In daylight, $R_d >> R_u$, so this assumption should not lead to a big bias, however this would imply that $S_b = R_{ld}$, which is certainly incorrect.</p>\n",
"<p>Unfortunately, the assumption $R_{lu} = R_{ld}$ does not help solve the problem as it just implies that $S_s = S_a + S_b$:</p>"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"eq_Rs_Rd = R_s == (1-alpha_l)*R_d\n",
"eq_Sa = S_a == R_d - R_lu\n",
"eq_Sb = S_b == R_ld - R_u\n",
"eq_Ss = S_s == R_d - R_u"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Assuming R_lu = R_ld\n",
"eq_assRldRlu = R_ld == R_lu\n",
"solve([eq_Sa, eq_Sb.subs(eq_assRldRlu), eq_Ss], R_d, R_lu, R_u)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[S_s == S_a + S_b]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# More specifically,\n",
"eq1 = solve(eq_Sa, R_d)[0]\n",
"eq2 = solve(eq_Sb, R_ld)[0].subs(eq_assRldRlu)\n",
"eq3 = solve(eq_Ss, R_u)[0] \n",
"solve(eq1.subs(eq2).subs(eq3), S_s)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
"[R_d == S_s, R_lu == -S_a + S_s, R_ld == S_b, R_u == 0]\n",
"]\n"
]
}
],
"source": [
"# Assuming that R_u = 0\n",
"eq_assRu0 = R_u == 0\n",
"soln = solve([eq_Sa, eq_Sb, eq_Ss, eq_assRu0], R_d, R_lu, R_ld, R_u)\n",
"print soln"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#eq_Rd = soln[0][0]\n",
"#eq_Rlu = soln[0][1]\n",
"#eq_Rld = soln[0][2]\n",
"#eq_Rlu"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>However, what we can do in any case is to quantify the net radiative energy absorbed by the leaf as</p>\n",
"<p>$\\alpha_l R_s - R_{ll} = S_a - S_b$:</p>"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Leaf radiation balance\n",
"eq_Rs_Rll = R_s - R_ll == R_d + R_u - R_lu - R_ld\n",
"eq_Rbalance = R_s - R_ll == S_a - S_b"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[[R_d == S_s + r1, R_lu == -S_a + S_s + r1, R_ld == S_b + r1, R_u == r1]]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"solve([eq_Sa, eq_Sb, eq_Ss, R_d + R_u - R_lu - R_ld == S_a - S_b], R_d, R_lu, R_ld, R_u)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Leaf water vapour exchange and energy balace\n",
"The leaf water vapour exchange and energy balance equations were imported from [leaf_enbalance_eqs](Leaf_enbalance_eqs.ipynb). Here we will use an additional equation to estimate the thickness of the leaf boundary layer and get a feeling for the minimum distance between leaf and sensors to avoid interference with the boundary layer conditions."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"B_l == 0.0106559443973775\n"
]
},
{
"data": {
"text/plain": [
"g_bw == 0.00626665953544432"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Blasius solution for BL thickness (http://en.wikipedia.org/wiki/Boundary-layer_thickness)\n",
"var2('B_l', 'Boundary layer thickness', meter)\n",
"vdict = cdict.copy()\n",
"Ta = 300\n",
"vdict[T_a] = Ta\n",
"vdict[T_l] = Ta\n",
"vdict[L_l] = 0.15\n",
"vdict[v_w] = 0.5\n",
"vdict[Re_c] = 3000\n",
"vdict[a_s] = 1\n",
"vdict[P_a] = 101325\n",
"vdict[P_wa] = 0\n",
"eq_Bl = B_l == 4.91*sqrt(nu_a*L_l/v_w)\n",
"print eq_Bl.subs(eq_nua).subs(vdict)\n",
"eq_gbw_hc.subs(eq_hc).subs(eq_Nu_forced_all).subs(eq_Re).subs(eq_rhoa).subs(eq_Le).subs(eq_Dva).subs(eq_alphaa, eq_nua, eq_ka).subs(vdict)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"B_l == 0.00119137080184970\n"
]
},
{
"data": {
"text/plain": [
"g_bw == 0.0618819100593627"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vdict[L_l] = 0.03\n",
"vdict[v_w] = 8\n",
"print eq_Bl.subs(eq_nua).subs(vdict)\n",
"eq_gbw_hc.subs(eq_hc).subs(eq_Nu_forced_all).subs(eq_Re).subs(eq_rhoa).subs(eq_Le).subs(eq_Dva).subs(eq_alphaa, eq_nua, eq_ka).subs(vdict)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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RRroOYq/WAXfz9zc211VHbxERkYxJz++0ayZlyrWCg4NxdXUlJCSEkJCQDI4FQ4bA339D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\n1agfExEREVFdUqmwlJ+fbzTiNQD9oI1ubm5lHteiRQvcvXu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"text/plain": [
"Graphics object consisting of 1 graphics primitive"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# How does B_l scale with wind speed?\n",
"vdict[v_w] = v_w\n",
"P = plot(eq_Bl.rhs().subs(eq_nua).subs(vdict), (v_w, 0.5,5))\n",
"P.axes_labels(['Wind speed (m s$^{-1}$)', 'Boundary layer thickness (m)'])\n",
"P"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.540000000000000"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Maximum sensible heat flux of a 3x3 cm leaf irradiated by 600 W/m2\n",
"600*0.03^2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1>Chamber mass and energy balance</h1>\n",
"<p>Usually, we know the volumetric inflow into the chamber, so to convert to molar inflow (mol s$^{-1}$), we will use the ideal gas law: $P_a V_c = n R_{mol} T_{in}$, where $n$ is the amount of matter in the chamber (mol). To convert from a volume to a flow rate, we replace $V_c$ by $F_{in,v}$. Note that partial pressures of dry air and vapour are additive, such that</p>\n",
"<p>$P_a = P_w + P_d$</p>\n",
"<p>However, the volumes are not additive, meaning that:</p>\n",
"<p>$P_d V_a = n_d R_{mol} T_{a}$</p>\n",
"<p>$(P_a - P_d) V_a = n_a R_{mol} T_{a}$</p>\n",
"<p>i.e. we use the same volume ($V_a$) for both the vapour and the dry air. This is because both the vapour and the dry air are well mixed and occupy the same total volume. Their different amounts are expressed in their partial pressures. If we wanted to calculate the partial volumes they would take up in isolation from each other, we would need to specify at which pressure this volume is taken up and if we used the same pressure for both (e.g. $P_a$), we would obtain a volume fraction for water vapour equivalent to its partial pressure fraction in the former case.</p>\n",
"<p>Therefore, we will distinguish the molar flow rates of water vapour ($F_{in,mol,v}$) and dry air ($F_{in,mol,a}$) but they share a common volumetric flow rate ($F_{in,v}$).</p>"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"L_A"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"var2('W_c', 'Chamber width', meter)\n",
"var2('L_c', 'Chamber length', meter)\n",
"var2('H_c', 'Chamber height', meter)\n",
"var2('V_c', 'Chamber volume', meter^3)\n",
"var2('n_c', 'molar mass of gas in chamber', mole)\n",
"var2('F_in_v', 'Volumetric flow rate into chamber', meter^3/second, latexname='F_{in,v}')\n",
"var2('F_in_mola', 'Molar flow rate of dry air into chamber', mole/second, latexname='F_{in,mol,a}')\n",
"var2('F_in_molw', 'Molar flow rate of water vapour into chamber', mole/second, latexname='F_{in,mol,w}')\n",
"var2('F_out_mola', 'Molar flow rate of dry air out of chamber', mole/second, latexname='F_{out,mol,a}')\n",
"var2('F_out_molw', 'Molar flow rate of water vapour out of chamber', mole/second, latexname='F_{out,mol,w}')\n",
"var2('F_out_v', 'Volumetric flow rate out of chamber', meter^3/second, latexname='F_{out,v}')\n",
"var2('T_d', 'Dew point temperature of incoming air', kelvin)\n",
"var2('T_in', 'Temperature of incoming air', kelvin, latexname='T_{in}')\n",
"var2('T_out', 'Temperature of outgoing air (= chamber T_a)', kelvin, latexname='T_{out}')\n",
"var2('T_room', 'Lab air temperature', kelvin, latexname='T_{room}')\n",
"var2('P_w_in', 'Vapour pressure of incoming air', pascal, latexname='P_{w,in}')\n",
"var2('P_w_out', 'Vapour pressure of outgoing air', pascal, latexname='P_{w,out}')\n",
"var2('R_H_in', 'Relative humidity of incoming air', latexname='R_{H,in}')\n",
"var2('L_A', 'Leaf area', meter^2)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}V_{c} = H_{c} L_{c} W_{c}</script></html>"
],
"text/plain": [
"V_c == H_c*L_c*W_c"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"meter^3 == meter^3\n"
]
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,a}} = \\frac{{F_{in,v}} {\\left(P_{a} - {P_{w,in}}\\right)}}{{R_{mol}} {T_{in}}}</script></html>"
],
"text/plain": [
"F_in_mola == F_in_v*(P_a - P_w_in)/(R_mol*T_in)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mole/second == mole/second\n"
]
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,w}} = \\frac{{F_{in,v}} {P_{w,in}}}{{R_{mol}} {T_{in}}}</script></html>"
],
"text/plain": [
"F_in_molw == F_in_v*P_w_in/(R_mol*T_in)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mole/second == mole/second\n"
]
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,mol,a}} = \\frac{{F_{out,v}} {\\left(P_{a} - {P_{w,out}}\\right)}}{{R_{mol}} {T_{out}}}</script></html>"
],
"text/plain": [
"F_out_mola == F_out_v*(P_a - P_w_out)/(R_mol*T_out)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mole/second == mole/second\n"
]
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,mol,w}} = \\frac{{F_{out,v}} {P_{w,out}}}{{R_{mol}} {T_{out}}}</script></html>"
],
"text/plain": [
"F_out_molw == F_out_v*P_w_out/(R_mol*T_out)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mole/second == mole/second\n"
]
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,v}} = \\frac{{\\left({F_{out,mol,a}} + {F_{out,mol,w}}\\right)} {R_{mol}} {T_{out}}}{P_{a}}</script></html>"
],
"text/plain": [
"F_out_v == (F_out_mola + F_out_molw)*R_mol*T_out/P_a"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"meter^3/second == meter^3/second\n"
]
}
],
"source": [
"eq_V_c = fun_eq(V_c == W_c*L_c*H_c)\n",
"eq_F_in_mola = fun_eq(F_in_mola == (P_a - P_w_in)*F_in_v/(R_mol*T_in))\n",
"eq_F_in_molw = fun_eq(F_in_molw == (P_w_in)*F_in_v/(R_mol*T_in))\n",
"eq_F_out_mola = fun_eq(F_out_mola == (P_a - P_w_out)*F_out_v/(R_mol*T_out))\n",
"eq_F_out_molw = fun_eq(F_out_molw == (P_w_out)*F_out_v/(R_mol*T_out))\n",
"eq_F_out_v = fun_eq(F_out_v == (F_out_mola + F_out_molw)*R_mol*T_out/P_a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>At steady state, $F_{out,mola} = F_{in,mola}$ and $F_{out,molw} = F_{in,molw} + E_{l,mol} L_A$. In the presence of evaporation, we can simply add Elmol to get F_out_v as a function of F_in_v</p>\n",
"<p>Assuming that the pressure inside the chamber is constant and equal to the pressure outside, we compute the change in volumetric outflow due to a change in temperature and due to the input of water vapour by transpiration as:</p>"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,v}} = \\frac{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} P_{a} {T_{out}}}{P_{a} {T_{in}}}</script></html>"
],
"text/plain": [
"F_out_v == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)/(P_a*T_in)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"meter^3/second == meter^3/second"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eq_Foutv_Finv_Tout = eq_F_out_v.subs(F_out_mola = F_in_mola, F_out_molw = F_in_molw + E_lmol*L_A).subs(eq_F_in_mola, eq_F_in_molw).simplify_full()\n",
"units_check(eq_Foutv_Finv_Tout)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"F_out_v == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)/(P_a*T_in)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eq_Foutv_Finv_Tout"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,mol,w}} = {E_{l,mol}} L_{A} + {F_{in,mol,w}}</script></html>"
],
"text/plain": [
"F_out_molw == E_lmol*L_A + F_in_molw"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"mole/second == mole/second"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Other way, using molar in and outflow\n",
"eq_Foutmolw_Finmolw_Elmol = F_out_molw == (F_in_molw + E_lmol*L_A)\n",
"units_check(eq_Foutmolw_Finmolw_Elmol)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2>Change in air temperature</h2>\n",
"<p>See also <a href=\"http://www.engineeringtoolbox.com/mixing-humid-air-d_694.html\">http://www.engineeringtoolbox.com/mixing-humid-air-d_694.html</a> and <a href=\"http://www.engineeringtoolbox.com/enthalpy-moist-air-d_683.html\">http://www.engineeringtoolbox.com/enthalpy-moist-air-d_683.html</a> for reference.</p>\n",
"<p>We will assume that the air entering the chamber mixes with the air inside the chamber at constant pressure, i.e. the volume of the mixed air becomes the chamber volume plus the volume of the air that entered. The temperature of the mixed air is then the sum of their enthalpies plus the heat added by the fan and by sensible heaflux, divided by the sum of their heat capacities. The addition of water vapour through evaporation by itself should not affect the air temperature, but the volume of the air.</p>\n",
"<p> </p>\n",
"<p>Alternatively, we could assume that a given amount of air is added to a constant volume, leading to an increase in pressure. Addition of water vapour would lead to an additional increase in pressure. In addition, addition/removal of heat by sensible heat flux and the chamber fan would affect both temperature and pressure.To calculate both temperature and pressure, we need to track the internal energy in addition to the mole number. According to Eq. 6.1.3 in Kondepudi & Prigogine (2006), the internal energy of an ideal gas is given by (see also Eq. 2.2.15 in Kondepuid & Prigogine):</p>\n",
"<p>$U = N(U_0 + C_v T)$</p>\n",
"<p>where</p>\n",
"<p>$U_0 = M c^2$</p>\n",
"<p>The relation between molar heat capacities at constant pressure and volume is given as :</p>\n",
"<p>$C_v = C_p - R_{mol}$</p>\n",
"<p>Any heat exchanged by sensible heat flux, across the walls and the fan can be added to total $U$, and then knowledge about total $C_v$ will let us calculate air temperature inside the chamber. After that, we can use the ideal gas law to calculate volume or pressure, depending in which of those we fixed:</p>\n",
"<p>$P V = n R T$</p>\n",
"<p> </p>\n",
"<p>The difference in water vapour pressure and temperature between the incoming and outgoing air is a function of the latent and sensible heat flux, as well as the flow rate. The heat fluxes associated with the incoming and outgoing air are $T_{in} (c_{pa} F_{in,mola} M_{air} + c_{pv} F_{in,molw} M_{w})$ and $T_{out} (c_{pa} F_{out,mola} M_{air} + c_{pv} F_{out,molw} M_{w})$ respectively. The difference between the two plus any additional heat sources/sinks ($Q_{in}$) equals the sensible heat flux at constant air temperature (steady state).</p>"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,v}} = \\frac{{\\left({F_{out,mol,a}} + {F_{out,mol,w}}\\right)} {R_{mol}} {T_{out}}}{P_{a}}</script></html>"
],
"text/plain": [
"F_out_v == (F_out_mola + F_out_molw)*R_mol*T_out/P_a"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"meter^3/second == meter^3/second"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"units_check(eq_F_out_v)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}0 = H_{l} L_{A} + {\\left({F_{in,mol,a}} {M_{air}} {c_{pa}} + {F_{in,mol,w}} M_{w} {c_{pv}}\\right)} {T_{in}} - {\\left({F_{out,mol,a}} {M_{air}} {c_{pa}} + {F_{out,mol,w}} M_{w} {c_{pv}}\\right)} {T_{out}} + {Q_{in}}</script></html>"
],
"text/plain": [
"0 == H_l*L_A + (F_in_mola*M_air*c_pa + F_in_molw*M_w*c_pv)*T_in - (F_out_mola*M_air*c_pa + F_out_molw*M_w*c_pv)*T_out + Q_in"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = {E_{l,mol}} M_{w} {T_{out}} {c_{pv}} - \\frac{{F_{in,mol,a}} {M_{air}} {T_{in}} {c_{pa}}}{L_{A}} + \\frac{{F_{in,mol,a}} {M_{air}} {T_{out}} {c_{pa}}}{L_{A}} - \\frac{{F_{in,mol,w}} M_{w} {T_{in}} {c_{pv}}}{L_{A}} + \\frac{{F_{in,mol,w}} M_{w} {T_{out}} {c_{pv}}}{L_{A}} - \\frac{{Q_{in}}}{L_{A}}</script></html>"
],
"text/plain": [
"H_l == E_lmol*M_w*T_out*c_pv - F_in_mola*M_air*T_in*c_pa/L_A + F_in_mola*M_air*T_out*c_pa/L_A - F_in_molw*M_w*T_in*c_pv/L_A + F_in_molw*M_w*T_out*c_pv/L_A - Q_in/L_A"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"kilogram/second^3 == kilogram/second^3\n"
]
}
],
"source": [
"var2('M_air', 'Molar mass of air (kg mol-1)', kilogram/mole, value = 0.02897, latexname='M_{air}') # http://www.engineeringtoolbox.com/molecular-mass-air-d_679.html\n",
"var2('c_pv', 'Specific heat of water vapour at 300 K', joule/kilogram/kelvin, latexname = 'c_{pv}', value = 1864) # source: http://www.engineeringtoolbox.com/water-vapor-d_979.html\n",
"var2('Q_in', 'Internal heat sources, such as fan', joule/second, latexname = 'Q_{in}')\n",
"eq_chamber_energy_balance = 0 == H_l*L_A + Q_in + T_in*(c_pa*M_air*F_in_mola + c_pv*M_w*F_in_molw) - (T_out*(c_pa*M_air*F_out_mola + c_pv*M_w*F_out_molw)); show(eq_chamber_energy_balance)\n",
"eq_Hl_enbal = solve(eq_chamber_energy_balance.subs(F_out_mola == F_in_mola, F_out_molw == F_in_molw + L_A*E_lmol), H_l)[0].expand()\n",
"print units_check(eq_Hl_enbal)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
"T_out == (F_in_mola*M_air*T_in*c_pa + F_in_molw*M_w*T_in*c_pv + H_l*L_A + Q_in)/(E_lmol*L_A*M_w*c_pv + F_in_mola*M_air*c_pa + F_in_molw*M_w*c_pv)\n",
"]\n"
]
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{T_{out}} = \\frac{{F_{in,mol,a}} {M_{air}} {T_{in}} {c_{pa}} + {F_{in,mol,w}} M_{w} {T_{in}} {c_{pv}} + H_{l} L_{A} + {Q_{in}}}{{E_{l,mol}} L_{A} M_{w} {c_{pv}} + {F_{in,mol,a}} {M_{air}} {c_{pa}} + {F_{in,mol,w}} M_{w} {c_{pv}}}</script></html>"
],
"text/plain": [
"T_out == (F_in_mola*M_air*T_in*c_pa + F_in_molw*M_w*T_in*c_pv + H_l*L_A + Q_in)/(E_lmol*L_A*M_w*c_pv + F_in_mola*M_air*c_pa + F_in_molw*M_w*c_pv)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"kelvin == kelvin"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"soln = solve(eq_chamber_energy_balance.subs(eq_Foutmolw_Finmolw_Elmol, F_out_mola == F_in_mola), T_out)\n",
"print soln\n",
"eq_Tout_Finmol_Tin = soln[0]\n",
"units_check(eq_Tout_Finmol_Tin).simplify_full().convert().simplify_full()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"T_out == (F_in_v*M_w*P_w_in*T_in*c_pv + H_l*L_A*R_mol*T_in + Q_in*R_mol*T_in + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_in*c_pa)/(E_lmol*L_A*M_w*R_mol*T_in*c_pv + F_in_v*M_w*P_w_in*c_pv + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*c_pa)\n"
]
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{T_{out}} = \\frac{{F_{in,v}} M_{w} {P_{w,in}} {T_{in}} {c_{pv}} + H_{l} L_{A} {R_{mol}} {T_{in}} + {Q_{in}} {R_{mol}} {T_{in}} + {\\left({F_{in,v}} {M_{air}} P_{a} - {F_{in,v}} {M_{air}} {P_{w,in}}\\right)} {T_{in}} {c_{pa}}}{{E_{l,mol}} L_{A} M_{w} {R_{mol}} {T_{in}} {c_{pv}} + {F_{in,v}} M_{w} {P_{w,in}} {c_{pv}} + {\\left({F_{in,v}} {M_{air}} P_{a} - {F_{in,v}} {M_{air}} {P_{w,in}}\\right)} {c_{pa}}}</script></html>"
],
"text/plain": [
"T_out == (F_in_v*M_w*P_w_in*T_in*c_pv + H_l*L_A*R_mol*T_in + Q_in*R_mol*T_in + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_in*c_pa)/(E_lmol*L_A*M_w*R_mol*T_in*c_pv + F_in_v*M_w*P_w_in*c_pv + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*c_pa)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{T_{out}} = \\frac{{F_{in,v}} M_{w} {P_{w,in}} {T_{in}} {c_{pv}} + H_{l} L_{A} {R_{mol}} {T_{in}} + {Q_{in}} {R_{mol}} {T_{in}} + {\\left({F_{in,v}} {M_{air}} P_{a} - {F_{in,v}} {M_{air}} {P_{w,in}}\\right)} {T_{in}} {c_{pa}}}{{E_{l,mol}} L_{A} M_{w} {R_{mol}} {T_{in}} {c_{pv}} + {F_{in,v}} M_{w} {P_{w,in}} {c_{pv}} + {\\left({F_{in,v}} {M_{air}} P_{a} - {F_{in,v}} {M_{air}} {P_{w,in}}\\right)} {c_{pa}}}</script></html>"
],
"text/plain": [
"T_out == (F_in_v*M_w*P_w_in*T_in*c_pv + H_l*L_A*R_mol*T_in + Q_in*R_mol*T_in + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_in*c_pa)/(E_lmol*L_A*M_w*R_mol*T_in*c_pv + F_in_v*M_w*P_w_in*c_pv + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*c_pa)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"kelvin == kelvin"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eq_Tout_Finv_Tin = eq_Tout_Finmol_Tin.subs(eq_F_in_mola, eq_F_in_molw).simplify_full()\n",
"print eq_Tout_Finv_Tin\n",
"show(eq_Tout_Finv_Tin)\n",
"units_check(eq_Tout_Finv_Tin).simplify_full().convert()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>The molar outflux of dry air equals the molar influx of dry air, while the molar outflux of water vapour equals the molar influx plus the evaporation rate. The sum of both can be used to obtain the volumetric outflow:</p>"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,mol,a}} + {F_{out,mol,w}} = \\frac{{F_{out,v}} P_{a}}{{R_{mol}} {T_{out}}}</script></html>"
],
"text/plain": [
"F_out_mola + F_out_molw == F_out_v*P_a/(R_mol*T_out)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{E_{l,mol}} L_{A} + \\frac{{F_{in,v}} {\\left(P_{a} - {P_{w,in}}\\right)}}{{R_{mol}} {T_{in}}} + \\frac{{F_{in,v}} {P_{w,in}}}{{R_{mol}} {T_{in}}} = \\frac{{F_{out,v}} P_{a}}{{R_{mol}} {T_{out}}}</script></html>"
],
"text/plain": [
"E_lmol*L_A + F_in_v*(P_a - P_w_in)/(R_mol*T_in) + F_in_v*P_w_in/(R_mol*T_in) == F_out_v*P_a/(R_mol*T_out)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
"F_out_v == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)/(P_a*T_in)\n",
"]\n"
]
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,v}} = \\frac{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} P_{a} {T_{out}}}{P_{a} {T_{in}}}</script></html>"
],
"text/plain": [
"F_out_v == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)/(P_a*T_in)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,v}} = \\frac{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} P_{a} {T_{out}}}{P_{a} {T_{in}}}</script></html>"
],
"text/plain": [
"F_out_v == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)/(P_a*T_in)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"meter^3/second == meter^3/second"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# F_out_v as function of F_inv and T_in\n",
"eq1 = (eq_F_out_molw + eq_F_out_mola).simplify_full(); show(eq1)\n",
"eq2 = eq1.subs(F_out_mola == F_in_mola, eq_Foutmolw_Finmolw_Elmol).subs(eq_F_in_mola, eq_F_in_molw); show(eq2)\n",
"soln = solve(eq2,F_out_v); print soln\n",
"eq_Foutv_Finv = soln[0]; show(eq_Foutv_Finv)\n",
"units_check(eq_Foutv_Finv)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"F_out_v == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)/(P_a*T_in)"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eq_Foutv_Finv"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"F_out_v == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)/(P_a*T_in)"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eq_Foutv_Finv_Tout"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"T_out == (F_in_mola*M_air*T_in*c_pa + F_in_molw*M_w*T_in*c_pv + H_l*L_A + Q_in)/(E_lmol*L_A*M_w*c_pv + F_in_mola*M_air*c_pa + F_in_molw*M_w*c_pv)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eq_Tout_Finmol_Tin"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}0 = H_{l} L_{A} + {\\left({F_{in,mol,a}} {M_{air}} {c_{pa}} + {F_{in,mol,w}} M_{w} {c_{pv}}\\right)} {T_{in}} - {\\left({F_{out,mol,a}} {M_{air}} {c_{pa}} + {F_{out,mol,w}} M_{w} {c_{pv}}\\right)} {T_{out}} + {Q_{in}}</script></html>"
],
"text/plain": [
"0 == H_l*L_A + (F_in_mola*M_air*c_pa + F_in_molw*M_w*c_pv)*T_in - (F_out_mola*M_air*c_pa + F_out_molw*M_w*c_pv)*T_out + Q_in"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
"T_in == (F_in_v*M_w*P_w_in*T_out*c_pv + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_out*c_pa)/(F_in_v*M_w*P_w_in*c_pv - (E_lmol*M_w*R_mol*T_out*c_pv - H_l*R_mol)*L_A + Q_in*R_mol + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*c_pa)\n",
"]\n"
]
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{T_{in}} = \\frac{{F_{in,v}} M_{w} {P_{w,in}} {T_{out}} {c_{pv}} + {\\left({F_{in,v}} {M_{air}} P_{a} - {F_{in,v}} {M_{air}} {P_{w,in}}\\right)} {T_{out}} {c_{pa}}}{{F_{in,v}} M_{w} {P_{w,in}} {c_{pv}} - {\\left({E_{l,mol}} M_{w} {R_{mol}} {T_{out}} {c_{pv}} - H_{l} {R_{mol}}\\right)} L_{A} + {Q_{in}} {R_{mol}} + {\\left({F_{in,v}} {M_{air}} P_{a} - {F_{in,v}} {M_{air}} {P_{w,in}}\\right)} {c_{pa}}}</script></html>"
],
"text/plain": [
"T_in == (F_in_v*M_w*P_w_in*T_out*c_pv + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_out*c_pa)/(F_in_v*M_w*P_w_in*c_pv - (E_lmol*M_w*R_mol*T_out*c_pv - H_l*R_mol)*L_A + Q_in*R_mol + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*c_pa)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Finding the T_in that would balance sensible heat release by the plate for given F_inv\n",
"assume(F_in_v > 0)\n",
"assume(F_out_v > 0)\n",
"assume(E_lmol >=0)\n",
"show(eq_chamber_energy_balance)\n",
"soln = solve(eq_chamber_energy_balance.subs(F_out_mola == F_in_mola).subs(eq_Foutmolw_Finmolw_Elmol).subs(eq_F_in_mola, eq_F_in_molw), T_in)\n",
"print soln\n",
"eq_T_in_ss = soln[0]\n",
"show(eq_T_in_ss)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
"Q_in == ((E_lmol*M_w*R_mol*T_in*T_out*c_pv - H_l*R_mol*T_in)*L_A - ((F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_in - (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_out)*c_pa - (F_in_v*M_w*P_w_in*T_in - F_in_v*M_w*P_w_in*T_out)*c_pv)/(R_mol*T_in)\n",
"]\n"
]
}
],
"source": [
"# Calculating Q_in from T_in, F_in_v and T_out\n",
"soln = solve(eq_T_in_ss,Q_in)\n",
"print soln\n",
"eq_Qin_Tin_Tout = soln[0]"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
"H_l == (E_lmol*L_A*M_w*R_mol*T_in*T_out*c_pv - Q_in*R_mol*T_in - ((F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_in - (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_out)*c_pa - (F_in_v*M_w*P_w_in*T_in - F_in_v*M_w*P_w_in*T_out)*c_pv)/(L_A*R_mol*T_in)\n",
"]\n"
]
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = \\frac{{E_{l,mol}} L_{A} M_{w} {R_{mol}} {T_{in}} {T_{out}} {c_{pv}} - {Q_{in}} {R_{mol}} {T_{in}} - {\\left({\\left({F_{in,v}} {M_{air}} P_{a} - {F_{in,v}} {M_{air}} {P_{w,in}}\\right)} {T_{in}} - {\\left({F_{in,v}} {M_{air}} P_{a} - {F_{in,v}} {M_{air}} {P_{w,in}}\\right)} {T_{out}}\\right)} {c_{pa}} - {\\left({F_{in,v}} M_{w} {P_{w,in}} {T_{in}} - {F_{in,v}} M_{w} {P_{w,in}} {T_{out}}\\right)} {c_{pv}}}{L_{A} {R_{mol}} {T_{in}}}</script></html>"
],
"text/plain": [
"H_l == (E_lmol*L_A*M_w*R_mol*T_in*T_out*c_pv - Q_in*R_mol*T_in - ((F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_in - (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_out)*c_pa - (F_in_v*M_w*P_w_in*T_in - F_in_v*M_w*P_w_in*T_out)*c_pv)/(L_A*R_mol*T_in)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Calculating H_l from T_in, F_in_v and T_out\n",
"soln = solve(eq_T_in_ss,H_l)\n",
"print soln\n",
"eq_Hl_Tin_Tout = soln[0]\n",
"eq_Hl_Tin_Tout.show()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
"H_l == -((F_in_mola*M_air*T_in - F_in_mola*M_air*T_out)*c_pa + (F_in_molw*M_w*T_in - F_out_molw*M_w*T_out)*c_pv + Q_in)/L_A\n",
"]\n"
]
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -\\frac{{\\left({F_{in,mol,a}} {M_{air}} {T_{in}} - {F_{in,mol,a}} {M_{air}} {T_{out}}\\right)} {c_{pa}} + {\\left({F_{in,mol,w}} M_{w} {T_{in}} - {F_{out,mol,w}} M_{w} {T_{out}}\\right)} {c_{pv}} + {Q_{in}}}{L_{A}}</script></html>"
],
"text/plain": [
"H_l == -((F_in_mola*M_air*T_in - F_in_mola*M_air*T_out)*c_pa + (F_in_molw*M_w*T_in - F_out_molw*M_w*T_out)*c_pv + Q_in)/L_A"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Calculating H_l from T_in, T_out and Fmol\n",
"soln = solve(eq_chamber_energy_balance.subs(F_out_mola == F_in_mola), H_l)\n",
"print soln\n",
"eq_Hl_Tin_Tout_Fmol = soln[0].simplify_full()\n",
"eq_Hl_Tin_Tout_Fmol.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2>Calculation of volumetric flow rate based on Cellkraft measurements</h2>\n",
"<p>Cellcraft uses Arden-Buck equation to convert between vapour pressure and dew point (<a href=\"http://en.wikipedia.org/wiki/Arden_Buck_Equation\">http://en.wikipedia.org/wiki/Arden_Buck_Equation</a>).<br />The air flow rate is given by the Cellkraft humidifier in l/min, but it refers to dry air at 0 $^o$C and 101300 Pa.</p>\n",
"<p>We will use the reported dew point temperature to obtain the vapour pressure of the air coming out from the Cellkraft humidifier, then the ideal gas law to obtain the molar flow of dry air, leading to three equations with three unknowns:</p>\n",
"<p>$F_{\\mathit{in}_{\\mathit{mola}}} = \\frac{F_{\\mathit{in}_{\\mathit{va}_{n}}} P_{r}}{{R_{mol}} T_{r}}$</p>\n",
"<p>$F_{\\mathit{in}_{v}} = \\frac{{\\left(F_{\\mathit{in}_{\\mathit{mola}}} + F_{\\mathit{in}_{\\mathit{molw}}}\\right)} {R_{mol}} T_{\\mathit{in}}}{P_{a}}$</p>\n",
"<p>$F_{\\mathit{in}_{\\mathit{molw}}} = \\frac{F_{\\mathit{in}_{v}} P_{v_{\\mathit{in}}}}{{R_{mol}} T_{\\mathit{in}}}$</p>"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[19.1756620432331, 35.0631107274011, 62.0367042507648, 106.473009443655, 177.667645607840, 288.831780737493, 458.305321164456, 710.999051614674, 1080.07052944457, 1608.82992645281, 2352.86266113812, 3382.34604242497, 4784.52773726516, 6666.32514525647, 9156.99713166349]\n"
]
},
{
"data": {
"image/png": 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q1Chs2rQJW7duRcuWLWFvb4/t27fLvb7/sTfWCjNv3jyYmJigbt26cHFxQfXq1XH8+HEY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\nAABJRU5ErkJggg==\n",
"text/plain": [
"Graphics object consisting of 3 graphics primitives"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calculating vapour pressure in the incoming air from reported dew point by the Cellkraft humidifier\n",
"var2('T0', 'Freezing point in kelvin',kelvin, value = 273.15)\n",
"eq_Pwin_Tdew = P_w_in == 611.21*exp((18.678 - (T_d - T0)/234.5)*((T_d - T0)/(257.14-T0+T_d))) # c\n",
"list_Tdew = np.array(srange(-40,50,6))+273.25\n",
"list_Pwin = [eq_Pwin_Tdew.rhs()(T_d = dummy).subs(cdict) for dummy in list_Tdew]\n",
"print list_Pwin\n",
"P = plot(eq_Pwin_Tdew.rhs().subs(cdict), (T_d, 253,303), frame = True, axes = False, legend_label = 'Arden Buck')\n",
"P += plot(eq_Pwl.rhs().subs(cdict), (T_l, 253,303), color = 'red', linestyle = '--', legend_label = 'Clausius-Clapeyron')\n",
"P += list_plot(zip(list_Tdew,list_Pwin), legend_label = 'Cellcraft')\n",
"P.axes_labels(['Dew point (K)', 'Saturation vapour pressure (Pa)'])\n",
"P"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,w}} = \\frac{{F_{in,v}} {P_{w,in}}}{{R_{mol}} {T_{in}}}</script></html>"
],
"text/plain": [
"F_in_molw == F_in_v*P_w_in/(R_mol*T_in)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"eq_F_in_molw.show()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,v}} = \\frac{{\\left({F_{in,mol,a}} + {F_{in,mol,w}}\\right)} {R_{mol}} {T_{in}}}{P_{a}}</script></html>"
],
"text/plain": [
"F_in_v == (F_in_mola + F_in_molw)*R_mol*T_in/P_a"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"meter^3/second == meter^3/second"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eq_Finv_Finmol = F_in_v == (F_in_mola + F_in_molw)*R_mol*T_in/P_a\n",
"units_check(eq_Finv_Finmol)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,a}} = \\frac{{F_{in,v,a,n}} P_{r}}{{R_{mol}} T_{r}}</script></html>"
],
"text/plain": [
"F_in_mola == F_in_va_n*P_r/(R_mol*T_r)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mole/second == mole/second\n"
]
}
],
"source": [
"var2('F_in_va_n', 'Volumetric inflow of dry air at 0oC and 101325 Pa', meter^3/second, latexname='F_{in,v,a,n}') \n",
"var2('P_r', 'Reference pressure',pascal, value = 101325)\n",
"var2('T_r', 'Reference temperature', kelvin)\n",
"\n",
"eq_Finmola_Finva_ref = fun_eq(F_in_mola == F_in_va_n * P_r/(R_mol*T_r))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>To get $F_{in,v}$ and $F_{in,mol,w}$, we will consider that:</p>\n",
"<p>$P_d = P_a - P_w$</p>\n",
"<p>$P_w F_{in,v} = F_{in,mol,w} R_{mol} T_{in}$</p>\n",
"<p>$(P_a - P_w) F_{in,v} = F_{in,mol,a} R_{mol} T_{a}$</p>"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,w}} = \\frac{{F_{in,v}} {P_{w,in}}}{{R_{mol}} {T_{in}}}</script></html>"
],
"text/plain": [
"F_in_molw == F_in_v*P_w_in/(R_mol*T_in)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mole/second == mole/second\n"
]
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,v}} = \\frac{{F_{in,mol,a}} {R_{mol}} {T_{in}}}{P_{a} - {P_{w,in}}}</script></html>"
],
"text/plain": [
"F_in_v == F_in_mola*R_mol*T_in/(P_a - P_w_in)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"meter^3/second == meter^3/second\n"
]
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,w}} = \\frac{{F_{in,mol,a}} {P_{w,in}}}{P_{a} - {P_{w,in}}}</script></html>"
],
"text/plain": [
"F_in_molw == F_in_mola*P_w_in/(P_a - P_w_in)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mole/second == mole/second\n"
]
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,v}} = \\frac{{F_{in,v,a,n}} P_{r} {T_{in}}}{{\\left(P_{a} - {P_{w,in}}\\right)} T_{r}}</script></html>"
],
"text/plain": [
"F_in_v == F_in_va_n*P_r*T_in/((P_a - P_w_in)*T_r)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"meter^3/second == meter^3/second\n"
]
}
],
"source": [
"eq_Finmolw_Finv = F_in_molw == (P_w_in*F_in_v)/(R_mol*T_in)\n",
"print units_check(eq_Finmolw_Finv)\n",
"eq_Finv_Finmola = F_in_v == F_in_mola*R_mol*T_in/(P_a - P_w_in)\n",
"print units_check(eq_Finv_Finmola)\n",
"eq_Finmolw_Finmola_Pwa = fun_eq(eq_Finmolw_Finv.subs(eq_Finv_Finmola))\n",
"eq_Finv_Finva_ref = fun_eq(eq_Finv_Finmola.subs(eq_Finmola_Finva_ref))"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1227.86016957787\n",
"0.000166666666666667\n",
"Volumentric flow at 0 oC: 0.000168711114309656 m3/s\n",
"Volumentric flow at 25 oC: 0.000184152365848157 m3/s\n",
"25oC/0oC: 1.09152480322167\n",
"Volumentric flow at 25 oC without added vapour: 0.000181920800536946 m3/s\n"
]
}
],
"source": [
"vdict = cdict.copy()\n",
"vdict[F_in_va_n] = 10e-3/60 # 10 l/min reported by Cellkraft\n",
"vdict[T_d] = 273.15 + 10 # 10oC dew point\n",
"vdict[P_a] = 101325.\n",
"vdict[T_r] = 273.15\n",
"vdict[P_w_in] = eq_Pwin_Tdew.rhs().subs(vdict)\n",
"print vdict[P_w_in]\n",
"print vdict[F_in_va_n]\n",
"\n",
"vdict[T_in] = 273.15+0 \n",
"inflow0 = eq_Finv_Finva_ref.rhs().subs(vdict)\n",
"print 'Volumentric flow at 0 oC: ' + str(inflow0) + ' m3/s'\n",
"\n",
"vdict[T_in] = 273.15+25 \n",
"inflow25 = eq_Finv_Finva_ref.rhs().subs(vdict)\n",
"print 'Volumentric flow at 25 oC: ' + str(inflow25) + ' m3/s'\n",
"\n",
"\n",
"print '25oC/0oC: ' + str(inflow25/inflow0)\n",
"\n",
"vdict[T_in] = 273.15+25 \n",
"vdict[P_w_in] = 0. \n",
"inflow25 = eq_Finv_Finva_ref.rhs().subs(vdict)\n",
"print 'Volumentric flow at 25 oC without added vapour: ' + str(inflow25) + ' m3/s'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2>Vapour pressure</h2>\n",
"<p>The water fluxes associated with the incoming and the outgoing air according to the ideal gas law are $P_{v,in} F_{in,v}/(R_{mol} T_{in})$ and $P_{v,out} F_{out,v}/(R_{mol} T_{out})$ respectively.</p>"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,v}} = \\frac{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} P_{a} {T_{out}}}{P_{a} {T_{in}}}</script></html>"
],
"text/plain": [
"F_out_v == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)/(P_a*T_in)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"eq_Foutv_Finv_Tout.show()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,v}} = \\frac{{\\left({F_{out,mol,a}} + {F_{out,mol,w}}\\right)} {R_{mol}} {T_{out}}}{P_{a}}</script></html>"
],
"text/plain": [
"F_out_v == (F_out_mola + F_out_molw)*R_mol*T_out/P_a"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"eq_F_out_v.show()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,a}} = \\frac{{F_{in,v,a,n}} P_{r}}{{R_{mol}} T_{r}}</script></html>"
],
"text/plain": [
"F_in_mola == F_in_va_n*P_r/(R_mol*T_r)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"eq_F_in_mola.subs(eq_Finv_Finva_ref).show()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,v}} = \\frac{{F_{in,v,a,n}} P_{r} {T_{in}}}{{\\left(P_{a} - {P_{w,in}}\\right)} T_{r}}</script></html>"
],
"text/plain": [
"F_in_v == F_in_va_n*P_r*T_in/((P_a - P_w_in)*T_r)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"eq_Finv_Finva_ref.show()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,w}} = \\frac{{F_{in,v}} {P_{w,in}}}{{R_{mol}} {T_{in}}}</script></html>"
],
"text/plain": [
"F_in_molw == F_in_v*P_w_in/(R_mol*T_in)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,mol,w}} = \\frac{{F_{out,v}} {P_{w,out}}}{{R_{mol}} {T_{out}}}</script></html>"
],
"text/plain": [
"F_out_molw == F_out_v*P_w_out/(R_mol*T_out)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,mol,w}} = {E_{l,mol}} L_{A} + {F_{in,mol,w}}</script></html>"
],
"text/plain": [
"F_out_molw == E_lmol*L_A + F_in_molw"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"eq_F_in_molw.show()\n",
"eq_F_out_molw.show()\n",
"eq_Foutmolw_Finmolw_Elmol.show()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\frac{{F_{out,v}} {P_{w,out}}}{{R_{mol}} {T_{out}}} = {E_{l,mol}} L_{A} + \\frac{{F_{in,v}} {P_{w,in}}}{{R_{mol}} {T_{in}}}</script></html>"
],
"text/plain": [
"F_out_v*P_w_out/(R_mol*T_out) == E_lmol*L_A + F_in_v*P_w_in/(R_mol*T_in)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,out}} = \\frac{{E_{l,mol}} L_{A} P_{a} {R_{mol}} {T_{in}} + {F_{in,v}} P_{a} {P_{w,in}}}{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} + {F_{in,v}} P_{a}}</script></html>"
],
"text/plain": [
"P_w_out == (E_lmol*L_A*P_a*R_mol*T_in + F_in_v*P_a*P_w_in)/(E_lmol*L_A*R_mol*T_in + F_in_v*P_a)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"kilogram/(meter*second^2) == kilogram/(meter*second^2)\n"
]
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,out}} = \\frac{{E_{l,mol}} L_{A} P_{a} {R_{mol}} {T_{in}} + {F_{in,v}} P_{a} {P_{w,in}}}{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} + {F_{in,v}} P_{a}}</script></html>"
],
"text/plain": [
"P_w_out == (E_lmol*L_A*P_a*R_mol*T_in + F_in_v*P_a*P_w_in)/(E_lmol*L_A*R_mol*T_in + F_in_v*P_a)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"kilogram/(meter*second^2) == kilogram/(meter*second^2)"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eq1 = eq_F_out_molw.rhs() == eq_Foutmolw_Finmolw_Elmol.rhs().subs(eq_F_in_molw)\n",
"eq1.show()\n",
"soln = solve(eq1, P_w_out)\n",
"eq_Pwout_Elmol = fun_eq(soln[0].subs(eq_Foutv_Finv_Tout.subs(eq_F_in_molw)).simplify_full())\n",
"units_check(eq_Pwout_Elmol)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p><span style=\"color: #ff0000;\">It is a bit surprising that steady-state $P_{w_{out}}$ does not depend on $T_{out}$.</span></p>"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,out}} = \\frac{{E_{l,mol}} L_{A} P_{a} {R_{mol}} {T_{in}} + {F_{in,v}} P_{a} {P_{w,in}}}{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} + {F_{in,v}} P_{a}}</script></html>"
],
"text/plain": [
"P_w_out == (E_lmol*L_A*P_a*R_mol*T_in + F_in_v*P_a*P_w_in)/(E_lmol*L_A*R_mol*T_in + F_in_v*P_a)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,out}} = \\frac{{E_{l,mol}} L_{A} P_{a} {R_{mol}} {T_{in}} + {F_{in,v}} P_{a} {P_{w,in}}}{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} + {\\left({F_{in,mol,a}} + {F_{in,mol,w}}\\right)} {R_{mol}} {T_{in}}}</script></html>"
],
"text/plain": [
"P_w_out == (E_lmol*L_A*P_a*R_mol*T_in + F_in_v*P_a*P_w_in)/(E_lmol*L_A*R_mol*T_in + (F_in_mola + F_in_molw)*R_mol*T_in)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"soln[0].subs(eq_Foutv_Finv_Tout.subs(eq_F_in_molw)).simplify_full().show()\n",
"soln[0].subs(eq_F_out_v).subs(F_out_mola = F_in_mola, F_out_molw = F_in_molw + E_lmol*L_A).simplify_full().show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p><span style=\"color: #ff0000;\">The above are equivalent, because $F_{in,v} P_a = (F_{in,mol,a} + F_{in,mol,w}) R_{mol} T_{in}$<br /></span></p>"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,out}} = \\frac{{F_{in,v,a,n}} P_{a} P_{r} {P_{w,in}} + {\\left({E_{l,mol}} P_{a}^{2} - {E_{l,mol}} P_{a} {P_{w,in}}\\right)} L_{A} {R_{mol}} T_{r}}{{\\left({E_{l,mol}} P_{a} - {E_{l,mol}} {P_{w,in}}\\right)} L_{A} {R_{mol}} T_{r} + {F_{in,v,a,n}} P_{a} P_{r}}</script></html>"
],
"text/plain": [
"P_w_out == (F_in_va_n*P_a*P_r*P_w_in + (E_lmol*P_a^2 - E_lmol*P_a*P_w_in)*L_A*R_mol*T_r)/((E_lmol*P_a - E_lmol*P_w_in)*L_A*R_mol*T_r + F_in_va_n*P_a*P_r)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"eq_Pwout_Elmol.subs(eq_Finv_Finva_ref).simplify_full().show()"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,out}} = \\frac{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} {P_{w,in}} {T_{out}}}{{F_{out,v}} {T_{in}}}</script></html>"
],
"text/plain": [
"P_w_out == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_w_in*T_out)/(F_out_v*T_in)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,v}} = \\frac{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} P_{a} {T_{out}}}{P_{a} {T_{in}}}</script></html>"
],
"text/plain": [
"F_out_v == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)/(P_a*T_in)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,w}} = \\frac{{F_{in,v}} {P_{w,in}}}{{R_{mol}} {T_{in}}}</script></html>"
],
"text/plain": [
"F_in_molw == F_in_v*P_w_in/(R_mol*T_in)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show(soln[0])\n",
"show(eq_Foutv_Finv_Tout)\n",
"show(eq_F_in_molw)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,out}} = \\frac{{\\left({E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} {P_{w,in}} {T_{out}}\\right)} P_{a}}{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} P_{a} {T_{out}}}</script></html>"
],
"text/plain": [
"P_w_out == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_w_in*T_out)*P_a/(E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show(soln[0].subs(eq_Foutv_Finv_Tout.subs(eq_F_in_molw)).simplify())"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,out}} = \\frac{{E_{l,mol}} L_{A} P_{a} {R_{mol}} {T_{in}} {T_{out}}}{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} P_{a} {T_{out}}} + \\frac{{F_{in,v}} P_{a} {P_{w,in}} {T_{out}}}{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} P_{a} {T_{out}}}</script></html>"
],
"text/plain": [
"P_w_out == E_lmol*L_A*P_a*R_mol*T_in*T_out/(E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out) + F_in_v*P_a*P_w_in*T_out/(E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# T_out cancels out when the above is expanded\n",
"show(soln[0].subs(eq_Foutv_Finv_Tout.subs(eq_F_in_molw)).expand())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>To convert from energetic to molar units, we need to divide $E_l$ by $\\lambda_E M_w$:</p>"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{E_{l,mol}} = \\frac{E_{l}}{M_{w} \\lambda_{E}}</script></html>"
],
"text/plain": [
"E_lmol == E_l/(M_w*lambda_E)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mole/(meter^2*second) == mole/(meter^2*second)\n"
]
}
],
"source": [
"eq_Elmol_El = E_lmol == E_l/(lambda_E*M_w)\n",
"print units_check(eq_Elmol_El)\n",
"eq_El_Elmol = E_l == E_lmol*lambda_E*M_w"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,in}} = -\\frac{{\\left({E_{l,mol}} P_{a} - {E_{l,mol}} {P_{w,out}}\\right)} L_{A} {R_{mol}} {T_{in}} - {F_{in,v}} P_{a} {P_{w,out}}}{{F_{in,v}} P_{a}}</script></html>"
],
"text/plain": [
"P_w_in == -((E_lmol*P_a - E_lmol*P_w_out)*L_A*R_mol*T_in - F_in_v*P_a*P_w_out)/(F_in_v*P_a)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# In order to keep P_w_out = P_wa = const., we need to adjust P_w_in accordingly.\n",
"soln = solve(eq_Pwout_Elmol, P_w_in)\n",
"eq_Pwin_Elmol = soln[0].simplify_full()\n",
"show(eq_Pwin_Elmol)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1227.86016957787\n",
"0.000166666666666667\n",
"Volumentric flow at 0 oC: 0.000168711114309656 m3/s\n",
"Volumentric flow at 25 oC: 0.000184152365848157 m3/s\n",
"25oC/0oC: 1.09152480322167\n",
"Volumentric flow at 25 oC without added vapour: 0.000181920800536946 m3/s\n"
]
},
{
"data": {
"text/plain": [
"P_w_out == 27.4649220798963"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vdict = cdict.copy()\n",
"vdict[F_in_va_n] = 10e-3/60 # 10 l/min reported by Cellkraft\n",
"vdict[T_d] = 273.15 + 10 # 10oC dew point\n",
"vdict[P_a] = 101325.\n",
"vdict[T_r] = 273.15\n",
"vdict[P_w_in] = eq_Pwin_Tdew.rhs().subs(vdict)\n",
"print vdict[P_w_in]\n",
"print vdict[F_in_va_n]\n",
"\n",
"vdict[T_in] = 273.15+0 \n",
"inflow0 = eq_Finv_Finva_ref.rhs().subs(vdict)\n",
"print 'Volumentric flow at 0 oC: ' + str(inflow0) + ' m3/s'\n",
"\n",
"vdict[T_in] = 273.15+25 \n",
"inflow25 = eq_Finv_Finva_ref.rhs().subs(vdict)\n",
"vdict[F_in_v] = eq_Finv_Finva_ref.rhs().subs(vdict)\n",
"print 'Volumentric flow at 25 oC: ' + str(inflow25) + ' m3/s'\n",
"\n",
"\n",
"print '25oC/0oC: ' + str(inflow25/inflow0)\n",
"\n",
"vdict[T_in] = 273.15+25 \n",
"vdict[P_w_in] = 0. \n",
"inflow25 = eq_Finv_Finva_ref.rhs().subs(vdict)\n",
"print 'Volumentric flow at 25 oC without added vapour: ' + str(inflow25) + ' m3/s'\n",
"\n",
"vdict[E_l] = 100. # assuming 100 W/m2 El\n",
"vdict[L_A] = 0.03^2\n",
"vdict[E_lmol] = eq_Elmol_El.rhs().subs(vdict)\n",
"vdict[T_out] = 273+20. # Assuming 20oC T in chamber\n",
"\n",
"eq_Pwout_Elmol.subs(vdict)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2>Net radiation measurement</h2>\n",
"<p>According to Incropera_fundamentals, Table 13.1, the view factor (absorbed fraction of radiation emitted by another plate) of a small plate of width $w_i$ at a distance $L$ from a parallel larger plate of width $w_j$ is calculated as:</p>"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}F_{s} = \\frac{L_{\\mathit{ls}} {\\left(\\sqrt{{\\left(\\frac{L_{l}}{L_{\\mathit{ls}}} + \\frac{L_{s}}{L_{\\mathit{ls}}}\\right)}^{2} + 4} - \\sqrt{{\\left(\\frac{L_{l}}{L_{\\mathit{ls}}} - \\frac{L_{s}}{L_{\\mathit{ls}}}\\right)}^{2} + 4}\\right)}}{2 \\, L_{s}}</script></html>"
],
"text/plain": [
"F_s == 1/2*L_ls*(sqrt((L_l/L_ls + L_s/L_ls)^2 + 4) - sqrt((L_l/L_ls - L_s/L_ls)^2 + 4))/L_s"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"1 == 1"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"var2('L_s', 'Width of net radiation sensor', meter)\n",
"var2('L_ls', 'Distance between leaf and net radiation sensor', meter)\n",
"var2('F_s', 'Fraction of radiation emitted by leaf, absorbed by sensor', 1)\n",
"Wi = L_s/L_ls\n",
"Wj = L_l/L_ls\n",
"eq_Fs = F_s == (sqrt((Wi + Wj)^2 + 4) - sqrt((Wj - Wi)^2 + 4))/(2*Wi)\n",
"units_check(eq_Fs)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.821854415126695\n"
]
},
{
"data": {
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POp1KRMT3bG+Ke82mTZtYvnw5hw8fBqBIkSI0aNCAqlWreh1ORPxXQIApkurW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\n",
"text/plain": [
"Graphics object consisting of 1 graphics primitive"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vdict = cdict.copy()\n",
"vdict[L_l] = 0.03\n",
"vdict[L_s] = 0.01\n",
"print eq_Fs.rhs().subs(vdict)(L_ls = 0.01)\n",
"P = plot(eq_Fs.rhs().subs(vdict), (L_ls, 0.001, 0.02))\n",
"P.axes_labels(['Distance to leaf (m)', 'Fraction of radiation captured'])\n",
"P"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Saving definitions to separate file\n",
"In the below, we save the definitions and variables to separate files in the /temp directory, one with the extension .sage, from which we can selectively load functions using\n",
"`%load fun_name filenam.sage`\n",
"and one with the extension .sobj, to be loaded elsewhere using \n",
"`load_session()`"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"jupyter nbconvert --to=python 'leaf_chamber_eqs.ipynb'\n",
"Exporting specified worksheet to .py file...\n",
"Checking if specified ipynb file was run with sage kernel...\n",
"Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"nbconvert returned 0\n"
]
}
],
"source": [
"fun_export_ipynb('leaf_chamber_eqs', 'temp/')\n",
"save_session('temp/leaf_chamber_eqs.sobj')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Table of symbols"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div class=\"notruncate\">\n",
"<table class=\"table_form\">\n",
"<tbody>\n",
"<tr>\n",
"<th>Variable</th>\n",
"<th>Description (value)</th>\n",
"<th>Units</th>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">A_{i}</script></td>\n",
"<td>Conducting area of insulation material</td>\n",
"<td> m<script type=\"math/tex\">^{2}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">a_{s}</script></td>\n",
"<td>Fraction of one-sided leaf area covered by stomata (1 if stomata are on one side only, 2 if they are on both sides)</td>\n",
"<td>1 </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{a_{sh}}</script></td>\n",
"<td>Fraction of projected area exchanging sensible heat with the air (2)</td>\n",
"<td>1 </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">\\alpha_{a}</script></td>\n",
"<td>Thermal diffusivity of dry air</td>\n",
"<td> m<script type=\"math/tex\">^{2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">\\alpha_{l}</script></td>\n",
"<td>Leaf albedo, fraction of shortwave radiation reflected by the leaf</td>\n",
"<td>1 </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">B_{l}</script></td>\n",
"<td>Boundary layer thickness</td>\n",
"<td>m </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{c_{pa}}</script></td>\n",
"<td>Specific heat of dry air (1010) </td>\n",
"<td>J K<script type=\"math/tex\">^{-1}</script> kg<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{c_{pi}}</script></td>\n",
"<td>Heat capacity of insulation material</td>\n",
"<td>J K<script type=\"math/tex\">^{-1}</script> kg<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{c_{pv}}</script></td>\n",
"<td>Specific heat of water vapour at 300 K</td>\n",
"<td>J K<script type=\"math/tex\">^{-1}</script> kg<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{C_{wa}}</script></td>\n",
"<td>Concentration of water in the free air </td>\n",
"<td>mol m<script type=\"math/tex\">^{-3}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{C_{wl}}</script></td>\n",
"<td>Concentration of water in the leaf air space </td>\n",
"<td>mol m<script type=\"math/tex\">^{-3}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{D_{va}}</script></td>\n",
"<td>Binary diffusion coefficient of water vapour in air</td>\n",
"<td> m<script type=\"math/tex\">^{2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">\\mathit{dT}_{i}</script></td>\n",
"<td>Temperature increment of insulation material</td>\n",
"<td>K </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">E_{l}</script></td>\n",
"<td>Latent heat flux from leaf</td>\n",
"<td>J m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{E_{l,mol}}</script></td>\n",
"<td>Transpiration rate in molar units</td>\n",
"<td>mol m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">\\epsilon_{l}</script></td>\n",
"<td>Longwave emmissivity of the leaf surface (1.0)</td>\n",
"<td>1 </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{F_{in,mol,a}}</script></td>\n",
"<td>Molar flow rate of dry air into chamber</td>\n",
"<td>mol s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{F_{in,mol,w}}</script></td>\n",
"<td>Molar flow rate of water vapour into chamber</td>\n",
"<td>mol s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{F_{in,v}}</script></td>\n",
"<td>Volumetric flow rate into chamber</td>\n",
"<td> m<script type=\"math/tex\">^{3}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{F_{in,v,a,n}}</script></td>\n",
"<td>Volumetric inflow of dry air at 0oC and 101325 Pa</td>\n",
"<td> m<script type=\"math/tex\">^{3}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{F_{out,mol,a}}</script></td>\n",
"<td>Molar flow rate of dry air out of chamber</td>\n",
"<td>mol s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{F_{out,mol,w}}</script></td>\n",
"<td>Molar flow rate of water vapour out of chamber</td>\n",
"<td>mol s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{F_{out,v}}</script></td>\n",
"<td>Volumetric flow rate out of chamber</td>\n",
"<td> m<script type=\"math/tex\">^{3}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">F_{s}</script></td>\n",
"<td>Fraction of radiation emitted by leaf, absorbed by sensor</td>\n",
"<td>1 </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">g</script></td>\n",
"<td>Gravitational acceleration (9.81)</td>\n",
"<td>m s<script type=\"math/tex\">^{-2}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{g_{bw}}</script></td>\n",
"<td>Boundary layer conductance to water vapour </td>\n",
"<td>m s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{g_{bw,mol}}</script></td>\n",
"<td>Boundary layer conductance to water vapour </td>\n",
"<td>mol m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{g_{sw}}</script></td>\n",
"<td>Stomatal conductance to water vapour</td>\n",
"<td>m s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{g_{sw,mol}}</script></td>\n",
"<td>Stomatal conductance to water vapour</td>\n",
"<td>mol m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{g_{tw}}</script></td>\n",
"<td>Total leaf conductance to water vapour</td>\n",
"<td>m s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{g_{tw,mol}}</script></td>\n",
"<td>Total leaf layer conductance to water vapour</td>\n",
"<td>mol m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">H_{c}</script></td>\n",
"<td>Chamber height</td>\n",
"<td>m </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">h_{c}</script></td>\n",
"<td>Average 1-sided convective transfer coefficient</td>\n",
"<td>J K<script type=\"math/tex\">^{-1}</script> m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">H_{l}</script></td>\n",
"<td>Sensible heat flux from leaf</td>\n",
"<td>J m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">k_{a}</script></td>\n",
"<td>Thermal conductivity of dry air</td>\n",
"<td>J K<script type=\"math/tex\">^{-1}</script> m<script type=\"math/tex\">^{-1}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">L_{A}</script></td>\n",
"<td>Leaf area</td>\n",
"<td> m<script type=\"math/tex\">^{2}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">L_{c}</script></td>\n",
"<td>Chamber length</td>\n",
"<td>m </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">L_{i}</script></td>\n",
"<td>Thickness of insulation material</td>\n",
"<td>m </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">L_{l}</script></td>\n",
"<td>Characteristic length scale for convection (size of leaf)</td>\n",
"<td>m </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">L_{\\mathit{ls}}</script></td>\n",
"<td>Distance between leaf and net radiation sensor</td>\n",
"<td>m </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">L_{s}</script></td>\n",
"<td>Width of net radiation sensor</td>\n",
"<td>m </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">\\lambda_{E}</script></td>\n",
"<td>Latent heat of evaporation (2.45e6)</td>\n",
"<td>J kg<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">\\lambda_{i}</script></td>\n",
"<td>Heat conductivity of insulation material</td>\n",
"<td>J K<script type=\"math/tex\">^{-1}</script> m<script type=\"math/tex\">^{-1}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{M_{air}}</script></td>\n",
"<td>Molar mass of air (kg mol-1)</td>\n",
"<td>kg mol<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">M_{N_{2}}</script></td>\n",
"<td>Molar mass of nitrogen (0.028)</td>\n",
"<td>kg mol<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">M_{O_{2}}</script></td>\n",
"<td>Molar mass of oxygen (0.032)</td>\n",
"<td>kg mol<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">M_{w}</script></td>\n",
"<td>Molar mass of water (0.018)</td>\n",
"<td>kg mol<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">n_{c}</script></td>\n",
"<td>molar mass of gas in chamber</td>\n",
"<td>mol </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{N_{Gr_L}}</script></td>\n",
"<td>Grashof number</td>\n",
"<td>1 </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{N_{Le}}</script></td>\n",
"<td>Lewis number</td>\n",
"<td>1 </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{N_{Nu_L}}</script></td>\n",
"<td>Nusselt number</td>\n",
"<td>1 </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{N_{Re_c}}</script></td>\n",
"<td>Critical Reynolds number for the onset of turbulence</td>\n",
"<td>1 </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{N_{Re_L}}</script></td>\n",
"<td>Reynolds number</td>\n",
"<td>1 </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{N_{Sh_L}}</script></td>\n",
"<td>Sherwood number</td>\n",
"<td>1 </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">\\nu_{a}</script></td>\n",
"<td>Kinematic viscosity of dry air</td>\n",
"<td> m<script type=\"math/tex\">^{2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">P_{a}</script></td>\n",
"<td>Air pressure</td>\n",
"<td>Pa </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{P_{N2}}</script></td>\n",
"<td>Partial pressure of nitrogen in the atmosphere</td>\n",
"<td>Pa </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{P_{O2}}</script></td>\n",
"<td>Partial pressure of oxygen in the atmosphere</td>\n",
"<td>Pa </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">P_{r}</script></td>\n",
"<td>Reference pressure</td>\n",
"<td>Pa </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{P_{w,in}}</script></td>\n",
"<td>Vapour pressure of incoming air</td>\n",
"<td>Pa </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{P_{w,out}}</script></td>\n",
"<td>Vapour pressure of outgoing air</td>\n",
"<td>Pa </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{P_{wa}}</script></td>\n",
"<td>Vapour pressure in the atmosphere</td>\n",
"<td>Pa </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{P_{was}}</script></td>\n",
"<td>Saturation vapour pressure at air temperature</td>\n",
"<td>Pa </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{P_{wl}}</script></td>\n",
"<td>Vapour pressure inside the leaf</td>\n",
"<td>Pa </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{N_{Pr}}</script></td>\n",
"<td>Prandtl number (0.71)</td>\n",
"<td>1 </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">Q_{i}</script></td>\n",
"<td>Heat conduction through insulation material</td>\n",
"<td>J s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{Q_{in}}</script></td>\n",
"<td>Internal heat sources, such as fan</td>\n",
"<td>J s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{r_{bw}}</script></td>\n",
"<td>Boundary layer resistance to water vapour, inverse of <script type=\"math/tex\">g_{bw}</script></td>\n",
"<td>s m<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">R_{d}</script></td>\n",
"<td>Downwelling global radiation</td>\n",
"<td>J m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{R_{H,in}}</script></td>\n",
"<td>Relative humidity of incoming air</td>\n",
"<td>1 </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{R_{la}}</script></td>\n",
"<td>Longwave radiation absorbed by leaf</td>\n",
"<td>J m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{R_{ld}}</script></td>\n",
"<td>Downwards emitted/reflected global radiation from leaf</td>\n",
"<td>J m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{R_{ll}}</script></td>\n",
"<td>Longwave radiation away from leaf</td>\n",
"<td>J m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{R_{lu}}</script></td>\n",
"<td>Upwards emitted/reflected global radiation from leaf</td>\n",
"<td>J m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{R_{mol}}</script></td>\n",
"<td>Molar gas constant (8.314472)</td>\n",
"<td>J K<script type=\"math/tex\">^{-1}</script> mol<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">R_{s}</script></td>\n",
"<td>Solar shortwave flux</td>\n",
"<td>J m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">{r_{sw}}</script></td>\n",
"<td>Stomatal resistance to water vapour, inverse of <script type=\"math/tex\">g_{sw}</script></td>\n",
"<td>s m<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{r_{tw}}</script></td>\n",
"<td>Total leaf resistance to water vapour, <script type=\"math/tex\">r_{bv} + r_{sv}</script></td>\n",
"<td>s m<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">R_{u}</script></td>\n",
"<td>Upwelling global radiation</td>\n",
"<td>J m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">\\rho_{a}</script></td>\n",
"<td>Density of dry air</td>\n",
"<td>kg m<script type=\"math/tex\">^{-3}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">\\rho_{\\mathit{al}}</script></td>\n",
"<td>Density of air at the leaf surface</td>\n",
"<td>kg m<script type=\"math/tex\">^{-3}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">\\rho_{i}</script></td>\n",
"<td>Density of insulation material</td>\n",
"<td>kg m<script type=\"math/tex\">^{-3}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">S_{a}</script></td>\n",
"<td>Radiation sensor above leaf reading</td>\n",
"<td>J m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">S_{b}</script></td>\n",
"<td>Radiation sensor below leaf reading</td>\n",
"<td>J m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">S_{s}</script></td>\n",
"<td>Radiation sensor beside leaf reading</td>\n",
"<td>J m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{\\sigma}</script></td>\n",
"<td>Stefan-Boltzmann constant (5.67e-8)</td>\n",
"<td>J K<script type=\"math/tex\">^{-4}</script> m<script type=\"math/tex\">^{-2}</script> s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">T_{0}</script></td>\n",
"<td>Freezing point in kelvin</td>\n",
"<td>K </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">T_{a}</script></td>\n",
"<td>Air temperature</td>\n",
"<td>K </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">T_{d}</script></td>\n",
"<td>Dew point temperature of incoming air</td>\n",
"<td>K </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{T_{in}}</script></td>\n",
"<td>Temperature of incoming air</td>\n",
"<td>K </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">T_{l}</script></td>\n",
"<td>Leaf temperature</td>\n",
"<td>K </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{T_{out}}</script></td>\n",
"<td>Temperature of outgoing air (= chamber T_a)</td>\n",
"<td>K </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">T_{r}</script></td>\n",
"<td>Reference temperature</td>\n",
"<td>K </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">{T_{room}}</script></td>\n",
"<td>Lab air temperature</td>\n",
"<td>K </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">T_{w}</script></td>\n",
"<td>Radiative temperature of objects surrounding the leaf</td>\n",
"<td>K </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">V_{c}</script></td>\n",
"<td>Chamber volume</td>\n",
"<td> m<script type=\"math/tex\">^{3}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-a\">\n",
"<td><script type=\"math/tex\">v_{w}</script></td>\n",
"<td>Wind velocity</td>\n",
"<td>m s<script type=\"math/tex\">^{-1}</script> </td>\n",
"</tr>\n",
"<tr class =\"row-b\">\n",
"<td><script type=\"math/tex\">W_{c}</script></td>\n",
"<td>Chamber width</td>\n",
"<td>m </td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Variable Description (value) Units\n",
"+------------+---------------------------------------------------------------------------------------------------------------------+----------------------------------+\n",
" A_i Conducting area of insulation material m$^{2}$\n",
" a_s Fraction of one-sided leaf area covered by stomata (1 if stomata are on one side only, 2 if they are on both sides) 1\n",
" a_sh Fraction of projected area exchanging sensible heat with the air (2) 1\n",
" alpha_a Thermal diffusivity of dry air m$^{2}$ s$^{-1}$\n",
" alpha_l Leaf albedo, fraction of shortwave radiation reflected by the leaf 1\n",
" B_l Boundary layer thickness m\n",
" c_pa Specific heat of dry air (1010) J K$^{-1}$ kg$^{-1}$\n",
" c_pi Heat capacity of insulation material J K$^{-1}$ kg$^{-1}$\n",
" c_pv Specific heat of water vapour at 300 K J K$^{-1}$ kg$^{-1}$\n",
" C_wa Concentration of water in the free air mol m$^{-3}$\n",
" C_wl Concentration of water in the leaf air space mol m$^{-3}$\n",
" D_va Binary diffusion coefficient of water vapour in air m$^{2}$ s$^{-1}$\n",
" dT_i Temperature increment of insulation material K\n",
" E_l Latent heat flux from leaf J m$^{-2}$ s$^{-1}$\n",
" E_lmol Transpiration rate in molar units mol m$^{-2}$ s$^{-1}$\n",
" epsilon_l Longwave emmissivity of the leaf surface (1.0) 1\n",
" F_in_mola Molar flow rate of dry air into chamber mol s$^{-1}$\n",
" F_in_molw Molar flow rate of water vapour into chamber mol s$^{-1}$\n",
" F_in_v Volumetric flow rate into chamber m$^{3}$ s$^{-1}$\n",
" F_in_va_n Volumetric inflow of dry air at 0oC and 101325 Pa m$^{3}$ s$^{-1}$\n",
" F_out_mola Molar flow rate of dry air out of chamber mol s$^{-1}$\n",
" F_out_molw Molar flow rate of water vapour out of chamber mol s$^{-1}$\n",
" F_out_v Volumetric flow rate out of chamber m$^{3}$ s$^{-1}$\n",
" F_s Fraction of radiation emitted by leaf, absorbed by sensor 1\n",
" g Gravitational acceleration (9.81) m s$^{-2}$\n",
" g_bw Boundary layer conductance to water vapour m s$^{-1}$\n",
" g_bwmol Boundary layer conductance to water vapour mol m$^{-2}$ s$^{-1}$\n",
" g_sw Stomatal conductance to water vapour m s$^{-1}$\n",
" g_swmol Stomatal conductance to water vapour mol m$^{-2}$ s$^{-1}$\n",
" g_tw Total leaf conductance to water vapour m s$^{-1}$\n",
" g_twmol Total leaf layer conductance to water vapour mol m$^{-2}$ s$^{-1}$\n",
" H_c Chamber height m\n",
" h_c Average 1-sided convective transfer coefficient J K$^{-1}$ m$^{-2}$ s$^{-1}$\n",
" H_l Sensible heat flux from leaf J m$^{-2}$ s$^{-1}$\n",
" k_a Thermal conductivity of dry air J K$^{-1}$ m$^{-1}$ s$^{-1}$\n",
" L_A Leaf area m$^{2}$\n",
" L_c Chamber length m\n",
" L_i Thickness of insulation material m\n",
" L_l Characteristic length scale for convection (size of leaf) m\n",
" L_ls Distance between leaf and net radiation sensor m\n",
" L_s Width of net radiation sensor m\n",
" lambda_E Latent heat of evaporation (2.45e6) J kg$^{-1}$\n",
" lambda_i Heat conductivity of insulation material J K$^{-1}$ m$^{-1}$ s$^{-1}$\n",
" M_air Molar mass of air (kg mol-1) kg mol$^{-1}$\n",
" M_N2 Molar mass of nitrogen (0.028) kg mol$^{-1}$\n",
" M_O2 Molar mass of oxygen (0.032) kg mol$^{-1}$\n",
" M_w Molar mass of water (0.018) kg mol$^{-1}$\n",
" n_c molar mass of gas in chamber mol\n",
" Gr Grashof number 1\n",
" Le Lewis number 1\n",
" Nu Nusselt number 1\n",
" Re_c Critical Reynolds number for the onset of turbulence 1\n",
" Re Reynolds number 1\n",
" Sh Sherwood number 1\n",
" nu_a Kinematic viscosity of dry air m$^{2}$ s$^{-1}$\n",
" P_a Air pressure Pa\n",
" P_N2 Partial pressure of nitrogen in the atmosphere Pa\n",
" P_O2 Partial pressure of oxygen in the atmosphere Pa\n",
" P_r Reference pressure Pa\n",
" P_w_in Vapour pressure of incoming air Pa\n",
" P_w_out Vapour pressure of outgoing air Pa\n",
" P_wa Vapour pressure in the atmosphere Pa\n",
" P_was Saturation vapour pressure at air temperature Pa\n",
" P_wl Vapour pressure inside the leaf Pa\n",
" Pr Prandtl number (0.71) 1\n",
" Q_i Heat conduction through insulation material J s$^{-1}$\n",
" Q_in Internal heat sources, such as fan J s$^{-1}$\n",
" r_bw Boundary layer resistance to water vapour, inverse of $g_{bw}$ s m$^{-1}$\n",
" R_d Downwelling global radiation J m$^{-2}$ s$^{-1}$\n",
" R_H_in Relative humidity of incoming air 1\n",
" R_la Longwave radiation absorbed by leaf J m$^{-2}$ s$^{-1}$\n",
" R_ld Downwards emitted/reflected global radiation from leaf J m$^{-2}$ s$^{-1}$\n",
" R_ll Longwave radiation away from leaf J m$^{-2}$ s$^{-1}$\n",
" R_lu Upwards emitted/reflected global radiation from leaf J m$^{-2}$ s$^{-1}$\n",
" R_mol Molar gas constant (8.314472) J K$^{-1}$ mol$^{-1}$\n",
" R_s Solar shortwave flux J m$^{-2}$ s$^{-1}$\n",
" r_sw Stomatal resistance to water vapour, inverse of $g_{sw}$ s m$^{-1}$\n",
" r_tw Total leaf resistance to water vapour, $r_{bv} + r_{sv}$ s m$^{-1}$\n",
" R_u Upwelling global radiation J m$^{-2}$ s$^{-1}$\n",
" rho_a Density of dry air kg m$^{-3}$\n",
" rho_al Density of air at the leaf surface kg m$^{-3}$\n",
" rho_i Density of insulation material kg m$^{-3}$\n",
" S_a Radiation sensor above leaf reading J m$^{-2}$ s$^{-1}$\n",
" S_b Radiation sensor below leaf reading J m$^{-2}$ s$^{-1}$\n",
" S_s Radiation sensor beside leaf reading J m$^{-2}$ s$^{-1}$\n",
" sigm Stefan-Boltzmann constant (5.67e-8) J K$^{-4}$ m$^{-2}$ s$^{-1}$\n",
" T0 Freezing point in kelvin K\n",
" T_a Air temperature K\n",
" T_d Dew point temperature of incoming air K\n",
" T_in Temperature of incoming air K\n",
" T_l Leaf temperature K\n",
" T_out Temperature of outgoing air (= chamber T_a) K\n",
" T_r Reference temperature K\n",
" T_room Lab air temperature K\n",
" T_w Radiative temperature of objects surrounding the leaf K\n",
" V_c Chamber volume m$^{3}$\n",
" v_w Wind velocity m s$^{-1}$\n",
" W_c Chamber width m"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Creating dictionary to substitute names of units with shorter forms\n",
"var('m s J Pa K kg mol')\n",
"subsdict = {meter: m, second: s, joule: J, pascal: Pa, kelvin: K, kilogram: kg, mole: mol}\n",
"var('N_Re_L N_Re_c N_Le N_Nu_L N_Gr_L N_Sh_L')\n",
"dict_varnew = {Re: N_Re_L, Re_c: N_Re_c, Le: N_Le, Nu: N_Nu_L, Gr: N_Gr_L, Sh: N_Sh_L}\n",
"dict_varold = {v: k for k, v in dict_varnew.iteritems()}\n",
"variables = sorted([str(variable.subs(dict_varnew)) for variable in udict.keys()],key=str.lower)\n",
"tableheader = [('Variable', 'Description (value)', 'Units')]\n",
"tabledata = [('Variable', 'Description (value)', 'Units')]\n",
"for variable1 in variables:\n",
" variable2 = eval(variable1).subs(dict_varold)\n",
" variable = str(variable2)\n",
" tabledata.append((eval(variable),docdict[eval(variable)],fun_units_formatted(variable)))\n",
"\n",
"table(tabledata, header_row=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "SageMath 7.3",
"language": "",
"name": "sagemath"
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"language": "python",
"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"
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"nav_menu": {},
"toc": {
"navigate_menu": true,
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"toc_section_display": "block",
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}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
quaquel/EMAworkbench | docs/source/indepth_tutorial/directed-search.ipynb | 1 | 175041 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Directed search\n",
"\n",
"This is the third turial in a series showcasing the functionality of the Exploratory Modeling workbench. Exploratory modeling entails investigating the way in which uncertainty and/or policy levers map to outcomes. To investigate these mappings, we can either use sampling based strategies (open exploration) or optimization based strategies (directed search).\n",
"\n",
"In this tutorial, I will demonstrate in more detail how to use the workbench for directed search. We are using the same example as in the previous tutorials. When using optimization, it is critical that you specify for each Scalar Outcome the direction in which it should move. There are three possibilities: info which is ignored, maximize, and minimize. If the `kind` keyword argument is not specified, it defaults to info."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from ema_workbench import RealParameter, ScalarOutcome, Constant, Model\n",
"from dps_lake_model import lake_model\n",
"\n",
"model = Model(\"lakeproblem\", function=lake_model)\n",
"\n",
"# specify uncertainties\n",
"model.uncertainties = [\n",
" RealParameter(\"b\", 0.1, 0.45),\n",
" RealParameter(\"q\", 2.0, 4.5),\n",
" RealParameter(\"mean\", 0.01, 0.05),\n",
" RealParameter(\"stdev\", 0.001, 0.005),\n",
" RealParameter(\"delta\", 0.93, 0.99),\n",
"]\n",
"\n",
"# set levers\n",
"model.levers = [\n",
" RealParameter(\"c1\", -2, 2),\n",
" RealParameter(\"c2\", -2, 2),\n",
" RealParameter(\"r1\", 0, 2),\n",
" RealParameter(\"r2\", 0, 2),\n",
" RealParameter(\"w1\", 0, 1),\n",
"]\n",
"\n",
"# specify outcomes\n",
"model.outcomes = [\n",
" ScalarOutcome(\"max_P\", ScalarOutcome.MINIMIZE),\n",
" ScalarOutcome(\"utility\", ScalarOutcome.MAXIMIZE),\n",
" ScalarOutcome(\"inertia\", ScalarOutcome.MAXIMIZE),\n",
" ScalarOutcome(\"reliability\", ScalarOutcome.MAXIMIZE),\n",
"]\n",
"\n",
"# override some of the defaults of the model\n",
"model.constants = [\n",
" Constant(\"alpha\", 0.41),\n",
" Constant(\"nsamples\", 150),\n",
" Constant(\"myears\", 100),\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using directed search with the ema_workbench requires [platypus-opt](https://github.com/Project-Platypus/Platypus). Please check the installation suggestions provided in the readme of the github repository. I personally either install from github directly \n",
"\n",
"\n",
"```\n",
"pip git+https://github.com/Project-Platypus/Platypus.git\n",
"```\n",
"\n",
"or through pip\n",
"\n",
"```\n",
"pip install platypus-opt\n",
"```\n",
"\n",
"One note of caution: don't install platypus, but platypus-opt. There exists a python package on pip called platypus, but that is quite a different kind of libary. \n",
"\n",
"There are three ways in which we can use optimization in the workbench:\n",
"1. Search over the decision levers, conditional on a reference scenario\n",
"2. Search over the uncertain factors, conditional on a reference policy\n",
"3. Search over the decision levers given a set of scenarios"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Search over levers\n",
"Directed search is most often used to search over the decision levers in order to find good candidate strategies. This is for example the first step in the [Many Objective Robust Decision Making process](https://www.sciencedirect.com/science/article/pii/S1364815212003131). This is straightforward to do with the workbench using the optimize method. Note that I have kept the number of functional evaluations (nfe) very low. In real applications this should be substantially higher and be based on convergence considerations which are demonstrated below.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[MainProcess/INFO] pool started with 12 workers\n",
"298it [00:07, 42.43it/s] \n",
"[MainProcess/INFO] optimization completed, found 6 solutions\n",
"[MainProcess/INFO] terminating pool\n"
]
}
],
"source": [
"from ema_workbench import MultiprocessingEvaluator, ema_logging\n",
"\n",
"ema_logging.log_to_stderr(ema_logging.INFO)\n",
"\n",
"with MultiprocessingEvaluator(model) as evaluator:\n",
" results = evaluator.optimize(\n",
" nfe=250,\n",
" searchover=\"levers\",\n",
" epsilons=[\n",
" 0.1,\n",
" ]\n",
" * len(model.outcomes),\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"the results from optimize is a DataFrame with the decision variables and outcomes of interest. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>c1</th>\n",
" <th>c2</th>\n",
" <th>r1</th>\n",
" <th>r2</th>\n",
" <th>w1</th>\n",
" <th>max_P</th>\n",
" <th>utility</th>\n",
" <th>inertia</th>\n",
" <th>reliability</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.031532</td>\n",
" <td>0.184650</td>\n",
" <td>0.280885</td>\n",
" <td>0.410156</td>\n",
" <td>0.367589</td>\n",
" <td>0.094885</td>\n",
" <td>0.250233</td>\n",
" <td>0.990000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.625142</td>\n",
" <td>0.098499</td>\n",
" <td>1.047069</td>\n",
" <td>0.913895</td>\n",
" <td>0.079976</td>\n",
" <td>2.283907</td>\n",
" <td>1.378241</td>\n",
" <td>0.985533</td>\n",
" <td>0.206533</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.686334</td>\n",
" <td>0.557664</td>\n",
" <td>1.017384</td>\n",
" <td>0.807999</td>\n",
" <td>0.496470</td>\n",
" <td>2.283759</td>\n",
" <td>1.001528</td>\n",
" <td>0.970933</td>\n",
" <td>0.310533</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.435289</td>\n",
" <td>0.350622</td>\n",
" <td>0.319505</td>\n",
" <td>0.624951</td>\n",
" <td>0.026242</td>\n",
" <td>0.197131</td>\n",
" <td>0.537310</td>\n",
" <td>0.990000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.947359</td>\n",
" <td>-0.622528</td>\n",
" <td>1.619746</td>\n",
" <td>1.863076</td>\n",
" <td>0.418746</td>\n",
" <td>2.283625</td>\n",
" <td>1.778130</td>\n",
" <td>0.990000</td>\n",
" <td>0.070000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.629075</td>\n",
" <td>-0.076368</td>\n",
" <td>1.666986</td>\n",
" <td>0.488599</td>\n",
" <td>0.673219</td>\n",
" <td>2.283672</td>\n",
" <td>1.631001</td>\n",
" <td>0.980000</td>\n",
" <td>0.110000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" c1 c2 r1 r2 w1 max_P utility \\\n",
"0 0.031532 0.184650 0.280885 0.410156 0.367589 0.094885 0.250233 \n",
"1 -0.625142 0.098499 1.047069 0.913895 0.079976 2.283907 1.378241 \n",
"2 0.686334 0.557664 1.017384 0.807999 0.496470 2.283759 1.001528 \n",
"3 0.435289 0.350622 0.319505 0.624951 0.026242 0.197131 0.537310 \n",
"4 1.947359 -0.622528 1.619746 1.863076 0.418746 2.283625 1.778130 \n",
"5 0.629075 -0.076368 1.666986 0.488599 0.673219 2.283672 1.631001 \n",
"\n",
" inertia reliability \n",
"0 0.990000 1.000000 \n",
"1 0.985533 0.206533 \n",
"2 0.970933 0.310533 \n",
"3 0.990000 1.000000 \n",
"4 0.990000 0.070000 \n",
"5 0.980000 0.110000 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Specifying constraints\n",
"It is possible to specify a constrained optimization problem. A model can have one or more constraints. A constraint can be applied to the model input parameters (both uncertainties and levers), and/or outcomes. A constraint is essentially a function that should return the distance from the feasibility threshold. The distance should be 0 if the constraint is met.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from ema_workbench import Constraint\n",
"\n",
"constraints = [\n",
" Constraint(\"max pollution\", outcome_names=\"max_P\", function=lambda x: max(0, x - 1))\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[MainProcess/INFO] pool started with 12 workers\n",
"298it [00:07, 42.38it/s] \n",
"[MainProcess/INFO] optimization completed, found 3 solutions\n",
"[MainProcess/INFO] terminating pool\n"
]
}
],
"source": [
"from ema_workbench import MultiprocessingEvaluator\n",
"from ema_workbench import ema_logging\n",
"\n",
"ema_logging.log_to_stderr(ema_logging.INFO)\n",
"\n",
"with MultiprocessingEvaluator(model) as evaluator:\n",
" results = evaluator.optimize(\n",
" nfe=250,\n",
" searchover=\"levers\",\n",
" epsilons=[\n",
" 0.1,\n",
" ]\n",
" * len(model.outcomes),\n",
" constraints=constraints,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
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" <th></th>\n",
" <th>c1</th>\n",
" <th>c2</th>\n",
" <th>r1</th>\n",
" <th>r2</th>\n",
" <th>w1</th>\n",
" <th>max_P</th>\n",
" <th>utility</th>\n",
" <th>inertia</th>\n",
" <th>reliability</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.242378</td>\n",
" <td>0.463484</td>\n",
" <td>1.707600</td>\n",
" <td>0.404997</td>\n",
" <td>0.886919</td>\n",
" <td>0.215327</td>\n",
" <td>0.555460</td>\n",
" <td>0.99</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.242378</td>\n",
" <td>0.343609</td>\n",
" <td>1.707600</td>\n",
" <td>0.434568</td>\n",
" <td>0.949529</td>\n",
" <td>0.092854</td>\n",
" <td>0.234231</td>\n",
" <td>0.99</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.042579</td>\n",
" <td>0.672822</td>\n",
" <td>0.957689</td>\n",
" <td>1.814246</td>\n",
" <td>0.168521</td>\n",
" <td>0.146712</td>\n",
" <td>0.419054</td>\n",
" <td>0.99</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" c1 c2 r1 r2 w1 max_P utility \\\n",
"0 0.242378 0.463484 1.707600 0.404997 0.886919 0.215327 0.555460 \n",
"1 0.242378 0.343609 1.707600 0.434568 0.949529 0.092854 0.234231 \n",
"2 0.042579 0.672822 0.957689 1.814246 0.168521 0.146712 0.419054 \n",
"\n",
" inertia reliability \n",
"0 0.99 1.0 \n",
"1 0.99 1.0 \n",
"2 0.99 1.0 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tracking convergence \n",
"An important part of using many-objective evolutionary algorithms is to carefully monitor whether they have converged. Various different metrics can be used for this. The workbench supports two useful metrics known as hypervolume and epsilon progress. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[MainProcess/INFO] pool started with 12 workers\n",
"10010it [02:17, 73.02it/s] \n",
"[MainProcess/INFO] optimization completed, found 16 solutions\n",
"[MainProcess/INFO] terminating pool\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from ema_workbench.em_framework.optimization import HyperVolume, EpsilonProgress\n",
"\n",
"convergence_metrics = [\n",
" HyperVolume(minimum=[0, 0, 0, 0], maximum=[1, 1.01, 1.01, 1.01]),\n",
" EpsilonProgress(),\n",
"]\n",
"\n",
"with MultiprocessingEvaluator(model) as evaluator:\n",
" results, convergence = evaluator.optimize(\n",
" nfe=10000,\n",
" searchover=\"levers\",\n",
" epsilons=[\n",
" 0.05,\n",
" ]\n",
" * len(model.outcomes),\n",
" convergence=convergence_metrics,\n",
" constraints=constraints,\n",
" )\n",
"\n",
"fig, (ax1, ax2) = plt.subplots(ncols=2, sharex=True, figsize=(8, 4))\n",
"ax1.plot(convergence.nfe, convergence.epsilon_progress)\n",
"ax1.set_ylabel(\"$\\epsilon$-progress\")\n",
"ax2.plot(convergence.nfe, convergence.hypervolume)\n",
"ax2.set_ylabel(\"hypervolume\")\n",
"\n",
"ax1.set_xlabel(\"number of function evaluations\")\n",
"ax2.set_xlabel(\"number of function evaluations\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Changing the reference scenario \n",
"\n",
"The workbench offers control over the reference scenario or policy under which you are performing the optimization. This makes it easy to aply multi-scenario MORDM [(Watson & Kasprzyk, 2017)](https://www.sciencedirect.com/science/article/pii/S1364815216310593). Alternatively, you can also use it to change the policy for which you are applying worst case scenario discovery (see below). \n",
"\n",
"\n",
"```python\n",
"reference = Scenario('reference', b=0.4, q=2, mean=0.02, stdev=0.01)\n",
"\n",
"with MultiprocessingEvaluator(lake_model) as evaluator:\n",
" results = evaluator.optimize(searchover='levers', nfe=1000,\n",
" epsilons=[0.1, ]*len(lake_model.outcomes),\n",
" reference=reference)\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Search over uncertainties: worst case discovery\n",
"Up till now, the focus has been on applying search to find promising candidate strategies. That is, we search through the lever space. However, there might also be good reasons to search through the uncertainty space. For example to search for worst case scenarios [(Halim et al, 2015)](https://www.sciencedirect.com/science/article/pii/S0016328715001342). This is easily achieved as shown below. We change the kind attribute on each outcome so that we search for the worst outcome and specify that we would like to search over the uncertainties instead of the levers.\n",
"\n",
"Any of the foregoing additions such as constraints or converence work as shown above. Note that if you would like to to change the reference policy, reference should be a Policy object rather than a Scenario object.\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[MainProcess/INFO] pool started with 12 workers\n",
"1090it [00:18, 60.52it/s] \n",
"[MainProcess/INFO] optimization completed, found 7 solutions\n",
"[MainProcess/INFO] terminating pool\n"
]
}
],
"source": [
"# change outcomes so direction is undesirable\n",
"minimize = ScalarOutcome.MINIMIZE\n",
"maximize = ScalarOutcome.MAXIMIZE\n",
"\n",
"for outcome in model.outcomes:\n",
" if outcome.kind == minimize:\n",
" outcome.kind = maximize\n",
" else:\n",
" outcome.kind = minimize\n",
"\n",
"with MultiprocessingEvaluator(model) as evaluator:\n",
" results = evaluator.optimize(\n",
" nfe=1000,\n",
" searchover=\"uncertainties\",\n",
" epsilons=[\n",
" 0.1,\n",
" ]\n",
" * len(model.outcomes),\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Parallel coordinate plots\n",
"\n",
"The workbench comes with support for making parallel axis plots through the parcoords module. This module offers a parallel axes object on which we can plot data. The typical workflow is to first instantiate this parallel axes object given a pandas dataframe with the upper and lower limits for each axes. Next, one or more datasets can be plotted on this axes. Any dataframe passed to the plot method will be normalized using the limits passed first. We can also invert any of the axes to ensure that the desirable direction is the same for all axes."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from ema_workbench.analysis import parcoords\n",
"\n",
"data = results.loc[:, [o.name for o in model.outcomes]]\n",
"limits = parcoords.get_limits(data)\n",
"limits.loc[0, [\"utility\", \"inertia\", \"reliability\", \"max_P\"]] = 0\n",
"limits.loc[1, [\"utility\", \"inertia\", \"reliability\"]] = 1\n",
"\n",
"paraxes = parcoords.ParallelAxes(limits)\n",
"paraxes.plot(data)\n",
"paraxes.invert_axis(\"max_P\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Robust Search\n",
"\n",
"In the foregoing, we have been using optimization over levers or uncertainties, while assuming a reference scenario or policy. However, we can also formulate a robust many objective optimization problem, were we are going to search over the levers for solutions that have robust performance over a set of scenarios. To do this with the workbench, there are several steps that one has to take.\n",
"\n",
"First, we need to specify our robustness metrics. A robustness metric takes as input the performance of a candidate policy over a set of scenarios and returns a single robustness score. For a more in depth overview, see[McPhail et al. (2018)](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017EF000649). In case of the workbench, we can use the ScalarOutcome class for this. We need to specify the name of the robustness metric a function that takes as input a numpy array and returns a single number, and the model outcome to which this function should be applied. \n",
"\n",
"Below, we use a percentile based robustness metric, which we apply to each model outcome. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import functools\n",
"\n",
"percentile10 = functools.partial(np.percentile, q=10)\n",
"percentile90 = functools.partial(np.percentile, q=90)\n",
"\n",
"MAXIMIZE = ScalarOutcome.MAXIMIZE\n",
"MINIMIZE = ScalarOutcome.MINIMIZE\n",
"robustnes_functions = [\n",
" ScalarOutcome(\n",
" \"90th percentile max_p\",\n",
" kind=MINIMIZE,\n",
" variable_name=\"max_P\",\n",
" function=percentile90,\n",
" ),\n",
" ScalarOutcome(\n",
" \"10th percentile reliability\",\n",
" kind=MAXIMIZE,\n",
" variable_name=\"reliability\",\n",
" function=percentile10,\n",
" ),\n",
" ScalarOutcome(\n",
" \"10th percentile inertia\",\n",
" kind=MAXIMIZE,\n",
" variable_name=\"inertia\",\n",
" function=percentile10,\n",
" ),\n",
" ScalarOutcome(\n",
" \"10th percentile utility\",\n",
" kind=MAXIMIZE,\n",
" variable_name=\"utility\",\n",
" function=percentile10,\n",
" ),\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we have to generate the scenarios we want to use. Below we generate 50 scenarios, which we will keep fixed over the optimization. The exact number of scenarios to use is to be established through trial and error. Typically it involves balancing computational costs of more scenarios, with the stability of the robustness metric over the number of scenarios"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from ema_workbench.em_framework import sample_uncertainties\n",
"\n",
"n_scenarios = 50\n",
"scenarios = sample_uncertainties(model, n_scenarios)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the robustness metrics specified, and the scenarios, sampled, we can now perform robust many-objective optimization. Below is the code that one would run. Note that this is computationally very expensive since each candidate solution is going to be run for fifty scenarios before we can calculate the robustness for each outcome of interest."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"```python\n",
"nfe = int(1e6)\n",
"with MultiprocessingEvaluator(model) as evaluator:\n",
" robust_results = evaluator.robust_optimize(robustnes_functions, scenarios, \n",
" nfe=nfe, epsilons=[0.05,]*len(robustnes_functions))\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| bsd-3-clause |
skrzym/monday-morning-quarterback | Research/CrowdFlower/cf2mongo execution.ipynb | 1 | 1666 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import cf2mongo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Start MongoDB server! \n",
"Local data files are located in C:\\Data\\db \n",
"This directory must exist in order for the following to work! \n",
"*If a custom location is desired, see mongoDB documentation online.*\n",
" \n",
"Do the following to initialize server on local Windows PC:\n",
"1. Open a command prompt\n",
"2. Navigate to C:\\Program Files\\MongoDB\\3.4\\bin\n",
"3. Execute mongod.exe\n",
"\n",
"The cf2mongo.run_conversion function will now be able to connect to the database."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"cf2mongo.run_conversion()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#cf2mongo.convert_and_insert()"
]
}
],
"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 |
james-prior/euler | euler-025-1000-digit-fibonacci-number-20130808.ipynb | 1 | 5104 | {
"metadata": {
"name": "euler-025-1000-digit-fibonacci-number-20130808"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Fibonacci sequence is defined by the recurrence relation: \n",
"\n",
"> F<sub>*n*</sub> = F<sub>*n*\u22121</sub> + F<sub>*n*\u22122</sub>, where F<sub>1</sub> = 1 and F<sub>2</sub> = 1. \n",
"\n",
"Hence the first 12 terms will be: \n",
"\n",
"> F<sub>1</sub> = 1 \n",
"> F<sub>2</sub> = 1 \n",
"> F<sub>3</sub> = 2 \n",
"> F<sub>4</sub> = 3 \n",
"> F<sub>5</sub> = 5 \n",
"> F<sub>6</sub> = 8 \n",
"> F<sub>7</sub> = 13 \n",
"> F<sub>8</sub> = 21 \n",
"> F<sub>9</sub> = 34 \n",
"> F<sub>10</sub> = 55 \n",
"> F<sub>11</sub> = 89 \n",
"> F<sub>12</sub> = 144 \n",
"\n",
"The 12th term, F12, is the first term to contain three digits. \n",
"\n",
"What is the first term in the Fibonacci sequence to contain 1000 digits? "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from euler import gen_fibonacci, gen_lte, term_of_ndigits_fibonacci, phi"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"list(gen_lte(gen_fibonacci(), 100))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 2,
"text": [
"[1, 2, 3, 5, 8, 13, 21, 34, 55, 89]"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def gen_fib2():\n",
" yield 1\n",
" for i in gen_fibonacci():\n",
" yield i"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"list(gen_lte(gen_fib2(), 100))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 4,
"text": [
"[1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def gen_fib3():\n",
" yield 0\n",
" yield 1\n",
" for i in gen_fibonacci():\n",
" yield i"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"list(gen_lte(gen_fib3(), 100))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 6,
"text": [
"[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def foo(n):\n",
" for i, fib in enumerate(gen_fib2(), 1):\n",
" if len(str(fib)) >= n:\n",
" return i"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo(3)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 8,
"text": [
"12"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n = 1000\n",
"%timeit foo(n)\n",
"foo(n)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1 loops, best of 3: 250 ms per loop\n"
]
},
{
"output_type": "pyout",
"prompt_number": 9,
"text": [
"4782"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For extra bonus points, directly calculate the number without generating any Fibonacci numbers. \n",
"\n",
"Hint: Read Knuth. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n = 1000\n",
"%timeit term_of_ndigits_fibonacci(n)\n",
"term_of_ndigits_fibonacci(n)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"100000 loops, best of 3: 1.93 us per loop\n"
]
},
{
"output_type": "pyout",
"prompt_number": 10,
"text": [
"4782"
]
}
],
"prompt_number": 10
}
],
"metadata": {}
}
]
} | mit |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/08_image_keras/flowers_fromscratch.ipynb | 2 | 10083 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Flowers Image Classification with TensorFlow on Cloud ML Engine\n",
"\n",
"This notebook demonstrates how to do image classification from scratch on a flowers dataset using the Estimator API."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import os\n",
"PROJECT = \"cloud-training-demos\" # REPLACE WITH YOUR PROJECT ID\n",
"BUCKET = \"cloud-training-demos-ml\" # REPLACE WITH YOUR BUCKET NAME\n",
"REGION = \"us-central1\" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1\n",
"MODEL_TYPE = \"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[\"TFVERSION\"] = \"1.13\" # Tensorflow version"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%%bash\n",
"gcloud config set project $PROJECT\n",
"gcloud config set compute/region $REGION"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Input functions to read JPEG images\n",
"\n",
"The key difference between this notebook and [the MNIST one](./mnist_models.ipynb) is in the input function.\n",
"In the input function here, we are doing the following:\n",
"* Reading JPEG images, rather than 2D integer arrays.\n",
"* Reading in batches of batch_size images rather than slicing our in-memory structure to be batch_size images.\n",
"* Resizing the images to the expected HEIGHT, WIDTH. Because this is a real-world dataset, the images are of different sizes. We need to preprocess the data to, at the very least, resize them to constant size."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Run as a Python module\n",
"\n",
"Let's first run it locally for a short while to test the code works. Note the --model parameter"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%%bash\n",
"rm -rf flowersmodel.tar.gz flowers_trained\n",
"gcloud ml-engine local train \\\n",
" --module-name=flowersmodel.task \\\n",
" --package-path=${PWD}/flowersmodel \\\n",
" -- \\\n",
" --output_dir=${PWD}/flowers_trained \\\n",
" --train_steps=5 \\\n",
" --learning_rate=0.01 \\\n",
" --batch_size=2 \\\n",
" --model=$MODEL_TYPE \\\n",
" --augment \\\n",
" --train_data_path=gs://cloud-ml-data/img/flower_photos/train_set.csv \\\n",
" --eval_data_path=gs://cloud-ml-data/img/flower_photos/eval_set.csv"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now, let's do it on ML Engine. Note the --model parameter"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%%bash\n",
"OUTDIR=gs://${BUCKET}/flowers/trained_${MODEL_TYPE}\n",
"JOBNAME=flowers_${MODEL_TYPE}_$(date -u +%y%m%d_%H%M%S)\n",
"echo $OUTDIR $REGION $JOBNAME\n",
"gsutil -m rm -rf $OUTDIR\n",
"gcloud ml-engine jobs submit training $JOBNAME \\\n",
" --region=$REGION \\\n",
" --module-name=flowersmodel.task \\\n",
" --package-path=${PWD}/flowersmodel \\\n",
" --job-dir=$OUTDIR \\\n",
" --staging-bucket=gs://$BUCKET \\\n",
" --scale-tier=BASIC_GPU \\\n",
" --runtime-version=$TFVERSION \\\n",
" -- \\\n",
" --output_dir=$OUTDIR \\\n",
" --train_steps=1000 \\\n",
" --learning_rate=0.01 \\\n",
" --batch_size=40 \\\n",
" --model=$MODEL_TYPE \\\n",
" --augment \\\n",
" --batch_norm \\\n",
" --train_data_path=gs://cloud-ml-data/img/flower_photos/train_set.csv \\\n",
" --eval_data_path=gs://cloud-ml-data/img/flower_photos/eval_set.csv"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Monitoring training with TensorBoard\n",
"\n",
"Use this cell to launch tensorboard"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from google.datalab.ml import TensorBoard\n",
"TensorBoard().start(\"gs://{}/flowers/trained_{}\".format(BUCKET, MODEL_TYPE))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"for pid in TensorBoard.list()[\"pid\"]:\n",
" TensorBoard().stop(pid)\n",
" print(\"Stopped TensorBoard with pid {}\".format(pid))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Here are my results:\n",
"\n",
"Model | Accuracy | Time taken | Run time parameters\n",
"--- | :---: | ---\n",
"cnn with batch-norm | 0.582 | 47 min | 1000 steps, LR=0.01, Batch=40\n",
"as above, plus augment | 0.615 | 3 hr | 5000 steps, LR=0.01, Batch=40"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Deploying and predicting with model\n",
"\n",
"Deploy the model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%%bash\n",
"MODEL_NAME=\"flowers\"\n",
"MODEL_VERSION=${MODEL_TYPE}\n",
"MODEL_LOCATION=$(gsutil ls gs://${BUCKET}/flowers/trained_${MODEL_TYPE}/export/exporter | tail -1)\n",
"echo \"Deleting and deploying $MODEL_NAME $MODEL_VERSION from $MODEL_LOCATION ... this will take a few minutes\"\n",
"#gcloud ml-engine versions delete --quiet ${MODEL_VERSION} --model ${MODEL_NAME}\n",
"#gcloud ml-engine models delete ${MODEL_NAME}\n",
"gcloud ml-engine models create ${MODEL_NAME} --regions $REGION\n",
"gcloud ml-engine versions create ${MODEL_VERSION} --model ${MODEL_NAME} --origin ${MODEL_LOCATION} --runtime-version=$TFVERSION"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"To predict with the model, let's take one of the example images that is available on Google Cloud Storage <img src=\"http://storage.googleapis.com/cloud-ml-data/img/flower_photos/sunflowers/1022552002_2b93faf9e7_n.jpg\" />"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"The online prediction service expects images to be base64 encoded as described [here](https://cloud.google.com/ml-engine/docs/tensorflow/online-predict#binary_data_in_prediction_input)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%%bash\n",
"IMAGE_URL=gs://cloud-ml-data/img/flower_photos/sunflowers/1022552002_2b93faf9e7_n.jpg\n",
"\n",
"# Copy the image to local disk.\n",
"gsutil cp $IMAGE_URL flower.jpg\n",
"\n",
"# Base64 encode and create request message in json format.\n",
"python -c 'import base64, sys, json; img = base64.b64encode(open(\"flower.jpg\", \"rb\").read()).decode(); print(json.dumps({\"image_bytes\":{\"b64\": img}}))' &> request.json"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Send it to the prediction service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%%bash\n",
"gcloud ml-engine predict \\\n",
" --model=flowers \\\n",
" --version=${MODEL_TYPE} \\\n",
" --json-instances=./request.json"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"<pre>\n",
"# Copyright 2017 Google Inc. All Rights Reserved.\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# http://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\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",
"</pre>"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
mne-tools/mne-tools.github.io | stable/_downloads/46108e44d5660a8696d75855ed89721d/hf_sef_data.ipynb | 2 | 2735 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n\n# HF-SEF dataset\n\nThis example looks at high-frequency SEF responses.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Author: Jussi Nurminen ([email protected])\n#\n# License: BSD-3-Clause"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import mne\nimport os\nfrom mne.datasets import hf_sef\n\nfname_evoked = os.path.join(hf_sef.data_path(),\n 'MEG/subject_b/hf_sef_15min-ave.fif')\n\nprint(__doc__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read evoked data\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"evoked = mne.Evoked(fname_evoked)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a highpass filtered version\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"evoked_hp = evoked.copy()\nevoked_hp.filter(l_freq=300, h_freq=None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare high-pass filtered and unfiltered data on a single channel\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ch = 'MEG0443'\npick = evoked.ch_names.index(ch)\nedi = {'HF': evoked_hp, 'Regular': evoked}\nmne.viz.plot_compare_evokeds(edi, picks=pick)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
MarcKjerland/Worst-Winter-Ever | worst-winter-chicago.ipynb | 1 | 632582 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Determining the worst winter ever in Chicago\n",
"===\n",
"\n",
"The object of this exercise is to take weather observations from past winters in Chicago and determine which of them could be considered the worst winter ever. Various criteria will be used, such as the number of days below zero degrees (F) and the number of days with heavy snowfall, and a Badness Index will be assigned to each winter using these criteria."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Collecting data from NOAA National Climate Data Center\n",
"---\n",
"\n",
"NOAA has some great weather records. These data points come from the weather station at Midway airport, starting in 1928 (measurements at O'Hare start around 1962). I pulled the dataset from NOAA-NCDC at http://www.ncdc.noaa.gov/cdo-web/datatools, specifically the Monthly Summaries data from CHICAGO MIDWAY AIRPORT 3 SW. The data is directly available here: https://github.com/MarcKjerland/Worst-Winter-Ever/blob/master/chicago-midway-noaa.csv.\n",
"\n",
"Here I've defined winter as November through March. Your definition may vary! Some of the variables would translate well to an expanded winter season. Further criteria could be added to highlight painfully long winters or miserable holiday travel conditions, for example.\n",
"\n",
"\n",
"In this first code section I do some data wrangling to prepare it for the analysis."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"# Read data, sort by year & month\n",
"dateparse = lambda x: pd.datetime.strptime(x, '%Y%m%d')\n",
"noaa_monthly = pd.read_csv('chicago-midway-noaa.csv', index_col=2,\n",
" parse_dates=True, date_parser=dateparse, na_values=-9999)\n",
"noaa_monthly = noaa_monthly.groupby([noaa_monthly.index.year, noaa_monthly.index.month]).sum()\n",
"# Fix \"suspicious\" entry in January 1930, based on a NOAA source\n",
"noaa_monthly.loc[(1930, 1), 'MXSD'] = 268 # conversion: 268 mm == 11 in\n",
"# Sum seasonal totals\n",
"winter_vars = ['MNTM','EMNT','DT00','DX32','MXSD','EMXP','TSNW','DP10']\n",
"year_start = 1928\n",
"year_end = 2014\n",
"season_start = 11 #November\n",
"season_end = 3 #March\n",
"noaa_winters = pd.concat(\n",
" [noaa_monthly.loc[(year, season_start):(year+1, season_end), winter_vars].sum(axis=0)\n",
" for year in range(year_start, year_end+1)], axis=1).transpose()\n",
"noaa_winters.index = range(year_start, year_end+1)\n",
"# Fix variables that should have been handled differently\n",
"noaa_winters['TSNW'] /= 24.4\n",
"for year in noaa_winters.index:\n",
" noaa_winters.loc[year, 'MNTM'] = \\\n",
" noaa_monthly.loc[(year, season_start):(year+1, season_end), 'MNTM'].mean() * 0.18 + 32\n",
" noaa_winters.loc[year, 'EMNT'] = \\\n",
" noaa_monthly.loc[(year, season_start):(year+1, season_end), 'EMNT'].min() * 0.18 + 32\n",
" noaa_winters.loc[year, 'MXSD'] = \\\n",
" noaa_monthly.loc[(year, season_start):(year+1, season_end), 'MXSD'].max() / 24.4\n",
" noaa_winters.loc[year, 'EMXP'] = \\\n",
" noaa_monthly.loc[(year, season_start):(year+1, season_end), 'EMXP'].max() / 24.4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Definition of variables\n",
"---\n",
"Here are the variables used to determine which Chicago winter was the worst. In the future I'd love to include others related to wind chill, cloud cover, high gusts, freezing rain, and other wintery hazards, but this monthly NCDC dataset didn't include them. Perhaps the daily figures are worth looking into.\n",
"\n",
"The units are American: inches and Fahrenheit.\n",
"\n",
"(Note: the max snow depth in 1929-30 appears to be incorrect, although there was a lot of snow that winter.)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ug+3cFgRBEPLLJl+Yt1p6uGxuRcrlzzb5qXbbOa42dbCfjuZAlOd2BrhyXgWr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\nWYoI5gUCgUAgEAgEglnK/w/2HhSiOJSAPQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fccff641150>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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6178mHA7zxBNP8O6776Zd3+v1UlpaitPpZO/evdxxxx0D3q+pqWHx4sWsWrWK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\nQboBOm8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fccfd26de90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Dzrc/fzrXAluwtvOfsBrTPh1/U6xeco6zt8FGrHSqe7EuIkqAr+VYTqXGHNmfXpnmTREf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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fccfd1d7dd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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hapRSRu6bQ6374vBMhtPdAPmwT5ZEZhO3FJES97feSOXpKY0xXyMjbiabXLpD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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fccfd078d10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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H3LhSMmwyCFaMpW3btnH77bezfv16Zs2aNVw2kyz1aG1t5f777+faa6+lubmZ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\nGo1Go9EsUbQzr9FoNBqNRqPRLFH+f3XNVuchGQR1AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fccfd1a7090>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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dO3cO7HfOnDkjvo/UNq2trYMcagDmz59Pa2trLocGgJkzZw7cLykpIRgMEo/H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\n4XA4HE6D8v8BS1BEXPEbDUEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fccff40d510>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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t2rU8/fTTVFdXc9lll3HPPffEDlD6Lt/f+hO9dvS2x+Ph4Ycf5n//938pLy/n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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fccfd0c7510>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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/fj0HH3wwAGvWrOEd73jH0Pd1dXXsu+++vPTSSyN+O2vWLNrb2/nZz3429Nnt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"text/plain": [
"<matplotlib.figure.Figure at 0x7fccfcfebe10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"acronym = { 'DP10': 'Number of days with greater than or equal to 1.0 inch of precipitation',\n",
" 'MXSD': 'Maximum snow depth, inches',\n",
" 'EMXP': 'Extreme maximum daily precipitation, inches',\n",
" 'DT00': 'Number days with minimum temperature less than or equal to 0.0 F',\n",
" 'DX32': 'Number days with maximum temperature less than or equal to 32.0 F',\n",
" 'EMNT': 'Extreme minimum daily temperature',\n",
" 'TSNW': 'Total snow fall, inches',\n",
" 'MNTM': 'Mean temperature'}\n",
"# Plot variables\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"for v in noaa_winters.columns:\n",
" noaa_winters[v].plot(figsize=(13,3), color='skyblue');\n",
" pd.rolling_mean(noaa_winters[v], 20).plot(color='blue')\n",
" plt.title(acronym[v])\n",
" plt.legend([\"observed data\", \"20-year rolling average\"], loc='best')\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Badness Index of each winter\n",
"---\n",
"\n",
"To determine the badness index for a particular winter, first we assign to each of its variables a score from 0 to 100. A score of 100 means it was the worst or coldest recorded value (for example, more snowfall than any other winter) and a score of 0 means it was the least bad or warmest recorded value (for example, less snowfall than any winter); otherwise the variable will get a score somewhere in between (on a linear scale). Then each winter is assigned a badness index equal to the average of each of its variable scores, ranging from 0 to 100."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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g06ZNvR4338fKNe6UVatWccstt6Qfq7m5mffee49169bl/Zr1xwIBY4wxph9t\n8SRjq4LELBAYFVSL8zW4x1bOO+88Nm3axP33308wuKMWZb/99kvPfoO7QvDWW2+x3377seuuu9Le\n3k57e3s6WKiurubkk09m6dKlLF26lHPOOSd932nTpnHbbbexbdu29FdnZyeHHXYYzz77LDfddBO/\n+tWv2L59O9u2baOpqanXCXWuDj+ZMm8zefJkVq9e3esYq1at6nUCnM8x+zNp0qR0IJPy7rvv5jzu\n5ZdfTjAY5OWXX6a1tZW77767V1HzSSedxPLly1mzZg0PPvggZ5xxBuCepFdVVbFly5b0a9fa2tor\nb7/vY/b3WLnGnTJt2jSuuOKKXr+rjo6OohWKWyBgjDHG5KCqtMccxlYFiSctEDCldeGFF/L666/z\n8MMPU1VV1eu6E044gZdffplly5bR09PDNddcwwEHHJBOHcrmnHPO4fbbb+fhhx9OpwUBXHDBBXzn\nO9/h1VdfBdyVhV/96lcAtLe3EwqFaGlpIRaLsWjRop1WIgbSdxb+sMMOo7a2lhtvvJF4PM7y5ct5\n9NFHOe2009K3zzVzn6/DDz+cYDDI9773PRKJBA899BB//vOfc96+o6ODuro6GhsbWbNmDTfddFOv\n68ePH8/MmTM599xz2W233dh7770B98T96KOPZt68ebS3t+M4Dm+99Va/ex3091gDjfv888/nRz/6\nES+88AKqSmdnJ7/+9a9zpoQVygIBY4wxJoeuhBIOCrWhgKUGmZJatWoVt912GytWrGDixInpVJ9U\nekpLSwv3338/V1xxBWPHjuUvf/lLr7z0bI444ggCgQAHHXRQr3STWbNmcemll3LaaafR1NTEBz7w\nAX77298CcMwxx3DMMcew1157MWPGDGpqatKdjKD/nv+5bhMOh3nkkUd47LHHGD9+PF//+te5++67\n00FM39vnOn62x079HIlEWLZsGT/72c9obm7mnnvu4bjjjiMSiWQ91oIFC3jppZdoamri85//PCee\neOJOxz7jjDN48skn06sBKXfddRexWCzddenkk09m/fr1OcfY32MNNO6DDjqIn/zkJ3z9619n7Nix\n7Lnnntx1111Zn9NgyFAjsFIREfXr2IwxxowO6zrjPL66g2n1YerDAT4yobbcQzJDJCIjtmd9Np/+\n9Kc544wz+PKXv1zuoQy7j3zkI1x00UXMnj273EMpyGDHnetv27s8a3RlKwLGGGNMDq1xh8ZIkEhQ\nrEbAVJyY5KzlAAAgAElEQVQ///nPvPTSS77YeGw4PPPMM6xfv55EIsGdd97Jyy+/zDHHHFPuYQ2o\nnOMODcujGGOMMRWoLebQFAkQDghdCQsETOWYPXs2Dz30ELfeeit1dXXlHs6wWLlyJaeccgqdnZ3s\nvvvu3Hfffb26DPlVOcdd0tQgEdkbyExg2w24ClgK/A8wHXgHOEVVt/e5r6UGGWOMKavfv9dBYyRI\nUGBzT5LP7Fpf7iGZIRptqUFm9PBdapCqrlTVA1X1QOAgoAt4ALgM+J2q7gU86f1sjDHG+EpbzKEx\n7K4IxKxrkDFmhBnOGoFPA2+q6mrgeOBO7/I7gVnDOA5jjDEmL20xh8ZIgEhArGuQMWbEGc5A4DQg\ntZf0BFXd4H2/AfB/ApcxxphRpy2epDESJGyBgDFmBBqWQEBEIsDngV/1vc4rBLB3V2OMMb4Sd5Ro\nUqkLCeGgBQLGmJFnuLoGfRZ4UVU3eT9vEJGJqrpeRCYBG7PdaeHChenvZ86cycyZM0s9TmOMMQaA\ndi8tSEQsNWiEGWhDLGMq2fLly1m+fHletx2WDcVE5F7gMVW90/v5RmCLqt4gIpcBY1T1sj73sa5B\nxhhjyuZfbTGe39DN6Xs2saUnwX1vt/G1fceWe1jGGFOQsm4oJiJ1uIXCyzIu/i7w/0TkDeAo72dj\njDHGN9ri7ooA4NUIlHlAxhhTZCVPDVLVTqClz2VbcYMDY4wxxpfaYsk+gYCtUhtjRpbh7BpkjDHG\nVAy3dWgQwK0RsH0EjDEjjAUCxhhjTBZtMYemsPsxGQy46bVJWxUwxowgFggYY4wxWbTGkukVAYBw\nUIhZIGCMGUEsEDDGGGP6UFXa4w4NkR0fk1YnYIwZaSwQMMYYY/roTChVQSEc2NFxLxzAAgFjzIhi\ngYAxxhjTR1ssSWM42OuyiLUQNcaMMBYIGGOMMX20xXbsIZASDliNgDFmZLFAwBhjjOmjNWMPgZSw\ntRA1xowwFggYY4wxfbi7CvdODbJiYWPMSGOBgDHGGNOHpQYZY0YDCwSMMcaYPtpiyfRmYimRoK0I\nGGNGFgsEjDHGmD4sNcgYMxpYIGCMMcZkiCWVeFKpDUmvy8MBLDXIGDOiWCBgjDHGZGiPJ2mIBBDp\nHQhErGuQMWaEsUDAGGOMydAWc3baTAxSqUFlGJAxxpSIBQLGGGNMhmwdg8BqBIwxI48FAsYYY0yG\n1vjOm4kBhK1rkDFmhLFAwBhjjMngrgjsnBoUsX0EjDEjjAUCxhhjTIa2mEOTpQYZY0YBCwSMMcaY\nDG2xZD/FwhYIGGNGDgsEjDHGGI+q0h53aMiyIhCxQMAYM8JYIGCMMcZ4OhNKVVAIB2Sn68IBiCfL\nMChjjCkRCwSMMcYYT1ssmbVQGNzUICsWNsaMJBYIGGOMMZ7WmENjOPtHY6p9qKoFA8aYkcECAWOM\nMcbTFktm7RgEEBRBBJIWBxhjRggLBIwxxhhPWzz7HgIpVjBsjBlJLBAwxhhjPO5mYrk/Gq1OwBgz\nklggYIwxxnjcYuH+A4G45QYZY0YICwSMMcYYj1ssnDs1KBzAUoOMMSOGBQLGGGMMEEsqCUepDe28\nh0CKpQYZY0aSULkHYIwxxvhBW9zdQ0AkdyAQCQpxZxgHZYwxWcydfxWtsZ13OGyKBFly/bV5H8cC\nAWOMMYaBC4XBqxGwFQFjTJm1xpLsfda8nS5fuXRxQcex1CBjjDEGLxDIsZlYiqUGGWNGEgsEjDHG\nGFIdg3IXCoPtI2CMGVksEDDGGGPwOgblkxpk7UONMSOEBQLGGGMMqWJhqxEwxoweVixsjDHG4NYI\nNA2QGhQOQEfcAgFjTHk1RYKsXLqY9zoS1IWF5qpg+vJCWCBgjDFm1HNU6Yg7NAxQLBwJCvHoMA3K\nGGNyWHL9tTiq3Pi3LezbXMXxMxoGdRxLDTLGGDPqdcYdqoNCKJB7DwGwrkHGGP9IvRd1DGFzk5IH\nAiIyRkTuE5HXRORVEfmIiIwVkd+JyBsi8oSIjCn1OIwxxphc2uLOgB2DwGoEjDH+EfMaF3QmfBwI\nAP8F/EZV3w98EHgduAz4naruBTzp/WyMMcaURT6biYEFAsYY/4g5SnVQ6PTrioCINAEfV9WfA6hq\nQlVbgeOBO72b3QnMKuU4jDHGmP60xZIDbiYGto+AMcY/YkmlKRIg5ijJQb4vlXpF4H3AJhG5XURe\nEpGfiEgdMEFVN3i32QBMKPE4jDHGmJzcPQTySw2K2T4CxhgfiDlKVTBAbSgw6PSgUgcCIeDDwA9U\n9cNAJ33SgFRVAXtXNcYYUzaWGmSMqTSxpBIJCHUhGXQgUOr2oe8B76nqn72f7wPmA+tFZKKqrheR\nScDGbHdeuHBh+vuZM2cyc+bM0o7WGGPMqNQWT+bVfzscxAIBY4wvxBwlEhTqCNCZsb/J8uXLWb58\neV7HEHdCvnRE5BngK6r6hogsBGq9q7ao6g0ichkwRlUv63M/LfXYjDHGL+bOv4rWWHKny5siQZZc\nf20ZRjS6LPn7Fr76/mZqB6gT6Ek6/ODlbcz70LhhGpkxxmT30qZuNnYnSaoytT7Mh8ZVZ72diKCq\nWXsjD8eGYt8A7hGRCPAW8CUgCPxSRM4D3gFOGYZxGGOMb7XGkux91rydLl+5dHEZRjO6RJMOCUep\nCfW/hwDsKBZWVUQGvr0xxpRKakUgKIPvHFTyQEBVVwCHZLnq06V+bGOMMSaX1CpM3FE29yR5pdb9\nSOxvFSYgQkAgoRC2OMAYU0apGoGqoLA9y4pyPoZjRcAYY4zxncGuwoQDQjyphAfYhdgYY0op5iiN\nkQD14QBrOuODOsZwbChmjDHGjBiRgBCzgmFjTJmlVgRqQ0LHIFODLBAwxhhjCmAtRI0xfpCqEagP\nB+hKDO49yVKDjDHGB5oiQVYuXUxrzKEt5rBr/Y58dVMa3YP84AwHLRAwxpTfjn0EAv4tFjbGGDOw\nVHHqb1a18/etUS754FiqgrZoWyr/aouxNTq44rpwwPYSMMaUX2pFoCooJFSJO4XXLlkgYIwxPpLa\nHTKaVKpsMaAk3u2I88iqdmY0hLMWBg+0CmM1AsYYP4h6KwIiO1YFxhT4wWGBgDHG+Ehqd8iepNJY\n5rGMRGs74zz4rzaOn9HAN2/69qCO4dYIFHlgxhhToJijVAXdFYC6cIDOROGBgK07G2OMj3QmHBrD\nAaJJm3EutvVdCe57u43PTWtgRkNk0MexYmFjjB/EHHdFABh0nYCtCBhjjE+oKp0Jh13rwvQkbcp5\nqFIbhoGb07+xO8nYqiBv14ZybhiWj9Q+An6S+Vwz9bc5mjGmssWSbo0AQF1Y0qmlhbBAwBhjfKLH\n26Sq3lYEimKwG4YNJOLDFYFSPVdjRptKCaqTqjgKIa822F0RKPx9yQIBY4zxic64Q10oQFVQLBDw\nsXDQioWNGakqJahOrQaI7KgR2NxTeCe0igoEKiVKM8aYwehIONSFheqg0GOBgG+FA0L3IJbgjTGm\nWDLrA8BdEVgVjxd8nIoKBColSjPGmMHoimt6RaBzkJtdmdKzfQSMMeWW2kwsJdU1qFDWNcgYY3zC\nXREIUBUMWLGwj0WsfagxpsxSm4mljIquQWoTMMaYEazLqxGothqBomiKBHnlrsVs6kkwqTbU6/Kh\nCPtwQ7GmSJCVSxezuiPB1PoQG7oSNEQCTK4Nl3toxpgS2HlFYIR3DWqLJdnQnWCfcg+kSKzewRjT\nV0fCYdfqMFVBocdSg4ZsyfXXsqYzzu/f62T23mOKdlw/tg9dcv219CQcfvjqNi754Dg2dCW4961W\nvlTE523MaNAUCfLXO25hS0+SXWqC6Q27hjqBUGx9VwRSQUFmS9F8VEQgsK4zzrJ/tVMXGjmZTFbv\nYIzpq9eKgM9mnCtVT0KpKeBDMR9+3VCsJ6lUe891Qm2IQ8bX8OtVHZy2R2O6s4gxpn9Lrr+WFzZ2\n89SaTk7crYE9m6rKPaSs+q4IiAi1IbdOIBLMP2jxfSDw2rYoT7zXwbHT6vlnfZiVSxfjKKzrSjCl\nzh2+36I0Y4wZjI6EQ304QCQgRK1GoCi6kw41RZ5E8nMgUJUR9Bw2oYa32mK8uKmHg3epKePIjKks\n7V7GRsxnK3+Z+q4IANSH3TqB5qoREgic+I3L6Ig7vK8xzJwbv51OmUk4yuIVW/jWAeNslsMYM2J0\nxh1qQ0JQrH1osfQklOpQcT8nIj7dRyCadHoFAgERPjetgbvf2M6MxjAt1b7+yDfGN9rjju/3c+m7\nIgBQGwrQUWCdgK9zbQ758r/zya/9x06/iFBAEAFLoTXGjBSOKt2JHe1DowlFrUPCkHUnnXS6TLH4\ndUUgmlSqg70/1sdWB/n4pFoeXdVB0v6ejMlLe9yhpTro70Agx4pAV4Gdg3wdCPTH75GaMcYUojvh\npnUEA0IoIAQEa1FZBN0JpSZY7NQgf+4j0Dc1KOXAlmpqgsKf1neXYVTGVJ72mBcI+PD/eUo0y4pA\n3SBWBCp2ndANBNx82krUFAny+tLFvNeRoDESoCkSSF9ujBl9Or09BFKqgwGiTmFFX2ZnPcnipwaF\nvX0EVNVX6anRHIGAiPDsT27ijdYY46uDvWYRrVOdMb05qnQkHMZVh9ge3bm7o19kWxGoCwvruwob\nc+UGAoGAr4s4BrLk+mtpjSX54SvbOGR8NZ+aWl/uIRljyqjT6xiUkkoParA28EPSk3CKviIQECHk\nrdj4ae4mmtE1qK/OhMNRX/uPnS63TnXG9NaZcKgJCrUhYUOXf88zY0mlKsuKQKG70lfmdDojIzWo\ny1u+8WPRmTFmePVdEagKWsFwMXQnlZoirwiAP+sEepIOVUUOeowZbdpjDg3hoNe9zV//xzPFHSXc\nNxAIF767sK9XBFIzFdnSZaqCQo/P3oQL1RV3x1/JKxvGmOJwVwR2vKnb7sLF0ZN0diqgLYZw0H+B\nQK7UIGNM/triDg0Rr2mD499CraypQd4+AoXwdSBw+y3fyXldJCgVfwLdlXDbU9mKgDGmM6FZVgT8\n+yFUKboTpVkRiAT8997dX2qQMSY/7opAgKqgv1PQs7UPTa0IFFK/VLFriCMlNWhMJOC7DxNjzPDb\nuUYgUPHvceXmqJZsltyfqUG2ImDMULXHHRpTKwI+fg+OOjv/fw8H3H1oChm3r1cE+uP3X1A+uhJK\nc1WQbT6uSjfGDI+duwZV/ntcuUWT7tJ5oASdffwYCET7SYNqigTT6bZrOhNMrA0RFOtUZ0xf7bEk\nE2oiVAXE1+1Ds60IgNs5qDPhUJ3njuqVGwgEhI4Kb7LtrggE2dCdKPdQjDFllq1rkBULD01PUqkp\n0Qy5H/cS6G/1I7NF6NI3tnPk5Dqm1VtLKmP6ao97xcLeZIzf2gSnZKsRAK9OIK6Mq87vOBWcGhTw\ndaSWj66Ew5iqoK9z0Iwxw2OnFYGQrQgMVXcBs2KFigSEuM8Wc3vyrBFoigR93R/dmHJq94qFQwFB\nAD++DSccBYVs/93rwoUVDFdwIFD5H5JdCWVMJGC7hxozyjmq9CSU2oyi1qpgwIqFh6i0KwL+Sg1S\nrx4i2wxhX01VAVpjFggY05eq0hF3i4XBv+eaqdWAbCsV7orAKAkEKn0mvTPh0FQVJO64S0/GmNGp\nK+HufpuZy17l8x7WlaA74VBTohWBsM86vsUdCAUgmEcKw5hIkO1RCzKN6asz4abXhbzce7/uJZCr\nPgBKsCIgIp/OctnsvB+hRPwapRWiy8sJDgVsUzFjRrOOPvUBYKlBxdBdwnaaflsRKGS/hDGRoK0I\nGJNFezyZXg0AfNviPVd9AEB9CVYEFojID0WkTkQmisgjwPF5P0KJ+DVKy1dqNSMc8HJNbXLGmFGr\nq099AFixcDH0eCstpeC3QKCQNqljqgK2ImBMFqldhVP8mqLZ34pArdc1KF/5BAKfAN4GVgDPAv+t\nqifm/Qgl4u745p834UJ1JRxqQwFEZERsjmaMGbysKwLBAFEffgBVku6kQ00JdhUG/20oVkggUB8O\n0J10fBXIGOMHqULhFL+enw28IpD/mPN5h2wGDgHeAmLANPFBHyU3NahyPyS7Eg613gyg3z5QjDHD\ny1YESqMnMZpSg/J/rgERGiMB2iw9yJhe2mJO79Qgn2afDGuNAPAn4Leq+hncgGAK8Me8H6FEIgEh\n4bjdNipRV0aHkEiFBzXGmKFxVwR6v6mHBBSvTZwZlJ5kCYuFAxD30QlCNOlQVcDqhxUMG7Oz1K7C\nKX7NPulvRaA25AYC+TahyWdDsf+nqqsAVLUL+IaIHJnvYEslM6WmVDmgpZRKDQKrETBmtOtKKBNr\ne5/EiUh6d+FQjpkf07/uhFJTwhoBP63k9hSQGgTeXgK2ImBML26xcFX6Z792qOxvRSAUECIBd0U5\nn/e/fKYPNovIVSLyEwAR2RNoLGTApVIVqNyl876BgB//0Iwxw6Mj7lCfZebaTQ+yWYLBKiRdplB+\nm8CJFvhc3YJhCwSMybRzsbBPU4P6WREAdy+BjjzfoPIJBG7HrQ34qPfzWuDbeR0dEJF3ROTvIvJX\nEXnBu2ysiPxORN4QkSdEZEy+x8vk17ZO+ehKaDoVIFLBz8MYM3TZagQgVTBs7w2D1Z0oXbFwOOiv\nGoFCioUh1ULUR5GMMWWmqjsXC1dgjQAUVieQzzvk7qp6A24wgKp25nXkHRSYqaoHquqh3mWXAb9T\n1b2AJ72fC+bXSC0fnfEduavuH5q9IRszWmXrGgRWMDwUququCIyS9qE9SaewQKDKUoOMydSdUMIB\nIZxxgu3XCeeBVwQk770E8gkEoiJSk/pBRHYHonkdfYe+oz0euNP7/k5gVoHHAyo7EOhK7PjgjwT9\ntcRsjBk+SUeJ5cjlrOT3uHKLejNmgRI1ufNbtzc3NSj/1Y+mSIDWaP4FhcaMdG3x3h2DwN1HwI8T\ntdG8VgTy+7+dz7vGQuBxYKqI/AJ4Crg0r6O7FPi9iPxFRM73Lpugqhu87zcAEwo4Xlolz6S7NQI7\ntrD20weKMWb4dGbsKdJXtQUCg1bK1QDw34pAoalB1UEBwVacjPG0x5O9OgZBqquj//6P5FMjkO+K\nwIBdg1T1CRF5CTjMu+ibqro5r6O7jlDVdSIyHvidiLze5/gqIllf5YULF6a/nzlzJjNnzux1fVUF\n5892JXTHPgJBYZu1cTNmVOpMONSFs7+h+3VXy0rQnXRKVigMXiDgo8+fQrsGiQhNkQDbY8mStVg1\nppL0LRQGf+8jUNXPisCr//ccT/7hDzw9pirnbVJyBgIichDubH7KWtwUn2kiMk1VX8pnsKq6zvt3\nk4g8ABwKbBCRiaq6XkQmARuz3TczEMimUpfNVXXnrkE+mlkarebOv4rWLDmzTZEgS66/tgwjMqNB\nZ1yz1geArQgMRU9CS3qCGw5AQt33cx/ssVlw1yDwCoajDpNqSzQoYypI30JhqMx9BMCdPK/f7yOc\nukcTANdcc03O2/a3InALbiBQAxwE/N27/IPAX4DDBxqoiNQCQVVtF5E64GjgGuBhYDZwg/fvgwMd\nKxu/FnEMJOYoASFdkOLXLaxHm9ZYkr3PmrfT5SuXLi7DaMxo0ZmjYxC473HtVkA0KN1JpaaEKwIi\n4m4q5kAkOPDtS63QDcXACoaNyybBXG0xh+kN4V6X+XYfAad4XYNyBgKqOhNARJYB56vqP7yf98c9\nmc/HBOABb7YkBNzjpRr9BfiliJwHvAOckufxeqkKSt59Uv3E3VW4d3uqSgxo8mVvMsbk1hnvPxCw\nFYHB6U44VJc45SW1qVh/M3PDpdDUIHALhjd1WyAw2tkkmKs97tAYzl4j4JeVv5RYcoAagXD+XYPy\n2Vl4n1QQAKCqL4vI+/M5uKr+Czggy+VbgU/nNcJ++LW/60Ay04Jg5G8oZm8yO1hQZPrqTDiMyTGl\nXO3TjhWVoKfEKwLgn4LhhDeGQmujx0SCvNkaK8GIjKk87fHkTqlBQRGC4p+Vv5SBVgRqQwG6E4qj\nOmDntHwCgb+LyE+Bpbg1AmcAKwoYb8lU6mxZZscgsA3FRhMLikxfnXGHKXXhrNfZPgKD151waCzx\nJ7dfAoFUx6BCZyzHVAUsNaiEbOKncqhq1mJh2JGG7oeVP3DHOtCGYkERqkJCd0JzNqNIyScQ+BJw\nITDH+/kZ4Id5jrek3CKOypst64qPrtQgY0xunQknvct4X1YsPHg9SWWXEn9wR3wSCPQknYL2EEhp\nigRpizl5zRqawtnET+XoSSrBgGQ92U/tJVCfI4VzuCUUAgLBfgIBgPpQoN8atJR82od2A4u9L1+p\n1PahmZuJgbtV/UhODaoUTZEgK5cuZnVHgkgAJtSG0pcbUyqdce23RsBWBAanO+Fk3aStmPzSQrTQ\nPQRSQgGhJhSgPe7Y+5wZ1dpiO9cHpPhtL4GBVgNS6sLeXgI1/d9uwEBARD4GLABmZNxeVXW3AUdR\nYpWaGtSZ6L17XWpWyW/FKKPNkuuvpSPu8L2Xt9JcFeBr+44dtsfeHk2ScJRQHv+5zcjS2WdiIJP7\nHld5q55+4NYIDE+x8FANNYVksIEAwBhvh2ELBEav1CTYlh73c2g0ToK1Z9lVOMVvewnkm6ZUFwrk\n1VAnn9SgnwFzgZcAXyUTlioQKHVeX1dCmVi74w8uIELIR23oiq0pEuT1pYt5ryNBbUgYVx1MX+43\n26JJmqvcIpvhlFC4Y+V2Pj+9If0mbEa+hKMknNz93yMBIeFAUpWgTRIUpCdR2p2FAa996NDfK4aa\nQtIziD0EUpoibgvRaWSvUzHFl2dXx2GTOq/51VutbI85nP/+5jKPaPhlKxRO8dteAoWsCHTl8ceW\nzxnHdlV9LI/bDbsqr9tOsWfSS53X17drEOyoE/BLMUoxLbn+WrZFk/z41W3MaAhzmrfBhR9tjyaZ\nWBPi9e2xkuTNpmZeNvckqQsF0qkLuzdG+MguNdz7Visv/PzmrP/JrcBs5EmtBuR6/xKR9IRHbYlP\nakea7qRT+hWBoOCHDtZDWhGwguFht6E7war2GNMbIuUeSi/RpI7aNOVchcLgv70E8l8RyK/Ffj6B\nwB9E5CZgGRBNXZjvzsKlFAwIAXFnUwcoivaVbIFAONVCdIROyrTFklQF8u9rWy7bY0maq4PpnMBi\n5xinTuR/+tq2rLP/u9aH+W00yZHn/8dO97UCs5GnM+5QO0AhV3U6EBimQY0AququCAxDsbAfGj0M\nZjOxlDGRIO+0x4s8IgM7Jn7WdiaYUBsi9ec4rT7MQ++0c+SkOg5oqS7vIDOM5kCgLe6wa332E7BK\nrhHYmMc+Ifl8tByGu8PwwX0u/2Qe9y251GxZuIJyq90NxXqPd6S3EG2LOUyoDbGlJ1HuofRre9Td\nWbDGK9KsKcHJl6qyPZqkuWrn2YcxVUEmlOJBTb/K1eavI567Y1CKWzDsAP5LpfOrmFdvM1BXjaHy\nS/vQIaUGVQXZvqWnyCMysGPi5+a/bWbOB8f1Ok/Z2pPkrHmXEwmw02dBuVZ/o44SHaX1iu39FAuP\n+hqB1A7DfhXxiun80tZpIKrab2rQSNUed5hYG+K9jrivW9W1xpKMqaqmJhSgO+FkPVkfqo64Q1Uw\ne5syUx7lavPXlcjdMSilUrujlVP3MKwGQMZKbplFkzroz8BUsbApjVSg2Heycmx1kPHVQd5/tn/a\ni6b+lmPO4FPNKlW/xcLB/FJsUko9sVRQ16Ch1AiIyNmqereI/BvuikD6KtyuQb7IU6i0D8loUgmL\n7NQdJuKzHLRia4s57FITpCqY3wYX5bI96jAmEqDaG2cpbIuWJsAwlacj7lCfo2NQSrW1EC1YTwnS\n+rIJBwo7QcilKRLktbsXs6YzQX1Y0u8P+TZU6BlCjUBDOEB30iHuVNbKeqXoTjhU5/g/7qeXW1XT\n6bBuIFDuEQ0fVR2gWDjA1mj+dTSlnliKFrAiMKRAAKj1/m2gdyDgK6VYsmmKBPn7nbewpSeJAlPq\nitdKqyuR/QNqpK8ItMWT7NEUSUeoA82ClkPcUbqT7qxATSjgpWMU37ZYkjGj6V3W5NSVcNJdtHKp\n1DbJ5dSTGNwGW4Uq1oZiS66/lk3dCX72+nb2aorwxd0aC7p/dJAbioFbkN4YCdAaS9JSbWmJxdad\nUGoqYHY97kBQoCYYGNH1itlEk4ogOets/PYeHEsqVXlEkTUhIZpQktr/2HP+r1fVH3v/LixwjMOq\nFG2dllx/LW9sj7JiSw8bupKctVdT0U7cunKcBEeC/tiYplTcivwAtaEAXXlscFEOrdEkTZEgIuKu\nCJTo95GrPiAlVWDWGnMQoNGbpfBju1UzNB1xh2k5CtRSqoNCj9/6Dfpc93CtCASLVyPQFnOIBCSv\nGby+htI1CNyC4daog4/qVkeM7qRDzQCrfn6QSgfyW2HscGiLO+nP2WwiPqwRyKeLXECEmpAM2EK0\n4sP/UkVq0aRSHQwwpV5Y0xkvWiDQmcj+phAJ+KtPbbG1xRyaIgHqQoP7oBsO22MOY6rc301NSOgp\nWWpQkr3GVOW8PpVD+PyGLroSylFT6koyDrNDMdI7BiPXxECmSkt/9IPuYVoRKNY+AuCumk6oDQ7q\nb3EoqUHgNimwFqKl0ZMjC8BvokmHSFDctuwj+Fwkm9REZS5VPmvmEksqTf0ELpnc3YUHuSJQKUoV\nvfYk3c1omiIh1nQm2K9Im8x2JbJ3CYn4pOisFKJJBwf3g8pNDfLn89weTTLGm3WvDpaut/a2aJLm\nPP4TVwWFbQXkJZrB6Yg7tMUcXr7rFjriDqGApD8USr0K43YNGigQELbH/Bk8+9Vw1ggU6wShLeYw\nqTbM3zYX3sEnOoSuQeAWDG+395qS6E46OX83qdVfgDWdCSZ6LUbLsfrrrioFRuWKQH+FwuC/1KC4\nk1+xMHh1AgNMLlR8IFCqjR5SS61T60K8vLV4rdXc1qHZU4OGezfb4dIWc2j0Um7y+aMsl+0Zufs1\nIXpGb94AACAASURBVGF9V/F/H27r0PyKhW0meHg8+V4Hl1+9gE9Mrhv2VRi3Xqb/N3Q3Ncj+DgrR\nnRieTnJujUBxjtUWc1sXJ1ULLtwdampQUyTIe53+bu1cqboTmjM1KLODzE9f28YXZjQwvkzto1N5\n5yN5UjKX/gqFofAJ51SA15lw2NrjMLmuuAFevsXCkF/noAH/4kRkLnA70Ab8FPgwcJmq/javUZRY\nVZG6NvTVk3RPXifUhNgWTQ5pw5ZMXQknPeucKRIQWh1/niAPVVtGf97acIAtUX9uXrM9uiNfuzro\ndtIotu6EIkLOLhKZ/Na7eCR6uy3Guq4Ex05vAKAxEmRdV3SAexWHuys6A87s+G02qhL0JJWWmuFI\nDSpijUA8SWOkKj1Zkm86quMFDvnOEGYzpiqYtd2hGbp8g9Jydwfr8U4u/ZYGMxzaYw6T63LXalUV\nGBylArwXNnbz1JpOzn//GMYVsRA/32JhKN6KwJdVdYmIfAYYC5wN3A34IxAIBtjSU/w3sNRSazAg\nTKgJsa4zwYzGoW8H3hV3mJxli9CR3D60Pe6ko+26VLGwD/VdESjFLOy2WP+FwpnsBLC04o7y29Ud\nfGbX+vTsa2M4QNsg0nAG0zc61T1roI17qoOl62A1UnUnh6dTSzhQvCYPbh1VkNpwwJ0wyvN9IrUa\nMJQNoNzUIGdUbiRVat1JZXweQWm1t3dNucSSo7tYeO9+grXUhq+F/v9I/T6LfW6X74Zi4K4ItA0Q\n5OcTCKQe7XPA3ar6sp/eKErRNQh6F19NrQvzXrECgVypQSO4WLgtlqQx7H6o1YX9WSysqu5mYt5q\nTU2JVgS2DdAxKJMFAqX1x3VdTKkLs1vG/+umyMBvmtkMpm90Zx71AVD8v4Ny7aI8nHpyNGUotmK1\nD1VVOrw85UIbKgw1LQjck1CR4autGE26E05er2m5VwRSf0dVI/hcJJf2AboGBUTS9UCF/F9Ldesp\n9uuZ74ZiAPWhAOs6+8/CyCcQeFFEngB2A+aLSCPgmzO50nYNcl/oKfUhXtpUnDqBbLsKw8huH9qW\n0SLR3eDCf8+zM+Hm5Kai7OpQaWo2tkWT6c5EA7FAoHQ2dif4+9YevrxPc6/L68IBepJKwtGdNv0r\ntnz30yj230G5dlEeTj1DLJ7NV7hIaRSdCfcEIxQQb9U0/2MOtWNQypiI2yChElpdVpIerwPhQGr8\nEAh4n4HtPl21L5WBugbBjvfhQhpIdiUUobwrArVhoWOAiYV8/sd/GZgPHKyqnbjbTHwprxEMg1IV\nC/dk1ARMqQ2ztiuBDrApQz66Eg61WYoDR/KGYm6xsFcjEHKXvYvxWhZTZscgcGdnokkt+ji3Rx2a\n8ywYcle7Rtcb8nBQVR5/t4MjJ9XtlLsbEKE+HBiWD8J8VwSqLSAsWPcwrQiEBJLq5ukPRVssSaP3\nvlCbR3Ffpp4hbCaWqSkSZHvU3m+KrTvP9qHVoUBZ9wtJ7yMwymrTokkHZeBgejApU10JNy262Od2\nha4IDDSxkM+KwOHAClXtEJGzcYuFl+Q1gmFQqoLKzBml2nCA2pCwqSfJLkOo6FdVuhNKbZY37ZFc\nqZ+ZGhQKuEtsfluC3t5nt9+A7MiVrC7iOLdFkxyY5649kYCQcCCpStBH6XiV7m9behCBD43LvpdD\napfVfFO4BiufjkGwo4e1o0rA/g4GpKp0D9OKgIik9xIYyqx8ZkOFulBhrYuLkRoEVjDcV7FS6LqT\nDjV5BGrVQWFzGWfio0mH5qrQqCsWdlcDggPm/g9mf4WuhDvxV8xzO001ByigRmCgFYF8zmp/BHxQ\nRD4EzMPtHHQX8Im8RlFiw5EaBDClLsyazviQAoGepBL2CpD7iozQ/3yq2qtYGHZUsftpCXp71GFM\nnxzBGm934SIW+xdUIyBeMBLzWdBUaTI/0JMK67sS7FIT5MXqUNYP9MZwcFAFw4XqjCu71Az8t5D5\nd1DMoHSkijsQFEqe2pUS9lqIDiVuzNzZtC4UYM0AOb2ZihYIRAJs7LZAIKUYKXSqSjTPFYGaYICe\nRPk66o3WfQQG2kMgZTDnml0JZVp9cVcEUu9v+U4KVeex+3k+pzgJVVURmQV8X1V/KiLn5TWCYRAp\nQfqEqrpLLxlvrm7BcJwDWwZ/3M5E7lSAkZoa1JVwX8fMntipguEhvJRFtz2WTNcxpKS6OBRrZrgn\n4ZBU8toaPCX15lOm1tIjQt8P9H29f3N9oDd5KwKFSPWN3tqTpMrLsR1T5bYfzsVdEcjdsi5TlZc/\nXMygdKTqLlKqTL6KUTDcOzVI6CqgPqlY9RBjqoK80Rob8nHMDlFv8i+fk7bqUHlrBFItKUfbzsJt\nfSYqcyk0QEqqEk8qDeFgUQOrQuoDgPT+Tf3J52OlXUQuB84CPi4iQdw6AV9IpU8Uc9k86uVfZR5v\nSl2I/9vYNaTjuh2Dso8xNeM30tq3tcWT6SXvFD8WDG+PJvng2N4pO8Uu3toecxhTNXC7yExVgfJ+\nOIxGjZFgQTOysKNv9D3/3M7HJtayrivB9qjDMdPqc94n3xoBKG4KZF0owJM/vpExEXfDunHV7glo\nOXYzLYWePGdgiyVchLTOtpjD1LrMhgqFdA1yirIiMJgA2PSvkBS1am8FulxSm1SN5Fbm2bRnpOX1\np9B61FRtSLF3hi+kPiClGIHAqcDpuPsJrBeRacBNBY2ihCQjl7tYb/7ZujC0VAfpSqj74T3IHSu7\n4tk7BoG7zBOU/5+97w6Tqyzbv99zzvTt6b2QQhMQUemEJglN2ocoKEpTsaGAlA8URYlSBMUuIEUs\nKIr4A6RJEBDlkxKlJtSEbLIpu7MzO+209/fHM2d3dvaU95SZLXBf11zZzO7MmXLK+zzPXQCdAwK0\n4XEDK1W4FukxmC5sLdJrkYrY19kPLcgCTQTG1mcVJcailWVbXMJLfcE+82yFJgHtcRm3rsniQzzj\n2KAo+Ei/pW5hNPvBp877X3QXdOw2OYlV3QV8fElHJM87VtDsiUAUoWL11CA/58eKwUecY4OgPU6U\nuHe1KNHBj2g9NcpiYYtiFp/g15x65DUD022ynepBTTnxz6VY/e6jLqz8TgQAeGrRPN8953wjY+yP\nABZV79oK4C5fr6LBsLplUdEn7DiXjDHMSivYUNCwpMNeZOgFJ+tQC9YO4ydafqwjZ2PLlakG5owV\naCa3TX+MukPTVzGEHYMsTHQL0bFoZdkel5DT/HdGdZOTS0RMgsQY2uIy1uU12/wRzqmp4HY+qEVC\nliLbD17JqthjShJdCRm9DQhjHG2MxkQgSmpQSqHrgKhJQNngmBrBRECRGNIKOWZNlOnQaKOkiwfb\njXaOgBUolpCid7kZy8irJha3e+/vfnWc1novakMbPxMBq9HmdZ73XDozxs4CcCYoVXg7ALMB/ATA\nwUKvpAmIerFUNkxbUd6slhg2FPQQhYAzNQgY0glkAj372IRdUEdGkdDtk3rRSPSrBtrj8oguWDLi\ndOG+ijE4/hdFMsIF4LsQQ2tVLOyXptevGmiLS4P70fYdcbycVW0LAdXkg85UIohqkVDUTfSUdCxo\ni0NhgMmbZ7XZLNBEoImFQEijB93kqBgcmeq1gTGGlMJQ1Ey0CizIoxILA1QEZyvGu4UAaELyxA1X\nISYxFDQTMzPK4P2iKBnix5a1jhmtiUzFJI1ATELkdOuxDHGxsL+wSWu9F7URjJ+JQG2j7e4fX+n4\ndyI99M8B+ACAfwIA53wNY2yq0KtoEqJOF7bU8/WYlVHw+MbgOoGibqIr6XwSiYJrOtaQUw1MTw8v\nnMZaurCdYxBALg5+bPy80FcxsHOXvyLynWbl1ghYQt5NRR2TkvLgxM3pgm7xZIs6F7L3tED70dBz\nbt+RwC0O9KCC5t4UqEdUzY61/SoWtMYGP4POpIy+ysQKkSrrXMiuMSqEFQtbC5HaojMTIx1Vq0CY\nvUghwDmwZg3wyCN0W7MG0DS6qerQz8VKO0wdSCWBP/wBWLYs8Nsa97j629/E9c/34lNLO3DDS304\nd9dJvvV7JV1cIyA1gOYsilqDFMbYoJ35O8GlLOeRKmzB7znYmghEbQ0fRCPgBZFCoMI5r1gHAGNM\nATCmViZRh4o5uTDMTMfQU9IDp44WdROzFeeO8ERc9OVshDhjTSxcnyFgIaUwbCpGKBau+HcgmujU\noGbA0hpcu3obPrtTJ5ICi962GAkn/eiB+ur2I0srYEcP8qMPAKLbD17JVrBzjSi+KyGjt2Jgps9J\n1VhGyfBXZIWFlSMQFP2qMcK1JKOI0yftAsU4B15/fWjh/8gjgKIABx4IrFgBfPWrQDwOxGJD/8Zi\nwL+3FaHEgNj6DE48Efjzn4G99gr81sY11g1omJyU0ZGQwdhQ4JYf0ERA/DGWQUWzXeJUk0ORhiwp\nrS62WOLN+EXFMGGYYsWaXzcl0giwyNd1QTQCXhDZ3R5ljP0vgDRj7FAAZwP4S6SvIiSIPxtdh7k+\nQ8BCXGboSsjoKemYFeDCKUQNmmCLPrtqO61IKI4hsTCNwkcuypKyhFJE+5VqcJQNsRFkLRIyQ2kM\nFU3NQn/EosWyboIDwhfytqpwcqYPnh6lUw//fp3oQX70AQDti37G0nYoGybeHtDx4flD567OhDTh\ndAJl3cSkRPMKm7CTXGqWBDdUqL1eZbPAJZcAd99NHf6DDqLF/ze/CSxcCHgdTvNiMt7Ma1h+MHDr\nrcAxxwD33QfsvnugtzausbZfxeJ2Om7TioSizn1nRZR17soCqMdgunCDwwzrUTE4EtLQ+eidkiUw\nULUOFZn0+G3GlHSOyUk5crFwxTBHZSJwIYDTAfwXwKcB3AsKFRsziDoSu+xixzYrE8PbA1rAQkBA\nLDyBJgIGJ/FkfeeTxt7+OdiNQlY1R2QIADQRiEojkFUNtCe80wvrkZAZspWxUzRFjfa4jJd/9T28\nPaBjTgudjkwOGCbHHa/lcPT8Vl8LZidYBano5x/ESjGrmphTtx850YMGRmEi8Fq/irktsWG0x66E\njLUTzDu+1ABKw6ZNtJhetAg44ABg112pww5Y1KDgz51TTUglCS++CGzcSItu6xwpAosa9MgjwCc/\nCRxxBPDAA8DSpd4L/3p0JGRkt5UBAMuXAz/7GXD44cBDDwE77+zzjY1jcM7xar+Kkxa1ARhycvI7\n0S3pJlKyeHt/tCxELaGwhXdKloCVKiwCv8URrfditD4d7xMBzrkB4OfV25hE9GJhPozrW4tZGQWv\nZINdON0CxYBqZ2kCHXwDVZ/0+q5uTGKQGX1nY4GDmK3YU4PopBzNIjyIYxAAJCQJFWPsCKujxnUr\nL0e2YuDXr/bj7J26Bu83Ocffu4s4/gsXoishjzjx+bUXJRtb8YV3W1z2XwhUjBHnDSd6UNGnDXEU\nYuFXsiqWdAyfTHQlZfRunmATAcOMVCPw/PPAkUcCH/4w8OqrwI03Ahs2AHvvTUVB504yFuw88jPk\nHMjnaXG/cSMVExs30mO7u4f+Xb8hhZgCzJ4FTJoEvPIKcNJnY1jxcRWY5v7aOOcolDguvZDhN78B\nbriBqD9B0VEVC1s45higVAIOO4zoRUuWBH/u8YSekgFFokIZANI+CrNalAzuS38TdXaNKCp1i8t3\nSpZATlAoDATVCLDIM6JUgws3kSx9nBdEXIP2BfB1APNr/p5zzhcKvZImoDFiYfsvbHZLDH/bUPD9\npZoCUeMT7eBzW3xlqumZo52UyjmvugbZiIUVKbqJQMVAZ8L/4uSdoBGwc22QGMOyWRn8NC7jPaee\nO+Ixfu1Fc6oxgn7hhra4hPUD4gWYtR/VZ1EAwA4dcbyUrQwrBAZ0EzPT4lPFsPuBanC8ldewoi7g\nrDNBYuGxMp2LAiU9ugbDgw8CJ58MXHst/WthyxbgsceARx8FbvpVHOvfYNh7T6Czc/iinzFgxozh\nt1mzgPe+l/6dORN4spTDvvNT2K5KQ3n5ZeDz58v47Y1pXPkt4BOfAGSHXfeZ1Rw/+ngH9tyZYfVq\nYHLIuPaWGLmUaeaQjfVHP0rFwKGH0vudPz/cNsYD1vZXsLg9MXhMZBQWyPKaJgLi+2JylLIE6tc8\nUbMsxirsXA2dkPBJASQquAQ54owoPxOB2mbZzd9b6fh3IsuwGwGcA+AZAGOydZSQGQYi5Jy7Rba3\nVd0d+lXTtovshJJOB5ob53mijeNo8eVQCFTTM7swujZ1BZ0ueHYuUVYXNopFUl/FxNRUgIlAxEXu\nWMSA5kyTico9w+9EoD3mj5Pvth8trdKDDqvRPFAwoY8FQkgd1Ot5FTMyyojuZFImV4sBXXxEPtZR\n9uHd7oZf/AK49FJyz9l//+G/mzIFOO44uq3epuLlDRoy61pRKAwt+KdPB1pbvbfz6EvD983ttwd+\nfruB2+9XcfMPW3D11cDKlcBRRw1RfUyTipOV32FY8aUybv3fFt80IDuwav5Ff8XA5BrF6mmnAcUi\ncPDBwN//TkXMRMbafhWHzh4qmoka5P887HciMGaoQROMpuyEvCp+XY7LDBXTX6CYRWuNMiNqtFyD\nspzz+yLdasSIumvqFtnOGMOsjIK3C5qvQqCom0h7jHNi0ugGikQNu1RhC2MlXdiOzmGh1s4tbIex\nr2KMoGWI4J0yEfDDlw+CnGZiYUq8A2+JhUXhth91JGR0xGW8ldewoDoVKOjcM/a9FomQlIE1WRVL\nHfa/zmqw2EQoBDjnlCMQQldimsDFFwN33kmLXi86TFxiiLdyHHmk/21xzqthYiNdg2burGHVKhLr\nXnABcOWVwHe/C8ydC5x6Ktl+3vuogf8wLZIiwEJHXEJWNTE5Nfz+z38eKBSAQw6hycDUOhPxchl4\n4QXgueeA1auJ9nTooUQvmuZBcRpL6FcN5DUTszJDy6N0LJiovuxzOpWMuKkpioo5fHH5ThEL5zUD\n27WLXRfiEhPOVzA5HxZsaE1YojBna4RGQORs+Qhj7CrG2F6Msd2tW6SvIiToIhnhREDnrhH1szIU\nLOYHFl/MDRNNLJyvKvLt4EcM10g4WYdaiKpD06cavoVmwDujEBjwwdMMitrkVhGkFfKGFx0Fe+1H\n5B5UGfx/EI1A0P1ANzley6lY3G6fYdGVkNE3QQTpOgcYELjzVioBH/kI8PjjwJNPinHiwyQLlw1a\nVNRPktIxoqIwRmLd554DzjoL+NjHSLRr0XRmzBX3qRdFe0J2zE+54ALgxBNp+/fcQ8XJyScDO+1E\ntKhPfQpYtYqKlaOPpp+XLiXnoh/9iIqDsY5X+1Vs1xYfttizJth+YJgcejWkSxQpRRodjcA7VCyc\n8yEWZowJ07fLxnAGSJRrO9UcnYnAnqDcgD3q7j8w0lcSAvGId1qvgJbZGQXP95Z9PafFF3PDRLMP\nzakm5rfal8BBTqyNgFOYmIVUBHZuuslR0ExbHYIX3imFwFQf3fogsMuzcANjDK1xCTnNwGQB148+\nG+vQWtTSgxi8jQPqYe0HQWhqb+Y1TEnKjlOXriRlCYjCiq2vh18BdyMQJiV582ZavC5cSC45SUET\n9TA5Ak77ZVqRUNKHUmZlmbQCJ54IbNs2RM2JMlXYQkdcQr/L/nDZZYAkAddcQ+5Jhx4KnH8+sMMO\nQKKu1vzEJ6i4euABolhdcgkVDSecABx/PDBnTqQvPRKs7Vfx3snDv/xMgAm25V7l53gdLWpQPQsi\n/g6xrc5r/q4LiSprw0vbWO8QGWXWlRX8FiVEXIOWRbrFBoByBKItBNy6LNNSCvoqRvXgEduJvKxD\ngYk3Echpzl3YTIyhpzgGCgHVGGH5WItUBCfmftVAa2yke5IIrNTSiRz37qYRqHU96Cnp6IjLSMjM\nMRXYDgbnxIH3WYi1V+lBkwUWhNmKiXkORS8wnB40Pa1AkZivUEKJMShSsFCjV7IVLO1wTrTuTMh4\ne6Di+Pt61MbWD9uOTwF3I+Cm73LDyy9T5/2UU4BvfMOf7WZcYtACqufoHDlyv5QZBRGVdY50jZYk\nmRzOzy/7uAaJ4JyLLsXGoo6ibuJXNf73tUUeY8DXvy7+nKkUOS59+MNApQI8/DAVBZdfTu/lwAMp\n72D//WmqMJooGya6CzqOWzCcRpeJMd+NKxIK+/tuRkssXO9EE5cYshGyLMYiNJNE8X50aKLaifrM\nKLIQjSiTqJkTAcaYZdVh+64556N/1q8iyq6pybnnxVaWGKalFGws6COCgpwgRA2agBMBp2o7rUgo\n6KNvi5mtGHhPl/MiKSkzlEKemPsCJApbqB1HjgWr1UYgrxmO1KDaDvP/eyuPOS0x7DrJX96lZWMr\n+yyk2mKSsE6AHIPcX5dFD2qNS76mARaS1YaHn13JqPqh7zsj7fg3VrrwREBJN30fJ089RZOAlSuJ\n2uIXYahBbjoqiz7ppi3zalr5Rb9q4L2fDO/S5YREggquww8HdB14+mmyJf3Rj6gIW7KECoMDDwT2\n2w9oIxt/cE5i5d7eoVtfH5DLkbVrWLckC2/kNMxpUUZ0XK1AMT8gobC/72bU7EMNjsnJug72BGpK\n1sKaaOomsLmk48WqFkRkoimqnahv/Ea5tmu2WLgVVAQsBfB+AHeD6JdHAnhKdAOMMRnAvwG8zTk/\nijHWBeB3AOYBeBPAiZzzbKBXX0WUhYA1dvEa583OxPC2r0KAUubcMJEmAqpB/EinE2GQUWsj4OX+\nFAVns68STB9gQXQcOR7BOXedCNSiMyEP8zgXhV/HIAskGBbbnhc1CBiiB23fkfDlGGTBEgy3+XjM\n+gFtMMvACZ0JykyYCFOnksF9dWEffhg46STKBjj66GDbjIU4b3s3S0xMcXl8uQHUoGZBUYAPfpBu\nF15I4uennqLC4Jpr6HuZMYOyGHp7yUK1q4tunZ30L2Mk7P75zxFIrF2Ptf0qFrWPvKYnZSr2dJML\nT/LKOonWOQeeeIKKmT33BGIuLMhkhCGWfmCXIxDGpWwso3aiuVPN/SLFruhaszSCGiRFqxFoFjWI\nc34ZADDGHgOwO+c8X/3/10HpwqL4EoAXQYUFQEnFD3LOr2SMXVD9/4X+X/oQElJw/mw9REfLs1oU\nPLNFXCdQ0EzMc6GgAEM0kImAvGa4RnePBbGwblLysZtQNamE50qGLgQmsE5ANTkY7G0369ERlwKl\n4LrZ2LqhLS7hrbz31EozOSoG9xQ8W/Sgl/oqAScC/veDV7IqltgsbGqhSAyZmIR+NfjkaqzAj0vL\nnXcCn/0s0VQOOCD4NsNMBPKaiSkO+piMwjybJRWDe06axwvicWDffel26aXkQvTGG0BHBy38nTQb\njz5Kk5w//YksVdv8VMo1MDjH6zkVB84aOT1jjA3q2kRpiQWN49kHY7jqJppetLYCr71GNKjDDqNb\nfSZDUpYiC7H0g3r70Kh1lxMFolkCI6hBEWkEOOfQTIyKWHgqgNqroVa9zxOMsdkADgfwbQAWqfRo\nANZp9xYAqxCyEJAlCmzQTCBAeOswiHRYzrnoUvRVDHQXdfy1xmLMbbQkrBGYIAs+6nQ5fxkZRQoU\n0BIl+qu2fW5d0JQsOTpoiCKrGljQFlwMO5GzBESnAYA1EfC/z7jRL9zQVhULeyFbMdAelz2bEOdc\ndCneHtCQVU20xBj+XF10i4ps/bqjcc6xJlvByYs7PP+2q2oh2qxCoFGCY9FU4V/8gnju999P4V5h\nEI4aZKAtbr/CpTRb9+etGCa6Eo0V2o8WkkkSIHvhgAPIrvTcc4FddgFuvhlYtsz/9tYPaGhlMjav\nk/HUm8CbNbeWFkDaIY7uDhPts92PEVUFbr8duOyKOFIZjiu+RvoIWQZ6eiik7q9/pWKnqwtYvpyK\nggMOANJpBgYMC3RrBka4BsnShFmLRImELAlx/Yu6OcxOmjQC4T9P1eSISYg8/FGkELgVwFOMsT+C\nqEHHgBbwIrgWwPnAsGn2NM55T/XnHniGqIvB4rSFHZlUDNPVOhSgBeQOH/8K6s9RbqOl+grRDlHt\nLGMBOY/EvphEo9JGKOBFka2Y6PRYICbl8KPaaCYCE3NM6ydDoCMhoy9AUZbTTE9anh3aBbMEsg6J\nwvXoVw3scdp5I+4X5V/7NUXYUNCRViR0Cbx3SyewncDzWgLu7oI+SP3r9KAf1aNRguOS7j3R/e53\ngZ/+lDrJixeH2hwAQGGAycX8xevhRg0SaZaMZ2pQlGhtJXrQvfeSnen//A9pPlIp+7/nnATijz1G\nNrGvvgq88pqC7LZ2zJpJnXrrdsgh5NR02x8TuOUSBTtuT05JhxwC7L33kFNSPk8F5ve+B+y4I/DF\nb1ew134ce9foc6ZNIy3EKadQXsWzz1Ix+p3vAGecQbQoSzAcC9vV9IEROQLvkGRhvxDWCGgmZqaH\nltdxmfnKpXFCI/QBgJhr0LcZY38FsB9IM/BJzvmzXo9jjB0JYDPn/FnG2DKH5+aMMcdP9bLLLhv8\nedmyZVjmUuYnqsmbYYOJGnViFQkUi8sMWkQUp9FGTjVcXVoYY4P0oLg8OnQEL+93wNIIBD+ATc6R\nU03HsCkRRO2KNZbgZyKQkhnA/VtE5lQDCwNMZFpjEvKa6bnAy1b8pYwHhQg1qLbTnq0YYIzh8bjk\n2Wn3Ixi+buXlyKsGbnw5iw/Pb8WTPSV8bHG7+BtpIMqGiU6HDjnnwFe/SovFxx+PLh2XMYZYlUrh\nR7hrerhZZWIS+gbcqWlR24daRd62soGUwgan2H6KvNHE4YcD//kPhZ+9973ArbcCH/gAoGm06H7s\nMbo9/jgVD/vtB+yzD3DqqRyPazmctmcLZrbaL4uWHF/GtFgMhTVJPPQQ6RpefJEev3gx8NvfEu3n\n7ruB3XcH7l2nI+0iCJAk4H3vo9vFF5NO5dBDgbNvlFHajkMglDoyvFOThf1C1Aa03i5elFLkBT/N\n7lWrVmHVqlVCfyskP+ScP80YextAEgBnjM3lnK/zeNjeAI5mjB1efVwbY+w2AD2Msemc802MuZcJ\nIAAAIABJREFUsRkANjs9QW0h4IWokvCC2s+5weDEIfaKvZcZA2OAwanLNJ6RU03M9ojRswTDo8VL\nJkqH+4IyFVIjkFPJscaPVWQ9JrJGwE+YGGMMHQkJ2YrhsxBwp6k5QZEYUtWkTzdqUVZ1ThWOEiLp\nwkE77V1JGa/mxPUXG4o6ZmUUTE7K2Fb2F67YSJQcNAK6ToFcL75IacGTJkW73ZgEaAaHn8HTgEZ0\nUSc3K5GJQNSuQVax+PDbA2iJSfjgNGe3qbGKSZOA3/wGuOMO4KijyInoueeou7/ffhQY98MfArNn\nDz1mc8nAS6+bmNHiTmfVJRPLlhH16FvfIu7/I48Azz8P/OMfwydMflOFTz8dGBgAVp7RgoMeMjF1\nke+3Hhj1BeVEoinXIykz/O1nV2JmRkHttyNS7CYkhl4Bk5NiXbMqKiMYPxOB+ub5N77xDce/9SwE\nGGNHA7gGwEzQon0egJcwXHA9ApzziwFcXH2OAwCcxzn/OGPsSgCnAvhu9d+7vF6DCBIRjbIaEdBS\nqkZNi3T5LZupMAvHsQC3VGEL6VEWDPepJmZ7CLgp4CX4a+yreE8dvDCRC4G85m9aQvQgEzMy4tvI\nqcHC3IAhepBrIVAxHIPzokRSZp6c8aCwNAKi6C7omJmJoSUmQTfDBXlFibJNw6VYJLpIoUBBYS0t\n0W+XdAL+HuMVcpdWGAqa+/ddrguCigotAvqEsY4TT6RsgueeI2cit4yCV6tuQW7X6ExMGuEi1tkJ\nHHcc3epRMkzP5l89vvQl4J9vaTj5qDiefDz6gtUOJic//doFplJtSBqc+7ZdHus48nMXoiMhY5/p\n/otc0QV9ySZHIIrCqt7dKSqInLm/BWAvAGs45wsAHAzgXwG2ZX0K3wFwKGNsDYCDqv8PjagWS2UB\njYBfiAiFLUwUC1GRJNeMwkZVMNxf8e7kpmQplEaA9AHh9qeoityxCD/UIADojPuzEC3rJjgQeLHU\nFvfOEqB06mZMBMLR1NzQFqeiXFT02l3QMCutgDGGSUkZW30UEY1ESR9+/t64kUSYLS3AX/7SmCIA\nwCA1yA+8dFQizmo0EYi+AMvEJAyMAXvnsJg+ncS4XkFla/tVLPZw10oLuDjVghqA/r+bk7+oYZ9D\nTSxfTjkJjYbVZa4tglg10G6iTQUGNBNr+1XsLpISaQORdSbnHEWjLkcgIsOPUdMIANA451sZYxJj\nTOacP8IY+76fjXDOHwXwaPXnXgCHBHitrojKWaVicE8/8Nq004JuoqhxTEk5i+V8FQITwLaLc+6p\nEQAsatDovFfOuZDIM6kQHSOobiOsUBigfbs/AqHRWITfQqAjIWNDQTyIzlpsBdXctMVlV+cgznk1\nTMz7O649b9TfL4JGToYkxtBRLbKmpNwvC4bJ0VPSMaPqmEb0IPeE7npYn8XGoo7OBBUSszNKaC56\nuSbE6b//JW/5M84ALrnEX1qwX8Rl/85B5Bjk7azmdO7hVcppQyYCYyTnpZGw9DQGBzYWdazyCJbK\n+AwVo2Rh/99NSmH49MUqbrtcwZFHksNQuoEMLacusyUY9jgdjCv8e0sJO3YmAk8vRc7BFYNDYcOT\n46OaCFDYbfSFv8hX3McYawXwGIDbGWObAQxE/kpCIrKJgM6RTLkfvLUnibJu4scv9OFzO3c6fkFF\nTdzreSJw88oGhyx5e8NnYtKodRKLOtGvvF6jVE32rQRM9u1TTczysUCyw0R3DRLVCABAR0LCC33i\n+0zQMDELbXHJlTKT14iaIWL1F8YWEwiWI+AHnUkSDHsVApvLOjri8uCxQxMBfzqB61ZeDs3k+P5/\ntuHLu07Cra9kcfDsFswNeaxYE4H77gM+8QngBz8APvrRUE8phCAWojnVRJdLAalIDApzPvdYa9JG\n0EgzMQkDo2zv3GjU6ml2rLnfSU/jJ/uGc15NFvZ/7kkpEiomx/XXUz7CsceSANlyJ4oa9UJhCxNN\nMFwxTKzeWsapS73tlJ0gIvq1c4iM6rMczYnAMQBKAL4M4GSQFaiz6mCUEFUSnt8OS1KRMLtFwWv9\nGnbssj9S32kTgX4BWhBAHZa3NPHubpTwI/AknUCwZN9sxfC0KPUCeReP733CDpxzFPxSg3xmCVCY\nWPDPvy0m4Q0XEW22iSFcImLh9riMl2/7HjYUdMxuGZ5x4gVRncCGgo6ZNfkpk5MK3siXPB9Xjy0l\nHV1JGTJjWNQex2v9aqhCQDM5OICf/4SEnHfdRY4uzUAgapBqempL0jGGQjWhth5RC4Vr0TJBqEFR\nwgoUE4FmAjILVqQlZYa8ZkKSyEnopJOomL3jDkpjjhpOa56J0JSsxX+2VTCvNRZKsyeSI2C33ovK\nzKYRqcKAmH2o1f03GGP3ANjGOR9zZ4iExCI5cQXRCCztSOCV/oprIZARXOxMhIMvrxlCXdh0bPRC\nxbIVw5MCZoF0Aibg8wTCOadC4F2xsC1KOoXm+LlYtlb3GdHAnbATAa8sAStMrBlIVi2S3XDdysux\nfkDD3zYUfHe+ugRpV90FHfNqFrCTqtQgv9hSMjC1On1Y1BbH/1s3gANn+VCB16Ggmrj36hZs/DfD\nE08A24mEIkSEuETWz36Q09ypQYC1+OSw04xWDLMhNAGAFqO6yaGb49+4IiqkFMqUEcmLKAkG29kh\nqUgo6XQcKgrw619TINlpp1FYmhTxV15x6DJPpCwBk3P835YSjpkfzpRVZEFPjkHDP8+oGrxWoFjU\ncHxKxthejLFVjLE/MsZ2Z4w9D+C/ADYxxlZE/1LCIRmR13oQzuXi9jjezGmOo+GCbopTgybAREA0\nyTWjsFFzDcqq4t7vSYUmAn5h0UbCVvATtRDwSwsCiKrVHpfRLygY9hJkesFLLJytiIWJRQGRiQAA\nbCrqmJ723zrsTEjoE/hcNxQ0zBqWqE4iZr8T2Z6SPlgITE8rKOum0PbtMDAAnHQ8w8ZXZTz5ZHOL\nACA4NchrcppWJBQdGlyNDBNjjCGjvDsVqIXEGFIKE9IJONnYiiBVd5zH48CddwLr1gGf/SzlYUQJ\n4p1PbGrQy1kVbTEJMz0szb0gIqAu1WUIADQZYgD0kJ/naFCDfgjgIgDtAP4GYDnn/J+Mse0B/BbA\nfZG/mhCISpUdhAueViRMTyt4PadiacfIqUB9uIQbYhGJSkYTIhc4YHTFwtmK4WkdaiEVMF04W4mG\nNjJRCwG/QmELHQkJfaqByQIqNhJkBnOIAKgzaoI7TgqzqhkorCwIEjJDRfcWrveUdMwJcMHrSnqH\nihU0E2WDY1LNfs0Yw6SEgm1lAzMz4t/nlrKOJVWnFsYYFrYRPWiPqQ5xsDYwDAqHOuccYMl7gAu/\nW0BnZ3AOcFDEJPgqBFSDuu31ncN6uPHSG0kNqt12M8Lyxgus7Buv8xYJhYNOBEZeb9Jpcr067DDg\ni18k7Yt1CqgNEayFV4igBUdq0ARYiwA0mf9XTxH7zgivuLZSxA2TdJB2cKKCR2ENPxpiYZlz/gAA\nMMa+yTn/JwBwzl92SwMeLURnHxrs5Lq0I45Xsk6FgLhGYCJU4XnNxJSU90IkITMYVQ9jEZpHlMiq\nBnaOi6mvUooUKEugT9BNxgsT1T50QA9YCMTFdQKiRakTWHUCkVNNJFM2hUDFQEeIQsMPFIlBYiQS\njbkcLpuKOvaYIr6YttCiSNBMjrIDJx2gacDMqm1oLSwLUdGOG+ccm2uoQQCwqD2O57aWPQuBchl4\n8EHSAPzlL5QQfOaZwEEf1fBC3+hkGcRkfzkCeY1c1bzcrNx46Y1yDLIw0XUC7XEZz996DbaWDcxI\ni+lp0gIhb8Bw9yq/SMr215vWVuC++yh9+NxzgWuuoWIgaIighYph2lODImqujjbWDWjQTKIfhgWz\nzENMjrRLIWB3XbOs4cOUI6MxEajdA8qRbzliRFEI2AVriGJJRwKPbizaciqLPqlB4/3gE+3CWuPn\ngtb8rlO24oMaJAdLF47COhQYsiYU4aaOJ+RV/9QggATDIhQSg3MM6N7Bdl5oixE9yG59mo2o2BMF\n0YNMxCT7bWom6VIm+4m4rYIxhq7qZzvDoRDoLg4XCluY7DNLIKeZUBjphCzMb43hnrcGbLnv2Sxw\nzz20+H/gAWC33chN5ZJLgAUL6G9Wb2tsh9wNcUmMMmJBNO06E2PoKTpRgxoTJja47QluIXrdysvx\nVl7F45uKOHmx2BRJ1DkoTMBePTWoFu3twP33AwcfDFx8MXDFFYE2MQwVhy7zRMkReGpzCR+Ymgps\nIV0Pa63pxL4s6hxTbZpGUWguRkMsvAtjLF/9OVXzMwD4bzc1GFF0Ta0OS5AdpiUmYUpSxpt5DYvq\ngkn8UIPiVceA8Qw/XVhLMNzMxZRuchR18UVoUhmZKCmCvoqBpe3hPd8kxgYpY0F5p2MRA5qJqSn/\n33tHQsIbeWcnn9rnzyhS6GTMtrhs+/1XDBItZ5r4nSRlCRWdw8lsZnNJx6SkHHj83JUgetAMh87+\nhoKGvaaN7GlNSspYvU28X7SlbhoAkCPHzIyCN/MaFrcl8Nxz1Pl/8EHgqaeAZcuAY44BfvxjYMqU\nkc9ZHsV0Y9II+HGzEtOupBUJBd1ewN2oMDELLQEsRMPSVJoNP9dmQDxUrGSTcC0KawHu1Pjp7KRi\n+KCDorEUdcpOikdkwDKa2FLSsamo49gFbZE9p1fTueTAAImC7dH0iQDnfFwRA6OoXsOKr5Z0JPBK\ntjKsEDBMDs0H3SiIDd1Ygml1YQUX2aMhGO5XydVItLuekhl6AmkEwqcKW7AC85pDQmkOBrRg/PpO\nQWpQWMcgC21xyTbQLVsx0R6XI+s0icBLMBxUKGyh00UnYHKOnqKBmTbPPzmp+JoIbK4RClt44w3g\nv39O4UePMLz0T1rsH3II8IUvUAfUKxW41GDOvBv8ioVzgs5qbl35RlODMjGGTUV/DZCwNJVmww9t\nFxAPFSvpYoYZdrBSfSsu9KLJk4GHHqLiONm1DEtPCbQpANXFpa1YWPLUDI1F1BajvWUDisTwTFyK\nrBj10k7Y5QiIPE4Eo2YfOl4g4u/qhbDiq6UdcTy5qQiD88EupGUlJbpYGO/2oQOaibQsOQpp6jEa\nguFsxRTOEABII1D2qRHgnKMvIrEwMDEFwwMBXIMAoD0ho181PKlSlCEQTSHwWv/ICYSfLIqo4LUf\n9BSHEn/dsGUL8NxzwN57A5kax86uhGz7XgHq4rfGJVv9QEeCFqxOi4p6bC7pmCHFcdddlJz64INA\noQDsf6CCeXsW8eufKJgzx9+5uKxztKdHcyLgjxokYlaQcbFYLhs8kMZGFBNdIwDYWz26IROTsK3i\nbbFb0jmmeQSTuiFZLfjd/BCmTgUefhjYfsfd8fdbUtj/VP9ZHsDEEws3uhilppzzcVF0mEzGx+NE\nYLwhJgG6iWGLcL8oh/Rlbo/LaE/IWJ/XML8qTPE7ekyM84lAXvPHyfaT1hgV/PK6g2gEijqHxOAo\nuvSLiVgI5DUj0EImJjGkFQl5zXQV9ona2HqhPS4jZ7MgapR1KOdAPg+02UyzvdKFN5V07DZ55Nxo\n61bg738HHnkEWLWKrAh32AF47TXgjDOAz3+eRLddCRn/dpi2WEJhO0iMobNKK3KaSHAOvPwycO+9\nwA2/T2H9Cwr22hNYvhz43OeAnXcGGJPwixc1KF06AH/TIvJub46DUz385giI0ifTLhPThrsGjaKr\nW7NQ0jm6fOhpRHUTZSMcTS2lSCjp3o2kGTOAEz72S/z+5tPw1r//gRk7PY451SBB0XyTisGRmMBi\n4aiR8LCqd3QNiuAa/u5EwAOWmlsNodYP6hhUi6XtcbzSr9YUAuJhYsD4tw/1S8dIK2Le5VHAGhlm\nKwZkxvDb6uv0GhmmFDH/9tptVAwSba5OD52Uw4wlJ1ohYHKOks59HRu16Kh63rsWApoZSDRbD6cs\ngaxqYlIEz1+L118nB5wnnqAF+qWXAtOmDf0+ITtPp3STo7dsYEpKQaFAPOJVq+j25puUsrtsGXDT\nTcB730thRa+/Dnz/+8B73gMccQTw2S/I6E0Ythal3UUds11cgUgwPJyaVCwCf/sbuZ3cey/ZfS5f\nwbHnSSX887RWtNsUO9u1x/FqTnXUKTihHMK7PSz8UjpFqUFxiYFze/pG2MaVF1pGoUnTbBR1E7MV\n8f3MSnr2QkkPvg4BhiYCIrjxR+fja18F9tjnSMze7WBc9bUUJreL7xdWjsDWrcAPf0gUvHPPjV4s\nPN70I05wuxZbn5fdZS2KjKh3JwICGOLVBXt8FJzLpR0J3L42i0NnZyAxVg0TEz8ooxgfjSb80jEy\nMUkozTQKBB0Zphzs3KLchhdIDD9xLsoFjY6LoC5IgxaiLkGROdWIxOO/tbogqp82ZisGtovAkg6g\nBfL11wPf+hZw4YXA7bcD3/0usOOOwNlnA+edR44hbhOBLWUd6sYYvnoLw623Au97H3Hrf/5zYPfd\ngZjNR7FwIRUC3/gG8ItfAB85XkJyRhtmX8RxwofZsBTTDQUNH3Cx9pyUpCwBw6Di47bbyOVnt92A\nww8nq8+ddqKpxb1vGWhvs//uF7XF8bcNBew3w88nSBOBRopn3RCTxXMEOOdVxyzvIpIxNkgPisvD\n/77hGoGqdalXbkXUaOaC0YnP7QRhjYBhhmoq0hRa/Hw/bx7whRtyuOO7KeywmOGsM2nKNnOm92M3\nvg1883oZd/waOOEEogy+9BLwzWujbT6NN/2IExIuzVprGmB3vIQtrEzOYXD7IiMsJlYhENI5qKyH\nO3gBCuVJKxI2FHTMaYn5PtGM92ThnAddox6jmS7shNWrAVUF3v9++r8V8NLMC6Jp0gJu2TJg++29\nx5HjDUHDxCx0JmRkPSZJohaNXpAYQ4siIV+XRp1VoxGDv/QScNpplCD65JPA4sV0/7XXAl/6EvD1\nr9N9F14I7H4cQxnD9wPTpI77Fd+T8MLqVnz2TOCZZ2hxIIqODuD88ymY60vXq/j2t1K45ALgox+l\nhfzSnUwMqNx1wtL3hoKbbuU45R4SM37848DKlURfqEV9fkA9ZrUoyKoGee37+P7KIbuwYUAaAbG/\nLeqUmyI64k8r9sFejaYGyRJDQiJaZNotuKIG7XEZr/zqe8hWTJSq/vSTkrKva0IzF4x+g7+sHAGv\nawFNBMJRg0QnAgAtEpPTdVzwsxJmFxjuvDGOnXYCjj4a+PKX6Riux8svU7Phjj+14YzTgeefp8Jh\nYAD4n/8BTv+ojBXfmDjXnKjgRpkquWhOwrowWdOARqxBJlYhEJLTRh2W8Bf2pVX3ICoE/E0Exrt3\nb041MUcwsRcYmzzUDRuAU08FfvYz4LjjaCFoeQA3g3qQzdIiat06WgT+9rdAYsnEogblQxYCHQkZ\nr2Qrrn+TU020R+AaBAzRg6zFmMl59fmDFxqaBlx5JXDddcA3vwl8+tMY1oEHgPnzgVtuoYv0xRcD\nV12bxMfOUbH/OUB/P/DLX5KdZmcncMDHNFx1M8fec4K7O8diwBHHmzjr4yryLyfx178CN9wAPP0c\nQy7Xhbt2Y9h1Vwzepk0D/vQn6v53b4xhl+UV3HsvUY2cQI5Bzp+bzBgWtMbwer+GXSeLf75hu7Bh\n4EcsnKuGiYnCiZce1uVOBJaFaFrwWLU69n9+I4f2hIw1WRVn7dgZ+euKampQ1E3hIgegUL+YxFwD\nw0zOQxdpfqhBAFCoNjHbYhKmzjPxwx/SOeXnPweOPBJYsgT4yldoMvf008B3vkOJ3F/4AnDhX/pw\nwd5dsE5lLS3A3XcDnzwNuPb0Vpz8MBX24wXtcRn/ueUaZFUD01JiQXF+kHCxeHfThMZlBrUS/Bqu\nmhyxBh3vE68QCEGfKBscXRF0+JZ2xPH713I4eBb51XekxRfG490+NO8zydXNFWO0cPjh5GTy4Q8T\nh/rcc6tTAYMj2eAj5r//peJjxQrgzjvpZH3SScCZ/6vgkOP1xm68iQg9EYhLrhOBsm6CA5EtlNri\nMnKaAUvAmq9Sm4L69T/zDE0BZsygC/Pcue5/v/POdHH+1X06rviagrt+DPT2Eq//9tuBD34QuGVN\nGXM7M+5PJIDOhIw+1cCB+wH77Uf3/b27iP5eILM5g9WrScPw4x8D69fT8fLd7wL7HwD84IUB7LBT\nAoDz57KlZHimfC5qj+PlrIpdbYTPdtBNGps3gj8rAj+TXL8i9kzMPqys0uBAMdq2VM378Pe4nGZi\nt8lJPL2lZBsQFwSbSwb+1VPEdu3xSKYGvKpTSvt8bVZh5tTxtyhbYcIf/WbX5KvTz3RMQqG6r3R1\n0RTxK18Bfv97aiqdfjplD5x3HnDrrUAqzXHVc3wE3SQWA26+GfjQGRr23VfBX//KMH9+4LfTVFy3\n8nL8d1sZb+Y1HDXfhTsaEG7NWrfGb0KSQjV5G6UPACZcIRDug46q4zu5GuizsaijqPmjBlkLC7uE\n4vEAEsH5c+RRTQ7D5MKWo83A+94H/OMf1E159VVgj8+RTqADjbOL/O1vqUNz7bXAKVVf6IMOIr71\noctjePN1hgOupVj58Y4Bn+5S9ehIkEbAaUSf00i0HtUYtT5LIIhjEOdUANx0E12Yr76aJj9+XuJe\newNf+3UBcza3Y9GiISGxYXJstfHmD4KuhIwX+oZPWzYUdLx/TgqLdqZ90h4M7XFyDnJ6HZxz2wyB\neixsi+OB9QXh82C5GuDUTC57LXxNBPw2S5SRol2Dc+hm4wufoOnC1vRsclLB5pKBOS3hC4HWmISs\nauKOV3PoLuhYGvL5KgZ1WP1edywnJ6cmeVihMEDXxc0+1jKWW19GGdlYi8eBk08GPvYx4MUXiWYY\njw+91rjDcROTGQ7/UhHyrinsuy+J/XfZJdTbahr6I5wG18MtIZgKAQdqUEj9Z6Mcg4AJVwiE1AhE\n1GFhjGFpRxyvZFXf1CCgakU3DgsBzaSRqJ+kVcbYIAc2CqtHN7THZfzzxqugMDZsEeo0Mpw7lzry\nH/kI8OTZGbz/1xwzRoaqjtjGi7d9D5uKOmZlxMaSug5ccAFRLB58cCSfc4cdgNsfUHHWSTGccgot\nJKNIlBxN5DUxH3UnpBQJYBQiZXfijSpMzEJbTEJPaWgi4yeL4s03qWv/q1+R9uSUU4D//AeYPt3/\n60gqdDHZZ5/h928tkyVuFBeKrqQ8zMnL5BybijpmCuQTTErK2FZ2LgTymgmJwdMtKqVImJKSsW5A\nw0IBQXZZNyOz6g0CmVGhJ9LQyKlijkEW0jEJvXVhbVbXudGFT5AsAcPkKFRDJaenFfQUdV90USek\nFIbD5rSAz+b4R4BE8noU9WDpvzTFdkmWjUC0nqrq0kRB4nMqBPoczDcYI7F+LZysQy3EJYYzP8cx\nawbDIYcAd9xBurUgaI/L+O8t16C3YgxqvKallcgoO7XIqkYk+5wd3HIEXKlBITWsQScCzz5LtFI3\nTKhCIOwHHWVk+9KOBO56IwcAgQqBMO5Ho4W8SnQPvxenjEKj74gMWBxx3crLcduaLPafkca8VrGN\ntbWR28nRn+I44UMKHvqruwjzupWX4828iic2FXHy4g7P59+8mQqNRAL4979pnGuHmdMZvvarAdx1\nWTsOPZSKhkmThN7CmMSAZqIl5MKNEoYN2+OL3Kuiu8C0x2WsrQna8sqi6OsD/vAH4s6/+CJw4onA\njTcCe+0VbqLjlCy8qaQP48OGgXWRtgLbtpYNpGNM6DxmWYgC9pXqFg+hcC22a4vj1X5VqBAoGcEW\ndVGBVXVEmkAhkNdMX9aoGUXCen344q7RjkGD2475o6gAVf1P1RFsWkrx7QrXHpfx8m3fw4aCjlkt\nyiDJzFowMkY8/bAI0qQDvKck0UwE/IVY5qrhjOkY80W19dqPLGHsRz5CSd8nnghcdRU1p3p76TzX\n2zv8574+Os+dcw6QrGH2XbfycqzaUIDEgL2np/H9/27D53fuaogFbr9qYOd4Y7plbsYdRd1EV8L+\n2A47Eaj4nAisWUP20489BlxyCdGdnTDOlpruiEYsHP4EYwmZugs6DA48mVEgMXEh03i1EBX1xq5H\n0PGzX5hVWsI0h8AjJygK8MVvq3jwVmDvvSkN1XIUssO2soEugcCyf/2L3Bk+8Qmyb5RdHpKQJfAY\nx+9+B1x0EZ1o770XWLTI11sZMwirEQCGsgRm2iyqIp8I1GUJZCsGFrUPX6AWCvSd/Pa3wEMPAR/6\nEOlLVqwYGsWHhZMOqqeoOwZ5+YUV2GbRO7oLOmYK6pwmJxWs6XcWcYvQgiwsao/jD6/nhNy6Svro\nWYdasPRdXqoGv9SgtDJycddoxyALLTEJ3T4X8v01E49paRlPb/GXeHvdysuxLq9hVXcBn1jq3UwJ\nisCFgEe+gl8nIjv4FQvnVQPT2uO+zTesDAEn1LIsDjqIsknOPJN+19VFJgVdXXSbNYtMAtraaPq5\n007A975HzkXW4bu2X8UR81qgSAwz0jG8PaBju/boO4D9Pia2fuHGPHFLqg5rBKN6TG8svP02rSfu\nuouuPzfdROnxn/uc82MmXCHgpOYWQRSBYsCQ/Vk9h1FUyDReo73zAe0a000KrtlWpiTbIAuGtMJw\n3Bk6DtgljiOOAH76UxL12qG3YmCSi6pY14FrriGO+C9+ARxzjPf2LWtcSSJh5nbbAfvuS13mww6j\nYmU8wW8CtR06EzKyNkFfAHXIFqaiGw23xSXktKGgrb7qIrlYpMX/HXcA998P7LknFXc33kiWnFEj\n6dCN2lTUsX1ndB0wKyWYCgFtGM3NDZOSMrb2OHeQN5d0oQ4/QNMFDqI9TfEoHspNcvRyg2iWgN8i\nNRMbubhrdJjY4LYD2DvXumlNSSroqxi+NW/dRc2VimbZlOZUEybHoF7HD82k5NPa20JaYdhYdN7H\nSyFCTS1YycKiyGuUS+FVpNTDcyJQJ4LfbTfg//7P+3mPP55orl/6EpkKXHcdMG0BBW0B5h7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glMmMJoLrwCxcJg7F1BQmJQ5e5zHdBMzqUXxpN9aMXgeOKGq0Z0YP1y9Lx4jTJjePTnV2J61Yqu\nMyEjITOh7fRWDKQUFknIS8kw0QH7bQ5oJhQJkdI0apGQpUis3Jywyy7AQw+RU4Kuk8tRWHBONmmP\nPkr0n95ejvecoOL+3ymY3BH+eOtMSMjWuUnlAiZcu8HizXcXyAbxxaqY0U9XNyokZYat1UVIT8nA\nXtNGj6bkhElJ4oQvbqf/bynrkekqZqRjOHpeK/70Rg4nLWpHRpGQVEbP+tnJHGJmJoYztu/AwxFM\nj0aVGuSj457T7CcCVpaAF7Wyu6D5FnpmfEwsLISl72SqGoERzyvQFRZFSmHIFcUmArVUyEzValYE\nFdNEwoULE7Yp2V3UkVYkV3OF6WkFOdUcLM4+8QmaEl9xBdlinnUWcO65YgVBv2oi+5qCz/yAioq2\nNnLhmTlz6HbAAUM/L1oUjIaUkEcu6EXsaN+dCDQJQbumZYNj0ij7UFto5kQgjC3ntrKOPU8/D6ct\n7UBryIudlyBtxdkXYLu2OHabnMSq7gIkAPvPFBOU9RSDBZ3Vg0a1zt/LNh9hSUHQyImAhR13JLee\nQw4hzv6ZZ4o9zjTJhejFF+n2wgv070svkdXn7rsDl1wCHLKc4ycvlTGpXey780J7XEZONYfx0XOq\nGXnHPixvPkpY+wFNusYeNQgAJqeUYanPm0sG5vslcbtgflscz958De4pEJWkTzXwbHp0ijOvbUXB\nF8/Ehi/umioW9qURMDG3ZeT3nKoWa30VE10u58jugo79Z/g7N7Qo0gh6oBeKGg+sDwDoM9laHrnN\nkm5GVqCRWNi9s2+FibUM0wiIF0ZCOQIhrjlrs6pnYScxhlktCtYNaNi+SvebM4c0axddRAXBkiXk\nfOdUEJTLwB13AFdfL2Pjhgy++Fm6/kTh1meHhDzSRZAKGfdzXCLE2u7diYAPxAPyqMeSWDjWRPvQ\noLacnHPcv76AfaalQxcBAI1anboY/aqB9QMajqrGyC9sjeNvGwrYX/Ag31SKhj6RkhlKLt9Lb9lo\nGC0IaE4hAFAX5pFHgIMPJs3A2WcP/325TJ7+zzxDt2efpYV/ZycVEjvuSH7IZ5xBbj9dXUOP3Vam\ni1ZU3VtFYsgoJOLtSMgo6yY4MGaO5UYgKUuoGCayKvHEwyxo7BDW/hgAJiVkPLe1PPj/zSUdH5ga\nQgHogAM//dUR941GcdZo1Dqr8WrA19jUCNhTg4DqVKCkOxYCec2AanJ0+hB6ArQoX18Qo8JYCCMU\nBuj7eEsbuc1ShAnXSZvFZj2KOnWJa4OtrEAxEXgFxYWlBq3tV3HEPG/Xh3ktMazLDxUCFubOJf7+\nRRcBK1fStckqCCZNAtaupUyCW24hrdsJn1Fx8HKOfWZFa6dcj4TMRoRZiuxTcYHv1A6GyWFyoFEy\nqAlXCARdLFXGkEbAj7J8tPB8bwUVw8TuEQljiRpk/56f21rGTl2JwRPWrBYFfaqBgjaUruqGTUUd\ne00LvwhJeoi3tlUaXwg0y01q0SIKOTnoIEpW7OwcWvSvXQssXjzkaHDKKcB73gO0t3s/L1mHRvsZ\ndSRkZCsGOhIyclUrvbGQEN4oWPahjRIKR9FNn1ylBnFO4slsxWiIgPudglSNX75qkkVnoxzW6pGu\nZqiIuLrlVBPtDsf39LSCnqKOHTrthaMUXudfRxKEGkTWocE/v7QD/aas80gmQMBQsrAb8pqJ1rrC\nKy4xcO69yBdZXIbhtPeWDVQMjhkC56i5rTHc89aA4+/nzRsqCKwJwY47Aq+8AnzqU8C//kWZPX98\nXcOkdHThZE6ghnN9IeCdSxGXGLIe36kdrGlAo65rE7MQCLDjjqWJwFgPFCvpJlZ1F3DCdm2RXYwy\nioQtpZEdFt3k+M+2Mj62eGiVKTOGeS0xvJ5T8Z5J7oVIlPQJkYnAgtbG8bWbNRGwsGABFQNf/CKN\nWPfZB/j85ylUJRmw/vObKiyCjoSEPtXAfFjWoWOjoG8UrP0grCVuI5FUJMQlhpxmoqxTAJsyxsS8\nzUIUExaJsWrAFIfOm3utEnV1Uw0SbTsthKelFPzbJl/CQneBEoX9okWRfDdIKEwsDDXIvute0k2k\nImooioiFSSg8fHuMMaSrVDI305RKNUPAbXFpcdqDWLKv7a9gUXtc6HHTUgrymunZ3Js3jyYAF11E\nQZ0rVgCJmnW/X+vQoCC9nh01SEAjEGB9WjEaa4YwNq8iIRB8IjB2fKjHeqDYqu4Ctu9MYEY6Os6v\nk1h4TVbF5KSCScnhu+rCtjjeyGuehUBfJTr6RFIh+0gnNEMj4NUhihrz5gF//nN0z9eIQqAzLg+O\naXOqEblQeKzBWiD0lHS8f0r0dJuoYAmGC5rpKz9goiEqvUItPajZ16pMNV3Y7ditdWyxw7S0jJ6S\n7rio3FDQsO90/5QOa1HuZ7FaNEx0JIJfvzKKhKJmIxY2gqUV2yEpM1dNGjBSKFz7+gq66SrS9coQ\nAMJZsq/tV7GXi1tQLSTGMCcTw7oBzXFiVIv58+3d5vpVsynWzvWUKZNzIQv6oFSrRuoDgAlbCPhf\nLI2liYDMAJNDOJI9DHSHgmNLybCtcN8e0PB6TsPpO3RE+jqcBE7PbC3h/Tbc4oVtMTzaXfAcV/eU\nouuapmSGzQ4HsWZyFLToRaq1SEhSUycCjUDewVUkDDoSMl7qqwBo3EQgiq5uVLDOcZuKfEwKhS1M\nTsnYWjaQVw1MeZcWFBrWOVKWWNOvVSLOQV4i/RZFggT7xavBqbCdnvG/P1tOKl4pubUohqTwJGQG\nrWpbWzvpKukmJoUoMOq3oZrulKz6MDELTvamtRAVnFuNST8L0aJmYkvJwDwfBgFzW8ULATuUdROc\nN6dIrteilqo5Jl4MCaJaBaAGNdAxCJiQhYB719YJY0kjwBgbHMlFxTd0QsXgeOqmq0ecwFtiEm5+\nOYvVt1wzbDqxqaijLS5jbVqJ1JkjExspFu4p6uhXTSy2cR1oi8toiUnYVHQfJ0dJn6DRvP1B3Fs2\n0JmQG8rbbTY1qBEY0EzMDjD+d0NnQh60EM1pJhamos8QaLZFqBviEoNuAokYa0juRlSYlKAOcLZi\n4gNTo6fMjaXirBmwpqYJufmFgIhzUE5zFgoDdF2bVrV/ri8EtpQMtMflQNdgxljVdY5DVKIl4vku\ntE19eGOjFGEwKWNsUA/kNGXIaybmJ0ee78hlyv1aISo4H+xi+zitvppTMb8t5osOOLclNsxgwC+y\n1UK0Gfqw+muxqPg8qH3ouJ0IMMaSAB4FkAAQB/BnzvlFjLEuAL8DMA/AmwBO5Jxno9pukPRVw+TQ\nzeApg42AZTPVyMF/WTex1xnn44wdOmwFnGuyFdyV13BQjTOHZZ8YtTNHWpFGCNKe3VrGbpOTjovr\nBW1xvJZTPQuBD0YgFAaAlCw5cjZ7GywUBoLrX8YSGqIRiEvIVogakFMNtMUbk+w8FmDZ/b5d0JGQ\nGJ6uevOPRp6BFyYnFbzQV0FvxYgsQ6AWY+39NhqZwS6v1PSmVUvMm4ffr9rTVGphOQctqXOH2VDQ\nMCvANGDY69NNdDlkvNSDFm7hFlZW1722ECjrZmSuQcCQSNypl5XTDLTGR3bQa2lkTqiYplCXWdTO\nvNaKfGuZsnvuViThc9O0lIyibiKvGYEMJbKq4UqFihL112LR/SkhSYFo3+N2IsA5LzPGDuScFxlj\nCoDHGWP7AjgawIOc8ysZYxcAuLB6iwRBHHcsfcBYchppRqjY6m1lLGqLOx50SzoSmNYkbq9c7X6U\ndI5MjLjwL2UrOHOHTsfHED2oiP1m2P+ec45NJT2y92CJ9eywrWy4+mNHgYkwESDXoGgXMUlFgsRo\n3E9hYmOooo8YYynPwA3nXHQp+ioGNhR0MAY8N4oBbBMF6WqIlSw1n8aaiUnotfHNr0VONT0Tvael\nFTy/rTLi/u6Cjrkhcib8OgcVQ4qFrW3Wd91LBkcqwu+GsgRMwKHAyTuEJ2YEshVE06njgnTrWivy\nIAnajDHMqdqI7tTl/1raXzGalvheL6IW3Z/CTARi47EQAADOuZUpHgftyX2gQuCA6v23AFiFCAuB\nIAEY5QjHeVEh1uBQMZNzPL21jGPnt7r+XYNCcm1hjb4zMQnP91awoDXm2j2enYmht2yvZQCoQxWX\nmJDFqAhSsrOdW2/FwIIIA5PskJCDOziMBXDOhS1f/aIzIaO3YmBAH2mn9y6aj37VwA4f/wp2qLt/\nrBUs4wmZmIRtFQ1xmTVdLNyiSFhn45tfC5Fp3PSUgodLhRH3dxc17BlicusVSFkLg3NoEZiD2HXd\nSxFPBNyyBDjntvahAO0rb3tkKwhTg5qoTZvXQjqBnbr8T3X7VbPhU3kLMmNQpCERtTA1KOC6zo/+\nJQgaWggwxiQAzwDYDsBPOOcvMMamcc57qn/SA2BalNsM0jWtGKZQZdxMNNpCdE2/itaYhBkR87XD\nIK1IKGomeJLj2S1lHDbXPYhEkRjmtMbwRk61PXFEba+YrI5p7RbivWUD74soU8EJUvXkI3JSCJMY\n3SiUDOpqNKKz0RGXsH5AQ0aRGi6wfxfvYjSQqZ4fU3LztSFOrm616BeYxrXHiRpR2xAo6iaKOg+V\nM9Ei8PosWMLOsM2U+uKDc045AhEu2NyyBEq68/k0rXiHiolaUjbTxXBuawxPb3W2mHVDv2pggcdE\nKkpYwt+4LKMkKD6PSYBuQiiToxbjlhoEAJxzE8BujLF2APczxg6s+z1njEW6hwUpBMpjyDrUQqMP\nvn9vLmGPMWY9mKmevNYNaGAMmCPAGd2uLYbXc/YdhKgLAeoCEDewdn/hnKO3YmBSE7oRCVmqjnTd\n/y5oYnQjMdAAWpCFzoSMt/LahM8QeBfvXFiL8RZTwqQxphEwOEdBYBrHGBvUCSyMkYC8u6BjRtp/\nkFgtMjEJbw+IpQuHTRW2kI5Jw5otlaqDkBzhgs0tSyDncj61xNNuqAh2mcOmC/vBlKSMsmFpvfxd\nT0ks3DyjAOta3BqjfUqEGswYG2zy+mGhjFuxcC045/2MsXsAvA9AD2NsOud8E2NsBoDNTo+77LLL\nBn9etmwZli1b5rmthE3imxdEbbSaiUZOBDZV3XiWdHg7eTTTmcNKF17TX8buk5NCF4YFrXE8trFo\n26Xf1ACfdWsqUHvMD2gmFIm46o3GeNYJ5NXohcIWOhIyntpcsnWYehfvYiKAurx8VDJvrO63Ey0x\nr5poEZzGTUvJ6CnqWNhmFQIaZoYQCte+PhFEVQhklP/f3plHSW5X9/5zpVq6u3oW2+NlPDa2Bxvb\ngEmMwTzC5kAgvBAIWSB5YQuPl5PtJDiGEJyDEwwkAbKR5B3CS14SlgCBLI9AdhYPxiwmGO8L2AaD\nZ8Yz9ni6q/dSlfR7f0iaVlfXIlVJKlXV/ZwzZ6pVpdKtX6mk3/3d5Ss8tLp5zI1W+inGvVKDlnt0\naYoTwXFcE2thpr1VZpaICI8J0oOemKBOwBhDveHmoiEQEnWQ1loeZ5XiRSPCRd4k+QOOa5hPeM4e\nOHCAAwcOxHptll2D9gAtY8yiiMwCzweuBT4JvAZ4V/D/J7q9R9QRiEvVTl6VPW0Rga89ss5lp87E\numjnkUYSprEsOR5Nz7DuGvbNldhd7Z/GsrtqM2tbHFlvbRE4M8ZXXk27z/pshxWaR3PoGBQySFes\norDSytYRaBkSryKNG9PWMlPZxC8W9pWa8164qth+j/T2aGhIEv2OM+ZK3Ft3Tvx9aLXF5R20YpIQ\ndg2Kg1/YOfz4+TUCkV7yrpdqWhD0FrH0NQQ6/+5nbKHpbdc5iJJUR6Af4bXp4GqLM+dKhIdNcm26\n8uprOLTaxHHNlhX2fimtqy1D2ZZMV83biTpIay3DXMxjD1IwPEhEoH3x/Nprr+362iwjAnuBDwR1\nAhbwIWPMZ0XkZuDjIvI6gvahaR40zMFKIsa1MUU1AqtNj3vrDs/b170bT94Mm8ayP0gPijoCdcej\nZKWfSztbsrZpCRzfyFZROMo4RwSySg268uprON5wObza4qaqxQeDY0xih5px+Tg80gwAACAASURB\nVDzqsKRPKcgHrzvuSCLYtbLfLrNT61JfQyDed3v6XIkvPOT3EfGCBZuhIwLl/gJaIcNqCITMla0t\n2jd+nnjK9xtbONo1ItA9FUtETjiO3b6XuI5A1Yo3tu/53bez0fJ4750L/OqTTh4o1avuuFz22jdu\n295vLlB38o0GwNZ7cZIi8YqVvA342NYIGGNuB57cYftx4AeyOu4gYlyjCLX2oxJ49Glz87ENLt5d\nTf2CNUr276xww5E1nhGRpz+y3uKMDFqfztjC+igjAmOsJbDS9DJRmK07Lo9/1VU8vm27dqgZHePi\nsIwbtZLFQsMdifhlWCdwSoechnofVeEoJ1dtVlseG67HsuMxV5ah70e1Djo03VhreakIdbanI623\nsogISNdi4SXH66ncO1fy21ru7JItGVtHIEF2Qj1HUa8oizm2Dg2J3ovXWv55HIdBFnknokYgb0JP\nLe48cMM1hVPorFjCYsxQZ1xanuHmY+v8j/N3pfq+o+bs+TKPrLtbvPKjKRcKh8yWLDY6RATO25FP\nbno1Zk/nXRWbW97/B6w0/XSrPbM2FUtGuiK77HiZt1hVlEmmVhaONxhNRKBHAeqS48a+3loinDZb\n4uE1l+MNl30pdK6zRJgJJr7zfSZkw3YoCpkt+WmiYfbBupt+RGCmh4jlcrN3Ola/uokkOgJxJ651\np7e6dFbUHS/3iEDYqt4Yw3rLMBfTOR9E62psIwKjJGkedVEjAmmnBt290OC02RJ7chIJy4uSJZw9\nX+KB5SYXn+SrLB5Za/HkDNp5dosI5JcaFK+n83t+9+3ceHSN5aZH2RI8A9+/r5aDhd3JQlVYUaaJ\nWjDRHIUj0CsPf8nxeNyu+NfA02dLHFlvcWzdzydPg1rJinWN8YuF03E+ZgORyfmyZFIsPGv779sJ\nX4G3hyPQp2A4vo5A/PnUUs6de0IWHZe9GSz89SIsFt5w/fqEuN2iBtG6yjoiMJF35aR51EWtEUgz\nNcgYw9ceKV7L0LTYv7PCt5b8ArRQUTiPiEAz6ImdV1gyybm96HicVLW5aHeVuxcbGDPalKIs24cq\nyjQwV7IoCV0LQLOk1wpzktQg8OsEjq61OLzW4syUtGzmY6oLp5UaBFvHxC8WTl81vVNqkDGmZ7Fw\nu22daHjp6wjUnfxTdADqjfwdkLBDpe9Yxj+fBhEV04jAAPi5W/HTagoZEUihWDgqKtVw/V7318+V\nCldEmUZh4f6dFb50xG8jutT0sKDnRXJQZmzh4cj3stBw2V21E4mDDEPcwi3wbbtgV4XTZm1KIjyU\n4k03KZ4xrLnZqAoryqQT7ay23PS4MyiuzfNaPl+2OLaxvVe/Mcn6vl959TU8stHi2LqLZ+CL8+l8\nljgtM8Ev6k2jWBh8xyw85nrLsHcug/ahHdTk112DbfXukjNXkq7aD8b4bWjjrDJXE8xF6o43VKpX\ndC5wZK3FSVWbqt0/pXU0xcIWjzbcoAtV/PNpEF2GuJoPgzKhjkAySeyNIuoIpNA+tIiiUp1I40Z2\nUtWmYgsPr7ssJshXTYofCt68uD66kY+QWEiSiMDxhstJVRsR4eKTKty90BiZI7AaKKJm4TBphxpl\n0inCtbybeu96y29RGTd1oe64XPLqN2zbPuxnmS/1Fj0LSUtHAALnI4wItNKPCJQswRZoehC9nC3H\nUHGulS2OrnduPeoaEOJFlioJGlQMGxGIzgU+f3gVgOec2Tul1TOmb71EFoQT+rUEHYMguS6DMQbH\nNR0VpNNiQh2BhDUCGeT2DUueIh6TQpge1PRMZo5Ae/HW8YYbS1EwLeKe2622lKWLdlf5+P1LPHdf\nZ0GgrMmyPqBI0S1FmVRqXVJv6s3RpIO0UytbLHbpuR9ijGGjFb+jYN9jluREC9H1BJ0Kk+Dfczwq\n9uZ9plfr0E3buqcGJRFRTVKv6OtJpHM/3L+zwqcPrvR1BJabvmOXd7pcOEdbT6hLUbGF5ZhRfQic\ntozTASfTEUiYVrPhdu6NPEqyah86yezfUeErD69RFuF796RfKAxBp4hI8dbxDbdnC7e0iZv2FnZv\nCFfgT50tUbWFQ6stzprPPyqw3Oydz6ooSrHptuJeT3HyNwzzZYtDq9tTl6KcKOxMaTHET0fy7wcb\nCVeG4xKqC0fbgPYrFIZNAbpONBLknIf1it1UpUMc19D00hFrA9hXK7HkeH0XkUbROhQ2F+WSRpji\nLvKG6YCegcNrLe7OMB1wIh2BJFXZLc/gAQULCAxUUDLNXHn1NSw6LodWWiCwd66ELen/aGZti3V3\na2pQFt2JuhE37S1MC4py8Ul+0fAoHAHtGKQo481syU8RcT2zpUNKElXhLKnFSA1KWtjZj7mSxSPr\nvvOx7prUdQSgs5ZAnJX3XjUTToKcc0uEktV/Hz8tyE4t4myJcO6OMt9acnhSJ/GKE8cdTaeisA1o\nL9G2TsRN+46mA14c2Z5FOuBEOgLVBKGXsFB4FOkSvciifegkU3dcLnrlVVzUtj3tH81M0M4t7MBz\nvFHMGoGFhrfdEdhd5cP3LvK8fbXciptD1BFQlPFGRPx0k7aJz5Lj9s1Xz4NaWfoWCyct7Ox7zGDV\n3QvyuLOoNfQXn7Ze85ebHo/ps6AzF7Q27SSylrRTYtWygs/X/TVJO0fFIUz37eUILDouu6v5n3+V\nExEBwxkJisSTZqzkwcQ6AnEjAv4PolhOAPgRCtcQSymxG7sqNnd88A94ZN3dIuGuRZSDY1tCKYjW\nOJ6hZPkt3vIi7rm92EHt+OQZm1rZ4sGVJuekIIAW7UoVpVMUZrnpcdaICpUVZdwpSkF8uOoedQTq\nTrLfdlafxW8f2vvamLSwsx/hqvt6K7sFxRlbaLRpCfitQ3t/jlBkbb1lqLWJrCV1WsKC4R09XpOk\nc1Rc9u+s8LlDqz3nQfVGb4XlrBg4NSiFRjBpM8GOQJKIwOhXM9oRkRMtRActZH7P776dW45tcHC1\nyQ+f0+snrCQhvLguOtsn21kTXnz65Wseb7g8toO2/MW7q9yz6KTiCCTpZKIRAUUZnKIUxHdadV9K\n2Ckmq89SseTEyny3DkZJCzv7MVcS1prG1xDIaEFoprQ1HRXiFQsDJyI47W2bk9QIQLwMhSwiAvNl\ni50Vi0OrLc7uEgHxU5KqqR43DmHtxGozuSNQtEYwE3lnrlrx24cWsXVoSBqe44MrTV2JTZlZ25eW\nP76Rn6JwiC2b7eR6sdChRgD8OoFvLDbwchYXU0dAUcaf+fL2PPw0O8UMg4j01RJIs3UobKYGrafY\niaiddnXhULchjjhjt85BSfvSx0lnCWsE0uaxEbHQzsf12J3zghz4EZeyJdSdZAJ1Raz/nOCIQLyB\nLqKYWEgaomIHV5s8/YzJVBMeFTO2xXrL81uHjuACFEa8ou3korieYaXpsatD3uTuqs3Ois13l5uc\n2yFikAaPrLvc/ugGf/F77zhRq3NwtcX1cyWsDAq4FUXJh1pb+k3anWKGJXRUOi2CAIkLO/thW0LZ\nFhYabmbziJmSP9kM2QjExOLk+HdzjAZNDerFktP5njMs+3dW+EyXNqKtoFh3VIr1YT1qFoJiYQrd\noxv+uRVGdbJwtibXEYjpcRW1RgCGjwgsN10c1+RazDoq8syhnS35EYFHN1zOTSHFJilh56BuyV6L\nwWpRtxZ5F++ucPdiIzNHoFa2+Ebd4e4Fh+f9/JsAuDDyfNEE7RRFicd8yeLhiEjVUtPPCy9Ks41e\nvfPBTw06fTZdW2sli2MbbnapQbbFurvZFtVvxRzvWHMlYa21fQ7RSKh5EGdRMmxZnTb7aiUWu7QR\nXQpqJfJufhFSsYWySyKxr7jtWMPFsr/55iLP2juXSjpvNybWEYi7kl7UGgHwT65hHIFDKy32zZcL\nc5HOkjxXmGdLBYgI9DgvOnUMinLRSVXef88iLzjbpNZPO8pcSfiJ/Tv5VG0iLy+KMrXUyhYry5uT\n0qK0Dg2ZL1us5JgaBH7dxKMbrczuBe2pQUsxCoVP2NYtNcg1ifL5+91zmp5hwzXMZ+AM9WojuphR\nOlJcqpYkdgAlZjvWkKUc2qNO3J067GTy4EqLW+e3dsrpNFksfI3AEKlBD642OVsnY6kzYwsrTY/V\npjeStmX9Ut+61QeE7KrYnDxj853lJvuHiArsqthc/xfvZlfF3hIWDy9aOQs9KoqSMfNt6sJZrQIP\nSjf145C1lsdcOf2IwENrLfZlVIs3U7K26AgsN+OP+VzZ4tHGdpG1JDoC0H8ushScB1ktOu7fWeHb\nHRyB+ohah4ZUbcEboNQ2TjtWANcYVnJIfZq4WWLYyeTCtu3d0hEarilED+ROVIeMCBxcafKCs+dT\ntEgB3xH49nKTXVV7JCHJYR0BgIt2V7l7oTGUI/A7b7uW/3PXAr98ycm5y7sripI/7SvMeaxWJmG+\n1FtdOG0dAfBFxZIWjCZhxpYtOgJ+69B4Y14rWax1jAgk1RHofc/J+jzYv7PMdR3aiC42RnP+hQvO\nxzZcjIHPz/o2xK1/i5v2vex41ErWFgG/LJg4RyApGy2PGbuYXXXKQxQLN1w/deX02an/ilNntmRx\neLU1kt7FEM8R6DfBv2h3hS8eWaPlmYEn8fcvOZyzo6xOgKJMCfNB8WmY37zkeJw7outgJ2o9UoOM\nMaxnkhrkv19WKcYzpa2pQctNL7Y6fDeRtUaPFqudqNjCUo9ISz3jFLEdZZudFf++G/3sdcflgl35\n1+klXXBuJ24jmHrC1ryDMvWzxEbRU4MGjAgcXm1xxlxJJ2kZMFvyv5e8W4eG+Ksz3S/KCzFqF3ZU\nbPbM2Dyw3OT8AS+k9y05PfctigiSoijpULL8lokbQbGpnxqUfw/3brSnLkUJNycp7IxDLXAssooI\nVC2h5Rlc49d0LTle7CyGuZLVtVi4mrDAtdfENavWoVH27/DbiEYdgcURtQ4dljhdmCC/iNvUOwIb\nQwh2Zc0w7UMPrqp+QNqE4cCGa3h43eXrVYsPlK3c22GGXYM64XqG5WZ/YZcrr76GQ6tNPu5udWji\nfpaWZ3hguckLzuqeeqYtQhVl8qgFLTpnS1bhUoNqJemqLpxFoTBwonXqbEYRARHxo8Atw1xZEnUN\nCgXF2jvUJK0R6FcsXHc89u/Mdr4Rqgw/+8zocUdbLDwoces/s460hEy9I1D0iMCSE08huZ2DKy0u\nP031A9IkiZJullRtYaHR+byoO36LtX45hXXH5bLXvnHb9rif5cGVJqdU7W2KlYqiTDbzQZ3AKTP5\nFDImYa7sC3y155JD+o5AuDDkuIaj6y531UrYGemkzJQkUC8WlptuLFVh2B7BCUk674lXLDzT9fk0\n2DdfYsFxWW36SsmOa2i6hlpBF3J7Ebf+s+64mRWhR5k4RyCajvDQWotTZmwqlnT1Gjdcr7DtQwdV\noHONCboYTNzXq9C7RiBOWlAa3FvvnRakKMpkEubhLzfzKWRMgi3iT5pbhlq53RFIV/gsujB0SWR7\nFgtDs7bFhmtouAYhnphYSBgVCNtcGmMS1wj0UxauO/2j0MNii3DOvN9G9JJTZvzWodXiaFgkoRJT\n9HbJ8Xj8SRoRSEzUE//coVVKFjx773ZFupBJbB/68FqLXRWLmYwETpTRUu1ROxKnY9CwGGO4b8nh\nJ/bvzPQ4iqIUjzAPv2gaAiG1kp+61B6tXI9MhseNmUBLYMlKPuZzJWG16bEnWLBvGbCFRBoyfk57\n5yi0m6O672N3Vk44AnXHZfeIzr9h69+SFAtratCQXLCrwqcPrnR1BFrBZKqokaVBIwIPtlXWK5NF\nz4iA42ZePPXIhq8seuqIiqUVRRkdtZKvo1J33EK23q4FnY3a8VODCnqz78NMyWLd9cAh8YS7Vt5a\nMDxIOnSvRcmlQPE3j1ba5+0sc91hv43oqFqHwvD1b3EawRjj1/vtzOEzFu9XnCL7aiWWHf+C1Ykw\nGlDU0NKgEYGDK03O0rSgiWXGtrp2DVrYyD416L66w/k7K4X93SiKkh1+C1FTuELhkPmgmLmdLDQE\n8iKMCCQpFA4JU4NCkmoIQCCA1WXimqeo3M6KzY6yL+CWV2vNLOinywCw0vSYsSX1LledmOjZoiXC\n/p0V7q87PPnU7YWzjQLXB8BgEQFjDAdXmzzvrO7pUMpgFKUdZr+IwEkxlBajn8Xgt5s9bdZmz0z/\nS8J9dYdn7Z1LZLOiKJNB2DWoYgmnzRbPEWgXPQtZa3mcXB3PSPlsyS/49VwvdqFwyFybqFjS+gCA\nsgUtj45F2PWcHcL9Oyvcv+Sw6MTXUygacSICfsegfMZ1oh0B8NODbn10o6MjUOT6ABisfehCw6Mk\n3YujlcEpSjvMbqsJrom/Stf+WW48usZDay1eel7vvP/VpsejDZfHjOkFWFGU4Qi7BtlCIRsG1MpW\nxyyAtVa66r95LgzN2BaLQYeipG3Ba2XhyNrmeDgJNQTAb2Eazkfa260v5bwyv39nmQOHfTHM3WM6\nz4kzt8sz4jHxjsB5O8v863dXOobDGq5hpsiOwACCYgdXm2PrJSvxCCMC7b2hl4LWoYOIyF26Z5Yb\n7zrOsY1Wz6jA/UsO5+4oF6pTiKIo+TEfqvcKhUzNmC9bHF5tbtu+nnJqUJ4LQ2Fq0GprsIhAVFuh\nkVBDICQUwWpvElp3vNwWhsKWrQdXWxgD19dKWBm1bM2SXg0/QvJMvZt4R6BqW+yrlfj2cpOLdm9V\nQNwouiNgCc2kjoDWB0w8tiVY4nd/iHbIG6ZjUMUWnnLqLF8+ss6Lz93R9XX31Z2RSLorilIMqrav\ndLvYyC83PAm1kvQoFi6evXGYLVlsuN7ANQJrreFSg6B7zWKe6tJhy9YL27bnreUzLHHqP+uOx6k5\npd6N568iIefvqnBf3dm2fZCimTyJ5uXF5aB2DJoKOtUJDNs69LJTZ/jWssPxjc7F9S3P8J3lJo9V\nR0BRphYRoRZEHot4/+xWLLyeso5AnszYvjbCspM8ItDeRWlQEdVuIlhLjje2KTqjIk79Z56qyVOx\ndHz+rgo3HFnbVuiy0Sp2REDErxh3vHh2rjY9Vlsee7St48RTDToHzUdWh44P6QhUbYsn75nhy0fX\neNE526MC311pcuqsPbaraoqiDEeYmnF0rYUBbpvzpxBFSs2olbemwoC/iNEcMCWmCMyU5ETdQ9L8\n/rB4OkwldbzkNQLQWQTLM4aVAaIU004cQbGlHETaQqbCEdgVtJw6tNri7Mhq+aCecZ5UbKHpGuLM\n7Q+uNtlXK+XSz1cZLZ0iAosNl3N3DBcNeuqps7zvrgUWG9v1CO5TNWFFmWrGITWjagmeMTiRFJj1\noFB4XFsez9p+y9ZTBlDSrdiCCL4DENw3agNERjoVuC43/XQrrRlLRrfoSogxJte2rFPjxp2/c3t6\n0EaHCviikaSF6KHVVuKOAsp40skRON5wOWnIUOJMyeLSPTN85ej6lu3GmBP6AYqiKEUlTF2KpsOM\ns4YAcCIjIGlaUMhcaVNUbNCU6GpQLBylnuOq9SQR1giYLmnfay2Ta+rdVEQEwG8j+s/fWeH79232\n199wPap2sSfOSVqIHlxpcsU+1Q+YBtodAS9oHZqGqvBTT53lz+9e4PvOmD3Rx/jhdRdL4BRNO1MU\npeDMl/10mDBV0o8IjO+E1baEspVcVTgkTA86qWpviZQkoVOB61KOeexQHC2fYbFEsAWaHnQyPe+W\nrFPjCJwxV2LD9Ti+4XJyMJkpevtQ2GzZ1Y+mZ3hko8Xeuan5Sqeadi2BJcejVhqsdWg7c2WLJ50y\nw40Pr/P8s+YBuG/JTwsa19C6oijTQ60UtDgNWBvjQuGwLuPwaosvli3+PpggJqnLmItESBoD6AhA\n53QWX/QqvwlrUepQ0iBsD9/JKctTTAymyBEQEb970JLD5TO+uFjRBcXA7xwUp4Xo4dUmp82WcpGj\nVkZP1RY23M0b3bCFwu1cftos//fuBZ5++hzzZYv76g7POVPVhBVFKT618lZ14XFuHZpGXUatJCda\niA6jIxCmF0VtO3Ou2FkVRaUaRlg6DF+eYmIwRY4AwAW7qtz48BqXn+Y7AuMQEajaVqzUoIOrLfZp\nfcDUULWtLZGiYVuHtjNftnjCyVVuPLrG006f43jD5Ww9vxRlqhmX1Iz5CXIE0qAWERUbODXIEhbd\nrW1Z6w2Pi3dP77gOQ6/6z7rjpXo/78dUOQLn7CjzyQfcE/mCGwXXEYD4xcIHV5pcuqdd80+ZVKq2\nsOhsXpR9RyDdc/kTf/pOvll32Fm2cDzDbUFKXZFaBSqKkh/j8ruvlSwOr22qC6+7hj0zxb7XZ8lc\n2eLRQB9mYB2BDmq4S81iisqNA34L0e16F+Cn+p4zZAfAJEyVI1C2hMfsKPOtJYfHn1Qdi4hAHAU6\nzxgOr7Z48Tm6YjsttBcLLzTc1GXe11oez/u5N23bXqRWgYqiKO3UytIhIjC998dayeLBlu8YNYbS\nEdgcUxM0qMgzl32S6B0RyLcIO1NHQETOBj4InAYY4M+NMX8iIicDHwPOAR4AXm6MWczSlpALgjai\nj9vtS2KnUVyZJWWLridLWETU9AzHNtxCirso2bDdEcg3lKgoilJU2tWFfUeg2Pf6LImKig2TGhSd\ni6y0PGZs0brEAemV9p2nmBhkHxFoAr9qjLlFROaBm0Tk08BrgU8bY94tIr8OvDn4lzmP3VXhc4dX\nWQtO4qJTsaSjXDpsFhG1oyu2k0/UEfAC8ZE0WocqiqKMO7WSL8AVMs46AmnUZdTKwmrLw/EMZUsG\nEh311ew3x7Te8ApXGzJOdIsIbLQ8DOQ6P83UETDGHAGOBI9XRORuYB/wEuA5wcs+ABwgJ0dgvmxx\nStXmvrrDTIHrA8LV/pWm/+P9m6rmZyubVCNh2rB1qK7MKIqi+I7AWstfAReRsS4WTuN+XytZrDUH\njwbAdk2jpZxbh04a3dK+Q5G2PFt151YjICLnApcCNwKnG2OOBk8dBU7Pyw6A83dVuON4o9CtQ7ut\n9t/zoT/kgWWHgystHl53t7UUU6aDqrW5OrPQ0GiAoihKiG0J1aDd5WwJNlqGmSlODaraQtMYVluD\n1QeE7xFdwc47j33SqLRpAYXUnfwLsHNxBIK0oH8AXm+MWY56OsYYIyLxpHNT4Mqrr+HYRosjay4z\ntnDd7HittB9abfGFh9Y4q1YeWGVQGX+icu9ZdAyC8WkVqCiK0s58yRfRErGo2oI9xWKIIkKtZLHQ\ncAdeAK201aXVHY/TZvVeMCgVW1hytqd9+/UB+Y5r5o6AiJTxnYAPGWM+EWw+KiJnGGOOiMhe4OFO\n+771rW898fiKK67giiuuGNqeuuPyxFe/gSe2bR+XvPp98yVe9bjdAHxwilc4pp2wRsAYw0LD5eQM\nIgLj4BgriqJ0IhQVs4SxTQtKk7mSsNBwB04NKgl4BlzPYFvCkuNywa5KylZOD9WuqUHpiIkdOHCA\nAwcOxHpt1l2DBPhL4C5jzHsiT30SeA3wruD/T3TYfYsjoPhEf8K6Yju9lCxBgJbxOwadlXLrUEVR\nlHGmVvI7B9mWTHXHoJBa2eL4EBEBETmRHjRrCXWtERiKiiVbREFD6o7HmSmId7Yvnl977bVdX5t1\nROAZwCuB20Tk5mDb1cA7gY+LyOsI2odmbMdEoiu2000YFVhw0lUVVhRFGXfmy35qUNkWZjUiwFyQ\nGnTKEPeKMD1oxjZaIzAk7cXXIXm3DoXsuwbdAHT7RD+Q5bHHHV3tV/pRtYUN16PeUEdAURQlSq1s\nseT4K+AaEfAjJPcvOeydG3zaVw0KXNdafhvSQdOMlM5KzTCaIuypUhYeJ3S1X+lH1bI4tu4yq61D\nFUVRtjBfsnhotclsydIaAXzHaL1lhuqWWAkmr0saDRiaTu1DHdfQ9EzujuvUOQK60q5MClVbOLLW\nYncGHYMURVHGmVpZWGl5zLVU+AqgFkwuq0PoJ4XpLKue0fqAIelUI7DkuOys2LlqCMAUOgK60q5M\nClVbOLLeyqRjkKIoyjjjdw0yrJcNe+c0YloLoiKD6gjAZjrLUkqdbaaZTl2D6iOoD4Du+fuKohSc\nMCKg9QGKoihbmS/57UPHWVU4TeYC3aFhU4MargkmrHrfGYYwumLMVpG2UURapi4ioCiTgl8sbFRV\nWFEUpY2qLbSMYclRR+DKq69hoeFyaLXFrbM2M4EzkFRI1VfD9ag7Lufu0JbVw2Bbggi4xtdoAEbm\nYKkjoChjSriyo6lBiqIoWxERamW/ZebslHcNqjsuF7/qKi5u255USHUzNUgjAmkQFgyXgnStJcfl\n/BGItKkjoChjxpVXX0PdcVlueiw2PO6plRBJvrqjKIoyycyXLI0IpEjFEpab3shy2SeNsGB4Lvjb\nF2nTiICiKH2oOy4XvvKqbduTru4oiqJMMrWyRdlC2yunRNW2OLTaQgRm1LkamnZRsfqIirDVEVAU\nRVEUZWIIo6YLDZf1luGOmj/V0ajpcFRs4ZGNFjvL6gSkQVRUrOUZNlzD/AjGVh0BRVEURVEmBo2a\nZkPFEhYa3kjy2CeRsAsTwJLjMV+2sHLWEAB1BBRFURRFUSaWtIRUqye6DWlEIA0q1mZEoD5CtWZ1\nBBRFURRFUSaUtNKhKtZm21FleKKiYksjLMBWR0BRxoy0VncURVEUJQ5XXn0Nj264PLTW4uYZm7nS\nYFoEyiahLgOMTkwM1BFQlLFDL7qKoihKntQdlye8+iqe0LZd6y4Gp2JHU4M8zhmRSJs6AoqiKIqi\nTAwaNVXGgYolrDQ3IwK7KtWR2KGOgKIoiqIoE4NGTZVxoGpbHG+4ACNVa9bSb0VRFEVRFEXJkVBQ\nzDWGlZbHjhHpM6gjoCiKoiiKoig5UrGFhmdYdjxqJQt7RArYmhqkKIqiKIqidEXrLtKnErQPHWXr\nUFBHQFEURVEURemB1l2kTygoNkoxMdDUIEVRFEVRFEXJlaotNFxD3fFG62a8LAAADxhJREFUpiEA\n6ggoiqIoiqIoSq6EEYEljQgoiqIoiqIoyvQQ1gjUR1wjoI6AoiiKoiiKouRIScAYWGi4mhqkKIqi\nKIqiKNOCiFCxhaWmx84RpgZp1yBFURRFURRFyZmKLdgC5RFpCIA6AoqiKIqiKIqSO1VLKJVGm5wj\nxpiRGtANETFFtU1RFEVRFEVRBuHKq6+h7rgcXW9hi7Bnxk8N2lWxM9FsEBGMMR3DDhoRUBRFURRF\nUZScqDsuF77yKi5s295JvTlrtFhYURRFURRFUaYQdQQURVEURVEUZQpRR0BRFEVRFEVRphB1BBRF\nURRFURRlCtFiYUVRFEVRFEXJiV0Vu2Nh8K4RCItp+1BFURRFURRFmVB6tQ/V1CBFURRFURRFmULU\nEVAURVEURVGUKUQdAUVRFEVRFEWZQtQRUBRFURRFUZQpJFNHQET+SkSOisjtkW0ni8inReSbIvKf\nIrI7SxsURVEURVEURdlO1hGBvwZe2LbtzcCnjTGPAz4b/J0JBw4cyOqtU0XtTBe1M33GxVa1M13U\nznQZFzthfGxVO9NF7UyXcbAzU0fAGPMFYKFt80uADwSPPwC8NKvjj8MXAGpn2qid6TMutqqd6aJ2\npsu42AnjY6vamS5qZ7qMg52jqBE43RhzNHh8FDh9BDYoiqIoiqIoylQz0mLhQDFMVcMURVEURVEU\nJWcyVxYWkXOBTxljLgn+vge4whhzRET2AtcZYy7qsJ86CIqiKIqiKIoyJN2UhUt5GwJ8EngN8K7g\n/090elE3gxVFURRFURRFGZ5MIwIi8lHgOcAe/HqA3wT+Cfg48BjgAeDlxpjFzIxQFEVRFEVRFGUb\nmacGKYqiKIqiKIpSPMZKWbiLQNn3iMiXReQ2EfmkiOwItl8uIjcH/24TkZ+M7HOZiNwuIveKyB8X\n2M4DInJP5Pk9o7Iz8vxjRGRFRN4Q2VaY8exjZ6bjmdRWETlXRNYj9rw3sk9hxrSPnYU6R0XkScFz\ndwTPV4LthRnPPnYWZjxF5BURO24WEVdEnhQ8V5jx7GNn0X7zMyLy0WD7XSLy5sg+RRrTXnYW6Ryt\niMhfB9tvEZHnRPYp0nj2sjPr8TxbRK4TkTuD682vBNu7iruKyNXBuN0jIi+IbM9sTFO2M7MxTWpn\nsP06EVkWkT9te69Mz9HYGGPG5h/wLOBS4PbItv8CnhU8fi3wtuDxLGAFj88AjgF28PdXgcuDx/8K\nvLCgdl4HPLkI4xl5/u+BjwFviGwrzHj2sTPT8Rzguz83+rq29ynMmPaxszDnKH7N063AJcHfJ0V+\nW0Uaz152FmY82/Z7InBfEc/PDnbem9f5OcB3/zPAR4PHs8C3gccUbUz72FmYcxT4JeAvg8enAl8r\n4jnax86sx/MM4HuDx/PAN4CLgXcDbwq2/zrwzuDx44FbgDL+tf8+NrNHMhvTlO3MbEwHsHMOeAbw\nc8Cftr1Xpudo3H9jFREwnQXKLgi2A3wG+PHgtevGGC/YPgvUjTGu+J2Kdhhjvho890FSFjVLw87I\nfpkVTSexE0BEXgp8C7grsq1Q49nNzgiZFqEntbUTRRzTPhTlHH0BcJsx5vZg3wVjjFfA8exoZ2S/\nooxnlJ8GPgqFPz9/Gvjbtm1F+s0/BNRExAZqgAMsFXBMO9oZ2a8o5+jF+JM+jDGPAIsi8tQCjmcn\nO58S2S/L8TxijLkleLwC3A3so7u464/gO4FNY8wD+BPsp2U9pmnZGXnLTMY0qZ3GmDVjzBeBRvR9\n8jhH4zJWjkAX7hSRHwkevww4O3xC/LSbO4E7gauCzfuAg5H9DwXbimZnyAeC0NZbcrCxq50iMg+8\nCXhr2+sLNZ497AzJezyhx3cPnBfYc0BEnhlsK9SY9rAzpBDnKPA4wIjIv4vITSLya8H2oo1nNztD\nijKeUV5O4AhQvPGMErUzpDC/eWPMf+BPqB/Cb5bxe8ZvllGoMe1hZ0hRztFbgZeIiC0i5wGXAWdR\nsPHsYmf0/M1lPMVv534pcCPdxV3PZOvYHcQfu/btmY3pEHaeGfk78zGNaWdIe0HuqM7RbUyCI/A/\ngV8Uka/hh2mc8AljzFeNMU8Angz8sYjsGpGNMJidrzDGPBE/BPksEXnVCO18K/BHxpg1Ml5hi8kg\ndo5iPHvZehg42xhzKb4D+BFpq3XImSR2zgfPFekcLQHPxF8VfibwoyLyXEYnWpjUTijWeAIgIk8D\n1owxnSJseTKInYX6zYvIK/Ejv3uB84A3BhPDUTGInUU6R/8KfzL1NeCPgC8BLsX7zXezE3Iaz+Ca\n/Q/A640xy9HnjJ+bUojOMSnZmfmYjst4xmEUOgKpYoz5BvCDACLyOOBFHV5zj4jcD5yP/2M8K/L0\nWfieWNHsvMkYczjYviIiHwEuBz6Us50/FDx1OfDjIvJuYDfgicg68I8UYzx72mmMee8oxrOLrS8K\ntjsENwpjzNeD7/4C/PErwpj2svNxwNcLc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"text/plain": [
"<matplotlib.figure.Figure at 0x7fccff641650>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Find the best & worst for each variable\n",
"winter_coldest = pd.Series(index=noaa_winters.columns)\n",
"winter_warmest = pd.Series(index=noaa_winters.columns)\n",
"# For these variables, big is bad\n",
"for v in ['MXSD','EMXP','DT00','DX32','TSNW','DP10']:\n",
" winter_coldest[v] = noaa_winters[v].max()\n",
" winter_warmest[v] = noaa_winters[v].min()\n",
"# For these variables, small (or negative) is bad\n",
"for v in ['MNTM','EMNT']:\n",
" winter_coldest[v] = noaa_winters[v].min()\n",
" winter_warmest[v] = noaa_winters[v].max()\n",
"# Assign scores to each year\n",
"winter_score = 100 * (noaa_winters-winter_warmest).abs() / (winter_coldest-winter_warmest).abs()\n",
"badness = winter_score.mean(axis=1)\n",
"# Plot the Badness Index\n",
"badness.plot(figsize=(13,6), marker='s', color='skyblue', xticks=badness.index[2::5])\n",
"pd.rolling_mean(badness, 20).plot(color='blue')\n",
"plt.title(\"Badness Index of each Chicago winter\")\n",
"plt.ylabel(\"Badness index\")\n",
"plt.xlabel(\"Year (start of winter)\")\n",
"plt.legend([\"Computed Badness\", \"20-year rolling average\"])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"There you have it! Some candidates for Worst Winter Ever can be determined by the highest peaks. The winter of 2013-14 was pretty bad, but it paled in comparison to the winter of 1978-79."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bonus Statistical Analysis [UNDER CONSTRUCTION]\n",
"---\n",
"\n",
"Here we'll dive into some Principal Component Analysis, which basically extracts the most prominent trends from all the variables for further inspection. Check Wikipedia for more info."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Explained variance ratios: [ 0.44403983 0.20300119 0.13699109 0.08643274]\n"
]
},
{
"data": {
"image/png": 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lpqNDHc+OjWWgjxTah4YQKtCb1HbjWNVLSWHLjSdamN+/H7jkktAeuWxvVztY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\nL5vLyqBP49BoHfbAJdbXrsc8e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KcOlEXoc5Nujh1D0VDqRrcbj8HA342P5xfiAbonf8Wr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f+Q6J+z+RaS51evI0LeXaFHrJbqPTUVShv+GNSW770bPsa21lNB5nRZrNvtp/4Qn9MpuN4Vgs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\nv2FBT0REvtPcDLS1AUeP5p/HDj0R+Q0LeiIi8iWj2A0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"text/plain": [
"<matplotlib.figure.Figure at 0x7fcce9eed510>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"z = (noaa_winters - noaa_winters.mean()) / noaa_winters.std()\n",
"from sklearn.decomposition import PCA\n",
"pca = PCA(n_components=4)\n",
"pca.fit(z)\n",
"pca_components = pd.DataFrame(pca.components_, index=['PC'+str(i) for i in range(1,pca.n_components_+1)], \\\n",
" columns=z.columns)\n",
"pca_scores = pd.DataFrame(pca.transform(z), index=z.index, columns=pca_components.index )\n",
"print \"Explained variance ratios:\", pca.explained_variance_ratio_\n",
"pca_scores.plot(figsize=(13,8))\n",
"plt.legend(loc='best')\n",
"plt.title('Principal component scores')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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Hcu6553LCCSfQv39/xo4dy2mnnQbAk08+yamnnsqyZcsYMmQIkyZN4utf/3q3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\n/j/HWuzE2rdlZgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcce9e5f350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Cluster analysis\n",
"import numpy as np\n",
"from scipy.spatial.distance import squareform\n",
"from scipy.cluster.hierarchy import linkage, dendrogram\n",
"dissimilarity = 1 - noaa_winters.corr().abs()\n",
"row_distance = np.clip(squareform(dissimilarity),0,1)\n",
"L = linkage(row_distance, method='average')\n",
"plt.figure(figsize=(13,9), dpi=100)\n",
"plt.subplot(212)\n",
"R = dendrogram(L, orientation='bottom')\n",
"plt.ylabel('Cluster distance (UPGMA)')\n",
"# Matched up with PC loadings (scaled by corresponding PC variances)\n",
"leaves = [pca_components.columns[i] for i in R['leaves']]\n",
"plt.subplot(211)\n",
"(pca_components[leaves].iloc[0] * pca.explained_variance_[0]).plot(kind='bar', color='blue')\n",
"(pca_components[leaves].iloc[1] * pca.explained_variance_[1]).plot(kind='bar', color='green')\n",
"(pca_components[leaves].iloc[2] * pca.explained_variance_[2]).plot(kind='bar', color='red')\n",
"(pca_components[leaves].iloc[3] * pca.explained_variance_[3]).plot(kind='bar', color='cyan')\n",
"plt.ylabel('PC loadings times PC variance')\n",
"plt.legend(loc='best')\n",
"plt.title('Components of each variable: PC loadings scaled by corresponding PC variances')\n",
"plt.show()"
]
},
{
"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 |
auag92/n2dm | .ipynb_checkpoints/index-checkpoint.ipynb | 1 | 1652 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello, World!\n"
]
}
],
"source": [
"print(\"Hello, World!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Importing Relevant Modules"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import scipy"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import asap3"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
araichev/affordability_nz | notebooks/prepare_geodata.ipynb | 1 | 10618 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pathlib import Path\n",
"import json\n",
"from functools import reduce\n",
"import math\n",
"import datetime as dt\n",
"import pytz \n",
"from itertools import product\n",
"from collections import OrderedDict\n",
"import time\n",
"import sys\n",
"\n",
"import requests\n",
"import numpy as np\n",
"import pandas as pd\n",
"import geopandas as gpd\n",
"import shapely.ops as so\n",
"\n",
"import helpers as hp\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prepare table of 2001 area units and rental area units"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 2001 census area units\n",
"path = hp.DATA_DIR/'collected'/'Geographical Table.csv'\n",
"f = pd.read_csv(path, dtype={'SAU': str})\n",
"f = f.rename(columns={\n",
" 'SAU': 'au2001', \n",
" 'SAU.Desc': 'au_name', \n",
" 'TA': 'territory',\n",
" 'Region': 'region',\n",
"})\n",
"del f['Water']\n",
"f.head()\n",
"\n",
"# rental area units\n",
"path = hp.DATA_DIR/'collected'/'Market Rent Areas.csv'\n",
"g = pd.read_csv(path, dtype={'SAU': str})\n",
"g = g.rename(columns={\n",
" 'SAU': 'au2001', \n",
" 'MARKET RENT DESCRIPTION': 'rental_area',\n",
" 'TA': 'territory',\n",
" 'AU NAME': 'au_name',\n",
"})\n",
"\n",
"# Clean rental areas\n",
"def clean(x):\n",
" y = x.split(' - ')\n",
" y = y[1] if 'District' not in y[1] else y[0]\n",
" return y\n",
"\n",
"g['rental_area'] = g['rental_area'].map(clean)\n",
"\n",
"\n",
"f = f.merge(g[['au2001', 'rental_area']])\n",
"\n",
"path = hp.get_path('au2001_csv')\n",
"f.to_csv(path, index=False)\n",
"f.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Process area units and rental areas into GeoJSON"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Read Shapefile\n",
"\n",
"path = hp.DATA_DIR/'collected'/'NZ_AU01_region_simplified'/'NZ_AU01_region.shp'\n",
"au = gpd.read_file(str(path))\n",
"au.crs = hp.CRS_NZGD49\n",
"au = au.to_crs(hp.CRS_WGS84)\n",
"au = au.rename(columns={'AU01': 'au2001', 'AU_DESC': 'au_name'})\n",
"print(au.shape)\n",
"print(au.head())\n",
"au.head().plot()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Remove water area units\n",
"\n",
"pattern = r'ocean|strait|inlet|harbour'\n",
"cond = au['au_name'].str.contains(pattern, case=False)\n",
"au = au[~cond].copy()\n",
"print(au.shape)\n",
"au.head().plot()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Merge geodata and metadata, drop null regions, and write to file\n",
"\n",
"f = hp.get_data('au2001_csv')\n",
"\n",
"g = au.merge(f[['au2001', 'territory', 'region', 'rental_area']])\n",
"g = g[g['region'].notnull()].copy()\n",
"\n",
"path = hp.get_path('au2001')\n",
"with path.open('w') as tgt:\n",
" tgt.write(g.to_json())\n",
"\n",
"g.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create geodata for rental areas "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Dissolve area units by area unit group\n",
"\n",
"au = get_data('au2001')\n",
"ra = au[['rental_area', 'region', 'territory', 'geometry']].dissolve(by='rental_area').reset_index()\n",
"\n",
"path = hp.get_path('rental_areas')\n",
"with path.open('w') as tgt:\n",
" tgt.write(ra.to_json())\n",
"\n",
"ra.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Choose representative points for rental areas using approximate centroids of property titles"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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" <th>LGD_ID</th>\n",
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" <td>21149591</td>\n",
" <td>Yeung J</td>\n",
" <td>6683994</td>\n",
" <td>122991</td>\n",
" <td>POINT (174.9064763832665 -36.95116400076868)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3410291</td>\n",
" <td>Vajsakovic D</td>\n",
" <td>5154438</td>\n",
" <td>NA11A/102</td>\n",
" <td>POINT (174.6539389831913 -36.82909730112425)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3414488</td>\n",
" <td>Grbic I L:Lowe J D</td>\n",
" <td>4826167</td>\n",
" <td>NA32C/33</td>\n",
" <td>POINT (173.3733926499484 -34.87991641687911)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3421826</td>\n",
" <td>Kim K</td>\n",
" <td>5013176</td>\n",
" <td>NA459/84</td>\n",
" <td>POINT (174.7440597997697 -36.79408678372536)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3438504</td>\n",
" <td>Skeen R</td>\n",
" <td>5065364</td>\n",
" <td>NA22A/1323</td>\n",
" <td>POINT (174.6284382829138 -36.85095825040765)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" LGD_ID OWNERS PAR_ID TTL_TITLE \\\n",
"0 21149591 Yeung J 6683994 122991 \n",
"1 3410291 Vajsakovic D 5154438 NA11A/102 \n",
"2 3414488 Grbic I L:Lowe J D 4826167 NA32C/33 \n",
"3 3421826 Kim K 5013176 NA459/84 \n",
"4 3438504 Skeen R 5065364 NA22A/1323 \n",
"\n",
" geometry \n",
"0 POINT (174.9064763832665 -36.95116400076868) \n",
"1 POINT (174.6539389831913 -36.82909730112425) \n",
"2 POINT (173.3733926499484 -34.87991641687911) \n",
"3 POINT (174.7440597997697 -36.79408678372536) \n",
"4 POINT (174.6284382829138 -36.85095825040765) "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ra = hp.get_data('rental_areas')\n",
"t = hp.get_data('property_titles')\n",
"t.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Spatial-join titles to rental areas\n",
"\n",
"%time f = gpd.sjoin(t[['geometry', 'fid']], ra, op='intersects')\n",
"f.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Choose representative points for rental areas\n",
"\n",
"def pt(group):\n",
" d = {}\n",
" d['geometry'] = so.unary_union(group['geometry']).representative_point()\n",
" d['territory'] = group['territory'].iat[0]\n",
" d['region'] = group['region'].iat[0]\n",
" return pd.Series(d)\n",
"\n",
"g = gpd.GeoDataFrame(f.groupby('rental_area').apply(pt).reset_index())\n",
"\n",
"path = hp.get_path('rental_points')\n",
"with path.open('w') as tgt:\n",
" tgt.write(g.to_json())\n",
"\n",
"g.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prepare regional slices of data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ra = hp.get_data('rental_areas')\n",
"rap = hp.get_data('rental_points')\n",
"\n",
"for region in hp.REGIONS:\n",
" region_root = hp.DATA_DIR/region\n",
" if not region_root.exists():\n",
" region_root.mkdir()\n",
" \n",
" region_c = region.capitalize()\n",
"\n",
" # Rental areas slice\n",
" f = ra[ra['region'] == region_c].copy()\n",
" path = hp.get_path('rental_areas', region)\n",
" with path.open('w') as tgt:\n",
" tgt.write(f.to_json())\n",
" \n",
" # Rental area points slice\n",
" f = rap[rap['region'] == region_c].copy()\n",
" path = hp.get_path('rental_points', region)\n",
" with path.open('w') as tgt:\n",
" tgt.write(f.to_json())\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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
PMEAL/OpenPNM | examples/simulations/multiphysics/advection_diffusion.ipynb | 1 | 62910 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Advection-Diffusion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this example, we will learn how to perform an advection-diffusion simulation of a given chemical species through a `Cubic` network. The algorithm can be applied to more complex networks in the same manner as described in this example. For the sake of simplicity, a one layer 3D cubic network is used here. On `OpenPNM`, 4 different space discretization schemes for the advection-diffusion problem are available and consist of:\n",
"\n",
"1. Upwind\n",
"2. Hybrid\n",
"3. Powerlaw\n",
"4. Exponential\n",
"\n",
"Depending on the Peclet number characterizing the transport (ratio of advective to diffusive fluxes), the solutions obtained using these schemes may differ. In order to achive a high numerical accuracy, the user should use either the `powerlaw` or the `exponential` schemes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generating network\n",
"First, we need to generate a `Cubic` network. For now, we stick to a one layer 3d network, but you might as well try more complex networks!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:31:04.826154Z",
"iopub.status.busy": "2021-06-24T11:31:04.824562Z",
"iopub.status.idle": "2021-06-24T11:31:05.555611Z",
"shell.execute_reply": "2021-06-24T11:31:05.554119Z"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import openpnm as op\n",
"%config InlineBackend.figure_formats = ['svg']\n",
"np.random.seed(10)\n",
"%matplotlib inline\n",
"ws = op.Workspace()\n",
"ws.settings[\"loglevel\"] = 40\n",
"np.set_printoptions(precision=5)\n",
"shape = [1, 20, 30]\n",
"net = op.network.Cubic(shape=shape, spacing=1e-4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Adding geometry\n",
"Next, we need to add a geometry to the generated network. A geometry contains information about size of the pores/throats in a network. `OpenPNM` has tons of prebuilt geometries that represent the microstructure of different materials such as Toray090 carbon papers, sand stone, electrospun fibers, etc. For now, we stick to a sample geometry called `SpheresAndCylinders` that assigns random values to pore/throat diameters."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:31:05.562675Z",
"iopub.status.busy": "2021-06-24T11:31:05.561281Z",
"iopub.status.idle": "2021-06-24T11:31:05.581828Z",
"shell.execute_reply": "2021-06-24T11:31:05.580666Z"
}
},
"outputs": [],
"source": [
"geom = op.geometry.SpheresAndCylinders(network=net, pores=net.Ps, throats=net.Ts)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Adding phase\n",
"Next, we need to add a phase to our simulation. A phase object(s) contain(s) thermophysical information about the working fluid(s) in the simulation. `OpenPNM` has tons of prebuilt phases as well! For this simulation, we use air as our working fluid."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:31:05.589007Z",
"iopub.status.busy": "2021-06-24T11:31:05.587632Z",
"iopub.status.idle": "2021-06-24T11:31:05.591339Z",
"shell.execute_reply": "2021-06-24T11:31:05.592328Z"
}
},
"outputs": [],
"source": [
"air = op.phases.Air(network=net)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Adding physics\n",
"Finally, we need to add a physics. A physics object contains information about the working fluid in the simulation that depend on the geometry of the network. A good example is diffusive conductance, which not only depends on the thermophysical properties of the working fluid, but also depends on the geometry of pores/throats."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:31:05.598933Z",
"iopub.status.busy": "2021-06-24T11:31:05.597671Z",
"iopub.status.idle": "2021-06-24T11:31:05.681466Z",
"shell.execute_reply": "2021-06-24T11:31:05.680209Z"
}
},
"outputs": [],
"source": [
"phys_air = op.physics.Standard(network=net, phase=air, geometry=geom)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Performing Stokes flow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the advection diffusion algorithm assumes that velocity field is given. Naturally, we solve Stokes flow inside a pore network model to obtain the pressure field, and eventually the velocity field. Therefore, we need to run the `StokesFlow` algorithm prior to running our advection diffusion. There's a separate tutorial on how to run `StokesFlow` in `OpenPNM`, but here's a simple code snippet that does the job for us."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:31:05.689592Z",
"iopub.status.busy": "2021-06-24T11:31:05.688199Z",
"iopub.status.idle": "2021-06-24T11:31:05.840759Z",
"shell.execute_reply": "2021-06-24T11:31:05.841382Z"
}
},
"outputs": [],
"source": [
"sf = op.algorithms.StokesFlow(network=net, phase=air)\n",
"sf.set_value_BC(pores=net.pores('front'), values=200.0)\n",
"sf.set_value_BC(pores=net.pores('back'), values=0.0)\n",
"sf.run();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is essential that you attach the results from `StokesFlow` (i.e. pressure field) to the corresponding phase, since the results from any algorithm in `OpenPNM` are by default only attached to the algorithm object (in this case to `sf`). Here's how you can update your phase:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:31:05.845209Z",
"iopub.status.busy": "2021-06-24T11:31:05.844634Z",
"iopub.status.idle": "2021-06-24T11:31:05.846920Z",
"shell.execute_reply": "2021-06-24T11:31:05.846327Z"
}
},
"outputs": [],
"source": [
"air.update(sf.results())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Performing advection-diffusion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that everything is set up, it's time to perform our advection-diffusion simulation. For this purpose, we need to add corresponding algorithm to our simulation. As mentioned above, `OpenPNM` supports 4 different discretizations that may be used with the `AdvectionDiffusion` and `Dispersion` algorithms.\n",
"Setting the discretization scheme can be performed when defining the physics model as follows:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:31:05.850781Z",
"iopub.status.busy": "2021-06-24T11:31:05.850199Z",
"iopub.status.idle": "2021-06-24T11:31:05.855372Z",
"shell.execute_reply": "2021-06-24T11:31:05.854856Z"
}
},
"outputs": [],
"source": [
"mod = op.models.physics.ad_dif_conductance.ad_dif\n",
"phys_air.add_model(propname='throat.ad_dif_conductance', model=mod, s_scheme='powerlaw')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, the advection-diffusion algorithm is defined by:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:31:05.859588Z",
"iopub.status.busy": "2021-06-24T11:31:05.858997Z",
"iopub.status.idle": "2021-06-24T11:31:05.861711Z",
"shell.execute_reply": "2021-06-24T11:31:05.861172Z"
}
},
"outputs": [],
"source": [
"ad = op.algorithms.AdvectionDiffusion(network=net, phase=air)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that `network` and `phase` are required parameters for pretty much every algorithm we add, since we need to specify on which network and for which phase do we want to run the algorithm."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that you can also specify the discretization scheme by modifying the `settings` of our `AdvectionDiffusion` algorithm. You can choose between `upwind`, `hybrid`, `powerlaw`, and `exponential`.\n",
"It is important to note that the scheme specified within the algorithm's settings is only used when calling the `rate` method for post processing."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Adding boundary conditions\n",
"Next, we need to add some boundary conditions to the simulation. By default, `OpenPNM` assumes zero flux for the boundary pores."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:31:05.866571Z",
"iopub.status.busy": "2021-06-24T11:31:05.865979Z",
"iopub.status.idle": "2021-06-24T11:31:05.867689Z",
"shell.execute_reply": "2021-06-24T11:31:05.868153Z"
}
},
"outputs": [],
"source": [
"inlet = net.pores('front') \n",
"outlet = net.pores(['back', 'top', 'bottom'])\n",
"ad.set_value_BC(pores=inlet, values=100.0)\n",
"ad.set_value_BC(pores=outlet, values=0.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`set_value_BC` applies the so-called \"Dirichlet\" boundary condition to the specified pores. Note that unless you want to apply a single value to all of the specified pores (like we just did), you must pass a list (or `ndarray`) as the `values` parameter."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running the algorithm\n",
"Now, it's time to run the algorithm. This is done by calling the `run` method attached to the algorithm object."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:31:05.871743Z",
"iopub.status.busy": "2021-06-24T11:31:05.871174Z",
"iopub.status.idle": "2021-06-24T11:31:05.890153Z",
"shell.execute_reply": "2021-06-24T11:31:05.889623Z"
}
},
"outputs": [],
"source": [
"ad.run();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Post processing\n",
"When an algorithm is successfully run, the results are attached to the same object. To access the results, you need to know the quantity for which the algorithm was solving. For instance, `AdvectionDiffusion` solves for the quantity `pore.concentration`, which is somewhat intuitive. However, if you ever forget it, or wanted to manually check the quantity, you can take a look at the algorithm `settings`:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:31:05.893618Z",
"iopub.status.busy": "2021-06-24T11:31:05.893059Z",
"iopub.status.idle": "2021-06-24T11:31:05.896109Z",
"shell.execute_reply": "2021-06-24T11:31:05.896579Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"Settings Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"name ad_02\n",
"prefix ad\n",
"uuid 7915931e-b8dc-415e-bae4-6ed58311114e\n",
"cache True\n",
"conductance throat.ad_dif_conductance\n",
"diffusive_conductance throat.diffusive_conductance\n",
"f_rtol 1e-06\n",
"hydraulic_conductance throat.hydraulic_conductance\n",
"newton_maxiter 5000\n",
"nlin_max_iter 5000\n",
"phase phase_01\n",
"pressure pore.pressure\n",
"quantity pore.concentration\n",
"relaxation_quantity 1.0\n",
"relaxation_source 1.0\n",
"sources []\n",
"variable_props []\n",
"x_rtol 1e-06\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
],
"source": [
"print(ad.settings)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we know the quantity for which `AdvectionDiffusion` was solved, let's take a look at the results:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:31:05.899659Z",
"iopub.status.busy": "2021-06-24T11:31:05.899092Z",
"iopub.status.idle": "2021-06-24T11:31:05.901305Z",
"shell.execute_reply": "2021-06-24T11:31:05.900810Z"
}
},
"outputs": [],
"source": [
"c = ad['pore.concentration']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Heatmap\n",
"Since the network is 2d, we can simply reshape the results in form of a 2d array similar to the shape of the network and plot the heatmap of it using `matplotlib`."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:31:05.904868Z",
"iopub.status.busy": "2021-06-24T11:31:05.904302Z",
"iopub.status.idle": "2021-06-24T11:31:05.906643Z",
"shell.execute_reply": "2021-06-24T11:31:05.907121Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Network shape: [1, 20, 30]\n"
]
}
],
"source": [
"print('Network shape:', shape)\n",
"c2d = c.reshape(shape)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:31:05.925038Z",
"iopub.status.busy": "2021-06-24T11:31:05.921263Z",
"iopub.status.idle": "2021-06-24T11:31:06.269123Z",
"shell.execute_reply": "2021-06-24T11:31:06.270268Z"
}
},
"outputs": [
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]
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| mit |
rougier/Neurosciences | basal-ganglia/guthrie-et-al-2013/model-single-trial.ipynb | 1 | 241486 | {
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"<div class=\"alert\" style=\"margin: 0px\">\n",
"**A Long Journey into Reproducible Computational Neurosciences** \n",
"\n",
"Meropi Topalidou\u00b9\u00b2\u00b3, Arthur Leblois\u2074, Thomas Boraud\u00b3 and Nicolas P. Rougier\u00b9\u00b2\u00b3\u207a \n",
"\u00b9 INRIA Bordeaux Sud-Ouest, France \n",
"\u00b2 LaBRI, UMR 5800 CNRS, Talence, France \n",
"\u00b3 Institute of Neurodegenerative Diseases, UMR 5293, Bordeaux, France \n",
"\u2074 Laboratoire de Neurophysique et Physiologie, UMR 8119, Paris, France \n",
"\u207a Corresponding author ([[email protected]](mailto:[email protected]))\n",
"</div>\n",
"\n",
"<small>\n",
"<div style=\"line-height:1em; text-align:justify\">\n",
"**Abstract** Computational neuroscience, a relatively recent field, has gained fast ground and modelling is now widely used to better understand individual and collective neuronal dynamics, and to propose new functions relying on neural substrate. While the development of a model is initially tightly linked to the specific question asked by a given group of researchers, further development of the model in different contexts is often possible and desired. When such further development relies on new players, the continuity of the work requires proper validation, reproduction and sharing of the model\u2019s original equations and code. However, as they are published today, computational neuroscience papers rarely include the sufficient material for\n",
"the reproduction and sharing of the underlying model as a whole. Here, we aim at showing the full extent of the problem, as well as state-of-the-art solutions, through the detailed story of the reproduction of a computational modelling study by Guthrie et al. (2013) investigating the dynamics of basal ganglia circuits and their function in multiple level action selection. In collaboration with the authors of the original work, we first explain the difficulties encountered during the reproduction and validation of the initial model and results. These difficulties led us to completely rewrite the model enforcing best software practices, relying on previous attempts to provide a common framework for reproducible computational science and software sustainability. We hereby detail these practices in the face of our practical example: the reproduction of the results from Guthrie et al. (2013). In particular, these practices include: (i) a template for formal description of the model in a single table, (ii) a public repository for shared software a proper version control, and (iii) an easy interface to run the underlying code and reproduce figures. We finally propose new formats for communicating results allowing the replay of a code while reading a computational study, in order to get a deeper understanding of the concepts being introduced.\n",
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},
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"metadata": {},
"source": [
"### Packages import"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"from dana import *\n",
"import matplotlib.pyplot as plt"
],
"language": "python",
"metadata": {},
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"prompt_number": 1
},
{
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"### Simulation parameters"
]
},
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"input": [
"# Population size\n",
"n = 4\n",
"\n",
"# Default trial duration\n",
"duration = 3.0*second\n",
"\n",
"# Default Time resolution\n",
"dt = 1.0*millisecond\n",
"\n",
"# Initialization of the random generator (reproductibility !)\n",
"np.random.seed(1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
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"input": [
"# Threshold\n",
"Cortex_h = -2.0\n",
"Striatum_h = 0.0\n",
"STN_h = -10.0\n",
"GPi_h = 10.0\n",
"Thalamus_h = -40.0"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
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"input": [
"# Time constants\n",
"Cortex_tau = 10*millisecond\n",
"Striatum_tau = 10*millisecond\n",
"STN_tau = 10*millisecond\n",
"GPi_tau = 10*millisecond\n",
"Thalamus_tau = 10*millisecond"
],
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"prompt_number": 4
},
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"# Noise level (%)\n",
"Cortex_N = 0.01\n",
"Striatum_N = 0.001\n",
"STN_N = 0.001\n",
"GPi_N = 0.03\n",
"Thalamus_N = 0.001"
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"prompt_number": 5
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{
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"# Sigmoid parameters\n",
"Vmin = 0.0\n",
"Vmax = 20.0\n",
"Vh = 16.0\n",
"Vc = 3.0"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
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"metadata": {},
"source": [
"### Helper functions"
]
},
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"def sigmoid(V,Vmin=Vmin,Vmax=Vmax,Vh=Vh,Vc=Vc):\n",
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"The actual sigmoid equation is sigmoid(V) = $V_{min} + \\frac{V_{max}-V_{min}}{1+\\exp\\left(\\frac{V_h-V}{V_c}\\right)}$"
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"def noise(Z, level):\n",
" Z = (1+np.random.uniform(-level/2,level/2,Z.shape))*Z\n",
" return np.maximum(Z,0.0)"
],
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"prompt_number": 8
},
{
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"**WARNING**: We also use the noise function to bound the minimal activity to 0"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def init_weights(L, gain=1):\n",
" Wmin, Wmax = 0.25, 0.75\n",
" W = L._weights\n",
" N = np.random.normal(0.5, 0.005, W.shape)\n",
" N = np.minimum(np.maximum(N, 0.0),1.0)\n",
" L._weights = gain*W*(Wmin + (Wmax - Wmin)*N)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Populations"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Cortex_cog = zeros((n,1), \"\"\"dV/dt = (-V + I + Iext - Cortex_h)/Cortex_tau;\n",
" U = noise(V,Cortex_N); I; Iext\"\"\")\n",
"Cortex_mot = zeros((1,n), \"\"\"dV/dt = (-V + I + Iext - Cortex_h)/Cortex_tau;\n",
" U = noise(V,Cortex_N); I; Iext\"\"\")\n",
"Cortex_ass = zeros((n,n), \"\"\"dV/dt = (-V + I + Iext - Cortex_h)/Cortex_tau;\n",
" U = noise(V,Cortex_N); I; Iext\"\"\")\n",
"Striatum_cog = zeros((n,1), \"\"\"dV/dt = (-V + I - Striatum_h)/Striatum_tau;\n",
" U = noise(sigmoid(V), Striatum_N); I\"\"\")\n",
"Striatum_mot = zeros((1,n), \"\"\"dV/dt = (-V + I - Striatum_h)/Striatum_tau;\n",
" U = noise(sigmoid(V), Striatum_N); I\"\"\")\n",
"Striatum_ass = zeros((n,n), \"\"\"dV/dt = (-V + I - Striatum_h)/Striatum_tau;\n",
" U = noise(sigmoid(V), Striatum_N); I\"\"\")\n",
"STN_cog = zeros((n,1), \"\"\"dV/dt = (-V + I - STN_h)/STN_tau;\n",
" U = noise(V,STN_N); I\"\"\")\n",
"STN_mot = zeros((1,n), \"\"\"dV/dt = (-V + I - STN_h)/STN_tau;\n",
" U = noise(V,STN_N); I\"\"\")\n",
"GPi_cog = zeros((n,1), \"\"\"dV/dt = (-V + I - GPi_h)/GPi_tau;\n",
" U = noise(V,GPi_N); I\"\"\")\n",
"GPi_mot = zeros((1,n), \"\"\"dV/dt = (-V + I - GPi_h)/GPi_tau;\n",
" U = noise(V,GPi_N); I\"\"\")\n",
"Thalamus_cog = zeros((n,1), \"\"\"dV/dt = (-V + I - Thalamus_h)/Thalamus_tau;\n",
" U = noise(V,Thalamus_N); I\"\"\")\n",
"Thalamus_mot = zeros((1,n), \"\"\"dV/dt = (-V + I - Thalamus_h)/Thalamus_tau;\n",
" U = noise(V, Thalamus_N); I\"\"\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Connectivity"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"L = DenseConnection( Cortex_cog('U'), Striatum_cog('I'), 1.0)\n",
"init_weights(L)\n",
"L = DenseConnection( Cortex_mot('U'), Striatum_mot('I'), 1.0)\n",
"init_weights(L)\n",
"L = DenseConnection( Cortex_ass('U'), Striatum_ass('I'), 1.0)\n",
"init_weights(L)\n",
"L = DenseConnection( Cortex_cog('U'), Striatum_ass('I'), np.ones((1,2*n+1)))\n",
"init_weights(L,0.2)\n",
"L = DenseConnection( Cortex_mot('U'), Striatum_ass('I'), np.ones((2*n+1,1)))\n",
"init_weights(L,0.2)\n",
"\n",
"DenseConnection( Cortex_cog('U'), STN_cog('I'), 1.0 )\n",
"DenseConnection( Cortex_mot('U'), STN_mot('I'), 1.0 )\n",
"DenseConnection( Striatum_cog('U'), GPi_cog('I'), -2.0 )\n",
"DenseConnection( Striatum_mot('U'), GPi_mot('I'), -2.0 )\n",
"DenseConnection( Striatum_ass('U'), GPi_cog('I'), -2.0*np.ones((1,2*n+1)))\n",
"DenseConnection( Striatum_ass('U'), GPi_mot('I'), -2.0*np.ones((2*n+1,1)))\n",
"DenseConnection( STN_cog('U'), GPi_cog('I'), 1.0*np.ones((2*n+1,1)) )\n",
"DenseConnection( STN_mot('U'), GPi_mot('I'), 1.0*np.ones((1,2*n+1)) )\n",
"DenseConnection( GPi_cog('U'), Thalamus_cog('I'), -0.5 )\n",
"DenseConnection( GPi_mot('U'), Thalamus_mot('I'), -0.5 )\n",
"DenseConnection( Thalamus_cog('U'), Cortex_cog('I'), 1.0 )\n",
"DenseConnection( Thalamus_mot('U'), Cortex_mot('I'), 1.0 )\n",
"DenseConnection( Cortex_cog('U'), Thalamus_cog('I'), 0.4 )\n",
"DenseConnection( Cortex_mot('U'), Thalamus_mot('I'), 0.4 )\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"<dana.dense_connection.DenseConnection at 0x11115dd50>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Trial setup"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"@clock.at(500*millisecond)\n",
"def set_trial(t):\n",
" m1,m2 = np.random.randint(0,4,2)\n",
" while m2 == m1:\n",
" m2 = np.random.randint(4)\n",
" c1,c2 = np.random.randint(0,4,2)\n",
" while c2 == c1:\n",
" c2 = np.random.randint(4)\n",
" Cortex_mot['Iext'] = 0\n",
" Cortex_cog['Iext'] = 0\n",
" Cortex_ass['Iext'] = 0\n",
" v = 7\n",
" Cortex_mot['Iext'][0,m1] = v + np.random.normal(0,v*Cortex_N)\n",
" Cortex_mot['Iext'][0,m2] = v + np.random.normal(0,v*Cortex_N)\n",
" Cortex_cog['Iext'][c1,0] = v + np.random.normal(0,v*Cortex_N)\n",
" Cortex_cog['Iext'][c2,0] = v + np.random.normal(0,v*Cortex_N)\n",
" Cortex_ass['Iext'][c1,m1] = v + np.random.normal(0,v*Cortex_N)\n",
" Cortex_ass['Iext'][c2,m2] = v + np.random.normal(0,v*Cortex_N)\n",
"\n",
"@clock.at(2500*millisecond)\n",
"def unset_trial(t):\n",
" Cortex_mot['Iext'] = 0\n",
" Cortex_cog['Iext'] = 0\n",
" Cortex_ass['Iext'] = 0"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Recording setup"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dtype = [ (\"cortex\", [(\"mot\", float, 4),\n",
" (\"cog\", float, 4),\n",
" (\"ass\", float, 16)]),\n",
" (\"striatum\", [(\"mot\", float, 4),\n",
" (\"cog\", float, 4),\n",
" (\"ass\", float, 16)]),\n",
" (\"GPi\", [(\"mot\", float, 4),\n",
" (\"cog\", float, 4)]),\n",
" (\"thalamus\", [(\"mot\", float, 4),\n",
" (\"cog\", float, 4)]),\n",
" (\"STN\", [(\"mot\", float, 4),\n",
" (\"cog\", float, 4)])]\n",
"n = int(duration/dt)+1\n",
"timesteps = np.zeros(n)\n",
"records = np.zeros(n, dtype=dtype)\n",
"record_index = 0\n",
"\n",
"@after(clock.tick)\n",
"def register(t):\n",
" global record_index\n",
"\n",
" i = record_index\n",
" timesteps[i] = t\n",
" records[\"cortex\"][\"mot\"][i] = Cortex_mot['U'].ravel()\n",
" records[\"cortex\"][\"cog\"][i] = Cortex_cog['U'].ravel()\n",
" records[\"cortex\"][\"ass\"][i] = Cortex_ass['U'].ravel()\n",
" records[\"striatum\"][\"mot\"][i] = Striatum_mot['U'].ravel()\n",
" records[\"striatum\"][\"cog\"][i] = Striatum_cog['U'].ravel()\n",
" records[\"striatum\"][\"ass\"][i] = Striatum_ass['U'].ravel()\n",
" records[\"STN\"][\"mot\"][i] = STN_mot['U'].ravel()\n",
" records[\"STN\"][\"cog\"][i] = STN_cog['U'].ravel()\n",
" records[\"GPi\"][\"mot\"][i] = GPi_mot['U'].ravel()\n",
" records[\"GPi\"][\"cog\"][i] = GPi_cog['U'].ravel()\n",
" records[\"thalamus\"][\"mot\"][i] = Thalamus_mot['U'].ravel()\n",
" records[\"thalamus\"][\"cog\"][i] = Thalamus_cog['U'].ravel()\n",
" record_index += 1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run simulation"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"run(time=duration, dt=dt)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Display results"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(17,7))\n",
"fig.patch.set_facecolor('1.0')\n",
"\n",
"plt.subplots_adjust(bottom=0.15)\n",
"ax = plt.subplot(1,1,1)\n",
"\n",
"plt.plot(timesteps, records[\"cortex\"][\"cog\"][:,0],c='r', label=\"Cognitive Cortex\")\n",
"plt.plot(timesteps, records[\"cortex\"][\"cog\"][:,1],c='r')\n",
"plt.plot(timesteps, records[\"cortex\"][\"cog\"][:,2],c='r')\n",
"plt.plot(timesteps, records[\"cortex\"][\"cog\"][:,3],c='r')\n",
"plt.plot(timesteps, records[\"cortex\"][\"mot\"][:,0],c='b', label=\"Motor Cortex\")\n",
"plt.plot(timesteps, records[\"cortex\"][\"mot\"][:,1],c='b')\n",
"plt.plot(timesteps, records[\"cortex\"][\"mot\"][:,2],c='b')\n",
"plt.plot(timesteps, records[\"cortex\"][\"mot\"][:,3],c='b')\n",
"\n",
"plt.xlabel(\"Time (seconds)\")\n",
"plt.ylabel(\"Activity (Hz)\")\n",
"plt.legend(frameon=False, loc='upper left')\n",
"plt.xlim(0.0,duration)\n",
"plt.ylim(0.0,60.0)\n",
"\n",
"plt.xticks([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0],\n",
" ['0.0','0.5\\n(Trial start)','1.0','1.5', '2.0','2.5\\n(Trial stop)','3.0'])\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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3bw6enmmHtW2bVf7OO1MfDwkBX19rTqwKFeDkSShSxKqrYEFrjriDB6FZM+tY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/RYuxbOOw/i4uCee6JfCyGEEG1WUxPdg/yBB1BFRcy3n8v/hv6LDzgD0NAxueSCMJf/zMEpp+y9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\nRERERMQN/A+qinAyZaI1oAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x11319b1d0>"
]
}
],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Display activity for all groups at once"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(18,12))\n",
"fig.patch.set_facecolor('1.0')\n",
"\n",
"def subplot(rows,cols,n, alpha=0.0):\n",
" ax = plt.subplot(rows,cols,n)\n",
" ax.patch.set_facecolor(\"k\")\n",
" ax.patch.set_alpha(alpha)\n",
"\n",
" ax.spines['right'].set_color('none')\n",
" ax.spines['top'].set_color('none')\n",
" ax.spines['bottom'].set_color('none')\n",
" ax.yaxis.set_ticks_position('left')\n",
" ax.yaxis.set_tick_params(direction=\"outward\")\n",
" return ax\n",
"\n",
"ax = subplot(5,3,1)\n",
"ax.set_title(\"MOTOR\", fontsize=24)\n",
"ax.set_ylabel(\"STN\", fontsize=24)\n",
"for i in range(4):\n",
" plt.plot(timesteps, records[\"STN\"][\"mot\"][:,i], c='k', lw=.5)\n",
"ax.set_xticks([])\n",
"\n",
"ax = subplot(5,3,2)\n",
"ax.set_title(\"COGNITIVE\", fontsize=24)\n",
"for i in range(4):\n",
" plt.plot(timesteps, records[\"STN\"][\"cog\"][:,i], c='k', lw=.5)\n",
"ax.set_xticks([])\n",
"\n",
"ax = subplot(5,3,3,alpha=0)\n",
"ax.set_title(\"ASSOCIATIVE\", fontsize=24)\n",
"ax.set_xticks([])\n",
"ax.set_yticks([])\n",
"ax.spines['left'].set_color('none')\n",
"\n",
"\n",
"ax = subplot(5,3,4)\n",
"ax.set_ylabel(\"CORTEX\", fontsize=24)\n",
"for i in range(4):\n",
" plt.plot(timesteps, records[\"cortex\"][\"mot\"][:,i], c='k', lw=.5)\n",
"ax.set_xticks([])\n",
"\n",
"ax = subplot(5,3,5)\n",
"for i in range(4):\n",
" plt.plot(timesteps, records[\"cortex\"][\"cog\"][:,i], c='k', lw=.5)\n",
"ax.set_xticks([])\n",
"\n",
"ax = subplot(5,3,6)\n",
"for i in range(16):\n",
" plt.plot(timesteps, records[\"cortex\"][\"ass\"][:,i], c='k', lw=.5)\n",
"ax.set_xticks([])\n",
"\n",
"ax = subplot(5,3,7)\n",
"ax.set_ylabel(\"STRIATUM\", fontsize=24)\n",
"for i in range(4):\n",
" plt.plot(timesteps, records[\"striatum\"][\"mot\"][:,i], c='k', lw=.5)\n",
"ax.set_xticks([])\n",
"\n",
"ax = subplot(5,3,8)\n",
"for i in range(4):\n",
" plt.plot(timesteps, records[\"striatum\"][\"cog\"][:,i], c='k', lw=.5)\n",
"ax.set_xticks([])\n",
"\n",
"ax = subplot(5,3,9)\n",
"for i in range(16):\n",
" plt.plot(timesteps, records[\"striatum\"][\"ass\"][:,i], c='k', lw=.5)\n",
"ax.set_xticks([])\n",
"\n",
"ax = subplot(5,3,10)\n",
"ax.set_ylabel(\"GPi\", fontsize=24)\n",
"for i in range(4):\n",
" plt.plot(timesteps, records[\"GPi\"][\"mot\"][:,i], c='k', lw=.5)\n",
"ax.set_xticks([])\n",
"\n",
"ax = subplot(5,3,11)\n",
"for i in range(4):\n",
" plt.plot(timesteps, records[\"GPi\"][\"cog\"][:,i], c='k', lw=.5)\n",
"ax.set_xticks([])\n",
"\n",
"ax = subplot(5,3,13)\n",
"ax.set_ylabel(\"THALAMUS\", fontsize=24)\n",
"for i in range(4):\n",
" plt.plot(timesteps, records[\"thalamus\"][\"mot\"][:,i], c='k', lw=.5)\n",
"ax.set_xticks([])\n",
"\n",
"ax = subplot(5,3,14)\n",
"for i in range(4):\n",
" plt.plot(timesteps, records[\"thalamus\"][\"cog\"][:,i], c='k', lw=.5)\n",
"ax.set_xticks([])\n",
"\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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qt/mjJgaxGIZmxdatW6FSqbB7925YW1uXWfCeZOgcV6fbaSiSHMeZpPP85Bg1yfH0GAYc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"text": [
"<matplotlib.figure.Figure at 0x1115c79d0>"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | bsd-3-clause |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/copula.ipynb | 2 | 237428 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Copula - Multivariate joint distribution"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2021-10-06T09:58:45.223674Z",
"iopub.status.busy": "2021-10-06T09:58:45.223303Z",
"iopub.status.idle": "2021-10-06T09:58:47.565448Z",
"shell.execute_reply": "2021-10-06T09:58:47.566167Z"
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns\n",
"from scipy import stats\n",
"\n",
"sns.set_style(\"darkgrid\")\n",
"sns.mpl.rc(\"figure\", figsize=(8, 8))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2021-10-06T09:58:47.571184Z",
"iopub.status.busy": "2021-10-06T09:58:47.569220Z",
"iopub.status.idle": "2021-10-06T09:58:47.587996Z",
"shell.execute_reply": "2021-10-06T09:58:47.588916Z"
}
},
"outputs": [
{
"data": {
"application/javascript": [
"IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
" return false;\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%javascript\n",
"IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
" return false;\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When modeling a system, there are often cases where multiple parameters are involved. Each of these parameters could be described with a given Probability Density Function (PDF). If would like to be able to generate a new set of parameter values, we need to be able to sample from these distributions-also called marginals. There are mainly two cases: *(i)* PDFs are independent; *(ii)* there is a dependency. One way to model the dependency it to use a **copula**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sampling from a copula\n",
"\n",
"Let's use a bi-variate example and assume first that we have a prior and know how to model the dependence between our 2 variables.\n",
"\n",
"In this case, we are using the Gumbel copula and fix its hyperparameter `theta=2`. We can visualize it's 2-dimensional PDF."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2021-10-06T09:58:47.593596Z",
"iopub.status.busy": "2021-10-06T09:58:47.591931Z",
"iopub.status.idle": "2021-10-06T09:58:48.720449Z",
"shell.execute_reply": "2021-10-06T09:58:48.720893Z"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from statsmodels.distributions.copula.api import (\n",
" CopulaDistribution, GumbelCopula, IndependenceCopula)\n",
"\n",
"copula = GumbelCopula(theta=2)\n",
"_ = copula.plot_pdf() # returns a matplotlib figure"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And we can sample the PDF."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2021-10-06T09:58:48.724279Z",
"iopub.status.busy": "2021-10-06T09:58:48.723592Z",
"iopub.status.idle": "2021-10-06T09:58:50.365970Z",
"shell.execute_reply": "2021-10-06T09:58:50.366380Z"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/kevin/git/statsmodels/statsmodels/tools/rng_qrng.py:54: FutureWarning: Passing `None` as the seed currently return the NumPy singleton RandomState\n",
"(np.random.mtrand._rand). After release 0.13 this will change to using the\n",
"default generator provided by NumPy (np.random.default_rng()). If you need\n",
"reproducible draws, you should pass a seeded np.random.Generator, e.g.,\n",
"\n",
"import numpy as np\n",
"seed = 32839283923801\n",
"rng = np.random.default_rng(seed)\"\n",
"\n",
" warnings.warn(_future_warn, FutureWarning)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sample = copula.rvs(10000)\n",
"h = sns.jointplot(x=sample[:, 0], y=sample[:, 1], kind=\"hex\")\n",
"_ = h.set_axis_labels(\"X1\", \"X2\", fontsize=16)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's come back to our 2 variables for a second. In this case we consider them to be gamma and normally distributed. If they would be independent from each other, we could sample from each PDF individually. Here we use a convenient class to do the same operation.\n",
"\n",
"### Reproducibility\n",
"\n",
"Generating reproducible random values from copulas required explicitly setting the `seed` argument.\n",
"`seed` accepts either an initialized NumPy `Generator` or `RandomState`, or any argument acceptable\n",
"to `np.random.default_rng`, e.g., an integer or a sequence of integers. This example uses an\n",
"integer.\n",
"\n",
"The singleton `RandomState` that is directly exposed in the `np.random` distributions is\n",
"not used, and setting `np.random.seed` has no effect on the values generated."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2021-10-06T09:58:50.368720Z",
"iopub.status.busy": "2021-10-06T09:58:50.368097Z",
"iopub.status.idle": "2021-10-06T09:58:51.500918Z",
"shell.execute_reply": "2021-10-06T09:58:51.501405Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"marginals = [stats.gamma(2), stats.norm]\n",
"joint_dist = CopulaDistribution(copula=IndependenceCopula(), marginals=marginals)\n",
"sample = joint_dist.rvs(512, random_state=20210801)\n",
"h = sns.jointplot(x=sample[:, 0], y=sample[:, 1], kind=\"scatter\")\n",
"_ = h.set_axis_labels(\"X1\", \"X2\", fontsize=16)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, above we have expressed the dependency between our variables using a copula, we can use this copula to sample a new set of observation with the same convenient class."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2021-10-06T09:58:51.504408Z",
"iopub.status.busy": "2021-10-06T09:58:51.503362Z",
"iopub.status.idle": "2021-10-06T09:58:53.337165Z",
"shell.execute_reply": "2021-10-06T09:58:53.338184Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"joint_dist = CopulaDistribution(copula, marginals)\n",
"# Use an initialized Generator object\n",
"rng = np.random.default_rng([2, 0, 2, 1, 0, 8, 0, 1])\n",
"sample = joint_dist.rvs(512, random_state=rng)\n",
"h = sns.jointplot(x=sample[:, 0], y=sample[:, 1], kind=\"scatter\")\n",
"_ = h.set_axis_labels(\"X1\", \"X2\", fontsize=16)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are two things to note here. *(i)* as in the independent case, the marginals are correctly showing a gamma and normal distribution; *(ii)* the dependence is visible between the two variables."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Estimating copula parameters\n",
"\n",
"Now, imagine we already have experimental data and we know that there is a dependency that can be expressed using a Gumbel copula. But we don't know what is the hyperparameter value for our copula. In this case, we can estimate the value.\n",
"\n",
"We are going to use the sample we just generated as we already know the value of the hyperparameter we should get: `theta=2`."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2021-10-06T09:58:53.345392Z",
"iopub.status.busy": "2021-10-06T09:58:53.341880Z",
"iopub.status.idle": "2021-10-06T09:58:53.359549Z",
"shell.execute_reply": "2021-10-06T09:58:53.360313Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.049379621506455\n"
]
}
],
"source": [
"copula = GumbelCopula()\n",
"theta = copula.fit_corr_param(sample)\n",
"print(theta)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that the estimated hyperparameter value is close to the value set previously."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.11"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| bsd-3-clause |
oroszl/statfiz-gyak | Fig/.ipynb_checkpoints/01-Gamma_Stirling_d-Sphere-checkpoint.ipynb | 1 | 149927 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"hide_input": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline\n",
"from scipy.special import *"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# $\\Gamma$-függvény "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"hide_input": true
},
"outputs": [
{
"data": {
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Omz3ySPjrXzUlqSlKGhJCIw35aSxpiJdAy5Q5mrTLeLvSEUfAdtu59vLlDeeN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\n3HMPzJrl9l/YaSfVLFQjJQ2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ShuestQc2c+6dwEn1555krb2nifOarWlozty58Pjj8MIL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\nBhERERERaen/A/zBzkcYBJD+AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f67ce3902e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x=linspace(-4,4,1000)\n",
"figure(figsize=(12,6))\n",
"ylim(-6,6)\n",
"grid()\n",
"fs=30\n",
"xticks(linspace(-4,4,9),fontsize=fs);yticks(fontsize=fs);\n",
"xlabel(r'$x$',fontsize=fs)\n",
"ylabel(r'$\\Gamma(x)$',fontsize=fs)\n",
"plot([-4,4],[0,0],'k-')\n",
"\n",
"plot(x,gamma(x),lw=3);\n",
"savefig('Gamma.png',pad_inches=0.0,bbox_inches='tight')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Stirling-formula"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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rFrV7NJbu0niKiEiki/Y6gKIqojaOOxgwk14+nNfliIiIiBQPWpMuMK8V7P3W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CBcdrau7dGmjY7k27QWUxX9+9//Du475zj11FM51Svl5Jzjq6++Ci6aBLBgwQKGDx8e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\n8w7wb/YOAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f67cb715160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"t=linspace(0,8,1000)\n",
"figure(figsize=(12,6))\n",
"fs=30\n",
"nn=2.3\n",
"plot(t,exp(-t)*t**nn,'r-',label=r'$e^{-t}t^n$',lw=3)\n",
"plot(t,exp(-t),'b--',label=r'$e^{-t}$',lw=3)\n",
"plot(t,t**nn,'-',color='orange',label=r'$t^n$',lw=3)\n",
"plot(t,exp(-(nn-nn*log(nn)))*exp(-(t-nn)**2/(2*nn)),'k:',lw=4,label=r'$e^{-(n-n\\log n)}e^{-\\frac{(t-n)^2}{2n}}$')\n",
"ylim(0,1)\n",
"grid()\n",
"xlabel(r'$t$',fontsize=fs)\n",
"xticks(fontsize=fs);yticks(fontsize=fs);\n",
"legend(fontsize=fs);\n",
"savefig('Stirling.png',pad_inches=0.0,bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"image/png": 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5rfjOmDFjrDEm/qt79+52wYIFNjIyMtPHWr58uTXG2JCQEDty5Mh0x0+dOjV+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\nGWAVyy1rAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f67cb8d9400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x=linspace(0,4,1000)\n",
"figure(figsize=(12,6))\n",
"ylim(0,6)\n",
"grid()\n",
"fs=30\n",
"xticks(linspace(0,4,5),fontsize=fs);yticks(fontsize=fs);\n",
"xlabel(r'$x$',fontsize=fs)\n",
"#ylabel(r'$\\Gamma(x)$',fontsize=fs)\n",
"#plot([0,4],[0,0],'k-')\n",
"\n",
"plot(x,gamma(x),lw=3,label=r\"$\\Gamma(x)$\");\n",
"x=linspace(1.0001,4,1000)\n",
"plot(x,(x-1)**(x-1)*exp(-(x-1))*sqrt(2*pi*(x-1)),'r--',lw=5,label=\"Stirling-formula\")\n",
"legend(fontsize=fs,loc=2)\n",
"savefig('Stirling-approx.png',pad_inches=0.0,bbox_inches='tight')"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"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"
},
"latex_envs": {
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 0
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
hpssjellis/forth-tensorflow | skflow-examples/broken/a15-language_model.ipynb | 2 | 946 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [
],
"source": [
"%autosave 0\n",
"!python /home/ubuntu/workspace/tensorflow/tensorflow/examples/skflow/language_model.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%load /home/ubuntu/workspace/tensorflow/tensorflow/examples/skflow/language_model.py"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
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| mit |
michigraber/neuralyzer | notebooks/doc/LookingAtImageStacks_#1.ipynb | 1 | 1198357 | null | mit |
jkirsch/hashbucketscreening | docs/Density Sensitive Projection.ipynb | 1 | 983545 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Density Sensitive projection"
]
},
{
"cell_type": "code",
"execution_count": 424,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import math\n",
"import numpy as np\n",
"from sklearn import random_projection\n",
"from itertools import groupby\n",
"from heapq import heappush, heappop\n",
"import random\n",
"from sklearn import svm\n",
"from pylab import rcParams\n",
"from sklearn.utils.extmath import safe_sparse_dot\n",
"\n",
"# Use seaborn styles for nice-looking plots\n",
"import seaborn; seaborn.set()\n",
"\n",
"# Define some functions\n",
"def plotHyperplane(normal, intercept=0, label='', plt=plt):\n",
" \"\"\" Plot a hyperplane given a normal and an intercept\n",
" \"\"\"\n",
" a = -normal[0] / normal[1]\n",
" xx = np.linspace(-10, 10)\n",
" yy = a * xx - (intercept) / normal[1] \n",
" plt.plot(xx, yy, label=label) \n",
" return 0\n",
"\n",
"def hashWithMedianPlane(p, X):\n",
" \"\"\" hash using median plane\n",
" \"\"\"\n",
" w = p[0] - p[1] \n",
" t = np.dot((p[0]+p[1])/2,w)\n",
" \n",
" res = np.inner(w, X) \n",
" \n",
" return ['1' if elem >= t else '0' for elem in res]\n",
"\n",
"\n",
"def medianPlane(p):\n",
" \"\"\"Generate median plane given two points\n",
" \"\"\" \n",
" median = (p[0]+p[1]) / 2\n",
" diff = p[0] - p[1]\n",
" \n",
" # hyperplane \n",
" intercept = -np.dot(median,diff) \n",
"\n",
" return (diff, intercept)\n",
"\n",
"def generateBitString( x ):\n",
" return \"\".join(['1' if i > 0 else '0' for i in x])\n",
"\n",
"class HashBucketScreening:\n",
" \"\"\"HashBucketScreening class\"\"\" \n",
" \n",
" def __init__(self, features, rnd=42,maxplanes=40,strategy='median',maxCutoff=0.5):\n",
" self.maxplanes = maxplanes\n",
" rng = np.random.RandomState(rnd) \n",
" self.randomProjection = random_projection.gaussian_random_matrix(1, features, random_state=rng) \n",
" self.strategy = strategy\n",
" self.maxCutoff = maxCutoff\n",
" \n",
" def _generateHyperplaneUsingSVM(self, X, Y):\n",
" \"\"\" Here we estimate the seperating hyperplane using svm\n",
" \"\"\"\n",
" plane = svm.SVC(kernel='linear', C=2)\n",
" plane.fit(X,Y)\n",
" \n",
" return (plane, plane.coef_[0], plane.intercept_[0])\n",
" \n",
" def screen(self, X, Y):\n",
"\n",
" # Now generate a random plane through the origin - start of the algorithm\n",
" # medians stores the hyperplane normals as well as intercepts \n",
" self.medians = [(self.randomProjection[0], 0)] \n",
" \n",
" # Project #1\n",
" projected = safe_sparse_dot(X, self.randomProjection.T)\n",
" self.buckets = np.apply_along_axis( generateBitString, axis=1, arr=projected )\n",
"\n",
" plane = 1 \n",
" \n",
" self.select = []\n",
"\n",
" # This is the algorithm\n",
" while True: \n",
" \n",
" heap = []\n",
" \n",
" for key in np.unique(self.buckets):\n",
" # estimate pos ratio \n",
" qualifying = Y[self.buckets == key]\n",
" length = qualifying.shape[0] \n",
" pos = qualifying[qualifying == 1].shape[0]\n",
" #print('Buckets', key, pos) \n",
" ratio = min(pos / length, (length - pos) / length)\n",
" #print(key,ratio,length)\n",
" if 0 < ratio < self.maxCutoff:\n",
" # Take buckets which need splitting - weight them by the number of points\n",
" heap.append(( length * ratio, key, pos, length)) \n",
"\n",
" if (len(heap) == 0) or (plane > self.maxplanes) :\n",
" print('No more elements')\n",
" break; \n",
"\n",
" heap = sorted(heap, key=lambda x: x[0]) \n",
" \n",
" #print(heap)\n",
" \n",
" # select the bucket that needs splitting\n",
" # get the smalles element \n",
" #largest = heap[0]#.pop()\n",
" largest = heap.pop()\n",
"\n",
" # 3 strategies\n",
" # Take random points 1) or 2) take the median of all pos/neg points each, simulates k-means\n",
"\n",
" # now from this \"largest bucket\" sample 1 pos and 1 neg point\n",
" #qualifyingPos = random.choice(X[(buckets == largest[1]) & (Y == 1)])\n",
" #qualifyingNeg = random.choice(X[(buckets == largest[1]) & (Y != 1)])\n",
"\n",
" # Strategy 2 take median of qualifying points\n",
" if(self.strategy == 'median'):\n",
" qualifyingPos = np.median(X[(self.buckets == largest[1]) & (Y == 1)],axis=0)\n",
" qualifyingNeg = np.median(X[(self.buckets == largest[1]) & (Y != 1)],axis=0)\n",
" median = (qualifyingPos, qualifyingNeg)\n",
" \n",
" # Store the median points\n",
" self.medians.append(medianPlane(median)) \n",
"\n",
" # add the new bitstring to the hash\n",
" self.buckets = np.core.defchararray.add(self.buckets, hashWithMedianPlane(median, X))\n",
"\n",
" \n",
" if(self.strategy == 'SVM'):\n",
" # Strategy 3 use SVM to find seperating hyperplane per bucket\n",
" (svm, point, intercept) = self._generateHyperplaneUsingSVM(\n",
" X[self.buckets == largest[1]], Y[self.buckets == largest[1]])\n",
" \n",
" if(np.array_equal(point, self.medians[-1][0]) & (intercept == self.medians[-1][1])):\n",
" print('We have the same hyperplane .. going in circles, breaking')\n",
" break\n",
" \n",
" self.medians.append((point,intercept))\n",
" \n",
" newbit = ['1' if elem > 0 else '0' for elem in svm.decision_function(X)] \n",
"\n",
" self.select.append(X[self.buckets == largest[1]])\n",
" \n",
" self.buckets = np.core.defchararray.add(self.buckets, newbit) \n",
"\n",
" \n",
" plane = plane + 1 \n",
" \n",
" print('Done after %d planes' % (plane))\n",
" \n",
" def getBucketSamples(self,X,Y,samples=2):\n",
" \"\"\" Samples from all buckets #samples points\n",
" \"\"\"\n",
" # Also just sample n point from all buckets\n",
" sampledPoints = []\n",
" data = np.column_stack((self.buckets, Y, X)) \n",
" for key, rows in groupby(data[np.argsort(data[:,0])], lambda x: x[0]):\n",
" #sampledPoints.append(random.choice(list(rows)))\n",
" l = list(rows)\n",
" sampledPoints.extend(random.sample(l,min(samples,len(l))))\n",
"\n",
" sampledPoints = np.asarray(sampledPoints) \n",
" return sampledPoints\n",
" \n",
" def getHammingSamples(self,X,Y):\n",
" \"\"\" Get all points that belong to hamming buckets that are support buckets\n",
" \"\"\" \n",
" data = np.column_stack((self.buckets, Y)) \n",
" keySize = len(list(groupby(data[np.argsort(data[:,0])], lambda x: x[0])))\n",
"\n",
" X_hamming = np.empty([keySize, len(self.buckets[0])])\n",
" Y_hamming = [0] * keySize\n",
" counter = 0\n",
"\n",
" for key, rows in groupby(data[np.argsort(data[:,0])], lambda x: x[0]):\n",
" row = list(rows) \n",
" pos = len(list(filter(lambda x: x[1] == '1.0', row)))\n",
" neg = len(list(filter(lambda x: x[1] != '1.0', row)))\n",
" \n",
" X_hamming[counter:] = np.array(list(map(int, key))) \n",
" Y_hamming[counter] = 1 if pos > neg else 0\n",
" counter = counter + 1 \n",
" \n",
" # Create Hamming Points and run SVM\n",
" clf = svm.SVC(kernel='linear', C=2)\n",
" clf.fit(X_hamming, Y_hamming)\n",
"\n",
" #print('Support Vectors:\\n', clf.support_vectors_)\n",
" print('All hamming points %d - reduced to %d' % (keySize, len(clf.support_vectors_)))\n",
" \n",
" self.selected = np.apply_along_axis( generateBitString, axis=1, arr=clf.support_vectors_)\n",
"\n",
" reduced_x = X[np.in1d(self.buckets, self.selected)]\n",
" reduced_y = Y[np.in1d(self.buckets, self.selected)] \n",
" \n",
" return (reduced_x, reduced_y)\n",
" \n",
" def getHammingSupportSamples(self, X, Y, samples): \n",
" data = np.column_stack((screen.buckets, Y, X)) \n",
" sampledSupportBucket = []\n",
" # Sample points from the support buckets only\n",
" for key, rows in groupby(data[np.argsort(data[:,0])], lambda x: x[0]):\n",
" if key in self.selected:\n",
" l = list(rows)\n",
" sampledSupportBucket.extend(random.sample(l,min(samples,len(l))))\n",
"\n",
" sampledSupportBucket = np.asarray(sampledSupportBucket) \n",
" \n",
" return (sampledSupportBucket[:,2:], sampledSupportBucket[:,1])\n",
" \n",
" def getPointsPerBucket(self, X, Y):\n",
" dist = []\n",
" for key in np.unique(self.buckets):\n",
" qualifying = Y[self.buckets == key]\n",
" pos = qualifying[qualifying == 1].shape[0]\n",
" dist.append((key, qualifying.shape[0], pos, qualifying.shape[0] - pos))\n",
" \n",
" return dist\n"
]
},
{
"cell_type": "code",
"execution_count": 402,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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AgA5cvaoniyJimR5axhEREZQrV44GDRoAZLsmcFJSEo6OjllflyxZkhs3buRR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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb755a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Show the base of the algorithm\n",
"p = np.array( [ (1.5,2), (4,5) ] )\n",
"\n",
"plt.scatter(p[:, 0], p[:, 1], s=80,\n",
" zorder=10, label='Points $\\mu_i$')\n",
"\n",
"# median of the centers\n",
"median = (p[0]+p[1]) / 2\n",
"diff = p[0] - p[1]\n",
"plt.plot(median[0],median[1], 'ro', label='Median')\n",
"\n",
"# hyperplane\n",
"a = -diff[0] / diff[1]\n",
"intercept = -np.dot(median,diff)\n",
"\n",
"xx = np.linspace(1, 5)\n",
"yy = a * xx - (intercept) / diff[1]\n",
"\n",
"plt.plot(xx, yy, 'k-', label='Median Plane')\n",
"plt.title('Median Plane')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define Median Plane as $(x - \\frac{\\mu_i+\\mu_j}{2})^T (\\mu_i - \\mu_j) = 0$"
]
},
{
"cell_type": "code",
"execution_count": 403,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Now generate some random points\n",
"rng = np.random.RandomState(42)\n",
"n_samples_1 = 200\n",
"n_samples_2 = 200\n",
"X = np.r_[2 * rng.randn(n_samples_1, 2) - [2,2],\n",
" 2 * rng.randn(n_samples_2, 2) + [2, 2]]\n",
"Y = np.array([0] * (n_samples_1) + [1] * (n_samples_2))"
]
},
{
"cell_type": "code",
"execution_count": 404,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No more elements\n",
"Done after 32 planes\n",
"Wall time: 59 ms\n"
]
}
],
"source": [
"# Screen\n",
"screen = HashBucketScreening(X.shape[1], strategy='median')\n",
"\n",
"%time screen.screen(X,Y)"
]
},
{
"cell_type": "code",
"execution_count": 405,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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MC0XPPwaGv1GxF9759ig7S/yN7GbgdVuRJAcy+TfI5FrgzFTWtNw4bjg/u9erCILA1KnT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b+Ymx78zgV/QaNC1YxLDuVwVTcHK3s4VNWDRID7r8nh+lmpYxa8CYKATq+kprwbp8NLxlC1tNvW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jV3BT0eJxutEWq+Fd689H1SjNrjaO91ZzYqCGNnfHyD6b3sq81EXMSCylNLEY4xiJz4+6RDzO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S9SNjCXAEFZz0vfDU10CU5rHXZDKbqUq5uAfBxIUMfxyKT7ebHqDV6oeJXvlnyV7z9azHNv\nlfH2gQYGXR7uXZgx7vstF4tYnhTFrNgI9ncNcLrfwhuNPSSr5axOjiYj/JMxW7gcCqmEDQXpZBms\nbG7tZ++81cy+4y4WmLsYuljDrq3BWVMd3EEQkOtTQr3YyuxsJBGRVz/IZ7hp+EQDdGXLAH/aXInN\n4aEkN4an7shDpfhop+QZ8lJ9vofzpzuwWYIs09SsKIpm6scIi5RMncr7R0+QM3O45lZbQXHmlYk9\nf28IBAL0Ow0hoZB6cyMef7DVRyaWMTk6PxiUdblEKXUTGlMqlRIZOTHRkIuwdB1gyNGJSluIWnd1\n9u31CJMY3n8Xd1sr4fMWED5r9jW3nyhO1/QRAKblZ3N892bi9Gl4h4Zob6pj/RPPAhCXmsP5xspR\nHslXg8doxPDBe6BSs1E5hbxUbcjsZV7iLA52HOVkdynzkmZdY6SPDmlMDEnf/Ac6/utn9LzwJxK/\n9R1cKhnxRi+JCWPlMy/6mYvFAhfOdFI0U4/8MiZzeNwcxNIwjK0bKQj/kO70AkpyR0/AFOGZiKUR\nDJoqiExagUh86ywbJ4oZ8dNosDRzpPMEb9Vt5HN5D/KDx4p57s0yth1vxeHy8ujKnCu294VJJdyV\nGsu8uEj2dBi5YLLzQm0nuREqViZHk6D65K9REASmx0SQrA4Km5ywuukIS+ShB0qIk0vxDQ7ibBwR\nUHG3NONua8W8dw8A0ti44ZR4MDUujR2/K+Mz3Dg+sQD9+q4a3txdi0gk8MjybJaVJH+kh2y3uako\n7aDyXDdDbi9iiYj8ogSmzNCjjRp/1hofF8/83HROH98LgkBOUgJTriJg8vcAt2+IelPjcFCuweAa\nCH2WqI4nLyqHAt0kMiPTbpnw+6VwWhuw9h1DIteh06+56rPvdbqptThI1ShG2QKOB/v5Msx7diGN\njyf2kcdu6jmfqu5FEGD57EJ+93oZgxYzMUl6ZO2jV/Qe78SkTwOBAH2vvUzA7aZ51gocRiVLpo2k\ns1emLuZY10l2tOxjVkLJLX0uipRUEr7ydTp/9b90/e5X9IcF0Pf5ifaMTW8qhr2wVRo5NouL82c6\nmTE/bcx2al0hZ+rtJIr2sTqngkFDIuGxIxMmQRChiZ6GpftDBk0XCIuefsuu73pwf9Y62qztnOg+\nQ2ZEOnMTZ/D9x0r437fKOHCuE4fbyxfW5CG5ChksWiHj4awEFthd7OwwUGtx/H/2zjOwrbNu+7+j\nLVmyZEuW9x6xYztx4uydJmmS7pWOdNHSTdsHKDwF3hd4oUCB52EUCpQyCl1pId0js9nTcews772n\nbFmStaXzfpDjJM2yEztJgd83W2dJOjr3ff/HdVE90EyBUcfSeOMV0e4Uo1HyxMQkPmzqptRi58Wy\nZm5NjSY3Qot2UgHaSSGHsqDPi7uhAXdtDc7qatx1Ndh278S2eycA0vDwYZtNdWYWysQkhBFqmP+H\nc3PZBug1G6swhqt4/KY80uLCR72/pdvBoaIWasu7CQZFVBo50+alkDc1bniGfy4m5eb9Sw/KoijS\n5eweXiXXDjTgH1olq6QqCqLyhvuSI1SXNmRl7Wvn9X/8DUGhxWiI5PZsOcFgkKamBsLCdKeppu3q\ntALnXz37rf10/fUvCDIZcY8+gUQ5dquVHquL+nYbOUnhfLBlEyvueRSJVEpZ8R78Xg/Vh4vJmjyN\nnvZWupobRjTZdBw8wOCRw6gmZPORPQp9mJSCzBPtdAalnnlxs9jauou9HcXMjx+7aMCZCJuYS8yD\nD9H5pz9i9oWu393QgHbKqZ+7RCKg0siRSAVUajlHDrQyeXrCaYYx/kCQD4sDaGUFPDynCmvbRgI+\nG4a4ZcOfT5hxCgMd23H0lqA1Fl4RKzG5VM6X8+7lpwde4B/V75GkiydBF8ezq6fw67VH2F/ehcvj\n5/Gb8lCexz0vQaviyxPiqbE5Wd/SS6nFzpE+B7PNehbFRZ63lmK8UUol3JYaTZpOzYfNPbxR28Fs\ns4GVicZh/XGJXIEmawKarAlEXhOqmfC2tQ0LqLhqqnEcLMZxMOSlLCiVqNMyhsLimajS0sf0t/jv\nxGUboG+Yn8aywnjCRtHkHxIW6eNwUSutjaGQp8GoYfL0hLMIi/x74fa7qeqvo3yo4rrP3T/8WoI2\nbnhATtMnn7cyeLwQxSB/XvM3Jl3/DBKJBIfNylvvvUtXv42IjFzcgw1E+Ae548aQSIfN6+eQxY5R\nKSf7HMb0YjBIx59fJuCwY159D8pRyGCejMfjobu7C7M5GuVJD5WiilD1ekpkgEFVLpKhFcLEwtk4\nLN2ERxg5uH0j4REmVPpTi7q6u7vp7u4iPT0DtTrUehQYHKT7zdcR5HLaZl2Lc3cP1xYmnLYqW5a8\niF3t+9jQuIVZsdOQn2MVvbdoP+9v2Y4gV6LEzxP33EN0dPRZtz8T2hkzOXjoXQoP9ADgrK4ctlQ8\nGbVGjtPhpWBmIvu3N3D0YBuFc04k3g8eKmX7wQraGu3ccPViYibMpbv2Dezd+wj4HBiTbkCQyJDJ\ndaj1E3ANVOJ1tqMMO79OwKXApI7kvpzb+ePRv/PnY6/x7PSn0ajUPHNHAb977yhH6iz86u1DPH3b\n5POm5QRBIEsfRka4hsMWO5vaLOzqslLca2NhbARzog0j6ukfLwRBoDBKT4JWxZu1neztttLscHFX\neuwZRVgEiQRlYiLKxEQMVy1FFEX8lt5TjUAqynBWlIV2kEpRJSUP222qMzOR6Ua/KPt35LJVcRdm\nR+Pz+ke0bcAfpPpYJ1s+qeTIgVZsVjdxSQbmL8tk7tIMzLHhSMag97CkpJiHH76fvXt3s379J7z/\n/jt4vR4mTszjyScfIS8vH/04FEi43W6eeuoRJk2aPHz81157hT/84be8//47yOVyMjOzTttPFEXa\nBzvZ33GQj+s38I/qDyjuKqXZ3oaAwCRTLkuSFnLnhJtZmryQ7MhMjOqIMbFCG00l6ckMdO6ktNVD\nbFqobUehVLF360am33AXhqhoImPi6bYOECkNYjBEsL2jjwa7i6sTTOcMb/d98hG2XTsJmzKVqNvv\nvKCV2NHyMv6xZQftXgm79+9Dr5QRZQqtaN/YVM2gy8fdyzI4UtdAVFyoj1kURer2byMucyLpeVNo\nq6+jotbDvMIJaNVyNmzdyp76VmxKPdt2bCMpyki4Lpyet97AVV2N6aZb+Ge7Gqvdw0PXTUTzuQei\nSqZk0Oekoq8avSKc5PAz+yf39Vl4c9N2Fq+6j7S8AlxeH/t272D+zNNz1+f67qr6a1nrPUiKxIi2\ny4a3swPDkmWnhSzrKnuwWpwsu3EiFYc76GqzkTslDqlMwqZt22jwysiYPpu4+HB83VVMnTQFTUQe\nnsFm3LZaPIMtaAzZCBIZEqkSZ/9REEU0htEXuo0X0WFmfAEfRy0VdDstTDVPQiaVMD3bTGefk6P1\nfRxrsDA1Kwql4vwTXkEQiNUomWHWo5FJabS7qBxwUtJrRyWVEK1Rjki69kJ/e+dDK5dRaArH5vVT\nbXNy0GLDpJRjVp979SsIAlJNGMrEJLSTCzAsXoJh8RLUmVnIIiIgEMTd3IS7tgbHgSL6N6zDVrQP\nT0szgcFBJGoVEo3mioieXAq+UFXcZ2PPljrqKrrxegP4vH6Od2vL5BIUChl2q4udG6tHdcy0bDNz\nrko/6+snu1kB+Hw+Vq++leXLj+dIx/4GOpObVUlJMceOHeGll/6Ky+XizTdfHd7e6XNR2V9DhaWK\n8r5qrJ4TloBJuoThiutkXeJlWyWfDbejGVvndkTXCSUjURSRSCTITurZ1RnNWAcG8AaC7O8eQCOT\nMtV09sp+V00Nlg/fRxYRScz9D17wD33noaPkDfVzkzWRbTvWkzMhm/beQVq6HRRkmIiNiabO0of/\nwF4iTVFYqo/w7Fee4EhZGa37iohwtqPUz2LdvmbuvTqT8vZu8heuACAqLpENuzZyZ8EkBnZsRxGf\ngL1gHnWvljAp3YjJoD7jdS1LXsTOtn1saNrC7LjpZ1xFHygpZcbVJ1q/sqfOZGNZ6ag/g70dB0AQ\niLvnAcTv/oyg00n7y38g/omnhiVSATRhoe8rGBSZNC2BA7saKSttZ8qsJBp6+kibPQWAmMQkqppD\nHRJSmQZzxr1YGt/FNVBFV83fiEpfjUqXjlRhwGktIyLhaiTSsdW4vhiuT1tOg62JQz1H2dq6i6sS\n5yOTSnj0hlw0KhnbD7Xz/BslfOOOAoz6kV23XCJhXkwE00zhbO/sZ0+XlXcbu9nVaWV5gpFsQ9hl\nG6wUUgm3pcWQGq7hw6Zu3qzrZLbdxcpE06gsN6U6HdqCKWgLQvdB0OPB3VAfWmXX1uCqrcW2cwe2\nnTtC2+sNpxSeKRMST7nf/l25IgfogX4nbY19OOwnrP7kCikKpfSC7d5GgiiKnKzbMjg4iFQqRXrS\n6qG7u4tf/OKneL1eLJZeHn74cebPX8T999/JlCmF1NbWIAgCP/3pLwgL0/LSSy9y5MghgsEgd9yx\nmsWLl55yTp/Px/PP/y/PPXfC77moaB9paRl8+9vPMDg4yK1fuov1jVsot1TRYGsiKIaKkMLkGqZF\nF5BrzCYnMgud4sqS8LNa+1nz8aeICjWiZ5C5yc1E6mBx4WR2bPkYiVINTjs3XLWII4eKSCuYQTAY\npPXQXm66ZzUHe224AkGWxEWeNQQYcDjo+NNLIIrEPPzoiDx0z4rsc7ULQ5OG4+HtGTlmSnptaCbN\nIFrmZ4ZORsrse5HJZMydNRvXgJGuureoHBTYfbSD5dNikCtPHXQFiZSuV18BQSD6/gdYezR07IVn\nlZeFcIWOBfGz+axlB3vai1iYMOe0beJjYznQ3EBiZkiC1GGzIguOLEJ1HKfPyaGeY0RrokiPSKUj\nfzKO4iKch0rpfvN1zHffOzxwHK/zcA36yJ8Wz+EDLRwqaiGvMB4xeGqR3MmmJxKJHFPqKvpb1+Ho\nPUhX9V8xp9+N1jiVgY4tDPYdRRd1euX45UIqkfJg7t08f+DXvFf7CSnhSaTpk5FIBO5bPgGNSsa6\nfc08/8ZBnrmjYFTe9CqZlOUJJmaZDXzWZuFgr43XajtI1qpYkWAiWXfmCduloNAUTkKYkjW1nezt\nHqB5SMv7Qp28JEolmuwcNNmh+1MMBPC0tgyHxF011TiKi3AUF4W2V6lQpZ8wAlGlpiEZobbCvxJX\nzAAtiiKdbTYOF7XQUB3qw9WGK5k0LYGcybGnFaCMFyUlxTz11KNIJBKkUhlf/eo3h/OGl8rNqre/\nl8a2BuY9uoLS2kP88IffJfvpWQgIpIQnkmOcQK5xAkm6hDEJV48X//x0AxkLT1Rob37/lzx82xKS\nYhZQOC1kHnJ88hNWXsaR4m0QDPDlW29CrlCwq6sdmXB2WU9RFOn6+yv4+ywYb7wZzQX0AZ9MGAEG\n7QOE6fQMDvQTLglN2IoqulHIJORnGPldZSsyQeC63EzCFZ/Xmw4iEWDJJDlv7fKy9XA3gqMfj8uF\nUq2mo74Kc3szvs5ODFctRUhIZu/7u4nQKZmUfmav3+OEVtF72di0lTmx04f70Y+Tl5vL4cp3KN7S\niFyhpLPmGD/8xjdG9f4PnGSMIQgC6vR0HMVFSCMjGdi2BZnBgPG6G4BQDhrA5fRiitaSX5jAwT1N\nlJe2kxoTy4Ht2yiYPZu2mnImxp/qWCUIEiISrkEqD2egYytd1a8QmXwjIMHRexCtadoVFe7UK8N5\nMHc1vyn9E3859jrfnv5VtIrQKnfVogzCVHLWbqvj+ddDntKj1XHQK2TckhrNvJgINrb2Um4d5I+V\nreQYwlieYMKsvjwDU7RayRMTE/mouYeDvTZeLG/hlhQz+ZEXLyojSKWoklNQJacQsXRZyAikpydU\neDaUy3aWHcNZdiy0g1SKKiX1hOpZRubFTca/IFz2Afq4sMihoha620PCF1ExOgpmJpI2wXTJNain\nTp3GD37wk7O8Oj5uVkExiDvgYUfrHjq7P6bEdhRZtJwDPaXoTFq0qjBWJVzHtNRCtPKRz9DHm8qq\nSgYHB5mUP+mMspJBufKUB60sPInw6HnDf58cmcifmEv+xBNykkf77PQ6XEzWKQg7S6XrwLatOEoP\nos6aQOS1159xm9Fw36rb+GD9ejrcXgxqJatvuYWWbgedfU6mZZupsDmxev3MNutPG5whJLwCMD1D\nwcYjAjsOtfOTh1exfddWbP4AaWoVUWVHkUVEYrrlVnaWd+H2BlgxIwnpee5znULLgoQ5bG7ezu6O\nIhYlzD1tm7tvvRWXy0UgEECrvXnU739vexESQcKMmEIAVCkhAZKw3Dyc5WVY3n8XmSEC/bz51DSX\nUd9TS3mlgsTUOUyansCR4lYO7W9BmmXiaEU7Ot92Vi6cSnrq6WklQRDQx8xHKtfR1/wRloa1KDRx\neJ2teJ2tKMPOnGu/XGRFZHBd2nI+ql/P38rX8MTkB4cnx9fMSkajkvHa+ip+vqaEp2+dxISk0WkC\nAJjVCu7JjKPJ7mJ9ay8V1pAHdaEpnCXxRvRj6OQ3UhRSCbemRpOqU/NBUzdr6jppsLu4ZpQh7/Mh\nCAIKsxmF2Yx+bsg8xm+znQiJD+mKu+tq6d+wLnRtcXFDg3UW6qwsZJHGK2piNxZctgHa6/FzpLj1\nFGGRlAwjk88gLHLlMHZuVnavg4q+asotVVT0VdNsa2Vb625UpjDSc7Lo3NPIs9OeRuWR8zSlLMyc\nf0V9Ji/86RV8kcmotTq2//1VHr979UmRhhBSv4dgIIBEKkUUReSi9KxWhCcjiiJvbdyM2u3GGhnJ\nC0U7eXT1nacc39PSQs/bbyLRaol5+LExyVcJgsBNK1ee8r/9Q+Ht6dlRbO3oQyoILIg9Xbxlw9at\nNHc24x7wcs2CAVbOzOGNTdVsPdTBrddeixgM0vo/P8UVCGC++14kKjXbStuRCALzz6Lz/HmWJi1k\nR9teNjZuZW7sjNNW0cBp38FIabG30eJoJ980Eb0ytEJSJiWBRIK3vX1IbezHdL36Ch+UV6CaMo+l\ns2ex5Z3XqWg8xERVP0gAACAASURBVIN3303e1HhK9zXTdriD2Ng4Hrt3NvuLD7D/8DE0Cjk3rFhx\n2oRbayxAKgujt3EtXmfIM9zRW3LFDdAQ6kuvH2ikzFLJhsYtrEw9ka5aVBCPRinjTx+V88t/HOaJ\nm/KYnHFhDnTJOjWPZCdQaR1kfauF4l4bhyx25kYbWBA7+oF/LJhqCic+TMmauk72DYW877qIkPdI\nkIWHoyuchq4w1B8fdLtP5LFrqnHV1eJtb2dg+7bQ9hGRwzlsdUYWivj4L3wee8wGaFEUWbBgASkp\nKQBMmTKFr3/962fd/tfPbcbt8o1IWORSIQjCeQbBC3ezcjqdTJo1lS2duyi3VNFsb0UklO/WK3To\nleHclnUDC3LnoZFr+L3jNzz3zHcIBkWeeebZK2pwPlZ2jGBUGvHJoZWRfulNfLJ5M7ddf+oq9s7r\nlvPX138FaiNyQc3qG0ZmoFHW0U2YKDJjacgr2O/z8d66day+5RYgVHDS8fIfEP1+Yr/0ZeQRo3to\n+Xw+PtqwAW8gwMSM9LP2w4uiSFF5NyqFlKBBSX+LjZlm/Wkrma27dtKniiB1fqgd6b11a3jingV8\ntLuBLSWtrJyZhHf/Llw11WgLp6EtmEJDh42mLjtTMk1E6EZW1alTaFkYP4dNzdvY1b6fxYnzzr/T\nCNnbcQCA2Scph0mUSpTx8Xiam5BHmYl/6qu0/O/P6Hb5mGSOYcdH/2T+DbejUCj50zvvs3rFSg4V\nCUQFg2ROiWfrzh10K/RET1uEa9DBX9as4eG7T7f3VOszMWfcR0/9GoJ+55AN5TKk8itDHvM4EkHC\nfRPv4KdFL/BJwyZS9clkR56Ihs3IiUatlPG7d4/y4rtH+fJ1OcyaGHOOI54dQRDIidCSZQijtNfG\n5rY+tnf2U9QzwHWuWPLCVJe8NStareSJnPEJeY8EiUqFJmcimpyQ253o9+NpaT4lj20v2o+9aH9o\ne7X6FCMQZUoKEvkXK489Zm5WTU1NPP/887z00ksj2v6FH20mKzea3BEKi3wRGfDYqeirGl4lO/0u\nIPRDT9enDPclx2u/WJZve4v2064yoY88sUKwlGznjhtuGP5bFEUsTe/j7D+KzjybiPhlZzrUGXlh\nRxEaf4DUrInD/+ss3sY9N4V6ozv//ldsO3dgWLoM852j83MWRZHfvvI3MhZeg0KpoqmshIJoA4VD\n1aYnU9c2wI9fO8is3BhsyRoGvD6eyU/B8DkVqNff/4CYaYuG/26uLmdpRhzH2mHttjpunB5L7vu/\nASDlueeRGQy88mkFO4908PXbJ5OXdu7888k4vIN8d+/zqKRKfjD7WyjOsIo+H593Q/IFfHxn94+Q\nSqT8eM7/OaX6//hnnfS9H6BKSsZeWsJLW3ehzc4nKi4BgykkKiOKIpbibTQ0RGLwBpl1VTqlLcUk\nzbxq+Fjluzfz1O23nPVe93n66Kr6C8GAC4UmjuisBxCEK6sTAaDR1swvD/4BtUzFt2d8FYPy1BqJ\n6hYrL6w9gtvj556rs1g8NeGiz+kNBNnbbWV7Rz/uQBCDQsbSeCMFRt24Fs6ejdJeGx80deMNisw0\n67km0XRZe7khdA/6urpCg3V1aMD29XQPvy7IZKhS01AN57EzkGoufcrwsrhZ7dmzh507d/LJJ5+w\nbt068vLyiDjHymbWgjQMJs2wE86/AoFggPqBJna27eP92k94r+4TjvSW0THYhVaupTB6MitSlnDX\nhFuYHz+LdEMq4UrdF2pwBjCboti6+VOMyZkIEgmVe7dw1bQpGE7qER/sO4ytaycKTTymlJtHFNoG\n6HF52dQ7SM/hItIn5CIIAjs+eAuXL0BpVTUdRXsJ370TZVIysY88PmpJwd7eXmptXkzxoRCqwRxL\nQ9lhJuec7j61vqiZ+nYbhbPjqXV5mB4VToHpdIGF8soK5KY4pNLQyrqtopQ5k/NJi49kW2kb9S19\nFHQfJebOu9Bk5+B0+/nrpxVE6JTctSxrVN+/QqrAE/BQ3leFVhFGqn70Dmyf76Mt7TnKga5SFsTP\nZqLx1EI7v22AwcOHUCaloEpJQRkbS0vJAVpsdqISU1Cd9ICrO1JGrSOMWEFgwOLEp7JiSDqRf+5u\nqGbWpLOr90llalThWTh6iwn47HgHO1DrJyBcYe2CBqUetVzNoZ6jNNpamBkz9ZRiTaNeRX5aJCXV\nPRyo7EEqEchMuLi0nVQikKJTMz1Kj0otp7LPQVm/g/J+BwaFHKNSfkmfI7EaJRMjtDTYXUMypk7S\nw9WXVRlNEASkWi2qpGS0U6YSsXQZ+gWLUKWlIzMYEL0+3I0NuGuqse/fR//6ddhLDuJpayXodiFR\na5BeYIpoNIx7H/Q///lPXn311VP+9/3vf59HH32U5cuXc/DgQb75zW+ydu3aCzn8FwqrZ4BySxVl\nliqq+mtw+UP5dJkgZUJExvAqOTbsX0dQXqlU8uxjX+Jvb70HgoTrpk8hJenEQOFz99Lfug5BqsSU\ncsuoVkG7u/oRpDJuXLmC2n2baW1vY8L0hUQnhcxMmg8X0xDcweJHHkdyFr/jc6FWq/A4T6weRVEk\nGDi9HSkYFDlQ2U2YSkat34dEgIVnyD0D3HLNtbz85hoCag0eexdTUmLRakOz5AUJMtbXyShLn03u\n/IUA7C3rxOsLsrAg7oJWP0uSFrC9dTcbm7YyL24mCunFRaD2tp8e3j6OKiX0ubsb62HhIgDu/MqT\nbH3xBT58+Vcs/dr3kCuVHPvsIyz+BAJAem40dce6yEhLpmzbOsxZefS3N5OffP5cu0IdhVqfjWug\nEre9lu7aV4lKuwvpFVQcCbAwfg511gZKuo/wQd06bsk81Y0rKVrHt+4p5BdvlfLujnqcbj+rFqdf\n9DNAI5OyKieBAp2GzW0WSi12/l7TTqpOzcoEEwnn0aofS8xqBY/nJPJxcw/FvTZeLGvh5lQzky5R\nyHskyAwGdNOmo5sWureDbheuurrhPLa7vg5vawsDW7eEtjcah0Pi6owsFLGxlzWPPWYhbrfbjVQq\nHa7mXbBgATt27BiLQ19R+AN+qiz1lHaUcaijjOaBtuHXzGFGCmJzKYjJJc+chUp+5QguXCqCAR+V\n+3+Ly9FB2qR7iYiZNOJ9bR4f39p6DL1Szo8X5SIRBF59+x3kWSfUsILBIK4Na3joO89e8DW+sfZ9\nWjwywqNi6Cgr5iurbybmc5KYR2t7+c4fdjNjbhLNKpH5iUbuyz/3arWleiNdDRvJnPpl9FE5+J0u\ndj/1DV4IX4xWp+Ev31uOTCrhqf/dSmu3g1e+dzURugu7R9Yc+YD3KtZzX8GtXDdh6fl3OAs9gxae\n/Pi7ZBlTeW7pN097Pej3s/+ue1HHx1Hw618M/18URd598DvUth5GnpjI7K88w0/erGBmbgxP3zqJ\n3/54C+EGFV96cgY1dXUkJsQTFXV2O9GTsffVUV38Egp1JF5XH0q1kYzCh1BpLqzoarxw+dx8e9NP\nabd38Y25jzIjoeC0bXqtLr77xz20djtYNiOJr6wqQCoZu4l6q83Fu1VtHO0J1cIUxhi4eUIc0WGX\n9tmzv62P14414wkEWZRk4vacBORjoO443gR9PgbrG7CVV2CrqMBWXonffmICL9Np0WVnEz4xh/CJ\nOWjT0y5oYXChjFmR2O9+9zv0ej0PPfQQlZWVxMWdf7Z8ch7sSqbP3U/5kOlEVX8t7kBIQEUmkZET\nmUWuMZuJkVmYNVHDM2S71Ycd3+W87HHl83nM4/S1fIrL0YHWVIhfmjqq73hzmwVfUGR2lB5LrwOA\nhOg4SqrLiR/KR9fv38mK5dde1L1z9cIlNDc30WuxcN2ttyCVqE873sa9DQBYwyRIggFmRpz5/Z6M\nx+NDEARsNi9e7HSveQNZbyez04Ls6PPx/tYaEkxamjrtTM8243f76HGP/h6pb2igbGc7Ef40/nbo\nA9IfzMBkGHke++Tv7pOG7YiITIsqPOv7UyQmMdhQT1dr7ymmB13ZS8nxujBaWmj986ugnsaiybF4\nvH6yJ8dQVtLO4eJOJuSHwtwj/c5EMQqZ0oTX1Ycuahb2nn1U7PstUemrUWpGVvF+qXgg525+Xvxb\nXtz3d7413UCU5vTv4Rt3FvCrfxxmU1Ez/QMuHr4+F7nswgevk78/JXBXSjQzI3Wsb+3lYKeV0i4r\n0016roqPRCe/NI06aQo5T+Qksqaug23NvVT12rkrPQbTOFZ5jxmRsSjmxWKadxXGYBBvZ+dQe1c1\n7poa+g8U039gyAhELkeVmnZCQCU9Y9Rh8cuSg87Pz+fvf/87b7/9Nnv37uW55547Zw4aGBc92bHA\nF/RT01/H9tY9vFP7MR/Vr+eYpYIuZw8RKgPTY6Zybeoy7pxwM7PjppOqTxoWLvh34Ux6wE5rBdb2\nzchVZkypq0YV2vYGgrxd34VMEFiVFjO8yog2m+ncv4uyjZ/Sc/gghZNzyM/Lv+jr1+sNxMbGnrF/\n2x8I8sq6SsLiwghEKJlqCqfwDLnnz+O21eMZbCbMNAVfq4Xu1/6GPCaGyQ/dy5ZD7bT2OBhweGnt\nGeTupZlEnUXa83y8/vE6Jl51HSnp+WTnzOGDj19jwdSRO10d/+6CYpDXK/5JQAxyb86qs9pZetpa\ncNfVEZY/GbnxxADUUGOh1mMiXWtH21ZLpEbK3BsXIggCkaYwjpW0YekdJHdK/Kh+G4IgIIoB3PZa\nNBE5oZC3tQJn/1EU6hjkqpFPRsYbnUJLhNLAwe7D1FobmBlTeJrErlIuZWZONHVtAxyt76Ohw0Zh\nVtQ57SrPxZl+exFKOdNM4USrFbQPeqixOSnqHsAnisSHKce0Z/ms1yWXMtUUzqA/QNWAk4O9NiKU\ncmKuAA/skSIIAjKdDlVKCrqphUQsvZrweQtQpaYiDdcjej24GxtwVVdh37eX/vWf4jhUiretjaDH\njVQThkR17ujFZdHi1ul0I67gvhLpdVmGc8nV/bV4g6GVjVwiJ8+YTc5QLtl8hYXZrhT8HiuW5g8R\nJHJMqbcikYwuDFRqseH0B1gUG4HipAeXf2CAzKK9pA0OkvTt76IaauMbTyqa+nG4fCSnReMDFo20\n93RIqIRgSOEMUST6vgfQRGiZNymObaVtWAY8REdqyE6+iH5WxYkfuEQiYSDoxRPwohxlLrq6v44+\ndz+zY6ejkp39oaJKDQmWuBvqUWeeaCtSa+QEJXL25V9HeuerTGwrxfrZJiKWXo1Or2JCfgwVhzuo\nq+wmc+LoXLXCIidjbf8MR28JsTlPIJVrsTS+S0/9W0QmXY/WeHo4+XIxM7aQuoEGdrcXsbbmA1Zn\nn95OqFbK+Nrtk3npgzIO1fbyv2+X8tVVk0fl5nc+BEEgP1LHRIOW4t4BPmvrY2t7H0XdAyyOi2RG\nlB7ZGIbXz4RcIuHmlGjSdBrea+zi7fpOGuxOrk2KuuxV3heKPDIS+YxZhM8ITYIDzkHcJ+exG+rx\nNDdh3bI5tH2UOdSPPSSgIo+OueDF22VXErtceAM+aqz1Q9aMVXQ7e3E09NP0j2PoYiLQyjXIRRnX\nrbiB2xet5sknH+G///s7kDT2A7Tb7eZrX3uCb3/7+yQlJbNu3cd8+ulHQMj+sLa2ho8+2kBY2JUp\nbSeKAXob30EMeIhMugG5amS5xuMERZFdnVakgsDs6BOV4GIwSOdf/0TAZiPq9rsuyeAMUFTehdKs\nxiMVmGrUjViM4biSmG3rXrxtregXLByWH105M4nth9oIiiILJ19cW53gcSKKIoIg4PW46bW3sqN1\nD8uSF43qOHvaQ7rHc+LOrX19olCs4ZT/qzUK/IjsqndQm7mSe1vX0/P2GmR6A7rpM5g6O4nKIx0c\n3NNERo55VO9ZKlOjMUzE2X8Uj6MJjSEbacY99NS/RV/zhwR8dsKj510xUatVmTfSZGtld3sR6fpU\nZsYWnraNQi7liZvz+OunFewr6+Jnb5Tw9TsKMGjHdoUplQjMNBsoMIazu6ufHR39fNzcw54uK8vi\njeRHase9NWuyURcSNqntoKjHRovDzV0ZsV+MkPd5kGrCCMufRFh+qL4m6PPhaWw84Y9dW4Ntz25s\ne3aHttfpQoN1ZibqrGyIGnkE8IodoPvbNuG0lo/dAUVAm0K11Ei5pYoaax2+ITMBpVRBvmki6oCE\nqhkGfvLc/wAn3KyuWXnDJXKzCrFy5XWsXBmqCv3lL3/G9dffdMUOzgAD7VvxOtvQROQRFnm6tvj5\nqLQOYvH4mGYKPyVn1r9hPc6yY4RNmoxh2dVjeclnxecPUFLTg2GqGQFYFHfmyu0zIYpBglYfA+s/\nQxoejunW24dfM+lVKOVS3N4AOo2CYDDIjt07cXu8LJgzF41m5KIcd157De9uWI+oUCL1uYgo0LK5\neTvz42ejko3sYT/oc3K4t4xoTRSp4ecufpObo5FoNLgb6k/5vzpMTi/g9QeZvjCH+PiJtP78eTr/\n8jJSnY7w7Byy8mKoOtpJfVUP6dnmEb9HAK2pEGf/URy9B1HpUlBqk4jOeoDu2jcZ6NhKwGcnImHF\niFv4xhO5VM5Deffys+IXWFP1Lgm6OOK1sadtJ5NKQraiShlbStr46eslPHNnwQWnO86FUirhqjgj\nM6L0bG3vp6jHytv1nezsVLIiwUSGfnyFYEwqBY9NTOST5h6Kemy8WNbMTSlmCoz/Wl7QErl8SMEs\nFF0Sg0G87W2nGoGUHsRRehCAxA/eGfGxr9gBeiwQRRF/0I836MMX8FFuP8A2Vyh3ExcWM9wClW5I\nQSaRUVJSTL2kcnj/y+VmdZzKynIaGur5+tcvvGJ5vHHZarF170GmjCQy8doLWtHs7OwHYG7MidWz\nq76O3vffQao3EP3Aly/ZSulofR/BcAWCRsZko25UM34x6Me/vRfR58f84D1Iw060BlU2W3F7Qyvs\nzQeaKS3eQPz0RSjVav6w5m0euf1WdCM0sTcZjTyy+s7hvz9p2MSnDZvY0baHq5MXj+gYB7pKTzHG\nOBeCIKBKScVZXkbA4Rg2KVCq5HQhIpcKLCyIR6WWE/eVp2n99S9o/91vSHz2O0ydnUT1sU4O7m4i\nbULUqL5HZVgiclUUzoEKAr5BpPIw5Koooic8SE/tm8P90saUW0adUhkPojRG7sm5nT8dfZW/HHud\n/5721BlTBxJB4O5lWYSp5Hy0p5HnXw85YcVHjc8kXCuXcX1yFHOjDWxqs3C4z85fq9vIDNewPMFI\n3DhWfMslEm5KiSZ1KOT9j/ouGuwurvsCh7zPhyCRoExIRJmQiGFxSKjHZ7Hgqq3G29Z2nr1P5Yod\noCPil41KfQqGHLGc3cMV17XWevxDYUeVVEV2ZBark0ODcoTKcMZjXAluVsd59dVXePDBR0b1GVxK\nAj47lqb3QZBgSrkViXT0obpmh4smh5sJeg3RQ8bwAaeTzpdfgmCQ2IcfRTbCgWss2F/RhTY1dL7F\nZ+l7Phuew40EW12o8yainXZq2Hj7odAPc0KSgeKDxaxavQCdIZSHzl92E+u2bOX2G2+8oGtenDCP\nrS272Ny8nQXxs8+ZTz7OvvYDpxhjnA9VahrO8jLcTY2EDUmjNvY78QL50Tq06tAAqcmZSMyDD9P5\np5do/fUvSPr2d8mYaKamrJvGGgupWSNPEQmCgNY4lf62DQz2HSY8OmSzKZPriM66n576f+AaqKK7\n9rVQr7Ts8tkzHqcgKo8liQv4rGUHb1Su5cHcu884KREEgZsXpBGmkvHWllp++kYJX7u9gLS48bvX\nI1Vy7kiPYV6MgQ2tFmpsTmrKnUyO1LEswUikcvwmOcMh77pODgyFvO9Mj71sTl2XGrnRiNw4e9T7\nXbED9Ehx+91U9dcOF3j1e6zDryVo44ZWyVmk6VNOq648E5fDzepM2O12WlqamDJlZA/QS40oBult\nfI+g34khfjkKzenhvJGwqzP0fc2PiRg6rkj3a3/D19tD5HXXD/vHXgo83gDl/Q50cUYmRWqJGsXD\nw2+3MbjpKMgEou5cdcpDeWDQy8GqHuKjwrjjqgyKi/cjO8nbVhCEi8qeaORqrkqcxycNm9jeuofl\nKVedc/uG/hZaHO1MMuUOG2Ocj+E8dEP98ABdVBeyhZ3wOQ/k8Jmz8Fv76f3n27T9+hcUPPw1asq6\nKd7dSErm6ByHwiInDRWLHURnnj28r0Sqwpy+Gkvzhzj7j9FV/QrmjNXIFCcm3gMDVrxeHyaT6ZLm\nqm9MX0mDrZmS7iOkG1LP6Dx2nKtnJKFWyfjbukr+561Snr4ln5yU0U0MR0t8mIoHJ8RTMzDIhtbQ\nivpYv52ZZgOLYiPQjlNrlkml4LGcBD5t7mV/zwC/L//XDHmPJV+4AVoURdoHO4dXyXUDjQSGVslq\nmZqp5klMHOpL1ivH+osfOzer83H4cAmFhTPG9OrHks6GrXgcjaj1WeiiLuw6LW4vZf0O4jVKUofM\n6W27dmA/UIQqIxPj9TeN5SWfl9KaHlSJOhBh8ShyzwA9b69BdHmRzTMiN55aJLfrSDuBoMiignhS\nYsKZNX0GH695h1UP3Y9MruDY1k+5e+XookWfZ3HiPLa07OKz5h0sSJiD+hyr6K31ewCYHTttxMdX\npZ5aKFbbNkBzzyAGQBE8XesocvlK/FYr1k0bcL75MmlZN1BfbaG5vo/k83hfn4xEpkYTkctg32E8\n9gZU4WnDrwkSGcbkm5HKtdi799FV9Vei0lej0MSw5r336JeokMlV+LubefTee05JVY0nUomUL+fd\nzfNFv+bdmo9J1iWSqk866/bzJ8WhUcr444dl/Oqfh3nsxjymZo2u0PJCyNSHkR6u4Wifg41tvezp\nsnKwx8aC2AjmRhtO6aYYK+QSCTemmEkNV/NeQzf/qO+i3hYKeY/H+b7ofCEGaKfPRWV/DRWWKsr7\nqrF6BoZfS9LFM9GYTa5xAsm6xBGtks/GeLpZuVxOFixYPOJioObmZuLjL15kfzxwO5rprt2AVB5O\nZNINF7w62d1lRQTmxUQgCAKe9ja617yBRKMh9uHHRq2zfbHsaOxFblaSEaYaDrePhMGyY9j37UUa\nq0eaHw4n9X8HRZGtJS34rUeprx+grX4vy6fP52j9JDa8+T7zJ8dy37XLMRovrrdXLVOzJHEBHzds\nYHvrblakLDnjdr6Aj53NRYQrdOQaT9cfPxsyQwSyiAjcDfWIosjGAy0ARCPgGjyz2ErUqjsIDFix\nF+0nSbOXerIo3t1IUlrkqO4ZrWkqg32HsVsOnjJAQ+g3GxF/NVK5DmvbJrpq/k63dwp+UyJZqaFI\nlds5gU83beL6FStGfM6LxaDU80Dual489Gf+cux1vjXjv87p5V44wcx/rZLx4jtH+d17R3nwmhzm\n5l9YVGo0SASByUYduRFainoG2NLex6Y2C/u6rVwVZ2RaVDjScYg+TIrUEa8JhbyLe220DLq5698o\n5D1Sxkzq80I4m7JQUAzS5uigzFJFuaWSBlszQTEIQJhcM6zelROZhU5x5VY3/ysS8DvprHyZgM+O\nOfM+VNrRmzUAOP0Bfna4gTCZlGcmpSD4fDT/+Id421qJfeIpdFMvbWjf4fLygz01yMMVPJ2bNGJx\nhaDHQ9P3/y++PgvaL83Er+4iYdKzw/n4o3W9fO+nP2fu0jnkTp+DIEho3L0Bq5BDdYuV739pOskx\nY6Nd7PK7+f6enyIi8sM530J9hpxscdchXil7k2VJi7gp45pRHb/td79hsLQEw//9Cd9+u5JEs5aE\nfjfhBjW3P3jmVq2gz0fbC7/EVVlBRd4q2t1hXHfHJBJTR1MdL9JZ+TI+dw/xeV9FKtfS0dnBtn0h\nW8Elc+dijopisP8YlqYP2HOoE+3c76BSn5gM95dsZ9VJbmuXinUNm/m4YSMTjRN4fNIDp5hqnIm6\ntgF+/c/DDLr93LUkk2XTT/fFPpuK31jgDgTY2WllV2c/vqCISSXn6ngTuRHjI8TkCwb5tKWX/d0D\nKCQCNyabmTICUaAvMpdFSexCOFkNZ9Dn5EhvGZubt/NW9XtsadlJdX8tVs8AKeGJzImbwU0Z13Br\n5vVMNU8iXhs7amGG/3BxhCwk38XrbCcuYzkKXe4FH2tXp5Uam5MlcZEk69R0r3kD59HD6BddReTy\nlWN41SPjo2NtdMnBGBS4OnXk7UC977/L4JHDRCxfSY9+kB0lbbT2BElPSUMikfCT3/2Za1bfTnRC\nMvs2fUx8aiZ93R1cPauAvWVdDLp8TM8ZnYjH2ZBLZATEIGWWShQSBZkRaadt807NR/S6+7gn+za0\nitEZUPh7e3BWlFMWNFDmULBqcTqDnYN4PH4KZpw+kAAIUinaKYU4jx1B1lxFm34CtgEX2fkjF28I\nbSfittUgkaqxuzWs2biF1DlXo4lN4bON65iYmoTOkIQyLBFFoJbdRTXEZoYsROtK9jI7dwLGyPHN\n7Z6JdEMKjbZmKvqqkUlkZBhSz7l9ZLiKSelGSmp6KK7qQRRFJiQZTvmszqQkNlbIJBLSwzVMiwrH\nGxCpszk50u+gZsCJSaUgYowLyaSCQLYhDLNaQaU1dC6r109GuGZMNcuvJEajJHbZBuigGKSys549\n7UV8ULeetTUfUtpzlDZHB0qJginmfK5OWsSd2bewKHEeWRHpGJQXZ9n2Hy4OR08R9p4ilNoU0vJv\nx+m8MK1xXzDI23WdSASB29NicJUepHft2yjiE4h7/CuXPLQtiiJv1XYSlEm4JdmMeYSOQO7mJrpe\n+Qtyo4nA9Tfy8cFmJqz4ChiiWf/+WuwDdqLyJ2OMjkWuVJKYkc2RfdtRBLxcu2AGR+osVDT1Mz3b\njG6MPNETtLHsbt9P/UAT8+JmIT/JL9ri6mNtzUdMMKWzJHHhqI8d9Pmw791D3aCMvqhkHliZQ31l\nDwP9LgrnJJ/1tymRy9EWTMVbtIOBoJput4q4JAPho+j9lauM2HuK8Hks7DnqIGHWkuGUVFRyBvUl\ne5mQmYlMOB+GYAAAIABJREFUGYHelI3WvZcje7fh6GhkVu5kciaMPJw/lgiCwMTICRR3HeJobznp\n+lRM6nNPFMLDFEzNiuJQTS+lNb0Muv3knZQWGM8B+jhKqYRsQxiTjDrsvgC1NiclvTbaBj1EqxVj\nXkgWrVaSH6mlyeGmesBJuXWQVJ163ArWLidfiAH64Q/+my0tu6ix1jPgsZGqT2Ze/ExuzriWmzOu\npcCcT5w29qKt9P7D2OB1dtDb+A4SmZrojHvQhYdf8EOitNfG4T4Hc6INpAdctL3wSxAEEr7+TeSG\nM7e/jSeHu20cHXQhsfm4M//MK8HPIwaDtL/4GwL9/cQ++gSfVVSSOu9aAKQyGYJGx64tu0kvmIxC\nGRrwBUGgdMun3LlyBSajEa1aQVFlN25vYMyKgmQSGaIocsxSgVwqJyvihB/z1pad1FjruT33OszK\n0a/apWFh9K/7FB8SUpZfRXZSBE21FvotTvKnJSA7h7e7RKUiLH8Svh3radOkYe2wkFN49sKpzyNI\nZPi9VjyORrqsGoIRiciGdNQHHTa0XgdpQ5XmUrmWqLgCEsObSI2wEKmToNZnXTZBE4VUQao+mX2d\nxZRZKpkWU3DeVrgwlZxp2WbKGvs4XGuhx+pmcoYRiUS4JAP0cTQyKfmROrL0YVg8PmptTop6Buj3\n+ojTKFGNof+zRiZlqkmHKxCkaiA0IdArZMR+gbS8R8JoBujLVjanlqmYFTuNL+fdw8/nf59nCp9g\nRcoSknQJ583T/IdLSzDgobfxHRADGJNvQiq/8JxpUBTZ2WlFIsAsk46Ol18i6HRivvNulCNwQBtr\nRFFkQ3OoXWiKbuTKStbPNuFpbEA3e06o7SgY5ORyDp/HQ5/PyKdvrSXg94cGzW2f8q3HHiUrIyN0\nviwT8aYw9pV10Wt1jdl7WpAwB608jK0tO3H6QscNikH2dhSjlCqYnTj1wg6sUmNV6on1WFgwKVTA\npA4LDZKuEQwYiphYJj7+JYyudrosfhr3j04pUGsMXffMiTIadq6ns7mBjsY62g9s46ohr+rjyBR6\nojMfQBmWiNNaRnfdmwQD7lGdbyxJ0ydzS8Z12H0O/nrsTQLBwHn3idApeXb1VNLiwtlb1snv3zuG\nz3/+/caDRK2KhybEc39mHGa1gpJeO7882sS6ll6cY3hNMomEG5LN3JUeg0QQWNvQxdqGTryB4Jid\n44vEZVtBX5N1FRmaTGLDok8Jw/2HKwtRFOlr/giPowmdeTa6qFAx0IXO4qsGnOzttlJg1JG8+zPs\n+/ehmz4D4y23XZb0RZ3NxT6LDXePi/unJqNWnj+k5rP00v6H3yFRq4l/6r+QKJXEx0Tz0Tt/xpQ8\nEXt/H43FRViCScwpyEPSfQx3RzPXLVpAtPlEflsQBNRKGQerevAHRCZnjI3O+3FXqmOWCuQSGVkR\n6VT217CjbQ8zY6YyL236BX13JVU9tJSWEeexYJo3F6lWR1e7jY6WAdKzzej0508NyCMjUeOmrs1H\nf1U9mdmmYWWy8yGV63ANVONzNrNoyZdQOB3EhclZuWTpGe8diUSOJiIPn7sXt70W10AtasOECxLU\nGQtSwhPpGOyivK8KfzBAduT59RAUcikzcsw0dNg4Wt9HbesA8wri8Xn9l+CKT0UQBEwqBTOi9EQq\n5bQMhsLRRT0DCEBcmHLMKr6j1UryI7Q0Hw959w+SGv6vEfL+Qqyg/8MXg8G+wyGbP008hrhzC2CM\nhOOynrNsXfR9+jFyUxTme790WQZnURTZ0BLSQDc6g0SGn3+AEUWR7jdeQ/R4MN9+17DKWUREJKtm\nyhnY+TPiPBZUUYUIgsDSGWncev313HbD9acMzseZkWMmyqBi55EOrA7PmL2346voLS27cPqc7G0/\nAMDsuAvvrd94oIUOVWgScVyXWz2UOx/JCvo4aYunE60HizKast/+Bf+A9fw7MaQsZioERFz9h8nN\nzWNizsRz3jsSiRxT6m1oTdPwubvoqvorPnfPWbcfTwRB4O6cVZjVJjY1b+NIT9mI9lMpZPzXbZMp\nzIqistnK/3lpD/bLaNUrEQSmmsL5en4yKxNMCMD6Vgu/PNJEcc8AwTFqDDKqFDyak8Bss55ut5ff\nl7dwsPfsba3/ivxngD6JkpJirrtuGU899ShPP/0Yjz76AO+88zYATz75CM3NjeNyXrfbzeOPPzh8\n/GAwyE9+8gMef/zLfOUrD4/bec+Hz91Df+s6BKkSU8oto/J3PhOtg24a7C5yZCLe114BiYSYRx5D\nOgqjiLGk3u6izeXF3etiTtrIVq+O4gMMHjmMJicX3ew5p7ymVstYMC2RrJwCjtX3kR4XTlL0udMB\nUomElbOS8QeCbCxqueD38nmUUgXLkhfhDrhZ37hlyBjDTGr4yPO+J1PXPkBt2wDa9OPWkyHBErXm\neIh7dAWDs64N2UXWSJJoe+FXBN0jC/GHReQhSBQ4eksQxZGFPQVBQkTCSvSxVxHwDdBV/QpuR/Oo\nrnesUMtUPJR/L3KJjFcr/kGvq29E+8llEh67KZd5+bHUtlj56Rsl9NkuX8geQqIj82Mj+MakFBbE\nRDDoD/BuYze/OdZMhdXBWHTwyiQSrk82szo9Fqkg8E5DF2vr/31C3ldsvGBdSw9H+xxjesz8SC0r\nE89ejCMIAtOmzeD//b8fAyfcrJYvP24CMd5uVqHjFxXtw+1284c//IUDB/bz8su/50c/+vmYn/tc\nBIM+ehveQQz6MKXchkx5Ef7FQ+zq7AdRZOaWDwgMWDHdejvqtPTz7zhObGkPPRydDTYKF5y/yjcw\nOEj3mtcR5HLM995/2spNFAMgSNlxuB0RWDRlZKpxc/Ni+XBXA1tL27hmdvKwrvXFMj9+NpubtrO9\nbQ/+oJ85cec3xjgbm4aESWYsngrFa4cVxYZX0IOjW9HFJRmITdTT0QI9LYeQ/v5F4p/+GoLs3I8k\niVRJWEQeDksJblstan3WiM4nCAL6mHlI5Tr6mj+ip/Z1jCm3oDFc+urueG0sd2TdzOuV/+TPx17j\nmalPjCjNJ5VI+NI12RgjNHywo47nXy/hG3cWEB15eSa4x1HLpKxINDE7Ws9nbX0c7LXxWk0HyVoV\nKxJMJOsuXiM9L1JLnEbJmroOSix2WgY93JUeM2K9gi8q/1lBn4QoiqfM+s7mZvXss1/ja1/7Cvfd\ndwc7d24D4P777+TXv/4fnnzyEZ566lEGB0OTi5deepEnnniIxx57kK1bN592zuNuVklJJwQ/lEol\nDkdoBjo46EAmu/Q5emvbJnzubrSmQjQREy/6eP0eH8f6HMysPAgVZWhy84hYfumUnT5Pvc1Jg92F\np9dFplFLeNj5uwV61r5NwGbDeMNNKM4QrkYMgiBh5+EONEoZ00doryiXSVgxIwmPL8Dm4rFdRS9N\nXoh/yFZ1RsyFFYdZBtwUV/aQaNaSnW5GGZ+Ap7kJ0e8/qUhs9C130+amANCSsgBneRmdf/sLYvD8\nK6NQmBscvSWjPqfWOJmo9DtBEOht+Cf2nuJRH2MsmB03ndmx02mxt7G29qMR7ycRBL58Qy43z0/F\nYnPz/BslNHeNj2jJaNEr5NySGs3TeUnkGMJocrj5Y2Urr9e00+26+JB8pErOozkJzIk20OP28oeK\nFg72DIzJSv1K5YpdQa9MjDrnane8uBLcrPLzJ+P1eli9+lZstgF+9rNfXaJ3H8JprcDRW4xcZcYQ\nPzY+zHu6rET0dJC9ezNSXTgxDz6EcBnt5o6vnh0NNmbMO13Q4/M4qyqx7dyBIiGRiGXLz7iNKAbw\n+mUMDHpZOi0BxTnajj7PwoJ4Pt7bxObiVpbPSBpRsdpISBvyepYIkgvujvjsYCtBUeTq6Ykh68nU\nVDzNTXjaWlGbQpX3o8lBHyc+2UB0fDidbZCZPhn7vr3IDBFE3Xb7OfdTaGJRaOJw2WrweweQKfSj\nOq86PIPozPvprltDf+unBHw29LGLL3kdxO1ZN9Fsb2VX2z7S9SkjnkAJgsD1c1PRqOS8saman79Z\nyldXTSYjYXSfw3gRrVZyb2YcjXYX61t7KbcOUmEdZFpUOEvijIQrLvzelkkkXJcURapOzTsNXbzT\n2E293cUNyWaU/4Ja3v967+gimTp1Gr/97R954YU/8Mtf/pZZs07OM4bcrD744F2ee+57vP/+O+d0\ns2poqBt2s/rGN54edrM6H2+++Sr5+ZNZs+ZdXnnlTX70o/+Hz3dhoiCjxe/px9L8IYJEjin11jHx\n2XX5A5S2dbP4s/cRAgFiHnoEmf7S9zsfp9Huot7uQrD7CDp8FJ6nBzno89L12t9AEIi+74GzhmFF\nMYDDFbofFhWMLLx9HKVCyrLpiTg9fraVjs4z9lwc6DoEhNqsPmveMer9XR4/2w+3oQ9TMHNiqHf6\nZGcr1VA4/mx63OdCEASmzQ1NIFozrkIeE0P/+k/p37zxvPseLxZzWEpHfV4AhSaOmKwHkSkjsXXt\noq/5w1CK4hKikMp5KO8eVFIlayrfoWOwa1T7LylM4OHrJ+L2Bvjft0o5Vm8Zpyu9MFJ0ah7NTuCe\njFhMKjkHemz84mgjG1p7cV9ka1ZuhJYnc5NICFNSarHz+/JmOp1jV2R5pfCfAXpUhNysVqy4lu9+\n94dMmVJI8KSQ3NncrH772z/yq1/9jsWLl47IzcrlchEWFpJh1OnCCQT8BEfQN3mxiGKA3sZ3EQMe\nIhJWIleNTQTjQM8AhTvWoR3oI2LFNcN2hZeL46vn3qo+8tOMaFTnnoT0ffr/2Xvv8KbObG/73lu9\nWJYs914AA6b3FkJIQnomZUIK6b1OZpIpZ855z/nme898JzNzUmcyKZPeE0gmhAmkkBBK6MUGbEyx\nwd2SLVmyLVld+/tDNhhwt2yThPu6cl0Bbz37Eba19lrPWr/fagIWC8bzL0CT2322LYVDuH1hxmQY\nSY3vn4wmwPnT0tCoZHy1sxp/YPDf70AowE5rITEKPQZlDBtqNuPyu/u1xvf76/H4Qiyeno68PUNR\nZ7c3ilUcQxQF1FrFgDJogIycOBJTYjhW7kB320PIYmNp/OgDWnfu6PF1WmMBgqjCbS/sc7PYqchV\nJpJG34FSm4q7aS+N5R8SDg1vd3SiNoGbxy3FHw7w6v538Ab7F2TmFiTz8LUTkYDnPt7HjtL+Bfmh\nRhAExpv0/GJCFtdkJ6KRiWyod/Dk/go2WxwE+3Ck0R1xKgX3js1gfpKRRm+AF0ur2fUjK3mfDdCd\n6I+b1eOP/wKr1dKrm5VGo+Whh+7h3nsjTUV9cbO66aZbKSkp5sEH7+bRRx/gvvseQqXqm/zkYHDW\nfYe/rRataQK6uNNL7wMhGJao2bCBvCPFKLNziL/qmqisO1CqXB7KWtowhCDQ7GfW+J7PiX11tTSt\n+Rx5XFyvew+HQ4TDAoumDExwRatWsHhaOi1uP5v29V5p6Y29jcV4gh7mpMxgSdZ5+EJ+vqna0OfX\nh8MSa3dWo5CLJ70nZWoqglJ5Uif3QM6gIfI7N31eJIveW9pK2qOPIapUWF77B20HS7t9nShTooub\nSCjQiqflyIDuDSBT6EgcdStqwyi8reU0HHmLUCC6zam9MTVxIudlLMDS1sAHhz7pd4CZMiqex5ZO\nRiEXefmzEjYURa8CEy1kgsCMhFgem5jNRelmwhKsrrbx9P5KCm0tAx7NkosCl2UmcPOoSJf3Pysa\nWHHMiu9H0uV9RrpZnWX48bSU0Vj+PnJVHMn59/Qq5tBXR53C0jKUz/0ZUSYj7w//F2VC340ohoI3\nD9dyuLkN6XAzjnoXz/5iAepuzsSkcJjqvzyBt+wIqQ8/in7K1G7X9QdCWIr/P6qdRmad8zAK+cCe\nfVva/Pz2hS3EaBU8cd/c41nrQPhb4SscdBzhv+b8BpPKyB+2/glPyMcLl/8RXx9+9XYfauDvnxaz\naEoqt158crdz9Z//B0/ZEUY9/xL/+qSUuion9/5mIbIB7FeSJD5+Yze2Bhc33DMLVUMFNc8+hahU\nkvHbf0eV0bX8qt9jxXLwZdSGUSTm3dTv+568hzBNVZ/jbipCrjSRMGoZCtXwmWsEw0Ge3fMyx1oq\nuX7M1SxMn9vttd397lVYWnj6o724PAGuW5THJXMG5jQ3HLQFQ6yva2JrQzMhSSJFo+SijHhGG7QD\n7gVw+AJ8WG6h2u0lXq3gpryUM7LLuz9uVmcz6LMQCrRir1wJgkh89rVRU1oK+f0E334NRTCAadmt\nIx6cq9tViVJVSqzVLUweFd9tcAZo3rgeb9kR9DNm9hicAfYcsgAQo1MPODgDGLRKFk5Jxd7iY2uJ\nZcDr2D1NHHQcIS82myRtAkqZgiVZi/GH/Kw6tLZPa3R4PndleajOzgFJwltZgba9k9vrGUQW3X4W\nvWdrJdpx40m+6x7CHg81zz1FwN712apSk4RSm4a3pYygr29iJ93vQSQu8woMyQsJ+h1YD7+Ozz18\nmahclHPXhGXoFFo+ObKKypb+d/NnJxv4/c3TMMWoWLG+nI/Xl5+x5V6tXMalmQk8NjGLqeYYLB4/\nbx6u47VDtdS4BzbfbVIpuGdsOguSjNi8AV44UM3OH3jJ+2yA/okjSWFsFZ8SDrZhTL0QpTZ6JvHl\nH3xAbGM9tskzSZ43r/cXDDHr6iIf9Fpn5JxxVg82j0GnA9snKxA1GhJvWNbr2t/vi3yYxxkGP/N5\n8axMZKLAmm1VhMMD+3DZVh8ZH+qsHDY/dRZGVSxfHdlAq7/nMu6x+haO1DQzKc9Mivn083RVzolG\nsROz0ANvZMwZE09cgo4jJVaaHR4Ms+aQsPRGQk4ntc8+RcjV9X6Pj1zZ+z9ydSqCIGBMWURcxmWE\ngx4ayt7G0zzw8nl/MamN3D7+RkJSmNeK36Ut0NbvNVLMOn5/8zSSTBrWbKvkna8ODfhnaDgwqRRc\nl5vMwwWZjInVcrTVwwsHqvmgrB67t//9AHJR4NLMBG4ZlYJCFPi0ooHlR3+4Je+zAfonTot1Mz5X\nBRrDGGISBi4DeSquokLY9B1OYzyZN98StXUHSo3by6HmNrL1akqLG1ArZUzK676E2fDBe4Q9HuJ/\nfj3yXhy2am1ujtZFMjiVcvDua3EGNfMnJmNtamPXoYZ+v76zMcbUhInH/14hUxw/i15bub7HNXrK\nngHUOZFGMV/FsU5qYgNvsOo4i5akSBYNYFpyEaYlF+Ovr6P2b88S9p++vtZUgCBT4bIXRa0LWx8/\nnfjcpSBJNB79EJe9KCrr9oXx5nwuzj4fu9fBWwc+IjyABrj4WA2/v3k6mYl61hfV8Y9/lRA8wwNU\nilbF7WPSuCs/jXSdiv0OF88UV7KqsoHWQP91x8e1d3ln6NTsbWrl7weqqP8BdnmfDdA/YbyuKprr\n1yNTGIjLujJqc6ABh4P6N14lJJNR9rMbSDcZorLuYPiuvXN7rEqNvcXHtDEJKLqxynMV7sG1exea\n0WOIPWdhr2tvKKxFJkY+AAcrh9rBJXOyEARYvbWy3yW6Q44yHD4n0xOnoJaffFwxL3UWZo2JjbVb\nae7mILqpxcvO0gbSE3SMz+paQU4Rn4Co1+M9dgyNrkOPe3CjgLn5CRjNWg4XW2lpd/eK//lSYmbP\nwVteRv0/XkQKnRyERVGBLm4y4aALT/PhQd2/M9rYfBJH34IoU9NUtYpmy8ZhK5VemnMBY02jKbaX\n9quprzMGnZLf3jSN0emx7Cht4G+f7McXhcmAoSbPoOWBcRncmJeMSalgW0MzT+2r4Jtae7+zYJNK\nwb1j0zknOVLyfvFANTsaflgl77MB+idKKOjBXvFPAMzZVyOTR0cuUAqHsbzyEpLbzY65FzJ94rio\nrDsY6txeSp1uMvVqqssjZh3dlbdDHg8N77+DIJdHTDx6EVPxBUJsKbZg1LWfZUfJKjXJpGXWuCSq\nG1zsK+/ffOsJY4yZp31NIcq5evxFBMIBvqla3+XrTwiTZHb70CYIAuqsbAK2RtREMtv+yn2eiihG\nsuhwWKJwW0QrWxBFku+4G+248biLCml4/53TPmA7bChdtt2Duv+pqHQZJI25A5kylub69Thq1gx4\npKs/iILI7QU3Eqs0sKr8S444yge0jlYt57HrpzAx18z+o3ae+qiINu/w6CkMBkEQmBgXwy8nZHFl\nVgJKUWRdXRNP7qtgq9VJsB8le5kocElGAreOjpS8V1Y28NFRyw+m5H02QP8EiVhIftauoHQuan30\nuj2bVv8Lz+FDVObk45g2h9GxI6sTDCfmns9LiWPXwQZ0ajnjs7vODO2ffkzQ4SDu0sv75E+9s7SB\nNl+Q2eMjZhvRyqABLpsb+b58vrWiz0/97kAbexuLSe7BGOO8nHmYVEY21W6l2XfymKDXH2R9UR2G\nTsIk3dFR5lY6Is1sg82gAUaNSyDWpOHgPguudjMIQS4n5cFHUGVk0rxhPU2frzrpNUpNIipdBt7W\nowR9jkHvoTMKdTzJY+5EoUnGZduN7dgKwuGhD3IxSj13TbgZQRB4veT9bqsdvaFSyHjk2onMGpdI\nWU0zf3m/kOZBPkgNFzJRYE6ikccnZXN+ahyBcJh/VTXybHEl++yt/RrNGmvU80hBJpk6NfuaXDxf\n8sMoeZ8N0J04U9ysAoEA//3f/8X999/Jww/fy5Ej0SvdAbgad+BpPoxKn40haUHU1m07fAj7qpUE\nDEY2L7yMBSkmxBGwkexMfZuPA043GTo1IaePZrefGWMTuxxf8pSX4fxuHcrkFEyXXNan9TcU1SIA\ns8a2i7pEMUCnJ+iZOjqe8toWDlb1rUt5p6WQoBRibg/GGAqZgouyFxMIB087i96834LHF2TxtLRe\nu9E7FMWExhpgcGfQHYiiyLS5me1Z9IlOZplGQ9qjjyGPj8f+2ac0bzq59BvNZrFTkSliSBp9Gyp9\nDp7mQzSUvUMo2P8Grv6SZ8zmqrxLafG38kbJe4QGKFYkl4nce0UBi6amUdXg4k/v7sbW3Df3sDMB\nlUzk/DQzj0/KZk5iLE5/gA+PWnjxQDXlLX3/Phjbu7zPSTZh90VK3tvP8JL3GavFvXxdGTsP9r9B\npidmjk1k6eJR3X79THGzWrXqU9RqNS+99DpVVZX84Q//weuvvxuV+/nb6nHUfYMo1xKffTVClEqy\nIZcLyysvgyCw/vyrUOr1TDH3fd5vqOg4e16cGse29g/8rsrbUjCI9e03QZJIvPV2REXvEqdV1lbK\n61qYlGfGqJdjIboZNMBlc7MpPGLj8y0VjOvmPLgzW+t3Igpir7rOc1Nm8HXld2yq28YFWediVMUe\nFyaRy8Q+OXGp2zu5w3VVQPygurg7M7ogiV2bKyndW8e0eZno9JFzdLnRSPovf03Vn/6I9Z23kMXG\nop8Usa3UGsfjqPkKl72I2ORFCGJ0vw+iTEVi3k3Yqz6jzVGM9fAbJOYtQ64aWsnaxRnnUN5cwd7G\nYlYfW8uVeQMzmBFFgVuWjEGnlrN6a+VxJ6yuOvTPVGIUcq7MSmR+kpGva+3sb3Lx2qFaRhu0XJwR\nT0ofZp4jJe94cmI0rDhq4bPKBo62tnF1diJqWXR/ZqLB2Qy6E2eKm1VFxTFmz46MJWVmZmGzNR5f\nbzCEQz5sFZ+AFMKcdRUyRXQCqCRJWN58jaCjCdd5F1GbmMa8JCPyETTDALC0+Sh2uEjTqsjVq9l1\nsIFYnZL8jNM/VB1ff4m/tobYhYvQtmuq98b6ojqgXXdb6mgSi+57zk01MD7bRGmlg/K65h6vrWqt\nocZVx0TzOAzKnr+3clHOxVmLCYaDfN2eRe8ts9Hg9DBvQjIGbe/d6PJYI/K4OPyVFcjE6GTQADJZ\nJIsOhSSKtp88D6xMTj5uS1n/0gt4yssAEEQ5urhJhINu2poPRmUfpyKIMsxZVxOTOJegz47l8Ov4\n2wY+q96newoCN4+9jniNma8q11Fs615drS9rXXtuHtedl4ej1ccT7+6hwtK9EuKZilmt5Ma8FB4c\nn0FujIYjLW08X1LF8qMWHL6+PSSONeoiJW+9mv1NLv5eUk3dAOevh5IzNoNeunhUj9nuUHEmuFmN\nHj2GLVs2sXDhIoqL9+N0OvB4vOh0+tOu7SuSJNFUvYagr4mYxLloDNH7t23+7lvcRYWox4zl07Ez\nUIQlZiWOvLNOR/Z8flocpZVO3N4gF0xPRxRProT4rRbsq1Yii40l/ufX9Wltjy/I1hILcQYVk/LM\nBDztohZRzqABLp+bzYEKB6u3VPKLn0/q9rqtdR2zz6c3h3XF7JTpfFW5js1121mStYivehmt6gp1\nTi6u3buIzfJH5Qy6g/yJyezeUsmBwjqmzslE28kOVJObR8p9D1L3979S+7dnyfy3/0CZnII+fjqt\njdtx2XajMxVEbS+dEQQBU9qFyBQGnLVfYT3yJgm5S1HH9O6INlC0Cg13T7iFJ3c/z1sHPqQgMw+B\ngY/zXTI7C61KzttfHuIv7xfy6M8nkZ85eL/34SZdp+au/DTKWtr4ssZOkb2V/U0u5iTGsiglDl0v\nbnJGlYJ78tNZW2tno8XBS6U1XJoZz+yE2GF3NuuOsxn0KZwJblaXXXYlOp2OBx+8m02b1pORkYnB\nMLhRJXfTXtoc+1Fq0zCmLh7UWp3xVVfRuPxDZPoYHNctozkYZnq8AW03I0zDhdUTyZ5TtSryY3XH\nTQRmndL4JEkS1nfeQgoGSbxxGTJt30p+20ut+PwhFk5ORRSF4zO40c6gAfIzjeSlGSgqs1HT0HUl\nxd9ujBGrjGF8XN8qAHJRzkXZkSz64wNfcbjayYTcONL6YfTRcQ4dF2yKWgYNkSx66pxMgsEwe3ec\nrqqlnzyFpJtvI+xyUfPsUwSdThTqeFT6LHyuCgLeoXV2MiTOxpx9LZIUoqH8fdxN+4f0fhkxqSwd\n8zPagh6e2fIKgXD/Z4M7c+6UNO6/agKBYJinl++lqMwWpZ0OL4IgMDpWx0PjM1iam4RBIWOz1cmT\n+ytLS5jpAAAgAElEQVRYX9eEv5dubZkocHFGPLeNTkUpE1hV2ciH5Ra8oTNjJO1sgO4Xw+NmVVpa\nwrRpM3nhhVc577zzMZvjUQ5CACPgteGo+QJBpiI++5qonZOGfT7qXn4BKRgk6Y672NQmIQALkkb+\nafy7uiYkImfPgWCYPYcbMRvU5KWe/KDTsuV7PAdL0U2ajH563zJPSZJYX1iLKAicM6m90/t4gI7+\ng4kgCFw+NxuA1dsqu7xmX7sxxuyUGcj6cf46J3kGZnUcRU17EJQelvQje4YTATrGayMYCBPwR++D\nbeykZHR6JcV7arsM/rELz8X8s6sJ2mzUPvc0IY8HvXnomsVORWcqIDFvGYKowF75KS3WLUPacDQv\nZRazk6dT3lTJP498Puj1Zo5N5NGfT0IQ4PlP9g9KWnakEQWBKWYDv5qYxWUZ8cgE+LrWzlP7K9jZ\nGNH77ol8o46Hx2eSpVez3+Hi+TOk5H02QHfiTHGzyszMYsWKD7j//jt54YW/8rvf/Z8BvJsI4XAA\n27FPkMIBzBlXIFdFL3g2fPBuxIbxgiVYc8ZQ1+ajwKQnrhf7xqGmweNnf5OLFK2KcUYd+4/a8fpD\nzBqXeNL3N9jSQuPyDxFUahJvvrXPZa0KSytVVhdTRsdjiok0phyfjx2CAA0wKc9MRqKeHaVWrI7T\nO1e3tkt7zkmZ0a91ZaKMhckLkYQwsTnVFGT3zyBClZUNgoDWFWnojGYWLZfLmDI7k2AgzL6dNV1e\nE3f5lcQuXISvuor6F/6GRj8KUa7F3bQXaZBZZl9Qx2STNPp2ZIoYnHXf4Kz9esiCtCAI3JB/NRmx\nqWys3cIu6+AVzibkmvn19VNRKWW88q8DfLu763/nHwpyUWR+solfT8xmUYoJbyjMpxUNPFdcSYnD\n1eP3xqhScHd+Oucmm2jyBXixtIZtDc4R7fI+62b1I6epeg0u2y708dOJy+jb6FCfKC3i8FPPosrM\nIuP3/4d3jjVwqLmNB8ZlkKEfemvMnlh+1EKRvZVlo1IoMOl5YWUxuw428P/cPpOs5BPNU/WvvEzr\n9q0k3LAM0wUX9nn919eU8v2+eh67fjITcswAtDUfwnb0I4ypF2JI6t6JaDDsKLXy0mclnDMphTsu\nPSEAY/c08V9b/0RebA6PTX+g13VOdUNa/t1hvvO8h1zt5//O+x0mdf86kyv+z+/x2ux8l3Uj19w2\nnaTU6CnHBQIh3ntpG8FAmFsenIOqi4c/KRSi7sXncRcVEjN7DupLcmlt3IY56xp0ccPjPR70N9NY\n/j4BbyNa43jMWVchiEPT4hNQufnd108gAb+b8QjJup7n1ftClbWVp5fvpcXt5+pzcrh8XvYZcw47\nGFr8QdbV2dnV2EIYyNSpuTgjnuyYnjXzDzndrDhmoS0YZoJJzzXZiaijdGx31s3qLAC0OUtx2Xah\nUCdiTFsStXX9DQ2Uv/AygkpNyn0P0BgMc6i5jSy9esSDs83rZ6+9lWSNknFGHR5fkH1lNpLitGQm\nnWiycxfvp3X7VlTZORgXn9/n9du8AXYcsJJgVDO+c7Y5RF3cnZmRn0hynJYtxRaaWk6U304YY/St\nRN8Znz/Epr0W5LZ8woT4snJdv9dQ5+QiBv1oA82DVhM7FYVCxpRZmQT8oW6zaEEmI+We+1HnjaJ1\n+zb8uyKlWpc9uspiPSFXxpI4+nZUugzanAdoKH+PcHBoSqSphmSWjb0Of8jPK8Xv4gsN/t88MymG\n3y+bhtmg5tNNx/hoXdkZPR/cVwxKOVdlJ/HohCwKTHqq3F7+cbCGt4/UYfV0L1SS397lnaVXU+xw\n8fyBampHoOR9NkD/SAn6nNirViGICuJzrkUUo1N2loJB6v/xIiGPh6Sbb0GZlMz3loiIxjnJI3/2\nvL797Pm81DhEQWBvmQ1/MMzsTuXtsM+H9d23QBRJvu2OXuU8O7O1xIo/GObcKWknibBIHSISUZ6/\n7YwoClw6J4tQWOLL7REpzM7GGNMSu+/w7o7NxfW4vUHOy55FgsbM1rqd2D39U+PqcLYyeG1R7eTu\noGBqKmqNgn27avB5uy5biyoVaY/8EmVyCs2fr0PmN+BzVRLwNkZ9P90hk2tIHHULmtix+FyVWI+8\nSdA/NGNM05Mmc276fCxuKx8c/GdUgmlSnJZ/v2U6KWYtX++s5o01BwmFfxiSmL2RoFGybFQK949L\nJ1uv5qDTzV+Lq/jkmJVmf9c/s7FKBXePTefclEjJ+6XSGrZah7fkfTZA/wiRpBC2ik+QQj5M6Zeg\nUCdEbW3bpx/jqzhGwnmLMMydT4s/SJG9FbNKwVjjyIoe2L1+iuytJGqUFJgi2fKO0sjZ6MxO4iT2\nVSsJ2myYllyMKqNrOcyu6GgOk4kCCyamnPK1oWsS68ycgiTMBhUb99bR4vafZIyhkvWvkTAsnRAm\nOX96JpdkX0BICvFVP7NodXZkxMjgs0X1DLoDhVLG5Fnp+H0hivd079Es0+tJ+9XjyIxGPOsiNpEu\n29A3i3VGEOXE5/wcffxMAt4GrIdfx++JruBSB9eMuoxsQyY7rXvYXLc9KmuaYlT827JpZCfH8P3+\nel5aWUIg+OMI0gCZeg33jE3n1tGpJGqU7La18NS+Sr6stuEJnt7gKBMELkqP5/YxqahkIv+qauSD\ncgveLq4dCs4G6B8hzXXf4W+rRWuagC7u9DnrgeIu3ofjqy9RJCWRd9/dAGxrcBKSJBYkj7ys5/p6\nB2FgcUoke3Z7A+w/aic9QX98dMhbVYlj7VcoEhIwX/Gzfq1/pKaZWpub6fkJGHSnBMNhCtBymcjF\ns7PwB8Os3VXdozFGb+wrs2N1eJhbkESsTsmMpCkkauLZWr8Tu6epz+uoMjJAJotk0FFSEzuVCdPS\nUKnl7N1Rjd/XffOXwhxP+qOPgyWM1BbC1bhnWLSzOyMIIqb0izGmnk8o0IL1yJt4XV133w8GuSjn\nrgnL0Mm1rDiyiqrW6DR4xWiV/ObGqYzNNLL7cCPPfbwXr3/oG+6GC0EQjguVXJuThE4uY6PFwZP7\nKthU7yDQRdVgTGzk+uxhLnmfDdA/MjwtZbQ0bEGuiiMu47KoNXoEm51YXnsFZDJS7n0AmUaDLxRm\ne0MzWrmMafEjK+vZ5AtQaG8hQa1kQlwke95zqJFQWGL2+EQg0kxkfesNCIdJvPk2RFXv0oCd2VAU\nyd4WTTl9VO5EF/fQ/0qdMykFg07Jt7trKKo/2KMxRk98vTNSJu8QJpGJMi7JuYCwFObLir5n0aJC\ngSI1nRhfEx7X0Gg8K1VyJs1Mx+cNUlJY1+O1qowMUh/4BaFDLiQCtJR/PyR76glBEDAkzcecdRVS\nyE9D2bu0OQeuAtYdcWoTtxXcQDAc5NX979IWiM6/v0Yl51dLJzNlVDwHKhw8+WERLs+Z74TVH0RB\nYHq8gccmZXFJesTs5osaG0/vr2S3reU0M45YpZy7xqazKMWEo73kvWWIS95nA/SPiFCgFXvlShBE\n4rOvQZT1LwB1hxQOY3n1FUKtrST8fCnqrGwAdtta8ITCzE2MRTHCsp4b6psIS5G5545M/rg4SXt5\n2/ntN/gqKzDMnY+uoH/dvS5PgJ0HG0mO05KfeXqX83CVuAGUChkXzczA6w/htaT1aIzRHZWWVg5W\nOSnIiSM94UTz3IykKSRpE9hm2YWtH1m0JicHkTDhhp6D52CYOD0NpUpG0Y7qXuettWPHETfhcgCc\nR9cRsA3fWXRndHGTSMi7EUGQYTu2gtbGHVG/R4F5LBdnLcbubeKd0uVRCxgKuYyHrpnA3IJkjta1\n8Of39+B0nfkOUP1FIYqck2Li15OyWZhswh0I8ckxK38tqeKg8+TRLJkgsKS95K2WiXxe1cj75fVd\nlsejwdkA3YmRcLNau/ZL7r33dh544C6efPIJJEkiHA7zv//7P9x//5088sh91Nb2XrqSpDC2ipWE\ng20YUy9Eqe3dKrGvOL5cQ1tpCbpJkzFeEOkGD4UlNlsdyAWB2SMs6+nwBdhtayFerWBie/bc4vZz\noNJBbqqBBKOGgK0R28pPEPV6Epbe0O97bN5fTzAUZtGU1K6DYXuAHo4MGmDR1DREeZCgNZvJ5v4f\nY3zdLut5qjCJKIhckt2RRX/b5/U0uXkAyBq7PyMeLCq1gonT0/G2BThQ1PuDgHHWecj8sYiJCmpe\ne5qQa/B69gNBY8gjafRtiHIdjpovcdZ+E/Ws67LcJYwxjWKfrYRvqzdGbV2ZKHLX5eM4f3o6tY1u\nnnh3Nw3OH44TVn/QyGVcnBHPYxOzmB5voNHj5+0j9bxyqJaqUypDo9tL3jkxGkocbp4/UEWNK/ol\n7zNWi/ufZZ9T2BBd+bypiRO5ZtTl3X59uN2sfD4vr776Em+//REqlYo//OE/2Lx5E6FQkEAgwEsv\nvU5JSTHPP/8MTzzxVI9rtVg343MdQ2MYQ0zCrKjt0VNehm3lP5EZjSTfcffx4FRodeLwBZmVEIte\nMbI/Rh3Z83kpJ7LnXYcakKRI9ixJEtZ330Hy+0m65TZkMf0rx3c0h8llIvNOaQ47cc3wZdAAjX4L\nYmIF4bpRFJa0sGRW32eX7c0edpRaSY3XMSHndGGS6UmT+bLiW7ZbdnNR1mIStOZe1+zwhlY1D60a\n1aSZ6ezbVUPh9ioKpqYi70Vv2ZS/BNuxFUjJfmr/+gzpj/+230cb0UCpTSF5zJ00lL9HS8MWggEX\n5swroua6JQoidxTcyJ92PMtn5V+QbchklDEnSmsL3HTBaHRqOas2V/DEu7t5/PopJ1VefkwYVQqu\nzUk67pp10OnmpdIaCkw6lqTFk6CJ9J8YlHLuzE9jXW0T6+ubePlgNRenxzMvyRi1o8WzGXQnhtvN\nSqlU8dJLb6Bq/8AIhUKoVEr27dt73M2qoGACBw/2fHblc1XRXL8emcJAXNaVUfvhCLW5qf/HiyBJ\npNx93/HAJkkSXx21RmQ9k4fWbq83nO3Zs1mlYFIne8sdByL7mzk2EdfOHbQV70M7roCYk7TV+8bB\nSgdWh4eZYxPRa7oZVzs+Bz08AXpr3S7kSZUo5AJf7qjqV6ft6s3HCIUlLpyR3uXPiiiInc6i+5ZF\nK1NSCIkKdK6GIT2TU2sUTJiWhscdoHRv77r2mtgxiHI98vFGvFVHqf/Hi0gjpLMsV5lIGnMnSm0a\nbY59NB79gHAoeiVjgzKGOwqWAfB68bu0+KMnBCUIAledk8uN54+m2eXnz+/t6dVd7YdOslbFraNT\nuWdsOhk6NSUON88VV7KywkpLe9OcTBC4MN3cXvKWsbraxntl0St5n7EZ9DWjLu8x2x0qhtPNShAE\nTKbI7PDHH3+I1+th5sw5rFv3DTrdiZElURQJh8OIXZzzhoIebBX/BMCcfTUyee9Son1BkiSsb79J\n0G4n7vIr0Y49oVxV4fJS0dzGeKOOePXANcKjwQaLg5AUmXuWtQebphYvh2uayc8wYhCDVHzwHoJC\nQeIttw3o4aXDVvK8HjySpWEscXcYYxh1aiZNTePrnTVsLq7vsnntVHyBEF9urUCvUTC3ILnb66Yl\nTuKLim/ZYd3DRdmLSdTG97iuIIr4jUlom2rwOF1oTUPXNDh5Vjr7d9dQuK2KcVNSkPeg8CQIMvTm\nKbRYv0dzzhjc3xXR8N7bJN5y+4goZcnkWhJH3YKt4hO8LUewHnmbxLwbkSmik42ONuVyZe7FrCxf\nwxslH/DIlLsRo/gzeeHMDLRqOa+vKeXJD4p4+NqJ/ZaH/aGRE6Ph/nHpHHC6+arGxo7GFgrtrcxP\nMrIw2YRaLjte8v7oqIUDTjf1JVXckJcyaOGmsxn0KQy3m1U4HOb5559l9+6d/PGPfwFAq9XR1nZC\nb1mSpC6DsyRJNFWtIhRoITblXNT6rNOuGSjNGzfg2rUTzegxp40jfW+JCFmMtDBJsz/IrsYW4lQK\nJnfKnncejMydzhqfROPHHxFqbcF85VUoExP7fw+3nz2HG0lP0JGX1r2E5XCWuDsbY1w8Owu5TOSL\nbZV9EpXYUmyhtS3AeVPTUPZQHhYFkUv7eRYdik9DAFrLyvv6VgaERqukYGoabpefg/t6L6nr46cB\nIJ8Wjyojk+aNG2j612dDuseeEGVKEnKvR2eeSsBTj+Xw61F13zo/cyET48dz2FHGmmNro7ZuB/Mn\npvDQ1RMJhcM8t2Ivuw8NzZz3mYQgCBSY9Dw6IYursxNRy0TW1zt4cn8Fmy0OguEwBqWcu/LTWJwa\nh9Mf5B8Hq9lscQyqonQ2QPeL6LtZ/e///g+BgJ//+Z8nj5e6J02azLZtmwEoLt5PXl7X3s0u2048\nzYdQ6bMxJC2I2rv01dbS+OF7iFodyffch9CpxN/o8XPQ6SbXqCNzhGU9N9Y3EZIkFqWYjmfPEOne\nFgWBiWITLZs2okzPwHThRQO6x/f76giFJc6dktZzxjWMJe4t9ZHZ5zkpMzDqVZwzKYVGp5cdB3r+\noAxLEl+3C5MsntZ7tj01cSKpumR2WPbQ0NZ7F7SQHGk485Qf7cO7GBxTZmcgk4sUbqsi1IuloFxp\nRG0YRcBbT+IDtyCPj8e+aiXOjeuHfJ/dIQgicRmXE5t8LiG/E+uRN/C5ozPHLAoit45billt4ouK\nbymxH4rKup2ZNiaBX143GZko8sLKYjbtG7ru/TMJmSAwMyGWxydmsyTNTEiC1dU2ntlfSZE9ohp3\nQZqZO8akRaXkfTZAd2K43awOHTrI6tWrOHq0nF/84n4eeeQ+Nm1az8KF56FUKnnggTv5+9+f4ZFH\nHjttbX9bPY7atYhyLebsq6OmAR32+6l/+QWkQIDkO+5EEXdyg9BmqwMJWJKTOCIlwg5a/EF2NrZg\nUsqZaj6R2TY42jhW38qEjBhal78HghCR85T3/zQnLElsKKpDqRB7LAdD5xL30AZou6eJQ44y8mJz\nSNJGFOIumZ2JKAis3lZ52uxmZ/aX27E2tXHutDRi9b03SnWcRUtIfNGHLFqeHqngBIZg2uFUtDol\n46ek4Grxcai4L1l0xIbS4ztC+i9/jajX0/DOW7iKCod6q90iCAKxKecSl3E54aCHhiNv42k+HJW1\ntQotd0+4Bbkg460DH9Dk7Z98a18Ynx3Hb26cilYl5401B/l6R1XU73GmopSJLEqN49cTs5mfZKQl\nEGL5USt/P1DNkWY3o2K1PFKQSW6MhgNON38rqaJ6AF3eZ92sfoCEQz4sh14h6GsiIe8mNIauM+yB\nYH3nTZo3rCf2vPNJWnbLSV9zBYL8ZW8FBqWcJxZPwG4bmbEVgNVVjWy2Ork6O5GZCSfGvD7fUsE/\nNx7lkbgadDvWYbzgQhJvWDage+w/aueZ5XtZODmF2y8Z1+O19qp/4bYXkjLuQRTqns9rB8PnR7/m\ni4pvuHncUuZ2spZ87fMDbC628NDVE5me37W06/9+UEhppYO/Pr4IvaJvD3RhKcyfdj5HncvCf85+\nnCRd98cERw5Y8T37Xyh0GvKfebZ/b2wAuFp9vPfSNnR6FTfeOwuZrPv3JElh6kqeIxzykTbhMXyV\n1dQ8+WcA0h//LZpuqlTDhaf5MLZjHyNJIeIyLjtelu+KU93IemJT7VY+PPQp2YZMfjXtfuRD4LBV\n2+jiyY+KaHb5uXxeNlefk/OjcMLqDw5fgLW1dvbaW5GAUQYNF6XHk6JVsa6uie/qmhAEuDg9nqsn\n9l1U6GwG/QNDkiSaqtcQ9DURkzg3qsG5dfdOmjesR5meQcLS60/7+raGZoKSxPwk44jKerYGgmxv\naMZ4SvYMkfJ2UtCJbvcG5HFxxF91zYDvs74wMtN7bh+ar4ajxB2Wwmzrxhjj0rlZCMDnWyu6PPOq\nsrZSWulgXJaJnNS+z613nEX3JYvW6pS0qBMQWp0Ee6gsRQt9jIpxk1NobfZypJfyviCI6MxTkcJ+\n2hzFaHLzSLn/QaRgkNq/PoO/fmRLtJrYMSSOvhVRpqap+nOa6zdEpRt+QeocZiZNpaKlipVla6Kw\n09NJS9Dz7zdPJ8Go5vMtFby/9kiPlZwfIyaVgqW5yTxUkMmYWC1lLR7+fqCa5UctTDXHcEd+GlqZ\njDXVtn6tezZA/8BwN+2jzbEfpTYNY+riqK0bsDViffN1BKWS1PseQFSc3J3tD4XZ1tCMRiYyPT56\nfr8DYVO9g6AkcW5KHHLxxINCbaOLmgYXVzl3QShE4k23IKp79n3tjqYWL0VlNrKSY8hJ6f39DkeJ\n+1BT98YYKWYd0/MTqLS0UlJxugLY2nZhkotmZZz2td6YlFBAmj6FXdYiLO7uA6FGq6RFFTkS8R47\n1u/7DISpszMRRYE9WyoJh3sOCnrzNEDAZY8YaOgnTSHp1tsJu93UPPsUQadzGHbcPSpdOklj7kSm\nNNJs2UBT9eoTErIDRBAEbsi/hmRdEt/VfM+ehn1R2u3JJBg1/P7m6aQn6Ph2Tw2vfn6AYC+9AT9G\nUrUqbh+Txl35aaRpVexrcvFscSWlDhd35qeyOLV/He9nA/QPiIDXhqNmDYJMRXz2NVHL1qRgkPpX\nXibs8ZB40y0oU05XISu0t9AWDDE7MRZlD6XEocYVCLK9sZlYhZzpp+h/7yhtYGrLYUzOOvQzZqKf\nMnXA99m0rx5J6nm0qjPD0cW9tb05bF43xhiXzc0G4PMtJxszOF0+th2wkmLWMiG3d9GRUxEFkcty\nLmzPor/p9jqNTkFLu3Oa99jQN4oBxMSqyZ+YTLPDQ1lpz1m0XGlAYxiNv60Of1tkmiJ2wULMP7ua\noN1O7XNPEfKMrEqWQm0mecydKDTJuO17sB1dPmizD7VcxT0TbkYpU/Je6QqsfWj4GwhGvYrf3jSN\nvFQD20qsvPBpMf7AyMycjzR5Bi0PjM/ghtxkYpUKtjY081JpTb+lrs4G6B8I4XAA27FPkMIBzBlX\nIFdFb8TJvmol3vIyYmbNwTD/9G7wsCTxvcWJTBCYmzSywiSbLE4CYYlzU03IO42eSZJE8d6jLLLv\nQdBoBnzuDBAKh9m4tw61UsascX0czToeoIfmV8oVcLO3sZhkbSLZ3RhjZCXHMDHXzOFqJ4erT2SD\n6/bUtguTZAz4aGJSfAEZ+lR2W/dS77Z2eY1KraCl/fzdVzE8GTTAtLmZCALs2VLZa1m442zXZdt9\n/O/iLr+S2HMX4auupv6FvyEFR9a5SabQkzT6NtQxuXhaDtNw5G1CwbbeX9gDybokbsq/Fm/Ix6v7\n38Efir4tKIBeo+DxG6ZQkG2iqMzGM8v34unBfezHjCgITDLH8MsJWVyRmYBCFPm2ru/69nA2QP9g\ncNauJeC1oo+fjtY0PmrrtpUeoOmL1SgSEroV8jjodGP3BZhqjiFmBGU9XYEg2xqcGBRyZpxSZq+y\nuphSthFVOEDCddcjNw78QWJfmR1Hq4+5E5JRK/v2fk+4WQ1NBr3LUkRQCvVqjHH5vEgn9eqtkSza\nHwixvrA2IkwyoedO9J4QBIFLO7LoY11n0aIoIIuJwaeMwXvs2LAZ2xuMGvInJOOwt3H0UM/Zodow\nCpkiFrej+LiKlyAIJC67Fd2UqbSVHsDy+qtIfZgpH0pEmYqE3BvRmibib6vFevgNgr7BdWLPTJ7K\nOWlzqXNb+Ojwyijt9HTUSjm/+PlkpucncKjayV8+KKRlCHzCfyjIxUhi8+tJ2Vyf27/fwbMB+gdA\nm7MUl20XCnUixrQlUVs32NJC/asvgyget5Dsik3twiTzR1jWc3N79rww5eTsGeDg1xvJd1cRSs8h\ndsHCQd2nQzmsL8pcHUjhoStxS5LElvodiILI7OTpPV47Ot1IfoaR/UftVFpa2VJiweUJsGhqKqpe\ndKt7Y2L8eDJi0tjTsI86V9ejTRptJIsOuVoJ2vvXEDMYps2LZNG7NvecRQuCiD4+0izmdhSf+Pv2\n3wF13ihad2zDtuKj4dh2jwiiDHPWVRgS5xH02bEcfuN4aX6gXDv6CjJj0tlWv4st7V7iQ4FCLnL/\nzwpYMCmFSksrf35vD00tQ++ffCajkoknCSr1hbMBuhNniptVByUlxTz80F3Yq1YhiAric65FFLvR\ngu4nUjiM5fVXCTU3E3/Nz4+bHZxKlctDpctLfqyWJM3wmwx00BYMsbXBSYxCxsyEk7PnYFsbiVtX\nExREMu+6C2EQ1peNTg/FR+3kpRnISOyP/OLQSX1Wu2qpddUzMX48Mcre93RZexb9+ZYK1u6sRiYK\nLJ6WPuh9CIJw/Cx6TTdn0RqtEqdieBvFAGJNWkaPT6Kp0U3FkZ4fDHTmqYCAy7b7pN83Uakk7ZFf\nokxOwbH2KxxffznEu+4dQRAwpl2AKf1iwkEX1iNv0WIf+Ky0QpRz14Sb0cg1LD/8KTWtQ9e9LhNF\n7rhkLBfNyqDe3sYT7+7G2jS4Uv1PjTNWi7txxYe07oruE17MjJkkXNe91eCZ4ma1YMFC3nvvLb76\n6gvkQitSKIu4zCtRqLuebx0Izm++jhhITJjYo8rWJkvkLHOkZT2/tzjwhyUuTDOd5j199J0P0AXa\nqBw7n/EZgwtEG/fWIdG/7Bk6MmhxSOY/t7ZnOp3nnnuiIDuO7OQYdh+OlHvnTUjG2Adhkr4wwTyO\nzJh0Ctuz6FT9ySU7rU6Brf0c2ltxlJiZ0XNW641p8zI5XGJl1+ZKskfHd/u9kCti0MSOwdN8CH9b\nHSrdie+1TK8n7VePU/XEH2lc/iGyWCOG2XOG6y10S0zCLGRyPbbKTzmy5zXMmT9DFzdxQGvFa+K4\nbfz1vLTvTV4tfoffzfwFGvnAph16QxAElp43Cq1awacbj/LEu7t57PopZCYNnVb7j4mzGXQnzgw3\nq8j/p6dn8G+PXkU45EdrmoAurv+ev93hrThG4ycrkMXGknznPd1mnHavnwMOF2laFTkxQ/ML3Bc8\nwRBbrc3o5TJmneI97SkvQ9q5CZsilpQrrxzUfYKhMJv21aNTy5k5tp+63VI4ataBnYkYYxQRq+Kt\njU0AACAASURBVIxhfFx+n14jCAKXz8s+/udTPZ8HQ0cWDXSp86zRKmlVmUEQhjWDBjCZdYwal4DN\n6qKyvGdt6w5lsc7NYh0ozPGkP/o4okaD5fVXaCs9MCT77S9a03gSR92MTKbCXvkpLdbNAz7nnxg/\nngszF9HosfNu6Yoh7RcQBIEr5mVz85IxtLYF+PP7hSc1MZ6le87YDDrhuht6zHaHipF3s5oNwKyp\nWRzY8TWCKCcu47KoZWYhj4f6l1+EcJjku+5Fbuh+xnez1YkELEg2jagy0GarE184zPlp8Sdlz1Iw\niPWtNxCADRkL+E3e4CoMhUdstLj9XDgjo0cjia6QpNCQlLf3thtjnJN1HrJ+PADEx57QSdeoovtr\nXmAeS5Yhg8LG/dS66knTn/DI1mgVhEQFQnwS3soKpHB4UEcO/WXavCzKShvZvbmSrDxztz+36pg8\nZEojbc4STOlLEGUn68qrMjJIfegX1D77FHV//yvpv/096szomdEMFLU+i/xZD3Jo5ys4674lGGjF\nlLZkQNMDV+RexLGWSooai/muehOLMwfXu9Ebi6elo1XJeW11KU9/VMSDV09kUl7/x/5+SpzNoE/h\nTHCzCgVc2CtXgiCgUMUhyqJTnpQkiYZ33yLQ2EDcJZehG1/Q7bVtwRC7bS0YlXImxI2cMbsnGGKL\n1YlOLmNWwsnZc9OXa/DX1VJoGEPa9EnIBzmf3aEctmjq6XPgvSFJ4SFpEOuYfe5rebuDb3adMF74\nYnt0NZJ7yqI1uoiAipSQhuTz4a8fXFNTfzEn6MkZE09DfSvVx7rvehYEAb15GlI4gLtpf5fXaMeO\nI/nOewh7vdQ+9zQB29DMD/cXjT6ZpDF3olAn4mrcga3iE6Rw/0eZZKKMOwuWEaPU82n5Go42V0R/\ns6cwpyCZh6+ZiAT87ZN97CjtemTvLBHOBuh+MfRuVpIUxlbxKeGgG0PSOQhi9PyWW7Z8T+v2bahz\n8zBfeVWP125raCYQjsh6ykYwe95ideINhTkn2XSSQIrfYqHp81X41TrWm6cxu6/zyt1gaWqjtNJB\nfoaRFLOu9xecihSKeoC2dTLGSNT2vTrQ7Paz7YCFRJOGBKOG7/fV43T5orq38XH5ZBsyKWosprpT\no5FGG2li9MdFHnKGS7CkMzPmRzLd3Zu7lj3tQG+eAoinNYt1JmbWbBJuuIlQczM1zzxFqPXM8A+Q\nKw0kjb4dlT4Tj7OUhvL3CAf73yUdqzJwZ8EyJEniteL3aPUPvb7+5FHxPLZ0MkqFyMuflbC+qHbI\n7/lD5WyA7sSZ4Gb11b9ewuc6hsYwBl3c5KiVlv2WehreewdRoyHl3vt7dHcKhMNstTpRy0RmJPRd\ntznaeIMhNludaOUyZnc6e5YkCes7byIFg3yTMAtNrJ7R6YMbAdtQ1JE996857MSeQlGfgd5Wvwvo\nXjmsO77bU0MwJHHRzAwunZNJMBTmqyg7DXXOor/olEVrtJEHSk9M5IHJO4yCJR3EJ8WQPcqMpbaF\n2sruzzplCj0aYz4BbwP+tu6tHk0XLMF00SUErBZq//YsYV90H3YGiihXk5h3M1rjeHyuSqxH3iDo\n778G+hhTHlfkXoTT18xbBz4kPEh50b6Qn2nitzdOQ69V8PaXh1izrbL3F/0EOWPPoEeCqVOnM3Vq\n13Omf/vbywBkZmZxwQUnup7vvPNeAFasWHX87+6//+Hj///II7/q9n75+WPZuHHH8T97XVU0HHkL\nmcJAXNaVyORaXnrp9YG9mU6EA+0Wkn4/yfc/hCK+52ysyN6KOxhiYbIJ1QjKem5taMYbCnNRuvmk\nfbRs/h7PoYOERo1nn5TOhWOTEMWBP8gEgiG+31dPjFbBtDEDO8eWpBBiFJ2COowx1DIVU08xxugJ\nfyDEuj216NRy5k1IQRQFVm2uYH1hHZfNzUavic6YHsC4uDHkGLLYayuhurWWjJg0NLrI+i5lHEa5\nfEQCNMD0+VlUlNnZvaWS9OzuJxBizNPxOEtx2fag0nXfTBd/7XUEnU5at2+l/h8vkvrgIyf5pI8U\ngijHnH0tYq0eV+MOrIdfJyHvJpSa/lWULsxaRHlzBSX2g3xR8e3xh6+hJCs5hn9bNo2nPiri4/Xl\nuL0Bfn5u3k/OCasnzmbQZwihoAd7xT8BMGdfjUyu7eUVfce2Yjm+6mpiFy4iZkbP2VhE1tOBTGBE\nZT19oTDfWxxoZCJzEk/sI9jSQuPyDxFUanbmLgRBYNb4wZW3dx1sxO0NsmBiCgr5AH8lonwGfdwY\nI2nyacYYPbHtgLVdmCQNlVKGQi5y0axMfIEQ3+yqjtr+oD2Lzo18kK9uz6KPZ9DeEKqMTHzVVYQD\ng9OSHgiJKQYyc+Ooq3JSV9V9Fq2KyUGuNNHmKCEc7F6HWxBFku+4C+34Atx7i2h47+1hU0rrDUEQ\nMKVdhDH1AkKBFqxH3sTr6l9GKgoit42/gTi1iS+OfUNpU3R8qXsjxazj98umkxSn5YttVbz91aFe\nTU9+SpwN0GcAkiTRVPUZoUALsSnnotZHr1vUVVSIc903KFPTSLj+xl6vP9zsptEbYHJcDLF9lLkc\nCrZanXhCYRacksU3fvg+4TY3pp9dw7YaH/GxanL74DbVEx1nYOdO6X9zWAfRLnFvqY9UVuam9L28\nLUkSX3chTHLu5FT0GgXf7KqJui7yWNNocmOz2W87QFVLDQqlDJlMwNMWQJWdA6EQvuroPhj0lekd\nZ9Fbug9WgiCgj5+GJAVxN/Xs9CTI5aQ++DCqzCyaN26g6V+fRXW/g0EQBAxJ8zBnXYUU8tNQ9i5t\njv6Nh+kUWu6ecDOiIPJmyQc4vMMzCmWOVfP7ZdPITNKzoaiOl1eV/CSdsLribIA+A3DZduJpPoxK\nn40h6XSzioESaGrC8sarCAoFKfc9gKjqvRu8Q5hkwQgKk/hCYb63OlDLROYmnTh7du/fR+uObahz\ncqnMmITXH2L2+KRBlcRqGl0cqWmmICeORNPAqxaSFIqaUYYr4GZfYwnJuqRujTG6ouRYE3U2N7PG\nJWKKOfG9VillXDgzgzZfkO8Ko9uQ0/ksevWxtQiCgEanxOP2o2lXp/NWDH+jGEByWizp2SZqKhxY\napu7vU4XNwUEkVZ7981iHYhqDWmP/gpFfAL2VStxblwf5V0PDl3cJBLybkQQZNgqPqa1YXu/Xp9l\nyODa0VfgCrh5veQ9QuHhcaMy6JT89sZpjMkwsvNgA3/9ZB8+/0/TCaszZwP0CONvq8dRuxZRrsWc\nfXXUPuSlUAjLKy8RdrtJuOEmVGm9K2zVuLwca/Uw2qAlWTtysp7bG5y0BcPMTzKibj/nC/t8WN99\nC2Qykm69g+2HInKOs8YlDepeGwo7dLcHnj0DUe3i3mkpjBhjpMzo18PHV+2ez0tmnh7Uz5+WhkYl\n4+sdVfiibAGYbxpFXmw2xfZSKluq0WgV7Rl0NgC+YRYs6czxLHpz91m0TKFDGzuOoNeGz917M508\n1kjaLx9Hpo+h4Z23cBUVRm2/0UBjyCNp9G2Ich2O2q9w1H7Tr3L8wrS5TE+czNHmSlaWrxnCnZ6M\nVi3nsaWTmZRnpvhoE08tL6LNO/zHI2cSZwP0CBIO+bBVfAJSCHPWVcgV0ZO/s3++Cs+Rw+inzyB2\n4aI+vWaTNTI3OpKynv5QmE2WSAf5vE5n4PbPPiVot2NacjHhxBT2ldlIMWtJTxjASFQ7Pn+ILSUW\nYvVKJo+KH/A60XSykiSJrfU7+2SM0ZmaRhclx5rIzzCSlXz6z5FWrWDxtHRa2gKs3R7djtlIFh0x\ncVl9bC0arZJgMIwQl4CoVo9YoxhAaoaR1IxYqo420VDffYfzCWWxPX1aV5mcTOovfoWgUFD/jxfx\nlJdFZb/RQqlNIXnMnchVZlobtmCvXHnc0KU3BEHgprHXkqRNZF31Jooaup4THwqUChkPXzOR2eOT\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WYwPacV/Xeme73mJCm9XWrVvVoUMHDRkyRBkZGfWu9w0AuDUZG9C+EhERUWublVQdqB07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\nAICBCGgAAAz0/wHdz+zj5/rFkAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xae7d978>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"We need 32 bits to seperate all points\n"
]
}
],
"source": [
"plt.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=plt.cm.Paired, label='Points')\n",
" \n",
"plane = 0\n",
"for (point, intercept) in screen.medians:\n",
" plotHyperplane(point, intercept, 'Plane ' + str(plane + 1))\n",
" plane = plane + 1\n",
" \n",
"plt.legend(loc='upper left')\n",
"plt.ylim([-10,10])\n",
"plt.xlim([-10,10])\n",
"plt.show()\n",
"\n",
"print('We need %d bits to seperate all points' % (plane))"
]
},
{
"cell_type": "code",
"execution_count": 406,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Gn3O5mpIjRw4xdepEnnnmCZ599kmeeOIRVqxYCsCcOY+Tl3fe5DIBWlpa+N3vfmO2+rsa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\nfG6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dmYbt4ik6JAqapCrKO3tJMOowKuX3GOnRQCIRsMboyC1y4EoyExwP09Xup7nOQ21F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\n9ItuwS8xafL48JiFz2p6iI/UMz8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"text/plain": [
"<matplotlib.figure.Figure at 0xae7d6a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Display using #planes random projections\n",
"rng = np.random.RandomState(42) \n",
"randomProjection = random_projection.gaussian_random_matrix(10, X.shape[1], random_state=rng) \n",
"# Plot\n",
"plt.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=plt.cm.Paired, label='Points')\n",
" \n",
"plane = 0\n",
"for (point) in randomProjection:\n",
" plotHyperplane(point, 0, 'Plane ' + str(plane + 1))\n",
" plane = plane + 1\n",
" \n",
"plt.legend(loc='upper left')\n",
"plt.title('Partitioning using random projection')\n",
"plt.ylim([-10,10])\n",
"plt.xlim([-10,10])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 407,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Random SVM Bucket 0.915000 with 341 points\n",
"Entire Dataset 0.915000\n"
]
}
],
"source": [
"# What if we use the random projection idea to generate the buckets\n",
"buckets = np.apply_along_axis( generateBitString, axis=1, arr=np.dot(X, randomProjection.T ))\n",
"heap = []\n",
"\n",
"X_selected = []\n",
"Y_selected = []\n",
"\n",
"for key in np.unique(buckets):\n",
" # estimate pos ratio \n",
" qualifying = Y[buckets == key]\n",
" length = qualifying.shape[0] \n",
" pos = qualifying[qualifying == 1].shape[0]\n",
" #print('Buckets', key, pos) \n",
" ratio = min(pos / length, (length - pos) / length)\n",
" #print(key,ratio,length)\n",
" if 0 < ratio < 0.5:\n",
" # Take buckets which need splitting - weight them by the number of points\n",
" X_selected.extend(X[buckets == key])\n",
" Y_selected.extend(Y[buckets == key])\n",
" heap.append(( ratio, key, pos, length)) \n",
" \n",
"X_selected = np.array(X_selected) \n",
" \n",
"# Now check if we still hit the same accuray\n",
"entire = svm.SVC(kernel='linear')\n",
"entire.fit(X,Y) \n",
" \n",
"# Now generate an svm with the points in the buckets\n",
"projectionSVM = svm.SVC(kernel='linear')\n",
"projectionSVM.fit(X_selected, Y_selected)\n",
"print('Random SVM Bucket %f with %d points' % (projectionSVM.score(X,Y),X_selected.shape[0] ))\n",
"print('Entire Dataset %f'% (entire.score(X,Y)))"
]
},
{
"cell_type": "code",
"execution_count": 408,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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HjgEsWLAYW1s7jh07wtChw7G0tOTTT78kJOQE9es35KeffmX48FHk5OSUWPed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nHBoaxNrahqjwcFYnRhjpaMRFCmhU7NuXSmdnBzWtbZTX1jIzP09bt5K4k5+no76ayISdDI6P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\nBUmBFkIIIbqg/w+WhFQ8d4JFowAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb0b92b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot the random svm \n",
"plt.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=plt.cm.Paired, label='Points')\n",
"plt.title('Random projection results')\n",
"\n",
"plotHyperplane(entire.coef_[0],entire.intercept_[0],plt=plt,label='Entire Dataset Hyperplane')\n",
"plotHyperplane(projectionSVM.coef_[0],projectionSVM.intercept_[0],plt=plt,label='Mixed Bucket Hyperplane')\n",
"\n",
"plt.scatter(X_selected[:,0], X_selected[:,1], s=80,\n",
" facecolors='none', zorder=10, label='Selected Points')\n",
"\n",
"plt.xlim([-10,10])\n",
"plt.ylim([-10,10]) \n",
"plt.legend(loc='upper left')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate Hamming points and label based on majority"
]
},
{
"cell_type": "code",
"execution_count": 409,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"All hamming points 159 - reduced to 64\n"
]
}
],
"source": [
"keySize = np.unique(screen.buckets).shape[0]\n",
"\n",
"X_hamming = np.empty([keySize, len(screen.buckets[0])])\n",
"Y_hamming = [0] * keySize\n",
"counter = 0\n",
"\n",
"for key in np.unique(screen.buckets):\n",
" qualifying = Y[screen.buckets == key] \n",
" pos = qualifying[qualifying == 1].shape[0]\n",
" neg = qualifying[qualifying != 1].shape[0]\n",
" X_hamming[counter:] = np.array(list(map(int, key))) \n",
" Y_hamming[counter] = 1 if pos > neg else 0\n",
" counter = counter + 1 \n",
" \n",
"# Create Hamming Points and run SVM\n",
"clf = svm.SVC(kernel='linear')\n",
"clf.fit(X_hamming, Y_hamming)\n",
"\n",
"#print('Support Vectors:\\n', clf.support_vectors_)\n",
"print('All hamming points %d - reduced to %d' % (keySize, len(clf.support_vectors_)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Testing on generated data"
]
},
{
"cell_type": "code",
"execution_count": 410,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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yWBIGoxG42SWlM+qIST9VxdbKyNzEoXlzFPYOKKN3Y+9sgwuwK/Jmd2pNx+rU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Hh+DrH4Cbty+tjXV4BQRyet82xg1fiKdfAAdWLkNYfoE/7VuNb9xwFt9xNydPniA4OJiNG9cz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\nIiIiyTgoJyIiIiKS7P9d+CwXaXpNkwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb2e2550>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"We have 76 support vectors in hamming space (down from 159)\n",
"Stats\n",
"All points 400 - hamming buckets 83 (20.75 %) - hamming sampled 64 (16.00 %) - bucket sampled 159 (39.75 %)\n"
]
}
],
"source": [
"selected = np.apply_along_axis( generateBitString, axis=1, arr=clf.support_vectors_)\n",
"\n",
"reduced_x = X[np.in1d(screen.buckets, selected)]\n",
"reduced_y = Y[np.in1d(screen.buckets, selected)]\n",
"\n",
"\n",
"sample_Number_of_points_hamming = 1\n",
"\n",
"# Also just sample n point from all buckets\n",
"sampledPoints = []\n",
"data = np.column_stack((screen.buckets, X, Y)) \n",
"for key, rows in groupby(data[np.argsort(data[:,0])], lambda x: x[0]):\n",
" #sampledPoints.append(random.choice(list(rows)))\n",
" l = list(rows)\n",
" sampledPoints.extend(random.sample(l,min(1,len(l))))\n",
"\n",
"sampledPoints = np.asarray(sampledPoints) \n",
"\n",
"sampledSupportBucket = []\n",
"# Sample points from the support buckets only\n",
"for key, rows in groupby(data[np.argsort(data[:,0])], lambda x: x[0]):\n",
" if key in selected:\n",
" l = list(rows)\n",
" sampledSupportBucket.extend(random.sample(l,min(sample_Number_of_points_hamming,len(l))))\n",
"\n",
"sampledSupportBucket = np.asarray(sampledSupportBucket) \n",
"\n",
"rcParams['figure.figsize'] = (12.0, 6.0)\n",
"f, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, sharey=True, sharex=True)\n",
"\n",
"# Now check if we still hit the same accuray\n",
"entire = svm.SVC(kernel='linear')\n",
"entire.fit(X,Y)\n",
"reduced = svm.SVC(kernel='linear')\n",
"reduced.fit(reduced_x, reduced_y)\n",
"\n",
"sSampled = svm.SVC(kernel='linear')\n",
"sSampled.fit(sampledSupportBucket[:, [1,2]].astype(float), sampledSupportBucket[:,3].astype(int))\n",
"\n",
"bSampled = svm.SVC(kernel='linear')\n",
"bSampled.fit(sampledPoints[:, [1,2]].astype(float), sampledPoints[:,3].astype(int))\n",
"\n",
"ax1.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=plt.cm.Paired, label='Points')\n",
"ax1.set_title('All Points')\n",
"\n",
"ax2.scatter(reduced_x[:, 0], reduced_x[:, 1], c=reduced_y, zorder=10, cmap=plt.cm.Paired, label='Reduced Points')\n",
"ax2.set_title('Hamming Sampling')\n",
"\n",
"ax3.scatter(sampledSupportBucket[:, 1], sampledSupportBucket[:, 2], c=sampledSupportBucket[:,3].astype(int), zorder=10, cmap=plt.cm.Paired, label='Reduced Points')\n",
"ax3.set_title('Sampled Hamming Buckets')\n",
"\n",
"ax4.scatter(sampledPoints[:, 1], sampledPoints[:, 2], c=sampledPoints[:,3].astype(int),\n",
" zorder=10, cmap=plt.cm.Paired, label='Sampled Points')\n",
"ax4.set_title('Bucket Sampled Points')\n",
"\n",
"\n",
"for plot in [ax1, ax2, ax3, ax4]: \n",
" plotHyperplane(entire.coef_[0],entire.intercept_[0],plt=plot,label='Entire Dataset Hyperplane')\n",
" plotHyperplane(reduced.coef_[0],reduced.intercept_[0], plt=plot, label='Hamming Buckets')\n",
" plotHyperplane(sSampled.coef_[0],sSampled.intercept_[0], plt=plot, label='Hamming sampling Hyperplane')\n",
" plotHyperplane(bSampled.coef_[0],bSampled.intercept_[0], plt=plot, label='Bucket sampling Hyperplane')\n",
" plot.scatter(entire.support_vectors_[:, 0], entire.support_vectors_[:, 1], s=80,\n",
" facecolors='none', zorder=10, label='Support vector')\n",
" \n",
" plot.legend(loc='upper left')\n",
" \n",
"plt.xlim([-10,10])\n",
"plt.ylim([-10,10]) \n",
"plt.show()\n",
"\n",
"\n",
"print('We have %d support vectors in hamming space (down from %d)' % (len(entire.support_vectors_), keySize))\n",
"\n",
"print('Stats\\nAll points %d - hamming buckets %d (%.2f %%) - hamming sampled %d (%.2f %%) - bucket sampled %d (%.2f %%)' % \n",
" (len(X), \n",
" len(reduced_x), \n",
" (len(reduced_x)/len(X)) * 100, \n",
" len(sampledSupportBucket),\n",
" (len(sampledSupportBucket)/len(X)) * 100,\n",
" len(sampledPoints), \n",
" (len(sampledPoints)/len(X)) * 100))\n"
]
},
{
"cell_type": "code",
"execution_count": 411,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Classification accuracy on the entire dataset with all points 0.915000 \n",
"Classification accuracy on the entire dataset with hamming points 0.912500 \n",
"Classification accuracy on the entire dataset with hamming sampled 0.917500 \n",
"Classification accuracy on the entire dataset with buckets sampling 0.910000 \n"
]
}
],
"source": [
"# Now check if we still hit the same accuray\n",
"\n",
"print ('Classification accuracy on the entire dataset with all points %f ' % (entire.score(X, Y)))\n",
"print ('Classification accuracy on the entire dataset with hamming points %f ' % (reduced.score(X, Y)))\n",
"print ('Classification accuracy on the entire dataset with hamming sampled %f ' % (sSampled.score(X, Y)))\n",
"print ('Classification accuracy on the entire dataset with buckets sampling %f ' % (bSampled.score(X, Y)))"
]
},
{
"cell_type": "code",
"execution_count": 412,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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IIaBSqbBu3TrodLpqP081/Xzd7OctNjYWc+fOxfDhw+Hr64v27dvX+rNCRK4liboOvRAR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\nYlsIIYQQQohC8v8bU3Hfs4RFmgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb224400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Test against random sampling\n",
"randomSamplingResult = []\n",
"for sample in np.arange(0.01, 0.4, 0.01):\n",
" # generate random subset multiple times\n",
" x = []\n",
" for tries in np.arange(0, 100): \n",
" selected = np.random.choice(np.arange(0, X.shape[0]), math.floor(X.shape[0] * sample), replace=False)\n",
" sampled_X = X[selected]\n",
" sampled_Y = Y[selected]\n",
" # Now check if we still hit the same accuray\n",
" randomSampling = svm.SVC(kernel='linear')\n",
" try:\n",
" randomSampling.fit(sampled_X,sampled_Y)\n",
" x.append(randomSampling.score(X, Y))\n",
" except ValueError:\n",
" pass\n",
" \n",
" #randomSamplingResult.append([sample,sampled_X.shape[0], randomSampling.score(X, Y)])\n",
" #print(sample,sampled_X.shape[0], randomSampling.score(X, Y))\n",
" #print('sample',sample,x,np.median(x))\n",
" randomSamplingResult.append([sample,sampled_X.shape[0], np.mean(x)])\n",
"\n",
"randomSamplingResult = np.array(randomSamplingResult) \n",
" \n",
"plt.title('Random sampling vs hash bucket screening')\n",
"plt.plot(randomSamplingResult[:,0], randomSamplingResult[:,2], '-o', label='Random sampling')\n",
"plt.plot(len(sampledSupportBucket)/len(X), sSampled.score(X, Y), 'ro',label='Hamming suppert bucket')\n",
"plt.plot((len(sampledPoints)/len(X)), bSampled.score(X, Y), 'go', label='All bucket sampling')\n",
"\n",
"# random Projection results\n",
"plt.plot((len(X_selected)/len(X)), projectionSVM.score(X,Y), 'yo', label='Random Projection')\n",
"\n",
"\n",
"plt.legend(loc='lower right')\n",
"plt.ylabel('Accuracy')\n",
"plt.xlabel('Percentage of points')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 413,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No more elements\n",
"Done after 30 planes\n",
"Wall time: 79.1 ms\n"
]
},
{
"data": {
"image/png": 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gVk4CN3+9kOpD7Rza08S7r1ZRtruR0k1zSZrElrIlKSvZ3vQxqTIPx2tOUfzj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Ev/esfnnvsLr7/+Dmq1mltuuZZFixaj0+lYkDGXD48uY0PtFi7MWTTgz9C9AjycEBZF3l9TAsD1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825508jzV3GncseRfH5UAIjshHF5wuz2ocvldESUGjrbcIcbOKOrAVYBf3IegE/HT09tCXn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bO4rVP3mB6JgrnzMHtq+ntug0pqhElvaxRNv+6ZvU73ie1YlG2px6NlR0k73wTm5ZfQcd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\nIiIiklYUgEVEREQkrSgAi4iIiEha+X/2xCsQkOGisgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb0b9198>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"We need 31 bits to seperate all points\n"
]
}
],
"source": [
"import scipy.io\n",
"data2 = scipy.io.loadmat('ex6data2.mat') \n",
" \n",
"# Screen\n",
"screen = HashBucketScreening(X.shape[1], strategy='median')\n",
"\n",
"%time screen.screen(data2['X'], data2['y'].ravel())\n",
"\n",
"plt.scatter(data2['X'][:,0], data2['X'][:,1], c=(data2['y'].flatten()), zorder=10, cmap=plt.cm.Paired, label='Points')\n",
" \n",
"plane = 1\n",
"for (point, intercept) in screen.medians:\n",
" plotHyperplane(point, intercept, 'Plane ' + str(plane))\n",
" \n",
" plane = plane + 1\n",
" \n",
"plt.legend(loc='upper left')\n",
"plt.xlim([0, 1.5])\n",
"plt.ylim([0, 1.5])\n",
"plt.show()\n",
"\n",
"print('We need %d bits to seperate all points' % (plane ))"
]
},
{
"cell_type": "code",
"execution_count": 418,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing on http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/mushrooms\n",
"(9999, 21)\n",
"(999, 21)\n"
]
}
],
"source": [
"# Try a larger dataset\n",
"import urllib\n",
"from sklearn.datasets import load_svmlight_file\n",
"\n",
"#training = 'http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/liver-disorders_scale'\n",
"#training = 'http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/mushrooms'\n",
"#training = 'http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/skin_nonskin'\n",
"#training = 'http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/svmguide1'\n",
"\n",
"#training = 'http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/splice_scale'\n",
"#testing = 'http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/splice.t'\n",
"\n",
"#training = 'http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/svmguide3'\n",
"#testing = 'http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/svmguide3.t'\n",
"\n",
"#training = 'http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/madelon'\n",
"#testing = 'http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/madelon.t'\n",
"\n",
"#(X_train, Y_train) = load_svmlight_file(urllib.request.urlopen(training))\n",
"#(X_test, Y_test) = (X_train, Y_train)\n",
"#(X_test, Y_test) = load_svmlight_file(urllib.request.urlopen(testing))\n",
"\n",
"(X_train, Y_train) = load_svmlight_file('./data/higg_lo_svm.csv')\n",
"(X_test, Y_test) = load_svmlight_file('./data/higg_lo_svm_test.csv')\n",
"\n",
"\n",
"#load_svmlight_file()\n",
"print('Testing on ' + training)\n",
"\n",
"print(X_train.shape)\n",
"print(X_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 427,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No more elements\n",
"Done after 33 planes\n",
"Wall time: 30.7 s\n",
"All hamming points 9889 - reduced to 9300\n",
"Wall time: 9.6 s\n",
"Classification accuracy on the entire dataset with all points 0.557558 \n",
"Classification accuracy on the entire dataset with hamming points 0.573574 \n",
"Classification accuracy on the entire dataset with sampled hamming 0.565566 \n",
"Classification accuracy on the entire dataset with buckets points 0.547548 \n",
"Stats\n",
"All points 9999 - hamming points 9405 (94.06 %) - sampled hamming 9300 (93.01 %) - bucket sampled 9889 (98.90 %)\n"
]
}
],
"source": [
"import math\n",
"screen = HashBucketScreening(X_train.shape[1],strategy='median',maxCutoff=0.49)\n",
"\n",
"%time screen.screen(X_train.toarray(), Y_train)\n",
"\n",
"sPoints = screen.getBucketSamples(X_train.toarray(), Y_train,samples = 1)\n",
" \n",
"(r_x, r_y) = screen.getHammingSamples(X_train.toarray(), Y_train)\n",
"\n",
"(s_x, s_y) = screen.getHammingSupportSamples(X_train.toarray(), Y_train, 1)\n",
"\n",
" \n",
"reducedPoints = svm.SVC(kernel='linear')\n",
"reducedPoints.fit(r_x, r_y) \n",
"\n",
"supportBucketSampled = svm.SVC(kernel='linear')\n",
"supportBucketSampled.fit(s_x, s_y.astype(float)) \n",
" \n",
"bs = svm.SVC(kernel='linear')\n",
"bs.fit(sPoints[:, 2:].astype(float), sPoints[:,1].astype(float)) \n",
"\n",
"d = svm.SVC(kernel='linear')\n",
"%time d.fit(X_train,Y_train)\n",
"\n",
"print ('Classification accuracy on the entire dataset with all points %f ' % (d.score(X_test.toarray(), Y_test)))\n",
"print ('Classification accuracy on the entire dataset with hamming points %f ' % (reducedPoints.score(X_test.toarray(), Y_test)))\n",
"print ('Classification accuracy on the entire dataset with sampled hamming %f ' % (supportBucketSampled.score(X_test.toarray(), Y_test)))\n",
"print ('Classification accuracy on the entire dataset with buckets points %f ' % (bs.score(X_test.toarray(), Y_test)))\n",
"\n",
"print('Stats\\nAll points %d - hamming points %d (%.2f %%) - sampled hamming %d (%.2f %%) - bucket sampled %d (%.2f %%)' % \n",
" (X_train.shape[0], \n",
" len(r_x),(len(r_x)/X_train.shape[0]) * 100,\n",
" len(s_x),(len(s_x)/X_train.shape[0]) * 100, \n",
" len(sPoints),(len(sPoints)/X_train.shape[0]) * 100))\n"
]
}
],
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| gpl-3.0 |
lantonov/Rockstar | bayesopt.ipynb | 1 | 18886 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Import functions\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import timeit\n",
"import subprocess\n",
"import random\n",
"import numpy as np\n",
"import scipy as sp\n",
"import math\n",
"import re\n",
"import chess\n",
"from bayes_opt import BayesianOptimization\n",
"from operator import itemgetter\n",
"from chess import uci\n",
"from chess import Board\n",
"from chess import Move\n",
"from chess import syzygy\n",
"from numpy import sqrt\n",
"from scipy.stats import chi2\n",
"from scipy.stats import norm\n",
"from statistics import median"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Getting Started\n",
"\n",
"All we need to get started is to instanciate a `BayesianOptimization` object specifying a function to be optimized `f`, and its parameters with their corresponding bounds, `pbounds`. This is a constrained optimization technique, so you must specify the minimum and maximum values that can be probed for each parameter in order for it to work"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"Engines = [\n",
" {'file': 'C:\\\\msys2\\\\home\\\\lanto\\\\safechecks\\\\tune.exe', 'name': 'test'},\n",
" {'file': 'C:\\\\msys2\\\\home\\\\lanto\\\\safechecks\\\\tune.exe', 'name': 'base'}\n",
" ]\n",
"\n",
"Draw = {'movenumber': 40, 'movecount': 8, 'score': 20}\n",
"Resign = {'movecount': 3, 'score': 400}\n",
"population_size=40\n",
"iterations=200\n",
"dynamic_rate=5\n",
"Openings = 'C:\\\\Cutechess\\\\2moves.epd'\n",
"Games = 50\n",
"UseEngine = False\n",
"Syzygy = 'C:\\\\Winboard\\\\Syzygy'\n",
"ParametersFile = 'C:\\\\Rockstar\\\\safechecks.txt'\n",
"LogFile = 'tuning.txt'\n",
"DynamicConstraints = True\n",
"\n",
"Options = {'Clear Hash': True, 'Hash': 16, 'SyzygyPath': Syzygy, \\\n",
" 'SyzygyProbeDepth': 10, 'Syzygy50MoveRule': True, 'SyzygyProbeLimit': 5}\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preparatory phase\n",
"\n",
" takes parameters from the engine"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def getPars():\n",
" sf = subprocess.Popen(Engines[0]['file'], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True, bufsize=1)\n",
" sf.stdin.write('isready' + '\\n')\n",
" pars = []\n",
" outline = []\n",
" while outline is not '':\n",
" outline = sf.stdout.readline().rstrip()\n",
" if not (outline.startswith('Stockfish ') or outline.startswith('Unknown ') or outline == ''):\n",
" pars.append(outline.split(','))\n",
" sf.terminate()\n",
" sf.wait()\n",
" return pars"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"takes parameters from file that is copied from engine output"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def get_pars():\n",
" params = []\n",
" f = open(ParametersFile)\n",
" lines = f.read().split('\\n')\n",
" if lines[-1] == '':\n",
" lines.remove('')\n",
" for p in lines:\n",
" params.append(p.split(','))\n",
" \n",
" return sorted(params)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"if UseEngine:\n",
" Pars = getPars()\n",
"else:\n",
" Pars = get_pars()\n",
"\n",
"# openings\n",
"def get_fens():\n",
" fens = []\n",
" lines = open(Openings).read().splitlines()\n",
" for i in range(0, Games, 1):\n",
" fen =random.choice(lines)\n",
" fens.append(fen)\n",
"# print(fens)\n",
" return fens\n",
"\n",
"def shuffled(x):\n",
" y = x[:]\n",
" random.shuffle(y)\n",
" return y"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def init_engines(pars):\n",
" info_handlers = []\n",
" uciEngines = []\n",
" for e in Engines:\n",
" uciEngines.append(uci.popen_engine(e['file']))\n",
"\n",
" for e,u in enumerate(uciEngines):\n",
" u.uci()\n",
" u.setoption(Options)\n",
" u.setoption(pars[e])\n",
" u.isready()\n",
"\n",
" return uciEngines"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"class DifferentialEvolution():\n",
"\n",
" def __init__(self):\n",
" self.nameArray = [str(par[0]) for par in Pars]\n",
" self.parsArray = [int(par[1]) for par in Pars]\n",
" self.bounds = [(int(p[2]), int(p[3])) for p in Pars]\n",
" self.n_parameters = len(self.nameArray)\n",
" self.current = [int(par[1]) for par in Pars]\n",
" self.trial = [int(par[1]) for par in Pars]\n",
" self.pbounds = dict(zip(self.nameArray, self.bounds))\n",
"\n",
"### Evaluation\n",
" def evaluate(self, variables):\n",
" num = 0\n",
" fens = get_fens()\n",
" trial = dict(zip(self.nameArray, self.trial))\n",
" result = []\n",
" with syzygy.open_tablebases(Syzygy) as tablebases:\n",
" for fen in fens:\n",
" result1 = self.trans_result(self.launchSf([variables, trial], fen, tablebases,))\n",
" result2 = self.trans_result(self.launchSf([trial, variables], fen, tablebases,))\n",
" result.append(result1 + result2)\n",
" pentares = self.pentanomial(result)\n",
" curr = float(self.calc_los(pentares))\n",
" return curr\n",
"\n",
" def trans_result(self, score):\n",
" return {'1-0': 2, '1/2-1/2': 1, '0-1': 0}[score]\n",
"\n",
" def pentanomial(self, result):\n",
" pentares = []\n",
" for i in range(0,5):\n",
" pentares.append(result.count(i))\n",
" return pentares\n",
"\n",
" def calc_los(self, pentares):\n",
" sumi, sumi2 = 0, 0\n",
" for i in range(0,5):\n",
" res = 0.5 * i\n",
" N = sum(pentares)\n",
" sumi += pentares[i] * res / N\n",
" sumi2 += pentares[i] * res * res / N\n",
" sigma = math.sqrt(sumi2 - sumi * sumi)\n",
" try:\n",
" t = math.sqrt(N) * (sumi - 1) / sigma * 100\n",
" except ZeroDivisionError:\n",
" t = 0.0\n",
"# los = norm.cdf(t) * 100\n",
"# return '{0:.2f}'.format(round(t, 2))\n",
" return t\n",
"\n",
"### Game playing\n",
" def launchSf(self, pars, fen, tablebases,):\n",
" try:\n",
" board = Board(fen,chess960=False)\n",
" except BaseException:\n",
" try:\n",
" board.set_epd(fen)\n",
" except BaseException:\n",
" board = Board(chess960=False)\n",
" wdl = None\n",
" drawPlyCnt, resignPlyCnt = 0, 0\n",
" whiteIdx = 1\n",
" turnIdx = whiteIdx ^ (board.turn == chess.BLACK)\n",
" uciEngines = init_engines(pars)\n",
" info_handler = uci.InfoHandler()\n",
" for u in uciEngines:\n",
" u.info_handlers.append(info_handler)\n",
" u.ucinewgame()\n",
"\n",
" try:\n",
" while (not board.is_game_over(claim_draw=True)):\n",
"\n",
" if board.castling_rights == 0:\n",
"\n",
"# if len(re.findall(r\"[rnbqkpRNBQKP]\", board.board_fen())) < 6:\n",
"# wdl = tablebases.probe_wdl(board)\n",
"# if wdl is not None:\n",
"# break # ~ 1.5 ms\n",
"\n",
" try:\n",
" wdl = tablebases.probe_wdl(board)\n",
" if wdl is not None:\n",
" break\n",
" except KeyError:\n",
" pass # < 1 ms\n",
"\n",
" uciEngines[turnIdx].position(board)\n",
" bestmove, score = uciEngines[turnIdx].go(depth=9)\n",
" score = info_handler.info[\"score\"][1].cp\n",
"# print(score)\n",
"\n",
" if score is not None:\n",
" # Resign adjudication\n",
" if abs(score) >= Resign['score']:\n",
" resignPlyCnt += 1\n",
" if resignPlyCnt >= 2 * Resign['movecount']:\n",
" break\n",
" else:\n",
" resignPlyCnt = 0\n",
"\n",
" # Draw adjudication\n",
" if abs(score) <= Draw['score'] and board.halfmove_clock > 0:\n",
" drawPlyCnt += 1\n",
" if drawPlyCnt >= 2 * Draw['movecount'] \\\n",
" and board.fullmove_number >= Draw['movenumber']:\n",
" break\n",
" else:\n",
" drawPlyCnt = 0\n",
" else:\n",
" # Disable adjudication over mate scores\n",
" drawPlyCnt, resignPlyCnt = 0, 0\n",
"\n",
" board.push(bestmove)\n",
" turnIdx ^= 1\n",
"\n",
" result = board.result(True)\n",
" if result == '*':\n",
" if resignPlyCnt >= 2 * Resign['movecount']:\n",
" if score > 0:\n",
" result = '1-0' if board.turn == chess.WHITE else '0-1'\n",
" else:\n",
" result = '0-1' if board.turn == chess.WHITE else '1-0'\n",
" elif wdl is not None:\n",
" if wdl <= -1:\n",
" result = '1-0' if board.turn == chess.WHITE else '0-1'\n",
" elif wdl >= 1:\n",
" result = '0-1' if board.turn == chess.WHITE else '1-0'\n",
" else:\n",
" result = '1/2-1/2'\n",
"# print('tb draw')\n",
" else:\n",
" result = '1/2-1/2'\n",
"# print('draw')\n",
"\n",
" # print(board.fen())\n",
" # print(re.findall(r\"[rnbqkpRNBQKP]\", board.board_fen()))\n",
" for u in uciEngines:\n",
" u.quit(0)\n",
" except (MemoryError, SystemError, KeyboardInterrupt,\n",
" OverflowError, OSError, ResourceWarning):\n",
" pass\n",
"# print(result)\n",
" return result\n",
" exit(0)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"| iter | target | Bishop... | Knight... | QueenS... | RookSa... |\n",
"-------------------------------------------------------------------------\n",
"| \u001b[0m 1 \u001b[0m | \u001b[0m 32.57 \u001b[0m | \u001b[0m 436.0 \u001b[0m | \u001b[0m 794.3 \u001b[0m | \u001b[0m 782.1 \u001b[0m | \u001b[0m 880.9 \u001b[0m |\n",
"| \u001b[95m 2 \u001b[0m | \u001b[95m 81.36 \u001b[0m | \u001b[95m 433.5 \u001b[0m | \u001b[95m 792.9 \u001b[0m | \u001b[95m 778.8 \u001b[0m | \u001b[95m 887.8 \u001b[0m |\n",
"| \u001b[95m 3 \u001b[0m | \u001b[95m 125.7 \u001b[0m | \u001b[95m 434.4 \u001b[0m | \u001b[95m 796.2 \u001b[0m | \u001b[95m 780.7 \u001b[0m | \u001b[95m 874.3 \u001b[0m |\n",
"| \u001b[0m 4 \u001b[0m | \u001b[0m 36.16 \u001b[0m | \u001b[0m 433.5 \u001b[0m | \u001b[0m 782.5 \u001b[0m | \u001b[0m 772.5 \u001b[0m | \u001b[0m 875.9 \u001b[0m |\n",
"| \u001b[0m 5 \u001b[0m | \u001b[0m 118.8 \u001b[0m | \u001b[0m 435.0 \u001b[0m | \u001b[0m 781.6 \u001b[0m | \u001b[0m 784.3 \u001b[0m | \u001b[0m 884.9 \u001b[0m |\n",
"| \u001b[0m 6 \u001b[0m | \u001b[0m-91.9 \u001b[0m | \u001b[0m 426.0 \u001b[0m | \u001b[0m 789.9 \u001b[0m | \u001b[0m 790.0 \u001b[0m | \u001b[0m 889.7 \u001b[0m |\n",
"| \u001b[0m 7 \u001b[0m | \u001b[0m 71.8 \u001b[0m | \u001b[0m 425.8 \u001b[0m | \u001b[0m 787.2 \u001b[0m | \u001b[0m 784.3 \u001b[0m | \u001b[0m 889.3 \u001b[0m |\n",
"| \u001b[95m 8 \u001b[0m | \u001b[95m 197.0 \u001b[0m | \u001b[95m 425.4 \u001b[0m | \u001b[95m 792.4 \u001b[0m | \u001b[95m 779.7 \u001b[0m | \u001b[95m 886.7 \u001b[0m |\n",
"| \u001b[0m 9 \u001b[0m | \u001b[0m 49.36 \u001b[0m | \u001b[0m 431.8 \u001b[0m | \u001b[0m 788.6 \u001b[0m | \u001b[0m 777.4 \u001b[0m | \u001b[0m 879.5 \u001b[0m |\n",
"| \u001b[0m 10 \u001b[0m | \u001b[0m 133.6 \u001b[0m | \u001b[0m 431.6 \u001b[0m | \u001b[0m 786.2 \u001b[0m | \u001b[0m 770.5 \u001b[0m | \u001b[0m 885.0 \u001b[0m |\n",
"| \u001b[0m 11 \u001b[0m | \u001b[0m 48.62 \u001b[0m | \u001b[0m 427.8 \u001b[0m | \u001b[0m 785.6 \u001b[0m | \u001b[0m 773.1 \u001b[0m | \u001b[0m 887.8 \u001b[0m |\n",
"| \u001b[0m 12 \u001b[0m | \u001b[0m-50.13 \u001b[0m | \u001b[0m 427.2 \u001b[0m | \u001b[0m 785.8 \u001b[0m | \u001b[0m 789.5 \u001b[0m | \u001b[0m 877.1 \u001b[0m |\n",
"| \u001b[0m 13 \u001b[0m | \u001b[0m-65.37 \u001b[0m | \u001b[0m 427.6 \u001b[0m | \u001b[0m 787.1 \u001b[0m | \u001b[0m 784.5 \u001b[0m | \u001b[0m 876.7 \u001b[0m |\n",
"| \u001b[0m 14 \u001b[0m | \u001b[0m 0.0 \u001b[0m | \u001b[0m 425.5 \u001b[0m | \u001b[0m 793.0 \u001b[0m | \u001b[0m 779.9 \u001b[0m | \u001b[0m 887.1 \u001b[0m |\n",
"| \u001b[0m 15 \u001b[0m | \u001b[0m 36.7 \u001b[0m | \u001b[0m 437.3 \u001b[0m | \u001b[0m 786.1 \u001b[0m | \u001b[0m 789.7 \u001b[0m | \u001b[0m 886.7 \u001b[0m |\n",
"| \u001b[0m 16 \u001b[0m | \u001b[0m 36.16 \u001b[0m | \u001b[0m 433.3 \u001b[0m | \u001b[0m 799.0 \u001b[0m | \u001b[0m 774.5 \u001b[0m | \u001b[0m 877.1 \u001b[0m |\n",
"| \u001b[0m 17 \u001b[0m | \u001b[0m 12.6 \u001b[0m | \u001b[0m 444.8 \u001b[0m | \u001b[0m 797.2 \u001b[0m | \u001b[0m 788.6 \u001b[0m | \u001b[0m 880.0 \u001b[0m |\n",
"| \u001b[0m 18 \u001b[0m | \u001b[0m 116.2 \u001b[0m | \u001b[0m 433.0 \u001b[0m | \u001b[0m 799.5 \u001b[0m | \u001b[0m 770.6 \u001b[0m | \u001b[0m 887.2 \u001b[0m |\n",
"| \u001b[0m 19 \u001b[0m | \u001b[0m 125.6 \u001b[0m | \u001b[0m 426.4 \u001b[0m | \u001b[0m 781.0 \u001b[0m | \u001b[0m 785.1 \u001b[0m | \u001b[0m 882.0 \u001b[0m |\n",
"| \u001b[0m 20 \u001b[0m | \u001b[0m-86.15 \u001b[0m | \u001b[0m 425.4 \u001b[0m | \u001b[0m 783.3 \u001b[0m | \u001b[0m 781.3 \u001b[0m | \u001b[0m 885.1 \u001b[0m |\n",
"| \u001b[0m 21 \u001b[0m | \u001b[0m 123.2 \u001b[0m | \u001b[0m 436.7 \u001b[0m | \u001b[0m 795.8 \u001b[0m | \u001b[0m 770.6 \u001b[0m | \u001b[0m 887.4 \u001b[0m |\n",
"| \u001b[95m 22 \u001b[0m | \u001b[95m 242.3 \u001b[0m | \u001b[95m 434.1 \u001b[0m | \u001b[95m 784.4 \u001b[0m | \u001b[95m 787.9 \u001b[0m | \u001b[95m 881.3 \u001b[0m |\n",
"| \u001b[0m 23 \u001b[0m | \u001b[0m 136.9 \u001b[0m | \u001b[0m 439.3 \u001b[0m | \u001b[0m 797.5 \u001b[0m | \u001b[0m 774.5 \u001b[0m | \u001b[0m 879.8 \u001b[0m |\n",
"| \u001b[0m 24 \u001b[0m | \u001b[0m-86.15 \u001b[0m | \u001b[0m 429.7 \u001b[0m | \u001b[0m 799.2 \u001b[0m | \u001b[0m 779.8 \u001b[0m | \u001b[0m 888.7 \u001b[0m |\n",
"| \u001b[0m 25 \u001b[0m | \u001b[0m 65.37 \u001b[0m | \u001b[0m 441.3 \u001b[0m | \u001b[0m 795.3 \u001b[0m | \u001b[0m 770.7 \u001b[0m | \u001b[0m 874.8 \u001b[0m |\n",
"| \u001b[0m 26 \u001b[0m | \u001b[0m 165.6 \u001b[0m | \u001b[0m 438.9 \u001b[0m | \u001b[0m 786.7 \u001b[0m | \u001b[0m 771.7 \u001b[0m | \u001b[0m 871.0 \u001b[0m |\n",
"| \u001b[0m 27 \u001b[0m | \u001b[0m 65.37 \u001b[0m | \u001b[0m 429.7 \u001b[0m | \u001b[0m 798.7 \u001b[0m | \u001b[0m 775.9 \u001b[0m | \u001b[0m 883.6 \u001b[0m |\n",
"| \u001b[95m 28 \u001b[0m | \u001b[95m 266.3 \u001b[0m | \u001b[95m 426.5 \u001b[0m | \u001b[95m 784.4 \u001b[0m | \u001b[95m 781.8 \u001b[0m | \u001b[95m 874.2 \u001b[0m |\n",
"| \u001b[0m 29 \u001b[0m | \u001b[0m 59.55 \u001b[0m | \u001b[0m 438.5 \u001b[0m | \u001b[0m 795.2 \u001b[0m | \u001b[0m 788.6 \u001b[0m | \u001b[0m 878.2 \u001b[0m |\n",
"| \u001b[0m 30 \u001b[0m | \u001b[0m-36.7 \u001b[0m | \u001b[0m 432.6 \u001b[0m | \u001b[0m 797.2 \u001b[0m | \u001b[0m 784.3 \u001b[0m | \u001b[0m 887.9 \u001b[0m |\n",
"| \u001b[0m 31 \u001b[0m | \u001b[0m-35.16 \u001b[0m | \u001b[0m 435.7 \u001b[0m | \u001b[0m 786.9 \u001b[0m | \u001b[0m 787.1 \u001b[0m | \u001b[0m 889.9 \u001b[0m |\n",
"| \u001b[0m 32 \u001b[0m | \u001b[0m 11.87 \u001b[0m | \u001b[0m 438.9 \u001b[0m | \u001b[0m 797.0 \u001b[0m | \u001b[0m 782.3 \u001b[0m | \u001b[0m 884.0 \u001b[0m |\n",
"=========================================================================\n",
"{'target': 266.31396209587814, 'params': {'BishopSafeCheck': 426.5468446744946, 'KnightSafeCheck': 784.4251834641562, 'QueenSafeCheck': 781.8090358145664, 'RookSafeCheck': 874.2404789009539}}\n"
]
}
],
"source": [
"if __name__ == '__main__':\n",
" de = DifferentialEvolution()\n",
"\n",
" variables = dict(zip(de.nameArray, de.current))\n",
" def black_box_function(**variables):\n",
" f = de.evaluate(variables)\n",
" return f\n",
"\n",
" pbounds = dict(zip(de.nameArray, de.bounds))\n",
" \n",
" optimizer = BayesianOptimization(\n",
" f=black_box_function,\n",
" pbounds=pbounds,\n",
" verbose=2, # verbose = 1 prints only when a maximum is observed, verbose = 0 is silent\n",
" random_state=0,\n",
" )\n",
"\n",
" optimizer.maximize(\n",
" init_points=2,\n",
" n_iter=30,\n",
" acq='poi', \n",
" )\n",
" print(optimizer.max)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
raul-jr3/dope-learning | pokemon classification.ipynb | 1 | 276623 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataset = pd.read_csv('Pokemon.csv')"
]
},
{
"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>#</th>\n",
" <th>Name</th>\n",
" <th>Type 1</th>\n",
" <th>Type 2</th>\n",
" <th>Total</th>\n",
" <th>HP</th>\n",
" <th>Attack</th>\n",
" <th>Defense</th>\n",
" <th>Sp. Atk</th>\n",
" <th>Sp. Def</th>\n",
" <th>Speed</th>\n",
" <th>Generation</th>\n",
" <th>Legendary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Bulbasaur</td>\n",
" <td>Grass</td>\n",
" <td>Poison</td>\n",
" <td>318</td>\n",
" <td>45</td>\n",
" <td>49</td>\n",
" <td>49</td>\n",
" <td>65</td>\n",
" <td>65</td>\n",
" <td>45</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>Ivysaur</td>\n",
" <td>Grass</td>\n",
" <td>Poison</td>\n",
" <td>405</td>\n",
" <td>60</td>\n",
" <td>62</td>\n",
" <td>63</td>\n",
" <td>80</td>\n",
" <td>80</td>\n",
" <td>60</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Venusaur</td>\n",
" <td>Grass</td>\n",
" <td>Poison</td>\n",
" <td>525</td>\n",
" <td>80</td>\n",
" <td>82</td>\n",
" <td>83</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>80</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>VenusaurMega Venusaur</td>\n",
" <td>Grass</td>\n",
" <td>Poison</td>\n",
" <td>625</td>\n",
" <td>80</td>\n",
" <td>100</td>\n",
" <td>123</td>\n",
" <td>122</td>\n",
" <td>120</td>\n",
" <td>80</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>Charmander</td>\n",
" <td>Fire</td>\n",
" <td>NaN</td>\n",
" <td>309</td>\n",
" <td>39</td>\n",
" <td>52</td>\n",
" <td>43</td>\n",
" <td>60</td>\n",
" <td>50</td>\n",
" <td>65</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" # Name Type 1 Type 2 Total HP Attack Defense \\\n",
"0 1 Bulbasaur Grass Poison 318 45 49 49 \n",
"1 2 Ivysaur Grass Poison 405 60 62 63 \n",
"2 3 Venusaur Grass Poison 525 80 82 83 \n",
"3 3 VenusaurMega Venusaur Grass Poison 625 80 100 123 \n",
"4 4 Charmander Fire NaN 309 39 52 43 \n",
"\n",
" Sp. Atk Sp. Def Speed Generation Legendary \n",
"0 65 65 45 1 False \n",
"1 80 80 60 1 False \n",
"2 100 100 80 1 False \n",
"3 122 120 80 1 False \n",
"4 60 50 65 1 False "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train_data = dataset[[\"Total\", \"HP\", \"Attack\", \"Defense\", \"Sp. Atk\", \"Sp. Def\", \"Speed\"]]\n",
"train_labels = dataset[[\"Type 1\"]]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(800, 7)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(800, 1)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_labels.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"outs = np.zeros((800, 19))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(800, 19)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"outs.shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def vectorize_data(values, dimension = 19):\n",
" results = np.zeros((len(values), dimension))\n",
" for i, phrase in enumerate(values):\n",
" results[i, phrase] = 1.\n",
" return results"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mean = train_data.mean()\n",
"std = train_data.std()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_data -= mean\n",
"train_data /= std"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>Total</th>\n",
" <th>HP</th>\n",
" <th>Attack</th>\n",
" <th>Defense</th>\n",
" <th>Sp. Atk</th>\n",
" <th>Sp. Def</th>\n",
" <th>Speed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.976155</td>\n",
" <td>-0.950032</td>\n",
" <td>-0.924328</td>\n",
" <td>-0.796655</td>\n",
" <td>-0.238981</td>\n",
" <td>-0.248033</td>\n",
" <td>-0.801002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.250931</td>\n",
" <td>-0.362595</td>\n",
" <td>-0.523803</td>\n",
" <td>-0.347700</td>\n",
" <td>0.219422</td>\n",
" <td>0.290974</td>\n",
" <td>-0.284837</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.749377</td>\n",
" <td>0.420654</td>\n",
" <td>0.092390</td>\n",
" <td>0.293665</td>\n",
" <td>0.830626</td>\n",
" <td>1.009651</td>\n",
" <td>0.403383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.582967</td>\n",
" <td>0.420654</td>\n",
" <td>0.646964</td>\n",
" <td>1.576395</td>\n",
" <td>1.502951</td>\n",
" <td>1.728328</td>\n",
" <td>0.403383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1.051178</td>\n",
" <td>-1.185007</td>\n",
" <td>-0.831899</td>\n",
" <td>-0.989065</td>\n",
" <td>-0.391782</td>\n",
" <td>-0.787041</td>\n",
" <td>-0.112782</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Total HP Attack Defense Sp. Atk Sp. Def Speed\n",
"0 -0.976155 -0.950032 -0.924328 -0.796655 -0.238981 -0.248033 -0.801002\n",
"1 -0.250931 -0.362595 -0.523803 -0.347700 0.219422 0.290974 -0.284837\n",
"2 0.749377 0.420654 0.092390 0.293665 0.830626 1.009651 0.403383\n",
"3 1.582967 0.420654 0.646964 1.576395 1.502951 1.728328 0.403383\n",
"4 -1.051178 -1.185007 -0.831899 -0.989065 -0.391782 -0.787041 -0.112782"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.preprocessing import LabelEncoder"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/rahul/.local/lib/python3.5/site-packages/sklearn/preprocessing/label.py:112: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n",
"/home/rahul/.local/lib/python3.5/site-packages/sklearn/preprocessing/label.py:147: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n"
]
}
],
"source": [
"encoder = LabelEncoder()\n",
"encoder.fit(train_labels)\n",
"encoded_labels = encoder.transform(train_labels)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"17"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"encoded_labels[10]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"labels = vectorize_data(encoded_labels)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 1., 0.])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"labels[10]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(800, 7)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data.shape"
]
},
{
"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>Total</th>\n",
" <th>HP</th>\n",
" <th>Attack</th>\n",
" <th>Defense</th>\n",
" <th>Sp. Atk</th>\n",
" <th>Sp. Def</th>\n",
" <th>Speed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.976155</td>\n",
" <td>-0.950032</td>\n",
" <td>-0.924328</td>\n",
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" <td>-0.238981</td>\n",
" <td>-0.248033</td>\n",
" <td>-0.801002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.250931</td>\n",
" <td>-0.362595</td>\n",
" <td>-0.523803</td>\n",
" <td>-0.347700</td>\n",
" <td>0.219422</td>\n",
" <td>0.290974</td>\n",
" <td>-0.284837</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.749377</td>\n",
" <td>0.420654</td>\n",
" <td>0.092390</td>\n",
" <td>0.293665</td>\n",
" <td>0.830626</td>\n",
" <td>1.009651</td>\n",
" <td>0.403383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.582967</td>\n",
" <td>0.420654</td>\n",
" <td>0.646964</td>\n",
" <td>1.576395</td>\n",
" <td>1.502951</td>\n",
" <td>1.728328</td>\n",
" <td>0.403383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1.051178</td>\n",
" <td>-1.185007</td>\n",
" <td>-0.831899</td>\n",
" <td>-0.989065</td>\n",
" <td>-0.391782</td>\n",
" <td>-0.787041</td>\n",
" <td>-0.112782</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Total HP Attack Defense Sp. Atk Sp. Def Speed\n",
"0 -0.976155 -0.950032 -0.924328 -0.796655 -0.238981 -0.248033 -0.801002\n",
"1 -0.250931 -0.362595 -0.523803 -0.347700 0.219422 0.290974 -0.284837\n",
"2 0.749377 0.420654 0.092390 0.293665 0.830626 1.009651 0.403383\n",
"3 1.582967 0.420654 0.646964 1.576395 1.502951 1.728328 0.403383\n",
"4 -1.051178 -1.185007 -0.831899 -0.989065 -0.391782 -0.787041 -0.112782"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x_train_data = train_data[:700]\n",
"train_labels = labels[:700]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x_test_data = train_data[700:]\n",
"test_labels = labels[700:]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(100, 7)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_test_data.shape"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(700, 7)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train_data.shape"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x_train_data = np.array(x_train_data)\n",
"train_labels = np.array(train_labels)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x_test_data = np.array(x_test_data)\n",
"test_labels = np.array(test_labels)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from keras import models\n",
"from keras import layers"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def build_model():\n",
" model = models.Sequential()\n",
" model.add(layers.Dense(64, activation = \"relu\", input_shape = (x_train_data.shape[1],)))\n",
" model.add(layers.Dense(64, activation = \"relu\"))\n",
" model.add(layers.Dense(19, activation = \"softmax\"))\n",
" model.compile(loss = \"categorical_crossentropy\", optimizer = \"rmsprop\", metrics = [\"accuracy\"])\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_17 (Dense) (None, 64) 512 \n",
"_________________________________________________________________\n",
"dense_18 (Dense) (None, 64) 4160 \n",
"_________________________________________________________________\n",
"dense_19 (Dense) (None, 19) 1235 \n",
"=================================================================\n",
"Total params: 5,907\n",
"Trainable params: 5,907\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model = build_model()\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/750\n",
"700/700 [==============================] - 0s - loss: 0.2195 - acc: 0.9486 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 2/750\n",
"700/700 [==============================] - 0s - loss: 0.2224 - acc: 0.9486 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 3/750\n",
"700/700 [==============================] - 0s - loss: 0.2215 - acc: 0.9471 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 4/750\n",
"700/700 [==============================] - 0s - loss: 0.2267 - acc: 0.9457 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 5/750\n",
"700/700 [==============================] - 0s - loss: 0.2217 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 6/750\n",
"700/700 [==============================] - 0s - loss: 0.2189 - acc: 0.9443 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 7/750\n",
"700/700 [==============================] - 0s - loss: 0.2175 - acc: 0.9443 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 8/750\n",
"700/700 [==============================] - 0s - loss: 0.2183 - acc: 0.9543 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 9/750\n",
"700/700 [==============================] - 0s - loss: 0.2202 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 10/750\n",
"700/700 [==============================] - 0s - loss: 0.2270 - acc: 0.9443 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 11/750\n",
"700/700 [==============================] - 0s - loss: 0.2154 - acc: 0.9514 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 12/750\n",
"700/700 [==============================] - 0s - loss: 0.2213 - acc: 0.9443 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 13/750\n",
"700/700 [==============================] - 0s - loss: 0.2134 - acc: 0.9471 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 14/750\n",
"700/700 [==============================] - 0s - loss: 0.2205 - acc: 0.9443 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 15/750\n",
"700/700 [==============================] - 0s - loss: 0.2165 - acc: 0.9486 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 16/750\n",
"700/700 [==============================] - 0s - loss: 0.2170 - acc: 0.9514 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 17/750\n",
"700/700 [==============================] - 0s - loss: 0.2157 - acc: 0.9471 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 18/750\n",
"700/700 [==============================] - 0s - loss: 0.2173 - acc: 0.9414 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 19/750\n",
"700/700 [==============================] - 0s - loss: 0.2147 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 20/750\n",
"700/700 [==============================] - 0s - loss: 0.2182 - acc: 0.9414 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 21/750\n",
"700/700 [==============================] - 0s - loss: 0.2191 - acc: 0.9471 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 22/750\n",
"700/700 [==============================] - 0s - loss: 0.2188 - acc: 0.9457 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 23/750\n",
"700/700 [==============================] - 0s - loss: 0.2184 - acc: 0.9471 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 24/750\n",
"700/700 [==============================] - 0s - loss: 0.2120 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 25/750\n",
"700/700 [==============================] - 0s - loss: 0.2106 - acc: 0.9486 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 26/750\n",
"700/700 [==============================] - 0s - loss: 0.2165 - acc: 0.9500 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 27/750\n",
"700/700 [==============================] - 0s - loss: 0.2119 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 28/750\n",
"700/700 [==============================] - 0s - loss: 0.2117 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 29/750\n",
"700/700 [==============================] - 0s - loss: 0.2136 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 30/750\n",
"700/700 [==============================] - 0s - loss: 0.2170 - acc: 0.9457 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 31/750\n",
"700/700 [==============================] - 0s - loss: 0.2048 - acc: 0.9500 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 32/750\n",
"700/700 [==============================] - 0s - loss: 0.2161 - acc: 0.9486 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 33/750\n",
"700/700 [==============================] - 0s - loss: 0.2142 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 34/750\n",
"700/700 [==============================] - 0s - loss: 0.2103 - acc: 0.9500 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 35/750\n",
"700/700 [==============================] - 0s - loss: 0.2124 - acc: 0.9500 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 36/750\n",
"700/700 [==============================] - 0s - loss: 0.2102 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 37/750\n",
"700/700 [==============================] - 0s - loss: 0.2118 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 38/750\n",
"700/700 [==============================] - 0s - loss: 0.2115 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 39/750\n",
"700/700 [==============================] - 0s - loss: 0.2036 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 40/750\n",
"700/700 [==============================] - 0s - loss: 0.2080 - acc: 0.9543 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 41/750\n",
"700/700 [==============================] - 0s - loss: 0.2105 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 42/750\n",
"700/700 [==============================] - 0s - loss: 0.2148 - acc: 0.9457 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 43/750\n",
"700/700 [==============================] - 0s - loss: 0.2081 - acc: 0.9457 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 44/750\n",
"700/700 [==============================] - 0s - loss: 0.2044 - acc: 0.9514 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 45/750\n",
"700/700 [==============================] - 0s - loss: 0.2074 - acc: 0.9500 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 46/750\n",
"700/700 [==============================] - 0s - loss: 0.2052 - acc: 0.9514 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 47/750\n",
"700/700 [==============================] - 0s - loss: 0.2077 - acc: 0.9471 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 48/750\n",
"700/700 [==============================] - 0s - loss: 0.2139 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 49/750\n",
"700/700 [==============================] - 0s - loss: 0.2004 - acc: 0.9543 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 50/750\n",
"700/700 [==============================] - 0s - loss: 0.2062 - acc: 0.9500 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 51/750\n",
"700/700 [==============================] - 0s - loss: 0.2043 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 52/750\n",
"700/700 [==============================] - 0s - loss: 0.2112 - acc: 0.9471 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 53/750\n",
"700/700 [==============================] - 0s - loss: 0.2102 - acc: 0.9443 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 54/750\n",
"700/700 [==============================] - 0s - loss: 0.2011 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 55/750\n",
"700/700 [==============================] - 0s - loss: 0.2030 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 56/750\n",
"700/700 [==============================] - 0s - loss: 0.2018 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 57/750\n",
"700/700 [==============================] - 0s - loss: 0.1983 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 58/750\n",
"700/700 [==============================] - 0s - loss: 0.2114 - acc: 0.9543 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 59/750\n",
"700/700 [==============================] - 0s - loss: 0.1971 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 60/750\n",
"700/700 [==============================] - 0s - loss: 0.2039 - acc: 0.9486 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 61/750\n",
"700/700 [==============================] - 0s - loss: 0.2020 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 62/750\n",
"700/700 [==============================] - 0s - loss: 0.2081 - acc: 0.9414 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 63/750\n",
"700/700 [==============================] - 0s - loss: 0.2017 - acc: 0.9471 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 64/750\n",
"700/700 [==============================] - 0s - loss: 0.2047 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 65/750\n",
"700/700 [==============================] - 0s - loss: 0.2012 - acc: 0.9500 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 66/750\n",
"700/700 [==============================] - 0s - loss: 0.1957 - acc: 0.9514 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 67/750\n",
"700/700 [==============================] - 0s - loss: 0.2005 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 68/750\n",
"700/700 [==============================] - 0s - loss: 0.1981 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 69/750\n",
"700/700 [==============================] - 0s - loss: 0.1974 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 70/750\n",
"700/700 [==============================] - 0s - loss: 0.2024 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 71/750\n",
"700/700 [==============================] - 0s - loss: 0.2005 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 72/750\n",
"700/700 [==============================] - 0s - loss: 0.1974 - acc: 0.9543 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 73/750\n",
"700/700 [==============================] - 0s - loss: 0.1952 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 74/750\n",
"700/700 [==============================] - 0s - loss: 0.2007 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 75/750\n",
"700/700 [==============================] - 0s - loss: 0.1985 - acc: 0.9500 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 76/750\n",
"700/700 [==============================] - 0s - loss: 0.1940 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 77/750\n",
"700/700 [==============================] - 0s - loss: 0.2056 - acc: 0.9429 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 78/750\n",
"700/700 [==============================] - 0s - loss: 0.1939 - acc: 0.9514 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 79/750\n",
"700/700 [==============================] - 0s - loss: 0.1925 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 80/750\n",
"700/700 [==============================] - 0s - loss: 0.1938 - acc: 0.9543 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 81/750\n",
"700/700 [==============================] - 0s - loss: 0.2015 - acc: 0.9471 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 82/750\n",
"700/700 [==============================] - 0s - loss: 0.1983 - acc: 0.9514 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 83/750\n",
"700/700 [==============================] - 0s - loss: 0.1904 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 84/750\n",
"700/700 [==============================] - 0s - loss: 0.1955 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 85/750\n",
"700/700 [==============================] - 0s - loss: 0.1976 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 86/750\n",
"700/700 [==============================] - 0s - loss: 0.1965 - acc: 0.9514 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 87/750\n",
"700/700 [==============================] - 0s - loss: 0.1917 - acc: 0.9543 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 88/750\n",
"700/700 [==============================] - 0s - loss: 0.1893 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 89/750\n",
"700/700 [==============================] - 0s - loss: 0.1944 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 90/750\n",
"700/700 [==============================] - 0s - loss: 0.1926 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 91/750\n",
"700/700 [==============================] - 0s - loss: 0.1992 - acc: 0.9500 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 92/750\n",
"700/700 [==============================] - 0s - loss: 0.1938 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 93/750\n",
"700/700 [==============================] - 0s - loss: 0.1953 - acc: 0.9543 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 94/750\n",
"700/700 [==============================] - 0s - loss: 0.1904 - acc: 0.9514 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 95/750\n",
"700/700 [==============================] - 0s - loss: 0.1871 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 96/750\n",
"700/700 [==============================] - 0s - loss: 0.1956 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 97/750\n",
"700/700 [==============================] - 0s - loss: 0.1991 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 98/750\n",
"700/700 [==============================] - 0s - loss: 0.1879 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 99/750\n",
"700/700 [==============================] - 0s - loss: 0.1846 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 100/750\n",
"700/700 [==============================] - 0s - loss: 0.1895 - acc: 0.9457 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 101/750\n",
"700/700 [==============================] - 0s - loss: 0.1942 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 102/750\n",
"700/700 [==============================] - 0s - loss: 0.1917 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 103/750\n",
"700/700 [==============================] - 0s - loss: 0.1861 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 104/750\n",
"700/700 [==============================] - 0s - loss: 0.1880 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 105/750\n",
"700/700 [==============================] - 0s - loss: 0.1837 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 106/750\n",
"700/700 [==============================] - 0s - loss: 0.1954 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 107/750\n",
"700/700 [==============================] - 0s - loss: 0.1865 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 108/750\n",
"700/700 [==============================] - 0s - loss: 0.1899 - acc: 0.9543 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 109/750\n",
"700/700 [==============================] - 0s - loss: 0.1830 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 110/750\n",
"700/700 [==============================] - 0s - loss: 0.1859 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 111/750\n",
"700/700 [==============================] - 0s - loss: 0.1843 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 112/750\n",
"700/700 [==============================] - 0s - loss: 0.1833 - acc: 0.9543 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 113/750\n",
"700/700 [==============================] - 0s - loss: 0.1872 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 114/750\n",
"700/700 [==============================] - 0s - loss: 0.1820 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 115/750\n",
"700/700 [==============================] - 0s - loss: 0.1846 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 116/750\n",
"700/700 [==============================] - 0s - loss: 0.1871 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 117/750\n",
"700/700 [==============================] - 0s - loss: 0.1858 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 118/750\n",
"700/700 [==============================] - 0s - loss: 0.1844 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 119/750\n",
"700/700 [==============================] - 0s - loss: 0.1806 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 120/750\n",
"700/700 [==============================] - 0s - loss: 0.1842 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 121/750\n",
"700/700 [==============================] - 0s - loss: 0.1822 - acc: 0.9543 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 122/750\n",
"700/700 [==============================] - 0s - loss: 0.1875 - acc: 0.9514 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 123/750\n",
"700/700 [==============================] - 0s - loss: 0.1810 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 124/750\n",
"700/700 [==============================] - 0s - loss: 0.1816 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 125/750\n",
"700/700 [==============================] - 0s - loss: 0.1756 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 126/750\n",
"700/700 [==============================] - 0s - loss: 0.1842 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 127/750\n",
"700/700 [==============================] - 0s - loss: 0.1843 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 128/750\n",
"700/700 [==============================] - 0s - loss: 0.1851 - acc: 0.9500 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 129/750\n",
"700/700 [==============================] - 0s - loss: 0.1820 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 130/750\n",
"700/700 [==============================] - 0s - loss: 0.1793 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 131/750\n",
"700/700 [==============================] - 0s - loss: 0.1796 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 132/750\n",
"700/700 [==============================] - 0s - loss: 0.1853 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 133/750\n",
"700/700 [==============================] - 0s - loss: 0.1810 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 134/750\n",
"700/700 [==============================] - 0s - loss: 0.1873 - acc: 0.9543 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 135/750\n",
"700/700 [==============================] - 0s - loss: 0.1821 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 136/750\n",
"700/700 [==============================] - 0s - loss: 0.1809 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 137/750\n",
"700/700 [==============================] - 0s - loss: 0.1802 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 138/750\n",
"700/700 [==============================] - 0s - loss: 0.1713 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 139/750\n",
"700/700 [==============================] - 0s - loss: 0.1762 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 140/750\n",
"700/700 [==============================] - 0s - loss: 0.1849 - acc: 0.9543 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 141/750\n",
"700/700 [==============================] - 0s - loss: 0.1765 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 142/750\n",
"700/700 [==============================] - 0s - loss: 0.1795 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 143/750\n",
"700/700 [==============================] - 0s - loss: 0.1711 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 144/750\n",
"700/700 [==============================] - 0s - loss: 0.1782 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 145/750\n",
"700/700 [==============================] - 0s - loss: 0.1828 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 146/750\n",
"700/700 [==============================] - 0s - loss: 0.1699 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 147/750\n",
"700/700 [==============================] - 0s - loss: 0.1780 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 148/750\n",
"700/700 [==============================] - 0s - loss: 0.1765 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 149/750\n",
"700/700 [==============================] - 0s - loss: 0.1847 - acc: 0.9486 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 150/750\n",
"700/700 [==============================] - 0s - loss: 0.1701 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 151/750\n",
"700/700 [==============================] - 0s - loss: 0.1772 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 152/750\n",
"700/700 [==============================] - 0s - loss: 0.1779 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 153/750\n",
"700/700 [==============================] - 0s - loss: 0.1807 - acc: 0.9500 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 154/750\n",
"700/700 [==============================] - 0s - loss: 0.1777 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 155/750\n",
"700/700 [==============================] - 0s - loss: 0.1716 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 156/750\n",
"700/700 [==============================] - 0s - loss: 0.1762 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 157/750\n",
"700/700 [==============================] - 0s - loss: 0.1726 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 158/750\n",
"700/700 [==============================] - 0s - loss: 0.1727 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 159/750\n",
"700/700 [==============================] - 0s - loss: 0.1714 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 160/750\n",
"700/700 [==============================] - 0s - loss: 0.1710 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 161/750\n",
"700/700 [==============================] - 0s - loss: 0.1756 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 162/750\n",
"700/700 [==============================] - 0s - loss: 0.1740 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 163/750\n",
"700/700 [==============================] - 0s - loss: 0.1751 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 164/750\n",
"700/700 [==============================] - 0s - loss: 0.1723 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 165/750\n",
"700/700 [==============================] - 0s - loss: 0.1763 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 166/750\n",
"700/700 [==============================] - 0s - loss: 0.1659 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 167/750\n",
"700/700 [==============================] - 0s - loss: 0.1788 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 168/750\n",
"700/700 [==============================] - 0s - loss: 0.1664 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 169/750\n",
"700/700 [==============================] - 0s - loss: 0.1801 - acc: 0.9486 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 170/750\n",
"700/700 [==============================] - 0s - loss: 0.1670 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 171/750\n",
"700/700 [==============================] - 0s - loss: 0.1723 - acc: 0.9543 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 172/750\n",
"700/700 [==============================] - 0s - loss: 0.1729 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 173/750\n",
"700/700 [==============================] - 0s - loss: 0.1703 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 174/750\n",
"700/700 [==============================] - 0s - loss: 0.1647 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 175/750\n",
"700/700 [==============================] - 0s - loss: 0.1642 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 176/750\n",
"700/700 [==============================] - 0s - loss: 0.1823 - acc: 0.9543 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 177/750\n",
"700/700 [==============================] - 0s - loss: 0.1610 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 178/750\n",
"700/700 [==============================] - 0s - loss: 0.1609 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 179/750\n",
"700/700 [==============================] - 0s - loss: 0.1692 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 180/750\n",
"700/700 [==============================] - 0s - loss: 0.1677 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 181/750\n",
"700/700 [==============================] - 0s - loss: 0.1620 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 182/750\n",
"700/700 [==============================] - 0s - loss: 0.1677 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 183/750\n",
"700/700 [==============================] - 0s - loss: 0.1797 - acc: 0.9543 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 184/750\n",
"700/700 [==============================] - 0s - loss: 0.1615 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 185/750\n",
"700/700 [==============================] - 0s - loss: 0.1616 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 186/750\n",
"700/700 [==============================] - 0s - loss: 0.1617 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 187/750\n",
"700/700 [==============================] - 0s - loss: 0.1747 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 188/750\n",
"700/700 [==============================] - 0s - loss: 0.1660 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 189/750\n",
"700/700 [==============================] - 0s - loss: 0.1662 - acc: 0.9529 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 190/750\n",
"700/700 [==============================] - 0s - loss: 0.1638 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 191/750\n",
"700/700 [==============================] - 0s - loss: 0.1694 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 192/750\n",
"700/700 [==============================] - 0s - loss: 0.1627 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 193/750\n",
"700/700 [==============================] - 0s - loss: 0.1645 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 194/750\n",
"700/700 [==============================] - 0s - loss: 0.1698 - acc: 0.9500 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 195/750\n",
"700/700 [==============================] - 0s - loss: 0.1636 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 196/750\n",
"700/700 [==============================] - 0s - loss: 0.1625 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 197/750\n",
"700/700 [==============================] - 0s - loss: 0.1601 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 198/750\n",
"700/700 [==============================] - 0s - loss: 0.1633 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 199/750\n",
"700/700 [==============================] - 0s - loss: 0.1647 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 200/750\n",
"700/700 [==============================] - 0s - loss: 0.1612 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 201/750\n",
"700/700 [==============================] - 0s - loss: 0.1725 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 202/750\n",
"700/700 [==============================] - 0s - loss: 0.1585 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 203/750\n",
"700/700 [==============================] - 0s - loss: 0.1586 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 204/750\n",
"700/700 [==============================] - 0s - loss: 0.1670 - acc: 0.9557 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 205/750\n",
"700/700 [==============================] - 0s - loss: 0.1655 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 206/750\n",
"700/700 [==============================] - 0s - loss: 0.1595 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 207/750\n",
"700/700 [==============================] - 0s - loss: 0.1656 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 208/750\n",
"700/700 [==============================] - 0s - loss: 0.1624 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 209/750\n",
"700/700 [==============================] - 0s - loss: 0.1691 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 210/750\n",
"700/700 [==============================] - 0s - loss: 0.1554 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 211/750\n",
"700/700 [==============================] - 0s - loss: 0.1711 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 212/750\n",
"700/700 [==============================] - 0s - loss: 0.1515 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 213/750\n",
"700/700 [==============================] - 0s - loss: 0.1670 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 214/750\n",
"700/700 [==============================] - 0s - loss: 0.1607 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 215/750\n",
"700/700 [==============================] - 0s - loss: 0.1582 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 216/750\n",
"700/700 [==============================] - 0s - loss: 0.1566 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 217/750\n",
"700/700 [==============================] - 0s - loss: 0.1620 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 218/750\n",
"700/700 [==============================] - 0s - loss: 0.1607 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 219/750\n",
"700/700 [==============================] - 0s - loss: 0.1600 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 220/750\n",
"700/700 [==============================] - 0s - loss: 0.1623 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 221/750\n",
"700/700 [==============================] - 0s - loss: 0.1541 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 222/750\n",
"700/700 [==============================] - 0s - loss: 0.1604 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 223/750\n",
"700/700 [==============================] - 0s - loss: 0.1558 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 224/750\n",
"700/700 [==============================] - 0s - loss: 0.1565 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 225/750\n",
"700/700 [==============================] - 0s - loss: 0.1676 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 226/750\n",
"700/700 [==============================] - 0s - loss: 0.1535 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 227/750\n",
"700/700 [==============================] - 0s - loss: 0.1534 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 228/750\n",
"700/700 [==============================] - 0s - loss: 0.1557 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 229/750\n",
"700/700 [==============================] - 0s - loss: 0.1551 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 230/750\n",
"700/700 [==============================] - 0s - loss: 0.1514 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 231/750\n",
"700/700 [==============================] - 0s - loss: 0.1616 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 232/750\n",
"700/700 [==============================] - 0s - loss: 0.1550 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 233/750\n",
"700/700 [==============================] - 0s - loss: 0.1530 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 234/750\n",
"700/700 [==============================] - 0s - loss: 0.1580 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 235/750\n",
"700/700 [==============================] - 0s - loss: 0.1596 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 236/750\n",
"700/700 [==============================] - 0s - loss: 0.1501 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 237/750\n",
"700/700 [==============================] - 0s - loss: 0.1494 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 238/750\n",
"700/700 [==============================] - 0s - loss: 0.1583 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 239/750\n",
"700/700 [==============================] - 0s - loss: 0.1532 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 240/750\n",
"700/700 [==============================] - 0s - loss: 0.1616 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 241/750\n",
"700/700 [==============================] - 0s - loss: 0.1477 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 242/750\n",
"700/700 [==============================] - 0s - loss: 0.1570 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 243/750\n",
"700/700 [==============================] - 0s - loss: 0.1534 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 244/750\n",
"700/700 [==============================] - 0s - loss: 0.1513 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 245/750\n",
"700/700 [==============================] - 0s - loss: 0.1522 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 246/750\n",
"700/700 [==============================] - 0s - loss: 0.1546 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 247/750\n",
"700/700 [==============================] - 0s - loss: 0.1505 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 248/750\n",
"700/700 [==============================] - 0s - loss: 0.1602 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 249/750\n",
"700/700 [==============================] - 0s - loss: 0.1499 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 250/750\n",
"700/700 [==============================] - 0s - loss: 0.1475 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 251/750\n",
"700/700 [==============================] - 0s - loss: 0.1558 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 252/750\n",
"700/700 [==============================] - 0s - loss: 0.1496 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 253/750\n",
"700/700 [==============================] - 0s - loss: 0.1581 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 254/750\n",
"700/700 [==============================] - 0s - loss: 0.1488 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 255/750\n",
"700/700 [==============================] - 0s - loss: 0.1530 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 256/750\n",
"700/700 [==============================] - 0s - loss: 0.1480 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 257/750\n",
"700/700 [==============================] - 0s - loss: 0.1542 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 258/750\n",
"700/700 [==============================] - 0s - loss: 0.1476 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 259/750\n",
"700/700 [==============================] - 0s - loss: 0.1510 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 260/750\n",
"700/700 [==============================] - 0s - loss: 0.1500 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 261/750\n",
"700/700 [==============================] - 0s - loss: 0.1476 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 262/750\n",
"700/700 [==============================] - 0s - loss: 0.1516 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 263/750\n",
"700/700 [==============================] - 0s - loss: 0.1448 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 264/750\n",
"700/700 [==============================] - 0s - loss: 0.1528 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 265/750\n",
"700/700 [==============================] - 0s - loss: 0.1558 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 266/750\n",
"700/700 [==============================] - 0s - loss: 0.1442 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 267/750\n",
"700/700 [==============================] - 0s - loss: 0.1568 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 268/750\n",
"700/700 [==============================] - 0s - loss: 0.1439 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 269/750\n",
"700/700 [==============================] - 0s - loss: 0.1467 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 270/750\n",
"700/700 [==============================] - 0s - loss: 0.1518 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 271/750\n",
"700/700 [==============================] - 0s - loss: 0.1528 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 272/750\n",
"700/700 [==============================] - 0s - loss: 0.1444 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 273/750\n",
"700/700 [==============================] - 0s - loss: 0.1543 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 274/750\n",
"700/700 [==============================] - 0s - loss: 0.1507 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 275/750\n",
"700/700 [==============================] - 0s - loss: 0.1420 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 276/750\n",
"700/700 [==============================] - 0s - loss: 0.1483 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 277/750\n",
"700/700 [==============================] - 0s - loss: 0.1502 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 278/750\n",
"700/700 [==============================] - 0s - loss: 0.1432 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 279/750\n",
"700/700 [==============================] - 0s - loss: 0.1505 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 280/750\n",
"700/700 [==============================] - 0s - loss: 0.1539 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 281/750\n",
"700/700 [==============================] - 0s - loss: 0.1439 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 282/750\n",
"700/700 [==============================] - 0s - loss: 0.1520 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 283/750\n",
"700/700 [==============================] - 0s - loss: 0.1479 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 284/750\n",
"700/700 [==============================] - 0s - loss: 0.1516 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 285/750\n",
"700/700 [==============================] - 0s - loss: 0.1418 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 286/750\n",
"700/700 [==============================] - 0s - loss: 0.1534 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 287/750\n",
"700/700 [==============================] - 0s - loss: 0.1366 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 288/750\n",
"700/700 [==============================] - 0s - loss: 0.1445 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 289/750\n",
"700/700 [==============================] - 0s - loss: 0.1482 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 290/750\n",
"700/700 [==============================] - 0s - loss: 0.1487 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 291/750\n",
"700/700 [==============================] - 0s - loss: 0.1424 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 292/750\n",
"700/700 [==============================] - 0s - loss: 0.1490 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 293/750\n",
"700/700 [==============================] - 0s - loss: 0.1422 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 294/750\n",
"700/700 [==============================] - 0s - loss: 0.1488 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 295/750\n",
"700/700 [==============================] - 0s - loss: 0.1411 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 296/750\n",
"700/700 [==============================] - 0s - loss: 0.1493 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 297/750\n",
"700/700 [==============================] - 0s - loss: 0.1368 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 298/750\n",
"700/700 [==============================] - 0s - loss: 0.1394 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 299/750\n",
"700/700 [==============================] - 0s - loss: 0.1488 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 300/750\n",
"700/700 [==============================] - 0s - loss: 0.1338 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 301/750\n",
"700/700 [==============================] - 0s - loss: 0.1535 - acc: 0.9571 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 302/750\n",
"700/700 [==============================] - 0s - loss: 0.1426 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 303/750\n",
"700/700 [==============================] - 0s - loss: 0.1409 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 304/750\n",
"700/700 [==============================] - 0s - loss: 0.1410 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 305/750\n",
"700/700 [==============================] - 0s - loss: 0.1365 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 306/750\n",
"700/700 [==============================] - 0s - loss: 0.1412 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 307/750\n",
"700/700 [==============================] - 0s - loss: 0.1351 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 308/750\n",
"700/700 [==============================] - 0s - loss: 0.1389 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 309/750\n",
"700/700 [==============================] - 0s - loss: 0.1388 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 310/750\n",
"700/700 [==============================] - 0s - loss: 0.1463 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 311/750\n",
"700/700 [==============================] - 0s - loss: 0.1366 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 312/750\n",
"700/700 [==============================] - 0s - loss: 0.1405 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 313/750\n",
"700/700 [==============================] - 0s - loss: 0.1377 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 314/750\n",
"700/700 [==============================] - 0s - loss: 0.1438 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 315/750\n",
"700/700 [==============================] - 0s - loss: 0.1385 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 316/750\n",
"700/700 [==============================] - 0s - loss: 0.1362 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 317/750\n",
"700/700 [==============================] - 0s - loss: 0.1367 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 318/750\n",
"700/700 [==============================] - 0s - loss: 0.1407 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 319/750\n",
"700/700 [==============================] - 0s - loss: 0.1399 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 320/750\n",
"700/700 [==============================] - 0s - loss: 0.1432 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 321/750\n",
"700/700 [==============================] - 0s - loss: 0.1383 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 322/750\n",
"700/700 [==============================] - 0s - loss: 0.1406 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 323/750\n",
"700/700 [==============================] - 0s - loss: 0.1413 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 324/750\n",
"700/700 [==============================] - 0s - loss: 0.1359 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 325/750\n",
"700/700 [==============================] - 0s - loss: 0.1369 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 326/750\n",
"700/700 [==============================] - 0s - loss: 0.1332 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 327/750\n",
"700/700 [==============================] - 0s - loss: 0.1365 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 328/750\n",
"700/700 [==============================] - 0s - loss: 0.1359 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 329/750\n",
"700/700 [==============================] - 0s - loss: 0.1425 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 330/750\n",
"700/700 [==============================] - 0s - loss: 0.1312 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 331/750\n",
"700/700 [==============================] - 0s - loss: 0.1388 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 332/750\n",
"700/700 [==============================] - 0s - loss: 0.1373 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 333/750\n",
"700/700 [==============================] - 0s - loss: 0.1316 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 334/750\n",
"700/700 [==============================] - 0s - loss: 0.1320 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 335/750\n",
"700/700 [==============================] - 0s - loss: 0.1455 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 336/750\n",
"700/700 [==============================] - 0s - loss: 0.1322 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 337/750\n",
"700/700 [==============================] - 0s - loss: 0.1350 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 338/750\n",
"700/700 [==============================] - 0s - loss: 0.1404 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 339/750\n",
"700/700 [==============================] - 0s - loss: 0.1374 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 340/750\n",
"700/700 [==============================] - 0s - loss: 0.1381 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 341/750\n",
"700/700 [==============================] - 0s - loss: 0.1328 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 342/750\n",
"700/700 [==============================] - 0s - loss: 0.1307 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 343/750\n",
"700/700 [==============================] - 0s - loss: 0.1263 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 344/750\n",
"700/700 [==============================] - 0s - loss: 0.1345 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 345/750\n",
"700/700 [==============================] - 0s - loss: 0.1495 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 346/750\n",
"700/700 [==============================] - 0s - loss: 0.1365 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 347/750\n",
"700/700 [==============================] - 0s - loss: 0.1335 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 348/750\n",
"700/700 [==============================] - 0s - loss: 0.1315 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 349/750\n",
"700/700 [==============================] - 0s - loss: 0.1331 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 350/750\n",
"700/700 [==============================] - 0s - loss: 0.1353 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 351/750\n",
"700/700 [==============================] - 0s - loss: 0.1292 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 352/750\n",
"700/700 [==============================] - 0s - loss: 0.1290 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 353/750\n",
"700/700 [==============================] - 0s - loss: 0.1335 - acc: 0.9614 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 354/750\n",
"700/700 [==============================] - 0s - loss: 0.1302 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 355/750\n",
"700/700 [==============================] - 0s - loss: 0.1369 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 356/750\n",
"700/700 [==============================] - 0s - loss: 0.1327 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 357/750\n",
"700/700 [==============================] - 0s - loss: 0.1323 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 358/750\n",
"700/700 [==============================] - 0s - loss: 0.1299 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 359/750\n",
"700/700 [==============================] - 0s - loss: 0.1307 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 360/750\n",
"700/700 [==============================] - 0s - loss: 0.1280 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 361/750\n",
"700/700 [==============================] - 0s - loss: 0.1327 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 362/750\n",
"700/700 [==============================] - 0s - loss: 0.1291 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 363/750\n",
"700/700 [==============================] - 0s - loss: 0.1303 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 364/750\n",
"700/700 [==============================] - 0s - loss: 0.1317 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 365/750\n",
"700/700 [==============================] - 0s - loss: 0.1355 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 366/750\n",
"700/700 [==============================] - 0s - loss: 0.1324 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 367/750\n",
"700/700 [==============================] - 0s - loss: 0.1291 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 368/750\n",
"700/700 [==============================] - 0s - loss: 0.1356 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 369/750\n",
"700/700 [==============================] - 0s - loss: 0.1253 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 370/750\n",
"700/700 [==============================] - 0s - loss: 0.1259 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 371/750\n",
"700/700 [==============================] - 0s - loss: 0.1286 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 372/750\n",
"700/700 [==============================] - 0s - loss: 0.1298 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 373/750\n",
"700/700 [==============================] - 0s - loss: 0.1408 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 374/750\n",
"700/700 [==============================] - 0s - loss: 0.1216 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 375/750\n",
"700/700 [==============================] - 0s - loss: 0.1300 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 376/750\n",
"700/700 [==============================] - 0s - loss: 0.1362 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 377/750\n",
"700/700 [==============================] - 0s - loss: 0.1282 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 378/750\n",
"700/700 [==============================] - 0s - loss: 0.1323 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 379/750\n",
"700/700 [==============================] - 0s - loss: 0.1284 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 380/750\n",
"700/700 [==============================] - 0s - loss: 0.1234 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 381/750\n",
"700/700 [==============================] - 0s - loss: 0.1294 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 382/750\n",
"700/700 [==============================] - 0s - loss: 0.1283 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 383/750\n",
"700/700 [==============================] - 0s - loss: 0.1380 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 384/750\n",
"700/700 [==============================] - 0s - loss: 0.1287 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 385/750\n",
"700/700 [==============================] - 0s - loss: 0.1268 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 386/750\n",
"700/700 [==============================] - 0s - loss: 0.1278 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 387/750\n",
"700/700 [==============================] - 0s - loss: 0.1333 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 388/750\n",
"700/700 [==============================] - 0s - loss: 0.1282 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 389/750\n",
"700/700 [==============================] - 0s - loss: 0.1271 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 390/750\n",
"700/700 [==============================] - 0s - loss: 0.1231 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 391/750\n",
"700/700 [==============================] - 0s - loss: 0.1167 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 392/750\n",
"700/700 [==============================] - 0s - loss: 0.1249 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 393/750\n",
"700/700 [==============================] - 0s - loss: 0.1262 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 394/750\n",
"700/700 [==============================] - 0s - loss: 0.1302 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 395/750\n",
"700/700 [==============================] - 0s - loss: 0.1233 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 396/750\n",
"700/700 [==============================] - 0s - loss: 0.1320 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 397/750\n",
"700/700 [==============================] - 0s - loss: 0.1205 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 398/750\n",
"700/700 [==============================] - 0s - loss: 0.1218 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 399/750\n",
"700/700 [==============================] - 0s - loss: 0.1281 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 400/750\n",
"700/700 [==============================] - 0s - loss: 0.1212 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 401/750\n",
"700/700 [==============================] - 0s - loss: 0.1298 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 402/750\n",
"700/700 [==============================] - 0s - loss: 0.1202 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 403/750\n",
"700/700 [==============================] - 0s - loss: 0.1225 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 404/750\n",
"700/700 [==============================] - 0s - loss: 0.1271 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 405/750\n",
"700/700 [==============================] - 0s - loss: 0.1279 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 406/750\n",
"700/700 [==============================] - 0s - loss: 0.1183 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 407/750\n",
"700/700 [==============================] - 0s - loss: 0.1237 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 408/750\n",
"700/700 [==============================] - 0s - loss: 0.1315 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 409/750\n",
"700/700 [==============================] - 0s - loss: 0.1191 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 410/750\n",
"700/700 [==============================] - 0s - loss: 0.1282 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 411/750\n",
"700/700 [==============================] - 0s - loss: 0.1257 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 412/750\n",
"700/700 [==============================] - 0s - loss: 0.1230 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 413/750\n",
"700/700 [==============================] - 0s - loss: 0.1229 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 414/750\n",
"700/700 [==============================] - 0s - loss: 0.1225 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 415/750\n",
"700/700 [==============================] - 0s - loss: 0.1357 - acc: 0.9586 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 416/750\n",
"700/700 [==============================] - 0s - loss: 0.1232 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 417/750\n",
"700/700 [==============================] - 0s - loss: 0.1176 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 418/750\n",
"700/700 [==============================] - 0s - loss: 0.1217 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 419/750\n",
"700/700 [==============================] - 0s - loss: 0.1263 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 420/750\n",
"700/700 [==============================] - 0s - loss: 0.1220 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 421/750\n",
"700/700 [==============================] - 0s - loss: 0.1257 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 422/750\n",
"700/700 [==============================] - 0s - loss: 0.1201 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 423/750\n",
"700/700 [==============================] - 0s - loss: 0.1256 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 424/750\n",
"700/700 [==============================] - 0s - loss: 0.1241 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 425/750\n",
"700/700 [==============================] - 0s - loss: 0.1225 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 426/750\n",
"700/700 [==============================] - 0s - loss: 0.1210 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 427/750\n",
"700/700 [==============================] - 0s - loss: 0.1171 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 428/750\n",
"700/700 [==============================] - ETA: 0s - loss: 0.0683 - acc: 0.9900\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b - 0s - loss: 0.1254 - acc: 0.9743 \n",
"Epoch 429/750\n",
"700/700 [==============================] - 0s - loss: 0.1198 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 430/750\n",
"700/700 [==============================] - 0s - loss: 0.1176 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 431/750\n",
"700/700 [==============================] - 0s - loss: 0.1170 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 432/750\n",
"700/700 [==============================] - 0s - loss: 0.1174 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 433/750\n",
"700/700 [==============================] - 0s - loss: 0.1239 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 434/750\n",
"700/700 [==============================] - 0s - loss: 0.1304 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 435/750\n",
"700/700 [==============================] - 0s - loss: 0.1233 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 436/750\n",
"700/700 [==============================] - 0s - loss: 0.1203 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 437/750\n",
"700/700 [==============================] - 0s - loss: 0.1108 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 438/750\n",
"700/700 [==============================] - 0s - loss: 0.1195 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 439/750\n",
"700/700 [==============================] - 0s - loss: 0.1287 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 440/750\n",
"700/700 [==============================] - 0s - loss: 0.1281 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 441/750\n",
"700/700 [==============================] - 0s - loss: 0.1162 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 442/750\n",
"700/700 [==============================] - 0s - loss: 0.1192 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 443/750\n",
"700/700 [==============================] - 0s - loss: 0.1112 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 444/750\n",
"700/700 [==============================] - 0s - loss: 0.1235 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 445/750\n",
"700/700 [==============================] - 0s - loss: 0.1229 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 446/750\n",
"700/700 [==============================] - 0s - loss: 0.1153 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 447/750\n",
"700/700 [==============================] - 0s - loss: 0.1093 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 448/750\n",
"700/700 [==============================] - 0s - loss: 0.1224 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 449/750\n",
"700/700 [==============================] - 0s - loss: 0.1250 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 450/750\n",
"700/700 [==============================] - 0s - loss: 0.1172 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 451/750\n",
"700/700 [==============================] - 0s - loss: 0.1154 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 452/750\n",
"700/700 [==============================] - 0s - loss: 0.1171 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 453/750\n",
"700/700 [==============================] - 0s - loss: 0.1089 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 454/750\n",
"700/700 [==============================] - 0s - loss: 0.1230 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 455/750\n",
"700/700 [==============================] - 0s - loss: 0.1238 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 456/750\n",
"700/700 [==============================] - 0s - loss: 0.1183 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 457/750\n",
"700/700 [==============================] - 0s - loss: 0.1136 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 458/750\n",
"700/700 [==============================] - 0s - loss: 0.1131 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 459/750\n",
"700/700 [==============================] - 0s - loss: 0.1249 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 460/750\n",
"700/700 [==============================] - 0s - loss: 0.1180 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 461/750\n",
"700/700 [==============================] - 0s - loss: 0.1140 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 462/750\n",
"700/700 [==============================] - 0s - loss: 0.1165 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 463/750\n",
"700/700 [==============================] - 0s - loss: 0.1210 - acc: 0.9643 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 464/750\n",
"700/700 [==============================] - 0s - loss: 0.1139 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 465/750\n",
"700/700 [==============================] - 0s - loss: 0.1190 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 466/750\n",
"700/700 [==============================] - 0s - loss: 0.1110 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 467/750\n",
"700/700 [==============================] - 0s - loss: 0.1153 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 468/750\n",
"700/700 [==============================] - 0s - loss: 0.1136 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 469/750\n",
"700/700 [==============================] - 0s - loss: 0.1160 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 470/750\n",
"700/700 [==============================] - 0s - loss: 0.1224 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 471/750\n",
"700/700 [==============================] - 0s - loss: 0.1136 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 472/750\n",
"700/700 [==============================] - 0s - loss: 0.1218 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 473/750\n",
"700/700 [==============================] - 0s - loss: 0.1130 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 474/750\n",
"700/700 [==============================] - 0s - loss: 0.1130 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 475/750\n",
"700/700 [==============================] - 0s - loss: 0.1141 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 476/750\n",
"700/700 [==============================] - 0s - loss: 0.1284 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 477/750\n",
"700/700 [==============================] - 0s - loss: 0.1128 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 478/750\n",
"700/700 [==============================] - 0s - loss: 0.1137 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 479/750\n",
"700/700 [==============================] - 0s - loss: 0.1099 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 480/750\n",
"700/700 [==============================] - 0s - loss: 0.1186 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 481/750\n",
"700/700 [==============================] - 0s - loss: 0.1146 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 482/750\n",
"700/700 [==============================] - 0s - loss: 0.1181 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 483/750\n",
"700/700 [==============================] - 0s - loss: 0.1088 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 484/750\n",
"700/700 [==============================] - 0s - loss: 0.1057 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 485/750\n",
"700/700 [==============================] - 0s - loss: 0.1126 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 486/750\n",
"700/700 [==============================] - 0s - loss: 0.1109 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 487/750\n",
"700/700 [==============================] - 0s - loss: 0.1213 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 488/750\n",
"700/700 [==============================] - 0s - loss: 0.1192 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 489/750\n",
"700/700 [==============================] - 0s - loss: 0.1092 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 490/750\n",
"700/700 [==============================] - 0s - loss: 0.1110 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 491/750\n",
"700/700 [==============================] - 0s - loss: 0.1078 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 492/750\n",
"700/700 [==============================] - 0s - loss: 0.1250 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 493/750\n",
"700/700 [==============================] - ETA: 0s - loss: 0.1141 - acc: 0.9700\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b - 0s - loss: 0.1080 - acc: 0.9743 \n",
"Epoch 494/750\n",
"700/700 [==============================] - 0s - loss: 0.1115 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 495/750\n",
"700/700 [==============================] - 0s - loss: 0.1097 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 496/750\n",
"700/700 [==============================] - 0s - loss: 0.1094 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 497/750\n",
"700/700 [==============================] - 0s - loss: 0.1093 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 498/750\n",
"700/700 [==============================] - 0s - loss: 0.1054 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 499/750\n",
"700/700 [==============================] - 0s - loss: 0.1128 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 500/750\n",
"700/700 [==============================] - 0s - loss: 0.1133 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 501/750\n",
"700/700 [==============================] - 0s - loss: 0.1097 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 502/750\n",
"700/700 [==============================] - 0s - loss: 0.1154 - acc: 0.9629 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 503/750\n",
"700/700 [==============================] - 0s - loss: 0.1116 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 504/750\n",
"700/700 [==============================] - 0s - loss: 0.1078 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 505/750\n",
"700/700 [==============================] - 0s - loss: 0.1128 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 506/750\n",
"700/700 [==============================] - 0s - loss: 0.1185 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 507/750\n",
"700/700 [==============================] - 0s - loss: 0.1093 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 508/750\n",
"700/700 [==============================] - 0s - loss: 0.1159 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 509/750\n",
"700/700 [==============================] - 0s - loss: 0.1110 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 510/750\n",
"700/700 [==============================] - 0s - loss: 0.1054 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 511/750\n",
"700/700 [==============================] - 0s - loss: 0.1103 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 512/750\n",
"700/700 [==============================] - 0s - loss: 0.1134 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 513/750\n",
"700/700 [==============================] - 0s - loss: 0.1091 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 514/750\n",
"700/700 [==============================] - 0s - loss: 0.1127 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 515/750\n",
"700/700 [==============================] - 0s - loss: 0.1114 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 516/750\n",
"700/700 [==============================] - 0s - loss: 0.1027 - acc: 0.9800 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 517/750\n",
"700/700 [==============================] - 0s - loss: 0.1154 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 518/750\n",
"700/700 [==============================] - 0s - loss: 0.1049 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 519/750\n",
"700/700 [==============================] - 0s - loss: 0.1102 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 520/750\n",
"700/700 [==============================] - 0s - loss: 0.1110 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 521/750\n",
"700/700 [==============================] - 0s - loss: 0.1126 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 522/750\n",
"700/700 [==============================] - 0s - loss: 0.1104 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 523/750\n",
"700/700 [==============================] - 0s - loss: 0.1118 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 524/750\n",
"700/700 [==============================] - 0s - loss: 0.1073 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 525/750\n",
"700/700 [==============================] - 0s - loss: 0.1048 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 526/750\n",
"700/700 [==============================] - 0s - loss: 0.1029 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 527/750\n",
"700/700 [==============================] - 0s - loss: 0.1119 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 528/750\n",
"700/700 [==============================] - 0s - loss: 0.1045 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 529/750\n",
"700/700 [==============================] - 0s - loss: 0.1036 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 530/750\n",
"700/700 [==============================] - 0s - loss: 0.1135 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 531/750\n",
"700/700 [==============================] - 0s - loss: 0.1113 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 532/750\n",
"700/700 [==============================] - 0s - loss: 0.1056 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 533/750\n",
"700/700 [==============================] - 0s - loss: 0.1092 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 534/750\n",
"700/700 [==============================] - 0s - loss: 0.1030 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 535/750\n",
"700/700 [==============================] - 0s - loss: 0.1068 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 536/750\n",
"700/700 [==============================] - 0s - loss: 0.1124 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 537/750\n",
"700/700 [==============================] - 0s - loss: 0.1008 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 538/750\n",
"700/700 [==============================] - 0s - loss: 0.1069 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 539/750\n",
"700/700 [==============================] - 0s - loss: 0.1152 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 540/750\n",
"700/700 [==============================] - 0s - loss: 0.0972 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 541/750\n",
"700/700 [==============================] - 0s - loss: 0.1110 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 542/750\n",
"700/700 [==============================] - 0s - loss: 0.1089 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 543/750\n",
"700/700 [==============================] - 0s - loss: 0.1020 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 544/750\n",
"700/700 [==============================] - 0s - loss: 0.1141 - acc: 0.9657 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 545/750\n",
"700/700 [==============================] - 0s - loss: 0.1057 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 546/750\n",
"700/700 [==============================] - 0s - loss: 0.1039 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 547/750\n",
"700/700 [==============================] - 0s - loss: 0.0972 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 548/750\n",
"700/700 [==============================] - 0s - loss: 0.1105 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 549/750\n",
"700/700 [==============================] - 0s - loss: 0.1065 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 550/750\n",
"700/700 [==============================] - 0s - loss: 0.1010 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 551/750\n",
"700/700 [==============================] - 0s - loss: 0.1039 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 552/750\n",
"700/700 [==============================] - 0s - loss: 0.1053 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 553/750\n",
"700/700 [==============================] - 0s - loss: 0.0999 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 554/750\n",
"700/700 [==============================] - 0s - loss: 0.1011 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 555/750\n",
"700/700 [==============================] - 0s - loss: 0.1127 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 556/750\n",
"700/700 [==============================] - 0s - loss: 0.0992 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 557/750\n",
"700/700 [==============================] - 0s - loss: 0.1106 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 558/750\n",
"700/700 [==============================] - 0s - loss: 0.1039 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 559/750\n",
"700/700 [==============================] - 0s - loss: 0.1090 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 560/750\n",
"700/700 [==============================] - 0s - loss: 0.1033 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 561/750\n",
"700/700 [==============================] - 0s - loss: 0.1049 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 562/750\n",
"700/700 [==============================] - 0s - loss: 0.0983 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 563/750\n",
"700/700 [==============================] - 0s - loss: 0.1038 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 564/750\n",
"700/700 [==============================] - 0s - loss: 0.1003 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 565/750\n",
"700/700 [==============================] - 0s - loss: 0.1101 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 566/750\n",
"700/700 [==============================] - 0s - loss: 0.1047 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 567/750\n",
"700/700 [==============================] - 0s - loss: 0.0991 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 568/750\n",
"700/700 [==============================] - 0s - loss: 0.1080 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 569/750\n",
"700/700 [==============================] - 0s - loss: 0.1005 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 570/750\n",
"700/700 [==============================] - 0s - loss: 0.0994 - acc: 0.9829 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 571/750\n",
"700/700 [==============================] - 0s - loss: 0.1004 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 572/750\n",
"700/700 [==============================] - 0s - loss: 0.1041 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 573/750\n",
"700/700 [==============================] - 0s - loss: 0.0957 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 574/750\n",
"700/700 [==============================] - 0s - loss: 0.1129 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 575/750\n",
"700/700 [==============================] - 0s - loss: 0.0926 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 576/750\n",
"700/700 [==============================] - 0s - loss: 0.0990 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 577/750\n",
"700/700 [==============================] - 0s - loss: 0.1041 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 578/750\n",
"700/700 [==============================] - 0s - loss: 0.1015 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 579/750\n",
"700/700 [==============================] - 0s - loss: 0.1019 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 580/750\n",
"700/700 [==============================] - 0s - loss: 0.1035 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 581/750\n",
"700/700 [==============================] - 0s - loss: 0.1056 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 582/750\n",
"700/700 [==============================] - 0s - loss: 0.1019 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 583/750\n",
"700/700 [==============================] - 0s - loss: 0.0937 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 584/750\n",
"700/700 [==============================] - 0s - loss: 0.1092 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 585/750\n",
"700/700 [==============================] - 0s - loss: 0.0999 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 586/750\n",
"700/700 [==============================] - 0s - loss: 0.0935 - acc: 0.9814 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 587/750\n",
"700/700 [==============================] - 0s - loss: 0.1138 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 588/750\n",
"700/700 [==============================] - 0s - loss: 0.1004 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 589/750\n",
"700/700 [==============================] - 0s - loss: 0.1024 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 590/750\n",
"700/700 [==============================] - 0s - loss: 0.1001 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 591/750\n",
"700/700 [==============================] - 0s - loss: 0.1032 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 592/750\n",
"700/700 [==============================] - 0s - loss: 0.0938 - acc: 0.9800 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 593/750\n",
"700/700 [==============================] - 0s - loss: 0.0937 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 594/750\n",
"700/700 [==============================] - 0s - loss: 0.1057 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 595/750\n",
"700/700 [==============================] - 0s - loss: 0.0968 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 596/750\n",
"700/700 [==============================] - 0s - loss: 0.1015 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 597/750\n",
"700/700 [==============================] - 0s - loss: 0.1028 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 598/750\n",
"700/700 [==============================] - 0s - loss: 0.1012 - acc: 0.9671 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 599/750\n",
"700/700 [==============================] - ETA: 0s - loss: 0.0564 - acc: 0.9900\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b - 0s - loss: 0.1001 - acc: 0.9771 \n",
"Epoch 600/750\n",
"700/700 [==============================] - 0s - loss: 0.0961 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 601/750\n",
"700/700 [==============================] - 0s - loss: 0.1056 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 602/750\n",
"700/700 [==============================] - 0s - loss: 0.1013 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 603/750\n",
"700/700 [==============================] - 0s - loss: 0.0935 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 604/750\n",
"700/700 [==============================] - 0s - loss: 0.0896 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 605/750\n",
"700/700 [==============================] - 0s - loss: 0.1009 - acc: 0.9814 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 606/750\n",
"700/700 [==============================] - 0s - loss: 0.0967 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 607/750\n",
"700/700 [==============================] - 0s - loss: 0.1012 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 608/750\n",
"700/700 [==============================] - 0s - loss: 0.0986 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 609/750\n",
"700/700 [==============================] - 0s - loss: 0.1023 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 610/750\n",
"700/700 [==============================] - 0s - loss: 0.0952 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 611/750\n",
"700/700 [==============================] - 0s - loss: 0.0938 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 612/750\n",
"700/700 [==============================] - 0s - loss: 0.1010 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 613/750\n",
"700/700 [==============================] - 0s - loss: 0.0995 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 614/750\n",
"700/700 [==============================] - 0s - loss: 0.1043 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 615/750\n",
"700/700 [==============================] - 0s - loss: 0.1027 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 616/750\n",
"700/700 [==============================] - 0s - loss: 0.0971 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 617/750\n",
"700/700 [==============================] - 0s - loss: 0.0968 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 618/750\n",
"700/700 [==============================] - 0s - loss: 0.0962 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 619/750\n",
"700/700 [==============================] - 0s - loss: 0.0997 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 620/750\n",
"700/700 [==============================] - 0s - loss: 0.0956 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 621/750\n",
"700/700 [==============================] - 0s - loss: 0.1012 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 622/750\n",
"700/700 [==============================] - 0s - loss: 0.1023 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 623/750\n",
"700/700 [==============================] - 0s - loss: 0.1004 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 624/750\n",
"700/700 [==============================] - 0s - loss: 0.0943 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 625/750\n",
"700/700 [==============================] - 0s - loss: 0.1011 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 626/750\n",
"700/700 [==============================] - 0s - loss: 0.0966 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 627/750\n",
"700/700 [==============================] - 0s - loss: 0.0944 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 628/750\n",
"700/700 [==============================] - 0s - loss: 0.0930 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 629/750\n",
"700/700 [==============================] - ETA: 0s - loss: 0.0902 - acc: 0.9800\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b - 0s - loss: 0.0980 - acc: 0.9771 \n",
"Epoch 630/750\n",
"700/700 [==============================] - 0s - loss: 0.1041 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 631/750\n",
"700/700 [==============================] - 0s - loss: 0.0983 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 632/750\n",
"700/700 [==============================] - 0s - loss: 0.0945 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 633/750\n",
"700/700 [==============================] - 0s - loss: 0.0947 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 634/750\n",
"700/700 [==============================] - 0s - loss: 0.0966 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 635/750\n",
"700/700 [==============================] - 0s - loss: 0.0986 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 636/750\n",
"700/700 [==============================] - 0s - loss: 0.0987 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 637/750\n",
"700/700 [==============================] - 0s - loss: 0.0969 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 638/750\n",
"700/700 [==============================] - 0s - loss: 0.1004 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 639/750\n",
"700/700 [==============================] - 0s - loss: 0.0906 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 640/750\n",
"700/700 [==============================] - 0s - loss: 0.0930 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 641/750\n",
"700/700 [==============================] - 0s - loss: 0.0993 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 642/750\n",
"700/700 [==============================] - 0s - loss: 0.0952 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 643/750\n",
"700/700 [==============================] - 0s - loss: 0.0953 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 644/750\n",
"700/700 [==============================] - 0s - loss: 0.0950 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 645/750\n",
"700/700 [==============================] - 0s - loss: 0.0932 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 646/750\n",
"700/700 [==============================] - 0s - loss: 0.0906 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 647/750\n",
"700/700 [==============================] - 0s - loss: 0.0974 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 648/750\n",
"700/700 [==============================] - 0s - loss: 0.0979 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 649/750\n",
"700/700 [==============================] - ETA: 0s - loss: 0.0894 - acc: 0.9700\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b - 0s - loss: 0.0919 - acc: 0.9771 \n",
"Epoch 650/750\n",
"700/700 [==============================] - 0s - loss: 0.0955 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 651/750\n",
"700/700 [==============================] - 0s - loss: 0.0916 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 652/750\n",
"700/700 [==============================] - 0s - loss: 0.1022 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 653/750\n",
"700/700 [==============================] - 0s - loss: 0.0922 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 654/750\n",
"700/700 [==============================] - 0s - loss: 0.0977 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 655/750\n",
"700/700 [==============================] - 0s - loss: 0.0949 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 656/750\n",
"700/700 [==============================] - 0s - loss: 0.0895 - acc: 0.9800 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 657/750\n",
"700/700 [==============================] - 0s - loss: 0.0975 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 658/750\n",
"700/700 [==============================] - 0s - loss: 0.0999 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 659/750\n",
"700/700 [==============================] - 0s - loss: 0.0956 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 660/750\n",
"700/700 [==============================] - 0s - loss: 0.0936 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 661/750\n",
"700/700 [==============================] - 0s - loss: 0.0937 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 662/750\n",
"700/700 [==============================] - 0s - loss: 0.0949 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 663/750\n",
"700/700 [==============================] - 0s - loss: 0.0965 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 664/750\n",
"700/700 [==============================] - 0s - loss: 0.0902 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 665/750\n",
"700/700 [==============================] - 0s - loss: 0.0961 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 666/750\n",
"700/700 [==============================] - 0s - loss: 0.0909 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 667/750\n",
"700/700 [==============================] - 0s - loss: 0.0898 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 668/750\n",
"700/700 [==============================] - 0s - loss: 0.0981 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 669/750\n",
"700/700 [==============================] - 0s - loss: 0.0923 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 670/750\n",
"700/700 [==============================] - 0s - loss: 0.0871 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 671/750\n",
"700/700 [==============================] - 0s - loss: 0.1010 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 672/750\n",
"700/700 [==============================] - 0s - loss: 0.0926 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 673/750\n",
"700/700 [==============================] - 0s - loss: 0.0910 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 674/750\n",
"700/700 [==============================] - 0s - loss: 0.0926 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 675/750\n",
"700/700 [==============================] - 0s - loss: 0.0978 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 676/750\n",
"700/700 [==============================] - 0s - loss: 0.0947 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 677/750\n",
"700/700 [==============================] - 0s - loss: 0.0867 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 678/750\n",
"700/700 [==============================] - 0s - loss: 0.0985 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 679/750\n",
"700/700 [==============================] - 0s - loss: 0.0874 - acc: 0.9814 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 680/750\n",
"700/700 [==============================] - 0s - loss: 0.0874 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 681/750\n",
"700/700 [==============================] - 0s - loss: 0.0933 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 682/750\n",
"700/700 [==============================] - 0s - loss: 0.0887 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 683/750\n",
"700/700 [==============================] - 0s - loss: 0.0946 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 684/750\n",
"700/700 [==============================] - 0s - loss: 0.0881 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 685/750\n",
"700/700 [==============================] - 0s - loss: 0.0874 - acc: 0.9829 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 686/750\n",
"700/700 [==============================] - 0s - loss: 0.0872 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 687/750\n",
"700/700 [==============================] - 0s - loss: 0.0985 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 688/750\n",
"700/700 [==============================] - 0s - loss: 0.0904 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 689/750\n",
"700/700 [==============================] - 0s - loss: 0.0927 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 690/750\n",
"700/700 [==============================] - 0s - loss: 0.0879 - acc: 0.9800 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 691/750\n",
"700/700 [==============================] - 0s - loss: 0.0894 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 692/750\n",
"700/700 [==============================] - 0s - loss: 0.0819 - acc: 0.9843 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 693/750\n",
"700/700 [==============================] - 0s - loss: 0.0859 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 694/750\n",
"700/700 [==============================] - 0s - loss: 0.1009 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 695/750\n",
"700/700 [==============================] - 0s - loss: 0.0838 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 696/750\n",
"700/700 [==============================] - 0s - loss: 0.0870 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 697/750\n",
"700/700 [==============================] - 0s - loss: 0.0914 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 698/750\n",
"700/700 [==============================] - 0s - loss: 0.0891 - acc: 0.9814 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 699/750\n",
"700/700 [==============================] - 0s - loss: 0.0894 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 700/750\n",
"700/700 [==============================] - 0s - loss: 0.0905 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 701/750\n",
"700/700 [==============================] - 0s - loss: 0.0877 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 702/750\n",
"700/700 [==============================] - 0s - loss: 0.0832 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 703/750\n",
"700/700 [==============================] - 0s - loss: 0.1010 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 704/750\n",
"700/700 [==============================] - 0s - loss: 0.0861 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 705/750\n",
"700/700 [==============================] - 0s - loss: 0.0996 - acc: 0.9686 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 706/750\n",
"700/700 [==============================] - 0s - loss: 0.0895 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 707/750\n",
"700/700 [==============================] - 0s - loss: 0.0851 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 708/750\n",
"700/700 [==============================] - 0s - loss: 0.0917 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 709/750\n",
"700/700 [==============================] - 0s - loss: 0.0937 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 710/750\n",
"700/700 [==============================] - 0s - loss: 0.0891 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 711/750\n",
"700/700 [==============================] - 0s - loss: 0.0858 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 712/750\n",
"700/700 [==============================] - 0s - loss: 0.0907 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 713/750\n",
"700/700 [==============================] - 0s - loss: 0.0909 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 714/750\n",
"700/700 [==============================] - 0s - loss: 0.0933 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 715/750\n",
"700/700 [==============================] - 0s - loss: 0.0889 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 716/750\n",
"700/700 [==============================] - 0s - loss: 0.0905 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 717/750\n",
"700/700 [==============================] - 0s - loss: 0.0987 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 718/750\n",
"700/700 [==============================] - 0s - loss: 0.0821 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 719/750\n",
"700/700 [==============================] - 0s - loss: 0.0890 - acc: 0.9814 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 720/750\n",
"700/700 [==============================] - 0s - loss: 0.0880 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 721/750\n",
"700/700 [==============================] - 0s - loss: 0.0888 - acc: 0.9800 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 722/750\n",
"700/700 [==============================] - 0s - loss: 0.0928 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 723/750\n",
"700/700 [==============================] - 0s - loss: 0.0924 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 724/750\n",
"700/700 [==============================] - 0s - loss: 0.0880 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 725/750\n",
"700/700 [==============================] - 0s - loss: 0.0937 - acc: 0.9700 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 726/750\n",
"700/700 [==============================] - 0s - loss: 0.0843 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 727/750\n",
"700/700 [==============================] - 0s - loss: 0.0852 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 728/750\n",
"700/700 [==============================] - 0s - loss: 0.0962 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 729/750\n",
"700/700 [==============================] - 0s - loss: 0.0861 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 730/750\n",
"700/700 [==============================] - 0s - loss: 0.0890 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 731/750\n",
"700/700 [==============================] - 0s - loss: 0.0907 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 732/750\n",
"700/700 [==============================] - 0s - loss: 0.0860 - acc: 0.9829 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 733/750\n",
"700/700 [==============================] - 0s - loss: 0.0926 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 734/750\n",
"700/700 [==============================] - 0s - loss: 0.0912 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 735/750\n",
"700/700 [==============================] - 0s - loss: 0.0857 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 736/750\n",
"700/700 [==============================] - 0s - loss: 0.0864 - acc: 0.9800 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 737/750\n",
"700/700 [==============================] - 0s - loss: 0.0935 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 738/750\n",
"700/700 [==============================] - 0s - loss: 0.0991 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 739/750\n",
"700/700 [==============================] - 0s - loss: 0.0807 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 740/750\n",
"700/700 [==============================] - 0s - loss: 0.0892 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 741/750\n",
"700/700 [==============================] - 0s - loss: 0.0876 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 742/750\n",
"700/700 [==============================] - 0s - loss: 0.0911 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 743/750\n",
"700/700 [==============================] - 0s - loss: 0.0854 - acc: 0.9729 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 744/750\n",
"700/700 [==============================] - 0s - loss: 0.0820 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 745/750\n",
"700/700 [==============================] - 0s - loss: 0.0854 - acc: 0.9757 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 746/750\n",
"700/700 [==============================] - 0s - loss: 0.0827 - acc: 0.9814 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 747/750\n",
"700/700 [==============================] - 0s - loss: 0.0909 - acc: 0.9714 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 748/750\n",
"700/700 [==============================] - 0s - loss: 0.0854 - acc: 0.9743 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 749/750\n",
"700/700 [==============================] - 0s - loss: 0.0857 - acc: 0.9786 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 750/750\n",
"700/700 [==============================] - 0s - loss: 0.0847 - acc: 0.9771 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
]
}
],
"source": [
"history = model.fit(x_train_data, train_labels, epochs = 750, batch_size = 100)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"history = history.history"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<function dict.keys>"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"history.keys"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['loss', 'acc'])"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"history.keys()"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"epochs = range(1, 751)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
" warnings.warn(\"No labelled objects found. \"\n"
]
}
],
"source": [
"train_loss = history[\"loss\"]\n",
"\n",
"plt.plot(epochs, train_loss, 'b')\n",
"plt.xlabel(\"Epochs\")\n",
"plt.ylabel(\"Loss\")\n",
"plt.title(\"Training loss\")\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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CPXsw35o12XNRQflG8SVLQt7++tfm50ekFAULkQK6doXPfS47rXfvsM0dIHfS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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff346cb1748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 32/100 [========>.....................] - ETA: 0s"
]
}
],
"source": [
"evaluation_results = model.evaluate(x_test_data, test_labels)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[10.768644618988038, 0.16]"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"evaluation_results"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"list"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(evaluation_results)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
" warnings.warn(\"No labelled objects found. \"\n"
]
}
],
"source": [
"train_acc = history[\"acc\"]\n",
"\n",
"plt.plot(epochs, train_acc, 'g')\n",
"plt.xlabel(\"Epochs\")\n",
"plt.ylabel(\"Accuracy\")\n",
"plt.title(\"Training accuracy\")\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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pojSjWdW5zmK22b3L7r7x9F0775oMkYxNvyYoTFRRWkFlTSUVpRXs03UfZ/+2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\nNCtpxuMnPr7ZFhEePeFRjt3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0YBeSpTh+A3QWkXtF5H/qy8CmRtycRX0TNNPbJiwM1ba8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\niqIoSiQqFoqiKEok/w+tADdkIf+JSgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff346c138d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
hall1467/wikidata_usage_tracking | jupyter_notebooks/misalignment/dissonance-201701.ipynb | 2 | 737231 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"library(data.table)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Using a table of article assessments and views, build tables\n",
"## (matrices) that shows the number of dissonant articles per\n",
"## assessment category based on sorting by popularity.\n",
"##\n",
"## The underlying assumption is that in an ideal system with a limited\n",
"## and fixed amount of resources (in other words, popularity and high quality\n",
"## artefacts does not increase the amount of resources in the system),\n",
"## popularity ranking and assessment class follow a 1-to-1 relationship.\n",
"## We can therefore sort by popularity and group articles that way\n",
"## because work will be prioritised by popularity.\n",
"\n",
"## DATA ASSUMPTION: views_with_redirects from resolve-redirects.R\n",
"## is loaded into memory.\n",
"\n",
"## 3: build a 2x2 matrix of assessment classes and popularity classes\n",
"## \n",
"\n",
"## Assessment classes in ascending order of quality."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"assessment_classes = c('E', 'D', 'C', 'B', 'A');"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"quality_prediction_and_page_views <- read.table(\"../../results/sql_queries/entity_views_and_aggregated_revisions/entity_views_and_aggregated_revisions_and_quality_scoring_20170101.tsv\", header=FALSE, sep=\"\\t\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"quality_prediction_and_page_views <- data.table(quality_prediction_and_page_views)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"colnames(quality_prediction_and_page_views) <- c('entity_id','number_of_revisions', 'page_views', 'prediction')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" entity_id number_of_revisions page_views prediction \n",
" Q1 : 1 Min. : 1.00 Min. :0.000e+00 A: 1006 \n",
" Q100 : 1 1st Qu.: 6.00 1st Qu.:1.300e+01 B: 389021 \n",
" Q1000 : 1 Median : 13.00 Median :1.430e+02 C: 5097626 \n",
" Q10000 : 1 Mean : 18.24 Mean :2.525e+04 D: 3694305 \n",
" Q100000 : 1 3rd Qu.: 23.00 3rd Qu.:1.039e+03 E:12355934 \n",
" Q1000000: 1 Max. :21863.00 Max. :1.253e+10 \n",
" (Other) :21537886 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"summary(quality_prediction_and_page_views)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## 0: calculate number of articles in each assessment class\n",
"n_per_class = quality_prediction_and_page_views[, list(narticles=sum(.N)), by='prediction']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"setkey(n_per_class, prediction);\n",
"## NOTE: setkey allows us to do n_per_class['GA']$narticles to get counts"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## 1: order articles by popularity\n",
"articles_by_pop = quality_prediction_and_page_views[order(quality_prediction_and_page_views$page_views)][,list(entity_id, prediction, page_views)];\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10750354</td><td>C </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10766855</td><td>D </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12443525</td><td>D </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td> </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818</td><td> </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td> </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td> </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927</td><td> </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td> </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224</td><td> </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td> </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td> </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td> </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td> </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157</td><td> </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td> </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td> </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498</td><td> </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td> </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td> </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td> </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td> </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td> </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td> </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td> </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td> </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td> </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td> </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td> </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td> </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td> </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721</td><td> </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td> </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & \\\\\n",
"\t Q10069140 & C & 0 & \\\\\n",
"\t Q10081695 & C & 0 & \\\\\n",
"\t Q10092002 & E & 0 & \\\\\n",
"\t Q10111267 & E & 0 & \\\\\n",
"\t Q10149726 & E & 0 & \\\\\n",
"\t Q10180230 & E & 0 & \\\\\n",
"\t Q10185035 & E & 0 & \\\\\n",
"\t Q10205202 & E & 0 & \\\\\n",
"\t Q10252966 & E & 0 & \\\\\n",
"\t Q10444494 & C & 0 & \\\\\n",
"\t Q10624171 & C & 0 & \\\\\n",
"\t Q10704108 & C & 0 & \\\\\n",
"\t Q10750354 & C & 0 & \\\\\n",
"\t Q10766855 & D & 0 & \\\\\n",
"\t Q10827611 & E & 0 & \\\\\n",
"\t Q11093044 & E & 0 & \\\\\n",
"\t Q11934537 & E & 0 & \\\\\n",
"\t Q12133466 & E & 0 & \\\\\n",
"\t Q12264503 & E & 0 & \\\\\n",
"\t Q12267516 & E & 0 & \\\\\n",
"\t Q12304084 & E & 0 & \\\\\n",
"\t Q12443525 & D & 0 & \\\\\n",
"\t Q12543904 & E & 0 & \\\\\n",
"\t Q12890205 & E & 0 & \\\\\n",
"\t Q12891524 & E & 0 & \\\\\n",
"\t Q12918202 & E & 0 & \\\\\n",
"\t Q13005653 & E & 0 & \\\\\n",
"\t Q13073896 & E & 0 & \\\\\n",
"\t Q13163823 & E & 0 & \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & \\\\\n",
"\t Q31165 & B & 2048330818 & \\\\\n",
"\t Q40629 & C & 2049755644 & \\\\\n",
"\t Q105584 & C & 2049926923 & \\\\\n",
"\t Q4584301 & C & 2052339927 & \\\\\n",
"\t Q565 & C & 2052996261 & \\\\\n",
"\t Q1868372 & D & 2056080224 & \\\\\n",
"\t Q209330 & C & 2060928966 & \\\\\n",
"\t Q14005 & D & 2063120071 & \\\\\n",
"\t Q918 & C & 2063217449 & \\\\\n",
"\t Q150248 & C & 2068796814 & \\\\\n",
"\t Q866 & B & 2079749157 & \\\\\n",
"\t Q477675 & C & 2080785713 & \\\\\n",
"\t Q1967876 & C & 2084215818 & \\\\\n",
"\t Q750403 & B & 2084693498 & \\\\\n",
"\t Q355 & C & 2093900731 & \\\\\n",
"\t Q623578 & C & 2097991400 & \\\\\n",
"\t Q17299517 & D & 2105487660 & \\\\\n",
"\t Q33999 & C & 2108672678 & \\\\\n",
"\t Q2494649 & C & 2114531894 & \\\\\n",
"\t Q2597810 & C & 2128920607 & \\\\\n",
"\t Q193563 & C & 2130725560 & \\\\\n",
"\t Q423048 & C & 2136131564 & \\\\\n",
"\t Q37312 & C & 2142913121 & \\\\\n",
"\t Q54919 & C & 2148531382 & \\\\\n",
"\t Q36578 & C & 2229315598 & \\\\\n",
"\t Q30 & A & 2277746226 & \\\\\n",
"\t Q6581097 & D & 3273952711 & \\\\\n",
"\t Q5 & C & 5668008721 & \\\\\n",
"\t Q5296 & C & 12530369761 & \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | | \n",
"| Q10069140 | C | 0 | | \n",
"| Q10081695 | C | 0 | | \n",
"| Q10092002 | E | 0 | | \n",
"| Q10111267 | E | 0 | | \n",
"| Q10149726 | E | 0 | | \n",
"| Q10180230 | E | 0 | | \n",
"| Q10185035 | E | 0 | | \n",
"| Q10205202 | E | 0 | | \n",
"| Q10252966 | E | 0 | | \n",
"| Q10444494 | C | 0 | | \n",
"| Q10624171 | C | 0 | | \n",
"| Q10704108 | C | 0 | | \n",
"| Q10750354 | C | 0 | | \n",
"| Q10766855 | D | 0 | | \n",
"| Q10827611 | E | 0 | | \n",
"| Q11093044 | E | 0 | | \n",
"| Q11934537 | E | 0 | | \n",
"| Q12133466 | E | 0 | | \n",
"| Q12264503 | E | 0 | | \n",
"| Q12267516 | E | 0 | | \n",
"| Q12304084 | E | 0 | | \n",
"| Q12443525 | D | 0 | | \n",
"| Q12543904 | E | 0 | | \n",
"| Q12890205 | E | 0 | | \n",
"| Q12891524 | E | 0 | | \n",
"| Q12918202 | E | 0 | | \n",
"| Q13005653 | E | 0 | | \n",
"| Q13073896 | E | 0 | | \n",
"| Q13163823 | E | 0 | | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | | \n",
"| Q31165 | B | 2048330818 | | \n",
"| Q40629 | C | 2049755644 | | \n",
"| Q105584 | C | 2049926923 | | \n",
"| Q4584301 | C | 2052339927 | | \n",
"| Q565 | C | 2052996261 | | \n",
"| Q1868372 | D | 2056080224 | | \n",
"| Q209330 | C | 2060928966 | | \n",
"| Q14005 | D | 2063120071 | | \n",
"| Q918 | C | 2063217449 | | \n",
"| Q150248 | C | 2068796814 | | \n",
"| Q866 | B | 2079749157 | | \n",
"| Q477675 | C | 2080785713 | | \n",
"| Q1967876 | C | 2084215818 | | \n",
"| Q750403 | B | 2084693498 | | \n",
"| Q355 | C | 2093900731 | | \n",
"| Q623578 | C | 2097991400 | | \n",
"| Q17299517 | D | 2105487660 | | \n",
"| Q33999 | C | 2108672678 | | \n",
"| Q2494649 | C | 2114531894 | | \n",
"| Q2597810 | C | 2128920607 | | \n",
"| Q193563 | C | 2130725560 | | \n",
"| Q423048 | C | 2136131564 | | \n",
"| Q37312 | C | 2142913121 | | \n",
"| Q54919 | C | 2148531382 | | \n",
"| Q36578 | C | 2229315598 | | \n",
"| Q30 | A | 2277746226 | | \n",
"| Q6581097 | D | 3273952711 | | \n",
"| Q5 | C | 5668008721 | | \n",
"| Q5296 | C | 12530369761 | | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class\n",
"1 Q10040378 E 0 \n",
"2 Q10069140 C 0 \n",
"3 Q10081695 C 0 \n",
"4 Q10092002 E 0 \n",
"5 Q10111267 E 0 \n",
"6 Q10149726 E 0 \n",
"7 Q10180230 E 0 \n",
"8 Q10185035 E 0 \n",
"9 Q10205202 E 0 \n",
"10 Q10252966 E 0 \n",
"11 Q10444494 C 0 \n",
"12 Q10624171 C 0 \n",
"13 Q10704108 C 0 \n",
"14 Q10750354 C 0 \n",
"15 Q10766855 D 0 \n",
"16 Q10827611 E 0 \n",
"17 Q11093044 E 0 \n",
"18 Q11934537 E 0 \n",
"19 Q12133466 E 0 \n",
"20 Q12264503 E 0 \n",
"21 Q12267516 E 0 \n",
"22 Q12304084 E 0 \n",
"23 Q12443525 D 0 \n",
"24 Q12543904 E 0 \n",
"25 Q12890205 E 0 \n",
"26 Q12891524 E 0 \n",
"27 Q12918202 E 0 \n",
"28 Q13005653 E 0 \n",
"29 Q13073896 E 0 \n",
"30 Q13163823 E 0 \n",
"⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 \n",
"21537864 Q31165 B 2048330818 \n",
"21537865 Q40629 C 2049755644 \n",
"21537866 Q105584 C 2049926923 \n",
"21537867 Q4584301 C 2052339927 \n",
"21537868 Q565 C 2052996261 \n",
"21537869 Q1868372 D 2056080224 \n",
"21537870 Q209330 C 2060928966 \n",
"21537871 Q14005 D 2063120071 \n",
"21537872 Q918 C 2063217449 \n",
"21537873 Q150248 C 2068796814 \n",
"21537874 Q866 B 2079749157 \n",
"21537875 Q477675 C 2080785713 \n",
"21537876 Q1967876 C 2084215818 \n",
"21537877 Q750403 B 2084693498 \n",
"21537878 Q355 C 2093900731 \n",
"21537879 Q623578 C 2097991400 \n",
"21537880 Q17299517 D 2105487660 \n",
"21537881 Q33999 C 2108672678 \n",
"21537882 Q2494649 C 2114531894 \n",
"21537883 Q2597810 C 2128920607 \n",
"21537884 Q193563 C 2130725560 \n",
"21537885 Q423048 C 2136131564 \n",
"21537886 Q37312 C 2142913121 \n",
"21537887 Q54919 C 2148531382 \n",
"21537888 Q36578 C 2229315598 \n",
"21537889 Q30 A 2277746226 \n",
"21537890 Q6581097 D 3273952711 \n",
"21537891 Q5 C 5668008721 \n",
"21537892 Q5296 C 12530369761 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td> </td><td> 1 </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td> </td><td> 2 </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td> </td><td> 3 </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td> </td><td> 4 </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td> </td><td> 5 </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td> </td><td> 6 </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td> </td><td> 7 </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td> </td><td> 8 </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td> </td><td> 9 </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td> </td><td>10 </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td> </td><td>11 </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td> </td><td>12 </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td> </td><td>13 </td></tr>\n",
"\t<tr><td>Q10750354</td><td>C </td><td>0 </td><td> </td><td>14 </td></tr>\n",
"\t<tr><td>Q10766855</td><td>D </td><td>0 </td><td> </td><td>15 </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td> </td><td>16 </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td> </td><td>17 </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td> </td><td>18 </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td> </td><td>19 </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td> </td><td>20 </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td> </td><td>21 </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td> </td><td>22 </td></tr>\n",
"\t<tr><td>Q12443525</td><td>D </td><td>0 </td><td> </td><td>23 </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td> </td><td>24 </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td> </td><td>25 </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td> </td><td>26 </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td> </td><td>27 </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td> </td><td>28 </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td> </td><td>29 </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td> </td><td>30 </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td> </td><td>21537863 </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818</td><td> </td><td>21537864 </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td> </td><td>21537865 </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td> </td><td>21537866 </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927</td><td> </td><td>21537867 </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td> </td><td>21537868 </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224</td><td> </td><td>21537869 </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td> </td><td>21537870 </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td> </td><td>21537871 </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td> </td><td>21537872 </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td> </td><td>21537873 </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157</td><td> </td><td>21537874 </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td> </td><td>21537875 </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td> </td><td>21537876 </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498</td><td> </td><td>21537877 </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td> </td><td>21537878 </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td> </td><td>21537879 </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td> </td><td>21537880 </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td> </td><td>21537881 </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td> </td><td>21537882 </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td> </td><td>21537883 </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td> </td><td>21537884 </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td> </td><td>21537885 </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td> </td><td>21537886 </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td> </td><td>21537887 </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td> </td><td>21537888 </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td> </td><td>21537889 </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td> </td><td>21537890 </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721</td><td> </td><td>21537891 </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td> </td><td>21537892 </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & & 1 \\\\\n",
"\t Q10069140 & C & 0 & & 2 \\\\\n",
"\t Q10081695 & C & 0 & & 3 \\\\\n",
"\t Q10092002 & E & 0 & & 4 \\\\\n",
"\t Q10111267 & E & 0 & & 5 \\\\\n",
"\t Q10149726 & E & 0 & & 6 \\\\\n",
"\t Q10180230 & E & 0 & & 7 \\\\\n",
"\t Q10185035 & E & 0 & & 8 \\\\\n",
"\t Q10205202 & E & 0 & & 9 \\\\\n",
"\t Q10252966 & E & 0 & & 10 \\\\\n",
"\t Q10444494 & C & 0 & & 11 \\\\\n",
"\t Q10624171 & C & 0 & & 12 \\\\\n",
"\t Q10704108 & C & 0 & & 13 \\\\\n",
"\t Q10750354 & C & 0 & & 14 \\\\\n",
"\t Q10766855 & D & 0 & & 15 \\\\\n",
"\t Q10827611 & E & 0 & & 16 \\\\\n",
"\t Q11093044 & E & 0 & & 17 \\\\\n",
"\t Q11934537 & E & 0 & & 18 \\\\\n",
"\t Q12133466 & E & 0 & & 19 \\\\\n",
"\t Q12264503 & E & 0 & & 20 \\\\\n",
"\t Q12267516 & E & 0 & & 21 \\\\\n",
"\t Q12304084 & E & 0 & & 22 \\\\\n",
"\t Q12443525 & D & 0 & & 23 \\\\\n",
"\t Q12543904 & E & 0 & & 24 \\\\\n",
"\t Q12890205 & E & 0 & & 25 \\\\\n",
"\t Q12891524 & E & 0 & & 26 \\\\\n",
"\t Q12918202 & E & 0 & & 27 \\\\\n",
"\t Q13005653 & E & 0 & & 28 \\\\\n",
"\t Q13073896 & E & 0 & & 29 \\\\\n",
"\t Q13163823 & E & 0 & & 30 \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & & 21537863 \\\\\n",
"\t Q31165 & B & 2048330818 & & 21537864 \\\\\n",
"\t Q40629 & C & 2049755644 & & 21537865 \\\\\n",
"\t Q105584 & C & 2049926923 & & 21537866 \\\\\n",
"\t Q4584301 & C & 2052339927 & & 21537867 \\\\\n",
"\t Q565 & C & 2052996261 & & 21537868 \\\\\n",
"\t Q1868372 & D & 2056080224 & & 21537869 \\\\\n",
"\t Q209330 & C & 2060928966 & & 21537870 \\\\\n",
"\t Q14005 & D & 2063120071 & & 21537871 \\\\\n",
"\t Q918 & C & 2063217449 & & 21537872 \\\\\n",
"\t Q150248 & C & 2068796814 & & 21537873 \\\\\n",
"\t Q866 & B & 2079749157 & & 21537874 \\\\\n",
"\t Q477675 & C & 2080785713 & & 21537875 \\\\\n",
"\t Q1967876 & C & 2084215818 & & 21537876 \\\\\n",
"\t Q750403 & B & 2084693498 & & 21537877 \\\\\n",
"\t Q355 & C & 2093900731 & & 21537878 \\\\\n",
"\t Q623578 & C & 2097991400 & & 21537879 \\\\\n",
"\t Q17299517 & D & 2105487660 & & 21537880 \\\\\n",
"\t Q33999 & C & 2108672678 & & 21537881 \\\\\n",
"\t Q2494649 & C & 2114531894 & & 21537882 \\\\\n",
"\t Q2597810 & C & 2128920607 & & 21537883 \\\\\n",
"\t Q193563 & C & 2130725560 & & 21537884 \\\\\n",
"\t Q423048 & C & 2136131564 & & 21537885 \\\\\n",
"\t Q37312 & C & 2142913121 & & 21537886 \\\\\n",
"\t Q54919 & C & 2148531382 & & 21537887 \\\\\n",
"\t Q36578 & C & 2229315598 & & 21537888 \\\\\n",
"\t Q30 & A & 2277746226 & & 21537889 \\\\\n",
"\t Q6581097 & D & 3273952711 & & 21537890 \\\\\n",
"\t Q5 & C & 5668008721 & & 21537891 \\\\\n",
"\t Q5296 & C & 12530369761 & & 21537892 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | | 1 | \n",
"| Q10069140 | C | 0 | | 2 | \n",
"| Q10081695 | C | 0 | | 3 | \n",
"| Q10092002 | E | 0 | | 4 | \n",
"| Q10111267 | E | 0 | | 5 | \n",
"| Q10149726 | E | 0 | | 6 | \n",
"| Q10180230 | E | 0 | | 7 | \n",
"| Q10185035 | E | 0 | | 8 | \n",
"| Q10205202 | E | 0 | | 9 | \n",
"| Q10252966 | E | 0 | | 10 | \n",
"| Q10444494 | C | 0 | | 11 | \n",
"| Q10624171 | C | 0 | | 12 | \n",
"| Q10704108 | C | 0 | | 13 | \n",
"| Q10750354 | C | 0 | | 14 | \n",
"| Q10766855 | D | 0 | | 15 | \n",
"| Q10827611 | E | 0 | | 16 | \n",
"| Q11093044 | E | 0 | | 17 | \n",
"| Q11934537 | E | 0 | | 18 | \n",
"| Q12133466 | E | 0 | | 19 | \n",
"| Q12264503 | E | 0 | | 20 | \n",
"| Q12267516 | E | 0 | | 21 | \n",
"| Q12304084 | E | 0 | | 22 | \n",
"| Q12443525 | D | 0 | | 23 | \n",
"| Q12543904 | E | 0 | | 24 | \n",
"| Q12890205 | E | 0 | | 25 | \n",
"| Q12891524 | E | 0 | | 26 | \n",
"| Q12918202 | E | 0 | | 27 | \n",
"| Q13005653 | E | 0 | | 28 | \n",
"| Q13073896 | E | 0 | | 29 | \n",
"| Q13163823 | E | 0 | | 30 | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | | 21537863 | \n",
"| Q31165 | B | 2048330818 | | 21537864 | \n",
"| Q40629 | C | 2049755644 | | 21537865 | \n",
"| Q105584 | C | 2049926923 | | 21537866 | \n",
"| Q4584301 | C | 2052339927 | | 21537867 | \n",
"| Q565 | C | 2052996261 | | 21537868 | \n",
"| Q1868372 | D | 2056080224 | | 21537869 | \n",
"| Q209330 | C | 2060928966 | | 21537870 | \n",
"| Q14005 | D | 2063120071 | | 21537871 | \n",
"| Q918 | C | 2063217449 | | 21537872 | \n",
"| Q150248 | C | 2068796814 | | 21537873 | \n",
"| Q866 | B | 2079749157 | | 21537874 | \n",
"| Q477675 | C | 2080785713 | | 21537875 | \n",
"| Q1967876 | C | 2084215818 | | 21537876 | \n",
"| Q750403 | B | 2084693498 | | 21537877 | \n",
"| Q355 | C | 2093900731 | | 21537878 | \n",
"| Q623578 | C | 2097991400 | | 21537879 | \n",
"| Q17299517 | D | 2105487660 | | 21537880 | \n",
"| Q33999 | C | 2108672678 | | 21537881 | \n",
"| Q2494649 | C | 2114531894 | | 21537882 | \n",
"| Q2597810 | C | 2128920607 | | 21537883 | \n",
"| Q193563 | C | 2130725560 | | 21537884 | \n",
"| Q423048 | C | 2136131564 | | 21537885 | \n",
"| Q37312 | C | 2142913121 | | 21537886 | \n",
"| Q54919 | C | 2148531382 | | 21537887 | \n",
"| Q36578 | C | 2229315598 | | 21537888 | \n",
"| Q30 | A | 2277746226 | | 21537889 | \n",
"| Q6581097 | D | 3273952711 | | 21537890 | \n",
"| Q5 | C | 5668008721 | | 21537891 | \n",
"| Q5296 | C | 12530369761 | | 21537892 | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum \n",
"1 Q10040378 E 0 1 \n",
"2 Q10069140 C 0 2 \n",
"3 Q10081695 C 0 3 \n",
"4 Q10092002 E 0 4 \n",
"5 Q10111267 E 0 5 \n",
"6 Q10149726 E 0 6 \n",
"7 Q10180230 E 0 7 \n",
"8 Q10185035 E 0 8 \n",
"9 Q10205202 E 0 9 \n",
"10 Q10252966 E 0 10 \n",
"11 Q10444494 C 0 11 \n",
"12 Q10624171 C 0 12 \n",
"13 Q10704108 C 0 13 \n",
"14 Q10750354 C 0 14 \n",
"15 Q10766855 D 0 15 \n",
"16 Q10827611 E 0 16 \n",
"17 Q11093044 E 0 17 \n",
"18 Q11934537 E 0 18 \n",
"19 Q12133466 E 0 19 \n",
"20 Q12264503 E 0 20 \n",
"21 Q12267516 E 0 21 \n",
"22 Q12304084 E 0 22 \n",
"23 Q12443525 D 0 23 \n",
"24 Q12543904 E 0 24 \n",
"25 Q12890205 E 0 25 \n",
"26 Q12891524 E 0 26 \n",
"27 Q12918202 E 0 27 \n",
"28 Q13005653 E 0 28 \n",
"29 Q13073896 E 0 29 \n",
"30 Q13163823 E 0 30 \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 21537863\n",
"21537864 Q31165 B 2048330818 21537864\n",
"21537865 Q40629 C 2049755644 21537865\n",
"21537866 Q105584 C 2049926923 21537866\n",
"21537867 Q4584301 C 2052339927 21537867\n",
"21537868 Q565 C 2052996261 21537868\n",
"21537869 Q1868372 D 2056080224 21537869\n",
"21537870 Q209330 C 2060928966 21537870\n",
"21537871 Q14005 D 2063120071 21537871\n",
"21537872 Q918 C 2063217449 21537872\n",
"21537873 Q150248 C 2068796814 21537873\n",
"21537874 Q866 B 2079749157 21537874\n",
"21537875 Q477675 C 2080785713 21537875\n",
"21537876 Q1967876 C 2084215818 21537876\n",
"21537877 Q750403 B 2084693498 21537877\n",
"21537878 Q355 C 2093900731 21537878\n",
"21537879 Q623578 C 2097991400 21537879\n",
"21537880 Q17299517 D 2105487660 21537880\n",
"21537881 Q33999 C 2108672678 21537881\n",
"21537882 Q2494649 C 2114531894 21537882\n",
"21537883 Q2597810 C 2128920607 21537883\n",
"21537884 Q193563 C 2130725560 21537884\n",
"21537885 Q423048 C 2136131564 21537885\n",
"21537886 Q37312 C 2142913121 21537886\n",
"21537887 Q54919 C 2148531382 21537887\n",
"21537888 Q36578 C 2229315598 21537888\n",
"21537889 Q30 A 2277746226 21537889\n",
"21537890 Q6581097 D 3273952711 21537890\n",
"21537891 Q5 C 5668008721 21537891\n",
"21537892 Q5296 C 12530369761 21537892"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## 2: assign popularity assessment class based on rank\n",
"## (buckets based on number of articles in each class)\n",
"articles_by_pop[, pop_class := ''];\n",
"articles_by_pop[, seqNum := seq_len(nrow(articles_by_pop))];"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"assign_pop_class = function(dataset, classes, class_n) {\n",
" ## Based on the per-class number of articles in class_n\n",
" ## assign popularity based on classes to dataset.\n",
" prev_idx = 0;\n",
" for(rating in classes) {\n",
" start_idx = prev_idx + 1;\n",
" end_idx = start_idx + class_n[prediction == rating]$narticles;\n",
" print(paste('start_idx =', start_idx, ', end_idx = ', end_idx));\n",
" dataset[seqNum >= start_idx & seqNum <= end_idx, pop_class := rating];\n",
" prev_idx = end_idx -1;\n",
" }\n",
" dataset;\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1] \"start_idx = 1 , end_idx = 12355935\"\n",
"[1] \"start_idx = 12355935 , end_idx = 16050240\"\n",
"[1] \"start_idx = 16050240 , end_idx = 21147866\"\n",
"[1] \"start_idx = 21147866 , end_idx = 21536887\"\n",
"[1] \"start_idx = 21536887 , end_idx = 21537893\"\n"
]
}
],
"source": [
"articles_by_pop = assign_pop_class(articles_by_pop,\n",
" assessment_classes, n_per_class);"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"create_dissonance_matrix = function(articledata, classes) {\n",
" d_mtrx = matrix(0, nrow=length(classes), ncol=length(classes));\n",
" rownames(d_mtrx) = classes;\n",
" colnames(d_mtrx) = classes;\n",
"\n",
" for(real_rating in classes) {\n",
" for(pop_rating in classes) {\n",
" d_mtrx[real_rating, pop_rating] = length(articledata[prediction == real_rating & pop_class == pop_rating]$entity_id);\n",
" }\n",
" }\n",
" d_mtrx;\n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>E</th><th scope=col>D</th><th scope=col>C</th><th scope=col>B</th><th scope=col>A</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>E</th><td>7859313</td><td>1997767</td><td>2421894</td><td> 76926 </td><td> 34 </td></tr>\n",
"\t<tr><th scope=row>D</th><td>1918538</td><td> 759916</td><td> 946779</td><td> 68933 </td><td>139 </td></tr>\n",
"\t<tr><th scope=row>C</th><td>2425912</td><td> 891534</td><td>1583506</td><td>196090 </td><td>584 </td></tr>\n",
"\t<tr><th scope=row>B</th><td> 152171</td><td> 45088</td><td> 145426</td><td> 46246 </td><td> 90 </td></tr>\n",
"\t<tr><th scope=row>A</th><td> 0</td><td> 0</td><td> 21</td><td> 826 </td><td>159 </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" & E & D & C & B & A\\\\\n",
"\\hline\n",
"\tE & 7859313 & 1997767 & 2421894 & 76926 & 34 \\\\\n",
"\tD & 1918538 & 759916 & 946779 & 68933 & 139 \\\\\n",
"\tC & 2425912 & 891534 & 1583506 & 196090 & 584 \\\\\n",
"\tB & 152171 & 45088 & 145426 & 46246 & 90 \\\\\n",
"\tA & 0 & 0 & 21 & 826 & 159 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | E | D | C | B | A | \n",
"|---|---|---|---|---|\n",
"| E | 7859313 | 1997767 | 2421894 | 76926 | 34 | \n",
"| D | 1918538 | 759916 | 946779 | 68933 | 139 | \n",
"| C | 2425912 | 891534 | 1583506 | 196090 | 584 | \n",
"| B | 152171 | 45088 | 145426 | 46246 | 90 | \n",
"| A | 0 | 0 | 21 | 826 | 159 | \n",
"\n",
"\n"
],
"text/plain": [
" E D C B A \n",
"E 7859313 1997767 2421894 76926 34\n",
"D 1918538 759916 946779 68933 139\n",
"C 2425912 891534 1583506 196090 584\n",
"B 152171 45088 145426 46246 90\n",
"A 0 0 21 826 159"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Based on direct hits to articles:\n",
"create_dissonance_matrix(articles_by_pop, assessment_classes)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dissonance_matrix = create_dissonance_matrix(articles_by_pop,\n",
" assessment_classes);"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0.475865511815177"
],
"text/latex": [
"0.475865511815177"
],
"text/markdown": [
"0.475865511815177"
],
"text/plain": [
"[1] 0.4758655"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Total misaligned entities\n",
"(dissonance_matrix[1,1]+dissonance_matrix[2,2]+dissonance_matrix[3,3]+dissonance_matrix[4,4]+dissonance_matrix[5,5])/sum(dissonance_matrix[,])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0.158051689860835"
],
"text/latex": [
"0.158051689860835"
],
"text/markdown": [
"0.158051689860835"
],
"text/plain": [
"[1] 0.1580517"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# A class quality and A class views over A class quality\n",
"dissonance_matrix[5,5]/sum(dissonance_matrix[5,])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0"
],
"text/latex": [
"0"
],
"text/markdown": [
"0"
],
"text/plain": [
"[1] 0"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# A class quality and E and D class views over A class quality\n",
"(dissonance_matrix[5,1]+dissonance_matrix[5,2])/sum(dissonance_matrix[5,])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0.841948310139165"
],
"text/latex": [
"0.841948310139165"
],
"text/markdown": [
"0.841948310139165"
],
"text/plain": [
"[1] 0.8419483"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# A class quality and < A class views\n",
"(dissonance_matrix[5,1]+dissonance_matrix[5,2]+dissonance_matrix[5,3]+dissonance_matrix[5,4])/sum(dissonance_matrix[5,])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0.841948310139165"
],
"text/latex": [
"0.841948310139165"
],
"text/markdown": [
"0.841948310139165"
],
"text/plain": [
"[1] 0.8419483"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# < A class quality and A class views\n",
"(dissonance_matrix[1,5]+dissonance_matrix[2,5]+dissonance_matrix[3,5]+dissonance_matrix[4,5])/sum(dissonance_matrix[,5])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"prediction_e_pop_class_a <- merge(articles_by_pop[prediction == 'E' & pop_class == 'A'],quality_prediction_and_page_views, by='entity_id')[, c(\"entity_id\",\"page_views.x\", \"number_of_revisions\")]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>page_views.x</th><th scope=col>number_of_revisions</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q1005887 </td><td> 21374143</td><td>29 </td></tr>\n",
"\t<tr><td>Q1097348 </td><td> 76241976</td><td>30 </td></tr>\n",
"\t<tr><td>Q1329615 </td><td> 36240870</td><td>25 </td></tr>\n",
"\t<tr><td>Q1450568 </td><td> 17053817</td><td>41 </td></tr>\n",
"\t<tr><td>Q15401930</td><td>215204156</td><td>21 </td></tr>\n",
"\t<tr><td>Q15958642</td><td> 24686117</td><td>54 </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lll}\n",
" entity\\_id & page\\_views.x & number\\_of\\_revisions\\\\\n",
"\\hline\n",
"\t Q1005887 & 21374143 & 29 \\\\\n",
"\t Q1097348 & 76241976 & 30 \\\\\n",
"\t Q1329615 & 36240870 & 25 \\\\\n",
"\t Q1450568 & 17053817 & 41 \\\\\n",
"\t Q15401930 & 215204156 & 21 \\\\\n",
"\t Q15958642 & 24686117 & 54 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | page_views.x | number_of_revisions | \n",
"|---|---|---|---|---|---|\n",
"| Q1005887 | 21374143 | 29 | \n",
"| Q1097348 | 76241976 | 30 | \n",
"| Q1329615 | 36240870 | 25 | \n",
"| Q1450568 | 17053817 | 41 | \n",
"| Q15401930 | 215204156 | 21 | \n",
"| Q15958642 | 24686117 | 54 | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id page_views.x number_of_revisions\n",
"1 Q1005887 21374143 29 \n",
"2 Q1097348 76241976 30 \n",
"3 Q1329615 36240870 25 \n",
"4 Q1450568 17053817 41 \n",
"5 Q15401930 215204156 21 \n",
"6 Q15958642 24686117 54 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"head(prediction_e_pop_class_a)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Q: why do I get _two_ pageid columns? Solution is to do the selection\n",
"## on the joined table, not as a select _in_ the join.\n",
"\n",
"## Dissonance matrix proportions by row (..., 1) and column (..., 2)\n",
"## rounded to 1 decimal places."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>E</th><th scope=col>D</th><th scope=col>C</th><th scope=col>B</th><th scope=col>A</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>E</th><td>63.6</td><td>16.2</td><td>19.6</td><td> 0.6</td><td> 0.0</td></tr>\n",
"\t<tr><th scope=row>D</th><td>51.9</td><td>20.6</td><td>25.6</td><td> 1.9</td><td> 0.0</td></tr>\n",
"\t<tr><th scope=row>C</th><td>47.6</td><td>17.5</td><td>31.1</td><td> 3.8</td><td> 0.0</td></tr>\n",
"\t<tr><th scope=row>B</th><td>39.1</td><td>11.6</td><td>37.4</td><td>11.9</td><td> 0.0</td></tr>\n",
"\t<tr><th scope=row>A</th><td> 0.0</td><td> 0.0</td><td> 2.1</td><td>82.1</td><td>15.8</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" & E & D & C & B & A\\\\\n",
"\\hline\n",
"\tE & 63.6 & 16.2 & 19.6 & 0.6 & 0.0\\\\\n",
"\tD & 51.9 & 20.6 & 25.6 & 1.9 & 0.0\\\\\n",
"\tC & 47.6 & 17.5 & 31.1 & 3.8 & 0.0\\\\\n",
"\tB & 39.1 & 11.6 & 37.4 & 11.9 & 0.0\\\\\n",
"\tA & 0.0 & 0.0 & 2.1 & 82.1 & 15.8\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | E | D | C | B | A | \n",
"|---|---|---|---|---|\n",
"| E | 63.6 | 16.2 | 19.6 | 0.6 | 0.0 | \n",
"| D | 51.9 | 20.6 | 25.6 | 1.9 | 0.0 | \n",
"| C | 47.6 | 17.5 | 31.1 | 3.8 | 0.0 | \n",
"| B | 39.1 | 11.6 | 37.4 | 11.9 | 0.0 | \n",
"| A | 0.0 | 0.0 | 2.1 | 82.1 | 15.8 | \n",
"\n",
"\n"
],
"text/plain": [
" E D C B A \n",
"E 63.6 16.2 19.6 0.6 0.0\n",
"D 51.9 20.6 25.6 1.9 0.0\n",
"C 47.6 17.5 31.1 3.8 0.0\n",
"B 39.1 11.6 37.4 11.9 0.0\n",
"A 0.0 0.0 2.1 82.1 15.8"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"round(100*prop.table(dissonance_matrix, 1), 1);"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>E</th><th scope=col>D</th><th scope=col>C</th><th scope=col>B</th><th scope=col>A</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>E</th><td>63.6</td><td>54.1</td><td>47.5</td><td>19.8</td><td> 3.4</td></tr>\n",
"\t<tr><th scope=row>D</th><td>15.5</td><td>20.6</td><td>18.6</td><td>17.7</td><td>13.8</td></tr>\n",
"\t<tr><th scope=row>C</th><td>19.6</td><td>24.1</td><td>31.1</td><td>50.4</td><td>58.1</td></tr>\n",
"\t<tr><th scope=row>B</th><td> 1.2</td><td> 1.2</td><td> 2.9</td><td>11.9</td><td> 8.9</td></tr>\n",
"\t<tr><th scope=row>A</th><td> 0.0</td><td> 0.0</td><td> 0.0</td><td> 0.2</td><td>15.8</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" & E & D & C & B & A\\\\\n",
"\\hline\n",
"\tE & 63.6 & 54.1 & 47.5 & 19.8 & 3.4\\\\\n",
"\tD & 15.5 & 20.6 & 18.6 & 17.7 & 13.8\\\\\n",
"\tC & 19.6 & 24.1 & 31.1 & 50.4 & 58.1\\\\\n",
"\tB & 1.2 & 1.2 & 2.9 & 11.9 & 8.9\\\\\n",
"\tA & 0.0 & 0.0 & 0.0 & 0.2 & 15.8\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | E | D | C | B | A | \n",
"|---|---|---|---|---|\n",
"| E | 63.6 | 54.1 | 47.5 | 19.8 | 3.4 | \n",
"| D | 15.5 | 20.6 | 18.6 | 17.7 | 13.8 | \n",
"| C | 19.6 | 24.1 | 31.1 | 50.4 | 58.1 | \n",
"| B | 1.2 | 1.2 | 2.9 | 11.9 | 8.9 | \n",
"| A | 0.0 | 0.0 | 0.0 | 0.2 | 15.8 | \n",
"\n",
"\n"
],
"text/plain": [
" E D C B A \n",
"E 63.6 54.1 47.5 19.8 3.4\n",
"D 15.5 20.6 18.6 17.7 13.8\n",
"C 19.6 24.1 31.1 50.4 58.1\n",
"B 1.2 1.2 2.9 11.9 8.9\n",
"A 0.0 0.0 0.0 0.2 15.8"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"round(100*prop.table(dissonance_matrix, 2), 1);"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>pop_class</th><th scope=col>prediction.x</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q100 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q10000 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1002972 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1005887 </td><td>A </td><td>E </td></tr>\n",
"\t<tr><td>Q1028181 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q103204 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q103618 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q10379 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q103824 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q103916 </td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q1043527 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1044427 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1048694 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q104994 </td><td>A </td><td>B </td></tr>\n",
"\t<tr><td>Q10511368</td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1054574 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q105584 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q105650 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1058 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1061 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1061257 </td><td>A </td><td>B </td></tr>\n",
"\t<tr><td>Q1061861 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q106291 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1063819 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1065 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q10683 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q10693 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1071953 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q10726338</td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q10738 </td><td>A </td><td>B </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q891723 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q899770 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q901 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q9017214</td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q9067 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q907311 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q9081 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q9087655</td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q908858 </td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q911554 </td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q918 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q9212 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q9268 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q9288 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q9332 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q937228 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q937857 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q9384291</td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q938726 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q941023 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q947873 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q948329 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q953058 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q9592 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q959790 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q9616 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q9696 </td><td>A </td><td>B </td></tr>\n",
"\t<tr><td>Q970153 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q974144 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q999897 </td><td>A </td><td>E </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lll}\n",
" entity\\_id & pop\\_class & prediction.x\\\\\n",
"\\hline\n",
"\t Q100 & A & C \\\\\n",
"\t Q10000 & A & C \\\\\n",
"\t Q1002972 & A & C \\\\\n",
"\t Q1005887 & A & E \\\\\n",
"\t Q1028181 & A & C \\\\\n",
"\t Q103204 & A & C \\\\\n",
"\t Q103618 & A & C \\\\\n",
"\t Q10379 & A & C \\\\\n",
"\t Q103824 & A & C \\\\\n",
"\t Q103916 & A & D \\\\\n",
"\t Q1043527 & A & C \\\\\n",
"\t Q1044427 & A & C \\\\\n",
"\t Q1048694 & A & C \\\\\n",
"\t Q104994 & A & B \\\\\n",
"\t Q10511368 & A & C \\\\\n",
"\t Q1054574 & A & C \\\\\n",
"\t Q105584 & A & C \\\\\n",
"\t Q105650 & A & C \\\\\n",
"\t Q1058 & A & C \\\\\n",
"\t Q1061 & A & C \\\\\n",
"\t Q1061257 & A & B \\\\\n",
"\t Q1061861 & A & C \\\\\n",
"\t Q106291 & A & C \\\\\n",
"\t Q1063819 & A & C \\\\\n",
"\t Q1065 & A & C \\\\\n",
"\t Q10683 & A & C \\\\\n",
"\t Q10693 & A & C \\\\\n",
"\t Q1071953 & A & C \\\\\n",
"\t Q10726338 & A & D \\\\\n",
"\t Q10738 & A & B \\\\\n",
"\t ⋮ & ⋮ & ⋮\\\\\n",
"\t Q891723 & A & C \\\\\n",
"\t Q899770 & A & C \\\\\n",
"\t Q901 & A & C \\\\\n",
"\t Q9017214 & A & D \\\\\n",
"\t Q9067 & A & C \\\\\n",
"\t Q907311 & A & C \\\\\n",
"\t Q9081 & A & C \\\\\n",
"\t Q9087655 & A & C \\\\\n",
"\t Q908858 & A & D \\\\\n",
"\t Q911554 & A & D \\\\\n",
"\t Q918 & A & C \\\\\n",
"\t Q9212 & A & C \\\\\n",
"\t Q9268 & A & C \\\\\n",
"\t Q9288 & A & C \\\\\n",
"\t Q9332 & A & C \\\\\n",
"\t Q937228 & A & C \\\\\n",
"\t Q937857 & A & C \\\\\n",
"\t Q9384291 & A & D \\\\\n",
"\t Q938726 & A & C \\\\\n",
"\t Q941023 & A & C \\\\\n",
"\t Q947873 & A & C \\\\\n",
"\t Q948329 & A & C \\\\\n",
"\t Q953058 & A & C \\\\\n",
"\t Q9592 & A & C \\\\\n",
"\t Q959790 & A & C \\\\\n",
"\t Q9616 & A & C \\\\\n",
"\t Q9696 & A & B \\\\\n",
"\t Q970153 & A & C \\\\\n",
"\t Q974144 & A & C \\\\\n",
"\t Q999897 & A & E \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | pop_class | prediction.x | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q100 | A | C | \n",
"| Q10000 | A | C | \n",
"| Q1002972 | A | C | \n",
"| Q1005887 | A | E | \n",
"| Q1028181 | A | C | \n",
"| Q103204 | A | C | \n",
"| Q103618 | A | C | \n",
"| Q10379 | A | C | \n",
"| Q103824 | A | C | \n",
"| Q103916 | A | D | \n",
"| Q1043527 | A | C | \n",
"| Q1044427 | A | C | \n",
"| Q1048694 | A | C | \n",
"| Q104994 | A | B | \n",
"| Q10511368 | A | C | \n",
"| Q1054574 | A | C | \n",
"| Q105584 | A | C | \n",
"| Q105650 | A | C | \n",
"| Q1058 | A | C | \n",
"| Q1061 | A | C | \n",
"| Q1061257 | A | B | \n",
"| Q1061861 | A | C | \n",
"| Q106291 | A | C | \n",
"| Q1063819 | A | C | \n",
"| Q1065 | A | C | \n",
"| Q10683 | A | C | \n",
"| Q10693 | A | C | \n",
"| Q1071953 | A | C | \n",
"| Q10726338 | A | D | \n",
"| Q10738 | A | B | \n",
"| ⋮ | ⋮ | ⋮ | \n",
"| Q891723 | A | C | \n",
"| Q899770 | A | C | \n",
"| Q901 | A | C | \n",
"| Q9017214 | A | D | \n",
"| Q9067 | A | C | \n",
"| Q907311 | A | C | \n",
"| Q9081 | A | C | \n",
"| Q9087655 | A | C | \n",
"| Q908858 | A | D | \n",
"| Q911554 | A | D | \n",
"| Q918 | A | C | \n",
"| Q9212 | A | C | \n",
"| Q9268 | A | C | \n",
"| Q9288 | A | C | \n",
"| Q9332 | A | C | \n",
"| Q937228 | A | C | \n",
"| Q937857 | A | C | \n",
"| Q9384291 | A | D | \n",
"| Q938726 | A | C | \n",
"| Q941023 | A | C | \n",
"| Q947873 | A | C | \n",
"| Q948329 | A | C | \n",
"| Q953058 | A | C | \n",
"| Q9592 | A | C | \n",
"| Q959790 | A | C | \n",
"| Q9616 | A | C | \n",
"| Q9696 | A | B | \n",
"| Q970153 | A | C | \n",
"| Q974144 | A | C | \n",
"| Q999897 | A | E | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id pop_class prediction.x\n",
"1 Q100 A C \n",
"2 Q10000 A C \n",
"3 Q1002972 A C \n",
"4 Q1005887 A E \n",
"5 Q1028181 A C \n",
"6 Q103204 A C \n",
"7 Q103618 A C \n",
"8 Q10379 A C \n",
"9 Q103824 A C \n",
"10 Q103916 A D \n",
"11 Q1043527 A C \n",
"12 Q1044427 A C \n",
"13 Q1048694 A C \n",
"14 Q104994 A B \n",
"15 Q10511368 A C \n",
"16 Q1054574 A C \n",
"17 Q105584 A C \n",
"18 Q105650 A C \n",
"19 Q1058 A C \n",
"20 Q1061 A C \n",
"21 Q1061257 A B \n",
"22 Q1061861 A C \n",
"23 Q106291 A C \n",
"24 Q1063819 A C \n",
"25 Q1065 A C \n",
"26 Q10683 A C \n",
"27 Q10693 A C \n",
"28 Q1071953 A C \n",
"29 Q10726338 A D \n",
"30 Q10738 A B \n",
"⋮ ⋮ ⋮ ⋮ \n",
"818 Q891723 A C \n",
"819 Q899770 A C \n",
"820 Q901 A C \n",
"821 Q9017214 A D \n",
"822 Q9067 A C \n",
"823 Q907311 A C \n",
"824 Q9081 A C \n",
"825 Q9087655 A C \n",
"826 Q908858 A D \n",
"827 Q911554 A D \n",
"828 Q918 A C \n",
"829 Q9212 A C \n",
"830 Q9268 A C \n",
"831 Q9288 A C \n",
"832 Q9332 A C \n",
"833 Q937228 A C \n",
"834 Q937857 A C \n",
"835 Q9384291 A D \n",
"836 Q938726 A C \n",
"837 Q941023 A C \n",
"838 Q947873 A C \n",
"839 Q948329 A C \n",
"840 Q953058 A C \n",
"841 Q9592 A C \n",
"842 Q959790 A C \n",
"843 Q9616 A C \n",
"844 Q9696 A B \n",
"845 Q970153 A C \n",
"846 Q974144 A C \n",
"847 Q999897 A E "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Let's write the stubs out to a file\n",
"write.table(merge(articles_by_pop[(prediction == 'E' | prediction == 'D' | prediction == 'C' | prediction == 'B') & pop_class == 'A'], quality_prediction_and_page_views, by='entity_id')[, c(\"entity_id\",\"pop_class\", \"prediction.x\")],\n",
" '../../results/entity_categorization/201701_a_class_views_less_than_a_quality.tsv', row.names=FALSE, col.names=FALSE, quote=FALSE, sep='\\t');\n",
"merge(articles_by_pop[(prediction == 'E' | prediction == 'D' | prediction == 'C' | prediction == 'B') & pop_class == 'A'], quality_prediction_and_page_views, by='entity_id')[, c(\"entity_id\",\"pop_class\", \"prediction.x\")]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"write.table(merge(articles_by_pop[prediction == 'A' & (pop_class == 'B' | pop_class == 'C' | pop_class == 'D' | pop_class == 'E')], quality_prediction_and_page_views, by='entity_id')[, c(\"entity_id\",\"pop_class\", \"prediction.x\")],\n",
" '../../results/entity_categorization/201701_a_class_quality_less_than_a_views.tsv', row.names=FALSE, col.names=FALSE, quote=FALSE, sep='\\t');"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"write.table(merge(articles_by_pop[(prediction == 'A' & pop_class == 'A') | (prediction == 'B' & pop_class == 'B') | (prediction == 'C' & pop_class == 'C') | (prediction == 'D' & pop_class == 'D') | (prediction == 'E' & pop_class == 'E')], quality_prediction_and_page_views, by='entity_id')[, c(\"entity_id\",\"pop_class\", \"prediction.x\")],\n",
" '../../results/entity_categorization/201701_aligned.tsv', row.names=FALSE, col.names=FALSE, quote=FALSE, sep='\\t');"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"write.table(merge(articles_by_pop[(prediction == 'A' & pop_class != 'A') | (prediction == 'B' & pop_class != 'B') | (prediction == 'C' & pop_class != 'C') | (prediction == 'D' & pop_class != 'D') | (prediction == 'E' & pop_class != 'E')], quality_prediction_and_page_views, by='entity_id')[, c(\"entity_id\",\"pop_class\", \"prediction.x\")],\n",
" '../../results/entity_categorization/201701_misaligned.tsv', row.names=FALSE, col.names=FALSE, quote=FALSE, sep='\\t');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dissonance Measures (was seperate file)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Various ways of measuring dissonance.\n",
"\n",
"## DATA ASSUMPTION: articles_by_pop from build-dissonance-table.R\n",
"## is loaded into memory.\n",
"\n",
"## None/Moderate/High measure of dissonance"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td></tr>\n",
"\t<tr><td>Q10750354</td><td>C </td><td>0 </td><td>E </td><td>14 </td></tr>\n",
"\t<tr><td>Q10766855</td><td>D </td><td>0 </td><td>E </td><td>15 </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td></tr>\n",
"\t<tr><td>Q12443525</td><td>D </td><td>0 </td><td>E </td><td>23 </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>21537863 </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818</td><td>A </td><td>21537864 </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>21537865 </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>21537866 </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927</td><td>A </td><td>21537867 </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>21537868 </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224</td><td>A </td><td>21537869 </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>21537870 </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>21537871 </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>21537872 </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>21537873 </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157</td><td>A </td><td>21537874 </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>21537875 </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>21537876 </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498</td><td>A </td><td>21537877 </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>21537878 </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>21537879 </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>21537880 </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>21537881 </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>21537882 </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>21537883 </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>21537884 </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>21537885 </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>21537886 </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>21537887 </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>21537888 </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>21537889 </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>21537890 </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721</td><td>A </td><td>21537891 </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>21537892 </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 \\\\\n",
"\t Q10069140 & C & 0 & E & 2 \\\\\n",
"\t Q10081695 & C & 0 & E & 3 \\\\\n",
"\t Q10092002 & E & 0 & E & 4 \\\\\n",
"\t Q10111267 & E & 0 & E & 5 \\\\\n",
"\t Q10149726 & E & 0 & E & 6 \\\\\n",
"\t Q10180230 & E & 0 & E & 7 \\\\\n",
"\t Q10185035 & E & 0 & E & 8 \\\\\n",
"\t Q10205202 & E & 0 & E & 9 \\\\\n",
"\t Q10252966 & E & 0 & E & 10 \\\\\n",
"\t Q10444494 & C & 0 & E & 11 \\\\\n",
"\t Q10624171 & C & 0 & E & 12 \\\\\n",
"\t Q10704108 & C & 0 & E & 13 \\\\\n",
"\t Q10750354 & C & 0 & E & 14 \\\\\n",
"\t Q10766855 & D & 0 & E & 15 \\\\\n",
"\t Q10827611 & E & 0 & E & 16 \\\\\n",
"\t Q11093044 & E & 0 & E & 17 \\\\\n",
"\t Q11934537 & E & 0 & E & 18 \\\\\n",
"\t Q12133466 & E & 0 & E & 19 \\\\\n",
"\t Q12264503 & E & 0 & E & 20 \\\\\n",
"\t Q12267516 & E & 0 & E & 21 \\\\\n",
"\t Q12304084 & E & 0 & E & 22 \\\\\n",
"\t Q12443525 & D & 0 & E & 23 \\\\\n",
"\t Q12543904 & E & 0 & E & 24 \\\\\n",
"\t Q12890205 & E & 0 & E & 25 \\\\\n",
"\t Q12891524 & E & 0 & E & 26 \\\\\n",
"\t Q12918202 & E & 0 & E & 27 \\\\\n",
"\t Q13005653 & E & 0 & E & 28 \\\\\n",
"\t Q13073896 & E & 0 & E & 29 \\\\\n",
"\t Q13163823 & E & 0 & E & 30 \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 \\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 \\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 \\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | \n",
"| Q10069140 | C | 0 | E | 2 | \n",
"| Q10081695 | C | 0 | E | 3 | \n",
"| Q10092002 | E | 0 | E | 4 | \n",
"| Q10111267 | E | 0 | E | 5 | \n",
"| Q10149726 | E | 0 | E | 6 | \n",
"| Q10180230 | E | 0 | E | 7 | \n",
"| Q10185035 | E | 0 | E | 8 | \n",
"| Q10205202 | E | 0 | E | 9 | \n",
"| Q10252966 | E | 0 | E | 10 | \n",
"| Q10444494 | C | 0 | E | 11 | \n",
"| Q10624171 | C | 0 | E | 12 | \n",
"| Q10704108 | C | 0 | E | 13 | \n",
"| Q10750354 | C | 0 | E | 14 | \n",
"| Q10766855 | D | 0 | E | 15 | \n",
"| Q10827611 | E | 0 | E | 16 | \n",
"| Q11093044 | E | 0 | E | 17 | \n",
"| Q11934537 | E | 0 | E | 18 | \n",
"| Q12133466 | E | 0 | E | 19 | \n",
"| Q12264503 | E | 0 | E | 20 | \n",
"| Q12267516 | E | 0 | E | 21 | \n",
"| Q12304084 | E | 0 | E | 22 | \n",
"| Q12443525 | D | 0 | E | 23 | \n",
"| Q12543904 | E | 0 | E | 24 | \n",
"| Q12890205 | E | 0 | E | 25 | \n",
"| Q12891524 | E | 0 | E | 26 | \n",
"| Q12918202 | E | 0 | E | 27 | \n",
"| Q13005653 | E | 0 | E | 28 | \n",
"| Q13073896 | E | 0 | E | 29 | \n",
"| Q13163823 | E | 0 | E | 30 | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | \n",
"| Q565 | C | 2052996261 | A | 21537868 | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | \n",
"| Q918 | C | 2063217449 | A | 21537872 | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | \n",
"| Q866 | B | 2079749157 | A | 21537874 | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | \n",
"| Q355 | C | 2093900731 | A | 21537878 | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | \n",
"| Q30 | A | 2277746226 | A | 21537889 | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | \n",
"| Q5 | C | 5668008721 | A | 21537891 | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum \n",
"1 Q10040378 E 0 E 1 \n",
"2 Q10069140 C 0 E 2 \n",
"3 Q10081695 C 0 E 3 \n",
"4 Q10092002 E 0 E 4 \n",
"5 Q10111267 E 0 E 5 \n",
"6 Q10149726 E 0 E 6 \n",
"7 Q10180230 E 0 E 7 \n",
"8 Q10185035 E 0 E 8 \n",
"9 Q10205202 E 0 E 9 \n",
"10 Q10252966 E 0 E 10 \n",
"11 Q10444494 C 0 E 11 \n",
"12 Q10624171 C 0 E 12 \n",
"13 Q10704108 C 0 E 13 \n",
"14 Q10750354 C 0 E 14 \n",
"15 Q10766855 D 0 E 15 \n",
"16 Q10827611 E 0 E 16 \n",
"17 Q11093044 E 0 E 17 \n",
"18 Q11934537 E 0 E 18 \n",
"19 Q12133466 E 0 E 19 \n",
"20 Q12264503 E 0 E 20 \n",
"21 Q12267516 E 0 E 21 \n",
"22 Q12304084 E 0 E 22 \n",
"23 Q12443525 D 0 E 23 \n",
"24 Q12543904 E 0 E 24 \n",
"25 Q12890205 E 0 E 25 \n",
"26 Q12891524 E 0 E 26 \n",
"27 Q12918202 E 0 E 27 \n",
"28 Q13005653 E 0 E 28 \n",
"29 Q13073896 E 0 E 29 \n",
"30 Q13163823 E 0 E 30 \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863\n",
"21537864 Q31165 B 2048330818 A 21537864\n",
"21537865 Q40629 C 2049755644 A 21537865\n",
"21537866 Q105584 C 2049926923 A 21537866\n",
"21537867 Q4584301 C 2052339927 A 21537867\n",
"21537868 Q565 C 2052996261 A 21537868\n",
"21537869 Q1868372 D 2056080224 A 21537869\n",
"21537870 Q209330 C 2060928966 A 21537870\n",
"21537871 Q14005 D 2063120071 A 21537871\n",
"21537872 Q918 C 2063217449 A 21537872\n",
"21537873 Q150248 C 2068796814 A 21537873\n",
"21537874 Q866 B 2079749157 A 21537874\n",
"21537875 Q477675 C 2080785713 A 21537875\n",
"21537876 Q1967876 C 2084215818 A 21537876\n",
"21537877 Q750403 B 2084693498 A 21537877\n",
"21537878 Q355 C 2093900731 A 21537878\n",
"21537879 Q623578 C 2097991400 A 21537879\n",
"21537880 Q17299517 D 2105487660 A 21537880\n",
"21537881 Q33999 C 2108672678 A 21537881\n",
"21537882 Q2494649 C 2114531894 A 21537882\n",
"21537883 Q2597810 C 2128920607 A 21537883\n",
"21537884 Q193563 C 2130725560 A 21537884\n",
"21537885 Q423048 C 2136131564 A 21537885\n",
"21537886 Q37312 C 2142913121 A 21537886\n",
"21537887 Q54919 C 2148531382 A 21537887\n",
"21537888 Q36578 C 2229315598 A 21537888\n",
"21537889 Q30 A 2277746226 A 21537889\n",
"21537890 Q6581097 D 3273952711 A 21537890\n",
"21537891 Q5 C 5668008721 A 21537891\n",
"21537892 Q5296 C 12530369761 A 21537892"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"articles_by_pop[, pop_class := ordered(pop_class, assessment_classes)];"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dissonance_metric = c('High negative', 'Moderate negative',\n",
" 'None', 'Moderate positive', 'High positive');"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10750354</td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855</td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525</td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>21537863 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818</td><td>A </td><td>21537864 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>21537865 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>21537866 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927</td><td>A </td><td>21537867 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>21537868 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224</td><td>A </td><td>21537869 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>21537870 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>21537871 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>21537872 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>21537873 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157</td><td>A </td><td>21537874 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>21537875 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>21537876 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498</td><td>A </td><td>21537877 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>21537878 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>21537879 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>21537880 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>21537881 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>21537882 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>21537883 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>21537884 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>21537885 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>21537886 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>21537887 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>21537888 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>21537889 </td><td>NA </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>21537890 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721</td><td>A </td><td>21537891 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>21537892 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & NA \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & NA \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & NA \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & NA \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & NA \\\\\n",
"\t Q10750354 & C & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & D & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & NA \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & NA \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & NA \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & NA \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & NA \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & NA \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & NA \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & NA \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | NA | \n",
"| Q10081695 | C | 0 | E | 3 | NA | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | NA | \n",
"| Q10624171 | C | 0 | E | 12 | NA | \n",
"| Q10704108 | C | 0 | E | 13 | NA | \n",
"| Q10750354 | C | 0 | E | 14 | NA | \n",
"| Q10766855 | D | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | D | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | NA | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | NA | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | NA | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | NA | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | NA | \n",
"| Q565 | C | 2052996261 | A | 21537868 | NA | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | NA | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | NA | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | NA | \n",
"| Q918 | C | 2063217449 | A | 21537872 | NA | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | NA | \n",
"| Q866 | B | 2079749157 | A | 21537874 | NA | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | NA | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | NA | \n",
"| Q355 | C | 2093900731 | A | 21537878 | NA | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | NA | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | NA | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | NA | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | NA | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | NA | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | NA | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | NA | \n",
"| Q30 | A | 2277746226 | A | 21537889 | NA | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | NA | \n",
"| Q5 | C | 5668008721 | A | 21537891 | NA | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance\n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 NA \n",
"3 Q10081695 C 0 E 3 NA \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 NA \n",
"12 Q10624171 C 0 E 12 NA \n",
"13 Q10704108 C 0 E 13 NA \n",
"14 Q10750354 C 0 E 14 NA \n",
"15 Q10766855 D 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 D 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 NA \n",
"21537864 Q31165 B 2048330818 A 21537864 NA \n",
"21537865 Q40629 C 2049755644 A 21537865 NA \n",
"21537866 Q105584 C 2049926923 A 21537866 NA \n",
"21537867 Q4584301 C 2052339927 A 21537867 NA \n",
"21537868 Q565 C 2052996261 A 21537868 NA \n",
"21537869 Q1868372 D 2056080224 A 21537869 NA \n",
"21537870 Q209330 C 2060928966 A 21537870 NA \n",
"21537871 Q14005 D 2063120071 A 21537871 NA \n",
"21537872 Q918 C 2063217449 A 21537872 NA \n",
"21537873 Q150248 C 2068796814 A 21537873 NA \n",
"21537874 Q866 B 2079749157 A 21537874 NA \n",
"21537875 Q477675 C 2080785713 A 21537875 NA \n",
"21537876 Q1967876 C 2084215818 A 21537876 NA \n",
"21537877 Q750403 B 2084693498 A 21537877 NA \n",
"21537878 Q355 C 2093900731 A 21537878 NA \n",
"21537879 Q623578 C 2097991400 A 21537879 NA \n",
"21537880 Q17299517 D 2105487660 A 21537880 NA \n",
"21537881 Q33999 C 2108672678 A 21537881 NA \n",
"21537882 Q2494649 C 2114531894 A 21537882 NA \n",
"21537883 Q2597810 C 2128920607 A 21537883 NA \n",
"21537884 Q193563 C 2130725560 A 21537884 NA \n",
"21537885 Q423048 C 2136131564 A 21537885 NA \n",
"21537886 Q37312 C 2142913121 A 21537886 NA \n",
"21537887 Q54919 C 2148531382 A 21537887 NA \n",
"21537888 Q36578 C 2229315598 A 21537888 NA \n",
"21537889 Q30 A 2277746226 A 21537889 NA \n",
"21537890 Q6581097 D 3273952711 A 21537890 NA \n",
"21537891 Q5 C 5668008721 A 21537891 NA \n",
"21537892 Q5296 C 12530369761 A 21537892 NA "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"articles_by_pop[, dissonance := factor(NA, dissonance_metric)];"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## NOTE: because pop_class is of class ordered, we can use\n",
"## expressions like \"pop_class < 'C'\" as expected"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10750354</td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855</td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525</td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>21537863 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818</td><td>A </td><td>21537864 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>21537865 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>21537866 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927</td><td>A </td><td>21537867 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>21537868 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224</td><td>A </td><td>21537869 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>21537870 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>21537871 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>21537872 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>21537873 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157</td><td>A </td><td>21537874 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>21537875 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>21537876 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498</td><td>A </td><td>21537877 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>21537878 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>21537879 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>21537880 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>21537881 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>21537882 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>21537883 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>21537884 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>21537885 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>21537886 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>21537887 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>21537888 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>21537889 </td><td>NA </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>21537890 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721</td><td>A </td><td>21537891 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>21537892 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & NA \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & NA \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & NA \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & NA \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & NA \\\\\n",
"\t Q10750354 & C & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & D & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & NA \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & NA \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & NA \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & NA \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & NA \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & NA \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & NA \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & NA \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | NA | \n",
"| Q10081695 | C | 0 | E | 3 | NA | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | NA | \n",
"| Q10624171 | C | 0 | E | 12 | NA | \n",
"| Q10704108 | C | 0 | E | 13 | NA | \n",
"| Q10750354 | C | 0 | E | 14 | NA | \n",
"| Q10766855 | D | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | D | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | NA | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | NA | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | NA | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | NA | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | NA | \n",
"| Q565 | C | 2052996261 | A | 21537868 | NA | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | NA | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | NA | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | NA | \n",
"| Q918 | C | 2063217449 | A | 21537872 | NA | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | NA | \n",
"| Q866 | B | 2079749157 | A | 21537874 | NA | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | NA | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | NA | \n",
"| Q355 | C | 2093900731 | A | 21537878 | NA | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | NA | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | NA | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | NA | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | NA | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | NA | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | NA | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | NA | \n",
"| Q30 | A | 2277746226 | A | 21537889 | NA | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | NA | \n",
"| Q5 | C | 5668008721 | A | 21537891 | NA | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance\n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 NA \n",
"3 Q10081695 C 0 E 3 NA \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 NA \n",
"12 Q10624171 C 0 E 12 NA \n",
"13 Q10704108 C 0 E 13 NA \n",
"14 Q10750354 C 0 E 14 NA \n",
"15 Q10766855 D 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 D 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 NA \n",
"21537864 Q31165 B 2048330818 A 21537864 NA \n",
"21537865 Q40629 C 2049755644 A 21537865 NA \n",
"21537866 Q105584 C 2049926923 A 21537866 NA \n",
"21537867 Q4584301 C 2052339927 A 21537867 NA \n",
"21537868 Q565 C 2052996261 A 21537868 NA \n",
"21537869 Q1868372 D 2056080224 A 21537869 NA \n",
"21537870 Q209330 C 2060928966 A 21537870 NA \n",
"21537871 Q14005 D 2063120071 A 21537871 NA \n",
"21537872 Q918 C 2063217449 A 21537872 NA \n",
"21537873 Q150248 C 2068796814 A 21537873 NA \n",
"21537874 Q866 B 2079749157 A 21537874 NA \n",
"21537875 Q477675 C 2080785713 A 21537875 NA \n",
"21537876 Q1967876 C 2084215818 A 21537876 NA \n",
"21537877 Q750403 B 2084693498 A 21537877 NA \n",
"21537878 Q355 C 2093900731 A 21537878 NA \n",
"21537879 Q623578 C 2097991400 A 21537879 NA \n",
"21537880 Q17299517 D 2105487660 A 21537880 NA \n",
"21537881 Q33999 C 2108672678 A 21537881 NA \n",
"21537882 Q2494649 C 2114531894 A 21537882 NA \n",
"21537883 Q2597810 C 2128920607 A 21537883 NA \n",
"21537884 Q193563 C 2130725560 A 21537884 NA \n",
"21537885 Q423048 C 2136131564 A 21537885 NA \n",
"21537886 Q37312 C 2142913121 A 21537886 NA \n",
"21537887 Q54919 C 2148531382 A 21537887 NA \n",
"21537888 Q36578 C 2229315598 A 21537888 NA \n",
"21537889 Q30 A 2277746226 A 21537889 NA \n",
"21537890 Q6581097 D 3273952711 A 21537890 NA \n",
"21537891 Q5 C 5668008721 A 21537891 NA \n",
"21537892 Q5296 C 12530369761 A 21537892 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10750354</td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855</td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525</td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>21537863 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818</td><td>A </td><td>21537864 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>21537865 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>21537866 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927</td><td>A </td><td>21537867 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>21537868 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224</td><td>A </td><td>21537869 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>21537870 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>21537871 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>21537872 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>21537873 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157</td><td>A </td><td>21537874 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>21537875 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>21537876 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498</td><td>A </td><td>21537877 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>21537878 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>21537879 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>21537880 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>21537881 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>21537882 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>21537883 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>21537884 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>21537885 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>21537886 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>21537887 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>21537888 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>21537889 </td><td>NA </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>21537890 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721</td><td>A </td><td>21537891 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>21537892 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & NA \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & NA \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & NA \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & NA \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & NA \\\\\n",
"\t Q10750354 & C & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & D & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & NA \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & NA \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & NA \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & NA \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & NA \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & NA \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & NA \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & NA \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | NA | \n",
"| Q10081695 | C | 0 | E | 3 | NA | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | NA | \n",
"| Q10624171 | C | 0 | E | 12 | NA | \n",
"| Q10704108 | C | 0 | E | 13 | NA | \n",
"| Q10750354 | C | 0 | E | 14 | NA | \n",
"| Q10766855 | D | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | D | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | NA | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | NA | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | NA | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | NA | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | NA | \n",
"| Q565 | C | 2052996261 | A | 21537868 | NA | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | NA | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | NA | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | NA | \n",
"| Q918 | C | 2063217449 | A | 21537872 | NA | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | NA | \n",
"| Q866 | B | 2079749157 | A | 21537874 | NA | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | NA | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | NA | \n",
"| Q355 | C | 2093900731 | A | 21537878 | NA | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | NA | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | NA | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | NA | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | NA | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | NA | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | NA | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | NA | \n",
"| Q30 | A | 2277746226 | A | 21537889 | NA | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | NA | \n",
"| Q5 | C | 5668008721 | A | 21537891 | NA | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance\n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 NA \n",
"3 Q10081695 C 0 E 3 NA \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 NA \n",
"12 Q10624171 C 0 E 12 NA \n",
"13 Q10704108 C 0 E 13 NA \n",
"14 Q10750354 C 0 E 14 NA \n",
"15 Q10766855 D 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 D 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 NA \n",
"21537864 Q31165 B 2048330818 A 21537864 NA \n",
"21537865 Q40629 C 2049755644 A 21537865 NA \n",
"21537866 Q105584 C 2049926923 A 21537866 NA \n",
"21537867 Q4584301 C 2052339927 A 21537867 NA \n",
"21537868 Q565 C 2052996261 A 21537868 NA \n",
"21537869 Q1868372 D 2056080224 A 21537869 NA \n",
"21537870 Q209330 C 2060928966 A 21537870 NA \n",
"21537871 Q14005 D 2063120071 A 21537871 NA \n",
"21537872 Q918 C 2063217449 A 21537872 NA \n",
"21537873 Q150248 C 2068796814 A 21537873 NA \n",
"21537874 Q866 B 2079749157 A 21537874 NA \n",
"21537875 Q477675 C 2080785713 A 21537875 NA \n",
"21537876 Q1967876 C 2084215818 A 21537876 NA \n",
"21537877 Q750403 B 2084693498 A 21537877 NA \n",
"21537878 Q355 C 2093900731 A 21537878 NA \n",
"21537879 Q623578 C 2097991400 A 21537879 NA \n",
"21537880 Q17299517 D 2105487660 A 21537880 NA \n",
"21537881 Q33999 C 2108672678 A 21537881 NA \n",
"21537882 Q2494649 C 2114531894 A 21537882 NA \n",
"21537883 Q2597810 C 2128920607 A 21537883 NA \n",
"21537884 Q193563 C 2130725560 A 21537884 NA \n",
"21537885 Q423048 C 2136131564 A 21537885 NA \n",
"21537886 Q37312 C 2142913121 A 21537886 NA \n",
"21537887 Q54919 C 2148531382 A 21537887 NA \n",
"21537888 Q36578 C 2229315598 A 21537888 NA \n",
"21537889 Q30 A 2277746226 A 21537889 NA \n",
"21537890 Q6581097 D 3273952711 A 21537890 NA \n",
"21537891 Q5 C 5668008721 A 21537891 NA \n",
"21537892 Q5296 C 12530369761 A 21537892 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10750354</td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855</td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525</td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>21537863 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818</td><td>A </td><td>21537864 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>21537865 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>21537866 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927</td><td>A </td><td>21537867 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>21537868 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224</td><td>A </td><td>21537869 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>21537870 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>21537871 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>21537872 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>21537873 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157</td><td>A </td><td>21537874 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>21537875 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>21537876 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498</td><td>A </td><td>21537877 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>21537878 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>21537879 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>21537880 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>21537881 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>21537882 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>21537883 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>21537884 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>21537885 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>21537886 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>21537887 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>21537888 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>21537889 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>21537890 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721</td><td>A </td><td>21537891 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>21537892 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & NA \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & NA \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & NA \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & NA \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & NA \\\\\n",
"\t Q10750354 & C & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & D & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & NA \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & NA \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & NA \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & NA \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & NA \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & NA \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & NA \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | NA | \n",
"| Q10081695 | C | 0 | E | 3 | NA | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | NA | \n",
"| Q10624171 | C | 0 | E | 12 | NA | \n",
"| Q10704108 | C | 0 | E | 13 | NA | \n",
"| Q10750354 | C | 0 | E | 14 | NA | \n",
"| Q10766855 | D | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | D | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | NA | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | NA | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | NA | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | NA | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | NA | \n",
"| Q565 | C | 2052996261 | A | 21537868 | NA | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | NA | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | NA | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | NA | \n",
"| Q918 | C | 2063217449 | A | 21537872 | NA | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | NA | \n",
"| Q866 | B | 2079749157 | A | 21537874 | NA | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | NA | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | NA | \n",
"| Q355 | C | 2093900731 | A | 21537878 | NA | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | NA | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | NA | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | NA | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | NA | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | NA | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | NA | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | NA | \n",
"| Q30 | A | 2277746226 | A | 21537889 | None | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | NA | \n",
"| Q5 | C | 5668008721 | A | 21537891 | NA | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance\n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 NA \n",
"3 Q10081695 C 0 E 3 NA \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 NA \n",
"12 Q10624171 C 0 E 12 NA \n",
"13 Q10704108 C 0 E 13 NA \n",
"14 Q10750354 C 0 E 14 NA \n",
"15 Q10766855 D 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 D 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 NA \n",
"21537864 Q31165 B 2048330818 A 21537864 NA \n",
"21537865 Q40629 C 2049755644 A 21537865 NA \n",
"21537866 Q105584 C 2049926923 A 21537866 NA \n",
"21537867 Q4584301 C 2052339927 A 21537867 NA \n",
"21537868 Q565 C 2052996261 A 21537868 NA \n",
"21537869 Q1868372 D 2056080224 A 21537869 NA \n",
"21537870 Q209330 C 2060928966 A 21537870 NA \n",
"21537871 Q14005 D 2063120071 A 21537871 NA \n",
"21537872 Q918 C 2063217449 A 21537872 NA \n",
"21537873 Q150248 C 2068796814 A 21537873 NA \n",
"21537874 Q866 B 2079749157 A 21537874 NA \n",
"21537875 Q477675 C 2080785713 A 21537875 NA \n",
"21537876 Q1967876 C 2084215818 A 21537876 NA \n",
"21537877 Q750403 B 2084693498 A 21537877 NA \n",
"21537878 Q355 C 2093900731 A 21537878 NA \n",
"21537879 Q623578 C 2097991400 A 21537879 NA \n",
"21537880 Q17299517 D 2105487660 A 21537880 NA \n",
"21537881 Q33999 C 2108672678 A 21537881 NA \n",
"21537882 Q2494649 C 2114531894 A 21537882 NA \n",
"21537883 Q2597810 C 2128920607 A 21537883 NA \n",
"21537884 Q193563 C 2130725560 A 21537884 NA \n",
"21537885 Q423048 C 2136131564 A 21537885 NA \n",
"21537886 Q37312 C 2142913121 A 21537886 NA \n",
"21537887 Q54919 C 2148531382 A 21537887 NA \n",
"21537888 Q36578 C 2229315598 A 21537888 NA \n",
"21537889 Q30 A 2277746226 A 21537889 None \n",
"21537890 Q6581097 D 3273952711 A 21537890 NA \n",
"21537891 Q5 C 5668008721 A 21537891 NA \n",
"21537892 Q5296 C 12530369761 A 21537892 NA "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## A: None if A, Moderate if A, High elsewhere\n",
"articles_by_pop[prediction == 'A' & pop_class <= 'C',\n",
" dissonance := 'High negative'];\n",
"articles_by_pop[prediction == 'A' & pop_class == 'B',\n",
" dissonance := 'Moderate negative'];\n",
"articles_by_pop[prediction == 'A' & pop_class == 'A',\n",
" dissonance := 'None'];\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10750354</td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855</td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525</td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>21537863 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818</td><td>A </td><td>21537864 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>21537865 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>21537866 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927</td><td>A </td><td>21537867 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>21537868 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224</td><td>A </td><td>21537869 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>21537870 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>21537871 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>21537872 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>21537873 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157</td><td>A </td><td>21537874 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>21537875 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>21537876 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498</td><td>A </td><td>21537877 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>21537878 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>21537879 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>21537880 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>21537881 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>21537882 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>21537883 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>21537884 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>21537885 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>21537886 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>21537887 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>21537888 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>21537889 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>21537890 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721</td><td>A </td><td>21537891 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>21537892 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & NA \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & NA \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & NA \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & NA \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & NA \\\\\n",
"\t Q10750354 & C & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & D & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & NA \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & NA \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & NA \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & NA \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & NA \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & NA \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & NA \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | NA | \n",
"| Q10081695 | C | 0 | E | 3 | NA | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | NA | \n",
"| Q10624171 | C | 0 | E | 12 | NA | \n",
"| Q10704108 | C | 0 | E | 13 | NA | \n",
"| Q10750354 | C | 0 | E | 14 | NA | \n",
"| Q10766855 | D | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | D | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | NA | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | NA | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | NA | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | NA | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | NA | \n",
"| Q565 | C | 2052996261 | A | 21537868 | NA | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | NA | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | NA | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | NA | \n",
"| Q918 | C | 2063217449 | A | 21537872 | NA | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | NA | \n",
"| Q866 | B | 2079749157 | A | 21537874 | NA | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | NA | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | NA | \n",
"| Q355 | C | 2093900731 | A | 21537878 | NA | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | NA | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | NA | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | NA | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | NA | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | NA | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | NA | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | NA | \n",
"| Q30 | A | 2277746226 | A | 21537889 | None | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | NA | \n",
"| Q5 | C | 5668008721 | A | 21537891 | NA | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance\n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 NA \n",
"3 Q10081695 C 0 E 3 NA \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 NA \n",
"12 Q10624171 C 0 E 12 NA \n",
"13 Q10704108 C 0 E 13 NA \n",
"14 Q10750354 C 0 E 14 NA \n",
"15 Q10766855 D 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 D 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 NA \n",
"21537864 Q31165 B 2048330818 A 21537864 NA \n",
"21537865 Q40629 C 2049755644 A 21537865 NA \n",
"21537866 Q105584 C 2049926923 A 21537866 NA \n",
"21537867 Q4584301 C 2052339927 A 21537867 NA \n",
"21537868 Q565 C 2052996261 A 21537868 NA \n",
"21537869 Q1868372 D 2056080224 A 21537869 NA \n",
"21537870 Q209330 C 2060928966 A 21537870 NA \n",
"21537871 Q14005 D 2063120071 A 21537871 NA \n",
"21537872 Q918 C 2063217449 A 21537872 NA \n",
"21537873 Q150248 C 2068796814 A 21537873 NA \n",
"21537874 Q866 B 2079749157 A 21537874 NA \n",
"21537875 Q477675 C 2080785713 A 21537875 NA \n",
"21537876 Q1967876 C 2084215818 A 21537876 NA \n",
"21537877 Q750403 B 2084693498 A 21537877 NA \n",
"21537878 Q355 C 2093900731 A 21537878 NA \n",
"21537879 Q623578 C 2097991400 A 21537879 NA \n",
"21537880 Q17299517 D 2105487660 A 21537880 NA \n",
"21537881 Q33999 C 2108672678 A 21537881 NA \n",
"21537882 Q2494649 C 2114531894 A 21537882 NA \n",
"21537883 Q2597810 C 2128920607 A 21537883 NA \n",
"21537884 Q193563 C 2130725560 A 21537884 NA \n",
"21537885 Q423048 C 2136131564 A 21537885 NA \n",
"21537886 Q37312 C 2142913121 A 21537886 NA \n",
"21537887 Q54919 C 2148531382 A 21537887 NA \n",
"21537888 Q36578 C 2229315598 A 21537888 NA \n",
"21537889 Q30 A 2277746226 A 21537889 None \n",
"21537890 Q6581097 D 3273952711 A 21537890 NA \n",
"21537891 Q5 C 5668008721 A 21537891 NA \n",
"21537892 Q5296 C 12530369761 A 21537892 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10750354</td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855</td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525</td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>21537863 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818</td><td>A </td><td>21537864 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>21537865 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>21537866 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927</td><td>A </td><td>21537867 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>21537868 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224</td><td>A </td><td>21537869 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>21537870 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>21537871 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>21537872 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>21537873 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157</td><td>A </td><td>21537874 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>21537875 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>21537876 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498</td><td>A </td><td>21537877 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>21537878 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>21537879 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>21537880 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>21537881 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>21537882 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>21537883 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>21537884 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>21537885 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>21537886 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>21537887 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>21537888 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>21537889 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>21537890 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721</td><td>A </td><td>21537891 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>21537892 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & NA \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & NA \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & NA \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & NA \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & NA \\\\\n",
"\t Q10750354 & C & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & D & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & NA \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & NA \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & NA \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & NA \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & NA \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & NA \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & NA \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | NA | \n",
"| Q10081695 | C | 0 | E | 3 | NA | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | NA | \n",
"| Q10624171 | C | 0 | E | 12 | NA | \n",
"| Q10704108 | C | 0 | E | 13 | NA | \n",
"| Q10750354 | C | 0 | E | 14 | NA | \n",
"| Q10766855 | D | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | D | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | NA | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | NA | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | NA | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | NA | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | NA | \n",
"| Q565 | C | 2052996261 | A | 21537868 | NA | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | NA | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | NA | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | NA | \n",
"| Q918 | C | 2063217449 | A | 21537872 | NA | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | NA | \n",
"| Q866 | B | 2079749157 | A | 21537874 | NA | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | NA | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | NA | \n",
"| Q355 | C | 2093900731 | A | 21537878 | NA | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | NA | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | NA | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | NA | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | NA | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | NA | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | NA | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | NA | \n",
"| Q30 | A | 2277746226 | A | 21537889 | None | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | NA | \n",
"| Q5 | C | 5668008721 | A | 21537891 | NA | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance\n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 NA \n",
"3 Q10081695 C 0 E 3 NA \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 NA \n",
"12 Q10624171 C 0 E 12 NA \n",
"13 Q10704108 C 0 E 13 NA \n",
"14 Q10750354 C 0 E 14 NA \n",
"15 Q10766855 D 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 D 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 NA \n",
"21537864 Q31165 B 2048330818 A 21537864 NA \n",
"21537865 Q40629 C 2049755644 A 21537865 NA \n",
"21537866 Q105584 C 2049926923 A 21537866 NA \n",
"21537867 Q4584301 C 2052339927 A 21537867 NA \n",
"21537868 Q565 C 2052996261 A 21537868 NA \n",
"21537869 Q1868372 D 2056080224 A 21537869 NA \n",
"21537870 Q209330 C 2060928966 A 21537870 NA \n",
"21537871 Q14005 D 2063120071 A 21537871 NA \n",
"21537872 Q918 C 2063217449 A 21537872 NA \n",
"21537873 Q150248 C 2068796814 A 21537873 NA \n",
"21537874 Q866 B 2079749157 A 21537874 NA \n",
"21537875 Q477675 C 2080785713 A 21537875 NA \n",
"21537876 Q1967876 C 2084215818 A 21537876 NA \n",
"21537877 Q750403 B 2084693498 A 21537877 NA \n",
"21537878 Q355 C 2093900731 A 21537878 NA \n",
"21537879 Q623578 C 2097991400 A 21537879 NA \n",
"21537880 Q17299517 D 2105487660 A 21537880 NA \n",
"21537881 Q33999 C 2108672678 A 21537881 NA \n",
"21537882 Q2494649 C 2114531894 A 21537882 NA \n",
"21537883 Q2597810 C 2128920607 A 21537883 NA \n",
"21537884 Q193563 C 2130725560 A 21537884 NA \n",
"21537885 Q423048 C 2136131564 A 21537885 NA \n",
"21537886 Q37312 C 2142913121 A 21537886 NA \n",
"21537887 Q54919 C 2148531382 A 21537887 NA \n",
"21537888 Q36578 C 2229315598 A 21537888 NA \n",
"21537889 Q30 A 2277746226 A 21537889 None \n",
"21537890 Q6581097 D 3273952711 A 21537890 NA \n",
"21537891 Q5 C 5668008721 A 21537891 NA \n",
"21537892 Q5296 C 12530369761 A 21537892 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10750354</td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855</td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525</td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>21537863 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818</td><td>A </td><td>21537864 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>21537865 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>21537866 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927</td><td>A </td><td>21537867 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>21537868 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224</td><td>A </td><td>21537869 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>21537870 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>21537871 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>21537872 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>21537873 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157</td><td>A </td><td>21537874 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>21537875 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>21537876 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498</td><td>A </td><td>21537877 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>21537878 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>21537879 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>21537880 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>21537881 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>21537882 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>21537883 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>21537884 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>21537885 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>21537886 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>21537887 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>21537888 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>21537889 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>21537890 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721</td><td>A </td><td>21537891 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>21537892 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & NA \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & NA \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & NA \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & NA \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & NA \\\\\n",
"\t Q10750354 & C & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & D & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & NA \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & NA \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & NA \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & NA \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & NA \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & NA \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & NA \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | NA | \n",
"| Q10081695 | C | 0 | E | 3 | NA | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | NA | \n",
"| Q10624171 | C | 0 | E | 12 | NA | \n",
"| Q10704108 | C | 0 | E | 13 | NA | \n",
"| Q10750354 | C | 0 | E | 14 | NA | \n",
"| Q10766855 | D | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | D | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | NA | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | NA | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | NA | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | NA | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | NA | \n",
"| Q565 | C | 2052996261 | A | 21537868 | NA | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | NA | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | NA | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | NA | \n",
"| Q918 | C | 2063217449 | A | 21537872 | NA | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | NA | \n",
"| Q866 | B | 2079749157 | A | 21537874 | NA | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | NA | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | NA | \n",
"| Q355 | C | 2093900731 | A | 21537878 | NA | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | NA | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | NA | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | NA | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | NA | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | NA | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | NA | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | NA | \n",
"| Q30 | A | 2277746226 | A | 21537889 | None | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | NA | \n",
"| Q5 | C | 5668008721 | A | 21537891 | NA | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance\n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 NA \n",
"3 Q10081695 C 0 E 3 NA \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 NA \n",
"12 Q10624171 C 0 E 12 NA \n",
"13 Q10704108 C 0 E 13 NA \n",
"14 Q10750354 C 0 E 14 NA \n",
"15 Q10766855 D 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 D 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 NA \n",
"21537864 Q31165 B 2048330818 A 21537864 NA \n",
"21537865 Q40629 C 2049755644 A 21537865 NA \n",
"21537866 Q105584 C 2049926923 A 21537866 NA \n",
"21537867 Q4584301 C 2052339927 A 21537867 NA \n",
"21537868 Q565 C 2052996261 A 21537868 NA \n",
"21537869 Q1868372 D 2056080224 A 21537869 NA \n",
"21537870 Q209330 C 2060928966 A 21537870 NA \n",
"21537871 Q14005 D 2063120071 A 21537871 NA \n",
"21537872 Q918 C 2063217449 A 21537872 NA \n",
"21537873 Q150248 C 2068796814 A 21537873 NA \n",
"21537874 Q866 B 2079749157 A 21537874 NA \n",
"21537875 Q477675 C 2080785713 A 21537875 NA \n",
"21537876 Q1967876 C 2084215818 A 21537876 NA \n",
"21537877 Q750403 B 2084693498 A 21537877 NA \n",
"21537878 Q355 C 2093900731 A 21537878 NA \n",
"21537879 Q623578 C 2097991400 A 21537879 NA \n",
"21537880 Q17299517 D 2105487660 A 21537880 NA \n",
"21537881 Q33999 C 2108672678 A 21537881 NA \n",
"21537882 Q2494649 C 2114531894 A 21537882 NA \n",
"21537883 Q2597810 C 2128920607 A 21537883 NA \n",
"21537884 Q193563 C 2130725560 A 21537884 NA \n",
"21537885 Q423048 C 2136131564 A 21537885 NA \n",
"21537886 Q37312 C 2142913121 A 21537886 NA \n",
"21537887 Q54919 C 2148531382 A 21537887 NA \n",
"21537888 Q36578 C 2229315598 A 21537888 NA \n",
"21537889 Q30 A 2277746226 A 21537889 None \n",
"21537890 Q6581097 D 3273952711 A 21537890 NA \n",
"21537891 Q5 C 5668008721 A 21537891 NA \n",
"21537892 Q5296 C 12530369761 A 21537892 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10750354</td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855</td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525</td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>21537863 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818 </td><td>A </td><td>21537864 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>21537865 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>21537866 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927 </td><td>A </td><td>21537867 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>21537868 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224 </td><td>A </td><td>21537869 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>21537870 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>21537871 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>21537872 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>21537873 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157 </td><td>A </td><td>21537874 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>21537875 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>21537876 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498 </td><td>A </td><td>21537877 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>21537878 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>21537879 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>21537880 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>21537881 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>21537882 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>21537883 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>21537884 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>21537885 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>21537886 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>21537887 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>21537888 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>21537889 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>21537890 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721 </td><td>A </td><td>21537891 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>21537892 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & NA \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & NA \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & NA \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & NA \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & NA \\\\\n",
"\t Q10750354 & C & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & D & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & NA \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & Moderate positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & NA \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & NA \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & NA \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & Moderate positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & NA \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & Moderate positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & NA \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & NA \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | NA | \n",
"| Q10081695 | C | 0 | E | 3 | NA | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | NA | \n",
"| Q10624171 | C | 0 | E | 12 | NA | \n",
"| Q10704108 | C | 0 | E | 13 | NA | \n",
"| Q10750354 | C | 0 | E | 14 | NA | \n",
"| Q10766855 | D | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | D | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | NA | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | Moderate positive | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | NA | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | NA | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | NA | \n",
"| Q565 | C | 2052996261 | A | 21537868 | NA | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | NA | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | NA | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | NA | \n",
"| Q918 | C | 2063217449 | A | 21537872 | NA | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | NA | \n",
"| Q866 | B | 2079749157 | A | 21537874 | Moderate positive | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | NA | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | Moderate positive | \n",
"| Q355 | C | 2093900731 | A | 21537878 | NA | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | NA | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | NA | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | NA | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | NA | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | NA | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | NA | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | NA | \n",
"| Q30 | A | 2277746226 | A | 21537889 | None | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | NA | \n",
"| Q5 | C | 5668008721 | A | 21537891 | NA | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 NA \n",
"3 Q10081695 C 0 E 3 NA \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 NA \n",
"12 Q10624171 C 0 E 12 NA \n",
"13 Q10704108 C 0 E 13 NA \n",
"14 Q10750354 C 0 E 14 NA \n",
"15 Q10766855 D 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 D 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 NA \n",
"21537864 Q31165 B 2048330818 A 21537864 Moderate positive\n",
"21537865 Q40629 C 2049755644 A 21537865 NA \n",
"21537866 Q105584 C 2049926923 A 21537866 NA \n",
"21537867 Q4584301 C 2052339927 A 21537867 NA \n",
"21537868 Q565 C 2052996261 A 21537868 NA \n",
"21537869 Q1868372 D 2056080224 A 21537869 NA \n",
"21537870 Q209330 C 2060928966 A 21537870 NA \n",
"21537871 Q14005 D 2063120071 A 21537871 NA \n",
"21537872 Q918 C 2063217449 A 21537872 NA \n",
"21537873 Q150248 C 2068796814 A 21537873 NA \n",
"21537874 Q866 B 2079749157 A 21537874 Moderate positive\n",
"21537875 Q477675 C 2080785713 A 21537875 NA \n",
"21537876 Q1967876 C 2084215818 A 21537876 NA \n",
"21537877 Q750403 B 2084693498 A 21537877 Moderate positive\n",
"21537878 Q355 C 2093900731 A 21537878 NA \n",
"21537879 Q623578 C 2097991400 A 21537879 NA \n",
"21537880 Q17299517 D 2105487660 A 21537880 NA \n",
"21537881 Q33999 C 2108672678 A 21537881 NA \n",
"21537882 Q2494649 C 2114531894 A 21537882 NA \n",
"21537883 Q2597810 C 2128920607 A 21537883 NA \n",
"21537884 Q193563 C 2130725560 A 21537884 NA \n",
"21537885 Q423048 C 2136131564 A 21537885 NA \n",
"21537886 Q37312 C 2142913121 A 21537886 NA \n",
"21537887 Q54919 C 2148531382 A 21537887 NA \n",
"21537888 Q36578 C 2229315598 A 21537888 NA \n",
"21537889 Q30 A 2277746226 A 21537889 None \n",
"21537890 Q6581097 D 3273952711 A 21537890 NA \n",
"21537891 Q5 C 5668008721 A 21537891 NA \n",
"21537892 Q5296 C 12530369761 A 21537892 NA "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## B: \n",
"articles_by_pop[prediction == 'B' & pop_class <= 'D',\n",
" dissonance := 'High negative'];\n",
"articles_by_pop[prediction == 'B' & pop_class == 'C',\n",
" dissonance := 'Moderate negative'];\n",
"articles_by_pop[prediction == 'B' & pop_class == 'B',\n",
" dissonance := 'None'];\n",
"articles_by_pop[prediction == 'B' & pop_class == 'A',\n",
" dissonance := 'Moderate positive'];"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10750354 </td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10766855 </td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>21537863 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818 </td><td>A </td><td>21537864 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>21537865 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>21537866 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927 </td><td>A </td><td>21537867 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>21537868 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224 </td><td>A </td><td>21537869 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>21537870 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>21537871 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>21537872 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>21537873 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157 </td><td>A </td><td>21537874 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>21537875 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>21537876 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498 </td><td>A </td><td>21537877 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>21537878 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>21537879 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>21537880 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>21537881 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>21537882 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>21537883 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>21537884 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>21537885 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>21537886 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>21537887 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>21537888 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>21537889 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>21537890 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721 </td><td>A </td><td>21537891 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>21537892 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative\\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative\\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative\\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative\\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative\\\\\n",
"\t Q10750354 & C & 0 & E & 14 & High negative\\\\\n",
"\t Q10766855 & D & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & NA \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & Moderate positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & NA \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & NA \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & NA \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & Moderate positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & NA \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & Moderate positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & NA \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & NA \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | C | 0 | E | 14 | High negative | \n",
"| Q10766855 | D | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | D | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | NA | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | Moderate positive | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | NA | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | NA | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | NA | \n",
"| Q565 | C | 2052996261 | A | 21537868 | NA | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | NA | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | NA | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | NA | \n",
"| Q918 | C | 2063217449 | A | 21537872 | NA | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | NA | \n",
"| Q866 | B | 2079749157 | A | 21537874 | Moderate positive | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | NA | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | Moderate positive | \n",
"| Q355 | C | 2093900731 | A | 21537878 | NA | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | NA | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | NA | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | NA | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | NA | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | NA | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | NA | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | NA | \n",
"| Q30 | A | 2277746226 | A | 21537889 | None | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | NA | \n",
"| Q5 | C | 5668008721 | A | 21537891 | NA | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative \n",
"3 Q10081695 C 0 E 3 High negative \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative \n",
"12 Q10624171 C 0 E 12 High negative \n",
"13 Q10704108 C 0 E 13 High negative \n",
"14 Q10750354 C 0 E 14 High negative \n",
"15 Q10766855 D 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 D 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 NA \n",
"21537864 Q31165 B 2048330818 A 21537864 Moderate positive\n",
"21537865 Q40629 C 2049755644 A 21537865 NA \n",
"21537866 Q105584 C 2049926923 A 21537866 NA \n",
"21537867 Q4584301 C 2052339927 A 21537867 NA \n",
"21537868 Q565 C 2052996261 A 21537868 NA \n",
"21537869 Q1868372 D 2056080224 A 21537869 NA \n",
"21537870 Q209330 C 2060928966 A 21537870 NA \n",
"21537871 Q14005 D 2063120071 A 21537871 NA \n",
"21537872 Q918 C 2063217449 A 21537872 NA \n",
"21537873 Q150248 C 2068796814 A 21537873 NA \n",
"21537874 Q866 B 2079749157 A 21537874 Moderate positive\n",
"21537875 Q477675 C 2080785713 A 21537875 NA \n",
"21537876 Q1967876 C 2084215818 A 21537876 NA \n",
"21537877 Q750403 B 2084693498 A 21537877 Moderate positive\n",
"21537878 Q355 C 2093900731 A 21537878 NA \n",
"21537879 Q623578 C 2097991400 A 21537879 NA \n",
"21537880 Q17299517 D 2105487660 A 21537880 NA \n",
"21537881 Q33999 C 2108672678 A 21537881 NA \n",
"21537882 Q2494649 C 2114531894 A 21537882 NA \n",
"21537883 Q2597810 C 2128920607 A 21537883 NA \n",
"21537884 Q193563 C 2130725560 A 21537884 NA \n",
"21537885 Q423048 C 2136131564 A 21537885 NA \n",
"21537886 Q37312 C 2142913121 A 21537886 NA \n",
"21537887 Q54919 C 2148531382 A 21537887 NA \n",
"21537888 Q36578 C 2229315598 A 21537888 NA \n",
"21537889 Q30 A 2277746226 A 21537889 None \n",
"21537890 Q6581097 D 3273952711 A 21537890 NA \n",
"21537891 Q5 C 5668008721 A 21537891 NA \n",
"21537892 Q5296 C 12530369761 A 21537892 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10750354 </td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10766855 </td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>21537863 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818 </td><td>A </td><td>21537864 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>21537865 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>21537866 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927 </td><td>A </td><td>21537867 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>21537868 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224 </td><td>A </td><td>21537869 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>21537870 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>21537871 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>21537872 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>21537873 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157 </td><td>A </td><td>21537874 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>21537875 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>21537876 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498 </td><td>A </td><td>21537877 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>21537878 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>21537879 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>21537880 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>21537881 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>21537882 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>21537883 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>21537884 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>21537885 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>21537886 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>21537887 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>21537888 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>21537889 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>21537890 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721 </td><td>A </td><td>21537891 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>21537892 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative\\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative\\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative\\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative\\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative\\\\\n",
"\t Q10750354 & C & 0 & E & 14 & High negative\\\\\n",
"\t Q10766855 & D & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & NA \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & Moderate positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & NA \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & NA \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & NA \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & Moderate positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & NA \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & Moderate positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & NA \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & NA \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | C | 0 | E | 14 | High negative | \n",
"| Q10766855 | D | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | D | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | NA | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | Moderate positive | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | NA | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | NA | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | NA | \n",
"| Q565 | C | 2052996261 | A | 21537868 | NA | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | NA | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | NA | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | NA | \n",
"| Q918 | C | 2063217449 | A | 21537872 | NA | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | NA | \n",
"| Q866 | B | 2079749157 | A | 21537874 | Moderate positive | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | NA | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | Moderate positive | \n",
"| Q355 | C | 2093900731 | A | 21537878 | NA | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | NA | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | NA | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | NA | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | NA | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | NA | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | NA | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | NA | \n",
"| Q30 | A | 2277746226 | A | 21537889 | None | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | NA | \n",
"| Q5 | C | 5668008721 | A | 21537891 | NA | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative \n",
"3 Q10081695 C 0 E 3 High negative \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative \n",
"12 Q10624171 C 0 E 12 High negative \n",
"13 Q10704108 C 0 E 13 High negative \n",
"14 Q10750354 C 0 E 14 High negative \n",
"15 Q10766855 D 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 D 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 NA \n",
"21537864 Q31165 B 2048330818 A 21537864 Moderate positive\n",
"21537865 Q40629 C 2049755644 A 21537865 NA \n",
"21537866 Q105584 C 2049926923 A 21537866 NA \n",
"21537867 Q4584301 C 2052339927 A 21537867 NA \n",
"21537868 Q565 C 2052996261 A 21537868 NA \n",
"21537869 Q1868372 D 2056080224 A 21537869 NA \n",
"21537870 Q209330 C 2060928966 A 21537870 NA \n",
"21537871 Q14005 D 2063120071 A 21537871 NA \n",
"21537872 Q918 C 2063217449 A 21537872 NA \n",
"21537873 Q150248 C 2068796814 A 21537873 NA \n",
"21537874 Q866 B 2079749157 A 21537874 Moderate positive\n",
"21537875 Q477675 C 2080785713 A 21537875 NA \n",
"21537876 Q1967876 C 2084215818 A 21537876 NA \n",
"21537877 Q750403 B 2084693498 A 21537877 Moderate positive\n",
"21537878 Q355 C 2093900731 A 21537878 NA \n",
"21537879 Q623578 C 2097991400 A 21537879 NA \n",
"21537880 Q17299517 D 2105487660 A 21537880 NA \n",
"21537881 Q33999 C 2108672678 A 21537881 NA \n",
"21537882 Q2494649 C 2114531894 A 21537882 NA \n",
"21537883 Q2597810 C 2128920607 A 21537883 NA \n",
"21537884 Q193563 C 2130725560 A 21537884 NA \n",
"21537885 Q423048 C 2136131564 A 21537885 NA \n",
"21537886 Q37312 C 2142913121 A 21537886 NA \n",
"21537887 Q54919 C 2148531382 A 21537887 NA \n",
"21537888 Q36578 C 2229315598 A 21537888 NA \n",
"21537889 Q30 A 2277746226 A 21537889 None \n",
"21537890 Q6581097 D 3273952711 A 21537890 NA \n",
"21537891 Q5 C 5668008721 A 21537891 NA \n",
"21537892 Q5296 C 12530369761 A 21537892 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10750354 </td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10766855 </td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>21537863 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818 </td><td>A </td><td>21537864 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>21537865 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>21537866 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927 </td><td>A </td><td>21537867 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>21537868 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224 </td><td>A </td><td>21537869 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>21537870 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>21537871 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>21537872 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>21537873 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157 </td><td>A </td><td>21537874 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>21537875 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>21537876 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498 </td><td>A </td><td>21537877 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>21537878 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>21537879 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>21537880 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>21537881 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>21537882 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>21537883 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>21537884 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>21537885 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>21537886 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>21537887 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>21537888 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>21537889 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>21537890 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721 </td><td>A </td><td>21537891 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>21537892 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative\\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative\\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative\\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative\\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative\\\\\n",
"\t Q10750354 & C & 0 & E & 14 & High negative\\\\\n",
"\t Q10766855 & D & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & NA \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & Moderate positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & NA \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & NA \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & NA \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & Moderate positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & NA \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & Moderate positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & NA \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & NA \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | C | 0 | E | 14 | High negative | \n",
"| Q10766855 | D | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | D | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | NA | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | Moderate positive | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | NA | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | NA | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | NA | \n",
"| Q565 | C | 2052996261 | A | 21537868 | NA | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | NA | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | NA | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | NA | \n",
"| Q918 | C | 2063217449 | A | 21537872 | NA | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | NA | \n",
"| Q866 | B | 2079749157 | A | 21537874 | Moderate positive | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | NA | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | Moderate positive | \n",
"| Q355 | C | 2093900731 | A | 21537878 | NA | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | NA | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | NA | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | NA | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | NA | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | NA | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | NA | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | NA | \n",
"| Q30 | A | 2277746226 | A | 21537889 | None | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | NA | \n",
"| Q5 | C | 5668008721 | A | 21537891 | NA | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative \n",
"3 Q10081695 C 0 E 3 High negative \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative \n",
"12 Q10624171 C 0 E 12 High negative \n",
"13 Q10704108 C 0 E 13 High negative \n",
"14 Q10750354 C 0 E 14 High negative \n",
"15 Q10766855 D 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 D 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 NA \n",
"21537864 Q31165 B 2048330818 A 21537864 Moderate positive\n",
"21537865 Q40629 C 2049755644 A 21537865 NA \n",
"21537866 Q105584 C 2049926923 A 21537866 NA \n",
"21537867 Q4584301 C 2052339927 A 21537867 NA \n",
"21537868 Q565 C 2052996261 A 21537868 NA \n",
"21537869 Q1868372 D 2056080224 A 21537869 NA \n",
"21537870 Q209330 C 2060928966 A 21537870 NA \n",
"21537871 Q14005 D 2063120071 A 21537871 NA \n",
"21537872 Q918 C 2063217449 A 21537872 NA \n",
"21537873 Q150248 C 2068796814 A 21537873 NA \n",
"21537874 Q866 B 2079749157 A 21537874 Moderate positive\n",
"21537875 Q477675 C 2080785713 A 21537875 NA \n",
"21537876 Q1967876 C 2084215818 A 21537876 NA \n",
"21537877 Q750403 B 2084693498 A 21537877 Moderate positive\n",
"21537878 Q355 C 2093900731 A 21537878 NA \n",
"21537879 Q623578 C 2097991400 A 21537879 NA \n",
"21537880 Q17299517 D 2105487660 A 21537880 NA \n",
"21537881 Q33999 C 2108672678 A 21537881 NA \n",
"21537882 Q2494649 C 2114531894 A 21537882 NA \n",
"21537883 Q2597810 C 2128920607 A 21537883 NA \n",
"21537884 Q193563 C 2130725560 A 21537884 NA \n",
"21537885 Q423048 C 2136131564 A 21537885 NA \n",
"21537886 Q37312 C 2142913121 A 21537886 NA \n",
"21537887 Q54919 C 2148531382 A 21537887 NA \n",
"21537888 Q36578 C 2229315598 A 21537888 NA \n",
"21537889 Q30 A 2277746226 A 21537889 None \n",
"21537890 Q6581097 D 3273952711 A 21537890 NA \n",
"21537891 Q5 C 5668008721 A 21537891 NA \n",
"21537892 Q5296 C 12530369761 A 21537892 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10750354 </td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10766855 </td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>21537863 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818 </td><td>A </td><td>21537864 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>21537865 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>21537866 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927 </td><td>A </td><td>21537867 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>21537868 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224 </td><td>A </td><td>21537869 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>21537870 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>21537871 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>21537872 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>21537873 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157 </td><td>A </td><td>21537874 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>21537875 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>21537876 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498 </td><td>A </td><td>21537877 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>21537878 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>21537879 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>21537880 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>21537881 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>21537882 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>21537883 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>21537884 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>21537885 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>21537886 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>21537887 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>21537888 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>21537889 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>21537890 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721 </td><td>A </td><td>21537891 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>21537892 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative\\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative\\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative\\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative\\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative\\\\\n",
"\t Q10750354 & C & 0 & E & 14 & High negative\\\\\n",
"\t Q10766855 & D & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & NA \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & Moderate positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & NA \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & NA \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & NA \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & Moderate positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & NA \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & Moderate positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & NA \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & NA \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | C | 0 | E | 14 | High negative | \n",
"| Q10766855 | D | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | D | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | NA | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | Moderate positive | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | NA | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | NA | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | NA | \n",
"| Q565 | C | 2052996261 | A | 21537868 | NA | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | NA | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | NA | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | NA | \n",
"| Q918 | C | 2063217449 | A | 21537872 | NA | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | NA | \n",
"| Q866 | B | 2079749157 | A | 21537874 | Moderate positive | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | NA | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | Moderate positive | \n",
"| Q355 | C | 2093900731 | A | 21537878 | NA | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | NA | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | NA | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | NA | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | NA | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | NA | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | NA | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | NA | \n",
"| Q30 | A | 2277746226 | A | 21537889 | None | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | NA | \n",
"| Q5 | C | 5668008721 | A | 21537891 | NA | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative \n",
"3 Q10081695 C 0 E 3 High negative \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative \n",
"12 Q10624171 C 0 E 12 High negative \n",
"13 Q10704108 C 0 E 13 High negative \n",
"14 Q10750354 C 0 E 14 High negative \n",
"15 Q10766855 D 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 D 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 NA \n",
"21537864 Q31165 B 2048330818 A 21537864 Moderate positive\n",
"21537865 Q40629 C 2049755644 A 21537865 NA \n",
"21537866 Q105584 C 2049926923 A 21537866 NA \n",
"21537867 Q4584301 C 2052339927 A 21537867 NA \n",
"21537868 Q565 C 2052996261 A 21537868 NA \n",
"21537869 Q1868372 D 2056080224 A 21537869 NA \n",
"21537870 Q209330 C 2060928966 A 21537870 NA \n",
"21537871 Q14005 D 2063120071 A 21537871 NA \n",
"21537872 Q918 C 2063217449 A 21537872 NA \n",
"21537873 Q150248 C 2068796814 A 21537873 NA \n",
"21537874 Q866 B 2079749157 A 21537874 Moderate positive\n",
"21537875 Q477675 C 2080785713 A 21537875 NA \n",
"21537876 Q1967876 C 2084215818 A 21537876 NA \n",
"21537877 Q750403 B 2084693498 A 21537877 Moderate positive\n",
"21537878 Q355 C 2093900731 A 21537878 NA \n",
"21537879 Q623578 C 2097991400 A 21537879 NA \n",
"21537880 Q17299517 D 2105487660 A 21537880 NA \n",
"21537881 Q33999 C 2108672678 A 21537881 NA \n",
"21537882 Q2494649 C 2114531894 A 21537882 NA \n",
"21537883 Q2597810 C 2128920607 A 21537883 NA \n",
"21537884 Q193563 C 2130725560 A 21537884 NA \n",
"21537885 Q423048 C 2136131564 A 21537885 NA \n",
"21537886 Q37312 C 2142913121 A 21537886 NA \n",
"21537887 Q54919 C 2148531382 A 21537887 NA \n",
"21537888 Q36578 C 2229315598 A 21537888 NA \n",
"21537889 Q30 A 2277746226 A 21537889 None \n",
"21537890 Q6581097 D 3273952711 A 21537890 NA \n",
"21537891 Q5 C 5668008721 A 21537891 NA \n",
"21537892 Q5296 C 12530369761 A 21537892 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10750354 </td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10766855 </td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>21537863 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818 </td><td>A </td><td>21537864 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>21537865 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>21537866 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927 </td><td>A </td><td>21537867 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>21537868 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224 </td><td>A </td><td>21537869 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>21537870 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>21537871 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>21537872 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>21537873 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157 </td><td>A </td><td>21537874 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>21537875 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>21537876 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498 </td><td>A </td><td>21537877 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>21537878 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>21537879 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>21537880 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>21537881 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>21537882 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>21537883 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>21537884 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>21537885 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>21537886 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>21537887 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>21537888 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>21537889 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>21537890 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721 </td><td>A </td><td>21537891 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>21537892 </td><td>High positive </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative\\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative\\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative\\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative\\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative\\\\\n",
"\t Q10750354 & C & 0 & E & 14 & High negative\\\\\n",
"\t Q10766855 & D & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & High positive \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & Moderate positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & High positive \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & High positive \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & High positive \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & High positive \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & High positive \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & High positive \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & High positive \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & Moderate positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & High positive \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & High positive \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & Moderate positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & High positive \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & High positive \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & High positive \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & High positive \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & High positive \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & High positive \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & High positive \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & High positive \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & High positive \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & High positive \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & NA \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & High positive \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & High positive \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | C | 0 | E | 14 | High negative | \n",
"| Q10766855 | D | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | D | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | High positive | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | Moderate positive | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | High positive | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | High positive | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | High positive | \n",
"| Q565 | C | 2052996261 | A | 21537868 | High positive | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | NA | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | High positive | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | NA | \n",
"| Q918 | C | 2063217449 | A | 21537872 | High positive | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | High positive | \n",
"| Q866 | B | 2079749157 | A | 21537874 | Moderate positive | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | High positive | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | High positive | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | Moderate positive | \n",
"| Q355 | C | 2093900731 | A | 21537878 | High positive | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | High positive | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | NA | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | High positive | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | High positive | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | High positive | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | High positive | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | High positive | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | High positive | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | High positive | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | High positive | \n",
"| Q30 | A | 2277746226 | A | 21537889 | None | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | NA | \n",
"| Q5 | C | 5668008721 | A | 21537891 | High positive | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | High positive | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative \n",
"3 Q10081695 C 0 E 3 High negative \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative \n",
"12 Q10624171 C 0 E 12 High negative \n",
"13 Q10704108 C 0 E 13 High negative \n",
"14 Q10750354 C 0 E 14 High negative \n",
"15 Q10766855 D 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 D 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 High positive \n",
"21537864 Q31165 B 2048330818 A 21537864 Moderate positive\n",
"21537865 Q40629 C 2049755644 A 21537865 High positive \n",
"21537866 Q105584 C 2049926923 A 21537866 High positive \n",
"21537867 Q4584301 C 2052339927 A 21537867 High positive \n",
"21537868 Q565 C 2052996261 A 21537868 High positive \n",
"21537869 Q1868372 D 2056080224 A 21537869 NA \n",
"21537870 Q209330 C 2060928966 A 21537870 High positive \n",
"21537871 Q14005 D 2063120071 A 21537871 NA \n",
"21537872 Q918 C 2063217449 A 21537872 High positive \n",
"21537873 Q150248 C 2068796814 A 21537873 High positive \n",
"21537874 Q866 B 2079749157 A 21537874 Moderate positive\n",
"21537875 Q477675 C 2080785713 A 21537875 High positive \n",
"21537876 Q1967876 C 2084215818 A 21537876 High positive \n",
"21537877 Q750403 B 2084693498 A 21537877 Moderate positive\n",
"21537878 Q355 C 2093900731 A 21537878 High positive \n",
"21537879 Q623578 C 2097991400 A 21537879 High positive \n",
"21537880 Q17299517 D 2105487660 A 21537880 NA \n",
"21537881 Q33999 C 2108672678 A 21537881 High positive \n",
"21537882 Q2494649 C 2114531894 A 21537882 High positive \n",
"21537883 Q2597810 C 2128920607 A 21537883 High positive \n",
"21537884 Q193563 C 2130725560 A 21537884 High positive \n",
"21537885 Q423048 C 2136131564 A 21537885 High positive \n",
"21537886 Q37312 C 2142913121 A 21537886 High positive \n",
"21537887 Q54919 C 2148531382 A 21537887 High positive \n",
"21537888 Q36578 C 2229315598 A 21537888 High positive \n",
"21537889 Q30 A 2277746226 A 21537889 None \n",
"21537890 Q6581097 D 3273952711 A 21537890 NA \n",
"21537891 Q5 C 5668008721 A 21537891 High positive \n",
"21537892 Q5296 C 12530369761 A 21537892 High positive "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## C: \n",
"articles_by_pop[prediction == 'C' & pop_class == 'E',\n",
" dissonance := 'High negative'];\n",
"articles_by_pop[prediction == 'C' & pop_class == 'D',\n",
" dissonance := 'Moderate negative'];\n",
"articles_by_pop[prediction == 'C' & pop_class == 'C',\n",
" dissonance := 'None'];\n",
"articles_by_pop[prediction == 'C' & pop_class == 'B',\n",
" dissonance := 'Moderate positive'];\n",
"articles_by_pop[prediction == 'C' & pop_class == 'A',\n",
" dissonance := 'High positive'];"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10750354 </td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>Moderate negative</td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>Moderate negative</td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>21537863 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818 </td><td>A </td><td>21537864 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>21537865 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>21537866 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927 </td><td>A </td><td>21537867 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>21537868 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224 </td><td>A </td><td>21537869 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>21537870 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>21537871 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>21537872 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>21537873 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157 </td><td>A </td><td>21537874 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>21537875 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>21537876 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498 </td><td>A </td><td>21537877 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>21537878 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>21537879 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>21537880 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>21537881 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>21537882 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>21537883 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>21537884 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>21537885 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>21537886 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>21537887 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>21537888 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>21537889 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>21537890 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721 </td><td>A </td><td>21537891 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>21537892 </td><td>High positive </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative \\\\\n",
"\t Q10750354 & C & 0 & E & 14 & High negative \\\\\n",
"\t Q10766855 & D & 0 & E & 15 & Moderate negative\\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & Moderate negative\\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & High positive \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & Moderate positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & High positive \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & High positive \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & High positive \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & High positive \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & High positive \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & High positive \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & High positive \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & Moderate positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & High positive \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & High positive \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & Moderate positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & High positive \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & High positive \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & High positive \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & High positive \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & High positive \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & High positive \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & High positive \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & High positive \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & High positive \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & High positive \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & NA \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & High positive \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & High positive \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | C | 0 | E | 14 | High negative | \n",
"| Q10766855 | D | 0 | E | 15 | Moderate negative | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | D | 0 | E | 23 | Moderate negative | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | High positive | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | Moderate positive | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | High positive | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | High positive | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | High positive | \n",
"| Q565 | C | 2052996261 | A | 21537868 | High positive | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | NA | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | High positive | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | NA | \n",
"| Q918 | C | 2063217449 | A | 21537872 | High positive | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | High positive | \n",
"| Q866 | B | 2079749157 | A | 21537874 | Moderate positive | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | High positive | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | High positive | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | Moderate positive | \n",
"| Q355 | C | 2093900731 | A | 21537878 | High positive | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | High positive | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | NA | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | High positive | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | High positive | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | High positive | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | High positive | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | High positive | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | High positive | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | High positive | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | High positive | \n",
"| Q30 | A | 2277746226 | A | 21537889 | None | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | NA | \n",
"| Q5 | C | 5668008721 | A | 21537891 | High positive | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | High positive | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative \n",
"3 Q10081695 C 0 E 3 High negative \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative \n",
"12 Q10624171 C 0 E 12 High negative \n",
"13 Q10704108 C 0 E 13 High negative \n",
"14 Q10750354 C 0 E 14 High negative \n",
"15 Q10766855 D 0 E 15 Moderate negative\n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 D 0 E 23 Moderate negative\n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 High positive \n",
"21537864 Q31165 B 2048330818 A 21537864 Moderate positive\n",
"21537865 Q40629 C 2049755644 A 21537865 High positive \n",
"21537866 Q105584 C 2049926923 A 21537866 High positive \n",
"21537867 Q4584301 C 2052339927 A 21537867 High positive \n",
"21537868 Q565 C 2052996261 A 21537868 High positive \n",
"21537869 Q1868372 D 2056080224 A 21537869 NA \n",
"21537870 Q209330 C 2060928966 A 21537870 High positive \n",
"21537871 Q14005 D 2063120071 A 21537871 NA \n",
"21537872 Q918 C 2063217449 A 21537872 High positive \n",
"21537873 Q150248 C 2068796814 A 21537873 High positive \n",
"21537874 Q866 B 2079749157 A 21537874 Moderate positive\n",
"21537875 Q477675 C 2080785713 A 21537875 High positive \n",
"21537876 Q1967876 C 2084215818 A 21537876 High positive \n",
"21537877 Q750403 B 2084693498 A 21537877 Moderate positive\n",
"21537878 Q355 C 2093900731 A 21537878 High positive \n",
"21537879 Q623578 C 2097991400 A 21537879 High positive \n",
"21537880 Q17299517 D 2105487660 A 21537880 NA \n",
"21537881 Q33999 C 2108672678 A 21537881 High positive \n",
"21537882 Q2494649 C 2114531894 A 21537882 High positive \n",
"21537883 Q2597810 C 2128920607 A 21537883 High positive \n",
"21537884 Q193563 C 2130725560 A 21537884 High positive \n",
"21537885 Q423048 C 2136131564 A 21537885 High positive \n",
"21537886 Q37312 C 2142913121 A 21537886 High positive \n",
"21537887 Q54919 C 2148531382 A 21537887 High positive \n",
"21537888 Q36578 C 2229315598 A 21537888 High positive \n",
"21537889 Q30 A 2277746226 A 21537889 None \n",
"21537890 Q6581097 D 3273952711 A 21537890 NA \n",
"21537891 Q5 C 5668008721 A 21537891 High positive \n",
"21537892 Q5296 C 12530369761 A 21537892 High positive "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10750354 </td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>Moderate negative</td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>Moderate negative</td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>21537863 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818 </td><td>A </td><td>21537864 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>21537865 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>21537866 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927 </td><td>A </td><td>21537867 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>21537868 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224 </td><td>A </td><td>21537869 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>21537870 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>21537871 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>21537872 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>21537873 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157 </td><td>A </td><td>21537874 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>21537875 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>21537876 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498 </td><td>A </td><td>21537877 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>21537878 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>21537879 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>21537880 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>21537881 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>21537882 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>21537883 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>21537884 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>21537885 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>21537886 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>21537887 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>21537888 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>21537889 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>21537890 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721 </td><td>A </td><td>21537891 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>21537892 </td><td>High positive </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative \\\\\n",
"\t Q10750354 & C & 0 & E & 14 & High negative \\\\\n",
"\t Q10766855 & D & 0 & E & 15 & Moderate negative\\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & Moderate negative\\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & High positive \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & Moderate positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & High positive \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & High positive \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & High positive \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & High positive \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & High positive \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & High positive \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & High positive \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & Moderate positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & High positive \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & High positive \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & Moderate positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & High positive \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & High positive \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & High positive \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & High positive \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & High positive \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & High positive \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & High positive \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & High positive \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & High positive \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & High positive \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & NA \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & High positive \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & High positive \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | C | 0 | E | 14 | High negative | \n",
"| Q10766855 | D | 0 | E | 15 | Moderate negative | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | D | 0 | E | 23 | Moderate negative | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | High positive | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | Moderate positive | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | High positive | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | High positive | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | High positive | \n",
"| Q565 | C | 2052996261 | A | 21537868 | High positive | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | NA | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | High positive | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | NA | \n",
"| Q918 | C | 2063217449 | A | 21537872 | High positive | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | High positive | \n",
"| Q866 | B | 2079749157 | A | 21537874 | Moderate positive | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | High positive | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | High positive | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | Moderate positive | \n",
"| Q355 | C | 2093900731 | A | 21537878 | High positive | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | High positive | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | NA | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | High positive | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | High positive | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | High positive | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | High positive | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | High positive | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | High positive | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | High positive | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | High positive | \n",
"| Q30 | A | 2277746226 | A | 21537889 | None | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | NA | \n",
"| Q5 | C | 5668008721 | A | 21537891 | High positive | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | High positive | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative \n",
"3 Q10081695 C 0 E 3 High negative \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative \n",
"12 Q10624171 C 0 E 12 High negative \n",
"13 Q10704108 C 0 E 13 High negative \n",
"14 Q10750354 C 0 E 14 High negative \n",
"15 Q10766855 D 0 E 15 Moderate negative\n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 D 0 E 23 Moderate negative\n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 High positive \n",
"21537864 Q31165 B 2048330818 A 21537864 Moderate positive\n",
"21537865 Q40629 C 2049755644 A 21537865 High positive \n",
"21537866 Q105584 C 2049926923 A 21537866 High positive \n",
"21537867 Q4584301 C 2052339927 A 21537867 High positive \n",
"21537868 Q565 C 2052996261 A 21537868 High positive \n",
"21537869 Q1868372 D 2056080224 A 21537869 NA \n",
"21537870 Q209330 C 2060928966 A 21537870 High positive \n",
"21537871 Q14005 D 2063120071 A 21537871 NA \n",
"21537872 Q918 C 2063217449 A 21537872 High positive \n",
"21537873 Q150248 C 2068796814 A 21537873 High positive \n",
"21537874 Q866 B 2079749157 A 21537874 Moderate positive\n",
"21537875 Q477675 C 2080785713 A 21537875 High positive \n",
"21537876 Q1967876 C 2084215818 A 21537876 High positive \n",
"21537877 Q750403 B 2084693498 A 21537877 Moderate positive\n",
"21537878 Q355 C 2093900731 A 21537878 High positive \n",
"21537879 Q623578 C 2097991400 A 21537879 High positive \n",
"21537880 Q17299517 D 2105487660 A 21537880 NA \n",
"21537881 Q33999 C 2108672678 A 21537881 High positive \n",
"21537882 Q2494649 C 2114531894 A 21537882 High positive \n",
"21537883 Q2597810 C 2128920607 A 21537883 High positive \n",
"21537884 Q193563 C 2130725560 A 21537884 High positive \n",
"21537885 Q423048 C 2136131564 A 21537885 High positive \n",
"21537886 Q37312 C 2142913121 A 21537886 High positive \n",
"21537887 Q54919 C 2148531382 A 21537887 High positive \n",
"21537888 Q36578 C 2229315598 A 21537888 High positive \n",
"21537889 Q30 A 2277746226 A 21537889 None \n",
"21537890 Q6581097 D 3273952711 A 21537890 NA \n",
"21537891 Q5 C 5668008721 A 21537891 High positive \n",
"21537892 Q5296 C 12530369761 A 21537892 High positive "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10750354 </td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>Moderate negative</td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>Moderate negative</td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>21537863 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818 </td><td>A </td><td>21537864 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>21537865 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>21537866 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927 </td><td>A </td><td>21537867 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>21537868 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224 </td><td>A </td><td>21537869 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>21537870 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>21537871 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>21537872 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>21537873 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157 </td><td>A </td><td>21537874 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>21537875 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>21537876 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498 </td><td>A </td><td>21537877 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>21537878 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>21537879 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>21537880 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>21537881 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>21537882 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>21537883 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>21537884 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>21537885 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>21537886 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>21537887 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>21537888 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>21537889 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>21537890 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721 </td><td>A </td><td>21537891 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>21537892 </td><td>High positive </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative \\\\\n",
"\t Q10750354 & C & 0 & E & 14 & High negative \\\\\n",
"\t Q10766855 & D & 0 & E & 15 & Moderate negative\\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & Moderate negative\\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & High positive \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & Moderate positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & High positive \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & High positive \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & High positive \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & High positive \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & High positive \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & High positive \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & High positive \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & Moderate positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & High positive \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & High positive \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & Moderate positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & High positive \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & High positive \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & High positive \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & High positive \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & High positive \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & High positive \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & High positive \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & High positive \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & High positive \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & High positive \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & NA \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & High positive \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & High positive \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | C | 0 | E | 14 | High negative | \n",
"| Q10766855 | D | 0 | E | 15 | Moderate negative | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | D | 0 | E | 23 | Moderate negative | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | High positive | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | Moderate positive | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | High positive | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | High positive | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | High positive | \n",
"| Q565 | C | 2052996261 | A | 21537868 | High positive | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | NA | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | High positive | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | NA | \n",
"| Q918 | C | 2063217449 | A | 21537872 | High positive | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | High positive | \n",
"| Q866 | B | 2079749157 | A | 21537874 | Moderate positive | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | High positive | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | High positive | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | Moderate positive | \n",
"| Q355 | C | 2093900731 | A | 21537878 | High positive | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | High positive | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | NA | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | High positive | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | High positive | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | High positive | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | High positive | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | High positive | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | High positive | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | High positive | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | High positive | \n",
"| Q30 | A | 2277746226 | A | 21537889 | None | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | NA | \n",
"| Q5 | C | 5668008721 | A | 21537891 | High positive | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | High positive | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative \n",
"3 Q10081695 C 0 E 3 High negative \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative \n",
"12 Q10624171 C 0 E 12 High negative \n",
"13 Q10704108 C 0 E 13 High negative \n",
"14 Q10750354 C 0 E 14 High negative \n",
"15 Q10766855 D 0 E 15 Moderate negative\n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 D 0 E 23 Moderate negative\n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 High positive \n",
"21537864 Q31165 B 2048330818 A 21537864 Moderate positive\n",
"21537865 Q40629 C 2049755644 A 21537865 High positive \n",
"21537866 Q105584 C 2049926923 A 21537866 High positive \n",
"21537867 Q4584301 C 2052339927 A 21537867 High positive \n",
"21537868 Q565 C 2052996261 A 21537868 High positive \n",
"21537869 Q1868372 D 2056080224 A 21537869 NA \n",
"21537870 Q209330 C 2060928966 A 21537870 High positive \n",
"21537871 Q14005 D 2063120071 A 21537871 NA \n",
"21537872 Q918 C 2063217449 A 21537872 High positive \n",
"21537873 Q150248 C 2068796814 A 21537873 High positive \n",
"21537874 Q866 B 2079749157 A 21537874 Moderate positive\n",
"21537875 Q477675 C 2080785713 A 21537875 High positive \n",
"21537876 Q1967876 C 2084215818 A 21537876 High positive \n",
"21537877 Q750403 B 2084693498 A 21537877 Moderate positive\n",
"21537878 Q355 C 2093900731 A 21537878 High positive \n",
"21537879 Q623578 C 2097991400 A 21537879 High positive \n",
"21537880 Q17299517 D 2105487660 A 21537880 NA \n",
"21537881 Q33999 C 2108672678 A 21537881 High positive \n",
"21537882 Q2494649 C 2114531894 A 21537882 High positive \n",
"21537883 Q2597810 C 2128920607 A 21537883 High positive \n",
"21537884 Q193563 C 2130725560 A 21537884 High positive \n",
"21537885 Q423048 C 2136131564 A 21537885 High positive \n",
"21537886 Q37312 C 2142913121 A 21537886 High positive \n",
"21537887 Q54919 C 2148531382 A 21537887 High positive \n",
"21537888 Q36578 C 2229315598 A 21537888 High positive \n",
"21537889 Q30 A 2277746226 A 21537889 None \n",
"21537890 Q6581097 D 3273952711 A 21537890 NA \n",
"21537891 Q5 C 5668008721 A 21537891 High positive \n",
"21537892 Q5296 C 12530369761 A 21537892 High positive "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10750354 </td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>Moderate negative</td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>Moderate negative</td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>21537863 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818 </td><td>A </td><td>21537864 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>21537865 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>21537866 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927 </td><td>A </td><td>21537867 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>21537868 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224 </td><td>A </td><td>21537869 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>21537870 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>21537871 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>21537872 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>21537873 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157 </td><td>A </td><td>21537874 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>21537875 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>21537876 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498 </td><td>A </td><td>21537877 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>21537878 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>21537879 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>21537880 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>21537881 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>21537882 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>21537883 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>21537884 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>21537885 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>21537886 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>21537887 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>21537888 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>21537889 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>21537890 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721 </td><td>A </td><td>21537891 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>21537892 </td><td>High positive </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative \\\\\n",
"\t Q10750354 & C & 0 & E & 14 & High negative \\\\\n",
"\t Q10766855 & D & 0 & E & 15 & Moderate negative\\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & Moderate negative\\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & High positive \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & Moderate positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & High positive \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & High positive \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & High positive \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & High positive \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & High positive \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & High positive \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & High positive \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & High positive \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & High positive \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & Moderate positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & High positive \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & High positive \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & Moderate positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & High positive \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & High positive \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & High positive \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & High positive \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & High positive \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & High positive \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & High positive \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & High positive \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & High positive \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & High positive \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & High positive \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & High positive \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & High positive \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & High positive \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | C | 0 | E | 14 | High negative | \n",
"| Q10766855 | D | 0 | E | 15 | Moderate negative | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | D | 0 | E | 23 | Moderate negative | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | High positive | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | Moderate positive | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | High positive | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | High positive | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | High positive | \n",
"| Q565 | C | 2052996261 | A | 21537868 | High positive | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | High positive | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | High positive | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | High positive | \n",
"| Q918 | C | 2063217449 | A | 21537872 | High positive | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | High positive | \n",
"| Q866 | B | 2079749157 | A | 21537874 | Moderate positive | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | High positive | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | High positive | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | Moderate positive | \n",
"| Q355 | C | 2093900731 | A | 21537878 | High positive | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | High positive | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | High positive | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | High positive | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | High positive | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | High positive | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | High positive | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | High positive | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | High positive | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | High positive | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | High positive | \n",
"| Q30 | A | 2277746226 | A | 21537889 | None | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | High positive | \n",
"| Q5 | C | 5668008721 | A | 21537891 | High positive | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | High positive | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative \n",
"3 Q10081695 C 0 E 3 High negative \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative \n",
"12 Q10624171 C 0 E 12 High negative \n",
"13 Q10704108 C 0 E 13 High negative \n",
"14 Q10750354 C 0 E 14 High negative \n",
"15 Q10766855 D 0 E 15 Moderate negative\n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 D 0 E 23 Moderate negative\n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 High positive \n",
"21537864 Q31165 B 2048330818 A 21537864 Moderate positive\n",
"21537865 Q40629 C 2049755644 A 21537865 High positive \n",
"21537866 Q105584 C 2049926923 A 21537866 High positive \n",
"21537867 Q4584301 C 2052339927 A 21537867 High positive \n",
"21537868 Q565 C 2052996261 A 21537868 High positive \n",
"21537869 Q1868372 D 2056080224 A 21537869 High positive \n",
"21537870 Q209330 C 2060928966 A 21537870 High positive \n",
"21537871 Q14005 D 2063120071 A 21537871 High positive \n",
"21537872 Q918 C 2063217449 A 21537872 High positive \n",
"21537873 Q150248 C 2068796814 A 21537873 High positive \n",
"21537874 Q866 B 2079749157 A 21537874 Moderate positive\n",
"21537875 Q477675 C 2080785713 A 21537875 High positive \n",
"21537876 Q1967876 C 2084215818 A 21537876 High positive \n",
"21537877 Q750403 B 2084693498 A 21537877 Moderate positive\n",
"21537878 Q355 C 2093900731 A 21537878 High positive \n",
"21537879 Q623578 C 2097991400 A 21537879 High positive \n",
"21537880 Q17299517 D 2105487660 A 21537880 High positive \n",
"21537881 Q33999 C 2108672678 A 21537881 High positive \n",
"21537882 Q2494649 C 2114531894 A 21537882 High positive \n",
"21537883 Q2597810 C 2128920607 A 21537883 High positive \n",
"21537884 Q193563 C 2130725560 A 21537884 High positive \n",
"21537885 Q423048 C 2136131564 A 21537885 High positive \n",
"21537886 Q37312 C 2142913121 A 21537886 High positive \n",
"21537887 Q54919 C 2148531382 A 21537887 High positive \n",
"21537888 Q36578 C 2229315598 A 21537888 High positive \n",
"21537889 Q30 A 2277746226 A 21537889 None \n",
"21537890 Q6581097 D 3273952711 A 21537890 High positive \n",
"21537891 Q5 C 5668008721 A 21537891 High positive \n",
"21537892 Q5296 C 12530369761 A 21537892 High positive "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## D\n",
"articles_by_pop[prediction == 'D' & pop_class == 'E',\n",
" dissonance := 'Moderate negative'];\n",
"articles_by_pop[prediction == 'D' & pop_class == 'D',\n",
" dissonance := 'None'];\n",
"articles_by_pop[prediction == 'D' & pop_class == 'C',\n",
" dissonance := 'Moderate positive'];\n",
"articles_by_pop[prediction == 'D' & pop_class >= 'B',\n",
" dissonance := 'High positive'];"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>None </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>None </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>None </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>None </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>None </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>None </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>None </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>None </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10750354 </td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>Moderate negative</td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>None </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>None </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>None </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>None </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>None </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>None </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>None </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>Moderate negative</td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>None </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>None </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>None </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>None </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>None </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>None </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>None </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>21537863 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818 </td><td>A </td><td>21537864 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>21537865 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>21537866 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927 </td><td>A </td><td>21537867 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>21537868 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224 </td><td>A </td><td>21537869 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>21537870 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>21537871 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>21537872 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>21537873 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157 </td><td>A </td><td>21537874 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>21537875 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>21537876 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498 </td><td>A </td><td>21537877 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>21537878 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>21537879 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>21537880 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>21537881 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>21537882 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>21537883 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>21537884 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>21537885 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>21537886 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>21537887 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>21537888 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>21537889 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>21537890 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721 </td><td>A </td><td>21537891 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>21537892 </td><td>High positive </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & None \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & None \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & None \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & None \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & None \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & None \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & None \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & None \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative \\\\\n",
"\t Q10750354 & C & 0 & E & 14 & High negative \\\\\n",
"\t Q10766855 & D & 0 & E & 15 & Moderate negative\\\\\n",
"\t Q10827611 & E & 0 & E & 16 & None \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & None \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & None \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & None \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & None \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & None \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & None \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & Moderate negative\\\\\n",
"\t Q12543904 & E & 0 & E & 24 & None \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & None \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & None \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & None \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & None \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & None \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & None \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & High positive \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & Moderate positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & High positive \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & High positive \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & High positive \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & High positive \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & High positive \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & High positive \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & High positive \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & High positive \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & High positive \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & Moderate positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & High positive \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & High positive \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & Moderate positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & High positive \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & High positive \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & High positive \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & High positive \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & High positive \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & High positive \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & High positive \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & High positive \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & High positive \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & High positive \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & High positive \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & High positive \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & High positive \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & High positive \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | None | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | None | \n",
"| Q10111267 | E | 0 | E | 5 | None | \n",
"| Q10149726 | E | 0 | E | 6 | None | \n",
"| Q10180230 | E | 0 | E | 7 | None | \n",
"| Q10185035 | E | 0 | E | 8 | None | \n",
"| Q10205202 | E | 0 | E | 9 | None | \n",
"| Q10252966 | E | 0 | E | 10 | None | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | C | 0 | E | 14 | High negative | \n",
"| Q10766855 | D | 0 | E | 15 | Moderate negative | \n",
"| Q10827611 | E | 0 | E | 16 | None | \n",
"| Q11093044 | E | 0 | E | 17 | None | \n",
"| Q11934537 | E | 0 | E | 18 | None | \n",
"| Q12133466 | E | 0 | E | 19 | None | \n",
"| Q12264503 | E | 0 | E | 20 | None | \n",
"| Q12267516 | E | 0 | E | 21 | None | \n",
"| Q12304084 | E | 0 | E | 22 | None | \n",
"| Q12443525 | D | 0 | E | 23 | Moderate negative | \n",
"| Q12543904 | E | 0 | E | 24 | None | \n",
"| Q12890205 | E | 0 | E | 25 | None | \n",
"| Q12891524 | E | 0 | E | 26 | None | \n",
"| Q12918202 | E | 0 | E | 27 | None | \n",
"| Q13005653 | E | 0 | E | 28 | None | \n",
"| Q13073896 | E | 0 | E | 29 | None | \n",
"| Q13163823 | E | 0 | E | 30 | None | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | High positive | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | Moderate positive | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | High positive | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | High positive | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | High positive | \n",
"| Q565 | C | 2052996261 | A | 21537868 | High positive | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | High positive | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | High positive | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | High positive | \n",
"| Q918 | C | 2063217449 | A | 21537872 | High positive | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | High positive | \n",
"| Q866 | B | 2079749157 | A | 21537874 | Moderate positive | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | High positive | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | High positive | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | Moderate positive | \n",
"| Q355 | C | 2093900731 | A | 21537878 | High positive | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | High positive | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | High positive | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | High positive | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | High positive | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | High positive | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | High positive | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | High positive | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | High positive | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | High positive | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | High positive | \n",
"| Q30 | A | 2277746226 | A | 21537889 | None | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | High positive | \n",
"| Q5 | C | 5668008721 | A | 21537891 | High positive | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | High positive | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 None \n",
"2 Q10069140 C 0 E 2 High negative \n",
"3 Q10081695 C 0 E 3 High negative \n",
"4 Q10092002 E 0 E 4 None \n",
"5 Q10111267 E 0 E 5 None \n",
"6 Q10149726 E 0 E 6 None \n",
"7 Q10180230 E 0 E 7 None \n",
"8 Q10185035 E 0 E 8 None \n",
"9 Q10205202 E 0 E 9 None \n",
"10 Q10252966 E 0 E 10 None \n",
"11 Q10444494 C 0 E 11 High negative \n",
"12 Q10624171 C 0 E 12 High negative \n",
"13 Q10704108 C 0 E 13 High negative \n",
"14 Q10750354 C 0 E 14 High negative \n",
"15 Q10766855 D 0 E 15 Moderate negative\n",
"16 Q10827611 E 0 E 16 None \n",
"17 Q11093044 E 0 E 17 None \n",
"18 Q11934537 E 0 E 18 None \n",
"19 Q12133466 E 0 E 19 None \n",
"20 Q12264503 E 0 E 20 None \n",
"21 Q12267516 E 0 E 21 None \n",
"22 Q12304084 E 0 E 22 None \n",
"23 Q12443525 D 0 E 23 Moderate negative\n",
"24 Q12543904 E 0 E 24 None \n",
"25 Q12890205 E 0 E 25 None \n",
"26 Q12891524 E 0 E 26 None \n",
"27 Q12918202 E 0 E 27 None \n",
"28 Q13005653 E 0 E 28 None \n",
"29 Q13073896 E 0 E 29 None \n",
"30 Q13163823 E 0 E 30 None \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 High positive \n",
"21537864 Q31165 B 2048330818 A 21537864 Moderate positive\n",
"21537865 Q40629 C 2049755644 A 21537865 High positive \n",
"21537866 Q105584 C 2049926923 A 21537866 High positive \n",
"21537867 Q4584301 C 2052339927 A 21537867 High positive \n",
"21537868 Q565 C 2052996261 A 21537868 High positive \n",
"21537869 Q1868372 D 2056080224 A 21537869 High positive \n",
"21537870 Q209330 C 2060928966 A 21537870 High positive \n",
"21537871 Q14005 D 2063120071 A 21537871 High positive \n",
"21537872 Q918 C 2063217449 A 21537872 High positive \n",
"21537873 Q150248 C 2068796814 A 21537873 High positive \n",
"21537874 Q866 B 2079749157 A 21537874 Moderate positive\n",
"21537875 Q477675 C 2080785713 A 21537875 High positive \n",
"21537876 Q1967876 C 2084215818 A 21537876 High positive \n",
"21537877 Q750403 B 2084693498 A 21537877 Moderate positive\n",
"21537878 Q355 C 2093900731 A 21537878 High positive \n",
"21537879 Q623578 C 2097991400 A 21537879 High positive \n",
"21537880 Q17299517 D 2105487660 A 21537880 High positive \n",
"21537881 Q33999 C 2108672678 A 21537881 High positive \n",
"21537882 Q2494649 C 2114531894 A 21537882 High positive \n",
"21537883 Q2597810 C 2128920607 A 21537883 High positive \n",
"21537884 Q193563 C 2130725560 A 21537884 High positive \n",
"21537885 Q423048 C 2136131564 A 21537885 High positive \n",
"21537886 Q37312 C 2142913121 A 21537886 High positive \n",
"21537887 Q54919 C 2148531382 A 21537887 High positive \n",
"21537888 Q36578 C 2229315598 A 21537888 High positive \n",
"21537889 Q30 A 2277746226 A 21537889 None \n",
"21537890 Q6581097 D 3273952711 A 21537890 High positive \n",
"21537891 Q5 C 5668008721 A 21537891 High positive \n",
"21537892 Q5296 C 12530369761 A 21537892 High positive "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>None </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>None </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>None </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>None </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>None </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>None </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>None </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>None </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10750354 </td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>Moderate negative</td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>None </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>None </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>None </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>None </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>None </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>None </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>None </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>Moderate negative</td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>None </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>None </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>None </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>None </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>None </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>None </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>None </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>21537863 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818 </td><td>A </td><td>21537864 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>21537865 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>21537866 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927 </td><td>A </td><td>21537867 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>21537868 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224 </td><td>A </td><td>21537869 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>21537870 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>21537871 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>21537872 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>21537873 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157 </td><td>A </td><td>21537874 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>21537875 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>21537876 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498 </td><td>A </td><td>21537877 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>21537878 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>21537879 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>21537880 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>21537881 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>21537882 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>21537883 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>21537884 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>21537885 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>21537886 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>21537887 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>21537888 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>21537889 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>21537890 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721 </td><td>A </td><td>21537891 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>21537892 </td><td>High positive </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & None \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & None \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & None \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & None \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & None \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & None \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & None \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & None \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative \\\\\n",
"\t Q10750354 & C & 0 & E & 14 & High negative \\\\\n",
"\t Q10766855 & D & 0 & E & 15 & Moderate negative\\\\\n",
"\t Q10827611 & E & 0 & E & 16 & None \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & None \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & None \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & None \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & None \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & None \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & None \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & Moderate negative\\\\\n",
"\t Q12543904 & E & 0 & E & 24 & None \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & None \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & None \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & None \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & None \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & None \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & None \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & High positive \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & Moderate positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & High positive \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & High positive \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & High positive \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & High positive \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & High positive \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & High positive \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & High positive \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & High positive \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & High positive \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & Moderate positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & High positive \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & High positive \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & Moderate positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & High positive \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & High positive \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & High positive \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & High positive \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & High positive \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & High positive \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & High positive \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & High positive \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & High positive \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & High positive \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & High positive \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & High positive \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & High positive \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & High positive \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | None | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | None | \n",
"| Q10111267 | E | 0 | E | 5 | None | \n",
"| Q10149726 | E | 0 | E | 6 | None | \n",
"| Q10180230 | E | 0 | E | 7 | None | \n",
"| Q10185035 | E | 0 | E | 8 | None | \n",
"| Q10205202 | E | 0 | E | 9 | None | \n",
"| Q10252966 | E | 0 | E | 10 | None | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | C | 0 | E | 14 | High negative | \n",
"| Q10766855 | D | 0 | E | 15 | Moderate negative | \n",
"| Q10827611 | E | 0 | E | 16 | None | \n",
"| Q11093044 | E | 0 | E | 17 | None | \n",
"| Q11934537 | E | 0 | E | 18 | None | \n",
"| Q12133466 | E | 0 | E | 19 | None | \n",
"| Q12264503 | E | 0 | E | 20 | None | \n",
"| Q12267516 | E | 0 | E | 21 | None | \n",
"| Q12304084 | E | 0 | E | 22 | None | \n",
"| Q12443525 | D | 0 | E | 23 | Moderate negative | \n",
"| Q12543904 | E | 0 | E | 24 | None | \n",
"| Q12890205 | E | 0 | E | 25 | None | \n",
"| Q12891524 | E | 0 | E | 26 | None | \n",
"| Q12918202 | E | 0 | E | 27 | None | \n",
"| Q13005653 | E | 0 | E | 28 | None | \n",
"| Q13073896 | E | 0 | E | 29 | None | \n",
"| Q13163823 | E | 0 | E | 30 | None | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | High positive | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | Moderate positive | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | High positive | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | High positive | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | High positive | \n",
"| Q565 | C | 2052996261 | A | 21537868 | High positive | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | High positive | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | High positive | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | High positive | \n",
"| Q918 | C | 2063217449 | A | 21537872 | High positive | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | High positive | \n",
"| Q866 | B | 2079749157 | A | 21537874 | Moderate positive | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | High positive | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | High positive | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | Moderate positive | \n",
"| Q355 | C | 2093900731 | A | 21537878 | High positive | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | High positive | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | High positive | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | High positive | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | High positive | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | High positive | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | High positive | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | High positive | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | High positive | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | High positive | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | High positive | \n",
"| Q30 | A | 2277746226 | A | 21537889 | None | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | High positive | \n",
"| Q5 | C | 5668008721 | A | 21537891 | High positive | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | High positive | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 None \n",
"2 Q10069140 C 0 E 2 High negative \n",
"3 Q10081695 C 0 E 3 High negative \n",
"4 Q10092002 E 0 E 4 None \n",
"5 Q10111267 E 0 E 5 None \n",
"6 Q10149726 E 0 E 6 None \n",
"7 Q10180230 E 0 E 7 None \n",
"8 Q10185035 E 0 E 8 None \n",
"9 Q10205202 E 0 E 9 None \n",
"10 Q10252966 E 0 E 10 None \n",
"11 Q10444494 C 0 E 11 High negative \n",
"12 Q10624171 C 0 E 12 High negative \n",
"13 Q10704108 C 0 E 13 High negative \n",
"14 Q10750354 C 0 E 14 High negative \n",
"15 Q10766855 D 0 E 15 Moderate negative\n",
"16 Q10827611 E 0 E 16 None \n",
"17 Q11093044 E 0 E 17 None \n",
"18 Q11934537 E 0 E 18 None \n",
"19 Q12133466 E 0 E 19 None \n",
"20 Q12264503 E 0 E 20 None \n",
"21 Q12267516 E 0 E 21 None \n",
"22 Q12304084 E 0 E 22 None \n",
"23 Q12443525 D 0 E 23 Moderate negative\n",
"24 Q12543904 E 0 E 24 None \n",
"25 Q12890205 E 0 E 25 None \n",
"26 Q12891524 E 0 E 26 None \n",
"27 Q12918202 E 0 E 27 None \n",
"28 Q13005653 E 0 E 28 None \n",
"29 Q13073896 E 0 E 29 None \n",
"30 Q13163823 E 0 E 30 None \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 High positive \n",
"21537864 Q31165 B 2048330818 A 21537864 Moderate positive\n",
"21537865 Q40629 C 2049755644 A 21537865 High positive \n",
"21537866 Q105584 C 2049926923 A 21537866 High positive \n",
"21537867 Q4584301 C 2052339927 A 21537867 High positive \n",
"21537868 Q565 C 2052996261 A 21537868 High positive \n",
"21537869 Q1868372 D 2056080224 A 21537869 High positive \n",
"21537870 Q209330 C 2060928966 A 21537870 High positive \n",
"21537871 Q14005 D 2063120071 A 21537871 High positive \n",
"21537872 Q918 C 2063217449 A 21537872 High positive \n",
"21537873 Q150248 C 2068796814 A 21537873 High positive \n",
"21537874 Q866 B 2079749157 A 21537874 Moderate positive\n",
"21537875 Q477675 C 2080785713 A 21537875 High positive \n",
"21537876 Q1967876 C 2084215818 A 21537876 High positive \n",
"21537877 Q750403 B 2084693498 A 21537877 Moderate positive\n",
"21537878 Q355 C 2093900731 A 21537878 High positive \n",
"21537879 Q623578 C 2097991400 A 21537879 High positive \n",
"21537880 Q17299517 D 2105487660 A 21537880 High positive \n",
"21537881 Q33999 C 2108672678 A 21537881 High positive \n",
"21537882 Q2494649 C 2114531894 A 21537882 High positive \n",
"21537883 Q2597810 C 2128920607 A 21537883 High positive \n",
"21537884 Q193563 C 2130725560 A 21537884 High positive \n",
"21537885 Q423048 C 2136131564 A 21537885 High positive \n",
"21537886 Q37312 C 2142913121 A 21537886 High positive \n",
"21537887 Q54919 C 2148531382 A 21537887 High positive \n",
"21537888 Q36578 C 2229315598 A 21537888 High positive \n",
"21537889 Q30 A 2277746226 A 21537889 None \n",
"21537890 Q6581097 D 3273952711 A 21537890 High positive \n",
"21537891 Q5 C 5668008721 A 21537891 High positive \n",
"21537892 Q5296 C 12530369761 A 21537892 High positive "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>None </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>None </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>None </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>None </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>None </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>None </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>None </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>None </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10750354 </td><td>C </td><td>0 </td><td>E </td><td>14 </td><td>High negative </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>D </td><td>0 </td><td>E </td><td>15 </td><td>Moderate negative</td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>None </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>None </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>None </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>None </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>None </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>None </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>None </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>D </td><td>0 </td><td>E </td><td>23 </td><td>Moderate negative</td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>None </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>None </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>None </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>None </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>None </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>None </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>None </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>21537863 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q31165 </td><td>B </td><td> 2048330818 </td><td>A </td><td>21537864 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>21537865 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>21537866 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>C </td><td> 2052339927 </td><td>A </td><td>21537867 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>21537868 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>D </td><td> 2056080224 </td><td>A </td><td>21537869 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>21537870 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>21537871 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>21537872 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>21537873 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q866 </td><td>B </td><td> 2079749157 </td><td>A </td><td>21537874 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>21537875 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>21537876 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q750403 </td><td>B </td><td> 2084693498 </td><td>A </td><td>21537877 </td><td>Moderate positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>21537878 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>21537879 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>21537880 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>21537881 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>21537882 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>21537883 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>21537884 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>21537885 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>21537886 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>21537887 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>21537888 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>21537889 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>21537890 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q5 </td><td>C </td><td> 5668008721 </td><td>A </td><td>21537891 </td><td>High positive </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>21537892 </td><td>High positive </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & None \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & None \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & None \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & None \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & None \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & None \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & None \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & None \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative \\\\\n",
"\t Q10750354 & C & 0 & E & 14 & High negative \\\\\n",
"\t Q10766855 & D & 0 & E & 15 & Moderate negative\\\\\n",
"\t Q10827611 & E & 0 & E & 16 & None \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & None \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & None \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & None \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & None \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & None \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & None \\\\\n",
"\t Q12443525 & D & 0 & E & 23 & Moderate negative\\\\\n",
"\t Q12543904 & E & 0 & E & 24 & None \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & None \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & None \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & None \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & None \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & None \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & None \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 21537863 & High positive \\\\\n",
"\t Q31165 & B & 2048330818 & A & 21537864 & Moderate positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 21537865 & High positive \\\\\n",
"\t Q105584 & C & 2049926923 & A & 21537866 & High positive \\\\\n",
"\t Q4584301 & C & 2052339927 & A & 21537867 & High positive \\\\\n",
"\t Q565 & C & 2052996261 & A & 21537868 & High positive \\\\\n",
"\t Q1868372 & D & 2056080224 & A & 21537869 & High positive \\\\\n",
"\t Q209330 & C & 2060928966 & A & 21537870 & High positive \\\\\n",
"\t Q14005 & D & 2063120071 & A & 21537871 & High positive \\\\\n",
"\t Q918 & C & 2063217449 & A & 21537872 & High positive \\\\\n",
"\t Q150248 & C & 2068796814 & A & 21537873 & High positive \\\\\n",
"\t Q866 & B & 2079749157 & A & 21537874 & Moderate positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 21537875 & High positive \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 21537876 & High positive \\\\\n",
"\t Q750403 & B & 2084693498 & A & 21537877 & Moderate positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 21537878 & High positive \\\\\n",
"\t Q623578 & C & 2097991400 & A & 21537879 & High positive \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 21537880 & High positive \\\\\n",
"\t Q33999 & C & 2108672678 & A & 21537881 & High positive \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 21537882 & High positive \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 21537883 & High positive \\\\\n",
"\t Q193563 & C & 2130725560 & A & 21537884 & High positive \\\\\n",
"\t Q423048 & C & 2136131564 & A & 21537885 & High positive \\\\\n",
"\t Q37312 & C & 2142913121 & A & 21537886 & High positive \\\\\n",
"\t Q54919 & C & 2148531382 & A & 21537887 & High positive \\\\\n",
"\t Q36578 & C & 2229315598 & A & 21537888 & High positive \\\\\n",
"\t Q30 & A & 2277746226 & A & 21537889 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 21537890 & High positive \\\\\n",
"\t Q5 & C & 5668008721 & A & 21537891 & High positive \\\\\n",
"\t Q5296 & C & 12530369761 & A & 21537892 & High positive \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | None | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | None | \n",
"| Q10111267 | E | 0 | E | 5 | None | \n",
"| Q10149726 | E | 0 | E | 6 | None | \n",
"| Q10180230 | E | 0 | E | 7 | None | \n",
"| Q10185035 | E | 0 | E | 8 | None | \n",
"| Q10205202 | E | 0 | E | 9 | None | \n",
"| Q10252966 | E | 0 | E | 10 | None | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | C | 0 | E | 14 | High negative | \n",
"| Q10766855 | D | 0 | E | 15 | Moderate negative | \n",
"| Q10827611 | E | 0 | E | 16 | None | \n",
"| Q11093044 | E | 0 | E | 17 | None | \n",
"| Q11934537 | E | 0 | E | 18 | None | \n",
"| Q12133466 | E | 0 | E | 19 | None | \n",
"| Q12264503 | E | 0 | E | 20 | None | \n",
"| Q12267516 | E | 0 | E | 21 | None | \n",
"| Q12304084 | E | 0 | E | 22 | None | \n",
"| Q12443525 | D | 0 | E | 23 | Moderate negative | \n",
"| Q12543904 | E | 0 | E | 24 | None | \n",
"| Q12890205 | E | 0 | E | 25 | None | \n",
"| Q12891524 | E | 0 | E | 26 | None | \n",
"| Q12918202 | E | 0 | E | 27 | None | \n",
"| Q13005653 | E | 0 | E | 28 | None | \n",
"| Q13073896 | E | 0 | E | 29 | None | \n",
"| Q13163823 | E | 0 | E | 30 | None | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 21537863 | High positive | \n",
"| Q31165 | B | 2048330818 | A | 21537864 | Moderate positive | \n",
"| Q40629 | C | 2049755644 | A | 21537865 | High positive | \n",
"| Q105584 | C | 2049926923 | A | 21537866 | High positive | \n",
"| Q4584301 | C | 2052339927 | A | 21537867 | High positive | \n",
"| Q565 | C | 2052996261 | A | 21537868 | High positive | \n",
"| Q1868372 | D | 2056080224 | A | 21537869 | High positive | \n",
"| Q209330 | C | 2060928966 | A | 21537870 | High positive | \n",
"| Q14005 | D | 2063120071 | A | 21537871 | High positive | \n",
"| Q918 | C | 2063217449 | A | 21537872 | High positive | \n",
"| Q150248 | C | 2068796814 | A | 21537873 | High positive | \n",
"| Q866 | B | 2079749157 | A | 21537874 | Moderate positive | \n",
"| Q477675 | C | 2080785713 | A | 21537875 | High positive | \n",
"| Q1967876 | C | 2084215818 | A | 21537876 | High positive | \n",
"| Q750403 | B | 2084693498 | A | 21537877 | Moderate positive | \n",
"| Q355 | C | 2093900731 | A | 21537878 | High positive | \n",
"| Q623578 | C | 2097991400 | A | 21537879 | High positive | \n",
"| Q17299517 | D | 2105487660 | A | 21537880 | High positive | \n",
"| Q33999 | C | 2108672678 | A | 21537881 | High positive | \n",
"| Q2494649 | C | 2114531894 | A | 21537882 | High positive | \n",
"| Q2597810 | C | 2128920607 | A | 21537883 | High positive | \n",
"| Q193563 | C | 2130725560 | A | 21537884 | High positive | \n",
"| Q423048 | C | 2136131564 | A | 21537885 | High positive | \n",
"| Q37312 | C | 2142913121 | A | 21537886 | High positive | \n",
"| Q54919 | C | 2148531382 | A | 21537887 | High positive | \n",
"| Q36578 | C | 2229315598 | A | 21537888 | High positive | \n",
"| Q30 | A | 2277746226 | A | 21537889 | None | \n",
"| Q6581097 | D | 3273952711 | A | 21537890 | High positive | \n",
"| Q5 | C | 5668008721 | A | 21537891 | High positive | \n",
"| Q5296 | C | 12530369761 | A | 21537892 | High positive | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 None \n",
"2 Q10069140 C 0 E 2 High negative \n",
"3 Q10081695 C 0 E 3 High negative \n",
"4 Q10092002 E 0 E 4 None \n",
"5 Q10111267 E 0 E 5 None \n",
"6 Q10149726 E 0 E 6 None \n",
"7 Q10180230 E 0 E 7 None \n",
"8 Q10185035 E 0 E 8 None \n",
"9 Q10205202 E 0 E 9 None \n",
"10 Q10252966 E 0 E 10 None \n",
"11 Q10444494 C 0 E 11 High negative \n",
"12 Q10624171 C 0 E 12 High negative \n",
"13 Q10704108 C 0 E 13 High negative \n",
"14 Q10750354 C 0 E 14 High negative \n",
"15 Q10766855 D 0 E 15 Moderate negative\n",
"16 Q10827611 E 0 E 16 None \n",
"17 Q11093044 E 0 E 17 None \n",
"18 Q11934537 E 0 E 18 None \n",
"19 Q12133466 E 0 E 19 None \n",
"20 Q12264503 E 0 E 20 None \n",
"21 Q12267516 E 0 E 21 None \n",
"22 Q12304084 E 0 E 22 None \n",
"23 Q12443525 D 0 E 23 Moderate negative\n",
"24 Q12543904 E 0 E 24 None \n",
"25 Q12890205 E 0 E 25 None \n",
"26 Q12891524 E 0 E 26 None \n",
"27 Q12918202 E 0 E 27 None \n",
"28 Q13005653 E 0 E 28 None \n",
"29 Q13073896 E 0 E 29 None \n",
"30 Q13163823 E 0 E 30 None \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"21537863 Q1048694 C 2048095025 A 21537863 High positive \n",
"21537864 Q31165 B 2048330818 A 21537864 Moderate positive\n",
"21537865 Q40629 C 2049755644 A 21537865 High positive \n",
"21537866 Q105584 C 2049926923 A 21537866 High positive \n",
"21537867 Q4584301 C 2052339927 A 21537867 High positive \n",
"21537868 Q565 C 2052996261 A 21537868 High positive \n",
"21537869 Q1868372 D 2056080224 A 21537869 High positive \n",
"21537870 Q209330 C 2060928966 A 21537870 High positive \n",
"21537871 Q14005 D 2063120071 A 21537871 High positive \n",
"21537872 Q918 C 2063217449 A 21537872 High positive \n",
"21537873 Q150248 C 2068796814 A 21537873 High positive \n",
"21537874 Q866 B 2079749157 A 21537874 Moderate positive\n",
"21537875 Q477675 C 2080785713 A 21537875 High positive \n",
"21537876 Q1967876 C 2084215818 A 21537876 High positive \n",
"21537877 Q750403 B 2084693498 A 21537877 Moderate positive\n",
"21537878 Q355 C 2093900731 A 21537878 High positive \n",
"21537879 Q623578 C 2097991400 A 21537879 High positive \n",
"21537880 Q17299517 D 2105487660 A 21537880 High positive \n",
"21537881 Q33999 C 2108672678 A 21537881 High positive \n",
"21537882 Q2494649 C 2114531894 A 21537882 High positive \n",
"21537883 Q2597810 C 2128920607 A 21537883 High positive \n",
"21537884 Q193563 C 2130725560 A 21537884 High positive \n",
"21537885 Q423048 C 2136131564 A 21537885 High positive \n",
"21537886 Q37312 C 2142913121 A 21537886 High positive \n",
"21537887 Q54919 C 2148531382 A 21537887 High positive \n",
"21537888 Q36578 C 2229315598 A 21537888 High positive \n",
"21537889 Q30 A 2277746226 A 21537889 None \n",
"21537890 Q6581097 D 3273952711 A 21537890 High positive \n",
"21537891 Q5 C 5668008721 A 21537891 High positive \n",
"21537892 Q5296 C 12530369761 A 21537892 High positive "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## E\n",
"articles_by_pop[prediction == 'E' & pop_class == 'E',\n",
" dissonance := 'None'];\n",
"articles_by_pop[prediction == 'E' & pop_class == 'D',\n",
" dissonance := 'Moderate positive'];\n",
"articles_by_pop[prediction == 'E' & pop_class >= 'C',\n",
" dissonance := 'High positive'];"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Build a matrix where columns are the metric and rows are classes\n",
"create_alt_diss_matrix = function(articledata, metric, classes) {\n",
" d_mtrx = matrix(0, nrow=length(classes), ncol=length(metric));\n",
" rownames(d_mtrx) = classes;\n",
" colnames(d_mtrx) = metric;\n",
"\n",
" ## NOTE: R matrix values are [row,col] dimensions\n",
" for(real_rating in classes) {\n",
" for(diss_rating in metric) {\n",
" d_mtrx[real_rating, diss_rating] = length(articledata[prediction == real_rating & dissonance == diss_rating]$entity_id);\n",
" }\n",
" }\n",
" d_mtrx;\n",
"}\n",
"\n",
"alternative_dissonance_matrix.1 = create_alt_diss_matrix(articles_by_pop,\n",
" dissonance_metric, assessment_classes);\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>High negative</th><th scope=col>Moderate negative</th><th scope=col>None</th><th scope=col>Moderate positive</th><th scope=col>High positive</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>E</th><td> 0.0</td><td> 0.0</td><td>63.6</td><td>16.2</td><td>20.2</td></tr>\n",
"\t<tr><th scope=row>D</th><td> 0.0</td><td>51.9</td><td>20.6</td><td>25.6</td><td> 1.9</td></tr>\n",
"\t<tr><th scope=row>C</th><td>47.6</td><td>17.5</td><td>31.1</td><td> 3.8</td><td> 0.0</td></tr>\n",
"\t<tr><th scope=row>B</th><td>50.7</td><td>37.4</td><td>11.9</td><td> 0.0</td><td> 0.0</td></tr>\n",
"\t<tr><th scope=row>A</th><td> 2.1</td><td>82.1</td><td>15.8</td><td> 0.0</td><td> 0.0</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" & High negative & Moderate negative & None & Moderate positive & High positive\\\\\n",
"\\hline\n",
"\tE & 0.0 & 0.0 & 63.6 & 16.2 & 20.2\\\\\n",
"\tD & 0.0 & 51.9 & 20.6 & 25.6 & 1.9\\\\\n",
"\tC & 47.6 & 17.5 & 31.1 & 3.8 & 0.0\\\\\n",
"\tB & 50.7 & 37.4 & 11.9 & 0.0 & 0.0\\\\\n",
"\tA & 2.1 & 82.1 & 15.8 & 0.0 & 0.0\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | High negative | Moderate negative | None | Moderate positive | High positive | \n",
"|---|---|---|---|---|\n",
"| E | 0.0 | 0.0 | 63.6 | 16.2 | 20.2 | \n",
"| D | 0.0 | 51.9 | 20.6 | 25.6 | 1.9 | \n",
"| C | 47.6 | 17.5 | 31.1 | 3.8 | 0.0 | \n",
"| B | 50.7 | 37.4 | 11.9 | 0.0 | 0.0 | \n",
"| A | 2.1 | 82.1 | 15.8 | 0.0 | 0.0 | \n",
"\n",
"\n"
],
"text/plain": [
" High negative Moderate negative None Moderate positive High positive\n",
"E 0.0 0.0 63.6 16.2 20.2 \n",
"D 0.0 51.9 20.6 25.6 1.9 \n",
"C 47.6 17.5 31.1 3.8 0.0 \n",
"B 50.7 37.4 11.9 0.0 0.0 \n",
"A 2.1 82.1 15.8 0.0 0.0 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Normalise by row\n",
"round(100*prop.table(alternative_dissonance_matrix.1, 1), 1);\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>prediction</th><th scope=col>dissonance</th><th scope=col>dissonant_views</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>E </td><td>None </td><td> 400329769 </td></tr>\n",
"\t<tr><td>C </td><td>High negative </td><td> 123189665 </td></tr>\n",
"\t<tr><td>D </td><td>Moderate negative</td><td> 114004506 </td></tr>\n",
"\t<tr><td>B </td><td>High negative </td><td> 32342610 </td></tr>\n",
"\t<tr><td>D </td><td>None </td><td> 408528194 </td></tr>\n",
"\t<tr><td>E </td><td>Moderate positive</td><td> 1074472055 </td></tr>\n",
"\t<tr><td>C </td><td>Moderate negative</td><td> 489626919 </td></tr>\n",
"\t<tr><td>E </td><td>High positive </td><td> 52243496234 </td></tr>\n",
"\t<tr><td>D </td><td>Moderate positive</td><td> 8106556935 </td></tr>\n",
"\t<tr><td>C </td><td>None </td><td> 16430928920 </td></tr>\n",
"\t<tr><td>B </td><td>Moderate negative</td><td> 2037008974 </td></tr>\n",
"\t<tr><td>A </td><td>High negative </td><td> 641770 </td></tr>\n",
"\t<tr><td>B </td><td>None </td><td> 34201368390 </td></tr>\n",
"\t<tr><td>D </td><td>High positive </td><td> 91682866317 </td></tr>\n",
"\t<tr><td>C </td><td>Moderate positive</td><td> 96979995782 </td></tr>\n",
"\t<tr><td>A </td><td>Moderate negative</td><td> 3635787187 </td></tr>\n",
"\t<tr><td>C </td><td>High positive </td><td>206485542115 </td></tr>\n",
"\t<tr><td>A </td><td>None </td><td> 14171655704 </td></tr>\n",
"\t<tr><td>B </td><td>Moderate positive</td><td> 15140351768 </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lll}\n",
" prediction & dissonance & dissonant\\_views\\\\\n",
"\\hline\n",
"\t E & None & 400329769 \\\\\n",
"\t C & High negative & 123189665 \\\\\n",
"\t D & Moderate negative & 114004506 \\\\\n",
"\t B & High negative & 32342610 \\\\\n",
"\t D & None & 408528194 \\\\\n",
"\t E & Moderate positive & 1074472055 \\\\\n",
"\t C & Moderate negative & 489626919 \\\\\n",
"\t E & High positive & 52243496234 \\\\\n",
"\t D & Moderate positive & 8106556935 \\\\\n",
"\t C & None & 16430928920 \\\\\n",
"\t B & Moderate negative & 2037008974 \\\\\n",
"\t A & High negative & 641770 \\\\\n",
"\t B & None & 34201368390 \\\\\n",
"\t D & High positive & 91682866317 \\\\\n",
"\t C & Moderate positive & 96979995782 \\\\\n",
"\t A & Moderate negative & 3635787187 \\\\\n",
"\t C & High positive & 206485542115 \\\\\n",
"\t A & None & 14171655704 \\\\\n",
"\t B & Moderate positive & 15140351768 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"prediction | dissonance | dissonant_views | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| E | None | 400329769 | \n",
"| C | High negative | 123189665 | \n",
"| D | Moderate negative | 114004506 | \n",
"| B | High negative | 32342610 | \n",
"| D | None | 408528194 | \n",
"| E | Moderate positive | 1074472055 | \n",
"| C | Moderate negative | 489626919 | \n",
"| E | High positive | 52243496234 | \n",
"| D | Moderate positive | 8106556935 | \n",
"| C | None | 16430928920 | \n",
"| B | Moderate negative | 2037008974 | \n",
"| A | High negative | 641770 | \n",
"| B | None | 34201368390 | \n",
"| D | High positive | 91682866317 | \n",
"| C | Moderate positive | 96979995782 | \n",
"| A | Moderate negative | 3635787187 | \n",
"| C | High positive | 206485542115 | \n",
"| A | None | 14171655704 | \n",
"| B | Moderate positive | 15140351768 | \n",
"\n",
"\n"
],
"text/plain": [
" prediction dissonance dissonant_views\n",
"1 E None 400329769 \n",
"2 C High negative 123189665 \n",
"3 D Moderate negative 114004506 \n",
"4 B High negative 32342610 \n",
"5 D None 408528194 \n",
"6 E Moderate positive 1074472055 \n",
"7 C Moderate negative 489626919 \n",
"8 E High positive 52243496234 \n",
"9 D Moderate positive 8106556935 \n",
"10 C None 16430928920 \n",
"11 B Moderate negative 2037008974 \n",
"12 A High negative 641770 \n",
"13 B None 34201368390 \n",
"14 D High positive 91682866317 \n",
"15 C Moderate positive 96979995782 \n",
"16 A Moderate negative 3635787187 \n",
"17 C High positive 206485542115 \n",
"18 A None 14171655704 \n",
"19 B Moderate positive 15140351768 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Number of dissonant views per assessment class and amount of dissonance\n",
"articles_by_pop[, list(dissonant_views=sum(page_views)), by=list(prediction, dissonance)];\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>dissonance</th><th scope=col>dissonant_views</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>None </td><td> 65612810977 </td></tr>\n",
"\t<tr><td>High negative </td><td> 156174045 </td></tr>\n",
"\t<tr><td>Moderate negative</td><td> 6276427586 </td></tr>\n",
"\t<tr><td>Moderate positive</td><td>121301376540 </td></tr>\n",
"\t<tr><td>High positive </td><td>350411904666 </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|ll}\n",
" dissonance & dissonant\\_views\\\\\n",
"\\hline\n",
"\t None & 65612810977 \\\\\n",
"\t High negative & 156174045 \\\\\n",
"\t Moderate negative & 6276427586 \\\\\n",
"\t Moderate positive & 121301376540 \\\\\n",
"\t High positive & 350411904666 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"dissonance | dissonant_views | \n",
"|---|---|---|---|---|\n",
"| None | 65612810977 | \n",
"| High negative | 156174045 | \n",
"| Moderate negative | 6276427586 | \n",
"| Moderate positive | 121301376540 | \n",
"| High positive | 350411904666 | \n",
"\n",
"\n"
],
"text/plain": [
" dissonance dissonant_views\n",
"1 None 65612810977 \n",
"2 High negative 156174045 \n",
"3 Moderate negative 6276427586 \n",
"4 Moderate positive 121301376540 \n",
"5 High positive 350411904666 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Calculations of total number of dissonant views per dissonance\n",
"articles_by_pop[, list(dissonant_views=sum(page_views)), by=list(dissonance)];"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"543758693814"
],
"text/latex": [
"543758693814"
],
"text/markdown": [
"543758693814"
],
"text/plain": [
"[1] 543758693814"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"articles_by_pop[,sum(as.numeric(page_views))];"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"12.0947727255594"
],
"text/latex": [
"12.0947727255594"
],
"text/markdown": [
"12.0947727255594"
],
"text/plain": [
"[1] 12.09477"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"0.0229368304409811"
],
"text/latex": [
"0.0229368304409811"
],
"text/markdown": [
"0.0229368304409811"
],
"text/plain": [
"[1] 0.02293683"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"1.23145971375505"
],
"text/latex": [
"1.23145971375505"
],
"text/markdown": [
"1.23145971375505"
],
"text/plain": [
"[1] 1.23146"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"22.107092420945"
],
"text/latex": [
"22.107092420945"
],
"text/markdown": [
"22.107092420945"
],
"text/plain": [
"[1] 22.10709"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"64.5437383092995"
],
"text/latex": [
"64.5437383092995"
],
"text/markdown": [
"64.5437383092995"
],
"text/plain": [
"[1] 64.54374"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Proportions\n",
"100*65938379920/545180810059;\n",
"100*125047198/545180810059;\n",
"100*6713682043/545180810059;\n",
"100*120523625541/545180810059;\n",
"100*351880075357/545180810059;"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 87% of views are high positive"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>dissonant_views</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>12.06653</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|l}\n",
" dissonant\\_views\\\\\n",
"\\hline\n",
"\t 12.06653\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"dissonant_views | \n",
"|---|\n",
"| 12.06653 | \n",
"\n",
"\n"
],
"text/plain": [
" dissonant_views\n",
"1 12.06653 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"100*(articles_by_pop[, list(dissonant_views=sum(page_views)), by=list(dissonance)][1][,c('dissonant_views')]/articles_by_pop[,sum(as.numeric(page_views))])"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>dissonant_views</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>0.0287212</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|l}\n",
" dissonant\\_views\\\\\n",
"\\hline\n",
"\t 0.0287212\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"dissonant_views | \n",
"|---|\n",
"| 0.0287212 | \n",
"\n",
"\n"
],
"text/plain": [
" dissonant_views\n",
"1 0.0287212 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"100*(articles_by_pop[, list(dissonant_views=sum(page_views)), by=list(dissonance)][2][,c('dissonant_views')]/articles_by_pop[,sum(as.numeric(page_views))])"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>dissonant_views</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>1.154267</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|l}\n",
" dissonant\\_views\\\\\n",
"\\hline\n",
"\t 1.154267\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"dissonant_views | \n",
"|---|\n",
"| 1.154267 | \n",
"\n",
"\n"
],
"text/plain": [
" dissonant_views\n",
"1 1.154267 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"100*(articles_by_pop[, list(dissonant_views=sum(page_views)), by=list(dissonance)][3][,c('dissonant_views')]/articles_by_pop[,sum(as.numeric(page_views))])"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>dissonant_views</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>22.30794</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|l}\n",
" dissonant\\_views\\\\\n",
"\\hline\n",
"\t 22.30794\\\\\n",
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],
"text/markdown": [
"\n",
"dissonant_views | \n",
"|---|\n",
"| 22.30794 | \n",
"\n",
"\n"
],
"text/plain": [
" dissonant_views\n",
"1 22.30794 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"100*(articles_by_pop[, list(dissonant_views=sum(page_views)), by=list(dissonance)][4][,c('dissonant_views')]/articles_by_pop[,sum(as.numeric(page_views))])"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>dissonant_views</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>64.44254</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|l}\n",
" dissonant\\_views\\\\\n",
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"\t 64.44254\\\\\n",
"\\end{tabular}\n"
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"text/markdown": [
"\n",
"dissonant_views | \n",
"|---|\n",
"| 64.44254 | \n",
"\n",
"\n"
],
"text/plain": [
" dissonant_views\n",
"1 64.44254 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"100*(articles_by_pop[, list(dissonant_views=sum(page_views)), by=list(dissonance)][5][,c('dissonant_views')]/articles_by_pop[,sum(as.numeric(page_views))])"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "R [r]",
"language": "R",
"name": "R [r]"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.3.2"
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"nbformat": 4,
"nbformat_minor": 0
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| mit |
kingmolnar/DataScienceProgramming | 12-Probability-Based-Learning/plot_classifier_comparison_orig.ipynb | 1 | 8854 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Classifier comparison\n",
"\n",
"\n",
"A comparison of a several classifiers in scikit-learn on synthetic datasets.\n",
"The point of this example is to illustrate the nature of decision boundaries\n",
"of different classifiers.\n",
"This should be taken with a grain of salt, as the intuition conveyed by\n",
"these examples does not necessarily carry over to real datasets.\n",
"\n",
"Particularly in high-dimensional spaces, data can more easily be separated\n",
"linearly and the simplicity of classifiers such as naive Bayes and linear SVMs\n",
"might lead to better generalization than is achieved by other classifiers.\n",
"\n",
"The plots show training points in solid colors and testing points\n",
"semi-transparent. The lower right shows the classification accuracy on the test\n",
"set.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-08T18:35:01.207911",
"start_time": "2017-11-08T18:35:01.203728"
},
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Automatically created module for IPython interactive environment\n"
]
}
],
"source": [
"print __doc__ "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-08T18:35:16.613412",
"start_time": "2017-11-08T18:35:15.412063"
},
"collapsed": true
},
"outputs": [],
"source": [
"# Code source: Gaël Varoquaux\n",
"# Andreas Müller\n",
"# Modified for documentation by Jaques Grobler\n",
"# License: BSD 3 clause\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.colors import ListedColormap\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.datasets import make_moons, make_circles, make_classification\n",
"from sklearn.neural_network import MLPClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.svm import SVC\n",
"from sklearn.gaussian_process import GaussianProcessClassifier\n",
"from sklearn.gaussian_process.kernels import RBF\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-08T18:35:32.572274",
"start_time": "2017-11-08T18:35:32.534985"
},
"collapsed": true
},
"outputs": [],
"source": [
"h = .02 # step size in the mesh\n",
"\n",
"names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
" \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
" \"Naive Bayes\", \"QDA\"]\n",
"\n",
"classifiers = [\n",
" KNeighborsClassifier(3),\n",
" SVC(kernel=\"linear\", C=0.025),\n",
" SVC(gamma=2, C=1),\n",
" GaussianProcessClassifier(1.0 * RBF(1.0)),\n",
" DecisionTreeClassifier(max_depth=5),\n",
" RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n",
" MLPClassifier(alpha=1),\n",
" AdaBoostClassifier(),\n",
" GaussianNB(),\n",
" QuadraticDiscriminantAnalysis()]\n",
"\n",
"X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,\n",
" random_state=1, n_clusters_per_class=1)\n",
"rng = np.random.RandomState(2)\n",
"X += 2 * rng.uniform(size=X.shape)\n",
"linearly_separable = (X, y)\n",
"\n",
"datasets = [make_moons(noise=0.3, random_state=0),\n",
" make_circles(noise=0.2, factor=0.5, random_state=1),\n",
" linearly_separable\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"start_time": "2017-11-08T23:35:37.289Z"
},
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib64/python2.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.\n",
" % (), ConvergenceWarning)\n"
]
}
],
"source": [
"figure = plt.figure(figsize=(27, 9))\n",
"i = 1\n",
"# iterate over datasets\n",
"for ds_cnt, ds in enumerate(datasets):\n",
" # preprocess dataset, split into training and test part\n",
" X, y = ds\n",
" X = StandardScaler().fit_transform(X)\n",
" X_train, X_test, y_train, y_test = \\\n",
" train_test_split(X, y, test_size=.4, random_state=42)\n",
"\n",
" x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
" y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
" xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
" np.arange(y_min, y_max, h))\n",
"\n",
" # just plot the dataset first\n",
" cm = plt.cm.RdBu\n",
" cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
" ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
" if ds_cnt == 0:\n",
" ax.set_title(\"Input data\")\n",
" # Plot the training points\n",
" ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,\n",
" edgecolors='k')\n",
" # and testing points\n",
" ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n",
" edgecolors='k')\n",
" ax.set_xlim(xx.min(), xx.max())\n",
" ax.set_ylim(yy.min(), yy.max())\n",
" ax.set_xticks(())\n",
" ax.set_yticks(())\n",
" i += 1\n",
"\n",
" # iterate over classifiers\n",
" for name, clf in zip(names, classifiers):\n",
" ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
" clf.fit(X_train, y_train)\n",
" score = clf.score(X_test, y_test)\n",
"\n",
" # Plot the decision boundary. For that, we will assign a color to each\n",
" # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
" if hasattr(clf, \"decision_function\"):\n",
" Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
" else:\n",
" Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
"\n",
" # Put the result into a color plot\n",
" Z = Z.reshape(xx.shape)\n",
" ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n",
"\n",
" # Plot also the training points\n",
" ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,\n",
" edgecolors='k')\n",
" # and testing points\n",
" ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,\n",
" edgecolors='k', alpha=0.6)\n",
"\n",
" ax.set_xlim(xx.min(), xx.max())\n",
" ax.set_ylim(yy.min(), yy.max())\n",
" ax.set_xticks(())\n",
" ax.set_yticks(())\n",
" if ds_cnt == 0:\n",
" ax.set_title(name)\n",
" ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),\n",
" size=15, horizontalalignment='right')\n",
" i += 1\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
}
],
"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": "2.7.5"
},
"toc": {
"nav_menu": {
"height": "30px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| cc0-1.0 |
zekearneodo/ephys-tools | ephysScripts/plot_playground.ipynb | 1 | 100206 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computer: server\n"
]
}
],
"source": [
"%matplotlib inline\n",
"import pdb\n",
"import sys\n",
"import pandas as pd\n",
"import numpy as np\n",
"import scipy.io as sio\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"import scipy.signal as sg\n",
"import math\n",
"import scipy as sp\n",
"import socket\n",
"import os\n",
"\n",
"matplotlib.style.use('ggplot')\n",
"\n",
"comp_name=socket.gethostname()\n",
"if comp_name == 'Ezequiels-MacBook-Pro.local':\n",
" print 'Computer: ' + comp_name\n",
" sys.path.append('/Users/zeke/experiment/ephysDataManagement/ephysScripts')\n",
" experiment_folder = os.path.join('/Users','zeke','experiment')\n",
"else:\n",
" print 'Computer: ' + 'server'\n",
" sys.path.append('/experiment/ephysDataManagement/ephysScripts')\n",
" experiment_folder = os.path.join('/','experiment')\n",
" \n",
"import unitToolsv2\n",
"from data_handling import ephys_names as en\n",
"from data_handling.basic_plot import decim, plot_raster\n",
"from data_handling import data_load as dl"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mouse = 'ZKawakeM72'\n",
"sess = 13\n",
"rec = 'e'\n",
"fn = en.file_names(mouse,sess,rec,root=experiment_folder)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['ZKawakeM72_013_f_010', 'ZKawakeM72_013_e_010']\n"
]
}
],
"source": [
"mat_file = os.path.join(fn.fold_exp_data,'ZKawakeM72_013_010_cell.mat')\n",
"r=dl.load_cell(mat_file, as_dict=True)\n",
"print r.keys()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'mat_struct' object has no attribute 'sniffPhase'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-24-01a5655f4c8f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mmat_file\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfold_exp_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'ZKawakeM72_013_e_trialsBase.mat'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0msniffs\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mdl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_sniff_base\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmat_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mas_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[1;31m#plot all the baseline sniffs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0msnif_plot\u001b[0m \u001b[1;33m=\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[0m\n",
"\u001b[1;32m/experiment/ephysDataManagement/ephysScripts/data_handling/data_load.py\u001b[0m in \u001b[0;36mload_sniff_base\u001b[1;34m(mat_file_path, as_dict)\u001b[0m\n\u001b[0;32m 39\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrialsBase\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 40\u001b[0m \u001b[0msniff_flow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msniffFlow\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m \u001b[0msniff_phase\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msniffPhase\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_tpoints\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 42\u001b[0m \u001b[0msniff_start\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAttributeError\u001b[0m: 'mat_struct' object has no attribute 'sniffPhase'"
]
}
],
"source": [
"#Load a baseline sniff file\n",
"mat_file = os.path.join(fn.fold_exp_data,'ZKawakeM72_013_e_trialsBase.mat')\n",
"\n",
"sniffs= dl.load_sniff_base(mat_file, as_dict=False)\n",
"#plot all the baseline sniffs\n",
"snif_plot = plt.figure()\n",
"all_ax = snif_plot.add_axes([0, 0, 1, .4])\n",
"avg_ax = snif_plot.add_axes([0, .5, 1, .4])\n",
"\n",
"t=np.arange(0, 450, 1)\n",
"avg_line = avg_ax.plot(np.average(-sniffs['flow'][250:750,:],axis=1))\n",
"lines=all_ax.plot(-sniffs['flow'][250:750,:50])"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'trialsBase'"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.path.split(mat_file)[-1].split('.')[0].split('_')[-1]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AssertionError",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-19-a8b837c985ed>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mmat_file\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfold_exp_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'ZKawakeM72_010_001_spikesBase.mat'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mbases\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_baseline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmat_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 6\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mbases\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[0mspikes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mbases\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ma_key\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ma_key\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mbases\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/experiment/ephysDataManagement/ephysScripts/data_handling/data_load.py\u001b[0m in \u001b[0;36mload_baseline\u001b[1;34m(mat_file_path, as_dict)\u001b[0m\n\u001b[0;32m 87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 88\u001b[0m \u001b[1;31m#Load a baseline raster file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 89\u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0mload_baseline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmat_file_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mas_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\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 90\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 91\u001b[0m \u001b[1;32massert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmat_file_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAssertionError\u001b[0m: "
]
}
],
"source": [
"#load the spikes baseline raster file\n",
"'ZKawakeM72_010_001_cell.mat'\n",
"mat_file = os.path.join(fn.fold_exp_data,'ZKawakeM72_010_001_spikesBase.mat')\n",
"\n",
"bases = dl.load_baseline(mat_file)\n",
"print bases.keys()\n",
"spikes = [bases[a_key] for a_key in bases.keys()]\n",
"\n",
"#spikes = load_baseline(mat_file, as_dict = False)\n",
"#print spikes\n",
"#print spikes[0]['u_id']\n",
"#print spikes[0]['id']\n",
"\n",
"sr_spikes = spikes[0]['spikes']\n",
"sr_t0 = spikes[0]['t_0']\n",
"\n",
"#plot all the baseline sniffs\n",
"sr_plot = plt.figure()\n",
"ras_ax = sr_plot.add_axes([0, 0, 1, .4])\n",
"hist_ax = sr_plot.add_axes([0, .5, 1, .4])\n",
"t0 = 500\n",
"t1 = 0\n",
"t2= 2500\n",
"bin_size=10\n",
"\n",
"#plot the raster\n",
"lines,_ = plot_raster(sr_spikes,t0=t0,t1=t1,t2=t2,ax=ras_ax)\n",
"ras_ax.set_xlim(-500,2500)\n",
"\n",
"#the psth\n",
"hist_line, hist_ax = plot_raster(sr_spikes,t0=t0, t1=t1,t2=t2, bin_size=15, ax=hist_ax)\n",
"#hist_ax.set_ylim(0.9,1.1)\n",
"hist_ax.set_xlim(-500,2500)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['ZKawakeM72_013_f_010', 'ZKawakeM72_013_e_010']\n",
"['/experiment/export_data/ZKawakeM72_013_f_trial.mat', '/experiment/export_data/ZKawakeM72_013_e_trial.mat']\n",
"/experiment/export_data/ZKawakeM72_013_f_trial.mat\n",
"/experiment/export_data/ZKawakeM72_013_e_trial.mat\n",
"['/experiment/export_data/ZKawakeM72_013_f_noStimSniff.mat', '/experiment/export_data/ZKawakeM72_013_e_noStimSniff.mat']\n",
"/experiment/export_data/ZKawakeM72_013_f_noStimSniff.mat\n"
]
},
{
"ename": "ValueError",
"evalue": "dictionary update sequence element #0 has length 4; 2 is required",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-4-14c228bd9854>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# get all the cells\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mcells_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfold_exp_data\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mrecords\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_cells\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcells_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m/experiment/ephysDataManagement/ephysScripts/data_handling/data_load.py\u001b[0m in \u001b[0;36mload_cells\u001b[1;34m(cells_path)\u001b[0m\n\u001b[0;32m 308\u001b[0m for a_rec in unit_recs.itervalues() if a_rec['rec_id'] not in load_dict]\n\u001b[0;32m 309\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mpaths\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 310\u001b[1;33m \u001b[1;33m[\u001b[0m\u001b[0mload_dict\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mload_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ma_path\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpaths\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 311\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 312\u001b[0m records = {'responses': responses,\n",
"\u001b[1;31mValueError\u001b[0m: dictionary update sequence element #0 has length 4; 2 is required"
]
}
],
"source": [
"# get all the cells\n",
"cells_path = fn.fold_exp_data\n",
"records = dl.load_cells(cells_path)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"['KPawakeM72_016_a_023',\n",
" 'ZKawakeM72_009_a_005',\n",
" 'KPawakeM72_016_a_021',\n",
" 'KPawakeM72_016_a_020',\n",
" 'ZKawakeM72_009_a_001',\n",
" 'KPawakeM72_016_a_025',\n",
" 'ZKawakeM72_009_a_002',\n",
" 'KPawakeM72_021_b_023',\n",
" 'KPawakeM72_021_b_026',\n",
" 'KPawakeM72_021_b_028',\n",
" 'KPawakeM72_021_b_029',\n",
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" 'KPawakeM72_016_a_018',\n",
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" 'ZKawakeM72_004_h_019',\n",
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" 'ZKawakeM72_004_h_017',\n",
" 'KPawakeM72_019_a_007',\n",
" 'ZKawakeM72_020_c_024',\n",
" 'ZKawakeM72_022_d_004',\n",
" 'ZKawakeM72_004_h_011',\n",
" 'ZKawakeM72_022_d_002',\n",
" 'ZKawakeM72_011_b_005',\n",
" 'ZKawakeM72_004_c_004',\n",
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" 'KPawakeM72_014_a_001',\n",
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" 'ZKawakeM72_020_g_001',\n",
" 'KPawakeM72_014_a_004',\n",
" 'KPawakeM72_019_d_010',\n",
" 'KPawakeM72_019_d_011',\n",
" 'ZKawakeM72_020_d_001',\n",
" 'KPawakeM72_019_d_013',\n",
" 'KPawakeM72_019_d_014',\n",
" 'KPawakeM72_019_d_015',\n",
" 'KPawakeM72_019_d_018',\n",
" 'ZKawakeM72_020_d_009',\n",
" 'ZKawakeM72_020_c_008',\n",
" 'ZKawakeM72_020_e_001',\n",
" 'KPawakeM72_014_c_012',\n",
" 'ZKawakeM72_020_c_001',\n",
" 'ZKawakeM72_020_c_002',\n",
" 'ZKawakeM72_020_c_003',\n",
" 'ZKawakeM72_020_c_004',\n",
" 'ZKawakeM72_010_b_001',\n",
" 'ZKawakeM72_020_c_006',\n",
" 'ZKawakeM72_010_b_003',\n",
" 'ZKawakeM72_010_b_002',\n",
" 'ZKawakeM72_027_d_005',\n",
" 'KPawakeM72_019_b_001',\n",
" 'KPawakeM72_019_b_003',\n",
" 'KPawakeM72_019_b_002',\n",
" 'KPawakeM72_019_b_005',\n",
" 'KPawakeM72_019_b_004',\n",
" 'KPawakeM72_019_b_007',\n",
" 'KPawakeM72_019_b_006',\n",
" 'KPawakeM72_021_a_010',\n",
" 'KPawakeM72_021_a_011',\n",
" 'ZKawakeM72_004_g_016',\n",
" 'KPawakeM72_021_a_014',\n",
" 'KPawakeM72_019_a_002',\n",
" 'KPawakeM72_021_a_016',\n",
" 'KPawakeM72_021_a_017',\n",
" 'KPawakeM72_021_a_018',\n",
" 'ZKawakeM72_020_g_016',\n",
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" 'ZKawakeM72_021_a_002',\n",
" 'KPawakeM72_019_d_002',\n",
" 'KPawakeM72_019_d_007',\n",
" 'ZKawakeM72_010_c_001',\n",
" 'KPawakeM72_014_a_006',\n",
" 'ZKawakeM72_013_f_010',\n",
" 'KPawakeM72_019_a_018',\n",
" 'KPawakeM72_019_a_011',\n",
" 'KPawakeM72_019_a_010',\n",
" 'KPawakeM72_019_a_013',\n",
" 'ZKawakeM72_020_e_011',\n",
" 'KPawakeM72_019_a_014',\n",
" 'ZKawakeM72_020_e_013',\n",
" 'ZKawakeM72_020_e_012',\n",
" 'ZKawakeM72_020_c_026',\n",
" 'KPawakeM72_817_f_001',\n",
" 'ZKawakeM72_011_b_011',\n",
" 'ZKawakeM72_020_c_022',\n",
" 'ZKawakeM72_020_c_023',\n",
" 'KPawakeM72_021_a_003',\n",
" 'ZKawakeM72_023_b_002',\n",
" 'KPawakeM72_021_a_001',\n",
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" 'KPawakeM72_021_a_006',\n",
" 'KPawakeM72_021_a_005',\n",
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" 'ZKawakeM72_020_g_030',\n",
" 'KPawakeM72_019_a_015',\n",
" 'ZKawakeM72_004_i_002',\n",
" 'ZKawakeM72_023_b_003',\n",
" 'ZKawakeM72_013_e_011',\n",
" 'ZKawakeM72_013_e_010',\n",
" 'ZKawakeM72_023_b_004',\n",
" 'ZKawakeM72_005_a_001',\n",
" 'ZKawakeM72_011_a_003',\n",
" 'ZKawakeM72_023_b_005',\n",
" 'ZKawakeM72_011_a_006',\n",
" 'ZKawakeM72_011_a_007',\n",
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" 'ZKawakeM72_011_d_001',\n",
" 'KPawakeM72_021_b_006',\n",
" 'KPawakeM72_021_b_001',\n",
" 'KPawakeM72_021_b_003',\n",
" 'ZKawakeM72_020_c_025',\n",
" 'ZKawakeM72_020_f_014',\n",
" 'ZKawakeM72_004_c_012',\n",
" 'KPawakeM72_817_e_004',\n",
" 'ZKawakeM72_027_c_003',\n",
" 'KPawakeM72_817_e_006',\n",
" 'ZKawakeM72_004_a_025',\n",
" 'KPawakeM72_817_e_001',\n",
" 'KPawakeM72_817_e_002',\n",
" 'KPawakeM72_817_e_003',\n",
" 'ZKawakeM72_006_a_001',\n",
" 'KPawakeM72_021_b_016',\n",
" 'KPawakeM72_021_b_012',\n",
" 'KPawakeM72_021_b_011',\n",
" 'KPawakeM72_021_b_010',\n",
" 'ZKawakeM72_027_a_001',\n",
" 'ZKawakeM72_004_h_001',\n",
" 'ZKawakeM72_004_f_011',\n",
" 'KPawakeM72_021_b_030',\n",
" 'KPawakeM72_014_b_017',\n",
" 'ZKawakeM72_004_h_002',\n",
" 'ZKawakeM72_020_g_031',\n",
" 'ZKawakeM72_011_c_012',\n",
" 'ZKawakeM72_020_g_033',\n",
" 'ZKawakeM72_020_g_032',\n",
" 'ZKawakeM72_004_f_014',\n",
" 'ZKawakeM72_020_g_028',\n",
" 'ZKawakeM72_004_i_022',\n",
" 'ZKawakeM72_004_i_023',\n",
" 'ZKawakeM72_004_i_020',\n",
" 'ZKawakeM72_004_i_021',\n",
" 'KPawakeM72_021_b_031',\n",
" 'ZKawakeM72_020_c_007',\n",
" 'ZKawakeM72_020_h_021',\n",
" 'ZKawakeM72_020_h_020',\n",
" 'KPawakeM72_014_b_012']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"records['responses'].keys()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'records' 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-14-a4e1f83bff09>\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[0mrecords\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'meta'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'id'\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[0mrecords\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'rec_id'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m#baselines.keys()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrecords\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m#records[4][0].keys()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'records' is not defined"
]
}
],
"source": [
"print records[5]['meta']['id']\n",
"print records[5]['rec_id']\n",
"#baselines.keys()\n",
"print len(records)\n",
"#records[4][0].keys()\n",
"baselines[records[5]['meta']['id']].keys()\n",
"\n",
"unit_recs = records[5:8]\n",
"print base_sniff.keys()\n",
"print 'rec'\n",
"#rec_trials.keys()\n",
"print rec_trials[records[5]['rec_id']].keys()\n",
"print base_sniff[records[5]['rec_id']].keys()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<type 'numpy.ndarray'>\n"
]
},
{
"data": {
"text/plain": [
"'ZKawakeM72_010_c'"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"u_f = unit_files[-4]\n",
"#print u_f\n",
"rec_file = os.path.join(fn.fold_exp_data,u_f)\n",
"#records = load_cell(rec_file)\n",
"#print sio.whosmat(mat_file)\n",
"#print records\n",
"\n",
"cell_data = sio.loadmat(rec_file, struct_as_record=False, squeeze_me=True)\n",
"\n",
"records = []\n",
"print type(cell_data['raster'])\n",
"#print len(cell_data['raster'])\n",
"#num_recs = len(cell_data['raster']) #num of recs the cell spans\n",
"\n",
"if type(cell_data['raster']) == np.ndarray:\n",
" for rec in cell_data['raster']:\n",
" #print rec\n",
" record = get_rec(rec)\n",
" records.append(record)\n",
"else:\n",
" records.append(get_rec(cell_data['raster']))\n",
" \n",
"#records = load_cell(rec_file)\n",
"\n",
"#print one_cell['uid']\n",
"#cell_data['raster'][0][1]['odors'].shape\n",
"records[1]['rec_id']"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('unit', (1, 1), 'struct')]\n"
]
}
],
"source": [
"# Load a unit array\n",
"# todo: complete\n",
"mat_file = os.path.join(fn.fold_exp_data,'ZKawakeM72_027_e_spikes.mat')\n",
"cell_data = sio.loadmat(mat_file, struct_as_record=False, squeeze_me=True)\n",
"print sio.whosmat(mat_file)\n",
"unit = cell_data['unit']\n",
"unit.times\n",
"u=np.array(unit.times,dtype=np.float)\n",
"unit = load_unit(mat_file)\n",
"unit.keys()\n",
"unit['ZKawakeM72_027_e_004']['times']\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ZKawakeM72_020_c\n",
"['trialId', 't_0', 'concs', 'odors', 'spikes']\n"
]
}
],
"source": [
"## plot the psth of an odor, by odorName and concentration\n",
"def conc_compare(conc1, conc2, tolerance=1.5):\n",
" return 1./float(tolerance) < float(conc1)/float(conc2) and float(conc1)/float(conc2) < float(tolerance)\n",
"\n",
"odor_name = ['2-hydroxyacetophenone','2hydroxyacetophenone']\n",
"odor_conc = 0.0051\n",
"cell_id = 'ZKawakeM72_020_c_001'\n",
"#get the rec\n",
"a_record = records['responses'][cell_id]\n",
"print a_record['rec_id']\n",
"print a_record['odor_resp'].keys()\n",
"#get the indexes of the trials with this odor\n",
"this_odor_conc = [i for i in range(len(a_record['odor_resp']['odors'])) if a_record['odor_resp']['odors'][i].lower() in odor_name \\\n",
" and conc_compare(a_record['odor_resp']['concs'][i], odor_conc)\n",
" ]\n",
"#plot the psth\n",
"#\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['trialId', 't_0', 'concs', 'odors', 'spikes']\n"
]
}
],
"source": [
"print a_record['odor_resp'].keys()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb9e1c8f5d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot a raster for a record, odor, concentration\n",
"\n",
"sr_spikes = a_record['odor_resp']['spikes'][this_odor_conc,:]\n",
"sr_t0 = a_record['odor_resp']['t_0']\n",
"\n",
"#plot the psth\n",
"sr_plot = plt.figure()\n",
"ras_ax = sr_plot.add_axes([0, 0, 1, .45])\n",
"hist_ax = sr_plot.add_axes([0, .5, 1, .45])\n",
"t0 = 200\n",
"t1 = 2800\n",
"t2= 3400\n",
"bin_size=15\n",
"\n",
"#plot the raster\n",
"lines,_ = plot_raster(sr_spikes,t0=t0,t1=t1,t2=t2,ax=ras_ax)\n",
"ras_ax.set_xlim(-t0,t2-t1-t0)\n",
"\n",
"#the psth\n",
"hist_line, hist_ax = plot_raster(sr_spikes,t0=t0, t1=t1,t2=t2, bin_size=bin_size, ax=hist_ax)\n",
"#hist_ax.set_ylim(0.9,1.1)\n",
"hist_ax.set_xlim(-t0,t2-t1-t0)\n",
"hist_ax.set_xticklabels([])\n",
"\n",
"#the baseline\n",
"#get the baseline for the cell\n",
"bl_spikes = records['baselines'][a_record['meta']['id']]['spikes']\n",
"bl_t0 = records['baselines'][a_record['meta']['id']]['t_0']\n",
"t1=300\n",
"t0=200\n",
"t2=900\n",
"base_line, hist_ax = plot_raster(bl_spikes,t0=t0, t1=t1,t2=t2, bin_size=bin_size, ax=hist_ax)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def cells_for_odor(responses, odor_aliases, odor_conc = ''):\n",
" #finds all the cells that respond to an odor, and set of concentrations\n",
" #returns sub set of responses\n",
" odor_responses = {};\n",
" for key, response in responses.iteritems():\n",
" is_right_odor = any([x in response['odor_resp']['odors'] for x in aliases])\n",
" if is_right_odor:\n",
" if odor_conc == '':\n",
" is_right_conc = True\n",
" else:\n",
" is_right_conc = any([conc_compare(x, y) for x in response['odor_resp']['concs'] for y in odor_conc])\n",
" if is_right_conc:\n",
" odor_responses.update(responses[key])\n",
" return odor_responses\n"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0012144000502303243"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a_record['odor_resp']['concs'][3]"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"aliases = ['menthone', 'mentone']\n",
"odor_conc = [0.0051]\n",
"conc = [a_record['odor_resp']['concs'][0], a_record['odor_resp']['concs'][0]]\n",
"\n",
"any([x in records['responses']['ZKawakeM72_027_d_003']['odor_resp']['o'] for x in aliases])"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"is_right_conc = False\n",
"for conc in odor_conc:\n",
" is_right_conc = is_right_conc & any([conc_compare(x, conc) for x in a_record['odor_resp']['concs']])"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"any([conc_compare(x, y) for x in a_record['odor_resp']['concs'] for y in odor_conc])"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ment_cells = cells_for_odor(records['responses'],aliases)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['meta', 'rec_id', 'odor_resp', 'light_resp']\n",
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"['meta', 'rec_id', 'odor_resp', 'light_resp']\n",
"['meta', 'rec_id', 'odor_resp', 'light_resp']\n",
"['meta', 'rec_id', 'odor_resp', 'light_resp']\n"
]
}
],
"source": [
"sel_resp = {};\n",
"for response in records['responses']:\n",
" print records['responses'][response].keys()\n",
" sel_resp.update(records['responses'][response])\n"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "TypeError",
"evalue": "'NoneType' object has no attribute '__getitem__'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-101-d2a2ea8f2ada>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mment_cells\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m: 'NoneType' object has no attribute '__getitem__'"
]
}
],
"source": [
"one_fieldment_cells[1]"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('noStimSniffs', (1, 2336), 'struct')]\n",
"<scipy.io.matlab.mio5_params.mat_struct object at 0x7fbd7c1540d0>\n"
]
}
],
"source": [
"mat_file = os.path.join(fn.fold_exp_data,'ZKawakeM72_013_010_noStimSniff.mat')\n",
"cell_data = sio.loadmat(mat_file, struct_as_record=False, squeeze_me=True)\n",
"print sio.whosmat(mat_file)\n",
"print cell_data['noStimSniffs'][1]\n",
"one_sniff = cell_data['noStimSniffs'][1]"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2336"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cell_data['noStimSniffs'].shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "TypeError",
"evalue": "data type not understood",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-71-d93c4c5abcda>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Hello'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'World'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m: data type not understood"
]
}
],
"source": [
"x=np.array((np.array([]), 'Hello'), (np.array([]), 'World'))"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "TypeError",
"evalue": "data type \"np.ndarray\" not understood",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-75-17f618be52b8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'np.ndarray,a10'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m: data type \"np.ndarray\" not understood"
]
}
],
"source": [
"x = np.zeros((2,), dtype=('np.ndarray,a10'))"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3.0"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x[1][1]"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y=np.array([])\n",
"type(y)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.ndarray"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "Required argument 'shape' (pos 1) not found",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-136-129d1c985431>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mones\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'foo'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'bar'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mones\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'foo'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'bar'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: Required argument 'shape' (pos 1) not found"
]
}
],
"source": [
"x=np.ones(3, dtype=np.dtype([('foo', int), ('bar', np.ndarray(dtype=np.float))]))\n",
"y=np.ones(3, dtype=np.dtype([('foo', int), ('bar', np.ndarray(dtype=np.float))]))"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x[1]['bar']=np.ndarray([1,2])"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"z=np.concatenate((x,y))"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"z[3]['bar']"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 1, 1, 1])"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"z['foo']"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y=np.ndarray((3,), dtype=np.dtype([('foo', int), ('bar', np.ndarray)]))"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array((0, None), \n",
" dtype=[('foo', '<i8'), ('bar', 'O')])"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y[0]=(3,[1,2,5])"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([(3, [1, 2, 5]), (0, None), (0, None)], \n",
" dtype=[('foo', '<i8'), ('bar', 'O')])"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "'float' object is not callable",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-134-33d9c10faeeb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m: 'float' object is not callable"
]
}
],
"source": [
"y=np.nan([2,3])"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0., 0., 0.])"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.zeros((3,),dtype=np.float)"
]
},
{
"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 |
ralph-group/pymeasure | examples/Notebook Experiments/script.ipynb | 1 | 3125 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ```Experiment``` class for live in-line plotting with jupyter\n",
"This example uses the ```Experiment``` class to create a measurement from a ```procedure``` object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile procedures.py\n",
"import random\n",
"from time import sleep\n",
"\n",
"import logging\n",
"log = logging.getLogger('')\n",
"log.addHandler(logging.NullHandler())\n",
"\n",
"from pymeasure.experiment import Procedure, IntegerParameter, Parameter, FloatParameter\n",
"\n",
"class TestProcedure(Procedure):\n",
"\n",
" iterations = IntegerParameter('Loop Iterations', default=100)\n",
" delay = FloatParameter('Delay Time', units='s', default=0.2)\n",
" seed = Parameter('Random Seed', default='12345')\n",
"\n",
" DATA_COLUMNS = ['Iteration', 'Random Number']\n",
"\n",
" def startup(self):\n",
" log.info(\"Setting up random number generator\")\n",
" random.seed(self.seed)\n",
"\n",
" def execute(self):\n",
" log.info(\"Starting to generate numbers\")\n",
" for i in range(self.iterations):\n",
" data = {\n",
" 'Iteration': i,\n",
" 'Random Number': random.random()\n",
" }\n",
" log.debug(\"Produced numbers: %s\" % data)\n",
" self.emit('results', data)\n",
" self.emit('progress', 100.*i/self.iterations)\n",
" sleep(self.delay)\n",
" if self.should_stop():\n",
" log.warning(\"Catch stop command in procedure\")\n",
" break\n",
"\n",
" def shutdown(self):\n",
" log.info(\"Finished\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pymeasure.experiment import Experiment\n",
"from procedures import TestProcedure\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment = Experiment('test', TestProcedure(iterations=100, delay=.1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment.start()\n",
"experiment.plot_live('Iteration', 'Random Number')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
smalladi78/SEF | notebooks/41_ExtractEventData.ipynb | 1 | 84457 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extract events data\n",
"The donations dataframe has event related information. Pull that out into a separate dataframe."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.read_pickle('out/21/donations.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'activity_date', u'city', u'fund', u'batch_num', u'amount_initial',\n",
" u'amount_cleanup', u'zipcode', u'longitude', u'sales', u'county',\n",
" u'charitable', u'amount', u'state', u'donor_id', u'timezone',\n",
" u'latitude', u'appeal', u'activity_year', u'activity_month',\n",
" u'activity_dow', u'activity_ym', u'activity_yq', u'activity_ymd',\n",
" u'county_norm', u'census_region_name', u'state_name', u'county_id',\n",
" u'is_service', u'channel', u'campaign_location_id',\n",
" u'is_location_center', u'campaign_month_id', u'is_month_center'],\n",
" dtype='object')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### We have 91 appeal codes that are for events\n",
"#### These break down into 125 events since appeal codes can be re-used"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"155"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df.is_service==True)].appeal.nunique()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# We will only consider services for this step, so limit what we need\n",
"dfs = df[df.is_service==True]\n",
"del df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"events_by_attendance = dfs\\\n",
" .groupby(['appeal', 'campaign_location_id', 'campaign_month_id'])\\\n",
" .size()\\\n",
" .to_frame()\\\n",
" .reset_index()\\\n",
" .rename(columns={0: 'transaction_count'})"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"events_by_amount = dfs\\\n",
" .groupby(['appeal', 'campaign_location_id', 'campaign_month_id', ])\\\n",
" .amount\\\n",
" .sum()\\\n",
" .to_frame()\\\n",
" .reset_index()\\\n",
" .rename(columns={0: 'amount'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Histogram plots"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xa438a36c>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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7CRDgdcC5Sd4EHAm8NMmngEeTrKmqvUnWAPvmW8GqVafxxBNbxlOtJC1DU1NTTE1NPT29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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa438a78c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"events_by_attendance[events_by_attendance.transaction_count > 10].transaction_count.plot(kind='hist', bins=30)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xa436ca4c>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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mkqTWDBNJUmuGyRisX7+JJIve1q/f1HV5kjRynX051vHs8OH7Weo7UA4fHstXCUhSpwwT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\nkSS1ZphIklozTCRJrf0/eV4dn1hCVv4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa437e08c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"events_by_amount.amount[events_by_amount.amount < 10000].plot(kind='hist', bins=50)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check for data correctness"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(161, 33)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfs[(dfs.appeal=='Event_TollywoodThriller')].shape"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"appeal campaign_location_id campaign_month_id\n",
"Event_TollywoodThriller 0 0 48\n",
" 1 41\n",
" 2 33\n",
" 3 39\n",
"dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfs[(dfs.appeal=='Event_TollywoodThriller')]\\\n",
" .groupby(['appeal', 'campaign_location_id', 'campaign_month_id']).size()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>appeal</th>\n",
" <th>campaign_location_id</th>\n",
" <th>campaign_month_id</th>\n",
" <th>transaction_count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>349</th>\n",
" <td>Event_TollywoodThriller</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>350</th>\n",
" <td>Event_TollywoodThriller</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>351</th>\n",
" <td>Event_TollywoodThriller</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352</th>\n",
" <td>Event_TollywoodThriller</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>39</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" appeal campaign_location_id campaign_month_id \\\n",
"349 Event_TollywoodThriller 0 0 \n",
"350 Event_TollywoodThriller 0 1 \n",
"351 Event_TollywoodThriller 0 2 \n",
"352 Event_TollywoodThriller 0 3 \n",
"\n",
" transaction_count \n",
"349 48 \n",
"350 41 \n",
"351 33 \n",
"352 39 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events_by_attendance[events_by_attendance.appeal=='Event_TollywoodThriller']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"appeal campaign_location_id campaign_month_id\n",
"Event_TollywoodThriller 0 0 3781\n",
" 1 5054\n",
" 2 6854\n",
" 3 5415\n",
"Name: amount, dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfs[(dfs.appeal=='Event_TollywoodThriller')]\\\n",
" .groupby(['appeal', 'campaign_location_id', 'campaign_month_id']).amount.sum()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>appeal</th>\n",
" <th>campaign_location_id</th>\n",
" <th>campaign_month_id</th>\n",
" <th>amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>349</th>\n",
" <td>Event_TollywoodThriller</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>350</th>\n",
" <td>Event_TollywoodThriller</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>351</th>\n",
" <td>Event_TollywoodThriller</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352</th>\n",
" <td>Event_TollywoodThriller</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>5415</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" appeal campaign_location_id campaign_month_id amount\n",
"349 Event_TollywoodThriller 0 0 3781\n",
"350 Event_TollywoodThriller 0 1 5054\n",
"351 Event_TollywoodThriller 0 2 6854\n",
"352 Event_TollywoodThriller 0 3 5415"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events_by_amount[events_by_amount.appeal=='Event_TollywoodThriller']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Filter the data - we will consider only the events with atleast 10 transactions - which results in 70 events"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(178, 4)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events_by_attendance[events_by_attendance.transaction_count >= 10].shape"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"608L"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events_by_attendance[events_by_attendance.transaction_count < 10].transaction_count.sum()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Trim the event attendance data to atleast 20 transactions and merge with the other dataframe to get the amounts\n",
"events = events_by_attendance[events_by_attendance.transaction_count >= 10]\\\n",
".merge(events_by_amount, how='left', on=['appeal','campaign_location_id','campaign_month_id'])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Merge with the original dataframe to get the month centers\n",
"events = events.merge(\n",
" dfs[(dfs.is_month_center==True)]\\\n",
" [['appeal', 'campaign_month_id', 'activity_year', 'activity_month', 'activity_ym']],\n",
" how='left', on=['appeal', 'campaign_month_id']).drop_duplicates()\n",
"\n",
"# Merge with the original dataframe to get the location centers\n",
"events = events.merge(\n",
" dfs[(dfs.is_location_center==True)]\\\n",
" [['appeal', 'campaign_location_id', 'county', 'state']],\n",
" how='left', on=['appeal', 'campaign_location_id']).drop_duplicates()"
]
},
{
"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>appeal</th>\n",
" <th>campaign_location_id</th>\n",
" <th>campaign_month_id</th>\n",
" <th>transaction_count</th>\n",
" <th>amount</th>\n",
" <th>activity_year</th>\n",
" <th>activity_month</th>\n",
" <th>activity_ym</th>\n",
" <th>county</th>\n",
" <th>state</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>8727</th>\n",
" <td>Event_TollywoodThriller</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>3781</td>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>201411</td>\n",
" <td>King</td>\n",
" <td>WA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8773</th>\n",
" <td>Event_TollywoodThriller</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>41</td>\n",
" <td>5054</td>\n",
" <td>2012</td>\n",
" <td>12</td>\n",
" <td>201212</td>\n",
" <td>King</td>\n",
" <td>WA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8819</th>\n",
" <td>Event_TollywoodThriller</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>33</td>\n",
" <td>6854</td>\n",
" <td>2009</td>\n",
" <td>8</td>\n",
" <td>200908</td>\n",
" <td>King</td>\n",
" <td>WA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8865</th>\n",
" <td>Event_TollywoodThriller</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>39</td>\n",
" <td>5415</td>\n",
" <td>2011</td>\n",
" <td>7</td>\n",
" <td>201107</td>\n",
" <td>King</td>\n",
" <td>WA</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" appeal campaign_location_id campaign_month_id \\\n",
"8727 Event_TollywoodThriller 0 0 \n",
"8773 Event_TollywoodThriller 0 1 \n",
"8819 Event_TollywoodThriller 0 2 \n",
"8865 Event_TollywoodThriller 0 3 \n",
"\n",
" transaction_count amount activity_year activity_month activity_ym \\\n",
"8727 48 3781 2014 11 201411 \n",
"8773 41 5054 2012 12 201212 \n",
"8819 33 6854 2009 8 200908 \n",
"8865 39 5415 2011 7 201107 \n",
"\n",
" county state \n",
"8727 King WA \n",
"8773 King WA \n",
"8819 King WA \n",
"8865 King WA "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Sanity check what we got thus far\n",
"events[events.appeal == 'Event_TollywoodThriller']"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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>county</th>\n",
" <th>state</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>115022</th>\n",
" <td>King</td>\n",
" <td>WA</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" county state\n",
"115022 King WA"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfs[(dfs.is_location_center==True) & (dfs.appeal=='Event_TollywoodThriller')]\\\n",
" [['county', 'state']].drop_duplicates()"
]
},
{
"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>activity_year</th>\n",
" <th>activity_month</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>66124</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115130</th>\n",
" <td>2009</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116918</th>\n",
" <td>2012</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117693</th>\n",
" <td>2011</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" activity_year activity_month\n",
"66124 2014 11\n",
"115130 2009 8\n",
"116918 2012 12\n",
"117693 2011 7"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfs[(dfs.is_month_center==True) & (dfs.appeal=='Event_TollywoodThriller')]\\\n",
" [['activity_year', 'activity_month']].drop_duplicates()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Understand the data\n",
"\n",
"We had events in a total of 13 different states. \n",
"California had the highest number of events (33). Washington has the next highest number of events (11). \n",
"The events were more or less distributed throughout the year. \n",
"March 2014 was by far the busiest year/month for the organization (8 events). \n",
"2014 was the busiest year in terms of number of events (22). \n",
"Other than that, in general events are spread out quite evenly over the year across the board. "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xa43bee2c>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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mGBsbm4OmRUQMj75kPc3qC17MdtoOOMr26WVkR8tsJ0lvAb4OvNX2nVOO1ZT1lCyniIhu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\nKRQREdHR/wcDk25KJgw05gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa43bf9cc>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"events.groupby('state').size().plot(kind='barh')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xa43a742c>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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zljrbI0njrNbJ7TLzMeCUiDgMuDYilmXmpsevOdn2fAIrpknSdCNTMQ0gIj4EPJSZl3Qs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\ngSBJAgwESVLBQJAkAfD/BedR4TcvbzoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa441d36c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"events.groupby('activity_month').size().plot(kind='barh')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xa44015cc>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RJhRJUiNKTl+/S0R8LyKui4jrI+KsOdoO7LH//ssX8f9akkZXsbm8oioUv3tm/ioidgT+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\nEAWcAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa4367dac>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"events.groupby('activity_month').amount.sum().plot(kind='barh')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xa44031cc>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0xa43faa0c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"events.groupby('activity_year').size().plot(kind='barh')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xa445c9ac>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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sLhpmZpat06Ih6T5JWyWtKmkbImmRpDXp3yNLlt0oqVnSaklTStonSlqZlt2l4mwQkvpL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\nD0wNINtNDGCIAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa43e514c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"events.groupby(['activity_year']).agg({'amount': sum}).plot(kind='bar', legend=True)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"events['event_name'] = \\\n",
" events.apply(\n",
" lambda row:\n",
" row.appeal + '_' +\n",
" str(int(row.campaign_location_id)) + '_' +\n",
" str(int(row.campaign_month_id)),\n",
" axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save the data"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!mkdir -p out/41\n",
"\n",
"events\\\n",
" .reset_index()\\\n",
" .drop('index', axis=1)\\\n",
" .to_pickle('out/41/events.pkl')"
]
},
{
"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
}
| unlicense |
km-Poonacha/python4phd | Session 1/ipython/Lesson 1 - Data and Types.ipynb | 2 | 10584 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "YOCVlkTDtqZr"
},
"source": [
"# Lesson 1: Data and Types"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "bhN5fgnltqZt"
},
"source": [
"In this lesson we learn about the basic data types and data structures and play with them a little. \n",
"\n",
"1. Defines the format by which you input data to a program, modify it and output it in the consol\n",
"2. Data types: integer, float, string and boolean \n",
"3. Data structures: Lists, dictionaries, tuples etc\n",
"\n",
"Lets start by initializing integer, string, list and dictionary \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "UJvZ5CPHtqZu"
},
"source": [
"## Data Types\n",
"*Let us create an integer and string variable and see its output.*"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "1vccnIIKtqZv",
"outputId": "0cc1a0ec-bb76-4b3c-8a12-8634aa56a324"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is an integer: 99\n",
"This is a string: poonacha\n"
]
}
],
"source": [
"integer = 99\n",
"string = 'poonacha'\n",
"\n",
"print('This is an integer: ',integer )\n",
"print('This is a string: ',string )"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Dz6JYxBEtqaB"
},
"source": [
"### Some Simple Data Operations \n",
"\n",
"Most integer/floating point operators that you use in stata or other languages work in python as well - eg (+,-,*,/).\n",
"\n",
"*Let us write a code to find the quotient and reminder of a number given a divisor. The operator '//' can be used to find the quotient and '%' can be used to find the reminder.*"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "YDcS-IHVtqaC",
"outputId": "a336a376-f072-4f1e-da91-c54a95caa43d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"reminder = 9 quotient = 19\n"
]
}
],
"source": [
"number = 199\n",
"divisor = 10\n",
"rem = 199 % 10\n",
"quotient = 199 // 10\n",
"print('reminder = ', rem,'quotient = ', quotient)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "sevq3rNNtqZ1"
},
"source": [
"## Data Structures\n",
"Data structures combine the basic datatypes (integer, float, string, boolean) to create more complex types. There are several types of data structures but the most commonly used ones are \"lists\" and \"dictionaries\"\n",
"\n",
"### Lists\n",
"We start with a list. A list is a sequence of integer / character data types.\n",
"Let us now create two list's one with only integer data \"i_list\" and the other with only character data \"c_list\". "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "t-8JCOritqZ1",
"outputId": "9bdca510-b9d3-45e6-9d71-8493c2429e50"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is an integer list: [1, 2, 3, 4]\n",
"This is a char list: ['day', 'month', 'year']\n"
]
}
],
"source": [
"i_list = [1,2,3,4]\n",
"c_list = ['day','month','year']\n",
"\n",
"print('This is an integer list: ',i_list )\n",
"print('This is a char list: ',c_list )"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "qnPkgkzctqZ5"
},
"source": [
"The elements in the lists are numbered upwards from 0. Hence, we can access each element in the list using its index number. The following code prints the first element of c_list."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "10EZc8KPtqZ6",
"outputId": "9743e6ca-e857-428c-a2c8-c83166e41c88"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The first element is: day\n"
]
}
],
"source": [
"print('The first element is: ', c_list[0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "KUh0UKU8tqaT"
},
"source": [
"#### List Operations\n",
"Lists are mutable i.e. we can append, insert and delete elements from the list. \n",
"\n",
"Syntax - append(*data to be entered*), insert(*index*, *data*), del(*index*)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Sk62ZRPAtqaU",
"outputId": "ab58b6df-16eb-4426-ca09-fb86dec6536d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2, 3, 4, 5]\n",
"[1, 1.5, 2, 3, 4, 5]\n",
"[1, 2, 3, 4, 5]\n",
"[3, 4, 5]\n",
"[]\n"
]
}
],
"source": [
"i_list = [1,2,3,4]\n",
"\n",
"# Append the number 5 at the end\n",
"i_list.append(5)\n",
"print(i_list)\n",
"\n",
"# Insert the 1.5 between 1 and 2\n",
"i_list.insert(1,1.5)\n",
"print(i_list)\n",
"\n",
"#delete the number 1.5\n",
"del i_list[1]\n",
"print(i_list)\n",
"\n",
"#delete the number between position 0 and 2\n",
"del i_list[0:2]\n",
"print(i_list)\n",
"\n",
"#delete the entire list\n",
"del i_list[:]\n",
"print(i_list)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "NOTn1slXtqaH"
},
"source": [
"### String\n",
"Strings are used to input and read sentences and words. There are several operations that can be done on a string. \n",
"1. Concatinating - We can use either \"+\" operator combine two strings. Join() can be used to combine a list of strings into a single string."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "9d6BE2cDtqaH",
"outputId": "33e4883f-d823-4293-eb2c-b2f847657cce"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"poonacha medappa\n",
"poo\n"
]
}
],
"source": [
"string = 'poonacha'\n",
"string = string + ' medappa' \n",
"print(string)\n",
"print(string[0:3])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "YRNOVwXatqaL"
},
"source": [
"1. split('*seperator*') - We can use the split() command to split a string into a list of smaller strings depending on a specified seperator \n",
"2. Conversely, join('*seperator*') can be used to combine a list of strings into a single string using the seperator\n",
"3. If no seperator is specified, it assumes the seperator is a space or a tab\n",
"4. The input('*input message*') function is used to enter data from the consol\n",
"\n",
"*Let us write a code to enter a sentence and split it into a list of words.* "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "OZyGSvGDtqaM",
"outputId": "c106031f-6100-4cac-ff39-3bd9a0abdbac"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Enter the sentence to be split: My name is poonacha\n",
"['My', 'name', 'is', 'poonacha']\n",
"My\n"
]
}
],
"source": [
"s_input = input('Enter the sentence to be split: ')\n",
"words = s_input.split()\n",
"print(words)\n",
"print(words[0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "9GZkvOmKtqaP"
},
"source": [
"##### Excercise 1:\n",
"*Write a small code to enter a date in \"dd/mm/yyyy\" format. Use the split() command to split the entered date into day, month and year. Hint. you need to specify the seperater as '/'.*"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "On-RkoSttqaQ",
"outputId": "23b8cbf7-2e02-47a5-d5f6-7ee686bd2bff"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Enter date in dd/mm/yyyy format:25/09/1984\n",
"the entered date is; day 25 month: 09 year: 1984\n"
]
}
],
"source": [
"# Enter Code\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "q-d2G3m0tqZ9"
},
"source": [
"### Dictionaries\n",
"A dictionary is similar to a list but rather than an index which represents each element, the dictionary uses a unique user defined key. The following code creates a dictionary by the name \"dict_date\" with three elements representing the current day, month and year."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ZVTufy_3tqZ-",
"outputId": "25e5ab2a-9f0b-45fd-ab27-2ce7de0ce861"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is a dictionary: {'day': 14, 'month': 3, 'year': 2018}\n",
"Today in dictionary is: day - 14 month - 3 year - 2018\n"
]
}
],
"source": [
"dict_date = {'day' : 14 , 'month' : 3, 'year' : 2018} \n",
"print('This is a dictionary: ',dict_date)\n",
"print('Today in dictionary is: day - ',dict_date['day'],' month - ',dict_date['month'],' year - ', dict_date['year'] )"
]
}
],
"metadata": {
"colab": {
"name": "Lesson 1 - Data and Types.ipynb",
"provenance": [],
"version": "0.3.2"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
oscarbranson/latools | latools/dev.ipynb | 1 | 85831 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from objects import *\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4 Analysis Files Loaded:\n",
"1 standards, 3 samples\n",
"Analytes: Mg24 Mg25 Al27 Ca43 Ca44 Mn55 Sr88 Ba137 Ba138\n"
]
}
],
"source": [
"cal = analyse('test_data/')\n",
"cal.autorange()\n",
"cal.bkgcorrect()\n",
"cal.ratio()\n",
"# cal.srm_id()\n",
"cal.load_srm_ids('params/srm.rng')\n",
"cal.calibrate()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# cal.trace_plots()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cal.stat_samples(filt=False)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = cal.getstats()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>Mg24</th>\n",
" <th>Mg25</th>\n",
" <th>Al27</th>\n",
" <th>Ca43</th>\n",
" <th>Ca44</th>\n",
" <th>Mn55</th>\n",
" <th>Sr88</th>\n",
" <th>Ba137</th>\n",
" <th>Ba138</th>\n",
" </tr>\n",
" <tr>\n",
" <th>statistic</th>\n",
" <th>sample</th>\n",
" <th>rep</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 rowspan=\"12\" valign=\"top\">nanmean</th>\n",
" <th rowspan=\"4\" valign=\"top\">A1-1</th>\n",
" <th>0</th>\n",
" <td>0.010236</td>\n",
" <td>0.009674</td>\n",
" <td>0.000066</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.988676</td>\n",
" <td>0.000016</td>\n",
" <td>0.000672</td>\n",
" <td>9.449396e-07</td>\n",
" <td>8.617774e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.011281</td>\n",
" <td>0.011020</td>\n",
" <td>0.000057</td>\n",
" <td>1.000000e+00</td>\n",
" <td>1.021726</td>\n",
" <td>0.000027</td>\n",
" <td>0.000720</td>\n",
" <td>9.026532e-07</td>\n",
" <td>9.374890e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.010971</td>\n",
" <td>0.010632</td>\n",
" <td>0.000082</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.990566</td>\n",
" <td>0.000020</td>\n",
" <td>0.000471</td>\n",
" <td>7.679670e-07</td>\n",
" <td>6.965433e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.009183</td>\n",
" <td>0.008915</td>\n",
" <td>0.000057</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.986902</td>\n",
" <td>0.000024</td>\n",
" <td>0.001062</td>\n",
" <td>7.592581e-07</td>\n",
" <td>7.150529e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">A1-10</th>\n",
" <th>0</th>\n",
" <td>0.007424</td>\n",
" <td>0.007337</td>\n",
" <td>0.000075</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.987353</td>\n",
" <td>0.000019</td>\n",
" <td>0.001026</td>\n",
" <td>7.667144e-07</td>\n",
" <td>7.340243e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.006700</td>\n",
" <td>0.006612</td>\n",
" <td>0.000050</td>\n",
" <td>1.000000e+00</td>\n",
" <td>1.002254</td>\n",
" <td>0.000019</td>\n",
" <td>0.001011</td>\n",
" <td>8.101797e-07</td>\n",
" <td>7.048347e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.006360</td>\n",
" <td>0.006247</td>\n",
" <td>0.000060</td>\n",
" <td>1.000000e+00</td>\n",
" <td>1.011480</td>\n",
" <td>0.000018</td>\n",
" <td>0.000858</td>\n",
" <td>7.712401e-07</td>\n",
" <td>7.319014e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">A1-5</th>\n",
" <th>0</th>\n",
" <td>0.009413</td>\n",
" <td>0.009403</td>\n",
" <td>0.000104</td>\n",
" <td>1.000000e+00</td>\n",
" <td>1.015699</td>\n",
" <td>0.000030</td>\n",
" <td>0.000902</td>\n",
" <td>8.537106e-07</td>\n",
" <td>8.308556e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.009001</td>\n",
" <td>0.009059</td>\n",
" <td>0.000097</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.994554</td>\n",
" <td>0.000030</td>\n",
" <td>0.000910</td>\n",
" <td>8.310771e-07</td>\n",
" <td>7.502507e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.008242</td>\n",
" <td>0.008131</td>\n",
" <td>0.000068</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.988699</td>\n",
" <td>0.000033</td>\n",
" <td>0.000473</td>\n",
" <td>8.350798e-07</td>\n",
" <td>8.388164e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.006562</td>\n",
" <td>0.006600</td>\n",
" <td>0.000064</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.993341</td>\n",
" <td>0.000032</td>\n",
" <td>0.000502</td>\n",
" <td>7.951983e-07</td>\n",
" <td>7.551572e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.007395</td>\n",
" <td>0.007076</td>\n",
" <td>0.000291</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.968223</td>\n",
" <td>0.000042</td>\n",
" <td>0.001012</td>\n",
" <td>9.175850e-07</td>\n",
" <td>9.042763e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"12\" valign=\"top\">nanstd</th>\n",
" <th rowspan=\"4\" valign=\"top\">A1-1</th>\n",
" <th>0</th>\n",
" <td>0.005116</td>\n",
" <td>0.004599</td>\n",
" <td>0.000083</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.101048</td>\n",
" <td>0.000003</td>\n",
" <td>0.000364</td>\n",
" <td>2.905857e-07</td>\n",
" <td>1.944963e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.003229</td>\n",
" <td>0.002628</td>\n",
" <td>0.000018</td>\n",
" <td>1.110223e-16</td>\n",
" <td>0.136210</td>\n",
" <td>0.000003</td>\n",
" <td>0.000408</td>\n",
" <td>3.219120e-07</td>\n",
" <td>1.723416e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.002829</td>\n",
" <td>0.002441</td>\n",
" <td>0.000011</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.074711</td>\n",
" <td>0.000004</td>\n",
" <td>0.000124</td>\n",
" <td>2.703543e-07</td>\n",
" <td>1.164669e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.002768</td>\n",
" <td>0.002525</td>\n",
" <td>0.000025</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.071980</td>\n",
" <td>0.000003</td>\n",
" <td>0.000420</td>\n",
" <td>2.683448e-07</td>\n",
" <td>1.359123e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">A1-10</th>\n",
" <th>0</th>\n",
" <td>0.001794</td>\n",
" <td>0.001847</td>\n",
" <td>0.000053</td>\n",
" <td>1.110223e-16</td>\n",
" <td>0.078443</td>\n",
" <td>0.000004</td>\n",
" <td>0.000485</td>\n",
" <td>3.203150e-07</td>\n",
" <td>1.538813e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.001607</td>\n",
" <td>0.001606</td>\n",
" <td>0.000047</td>\n",
" <td>1.110223e-16</td>\n",
" <td>0.119400</td>\n",
" <td>0.000004</td>\n",
" <td>0.000477</td>\n",
" <td>6.569413e-07</td>\n",
" <td>1.558993e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.001645</td>\n",
" <td>0.001631</td>\n",
" <td>0.000060</td>\n",
" <td>1.110223e-16</td>\n",
" <td>0.126664</td>\n",
" <td>0.000003</td>\n",
" <td>0.000483</td>\n",
" <td>2.694504e-07</td>\n",
" <td>1.500881e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">A1-5</th>\n",
" <th>0</th>\n",
" <td>0.001673</td>\n",
" <td>0.001854</td>\n",
" <td>0.000045</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.154668</td>\n",
" <td>0.000006</td>\n",
" <td>0.000492</td>\n",
" <td>4.286337e-07</td>\n",
" <td>1.719102e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.001775</td>\n",
" <td>0.001825</td>\n",
" <td>0.000079</td>\n",
" <td>1.110223e-16</td>\n",
" <td>0.115530</td>\n",
" <td>0.000006</td>\n",
" <td>0.000524</td>\n",
" <td>3.635392e-07</td>\n",
" <td>1.398156e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.001017</td>\n",
" <td>0.000882</td>\n",
" <td>0.000009</td>\n",
" <td>1.110223e-16</td>\n",
" <td>0.133417</td>\n",
" <td>0.000012</td>\n",
" <td>0.000177</td>\n",
" <td>2.455916e-07</td>\n",
" <td>1.689928e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.001138</td>\n",
" <td>0.001429</td>\n",
" <td>0.000012</td>\n",
" <td>1.110223e-16</td>\n",
" <td>0.073219</td>\n",
" <td>0.000002</td>\n",
" <td>0.000217</td>\n",
" <td>2.577513e-07</td>\n",
" <td>1.239379e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.002104</td>\n",
" <td>0.001384</td>\n",
" <td>0.000184</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.080313</td>\n",
" <td>0.000010</td>\n",
" <td>0.000497</td>\n",
" <td>2.968008e-07</td>\n",
" <td>1.818601e-07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Mg24 Mg25 Al27 Ca43 Ca44 \\\n",
"statistic sample rep \n",
"nanmean A1-1 0 0.010236 0.009674 0.000066 1.000000e+00 0.988676 \n",
" 1 0.011281 0.011020 0.000057 1.000000e+00 1.021726 \n",
" 2 0.010971 0.010632 0.000082 1.000000e+00 0.990566 \n",
" 3 0.009183 0.008915 0.000057 1.000000e+00 0.986902 \n",
" A1-10 0 0.007424 0.007337 0.000075 1.000000e+00 0.987353 \n",
" 1 0.006700 0.006612 0.000050 1.000000e+00 1.002254 \n",
" 2 0.006360 0.006247 0.000060 1.000000e+00 1.011480 \n",
" A1-5 0 0.009413 0.009403 0.000104 1.000000e+00 1.015699 \n",
" 1 0.009001 0.009059 0.000097 1.000000e+00 0.994554 \n",
" 2 0.008242 0.008131 0.000068 1.000000e+00 0.988699 \n",
" 3 0.006562 0.006600 0.000064 1.000000e+00 0.993341 \n",
" 4 0.007395 0.007076 0.000291 1.000000e+00 0.968223 \n",
"nanstd A1-1 0 0.005116 0.004599 0.000083 0.000000e+00 0.101048 \n",
" 1 0.003229 0.002628 0.000018 1.110223e-16 0.136210 \n",
" 2 0.002829 0.002441 0.000011 0.000000e+00 0.074711 \n",
" 3 0.002768 0.002525 0.000025 0.000000e+00 0.071980 \n",
" A1-10 0 0.001794 0.001847 0.000053 1.110223e-16 0.078443 \n",
" 1 0.001607 0.001606 0.000047 1.110223e-16 0.119400 \n",
" 2 0.001645 0.001631 0.000060 1.110223e-16 0.126664 \n",
" A1-5 0 0.001673 0.001854 0.000045 0.000000e+00 0.154668 \n",
" 1 0.001775 0.001825 0.000079 1.110223e-16 0.115530 \n",
" 2 0.001017 0.000882 0.000009 1.110223e-16 0.133417 \n",
" 3 0.001138 0.001429 0.000012 1.110223e-16 0.073219 \n",
" 4 0.002104 0.001384 0.000184 0.000000e+00 0.080313 \n",
"\n",
" Mn55 Sr88 Ba137 Ba138 \n",
"statistic sample rep \n",
"nanmean A1-1 0 0.000016 0.000672 9.449396e-07 8.617774e-07 \n",
" 1 0.000027 0.000720 9.026532e-07 9.374890e-07 \n",
" 2 0.000020 0.000471 7.679670e-07 6.965433e-07 \n",
" 3 0.000024 0.001062 7.592581e-07 7.150529e-07 \n",
" A1-10 0 0.000019 0.001026 7.667144e-07 7.340243e-07 \n",
" 1 0.000019 0.001011 8.101797e-07 7.048347e-07 \n",
" 2 0.000018 0.000858 7.712401e-07 7.319014e-07 \n",
" A1-5 0 0.000030 0.000902 8.537106e-07 8.308556e-07 \n",
" 1 0.000030 0.000910 8.310771e-07 7.502507e-07 \n",
" 2 0.000033 0.000473 8.350798e-07 8.388164e-07 \n",
" 3 0.000032 0.000502 7.951983e-07 7.551572e-07 \n",
" 4 0.000042 0.001012 9.175850e-07 9.042763e-07 \n",
"nanstd A1-1 0 0.000003 0.000364 2.905857e-07 1.944963e-07 \n",
" 1 0.000003 0.000408 3.219120e-07 1.723416e-07 \n",
" 2 0.000004 0.000124 2.703543e-07 1.164669e-07 \n",
" 3 0.000003 0.000420 2.683448e-07 1.359123e-07 \n",
" A1-10 0 0.000004 0.000485 3.203150e-07 1.538813e-07 \n",
" 1 0.000004 0.000477 6.569413e-07 1.558993e-07 \n",
" 2 0.000003 0.000483 2.694504e-07 1.500881e-07 \n",
" A1-5 0 0.000006 0.000492 4.286337e-07 1.719102e-07 \n",
" 1 0.000006 0.000524 3.635392e-07 1.398156e-07 \n",
" 2 0.000012 0.000177 2.455916e-07 1.689928e-07 \n",
" 3 0.000002 0.000217 2.577513e-07 1.239379e-07 \n",
" 4 0.000010 0.000497 2.968008e-07 1.818601e-07 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df.to_csv('test.csv')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"self = cal"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['A1-1', 'A1-10', 'A1-5']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[n for n in self.samples if 'STD' not in n.upper()]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['nanmean', 'nanstd']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"self.stats_calced"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/oscarbranson/anaconda/envs/py3/lib/python3.5/site-packages/pandas/__init__.py:7: DeprecationWarning: bad escape \\s\n",
" from pandas import hashtable, tslib, lib\n"
]
}
],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"slst = []\n",
"\n",
"for s in self.stats_calced:\n",
" for nm in [n for n in self.samples if 'STD' not in n.upper()]:\n",
" # make multi-index\n",
" reps = np.arange(self.stats[nm][s].shape[1])\n",
" ss = np.array([s] * reps.size)\n",
" nms = np.array([nm] * reps.size)\n",
" # make sub-dataframe\n",
" stdf = pd.DataFrame(self.stats[nm][s].T, columns=self.stats[nm]['analytes'], index=[ss, nms, reps])\n",
" \n",
" slst.append(stdf)\n",
"\n",
"sdf = pd.concat(slst)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'sdf' 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-6-dfe66f866a69>\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[0msdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'nanmean'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'sdf' is not defined"
]
}
],
"source": [
"df = sdf.loc['nanmean',:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"sdf.index.name = 'rep'"
]
},
{
"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>Mg24</th>\n",
" <th>Mg25</th>\n",
" <th>Al27</th>\n",
" <th>Ca43</th>\n",
" <th>Ca44</th>\n",
" <th>Mn55</th>\n",
" <th>Sr88</th>\n",
" <th>Ba137</th>\n",
" <th>Ba138</th>\n",
" </tr>\n",
" <tr>\n",
" <th>rep</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>100052.022399</td>\n",
" <td>14322.767928</td>\n",
" <td>4814.579694</td>\n",
" <td>76099.056349</td>\n",
" <td>1861392.439470</td>\n",
" <td>2267.161472</td>\n",
" <td>365224.020158</td>\n",
" <td>87.639904</td>\n",
" <td>239.521420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>90630.049871</td>\n",
" <td>11977.802651</td>\n",
" <td>8704.218060</td>\n",
" <td>99698.507814</td>\n",
" <td>1703461.854701</td>\n",
" <td>2889.532649</td>\n",
" <td>330930.143916</td>\n",
" <td>80.888477</td>\n",
" <td>284.507859</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>162210.364555</td>\n",
" <td>19154.807692</td>\n",
" <td>2114.556326</td>\n",
" <td>76103.940386</td>\n",
" <td>1181478.794719</td>\n",
" <td>5970.078179</td>\n",
" <td>163452.656806</td>\n",
" <td>62.022676</td>\n",
" <td>218.443147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>93382.319726</td>\n",
" <td>15381.524930</td>\n",
" <td>3613.108338</td>\n",
" <td>76301.845740</td>\n",
" <td>1292146.393199</td>\n",
" <td>2223.072497</td>\n",
" <td>203039.795400</td>\n",
" <td>76.284942</td>\n",
" <td>249.107369</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>128229.587861</td>\n",
" <td>10344.545233</td>\n",
" <td>22866.796166</td>\n",
" <td>73788.806336</td>\n",
" <td>1331440.908684</td>\n",
" <td>3937.652192</td>\n",
" <td>367893.852957</td>\n",
" <td>65.072075</td>\n",
" <td>290.230239</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Mg24 Mg25 Al27 Ca43 Ca44 \\\n",
"rep \n",
"0 100052.022399 14322.767928 4814.579694 76099.056349 1861392.439470 \n",
"1 90630.049871 11977.802651 8704.218060 99698.507814 1703461.854701 \n",
"2 162210.364555 19154.807692 2114.556326 76103.940386 1181478.794719 \n",
"3 93382.319726 15381.524930 3613.108338 76301.845740 1292146.393199 \n",
"4 128229.587861 10344.545233 22866.796166 73788.806336 1331440.908684 \n",
"\n",
" Mn55 Sr88 Ba137 Ba138 \n",
"rep \n",
"0 2267.161472 365224.020158 87.639904 239.521420 \n",
"1 2889.532649 330930.143916 80.888477 284.507859 \n",
"2 5970.078179 163452.656806 62.022676 218.443147 \n",
"3 2223.072497 203039.795400 76.284942 249.107369 \n",
"4 3937.652192 367893.852957 65.072075 290.230239 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"self.sample_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1.11152913e+06, 9.64727344e+05, 1.07048655e+06,\n",
" 7.46769938e+05],\n",
" [ 1.44583371e+05, 1.30092186e+05, 1.43071036e+05,\n",
" 9.98420605e+04],\n",
" [ 1.41298241e+04, 1.10178482e+04, 1.79908715e+04,\n",
" 1.04014157e+04],\n",
" [ 6.52986671e+05, 5.42458146e+05, 6.19350704e+05,\n",
" 5.04658881e+05],\n",
" [ 1.02579258e+07, 8.80931599e+06, 9.75783611e+06,\n",
" 7.91623456e+06],\n",
" [ 9.17585258e+03, 1.28162268e+04, 1.06710657e+04,\n",
" 1.04538116e+04],\n",
" [ 7.67216878e+05, 7.17557999e+05, 5.44406859e+05,\n",
" 9.69375520e+05],\n",
" [ 2.49529946e+02, 1.99561404e+02, 1.92361111e+02,\n",
" 1.55221519e+02],\n",
" [ 1.47346022e+03, 1.32439807e+03, 1.12156815e+03,\n",
" 9.35787595e+02]])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cal.stats['A1-1']['nanmean']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def getstat(self, analyte=None, sample=None):\n",
" if analyte is None:\n",
" analyte = self.analytes\n",
" if sample is None:\n",
" sample = self.samples\n",
" \n",
" if type(analyte) is str:\n",
" analyte = [analyte]\n",
" if type(sample) is str:\n",
" sample = [sample]\n",
" \n",
" ankey = [a in self.analytes for a in analyte]\n",
" \n",
" ss = [s for s in list(self.stats.values())[0].keys() if s is not 'analytes']\n",
" out = {}\n",
" out['analyte'] = analyte\n",
" \n",
" for s in ss:\n",
" out[s] = []\n",
" \n",
" for k,v in self.stats.items():\n",
" if k in sample:\n",
" for s in ss:\n",
" out[s].append(v[s][ankey])\n",
" \n",
" return out"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/oscarbranson/anaconda/envs/py3/lib/python3.5/site-packages/ipykernel/__main__.py:23: FutureWarning: in the future, boolean array-likes will be handled as a boolean array index\n"
]
},
{
"data": {
"text/plain": [
"{'analyte': array(['Mg24', 'Mg25', 'Al27', 'Ca43', 'Ca44', 'Mn55', 'Sr88', 'Ba137',\n",
" 'Ba138'], \n",
" dtype='<U10'),\n",
" 'nanmean': [array([[ 144583.37069892, 130092.18596491, 143071.0362963 ,\n",
" 99842.06050633],\n",
" [ 144583.37069892, 130092.18596491, 143071.0362963 ,\n",
" 99842.06050633],\n",
" [ 144583.37069892, 130092.18596491, 143071.0362963 ,\n",
" 99842.06050633],\n",
" [ 144583.37069892, 130092.18596491, 143071.0362963 ,\n",
" 99842.06050633],\n",
" [ 144583.37069892, 130092.18596491, 143071.0362963 ,\n",
" 99842.06050633],\n",
" [ 144583.37069892, 130092.18596491, 143071.0362963 ,\n",
" 99842.06050633],\n",
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" 99842.06050633],\n",
" [ 144583.37069892, 130092.18596491, 143071.0362963 ,\n",
" 99842.06050633],\n",
" [ 144583.37069892, 130092.18596491, 143071.0362963 ,\n",
" 99842.06050633]]),\n",
" array([[ 81385.13792453, 71225.28571429, 71170.30742574],\n",
" [ 81385.13792453, 71225.28571429, 71170.30742574],\n",
" [ 81385.13792453, 71225.28571429, 71170.30742574],\n",
" [ 81385.13792453, 71225.28571429, 71170.30742574],\n",
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" [ 81385.13792453, 71225.28571429, 71170.30742574],\n",
" [ 81385.13792453, 71225.28571429, 71170.30742574],\n",
" [ 81385.13792453, 71225.28571429, 71170.30742574]]),\n",
" array([[ 101546.62879518, 95273.85347826, 107981.86555556,\n",
" 88966.55055556, 73850.23475 ],\n",
" [ 101546.62879518, 95273.85347826, 107981.86555556,\n",
" 88966.55055556, 73850.23475 ],\n",
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" 88966.55055556, 73850.23475 ],\n",
" [ 101546.62879518, 95273.85347826, 107981.86555556,\n",
" 88966.55055556, 73850.23475 ],\n",
" [ 101546.62879518, 95273.85347826, 107981.86555556,\n",
" 88966.55055556, 73850.23475 ],\n",
" [ 101546.62879518, 95273.85347826, 107981.86555556,\n",
" 88966.55055556, 73850.23475 ],\n",
" [ 101546.62879518, 95273.85347826, 107981.86555556,\n",
" 88966.55055556, 73850.23475 ],\n",
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" 88966.55055556, 73850.23475 ],\n",
" [ 101546.62879518, 95273.85347826, 107981.86555556,\n",
" 88966.55055556, 73850.23475 ]])],\n",
" 'nanstd': [array([[ 79816.60349358, 30083.50378255, 31781.51207561, 34478.12520452],\n",
" [ 79816.60349358, 30083.50378255, 31781.51207561, 34478.12520452],\n",
" [ 79816.60349358, 30083.50378255, 31781.51207561, 34478.12520452],\n",
" [ 79816.60349358, 30083.50378255, 31781.51207561, 34478.12520452],\n",
" [ 79816.60349358, 30083.50378255, 31781.51207561, 34478.12520452],\n",
" [ 79816.60349358, 30083.50378255, 31781.51207561, 34478.12520452],\n",
" [ 79816.60349358, 30083.50378255, 31781.51207561, 34478.12520452],\n",
" [ 79816.60349358, 30083.50378255, 31781.51207561, 34478.12520452],\n",
" [ 79816.60349358, 30083.50378255, 31781.51207561, 34478.12520452]]),\n",
" array([[ 23980.79403935, 16468.60805268, 16149.89556004],\n",
" [ 23980.79403935, 16468.60805268, 16149.89556004],\n",
" [ 23980.79403935, 16468.60805268, 16149.89556004],\n",
" [ 23980.79403935, 16468.60805268, 16149.89556004],\n",
" [ 23980.79403935, 16468.60805268, 16149.89556004],\n",
" [ 23980.79403935, 16468.60805268, 16149.89556004],\n",
" [ 23980.79403935, 16468.60805268, 16149.89556004],\n",
" [ 23980.79403935, 16468.60805268, 16149.89556004],\n",
" [ 23980.79403935, 16468.60805268, 16149.89556004]]),\n",
" array([[ 14322.767928 , 11977.80265064, 19154.8076919 , 15381.52492972,\n",
" 10344.54523308],\n",
" [ 14322.767928 , 11977.80265064, 19154.8076919 , 15381.52492972,\n",
" 10344.54523308],\n",
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" 10344.54523308],\n",
" [ 14322.767928 , 11977.80265064, 19154.8076919 , 15381.52492972,\n",
" 10344.54523308],\n",
" [ 14322.767928 , 11977.80265064, 19154.8076919 , 15381.52492972,\n",
" 10344.54523308],\n",
" [ 14322.767928 , 11977.80265064, 19154.8076919 , 15381.52492972,\n",
" 10344.54523308],\n",
" [ 14322.767928 , 11977.80265064, 19154.8076919 , 15381.52492972,\n",
" 10344.54523308],\n",
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" 10344.54523308],\n",
" [ 14322.767928 , 11977.80265064, 19154.8076919 , 15381.52492972,\n",
" 10344.54523308]])]}"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"getstat(self)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/oscarbranson/anaconda/envs/py3/lib/python3.5/site-packages/ipykernel/__main__.py:22: FutureWarning: in the future, boolean array-likes will be handled as a boolean array index\n"
]
}
],
"source": [
"out = getstat(self, analyte='Mg24')"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'nanmean': [array([[ 0.00969654, 0.0110457 , 0.01065669, 0.00893574]]),\n",
" array([[ 0.00735398, 0.00662702, 0.00626134]]),\n",
" array([[ 0.00942511, 0.00907992, 0.00814972, 0.00661564, 0.00709262]])],\n",
" 'nanstd': [array([[ 0.00460944, 0.00263389, 0.00244689, 0.00253102]]),\n",
" array([[ 0.00185154, 0.00161013, 0.00163441]]),\n",
" array([[ 0.001858 , 0.0018291 , 0.00088382, 0.00143212, 0.00138694]])]}"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"out"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/oscarbranson/anaconda/envs/py3/lib/python3.5/site-packages/ipykernel/__main__.py:22: FutureWarning: in the future, boolean array-likes will be handled as a boolean array index\n"
]
},
{
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" [ 0.00969654, 0.0110457 , 0.01065669, 0.00893574],\n",
" [ 0.00969654, 0.0110457 , 0.01065669, 0.00893574],\n",
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" [ 0.00735398, 0.00662702, 0.00626134],\n",
" [ 0.00735398, 0.00662702, 0.00626134],\n",
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" array([[ 0.00942511, 0.00907992, 0.00814972, 0.00661564, 0.00709262],\n",
" [ 0.00942511, 0.00907992, 0.00814972, 0.00661564, 0.00709262],\n",
" [ 0.00942511, 0.00907992, 0.00814972, 0.00661564, 0.00709262],\n",
" [ 0.00942511, 0.00907992, 0.00814972, 0.00661564, 0.00709262],\n",
" [ 0.00942511, 0.00907992, 0.00814972, 0.00661564, 0.00709262],\n",
" [ 0.00942511, 0.00907992, 0.00814972, 0.00661564, 0.00709262],\n",
" [ 0.00942511, 0.00907992, 0.00814972, 0.00661564, 0.00709262],\n",
" [ 0.00942511, 0.00907992, 0.00814972, 0.00661564, 0.00709262],\n",
" [ 0.00942511, 0.00907992, 0.00814972, 0.00661564, 0.00709262]])],\n",
" 'nanstd': [array([[ 0.00460944, 0.00263389, 0.00244689, 0.00253102],\n",
" [ 0.00460944, 0.00263389, 0.00244689, 0.00253102],\n",
" [ 0.00460944, 0.00263389, 0.00244689, 0.00253102],\n",
" [ 0.00460944, 0.00263389, 0.00244689, 0.00253102],\n",
" [ 0.00460944, 0.00263389, 0.00244689, 0.00253102],\n",
" [ 0.00460944, 0.00263389, 0.00244689, 0.00253102],\n",
" [ 0.00460944, 0.00263389, 0.00244689, 0.00253102],\n",
" [ 0.00460944, 0.00263389, 0.00244689, 0.00253102],\n",
" [ 0.00460944, 0.00263389, 0.00244689, 0.00253102]]),\n",
" array([[ 0.00185154, 0.00161013, 0.00163441],\n",
" [ 0.00185154, 0.00161013, 0.00163441],\n",
" [ 0.00185154, 0.00161013, 0.00163441],\n",
" [ 0.00185154, 0.00161013, 0.00163441],\n",
" [ 0.00185154, 0.00161013, 0.00163441],\n",
" [ 0.00185154, 0.00161013, 0.00163441],\n",
" [ 0.00185154, 0.00161013, 0.00163441],\n",
" [ 0.00185154, 0.00161013, 0.00163441],\n",
" [ 0.00185154, 0.00161013, 0.00163441]]),\n",
" array([[ 0.001858 , 0.0018291 , 0.00088382, 0.00143212, 0.00138694],\n",
" [ 0.001858 , 0.0018291 , 0.00088382, 0.00143212, 0.00138694],\n",
" [ 0.001858 , 0.0018291 , 0.00088382, 0.00143212, 0.00138694],\n",
" [ 0.001858 , 0.0018291 , 0.00088382, 0.00143212, 0.00138694],\n",
" [ 0.001858 , 0.0018291 , 0.00088382, 0.00143212, 0.00138694],\n",
" [ 0.001858 , 0.0018291 , 0.00088382, 0.00143212, 0.00138694],\n",
" [ 0.001858 , 0.0018291 , 0.00088382, 0.00143212, 0.00138694],\n",
" [ 0.001858 , 0.0018291 , 0.00088382, 0.00143212, 0.00138694],\n",
" [ 0.001858 , 0.0018291 , 0.00088382, 0.00143212, 0.00138694]])]}"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"getstat(self)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def getstat(self, analyte=None, sample=None):\n",
" \"\"\"\n",
" Function to grab stats for a particular analyte and/or sample.\n",
" \"\"\"\n",
"\n",
" # make logical indices for analyte and sample axes\n",
" if analyte is not None:\n",
" aind = self.stats['analytes'] == analyte\n",
" else:\n",
" aind = np.array([True] * self.stats['analytes'].size)\n",
"\n",
" if sample is not None:\n",
" sind = self.stats['samples'] == sample\n",
" else:\n",
" sind = np.array([True] * self.stats['samples'].size)\n",
"\n",
" # combine them into a 2D array, the same shape as the Statistics\n",
" ind = aind & sind[:, np.newaxis]\n",
"\n",
" # get the name of the stat functions\n",
" fns = [k for k in self.stats.keys() if k is not 'analytes' and k\n",
" is not 'samples']\n",
"\n",
" # apply the 2D index to grab the stats\n",
" s = {}\n",
" for f in fns:\n",
" s[f] = self.stats[f][ind]\n",
"\n",
" if analyte is None:\n",
" s['analytes'] = self.stats['analytes']\n",
" if sample is None:\n",
" s['samples'] = self.stats['samples']\n",
"\n",
" return s\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"stat_samples(self, filt=False)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'A1-1': {'analytes': array(['Mg24', 'Mg25', 'Al27', 'Ca43', 'Ca44', 'Mn55', 'Sr88', 'Ba137',\n",
" 'Ba138'], \n",
" dtype='<U10'),\n",
" 'nanmean': array([[ 1.02666657e-02, 1.13151149e-02, 1.10040124e-02,\n",
" 9.21102544e-03],\n",
" [ 9.69653803e-03, 1.10457004e-02, 1.06566908e-02,\n",
" 8.93574008e-03],\n",
" [ 6.64737398e-05, 5.74684300e-05, 8.25550864e-05,\n",
" 5.72909376e-05],\n",
" [ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
" 1.00000000e+00],\n",
" [ 9.90080334e-01, 1.02317768e+00, 9.91973728e-01,\n",
" 9.88304284e-01],\n",
" [ 1.64874390e-05, 2.69021254e-05, 1.95726156e-05,\n",
" 2.38475172e-05],\n",
" [ 6.72789476e-04, 7.20305633e-04, 4.72012757e-04,\n",
" 1.06328767e-03],\n",
" [ 9.45856195e-07, 9.03528776e-07, 7.68711926e-07,\n",
" 7.59994551e-07],\n",
" [ 8.62438203e-07, 9.38207913e-07, 6.97077454e-07,\n",
" 7.15601244e-07]]),\n",
" 'nanstd': array([[ 5.13135206e-03, 3.23836676e-03, 2.83736151e-03,\n",
" 2.77676018e-03],\n",
" [ 4.60943712e-03, 2.63388671e-03, 2.44689406e-03,\n",
" 2.53101810e-03],\n",
" [ 8.29487782e-05, 1.80515346e-05, 1.11809813e-05,\n",
" 2.52948291e-05],\n",
" [ 2.22044605e-16, 2.22044605e-16, 2.22044605e-16,\n",
" 6.66133815e-16],\n",
" [ 1.01192000e-01, 1.36403134e-01, 7.48174351e-02,\n",
" 7.20826598e-02],\n",
" [ 3.26716099e-06, 3.47316061e-06, 3.95332843e-06,\n",
" 2.93844946e-06],\n",
" [ 3.64867396e-04, 4.08176227e-04, 1.24587690e-04,\n",
" 4.20872843e-04],\n",
" [ 2.90867544e-07, 3.22224244e-07, 2.70616530e-07,\n",
" 2.68605101e-07],\n",
" [ 1.94645431e-07, 1.72473793e-07, 1.16556179e-07,\n",
" 1.36016528e-07]])},\n",
" 'A1-10': {'analytes': array(['Mg24', 'Mg25', 'Al27', 'Ca43', 'Ca44', 'Mn55', 'Sr88', 'Ba137',\n",
" 'Ba138'], \n",
" dtype='<U10'),\n",
" 'nanmean': array([[ 7.44675257e-03, 6.72032613e-03, 6.37948568e-03],\n",
" [ 7.35397502e-03, 6.62701714e-03, 6.26133846e-03],\n",
" [ 7.50931489e-05, 5.04319945e-05, 6.04858215e-05],\n",
" [ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00],\n",
" [ 9.88756400e-01, 1.00367790e+00, 1.01291699e+00],\n",
" [ 1.87358825e-05, 1.87887042e-05, 1.82445856e-05],\n",
" [ 1.02731246e-03, 1.01229211e-03, 8.59294671e-04],\n",
" [ 7.67458122e-07, 8.10965568e-07, 7.71988173e-07],\n",
" [ 7.34587155e-07, 7.05375147e-07, 7.32462622e-07]]),\n",
" 'nanstd': array([[ 1.79910910e-03, 1.61152658e-03, 1.65009094e-03],\n",
" [ 1.85153595e-03, 1.61012553e-03, 1.63440947e-03],\n",
" [ 5.25721995e-05, 4.67675076e-05, 5.96298594e-05],\n",
" [ 4.44089210e-16, 4.44089210e-16, 4.44089210e-16],\n",
" [ 7.85548962e-02, 1.19569375e-01, 1.26843566e-01],\n",
" [ 4.23675690e-06, 4.01396588e-06, 3.41625877e-06],\n",
" [ 4.85961434e-04, 4.77624013e-04, 4.83754557e-04],\n",
" [ 3.20625732e-07, 6.57578464e-07, 2.69711708e-07],\n",
" [ 1.53999349e-07, 1.56018849e-07, 1.50203234e-07]])},\n",
" 'A1-5': {'analytes': array(['Mg24', 'Mg25', 'Al27', 'Ca43', 'Ca44', 'Mn55', 'Sr88', 'Ba137',\n",
" 'Ba138'], \n",
" dtype='<U10'),\n",
" 'nanmean': array([[ 9.44127696e-03, 9.02800081e-03, 8.26718298e-03,\n",
" 6.58131864e-03, 7.41705188e-03],\n",
" [ 9.42511031e-03, 9.07991581e-03, 8.14972299e-03,\n",
" 6.61563762e-03, 7.09261523e-03],\n",
" [ 1.03935339e-04, 9.70348925e-05, 6.84368290e-05,\n",
" 6.40761122e-05, 2.91289701e-04],\n",
" [ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
" 1.00000000e+00, 1.00000000e+00],\n",
" [ 1.01714203e+00, 9.95967452e-01, 9.90104291e-01,\n",
" 9.94752090e-01, 9.69598958e-01],\n",
" [ 3.03086385e-05, 2.98241443e-05, 3.26129439e-05,\n",
" 3.18561859e-05, 4.24865579e-05],\n",
" [ 9.03182427e-04, 9.10879426e-04, 4.73824219e-04,\n",
" 5.02328066e-04, 1.01346459e-03],\n",
" [ 8.54538646e-07, 8.31883261e-07, 8.35889835e-07,\n",
" 7.95969635e-07, 9.18475039e-07],\n",
" [ 8.31492730e-07, 7.50825980e-07, 8.39459596e-07,\n",
" 7.55736272e-07, 9.04969770e-07]]),\n",
" 'nanstd': array([[ 1.67779264e-03, 1.78020568e-03, 1.01965344e-03,\n",
" 1.14116609e-03, 2.11012077e-03],\n",
" [ 1.85800108e-03, 1.82909924e-03, 8.83820905e-04,\n",
" 1.43211536e-03, 1.38694375e-03],\n",
" [ 4.47109022e-05, 7.92512386e-05, 9.42345275e-06,\n",
" 1.18915512e-05, 1.83936970e-04],\n",
" [ 4.44089210e-16, 4.44089210e-16, 4.44089210e-16,\n",
" 4.44089210e-16, 0.00000000e+00],\n",
" [ 1.54887843e-01, 1.15693678e-01, 1.33606948e-01,\n",
" 7.33227366e-02, 8.04275766e-02],\n",
" [ 5.52727221e-06, 6.39895230e-06, 1.18585279e-05,\n",
" 2.43739885e-06, 1.03143653e-05],\n",
" [ 4.92686473e-04, 5.24779242e-04, 1.77447460e-04,\n",
" 2.17168058e-04, 4.97523381e-04],\n",
" [ 4.29049510e-07, 3.63891830e-07, 2.45829861e-07,\n",
" 2.58001300e-07, 2.97088722e-07],\n",
" [ 1.72042054e-07, 1.39922805e-07, 1.69122354e-07,\n",
" 1.24032973e-07, 1.81999520e-07]])}}"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"self.stats"
]
},
{
"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": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cal.load_calibration('test_data.calibdat')"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d = D('test_data/STD-3.csv')"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d.autorange()\n",
"d.separate()\n",
"d.bkg_correct()\n",
"d.ratio()\n",
"d.calibrate(cal.calib_dict)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d.sample_stats(filt=False, eachtrace=True)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/oscarbranson/anaconda/envs/py3/lib/python3.5/site-packages/matplotlib/scale.py:101: RuntimeWarning: invalid value encountered in less_equal\n",
" a[a <= 0.0] = 1e-300\n"
]
},
{
"data": {
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p+6qXCwwzxgw2xsQBs4CF7Q2kMfWIiYiISCyLaI+YMeZZIJvwht17gLnW2n8Y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\ns7LLYDnsAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109cbf550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = d.tplot(['Ba138', 'Ba137', 'Mn55', 'Al27'], stats=True, scale='log')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Lkr6TvkjSvyR8TgAAMmmPuD/QzKZKWiTJJVn1z7MkHSPpUUlvqu4HAABDiD2k3X2tpBn1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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109bd5c88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = d.statplot([a for a in d.analytes if 'Ca' not in a], scale='log')"
]
}
],
"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 |
aflaxman/AI4HM | Week_4/Exercise_4_Solutions.ipynb | 1 | 380413 | {
"metadata": {
"name": "",
"signature": "sha256:15b96bd59ad6f6b66c1e79053cf93ddd1aa1c3f7ec1a0b059877bb32f59ca6eb"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"!date\n",
"import numpy as np, pandas as pd, matplotlib.pyplot as plt, seaborn as sns\n",
"%matplotlib inline\n",
"sns.set_context('poster')\n",
"sns.set_style('darkgrid')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Sun Jan 25 21:30:05 PST 2015\r\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"# set random seed, for reproducibility\n",
"np.random.seed(12345)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load and clean PHMRC VA data:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pd.read_csv('IHME_PHMRC_VA_DATA_ADULT_Y2013M09D11_0.csv')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/snfs2/HOME/abie/pandas/pandas/io/parsers.py:1150: DtypeWarning: Columns (18,29,38,41,60,96) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What do you think we should do about that warning?"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"# just do what the warning recommends\n",
"df = pd.read_csv('IHME_PHMRC_VA_DATA_ADULT_Y2013M09D11_0.csv', low_memory=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# take a look at what is in the DataFrame\n",
"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>site</th>\n",
" <th>module</th>\n",
" <th>gs_code34</th>\n",
" <th>gs_text34</th>\n",
" <th>va34</th>\n",
" <th>gs_code46</th>\n",
" <th>gs_text46</th>\n",
" <th>va46</th>\n",
" <th>gs_code55</th>\n",
" <th>gs_text55</th>\n",
" <th>...</th>\n",
" <th>word_woman</th>\n",
" <th>word_womb</th>\n",
" <th>word_worri</th>\n",
" <th>word_wors</th>\n",
" <th>word_worsen</th>\n",
" <th>word_worst</th>\n",
" <th>word_wound</th>\n",
" <th>word_xray</th>\n",
" <th>word_yellow</th>\n",
" <th>newid</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> Mexico</td>\n",
" <td> Adult</td>\n",
" <td> K71</td>\n",
" <td> Cirrhosis</td>\n",
" <td> 6</td>\n",
" <td> K71</td>\n",
" <td> Cirrhosis</td>\n",
" <td> 8</td>\n",
" <td> K71</td>\n",
" <td> Cirrhosis</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> AP</td>\n",
" <td> Adult</td>\n",
" <td> G40</td>\n",
" <td> Epilepsy</td>\n",
" <td> 12</td>\n",
" <td> G40</td>\n",
" <td> Epilepsy</td>\n",
" <td> 16</td>\n",
" <td> G40</td>\n",
" <td> Epilepsy</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> AP</td>\n",
" <td> Adult</td>\n",
" <td> J12</td>\n",
" <td> Pneumonia</td>\n",
" <td> 26</td>\n",
" <td> J12</td>\n",
" <td> Pneumonia</td>\n",
" <td> 37</td>\n",
" <td> J12</td>\n",
" <td> Pneumonia</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> Mexico</td>\n",
" <td> Adult</td>\n",
" <td> J33</td>\n",
" <td> COPD</td>\n",
" <td> 8</td>\n",
" <td> J33</td>\n",
" <td> COPD</td>\n",
" <td> 10</td>\n",
" <td> J33</td>\n",
" <td> COPD</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> UP</td>\n",
" <td> Adult</td>\n",
" <td> I21</td>\n",
" <td> Acute Myocardial Infarction</td>\n",
" <td> 17</td>\n",
" <td> I21</td>\n",
" <td> Acute Myocardial Infarction</td>\n",
" <td> 23</td>\n",
" <td> I21</td>\n",
" <td> Acute Myocardial Infarction</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 946 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
" site module gs_code34 gs_text34 va34 gs_code46 \\\n",
"0 Mexico Adult K71 Cirrhosis 6 K71 \n",
"1 AP Adult G40 Epilepsy 12 G40 \n",
"2 AP Adult J12 Pneumonia 26 J12 \n",
"3 Mexico Adult J33 COPD 8 J33 \n",
"4 UP Adult I21 Acute Myocardial Infarction 17 I21 \n",
"\n",
" gs_text46 va46 gs_code55 gs_text55 \\\n",
"0 Cirrhosis 8 K71 Cirrhosis \n",
"1 Epilepsy 16 G40 Epilepsy \n",
"2 Pneumonia 37 J12 Pneumonia \n",
"3 COPD 10 J33 COPD \n",
"4 Acute Myocardial Infarction 23 I21 Acute Myocardial Infarction \n",
"\n",
" ... word_woman word_womb word_worri word_wors word_worsen word_worst \\\n",
"0 ... 0 0 0 0 0 0 \n",
"1 ... 0 0 0 0 0 0 \n",
"2 ... 0 0 0 0 0 0 \n",
"3 ... 0 0 0 0 0 0 \n",
"4 ... 0 0 0 0 0 0 \n",
"\n",
" word_wound word_xray word_yellow newid \n",
"0 0 0 0 1 \n",
"1 0 0 0 2 \n",
"2 0 0 0 3 \n",
"3 0 0 0 4 \n",
"4 0 0 0 5 \n",
"\n",
"[5 rows x 946 columns]"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"Index([u'site', u'module', u'gs_code34', u'gs_text34', u'va34', u'gs_code46', u'gs_text46', u'va46', u'gs_code55', u'gs_text55', u'va55', u'gs_comorbid1', u'gs_comorbid2', u'gs_level', u'g1_01d', u'g1_01m', u'g1_01y', u'g1_05', u'g1_06d', u'g1_06m', u'g1_06y', u'g1_07a', u'g1_07b', u'g1_07c', u'g1_08', u'g1_09', u'g1_10', u'g2_01', u'g2_02', u'g2_03ad', u'g2_03am', u'g2_03ay', u'g2_03bd', u'g2_03bm', u'g2_03by', u'g2_03cd', u'g2_03cm', u'g2_03cy', u'g2_03dd', u'g2_03dm', u'g2_03dy', u'g2_03ed', u'g2_03em', u'g2_03ey', u'g2_03fd', u'g2_03fm', u'g2_03fy', u'g3_01', u'g4_02', u'g4_03a', u'g4_03b', u'g4_04', u'g4_05', u'g4_06', u'g4_07', u'g4_08', u'g5_01d', u'g5_01m', u'g5_01y', u'g5_02', u'g5_03d', u'g5_03m', u'g5_03y', u'g5_04a', u'g5_04b', u'g5_04c', u'g5_05', u'g5_06a', u'g5_06b', u'g5_07', u'g5_08', u'a1_01_1', u'a1_01_2', u'a1_01_3', u'a1_01_4', u'a1_01_5', u'a1_01_6', u'a1_01_7', u'a1_01_8', u'a1_01_9', u'a1_01_10', u'a1_01_11', u'a1_01_12', u'a1_01_13', u'a1_01_14', u'a2_01', u'a2_02', u'a2_03', u'a2_04', u'a2_05', u'a2_06', u'a2_07', u'a2_08', u'a2_09_1a', u'a2_09_1b', u'a2_09_2a', u'a2_09_2b', u'a2_10', u'a2_11', u'a2_12', ...], dtype='object')"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# also load codebook (excel doc)\n",
"cb = pd.read_excel('IHME_PHMRC_VA_DATA_CODEBOOK_Y2013M09D11_0.xlsx')\n",
"cb"
],
"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>variable</th>\n",
" <th>question</th>\n",
" <th>module</th>\n",
" <th>health_care_experience</th>\n",
" <th>coding</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> site</td>\n",
" <td> Site</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> newid</td>\n",
" <td> Study ID</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> gs_diagnosis</td>\n",
" <td> Gold Standard Diagnosis Code</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> gs_comorbid1</td>\n",
" <td> Gold Standard Comorbid Conditions 1</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> gs_comorbid2</td>\n",
" <td> Gold Standard Comorbid Conditions 2</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> gs_level</td>\n",
" <td> Gold Standard Diagnosis Level</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 1 \"GS Level 1\" 2 \"GS Level 2\" 3 \"GS Level 2B\" ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> g1_01d</td>\n",
" <td> Date of birth [day]</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 99 \"Don't Know\"</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> g1_01m</td>\n",
" <td> Date of birth [month]</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 1 \"January\" 2 \"February\" 3 \"March\" 4 \"April\" 5...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> g1_01y</td>\n",
" <td> Date of birth [year]</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 9999 \"Don't Know\"</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> g1_05</td>\n",
" <td> Sex of deceased</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 1 \"Male\" 2 \"Female\" 8 \"Refused to Answer\" 9 \"D...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10 </th>\n",
" <td> g1_06d</td>\n",
" <td> Date of death [day]</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 99 \"Don't Know\"</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11 </th>\n",
" <td> g1_06m</td>\n",
" <td> Date of death [month]</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 1 \"January\" 2 \"February\" 3 \"March\" 4 \"April\" 5...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12 </th>\n",
" <td> g1_06y</td>\n",
" <td> Date of death [year]</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 9999 \"Don't Know\"</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13 </th>\n",
" <td> g1_07a</td>\n",
" <td> Last known age of the deceased [years]</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 999 \"Don't Know\"</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14 </th>\n",
" <td> g1_07b</td>\n",
" <td> Last known age of the deceased [months]</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 99 \"Don't Know\"</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15 </th>\n",
" <td> g1_07c</td>\n",
" <td> Last known age of the deceased [days]</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 99 \"Don't Know\"</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16 </th>\n",
" <td> g1_08</td>\n",
" <td> Marital status of deceased</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 1 \"Never Married\" 2 \"Married\" 3 \"Separated\" 4 ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17 </th>\n",
" <td> g1_09</td>\n",
" <td> Last known level of education completed</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 1 \"No Schooling\" 2 \"Primary School\" 3 \"High Sc...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18 </th>\n",
" <td> g1_10</td>\n",
" <td> Number of years of education completed</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 99 \"Don't Know\"</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19 </th>\n",
" <td> g2_01</td>\n",
" <td> Language of interview</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20 </th>\n",
" <td> g2_02</td>\n",
" <td> Interviewer ID number</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21 </th>\n",
" <td> g2_03ad</td>\n",
" <td> Date of first interview attempt [day]</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 99 \"Don't Know\"</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22 </th>\n",
" <td> g2_03am</td>\n",
" <td> Date of first interview attempt [month]</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 1 \"January\" 2 \"February\" 3 \"March\" 4 \"April\" 5...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23 </th>\n",
" <td> g2_03ay</td>\n",
" <td> Date of first interview attempt [year]</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 9999 \"Don't Know\"</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 </th>\n",
" <td> g2_03bd</td>\n",
" <td> Date and time arranged for second interview at...</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 99 \"Don't Know\"</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25 </th>\n",
" <td> g2_03bm</td>\n",
" <td> Date and time arranged for second interview at...</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 1 \"January\" 2 \"February\" 3 \"March\" 4 \"April\" 5...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26 </th>\n",
" <td> g2_03by</td>\n",
" <td> Date and time arranged for second interview at...</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 9999 \"Don't Know\"</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27 </th>\n",
" <td> g2_03cd</td>\n",
" <td> Date and time arranged for third interview att...</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 99 \"Don't Know\"</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28 </th>\n",
" <td> g2_03cm</td>\n",
" <td> Date and time arranged for third interview att...</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 1 \"January\" 2 \"February\" 3 \"March\" 4 \"April\" 5...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29 </th>\n",
" <td> g2_03cy</td>\n",
" <td> Date and time arranged for third interview att...</td>\n",
" <td> General</td>\n",
" <td> 0</td>\n",
" <td> 9999 \"Don't Know\"</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1619</th>\n",
" <td> word_stomach</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1620</th>\n",
" <td> word_test</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1621</th>\n",
" <td> word_deliv</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1622</th>\n",
" <td> word_asphyxia</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1623</th>\n",
" <td> word_wife</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1624</th>\n",
" <td> word_renal</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1625</th>\n",
" <td> word_surviv</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1626</th>\n",
" <td> word_visit</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1627</th>\n",
" <td> word_continu</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1628</th>\n",
" <td> word_husband</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1629</th>\n",
" <td> word_reach</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1630</th>\n",
" <td> word_provinci</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1631</th>\n",
" <td> word_servic</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1632</th>\n",
" <td> word_deceas</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1633</th>\n",
" <td> word_due</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1634</th>\n",
" <td> word_vomit</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1635</th>\n",
" <td> word_bad</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1636</th>\n",
" <td> word_nilof</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1637</th>\n",
" <td> word_male</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1638</th>\n",
" <td> word_milk</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1639</th>\n",
" <td> word_incub</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1640</th>\n",
" <td> word_aliv</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1641</th>\n",
" <td> word_doctor</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1642</th>\n",
" <td> word_cord</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1643</th>\n",
" <td> word_born</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1644</th>\n",
" <td> word_brought</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1645</th>\n",
" <td> word_look</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1646</th>\n",
" <td> word_week</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1647</th>\n",
" <td> word_babi</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1648</th>\n",
" <td> word_difficulti</td>\n",
" <td> The open narratives contained the word or stem...</td>\n",
" <td> Neonate</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1649 rows \u00d7 5 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
" variable question \\\n",
"0 site Site \n",
"1 newid Study ID \n",
"2 gs_diagnosis Gold Standard Diagnosis Code \n",
"3 gs_comorbid1 Gold Standard Comorbid Conditions 1 \n",
"4 gs_comorbid2 Gold Standard Comorbid Conditions 2 \n",
"5 gs_level Gold Standard Diagnosis Level \n",
"6 g1_01d Date of birth [day] \n",
"7 g1_01m Date of birth [month] \n",
"8 g1_01y Date of birth [year] \n",
"9 g1_05 Sex of deceased \n",
"10 g1_06d Date of death [day] \n",
"11 g1_06m Date of death [month] \n",
"12 g1_06y Date of death [year] \n",
"13 g1_07a Last known age of the deceased [years] \n",
"14 g1_07b Last known age of the deceased [months] \n",
"15 g1_07c Last known age of the deceased [days] \n",
"16 g1_08 Marital status of deceased \n",
"17 g1_09 Last known level of education completed \n",
"18 g1_10 Number of years of education completed \n",
"19 g2_01 Language of interview \n",
"20 g2_02 Interviewer ID number \n",
"21 g2_03ad Date of first interview attempt [day] \n",
"22 g2_03am Date of first interview attempt [month] \n",
"23 g2_03ay Date of first interview attempt [year] \n",
"24 g2_03bd Date and time arranged for second interview at... \n",
"25 g2_03bm Date and time arranged for second interview at... \n",
"26 g2_03by Date and time arranged for second interview at... \n",
"27 g2_03cd Date and time arranged for third interview att... \n",
"28 g2_03cm Date and time arranged for third interview att... \n",
"29 g2_03cy Date and time arranged for third interview att... \n",
"... ... ... \n",
"1619 word_stomach The open narratives contained the word or stem... \n",
"1620 word_test The open narratives contained the word or stem... \n",
"1621 word_deliv The open narratives contained the word or stem... \n",
"1622 word_asphyxia The open narratives contained the word or stem... \n",
"1623 word_wife The open narratives contained the word or stem... \n",
"1624 word_renal The open narratives contained the word or stem... \n",
"1625 word_surviv The open narratives contained the word or stem... \n",
"1626 word_visit The open narratives contained the word or stem... \n",
"1627 word_continu The open narratives contained the word or stem... \n",
"1628 word_husband The open narratives contained the word or stem... \n",
"1629 word_reach The open narratives contained the word or stem... \n",
"1630 word_provinci The open narratives contained the word or stem... \n",
"1631 word_servic The open narratives contained the word or stem... \n",
"1632 word_deceas The open narratives contained the word or stem... \n",
"1633 word_due The open narratives contained the word or stem... \n",
"1634 word_vomit The open narratives contained the word or stem... \n",
"1635 word_bad The open narratives contained the word or stem... \n",
"1636 word_nilof The open narratives contained the word or stem... \n",
"1637 word_male The open narratives contained the word or stem... \n",
"1638 word_milk The open narratives contained the word or stem... \n",
"1639 word_incub The open narratives contained the word or stem... \n",
"1640 word_aliv The open narratives contained the word or stem... \n",
"1641 word_doctor The open narratives contained the word or stem... \n",
"1642 word_cord The open narratives contained the word or stem... \n",
"1643 word_born The open narratives contained the word or stem... \n",
"1644 word_brought The open narratives contained the word or stem... \n",
"1645 word_look The open narratives contained the word or stem... \n",
"1646 word_week The open narratives contained the word or stem... \n",
"1647 word_babi The open narratives contained the word or stem... \n",
"1648 word_difficulti The open narratives contained the word or stem... \n",
"\n",
" module health_care_experience \\\n",
"0 General 0 \n",
"1 General 0 \n",
"2 General 0 \n",
"3 General 0 \n",
"4 General 0 \n",
"5 General 0 \n",
"6 General 0 \n",
"7 General 0 \n",
"8 General 0 \n",
"9 General 0 \n",
"10 General 0 \n",
"11 General 0 \n",
"12 General 0 \n",
"13 General 0 \n",
"14 General 0 \n",
"15 General 0 \n",
"16 General 0 \n",
"17 General 0 \n",
"18 General 0 \n",
"19 General 0 \n",
"20 General 0 \n",
"21 General 0 \n",
"22 General 0 \n",
"23 General 0 \n",
"24 General 0 \n",
"25 General 0 \n",
"26 General 0 \n",
"27 General 0 \n",
"28 General 0 \n",
"29 General 0 \n",
"... ... ... \n",
"1619 Neonate 1 \n",
"1620 Neonate 1 \n",
"1621 Neonate 1 \n",
"1622 Neonate 1 \n",
"1623 Neonate 1 \n",
"1624 Neonate 1 \n",
"1625 Neonate 1 \n",
"1626 Neonate 1 \n",
"1627 Neonate 1 \n",
"1628 Neonate 1 \n",
"1629 Neonate 1 \n",
"1630 Neonate 1 \n",
"1631 Neonate 1 \n",
"1632 Neonate 1 \n",
"1633 Neonate 1 \n",
"1634 Neonate 1 \n",
"1635 Neonate 1 \n",
"1636 Neonate 1 \n",
"1637 Neonate 1 \n",
"1638 Neonate 1 \n",
"1639 Neonate 1 \n",
"1640 Neonate 1 \n",
"1641 Neonate 1 \n",
"1642 Neonate 1 \n",
"1643 Neonate 1 \n",
"1644 Neonate 1 \n",
"1645 Neonate 1 \n",
"1646 Neonate 1 \n",
"1647 Neonate 1 \n",
"1648 Neonate 1 \n",
"\n",
" coding \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"5 1 \"GS Level 1\" 2 \"GS Level 2\" 3 \"GS Level 2B\" ... \n",
"6 99 \"Don't Know\" \n",
"7 1 \"January\" 2 \"February\" 3 \"March\" 4 \"April\" 5... \n",
"8 9999 \"Don't Know\" \n",
"9 1 \"Male\" 2 \"Female\" 8 \"Refused to Answer\" 9 \"D... \n",
"10 99 \"Don't Know\" \n",
"11 1 \"January\" 2 \"February\" 3 \"March\" 4 \"April\" 5... \n",
"12 9999 \"Don't Know\" \n",
"13 999 \"Don't Know\" \n",
"14 99 \"Don't Know\" \n",
"15 99 \"Don't Know\" \n",
"16 1 \"Never Married\" 2 \"Married\" 3 \"Separated\" 4 ... \n",
"17 1 \"No Schooling\" 2 \"Primary School\" 3 \"High Sc... \n",
"18 99 \"Don't Know\" \n",
"19 NaN \n",
"20 NaN \n",
"21 99 \"Don't Know\" \n",
"22 1 \"January\" 2 \"February\" 3 \"March\" 4 \"April\" 5... \n",
"23 9999 \"Don't Know\" \n",
"24 99 \"Don't Know\" \n",
"25 1 \"January\" 2 \"February\" 3 \"March\" 4 \"April\" 5... \n",
"26 9999 \"Don't Know\" \n",
"27 99 \"Don't Know\" \n",
"28 1 \"January\" 2 \"February\" 3 \"March\" 4 \"April\" 5... \n",
"29 9999 \"Don't Know\" \n",
"... ... \n",
"1619 NaN \n",
"1620 NaN \n",
"1621 NaN \n",
"1622 NaN \n",
"1623 NaN \n",
"1624 NaN \n",
"1625 NaN \n",
"1626 NaN \n",
"1627 NaN \n",
"1628 NaN \n",
"1629 NaN \n",
"1630 NaN \n",
"1631 NaN \n",
"1632 NaN \n",
"1633 NaN \n",
"1634 NaN \n",
"1635 NaN \n",
"1636 NaN \n",
"1637 NaN \n",
"1638 NaN \n",
"1639 NaN \n",
"1640 NaN \n",
"1641 NaN \n",
"1642 NaN \n",
"1643 NaN \n",
"1644 NaN \n",
"1645 NaN \n",
"1646 NaN \n",
"1647 NaN \n",
"1648 NaN \n",
"\n",
"[1649 rows x 5 columns]"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Every column that starts with `a{NUMBER}_stuff` is part of the signs and symptoms reported about an adult death:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.filter(regex='a\\d_')"
],
"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>a1_01_1</th>\n",
" <th>a1_01_2</th>\n",
" <th>a1_01_3</th>\n",
" <th>a1_01_4</th>\n",
" <th>a1_01_5</th>\n",
" <th>a1_01_6</th>\n",
" <th>a1_01_7</th>\n",
" <th>a1_01_8</th>\n",
" <th>a1_01_9</th>\n",
" <th>a1_01_10</th>\n",
" <th>...</th>\n",
" <th>a6_06_2y</th>\n",
" <th>a6_07d</th>\n",
" <th>a6_07m</th>\n",
" <th>a6_07y</th>\n",
" <th>a6_09</th>\n",
" <th>a6_10</th>\n",
" <th>a7_11</th>\n",
" <th>a7_12</th>\n",
" <th>a7_13</th>\n",
" <th>a7_14</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</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> Yes</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12 </th>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16 </th>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20 </th>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21 </th>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22 </th>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27 </th>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29 </th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7811</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7812</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7813</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7814</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> 2009</td>\n",
" <td> 16</td>\n",
" <td> April</td>\n",
" <td> 2009</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7815</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7816</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7817</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7818</th>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7819</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7820</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7821</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> 3</td>\n",
" <td> September</td>\n",
" <td> 2009</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7822</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7823</th>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7824</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7825</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7826</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7827</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7828</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7829</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7830</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7831</th>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7832</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7833</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7834</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7835</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7836</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td>...</td>\n",
" <td> 2009</td>\n",
" <td> 9</td>\n",
" <td> September</td>\n",
" <td> 2009</td>\n",
" <td> Yes</td>\n",
" <td> Yes</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7837</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7838</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7839</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7840</th>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td> Yes</td>\n",
" <td>...</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> Don't Know</td>\n",
" <td> No</td>\n",
" <td> No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>7841 rows \u00d7 195 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
" a1_01_1 a1_01_2 a1_01_3 a1_01_4 a1_01_5 a1_01_6 a1_01_7 a1_01_8 \\\n",
"0 No No No No No No No No \n",
"1 Yes Yes No Yes No Yes No No \n",
"2 Yes Yes No No No No Yes No \n",
"3 Yes No No Yes No Yes Yes No \n",
"4 No No No No No No No No \n",
"5 No No No No Yes No No No \n",
"6 No No No No No No Yes No \n",
"7 No No No No No No No No \n",
"8 Yes Yes No No Yes Yes No No \n",
"9 No No No No No No No No \n",
"10 No No No No No No No No \n",
"11 No No No No No No No No \n",
"12 Yes Yes No Yes No No No No \n",
"13 No No No No No No No No \n",
"14 No No No No No No No No \n",
"15 No No No No No No No No \n",
"16 No Yes No No No No No No \n",
"17 No No No No No No No No \n",
"18 No No No No No No No No \n",
"19 No No No No No No No No \n",
"20 Yes Yes No No No No No No \n",
"21 Yes No Yes No Yes Yes No No \n",
"22 No Yes No No No No Yes No \n",
"23 No No No No No No No No \n",
"24 No No No No No No No No \n",
"25 No No No No No No Yes No \n",
"26 No No No Yes Yes Yes No No \n",
"27 No Yes No No No Yes No No \n",
"28 No No No Yes No Yes No No \n",
"29 No No No No No No No No \n",
"... ... ... ... ... ... ... ... ... \n",
"7811 No No Yes No No No No No \n",
"7812 No No No No No No No No \n",
"7813 No No No No No No No No \n",
"7814 No No No No No No No No \n",
"7815 No No No No No No No No \n",
"7816 No No No No No No No No \n",
"7817 No No No No No No No No \n",
"7818 No Yes No No Yes No No No \n",
"7819 No No No No No Yes No No \n",
"7820 No No No No No No No No \n",
"7821 No No No No No No Yes No \n",
"7822 No No No No No No No No \n",
"7823 Yes No Yes Yes No Yes No No \n",
"7824 No No No No No No No No \n",
"7825 No No No No Yes No No Yes \n",
"7826 No No No No No No No No \n",
"7827 No No No No No No No No \n",
"7828 No No No No Yes No No No \n",
"7829 No No No No No No Yes No \n",
"7830 No No No No No No No No \n",
"7831 No Yes No No No No No Yes \n",
"7832 No No Yes No No No No No \n",
"7833 No No No No No No No No \n",
"7834 No No No No No No No No \n",
"7835 No No No No No No No No \n",
"7836 No No Yes No No Yes Yes No \n",
"7837 No No No No No No Yes No \n",
"7838 No No No No No No No No \n",
"7839 No No No No No No No No \n",
"7840 No No Yes No No No No No \n",
"\n",
" a1_01_9 a1_01_10 ... a6_06_2y a6_07d a6_07m \\\n",
"0 Don't Know Don't Know ... Don't Know Don't Know Don't Know \n",
"1 Yes No ... Don't Know Don't Know Don't Know \n",
"2 No Yes ... Don't Know Don't Know Don't Know \n",
"3 No No ... Don't Know Don't Know Don't Know \n",
"4 Yes No ... Don't Know Don't Know Don't Know \n",
"5 No No ... Don't Know Don't Know Don't Know \n",
"6 No No ... Don't Know Don't Know Don't Know \n",
"7 No No ... Don't Know Don't Know Don't Know \n",
"8 No No ... Don't Know Don't Know Don't Know \n",
"9 No No ... Don't Know Don't Know Don't Know \n",
"10 No Yes ... Don't Know Don't Know Don't Know \n",
"11 No No ... Don't Know Don't Know Don't Know \n",
"12 No No ... Don't Know Don't Know Don't Know \n",
"13 Yes No ... Don't Know Don't Know Don't Know \n",
"14 No No ... Don't Know Don't Know Don't Know \n",
"15 No No ... Don't Know Don't Know Don't Know \n",
"16 No No ... Don't Know Don't Know Don't Know \n",
"17 No No ... Don't Know Don't Know Don't Know \n",
"18 No No ... Don't Know Don't Know Don't Know \n",
"19 No No ... Don't Know Don't Know Don't Know \n",
"20 No No ... Don't Know Don't Know Don't Know \n",
"21 No Yes ... Don't Know Don't Know Don't Know \n",
"22 No No ... Don't Know Don't Know Don't Know \n",
"23 No Yes ... Don't Know Don't Know Don't Know \n",
"24 No No ... Don't Know Don't Know Don't Know \n",
"25 No No ... Don't Know Don't Know Don't Know \n",
"26 No No ... Don't Know Don't Know Don't Know \n",
"27 Yes Yes ... Don't Know Don't Know Don't Know \n",
"28 No No ... Don't Know Don't Know Don't Know \n",
"29 No No ... Don't Know Don't Know Don't Know \n",
"... ... ... ... ... ... ... \n",
"7811 No Yes ... Don't Know Don't Know Don't Know \n",
"7812 No No ... Don't Know Don't Know Don't Know \n",
"7813 No No ... Don't Know Don't Know Don't Know \n",
"7814 No No ... 2009 16 April \n",
"7815 Yes No ... Don't Know Don't Know Don't Know \n",
"7816 No No ... Don't Know Don't Know Don't Know \n",
"7817 No Yes ... Don't Know Don't Know Don't Know \n",
"7818 No Yes ... Don't Know Don't Know Don't Know \n",
"7819 No No ... Don't Know Don't Know Don't Know \n",
"7820 No No ... Don't Know Don't Know Don't Know \n",
"7821 No No ... Don't Know 3 September \n",
"7822 No No ... Don't Know Don't Know Don't Know \n",
"7823 No No ... Don't Know Don't Know Don't Know \n",
"7824 No No ... Don't Know Don't Know Don't Know \n",
"7825 Yes Yes ... Don't Know Don't Know Don't Know \n",
"7826 No No ... Don't Know Don't Know Don't Know \n",
"7827 Don't Know No ... Don't Know Don't Know Don't Know \n",
"7828 No No ... Don't Know Don't Know Don't Know \n",
"7829 No No ... Don't Know Don't Know Don't Know \n",
"7830 No Yes ... Don't Know Don't Know Don't Know \n",
"7831 No Yes ... Don't Know Don't Know Don't Know \n",
"7832 No No ... Don't Know Don't Know Don't Know \n",
"7833 No Yes ... Don't Know Don't Know Don't Know \n",
"7834 Yes Yes ... Don't Know Don't Know Don't Know \n",
"7835 No No ... Don't Know Don't Know Don't Know \n",
"7836 No Yes ... 2009 9 September \n",
"7837 No Yes ... Don't Know Don't Know Don't Know \n",
"7838 No No ... Don't Know Don't Know Don't Know \n",
"7839 No No ... Don't Know Don't Know Don't Know \n",
"7840 No Yes ... Don't Know Don't Know Don't Know \n",
"\n",
" a6_07y a6_09 a6_10 a7_11 a7_12 a7_13 a7_14 \n",
"0 Don't Know Yes No NaN NaN NaN NaN \n",
"1 Don't Know Yes No NaN NaN NaN NaN \n",
"2 Don't Know Yes No NaN NaN NaN NaN \n",
"3 Don't Know Yes No NaN NaN NaN NaN \n",
"4 Don't Know Yes No NaN NaN NaN NaN \n",
"5 Don't Know No No NaN NaN NaN NaN \n",
"6 Don't Know No No NaN NaN NaN NaN \n",
"7 Don't Know No No NaN NaN NaN NaN \n",
"8 Don't Know Yes No NaN NaN NaN NaN \n",
"9 Don't Know Yes No NaN NaN NaN NaN \n",
"10 Don't Know Yes No NaN NaN NaN NaN \n",
"11 Don't Know Yes No NaN NaN NaN NaN \n",
"12 Don't Know Yes No NaN NaN NaN NaN \n",
"13 Don't Know No No NaN NaN NaN NaN \n",
"14 Don't Know Yes Yes NaN NaN NaN NaN \n",
"15 Don't Know Yes No NaN NaN NaN NaN \n",
"16 Don't Know Yes No NaN NaN NaN NaN \n",
"17 Don't Know Yes No NaN NaN NaN NaN \n",
"18 Don't Know Yes No NaN NaN NaN NaN \n",
"19 Don't Know Yes No NaN NaN NaN NaN \n",
"20 Don't Know No No NaN NaN NaN NaN \n",
"21 Don't Know Yes Yes NaN NaN NaN NaN \n",
"22 Don't Know Yes No NaN NaN NaN NaN \n",
"23 Don't Know No No NaN NaN NaN NaN \n",
"24 Don't Know No No NaN NaN NaN NaN \n",
"25 Don't Know No No NaN NaN NaN NaN \n",
"26 Don't Know Yes No NaN NaN NaN NaN \n",
"27 Don't Know Yes Yes NaN NaN NaN NaN \n",
"28 Don't Know Yes No NaN NaN NaN NaN \n",
"29 Don't Know No No NaN NaN NaN NaN \n",
"... ... ... ... ... ... ... ... \n",
"7811 Don't Know Yes No NaN NaN NaN NaN \n",
"7812 Don't Know Yes No NaN NaN NaN NaN \n",
"7813 Don't Know No No NaN NaN NaN NaN \n",
"7814 2009 Yes No NaN NaN NaN NaN \n",
"7815 Don't Know Yes Yes NaN NaN NaN NaN \n",
"7816 Don't Know Yes No NaN NaN NaN NaN \n",
"7817 Don't Know Yes Yes NaN NaN NaN NaN \n",
"7818 Don't Know Yes No NaN NaN NaN NaN \n",
"7819 Don't Know No No NaN NaN NaN NaN \n",
"7820 Don't Know Yes No NaN NaN NaN NaN \n",
"7821 2009 No No NaN NaN NaN NaN \n",
"7822 Don't Know Don't Know No NaN NaN NaN NaN \n",
"7823 Don't Know Yes No NaN NaN NaN NaN \n",
"7824 Don't Know Yes No NaN NaN NaN NaN \n",
"7825 Don't Know Yes Yes NaN NaN NaN NaN \n",
"7826 Don't Know No No NaN NaN NaN NaN \n",
"7827 Don't Know Yes Yes NaN NaN NaN NaN \n",
"7828 Don't Know Yes Yes NaN NaN NaN NaN \n",
"7829 Don't Know Yes No NaN NaN NaN NaN \n",
"7830 Don't Know Yes No NaN NaN NaN NaN \n",
"7831 Don't Know Yes No NaN NaN NaN NaN \n",
"7832 Don't Know No No NaN NaN NaN NaN \n",
"7833 Don't Know No No NaN NaN NaN NaN \n",
"7834 Don't Know Yes No NaN NaN NaN NaN \n",
"7835 Don't Know No No NaN NaN NaN NaN \n",
"7836 2009 Yes Yes NaN NaN NaN NaN \n",
"7837 Don't Know Yes No NaN NaN NaN NaN \n",
"7838 Don't Know No No NaN NaN NaN NaN \n",
"7839 Don't Know Yes No NaN NaN NaN NaN \n",
"7840 Don't Know No No NaN NaN NaN NaN \n",
"\n",
"[7841 rows x 195 columns]"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is also a \"bag of words\" section from the processed results of the free-text response section:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.filter(like='word_')"
],
"language": "python",
"metadata": {},
"outputs": [
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" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27 </th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28 </th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29 </th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7811</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7812</th>\n",
" <td> 6</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7813</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7814</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7815</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7816</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7817</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7818</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7819</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7820</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7821</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7822</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7823</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7824</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7825</th>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7826</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7827</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7828</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7829</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7830</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7831</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7832</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7833</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7834</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7835</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>7836</th>\n",
" <td> 0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>7837</th>\n",
" <td> 0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>7838</th>\n",
" <td> 0</td>\n",
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" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>7839</th>\n",
" <td> 0</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>7841 rows \u00d7 679 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
" word_abdomen word_abl word_accid word_accord word_ach word_acidosi \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
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"6 0 0 0 0 0 0 \n",
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"8 0 2 0 1 0 0 \n",
"9 1 0 0 0 0 0 \n",
"10 0 0 0 0 0 0 \n",
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"12 0 0 0 0 0 0 \n",
"13 0 0 0 0 0 0 \n",
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"18 0 0 0 0 0 0 \n",
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"20 0 0 0 0 0 0 \n",
"21 0 0 0 0 0 0 \n",
"22 0 0 1 0 0 0 \n",
"23 0 0 0 0 0 0 \n",
"24 0 0 0 0 0 0 \n",
"25 0 0 0 0 0 0 \n",
"26 0 0 0 0 0 0 \n",
"27 0 0 0 0 0 0 \n",
"28 0 0 0 0 0 0 \n",
"29 0 0 0 0 0 0 \n",
"... ... ... ... ... ... ... \n",
"7811 0 0 0 0 0 0 \n",
"7812 6 0 0 0 0 0 \n",
"7813 0 0 0 0 0 0 \n",
"7814 1 0 0 0 0 0 \n",
"7815 0 0 0 0 0 0 \n",
"7816 0 0 0 0 0 0 \n",
"7817 0 0 0 0 0 0 \n",
"7818 0 0 0 0 0 0 \n",
"7819 0 0 0 0 0 0 \n",
"7820 0 0 0 0 0 0 \n",
"7821 0 0 0 0 0 0 \n",
"7822 0 0 0 0 0 0 \n",
"7823 0 0 0 0 0 0 \n",
"7824 0 0 4 0 0 0 \n",
"7825 2 0 0 0 0 0 \n",
"7826 0 0 0 0 0 0 \n",
"7827 0 0 0 0 0 0 \n",
"7828 0 0 0 0 0 0 \n",
"7829 0 0 0 0 1 0 \n",
"7830 0 0 0 0 0 0 \n",
"7831 0 0 0 0 0 0 \n",
"7832 0 0 0 0 0 0 \n",
"7833 0 0 0 0 0 0 \n",
"7834 0 0 0 0 0 0 \n",
"7835 0 0 0 0 0 0 \n",
"7836 0 0 0 0 0 0 \n",
"7837 0 0 0 0 0 0 \n",
"7838 0 0 0 0 0 0 \n",
"7839 0 0 0 0 0 0 \n",
"7840 0 0 0 0 0 0 \n",
"\n",
" word_acquir word_activ word_acut word_add ... word_wit \\\n",
"0 0 0 0 0 ... 0 \n",
"1 0 0 0 0 ... 0 \n",
"2 0 0 0 0 ... 0 \n",
"3 0 0 0 0 ... 0 \n",
"4 0 0 0 0 ... 0 \n",
"5 0 0 0 0 ... 0 \n",
"6 0 0 0 0 ... 0 \n",
"7 0 0 0 0 ... 0 \n",
"8 0 0 0 0 ... 0 \n",
"9 0 0 0 0 ... 0 \n",
"10 0 0 0 0 ... 0 \n",
"11 0 0 0 0 ... 0 \n",
"12 0 0 0 0 ... 0 \n",
"13 0 0 0 0 ... 0 \n",
"14 0 0 1 0 ... 0 \n",
"15 0 0 0 0 ... 0 \n",
"16 0 0 0 0 ... 0 \n",
"17 0 0 0 0 ... 0 \n",
"18 0 0 0 0 ... 0 \n",
"19 0 0 0 0 ... 0 \n",
"20 0 0 0 0 ... 0 \n",
"21 1 0 0 0 ... 0 \n",
"22 0 0 0 0 ... 0 \n",
"23 0 0 0 0 ... 0 \n",
"24 0 0 0 0 ... 0 \n",
"25 0 0 0 0 ... 0 \n",
"26 0 0 0 0 ... 0 \n",
"27 0 0 1 0 ... 0 \n",
"28 0 0 0 0 ... 0 \n",
"29 0 0 0 0 ... 0 \n",
"... ... ... ... ... ... ... \n",
"7811 0 0 0 0 ... 0 \n",
"7812 0 1 0 0 ... 0 \n",
"7813 0 1 0 0 ... 0 \n",
"7814 0 0 0 0 ... 0 \n",
"7815 0 0 0 0 ... 0 \n",
"7816 0 0 0 0 ... 0 \n",
"7817 0 0 0 0 ... 0 \n",
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"7819 0 0 0 0 ... 0 \n",
"7820 0 0 0 0 ... 1 \n",
"7821 0 0 0 0 ... 0 \n",
"7822 0 0 0 0 ... 0 \n",
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"7825 0 0 0 0 ... 0 \n",
"7826 0 0 0 0 ... 0 \n",
"7827 1 0 0 0 ... 0 \n",
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"7830 0 0 0 0 ... 0 \n",
"7831 0 0 0 0 ... 0 \n",
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"7837 0 0 0 0 ... 0 \n",
"7838 0 0 0 0 ... 0 \n",
"7839 0 0 0 0 ... 0 \n",
"7840 0 0 0 0 ... 0 \n",
"\n",
" word_woman word_womb word_worri word_wors word_worsen word_worst \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 \n",
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"... ... ... ... ... ... ... \n",
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"7837 0 0 0 0 0 0 \n",
"7838 0 0 0 0 0 0 \n",
"7839 0 0 0 0 0 0 \n",
"7840 0 0 0 0 0 0 \n",
"\n",
" word_wound word_xray word_yellow \n",
"0 0 0 0 \n",
"1 0 0 0 \n",
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"3 0 0 0 \n",
"4 0 0 0 \n",
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"... ... ... ... \n",
"7811 0 0 0 \n",
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"\n",
"[7841 rows x 679 columns]"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make the feature vectors, by putting together everything from the adult module, and everything from the free response section."
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"X = np.hstack((np.array(df.filter(regex='a\\d_')), np.array(df.filter(like='word_'))))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Did I get that right?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.array(df.filter(regex='a\\d_')).shape, np.array(df.filter(like='word_')).shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"((7841, 195), (7841, 679))"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"(7841, 874)"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Actually, that won't work without more data cleaning, because `sklearn` expects the feature vectors to be numbers, not answers like \"Yes\" and \"Don't know\".\n",
"\n",
"So let's just use the open-response text, which has already been processed."
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"X = np.array(df.filter(like='word_'))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The causes of death appear in the `gs_text34` column (obviously...)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.gs_text34"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"0 Cirrhosis\n",
"1 Epilepsy\n",
"2 Pneumonia\n",
"3 COPD\n",
"4 Acute Myocardial Infarction\n",
"5 Fires\n",
"6 Renal Failure\n",
"7 AIDS\n",
"8 Lung Cancer\n",
"9 Maternal\n",
"10 Maternal\n",
"11 Drowning\n",
"12 Renal Failure\n",
"13 Other Cardiovascular Diseases\n",
"14 Renal Failure\n",
"...\n",
"7826 Diarrhea/Dysentery\n",
"7827 Cirrhosis\n",
"7828 Cirrhosis\n",
"7829 Diabetes\n",
"7830 Maternal\n",
"7831 Maternal\n",
"7832 Breast Cancer\n",
"7833 AIDS\n",
"7834 Cirrhosis\n",
"7835 Homicide\n",
"7836 Cervical Cancer\n",
"7837 Other Cardiovascular Diseases\n",
"7838 Poisonings\n",
"7839 Fires\n",
"7840 Esophageal Cancer\n",
"Name: gs_text34, Length: 7841, dtype: object"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For class purposes, we will simplify the prediction task: was the death due to stroke, diabetes, or something else?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['Cause'] = df.gs_text34.map({'Stroke':'Stroke', 'Diabetes':'Diabetes'}).fillna('Other')\n",
"y = np.array(df.Cause)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Did that work?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.unique(y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"array(['Diabetes', 'Other', 'Stroke'], dtype=object)"
]
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's train one of our (by now) standard classifiers to predict the underlying cause of death from the restricted cause list:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sklearn.tree\n",
"\n",
"clf = sklearn.tree.DecisionTreeClassifier()\n",
"clf.fit(X,y)\n",
"\n",
"y_pred = clf.predict(X)\n",
"np.mean(y == y_pred)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"0.98125239127662289"
]
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Does that look good? Too good? If it looks too good to be true, it probably is..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sklearn.cross_validation\n",
"\n",
"scores = sklearn.cross_validation.cross_val_score(clf, X, y)\n",
"\n",
"print scores.mean(), scores.std()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.833696270575 0.00883096018699\n"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.distplot(scores, rug=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"<matplotlib.axes.AxesSubplot at 0x7ffcaf31f2d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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QSo/y7/58L71joWDpwgr+yc9tnZFeA7FYjK1rG9i2tmH8vrcPd3DaBcmSpDnEYCAp72Wz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\ntZXFfOax1XzioeX8ePc5nnv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qywiW2ulYkq5nYW0ZPzdhC9Nv/CDkYk8ywoqkucdgIEXo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\nAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7ffcaf190150>"
]
}
],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What have we done here?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"help(sklearn.cross_validation.cross_val_score)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Help on function cross_val_score in module sklearn.cross_validation:\n",
"\n",
"cross_val_score(estimator, X, y=None, scoring=None, cv=None, n_jobs=1, verbose=0, fit_params=None, score_func=None, pre_dispatch='2*n_jobs')\n",
" Evaluate a score by cross-validation\n",
" \n",
" Parameters\n",
" ----------\n",
" estimator : estimator object implementing 'fit'\n",
" The object to use to fit the data.\n",
" \n",
" X : array-like\n",
" The data to fit. Can be, for example a list, or an array at least 2d.\n",
" \n",
" y : array-like, optional, default: None\n",
" The target variable to try to predict in the case of\n",
" supervised learning.\n",
" \n",
" scoring : string, callable or None, optional, default: None\n",
" A string (see model evaluation documentation) or\n",
" a scorer callable object / function with signature\n",
" ``scorer(estimator, X, y)``.\n",
" \n",
" cv : cross-validation generator or int, optional, default: None\n",
" A cross-validation generator to use. If int, determines\n",
" the number of folds in StratifiedKFold if y is binary\n",
" or multiclass and estimator is a classifier, or the number\n",
" of folds in KFold otherwise. If None, it is equivalent to cv=3.\n",
" \n",
" n_jobs : integer, optional\n",
" The number of CPUs to use to do the computation. -1 means\n",
" 'all CPUs'.\n",
" \n",
" verbose : integer, optional\n",
" The verbosity level.\n",
" \n",
" fit_params : dict, optional\n",
" Parameters to pass to the fit method of the estimator.\n",
" \n",
" pre_dispatch : int, or string, optional\n",
" Controls the number of jobs that get dispatched during parallel\n",
" execution. Reducing this number can be useful to avoid an\n",
" explosion of memory consumption when more jobs get dispatched\n",
" than CPUs can process. This parameter can be:\n",
" \n",
" - None, in which case all the jobs are immediately\n",
" created and spawned. Use this for lightweight and\n",
" fast-running jobs, to avoid delays due to on-demand\n",
" spawning of the jobs\n",
" \n",
" - An int, giving the exact number of total jobs that are\n",
" spawned\n",
" \n",
" - A string, giving an expression as a function of n_jobs,\n",
" as in '2*n_jobs'\n",
" \n",
" Returns\n",
" -------\n",
" scores : array of float, shape=(len(list(cv)),)\n",
" Array of scores of the estimator for each run of the cross validation.\n",
"\n"
]
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you read through that, you will find out: we have done stratified K-Fold cross validation with $K = 3$. In other words, split the data into 3 equal parts (what if it's not divisible by three?) and then use two for training and the third for testing, in all three ways.\n",
"\n",
"So how to do 10-fold CV as in the reading?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%time\n",
"\n",
"n = len(y)\n",
"cv = sklearn.cross_validation.KFold(n, n_folds=10, shuffle=True)\n",
"scores = sklearn.cross_validation.cross_val_score(clf, X, y, cv=cv)\n",
"\n",
"print scores.mean(), scores.std()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.833949369557 0.00660735684823\n",
"CPU times: user 19.4 s, sys: 67 ms, total: 19.5 s\n",
"Wall time: 19.5 s\n"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.distplot(scores, rug=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
"<matplotlib.axes.AxesSubplot at 0x7ffce65024d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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1uBJhNZIylcFAkmbR4PAYLx+4QO/ACAAlhTk8uquaRNynW92aDdXF5OemO1k1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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7ffca9d32610>"
]
}
],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I think it is important to stratify the sampling, although our book does not make a big deal about that:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv = sklearn.cross_validation.StratifiedKFold(y, n_folds=10, shuffle=True)\n",
"scores = sklearn.cross_validation.cross_val_score(clf, X, y, cv=cv)\n",
"\n",
"print scores.mean(), scores.std()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.83688337277 0.0111495762856\n"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.distplot(scores, rug=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
"<matplotlib.axes.AxesSubplot at 0x7ffca9d86490>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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bLQPDE1zqHOZS5zX6hm6EhLGJ6eyiZYCaiiIaa0vZWFPCxpoSg4KkdcdnNUmr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\nrxLMjkIAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7ffca9b84690>"
]
}
],
"prompt_number": 25
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And I don't want you to ever rely on just 10 samples. How can you do ten repetitions of 10-fold cross validation?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scores = []\n",
"for rep in range(10):\n",
" print rep,\n",
" cv = sklearn.cross_validation.StratifiedKFold(y, n_folds=10, shuffle=True)\n",
" scores += list(sklearn.cross_validation.cross_val_score(clf, X, y, cv=cv))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0 1 2 3 4 5 6 7 8 9\n"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.mean(scores), np.std(scores)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": [
"(0.83225215827478494, 0.010111713947456653)"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.distplot(scores, rug=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
"<matplotlib.axes.AxesSubplot at 0x7ffca99eebd0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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P8e7+8wCUFBfx4O2LIk6kfNJYW87qT0xEbo84kSRNj8VAkmbo7Y/OMzqW3sTq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\nREREREREREREZAL+P3BSt5O+thF/AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7ffca99ee610>"
]
}
],
"prompt_number": 30
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Although our books make a big deal about K-Fold cross-validation, and we have gone through all of the trouble to understand what it is doing, I am partial to what is sometimes called leave-group-out cross-validation, Monte Carlo cross-validation, or just plain-old cross-validation: split the data into training and test sets randomly a bunch of times.\n",
"\n",
"Just to be perverse, in `sklearn` this is called `ShuffleSplit` cross-validation:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%time\n",
"\n",
"n = len(y)\n",
"cv = sklearn.cross_validation.ShuffleSplit(n, n_iter=10, test_size=.25)\n",
"scores = sklearn.cross_validation.cross_val_score(clf, X, y, cv=cv)\n",
"\n",
"print scores.mean(), scores.std()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.833197348292 0.00925643009122\n",
"CPU times: user 15.8 s, sys: 16 ms, total: 15.8 s\n",
"Wall time: 15.8 s\n"
]
}
],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of course, you can also do a stratified version of this:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv = sklearn.cross_validation.StratifiedShuffleSplit(y, n_iter=10, test_size=.25)\n",
"scores = sklearn.cross_validation.cross_val_score(clf, X, y, cv=cv)\n",
"\n",
"print scores.mean(), scores.std()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.831345565749 0.00604550537699\n"
]
}
],
"prompt_number": 34
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One last way to do cross-validation, which (in my humble opinion) was not emphasized sufficiently in the reading: split based on what you are actually interested in. In the case of VA, I am very interested in how the approach will generalize to other parts of the world. So I could hold-out based on site:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.site.value_counts()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 35,
"text": [
"Dar 1726\n",
"Mexico 1586\n",
"AP 1554\n",
"UP 1419\n",
"Bohol 1259\n",
"Pemba 297\n",
"dtype: int64"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv = sklearn.cross_validation.LeaveOneLabelOut(df.site)\n",
"scores = sklearn.cross_validation.cross_val_score(clf, X, y, cv=cv)\n",
"\n",
"print scores.mean(), scores.std()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.800788863972 0.0594346454159\n"
]
}
],
"prompt_number": 36
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is also a version that holds out $P$ labels, and lets you choose $P$:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv = sklearn.cross_validation.LeavePLabelOut(df.site, p=2)\n",
"scores = sklearn.cross_validation.cross_val_score(clf, X, y, cv=cv)\n",
"\n",
"print scores.mean(), scores.std()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.786904119569 0.057943850751\n"
]
}
],
"prompt_number": 37
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What did I not mention that whole time? Leave-one-out cross-validation. That is because it takes forever."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%time\n",
"\n",
"n = len(y)\n",
"cv = sklearn.cross_validation.LeaveOneOut(n)\n",
"scores = sklearn.cross_validation.cross_val_score(clf, X, y, cv=cv)\n",
"\n",
"print scores.mean(), scores.std()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-38-9f6ce4f81f80>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'time'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mu''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mu'\\nn = len(y)\\ncv = sklearn.cross_validation.LeaveOneOut(n)\\nscores = sklearn.cross_validation.cross_val_score(clf, X, y, cv=cv)\\n\\nprint scores.mean(), scores.std()'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m/snfs2/HOME/abie/ipython/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[1;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[0;32m 2231\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2232\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2233\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2234\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/snfs2/HOME/abie/ipython/IPython/core/magics/execution.pyc\u001b[0m in \u001b[0;36mtime\u001b[1;34m(self, line, cell, local_ns)\u001b[0m\n",
"\u001b[1;32m/snfs2/HOME/abie/ipython/IPython/core/magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(f, *a, **k)\u001b[0m\n\u001b[0;32m 191\u001b[0m \u001b[1;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 192\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 193\u001b[1;33m \u001b[0mcall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 194\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 195\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/snfs2/HOME/abie/ipython/IPython/core/magics/execution.pyc\u001b[0m in \u001b[0;36mtime\u001b[1;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[0;32m 1145\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1146\u001b[0m \u001b[0mst\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1147\u001b[1;33m \u001b[1;32mexec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1148\u001b[0m \u001b[0mend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1149\u001b[0m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32m/homes/abie/anaconda/lib/python2.7/site-packages/sklearn/cross_validation.pyc\u001b[0m in \u001b[0;36mcross_val_score\u001b[1;34m(estimator, X, y, scoring, cv, n_jobs, verbose, fit_params, score_func, pre_dispatch)\u001b[0m\n\u001b[0;32m 1149\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1150\u001b[0m fit_params)\n\u001b[1;32m-> 1151\u001b[1;33m for train, test in cv)\n\u001b[0m\u001b[0;32m 1152\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/homes/abie/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m 651\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 652\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 653\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 654\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 655\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpre_dispatch\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"all\"\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/homes/abie/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36mdispatch\u001b[1;34m(self, func, args, kwargs)\u001b[0m\n\u001b[0;32m 398\u001b[0m \"\"\"\n\u001b[0;32m 399\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pool\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 400\u001b[1;33m \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateApply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 401\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 402\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0m_verbosity_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/homes/abie/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, func, args, kwargs)\u001b[0m\n\u001b[0;32m 136\u001b[0m \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 137\u001b[0m \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 139\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/homes/abie/anaconda/lib/python2.7/site-packages/sklearn/cross_validation.pyc\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters)\u001b[0m\n\u001b[0;32m 1237\u001b[0m \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1238\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1239\u001b[1;33m \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1240\u001b[0m \u001b[0mtest_score\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1241\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/homes/abie/anaconda/lib/python2.7/site-packages/sklearn/tree/tree.pyc\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_mask, X_argsorted, check_input, sample_weight)\u001b[0m\n\u001b[0;32m 265\u001b[0m max_leaf_nodes)\n\u001b[0;32m 266\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 267\u001b[1;33m \u001b[0mbuilder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 268\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 269\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"prompt_number": 38
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's return to the stratified, repeated, 10-fold cross-validation approach:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%time\n",
"\n",
"scores = []\n",
"for rep in range(10):\n",
" print rep,\n",
" cv = sklearn.cross_validation.StratifiedKFold(y, n_folds=10, shuffle=True)\n",
" scores += [sklearn.cross_validation.cross_val_score(clf, X, y, cv=cv)]\n",
" \n",
"print"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0 1 2 3 4 5 6 7 8 9\n",
"CPU times: user 3min 6s, sys: 626 ms, total: 3min 7s\n",
"Wall time: 3min 7s\n"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.mean(scores), np.std(scores)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 40,
"text": [
"(0.83329775204490886, 0.010930491665794614)"
]
}
],
"prompt_number": 40
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sure, we can have a couple of minutes of break-time running this thing, but how do you think this is actually doing? Last week, we used a random prediction method as a baseline, and that is reasonable, but this week let's use something even simpler. Predicting a single class:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y_single = 'Diabetes'\n",
"np.mean(y == y_single)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 41,
"text": [
"0.052799387833184545"
]
}
],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y_single = 'Stroke'\n",
"np.mean(y == y_single)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 42,
"text": [
"0.08034689452875908"
]
}
],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y_single = 'Other'\n",
"np.mean(y == y_single)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 43,
"text": [
"0.86685371763805641"
]
}
],
"prompt_number": 43
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Uh, oh... we don't need any fancy classifiers to get 80% accuracy, and we don't need to wait around for five minutes to see how well that fancy classifier does. We could just say \"other\" and call it a day. To be a really fair (yet still damning) comparison, let's make a stratified, repeated 10-fold cross-validation of the baseline classifier that always says \"other\".\n",
"\n",
"To do so, it will help you to know how to use a `sklearn.cross_validation` object a little bit more:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv = sklearn.cross_validation.KFold(10, n_folds=3, shuffle=False)\n",
"for train, test in cv:\n",
" print train, test"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[4 5 6 7 8 9] [0 1 2 3]\n",
"[0 1 2 3 7 8 9] [4 5 6]\n",
"[0 1 2 3 4 5 6] [7 8 9]\n"
]
}
],
"prompt_number": 44
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use that to make a fair comparison of the decision tree to the baseline classifier that always predicts \"Other\":"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scores = []\n",
"for rep in range(10):\n",
" cv = sklearn.cross_validation.StratifiedKFold(y, n_folds=10, shuffle=True)\n",
" for train, test in cv:\n",
" scores.append(np.mean(y[test] == 'Other'))\n",
"np.mean(scores)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 45,
"text": [
"0.86685415362425799"
]
}
],
"prompt_number": 45
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To make this really, really fair, we should use the same random splits in each experiment..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%time\n",
"\n",
"scores = []\n",
"for rep in range(10):\n",
" print rep,\n",
" cv = sklearn.cross_validation.StratifiedKFold(y, n_folds=10, shuffle=True, random_state=123456+rep)\n",
" scores += [sklearn.cross_validation.cross_val_score(clf, X, y, cv=cv)]\n",
" \n",
"print\n",
"print np.mean(scores)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0 1 2 3 4 5 6 7 8 9\n",
"0.833157772844\n",
"CPU times: user 2min 51s, sys: 570 ms, total: 2min 51s\n",
"Wall time: 2min 52s\n"
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scores = []\n",
"for rep in range(10):\n",
" cv = sklearn.cross_validation.StratifiedKFold(y, n_folds=10, shuffle=True, random_state=123456+rep)\n",
" for train, test in cv:\n",
" scores.append(np.mean(y[test] == 'Other'))\n",
"np.mean(scores)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 47,
"text": [
"0.86685415362425799"
]
}
],
"prompt_number": 47
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But that is just practicing \"defensive research\", to prepare for the pickiest of critics...\n",
"\n",
"What we really need to be thinking about now is what was called \"cost-sensitive learning\" in the Data Mining book."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.Cause.value_counts()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 48,
"text": [
"Other 6797\n",
"Stroke 630\n",
"Diabetes 414\n",
"dtype: int64"
]
}
],
"prompt_number": 48
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make weights for examples proportional to inverse of counts:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"weights = 1000. / df.Cause.value_counts()\n",
"weights"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 49,
"text": [
"Other 0.147124\n",
"Stroke 1.587302\n",
"Diabetes 2.415459\n",
"dtype: float64"
]
}
],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"sample_weight = np.array(weights[y])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 50
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What is in `sample_weights`?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sklearn.metrics\n",
"y_pred = ['Other' for i in range(len(y))]\n",
"sklearn.metrics.accuracy_score(y, y_pred, sample_weight=sample_weight)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 51,
"text": [
"0.33333333333334975"
]
}
],
"prompt_number": 51
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Is that what you expected?\n",
"\n",
"This is a better metric for our classification challenge, and we should use it when building the classifier and when measuring its performance:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%time\n",
"\n",
"scores = []\n",
"for rep in range(10):\n",
" print rep,\n",
" cv = sklearn.cross_validation.StratifiedKFold(y, n_folds=10, shuffle=True, random_state=123456+rep)\n",
" for train, test in cv:\n",
" clf = sklearn.tree.DecisionTreeClassifier()\n",
" clf.fit(X[train], y[train], sample_weight=sample_weight[train])\n",
"\n",
" y_pred = clf.predict(X[test])\n",
" scores.append(sklearn.metrics.accuracy_score(y[test], y_pred, sample_weight=sample_weight[test]))\n",
" \n",
"print\n",
"print np.mean(scores)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0 1 2 3 4 5 6 7 8 9\n",
"0.508593929492\n",
"CPU times: user 1min 54s, sys: 609 ms, total: 1min 55s\n",
"Wall time: 1min 55s\n"
]
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scores = []\n",
"for rep in range(10):\n",
" cv = sklearn.cross_validation.StratifiedKFold(y, n_folds=10, shuffle=True, random_state=123456+rep)\n",
" for train, test in cv:\n",
" y_pred = ['Other' for i in range(len(test))]\n",
" scores.append(sklearn.metrics.accuracy_score(y[test], y_pred, sample_weight=sample_weight[test]))\n",
"np.mean(scores)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 53,
"text": [
"0.33333831953558202"
]
}
],
"prompt_number": 53
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The moral is: ML methods can do something, but you have to be careful about it!\n",
"\n",
"This relates to a subtle point, that I think may be somewhat unique to population health metrics, where we are interested in the population-level mean of the predictions more than the individual-level predictions themselves. I will see how far we make it through this exercise and decide if we have time to really dig into it.\n",
"\n",
"In short, the percent of test set examples with underlying cause Diabetes is equal to the percent of the training set with this underlying cause. So it is not really out-of-sample, even though the actual examples are completely disjoint."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def csmf_accuracy(y_true, y_pred):\n",
" csmf_diff = 0\n",
" csmf_true_min = 1.\n",
" for j in ['Stroke', 'Diabetes', 'Other']:\n",
" csmf_true_j = np.mean(np.array(y_true) == j)\n",
" csmf_pred_j = np.mean(np.array(y_pred) == j)\n",
" csmf_diff += np.abs(csmf_true_j - csmf_pred_j)\n",
" \n",
" if csmf_true_j < csmf_true_min:\n",
" csmf_true_min = csmf_true_j\n",
" #print csmf_true_j, csmf_pred_j, csmf_diff, csmf_true_min\n",
" return 1 - csmf_diff / (2 * (1 - csmf_true_min))\n",
"\n",
"\n",
"# test this function\n",
"y_true = ['Stroke', 'Diabetes', 'Other']\n",
"y_pred = ['Diabetes', 'Other', 'Stroke']\n",
"assert np.allclose(csmf_accuracy(y_true, y_pred), 1)\n",
"\n",
"y_true = ['Stroke', 'Stroke', 'Stroke']\n",
"y_pred = ['Diabetes', 'Other', 'Diabetes']\n",
"assert np.allclose(csmf_accuracy(y_true, y_pred), 0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 54
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The baseline predictor is pretty good:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scores = []\n",
"for rep in range(10):\n",
" cv = sklearn.cross_validation.StratifiedKFold(y, n_folds=10, shuffle=True, random_state=123456+rep)\n",
" for train, test in cv:\n",
" y_pred = ['Other' for i in range(len(test))]\n",
" scores.append(csmf_accuracy(y[test], y_pred))\n",
"np.mean(scores)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 55,
"text": [
"0.85943196700199098"
]
}
],
"prompt_number": 55
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But because the validation environment is leaking information, a different silly predictor is even better. I call this \"Random-From-Train\":"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scores = []\n",
"for rep in range(10):\n",
" cv = sklearn.cross_validation.StratifiedKFold(y, n_folds=10, shuffle=True, random_state=123456+rep)\n",
" for train, test in cv:\n",
" y_pred = np.random.choice(y[train], size=len(test), replace=True)\n",
" scores.append(csmf_accuracy(y[test], y_pred))\n",
"np.mean(scores)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 56,
"text": [
"0.98696647596797393"
]
}
],
"prompt_number": 56
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To fix it, you must resample the cause distribution of the test dataset. Super-hard extra bonus homework: figure out how to do this."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scores = []\n",
"for rep in range(10):\n",
" print rep,\n",
" \n",
" cv = sklearn.cross_validation.StratifiedKFold(y, n_folds=10, shuffle=True, random_state=123456+rep)\n",
" for train, test in cv:\n",
" clf = sklearn.tree.DecisionTreeClassifier()\n",
" clf.fit(X[train], y[train], sample_weight=sample_weight[train])\n",
"\n",
" # resample y[test] here to have a distribution chosen uniformly at random\n",
" rows = []\n",
" for j in np.unique(y[test]):\n",
" test_rows_with_cause_j = np.where(y[test] == j)[0] # np.where returns a tuple, just to be annoying \n",
" rows += list(np.random.choice(test_rows_with_cause_j, size=1000))\n",
" X_test = X[rows]\n",
" y_pred = clf.predict(X_test)\n",
" scores.append(csmf_accuracy(y[rows], y_pred))\n",
" \n",
"print\n",
"np.mean(scores)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"4\n",
"5\n",
"6\n",
"7\n",
"8\n",
"9\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 60,
"text": [
"0.92179468391231179"
]
}
],
"prompt_number": 60
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Did I promise a little more for this week in the syllabus?\n",
"\n",
"Probability prediction:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf.predict_proba(X[test[:10]])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 61,
"text": [
"array([[ 0. , 1. , 0. ],\n",
" [ 0. , 1. , 0. ],\n",
" [ 0. , 1. , 0. ],\n",
" [ 0. , 1. , 0. ],\n",
" [ 0. , 1. , 0. ],\n",
" [ 0. , 1. , 0. ],\n",
" [ 0. , 1. , 0. ],\n",
" [ 0. , 1. , 0. ],\n",
" [ 0.35442687, 0.31950041, 0.32607272],\n",
" [ 0. , 0. , 1. ]])"
]
}
],
"prompt_number": 61
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Maybe more convincing with Naive Bayes:"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"import sklearn.naive_bayes"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf = sklearn.naive_bayes.MultinomialNB()\n",
"clf.fit(X[train], y[train], )"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 63,
"text": [
"MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
]
}
],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf.predict_proba(X[test[:10]])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 64,
"text": [
"array([[ 1.81132527e-03, 9.97964902e-01, 2.23772715e-04],\n",
" [ 2.69863763e-02, 9.32236682e-01, 4.07769417e-02],\n",
" [ 5.79002201e-03, 2.09617280e-01, 7.84592698e-01],\n",
" [ 2.74315671e-03, 9.95858778e-01, 1.39806538e-03],\n",
" [ 1.22831768e-03, 9.98730068e-01, 4.16145637e-05],\n",
" [ 2.75318783e-03, 9.89736195e-01, 7.51061729e-03],\n",
" [ 3.35403449e-06, 9.99958398e-01, 3.82481162e-05],\n",
" [ 7.90692631e-06, 9.99983598e-01, 8.49518171e-06],\n",
" [ 5.92856078e-03, 9.86574873e-01, 7.49656648e-03],\n",
" [ 1.10081195e-03, 1.46315277e-01, 8.52583911e-01]])"
]
}
],
"prompt_number": 64
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use `np.round` to make this look nicer:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.round(clf.predict_proba(X[test[:10]]), 2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 65,
"text": [
"array([[ 0. , 1. , 0. ],\n",
" [ 0.03, 0.93, 0.04],\n",
" [ 0.01, 0.21, 0.78],\n",
" [ 0. , 1. , 0. ],\n",
" [ 0. , 1. , 0. ],\n",
" [ 0. , 0.99, 0.01],\n",
" [ 0. , 1. , 0. ],\n",
" [ 0. , 1. , 0. ],\n",
" [ 0.01, 0.99, 0.01],\n",
" [ 0. , 0.15, 0.85]])"
]
}
],
"prompt_number": 65
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is a bit hard to understand those probabilities. What do they mean?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y[test[:10]]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 66,
"text": [
"array(['Other', 'Other', 'Stroke', 'Other', 'Other', 'Other', 'Other',\n",
" 'Other', 'Other', 'Other'], dtype=object)"
]
}
],
"prompt_number": 66
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us hope that the middle column is the probability of 'Other'. The column names must be stored in the `clf` instance somewhere:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf.classes_"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 67,
"text": [
"array(['Diabetes', 'Other', 'Stroke'], \n",
" dtype='|S8')"
]
}
],
"prompt_number": 67
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make that into a pretty DataFrame"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t = pd.DataFrame(clf.predict_proba(X[test[:10]]), columns=clf.classes_)\n",
"np.round(t, 2)"
],
"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>Diabetes</th>\n",
" <th>Other</th>\n",
" <th>Stroke</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 0.00</td>\n",
" <td> 1.00</td>\n",
" <td> 0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 0.03</td>\n",
" <td> 0.93</td>\n",
" <td> 0.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 0.01</td>\n",
" <td> 0.21</td>\n",
" <td> 0.78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 0.00</td>\n",
" <td> 1.00</td>\n",
" <td> 0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 0.00</td>\n",
" <td> 1.00</td>\n",
" <td> 0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> 0.00</td>\n",
" <td> 0.99</td>\n",
" <td> 0.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td> 0.00</td>\n",
" <td> 1.00</td>\n",
" <td> 0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td> 0.00</td>\n",
" <td> 1.00</td>\n",
" <td> 0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td> 0.01</td>\n",
" <td> 0.99</td>\n",
" <td> 0.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td> 0.00</td>\n",
" <td> 0.15</td>\n",
" <td> 0.85</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 69,
"text": [
" Diabetes Other Stroke\n",
"0 0.00 1.00 0.00\n",
"1 0.03 0.93 0.04\n",
"2 0.01 0.21 0.78\n",
"3 0.00 1.00 0.00\n",
"4 0.00 1.00 0.00\n",
"5 0.00 0.99 0.01\n",
"6 0.00 1.00 0.00\n",
"7 0.00 1.00 0.00\n",
"8 0.01 0.99 0.01\n",
"9 0.00 0.15 0.85"
]
}
],
"prompt_number": 69
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And what about numeric prediction? I'll give us a little something to show the mechanics, although it is a weird example. Can you figure out what `g1_07a` is? Hint: remember that `cb` is the codebook."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def float_or_100(x):\n",
" try:\n",
" return float(x)\n",
" except:\n",
" return 100.\n",
" \n",
"y = np.array(df.g1_07a.map(float_or_100))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 70
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is some MSE cross-validation:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scores = []\n",
"cv = sklearn.cross_validation.KFold(len(y), n_folds=10, shuffle=True, random_state=123456)\n",
"\n",
"for train, test in cv:\n",
" clf = sklearn.tree.DecisionTreeRegressor()\n",
" clf.fit(X[train], y[train])\n",
"\n",
" y_pred = clf.predict(X[test])\n",
" scores.append(sklearn.metrics.mean_squared_error(y[test], y_pred))\n",
" \n",
"print\n",
"print np.mean(scores)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"592.67874886\n"
]
}
],
"prompt_number": 78
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I prefer root mean squared error, myself. I find it more interpretable:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.mean(np.sqrt(scores))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 79,
"text": [
"24.33928481528179"
]
}
],
"prompt_number": 79
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is also a mean absolute error in `sklearn`. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sklearn.metrics.mean_absolute_error"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 80,
"text": [
"<function sklearn.metrics.metrics.mean_absolute_error>"
]
}
],
"prompt_number": 80
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Can you make a custom error function that doesn't charge for the missing values we clipped to 100?"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"def rmse_when_not_100(a, b):\n",
" diff = a-b\n",
" rows = np.where((a != 100) & (b != 100))[0]\n",
" return np.sqrt(np.mean(diff[rows]**2))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 85
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv = sklearn.cross_validation.KFold(len(y), n_folds=10, shuffle=True, random_state=123456)\n",
"\n",
"for train, test in cv:\n",
" clf = sklearn.tree.DecisionTreeRegressor()\n",
" clf.fit(X[train], y[train], sample_weight=sample_weight[train])\n",
"\n",
" y_pred = clf.predict(X[test])\n",
" scores.append(rmse_when_not_100(y[test], y_pred))\n",
" \n",
"print np.mean(scores)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"31.0586305975\n"
]
}
],
"prompt_number": 86
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Is this any good? How can you tell?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Answer: compare to a baseline method that makes a numeric prediction without looking at the feature vector"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scores = []\n",
"\n",
"for val in np.unique(y):\n",
" if val != 100:\n",
" scores.append(rmse_when_not_100(y, val))\n",
"max(scores)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 87,
"text": [
"57.198502197364171"
]
}
],
"prompt_number": 87
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Yes, about twice as good as random."
]
}
],
"metadata": {}
}
]
} | mit |
root-osp/time_series | linear_exponential.ipynb | 2 | 2601 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import xlrd as xlrd\n",
"import datetime as dt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"book = xlrd.open_workbook(\"data.xlsx\")\n",
"sh = book.sheet_by_index(0)\n",
"sh21= book.sheet_by_index(1)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ca1 = sh.cell_value(1,0)\n",
"i=1\n",
"a1_time=[]\n",
"a1_value=[]\n",
"while sh.cell_value(i,0)==ca1:\n",
" a1_time.append(dt.datetime(*xlrd.xldate_as_tuple(sh.cell_value(i,1), book.datemode)).timestamp())\n",
" a1_value.append(sh.cell_value(i,2))\n",
" i+=1"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ca12 = sh.cell_value(i,0)\n",
"a2_time=[]\n",
"a2_value=[]\n",
"while sh.cell_value(i,0)==ca12:\n",
" a2_time.append(dt.datetime(*xlrd.xldate_as_tuple(sh.cell_value(i,1), book.datemode)).timestamp())\n",
" a2_value.append(sh.cell_value(i,2))\n",
" i+=1"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"i+=1\n",
"ca13 = sh.cell_value(i,0)\n",
"a3_time=[]\n",
"a3_value=[]\n",
"while i<sh.nrows-1 and sh.cell_value(i,0)==ca13:\n",
" a3_time.append(dt.datetime(*xlrd.xldate_as_tuple(sh.cell_value(i,1), book.datemode)).timestamp())\n",
" a3_value.append(sh.cell_value(i,2))\n",
" i+=1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"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 |
leriomaggio/deep-learning-keras-tensorflow | 2. Deep Learning Frameworks/2.3 Introduction to Keras.ipynb | 1 | 57543 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"../imgs/keras-logo-small.jpg\" width=\"20%\" />\n",
"\n",
"## Keras: Deep Learning library for Theano and TensorFlow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. \n",
"\n",
">It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.\n",
"ref: https://keras.io/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"kaggle\"></a>\n",
"### Kaggle Challenge Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">The Otto Group is one of the world’s biggest e-commerce companies, A consistent analysis of the performance of products is crucial. However, due to diverse global infrastructure, many identical products get classified differently.\n",
"For this competition, we have provided a dataset with 93 features for more than 200,000 products. The objective is to build a predictive model which is able to distinguish between our main product categories. \n",
"Each row corresponds to a single product. There are a total of 93 numerical features, which represent counts of different events. All features have been obfuscated and will not be defined any further.\n",
"\n",
"https://www.kaggle.com/c/otto-group-product-classification-challenge/data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### For this section we will use the Kaggle Otto Group Challenge Data. You will find these data in \n",
"`../data/kaggle_ottogroup/` folder."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Logistic Regression\n",
"\n",
"This algorithm has nothing to do with the canonical _linear regression_, but it is an algorithm that allows us to solve problems of classification (supervised learning). \n",
"\n",
"In fact, to estimate the dependent variable, now we make use of the so-called **logistic function** or **sigmoid**. \n",
"\n",
"It is precisely because of this feature we call this algorithm logistic regression.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Preparation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from kaggle_data import load_data, preprocess_data, preprocess_labels\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9 classes\n",
"93 dims\n"
]
}
],
"source": [
"X_train, labels = load_data('../data/kaggle_ottogroup/train.csv', train=True)\n",
"X_train, scaler = preprocess_data(X_train)\n",
"Y_train, encoder = preprocess_labels(labels)\n",
"\n",
"X_test, ids = load_data('../data/kaggle_ottogroup/test.csv', train=False)\n",
"X_test, _ = preprocess_data(X_test, scaler)\n",
"\n",
"nb_classes = Y_train.shape[1]\n",
"print(nb_classes, 'classes')\n",
"\n",
"dims = X_train.shape[1]\n",
"print(dims, 'dims')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Class_1', 'Class_2', 'Class_3', 'Class_4', 'Class_5', 'Class_6',\n",
" 'Class_7', 'Class_8', 'Class_9'], dtype=object)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.unique(labels)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 1., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" ..., \n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 1., ..., 0., 0., 0.]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Y_train # one-hot encoding"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using Theano"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import theano as th\n",
"import theano.tensor as T"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1\n",
"Epoch 2\n",
"Epoch 3\n",
"Epoch 4\n",
"Epoch 5\n",
"Epoch 6\n",
"Epoch 7\n",
"Epoch 8\n",
"Epoch 9\n",
"Epoch 10\n",
"target values for Data:\n",
"[ 0. 0. 0. ..., 0. 0. 0.]\n",
"prediction on training set:\n",
"[ True True False ..., True True True]\n"
]
}
],
"source": [
"#Based on example from DeepLearning.net\n",
"rng = np.random\n",
"N = 400\n",
"feats = 93\n",
"training_steps = 10\n",
"\n",
"# Declare Theano symbolic variables\n",
"x = T.matrix(\"x\")\n",
"y = T.vector(\"y\")\n",
"w = th.shared(rng.randn(feats), name=\"w\")\n",
"b = th.shared(0., name=\"b\")\n",
"\n",
"# Construct Theano expression graph\n",
"p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b)) # Probability that target = 1\n",
"prediction = p_1 > 0.5 # The prediction thresholded\n",
"xent = -y * T.log(p_1) - (1-y) * T.log(1-p_1) # Cross-entropy loss function\n",
"cost = xent.mean() + 0.01 * (w ** 2).sum() # The cost to minimize\n",
"gw, gb = T.grad(cost, [w, b]) # Compute the gradient of the cost\n",
" \n",
"\n",
"# Compile\n",
"train = th.function(\n",
" inputs=[x,y],\n",
" outputs=[prediction, xent],\n",
" updates=((w, w - 0.1 * gw), (b, b - 0.1 * gb)),\n",
" allow_input_downcast=True)\n",
"predict = th.function(inputs=[x], outputs=prediction, allow_input_downcast=True)\n",
"\n",
"#Transform for class1\n",
"y_class1 = []\n",
"for i in Y_train:\n",
" y_class1.append(i[0])\n",
"y_class1 = np.array(y_class1)\n",
"\n",
"# Train\n",
"for i in range(training_steps):\n",
" print('Epoch %s' % (i+1,))\n",
" pred, err = train(X_train, y_class1)\n",
"\n",
"print(\"target values for Data:\")\n",
"print(y_class1)\n",
"print(\"prediction on training set:\")\n",
"print(predict(X_train))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using Tensorflow"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Parameters\n",
"learning_rate = 0.01\n",
"training_epochs = 25\n",
"display_step = 1"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# tf Graph Input\n",
"x = tf.placeholder(\"float\", [None, dims]) \n",
"y = tf.placeholder(\"float\", [None, nb_classes])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'Placeholder:0' shape=(?, 93) dtype=float32>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model (Introducing Tensorboard)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Construct (linear) model\n",
"with tf.name_scope(\"model\") as scope:\n",
" # Set model weights\n",
" W = tf.Variable(tf.zeros([dims, nb_classes]))\n",
" b = tf.Variable(tf.zeros([nb_classes]))\n",
" activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\n",
"\n",
" # Add summary ops to collect data\n",
" w_h = tf.summary.histogram(\"weights_histogram\", W)\n",
" b_h = tf.summary.histogram(\"biases_histograms\", b)\n",
" tf.summary.scalar('mean_weights', tf.reduce_mean(W))\n",
" tf.summary.scalar('mean_bias', tf.reduce_mean(b))\n",
"\n",
"# Minimize error using cross entropy\n",
"# Note: More name scopes will clean up graph representation\n",
"with tf.name_scope(\"cost_function\") as scope:\n",
" cross_entropy = y*tf.log(activation)\n",
" cost = tf.reduce_mean(-tf.reduce_sum(cross_entropy,reduction_indices=1))\n",
" # Create a summary to monitor the cost function\n",
" tf.summary.scalar(\"cost_function\", cost)\n",
" tf.summary.histogram(\"cost_histogram\", cost)\n",
"\n",
"with tf.name_scope(\"train\") as scope:\n",
" # Set the Optimizer\n",
" optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Accuracy"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with tf.name_scope('Accuracy') as scope:\n",
" correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1))\n",
" # Calculate accuracy\n",
" accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
" # Create a summary to monitor the cost function\n",
" tf.summary.scalar(\"accuracy\", accuracy)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Learning in a TF Session"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"LOGDIR = \"/tmp/logistic_logs\"\n",
"import os, shutil\n",
"if os.path.isdir(LOGDIR):\n",
" shutil.rmtree(LOGDIR)\n",
"os.mkdir(LOGDIR)\n",
"\n",
"# Plug TensorBoard Visualisation \n",
"writer = tf.summary.FileWriter(LOGDIR, graph=tf.get_default_graph())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"model/weights_histogram:0\n",
"model/biases_histograms:0\n",
"model/mean_weights:0\n",
"model/mean_bias:0\n",
"cost_function/cost_function:0\n",
"cost_function/cost_histogram:0\n",
"Accuracy/accuracy:0\n",
"Tensor(\"add:0\", shape=(), dtype=string)\n"
]
}
],
"source": [
"for var in tf.get_collection(tf.GraphKeys.SUMMARIES):\n",
" print(var.name)\n",
" \n",
"summary_op = tf.summary.merge_all()\n",
"print('Summary Op: ' + summary_op)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"accuracy epoch 0:0.6649535894393921\n",
"accuracy epoch 1:0.665276825428009\n",
"accuracy epoch 2:0.6657131910324097\n",
"accuracy epoch 3:0.6659556031227112\n",
"accuracy epoch 4:0.6662949919700623\n",
"accuracy epoch 5:0.6666181683540344\n",
"accuracy epoch 6:0.6668121218681335\n",
"accuracy epoch 7:0.6671029925346375\n",
"accuracy epoch 8:0.6674585342407227\n",
"accuracy epoch 9:0.6678463816642761\n",
"accuracy epoch 10:0.6680726408958435\n",
"accuracy epoch 11:0.6682504415512085\n",
"accuracy epoch 12:0.6684605479240417\n",
"accuracy epoch 13:0.6687514185905457\n",
"accuracy epoch 14:0.6690422892570496\n",
"accuracy epoch 15:0.6692523956298828\n",
"accuracy epoch 16:0.6695109605789185\n",
"accuracy epoch 17:0.6697695255279541\n",
"accuracy epoch 18:0.6699796319007874\n",
"accuracy epoch 19:0.6702220439910889\n",
"accuracy epoch 20:0.6705452799797058\n",
"accuracy epoch 21:0.6708361506462097\n",
"accuracy epoch 22:0.6710785627365112\n",
"accuracy epoch 23:0.671385645866394\n",
"accuracy epoch 24:0.6716926693916321\n",
"Training phase finished\n"
]
},
{
"data": {
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kOoyUdfrpp7NkyZIa22RlZSX86qI+aNA7BomcfSPBZ6OIyKHq4+dHCSMGqgxPnIYNG7J5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\nQJQwREQkECUMEREJ5H8BqFoC5NsZZtAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11e8e3080>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 5 5 ..., 2 1 1]\n"
]
}
],
"source": [
"# Launch the graph\n",
"with tf.Session() as session:\n",
" # Initializing the variables\n",
" session.run(tf.global_variables_initializer())\n",
" \n",
" cost_epochs = []\n",
" # Training cycle\n",
" for epoch in range(training_epochs):\n",
" _, summary, c = session.run(fetches=[optimizer, summary_op, cost], \n",
" feed_dict={x: X_train, y: Y_train})\n",
" cost_epochs.append(c)\n",
" writer.add_summary(summary=summary, global_step=epoch)\n",
" print(\"accuracy epoch {}:{}\".format(epoch, accuracy.eval({x: X_train, y: Y_train})))\n",
" \n",
" print(\"Training phase finished\")\n",
" \n",
" #plotting\n",
" plt.plot(range(len(cost_epochs)), cost_epochs, 'o', label='Logistic Regression Training phase')\n",
" plt.ylabel('cost')\n",
" plt.xlabel('epoch')\n",
" plt.legend()\n",
" plt.show()\n",
" \n",
" prediction = tf.argmax(activation, 1)\n",
" print(prediction.eval({x: X_test}))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Process is terminated.\n"
]
}
],
"source": [
"%%bash\n",
"python -m tensorflow.tensorboard --logdir=/tmp/logistic_logs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using Keras"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from keras.models import Sequential\n",
"from keras.layers import Dense, Activation"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"93 dims\n",
"Building model...\n",
"9 classes\n",
"Epoch 1/10\n",
"61878/61878 [==============================] - 3s - loss: 1.9845 \n",
"Epoch 2/10\n",
"61878/61878 [==============================] - 2s - loss: 1.8337 \n",
"Epoch 3/10\n",
"61878/61878 [==============================] - 2s - loss: 1.7779 \n",
"Epoch 4/10\n",
"61878/61878 [==============================] - 3s - loss: 1.7432 \n",
"Epoch 5/10\n",
"61878/61878 [==============================] - 2s - loss: 1.7187 \n",
"Epoch 6/10\n",
"61878/61878 [==============================] - 3s - loss: 1.7002 \n",
"Epoch 7/10\n",
"61878/61878 [==============================] - 2s - loss: 1.6857 \n",
"Epoch 8/10\n",
"61878/61878 [==============================] - 2s - loss: 1.6739 \n",
"Epoch 9/10\n",
"61878/61878 [==============================] - 2s - loss: 1.6642 \n",
"Epoch 10/10\n",
"61878/61878 [==============================] - 2s - loss: 1.6560 \n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x123026dd8>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dims = X_train.shape[1]\n",
"print(dims, 'dims')\n",
"print(\"Building model...\")\n",
"\n",
"nb_classes = Y_train.shape[1]\n",
"print(nb_classes, 'classes')\n",
"\n",
"model = Sequential()\n",
"model.add(Dense(nb_classes, input_shape=(dims,), activation='sigmoid'))\n",
"model.add(Activation('softmax'))\n",
"\n",
"model.compile(optimizer='sgd', loss='categorical_crossentropy')\n",
"model.fit(X_train, Y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Simplicity is pretty impressive right? :)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Theano**:\n",
"\n",
"`shape = (channels, rows, cols)`\n",
"\n",
"**Tensorflow**:\n",
"\n",
"`shape = (rows, cols, channels)`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`image_data_format` : `channels_last | channels_first`"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\r\n",
"\t\"epsilon\": 1e-07,\r\n",
"\t\"backend\": \"tensorflow\",\r\n",
"\t\"floatx\": \"float32\",\r\n",
"\t\"image_data_format\": \"channels_last\"\r\n",
"}\r\n"
]
}
],
"source": [
"!cat ~/.keras/keras.json"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now lets understand:\n",
"<pre>The core data structure of Keras is a <b>model</b>, a way to organize layers. The main type of model is the <b>Sequential</b> model, a linear stack of layers.</pre>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What we did here is stacking a Fully Connected (<b>Dense</b>) layer of trainable weights from the input to the output and an <b>Activation</b> layer on top of the weights layer."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Dense"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"```python\n",
"from keras.layers.core import Dense\n",
"\n",
"Dense(units, activation=None, use_bias=True, \n",
" kernel_initializer='glorot_uniform', bias_initializer='zeros', \n",
" kernel_regularizer=None, bias_regularizer=None, \n",
" activity_regularizer=None, kernel_constraint=None, bias_constraint=None)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* `units`: int > 0.\n",
"\n",
"* `init`: name of initialization function for the weights of the layer (see initializations), or alternatively, Theano function to use for weights initialization. This parameter is only relevant if you don't pass a weights argument.\n",
"\n",
"* `activation`: name of activation function to use (see activations), or alternatively, elementwise Theano function. If you don't specify anything, no activation is applied (ie. \"linear\" activation: a(x) = x).\n",
"\n",
"* `weights`: list of Numpy arrays to set as initial weights. The list should have 2 elements, of shape (input_dim, output_dim) and (output_dim,) for weights and biases respectively.\n",
"\n",
"* `kernel_regularizer`: instance of WeightRegularizer (eg. L1 or L2 regularization), applied to the main weights matrix.\n",
"\n",
"* `bias_regularizer`: instance of WeightRegularizer, applied to the bias.\n",
"\n",
"* `activity_regularizer`: instance of ActivityRegularizer, applied to the network output.\n",
"\n",
"* `kernel_constraint`: instance of the constraints module (eg. maxnorm, nonneg), applied to the main weights matrix.\n",
"\n",
"* `bias_constraint`: instance of the constraints module, applied to the bias.\n",
"\n",
"* `use_bias`: whether to include a bias (i.e. make the layer affine rather than linear)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## (some) others `keras.core.layers`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* `keras.layers.core.Flatten()`\n",
"* `keras.layers.core.Reshape(target_shape)`\n",
"* `keras.layers.core.Permute(dims)`\n",
"\n",
"```python\n",
"model = Sequential()\n",
"model.add(Permute((2, 1), input_shape=(10, 64)))\n",
"# now: model.output_shape == (None, 64, 10)\n",
"# note: `None` is the batch dimension\n",
"```\n",
"\n",
"* `keras.layers.core.Lambda(function, output_shape=None, arguments=None)`\n",
"* `keras.layers.core.ActivityRegularization(l1=0.0, l2=0.0)`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"../imgs/dl_overview.png\" >\n",
"\n",
"Credits: Yam Peleg ([@Yampeleg](https://twitter.com/yampeleg))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Activation"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"```python\n",
"from keras.layers.core import Activation\n",
"\n",
"Activation(activation)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Supported Activations** : [https://keras.io/activations/]\n",
"\n",
"**Advanced Activations**: [https://keras.io/layers/advanced-activations/]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Optimizer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you need to, you can further configure your optimizer. A core principle of Keras is to make things reasonably simple, while allowing the user to be fully in control when they need to (the ultimate control being the easy extensibility of the source code).\n",
"Here we used <b>SGD</b> (stochastic gradient descent) as an optimization algorithm for our trainable weights. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"http://sebastianruder.com/content/images/2016/09/saddle_point_evaluation_optimizers.gif\" width=\"40%\">\n",
"\n",
"Source & Reference: http://sebastianruder.com/content/images/2016/09/saddle_point_evaluation_optimizers.gif"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\"Data Sciencing\" this example a little bit more\n",
"====="
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What we did here is nice, however in the real world it is not useable because of overfitting.\n",
"Lets try and solve it with cross validation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Overfitting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In overfitting, a statistical model describes random error or noise instead of the underlying relationship. Overfitting occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. \n",
"\n",
"A model that has been overfit has poor predictive performance, as it overreacts to minor fluctuations in the training data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"<img src=\"../imgs/overfitting.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<pre>To avoid overfitting, we will first split out data to training set and test set and test out model on the test set.\n",
"Next: we will use two of keras's callbacks <b>EarlyStopping</b> and <b>ModelCheckpoint</b></pre>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see first the model we implemented"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_1 (Dense) (None, 9) 846 \n",
"_________________________________________________________________\n",
"activation_1 (Activation) (None, 9) 0 \n",
"=================================================================\n",
"Total params: 846\n",
"Trainable params: 846\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from keras.callbacks import EarlyStopping, ModelCheckpoint"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 52596 samples, validate on 9282 samples\n",
"Epoch 1/50\n",
"52596/52596 [==============================] - 1s - loss: 1.6516 - val_loss: 1.6513\n",
"Epoch 2/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6501 - val_loss: 1.6499\n",
"Epoch 3/50\n",
"52596/52596 [==============================] - 1s - loss: 1.6488 - val_loss: 1.6486\n",
"Epoch 4/50\n",
"52596/52596 [==============================] - 1s - loss: 1.6474 - val_loss: 1.6473\n",
"Epoch 5/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6462 - val_loss: 1.6461\n",
"Epoch 6/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6449 - val_loss: 1.6448\n",
"Epoch 7/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6437 - val_loss: 1.6437\n",
"Epoch 8/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6425 - val_loss: 1.6425\n",
"Epoch 9/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6414 - val_loss: 1.6414\n",
"Epoch 10/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6403 - val_loss: 1.6403\n",
"Epoch 11/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6392 - val_loss: 1.6393\n",
"Epoch 12/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6382 - val_loss: 1.6383\n",
"Epoch 13/50\n",
"52596/52596 [==============================] - 1s - loss: 1.6372 - val_loss: 1.6373\n",
"Epoch 14/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6362 - val_loss: 1.6363\n",
"Epoch 15/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6352 - val_loss: 1.6354\n",
"Epoch 16/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6343 - val_loss: 1.6345\n",
"Epoch 17/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6334 - val_loss: 1.6336\n",
"Epoch 18/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6325 - val_loss: 1.6327\n",
"Epoch 19/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6316 - val_loss: 1.6319\n",
"Epoch 20/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6308 - val_loss: 1.6311\n",
"Epoch 21/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6300 - val_loss: 1.6303\n",
"Epoch 22/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6292 - val_loss: 1.6295\n",
"Epoch 23/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6284 - val_loss: 1.6287\n",
"Epoch 24/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6276 - val_loss: 1.6280\n",
"Epoch 25/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6269 - val_loss: 1.6273\n",
"Epoch 26/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6262 - val_loss: 1.6265\n",
"Epoch 27/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6254 - val_loss: 1.6258\n",
"Epoch 28/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6247 - val_loss: 1.6252\n",
"Epoch 29/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6241 - val_loss: 1.6245\n",
"Epoch 30/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6234 - val_loss: 1.6238\n",
"Epoch 31/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6227 - val_loss: 1.6232\n",
"Epoch 32/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6221 - val_loss: 1.6226\n",
"Epoch 33/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6215 - val_loss: 1.6220\n",
"Epoch 34/50\n",
"52596/52596 [==============================] - 1s - loss: 1.6209 - val_loss: 1.6214\n",
"Epoch 35/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6203 - val_loss: 1.6208\n",
"Epoch 36/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6197 - val_loss: 1.6202\n",
"Epoch 37/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6191 - val_loss: 1.6197\n",
"Epoch 38/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6186 - val_loss: 1.6191\n",
"Epoch 39/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6180 - val_loss: 1.6186\n",
"Epoch 40/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6175 - val_loss: 1.6181\n",
"Epoch 41/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6170 - val_loss: 1.6175\n",
"Epoch 42/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6165 - val_loss: 1.6170\n",
"Epoch 43/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6160 - val_loss: 1.6166\n",
"Epoch 44/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6155 - val_loss: 1.6161\n",
"Epoch 45/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6150 - val_loss: 1.6156\n",
"Epoch 46/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6145 - val_loss: 1.6151\n",
"Epoch 47/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6141 - val_loss: 1.6147\n",
"Epoch 48/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6136 - val_loss: 1.6142\n",
"Epoch 49/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6132 - val_loss: 1.6138\n",
"Epoch 50/50\n",
"52596/52596 [==============================] - 0s - loss: 1.6127 - val_loss: 1.6134\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x11e7a2710>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size=0.15, random_state=42)\n",
"\n",
"fBestModel = 'best_model.h5' \n",
"early_stop = EarlyStopping(monitor='val_loss', patience=2, verbose=1) \n",
"best_model = ModelCheckpoint(fBestModel, verbose=0, save_best_only=True)\n",
"\n",
"model.fit(X_train, Y_train, validation_data = (X_val, Y_val), epochs=50, \n",
" batch_size=128, verbose=True, callbacks=[best_model, early_stop]) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multi-Layer Fully Connected Networks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"../imgs/MLP.png\" width=\"45%\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Forward and Backward Propagation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"../imgs/backprop.png\" width=\"50%\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q:** _How hard can it be to build a Multi-Layer Fully-Connected Network with keras?_\n",
"\n",
"**A:** _It is basically the same, just add more layers!_"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_2 (Dense) (None, 100) 9400 \n",
"_________________________________________________________________\n",
"dense_3 (Dense) (None, 9) 909 \n",
"_________________________________________________________________\n",
"activation_2 (Activation) (None, 9) 0 \n",
"=================================================================\n",
"Total params: 10,309\n",
"Trainable params: 10,309\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model = Sequential()\n",
"model.add(Dense(100, input_shape=(dims,)))\n",
"model.add(Dense(nb_classes))\n",
"model.add(Activation('softmax'))\n",
"model.compile(optimizer='sgd', loss='categorical_crossentropy')\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 52596 samples, validate on 9282 samples\n",
"Epoch 1/20\n",
"52596/52596 [==============================] - 1s - loss: 1.2113 - val_loss: 0.8824\n",
"Epoch 2/20\n",
"52596/52596 [==============================] - 0s - loss: 0.8229 - val_loss: 0.7851\n",
"Epoch 3/20\n",
"52596/52596 [==============================] - 0s - loss: 0.7623 - val_loss: 0.7470\n",
"Epoch 4/20\n",
"52596/52596 [==============================] - 1s - loss: 0.7329 - val_loss: 0.7258\n",
"Epoch 5/20\n",
"52596/52596 [==============================] - 1s - loss: 0.7143 - val_loss: 0.7107\n",
"Epoch 6/20\n",
"52596/52596 [==============================] - 0s - loss: 0.7014 - val_loss: 0.7005\n",
"Epoch 7/20\n",
"52596/52596 [==============================] - 1s - loss: 0.6918 - val_loss: 0.6922\n",
"Epoch 8/20\n",
"52596/52596 [==============================] - 0s - loss: 0.6843 - val_loss: 0.6868\n",
"Epoch 9/20\n",
"52596/52596 [==============================] - 0s - loss: 0.6784 - val_loss: 0.6817\n",
"Epoch 10/20\n",
"52596/52596 [==============================] - 0s - loss: 0.6736 - val_loss: 0.6773\n",
"Epoch 11/20\n",
"52596/52596 [==============================] - 0s - loss: 0.6695 - val_loss: 0.6739\n",
"Epoch 12/20\n",
"52596/52596 [==============================] - 1s - loss: 0.6660 - val_loss: 0.6711\n",
"Epoch 13/20\n",
"52596/52596 [==============================] - 1s - loss: 0.6631 - val_loss: 0.6688\n",
"Epoch 14/20\n",
"52596/52596 [==============================] - 1s - loss: 0.6604 - val_loss: 0.6670\n",
"Epoch 15/20\n",
"52596/52596 [==============================] - 1s - loss: 0.6582 - val_loss: 0.6649\n",
"Epoch 16/20\n",
"52596/52596 [==============================] - 1s - loss: 0.6563 - val_loss: 0.6626\n",
"Epoch 17/20\n",
"52596/52596 [==============================] - 1s - loss: 0.6545 - val_loss: 0.6611\n",
"Epoch 18/20\n",
"52596/52596 [==============================] - 1s - loss: 0.6528 - val_loss: 0.6598\n",
"Epoch 19/20\n",
"52596/52596 [==============================] - 1s - loss: 0.6514 - val_loss: 0.6578\n",
"Epoch 20/20\n",
"52596/52596 [==============================] - 1s - loss: 0.6500 - val_loss: 0.6571\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x12830b978>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(X_train, Y_train, validation_data = (X_val, Y_val), epochs=20, \n",
" batch_size=128, verbose=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Your Turn!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hands On - Keras Fully Connected\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Take couple of minutes and try to play with the number of layers and the number of parameters in the layers to get the best results. "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_4 (Dense) (None, 100) 9400 \n",
"_________________________________________________________________\n",
"dense_5 (Dense) (None, 9) 909 \n",
"_________________________________________________________________\n",
"activation_3 (Activation) (None, 9) 0 \n",
"=================================================================\n",
"Total params: 10,309\n",
"Trainable params: 10,309\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model = Sequential()\n",
"model.add(Dense(100, input_shape=(dims,)))\n",
"\n",
"# ...\n",
"# ...\n",
"# Play with it! add as much layers as you want! try and get better results.\n",
"\n",
"model.add(Dense(nb_classes))\n",
"model.add(Activation('softmax'))\n",
"model.compile(optimizer='sgd', loss='categorical_crossentropy')\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 52596 samples, validate on 9282 samples\n",
"Epoch 1/20\n",
"52596/52596 [==============================] - 1s - loss: 1.2107 - val_loss: 0.8821\n",
"Epoch 2/20\n",
"52596/52596 [==============================] - 1s - loss: 0.8204 - val_loss: 0.7798\n",
"Epoch 3/20\n",
"52596/52596 [==============================] - 1s - loss: 0.7577 - val_loss: 0.7393\n",
"Epoch 4/20\n",
"52596/52596 [==============================] - 0s - loss: 0.7280 - val_loss: 0.7176\n",
"Epoch 5/20\n",
"52596/52596 [==============================] - 1s - loss: 0.7097 - val_loss: 0.7028\n",
"Epoch 6/20\n",
"52596/52596 [==============================] - 1s - loss: 0.6973 - val_loss: 0.6929\n",
"Epoch 7/20\n",
"52596/52596 [==============================] - 1s - loss: 0.6883 - val_loss: 0.6858\n",
"Epoch 8/20\n",
"52596/52596 [==============================] - 1s - loss: 0.6813 - val_loss: 0.6804\n",
"Epoch 9/20\n",
"52596/52596 [==============================] - 1s - loss: 0.6757 - val_loss: 0.6756\n",
"Epoch 10/20\n",
"52596/52596 [==============================] - 1s - loss: 0.6711 - val_loss: 0.6722\n",
"Epoch 11/20\n",
"52596/52596 [==============================] - 1s - loss: 0.6672 - val_loss: 0.6692\n",
"Epoch 12/20\n",
"52596/52596 [==============================] - 0s - loss: 0.6641 - val_loss: 0.6667\n",
"Epoch 13/20\n",
"52596/52596 [==============================] - 0s - loss: 0.6613 - val_loss: 0.6636\n",
"Epoch 14/20\n",
"52596/52596 [==============================] - 0s - loss: 0.6589 - val_loss: 0.6620\n",
"Epoch 15/20\n",
"52596/52596 [==============================] - 0s - loss: 0.6568 - val_loss: 0.6606\n",
"Epoch 16/20\n",
"52596/52596 [==============================] - 0s - loss: 0.6546 - val_loss: 0.6589\n",
"Epoch 17/20\n",
"52596/52596 [==============================] - 0s - loss: 0.6531 - val_loss: 0.6577\n",
"Epoch 18/20\n",
"52596/52596 [==============================] - 0s - loss: 0.6515 - val_loss: 0.6568\n",
"Epoch 19/20\n",
"52596/52596 [==============================] - 0s - loss: 0.6501 - val_loss: 0.6546\n",
"Epoch 20/20\n",
"52596/52596 [==============================] - 0s - loss: 0.6489 - val_loss: 0.6539\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x1285bae80>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(X_train, Y_train, validation_data = (X_val, Y_val), epochs=20, \n",
" batch_size=128, verbose=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Building a question answering system, an image classification model, a Neural Turing Machine, a word2vec embedder or any other model is just as fast. The ideas behind deep learning are simple, so why should their implementation be painful?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Theoretical Motivations for depth"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">Much has been studied about the depth of neural nets. Is has been proven mathematically[1] and empirically that convolutional neural network benifit from depth! "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[1] - On the Expressive Power of Deep Learning: A Tensor Analysis - Cohen, et al 2015"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Theoretical Motivations for depth"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One much quoted theorem about neural network states that:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">Universal approximation theorem states[1] that a feed-forward network with a single hidden layer containing a finite number of neurons (i.e., a multilayer perceptron), can approximate continuous functions on compact subsets of $\\mathbb{R}^n$, under mild assumptions on the activation function. The theorem thus states that simple neural networks can represent a wide variety of interesting functions when given appropriate parameters; however, it does not touch upon the algorithmic learnability of those parameters."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[1] - Approximation Capabilities of Multilayer Feedforward Networks - Kurt Hornik 1991"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Addendum\n",
"\n",
"[2.3.1 Keras Backend](2.3.1 Keras Backend.ipynb)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
SimonTheCoder/server_monitor | play_manual.ipynb | 1 | 9177 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# import "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import auto_swap\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# set up your args"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['/usr/bin/sslocal']\n"
]
}
],
"source": [
"sslocal_path = !which sslocal\n",
"print sslocal_path"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"exec_path = sslocal_path[0]\n",
"config_path = \"ssproxy.json\"\n",
"duration = 1\n",
"host_list = \"servers.list\"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# run"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ping...\n",
"1/27\n",
"2/27\n",
"3/27\n",
"4/27\n",
"5/27\n",
"6/27\n",
"7/27\n",
"8/27\n",
"9/27\n",
"13/27\n",
"10/27\n",
"11/27\n",
"16/27\n",
"19/27\n",
"21/27\n",
"18/27\n",
"20/27\n",
"15/27\n",
"17/27\n",
"14/27\n",
"22/27\n",
"13/27\n",
"24/27\n",
"23/27\n",
"27/27\n",
"26/27\n",
"25/27\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-234be5c28b2d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mlooper\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauto_swap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLooper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexec_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mduration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhost_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlooper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_looper\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/home/lordsimon/4fun/server_monitor/auto_swap.pyc\u001b[0m in \u001b[0;36mstart_looper\u001b[0;34m(self, using_thread)\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m\"still in working...\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloop\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 91\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/lordsimon/4fun/server_monitor/auto_swap.pyc\u001b[0m in \u001b[0;36mloop\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstarted\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;31m#find fastest server\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0mstates\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonitor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_all\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 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstates\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\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/home/lordsimon/4fun/server_monitor/monitor.pyc\u001b[0m in \u001b[0;36mcheck_all\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mthreads\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\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 98\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_all_res_up\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_all_res_up\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m\"result:\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python2.7/threading.pyc\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 938\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\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 939\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__stopped\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 940\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__block\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\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 941\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__debug__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 942\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_note\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s.join(): thread stopped\"\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[0;32m/usr/lib/python2.7/threading.pyc\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 338\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 339\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 340\u001b[0;31m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\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 341\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__debug__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 342\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_note\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s.wait(): got it\"\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[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"looper = auto_swap.Looper(exec_path, config_path, duration, host_list)\n",
"looper.start_looper()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-2.0 |
Caranarq/01_Dmine | Datasets/INEGI/Defunciones/Mortalidad.ipynb | 1 | 67615 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Limpieza del dataset de Mortalidad de INEGI\n",
"\n",
"## 1. Introduccion\n",
"Indicadores que salen de este dataset:\n",
"\n",
"ID | DESCRIPCION\n",
"----- | --------------\n",
"P0813 | Homicidios Intencionales\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"descripciones = {\n",
" 'P0813' : 'Homicidios Intencionales',\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Librerías utilizadas\n",
"import pandas as pd\n",
"import sys\n",
"import urllib\n",
"import os\n",
"import csv\n",
"import zipfile\n",
"from simpledbf import Dbf5\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Python 3.6.1 |Anaconda 4.4.0 (64-bit)| (default, May 11 2017, 13:25:24) [MSC v.1900 64 bit (AMD64)] on win32\n",
"Pandas version: 0.20.1\n",
"Running on Windows 8.1\n"
]
}
],
"source": [
"# Configuracion del sistema\n",
"print('Python {} on {}'.format(sys.version, sys.platform))\n",
"print('Pandas version: {}'.format(pd.__version__))\n",
"import platform; print('Running on {} {}'.format(platform.system(), platform.release()))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# URL Fuente\n",
"remote_path = r'http://www.beta.inegi.org.mx/contenidos/proyectos/registros/vitales/mortalidad/microdatos/defunciones/2016/defunciones_base_datos_2016_dbf.zip'\n",
"\n",
"# Carpeta destino Local\n",
"local_path = r'D:\\PCCS\\00_RawData\\01_CSV\\INEGI\\Defunciones\\defunciones_base_datos_2016_dbf.zip'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Descarga de datos\n",
"Todos los datos se encuentran en un solo archivo"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ya existe el archivo: D:\\PCCS\\00_RawData\\01_CSV\\INEGI\\Defunciones\\defunciones_base_datos_2016_dbf.zip\n"
]
}
],
"source": [
"# Descarga de archivo\n",
"if os.path.isfile(local_path):\n",
" print('Ya existe el archivo: {}'.format(local_path))\n",
"else:\n",
" print('Descargando {} ... ... ... ... ... '.format(local_path))\n",
" urllib.request.urlretrieve(remote_path, local_path) #\n",
" print('se descargó {}'.format(local_path))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Iniciando descompresión\n",
"Descompresión terminada en D:\\PCCS\\00_RawData\\01_CSV\\INEGI\\Defunciones\n"
]
}
],
"source": [
"# Descompresión de archivo\n",
"target = r'D:\\PCCS\\00_RawData\\01_CSV\\INEGI\\Defunciones'\n",
"descomprimir = zipfile.ZipFile(local_path, 'r')\n",
"print('Iniciando descompresión')\n",
"descomprimir.extractall(target)\n",
"descomprimir.close\n",
"print('Descompresión terminada en {}'.format(target))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 - .ipynb_checkpoints\n",
"1 - CAPGPO.dbf\n",
"2 - CATEMLDE16.dbf\n",
"3 - CATMINDE.dbf\n",
"4 - CATMINDE.xlsx\n",
"5 - DEFUN16.dbf\n",
"6 - DEFUN16.dbf.xlsx\n",
"7 - defunciones_base_datos_2016_dbf.zip\n",
"8 - Descripcion BD_Defunciones 2016(LOC).pdf\n",
"9 - GPOLIMEX.dbf\n",
"10 - LISTA1.dbf\n",
"11 - LISTAMEX.dbf\n",
"12 - PARENTESCO.dbf\n"
]
}
],
"source": [
"# Listado de archivos\n",
"files = os.listdir(target)\n",
"x = 0\n",
"for file in files:\n",
" print('{} - {}'.format(x, file))\n",
" x += 1"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"scrolled": true
},
"outputs": [
{
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" <th>DIS_RE_OAX</th>\n",
" </tr>\n",
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" <td>01</td>\n",
" <td>001</td>\n",
" <td>01</td>\n",
" <td>010</td>\n",
" <td>1</td>\n",
" <td>7777</td>\n",
" <td>01</td>\n",
" <td>010</td>\n",
" <td>1</td>\n",
" <td>7777</td>\n",
" <td>...</td>\n",
" <td>2016</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>888</td>\n",
" <td>8888</td>\n",
" <td>NaN</td>\n",
" <td>999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>1</td>\n",
" <td>0444</td>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>1</td>\n",
" <td>7777</td>\n",
" <td>...</td>\n",
" <td>2016</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>888</td>\n",
" <td>8888</td>\n",
" <td>NaN</td>\n",
" <td>999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>01</td>\n",
" <td>006</td>\n",
" <td>01</td>\n",
" <td>006</td>\n",
" <td>8</td>\n",
" <td>0001</td>\n",
" <td>01</td>\n",
" <td>006</td>\n",
" <td>8</td>\n",
" <td>0001</td>\n",
" <td>...</td>\n",
" <td>2015</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>888</td>\n",
" <td>8888</td>\n",
" <td>NaN</td>\n",
" <td>999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 59 columns</p>\n",
"</div>"
],
"text/plain": [
" ENT_REGIS MUN_REGIS ENT_RESID MUN_RESID TLOC_RESID LOC_RESID ENT_OCURR \\\n",
"0 01 003 01 001 15 0001 01 \n",
"1 01 001 01 006 8 0001 01 \n",
"2 01 001 01 010 1 7777 01 \n",
"3 01 001 01 001 1 0444 01 \n",
"4 01 006 01 006 8 0001 01 \n",
"\n",
" MUN_OCURR TLOC_OCURR LOC_OCURR ... ANIO_CERT MATERNAS LENGUA \\\n",
"0 001 15 0001 ... 2016 NaN 9 \n",
"1 001 15 0001 ... 2016 NaN 9 \n",
"2 010 1 7777 ... 2016 NaN 9 \n",
"3 001 1 7777 ... 2016 NaN 9 \n",
"4 006 8 0001 ... 2015 NaN 2 \n",
"\n",
" COND_ACT PAR_AGRE ENT_OCULES MUN_OCULES LOC_OCULES RAZON_M DIS_RE_OAX \n",
"0 2 88 88 888 8888 NaN 999 \n",
"1 2 88 88 888 8888 NaN 999 \n",
"2 2 88 88 888 8888 NaN 999 \n",
"3 2 88 88 888 8888 NaN 999 \n",
"4 2 88 88 888 8888 NaN 999 \n",
"\n",
"[5 rows x 59 columns]"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Se utilizará el dataset contenido en el archivo DEFUN16.dbf (Posición 5)\n",
"path_to_dbf = r'{}\\{}'.format(target,files[5])\n",
"dataset = Dbf5(path_to_dbf, codec='mbcs').to_dataframe()\n",
"dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"685766"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dataset)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 - ENT_REGIS\n",
"1 - MUN_REGIS\n",
"2 - ENT_RESID\n",
"3 - MUN_RESID\n",
"4 - TLOC_RESID\n",
"5 - LOC_RESID\n",
"6 - ENT_OCURR\n",
"7 - MUN_OCURR\n",
"8 - TLOC_OCURR\n",
"9 - LOC_OCURR\n",
"10 - CAUSA_DEF\n",
"11 - LISTA_MEX\n",
"12 - SEXO\n",
"13 - EDAD\n",
"14 - DIA_OCURR\n",
"15 - MES_OCURR\n",
"16 - ANIO_OCUR\n",
"17 - DIA_REGIS\n",
"18 - MES_REGIS\n",
"19 - ANIO_REGIS\n",
"20 - DIA_NACIM\n",
"21 - MES_NACIM\n",
"22 - ANIO_NACIM\n",
"23 - OCUPACION\n",
"24 - ESCOLARIDA\n",
"25 - EDO_CIVIL\n",
"26 - PRESUNTO\n",
"27 - OCURR_TRAB\n",
"28 - LUGAR_OCUR\n",
"29 - NECROPSIA\n",
"30 - ASIST_MEDI\n",
"31 - SITIO_OCUR\n",
"32 - COND_CERT\n",
"33 - NACIONALID\n",
"34 - DERECHOHAB\n",
"35 - EMBARAZO\n",
"36 - REL_EMBA\n",
"37 - HORAS\n",
"38 - MINUTOS\n",
"39 - CAPITULO\n",
"40 - GRUPO\n",
"41 - LISTA1\n",
"42 - GR_LISMEX\n",
"43 - VIO_FAMI\n",
"44 - AREA_UR\n",
"45 - EDAD_AGRU\n",
"46 - COMPLICARO\n",
"47 - DIA_CERT\n",
"48 - MES_CERT\n",
"49 - ANIO_CERT\n",
"50 - MATERNAS\n",
"51 - LENGUA\n",
"52 - COND_ACT\n",
"53 - PAR_AGRE\n",
"54 - ENT_OCULES\n",
"55 - MUN_OCULES\n",
"56 - LOC_OCULES\n",
"57 - RAZON_M\n",
"58 - DIS_RE_OAX\n"
]
}
],
"source": [
"# Seleccion de variables\n",
"x = 0\n",
"for i in dataset:\n",
" print('{} - {}'.format(x, i))\n",
" x += 1"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"['ENT_REGIS',\n",
" 'MUN_REGIS',\n",
" 'ENT_OCURR',\n",
" 'MUN_OCURR',\n",
" 'CAUSA_DEF',\n",
" 'SEXO',\n",
" 'ANIO_OCUR',\n",
" 'ESCOLARIDA',\n",
" 'PRESUNTO',\n",
" 'ASIST_MEDI',\n",
" 'VIO_FAMI',\n",
" 'ENT_OCULES',\n",
" 'MUN_OCULES']"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# lista de variables seleccionadas\n",
"Variables = [0, 1, 6, 7, 10, 12, 16, 24, 26, 30, 43, 54, 55]\n",
"Variables = list(list(dataset)[i] for i in Variables)\n",
"Variables"
]
},
{
"cell_type": "code",
"execution_count": 50,
"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>ENT_REGIS</th>\n",
" <th>MUN_REGIS</th>\n",
" <th>ENT_OCURR</th>\n",
" <th>MUN_OCURR</th>\n",
" <th>CAUSA_DEF</th>\n",
" <th>SEXO</th>\n",
" <th>ANIO_OCUR</th>\n",
" <th>ESCOLARIDA</th>\n",
" <th>PRESUNTO</th>\n",
" <th>ASIST_MEDI</th>\n",
" <th>VIO_FAMI</th>\n",
" <th>ENT_OCULES</th>\n",
" <th>MUN_OCULES</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>01</td>\n",
" <td>003</td>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>C539</td>\n",
" <td>2</td>\n",
" <td>1993</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>88</td>\n",
" <td>888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>I259</td>\n",
" <td>2</td>\n",
" <td>2010</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>88</td>\n",
" <td>888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>01</td>\n",
" <td>010</td>\n",
" <td>K729</td>\n",
" <td>2</td>\n",
" <td>2010</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>88</td>\n",
" <td>888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>E112</td>\n",
" <td>2</td>\n",
" <td>2007</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>88</td>\n",
" <td>888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>01</td>\n",
" <td>006</td>\n",
" <td>01</td>\n",
" <td>006</td>\n",
" <td>X590</td>\n",
" <td>2</td>\n",
" <td>2015</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>88</td>\n",
" <td>888</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ENT_REGIS MUN_REGIS ENT_OCURR MUN_OCURR CAUSA_DEF SEXO ANIO_OCUR \\\n",
"0 01 003 01 001 C539 2 1993 \n",
"1 01 001 01 001 I259 2 2010 \n",
"2 01 001 01 010 K729 2 2010 \n",
"3 01 001 01 001 E112 2 2007 \n",
"4 01 006 01 006 X590 2 2015 \n",
"\n",
" ESCOLARIDA PRESUNTO ASIST_MEDI VIO_FAMI ENT_OCULES MUN_OCULES \n",
"0 1 8 1 8 88 888 \n",
"1 3 8 1 8 88 888 \n",
"2 3 8 1 8 88 888 \n",
"3 1 8 1 8 88 888 \n",
"4 9 1 1 8 88 888 "
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset = dataset[Variables]\n",
"dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"ENT_REGIS object\n",
"MUN_REGIS object\n",
"ENT_OCURR object\n",
"MUN_OCURR object\n",
"CAUSA_DEF object\n",
"SEXO int64\n",
"ANIO_OCUR int64\n",
"ESCOLARIDA int64\n",
"PRESUNTO int64\n",
"ASIST_MEDI int64\n",
"VIO_FAMI int64\n",
"ENT_OCULES object\n",
"MUN_OCULES object\n",
"dtype: object"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Tipos de datos en variables\n",
"dataset.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploracion del Dataset"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"years = sorted(dataset['ANIO_OCUR'].unique())"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"yearsize = {}\n",
"for year in years:\n",
" yearsize[year] = len(dataset[dataset['ANIO_OCUR'] == year])"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"685766"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dataset)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"148"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# El set1 tiene la suma de las defunciones registradas entre 1923 y 1990\n",
"set1 = 0\n",
"for year in years:\n",
" # print(year)\n",
" if year > 1990:\n",
" break\n",
" set1 += yearsize[year]\n",
"set1"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"171"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# El set 2 tiene la suma de las defunciones registradas entre 1991 y 2000\n",
"set2 = 0\n",
"for year in years:\n",
" if year < 1991:\n",
" continue\n",
" if year > 2000:\n",
" break\n",
" # print(year)\n",
" set2 += yearsize[year]\n",
"set2"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"yearsize2 = {'1923-1990':set1,\n",
" '1991-2000':set2}\n",
"for year in years:\n",
" if year < 2001:\n",
" continue\n",
" # print(year)\n",
" yearsize2[str(year)] = yearsize[year]"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1923-1990 : 148\n",
"1991-2000 : 171\n",
"2001 : 31\n",
"2002 : 37\n",
"2003 : 35\n",
"2004 : 29\n",
"2005 : 59\n",
"2006 : 59\n",
"2007 : 66\n",
"2008 : 75\n",
"2009 : 114\n",
"2010 : 140\n",
"2011 : 145\n",
"2012 : 234\n",
"2013 : 333\n",
"2014 : 531\n",
"2015 : 11884\n",
"2016 : 671536\n",
"9999 : 139\n"
]
}
],
"source": [
"# Numero de defunciones registradas en cada periodo\n",
"for k,v in yearsize2.items():\n",
" print('{} : {}'.format(k, v))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Subconjunto de trabajo"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Subconjunto de años para el estudio\n",
"dataset = dataset.loc[dataset['ANIO_OCUR'].isin(range(2010, 2017))]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En el campo \"PRESUNTO\", la clave 2 identifica homicidios."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Subconjunto de homicidios (El identificador 2 corresponde a homicidios) \n",
"dataset = dataset.loc[dataset['PRESUNTO'] == 2]"
]
},
{
"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>ENT_REGIS</th>\n",
" <th>MUN_REGIS</th>\n",
" <th>ENT_OCURR</th>\n",
" <th>MUN_OCURR</th>\n",
" <th>CAUSA_DEF</th>\n",
" <th>SEXO</th>\n",
" <th>ANIO_OCUR</th>\n",
" <th>ESCOLARIDA</th>\n",
" <th>PRESUNTO</th>\n",
" <th>ASIST_MEDI</th>\n",
" <th>VIO_FAMI</th>\n",
" <th>ENT_OCULES</th>\n",
" <th>MUN_OCULES</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>796</th>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>X954</td>\n",
" <td>2</td>\n",
" <td>2016</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>88</td>\n",
" <td>888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>797</th>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>X954</td>\n",
" <td>1</td>\n",
" <td>2016</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>88</td>\n",
" <td>888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>798</th>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>X998</td>\n",
" <td>1</td>\n",
" <td>2016</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>88</td>\n",
" <td>888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>810</th>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>X954</td>\n",
" <td>1</td>\n",
" <td>2016</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>88</td>\n",
" <td>888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1402</th>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>X979</td>\n",
" <td>1</td>\n",
" <td>2016</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>88</td>\n",
" <td>888</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ENT_REGIS MUN_REGIS ENT_OCURR MUN_OCURR CAUSA_DEF SEXO ANIO_OCUR \\\n",
"796 01 001 01 001 X954 2 2016 \n",
"797 01 001 01 001 X954 1 2016 \n",
"798 01 001 01 001 X998 1 2016 \n",
"810 01 001 01 001 X954 1 2016 \n",
"1402 01 001 01 001 X979 1 2016 \n",
"\n",
" ESCOLARIDA PRESUNTO ASIST_MEDI VIO_FAMI ENT_OCULES MUN_OCULES \n",
"796 6 2 2 9 88 888 \n",
"797 8 2 1 9 88 888 \n",
"798 6 2 2 9 88 888 \n",
"810 6 2 2 9 88 888 \n",
"1402 4 2 2 9 88 888 "
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2010 : 8\n",
"2011 : 14\n",
"2012 : 25\n",
"2013 : 22\n",
"2014 : 64\n",
"2015 : 318\n",
"2016 : 24029\n"
]
}
],
"source": [
"for year in sorted(list(dataset['ANIO_OCUR'].unique())):\n",
" print('{} : {}'.format(year, len(dataset[dataset['ANIO_OCUR'] == year])))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Asignación de causas de defuncion\n",
"El dataset tiene identificadas 127 causas de defunción (Por homicidio, pues ya se identificaron únicamente los casos donde el PRESUNTO es homicidio)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"127"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dataset['CAUSA_DEF'].unique())"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array(['X954', 'X998', 'X979', 'X950', 'Y004', 'Y009', 'Y007', 'X994',\n",
" 'Y099', 'Y094', 'X990', 'X959', 'X999', 'X997', 'Y090', 'X955',\n",
" 'X957', 'X910', 'X949', 'X934', 'X919', 'Y095', 'X939', 'X914',\n",
" 'X935', 'X970', 'X930', 'X958', 'Y097', 'Y098', 'Y079', 'X992',\n",
" 'X918', 'X938', 'X956', 'X940', 'X978', 'Y000', 'X909', 'X995',\n",
" 'X936', 'X915', 'X900', 'X944', 'Y038', 'X899', 'Y071', 'X928',\n",
" 'X937', 'X945', 'Y046', 'Y008', 'X996', 'Y044', 'Y089', 'X993',\n",
" 'Y040', 'X974', 'X920', 'Y096', 'X977', 'X952', 'X912', 'X951',\n",
" 'X916', 'X917', 'Y091', 'X947', 'Y068', 'Y048', 'Y088', 'Y002',\n",
" 'Y070', 'Y069', 'Y054', 'Y034', 'X948', 'Y093', 'X953', 'X991',\n",
" 'Y092', 'X924', 'X913', 'Y039', 'Y084', 'Y049', 'X889', 'X911',\n",
" 'Y047', 'Y020', 'X929', 'X976', 'Y018', 'Y080', 'Y005', 'X890',\n",
" 'Y014', 'Y041', 'X926', 'Y006', 'Y043', 'Y042', 'X859', 'Y001',\n",
" 'X907', 'Y010', 'X927', 'X850', 'Y078', 'Y016', 'X960', 'Y087',\n",
" 'X870', 'Y019', 'Y871', 'X975', 'X885', 'X905', 'X964', 'Y003',\n",
" 'Y058', 'Y029', 'X923', 'Y045', 'Y028', 'Y030', 'X879'], dtype=object)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset['CAUSA_DEF'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CLAVE</th>\n",
" <th>NOMBRE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>Fiebre tifoidea</td>\n",
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" <td>A011</td>\n",
" <td>Fiebre paratifoidea A</td>\n",
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"</table>\n",
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],
"text/plain": [
" CLAVE NOMBRE\n",
"0 A000 C¢lera debido a Vibrio cholerae 01, biotipo ch...\n",
"1 A001 C¢lera debido a Vibrio cholerae 01, biotipo el...\n",
"2 A009 C¢lera, no especificado\n",
"3 A010 Fiebre tifoidea\n",
"4 A011 Fiebre paratifoidea A"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Se utilizará el dataset contenido en el archivo CATMINDE.dbf (Posición 3)\n",
"path_to_desc = r'{}\\{}'.format(target,files[3])\n",
"descripciones = Dbf5(path_to_desc, codec='mbcs').to_dataframe()\n",
"descripciones.head()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
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" <th>MUN_OCURR</th>\n",
" <th>CAUSA_DEF</th>\n",
" <th>SEXO</th>\n",
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" <td>2016</td>\n",
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" <td>2</td>\n",
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" <td>88</td>\n",
" <td>888</td>\n",
" <td>X954</td>\n",
" <td>Agresi¢n con disparo de otras armas de fuego, ...</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>X954</td>\n",
" <td>1</td>\n",
" <td>2016</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>88</td>\n",
" <td>888</td>\n",
" <td>X954</td>\n",
" <td>Agresi¢n con disparo de otras armas de fuego, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>01</td>\n",
" <td>001</td>\n",
" <td>X954</td>\n",
" <td>1</td>\n",
" <td>2016</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>88</td>\n",
" <td>888</td>\n",
" <td>X954</td>\n",
" <td>Agresi¢n con disparo de otras armas de fuego, ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ENT_REGIS MUN_REGIS ENT_OCURR MUN_OCURR CAUSA_DEF SEXO ANIO_OCUR \\\n",
"0 01 001 01 001 X954 2 2016 \n",
"1 01 001 01 001 X954 1 2016 \n",
"2 01 001 01 001 X954 1 2016 \n",
"3 01 001 01 001 X954 1 2016 \n",
"4 01 001 01 001 X954 1 2016 \n",
"\n",
" ESCOLARIDA PRESUNTO ASIST_MEDI VIO_FAMI ENT_OCULES MUN_OCULES CLAVE \\\n",
"0 6 2 2 9 88 888 X954 \n",
"1 8 2 1 9 88 888 X954 \n",
"2 6 2 2 9 88 888 X954 \n",
"3 6 2 2 9 88 888 X954 \n",
"4 7 2 1 9 88 888 X954 \n",
"\n",
" NOMBRE \n",
"0 Agresi¢n con disparo de otras armas de fuego, ... \n",
"1 Agresi¢n con disparo de otras armas de fuego, ... \n",
"2 Agresi¢n con disparo de otras armas de fuego, ... \n",
"3 Agresi¢n con disparo de otras armas de fuego, ... \n",
"4 Agresi¢n con disparo de otras armas de fuego, ... "
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Asignacion de columna con descripciones de causa de defunción\n",
"dataframe = dataset.merge(descripciones, left_on='CAUSA_DEF', right_on = 'CLAVE')\n",
"dataframe.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creación del dataset estándar y exportación a Excel"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
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" <td>888</td>\n",
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" <td>888</td>\n",
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" <td>01001</td>\n",
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" <th>4</th>\n",
" <td>01</td>\n",
" <td>001</td>\n",
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],
"text/plain": [
" ENT_REGIS MUN_REGIS ENT_OCURR MUN_OCURR CAUSA_DEF SEXO ANIO_OCUR \\\n",
"0 01 001 01 001 X954 2 2016 \n",
"1 01 001 01 001 X954 1 2016 \n",
"2 01 001 01 001 X954 1 2016 \n",
"3 01 001 01 001 X954 1 2016 \n",
"4 01 001 01 001 X954 1 2016 \n",
"\n",
" ESCOLARIDA PRESUNTO ASIST_MEDI VIO_FAMI ENT_OCULES MUN_OCULES CLAVE \\\n",
"0 6 2 2 9 88 888 X954 \n",
"1 8 2 1 9 88 888 X954 \n",
"2 6 2 2 9 88 888 X954 \n",
"3 6 2 2 9 88 888 X954 \n",
"4 7 2 1 9 88 888 X954 \n",
"\n",
" NOMBRE CVE_MUN_REGIS \\\n",
"0 Agresi¢n con disparo de otras armas de fuego, ... 01001 \n",
"1 Agresi¢n con disparo de otras armas de fuego, ... 01001 \n",
"2 Agresi¢n con disparo de otras armas de fuego, ... 01001 \n",
"3 Agresi¢n con disparo de otras armas de fuego, ... 01001 \n",
"4 Agresi¢n con disparo de otras armas de fuego, ... 01001 \n",
"\n",
" CVE_MUN_OCURR CVE_MUN_OCULES \n",
"0 01001 88888 \n",
"1 01001 88888 \n",
"2 01001 88888 \n",
"3 01001 88888 \n",
"4 01001 88888 "
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Concatenar claves estatales y municipales para obtener CVE_MUN\n",
"# Municipio donde se registró el deceso\n",
"dataframe['CVE_MUN_REGIS'] = dataframe.ENT_REGIS.map(str)+dataframe.MUN_REGIS\n",
"# Municipio donde ocurrió el deceso\n",
"dataframe['CVE_MUN_OCURR'] = dataframe.ENT_OCURR.map(str)+dataframe.MUN_OCURR\n",
"# Municipio donde ocurrió la lesión que provocó el deceso\n",
"dataframe['CVE_MUN_OCULES'] = dataframe.ENT_OCULES.map(str)+dataframe.MUN_OCULES\n",
"\n",
"dataframe.head()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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" <th>01001</th>\n",
" <td>X954</td>\n",
" <td>1</td>\n",
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" <td>6</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
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" <td>Agresi¢n con disparo de otras armas de fuego, ...</td>\n",
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" <th>01001</th>\n",
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" <td>9</td>\n",
" <td>X954</td>\n",
" <td>Agresi¢n con disparo de otras armas de fuego, ...</td>\n",
" <td>01001</td>\n",
" <td>88888</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CAUSA_DEF SEXO ANIO_OCUR ESCOLARIDA PRESUNTO ASIST_MEDI \\\n",
"CVE_MUN_OCURR \n",
"01001 X954 2 2016 6 2 2 \n",
"01001 X954 1 2016 8 2 1 \n",
"01001 X954 1 2016 6 2 2 \n",
"01001 X954 1 2016 6 2 2 \n",
"01001 X954 1 2016 7 2 1 \n",
"\n",
" VIO_FAMI CLAVE \\\n",
"CVE_MUN_OCURR \n",
"01001 9 X954 \n",
"01001 9 X954 \n",
"01001 9 X954 \n",
"01001 9 X954 \n",
"01001 9 X954 \n",
"\n",
" NOMBRE_CAUSA_DEF \\\n",
"CVE_MUN_OCURR \n",
"01001 Agresi¢n con disparo de otras armas de fuego, ... \n",
"01001 Agresi¢n con disparo de otras armas de fuego, ... \n",
"01001 Agresi¢n con disparo de otras armas de fuego, ... \n",
"01001 Agresi¢n con disparo de otras armas de fuego, ... \n",
"01001 Agresi¢n con disparo de otras armas de fuego, ... \n",
"\n",
" CVE_MUN_REGIS CVE_MUN_OCULES \n",
"CVE_MUN_OCURR \n",
"01001 01001 88888 \n",
"01001 01001 88888 \n",
"01001 01001 88888 \n",
"01001 01001 88888 \n",
"01001 01001 88888 "
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Eliminar columnas redundantes\n",
"del(dataframe['ENT_REGIS'])\n",
"del(dataframe['MUN_REGIS'])\n",
"del(dataframe['ENT_OCURR'])\n",
"del(dataframe['MUN_OCURR'])\n",
"del(dataframe['ENT_OCULES'])\n",
"del(dataframe['MUN_OCULES'])\n",
"del(dataframe['CLAVE'])\n",
"\n",
"# Renombrar nombre de la causa de defuncion\n",
"dataframe.rename(columns={'NOMBRE' : 'NOMBRE_CAUSA_DEF'}, inplace = True)\n",
"\n",
"# Se asigna el municipio de ocurrencia como indice de la tabla\n",
"dataframe.set_index('CVE_MUN_OCURR', inplace=True)\n",
"dataframe.head()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['CAUSA_DEF',\n",
" 'SEXO',\n",
" 'ANIO_OCUR',\n",
" 'ESCOLARIDA',\n",
" 'PRESUNTO',\n",
" 'ASIST_MEDI',\n",
" 'VIO_FAMI',\n",
" 'CLAVE',\n",
" 'NOMBRE_CAUSA_DEF',\n",
" 'CVE_MUN_REGIS',\n",
" 'CVE_MUN_OCULES']"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Reordenar Columnas\n",
"list(dataframe)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"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>Descripcion</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Metadato</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Nombre del Dataset</th>\n",
" <td>INEGI - Registros administrativos de mortalida...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Descripcion del dataset</th>\n",
" <td>Originalmente, el formato de captación para la...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Disponibilidad Temporal</th>\n",
" <td>1923 a 2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Periodo de actualizacion</th>\n",
" <td>Anual</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nivel de Desagregacion</th>\n",
" <td>Caso</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Notas</th>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fuente</th>\n",
" <td>INEGI</td>\n",
" </tr>\n",
" <tr>\n",
" <th>URL_Fuente</th>\n",
" <td>http://www.beta.inegi.org.mx/proyectos/registr...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dataset base</th>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Descripcion\n",
"Metadato \n",
"Nombre del Dataset INEGI - Registros administrativos de mortalida...\n",
"Descripcion del dataset Originalmente, el formato de captación para la...\n",
"Disponibilidad Temporal 1923 a 2016\n",
"Periodo de actualizacion Anual\n",
"Nivel de Desagregacion Caso\n",
"Notas None\n",
"Fuente INEGI\n",
"URL_Fuente http://www.beta.inegi.org.mx/proyectos/registr...\n",
"Dataset base None"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Metadatos estándar\n",
"metadatos = {\n",
" 'Nombre del Dataset': 'INEGI - Registros administrativos de mortalidad al año 2016',\n",
" 'Descripcion del dataset': 'Originalmente, el formato de captación para las defunciones generales era una boleta colectiva, en la cual las fuentes informantes reportaban las defunciones que registraban durante el mes. A partir del año 1987, el formato principal es el certificado o acta de defunción y el cuaderno para defunciones accidentales y violentas del Ministerio Público.',\n",
" 'Disponibilidad Temporal': '1923 a 2016',\n",
" 'Periodo de actualizacion': 'Anual',\n",
" 'Nivel de Desagregacion': 'Caso',\n",
" 'Notas': None,\n",
" 'Fuente': 'INEGI',\n",
" 'URL_Fuente': 'http://www.beta.inegi.org.mx/proyectos/registros/vitales/mortalidad/',\n",
" 'Dataset base': None,\n",
"}\n",
"\n",
"metadatos = pd.DataFrame.from_dict(metadatos, orient='index', dtype=None)\n",
"metadatos.columns = ['Descripcion']\n",
"metadatos = metadatos.rename_axis('Metadato')\n",
"metadatos"
]
},
{
"cell_type": "code",
"execution_count": 79,
"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>Descripcion</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Mnemonico</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ENT_REGIS</th>\n",
" <td>Entidad de registro.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MUN_REGIS</th>\n",
" <td>Municipio de registro.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ENT_OCURR</th>\n",
" <td>Entidad de ocurrencia.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MUN_OCURR</th>\n",
" <td>Municipio de ocurrencia.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CAUSA_DEF</th>\n",
" <td>Causa de la defunción (clave).</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SEXO</th>\n",
" <td>Sexo del (la) fallecido (a).\\n1: Hombre\\n2: Mu...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ANIO_OCUR</th>\n",
" <td>Año de ocurrencia.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ESCOLARIDA</th>\n",
" <td>Nivel de escolaridad del (la) fallecido (a) (e...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PRESUNTO</th>\n",
" <td>Tipo de defunción (presunto). 2: Homicidio</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ASIST_MEDI</th>\n",
" <td>Condición de atención médica.\\n1: Con Asistenc...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>VIO_FAMI</th>\n",
" <td>Condición de violencia familiar.\\n1: Hubo viol...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ENT_OCULES</th>\n",
" <td>Entidad de ocurrencia de la lesión.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MUN_OCULES</th>\n",
" <td>Municipio de ocurrencia de la lesión.</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Descripcion\n",
"Mnemonico \n",
"ENT_REGIS Entidad de registro.\n",
"MUN_REGIS Municipio de registro.\n",
"ENT_OCURR Entidad de ocurrencia.\n",
"MUN_OCURR Municipio de ocurrencia.\n",
"CAUSA_DEF Causa de la defunción (clave).\n",
"SEXO Sexo del (la) fallecido (a).\\n1: Hombre\\n2: Mu...\n",
"ANIO_OCUR Año de ocurrencia.\n",
"ESCOLARIDA Nivel de escolaridad del (la) fallecido (a) (e...\n",
"PRESUNTO Tipo de defunción (presunto). 2: Homicidio\n",
"ASIST_MEDI Condición de atención médica.\\n1: Con Asistenc...\n",
"VIO_FAMI Condición de violencia familiar.\\n1: Hubo viol...\n",
"ENT_OCULES Entidad de ocurrencia de la lesión.\n",
"MUN_OCULES Municipio de ocurrencia de la lesión."
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"variables = {\n",
" 'ENT_REGIS': 'Entidad de registro.',\n",
" 'MUN_REGIS': 'Municipio de registro.',\n",
" 'ENT_OCURR': 'Entidad de ocurrencia.',\n",
" 'MUN_OCURR': 'Municipio de ocurrencia.',\n",
" 'CAUSA_DEF': 'Causa de la defunción (clave).',\n",
" 'SEXO': 'Sexo del (la) fallecido (a).\\n'\n",
" '1: Hombre\\n'\n",
" '2: Mujer\\n'\n",
" '9: No especificado',\n",
" 'ANIO_OCUR': 'Año de ocurrencia.',\n",
" 'ESCOLARIDA': 'Nivel de escolaridad del (la) fallecido (a) (escolaridad).\\n'\n",
" '1: Sin escolaridad\\n'\n",
" '2: Preescolar\\n'\n",
" '3: Primaria incompleta\\n'\n",
" '4: Primaria completa\\n'\n",
" '5: Secundaria incompleta\\n'\n",
" '6: Secundaria completa\\n'\n",
" '7: Bachillerato o preparatoria incompleto\\n'\n",
" '8: Bachillerato o preparatoria completo\\n'\n",
" '9: Profesional\\n'\n",
" '10: Posgrado\\n'\n",
" '88: No aplica a menores de 3 años\\n'\n",
" '99: No especificado',\n",
" 'PRESUNTO': 'Tipo de defunción (presunto). 2: Homicidio',\n",
" 'ASIST_MEDI': 'Condición de atención médica.\\n'\n",
" '1: Con Asistencia Medica\\n'\n",
" '2: Sin Asistencia Medica\\n'\n",
" '9: No especificada',\n",
" 'VIO_FAMI': 'Condición de violencia familiar.\\n'\n",
" '1: Hubo violencia familiar\\n'\n",
" '2: No hubo violencia familiar\\n'\n",
" '2: No aplica cuando no es homicidio\\n'\n",
" '9: No especificado',\n",
" 'ENT_OCULES': 'Entidad de ocurrencia de la lesión.',\n",
" 'MUN_OCULES': 'Municipio de ocurrencia de la lesión.',\n",
"}\n",
"\n",
"\n",
"variables = pd.DataFrame.from_dict(variables, orient='index', dtype=None)\n",
"variables.columns = ['Descripcion']\n",
"variables = variables.rename_axis('Mnemonico')\n",
"variables"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Condición de violencia familiar.\\n1: Hubo violencia familiar\\n2: No hubo violencia familiar\\n2: No aplica cuando no es homicidio\\n9: No especificado'"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"variables['VIO_FAMI']"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"---------------TERMINADO---------------\n"
]
}
],
"source": [
"# Guardar el dataset\n",
"file = r'D:\\PCCS\\01_Dmine\\Datasets\\INEGI\\Defunciones\\defunciones.xlsx'\n",
"writer = pd.ExcelWriter(file)\n",
"dataframe.to_excel(writer, sheet_name = 'DATOS')\n",
"metadatos.to_excel(writer, sheet_name = 'METADATOS')\n",
"variables.to_excel(writer, sheet_name = 'VARIABLES')\n",
"writer.save()\n",
"print('---------------TERMINADO---------------')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
KMFleischer/PyEarthScience | Visualization/Cartopy/rotated_curvilinear_grid_1.ipynb | 1 | 55157 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot data on a curvilinear grid using a rotated pole\n",
"\n",
"In this example we show how to work with a curvilinear grid using a rotated pole (rotated grid), which is often used by regional models. In this case you have to look carefully at the dimensions, because the dimensions of the data variable seem at first sight to be defined on a regular grid, since the coordinate variables of the data variables are 1-dimensional.\n",
"\n",
"E.g. for a variable tas:\n",
"\n",
" tas(time, rlat, rlon)\n",
"\n",
"But if you look at the names of the dimensions of the data variable more precisely, they usually do not depend on lat and lon or latitude and longitude, but rather rlat and rlon.\n",
"\n",
" rlat(rlat)\n",
" rlon(rlon)\n",
" \n",
"With a little luck the variables lat and lon of the regular non-rotated grid are included in the file, if not, the variable rotated_pole with the attributes grid_north_pole_latitude and grid_north_pole_longitude must be included to transform the rotated to the non-rotated grid.\n",
"\n",
"As an example of a curvilinear grid with rotated pole we use **CORDEX EUR-11** data. With the command line program `ncdump` we can take a look at the metadata of the file contents.\n",
"\n",
"`ncdump -h rotated_curvilinear_data.nc`\n",
"\n",
"```\n",
"netcdf rotated_curvilinear_data {\n",
"dimensions:\n",
"\ttime = UNLIMITED ; // (1 currently)\n",
"\tbnds = 2 ;\n",
"\trlon = 424 ;\n",
"\trlat = 412 ;\n",
"variables:\n",
"\tdouble time(time) ;\n",
"\t\ttime:standard_name = \"time\" ;\n",
"\t\ttime:long_name = \"time\" ;\n",
"\t\ttime:bounds = \"time_bnds\" ;\n",
"\t\ttime:units = \"days since 1949-12-01 00:00:00\" ;\n",
"\t\ttime:calendar = \"standard\" ;\n",
"\t\ttime:axis = \"T\" ;\n",
"\tdouble time_bnds(time, bnds) ;\n",
"\tdouble lon(rlat, rlon) ;\n",
"\t\tlon:standard_name = \"longitude\" ;\n",
"\t\tlon:long_name = \"longitude\" ;\n",
"\t\tlon:units = \"degrees_east\" ;\n",
"\t\tlon:_CoordinateAxisType = \"Lon\" ;\n",
"\tdouble lat(rlat, rlon) ;\n",
"\t\tlat:standard_name = \"latitude\" ;\n",
"\t\tlat:long_name = \"latitude\" ;\n",
"\t\tlat:units = \"degrees_north\" ;\n",
"\t\tlat:_CoordinateAxisType = \"Lat\" ;\n",
"\tdouble rlon(rlon) ;\n",
"\t\trlon:standard_name = \"grid_longitude\" ;\n",
"\t\trlon:long_name = \"longitude in rotated pole grid\" ;\n",
"\t\trlon:units = \"degrees\" ;\n",
"\t\trlon:axis = \"X\" ;\n",
"\tdouble rlat(rlat) ;\n",
"\t\trlat:standard_name = \"grid_latitude\" ;\n",
"\t\trlat:long_name = \"latitude in rotated pole grid\" ;\n",
"\t\trlat:units = \"degrees\" ;\n",
"\t\trlat:axis = \"Y\" ;\n",
"\tint rotated_pole ;\n",
"\t\trotated_pole:grid_mapping_name = \"rotated_latitude_longitude\" ;\n",
"\t\trotated_pole:grid_north_pole_latitude = 39.25 ;\n",
"\t\trotated_pole:grid_north_pole_longitude = -162. ;\n",
"\tdouble height ;\n",
"\t\theight:standard_name = \"height\" ;\n",
"\t\theight:long_name = \"height\" ;\n",
"\t\theight:units = \"m\" ;\n",
"\t\theight:positive = \"up\" ;\n",
"\t\theight:axis = \"Z\" ;\n",
"\tfloat tas(time, rlat, rlon) ;\n",
"\t\ttas:standard_name = \"air_temperature\" ;\n",
"\t\ttas:long_name = \"Near-Surface Air Temperature\" ;\n",
"\t\ttas:units = \"K\" ;\n",
"\t\ttas:grid_mapping = \"rotated_pole\" ;\n",
"\t\ttas:coordinates = \"height lat lon\" ;\n",
"\t\ttas:_FillValue = 1.e+20f ;\n",
"\t\ttas:missing_value = 1.e+20f ;\n",
"\t\ttas:cell_methods = \"time: mean\" ;\n",
"\n",
"// global attributes:\n",
"\t\t:Conventions = \"CF-1.4\" ;\n",
"\t\t:project_id = \"CORDEX\" ;\n",
"\t\t:CORDEX_domain = \"EUR-11\" ;\n",
"}\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import python modules\n",
"\n",
"#### Used modules\n",
" xarray: read netCDF file\n",
" numpy: handle data arrays\n",
" matplotlib: create the plot\n",
" cartopy: use projection RotatedPole"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import xarray as xr\n",
"import numpy as np\n",
"import os, sys\n",
"import matplotlib.pyplot as plt\n",
"import cartopy\n",
"import cartopy.crs as ccrs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Allow to display the output of plotting commands in notebook"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Function read_data\n",
"\n",
"The function read_data opens the data file, read the variable data, and return relevant variables to the main function.\n",
"\n",
"- open and decode a dataset from the netCDF file\n",
"- select variable tas, first time step\n",
"- select coordinate variables rlat and rlon of the rotated grid\n",
"- select rotated_pole variable to get the used pole coordinates"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def read_data(file_name):\n",
" \"\"\"\n",
" Read netcdf file and return variables: \n",
" rlat, rlon, var, px and py.\n",
" \"\"\"\n",
" # read the dataset\n",
" ds = xr.open_dataset(file_name)\n",
" \n",
" # retrieve the data of variable tas, coordinate variables rlat and rlon, and rotated pole\n",
" var = ds.tas[0,:,:]\n",
" rlat = ds.rlat[:]\n",
" rlon = ds.rlon[:]\n",
" pole = ds.rotated_pole\n",
" \n",
" try:\n",
" # retrieve attribute grid_north_pole_longitude\n",
" if hasattr(pole,'grid_north_pole_longitude'):\n",
" px = pole.attrs['grid_north_pole_longitude']\n",
" \n",
" # retrieve attribute grid_north_pole_latitude\n",
" if hasattr(pole,'grid_north_pole_latitude'):\n",
" py = pole.attrs['grid_north_pole_latitude']\n",
" except:\n",
" print('Unexpected error:', sys.exc_info()[0])\n",
" raise\n",
" \n",
" return rlon, rlat, var, px, py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Function main\n",
"Create the contour fill plot of variable tas. The cartopy projection RotatedPole is used to draw the data above the original region of Europe. Save the plot output to a PNG file."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def main():\n",
" \"\"\"\n",
" Draw variable tas on map using the RotatedPole projection.\n",
" The coordinate variables rlat and rlon are required.\n",
" \"\"\"\n",
" dir_name = 'data/'\n",
" file_name = 'rotated_curvilinear_data.nc'\n",
" fname = os.path.join(dir_name,file_name)\n",
" \n",
" # read file content and return relevant variables\n",
" rlon, rlat, var, pole_lon, pole_lat = read_data(fname)\n",
" \n",
" # initialize plot\n",
" ax = plt.axes(projection=ccrs.PlateCarree())\n",
" ax.set_extent([-46, 70, 20, 75], crs=ccrs.PlateCarree())\n",
" \n",
" # set fill colors for ocean and land areas\n",
" ax.add_feature(cartopy.feature.OCEAN, color='white', zorder=0)\n",
" ax.add_feature(cartopy.feature.LAND, color='lightgray',zorder=0, \n",
" linewidth=0.5, edgecolor='black')\n",
" \n",
" # add gridlines\n",
" ax.gridlines(draw_labels=True, linewidth=0.5, color='gray', xlocs=range(-180,180,15), ylocs=range(-90,90,15))\n",
" \n",
" # add coastlines\n",
" ax.coastlines(resolution='50m', linewidth=0.3, color='black')\n",
" \n",
" # add title\n",
" ax.set_title('Python: rotated curvilinear grid', fontsize=10, fontweight='bold')\n",
"\n",
" # set projection\n",
" crs = ccrs.RotatedPole(pole_longitude=pole_lon, pole_latitude=pole_lat)\n",
" \n",
" # contour fill plot\n",
" ax.contourf(rlon, rlat, var, levels=15, cmap='RdYlBu_r', transform=crs)\n",
"\n",
" # save the plot output to PNG file (and display it in notebook if '%matplotlib inline' is set)\n",
" plt.savefig('Py_rotated_curvilinear_grid_1.png', bbox_inches='tight', dpi=200)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run main\n",
"\n",
"And here we go ..."
]
},
{
"cell_type": "code",
"execution_count": 5,
"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": [
"if __name__ == '__main__':\n",
" main()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
TheKingInYellow/PySeidon | PySeidon_tuto_3.ipynb | 2 | 219062 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PySeison - Tutorial 3: ADCP class"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. PySeidon - ADCP object initialisation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similarly to the \"TideGauge class\" and the \"Drifter class\", the \"ADCP class\" is a measurement-based object."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.1. Package importation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As any other library in *Python*, PySeidon has to be first imported before to be used. Here we will use an alternative *import* statement compared to the one previoulsy presented: "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from pyseidon import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*** Star *** here means *all*. Usually this form of statements would import the entire library. In the case of *PySeidon*, this statement will import the following object classes: FVCOM, Station, Validation, ADCP, Tidegauge and Drifter. Only the ADCP class will be tackle in this tutorial. However note should note that the architecture design and functioning between each classes are very similar. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2. Object definition"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Python* is by definition an [**object oriented language**](http://www.tutorialspoint.com/python/python_classes_objects.htm)...and so is *matlab*. *PySeidon* is based on this notion of object, so let us define our first \"ADCP\" object."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Exercise 1: ***\n",
"- Unravel ADCP documentation with Ipython ***shortcuts***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Answer: ***"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ADCP?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"According to the documentation, in order to define a ADCP object, the only required input is a ***filename**. This string input represents path to a file (e.g. *testAdcp=ADCP('./path_to_matlab_file/filename')* and whose file must be a matlab file (i.e. *.mat).\n",
"\n",
"**Note** that, at the current stage, the package only handle fully processed ADCP matlab data previously quality-controlled as well as formatted through \"EnsembleData_FlowFile\" matlab script at the mo. All the tool necessary for this processing and quality-control can be found in *./pyseidon/utilities/BP_tools.py and save_FlowFile_BPFormat.py*. Additionally, a template for the ADCP file is provided in the package under *data4tutorial*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Exercise 2: ***\n",
"- define a adcp object named *adcp* from the following template: **./data4tutorial/adcp_GP_01aug2013.mat**\n",
"- Tip: adapt the file's path to your local machine. \n",
"\n",
"***Answer: ***"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"adcp=ADCP('./data4tutorial/adcp_GP_01aug2013.mat')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.3. Object attributes, functions, methods & special methods"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ADCP object possesses 3 attributes and 3 methods. They would appear by typing ***adcp. Tab*** for instance.\n",
"\n",
"An *attribute* is a quantity intrinsic to its *object*. A *method* is an intrinsic *function* which changes an *attribute* of its object. Contrarily a *function* will generate its own *output*:"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"object.method(inputs)\n",
"output = object.function(inputs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Station attributes are:\n",
"- ***History***: history metadata that keeps track of the object changes\n",
"- ***Data***: gathers the raw/unchanged data of the specified *.mat file\n",
"- ***Variables***: gathers the hydrodynamics related data. Note that methods will generate new fields in this attribute\n",
"\n",
"The Station methods & functions are:\n",
"- ***Utils***: gathers utility methods and functions for use with 2D and 3D variables\n",
"- ***Plots***: gathers plotting methods for use with 2D and 3D variables\n",
"- ***dump_profile_data***: dumps profile data (x,y) in a *.csv file."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. PySeidon - Hands-on (15 mins)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Utils"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Exercise 3: ***\n",
"- Print the (lon,lat) coordinates of the *adcp* object (hint: look into ***Variables***)\n",
"- Use ***Utils.velo_norm*** to compute the velocity norm over all time steps and all vertical levels...and accessorily plot the mean velocity vertical profile. \n",
"- Use ***Utils.ebb_flood_split*** function to get the ebb and flood time indices of the time series.\n",
"- Plot the **flood** flow speed ***hitogram***\n",
"- Compute & Plot the **ebb** ***vertical shear***\n",
"- Perform a harmonic analysis of the velocities and print out the result\n",
"- Reconstruction these velocities based on the harmonic results of the previous question\n",
"\n",
"***Answer: ***"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(lon, lat) coordinates: (-66.3390666667, 44.2799166667)\n",
"solve: \n",
"matrix prep ... \n",
"Solution ...\n",
"diagnostics...\n",
"Done.\n",
"\n",
"Lsmaj : [ 1.92087736]\n",
"Lsmaj_ci : [ 0.02495443]\n",
"Lsmin : [ 0.02102549]\n",
"Lsmin_ci : [ 0.01190183]\n",
"aux : frq : [ 0.0805114]\n",
"lat : 44.2799166667\n",
"lind : [47]\n",
"opt : cnstit : auto\n",
"conf_int : True\n",
"diagnminsnr : 2\n",
"diagnplots : 0\n",
"equi : False\n",
"gwchlint : False\n",
"gwchnone : False\n",
"infer : []\n",
"inferaprx : 0\n",
"linci : True\n",
"lsfrqosmp : 1\n",
"method : ols\n",
"newopts : MC_n : 200\n",
"Rayleigh_min : 1\n",
"conf_int : linear\n",
"constit : auto\n",
"infer : none\n",
"method : ols\n",
"nodal : True\n",
"phase : Greenwich\n",
"robust_kw : {'weight_function': 'cauchy'}\n",
"trend : True\n",
"white : False\n",
"\n",
"nodesatlint : False\n",
"nodesatnone : False\n",
"nodiagn : 0\n",
"nodsatlint : 0\n",
"nodsatnone : 0\n",
"notrend : False\n",
"nrlzn : 200\n",
"ordercnstit : None\n",
"prefilt : []\n",
"rmin : 1\n",
"runtimedisp : yyy\n",
"tunrdn : 1\n",
"twodim : True\n",
"white : False\n",
"\n",
"reftime : 735448.001987\n",
"\n",
"diagn : {'SNR': array([ 18546.23695224]), 'name': array([u'M2 '], \n",
" dtype='<U4'), 'PE': array([ 100.])}\n",
"g : [ 347.97287404]\n",
"g_ci : [ 0.72494198]\n",
"name : [u'M2 ']\n",
"theta : [ 83.74403207]\n",
"theta_ci : [ 0.36470506]\n",
"umean : 0.0151540354014\n",
"uslope : -0.00784641470595\n",
"vmean : 0.0254322275605\n",
"vslope : -0.0182159475527\n",
"weights : [ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1. 1.]\n",
"\n",
"reconstruct:\n",
"prep/calcs...\n",
"Done.\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/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"
]
},
{
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EAOCcpguMnACQX1O1ilZWO+oR1ALIqcKI04KxvFqHMUZRVHBWPwx9Z/Vd1nZZ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\nEwC6143Jef3xZ7+rWj3Vp54+rgP7xl2nBABNYWpWw/W6jox7iiJmTKD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"text/plain": [
"<matplotlib.figure.Figure at 0x56ec350>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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KhBxBfUTJxB065DLOky/HcXZ5GCYUQqcEY4Bl6eh0apibq0FVGXw/wsKCD9+P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XAAAg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\nkiRJkhYhhWiSJEmSJEnSIvwX23OEJpQ+6q0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x583f9d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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AAABXzZlv92itPae19k9bazdz/OGZP9pae/Z6RgMAAACukicsKVprfyzJ30vy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\n8G8wTAth39haVQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x5a93d50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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r1dPPb9HuvQOH37++uCd1QxdJiiKjuBwp8KQBBhUAAAAATlH6zpUHjhBFRvuH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\nwfj/hlcTQhimTjYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x583fa50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print \"(lon, lat) coordinates: (\"+str(adcp.Variables.lon)+\", \"+str(adcp.Variables.lat)+\")\"\n",
"vel = adcp.Utils.velo_norm()\n",
"fI, eI, pa, pav = adcp.Utils.ebb_flood_split()\n",
"adcp.Utils.speed_histogram(time_ind=fI)\n",
"dveldz = adcp.Utils.verti_shear(time_ind=eI)\n",
"harmo = adcp.Utils.Harmonic_analysis(elevation=False, velocity=True)\n",
"print harmo\n",
"velos = adcp.Utils.Harmonic_reconstruction(harmo)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Exercise 5: ***\n",
"- ***Dump*** depth-averaged velocity and time step data in a *.csv file\n",
"- ***Hint:*** use numpy for averaging\n",
"\n",
"***Answer: ***"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"da_vel = np.nanmean(vel,axis=1)\n",
"station.dump_profile_data(adcp.Variables.matlabTime[:], da_vel, title='flow_speed_time_series', xlabel='matlab time', ylabel='speed')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Bug patrol & steering committee"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.1. Bug report"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As beta tester, your first assignement is to report bugs...yet not everything is a bug. The first thing to check before to report a bug is to verify that your version of *PySeidon* is up-to-date. The best way to keep up with the package evolution is to [***git***](http://git-scm.com/) to ***clone*** the repository, use ***pull*** to update it and ***re-install*** it if needed. \n",
"\n",
"The second thing to check before to report a bug is to verify that the bug is ***reproducible***. When running into a bug, double check that your inputs fit the description of the documentation then turn the ***debug flag on*** (e.g. *output = adcpobject.function(inputs, debug=True)*) and submit the command again. If the error re-occurs then report it (i.e. copy entire error message + command and send it to package administrator)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.2. Suggestions & critics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Your second role as beta-tester is to submit suggestions and critics to the developpers regarding the functioning and functionality of the package. Beta testing phase is the best opportunity to steer a project towards the applications you would like to be tackled..."
]
}
],
"metadata": {
"celltoolbar": "Raw Cell Format",
"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": "2.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| agpl-3.0 |
fabriziocosta/pyMotif | meme_example.ipynb | 1 | 198195 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from meme_wrapper import Meme\n",
"import logging"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train = [\n",
" ('ce1cg', \n",
" 'TAATGTTTGTGCTGGTTTTTGTGGCATCGGGCGAGAATAGCGCGTGGTGTGAAAGACTGTTTTTTTGATCGTTTTCACAAAAATGGAAGTCCACAGTCTTGACAG'),\n",
" ('ara', \n",
" 'GACAAAAACGCGTAACAAAAGTGTCTATAATCACGGCAGAAAAGTCCACATTGATTATTTGCACGGCGTCACACTTTGCTATGCCATAGCATTTTTATCCATAAG'),\n",
" ('bglr1', \n",
" 'ACAAATCCCAATAACTTAATTATTGGGATTTGTTATATATAACTTTATAAATTCCTAAAATTACACAAAGTTAATAACTGTGAGCATGGTCATATTTTTATCAAT'),\n",
" ('crp', \n",
" 'CACAAAGCGAAAGCTATGCTAAAACAGTCAGGATGCTACAGTAATACATTGATGTACTGCATGTATGCAAAGGACGTCACATTACCGTGCAGTACAGTTGATAGC'),\n",
" ('cya', \n",
" 'ACGGTGCTACACTTGTATGTAGCGCATCTTTCTTTACGGTCAATCAGCAAGGTGTTAAATTGATCACGTTTTAGACCATTTTTTCGTCGTGAAACTAAAAAAACC'),\n",
" ('deop2', \n",
" 'AGTGAATTATTTGAACCAGATCGCATTACAGTGATGCAAACTTGTAAGTAGATTTCCTTAATTGTGATGTGTATCGAAGTGTGTTGCGGAGTAGATGTTAGAATA'),\n",
" ('gale', \n",
" 'GCGCATAAAAAACGGCTAAATTCTTGTGTAAACGATTCCACTAATTTATTCCATGTCACACTTTTCGCATCTTTGTTATGCTATGGTTATTTCATACCATAAGCC'),\n",
" ('ilv', \n",
" 'GCTCCGGCGGGGTTTTTTGTTATCTGCAATTCAGTACAAAACGTGATCAACCCCTCAATTTTCCCTTTGCTGAAAAATTTTCCATTGTCTCCCCTGTAAAGCTGT'),\n",
" ('lac', \n",
" 'AACGCAATTAATGTGAGTTAGCTCACTCATTAGGCACCCCAGGCTTTACACTTTATGCTTCCGGCTCGTATGTTGTGTGGAATTGTGAGCGGATAACAATTTCAC'),\n",
" ('male', \n",
" 'ACATTACCGCCAATTCTGTAACAGAGATCACACAAAGCGACGGTGGGGCGTAGGGGCAAGGAGGATGGAAAGAGGTTGCCGTATAAAGAAACTAGAGTCCGTTTA'),\n",
" ('malk', \n",
" 'GGAGGAGGCGGGAGGATGAGAACACGGCTTCTGTGAACTAAACCGAGGTCATGTAAGGAATTTCGTGATGTTGCTTGCAAAAATCGTGGCGATTTTATGTGCGCA'),\n",
" ('malt', \n",
" 'GATCAGCGTCGTTTTAGGTGAGTTGTTAATAAAGATTTGGAATTGTGACACAGTGCAAATTCAGACACATAAAAAAACGTCATCGCTTGCATTAGAAAGGTTTCT'),\n",
" ('ompa', \n",
" 'GCTGACAAAAAAGATTAAACATACCTTATACAAGACTTTTTTTTCATATGCCTGACGGAGTTCACACTTGTAAGTTTTCAACTACGTTGTAGACTTTACATCGCC'),\n",
" ('tnaa', \n",
" 'TTTTTTAAACATTAAAATTCTTACGTAATTTATAATCTTTAAAAAAAGCATTTAATATTGCTCCCCGAACGATTGTGATTCGATTCACATTTAAACAATTTCAGA'),\n",
" ('uxu1', \n",
" 'CCCATGAGAGTGAAATTGTTGTGATGTGGTTAACCCAATTAGAATTCGGGATTGACATGTCTTACCAAAAGGTAGAACTTATACGCCATCTCATCCGATGCAAGC'),\n",
" ('pbr322', \n",
" 'CTGGCTTAACTATGCGGCATCAGAGCAGATTGTACTGAGAGTGCACCATATGCGGTGTGAAATACCGCACAGATGCGTAAGGAGAAAATACCGCATCAGGCGCTC'),\n",
" ('trn9cat', \n",
" 'CTGTGACGGAAGATCACTTCGCAGAATAAATAAATCCTGGTGTCCCTGTTGATACCGGGAAGCCCTGGGCCAACTTTTGGCGAAAATGAGACGTTGATCGGCACG'),\n",
" ('tdc', \n",
" 'GATTTTTATACTTTAACTTGTTGATATTTAAAGGTATTTAATTGTAATAACGATACTCTGGAAAGTATTGAAAGTTAATTTGTGAGTGGTCGCACATATCCTGTT'),\n",
" ]\n",
"\n",
"# test data consists of first 9 sequences of training data\n",
"test = train[:9]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# saving data as fasta files\n",
"with open('seq18.fa','w') as f_train:\n",
" for seq in train:\n",
" f_train.write('>' + seq[0] + ' \\n')\n",
" f_train.write(seq[1] + '\\n')\n",
" \n",
"with open('seq9.fa','w') as f_test:\n",
" for seq in test:\n",
" f_test.write('>' + seq[0] + ' \\n')\n",
" f_test.write(seq[1] + '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1>MEME Wrapper Example</h1>"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Meme().display_meme_help()\n",
"from eden.util import configure_logging\n",
"import logging\n",
"configure_logging(logging.getLogger(),verbosity=2)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The output directory 'meme_anr' already exists.\n",
"Its contents will be overwritten.\n",
"Initializing the motif probability tables for 2 to 50 sites...\n",
"nsites = 2\r",
"nsites = 3\r",
"nsites = 4\r",
"nsites = 5\r",
"nsites = 6\r",
"nsites = 7\r",
"nsites = 8\r",
"nsites = 9\r",
"nsites = 10\r",
"nsites = 11\r",
"nsites = 12\r",
"nsites = 13\r",
"nsites = 14\r",
"nsites = 15\r",
"nsites = 16\r",
"nsites = 17\r",
"nsites = 18\r",
"nsites = 19\r",
"nsites = 20\r",
"nsites = 21\r",
"nsites = 22\r",
"nsites = 23\r",
"nsites = 24\r",
"nsites = 25\r",
"nsites = 26\r",
"nsites = 27\r",
"nsites = 28\r",
"nsites = 29\r",
"nsites = 30\r",
"nsites = 31\r",
"nsites = 32\r",
"nsites = 33\r",
"nsites = 34\r",
"nsites = 35\r",
"nsites = 36\r",
"nsites = 37\r",
"nsites = 38\r",
"nsites = 39\r",
"nsites = 40\r",
"nsites = 41\r",
"nsites = 42\r",
"nsites = 43\r",
"nsites = 44\r",
"nsites = 45\r",
"nsites = 46\r",
"nsites = 47\r",
"nsites = 48\r",
"nsites = 49\r",
"nsites = 50\r\n",
"Done initializing.\n",
"SEEDS: highwater mark: seq 17 pos 105\n",
"\n",
"seqs= 18, min= 105, max= 105, total= 1890\n",
"\n",
"motif=1\n",
"SEED WIDTHS: 8 11 15 21 29 41 50\n",
"starts: w=8, seq=0, l=105 \r",
"starts: w=8, seq=5, l=105 \r",
"starts: w=8, seq=10, l=105 \r",
"starts: w=8, seq=15, l=105 \r",
"starts: w=11, seq=0, l=105 \r",
"starts: w=11, seq=5, l=105 \r",
"starts: w=11, seq=10, l=105 \r",
"starts: w=11, seq=15, l=105 \r",
"starts: w=15, seq=0, l=105 \r",
"starts: w=15, seq=5, l=105 \r",
"starts: w=15, seq=10, l=105 \r",
"starts: w=15, seq=15, l=105 \r",
"starts: w=21, seq=0, l=105 \r",
"starts: w=21, seq=5, l=105 \r",
"starts: w=21, seq=10, l=105 \r",
"starts: w=21, seq=15, l=105 \r",
"starts: w=29, seq=0, l=105 \r",
"starts: w=29, seq=5, l=105 \r",
"starts: w=29, seq=10, l=105 \r",
"starts: w=29, seq=15, l=105 \r",
"starts: w=41, seq=0, l=105 \r",
"starts: w=41, seq=5, l=105 \r",
"starts: w=41, seq=10, l=105 \r",
"starts: w=41, seq=15, l=105 \r",
"starts: w=50, seq=0, l=105 \r",
"starts: w=50, seq=5, l=105 \r",
"starts: w=50, seq=10, l=105 \r",
"starts: w=50, seq=15, l=105 \r",
"\r",
"em: w= 8, psites= 2, iter= 0 \r",
"em: w= 8, psites= 2, iter= 10 \r",
"em: w= 8, psites= 2, iter= 20 \r",
"em: w= 8, psites= 4, iter= 0 \r",
"em: w= 8, psites= 4, iter= 10 \r",
"em: w= 8, psites= 4, iter= 20 \r",
"em: w= 8, psites= 4, iter= 30 \r",
"em: w= 8, psites= 4, iter= 40 \r",
"em: w= 8, psites= 8, iter= 0 \r",
"em: w= 8, psites= 8, iter= 10 \r",
"em: w= 8, psites= 8, iter= 20 \r",
"em: w= 8, psites= 8, iter= 30 \r",
"em: w= 8, psites= 8, iter= 40 \r",
"em: w= 8, psites= 16, iter= 0 \r",
"em: w= 8, psites= 16, iter= 10 \r",
"em: w= 8, psites= 16, iter= 20 \r",
"em: w= 8, psites= 16, iter= 30 \r",
"em: w= 8, psites= 16, iter= 40 \r",
"em: w= 8, psites= 32, iter= 0 \r",
"em: w= 8, psites= 32, iter= 10 \r",
"em: w= 8, psites= 32, iter= 20 \r",
"em: w= 8, psites= 32, iter= 30 \r",
"em: w= 8, psites= 32, iter= 40 \r",
"em: w= 8, psites= 50, iter= 0 \r",
"em: w= 8, psites= 50, iter= 10 \r",
"em: w= 8, psites= 50, iter= 20 \r",
"em: w= 8, psites= 50, iter= 30 \r",
"em: w= 8, psites= 50, iter= 40 \r",
"em: w= 11, psites= 2, iter= 0 \r",
"em: w= 11, psites= 2, iter= 10 \r",
"em: w= 11, psites= 2, iter= 20 \r",
"em: w= 11, psites= 2, iter= 30 \r",
"em: w= 11, psites= 2, iter= 40 \r",
"em: w= 11, psites= 4, iter= 0 \r",
"em: w= 11, psites= 4, iter= 10 \r",
"em: w= 11, psites= 4, iter= 20 \r",
"em: w= 11, psites= 4, iter= 30 \r",
"em: w= 11, psites= 8, iter= 0 \r",
"em: w= 11, psites= 8, iter= 10 \r",
"em: w= 11, psites= 8, iter= 20 \r",
"em: w= 11, psites= 16, iter= 0 \r",
"em: w= 11, psites= 16, iter= 10 \r",
"em: w= 11, psites= 16, iter= 20 \r",
"em: w= 11, psites= 16, iter= 30 \r",
"em: w= 11, psites= 16, iter= 40 \r",
"em: w= 11, psites= 32, iter= 0 \r",
"em: w= 11, psites= 32, iter= 10 \r",
"em: w= 11, psites= 32, iter= 20 \r",
"em: w= 11, psites= 32, iter= 30 \r",
"em: w= 11, psites= 32, iter= 40 \r",
"em: w= 11, psites= 50, iter= 0 \r",
"em: w= 11, psites= 50, iter= 10 \r",
"em: w= 11, psites= 50, iter= 20 \r",
"em: w= 11, psites= 50, iter= 30 \r",
"em: w= 11, psites= 50, iter= 40 \r",
"em: w= 15, psites= 2, iter= 0 \r",
"em: w= 15, psites= 4, iter= 0 \r",
"em: w= 15, psites= 4, iter= 10 \r",
"em: w= 15, psites= 4, iter= 20 \r",
"em: w= 15, psites= 4, iter= 30 \r",
"em: w= 15, psites= 8, iter= 0 \r",
"em: w= 15, psites= 8, iter= 10 \r",
"em: w= 15, psites= 8, iter= 20 \r",
"em: w= 15, psites= 8, iter= 30 \r",
"em: w= 15, psites= 8, iter= 40 \r",
"em: w= 15, psites= 16, iter= 0 \r",
"em: w= 15, psites= 16, iter= 10 \r",
"em: w= 15, psites= 16, iter= 20 \r",
"em: w= 15, psites= 16, iter= 30 \r",
"em: w= 15, psites= 32, iter= 0 \r",
"em: w= 15, psites= 32, iter= 10 \r",
"em: w= 15, psites= 32, iter= 20 \r",
"em: w= 15, psites= 32, iter= 30 \r",
"em: w= 15, psites= 32, iter= 40 \r",
"em: w= 15, psites= 50, iter= 0 \r",
"em: w= 15, psites= 50, iter= 10 \r",
"em: w= 15, psites= 50, iter= 20 \r",
"em: w= 15, psites= 50, iter= 30 \r",
"em: w= 15, psites= 50, iter= 40 \r",
"em: w= 21, psites= 2, iter= 0 \r",
"em: w= 21, psites= 4, iter= 0 \r",
"em: w= 21, psites= 4, iter= 10 \r",
"em: w= 21, psites= 4, iter= 20 \r",
"em: w= 21, psites= 4, iter= 30 \r",
"em: w= 21, psites= 8, iter= 0 \r",
"em: w= 21, psites= 8, iter= 10 \r",
"em: w= 21, psites= 8, iter= 20 \r",
"em: w= 21, psites= 16, iter= 0 \r",
"em: w= 21, psites= 16, iter= 10 \r",
"em: w= 21, psites= 16, iter= 20 \r",
"em: w= 21, psites= 32, iter= 0 \r",
"em: w= 21, psites= 32, iter= 10 \r",
"em: w= 21, psites= 32, iter= 20 \r",
"em: w= 21, psites= 50, iter= 0 \r",
"em: w= 21, psites= 50, iter= 10 \r",
"em: w= 21, psites= 50, iter= 20 \r",
"em: w= 21, psites= 50, iter= 30 \r",
"em: w= 21, psites= 50, iter= 40 \r",
"em: w= 29, psites= 2, iter= 0 \r",
"em: w= 29, psites= 4, iter= 0 \r",
"em: w= 29, psites= 8, iter= 0 \r",
"em: w= 29, psites= 8, iter= 10 \r",
"em: w= 29, psites= 16, iter= 0 \r",
"em: w= 29, psites= 16, iter= 10 \r",
"em: w= 29, psites= 16, iter= 20 \r",
"em: w= 29, psites= 32, iter= 0 \r",
"em: w= 29, psites= 32, iter= 10 \r",
"em: w= 29, psites= 32, iter= 20 \r",
"em: w= 41, psites= 2, iter= 0 \r",
"em: w= 41, psites= 4, iter= 0 \r",
"em: w= 41, psites= 8, iter= 0 \r",
"em: w= 41, psites= 16, iter= 0 \r",
"em: w= 41, psites= 32, iter= 0 \r",
"em: w= 41, psites= 32, iter= 10 \r",
"em: w= 50, psites= 2, iter= 0 \r",
"em: w= 50, psites= 4, iter= 0 \r",
"em: w= 50, psites= 8, iter= 0 \r",
"em: w= 50, psites= 16, iter= 0 \n",
"motif=2\n",
"SEED WIDTHS: 8 11 15 21 29 41 50\n",
"starts: w=8, seq=0, l=105 \r",
"starts: w=8, seq=5, l=105 \r",
"starts: w=8, seq=10, l=105 \r",
"starts: w=8, seq=15, l=105 \r",
"starts: w=11, seq=0, l=105 \r",
"starts: w=11, seq=5, l=105 \r",
"starts: w=11, seq=10, l=105 \r",
"starts: w=11, seq=15, l=105 \r",
"starts: w=15, seq=0, l=105 \r",
"starts: w=15, seq=5, l=105 \r",
"starts: w=15, seq=10, l=105 \r",
"starts: w=15, seq=15, l=105 \r",
"starts: w=21, seq=0, l=105 \r",
"starts: w=21, seq=5, l=105 \r",
"starts: w=21, seq=10, l=105 \r",
"starts: w=21, seq=15, l=105 \r",
"starts: w=29, seq=0, l=105 \r",
"starts: w=29, seq=5, l=105 \r",
"starts: w=29, seq=10, l=105 \r",
"starts: w=29, seq=15, l=105 \r",
"starts: w=41, seq=0, l=105 \r",
"starts: w=41, seq=5, l=105 \r",
"starts: w=41, seq=10, l=105 \r",
"starts: w=41, seq=15, l=105 \r",
"starts: w=50, seq=0, l=105 \r",
"starts: w=50, seq=5, l=105 \r",
"starts: w=50, seq=10, l=105 \r",
"starts: w=50, seq=15, l=105 \r",
"\r",
"em: w= 8, psites= 2, iter= 0 \r",
"em: w= 8, psites= 2, iter= 10 \r",
"em: w= 8, psites= 2, iter= 20 \r",
"em: w= 8, psites= 4, iter= 0 \r",
"em: w= 8, psites= 4, iter= 10 \r",
"em: w= 8, psites= 4, iter= 20 \r",
"em: w= 8, psites= 4, iter= 30 \r",
"em: w= 8, psites= 4, iter= 40 \r",
"em: w= 8, psites= 8, iter= 0 \r",
"em: w= 8, psites= 8, iter= 10 \r",
"em: w= 8, psites= 8, iter= 20 \r",
"em: w= 8, psites= 8, iter= 30 \r",
"em: w= 8, psites= 8, iter= 40 \r",
"em: w= 8, psites= 16, iter= 0 \r",
"em: w= 8, psites= 16, iter= 10 \r",
"em: w= 8, psites= 16, iter= 20 \r",
"em: w= 8, psites= 16, iter= 30 \r",
"em: w= 8, psites= 16, iter= 40 \r",
"em: w= 8, psites= 32, iter= 0 \r",
"em: w= 8, psites= 32, iter= 10 \r",
"em: w= 8, psites= 32, iter= 20 \r",
"em: w= 8, psites= 32, iter= 30 \r",
"em: w= 8, psites= 32, iter= 40 \r",
"em: w= 8, psites= 50, iter= 0 \r",
"em: w= 8, psites= 50, iter= 10 \r",
"em: w= 8, psites= 50, iter= 20 \r",
"em: w= 8, psites= 50, iter= 30 \r",
"em: w= 8, psites= 50, iter= 40 \r",
"em: w= 11, psites= 2, iter= 0 \r",
"em: w= 11, psites= 2, iter= 10 \r",
"em: w= 11, psites= 4, iter= 0 \r",
"em: w= 11, psites= 4, iter= 10 \r",
"em: w= 11, psites= 4, iter= 20 \r",
"em: w= 11, psites= 4, iter= 30 \r",
"em: w= 11, psites= 8, iter= 0 \r",
"em: w= 11, psites= 8, iter= 10 \r",
"em: w= 11, psites= 8, iter= 20 \r",
"em: w= 11, psites= 8, iter= 30 \r",
"em: w= 11, psites= 16, iter= 0 \r",
"em: w= 11, psites= 16, iter= 10 \r",
"em: w= 11, psites= 16, iter= 20 \r",
"em: w= 11, psites= 16, iter= 30 \r",
"em: w= 11, psites= 16, iter= 40 \r",
"em: w= 11, psites= 32, iter= 0 \r",
"em: w= 11, psites= 32, iter= 10 \r",
"em: w= 11, psites= 32, iter= 20 \r",
"em: w= 11, psites= 32, iter= 30 \r",
"em: w= 11, psites= 32, iter= 40 \r",
"em: w= 11, psites= 50, iter= 0 \r",
"em: w= 11, psites= 50, iter= 10 \r",
"em: w= 11, psites= 50, iter= 20 \r",
"em: w= 11, psites= 50, iter= 30 \r",
"em: w= 11, psites= 50, iter= 40 \r",
"em: w= 15, psites= 2, iter= 0 \r",
"em: w= 15, psites= 4, iter= 0 \r",
"em: w= 15, psites= 4, iter= 10 \r",
"em: w= 15, psites= 8, iter= 0 \r",
"em: w= 15, psites= 16, iter= 0 \r",
"em: w= 15, psites= 16, iter= 10 \r",
"em: w= 15, psites= 16, iter= 20 \r",
"em: w= 15, psites= 16, iter= 30 \r",
"em: w= 15, psites= 32, iter= 0 \r",
"em: w= 15, psites= 32, iter= 10 \r",
"em: w= 15, psites= 32, iter= 20 \r",
"em: w= 15, psites= 50, iter= 0 \r",
"em: w= 15, psites= 50, iter= 10 \r",
"em: w= 15, psites= 50, iter= 20 \r",
"em: w= 15, psites= 50, iter= 30 \r",
"em: w= 15, psites= 50, iter= 40 \r",
"em: w= 21, psites= 2, iter= 0 \r",
"em: w= 21, psites= 4, iter= 0 \r",
"em: w= 21, psites= 8, iter= 0 \r",
"em: w= 21, psites= 8, iter= 10 \r",
"em: w= 21, psites= 16, iter= 0 \r",
"em: w= 21, psites= 16, iter= 10 \r",
"em: w= 21, psites= 32, iter= 0 \r",
"em: w= 21, psites= 32, iter= 10 \r",
"em: w= 21, psites= 50, iter= 0 \r",
"em: w= 21, psites= 50, iter= 10 \r",
"em: w= 21, psites= 50, iter= 20 \r",
"em: w= 21, psites= 50, iter= 30 \r",
"em: w= 21, psites= 50, iter= 40 \r",
"em: w= 29, psites= 2, iter= 0 \r",
"em: w= 29, psites= 4, iter= 0 \r",
"em: w= 29, psites= 8, iter= 0 \r",
"em: w= 29, psites= 8, iter= 10 \r",
"em: w= 29, psites= 16, iter= 0 \r",
"em: w= 29, psites= 32, iter= 0 \r",
"em: w= 29, psites= 32, iter= 10 \r",
"em: w= 41, psites= 2, iter= 0 \r",
"em: w= 41, psites= 4, iter= 0 \r",
"em: w= 41, psites= 8, iter= 0 \r",
"em: w= 41, psites= 16, iter= 0 \r",
"em: w= 41, psites= 32, iter= 0 \r",
"em: w= 50, psites= 2, iter= 0 \r",
"em: w= 50, psites= 4, iter= 0 \r",
"em: w= 50, psites= 8, iter= 0 \r",
"em: w= 50, psites= 16, iter= 0 \n",
"motif=3\n",
"SEED WIDTHS: 8 11 15 21 29 41 50\n",
"starts: w=8, seq=0, l=105 \r",
"starts: w=8, seq=5, l=105 \r",
"starts: w=8, seq=10, l=105 \r",
"starts: w=8, seq=15, l=105 \r",
"starts: w=11, seq=0, l=105 \r",
"starts: w=11, seq=5, l=105 \r",
"starts: w=11, seq=10, l=105 \r",
"starts: w=11, seq=15, l=105 \r",
"starts: w=15, seq=0, l=105 \r",
"starts: w=15, seq=5, l=105 \r",
"starts: w=15, seq=10, l=105 \r",
"starts: w=15, seq=15, l=105 \r",
"starts: w=21, seq=0, l=105 \r",
"starts: w=21, seq=5, l=105 \r",
"starts: w=21, seq=10, l=105 \r",
"starts: w=21, seq=15, l=105 \r",
"starts: w=29, seq=0, l=105 \r",
"starts: w=29, seq=5, l=105 \r",
"starts: w=29, seq=10, l=105 \r",
"starts: w=29, seq=15, l=105 \r",
"starts: w=41, seq=0, l=105 \r",
"starts: w=41, seq=5, l=105 \r",
"starts: w=41, seq=10, l=105 \r",
"starts: w=41, seq=15, l=105 \r",
"starts: w=50, seq=0, l=105 \r",
"starts: w=50, seq=5, l=105 \r",
"starts: w=50, seq=10, l=105 \r",
"starts: w=50, seq=15, l=105 \r",
"\r",
"em: w= 8, psites= 2, iter= 0 \r",
"em: w= 8, psites= 2, iter= 10 \r",
"em: w= 8, psites= 4, iter= 0 \r",
"em: w= 8, psites= 4, iter= 10 \r",
"em: w= 8, psites= 4, iter= 20 \r",
"em: w= 8, psites= 4, iter= 30 \r",
"em: w= 8, psites= 4, iter= 40 \r",
"em: w= 8, psites= 8, iter= 0 \r",
"em: w= 8, psites= 8, iter= 10 \r",
"em: w= 8, psites= 8, iter= 20 \r",
"em: w= 8, psites= 8, iter= 30 \r",
"em: w= 8, psites= 8, iter= 40 \r",
"em: w= 8, psites= 16, iter= 0 \r",
"em: w= 8, psites= 16, iter= 10 \r",
"em: w= 8, psites= 16, iter= 20 \r",
"em: w= 8, psites= 16, iter= 30 \r",
"em: w= 8, psites= 16, iter= 40 \r",
"em: w= 8, psites= 32, iter= 0 \r",
"em: w= 8, psites= 32, iter= 10 \r",
"em: w= 8, psites= 32, iter= 20 \r",
"em: w= 8, psites= 32, iter= 30 \r",
"em: w= 8, psites= 32, iter= 40 \r",
"em: w= 8, psites= 50, iter= 0 \r",
"em: w= 8, psites= 50, iter= 10 \r",
"em: w= 8, psites= 50, iter= 20 \r",
"em: w= 8, psites= 50, iter= 30 \r",
"em: w= 8, psites= 50, iter= 40 \r",
"em: w= 11, psites= 2, iter= 0 \r",
"em: w= 11, psites= 4, iter= 0 \r",
"em: w= 11, psites= 4, iter= 10 \r",
"em: w= 11, psites= 8, iter= 0 \r",
"em: w= 11, psites= 8, iter= 10 \r",
"em: w= 11, psites= 8, iter= 20 \r",
"em: w= 11, psites= 16, iter= 0 \r",
"em: w= 11, psites= 16, iter= 10 \r",
"em: w= 11, psites= 16, iter= 20 \r",
"em: w= 11, psites= 16, iter= 30 \r",
"em: w= 11, psites= 16, iter= 40 \r",
"em: w= 11, psites= 32, iter= 0 \r",
"em: w= 11, psites= 32, iter= 10 \r",
"em: w= 11, psites= 32, iter= 20 \r",
"em: w= 11, psites= 32, iter= 30 \r",
"em: w= 11, psites= 32, iter= 40 \r",
"em: w= 11, psites= 50, iter= 0 \r",
"em: w= 11, psites= 50, iter= 10 \r",
"em: w= 11, psites= 50, iter= 20 \r",
"em: w= 11, psites= 50, iter= 30 \r",
"em: w= 11, psites= 50, iter= 40 \r",
"em: w= 15, psites= 2, iter= 0 \r",
"em: w= 15, psites= 4, iter= 0 \r",
"em: w= 15, psites= 8, iter= 0 \r",
"em: w= 15, psites= 8, iter= 10 \r",
"em: w= 15, psites= 16, iter= 0 \r",
"em: w= 15, psites= 16, iter= 10 \r",
"em: w= 15, psites= 16, iter= 20 \r",
"em: w= 15, psites= 32, iter= 0 \r",
"em: w= 15, psites= 32, iter= 10 \r",
"em: w= 15, psites= 32, iter= 20 \r",
"em: w= 15, psites= 32, iter= 30 \r",
"em: w= 15, psites= 32, iter= 40 \r",
"em: w= 15, psites= 50, iter= 0 \r",
"em: w= 15, psites= 50, iter= 10 \r",
"em: w= 15, psites= 50, iter= 20 \r",
"em: w= 15, psites= 50, iter= 30 \r",
"em: w= 15, psites= 50, iter= 40 \r",
"em: w= 21, psites= 2, iter= 0 \r",
"em: w= 21, psites= 4, iter= 0 \r",
"em: w= 21, psites= 8, iter= 0 \r",
"em: w= 21, psites= 8, iter= 10 \r",
"em: w= 21, psites= 16, iter= 0 \r",
"em: w= 21, psites= 16, iter= 10 \r",
"em: w= 21, psites= 16, iter= 20 \r",
"em: w= 21, psites= 32, iter= 0 \r",
"em: w= 21, psites= 32, iter= 10 \r",
"em: w= 21, psites= 32, iter= 20 \r",
"em: w= 21, psites= 32, iter= 30 \r",
"em: w= 21, psites= 50, iter= 0 \r",
"em: w= 21, psites= 50, iter= 10 \r",
"em: w= 21, psites= 50, iter= 20 \r",
"em: w= 21, psites= 50, iter= 30 \r",
"em: w= 21, psites= 50, iter= 40 \r",
"em: w= 29, psites= 2, iter= 0 \r",
"em: w= 29, psites= 4, iter= 0 \r",
"em: w= 29, psites= 8, iter= 0 \r",
"em: w= 29, psites= 16, iter= 0 \r",
"em: w= 29, psites= 32, iter= 0 \r",
"em: w= 41, psites= 2, iter= 0 \r",
"em: w= 41, psites= 4, iter= 0 \r",
"em: w= 41, psites= 8, iter= 0 \r",
"em: w= 41, psites= 16, iter= 0 \r",
"em: w= 41, psites= 32, iter= 0 \r",
"em: w= 50, psites= 2, iter= 0 \r",
"em: w= 50, psites= 4, iter= 0 \r",
"em: w= 50, psites= 8, iter= 0 \r",
"em: w= 50, psites= 16, iter= 0 \n",
"\n"
]
}
],
"source": [
"from utilities import Weblogo\n",
"wl = Weblogo(color_scheme='classic')\n",
"meme1 = Meme(alphabet=\"dna\", # {ACGT}\n",
" gap_in_alphabet=False,\n",
" mod=\"anr\", # Any number of repititions\n",
" output_dir=\"meme_anr\", \n",
" nmotifs=3, # Number of motives to be found\n",
" \n",
" weblogo_obj = wl\n",
" )\n",
"\n",
"meme1.fit(fasta_file=\"seq18.fa\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 1, 2]\n",
"[0, 1]\n",
"[0]\n",
"[0]\n",
"[0]\n",
"[0]\n",
"[0, 0, 1]\n",
"[0]\n",
"[0, 2]\n"
]
}
],
"source": [
"predictions = meme1.predict(input_seqs=test, return_list=True)\n",
"for p in predictions: print p"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n",
"2\n",
"1\n",
"1\n",
"1\n",
"1\n",
"3\n",
"1\n",
"2\n"
]
}
],
"source": [
"predictions = meme1.predict(input_seqs=\"seq9.fa\", return_list=False)\n",
"for p in predictions: print p"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[(64, 83, 8.78949776345788e-07)], [(30, 44, 0.0008573388203017831)], [(91, 105, 0.125)]]\n",
"[[(58, 77, 5.4842034364432564e-08)], [(36, 50, 0.000285779606767261)], []]\n",
"[[(79, 98, 1.7480898453662876e-07)], [], []]\n",
"[[(66, 85, 7.731576313195523e-08)], [], []]\n",
"[[(53, 72, 6.40325950683622e-08)], [], []]\n",
"[[(10, 29, 5.043839197613452e-07)], [], []]\n",
"[[(27, 46, 8.238203820897798e-08), (54, 73, 1.180518625128653e-08)], [(3, 17, 0.0004572473708276176)], []]\n",
"[[(42, 61, 6.830143473958632e-09)], [], []]\n",
"[[(12, 31, 8.324237358887084e-07)], [], [(37, 51, 0.125)]]\n"
]
}
],
"source": [
"match = meme1.transform(input_seqs=test, return_match=True)\n",
"for m in match: print m"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 1, 1]\n",
"[1, 1, 0]\n",
"[1, 0, 0]\n",
"[1, 0, 0]\n",
"[1, 0, 0]\n",
"[1, 0, 0]\n",
"[1, 1, 0]\n",
"[1, 0, 0]\n",
"[1, 0, 1]\n"
]
}
],
"source": [
"match = meme1.transform(input_seqs=test, return_match=False)\n",
"for m in match: print m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>E-value of each motif</h3>"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1.4e-05, 310.0, 1300.0]\n"
]
}
],
"source": [
"print meme1.e_values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2>fit_predict() and fit_transform() example</h2>"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"meme2 = Meme(alphabet=\"dna\", mod=\"anr\", nmotifs=3)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The output directory 'meme_out' already exists.\n",
"Its contents will be overwritten.\n",
"Initializing the motif probability tables for 2 to 50 sites...\n",
"nsites = 50\n",
"Done initializing.\n",
"SEEDS: highwater mark: seq 17 pos 105\n",
"\n",
"seqs= 18, min= 105, max= 105, total= 1890\n",
"\n",
"motif=1\n",
"SEED WIDTHS: 8 11 15 21 29 41 50\n",
"em: w= 50, psites= 16, iter= 0 \n",
"motif=2\n",
"SEED WIDTHS: 8 11 15 21 29 41 50\n",
"em: w= 50, psites= 16, iter= 0 \n",
"motif=3\n",
"SEED WIDTHS: 8 11 15 21 29 41 50\n",
"em: w= 50, psites= 16, iter= 0 \n",
"\n",
"[0, 1, 2]\n",
"[0, 1]\n",
"[0]\n",
"[0]\n",
"[0]\n",
"[0]\n",
"[0, 0, 1]\n",
"[0]\n",
"[0, 2]\n",
"[0]\n",
"[0, 1]\n",
"[0]\n",
"[0]\n",
"[0]\n",
"[0]\n",
"[0, 1]\n",
"[1]\n",
"[0]\n"
]
}
],
"source": [
"predictions = meme2.fit_predict(fasta_file=\"seq18.fa\", return_list=True)\n",
"for p in predictions: print p"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The output directory 'meme_out' already exists.\n",
"Its contents will be overwritten.\n",
"Initializing the motif probability tables for 2 to 50 sites...\n",
"nsites = 50\n",
"Done initializing.\n",
"SEEDS: highwater mark: seq 17 pos 105\n",
"\n",
"seqs= 18, min= 105, max= 105, total= 1890\n",
"\n",
"motif=1\n",
"SEED WIDTHS: 8 11 15 21 29 41 50\n",
"em: w= 50, psites= 16, iter= 0 \n",
"motif=2\n",
"SEED WIDTHS: 8 11 15 21 29 41 50\n",
"em: w= 50, psites= 16, iter= 0 \n",
"motif=3\n",
"SEED WIDTHS: 8 11 15 21 29 41 50\n",
"em: w= 50, psites= 16, iter= 0 \n",
"\n",
"[[(64, 83, 8.78949776345788e-07)], [(30, 44, 0.0008573388203017831)], [(91, 105, 0.125)]]\n",
"[[(58, 77, 5.4842034364432564e-08)], [(36, 50, 0.000285779606767261)], []]\n",
"[[(79, 98, 1.7480898453662876e-07)], [], []]\n",
"[[(66, 85, 7.731576313195523e-08)], [], []]\n",
"[[(53, 72, 6.40325950683622e-08)], [], []]\n",
"[[(10, 29, 5.043839197613452e-07)], [], []]\n",
"[[(27, 46, 8.238203820897798e-08), (54, 73, 1.180518625128653e-08)], [(3, 17, 0.0004572473708276176)], []]\n",
"[[(42, 61, 6.830143473958632e-09)], [], []]\n",
"[[(12, 31, 8.324237358887084e-07)], [], [(37, 51, 0.125)]]\n",
"[[(17, 36, 1.369133305461708e-06)], [], []]\n",
"[[(64, 83, 8.408320564532407e-09)], [(15, 29, 0.00019051973784484067)], []]\n",
"[[(44, 63, 3.42283326365427e-08)], [], []]\n",
"[[(51, 70, 3.2631010446837377e-07)], [], []]\n",
"[[(74, 93, 9.668447539803668e-07)], [], []]\n",
"[[(20, 39, 1.9619414650575618e-08)], [], []]\n",
"[[(56, 75, 3.9842503598092026e-07)], [(81, 95, 0.010288065843621397)], []]\n",
"[[], [(79, 93, 0.00021433470507544578)], []]\n",
"[[(81, 100, 5.949813870648408e-07)], [], []]\n"
]
}
],
"source": [
"matches = meme2.fit_transform(fasta_file=\"seq18.fa\", return_match=True)\n",
"for m in matches: print m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>Print motives as lists</h3>"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('male', 'TGTAACAGAGATCACACAA')\n",
"('ompa', 'CCTGACGGAGTTCACACTT')\n",
"('lac', 'TGTGAGTTAGCTCACTCAT')\n",
"('tdc', 'TGTGAGTGGTCGCACATAT')\n",
"('pbr322', 'TGTGAAATACCGCACAGAT')\n",
"('tnaa', 'TGTGATTCGATTCACATTT')\n",
"('deop2', 'TTTGAACCAGATCGCATTA')\n",
"('ce1cg', 'TTTGATCGTTTTCACAAAA')\n",
"('ara', 'TTTGCACGGCGTCACACTT')\n",
"('bglr1', 'TGTGAGCATGGTCATATTT')\n",
"('crp', 'TGCAAAGGACGTCACATTA')\n",
"('malt', 'TGTGACACAGTGCAAATTC')\n",
"('gale', 'TGTAAACGATTCCACTAAT')\n",
"('cya', 'TGTTAAATTGATCACGTTT')\n",
"('uxu1', 'TGTGATGTGGTTAACCCAA')\n",
"('ilv', 'CGTGATCAACCCCTCAATT')\n",
"('gale', 'TGTCACACTTTTCGCATCT')\n",
"('malk', 'CGTGATGTTGCTTGCAAAA')\n",
"\n",
"('pbr322', 'GGAGAAAATACCGC')\n",
"('ce1cg', 'GGCGAGAATAGCGC')\n",
"('gale', 'GCATAAAAAACGGC')\n",
"('malk', 'GATGAGAACACGGC')\n",
"('ara', 'GCAGAAAAGTCCAC')\n",
"('trn9cat', 'GGCGAAAATGAGAC')\n",
"\n",
"('lac', 'CCCCAGGCTTTACA')\n",
"('ce1cg', 'CCACAGTCTTGACA')\n",
"\n"
]
}
],
"source": [
"#printing motives as lists\n",
"for motif in meme1.motives_list:\n",
" for m in motif:\n",
" print m\n",
" print"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>Display Sequence logo of un-aligned motives</h3>"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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QaCE7nLBw8GGtwyIOPhBkq+14M2MS+wnEAVjHi9SSanE/qivnzBYF66lW2eXJ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\njHWJX/+sq3d17ZWqUMQywQ8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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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ZzyVmStE1I/2yVDYIR8RasabFxcOhobRwzjNdwQHGrutiFAQbJIdhyBiLosiy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\nJHpRueYB3lmTNiYLIn3C0u1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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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PEXRGqW/Zpy1ZliIGJPNttA2r0W51ts4UfUxfITYQz/PW6/V6vcbI3gN0i4RGXR5KeJLx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\n0IeHkp4AAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"meme1.display_logo(do_alignment=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>Display Logo of specified motif</h3>"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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QaCE7nLBw8GGtwyIOPhBkq+14M2MS+wnEAVjHi9SSanE/qivnzBYF66lW2eXJ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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"meme1.display_logo(motif_num=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>Multiple Sequence Alignment of motives with Muscle</h3>\n",
"Note: Motives in this example were already aligned, hence no dashes appear in the alignment"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('male', 'TGTAACAGAGATCACACAA')\n",
"('ompa', 'CCTGACGGAGTTCACACTT')\n",
"('lac', 'TGTGAGTTAGCTCACTCAT')\n",
"('tdc', 'TGTGAGTGGTCGCACATAT')\n",
"('pbr322', 'TGTGAAATACCGCACAGAT')\n",
"('tnaa', 'TGTGATTCGATTCACATTT')\n",
"('deop2', 'TTTGAACCAGATCGCATTA')\n",
"('ce1cg', 'TTTGATCGTTTTCACAAAA')\n",
"('ara', 'TTTGCACGGCGTCACACTT')\n",
"('bglr1', 'TGTGAGCATGGTCATATTT')\n",
"('crp', 'TGCAAAGGACGTCACATTA')\n",
"('malt', 'TGTGACACAGTGCAAATTC')\n",
"('gale', 'TGTAAACGATTCCACTAAT')\n",
"('cya', 'TGTTAAATTGATCACGTTT')\n",
"('uxu1', 'TGTGATGTGGTTAACCCAA')\n",
"('ilv', 'CGTGATCAACCCCTCAATT')\n",
"('gale', 'TGTCACACTTTTCGCATCT')\n",
"('malk', 'CGTGATGTTGCTTGCAAAA')\n",
"\n",
"('pbr322', 'GGAGAAAATACCGC')\n",
"('ce1cg', 'GGCGAGAATAGCGC')\n",
"('gale', 'GCATAAAAAACGGC')\n",
"('malk', 'GATGAGAACACGGC')\n",
"('ara', 'GCAGAAAAGTCCAC')\n",
"('trn9cat', 'GGCGAAAATGAGAC')\n",
"\n",
"('lac', 'CCCCAGGCTTTACA')\n",
"('ce1cg', 'CCACAGTCTTGACA')\n",
"\n"
]
}
],
"source": [
"meme1.align_motives() #MSA with Muscle\n",
"motives1=meme1.aligned_motives_list\n",
"for m in motives1:\n",
" for i in m:\n",
" print i\n",
" print"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>Display sequence logo of aligned motives</h3>"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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QaCE7nLBw8GGtwyIOPhBkq+14M2MS+wnEAVjHi9SSanE/qivnzBYF66lW2eXJ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\njHWJX/+sq3d17ZWqUMQywQ8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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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ZzyVmStE1I/2yVDYIR8RasabFxcOhobRwzjNdwQHGrutiFAQbJIdhyBiLosiy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\nJHpRueYB3lmTNiYLIn3C0u1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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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PEXRGqW/Zpy1ZliIGJPNttA2r0W51ts4UfUxfITYQz/PW6/V6vcbI3gN0i4RGXR5KeJLx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\n4lGf8tvtNsuyKIr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A2l/NHirr0SPtrxtjx5ko0mB1xt7BMRcx5Fpa6HTLp/qy9OTtRdsB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\n0IeHkp4AAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"meme1.display_logo(do_alignment=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>Position Weight Matrices for motifs</h3>"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18\n",
"A: 0.05 0.05 0.05 0.18 0.82 0.32 0.27 0.14 0.45 0.09 0.18 0.05 0.09 0.68 0.09 0.68 0.23 0.41 0.32\n",
"C: 0.18 0.09 0.09 0.09 0.09 0.23 0.32 0.23 0.05 0.23 0.27 0.14 0.77 0.05 0.77 0.09 0.27 0.09 0.09\n",
"G: 0.05 0.68 0.05 0.64 0.05 0.18 0.23 0.36 0.23 0.45 0.18 0.18 0.05 0.18 0.05 0.09 0.09 0.05 0.05\n",
"T: 0.73 0.18 0.82 0.09 0.05 0.27 0.18 0.27 0.27 0.23 0.36 0.64 0.09 0.09 0.09 0.14 0.41 0.45 0.55\n",
"\n",
" 0 1 2 3 4 5 6 7 8 9 10 11 12 13\n",
"A: 0.10 0.20 0.40 0.10 0.70 0.50 0.70 0.70 0.20 0.50 0.20 0.10 0.30 0.10\n",
"C: 0.10 0.30 0.30 0.10 0.10 0.10 0.10 0.10 0.20 0.10 0.50 0.40 0.10 0.70\n",
"G: 0.70 0.40 0.10 0.60 0.10 0.30 0.10 0.10 0.20 0.20 0.20 0.40 0.50 0.10\n",
"T: 0.10 0.10 0.20 0.20 0.10 0.10 0.10 0.10 0.40 0.20 0.10 0.10 0.10 0.10\n",
"\n",
" 0 1 2 3 4 5 6 7 8 9 10 11 12 13\n",
"A: 0.17 0.17 0.33 0.17 0.50 0.17 0.17 0.17 0.17 0.17 0.17 0.50 0.17 0.50\n",
"C: 0.50 0.50 0.33 0.50 0.17 0.17 0.17 0.50 0.17 0.17 0.17 0.17 0.50 0.17\n",
"G: 0.17 0.17 0.17 0.17 0.17 0.50 0.33 0.17 0.17 0.17 0.33 0.17 0.17 0.17\n",
"T: 0.17 0.17 0.17 0.17 0.17 0.17 0.33 0.17 0.50 0.50 0.33 0.17 0.17 0.17\n",
"\n"
]
}
],
"source": [
"meme1.display()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[array([[ 0. , 0. , 0. , 0.16666667, 0.94444444,\n",
" 0.33333333, 0.27777778, 0.11111111, 0.5 , 0.05555556,\n",
" 0.16666667, 0. , 0.05555556, 0.77777778, 0.05555556,\n",
" 0.77777778, 0.22222222, 0.44444444, 0.33333333],\n",
" [ 0.16666667, 0.05555556, 0.05555556, 0.05555556, 0.05555556,\n",
" 0.22222222, 0.33333333, 0.22222222, 0. , 0.22222222,\n",
" 0.27777778, 0.11111111, 0.88888889, 0. , 0.88888889,\n",
" 0.05555556, 0.27777778, 0.05555556, 0.05555556],\n",
" [ 0. , 0.77777778, 0. , 0.72222222, 0. ,\n",
" 0.16666667, 0.22222222, 0.38888889, 0.22222222, 0.5 ,\n",
" 0.16666667, 0.16666667, 0. , 0.16666667, 0. ,\n",
" 0.05555556, 0.05555556, 0. , 0. ],\n",
" [ 0.83333333, 0.16666667, 0.94444444, 0.05555556, 0. ,\n",
" 0.27777778, 0.16666667, 0.27777778, 0.27777778, 0.22222222,\n",
" 0.38888889, 0.72222222, 0.05555556, 0.05555556, 0.05555556,\n",
" 0.11111111, 0.44444444, 0.5 , 0.61111111]]),\n",
" array([[ 0. , 0.16666667, 0.5 , 0. , 1. ,\n",
" 0.66666667, 1. , 1. , 0.16666667, 0.66666667,\n",
" 0.16666667, 0. , 0.33333333, 0. ],\n",
" [ 0. , 0.33333333, 0.33333333, 0. , 0. ,\n",
" 0. , 0. , 0. , 0.16666667, 0. ,\n",
" 0.66666667, 0.5 , 0. , 1. ],\n",
" [ 1. , 0.5 , 0. , 0.83333333, 0. ,\n",
" 0.33333333, 0. , 0. , 0.16666667, 0.16666667,\n",
" 0.16666667, 0.5 , 0.66666667, 0. ],\n",
" [ 0. , 0. , 0.16666667, 0.16666667, 0. ,\n",
" 0. , 0. , 0. , 0.5 , 0.16666667,\n",
" 0. , 0. , 0. , 0. ]]),\n",
" array([[ 0. , 0. , 0.5, 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. ,\n",
" 1. , 0. , 1. ],\n",
" [ 1. , 1. , 0.5, 1. , 0. , 0. , 0. , 1. , 0. , 0. , 0. ,\n",
" 0. , 1. , 0. ],\n",
" [ 0. , 0. , 0. , 0. , 0. , 1. , 0.5, 0. , 0. , 0. , 0.5,\n",
" 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0.5, 0. , 1. , 1. , 0.5,\n",
" 0. , 0. , 0. ]])]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"meme1.matrix()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h4>Display PWM of single motif</h4>"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0 1 2 3 4 5 6 7 8 9 10 11 12 13\n",
"A: 0.00 0.00 0.50 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 1.00\n",
"C: 1.00 1.00 0.50 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 0.00\n",
"G: 0.00 0.00 0.00 0.00 0.00 1.00 0.50 0.00 0.00 0.00 0.50 0.00 0.00 0.00\n",
"T: 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00 1.00 1.00 0.50 0.00 0.00 0.00\n",
"\n"
]
}
],
"source": [
"meme1.display(motif_num=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h4>Scoring a sequence w.r.t a motif</h4>"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.010288065843621397, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.010288065843621397, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.010288065843621397, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.010288065843621397, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.010288065843621397, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.010288065843621397, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.010288065843621397, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.010288065843621397, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.010288065843621397, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.010288065843621397, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
]
}
],
"source": [
"test_seq = 'GGAGAAAATACCGC' * 10\n",
"seq_score = meme1.score(motif_num=2, seq=test_seq)\n",
"print seq_score"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"<h3> Transform with HMM as scoring criteria</h3>"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[(75, 84, 1.149785137630629)], [(79, 88, 1.7480177623272883)], [(58, 67, 1.9982163169366849)]]\n",
"[[(1, 10, 1.149785137630629)], [(13, 22, 1.6640484225879693)], [(92, 101, 1.6588939715039304)]]\n",
"[[(57, 66, 1.149785137630629)], [(67, 76, 1.6861238904884241)], [(23, 32, 1.6227737703669607)]]\n",
"[[(4, 13, 1.149785137630629)], [(4, 13, 1.6699541402508258)], [(82, 91, 1.4225932893334565)]]\n",
"[[(92, 101, 1.3414159939024004)], [(93, 102, 1.7141308591011202)], [(79, 88, 1.9072646534741875)]]\n",
"[[], [(44, 53, 1.64194717163813)], [(79, 88, 1.689241125412223)]]\n",
"[[(4, 13, 1.3414159939024004)], [(4, 13, 1.7141308591011202)], [(21, 30, 1.8345901441201993)]]\n",
"[[(33, 42, 1.149785137630629)], [(71, 80, 1.6861238904884241)], [(13, 22, 1.9982163169366849)]]\n",
"[[], [(6, 15, 1.6301357363124165)], [(71, 80, 1.7801927888747202)]]\n"
]
}
],
"source": [
"meme2 = Meme(alphabet=\"dna\", scoring_criteria=\"hmm\", k=1, threshold=1.0,mod=\"anr\", nmotifs=3, minw=7, maxw=9)\n",
"matches = meme2.fit_transform(fasta_file=\"seq9.fa\", return_match=True)\n",
"for m in matches: print m"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, -230.25850929940458, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"CPU times: user 56 ms, sys: 8 ms, total: 64 ms\n",
"Wall time: 58.2 ms\n"
]
}
],
"source": [
"%%time\n",
"# Markov Model score\n",
"mm_score = meme2.score(motif_num=2, seq=\"ACGT\"*10)\n",
"print mm_score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
maxentile/msm-learn | notebooks/Notes on metastable state identification.ipynb | 1 | 1912 | {
"metadata": {
"name": "",
"signature": "sha256:659fae4faa943c516c5a33e827a8e831c715e72acc8976d91aa18fd5466ce0e8"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*MSTIS*\n",
"\"Adaptive single replica multiple state transition interface sampling\" (Du and Bolhuis, 2013)\n",
"\n",
"- http://scitation.aip.org/content/aip/journal/jcp/139/4/10.1063/1.4813777"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Hierarchical uncoupling-coupling Monte Carlo*\n",
"\n",
"[Fischer, Sch\u00fctte, Deuflhard, Cordes, 2000](http://link.springer.com/chapter/10.1007%2F978-3-642-56080-4_10)\n",
"- Use perturbed dynamics (e.g. high-temperature dynamics) to identify weakly metastable states\n",
"- Explicitly decouple these states\n",
"- Repeat at lower temperature until converged"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Grid-free hierarchical conformational dynamics*\n",
"\n",
"see PhD thesis \"Statistical error estimation and grid-free hierarchical refinement in conformational dynamics\" - [Susanna R\u00f6blitz (ne\u00e9 Kube), 2008](http://www.diss.fu-berlin.de/diss/receive/FUDISS_thesis_000000008079)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Metadynamics*\n",
"\n",
"Bias dynamics away from regions of dense historical sampling via a kernel density estimator trained on all visited states:\n",
"\n",
"http://people.sissa.it/~laio/Research/Res_metadynamics.php"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
ecalio07/enron-paper | dev/svm_accuracy_vs_performance.ipynb | 2 | 2668 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## SVM Focus on Accuracy vs Performance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importing Modules"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"from sklearn import svm\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run Variables Setup If Necessary"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"if 'features_train' not in locals() or globals():\n",
" %run ../dev/environment_setup.ipynb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reduced Variables DataSet (1%)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"features_train_small = features_train[:len(features_train)/100]\n",
"labels_train_small = labels_train[:len(labels_train)/100]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load SVM Classifier with RBF Kernel Parameter"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"clf = svm.SVC(kernel='rbf', C=10000.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train and Predict Data with Full DataSet"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"train_predict_fulldataset (\"Train and Predict Data with Full DataSet\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train and Predict Data with 1% DataSet"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"train_predict (\"Train and Predict Data with 1% DataSet\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
icrtiou/coursera-ML | ex7-kmeans and PCA/4- 2D PCA.ipynb | 1 | 106437 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%reload_ext autoreload\n",
"%autoreload 2\n",
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"sns.set(context=\"notebook\", style=\"white\")\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import scipy.io as sio\n",
"\n",
"import sys\n",
"sys.path.append('..')\n",
"\n",
"from helper import general\n",
"from helper import pca"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# load data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(50, 2)\n"
]
},
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x11264a748>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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mzNGyZcuUk5Oja6+91nQ5AAA4zvgI+amnntKyZcv02GOPacCAAabLAQDACKMj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\nRRfKz2P6jOAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112647dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mat = sio.loadmat('./data/ex7data1.mat')\n",
"X = mat.get('X')\n",
"\n",
"# visualize raw data\n",
"print(X.shape)\n",
"\n",
"sns.lmplot('X1', 'X2', \n",
" data=pd.DataFrame(X, columns=['X1', 'X2']),\n",
" fit_reg=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# normalize data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x106c79668>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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P1Nb2+eMNhw8fVlpaWvtNAQDEiufd4UYxjYwrKir00EMPacmSJUpKSjrl9YkTJ2rbtm0a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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x106c795c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"X_norm = pca.normalize(X)\n",
"\n",
"sns.lmplot('X1', 'X2', \n",
" data=pd.DataFrame(X_norm, columns=['X1', 'X2']),\n",
" fit_reg=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# covariance matrix $\\Sigma$\n",
"<img style=\"float: left;\" src=\"../img/cov_mat.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"this is biased sample covariance matrix, for unbiased version, you need to divide it by $m-1$"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1. , 0.73553038],\n",
" [ 0.73553038, 1. ]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Sigma = pca.covariance_matrix(X_norm) # capital greek Sigma\n",
"Sigma # (n, n)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PCA\n",
"http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.svd.html"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"U, S, V = pca.pca(X_norm)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.70710678, -0.70710678],\n",
" [-0.70710678, 0.70710678]])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"U"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([-0.70710678, -0.70710678])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"u1 = U[0]\n",
"u1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# project data to lower dimension"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1.49631261],\n",
" [-0.92218067],\n",
" [ 1.22439232],\n",
" [ 1.64386173],\n",
" [ 1.2732206 ],\n",
" [-0.97681976],\n",
" [ 1.26881187],\n",
" [-2.34148278],\n",
" [-0.02999141],\n",
" [-0.78171789]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# show top 10 projected data\n",
"Z = pca.project_data(X_norm, U, 1)\n",
"Z[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"http://stackoverflow.com/a/23973562/3943702"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x1163d08d0>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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DIKADAAAAAGAABHQAAAAAAAyAgA4AAAAAgAEQ0AEAAAAAMIDrHF0AAADXylRc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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114636160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 4))\n",
"\n",
"sns.regplot('X1', 'X2', \n",
" data=pd.DataFrame(X_norm, columns=['X1', 'X2']),\n",
" fit_reg=False,\n",
" ax=ax1)\n",
"ax1.set_title('Original dimension')\n",
"\n",
"sns.rugplot(Z, ax=ax2)\n",
"ax2.set_xlabel('Z')\n",
"ax2.set_title('Z dimension')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# recover data to original dimension\n",
"Of course, there would be inevitable information loss if you boost data from lower to higher dimension"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x1166f0e80>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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qqoJMJmv1z12zZg2SkpIQHx9vcO93jo9EZGqmGh8bq0+fPhgyZAiio6Oxb98+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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1163e9fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"X_recover = pca.recover_data(Z, U)\n",
"\n",
"fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(12, 4))\n",
"\n",
"sns.rugplot(Z, ax=ax1)\n",
"ax1.set_title('Z dimension')\n",
"ax1.set_xlabel('Z')\n",
"\n",
"sns.regplot('X1', 'X2', \n",
" data=pd.DataFrame(X_recover, columns=['X1', 'X2']),\n",
" fit_reg=False,\n",
" ax=ax2)\n",
"ax2.set_title(\"2D projection from Z\")\n",
"\n",
"sns.regplot('X1', 'X2', \n",
" data=pd.DataFrame(X_norm, columns=['X1', 'X2']),\n",
" fit_reg=False,\n",
" ax=ax3)\n",
"ax3.set_title('Original dimension')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### the projection from `(X1, X2)` to `Z` could be visualized like this"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img style=\"float: central;\" src=\"../img/pca_projection.png\">"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
ereodeereigeo/dataTritiumWS22 | analisis promedios-Copy1.ipynb | 2 | 59849 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Importamos las librerías necesarias"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Importamos las librerías creadas para trabajar"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import ext_datos as ext\n",
"import procesar as pro\n",
"import time_plot as tplt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Generamos los datasets de todos los días"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* En primer lugar se extraen los datos de todos los archivos\n",
" de cada día y se genera una lista de tablas separadas por motor"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dia1 = ext.extraer_data('dia1')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/rodrigo/dataTritiumWS22\n"
]
}
],
"source": [
"cd .."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dia2 = ext.extraer_data('dia2')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/rodrigo/dataTritiumWS22\n"
]
}
],
"source": [
"cd .."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dia3 = ext.extraer_data('dia3')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/rodrigo/dataTritiumWS22\n"
]
}
],
"source": [
"cd .."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dia4 = ext.extraer_data('dia4')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Se procesan las listas anteriores, se concatenan por motor según\n",
" la hora de los registros y se rellenan los espacios vacíos con\n",
" datos NaN, luego se juntan de costado las tablas (join) y se\n",
" le añade el sufijo _m1 y _m2 para diferenciar las columnas"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"motoresdia1 = pro.procesar(dia1)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"motoresdia2 = pro.procesar(dia2)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"motoresdia3 = pro.procesar(dia3)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"motoresdia4 = pro.procesar(dia4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Cálculo de promedios"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Día 4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Se añade la potencia calculada como $V\\cdot{I}$"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"motoresdia4['pot_m2']=motoresdia4.busCurrent_m2*motoresdia4.busVoltage_m2"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"motoresdia4['pot_m1']=motoresdia4.busCurrent_m1*motoresdia4.busVoltage_m1"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"motoresdia3['pot_m2']=motoresdia3.busCurrent_m2*motoresdia3.busVoltage_m2\n",
"motoresdia3['pot_m1']=motoresdia3.busCurrent_m1*motoresdia3.busVoltage_m1"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"motoresdia2['pot_m2']=motoresdia2.busCurrent_m2*motoresdia2.busVoltage_m2\n",
"motoresdia2['pot_m1']=motoresdia2.busCurrent_m1*motoresdia2.busVoltage_m1"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"motoresdia1['pot_m2']=motoresdia1.busCurrent_m2*motoresdia1.busVoltage_m2\n",
"motoresdia1['pot_m1']=motoresdia1.busCurrent_m1*motoresdia1.busVoltage_m1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Se calcula la potencia promedio de todos los datos positivos que se obtienen de cada motor"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1343.2712991041869"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"promediodia4_pot_m1_todos_positivos=motoresdia4[motoresdia4.pot_m1>0].pot_m1.dropna().mean()\n",
"promediodia4_pot_m1_todos_positivos"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1340.9836630260627"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"promediodia4_pot_m1_algunos_positivos = motoresdia4[motoresdia4.pot_m1>0].dropna().pot_m1.mean()\n",
"promediodia4_pot_m1_algunos_positivos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Se observa que la variación de potencia promedio tomando los datos solo entre aquellos en donde existe información de ambos motores no es significativa respecto a la potencia promedio tomando el total de datos de cada motor por separado, sin usar la muestra que coincida en espacio temporal"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1130.389884273317"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"promediodia4_pot_m2_todos_positivos = motoresdia4[motoresdia4.pot_m2>0].pot_m2.dropna().mean()\n",
"promediodia4_pot_m2_todos_positivos"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1129.9650190058408"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"promediodia4_pot_m2_algunos_positivos = motoresdia4[motoresdia4.pot_m2>0].dropna().pot_m2.mean()\n",
"promediodia4_pot_m2_algunos_positivos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Se verifica con el segundo motor, que es el que tiene menos potencia y menos datos, en el día 2 es un 80% y el primero casi el 100%, en los otros días puede variar la apreciación"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Potencia promedio consumida con 360 Kg último día ambos motores solo acelerendo, pot > 0"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2470.9486820319034"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pot_prom_ambos_dia4 = promediodia4_pot_m1_algunos_positivos+promediodia4_pot_m2_algunos_positivos\n",
"pot_prom_ambos_dia4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Potencia promedio entregada por regeneración"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-749.66945667315588"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"promediodia4_pot_m1_algunos_negativos = motoresdia4[motoresdia4.pot_m1<0].dropna().pot_m1.mean()\n",
"promediodia4_pot_m1_algunos_negativos "
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-686.84717211990721"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"promediodia4_pot_m2_algunos_negativos = motoresdia4[motoresdia4.pot_m2<0].dropna().pot_m2.mean()\n",
"promediodia4_pot_m2_algunos_negativos "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-1436.5166287930631"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pot_prom_reg_ambos_dia4 = promediodia4_pot_m1_algunos_negativos+promediodia4_pot_m2_algunos_negativos\n",
"pot_prom_reg_ambos_dia4"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.58136238896381731"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"abs(pot_prom_reg_ambos_dia4/pot_prom_ambos_dia4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Potencia promedio dia 4 consumida y regenerada a la vez"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1109.9504543656908"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"promediodia4_pot_m1_algunos_ambos = motoresdia4[motoresdia4.pot_m1!=0].dropna().pot_m1.mean()\n",
"promediodia4_pot_m1_algunos_ambos "
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"904.29168993214194"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"promediodia4_pot_m2_algunos_ambos = motoresdia4[motoresdia4.pot_m2!=0].dropna().pot_m2.mean()\n",
"promediodia4_pot_m2_algunos_ambos "
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2014.2421442978327"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pot_prom_cons_y_reg = promediodia4_pot_m1_algunos_ambos+promediodia4_pot_m2_algunos_ambos\n",
"pot_prom_cons_y_reg"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Impacto uso de regenerativo ultimo dia de carrera"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.18483044227309209"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1 - pot_prom_cons_y_reg/(pot_prom_ambos_dia4)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f56a18f3410>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"EJFJIvJxez5FRH5St0wOR5m4IaIORwwisjlmL4kdahbF4aiEcXUL4HA0nG8BW4nIHcBC4BWquoOI\n",
"HILZKGl1YCrwbeBlwHswq9C+XVWfFJGtMAuUrYfZMvLDqnpf/9VwOMJxzUEORzxHAX9W1VcDnw+E\n",
"bY/dZQ74BvCMqr4Gs+z4wTbO2cAnVPW19voz+yK1w5ESVxNwOOKRiHOAG1T1H8A/ROQp4HLrPx/Y\n",
"UUTWAN4I/ERk6NLxVQrrcGTFGQGHIz/+xcdW+twrMf+tMcCTthbhcDQS1xzkcMTzLGYv6iwIgKo+\n",
"C/xVRN4NZi17EdmxZPkcjkI4I+BwxKCqjwO/FZH5wEmAN5xOfeeEnHvu9wKHicidwF3AzGoldjiy\n",
"4YaIOhwOxyjG1QQcDodjFOOMgMPhcIxinBFwOByOUYwzAg6HwzGKcUbA4XA4RjHOCDgcDscoxhkB\n",
"h8PhGMU4I+BwOByjmP8HornOhpBDSksAAAAASUVORK5CYII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x7f56a18ddb90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"motoresdia4.pot_m2[10000:30000].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"~~Promedio día 4 incluyendo recargo de celdas.. solo en intervalo donde existen datos de ambos motores~~"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"813.6939774760142"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"promediodia4_pot_m1_algunos_todo = motoresdia4.dropna().pot_m1.mean()\n",
"promediodia4_pot_m1_algunos_todo "
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"662.89368226963484"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"promediodia4_pot_m2_algunos_todo = motoresdia4.dropna().pot_m2.mean()\n",
"promediodia4_pot_m2_algunos_todo "
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1476.587659745649"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"promediototalconsumo = promediodia4_pot_m2_algunos_todo+promediodia4_pot_m1_algunos_todo\n",
"promediototalconsumo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Impacto carga de celdas"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.26692643983954112"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1-promediototalconsumo/pot_prom_cons_y_reg"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Validación en un intervalo de competencia solamente, no luego de llegar y recargar.. solo en carrera"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"975.6521323211665"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"promediodia4_pot_m1_algunos_todo_val = motoresdia4.dropna().pot_m1[0:110000].mean()\n",
"promediodia4_pot_m1_algunos_todo_val "
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"794.53780352317597"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"promediodia4_pot_m2_algunos_todo_val = motoresdia4.dropna().pot_m2[0:110000].mean()\n",
"promediodia4_pot_m2_algunos_todo_val"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1770.1899358443425"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"promediototalconsumo_val = promediodia4_pot_m2_algunos_todo_val+promediodia4_pot_m1_algunos_todo_val\n",
"promediototalconsumo_val"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.12116329168485707"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1-promediototalconsumo_val/pot_prom_cons_y_reg"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'jdj' 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-42-ea27e7b61794>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mjdj\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'jdj' is not defined"
]
}
],
"source": [
"jdj"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1+1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Máximo absoluto día 4"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'maximo_m1' 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-44-ad61be28c1df>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmaximo_m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'maximo_m1' is not defined"
]
}
],
"source": [
"maximo_m1"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"11176"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(motoresdia4[motoresdia4.pot_m1<0].pot_m1.dropna())"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"promedio_pot_m1_algunos=motoresdia4[motoresdia4.pot_m1<0].dropna().pot_m1.mean()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"10825"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(motoresdia4[motoresdia4.pot_m1<0].dropna().pot_m1)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-749.66945667315588"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"motoresdia4[motoresdia4.pot_m1<0].dropna().pot_m1.mean()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-686.84717211990721"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"motoresdia4[motoresdia4.pot_m2<0].dropna().pot_m2.mean()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"904.88479222517333"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"motoresdia4[motoresdia4.pot_m2!=0].pot_m2.dropna().mean()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2473"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1343+1130"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"motoresdia3['pot_m2']=motoresdia3.busCurrent_m2*motoresdia3.busVoltage_m2"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"motoresdia3['pot_m1']=motoresdia3.busCurrent_m1*motoresdia3.busVoltage_m1"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-589.44628614468138"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"motoresdia3[motoresdia3.pot_m1<0].pot_m1.dropna().mean()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-510.71265518321326"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"motoresdia3[motoresdia3.pot_m2<0].pot_m2.dropna().mean()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"motoresdia2['pot_m2']=motoresdia2.busCurrent_m2*motoresdia2.busVoltage_m2"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"motoresdia2['pot_m1']=motoresdia2.busCurrent_m1*motoresdia2.busVoltage_m1"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-1057.9009694831727"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"motoresdia2[motoresdia2.pot_m1<0].pot_m1.dropna().mean()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-1102.4473712984573"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"motoresdia2[motoresdia2.pot_m2<0].pot_m2.dropna().mean()"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"motoresdia1['pot_m2']=motoresdia1.busCurrent_m2*motoresdia1.busVoltage_m2"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"motoresdia1['pot_m1']=motoresdia1.busCurrent_m1*motoresdia1.busVoltage_m1"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1206.3030021607772"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"motoresdia1[motoresdia1.pot_m1!=0].pot_m1.dropna().mean()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1093.9399017808028"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"motoresdia1[motoresdia1.pot_m2!=0].pot_m2.dropna().mean()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import scipy.integrate as integr"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"numpy.timedelta64(18394472149109125,'ns')"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"integr.trapz(motoresdia4.pot_m2.dropna(),motoresdia4.pot_m2.dropna().index.values)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy import integrate\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"def integrate_method(self, how='trapz', unit='s'):\n",
" '''Numerically integrate the time series.\n",
"\n",
" @param how: the method to use (trapz by default)\n",
" @return \n",
"\n",
" Available methods:\n",
" * trapz - trapezoidal\n",
" * cumtrapz - cumulative trapezoidal\n",
" * simps - Simpson's rule\n",
" * romb - Romberger's rule\n",
"\n",
" See http://docs.scipy.org/doc/scipy/reference/integrate.html for the method details.\n",
" or the source code\n",
" https://github.com/scipy/scipy/blob/master/scipy/integrate/quadrature.py\n",
" '''\n",
" available_rules = set(['trapz', 'cumtrapz', 'simps', 'romb'])\n",
" if how in available_rules:\n",
" rule = integrate.__getattribute__(how)\n",
" else:\n",
" print('Unsupported integration rule: %s' % (how))\n",
" print('Expecting one of these sample-based integration rules: %s' % (str(list(available_rules))))\n",
" raise AttributeError\n",
" \n",
" result = rule(self.values, self.index.astype(np.int64) / 10**9)\n",
" #result = rule(self.values)\n",
" return result\n",
"\n",
"pd.TimeSeries.integrate = integrate_method"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5104.5918135900893"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"energia1=motoresdia4.dropna().pot_m2.integrate()/3600\n",
"energia1"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6245.9719080816658"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"energia2=motoresdia4.dropna().pot_m1.integrate()/3600\n",
"energia2"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"11350.563721671755"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"energiatotal=energia1+energia2\n",
"energiatotal"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6433.7287677648956"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"energiaconsumida2=motoresdia4[motoresdia4.pot_m2>0].dropna().pot_m2.integrate()/3600\n",
"energiaconsumida2"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5895.2291752692927"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"energiaconsumida1=motoresdia4[motoresdia4.pot_m1>0].dropna().pot_m2.integrate()/3600\n",
"energiaconsumida1"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"12328.957943034187"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"energiaconsumidatotal=energiaconsumida1+energiaconsumida2\n",
"energiaconsumidatotal"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"7.9357414136952409"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(1-(energiatotal/energiaconsumidatotal))*100"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5104.5918135900893"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
tien-le/uranus | 05_model_evaluation.ipynb | 1 | 35801 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comparing machine learning models in scikit-learn\n",
"*From the video series: [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos)*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Agenda\n",
"\n",
"- How do I choose **which model to use** for my supervised learning task?\n",
"- How do I choose the **best tuning parameters** for that model?\n",
"- How do I estimate the **likely performance of my model** on out-of-sample data?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Review\n",
"\n",
"- Classification task: Predicting the species of an unknown iris\n",
"- Used three classification models: KNN (K=1), KNN (K=5), logistic regression\n",
"- Need a way to choose between the models\n",
"\n",
"**Solution:** Model evaluation procedures"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluation procedure #1: Train and test on the entire dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Train the model on the **entire dataset**.\n",
"2. Test the model on the **same dataset**, and evaluate how well we did by comparing the **predicted** response values with the **true** response values."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# read in the iris data\n",
"from sklearn.datasets import load_iris\n",
"iris = load_iris()\n",
"\n",
"# create X (features) and y (response)\n",
"X = iris.data\n",
"y = iris.target"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Logistic regression"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,\n",
" 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# import the class\n",
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"# instantiate the model (using the default parameters)\n",
"logreg = LogisticRegression()\n",
"\n",
"# fit the model with data\n",
"logreg.fit(X, y)\n",
"\n",
"# predict the response values for the observations in X\n",
"logreg.predict(X)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"150"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# store the predicted response values\n",
"y_pred = logreg.predict(X)\n",
"\n",
"# check how many predictions were generated\n",
"len(y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Classification accuracy:\n",
"\n",
"- **Proportion** of correct predictions\n",
"- Common **evaluation metric** for classification problems"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.96\n"
]
}
],
"source": [
"# compute classification accuracy for the logistic regression model\n",
"from sklearn import metrics\n",
"print(metrics.accuracy_score(y, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Known as **training accuracy** when you train and test the model on the same data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### KNN (K=5)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.966666666667\n"
]
}
],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"knn = KNeighborsClassifier(n_neighbors=5)\n",
"knn.fit(X, y)\n",
"y_pred = knn.predict(X)\n",
"print(metrics.accuracy_score(y, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### KNN (K=1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0\n"
]
}
],
"source": [
"knn = KNeighborsClassifier(n_neighbors=1)\n",
"knn.fit(X, y)\n",
"y_pred = knn.predict(X)\n",
"print(metrics.accuracy_score(y, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problems with training and testing on the same data\n",
"\n",
"- Goal is to estimate likely performance of a model on **out-of-sample data**\n",
"- But, maximizing training accuracy rewards **overly complex models** that won't necessarily generalize\n",
"- Unnecessarily complex models **overfit** the training data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Image Credit: [Overfitting](http://commons.wikimedia.org/wiki/File:Overfitting.svg#/media/File:Overfitting.svg) by Chabacano. Licensed under GFDL via Wikimedia Commons.*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluation procedure #2: Train/test split"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Split the dataset into two pieces: a **training set** and a **testing set**.\n",
"2. Train the model on the **training set**.\n",
"3. Test the model on the **testing set**, and evaluate how well we did."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(150, 4)\n",
"(150,)\n"
]
}
],
"source": [
"# print the shapes of X and y\n",
"print(X.shape)\n",
"print(y.shape)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"# STEP 1: split X and y into training and testing sets\n",
"from sklearn.cross_validation import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What did this accomplish?\n",
"\n",
"- Model can be trained and tested on **different data**\n",
"- Response values are known for the testing set, and thus **predictions can be evaluated**\n",
"- **Testing accuracy** is a better estimate than training accuracy of out-of-sample performance"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(90, 4)\n",
"(60, 4)\n"
]
}
],
"source": [
"# print the shapes of the new X objects\n",
"print(X_train.shape)\n",
"print(X_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(90,)\n",
"(60,)\n"
]
}
],
"source": [
"# print the shapes of the new y objects\n",
"print(y_train.shape)\n",
"print(y_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
" penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# STEP 2: train the model on the training set\n",
"logreg = LogisticRegression()\n",
"logreg.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.95\n"
]
}
],
"source": [
"# STEP 3: make predictions on the testing set\n",
"y_pred = logreg.predict(X_test)\n",
"\n",
"# compare actual response values (y_test) with predicted response values (y_pred)\n",
"print(metrics.accuracy_score(y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Repeat for KNN with K=5:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.966666666667\n"
]
}
],
"source": [
"knn = KNeighborsClassifier(n_neighbors=5)\n",
"knn.fit(X_train, y_train)\n",
"y_pred = knn.predict(X_test)\n",
"print(metrics.accuracy_score(y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Repeat for KNN with K=1:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.95\n"
]
}
],
"source": [
"knn = KNeighborsClassifier(n_neighbors=1)\n",
"knn.fit(X_train, y_train)\n",
"y_pred = knn.predict(X_test)\n",
"print(metrics.accuracy_score(y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Can we locate an even better value for K?"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# try K=1 through K=25 and record testing accuracy\n",
"k_range = list(range(1, 26))\n",
"scores = []\n",
"for k in k_range:\n",
" knn = KNeighborsClassifier(n_neighbors=k)\n",
" knn.fit(X_train, y_train)\n",
" y_pred = knn.predict(X_test)\n",
" scores.append(metrics.accuracy_score(y_test, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f7fbdf31160>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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dwNckHQ9bbmUlZaJO2GqX9nlInGt7yVUyCnA5RVsbI09Fm5mZCfqQ/Mq5mxqd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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7fc00e6320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# import Matplotlib (scientific plotting library)\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# allow plots to appear within the notebook\n",
"%matplotlib inline\n",
"\n",
"# plot the relationship between K and testing accuracy\n",
"plt.plot(k_range, scores)\n",
"plt.xlabel('Value of K for KNN')\n",
"plt.ylabel('Testing Accuracy')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- **Training accuracy** rises as model complexity increases\n",
"- **Testing accuracy** penalizes models that are too complex or not complex enough\n",
"- For KNN models, complexity is determined by the **value of K** (lower value = more complex)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Making predictions on out-of-sample data"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# instantiate the model with the best known parameters\n",
"knn = KNeighborsClassifier(n_neighbors=11)\n",
"\n",
"# train the model with X and y (not X_train and y_train)\n",
"knn.fit(X, y)\n",
"\n",
"# make a prediction for an out-of-sample observation\n",
"knn.predict([[3, 5, 4, 2]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Downsides of train/test split?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Provides a **high-variance estimate** of out-of-sample accuracy\n",
"- **K-fold cross-validation** overcomes this limitation\n",
"- But, train/test split is still useful because of its **flexibility and speed**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Resources\n",
"\n",
"- Quora: [What is an intuitive explanation of overfitting?](http://www.quora.com/What-is-an-intuitive-explanation-of-overfitting/answer/Jessica-Su)\n",
"- Video: [Estimating prediction error](https://www.youtube.com/watch?v=_2ij6eaaSl0&t=2m34s) (12 minutes, starting at 2:34) by Hastie and Tibshirani\n",
"- [Understanding the Bias-Variance Tradeoff](http://scott.fortmann-roe.com/docs/BiasVariance.html)\n",
" - [Guiding questions](https://github.com/justmarkham/DAT8/blob/master/homework/09_bias_variance.md) when reading this article\n",
"- Video: [Visualizing bias and variance](http://work.caltech.edu/library/081.html) (15 minutes) by Abu-Mostafa"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comments or Questions?\n",
"\n",
"- Email: <[email protected]>\n",
"- Website: http://dataschool.io\n",
"- Twitter: [@justmarkham](https://twitter.com/justmarkham)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>\n",
" @font-face {\n",
" font-family: \"Computer Modern\";\n",
" src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
" }\n",
" div.cell{\n",
" width: 90%;\n",
"/* margin-left:auto;*/\n",
"/* margin-right:auto;*/\n",
" }\n",
" ul {\n",
" line-height: 145%;\n",
" font-size: 90%;\n",
" }\n",
" li {\n",
" margin-bottom: 1em;\n",
" }\n",
" h1 {\n",
" font-family: Helvetica, serif;\n",
" }\n",
" h4{\n",
" margin-top: 12px;\n",
" margin-bottom: 3px;\n",
" }\n",
" div.text_cell_render{\n",
" font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
" line-height: 145%;\n",
" font-size: 130%;\n",
" width: 90%;\n",
" margin-left:auto;\n",
" margin-right:auto;\n",
" }\n",
" .CodeMirror{\n",
" font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
" }\n",
"/* .prompt{\n",
" display: None;\n",
" }*/\n",
" .text_cell_render h5 {\n",
" font-weight: 300;\n",
" font-size: 16pt;\n",
" color: #4057A1;\n",
" font-style: italic;\n",
" margin-bottom: 0.5em;\n",
" margin-top: 0.5em;\n",
" display: block;\n",
" }\n",
"\n",
" .warning{\n",
" color: rgb( 240, 20, 20 )\n",
" }\n",
"</style>\n",
"<script>\n",
" MathJax.Hub.Config({\n",
" TeX: {\n",
" extensions: [\"AMSmath.js\"]\n",
" },\n",
" tex2jax: {\n",
" inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
" },\n",
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
" \"HTML-CSS\": {\n",
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
" }\n",
" });\n",
"</script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.core.display import HTML\n",
"def css_styling():\n",
" styles = open(\"styles/custom.css\", \"r\").read()\n",
" return HTML(styles)\n",
"css_styling()"
]
}
],
"metadata": {
"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 |
cornhundred/ipywidgets | docs/source/examples/Lorenz Differential Equations.ipynb | 1 | 124075 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Exploring the Lorenz System of Differential Equations"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"In this Notebook we explore the Lorenz system of differential equations:\n",
"\n",
"$$\n",
"\\begin{aligned}\n",
"\\dot{x} & = \\sigma(y-x) \\\\\n",
"\\dot{y} & = \\rho x - y - xz \\\\\n",
"\\dot{z} & = -\\beta z + xy\n",
"\\end{aligned}\n",
"$$\n",
"\n",
"This is one of the classic systems in non-linear differential equations. It exhibits a range of different behaviors as the parameters ($\\sigma$, $\\beta$, $\\rho$) are varied."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Imports"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"First, we import the needed things from IPython, NumPy, Matplotlib and SciPy."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from ipywidgets import interact, interactive\n",
"from IPython.display import clear_output, display, HTML"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy import integrate\n",
"\n",
"from matplotlib import pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"from matplotlib.colors import cnames\n",
"from matplotlib import animation"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Computing the trajectories and plotting the result"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"We define a function that can integrate the differential equations numerically and then plot the solutions. This function has arguments that control the parameters of the differential equation ($\\sigma$, $\\beta$, $\\rho$), the numerical integration (`N`, `max_time`) and the visualization (`angle`)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def solve_lorenz(N=10, angle=0.0, max_time=4.0, sigma=10.0, beta=8./3, rho=28.0):\n",
"\n",
" fig = plt.figure()\n",
" ax = fig.add_axes([0, 0, 1, 1], projection='3d')\n",
" ax.axis('off')\n",
"\n",
" # prepare the axes limits\n",
" ax.set_xlim((-25, 25))\n",
" ax.set_ylim((-35, 35))\n",
" ax.set_zlim((5, 55))\n",
" \n",
" def lorenz_deriv(x_y_z, t0, sigma=sigma, beta=beta, rho=rho):\n",
" \"\"\"Compute the time-derivative of a Lorenz system.\"\"\"\n",
" x, y, z = x_y_z\n",
" return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]\n",
"\n",
" # Choose random starting points, uniformly distributed from -15 to 15\n",
" np.random.seed(1)\n",
" x0 = -15 + 30 * np.random.random((N, 3))\n",
"\n",
" # Solve for the trajectories\n",
" t = np.linspace(0, max_time, int(250*max_time))\n",
" x_t = np.asarray([integrate.odeint(lorenz_deriv, x0i, t)\n",
" for x0i in x0])\n",
" \n",
" # choose a different color for each trajectory\n",
" colors = plt.cm.viridis(np.linspace(0, 1, N))\n",
"\n",
" for i in range(N):\n",
" x, y, z = x_t[i,:,:].T\n",
" lines = ax.plot(x, y, z, '-', c=colors[i])\n",
" plt.setp(lines, linewidth=2)\n",
"\n",
" ax.view_init(30, angle)\n",
" plt.show()\n",
"\n",
" return t, x_t"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let's call the function once to view the solutions. For this set of parameters, we see the trajectories swirling around two points, called attractors. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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Bozrw9Fu3teh5CMJZRChej1bN383nr60guJU3n/9yppVYVlvHgE++pdZs5rsW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\nohBCCBEkoSiEEEIESSgKIYQQQf8HKcfOes5NrnwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10fcf1198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"t, x_t = solve_lorenz(angle=0, N=10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Using IPython's `interactive` function, we can explore how the trajectories behave as we change the various parameters."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2a0dede062494d4d8e2a39400b2f7cab"
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"w = interactive(solve_lorenz, angle=(0.,360.), max_time=(0.1, 4.0), \n",
" N=(0,50), sigma=(0.0,50.0), rho=(0.0,50.0))\n",
"display(w)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"The object returned by `interactive` is a `Widget` object and it has attributes that contain the current result and arguments:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"t, x_t = w.result"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"{'N': 10,\n",
" 'angle': 0.0,\n",
" 'beta': 2.6666666666666665,\n",
" 'max_time': 4.0,\n",
" 'rho': 28.0,\n",
" 'sigma': 10.0}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w.kwargs"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"After interacting with the system, we can take the result and perform further computations. In this case, we compute the average positions in $x$, $y$ and $z$."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"xyz_avg = x_t.mean(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"(10, 3)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xyz_avg.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Creating histograms of the average positions (across different trajectories) show that on average the trajectories swirl about the attractors."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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6acA1SSb3dUVVfXqUByBJmp2BwQ9QVduB7VPaNk9Zvhy4fErbbmD1EVUoSRopP7krSY0x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\nqj28zQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1135c3128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(xyz_avg[:,0])\n",
"plt.title('Average $x(t)$');"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x113e64a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(xyz_avg[:,1])\n",
"plt.title('Average $y(t)$');"
]
}
],
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"language": "python",
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},
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| bsd-3-clause |
Ledoux/ShareYourSystem | Pythonlogy/draft/Noders/Attentioner/Readme.ipynb | 1 | 8168 | {
"nbformat": 3,
"worksheets": [
{
"cells": [
{
"source": "\n<!--\nFrozenIsBool False\n-->\n\n#Attentioner\n\n##Doc\n----\n\n\n> \n> Attentioner instances grasp a Variable and make inside a catch to the original\n> instance\n> \n> \n\n----\n\n<small>\nView the Attentioner notebook on [NbViewer](http://nbviewer.ipython.org/url/shareyoursystem.ouvaton.org/Attentioner.ipynb)\n</small>\n\n",
"cell_type": "markdown",
"prompt_number": 0,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
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},
{
"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\nAttentioner instances grasp a Variable and make inside a catch to the original\ninstance\n\n\"\"\"\n\n#<DefineAugmentation>\nimport ShareYourSystem as SYS\nBaseModuleStr=\"ShareYourSystem.Standards.Noders.Catcher\"\nDecorationModuleStr=\"ShareYourSystem.Standards.Classors.Classer\"\nSYS.setSubModule(globals())\n#</DefineAugmentation>\n\n#<ImportSpecificModules>\nfrom ShareYourSystem.Standards.Classors import Doer\nfrom ShareYourSystem.Standards.Noders import Noder\n#</ImportSpecificModules>\n\n#<DefineClass>\n@DecorationClass()\nclass AttentionerClass(BaseClass):\n\t\n\t#Definition\n\tRepresentingKeyStrsList=[\n\t\t\t\t\t\t\t\t'AttentioningCollectionStr'\n\t\t\t\t\t\t\t]\n\n\tdef default_init(self,\n\t\t\t\t\t\t_AttentioningCollectionStr=\"\",\n\t\t\t\t\t\t**_KwargVariablesDict\n\t\t\t\t\t):\n\n\t\t#Call the parent init method\n\t\tBaseClass.__init__(self,**_KwargVariablesDict)\n\n\tdef do_attention(self):\n\t\t\n\t\t#debug\n\t\t'''\n\t\tself.debug(('self.',self,['CatchingCollectionStr']))\n\t\t'''\n\t\t\n\t\t#set\n\t\tif self.AttentioningCollectionStr==\"\":\n\t\t\tself.AttentioningCollectionStr=self.CollectingCollectionStr\n\n\t\t#debug\n\t\tself.debug(\n\t\t\t\t\t('self.',self,[\n\t\t\t\t\t\t\t'AttentioningCollectionStr',\n\t\t\t\t\t\t\t'GraspingClueVariable'\n\t\t\t\t\t\t\t])\n\t\t\t\t)\n\t\t\n\t\t#poitn in the other way\n\t\tself.GraspedAnswerVariable.grasp(\n\t\t\t\tself\n\t\t\t).catch(\n\t\t\t\tself.AttentioningCollectionStr\n\t\t\t)\n\n\t\t\n\n#</DefineClass>\n\n\n```\n\n<small>\nView the Attentioner sources on <a href=\"https://github.com/Ledoux/ShareYourSystem/tree/master/Pythonlogy/ShareYourSystem/Noders/Attentioner\" target=\"_blank\">Github</a>\n</small>\n\n",
"cell_type": "markdown",
"prompt_number": 1,
"metadata": {
"slideshow": {
"slide_type": "subslide"
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}
},
{
"source": "```python\n\n#ImportModules\nimport ShareYourSystem as SYS\nfrom ShareYourSystem.Applyiers import Producer\nfrom ShareYourSystem.Standards.Noders import Attentioner\n\n#Definition of an instance\nMyProducer=Producer.ProducerClass().produce(\n ['First','Second'],\n Attentioner.AttentionerClass,\n **{'CollectingCollectionStr':\"Pointome\"}\n )\n\n#attention\nMyProducer['<Pointome>FirstAttentioner'].grasp(\n '/NodePointDeriveNoder/<Pointome>SecondAttentioner'\n ).attention(\n 'BackRelatome'\n )\n\n#Definition the AttestedStr\nSYS._attest(\n [\n 'MyProducer is '+SYS._str(\n MyProducer,\n **{\n 'RepresentingBaseKeyStrsListBool':False,\n 'RepresentingAlineaIsBool':False\n }\n )\n ]\n) \n\n#Print\n\n\n\n```\n",
"cell_type": "markdown",
"metadata": {}
},
{
"source": "```console\n>>>\n \n xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n ////////////////////////////////\n Attentioner/__init__.py do_attention\n From Attentioner/__init__.py do_attention | site-packages/six.py exec_ | Celler/__init__.py do_cell | Notebooker/__init__.py do_notebook | Documenter/__init__.py do_inform | inform.py <module>\n ////////////////////////////////\n \n l.60 : \n *****\n I am with [('NodeKeyStr', 'FirstAttentioner')]\n *****\n self.AttentioningCollectionStr is BackRelatome\n self.GraspingClueVariable is /NodePointDeriveNoder/<Pointome>SecondAttentioner\n \n xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n \n\n\n*****Start of the Attest *****\n\nMyProducer is < (ProducerClass), 4555536272>\n /{ \n / '<New><Instance>IdInt' : 4555536272\n / '<New><Instance>NodeCollectionStr' : Globals\n / '<New><Instance>NodeIndexInt' : -1\n / '<New><Instance>NodeKeyStr' : TopProducer\n / '<New><Instance>NodePointDeriveNoder' : None\n / '<New><Instance>NodePointOrderedDict' : None\n / '<New><Instance>PointomeCollectionOrderedDict' : \n / /{ \n / / 'FirstAttentioner' : < (AttentionerClass), 4555633872>\n / / /{ \n / / / '<New><Instance>IdInt' : 4555633872\n / / / '<New><Instance>NodeCollectionStr' : Pointome\n / / / '<New><Instance>NodeIndexInt' : 0\n / / / '<New><Instance>NodeKeyStr' : FirstAttentioner\n / / / '<New><Instance>NodePointDeriveNoder' : {...}< (ProducerClass), 4555536272>\n / / / '<New><Instance>NodePointOrderedDict' : {...}< (OrderedDict), 4556380560>\n / / / '<Spe><Instance>AttentioningCollectionStr' : BackRelatome\n / / /}\n / / 'SecondAttentioner' : < (AttentionerClass), 4555634960>\n / / /{ \n / / / '<New><Instance>BackRelatomeCollectionOrderedDict' : \n / / / /{ \n / / / / 'SecondAttentioner>TopProducer<FirstAttentionerPointer' : < (PointerClass), 4555635216>\n / / / / /{ \n / / / / / '<New><Instance>IdInt' : 4555635216\n / / / / / '<New><Instance>CatchToPointVariable' : {...}< (AttentionerClass), 4555633872>\n / / / / / '<Spe><Class>PointedBackSetStr' : \n / / / / / '<Spe><Class>PointedPathBackVariable' : \n / / / / / '<Spe><Instance>PointedGetVariable' : {...}< (AttentionerClass), 4555633872>\n / / / / / '<Spe><Instance>PointedLocalSetStr' : CatchToPointVariable\n / / / / / '<Spe><Instance>PointingBackSetStr' : \n / / / / / '<Spe><Instance>PointingGetVariable' : {...}< (AttentionerClass), 4555633872>\n / / / / / '<Spe><Instance>PointingSetPathStr' : CatchToPointVariable\n / / / / /}\n / / / /}\n / / / '<New><Instance>IdInt' : 4555634960\n / / / '<New><Instance>NodeCollectionStr' : Pointome\n / / / '<New><Instance>NodeIndexInt' : 1\n / / / '<New><Instance>NodeKeyStr' : SecondAttentioner\n / / / '<New><Instance>NodePointDeriveNoder' : {...}< (ProducerClass), 4555536272>\n / / / '<New><Instance>NodePointOrderedDict' : {...}< (OrderedDict), 4556380560>\n / / / '<Spe><Class>AttentioningCollectionStr' : \n / / /}\n / /}\n / '<Spe><Class>ProducingUpdateVariable' : None\n / '<Spe><Instance>ProducedPushList' : \n / /[\n / / 0 : \n / / /[\n / / / 0 : First\n / / / 1 : {...}< (AttentionerClass), 4555633872>\n / / /]\n / / 1 : \n / / /[\n / / / 0 : Second\n / / / 1 : {...}< (AttentionerClass), 4555634960>\n / / /]\n / /]\n / '<Spe><Instance>ProducingCollectionKeyStrsList' : ['First', 'Second']\n / '<Spe><Instance>ProducingPushClass' : <class 'ShareYourSystem.Standards.Noders.Attentioner.AttentionerClass'>\n /}\n\n*****End of the Attest *****\n\n\n\n```\n",
"cell_type": "markdown",
"metadata": {}
}
]
}
],
"metadata": {
"name": "",
"signature": ""
},
"nbformat_minor": 0
} | mit |
biothings/biothings_explorer | jupyter notebooks/EXPLAIN_ACE2_hydroxychloroquine_demo.ipynb | 1 | 46382 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"\n",
"This notebook demonstrates basic usage of BioThings Explorer, an engine for autonomously querying a distributed knowledge graph. BioThings Explorer can answer two classes of queries -- \"PREDICT\" and \"EXPLAIN\". PREDICT queries are described in [PREDICT_demo.ipynb](PREDICT_demo.ipynb). Here, we describe EXPLAIN queries and how to use BioThings Explorer to execute them. A more detailed overview of the BioThings Explorer systems is provided in [these slides](https://docs.google.com/presentation/d/1QWQqqQhPD_pzKryh6Wijm4YQswv8pAjleVORCPyJyDE/edit?usp=sharing).\n",
"\n",
"EXPLAIN queries are designed to **identify plausible reasoning chains to explain the relationship between two entities**. For example, in this notebook, we explore the question:\n",
"\n",
" \"*Why does hydroxychloroquine have an effect on ACE2?*\" \n",
"\n",
"\n",
"\n",
"**To experiment with an executable version of this notebook, [load it in Google Colaboratory](https://colab.research.google.com/github/biothings/biothings_explorer/blob/master/jupyter%20notebooks/EXPLAIN_ACE2_hydroxychloroquine_demo.ipynb).**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 0: Load BioThings Explorer modules"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, install the `biothings_explorer` and `biothings_schema` packages, as described in this [README](https://github.com/biothings/biothings_explorer/blob/master/jupyter%20notebooks/README.md#prerequisite). This only needs to be done once (but including it here for compability with [colab](https://colab.research.google.com/))."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting biothings_explorer from git+https://github.com/biothings/biothings_explorer#egg=biothings_explorer\n",
" Cloning https://github.com/biothings/biothings_explorer to /private/var/folders/59/w2v_bg_d2rj_hg69468vdxzw0000gn/T/pip-install-87l7sgk0/biothings-explorer\n",
" Running command git clone -q https://github.com/biothings/biothings_explorer /private/var/folders/59/w2v_bg_d2rj_hg69468vdxzw0000gn/T/pip-install-87l7sgk0/biothings-explorer\n",
" Running command git submodule update --init --recursive -q\n",
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"Building wheels for collected packages: biothings-explorer\n",
" Building wheel for biothings-explorer (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25h Stored in directory: /private/var/folders/59/w2v_bg_d2rj_hg69468vdxzw0000gn/T/pip-ephem-wheel-cache-mp83lp3v/wheels/61/44/e1/901cb798059240028e8e2b5d8ed46a47aafa11af30a20c465a\n",
"Successfully built biothings-explorer\n",
"Installing collected packages: biothings-explorer\n",
"Successfully installed biothings-explorer-0.0.1\n",
"\u001b[33mWARNING: You are using pip version 19.1.1, however version 20.0.2 is available.\n",
"You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
]
}
],
"source": [
"!pip install git+https://github.com/biothings/biothings_explorer#egg=biothings_explorer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, import the relevant modules:\n",
"\n",
"* **Hint**: Find corresponding bio-entity representation used in BioThings Explorer based on user input (could be any database IDs, symbols, names)\n",
"* **FindConnection**: Find intermediate bio-entities which connects user specified input and output"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# import modules from biothings_explorer\n",
"from biothings_explorer.hint import Hint\n",
"from biothings_explorer.user_query_dispatcher import FindConnection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1: Find representation of \"ACE2\" and \"hydroxychloroquine\" in BTE\n",
"\n",
"In this step, BioThings Explorer translates our query strings \"ACE2\" and \"hydroxychloroquine \" into BioThings objects, which contain mappings to many common identifiers. Generally, the top result returned by the `Hint` module will be the correct item, but you should confirm that using the identifiers shown.\n",
"\n",
"Search terms can correspond to any child of [BiologicalEntity](https://biolink.github.io/biolink-model/docs/BiologicalEntity.html) from the [Biolink Model](https://biolink.github.io/biolink-model/docs/), including `DiseaseOrPhenotypicFeature` (e.g., \"lupus\"), `ChemicalSubstance` (e.g., \"acetaminophen\"), `Gene` (e.g., \"CDK2\"), `BiologicalProcess` (e.g., \"T cell differentiation\"), and `Pathway` (e.g., \"Citric acid cycle\")."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'NCBIGene': '59272',\n",
" 'name': 'angiotensin I converting enzyme 2',\n",
" 'SYMBOL': 'ACE2',\n",
" 'UMLS': 'C1422064',\n",
" 'HGNC': '13557',\n",
" 'UNIPROTKB': 'Q9BYF1',\n",
" 'ENSEMBL': 'ENSG00000130234',\n",
" 'primary': {'identifier': 'NCBIGene', 'cls': 'Gene', 'value': '59272'},\n",
" 'display': 'NCBIGene(59272) ENSEMBL(ENSG00000130234) HGNC(13557) UMLS(C1422064) UNIPROTKB(Q9BYF1) SYMBOL(ACE2)',\n",
" 'type': 'Gene'}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ht = Hint()\n",
"# find all potential representations of ACE2\n",
"ace2_hint = ht.query(\"ACE2\")\n",
"# select the correct representation of ACE2\n",
"ace2 = ace2_hint['Gene'][0]\n",
"ace2"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'DRUGBANK': 'DB01611',\n",
" 'CHEBI': 'CHEBI:5801',\n",
" 'name': 'hydroxychloroquine',\n",
" 'primary': {'identifier': 'CHEBI',\n",
" 'cls': 'ChemicalSubstance',\n",
" 'value': 'CHEBI:5801'},\n",
" 'display': 'CHEBI(CHEBI:5801) DRUGBANK(DB01611) name(hydroxychloroquine)',\n",
" 'type': 'ChemicalSubstance'}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# find all potential representations of hydroxychloroquine \n",
"hydroxychloroquine_hint = ht.query(\"hydroxychloroquine\")\n",
"# select the correct representation of hydroxychloroquine \n",
"hydroxychloroquine = hydroxychloroquine_hint['ChemicalSubstance'][0]\n",
"hydroxychloroquine"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2: Find intermediate nodes connecting ACE2 and hydroxychloroquine \n",
"\n",
"In this section, we find all paths in the knowledge graph that connect ACE2 and hydroxychloroquine . To do that, we will use `FindConnection`. This class is a convenient wrapper around two advanced functions for **query path planning** and **query path execution**. More advanced features for both query path planning and query path execution are in development and will be documented in the coming months. \n",
"\n",
"The parameters for `FindConnection` are described below:\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function __init__ in module biothings_explorer.user_query_dispatcher:\n",
"\n",
"__init__(self, input_obj, output_obj, intermediate_nodes, registry=None)\n",
" Find relationships in the Knowledge Graph between an Input Object and an Output Object.\n",
" \n",
" Args:\n",
" input_obj (required): must be an object returned from Hint corresponding to a specific biomedical entity.\n",
" Examples: \n",
" Hint().query(\"Fanconi anemia\")['DiseaseOrPhenotypicFeature'][0]\n",
" Hint().query(\"acetaminophen\")['ChemicalSubstance'][0]\n",
" \n",
" output_obj (required): must EITHER be an object returned from Hint corresponding to a specific biomedical\n",
" entity, OR be a string or list of strings corresponding to Biolink Entity classes.\n",
" Examples:\n",
" Hint().query(\"acetaminophen\")['ChemicalSubstance'][0]\n",
" 'Gene'\n",
" ['Gene','ChemicalSubstance']\n",
" \n",
" intermediate_nodes (required): the semantic type(s) of the intermediate node(s). Examples:\n",
" None : no intermediate node, find direct connections only\n",
" [] : no intermediate node, find direct connections only\n",
" ['BiologicalEntity'] : one intermediate node of any semantic type\n",
" ['Gene'] : one intermediate node that must be a Gene\n",
" [('Gene','Pathway')] : one intermediate node that must be a Gene or a Pathway\n",
" ['Gene','Pathway'] : two intermediate nodes, first must be a Gene, second must be a Pathway.\n",
" ['Gene',('Pathway','Gene')] : two intermediate nodes, first must be a Gene, second must be a Pathway or Gene.\n",
" **NOTE**: queries with more than one intermediate node are currently not supported\n",
"\n"
]
}
],
"source": [
"help(FindConnection.__init__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, we formulate a `FindConnection` query with \"CML\" as the `input_ojb`, \"imatinib\" as the `output_obj`. We further specify with the `intermediate_nodes` parameter that we are looking for paths joining chronic myelogenous leukemia and imatinib with *one* intermediate node that is a Gene. (The ability to search for longer reasoning paths that include additional intermediate nodes will be added shortly.)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"fc = FindConnection(input_obj=ace2, output_obj=hydroxychloroquine, intermediate_nodes=['BiologicalEntity'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We next execute the `connect` method, which performs the **query path planning** and **query path execution** process. In short, BioThings Explorer is deconstructing the query into individual API calls, executing those API calls, then assembling the results.\n",
"\n",
"A verbose log of this process is displayed below:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==========\n",
"========== QUERY PARAMETER SUMMARY ==========\n",
"==========\n",
"\n",
"BTE will find paths that join 'ACE2' and 'hydroxychloroquine'. Paths will have 1 intermediate node.\n",
"\n",
"Intermediate node #1 will have these type constraints: BiologicalEntity\n",
"\n",
"\n",
"==========\n",
"========== QUERY #1 -- fetch all Biological Entities linked to 'ACE2' ==========\n",
"==========\n",
"\n",
"==== Step #1: Query path planning ====\n",
"\n",
"Because ACE2 is of type 'Gene', BTE will query our meta-KG for APIs that can take 'Gene' as input and 'None' as output\n",
"\n",
"BTE found 17 apis:\n",
"\n",
"API 1. hmdb(1 API call)\n",
"API 2. mychem(3 API calls)\n",
"API 3. dgidb(1 API call)\n",
"API 4. cord_gene(1 API call)\n",
"API 5. ebi_gene2phenotype(1 API call)\n",
"API 6. scibite(2 API calls)\n",
"API 7. chembio(1 API call)\n",
"API 8. mydisease(1 API call)\n",
"API 9. biolink(4 API calls)\n",
"API 10. semmed_gene(13 API calls)\n",
"API 11. mygene(9 API calls)\n",
"API 12. DISEASES(1 API call)\n",
"API 13. pharos(2 API calls)\n",
"API 14. opentarget(1 API call)\n",
"API 15. scigraph(2 API calls)\n",
"API 16. hetio(1 API call)\n",
"API 17. ctd(1 API call)\n",
"\n",
"\n",
"==== Step #2: Query path execution ====\n",
"NOTE: API requests are dispatched in parallel, so the list of APIs below is ordered by query time.\n",
"\n",
"API 8.1: http://mydisease.info/v1/query?fields=disgenet.xrefs.umls&size=250 (POST -d q=59272&scopes=disgenet.genes_related_to_disease.gene_id)\n",
"API 11.3: https://mygene.info/v3/query?fields=ensembl.transcript (POST -d q=ENSG00000130234&scopes=ensembl.gene)\n",
"API 11.4: https://mygene.info/v3/query?fields=pathway.reactome&species=human (POST -d q=59272&scopes=entrezgene)\n",
"API 2.1: https://mychem.info/v1/query?fields=drugbank.id&size=250 (POST -d q=ACE2&scopes=drugbank.targets.gene_name)\n",
"API 9.1: https://api.monarchinitiative.org/api/bioentity/gene/NCBIGene:59272/phenotypes?rows=200\n",
"API 10.2: https://biothings.ncats.io/semmedgene/query?fields=related_to (POST -d q=C1422064&scopes=umls)\n",
"API 2.2: https://mychem.info/v1/query?fields=drugbank.id&size=250 (POST -d q=ACE2&scopes=drugbank.enzymes.gene_name)\n",
"API 10.12: https://biothings.ncats.io/semmedgene/query?fields=prevents (POST -d q=C1422064&scopes=umls)\n",
"API 11.1: https://mygene.info/v3/query?fields=ensembl.protein (POST -d q=ENSG00000130234&scopes=ensembl.gene)\n",
"API 10.3: https://biothings.ncats.io/semmedgene/query?fields=disrupts (POST -d q=C1422064&scopes=umls)\n",
"API 2.3: https://mychem.info/v1/query?fields=chembl.molecule_chembl_id&size=250 (POST -d q=ACE2&scopes=drugcentral.bioactivity.uniprot.gene_symbol)\n",
"API 11.9: https://mygene.info/v3/query?fields=pathway.wikipathways (POST -d q=59272&scopes=entrezgene)\n",
"API 10.9: https://biothings.ncats.io/semmedgene/query?fields=negatively_regulated_by (POST -d q=C1422064&scopes=umls)\n",
"API 10.7: https://biothings.ncats.io/semmedgene/query?fields=disrupted_by (POST -d q=C1422064&scopes=umls)\n",
"API 11.7: https://mygene.info/v3/query?fields=go.CC (POST -d q=59272&scopes=entrezgene)\n",
"API 9.2: https://api.monarchinitiative.org/api/bioentity/gene/NCBIGene:59272/diseases?rows=200\n",
"API 17.1: http://ctdbase.org/tools/batchQuery.go?inputType=gene&report=diseases_curated&format=json&inputTerms=59272\n",
"API 11.6: https://mygene.info/v3/query?fields=go.BP (POST -d q=59272&scopes=entrezgene)\n",
"API 11.8: https://mygene.info/v3/query?fields=pantherdb.ortholog (POST -d q=59272&scopes=entrezgene)\n",
"API 6.1: https://biothings.ncats.io/cord_gene/query?fields=associated_with (POST -d q=13557&scopes=hgnc)\n",
"API 11.2: https://mygene.info/v3/query?fields=uniprot.Swiss-Prot (POST -d q=ENSG00000130234&scopes=ensembl.gene)\n",
"API 7.1: https://pending.biothings.io/ebigene2phenotype/query?fields=gene2phenotype (POST -d q=13557&scopes=_id)\n",
"API 11.5: https://mygene.info/v3/query?fields=go.MF (POST -d q=59272&scopes=entrezgene)\n",
"API 10.4: https://biothings.ncats.io/semmedgene/query?fields=affected_by (POST -d q=C1422064&scopes=umls)\n",
"API 10.13: https://biothings.ncats.io/semmedgene/query?fields=treats (POST -d q=C1422064&scopes=umls)\n",
"API 10.8: https://biothings.ncats.io/semmedgene/query?fields=negatively_regulates (POST -d q=C1422064&scopes=umls)\n",
"API 10.1: https://biothings.ncats.io/semmedgene/query?fields=positively_regulates (POST -d q=C1422064&scopes=umls)\n",
"API 13.1: https://pending.biothings.io/DISEASES/query?fields=DISEASES.doid&size=250 (POST -d q=ACE2&scopes=DISEASES.associatedWith.symbol)\n",
"API 10.11: https://biothings.ncats.io/semmedgene/query?fields=positively_regulated_by (POST -d q=C1422064&scopes=umls)\n",
"API 10.6: https://biothings.ncats.io/semmedgene/query?fields=affects (POST -d q=C1422064&scopes=umls)\n",
"API 10.5: https://biothings.ncats.io/semmedgene/query?fields=physically_interacts_with (POST -d q=C1422064&scopes=umls)\n",
"API 10.10: https://biothings.ncats.io/semmedgene/query?fields=causes (POST -d q=C1422064&scopes=umls)\n",
"API 3.1: http://dgidb.genome.wustl.edu/api/v2/interactions.json?genes=ACE2\n",
"API 15.1: https://platform-api.opentargets.io/v3/platform/public/evidence/filter?target=ENSG00000130234&datasource=chembl&size=100&fields=drug\n",
"API 9.4: https://api.monarchinitiative.org/api/bioentity/gene/NCBIGene:59272/interactions?rows=200\n",
"API 9.3: https://api.monarchinitiative.org/api/bioentity/gene/NCBIGene:59272/anatomy?rows=200\n",
"API 4.2: https://automat.renci.org/cord19_scibite_v2/gene/disease/NCBIGene:59272\n",
"API 1.1: https://automat.renci.org/hmdb/gene/chemical_substance/NCBIGene:59272\n",
"API 14.1: https://automat.renci.org/cord19_scigraph_v2/gene/chemical_substance/NCBIGene:59272\n",
"API 14.2: https://automat.renci.org/cord19_scigraph_v2/gene/disease/NCBIGene:59272\n",
"API 12.2: https://automat.renci.org/pharos/gene/chemical_substance/NCBIGene:59272\n",
"API 12.1: https://automat.renci.org/pharos/gene/disease/NCBIGene:59272\n",
"API 16.1: https://automat.renci.org/hetio/gene/disease/NCBIGene:59272\n",
"API 4.1: https://automat.renci.org/cord19_scibite_v2/gene/chemical_substance/NCBIGene:59272\n",
"API 5.1: https://automat.renci.org/chembio/gene/chemical_substance/NCBIGene:59272\n",
"\n",
"\n",
"==== Step #3: Output normalization ====\n",
"\n",
"API 10.1 semmed_gene: 31 hits\n",
"API 10.2 semmed_gene: 57 hits\n",
"API 10.3 semmed_gene: 16 hits\n",
"API 6.1 cord_gene: 123 hits\n",
"API 9.1 biolink: No hits\n",
"API 15.1 opentarget: 1 hits\n",
"API 10.4 semmed_gene: 2 hits\n",
"API 7.1 ebi_gene2phenotype: No hits\n",
"API 10.5 semmed_gene: 43 hits\n",
"API 10.6 semmed_gene: 46 hits\n",
"API 4.1 scibite: 45 hits\n",
"API 4.2 scibite: 44 hits\n",
"API 10.7 semmed_gene: No hits\n",
"API 11.1 mygene: 2 hits\n",
"API 13.1 DISEASES: 31 hits\n",
"API 10.8 semmed_gene: 19 hits\n",
"API 11.2 mygene: 1 hits\n",
"API 10.9 semmed_gene: 9 hits\n",
"API 11.3 mygene: 5 hits\n",
"API 10.10 semmed_gene: 21 hits\n",
"API 10.11 semmed_gene: 15 hits\n",
"API 11.4 mygene: 3 hits\n",
"API 11.5 mygene: 11 hits\n",
"API 11.6 mygene: 19 hits\n",
"API 14.1 scigraph: 57 hits\n",
"API 12.1 pharos: 4 hits\n",
"API 16.1 hetio: 5 hits\n",
"API 9.2 biolink: No hits\n",
"API 3.1 dgidb: 4 hits\n",
"API 9.3 biolink: 20 hits\n",
"API 2.1 mychem: 2 hits\n",
"API 8.1 mydisease: 7 hits\n",
"API 10.12 semmed_gene: 6 hits\n",
"API 10.13 semmed_gene: 12 hits\n",
"API 12.2 pharos: 52 hits\n",
"API 14.2 scigraph: 41 hits\n",
"API 9.4 biolink: 21 hits\n",
"API 5.1 chembio: No hits\n",
"API 2.2 mychem: No hits\n",
"API 1.1 hmdb: 5 hits\n",
"API 2.3 mychem: 2 hits\n",
"API 11.7 mygene: 12 hits\n",
"API 11.8 mygene: 1 hits\n",
"API 11.9 mygene: 1 hits\n",
"API 17.1 ctd: 17 hits\n",
"\n",
"After id-to-object translation, BTE retrieved 624 unique objects.\n",
"\n",
"\n",
"==========\n",
"========== QUERY #2 -- fetch all Biological Entities linked to 'hydroxychloroquine' ==========\n",
"==========\n",
"\n",
"==== Step #1: Query path planning ====\n",
"\n",
"Because hydroxychloroquine is of type 'ChemicalSubstance', BTE will query our meta-KG for APIs that can take 'ChemicalSubstance' as input and 'None' as output\n",
"\n",
"BTE found 11 apis:\n",
"\n",
"API 1. hmdb(2 API calls)\n",
"API 2. mychem(5 API calls)\n",
"API 3. dgidb(2 API calls)\n",
"API 4. scibite(2 API calls)\n",
"API 5. chembio(1 API call)\n",
"API 6. mydisease(1 API call)\n",
"API 7. pharos(2 API calls)\n",
"API 8. cord_chemical(1 API call)\n",
"API 9. scigraph(2 API calls)\n",
"API 10. semmed_chemical(16 API calls)\n",
"API 11. ctd(1 API call)\n",
"\n",
"\n",
"==== Step #2: Query path execution ====\n",
"NOTE: API requests are dispatched in parallel, so the list of APIs below is ordered by query time.\n",
"\n",
"API 6.1: http://mydisease.info/v1/query?fields=disgenet.xrefs.mesh&size=250 (POST -d q=D006886&scopes=ctd.chemical_related_to_disease.mesh_chemical_id)\n",
"API 11.1: http://ctdbase.org/tools/batchQuery.go?inputType=chem&report=genes_curated&format=json&inputTerms=D006886\n",
"API 2.4: https://mychem.info/v1/query?fields=drugcentral.drug_use.indication (POST -d q=CHEMBL1535,CHEMBL1690&scopes=chembl.molecule_chembl_id)\n",
"API 10.15: https://biothings.ncats.io/semmedchemical/query?fields=produces (POST -d q=C0020336&scopes=umls)\n",
"API 2.1: https://mychem.info/v1/query?fields=drugbank.targets (POST -d q=DB01611&scopes=drugbank.id)\n",
"API 10.3: https://biothings.ncats.io/semmedchemical/query?fields=related_to (POST -d q=C0020336&scopes=umls)\n",
"API 10.12: https://biothings.ncats.io/semmedchemical/query?fields=disrupts (POST -d q=C0020336&scopes=umls)\n",
"API 10.10: https://biothings.ncats.io/semmedchemical/query?fields=produced_by (POST -d q=C0020336&scopes=umls)\n",
"API 10.1: https://biothings.ncats.io/semmedchemical/query?fields=positively_regulates (POST -d q=C0020336&scopes=umls)\n",
"API 10.14: https://biothings.ncats.io/semmedchemical/query?fields=prevents (POST -d q=C0020336&scopes=umls)\n",
"API 10.16: https://biothings.ncats.io/semmedchemical/query?fields=affected_by (POST -d q=C0020336&scopes=umls)\n",
"API 2.3: https://mychem.info/v1/query?fields=drugbank.enzymes (POST -d q=DB01611&scopes=drugbank.id)\n",
"API 10.13: https://biothings.ncats.io/semmedchemical/query?fields=disrupted_by (POST -d q=C0020336&scopes=umls)\n",
"API 10.11: https://biothings.ncats.io/semmedchemical/query?fields=negatively_regulates (POST -d q=C0020336&scopes=umls)\n",
"API 2.5: https://mychem.info/v1/query?fields=drugcentral.drug_use.contraindication (POST -d q=CHEMBL1535,CHEMBL1690&scopes=chembl.molecule_chembl_id)\n",
"API 3.2: http://dgidb.genome.wustl.edu/api/v2/interactions.json?drugs=CHEMBL1690\n",
"API 10.7: https://biothings.ncats.io/semmedchemical/query?fields=positively_regulated_by (POST -d q=C0020336&scopes=umls)\n",
"API 10.4: https://biothings.ncats.io/semmedchemical/query?fields=coexists_with (POST -d q=C0020336&scopes=umls)\n",
"API 8.1: https://biothings.ncats.io/cord_chemical/query?fields=associated_with (POST -d q=CHEBI:5801&scopes=chebi)\n",
"API 10.6: https://biothings.ncats.io/semmedchemical/query?fields=negatively_regulated_by (POST -d q=C0020336&scopes=umls)\n",
"API 10.2: https://biothings.ncats.io/semmedchemical/query?fields=physically_interacts_with (POST -d q=C0020336&scopes=umls)\n",
"API 10.5: https://biothings.ncats.io/semmedchemical/query?fields=causes (POST -d q=C0020336&scopes=umls)\n",
"API 2.2: https://mychem.info/v1/query?fields=drugcentral.bioactivity (POST -d q=CHEMBL1535,CHEMBL1690&scopes=chembl.molecule_chembl_id)\n",
"API 10.9: https://biothings.ncats.io/semmedchemical/query?fields=affects (POST -d q=C0020336&scopes=umls)\n",
"API 10.8: https://biothings.ncats.io/semmedchemical/query?fields=treats (POST -d q=C0020336&scopes=umls)\n",
"API 3.1: http://dgidb.genome.wustl.edu/api/v2/interactions.json?drugs=CHEMBL1535\n",
"API 9.2: https://automat.renci.org/cord19_scigraph_v2/chemical_substance/gene/CHEBI:5801\n",
"API 7.1: https://automat.renci.org/pharos/chemical_substance/disease/CHEBI:5801\n",
"API 7.1 pharos failed\n",
"API 5.1: https://automat.renci.org/chembio/chemical_substance/gene/CHEBI:5801\n",
"API 1.2: https://automat.renci.org/hmdb/chemical_substance/gene/CHEBI:5801\n",
"API 1.1: https://automat.renci.org/hmdb/chemical_substance/disease/CHEBI:5801\n",
"API 7.2: https://automat.renci.org/pharos/chemical_substance/gene/CHEBI:5801\n",
"API 4.2: https://automat.renci.org/cord19_scibite_v2/chemical_substance/disease/CHEBI:5801\n",
"API 4.1: https://automat.renci.org/cord19_scibite_v2/chemical_substance/gene/CHEBI:5801\n",
"API 9.1: https://automat.renci.org/cord19_scigraph_v2/chemical_substance/disease/CHEBI:5801\n",
"\n",
"\n",
"==== Step #3: Output normalization ====\n",
"\n",
"API 10.1 semmed_chemical: 28 hits\n",
"API 10.2 semmed_chemical: 57 hits\n",
"API 10.3 semmed_chemical: No hits\n",
"API 10.4 semmed_chemical: 42 hits\n",
"API 10.5 semmed_chemical: 62 hits\n",
"API 5.1 chembio: 2 hits\n",
"API 10.6 semmed_chemical: No hits\n",
"API 10.7 semmed_chemical: 6 hits\n",
"API 10.8 semmed_chemical: 231 hits\n",
"API 10.9 semmed_chemical: 89 hits\n",
"API 10.10 semmed_chemical: No hits\n",
"API 2.1 mychem: 2 hits\n",
"API 10.11 semmed_chemical: 53 hits\n",
"API 8.1 cord_chemical: 38 hits\n",
"API 1.1 hmdb: No hits\n",
"API 10.12 semmed_chemical: 40 hits\n",
"API 2.2 mychem: 6 hits\n",
"API 9.1 scigraph: 23 hits\n",
"API 2.3 mychem: 2 hits\n",
"API 10.13 semmed_chemical: No hits\n",
"API 10.14 semmed_chemical: 32 hits\n",
"API 4.1 scibite: No hits\n",
"API 10.15 semmed_chemical: No hits\n",
"API 7.2 pharos: 2 hits\n",
"API 10.16 semmed_chemical: No hits\n",
"API 2.4 mychem: 7 hits\n",
"API 9.2 scigraph: 2 hits\n",
"API 3.1 dgidb: 9 hits\n",
"API 3.2 dgidb: 2 hits\n",
"API 1.2 hmdb: 2 hits\n",
"API 4.2 scibite: 8 hits\n",
"API 11.1 ctd: 46 hits\n",
"API 6.1 mydisease: No hits\n",
"API 2.5 mychem: 13 hits\n",
"\n",
"After id-to-object translation, BTE retrieved 597 unique objects.\n",
"\n",
"==========\n",
"========== Final assembly of results ==========\n",
"==========\n",
"\n",
"\n",
"BTE found 69 unique intermediate nodes connecting 'ACE2' and 'hydroxychloroquine'\n"
]
}
],
"source": [
"# set verbose=True will display all steps which BTE takes to find the connection\n",
"fc.connect(verbose=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3: Display and Filter results\n",
"This section demonstrates post-query filtering done in Python. Later, more advanced filtering functions will be added to the **query path execution** module for interleaved filtering, thereby enabling longer query paths. More details to come...\n",
"\n",
"First, all matching paths can be exported to a data frame. Let's examine a sample of those results."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>input</th>\n",
" <th>input_type</th>\n",
" <th>pred1</th>\n",
" <th>pred1_source</th>\n",
" <th>pred1_api</th>\n",
" <th>pred1_pubmed</th>\n",
" <th>node1_type</th>\n",
" <th>node1_name</th>\n",
" <th>node1_id</th>\n",
" <th>pred2</th>\n",
" <th>pred2_source</th>\n",
" <th>pred2_api</th>\n",
" <th>pred2_pubmed</th>\n",
" <th>output_type</th>\n",
" <th>output_name</th>\n",
" <th>output_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>ACE2</td>\n",
" <td>Gene</td>\n",
" <td>positively_regulates</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Gene API</td>\n",
" <td>18978194,18978194</td>\n",
" <td>Gene</td>\n",
" <td>C3539645</td>\n",
" <td>UMLS:C3539645</td>\n",
" <td>positively_regulates</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Chemical API</td>\n",
" <td>10993122,10993122</td>\n",
" <td>Gene</td>\n",
" <td>HYDROXYCHLOROQUINE</td>\n",
" <td>name:HYDROXYCHLOROQUINE</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>ACE2</td>\n",
" <td>Gene</td>\n",
" <td>negatively_regulates</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Gene API</td>\n",
" <td>18156169</td>\n",
" <td>Gene</td>\n",
" <td>C1709136</td>\n",
" <td>UMLS:C1709136</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Chemical API</td>\n",
" <td>27939463</td>\n",
" <td>Gene</td>\n",
" <td>HYDROXYCHLOROQUINE</td>\n",
" <td>name:HYDROXYCHLOROQUINE</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>ACE2</td>\n",
" <td>Gene</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Gene API</td>\n",
" <td>25875512</td>\n",
" <td>Gene</td>\n",
" <td>C0014442</td>\n",
" <td>UMLS:C0014442</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Chemical API</td>\n",
" <td>9001825</td>\n",
" <td>Gene</td>\n",
" <td>HYDROXYCHLOROQUINE</td>\n",
" <td>name:HYDROXYCHLOROQUINE</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>ACE2</td>\n",
" <td>Gene</td>\n",
" <td>negatively_regulates</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Gene API</td>\n",
" <td>18156169</td>\n",
" <td>Gene</td>\n",
" <td>C0669365</td>\n",
" <td>UMLS:C0669365</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Chemical API</td>\n",
" <td>27939463</td>\n",
" <td>Gene</td>\n",
" <td>HYDROXYCHLOROQUINE</td>\n",
" <td>name:HYDROXYCHLOROQUINE</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>ACE2</td>\n",
" <td>Gene</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Gene API</td>\n",
" <td>19232015</td>\n",
" <td>ChemicalSubstance</td>\n",
" <td>C0309049</td>\n",
" <td>UMLS:C0309049</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Chemical API</td>\n",
" <td>24669876,26197707,26872459</td>\n",
" <td>ChemicalSubstance</td>\n",
" <td>HYDROXYCHLOROQUINE</td>\n",
" <td>name:HYDROXYCHLOROQUINE</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" input input_type pred1 pred1_source pred1_api \\\n",
"0 ACE2 Gene positively_regulates SEMMED SEMMED Gene API \n",
"1 ACE2 Gene negatively_regulates SEMMED SEMMED Gene API \n",
"2 ACE2 Gene physically_interacts_with SEMMED SEMMED Gene API \n",
"3 ACE2 Gene negatively_regulates SEMMED SEMMED Gene API \n",
"4 ACE2 Gene physically_interacts_with SEMMED SEMMED Gene API \n",
"\n",
" pred1_pubmed node1_type node1_name node1_id \\\n",
"0 18978194,18978194 Gene C3539645 UMLS:C3539645 \n",
"1 18156169 Gene C1709136 UMLS:C1709136 \n",
"2 25875512 Gene C0014442 UMLS:C0014442 \n",
"3 18156169 Gene C0669365 UMLS:C0669365 \n",
"4 19232015 ChemicalSubstance C0309049 UMLS:C0309049 \n",
"\n",
" pred2 pred2_source pred2_api \\\n",
"0 positively_regulates SEMMED SEMMED Chemical API \n",
"1 physically_interacts_with SEMMED SEMMED Chemical API \n",
"2 physically_interacts_with SEMMED SEMMED Chemical API \n",
"3 physically_interacts_with SEMMED SEMMED Chemical API \n",
"4 physically_interacts_with SEMMED SEMMED Chemical API \n",
"\n",
" pred2_pubmed output_type output_name \\\n",
"0 10993122,10993122 Gene HYDROXYCHLOROQUINE \n",
"1 27939463 Gene HYDROXYCHLOROQUINE \n",
"2 9001825 Gene HYDROXYCHLOROQUINE \n",
"3 27939463 Gene HYDROXYCHLOROQUINE \n",
"4 24669876,26197707,26872459 ChemicalSubstance HYDROXYCHLOROQUINE \n",
"\n",
" output_id \n",
"0 name:HYDROXYCHLOROQUINE \n",
"1 name:HYDROXYCHLOROQUINE \n",
"2 name:HYDROXYCHLOROQUINE \n",
"3 name:HYDROXYCHLOROQUINE \n",
"4 name:HYDROXYCHLOROQUINE "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = fc.display_table_view()\n",
"\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While most results are based on edges from [semmed](https://skr3.nlm.nih.gov/SemMed/), edges from [DGIdb](http://www.dgidb.org/), [biolink](https://monarchinitiative.org/), [disgenet](http://www.disgenet.org/), [mydisease.info](https://mydisease.info) and [drugcentral](http://drugcentral.org/) were also retrieved from their respective APIs. \n",
"\n",
"Next, let's look to see which genes are mentioned the most."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Gene', 'ChemicalSubstance', 'Disease', 'AnatomicalEntity',\n",
" 'GenomicEntity', 'BiologicalProcess', 'CellularComponent'],\n",
" dtype=object)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.node1_type.unique()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
santipuch590/deeplearning-tf | dl_tf_BDU/2.CNNs/CNN.ipynb | 1 | 58922 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow.examples.tutorials.mnist import input_data\n",
"import time\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"session = tf.InteractiveSession()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting ../../data/MNIST/train-images-idx3-ubyte.gz\n",
"Extracting ../../data/MNIST/train-labels-idx1-ubyte.gz\n",
"Extracting ../../data/MNIST/t10k-images-idx3-ubyte.gz\n",
"Extracting ../../data/MNIST/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"mnist = input_data.read_data_sets(\"../../data/MNIST\", one_hot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# General parameters"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"width = 28\n",
"height = 28\n",
"flat = width * height\n",
"n_classes = 10"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = tf.placeholder(tf.float32, shape=[None, flat])\n",
"y = tf.placeholder(tf.float32, shape=[None, n_classes])\n",
"\n",
"# Transform input into image tensor\n",
"x_image = tf.reshape(x, [-1, height, width, 1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Weights"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def init_weights(shape):\n",
" w_init = tf.truncated_normal(shape, dtype=tf.float32, stddev=0.5)\n",
" return tf.Variable(w_init)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def init_bias(shape):\n",
" bias_init = tf.constant(0.1, shape=shape)\n",
" return tf.Variable(bias_init)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2D Convolution"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def conv2d(x, W):\n",
" return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Max Pooling"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def max_pool_2x2(x):\n",
" return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Architecture"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conv Layer 1"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'add_4:0' shape=(?, 28, 28, 32) dtype=float32>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convolution 1: 3x3 kernels, 1 input channel, 32 feature maps\n",
"kernel_1 = init_weights([3, 3, 1, 32])\n",
"bias_1 = init_bias([32])\n",
"conv_1 = conv2d(x_image, kernel_1) + bias_1\n",
"conv_1"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'Relu_4:0' shape=(?, 28, 28, 32) dtype=float32>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ReLU (conv 1)\n",
"conv_1_a = tf.nn.relu(conv_1)\n",
"conv_1_a"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'MaxPool_3:0' shape=(?, 14, 14, 32) dtype=float32>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Max pooling (conv 1)\n",
"max_pool_1 = max_pool_2x2(conv_1_a)\n",
"max_pool_1"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Finished layer 1\n",
"layer_1 = max_pool_1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conv Layer 2"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'add_5:0' shape=(?, 14, 14, 64) dtype=float32>"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convolution 2: 3x3 kernels, 32 input channel, 64 feature maps\n",
"kernel_2 = init_weights([3, 3, 32, 64])\n",
"bias_2 = init_bias([64])\n",
"conv_2 = conv2d(layer_1, kernel_2) + bias_2\n",
"conv_2"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'Relu_5:0' shape=(?, 14, 14, 64) dtype=float32>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ReLU (conv 2)\n",
"conv_2_a = tf.nn.relu(conv_2)\n",
"conv_2_a"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'MaxPool_4:0' shape=(?, 7, 7, 64) dtype=float32>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Max pooling (conv 2)\n",
"max_pool_2 = max_pool_2x2(conv_2_a)\n",
"max_pool_2"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Finished layer 1\n",
"layer_2 = max_pool_2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fully Connected Layer"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"img_height, img_width, feature_maps = layer_2.get_shape().as_list()[1:]"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'Reshape_3:0' shape=(?, 3136) dtype=float32>"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Flatten image\n",
"flat_units = img_height * img_width * feature_maps\n",
"flat_image = tf.reshape(layer_2, [-1, flat_units])\n",
"flat_image"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'add_7:0' shape=(?, 1024) dtype=float32>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kernel_3 = init_weights([flat_units, 1024])\n",
"bias_3 = init_bias([1024])\n",
"fcn_1 = tf.matmul(flat_image, kernel_3) + bias_3\n",
"fcn_1"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'Relu_7:0' shape=(?, 1024) dtype=float32>"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ReLU (conv 2)\n",
"fcn_1_a = tf.nn.relu(fcn_1)\n",
"fcn_1_a"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'dropout_1/mul:0' shape=(?, 1024) dtype=float32>"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Dropout\n",
"keep_prob = tf.placeholder(dtype=tf.float32)\n",
"fcn_1_drop = tf.nn.dropout(fcn_1_a, keep_prob=keep_prob)\n",
"fcn_1_drop"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Finished layer 3\n",
"layer_3 = fcn_1_drop"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Softmax"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'add_8:0' shape=(?, 10) dtype=float32>"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kernel_4 = init_weights([1024, n_classes])\n",
"bias_4 = init_bias([n_classes])\n",
"pre_softmax = tf.matmul(fcn_1_drop, kernel_4) + bias_4\n",
"pre_softmax"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'Softmax:0' shape=(?, 10) dtype=float32>"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"softmax = tf.nn.softmax(pre_softmax)\n",
"softmax"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train configuration"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'Mean_1:0' shape=() dtype=float32>"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pre_softmax, labels=y))\n",
"loss"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"correct_predictions = tf.equal(tf.arg_max(softmax, dimension=1), tf.arg_max(y, dimension=1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss)"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Initialize variables\n",
"batch_size = 100\n",
"iterations = 2000\n",
"session.run(tf.global_variables_initializer())\n",
"train_losses = []\n",
"val_losses = []"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.00%] Validation loss: 695.941\n",
"[2.50%] Validation loss: 25.753\n",
"[5.00%] Validation loss: 12.167\n",
"[7.50%] Validation loss: 9.331\n",
"[10.00%] Validation loss: 5.484\n",
"[12.50%] Validation loss: 3.468\n",
"[15.00%] Validation loss: 2.621\n",
"[17.50%] Validation loss: 2.425\n",
"[20.00%] Validation loss: 2.325\n",
"[22.50%] Validation loss: 2.304\n",
"[25.00%] Validation loss: 2.094\n",
"[27.50%] Validation loss: 2.083\n",
"[30.00%] Validation loss: 2.387\n",
"[32.50%] Validation loss: 2.051\n",
"[35.00%] Validation loss: 1.968\n",
"[37.50%] Validation loss: 1.955\n",
"[40.00%] Validation loss: 1.994\n",
"[42.50%] Validation loss: 1.918\n",
"[45.00%] Validation loss: 1.932\n",
"[47.50%] Validation loss: 1.874\n",
"[50.00%] Validation loss: 1.940\n",
"[52.50%] Validation loss: 1.815\n",
"[55.00%] Validation loss: 1.856\n",
"[57.50%] Validation loss: 1.863\n",
"[60.00%] Validation loss: 1.794\n",
"[62.50%] Validation loss: 1.813\n",
"[65.00%] Validation loss: 1.773\n",
"[67.50%] Validation loss: 1.780\n",
"[70.00%] Validation loss: 1.747\n",
"[72.50%] Validation loss: 2.093\n",
"[75.00%] Validation loss: 1.798\n",
"[77.50%] Validation loss: 1.888\n",
"[80.00%] Validation loss: 1.785\n",
"[82.50%] Validation loss: 1.723\n",
"[85.00%] Validation loss: 1.913\n",
"[87.50%] Validation loss: 1.649\n",
"[90.00%] Validation loss: 1.658\n",
"[92.50%] Validation loss: 1.618\n",
"[95.00%] Validation loss: 1.663\n",
"[97.50%] Validation loss: 1.570\n",
"Training time: 187.33 s\n"
]
}
],
"source": [
"start = time.time()\n",
"for i in range(iterations): \n",
" batch = mnist.train.next_batch(batch_size)\n",
" train_dict = {\n",
" x: batch[0],\n",
" y: batch[1],\n",
" keep_prob: 0.8\n",
" }\n",
" session.run(train_step, feed_dict=train_dict)\n",
" # Evaluate train loss\n",
" train_loss = loss.eval(feed_dict=train_dict)\n",
" train_losses.append(train_loss)\n",
" \n",
" # Evaluate validation loss\n",
" if i % 50 == 0:\n",
" val_loss = loss.eval(feed_dict={\n",
" x: mnist.validation.images,\n",
" y: mnist.validation.labels,\n",
" keep_prob: 1.0\n",
" })\n",
" val_losses.append(val_loss)\n",
" print('[{:.2%}] Validation loss: {:.3f}'.format(i / iterations, val_loss))\n",
" \n",
"end = time.time()\n",
"print('Training time: {:.2f} s'.format(end - start))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training plot"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7ffb38239828>"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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a/uGciyWfviyp1KvvP1iYJAAAAPwW5D1on5H0t5TnM5PDm8+a2SndvcnMLjezZWa2rKqq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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ffb382eca90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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XF+eJiMOAtcDhwKnApRHR6OzHqVezUQQ0d3NKkqQStRPQlmbmVzNzpPj6C2Dp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\nPmNAkyRJ6jP/P9877wiSKMN4AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ffb382390b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 10))\n",
"plt.plot(train_losses[10:], label='Train loss')\n",
"plt.xlabel('Iterations')\n",
"plt.ylabel('Loss (x-entropy)')\n",
"plt.legend()\n",
"\n",
"plt.figure(figsize=(10, 10))\n",
"plt.plot(val_losses, color='green', label='Validation loss')\n",
"plt.xlabel('Iterations')\n",
"plt.ylabel('Loss (x-entropy)')\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test accuracy: 49.79%\n"
]
}
],
"source": [
"acc = accuracy.eval(feed_dict={\n",
" x: mnist.test.images,\n",
" y: mnist.test.labels,\n",
" keep_prob: 1\n",
"})\n",
"\n",
"print('Test accuracy: {:.2%}'.format(acc))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
alantian/polyglot | notebooks/Tokenization.ipynb | 6 | 8944 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tokenization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Toeknization is the process that identifies the text boundaries of words and sentences.\n",
"We can identify the boundaries of sentences first then tokenize each sentence to identify the words that compose the sentence.\n",
"Of course, we can do word tokenization first and then segment the token sequence into sentneces.\n",
"Tokenization in polyglot relies on the [Unicode Text Segmentation](http://www.unicode.org/reports/tr29/) algorithm as implemented by the [ICU Project](http://site.icu-project.org/).\n",
"\n",
"You can use C/C++ ICU library by installing the required package `libicu-dev`. For example, on ubuntu/debian systems you should use `apt-get` utility as the following:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sudo apt-get install libicu-dev"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from polyglot.text import Text"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Word Tokenization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To call our word tokenizer, first we need to construct a Text object."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"blob = u\"\"\"\n",
"两个月前遭受恐怖袭击的法国巴黎的犹太超市在装修之后周日重新开放，法国内政部长以及超市的管理者都表示，这显示了生命力要比野蛮行为更强大。\n",
"该超市1月9日遭受枪手袭击，导致4人死亡，据悉这起事件与法国《查理周刊》杂志社恐怖袭击案有关。\n",
"\"\"\"\n",
"text = Text(blob)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The property words will call the word tokenizer."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"WordList(['两', '个', '月', '前', '遭受', '恐怖', '袭击', '的', '法国', '巴黎', '的', '犹太', '超市', '在', '装修', '之后', '周日', '重新', '开放', '，', '法国', '内政', '部长', '以及', '超市', '的', '管理者', '都', '表示', '，', '这', '显示', '了', '生命力', '要', '比', '野蛮', '行为', '更', '强大', '。', '该', '超市', '1', '月', '9', '日', '遭受', '枪手', '袭击', '，', '导致', '4', '人', '死亡', '，', '据悉', '这', '起', '事件', '与', '法国', '《', '查理', '周刊', '》', '杂志', '社', '恐怖', '袭击', '案', '有关', '。'])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text.words"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since ICU boundary break algorithms are language aware, polyglot will detect the language used first before calling the tokenizer"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"name: code: zh confidence: 99.0 read bytes: 1920\n"
]
}
],
"source": [
"print(text.language)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Sentence Segementation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we are interested in segmenting the text first into sentences, we can query the `sentences` property"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[Sentence(\"两个月前遭受恐怖袭击的法国巴黎的犹太超市在装修之后周日重新开放，法国内政部长以及超市的管理者都表示，这显示了生命力要比野蛮行为更强大。\"),\n",
" Sentence(\"该超市1月9日遭受枪手袭击，导致4人死亡，据悉这起事件与法国《查理周刊》杂志社恐怖袭击案有关。\")]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text.sentences"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`Sentence` class inherits `Text`, therefore, we can tokenize each sentence into words using the same property `words`"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"WordList(['两', '个', '月', '前', '遭受', '恐怖', '袭击', '的', '法国', '巴黎', '的', '犹太', '超市', '在', '装修', '之后', '周日', '重新', '开放', '，', '法国', '内政', '部长', '以及', '超市', '的', '管理者', '都', '表示', '，', '这', '显示', '了', '生命力', '要', '比', '野蛮', '行为', '更', '强大', '。'])"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"first_sentence = text.sentences[0]\n",
"first_sentence.words"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Command Line"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The subcommand tokenize does by default sentence segmentation and word tokenization."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"usage: polyglot tokenize [-h] [--only-sent | --only-word] [--input [INPUT [INPUT ...]]]\r\n",
"\r\n",
"optional arguments:\r\n",
" -h, --help show this help message and exit\r\n",
" --only-sent Segment sentences without word tokenization\r\n",
" --only-word Tokenize words without sentence segmentation\r\n",
" --input [INPUT [INPUT ...]]\r\n"
]
}
],
"source": [
"! polyglot tokenize --help"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each line represents a sentence where the words are split by spaces."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Australia posted a World Cup record total of 417 - 6 as they beat Afghanistan by 275 runs .\r\n",
"David Warner hit 178 off 133 balls , Steve Smith scored 95 while Glenn Maxwell struck 88 in 39 deliveries in the Pool A encounter in Perth .\r\n",
"Afghanistan were then dismissed for 142 , with Mitchell Johnson and Mitchell Starc taking six wickets between them .\r\n",
"Australia's score surpassed the 413 - 5 India made against Bermuda in 2007 .\r\n",
"It continues the pattern of bat dominating ball in this tournament as the third 400 plus score achieved in the pool stages , following South Africa's 408 - 5 and 411 - 4 against West Indies and Ireland respectively .\r\n",
"The winning margin beats the 257 - run amount by which India beat Bermuda in Port of Spain in 2007 , which was equalled five days ago by South Africa in their victory over West Indies in Sydney .\r\n"
]
}
],
"source": [
"!polyglot --lang en tokenize --input testdata/cricket.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### References\n",
"\n",
"- [Unicode Text Segmentation Algorithm](http://www.unicode.org/reports/tr29/)\n",
"- [Unicode Line Breaking Algorithm](http://www.unicode.org/reports/tr14/)\n",
"- [Boundary Analysis](http://userguide.icu-project.org/boundaryanalysis)\n",
"- [ICU Homepage](http://site.icu-project.org/)\n",
"- [Python Wrapper for libicu](https://pypi.python.org/pypi/PyICU)"
]
}
],
"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
}
| gpl-3.0 |
metpy/MetPy | v0.4/_downloads/NEXRAD_Level_2_File.ipynb | 1 | 3236 | {
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"version": "3.5.3",
"pygments_lexer": "ipython3"
}
},
"cells": [
{
"execution_count": null,
"outputs": [],
"cell_type": "code",
"metadata": {
"collapsed": false
},
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\nNEXRAD Level 2 File\n===================\n\nUse MetPy to read information from a NEXRAD Level 2 (volume) file and plot\n\n"
]
},
{
"execution_count": null,
"outputs": [],
"cell_type": "code",
"metadata": {
"collapsed": false
},
"source": [
"import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom metpy.cbook import get_test_data\nfrom metpy.io import Level2File\nfrom metpy.plots import ctables"
]
},
{
"execution_count": null,
"outputs": [],
"cell_type": "code",
"metadata": {
"collapsed": false
},
"source": [
"# Open the file\nname = get_test_data('KTLX20130520_201643_V06.gz', as_file_obj=False)\nf = Level2File(name)\n\nprint(f.sweeps[0][0])"
]
},
{
"execution_count": null,
"outputs": [],
"cell_type": "code",
"metadata": {
"collapsed": false
},
"source": [
"# Pull data out of the file\nsweep = 0\n\n# First item in ray is header, which has azimuth angle\naz = np.array([ray[0].az_angle for ray in f.sweeps[sweep]])\n\n# 5th item is a dict mapping a var name (byte string) to a tuple\n# of (header, data array)\nref_hdr = f.sweeps[sweep][0][4][b'REF'][0]\nref_range = np.arange(ref_hdr.num_gates) * ref_hdr.gate_width + ref_hdr.first_gate\nref = np.array([ray[4][b'REF'][1] for ray in f.sweeps[sweep]])\n\nrho_hdr = f.sweeps[sweep][0][4][b'RHO'][0]\nrho_range = (np.arange(rho_hdr.num_gates + 1) - 0.5) * rho_hdr.gate_width + rho_hdr.first_gate\nrho = np.array([ray[4][b'RHO'][1] for ray in f.sweeps[sweep]])"
]
},
{
"execution_count": null,
"outputs": [],
"cell_type": "code",
"metadata": {
"collapsed": false
},
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(15, 8))\nfor var_data, var_range, ax in zip((ref, rho), (ref_range, rho_range), axes):\n # Turn into an array, then mask\n data = np.ma.array(var_data)\n data[np.isnan(data)] = np.ma.masked\n\n # Convert az,range to x,y\n xlocs = var_range * np.sin(np.deg2rad(az[:, np.newaxis]))\n ylocs = var_range * np.cos(np.deg2rad(az[:, np.newaxis]))\n\n # Plot the data\n cmap = ctables.registry.get_colortable('viridis')\n ax.pcolormesh(xlocs, ylocs, data, cmap=cmap)\n ax.set_aspect('equal', 'datalim')\n ax.set_xlim(-40, 20)\n ax.set_ylim(-30, 30)\n\nplt.show()"
]
}
],
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
daknuett/daknuett.github.io | topics/blogs/Blog3.ipynb | 1 | 84015 | {
"metadata": {
"name": "",
"signature": "sha256:3bac066170686a6084b778e76e04f426561b200986b23c1037e790c39ce67e95"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Rechener auf Speed -- Laufzeit, Laufzeitverhalten und Geschwindigkeit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\"Mein Algorithmus hat statt $$\\mathcal{O}(n^{2})$$ die Laufzeit $$\\mathcal{O}(n)$$\" kann man oft \n",
"in der Beschreibung von neuen / neu implementierten Algorithmen lesen, aber was bedeutet das eigentlich?\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Laufzeit\n",
"\n",
"Ein Algorithmus besitzt keine Laufzeit. Ein Algorithmus ist n\u00e4mlich eine *abstrakte Anweisung*, die an sich nicht ausgef\u00fchrt werden kann. Diese Anweisung kann in unterschiedlichen Formen (Medien) dargestellt werden, beispielsweise Flie\u00dftext:\n",
"\n",
" Fibonacci-Reihen-Berechnung:\n",
" Nimm eine nat\u00fcrliche Zahl n.\n",
" Wenn die Zahl Eins oder Null ist, gib 1 zur\u00fcck. Sonst gib die Summe aus den Fibonacci-Reihen-Berechnungen f\u00fcr\n",
" n minus Eins und n minus Zwei zur\u00fcck\n",
" \n",
"Alternativ kann auch Pseudo-Code genutzt werden:\n",
"\n",
" fibonacci_reihe(n):\n",
" n ist 1 oder n ist 0:\n",
" gib 1 zur\u00fcck\n",
" gib fibonacci_reihe(n - 1) + fibonacci_reihe(n - 2) zur\u00fcck\n",
" \n",
"Oder auch richtiger Code. Hier empfiehlt sich eine Sprache, die m\u00f6glichst wenig \"Drumherum\" braucht, z.B. Python:\n",
"\n",
" def fibonacci(n):\n",
" if( n in (1,0)):\n",
" return 1\n",
" return fibonacci(n - 1) + fibonacci(n - 2)\n",
" \n",
"Dabei w\u00e4re aber das Letzte auf alle F\u00e4lle bereits eine Implementation, je nach Auslegung auch das Zweite.\n",
"\n",
"### Implementation\n",
"\n",
"Eine Implementation ist die Umsetzung eines Algorithmusses (*abstrakte Anweisung*) in eine *konkrete ausf\u00fchrbare Form*.\n",
"Eine Implementation ist stets *Sprachabh\u00e4ngig*, d.h. sie basiert auf einer Programmiersprache. Die Implementation \n",
"ein und desselben Algorithmusses kann sehr unterschiedlich aussehen, je nach Sprache:\n",
"\n",
"In C:\n",
"\n",
" long int fibonacci(unsigned int n)\n",
" {\n",
" if(n == 1 || n == 0)\n",
" {\n",
" return 1;\n",
" }\n",
" return fibonacci(n - 1) + fibonacci(n - 2);\n",
" }\n",
" \n",
"Im python3:\n",
"\n",
" def fibonacci(n):\n",
" if( n in (1,0)):\n",
" return 1\n",
" return fibonacci(n - 1) + fibonacci(n - 2)\n",
" \n",
"In Haskell:\n",
"\n",
" fibonacci:: Int->Integer\n",
" fibonacci 0 = 1\n",
" fibonacci 1 = 1\n",
" fibonacci n = fibonacci(n - 1) + fibonacci(n - 2)\n",
" \n",
"Diese *Implementationen* besitzen eine Laufzeit, sobald man sie ausf\u00fchrt: \n",
"Die Laufzeit ist die Zeit, die eine *Implementation* zur Ausf\u00fchrung braucht. Also eigentlich eine Zeit in *s* oder *ms*.\n",
"Diese Laufzeit ist allerdings von sehr vielen Parametern abh\u00e4ngig, die man (meistens) nicht einstellen kann.\n",
"Beispielsweise das Betriebssystem."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Das l\u00e4sst sich sehr einfach testen: Man implementiert einen Algorithmus dessen Laufzeitverhalten (Dazu sp\u00e4ter mehr) bekannt ist,\n",
"beispielsweise die Traversierung einer Liste $$\\mathcal{O}(n)$$ hat ein Lineares Laufzeitverhalten,\n",
"testet man die Implementation allerdings, so kann man zu doch ganz anderen Ergebnissen f\u00fcr die Laufzeit kommen:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import time\n",
"\n",
"def traverse_list(length):\n",
" for i in range(length):\n",
" i = i + i\n",
" \n",
"def measure_time(funct, *args):\n",
" start = time.time()\n",
" funct(*args)\n",
" stop = time.time()\n",
" return stop - start\n",
"\n",
"trials = list(range(1000, 50000, 100))\n",
"results = [measure_time(traverse_list, trial) for trial in trials]\n",
"\n",
"plot = plt.plot(trials, results, \"r-\")\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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rluIkBN++/HK44w7PkdWrF7z7rg/pLSqC1q39M1OmeB/Kz34Gp5zi64n8+tf+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\nmIiISI0pmIiISI0pmIiISI39f4ZDCanCnZS6AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f3c29701438>"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Das sieht zwar wirlich aus wie eine Gerade, aber bei 10000 ist irgendwie ein Buckel reingeraten.\n",
"Der Graph zeigt die *Laufzeit der Implementation*, nicht *das Laufzeitverhalten des Algorithmusses*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Laufzeitverhalten\n",
"\n",
"Ein Algorithmus besitzt ein *Laufzeitverhalten*, d.h. eine Vorhersage, wie sich die Laufzeit einer Implementation verhalten wird.\n",
"Diese Vorhersage wird anhand einer Analyse der Algorithmusses (z.B. Rekursionen(Kaskadierend, Linear), Wiederholung, ...) bestimmt.\n",
"\n",
"Dabei wird das Ordnungssymbol $$\\mathcal{O}$$ genutzt.\n",
"\n",
"Das Laufzeitverhalten ist, anders als oft beschrieben, keine Funktion. Es beschreibt nur die Ordnung der Funkion. Betrachtet man den Graph oben, so k\u00f6nnte man aus der Laufzeit eine Funktion f\u00fcr das Laufzeitverhalten Approximieren, ungef\u00e4hr $$ \\mathcal{f}(n) = 0.5 \\cdot n $$\n",
"\n",
"Da diese Zeiten allerdings von vielen Umweltparametern abh\u00e4ngen ist es sinnvoller nur die Ordnung der Funktion an zu geben:\n",
"\n",
"$$ \\mathcal{O}(\\mathcal{f}(n)) = \\mathcal{O}(n) $$\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Laufzeit als Hinweis auf Laufzeitverhalten\n",
"\n",
"Nat\u00fcrlich ist es, besonders bei gro\u00dfen Programmen/Bibliotheken nicht mehr sinnvoll die Algorithmen nach Laufzeitverhalten zu analysieren.\n",
"Sinnvoller ist es das Laufzeitverhalten anhand der Laufzeit ab zu sch\u00e4tzen. Daf\u00fcr gibt es zwei M\u00f6glichkeiten:\n",
"\n",
"* Zeitmessungsapproximation\n",
"* Taktmessungsapproximation\n",
"\n",
"### Zeitmessungsapproximation\n",
"\n",
"Indem man die Laufzeit f\u00fcr viele Samples misst und graphisch darstellt kann man meist relativ gut zutreffende Aussagen \u00fcber\n",
"die Laufzeit eines Algorithmusses machen dabei wird der Graph auf unterschiedlich skalierte Achsen aufgetragen:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"def fibonacci(n):\n",
" if( n in (1,0)):\n",
" return 1\n",
" return fibonacci(n - 1) + fibonacci(n - 2)\n",
"\n",
"trials = list(range(25))\n",
"results = [min([measure_time(fibonacci, trial) for i in range(4)]) for trial in trials]\n",
"\n",
"def plot_all(trials, results):\n",
" plt.subplot(221)\n",
" plt.title(\"linear\")\n",
" plt.plot(trials, results, \"r-\")\n",
"\n",
" plt.subplot(222)\n",
" plt.xscale(\"log\")\n",
" plt.title(\"x log\")\n",
" plt.plot(trials, results, \"r-\")\n",
"\n",
" plt.subplot(223)\n",
" plt.yscale(\"log\")\n",
" plt.xscale(\"log\")\n",
" plt.title(\"both log\")\n",
" plt.plot(trials, results, \"r-\")\n",
"\n",
" plt.subplot(224)\n",
" plt.yscale(\"log\")\n",
" plt.title(\"y log\")\n",
" plt.plot(trials, results, \"r-\")\n",
"plot_all(trials, results)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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9zboW7Y647zbBLMq0a64BgflXJIMDgDelMJcnYL/rnY4brAySnby6eorIsThh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\nuMECQ8wILdmP9zT1johsC9w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"text": [
"<matplotlib.figure.Figure at 0x7f3c29964940>"
]
}
],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Die Gerade in logarithmischer Darstellung weist auf ein Laufzeitverhalten von\n",
"\n",
"$$\\mathcal{O}(x^{n})$$\n",
"\n",
"hin, was Sinn macht, da pro Rekursion 2 Kaskaden erzeugt werden (Kaskadierende Rekursion).\n",
"\n",
"### Taktmessungsapproximation\n",
"\n",
"Die Taktmessungsapproximation versucht so viele st\u00f6rende Umweltparameter wie m\u00f6glich aus zu schalten, indem auf alles was \u00fcberfl\u00fcssig sein k\u00f6nnte verzichtet wird und die Anzahl der Prozessortakte gemessen wird, die zur Ausf\u00fchrung n\u00f6tig sind.\n",
"\n",
"Dass das bei wirklich gro\u00dfen Bibliotheken nicht praktikabel ist, ist klar, allerdings kann es zu Optimierung von Quellcode sinnvoll sein. Am besten ist es dann gleich auf einen emulierten Prozessor, respektive auf eine Registermaschine um zu steigen.\n",
"\n",
"Dann kann die Messung sehr leicht durchgef\u00fchrt werden, hier am Beispiel eines iterativen Verfahrens zur Fibonacci-Reihen Berechnung:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from py_register_machine2.machines.small import small_register_machine\n",
"from py_register_machine2.tools.assembler.assembler import Assembler\n",
"from io import StringIO\n",
"proc, rom, ram, flash = small_register_machine()\n",
"proc.setup_done()\n",
"\n",
"\n",
"fib_asm = '''\\\n",
"\n",
"mov r0 r1\n",
"ldi 1 r2\n",
"ldi 0 r3\n",
"\n",
"loop:\n",
"mov r2 r4\n",
"add r3 r2\n",
"mov r4 r3\n",
"dec r1\n",
"jgt r1 loop\n",
"\n",
"ldi 0b1 ECR\n",
"'''\n",
"stream = StringIO(fib_asm)\n",
"assembler = Assembler(proc, stream)\n",
"code = assembler.assemble()\n",
"rom.program(code)\n",
"\n",
"trials = list(range(100))\n",
"results = []\n",
"\n",
"for trial in trials:\n",
" proc.register_interface.write(\"r0\", trial)\n",
" proc.run()\n",
" results.append(proc.cycles)\n",
" proc.reset()\n",
" proc.cycles = 0\n",
" \n",
"plot_all(trials, results)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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W+W1ntfDoqO/fEfgA2EI3bu9yQhJW6f8Iixx5sPrFCMLwZpAxXyhwpmbMFxpE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\nBrFkiU0WPW4c/OtfNuFIt27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"text": [
"<matplotlib.figure.Figure at 0x7f3c2a017c50>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Man sieht eine Gerade in linearer Darstellung, was sich mit der Code-Analyse deckt.\n",
"Wichtig ist allerdings zu erw\u00e4hnen, dass es keine \"Zacken\" gibt, d.h. Es wurde die naive Laufzeit gemessen,\n",
"also ohne Unterbrechungen durch Betriebssystem o.\u00e4..\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Geschwindigkeit\n",
"\n",
"Die Geschwindigkeit mit der ein Programm/eine Implementation abl\u00e4uft kann auf mehrere Arten erh\u00f6ht werden:\n",
"\n",
"* Objektcodeoptimierung \n",
" Wird vom Compiler durchgef\u00fchrt. Oft wird das allerdings durch Anweisungen in Quellcode unterst\u00fctzt\n",
" (z.B. ``__attribute__((cold))`` im GCC)\n",
"* Hardwarenahe Optimierung/Beschleunigung \n",
" Der Code wird an eine spezielle Architektur angepasst, evtl. werden Interrupts o.\u00e4. abgeschaltet\n",
"* Clever Code \n",
" Meist wird dadurch zwar der Algorithmus leicht ver\u00e4ndert aber durch geschickte Pufferung oder \u00e4hnliche Kniffe kann\n",
" die Geschwindigkeit (stark!) erh\u00f6ht werden\n",
" \n",
"Ein Beispiel f\u00fcr Clever Code, wieder bei der Fibonacci-Reihe:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"calculated = {0: 1, 1: 1}\n",
"\n",
"def clever_fibonacci(n):\n",
" calculated = {0: 1, 1: 1}\n",
" return __clever_fibonacci(n)\n",
"def __clever_fibonacci(n):\n",
" if(n in calculated):\n",
" return calculated[n]\n",
" calculated[n] = __clever_fibonacci(n - 1 ) + __clever_fibonacci(n - 2)\n",
" return calculated[n]\n",
"\n",
"trials = list(range(2500))\n",
"results = [min([measure_time(clever_fibonacci, trial) for i in range(4)]) for trial in trials]\n",
"\n",
"plot_all(trials, results)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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fpNfrOCoWzysE97Oi5wEcHsVXuMcLomda9f0lGI1lhXzXmO41hEb0RYU8x/7z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\nIYQQwP8HFX8tcNK61HoAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f3c29f91780>"
]
}
],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zeigt eine ganz erstaunliche Laufzeit, welche allerdings hervorragend ist.\n",
"Faktisch wurde zwar der Algorithmus ge\u00e4ndert, die \u00c4nderungen sind jedoch so gering, dass man\n",
"von einer Optimierung reden kann."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Funktion des Eintrags"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def runtime_test(function, *args, trials = 4):\n",
" res = None\n",
" times = []\n",
" for i in range(trials):\n",
" start = time.time()\n",
" res = function(*args)\n",
" stop = time.time()\n",
" times.append(stop - start)\n",
" return res, min(times)\n",
"runtime_test(fibonacci, 20)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"(10946, 0.0023140907287597656)"
]
}
],
"prompt_number": 23
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"bestimmt die Laufzeit einer Funktion unter den Parametern `*args`"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
}
],
"metadata": {}
}
]
} | agpl-3.0 |
wathen/PhD | MHD/FEniCS/MHD/Stabilised/SaddlePointForm/Test/SplitMatrix/ScottTest/Lshaped/Dominik/InterpTest.ipynb | 2 | 12636 | {
"metadata": {
"name": "",
"signature": "sha256:100973f4488f686b9eaf01cfffb164e9e1c0aa2f1046945ffdf7bc3ee69ad833"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sympy as sy\n",
"import numpy as np\n",
"from dolfin import *\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n = 2**2\n",
"mesh = RectangleMesh(-1,-1,1,1,n,n, 'left')\n",
"cell_f = CellFunction('size_t', mesh, 0)\n",
"for cell in cells(mesh):\n",
" v = cell.get_vertex_coordinates()\n",
" y = v[np.arange(0,6,2)]\n",
" x = v[np.arange(1,6,2)]\n",
" xone = np.ones(3)\n",
" xone[x > 0] = 0\n",
" yone = np.ones(3)\n",
" yone[y < 0] = 0\n",
" if np.sum(xone)+ np.sum(yone)>5.5:\n",
" cell_f[cell] = 1\n",
"mesh = SubMesh(mesh, cell_f, 0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = sy.symbols('x[0]')\n",
"y = sy.symbols('x[1]')\n",
"rho = sy.sqrt(x**2 + y**2)\n",
"phi = sy.atan2(y,x)\n",
"\n",
"f = rho**(2./3)*sy.sin((2./3)*phi)\n",
"b = sy.diff(f,x)\n",
"d = sy.diff(f,y)\n",
"print sy.ccode(b)\n",
"\n",
"b0Upper = Expression((sy.ccode(b),sy.ccode(d)))\n",
"b0Lower = Expression((str(sy.ccode(b)).replace('atan2(x[1], x[0])','(atan2(x[1], x[0])+2*pi)'),str(sy.ccode(d)).replace('atan2(x[1], x[0])','(atan2(x[1], x[0])+2*pi)')))\n",
"fUpper = Expression(str(sy.ccode(f)))\n",
"fLower = Expression(str(sy.ccode(f)).replace('atan2(x[1], x[0])','(atan2(x[1], x[0])+2*pi)'))\n",
"class b0(Expression):\n",
" def __init__(self, mesh, bu0, bb0):\n",
" self.mesh = mesh\n",
" self.b0 = bu0\n",
" self.bb0 = bb0\n",
" def eval_cell(self, values, x, ufc_cell):\n",
" if abs(x[0]) < 1e-8 and abs(x[1]) < 1e-8:\n",
" values[0] = 0.0\n",
" values[1] = 0.0\n",
" else:\n",
" if x[1] < 0:\n",
" values[0] = self.bb0(x[0], x[1])[0]\n",
" values[1] = self.bb0(x[0], x[1])[1]\n",
" else:\n",
" values[0] = self.b0(x[0], x[1])[0]\n",
" values[1] = self.b0(x[0], x[1])[1]\n",
" # print values\n",
" def value_shape(self):\n",
" return (2,)\n",
" \n",
"class f0(Expression):\n",
" def __init__(self, mesh, pu0, pb0):\n",
" self.mesh = mesh\n",
" self.p0 = pu0\n",
" self.b0 = pb0\n",
" def eval_cell(self, values, x, ufc_cell):\n",
" if abs(x[0]) < 1e-8 and abs(x[1]) < 1e-8:\n",
" values[0] = 0.0\n",
" else:\n",
" if x[1] < 0:\n",
" values[0] = self.b0(x[0], x[1])\n",
" else:\n",
" values[0] = self.p0(x[0], x[1])\n",
"\n",
"n = 2**2\n",
"for i in range(5):\n",
" n = 2**i\n",
" mesh = RectangleMesh(-1,-1,1,1,n,n, 'left')\n",
" cell_f = CellFunction('size_t', mesh, 0)\n",
" for cell in cells(mesh):\n",
" v = cell.get_vertex_coordinates()\n",
" y = v[np.arange(0,6,2)]\n",
" x = v[np.arange(1,6,2)]\n",
" xone = np.ones(3)\n",
" xone[x > 0] = 0\n",
" yone = np.ones(3)\n",
" yone[y < 0] = 0\n",
" if np.sum(xone)+ np.sum(yone)>5.5:\n",
" cell_f[cell] = 1\n",
" mesh = SubMesh(mesh, cell_f, 0)\n",
"\n",
" b = b0(mesh, b0Upper, b0Lower)\n",
" f = f0(mesh, fUpper, fLower)\n",
"\n",
"\n",
" # print \n",
" V = FunctionSpace(mesh, 'N1curl', 2)\n",
" Q = FunctionSpace(mesh, 'CG', 2)\n",
"\n",
" B = interpolate(b, V)\n",
" F = interpolate(f, Q)\n",
" BB = project(grad(F), V)\n",
"\n",
" print 'curl(BB)*curl(BB) ', assemble(curl(BB)*curl(BB)*dx)\n",
" print 'curl(B)*curl(B) ', assemble(curl(B)*curl(B)*dx)\n",
" print np.linalg.norm(BB.vector().array() - B.vector().array())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.666666666666667*x[0]*pow(pow(x[0], 2) + pow(x[1], 2), -0.666666666666667)*sin(0.666666666666667*atan2(x[1], x[0])) - 0.666666666666667*x[1]*pow(pow(x[0], 2) + pow(x[1], 2), -0.666666666666667)*cos(0.666666666666667*atan2(x[1], x[0]))\n",
"curl(BB)*curl(BB) "
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 4.07936614125e-28\n",
"curl(B)*curl(B) 1.85240155796\n",
"1.84536032594\n",
"curl(BB)*curl(BB) "
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 4.75734703589e-13\n",
"curl(B)*curl(B) 0.422372956612\n",
"0.394917888654\n",
"curl(BB)*curl(BB) "
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 2.3536775173e-12\n",
"curl(B)*curl(B) 0.675999149217\n",
"0.249098972069\n",
"curl(BB)*curl(BB) "
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1.01476697715e-11\n",
"curl(B)*curl(B) 1.07454911854\n",
"0.156964877931\n",
"curl(BB)*curl(BB) "
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 3.09607992316e-11\n",
"curl(B)*curl(B) 1.70612040611\n",
"0.0988864232664\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(B)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"<dolfin.cpp.io.VTKPlotter; proxy of <Swig Object of type 'std::shared_ptr< dolfin::VTKPlotter > *' at 0x109284ba0> >"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = sy.symbols('x[0]')\n",
"y = sy.symbols('x[1]')\n",
"rho = sy.sqrt(x**2 + y**2)\n",
"phi = sy.atan2(y,x)\n",
"\n",
"f = rho**(2./3)*sy.sin((2./3)*phi)\n",
"b = sy.diff(f,x)\n",
"d = sy.diff(f,y)\n",
"print sy.ccode(b)\n",
"\n",
"b0 = Expression((sy.ccode(b),sy.ccode(d)))\n",
"f = Expression(sy.ccode(f))\n",
"\n",
"mesh = UnitSquareMesh(500,500)\n",
"V = FunctionSpace(mesh, 'N1curl', 5)\n",
"Q = FunctionSpace(mesh, 'CG', 5)\n",
"\n",
"B = interpolate(b0, V)\n",
"F = interpolate(f, Q)\n",
"BB = project(grad(F), V)\n",
"\n",
"print np.linalg.norm(BB.vector().array() - B.vector().array())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"DEBUG:FFC:Reusing form from cache.\n"
]
}
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
}
],
"metadata": {}
}
]
} | mit |
nayutaya/tensorflow-rnn-sin | ex2/lstm_learning0.5/output.ipynb | 1 | 95582 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import yaml\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'forget_bias': 1.0,\n",
" 'learning_rate': 0.5,\n",
" 'length_of_sequences': 50,\n",
" 'num_of_hidden_nodes': 2,\n",
" 'num_of_input_nodes': 1,\n",
" 'num_of_output_nodes': 1,\n",
" 'num_of_prediction_epochs': 100,\n",
" 'num_of_training_epochs': 2000,\n",
" 'optimizer': 'GradientDescentOptimizer',\n",
" 'seed': 0,\n",
" 'size_of_mini_batch': 100,\n",
" 'train_data_path': '../train_data/normal.npy'}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open(\"param.yaml\", \"r\") as file:\n",
" param = yaml.load(file.read())\n",
"param"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.00000000e+00, 1.25333234e-01],\n",
" [ 1.25333234e-01, 2.48689887e-01],\n",
" [ 2.48689887e-01, 3.68124553e-01],\n",
" ..., \n",
" [ -3.68124553e-01, -2.48689887e-01],\n",
" [ -2.48689887e-01, -1.25333234e-01],\n",
" [ -1.25333234e-01, 3.92877345e-15]])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = np.load(param[\"train_data_path\"])\n",
"train"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.00000000e+00, 1.25333234e-01, 2.48689887e-01,\n",
" 3.68124553e-01, 4.81753674e-01, 5.87785252e-01,\n",
" 6.84547106e-01, 7.70513243e-01, 8.44327926e-01,\n",
" 9.04827052e-01, 9.51056516e-01, 9.82287251e-01,\n",
" 9.98026728e-01, 9.98026728e-01, 9.82287251e-01,\n",
" 9.51056516e-01, 9.04827052e-01, 8.44327926e-01,\n",
" 7.70513243e-01, 6.84547106e-01, 5.87785252e-01,\n",
" 4.81753674e-01, 3.68124553e-01, 2.48689887e-01,\n",
" 1.25333234e-01, -3.21624530e-16, -1.25333234e-01,\n",
" -2.48689887e-01, -3.68124553e-01, -4.81753674e-01,\n",
" -5.87785252e-01, -6.84547106e-01, -7.70513243e-01,\n",
" -8.44327926e-01, -9.04827052e-01, -9.51056516e-01,\n",
" -9.82287251e-01, -9.98026728e-01, -9.98026728e-01,\n",
" -9.82287251e-01, -9.51056516e-01, -9.04827052e-01,\n",
" -8.44327926e-01, -7.70513243e-01, -6.84547106e-01,\n",
" -5.87785252e-01, -4.81753674e-01, -3.68124553e-01,\n",
" -2.48689887e-01, -1.25333234e-01])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"initial = np.load(\"initial.npy\")\n",
"initial"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ -2.89351046e-02, 4.70549762e-02, 8.91841352e-02,\n",
" 9.56257880e-02, 6.58415854e-02, -9.80496407e-06,\n",
" -1.00909412e-01, -2.33765915e-01, -3.91262800e-01,\n",
" -5.60204148e-01, -7.23248959e-01, -8.65128160e-01,\n",
" -9.78696465e-01, -1.06517673e+00, -1.12993598e+00,\n",
" -1.17871284e+00, -1.21608496e+00, -1.24531591e+00,\n",
" -1.26863873e+00, -1.28756928e+00, -1.30314612e+00,\n",
" -1.31609678e+00, -1.32694495e+00, -1.33607936e+00,\n",
" -1.34379745e+00, -1.35033393e+00, -1.35587716e+00,\n",
" -1.36058152e+00, -1.36457598e+00, -1.36796820e+00,\n",
" -1.37084889e+00, -1.37329483e+00, -1.37537158e+00,\n",
" -1.37713468e+00, -1.37863076e+00, -1.37990046e+00,\n",
" -1.38097787e+00, -1.38189173e+00, -1.38266718e+00,\n",
" -1.38332486e+00, -1.38388264e+00, -1.38435566e+00,\n",
" -1.38475668e+00, -1.38509679e+00, -1.38538527e+00,\n",
" -1.38562977e+00, -1.38583720e+00, -1.38601303e+00,\n",
" -1.38616204e+00, -1.38628852e+00, -1.38639545e+00,\n",
" -1.38648617e+00, -1.38656342e+00, -1.38662851e+00,\n",
" -1.38668382e+00, -1.38673079e+00, -1.38677061e+00,\n",
" -1.38680422e+00, -1.38683283e+00, -1.38685703e+00,\n",
" -1.38687766e+00, -1.38689494e+00, -1.38690972e+00,\n",
" -1.38692224e+00, -1.38693273e+00, -1.38694191e+00,\n",
" -1.38694942e+00, -1.38695586e+00, -1.38696134e+00,\n",
" -1.38696587e+00, -1.38696969e+00, -1.38697302e+00,\n",
" -1.38697577e+00, -1.38697827e+00, -1.38698018e+00,\n",
" -1.38698196e+00, -1.38698339e+00, -1.38698471e+00,\n",
" -1.38698566e+00, -1.38698661e+00, -1.38698733e+00,\n",
" -1.38698792e+00, -1.38698852e+00, -1.38698924e+00,\n",
" -1.38698947e+00, -1.38698995e+00, -1.38699007e+00,\n",
" -1.38699031e+00, -1.38699043e+00, -1.38699067e+00,\n",
" -1.38699090e+00, -1.38699102e+00, -1.38699126e+00,\n",
" -1.38699138e+00, -1.38699138e+00, -1.38699150e+00,\n",
" -1.38699162e+00, -1.38699162e+00, -1.38699150e+00,\n",
" -1.38699162e+00])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output = np.load(\"output.npy\")\n",
"output"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1.00000000e+01, 3.29082876e-01],\n",
" [ 2.00000000e+01, 8.61236155e-02],\n",
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]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"losses = np.load(\"losses.npy\")\n",
"losses"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10fe909b0>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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yjV1sLa2GNWvWRIkSJbBp0ybZUVRhafUzN6yf9rGG2jd69GhMnToVjo6O2Lhx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\nIiIiHvD/AaYddP8wNFmAAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10fe01f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"train_df = pd.DataFrame(train[:len(initial) + len(output), 0], columns=[\"train\"])\n",
"initial_df = pd.DataFrame(initial, columns=[\"initial\"])\n",
"output_df = pd.DataFrame(output, columns=[\"output\"], index=range(len(initial), len(initial) + len(output)))\n",
"merged = pd.concat([train_df, initial_df, output_df])\n",
"merged.plot(figsize=(15, 5), grid=True, style=[\"-\", \"-\", \"k--\"])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10fe01780>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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j2Rp5QHgyeStWSEandu34z6lfv2r25Vk/RxWnJTcnBzjkkPRbRmHvXhlq9H//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\n3Dbnz5cT+2rVgHPOkUyh9TVnepC3fr1kjdxcZHET5LlhMru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hBum991iqGYURI6Rbb23b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\n/D/+AqVESFy0NwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10fefcb38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"losses_df = pd.DataFrame(losses, columns=[\"epoch\", \"loss\"])\n",
"losses_df.plot(figsize=(15, 5), grid=True, logy=True, x=\"epoch\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
adamwalz/Jupyter-Notebooks | machine_learning/tutorials/parallel_ml_tutorial/notebooks/04 - Text Feature Extraction for Classification and Clustering.ipynb | 1 | 283793 | {
"metadata": {
"name": "",
"signature": "sha256:66d499bfc20adfaa5cec475cec29fa28cf80f3b6d3cf1a8b7a53879bdac84260"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"# Some nice default configuration for plots\n",
"plt.rcParams['figure.figsize'] = 10, 7.5\n",
"plt.rcParams['axes.grid'] = True\n",
"plt.gray()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
"<matplotlib.figure.Figure at 0x1081a4ed0>"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Text Feature Extraction for Classification and Clustering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Outline of this section:\n",
"\n",
"- Turn a corpus of text documents into **feature vectors** using a **Bag of Words** representation,\n",
"- Train a simple text classifier on the feature vectors,\n",
"- Wrap the vectorizer and the classifier with a **pipeline**,\n",
"- Cross-validation and **model selection** on the pipeline."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Text Classification in 20 lines of Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's start by implementing a canonical text classification example:\n",
"\n",
"- The 20 newsgroups dataset: around 18000 text posts from 20 newsgroups forums\n",
"- Bag of Words features extraction with TF-IDF weighting\n",
"- Naive Bayes classifier or Linear Support Vector Machine for the classifier itself"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.datasets import load_files\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"\n",
"# Load the text data\n",
"categories = [\n",
" 'alt.atheism',\n",
" 'talk.religion.misc',\n",
" 'comp.graphics',\n",
" 'sci.space',\n",
"]\n",
"twenty_train_small = load_files('../datasets/20news-bydate-train/',\n",
" categories=categories, encoding='latin-1')\n",
"twenty_test_small = load_files('../datasets/20news-bydate-test/',\n",
" categories=categories, encoding='latin-1')\n",
"\n",
"# Turn the text documents into vectors of word frequencies\n",
"vectorizer = TfidfVectorizer(min_df=2)\n",
"X_train = vectorizer.fit_transform(twenty_train_small.data)\n",
"y_train = twenty_train_small.target\n",
"\n",
"# Fit a classifier on the training set\n",
"classifier = MultinomialNB().fit(X_train, y_train)\n",
"print(\"Training score: {0:.1f}%\".format(\n",
" classifier.score(X_train, y_train) * 100))\n",
"\n",
"# Evaluate the classifier on the testing set\n",
"X_test = vectorizer.transform(twenty_test_small.data)\n",
"y_test = twenty_test_small.target\n",
"print(\"Testing score: {0:.1f}%\".format(\n",
" classifier.score(X_test, y_test) * 100))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Training score: 95.1%\n",
"Testing score: 85.1%"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is a workflow diagram summary of what happened previously:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"files/images/supervised_scikit_learn.png\" />"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's now decompose what we just did to understand and customize each step."
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Loading the Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's explore the dataset loading utility without passing a list of categories: in this case we load the full 20 newsgroups dataset in memory. The source website for the 20 newsgroups already provides a date-based train / test split that is made available using the `subset` keyword argument: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ls -l ../datasets/"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"total 28376\r\n",
"drwxr-xr-x 22 awalz staff 748 Mar 18 2003 \u001b[34m20news-bydate-test\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 22 awalz staff 748 Mar 18 2003 \u001b[34m20news-bydate-train\u001b[m\u001b[m/\r\n",
"-rw-r--r-- 1 awalz staff 14464277 Jun 12 12:55 20news-bydate.tar.gz\r\n",
"-rw-r--r-- 1 awalz staff 61194 Jun 12 12:56 titanic_train.csv\r\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ls -lh ../datasets/20news-bydate-train"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"total 0\r\n",
"drwxr-xr-x 482 awalz staff 16K Mar 18 2003 \u001b[34malt.atheism\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 586 awalz staff 19K Mar 18 2003 \u001b[34mcomp.graphics\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 593 awalz staff 20K Mar 18 2003 \u001b[34mcomp.os.ms-windows.misc\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 592 awalz staff 20K Mar 18 2003 \u001b[34mcomp.sys.ibm.pc.hardware\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 580 awalz staff 19K Mar 18 2003 \u001b[34mcomp.sys.mac.hardware\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 595 awalz staff 20K Mar 18 2003 \u001b[34mcomp.windows.x\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 587 awalz staff 19K Mar 18 2003 \u001b[34mmisc.forsale\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 596 awalz staff 20K Mar 18 2003 \u001b[34mrec.autos\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 600 awalz staff 20K Mar 18 2003 \u001b[34mrec.motorcycles\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 599 awalz staff 20K Mar 18 2003 \u001b[34mrec.sport.baseball\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 602 awalz staff 20K Mar 18 2003 \u001b[34mrec.sport.hockey\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 597 awalz staff 20K Mar 18 2003 \u001b[34msci.crypt\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 593 awalz staff 20K Mar 18 2003 \u001b[34msci.electronics\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 596 awalz staff 20K Mar 18 2003 \u001b[34msci.med\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 595 awalz staff 20K Mar 18 2003 \u001b[34msci.space\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 601 awalz staff 20K Mar 18 2003 \u001b[34msoc.religion.christian\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 548 awalz staff 18K Mar 18 2003 \u001b[34mtalk.politics.guns\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 566 awalz staff 19K Mar 18 2003 \u001b[34mtalk.politics.mideast\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 467 awalz staff 16K Mar 18 2003 \u001b[34mtalk.politics.misc\u001b[m\u001b[m/\r\n",
"drwxr-xr-x 379 awalz staff 13K Mar 18 2003 \u001b[34mtalk.religion.misc\u001b[m\u001b[m/\r\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ls -lh ../datasets/20news-bydate-train/alt.atheism/"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"total 4480\r\n",
"-rw-r--r-- 1 awalz staff 12K Mar 18 2003 49960\r\n",
"-rw-r--r-- 1 awalz staff 31K Mar 18 2003 51060\r\n",
"-rw-r--r-- 1 awalz staff 4.0K Mar 18 2003 51119\r\n",
"-rw-r--r-- 1 awalz staff 1.6K Mar 18 2003 51120\r\n",
"-rw-r--r-- 1 awalz staff 773B Mar 18 2003 51121\r\n",
"-rw-r--r-- 1 awalz staff 4.8K Mar 18 2003 51122\r\n",
"-rw-r--r-- 1 awalz staff 618B Mar 18 2003 51123\r\n",
"-rw-r--r-- 1 awalz staff 1.4K Mar 18 2003 51124\r\n",
"-rw-r--r-- 1 awalz staff 2.7K Mar 18 2003 51125\r\n",
"-rw-r--r-- 1 awalz staff 427B Mar 18 2003 51126\r\n",
"-rw-r--r-- 1 awalz staff 742B Mar 18 2003 51127\r\n",
"-rw-r--r-- 1 awalz staff 650B Mar 18 2003 51128\r\n",
"-rw-r--r-- 1 awalz staff 1.3K Mar 18 2003 51130\r\n",
"-rw-r--r-- 1 awalz staff 2.3K Mar 18 2003 51131\r\n",
"-rw-r--r-- 1 awalz staff 2.6K Mar 18 2003 51132\r\n",
"-rw-r--r-- 1 awalz staff 1.5K Mar 18 2003 51133\r\n",
"-rw-r--r-- 1 awalz staff 1.2K Mar 18 2003 51134\r\n",
"-rw-r--r-- 1 awalz staff 1.6K Mar 18 2003 51135\r\n",
"-rw-r--r-- 1 awalz staff 2.1K Mar 18 2003 51136\r\n",
"-rw-r--r-- 1 awalz staff 1.3K Mar 18 2003 51139\r\n",
"-rw-r--r-- 1 awalz staff 409B Mar 18 2003 51140\r\n",
"-rw-r--r-- 1 awalz staff 940B Mar 18 2003 51141\r\n",
"-rw-r--r-- 1 awalz staff 9.0K Mar 18 2003 51142\r\n",
"-rw-r--r-- 1 awalz staff 632B Mar 18 2003 51143\r\n",
"-rw-r--r-- 1 awalz staff 1.2K Mar 18 2003 51144\r\n",
"-rw-r--r-- 1 awalz staff 609B Mar 18 2003 51145\r\n",
"-rw-r--r-- 1 awalz staff 631B Mar 18 2003 51146\r\n",
"-rw-r--r-- 1 awalz staff 1.0K Mar 18 2003 51147\r\n",
"-rw-r--r-- 1 awalz staff 1.8K Mar 18 2003 51148\r\n",
"-rw-r--r-- 1 awalz staff 405B Mar 18 2003 51149\r\n",
"-rw-r--r-- 1 awalz staff 696B Mar 18 2003 51150\r\n",
"-rw-r--r-- 1 awalz staff 5.5K Mar 18 2003 51151\r\n",
"-rw-r--r-- 1 awalz staff 1.4K Mar 18 2003 51152\r\n",
"-rw-r--r-- 1 awalz staff 5.0K Mar 18 2003 51153\r\n",
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"-rw-r--r-- 1 awalz staff 604B Mar 18 2003 51158\r\n",
"-rw-r--r-- 1 awalz staff 1.4K Mar 18 2003 51159\r\n",
"-rw-r--r-- 1 awalz staff 1.0K Mar 18 2003 51160\r\n",
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"-rw-r--r-- 1 awalz staff 1.1K Mar 18 2003 51163\r\n",
"-rw-r--r-- 1 awalz staff 2.3K Mar 18 2003 51164\r\n",
"-rw-r--r-- 1 awalz staff 4.8K Mar 18 2003 51165\r\n",
"-rw-r--r-- 1 awalz staff 1.2K Mar 18 2003 51169\r\n",
"-rw-r--r-- 1 awalz staff 868B Mar 18 2003 51170\r\n",
"-rw-r--r-- 1 awalz staff 721B Mar 18 2003 51171\r\n",
"-rw-r--r-- 1 awalz staff 3.0K Mar 18 2003 51172\r\n",
"-rw-r--r-- 1 awalz staff 1.9K Mar 18 2003 51173\r\n",
"-rw-r--r-- 1 awalz staff 645B Mar 18 2003 51174\r\n",
"-rw-r--r-- 1 awalz staff 2.4K Mar 18 2003 51175\r\n",
"-rw-r--r-- 1 awalz staff 2.9K Mar 18 2003 51176\r\n",
"-rw-r--r-- 1 awalz staff 1.3K Mar 18 2003 51177\r\n",
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"-rw-r--r-- 1 awalz staff 1.3K Mar 18 2003 51179\r\n",
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"-rw-r--r-- 1 awalz staff 949B Mar 18 2003 51186\r\n",
"-rw-r--r-- 1 awalz staff 1.9K Mar 18 2003 51187\r\n",
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"-rw-r--r-- 1 awalz staff 834B Mar 18 2003 51189\r\n",
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"-rw-r--r-- 1 awalz staff 1.6K Mar 18 2003 51192\r\n",
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"-rw-r--r-- 1 awalz staff 6.2K Mar 18 2003 51208\r\n",
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"-rw-r--r-- 1 awalz staff 636B Mar 18 2003 51214\r\n",
"-rw-r--r-- 1 awalz staff 989B Mar 18 2003 51215\r\n",
"-rw-r--r-- 1 awalz staff 668B Mar 18 2003 51216\r\n",
"-rw-r--r-- 1 awalz staff 2.8K Mar 18 2003 51217\r\n",
"-rw-r--r-- 1 awalz staff 1.7K Mar 18 2003 51218\r\n",
"-rw-r--r-- 1 awalz staff 905B Mar 18 2003 51219\r\n",
"-rw-r--r-- 1 awalz staff 2.4K Mar 18 2003 51220\r\n",
"-rw-r--r-- 1 awalz staff 1.0K Mar 18 2003 51221\r\n",
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"-rw-r--r-- 1 awalz staff 704B Mar 18 2003 51227\r\n",
"-rw-r--r-- 1 awalz staff 949B Mar 18 2003 51228\r\n",
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"-rw-r--r-- 1 awalz staff 871B Mar 18 2003 51232\r\n",
"-rw-r--r-- 1 awalz staff 1.0K Mar 18 2003 51233\r\n",
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"-rw-r--r-- 1 awalz staff 11K Mar 18 2003 51238\r\n",
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"-rw-r--r-- 1 awalz staff 456B Mar 18 2003 51266\r\n",
"-rw-r--r-- 1 awalz staff 816B Mar 18 2003 51267\r\n",
"-rw-r--r-- 1 awalz staff 2.4K Mar 18 2003 51268\r\n",
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"-rw-r--r-- 1 awalz staff 1.6K Mar 18 2003 51274\r\n",
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"-rw-r--r-- 1 awalz staff 1.5K Mar 18 2003 51277\r\n",
"-rw-r--r-- 1 awalz staff 6.2K Mar 18 2003 51278\r\n",
"-rw-r--r-- 1 awalz staff 963B Mar 18 2003 51279\r\n",
"-rw-r--r-- 1 awalz staff 2.0K Mar 18 2003 51280\r\n",
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"-rw-r--r-- 1 awalz staff 618B Mar 18 2003 51282\r\n",
"-rw-r--r-- 1 awalz staff 2.7K Mar 18 2003 51283\r\n",
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"-rw-r--r-- 1 awalz staff 5.4K Mar 18 2003 51318\r\n",
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]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `load_files` function can load text files from a 2 levels folder structure assuming folder names represent categories:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#print(load_files.__doc__)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_twenty_train = load_files('../datasets/20news-bydate-train/',\n",
" encoding='latin-1', random_state=42)\n",
"all_twenty_test = load_files('../datasets/20news-bydate-test/',\n",
" encoding='latin-1', random_state=42)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_target_names = all_twenty_train.target_names\n",
"all_target_names"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"['alt.atheism',\n",
" 'comp.graphics',\n",
" 'comp.os.ms-windows.misc',\n",
" 'comp.sys.ibm.pc.hardware',\n",
" 'comp.sys.mac.hardware',\n",
" 'comp.windows.x',\n",
" 'misc.forsale',\n",
" 'rec.autos',\n",
" 'rec.motorcycles',\n",
" 'rec.sport.baseball',\n",
" 'rec.sport.hockey',\n",
" 'sci.crypt',\n",
" 'sci.electronics',\n",
" 'sci.med',\n",
" 'sci.space',\n",
" 'soc.religion.christian',\n",
" 'talk.politics.guns',\n",
" 'talk.politics.mideast',\n",
" 'talk.politics.misc',\n",
" 'talk.religion.misc']"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_twenty_train.target"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"array([12, 6, 9, ..., 9, 1, 12])"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_twenty_train.target.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"(11314,)"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_twenty_test.target.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"(7532,)"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(all_twenty_train.data)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"11314"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(all_twenty_train.data[0])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"unicode"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def display_sample(i, dataset):\n",
" print(\"Class name: \" + dataset.target_names[dataset.target[i]])\n",
" print(\"Text content:\\n\")\n",
" print(dataset.data[i])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"display_sample(0, all_twenty_train)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Class name: sci.electronics\n",
"Text content:\n",
"\n",
"From: [email protected] (Bill Mayhew)\n",
"Subject: Re: How to the disks copy protected.\n",
"Organization: Northeastern Ohio Universities College of Medicine\n",
"Lines: 23\n",
"\n",
"Write a good manual to go with the software. The hassle of\n",
"photocopying the manual is offset by simplicity of purchasing\n",
"the package for only $15. Also, consider offering an inexpensive\n",
"but attractive perc for registered users. For instance, a coffee\n",
"mug. You could produce and mail the incentive for a couple of\n",
"dollars, so consider pricing the product at $17.95.\n",
"\n",
"You're lucky if only 20% of the instances of your program in use\n",
"are non-licensed users.\n",
"\n",
"The best approach is to estimate your loss and accomodate that into\n",
"your price structure. Sure it hurts legitimate users, but too bad.\n",
"Retailers have to charge off loss to shoplifters onto paying\n",
"customers; the software industry is the same.\n",
"\n",
"Unless your product is exceptionally unique, using an ostensibly\n",
"copy-proof disk will just send your customers to the competetion.\n",
"\n",
"\n",
"-- \n",
"Bill Mayhew NEOUCOM Computer Services Department\n",
"Rootstown, OH 44272-9995 USA phone: 216-325-2511\n",
"[email protected] (140.220.1.1) 146.580: N8WED\n",
"\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"display_sample(1, all_twenty_train)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Class name: misc.forsale\n",
"Text content:\n",
"\n",
"From: [email protected] (Andy Freeman)\n",
"Subject: Re: Catalog of Hard-to-Find PC Enhancements (Repost)\n",
"Organization: Computer Science Department, Stanford University.\n",
"Lines: 33\n",
"\n",
">[email protected] (Andy Freeman) writes:\n",
">> >In article <[email protected]> [email protected] (Joe Doll) wr\n",
">> >> \"The Catalog of Personal Computing Tools for Engineers and Scien-\n",
">> >> tists\" lists hardware cards and application software packages for \n",
">> >> PC/XT/AT/PS/2 class machines. Focus is on engineering and scien-\n",
">> >> tific applications of PCs, such as data acquisition/control, \n",
">> >> design automation, and data analysis and presentation. \n",
">> >\n",
">> >> If you would like a free copy, reply with your (U. S. Postal) \n",
">> >> mailing address.\n",
">> \n",
">> Don't bother - it never comes. It's a cheap trick for building a\n",
">> mailing list to sell if my junk mail flow is any indication.\n",
">> \n",
">> -andy sent his address months ago\n",
">\n",
">Perhaps we can get Portal to nuke this weasal. I never received a \n",
">catalog either. If that person doesn't respond to a growing flame, then \n",
">we can assume that we'yall look forward to lotsa junk mail.\n",
"\n",
"I don't want him nuked, I want him to be honest. The junk mail has\n",
"been much more interesting than the promised catalog. If I'd known\n",
"what I was going to get, I wouldn't have hesitated. I wouldn't be\n",
"surprised if there were other folks who looked at the ad and said\n",
"\"nope\" but who would be very interested in the junk mail that results.\n",
"Similarly, there are people who wanted the advertised catalog who\n",
"aren't happy with the junk they got instead.\n",
"\n",
"The folks buying the mailing lists would prefer an honest ad, and\n",
"so would the people reading it.\n",
"\n",
"-andy\n",
"--\n",
"\n"
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's compute the (uncompressed, in-memory) size of the training and test sets in MB assuming an 8-bit encoding (in this case, all chars can be encoded using the latin-1 charset)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def text_size(text, charset='iso-8859-1'):\n",
" return len(text.encode(charset)) * 8 * 1e-6\n",
"\n",
"train_size_mb = sum(text_size(text) for text in all_twenty_train.data) \n",
"test_size_mb = sum(text_size(text) for text in all_twenty_test.data)\n",
"\n",
"print(\"Training set size: {0} MB\".format(int(train_size_mb)))\n",
"print(\"Testing set size: {0} MB\".format(int(test_size_mb)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Training set size: 176 MB\n",
"Testing set size: 110 MB\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we only consider a small subset of the 4 categories selected from the initial example:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"train_small_size_mb = sum(text_size(text) for text in twenty_train_small.data) \n",
"test_small_size_mb = sum(text_size(text) for text in twenty_test_small.data)\n",
"\n",
"print(\"Training set size: {0} MB\".format(int(train_small_size_mb)))\n",
"print(\"Testing set size: {0} MB\".format(int(test_small_size_mb)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Training set size: 31 MB\n",
"Testing set size: 22 MB\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Extracting Text Features"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"\n",
"TfidfVectorizer()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"TfidfVectorizer(analyzer=u'word', binary=False, charset=None,\n",
" charset_error=None, decode_error=u'strict',\n",
" dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',\n",
" lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
" ngram_range=(1, 1), norm=u'l2', preprocessor=None, smooth_idf=True,\n",
" stop_words=None, strip_accents=None, sublinear_tf=False,\n",
" token_pattern=u'(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
" vocabulary=None)"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"vectorizer = TfidfVectorizer(min_df=1)\n",
"\n",
"%time X_train_small = vectorizer.fit_transform(twenty_train_small.data)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CPU times: user 635 ms, sys: 29.5 ms, total: 665 ms\n",
"Wall time: 658 ms\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The results is not a `numpy.array` but instead a `scipy.sparse` matrix. This datastructure is quite similar to a 2D numpy array but it does not store the zeros."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_train_small"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"<2034x34118 sparse matrix of type '<type 'numpy.float64'>'\n",
"\twith 323433 stored elements in Compressed Sparse Row format>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"scipy.sparse matrices also have a shape attribute to access the dimensions:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n_samples, n_features = X_train_small.shape"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This dataset has around 2000 samples (the rows of the data matrix):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n_samples"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
"2034"
]
}
],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the same value as the number of strings in the original list of text documents:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(twenty_train_small.data)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"2034"
]
}
],
"prompt_number": 23
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The columns represent the individual token occurrences:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n_features"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
"34118"
]
}
],
"prompt_number": 24
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This number is the size of the vocabulary of the model extracted during fit in a Python dictionary:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(vectorizer.vocabulary_)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
"dict"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(vectorizer.vocabulary_)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
"34118"
]
}
],
"prompt_number": 26
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The keys of the `vocabulary_` attribute are also called feature names and can be accessed as a list of strings."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(vectorizer.get_feature_names())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"34118"
]
}
],
"prompt_number": 27
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are the first 10 elements (sorted in lexicographical order):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"vectorizer.get_feature_names()[:10]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"text": [
"[u'00',\n",
" u'000',\n",
" u'0000',\n",
" u'00000',\n",
" u'000000',\n",
" u'000005102000',\n",
" u'000021',\n",
" u'000062david42',\n",
" u'0000vec',\n",
" u'0001']"
]
}
],
"prompt_number": 28
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's have a look at the features from the middle:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"vectorizer.get_feature_names()[n_features / 2:n_features / 2 + 10]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": [
"[u'inadequate',\n",
" u'inala',\n",
" u'inalienable',\n",
" u'inane',\n",
" u'inanimate',\n",
" u'inapplicable',\n",
" u'inappropriate',\n",
" u'inappropriately',\n",
" u'inaudible',\n",
" u'inbreeding']"
]
}
],
"prompt_number": 29
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have extracted a vector representation of the data, it's a good idea to project the data on the first 2D of a Principal Component Analysis to get a feel of the data. Note that the `TruncatedSVD` class can accept `scipy.sparse` matrices as input (as an alternative to numpy arrays):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.decomposition import TruncatedSVD\n",
"\n",
"%time X_train_small_pca = TruncatedSVD(n_components=2).fit_transform(X_train_small)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CPU times: user 101 ms, sys: 16 ms, total: 117 ms\n",
"Wall time: 112 ms\n"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from itertools import cycle\n",
"\n",
"colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n",
"for i, c in zip(np.unique(y_train), cycle(colors)):\n",
" plt.scatter(X_train_small_pca[y_train == i, 0],\n",
" X_train_small_pca[y_train == i, 1],\n",
" c=c, label=twenty_train_small.target_names[i], alpha=0.5)\n",
" \n",
"_ = plt.legend(loc='best')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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M9yMaXgZmrHMhRN51Lz8/n6ioKJKSkuq8B0C83zVLpVKhbNuW3dHRRGdk8Eds\nLFLjxvVmMVpjfb8ry9fXl0OHDlWorKHW48rOzsa3gpt/V6ZsbenVqxdXrlwxaAz3I+Z4CYIRUKvV\nbF25ElVWFhlaLU2GDmXIU0+JhRUfUDKZjLEzZnBwzx4OhYej6tKFicOGYWIi/kmvKfn5+ezau5ew\nhAQ8HR0ZOXQo9vb2NVpHZYdBa+IPJq1WW2YDbaHuiR4vA+tbid3sGxKRd936+csvGVRYyLNeXszy\n8iJu926uXbtWZ/WL97vmWVhYMOSpp3h23jyeHDsWa2vrWqursh6E9zs+Pp71W7aw+quvOPH332Ua\nNZIksf6bbziQlUVW586cNDFh1ZdflntyVKvVkpCQQGJiYqWHeSdMmEBsbCyPP/44tra2rFy5klGj\nRuHm5oaDgwN9+vTh0qVLFbrXRx99RMuWLVGr1eXOLV26lJEjRzJhwgTs7e3ZvHkzmZmZTJ06FXd3\ndzw9PVm0aNFd45fL5URGRgKQmprK448/jr29PZ07d+aNN94oMyxZumxmZiYTJ06kUaNG+Pr6snz5\ncv3PeNOmTfTs2ZPXXnsNlUqFv78/+/btu2t+crmcdevWERgYiJ2dHYsXLyYiIoJu3brh4ODAmDFj\n0Gg0QPlh2Pfeew9PT0/s7Oxo1qyZvodRq9XyzjvvEBAQgJ2dHR07diQ+Pr5CP+/qEn8eCYIRSIuP\nJ9DVFQAzhQJfuZy0tDQDRyUIhpGUlMSyDRuQOnTA3NqakCNHKCoqom/v3sDNRkPo9ev4TJiATCbD\n1sWF2IQE1Gq1figtPz+fj77+mitZWSBJtHV25v8mTcLMzKxCMWzZsoXjx4+zYcMGHnnkEeBmg2TT\npk2YmZnxv//9j3HjxhESEnLP+7z11lv8+uuvHD16FEdHxzuW+fXXX/nhhx/YsmULBQUFjB07FldX\nVyIiIsjJyWHo0KF4eXkxffr0e9Y1c+ZMbG1tSUpKIioqioEDB951aHH27NlkZ2cTFRVFSkoKAwYM\nwM3NjWeffRaA4OBgpkyZQmpqKp9//jlTp04lISHhrnUfOHCAkJAQYmNjadeuHcePH2fbtm2oVCq6\ndevGtm3bmDhxYplrrl69yqeffsrp06dxdXUlNjaW4uJiAFavXs327dv5/fffCQwM5Pz581hZWd0z\n/5oierwMzFjnQoi865ZLQABnr18HILeoiDBJwsXFpc7qF++3canveYdeuEBBQABuLVui8vXF5eGH\n2R8crD9qedHdAAAgAElEQVRvYmKCpNWiK+lFkXQ6dIWFZYbo9hw4wGUrK7xHj8Z7zBjOaLUcOnKk\nWnFNnjwZa2trTE1NWbJkCefOnSM7O/uOZSVJ4pVXXuHPP//k8OHDd210AXTv3p1hw4YBNxuVv//+\nO2vWrMHS0hJnZ2deeukltm/ffs/YtFotP/30E2+++SYWFhY0b96cSZMm3XH4U6vVsmPHDlasWIG1\ntTU+Pj7MnTuXLVu26Mv4+PgwdepUZDIZEydOJDExkeTk5LvW/7///Q8bGxtatGhB69atGTRoEL6+\nvtjZ2TFo0KA7NlAVCgWFhYVcvHgRjUaDt7c3/v7+AGzYsIHly5cTGBgIQOvWrVGpVPf8GdQU0fAS\nBCMwfOpU/nZ15cPYWD66fp0248bp/wESBGMjl8nQlfR8AGg1GkxKrX9mY2PDwLZtidqzB3VoKJH7\n9tGhUaMyS3XE3riBnZ+fftK7ja8v8TduVDkmrVbL/PnzCQgIwN7eHj8/PwBSUlLuWD4jI4OvvvqK\n+fPnY2tre897e3p66r+PiYlBo9Hg5uaGUqlEqVQyY8YMbtwn9hs3blBcXFxmGK/0fUtLSUlBo9Hg\n4+Ojf83b27tMj5ZrSQ88oO9pysnJuWv9pf9QtLS0LHNsYWFxx2sDAgJYu3YtS5cuxcXFhbFjx5KY\nmAhAXFwcjRs3vmt9tUk0vAzsQZgLURtE3nXLycmJmUuXMn7lSuZ88gl9+/ev0/pvzzspKYlLly7d\n8y/chkB8zuun9u3aoYqLIzY4mMSLF0k9fJhhJcOMt4wePpxZPXrQT6djauvWzJg4sczitL4uLmSG\nhyNJEjqtluyICHxLNSYqovTDLVu3buXXX3/l4MGDZGZmEhUVBZSdUF+6vFKp5LfffmPKlCmcOHHi\nnnWUvs7Lywtzc3NSU1NJT08nPT2dzMxMzp8/f89YnZ2dMTExIS4uTv9a6e9Lc3JywtTUtMzSErGx\nsXdtqFXXvR4SGjt2LMeOHSMmJgaZTMa8efOAmz+H8PDwWonnfkTDSxDqgCRJJCQkEBkZSX5+vkFi\nUCgUODo61tk8hrs5cfQo3y5YQOgHH/DNggUE3+OXhiDUBqVSyRszZjDYwoIeeXnMGz6cTh07likj\nl8vp2qULo4cPp2/v3piampY5P2TAANpJErFbtxK3dSvdra31c8QqysXFhYiICODmUgzm5uaoVCpy\nc3NZuHBhmbKSJJUb1uvduzffffcdI0aM4NSpU3es4/Zr3NzcGDBgAK+88grZ2dnodDoiIiI4evTo\nPWNVKBSMGDGCpUuXkp+fz5UrV9iyZcsdGz0KhYKnn36a119/nZycHGJiYlizZg3jx4+/78+kom5/\nGOJOrl27xqFDhygsLMTc3BwLCwv9cPFzzz3HokWLCC9pPIeGhtbZvFfR8DKw+j4XorYYU946nY6d\nmzbx4+uv88WsWaxbvJikpCRDh1Wnbr3fmZmZHP/mG6a5uDDGy4vnnJ059PXX9xxieJAZ0+e8tIrk\nnZmZyS/bt/PdJ59wPCiozhd/dXJyYuQTTzDx6adp2bJlpa83NzdnznPPsXLGDFbNnMmMSZMqvZzH\nggULWLZsGUqlkvT0dHx8fPDw8KBVq1Z069atTKPm9p6rW98/+uijfP311zz++OOcPXuW2NhYbG1t\n9U/o3Wn9r2+++YaioiJatGiBSqVi1KhRXC+ZA3q3egA++eQTMjMzcXV1ZdKkSYwdO7bMwwSly378\n8cdYW1vj7+9Pr169GDduHFOmTLlrTKWPX3jhBV544YU7nrvTa3eLubCwkAULFuDs7IybmxspKSms\nWLECgFdeeYWnn36aAQMGYG9vz7Rp0ygoKChXT22oT4v4GOWWQUFBQfW+W742GFPeISEhnF2zhom+\nvhyLjcXO3JyzgYE8++qrhg6tztx6v+Pi4ti3dCnTSs0TWRcXx5PLltX64p/Z2dlcunQJnU5H8+bN\ncXBwqNX6wLg+56XdL++8vDw+f+st2t24gZulJSczM3EePpwhI0bUeCxiy6DaM2/ePJKTk9m4caOh\nQ6lVYsugBsYY/1EG48o7PTUVP7kchVxOX19fAlQq0upovZj64tb77eTkRLqlJdEZGQBEpKWRY2NT\n608TZWRk8MVbb5H42WekrF/Pl0uW1Mn8MmP6nJd2v7zDwsJwT0qir5cXTZ2cGO3tTcjevfft9crN\nzSUlJQWtVluD0QoVdfXqVUJDQ5EkieDgYL7++muGDx9u6LAeOGIdL0GoZa7u7gTpdHTRaLAwMeFM\ncjJut80naYhyc3MJCQlBU1hI0+bNcXd3x9LSkpGvvMLOjz9GiolBrlIxavZszM3NazWWY3/8QfuU\nFB4ueVLMOT6eI3v2MKpk6KMqsrKyCD5xgoKcHJq0aUOTJk1qKlyjUK4H4T67KBw9eJC/tm7FWpKQ\n3Nx45qWXcHZ2rsUIhdtlZ2czduxY1Go1Li4uvPrqq/plKoSKEw0vAxNDEQ1f8+bNSRg9mrU//kic\nWk3rnj0ZN26cocOqVTk5OXz1zjv4x8djI5fz1o0bzF27lsDAQPz9/Zn7wQfk5eVhZWVV5kmx2lKQ\nlYWvhYX+2MnKimslvW5VkZOTw4Z33qF5YiLOpqbs3b2bnNmzaX9bg9qYPuel3S/vwMBADrm6cig2\nFncrK05mZtLhqafu+lmIjo4m5JtvmO3hgY2ZGf8mJvLTl1/y/G0T0IXa1bFjR8LCwgwdxgNPNLwE\noZbJZDL6DxlCj4cf5uDBgwwZMqROGhuG9O+pUwTExzO0ZK2whMxMDn//PYGvvw7cfGLMxsamzuJp\n3LYtx48cwT0vD4VczpH0dJo99VSV73fu3Dkaq9U8VpKfZ1YWP/74Y7mGl3BnVlZWPLtgAUf27eNM\nSgpNW7ema8+edy2flJREgFyOTclE7rYuLuyJiECSJLHfqPDAEQ0vAzPGv4bBOPO2srLi8ccfN3QY\ndaIwPx/7Uqt8DwwI4Pu8PIPF065DB3KnTGHjL7+g0+loP3Ys3UrtMVdZxRoNlqV+4VuamlJ82z5+\nYJyfc6hY3vb29gwbPbpC91OpVPyr01FYXIy5iQnXUlNReXqKRpfwQBINL0GoR4qLi/lzzx7C/v4b\nCzs7Hnn6aYOtrlwdTVq04EfAOyMDGzMz9l2/TtMxYwwWj0wmo9fDD9Pr4Ydr5H7Nmjdns4UFbsnJ\nKC0s+OPGDVpVsBFR2zQaDb//9BPhwcFY2NrSf9w4/bYoD6qAgADCnniCT3fvxkEuJ83WljHTphk6\nLEGokvr054JYTsKIiLzvbM+PP5Lx888McHUlLT+fX4uKmPj22+X2VdRoNGRnZ2Nra1tuYcf64sL5\n8xz5/nuK8vPRKJXMnT+/zF53FaHRaPjzt9+ICgnBWqXi0VGjymzbYkgxMTEc/vFHCnNyaNKlC336\n9y83hGyIz/nP27ZRtHcvAzw8SM3L46f8fCa8/XaZLVpqW23lnZycTF5eHi4uLlhaWlboGpVKRXp6\neo3HIhgPpVJ5x8VVq7qchOjxEoQSkiRx8PffOb1nDzKZjE6PP87DAwbU6XDGpaNHed7TEztzc5yt\nrWkbHc21a9fKNLzCwsLY9fHHmOXkUGhlxYg5c+plj0ar1q1p1bo1cPMXcWUbXQC/ff89hfv2MbxR\nI5KSk9n67rtMe/vtOlmD6358fHyY/MordVrnxQsX+PePP5AkiY4DBtCyVatyZa7+9RczvbywMTPD\nwcKCNtHRhIeH12nDq7Y0atSo0tfU1WrkIP6gFCqmYc/wfQAY64e1qnnn5ORwOOgw+/bvIzY2tkZj\n+vv4caK3beP/7OyYYWND+Lffcurvv2u0jvvlbWZpSXZhof44W6cr06OVn5/PrrVrGatQ8JK3N8+Y\nmrJr7VqDbUNUUVV5vyVJ4mJQEMN9fHCzteUhV1cCc3Kqvb9aXfas1+T/31euXGH/qlV0joigS2Qk\nB1at4vLly+XKmVtbk1FqBe4Mna7Wl+u4nfh3zbgYa95VJXq8hAdGTk4Oy9YsQ22uRmGuQH5IzmtT\nXqNZs2Y1cv/Ic+foZW+PXckvqZ52dpw7e5bO3brVyP0BtFotKSkpmJqaolQqy/Wm9R0zhh1r19Ip\nPZ10rZYEX18Gt22rP5+WloZ9QQFeJesXednbYx8XR1paWr0ZgruX4uJigg4cIPrsWaydnHh0+PB7\nrsWkMDUlT6PBvGQrljydrtLbstySnZ3Nj19/Tey5c1gplQydNq3GPjt14eyRI/S3tKSZkxMAGq2W\nc0eP0rx58zLl+o0bx441a2ifnk6qVktqkyY82aaNIUIWBOEORI+XgYm93Cru1OlTqM3V+Hf2x6et\nD9ZtrNm5d2eNxWRpb8+NUj1HN/LzsVIqa+z+WVlZzJ0+ne/nz+fruXPZtXVruZW62z70EE8uXkz+\nqFE4TJ3Kc/Pnl5nLYm9vT4ZcTlpJnGn5+WTI5djb29dYnNWl0+nK9Srder/3/PADSdu20T85Gf9T\np9i8YgXZ2dl3vI9MJqP36NF8q1bzT3w8v0RGkubvX66hUVE/fPUVXmfP8rqnJ2Mkid0ffMCNGzeq\ndK+Kqur/32FhYfy6Ywf7du/Wz09SmJpSVGrFdo1Oh/wOjdDWbdow6s03Yfx4PF54gamvvVbnPV7i\n3zXjYqx5V5Xo8RIeGIWFhSjM/5snZG5lTl5BzS1R0GfwYDaGhJASFYUERDo78+zAgTV2/9+//x5v\ntZpZnTtTrNPx7b59hDRrRocOHcqU8/f3x79kfajb2djY8Oi0aXz1xRe4ShLXZTIenT69TtfEupv8\n/Hx+2ryZiOBgTC0t6T9pEh07d9af1+l0nP/zT17z8cHcxAQfBwfiY2IICwujffv2d7xnjz59UDo7\nE3XlCvb29gzo3r1KjQitVktcaCiTvL2Ry2R42tkRmJFBXFxcvVv9PPTcOQ6uWUNPU1Oyi4vZcPgw\nzy1eTOd+/dhx8iSaku2mjsnlPP3oo3e8h7e3N97e3nUZtiAIFSQaXgZmrGPjVcm7RfMWyA/JSXNK\nw9zKnMSQRJ7p8UyNxeTo6Mjzb76pnzfTv0WLGm3QJEdEMKFVK2QyGaYKBc3MzEiOj4fbGl7306Fz\nZ/wDAzlz5gy5wcGcCwpCAjp06mTQdY1+274du5Mned3Hh4yCAr757DMcnZ3x8/Ojb9++Nxe7VCgo\n0mr1Q4dFknTfSfctWrSgRYsW1YpNLpdjbmtLcm4urjY26CSJG1otzaysqnXf+6nK5/yvXbsY7uCA\nb8kDBJqoKEL+/ZeH+/Vj7OLFhJw4gSRJjOnRA69Sm43XJ+LfNeNirHlXlWh4CQ8Mb29vXpvyGjv3\n7iSvII9nejzDwEdrrkcKwNbWls6lemlqkpOfH5f/+Qdna2uKdTquFRXRwt39vtcVFxcjl8vLLFWQ\nlZVFyLZtDDY3x1yhYN/HHyPNnEmnrl1rJfaKiD57lufd3VHI5ThaWdEWiImOxq9kf0SZTEb3p57i\nuy1b6GJlRWJhITe8vWnatGmtxyaTyRj83HNs+fBDmqWmkqTTYd2jR73cX1Gr0WBeqjFqLpNRXFwM\ngJeXF171ZL0wQRCqRjS8DMxYH8Otat7NmjVjUbNFNR/QPRQXF/PH7t1cPXECc2trHh4z5p6TsnNz\nc/n9hx9ICgtD5e3NY6NGoVQqGTx6NIv++otLsbHk63R49OtXbpixNI1Gw89bt3LlyBFkcjldR4yg\n36BByGQyzp86RQ9JokXJMNlgmYyDhw4ZtOFlo1JxPSUFO3NzJEkiUaulSUmP4a33u2///jg4OhJ9\n+TLWSiVT+/bFotQeirWpdZs2OC9fTlxcHI2trWnWrFmtb9303XffkRUeTkF2No07dWLQiBH3XXet\nTb9+7P76awZqtWQXFRFsZsb4kmU5HhTi3zXjYqx5V5VoeAnCffyxezdpP/3EBDc3MtPT+WnlSmze\nfBNPT89yZXU6Hd998gk+V67Qy9GRsOBgvomN5YXFi29ukTJhAs2bN8fU1BRnZ+d7Dg0e3LMH6c8/\nWeDnR2FxMd9s3YqDszMdO3W6OdG61MT8Iq0WhYEXUn1s4kR2vvceTWJiSNfp0LVrR7t27cqUkclk\ntOvQgXaVHF6tKa6urnW2nlVSUhLHtm1jrp8fTlZW/PHbb+zRannymXsPj/d6+GEUCgV//PUXZtbW\njBo2DPcK9IwKgvBgECvXC8J9rHn1VSYpFKhKni48HBMD48fzcL9+5cqmpaWxae5cXvb21jeqvoqN\npf+SJfj4+FSq3i/efpvB6el42NoSfvUqQSEhHHN1Zfjzz9PtkUf4ZtkyuubmYmFiwjGtliH/+5/B\nl0dIS0sjKioKCwsLmjZtWuWlH6ojOTmZf0+eRKfV0rpjR4NNMv/rr7/I/vJLHvP1BSC3qIiPMzOZ\n/+mnBolHEISaJVauF4RaYm5tTWZGhr7hlanV4nyX4TFTU1M03HzU30yhQCdJ5Fdx7SnbRo1IiIuD\nzEyyLl7E0dyc8Z6epPz+O5dUKiYvXsyp48fJ0GgY3qnTXZ+ErIr8/HwyMjJwcHBAp9MRFhYGQJMm\nTbC6x4R0lUqFSqWqsTgqKykpic1vvUXXvDxM5HJ27N3LiAULDLLfpbm5OfGl/pjMLCzE3Nq6zuMQ\nBKF+EQ0vAzPWsfEHKe+Hx4zhx1Wr6JiZSYZOR6yPDwNuG0K7xdbWlmYDB/Ltnj20MDcnorAQVY8e\n+qGiyuT96IgRbL56leJ//sGyqAhTd3ee9fNDnZ3N0QsXeOSxxxgyYkRNpal3+dIlfv34Y+yKirgu\nSWgkiYckCQkIcnfn2fnzsbOzq9Q9a/r9liSJ6OhoMjMzcXd3128lE3z0KN3z8+lZ0rtom5TEyb17\naTx7do3VXVGtW7fmWxMTFJGROMnl/CuT8eicOXUehyE8SP9/1ySRt1ARouElCPfRvHlzbJYuJezK\nFZwsLBjQocM9e32GjR7NmYAArsfG4u/qSucuXZDJZGRnZ3Pt2jWUSiVNmza976RyZ2dnZrz1Flu+\n/hrZwYM826YNZgoFCTk52NTS2lP5+fn8+vHHTLSyws3FhY3BweRERvLkiBGYmZlxMCaGI/v38/io\nUbVSf0VIksTu778ndu9e3BQK/pDLGTh7Nm3atqW4sBDLUr2LlqamFJfagqkumZubM3j0aGxtbcnP\nzeWpgAB8S4YdBUEwXmKOl2CU8vLyOHLgAJlJSbgHBtKjT587ridVXFzM8aAgkiIjUbq706d//yot\n4Hnjxg02r1iBX3o6RZLEDS8vps6fj3UFhp5yc3P5+v33UcbEYCKToXZ2ZsqCBSiruKq+RqPh8uXL\nFBYW4u/vj6Ojo/6cWq3m10WLmFGyPtSWv//GOiaGRwcNws7Ojks3bhDapg1jnn++SnXXhJiYGH5d\nsoQZXl6YKhQk5+ayITeXeZ98QmRkJL8sX87jNjaYyuXsSUuj24sv0qFTJ4PFKwhCw2TIOV6PAWsB\nBfAV8N5t58cB/+NmcNnAC0BoDdQrCFWi0WjYtHo1vuHhtLG25szx46QkJjJi3Lgy5SRJYuemTciO\nHqW1jQ1hf/3FlitXmPLyy/dd9PN2B3/+mZ7Z2XQtGQL7PSqKvw4fZsDQofoy+fn5ZGdn4+DggJmZ\nmf51a2trpi9cSHh4ODqdjmGNG9+zx+1eioqK2LRmDRaXLuEgl3PY3Jyn58/X98Q4ODiQaWpKcm4u\njaytcbCz40hxMf0UCvI0Gk5mZdGyVasq1V1TsrOzcVEoMC15DxpZW0NKCoWFhQQEBDDotdc4vmcP\nOo2GruPG0b5jR4PGKwiCUFp1F7FRAJ9ws/HVAhgL3L6RWiTQG2gDvA18Uc06GxRj3ePKkHnHxMRg\nERHBIB8fWjg7M9rXlyt//klBQUGZcllZWcQfP84oX19aNmrEE76+aC5eRK1WV7rO3NRU3KytCYqO\nBsDN0pKctDT9+TOnTvHhnDl8v2ABH/7vf0SXlLvF3Nycli1b0rp16yo3ugBCQkKwv3CBCb6+DPPx\nYZiJCfu//VZ/PjMzE5uAABacOcOKS5e44OCA3/PP83FGBquTk/EYNYou3btXut6afL/d3d2JVihQ\nZ2cjSRLBCQnY+frqh25btGjBs6+9xnMLF9Kxc2eDruYv/v82LiJvoSKq2+PVGQgHokuOtwNPAJdL\nlTlZ6vt/gPKLHwlCHZIkCRn6bmJkMhmyktfLlZPJyvzilt+hXEX4tm3LsYsXaQRkFxbyT24unUq2\nwUlNTeXg558zTaXC0cqKiLQ0dn70Ea+sXFnpnrX7yS3pLbqVk6uNDbklmzAnJiay5a236FFUxENe\nXvxRUMDwmTNp27atPmdDNmJuUalUDHv5ZbasX48mJgZV48aM+b//Q5Ik4uPjKS4uxsPDo0yvofBg\nOn/+PL/8+QuaYg39uvWjV49e9eIzKAjVUd2GlwcQV+o4Huhyj/JTgb3VrLNBMdYnQQyZt4+PD/u8\nvfkjJgZfGxv+zcyk8SOPYFmyXMQt9vb2NOrYkZ+Dg2ljb09YVhZSkyZVWsyy74AB7MnKIviPPziV\nlka38eP1i4impKTgLkk4lvRkNVapkMXEkJOTg729ffUTLsXX359fJIlWeXnYm5tzWK3Gr2Qj8FNH\nj9KzqIjuJfO77JOTOXv0KG3btq32L7uafr+bNW9O07Vr0Wg0mJmZodFo+OaTT8g9cwZzuZw8Dw8m\nvfpqjf/8Kkv8/111YWFhfPDtB9i3tUdhquCr379CIVfQo3uP6gdYS8T7LVREdRtelfnT/2HgWeCu\n/9dMnjy5zFyThx56SP+G3urKFMfiuLrHZmZmNO7ShWBJ4rqjI+5NmoCZWZlHom+VHzNtGoe9vNiw\nfz92fn7MeeklTExMKl3/sWPHsHNx4fUvvwTgyJEjHDlyhL59+6JSqTiuVqPMz2dwkybEZ2VxLSOD\n06dP069kkdaazL/XzJksXLGC4qIiBo4YwZMjRxIUFMS50FAGlfSwBUVHE5eZiS4goNbfj5o4/uyT\nT0jdu5el3bsjl8lYGxzM6uXLefP99+tFfOK48scHjxzE1M8UpbuS6LPR5MpzOf7vcXp071Ev4hPH\nxnd86/vbp4JUVnX7bLsCS7k5xwtgAaCj/AT7NsBPJeXC73Ivo3yqMajUL3tjIvIu68TRoxzfvBlH\nINXcnCdfeqnWN3C+NZR6S0REBD8vX85gS0tMFQr2ZmTQ+6WXeKh9+2rXdSvvGzducGzfPvIzMwlo\n357O3bpVqjftbkOev2zbhufhw3Qo6Y1MyMriNwcHnl+8uNqxV4f4nFfdrl93sTtqN95tb+48kByV\nTDNNM16c/mINRFg7xPttXAz1VONpIBDwBdTAaG5OsC/Nm5uNrvHcvdElCEate+/eNG/dmqysLJyc\nnCq0zER13d54ady4MUPnzeOf339HV1xMr0mTaqTRdUtmZiably+nW04OjhYWHP3nH/Jzc+nbv/99\nr9Vqtfy+axdn9+9HJpfTedgwHh08+L+5ar6+nM/Pp41Wi4lcTkhqKq6dO9dY7DUtMzOTr7d+zcXw\ni7g4uvDc2Ofw8/MjLi6O8//+i8LEhPZduuBcS+u1PQh6de9F0Kkgov6NQm4qx0RtwtDpQ+9/oSDU\nczUxS3EQ/y0nsQFYAdxa5Odzbi4xMRyILXlNw81J+bczyh4voe5ptVoO7dtH5L//YqVU8ujIkbi5\nuRk6rAbv5MmTpKxfz+N+fgCk5efzdWEhr65de99rj/z5JzGbN/O0jw9anY6tsbE8NGsWnbp2BW5u\nTv7L9u1cPXAAE5kMVZs2jJ0xo9y8vfpAkiTe/fBdwuXhuDd3JzM5k+KLxTw/+nkOfPIJ3bVaiiSJ\nU9bWTF68WL8qvzFKTU3l9L+n0RRraNe2HR4eHoYOSRD0DLmO1+8lX6V9Xur750q+BMHgCgoK+PHb\nb9EdPswQFxeSExL49to1pr39Ng4ODoYOz6hU5g+t6NBQeiqVWJSsSt/V1pbLFy7oG15yuZzhzzxD\nzrBhFBcXY29vX2+ffsvPz+da/DW8h9zcSF3loSIuOo79O3Yw2NSUViWNC5PYWIKPHmXoyJFVrkut\nVvPX/v1o8vJo3rUrD7VvX29/Lnfi6OjIwAEDDR2GINQouaEDMHalJ+0ZE0PkfS4khDVz5rB75Uo8\nw8OxlSTau7nRJDtbvwl0bbtX3pIkkZGRQWpqKjqdrk7iqStBQUG0bNmSq46OHIuL49KNG+xMTKTT\n449X6HprR0eu5+Xpj68XFGBTasX9W2xsbHBwcDBI4yI6OpofNm3ih40biYiIAO78fpubm2MqM6Uw\n9+ZWRjqtDm2uFoUkld3uyMSkWtsdJScn8+3bb+N74gTtLl3ir7VrOfX331W+X2WIf9eMi7HmXVVi\nr0bBKKSlpXHgs8+YplSywd4eL42Gi3//Tdf+/SmQpBpfL6uytFotP337LdGHD2Mik2HXqhXP/N//\n1cuhstLy8/NRKBQVWjPLzs6OKa+/zrH9+4nPyqJju3Z0qOA8rEcef5yN58+jjolBK0kkeXjw7KOP\nVjreoqIizp49S25ODn7+/jW2d2J0dDQ7ly3jEbkcmUzGrmPHeHLhwjuWVSgUTHlqCp/v+hyZswxt\nhpZHWj5CC/+m7F+3jiFyOUVaLUc1GoZ1udfqPPcWGhJCx7w8OpUM7dqam7N73z46d+tW5XsKglB9\n9anPWczxEmrN1atXOf3++4zz8uKvmBhCzpzBsqAAVfv2xAUGMn3hwvtuWl2bThw7RsQXXzDa15dj\nkZH8EhqKrGlTpi1ZQqvWreskhsoskpqSksLbL79M/N9/ozA1pdvYscycN69WFy3NyckhLCwMuVxO\nkyZNKt0o1Wg0bFy9GruLF2kkl3MWeHj2bP16anciSRIHf/+d03v2ANBh8OAyk/pv+WHTJvxOnNA/\nVRmalMTFtm0ZO2PGXe8dGxtLQkICdnZ2tChZTDf45EnOHTyI3NSUbkOHVmt7pj/370e2bRv9ShqX\nsQlX9a8AACAASURBVJmZ7LG354WlS6t8T0EQ/mPIOV6CUO8plUoSJYmcoiJ6+PhQqNXyXVISoydM\nYGqfPgZtdAEkx8bS0sqKE9HRxJw/z4tmZlxKSGD/6tVYLVqEv79/rdWt0+k4sHs3p3/7DYAOQ4Yw\ncNgw5HI5xcXFhISEkJWejqePD02bNkWSJD544w2c//6bd1xcKCou5qMtW/jJy4sxkydXuv6CggLO\nnj1Lfl4eAYGBeJUs4Ho7Gxsb2rVrV+U8L168iNWlS4z280Mmk9EyJ4dvtmy5Z8Mr+ORJorZu5f9K\nYvp++3b+sbena8+eZQtKEvLSOxzIZHCfPyS9vb3x9vYu81qX7t2rtCXTnbRt356Nv/6KdXw8NmZm\nHMrJofvY2x86FwShrok5XgZmrGPjdZ13o0aN6DpxIp8mJrLg5Ek2XLyIf/Pm+DRuXCdLN9xyt7xV\n7u6E5eVxMTaWx6ytket0BLi60k0u5+r587Ua09/Hj6PeuZNXGjXilUaNSPzhB/4+fhydTsd369Zx\n9aOPMNm2jQPLl3P00CEKCgpIunKFgba2WJuYoLSwoLeZGVeDg+9ax+15a7Va9u7axbIZM5jcty//\nLl6MbvNmdixZwsULF6qdU1JSEufPny+zr2ZhYSEOJUOBAEpLSwpzc+85yT/i7Fl62ttjZ26Onbk5\nPe3tiTh7tly5dr17c1CjITQpif9n77wDoyrTt31Nn8xMkkky6cmkkRASSIDQO1IVFMGGYu+oP113\n3V3bWtZeVl11dV1UUKmiooJ0kJDQSwKkkd57nUkmU8/5/iDmE6UkUcTdzPVfMnPOeZ8zc87c53mf\n936y6uvZ2tnJ8EsuueDfc0EQ2Jeezucffsimr7+mo6Oj+zV/f39u/tvfqJ48meyhQ5nypz8x4jey\n2HDf1/oX/TXuvuLOeLnpN0yYMoXGxkbKVqzgnnHjcLhcfPPqq3g8/fTPMg+/NaPHjePl9evJTEtD\n53QSFx3N8Ph4dlVXo/yV6rxcLhfSHwkPAIvFwsa1azE2N1Ou1xNvMDBOr+fIiRN4+frScfAg90ZH\nI5VIGG6z8c+VKxkzYQJyLy8KGhoYIYoIokiJ3Y5PL1op7d6+nYavvmKCTIbRbCa+s5OImBji1Gq+\nWrGCxJde6nOcB/fuZfeHHxLicnGkshJtQgKTL7uMQcnJpCqVDGxqIkCrZWd1NXFTppxzalXj40OD\n1cqgrr8bOjvR+Pj87H0xMTFc+fjjHNq+HVEQmDNtGgMHDqSmpqbPcfSETV9/Tf26dYz09KSio4OP\nMzO5+7HHUKlUAAQFBbHgppt6tU+n08nWHVs5kn2E5pJqQlRagiIi3LYrbtz8SriF10WmP7r9wsWL\nuyozk+tiYgjS6QAY3d5OXlbWbya8zhb39u++I7SujsihQ0k/dowOk4mGigoKgoO5q8syoa+0tray\n9j//oSY3Fw+9nsvvuYf4+HisVisfvfIKwZmZ+NfVsbWqirYRIzC7XBw9fJhj27fTmJ3NdpeLGbGx\n6JRKpC4XgiBw95NP8o/77ye/uhpRFGmIj+eZu+8+Z9wtLS3U19ej1+spOnKEGX5+lLe1EaRQEC6T\n0VJXR+jAgdja2/scq8ViYefSpdxtMFCSkUF8fT1fVldTVldHzdSpLPjzn9m2YgWW1laiZsxg3rXX\nnnN/k2fP5uMjR2jqahFS7OfHbZdeesb3DhgwgAFdLZZ+HPeFwul0kvHddzwSEYFaLmcw8GlpKUVF\nRd01Y31h7bq1bMzZSEdjI+EHCwmReBLTPJTlBQXc/dxzPep/6b6v9S/6a9x9xS283PQrFGo1HSZT\n99/tgoC6KztwsXA4HGRs2MCfo6JQyeVMi4ri7awsdDNmcNf8+Xh5ef2i/a95/30Si4q4w2ik2mxm\n1Rtv4P/SS5SWluJXVMTI8HDyGhqIaW/nw717kcfGMlGr5bLISFJzc0nfvRuJKOJSqwlOTkatVjNi\n1Che//prDh48iEKhICEhAU1Xk+8zceLYMTa9+y6hgkCtINDm6UmjzcYAX1+WS6XILRb0UimbKisZ\nOGcOcKqw3Ww2IwhCj3252tvb0QkCcqcToaGBEb6+ZJhMTAkK4quDB/FbuJDFTz/d43Pn4+PDPc88\nQ25uLgDT4+Px9PTs8fa9xeVy0drailqt7tkUuCgi+9F5kUskvfJH+ymCILBj/w6MM43kvJPNnLhg\nLNUmIhQKasxmCgsLSTlHTZwbN27Oj7vG6yLTX+fGL1bcE6+6inXt7aSXl7O5pIScgABSRo78zY5/\nprhFUTz1Ayo9dTmGeXuTHBbGiJEjf7HostvtNJw8yfjQUKQSCWFeXkQ7nVRWVtLZ2UlNXh6O7GyS\n1WoUgNVgIG7QIMYbDOQcOECEVIqhs5OX9+2jJCGB636U1QoNDWX8+PEU7tnDd88/z7sPPcSurVvP\nOIa3//Y3btXrWRQezr3Bwcjr6vjW6eRwYyNKo5Flfn5sDQpCPXcul111FS6Xi8+XLuXfDz3Ehw8/\nzGf/+hd2u/288fr4+GDT68lvbgag1GqlSaHAX6tFQu9MW39Ap9MxcuRIRo4c2WvR1ZvveUtLC+8/\n9xzL//xn3nnwQbZv3HjO8crlchKmTWNtaSlFzc2kVlRQFxj4ixZiSCQSZDIZLqcLiUyG3SWAcOr/\nnaKIXN6zZ3X3fa1/0V/j7ivujJeb/wpcLhcFBQXY7XYiIiJ6NN1xJgYNGoTm6afJO3ECpUrFXaNH\n/2Jx80tRKpUMnDKFL7ZvZ7SvLxXt7dQHBhLV5b/UE2xdRpuZmZndKxDj4+NRKBTItFoaLBYCtFpc\ngkC9IDBUp8NisZDV3s50rRaFUkmN04lUocAvLIxDGzYw3GIhyt+ffLmcG6OikKvViKJ4WnPtr5Ys\nYVJzM8ONRjrsdj5auZKI2NjTxt7R0YHS4SCgK4OjVSqJ0ukYtHgxTqeTIJmMuxITT8uY7d65E9f3\n3/PHqCikEgnfHDrEzvBwZs+bd87zoFAouO7hh1n9z3+SJZfjYTZz9YgRbK2qwn/0aHzOUJ91IRFF\nEYvFglqtRio993PuN598wrDKSsYbjVgcDpauWUN4TAwDBw486zZXXHcdu/38SM/JwdPfn9suv/wX\neb9JJBKumnkVy79fjjAgiJXf5zBB5sVes5nmuDji4+P7vG83btycwi28LjL9dW68N3E7HA4+e/dd\nxMxMvGQytmi13PDoo33u2xYREUFERESftv2lnC3uwaNG8W1ZGZk1NQwcPpxbrr++Rz+gFouFz5cs\noeLoUQry84nS6ZgeFsZOl4u6m25iyowZzLn7bj59+21im5qocbnwnzaN6OhoOjo6GJWUREZbGxar\nlcjERKLUamZecw3Pbt5MVWcnGkAaHIy/hwdfrFhB9dGjaIxGFj7wAAaDgfriYpK6PgetUkmMREJd\nXd1pwsvLy4voxETyGhuJNxioMZupkcu5Ijb2rAK6triYJE9P5F1iJVmvZ3cPuwuEhYXxp1dfpaWl\nhX3ff09NVRWBMTFcPmPGb+poX1VVReaWLRz49FOk3t7Mv/9+YmNjz/r+2oICrg0MBECjUDCw61ye\nS3jJ5XIumT0bZs/+1cY9Y9oM/P38ySnIoS22jUBPb3wCA7l0/Pjuov3z4b6v9S/6a9x9xS283Pzu\nOXr0KB5Hj7IwOhqJRMKJujo2LV/OnX/968Ue2jlpaWnhu1WraCwpISAmhjnXX39GoXFg7172f/AB\nCSYTh3Ny2H7oEA1FRVz/8MNn9bT6gQ2rVxOYkcHlXl5kt7eTabEQGB/PLZ6evLVmDROmTiUpOZmA\nF16gqqqKQZ6exMbGIpFIiIqKYmtICKN9fAj19GRPfT0DJ0wgKCiIO599lq+ee47L/PzQq1S8uW0b\nD48YwbCICI7U1PD5e+9x/9NP4xsWxsmGBhIDAuh0OMi1WrnsJxlEmUzGdX/4A2v++U++Ky/H4eHB\nFQ8+eM6spW9ICAW7d5Po7w9AgcmET1hYj8+9RCLB19eXOVdd1eNtfk2cTier33qLyywWBkVEUN7W\nxuo33mDxq6+edbrSNzyc/NJShgYF4XC5KBEExvr6/sYjP3Xuhg0b9os809y4cXN23DVeF5n+Ojfe\nm7jNra2EyuXd2YowLy/MDQ0XaGS/Dg6Hg8/eeIOoo0e5WSol9NAhlr/1Fjt27DjtfaIosnPFCq71\n8cG3ooI/+fgwy+EgvqaGZa+8QnZ2NnV1dWc9TkVWFuOCghAEAR+5nCSJhMq2NjQKBVKXC6fTCZyy\nFUhJSSEuLq77PHp6enLjo4+SlZjIF2o1irlzWXDzzQCkjBjBlY8/zpHAQJZ2dJAUHc3QrszL8KAg\nWkpKsNlszL/7bjap1bxx8iS3r19PZX096956i6OHD582zsLCQv7w8svc+eabPPLPf5KQmHjO8zdx\n2jSakpL4d3k5S8rLKYqJYXoP+zr+Hmhra0Pe1ERdl6+W0dubALudhnN8b6+49VZ2aLV8XFHBuxUV\nGGbPJvE85+n3ivu+1r/or3H3FXfGy83vHmNUFFsEgaE2GzqlkvTaWozTpl3sYZ2Turo6VFVVjO+y\nqZgcHs6x0lLq4+IQBKG73kcURZxWK1JA53JRL5OR1dSEPSeHwtZWQltbaVEoSLn+eqbMnPmz43gF\nBFBeVsZAHx/ylUqOt7SQAGwuKyNk2LDzOvIHBQVx84MPnvG1kaNHM3L0aEpLS1n/9NM4BAGlTEa1\n2YzC2xulUkloaCgPvPgi/3j0Uf5PpWJcZCRNFgtL33+f0BdfJLBr6gxOZb7OV5snCAIlJSV0dnYy\n/7bbsFgsCIJAaGhojwu7LzYZR46wb/16jp44gd7PDyIjsTgcNIriOesJg4KCuP+FF6itrUWtVhMY\nGHhRmn2fC1EUEQThovc2dePmv5nf09XzzDP9sIfYr9Wk97+N3sTt5+eH09eXzw8eZHdzMx6jRzNv\n0SIUCsWFG+AvxGq1cnTrVkZ6eSGVSKhtaOCT9HREk4kj+/cTFh/fbZFQ29hI4fHjVFZWsr2ykkRB\nIKC1FdFmY3pSEpcGB/Ptvn3EjB2Lrst/7AeCoqJYt28fNS0tnPTwINfXF0dYGNrRo7ny5pt/lXPk\n7e1NoyCw9dAhSs1m0l0urnzwQQwGA3BKLO1Zu5YbIiKQSCRoFApq2tpQDh5MUFAQ0LPPWxAE1nz0\nETnLl9N+4ADf795NwrhxREZGnrcw/ffC8WPH2P3mm8wBfBUKNuXkUCeRsN9uZ/jChSQmJZ1ze7lc\njl6vR6fT/e5EV0FBAUtffJGdq1eTl5tLdGLiWesQ3fe1/kV/jfvZZ58FeLa32/2ermx3k2w350QQ\nBFwu1+9acP2AKIp8+dlnmLdtIxL45uBBhsTEcPvIkRQ2N/ONQsGDL7+MUqnEZrOxed06PnvrLVIK\nChju7Y2ppYXYkBAyQ0O5ddw4lldUMOqvfyUuLu5nxzKbzZSXl6NUKomJibkgIkUURSorKzGbzQQH\nB5+2OlAURV57+GEWSiQYvb2xOZ38p7KSy595plc35BMnTnDotde4tWslY0FTE1t8fHjg73//1eO5\nUKz8178YmpNDQldt2velpRyJjOSGu+8mpBfO/r83Wltb+c+jj7LQw4NwLy8OVFdzJDyc+/72t9+d\nQHTj5reir02y/zseI/+H6a9z432JWyqV/leILjh1QS648UaSH36YyksuwXvQIG4fOZLUsjJi/fxQ\nt7XR2toKgEqlYt7ChcxYuJB2jYadbW1s6+xkRW0tLqC2vZ1KqZSso0d585FHePfJJzl+7Fj3sTw9\nPUlMTCQ2Nvacois/P59tmzezd+/eHnli/TSe8PBwEhISfmbJIJFImH///ay2WllRUcH7VVUMmDfv\ntJWjPfm8TSYToTJZd7PpcG9vTPX1vRrnxUauUmHtqqsDyG1oICo6+ryiy2QysebDD3n3iSdYvWQJ\nbW1tF3qoZ8VqtdLZ2Xna/6qqqjDa7Ri7srRjQkMxd00Jnwn3fa1/0V/j7iv/HUUTbtxwKrOyd/du\nsnbvRqFWM2HevDNmgH4vSKVShg8fTlRUFP9OTeW7vDz2FBdT2dREk5/fadOGoiiSfeAADR0dPCST\n4a3T8b7JxLqaGhpiYwlIScH83XfcHBxMR0cHa998E+kjjxAfH9+j2qd9aWkc+vBDhkmlVDqdZA0e\nzG1//GOfhGxTUxPp27Zha28nbvhwkocNIzY2lntefpna2lqqq6qoys5m1fvvM2rmzJ+10TkbYWFh\nfCWKjLZa8Vap2FtdTdjw4b0e38Vk7KxZrDp0iI6yMkTgmEzG09Onn3Mbl8vF8n/+k0ElJUz18yN3\n/34+q6ri3ief/E3r2gRB4Ns1a8jZuhUJEDl+PFffcgsKhQKdTkeDIOBwuVDIZDRZLAgqVY/tJdy4\ncfP/cdd4XWT669x4X+Lek5pKzkcfcZlEQlBzM+tTUwkbOrTPZqoXArvdzuavv2bn2rXk5+URHBmJ\nn58fu/fto3T7diYIAhX19bRGRnLJ5Zd3/7Da7Xa+XbaMeYJAuIcHaLUYfXyoi4/n2Y8/5sCGDcxV\nKgnQ6VABBfv28cWuXWTv24fKz4+wc9hOiKLIZy+/zB1+fgw0GEjw8SG7oADlwIEEBAT0Kr62tjY+\n+vvfic3NxdjQQFpqKk6DgfCICNRqNXW1tex57z0mmUz419XxXWoqwUlJPbIm0Ov1yAIDWb1/P+nN\nzdgTErj6zjv/q37cvb29iUpJocLLC0d8PNcvXnzextL19fVkf/EF1xqN6JRKIr29OVxeTvjo0Re0\nPdFP2ZeeTvXq1dxjNDLR25uTx45Ro1IRM3Ag3t7e1FitpB45QrXZzA6LhZn33kvIWbz03Pe1/kV/\njbuvNV7ujJeb/xqyUlOZ6+9PWNfKsMbycnKOHetucG21Wtny9ddUnDiBZ0AAsxcuPG1V3U+x2+2k\n7dhBQ3k5hvBwJk2fjlKp/EVj/PKTT1ClpXGFvz8VVVV8UljIbY8/DnV1PDJvHg6bjckaDV80NlJa\nWtptjqlUKtGHhVFVVMT84GA6nU5yW1sJ6FrNp9LpaGtuJtjTk9yMDKQmEzckJDDc15el//kPwWFh\nZ230LYoiLrsdzy4BI5FI8JJKezTdKIoiNpsNpVKJVCrlxIkTDGpqYmJXW5oArZaV69czbtIkAI7u\n2MGlnp7EdxXe26qryUhP77EL/6ixYxkxejR2u717RWZJSQkZ6elIJBJSJk36zRqa95WQkBBCzuOw\n/2MUCgU2wCkIKGQynIKAVRB+82n1qvx8hut0KLtWLI7w8WFXXh5cfjkSiYR5CxdSNHIkbW1tjAoJ\nOa+gdOPGzZlx13hdZPrr3Hhf4pYrlXQ6HN1/d7pcyH8klL5ctgw2beJau50hOTl89vLLtLe3n3Ff\noiiy6oMPaFm1iqTjx2lbvZqV//43giD0elw/YLfbKdqzhysiIhBbW1EWF2NJS2Pv3r0gkaDRasnu\n6ECj0SDAaUXJEomEh55+mkNhYTxRVcV7jY0cGjCA+ffeC8CUq65ivc3GjtJSNhQX0+Tvz6jwcPRq\nNfESCVVVVWcdl1QqJXbsWNaXltJksZBVX0+BRoOXlxf/efllnr/rLv7dZWPwY9ra2vjPyy/zxuLF\nvHzffRw5dAhRELprsACkP2nKLJFKcf3oHLoEAalM1qvPWyqVdouuoqIivnj+eYxpaYSmprLmueco\nKyvr8b4uNj2J29fXl8hLLmFFaSn7KipYUVJC+NSp3atGfyv0QUGUWCzdn2ep2Yx316pUOPUdHTBg\nACkpKecVXe77Wv+iv8bdV9wZLzcXlcrKSgpOnkTl4cGwYcPO2SZn/JVX8s2rrzLeYqHD5eKYry93\njBoFnBI9Jfv383hkJFKJhACtloLyckpLSxk8ePDP9tXQ0EDL0aPc1LWCLt5g4J2MDBoaGs6ZJTsX\nUqkUZDJKyspoPXaMGJUKTVsbqZ98QvyUKazevx+XyYQFsERH/yw9HxYWxpJNm9i/fz+iKBIXF9dd\nlB0YGEjyNddQUlZGncPBnTodOqUSlyBQ4XIRcZ7p1vk33cQmjYblmZloIyK4+ppr+HbJEiY2NzMk\nKIjcykpWvP46D7zwQvfU3hcffkh8QQEjgoPpdDj45L33mPWnP7Hfywvfqip81Gq+b21l+K23dh9n\n1KxZfJuRga2mBqcgkCqVcsOkSRQWFvbpnB7avp2ZCgXJXQJAWl3N4V27iLjllj7t7/eIRCJh/qJF\nHB00iIaqKhJCQkhJSfnNVwtOnDaNZceO8VF+PnKJBFNYGLf2InPnxo2bnuEWXheZ/trjasqUKeTm\n5vLd668z3OWiThBYEhnJXY89dlbxNWjQINRPPUVORgYKlYo7xo3rXmEnk8lALqfT4UCrVCKKIu3n\nsZ6Q8P/XAUs4lf4VRRG73Y5MJuu1SaRcLmf0ggX8+/HHmety8UVzM0ftdnyOH6ciIoIpt95KXVER\n0sBAbpsx44zTmiqVismTJ5/2v6amJpa9+CIRTU3oAH8fH9bb7eRWVNDschEwbRqDBg0645gEQSAr\nK4vW1lYGjxrFlTfcAJwyeFXU15PSVRuWHBjIgfJy6uvrCQ8PRxRFTuzbR0NFBfuOHkWh1eIbGorF\nYuHmJ59k98aN5JtMDB05klFjx3YfLy4ujvlPPEFmejpSmYzrJ08mLCyMsF60+zlt/C5Xd79GAJlE\nguBy9WlfF4OeXt9SqZQRI0bAiBEXdkDnQK1Wc+df/kJpaSmiKGI0GvtcX9ef72v9kf4ad19xCy83\nF43vV6/mKp2OqC7xtK6oiKNHjzJ+/PizbhMVFXXGeiGZTMb4a6/lk88+Y5hSSaXdjpicTExMzBn3\nYzAY8ExK4tvMTBK9vTnW1IQ1OppNa9ZQlZkJcjmTb7iBiVOn9iqm6ZddRtqmTew9doy6qir+7u9P\ni9nMln37sE6cyHV3333a+x0OB/IftUM6Ezu//ZaxbW2M68qQbSktpX3WLBKGDsXDw4OILuPSnyKK\nImuXLaN91y4ipFI2iiLDbr2ViVOn4uHhQbso0ulw4KFQYHM6MYkiGo0GONV8u76oiNtFkSS9ntyO\nDl7KyiKly1H9mttuA07V1VksFjQaTfcYYmJiznree8uwKVPYdPAgkoaGU+2V7HaumDjxvNsJgsD+\n9HQq8vLwNBiYNHPmz8xn3fwcuVze41Wobty46RvuVY0XmV27dvXLFSG7du2iMjub0UolHgoFoihS\n1dqKOHgwkVFRfZpmiYyORhMXR31AAPpx41B7efGvt15nyZqlHMs5jjHYiJ+fH3BqemfQsGFUKBRs\nKS7mcGkpjQUFqPbu5f7Bgxnr5cWWtDS8Bg3qVa2NRCIhwGhk69atTG1qQlFXR63NhkEU2d3UhNJg\nIDIyErPZzPJ332XzRx+xZ+tWdEFBBJ/F6+ng9u0Mtlrx8fBAFEUsdjtNgYFMnjEDvV7ffa5OnjzJ\nhk8/5eju3biUShxOJ5mffMJdEREM8PEhQaPh8/37GTN7NhqNBqtCwbZ9+2g2mdjZ1kbcvHkkddk3\nVFdXYzp8GH+TCXNnJx0uFyU+Psy67TZ0Ol239cC3//wnhzZupLy2lvikpLNmCfv6Pff398c7Npaj\nJhO1/v5MveWWHlmIbFy3jvIVKxjW0kJ7djbbs7IYOm7cafYMVquVvLw86uvr8fT0pKmpiYqKCqRS\nabcA/aX05+vbHXf/ob/G7V7V6Oa/joHjxrF53TpSFAr2nDjC52YzqspStn70EUFRUVx5991ERETg\ncrnYk5pKRU4OXoGBTL300jNmLyQSCYMHDyYhIYHl779P8WefECG2ItUpyfbs4NUPG3j2oWdpaGjA\nbrcTHR1N8qhR5H7zDW+MGMHS3buZLZeTd/gwoy+5hGSplIrSUuLj43sVV+LgwYxZtIjDTzxBfEAA\n03x9ybda+bqwsNs09aulS4nKzuam4GCaOjtZ+e67BDz/PKFnWJ4flpjI9sOHUZw8SXFNDaU2G5f+\nJCtYXFzM+ldeYa5Gg1ImY+M77xA8bx56qRRZ11SdTqlE4XJx7NgxCg8fRiqXk3DLLajVaqZ1CdK0\ntDR8fHwIDg7G6e3NwIkTEWw2LIKAweWis7OTY8eOUZSfT9uGDTwSFYVMIuHrvXvZERjIpfPn9+pc\n9YSExMTzNtX+MS6Xi4zvvuPPERGo5HISgYbSUgoLC7ubTre3t/PxK6/gV1GBVCLhI4sFP5mMGA8P\nKqVSZt53H8k9sMBw48aNm97iFl4Xmf46Nz5lyhScTidrTSYeeu8NlJEacCiZ0NhASoOUcD8/Pn/t\nNe596SV2bNiAeeNGhup0VB06xMcnTnDPE0+ctf6kpqaG1oMHScaOfmAwyYh8VFKPeYQv773wAvHt\n7XhJpXyvVpM4dy5REgneajWeWi02mw2ryYTT6aTS6SRKr+9TfDNmzWL7u++SKwiUWyxkKxQkR0Ux\nZMgQAEqPHWNYczMHjh4FQKXVUlJScprwstvtfPnJJxSkp5N27BhjzGamBgaiS0hg986dlIwfj7+/\nP1qtlqxDh5gkk3XbOFwqimzNzaXd05Ps+noi9XoO1dXR6e1N2jvvMEunwyEIbDtwgOueeorCnBxy\nVq1ioETCPkHAMHs2o66/nk9XrCBUKqUCiJg8mbXPP0+0ILAtP5/ZHh4oYmKQSCSM8PVl+8mT5/y8\nfytEUUQUxdOypj9dfZm6dSvxFRXMjIyko6ODxq+/xjBoENfHxdHQ0cGHH3zAoLff/sX2Iv35+u6P\nuON20xPcwsvNBcflcpGRkUFbczMh4eHEx8cjkUiQy+UMTkkh5MoRBCQFUPGvLYyICaAtv5Uob2+C\na2ooLy/n6MaNGOvqWJ+VhSCR0FlbS3Fx8VkLyl0uFyqpFJVCicPmQKFWIBOhtqiR6e0WFg0bhkQi\nIbahgfW7d6Nyueh0OLg0KYn3tm9H4nKRX1GBbPhwUlJS+hRzcHAwSVOnQkUFPp6eTHQ42CiKAjxO\nJgAAIABJREFU3YsBTBYLVXl5zAwIQAQ2V1bikZXFhAkTuvex5euvUaWlcblaTVFHB8OAiMREYmJi\nyDtwgJcefJABBgOeMTH4Go10OhwU5OXRUFpKmc1G89SpXP/QQ6x57z06qquJTUlBLCggNCuLVoUC\ng9HIRC8v9u3YQXFaGg+FhaFRKHC4XPxr2zbGvPgicS++SHNzM5P9/Fj6979zh48PAVotOquVowcP\nMiUxEb1eT4nZjP48LvOiKLJ7504ytmxBKpMx5oorTivM/7WQy+UMmTGDzzduZIyPD5UdHdQFBZ1W\nd9be2MjgrulEm81GpEJBi80GgL9Wi7qpiY6Ojl8svNy4cePmp7iF10Vm165d/9NPC4IgsHrJElx7\n9mCUStnmcFB5440oNBqmTJmCp6cnQruAVCbFKpXQaupEKVMgAE0uF9quTNBIs5k79Ho6BIG/FRWd\nU3gFBwdjCQ1lZ2YGtTsLaJeKVEcFM2JADAMU1u5MSKBOh1IQSLzuOv61di2+EgmqMWMYOX8+UVFR\nDBgwAJlMhsvlYv+ePdSVluIbEsL4yZPPa24pk8m46ZFHWPnWW2xJTaXTZGLQoEE8+8c/8vw77xCe\nkEDq8eNYzGYaRRH/yEjkFstp+6jIyiJGEEg/cIBwpxPvzk7y9u1DrVaTnZ/PTZMmMcFoZE9pKYdc\nLtIbGxmam0usTkeBy0XbyZMse+01jDYbnkBLaysl+/YxRxBIUijIP3mSioAAHGPG4AFoumJSyGR4\nS6W0tbVhNpv5/ptvsDc1UXzkCLqu7+qU6Gi+zc/nP6WlBPj40BYaek7rgV27dqGSychdupRJDgcN\nlZWs3L6dlueeY9bs2T36LvWGy6+9ljSDgb1ZWXgGBHD73LmnrZYNj4/nYFoaMb6+KNRq0h0ORna5\nxOc1NiL4+f0qrvG/l+tbFEVaW1u72/9caH4vcf/WuON20xPcwsvNBaWiooK2/fu5wWDgUMZBAjvN\nvP3Yn1nwh0eYMmUKERERzB4+m81pm3GEBbNibz7zYhL5qKKCyEsvJSIiAk9vb/QtLTR1dtJmtzNQ\nq0Xyo2mjn6JQKPALDUWm0TI3Oo42qZS9vr5cevX1ZC5dymCLBS+Viu9raoieM4fpl13G0FGjMJvN\nBAQEoNVqKSgoYNeOHWi9vCjOykJITWWwVkuBxcKKnBxufuCBczakhlMO5ikzZxJYUcENsbHIpFL+\nceAA277+mvikJIKys4nx9iZJqaTYbMb2k/ouT39/0r/7jrs8POgMC2NZURGBnZ3syshAYjAwLjwc\niUTCqOBgdlVWEh4fj1IqpU2l4ubQUFYePkxkfj5Xjx5Na1sbSzZtIlylIs3hQGm1YpZIWN/QwPOz\nZvFtVRV7KisZFhhIQXMzNR4efPPRRzRv24Z3Rwfa4GD8HA5Wbd3KLXPn0tTZSdjQoUy/7z68vLwI\nDw8/r/VA/qFDDDSZoLSUMRoNio4Olr/8MkOHDev2Tqurq6OtrY2AgAD0XdO8oijS3NyMy+XCYDCc\n97zDKeE7ZcYMmDHjjK+PmTCBlsZGXvvuOxBFjPfcQ35xMa+VlyMzGLjuwQcveJ9Eu91OfX09arUa\nPz+/C+bbZbFYWPnee7RlZ+MAEi69lMuvueY39wlz48bNKdzC6yLzv/6UYLPZ0EokHMjYj8vHRUCE\nD8ryRvZk7eGa9mvQ6XRcM/8a4qLj6OzsRHOvBrvdjl6vJy4uDolEwrBJkzDLZNQ4nSg0GjwUCgzn\n6TFYm5PDfZMno+uaKtKWlSGVShm/eDEfrViBo6WFgZdcwuyuYnCDwdC9enFfWhoHlyxhqEzGSYuF\nbcXFfDhrFmq5nCRR5L2jR6mpqTljIfwPiKLI0SNH2LFuHZFmMy5RRAZcEx/P1ooKZv/hD3ySlcWR\nwkKkdjvtERHcNmfOafuYdd11bF25kpNtbRiUSqYNHsxJrRbtiBEElZQgiCL7SkvZmJFBqUJB8JAh\npCgUaKqqqCwtpbCujrGDB3N45050Nhs+DQ1kenjw95kzyaqspK6jg0FDh2I0Gln00EN8vWwZaYWF\n+ISHE+jpSci+ffhJJIwMCWFDayvGAQPYUFJC4cmT6ENCmP+nPxF/lqzjT5kyZQqr8/MpKy3lWk9P\nNAoFMpuNGFEkNzubwMBAdm3dypFVqwiUSqmWyZjz4IMMjI/n86VLqd27FxmgSUjgxgceOKfRbk+Q\nSqXMWbCA2V1Zuh8ymxaLBa1W2yNx1xPOdn03Njby2euvo6mvp10Uibv0UuZeIDG06YsvCMvO5g6j\nEYcg8Nn69RyNjiblAnqG/a/f186GO243PcEtvNxcUMLCwihXq2lqayYuxJ9D9W1I40IQPAUaGhqQ\nSCSsePddOvLycACxM2Ywb+HC03745t58M8urqzG1t2NyuVAOH87w89QTafR66k0mdF1mqvUuF3Ea\nDSNGjGDkmDE/K77+AVEU2bl8OfeHhKBXqxlpsXD8yBGKm5tJCAhAAiik0vO2Fvp27VrS/v1vVE1N\nHKmvx2G1cn1KCitzcnCOHElzczN3/uUvlJWVIYoiERERP6snCggI4K6XXmLXG29wiVaLVqulQ6lk\n8QMPkL51K39buxZnTg7TvLxYPGoUKwsKeCE7m/v0ekSZjDpBIDU3l3t8ffHz9OSAw4HGamV9aSlD\n/f0pDQzkmq6WRD4+Ptz28MPdx1762mtEeXnRJIq4RJEIhYIcu50hyclc9/zzhISEnFcktLe3U1lZ\niYeHB0ajkUlz5/KXDz7Au7UVlVxOjkZDdFAQMrmcuro6jqxaxeKQEDQKBbXt7Sx7/33qFyxAkpbG\nQ10dCTZmZ7P1m2+ITUqitroava8vQ4cO7bNQ+rH9RVVVFWnr12O3WBg0diyjx4+/YFmhb5YtY0Jz\nMyONRuwuF8s2bCAnMbF71eWvSW1+Pld3ZdSUMhmDVSpqy8svqlmrGzf9Gbfwusj8r8+NazQabvrr\nX1l821FSO6yo4oMxTkggZ20OXl5ebP7ySyLy8phpNOIUBJZv2sSRAQMY2dUKCE7VbC1+4QXKy8tR\nKpVERUWd11V+1s038+Vrr5FgMtEsCFiHDCE5Obn79bP9oLpcLgS7Hc8uEeTh4YHB15dtFRXoVCpO\ntrXhioo6Z6+6zs5OVr3+OldZrQxUKkmTy9mQmcmm8nJCJBLm+/uz6umnmffXv57Xk2rSlCnofXzI\n2bcPhYcHN86YQWBgIAtuvJHmpiZiPDwYHx2N2WTCv7AQz85O0n18mJyQwD8MBhZ/8w0qhwOJycSg\n+HgWqVQUjhuHR1ISN8XGEvSjXnw/JjQ+nuPHjzMkNpZDeXmkWiyY/PwYNXNmj0RXZWUlq159lZD2\ndloFgTp/f5555RVuf/VVvnr1VZIUCgZ4eVEUGsrcYcOorq4mSCrtrjML0umQNzdTcfIkyRpNtyXG\nYL2edzdvpmrjRhKkUo65XBRMncq1t932i0RSbW0tq198kZmAl0rFtg8+wOF0MvEXXptnu76bysoY\n1JVhVcpkxEilNDY2/qJjnQ2f8HAKDh0iUKdDEEWKbDYi+9gWq6f8r9/XzoY7bjc9wS283FxwIiMj\nuf7O+3nxoxdxFbeRf7KG+WPn4+fnR11hIeO7nsYVMhkJKhV15eXwI+EFoNPpSEhI6PExY2NjueX5\n5ykpKSFMrSYxMbFHNTtyuZzo0aPZuG8fE4ODqTKZICWFgFGj2FpTg19KCjdfeeU591VZWYm+uZmr\nQkKQSqUM0mjYlZ9PoIcHlo4ODuXkEBQUxI7Vq4l76qnzjikpOZmkH4lGOCUcw6OjcWZmAnDywAGC\npFImazRcodPxdXk5A3x9GZiSQrxWyzSjEZlUytLKSmZMn87AgQPPecyps2fzZV0dX6Wn0xITg8/g\nwVx79dUkJyf3SOBsWLaMOS4XCUYjLkHgsf37ycrK4pJp0wgNCyP/2DGUGg13TpyIp6cnAQEBVMlk\n1LW3E6jTkdfYiNRgICw2lrx9+xgiikiAzKYmTBUV3Dp+PBqFgomCwHu7d1M1c2af2xIBrP/6a7wP\nH0b08UEbE8MVAQF8uW3bLxZeZ8M/OpqsnBzGhIZiczopEAQm+ftfkGNdeu21fFpezsnycjoFAe+x\nYxk1evQFOZYbN27Oj1t4XWT6w1NCXV0dqSdSmX7rdASXgKnB1N0k0Tc8nPwDBwjqehovtNmIPouD\ne28JCAgg4Dy1YGdiwS238J2HBx8fP46n0ciNixYR3tXTsCcolUo8/f3Ja20lTKul2WqlERjc2spf\njEYkEglLS0sp6LKW6C3FxcVk7N6N1W7npEZD9cmTNLe14fD1JUmhwNbRQZ3FwhcWCw+88AL7Nm7k\n/dxcnFIp42+66Weiy+l00tDQcGpRwg8iWKFg4Z130rloEVKptNc9+1praojsKo6XSaXMDAnpNo8d\nOHDgz8ag1+sJGDWKB956Cw+nE93AgTz6zjsEBwezuqCAdw4dQg7Yo6KIAjy6hK9MKkUnlZKdnU1+\nXh4+fn4kJyf3auoxJzubzK++YnJ7OxEyGfn79qFMSkJ6htZUveVs1/cVN9/Mijff5Eh5OR2iyOAr\nrzzrKt3zYbPZOHzoEJb2dqIGDPhZyx8fHx/ufeopqqurUSgUhHQ9EFxI+sN97Uy443bTE9zCy80F\np6amBlEvYjAa6OjooKC+gKObjmLQG7hi9hWs+9HTuN+ECRf9adzDw4Orb7ml++9jx46x/KvliIjM\nmjiLlOHn9vYKDQ0lfOZM9qamYrBaKVarCU9KIra8HKvdjkouJ9jppLoHdgUul4vMzExaW1sJCAhg\nz44dHPj4Y6ZrNITFxFCmVCJfsIBih4O/DhhAsFZLXnk5DpOJO59/nvDwcBITE7s9qZRdNW+ZmZnU\nlpej1GrJSU9HVl6OVRQJnzKFq266qfuHua9F7KEJCRw4coQp4eG02+3kiCJzzrEY4fixY1h27+Zf\ns2fjEkW21tZSmJOD0WjkxsWLqVuwAEEQCAgI4MNXX+X7wkJSAgIoamkho7WVtk8+YahSyTGXi5NT\npjBrwQLy8/ORyWQkJiaeM47j6encEBbGXpOJ41YrToeD9YWF3PqHP/Qp9p7g5+fHfc88Q1NTE2q1\nGm9v7z7tx263s/Qf/8CQm0ugXM4GQWD84sWMHDPmtPepVKoz9jj9rWlpaeG7lStpLC3FPzqauTfc\n0OfY3bj5b8UtvC4y/WFu3NvbG8EkYLVYST+UTrurHZfMxeH2w5i+NPHHJ5+krq4OuVxOcHDwr1LQ\n7HQ62fLNN+Tt2YNKq+WShQt71XbmB7Kzs3lj5Rt4J3kjkUh4+/O3+ZP8TyQlJZ11G7lczh1/+Qs7\nEhJorqhgUEwMxvZ2pOvWsaaoiKE6HWajkeGTJp22nSAINDU1IZfL0ev1iKLIkk+WsDlzM9bmJppP\n1BHfIXC/VkuE00lRQQGTBw2i2ukkZswY/rx8OVqZDI+oKO76+9/p6Ohg/eefo/b0ZMyECd3F+xvX\nraN63ToGK5Wsyc5msFTKHTNm4BJFPty4kRdOnEDe0YF3cDCX33Zbr7J9PzDvpptYZTJxMD8fp0yG\n17Bh52ycXZyVxRgPDwK6PKamCgJbjx7lktmzkUgkp9Wi3fDAA6xfsYIlJ0+ijYhAZzJxd1QUGoUC\nlyDw0pYt5KSnM0IUsYkie8LCuOOxx87qXyVTKFDJ5dw+aRL7y8o4WV9P5CWXMKyP5rk/5lzXt1wu\n77bR6Cu5ubl4njzJVdHRp/qPWix8vHLlz4TXb82Z4nY4HHz2xhuk1NRwmZ8fWUeOsLy+nnuffPK8\nNZvn4wfPOT8/v1+84vWX0B/u52eiv8bdV9zCy80FJzIykrkj57Lyu5U01TbhpfNiwKgBGIcaKdhc\ngNVqxWg09mnfu3btYt2//oXLZmPMggVERUVRnJlJflERYTU13BETQ5vZzNp//APdM8/0+jh7Du9B\nE6fBN9QXAKfdSdqhtHMKLwCtVssV113X/XdbWxsfZ2fTYDJhVyop0eu5/0d1bO3t7bzz4TsU1Bcg\nOkWmDpvK+FHj2XJwC5qyMmZLJdQ3NHLQbEeMiiZIp6O5rY2qjg7ysrIIr6vjzRkzMFksbGhvJzcn\nh8zPPyewo4MKi4XPDQauvu8+Ro4axYkNG3i4q4/h4fx8wpqbMZlMeHl5UVFUxMDGRq4ZM4bSujpW\nv/oq97zwAkVFRdRVVuIbGEhKSsp5fyi9vLy4+9FHsVgsKJVK9uzZc+7zpddTb7UC4HC5yKitpUIm\nIysrC6PRiJeX12n7XrR4MSUlJeTl5ZG/fz/KrgydTCqlrqKCKyMjmdY1nbm5tJS9qanM/Ildxw+M\nmTGDVQcOML6xER+dDqdez/W33nrO8f5esNvteEql3Q8rnkol9paWs67avZjU19ejrKpifNc1OCks\njONlZTQ2Nv4iAZq2Zw/Ltm4FT080nZ08vGgR0dHRv9aw3bj51XELr4tMf3hKkEgkXHXlVUSERfDC\nf14gbm4cGm8NDqsDXPS5Lcv+/fv5+J57uFMuRyeT8e6jj6KJiuLBESOw7NpFg0qFdMAAIvR6RrS2\nUnjyZK+Fl8vpoqG0AaVGiT5Ij8PmQKU5f72T2Wxm45o11BUW4ms0ctnChSx86CFe/+tfkTQ3c4lE\nwlevv871TzxBeHg4X63/inxXPsYZRkRBZFvaNqxmK20nS5mrkRBt8ESvU6HqdLKqtRWdUkluRweZ\nLhc6iYSpfn4E6/UE+/rSUVPDc2+/zTizmUi7HWtTE51SKUVWKzljxyJzuVB2CacAHx/KamsZ5XLR\nZrNR19LC4qFD8VAoGOTvT0ZFBcs/+ghVRgaDFAry7HaKJk9m4R13nPeHXSKRoNVqgfN/z+OTknh9\nzRpSd++moa6OzuZmPFUqPty7l6Bhw7jxscdOK54/sHcvez/4gETAo6KCZxoaeGTyZEpaWzHJZER2\nNf0G8FcqqTSZznrs8PBwFj31FJkHDiCKIovGjDmnR9sPn6/L5cLb2/uc5+FCX9/R0dF8r1YzoKGB\nQK2W72triZ8+/aKLrjPFrVQqsQgCTkFALpVid7noFMVf1Japrq6OpTt2EHjVVag8PWmpqOCdlSv5\nx+OPX/A6tjPRH+7nZ6K/xt1X3MLLzW+CRCJhxIgRzM2dS+rRVOQ+clz1LhbOWNjnqYHNX3zBfFFk\nosGAw+HgkspKcmprifPzo83Pj7z6evIaGxkVGkqLy0VAV2++nlJSUkJN6j5CjmXRsOUYubGBGEPi\nmfXgrHNuJwgCK955h9jCQqYZDOQfPcpnVVVEDx/OZVIp07r8k4KqqljyxhtMnDWLQ5mH8B3li0Qi\nQSKT0FpfR8b3y9EU1NDg6oQAL6yddkSkSP39edliweTlxfzLLgOLhaacHCL0eiwWCydraqivqOCm\n8HDyy8v5o07HB2Yz3u3tFKem0hIdzdbycoYbDPjq9azV6ynJzcUuleIICsKzy+bAJQjUWa3U793L\ns/HxKGUyRgsC76anUztnzjktNXpDbW0ta157jZmiyLbGRsJbW7nU25uUgAA+b2vDWVXFd59+yj2P\nP959frd/8gn3BwfjrVIRolbz+t69PFlczLBRo7hi8mT2btxIkJcXNpeL/VYrk84zzRwaGkroggXn\nHasgCCx97z0yvvoKOWCcOJH7HnsMtVr9a5yKXuPn58d1jz7K1pUrsbS2EjVnTrcp8O8Ng8FAxLRp\nfLZtG7FyOXkOB3Fz5nR3KOgLTU1NSAwGVF31kj7h4ZQ5HFgslt+kNZIbN33BLbwuMv1pblwikXD7\njbczPHM4O7/fyWU3XtbnlVwAUrmcDqeTqtJSJDYbpvZ2rF3F41GDB7NxyxZMNTVU2mxURkUx+zym\nqz/l2yVLuEWvJ2D6HI5kHOGT7/ORJOlI37yZBTfffMaVfqIocvDgQXL37GF6dDQGjQaDRkNORQW1\nVVUIjY1Mi4zE7nKxPScHD4sFVWsrQmE+hXZfhs1LoamyCfXeIh5OHk+O2oPPdu1gYosZDw8laRIV\n+vBwBnV2MjM8nJq0NPKCg9kilZKbmUlhTg6VKhXeLheHq6pQOZ10iiJ2mw11XR2+MhlNGg07oqPZ\nUFmJyWrFx8ODhsJCdBIJjqAg3szJYZyvLxWCgNfo0ZCRgUIqpdFiobytDZPZjLVrWvBMmM1mMjIy\nqKmpIS4ujiFDhpCenn7W7/nujRuZZLUyesAAKiorCTeZUHZ2IpNKGSCTUQLU1tV1v9/pdCLabHgp\nlXx1/Dj1JSUk2Ww0OJ2MmjmThMRENqtUvL1tGzKFgvF33MHgIUN69dmfjQ3r15P39ts8rNEgB1au\nWsUKf3/ueOCBM76/J9f3D0azKpWKiIiIXmdqIiIiuOuxx3q1zYXmTHFLJBLmL1pEZmIijbW1jAgJ\n6bE9ydkwGAyIDQ1YTSbUXl40l5Xho1Cg6eVD1q9Ff7qf/5j+GndfcQsvN78pUqmU4cOHYzKZeuXL\ndSbmL1rEH5ctw9XZiUGhYJtSiVoU2ZOfj9PDA3HaNKKvuAJvvZ5Lhw3rUWbNZrNhs9nw9PTEVF+P\nMTiY1uZmNE0tzDMY8A8JoT4tja9kMkKio3Ha7SQMGUJwcDCiKLLhiy8oWbeO0JMn2VBRQXJyMpOi\nouhwuRiWnMzq9es5XF1NRVsb1upqbps4kciICKK9vHjw0H7KteU0VjYyziuIARGR7M7OxOCro9Tu\nwismkCkuSM3M5A/XX49OpUIURVrKyhh4zz189uabXJqSwiNRUXxz4gT7Dx8m0sODYrOZMpWKkZ6e\nFMjlJAP7d+5k3pAh7MzIoNbh4P2oKBTA1rY2dhmNyG++mVH+/gwaNIgPX32VTw4fpqywkECrlWa1\nmt0bNxKxePHPREJzczPv/e1vkJqKh9XKOsAjLo6Q2FgiIiLOuLLOZjbj05UxCvTxobq8HJnNxgC7\nnSynE7PDQfjgwd3vVyqVhCQn82laGqbCQq5Vqyny9iYsNJSVH3zA4HffZc6CBczpQQarp/zQL/L7\ndeuYIZUS05WlmelysWzbNjiL8DofNTU1vPL+K5gUJgSrwOjo0dx9692/uNj898oP1/+vRUBAAHfM\nnMnSL79E1GrR2e08+KNVuW7c/B5xC6+LTH99SvghbkEQ+nyTHDJkCAmTJ5NbXIxEELgvPp78lha+\nDwsjecQI/jBrVq+mMdJ37WL38uUoBQFNVBS+0dHsKyoiuKMDD0GgWCYjxdubAJuNP771FldERxPp\n6clyDw+ufuwxNBoNR9as4Uq7nWYfH1xtbXyzfz+FTieB06YxJCmJTUYjnx44QJnZTIpWizEyEgB/\nT0+GDhzM7fc+S2NjIzveeguz3U6n00GIXMagAG8CEsJoKGtmR20Te7t6TyYEBqKWSPDw8CBIo2Fu\nRAQSiYSrhw7l+fZ2iqOiqKqtRVVSwiG5jAA62X1kP5NkCpI8PfH08OBbk4l6h4MYDw/85XL85XKG\nDx/efe4W/d//8adrruEypZLo8HBuSkxk9cGD5E6Z8rMWN7s3byYsM5NpSiU+ajURRUWkZWQQJZPx\nxQsvcN1TT2E0GnE4HMjlciQSCXEjR/L94cP4qNUMMxp5tqwMjVrNZ42NKIKCmHjJJVx+ww2nHefa\nu+7ineZmOnNzKddqSRw6FG9vb1xlZTgcjl9UN+RwOGhsbESj0eDt7Y3T6WTtsmVUp6dTefgwexob\nmW4woJTLKXc6UZzDFuR81/enX3yK1WjFOOBUbd/e1L2MzBjJiIvYzkcURY4cOkR+ZiaN7e0kJieT\nlJSEfy8MXn/L+9r4sWNJHjIEs9mMr69vrz3nfk36+/3cTc/4NYTXbOAtQAZ8CLzyk9fjgaXAMOAJ\n4B+/wjHd/JeTl5fHhiVLsDQ3EzZkCFfdcUef/HwGDx9Oil7P8OBg7C4XxWVlLLj11l71vBMEgfz8\nfA4vXcr/hYSgUyrZU1ZGZlQUuQMG8FVaGmarlXsmTUKnVPLSt98S1daGVCql2GBgWnQ0qevWETl8\nOG3HjhHo5YW/KJIrleL08yPy9tuZNm0aqz74gDkSCRPmz6euvZ3Hvv2WtNxchkRHs7OmhoTp0wkL\nCyMsLIzOO+7gvU8/pdTTl+P1dQT4eSJpMHGk3oKo0VB44ABGlYo3BAHlhAlcHhtL4MCB7CsqYmxo\nKK1WK14xMdz0wgtIpVLefPBB5HlHUUZ40VotogzWUlhaiMTDA1EUqejsxMPppEwmQxEYiOePxIRO\npyM8LIw5I0ei6MrEBEmltLe3/+xcWlpa0Dkc6FUq2uvriVEoKJDLCVSpGAJ8v2kTbZWVtFVU4GEw\ncNX99zNq7FhsVivLN25EolJx55tvMmjwYCQSCRqN5oz1UzqdjrseeoildXWEeXr+P/bOOzyqMv37\nnzO9ZiaT3iuBAAkJECBIk64gIihWVlzWdcW+7+q6q/5WX1111XV3Lbv2hlgoonSkhRZqCKSQ3nsy\nyUwm0+v7BzErC7hY3vXnks915bpypjxznplznnOfu3xvdBoN+S0thA0d+r2TtVf9+c8ojEb6gLHX\nXYdMqSSwdy/3JyXRLpfz23Xr+FV1NSKRiEaRiFvnfHPO3zfRbmxHn3PGwBVEApJgCSaz6TuP90Ow\nc8sWKlaupLWqijCzmZNaLUdycrj50UdJSEj4UfftQmg0msGcrkF+Mnxff7YY2ArMBp4FXgL2Al9v\nOiYA+UAP4AQOXWCsxx9//PHvuTs/PfLy8kjs93pcKhiNRp5esYIVISHMCw+nr7qa/Y2NjL7ssm89\nVlxaGhsLCylua2O/xULCvHlMmjbtrLyRb2qIXVhQwHtPPsm21asJr61lXEoKUqmUCJWKPW1t3P/s\ns0y86ip6vF5MXV18duoUUT093BoayszgYJp7euiQy7GHhmLt7aWnuJghUilxajVVdjsVMTHc9bvf\nIRaLyVu7FllXF8NCQ9HIZHjFYnarVNSpVIROm8a8664bCDHFJSQwdtYschdcjTQikp0iYAzvAAAg\nAElEQVTldVQ6JEhHZHFzZCTjoqKQCQI6jQayspg0axYpI0eyq7qabdXVnBKJuGLFClJSUjCZTDRY\nLHzRVkpzqJZenYpgmQSpxUnw0BFstFo55HZzRCbDP2ECSx988CxPoSAINDQ00FNRQaJOR5fdzi6H\ng0nXXHOWgWY2mykoLmbXkSME22zIvV52OZ2Y1GokwcGEKJVsKyriWrGY62JiiLLbWXvwIFlTpzJk\n6FByZ89mwqxZxCcmolKpUCqV39iaSaVSYUhJYc2xY3xaUsJpQSBr+nQSk5O/c6hu5V/+whSjkati\nYhijUrH90CE63W7G2mxEabUEabUoFQq+bG5mVlgY8zMyqG9pQZmUNKA11traOtD8/Pjx4994fjc1\nNlHeVI4uSofH6aH3dC/zp8wntL/A4T+N3+/nkxdeYITXS0xHB7dGRKBxOgnTaikwm8meOPGixrkU\n1zUYnPelxhNPPAHwxLd93/f1eI0DqoH6/u1PgKuBsq+9pqv/7/wiOoNccjQ3NxPt8xHbr800JS6O\nfadP4/F4kPY3Sb5YwsPDWfF//y+dnZ0oFApCQ0MHjKyuri7WvfEGHTU1GGJiuOaOO4iNjcXpdPLF\nqlUU7txJ7alTLBw6lOzgYI5VVFB0/DjjJk2i1mRC3y/mqtPp+MWDD1JZWUnj3//OzIQEuktLCbZa\nkZpNrCw6RaRajaO1lfEyGTtlMj63WvEFBZEza9aAERCWkkLFiRM0NTXhFwS6JRJuvO++C+a8KJVK\nlEold/3qLu761V0AbFq7ltBt2xgSGwvp6QR3d1NjtWK32zldWkpJwXHclj6GT5xIfEICX27cyOl1\n61BZrajabegX5pCaGEb+Jwc55fYyKzOTvz/3HHq9nrKyMiQSyXk9j9csW8Zan4+nTp5EptVyxb33\nEv211k7d3d28+9RTDO/uRhYayovl5fglEvxyOdekpuINBNjh9xOrVJLZr9mUHBxMeFMTnZ2dF/RW\nGI1GNn/0EaamJiLT0ph3ww1nGXvpw4dTPWkSBqORMRoNdevW8UFVFbc98MC3Nr4CgQBd9fWM7JeS\nUEqlpAgC9SoV5TbbQL/IE2YzS9PSWHr55QAk9fSwZ+tWRmVlsW/XLo5/+CGxIhGNgQDq0aO/MQxz\n07U3YXnPQsnmEsSIWXrFUoYNG/at9vuHJuD34/F6Cf1KGw0wyOWU9Ld8GmSQQb4f39fwigGavrbd\nDAx2X/0WXIqxcbVaTXh4OD6/H7FIRJfdjlSjuagm1udDLpefo67u8/n46K9/ZZLRSFZsLFU9PXzy\nwgusePpptqxZg2LvXm6VSvnAaORIezsJERE0S6X8va6Oivh42jUabli+fGA8iUTC8OHDyZkxg4b1\n68nOzGTLnp1s99qpDgpi5KlDjEvNIL+lhXSPhynjxrHb52Pq10Q7R0+dyspVb3P81D6c/gDqtJHc\nlJb2b+fn9XrZnbebyvpKBK9AqdOJvqMDp8XCu8eOIU1N5bFlyyg4vJ+FXifBgkB1dTkvmEyE9PVx\nZ2wscrGYkX4vj/59D40pMchdIkZOnIJELObk0aOUNFZyuvs0IrmIIGcQD694+Cy5CK1Wy23334/P\n50P0NcHOr8jftYtxvb1MSU6G5GQyU1Mpy8pi9NSplB05gkgqZXZuLmv+9CfMTid6hQKn10u3339e\no8vn87F5/Xo+ee45Zng8ZKelUV9by3vt7dz1yCMDeYE2m41Dn33GfTExhKvVZANvlpTQ0NDwrUU0\nBUEgNCGBsrY2MiIicHq91Pr9zJgxg0N+P4/t2YNYEOgODyf3azcIIkGA/uT7I6tWsSIqCrVMhtnp\n5LVTp7Db7ResslOr1fx6xa9xOBxIpdJvfePxQyMSiRg5YwbVn37KKacTudeLSaWixeMhJSfnose5\nFNc1GJz3IBfH9zW8Aj/IXvSzbNmyAXelXq8nKytr4AfNy8sDGNz+L9hOTU1lZUQEDx05wtSYGCoF\ngbDcXPbu3fuDfd6mTZuoOnGC+/r7PrZbrXR1dtLR0UHdiROM8Hp57fRpHBYLYYJAg9FImEJB3bBh\njH/4YWpqaqipqRkw6L4af8a8eazu7GTFK6/QLvGSct0kRpxuRuVwUdlYx9JZs/h7QQGHPR4mzJw5\n0CYnLy+P9ZvXE31DBqogFR01HXQ3mzhw8ABz58w9a//Lysp46LGHsNgsLLxqIUq5knX716GOVKPS\nqOhpqGd7cxtmo5GrMjL4w9ixLP3oQ3TtncwdEoVELKK1pYe16z/jgVmzUUgk5NXX41aoiPJIuIpg\nNteW0X3qfS6fMIGinTv51NLIyGvHkjA6gfbqdp7805MsWbjkor/vEwUFxBiNTOkXqC1pb+fk8ePc\ncPvtjBgxgry8PJqbm5m+bBlvv/UWtvZ2ugIBFtx9N+Hh4eeM9+dnnqF+2zYm2u1gs/H2li2EhYTQ\nWl7OnOuvp7W19Uzfz8OH6Tx+nP85doy4mBgemTYNuUhEfn4+jY2N3/r4ueb22/noz3/m3aNHsQcC\nLFqxgoSEBF4/dYqA2czwiAiEkBA+rKmhymRickICO/v6UA4fzpdffkmIIKCWycirrwdAIxZjtVo5\nevToBT9fEIRvfP4/vX3VkiX8ramJKkHgdYeDmLg4pLGxxH7NQP7ftJ4Mbg9u/6e2v/q/vv/8/q58\nX3njCcDjnEmwB/gd4OfcBHuAPwBWLpxcHwgEflA77idB3iWqf7Jnzx6ioqKwWq3Exsae1YsPzoR9\n3G43MpnsO+n82O12/nbPPdwVFoZWLsft8/FqSwu3/OlPrHn1VXLb2/n7tm3c1NNDmNvNMYWCQpWK\npOXL+fVTT11wXJ/Px9t//Sv7Vn6AxdqBMz4UhT/AcoMahVlKSMpwPmhowJCZibi3l6CwMOYvX05K\nSgq//+Pv2bPrC5LkUlwiEb6h0dx42VKuX/zP1kKtra3cdsti4ny9BCmknLK5MSmVpI9Pwn6qHmNz\nD+P73Nw9aRathYWUAONzc3l/726qq2sYo1OhV8iIA16SaLhi9hxuVCqJ1enYWVzM59XVPDBtGvsP\nHOAGlYqTPh/BSYk8WFfAyMcWU9tUS21tLfIyOW8999ZF66wVFxWR98ILLA4Lo7ujgzeOHEE+dCiR\naWlc/8AD1NTUDBznra2tdHR0YDAYLpis/bff/pbLLRY+3LWLYb29XA34w8P5wu3Gs2wZd/7ud3z8\nxhtEHj5McHs7oqYm8rxePPHxtEZHs/zBB0lPT/9Ox47L5aKrqwubzYZSqaT05Em869YxPzERQRDY\n19TE6eHDMeh0eF0uMiZPJiMzE5vNxqu//S1LJBIS9XoqjEb+2tHBSx999KN7sv7TXKrr2uC8Ly36\n15dvvch8X4/XcWAIkAi0AtcDN17gtf+7GocN8qMiCMIFc1mqq6v57JVX8JjNaGJjuf7uu88xzP4d\nIpEIp07HfWvXkioW40lOZtzttxMeHs7cn/2Mlx96iGivF7FEgkOpRGO3U9jdTf2uXaSOG0dUVBQZ\nGRkDFXVfXYyLi4s59M47XA/Y+rzsPt3MkYggPuq0E+KTEVG/iyC/H3N9PbfPmoXSZmPt889z+zPP\n4Gk0kt5o5qbxSdhcXt7ZUkRzTDNFRUWMHDkSkUjEtq1bGWbuZGFGLIIgkNJu4oWT1cQEPMyMCmZj\ndTsjeq047Hba/H6au7upPHCA4pZ2srx+0vscmB1uXvEGEKVFs/fwYba3tRGi1aJITWV2ejpikQhf\nIAD9Nzo6nR6f3U9paSn1tnoCtgAhKSE8/vLjzE6fRLBejyEujuDgYOLi4jAYDOd83xmZmdh/9Ss+\n+PRTioqKuGnMGOYNHUpVTw+rX3qJ7CuuGHhtdHT0Wflh50OmUqF2OJAZDAS6umgVBHrsdkZnZrKn\nsxOAzpoaZoeGoouOplajwXjqFNVNTSwMDWXPn/5Ezfz5zL/22m9tfEmlUg7v3k3znj2oBYGCzk5u\n/FruYLxWS7XXy5Jf/AI4Y4zvz8ujtbKS8FGj+KioCKG+Hnl4ONOXLLnkjK5BBhnkm/m+VY1+oApY\nBdwDrATWA3cAY4ECIJIzyfbTgMnAXcCbgPtfxrokqxovxUoQuPC8+/r6WPnkk1wvFjMvIoLakhLe\n+/xzlCEhxMbH/9uEaavVyprP1/DS88+SUlHNbbm5aMPCqPX7mX7LLYSEhGAwGAiKi6O5ooIolYq6\nxkaSfT4apFIaZDI2WK20CAIlhw4xISsLo9HI208+ScPWraxfs4aRbW0sT0hgSJCOcJeHI84A42+9\nnUiTiZuzsjD09DBTo+Hj1lZMFgsV1dVYNBqcTU0s1Ydgau7G3WFFbnYgs9vpOXmScqOREdnZHDt0\nCEnBEWIjzxQeuHrtFDaama1RolVIaOrsI8IZQBkWzqGeHqKNRiQWCx0OBzPkcnSABIE6iZT0yEiu\n9fu5U68nSqNBHBFBjVJJlNfLabOZEx0d6BMTKRWL0YzNJX/vUbBBYmQiqVmpdGw4wMTmbtyHD/PZ\nRx/hrazkSH4+4cOGEfK1XohfERsXR0hiIrqGBhanpCAIAiEqFQfa2ph0zTXk79nD6ZMnkSiV5zXe\n4IygaH19PdrISHYdO0aUIFDY3Y1Kq4UwA4d7OmmLiGD6nLnU1dTgqa0lUa9HpdezqqSE30+YwLSU\nFLKCgthRUEBcTs5ZDbYvhpMnT9KwahW/iI8n2mbDXFLC2qoqpicmIpPJ2NnaStjUqai0WsrKylj/\n0Uc4t28ny2TCVVODJyGB5Y8/ztR5876VrMnF0NPTw+o332TXJ59QVVlJ/JAh36rl1leVvNtWraL4\n2DGUBsN5f8vvy+C6dmlxqc77u1Y1/hDyyNXAK5yRkjjQ/1hB/x+cCS/+hTNyE3/q//9fjS64RA2v\nQc6mubmZnj17mBoRwcbTp7FWVRHf2YmzrY3jzc2MGj/+goKrXq+X5195ngJLAd1lZeS4+1A6fEwe\nPhKJy4UpLo7k/pyr6Oho6kwmSisrETo7adBqsUREED59Oi06HWMWLaKpo4MIp5N9n33GdJOJK2Ni\n8Fss9FRVkarVolMqaff6aEhKYf7NNxNWW8uo+Hgaa2oIeDxsbWzkJqWSUIeD5u5uOiQSxhtCmDgy\ni47qOvokEmZmZTErJoZDxcUEZ2YSHhnJgW070ZstdDZ0sLehnbYwHYlWLwaJHpEymB12B8UeD0Oc\nTrKHDcPhdlNpt2MSiXAFB6PUaulUqQhXq1mkVOJ0uWhuasJuseDPzUU9ZgyypCT8Y8YgGTWKuJkz\nufm222hvbyciJ4LUMam0HqpkQnUn40Ji0Le1MUGpxK5UMic8nA1FReTOmjXwvQcCAbxeL2KxGJ/P\nR/7OnWSqVMjEYpp6ezni81F59ChJxcWENDSwIy8PbUoK4f3VjV9xaP9+trzwAp4jRygvLCRp7lwi\np0zBEhrKxqpSilUeGtNCcMUocHe7mbdwEdtLSjjZ3MweoxGH38+t2dkIgoBYJKKmr4/QnJwBaYbC\nEyfYtXEjzU1NhEdFXbC/YmlREWGnTyPu7aXj+HFGSCTstdnY2NhIiUxG+IwZxCQns/G55xAdOsTe\nL75ggUZDRmoqaXo9J+vqiMzJuaBx+V3xer28/eyzZNTUMDcoCH99PduKixk9efJFV3AWFhRw8KWX\nmOF2E2E0sikvj6jMTIKDg3/QfR1kkEuBH0tOYpDvyU89Nv7VRffbhlPON+9AIEBVeTkHCwuJr6zk\nWFcXvzEYKJJKmZCayjslJTQ2Np637QycyR2qNlWjzdbSGSTDLRZo727H4XDQ5vOR9DXPh1gs5pYV\nK3g1EOBEdTW/j4nhHa+XbpmMgEiERCpFotNhtdsx1teTHhtLb28vUT4fG2QyVnd2Eud0UqBUMvWm\nm/D5/RxyOkm3WonJyOCprVsZKxLhcLtJSE0lOzmZvxiN/Ob4cTL1etpMJuZnZjI8LAxBEDCIxTgc\nDkaNGsUVj/4P6/72N6q6zGiuHMOU6Znsf2s3BUUVzNEFk6vTccRgQBcTQ3JaGquLi1kukyH1ePC4\nXLxps6FKTSVMr6eguRl9Tw8jgHa/n9b9+5k6fTrX3nrrOd/fTQtv4oX3X6DeWE9XRQd6URBBQUG4\nBQGL30+n1UqwQkFfV9eANlpTUxNrX30Va3s72uhorrvrLsYtXco/PvyQUEGgSyYjMSeHnnXrmDZh\nAgCGnh52b9hwVv/Evr4+9r3/PndGRhIkl2N1u/n79u3c8eKLaPV6yqS1xGTFEAgEqN9dwqq//Bmp\nzc0tDzyA3W5HKpXy6auvcrytjbFRUTT09tIil3NVf2XmynffZeszz5AgCNhkMo5OnsxvnnvuvBWV\nEdHRHPL5oKqK0Wo1BU4n8zIy0AUHo7j1VmbMnMmzd9/NL0JCUEmlVBQXY6mtpUQmI9hgIODz4ff7\nL3icf1e6urqQtLQwsb+AITcmhpONjXR1df3b0O1XnNqzhyv0elL7jUJrSwtFR45c8Jz6rvzU17Xv\nyuC8B7kYBg2vQb4zFRUVfPGPf+AymwlJSWHJnXeeJfzo8XjweDwolcqLyrMpLCig5pNPmBofz+7y\ncqpaWzkmCAyfNAmpVIpMEPD5fBd8v9frpfR0KR6xB2+kl9f3dzPepqKmuhpjTAxJQGdnJ4VHj+Ky\n2UgbNYrbb7+dh/bv5/PCQtxeL3tKSgiZN4+D69fjOHSIxT//OWFJSRyqrERUWkqU349eq2WfVsuo\nKVNw+HxsfP55dGIxfTodLwoCBp2O4IUL0dfXE5ucTFhYGAdLSzHW1DA7LAylIHBKr8cUCOD0emnp\n66NOLmdWXByCIDBn/nz0YWG8+uWrJE8+I4mgG51Een0vV02fTkREBPsaG/m4rY1AQwMhMhmC14s0\nLIyAIKAPDmbe/fdTtWED20tLGet00iQWU9XRQYLFwpuPPEJ8UtI5cgvp6ek8cfcTVFRWYIw2Ur9p\nEx1+P4Xd3Ry2WkmNjOR/vviC6MWLEQQBp9PJp3/+M1d5vaQlJFBuNPLJiy9yzzPPMHzUKMrKyrDu\n3MmRTZswtLTg9XqRSCTIJRJ8Xu9Zn221WgkKBAjqb/eikcnQc8Ygk8lkiPwipAopp1fuY3yLiVgk\nhOzfz+r2dm7/7W8Ri8XcdO+9rHntNbbW1KAODWXxPfcQFBREV1cXO158kWd0OhLUaoqtVv6xfz/5\n+fm0lZfTVlFBcEwMC267jaioKEaOHEnjwoW8/Yc/kB8IoA0N5ZaMDPLb2pDJZHi9Xrw228CxrjYY\n2HD8ODMcDg6LxeyLjmbJ/wcBVLlcjj0QwO3zIROLcft82AKBb9UiRyyV4v7aOeT2+RD/SDlogUAA\ni8WC9Edsaj3IID8G/5s6sV6SocafamzcZDLx0ZNPcpNczvzwcISWFraXlZEzdSqCIJC3N49n//Es\nG/dspKqiCoVEwYa33iJ/61bMdjuXz5x5Tshwx6efMtXh4PLkZFLi4iju68OuVDJiyBBOdnXREB1N\nQno6769+n537duJ3+Uno700IcKLwBLuO7cIr9aIIUWB0evCokwhWB5Hr89GSn88rr7/OiJYWopqb\n2ZOXhzIxkRt/+UtaIiIQDRuGzOvFeeAA6S0t/CIoiIqKCsbfcAPvb92KsaODKpmMmWPHMiclhWKp\nFM+XX/I/QUFMk8vpsliwBAUx+cYbUUskFDQ3ow4EMNrtvFFYyC9zcpgcEoK0shKPycQuo5F17e24\nhw1j8b33nqWbJZVKyduXhyRUglQupWFfGZf5VEzJGo1IJMLt8+EcNQpZVhY1jY1kDB1KqSDQpFDQ\nGx7OfU88QcL48fQIAlWtrUh6e/mjRkOi10uD00mtycSUK644yyA2m800Njai1WiZNGkSkZmZbK6q\nYmtBAeN8PoYCSeHhtAcHM/nKK+no6KBx+3bm9AvNhqnVnOrsJCE3F5lMxud/+xtz7XbGabUcrKzE\nZTKh1OvZ2tND5uLFxPVXNOYfyue1Va9x5NBhDOY+UqKiqDWZOCmTcfnVVxMeHk75iXKKi4tRFFQz\n2S8jd/gYsuLiOFRXR+qkSajValQqFTlTpjDpqqu4bM6cgVBfXV0dzWvWcIVGg0QkIkImY0t3N3UW\nCxM7O7kmNBR1RwefHz1K9pQpSKVS0tLT0aak0NbUxPT4eJr6+jgRFMQV11+PWq2mqrqavtpa4rRa\n9lZU4Pd4kKSkEJ6YSEpwMPa4OBISE3/Q81uhUNDtcnHw2DH6envZbTaTcOWVZI0de9EFBEqDgU15\neUisVurMZvIVCq5YuvS8nr+GhgY+fukl9qxdS1NLC0nDhl10O6Z/N2+bzcYHL71E/sqV5G/ZQp/f\nT+qwYd+pCvV/Ez/V9fz7cqnOezDUOMh/lLa2NuK93gH1+ZzoaHbV12O322lra+Pdre8SfXk0MqWM\n43uOU37/5zySPRadQsHWNWvYCcxZsOCsMaVKJXaPB4AEvZ6Fo0aRFxzMttBQ9JmZzBo/nhfffxHF\nCAWyCBnv7HoHn9/HrBln8o26zd1kTM/A4/LQ19tHSnYKgfxubjEYGBkeTmNjI5a2NrSpqYyNjia6\nr481n33G6LFjCQkKojE/H39JCXfGxbG4v1egoamJ7tZWpi5YQNDevUhEIhCJMDudNFVWMt3hoFcQ\nCNFqmSaV8sfychrefpvLIiORKRTskEoZkpmJq6wMb2Mj+dXVJMpkCH19XBYSgrm7Gw8QGxsLnPEC\nVFZWYjabWTx1MZv2bsLoMZIUkYnRa6TZYkEsCOzq7WXc0qWMGTcOpULB208/zVVeL1KJBAWwc+NG\nFlx/PXf++tf8bPVqHhSLMfh8KBQKpgYFsbOuDq/Xi9/v5/ixYzTW1bHl5Ekk6en4HQ5Sdu9mxdKl\ndLe2sjgkhGUhIbRYrfSo1ZTZbDidTjQaDeZAAJvbjVomw+p2Y+FM37zq6mpSHQ6G94fF7ps7l8eP\nHsUUGUnWkiWM7289s2nTJp79n98g04gQpYfxp8paNh5ykTwqi+vuvnsgD+s3d/+G7du3s+/Ey1yW\nNpSw0FBcHg+uQOAc4d3ysjJ2rFyJw2JhyIQJZE+ahDMqisL2doaoVNTabLTK5YxxuZg8dCgAmRER\nnGhspK2tbcATOGP2bELDwig/fhy5RsNts2cP5EIt+eUvWff22+wrLqbK7+fR2bMZ2v8b7m9owGGz\n/UBn2j8RBIGrliyhOD0dY2cn48PDycjI+FbGSlpaGosfe4yiw4cRS6X8bNKk81YMm81mPn3uORYI\nArFBQRw4eJC1Dge33nsvcMabvXntWkrz8pDK5Uy94YaB3/Ri2LJ6NXGlpSyPj8fl87Fy/XpOJSeT\nlZV10WMMMshPlUHD60fmpxob12g0dPp8eHw+pGIxRrsdv0KBQqGgpaUFIVxArjoTApHIRQwx95DS\n74G4IiqKxz/++BzDa+IVV/BpYSGWhgZ8gQDHNBpWPPwwEf1J2Fu3bcUX5SM0/kwYRzRaRN7RvAHD\nKzUxFU+Bh/gp8YglYuqP1KNRagnrD2P4/X4Mfj/tRiOu6GgUEgk+j4cDeXm0r13LPXFxnFCpyC8u\npjAkhJGhodR2dGBuaiIzO5uXX3qJmTYbamCjSIRFo0FqsxHkdtPc28sJuZw+v5+laWmoFApGhIVR\nd+wY7Xl5qEwmthUVoXa7aRWLaRKLeSY0lFa7nf3FxRQUFDB+/Hg2fPoprVu3Ei8SURkIcPPPf86Y\nceOQy+UcP3qUzzdsAGD07bczZtw4BEEgOT2dWcOHM9lgQKvVIlUqeWHnTq5asoSQkBBGjhuH6fBh\n/Go10UFBdHd1IQ0OZu1777F73TqyfT7KfT4qs7LQSaWo1Gr2V1QQvWEDyT4ffQoFDiBJp+NQSwvi\nUaNQKBQolUrG3XADb378MQmCQL3fz8Sf/Yzamho2vfMOTQUFRPj9TOwPQ44YPZrlDz88YCg0Njby\nwUMPckt3G7FKGRur2qi6LI3QsRP5P799AofDQUNDAxqNhpCQEBYsWICjq4t1mzcj3rePcrsdW0YG\nTqdz4Bhqbm7m7UcfZZxYTJLBQMvOnZwUBGbcey+vPfoo8tpaWsRiMq+/noDFMmA0ev1+ev3+sxLu\nBUEga/RoskaP5mRhIRvefhtBJCJn7lxGZmRw2wMPEAgE2L5hA8fXriXS6aTP7eYYcE2//tn5zu/G\nxkaOFhYiEgQmjhv3raRSBEEgMzPznMftdjs1NTWIRCJSU1MvGH602Wx4vV7GTJ5MTEzMBY22hoYG\nkh0OhvUbzrMTEni6sHCgrdfOTZtwbtnCr+PjsXk8rHrtNfQhIQztN2T/3brWXlnJtH6JDoVEwgiZ\njLaGhp+84fVTXc+/L5fqvL8rg4bXJY7f76e4uBiz2Ux0dDRDhgy5qPfFxcWRdOWVvL55M9FiMbWC\nwJV33YVYLEan0+E3+wn4AwgiAYfViU/+zwuaxeVCep4LQ0JCArf84Q8UnziBIBJxW04OYWFhA89L\npVL8Hj8ep4eW0iYsTd3ESP+Zp5Sdnc2S9iWs/3I9AQLkDs8l5voQ9mzcyAKZjPq2Nj7r6WFyWRmb\nmppoTkoi8447qD1xgikGAxqZjIxRo6jr6ODzU6fY4LRyIEggvPswX770JaNtNpK8XgKBABMkEoq8\nXmpjYwm0t9PndrPS7SYmKYGa2hpSk5JRKpUY6+u5OieHfLmcjPh43q+rQwAWyeXYnU56xWKStVqs\nvb20trZSv20bK+LjkYrFTHa5eGXlSnImTEAQBHLGjydn/PgzRQhVVeTn52MwGBCJRMg1moEE6z6X\nC9HXqtzmLV9OXnc3PQ0NmNva2C2XY+jpwbh+PaPr65kglVIUFIQsEEAoKSE+OZkSk4nDjY1kyWRM\nys7mpePHkbpc7Pf5mBgI8LeHHiIlJ4e511xDSno6RqORnLAwbDYbW55+mpt1Oir1er7Yu5fi4cMp\n8Xq55rbbKC8vR6lUkpCQQP7+/Yw3mZmiUqDSKQlzeXnwcDXaKddSX1/Ps/94lhLkPPIAACAASURB\nVC5bFyK3iJ9f+3OunHMlVy5ZwiPbtpGdkMCNkZFIJRI++etfueePf0QsFrPyzTfRFRcTpdVSVFVF\nUHIy9UePMuWGGxidlsaMSZOI1mjY095OTVIS79bXM6zfaIydNYvIyEgqKiowmUxERESQlJREcVER\ne//yF+bp9QQCATb9+c9IHn6YYf2hsZnz5rHd5+PNffuQKZXMuO++Cyar19XV8fQHH+BJT6etpYWX\nP/2U+667jnnz5n1nvS+TycSzrzxLJ50EAgESZAk8dM9DqNXqs17X3NzM868/j1VmxWf3MWPUDG65\n/pbzGl8KhQKTzzdQRGFxuRBksoHqydqCAq6NiEAplaKUShknlVJbXj5geP07gmNjqTl5kjC1Gn8g\nQJ3bTUp4+Hea/yCD/NQYNLx+ZH6ouwSz2cyWHVswmo1kDMlg6pSpF5Rd+IpAIMB7q94jryoPkV5E\nYGuApbOXMnzYcDa88w6mlhaihg5l4bJl6PX6s94rCALzr7uOurFj6ejoYHx8PDH9zYUzMzOZcmoK\nB3YdQKQUEemLwp8bx4baWnRiMcdEIn7529+ed5++SVwzZ2wOG/ds5NCLm8jptTHcIeBKEJG/bx8T\np0w5E4q58irmzpqLrz+s5vF42Ozz8cSGDbQ0NXHTnDnYrFZqentpkMu5Z+5c1ra10VleTnJwMMHB\nwQRnZlLj92KOcjB6xkh8Xh/7dhaw2O1i1tAzoq+ehgaK7XbuWLKEkvZ2qpoakfa2YBup4+2yE2Q1\n1BA+ZDitajUJej0FwPywMJJEIj6z2yk2GgkJBEgdO5ZNLhdXJCdjs9kwiERI+y9uQXI5YpeL48eP\no9FoSEhI4Mv169n28cdIGhqYnJJCcWgoYbNnU6JQ8O7hwyTodFRIpeT+7GcDF9TJl1+OVq8nb8sW\nju7bxzSvF21ZGQftNrJ8XvQ6HfK+Przl5USMHInS7yemrQ2N241m1ix2rP8Mt9fKIby4/AFmdXUx\nJCaGHRs3stnnY+GNNw60Vlr/4YdMEAQMIhHTcnPRRESwVa/nuptv5uiaNXTa7fT4/YROmYJfLkek\nUqHwe3D0OjB7vYg9IuZePpfnXn2OltKjpAMWr4+/PP8Ew4cOx+/3M0anY9nIkQPHxd7GRnp7ewGw\nnDjBdWo12ToduYEAf6ysRJmURGt1NdNCQxnZf3EfYzDQq1Ix6ZFHaG9vZ3xwMCNGjGDz2rU0b95M\ngkjEEb+f0bfeSuPp08zSagcqAad7PBQfPDggACyRSJi3aBHzFi0655j91/N76759+EaNoqCxlmaf\nE19cFL9+922q29p44Je/PK8sRCAQoKioiJKKEnQaHVMnTz2rUfjGbRsx6owkZJ7Jmas/Xs+O3TtY\neNXCs8Z5++O38aZ4iUuMw+/zs2PPDkZnjD6v1lhqaiqHx45l5dGjxIjFFAcCzLzjjoE1RRUcTGd1\nNRH9uWGdHg/6r1UN/7t17YobbuCDxkbKGhux+/3oJk4kZ9y4b3zPT4FL1etzqc77uzJoeP0XYLPZ\nePqlpzHqjKgNao7tPoap18Siq8+9EHydpqYm9pXtI3FGIiKxCLfDzYdffMiwz4OY5/GQajBQUFLC\nqpde4s5HHz3HkOvt7WX7xx/TW1WFTyZj5rJljJ84EZFIxPKfLWdG/QycTiexsbGIRCJOFBTgdjq5\nPj39nKbW5yMQCFBXV4fZbCYqKoqoqCgWzlhI6IGTXJ2YRlxkNAGFgjdWryZ38uQBQ+PrzYalUilX\nLVlCRUsjniOHkJQUERkbT8bIkXxssRAIBLh8wQLeLy6mo74eh9dLZVgYGcmxlIvKqVl1AIPVgbTR\nyHabhzFGIwaVipN+P6VeNx/u2EJSWATHjB1EX5dN6mXDaDpWw3u7Skn1eEgeN4519fWIdDpWtrWh\nVSpJGzmSD9vaMIWEEOp2c+UvfsGQIUOwWq20q9VUGo2oPR521dSws7eb0i9fQywRYy/qZo5PSnJH\nB3fo9VQ3N5OQkMBjb7zBsJAQOj0ejrW0kLlkCdO+prPl8/koOniQ1gMHGFdTwwiVCondhkHs4SO3\nmxSXBYPdT2hxMbbubvpKS7ktIoIjajWXzZ7NA0e2Is/NRur0MOFoNT0NVQSnpDA/Lo5XDh6EG//Z\nrKKqvpbTBfl0hqiR+CRoYlMZPmoUxbt2cbUgMCQujnarlQ+3bGHMihVsDAnB0NODARlbvS7GLphL\namoqNceOcZ1OzBCDhkAgwLqiZg4cOMCcOXPo9vtxer0oJBJ6nU7sYjFqtZqenh5iQ0MJOByUdHWh\nFATavV5uWbQIl9VKncNBZr8Hp85iQZuVhbGjg56mJkSBwBk5ki1buCs+HplYjNXt5uVVq4jJzsb1\ntSpMl8+H+CKTzP8Vt9dLbXMDrfZetGMy8NTW47J1s7O2lqtra8/rcd53YB9vbXkLVZIKV6OL/BP5\nPPrrRwcqAY1mI+qQf3q3lAYl3b3d54zTZmwjdFR/mF4sQhwsxmQynXc/v5JbOXXZZfRZLCyIjz+r\nCnbmkiV8/Mwz1Dc0YAsEMKakMLtfLuRiCAkJ4c4nnqC1tRWpVEpMTMy/vVEcZJD/FgYNrx+ZHyI2\nXlVVRZe4i4RRZ+54g8KD2LJjCwuvWviNi5nT6USsFCMSn3mNVCHFarcS4vKT0b/IXhYTw9H6eiwW\nyzler8/eeYeRtbVMSkig1+Xi3TffJCo2lvj4ePx+P0qlEq1Wi0ajQRAEJk+ZctHzDgQCbFq7lsbN\nm4kRidgjCKRfcw17PvkEd3MLW2x25htCSdVq8bpc3/j9fLLmEz5f8w4Rfd1ITX7cdfWEnDxJZ3w8\nJUVFjMrO5vYnnmDjhg0cWb+eVEGgbn8LlQ0l/DxKS5xGQbLdyVaniyfbGtBK5NSIxASS9XzR04ur\nsgtLsJ752UmIJWLCh8fSuqmQOVYrcRoNa+x2pEOGUGowUFVTTV9xAeNVcqJ8LoiKQiyX8+zfnqXL\n1EV0aiKPbdpC7YmjgA+jXozMFs3YK8dyePdxwmRR2ASBUIUCm9NJQ0cHstZWlo0aRWhmJg6Ph7+d\nPInNZhuoVCsoKEB69CgzQ0IQNTUR53azz24jNkSJTKLkTamYVrEfhUTHXKfAdJ2Oeq+XoXPn0tXV\nhau5hYgu6DHbKLM6ydRI8Xq99LpcyL4mA9DV1cWpjgpsSUHIXR78bjsHy4t58amnePr++1memcm6\noiIa6+rotdnY+sEHPPT663zy+uuY29pIHzuW2+69F4VCgcYnQes987v6PD6ivSIkgkBYWBiZixfz\nxtq1xIpE1AUCTP/FL1AoFISFheGNjsYhCGji4ijq7kauUpG/di0uu51uj4f2ujrkYjGW2FiCbDZq\n33iD4SoVFXY7R9PSCBaJkPV7naSBAC21VbSFqjlUW8ONLhcikYh8mYybp0//xmPuQsf5lNGjeftP\nfySQHIe3rYPAqSJ0w+JxNbtwu8+nKw3rtq8jOjcaZdAZhfq6g3WcPn2asWPHAjA8dTiF+YXownUE\nAgGsdVbSrzy3x+bQxKGUVpQSMzIGt8ONv8v/jfpfYrGY0aNHn/e5uLg4fvHUU1RXVyORSLhmxIiz\n8souZl1TKBTnSJr81LlUc50u1Xl/VwYNr/8CBEHgrAbjARAQ/m21U0xMDFqPlo7aDoKjgumo7CA1\nJhVbXRtevx+JSITd48EpCOdN1m05fZql0dEIgoBeoWAo0NLSQkhICB++9BLuykraLBacSiWjx49n\nzMyZpKen43a7OXXyJNWlpSCTkZWdTXZ29llhlpaWFmo3b+bOuDikIhF7amt58s47WTx0KGk6HVFu\nN2sOHiQuPZ2RCxdecK6BQIDPv/wcvbmPMLGYepOduQQQezzcOHYsq19/neEvv4zP56Nxzx7+kJpK\nqEpFo9nMHceOofPLONVYji/ITZRazFECyH1uLA4vKwJ6hg6PYV9VG8drmsl/eiOpN4ynYmcxM5os\njEsLJz42Fnt3N3/YvBl5Xx+9LgehGgUZQ4cQKwj0VVfw1CO/JvamTORRcjZu3EjvyRNcGy9FKRWo\nd7rY/MV+hk0YhjREg6XTjl+r5YjFgszno6KvD7lcPhDmUUqlKDnTW/Irw8tsNGI3m6lubqamtZGb\nFFL6/F6+kMswD4smMERP7yEnY4a9wMn2U+yv+YJFS+eSO348G1et4nKjnZzhofiD1bx7oIzNbhnB\nLS2cEARm3HffwHfd09ODIkJBzMzplFS2EfD7UTb6CA0NJSwxkZXFxUjq6/mlVksJYHI4KD10iD+9\n9to5v9vcxddT8d5byC1m3N4ArphkMrOzAZg1fz5pGRmYTCZyIyIGZDikUik3P/AAL/7xj1TW1KBS\nqUj2eLhNJkOj0fBZfT3u3Fwmz5qFWq3mo9//nnsTE/F5PAw3GHi5spJGhYKyri6SdDre2bWdE/QR\nEtBhjwvwkRjmz53H0okTz5L++DaMzs7m+tGjeX3zGrw1FYRPHIWz206kw3PBhuE+nw+x9J/nhiA5\nW89u1vRZmMwmdmzdgSAILJ62mNwJueeMc9uNt/HyWy9Tu7UWsV/MsvnLvpfhYzAYGPdfEB4cZJD/\nNIOG14/MD3GXkJaWRjTRNJ5sRBmsxFJt4dpp/745sFqt5qFfPcR7n75HW00bo5NGc8uyW9i5YQPv\n7thBokhEeSDAxFtuOW8/OF1EBA29vaQaDHj9fpp9PhJ1Or5cv57EykpStVrWFRaS0NeHpLWV93bs\nYPyyZVQXFKApKKC7rYZSiZ81wxKYP/UaVixfMWB8Wa1WwsRiZGIxe2trKT5xggW9vaR2dnJCoWCa\nwYDXaISpUxmSmcn2TZtQ63Tk9FcAfp325nbUTd0opWKcEjHJIoFWiYT40FCkra18sekLPlj3AdKD\nxzkcEc2kCZOI1+sJNhhocTuQGWR4wqHB5KPV4EVvDpDQ48ZbVMu7p0QsUMq4XCrFIjbw3st7GSmR\nMFqqoPPECawWC9WHD5NksfCcUkGX18W7djcFZS2EZyfhbuvFJZGgMWjY/8V+Ot2dpIisyO0qFBEq\n4sQ+ND1O2hrakEZHctTtZ4xCwUsnCjAG/IyIjkKfnEyJ0cgQg4FTnZ2I4+LO8k7WVFZy8OhRElxW\nAlIRz7pc9IWr6ZUKTJw6nCM7i0kz3EpU1BisHcUEOvrY//KLfPbJW2Bz8/uoROxN3fgEH2ODI7Ff\ncx2+iRNZlJZ2ln5PaGgoQt8Zgz8xJwVTqwmRTYRGo+F3Tz3FH+67j7TTpzkhlZKUnY3a7Wbj5s3E\nDh3KxMmTz9KIuuWOO/gYOHHkCGKFgstvvpn09H96cRISEs4yVHw+Hzs3b2b1hx9SolaTcssttNXU\nIGzbRlB6OgqJhFlRUaxpbiY1NZXTp09TUnKKT/fsQO31o9cb6AkLY9Fzz7Fr0yYay8v50taNO0eN\nQ9qMT/ARMAWYNGPGOZ7fb+J85/f/uecBpDIxm/bvwLztELmpI3j294+eV0sLYGbuTNYdWkfo8FDs\nvXa0Fi1paWkDz4vFYm687kaWLFoysH0+9Ho9j/z6Efr6+lAoFN9KePXbcql6PwbnPcjFMGh4/Reg\nVCp5+N6H2b5zO9293YyYO4LLci+7qPdGR0fz+wd+f9Zji265hdLsbEwmE3MjIy9Y6bjg9ttZ/fzz\nxDU10ePzEdHv0Tqwbh1XBgdzpLaWy9xuVL29HD59GrPDwd7iYtwiEbFSyEo3MFwq5h2TiaMNR5ld\nPXugKioqKopmqZQGs5n8sjKulkg4pVQS63ZTZbNhTk3FKQgUHzpEyfvvMy8piQ6plHf272f5b34z\ncBHv6+sj1uJmPgKj/X72eb18FhC4zKCgvKeHbkHg4O5V1IXUoQxzUdRXTc+XPUyYPAtJWBhvVhYQ\n7LfT2x2g0SBBHOxjemcAk0pKR5+HMV6BSd4AZXIFQ9RqhtqdTB8xgrL2dpLEYg4VFrLfbidHJkMj\niPAHIM3rI9/uormzlzbkCOEGak7W4Ix0EqQOovuwgEvuRWb14vZJsNm82E/aWbpoKZNyJ/G7P/yO\nlkmxxGfF09HlxWuX8WpzC7K2VkZPmcrNP//5wMXX4XBw5PPPGSkWE+X2InIHOCYRE6QLwtDrIcUY\nA6Eh6PVX095+gtiGPBI8HUSkKemQiVkrhvy2WuJTRlLqdtMgsbH8ssuY0Z9DFggEaG9vx+v1EhkZ\nyR3X3cGba97EKDWiETTct/y+gUrXn917L8dcLsYlJ1NXXs7hkyeJj46m+/33+aCwkNseeGBgv1Uq\nFcsfeACn04lEIjlHq+tf2bFpE52rV+MwmRgzaRJVbW0EZ2bSVFhIdU8PI8PDMdrtqCIjcbvdvL/m\nfcr8Zoa6bOQqpNQaO2kXiwkEAtz9xBMcPXqUzx67iejJoYjEInzxPpo+asLhcKDX6yk6dYrVr75K\nX6+ZzOkzuO322y86R0mpVPLQfQ9x58/vRNyfn/ZNLJi3AJVKRUFpAcFBwVxzzzXn7a14Mf0aRSIR\nOp3uovZzkEEG+f/DoOH1I/NDxcaDgoK4btF133+HOBO6HPm1qrELkZiYyK+eeYaWlhZUKhXx8fEI\ngkBoYiKna2vx+/10d3YiBpo9Hu5VKCix24nUanmutg51dzMqQaA7WId2fAaur+Vq6XQ6Fv3mN6z5\nxz84ZrGQKhYjBppaWugFnty9mwlpaaiLirgFMNXWctm0aXxSUsJf/vhHVG43IYmJDMvJYWJcPFHd\nPbjNJob6rXzu8VLgdzCyq4Oc+fPYf/JdZHEy/MGJbPyyGV2bmW2ni+lN0OMLiqTWa8ESsODudKNp\n9ZGk0KIPU5FmN1Lv8+FEIE6ppKC0iEqrGXfVSXpsPppCI2iSyxGFh6MyGumx2Sj0BWgOQInFQZtd\nyrJ770fSWM2WT9/HJ7PjiVbjHRHN5oouom1uOnUahs+dwjt/fQeDwcCb77zJ/sb9qCeo6WnuQThq\nx9PWTULOcIKGxOLSKdHr9TgcDpqbm+nq6sLf0sKK2FiaRF4OCOCMjMeQGI22pRPRqSJy58xl1743\n6WrzMdPajERmJSgskZZuKwGJmA8DLhRSKbrsbBRBQWw4fJhx48ahVqt5881POHq0h0BAQnS0kxtv\nnMWczKm4PB4mz5gxUESRl5fH1KlTaVywgL9s2UJNUREZsbHcmZtLkFzOG6WlNDU1naOAfaFG1v9K\n+YED3BQVRZ7JRIhKhc1qxSeTYQsKYmtzMw0OB6UKBTcsWUJXVxdd3i6ih0TSJxWzzeVBJAtwbVoa\nxpYW4EwYLUQfQm9LL1KNFFevi8iwSBQKBTU1Nbx69wqy3N0o1RLyXjlGa0cbj/3P4+fs14XOb0EQ\nCPpaFeA3IRaLmTNzDnNmzrmo1/+n+EoTLCgo6Bzv+ndZ106XlnIyLw+RRMK4WbN+kvlfl2qu06U6\n7+/KoOE1yPciKCjonAvInEWLWNnURIvZTKHbzcKgIOhXeI/R66nu7ETjC6BFoEsi4Df10lXcSfx9\n8WeNk5KSwpXLl1PR2ckXX3zBLyUS2iUSTggCo/0+RkgkNAkCqUFB1PX2YjQaaS8rQ9/Xxxi5nL2b\nN7JSrWJkcCgZsbEc62jDoZHhCg1l9COL6DnWg9flpa+lD4fGgdfrxZUsITQkkqHpOciiegn2Gygq\nLELaI8VishAWFkKXyIvMZiNULscU8LBBLEZlMrFa5EcnwJg2E2EiEevMfTTGRhCSGMuHHe0c8/up\nBRQGA9elpzP2kUcYO348TQ8/zBPR6XR2VFNvlrAvQkVjtAivOILxE8Zz/+33YzAYaG1tZevhrahi\nVQTFBNGzuoax1UaGxwYh67GyjxYOuQ4xvWI676x+ByNGrN1WBJ+XNpeLCqmKPQYD2pEZaMJCSRs3\nmfKtW5Gu/4zckBCqvc2Uyc0sDI5iVXU3heGxGIMU9HXUMOnqqxmVlYVSpaJxxw6+/PJLtu//kry8\nasQSHTZPB/4jHgo+fYXfTbwMQSLh4+PHufWxxwYEcAVB4OobbmDMpEm8/uCDrBgyZEA6Q8oZYdHe\n3t7/x955hsl1l2f/d86ZXnZmZ2Z3Z/tqm7SrVe+2mi3LvYINxoYYTAuEzgt5EwJOCAnFQAiEEIwx\nLi/V2MZFbrIsWbJ62V1ppe29Tu/1tPeDjIJiAyYxscH7+7LXXjPnnPnvdf5nn3nKfeP3+88d85uM\njIyctTRyOlm+fPl5GR6j1Uo2meRyt5vHDxwg+tLxmxYs4IL3vAdJkri9uRmfz0c0GkUv6BjLXfhT\neRZ7HMSHEoREkYUvHVdbW8umtk2cKZxBN+qQh80XbsbtdvPEQw/RmAyyaFkVgihwhdvOj559lNQn\nP32ezMPriSzL5yRVXmt0XefxBx/kzNNPY9B1Sjs6eMeHPvQ/8ls83dPDs3feyaV2O4qm8dDRo7zt\n85//rX1v88zzp8y8V+PrzJ+jx5XJZKKioYFgKsVYKETBYiEhiiy02zHabAylEwwYBCImE0WjgQaj\niap121i+evVZY+V0mmQyyb3f/z69DzyAr7OTQiZDr8GATRIRlTxxvUhWyGLDSCCbIxYM8vjAGZ4L\nBlmcSzMxcBJXIsTQ9AwvTE0SiyXQUTlhEimubsbfUcfog/tpCieJnxjhzOkZ8gYFLaQhZASu23wd\nk4lJmtY3UVVThV7UMWVMWA02TkWizKbz9CdyXOd24ywv54lciqRd4DK7kbUieAsapQaBSUkjvthD\nJJSiwiDhE2UWW1QC0RjRqmoqK6sQDhzgmrY2TJpE5MwAB0ZCeJY0sah9ER+/7eO0trYiyzKzs7Mc\nGj5EZCLC4P5BynrjrFdkfGUu6j1uZhI5JhXoOt7FjDhDy0UteFu8nDjYx2Akw65oDHHBAhoqKsBs\nQbDb6T18mBtFkQ9v2MDljY0Mh0L8LJnhgMePVtfIwlIfxVyBhMFA9YIFOB0OJo4c4eSRpxljkrHk\nKCF/L0KdiGkqzFV6jja7i43tHWjhMCMWC63t7efu819neqYDASZ6e7GLIl2hELtTKbInT6IdPcoL\nzz+PtaaGyt+YuDtw8ADf+Mk36E52c/DUQWaGZli9fPW58p6jvJxH9+yhSRDIzs0RSqW4Ye1a/vLW\nW2lpaaGmpuZcYGC1WtGyGmcmh+mcnmN0NEbMVYn/iiu57NprEUURg8HAqiWrKMwWsKVtbG7dzO23\n3o7ZbObUyZMEDu1lQdXZkl04W+SUKnLDjbe8rBfyf3t/67rOs088wS/uvJNDTzzBxNwcC5cs+b2l\n2j+E48eOMfrAA3ywro6NbjdzAwP0Fwq0/Yai/h+67qd/8hO2ZjK0l5XhdzgwplIMiiKLXkGl/43M\nn+Pz/NXwZl33vFfjPK87sViMR++7j5GeHiZOn+b2piZWt7dz79GjFGpr+X4iQV1NDbunJ2h3CKyr\nsCJpBrqKEh6Hjc//2+fBA1P9UyTnkvgDEW41WTErMm+vrOTO8TFqBYVxk85+k0ClkmQbIg+HMvRk\nE9htBryqwp7ZSS4CFkoC5arG+mIeigrDNjtWUSA7GqDz3ud5myLynuZmWkMz2OIqp+yleJd7KZFL\nqKuvI5qLMrBvgFAsROdznZTVlpEihWmZCd+4BU3R+SkqVzY0cL0Tfjo3hVzUsLhMFLIZchYJs1ll\nMj9BxppjOCuzodnIrKrxgqygPvNz8kqR/JkejL1dhCNBhHiCRquV+niGUdck/3H/f2CymJgJz5CN\nZDnWf4xIKoIqquQ0HUXXCUwGCMXCdEUFYq0F5lrmUCIKxWeK1DbXUKmr2NFZDxyam0NcvJjSYpEX\n9+7Fkclw4UuTaZIocuGCBew8fZq6mlraSlw4RBFzbS1DR47QKYr4PR5MI8OULHMze2oOmhQMpSa0\njIxYomDJSISiIQAskkT0FSQSBEHg7e9/Pzv9fp7u74e6OvxHj/JX9fWYDQYi2Sx3/eAHdCxdetat\nQNO4/5H78W/yoxQV0tE0e07uOa8nsH3xYux33MFgXx9brVY+tWrV7+yduuHaG2hrbWNmZgZN01i4\ncOE5vblf4/V6+cj7P/KyY7dfcQUP/uDf0XunKXGY2ZMp0rHtmt/adD80NEQwGMTr9dLa2vpHNYLu\nPHGC8Z//nE/X12OSJB47eJCdXi9X3/TatCEAzI2P02GxnJPdWO7xcO+RI6zavJmqqqr/VpD3Xyez\ndV1HmNf1mufPlPmM1+vMnj17/iy+Laiqyg+/+lUW9PZiSyRoHhvDWyzSWFdHiaZw2mzk0ve9nxOh\ncUacUUayOZKSTmcqR8hbj9HvpGxzGSU1JXQHu8kms7SoRjY0eIjMxdHzCl2FLGdUmWdLRWavthKv\ntXBqRmbcYWCVV+TmJV5qCgUcIZneok5a0SmKENdgnQofKS9npWRk/3SUYFpjvdHA4MQwqVgUo9uG\nedtqVqxfQT6SZ3X9at5x4ztwqS4ef/hx1CUq5iozc6OzLOtJcl04wyoZlLxMXXMLxXSSTqeZvkwe\nczRPSNXZ4xAZ9QgEFqpoQZ05n8iptMZJSSNcKhCeDDNpnOTk2ATBdAQlmWfcCPVuMxfVVjA8GuD5\n4QEKjQWOB4/T1dNF0pBEa9HQF+okMxpaCkyySH/RQGe2iKnJQtOqVjLODOnZNEJngKuzKgvrWqjU\nNDaqKmPxONMnT6IMDlKVzxPs76fW6cRZWsrDo6PYBYFYOo3d46HSamV6eJi2yQlmJvqwGRKouQy6\nVUcySsT0MBkliaCoYJSQwhLNNjdml4edhQKb3/EOPB7Py+5zg8FAa3s7qzZvxuHxkD1yhBUvNYzb\njEYOR6Msu+QSzGYzqqryyNOPkNczBO97gYqT48QHp5GcPlavW3funG63m8bmZurr68+bkHwlhJc0\nwRobG2lqasLlcr3qgMjhcLBh23Z2z4YYNFhYv/0aPvOxz7xiWe+rd36Vhw48RGekk72H9qJnddoW\nvlxj67Xi6L59tExOUu92IwoCLoOBo8kkq1/D/ptQJML4kSO0u92oqsqDpBVtOQAAIABJREFUu3fT\nOzdH+tQpjvf1sXjVKvbv3/8HPdfMbjdP7t6NLZ9nKplkjyBw6bve9Sc3CPBGe55PTk7S19dHJpPB\n6/X+0YL+N9q6/7eYz3jN87oSiUTo37+fbCTCdCpFYyp1Vvj00G4ybpHBYp69936RhtYGzO1mlJJS\nxGXLqDDbqU5UE86EsTgsZLNZBKOAqcLEXE5iMJ7BajPzTDzGc7pM0AmaWaPkUIacmiEWljBZBTyy\nRsotoOZlXBrsAtwa1CtwTIMFJgM5WcZaVsYWewmnEkEemUkQdLrIaBrF03ME1FlMD7tZ6Gqm6poq\nTp8+zT999Q6ik/1kCxpSRyXuk1msaZ2AVeBikwFBk7i3t5eFq1cTGO+kuNLHN05MY4qLGJ0Gsqts\naMU8kiCh53XSNSqGTUaK0SJaSsNcbSaRNPK8R2Gk08BbSsw4FOgenGJ2Lkq4xszM4AyGnAHNq6EX\ndPQWHUESyDfAHgcc7lNpycDFMqSPBNCEaXzrfEwEJkiPgcfhphCfZl8qzOKszCpNozydZpHDwdUV\nFXx2bIwv7t9PTaFA9ZYtLO3pYass85XnniOp61Qkk0R9Zi786BWUNZQx2TvJwI4BvIu9WMfNuM1O\nTEYT1owVbUkLc40dHPD5uPLqq2lqavq9947f72faaGQykaCmpITuQICc3U5/fz+lpaU0NTWxqH4R\nD33r67zNrmMRoT5pYG7fPoI330z5H9njT1VVHt2xg2ePHcMgSbxlyxYu2rKFb3/zO7/zuFQqxZ5j\ne1h5+0qMZiNKUeHxXY+z+YLN+Hy+1/xz6rqO3eNholBg9UsK/ePJJCWv0n/11bJuwwaGu7v53rFj\nRKenmc3n+fIVV+C2WLjn2DE+87nPYbbZqKqqOk/24nexaNEixL/9W7r37UM0GLj5ootelbvFPL+d\nQy++yMEf/pBWoFPT6L38cq67+eY/asZ1nlfHfOD1OvNGnQRJp9NnvQM9nldl3js1NYUyNsaHKyqQ\nbTa+lEgQmZnG01HFadFKcYkbiz2HHJOREhKly0sRHSKmnImOhR1Mzk4ydnoM/0I/hpSBzHSGxrdv\n5Ed37SQ+PU2+3MDkGh37CFw+o9MgQTQPT1oUIjaJ/riGvz/JgrhOToQyAWIq1GrgBbrQMXk8NDQ1\nERobo1ItEPb7ca1cRU6UGB0awlCIoqxcSV/ByEf+8R8pTg7QFpljqyYwFlI4/v+GuSWns1aDiazO\nTkWn2W6haflyVl1/FfGAhXw0SXw6R4k9zWhWJ1pwYivYcVlcTAQmUC0qHAAhrWF0G4mGo4g2ETmu\nM1Pi5Zd5lWK6gFRRwUS1m0x8HLQ8ZtmMltXO5qgV0BUdVCAIq2SRz9rAlZHoU2BP/xyJ5kquXHYl\ndRf6eeQ/vsVaj4nqMhs/y8qQzXGZrtJRSDPeG2KdrHHC46Higgv44Mc/zt1f+xqlAwP83YoVPDE+\nTnHpUkxNMt5aL6P7+0h2jaGMhplMZrHKVpbULeHirRezbPEyli9f/orTer/rPne5XFz/qU/x03//\nd+TxcZIGiQlDgtHnv4eSUPDpPk70nEDPpzD6rDicLhzlDqYDs6RSqZcFXpFIhEOHDmG1WlmxYsU5\n6YV0Os2+F/eRyqZY0rbkPF2w38WuPXt4ZGSE2re/HVWW+dFTT+Fxu1m+fDlwNuDp6+sjHo/j9/vP\nGWTncjmqmqswms/uH4PJgGgRyeVyr+q6fwjHjh/jngfvIZVJoSdjBIZUXGYzAa+X2/5LmbGrq4sH\nnnySdC7HurY2bn3rW1+1ppeu65w4ehQlm0Vtbkb1+fhAOk2p1cpwNMpTuRzRigoWLlvGl3/2M/7m\n5ptfdfDV2tr6qt/7RuWN8jwvFos8f999/FVFBS6LBVlV+d4zzzC9aRM1NTWv+fXeKOv+U2G+1DjP\ny9j3/PM89LWvMfTccxw8dIgFS5a8TNxR0zSSySSiKCJJEkNDQ9i7uhDjcURZxmE2cx8QWFNP6ZUr\nKNoVYskY/hI/VeVVjO8bR5qWcCUc+C1OFra0o0Q1xo6P0WRtosnbxPDxYUaDcwhVThwXl5N35qk6\nVeAiB5jz4K6CtAPG6nTmUjAdFUjqJhIWI16jxAdUlZwODquRFw0iss9Hrq6eJ2dnsIdnSGxci7my\nFAwCgdJyjA4Jx9JmSrICxXSW5PMv8E5ZpiGrEkuq+NMqv3ZALBPgcR3OmIxcdNu76ZscYTg1jGn/\nMLeWOfEpOToEG9NhAZetgZSSQjGnKJ+WqR+CNWmB1rDKTEom7dDIJxbguuZqokWR8PJVxGsr0Lc1\nossa+plpKABhzu7YSSACzIIwI3BRWqcuo6Mh4TbZOZpXqVlyIZ//7B08uecZHo91cZI8x7U8NFVR\nzKjUaEWqkymsSpGDikwuX6RnLsCW667lgm3bmHO5iPp8bL31VrZddRWHTxwmPDlN5Yv9rJmOkQ2E\nKY8nuLjcztzEJCnMLKipRTKZKCsrAyCbzTI3N0dvby+Hjh5idnaWqspX7gHyer1ccNllrNy+nUcP\n78R/sR9fk48YMXbu2kl5aznxbJwaVafO62N/XOaJlIxud1BXXk5paSm5XI7jx4/zyb+4mfxTjzLx\n1A6eP3KcZZs3I4oi//Stf+Jg+CDjyjh79uzBptvY++hjPPHDH9J95AgVjY2v2Kf14DPPoC1fjt3r\nxWixUBAErIEASxcvRtd1fvrQQ9x3+DAn8nn27N+PG2ior8dsNnP08FHmsnOko2kGd5+GsMjbbnjb\naypeOjU1xVd/9FVKLyilfHk5KZOMUFLPZe//Sy65/vrzynUTExN85ec/x7p9O641a+geHCQ3Ps6y\nVzDKfiUOvfgind//PtuKRapiMQ4ND5OVZVb6fPxqfJzjTU1UbdpE7cKFFMxmcoODrF627DVb6zyv\njkwmQ+eTT3LxS5lVSRQZSqUoW7v2j5JtfbMyX2r8E+WNpn8yMTHB8Qce4COVlThMJk4GAvzye9/j\nI//wn/dWJBLhOz/8DuPhcQy6gdtuuA2v10tPdTVVLS3I+TyWTIbtfj+nCzPkMnm8Bi8jnSMY1xqx\nmCw0WZogkcY1dJh81zGOW92wcTO3XXcbNTU13P2Tu8mmonTMRViAxkwyTnKBGUUArQCSGXQTFDSg\nEZSoxIRiYot/KYwM0ROOcJcOg2Uu8j4vBUHnp7rM5dXVDBx7kXp0lEga1aOSzoGqFDAKAva5LAsN\nEuFsBpeqosfT+J0WFhRU9ulgsINJhngR8mjkKqvonZmgJ9JD72Qvy0MpJGs1bs1NqbuU2nyBwUyB\nnDXKNZoVs5xlq1FlRxzKbEaIi+woiBjWt2C228kJInpDPea5CbwzBQJWD1mHAFPg8DnI63nMGTPp\nRBpDkwFTlYnI7iyNmkCj08VjmQzTZjPvvuSKsxN446fIL1Kh/uy0XeSZcWpdtRyqt7P3+SgVgo4q\nCqQ0FbHnDO++7i185fv/zsCRI2RHRhg9cICN73gHt112G1/7zMe5QtDJFgyUGSWuMxkJmCSm5CKW\nxx/HpcFjP/4xP3K5ELJZTocnKfisjMfGKa8sp6m1iYOdB/nsRz/7ij1Yv26wVgTlnC9hJBnB5DdR\n4i5hotHDY4E4jwwEiTYtoe09NxJqbOaf77uP1tJSHj16lNN9Z2hPhti6rgFJ1ek608WOn/2Mjg0b\nmGKKxrVntaHS5Wn+7Stf5K8b23hXZSVj8TgPfv3rfPCf//llGbtSh4ORaBT3S5mCQjSKy+cjmUzy\nze9+kx8fOoz3mmtZ2d5O3OPh77/1LT42NcVl113H2o61PPXs42T37OVyZwm2Wgc///73efcnPvGa\nTRpOTU2BD2yus1ObtctqmX1q9hUzeoODg+hNTThfyhJWb9jA8V/9ir94ldfqeu45rvH5qHnpb3Rt\nLsdzLhffnJzkaCyGsnIlDY2NjB06hPUP6Jv7c+GN8jx3Op1YGxo4ODXFmspKRmMxpi0Wrv0d3pz/\nE94o6/5TYT7wmuc8gsEgjYKA46V/jEvKy3lkfPysX5wkkUgk+MCnPsCodZS6xXU01jRy96N38w8f\n/gdqrrmGnz35JCWSxITbTTYYZNF0gIH9Z5CbF2Aumjn65FGa/E2UukqRZ3vYVGJFNEkkh4Y5/rMZ\nJoaH+d7sNMF2O/5Cjot8LnJamoa0ylyPwmnJwt5gnlYrzKbgTBmQBi2i4Xf5UT0eqrrTNAvwq9JS\nZrZtxuNzM9Q7hu6vJLFmDYnhM5xIhXGf6mZGVkg6Lahn+pGdTnRKSGYKlJ44wTpN5Ukdkrk8YRX2\nG8AhQZMZjmagVwVfg5lf9fyKju0dbGjYwMCPnuORA6O0VjeQK+aYtmlMpiexxVK0em1Ma+AzwioB\nukwqLtWEo9xMUcuRklPkTDJCXy8WswlLvoivd4zUqI7X5adpQQfBdJC0lGbWO4toFxFSAkqpkR9m\nRaR0GkUUuUCS+OanPs4Xa/xE0kHKry0nqSZRsgq6rFNeVk6iNkGszUJ9QkcPKLRoEu2yTrqvjy+8\n9a3cvn49b120iIwsc8/993Pl5z/PFZdew/pEgvHJCU4fDnBU1Ribi1OVzLHM5mSpx0O6p4dns1nW\nOE347DIPnBnB8546AscDXLDsAoaODTEwMPBbBXodDge13lqme6epWlSFntHRQzqNb20EA3RFusiW\n2lj8tnew4YLNGAwGXty1i+eOHKHkppvQAjMM7t7N3b1dfGRJDU6rROQlZX3R+J9TcrqmYwhHuXBT\nNYIg0OL1Uj05yczMzMsCr+svvZQzd93FWCiELstUp1JsveEG7rr/Lk6lT+FoK4dKgWd37qDD5MCb\nz6M98QT3Dg3Rsn49tUWBT1x/Ew6TCV3XuefUKYaHh89NZP5P0HWdXC5HcjKJskzBYDKQDCXxlZ6f\n1Uin09z9k5+w68ABBoxGjA0NVFdVkYlEKP0tVkWvhGgwoGjaud814KIrr2Tthg1cOjbGtx95hGBv\nL9GxMXzFIttuvfV/vMZ5/nAEQeCWj36Uh+6+m539/bgqKnjbBz7whtGZe7MzH3i9zryRviXIskw8\nHqcrGmWrz0eJzUZ/JIK7uhpJkpBlmW987xucmjuF91ovY4kxUv0p6jx1zM3Ncc1NNxHaupVsNsv3\nvvAFLjhzhpWSRExW+eTO5xi62Ih1jZXj3cex7RG4VJJYkC0SiKRIFmQ0m8LE3l0syGQ41gOC3YZj\nQQXZ6RRSUcArWamu83Ig18/htEoBwAwcB7Nuxulzkjx2mLdJUPRbeLLEi+os5bScRfV5Mbe3sfv0\nEXI1FRTdPjLREawzB8AIQqWAadCBYSLEsnyem4oZxnToFOFXImCAyCK43yXgCOooecg5zDgNBUxe\nE4cOHaHcVoetwsWCuEqHovBiNETJpYtpkFxMHzjJ7FyKCofGXAjmREgYNY4nMiTG8+hTB1DSCYxJ\nlfLeo9SZLRQ0GWMwyFskEZMpR3dolLrNbfTv66fEXULBWEC36ShGHcFooM5ZwhJd42s2K8Lm9cwa\nJOJdCVz7kngWe7DsmqEtY0KJjjI8kCJgV1EiMlsUkeWaSptkQDWqJAOzjI0MI7S14TCZaBMEZmdn\nueSWW3jsm9/EVCgymsjTIoKUVwjlFVwr2pBlmXwmwwFNo1sFT1FED6QIPzeDdSLN6cgzCFVVyLL8\nW+9BQRD42Ps+xn/c9x8MPTZEs7WZ+oX1BLoC2LFz/drryWoWTK3t5zJGoakptJYWSuvrmStk0ZYv\no7uvh2Ayw+FUgRs3bGDhwoXYn7AzNzSHzWUj0B3A568hWSjgslhQNI2QLDMzM3PWa7Kh4Vw5sKKi\ngi9+7GMMDAwgiiJtbW2YTCZ6hnpo3d7K1GOnkOfCCIk41mKCFkliicdDb2cnps2biUcipKxWNIcD\np8mE46W99D9FVVV+8aMfEdi3j4aROQ6eGMR/8RIcRQcffd9Hz3vvfQ8+yAmzGfP1N6I8/Cse/86/\nccGG9fjicd71BwRH66+5hkf+5V/YksuRlmU6XS5uX7MGl8vFsmXL+LuSEnYfOoTe0MDWtWtf1XDF\nnxNvpOe5x+Ph/Z/97Flpjj9y5vGNtO4/BeYDr3mAs43AX//u1xlKDRF2ZHj/zqfYtHgZ45oGVhN/\n96EPUVFby0BggIqGCgqpAq5KF+HBMN6C91wDc1lZGblcjpmeHpoSMXRJxF6QWSYo9GLFWmlHSAp4\nXwyD1cLeSIoVmsaECgNKgrhJwK5DXVonk05xJJzC5ROJWW30q0WWv6WD4V8MU7CqkASGAR1KFpXQ\nfFEz2qkx7IBVEXCoRTRFRjeZMGhFDIKK4lRw+O1En1XoSMDGDBQ1eD6so8kKtzS1M3fqFL/UQDNC\nswkeVaGogGdSIGbQCbeAMCbgX+pBT+vEn5ugLVfEokaJ5TTqGxpZc8GFRA8+w6n+IUqvWE5kqpod\nx0ZYqsEBi8Bpq07OCNUyLI6rjBoSjA4epW5c5n0qlAG6Dkc0AX+Zi7IaO3VuGweNEu3b2tn96G6E\nlQK6pnPMKBCSizi0OA+qAuYN66h0lTKhg+vSi+DJ57DtTfEOQwnVbhu6GTwOByerXHQXe+gfyXCx\nICG4jehmEYOgcWKkn76+ZppaWhjXNNaXlrK4owPHHXfwjU9/mk9fsh09GKQ6leJnkQhJr5eZbJYv\nKwqe66/HbTbQ3X2ckUVuKiQrdtM4F0azDEyNk3+puTyfz7Nrxw4CQ0N46urYfu212O12vF4vn/vU\n585lWfP5PIODgwA0NzfT39/Ptx57jMSiRSipFJXFIqlUCl3XaahtoK+zk3C2yA/GM1z1nr/kymuu\nQRAE/ubDf8PDOx4mMZFg++btlFxq55577mGhIDBaKHBEzdG5934EQaDOXMdnPvKZc72NLpeLNWvW\nnNsvuq5jt9pRCgoXXNzC0T2HCO7up6mmAa+q8sK+fWTjcf7lgx9EURQ+uWsXVU4nRZuDbFMLq/4A\nRXlVVYGXezEeO3oU5YUX+OiCBYgNDXz7wAGe6cnRunkN+Xz+vPd2Dw8z27qE4KgRz9r3U9x1P+K+\nY3zx3+7E7/e/6s+ybPlyLH/7t5w5ehSjxcK7t2zB6/Wee33BggXnhgvmeWPwZiv3/ikw31z/OvNG\n0D/RdZ07v3kn9z59L9FsFF9rBaYl1YQKZgYyExhPHmNhYBzh9ABd41M0X72K2ROzxCZiJLuTvOvi\nd3HZ9svObfBMJsO3v/FVmoppKhwSs6kMO4sFZpssxAeyxI/FsWZgEwpJXWfCAH06bAHGVLhZgYuA\nvAAHgJ68zhm5SE7JExiLk20uoLfrYAfBJmBymnAudzLRM4E2WyCVLpLJFxlLpOhPpBCNZqyRWYRs\nGtElUjzZh+lYL9cWVWadboKeMsiZiMRTfHrDhURiMVamE1QAO0RIlUjcaDWzJWOgYkZhYgaKDgmT\nakLMwNrJOOusGvUlIjVpjb50DpdQJJJPcCiX5WR0GlwQyWr0KjBYqpP0CCyd0fmoDtd6jbQXNHpD\nGhYJrjZCmQazRTglgN9swmQQKRpN9CCQODZCyVSKcrkUZ0klMTLEygxMJDVimBmzWOgPRxEVhUJW\nZqndQ40scYFmIF/IEU/FUMwmrFeuYt01a+g5Pk0gncZtMzKoKez1Gugx6AwHg+yLJCi7eBvbr7wS\nURTPGkTv3cv1dXUsXLSIto4OCjYbp+rqGDSZ6LHZKGlvJx2O0q/oaEuX0uEoQZDM5OIFPrh4KVG/\nH5Pdzhc+/Qm0p59io6ZTHB5mT38/Ky644JyI6a9/GgwGKioqqKiowGg04vf7WVpbiy+VYm15Oe9/\n17vY/eCDTJw+jTI8TOnwKJ++5Va+dOe3uGjbtnP3ZUlJCetWr2PLBVtoaWqhrr6e6pUrkVtbmZQE\nghUxGi5owN3gZiIwAVFY3PbKTeeCIFDtq2bPU3vIFXOUilaWeOqxJ1N4JyfZIklI2SzGYhGtWOSv\nnE6kUJSg6mDAvo7+kWlWrGj4nX6Nmqax4+GHefAb32Df448Tz+VoaWs793c5dewYVcPD1LndnAwG\n+XGxSGzNGpwrV7Ln2Wdp8/vPNVI/t3cvnWEdb+UGjEYrwlwQV07imms2vGxw5vfh8/loW7qU1vb2\n3ypS+0Z4rr0ezK/7zcV8c/08fzDJZJKBgQFOnjzJ//vhd/CSQUuYOJ1P0bKojYGRAeqcBq5bUI7T\nJHG4Zw49L7HrZycQS5IUsnkq3ZUMTw8Ti8XweDzA2U04WS3wneECC8N50jL0qTDbk0AXgEUQckJP\nCC5KgGoSMQg6ZHVEQACarLBagUMSrJIhrkBrAcKnp3k6JRKwCRh0A/YFdrKHsoS6QyiSQsyhoSfg\nlARzaGQTozh7o7hqPCiRKOGfRnFnMtiSGqddbqwbN+PzlJJPZMju3c83dz1DpZznuFlgzCIwUiFx\nUVKiPizTIkhs9ruZTOeZWVpPcUolPxnHp6iI5UY0WUO3aQyks3SlI5wwm5kbiWLPauieAq5aD9Hy\nKFK9RHG6SGMRqk0GRFWgThKwazpGGe4vwgIdpoFBDTYvrGAyGuS502OIpyd5lwa9RY3ISJj+wTnU\nVQKOxS6yDiM5vZF0xzIkdynqgRfxdXZR5nKj+v2cygVZ31HG3FCSPVNzNOdlogNRLr7sCp557mn6\nCxFEj4h1Yx3mOehXLEQD0ySCnQT/9Svc/o7bKSsrY9HGjTyxYweXVFYSzmaZ8Xj4q899DrPZzMg3\nv4m4fDkBux2f10veYKDOWwZGC4Z0jpQokszl+MK/fIHs0FGuKHUwNtDFplWbGBkeZnZ29mX6Tel0\nmh8/+GN6hnqo9FVy29tuo7Gx8ZyJsq7rfOeOO3jk8cfRJYnNN9zAxgsvJB6P8/NHHiGeybC0pYX1\na9e+7Nt/XV0ddXV1nDhzAofrPwMQe5mdYDT4O/fPsmXL+FL5l5icnMThcODz+fjK3/896VAItbER\n3+AgvnicjKJQa7ISddSytmYD05IFRdnC7t1HeOc7b/it5z+4bx/hX/2Kz9bXIwgCP9+xg/1lZWze\ntg2A8tpauotFVqsqL4TDFBYtorytDW9jI3I+z4HOznN9ZLdccQVP/987SYbN6KkkNXkvbvd8JmSe\neV4P5jNerzOv17eEYDDIF7/1RfZN7uOX/3oX186FeZeqsTirMh7LM5aRKXeVYzOodAATqTyPii7U\nCzYy568hNh6kyVtCnc3C1MgAgXCKzRu3APDlf/0yIUeIZI2FvskE4waF6VoDWk4HB7AElGoYn4SB\nKMh5nS0SnFLPNuv6JSgKcBQYN0OpAmsssEWDJgGUnM6QSUK2gDguIiZEZFVGrVdRCxAsBUEHWylo\nOiS9GmpQRdJ0lg3HuM6q4yzAKY+fsoWL0FUYNphI5FKMl84wbYMRg85ku47gkyjpKXC5AF4j5ASF\nPlEiZRApqamiEFHIJWPUmDQw6fQYYJ8Ap3UJXzTNOzMF1uZ1imGF6VCKbB0YRAOCJGCZgvWYsRQU\nDhU1OovQrsEiDWZ1KAJRAUbbapjxeymG4myRDdQ63SzQNK4vqlTIMnMJjXRFCbLmgMZ2PDkFn1xA\nyOdZO9ZPQyrOzniAeFs5vWmZM0Yzfc4isZNx5IBMd7obsUMknM4TzRURFQMLqhcQt8extdhoW9HG\n40+9yFMHTnCs+yQXb9yI7PfzwswMM2VlXP3BD1JTU4PFYsFjMtH5wgtIisLcoUMsXreOSLFIcnyc\n0lAI09KlyCUWZu2ziFNR1vlKUEUFOSEzYbHRfskl52WB8vk8d3z5Do4kjlCxtoKwFubgswfZtHYT\nppea1X/8ix/zwNMPECOCmitw9SVXIYoiX/rudzlVUkLU5+PFw4ex5/M0vxSs/VfSiTSHug7hqnGh\nazqBkwG2LdtGU+Pv7lFyOp1UV1eTSqX4px/+kKnSUg7H43iMRiplmYlEgoQksUQXGJMNhJyVDDv8\njMU7GZ3dh9Vsormx+Tyrol9z4OmnWZlIUOl0IokiVl3njKqy9CW1/srKSiYKBXZ0dnIoGCTU0sKK\nrVuRJIn49DRNgsDS9nYAqquryQYixE6lqDG1YzcJrFljYOvWDX+UUtSbMfsB8+t+s/HfzXjNB15v\nUn7yy58wZh7D2+Ql+PP9vFdSKbdZ8Ao6gWiRbMNibrruJk5Hx5gaC3A6kCG8cAlTLjuyx0xa0yl5\n/gg3yjLLzEa6Tg6zbNt2TCYT9z14H+mucUx9ARSHyvQ6MJQYUJMqvKTF2noYbotCkwKjGsg6FAyQ\nMsKYAIessM8EIcCmwzoDuHSI6xAUYFizYCq6MMVMFCkiIyPUCughnbY4tBmNJCzlmLCTC2YpGDWs\ngSyb8xoGHZwWmFZMBGprkUwG7IpCYGoQoT1PplZHmlYpm9YxT6tMFsCsQl7W2Y2G5lAYVGRSGsxM\nzjBpVBnLQY8AJ80gtFpxD+e5JVdgqwLLRZhWwVqA1cmzNj+xMolQRmJMs9GtaezNK9QYYLsGa4G3\nAH3AYqPImM9J61svoPbMHDbRTrFY5EajEUUQCYgimhm6DBq5kIJa1YRmc1An6phnp+goBBDcAiMu\nB4lWaLp0DeP2GLoOza3N9AX6EBYK1K2pw93iJnMqg9VvRcpLBKNBGmsa6dk/Q7ptOdLCxTRt2sL+\nHTu4/eab2XrNNazatOlcfx9AfV0d6xctYk1tLVesWUOgu5u5I0ewhkIs37CBd3/84wyODDLLLJFC\njuDgLEJB4UxKw7vtEjZecsm5QCCdTvOlb3yJh198mHR9mnAwTMuiFmLBGB3VHZSVlXHmzBnu33U/\n9dvqcS9wkyTJ0NEh3HY3e1Ip6jdvxu71YqutpWfnTq78LU3AdXV1FMIFuvZ0kRxKsn3pdq696tpX\nDIheia/94AdoGzdSv3EjQlMTTx05QtRgYNrjIWEysT+Tok+y0GWpZIRJsvW9LLq4mp7+HpSoQkf7\ny6c8x8bGyJ45Q9NL2mInw2FYupRFHR2kUikSiQTLVq9m6bZtLF4LMulJAAAgAElEQVS1itHeXtKC\nQHJmBmNPD7ddd925IFYQBNatW05ZWRG3O8bGjT5uuumq19Q8e5553mzMlxr/RPnf0j+ZmJg4q9HV\ndZz+qX66e7pxr3ez8+hOSrUc03kNq5LD4SwhKRn4y9v/khtuuIEybxnPmp/h1K59zFFAcuoUi2nM\naoEmt0YhF8E2o3FVTRv7d++mc+I0E8c72RyNUGFVSAs6T5yA3iVF8AFz4DkKSzKwRQdFghoBVEEE\nSWPaAz8tQLgMsAGzkJPBnoVtIsRscEoBze/AZrDRsrmZ+DMncE5kCGUFeg3gSEuMdCzFvbwNe1pn\n8vhp5LluvAWdjAr1aejVoUkLcvrF/eD3o8WilIUmmWs1IHUV8FJDyZJalGACSR7ml2KBJTmolWEy\nJxA0g0PPoSxWUPMwngXagSzY/SLmTiNCWsEqQIqzQeMaYCwF2xwywSMq6qoKHC2rmDkTIX/qJKW5\nHD5Rw1iANFAigEnSCI/OMts3iw07ly1fyC8PHOCZdBqz0Yhe7SZrKOAot2C2yJhOD0B9A2cmx1g0\nPETBq3JUstJ66WqO7T7G8bnjZMQMjdWN1CyuYSA+QCwVQ9d1zCVmvDVe7IMCS/IxNukq3SNnSBlL\naGwQEQNhhoaG8Hm9BAKB8wKu3+TX/ViqqnJyzx4udThIAq6pKX55111suOoqvvd/7yFVauKU2Y5l\nMsbHP/xhbv3gB88LdJ7d9SzT5mmcFU5sFTZSiRQDgwM4c85zvoixWAyxVESUzh7nqfYw3Td9nuEy\nvNyE+b8iSRK3vO0Wbrz+RoDf6/X4m4TDYY73nMazZBlSKkXzwoUYrr2WdzQ3o6oqK1euJBKJ0NNz\nmief3E8su581V6+gvLwMuVZm967d3HzjzS8775bLLuOeri6C4+MIQLCykndffjk7duzkoYdOADaq\nq3U+8YlbWblyJXeUlXH4xAlEQWDDBz5AZWXleeczGo1cfvm2V72u30TXdTKZDAaD4RU9Kf8rb1Zd\np/l1z/NqmA+83gQcOHiAb//k24x2nSA9MYersZqS5fU8/6vn0VZoyI1Gft4n06EIJNMy2pp1XHfd\ndRiNRmora5gbGCeQnSTfkwVhCbpRw3y8E5+aJSdKGOIG9AqFJ59+DMcGF3ZFoVwpohXAJkGTCXpj\nnFVbLwHDDEgq5KzgtkIuDMNoOO1wJAuRZqAWGDxbettggCkFvi6KJJx2UrKCfzBLa7UB7eFOLvK7\n8ZcpRBJJ8iqMiFaqfHWIKYkCoDTXYxkZZJGS4WIj+DWoVeFO4H1TU9TOTLFfh0k7HOgWySg+fFsv\nxOawkmtWCWs6G4K9WB3wYhriK6wYCkZMS01IQQm9REc7oEEOsIB8VCfmr+e75iSngnPUqCongKut\nMFGEpsRZK6PQWAbfVT6W+5fzZDhGT98AjerZEqMuwpgBdIuZpNvAzIkZWpe1s78gEiv38a3pHEus\nZvKiSqiuhvWxPI26Tuf4GGFDDrOrwGlbkQmbkxVbVuA4Msz1SZFQRKGv3Ebd1jrsbjuOgoO0kibS\nHyE/l8eetHNldS2fX76c7lPdhA7sQ7Hq+FSJuqpqdvX0MHXyJPs0jZqamt/ZmB0OhykODLDSZuPx\n0WE6DCI9p09zuqGZGtM7kfNnS8+2xR7MdullwU4wGsRR4WDJmiV0He6i6CgyEZngfZe+j/r6euBs\nuU0P6RRzRUxWE7N9s7TXtbNo0SI8O3cydfw41tJS4p2d3Lxhw+/dK6824EqlUkxMTJBOp3nggT3E\nJssY29HPQGuINUtrEKamaLz0UoaGhnC5XLhcLhobG6mpqeZfn5ylvPysur+clzGbXlnF3ul08sG/\n+zuGh4fRdZ2mpiYmJib4xS8GqKn5GEajlZmZw9x776N8+tO3U1tb+0fxN8zn8/z8Bz9g7sQJVEFg\n2dVXc+UNN8xPy80zz3+T+VLj68wfozY+OTnJwMAA2WwWg8HAHXfewcxAJ2uD02wo0/HOZDk1FyWQ\nSaOndQpmiZTTxJhkJl1Vy90PP4rb7ebJRx7hmb+/A8fAYcodKiFbnnRnP3pwCKUiTTYC9oTOeCrP\nj5MzDDDLdP802ViUBoeKxQWaDF0xmMkDBahIQY0CcauTEd1AriBzRIZ9duhb46DfXoQ4kITGpIFr\nNSjXIOuSmG5fwtz2VYjuGlriBd7rtbE6WyCdypMURZLxIsWCjslsYM5XSbDUSaAU8skQFX0jbMlr\nrBdhVgRZOzsxGBUgYxDRzBIVdiv9cRNpi5Py9oWYgLgs4E6l6chP0ewCSw56ZR0hpmMqs2AxWsgU\nMiCC0C9gmbBjql7BRZ/5W4I1NewtFjlmNROy27CkU2wQYdYEokPiQqeH09EC9pjGe/01OMMRnsqk\neMIMe+ww7TXRiYYvo7LQbiKlJBkoZCm/pIWlH9jKeImRF0bGcKXSvKvUTlVeo65YJO21sur9V9B+\nUQejR8cxd09xVUFhudXHIouRg9Nj9ChzTHdPU1tSy2rvaqxBK1pAw+1245yYZmNlNbW1dfRMzbBZ\nhxlV40RvL8bOTt7rdLIgneaFoaHzphB/k1AoxN1f/zqHHn0UBntwVxgI5SLsGptBKa1iqmsYWyqK\n3eKnrHodojjEpk0rzztHOpXm0IlD1K+up8xbhjwgc8vGW/jAez9w7poej4cSqYRDzxxi9OAIhX2j\nOJJ55ubmuPntb0eYnsYWCnH18uVs27qVVCrFXffdxb0P3svxzuM01TWdZ6nzapiZmeEfv/c9XgiF\neHDXHsbPKGxd89dkRnuInOom3bmXz93+F7S3tb1sf5eWltJztIfRqVEyyQyJngS3XXMbdbV1qKqK\nIAjnsnOCIGAwGCgvL6e8vByj0Uhvby+dnS683rNN8xaLl+npXVx99aZX/fkHBwc5efIU8XiMioqK\n3xtAPfnQQ1hfeIH31NezzuHgwKFDaLW1VFVX/9Zj3qg9P0ePHeOehx5i79Gj2CWJqtdYyf2Nuu4/\nNm/Wdc+XGucBznqpHbj7bloEgWfCYfbrKToDfTRNxaitNJMXFYLpNLZghjqLQGiBEcdGF7mZAnqn\nhdtvuo2Kigry+Txdjz/OkmyMoN9ElVQklFIIejWYAe8UzBrgfhtnsxZbjGiZPPHDcTRB46ECtBog\nkoNeM1AGxglYkRGYXtCCurSD7pzC7q5OiqE5TG1mtFoNyS7iHdGoTYPDqLBIgawCQ+5SnB3tGDQz\n9goPUptAfGqSElnFEYox7IK0WeQSHfK6wvNdx+jJNpL06VR1D3GJR+FoGhoUaNNhHPCLEBbgJquD\nOauFPSaVhMuFWXHQFwhQVltOKpGhcnISVxEsYajIwGbVwga3m+MH8pxZaaWmooZwfxjnQidiwIFl\n1Vq6J3qZVQsI2y+m0HMMeUEFD/1iJ4HgDFVGI8slBzbBijqSorEqxua2NuTqUizmLN8WRaI1HpRY\njsq5ADe5oaqQRdRsfL3rJP42D3JBJufNIXkktDMpZgJpyINm0whNZgjd9zwlFjNioogpmUP0CcxG\nZzF5TSxprSOwop5kKMka3xo+9N4P8Vef+Sjudh+Ny+sY/nGI+3Y9yVWrL0R1OgkYDLy7rIyH9+5l\nu8fDpStWnA0CBgeJRCIvM6mWZZm///CHqOk5gSsfp0eR8QxqJJr8zNRZkfY9x+UJgeayNfRMHuDZ\n2f1s//wtL7uXN2/cTDAU5Omnn0bXdd5z5Xt4+1vffl6gNzQ0hFaQuWL9FZx++GHev/YC/E4nL/T0\nsFsQeM8nP3nuvbqu8917vsuQMIT/Ij/BuSBf+/7X+Oe//ufzFL11XScQCCDLMn6//2Um8T9+7DGK\nq1ZR29ZGtKqBqehhotEhNi77COFwL3V1B1n3G5pfv4nFYuGvP/rXHDx0kGQ6yaKLFrFgwQJ+8MAD\nHOztJZdOQzaKtcREe2M773vn+87zj/R6vWjaPlS1iCSZiET6aGjwnncNWZYZGRlBVVUWLFiA1Wo9\n99qePS/yox+dQBA6UNVuNm3q5X3vu/l39rLN9PVxrc+HKAiYDQaWWizMjo7Cb1njG5Xu7m7+7Zln\nKN2yBXSdbz/9NP/HaGTp0qWv90eb503GfOD1OvNa1sbPOdL7/ZgBpa+bPmsRQ5OBTEqnkFQYDKks\nSmlsEARSthJ29Cgcn8tTNPspbVjMSDzNvn37yOfzTI2PUxIJUSwtoioqJGWE/8/eecfHdZV5/3vv\n9KoZ1VFvVrNsuck1LonjOHZiJ8Qlm06AEMrLhkBgafvusi+7C4QSYMOyEDaQGKcQkjghxY7j3otk\nW7Z6LyNpNDOa3ufe+/4hY2KcRshCFvz9fPTHSGfuPUcz597ffc5zfo88lau0yAJpK+yKQ1etgDQu\nofQaKKiejyhIeLva2aX2gh2oBMEiYDCrmGw3YZw1k5QUwz4tH68yk+z9k0hdEVxDCrUSfCgIcxR4\nKQkHtFqWGyGpCDhVAmSqkXwC4USSoNXOTkXknKQhkQhQHI2js2qIyzAr4id+8iTNpSJVaihPqgiJ\nEs/IUCRCRAVzgO1p+Loso8u2MWT1EI6MoYloEcYnCfozkYJhbN4xypMiBqAFgU05DlYtXkzN6Wa+\ndWQY2w2zmH/LfAS9wJnH2nGNO4lbdCQNOgiG0SVVlORUEa4cQEor3L94CSa9gS2HDlFhVCH29HBq\nYoKJ4AQtgkK0ejZSQymIaZQ9Z1DEUaSkhK5tmIZwiplHOznR0ktfZQJboQ21lMFgzyCOLJmxqIhr\nLMj1Qi92QU26b5IzRoG2dIS8tIhvUo03P4dZc2cRdAdRjan4px/9iFNZdsxlFTgPtKAqEXmwy8v3\nDu1BUulomDmLpokJBIOB2vnz0Wq1pGWZJLxpcvZzzz1Hx8GdzMjTU6NXGDfr2OqL0LCsFksozuxz\nKa6dm0Vn50mqUWjXy1x99RWXHEcURW7eeDM33TBlufA7AaQoytSDQVMTxx55hHmiSLPLhd7lIq+y\nElEQWFFczIGWFmRZviAqotEoXc4u9I0G2jp60GhUaBAYGRm5UNdQkiR+sfUXHGo7hKARKDQW8sCn\nHrhI/Lh8PqyLFgFQVJjD2UwZj7MTi6WAUGgfy5fPu9D2zea30Wjk6pW/z7Xa+pvfcCCRIO+229i5\n+xUSLUOsnFVIR7CD//zFf/KV+79yISpVV1fH9df3sH37wwiChaysEB/5yO+d52OxGN/73i/o7TUi\nCBpyc3fw5S9/BJvNRiqVYuvWfRQU3IdOZ0FRZA4f/inXXDP4tqantoIC+oeHKbBYUBSFgXicvPOF\n0N+KD2LOz8HTpzHPn4/tfKQuOW8eR86efVPhlU6nGRoaQlEUiouL3/US9Adx3H8O/lbH/V65LLz+\niojFYugkiQy9firpWKeQa9CSbc9mIM/Lyy0JrEmRZVo9gjWTvDI7DR4fbY5pzN54K1evvIaWgwf4\nzLf/kYZlJRweOYesFyiO6xlIhOlIQVYSFqnBrgUEWKrAkB9inhRFNVeQazcw6g6jWbQCVWsrUrIX\nEmkslVbQGhnvSJKjViGnNXgjkK0WWJWhxyomecKVYoEJMrPAGofNSZGfCyI7rQYGwgH8Z85ATSVh\nd4zelpP8rKIazcL5jMkJzN2dDLT3IDTOB7Wa3uEuvGPdyDqZnh6BuWoDK6qK2dXTwTQN1KjAogic\nEFWMaSwMqOIkU3EyK80kPAk0lTHWXTmXAkcB2z7yn+zxxxAkiTxBoWxwkFddo1h1KnShCG3n2hhM\nRBDcMuFIknDPALKogkAQFIlUcSFZaj3XLL+KvMVLecnp5OiRwwSDPpZoZboTCXJcMbxSjJYMC6pZ\n5ahlEbVBj1xeQc+xbjISMvaQQGl+ETVpLaG+IY4PpUg4skhZ9fSbRSyiQDrfxBxnCPNEFEGvZVaJ\nwqmowFOKjFFJEw2mWXttA7Ik4+v0kWcuJFJdgnrSzLg6RjS3gMSOZnSLFqKtmUFaFjh+ph1rdjZW\nm417Ojv5pMdDwmCg7JprLkmwj8ViPPHyE8SyRNTFekwWmay+EGhUaK0GhFE1olWiumYa1dWVBOJx\nzk1OMjIygiRJlJaWXog+SZLEwMAAqVSKkpISNBoNwWCQn2zZQsfYGG2HD/MvhYUsKy8n22jkl52d\nuCYmKMjPZywUwmCzXRTJ0Wq1+Nx+evYOozNVIqXiJFtbSW5OXmhz8uRJ9vXuo/yackSVyPDZYX69\n7dfcsuEWPB4PdrudmeXl7D51irLlyzHrdNTp45SVDGCzvczGjbNZtOjiSJCiKExMTBCPx3E4HBdK\nEf2O07295K9eTSgSQbDr0DVMJ+AeoXJeOd0vdROPxy9ErQRB4Oab17NypYdYLEZeXt5Fx9u9+yDd\n3SWUlV2PIAgMDx9g27bXufvuTaRSKdJpFVqt+fyxREQx4xKX+z9k9caNPNbbS8/QEHFFQTN3Ljde\ncalQ/qBj1GpJRqMXXqdiMQxv8uAQj8d57Ac/QOzsRAVES0v58AMPXK5zeJn3jcvC6y/M+/mUYLFY\n0BYXc9zpZEZmJuPBJB1qgbgujTjdwNmYFnN/gvq0wLKCXHKNuURFifKGBVy5/CpGnU5aRwfRGASK\nZhRhmJvLK95x7JNaJiJa3CYJW1JBkpky3FJDSoSUVwGvCp1Gy9iEH/Xq1eg0GmIZNjL2pshu7mPM\nEyA9XY9HK+I92ox6VgOyc4wZfa3UGdWktSqKQykiUYhlwYAftAIoikyHIKBdaENt7ELqHUAJpZBK\n9Tjn5CPYZUx+EXtxMaG0RDDfQQJwZxtIZQZxVAl4oj6elNKoR3uIZQmIUcizaDkah+pMO7Mqa9ni\nG0S9opCCGQUEB4J07+imTWgjmZ/EpNFzTa6aRqOOX49N0heMYU8qTOi1VJgEhJYQnUEvmlgmyoKV\nUF+AKIbhbCdyzyDC8BBdew9TtXotJZWV5FRUcKKrjYqljTSl4pjOnGH7iJc2vYaJpExsKIy6tIiU\nL0gk6eeUJOD2yxSZbHz1uusYONtMgRwjV5dAvDab0UOjxI0qNCtzyLRlkny6C5U/hWSccv+XMxTG\nJQnFqGBKGKATJnsn2XDFBjq6+xgYHSUcgZSsJtnnJxU1o6uuQVVTjWA0EnL5KV60CHNvL/mbN/Pk\nCy/w1U99ivnnDUllWebg3r30NjWRUBTiQpzxMiMvuCJUa1WcEVSosgtoNDWy/svrObR9O1uOHKFI\nFDkjSXgyCvnWtw6hUlmwWF7ly1++g+zsbH700x+x78w+kskk+ZZ8HvznB3li2zY6s7IoXruWHrOZ\nJ48dY3ogQE12NuTl8Uunk+mpFN0qFevesMwIUxGzDKEMuWsYqSCBElCwpZbjdnsvtBl3j6PN1f5+\nl2RRJif2nqBpcIx0Rgb4fNyxciWNPT00PfooakHgc9dfx8o3mceyLFNfX88jj2zh8GEPomglOzvI\nxz++nmQyidFopKysjGyrlUGXC01eHlJCQnF70Dk0xIIxdGrdJUJNEARy3iLi5HYHMRimXYiQWSzF\nuFxT5ZYMBgPTp2fR1raL/PxF+P2DmM1OiorWv+11JTMzk099/esMDw+jVqspKSm5pHzRH/JBjH6s\nXr6c4z//OYPxOApg6Oxk1cc/fkm7/a+/Tn57O+vLyhAEgdcHB9n10kt86NZb3/EcH8Rx/zn4Wx33\ne+Wy8PorQhRFbrvvPn7zs5/xalcXkYZGIr4BpOFRinXFiJUiE0UT7B1MIZhSuIe7cVy5Fr8CT/36\nV0wqISIDvdhGhxjpLCYyESFYkELO0hGrViNEBXyywp6jsCQNqVHYI0PaAqQTuNs7URcVodfqCY65\nyZRkbBYrq/qh7Rzs6nehUauZ7fJjnXQhR5OYBZni5bX0DvtRBIH9ZgV9AEYFOCIqjGcZECwagsNu\nsgWBqCFGfJYaqSWBkgigZNoRwwJSPIpGEclVw4RaJGwWMDrVuMfHUaIS2kkokiFoEjihFbBoJbI1\nOpbnFnHCZCRfzMO/q4twc5hEoZEccw7Lc5dz49ob+UbbGAeOnaJSr6HYpOPRUIzZahHRKDArz4hx\nUEO0fgnjbQOkjHqEUBipMA+hCrRjo6hco+Su3kTRRz5C/9AQv3zoIfSrl5EUROwGLeOxOAN9e8nT\nGhGnVzM+MYEnkQLXGFn4yL9uATqtkdDOblAUzjlH2J4KEq/KxNQlkGnJRJlQEPtEgtYgp0IC6ohI\nnt7MkWQCpyON4pEQSgVSQoqD+w+yZP4SmpuaafG0MDjqw20vRRocRTGawWonFpVQPAFEXQqSSbzj\n41gEgaqGBvoPH6agsJB4PI7RaOT1V17B+fTTXJmZSVNXB6Nnj5G61sJBTZpmSYtZKuS3v/wtFRUV\nUzfue++lZdEigsEgxRMTnHpRoqLi7xAEgfHx0zz55HbmNVbw6u5nmR4OUy6KnO7s5Rv/8o/EbDkU\n3nknoiDgqK1lqKeHtokJwskk9oYGrvrIR1Cr1SwpLr4k9wygqKASjeoWJCmJvjCDqH0CWf69zURR\nfhHJY0mkGglRJeLqdjHkTtB4+1rMOTnEg0G2Pv883/nsZ7nXaEStVr+pCInH4/zkscfY19nJ2bMT\n1FqvZl79nXR3v8xd//B/mb56BZLfz8qKCm5bt45v//KXBHNzsbb2ERvoRDKW4zns4dM3f/pde4kB\nVFcXsWtXE+l0DaKoxus9xqpVRcCUYPvUp25ly5YXaGv7TwoLM/jwh299V5EcvV5PVVXVu+7HB5HC\nwkL++ROf4OSpU4iCwLxPfpK8vLxL2vnHx5luMl0Qr+UWCwdGR//c3b3MXzGXdzX+hXm/a1wZjUYa\nly1jxY03suSaa0iFUoxPjrNs8zJG+0YxzDKgL3GQsawRb46ZG1Zu5sSBnXScPIBOmgBvF0pekEh/\nhFQqBRGIjkRJ5iVJxVKQC249tGmgJRd8+YAGLJNQFvLhDyaIoEFOJymORihrb+eWVJhpQLNewJoB\nm0URY0phkVGDJ5TgjDdCX1TDyVSSEg1kqSAhQ0olYlk4neDwOMu8KVbp1ZREJZx9MiF7GiHgQxhM\ng3MS6XgL6nAUvy0LVzKN3NqJNj0BZ+LMCcAnRFgtQkEEQmYto7JAblykzQTHNUnsg15yQkn8MT2B\ngRRG0cBXHvgqCxcu5PjpJg6ND3EuGCNl0pOMJJllM1FSqCUdSXMgCOnRUVRjg4QM4FhYRXRoHHnf\nUfTDvZgd5Vzz958nLcu4e3pwulxosjMQKvIYC8YYnfCSOeam1FFEvLYKR2EOiaRESJSxyB50Bg2V\nDZUMj8V5sb2VHcFRnEsNqOca8Az70I8Z+fcv/iuFGYXUmGtYe+0GtnWG6cjMoN8YJB2Kw3QBfaUO\nvV5NcbOHOQkfp3btQdtQiMEkM3joKFSUY79iKXIiQXrcjcrtxewcQz06itbjIb+uDs+ZMzi7u2md\nnGTX3r1U5Oay/6mnuDMrC6tKxUDPWUxWLb1pHTa7DXFU5KGvPcR/b32CF44cYd+RI0wrKGDGjBmU\nlZUxOOikszMLm23KHkIUNUQiJxBUYQZ37eDqLAuZBg2zLAYOnRvAMWsOEZsNg82GPTOTkeZmUkC4\nrIwPffrT1NfXU1BQwMDAAP+xZQsv79tHxOejqrISURRRqZKcONFCRsZs0uk4Gs0hbrll9QVbDIfD\nQXIyyakDpwj0BygSiyDLgWPJEgDUOh2BgQEWlJWRnZ39lqLoyWef5YmefvqiSaKlMwlEnRj8cQYC\nZ4g0lrNo4zqstbWcOnaMBWVl3Hj11dSazdy4YCE3rbqeOcVzuHHVjdTW1P5R87+oqBAYo6npOQKB\ng1x1VTY33bT2gjjUarXMn9/AddddwfLljW9bK/JP4YNUu09RFE6caGLv3uNEoyGuunI5dbW1b2mF\n4gsG6Th6lLrzu113j4+Ts2IFldXV73iuD9K4/5z8rY778q7Gy1yE1+vlmw9/E5fgwhV2sf2R7aCG\nZH+SRVcvIjs7m8hYBJWoQp+lpdSqxTJHQ0LOp31HBz3tYxhEDSs+tojDOw8Tk2MgAkEgCnEVkGJK\nukswQwfX2iFkHOfI0R00i2bESJzNES9GIygKaHQimgmJGVqRvniKPC2kHQYmVsxGG9JSMznOlT0j\nFCCRUqeo1Is8u+sUMySFu4GxhITZIDAjKDOeBp0UYF6sidr4lEVFkxoSJ/dzShBZWDubYVecUCCM\n32bjZFomL+ynSoJuT4Ins3SY1jVSvqCaEncA/092c65uDr7aKkgkCBw9yjNbn8CR5yAuWhCvuoEW\n1zhdLee4YuU89o70Yxl1M5YUiSeiVOrTBHJ0mDrPMfpfLqpnl6DYo6xfdg9nPZM0nT6GYlERbOtA\nE49h7BsnkEyTTCdRD48xa/oMYmNDmG0GrLnZ9GtUEPeiZKcIO8Ls+MUOambXYF5Qgzcu4h4aJNqh\nRaPKIBXKo6VlmK9//SuIokhHRwePPbGbQFIiJ1DHhPocpgodgkoifzDGGo3AwvIcbH4vx0/2omos\nwValwZerJpL2IMwoILd1lKrWdlZlZ6PXaDjucuHcsYOwzcb8++/HnpdHaGKCh595hmJFISXLyIkE\naECjMzB/ZQOWLAv+LD/7T5/B5XAw77bbCI2P870nn+Tbn/0sNpuN8vJi0undJJOz0WiMuFxHWLWq\nGEGIEhpw45wMERYEUho1eXkV3LpmDT9/6SWGOzpIBwLcsXgxd99660UCaHh4mO/95jdYV65EZ7Hw\n/IED8OqrbFi/nhUrrkCr1XD48AFMJi3r1t16UdRDFEVu3Xwra69ZSyqVwmKx8A8PPoh/ZARbURFh\ntxt1MHih+PSbEY/H+cVTLzFau4Lw+HEK7EOkCdFx+hGCGVYqK2+cOpdajZidTTAYpK6ujtmzZ//J\n814QBDZsuJ4bbrgWWZb/KCPYv1ZeeGE7zz7rxGCYQzw+RFPTYzzwwEff0rV/8bJluIaH+e7u3YhA\n8dKlXHXttX/eTl/mr5rLwusvzPu5Nh6JRFAUBZPJxLZXtlwmofYAACAASURBVBHMDTJt+jRKFpdw\naNshAk0BJtoneLrpaYpyirjhihuYPXs27kfceMIegpYgQXeYRKIC+00LEeIpdj6+B5U2Tqo9NeUk\nLzBlFCoDDqZ2LVrBOAI6vQ6jXcXV0QBj4QByEJw68CfhlEVFKE9FZUimOQVzRQGPItObUnHFjXMY\nOTLCpN9NzKjClKkn7AwTDEQp1YNJAnMaKhSFZEqDoktDWiI/ClcLUJiGmUYNQgrEeJIxrQGfzY4i\nS/jUZqzVVUzKMs+cOc3Knh68EiAItMsj+HpVqHwRPIIa78x67GWlpOSpXXOtTzzB/f392O+6i02N\njUiShLO5mVUqFQ01NXz7x98mePY0Ss8wnmXLMObnYvJMoN17jHmNi3GdVFM9rZqTrU8TPjmOcWYt\nRkIERnqIZubj7ulEE/Iwd24xQ64Uaq8H56u7aauuZlyJkeEdpv6q6RhyDfRk9VA6u5RkIonUJaHP\nycQUKCHQP8FkysMjzz1CaaWVj971UXbs3YFprp9IWkbrFxCPpLGG7fgVP2p3mhybDa1Wi0k0oAnE\nibqjJMZDiLFhzEULSSdjpCYjzM3I4GuLFyMKAr8dGMC9cCGH43Hs54WKJTcXn1aLpriYL+/cyWKD\ngRFvlNftIpFWDwnZi8mdIq24afzsZwGw5ucTyMlhdHQUm81GXV0dd901wVNP/QBJEpg/v5hNmzbz\ny+9/nxssmTSGQxhFgaekNMalNcyePZtvVlYyMjKCwWCgvLz8Eh+qjq4u5JoabEVTS2wFS5dy5LXX\n2LB+PYIgsGTJQpYsWfi2c+mNuxjvv+MOfrB1K0MqFbpkkr/ftOltl+d6enqQwrmkR3qYVzkDfVpg\nqOsVagIDtGUUkKfEpnZmBgIwPEzhNdf8UfP83fCXLgX0Qcn5SaVSvPhiE6WlX0Ct1qMoc+noeJT+\n/v63XDpVqVRsvPNOohs3IssypjcsO74TH5Rx/7l5v8b9u40oiUTiko0jf01cFl7/S/H7/Tz+68fp\nHuymIKcAs85Mc28zAIvqF+ENejHlmYCpp2BPykNEF6HoQ0UkhASRwQjxdJzHnn4MU7EJU6+JiX0T\nhHw6sm5fTlljHaN9o6S9dURP74AsoIKp+jdjQABElYgoiihOha4U1AXBGkvS7ROhKpNIpZWtnknU\nyERzdCSTSYrUKnwqiRfSEqQl/EaFx194HHWbmgwlg9dMSbyeINqUzAk1LDCLaDTwslemOixzhjQ9\nWUbMcxRUbVFEtUCiR0IfV8jSaxlUqxmtqkesnY56cBBppp1QIMAoMvGqWroGhylWEigGPe7+KBrJ\nSnTQRRAJlVpFVKUirVWRBIZ1OiqdTtp6eznUN4KIihKzFo9By0+f/Cn6Bj3FmmJOhFIUVZaQbbMj\n6fWEMweY+NEuym25POL+NYN6DRmREMXjHSTTKQZLi9E3zkRMRYh39tA22IpZpSOksmG3ZCF5fMiR\nDgw2K7MXzSYZSxK0BElFU5Q0lNDX3sfIuQmi3hGU2fOxzZhFvKeJ/3ruMeY2zOVU9ymWfmgJkUiE\nRCJBX7aV5FCSyegkYWmQCZMJW58XY1YZQ5NuApEAKY8C42OEhrdiVNnIVmXiLDHyteZmcrOzsc2e\nzQ2bN3Psxz8m4vViysoi6HIx1NZGbMYMErfeyuNnz5JrseANelE3LCMrK5dyvZW2LVso8XiIKgrJ\neJzo+DgWiwVJklCpVKxatYKrrlqKJEkXIjQRt5vb161ntK2NUCBApSjiuGGqduLvnOD/EEVRkCQJ\no16PHApd+H0sECDrXZS5eSsqKir43pe/TCAQwGKxvKubQYljBv6zrxDu7yQtqJg55uTO5XMZWrqU\nEZ+P7l/8Ai3wyRtuoOi8QLzMO6MoCoFAAI1Gg8lkesf2kiShKCKiOGVFMmVQq0OSpHd8r9Fo/JP7\ne5l3jyzLbN36PLt2DSGKZnJzw3zhC3e+bXT5fyuXhddfmPfifyLLMj/46Q846TyBFIyw58huokKM\nm79wM1qtlkNHD1FJJV6/F3OmGf+kn8hQBG2WlszaTAACmgDdrm60Hi2NGxqZHpiOZ8zDcz/dR3F1\nMWF/mJQphWhWoS5VI2WlUYIKlILQL2DRWsiUM8nyZzF3/lxGK0Y5cPYc6UCIuF1HTlExNYtq6Njd\nQWZOJo48B4JPoDe+nysLrPSMu+l0jTKhETAl9agqVLhOu0jPTTOm1iKPSCQCScwxWJ2lZSQZ55E0\n9JhENFYVljkGQiY1J88mqLGmGQvIdMaSnFHrkGfMQC4uQJ9Motfr8SQSaJNxkJJMNyr4RT3myhWk\nnG4CAxJ2Yw3hDDfJo0dJBgKYRBGjz4c4bRq9XZ10vfgMlmvXIspqXK8cZdFVS+lzDuD0qZEsOYQM\nLk74ExQTJSBrsepNfH76TLYmk6y89lpeHegnlq9l/MxxIkPjCNNnTglWvYJ2noN4vBW1OYHFVYtF\nXc1ArAOdZTGhyWE6Xu7AIBu44/o7eHXfq7x25DUUWaE0Wkx/ViamxQtQ1Ckcqxfhe2Y7586dwznk\nwncyTNm0QjLtmQQtQb76ra9SWlpKe3s7zz76KCfGx9FmZJCd5WfGTAdjyimYvRJ9MovkuWaCwTOw\ntJq+cYG5S65j88bNCILAp2+6iZ88/zwegwFffz+JdJrcVauwZ2Yir1jB6YceYs7ChVRcdx0qUQRB\nwLdrF7994EtIpfNgMkR2aJx7Ox4gKknUVlTw9c9/nvz8/IuS1PNraug4cYIrr7iCWDpNy/Dw2yZ3\n799/mK1b95BKycyZU0xRdJK+115DNJtRdXfzf97FjrREIsGe/ftxejxUFhSwbOnSC33SaDTv+gYw\nbdo0iotfx+deSPzMk6w125hZU06LSsWq5cu5c/p04vE4Wq32HXcH/m/lf8LXKRKJ8OPHHqPd44FU\nivULFnDTunVvG43S6/UsXlzGwYPbyM5eSDA4SE6O+0LJqfebv1U/q/dj3C0tLezc6aes7O8RRTWj\no8fZsuW3fO5zH3l/OvkB4rLw+l+Iz+fj8O7XKHYOkZOOok0mOKiXaT3cyryr52Ers2Hym7gu9zpe\n3/E66VSaMkMZk4ZJ5LSMLMsoCQWVokIlqogFY8hpmYLyAkqzrXi27SExrYxYwoe6sx1VsRbRpifl\nTUEaTGoT//Vv/0VNTQ2v7H6FNl8bOWU5OMpW8bVPf41UKsWz254llUhx39fvo6SkZMp4cWCAh3Jj\nHIqG6Q6F6ak1oEnpKJlXgj5Hz+HWw5AGfb0exaqg2q7idJbMqC+JAkzWGVDiFtIxGelkGLFc5FR9\nBh2JNPOsFhotCiOSQEROkjBqUKZVIDy3jcKSEjyuCTSnT9MqyHinWdAoQSzKPBw5nwdewmIfZiTs\nITDYh2KzY4jFyKyronmoFxJuQtuewazLwKoy09fjpscbxXbPjegzLeRLKsbbhlHmz6ckmSTTYCDX\nYoF0GrVaTVFmFuNaA64OL+JEAL1lAn1FOf64DIPjqMQEkiwSiY2hL2ig9paHCCc9EGumb/9OivKT\nPN/8PGcHz1KyoITc7FzirXHiflAMMewOO3qNFmckxYsvHkMdXUvbb3fQmnuI+pJCrqm7hqqqKkRR\nZNasWRg/8xmi0SjdPd24Rl/F4wyQdcUCfEY1sZEwUn0O5m49tfNqScfT7N6/m80bNwMwe9YsHiwr\n46EfP0TQ4GbcH2Ff0wEWzlhAWpLwRaNEOzqYzCpgyDlJ3DNB3vAIOfJM6o23IemSvN77RTS19cxo\nqOfc4CAPfOMbPPbDH17kEL/+9tt5wufjhaNHGUsmmbtiBSUlJW86H7q6uvj5z09SUPAZtFozJ0++\nzPLlOtbVlRJPJKi58koK36a8DUxFRv7j0UdpUakwFRezt6WFgdFRPnzLLRdu7L8rsv1Oy06SJHHz\nzVdSUnKa13RVDOiA8lKWbNjA9Pp6gIvc5N8LkiRx+vRpPJOTFBUUMH369L/62om/fvFF2i0WStas\nQUomef63v6WypIRZs2a97fvuvnsT2dmv09r6CnV1VjZu/PCf/P+/zPuPxzOJKFYiilOyJDOzhsHB\n/X/hXv3PcFl4/YV5L08JwWAQQ/8Yc/VJjDUWCv0qhsaDdJ/tpmFpA2FXmKKSIm7ZdAt/t3Fqm/72\nndv51k+/hfMlJ5hhmmEat627jT0H9vDcw8+htqsRgyJ3XHsHxfklvLj7dQ6ePQbVcYKyQvxknGx7\nNqYxE1/84hfZtGkTR48e5VzwHBUrKhAEAVefi+88/B3iqjiyQ0ZOyby06yW+dN+XLlzoLCkL5oX5\nZMwvpuPnvyKnxIYxz0jIHUKICYhOEeKgRo1QKpA1kcVkxiRKiRbRNgvtrGriPX7kY00Uy3pKiktY\n/onleNp7aT+zF7UkkBjsQ5TiJOJgTSWRj2zHkpPEvjAbyCM4EkbV7SXDWE8o9DqlpWf5h3/4d7Y8\nv4Xdpzsx1tQhl5TQnw4Qz8xEtWkxSlIk1tRHqr2dcYOJihkzcE1CMhzEWFbLtG4vC31BZlZXc7Kr\ni4df2sYBg5E9Q0PoS0rItdlQ5xWjndvIxPFjxH7xDCqzCkUYR10oow5rER1RRqQzpAIniaVHMBfq\nGFXcpIJJ7KKdjOUZuIIuMnWZpIpS1MQtDLaMEh4N4+vtp86Ui8m0ifr6K6mavI6h4UOUK/186qOf\nIp1O09nZyTPPvExPjwm9vpBo9CSJTD+afC16lUyWXoe+0IDP66aiogCVSoWski8Ijt8xODhIf6Kf\n+nX1BF5uYWy8l5cHx9Fr9JhFEVfPAJPNW5ENM2AiQNidoqBgGnl5s+joeJ7MrAxKcwvIM5sxV1Wx\n/9VX8fl8F9k/WK1WShsb2SvLmOvraZuc5Ls//Slf/j//55KE8f7+IVSq2ej1U8uPDsdSOjoe52Mf\ne+co1+9wOp2cCwYp3zwV2cuZNo19v/oVG0IhzGYzL77yCtuPH0cQBG5YupQ1q1a9qdAZGhriu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B4F2/ahXbvvxVBhN+JFM+lpEAebkrMJs7LplrH//4Rn74wycYGjqIKMb4yldufctNARMTE5w9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8JJK4kREUhEwRzcuhVtVBQebfX4hEiJDy0jJMST6dPv+0HT979GEAQee+xuli5dT0HBYQID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\n",
"text": [
"<matplotlib.figure.Figure at 0x10b5b3190>"
]
}
],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can observe that there is a large overlap of the samples from different categories. This is to be expected as the PCA linear projection projects data from a 34118 dimensional space down to 2 dimensions: data that is linearly separable in 34118D is often no longer linearly separable in 2D.\n",
" \n",
"Still we can notice an interesting pattern: the newsgroups on religion and atheism occupy the much the same region and computer graphics and space science / space overlap more together than they do with the religion or atheism newsgroups."
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Training a Classifier on Text Features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have previously extracted a vector representation of the training corpus and put it into a variable name `X_train_small`. To train a supervised model, in this case a classifier, we also need "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y_train_small = twenty_train_small.target"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y_train_small.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": [
"(2034,)"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y_train_small"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 34,
"text": [
"array([1, 2, 2, ..., 2, 1, 1])"
]
}
],
"prompt_number": 34
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can shape that we have the same number of samples for the input data and the labels:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_train_small.shape[0] == y_train_small.shape[0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 35,
"text": [
"True"
]
}
],
"prompt_number": 35
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can now train a classifier, for instance a Multinomial Naive Bayesian classifier:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.naive_bayes import MultinomialNB\n",
"\n",
"clf = MultinomialNB(alpha=0.1)\n",
"clf"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 36,
"text": [
"MultinomialNB(alpha=0.1, class_prior=None, fit_prior=True)"
]
}
],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf.fit(X_train_small, y_train_small)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 37,
"text": [
"MultinomialNB(alpha=0.1, class_prior=None, fit_prior=True)"
]
}
],
"prompt_number": 37
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can now evaluate the classifier on the testing set. Let's first use the builtin score function, which is the rate of correct classification in the test set:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_test_small = vectorizer.transform(twenty_test_small.data)\n",
"y_test_small = twenty_test_small.target"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_test_small.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 39,
"text": [
"(1353, 34118)"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y_test_small.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 40,
"text": [
"(1353,)"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf.score(X_test_small, y_test_small)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 41,
"text": [
"0.89652623798965259"
]
}
],
"prompt_number": 41
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also compute the score on the test set and observe that the model is both overfitting and underfitting a bit at the same time:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf.score(X_train_small, y_train_small)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 42,
"text": [
"0.99262536873156337"
]
}
],
"prompt_number": 42
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Introspecting the Behavior of the Text Vectorizer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The text vectorizer has many parameters to customize it's behavior, in particular how it extracts tokens:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"TfidfVectorizer()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 43,
"text": [
"TfidfVectorizer(analyzer=u'word', binary=False, charset=None,\n",
" charset_error=None, decode_error=u'strict',\n",
" dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',\n",
" lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
" ngram_range=(1, 1), norm=u'l2', preprocessor=None, smooth_idf=True,\n",
" stop_words=None, strip_accents=None, sublinear_tf=False,\n",
" token_pattern=u'(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
" vocabulary=None)"
]
}
],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(TfidfVectorizer.__doc__)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Convert a collection of raw documents to a matrix of TF-IDF features.\n",
"\n",
" Equivalent to CountVectorizer followed by TfidfTransformer.\n",
"\n",
" Parameters\n",
" ----------\n",
" input : string {'filename', 'file', 'content'}\n",
" If filename, the sequence passed as an argument to fit is\n",
" expected to be a list of filenames that need reading to fetch\n",
" the raw content to analyze.\n",
"\n",
" If 'file', the sequence items must have 'read' method (file-like\n",
" object) it is called to fetch the bytes in memory.\n",
"\n",
" Otherwise the input is expected to be the sequence strings or\n",
" bytes items are expected to be analyzed directly.\n",
"\n",
" encoding : string, 'utf-8' by default.\n",
" If bytes or files are given to analyze, this encoding is used to\n",
" decode.\n",
"\n",
" decode_error : {'strict', 'ignore', 'replace'}\n",
" Instruction on what to do if a byte sequence is given to analyze that\n",
" contains characters not of the given `encoding`. By default, it is\n",
" 'strict', meaning that a UnicodeDecodeError will be raised. Other\n",
" values are 'ignore' and 'replace'.\n",
"\n",
" strip_accents : {'ascii', 'unicode', None}\n",
" Remove accents during the preprocessing step.\n",
" 'ascii' is a fast method that only works on characters that have\n",
" an direct ASCII mapping.\n",
" 'unicode' is a slightly slower method that works on any characters.\n",
" None (default) does nothing.\n",
"\n",
" analyzer : string, {'word', 'char'} or callable\n",
" Whether the feature should be made of word or character n-grams.\n",
"\n",
" If a callable is passed it is used to extract the sequence of features\n",
" out of the raw, unprocessed input.\n",
"\n",
" preprocessor : callable or None (default)\n",
" Override the preprocessing (string transformation) stage while\n",
" preserving the tokenizing and n-grams generation steps.\n",
"\n",
" tokenizer : callable or None (default)\n",
" Override the string tokenization step while preserving the\n",
" preprocessing and n-grams generation steps.\n",
"\n",
" ngram_range : tuple (min_n, max_n)\n",
" The lower and upper boundary of the range of n-values for different\n",
" n-grams to be extracted. All values of n such that min_n <= n <= max_n\n",
" will be used.\n",
"\n",
" stop_words : string {'english'}, list, or None (default)\n",
" If a string, it is passed to _check_stop_list and the appropriate stop\n",
" list is returned. 'english' is currently the only supported string\n",
" value.\n",
"\n",
" If a list, that list is assumed to contain stop words, all of which\n",
" will be removed from the resulting tokens.\n",
"\n",
" If None, no stop words will be used. max_df can be set to a value\n",
" in the range [0.7, 1.0) to automatically detect and filter stop\n",
" words based on intra corpus document frequency of terms.\n",
"\n",
" lowercase : boolean, default True\n",
" Convert all characters to lowercase befor tokenizing.\n",
"\n",
" token_pattern : string\n",
" Regular expression denoting what constitutes a \"token\", only used\n",
" if `tokenize == 'word'`. The default regexp select tokens of 2\n",
" or more letters characters (punctuation is completely ignored\n",
" and always treated as a token separator).\n",
"\n",
" max_df : float in range [0.0, 1.0] or int, optional, 1.0 by default\n",
" When building the vocabulary ignore terms that have a term frequency\n",
" strictly higher than the given threshold (corpus specific stop words).\n",
" If float, the parameter represents a proportion of documents, integer\n",
" absolute counts.\n",
" This parameter is ignored if vocabulary is not None.\n",
"\n",
" min_df : float in range [0.0, 1.0] or int, optional, 1 by default\n",
" When building the vocabulary ignore terms that have a term frequency\n",
" strictly lower than the given threshold.\n",
" This value is also called cut-off in the literature.\n",
" If float, the parameter represents a proportion of documents, integer\n",
" absolute counts.\n",
" This parameter is ignored if vocabulary is not None.\n",
"\n",
" max_features : optional, None by default\n",
" If not None, build a vocabulary that only consider the top\n",
" max_features ordered by term frequency across the corpus.\n",
"\n",
" This parameter is ignored if vocabulary is not None.\n",
"\n",
" vocabulary : Mapping or iterable, optional\n",
" Either a Mapping (e.g., a dict) where keys are terms and values are\n",
" indices in the feature matrix, or an iterable over terms. If not\n",
" given, a vocabulary is determined from the input documents.\n",
"\n",
" binary : boolean, False by default.\n",
" If True, all non zero counts are set to 1. This is useful for discrete\n",
" probabilistic models that model binary events rather than integer\n",
" counts.\n",
"\n",
" dtype : type, optional\n",
" Type of the matrix returned by fit_transform() or transform().\n",
"\n",
" norm : 'l1', 'l2' or None, optional\n",
" Norm used to normalize term vectors. None for no normalization.\n",
"\n",
" use_idf : boolean, optional\n",
" Enable inverse-document-frequency reweighting.\n",
"\n",
" smooth_idf : boolean, optional\n",
" Smooth idf weights by adding one to document frequencies, as if an\n",
" extra document was seen containing every term in the collection\n",
" exactly once. Prevents zero divisions.\n",
"\n",
" sublinear_tf : boolean, optional\n",
" Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).\n",
"\n",
" See also\n",
" --------\n",
" CountVectorizer\n",
" Tokenize the documents and count the occurrences of token and return\n",
" them as a sparse matrix\n",
"\n",
" TfidfTransformer\n",
" Apply Term Frequency Inverse Document Frequency normalization to a\n",
" sparse matrix of occurrence counts.\n",
"\n",
" \n"
]
}
],
"prompt_number": 44
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The easiest way to introspect what the vectorizer is actually doing for a given test of parameters is call the `vectorizer.build_analyzer()` to get an instance of the text analyzer it uses to process the text:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"analyzer = TfidfVectorizer().build_analyzer()\n",
"analyzer(\"I love scikit-learn: this is a cool Python lib!\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 45,
"text": [
"[u'love', u'scikit', u'learn', u'this', u'is', u'cool', u'python', u'lib']"
]
}
],
"prompt_number": 45
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can notice that all the tokens are lowercase, that the single letter word \"I\" was dropped, and that hyphenation is used. Let's change some of that default behavior:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"analyzer = TfidfVectorizer(\n",
" preprocessor=lambda text: text, # disable lowercasing\n",
" token_pattern=ur'(?u)\\b[\\w-]+\\b', # treat hyphen as a letter\n",
" # do not exclude single letter tokens\n",
").build_analyzer()\n",
"\n",
"analyzer(\"I love scikit-learn: this is a cool Python lib!\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 46,
"text": [
"[u'I',\n",
" u'love',\n",
" u'scikit-learn',\n",
" u'this',\n",
" u'is',\n",
" u'a',\n",
" u'cool',\n",
" u'Python',\n",
" u'lib']"
]
}
],
"prompt_number": 46
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The analyzer name comes from the Lucene parlance: it wraps the sequential application of:\n",
"\n",
"- text preprocessing (processing the text documents as a whole, e.g. lowercasing)\n",
"- text tokenization (splitting the document into a sequence of tokens)\n",
"- token filtering and recombination (e.g. n-grams extraction, see later)\n",
"\n",
"The analyzer system of scikit-learn is much more basic than lucene's though."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise**:\n",
"\n",
"- Write a pre-processor callable (e.g. a python function) to remove the headers of the text a newsgroup post.\n",
"- Vectorize the data again and measure the impact on performance of removing the header info from the dataset.\n",
"- Do you expect the performance of the model to improve or decrease? What is the score of a uniform random classifier on the same dataset?\n",
"\n",
"Hint: the `TfidfVectorizer` class can accept python functions to customize the `preprocessor`, `tokenizer` or `analyzer` stages of the vectorizer.\n",
" \n",
"- type `TfidfVectorizer()` alone in a cell to see the default value of the parameters\n",
"\n",
"- type `TfidfVectorizer.__doc__` to print the constructor parameters doc or `?` suffix operator on a any Python class or method to read the docstring or even the `??` operator to read the source code."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Model Selection of the Naive Bayes Classifier Parameter Alone"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `MultinomialNB` class is a good baseline classifier for text as it's fast and has few parameters to tweak:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"MultinomialNB()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(MultinomialNB.__doc__)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By reading the doc we can see that the `alpha` parameter is a good candidate to adjust the model for the bias (underfitting) vs variance (overfitting) trade-off."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise**:\n",
" \n",
"- use the `sklearn.grid_search.GridSearchCV` or the `model_selection.RandomizedGridSeach` utility function from the previous chapters to find a good value for the parameter `alpha`\n",
"- plots the validation scores (and optionally the training scores) for each value of alpha and identify the areas where model overfits or underfits.\n",
" \n",
" \n",
"Hints:\n",
" \n",
"- you can search for values of alpha in the range [0.00001 - 1] using a logarithmic scale\n",
"- `RandomizedGridSearch` also has a `launch_for_arrays` method as an alternative to `launch_for_splits` in case the CV splits have not been precomputed in advance.\n",
"1"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Setting Up a Pipeline for Cross Validation and Model Selection of the Feature Extraction parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The feature extraction class has many options to customize its behavior:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(TfidfVectorizer.__doc__)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to evaluate the impact of the parameters of the feature extraction one can chain a configured feature extraction and linear classifier (as an alternative to the naive Bayes model):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.linear_model import PassiveAggressiveClassifier\n",
"from sklearn.pipeline import Pipeline\n",
"\n",
"pipeline = Pipeline((\n",
" ('vec', TfidfVectorizer(min_df=1, max_df=0.8, use_idf=True)),\n",
" ('clf', PassiveAggressiveClassifier(C=1)),\n",
"))"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Such a pipeline can then be cross validated or even grid searched:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.cross_validation import cross_val_score\n",
"from scipy.stats import sem\n",
"\n",
"scores = cross_val_score(pipeline, twenty_train_small.data,\n",
" twenty_train_small.target, cv=3, n_jobs=-1)\n",
"scores.mean(), sem(scores)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the grid search, the parameters names are prefixed with the name of the pipeline step using \"__\" as a separator:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.grid_search import GridSearchCV\n",
"\n",
"parameters = {\n",
" #'vec__min_df': [1, 2],\n",
" 'vec__max_df': [0.8, 1.0],\n",
" 'vec__ngram_range': [(1, 1), (1, 2)],\n",
" 'vec__use_idf': [True, False],\n",
"}\n",
"\n",
"gs = GridSearchCV(pipeline, parameters, verbose=2, refit=False)\n",
"_ = gs.fit(twenty_train_small.data, twenty_train_small.target)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gs.best_score_"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gs.best_params_"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Introspecting Model Performance"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Displaying the Most Discriminative Features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's fit a model on the small dataset and collect info on the fitted components:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"_ = pipeline.fit(twenty_train_small.data, twenty_train_small.target)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"vec_name, vec = pipeline.steps[0]\n",
"clf_name, clf = pipeline.steps[1]\n",
"\n",
"feature_names = vec.get_feature_names()\n",
"target_names = twenty_train_small.target_names\n",
"\n",
"feature_weights = clf.coef_\n",
"\n",
"feature_weights.shape"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By sorting the feature weights on the linear model and asking the vectorizer what their names is, one can get a clue on what the model did actually learn on the data:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def display_important_features(feature_names, target_names, weights, n_top=30):\n",
" for i, target_name in enumerate(target_names):\n",
" print(\"Class: \" + target_name)\n",
" print(\"\")\n",
" \n",
" sorted_features_indices = weights[i].argsort()[::-1]\n",
" \n",
" most_important = sorted_features_indices[:n_top]\n",
" print(\", \".join(\"{0}: {1:.4f}\".format(feature_names[j], weights[i, j])\n",
" for j in most_important))\n",
" print(\"...\")\n",
" \n",
" least_important = sorted_features_indices[-n_top:]\n",
" print(\", \".join(\"{0}: {1:.4f}\".format(feature_names[j], weights[i, j])\n",
" for j in least_important))\n",
" print(\"\")\n",
" \n",
"display_important_features(feature_names, target_names, feature_weights)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Displaying the per-class Classification Reports"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.metrics import classification_report\n",
"\n",
"predicted = pipeline.predict(twenty_test_small.data)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(classification_report(twenty_test_small.target, predicted,\n",
" target_names=twenty_test_small.target_names))"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Printing the Confusion Matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The confusion matrix summarize which class where by having a look at off-diagonal entries: here we can see that articles about atheism have been wrongly classified as being about religion 57 times for instance: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.metrics import confusion_matrix\n",
"\n",
"confusion_matrix(twenty_test_small.target, predicted)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"twenty_test_small.target_names"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | unlicense |
surchs/Logbooks | .ipynb_checkpoints/test_pymat_bridge-checkpoint.ipynb | 6 | 181 | {
"metadata": {
"name": "",
"signature": "sha256:57978910505ced8a0b8774db398ad42182de883fd8c6d3baa01d912b05e1de92"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": []
} | gpl-3.0 |
lijingpeng/kaggle | competitions/digit_recognizer/digit_recognizer.ipynb | 1 | 6966 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[kaggle地址](https://www.kaggle.com/c/digit-recognizer/) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 数据预览\n",
"\n",
"首先载入数据集"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" label pixel0 pixel1 pixel2 pixel3 pixel4 pixel5 pixel6 pixel7 \\\n",
"0 1 0 0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 0 0 0 \n",
"2 1 0 0 0 0 0 0 0 0 \n",
"3 4 0 0 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 0 0 0 \n",
"\n",
" pixel8 ... pixel774 pixel775 pixel776 pixel777 pixel778 \\\n",
"0 0 ... 0 0 0 0 0 \n",
"1 0 ... 0 0 0 0 0 \n",
"2 0 ... 0 0 0 0 0 \n",
"3 0 ... 0 0 0 0 0 \n",
"4 0 ... 0 0 0 0 0 \n",
"\n",
" pixel779 pixel780 pixel781 pixel782 pixel783 \n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
"[5 rows x 785 columns]\n",
" pixel0 pixel1 pixel2 pixel3 pixel4 pixel5 pixel6 pixel7 pixel8 \\\n",
"0 0 0 0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 0 0 0 \n",
"\n",
" pixel9 ... pixel774 pixel775 pixel776 pixel777 pixel778 \\\n",
"0 0 ... 0 0 0 0 0 \n",
"1 0 ... 0 0 0 0 0 \n",
"2 0 ... 0 0 0 0 0 \n",
"3 0 ... 0 0 0 0 0 \n",
"4 0 ... 0 0 0 0 0 \n",
"\n",
" pixel779 pixel780 pixel781 pixel782 pixel783 \n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
"[5 rows x 784 columns]\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"train = pd.read_csv('/Users/frank/Documents/workspace/kaggle/dataset/digit_recognizer/train.csv')\n",
"test = pd.read_csv('/Users/frank/Documents/workspace/kaggle/dataset/digit_recognizer/test.csv')\n",
"print train.head()\n",
"print test.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"分离训练数据和标签："
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train_data = train.values[:,1:]\n",
"label = train.ix[:,0]\n",
"test_data = test.values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"使用PCA来降维：[PCA文档](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)\n",
"使用SVM来训练：[SVM文档](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 降维"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA\n",
"from sklearn.svm import SVC\n",
"pca = PCA(n_components=0.8, whiten=True)\n",
"# pca.fit(train_data)\n",
"train_data = pca.fit_transform(train_data)\n",
"# pca.fit(test_data)\n",
"test_data = pca.transform(test_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## SVM训练"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"使用SVM进行训练...\n",
"训练结束.\n"
]
}
],
"source": [
"print('使用SVM进行训练...')\n",
"svc = SVC(kernel='rbf',C=2)\n",
"svc.fit(train_data, label)\n",
"print('训练结束.')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"对测试集进行预测...\n",
"预测结束.\n"
]
}
],
"source": [
"print('对测试集进行预测...')\n",
"predict = svc.predict(test_data)\n",
"print('预测结束.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"保存结果："
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"done.\n"
]
}
],
"source": [
"pd.DataFrame(\n",
" {\"ImageId\": range(1, len(predict) + 1), \"Label\": predict}\n",
").to_csv('output.csv', index=False, header=True)\n",
"\n",
"print 'done.'"
]
}
],
"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 |
mprego/NBA | Schedule/Untitled.ipynb | 1 | 47263 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"//anaconda/lib/python2.7/site-packages/pandas/computation/__init__.py:19: UserWarning: The installed version of numexpr 2.4.4 is not supported in pandas and will be not be used\n",
"\n",
" UserWarning)\n"
]
}
],
"source": [
"import pandas as pd\n",
"from Schedule import Schedule\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sched = Schedule('1/1/2015', '1/3/2015')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'GAME_DATE', u'H_WL', u'A_WL', u'Home Team', u'Away Team',\n",
" u'H_PTS', u'A_PTS', u'Pts_diff', u'FGM_home', u'FG3M_home',\n",
" u'FGA_home', u'OREB_home', u'DREB_away', u'TOV_home', u'FTM_home',\n",
" u'FTA_home', u'FGM_away', u'FG3M_away', u'FGA_away', u'OREB_away',\n",
" u'DREB_home', u'TOV_away', u'FTM_away', u'FTA_away', u'H_AST',\n",
" u'A_AST', u'H_STL', u'A_STL', u'H_BLK', u'A_BLK'],\n",
" dtype='object')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sched.games.columns"
]
},
{
"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>GAME_DATE</th>\n",
" <th>H_WL</th>\n",
" <th>A_WL</th>\n",
" <th>Home Team</th>\n",
" <th>Away Team</th>\n",
" <th>H_PTS</th>\n",
" <th>A_PTS</th>\n",
" <th>Pts_diff</th>\n",
" <th>FGM_home</th>\n",
" <th>FG3M_home</th>\n",
" <th>...</th>\n",
" <th>DREB_home</th>\n",
" <th>TOV_away</th>\n",
" <th>FTM_away</th>\n",
" <th>FTA_away</th>\n",
" <th>H_AST</th>\n",
" <th>A_AST</th>\n",
" <th>H_STL</th>\n",
" <th>A_STL</th>\n",
" <th>H_BLK</th>\n",
" <th>A_BLK</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>2015-01-02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>BOS</td>\n",
" <td>DAL</td>\n",
" <td>101</td>\n",
" <td>119</td>\n",
" <td>-18</td>\n",
" <td>38</td>\n",
" <td>12</td>\n",
" <td>...</td>\n",
" <td>29</td>\n",
" <td>11</td>\n",
" <td>16</td>\n",
" <td>17</td>\n",
" <td>22</td>\n",
" <td>24</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>2015-01-02</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>NOP</td>\n",
" <td>HOU</td>\n",
" <td>111</td>\n",
" <td>83</td>\n",
" <td>28</td>\n",
" <td>44</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>37</td>\n",
" <td>19</td>\n",
" <td>7</td>\n",
" <td>11</td>\n",
" <td>24</td>\n",
" <td>22</td>\n",
" <td>12</td>\n",
" <td>13</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>189</th>\n",
" <td>2015-01-03</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>CHI</td>\n",
" <td>BOS</td>\n",
" <td>109</td>\n",
" <td>104</td>\n",
" <td>5</td>\n",
" <td>37</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>35</td>\n",
" <td>20</td>\n",
" <td>9</td>\n",
" <td>11</td>\n",
" <td>19</td>\n",
" <td>26</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>2015-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>CHI</td>\n",
" <td>DEN</td>\n",
" <td>106</td>\n",
" <td>101</td>\n",
" <td>5</td>\n",
" <td>38</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>33</td>\n",
" <td>12</td>\n",
" <td>22</td>\n",
" <td>27</td>\n",
" <td>22</td>\n",
" <td>19</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>18</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>269</th>\n",
" <td>2015-01-03</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>DEN</td>\n",
" <td>MEM</td>\n",
" <td>114</td>\n",
" <td>85</td>\n",
" <td>29</td>\n",
" <td>41</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>9</td>\n",
" <td>13</td>\n",
" <td>21</td>\n",
" <td>20</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>314</th>\n",
" <td>2015-01-02</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>GSW</td>\n",
" <td>TOR</td>\n",
" <td>126</td>\n",
" <td>105</td>\n",
" <td>21</td>\n",
" <td>49</td>\n",
" <td>12</td>\n",
" <td>...</td>\n",
" <td>31</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>20</td>\n",
" <td>35</td>\n",
" <td>23</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>351</th>\n",
" <td>2015-01-03</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>HOU</td>\n",
" <td>MIA</td>\n",
" <td>115</td>\n",
" <td>79</td>\n",
" <td>36</td>\n",
" <td>41</td>\n",
" <td>13</td>\n",
" <td>...</td>\n",
" <td>31</td>\n",
" <td>21</td>\n",
" <td>15</td>\n",
" <td>25</td>\n",
" <td>21</td>\n",
" <td>20</td>\n",
" <td>13</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391</th>\n",
" <td>2015-01-03</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>LAC</td>\n",
" <td>PHI</td>\n",
" <td>127</td>\n",
" <td>91</td>\n",
" <td>36</td>\n",
" <td>46</td>\n",
" <td>15</td>\n",
" <td>...</td>\n",
" <td>34</td>\n",
" <td>21</td>\n",
" <td>18</td>\n",
" <td>26</td>\n",
" <td>33</td>\n",
" <td>17</td>\n",
" <td>13</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>435</th>\n",
" <td>2015-01-02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>LAL</td>\n",
" <td>MEM</td>\n",
" <td>106</td>\n",
" <td>109</td>\n",
" <td>-3</td>\n",
" <td>43</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>33</td>\n",
" <td>12</td>\n",
" <td>21</td>\n",
" <td>31</td>\n",
" <td>24</td>\n",
" <td>27</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>519</th>\n",
" <td>2015-01-02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>MIL</td>\n",
" <td>IND</td>\n",
" <td>91</td>\n",
" <td>94</td>\n",
" <td>-3</td>\n",
" <td>38</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>33</td>\n",
" <td>15</td>\n",
" <td>16</td>\n",
" <td>20</td>\n",
" <td>28</td>\n",
" <td>24</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>558</th>\n",
" <td>2015-01-03</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>MIN</td>\n",
" <td>UTA</td>\n",
" <td>89</td>\n",
" <td>101</td>\n",
" <td>-12</td>\n",
" <td>34</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>23</td>\n",
" <td>12</td>\n",
" <td>14</td>\n",
" <td>21</td>\n",
" <td>17</td>\n",
" <td>23</td>\n",
" <td>8</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>559</th>\n",
" <td>2015-01-01</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>MIN</td>\n",
" <td>SAC</td>\n",
" <td>107</td>\n",
" <td>110</td>\n",
" <td>-3</td>\n",
" <td>42</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>23</td>\n",
" <td>20</td>\n",
" <td>25</td>\n",
" <td>29</td>\n",
" <td>22</td>\n",
" <td>23</td>\n",
" <td>12</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>639</th>\n",
" <td>2015-01-02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>NYK</td>\n",
" <td>DET</td>\n",
" <td>81</td>\n",
" <td>97</td>\n",
" <td>-16</td>\n",
" <td>32</td>\n",
" <td>10</td>\n",
" <td>...</td>\n",
" <td>30</td>\n",
" <td>16</td>\n",
" <td>8</td>\n",
" <td>14</td>\n",
" <td>18</td>\n",
" <td>24</td>\n",
" <td>13</td>\n",
" <td>10</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>681</th>\n",
" <td>2015-01-03</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>ORL</td>\n",
" <td>CHA</td>\n",
" <td>90</td>\n",
" <td>98</td>\n",
" <td>-8</td>\n",
" <td>33</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>32</td>\n",
" <td>14</td>\n",
" <td>24</td>\n",
" <td>32</td>\n",
" <td>18</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>682</th>\n",
" <td>2015-01-02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>ORL</td>\n",
" <td>BKN</td>\n",
" <td>98</td>\n",
" <td>100</td>\n",
" <td>-2</td>\n",
" <td>37</td>\n",
" <td>11</td>\n",
" <td>...</td>\n",
" <td>29</td>\n",
" <td>22</td>\n",
" <td>11</td>\n",
" <td>18</td>\n",
" <td>22</td>\n",
" <td>26</td>\n",
" <td>13</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>805</th>\n",
" <td>2015-01-02</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PHX</td>\n",
" <td>PHI</td>\n",
" <td>112</td>\n",
" <td>96</td>\n",
" <td>16</td>\n",
" <td>42</td>\n",
" <td>14</td>\n",
" <td>...</td>\n",
" <td>35</td>\n",
" <td>16</td>\n",
" <td>15</td>\n",
" <td>25</td>\n",
" <td>24</td>\n",
" <td>20</td>\n",
" <td>9</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>843</th>\n",
" <td>2015-01-03</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>POR</td>\n",
" <td>ATL</td>\n",
" <td>107</td>\n",
" <td>115</td>\n",
" <td>-8</td>\n",
" <td>42</td>\n",
" <td>13</td>\n",
" <td>...</td>\n",
" <td>33</td>\n",
" <td>14</td>\n",
" <td>23</td>\n",
" <td>31</td>\n",
" <td>24</td>\n",
" <td>17</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>926</th>\n",
" <td>2015-01-03</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>SAS</td>\n",
" <td>WAS</td>\n",
" <td>101</td>\n",
" <td>92</td>\n",
" <td>9</td>\n",
" <td>43</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>29</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>13</td>\n",
" <td>27</td>\n",
" <td>23</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>967</th>\n",
" <td>2015-01-02</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>OKC</td>\n",
" <td>WAS</td>\n",
" <td>109</td>\n",
" <td>102</td>\n",
" <td>7</td>\n",
" <td>44</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>32</td>\n",
" <td>15</td>\n",
" <td>13</td>\n",
" <td>15</td>\n",
" <td>21</td>\n",
" <td>27</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1170</th>\n",
" <td>2015-01-02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>CHA</td>\n",
" <td>CLE</td>\n",
" <td>87</td>\n",
" <td>91</td>\n",
" <td>-4</td>\n",
" <td>32</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>35</td>\n",
" <td>7</td>\n",
" <td>21</td>\n",
" <td>30</td>\n",
" <td>20</td>\n",
" <td>16</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1214</th>\n",
" <td>2015-01-02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>UTA</td>\n",
" <td>ATL</td>\n",
" <td>92</td>\n",
" <td>98</td>\n",
" <td>-6</td>\n",
" <td>32</td>\n",
" <td>10</td>\n",
" <td>...</td>\n",
" <td>30</td>\n",
" <td>10</td>\n",
" <td>26</td>\n",
" <td>30</td>\n",
" <td>18</td>\n",
" <td>24</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>21 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" GAME_DATE H_WL A_WL Home Team Away Team H_PTS A_PTS Pts_diff \\\n",
"64 2015-01-02 0 1 BOS DAL 101 119 -18 \n",
"149 2015-01-02 1 0 NOP HOU 111 83 28 \n",
"189 2015-01-03 1 0 CHI BOS 109 104 5 \n",
"190 2015-01-01 1 0 CHI DEN 106 101 5 \n",
"269 2015-01-03 1 0 DEN MEM 114 85 29 \n",
"314 2015-01-02 1 0 GSW TOR 126 105 21 \n",
"351 2015-01-03 1 0 HOU MIA 115 79 36 \n",
"391 2015-01-03 1 0 LAC PHI 127 91 36 \n",
"435 2015-01-02 0 1 LAL MEM 106 109 -3 \n",
"519 2015-01-02 0 1 MIL IND 91 94 -3 \n",
"558 2015-01-03 0 1 MIN UTA 89 101 -12 \n",
"559 2015-01-01 0 1 MIN SAC 107 110 -3 \n",
"639 2015-01-02 0 1 NYK DET 81 97 -16 \n",
"681 2015-01-03 0 1 ORL CHA 90 98 -8 \n",
"682 2015-01-02 0 1 ORL BKN 98 100 -2 \n",
"805 2015-01-02 1 0 PHX PHI 112 96 16 \n",
"843 2015-01-03 0 1 POR ATL 107 115 -8 \n",
"926 2015-01-03 1 0 SAS WAS 101 92 9 \n",
"967 2015-01-02 1 0 OKC WAS 109 102 7 \n",
"1170 2015-01-02 0 1 CHA CLE 87 91 -4 \n",
"1214 2015-01-02 0 1 UTA ATL 92 98 -6 \n",
"\n",
" FGM_home FG3M_home ... DREB_home TOV_away FTM_away FTA_away \\\n",
"64 38 12 ... 29 11 16 17 \n",
"149 44 7 ... 37 19 7 11 \n",
"189 37 6 ... 35 20 9 11 \n",
"190 38 8 ... 33 12 22 27 \n",
"269 41 7 ... 40 12 9 13 \n",
"314 49 12 ... 31 15 15 20 \n",
"351 41 13 ... 31 21 15 25 \n",
"391 46 15 ... 34 21 18 26 \n",
"435 43 6 ... 33 12 21 31 \n",
"519 38 7 ... 33 15 16 20 \n",
"558 34 6 ... 23 12 14 21 \n",
"559 42 7 ... 23 20 25 29 \n",
"639 32 10 ... 30 16 8 14 \n",
"681 33 4 ... 32 14 24 32 \n",
"682 37 11 ... 29 22 11 18 \n",
"805 42 14 ... 35 16 15 25 \n",
"843 42 13 ... 33 14 23 31 \n",
"926 43 4 ... 29 6 8 13 \n",
"967 44 8 ... 32 15 13 15 \n",
"1170 32 6 ... 35 7 21 30 \n",
"1214 32 10 ... 30 10 26 30 \n",
"\n",
" H_AST A_AST H_STL A_STL H_BLK A_BLK \n",
"64 22 24 6 8 2 1 \n",
"149 24 22 12 13 8 3 \n",
"189 19 26 10 9 9 6 \n",
"190 22 19 7 4 18 7 \n",
"269 21 20 6 6 8 8 \n",
"314 35 23 8 7 8 1 \n",
"351 21 20 13 6 1 3 \n",
"391 33 17 13 7 3 1 \n",
"435 24 27 10 7 8 4 \n",
"519 28 24 10 7 7 3 \n",
"558 17 23 8 6 5 10 \n",
"559 22 23 12 7 6 7 \n",
"639 18 24 13 10 4 3 \n",
"681 18 16 9 4 3 4 \n",
"682 22 26 13 7 3 5 \n",
"805 24 20 9 12 12 7 \n",
"843 24 17 6 9 7 5 \n",
"926 27 23 4 6 5 6 \n",
"967 21 27 8 3 4 1 \n",
"1170 20 16 5 8 6 5 \n",
"1214 18 24 6 9 9 3 \n",
"\n",
"[21 rows x 30 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sched.games"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<div>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>GAME_DATE</th>\n",
" <th>H_WL</th>\n",
" <th>A_WL</th>\n",
" <th>Home Team</th>\n",
" <th>Away Team</th>\n",
" <th>H_PTS</th>\n",
" <th>A_PTS</th>\n",
" <th>Pts_diff</th>\n",
" <th>FGM_home</th>\n",
" <th>FG3M_home</th>\n",
" <th>...</th>\n",
" <th>H_BLK</th>\n",
" <th>A_BLK</th>\n",
" <th>H_FF_EFG</th>\n",
" <th>H_FF_ORB</th>\n",
" <th>H_FF_FTFGA</th>\n",
" <th>H_FF_TOV</th>\n",
" <th>A_FF_EFG</th>\n",
" <th>A_FF_ORB</th>\n",
" <th>A_FF_FTFGA</th>\n",
" <th>A_FF_TOV</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>2015-01-02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>BOS</td>\n",
" <td>DAL</td>\n",
" <td>101</td>\n",
" <td>119</td>\n",
" <td>-18</td>\n",
" <td>38</td>\n",
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" <td>...</td>\n",
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" <td>0.119570</td>\n",
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" <td>0.111698</td>\n",
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" <tr>\n",
" <th>149</th>\n",
" <td>2015-01-02</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>NOP</td>\n",
" <td>HOU</td>\n",
" <td>111</td>\n",
" <td>83</td>\n",
" <td>28</td>\n",
" <td>44</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
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" <td>3</td>\n",
" <td>0.572289</td>\n",
" <td>0.324324</td>\n",
" <td>0.192771</td>\n",
" <td>0.184049</td>\n",
" <td>0.431818</td>\n",
" <td>0.260000</td>\n",
" <td>0.079545</td>\n",
" <td>0.192230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>189</th>\n",
" <td>2015-01-03</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>CHI</td>\n",
" <td>BOS</td>\n",
" <td>109</td>\n",
" <td>104</td>\n",
" <td>5</td>\n",
" <td>37</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>0.400000</td>\n",
" <td>0.406780</td>\n",
" <td>0.290000</td>\n",
" <td>0.145985</td>\n",
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" <td>0.222222</td>\n",
" <td>0.090000</td>\n",
" <td>0.174155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>2015-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>CHI</td>\n",
" <td>DEN</td>\n",
" <td>106</td>\n",
" <td>101</td>\n",
" <td>5</td>\n",
" <td>38</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>18</td>\n",
" <td>7</td>\n",
" <td>0.461538</td>\n",
" <td>0.245283</td>\n",
" <td>0.241758</td>\n",
" <td>0.072917</td>\n",
" <td>0.429348</td>\n",
" <td>0.340000</td>\n",
" <td>0.239130</td>\n",
" <td>0.121359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>269</th>\n",
" <td>2015-01-03</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>DEN</td>\n",
" <td>MEM</td>\n",
" <td>114</td>\n",
" <td>85</td>\n",
" <td>29</td>\n",
" <td>41</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>0.563291</td>\n",
" <td>0.216216</td>\n",
" <td>0.316456</td>\n",
" <td>0.133142</td>\n",
" <td>0.441860</td>\n",
" <td>0.166667</td>\n",
" <td>0.104651</td>\n",
" <td>0.125366</td>\n",
" </tr>\n",
" <tr>\n",
" <th>314</th>\n",
" <td>2015-01-02</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>GSW</td>\n",
" <td>TOR</td>\n",
" <td>126</td>\n",
" <td>105</td>\n",
" <td>21</td>\n",
" <td>49</td>\n",
" <td>12</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>0.597826</td>\n",
" <td>0.350000</td>\n",
" <td>0.173913</td>\n",
" <td>0.093946</td>\n",
" <td>0.523256</td>\n",
" <td>0.261905</td>\n",
" <td>0.174419</td>\n",
" <td>0.151822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>351</th>\n",
" <td>2015-01-03</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>HOU</td>\n",
" <td>MIA</td>\n",
" <td>115</td>\n",
" <td>79</td>\n",
" <td>36</td>\n",
" <td>41</td>\n",
" <td>13</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0.572289</td>\n",
" <td>0.404762</td>\n",
" <td>0.240964</td>\n",
" <td>0.169635</td>\n",
" <td>0.470588</td>\n",
" <td>0.205128</td>\n",
" <td>0.220588</td>\n",
" <td>0.228261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391</th>\n",
" <td>2015-01-03</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>LAC</td>\n",
" <td>PHI</td>\n",
" <td>127</td>\n",
" <td>91</td>\n",
" <td>36</td>\n",
" <td>46</td>\n",
" <td>15</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0.629412</td>\n",
" <td>0.230769</td>\n",
" <td>0.235294</td>\n",
" <td>0.083822</td>\n",
" <td>0.462025</td>\n",
" <td>0.306122</td>\n",
" <td>0.227848</td>\n",
" <td>0.217752</td>\n",
" </tr>\n",
" <tr>\n",
" <th>435</th>\n",
" <td>2015-01-02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>LAL</td>\n",
" <td>MEM</td>\n",
" <td>106</td>\n",
" <td>109</td>\n",
" <td>-3</td>\n",
" <td>43</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>4</td>\n",
" <td>0.567901</td>\n",
" <td>0.131579</td>\n",
" <td>0.172840</td>\n",
" <td>0.133525</td>\n",
" <td>0.523810</td>\n",
" <td>0.282609</td>\n",
" <td>0.250000</td>\n",
" <td>0.124172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>519</th>\n",
" <td>2015-01-02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>MIL</td>\n",
" <td>IND</td>\n",
" <td>91</td>\n",
" <td>94</td>\n",
" <td>-3</td>\n",
" <td>38</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>0.466292</td>\n",
" <td>0.222222</td>\n",
" <td>0.089888</td>\n",
" <td>0.125786</td>\n",
" <td>0.481481</td>\n",
" <td>0.232558</td>\n",
" <td>0.197531</td>\n",
" <td>0.158228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>558</th>\n",
" <td>2015-01-03</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>MIN</td>\n",
" <td>UTA</td>\n",
" <td>89</td>\n",
" <td>101</td>\n",
" <td>-12</td>\n",
" <td>34</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>0.430233</td>\n",
" <td>0.280000</td>\n",
" <td>0.174419</td>\n",
" <td>0.090992</td>\n",
" <td>0.580000</td>\n",
" <td>0.323529</td>\n",
" <td>0.186667</td>\n",
" <td>0.140779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>559</th>\n",
" <td>2015-01-01</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>MIN</td>\n",
" <td>SAC</td>\n",
" <td>107</td>\n",
" <td>110</td>\n",
" <td>-3</td>\n",
" <td>42</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>0.500000</td>\n",
" <td>0.319149</td>\n",
" <td>0.175824</td>\n",
" <td>0.140449</td>\n",
" <td>0.574324</td>\n",
" <td>0.303030</td>\n",
" <td>0.337838</td>\n",
" <td>0.206697</td>\n",
" </tr>\n",
" <tr>\n",
" <th>639</th>\n",
" <td>2015-01-02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>NYK</td>\n",
" <td>DET</td>\n",
" <td>81</td>\n",
" <td>97</td>\n",
" <td>-16</td>\n",
" <td>32</td>\n",
" <td>10</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0.406593</td>\n",
" <td>0.298246</td>\n",
" <td>0.076923</td>\n",
" <td>0.120667</td>\n",
" <td>0.585526</td>\n",
" <td>0.230769</td>\n",
" <td>0.105263</td>\n",
" <td>0.179453</td>\n",
" </tr>\n",
" <tr>\n",
" <th>681</th>\n",
" <td>2015-01-03</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>ORL</td>\n",
" <td>CHA</td>\n",
" <td>90</td>\n",
" <td>98</td>\n",
" <td>-8</td>\n",
" <td>33</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>0.402299</td>\n",
" <td>0.244898</td>\n",
" <td>0.229885</td>\n",
" <td>0.076721</td>\n",
" <td>0.474359</td>\n",
" <td>0.333333</td>\n",
" <td>0.307692</td>\n",
" <td>0.155417</td>\n",
" </tr>\n",
" <tr>\n",
" <th>682</th>\n",
" <td>2015-01-02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>ORL</td>\n",
" <td>BKN</td>\n",
" <td>98</td>\n",
" <td>100</td>\n",
" <td>-2</td>\n",
" <td>37</td>\n",
" <td>11</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>0.482955</td>\n",
" <td>0.163265</td>\n",
" <td>0.147727</td>\n",
" <td>0.110220</td>\n",
" <td>0.618056</td>\n",
" <td>0.121212</td>\n",
" <td>0.152778</td>\n",
" <td>0.224673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>805</th>\n",
" <td>2015-01-02</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PHX</td>\n",
" <td>PHI</td>\n",
" <td>112</td>\n",
" <td>96</td>\n",
" <td>16</td>\n",
" <td>42</td>\n",
" <td>14</td>\n",
" <td>...</td>\n",
" <td>12</td>\n",
" <td>7</td>\n",
" <td>0.583333</td>\n",
" <td>0.342105</td>\n",
" <td>0.166667</td>\n",
" <td>0.195796</td>\n",
" <td>0.465517</td>\n",
" <td>0.313725</td>\n",
" <td>0.172414</td>\n",
" <td>0.163265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>843</th>\n",
" <td>2015-01-03</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>POR</td>\n",
" <td>ATL</td>\n",
" <td>107</td>\n",
" <td>115</td>\n",
" <td>-8</td>\n",
" <td>42</td>\n",
" <td>13</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>0.521505</td>\n",
" <td>0.234043</td>\n",
" <td>0.107527</td>\n",
" <td>0.170973</td>\n",
" <td>0.547619</td>\n",
" <td>0.232558</td>\n",
" <td>0.273810</td>\n",
" <td>0.137741</td>\n",
" </tr>\n",
" <tr>\n",
" <th>926</th>\n",
" <td>2015-01-03</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>SAS</td>\n",
" <td>WAS</td>\n",
" <td>101</td>\n",
" <td>92</td>\n",
" <td>9</td>\n",
" <td>43</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>0.576923</td>\n",
" <td>0.290323</td>\n",
" <td>0.141026</td>\n",
" <td>0.115048</td>\n",
" <td>0.512195</td>\n",
" <td>0.236842</td>\n",
" <td>0.097561</td>\n",
" <td>0.070822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>967</th>\n",
" <td>2015-01-02</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>OKC</td>\n",
" <td>WAS</td>\n",
" <td>109</td>\n",
" <td>102</td>\n",
" <td>7</td>\n",
" <td>44</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0.578313</td>\n",
" <td>0.162162</td>\n",
" <td>0.156627</td>\n",
" <td>0.104866</td>\n",
" <td>0.529762</td>\n",
" <td>0.200000</td>\n",
" <td>0.154762</td>\n",
" <td>0.153689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1170</th>\n",
" <td>2015-01-02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>CHA</td>\n",
" <td>CLE</td>\n",
" <td>87</td>\n",
" <td>91</td>\n",
" <td>-4</td>\n",
" <td>32</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>0.402299</td>\n",
" <td>0.312500</td>\n",
" <td>0.195402</td>\n",
" <td>0.143678</td>\n",
" <td>0.402299</td>\n",
" <td>0.270833</td>\n",
" <td>0.241379</td>\n",
" <td>0.074310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1214</th>\n",
" <td>2015-01-02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>UTA</td>\n",
" <td>ATL</td>\n",
" <td>92</td>\n",
" <td>98</td>\n",
" <td>-6</td>\n",
" <td>32</td>\n",
" <td>10</td>\n",
" <td>...</td>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" <td>0.430233</td>\n",
" <td>0.320755</td>\n",
" <td>0.209302</td>\n",
" <td>0.149637</td>\n",
" <td>0.444444</td>\n",
" <td>0.268293</td>\n",
" <td>0.320988</td>\n",
" <td>0.107296</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>21 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" GAME_DATE H_WL A_WL Home Team Away Team H_PTS A_PTS Pts_diff \\\n",
"64 2015-01-02 0 1 BOS DAL 101 119 -18 \n",
"149 2015-01-02 1 0 NOP HOU 111 83 28 \n",
"189 2015-01-03 1 0 CHI BOS 109 104 5 \n",
"190 2015-01-01 1 0 CHI DEN 106 101 5 \n",
"269 2015-01-03 1 0 DEN MEM 114 85 29 \n",
"314 2015-01-02 1 0 GSW TOR 126 105 21 \n",
"351 2015-01-03 1 0 HOU MIA 115 79 36 \n",
"391 2015-01-03 1 0 LAC PHI 127 91 36 \n",
"435 2015-01-02 0 1 LAL MEM 106 109 -3 \n",
"519 2015-01-02 0 1 MIL IND 91 94 -3 \n",
"558 2015-01-03 0 1 MIN UTA 89 101 -12 \n",
"559 2015-01-01 0 1 MIN SAC 107 110 -3 \n",
"639 2015-01-02 0 1 NYK DET 81 97 -16 \n",
"681 2015-01-03 0 1 ORL CHA 90 98 -8 \n",
"682 2015-01-02 0 1 ORL BKN 98 100 -2 \n",
"805 2015-01-02 1 0 PHX PHI 112 96 16 \n",
"843 2015-01-03 0 1 POR ATL 107 115 -8 \n",
"926 2015-01-03 1 0 SAS WAS 101 92 9 \n",
"967 2015-01-02 1 0 OKC WAS 109 102 7 \n",
"1170 2015-01-02 0 1 CHA CLE 87 91 -4 \n",
"1214 2015-01-02 0 1 UTA ATL 92 98 -6 \n",
"\n",
" FGM_home FG3M_home ... H_BLK A_BLK H_FF_EFG H_FF_ORB \\\n",
"64 38 12 ... 2 1 0.494382 0.200000 \n",
"149 44 7 ... 8 3 0.572289 0.324324 \n",
"189 37 6 ... 9 6 0.400000 0.406780 \n",
"190 38 8 ... 18 7 0.461538 0.245283 \n",
"269 41 7 ... 8 8 0.563291 0.216216 \n",
"314 49 12 ... 8 1 0.597826 0.350000 \n",
"351 41 13 ... 1 3 0.572289 0.404762 \n",
"391 46 15 ... 3 1 0.629412 0.230769 \n",
"435 43 6 ... 8 4 0.567901 0.131579 \n",
"519 38 7 ... 7 3 0.466292 0.222222 \n",
"558 34 6 ... 5 10 0.430233 0.280000 \n",
"559 42 7 ... 6 7 0.500000 0.319149 \n",
"639 32 10 ... 4 3 0.406593 0.298246 \n",
"681 33 4 ... 3 4 0.402299 0.244898 \n",
"682 37 11 ... 3 5 0.482955 0.163265 \n",
"805 42 14 ... 12 7 0.583333 0.342105 \n",
"843 42 13 ... 7 5 0.521505 0.234043 \n",
"926 43 4 ... 5 6 0.576923 0.290323 \n",
"967 44 8 ... 4 1 0.578313 0.162162 \n",
"1170 32 6 ... 6 5 0.402299 0.312500 \n",
"1214 32 10 ... 9 3 0.430233 0.320755 \n",
"\n",
" H_FF_FTFGA H_FF_TOV A_FF_EFG A_FF_ORB A_FF_FTFGA A_FF_TOV \n",
"64 0.146067 0.119570 0.536458 0.355556 0.166667 0.111698 \n",
"149 0.192771 0.184049 0.431818 0.260000 0.079545 0.192230 \n",
"189 0.290000 0.145985 0.475000 0.222222 0.090000 0.174155 \n",
"190 0.241758 0.072917 0.429348 0.340000 0.239130 0.121359 \n",
"269 0.316456 0.133142 0.441860 0.166667 0.104651 0.125366 \n",
"314 0.173913 0.093946 0.523256 0.261905 0.174419 0.151822 \n",
"351 0.240964 0.169635 0.470588 0.205128 0.220588 0.228261 \n",
"391 0.235294 0.083822 0.462025 0.306122 0.227848 0.217752 \n",
"435 0.172840 0.133525 0.523810 0.282609 0.250000 0.124172 \n",
"519 0.089888 0.125786 0.481481 0.232558 0.197531 0.158228 \n",
"558 0.174419 0.090992 0.580000 0.323529 0.186667 0.140779 \n",
"559 0.175824 0.140449 0.574324 0.303030 0.337838 0.206697 \n",
"639 0.076923 0.120667 0.585526 0.230769 0.105263 0.179453 \n",
"681 0.229885 0.076721 0.474359 0.333333 0.307692 0.155417 \n",
"682 0.147727 0.110220 0.618056 0.121212 0.152778 0.224673 \n",
"805 0.166667 0.195796 0.465517 0.313725 0.172414 0.163265 \n",
"843 0.107527 0.170973 0.547619 0.232558 0.273810 0.137741 \n",
"926 0.141026 0.115048 0.512195 0.236842 0.097561 0.070822 \n",
"967 0.156627 0.104866 0.529762 0.200000 0.154762 0.153689 \n",
"1170 0.195402 0.143678 0.402299 0.270833 0.241379 0.074310 \n",
"1214 0.209302 0.149637 0.444444 0.268293 0.320988 0.107296 \n",
"\n",
"[21 rows x 38 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sched.add_four_factors()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d1=pd.to_datetime('1/1/2001')\n",
"d2=pd.to_datetime('1/2/2001')\n",
"(d2-d1).days"
]
},
{
"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 |
femtotrader/barchart-ondemand-client-python | notebooks/example.ipynb | 1 | 46146 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import barchart\n",
"from barchart import getHistory, getQuote, CONFIG"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# API key setup"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#barchart.API_KEY = 'YOURAPIKEY'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also set an environment variable using Bash\n",
"\n",
"```\n",
"export BARCHART_API_KEY=\"YOURAPIKEY\"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cache queries\n",
"`requests_cache` is optional\n",
"\n",
"use it to have a cache mechanism\n",
"\n",
"a session parameter can be pass to functions"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import datetime\n",
"import requests_cache\n",
"session = requests_cache.CachedSession(cache_name='cache',\n",
" backend='sqlite', expire_after=datetime.timedelta(days=1))\n",
"#session = None # pass a None session to avoid caching queries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# getQuote with ONE symbol"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'close': '',\n",
" 'dayCode': 'S',\n",
" 'exchange': 'BATS',\n",
" 'flag': '',\n",
" 'high': 30.82,\n",
" 'lastPrice': 30.66,\n",
" 'low': 30.44,\n",
" 'mode': 'i',\n",
" 'name': 'Eni S.P.A',\n",
" 'netChange': 0.36,\n",
" 'open': 30.52,\n",
" 'percentChange': 1.19,\n",
" 'serverTimestamp': datetime.datetime(2015, 9, 29, 14, 40, 10, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400))),\n",
" 'symbol': 'E',\n",
" 'tradeTimestamp': datetime.datetime(2015, 9, 29, 15, 25, 10, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400))),\n",
" 'unitCode': None,\n",
" 'volume': 12620}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"symbol = \"^EURUSD\"\n",
"quote = getQuote(symbol, session=session)\n",
"quote # quote is a dict"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# getQuote with SEVERAL symbols"
]
},
{
"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>close</th>\n",
" <th>dayCode</th>\n",
" <th>exchange</th>\n",
" <th>flag</th>\n",
" <th>high</th>\n",
" <th>lastPrice</th>\n",
" <th>low</th>\n",
" <th>mode</th>\n",
" <th>name</th>\n",
" <th>netChange</th>\n",
" <th>open</th>\n",
" <th>percentChange</th>\n",
" <th>serverTimestamp</th>\n",
" <th>symbol</th>\n",
" <th>tradeTimestamp</th>\n",
" <th>unitCode</th>\n",
" <th>volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>389</td>\n",
" <td>S</td>\n",
" <td>CBOT</td>\n",
" <td>s</td>\n",
" <td>389.50000</td>\n",
" <td>389.00000</td>\n",
" <td>385.00000</td>\n",
" <td>d</td>\n",
" <td>Corn</td>\n",
" <td>2.25000</td>\n",
" <td>387.2500</td>\n",
" <td>0.58</td>\n",
" <td>2015-09-29 05:00:00</td>\n",
" <td>ZCZ15</td>\n",
" <td>2015-09-29 05:00:00</td>\n",
" <td>-1</td>\n",
" <td>117311</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td></td>\n",
" <td>S</td>\n",
" <td>BATS</td>\n",
" <td></td>\n",
" <td>142.54000</td>\n",
" <td>141.97000</td>\n",
" <td>141.17000</td>\n",
" <td>i</td>\n",
" <td>International Business Machines</td>\n",
" <td>-0.55000</td>\n",
" <td>142.0000</td>\n",
" <td>-0.39</td>\n",
" <td>2015-09-29 19:41:56</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-29 20:26:56</td>\n",
" <td>None</td>\n",
" <td>167005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td></td>\n",
" <td>S</td>\n",
" <td>BATS</td>\n",
" <td></td>\n",
" <td>634.67000</td>\n",
" <td>619.44000</td>\n",
" <td>618.49000</td>\n",
" <td>i</td>\n",
" <td>Google Inc</td>\n",
" <td>-4.81000</td>\n",
" <td>625.7600</td>\n",
" <td>-0.77</td>\n",
" <td>2015-09-29 19:41:49</td>\n",
" <td>GOOGL</td>\n",
" <td>2015-09-29 20:26:49</td>\n",
" <td>None</td>\n",
" <td>129034</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td></td>\n",
" <td>S</td>\n",
" <td>FOREX</td>\n",
" <td></td>\n",
" <td>1.12806</td>\n",
" <td>1.12604</td>\n",
" <td>1.11933</td>\n",
" <td>i</td>\n",
" <td>Euro Fx/U.S. Dollar</td>\n",
" <td>0.00172</td>\n",
" <td>1.1242</td>\n",
" <td>0.15</td>\n",
" <td>2015-09-29 19:42:04</td>\n",
" <td>^EURUSD</td>\n",
" <td>2015-09-29 19:32:04</td>\n",
" <td>5</td>\n",
" <td>488052</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" close dayCode exchange flag high lastPrice low mode \\\n",
"0 389 S CBOT s 389.50000 389.00000 385.00000 d \n",
"1 S BATS 142.54000 141.97000 141.17000 i \n",
"2 S BATS 634.67000 619.44000 618.49000 i \n",
"3 S FOREX 1.12806 1.12604 1.11933 i \n",
"\n",
" name netChange open percentChange \\\n",
"0 Corn 2.25000 387.2500 0.58 \n",
"1 International Business Machines -0.55000 142.0000 -0.39 \n",
"2 Google Inc -4.81000 625.7600 -0.77 \n",
"3 Euro Fx/U.S. Dollar 0.00172 1.1242 0.15 \n",
"\n",
" serverTimestamp symbol tradeTimestamp unitCode volume \n",
"0 2015-09-29 05:00:00 ZCZ15 2015-09-29 05:00:00 -1 117311 \n",
"1 2015-09-29 19:41:56 IBM 2015-09-29 20:26:56 None 167005 \n",
"2 2015-09-29 19:41:49 GOOGL 2015-09-29 20:26:49 None 129034 \n",
"3 2015-09-29 19:42:04 ^EURUSD 2015-09-29 19:32:04 5 488052 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"symbols = [\"ZC*1\", \"IBM\", \"GOOGL\" , \"^EURUSD\"]\n",
"quotes = getQuote(symbols, session=session)\n",
"quotes # quotes is a Pandas DataFrame\n",
"#print(quotes.dtypes)\n",
"#print(type(quotes['serverTimestamp'][0])) # should be a pandas.tslib.Timestamp"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'name': 'Corn', 'dayCode': 'S', 'close': 389, 'high': 389.5, 'open': 387.25, 'flag': 's', 'percentChange': 0.58, 'netChange': 2.25, 'unitCode': '-1', 'lastPrice': 389, 'symbol': 'ZCZ15', 'tradeTimestamp': datetime.datetime(2015, 9, 29, 0, 0, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400))), 'volume': 117311, 'low': 385, 'exchange': 'CBOT', 'mode': 'd', 'serverTimestamp': datetime.datetime(2015, 9, 29, 0, 0, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400)))}, {'name': 'International Business Machines', 'dayCode': 'S', 'close': '', 'high': 142.54, 'open': 142, 'flag': '', 'percentChange': -0.39, 'netChange': -0.55, 'unitCode': None, 'lastPrice': 141.97, 'symbol': 'IBM', 'tradeTimestamp': datetime.datetime(2015, 9, 29, 15, 26, 56, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400))), 'volume': 167005, 'low': 141.17, 'exchange': 'BATS', 'mode': 'i', 'serverTimestamp': datetime.datetime(2015, 9, 29, 14, 41, 56, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400)))}, {'name': 'Google Inc', 'dayCode': 'S', 'close': '', 'high': 634.67, 'open': 625.76, 'flag': '', 'percentChange': -0.77, 'netChange': -4.81, 'unitCode': None, 'lastPrice': 619.44, 'symbol': 'GOOGL', 'tradeTimestamp': datetime.datetime(2015, 9, 29, 15, 26, 49, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400))), 'volume': 129034, 'low': 618.49, 'exchange': 'BATS', 'mode': 'i', 'serverTimestamp': datetime.datetime(2015, 9, 29, 14, 41, 49, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400)))}, {'name': 'Euro Fx/U.S. Dollar', 'dayCode': 'S', 'close': '', 'high': 1.12806, 'open': 1.1242, 'flag': '', 'percentChange': 0.15, 'netChange': 0.00172, 'unitCode': '5', 'lastPrice': 1.12604, 'symbol': '^EURUSD', 'tradeTimestamp': datetime.datetime(2015, 9, 29, 14, 32, 4, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400))), 'volume': 488052, 'low': 1.11933, 'exchange': 'FOREX', 'mode': 'i', 'serverTimestamp': datetime.datetime(2015, 9, 29, 14, 42, 4, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400)))}]\n"
]
}
],
"source": [
"CONFIG.output_pandas = False\n",
"quotes = getQuote(symbols, session=session)\n",
"print(quotes) # quotes is a Pandas DataFrame\n",
"CONFIG.output_pandas = True"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# getHistory with ONE symbol"
]
},
{
"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>close</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>open</th>\n",
" <th>openInterest</th>\n",
" <th>symbol</th>\n",
" <th>tradingDay</th>\n",
" <th>volume</th>\n",
" </tr>\n",
" <tr>\n",
" <th>timestamp</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>2014-09-29 04:00:00</th>\n",
" <td>184.1057</td>\n",
" <td>184.4163</td>\n",
" <td>182.6300</td>\n",
" <td>183.0086</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-09-29</td>\n",
" <td>2415183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-09-30 04:00:00</th>\n",
" <td>184.2901</td>\n",
" <td>185.2803</td>\n",
" <td>183.6299</td>\n",
" <td>184.1057</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-09-30</td>\n",
" <td>3020653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-01 04:00:00</th>\n",
" <td>181.7077</td>\n",
" <td>184.8435</td>\n",
" <td>181.3388</td>\n",
" <td>184.3678</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-01</td>\n",
" <td>3835019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-02 04:00:00</th>\n",
" <td>181.4553</td>\n",
" <td>182.2999</td>\n",
" <td>180.8049</td>\n",
" <td>182.1834</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-02</td>\n",
" <td>2352246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-03 04:00:00</th>\n",
" <td>183.1640</td>\n",
" <td>183.8435</td>\n",
" <td>182.0863</td>\n",
" <td>182.6203</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-03</td>\n",
" <td>3163831</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-06 04:00:00</th>\n",
" <td>183.5231</td>\n",
" <td>185.3192</td>\n",
" <td>183.2028</td>\n",
" <td>184.3387</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-06</td>\n",
" <td>2163230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-07 04:00:00</th>\n",
" <td>180.2903</td>\n",
" <td>182.6300</td>\n",
" <td>180.1253</td>\n",
" <td>182.2902</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-07</td>\n",
" <td>3084001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-08 04:00:00</th>\n",
" <td>183.8338</td>\n",
" <td>184.0668</td>\n",
" <td>180.1933</td>\n",
" <td>180.5428</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-08</td>\n",
" <td>3075040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-09 04:00:00</th>\n",
" <td>180.9796</td>\n",
" <td>183.9697</td>\n",
" <td>180.6593</td>\n",
" <td>183.6008</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-09</td>\n",
" <td>2704527</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-10 04:00:00</th>\n",
" <td>180.5039</td>\n",
" <td>182.2611</td>\n",
" <td>179.6982</td>\n",
" <td>180.4360</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-10</td>\n",
" <td>5243318</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-13 04:00:00</th>\n",
" <td>178.1643</td>\n",
" <td>181.2029</td>\n",
" <td>178.0672</td>\n",
" <td>180.0768</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-13</td>\n",
" <td>3708115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-14 04:00:00</th>\n",
" <td>178.4361</td>\n",
" <td>180.3000</td>\n",
" <td>178.2322</td>\n",
" <td>179.4943</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-14</td>\n",
" <td>4042885</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-15 04:00:00</th>\n",
" <td>176.4459</td>\n",
" <td>178.4264</td>\n",
" <td>173.5335</td>\n",
" <td>177.3099</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-15</td>\n",
" <td>7104947</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-16 04:00:00</th>\n",
" <td>174.5916</td>\n",
" <td>176.1838</td>\n",
" <td>173.4752</td>\n",
" <td>174.5528</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-16</td>\n",
" <td>5746296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-17 04:00:00</th>\n",
" <td>176.7372</td>\n",
" <td>177.5041</td>\n",
" <td>174.9606</td>\n",
" <td>175.9508</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-17</td>\n",
" <td>4485296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-20 04:00:00</th>\n",
" <td>164.1651</td>\n",
" <td>165.3592</td>\n",
" <td>161.8254</td>\n",
" <td>161.9710</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-20</td>\n",
" <td>24120416</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-21 04:00:00</th>\n",
" <td>158.4664</td>\n",
" <td>161.8157</td>\n",
" <td>156.9616</td>\n",
" <td>161.5439</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-21</td>\n",
" <td>21583480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-22 04:00:00</th>\n",
" <td>157.0684</td>\n",
" <td>160.5828</td>\n",
" <td>156.3986</td>\n",
" <td>157.6606</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-22</td>\n",
" <td>11424816</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-23 04:00:00</th>\n",
" <td>157.4470</td>\n",
" <td>158.0781</td>\n",
" <td>156.8257</td>\n",
" <td>157.3888</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-23</td>\n",
" <td>7828667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-24 04:00:00</th>\n",
" <td>157.3499</td>\n",
" <td>157.6994</td>\n",
" <td>156.7383</td>\n",
" <td>157.3499</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-24</td>\n",
" <td>6852273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-27 04:00:00</th>\n",
" <td>157.1461</td>\n",
" <td>158.1557</td>\n",
" <td>157.0878</td>\n",
" <td>157.2723</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-27</td>\n",
" <td>5139385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-28 04:00:00</th>\n",
" <td>158.8256</td>\n",
" <td>158.8256</td>\n",
" <td>157.0781</td>\n",
" <td>157.2335</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-28</td>\n",
" <td>8134183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-29 04:00:00</th>\n",
" <td>158.6897</td>\n",
" <td>159.8158</td>\n",
" <td>158.0101</td>\n",
" <td>159.5343</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-29</td>\n",
" <td>4884136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-30 04:00:00</th>\n",
" <td>159.5537</td>\n",
" <td>159.8158</td>\n",
" <td>158.2625</td>\n",
" <td>158.7285</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-30</td>\n",
" <td>4013013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-31 04:00:00</th>\n",
" <td>159.6022</td>\n",
" <td>160.7575</td>\n",
" <td>158.8450</td>\n",
" <td>160.5536</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-10-31</td>\n",
" <td>5993202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-03 05:00:00</th>\n",
" <td>159.5634</td>\n",
" <td>159.7381</td>\n",
" <td>158.6120</td>\n",
" <td>159.4566</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-11-03</td>\n",
" <td>4833457</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-04 05:00:00</th>\n",
" <td>157.9033</td>\n",
" <td>159.5634</td>\n",
" <td>157.5053</td>\n",
" <td>159.5440</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-11-04</td>\n",
" <td>4374668</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-05 05:00:00</th>\n",
" <td>157.0975</td>\n",
" <td>158.7673</td>\n",
" <td>156.8451</td>\n",
" <td>158.3693</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-11-05</td>\n",
" <td>4228090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-06 05:00:00</th>\n",
" <td>157.8209</td>\n",
" <td>157.8893</td>\n",
" <td>156.4426</td>\n",
" <td>157.6449</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-11-06</td>\n",
" <td>4161393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-07 05:00:00</th>\n",
" <td>158.4171</td>\n",
" <td>158.5540</td>\n",
" <td>157.2246</td>\n",
" <td>157.7818</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2014-11-07</td>\n",
" <td>3575385</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",
" </tr>\n",
" <tr>\n",
" <th>2015-08-17 04:00:00</th>\n",
" <td>156.3100</td>\n",
" <td>156.6900</td>\n",
" <td>154.7000</td>\n",
" <td>155.2000</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-08-17</td>\n",
" <td>2249500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-08-18 04:00:00</th>\n",
" <td>156.0100</td>\n",
" <td>156.5200</td>\n",
" <td>155.2500</td>\n",
" <td>155.5100</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-08-18</td>\n",
" <td>2018300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-08-19 04:00:00</th>\n",
" <td>153.9400</td>\n",
" <td>155.6700</td>\n",
" <td>153.4100</td>\n",
" <td>155.1500</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-08-19</td>\n",
" <td>4206300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-08-20 04:00:00</th>\n",
" <td>152.6600</td>\n",
" <td>153.9100</td>\n",
" <td>152.5000</td>\n",
" <td>152.7400</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-08-20</td>\n",
" <td>4011500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-08-21 04:00:00</th>\n",
" <td>148.8500</td>\n",
" <td>153.1900</td>\n",
" <td>148.7000</td>\n",
" <td>151.5000</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-08-21</td>\n",
" <td>7362100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-08-24 04:00:00</th>\n",
" <td>143.4700</td>\n",
" <td>147.7600</td>\n",
" <td>142.3200</td>\n",
" <td>143.4700</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-08-24</td>\n",
" <td>10189600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-08-25 04:00:00</th>\n",
" <td>140.9600</td>\n",
" <td>147.1100</td>\n",
" <td>140.6200</td>\n",
" <td>146.9400</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-08-25</td>\n",
" <td>7073200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-08-26 04:00:00</th>\n",
" <td>146.7000</td>\n",
" <td>146.9800</td>\n",
" <td>142.1400</td>\n",
" <td>144.0900</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-08-26</td>\n",
" <td>6221700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-08-27 04:00:00</th>\n",
" <td>148.5400</td>\n",
" <td>148.9700</td>\n",
" <td>145.6600</td>\n",
" <td>148.5000</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-08-27</td>\n",
" <td>4976600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-08-28 04:00:00</th>\n",
" <td>147.9800</td>\n",
" <td>148.2000</td>\n",
" <td>147.1800</td>\n",
" <td>147.7500</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-08-28</td>\n",
" <td>4076200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-08-31 04:00:00</th>\n",
" <td>147.8900</td>\n",
" <td>148.4000</td>\n",
" <td>146.2600</td>\n",
" <td>147.3800</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-08-31</td>\n",
" <td>4093000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-01 04:00:00</th>\n",
" <td>142.6800</td>\n",
" <td>144.9800</td>\n",
" <td>141.8500</td>\n",
" <td>144.9100</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-01</td>\n",
" <td>5272000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-02 04:00:00</th>\n",
" <td>145.0500</td>\n",
" <td>145.0800</td>\n",
" <td>143.1800</td>\n",
" <td>144.7400</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-02</td>\n",
" <td>4252000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-03 04:00:00</th>\n",
" <td>146.7800</td>\n",
" <td>148.0300</td>\n",
" <td>145.7700</td>\n",
" <td>146.0500</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-03</td>\n",
" <td>3603500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-04 04:00:00</th>\n",
" <td>143.7000</td>\n",
" <td>145.4000</td>\n",
" <td>143.3200</td>\n",
" <td>144.5700</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-04</td>\n",
" <td>4201000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-08 04:00:00</th>\n",
" <td>147.2300</td>\n",
" <td>147.3400</td>\n",
" <td>145.6600</td>\n",
" <td>145.8600</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-08</td>\n",
" <td>3933200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-09 04:00:00</th>\n",
" <td>145.0500</td>\n",
" <td>149.0400</td>\n",
" <td>144.8500</td>\n",
" <td>148.7400</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-09</td>\n",
" <td>3407700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-10 04:00:00</th>\n",
" <td>146.2000</td>\n",
" <td>147.1600</td>\n",
" <td>144.5100</td>\n",
" <td>145.8500</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-10</td>\n",
" <td>3461600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-11 04:00:00</th>\n",
" <td>147.3700</td>\n",
" <td>147.5000</td>\n",
" <td>145.6700</td>\n",
" <td>145.7100</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-11</td>\n",
" <td>3115000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-14 04:00:00</th>\n",
" <td>145.6500</td>\n",
" <td>147.3700</td>\n",
" <td>145.4100</td>\n",
" <td>147.3700</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-14</td>\n",
" <td>3226700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-15 04:00:00</th>\n",
" <td>147.5300</td>\n",
" <td>147.9300</td>\n",
" <td>145.7600</td>\n",
" <td>146.6000</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-15</td>\n",
" <td>2717000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-16 04:00:00</th>\n",
" <td>148.4100</td>\n",
" <td>148.8900</td>\n",
" <td>147.5400</td>\n",
" <td>147.8400</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-16</td>\n",
" <td>2799200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-17 04:00:00</th>\n",
" <td>148.1400</td>\n",
" <td>149.6800</td>\n",
" <td>147.3000</td>\n",
" <td>148.1000</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-17</td>\n",
" <td>4002900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-18 04:00:00</th>\n",
" <td>144.5100</td>\n",
" <td>146.3800</td>\n",
" <td>143.9800</td>\n",
" <td>146.0500</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-18</td>\n",
" <td>7975800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-21 04:00:00</th>\n",
" <td>146.4800</td>\n",
" <td>146.9800</td>\n",
" <td>144.9200</td>\n",
" <td>145.3900</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-21</td>\n",
" <td>3825000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-22 04:00:00</th>\n",
" <td>144.4300</td>\n",
" <td>145.0600</td>\n",
" <td>143.7700</td>\n",
" <td>144.6200</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-22</td>\n",
" <td>3564000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-23 04:00:00</th>\n",
" <td>143.6600</td>\n",
" <td>144.5700</td>\n",
" <td>142.7500</td>\n",
" <td>144.2100</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-23</td>\n",
" <td>2674200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-24 04:00:00</th>\n",
" <td>144.4100</td>\n",
" <td>145.0700</td>\n",
" <td>141.9500</td>\n",
" <td>142.6000</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-24</td>\n",
" <td>3280200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-25 04:00:00</th>\n",
" <td>145.4200</td>\n",
" <td>146.2700</td>\n",
" <td>144.5300</td>\n",
" <td>145.5500</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-25</td>\n",
" <td>3474400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-28 04:00:00</th>\n",
" <td>142.5200</td>\n",
" <td>145.3800</td>\n",
" <td>142.4700</td>\n",
" <td>144.4200</td>\n",
" <td>None</td>\n",
" <td>IBM</td>\n",
" <td>2015-09-28</td>\n",
" <td>4317300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>252 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" close high low open openInterest \\\n",
"timestamp \n",
"2014-09-29 04:00:00 184.1057 184.4163 182.6300 183.0086 None \n",
"2014-09-30 04:00:00 184.2901 185.2803 183.6299 184.1057 None \n",
"2014-10-01 04:00:00 181.7077 184.8435 181.3388 184.3678 None \n",
"2014-10-02 04:00:00 181.4553 182.2999 180.8049 182.1834 None \n",
"2014-10-03 04:00:00 183.1640 183.8435 182.0863 182.6203 None \n",
"2014-10-06 04:00:00 183.5231 185.3192 183.2028 184.3387 None \n",
"2014-10-07 04:00:00 180.2903 182.6300 180.1253 182.2902 None \n",
"2014-10-08 04:00:00 183.8338 184.0668 180.1933 180.5428 None \n",
"2014-10-09 04:00:00 180.9796 183.9697 180.6593 183.6008 None \n",
"2014-10-10 04:00:00 180.5039 182.2611 179.6982 180.4360 None \n",
"2014-10-13 04:00:00 178.1643 181.2029 178.0672 180.0768 None \n",
"2014-10-14 04:00:00 178.4361 180.3000 178.2322 179.4943 None \n",
"2014-10-15 04:00:00 176.4459 178.4264 173.5335 177.3099 None \n",
"2014-10-16 04:00:00 174.5916 176.1838 173.4752 174.5528 None \n",
"2014-10-17 04:00:00 176.7372 177.5041 174.9606 175.9508 None \n",
"2014-10-20 04:00:00 164.1651 165.3592 161.8254 161.9710 None \n",
"2014-10-21 04:00:00 158.4664 161.8157 156.9616 161.5439 None \n",
"2014-10-22 04:00:00 157.0684 160.5828 156.3986 157.6606 None \n",
"2014-10-23 04:00:00 157.4470 158.0781 156.8257 157.3888 None \n",
"2014-10-24 04:00:00 157.3499 157.6994 156.7383 157.3499 None \n",
"2014-10-27 04:00:00 157.1461 158.1557 157.0878 157.2723 None \n",
"2014-10-28 04:00:00 158.8256 158.8256 157.0781 157.2335 None \n",
"2014-10-29 04:00:00 158.6897 159.8158 158.0101 159.5343 None \n",
"2014-10-30 04:00:00 159.5537 159.8158 158.2625 158.7285 None \n",
"2014-10-31 04:00:00 159.6022 160.7575 158.8450 160.5536 None \n",
"2014-11-03 05:00:00 159.5634 159.7381 158.6120 159.4566 None \n",
"2014-11-04 05:00:00 157.9033 159.5634 157.5053 159.5440 None \n",
"2014-11-05 05:00:00 157.0975 158.7673 156.8451 158.3693 None \n",
"2014-11-06 05:00:00 157.8209 157.8893 156.4426 157.6449 None \n",
"2014-11-07 05:00:00 158.4171 158.5540 157.2246 157.7818 None \n",
"... ... ... ... ... ... \n",
"2015-08-17 04:00:00 156.3100 156.6900 154.7000 155.2000 None \n",
"2015-08-18 04:00:00 156.0100 156.5200 155.2500 155.5100 None \n",
"2015-08-19 04:00:00 153.9400 155.6700 153.4100 155.1500 None \n",
"2015-08-20 04:00:00 152.6600 153.9100 152.5000 152.7400 None \n",
"2015-08-21 04:00:00 148.8500 153.1900 148.7000 151.5000 None \n",
"2015-08-24 04:00:00 143.4700 147.7600 142.3200 143.4700 None \n",
"2015-08-25 04:00:00 140.9600 147.1100 140.6200 146.9400 None \n",
"2015-08-26 04:00:00 146.7000 146.9800 142.1400 144.0900 None \n",
"2015-08-27 04:00:00 148.5400 148.9700 145.6600 148.5000 None \n",
"2015-08-28 04:00:00 147.9800 148.2000 147.1800 147.7500 None \n",
"2015-08-31 04:00:00 147.8900 148.4000 146.2600 147.3800 None \n",
"2015-09-01 04:00:00 142.6800 144.9800 141.8500 144.9100 None \n",
"2015-09-02 04:00:00 145.0500 145.0800 143.1800 144.7400 None \n",
"2015-09-03 04:00:00 146.7800 148.0300 145.7700 146.0500 None \n",
"2015-09-04 04:00:00 143.7000 145.4000 143.3200 144.5700 None \n",
"2015-09-08 04:00:00 147.2300 147.3400 145.6600 145.8600 None \n",
"2015-09-09 04:00:00 145.0500 149.0400 144.8500 148.7400 None \n",
"2015-09-10 04:00:00 146.2000 147.1600 144.5100 145.8500 None \n",
"2015-09-11 04:00:00 147.3700 147.5000 145.6700 145.7100 None \n",
"2015-09-14 04:00:00 145.6500 147.3700 145.4100 147.3700 None \n",
"2015-09-15 04:00:00 147.5300 147.9300 145.7600 146.6000 None \n",
"2015-09-16 04:00:00 148.4100 148.8900 147.5400 147.8400 None \n",
"2015-09-17 04:00:00 148.1400 149.6800 147.3000 148.1000 None \n",
"2015-09-18 04:00:00 144.5100 146.3800 143.9800 146.0500 None \n",
"2015-09-21 04:00:00 146.4800 146.9800 144.9200 145.3900 None \n",
"2015-09-22 04:00:00 144.4300 145.0600 143.7700 144.6200 None \n",
"2015-09-23 04:00:00 143.6600 144.5700 142.7500 144.2100 None \n",
"2015-09-24 04:00:00 144.4100 145.0700 141.9500 142.6000 None \n",
"2015-09-25 04:00:00 145.4200 146.2700 144.5300 145.5500 None \n",
"2015-09-28 04:00:00 142.5200 145.3800 142.4700 144.4200 None \n",
"\n",
" symbol tradingDay volume \n",
"timestamp \n",
"2014-09-29 04:00:00 IBM 2014-09-29 2415183 \n",
"2014-09-30 04:00:00 IBM 2014-09-30 3020653 \n",
"2014-10-01 04:00:00 IBM 2014-10-01 3835019 \n",
"2014-10-02 04:00:00 IBM 2014-10-02 2352246 \n",
"2014-10-03 04:00:00 IBM 2014-10-03 3163831 \n",
"2014-10-06 04:00:00 IBM 2014-10-06 2163230 \n",
"2014-10-07 04:00:00 IBM 2014-10-07 3084001 \n",
"2014-10-08 04:00:00 IBM 2014-10-08 3075040 \n",
"2014-10-09 04:00:00 IBM 2014-10-09 2704527 \n",
"2014-10-10 04:00:00 IBM 2014-10-10 5243318 \n",
"2014-10-13 04:00:00 IBM 2014-10-13 3708115 \n",
"2014-10-14 04:00:00 IBM 2014-10-14 4042885 \n",
"2014-10-15 04:00:00 IBM 2014-10-15 7104947 \n",
"2014-10-16 04:00:00 IBM 2014-10-16 5746296 \n",
"2014-10-17 04:00:00 IBM 2014-10-17 4485296 \n",
"2014-10-20 04:00:00 IBM 2014-10-20 24120416 \n",
"2014-10-21 04:00:00 IBM 2014-10-21 21583480 \n",
"2014-10-22 04:00:00 IBM 2014-10-22 11424816 \n",
"2014-10-23 04:00:00 IBM 2014-10-23 7828667 \n",
"2014-10-24 04:00:00 IBM 2014-10-24 6852273 \n",
"2014-10-27 04:00:00 IBM 2014-10-27 5139385 \n",
"2014-10-28 04:00:00 IBM 2014-10-28 8134183 \n",
"2014-10-29 04:00:00 IBM 2014-10-29 4884136 \n",
"2014-10-30 04:00:00 IBM 2014-10-30 4013013 \n",
"2014-10-31 04:00:00 IBM 2014-10-31 5993202 \n",
"2014-11-03 05:00:00 IBM 2014-11-03 4833457 \n",
"2014-11-04 05:00:00 IBM 2014-11-04 4374668 \n",
"2014-11-05 05:00:00 IBM 2014-11-05 4228090 \n",
"2014-11-06 05:00:00 IBM 2014-11-06 4161393 \n",
"2014-11-07 05:00:00 IBM 2014-11-07 3575385 \n",
"... ... ... ... \n",
"2015-08-17 04:00:00 IBM 2015-08-17 2249500 \n",
"2015-08-18 04:00:00 IBM 2015-08-18 2018300 \n",
"2015-08-19 04:00:00 IBM 2015-08-19 4206300 \n",
"2015-08-20 04:00:00 IBM 2015-08-20 4011500 \n",
"2015-08-21 04:00:00 IBM 2015-08-21 7362100 \n",
"2015-08-24 04:00:00 IBM 2015-08-24 10189600 \n",
"2015-08-25 04:00:00 IBM 2015-08-25 7073200 \n",
"2015-08-26 04:00:00 IBM 2015-08-26 6221700 \n",
"2015-08-27 04:00:00 IBM 2015-08-27 4976600 \n",
"2015-08-28 04:00:00 IBM 2015-08-28 4076200 \n",
"2015-08-31 04:00:00 IBM 2015-08-31 4093000 \n",
"2015-09-01 04:00:00 IBM 2015-09-01 5272000 \n",
"2015-09-02 04:00:00 IBM 2015-09-02 4252000 \n",
"2015-09-03 04:00:00 IBM 2015-09-03 3603500 \n",
"2015-09-04 04:00:00 IBM 2015-09-04 4201000 \n",
"2015-09-08 04:00:00 IBM 2015-09-08 3933200 \n",
"2015-09-09 04:00:00 IBM 2015-09-09 3407700 \n",
"2015-09-10 04:00:00 IBM 2015-09-10 3461600 \n",
"2015-09-11 04:00:00 IBM 2015-09-11 3115000 \n",
"2015-09-14 04:00:00 IBM 2015-09-14 3226700 \n",
"2015-09-15 04:00:00 IBM 2015-09-15 2717000 \n",
"2015-09-16 04:00:00 IBM 2015-09-16 2799200 \n",
"2015-09-17 04:00:00 IBM 2015-09-17 4002900 \n",
"2015-09-18 04:00:00 IBM 2015-09-18 7975800 \n",
"2015-09-21 04:00:00 IBM 2015-09-21 3825000 \n",
"2015-09-22 04:00:00 IBM 2015-09-22 3564000 \n",
"2015-09-23 04:00:00 IBM 2015-09-23 2674200 \n",
"2015-09-24 04:00:00 IBM 2015-09-24 3280200 \n",
"2015-09-25 04:00:00 IBM 2015-09-25 3474400 \n",
"2015-09-28 04:00:00 IBM 2015-09-28 4317300 \n",
"\n",
"[252 rows x 8 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"symbol = 'IBM'\n",
"startDate = datetime.date(year=2014, month=9, day=28)\n",
"history = getHistory(symbol, typ='daily', startDate=startDate, session=session)\n",
"history\n",
"#print(history.dtypes)\n",
"#print(type(history['timestamp'][0])) # should be a pandas.tslib.Timestamp\n",
"#print(type(history.index[0])) # should be a pandas.tslib.Timestamp\n",
"#print(type(history['tradingDay'][0])) # should be a pandas.tslib.Timestamp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# getHistory with SEVERAL symbols"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<class 'pandas.core.panel.Panel'>\n",
"Dimensions: 8 (items) x 511 (major_axis) x 4 (minor_axis)\n",
"Items axis: close to volume\n",
"Major_axis axis: 2014-09-29 04:00:00 to 2015-09-29 05:00:00\n",
"Minor_axis axis: ZC*1 to ^EURUSD"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"symbols = [\"ZC*1\", \"IBM\", \"GOOGL\" , \"^EURUSD\"]\n",
"histories = getHistory(symbols, typ='daily', startDate=startDate, session=session)\n",
"histories\n",
"#print(histories.dtypes)\n",
"#print(type(histories.index[0])) # should be a pandas.tslib.Timestamp\n",
"#print(type(histories['timestamp'][0])) # should be a pandas.tslib.Timestamp"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#histories.loc[:, :, \"IBM\"] #??"
]
}
],
"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 |
cydcowley/Imperial-Visualizations | visuals_mechanics/mechanics_pulse_at_interface/pulse_at_interface.ipynb | 1 | 11235 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"<img src=\"imperial_logo.png\" width=\"300\" align=\"left\"><p style=\"text-align: right\">Created by Celie Valentiny<br>Email: [email protected]<br><a>HTML Version (This will be a link)</a></p><br>\n",
"# Pulse at Interface\n",
"## Learning Objectives:\n",
"\n",
"* Understand the transmission and reflection of a pulse at a boundary between two mediums. \n",
"* Be able to show this phenomenon on `Python` by creating animations with Plotly`.\n",
"\n",
"\n",
"## Table of Contents\n",
"1. Introduction\n",
"2. Reflection and Transmission"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Introduction \n",
"When a wave approaches a boundary between two different mediums which have different propagation speeds some of its energy passes into the second medium and some of its energy is reflected from the boundary and remains in the first medium."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'NotebookApp' 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-125-b1bfb10b9f35>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mNotebookApp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miopub_data_rate_limit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1.0e10\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;31m# import libraries/packages to be used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure_factory\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mff\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mplotly\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moffline\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdownload_plotlyjs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minit_notebook_mode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0miplot\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph_objs\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'NotebookApp' is not defined"
]
}
],
"source": [
"\n",
"# import libraries/packages to be used\n",
"import plotly.figure_factory as ff\n",
"from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot\n",
"import plotly.graph_objs as go\n",
"init_notebook_mode(connected=True)\n",
"import numpy as np"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"The code below defines the different variables for the plot. You can modify some of these variables to obtain a different plot:\n",
"-v1: this is the velocity of the incident wave\n",
"-alpha: it is ratio of the velocity in the second medium and the velocity in the first medium. Changing alpha will therefore change the velocity of the second medium\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Variables\n",
"# x-coordinates\n",
"n = 200\n",
"x = np.linspace(-10, 10, n)\n",
"# Initialise incident, reflected and transmitted waves\n",
"y_i = [0]*n\n",
"y_r = [0]*n\n",
"y_t = [0]*n\n",
"# Velocity of incident wave\n",
"v1 = 10\n",
"# Velocity of transmitted wave\n",
"alpha = 0.5\n",
"v2 = alpha*v1\n",
"# Time\n",
"t_end = 19/v1 if v1 < v2 else (9 / v1 + 10 / v2)\n",
"times = np.linspace(0, t_end, 80)\n",
"# Transmitted and reflected equation constants\n",
"A = (v2-v1)/(v1+v2)\n",
"B = 2*v1/(v1+v2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The code snippet below is the equation for the pulse at time t=0, so prior to reaching the interface. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Initialise incident wave\n",
"for i in range(n):\n",
" y_i[i] = (x[i] + 6) * np.exp(-1*((x[i] + 6) * (x[i] + 6)) + (x[i] + 6))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Data: Initial plot\n",
"incident = dict(x=x, y=y_i,\n",
" name=\"incident\",\n",
" mode=\"line\",\n",
" line=dict(width=2, color=\"#960078\", simplify=False))\n",
"\n",
"\n",
"reflected = dict(x=x, y=y_r,\n",
" name=\"reflected\",\n",
" mode=\"line\",\n",
" line=dict(width=2, color='#E40043', simplify=False))\n",
"\n",
"\n",
"transmitted = dict(x=x, y=y_t,\n",
" name=\"transmitted\",\n",
" mode=\"line\",\n",
" line=dict(width=2, color='#00ACD7', simplify=False))\n",
"\n",
"\n",
"boundary = dict(x=[0, 0], y=[-4, 4],\n",
" name=\"boundary\",\n",
" mode=\"line\",\n",
" line=dict(width=2, color='black', simplify=False))\n",
"\n",
"\n",
"xline = dict(x=[-15, 15], y=[0, 0],\n",
" showlegend=False,\n",
" mode=\"line\",\n",
" line=dict(width=2, color='black', simplify=False))\n",
"\n",
"data = [incident, transmitted, reflected, boundary, xline]\n"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"The cell below is mainly plotly syntax to define the layout of the plot, there is no need to fully understand this bit of code, the most important is to understand the physical concept. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Plot layout\n",
"layout = dict(width=800, height=600,margin=dict(l=50,r=50,b=100,t=100, pad=4),\n",
" xaxis=dict(range=[-10, 10], autorange=False, zeroline=False),\n",
" yaxis=dict(range=[-1.5, 1.5], autorange=False, zeroline=False),\n",
" title='Travelling pulse at an interface', hovermode='closest', font=dict(family='Lato',size=18,color='#003E74'),\n",
" updatemenus=[{'buttons': [\n",
" {\n",
" 'args': [None, {'frame': {'duration': 500, 'redraw': False},\n",
" 'fromcurrent': True,\n",
" 'transition': {'duration': 300, 'easing': 'quadratic-in-out'}}],\n",
" 'label': 'Play',\n",
" 'method': 'animate'\n",
" },\n",
" {\n",
" 'args': [[None], {'frame': {'duration': 0, 'redraw': False}, 'mode': 'immediate',\n",
" 'transition': {'duration': 0}}],\n",
" 'label': 'Pause',\n",
" 'method': 'animate'\n",
" }\n",
" ],\n",
" 'direction': 'right',\n",
" 'pad': {'r': 10, 't': 87},\n",
" 'showactive': False,\n",
" 'type': 'buttons',\n",
" 'x': 5,\n",
" 'xanchor': 'right',\n",
" 'y': 0,\n",
" 'yanchor': 'top'}])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The functions below are used to compute the frames that show the travelling of the wave. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Data update functions over time\n",
"def compute_i(t):\n",
" for j in range(n):\n",
" if x[j] < 0:\n",
" y_i[j] = (x[j] + 6 - v1 * t) * np.exp(-1*((x[j] + 6 - v1 * t) * (x[j] + 6 - v1 * t)) + (x[j] + 6 - v1 * t))\n",
" else:\n",
" y_i[j] = 0\n",
" return y_i[:]\n",
"\n",
"\n",
"def compute_r(t):\n",
" for k in range(n):\n",
" if x[k] < 0:\n",
" y_r[k] = A * (-1*x[k] + 6 - v1 * t) * np.exp(-1*((-1*x[k] + 6 - v1 * t) * (-1*x[k] + 6 - v1 * t)) + (-1*x[k] + 6 - v1 * t))\n",
" else:\n",
" y_r[k] = 0\n",
" return y_r[:]\n",
"\n",
"\n",
"def compute_t(t):\n",
" for l in range(n):\n",
" if x[l] > 0:\n",
" y_t[l] = B * (v1 / v2 * x[l] + 6 - v1 * t) * np.exp(-1*((v1 / v2 * x[l] + 6 - v1 * t) * (v1 / v2 * x[l] + 6 - v1 * t)) + (v1 / v2 * x[l] + 6 - v1 * t))\n",
" else:\n",
" y_t[l] = 0\n",
" return y_t[:]\n",
"\n",
"frames = [dict(data=[dict(x=x,\n",
" y=compute_i(time),\n",
" mode='lines',\n",
" line=dict(color=\"#960078\", width=2)),\n",
" dict(x=x,\n",
" y=compute_t(time),\n",
" mode='lines',\n",
" line=dict(color='#00ACD7', width=2)),\n",
" dict(x=x,\n",
" y=compute_r(time),\n",
" mode='lines',\n",
" line=dict(color='#E40043', width=2))\n",
" ]) for time in times]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"figure = dict(data=data, layout=layout, frames=frames)\n",
"iplot(figure)\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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
odewahn/sklearn_pycon2015 | notebooks/Index.ipynb | 1 | 1917 | {
"metadata": {
"name": "",
"signature": "sha256:6d668c263bfa58b9d0af9434b267fd5a74e993eabb25e1847f689082a86eca80"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PyCon 2015 Scikit-Learn Tutorial Index\n",
"\n",
"This is the main index of the PyCon 2015 Introduction to Scikit-Learn tutorial, presented by [Jake VanderPlas](http://www.vanderplas.com).\n",
"\n",
"The following links are to notebooks containing the tutorial materials.\n",
"Note that many of these require files that are in the directory structure of the [github repository](http://github.com/jakevdp/sklearn_pycon2015) in which they are contained. We will not have time to cover all this material, but I left it here for reference.\n",
"\n",
"### 1. Preliminaries\n",
"\n",
" + [01-Preliminaries.ipynb](01-Preliminaries.ipynb)\n",
" \n",
"### 2. Introduction to Machine Learning with Scikit-Learn\n",
"\n",
" + [02.1-Machine-Learning-Intro.ipynb](02.1-Machine-Learning-Intro.ipynb)\n",
" + [02.2-Basic-Principles.ipynb](02.2-Basic-Principles.ipynb)\n",
" \n",
"### 3. Supervised Learning In-Depth\n",
"\n",
" + [03.1-Classification-SVMs.ipynb](03.1-Classification-SVMs.ipynb)\n",
" + [03.2-Regression-Forests.ipynb](03.2-Regression-Forests.ipynb)\n",
"\n",
"### 4. Unsupervised Learning In-Depth\n",
"\n",
" + [04.1-Dimensionality-PCA.ipynb](04.1-Dimensionality-PCA.ipynb)\n",
" + [04.2-Clustering-KMeans.ipynb](04.2-Clustering-KMeans.ipynb)\n",
" + [04.3-Density-GMM.ipynb](04.3-Density-GMM.ipynb)\n",
" \n",
"### 5. Model Validation In-Depth\n",
"\n",
" + [05-Validation.ipynb](05-Validation.ipynb)"
]
}
],
"metadata": {}
}
]
} | bsd-3-clause |
tsaqib/ml-playground | bike-sharing-nn/bike-sharing.ipynb | 1 | 114740 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A Neural Network using Numpy on Bike Sharing Time Series dataset\n",
"In this project, we'll build a neural network and use it to predict daily bike rental ridership. \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load and prepare the data\n",
"\n",
"A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_path = 'Bike-Sharing-Dataset/hour.csv'\n",
"\n",
"rides = pd.read_csv(data_path)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>instant</th>\n",
" <th>dteday</th>\n",
" <th>season</th>\n",
" <th>yr</th>\n",
" <th>mnth</th>\n",
" <th>hr</th>\n",
" <th>holiday</th>\n",
" <th>weekday</th>\n",
" <th>workingday</th>\n",
" <th>weathersit</th>\n",
" <th>temp</th>\n",
" <th>atemp</th>\n",
" <th>hum</th>\n",
" <th>windspeed</th>\n",
" <th>casual</th>\n",
" <th>registered</th>\n",
" <th>cnt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2011-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.24</td>\n",
" <td>0.2879</td>\n",
" <td>0.81</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2011-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.22</td>\n",
" <td>0.2727</td>\n",
" <td>0.80</td>\n",
" <td>0.0</td>\n",
" <td>8</td>\n",
" <td>32</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>2011-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.22</td>\n",
" <td>0.2727</td>\n",
" <td>0.80</td>\n",
" <td>0.0</td>\n",
" <td>5</td>\n",
" <td>27</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2011-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.24</td>\n",
" <td>0.2879</td>\n",
" <td>0.75</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>2011-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.24</td>\n",
" <td>0.2879</td>\n",
" <td>0.75</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" instant dteday season yr mnth hr holiday weekday workingday \\\n",
"0 1 2011-01-01 1 0 1 0 0 6 0 \n",
"1 2 2011-01-01 1 0 1 1 0 6 0 \n",
"2 3 2011-01-01 1 0 1 2 0 6 0 \n",
"3 4 2011-01-01 1 0 1 3 0 6 0 \n",
"4 5 2011-01-01 1 0 1 4 0 6 0 \n",
"\n",
" weathersit temp atemp hum windspeed casual registered cnt \n",
"0 1 0.24 0.2879 0.81 0.0 3 13 16 \n",
"1 1 0.22 0.2727 0.80 0.0 8 32 40 \n",
"2 1 0.22 0.2727 0.80 0.0 5 27 32 \n",
"3 1 0.24 0.2879 0.75 0.0 3 10 13 \n",
"4 1 0.24 0.2879 0.75 0.0 0 1 1 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rides.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Checking out the data\n",
"\n",
"This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the `cnt` column. You can see the first few rows of the data above.\n",
"\n",
"Below is a plot showing the number of bike riders over the first 10 days in the data set. You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x113fca978>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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SeMPpm/CGv17KB1hozJ5aKvjL2WhMCCnT2JhMQgiJge0gmYtN5y8fPj/ZvrtOnJvx24sH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\nC3xCSBRiDLpS77J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zwCckEqZSG9qD3zEU/JTWBMDVZOuxuDVy1gu6ms85pUVn+th6Bvpy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\nMzOzCnGBb2ZmZmZWIf8HXKqiIyqMTswAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x113f67eb8>"
]
},
"metadata": {
"image/png": {
"height": 263,
"width": 380
}
},
"output_type": "display_data"
}
],
"source": [
"rides[:24*10].plot(x='dteday', y='cnt')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dummy variables\n",
"Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to `get_dummies()`."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>yr</th>\n",
" <th>holiday</th>\n",
" <th>temp</th>\n",
" <th>hum</th>\n",
" <th>windspeed</th>\n",
" <th>casual</th>\n",
" <th>registered</th>\n",
" <th>cnt</th>\n",
" <th>season_1</th>\n",
" <th>season_2</th>\n",
" <th>...</th>\n",
" <th>hr_21</th>\n",
" <th>hr_22</th>\n",
" <th>hr_23</th>\n",
" <th>weekday_0</th>\n",
" <th>weekday_1</th>\n",
" <th>weekday_2</th>\n",
" <th>weekday_3</th>\n",
" <th>weekday_4</th>\n",
" <th>weekday_5</th>\n",
" <th>weekday_6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.24</td>\n",
" <td>0.81</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.22</td>\n",
" <td>0.80</td>\n",
" <td>0.0</td>\n",
" <td>8</td>\n",
" <td>32</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.22</td>\n",
" <td>0.80</td>\n",
" <td>0.0</td>\n",
" <td>5</td>\n",
" <td>27</td>\n",
" <td>32</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.24</td>\n",
" <td>0.75</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.24</td>\n",
" <td>0.75</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 59 columns</p>\n",
"</div>"
],
"text/plain": [
" yr holiday temp hum windspeed casual registered cnt season_1 \\\n",
"0 0 0 0.24 0.81 0.0 3 13 16 1 \n",
"1 0 0 0.22 0.80 0.0 8 32 40 1 \n",
"2 0 0 0.22 0.80 0.0 5 27 32 1 \n",
"3 0 0 0.24 0.75 0.0 3 10 13 1 \n",
"4 0 0 0.24 0.75 0.0 0 1 1 1 \n",
"\n",
" season_2 ... hr_21 hr_22 hr_23 weekday_0 weekday_1 weekday_2 \\\n",
"0 0 ... 0 0 0 0 0 0 \n",
"1 0 ... 0 0 0 0 0 0 \n",
"2 0 ... 0 0 0 0 0 0 \n",
"3 0 ... 0 0 0 0 0 0 \n",
"4 0 ... 0 0 0 0 0 0 \n",
"\n",
" weekday_3 weekday_4 weekday_5 weekday_6 \n",
"0 0 0 0 1 \n",
"1 0 0 0 1 \n",
"2 0 0 0 1 \n",
"3 0 0 0 1 \n",
"4 0 0 0 1 \n",
"\n",
"[5 rows x 59 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n",
"for each in dummy_fields:\n",
" dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n",
" rides = pd.concat([rides, dummies], axis=1)\n",
"\n",
"fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \n",
" 'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n",
"data = rides.drop(fields_to_drop, axis=1)\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scaling target variables\n",
"To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.\n",
"\n",
"The scaling factors are saved so we can go backwards when we use the network for predictions."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n",
"# Store scalings in a dictionary so we can convert back later\n",
"scaled_features = {}\n",
"for each in quant_features:\n",
" mean, std = data[each].mean(), data[each].std()\n",
" scaled_features[each] = [mean, std]\n",
" data.loc[:, each] = (data[each] - mean)/std"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Splitting the data into training, testing, and validation sets\n",
"\n",
"We'll save the last 21 days of the data to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Save the last 21 days \n",
"test_data = data[-21*24:]\n",
"data = data[:-21*24]\n",
"\n",
"# Separate the data into features and targets\n",
"target_fields = ['cnt', 'casual', 'registered']\n",
"features, targets = data.drop(target_fields, axis=1), data[target_fields]\n",
"test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Hold out the last 60 days of the remaining data as a validation set\n",
"train_features, train_targets = features[:-60*24], targets[:-60*24]\n",
"val_features, val_targets = features[-60*24:], targets[-60*24:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Time to build the network\n",
"\n",
"Below we'll build the network. We've built out the structure and the backwards pass. The forward pass through the network is to be implemented. We'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.\n",
"\n",
"The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called *forward propagation*.\n",
"\n",
"We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called *backpropagation*.\n",
"\n",
"We will need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. This function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"_VERBOSE = False\n",
"\n",
"\n",
"class NeuralNetwork(object):\n",
" def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n",
" # Set 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",
" # Initialize weights\n",
" self.weights_input_to_hidden = np.random.normal(0.0, self.hidden_nodes ** -0.5,\n",
" (self.hidden_nodes, self.input_nodes))\n",
"\n",
" self.weights_hidden_to_output = np.random.normal(0.0, self.output_nodes ** -0.5,\n",
" (self.output_nodes, self.hidden_nodes))\n",
" self.lr = learning_rate\n",
"\n",
" #### Set this to your implemented sigmoid function ####\n",
" # Activation function is the sigmoid function\n",
" self.activation_function = (lambda x: 1 / (1 + np.exp(-x)))\n",
"\n",
" # All shapes\n",
" if _VERBOSE:\n",
" print(\n",
" 'Inputs: {0}, Hidden: {1}, Output: {2}'.format(self.input_nodes, self.hidden_nodes, self.output_nodes))\n",
" print('Weights - Input-to-Hidden: {0}, Hidden-to-Output: {1}'.format(self.weights_input_to_hidden.shape,\n",
" self.weights_hidden_to_output.shape))\n",
"\n",
" def train(self, inputs_list, targets_list):\n",
"\n",
" # Convert inputs list to 2d array\n",
" inputs = np.array(inputs_list, ndmin=2).T\n",
" targets = np.array(targets_list, ndmin=2).T\n",
"\n",
" if _VERBOSE:\n",
" print('Input-list: {0}, Target-list: {1}'.format(inputs_list.shape, targets_list.shape))\n",
" print('Transposed - Input-list: {0}, Target-list: {1}'.format(inputs.shape, targets.shape))\n",
" print('Targets:', targets_list, targets)\n",
"\n",
" #### Implement the forward pass here ####\n",
" ### Forward pass ###\n",
" # Hidden layer (Input to Hidden)\n",
" hidden_inputs = np.dot(self.weights_input_to_hidden, inputs) # (2, 56) x (56, 1) -> (2, 1)\n",
" hidden_outputs = self.activation_function(hidden_inputs) # (2, 1) -> (2, 1)\n",
"\n",
" # Output layer (Hidden to Output)\n",
" final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs) # (1, 2) -> (2, 1) -> (1, 1)\n",
" final_outputs = final_inputs # signals from final output layer, eg. f(x)=x. (1, 1)\n",
"\n",
" if _VERBOSE:\n",
" print('Final inputs:', final_inputs.shape, 'Final outputs:', final_outputs.shape)\n",
"\n",
" #### Implement the backward pass here ####\n",
" ### Backward pass ###\n",
"\n",
" # Output error\n",
" output_errors = targets - final_outputs # Output layer error is the difference between desired target and actual output.\n",
" # (1, 1) - (1, 1) -> (1, 1)\n",
" if _VERBOSE:\n",
" print('Shapes - Targets:', targets.shape, 'Final outputs:', final_outputs.shape, 'Output errors:',\n",
" output_errors.shape)\n",
" print('Values - Targets:', targets, 'Final outputs:', final_outputs, 'Output errors:', output_errors)\n",
"\n",
" # Backpropagated error\n",
" hidden_errors = np.dot(self.weights_hidden_to_output.T, output_errors) # errors propagated to the hidden layer\n",
" # (1, 2) x (2, 1) -> (1, 1)\n",
" hidden_grad = hidden_outputs * (1 - hidden_outputs) # hidden layer gradients. (2, 1) -> (2, 1)\n",
"\n",
" if _VERBOSE:\n",
" print('Shapes - Output errors:', output_errors.shape, 'Weights/Hidden to Output:',\n",
" self.weights_hidden_to_output.shape, 'Hidden errors:', hidden_errors.shape)\n",
" print('Shapes - Hidden outputs:', hidden_outputs.shape, 'Hidden grad:', hidden_grad.shape)\n",
"\n",
" # Update the weights\n",
" self.weights_hidden_to_output += np.dot(output_errors,\n",
" hidden_outputs.T) * self.lr # update hidden-to-output weights with gradient descent step. (1, 2) x (2, 1) [transposed of (1, 2)] -> (1, 1)\n",
"\n",
" if _VERBOSE:\n",
" print('Shapes - Output errors:', output_errors.shape, 'Hidden errors:', hidden_outputs.T.shape,\n",
" 'Weights/Hidden to Output:', self.weights_hidden_to_output.shape)\n",
" print('Shapes - Hidden errors:', hidden_errors.shape, 'Hidden grad:', hidden_grad.shape, 'Input (trans):',\n",
" inputs.T.shape, 'Weights/Input to Hidden:', self.weights_input_to_hidden.shape)\n",
"\n",
" self.weights_input_to_hidden += np.dot(hidden_errors * hidden_grad,\n",
" inputs.T) * self.lr # update input-to-hidden weights with gradient descent step\n",
"\n",
" def run(self, inputs_list):\n",
" # Run a forward pass through the network\n",
" inputs = np.array(inputs_list, ndmin=2).T\n",
"\n",
" #### Implement the forward pass here ####\n",
" # Hidden layer\n",
" hidden_inputs = np.dot(self.weights_input_to_hidden, inputs) # signals into hidden layer\n",
" hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n",
"\n",
" # Output layer\n",
" final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs) # signals into final output layer\n",
" final_outputs = final_inputs # signals from final output layer\n",
"\n",
" return final_outputs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def MSE(y, Y):\n",
" return np.mean((y-Y)**2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training the network\n",
"\n",
"Here we'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.\n",
"\n",
"We'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.\n",
"\n",
"### Choose the number of epochs\n",
"This is the number of times the dataset will pass through the network, each time updating the weights. As the number of epochs increases, the network becomes better and better at predicting the targets in the training set. We'll need to choose enough epochs to train the network well but not too many or you'll be overfitting.\n",
"\n",
"### Choose the learning rate\n",
"This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.\n",
"\n",
"### Choose the number of hidden nodes\n",
"The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. We can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Progress: 16.2% ... Training loss: 0.200 ... Validation loss: 0.357"
]
}
],
"source": [
"import sys\n",
"\n",
"### Set the hyperparameters here ###\n",
"epochs = 3000\n",
"learning_rate = 0.01\n",
"hidden_nodes = 15\n",
"output_nodes = 1\n",
"\n",
"N_i = train_features.shape[1]\n",
"network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\n",
"\n",
"losses = {'train':[], 'validation':[]}\n",
"for e in range(epochs):\n",
" # Go through a random batch of 128 records from the training data set\n",
" batch = np.random.choice(train_features.index, size=128)\n",
" for record, target in zip(train_features.ix[batch].values, \n",
" train_targets.ix[batch]['cnt']):\n",
" network.train(record, target)\n",
" \n",
" # Printing out the training progress\n",
" train_loss = MSE(network.run(train_features), train_targets['cnt'].values)\n",
" val_loss = MSE(network.run(val_features), val_targets['cnt'].values)\n",
" sys.stdout.write(\"\\rProgress: \" + str(100 * e/float(epochs))[:4] \\\n",
" + \"% ... Training loss: \" + str(train_loss)[:5] \\\n",
" + \" ... Validation loss: \" + str(val_loss)[:5])\n",
" \n",
" losses['train'].append(train_loss)\n",
" losses['validation'].append(val_loss)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.plot(losses['train'], label='Training loss')\n",
"plt.plot(losses['validation'], label='Validation loss')\n",
"plt.legend()\n",
"plt.ylim(ymax=0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check out the predictions\n",
"\n",
"Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fig, ax = plt.subplots(figsize=(8,4))\n",
"\n",
"mean, std = scaled_features['cnt']\n",
"predictions = network.run(test_features)*std + mean\n",
"ax.plot(predictions[0], label='Prediction')\n",
"ax.plot((test_targets['cnt']*std + mean).values, label='Data')\n",
"ax.set_xlim(right=len(predictions))\n",
"ax.legend()\n",
"\n",
"dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])\n",
"dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
"ax.set_xticks(np.arange(len(dates))[12::24])\n",
"_ = ax.set_xticklabels(dates[12::24], rotation=45)\n",
"\n",
"accuracy = np.sum((predictions[0] > 0.5)) / len(predictions[0])\n",
"print('Accuracy:', accuracy)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Unit tests\n",
"\n",
"Run these unit tests to check the correctness of your network implementation. These tests must all be successful to pass the project."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import unittest\n",
"\n",
"inputs = [0.5, -0.2, 0.1]\n",
"targets = [0.4]\n",
"test_w_i_h = np.array([[0.1, 0.4, -0.3], \n",
" [-0.2, 0.5, 0.2]])\n",
"test_w_h_o = np.array([[0.3, -0.1]])\n",
"\n",
"class TestMethods(unittest.TestCase):\n",
" \n",
" ##########\n",
" # Unit tests for data loading\n",
" ##########\n",
" \n",
" def test_data_path(self):\n",
" # Test that file path to dataset has been unaltered\n",
" self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')\n",
" \n",
" def test_data_loaded(self):\n",
" # Test that data frame loaded\n",
" self.assertTrue(isinstance(rides, pd.DataFrame))\n",
" \n",
" ##########\n",
" # Unit tests for network functionality\n",
" ##########\n",
"\n",
" def test_activation(self):\n",
" network = NeuralNetwork(3, 2, 1, 0.5)\n",
" # Test that the activation function is a sigmoid\n",
" self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))\n",
"\n",
" def test_train(self):\n",
" # Test that weights are updated correctly on training\n",
" network = NeuralNetwork(3, 2, 1, 0.5)\n",
" network.weights_input_to_hidden = test_w_i_h.copy()\n",
" network.weights_hidden_to_output = test_w_h_o.copy()\n",
" \n",
" network.train(inputs, targets)\n",
" self.assertTrue(np.allclose(network.weights_hidden_to_output, \n",
" np.array([[ 0.37275328, -0.03172939]])))\n",
" self.assertTrue(np.allclose(network.weights_input_to_hidden,\n",
" np.array([[ 0.10562014, 0.39775194, -0.29887597],\n",
" [-0.20185996, 0.50074398, 0.19962801]])))\n",
"\n",
" def test_run(self):\n",
" # Test correctness of run method\n",
" network = NeuralNetwork(3, 2, 1, 0.5)\n",
" network.weights_input_to_hidden = test_w_i_h.copy()\n",
" network.weights_hidden_to_output = test_w_h_o.copy()\n",
"\n",
" self.assertTrue(np.allclose(network.run(inputs), 0.09998924))\n",
"\n",
"suite = unittest.TestLoader().loadTestsFromModule(TestMethods())\n",
"unittest.TextTestRunner().run(suite)"
]
}
],
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
aleph314/K2 | Foundations/Python CS/Activity 08.ipynb | 1 | 362322 | {
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from ipywidgets import interact\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 08.1 (function plotting)\n",
"\n",
"Consider the function \n",
"\n",
"$$\n",
"f(x) = e^{x/10} \\sin(\\omega_{1}x)\\cos(\\omega_{0}x)\n",
"$$\n",
"\n",
"from $x = -4\\pi$ to $x = 4\\pi$. \n",
"\n",
"1. Plot the function when $\\omega_{0} = \\omega_{1} = 1$. Label the axes.\n",
"1. Create an interactive plot with sliders for $\\omega_{0}$ and $\\omega_{1}$, varying from 0 to 2."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot for $\\omega_0 = \\omega_1 = 1$:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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97SEYVF492uY4rWWzckYuXf2D7KvvdNmyEznU2MXsgsjSWgB+XwJlOamedm7V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\niZmZJfk/KaRpozW6SBsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x28506924320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def function_plot(ω0=1, ω1=1):\n",
" # Define x axis range\n",
" x = np.linspace(-4*np.pi, 4*np.pi, 100)\n",
" # Add labels to x and y axis\n",
" plt.xlabel('$x$')\n",
" plt.ylabel('$\\exp(x/10) \\cdot \\sin(\\omega_{1}x) \\cdot \\cos(\\omega_{0}x)$')\n",
" # Limit x axis between start and end point of the range\n",
" plt.xlim(x[0], x[-1])\n",
" # Add a title\n",
" plt.title('Plot of $f$ for $ω_0 = {}$ and $ω_1 = {}$'.format(ω0, ω1))\n",
" # Plot the function\n",
" plt.plot(x, np.exp(x/10) * np.sin(ω1*x) * np.cos(ω0*x))\n",
"\n",
"plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot with sliders for $\\omega_0$ and $\\omega_1$ from 0 to 2 with steps of 0.25:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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oXidfh6cncvGc4Ty96hAVdc7u122EV7A5JD5jrH1MgimVUlrjJn9YvN9b/QYq\nPdFbJiUS/AkkZwMdwNMiUioiW0VkH7ALuBz4o6o+FsI2GkbQ/t+H+0iIieKyE0f0edxNnx9Hh0d5\n0PRKBrVgc0h8OqsABzFPUlrTHLLSKF1lJMVydKAObamqW1UfUNVTgFHA6cAsVR2lqteq6rqQt9Iw\ngnCkoYWX1pdy4exChlnr7XszIiORC2cV8tTKg1SaXsmgFWwOiU9ibDT5afFB9kiaKQzh0l+f9KRY\nappa8e62EV52lv/GAZfg3UzqVhG5XURuD1nLDMMhT608SGu7h6+fMtqv42/8/Dha2j28sK7HXZyN\nQaC4Orgckq7GZCexJ8AeSVuHh4o6d0hXbPlkJMbS1qE0tDizq6Mddt7ll4HzgHagscvDMAas1nYP\nT3x8gNPGZzMuJ8Wvc07ISmJqYSr/3loR4tYZoRJsDklX3iXAjQF906+oc+NRKAzxii34tHBjJOZJ\n7ASS4ap6qar+n6r+zvdwsjEicraI7BCR3SJyWw+vLxKRWhFZbz1Mj8jo0+uby6isb+EbfvZGfM6c\nlMfag9VU1pvhrcGouLqZERnBTbT7jM1Oor6lnaoG+8Ubw5GM6JOeaNXbisA8iZ1AskJEpoWqISIS\nBdwPLAEmA5eLyOQeDl2mqjOtx89D1R5jaHhy5UHGZCVxWlG2rfO+MCUXVXh7W2WIWmaEilM5JD6+\n4o17Ku0PwHyaQxKeORKA6ggsAbYTSBYCa6wew0Yrw32jg22ZB+xW1b2q2gr8He9QmmEEpLSmmVX7\nj3LBrELbSy8n5qUwIiPBDG8NQp/mkDjTIynK9QaS3ZX1ts8N9YZWXflKyUcil8RO0cYlIWuFVyFw\nqMvvxcBJPRy3wApgJcB/quqWni4mItcB1wGMHDmyp0OMIe7VjaWowjkzCmyfKyKcOSmPv608QGNL\ne0Q2CzIC82kOiTMf3nmp8aTER7Ojwn4gCfWGVl119kgG8tCWqh7o6RHKxvVgLTDSKtVyL/BSbweq\n6kOqOldV52Zn2xvWMIaGVzaUMmN4GqOzAtsH4gtTcmlt9/DBziqHW2aEklM5JD4iwoTcFHZW2F+5\nVVrTTGEYhrUAUuOjiXLJwAwkIrLc+m+9iNRZj3rf7w62pQTomi023Hquk6rWqWqD9fNSIEZEQlfk\n3xi09lY1sLmkLqDeiM/cUekMS4zhTTO8Naj4ckjyUp1bKVWUm8LOinrbK7dCvTNiVyK+wo0DcNWW\nqi60/pvSTuyuAAAgAElEQVSiqqnWI8X3u4NtWQUUicgJIhILXAa80vUAEcnz1fUSkXlW+4842AZj\niHhlQykigQ1r+URHuTh9Yi7vbK+krcMUuh4sSmuayU2JcySHxGdCbjI1TW1U1fu/csu3oVU4Jtp9\n0hNjIjLZ7vfAr4hcAryhqvUi8lNgNnCHU5ntqtpu7QH/LyAK+IuqbrF2ZPRt9XsxcIOItAPNwGUa\niTROo1+PLNvLo8v3kRQXTXpiDNkpcdz8+SImFzj53aNnqsorG0o56YQMcoP8Vnrm5FyeX1vMqn1H\ng9olzwif0tpm8h3+8B6f681B2lnRQI6ff6d8G1qFa2gLvPMkA33578+sILIQOAN4FHiwn3NsUdWl\nqjpeVceq6l3Wcw/69otX1ftUdYqqzlDVk1V1hZP3N5zx6PJ93PnaNkakJ1KUk0yUS/hozxGuenRl\n0Hs7+GNLaR17qxo5d0Zh0Nc6bXwWcdEuM7w1iJTXuoMujdLd+DxvILEz4R6OfUi6y0iMHfDLf30F\n+b8EPKSqrwF9Fy4yjjtPfHyAO17dypKpeTx17Un86atz+Pt183n+hgUAXPXoJ5TVhna/j1c2lBLt\nEpZMzQv6Womx0ZwyLov3dph8ksFAVSkLQSDJSo4jIymWXTYCSTiTEX28e5IMwDmSLkpE5M/ApcBS\nq/aWc4OQxqD33OpD/OylzZwxKYe7L5v1mTHqMdnJPPb1edQ2t3H1o5+E7FuTx6O8uqGU08Zndy6H\nDNapRVnsP9LEoaPh3YZ3Z0U9tzy9jvWHasJ638HsaGMrLe0e8kOQtzE+N9lejyRMG1p1lZEUQ3UE\nCjfaCQRfwTt/cZaq1gAZwA9C0ipj0Kmsc/PTlzZzyrhM7r9yNrHRx/7VmjY8jYevnsuBo03c+OTa\nkPxlX7nvKKW1bs6bGfgke3cLrbmR5bsPO3bN/tQ0tfKtx1fzzw2lXPDAh/z8n1tpjEAxvsGmrNZK\nAAzBh/f43BR2VTT4/fe2pKaZ2OjQb2jVVXpiLB0epc4d3r8rdvJImlT1BVXdZf1epqpvhq5pxmDy\nwHt7aPcov7hgGnHRvSdfzR+byc++PJmP9h5h6aZyx9vxwtpikmKj+MLk4Ie1fMblJJObGhe2QNLe\n4eGWp9dRXuvmiW/O46snjeIvH+7jC3/4gE3FtWFpw2DlCyR5IemRpNDQ0k5prX/110pr3BSkhX5D\nq67SEyNTJsUMTRlBK61p5qmVB7lkznBGZfaf/HfFvJFMzEvhF0u3BbUXdnfNrR28vrmcL07LdzST\nWERYOC6bFbsP4/GEfsjg129sZ9muw9x5wVROLcrmjvOn8tz182lp9/CrN7aF/P6DWblvOMnhORLo\nsnKr3L/hrd2VDYz049+DkzKs4dxwr9yyHUhEZKTZo93o6r53d6MoNy8e59fxUS7hv8+ZQklNMw9/\nsNexdry5tZyGlnYunD3csWv6LCzKpLqpjS2lTubgHuvl9SU8vGwf18wfxVfmfpqfe+LoDK48aSQr\n9hzpXA1kHKu01k20S8hKdn44abxVc2unH/Mk7rYOdlXUM60w9Mvdu4pU4UZbgUREEoCVQE5ommMM\nNoeONvHsqkNcduJIW0Xy5o/NZMnUPB54bw/lfg4V9OeFtSUUDkvgpBMyHLleV6eEYZ6kw6P83xs7\nmDE8jZ9++djC1xfNHo4qvGg23OpVWU0zuamhGU4alhhLTkqcXxPu28vrafco0wrTHG9HX3yFG8O9\ncstWIFHVZlXNV1WzqN4A4J63d+FyCTd93r/eSFc//uIkOlT59Rvbg25HZb2bZbuqOH9WQUg+RHJS\n4pmQm8Ly3aGru/XBzipKapq59rQxxPSQlT0yM5F5ozN4fm1xRLZTHQzKat0hXSU1Ic874d6fTSXe\nuaypYQ4k6UnePUkGdI/EMLo6cKSRF9aV8NWTRpEXwJj0iIxErjt1DC+uKwl6iesr60vxKFwwy/lh\nLZ+FRVms2l/t6LxOV0+uPEhWcmyfCwUunF3I3qpGsyS4F94cktDlbYzPTWFXZX2/c2Wbi2tJT4wJ\na1Y7QHJcNNEuGfhzJIbh8+TKgwhw/efGBHyN6xeNJSs5ll8u3RbUt+zn15YwY8QwxuUkB3yN/iws\nyqK13cOq/Ucdv3ZZbTPvbK/g4jkjelw67fPF6fnERbt4fm2x420Y7DweDUlWe1fjc5Nxt3k4VN13\nTtGmklqmFqYR7ulkEfEmJQ70HomIJFm7GRrHMXdbB8+tPsQXpuT6XXuoJ8lx0dx6ehEr9x3l3QCz\nx7eV1bGtrI6LZgdfEqUvJ52QQUyUsHyX8/Mkz6w6hEfh8nkj+jwuNT6Gs6bk8c8NZbS0h6ZnNFgd\nbWqltcMT4kBilUrpY+WWu62DnRX1YZ8f8clIjA375lb+lJF3icgVIvKaiFQC24EyEdkqIr8REfuD\n48ag968t5VQ3tXHFvFFBX+vyeSMZnZnIr1/fQUcAy2sf+3A/sdEuvjzduSTEniTGRjN7ZDrLHA4k\n7R0enll1iFOLsvxaPn3RnOHUNrfxjtkG+DPKrN0InS7Y2FWRFUh2VfY+T7IjQhPtPulWdns4+dMj\neRcYC/wXkKeqI1Q1B+/Wux8DvxaRr4awjcYA9OTHBxmVmciCsZlBXysmysUPzprIjop620M2B440\n8o+1xVwxb2TnGvpQOrUoi61ldRxu8L+ceH/e21FFWa2bK0/ybyfPheOyyEmJM8Nb3fhKkoSyR5Ic\nF82IjAQ29DFHtTFCE+0+GUkDsEcCnKGqd6jqRlXt3JRBVY+q6vOqehHwTOiaGFoej1JV38L28joz\nVOCnXRX1fLL/KFfMG+nYCqkvTstjxvA0/vDvnbYms+95ezfRLuHGRWMdaUd/Thvv3W3z/R3Ord56\n+pODZKfEcfqkXL+Oj3IJ58wo4P2dVSGb+B+MfMvIQznZDnBaUTbLdx/u9b3fXFzLsMQYx3ZotCs9\nMZaagbb8V1X7bZE/xwwkDS3t/O7NHSz45dsU/fR1TrzrLc7+4zJO+793eWTZXppaTU2jvjy58iCx\nUS4unuPcCikR4bYlkyirdfPg+3v8OmdvVQMvrivmqpNHBTVPY8fUgjSykuN4x6FqwOW1bt7dUcml\nc0f0uOS3NyedkEFbh7K5xJRM8SmtbSY2ykVmiHumZ07Opam1g4/29Lyn3qaSWqZFYKLdJz0xluqm\n1rBUYfDxa2MrEZkInAf4ZjNLgFdUdVDVa2jv8PDs6mJ+/++dHG5o4YxJOVyQl0JuajxJsdE8t+YQ\nd762jfvf3c13zxjPNQtGR7rJA05zawcvrC3m7Kl5ZDqcPTx/bCbnzCjg3nd2c2pRFnNG9Z1YeM/b\nu4iLjuLbnwtPbwTA5RI+PyGbN7aU09bhsfXh35N/bvAuW77IZlCePSodgDUHqpk72vkEzECoKqW1\n7rAvefUpr3WTmxYX8tpW88dmkhQbxZtbK/j8xM/mZvsm2q+dEPhKxmClJ8XiUahztzEsMTw7ffgz\n2f4j4O+AAJ9YDwGeFpHbnGyMiJwtIjtEZHdP1xave6zXN4rIbH+vXVLTzEV/WsGPX9zEqMxEXrxx\nAY9ccyI/OGsiV88fzUVzhlv7ZsxnamEa//3KFu5/d7eTf7wh4dWNpdS527nCz/F8u+66YCoFw+K5\n9en11PbRPd9dWc/LG0q5esEoslPCV10VYPHEHOrd7aw5UB30tV5aX8KM4WmckGWvJlNWchyjMxMd\naYNTHlm2j1N+9Q6/e3NHRBImy2pCm0PiExcdxecmZPP2topjvvVHeqIdvKXkgbDOk/jzdeqbwImq\n+itV/Zv1+BUwz3rNEdaS4vuBJcBk4HIR6V4nYglQZD2uA/7kz7UbWto5597l7K1q5J7LZ/GP6+cz\na2R6j8fOGZXBY1+fx/kzC/jNv3bwyDLnakE5KZzd1q6e+uQgY7OTQlKGBLzLW++9fDYVdW5+9PzG\nHj+QWto7uOPVbSTERPHt08LXG/FZWJRFTJTw7vbghrd2V9azpbSOc2cGtmx59qh01h6sHhBZ7lX1\nLdz99i4ykmK5953dfPeZ9WGfcyytbQ5JscaenDk5l8r6ls6JdR9fRnskA4mvAvCRARZIPEBP6yrz\nrdecMg/Yrap7VbUVby/ovG7HnAf8Vb0+BoaJSH5/F953uJHMpFheuvkUzp1R0O/YZZRL+O0lM/ji\ntDzufG0bT3y0P6A/kJO2ltbxg+c28JUHP+KkX7zFuJ8s5Ya/rQn5boPd27DuYA1XnDQqpOO/M0cM\n44dnT+CNLeX85cP9n/mgrKpv4YqHV/L+zip+eNaEsKzU6i4lPoYTR2fwTpCB5OX1pbgEzpne71/h\nHs0Zlc7hhlYOhnnDrZ783lok8dz18/nBWRN4eX0pVz3yCTVhWobq8SgVde6QlI/vyecn5BDlEv69\n9bNbIWwuiexEO9DZQz9cH/jKQruFQf0JJN8F3haR10XkIevxBvC29ZpTCoFDXX4v5tM5GTvHACAi\n14nIahFZHe/y8OJNpzA22/+s5+goF3+8dBZnTMrhZy9v4Z3tkSsv9vqmMi760wr+taUcBE4tyuar\nJ4/ine2VnPG793lk2V7aO5yM6T176pMDxEa7Qp74B/CthWNYNCGbO17dyhm/f5+HP9jLit2HOfe+\n5WwpreX+K2bztVNOCHk7erN4Yg67KhsC3jVRVXl5fSkLxmYFvFBgTpd5kkjaXl7HM6sOctX8UYzN\nTuamz4/jnstnse5QNX98a1dY2nC4sYW2Dg3bboTDEmM5cXQ6b2397JeJSE+0g7cuHEBlEIFk2S57\nqxL9WbX1BjAe+F+8OyT+C/gfYIKqvm6/ieGhqg+p6lxVnVuUn05ynF/rCj4jNtrFfVfMZmJeCj/8\nx0ZHcwf8oarc/dYubnhyLZPyU3j7+4t49tvz+e0lM/j5eVN563uf46Qxmdz52jaueHhlSJeCNra0\n89K6Ur48PT8sE3gul/DgV+fwfxdNJy0hhruWbuOKR1YiwD+uX8CXAvwW7xTfJGug2fjrDtVw8GhT\nUDs5FuWkkBIXHdFAoqrc8epWUuJj+M7pRZ3PnzujgM+Nz+HfWyvCMvTWmYwYph4JwJmT89hRUc/B\nI94vE+W1bnaU10csf8QnIymWKJdQFUQgKbNZkduvJSeq6lHVj628keetnztE5OsBtbJnJUDX+hDD\nrefsHuOo+Jgo/njZTOrc7dzWy5h9KKgqP/jHRv7w1k4unFXIU9eefMyk8oiMRB69Zi6/vWQGn+w/\nyo9f2BSy9r2yoZSGlna/k+acEB8TxVdOHMELN57Cv757Gv977hRevnlhxP+hAozJSmJUZmLAw1sv\nryshNtrF2VMD38kxyiXMHDksooHk7W2VfLj7CP9xRtExXzDOmJRDSU2zrX3OA1XWmUMSvv3Rz7Ty\nft7cWs6/tpRz9t0fEBPl4uwpzu3OGYgol5CZFEtlfeDbM1TUhSCQ9OF/gzy/q1VAkYicICKxwGXA\nK92OeQW42lq9dTJQq6plDrahRxPzUvnR2RN5a1slT39yqP8THPCXD/fzjzXF3LJ4HL/7ygziY3ou\nbyYiXDxnON87czwvrCvhzw5uFNXVUysPMiE3hdm9LFIItQl5KVyzYHTYV2j1RkT4/IQcPtpzhOZW\nez3B9g4Pr24s44xJOaTExwTVjjmj0tlZUU+9OzKpXA++v4cTspK48uRjS+Ustnptb20N/bBwWRiy\n2rsbmZnIhNwU7n57F99+Yg0j0hN59daFzBgxLGxt6E1OalxQQ1uO90isZbY9PTYB/qXi+kFV24Gb\n8Q6dbQOeVdUtInK9iFxvHbYU2AvsBh4GbnTq/v35+oLRLByXxR2vbmVvVf/7EQRj9f6j/HLpNr4w\nOZfvnTner/HWWxaP48vT8/n1G9sd/4e7qbiWTSW1XHnyyIiO/Q40iyfm0NLuYcUee7W3lu8+zJHG\nVs4LcLVWV3NGpeNR2HAo/ImJDS3trDtUwxen5fWYT5OTGs+MEcN4Kww1wcpq3cRGu8K++OJL0/Np\naGnnhkVjef6GBbbmYUMpJyU+qKEtu5vN+dMjyQWuBs7p4dFzameAVHWpqo5X1bGqepf13IOq+qD1\ns6rqTdbr01R1tZP374vLJfzuKzOIi3Hx/ec2BFRc0B9V9S3c9NRahqcn8NuvzPD7g1tE+M3FM5ha\nkMZ3/r6OfYcbHWvTU58cICEmivNnhX6SfTA5aUwGKXHRvLKh1NZ5f/v4IOmJMSyakB10G2aOGIZI\nZCbcV+07SodHWTA2q9djzpiYw4bimqCGWfxRZpWPD/cXnRsXjeXDHy3mR2dP7LP8f7hlJwfXIykP\nwdDWq0Cyqh7o9tgPvGe/iYNXbmo8/3POFNYdrOGxFfsdv357h4dbn15HbXMbf/rqHFJtDnskxEbx\n0NVziHIJ3392vSPBrrqxlZfXl3LOjHzb7Rnq4qKjuHjucJZuKqPSz394e6saeHt7BVedPIq46OB3\nY0iJj2FCbgprDoY/kKzYc5jYaFfn6rGenD4pF1WCzrnpT1lNc1iHtXyio1wURCiTvy85qXEcaWgJ\n6DPA3dZhu1aXP4HkEeDDnl5Q1Sts3W0IOG9mAadPzOE3/9rOgSPOfesHeOC9PXy09wh3nj+NSfmp\nAV0jPy2Bn583lbUHa3jYgWTKh5ftpbmtg2tPjVzJh4HsmvmjafcoT6486Nfxf/lwHzEuF1fNH+1Y\nG2aPSmfdgeqwJ6mu2HOEOSPTe52/A5iUn0LhsISQD2+V1bopCOOKrYEuJyUOj8KRRvu9ErvDWuBf\nILkaWCMifxeRr4lIZJckRJiIcNcF04hxubjt+U2O/eNde7Cau9/exfkzC4IuhnjezALOmpLL79/c\nyc4gVswcbWzl8RX7+fL0gs59GIzPGp2VxKLx2Ty58iCt7X3n8lQ3tvKPNcWcP6vA0UUDc0amU9/S\n3uceGU6rbmxla1ldv9sIiAinT8ph2a7QVSpu7/BQXucmP0w5JIOB7+9XZZ39QGJ3oh38yyO5QVVn\n480dSQceE5GPROQXInLa8bhbYl5aPD/50iQ+2nuEp1f59020Lw0t7fzHM+vJS43n5+dPDfp6vmCX\nHB/N955dT1uAyYoPL9tLU1sHty42e5f15WunnMDhhhaWbup7AeGTKw/gbvPwLYd7d7NGelcJrT8U\nvuGtj/ceQRUWjOt/P5rTJ+XibrO/KMFf5XVuOjzKiPTEkFx/MMq2khKrAsh9K6+zXy3D79khVd2u\nqn9Q1bOBxcBy4BJgpe27DgGXnjiCU8Zl8ovXtgW9iut/X9nCoaNN/OHSmY7NQ2Qlx3HX+VPZXFLH\nvW/bzy4+0tDC4yv2c47pjfTr1HFZjMlK6nPerKW9g8c/OsDnxmd3btfqlNGZSSTGRrGtLPT5Gj4r\n9hwhKTaK6cP7X+p68pgMkmKjQja8VVLt/eAbbgJJpxyrR1IVQI+kvNb+OX4HEhG5W6wlEarabK2w\nukVV59q+6xDgWyUVG+3ixifX2s4l8Hl1YynPrSnmxkXjmOdwIcQl0/K5aPZw7n13Nx/stFfy4CFr\nbuTWLtnKRs9cLuGaBaNZf6iGdb1Mer+8vpSq+ha+darzZV1cLmFSfipbS+scv3ZvVuw5zLwTMvwq\nox8XHcWpRdm8F6IJ92IrkBRGsL7VQOMb2gqoR1LbTEq8vUogdtar1QOviEgSgIicJSI9TsIfLwqG\nJfCHS2eyo6Ke21/ebPv8DYdq+M/nNjBr5DC+c0ZoPrDvOH8KRTnJfPeZ9X4XeDzS0MJfVxzg3BkF\njMsZGOviB7qL5gwnOS6ah5ftPaa6wKGjTdz91i4m5qWwcFzvS2WDMTk/la1ldWGZcK+oc7OnqrHP\nZb/dzTshg9Jat9+r2+zwBZJw1dkaDOJjokiNjw7o/S6vc9teAWdnaOunwNPAe1YA+R7g6H4kg9Gi\nCTncsriI59YU8+wq/7PeDx1t4puPryY7JY6Hr54b9AZJvUmMjeaBK+fQ0tbBzU+t63e+pK3Dw3ef\nWU9rh4dbFpveiL+S46K5ZsEolm4q58Yn11JnZZpvL6/joj+toN7dxi8vnBayPIcpBak0tLRzqDr0\nlYB9cx3z+5lo72r6cG9Zm43FzidOFlc3kZsa58hy6qEkOyWwXJLyWvtVlO0MbZ0OXAs0AlnAraq6\nzNbdhqjvnF7EwnFZ/Ozlzazc23+OZm1zG994bBWt7R38v6+dSJbDOw12Ny4nmV9eNJ01B6r5xdJt\nvdbjUlV+8uImlu06zC8umGp6Izb95xcm8OMvTuTNrRWce+9ynl11iK88+BEi8Nz1C3rdA8cJkwu8\ny8XDMby1YvcR0hJimGxjifrkglRcwjH7dzihuLrZzI/0INDs9rJaN3mp9j6T7HwN/gnwM1VdBFwM\nPCMii23dbYiKcgl3XzaTwmEJXPnISv760f5eP6xLapq59vHV7D/SyINXzWFcTngmss+dUcA180fx\n/z7cz3VPrOlxn4h73t7Ns6uLuXXxOC49MXzFGYcKEeG608byzHUn427z8MPnN5KVHMfzNyxgQl5o\n/z+Pz00hyiVsLQttIFFVVuw5wvwxmba2tE2MjWZ8bgobi2scb1NxTVNE9/8YqAKpt9XW4aGqocV2\nj8TvGRVVXdzl500isgR4Hlhg645DVGZyHC/edArfe2Y9t7+8hY3Ftdx5/tTOZK3Wdg+PLt/HPW/v\nQlF+e8kMW2PMTvjvc6YwIiORX7+xnSV3L+MPl3qD3+6qBlbtO8oD7+3hwtmF/MeZ48ParqFm7ugM\nXrt1Ic+uLuYrc4c7vrd9T+JjohibnRTyHklJTTMlNc18+3P2lzBPK0zjne2VqKpjQ3wdHqWsxk3h\ndBNIuvOWSXHber+r6ltQtV/8st9AIiKiPXy9VtUya7ir12OON2kJMTx89VzufnsXd7+9i39uKCUv\nLZ7clHiqGlrYd7iRMyfncvuXJzMiI/xdcZdL+NapYzjphExueXotlz308WdeXzwxh19dON0UZnRA\nZnIcNywK7zbAk/NTWbnvaEjv4SsOOWuE/WG66cPTeG5NMaW1bgodKitSUeem3aNmaKsHOalxuNs8\nNLS0+11l2ldjK8/mZmv+9EjeFZHngZdVtTP7zir1Pl9ErgHeBR6zdechyuUS/uPM8cw7IYP3dlRS\nUddCRZ2bjKRYfvqlSZw+ybGCyQGbNjyNV289lb9/cpDkuGjGZCczNjuJjKRYE0QGsckFqby0vpSj\nja0hq4K7saSG2CgX4/Psz5/5ck42HqpxLJAUd+aQmB5Jd113SvQ7kFhZ7XlO90iAs4FvAE+LyBig\nGkjAO7/yJvBHVV1n667HgVPGZXFKiJZ6OiE5LtrxDGsjsibne1dGbSurC9nfvU3FtUzMTwlohdTE\n/BRiooSNJbUsmebMDpfF1io1E0iO1ZlLUt/id3l7X3kUx3skquoGHgAeEJEYvCu2mlXV+VkzwzAC\nNinfO6G/pbQ2JIHE41E2ldRy7ozAtgeOi45iYl4qmxxcAvxpDokJJN35stvtTLhX1LmJi3YxLNFe\nhQ2/J9tFJB7vRlILARWRZcCDVqAxDCPCMpPjyEuND9mE+4GjTdS72ztzQgIxbXgar24odWzCvbi6\nieyUuD4rEB+vPi3c6P9HdKD7uthZ/vtXYApwL3Cf9fMTtu5mGEZITS5IDdkSYN/S3WmFgW8lO70w\njTp3OweOOJM4WVLTbIa1epGWEENstMtWmZTy2mZybQ5rgY0eCTBVVSd3+f1dEdlq+449EJEM4Blg\nNLAf+IqqHlO0SET24y3V0gG0H691vgyjN1MKUnl/p7dku9Pf0jcV1xIX7aIoN/BEVd+E+4biGkZn\nJQXdpuLqZr8KRx6PRITs5DhbhRvL69zMCSBx1k6PZK2InOz7RUROApza6vY24G1VLQLepu/SK59X\n1ZkmiBjGsSbnp9Lh0aD2oenNxpJaJhekBlXOpyg3mbholyPzJB0epdT0SPqUnRLnd49EVamotZ+M\nCPYCyRxghYjst3oGHwEnisgmEdlo+86fdR7wuPXz48D5QV7PMI5LoSqV0uFRtpTUMr0w8PkRgJgo\nF1MKUh0plVJZ76atQ00g6UNOSpzfm1sdbWyltcNjuzwK2BvaOtv21f2Xq6q+XYHKgd6SLRR4S0Q6\ngD+r6kO9XVBErgOuAxg50pT7MI4PI9ITSY6LdnyeZN/hBhpbO5jmwDDS9OHDeHb1ITo8SpSNMivd\ndZaPNyu2epWTGseq/f4lqXYu/Q1xjyRJVQ90fQAndPm5TyLylohs7uFxXtfjrAz53rLkF6rqTGAJ\ncJOInNbb/VT1IVWdq6pzs7OzbfwxDWPw8u5NksJmh4sj+qr2BrNiy2daYRpNrR1BbwhnNrTqX3Zy\nPNVNbf1uAw2BJyOCvUDyrIj8SLwSRORe4Jf+nqyqZ6jq1B4eLwMVIpIPYP23xx1wVLXE+m8l8CIw\nz0b7DeO4MKUgjW1l9XQ4uDfJxuJaEmKi/E5s64tTJeVNMmL/cqxhqsN+zJP4yqPYrbMF9gLJScAI\nYAWwCigFTrF9x569Alxj/XwN8HL3A0QkSURSfD8DXwDs7yZlGEPc1MI0mts62Hc4uG/8XW0qqWVq\nYWpQQ1E+Y7KTSYiJYnNpsIGkmaxkk0PSl5wu2e39Ka91E+WSgLa1sBNI2oBmvOVR4oF9qtp/f8k/\nvwLOFJFdwBnW74hIgYgstY7JBZaLyAbgE+A1VX3DofsbxpAxtdA74b65xJl5kvYOD1tKa4PKH+kq\nyiVMLkhlS5Dt8+5DYnojfcm2kd1eXucmJyUuoC8LdgLJKryB5ETgVOByEXnO9h17oKpHVPV0VS2y\nhsCOWs+XquoXrZ/3quoM6zFFVe9y4t6GMdSMy/YusXVqnmRXZQPuNg8zRgQ/P+IztSCVLaW1QW0N\nXFzdZPZp78enhRv7z24vCzAZEewFkm+q6u2q2qaqZap6Ht4hKcMwBpDoKBcT81ODHjry8eV8TAty\n6b9h8ksAAA0wSURBVG9XUwrTaGztYP+RxoDO93iU0hq36ZH0IzM5FhH/hrb2VDYyJsAkUTuBZI2I\nfFVEbgcQkZHAjoDuahhGSE21ho6C+cbvs6G4hpS4aEZnBp+J7jO1wBuUNgXYa6pqaKG1w2NWbPUj\nJspFZlJs54qs3tQ2tVFe5w54J087geQBYD5wufV7PXB/QHc1DCOkphamUd/SzqHq4GtarT9Uw/QR\naba21u1PUW4ysdEutgSYOGlWbPlvXE5yv5UOdlivjw9DIDlJVW8C3ABWLazQ7J5jGEZQfN/4g51w\nb27tYHt5PTNHOFvPKibKxaS8wPNd9lZ5h8RGRWCn0cFmYl4qO8rr++yd+gLJhNzQB5I2EYnCShYU\nkWzAqVVbhmE4aHxeMtEuCXqeZHNpLR0eZWYAW+v2Z0phGptLaglkl+7t5fXEx7gY5eBw21A1KT+F\nxtaOzkoAPdlZXk9KfHRAOSRgL5DcgzcJMEdE7gKWA78I6K6GYYRUXHQU43ODz3Bff9BbOt7pHgl4\ne0117nYOHe39A64328vrGJ+b4khey1A3Ic+7HHxbee+90x3l9UzITQl4jxi/A4mqPgn8EG82exlw\nvqo6svzXMAznTS1MZUtpXUDf+H3WF3v3V/flIzipM98lgF7TjvJ6JgY4nn+8GZ+bjAhsL+t5nkRV\n2VFRH/D8CNjrkaCq21X1flW9T1W3BXxXwzBCbmphGkcbWzuL8QVi/cEaZo4MzX4fE/JSvMNvNntN\nVfUtHG5o7fymbfQtMda74m57Lz2SyvoWapvbAp4fAZuBxDCMwWNK54R7gEts61soqWlmVgiGteDT\n4Te7S4B3lHu/WU8yPRK/TcxLYXt5zz0S3/OBLv0FE0gMY8ialJ+CS2BzgEts1x/yzo/MCFEggcCG\n33zfrIP54DveTMxLZf+RRppa2495bacVSMabHolhGN0lxkYzNjuZLQH2SNYfqibKJZ1LiUMhkOG3\nbWX1ZKfEkRlAccHj1cT8FFRhV8WxhTx3VHjfz4ykwLM5TCAxjCFsamFawEl/Gw7VMjEvhYTY0FXX\nDWT4bUdFnZlot2mSNZ/U0zyJEwsXTCAxjCFsSkEq5XVuKursTbh7PMqGQzUhWfbb1eT8VO/wm5+B\npL3Dw86KBhNIbBqenkBibBTbuq3c6vAouyrrgxrWAhNIDGNIO3lMJgAr9hy2dd7eww3Ut7SHPJAk\nxEZRlJPCOms+pj/7jzTS2u5holmxZYvLJUzISzmmR3LoaBPuNk9QK7bABBLDGNIm56cyLDGGD3cf\nsXXeOisRcVaIlv52NX9sJqv2H8Xd1tHvsb4VRhPzTY/Erol5qWwvr//Mwgbf+xlMDgmYQGIYQ5rL\nJcwfk8mK3YdtrYzyVfwdkxX81rr9WTguC3ebh7UHq/s9dntZPVEuYVxO6Ns11EzKT6GmqY2Kuk9L\nyvuKOY7PDe79HBCBREQuEZEtIuIRkbl9HHe2iOwQkd0icls422gYg9Up47IorXWz77D/e3+sO+h8\nxd/enDQmgyiX8OHu/offtpfXMyYribhos72uXRN7KJWyo6KekRmJJMZGB3XtARFI8O69fiHwQW8H\nWAUj7weWAJPx7tA4OTzNM4zB65RxWQB8uMe/4a3KOjdby+o46YTMUDarU0p8DDNHDGO5H8Nv28vr\nTP5IgHzvW9dSKTvKg59ohwESSFR1m6r2t0nWPGC3teVuK/B34LzQt84wBrfRmYkUDktghR/f+AHe\n2laJ6v9v795jq6zvOI6/P1AKcnGIpeWqXEqHOAcqcdMylISBl0yGi0aTZcYt0xndH25L5rIYF3WJ\nWVyWzDg3F9n8Y865ZEacbApsYsBlwhZRLjIrRS1QbiqCF0rpd3+cp7ZqhdKnPb9TzueVnPQ5z+nl\nm9952k+f3/P7PT9YcGZNH1fWob62ipea3mb/e4c/9XMOfHCYprfe54yxvtDeE585aRDjR5704QX3\n5uwstTdGwJVEkHTTeOCNTs+bsn1dknS9pHWS1u3Zs6fPizMrVZK4YOqpPPfqPo50Y8XE5ZuaOW3U\n0NwjeY7HnNoq2gL+tfXTz0ra+/M99Lfnpo8ZwZqGfXzt/uc4/+6VHGkLzj09/xIBRQsSSSskbeji\n0SdnFRHxQETMjojZo0eP7osfYdZv1NdWsf/9w2w6xuTEg4daWdOwjwUzanp8S/GemDVxJEMrBx71\nOkn7HAh3bfXcOaefwt6Dh3iv5Qi3zK9jxffmMm96de7vm+8Ky3GIiPk5v8V2YGKn5xOyfWZ2DBfU\nFq53rHl1L2dN+PRbnqzasoeWI20sOHNMsUoDoLJiAF+ccupRg2RL8wFGDK5g/Egvr9tTN8ydwlWz\nJ/b6sgD9qWtrLTBN0mRJlcDVwNLENZn1C9UjhlBXM/yYI6Oe3tTMqGGVvdLdcbzqa6vYuvddtr/9\nyYWuDh9p4+lNzZw76ZSinimdaCoGDuiTtWVKIkgkLZbUBJwPPCnpqWz/OEnLACKiFbgZeArYDDwa\nERtT1WzW39TXVrF225scau164l9Laxv/eHk388+oTrLy4Jz20WVdhN2yl3ay651DXHv+pCJXZd1R\nEkESEY9FxISIGBwRNRGxMNu/IyIu7fR5yyKiLiKmRsRP01Vs1v/UT80m/r3W9e1I/t24jwMftLJg\nRnG7tdrV1QynavjgTwRJRPDg6kamVA3jwjpf7yxFJREkZtb3vjBlFJUVA3j4+de7fP3pjbs4adBA\n5kyrKnJlBZKYU1u4TtLS2vbh/v++/hYvNu3nuvpJRZkgacfPQWJWJkYMGcSNF07lifU7WP3KR//r\nb2sLlm/axdy6KoYMSjdrfNGs8ew92MIP/ryetmyo8pLV2zh5SAVXnDMhWV12dA4SszJy40VTmXTq\nUG57fMNHbpL4xIs7aH7ng2TdWu3mTa/mhxdPZ+n6Hdz15Gaa3nqPv23YyTXnncawwUUbZGrHyUFi\nVkaGDBrIHYs+R+Ped/nNqq0APPL869zypxeYNXEkl5yVNkgAvnPhFK6rn8SSNY1c97u1SOIbF0xK\nXZYdhSPerMzMrRvNV2aO475nGtj//mGWrGlkbt1ofv31c3LfvK83SOK2y2aw72ALS9fv4LLPj/Xc\nkRKX/qgxs6K77bIzeObl3SxZ08jlM8dxz5UzqawonQ6KAQPEPVfO5LNjRnD5zHGpy7FjcJCYlaHq\nk4fwy2vO5n+7DvDtL00pydFQlRUDuGlebeoyrBscJGZlat706l65z5JZ6ZzLmplZv+QgMTOzXBwk\nZmaWi4PEzMxycZCYmVkuDhIzM8vFQWJmZrk4SMzMLBdFROoa+pykPcBrqesAqoCjr3VaHtwOHdwW\nHdwWBaXSDqdHRLdWEiuLICkVktZFxOzUdaTmdujgtujgtijoj+3gri0zM8vFQWJmZrk4SIrrgdQF\nlAi3Qwe3RQe3RUG/awdfIzEzs1x8RmJmZrk4SMzMLBcHSR+TdKWkjZLaJM3+2Gs/ktQgaYukhalq\nTEHSTyRtl/RC9rg0dU3FJOni7H1vkHRr6npSkrRN0kvZcbAudT3FJGmJpN2SNnTaN0rSckmvZB9P\nSVljdzhI+t4G4Arg2c47Jc0ArgbOBC4GfiVpYPHLS+oXETEreyxLXUyxZO/zfcAlwAzgmux4KGfz\nsuOgX82f6AW/p/D739mtwMqImAaszJ6XNAdJH4uIzRGxpYuXFgGPRMShiGgEGoDziludJXIe0BAR\nWyOiBXiEwvFgZSYingXe/NjuRcBD2fZDwFeLWlQPOEjSGQ+80el5U7avnHxX0ovZ6X3Jn773Ir/3\nHxXACkn/kXR96mJKQE1E7My2m4GalMV0R0XqAk4EklYAY7p46ccR8Xix6ykVR2sX4H7gTgp/RO4E\nfg58s3jVWQmZExHbJVUDyyW9nP2nXvYiIiSV/BwNB0kviIj5Pfiy7cDETs8nZPtOGN1tF0m/Bf7a\nx+WUkhP+vT8eEbE9+7hb0mMUuv7KOUh2SRobETsljQV2py7oWNy1lc5S4GpJgyVNBqYBzyeuqWiy\nX5B2iykMSigXa4FpkiZLqqQw6GJp4pqSkDRM0oj2bWAB5XUsdGUpcG22fS1Q8r0aPiPpY5IWA/cC\no4EnJb0QEQsjYqOkR4FNQCtwU0QcSVlrkf1M0iwKXVvbgBvSllM8EdEq6WbgKWAgsCQiNiYuK5Ua\n4DFJUPh79HBE/D1tScUj6Y/ARUCVpCbgduBu4FFJ36Kw/MVV6SrsHt8ixczMcnHXlpmZ5eIgMTOz\nXBwkZmaWi4PEzMxycZCYmVkuDhIzM8vFQWJmZrk4SMwSkPRPSV/Otu+SdG/qmsx6yjPbzdK4Hbgj\nu1Hh2cDliesx6zHPbDdLRNIqYDhwUUQcSF2PWU+5a8ssAUlnAWOBFoeI9XcOErMiy+58/AcKK+Ed\nlPTxpVbN+hUHiVkRSRoK/AX4fkRsprCo1+1pqzLLx9dIzMwsF5+RmJlZLg4SMzPLxUFiZma5OEjM\nzCwXB4mZmeXiIDEzs1wcJGZmlsv/AT2ZTDMM562TAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x28506dc6748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Add sliders for the two parameters\n",
"interact(function_plot, ω0=(0, 2, 0.25), ω1=(0, 2, 0.25));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 08.2 (multiple function plotting)\n",
"\n",
"1. Plot the function \n",
"\n",
" $$\n",
" f(x) = \\frac{\\sin(x)}{x}\n",
" $$\n",
"\n",
" from $x = -6\\pi$ to $x = 6\\pi$. Think carefully about which $x$ values you use when \n",
" $x$ is close to zero.\n",
" \n",
"1. Add to the above plot the graph of $1/ \\left| x \\right|$, and limit the range of the $y$ axis to 1 using\n",
" `plt.ylim`. (Hint: use `np.abs(x)` to return the absolute values of each \n",
" component of a NumPy array `x`. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot of $\\sin(x) / x$ between $-6\\pi$ and $6\\pi$:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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URVldM+2dXVaHogap/rRElgOjgLuAocaYTGNMKjAH+Az4vYh8248xKmULX+4jYq+WSJeB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\nAfCzSL9phMcnAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x28507190390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Define x axis range using an even number of points to avoid division by 0\n",
"x = np.linspace(-6*np.pi, 6*np.pi, 100)\n",
"# Add labels to x and y axis\n",
"plt.xlabel('$x$')\n",
"plt.ylabel('$\\sin(x)/x$')\n",
"# Limit x axis between start and end point of the range\n",
"plt.xlim(x[0], x[-1])\n",
"# Plot the function\n",
"plt.plot(x, np.sin(x)/x);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot of $\\sin(x) / x$ and $1/\\left| x \\right|$ between $-6\\pi$ and $6\\pi$:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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G+VMoI/GqL7BZGYoyFCoBKO6rw0hgWxn1PiMweNhuv14/bwOVXjEEtBRrewMripNQCUBx\nX/VFGDDRHmTdZJqBaPWPw9fUpJaFVpyKSgCK+zKPy/cIS+znROvJEDUXQHE+KgEobqvJPCzTP8r2\ns069I1IAaFcdwYoTUQlAcVt1Jdq21KFxI21eVlCsVkZtcY7Ny1KUgVIJQHFbbZWnKZfBJESF27ys\n2BHxNEtvNRlMcSoqAShuS9TmUygjSbThHACLpIgACmUkJrUchOJEVAJQ3JZvYyEVHtEE6rgRfF+i\nAn0oJgqv+kKbl6UoA6USgOKepCSkrZQGvzi7FOfhIaj1iSWwpdgu5SnKQKgEoLinxnK8acNohzkA\nFq0B8QSbaqCtyW5lKsq5qASguCVj1WkAPMNttw/AWUK1uQBSbQ+pOAldEoAQYokQ4pgQIlsI8WAf\n51wkhDgghMgSQnylR7mKMlTVRdoQUL/oFLuVadlzoN48/FRRHM3qBCCEMABPAEuB8cBKIcT4HueE\nAk8C35FSTgCutbZcRbFGfan2IRwel263MoPNcwFq1FwAxUnoUQOYBWRLKU9KKduAN4HlPc65EXhP\nSpkHIKUs06FcRRmy9spT1Ek/4mPtt0dvTHwK7dJAS8Upu5WpKOeiRwKIB7o2ahaYj3U1GggTQnwp\nhNgrhLi1r4sJIe4WQuwRQuwpLy/XITxFOZtHXT5FRNl0GeieEiOCKJHhyGrVB6A4B3t1AnsCM4Ar\ngMuAh4UQo3s7UUr5rJRyppRyZlRUlJ3CU9yNf1Mx1Z4xNl0Guic/bwNlhmi8G9VcAMU56JEACoGu\nyykmmI91VQBsllI2SikrgK+BKTqUrSiDJyWh7SU0+o+we9H1PrEEq7kAipPQIwHsBkYJIVKFEN7A\nDcD6Hud8CFwghPAUQvgDs4EjOpStKINXX0KAbKI12PaLwPXUFJRKhKkCWursXrai9GR1ApBSGoH7\ngM1oH+pvSSmzhBCrhRCrzeccATYBGcAu4HkpZaa1ZSvKUDTm7QOgI2ai3ctujdTKbC/KsHvZitKT\nLougSCk3ABt6HHu6x89/Bf6qR3mKYo2GU/sIAHwTptq9bEPcFDgC9af2ET7yAruXryhd2X4VLEVx\nNsUZ5JpiiI22/yCDyBFJlMsQZOEBu5etKD2ppSAUt+NXdZjDMpn4UD+7lx0f5sdhUzJe5Vl2L1tR\nelIJQHEvLbUENxdwXIwkPMDb7sWPCPEjS6YQXJ8Nxja7l68oXakEoLiXkkMAlAWMQQj7zQGw8Pb0\noMh3FAZphHI1EE5xLJUAFPdSrI2+aYoY38+JtlMTMq5bLIriKCoBKO6l5BDlhBIY0XO1EvvxihxJ\nE76dtRFFcRSVABS30lF8kMyOZBLCbL8PcF/iwgM4bEpGFh90WAyKAioBKO7E2IpHxTFtBFCY/UcA\nWSSE+ZNpSkaWHAKTyWFxKIpKAIr7KDuMMBnJMqWQ4MAEEB+qjQTyaG+E6lyHxaEoKgEo7sPc6Zol\nU0hwwBwAiwTzXAAtJtUMpDiOSgCK+yg5RKuHPyWGWCIDfRwWRlyoHydkAh3CoDqCFYdSCUBxHyUZ\n5HunExcagIcd9wHoydfLQEhQIKU+qVCihoIqjqMSgOIeTB1QkslRkh3a/m+REObHCY9UNRdAcSiV\nABT3UHUS2hvZ25boFAkgPtSPDGMSNJZBfYmjw1HclEoAinswd7bubE5wyCJwPSWE+bOjyTwZrUit\nDKo4hkoAins4+SUd3sEck4kOnQRmER/mxz5jKtLgA7lfOzocxU2pBKAMf1JCzhaqYubSgcGhk8As\nEsL8aMGH+pjzIOcLR4ejuCmVAJThr+I41BWQEzwbwCn6ACzzEPLC5mirgtYWOjgixR2pBKAMf+Y7\n7P1e0/D0EEQH+To4IDprIYd8Z2oHTm5xYDSKu1IJQBn+sj+HiHQON4cRF+qHwYFzACz8vT0JD/Am\noy0eAmO1GBXFzlQCUIY3Yyuc2gppiyisbnKKEUAWCWF+FNa2QNrFWg3A1OHokBQ3oxKAMrzl7QBj\nM6RdTEF1s1O0/1vEh/pRUN2kJYDmaihWw0EV+1IJQBnecr4ADy9aE+dSVt/qFCOALBLC/CisbkaO\nvEg7oEYDKXamEoAyvGV/AUlzKGryBHCKOQAWCWH+tBpNVMhgGDFFi1VR7EglAGX4qi+F0kOQdjGF\n1c0ATtUHYImlsKYZ0hZBwS5oqXNwVIo70SUBCCGWCCGOCSGyhRAPnuO884QQRiHENXqUqyjnZBla\nmXax1taOc8wBsEgI12Lp7AcwGeHUNw6OSnEnVicAIYQBeAJYCowHVgohxvdx3p+BT6wtU1EGJPtz\n8I+E2MkU1jTjISA2xPFzACw6awDVzZA4G7wC1HBQxa70qAHMArKllCellG3Am8DyXs77IfAuUKZD\nmYpybm1NcGwjjL4MPDwoqG5mRIgfXgbnafUM8vUixM+Lgupm8PSG9EVwZD10tDs6NMVN6PHXEA/k\nd/m5wHyskxAiHlgBPNXfxYQQdwsh9ggh9pSXl+sQnuKWjn4MbfUw9UZAa2ZxphFAFglhfp3NU0y9\nERrLIfszxwaluA173Q79E3hASmnq70Qp5bNSyplSyplRUVF2CE0Zlg6shdBkSJoHaM0sjtwHuC/x\noX5aJzBA+iUQEKXFrih2oEcCKAQSu/ycYD7W1UzgTSHEKeAa4EkhxFU6lK0oZ6vJh5NfaXfUHh60\nd5goqWtxqg5gi4Qwfwqqm5FSgsELJl8PxzZBY6WjQ1PcgB4JYDcwSgiRKoTwBm4A1nc9QUqZKqVM\nkVKmAO8AP5BSfqBD2Ypytow3AQlTbgCgpLYFk8Qpm4Diw/xoauugpsnc7j9lJZjaIfMdxwamuAWr\nE4CU0gjcB2wGjgBvSSmzhBCrhRCrrb2+ogyKlHDgdUi5EMJSAMjvHALqPJPALCy1kgLzPAViJ2qT\nwva/5sCoFHfhqcdFpJQbgA09jj3dx7mr9ChTUXqV9622/+/8+zsPWT5cnbMJ6MxcgEkJIdrBqTfB\nxl9CySGIneTA6JThznnGxCmKHg6sBe9AGH9mJHJhdTNCwIgQJ0wAoVqtpLMjGGDSteDhBQfecFBU\nirtQCUAZPtoaIesDGH8VeAd0Hi6obiYmyBdvT+f7dQ/28yTIx/NMExCAfziMWQoZ68DY5rjglGHP\n+f4iFGWo9rykjf2fcVu3w4U1zjkHAEAIQXyYX/cEADBjFTRVwEFVC1BsRyUAZXhob4bt/4bU+ZA4\nq9tTzrYPQE/dJoNZpF0McdPgm7+rmcGKzagEoAwP+16BhlJY8EC3w8YOE8W1zjkHwCIhzL9ztdJO\nQmj/lprTcOhtxwSmDHsqASiuz9gKW/8JyedDygXdniqtb6XDJIkPdb4hoBbxoX7Utxqpbe5xpz96\niTYK6Ou/QYfRMcEpw5pKAIrr2/8a1Bd1G/ppUVDlfMtA99R1KGg3QsD8X0JVDmS954DIlOFOJQDF\ntRnbYOtjkDALRl501tOW4ZXO2gkMZ2I7qxkIYOwyiB4PX/9VbRqv6E4lAMW1HVgLtflae7kQZz1d\n4IQ7gfVkmaF81kggAA8PrWZTcRwyVS1A0ZdKAIrraqyEz38HSXO1tfR7UVjdTFSQD75eBjsHN3Bh\n/l74exu6TwbravxyrS/g04fVlpGKrlQCUFzXpw9Dax0se6zXu3+Agpomp777B/NcgNBehoJaeBhg\n2b+gvgS++IN9g1OGNZUAFNeU+43W/DPvRxA9rs/TnH0OgEVCb5PBup0wA867C3Y9C4X77BeYMqyp\nBKC4HmMrfPxTbcOXXkb+WJhMkqKaZqdcBbSnhDD/vpuALBY9DIEx8PFP1LBQRRcqASiuZ9u/oPIE\nXPEP8O77w72svpX2DunUI4As4sP8qGlqp6H1HB/sviGw5E9QfBB2P2e/4JRhSyUAxbUU7IGv/gIT\nroZRl5zz1MIa558DYJFwrqGgXU1YAemXwmePQGmWHSJThjOVABTX0VgJb90GwSNg2T/6Pb1zHwAn\n7wSGM8NU++wIthAClj+h1QbW3aJGBSlWUQlAcQ0mE7z3PWgsg+teAb+wfl/SOQfAJWoA55gL0FNQ\nDFz7ElSfgg/v1XZBU5QhUAlAcQ1f/xVyPoelf9ZWyRyAgupmIgK88ffWZeM7m4oM9MbH06P/jmCL\n5Hlwyf/CkfXw7ZO2DE0ZxlQCUJzf0f/Cl3+CyTfAjNsH/LKCaufdB6CnM/sC9NME1NW8H2pLRXz6\nG8j5wnbBKcOWSgCKc8v9Gt6+HeKna+3+fUz46k1BdTOJLjAE1CIxzJ/8qgHWAEB7L656EiLHwJs3\nQ/5u2wWnDEsqASjOq3AfvLESwkfCTe902+axPx0mSUF1E0kRrpMAkiP8OV3ZOLgX+YbALe9BYDSs\nvQZKD9smOGVYUglAcU7lx+C174J/BNzyvrZP7iAU1zbT3iFJCnedBJAU7k9di5HapkHuABYUC7d+\nAF5+8OoKqDppmwCVYUclAMX5FO6Fl5aCwUv7YAseMehL5Jn3AXClBJBojtUS+6CEpWiJsqMVXrpc\nzRFQBkQlAMW5ZH8Ga64E70C4faPW/DME+S6YAJKsSQCgrYm0aoP2+MWlcGqbTpEpw5UuCUAIsUQI\ncUwIkS2EeLCX528SQmQIIQ4JIbYLIaboUa4yzGS8Ba9fr33o3/kpRKQN+VJ5VU14eghGhPjqGKBt\nWRLA6apB9gN0FTMe7vxE6xN4dQUc+Uin6JThyOoEIIQwAE8AS4HxwEohxPgep+UCC6SUk4DfA89a\nW64yjHQY4ZOHtIleSXPh9v9qk52scLpSGwLqaXCdSm6AjyeRgd6dtZchC02COzZrewisuwW2/FHt\nJqb0So+/jllAtpTypJSyDXgTWN71BCnldilltfnHb4EEHcpVhoP6Enj5Stj+H22545vf1Ua2WCm/\nqsmlmn8sEsP9h94E1FVABNz2EUxZCV/9WRsh1Fhp/XWVYUWPBBAP5Hf5ucB8rC93Ahv7elIIcbcQ\nYo8QYk95ebkO4SlOK/szePpCKD4AVz8PV/wdPH10uXReVVNnp6orSQr353SlDgkAtJVSr3oSrvyX\n1h/wzIVwaqs+11aGBbvWj4UQC9ESwAN9nSOlfFZKOVNKOTMqKsp+wSn201ILH96nDfP0C4PvfQGT\nr9Xt8nUt7VQ3tZPsggkgOdyfoppm2jtM+lxQCJixCu7cDAZvWHMFbLgfWhv0ub7i0vRIAIVAYpef\nE8zHuhFCTAaeB5ZLKVVd1F0d2wRPzNF287rgp/D9r8+5o9dQ5FW63gggi8Rwf0wSiga6JtBAxU2D\ne7bB7Htg13Pw1DzI2aJvGYrL0SMB7AZGCSFShRDewA3A+q4nCCGSgPeAW6SUx3UoU3E1FSdg7bXw\nxvXgGwx3faYtZual/ygdSyeqqzYBgRVDQc/FOwCW/h/cvkHbZ/jVq2DdzVCVq39ZikuweplEKaVR\nCHEfsBkwAC9KKbOEEKvNzz8N/AaIAJ4U2louRinlTGvLVlxAYyV883fY9Qx4+cPiR2HW3eDpbbMi\nOyeBudAyEBaWmE9XNnHhKBsVkjwP7tkOOx6Hbx6D45/A3Hvh/B+DX6iNClWckS7r5EopNwAbehx7\nusvju4C79ChLcRFNVbDjCdj5NLQ1wvRb4eKHIdD2/Tp5VU2E+XsR7Otl87L0FhPki7enh/VDQfvj\n5aftpzz1Jm13sa3/gN0vwLz7YPZqrZamDHvOv1C64lrqS2DnM7D7eWit07YwXPAgRI+1Wwh5LjoE\nFMDDQ5AY5mebJqDeBMfB1c9oH/xb/gRbHtX2F5i9WhuWGxBpnzgUh1AJQNFHSab2wZHxFpiMMO5K\nuOhBiJlg91DyqpqYFG/9XAJH0XUo6EDFToKVr0PRfvjyz9r+C1sfgyk3wJx7IWq0feNR7EIlAGXo\n2psh633Yuwbyd2pt/DNv1+4erVjGwRrGDhOF1c1cMWnwC8g5i6Rwf/acqkZKiRjE/ge6iJsGN76p\nrca64wk48Ib2/5t8gfZ/O+5K3eZqKI6nEoAyOFJqH/YZ6yDzXW1Mf8QorXN36o2DXrZZb8W1LRhN\nkmQX7AC2SIoIoL7VSE1TO2EBtussP6eoMfCdf2v9Ngde05LAu3eCXzhMukbbnS1++qA26FGcj0oA\nSv+k1Gbx5SSBAAAgAElEQVTrHl4Pme9ATZ52tz92mda5m3KB03wQ5LnwEFCLrkNBHZYALAKjtPka\n834MuV/Cvle0r13Paov2TbwGxi/Xmvqc5HdAGTiVAJTedbTTmL2Vk9veI63ic/ybCkEYYOQCWPg/\n2oe/T6CjozyLK+4D0NOZVUGbmJLoJMMyPTwg7WLtq6VWW2U0Yx188zf4+i80BCSRG3Ux6Rdci1/q\nHDCojxZXoP6XlDNqCyBnCzL7M4wnPiegvZ7R0pNtpolsNF1BUcxCVoyfzDVTnHctv7yqJrwMghEh\nrrEZfG8Sw7XYbT4UdKh8Q2DazbzWeiEbqjNILt/C0rpdzG14Fa9Ta2j3DsFr9KWQvghGLhzShj6K\nfagE4M7qS+H0Nu3r5FdQeQKAGkM4m1tnkB12PsuuupEQT3/iT1SQmVXKL94+SJCvJ5dNiHVw8L3L\nq2oiIcwfg4frNkf4e3sSFeTTuaSFM3pvXwEPfZDJlIQw4hfdQ/Co35DRVMOGD99gXP12Fh/+jODM\nd7STo8ZC6gJIOR+Sz1dDS52ISgDuwmSCiuNaB27BLsjb2fmBj1cAJM/DOH0VP9sdymdVEfxiyVge\nnJvcuZ7+jORwVi9I4/pndvDTdQd49555jBvhfJOF8ipdcxXQnpLC/a3bGMaG9udV8+B7h5gzMpxX\n75yNV+eeC6FM/vkDPP9NLud9dpSrRlTzpymVeJzcYu43eEY7LXIMJM2GRPNXRLrqP3AQlQCGIymh\n+hSUZEDhPijaB0UHtIlZoI3kSJwF02/RhveNmAwGL/7v48OsL8nl6ZunsmTi2dV2Xy8Dz946k+88\nvpW7Xt7D+vvOJyLQuYYE5lU1MSXRdecAWCSF+7Mrt8rRYZylpLaF77+6l5hgH568aUaXD3+Nl8GD\ney5KIyrIh1+8fZARY2bxk1t/DMY2bY7B6W1wejsc/lBLCqA1KcVNg7jp2sii2MnapjYqKdicSgCu\nrq0Ryo9qm4CXZmkTskoOQWut9ryHlzZCY9K1ED/DfMeVdtYf12eHS3l+ay63zU3u9cPfIibYl2dv\nmcl1z+zgntf28fr3ZjvNrlu1Te3UNre7dAewRWK4Px8cKKTNaMLb0zne35b2Du5+dQ+NrUZeu+t8\nws8xQumaGQlsz6ngX5+fYFZqOPPSIrW7/qTZcOHPtBpp5QmtRlq4V7tR2fYvkOady3xDtRuTmIna\n72/MBK0pyct1+3ackUoArqKpSltRs+K49lV+DMqPaEMyLbz8taWVJ31Xu4saMRmiJ/S74mZRTTO/\neOcgE+KC+dXl/S/NPCUxlEdXTOIXbx/kwwNFfHeGc3QKn6rUmkySIwIcHIn1UiP9kRLyqhpJjw5y\ndDgArNudT0ZBLU/fPIPRMf3H9PvlEzmQX8NP3jzAhh9fSGTX2qKHhzbXIGqMNpQYtImFJZlQchCK\nM7Qa7J6XwGhZGltAWIqWCKLGQORo81e6tq+EMmgqATgLKaGxXFuat/oUVOdC1UmozIGqHGiuPnOu\nwVv7xU84D6bdqq2zEzMBQlO0P6xBFSv56boDtBtNPH7jdHy9DAN63Xenx/P8Nyd58stsVkyLx8MJ\nOl2zy7RNTtKjnW946mClRWn/huwy50gAbUYTz3yVw8zkMC6bMLD9mgN8PHnixuksf2IbD7yTwQur\nzjv3C7z8IPE87cvC1KH9TZRlQdlR7aan/Ji2m5yp/cx5/hEQnqbVbsNHaokiLBXCU7XnVHNSr1QC\nsJcOIzSUQG0h1Oabvwq0O/jq09p3Y49NQIITIGKktqBaeJr5bmeU1j7qMbAP6v5sziplZ24Vj66Y\nSGrkwO+chRDcuzCdH76xn81ZJSx1gqUXcsob8PQQw6IJaKQ5AeSUO8fOXR8cKKSotoVHr540qOUp\nxo0I5heLR/PHDUfZeqKCC0YNcgSQh0G7w49M1yacWXQYoeb0mVpxVY52s3TyKzj4RvdreAVofzNh\nyRCSCKGJEJKgPQ6Oh6BY3f6eXI1KANaSUrs7byjVVsKsL9E+6OuKob5I+15XpB2TPbb58w3Vfhkj\nR0H6JdovaXiqducSmmSTzVK6au8w8ZdNRxkVHcj1MxP7f0EPl08awWOfHufxLdksmRhr/3Vresgu\nayAlMuCsjklXFOjjyYgQX3LKHJ8AOkySp77MYWJ8MBeNHvxy3rfNS+GVHaf508YjfJR2gT61RYOn\ndrcfkQZjlnR/rq3JfGN1SqtJd95knYa8HdpEtq6EAYJGaPMVgkacSQpBIyAoBgJjte++ocOuJqES\nQG+MbdBUCU0V0FihPW4s174ayjDWlVJZVoChqZyQjmq8MJ59DZ/gM79UIy+CkHjtF8ty5xESDz6O\nrdq/uTufkxWNvHDbzCF15Bo8BKsvSuOX72Tw5fFyFo6JtkGUA5dT3jAsmn8s0qICnaIGsOFQMbkV\njTx10/QhJXkfTwP3XzaGH795gPUHi7hqWrwNouzC219rFu1rCfKWOq32XVsAdQVarbyuULtRKz8K\nOV9A29nvexte1BrCMflHERGbgGdQNAREQ0CUNrchIEprbgqI1L4bnH8/iuGdAEwdWrZvqdHu0pst\n381fTVXQXNXle6X22DJcsgcpDDR5hVLQFkhpRzBGv4k0+EXS6BVBUUcwu8p9aPaNYsmcqdw0fzwh\nfs77C9DQauRfnx1ndmo4F48d+gf3VVPj+eenx3nii2wuGh3lsFpAe4eJ05VNTjtBbSjSogJ4d1+h\nY1YFNZNS8sSWbNKiAqx6b6+cHMdz35zkr5uPsWRi7ID7mmzCNxh8x0PM+F6frm5s45WvMvl010EC\n2yqYE9lGjKGWwPYqAtoq8KypILb+CAneu/Frr0b0rNlb+IRoiyP6R2jf/cK7fA/TOq79wrSahV+o\n9tgnZND9eNZw7gQgO7Ts3Fpv/qrVvrfUaR/Snd9rtccttd2/WmvPfX3voDP/Ef4RWueRf0T3L3Nm\nP1rvw6o3jlNS187CMVH85JLRZ63Tsvd0NU99mc1fthTwVkYVr901m4Qw52yPfvbrk1Q0tPH8beOs\n+nDx9vTg+wvS+O36LHbmVjFnZISOUQ7c6comjCY5rGoA6dGBNLQaKa1rJTbEts2BffniaBlHS+r5\n+7VTrGq68fAQ/GrpOG56fiev7jjN9+aP1DFK/eRWNHLTc99SXNfCkgmTuXdhOhN77C2x51QVj3x2\nnG3ZlSSG+vDyylGM9GvWWgiaLC0GleYbykrt5rKhVOvEbq7qtXZxhtBaD/xCtPkRvqHad59gc+Lq\n8tgnSHvc7efBtSo4dwIozoDHes/SnXzMb4rlKyQBYiea37jgszNs16w7wH1pvzlRzj2v7SPY15N3\n75nJjOTelzyekRzG87edx67cKu58eTfXPb2D1+6a3dmh5yzK6lt47uuTXDF5BFN1WGzs+vMS+dfn\nJ3hlxymHJQDLCKA0J3uvrZHWpSPYUQng5R2niQvx5TtT46y+1vnpkSwYHcV/vjjBdTMTCfF3rhry\nkeI6bnlhFyYp+eAH5/e5EN/MlHDW3jWH7TkV/OiNA6xYc4wXbpvJzNQB7npnbD27NaKlRjvWUnPm\nBrbZ/Lgq98yxtnod/8XOngCC4+HK33XPdJYs5xus3cHbuLr03r4CfvlOBunRgay5fdaA/hBnpYbz\n5t1zuPWFXVz3zA5evXO2Uy2b8MxXJ2nrMHH/4jG6XM/Xy8B3psTx+q48apvbHdL0ZWkrTxtmNQDQ\nktv56fZfP6esroWtJ8r5wUXpunWsP7BkLJf/+xte3JbLTy91nl3G9udVs+ql3fh5GXjtrtkDGno7\nLy2S938wj9te3MVNz+/kXzdMY8nEATSTefponcpBAxtO242pw9waUnemZaSlTksMlseP/HjAl3Pu\n4RKB0TBjFUz8Loy6VJtFGDNeGznja/u2sjd35fGztw5yXko4b62eO6i7sAlxIaz7/lw8PTy44dlv\nnWZhr/L6VtbuPM1VU+NJGcSwz/6smBZPm9HExkPFul1zMHLKG4gN9iXQx7nvaQYjKsiHIB9Ph3UE\nrz9YhEnCiun6ddqOjwvmsgkxvLgtl9rm9v5fYAcnSuu55YVdhPh58fbquYOad5EY7s8798xjfFww\n96zdy/qDRTaMFG24ql+oNkowZgIkzYHRi7XPyBmr4PwfDe5ytonS9X1+pJRfv3+IBaOjWHPHeQT7\nDv6uNj06kHXfn4OUknvW7qWlvcMGkQ7Oc9+cpM1o4t6F+m7ZODkhhJFRAby3v1DX6w5UTtnwGgEE\n2lyLtGjHjQR6f38hUxJCdG9W++HFo6hvMfLy9lO6XncoGlqNrH5tL75eHqz7/pwhLSQYHuDN63fN\n4byUcH7+1gG251TYIFLbUAmgFwfza7jv9f1MiAvhyZum4+M59BELyREB/OO6qWQV1fHIR1k6Rjl4\nlQ2tvLrjNN+ZEqd7v4QQgqunxbMrt8ru69hLKckpbyQtyvWXgOgpLSqws3/Dno6X1pNVVMcKGwzZ\nnBgfwiXjonlhay71LY6rBUgp+dV7h8itaOTfK6dZtYeEn7eB526ZSUpEAN9/dS9HS3ofSehsVALo\n4VRFI3es2U1kkDcvrjqPAB2aFC4ZH8M9F6Xxxq583tlboEOUQ/P81lxajB3cd/Eom1x/+VTtw+LD\nA/atBZTWtdLQahxW7f8WadEBlNa12v2D8r19hRg8BMumWN/525sfLRpFbXM7r+w4bZPrD8QrO07z\n0cEifr54jLZYnZVC/L1Yc8cs/L0NrHpxN8W1zf2/yMF0SQBCiCVCiGNCiGwhxIO9PC+EEP82P58h\nhJiuR7l6q21q5441uzFJyZrbZxEVpN9Sxz+/dDRzR0bw0AeHHHJ3UN3YxivbT7FscpzNmkoSw/2Z\nlRLOe/u1sev2YmkiSR9GI4As0jtHAtlvbwCTSfLhgUIWjI7qvoCbjiYnhLJwTBTPf3OSxtZeJlLa\n2IH8Gv7w38MsGhvNPQv0aw6ND/XjpVWzaGg1cvtLux3ybxsMqxOAEMIAPAEsBcYDK4UQPcduLgVG\nmb/uBp6ytly9tXeYuPf1feRXN/HMLTN1b/f0NHjw75XTCPTx4mfrDtLe0cfkERt5YWsujW0d/PDi\ndJuWs2J6PCfLGzlU2M8cDB11DgEdljUAcwKwYzPQt7mVFNe22KT5p6sfLhpFdZP9awEt7R38bN0B\nooN8+cd1U3VfyHB8XDBP3jSd46X1/HTdAUwm+90MDZYeNYBZQLaU8qSUsg14E1je45zlwCtS8y0Q\nKoRw/OphXfz+48Nsza7g0RWTmJXa+zh/a0UF+fDHFRM5XFzHE1uybVJGbyobWnlpWy5XTBoxoGV8\nrXH5pBF4Gzx4b5/9moFyyhsI8vEkWscam7NICvfHyyDs2hH8/r5CAn08uXT8EIYpDsL0pDAWjI7i\n2a9z7NrE9bfNxzhZ0chfrplss7kI80dH8fCy8XxyuJS/fXLMJmX0ZrA1bz0SQDyQ3+XnAvOxwZ5z\nltK6Frs0Jbz67Wle2XGa712YynVDWBRtMBZPiGXFtHge/yKbTDvdJT/z9Uma2zv46aW2afvvKsTP\ni0XjovnoYBEddrrzyS5rYGR0oMMXo7MFL4MHyREBdusIbmnvYGNmCUvttFzDzxePprqpnZe2nbJ5\nWQC7T1XxwrZcbpmTbPO5FavmpbByVhJPfpnD+/tt3/cnpeSRjw4P6jVO1wkshLhbCLFHCLGnrL6V\nv31yzKZJYMvRMv53fRYXj43mwaX9b4aih99eOZ7wAG9+8fZB2oy2bQoqq2vh5e2nuGpavN3WlV82\nOY7KxjZ2n7LPloY55Q3Dsv3fIt2Oi8Jty66godVos87fniYnhLJ4fAzPfX2SmqY2m5bV1Gbk/rcP\nkhDmx4NLBzhr1wpCCH63fAJzRobzwDuH2JFTabOyLB/+awY5tFaPBFAIdL1tTjAfG+w5AEgpn5VS\nzpRSzgwP8OaJLTn8dbNtksC+vGruWbuXcSOC+PfKaRjstKlJqL83f7p6EkdL6vn35ydsWtYTW7Lp\nMEl+vMj2d/8WF42JwtvTg02ZJTYvq76lndK6VtKih98QUIu06ABOVzbZpd9oU2YJQb6ezLXjkh4/\nWzyahjYjz31z0qbl/GXTMU5VNvHXa6boMrpvILwMHjx98wySIvy5+5U9HC7SfwCIlJL/XZ/Fmu2n\nuOuC1EG9Vo8EsBsYJYRIFUJ4AzcA63ucsx641TwaaA5QK6Xsd8pofKgfN87WqlB/3HBE186U7LJ6\n7lizm9hgX9bcPsvuM0gXjYvhmhkJPPVVDgfza2xSRkF1E6/vyuPamYl23SYxwMeT+aOi2JxVYvMm\nPMvomGFdA4gOxGiSnLbxbHJjh4lPj5RyybgYu+5DPDY2mGWT43hp2ykqGlptUsb2nArWbD/Fqnkp\ndl+vKtTfm1fumEWgrye3vbRL13kyHSbJQx9k8vKO09w9fyT/c8XgWjGs/l+WUhqB+4DNwBHgLSll\nlhBitRBitfm0DcBJIBt4DvjBQK//h+UTuXVuMs99k8vP3jqgS5NJUU0zt76wC08PD165Y7bNhrr1\n5+Fl44kO8uHnbx+0ySzhx7/IRiBsPvKnN0smxlJc20JGgW37OXKG8QggizQ77Q62M7eKmqZ2hyyp\n/ZNLRtHS3sHTX+bofu2GViP3v51BamQADyyxfdNPb+JC/Xjljlm0d5i45YWdlNdbn+ha2jv4wdq9\nrN2Zx+oFafxq6dhB94PpkuallBuklKOllGlSykfNx56WUj5tfiyllPean58kpdwz4AA9BI98ZwK/\nWDyaDw4Uccea3VaNGMgqquXqJ7drU9HvOI+kCMct1xzi58WfvzuZ7LIG/q7zSIGsolre3lvAjbOT\niAsd+gzHobpkXDSeHoJNWbZtBjpeVo+XYXhsA9kXy6zt4yX6rgTZ06bMEvy8DCwYwq5f1kqLCuTq\n6Qm88u1pTuqc6B7972GKa5v527WT8fN23D4Eo2KCeOG28yita+W7T223qmO/pqmNm5/fySeHS/nN\nsvE8OIQPf3DCTuDeCCG47+JR/PWayew4Wcl1z3zLidLB/zF8cbSUa5/egRDw1uq5TIgL6f9FNjZ/\ndBQ3z0ni+a257MrVp9O0wyT59fuZhPl78ZNL7Nf231Wovzdz0yLYlGnbZqCM/FrGxgYPi20g+xLo\n48nIyAAybDhqzGSSbM4q4aIxUQ77kPzlZWPw8fTgoQ8ydfud2XKsjDd25XP3/LQ+l3G3pxnJYbxx\n9xya2oxc/eQ2tmcPft2gzMJavvvUdjIKanl85XTuGGS7f1cu9Vdz7cxEXlx1HiW1zVzx76385/MT\nA+oYazOaeOarHO56eQ8jowL44N7znWp55l8tHUdimD+/ePsgDTrMHFy78zQH82t4eNl4Qv0HtueB\nLVw2IZbcikZO2GgIo8kkySysZUqi4xO5rU1OCCGjwDZ9RQD782soq28d2HLGNhId7MsDS8ayPaeS\n93VYVLCmqY0H381gdEygXYZAD9TUxFDe/8H5xAT7cuuLu1izLXdAn2Mt7R38dfNRlj+xjboWI6/c\nOYsrJls3ncqlEgDAgtFRfPqzBSyeEMPfPz3Odx7fxn8zinv94Gwzmli78zQL//Ylf9p4lEvGxfDW\n9+cSE+yYzTX6EuDjyd+unUJhTTM/f8u6mYMltS38ZdMxLhwVyXfsNJSvL4snxCAEbDxkm2agkxWN\n1LcamZxg/aY2zm5KYiilda2U1rXY5Pqbs0rwMggWWrE9qB5unJXE9KRQ/vDfI1Q1Dn1YqLHDxA/f\n2E91Yzv/uG6qVQs62kJiuD/v/mAe89Ij+d+PDnPJP77i7T35GHtJBLXN7Xx4oJBl/9nKE1tyWDEt\nns9+ukCXzmyXXDw9MtCHx2+czpVTSvjth1nc+/o+vA0ezEuPYExsEJUNbZTXt3K0pI7SulamJYXy\np6snceGoSKedLDQrNZxfXz6O3398mP98kc2Ph9h088hHWbR3mPjDVRMd/m+NDvJlZnIYm7JKhvzv\nORfLHfEUN0gAliR3ML+GxTp30kop2ZhZzAXpkUNa9lxPHh6CP149iWX/3sqfNhzhr9dOGdJ1/rzp\nKN+cqOAv35181paOziLY14uXbz+Pz4+U8c/Pj3P/Oxk89ulxRscGERXoQ3igN4eL6tiRU4nRJEkI\n8+PlO2bp2kfjkgnA4rIJsSwaG83e09V8eriUTw6Xsi27gshAH6KCfJiaGMqNs5OZ78Qf/F3dcX4K\nWYW1PPbZccbHBQ96Kv4H+wvZmFnC/ZeNseuwz3O5bEIsf/jvEU5XNuoeU0ZBLf7ehmG3D0BvJsQF\n4+khyCio1T0BHC6uI7+qmfsW2n+0WG/GxgbzvfkjeerLHC4dHzPof+8H+wt57ptcbp2bzHXn2XZm\nv7WEEFwyPoZF46L57EgZ63bnU1rXwtHieioaWkkK9+fOC1NZPD6GqYlhus9VcukEANoia7NHRjB7\nZAQPLRuPlNIlPux7I4R295Nd3sBP1x3gvR/MG/DaPV8cLeUXbx9kVmo4dzvRhtuWBLAps4Tv67jq\nImgrOk6MC7HbBD5H8vUyMDomiIM26AfYlFmCh4BLxtl27Z/B+PGiUWzLruC+N/az5vbzBrxcc0ZB\nDQ+8m8Gs1HAeXtbPfuJORAjBpeNjut302eOzzOX6APrjqh/+Fr5eBp6+eQa+XgaueWo7Xx8v7/c1\n23MqWP3aPsbHBfPCbTOdakRMYrg/k+JD2KjzrOA2o4nDxXVu0QFsMSUxhIyCWt1HVW3MLGF2agQR\nDpoP0xtfLwMv3z6LlAh/vvfyHvbnVff7mk+ySlj57LdEBvrw5E3TnervYCjs8Vnm2u/QMBUX6sf7\nP5hHXKgfq17axbNf5/T5R7/7VBXfe3kPKRH+vHz7LIIc3Ibbm6WTYjmQX0NRjX4bZBwvrafNaHKL\nDmCLyQmh1Da36zoj+ERpPdllDVw+yXGjf/oSFuDNa3fOJjLIh1Uv7eZQH5MKTSbJPz87zt2v7iU9\nOpB37pnrsMmdrkYlACeVGO7Pu/fM47IJsfxxw1HufHkPb+3OJ6e8gfYObfP1lc9+y7VP7yAi0IdX\n75xNWIDjhnyey9KJ2lA1PdcGOuhGHcAWln+rns1AGzNLEAKHzP4diOhgX167czZ+XgaufHwrt764\ni88Ol2LsMHGitJ43duVx20u7+OdnJ7h6ejzrvj/Xqq0d3Y3L9wEMZwE+njx503Se/DKH5745yRdH\nywDwMgjaOyTxoX48sGQsK2clOnS8f39SIwMYGxvExsxiqyatdJWRX0uYvxeJ4e7zxz46JhBfLw8y\nCmo7t9+01oZDxcxMDiPayYZGd5UY7s9/f3QBa3fm8frOPO56ZU/n3wBARIA3v1k2ntvPT3H5JmB7\nUwnAyQkhuHdhOvcsSONkRQN7TlVztKSe89MjuXhstMt0gC6dOIJ/fn6csroWXT5sDhbUMDkh1K3+\n4D0NHkyIC9Ft8cDcikaOltS7RGdpRKAPP1o0insuSuPTw6Xsyq1ifFww56WEkxLh71a/B3pSCcBF\neHgI0qOD7Lamv96WTorlsc+OszmrhFvmplh1raY2I8dL61ls4x2rnNHkhBDe2JWHscOEp5WdnBsz\ntQV5HTn7d7C8DB5cPmkEl09yqg0FXZbqA1DsYlR0IGlRAbqMBsoqqsMkcasOYIspCaG0tJt0WV5j\nU2YJUxJDiXfAYoGKc1AJQLELIQRLJ47g25OVVFq55rulCWSyGw0BtZicoP2brV0XKL+qiYyCWpa6\n0N2/oj+VABS7WTopFpOETw+XWnWdjIJaRoT4Eh3kvB2XtpISEUCQrycHrdxnYbN5mW6VANybSgCK\n3YwfEUxyhD/rDxYN+RpSSvbnV3feCbsbDw/BlIRQ9udZVwP46GCR+f/DOZYMURxDJQDFboQQXD0t\nge05lUPeFu9EWQP5Vc1ckD6wpQGGo/PTIzlSXEfhECfWHS2p42BBLd+dkaBzZIqrUQlAsatrZyZo\nG/LsyR/S6zcecu6JS/ZgabYZ6sS6dbvz8TZ4sGKaPnMJFNelEoBiV3GhfiwYHcXbewroGMK+Bxsz\ni5mR5NwTl2wtxTyxbpN5GOdgtBo7eH9/IYsnxBDupDPHFftRCUCxu+tnJlJS1zKghe66OmWeuORK\n49ZtZenEEew5XU3ZIDeI+SSrlJqmdq538mWSFftQCUCxu0XjYogI8ObN3XmDep1lDoFKANqIKinP\njOYZqHW784kP9eP8AS6vrAxvKgEoduft6cF3ZyTw+ZEyyusHPidgU2YxkxNCSAjzt2F0rmFUdCAj\nBzmxLr+qia3ZFVx/XiIeLrKEiGJbKgEoDnHdzESMJsl7+woGdH5hTTMHC2rV3b+ZNrEulp25VQPe\nO/ftPfkIAdeo0T+KmUoAikOkRwcyMzmMdbvzB7TBiWXEi2VpaUV7LzpMkk8P918LMHaYeHtvAQtG\nRxGnln5QzFQCUBzm5jnJnKxo5J29/dcCNmUWMzY2iNRINXHJYkJcMAlhfgNqBnrt29MU17Zw8+xk\nO0SmuAqrEoAQIlwI8akQ4oT5e1gv5yQKIbYIIQ4LIbKEED+2pkxl+PjOlDimJ4Xyp41HqT5HM0ZZ\nXQt7Tleru/8eLM1A27IrqG1u7/O80roW/vbJcS4cFcmicdF2jFBxdtbWAB4EPpdSjgI+N//ckxH4\nuZRyPDAHuFcI4fwLkCs25+EheHTFJGqb2/nzpqO9niOl5Lfrs/AQgmVTVALoafnUeIwmye8/Ptzn\nOb//+DBtHSZ+v3yiWjdf6cbaBLAceNn8+GXgqp4nSCmLpZT7zI/rgSOAmoKoADBuRDB3nJ/Cm7vz\n2Xu66qzn12w/xcbMEh5YMoa0qEAHROjcJsaH8MOLR/HO3oJeZ1d/fbycjzOKufeidFJU85nSg7UJ\nIEZKaZmOWAKcc4cOIUQKMA3YeY5z7hZC7BFC7CkvH9xEIcU1/eSS0YwI8eV/3s+kvcPUefxAfg1/\n3L4h6hwAAAZgSURBVHCES8ZF870LRzowQuf240WjmJcWwW8+zORoSV3n8Zb2Dh7+MJPUyABWX6Te\nP+Vsor8RGEKIz4Dext79D/CylDK0y7nVUsqz+gHMzwUCXwGPSinfG0hwM2fOlHv27BnIqYqL25RZ\nwurX9pIWFcAl42M4Py2SX713CID//ugCp97z2BmU1bdw+b+2EuznyW+vnMA3x8v5/GgZuRWNvHbn\nbC4YpSZ+uQshxF4p5cwBnTuQIXjnKOgYcJGUslgIMQL4Uko5ppfzvICPgc1Syn8M9PoqAbgPKSVv\n7cnnwwNF7MqtwmiSeBkEb6+ex9RE99v5ayi251Rw8/M7MUnwNngwe2Q4V0+PZ8U0Ne7fndgzAfwV\nqJRS/p8Q4kEgXEr5yx7nCLT+gSop5U8Gc32VANxTfUs7W09UEOLnxTw3XvZ5KL48Vkab0cT56ZEE\n+Kgtv92RPRNABPAWkAScBq6TUlYJIeKA56WUlwshLgC+AQ4BlgbeX0spN/R3fZUAFEVRBmcwCcCq\nWwQpZSWwqJfjRcDl5sdbATX2TFEUxcmomcCKoihuSiUARVEUN6USgKIoiptSCUBRFMVNqQSgKIri\nplQCUBRFcVMqASiKorgplQAURVHclEoAiqIobkolAEVRFDelEoCiKIqbsmoxOFsTQpSjLTLnaJFA\nhaODGARXixdcL2YVr+25WszOEm+ylDJqICc6dQJwFkKIPQNdXc8ZuFq84Hoxq3htz9VidrV4QTUB\nKYqiuC2VABRFUdyUSgAD86yjAxgkV4sXXC9mFa/tuVrMrhav6gNQFEVxV6oGoCiK4qZUAlAURXFT\nKgH0QQhxrRAiSwhhEkLM7HI8RQjRLIQ4YP562pFxdtVXzObnfiWEyBZCHBNCXOaoGPsihPhfIURh\nl/f1ckfH1BshxBLze5gthHjQ0fEMhBDilBDikPl93ePoeHojhHhRCFEmhMjscixcCPGpEOKE+XuY\nI2Psqo94XeJ3uCuVAPqWCVwNfN3LczlSyqnmr9V2jutceo1ZCDEeuAGYACwBnhRCGOwfXr8e6/K+\nbnB0MD2Z37MngKXAeGCl+b11BQvN76uzjlNfg/a72dWDwOdSylHA5+afncUazo4XnPx3uCeVAPog\npTwipTzm6DgG4xwxLwfelFK2SilzgWxgln2jGxZmAdlSypNSyjbgTbT3VrGSlPJroKrH4eXAy+bH\nLwNX2TWoc+gjXpejEsDQpJqreF8JIS50dDADEA/kd/m5wHzM2fxQCJFhrl47TXW/C1d5H3uSwGdC\niL1CiLsdHcwgxEgpi82PS4AYRwYzQM7+O9yNWycAIcRnQojMXr7OdVdXDCRJKacCPwNeF0IE2yfi\nIcfsFPqJ/SlgJDAV7T3+u0ODHV4uMP++LgXuFULMd3RAgyW18erOPmbd5X6HPR0dgCNJKS8Zwmta\ngVbz471CiBxgNGCXzrWhxAwUAoldfk4wH7OrgcYuhHgO+NjG4QyFU7yPgyWlLDR/LxNCvI/WlNVb\n35azKRVCjJBSFgshRgBljg7oXKSUpZbHTvw73I1b1wCGQggRZelAFUKMBEYBJx0bVb/WAzcIIXyE\nEKloMe9ycEzdmP/ALVagdWg7m93AKCFEqhDCG61jfb2DYzonIUSAECLI8hhYjHO+t71ZD9xmfnwb\n8KEDY+mXi/wOd+PWNYBzEUKsAP4DRAH/FUIckFJeBswHfieEaAdMwGoppVN0BvUVs5QySwjxFnAY\nMAL3Sik7HBlrL/4ihJiKVs0/BXzfseGcTUppFELcB2yG/2/vbnEqhqIoCq8dHEESCINAMIMaLJIJ\nMAQG8AQIPB4HJAiwOMIgCBNgBqAwF9Fn+AuvNaU561OVR7RdaZt7yxpw0Vp7mnisv2wDd0mgv96v\nWmv30470XZJroAM2k7wAC+AMuElyRL8t/OF0E372y7zdfz+Hv3IrCEkqyldAklSUAZCkogyAJBVl\nACSpKAMgSUUZAEkqygBIUlEGQBogyUOS/eXxaZLzqWeSxnIlsDTMgn4l+BawBxxMPI80miuBpYGS\nPAIbQNdae516HmksXwFJAyTZBXaAd2/+mjsDIK1oudvjJf2fqt6S/PRLQGk2DIC0giTrwC1w3Fp7\nBk7ovwdIs+U3AEkqyicASSrKAEhSUQZAkooyAJJUlAGQpKIMgCQVZQAkqagP8NUbnWZwbVYAAAAA\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x285069124a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Define x axis range using an even number of points to avoid division by 0\n",
"x = np.linspace(-6*np.pi, 6*np.pi, 100)\n",
"# Add label to x axis\n",
"plt.xlabel('$x$')\n",
"# Limit x axis between start and end point of the range\n",
"plt.xlim(x[0], x[-1])\n",
"# Limit y axis between -0.3 and 1\n",
"plt.ylim(-0.3, 1)\n",
"# Plot the first function\n",
"plt.plot(x, np.sin(x)/x, label='$\\sin(x)/x$')\n",
"# Plot the second function on the same plot\n",
"plt.plot(x, 1/np.abs(x), label='$1/|x|$')\n",
"# Add a legend\n",
"plt.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 08.3 (demographics and interactive plotting)\n",
"\n",
"A county planning body has requested an interactive tool to visualise the population distribution in Cambridgeshire (by district) from 2011 to 2021 for different population growth rate scenarios in each district. It can be assumed that:\n",
"\n",
"- the growth rates are constant in each district;\n",
"- the growth rate will not be negative in any district; and \n",
"- the annual growth rate in any one district will not exceed 10%.\n",
"\n",
"Building on the pie chart example with population data in the body of the notebook, create an interactive plot with:\n",
"\n",
"1. A slider for the year (from 2011 to 2021); and\n",
"2. Sliders for the annual population growth for each district (in percentage), with an \n",
" initial value of zero for each district."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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IX1YOPIjZtVEVYGYTU8a+ARwFvAysBMaYWUW4/VJgfvh6Lb7LCAcfBt2Kv4Z0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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x28506f065c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def demographics_plot(year=2011, grCC=0, grEX=0, grFL=0, grHS=0, grSC=0):\n",
" \n",
" # Initialize district tuple, population and annual growth arrays\n",
" district = ('Cambridge City', 'East Cambridgeshire', 'Fenland', 'Huntingdonshire',\n",
" 'South Cambridgeshire')\n",
" population = np.array((123900, 83800, 95300, 169500, 148800))\n",
" annual_growth = np.array((grCC, grEX, grFL, grHS, grSC))\n",
" \n",
" # Specify slice colours\n",
" colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral', 'red']\n",
"\n",
" # Explode the 1st slice (Cambridge City)\n",
" explode = (0.1, 0.0, 0, 0, 0)\n",
" \n",
" # Set figure size\n",
" plt.figure(figsize=(10,10))\n",
"\n",
" # Plot pie chart using a linear annual growth in population\n",
" plt.pie(population * (1 + (year-2011) * annual_growth / 100), explode=explode, labels=district, colors=colors,\n",
" autopct='%1.1f%%', shadow=True, startangle=90)\n",
"\n",
" # Add title\n",
" plt.title('{} population distribution in Cambridgeshire'.format(year))\n",
"\n",
"# Add sliders for the annual growth of each district\n",
"interact(demographics_plot,\n",
" year=(2011, 2021, 1),\n",
" grCC=(0, 10, 0.1),\n",
" grEX=(0, 10, 0.1),\n",
" grFL=(0, 10, 0.1),\n",
" grHS=(0, 10, 0.1),\n",
" grSC=(0, 10, 0.1));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 08.4 (crime reports by location)\n",
"\n",
"\n",
"### Background\n",
"\n",
"Your task is to produce a crime report data plot in the neighborhood of your college, by reported crime\n",
"category. It will be interesting to see how this varies between colleges!\n",
"\n",
"We can get crime data in the UK from the police data systems using what is known as a REST API,\n",
"and turn the data into a list of Python dictionaries. Each entry in the list is a police report \n",
"(an entry is a Python dictionary detailing the report).\n",
"\n",
"The first step is the import the modules we will be using:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import json\n",
"import requests"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The service https://data.police.uk has an interface where we can add specific strings to the URL (web address) to define what data we are intersted in, and the police server will return our requested data. The format is\n",
"\n",
" https://data.police.uk/api/crimes-street/all-crime?poly=[LAT0],[LON0]:[LAT1],[LON1]:[LAT2,LON2]&date=YYYY-MM\n",
" \n",
"This return crimes reports in the triangle given by the three geographic coordinate points `(latitude0, longitude0), (latitude1, longitude1) and (latitude2, longitude2)`, for the month `YYY-MM`. \n",
"\n",
"Below we create this URL string to include a large part of the Cambridge city centre. You can modify this for your own college or other area of interest (Google Maps is a handy way to get the geographic coordinates)."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# A triangle that includes most of the Cambridge city centre\n",
"# (long, lat) for three vertices of a triangle (no spaces!)\n",
"p0 = '52.211546,0.116465'\n",
"p1 = '52.203510,0.145500'\n",
"p2 = '52.189730,0.113050'\n",
"\n",
"# year-month of interest\n",
"year_month = '2016-05'\n",
"\n",
"# Construct request URL string using the above data\n",
"url = 'https://data.police.uk/api/crimes-street/all-crime?poly=' + p0 + ':' + p1 + ':' + p2 + '&date=' + year_month\n",
"\n",
"# Fetch data from https://data.police.uk\n",
"r = requests.get(url)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following converts the fetched data into a list of dictionaries:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"crime_data = r.json()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To get an idea of how the data is arranged, we can look at the first report in the list. To make the displayed data easier to read, we use the 'pretty print' module `pprint`."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'category': 'anti-social-behaviour',\n",
" 'context': '',\n",
" 'id': 48727186,\n",
" 'location': {'latitude': '52.205885',\n",
" 'longitude': '0.121266',\n",
" 'street': {'id': 560747, 'name': 'On or near Shopping Area'}},\n",
" 'location_subtype': '',\n",
" 'location_type': 'Force',\n",
" 'month': '2016-05',\n",
" 'outcome_status': None,\n",
" 'persistent_id': ''}\n"
]
}
],
"source": [
"import pprint\n",
"if crime_data:\n",
" pprint.pprint(crime_data[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Task\n",
"\n",
"\n",
"Produce a bar chart of the number of reports in different categories. The categories are: "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"categories = ('anti-social-behaviour', 'bicycle-theft', 'burglary', 'criminal-damage-arson', \\\n",
" 'drugs', 'other-crime', 'other-theft', 'public-order', 'shoplifting', \\\n",
" 'theft-from-the-person', 'vehicle-crime', 'violent-crime')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This function retrieves data from the UK police URL and returns it in a json; the default parameters for the coordinates are that of Cambridge used above:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_crime_data(year_month, p0='52.211546,0.116465', p1='52.203510,0.145500', p2='52.189730,0.113050'):\n",
" \"Get the crime data for a given year and month (in the format YYYY-MM) and coordinates\"\n",
" # Construct request URL string using the above data\n",
" url = 'https://data.police.uk/api/crimes-street/all-crime?poly=' + p0 + ':' + p1 + ':' + p2 + '&date=' + year_month\n",
" # Fetch data from https://data.police.uk\n",
" r = requests.get(url)\n",
" \n",
" return r.json()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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fpG90+4KSlgH2BabYXgMYB+wCHAhMs70yMC3fDiGE0JCRTHPdCni77eNsHwdsQflSG/MC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\nJXXEbH8F+EouQ7MzaU/tB22X2i+mV0U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"text/plain": [
"<matplotlib.figure.Figure at 0x285084c1cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def crime_plot(year_month, p0='52.211546,0.116465', p1='52.203510,0.145500', p2='52.189730,0.113050'):\n",
" \"Plot the crime data on a barplot for a given year and month (in the format YYYY-MM) and coordinates\"\n",
" # Get the crime data\n",
" crime_data = get_crime_data(year_month, p0, p1, p2)\n",
" # Initialize a dict for crime category frequencies\n",
" categories_freq = {}\n",
" # Count the frequencies\n",
" for crime in crime_data:\n",
" curr_category = crime['category']\n",
" if curr_category in categories_freq:\n",
" categories_freq[curr_category] += 1\n",
" else:\n",
" categories_freq[curr_category] = 1\n",
" \n",
" # Define values for x axis ticks\n",
" x_values = np.arange(len(categories_freq))\n",
" # Create barplot\n",
" plt.bar(x_values, categories_freq.values(), align='center')\n",
" # Add labels to x axis ticks\n",
" plt.xticks(x_values, categories_freq.keys(), rotation=90)\n",
" # Add axis labels\n",
" #plt.xlabel('Crime Category')\n",
" plt.ylabel('Number of Crimes')\n",
" # Add title\n",
" plt.title('Crimes in {}'.format(year_month))\n",
"\n",
"# Test for a month\n",
"crime_plot('2017-01')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run your program for different parts of Cambridge, starting with the area around your college, and for different months and years."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Hints:\n",
"\n",
"Create an empty dictionary, which will eventually map the report category to the number of incidents:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"categories_freq = {}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Iterate over all reports in the list, and extract the category string from each report. If the category string (the 'key') is already in the dictionary increment the associated counter. Otherwise add the key to the dictionary, and associate the value 1."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Iterate over all reports\n",
"for report in crime_data:\n",
" # Get category type from the report\n",
" category = report['category']\n",
" \n",
" if category in categories_freq:\n",
" # Increment counter here\n",
" pass # This can be removed once this 'if' block has a body\n",
" else:\n",
" # Add category to dictionary here\n",
" pass # This can be removed once this 'else' block has a body"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When adding the tick labels (crime categories), it may be necessary to rotate the labels, e.g.:\n",
"```python\n",
"plt.xticks(x_pos, categories, rotation='vertical')\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Extensions (optional)\n",
"\n",
"1. Probe the retrieved data to build a set of all crime categories in the data set.\n",
"2. Explore the temporal (time) aspect of the data. Thinks of ways to represent the change in reported incident \n",
" types over time."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a list containing all crimes in a given period:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Initialize the starting year and month and the number of months to retrieve\n",
"start_year, start_month, num_months = 2016, 1, 6\n",
"# Initialize an empty list for all crimes\n",
"all_crimes = []\n",
"#crime_freq = {}\n",
"\n",
"for unused in range(num_months):\n",
" # For every month in range get crime data\n",
" crime_data = get_crime_data(str(start_year) + '-' + str(start_month))\n",
" \n",
" # Append every crime retrieved to the list of all crimes\n",
" for crime in crime_data:\n",
" all_crimes.append([crime['id'], crime['month'], crime['category']])\n",
" \n",
" # Update month and year\n",
" start_month += 1\n",
" if start_month % 13 == 0:\n",
" start_month = 1\n",
" start_year += 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a plot to represent crimes by year-month and category (not sure about order of the data in the dictionary):"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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JgG3btrFz507c3NwICQlh/fr1dOjQodDf9bW63hVLK4CuAMaY5kBl4DTwFfCA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\nccmaTdVI2zYt0wh57GPHH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"text/plain": [
"<matplotlib.figure.Figure at 0x28509812c18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Initialize figure setting size\n",
"plt.figure(figsize=(20,20))\n",
"# for each category in the above list create a dict of frequencies and plot it on a line\n",
"for category in categories:\n",
" category_freq = {}\n",
" for crime in all_crimes:\n",
" if crime[2] == category:\n",
" if crime[1] in category_freq:\n",
" category_freq[crime[1]] += 1\n",
" else:\n",
" category_freq[crime[1]] = 1\n",
" # Define values for x axis ticks\n",
" x_values = np.arange(len(category_freq))\n",
" # Create a plot using dict values (not sure if they are in the right order here...)\n",
" plt.plot(x_values, list(category_freq.values()), '-o', label=category)\n",
" # Add x axis values labels using keys\n",
" plt.xticks(x_values, category_freq.keys(), rotation=90)\n",
"\n",
"# Add legend, title and labels\n",
"plt.legend()\n",
"plt.title('Crime categories in time')\n",
"plt.xlabel('Month')\n",
"plt.ylabel('Number of Crimes');"
]
}
],
"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"
},
"widgets": {
"state": {
"40ffa1766a434143b36dbfec987715b6": {
"views": [
{
"cell_index": 5
}
]
},
"ab0eb6706870489b910a631fd82c65d5": {
"views": [
{
"cell_index": 12
}
]
}
},
"version": "1.2.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
vadim-ivlev/STUDY | algorithms/WhatsUp2.ipynb | 1 | 2599 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## hello"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"code_folding": [],
"hide_input": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['b', 'c', 'd']\n",
"['b', 'd', 'c']\n"
]
}
],
"source": [
"class R:\n",
" \n",
" \n",
" def __init__(self, size=3):\n",
" self.__N=size\n",
" self.__h={}\n",
" self.__q=[]\n",
"\n",
" \n",
" \n",
" def __remove(self,k):\n",
" if k in self.__h:\n",
" del self.__h[k]\n",
" self.__q.remove(k)\n",
" \n",
" \n",
" def __make_recent(self,k):\n",
" if k in self.__q: \n",
" self.__q.remove(k)\n",
" self.__q.append(k)\n",
"\n",
" \n",
" def __append(self,k,v):\n",
" self.__h[k]=v\n",
" self.__q.append(k)\n",
"\n",
" \n",
" def __remove_oldest(self):\n",
" if len(self.__q) >0:\n",
" k = self.__q.pop(0)\n",
" del self.__h[k]\n",
" \n",
" \n",
" def put(self,k,v):\n",
" self.__remove(k)\n",
" \n",
" if len(self.__h) >= self.__N :\n",
" self.__remove_oldest()\n",
" \n",
" self.__append(k, v)\n",
" \n",
" \n",
" def get(self, k):\n",
" self.__make_recent(k)\n",
" return self.__h[k]\n",
" \n",
" @property\n",
" def q(self):\n",
" return self.__q\n",
" \n",
" \n",
" \n",
"r = R(3)\n",
"r.put('a','aa')\n",
"r.put('b','bb')\n",
"r.put('c','cc')\n",
"r.put('d','dd')\n",
"print(r.q)\n",
"r.get('c')\n",
"print(r.q)\n",
"\n",
"assert r.q == ['b', 'd', 'c']\n",
"\n",
" \n",
"# %save 'wu2.py'"
]
}
],
"metadata": {
"hide_input": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
happycube/kaggle2017 | twosigma/kernel-sdfoley.ipynb | 1 | 4481 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### This is my shortened version of sdfoley/sdf1's nice simple script. I was very impressed by it's simplicity, and decided to play code golf with it :)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'public_score': 0.026839613259479755}\n"
]
}
],
"source": [
"from collections import deque\n",
"from sklearn.linear_model import Ridge\n",
"from sklearn.linear_model import SGDRegressor\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"import kagglegym\n",
"import numpy as np\n",
"import pandas as pd\n",
"import random\n",
"import math\n",
"\n",
"random.seed(a=52)\n",
"\n",
"class Model:\n",
"\n",
" def __init__(self, df, model):\n",
"\n",
" self.cols = ['technical_20', 'technical_30']\n",
" self.scale = MinMaxScaler()\n",
" \n",
" self.regr = model\n",
"\n",
" df_cols = df[self.cols]\n",
" self.means = df_cols.mean()\n",
" \n",
" df_cols = df_cols.fillna(self.means)\n",
" x = self.scale.fit_transform(df_cols.values)\n",
"\n",
" self.regr.fit(x, df.y.values)\n",
"\n",
" # ------------------------------\n",
"\n",
" def predict(self, df):\n",
" df_cut = df[self.cols].fillna(self.means)\n",
" \n",
" x = self.scale.transform(df_cut.values)\n",
" \n",
" return self.regr.predict(x)\n",
"\n",
"env = kagglegym.make()\n",
"observation = env.reset()\n",
"\n",
"train = observation.train\n",
"\n",
"# build dataframe with volatility for each day\n",
"\n",
"df = train[['timestamp', 'y', 'technical_20', 'technical_30']].copy()\n",
"\n",
"grouped = df.groupby('timestamp')\n",
"\n",
"stdgroup = grouped[['technical_20', 'technical_30']].std()\n",
"stdgroup_mean = stdgroup.rolling(window=3, win_type='triang').mean().mean(axis=1)\n",
"\n",
"df_vol = pd.DataFrame({'vol': stdgroup_mean}, index=stdgroup.index)\n",
"\n",
"regressors = [(SGDRegressor(loss = 'epsilon_insensitive', fit_intercept = False, random_state = 52), [-math.inf, 33]),\n",
" (SGDRegressor(loss = 'huber', fit_intercept = False, random_state = 52), [33, 66]),\n",
" (Ridge(alpha = 200, random_state = 52), [66, math.inf])]\n",
"\n",
"models = []\n",
"\n",
"for reg, rawlimits in regressors:\n",
" limits = [0, 0]\n",
" limits[0] = np.nanpercentile(df_vol.vol, rawlimits[0]) if not (np.isinf(rawlimits[0])) else rawlimits[0]\n",
" limits[1] = np.nanpercentile(df_vol.vol, rawlimits[1]) if not (np.isinf(rawlimits[1])) else rawlimits[1]\n",
" \n",
" subset = (df_vol.vol > limits[0]) & (df_vol.vol <= limits[1])\n",
"\n",
" df_subset = train.loc[train.timestamp.isin(df_vol[subset].index)]\n",
" \n",
" models.append((Model(df_subset, reg), limits.copy()))\n",
"\n",
"vol = deque([0, 0, 0], 3)\n",
"\n",
"while True:\n",
" df = observation.features\n",
" target = observation.target\n",
" \n",
" vol.append((df.technical_20.std() + df.technical_30.std()) / 2)\n",
" curvol = sum(vol) / len(vol)\n",
" \n",
" for model, bounds in models:\n",
" if curvol > bounds[0] and curvol <= bounds[1]:\n",
" target.y = model.predict(df)\n",
" break\n",
" \n",
" observation, reward, done, info = env.step(target)\n",
" \n",
" if done:\n",
" break\n",
"\n",
"print(info)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
saketkc/notebooks | python/Mutation Logo.ipynb | 1 | 196878 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
},
"toc": "true"
},
"source": [
"# Table of Contents\n",
" <p>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [],
"source": [
"# Author: Saket Choudhar [saketkc\\\\gmail]\n",
"# License: GPL v3\n",
"# Copyright © 2017 Saket Choudhary<saketkc__AT__gmail>\n",
"\n",
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import set_matplotlib_formats\n",
"set_matplotlib_formats('png', 'pdf')\n",
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\"\n",
"import matplotlib as mpl\n",
"plt.rcParams['savefig.dpi'] = 80\n",
"plt.rcParams['figure.dpi'] = 80\n",
"plt.rcParams['figure.autolayout'] = False\n",
"plt.rcParams['figure.figsize'] = 12, 8\n",
"plt.rcParams['axes.labelsize'] = 18\n",
"plt.rcParams['axes.titlesize'] = 20\n",
"plt.rcParams['font.size'] = 16\n",
"plt.rcParams['lines.linewidth'] = 2.0\n",
"plt.rcParams['lines.markersize'] = 8\n",
"plt.rcParams['legend.fontsize'] = 14\n",
"import seaborn \n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('seaborn-ticks')\n",
"from matplotlib import transforms\n",
"import matplotlib.patheffects\n",
"from matplotlib.font_manager import FontProperties\n",
"from matplotlib.patches import Polygon, RegularPolygon\n",
"from matplotlib.collections import PatchCollection\n",
"import math\n",
"import numpy as np\n",
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
"\n",
"COLOR_SCHEME = {'G': 'orange',\n",
" 'A': 'red',\n",
" 'C': 'blue',\n",
" 'T': 'darkgreen'}\n",
"\n",
"BASES = list(COLOR_SCHEME.keys())\n",
"\n",
"\n",
"ALL_SCORES1 = [[('C', 0.02247014831444764),\n",
" ('T', 0.057903843733384308),\n",
" ('A', 0.10370837683591219),\n",
" ('G', 0.24803586793255664)],\n",
" [('T', 0.046608227674354567),\n",
" ('G', 0.048827667087419063),\n",
" ('A', 0.084338697696451109),\n",
" ('C', 0.92994511407402669)],\n",
" [('G', 0.0),\n",
" ('T', 0.011098351287382456),\n",
" ('A', 0.022196702574764911),\n",
" ('C', 1.8164301607015951)],\n",
" [('C', 0.020803153636453006),\n",
" ('T', 0.078011826136698756),\n",
" ('G', 0.11268374886412044),\n",
" ('A', 0.65529933954826969)],\n",
" [('T', 0.017393530660176126),\n",
" ('A', 0.030438678655308221),\n",
" ('G', 0.22611589858228964),\n",
" ('C', 0.45078233627623127)],\n",
" [('G', 0.022364103549245576),\n",
" ('A', 0.043412671595594352),\n",
" ('T', 0.097349627214363091),\n",
" ('C', 0.1657574733649966)],\n",
" [('C', 0.03264675899941203),\n",
" ('T', 0.045203204768416654),\n",
" ('G', 0.082872542075430544),\n",
" ('A', 1.0949220710572034)],\n",
" [('C', 0.0),\n",
" ('T', 0.0076232429756614498),\n",
" ('A', 0.011434864463492175),\n",
" ('G', 1.8867526364762088)],\n",
" [('C', 0.0018955903000026028),\n",
" ('T', 0.0094779515000130137),\n",
" ('A', 0.35637097640048931),\n",
" ('G', 0.58005063180079641)],\n",
" [('A', 0.01594690817903021),\n",
" ('C', 0.017541598996933229),\n",
" ('T', 0.2774762023151256),\n",
" ('G', 0.48638069946042134)],\n",
" [('A', 0.003770051401807444),\n",
" ('C', 0.0075401028036148881),\n",
" ('T', 0.011310154205422331),\n",
" ('G', 1.8624053924928772)],\n",
" [('C', 0.036479877757360731),\n",
" ('A', 0.041691288865555121),\n",
" ('T', 0.072959755514721461),\n",
" ('G', 1.1517218549109602)],\n",
" [('G', 0.011831087684038642),\n",
" ('T', 0.068620308567424126),\n",
" ('A', 0.10174735408273231),\n",
" ('C', 1.0009100180696691)],\n",
" [('C', 0.015871770937774379),\n",
" ('T', 0.018757547471915176),\n",
" ('A', 0.32176408355669878),\n",
" ('G', 0.36505073156881074)],\n",
" [('A', 0.022798100897300954),\n",
" ('T', 0.024064662058262118),\n",
" ('G', 0.24571286522646588),\n",
" ('C', 0.34070495229855319)]]\n",
"\n",
"ALL_SCORES2 = [[('A', 0.01653482213365913),\n",
" ('G', 0.026710097292833978),\n",
" ('C', 0.035613463057111966),\n",
" ('T', 0.057235922770358522)],\n",
" [('C', 0.020055669245080433),\n",
" ('G', 0.023816107228533015),\n",
" ('A', 0.031336983195438178),\n",
" ('T', 0.058913528407423782)],\n",
" [('T', 0.018666958185377256),\n",
" ('G', 0.084001311834197651),\n",
" ('A', 0.093334790926886277),\n",
" ('C', 0.30333807051238043)],\n",
" [('C', 0.0),\n",
" ('G', 0.0),\n",
" ('A', 0.32027512306044359),\n",
" ('T', 0.82203948252180525)],\n",
" [('C', 0.012698627658037786),\n",
" ('A', 0.053334236163758708),\n",
" ('T', 0.096509570201087178),\n",
" ('G', 0.10920819785912497)],\n",
" [('C', 0.0),\n",
" ('G', 0.089472611853783468),\n",
" ('A', 0.1930724782107959),\n",
" ('T', 0.22132698721725386)],\n",
" [('C', 0.020962390607965918),\n",
" ('A', 0.026202988259957396),\n",
" ('G', 0.066380903591892068),\n",
" ('T', 0.07336836712788071)],\n",
" [('G', 0.0),\n",
" ('A', 0.10236420974570831),\n",
" ('C', 0.15354631461856247),\n",
" ('T', 0.29173799777526871)],\n",
" [('G', 0.027681850851852024),\n",
" ('C', 0.089966015268519078),\n",
" ('A', 0.089966015268519078),\n",
" ('T', 0.53287562889815143)],\n",
" [('A', 0.034165612000664765),\n",
" ('C', 0.06833122400132953),\n",
" ('G', 0.072601925501412631),\n",
" ('T', 0.28186629900548432)],\n",
" [('G', 0.0),\n",
" ('A', 0.037325935579058833),\n",
" ('C', 0.23328709736911771),\n",
" ('T', 0.72785574379164719)],\n",
" [('A', 0.017470244196759552),\n",
" ('C', 0.062892879108334396),\n",
" ('G', 0.094339318662501587),\n",
" ('T', 0.19916078384305891)],\n",
" [('G', 0.0),\n",
" ('A', 0.096447131567581681),\n",
" ('C', 0.15844885900388422),\n",
" ('T', 0.48223565783790845)],\n",
" [('G', 0.0),\n",
" ('A', 0.069291952024925829),\n",
" ('C', 0.20787585607477749),\n",
" ('T', 0.46425607856700307)],\n",
" [('G', 0.0),\n",
" ('A', 0.0),\n",
" ('C', 0.21713201856318373),\n",
" ('T', 1.1495224512168551)],\n",
" [('G', 0.0),\n",
" ('A', 0.048934292002649343),\n",
" ('T', 0.27263391258618919),\n",
" ('C', 0.42642740173737281)],\n",
" [('A', 0.0),\n",
" ('G', 0.053607190685875404),\n",
" ('C', 0.2054942309625224),\n",
" ('T', 0.69689347891638032)],\n",
" [('G', 0.0),\n",
" ('A', 0.0),\n",
" ('C', 0.31312908494534769),\n",
" ('T', 0.84220926295645249)],\n",
" [('G', 0.0),\n",
" ('C', 0.068079835765814778),\n",
" ('A', 0.068079835765814778),\n",
" ('T', 1.3207488138568066)],\n",
" [('G', 0.020257705570431345),\n",
" ('A', 0.020257705570431345),\n",
" ('C', 0.048618493369035232),\n",
" ('T', 0.055371061892512348)],\n",
" [('G', 0.0),\n",
" ('A', 0.076286510680262556),\n",
" ('C', 0.20538675952378382),\n",
" ('T', 0.34622339462580698)]]\n",
"\n",
"\n",
"class Scale(matplotlib.patheffects.RendererBase):\n",
" def __init__(self, sx, sy=None):\n",
" self._sx = sx\n",
" self._sy = sy\n",
"\n",
" def draw_path(self, renderer, gc, tpath, affine, rgbFace):\n",
" affine = affine.identity().scale(self._sx, self._sy) + affine\n",
" renderer.draw_path(gc, tpath, affine, rgbFace)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [],
"source": [
"def draw_logo(all_scores, fontfamily='Arial', size=80):\n",
" mpl.rcParams['font.family'] = fontfamily\n",
"\n",
" fig, ax = plt.subplots(figsize=(len(all_scores), 2.5))\n",
"\n",
" font = FontProperties()\n",
" font.set_size(size)\n",
" font.set_weight('bold')\n",
" \n",
" #font.set_family(fontfamily)\n",
"\n",
" ax.set_xticks(range(1,len(all_scores)+1)) \n",
" ax.set_yticks(range(0,3))\n",
" ax.set_xticklabels(range(1,len(all_scores)+1), rotation=90)\n",
" ax.set_yticklabels(np.arange(0,3,1)) \n",
" seaborn.despine(ax=ax, trim=True)\n",
" \n",
" trans_offset = transforms.offset_copy(ax.transData, \n",
" fig=fig, \n",
" x=1, \n",
" y=0, \n",
" units='dots')\n",
" \n",
" for index, scores in enumerate(all_scores):\n",
" yshift = 0\n",
" for base, score in scores:\n",
" txt = ax.text(index+1, \n",
" 0, \n",
" base, \n",
" transform=trans_offset,\n",
" fontsize=80, \n",
" color=COLOR_SCHEME[base],\n",
" ha='center',\n",
" fontproperties=font,\n",
"\n",
" )\n",
" txt.set_path_effects([Scale(1.0, score)])\n",
" fig.canvas.draw()\n",
" window_ext = txt.get_window_extent(txt._renderer)\n",
" yshift = window_ext.height*score\n",
" trans_offset = transforms.offset_copy(txt._transform, \n",
" fig=fig,\n",
" y=yshift,\n",
" units='points')\n",
" trans_offset = transforms.offset_copy(ax.transData, \n",
" fig=fig, \n",
" x=1, \n",
" y=0, \n",
" units='points') \n",
" plt.tight_layout()\n",
" for o in fig.findobj():\n",
" o.set_clip_on(False)\n",
"def draw_logo_mutated(all_scores, fontfamily='Arial', size=80):\n",
" mpl.rcParams['font.family'] = fontfamily\n",
"\n",
" fig, ax = plt.subplots(figsize=(2.5*len(all_scores), 2.5))\n",
"\n",
" font = FontProperties()\n",
" font.set_size(size)\n",
" font.set_weight('bold')\n",
" \n",
" #font.set_family(fontfamily)\n",
"\n",
" \n",
" \n",
" trans_offset = transforms.offset_copy(ax.transData, \n",
" fig=fig, \n",
" x=2, \n",
" y=0, \n",
" units='dots')\n",
" \n",
" for index, scores in enumerate(all_scores):\n",
" yshift = 0\n",
" for base, score in scores:\n",
" txt = ax.text(2*index+1.25, \n",
" 0, \n",
" base, \n",
" transform=trans_offset,\n",
" fontsize=80, \n",
" color=COLOR_SCHEME[base],\n",
" ha='center',\n",
" fontproperties=font,\n",
" )\n",
" txt1 = ax.text(2*index+2.3, \n",
" 0, \n",
" base, \n",
" transform=trans_offset,\n",
" fontsize=80, \n",
" color=COLOR_SCHEME[base],\n",
" ha='center',\n",
" fontproperties=font,\n",
" )\n",
" ax.axvline(x=2*index+0.8, linewidth=1, color='grey')\n",
"\n",
" v = np.array([[2*index+1.7, 0.2], [2*index+1.85,0.4], [2*index+1.7, 0.6]])\n",
" go = Polygon(v, closed=False, fc='white', ec='black', \n",
" transform=trans_offset, linewidth=1)\n",
" ax.add_patch(go) \n",
" txt.set_path_effects([Scale(1.0, score)])\n",
" txt1.set_path_effects([Scale(1.0, score)])\n",
"\n",
" go.set_path_effects([Scale(1.0, score)])\n",
"\n",
" fig.canvas.draw()\n",
" \n",
" window_ext = txt.get_window_extent(txt._renderer) \n",
" yshift = window_ext.height*score\n",
" trans_offset = transforms.offset_copy(txt._transform, \n",
" fig=fig,\n",
" y=yshift,\n",
" units='points')\n",
" \n",
" \n",
" trans_offset = transforms.offset_copy(ax.transData, \n",
" fig=fig, \n",
" x=2, \n",
" y=0, \n",
" units='points') \n",
" ax.set_xticks([2*index+1.8 for index in np.arange(0, len(all_scores)+0.00)]) \n",
" ax.set_yticks(range(0,3))\n",
" ax.set_xticklabels(range(1,2*len(all_scores)), rotation=90)\n",
" ax.set_yticklabels(np.arange(0,3,1)) \n",
" seaborn.despine(ax=ax, bottom=True, trim=False)\n",
" plt.tight_layout()\n",
" for o in fig.findobj():\n",
" o.set_clip_on(False)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [],
"source": [
"def draw_conservation_scores(all_scores, conservation_scores, fontfamily='Arial', size=80):\n",
" mpl.rcParams['font.family'] = fontfamily\n",
" cms = mpl.cm\n",
" maps = [cms.jet, cms.gray, cms.autumn]\n",
" cmap = mpl.cm.seismic\n",
" fig, ax = plt.subplots(figsize=(len(all_scores)+0.5, 2.5))\n",
"\n",
" font = FontProperties()\n",
" font.set_size(size)\n",
" font.set_weight('bold')\n",
" \n",
" #font.set_family(fontfamily)\n",
" \n",
" \n",
" trans_offset = transforms.offset_copy(ax.transData, \n",
" fig=fig, \n",
" x=1, \n",
" y=0, \n",
" units='dots')\n",
" trans_offset2 = transforms.offset_copy(ax.transData, \n",
" fig=fig, \n",
" x=2, \n",
" y=0, \n",
" units='dots')\n",
" sc = ax.scatter(range(1, len(all_scores)+1), [-0.3]*len(all_scores), \n",
" s=100,\n",
" c=conservation_scores, \n",
" cmap=cmap, \n",
" transform=trans_offset )\n",
" \n",
" for index, scores in enumerate(all_scores):\n",
" yshift = 0\n",
" conservation_score = conservation_scores[index]\n",
" color = maps[0](conservation_score) \n",
" #circ = plt.Circle((index+1, -.25), 0.05, color=color, label=conservation_score)#, r=5)\n",
" #ax.add_artist(circ)#, clip_on=False)\n",
"\n",
" for base, score in scores:\n",
" txt = ax.text(index+1, \n",
" 0, \n",
" base, \n",
" transform=trans_offset,\n",
" fontsize=80, \n",
" color=COLOR_SCHEME[base],\n",
" ha='center',\n",
" fontproperties=font,\n",
"\n",
" )\n",
" \n",
" txt.set_path_effects([Scale(1.0, score)])\n",
" fig.canvas.draw()\n",
" window_ext = txt.get_window_extent(txt._renderer)\n",
" yshift = window_ext.height*score\n",
" trans_offset = transforms.offset_copy(txt._transform, \n",
" fig=fig,\n",
" y=yshift,\n",
" units='points')\n",
" trans_offset = transforms.offset_copy(ax.transData, \n",
" fig=fig, \n",
" x=1, \n",
" y=0, \n",
" units='points') \n",
" \n",
" ax.tick_params(axis='x', direction='out', pad=35, length=0)\n",
" #ax.scatter(conservation_scores, [0,0,0,0],\n",
" # c = conservation_scores)\n",
" #ax.margins(x=1)\n",
" \n",
" ax.set_xmargin(1)\n",
" ax.set_ymargin(1)\n",
" ax.set_xticks(range(1,len(all_scores)+1)) \n",
" ax.set_yticks(range(0,3))\n",
" ax.set_ylim(0,2)\n",
" ax.set_xticklabels(range(1,len(all_scores)+1), rotation=90)\n",
" ax.set_yticklabels(np.arange(0,3,1)) \n",
" seaborn.despine(ax=ax, bottom=True, trim=False, offset=15)\n",
" \n",
" #plt.legend()\n",
" #plt.colorbar()\n",
" \n",
" #norm = mpl.colors.Normalize(vmin=0.,vmax=1.)\n",
" #fig.colorbar(sc, cax=ax)\n",
" \n",
" divider = make_axes_locatable(ax)\n",
" cax = divider.append_axes('right', size='5%', pad=0.4)\n",
" \n",
" fig.colorbar(sc, cax=cax, orientation='vertical', cmap=cmap)\n",
"\n",
" #plt.legend(circ, 'Conservation')\n",
" #ax.colorbar()\n",
" #plt.tight_layout()\n",
" \n",
" for o in fig.findobj():\n",
" o.set_clip_on(False)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [],
"source": [
"def draw_conservation_scores2(all_scores, conservation_scores, padding=5,\n",
" fontfamily='Arial', size=80):\n",
" mpl.rcParams['font.family'] = fontfamily\n",
" cms = mpl.cm\n",
" maps = [cms.jet, cms.gray, cms.autumn]\n",
" cmap = mpl.cm.seismic\n",
" fig, (ax, bx) = plt.subplots(2, sharex=True, figsize=(len(all_scores)+0.5+2*padding, 2.5*2))\n",
"\n",
" font = FontProperties()\n",
" font.set_size(size)\n",
" font.set_weight('bold')\n",
" \n",
" #font.set_family(fontfamily)\n",
" \n",
" \n",
" trans_offset = transforms.offset_copy(ax.transData, \n",
" fig=fig, \n",
" x=1+padding, \n",
" y=0, \n",
" units='dots')\n",
" trans_offset2 = transforms.offset_copy(ax.transData, \n",
" fig=fig, \n",
" x=2+padding, \n",
" y=0, \n",
" units='dots')\n",
" #sc = ax.scatter(range(1+padding, len(all_scores)+1+padding), [-0.3]*len(all_scores), \n",
" # s=100,\n",
" # c=conservation_scores, \n",
" # cmap=cmap, \n",
" # transform=trans_offset )\n",
" sc = bx.bar(range(1, len(all_scores)+1+2*padding), [0.1]*padding + conservation_scores + [0.1]*padding)\n",
" \n",
" for index, scores in enumerate(all_scores):\n",
" yshift = 0\n",
" conservation_score = conservation_scores[index]\n",
" color = maps[0](conservation_score) \n",
" #circ = plt.Circle((index+1, -.25), 0.05, color=color, label=conservation_score)#, r=5)\n",
" #ax.add_artist(circ)#, clip_on=False)\n",
"\n",
" for base, score in scores:\n",
" txt = ax.text(index+1+padding, \n",
" 0, \n",
" base, \n",
" transform=trans_offset,\n",
" fontsize=80, \n",
" color=COLOR_SCHEME[base],\n",
" ha='center',\n",
" fontproperties=font,\n",
"\n",
" )\n",
" \n",
" txt.set_path_effects([Scale(1.0, score)])\n",
" fig.canvas.draw()\n",
" window_ext = txt.get_window_extent(txt._renderer)\n",
" yshift = window_ext.height*score\n",
" trans_offset = transforms.offset_copy(txt._transform, \n",
" fig=fig,\n",
" y=yshift,\n",
" units='points')\n",
" trans_offset = transforms.offset_copy(ax.transData, \n",
" fig=fig, \n",
" x=1+padding, \n",
" y=0, \n",
" units='points') \n",
" \n",
" ax.tick_params(axis='x', direction='out', pad=35, length=0)\n",
" #ax.scatter(conservation_scores, [0,0,0,0],\n",
" # c = conservation_scores)\n",
" #ax.margins(x=1)\n",
" \n",
" ax.set_xmargin(1)\n",
" ax.set_ymargin(1)\n",
" ax.set_xticks(range(1,len(all_scores)+1)) \n",
" ax.set_yticks(range(0,3))\n",
" ax.set_ylim(0,2)\n",
" ax.set_xticklabels(range(1,len(all_scores)+1), rotation=90)\n",
" ax.set_yticklabels(np.arange(0,3,1)) \n",
" seaborn.despine(ax=ax, bottom=True, trim=False, offset=15+padding)\n",
" \n",
" seaborn.despine(ax=bx, bottom=True, trim=False, offset=15+padding)\n",
"\n",
" #plt.legend()\n",
" #plt.colorbar()\n",
" \n",
" #norm = mpl.colors.Normalize(vmin=0.,vmax=1.)\n",
" #fig.colorbar(sc, cax=ax)\n",
" \n",
" #divider = make_axes_locatable(ax)\n",
" #cax = divider.append_axes('right', size='5%', pad=0.4)\n",
" \n",
" #fig.colorbar(sc, cax=cax, orientation='vertical', cmap=cmap)\n",
"\n",
" #plt.legend(circ, 'Conservation')\n",
" #ax.colorbar()\n",
" #plt.tight_layout()\n",
" \n",
" for o in fig.findobj():\n",
" o.set_clip_on(False)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
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QMA/7XeA5tSRNwgEfI3oUQEkYSKIfl3mMD4E0D4agAR6zl1eVEBVvZaO44cd2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\nRU9GCg==\n",
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5qzFxonTVVVJWVoMP/epX0iOPOOsttHWrKf/OO6G1XUIEtYZfJqH6+GOpsFDq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\ndDG/I65bM0NycDCYpPjoqXX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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc2f40a7d10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"draw_conservation_scores2(ALL_SCORES1[1:8], conservation_scores = [0.1,0.2,0.3,0.15,0.03,1,0.4])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"application/pdf": 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6ApuQqEDqK4UlJCobchIejC9CKtx67kbZLAOquEI3tWUkzXW6BqYtrdpHOxmJ\n9o+jf8NIhd8yjG30Pxip8O5nd/ZipMIPIKWJLUaysGEkSvBtwoORtC+UY8lSEvUqQ4ChJCYQglSd\nei1KKlobhQEOnimpsdgZAhYlcWV2w4a7LfMnZBvJkhJX/JEhWFLSXKWqZTYpFX6Isne2ScmCJjQM\nukjJJECblKiAG0FoSInK+u6xRUoFsZNHvC2mKamungwp0TCoQHRq7bYsjMaoFivRrmVNjsVKjCLX\nrbVpiY4JRR2zaakwAR00YGjJwoZ/GMl+jPcL2PCPZZ47LX13bzcX7hfF0N+A16iXa37VtRK1CniC\nEwfUlKIulZjlibtjvGWH8RHs1TJ/zw97StMnwkq1/HbMUy+dCzdFyep8bFx4u6eyqiR9Y0mA07hZ\nvECuNLF/esY25ZaGr0NTLC9FVzrGYlc/8rZJK7pSBH7CorflS4oYeLMu9NI76kNYQLz1ZMDMz/U4\nUKsK2HDha2R+9s5I5ZFL5GeVrAa8QVfJvrrYLm25Nx/p4OceFm/vSkVn1gIb/HhsY5mmy6i8pN1g\nf7H2520Z6O1mCjV8VfRLTVGXxelU7qmAsEDuxv96DhJKX+o3zJN1LOh6tU4loMIs/IorH5LwBjSv\ncjjFeDu/Oealmd9WisNSSNtDqiMqck06mfj5U9iNGRRW9TjBkBAsSZPtOtRaIOMCy0cfwW6aeCbW\nLeC5z1NBTEyrfO3+a7wEEDUx526fGjDr4WCmUUBGMlryuRwmTjxAGxUGdjyKTp7J0hTKooGPrsV2\nj9F5L14/HmBUJXEjlcgMijkmPqVtfJVth7/BkQB1U5m226jcjmihO2U5QOtS13pUTFfx9g64joXJ\ncimTLoQPEhtLCvqSXfTq6shx+FrB8dNDljB0KYPtSjSMwRQHJSM/ULAZgzUld5fSQRgkwdBfx2/G\nKMJnOJHg5gESNqgZv7eMUbg1339vmvITYDV0VQdjFD3DV/UNYeBv+ISE6OIL2FE3qpKhi43ZuDDo\nIostcXPF7ttSxdZyM4UZzp7/e+SWKYyRTNNtzs0V2/KWKoyTNlUYd26u2J63VLFRyxQqwGcVsKiC\nXcXcwcUV1AqB3Q21qIIDwLqbXDZUoUttm+CY/5rada0MUxT9vy7oA9hNmSQFNcBiCk12kvpuEwVz\njdjNt5kCaR8soS0XURjMxsNGF00YkZsndueWJraamyXMePbU30O3LGGsZJsue26W2KY3JGGctFnC\nuHPThGGCF5b4X55TgoEKZW5kc3RyZWFtCmVuZG9iagoxMSAwIG9iago3ODIwCmVuZG9iagoxNiAw\nIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDkwID4+CnN0cmVhbQp4nE2NQRLA\nIAgD77wiT1BE0P90etL/X6vUDr3ATgKJFkWC9DVqSzDuuDIVa1ApmJSXwFUwXAva7qLK/jJJTJ2G\n03u3A4Oy8XGD0kn79nF6AKv9egbdD9IcIlgKZW5kc3RyZWFtCmVuZG9iagoxNyAwIG9iago8PCAv\nRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDIxMCA+PgpzdHJlYW0KeJw1UMsNQzEIu2cKFqgU\nAoFknla9df9rbdA7YRH/QljIlAh5qcnOKelLPjpMD7Yuv7EiC611JezKmiCeK++hmbKx0djiYHAa\nJl6AFjdg6GmNGjV04YKmLpVCgcUl8Jl8dXvovk8ZeGoZcnYEEUPJYAlquhZNWLQ8n5BOAeL/fsPu\nLeShkvPKnhv5G5zt8DuzbuEnanYi0XIVMtSzNMcYCBNFHjx5RaZw4rPWd9U0EtRmC06WAa5OP4wO\nAGAiXlmA7K5EOUvSjqWfb7zH9w9AAFO0CmVuZHN0cmVhbQplbmRvYmoKMTggMCBvYmoKPDwgL0Zp\nbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAzMzggPj4Kc3RyZWFtCnicNVI5rt1ADOt9Cl0ggHbN\nnOcFqX7u34aUXwpDtFaKmo4WlWn5ZSFVLZMuv+1JbYkb8vfJCokTklcl2qUMkVD5PIVUv2fLvL7W\nnBEgS5UKk5OSxyUL/gyX3i4c52NrP48jdz16YFWMhBIByxQTo2tZOrvDmo38PKYBP+IRcq5Ytxxj\nFUgNunHaFe9D83nIGiBmmJaKCl1WiRZ+QfGgR61991hUWCDR7RxJcIyNUJGAdoHaSAw5sxa7qC/6\nWZSYCXTtiyLuosASScycYl06+g8+dCyovzbjy6+OSvpIK2tM2nejSWnMIpOul0VvN299PbhA8y7K\nf17NIEFT1ihpfNCqnWMomhllhXccmgw0xxyHzBM8hzMSlPR9KH5fSya6KJE/Dg2hf18eo4ycBm8B\nc9GftooDF/HZYa8cYIXSxZrkfUAqE3pg+v/X+Hn+/AMctoBUCmVuZHN0cmVhbQplbmRvYmoKMTkg\nMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyNDggPj4Kc3RyZWFtCnicLVE5\nkgNBCMvnFXpCc9PvscuR9//pCsoBg4ZDIDotcVDGTxCWK97yyFW04e+ZGMF3waHfynUbFjkQFUjS\nGFRNqF28Hr0HdhxmAvOkNSyDGesDP2MKN3pxeEzG2e11GTUEe9drT2ZQMisXccnEBVN12MiZw0+m\njAvtXM8NyLkR1mUYpJuVxoyEI00hUkih6iapM0GQBKOrUaONHMV+6csjnWFVI2oM+1xL29dzE84a\nNDsWqzw5pUdXnMvJxQsrB/28zcBFVBqrPBAScL/bQ/2c7OQ33tK5s8X0+F5zsrwwFVjx5rUbkE21\n+Dcv4vg94+v5/AOopVsWCmVuZHN0cmVhbQplbmRvYmoKMjAgMCBvYmoKPDwgL0ZpbHRlciAvRmxh\ndGVEZWNvZGUgL0xlbmd0aCA4MCA+PgpzdHJlYW0KeJxFjLsNwDAIRHumYAR+JmafKJWzfxsgStxw\nT7p7uDoSMlPeYYaHBJ4MLIZT8QaZo2A1uEZSjZ3so7BuX3WB5npTq/X3BypPdnZxPc3LGfQKZW5k\nc3RyZWFtCmVuZG9iagoxNCAwIG9iago8PCAvRm9udERlc2NyaXB0b3IgMTMgMCBSIC9OYW1lIC9E\nZWphVnVTYW5zCi9Gb250TWF0cml4IFsgMC4wMDEgMCAwIDAuMDAxIDAgMCBdIC9CYXNlRm9udCAv\nRGVqYVZ1U2FucyAvV2lkdGhzIDEyIDAgUgovU3VidHlwZSAvVHlwZTMgL0NoYXJQcm9jcyAxNSAw\nIFIgL1R5cGUgL0ZvbnQgL0ZpcnN0Q2hhciAwCi9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAx\nMjMzIF0KL0VuY29kaW5nIDw8IC9EaWZmZXJlbmNlcyBbIDQ4IC96ZXJvIC9vbmUgL3R3byAvdGhy\nZWUgL2ZvdXIgXSAvVHlwZSAvRW5jb2RpbmcgPj4KL0xhc3RDaGFyIDI1NSA+PgplbmRvYmoKMTMg\nMCBvYmoKPDwgL0Rlc2NlbnQgLTIzNiAvRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBd\nIC9TdGVtViAwIC9GbGFncyAzMgovWEhlaWdodCAwIC9UeXBlIC9Gb250RGVzY3JpcHRvciAvRm9u\ndE5hbWUgL0RlamFWdVNhbnMgL01heFdpZHRoIDEzNDIKL0NhcEhlaWdodCAwIC9JdGFsaWNBbmds\nZSAwIC9Bc2NlbnQgOTI5ID4+CmVuZG9iagoxMiAwIG9iagpbIDYwMCA2MDAgNjAwIDYwMCA2MDAg\nNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwCjYwMCA2\nMDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgMzE4IDQw\nMSA0NjAgODM4IDYzNgo5NTAgNzgwIDI3NSAzOTAgMzkwIDUwMCA4MzggMzE4IDM2MSAzMTggMzM3\nIDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYKNjM2IDYzNiAzMzcgMzM3IDgzOCA4Mzgg\nODM4IDUzMSAxMDAwIDY4NCA2ODYgNjk4IDc3MCA2MzIgNTc1IDc3NSA3NTIgMjk1CjI5NSA2NTYg\nNTU3IDg2MyA3NDggNzg3IDYwMyA3ODcgNjk1IDYzNSA2MTEgNzMyIDY4NCA5ODkgNjg1IDYxMSA2\nODUgMzkwIDMzNwozOTAgODM4IDUwMCA1MDAgNjEzIDYzNSA1NTAgNjM1IDYxNSAzNTIgNjM1IDYz\nNCAyNzggMjc4IDU3OSAyNzggOTc0IDYzNCA2MTIKNjM1IDYzNSA0MTEgNTIxIDM5MiA2MzQgNTky\nIDgxOCA1OTIgNTkyIDUyNSA2MzYgMzM3IDYzNiA4MzggNjAwIDYzNiA2MDAgMzE4CjM1MiA1MTgg\nMTAwMCA1MDAgNTAwIDUwMCAxMzQyIDYzNSA0MDAgMTA3MCA2MDAgNjg1IDYwMCA2MDAgMzE4IDMx\nOCA1MTggNTE4CjU5MCA1MDAgMTAwMCA1MDAgMTAwMCA1MjEgNDAwIDEwMjMgNjAwIDUyNSA2MTEg\nMzE4IDQwMSA2MzYgNjM2IDYzNiA2MzYgMzM3CjUwMCA1MDAgMTAwMCA0NzEgNjEyIDgzOCAzNjEg\nMTAwMCA1MDAgNTAwIDgzOCA0MDEgNDAxIDUwMCA2MzYgNjM2IDMxOCA1MDAKNDAxIDQ3MSA2MTIg\nOTY5IDk2OSA5NjkgNTMxIDY4NCA2ODQgNjg0IDY4NCA2ODQgNjg0IDk3NCA2OTggNjMyIDYzMiA2\nMzIgNjMyCjI5NSAyOTUgMjk1IDI5NSA3NzUgNzQ4IDc4NyA3ODcgNzg3IDc4NyA3ODcgODM4IDc4\nNyA3MzIgNzMyIDczMiA3MzIgNjExIDYwNQo2MzAgNjEzIDYxMyA2MTMgNjEzIDYxMyA2MTMgOTgy\nIDU1MCA2MTUgNjE1IDYxNSA2MTUgMjc4IDI3OCAyNzggMjc4IDYxMiA2MzQKNjEyIDYxMiA2MTIg\nNjEyIDYxMiA4MzggNjEyIDYzNCA2MzQgNjM0IDYzNCA1OTIgNjM1IDU5MiBdCmVuZG9iagoxNSAw\nIG9iago8PCAvZm91ciAxNiAwIFIgL3plcm8gMTcgMCBSIC9vbmUgMjAgMCBSIC90aHJlZSAxOCAw\nIFIgL3R3byAxOSAwIFIgPj4KZW5kb2JqCjMgMCBvYmoKPDwgL0YxIDE0IDAgUiA+PgplbmRvYmoK\nNCAwIG9iago8PCAvQTIgPDwgL0NBIDEgL1R5cGUgL0V4dEdTdGF0ZSAvY2EgMSA+PgovQTEgPDwg\nL0NBIDAgL1R5cGUgL0V4dEdTdGF0ZSAvY2EgMSA+PiA+PgplbmRvYmoKNSAwIG9iago8PCA+Pgpl\nbmRvYmoKNiAwIG9iago8PCA+PgplbmRvYmoKNyAwIG9iago8PCA+PgplbmRvYmoKMiAwIG9iago8\nPCAvQ291bnQgMSAvS2lkcyBbIDEwIDAgUiBdIC9UeXBlIC9QYWdlcyA+PgplbmRvYmoKMjEgMCBv\nYmoKPDwgL0NyZWF0aW9uRGF0ZSAoRDoyMDE3MDYwNDAxMzY0My0wNycwMCcpCi9Qcm9kdWNlciAo\nbWF0cGxvdGxpYiBwZGYgYmFja2VuZCkKL0NyZWF0b3IgKG1hdHBsb3RsaWIgMi4wLjEsIGh0dHA6\nLy9tYXRwbG90bGliLm9yZykgPj4KZW5kb2JqCnhyZWYKMCAyMgowMDAwMDAwMDAwIDY1NTM1IGYg\nCjAwMDAwMDAwMTYgMDAwMDAgbiAKMDAwMDAxMTUwNSAwMDAwMCBuIAowMDAwMDExMzExIDAwMDAw\nIG4gCjAwMDAwMTEzNDMgMDAwMDAgbiAKMDAwMDAxMTQ0MiAwMDAwMCBuIAowMDAwMDExNDYzIDAw\nMDAwIG4gCjAwMDAwMTE0ODQgMDAwMDAgbiAKMDAwMDAwMDA2NSAwMDAwMCBuIAowMDAwMDAwNDAy\nIDAwMDAwIG4gCjAwMDAwMDAyMDggMDAwMDAgbiAKMDAwMDAwODI5NyAwMDAwMCBuIAowMDAwMDEw\nMTcyIDAwMDAwIG4gCjAwMDAwMDk5NzIgMDAwMDAgbiAKMDAwMDAwOTY0NyAwMDAwMCBuIAowMDAw\nMDExMjI1IDAwMDAwIG4gCjAwMDAwMDgzMTggMDAwMDAgbiAKMDAwMDAwODQ4MCAwMDAwMCBuIAow\nMDAwMDA4NzYzIDAwMDAwIG4gCjAwMDAwMDkxNzQgMDAwMDAgbiAKMDAwMDAwOTQ5NSAwMDAwMCBu\nIAowMDAwMDExNTY1IDAwMDAwIG4gCnRyYWlsZXIKPDwgL0luZm8gMjEgMCBSIC9Sb290IDEgMCBS\nIC9TaXplIDIyID4+CnN0YXJ0eHJlZgoxMTcxMwolJUVPRgo=\n",
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BUaNcf35ERPEJ1DXXAI8+6nrbS5bIBlFuUqVKFcyfPx/fffcdZs+ejXbt2uH3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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc2f473ab50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"draw_logo_mutated(ALL_SCORES1[1:5])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/cmb-panasas2/skchoudh/software_frozen/anaconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family [u'Verdana'] not found. Falling back to DejaVu Sans\n",
" (prop.get_family(), self.defaultFamily[fontext]))\n"
]
},
{
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7piVZzmKTv5FggyLBQa7r24OwySJtmr8xIZHGYkt7M4sbqJotr/TiKUOi0sbJ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\nZWYKMjEzOTcKJSVFT0YK\n",
"image/png": 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LZBUSb74Jc+bYidWwIUydCn372ol3OE2bwjPPwAMPSGHIl1925ji6IixEpk6F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\nNjBpEhx7rJ15BcrthqOOCu0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Jcn2dkCnX3mG0KtvtlgUobdvKJZLHYAf76NFyzTB4MFx2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\nhmEYhmEYhmEYhmEYpkvw/wHHppgVDl9E5wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc66681c310>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"draw_logo(ALL_SCORES1, 'Verdana')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"run_control": {
"frozen": false,
"read_only": false
}
},
"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": "2.7.13"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "12px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": true,
"toc_section_display": "block",
"toc_window_display": true,
"widenNotebook": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-2-clause |
rougier/Neurosciences | basal-ganglia/Chakrabarthy-2013/BG_model.ipynb | 1 | 10940 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Distributed under the (new) BSD License.\n",
"Copyright (c) 2014, Nicolas P. Rougier\n",
"\n",
"Contributors: Nicolas P. Rougier ([email protected]),\n",
" Meropi Topalidou ([email protected])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Packages import"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from dana import *\n",
"import matplotlib.pyplot as plt\n",
"#%pylab --no-import-all"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Helper functions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def H(x):\n",
" return 1 if x > 0 else 0\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simulation parameters"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Population size\n",
"n = 20\n",
"\n",
"# Default Time resolution\n",
"dt = 1.0*millisecond\n",
"\n",
"# Default trial duration\n",
"duration = 100.0*dt\n",
"\n",
"# Initialization of the random generator (reproductibility !)\n",
"np.random.seed(1)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Dopamine levels\n",
"delta = -2"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Time constants\n",
"tau_GPe = 10\n",
"tau_STN = 10\n",
"tau_GPi = 10"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"theta_D1 =0.1\n",
"theta_D2 = 0.1\n",
"theta_GPe = 0.1\n",
"theta_STN = 0.1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Compute of parameters\n",
"lamda_D1 = 5*(1/(1+np.exp(-6*(delta - theta_D1))))\n",
"lamda_D2 = 5*(1/(1+np.exp(-6*(theta_D2 - delta))))\n",
"lamda_GPe = 4 * H(theta_GPe - delta) + 1; \n",
"lamda_STN = 4 * H(theta_STN - delta) + 1\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"r = np.zeros((n,n,n,n))\n",
"for i in range(0,n):\n",
" for j in range(0,n):\n",
" for p in range(0,n):\n",
" for q in range(0,n):\n",
" r[i,j,p,q] = np.sqrt((i-p)**2 + (j-q)**2)\n",
" \n",
"d = np.zeros((n,n))\n",
"for i in range(0,n):\n",
" for p in range(0,n):\n",
" d[i,p] = np.abs(i-p)\n",
"\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"k_x = 2*np.pi/n \n",
"A = 10 \n",
"sigma =1.2\n",
"C = 0.2\n",
"#parameters A, sigma, Ce{0.1,0.3}, k_x, tau are from the article from Standage "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"W_lat = 1\n",
"#W_lat = sigma_STN * np.exp(-r^2/sigma_lat^2 ) if r < R else -1 if r =0 else 0; r; R; sigma_STN = 1; sigma_lat = 0.2; \n",
"#w_sg = 1\n",
"#w_gs = 1\n",
"#W_GPe = np.ones((1,n))\n",
"W_GPi = A * np.exp(-d**2/(2*sigma**2)) - C;\n",
"#W_STN_GPi = np.ones((1,n))*1./n\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Populations"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Striatum_D1 = zeros((n,1), \"\"\"du/dt = -u + V + I_ext; \n",
" V = np.tanh(lamda_D1* u); \n",
" I_ext\"\"\")\n",
" \n",
"Striatum_D2 = zeros((n,1), \"\"\"du/dt = -u + V + I_ext;\n",
" V = np.tanh(lamda_D2* u); \n",
" I_ext\"\"\") \n",
"\n",
"GPe = zeros((n,n), \"\"\"dx/dt = (-x + W_lat * np.ones(U.shape) * np.sum(U) + I_STN + I_Str) / tau_GPe;\n",
" U = np.tanh(lamda_GPe * x); \n",
" I_STN; I_Str\"\"\")\n",
" #I_Str = W_GPe * V_D2; \n",
" \n",
" \n",
"STN = zeros((n,n), \"\"\"dx/dt = (-x + W_lat * np.ones(U.shape) * np.sum(U) + I_GPe) / tau_STN; \n",
" U = np.tanh(lamda_STN * x); I_GPe\"\"\")\n",
" \n",
"\n",
"GPi = zeros((n,1), \"\"\"du/dt = (-u + np.dot(W_GPi, S) * k_x + I) / tau_GPi; \n",
" S = u**2 / (1 + 1/2 * k_x * np.sum(u**2)); \n",
" I = V_D1 + U_STN; U_STN ; V_D1\"\"\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Connectivity"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"DenseConnection( Striatum_D2('V'), GPe('I_Str'), np.ones((1,n)) )\n",
"DenseConnection( STN('U'), GPe('I_STN'), 1.0 )\n",
"DenseConnection( GPe('U'), STN('I_GPe'), 1.0 )\n",
"DenseConnection( Striatum_D1('V'), GPi('V_D1'), 1.0 )\n",
"DenseConnection( STN('U'), GPi('U_STN'), np.ones((1,n))*1./n )\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<dana.dense_connection.DenseConnection at 0x106bc3290>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Stimulus"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"@clock.at(1*millisecond)\n",
"def stimulus(time):\n",
" sigma = 0.4 #sigmaE{0.3,0.5}\n",
" d = np.linspace(0,n-1,n).reshape((n,1))\n",
" I = np.zeros((n,1))\n",
" first_start = 3\n",
" first_num_neur = 1\n",
" first = np.arange(first_start, first_start + first_num_neur)\n",
" second_start = 13\n",
" second_num_neur = 1\n",
" second = np.arange(second_start, second_start + second_num_neur)\n",
" I[first] = 0.005\n",
" I[second] = 0.2\n",
" plt.plot(I)\n",
" plt.show()\n",
" Striatum_D1['I_ext'] = I\n",
" Striatum_D2['I_ext'] = I\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Save GPi's activity in each tick"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Gpi = np.zeros((int(duration*1000),n))\n",
"@after(clock.tick)\n",
"def GP_i(t):\n",
" index = int(t*1000)\n",
" Gpi[index,:] = GPi[\"S\"].T"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run simulation"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"run(time=duration, dt=dt)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Choice"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"choice = 1 if np.sum(GPi(\"S\")[0:10]) > np.sum(GPi(\"S\")[10:20]) else 2\n",
"print choice\n",
"print \"diff = \" ,np.abs(np.sum(GPi(\"S\")[0:10]) - np.sum(GPi(\"S\")[10:20]))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1\n",
"diff = 1.57462194108e-08\n"
]
}
],
"prompt_number": 45
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Plot of output of GPi"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(Striatum_D2(\"V\"))\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"test = (GPi(\"S\") - np.min(GPi(\"S\")))/(np.max(GPi(\"S\")) - np.min(GPi(\"S\")))\n",
"plt.plot(test)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(Gpi[:,2],\"b\")\n",
"plt.plot(Gpi[:,3],\"b\")\n",
"plt.plot(Gpi[:,12],\"r\")\n",
"plt.plot(Gpi[:,13],\"r\")\n",
"\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print Gpi[-1,4] - Gpi[-1,13]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-3.92778118003e-10\n"
]
}
],
"prompt_number": 19
}
],
"metadata": {}
}
]
} | bsd-3-clause |
jazracherif/algorithms | quicksort/quicksort.ipynb | 1 | 12330 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quicksort \n",
"\n",
"\n",
"## Question 1\n",
"The file quicksort.txt contains all of the integers between 1 and 10,000 (inclusive, with no repeats) in unsorted order. The integer in the ith row of the file gives you the ith entry of an input array.\n",
"\n",
"Your task is to compute the total number of comparisons used to sort the given input file by QuickSort. As you know, the number of comparisons depends on which elements are chosen as pivots, so we'll ask you to explore three different pivoting rules.\n",
"\n",
"You should not count comparisons one-by-one. Rather, when there is a recursive call on a subarray of length m, you should simply add m−1 to your running total of comparisons. (This is because the pivot element is compared to each of the other m−1 elements in the subarray in this recursive call.)\n",
"\n",
"WARNING: The Partition subroutine can be implemented in several different ways, and different implementations can give you differing numbers of comparisons. For this problem, you should implement the Partition subroutine exactly as it is described in the video lectures (otherwise you might get the wrong answer).\n",
"\n",
"DIRECTIONS FOR THIS PROBLEM:\n",
"\n",
"For the first part of the programming assignment, you should always use the first element of the array as the pivot element.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 353,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"myCount 162085\n",
"COUNT 162085\n"
]
}
],
"source": [
"COUNT = 0\n",
"\n",
"array = [5, 4, 10, 2, 6, 9]\n",
"# array = [5, 4, 10, 2, 6, 9, 60, 59, 58, 56]\n",
"# array = [2148, 9058, 7742, 3153, 6324, 609, 7628, 5469, 7017, 504]\n",
"\n",
"fp = open(\"quickSort.txt\", 'r')\n",
"data = fp.readlines()\n",
"array = [int(x.strip()) for x in data]\n",
"#array = array[:100]\n",
"\n",
"#print (\"START\", array)\n",
"\n",
"def quicksort(i, j):\n",
" #print (\"quicksort\",i, j)\n",
" if j-i < 1:\n",
" return 0\n",
" \n",
" # Count comparisons\n",
" k = partition(i, j)\n",
" \n",
" countleft = quicksort( i, k-1)\n",
" countright = quicksort( k+1, j)\n",
"\n",
" count = countleft + countright + (j-i)\n",
"\n",
" #print(array)\n",
" #print (\"countleft , countright, i, j\", countleft , countright, i, j)\n",
" return count\n",
"\n",
" \n",
"def swap(i, j):\n",
" #print(\"swap\", i, j, array)\n",
"\n",
" temp = array[j]\n",
" array[j] = array[i]\n",
" array[i] = temp\n",
"\n",
"def partition(i, j):\n",
" global COUNT\n",
" \n",
" #print (\"Partition\", i, j, array[i:j+1])\n",
" \n",
" START_IDX = i+1\n",
" END_IDX = j \n",
" PIVOT_IDX = i\n",
" \n",
" pivot = array[PIVOT_IDX]\n",
" s = i + 1\n",
" \n",
" #print (START_IDX, END_IDX+1)\n",
" for k in range(START_IDX, END_IDX+1): \n",
" COUNT += 1 \n",
" #print (k)\n",
"\n",
" if array[k] < pivot: \n",
" swap(s, k) \n",
" s+=1 \n",
" \n",
" swap(PIVOT_IDX, s-1) \n",
" \n",
" #print (\"DONE\", array)\n",
" return s-1\n",
"\n",
"\n",
"myCount = quicksort(0, len(array)-1) \n",
"#print (array)\n",
"print (\"myCount\", myCount)\n",
"print (\"COUNT\", COUNT)\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Question 2 \n",
"\n",
"GENERAL DIRECTIONS AND HOW TO GIVE US YOUR ANSWER:\n",
"\n",
"See the first question.\n",
"\n",
"DIRECTIONS FOR THIS PROBLEM:\n",
"\n",
"Compute the number of comparisons (as in Problem 1), always using the final element of the given array as the pivot element. Again, be sure to implement the Partition subroutine exactly as it is described in the video lectures.\n",
"\n",
"Recall from the lectures that, just before the main Partition subroutine, you should exchange the pivot element (i.e., the last element) with the first element.\n"
]
},
{
"cell_type": "code",
"execution_count": 350,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"myCount 164123\n",
"COUNT 164123\n"
]
}
],
"source": [
"COUNT = 0\n",
"\n",
"array = [5, 4, 10, 2, 6, 9]\n",
"# array = [5, 4, 10, 2, 6, 9, 60, 59, 58, 56]\n",
"# array = [2148, 9058, 7742, 3153, 6324, 609, 7628, 5469, 7017, 504]\n",
"\n",
"fp = open(\"quickSort.txt\", 'r')\n",
"data = fp.readlines()\n",
"array = [int(x.strip()) for x in data]\n",
"#array = array[:100]\n",
"\n",
"#print (\"START\", array)\n",
"\n",
"def quicksort(i, j):\n",
" #print (\"quicksort\",i, j)\n",
" if j-i < 1:\n",
" return 0\n",
" \n",
" # Count comparisons\n",
" k = partition(i, j)\n",
" \n",
" countleft = quicksort( i, k-1)\n",
" countright = quicksort( k+1, j)\n",
"\n",
" count = countleft + countright + (j-i)\n",
"\n",
" #print(array)\n",
" #print (\"countleft , countright, i, j\", countleft , countright, i, j)\n",
" return count\n",
"\n",
" \n",
"def swap(i, j):\n",
" #print(\"swap\", i, j, array)\n",
"\n",
" temp = array[j]\n",
" array[j] = array[i]\n",
" array[i] = temp\n",
"\n",
"def partition(i, j):\n",
" global COUNT\n",
" \n",
" #print (\"Partition\", i, j, array[i:j+1])\n",
" swap(i, j)\n",
" \n",
" START_IDX = i+1\n",
" END_IDX = j \n",
" PIVOT_IDX = i\n",
" \n",
" pivot = array[PIVOT_IDX]\n",
" s = i + 1\n",
" \n",
" #print (START_IDX, END_IDX+1)\n",
" for k in range(START_IDX, END_IDX+1): \n",
" COUNT += 1 \n",
" #print (k)\n",
"\n",
" if array[k] < pivot: \n",
" swap(s, k) \n",
" s+=1 \n",
" \n",
" swap(PIVOT_IDX, s-1) \n",
" \n",
" #print (\"DONE\", array)\n",
" return s-1\n",
"\n",
"\n",
"myCount = quicksort(0, len(array)-1) \n",
"#print (array)\n",
"print (\"myCount\", myCount)\n",
"print (\"COUNT\", COUNT)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 3 - \n",
"\n",
"GENERAL DIRECTIONS AND HOW TO GIVE US YOUR ANSWER:\n",
"\n",
"See the first question.\n",
"\n",
"DIRECTIONS FOR THIS PROBLEM:\n",
"\n",
"Compute the number of comparisons (as in Problem 1), using the \"median-of-three\" pivot rule. [The primary motivation behind this rule is to do a little bit of extra work to get much better performance on input arrays that are nearly sorted or reverse sorted.] \n",
"\n",
"In more detail, you should choose the pivot as follows. Consider the first, middle, and final elements of the given array. (If the array has odd length it should be clear what the \"middle\" element is; for an array with even length 2k, use the kth element as the \"middle\" element. So for the array 4 5 6 7, the \"middle\" element is the second one ---- 5 and not 6!) Identify which of these three elements is the median (i.e., the one whose value is in between the other two), and use this as your pivot. As discussed in the first and second parts of this programming assignment, be sure to implement Partition exactly as described in the video lectures (including exchanging the pivot element with the first element just before the main Partition subroutine).\n",
"\n",
"EXAMPLE: For the input array 8 2 4 5 7 1 you would consider the first (8), middle (4), and last (1) elements; since 4 is the median of the set {1,4,8}, you would use 4 as your pivot element.\n",
"\n",
"SUBTLE POINT: A careful analysis would keep track of the comparisons made in identifying the median of the three candidate elements. You should NOT do this. That is, as in the previous two problems, you should simply add m−1 to your running total of comparisons every time you recurse on a subarray with length m."
]
},
{
"cell_type": "code",
"execution_count": 348,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"START\n",
"myCount 138382\n",
"COUNT 138382\n"
]
}
],
"source": [
"COUNT = 0\n",
"\n",
"array = [5, 4, 10, 2, 6, 9]\n",
"# array = [5, 4, 10, 2, 6, 9, 60, 59, 58, 56]\n",
"array = [2148, 9058, 7742, 3153, 6324, 609, 7628, 5469, 7017, 504]\n",
"\n",
"fp = open(\"quickSort.txt\", 'r')\n",
"data = fp.readlines()\n",
"array = [int(x.strip()) for x in data]\n",
"\n",
"#array = array\n",
"\n",
"print (\"START\")\n",
"\n",
"def quicksort(i, j):\n",
" #print (\"quicksort\",i, j)\n",
" if j-i < 1:\n",
" return 0\n",
" \n",
" # Count comparisons\n",
" k = partition(i, j)\n",
" \n",
" countleft = quicksort( i, k-1)\n",
" countright = quicksort( k+1, j)\n",
"\n",
" count = countleft + countright + (j-i)\n",
"\n",
" #print(array)\n",
" #print (\"countleft , countright, i, j\", countleft , countright, i, j)\n",
" return count\n",
"\n",
" \n",
"def swap(i, j):\n",
" #print(\"swap\", i, j, array)\n",
"\n",
" temp = array[j]\n",
" array[j] = array[i]\n",
" array[i] = temp\n",
"\n",
"def find_median(i,j):\n",
" n = j - i + 1 # num of points\n",
" if n%2 == 0:\n",
" m= i + int(n/2) - 1\n",
" else:\n",
" m= i + int(n/2)\n",
"\n",
" #print (array[i:j+1], array[m], i, m, j)\n",
" \n",
" if (array[m] > array[i] and array[m] < array[j]) or (array[m] > array[j] and array[m] < array[i]): \n",
" return m\n",
" elif (array[i] > array[m] and array[i] < array[j]) or (array[i] > array[j] and array[i] < array[m]): \n",
" return i\n",
" else:\n",
" return j\n",
"\n",
"\n",
"def partition(i, j):\n",
" global COUNT\n",
" \n",
" #print (\"Partition\", i, j, array[i:j+1])\n",
" m = find_median(i,j)\n",
" #print(\"swap\", i, m, array)\n",
" swap(m, i)\n",
" \n",
" \n",
" START_IDX = i+1\n",
" END_IDX = j \n",
" PIVOT_IDX = i\n",
" \n",
" pivot = array[PIVOT_IDX]\n",
" s = i + 1\n",
" \n",
" #print (START_IDX, END_IDX+1)\n",
" for k in range(START_IDX, END_IDX+1): \n",
" COUNT += 1 \n",
" #print (k)\n",
"\n",
" if array[k] < pivot: \n",
" swap(s, k) \n",
" s+=1 \n",
" \n",
" swap(PIVOT_IDX, s-1) \n",
" \n",
" #print (\"DONE\", array)\n",
" return s-1\n",
"\n",
"\n",
"myCount = quicksort(0, len(array)-1) \n",
"#print (array)\n",
"print (\"myCount\", myCount)\n",
"print (\"COUNT\", COUNT)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
joshamilton/Hamilton_acI_2017 | code/figure2.ipynb | 1 | 10298 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reverse Ecology and Metatranscriptomics of Uncultivated Freshwater Actinobacteria"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Preliminaries\n",
"\n",
"This figures requires the user to have run our complete pipeline, including mapping of metatranscriptomic reads. This pipeline encompasses the following files:\n",
"\n",
" code/01-genomeAnnotationAndModelProcessing.ipynb\n",
" code/02-calculateSeedCompounds.ipynb\n",
" code/03-integrateREwithMTs.ipynb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Figure 3c: Metatranscriptomics of Clade acI-C"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Overview\n",
"\n",
"In the outer circle, this panel shows the Average Log2 RPKM for each COG in the composite acI-C genome. The inner circles show the presence/absence of each COG in each of the acI-C genomes. The visualization was constructed using [Anvio](http://merenlab.org/software/anvio/).\n",
"\n",
"To create the visualization, the following command is run from with anvio:\n",
"\n",
" anvi-interactive -p profile.db -t tree.txt -d view_data.txt -A additional_view_data.txt --manual --title \"Average RPKM of the acI-C Composite Genome\"\n",
"\n",
"where the files are:\n",
" * profile.db - profile file\n",
" * tree.txt - phylogenetic tree giving clustering of the COGs\n",
" * view_data.txt - presence/absence of COG in each genome\n",
" * additional_view_data.txt - Average Log2 RPKM for each COG\n",
"\n",
"Thus, creating the visualization requires the following steps:\n",
"\n",
"1. Creation of view_data.txt\n",
"2. Creation of additional_view_data.txt\n",
"3. Creation of tree.txt\n",
"4. Visualization!\n",
"\n",
"The visualization was then manually touched up using Adobe Illustrator."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 0: Import Packages and Initialize Variables"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Once deleted, variables cannot be recovered. Proceed (y/[n])? y\n"
]
}
],
"source": [
"%reset\n",
"################################################################################\n",
"### Import packages\n",
"################################################################################\n",
"\n",
"import os\n",
"import pandas as pd\n",
"from scipy.spatial import distance as dist\n",
"from scipy.cluster import hierarchy as sch\n",
"\n",
"################################################################################\n",
"### Define folder structure\n",
"################################################################################\n",
"\n",
"externalDataDir = '../data/externalData'\n",
"taxonFile = externalDataDir+'/taxonomy.csv'\n",
"orthomclDir = '../data/orthoMCL'\n",
"resultsDir = '../results/'\n",
"exprDir = resultsDir+'/expression'\n",
"\n",
"figureDir = '../figures/fig3-workflow'\n",
"\n",
"taxonLevel = 'Clade'\n",
"clade = 'acI-C'\n",
"\n",
"# Check that figureDir exists, results will be placed there\n",
"if not os.path.exists(figureDir):\n",
" os.makedirs(figureDir)\n",
"\n",
"################################################################################\n",
"### Create list of genomes in the specified clade\n",
"################################################################################\n",
"\n",
"# Define a function to import taxonomy files\n",
"def importTaxonomy(taxonFile, level):\n",
"\n",
"# Read in the taxonomic classification\n",
" taxonClass = pd.DataFrame.from_csv(taxonFile, sep=',')\n",
" taxonClass = taxonClass.dropna()\n",
" \n",
"# Extract the unique tribes found in the dataset\n",
" groupList = pd.unique(taxonClass[level].values)\n",
" groupList.sort(axis=0)\n",
" groupList = [ group for group in groupList if not group.startswith('Unknown') ]\n",
" \n",
"# For each tribe, return the list of samples. Creates a dict and adds an entry\n",
"# for each tribe.\n",
" groupSampleDict = {}\n",
"\n",
" for group in groupList:\n",
"\n",
"# Identify the samples belonging to this tribe\n",
" samples = taxonClass.loc[taxonClass[level] == group]\n",
" samples = [sample for sample in samples.index]\n",
" groupSampleDict[group] = samples\n",
" \n",
" return groupSampleDict\n",
" \n",
"genomeSampleDict = importTaxonomy(taxonFile, taxonLevel)\n",
"\n",
"genomeList = genomeSampleDict[clade]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 1: Creation of view_data.txt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Determine the set of COGs found within the specified clade\n",
"# Read in the cog table. Subset columns belonging to the clade. Subset rows\n",
"# with COGs in that clade.\n",
"cogTable = pd.read_csv(orthomclDir+'/cogTable.csv', delimiter=',', index_col=0)\n",
"cogTable = cogTable[genomeList]\n",
"cogTable = cogTable[~pd.isnull(cogTable).all(axis=1)]\n",
"\n",
"# Replace CDS with '1' and 'nan' with 0\n",
"cogTable = cogTable.fillna(0) \n",
"cogTable = cogTable.replace(to_replace='.+', value='1', regex=True)\n",
"\n",
"# Make index a column and rearrange. Anvio requires the first row/column of \n",
"# view data file to say 'contig'\n",
"cogTable['contig'] = cogTable.index\n",
"cogTable = cogTable[['contig']+genomeList]\n",
"\n",
"cogTable.to_csv(figureDir+'/view_data.txt', sep='\\t', index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 2: Creation of additional_view_data.txt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Determine the set of COGs found within the specified clade\n",
"# Read in the cog table. Subset columns belonging to the clade. Subset rows\n",
"# with COGs in that clade.\n",
"cogTable = pd.read_csv(orthomclDir+'/cogTable.csv', delimiter=',', index_col=0)\n",
"cogTable = cogTable[genomeList]\n",
"cogTable = cogTable[~pd.isnull(cogTable).all(axis=1)]\n",
"\n",
"# Establish data table of RPKM values\n",
"rpkmTable = pd.read_csv(exprDir+'/'+clade+'.norm', delimiter=',', index_col=1)\n",
"rpkmTable = rpkmTable['Log2 Avg RPKM']\n",
"\n",
"# Create empty dataframe and populate with values from rpkmTable\n",
"addlViewDataDF = pd.DataFrame(0, index=cogTable.index, columns=['log2_avg_rpkm'], dtype=float)\n",
"for cog in addlViewDataDF.index:\n",
" if cog in rpkmTable.index:\n",
" addlViewDataDF.set_value(cog, 'log2_avg_rpkm', rpkmTable.loc[cog])\n",
" \n",
"addlViewDataDF.to_csv(figureDir+'/additional_view_data.txt', sep='\\t')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 3: Creation of tree.txt"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import the observations\n",
"obsDF = pd.read_csv(figureDir+'/view_data.txt', sep='\\t', index_col=0)\n",
"\n",
"leafNames = obsDF.index.tolist()\n",
"obsMatrix = obsDF.values\n",
"\n",
"# Compute the distance matrix\n",
"distMatrix = dist.pdist(obsMatrix, metric='euclidean')\n",
"\n",
"# Compute the linkage matrix\n",
"linkMatrix = sch.linkage(distMatrix, method='single', metric='euclidean')\n",
"\n",
"# Export the linakge matrix as a newick file\n",
"# Stolen from StackOverflow: http://stackoverflow.com/questions/28222179/save-dendrogram-to-newick-format\n",
"def getNewick(node, newick, parentdist, leaf_names):\n",
" if node.is_leaf():\n",
" return \"%s:%.2f%s\" % (leaf_names[node.id], parentdist - node.dist, newick)\n",
" else:\n",
" if len(newick) > 0:\n",
" newick = \"):%.2f%s\" % (parentdist - node.dist, newick)\n",
" else:\n",
" newick = \");\"\n",
" newick = getNewick(node.get_left(), newick, node.dist, leaf_names)\n",
" newick = getNewick(node.get_right(), \",%s\" % (newick), node.dist, leaf_names)\n",
" newick = \"(%s\" % (newick)\n",
" return newick\n",
"\n",
"tree = sch.to_tree(linkMatrix, False)\n",
"newickTree = getNewick(tree, \"\", tree.dist, leafNames)\n",
"\n",
"# Write to file\n",
"with open(figureDir+'/tree.txt', 'w') as outFileHandle:\n",
" outFileHandle.write(newickTree)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 4: Visualization\n",
"\n",
"The final step must be performed manually. In the terminal, navigate to the `figureDir` defined above. Launch Anvi'o via the command `anvio`. Then, type the command\n",
"\n",
" anvi-interactive -p profile.db -t tree.txt -d view_data.txt -A additional_view_data.txt --manual --title \"Average RPKM of the acI-C Composite Genome\"\n",
" \n",
"Change the `DrawAngle` to 360, press `Draw` in the Anvi'o window and save to svg format."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
ClaudioESSilva/SQLServer-PowerShell | Presentations/GroupBy Conf Oct 2020/03-Copy-TableData.ipynb | 1 | 10809 | {
"metadata": {
"kernelspec": {
"name": "powershell",
"display_name": "PowerShell"
},
"language_info": {
"name": "powershell",
"codemirror_mode": "shell",
"mimetype": "text/x-sh",
"file_extension": ".ps1"
}
},
"nbformat_minor": 2,
"nbformat": 4,
"cells": [
{
"cell_type": "markdown",
"source": [
"<pre>\n",
"\n",
"██████╗ ██████╗ █████╗ ████████╗ ██████╗ ██████╗ ██╗ ███████╗ \n",
"██╔══██╗██╔══██╗██╔══██╗╚══██╔══╝██╔═══██╗██╔═══██╗██║ ██╔════╝ \n",
"██║ ██║██████╔╝███████║ ██║ ██║ ██║██║ ██║██║ ███████╗ \n",
"██║ ██║██╔══██╗██╔══██║ ██║ ██║ ██║██║ ██║██║ ╚════██║ \n",
"██████╔╝██████╔╝██║ ██║ ██║ ╚██████╔╝╚██████╔╝███████╗███████║ \n",
"╚═════╝ ╚═════╝ ╚═╝ ╚═╝ ╚═╝ ╚═════╝ ╚═════╝ ╚══════╝╚══════╝ \n",
" \n",
"██████╗ ███████╗ ██████╗██╗██████╗ ███████╗ ██╗ ██╗ ██████╗ ██████╗ \n",
"██╔══██╗██╔════╝██╔════╝██║██╔══██╗██╔════╝ ████████╗██╔═████╗╚════██╗\n",
"██████╔╝█████╗ ██║ ██║██████╔╝█████╗ ╚██╔═██╔╝██║██╔██║ █████╔╝\n",
"██╔══██╗██╔══╝ ██║ ██║██╔═══╝ ██╔══╝ ████████╗████╔╝██║ ╚═══██╗\n",
"██║ ██║███████╗╚██████╗██║██║ ███████╗ ╚██╔═██╔╝╚██████╔╝██████╔╝\n",
"╚═╝ ╚═╝╚══════╝ ╚═════╝╚═╝╚═╝ ╚══════╝ ╚═╝ ╚═╝ ╚═════╝ ╚═════╝ \n",
" \n",
"</pre>\n",
"# Recipe #03 - Let's cook!\n",
"## Main course: \n",
"### - Copy data between tables\n",
"> Under the hood it uses `SQLBulkCopy` which is one of the most efficient ways to copy batchs of data between tables\n",
"<hr>"
],
"metadata": {
"azdata_cell_guid": "6066a42f-a32e-404d-b4c2-ded7e6557985"
}
},
{
"cell_type": "markdown",
"source": [
"### Set variables"
],
"metadata": {
"azdata_cell_guid": "52acd561-24a1-486f-b121-f8ff4781790c"
}
},
{
"cell_type": "code",
"source": [
"$dbatools1 = \"localhost,1433\"\r\n",
"$dbatools2 = \"localhost,14333\"\r\n",
"$dbatoolsEdge = \"raspberrypi.lan\"\r\n",
"$secureString = ConvertTo-SecureString \"dbatools.IO\" -AsPlainText -Force\r\n",
"$cred = New-Object -TypeName System.Management.Automation.PSCredential -ArgumentList \"sqladmin\", $secureString\r\n",
"\r\n",
"$sourceDB = \"Northwind\"\r\n",
"$destinationDB = \"EmptyNorthwind\"\r\n",
"$sourceTable = \"[dbo].[Order Details]\""
],
"metadata": {
"azdata_cell_guid": "3ca56988-8ec0-4c48-9fb4-0701f33bbacc",
"tags": []
},
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"source": [
"### Create empty database on destination instance"
],
"metadata": {
"azdata_cell_guid": "aeaecd95-7ea8-4a34-961a-935b3ce702a4"
}
},
{
"cell_type": "code",
"source": [
"New-DbaDatabase -SqlInstance $dbatools2, $dbatoolsEdge -SqlCredential $cred -Name $destinationDB"
],
"metadata": {
"azdata_cell_guid": "79609297-78a2-4bc5-9af4-012db64f84c0"
},
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"source": [
"### Check table's content on source"
],
"metadata": {
"azdata_cell_guid": "cd02f963-37eb-4b68-b393-2379f750095b"
}
},
{
"cell_type": "code",
"source": [
"Invoke-DbaQuery -SqlInstance $dbatools1 -SqlCredential $cred -Database $sourceDB -Query \"SELECT TOP 10 * FROM $sourceTable\" | Format-Table"
],
"metadata": {
"azdata_cell_guid": "4be3fc0a-a7a4-40d6-8586-99ea8476a4f2"
},
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"source": [
"### Copy data\r\n",
"> Note: Table does not exist so it will be created. However without PK, FK, UQ, (non)Clustered indexes..etc. \r\n",
"> If you need to keep all the objects take a look at the [“UPS…I HAVE DELETED SOME DATA. CAN YOU PUT IT BACK?” – DBATOOLS FOR THE RESCUE](https://claudioessilva.eu/2019/05/17/ups-i-have-deleted-some-data-can-you-put-it-back-dbatools-for-the-rescue/) blog post to understand how you can create the object with same structure/properties before copying the data."
],
"metadata": {
"azdata_cell_guid": "2486ad27-2ec7-4153-9c46-1ef637e309cf"
}
},
{
"cell_type": "code",
"source": [
"# Copy all data within dbo.Categories to other instance\r\n",
"$copySplat = @{\r\n",
" SqlInstance = $dbatools1\r\n",
" SqlCredential = $cred\r\n",
" Destination = $dbatools2\r\n",
" DestinationSqlCredential = $cred\r\n",
" Database = $sourceDB\r\n",
" DestinationDatabase = $destinationDB\r\n",
" Table = $sourceTable\r\n",
" AutoCreateTable = $true \r\n",
" BatchSize = 1000\r\n",
"}\r\n",
"Copy-DbaDbTableData @copySplat"
],
"metadata": {
"azdata_cell_guid": "082e1c93-b233-42f6-92d1-fda17f4b252c"
},
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"source": [
"### Check table content on destination"
],
"metadata": {
"azdata_cell_guid": "cfdee541-4e7c-40ca-9a00-63a4c8dacf66"
}
},
{
"cell_type": "code",
"source": [
"Invoke-DbaQuery -SqlInstance $dbatools2 -SqlCredential $cred -Database $destinationDB -Query \"SELECT TOP 10 * FROM $sourceTable\" | Format-Table"
],
"metadata": {
"azdata_cell_guid": "ecf4b56a-fa41-42b9-b459-2701e5499738"
},
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"source": [
"<hr>\r\n",
"\r\n",
"# Another example\r\n",
"## Copy data based on a query"
],
"metadata": {
"azdata_cell_guid": "7daa73a7-1ac4-4277-bb81-9340e5c46c8d"
}
},
{
"cell_type": "code",
"source": [
"# Copy specific data (see query parameter) from [dbo].[Order Details] to [dbo].[CopyOf_Order Details]\r\n",
"$copySplat = @{\r\n",
" SqlInstance = $dbatools1\r\n",
" SqlCredential = $cred\r\n",
" Destination = $dbatoolsEdge\r\n",
" DestinationSqlCredential = $cred\r\n",
" Database = $sourceDB\r\n",
" DestinationDatabase = $destinationDB\r\n",
" Table = $sourceTable\r\n",
" DestinationTable = \"[dbo].[CopyOf_Order Details]\"\r\n",
" AutoCreateTable = $true \r\n",
" BatchSize = 1000\r\n",
" Query = \"SELECT * FROM $sourceDB.$sourceTable WHERE Quantity > 70 \"\r\n",
"}\r\n",
"Copy-DbaDbTableData @copySplat"
],
"metadata": {
"azdata_cell_guid": "c81d8652-caad-4532-9b8b-4e2a4ac8be24"
},
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"source": [
"Confirm that data is there"
],
"metadata": {
"azdata_cell_guid": "d67e76e5-0ec2-4ad8-8f85-7deaaf6dc30a"
}
},
{
"cell_type": "code",
"source": [
"Invoke-DbaQuery -SqlInstance $dbatoolsEdge -SqlCredential $cred -Database $destinationDB -Query \"SELECT * FROM [dbo].[CopyOf_Order Details]\" | Format-Table"
],
"metadata": {
"azdata_cell_guid": "85086324-3f6e-4c36-ab91-e737db1fdfb5"
},
"outputs": [],
"execution_count": null
}
]
} | gpl-3.0 |
camilogavo/Colombian_Energy_Forecasting | .ipynb_checkpoints/sacar_df1-checkpoint.ipynb | 1 | 1240183 | null | mit |
nbokulich/short-read-tax-assignment | ipynb/mock-community/taxonomy-assignment-vsearch.ipynb | 1 | 13120 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data generation: using python to sweep over methods and parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook demonstrates taxonomy classification using ``vsearch`` followed by consensus assignment in QIIME2's ``q2-feature-classifier``."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Environment preparation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from os.path import join, expandvars\n",
"from joblib import Parallel, delayed\n",
"from glob import glob\n",
"from os import system\n",
"from tax_credit.framework_functions import (parameter_sweep,\n",
" generate_per_method_biom_tables,\n",
" move_results_to_repository)\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"project_dir = expandvars(\"$HOME/Desktop/projects/short-read-tax-assignment\")\n",
"analysis_name= \"mock-community\"\n",
"data_dir = join(project_dir, \"data\", analysis_name)\n",
"\n",
"reference_database_dir = expandvars(\"$HOME/Desktop/ref_dbs/\")\n",
"results_dir = expandvars(\"$HOME/Desktop/projects/mock-community/\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preparing data set sweep"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we're going to define the data sets that we'll sweep over. The following cell does not need to be modified unless if you wish to change the datasets or reference databases used in the sweep."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dataset_reference_combinations = [\n",
" ('mock-1', 'gg_13_8_otus'), # formerly S16S-1\n",
" ('mock-2', 'gg_13_8_otus'), # formerly S16S-2\n",
" ('mock-3', 'gg_13_8_otus'), # formerly Broad-1\n",
" ('mock-4', 'gg_13_8_otus'), # formerly Broad-2\n",
" ('mock-5', 'gg_13_8_otus'), # formerly Broad-3\n",
" ('mock-6', 'gg_13_8_otus'), # formerly Turnbaugh-1\n",
" ('mock-7', 'gg_13_8_otus'), # formerly Turnbaugh-2\n",
" ('mock-8', 'gg_13_8_otus'), # formerly Turnbaugh-3\n",
" ('mock-9', 'unite_20.11.2016_clean_fullITS'), # formerly ITS1\n",
" ('mock-10', 'unite_20.11.2016_clean_fullITS'), # formerly ITS2-SAG\n",
" ('mock-12', 'gg_13_8_otus'), # Extreme\n",
" ('mock-13', 'gg_13_8_otus_full16S_clean'), # kozich-1\n",
" ('mock-14', 'gg_13_8_otus_full16S_clean'), # kozich-2\n",
" ('mock-15', 'gg_13_8_otus_full16S_clean'), # kozich-3\n",
" ('mock-16', 'gg_13_8_otus'), # schirmer-1\n",
" ('mock-18', 'gg_13_8_otus'),\n",
" ('mock-19', 'gg_13_8_otus'),\n",
" ('mock-20', 'gg_13_8_otus'),\n",
" ('mock-21', 'gg_13_8_otus'),\n",
" ('mock-22', 'gg_13_8_otus'),\n",
" ('mock-23', 'gg_13_8_otus'),\n",
" ('mock-24', 'unite_20.11.2016_clean_fullITS'),\n",
" ('mock-25', 'unite_20.11.2016_clean_fullITS'),\n",
" ('mock-26-ITS1', 'unite_20.11.2016_clean_fullITS'),\n",
" ('mock-26-ITS9', 'unite_20.11.2016_clean_fullITS'),\n",
"]\n",
"\n",
"reference_dbs = {'gg_13_8_otus_clean' : (join(reference_database_dir, 'gg_13_8_otus/99_otus_clean_515f-806r.qza'),\n",
" join(reference_database_dir, 'gg_13_8_otus/taxonomy/99_otu_taxonomy.qza')),\n",
" 'gg_13_8_otus' : (join(reference_database_dir, 'gg_13_8_otus/rep_set/99_otus_515f-806r_trim250.qza'), \n",
" join(reference_database_dir, 'gg_13_8_otus/taxonomy/99_otu_taxonomy.qza')),\n",
" 'gg_13_8_otus_full16S_clean' : (join(reference_database_dir, 'gg_13_8_otus/99_otus_clean.qza'), \n",
" join(reference_database_dir, 'gg_13_8_otus/taxonomy/99_otu_taxonomy.qza')),\n",
" 'gg_13_8_otus_full16S' : (join(reference_database_dir, 'gg_13_8_otus/rep_set/99_otus.qza'), \n",
" join(reference_database_dir, 'gg_13_8_otus/taxonomy/99_otu_taxonomy.qza')),\n",
" 'unite_20.11.2016_clean_fullITS' : (join(reference_database_dir, 'sh_qiime_release_20.11.2016/developer/sh_refs_qiime_ver7_99_20.11.2016_dev_clean.qza'), \n",
" join(reference_database_dir, 'sh_qiime_release_20.11.2016/developer/sh_taxonomy_qiime_ver7_99_20.11.2016_dev_clean.qza')),\n",
" 'unite_20.11.2016_clean' : (join(reference_database_dir, 'sh_qiime_release_20.11.2016/developer/sh_refs_qiime_ver7_99_20.11.2016_dev_clean_ITS1Ff-ITS2r.qza'), \n",
" join(reference_database_dir, 'sh_qiime_release_20.11.2016/developer/sh_taxonomy_qiime_ver7_99_20.11.2016_dev.qza')),\n",
" 'unite_20.11.2016' : (join(reference_database_dir, 'sh_qiime_release_20.11.2016/developer/sh_refs_qiime_ver7_99_20.11.2016_dev_ITS1Ff-ITS2r_trim250.qza'), \n",
" join(reference_database_dir, 'sh_qiime_release_20.11.2016/developer/sh_taxonomy_qiime_ver7_99_20.11.2016_dev.qza'))}\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preparing the method/parameter combinations and generating commands\n",
"\n",
"Now we set the methods and method-specific parameters that we want to sweep. Modify to sweep other methods. Note how method_parameters_combinations feeds method/parameter combinations to parameter_sweep() in the cell below."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"method_parameters_combinations = {\n",
" 'vsearch' : {'p-maxaccepts': [1, 10, 100],\n",
" 'p-perc-identity': [0.80, 0.90, 0.97, 0.99],\n",
" 'p-min-consensus': [0.51, 0.75, 0.99]}\n",
" }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now enter the template of the command to sweep, and generate a list of commands with ``parameter_sweep()``.\n",
"\n",
"Fields must adhere to following format:\n",
"\n",
" {0} = output directory\n",
" {1} = input data\n",
" {2} = reference sequences\n",
" {3} = reference taxonomy\n",
" {4} = method name\n",
" {5} = other parameters"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"command_template = \"mkdir -p {0}; qiime feature-classifier vsearch --i-query {1} --o-classification {0}/rep_seqs_tax_assignments.qza --i-reference-reads {2} --i-reference-taxonomy {3} {5}; qiime tools export {0}/rep_seqs_tax_assignments.qza --output-dir {0}\"\n",
" \n",
"commands = parameter_sweep(data_dir, results_dir, reference_dbs,\n",
" dataset_reference_combinations,\n",
" method_parameters_combinations, command_template,\n",
" infile='rep_seqs.qza', output_name='rep_seqs_tax_assignments.qza')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a sanity check, we can look at the first command that was generated and the number of commands generated."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"72\n"
]
},
{
"data": {
"text/plain": [
"'mkdir -p /Users/nbokulich/Desktop/projects/mock-community/mock-26-ITS1/unite_20.11.2016_clean_fullITS/vsearch/1:0.51:0.8; qiime feature-classifier vsearch --i-query /Users/nbokulich/Desktop/projects/short-read-tax-assignment/data/mock-community/mock-26-ITS1/rep_seqs.qza --o-classification /Users/nbokulich/Desktop/projects/mock-community/mock-26-ITS1/unite_20.11.2016_clean_fullITS/vsearch/1:0.51:0.8/rep_seqs_tax_assignments.qza --i-reference-reads /Users/nbokulich/Desktop/ref_dbs/sh_qiime_release_20.11.2016/developer/sh_refs_qiime_ver7_99_20.11.2016_dev_clean.qza --i-reference-taxonomy /Users/nbokulich/Desktop/ref_dbs/sh_qiime_release_20.11.2016/developer/sh_taxonomy_qiime_ver7_99_20.11.2016_dev_clean.qza --p-maxaccepts 1 --p-perc-identity 0.8 --p-min-consensus 0.51; qiime tools export /Users/nbokulich/Desktop/projects/mock-community/mock-26-ITS1/unite_20.11.2016_clean_fullITS/vsearch/1:0.51:0.8/rep_seqs_tax_assignments.qza --output-dir /Users/nbokulich/Desktop/projects/mock-community/mock-26-ITS1/unite_20.11.2016_clean_fullITS/vsearch/1:0.51:0.8'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(len(commands))\n",
"commands[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we run our commands."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[0,\n",
" 0,\n",
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" 0]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Parallel(n_jobs=4)(delayed(system)(command) for command in commands)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate per-method biom tables\n",
"\n",
"Modify the taxonomy_glob below to point to the taxonomy assignments that were generated above. This may be necessary if filepaths were altered in the preceding cells."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"taxonomy_glob = join(results_dir, '*', '*', '*', '*', 'taxonomy.tsv')\n",
"generate_per_method_biom_tables(taxonomy_glob, data_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Move result files to repository\n",
"\n",
"Add results to the short-read-taxa-assignment directory (e.g., to push these results to the repository or compare with other precomputed results in downstream analysis steps). The precomputed_results_dir path and methods_dirs glob below should not need to be changed unless if substantial changes were made to filepaths in the preceding cells."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"precomputed_results_dir = join(project_dir, \"data\", \"precomputed-results\", analysis_name)\n",
"method_dirs = glob(join(results_dir, '*', '*', '*', '*'))\n",
"move_results_to_repository(method_dirs, precomputed_results_dir)"
]
},
{
"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
}
| bsd-3-clause |
the-deep-learners/nyc-ds-academy | notebooks/deep_net_in_tensorflow.ipynb | 1 | 9852 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deep Neural Network in TensorFlow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook, we convert our [intermediate-depth MNIST-classifying neural network](https://github.com/the-deep-learners/TensorFlow-LiveLessons/blob/master/notebooks/intermediate_net_in_keras.ipynb) from Keras to TensorFlow (compare them side by side) following Aymeric Damien's [Multi-Layer Perceptron Notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb) style."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Load dependencies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"np.random.seed(42)\n",
"import tensorflow as tf\n",
"tf.set_random_seed(42)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Load data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"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/\", one_hot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Set neural network hyperparameters (tidier at top of file!)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"lr = 0.1\n",
"epochs = 10\n",
"batch_size = 128\n",
"weight_initializer = tf.contrib.layers.xavier_initializer()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Set number of neurons for each layer"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"n_input = 784\n",
"n_dense_1 = 64\n",
"n_dense_2 = 64\n",
"n_dense_3 = 64\n",
"n_classes = 10"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Define placeholders Tensors for inputs and labels"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"x = tf.placeholder(tf.float32, [None, n_input])\n",
"y = tf.placeholder(tf.float32, [None, n_classes])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Define types of layers"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# dense layer with ReLU activation:\n",
"def dense(x, W, b):\n",
" z = tf.add(tf.matmul(x, W), b)\n",
" a = tf.nn.relu(z)\n",
" return a"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Define dictionaries for storing weights and biases for each layer -- and initialize"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"bias_dict = {\n",
" 'b1': tf.Variable(tf.zeros([n_dense_1])),\n",
" 'b2': tf.Variable(tf.zeros([n_dense_2])),\n",
" 'b3': tf.Variable(tf.zeros([n_dense_3])),\n",
" 'b_out': tf.Variable(tf.zeros([n_classes]))\n",
"}\n",
"\n",
"weight_dict = {\n",
" 'W1': tf.get_variable('W1', [n_input, n_dense_1], initializer=weight_initializer),\n",
" 'W2': tf.get_variable('W2', [n_dense_1, n_dense_2], initializer=weight_initializer),\n",
" 'W3': tf.get_variable('W3', [n_dense_2, n_dense_3], initializer=weight_initializer),\n",
" 'W_out': tf.get_variable('W_out', [n_dense_3, n_classes], initializer=weight_initializer),\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Design neural network architecture"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def network(x, weights, biases):\n",
" \n",
" # two dense hidden layers:\n",
" dense_1 = dense(x, weights['W1'], biases['b1'])\n",
" dense_2 = dense(dense_1, weights['W2'], biases['b2'])\n",
" dense_3 = dense(dense_2, weights['W3'], biases['b3'])\n",
" \n",
" # linear output layer (softmax):\n",
" out_layer_z = tf.add(tf.matmul(dense_3, weights['W_out']), biases['b_out'])\n",
" \n",
" return out_layer_z"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Build model"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"predictions = network(x, weights=weight_dict, biases=bias_dict)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Define model's loss and its optimizer"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=predictions, labels=y))\n",
"optimizer = tf.train.GradientDescentOptimizer(learning_rate=lr).minimize(cost)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Define evaluation metrics"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# calculate accuracy by identifying test cases where the model's highest-probability class matches the true y label: \n",
"correct_prediction = tf.equal(tf.argmax(predictions, 1), tf.argmax(y, 1))\n",
"accuracy_pct = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) * 100"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create op for variable initialization"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"initializer_op = tf.global_variables_initializer()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Train the network in a session"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training for 10 epochs.\n",
"Epoch 001: cost = 0.542, accuracy = 83.35%\n",
"Epoch 002: cost = 0.211, accuracy = 93.73%\n",
"Epoch 003: cost = 0.158, accuracy = 95.32%\n",
"Epoch 004: cost = 0.130, accuracy = 96.14%\n",
"Epoch 005: cost = 0.110, accuracy = 96.68%\n",
"Epoch 006: cost = 0.096, accuracy = 97.16%\n",
"Epoch 007: cost = 0.085, accuracy = 97.50%\n",
"Epoch 008: cost = 0.076, accuracy = 97.77%\n",
"Epoch 009: cost = 0.067, accuracy = 97.91%\n",
"Epoch 010: cost = 0.061, accuracy = 98.19%\n",
"Training Complete. Testing Model.\n",
"\n",
"Test Cost: 0.095\n",
"Test Accuracy: 97.01%\n"
]
}
],
"source": [
"with tf.Session() as session:\n",
" session.run(initializer_op)\n",
" \n",
" print(\"Training for\", epochs, \"epochs.\")\n",
" \n",
" # loop over epochs: \n",
" for epoch in range(epochs):\n",
" \n",
" avg_cost = 0.0 # track cost to monitor performance during training\n",
" avg_accuracy_pct = 0.0\n",
" \n",
" # loop over all batches of the epoch:\n",
" n_batches = int(mnist.train.num_examples / batch_size)\n",
" for i in range(n_batches):\n",
" \n",
" batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
" \n",
" # feed batch data to run optimization and fetching cost and accuracy: \n",
" _, batch_cost, batch_acc = session.run([optimizer, cost, accuracy_pct], feed_dict={x: batch_x, y: batch_y})\n",
" \n",
" # accumulate mean loss and accuracy over epoch: \n",
" avg_cost += batch_cost / n_batches\n",
" avg_accuracy_pct += batch_acc / n_batches\n",
" \n",
" # output logs at end of each epoch of training:\n",
" print(\"Epoch \", '%03d' % (epoch+1), \n",
" \": cost = \", '{:.3f}'.format(avg_cost), \n",
" \", accuracy = \", '{:.2f}'.format(avg_accuracy_pct), \"%\", \n",
" sep='')\n",
" \n",
" print(\"Training Complete. Testing Model.\\n\")\n",
" \n",
" test_cost = cost.eval({x: mnist.test.images, y: mnist.test.labels})\n",
" test_accuracy_pct = accuracy_pct.eval({x: mnist.test.images, y: mnist.test.labels})\n",
" \n",
" print(\"Test Cost:\", '{:.3f}'.format(test_cost))\n",
" print(\"Test Accuracy: \", '{:.2f}'.format(test_accuracy_pct), \"%\", sep='')"
]
},
{
"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 |
phievo/phievo | Examples/AnalyzeNetwork.ipynb | 1 | 3916 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyse Network #\n",
"\n",
"This is a template notebook to read, study, modify and save a given network, usually the results of an evolutionnary simulation.\n",
"\n",
"These networks may be manually retrieved manually from a simulation by looking into the folder __my_simulation/Seed#n/Bests_#gen.net__. Where __#n__ is the seed number and __#gen__ the generation of the network. It is a better practice to save networks with a __.net__ extension.\n",
"\n",
"To run the integrator on a network, you have to define a model, this is done by giving the path of the model at the beginning of the __Definition and proxy__ cell.\n",
"\n",
"Please `Kernel>Restart & Run All` for every new project to make sure you start with a clean notebook."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import required libraries ##"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from ipywidgets import widgets \n",
"from ipywidgets import interact, interactive, fixed\n",
"from IPython.display import display,HTML,clear_output\n",
"import os\n",
"HTML('''<script>code_show=true;function code_toggle() {if (code_show){$('div.input').hide();} else {$('div.input').show();} code_show = !code_show} $( document ).ready(code_toggle);</script><form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')\n",
"import phievo.AnalysisTools as AT\n",
"from phievo.AnalysisTools.Notebook import Notebook\n",
"\n",
"notebook = Notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Definition and proxy for usefull functions ##"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"MODEL = \"adaptation\"\n",
"sim = AT.Simulation(MODEL)\n",
"\n",
"read_network = AT.main_functions.read_network\n",
"\n",
"def write_network(net,filename): net.store_to_pickle(filename)\n",
"\n",
"def draw(net,label=False): net.draw(edgeLegend=label)\n",
"\n",
"def fitness(net,trial): return sim.run_dynamics(net,trial,erase_buffer=False,return_treatment_fitness=True)\n",
"\n",
"def gene_knock_out(net,label): net.delete_clean(label,target='species')\n",
" \n",
"def remove_interaction(net,label): net.delete_clean(label,target='interaction')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analyse ##"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"net = read_network(\"test.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"draw(net)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gene_knock_out(net,1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"draw(net)"
]
},
{
"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",
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"name": "python",
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| lgpl-3.0 |
google-research/ott | docs/notebooks/application_biology.ipynb | 1 | 146747 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Single-cell application for OTT.ipynb",
"provenance": [],
"collapsed_sections": [],
"last_runtime": {
"build_target": "",
"kind": "local"
}
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "qL_G7B4fovBH"
},
"source": [
"# Single-cell genomics"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "y18hMXUWJxv6"
},
"source": [
"[Single-cell RNA sequencing](https://en.wikipedia.org/wiki/Single_cell_sequencing) (scRNA-seq) is a technology that provides the [gene expression profile](https://en.wikipedia.org/wiki/Gene_expression_profiling) of a single-cell. scRNA-seq can help describe a cell as a vector of features, where each feature represents the expression level of a gene in that cell upon measurement. Given a large population of cells observed through time, one can sample a few of these cells at different points in time, and produce at each time point as many feature vectors through scRNA-seq, and obtain a point-cloud. Each of these point-clouds provide a clear picture of the diversity of cells at each point time. Taken together, they describe the collective evolution of these cells along a developmental time-course. However, as measuring destroys the cells, one cannot follow gene expression of the exact same cell through time. \n",
"\n",
"Optimal transport can help us infer individual trajectories from the dynamics of the population overall, and answer questions such as what are the descendants or ancestors of each cell.\n",
"\n",
"In this notebook, we infer the ancestors of [induced Pluripotent Stem Cells](https://en.wikipedia.org/wiki/Induced_pluripotent_stem_cell) (iPSCs) using temporal snapshots sampled twice or four times a day for a period of 18 days. iPSCs are a type of [pluripotent stem cells](https://en.wikipedia.org/wiki/Cell_potency#Pluripotency) that can be generated from specialised cells. Identifying the ancestors of iPSCs would enable us to know which specialised cells are able to revert to iPSCs. \n",
"\n",
"**Reference**\\\n",
"The optimal transport pipeline of this notebook has been adapted from *Schiebinger, G. et al., Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming, Cell 2019* [1]. The data was downloaded from the corresponding [WOT tutorial](https://broadinstitute.github.io/wot/tutorial/). Downloaded data is capitalised."
]
},
{
"cell_type": "code",
"metadata": {
"id": "n8JBHUyPHSJE"
},
"source": [
"import matplotlib as mpl\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"from ott.core import sinkhorn\n",
"from ott.geometry import pointcloud"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "96rC8WsdHQEf"
},
"source": [
"## Calculates optimal transport on gene expression point clouds\n",
"\n",
"The optimal transport between pairs of gene expression matrices, measured at consecutive time points, is calculated. As a preprocessing step, the cell profiles are projected to a 30-dimensional space with PCA, as suggested in [1]. \n",
"\n",
"```DAYS``` is an array of measurement days from 0 to 18.\\\n",
"```PCA30_SERUM_DAY``` is a dictionary, which contains as keys the measurement days and as values the low-dimensional representation of gene expression matrices. \\\n",
"```CELL_GROWTH_RATE``` is a dictionary, which contains as keys the measurement days and as values the cell growth rate vectors. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IZVkjGuZ_7CZ"
},
"source": [
"The optimal transport is calculated between each consecutive pair of datasets."
]
},
{
"cell_type": "code",
"metadata": {
"id": "XQNQFhe2pKR7",
"executionInfo": {
"status": "ok",
"timestamp": 1613650194760,
"user_tz": -60,
"elapsed": 247851,
"user": {
"displayName": "Laetitia Papaxanthos",
"photoUrl": "",
"userId": "13824884068334195048"
}
},
"outputId": "6df64c9b-88bc-45cb-e62c-3eebdf5e6283"
},
"source": [
"# Defines the optimal transport regularisation parameter\n",
"epsilon = 5\n",
"\n",
"dict_results = {}\n",
"for i, day in enumerate(DAYS): \n",
" if day == 18.:\n",
" continue\n",
" print('\\r' + f'Executing Sinkhorn between the days {day} and {DAYS[i+1]} / 18', end='')\n",
" # Computes the marginals\n",
" delta_days = DAYS[i+1] - day \n",
" n = PCA30_SERUM_DAY[DAYS[i+1]].shape[0] \n",
" # The original data has been transformed with PCA as described in [1]\n",
" a = np.power(CELL_GROWTH_RATE[day], delta_days) / np.mean(\n",
" np.power(CELL_GROWTH_RATE[day], delta_days)) / n\n",
" b = np.ones(n) / n\n",
"\n",
" # Applies optimal transport\n",
" geom = pointcloud.PointCloud(\n",
" PCA30_SERUM_DAY[day], PCA30_SERUM_DAY[DAYS[i+1]], epsilon=epsilon)\n",
" out = sinkhorn.sinkhorn(geom, a, b, tau_a=1/(1+epsilon), tau_b=1)\n",
"\n",
" # Saves the geometry and the potentials to calculate the ancestors at a later time\n",
" dict_results[day] = [geom, out.f, out.g]"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Executing Sinkhorn on pair of datasets for the days 17.5 and 18.0 / 18"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mr9ogxkxHYsL"
},
"source": [
"## Infers cells' ancestors\n",
"\n",
"Cells that are identified as induced Pluripotent Stem Cells (iPSCs) at day 18 are selected, and its ancestors at each previous time points, from day 18 to day 0, are inferred. To do so, the method ```apply_transport_from_potentials``` is used, which enables to perform the pull back operation without instantiating directly the transport matrix. \n",
"\n",
"```CELL_DISTRIBUTION_IPSC_DAY18``` is a normalised indicator array of cells identified as iPSCs at day 18.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "IfARvic82UnF",
"executionInfo": {
"status": "ok",
"timestamp": 1613650660849,
"user_tz": -60,
"elapsed": 2069,
"user": {
"displayName": "Laetitia Papaxanthos",
"photoUrl": "",
"userId": "13824884068334195048"
}
},
"outputId": "de02d1e7-599e-4e53-a7f9-095e8c6aa3e5"
},
"source": [
"cell_distribution_ipsc = {}\n",
"reverse_days = DAYS[::-1]\n",
"for i, day in enumerate(reverse_days):\n",
" print('\\r' + f'Infering ancestor cells at day {day}', end='')\n",
" if day == 0.:\n",
" continue\n",
" if day == 18.:\n",
" cell_distribution_ipsc[day] = CELL_DISTRIBUTION_IPSC_DAY18\n",
" # Calculates cells' ancestors\n",
" geom = dict_results[reverse_days[i+1]][0]\n",
" f = dict_results[reverse_days[i+1]][1]\n",
" g = dict_results[reverse_days[i+1]][2]\n",
" cell_distribution = geom.apply_transport_from_potentials(\n",
" f, g, cell_distribution_ipsc[day], axis=1) \n",
" cell_distribution_ipsc[reverse_days[i+1]] = cell_distribution / np.sum(\n",
" cell_distribution)"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"Calculating ancestor cells at day 0.0"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7lnZ_-KIHiTo"
},
"source": [
"## Visualizes\n",
"\n",
"Once the ancestors of iPSCs are identified, they are represented on a low-dimensional space thanks to an FLE visualisation. The iPSCs and its ancestors are coloured while all the other cells remain in grey.\n",
"\n",
"```COORD_DF``` is a dataframe that contains the coordinates of all cells on the FLE visualisation. \\\n",
"```IDS_CELLS``` is a dictionary that contains as keys the measurement days and as values the IDs of the cells. \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fIFt6WeDADNp"
},
"source": [
"All the cells can be represented in a 2D space thanks to an FLE visualisation [1]. The cells' coordinates are binarised. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "AvCJXwxsF8wb"
},
"source": [
"nbins = 500\n",
"xrange = COORD_DF['x'].min(), COORD_DF['x'].max()\n",
"yrange = COORD_DF['y'].min(), COORD_DF['y'].max()\n",
"COORD_DF['x'] = np.floor(\n",
" np.interp(COORD_DF['x'], [xrange[0], xrange[1]], [0, nbins - 1])).astype(int)\n",
"COORD_DF['y'] = np.floor(\n",
" np.interp(COORD_DF['y'], [yrange[0], yrange[1]], [0, nbins - 1])).astype(int)"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "tSz-NCpiAF7E"
},
"source": [
"The coordinates of cells identified as iPSCs at day 18 and its ancestors are filtered."
]
},
{
"cell_type": "code",
"metadata": {
"id": "cwxHtlUiGvgK"
},
"source": [
"coord_ancestors_ipsc = dict()\n",
"for day in DAYS:\n",
" cell_ids = np.array(IDS_CELLS[day])\n",
" coord_ancestors_ipsc[day] = COORD_DF[COORD_DF.index.isin(\n",
" cell_ids)][['x', 'y']].values "
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "UH8lBYfPAHWK"
},
"source": [
"To be able to discriminate between likely and unlikely ancestors, ancestors are binarised according to their inferred distribution at each time point."
]
},
{
"cell_type": "code",
"metadata": {
"id": "Vr2lcp7dAX0u"
},
"source": [
"alpha_bins = [1, 0.5, 0.]\n",
"binned_cell_distribution_ipsc = {}\n",
"for day in DAYS:\n",
" tmp = cell_distribution_ipsc[day].copy()\n",
" tmp[tmp >= 1e-2] = alpha_bins[0]\n",
" tmp[np.logical_and(1e-2 > tmp, tmp >= 5e-4)] = alpha_bins[1]\n",
" tmp[5e-4 > tmp] = alpha_bins[2]\n",
" binned_cell_distribution_ipsc[day] = tmp"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "hOqccdlVAPLF"
},
"source": [
"iPSCs and its ancestors are coloured according to the day the cells are measured on the FLE visualisation (see colorbar). All other cells are represented in grey. "
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"height": 646
},
"id": "n7yB9dag77eg",
"executionInfo": {
"status": "ok",
"timestamp": 1613651475608,
"user_tz": -60,
"elapsed": 1536,
"user": {
"displayName": "Laetitia Papaxanthos",
"photoUrl": "",
"userId": "13824884068334195048"
}
},
"outputId": "787fd479-8a9c-4e3f-9809-c97d67d7c37a"
},
"source": [
"# Sets a matplotlib colormap\n",
"cm = plt.get_cmap('jet')\n",
"cNorm = mpl.colors.Normalize(vmin=0, vmax=len(DAYS))\n",
"scalarMap = mpl.cm.ScalarMappable(norm=cNorm, cmap=cm)\n",
"\n",
"fig = plt.figure(figsize=(13, 10))\n",
"plt.title(f'Cell type: iPSC, medium: serum, $\\epsilon$={epsilon}', fontsize=24)\n",
"plt.plot(COORD_DF['x'], COORD_DF['y'], marker='.', color='grey', ls='', \n",
" markersize=0.3, alpha=0.07)\n",
"for i, day in enumerate(DAYS):\n",
" colorVal = scalarMap.to_rgba(i)\n",
" for b in alpha_bins:\n",
" ind_alpha = np.where(binned_cell_distribution_ipsc[day] == b)[0]\n",
" colorVal = np.array(colorVal)\n",
" colorVal[3] = b\n",
" plt.plot(coord_ancestors_ipsc[day][ind_alpha, 0], \n",
" coord_ancestors_ipsc[day][ind_alpha, 1], \n",
" marker='.', color=colorVal, ls='', markersize=1)\n",
"plt.xlabel('FLE1', fontsize=24)\n",
"plt.ylabel('FLE2', fontsize=24)\n",
"ax, _ = mpl.colorbar.make_axes(plt.gca(), shrink=1)\n",
"cbar = mpl.colorbar.ColorbarBase(ax, cmap=cm,\n",
" norm=mpl.colors.Normalize(vmin=0, vmax=18))\n",
"plt.show()"
],
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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NuqpjmcTs6rAP+MVhtv941THefhT3v6+y7dSp3aunO55pave5nqvNVes3z/KczHSev1lZ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\nbiGEENWQgFusW26a2Kj9KnV62L6ttAezUgpN7aFWrwUWH2B5PB4KJ09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p06fn7GkN80/a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QqFafb3e9aedDpNrVbTAj2bzWrffaFQ4NSpU6TTafL5fN98w372uGtSr67rsrGxwdWrV0kkEkxP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GSLq6upiYmDBrBev1OoqioCgK2WzWnH7pcChkCwpud8vpf61um9V0YjEXaMfKYN8x3R4cdeAx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\nlXi+BY4/AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1300x1000 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
}
]
} | apache-2.0 |
google/or-tools | examples/notebook/contrib/nonogram_default_search.ipynb | 1 | 9157 | {
"cells": [
{
"cell_type": "markdown",
"id": "google",
"metadata": {},
"source": [
"##### Copyright 2021 Google LLC."
]
},
{
"cell_type": "markdown",
"id": "apache",
"metadata": {},
"source": [
"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"you may not use this file except in compliance with the License.\n",
"You may obtain a copy of the License at\n",
"\n",
" http://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software\n",
"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"See the License for the specific language governing permissions and\n",
"limitations under the License.\n"
]
},
{
"cell_type": "markdown",
"id": "basename",
"metadata": {},
"source": [
"# nonogram_default_search"
]
},
{
"cell_type": "markdown",
"id": "link",
"metadata": {},
"source": [
"<table align=\"left\">\n",
"<td>\n",
"<a href=\"https://colab.research.google.com/github/google/or-tools/blob/master/examples/notebook/contrib/nonogram_default_search.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/colab_32px.png\"/>Run in Google Colab</a>\n",
"</td>\n",
"<td>\n",
"<a href=\"https://github.com/google/or-tools/blob/master/examples/contrib/nonogram_default_search.py\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/github_32px.png\"/>View source on GitHub</a>\n",
"</td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "doc",
"metadata": {},
"source": [
"First, you must install [ortools](https://pypi.org/project/ortools/) package in this colab."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "install",
"metadata": {},
"outputs": [],
"source": [
"!pip install ortools"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "code",
"metadata": {},
"outputs": [],
"source": [
"# Copyright 2010 Hakan Kjellerstrand [email protected], [email protected]\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# http://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License.\n",
"\"\"\"\n",
"\n",
" Nonogram (Painting by numbers) in Google CP Solver.\n",
"\n",
" http://en.wikipedia.org/wiki/Nonogram\n",
" '''\n",
" Nonograms or Paint by Numbers are picture logic puzzles in which cells in a\n",
" grid have to be colored or left blank according to numbers given at the\n",
" side of the grid to reveal a hidden picture. In this puzzle type, the\n",
" numbers measure how many unbroken lines of filled-in squares there are\n",
" in any given row or column. For example, a clue of '4 8 3' would mean\n",
" there are sets of four, eight, and three filled squares, in that order,\n",
" with at least one blank square between successive groups.\n",
"\n",
" '''\n",
"\n",
" See problem 12 at http://www.csplib.org/.\n",
"\n",
" http://www.puzzlemuseum.com/nonogram.htm\n",
"\n",
" Haskell solution:\n",
" http://twan.home.fmf.nl/blog/haskell/Nonograms.details\n",
"\n",
" Brunetti, Sara & Daurat, Alain (2003)\n",
" 'An algorithm reconstructing convex lattice sets'\n",
" http://geodisi.u-strasbg.fr/~daurat/papiers/tomoqconv.pdf\n",
"\n",
"\"\"\"\n",
"import sys\n",
"\n",
"from ortools.constraint_solver import pywrapcp\n",
"\n",
"\n",
"#\n",
"# Make a transition (automaton) list of tuples from a\n",
"# single pattern, e.g. [3,2,1]\n",
"#\n",
"def make_transition_tuples(pattern):\n",
" p_len = len(pattern)\n",
" num_states = p_len + sum(pattern)\n",
"\n",
" tuples = []\n",
"\n",
" # this is for handling 0-clues. It generates\n",
" # just the minimal state\n",
" if num_states == 0:\n",
" tuples.append((1, 0, 1))\n",
" return (tuples, 1)\n",
"\n",
" # convert pattern to a 0/1 pattern for easy handling of\n",
" # the states\n",
" tmp = [0]\n",
" c = 0\n",
" for pattern_index in range(p_len):\n",
" tmp.extend([1] * pattern[pattern_index])\n",
" tmp.append(0)\n",
"\n",
" for i in range(num_states):\n",
" state = i + 1\n",
" if tmp[i] == 0:\n",
" tuples.append((state, 0, state))\n",
" tuples.append((state, 1, state + 1))\n",
" else:\n",
" if i < num_states - 1:\n",
" if tmp[i + 1] == 1:\n",
" tuples.append((state, 1, state + 1))\n",
" else:\n",
" tuples.append((state, 0, state + 1))\n",
" tuples.append((num_states, 0, num_states))\n",
" return (tuples, num_states)\n",
"\n",
"\n",
"#\n",
"# check each rule by creating an automaton and transition constraint.\n",
"#\n",
"def check_rule(rules, y):\n",
" cleaned_rule = [rules[i] for i in range(len(rules)) if rules[i] > 0]\n",
" (transition_tuples, last_state) = make_transition_tuples(cleaned_rule)\n",
"\n",
" initial_state = 1\n",
" accepting_states = [last_state]\n",
"\n",
" solver = y[0].solver()\n",
" solver.Add(\n",
" solver.TransitionConstraint(y, transition_tuples, initial_state,\n",
" accepting_states))\n",
"\n",
"\n",
"\n",
"# Create the solver.\n",
"solver = pywrapcp.Solver('Nonogram')\n",
"\n",
"#\n",
"# variables\n",
"#\n",
"board = {}\n",
"for i in range(rows):\n",
" for j in range(cols):\n",
" board[i, j] = solver.IntVar(0, 1, 'board[%i, %i]' % (i, j))\n",
"\n",
"board_flat = [board[i, j] for i in range(rows) for j in range(cols)]\n",
"\n",
"# Flattened board for labeling.\n",
"# This labeling was inspired by a suggestion from\n",
"# Pascal Van Hentenryck about my (hakank's) Comet\n",
"# nonogram model.\n",
"board_label = []\n",
"if rows * row_rule_len < cols * col_rule_len:\n",
" for i in range(rows):\n",
" for j in range(cols):\n",
" board_label.append(board[i, j])\n",
"else:\n",
" for j in range(cols):\n",
" for i in range(rows):\n",
" board_label.append(board[i, j])\n",
"\n",
"#\n",
"# constraints\n",
"#\n",
"for i in range(rows):\n",
" check_rule(row_rules[i], [board[i, j] for j in range(cols)])\n",
"\n",
"for j in range(cols):\n",
" check_rule(col_rules[j], [board[i, j] for i in range(rows)])\n",
"\n",
"#\n",
"# solution and search\n",
"#\n",
"parameters = pywrapcp.DefaultPhaseParameters()\n",
"parameters.heuristic_period = 200000\n",
"\n",
"db = solver.DefaultPhase(board_label, parameters)\n",
"\n",
"print('before solver, wall time = ', solver.WallTime(), 'ms')\n",
"solver.NewSearch(db)\n",
"\n",
"num_solutions = 0\n",
"while solver.NextSolution():\n",
" print()\n",
" num_solutions += 1\n",
" for i in range(rows):\n",
" row = [board[i, j].Value() for j in range(cols)]\n",
" row_pres = []\n",
" for j in row:\n",
" if j == 1:\n",
" row_pres.append('#')\n",
" else:\n",
" row_pres.append(' ')\n",
" print(' ', ''.join(row_pres))\n",
"\n",
" print()\n",
" print(' ', '-' * cols)\n",
"\n",
" if num_solutions >= 2:\n",
" print('2 solutions is enough...')\n",
" break\n",
"\n",
"solver.EndSearch()\n",
"print()\n",
"print('num_solutions:', num_solutions)\n",
"print('failures:', solver.Failures())\n",
"print('branches:', solver.Branches())\n",
"print('WallTime:', solver.WallTime(), 'ms')\n",
"\n",
"\n",
"#\n",
"# Default problem\n",
"#\n",
"# From http://twan.home.fmf.nl/blog/haskell/Nonograms.details\n",
"# The lambda picture\n",
"#rows = 12\n",
"row_rule_len = 3\n",
"row_rules = [[0, 0, 2], [0, 1, 2], [0, 1, 1], [0, 0, 2], [0, 0, 1], [0, 0, 3],\n",
" [0, 0, 3], [0, 2, 2], [0, 2, 1], [2, 2, 1], [0, 2, 3], [0, 2, 2]]\n",
"\n",
"cols = 10\n",
"col_rule_len = 2\n",
"col_rules = [[2, 1], [1, 3], [2, 4], [3, 4], [0, 4], [0, 3], [0, 3], [0, 3],\n",
" [0, 2], [0, 2]]\n",
"\n"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
| apache-2.0 |
mcm326/programingworkshop | Python/python_intro.ipynb | 16 | 33998 | {
"metadata": {
"name": "python_intro"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Introduction to Python, Numpy, and Matplotlib\n",
"============================================="
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You will start almost every script with import statements. They import libraries and modules that you will use. Some of the most common libraries are NumPy (numerics), SymPy (symbolics), Matplotlib (plotting), and Pandas (time series and data). The syntax is as follows"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to import the entire library, you do this:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to import a library and give it a different name for ease (you will see why you want to do this in a little bit) you simply use the \"as\" command"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to import just a specific module from a libary, you would do this:\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from numpy import cos "
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"or..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from numpy import cos as c"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now I have imported the cosine function from the numpy library and named it c (usually would just use \"cos\", but just an example). So what do I do with this? Simple, use \"c\" as the cosine function, giving it input within a set of parantheticals"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"c(180*2*np.pi/360) # cosine of 180 degrees"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"c(0)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Typically, would utilize the numpy library like this, calling the cosine function from the library with this syntax:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.cos(0)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So instead of calling \"numpy\" everytime you want to use one of the modules, you just call \"np\". This makes it easier when you want to use plotting functions from matplotlib that might have long names. For example, "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from matplotlib import pyplot as plt"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt #same thing"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You will find yourself importing many different libraries in more complicated scripts, that employ data processing, reading, and plotting. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you don't want to use the \"np\" or \"plt\" extensions, you can import everything from a library using this command:\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from numpy import *"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Imports all of numpy's modules and you can use \"cos\" just by itself"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cos(0)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You will most likely use Numpy and Matplotlib the most of any libraries (maybe Pandas as well)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"NUMPY\n",
"====="
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some real basic examples in numpy, you will get experience fast in manipulating this library for your specific tasks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make an array from a range of numbers"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.arange(40) # default is a range from 0 to the input value by 1\n",
"y = np.arange(0,40,2) # can specify the increment\n",
"z = np.arange(0,20+1,+1) #makes the range go TO 20, by default will stop one short of the given value (i.e. 20 values in the array)\n",
"print z"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b = y.reshape(4,5) # reshape the array into a 3x7 2d array\n",
"print b"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = y.reshape(2,2,5) # 3d array\n",
"print a"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
">>> a = array( [20,30,40,50] )\n",
">>> b = arange( 4 )\n",
">>> b\n",
"array([0, 1, 2, 3])\n",
">>> c = a-b\n",
">>> c\n",
"array([20, 29, 38, 47])\n",
">>> b**2\n",
"array([0, 1, 4, 9])\n",
">>> 10*sin(a)\n",
"array([ 9.12945251, -9.88031624, 7.4511316 , -2.62374854])\n",
">>> a<35\n"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Indexing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Arrays are easy to maneuver as well, indexing is very similar to other languages. It is 0 based FYI"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print a[2]\n",
"print a[1]\n",
"print a[0]"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print a[-1] # print the last entry in the array"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print a[0:2]"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print a[: :-1] #print a in reverse"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a.shape # what are the dimensions of the array? 4X1"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y = np.arange(0,40,2)\n",
"b = y.reshape(2,2,5)\n",
"b.shape # 3 dimensional"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### For Loops"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y = np.arange(0,40,2)\n",
"for numbers in y:\n",
" print numbers**2 # just print the square of each value\n",
" \n",
"#### INDENTATION IS IMPORTANT!! Whatever is indented will be included in the for loop"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's even easier than that thought, You can do for loops in just ONE LINE!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"test = [str(nums)+'*' for nums in y] # make a new array with the numbers from y with an added asteristic. Now an array of strings\n",
"print test"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you just want to mathematically alter the elements of an array, can just use array mathematics\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y*2"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(y*2 + 2)**3"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" #### Functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets say you want to make a unique manipulation of an array, or read in data files multiple times in a script, well you may think, \"A function would be really nice right now\". Python has those too! Here is the syntax\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### This is a stupid example, but you get the idea\n",
"def pass_to(filename,extension):\n",
" \"\"\" This is a docstring, which tells the user what the function is for. Often, will define what the inputs and outputs are\n",
" Inputs: Filename-string of the desired file to be read in\n",
" extension-string \n",
" Output: Return the array of the read in file \"\"\"\n",
" a=np.genfromtext(filename+extension)\n",
" print 'Filename is: '+filename+'.'+extension\n",
" return a"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MATPLOTLIB\n",
"=========="
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Introduction to basic plotting in python\n",
"----------------------------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"\n",
"Matplotlib is an excellent 2D and 3D graphics library for generating scientific figures. Some of the many advantages of this library includes:\n",
"\n",
"* Easy to get started\n",
"* Support for $\\LaTeX$ formatted labels and texts\n",
"* Great control of every element in a figure, including figure size and DPI. \n",
"* High-quality output in many formats, including PNG, PDF, SVG, EPS.\n",
"* GUI for interactively exploring figures *and* support for headless generation of figure files (useful for batch jobs).\n",
"\n",
"One of the of the key features of matplotlib that I would like to emphasize, and that I think makes matplotlib highly suitable for generating figures for scientific publications is that all aspects of the figure can be controlled *programmatically*. This is important for reproducibility, convenient when one need to regenerate the figure with updated data or changes its appearance. \n",
"\n",
"More information at the Matplotlib web page: http://matplotlib.org/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To get started using matplotlib in python, you must \"import\" the library using:\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pylab import *"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or you can import pyplot directly from the matplotlib library (typically what I do, since pyplot is what I use the most:\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline \n",
"# this just insets the plots into ipython notebook, won't use this in a normal python script"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"plt.subplot(1,2,1)\n",
"x = np.arange(0,10,1)\n",
"y = x**2\n",
"plt.plot(x,y,'r')\n",
"plt.title('Example Plot 1')\n",
"plt.xlabel('x')\n",
"plt.ylabel('y')\n",
"plt.subplot(1,2,2)\n",
"plt.plot(y,x,'b--')\n",
"plt.title('Example Plot 2')\n",
"plt.xlabel('y')\n",
"plt.ylabel('x')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The matplotlib object-oriented API\n",
"\n",
"The main idea with object-oriented programming is to have objects that one can apply functions and actions on, and no object or program states should be global (such as the MATLAB-like API). The real advantage of this approach becomes apparent when more than one figure is created, or when a figure contains more than one subplot. \n",
"\n",
"To use the object-oriented API we start out very much like in the previous example, but instead of creating a new global figure instance we store a reference to the newly created figure instance in the `fig` variable, and from it we create a new axis instance `axes` using the `add_axes` method in the `Figure` class instance `fig`."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure()\n",
"\n",
"axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1)\n",
"\n",
"axes.plot(x, y, 'r')\n",
"\n",
"axes.set_xlabel('x')\n",
"axes.set_ylabel('y')\n",
"axes.set_title('title');"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Although a little bit more code is involved, the advantage is that we now have full control of where the plot axes are place, and we can easily add more than one axis to the figure."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure()\n",
"\n",
"axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes\n",
"axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes\n",
"\n",
"# main figure\n",
"axes1.plot(x, y, 'r')\n",
"axes1.set_xlabel('x')\n",
"axes1.set_ylabel('y')\n",
"axes1.set_title('title')\n",
"\n",
"# insert\n",
"axes2.plot(y, x, 'g')\n",
"axes2.set_xlabel('y')\n",
"axes2.set_ylabel('x')\n",
"axes2.set_title('insert title');"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we don't care to be explicit about where our plot axes are placed in the figure canvas, then we can use one of the many axis layout managers in matplotlib. My favorite is subplots, which can be used like this:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, axes = plt.subplots()\n",
"\n",
"axes.plot(x, y, 'r')\n",
"axes.set_xlabel('x')\n",
"axes.set_ylabel('y')\n",
"axes.set_title('title');"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, axes = plt.subplots(nrows=1, ncols=2)\n",
"\n",
"for ax in axes:\n",
" ax.plot(x, y, 'r')\n",
" ax.set_xlabel('x')\n",
" ax.set_ylabel('y')\n",
" ax.set_title('title');"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That was easy, but it isn't so pretty with overlapping figure axes and labels, right?\n",
"\n",
"We can deal with that by using the `fig.tight_layout` method, which automatically adjusts the positions of the axes on the figure canvas so that there is no overlapping content:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, axes = plt.subplots(nrows=1, ncols=2)\n",
"\n",
"for ax in axes:\n",
" ax.plot(x, y, 'r')\n",
" ax.set_xlabel('x')\n",
" ax.set_ylabel('y')\n",
" ax.set_title('title')\n",
" \n",
"fig.tight_layout()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(8,4), dpi=100)\n",
"# Or you can pass to subplots\n",
"fig, axes = plt.subplots(figsize=(12,3))\n",
"\n",
"axes.plot(x, y, 'r')\n",
"axes.set_xlabel('x')\n",
"axes.set_ylabel('y')\n",
"axes.set_title('title');"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Very easy to save figures as well"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig.savefig(\"filename.png\")\n",
"\n",
"# or\n",
"\n",
"fig.savefig(\"filename.png\", dpi=200)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For a paper, use PDF whenever possible for best possible format (in my opinion, although I think this is generally accepted)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ax.plot(x, x**2, label=\"curve1\")\n",
"ax.plot(x, x**3, label=\"curve2\")\n",
"ax.legend();"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Line and marker styles\n",
"\n",
"To change the line width we can use the `linewidth` or `lw` keyword argument, and the line style can be selected using the `linestyle` or `ls` keyword arguments:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = subplots(figsize=(12,6))\n",
"\n",
"ax.plot(x, x+1, color=\"blue\", linewidth=0.25)\n",
"ax.plot(x, x+2, color=\"blue\", linewidth=0.50)\n",
"ax.plot(x, x+3, color=\"blue\", linewidth=1.00)\n",
"ax.plot(x, x+4, color=\"blue\", linewidth=2.00)\n",
"\n",
"# possible linestype options \u2018-\u2018, \u2018\u2013\u2019, \u2018-.\u2019, \u2018:\u2019, \u2018steps\u2019\n",
"ax.plot(x, x+5, color=\"red\", lw=2, linestyle='-')\n",
"ax.plot(x, x+6, color=\"red\", lw=2, ls='-.')\n",
"ax.plot(x, x+7, color=\"red\", lw=2, ls=':')\n",
"\n",
"# custom dash\n",
"line, = ax.plot(x, x+8, color=\"black\", lw=1.50)\n",
"line.set_dashes([5, 10, 15, 10]) # format: line length, space length, ...\n",
"\n",
"# possible marker symbols: marker = '+', 'o', '*', 's', ',', '.', '1', '2', '3', '4', ...\n",
"ax.plot(x, x+ 9, color=\"green\", lw=2, ls='*', marker='+')\n",
"ax.plot(x, x+10, color=\"green\", lw=2, ls='*', marker='o')\n",
"ax.plot(x, x+11, color=\"green\", lw=2, ls='*', marker='s')\n",
"ax.plot(x, x+12, color=\"green\", lw=2, ls='*', marker='1')\n",
"\n",
"# marker size and color\n",
"ax.plot(x, x+13, color=\"purple\", lw=1, ls='-', marker='o', markersize=2)\n",
"ax.plot(x, x+14, color=\"purple\", lw=1, ls='-', marker='o', markersize=4)\n",
"ax.plot(x, x+15, color=\"purple\", lw=1, ls='-', marker='o', markersize=8, markerfacecolor=\"red\")\n",
"ax.plot(x, x+16, color=\"purple\", lw=1, ls='-', marker='s', markersize=8, \n",
" markerfacecolor=\"yellow\", markeredgewidth=2, markeredgecolor=\"blue\");"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Control over axis apperance\n",
"\n",
"The appearance of the axes is an important aspect of a figure that we often need to modify to make a publication quality graphics. We need to be able to control where the ticks and labels are placed, modify the font size and possibly the labels used on the axes. In this section we will look at controling those properties in a matplotlib figure.\n",
"\n",
"### Plot range\n",
"\n",
"The first thing we might want to configure is the ranges of the axes. We can do it using the `set_ylim` and `set_xlim` methods in the axis object, or `axis('tight')` for automatrically getting \"tightly fitted\" axes ranges."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, axes = subplots(1, 3, figsize=(12, 4))\n",
"\n",
"axes[0].plot(x, x**2, x, x**3)\n",
"axes[0].set_title(\"default axes ranges\")\n",
"\n",
"axes[1].plot(x, x**2, x, x**3)\n",
"axes[1].axis('tight')\n",
"axes[1].set_title(\"tight axes\")\n",
"\n",
"axes[2].plot(x, x**2, x, x**3)\n",
"axes[2].set_ylim([0, 60])\n",
"axes[2].set_xlim([2, 5])\n",
"axes[2].set_title(\"custom axes range\");"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Placement of ticks and custom tick labels\n",
"\n",
"We can explicitly determine where we want the axis ticks using the `set_xticks` and `set_yticks`, which both takes a list of values for where on the axis the ticks are to be placed. We can also use the functions `set_xticklabels` and `set_yticklabels` to provide a list of custom text labels for each tick location:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = subplots(figsize=(10, 4))\n",
"\n",
"ax.plot(x, x**2, x, x**3, lw=2)\n",
"\n",
"ax.set_xticks([1, 2, 3, 4, 5])\n",
"ax.set_xticklabels([r'$\\alpha$', r'$\\beta$', r'$\\gamma$', r'$\\delta$', r'$\\epsilon$'], fontsize=18)\n",
"\n",
"yticks = [0, 50, 100, 150]\n",
"ax.set_yticks(yticks)\n",
"ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18); # use LaTeX formatted labels"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Turning on and off grid lines"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, axes = subplots(1, 2, figsize=(10,3))\n",
"\n",
"# default grid appearance\n",
"axes[0].plot(x, x**2, x, x**3, lw=2)\n",
"axes[0].grid(True)\n",
"\n",
"# custom grid appearance\n",
"axes[1].plot(x, x**2, x, x**3, lw=2)\n",
"axes[1].grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Twin axes\n",
"\n",
"Sometimes it is useful to have dual x or y axes in a figure, for example when plotting curves with differnt units together. Matplotlib supports this with the `twinx` and `twiny` functions:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax1 = subplots()\n",
"\n",
"ax1.plot(x, x**2, lw=2, color=\"blue\")\n",
"ax1.set_ylabel(r\"area $(m^2)$\", fontsize=18, color=\"blue\")\n",
"for label in ax1.get_yticklabels():\n",
" label.set_color(\"blue\")\n",
" \n",
"ax2 = ax1.twinx()\n",
"ax2.plot(x, x**3, lw=2, color=\"red\")\n",
"ax2.set_ylabel(r\"volume $(m^3)$\", fontsize=18, color=\"red\")\n",
"for label in ax2.get_yticklabels():\n",
" label.set_color(\"red\")"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Other 2D plot styles\n",
"\n",
"In addition to the function `plot`, there are a number of other functions for generating different kind of plots. See the matplotlib plot gallery for a complete list of avaiable plot types: http://matplotlib.org/gallery.html. Some of the more useful ones are show below:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n = array([0,1,2,3,4,5])\n",
"xx = np.linspace(-0.75, 1., 100)\n",
"fig, axes = subplots(1, 4, figsize=(12,3))\n",
"\n",
"axes[0].scatter(xx, xx + 0.25*randn(len(xx)))\n",
"\n",
"axes[1].step(n, n**2, lw=2)\n",
"\n",
"axes[2].bar(n, n**2, align=\"center\", width=0.5, alpha=0.5)\n",
"\n",
"axes[3].fill_between(x, x**2, x**3, color=\"green\", alpha=0.5);"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# polar plot using add_axes and polar projection\n",
"fig = plt.figure()\n",
"ax = fig.add_axes([0.0, 0.0, .6, .6], polar=True)\n",
"t = linspace(0, 2 * pi, 100)\n",
"ax.plot(t, t, color='blue', lw=3);"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Figures with Multiple Subplots and Insets"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure()\n",
"ax1 = plt.subplot2grid((3,3), (0,0), colspan=3)\n",
"ax2 = plt.subplot2grid((3,3), (1,0), colspan=2)\n",
"ax3 = plt.subplot2grid((3,3), (1,2), rowspan=2)\n",
"ax4 = plt.subplot2grid((3,3), (2,0))\n",
"ax5 = plt.subplot2grid((3,3), (2,1))\n",
"fig.tight_layout()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Colormap and contour figures\n",
"\n",
"Colormaps and contour figures are useful for plotting functions of two variables. In most of these functions we will use a colormap to encode one dimension of the data. There is a number of predefined colormaps, and it is relatively straightforward to define custom colormaps. For a list of pre-defined colormaps, see:\n",
"\n",
"http://www.scipy.org/Cookbook/Matplotlib/Show_colormaps"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"alpha = 0.7\n",
"phi_ext = 2 * pi * 0.5\n",
"\n",
"def flux_qubit_potential(phi_m, phi_p):\n",
" return 2 + alpha - 2 * cos(phi_p)*cos(phi_m) - alpha * cos(phi_ext - 2*phi_p)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"phi_m = linspace(0, 2*pi, 100)\n",
"phi_p = linspace(0, 2*pi, 100)\n",
"X,Y = meshgrid(phi_p, phi_m)\n",
"Z = flux_qubit_potential(X, Y).T"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Pcolor"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = subplots()\n",
"\n",
"p = ax.pcolor(X/(2*pi), Y/(2*pi), Z, cmap=cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max())\n",
"cb = fig.colorbar(p)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### imshow"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = subplots()\n",
"\n",
"im = imshow(Z, cmap=cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])\n",
"im.set_interpolation('bilinear')\n",
"\n",
"cb = fig.colorbar(im)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### contour\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = subplots()\n",
"\n",
"cnt = contour(Z, cmap=cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3D figures\n",
"\n",
"To use 3D graphics in matplotlib, we first need to create an axes instance of the class `Axes3D`. 3D axes can be added to a matplotlib figure canvas in exactly the same way as 2D axes, but a conventient way to create a 3D axis instance is to use the `projection='3d'` keyword argument to the `add_axes` or `add_subplot` functions."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from mpl_toolkits.mplot3d.axes3d import Axes3D\n",
"\n",
"fig = plt.figure(figsize=(14,6))\n",
"\n",
"# `ax` is a 3D-aware axis instance, because of the projection='3d' keyword argument to add_subplot\n",
"ax = fig.add_subplot(1, 2, 1, projection='3d')\n",
"\n",
"p = ax.plot_surface(X, Y, Z, rstride=4, cstride=4, linewidth=0)\n",
"\n",
"# surface_plot with color grading and color bar\n",
"ax = fig.add_subplot(1, 2, 2, projection='3d')\n",
"p = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=False)\n",
"cb = fig.colorbar(p, shrink=0.5)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Coutour plots with projections"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(8,6))\n",
"\n",
"ax = fig.add_subplot(1,1,1, projection='3d')\n",
"\n",
"ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)\n",
"cset = ax.contour(X, Y, Z, zdir='z', offset=-pi, cmap=cm.coolwarm)\n",
"cset = ax.contour(X, Y, Z, zdir='x', offset=-pi, cmap=cm.coolwarm)\n",
"cset = ax.contour(X, Y, Z, zdir='y', offset=3*pi, cmap=cm.coolwarm)\n",
"\n",
"ax.set_xlim3d(-pi, 2*pi);\n",
"ax.set_ylim3d(0, 3*pi);\n",
"ax.set_zlim3d(-pi, 2*pi);"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basemap"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Further reading\n",
"\n",
"* http://www.matplotlib.org - The project web page for matplotlib.\n",
"* https://github.com/matplotlib/matplotlib - The source code for matplotlib.\n",
"* http://matplotlib.org/gallery.html - A large gallery that showcase what kind of plots matplotlib can create. Highly recommended! \n",
"* http://www.loria.fr/~rougier/teaching/matplotlib/ - A good matplotlib tutorial.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ALSO UTILIZE STACKOVERFLOW.COM WHEN YOU HAVE ISSUES!\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
DouglasOrr/DeepLearnTute | reference/GAN.ipynb | 1 | 6592 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# An attempt at implementing a (W)GAN\n",
"\n",
"Based on:\n",
" - [Generate Adversarial Text to Image Synthesis](https://arxiv.org/pdf/1605.05396.pdf)\n",
" - [Improved Training of Wasserstien GANs](https://arxiv.org/pdf/1704.00028.pdf)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"%env CHAINER_TYPE_CHECK 0\n",
"import dlt\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import chainer as C\n",
"import matplotlib.pyplot as plt\n",
"import itertools as it\n",
"\n",
"train = dlt.load_hdf5('/data/uji/train.hdf')\n",
"valid = dlt.load_hdf5('/data/uji/valid.hdf')\n",
"print(\" Training: %s\" % train)\n",
"print(\"Validation: %s\" % valid)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Utility functions\n",
"\n",
"def show(batch, im_size=1.5):\n",
" '''Show a batch of images.\n",
" '''\n",
" if batch.ndim == 2:\n",
" batch = batch[np.newaxis, ...]\n",
" size = int(np.sqrt(batch.shape[-1]))\n",
" plt.figure(figsize=(im_size * batch.shape[1], im_size * batch.shape[0]))\n",
" for plot_index, x in zip(it.count(1), batch.reshape(-1, batch.shape[-1])):\n",
" plt.subplot(batch.shape[0], batch.shape[1], plot_index)\n",
" plt.imshow(x.reshape(size, size))\n",
" plt.gca().set_xticks([])\n",
" plt.gca().set_yticks([])\n",
" plt.gca().grid(False)\n",
" \n",
"class GradientFlip(C.Function):\n",
" def __init__(self, f=-1):\n",
" self.f = f\n",
" def forward(self, inputs):\n",
" return inputs\n",
" def backward(self, inputs, grad_outputs):\n",
" return tuple(self.f * x for x in grad_outputs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Optimization objective\n",
"# min_G [ max_C [ mean_{x~X} [ C(x) ] - mean_{z~G} [ C(z) ] ] ]\n",
"# Requiring C is 1-Libschitz\n",
"\n",
"class HiddenLayers(C.ChainList):\n",
" def __init__(self, d, n=1):\n",
" super().__init__(*(C.links.Linear(d, d) for _ in range(n)))\n",
" def __call__(self, x):\n",
" for layer in self:\n",
" x = C.functions.elu(layer(x))\n",
" return x\n",
"\n",
"class Generator(C.Chain):\n",
" def __init__(self, D_noise, D_hidden, D_output, nhidden=1):\n",
" self.D_noise = D_noise\n",
" super().__init__(\n",
" initial=C.links.Linear(D_noise, D_hidden),\n",
" hidden=HiddenLayers(D_hidden, nhidden),\n",
" final=C.links.Linear(D_hidden, D_output),\n",
" )\n",
" def __call__(self, N):\n",
" noise = C.Variable(np.random.rand(N, self.D_noise).astype(np.float32))\n",
" hidden = self.hidden(C.functions.elu(self.initial(noise)))\n",
" return C.functions.sigmoid(self.final(hidden))\n",
"\n",
"class Critic(C.Chain):\n",
" def __init__(self, D_input, D_hidden, nhidden=1):\n",
" super().__init__(\n",
" initial=C.links.Linear(D_input, D_hidden),\n",
" hidden=HiddenLayers(D_hidden, nhidden),\n",
" final=C.links.Linear(D_hidden, 1, nobias=True),\n",
" )\n",
" def __call__(self, batch):\n",
" hidden = self.hidden(C.functions.elu(self.initial(batch)))\n",
" return C.functions.sum(self.final(hidden)) / batch.shape[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"batch_size = 128\n",
"max_batches = 100000\n",
"clip = 1\n",
"backratio = -0.01\n",
"\n",
"sample_every = max_batches // 10\n",
"network = C.Chain(\n",
" generator=Generator(10, 128, 256, nhidden=1),\n",
" critic=Critic(256, 128, nhidden=2),\n",
")\n",
"opt = C.optimizers.Adam()\n",
"opt.use_cleargrads()\n",
"opt.setup(network)\n",
"\n",
"log = dlt.Log()\n",
"samples = []\n",
"for step, i in enumerate(it.islice(it.cycle(range(0, len(train.x), batch_size)), 0, max_batches)):\n",
" network.cleargrads()\n",
" real = C.Variable(train.x[i:i+batch_size])\n",
" fake = GradientFlip(backratio)(network.generator(batch_size))\n",
" loss = network.critic(fake) - network.critic(real)\n",
" for p in network.critic.params():\n",
" r = clip / np.sqrt(p.data.size)\n",
" p.data = np.clip(p.data, -r, r)\n",
" loss.backward()\n",
" opt.update()\n",
" log.add('loss', 'train', loss)\n",
" if step % sample_every == 0:\n",
" samples.append(fake.data)\n",
"\n",
"log.show()\n",
"show(train.x[:5])\n",
"show(np.stack(samples)[:, :5, :])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"n = sum(1 for _ in network.critic.params())\n",
"plt.figure(figsize=(12, 4*n))\n",
"for i, (name, param) in enumerate(network.critic.namedparams()):\n",
" plt.subplot(n, 1, i+1)\n",
" plt.title(name)\n",
" sns.distplot(param.data.flatten(), kde=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"show(network.generator(64).data.reshape((8,8,-1)))"
]
}
],
"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 |
shigh/dg-tinker | dg1d-isoT-plasma-langmuir.ipynb | 1 | 8593 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from numpy import newaxis\n",
"import scipy.sparse as sps\n",
"from scipy.sparse.linalg import spsolve\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from fem import *\n",
"eval_phi1d = eval_lagrange_d0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from poly import eval_P\n",
"from utils import minmod"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build Mesh and Operators"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"order = 1\n",
"semh = SEMhat(order)\n",
"\n",
"N = 100\n",
"n_dofs = (order+1)*N"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"L = 1.0\n",
"\n",
"vertices = np.linspace(0, L, N+1)\n",
"EtoV = np.zeros((N, 2), dtype=np.int)\n",
"EtoV[:,0] = np.arange(N)\n",
"EtoV[:,1] = np.arange(N)+1\n",
"\n",
"topo = Interval()\n",
"xq = topo.ref_to_phys(vertices[EtoV], semh.xgll)\n",
"jacb_det = topo.calc_jacb(vertices[EtoV])[0]\n",
"dx = np.min(xq[0,1:]-xq[0,:-1])\n",
"EtoV[-1,-1] = EtoV[0,0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Make elem to dof map\n",
"EtoD = np.arange(N*(order+1))\n",
"EtoD = EtoD.reshape((N, -1))\n",
"\n",
"dof_phys = xq.ravel()\n",
"\n",
"# Averaging operator\n",
"rows = EtoD[:,[0,-1]].ravel()\n",
"cols = EtoV.ravel()\n",
"vals = np.ones_like(cols)\n",
"\n",
"FtoD = sps.coo_matrix((vals, (rows, cols))).tocsr()\n",
"AVG = FtoD.dot(FtoD.T)/2.0\n",
"\n",
"# Extract face DOFS\n",
"vals = np.ones(len(rows))\n",
"FD = sps.coo_matrix((vals, (rows, rows))).tocsr()\n",
"# Set face signs\n",
"vals[::2] = -1\n",
"SD = sps.coo_matrix((vals, (rows, rows))).tocsr()\n",
"\n",
"# Jump operator\n",
"JUMP = FtoD.dot(SD.dot(FtoD).T)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Build Advection operator\n",
"S = sps.kron(sps.eye(N), semh.Ch).tocsr()\n",
"\n",
"# Differentiation matrix\n",
"Dr = sps.kron(sps.eye(N), semh.Dh)/jacb_det\n",
"Dr = Dr.tocsr()\n",
"\n",
"# Build full elemental mass matrix\n",
"x, w = topo.get_quadrature(order+1)\n",
"P = eval_phi1d(semh.xgll, x).T\n",
"G = sps.dia_matrix((w, 0), shape=(len(x), len(x)))\n",
"Bf = P.T.dot(G.dot(P))*jacb_det\n",
"B = sps.kron(sps.eye(N), Bf)\n",
"\n",
"# Using trick from book\n",
"V = eval_P(order, semh.xgll).T\n",
"Vinv = np.linalg.inv(V)\n",
"Minv = V.dot(V.T)/jacb_det\n",
"Binv = sps.kron(sps.eye(N), Minv).tocsr()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Build Poisson matrix\n",
"tau = 1.0\n",
"\n",
"FLUXU = AVG\n",
"Q = Dr-Binv.dot(SD.dot(FD-FLUXU))\n",
"FLUXQ = AVG.dot(Q)-tau*JUMP \n",
"A = S.dot(Q)-SD.dot(FD.dot(Q)-FLUXQ)\n",
"A = -A"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem Setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Isothermal Euler -- Gaussian bump\n",
"\n",
"a = 0.0\n",
"def calc_flux(u):\n",
" \n",
" f = np.zeros_like(u)\n",
" f[:,0] = u[:,1]\n",
" f[:,1] = u[:,1]**2/u[:,0]+a*a*u[:,0]\n",
" f[:,2] = 0.0\n",
" \n",
" return f\n",
"\n",
"def calc_eig(u):\n",
" #return a+np.abs(u[:,1]/u[:,0])\n",
" return 1.0\n",
" \n",
"u0 = np.zeros((n_dofs, 3))\n",
"alpha = 0.01\n",
"u0[:,0] = 2.0+alpha*np.sin(2*np.pi*dof_phys)\n",
"wp = np.sqrt(np.mean(u0[:,0]))\n",
"\n",
"ue = u0.copy()\n",
"\n",
"np.max(calc_eig(u0))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def phie(t):\n",
" return alpha*np.sin(2*np.pi*dof_phys)*np.cos(wp*t)/((2*np.pi)**2)\n",
"\n",
"def Ee(t):\n",
" return -alpha*np.cos(2*np.pi*dof_phys)*np.cos(wp*t)/(2*np.pi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute solution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Time stepping\n",
"def g(u):\n",
" f = calc_flux(u)\n",
" c = np.max(calc_eig(u))\n",
" flux = AVG.dot(f)+c/2.0*JUMP.dot(u)\n",
" return Binv.dot(-S.dot(f)+SD.dot(FD.dot(f)-flux))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Integrate with SSP-RK3\n",
"u = u0.copy()\n",
"\n",
"# One complete oscillation\n",
"Tfinal = 2*np.pi/wp\n",
"\n",
"dt = 0.001\n",
"nt = int(Tfinal/dt)\n",
"dt = Tfinal/nt\n",
"\n",
"for k in range(nt):\n",
" \n",
" # Step Euler equations\n",
" v1 = u+dt*g(u) \n",
" v2 = .25*(3*u+v1+dt*g(v1))\n",
" u = (u+2*v2+2*dt*g(v2))/3.0\n",
"\n",
" # Step field equations\n",
" phi = sps.linalg.spsolve(A, B.dot(u[:,0]-np.mean(u[:,0])))\n",
" E = -Dr.dot(phi)\n",
" u[:,1] += dt*u[:,0]*E\n",
"\n",
"phif = sps.linalg.spsolve(A, B.dot(u[:,0]-np.mean(u[:,0])))\n",
"Ef = -Dr.dot(phif)\n",
" \n",
"nt, dt*nt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"k = -1\n",
"plt.figure()\n",
"plt.plot(dof_phys[:k], ue[:,0][:k], 'g--',\n",
" label=\"$t=0$\",\n",
" linewidth=2)\n",
"plt.plot(dof_phys[:k], u[:,0][:k],\n",
" label=\"$t=2\\pi/\\omega_p$\")\n",
"plt.ylabel('$\\\\rho$', size=16)\n",
"plt.xlabel(\"$x$\", size=16)\n",
"plt.legend(loc='upper right', fontsize=16)\n",
"plt.title(\"One complete Langmuir Oscillation\", size=16)\n",
"plt.savefig(\"langmuir.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ps = lambda x:x.reshape((-1,order+1)).T\n",
"plt.figure()\n",
"plt.plot(ps(dof_phys), ps(u[:,1]/u[:,0]), 'b')\n",
"plt.plot(ps(dof_phys), ps(ue[:,1]), 'g--')\n",
"plt.ylabel('$u$', size=16)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(ps(dof_phys), ps(phif-np.mean(phif)), 'b');\n",
"plt.plot(dof_phys, phie(nt*dt), 'g--',\n",
" linewidth=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(ps(dof_phys), ps(Ef), 'b');\n",
"plt.plot(dof_phys, Ee(nt*dt), 'g--',\n",
" linewidth=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
biof-309-python/BIOF309-2016-Fall | Week_03/Week03 - 04 - Homework.ipynb | 1 | 4604 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Week 3 Homework"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Write a python program to save a well formatted fasta file"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sequence_description = \"gb|AF333238|A/Brevig Mission/1/1918(H1N1)|Segment:8|Subtype:H1N1|Host:Human\"\n",
"sequence = \"ATGGATTCCAACACTGTGTCAAGCTTTCAGGTAGACTGCTTTCTTTGGCATGTCCGCAAACGGTTTGCAGACCAAGAACTGGGTGATGCCCCATTCCTTGATCGGCTTCGCCGAGATCAGAAGTCCCTAAGAGGAAGAGGCAGCACTCTTGGTCTGGACATCGAGACAGCCACCCGTGCTGGAAAGCAGATAGTGGAGCGGATTCTGAAGGAAGAATCCGATGAGGCACTTAAAATGACCATTGCCTCTGTACCTGCTTCGCGCTACCTAACTGACATGACTCTTGAGGAGATGTCAAGGGACTGGTTCATGCTCATGCCCAAGCAGAAAGTGGCAGGCTCTCTTTGTATCAGAATGGACCAGGCGATCATGGATAAGAACATCATACTGAAAGCGAACTTCAGTGTGATTTTCGACCGGCTGGAGACTCTAATACTACTAAGGGCTTTCACCGAAGAGGGAGCAATTGTTGGCGAAATTTCACCATTGCCTTCTCTTCCAGGACATACTGATGAGGATGTCAAAAATGCAGTTGGGGTCCTCATCGGAGGACTTGAATGGAATGATAACACAGTTCGAGTCTCTGAAACTCTACAGAGATTCGCTTGGAGAAGCAGTAATGAGAATGGGAGACCTCCACTCCCTCCAAAACAGAAACGGAAAATGGCGAGAACAATTAAGTCAGAAGTTTGAAGAAATAAGATGGTTGATTGAAGAAGTGAGACATAGACTGAAGATAACAGAGAATAGTTTTGAGCAAATAACATTTATGCAAGCCTTACAACTATTGCTTGAAGTGGAGCAAGAGATAAGAACTTTCTCGTTTCAGCTTATTTAA\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pseudocode\n",
"\n",
"Pseudocode is the term used to describe a draft outline of a program written in plain English (or whatever language you write it in :-) ). We use pseudocode to discuss the functionality of the program as well as key elements in the program. Starting a program by using pseudocode can help to get your logic down quickly without having to be concerned with hte exact details or syntax of the programming language."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create a FASTA file of the data above and make the sequence lowercase.\n",
"\n",
"Requirements:\n",
"\n",
"- A \">\" sign at the beginning of the description line\n",
"- Make all the sequence lowercase\n",
"- Parse the accession number (AF333238) from teh description\n",
"- Used the parsed accession to save the file name as 'AF333238.fasta\"\n",
"\n",
"Want to know more about FASTA files? Check this [webpage](https://en.wikipedia.org/wiki/FASTA_format) out."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pseudocode:\n",
"\n",
"- Read the description\n",
"- Parse out the accession number form the description (Hint: use the sequence_description.split() function; user parsed_description[1] )\n",
"- Create a new file with the filename \"AF333238.fasta\" ## Do not hard code the file name\n",
"- Save the description but add a \">\" sign at the beginning of the description line\n",
"- Make the sequence lowercase\n",
"- Write the sequence data\n",
"\n",
"NOTE: Please get into the good habit of commenting your code and describing what you are going to do or are doing. There must be at least one comment in your code.\n",
"\n",
"NOTE: Next week we will see how to break the sequence into 80-mers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Write your code here (if you wish)\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you would like to create a file with your source doe paste it in the cell below and run. __Please remember__ to add your name to the file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%writefile FASTA_formatter.py\n",
"\n",
"#Paste Code here"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%python3 FASTA_formatter.py"
]
}
],
"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": 0
}
| mit |
cougarTech2228/Scouting-2016 | notebooks/robocop.ipynb | 1 | 33175 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# import libraries\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"matplotlib.style.use('ggplot')\n",
"import random as rng\n",
"import numpy as np\n",
"%matplotlib inline\n",
"\n",
"# take a url of the csv or can read the csv locally into a pandas data frame\n",
"data = pd.read_csv(\"robodummy.csv\", index_col=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"9 defenses \n",
"\n",
"1. Low Bar\n",
"2. ALLIANCE selected\n",
"3. Audience selected\n",
"4. ALLIANCE selected\n",
"5. ALLIANCE selected"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Data structure choices include:\n",
" - Pandas dataframes\n",
" - Numpy Arrays\n",
" - Object oriented\n",
" - Dictionary"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Object oriented approach, would have to feed csv data into objects\n",
"\n",
"# maybe get rid of this and just use library analysis tools\n",
"class Robot(object):\n",
"\n",
"\tdef __init__(self, name, alliance, auto_points, points):\n",
"\n",
"\t\tself.name = name\n",
"\t\tself.alliance = alliance\n",
"\t\tself.auto_points = auto_points\n",
"\t\tself.points = points\n",
"\n",
"\tdef points_per_sec(self):\n",
"\t\treturn self.points / 150\n",
"\n",
"\tdef auto_points_per_sec(self):\n",
"\t\treturn self.auto_points / 15\n",
"\n",
"\tdef get_name(self):\n",
"\t\treturn self.name\n",
"\n",
"\tdef get_alliance(self):\n",
"\t\treturn self.alliance"
]
},
{
"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>Cougar Tech</th>\n",
" <th>Webster</th>\n",
" <th>PIttsford</th>\n",
" <th>Rush Henrietta</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Auto Points</th>\n",
" <td>10.00</td>\n",
" <td>15.0</td>\n",
" <td>5.0</td>\n",
" <td>20.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Points</th>\n",
" <td>35.00</td>\n",
" <td>45.0</td>\n",
" <td>15.0</td>\n",
" <td>55.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Low Bar</th>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Portcullis</th>\n",
" <td>1.00</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>0.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cheval de Frise</th>\n",
" <td>1.00</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Moat</th>\n",
" <td>0.25</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ramparts</th>\n",
" <td>0.50</td>\n",
" <td>1.0</td>\n",
" <td>0.3</td>\n",
" <td>0.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Drawbridge</th>\n",
" <td>0.50</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>0.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sally Port</th>\n",
" <td>1.00</td>\n",
" <td>0.3</td>\n",
" <td>0.5</td>\n",
" <td>0.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rock Wall</th>\n",
" <td>0.75</td>\n",
" <td>0.7</td>\n",
" <td>0.9</td>\n",
" <td>0.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rough Terrain</th>\n",
" <td>0.30</td>\n",
" <td>0.5</td>\n",
" <td>0.9</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Cougar Tech Webster PIttsford Rush Henrietta\n",
"Auto Points 10.00 15.0 5.0 20.00\n",
"Points 35.00 45.0 15.0 55.00\n",
"Low Bar 1.00 1.0 1.0 1.00\n",
"Portcullis 1.00 0.5 1.0 0.50\n",
"Cheval de Frise 1.00 1.0 1.0 0.75\n",
"Moat 0.25 1.0 1.0 0.30\n",
"Ramparts 0.50 1.0 0.3 0.50\n",
"Drawbridge 0.50 1.0 0.5 0.30\n",
"Sally Port 1.00 0.3 0.5 0.90\n",
"Rock Wall 0.75 0.7 0.9 0.50\n",
"Rough Terrain 0.30 0.5 0.9 1.00"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(45.0, 0.23333333333333334, 0.66666666666666663)\n"
]
}
],
"source": [
"def analyze(dataframe, team):\n",
" total_points = dataframe[team]['Points'] + dataframe[team]['Auto Points']\n",
" cumulative_success_rate = 4\n",
" pps = dataframe[team]['Points'] / 150\n",
" auto_pps = dataframe[team]['Auto Points'] / 15\n",
" \n",
" return(total_points, pps, auto_pps)\n",
"\n",
"stuff = analyze(data, 'Cougar Tech')\n",
"print stuff"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Analysis Functions:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "KeyError",
"evalue": "'x'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/Users/dinbecevic/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1974\u001b[0m \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1975\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1976\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1977\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1978\u001b[0m \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/dinbecevic/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1089\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1090\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1091\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1092\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/dinbecevic/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m 3209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3210\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3211\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3212\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3213\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/dinbecevic/anaconda/lib/python2.7/site-packages/pandas/core/index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 1757\u001b[0m 'backfill or nearest lookups')\n\u001b[1;32m 1758\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1759\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1761\u001b[0m indexer = self.get_indexer([key], method=method,\n",
"\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3979)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3843)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12265)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12216)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'x'"
]
},
{
"data": {
"image/png": 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rVfV4kmt3fe3h85z9L0vYBQAAwDE2z6cBAwAAwJESqwAAALQjVgEAAGhHrAIAANCOWAUA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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1094436d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = pd.read_csv(\"robodummy.csv\")\n",
"fig, axs = plt.subplots(1, 4, sharey = True)\n",
"data.plot(kind='scatter', x = 'x', y = 'y', ax = axs[0], figsize = (16, 8))\n",
"data.plot(kind='scatter', x = 'x', y = 'y', ax = axs[1])\n",
"data.plot(kind='scatter', x = 'x', y = 'y', ax = axs[2])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 4],\n",
" [5, 6],\n",
" [3, 9]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(([1, 4], [6, 5], [9, 3]))\n",
"np.sort(a)"
]
}
],
"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 |
Teaonly/easyLearning.js | heatmap/heatmap.ipynb | 1 | 213476 | {
"metadata": {
"language": "lua",
"name": "",
"signature": "sha256:639cbe855630b29ca41ce6e5988722152e1dd3c72de45b15f10baaa1a97e903e"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# \u5377\u79ef\u7f51\u7edc\u7684\u70ed\u56fe\u751f\u6210\n",
"\n",
"\u9996\u5148\u67e5\u770b\u5206\u7c7b\u8f93\u51fa\uff0c\u53ea\u67e5\u770b\u8fd9\u4e2a\u8f93\u51fa\u503c\u3002\u6ed1\u52a8\u4e00\u4e2a\u5c0f\u7684\u56fe\u50cf\uff0c\u4fee\u6539\u56fe\u50cf\u8f93\u5165\u4e3a0\uff0c\u89c2\u5bdf\u5206\u7c7b\u8f93\u51fa\u7684\u503c\uff0c\u7531\u8fd9\u4e2a\u503c\u6765\u7ed8\u5236\u6240\u8c13\u70ed\u56fe\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"require('nn')\n",
"require('image')\n",
"require('loadcaffe')\n",
"require('cunn')\n",
"\n",
"torch.setdefaulttensortype('torch.FloatTensor')\n",
"\n",
"\n",
"-- \u51c6\u5907\u8f93\u5165\u56fe\u50cf\n",
"--imgFile = './dog.png'\n",
"imgFile = './lion.png'\n",
"ximg = image.loadPNG(imgFile, 3)\n",
"itorch.image(ximg)\n",
"ximg = ximg:float() * 256\n",
"ximg[1] = ximg[1] - 103.939\n",
"ximg[2] = ximg[2] - 116.779\n",
"ximg[3] = ximg[3] - 123.68\n",
"\n",
"\n",
"print(ximg:size())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {
"png": {
"height": 224,
"width": 224
}
},
"output_type": "display_data",
"png": 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",
"text": [
"Console does not support images"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 1,
"text": [
" 3\n",
" 224\n",
" 224\n",
"[torch.LongStorage of size 3]\n",
"\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"-- \u8f7d\u5165\u5206\u7c7b\u6587\u672c\n",
"classText = torch.load('synset.t7','ascii')\n",
"-- \u52a0\u8f7d\u6a21\u578b\u6587\u4ef6\n",
"cnn = loadcaffe.load('vgg19/VGG_ILSVRC_19_layers_deploy.prototxt', 'vgg19/VGG_ILSVRC_19_layers.caffemodel', 'nn')\n",
"cnn:evaluate()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"Successfully loaded vgg19/VGG_ILSVRC_19_layers.caffemodel\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"conv1_1: 64 3 3 3\n",
"conv1_2: 64 64 3 3\n",
"conv2_1: 128 64 3 3\n",
"conv2_2: 128 128 3 3\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"conv3_1: 256 128 3 3\n",
"conv3_2: 256 256 3 3\n",
"conv3_3: 256 256 3 3\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"conv3_4: 256 256 3 3\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"conv4_1: 512 256 3 3\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"conv4_2: 512 512 3 3\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"conv4_3: 512 512 3 3\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"conv4_4: 512 512 3 3\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"conv5_1: 512 512 3 3\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"conv5_2: 512 512 3 3\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"conv5_3: 512 512 3 3\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"conv5_4: 512 512 3 3\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"fc6: 1 1 25088 4096\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"fc7: 1 1 4096 4096\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"fc8: 1 1 4096 1000\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"-- \u76f4\u63a5\u5f97\u5230\u5206\u7c7b\u7ed3\u679c\n",
"local score, obj = cnn:forward(ximg):max(1)\n",
"targetObj = obj[1]\n",
"print(score[1] .. \" : \" .. classText[obj[1]])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"0.2681924700737 : lion, king of beasts, Panthera leo\t\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"local K = 32 --\u75288x8\u7684\u53bbmask\u539f\u56fe\n",
"local S = 3\n",
"local W,H = 224,224\n",
"heatMap = torch.zeros(math.floor((W-K+1)/S+1), math.floor((H-K+1)/S+1) )\n",
"local xx,yy = 1,1\n",
"\n",
"cnn:cuda()\n",
"ximg = ximg:cuda()\n",
"\n",
"for y = 1, H-K+1, S do\n",
" xx = 1\n",
" for x = 1, W-K+1, S do\n",
" local xximg = ximg:clone()\n",
" xximg[{{},{y,y+K-1},{x,x+K-1} }] = -128\n",
" local scores = cnn:forward(xximg)\n",
" \n",
" heatMap[yy][xx] = scores[targetObj]\n",
" \n",
" xx = xx + 1\n",
" end\n",
" yy = yy + 1\n",
"end"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"heatBMP = heatMap:clone()\n",
"\n",
"local maxv = heatBMP:max()\n",
"local minv = heatBMP:min()\n",
"heatBMP = heatBMP - minv\n",
"heatBMP = heatBMP / (maxv - minv)\n",
"heatBMP = heatBMP * (-1) + 1\n",
"\n",
"heatBMP = image.scale(heatBMP, 224, 224)\n",
"local showImage = image.loadPNG(imgFile, 3)\n",
"showImage = image.rgb2yuv(showImage)\n",
"showImage[1] = (heatBMP * 4 + showImage[1]) / 5\n",
"showImage = image.yuv2rgb(showImage)\n",
"\n",
"itorch.image(heatBMP)\n",
"itorch.image(showImage)\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {
"png": {
"height": 224,
"width": 224
}
},
"output_type": "display_data",
"png": 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",
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}
},
"output_type": "display_data",
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",
"text": [
"Console does not support images"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
avijaira/udacity | self-driving-car/P1/P1.ipynb | 1 | 269154 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Self-Driving Car Engineer Nanodegree\n",
"\n",
"\n",
"## Project: **Finding Lane Lines on the Road** \n",
"***\n",
"In this project, you will use the tools you learned about in the lesson to identify lane lines on the road. You can develop your pipeline on a series of individual images, and later apply the result to a video stream (really just a series of images). Check out the video clip \"raw-lines-example.mp4\" (also contained in this repository) to see what the output should look like after using the helper functions below. \n",
"\n",
"Once you have a result that looks roughly like \"raw-lines-example.mp4\", you'll need to get creative and try to average and/or extrapolate the line segments you've detected to map out the full extent of the lane lines. You can see an example of the result you're going for in the video \"P1_example.mp4\". Ultimately, you would like to draw just one line for the left side of the lane, and one for the right.\n",
"\n",
"In addition to implementing code, there is a brief writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a [write up template](https://github.com/udacity/CarND-LaneLines-P1/blob/master/writeup_template.md) that can be used to guide the writing process. Completing both the code in the Ipython notebook and the writeup template will cover all of the [rubric points](https://review.udacity.com/#!/rubrics/322/view) for this project.\n",
"\n",
"---\n",
"Let's have a look at our first image called 'test_images/solidWhiteRight.jpg'. Run the 2 cells below (hit Shift-Enter or the \"play\" button above) to display the image.\n",
"\n",
"**Note: If, at any point, you encounter frozen display windows or other confounding issues, you can always start again with a clean slate by going to the \"Kernel\" menu above and selecting \"Restart & Clear Output\".**\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**The tools you have are color selection, region of interest selection, grayscaling, Gaussian smoothing, Canny Edge Detection and Hough Tranform line detection. You are also free to explore and try other techniques that were not presented in the lesson. Your goal is piece together a pipeline to detect the line segments in the image, then average/extrapolate them and draw them onto the image for display (as below). Once you have a working pipeline, try it out on the video stream below.**\n",
"\n",
"---\n",
"\n",
"<figure>\n",
" <img src=\"examples/line-segments-example.jpg\" width=\"380\" alt=\"Combined Image\" />\n",
" <figcaption>\n",
" <p></p> \n",
" <p style=\"text-align: center;\"> Your output should look something like this (above) after detecting line segments using the helper functions below </p> \n",
" </figcaption>\n",
"</figure>\n",
" <p></p> \n",
"<figure>\n",
" <img src=\"examples/laneLines_thirdPass.jpg\" width=\"380\" alt=\"Combined Image\" />\n",
" <figcaption>\n",
" <p></p> \n",
" <p style=\"text-align: center;\"> Your goal is to connect/average/extrapolate line segments to get output like this</p> \n",
" </figcaption>\n",
"</figure>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Run the cell below to import some packages. If you get an `import error` for a package you've already installed, try changing your kernel (select the Kernel menu above --> Change Kernel). Still have problems? Try relaunching Jupyter Notebook from the terminal prompt. Also, consult the forums for more troubleshooting tips.** "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import Packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#importing some useful packages\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.image as mpimg\n",
"import numpy as np\n",
"import cv2\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read in an Image"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This image is: <class 'numpy.ndarray'> with dimensions: (540, 960, 3)\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x11a77b080>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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78JB8esFoa0SmFO88eZ+XL16wv+8thdwprAwxOvF6YsQfVGQB3ZbeGkYPdXhWA6aQ0twLzPQGRhhTzZgMCl1jr5EGUGlJJbPS1Iw5dra2qXa+dTDjiIFD6awLbRaPIwcH102OxUoQaY2JnKNBuoc+tU3pPuFxXV039U16fAHVjj1nAQ+BxaGpNQ8UrLG99VfhguWzYpu4LdQCMoZVBO947WM47X6J9MydYHn1fHQphQysKwRrdUBDcemynupIoXXvQZshqcA0y3Y65301bYw9bkNN5XdpC/DWGPRZQv7hOrzSr4tobvV9T2zkiJDw8UJxoKOti8aGp6r7olz1/nVztsvpWvUjek+kjgTzPrvSYZ8M2NrZY3tzSD6dInnG8xdPGQ4SHj98wvPnzxkkiiK3LIuCrckmw41t0jTFFRZnLDuTTS5Oztjdu8e9/Qccv36FHjjm8znLYsVwc4vxeIgMhIuTV1i34vLohO1Us7kx4eXzF2itOTg4YJFBMpiwOdlA6QGZyytRHfqjlOrkPX30VjH63g0Uyi8GcWCVrZylqmR4fjeiNJ73c0pFE9ZPTlFSKfrVwnXOhysFihiC7tGGXFmIFVdjulFYYuPZWGAoKTVk14o6KKuOGUkV003F8boifZLSeVrVJ+VC7LAkPAXnGYSHfPHldYUPNQNwCiXeZXVd1EZwTkOppVnbYPBl4zuFRTxcFYOLNFeBisdJqKsUcuEZW/P+BvSjOvYPeP1wXehXygQ9zCJiVOFSgC8o67QRCF21X/mXg4AJQtmoJpQQvoGLOxvKMLYKAXVZ7YC34iN1QmivK/tW7fJV4fs6pBU2JeJhxtC/agUInYqD1I4pROqNZw5dC8+o/wZKyyAqLN4Z2yo/jsMXPIYdqLEBLyoj1ti7qKnUmUatohzaGMQ5JNliOBih1CbFcooanKIUaC0MhwOW0ylKHLvbe2xsbeOGQ/K84LOPfwxFxnc/+pAiW7KaTcmzJXlmefjkMR+ffsxwOGRvb4eDvX1Ozq9YYkjNFO2mXJ5/zfFRxtHLE3b2DxltjRhNdkn0CNIRK1OUoedU/aj786vG6KX+IG3oIp4MjRjz8l5Y4I33aDKH8HygGNtU5UII2nVvyoMe0pFTpK2FNaRv2dYa3bk5rLIhMLq085LJFNQwx9oz0bPdVGpMzuPsAKawCCEWXiFYjCsaGGezbM/cPX54TRtuQUqkYQEF6hqvBiQW8cZm09bfUSU22wi3c25N4BA9c93caGLYN3/TqpiO+alUa/53WKHx5jZpb1Ki29qIo5Ua1yPBG9cdFI4uOCeMXZhzJvrmMdwSdtT2+sV+QWryiLgvHWN/TQSEiIDW4BQb2zsoB0meYQZjPvhQk81OSVLF3u4Gx8tLxuMNZpnfrJYOhuwfHDI9O+by5AW2WJAOLKvVGWmaMtCW5XLK9vY2IsLjh484Oz/i+OgYUY6T1+dsjIZoZ5hdnDEeQLGa8/rFUw4fjdjah6woEF1n3ornw5vSW8HoYydU16RoX/Prya0xzkBdmmfALnuRLa0qARHXp3uer5QjiZ1jTc06/BrrHLHjrWsXawNeaNVZWQ7VhYgBiEQYfUeDxVZarovM6bo8vbZQBA3ixZhoBT0WVwVR3SIKoGuhK/HQAlBZaxI7l1vvh8tvOt1jSM5AFd1Vt6sOa6m1eFvCZUCldVNpt1boFLK3WYxNR2F8vZ85xTtEY8baycQbjBe6sHqtwRjTC33Fu28bkZ8xg42spqhFtTXSMb9hfYz6MPR+as/HN5sRgvbIkDMY452cejBiiEMXV1yezrg6P2YjhWx1RWFypkvYf/Aum+MRzmZonbGzm4A7Q9yC3GTYwqcmmF7B/GrG4cNHnJ2fAgV5dopzjkEyxGY+Lt9lU5wVkjSlyGCxvMIph9KCiR3ZbzC32vRWMHoHlUaoIqesjTpUOW2girJo4p6147ViaFJd8b90rJ9O7SeajLY3dUDAn2vnonXd+LMqF1ysD8damkgTr+8k1SGiWphyrE13TYYqb0nHwnPONZ14ZY6SBiO5SRmLHZA9O0rDAyGUVSqJFoSe96WoBgzSxGkbFlT4xq3NI6Gp1TvStOS6fBsxll6NXylYrATGGeqOX7ye0cffua1ExPMvDi1svycqzA8PNYaRQASlqbZfNOautS3FZv2bGONKq7j1cSMoqES1GuRUlESu8a1lbWzjPscKdmO+6zL9SDRGfevBlYOknHTO5SZ15ywKgsvP6wSFQ7slA2V4/uXHvP7mpyg7x64uWJbQcJ4LhwePGSVw9PwTNiZjxF3gzCnLIgNjMMaQGwVugFKK/Xs7fPPNFyxWc7a2JgwHCkFjjGOQaA/3LKdsbOyQDhKKVcbroxc8fv+7WBngEsFZH8wbj8tauokb6K1g9A1yrlJv4lDL4CStrGQtjaRWNVlC/paKcd2UjqBFMQOhZzdgxTBdbD5KtWAa5nZUXttB2a63z8FSMaxYwtfVNhhEvFNzbdEExbVVboxfxrqdt4T8ZrbrQvWq+PbQlr5FGj2P42YB53tElza6FoceBL911QCrEutXrDPf4N+BUsnQZQ6csh++jjrENLS5Fk5l6yLLIC6/7nPPN1Wu4fzv7FRHeTfdjwVW470u4SvqxjRodRnda0GvzVk//rZy3t5QQURWIqjzFs/GbZGO+VnL6/Xx80LMoUq/iJiCVy+/5Nk3H6PzS0QKjLGIThmNUs4vl+T5gq+//pTHj+8zu3jJ4vKYhAWFK7wlahzK+uRoShVk+ZSNzRGJhmy1YjLcZLyxycujU5YLg5OCja1tTO7DbKfzOdsH+6xWS9KRh320ojEvfmU1+pi6TJQYzqhwROt6F1A90ddD+WIc+Vbt0d3ZJDsXh1YRk5WKKYcdjbeJwe+jWLNeH5fSIigfamuunkxHqxXN1Mp+aWmdVtpgwMuVu3nh3YoaWn+X9eGZuhU8Y1rbXalogmHhqlT/xRBSn8XW1vI8BLN+rz3DqneUi2Lpqyh6HB0J4dabsEZvsnTXhcgvJhzelGJhFsbYCSSlhF+HWDuY660grXXB0kdxNsyuOuIhiH0bXsEor4sC6xjolLOzC0ajlHS0RTafkSQJk0lCUWK1jx/e5+zygtOXLxHldybbvEBwDNMhJvNhlTiYz+eIThGluLw8ZX//PvP5kvFkl/29e2AtF9NzknTE/t4WGzsHTPYOGE52SLR4Z3DVifWxfJPv+9Yw+srMA3Bhg1TJhMpndMB4qpeaGnZNZQZKgAA/RNZBlRZAmpBFTOJaZmYDXhAcSRnVUGaQtFJmVYy1vcosKJOlCbYO0I5a68pU7154hWgJiUqRKLStDVd5I8hvUxeIIpBUFFOsq/dRYVwNiI6w+/KtIqtEgq4mmU8b0UUajyVWVov4usO4NZy0oY6IIa9P2LCVWNXpLzBlOGJsQtXfTodxd67XrxLeCgKrLTRVtMGsco4GK0eidlrnS6u2PIf+2c4oHxdZX7oDu9ZEqZNF1y1sbCPtOb+A1sBCrybTB210PutMU6BEzlhfVlxCBF+GmeOq/621obKmOiAVX4YnHUeS9Tp0u+po/R3eF1u9IX7LhS/LFAg5L189Z3dzQrr5BJvP2XqS8vnHP2Y6WzEej3n3ySMuL04ZDVMW5opROiQh5WqucMaRJiPUIOHy8oKd7Q1vyTu/sTFNEsbpgIvzM/T5OelozMGTh0y/WrKcGa4yxcHuAwbGMdzcx6ihnwuuDDlXpmGl4t4MpXgrGL1QZ5OTMGGiHC636VA766OT5jpp5HlvMRivkPSn8a1yZLhag3bOm94iqmTiDm6RZKgtQNbrC9yyFka3pe4ls54iN2IPnaz7TTXB2CkOTZ4TYv2D3mXC/XKRdQVKUD3rGUE7PW5DMN9S+Yv7pBrwWfi/70TQ9vrej+P8A8XwgOrYrLaetYe1Z2rmX2vofV26zqHZvt+YQx2hiF2CqXyxp/bWY1Eb2lp8F9b+i8AO7TLelPrqrOafCNOLK5QTtvf2SRlz9OKK6XRKouBqNmdzMiZfLUnSARcn51i3whSObLliuTBsTjaYXi3Z3N3h8GAMoskKg+gBWZahXMF0OkcpxWq1YL5acu/BA7QeYI1iNs8ZDDZZ5Auy2QI1VKRD65ev2DVY6jYoQExvBaOHOgGTRNBMbeJ3T5Su3CAignW2kSirTQqLjbP9SXs7eU3tON3gCCmcQUkwrViLoV4vSFXbl9cZRaS5UGs57UNInHONdsa1NeAaHDWDX2f2OirXNJTGIGyb9dbx7OuQkHLgtMMY2yg3hlxsxzfzAkCB9O+fqCJM4jEpLYFmtEw/Dh4/02DWHfBW0DTDSVllwVW7veNOSqPNn5/gb1qCKOtSTsJZCaofZUGV46yjMuO9IW8Cm8WC0akoYZy5Pub8OmozlT4IVGEbgRNlK6rfjB9IKPcVVK6jDl9YQw72rJd4jdwkoNvX41338+mMR/fuwWrK2dFrnFlxdPIc5RYMR7DMLlA4VhcrHBaVOLI8Z7VyDNNNNrfucXLymtFwB4die3eXy/kC44SXR6+gyNlWA5Ty4Zk7e7t89smnZKuCew+e8Pj93yBzA5zyCs7W1jZLY6CHofvNZL9izliRchu9UDs/ReqjBLu0FoBSmw5/S2m+KwSsw+qIgboadpESAnAuTgPQJ/XLvCo1WuCZQXDwSihX9QqLuhFSa5ORUHCuxoWN1LBFPPlVvCpKqqCPiIF4rTKYdTWzr5iZq8ei6lAFS5TtiI5wdKrJmyr/R+AZEuXssc0wvIpPxYw2Six3k3YXBCNSQ2xO1uEPD6V1M7Hq28bH2bWYfAwlrLVBB1jRYW0JZ2iFsvHO56CJx32q66gYb2t6NAR8gJzENr5F1c54LjSs01pABbKuPpbPmvpZ3TU/Y0u4gxk229uMrmkL21CGao1BY7yjspvfYb0ffRprH9PvYuoNayMqP8zREOmXJAnOWY6PX3J1esxqcYyQkQwMg4FgigXL1YqB1kiicVaRLXNMocFYLqdTnGheHR2zub1NdnrJb//OX2G2XPL64pKdnQMKnFd6nCNfLfzRhc75fEdKo3TKZJCih0NwiiRRZVbd9bTnxphfPejGuXohx5piV1y237Xpf28v+DgkDkDrZo7vWCsU/0tdbu+kShrmunPGM2yM/wAlg+mKAGn20ZXPhkpr7hyshMCUnMOfclPqRH3Qkgo7f4W6HVHEBbh6h2twgkpTpAXBEN71W6vrhREsLAdlkqmm9gv4TTfBaatUCb95gVo4v6Ozio2PcN7AKPo0et+GEJ5aC6RQbyLR9+3TlGONz9TlxnQd/BGSxYkDpXS9O7WjDt+222tZ7bN928wvdjT2LequfEW+iPI7NUN61t5vwGw9Kbmr9ikqgdnHY3ywRJgjZfRM5xnG3f1o+01uol8UCgoUNlweHByQ4njv/Y94nRQ8+/KMLDekicPavFwnCbPFkslkQqJHZHaFFiEXi3UFucnA5rx8OSM3YJOE9z76iP39fVbzFeSGq6sZ4/GQk5MTjDHs3nvI1s4mxhbooWOxWpKkKVYMWhL8YUzrAuxNkay3gtEL1EmdGjCG/1HKa+7iIHd1pjuraqbTiIkrSbn68IzG1vvy4yoBh0AZuw0grulQMxRIPXfrup2gESQM4TXaWqNN1cKMtJlyT78IaNJKu6yShYnFdHzZpMHgelI1BE2TpBYW0YKuXheHqryp6+UIoMMiF3zK2hCaSG1tlFlVsMpDLk3ds52TJ96VuZ5b36qQRsFFuzYdYqKkdNXjN2t/Sl/PFOJP1oZg4vBYEcGV29Lbcfi9EidqTyizfX5qH+7ahpo6YYg+LbcRlNLh0I01+kalTQ3ZW7GD2rpaa2bYwFVbVl15jTpTK7QY2ZtgzzaasgmyNlZtqu/5zYBFkaPEYmzOSByfffZztiYJ73/wPf7sX/xTBpupP5lVwWCgUSohSRJSnbB7f5dXr88oVnMGacpgMGA+XzJMB6jCks3POX75lDQRzhYXfOej3+R7P/g+n37yc55+/TkPHz8mHQ6w1vDNN18wzSxHxyckgyH/1t/8d1EqQVHGy0u078Y5lKg3Oj71rWD0Dr+Qqq3UsUkYNFQoGXS3176Lsbadrl3ky3Y+N0tHWZ3mLt00BZCFAAAgAElEQVQmcxfu29U2pyIIo6N91eT/yykrjbJdJLxKpbT5XCRAw6JdL6eOrPGLuruu2gbo6Fv0uyuTXrUx9EAxU2jcucZJ37XQb6v1rTkxnWtozAG/9zBL3bA+5tz1ezuR3U3MrT3H3rQfN83P6xyV1z27Xm4EF0p9glc8hm0Ypa/ON+lvY871QEldNBCw1rFaLVktZ7x+/pRH9/Y4O7tAs8GD3S2+8+Fvki1OuTh+jtIg4iiMQamE6XxOVljyPMfifIIz5+HgVZ6RpikAJycnnF9esb21i7WWLF8xHI9Y5TlnZxdsWeHpy4+59+AJzmm+8+FHDDfGmGJFOtnAZEWj3T6Dqc8t1BVa2kdvBaMP6vXars1y51/jUUeVD6W05suTV/z9eKFLx7VGWcEMl/hak8H2aubB5alUlUDquqyH4d+QOqF9vFzd51JLdoCqAgFjxCNu3LX1Na7R6GQdrNKFS3dgwP5G/V6cRCyut4txNdrTcqQFWKy0rYgH/6b1fhsM9zomfJPm2GbIcf8ap2LFQ2uv7/91DsPr6u97vs3U+hzNb0pdAiZWsGLGHY6jrN5x65Za+P02db7JGbzNlCP9ENzae86HPY4SjU0SJumAUarB5ShnOD855fj1Ce88OkA7i7MZq2zOEMtqtWC6WLKhNOPxmOWqYLVaoZIB4/GYw/sPuZjOGIxGjAdjzi4uyUzB8fEx89mUyWTCZHOLvYNDnFNobbg4O+ed9z9iYzxmulrx4ukz3nl/jJXEH3HIunD8lcPoxUX5qF3UAeUnbYzF+zlgy12MpQkptjLt/auuKgu6F4p3GvlIGQF0lWWvPoDiWuowQW/DeAOr7IpgCeVeF3LYV+7Nz1yPk3oGobwjrXG/+5CSvqqD9RUL2TftQ3WASMfkhiaMcpvy2syw75k+xhaXUY1FNCfjyXCTptrHhLuYep/mH1sYXXW1BVOXoLquvX3X+3Yx15Bb+3s1GfdtGNObzWmaaZrDzuZb1OOc9zkohDQd8ODwgIvjr3jycI/ZxSnT6RxjCz7//HNGA9jaGvsINWcwxrG1uYNzjiRNefDgAbmxLBcZo80NlnmBKI0jYWt7l4eF5fz8gqfffM2jR49wzjAYDNja2ub8csr+/j4/+dkn/OB3fpc0HTFSW0yXOQrtdxeLRqSJXoSsu7elt4PRizelKHO0B3PYriv0VfiZK3EqKOGcNX2wJe2rupqLp29zTfxMJ0XabVeoWeeCCLLkGqqEmjTPjbst4+yrO1wLKX4DmfJeteGoJYAk+v1Nji7rYlJ1P26nSYc0zTc+d8MzfZEZ1zH36xhgG7aKc9bc1KKbGPlt3uu7dp0m32cdXFdvu13tnaXxc3W50vt+Xz9u256bqEsh6XMECxbMimFiWE0v2BwKx4sTFvNLrs7OOHl5wvRqzubmJo8eHrDILS9evmY8Stne3iJRKYvlktl0wfnlFRuTHUajMascloUhHU24vFqyzF/z5N33WCwyitUKLbCYzUmSlE8++YwPPvqI169PeHB4j3/6T/4ffv/3/z0W0xm2EBIcKI3VGmuLmsFjMTecFd2mt4LRQ6mluegEKRGaWTi6tfLrcL3bmuZ910Wk/0SrVp4d5Wg4fBt4plrXDHuzaF4Xi99qc3/s/zUCLrovDpKgbfWGl3YziKoNXUIOCEcGRleiem7ntPxlkI3NC6gsuTZDCHVexzwb8y1K6hXKvA2190Z01XFbus0YdVkC8bs31d1n/bWjfdbKc6rSQpsnPvVbPzc5Um+iLtina7wBEpYsp8ecHj/j7MXXPD7co1hMyedTdjfHLMcTtjd3GKQjxpMdjyTImIuLGVdXGe+9+y5aD3n48JCLy5/z8OEj5suC+WJFAWTTDJIBH/zGv4ZSit/46Dv8+Ed/ztZkg9waNjZ3ePbiJVjL1tYG5xcztBi++OITruYLRpMdNra22bz3AOco80359WOtJUn6j1fsoreG0QcKB0J4s+R6JnPjAinPWRTrGhi600mplV0Xe90PT/S1qU9zbPwdDiy5ppwu0iWkFTLuhSgkJ/X2+bqO8jCSWBuzCie2YnwBo2ynJgAq4dawjpxDRyc+hXIMPrtMTP5et/AImH9zXNpjUGb7LOtzkQLQDwX0McimUAljV92KXvPwm79WjaW07ldjEW8OiwpRcVqKcv6G+yLNZyPBEvrXPjy7rqK194EwFt4h1/QT1M+qKjKtTufQR41DXLryn4W2u3DYjv9TW6nhzgo6NRjiPR3d1BZAjayYkc+heRZFd1nBVxDKq+ZhCUuKdWjjcLZgPj/mm8//JdnsGJtd8sVnX7M1HHJxcoK+9wASzYvjEw4PD3n15Rd8//vf48Pf+m1++uMfgTVcTeckWjg9PeXhg0NEhDRNyB1ML+ZYBZPRmIvTUx49fMinnzxjMt5gY2Ob5y9f8u77j8iyAlvknB6/onAOnOXHf/FnKKWYLguS0ZDz+ZQn73+EcVImR7RI4hhoA0V27feM6S1h9E2p3wt7XENd3z5MTKekjYSAWJztx2vjf7smapW2trVw2+0JMebtsn8R8hpnVE7bSmiVrRqatF3bWBVysMSMtF1OgLc6tT0opc3N7Q7vJ+VxblVoWOvdBsMSHz8fQyU3Cf22Zt0W5f6522Hq7fZED0ADEohuRRBi/fsbaOmxXykWvj3tC8Je9Tz7i1BIC7DWtqj+xoFuVT9tlT/db2xTFaO9jqRj3UDLAuiBpfqskMoHV2YS0s6gXI64gjxf8fyzn7C8OGasDavlksvj14zv3WdjnGKKJfcP9kmSlIurS/b2t7m8Oufs5Jg0TVnO5+wfHrA12eAvfvhnTCYTVlnBaLKBiGKQagbDDRarFefn5yzmU169esUwVVzOrtBa87Of/YTp7IqdnS3GI1+PyXP+2l/7PS5PLpDBiPvbWxSiWF2cMdzZw9ocxGKyJWcXJ1yevrp2XGN6Oxi9RAuqoTU1NSpHa6t/zwfv+vgBPhERkAhAiCCNG5vZ0kRva2r2af83mc9VVEO8+KmdgXH4aMxIqxQQ0bKp8VUwlMfSCYhb70fMNB1RKgK3fjxgzDavG4uqfiyCaoyfhGMSpXVmLzWzDIFkvu/+L2NrNi4qYrzR59fSCsftbWHzm96khfZ3NWjbQbPor1GLitJh16kDOoVP2/IoKT4hrdmCdbopXUA4Lc05n6h6zRqVkAxPN+aWhLBbF+0/kEhoUX/f6/wlbSHc7utNfYqjjkL7dSkwlV1wefyC41cvePzgIQ/2dpnpnEnqeD67ZH93j+VixvbuFpdXV3zno99id3eHZy8VG5tjdve3SdOCgU75l//8X/DixTO+XMx48uQhr14+5/LylN/9vb9KZhRJOmS+yBgoxfsfvMvx0WsKk/Fg9yEvXrxgf38fYwsUBmczvv/97/H02dd88flXaLvk5x//iL/+r/8b/PBP/wlbuw/48Dd/m+2dMU4Mq2zJxz/6U1YXpyizunZsYno7GH0HeWwvyjAp60umbzF2TaBG/HL5nFZSHdLdtyDDeazB+Rs0Ucv6JGwwL6kdVkmXheKiBRBZFrEGWsEvEjFfqd+pFhWUW6WlwkQ9U2svVl9WlXirHNOQtTEIFNWzZT/GsOv9BYK1carj8Oz6js3yBtByJNVRpLUm1rO+nXPoKqtpbPLX2TLD95Jq0nixXo/N+slelVBtYbwSjX0z6uR6LbVZTndnPESJd+yLVHmT2uTHvTtyxZVnKMeng6muwXM9lkUDo9FV2LAfr+hl4vHywrqK/qni5n07TSlQg5EYQorFNcelS9FRInW6h4ap1Mfwm/CdgsrS1QI4y0AKjl99zRcf/wUHe7ucvvySw8MHHOz/FqvllKurFS+efka+mrFpLTu7E7784scolbC7kZIkc85fvaYwBbNVzmSUcbCfYowmy855cH+b6XyJNXNMockXK06Oz7Au4fnTDZ9GQ8FyteD4+BilYDgcsLtzyEApPv7pX3D0+jnbm1t88+XHvP/efX72439ObuCdJ/eYDFf82T/7Y773/e9zdXHB3jjhfGrY3Bz1jMk63cjoReQfAf8BcOSc+0F5bR/4X4APgC+Bv+ucOxP/xf5b4N8H5sB/6pz701u3pkWWm2GB62ig/OYWZ5vMw2ObglK6Wrzt6Jtay3WNH6m0kvVJKuWB2PGPZ9Rd8ADUC6hmKCbaIVmlU3b1ogv/qnZed4LW6zBYf+AxtbCMT7+KxyIc6t3nSAbnHau0rKvqmXXLxPfZcu3Hi3LNi9RCp2ojNNLiVscuunXrD5qMzhU+pasDxJW7noOwb32PLu09/qZJzOjjfuoe5m2ud6Y3nr3BemgoEmUOnDVlh/K7SDwfui3E29JNFk2gGG5tOKY7ygvUcG3EZXVU17BA3jDiK8C2AyzT82NefPUpy6tT1O6E8WjCz372M/b373Fvdxc93GC8tYcSw8pYEpdjigKtNZPRBsqucHaKwrI1SSnuTUh0wWikyBaOPF+ysTliOjtnsXDMFoatjQlJOmY8HKAGmkGiWayW3H94yA9+8ANOj0+4ujhhkS04evWCoshYLea+r+YcYwzDwZCriyM+/ckUx4DXz7/iyXvvkq+uvL/uDU6Yuk18zn8P/K3WtX8I/LFz7rvAH5d/A/xt4Lvlzx8A/91tGiF47TURhaZ0NJYaa/gJ9/t+KuYYabXQ1HLDs4Pyd+W88/e6EMtQRigvtCe81/jRuuEQEpHe3Wtx3xoLpnw/nvjxhpTrftrP1bncAV3fC33SUi+mcHDJWkbQuB26qYXF5bXHKx636571BUd9oP6J39Oi0OLHSrSs/TSKSzSiy2dVKXTEVsnaQpvCmMf9ia8F6npGlOv8QYnvj1bVT1xf+7vH3/+6cRXtYR4XVOXyx2dMbf7E5d409u347K52dP6o+BQr1/StuPATZe38JVLvGLXWPYBQcHL0gmGiGCjDyetXXJ2fMxml7GzuAJrt7UMePnqXjZ1dLMLR6RFpqijyOeORZaBWbE2EydCSJhmPH2yiVUaxukTIwWUsZhdcXV1iTUGRLZldnVNkK1arJe88eozWmjRN+fDDD7l//z5JosiyFZeXl4g4BlpwpvBr0uRksytW03OK6RnF4opiccn5+RFaLO+/+w77+/ucnJzeesxu1Oidc/+3iHzQuvx3gL9Z/v4/AP8X8F+U1/9H52fMPxORXRF55Jx7cXNTYg0xMKA6f0a12GgyzsBUA1bZmFTSfCb8XtvHzaPk+nDzNikEp2ho2nF7wr/VAmqF4kE7N0mtbVdYdM/xhw2nW1R37DDVCO2ttA0nX/jbCU6MP4TBqQhrj7l7VJ9ziHizvS7LQzFrFPLiYJFKjRNQeXk4hSqdeC2mGn2zMk3bWju6GIeLsklWGSDFt6P69tW3dsF8aJRh+j951F8/DjZqULM967BQfUoQxCd6FRL7WdY16cYcdNGehkgxGQb4xEuARhugyhaxRlUAjUqia8FyreGYspTy/9FYlvV4K9RHtVQO2CqyLKnytIT+6LXUH/VaBBpZOpGw/V81joIUhHAoTbB8fVpnh1ifosD7+DKK4orXR0/ZGzmW8wWbowlFvuJ7v/U7TJcrlFZsbO7y01dfs7fzmI3HD1nMtpFizjI1zGYnDFKvMCixZFnGqlgiDLDWodWQ/f0DPv/sK3Z3RyzznHy1RMuIq5MjtsYpq+kpB/sbHB0d8f67/yanx6+xrNjaHDGfn3Gwv8PpyWsGiSLPV1gKJHFMdkYYu8DOMhxQyIr/7//9P3nv/d9gfzLkWPLuj9tBvyhG/yBi3i+BB+XvT4BvoueeltduZPSxZgkt5owm5MVd27Uq0gi3is/vXGfwgdY5hQtZ9yK8XJzXiNeYfqhj7Xrr6LsIWvHt6tOA64yWMSzxC1jdZTM6mEXol2syimo8q9hTtd6v6lndiNzBegdt7ICr6q3ktoa4ONGloIj2CHd11FEmLFtvi3Lr1xzlQdhim+MW9zcI83K4XUc5tyGvfNTU0GZVPc7QYSFFWcZU80apsMTpq2OmN+hsiy19IVriTJf97W7/3lQm6vumtV48PKNxrtacrfWRa0mVylphIiHjbCu3PyARx6lCRp1CBWFA7VSlLDO0N7aUvR/AK4OBEltwdf6a0WjCZDji6uKEn3/85wzEnx3xe3/1r/Dpzz9BJ7CcnZKohFcvvuE0cUzSgvnVKcvZkkGyJKFAJyBOWExn5Hnu+2IEISUdbaCUYrnMKHLD9vYu1sBqtcJZiwwcqR4wvTrhR39+iqFgf3+bn/74T5hNFwxSoVhl/N7v/oAf/tCj27PFnOFgxGisGU9SBgONTgwihsIahBSxwsunP2N3a5PJ8PbnC/ylnbHOOSfSns43k4j8AR7eYffwcX2UWhTDHcx371jU1fWojMa/cfyspsS46ZjQumZq8XwW8Zp6DGGoEuyNj5LrjbYRocoy56KIijICo+sQ6VYBVQ/b0Tddlka7HZVzLMrqGc6w7apXxMfu+DGuGb6LFpQW5fNlS8ANwDu9vNUQNK1wRGMv6Vgbq+svG77WLg+1dJ0ZG8apORaqgm9a6W7DUWwRo7BBU6w4ftBYu6qyjWcC2VbgZm2zlBFRt2C27Ye8ozhixvF7HQLPByo0o+O9IO5Lb9zVqHXnqH/Htp7yFoN/xDbz17ha8DWOjWxZKEopLLUWWg2FmCogwf/4odHUwq0WFn58VSlAtFicKxBnWV4dMT3+mleXV9w/PGBzNGQrMTx88pCL05dcnByxszFCYbk8eebPg15NMVmOsUsGKmNna4RzimJlsSbHmII8L1gtC0Qc2cowmaQMbFKuAsty5Y/knM2WWKsxVmEN6MTx+PFDzi9ee1+KueLV8xM/5iLMpwu++eYzwKL1gDwz4AqSJEUr2Nve4/zimHSoS59cxmgwZLE45/nFa9Lk298Z+ypAMiLyCDgqrz8D3o2ee6e8tkbOuT8E/hDgne/+oHNZxJpufa1fC4vvWeo8GAGXre+FxOTR6UuqTqNwkyrts0923alPu7RiImdj1GblowqagRHNxd+HH1/nNOz6u+9a+3psstf3XLWolfJwR4jwiOGQOoVCt5YYRsTHw9dam5QHMgPlaTkRwynzNKi1NKzBT7JeTwNqEF+fC8K+Z/w89KCb1sz6QJX/Rrn96c9q6jePeQxbVeWFtrUielT7e4IU61p32bio1VWT/JiWsrM6waoB/3Q2s6EdX09qDfYDXUFQfkwVteatS5XMVpFQ8TzWlUpvG5p+lZK7gjjpMuZ8ezDgLEoM2uU4t2J6fsKzT37IRmpxizNOX7xikSSk1nL64gxrMrY2xpzNfErh1eqMwqywtkCrgkFi0IljPr/E5BnGWJQkIIIpNEVeMBwOKfIleQbWLRiNRgzSCVdXM9557zu8eH7E6+NLBpMJG1t7vPPeEwZqwXB5jtaWPFuQLc5wkiIkZKul75ESBgPN9vYBgmZjvMlsfoWTBKUShumIxWKBcoLLM/LFnIFOSOXbz175vwP/CfDflP/+b9H1fyAi/xj4G8DFbfB5v/A7vmq1SOuDqa8Lu6vKqxwx3ZO55vn1BLU4D9MQNF0itLGccyLVOaax5l2X7wWHE78BoG5rvLkmKQ8Ij4/4q2d1yDkT4IEY67fONjQ7ke7zTbvG6DpnnHNN51awXoJnP8TTV3qblAwkPnKwHDMpZUaAvKoy8edehogc50CrcKhLCZtVfgY/PrVwjk8Bi79KA0Cpnq372/z21bb9+ADu5oGMneNT3Yuhjz4HvoiPvhJdPV9PTduAbtqGsIfB4iUZzWnXhMa6KBxQLqpbo+/KBd8dASs0TlYTvEXdWGN+DP38D/XVIa6gCTKrekviJGSaBgDW1vw7lZry+1g/h8QVrBZnXBw/59lXn5IWcy4vLxCXgU0pjPLRLKsVSZJwtoREK0y2wGJIBwq0xbLAFDnGGrJ8zmq1QquRF9paoZIhxhVczXKMFVa5IXEFShVcTedMNndYLXOQAbv3HjLPHYcP3uHJO+9xcvwFuXWIMihtGU+GCClZ7gM4dnZ2SIYJhTXkecH9wwO01pxevObrr085ONwjy3KWy2U51opElQ5w10xhfB3dJrzyf8Y7Xg9E5CnwX+IZ/P8qIn8f+Ar4u+Xjf4QPrfwUH175n92qFeW3DzlIgmNFdSxS27HAnDMMGlhgOUk0hNPSY2w/1u11OJgjivsKjD6w3+rIvrjSEoqI2+hcMDUtIg6NKU3xGDrIUWsMq65XlydalReq7jiJtB5Ks53cn7jlmkxVYvM/XLNAnJ8kaMBKV0xZIzhjq0yi/lGpHMNJudFKwKc+UJSYvgWMx+xrnu1HLThArYB1WFVmKg3wjEDiFKaEO0SkjMJJcKbAC854I4yr5EtwoHu4YJ25eadcgdZNy9AUqnTv2cqRBzWSE5dRuEhrkkhgxkzatpipSLkbuxTmURFWak24C+qqYCViC6mZ90nHArRyxtaatZNoI1k0adtrx8/Hbpw3OE2thLXX3L1YnY8srvSxlOMppU/GlY7XUlLYMtzWVDBZKSTAQ6iJt+rEqRKzL6uzBsRSUKDFYZcF1hYIjmFiuDx/zuLiGdqeY/IZNpuTJhZlDKIUyhQMZOl5CwOUDCjIcK4ASXyghzWY3J8itZjnrBYZ440Bq+WS8cYOWxsjZtMFRe6Fms0LktGIPF9hbOYhRuW4XFyh0y3uHT5mY3sHi+LJO+/x7PnnTKdTBgmMRilZAcppnnzwHmdnJ2glFPkK54RXr16xt7sLVtB6SLEyDBIwuSEnZ2Njg7DtT8vt9fTbRN38vZ5b/07Hsw74z29de/VinbXRKyOBIdkKP65jhZuQQwzJrGdziw4TKa/EjrJQRni3GRlTPy/R79oF7bUfJqkYlr9KLCJqrH09br3dtoYWLi1BI9X/6nbKej5v55xnREr71M7Vdam0tRjiCmPRGf3hutvoQnlE79OCoCRYLKHZtRXjc9vXVpJ3oFK+Iw0G62tZ55BdFotnCG2cGhIVNPoSPup4t/JRqPrvqklQKQ6xpu0T25WKRdUmV8X+V3MpbNLrUKe7LBAvE5ttrNsTmKwfHehG4kN58fvXxfGH6xopBXgzkKDO2NkMTq7LctH0tBV2H+znyhpQqkwTUr4ltQz1mxJBoRloh8nmvPzmM2w+J00Vm5sp07MXzC5PsEXGzuYO48EeF2dHGJNj7ArBonU4pENYLuZYVyAiZJnFYVAKitzzinzljwhNkyFzk4G1zBZLtra2WA3g9PScpTEYHEmSMB4PWS1mbGzuMBwoJpMRO5sj3nl4iB4ozs9ecnZyAvmUyTBhOBwiTrO9vc3l5SVZlmGsz1mzXOQolXByalksFgBkhaYoDMPxyM8ja/2mMhF0z16OLnordsZKwFIbOXRjRlNf13GTlRcGMSPpLF/8odyWUjh0aX9B0XU1MxER0M3j8Oql1GSOvh+61FKbuyKbf9cMoP67Lr9LCMUN9NZJXE43k6vKtiHM0ZQhgSGqKeK31AxbiSZOoLVWdnkilOD8PsoS+kGaDFhTpzqrrYrS0Vu1P5jihhgOKmHn0pNSHk4YhIY1NMM7S6vLRKF40Y7h7o1bUf9U/S1tvOmq/E9XGn0ICmiWECyKeLz8tTDOPlJFOXDWlqG2Qciua9jiQtxKU9haWYfo/Pm8gg/PrSNQ+hz+zZDJEJLs19Y6s1dlBFH5l7U+BDdqcnXmb9wNcRBp7eVFKLPOVNgegk4qtb2jnQqnCpT1wkFcDuaSgT1heXXMxWqG3R6TL+Ysr87RCMPtMZPxiHy5YLU8w5gVWisKU1AYsEbIsowk0YhoFovM74nRmiIXlosFWg1J0xRhwO72HoUJjlIhy3OSwZDRULOzu4VzhiRRTDbGnJ88J000Jj9jeQU//8kRKM1ycc69nU0uzqZ+rIw/kvD+wSGrpRcy09kFw+GAvb1drq6m6EQwxjDZGKESjbOWNB2ymM0xZsVgoEmUj8O/Lb0VjB5q5hc0+VrbaKbj7HNKdjkC289USzaGaapfbOOClFywvZW/wsVdyVBiM7nkVq5M0eoXa/OwhqBxxyFkzciEvnwsJWdWsS3Rc1pRZGV7uMMvxsZxgiKlRukXVRzWWjG1GA4SwUUHBCQVY4n3EvRrxqEMwWIrhhH7KcKBEa4KtUuUKnW/0ofgaMBPIn6DqoeVYuitZDaR8InnR2UVtoVy/B0q52A7eVkLpimVggrCc4YQeeWVCgXlHApWgD+dSpox41ElIb7MVhBfUHbaY+udkhLDgE41IoKagjpeR2UdffnaxR9S7zc9hTUYz00B8S1ccwlKFxwk6/eC+t5wgod+Gh8W6SyJGJZXrzl+8THz06/RqxVDHKwcO+MNJnrCvXuHFEXBanaJtVAUXjPPc4sxxjN0lVLkGavVnMlkE6UGOOeYz3Jc4bB2gDX+WxZ5QWENm5ubKJViE2G5nFMUhtFkxOXlOdZadrYmXOYz7/Zxju3NBOGC3FoSNUaPDYuFQcRbmM4K1mrOzo84P3vtoVdTIGiWiys2JiMcQpbP2dAjD22mI4bp0Dtkxa9VCyyXv2qMXijjaxU45/HZCM5oQxtSabfrGuf6xL6deRPOAoUIkxevlYZUq4HxqRJPxsZMrnSWhjjqCMagxWADPiuRVGkzxLbACqbybTIh6pKlOym1MlfDBmtpdyXEpwBWUIrywJf2pi7nMe/47wAXVSGItShRxAnKSquohJukFFoh3DRRAd7xw+aqXD2GEPUh4cs4R9Jg9j0RNQF3p6kEiEgU7dIK/+sCzdvjFcqhCh73AjB6RsXf0RqkPHw+HIHpwwKF9qY2P1Jxe+p50WCFkZJQKw218NIdIZPl6K0pRPHO7TUFyoW0C65cbGEvSwQdNazE9e+w7kBuC04fzyxhg13kM1DFgmI+Y3Z1yuXp15y+/pz86hiNQ6sRzipsMWS1ykEu2JiM/ZF+WLAaZIFN4jgAACAASURBVIhCUdgFDoWxCelgg1UmmMIxHI5JkgRrFswXM7RKGY1HTCYTrCuYTqdYNyDPHIt5xmKVM06HpKMhs/mMxWJOMnCMUhgPU3Q6IM+vyBZnrPKC0XiDdDxhPEn48KMnLKdXLBYLnMuZzs5IEyEdJoAiz3NMnmPQqIHwwYfvMh6PKYoCYwwOw3g8xjmHyQtMUUYF3ZLeDkYPOOVK51p9LSxwJWV8cYRstGGLeJLVTLOb0cdTMbxvS0wxMPJQTvu5KiRQQqRKUjEVJZp6F28NLzT6JN4sS8rIiFiAeGthvW9xvX0HKXT30NaMxyrvIKw055KU10nD+MUWR0wxTFK1IzCcwFRL+KWOpGn+7a0NwYnD9qVv9hheZZnU37FnXKoxrMd8DU5z4do139Up4Poohvhb2TAPojL83gKviVcWKQZFUuHyAo28Qm2qmF9JIXw1dvrH1ldt38Vzrrv8uK0N5SnqXyDFegCDrYQnEPxmweaqlJu2sGjCPS7enUztcwsqFniB65xjfn7G0dMvMMtLZtOn2OyCfDXDKMcg0Uhh2d7dYGd3iBokXJ6cc3ZyxNZEkWUZWg0YDkdsTiZkWUaeFx6OUykiCmOcj8BzwjAdsypyisIync7JssynDckseeFDZsfjDRaLGavXC9KhY3t3m92dDRJtgILFcoornN9EVVhOT0/Z3j/k8P5DtBiGowSdjClyi7FCsrXBfHGFUqBVmT3U5D6yb+FhHaUUznhndJqm5HlO4Rx5ll2z/tfpLWH0DmWajMwzHq9xWuewKmz8KSeIeGyvMvwbJnYd01tRvFt0ECIeVG0+23ISutqi9L6DVnhmKQyCFtsQGs42NLLK3A2wg285SietZ2pq9j9AK147b/ejsQ8gIqd8pEzQA60DpWyEvQshTUBhajxbRGMBXUbDxNSuyTlbydF4nIOzNUZnS0PMj4z2zC7BlDc70lWFHCkh1DCGhsrv3vgkzkHYXCO2NaamKqLSgm1SH6ASNlMptyaQ6543GufLqO6XYZdiMWIqp6mHooPV4zUYhSu1eLeWshdqvL9Ok6C9o7wsi6pGIWyKqjTgCm8H14KEKgHpbDVhq28ZpXpW0fNta9mHBEfrKYIYRbpCQusIuPhr1N0IAqeZ+VREISYHc8XF2VdMr56zOxkxMwVCitI7KIR0NAGlybKMoshIlOXo6AucLbi8UDg3ZrXy4cj37u2jZIGSJYVZ4JzDGoPgKPCwTqEs80VOlvjxVcrnxJrNl6xWGblxLJdL0jRhmCakKWxujLEuIys3FGaZ3xuQFQ7nFBub23z3O+9zMb3k8uqSRAnpcADK4TIYbjncYISyQ+bTBdYtyfOVt0DcBJ0IIhZrwRWWJMnRkqD1gPPFFGgt0mvoLWH0kabRuOolqXNlfmxpPhvvCYzv16U0NVegdU5GjNF6Ju2ca1gO5Vtr7Y3zda9d7xj/6xymjQZWXVCIWJwLYWtdlknLyRuEiQSBFW8iqpljQ3MLa9fVzrxOU1zCMzf3p3MvWdTqdbr9hF1700uoutQq3LOjZlf3L0TLhMgFLwTj9AM31Cv1HgxXJhaL02KE6BsRRbyfo7JIXeQHKNtW6h4eniytkMq6jL6d/y5NHLypjKzvKXBthWVtTN6MmmmmXdXG2pfQbSmsoWMNv4AXVkopisLx6tUrxPgPtbG5y/zigkGiuX//PotswWKeA5blao7Jl1hbMFAaxG9w0smIcXkcoLGWxWKGtQ5rwJbWaDhM3Fh8qLHxnCCkws7yDGcs4iwDLSgMo/GQg71NnDOsspwsW6K1ZjFdoAcpSvkcP4eHh6xWS8QakiRhPr0iGWyVQmFJkm4wHA558uADXjw9ojg5pigKH7RQOL8fQ/mw5SwryPOcNBkiEpIn9luGbXpLGH28sSZiQuVkinFEK2FjTqn1OUAsFtVgsgbXOE4vmLsOGrlSwnz1m4aaoYUhTjuOTKnJVtp9IGfLkDPnNbewWCkthU4s+aaRKaGLsIBcS7NrYO7huqOCacKNKsu4a+cZLyGNnq3+taZFBBOsk3PuBgbfbuU149HTlprijTk31BaVEfYhVFE7xFAga8zTX+/efVhft+DKg1yCb6cKIDBAC1LsUQR8mfXJW0Grdi4c+VifqbAOUTbnQn1aWPlcCUve5gNdJ8D77ilsGSIbqVhSf59Gdk5jG1E6DSi2dGILjtEoJUmHvPPkfShy7qWP+MZ8wfz8Ekk2GOsxeXGOaDh+cURezP1zj9/h8mzGo8fvkCQjlFLMFjPy5RKL8+GMxuPey9WUwWCAiDAcTdjeuYcpClarFdlyBXlGYTK08hutNiZjxpOUra0N8uwS51wJEaXgIM8shXE+nDPxCsf5+TnG5FiTA4rTk7NyxLwvUg80R0cvGQxHjMdjrLUsFqdsbW0xX0zZ2R1jrcEYHyqshyOUUyRJwsbGxs0ftKS3hNHXEEVDSpWOLAgajapCtjyWXsIHSOnX8iayK9MRB6enf1Oa7wHxVn6nXJ1Np7JJAdu3fX6dpCzDBOcVPiokLAJVlt1cb83NPNW1KnuYpc0cowGKHKwe89Z4c6/ttF3P4R4w5riz+MXm6rFqC5ZA8fWwkzZmif08JUrOVQ11EEitDWS9judKOke+g6g9DQimg1HHG8pcgK38BpW6jLKQXmFTWwDB0e17UydxC7tHqcYzfhZsj2Cp5qyYcr44xA1QlL4PCYJZqh2kPgKnLD9mpNGQdtpSaxr1m5MTjax9u6jGlk8kPkWsmo2uDAoQr544q3jw5COG6Qgxls3NMfeN8Eqe8tWzI7JixbtPHrOzt8PFxRWXV46zsyn7C4MeDDDGYFmxXM6Zz65IFeT5itwUzGbTCr7J85zRaMT5xSmbW4cslyvef/99sizj9csXZCuL0vb/p+5Nfm9Jsvu+T0TkdKff9MaaWN1kN5ttqgkLIE2aNmUCXskb7bQzLMOANvbCgBcW/BdoZUArAwS8sAADtmEbkBeWDcu2DNIkZUkNssfqqurqml698TfdMTNj8iJyiLw373uvCNp4DKDq3V9mZGRkZOSJE99zzvcwLVKE8Chh2WxWGFtiraMopiiZU5YlMilQSpEkkrv3zlBSsqvqZkFOwFqkzzDOIRPRUBcnrLc7tpsbqkqTJoqLO2dU1YbpTDGd5qzXK7TRCC+xbkKapFhX4cle+x29GYK+wWqJYIow6Vr3RBV9SI0G4yGSOz2I0RqFWg1TtLvhHs4ZGP46Ol3Ra+5EmoYMfthtFqb+NocCoGW7VM1kldHxcE3/FP3zxgLqkF4hFiaH92uTfTcCW/gWwu7rd3w7w/u0gydE16OuvpRDr46u05FcGvRnT+gTPft+3bHI5p7pUg77eUTQ78MZbcv9/UbaHtzQDhcwERsWh23Elw8Nl23dGFaTtBp8t0MVw6jW4RwaW4QizDyGR3xIlNMbv0Xfd+8H9xywTEeePWM5UY4J96+z8wxtqPbC5qAbvoj+McLp6B4hmEpEiWYkSiWc332HSZqBCxTBL5YfI/OC2+2WvMjIpidM53f4pW/mfPThj3j7nTOyrMDoHc8vnwWiO1+hqxonghNEXVfUJrjAKgIcM58W3CwNerfh7OSEm5srpJTMF1MeLV9w984piYIsl2y3W8o6BFwlSUaWLDDGcXpylzQp0G7HfFHgcDijw45is8VaD9Y1C0HCbDJDKUmtS5I0YTYvWK6uWVY10zRHJfDwwV2S1HF2uqCur/AGrNVcrTZkSYL5q5YcXCAaAqH+WIt3ukjwgcVGCnAcJTvmGjf8jAafbP+rcbXbT+MmRn5B/2G0Id1xHeV7b+5YhCsRb8d975C2pwXJJpgGem+W3o3tUCi0/OnBfTJEloQtYe/l4jtPEt/1odvu+8DdMxASDcHY/jcq23vslaCB9d4uY2IjFiZdIFH0Onz0R+sKGiJtj+0Lei+f/kL2NPVGvApD/A5bd89Y0LSXu4HRvO37uE96zzKposXJ470ZX2jEYZ/j7rd9ScThMQAvTXd9HBDVeuWMCdX9okbcOQdjE5OMjXkGHdvdRPMidjUdZUyNVa4u+5nHu/A9CwkogfMp2US0YVY4V5PPzthZy/13vsnJfMHdB2+HCFgUyAl3LuZMlOLy6glVuSERFiVEYLiUoK1FW4fWllrvuHdxjjU12+02wE++RIoC52C7qcJC6wTalMwWc9brJWVtkSLj/OKCutZ4crIsp6q3FNMCUdXoqsK6IOR17agrx2q1YjFdkKkCZx1OQ5Ik3F69wFrDvQcPEL4KlM+J5fT0jLKsyJ1jPk2Z/dIDnj65ZLO+RqgcmeajO8Jj5Y0Q9HHpNdFmtWcIA+xHIbZ1xjemI+0y1MbHNJd9F7T96w//7jbj4bq97ymGQF7ezv41UV86be3QhgHDj1RGwjzG2LvzUQcVe0LiJcN4zBXvVc8yVmdf0MJw5xNy+fbv4VX3bge9w4ZFCCoJ86VfkPdpdOPS0nB8rRJp12Nt9g/28oa7OXJkzn09y85frLx6jI/1YrzPY+uK93SQm/MWRGsbU51FoWsLFSkgCe/90jf44jPLxcUFSZKQT6dslktqY7m8vuXOxTnOe756/BTnNF5ZUAqVBC+aTAls7tFaU+0sdVVxtphjbI0EjPA4H8jFkiSMQV4okjTAPlmW4gVIkbI4mbFabtluN5ydFmjj2O7WpIlkt9t295xMJmw3NW1iGN1EcO+2Fduy5Orqhnv37lDtdty7f4e6MizmU4xxTIucslpias10tmA+P6GuPF7k4AXbzfa13iu8MYK+3Y4CoiHXwoMP3Ws14uEnNeZYuNfqqAYDMg5waesOqrZCGfaZHfsaI5Nettr3sIhWixSRoIsojOOPykWwCbGwbqGCKFtVvGDFBq79yNu2a61hd7Bz8YcL2jFrfmysjPst/yJiKF5fusW9hyGc20sgHl/6sgWlfbzOeH2Y3UtGqkPsK966Rh5jSB3cJgq6GpZRrOg16hweHz5nD3MM64y08jVeh4x3Da+AJo8H6x0ZsIjAbVjVh++8ydncGrJFlPwn9CFeQBVpUYBIObm4IE9SPI7KGJxIqJ0gyafocsP7v/xNnj/5nGpzS6lL0jQlcU30t3XoqiRPM1Ip0KYikZJslrHcbnCuRqpgZF2vt5ydnVHkHpUFe8t8MQ0MltUOhKWqN1zfam5vr/BUvP3WAxAu0BxLybYq8V6gVIoxliyd4LFYBLoylLuazWZDVZXMF1NmsxScBuewRlBkOavKsFqW1LVhcXqHzbpmV2pkZFN6VXlDBP2hNhHc34b4dMtzPXr9yLGBdX8w+w9rD6AEH4X/x1v9gWPmYWmz6Yx5RcTPcXDv6Hibdq4PilEIBKaBYIZaZ/xhHrY3jikHYRdrz69rhOvl8jBS2I2NxlgWKD+kf2jbUaoxnMsmEGkvE9e+e+BYb2OxO5g3vuf+74zJcdvNv4dREUfGYAx3HrznV+0Gw/jHyVDiNo69CSHEwC41rqA058YyjAGWl3sVvRqXP7LbOHJdCzcON7+uu8bFzyxE9BKH8zPIA4cSkqIosEawrUqc1dS1QUrJN77xTZCe+2895NHnK4qiQO82GB1oAnQdokudtVhtkIrGjdOQ5CESf1pkzKcF3ntWVjObKIqMoN3j8N5ibRCwzuoQY4PGWdD1FqmCC3OahiQk1nrqyuOcxLuGyiAPVAbehTFIkoz1ekuWKRYnE6QEW9Wd51aSJJye3OHDjz7h8sUt73/zW4DEGHc0H/VYeUMEvejcEUW0msuWIyQ2ko1AN20T+2Wo/bxa4A5qjwjLA4HcHWu19CBMOi5t0VMmIATORlpqq2HvPY9jKACgmRCRhtVm3nID42G8qrkDta63YchXPr+zR6hr9/PQdoJ3TNocHmrfZ1g/20hZDhfGBn7xR95Z2434uvaaFt9vAqk79kmQnTWyyzOy18cuM9fo0/f3C/cI9Axj/Ru58uDI6yywxzyehleN203+ImU410drfM17jG03eo8p1Xyg1vtAQUxk+/AS31BfO2cQ1iCk5/75Ob/46EPOL045O52zW1U8+uJTfu/3fo/N6gUf/PDP2W5umc6nbG5vUWnCarPh5GSBtWCdZn4yCwuAcCSJQiaiiWa23Nxekuc5xUSR5RM8hrqhMDbOURQKvGW3K5EyCOf1esnZ+YzTsxlJAklaIKTj+bMrVsuSPFtQTOcY43hxfUNd18xmOcUk5969B3hbofUO4RT1rma7WVHkU6wp0Hiquma31SwWZ5RljUoKrIe6+itmjO2Mj76POAxBJrZZ8UVnsDum8xzsCF7yIQ0UjLaOGx5r4QMh+zqDD6/xmQzCujnfaGkxb85gcXjFCtz5uHsffdhDnbM7JI6PRXzfvu+t8O796PeTWXRQjDoC3Tgb5fa1Xd9ab4mX2TRa2GSwNjcudaF+lJDDt883fJ6BcXIfbmpcbaUXgSunfa3ONGPpQnCY76kXgvZP91vE8RpmTPvdH+/wTlQT6dxGtobfUa3R3dZ4JO5RG0zU51eVY9r2WFYscUDa1lw3tut9jR3p4L1Id3AszromGobOLiOVDzmIFarR6gUCi7eG3XZFriTlZskvvXsPqRxXzz/lq89+TrlZ80/+l/8e5xw5jlpXlNuwgGjb5q313fcnFSymMxKlqHdbtJa4JpFJ2Gl5ptMCpUQIYGp21VprksThnEabCu8Ek8mEizunQMjEVkwyhPBI5VnmKfndOVVpqesaSdq4VGY450jT8LcuPdZq1qsNRZ5RTBacnZ5jrGa12bJd2yDgU8it59333ma9/fkrE9HE5Y0Q9EII8iZEU8awRUd0FIVb7weQdtrz3gkhwI97TAyybHoPDWrr9z4m0SRcGA/qCf/EW3ClxjDzY+naRj6ktr4HRJtkpWUIDFm2woLoukXRN+uMaLa7gfv8sG3ZvGoheo9z3wj9TtdvNekRgR0Cv/rpEj9rh3lH941Mw837a7NjRe9JQO/Js4/jDt9nDPco0YSwN88vhMc2s96JJszft/1M+8jNplU3gv3LvS60EJMaefVhzYrnUE+zIPrtRnc6cSF2IKbb9l5isAdzQ4zYV8LxsIPwrlVgekVgXxFxMUNk1Pwwu2zLW3PE0jU25UcUnmN1moptD6O7tnkLQqBZd38PqZDNwh+0a9koPjJNqb3DVRuq1TN84tjtbnn2+FOE3pDKOsx/qUlkSpIoTG3ZucBKKYSi0ppa7xDeMp1OQ6Ypq7FYnAvjnaigtftqhxCeNG1iPrzFWYVyKa4WCCUxZVgEMmWwwuKEI80S1psN2gSPnQdvPUCQsd2WfPH5M6RwPHj4PuvNkqLIEEJgnePy5poskUwmOdp4hHCs12vKssR4KKuak/OT4GefCrw3/Oq3vsVnX3xx9D3slzdC0Lel9RbZxztf5rUQQoH3zrV/DxIt79fZjyTp2wnwQTtJx7fn7cc49HWO+tXeT4ohP01bNf4ARUQo1lzd/b+FE3zzFAfP2vznY8F7CEtEPe9/SxjzbG/ZD8c00bEy6lR5sFi0fW/F7RAKGWt1uOC0r9U3uH6nBwZhMGY89O2i2YxhSww34ibovW/4XPrSBp+1fez6KwWDJN4jAnCAvUcbubBIhIC2NqjPx9c3VBWi73ZzruGdl43jbqcZhzEZjuHxnexB9K88XFQAhE8PjnW8QS/RJF+F83eBjd3E7XesB3kclEB4hxQOZzdcP/+Cyyefo+srdL1ju76mrnYsFouwdFiHRSJFinWCLJN47Vlt1sFnvS7Ji5T5dIY222bsJXWlUUqRZymr1YrTszlFUZAohdaaEAQXtHrnCDYkUpw17HY7hMxJUsA6qroKOL6USJGgdY0QKfP5lM1mx83ti0BMZmpmswUAVWmgyMiMIE1DcpLb22uSJGG93WCNZz4/4f79E1SScnl5ybaq0UbzuuWNEPSCod9uL3BDhKg/Gp0YtTEC3cQzcojnt/47bViT6ARFgxR2GvLA0yTyZW/1kg5rb1o68NhotPP9oqK8nu0OJiSS2HuuTs9vhWK86Mke4oggiPjftsjG3XAgjDwHQjawNbjueSAiKDvyhY9ueLq6vZm0N8aGcRfNFkQeNHDoMNsqAK3NRjCkGYjzHHXaP3SpFiV0hpWxPLtCMEhw3rbTQiltXoHQdjw6e8/cplMcjE8f+dt5lkT3ahf64BMf01X3bbiIEjssCEHDV/TwyKt28u2CO+YkEB/r298vr+ZQ2F+cxtuI51c/97132Pa7FK753jzO1VxdfsWzRx+wvXqMc1uUBFdtEKZme1uRJBngSNMcmYbF2ChLViSIbfguvVTUlQnC04aI1bq2WBuSquzKkizLkFKGyFrTUFY0MkJrj95VCKGx2lCVNbP5giRJEMJSVTWmrnHOk0xynAFrPKvVJadn59y5c87NcovwkpvVkkp7cAaVZnzzG9/i+vqS9abEGgEyAxV87T0ObTzXN7ecnV2wXC7RzuLcX2LO2P8/ivcenO0FQfuJNztd+TIVomuk/ykIeMQxOthRnaORsK3BUDYSVEYa6OGFYnhcuFHhKEc+wTaPkPCBJqFraK+qbLS3PtuS6HYjIYuT3KtPMGKN9KNNsR4fE37IIT+W+eiVtv1RjTYat+6gj+rHWl0LfXQMa52BdXiL1kVVgnONdhw/ZV+3M734IfVbWNwOIZMDbdy3MEuEKYvAcum83xuiXitutfGegA/ikOLQtI/66LvnSlrdo11cognXRrg6ZyP+nthN+NWwCo0YDfBgTy0yJvjViIeO3RPcMR1JNxIDw8Ph9+dEQ/62NzdsA8UJ77q0hd4FyM/bmkePfsHq2SPM9oYsEVgfAhS9A2ccVaU5OZ0jE0lVbVAqJc0SvHXMT+dUpaEqDSfnZwgU2nt0ZdBVzWSSAx4lEor5lDyVOGOx1qJEQlUHnhpBEvrjPMZZhAqcM8EmB2mSYjRc3lzDTYkQiizPUUka7qk1q/Utu20FImkSoijyyYxnzy9RUqK1p6pWTGcTEJIkzRHS89Xj5wDcrnYoFVg7j+X7HStvhKAXQgzJxGhC0l++Czxo47CMN9Brf6q9GEP/MbdZ5vt22w8++hhwBx9I7JOt4mNjnCYHP/a7Hu1wcM1isI/PRvj4EWbJfW1ZCNEdc40WKKNr4tb3qXTHshwBAyH36tJo80N1vTsXhGxPOtbfJPopomdsx8WPC5vYU6gz8npgTKulhTd6SOlAbnoJe0JwMA9G4Kh+3IY8/77lJJCHWvZ+6e1HarAA7MNGYSd7TPOO3WIb+KdxgnidVxffKxh229y7x0qvrXdtNIqE93SaM4264glyoI05F8I3iVuCetKm/ZNCIZXEagN4Km2apDgZzgahP5/nSAm+yNFeU61KtHHUOhCa1cbijAOZIITg3t2LsJvyFu8tzjqMdlRWY60lTfKwJIfk1dR1TZalOOewPkFIFVw4RYY1Ei8ki/kp1mk26x1KlkwmE87Pz5FixXZXd3Qp3ntWqxXn53dwXiCEwljLNM3Zbku8UB1U6b3HNhw9YzvTY+WNEPS77ZZESnTk1ideopF3dY5pEq8oHdkU0GlskUYUb5+PqP8H9z/WpxCGf1hP7kW+9H/K3u+7VYB9jKSPu5rGx/chj0F/pOwWVUkPc4Q7B4uEbWGiPe3+KH+NP5xwPsaT+qs6W0OriUvEofFYNEa6OLR+8H7jnrzcvXDs9UmRdfUGC9nIVa3nyPAphnucuJ2B11i3YrZtWPAeSa9Nd3VlC00dMf774c6iOzyysKhmV9o6CkSN9E/Z3KId+4O5PAaXti7Q3Y6HJrHIkdiWo1CfA1Q3Hz/+8CPee+898nyCda7hhRPdYiyaOetcGPskK6jLEu8klfaUpSHLFdYoSr3FOt+lD8yKjCSdoLWg1h5rPc5ZqtqQpxmTosDbirIswRmSpIEEpcJaA16SJil1bTBah8hYKZktFmhTk08nTCYZVVVxe3uN8ynWSayF5XKDtRrvHVKUaG1Ji5TF6Ql5EYzExhjeeettqkrz9KvHIU7AVejG///0/Jz1ekeaZzhHk/M2C5w+5q8YdDObTbFopOoTgVgBjAgQObJdDX+MLQrHhP/h8TTWtAZ4fvRRR5wm+0IsJCoZska2e4AxRdgdsTsI3KGnhzgWBTy816uKa8fUD+GYlv+lVe3aJxMiDtx6iXtjm9oxMlAEv33bC4w2IYjdh1mOvKNui98diPrrO5jJI7Biby4M2h96oAR3vggiisawh7T6S8ZkrkQM4DEh+kHrnYh65tSOk0gMhXlLlOdDVzooanj/YQcOpsaIa2v7s0uS3mnxcq+ujziehpxH8ffUYsFqhBM7eG5FtivhBu3v90141UBbrtGcLUmqyPIUIcM7l0JirUFJsMaCsUySCWU2RySKy8dPmc1mWGuxTqCSnO22wvsr0iQQolkjydIUfIqSCZN8znRiqeoQYepFwp2LC4o8RfoSgcaaCkkaoFLrqWrLZJLhyXGigjRQBRuruXd6l81mRZIkrFZr1psdngyB5Pz8LpcvrtltK1QimEwKimKGUgJbWUQCk0Jxu9wgZcKTZ08ptzuMNUxEjlKBSXW73VJVmrqymLIiSRKyXHF2tqCqKrR+na8+lDdC0HvCKot1JFKFrTlDob5f9rfdY8JbHHE0HWogQ00MGi1X9L+P3X8IPzT/2/sWlBgn2z3KpX78xJHjoQz4gI61EBnd2tb2oyUDP4zscNJW+4+NoXFboTRjqNg7NoQRpPd9tFNb62WQxZGx94TFqX3OoyNzhITtMOF324/DKFExxszFyxaWw/r7NqbOUcC3hunxPsdjvm9sbosT48cH92rrRhp+Hw8xHnQXE9i1rrTHd837i/dwl7APMXhvA/4uIZECnO2D2JzFusDfbk2FrXcINKauEEIwnZ+xnmwRKkVJj95phE+QwmOcwuwMeZGSGcV6a5HSM5vleA+L+R0Kp9nu1ty9e5dUCc7hbQAAIABJREFUBj/9REi22xtco6oZbdmsS8rSkGcSR0gwrlSC1prJdBaySImEFy9WaK1J05xv/vKv8MGPf0KWgRBJ4wppSZKMsiyp65IsSzk7PyHPU3SeUJUa7QzL5W1YvLA4YzBGk+UJ69UWrcP4ZVnCdDqhmKSoBDabzZH3cVjeCEEvvA8JogkGmOA4Hj68w8nV6V3h/3tCXhBN2COa0fCzO/QBjxS0wcc/nLCuq9QFyci9zz8Avl3/hj7G+wtV+8cxnplDXPZobtDIJjAQzC2NMb4bso6SgH7shPMIeuzWNU7m0suhujkQYKJpMwgnSyzgD55m2N8jgv6Y/G/fT3taHVSO58ieRux76oX9Nr0YWwDG+3Cs//HC8crr/H6OsOEC0v0eWyjae7RuwM2uxnt/QCcd139tiHN0kRypxnAnF387ru1InN6xObQrt0gJWaqQOKR3OOvYbjcUecqP//z7LKYKnMPUW7766kse3rvgnQcPKVclV1chOYeQuxAhnkCWTrDSkuUTdqVhV6351q+8T5oVnF3MKMua6+tLtPZMJyckEupqQ1lvqbXGa4uSntVqw66yCBSVdhhTY60ly3K01pS7uouW9d6jHWTFhI8/+gxrJLUP11qrqeuahw8fcnn1nO12S5LOqOsSY0pO5nNM4diVNe5sgZIptdHcOT1FCMF2u8F7SFRGogR5mqGkxNQagRsEu72qvBGC3nnXaUKqMcbIAEwfak6RtnDMpezl/tmHJRgoo+sjrXPgXhl9lXJkGxvw5rhOf27snl29mP/lmI/LnrtbZ1Br7jP0dnj1c3eLRSOcY7vAvig4otTGrQ3aDNe0v/2gTlg9Yvjt0KU06uTgz/YZ1b6QhuBy2Qm08Xcf7x7Gzo89pzvCG7N//+4eI3WPwVNtANWw3debs23ZT2kpEAMO+uH9Xo/XKOz4IgXHm4PrhspJdKKFonCdMiO87L5j53uPp9ubK/JEsVktefH8KcVkxqbcMp9dkOcZVy+ecn5xirE1WZbxi198wen8nNWqZja7YFfuUEmB0xXWWRApta7JjOV2tWK5vKWufsT9h2+xKS1aW4ypSRPFnbv3mc9yPvjJD3jx7AVF7pFCop3HIanrHeWuJs1zECAThbYGoQI5mrG+w8in0ymXz16QZ5NgS/AGYwM9gXOO588vKast8/mcaTFFeEFdVihRs9tVaGOxxiNzuLm+QTbBWlJKZJLhvWJxMqUNnizrislkQlZMXvku2/JGCPrr6+sQESuCP7vE4aRESjXQvroAmLbE29I97f2lE3O/7CW8CNGlL79u/2ibqSimwW2PD3cLh1tx2gVLCPyI8S/cb4i3QuQ91P7TGtgGbnnDSNSutJxCIRAg4u4JngX7+Xl9tDOJn6NvWO5BO6EnwydQ+Mb/W7SBOl4eddzYp2eIPYMOy5gAP9w5dP71+4ZYMeJhw/Fd0wCii+fhSFXpxxcLLw4hsaOCfi/oznvffDNjXXv9xeIYQ2h8P9kEC8Xj1boht13uz0V8QgfwanATFUKwWMzwruZ0PuPJ40fcvThDe5iePgBv+Vf+2q/zp3/4BIDtNtDxaq158fyKxdk5jx49oqVNyLKc1Id0gO+//z4/+MGfIaRjcXpCkkqmiyk36+fsaoOta0RRcH19ze2NZT6fY+1d6mrJYlKwXC6ZTCaEVO4bWgjSWtvZVlroTSlFniiwjjRNqOodeZpRlSWJlKg0JVUKXdXcv3+/cZWGYG9WVKXh4Vvv8umnn+IczOdzHj58SJ7nfP755yRJIC8zxoS+LqYhLaGzuCbJ+euWVwp6IcR7wD8EHjRP/Qfe+38ghLgA/lvgG8CnwN/23l+L8Eb/AfDvAFvg73jvv/+yeywWC4Tz5EmCaSxSotH8GoWz6QuMB2GAHHwIArzvvUeEHAiKY5GR0TNHVMDjEIsfcW1q/eXlfldaHaeBTSStf/7hvTuqAk/n3hgcEl3cYHOhoA9lj285fBYnQHjbwwRe9mPawDLCB4Y9icAltsMsegZCsUe309+lpWoIsE03QP0zdcLE9X13ohNIXsVRyo13iRcg42C1qMoeNOFdrzUHIT52VWSUdKKBpUS/OnoGCVA6Bs29Y2MdGrNXxOVYHEiwF4igaESBT80ADPu+P2V9O68aWu/4o/8aW/p921TXNxUt2o2HTPwYA2zf9+20TgZSSqw/TGovkx4qPFmccXN9SZHPMQ7yLJB1iVSinODuw4eczRfkSY7ZWU4m5xRZwuOnz9lpjaJhjZRzkkSSZRlPnoTFQSnFbJ6zWCyYFinT6ZSsECyvb3Des1kvcaZkUqRcnMxZLWusDlQKyCDEW84qbS22Dgm+hXSoJCwDWZ7hbNDoTR1sCiIPSkyW5g1XliXJPdMiZ73dkGVBU7eA95Lrq1uQCc7WrFYbTk7OUCpFV4ZEKoqi4Pb2lkpB4RxpEjx8yvIv373SAP+J9/77QogF8C+FEP8b8HeA/917//eFEH8P+HvAfwr8TeDbzX+/DfwXzb9HS5Zl3bxu80n6fTjiFWUgLGXwd1HdRO2jWIFxbGvEeyHUZdQwO7beHFOO+hysPZwz3PlGGqG33e6g0/GkOKL1HjOk9R1pdxkHeHpUo4tj6vm++vai4djX/noN2Tf3bHdexzXFUUil+xH5zx+DOzxIpQYLt2vC5181X7rdyWCR6/ujBp4mjXfSwFMl1tzHjcijx6LfB30Ufs843NYWvCzd+hB621NCjozDUe195LoBzNMseu4IdxQR9h9TdffcPrFSE/VbOM7Pz/nqy0coJUgajxntK54+fcpkMqPc1ZyeX7CYzcmVZL28ZVdXZLlCCc/JfMau3HD57CnFJOHJk2fcf3AHIS3z+QSpYLW6Jk0ltjQgLEZXGFNTbtcs5ncAh3MOXdckScKu1MEVsuG6kVLiZXAFtc7gjWU6LUjTlNIYNpsNxtYUaUKWSESWo7IEXVucTyiygrKs2Wx2WGuDx0xVM5meYLMwNnmeM5/Pg0tollFVO1QTSZkkCcYYnAtEbdZ4duXuL1fQe+8fA4+b3yshxE+Bd4C/Bfx+U+2/Av4pQdD/LeAf+vDm/1QIcSaEeKtpZ7QEXLHvSsCJHW4Er1aMSFjAj1EodDtLMSSLkv6wbvyB7WGerfTbhxLi5Bgvg4aUCGyKnVxxnv2gm74/fRo/QbNxjLSpwUcshwK9/f8grWJ3XXyPaFFAtJJ1eNPuOSM63hEQWwgQNmjUsRH860TttX7lQwOgIzaK9Cn5mnP0HkTBxmFDX/beQ/wtdO6GIhY8479Fa1VsUx+2hFsjgVnD8RwFb4btdtc3YxvDhu4QNmsqj7cR9aWDpI4tsiPyf7BJ2VsshGg9y5p5FdM6DSZU3LDr6tmRmICwOwhzI01TTFWTpopUqSBIRTDK5vmETz/5jIf37zJfnHP9/Aml15zfu4P6wDHJFc5qri4fs9ttcF7z+PEvePjgjLPzE7bbJULU6NrgSZhNz0jTlDyTCGdZ3bxgOkspdyuENxRFRrXbUNclUmYI4ZBSAQJd1lg8SgYWKG1qnEnZ2R1JkrHblpyeLlDC8+zJE6bzOd6E73aS5WFXoDXWeOrKYbREiJSyDJmnsknB7eqmMcBuKYqC+/fvY4zBGENVVRTJlLKsyZMUYxx4OcrvdKx8LYxeCPEN4K8D/wx4EAnvJwRoB8IiENOqfdkcGwh6IcTfBf4uwN0Hb+OcQ4nWuBo47joUIJ4rRyZ8jBm2W3LRbhPdUEMRjHzcxyCaSMi2H5JzrmOK7LBu2cMoh0VGO4Nw4RgtAgR3ubEz3TI0+Pjbju3ju9EYRYvd2PPh5OjupOv5QGiMP19Pa9wHob2O0a+3vdjo3cR5cqN6e+9HNLDWvuF1zJum671r866qqO74rqjTPDsbRUO6JWg0Vhud71fHcSPwuPvhwaqKRMjouQc2j9hjJl5Y9p+z5+d5nTKiE3TFWcvtcsn5+TlYh4vsR4PUlRy6pYYGHQfDYQMNg5BBUUqSWFEKIGWiFOdnF/zGr3+P29sbpJQkecpufcVXjz7i5vYp3hnmRUpVXSOE5eRkihApeSbAb0iT8I52uxIhM6zdcHbxgNurS4SSnJ5MeOet+2x3Sy6fX6O15vz8nGfPXqBrg9amkUkyyoIF89M508kp2/UmaNi6xugKW2dMT2aNVq4wRjOfnuIFLJdLtpuKqtKUpSZN08Cpg2A2m5HkGe+9/y4///nPA1eS99S6ZjabAXBzswzJSrwPCcaFYpJPsf7/g4ApIcQc+B+A/9h7v9z7uLw4GvkyXrz3fwD8AcCv/Npf80mSYG0TyrznsRH7wx+dmDISyJ2GGj7O8F1Hwn34YAftDk63OPKe5iNkq0E1xyQo0Q/nYFsVy+P2oiMf46vFY1y3x5fj8nV4qkW3sLhR3pPXKi4IzP6619tSdouP9PgOz40HuhVyjaDtTvR2A9EsnkeNpnE3991j9+CXeA5I0Qh72fCkN/PJOYcnyoDU9rjTF8Yiaff6NnhtLx+r1qg9xMhHdlaDJ+uD3F71Lo9t/4UIKRefP31MlsBsNhvaGga7EDsK+8QBWrFCFXZLDuFsTyboLZIU21AdSC+Yz+fkWUqaCqZFwsc/+Yhnzz7nxYsvmU8KclWQKEMxSciKICcUBl1XgZrAQZIoaq353d/9Tb744hFPtqsQiFVvePTlFoTFeRsgGS9wvuWgD84D1jq01tTWUFYbZLJgPgl+8JeXlyiVI0SgRLh6UZIkinv37rPZbNDG4VwwIq/XW7bbHVkx5TzPQ+SuSgIEVOR4a8gSFdIK6pIsSTvoqKoqjDEoFDttmOQTTk/PkeovEbppXlxKEPL/tff+f2wOP20hGSHEW8Cz5vgj4L3o8nebYy9rP6T0ShJoPiLvoGXoi7VRP6aN0wqNVhuMtbHXCSU67M/BMXoBKqUMxlQZ+/nva5Kx5t1c2wqulyyJx7TTV2G/fXGDngy9Zl5d9u/zWv7g7UfdCjkvvxZ+SMfL7gHZIURORPDIoH7Q/GKf+NcZt84Y3/z/IAfq3oAGds12LhEpIOOBWF/HpjRop2NqbBSAZhylCEu5b/DssXc5NpeUisfi689/CARrXoXvZzLJKcstk8msrzDYZRPiX2gWCBH2411UdfR/KRXG1ngpGocGj5AB2sjzSQicFCp43EmDzFPwmpvrS7abJQmGxcmESaooMkGiFF56jN4GBksRXCOVyqh2FRqHd5I/+eM/ZDqdMZ0maL1DZQnOa6py18idoHknScJyuSZLJ6Ft39CGeElt6gA3GUOqBKdnCy5f3CII1MKr5RLwLNcrjDForZEikJ7lecF2E2gNnHMoFQIMl8slaZ2yXMKdu+cgHFUddhPr9bphxhTcv3+f6xeXgCDLMmpdkrwGq2/3Pl9VofGi+S+Bn3rv//Po1P8E/HvA32/+/UfR8f9ICPHfEIywty/D58ODW9I0cCuKYMlDJhJbt4RGId9jbWuWVy+Yz+dUuubs5JQf/ehHPHv8hG//2nf45jd/GYfoOPoOeRebSe8OhbKPNcJxxSViQIgiZwdtDBod3HdwCo5qoHYP2z6aGALHGCc6yOgKF3dwcG1bBqH8CNjjLmmh6lB3vHjMEL/3/ljc13gZwEM+Yk0wewI2nEhobBwRAmKJxyJ+vpgcLrZkhOuGnlQjsMsgkbxHikCb0LYafNYj9d4r9oONjBzfYsu4Iy1972Dc+l2jH3yqLeTlkb7X3jso7OAx3N794k5ErUY2B68c3ljwhjRJUCIZejvF49pQObT0Ba5pq6Vn6BYo66jNFmMM3luUDMZXtGW73pBPJyRSYk2JMRVOl2hdocsVX376U+aFolwZ7p2f8uzpI3TpmU1zFAprLcaGEK0Auwis9RgHWtes1s+w7pRJkaFNSXl7i8oUSZIE7yCpGi8hRZoUgVfG2ZDEpNltqibH7Hpbk6WCPJ+QZQmKFOcNF3fPg7G1gVmss1TOUsymaKEQyyVKBStOkqRYB7vdDudr7t69w269od6VCJlw78Fdvvjii2BXyFO2qy1FMaWuNYvTE5yzHR/O65TX0ej/DeDfBX4ohPiz5th/RhDw/50Q4j8APgP+dnPufya4Vn5McK/89191Ayklui7JVELQ4BVG16SpY327DFw4TvPk80/50Q+/z71797i+vub3f//3+fyTH7NdrfkXt89Ilefh2++iVIYTkpiAd6jl7YVks68FviSIp2txgBOFn0dE4Z79t6k7Xg6zQ7UfX/sMvZ+yGywK49t031EAj9+xdd7sm3EDQfUyDLcrrWBueOYRHu/GA7/GPD8cL0ugELx5jiV46fp2yEfRVO3HyxOie48ljVcji+q+iO49d5rMYuxFm44M0jF7DEf6MV633y3JaBezb3s5tDtENxqxFbS8Ti1ZWWcrcQ6EaFgTPVIJnI3Gda+/B/NbSXB95i0A4zRluQOgrHZUu5JUSDbbNTc3OTJRbMo1p4sJn37yM3JhSRPJZn1DvV3y8MEpm9sgsiaTCViDd2FhscaDdBhdNQt2A++5JrDJAc7jtEYJ32jONZ6MRGVIlSNFimloIsqyQkqYTk8Ru22YfyLBGs8kz8jz0I+Liwts7alrTZ7nnRae5zmX11d4ghfSarVCiIDJZ1mG85ZNWSGVJ53McM5xefmCsizJ84Lzk1O+4AvwgQrZe4eznrOT1j5gcX+ZGL33/o84/o3/2yP1PfAfvnYPAKNrLh//gnfefpvPP/uy2erk/PDP/jlPHj/ibDEPZES2JpWKr9ZXVFXF//GP/xH1riSXkvff/SX+6J/+Y/LJgt/+1/8GMptw98G7LcLefOThfpbDCR/rwd71m3r1Cokfa1JHA1X2oOfmLqNVlRz/mKRzIS6g/eAFIKIX3WzzhRBNnMDeM0a7mKG/dNSjFjI5ZnSNunwQFNb+aLQfOZq4glGvlAO+l7g5b5FCDOMORiV9i4/tH+7rJkIFLf6Im2C80euNw8M2O/gnguzGoOsBlPNaiM4hLBS/p/1MB92iJA771/EXfT2z2bAdD95ZvNXgDIJh8GJ8W4XEqyjeQoB1DuXbTGCN+6LW1I3/d5qlFPOUaV6wW66ZFhPOT064e+eEDz/4Pi8efwJ6RZHlTIs8YOqf3+CNRTtLnuQkWYGxdUgcIpKg+HhFnmdY6ynrHd4FCrva1lS7Nd6E/sznU5wzZFlggqwNrNc16/UWKRXTaWCHXK1vkRLSNMG5EMB0sjhjs74BCHCWrUkSxWq1bHYTlsms4OTkhCTL2Wx2XURwWZYopbAuZKdKUgHMuF5ec3N7RZ6nCFKyVHJ+Ome7KRHCo3LFdDqnLGusdtR1zXqzfO33+UZExt7eXPGnf/i/kmUZkyxHSsnV1RUnsylv31uwmM549NU1qTXcOX2L5XLJd7/3G3z44YdM0oyiKFhev8CUW+qy4p//P3/Cb/z1fw21j0+2GPuI0I29KIa66Mux5kGcyjFGSuEHkbbtNne07sCGG8MOjoQ9ISd7LSy2FfTM6pEHixz/SIPLYMDGW4y8a/4lcmJf6LdRf+Ab78cj4zay6zjMMNVWFX396F2O5sRt792WTuiPQ1ZdtWN4d+sXfmQQurWNPaHePLePbi2PSPo2/+sAdonO77vBdu/Ph2cRQowuyj1Ev2dvGd1t+EHVLleBc4Fz3nm26w1FMUVmsjPIDsfNB34kIYjXo2BoDf2zNnDH52ka4ArtKPKCR599GjzmdIWrd6RJyixP2W2usLtb3HRKKk9QIqTcK8saY2qkFGyrHcbUaBsEthCCRIkmsAlUZnA2AL9tsg68Yrte8taD+wiRNVQDCdZrpPOoxiV0MT8N5xLFs2fPcMYiGsj2+fMrBBalBFovEcLjTeCUT9OU8/NzrNOkqSLJEurbCu8DS6fHcXK64Pr6GoRnu91h7SnTouDi4oxvvP8eqRRs1zdkqWBltzx8+JAsnfLs2Ytmjnis0wNbzKvKGyHoq3LH0y8+YzGd8bTecXp6GizVesfldsdsPoUmZ+bl82cIIfj05x8DjqvbKyb1hN/5rd/h08++QKSS+3fv8eDePXC9ke4YZ3ZbZDTfh4a98frjTkY9djqo6wWqVThbf+djQlQEH9lQYhe25ojvnyWmoT1WOshD+IGw6G5HA8J732HhY7ItQqIP79GuK4PHGIduxt7D8eka+fBHNRP7ql0WtL1tF4X4imORqgO2iKaOPgBvmrpNhia53zix22UoxzbYKsp33O7GYiP2ICissQ3EUc3HnsXH7zmaaGNRsG1cyhgUIwhG9fPTMyptEBH75fCuFqEEeNcZZRMp8T7EhFgcKEkmkpDYA8VmfcMkdaTKoesSrTVXz2ve+8Y7rNcvqMtb6nJJWW6o6x1FVpBkGXa3o9Q1eZIglAyeMXUVlA0vODk9x7mgia82a6QProzzeYBMtstbkqxAa8tkMqGuDc4ZdBUoiPMsQUjJnfMFFk9dGxIlAoOkC5Hrm/UOqVzjEaSQEparG6y1nJ4tuHvvjKdPn5JlGev1krquEN5yfnKGUorl9RXTPGe9NiglqLY7zuZT7t+9h3Aeh2K9XrNb76h3JeVmjZoJ3n37PnVlyLIJeTbh5vaKf/Yvfn5kdg3LGyHorTVU21seni0o1AyzqZnPJ03Eo2C1XDObzTB1jVAhQObxo694+PAhhUpxleaTjz9CScnf+Dd/j7ff/xaqyPBSAwmW8DIC+ZegJQgTQjQ/Jb6h2G0hnt63uxe6QX40H+eIeqSOaPSdcHuNnbSDoVbYNRL+iQWLc8cajBaIVlB0C9lx75DW8H1M9I5RR4TumIPOxkbeOMgt9glqbRfHSLh8FFg2WHzFYZBaCDdvvXAsvonmFG7I3rjvHTP0yjmETdSowZtmNWlpC5p8ssE5sBuDQdWR0sJYnah3IQFJ7IrYteIbpL/d4Ix6e/VCuIeY+pgDiLlxeqUk7GLjRcAFO4t3wfgsQvS61SYoKu3iIQXIgN0759huNhhjqCvNvTv3calFIZE2AKNWCFAan8BHH/w5u+0t33zvXb788kuunz/j7t07vHh+TlmtqPUGj8Zbw3ZjmRQZzhusCzBJl5wE0E6T2ASRZNyuV8FHXWSYymN9icOTInAesnxKmkCtK/A6+KzXdRMbESiZZSLZ7lYkeTDc5pOMJElw2oGUCAfGGKwTLIop1lY4Z1BCsVrd8umnVehDkvH0iy/I0pwiS6nKNZNJw/FzesbtChaLMwCePHvKW2+9xYubFd4GfpvJbMF3vvvrIZl4FSJ1Z1NJWZYYW4/a5I6VN0LQp0nKOw/f6tJlAdR1hUegtaYoCjabXfPwWWOwyFmtVmw2G1QieP5ckUrFn/zJH/Pe0xf89u/+W1jr8d6QZkUQ096hVI7vXPNaMjOBt64Riq7XuiECXZufe4yDA9c+O75reFlAz+uWMfKq17rOHS4ag/6MbP3liFEyXHik76OO+xEJ2QD7bv8IUFFItTjebmyAPRbB2hbVRMfiG54e38BSe1TXL4sT2G93v268ILnGNTA8kyC4SIrXxOOb0thYuvA54UmOAD0HEE0rvNtFP9rFDBdq3w2vE0Ph3vWdIMwHdhwkUgqs8Vgd/MqVajKTqUDy5ZzAO4kxmqresby9pSy3XF/ecH62QJEhRVDk0jRFOMvt8jnzxYRv/cq7/On//TFPn1oSYXF2y1df3fL8hcDbEu8M1tkQsOQNVbUjTVPu3jtjtd42fC+tQFUkWYoUiqquWa23gESohKSBL621zSKlyLMMXYXFOc9zTG0wziBEoB/I8xzrLbqskB5SlTDJM+pdGaJVrce6CmsdYHhw75wsgdVyycnpKTc3N1SVx/vgrrnZbJq5LDk9PaWqdnhHIDBLM+q6Dr77Ht57511MFcZrs9mwvF6SZVnA9a1ls9lQFEXzPl6Zzbkrb4SgV0pRlnVj+Vd479C6pqxc4JMoa6qqwnvBrrpiNpuRpim7coPHImVGWW2DnLWGD370Q15c3TCbn7LZVPzmb/0OD95+gBMCrYNxQwhBmqtGw2xpkluq4FbItImgRQMd+Z7lsfnXOd8x8h3nJom1wvbCcWFzxMtzzzu+Gbev4ao+uN0g/uCwHDOkjqaXO9KKFa1heHhayAZ+6IyY4ihGf2xnMUim3tYUEJPXhWTT6gBiCxr9kdsdvD8/MMa6ONWl6EP5Iew+hPCdkXSwuAw2ArGBpM3AZaOJ0XuIvUop8M51xvR4txVgjCFks99maydyot3B9tYfIUIOXuFDTtNyvSLNFEIFrNuToJDB7kPQ1PM04+Rkzr27Z7x4+hXbzTWJl5TlDmc1292Sqtry8Sc/RJsdXu8o15c8Wn/F2ckpihVKQb0rUanE2kb4pSlZWqDtFkQQijfLNbvdjrIsQ2RpmmMdGK8bKmJHWe6w1lNkKdtdxWyR4J1BW8ft8pJ75xcUk4yq1DhvSZKUNE2aPLDBAUAoRVVVKAGmKrFWhznhNWniSRLL3XunTHLFegOTSUaWp9y/fz8YdUXGNLd44/Au7HoUgvOTc+aLaVhImzllTDDO1puKqqrRiUaKkCM3kQlpmgZhr0NC8yzLKLL8pfMjLm+EoK+qurPKB44JSZKluN2uScJboFQa8iWmKcbWHd/zYjqjLEus8wiVIrxjUuTUuxXlZof1ij/+oz9ktpgxO1nw7V/9LhcXF/zgBz/gu9/9LtNpAYBIQv5G4TxZnnbZeJwFKRxKqUgzDUamPlqxgQRe41n7AJ9X146x8tb9LS6j3iev0dawQ2OrxTGs4bh3zMEx0Rj69rF70WyNvGw4fI4/xXAn8PItTU9U1midQgGys9MMrz/W1vD4y+65z+XTsWcONOWx66KH8r323tpHvLAgm3oxFj/S530K6m5BiWwYIrpnIiSmgUMVAmc13lpkkuNdxEvjPXhLkafsVjd4c5/VZsfTF4+pqor79x5wfn6H2eIcIRUqSzG1BSXRuw277ZJVoRyYAAAgAElEQVSnjz4lSRU/+9lP+a3f/FdJspqf/PTP2a5ekKQCIQzWrDDWUZUCJUJ0bSIDHXAiPV4JiiIjSQJ9hbGiiWLVDQ+MwxhHngePIGMMm/UudN8LJsUUgaOuN8waz19ja1DghaDSJVVd47wnTSRShffW8s3kacZah3uVu7pZ5CxpmrCYFzhdY+oS8hknJycs5nMSVXB7u2S73pCmjkk+IU9zsnzCbBbcKIuiwHmDt4FKZbVaNUFUqoHIPMILEpWQqAThBd4phEoxGoxxFHlG/ldN0KdJyOtoraMsNyAEk0YABy5q2WWA91icC9njFYJKVGRZYHdrqXzTRIKpcd6RqAxhalZXJdvVki9/8TlpnlKWFT//2Qf86q/+KifnZ9y5d4ef/OQn7DZb3n//fb7zne+EqDkUta66FTWQr7lBmDlCoI0eUCAcKz3+On5ejpwObobuwNXPH41Kejmx1rDmmCfK65NihRMjQuioi+YQ597nLR80e8wGMbLgDAKjOg224ac56Nt4u36kH/v5D9r31+6QesHdxtsetjG2G4PeduEEnYeVwTQL03BROLrkdN46feyHGMBhbkDXoLzDGg2pREnPZ598yPX1Nd/+9neYTCbBYwUJssbrHavbp/zR//UJ2+0abbdkScpXvyjIJxPee/9X+LXvfg9rQErPz3/2Y26uX/Dtb7zNJ7/4mM32kuubS/7J//kJkyLF1FuEr3Aa1qtbsBWz6RzRJh6Sgl1ZkRYpiVIspnMgBDztqirArk6w3ZRsNjuKfEqa5I2ATNmsK25ubgHJvbsPEELhDJyengdmzERSFCExvEoTqqpGKEeqgjeRtjVVXVPVGus9WVFQV4ZduSFRwbsvSSWLxRxP8HpZLE7w3pKqDJkmOOdRMuett95jPjtBV5pnz56B92xWwR/fWRug5/WqoUNOcKYmyzPqsiZTSVAGnQcb/P2RlsUiocgm+NSTJflfPYy+qit+/OOf8u1v/TKJKqh0zWZdkqZpEwLssVZjjKMyIXdkkiRIJcN2ylvSLKOuK6SVVEhkmoFMG6zWIaWg2lSk2RSnaxIlccby4Qc/wwvQjX9rmqZcP33Kz3/6U4qiYLu5prY1u92usaqfMZnMuHPxkPfff5+qqpjNZpydneHlEchjz2FTRIa8/dK+us7e1fw95lJ4NJXYSF7WY2V8shwRvEdbGbnPscrO07ty0hi3j/mljC84o7uTVpjJgJ9L2mcTg/MvK6/jdr5PubA/fm5krGM2URdTEjeG5mAwDS64ysc4f2xLOd6fULMfz9aI670dPJOzJav1LVdXL1jdXvHxRz/j8vlj6rrm45/9GWVZ8vDh23zve9/D1jvWt0tur75it1mSJBIhNHUt2OhLpFTU2zXPvvocJQTnd+5Qbjbo8poPP3zCZrOiKlfUuzV1taEuMrJEYl2NEJ7ZbIKazzAGvPVoE2weeTZB2wrrLX4XxrmqdtTGIho6gzTNmRSS2WyOUo1nj7UIIUmSjLPTi+AyaW0DxSbUtUYaUEnwrlpZCybkpg24vCSTBdvtlrRIcTr4/p+cnLDdbgPNgncUWUGe52TptGHfrJjOJlSuQoqUJE0QomJSzLh8fsnFxd0gzIUMTKrOs1uXmFqTpEFBLYqC2WSK94HoLU0ComBtMMA65ygywNXU5Tr4/icT0vT1xfcbIegFcHHvLgaPd2WgK7WETCwtfakNacCUookUE81/YetfVZq6DiHB0miM9UxP7kDqyZMC5xWZAkmN8iBUiu4izh0qCRFsykOiFLvbG7Y3ButDbktlLQqo1rfUmzXLyxs+/fgjLB7nA//E+ekF9x7c5+23H+K84O233gpGK++7JNNWNzBQs+02xgSaXRpsFEPQOQMEocPsQDR2BCllQ/zmuwVjoFX6Hq6gadN5j1EmuMw1oG6/kMTeLI0QaterONcnPbww/hL3mApbT5T9a3yDozcZpjx24NkyqEqTjo0YepEDqddFiFrT9V02TKKOI5sF32P5X4epoSviuOfS2ArX0yhIFD0E2EWltmc9IXoT36WzbBc14wI3Smtct9aSyiBATMTJg4DUS7zQYU5JhTE1td7x6NFn/ODPv8/jJ1+QJQTPEitxpuLq2RdIBI+rFTfPPicVgZBrt9uQ5xnGVjjTwKpKYEzNi2efsVk9Q2U52/I502nBrlqy2azZVWXw0nE+0PEai5xPAIFQovGg8eAU1nl0WeEFFEWBKR3GG3zhKaYTsmyCNlu8D1GuWZKTzhKE8g20m+Ed5HnGnTt32G5qlApcMc44ZrMpiECfnOdppwyAw1owWGQGdVWjbfDvr3XJtg5K3L2HD5gt5sEoKlqvO0ddGTabEufCYlPZLd4He4XXgbfeOUeiinDfxnakkjCfiywjX5x08q0V9F560qLA1zWrXUmaJtTWIKzg5HTG+fkpeZoxn89fe8q+EYI+zVLOzhakWbAsSy/AW5yrg1Xc2IYfIxjHkiSh2lb4NA3CsGGrWyxmwcLuHWCxZksmPUiFajLmtEYz4RzTPA++08JBGhYW5ypcbQENGHxV4oTAu6AOGOMRUiFlECQByhEYp7mxNZdXT/nJT34QXnieI2XCxcUZ8/mU+/fvk+c5xXTG+eIUKSVpHryI2pffuurZxgYh8Lx48YzJJGgSaZ7hfYhUDKyAdBTNojWA4rroX++DFqlc45IYYctxUnOg88HsYBc/xHiPp6jbd68krCSu1zSdGEIrUihc62HeUidEl4YlKMGLZj/UHGs62jxbZHCMPXQEhGRtDt+E4Q8ieVtjZQQbxTwvXTsv2QkdOzea2q8LdGpYHju33cMdoBAB0GnzDzvCFl4StvHeBrdf4VxwYm3dZwVIleKcwXqHtRUtHfajLz/nX/zLP2U6S/n0Fx+EpBsy8PZoYxp7lKOYFKTSIX3Z2MwsAsudixMeP3lEQhB0xtVIAsWwxCGEZre9xegtWpfkRXCUAFivN+ja4SwkiUUqx6KYYnSFc5ZEplij2ZWBd51TiROSRCWkSR4UERnUEWssiUqQzTfurANvECQhOEvKhlxsR7ndUe521LWmKHLSNGU+n3Jzc0Vdl9y9ezd42SWQZSnWwNnilKKYoLXm5CTkzZVSkqUSXXu8s0gcs8kEMJRlhVIJdWWQCrIsJCMxtcZUptmNBNi37e9ut2M+n1MUBVkDXTptkEKQZYHVcldXaGtI05ztZsX5yYKLO+dhoUozzhdzlAThX0YdMixvhKBPkgQvHV54VHCRxdtA8FVWG0IIqEPrCiVStqt10GxVgsAilWBxMum8aUTwkESIukseUO+2mDaYo6EWrfySPM9IUtmtplab5mM0CGcRRjdslQKta7SpkUmGVBW1DttFJdNgPK5T8ukUbz1ShQQBVbViu16RpJKPPvwAmSi8E+RZxnvvvcdkEibWW2+9xfnF/0vdu/zamp93Xp/f7b2s+76cW5Wr7HJsywoINTBCRGp1q0HMEEy6hWgmSP0H8A/AtJnAAARqMQkwaEUtMUItdQONIKMmIRGik47t2JXYrrjOZe+9bu/ld2Xw/NY6VY6dOBItOUs68jmnjvfae73v+/ye5/t8L7cAGAPOWunE/MQ0jfyjf/w/89f+2l+lFMWz5y95/fodtzf3LBYLipKbsuRCLolcvb4vm/7Zj+giXOiLUx9FhPLauPp5lSvi8+VF4+VAKaifx8b5acyjwg+XQysrfe1qtbLVBlboOIJHC+MJKpvl5w4OX4aqpLhnck58Uc/83g1Bo2qgpMrSJReFiNe4sEsu3zPkP0ND8KdfP7vQ/yxY6WLx8OXdx58O5fjSv6WagtXDIYwj//fv/BZv375lnmfu7u74tV/7NeF3l7q/KhXeIGKMZ/ZHwjzzh9/9Hb7/3f8HAD/PpJSwSqFVwWrp0pe9uDUaCutuQSExlIFQZLTerrakcLmGBdM21NqKbRSxBGY/EVIkTmfmeeR88igcm/WSGCPDNKFJNK6j63umccDPsnOLQZGi4nyecZ2hXy9pKuslhIBCEqVUtfdIwWOMo2tbmXRVIYTE+TShleTpWmMwnaZxjq7rIBc++erXaZrmCgvLgSaN4vl4ZNFvub254dNP/5jlcoVzDevN8kpnNFqLJ7x2PI5HnDPvl/1Fi9e8bQl65t3bB6zVAv2gcY1Aw0JNjSSlah0TYdo4STau954QAtutZtE5Xr54Ttdb+r7FjxNhOtMtF6wWi1/wXv0lKfQ+eF6/fs163bNeLckp0C5aYixYK5QqweZ6soIQUk0VCkQyfdfjWsdwOuO9R2tN2/dorRjP+4oDO3yMspHPDsg4Y5jOknTjnIxW3guG2HcdKcsFkU5fGt6YMjr79wsvZYhlpESHcR3Zg1ZOqJYJeqspJVOiJ8WInyLWNgz+zD//vQcSBYWwE1JKkkSlNX6aKGnGNYbDYY/Rmf/x1/87Vps1x8PE177+LayTLv/li6+wXm9pW8mp3G63GG3RWg6LGCOUyHKpaDNArvBX+dJi84s+NJeJ4WKcpJSimPdYeimXZKcCF+qYce+XlWi8j6DhfD5yOg9M08TT40m+HyQ+bblcst0suL+/F/qqeS/yUjW+7bKYKuVibnYJIpfCrJSpNMWfYcamZA65wCT58t8ExP4pSuF7KuKf19lff/afev2s8m+1u7JoMlwZXV/2jrlMFojYLGWhTJbMm3dv+cmP/ohnt1tKnHnzZiKHif/1H/1Dvvq1r9MvNvzKt77FNI3M/syn3/99fvjj7xH8kcPhxNPDEzChVY/FUnJBG5l4Uoi4rsEohzUOg2IaE9Zp2mZFySN9t+bzn7xjmoS/3rSWu7sd3aJlnkcOxyOJjDGKh6c9CccwTIRZY42m71q2mw3vHl7jfeTtmyfazrDdrIhA8IFUFCGBw9DaHpU0U04Y06K1IsaAQqGyEdfGLM+taRwpFrTSpDCiClit2Oy2PHv2jG65kD1fFivkvu/rYZfxYQJkUogx0nfLGtK9YbsVIZPR7gof5wzn0yjJUb3Dh8jkZ9brJdY5UlaMw8xquaTrOj766CN8imh9IoaMxsqcqhXkguveHzQXrD7GSNM61psV9/f3kCK325XAxxp2z25ZNI71akVr3c+4237265ei0KeY+KNPf8jt3YqvfvwRTWPxfhLaVgkoC9ZcOkHQRmNq9KBGeOzTNInCrUSM6fDe07aWnBMlRaxTGFWIZLRRIjVO0inMfiYmeRittaLARQ6TcZ7QCNacSiYVaGxLDIKla12x6FwoKVOKBRKq2AoRUcMqMilOnE4H2ranbR2nk1i2atuQFmvpMlzLaf/I8fCEn85Yq2k7Q4iJMJ9wasnNbk2aJ4JPkAuffvopWluMFu/qb//qv8RwntDastlsUDrRWMNitaYkWfL4KIueRP5CuPSFwSHMoi8VPKmW8t8rhFYyYgdLISdFzGJZUEphmmf2+z3ncWB/PHI+jyijiYFa0A2DH3i3P2E/S9zcvKOUwnqzlGVX09A1DuuEchZCoLFO2BOouk/4wlK7lOufv8RW0Zefqf5Z2vsvYfe6UH3QueKoX7SX+FkL68uU80W17c9d0H7B1kCWtVp6hLohubyn3CdS5HNOfPbjH3N7u+OHP/g+fj7TuB3vXr9mt9lgjOGDFy/47u//HqZdsNvd0LSG8Xzku9/5PeZwQDFzOh4JIcghHBJKF5yV63v5PlNKpJKJ1V63xMIwD3z88VcIKWMaB9qgrCPNE/vjkdV2CT5XyKLBJ5l8rWsIcyH4zOQTcZ5JSdE0ltnPQGG7WDLNRx7eHei6RRUeaVIqaGVZrreUFBjmgfMwkrJAFK3rxNaYywQjTZK1wsp7dvs+c1U8bDST9+QgjYVWihiCkDmMAWNIlVZ6yWWF/KXJ95J/2zQNKQVZ+pZC38s1yPH9lCsNlSLFjFUa6wxZF9abFaeDiLvk/ijM88yqt6AKRsN5GqrVQuDuxQeM48j+4ZFXL++5v7+lbSyrxVIm9uCBjK/w7i/y+qUo9DkX/OQ5HE6EFMFXnHoWSfWF2igb6MuHLoueohQpiJudtgajLF3X8fj4hDE9VhvQCtsYTM4QawycijhjKSXWRW/CGEPXN5jVksPTnsPhgM91ZKvQQNN1xChiBmtNXRAFwe5zIqaZOWTaVrIiF8uO0c94PxPDLGPZfGBUhRgzIRW6rufxtEcpw2a75fXnn/PhB8/543c/QveWhOU0eJwxfPbjH9G2a+azx/Urnp6euNk9p+2W5AqV/O7v/L+g7XtqoUqQPdvtVhgP/YL1ZsXLly+52a3AGEpKwqwAStayRFWZnN8bviXeF7WcxGYghMDxLF168LJYjjHz+s2j/F0W6KToyxLSUCiiT6jXJhXF64e9mNntD2gNIcxo3dAYXTF9KeJd17HuW25vb7m53cp/0+Ktcnl9Wfn75R5b8QW18GVSUKo+0Io4x2sITtM0sqf4KRwfLseLev+rHjSqJGkuvtjtZyXWuVoOxVKSwBFG0ocu34/3ns9/8hklRs6nE9Nw5g9+f5JlY/Ic9w88f3GPn2YWfcsfffp9zqcjNhQ++/GP+OCDl7SN45uffJ3/8zf/F5SLjGNgGgulaErylKwwyqApxBLYrBe0bUfTWB4f90zTRMmKEGce9094PzP5kcPpwDAMtXjOzMFjXIOttGdSYPKzEAVy5HA8Y3THenNLqDoZay3vHt6yaDsa16HIQp8ukgcxjiPb7ZrT/sj5fOLpfKw8ekspiWW3oTEti21P3/cUra7slBgjOrvrweOcwU8zXdNcJ0iyxmnZAyYSIST5fX7f1FyuhVxDKCVSlJHdS8oYq8hzZL1e8+GHH3I6nTidjxiT5DkpYlHQGIux+grNrhZG3DKNQDDjNEgwSd0r3N7e0vc9+/2eUorAuo3j5YtnbFYdJSRC9HLQ5CT7ib/A65ei0FOg79Z0zuHHiOobCokUIimHa4clcVqFXL2fnZXRf55nYvLV8rMQ08Rq1ctFrAVpDjNXx78sXhJzhQBCEIpVyprDIYDRzGEmGygpiPcz8qA7Km6qjIQEk4hJDos0JXI+oLVlGiTUYDjt0UYYMylHVI7korDWYUrCGYsfJ1IpDJOXjt8a3r35nLaRwAc/BXS9uCZDSTPn41tWGoxryOlMCNC1a0CDkVH3ah+uFEY73j3uUQbePjyh/0Tzne98et1PtM7ylQ8/4OOPP0YZwXGjD7II1JoYE1OoakVtGYaB4/HI7COoTCrvA95zUcSSa8dfMcxcrtTCS+G8RLapTHXXhIKI1JTuKLkQ6rSh6uQ0nUaOxzOfvX4rExOFzWbDYrFg1S+wTrNYLK7dk6ZcnRhTKeQMrkr4Y85YZSsDoiVmf/3+rgwJLTCfRhGrijEX8bUxxhBqJ36BkwgJHwOoS/Eo7B+fePP2NaUUPv74G2hnKSpTgqr6D/l8PvujP+awf0tjLMNwZB4HVIkY2zOMJ0IInElM08Th6UHgBGRBqGImz4GkCy9ffMC//+/9Lf7J//6POTx8Wr/3CEnhtCalSL9a0DY9TWso+b29g7WW4TzhfeTh3YHNdoWqZUIarETXLdg/nQihZbfb4P2Er52yRtFZw6prGc4R4kx3IVlkw4u7WygFZxzL1QI/zXz++WussiyXS7puhW1ayhC5vVnw4sUzttstq5VY9Po5CgQaE+v1Wpww04RCqJVyBzlutrec7Vm+r+LJOaKdxlhHmBLZSw1AvXcQVRUzP59lkayKwEoZTwpz3Q9AjhFjHE9PB2L0kMUmwjWG4BXDcc9qvRDRFQ3no6hew3xmveqxC8f9ZsXLV/fX+/TNm9e8ezhCNEzngR/sv8u/+lf+FYbhxDAc6JwcqKF+zsK//0tmgQDQdQtZQAZF0wgOa1Uhp0iY5YdTrSKqTAgD6/Ua1SseHs9f+oFzxe5yFvrTZfQqlU6ltQYtpk0xlkq1k+24qOs81lq6vqF1Cquaq6uhTBVarFErYySEcMlXINXu7+JbUbKoba0zaKuu9DDnnDAoSiHEwDQGUpEbKBaPaiyUhugj42mkbUUPYI2uotxMSpGnx8/5+OOv0ZhMjhNz5eAZ3Ql7o0gXmVUhZSWC/hoGnoshlIKfRe49TBPDD37IP//OD9ht11d5uVEa4+QBj4JtXD/rkMVmIEc5LMzFLA5DVqVi0e/ZQaCEMXSBOEohloLBCTym8nWBq5RCaU0S2lC1IEq0zmG0COdSCjgrTn+Hw1GuUWVldV1H17Xc7DbknHn9+jXn88g8eYy7FJWOrutwxrJdbQUWMoYQIzGKrsJaK790XZxpjfdVmVlm5nnmPAwcDod6MOirSVbOkUVn2T88EIJI8lvbs729o1+0pChsi2maMArWq57Pf3Rg1lJwcwzEMNM4UzvXwDCcAPA+Vp+Wmc39C9pO8fj0hhgDx+NbjFGkkJkGibM7n8/c3d2hSqLvHcYWcgnVwkOw4mEYeHzYV1hEFKJNa4kxo7VYk8hnYLDGkKJQJ8dhwlqLMaouxTOr1ZJ5OlCyHHqb1ZK+aSk60TQNj/snfvLdP0RhePHsGS+ev2K5XOPajuVyyX6/J6RQSQWGtlmSosbPZwyaxrXobGiMQ1mIMVzhnBDEntjPkZSjPD8IKpBDpLWOqUg61xfhNmOkKRmGkxz0OZJSJOeLRZ/EXKaUsE4xVk57qY2OKZqcCzEn5nmWGiWcWaIfWC46nt/fcne35e52x/1uQ9N0cgDpxNvXb3G6keZlveS73/0DmsZye3vLbr24QoWbzaZOhf8CwsH/Rb8uKeld19E3PTF6XGOIUexz+35Ru++Z1XJDKZmnpwfp7N17mOLCR5UCrOsyVVz4lCo426BqNq1zmjkUUpRu/fWbz8k58/L5M2LIuMbQV+c6W1kw8zzjw4gxjhACJWdhRxuD1fJwXArcxTjJBxnVu0UrQi+toaiKLxqszszDyBzD1VfbjxNzTeMxSmOMqIVjLkze0zqH6yzT6Yn94wN9v2b2qdLGMrlonJawBGOqL0nJMlWUgjKyd8hFeBpKGYIvKGV5PApneU7S2aQsKtyrjXG1qGiaRlz7jBysIQlX+QK9wHsdxMVNEhSkihMrKClKwUmVo13hMEohlvie718XpTlVh1GtMMqSUxZ/oLoTKUoi5IZh4ng88/btO4zSVww8FZinzGnYU9gLiwJw2ly/3wvmKwv3itdXz3OA0dcCVM+8eGEUaQ1JXxsGozPTac/x4TUxzRhj+PEPv8+7B+nuhQQgPPeb7RpVwCihhFqlJcS6/jwpVafFMKO1Zr1ZEnyk6w2v3/wxf/KTH8j3EgPr9U4miacjbSOT7Wq1wqhCKgGlJRbv4rUSY+Zmd8dHH33EcrHm7dsHIST0PVpZIUMUMTgLQfDy3c0Ca8AZR3ILQgikKI1PQQnnXhWCn9jsbvjwxXMMhW65wLUNL58/Z/z4E/rm8kzIYWRMZJqmethbcg6AKFzDHDHKksplMW+xRmDApBIlK2KSXc40yVSKEvpiv2hp25Zzyng/goJXr15xPp85nU5XckUpRdg6GkKJ5BJRF+JBbRpj9FAUzjaUUui6C/tFYWygVXLYHY6P9E2LM4UPXj3ng5fP+eRrX6FphC1YSqLoQggzqUSev7rn8eGAz55xDLTOEOaIpjB7zzQLdHZJN/tZaW0/7/VLU+hzEKzt9esHTqcBpROrvmO1XgKZnARiaTth44QQKAVWK6FXGSOhvVqJeCJFVTurRN+3khRvK/tBG7yXjbu1Ikl++/ZddbvMtes2aGVrNyCFSzA1S9v2KAyLfkXfyeJ3HEeGcaRzcvFTSijT0NiGogpBTHMopXA+TUzDjFUW51oa19L3S3SUgy7OU/1UlFiyIrTDlCSHUg4zSEPAj4Gma1HF0TmYh7ekbiJFKdooQ9N0YOoiKGeUcmQ/Y5oGo94vtQRlNtIVO0tBDs2ishwGRf5NSZoweuZR6GByAOSrTYXW72PkdGMBh9HS7eQsMMNF3CbXoMI7JaFr15iz8I6vE9mFBVS7tq5r6tdLtcu8LGNFb2rrorOURLw4WmpD4xxESCRiTsRUoSmEHTX7AJ73D1IuWCfFe05RcOGkySrBRZtQrS9iKlhtKSi6TkOamOYJTUDj0QXmAabpQNs6vNYCe3jP6x9n2SfJp4ZXtQCkzPl8JpWZmAIXxtQ4VqvcMHI+H65eKV3XEf2IUorWOZ6CSPW10RQCSkl4uHPCXlkut6zXayhSpGc/0jQWayorJkbmccJP83UjkRO8/fyRkkc+/vhDGteikkxRX1STuq+1jOPI1z7+qjwj52MVeVmU1UyjJ6fEYr0WuC/Ww/QigitCkA2zBI4InBbgsiPKYptsNEKEsPJsnGdfU6YSOXpM19D3HapA33VoDIfTkf3Tk8QJliLTVZ3IrdKoXHDaUIyEmMQYK+XV0HZiXuYay263I/qZx6d3MiG2Dc7V6SVH7u5u2CxXbLdbceRMnnEYq7I/k3OFDvuW9brHe086DvR9x3a7qdGDlrv7G37wgx+gtb7uSv4Cdf6Xo9Arpei6Tkai2vF2nVgLz0FO4L7vadoWow3n85mYPF3XMpwnuq6j1OJXciGlzHAa8EGCAPq+I6aJfWUVKGuEe60UrXW0jeHu+Q0lvReBtJ2jby0heEqRC+19xGgwxsruIBZSEvGUs1yVat5f4r4C3se6FygoqwkpoYqha1dXfDrUjv/d4wNd17FYLOpY+b4jDiEIPT0Lc8OHQMmRpsnE5Jn9ma5rMFYTj2+gWHKG4/HEi+evuL17QUqJMCdQTlLuM8SLDKroq4Co5EKMSgoEWXYJtRheoBiUsB9KrmHsSomHSrZ4CpYi01S6OO+5KyVSUWiN8JjjFbevS/KCJC8hMIyqfPJYO/LLzT1P03WB5rQi6nj9s1KKpHV1zpT/T6mKRqU0XSNdYUiRmNP1M03p/Q6hFF2vb0JXkZI1WoJfSqkWmvL3rl4jYw2qiN1G4y1fYc8AACAASURBVKSTjGFPyRFdO3UfA9Y6fJG0IemQZeltXIevi2BCvi5qS1EYJ4VwGE5st2tCKBwOJ4aTPAuLVS+H0RTIbqg/R+Hu7oZYl3izH+k6x267FgOw85n7u2X1jhlo25abmxt+9KMfEUMBhFnS9y3eTyy7JdvVmq4TGGG16ug7Ybf0fU8pcDoJnDENnnGcaGzHOIqdSbdY0WiFbRynYWK1WIPK9H1LiJmUWnm2UkJzUcCXq4pVfHsy2shNIAdhIsZAKZmcRZWqW1Gqq1LwdSSMXpoo5zqm8Umucco4KxOLtZZWS+1pXYNWYnTWuF4gnwptGe3ol4vKglJoFVitW9pW6JjLVc/tdie2Cs4yjwNdp5lnSZ/y84jWmnme2SxalMksFgte3j1j3S1pVEuIP2G3kwM4Rc/N7p6279nd3l4nKm34y9fRq7p5HgbB3vu+J+UoUMkUhLplLavNmmE4cRxGIGObhnkYadueVBQUc/0gYhKOeNOIQMF7cbsDZGutAORwaIxEiCmjaJxhmgJKiRQ9RtnOlyydnJ9n2rZFFU1QCWM8IYiFsnMi9rlgnHmaUQXmOBNipukXaG0wRqaElBKx1PzHk7BNdIEcPNrJ+Ccju2Co4zBh2wbQ0sWVSDkXVqslNkHXOwk4CJ4YAlYrFp3i8eFHtA3sdjvevf4TMoa+X7Nz9+RoyEWKixRWhbqo+IaZXPxVY4AyYA1GO7TuiNmAsrgii1RjhKWgshxmOQSCHymVwdI0DqUKKF0f1oxVl8IqdgglFqxzgEYr8MqQSSibsVwMoGTXIUtWTdG5+vsoYkwiQdeKojVWKUiXQO0kGzYlqw6LwIKFgrpypXOFBUzdQcghlovCS+4EqnKqxSkTQgKbM6Zm0joVyWEgTgcMAa2nKykg5yTddfGkVKBEgVByIkQFJZCLo7GGoiIxB5F8jRkfBqb5SDtrlNcMw5mYBIM3ypJzBBIpJNF8xEoSKPU9L81JnVovO6NxGIjBY5QUoOgDi7ZjGk5st1us1nzj65/QWPFqt7ap7B1pBg7nE+BZLBYY43Da8fLZS374wz+SXUCImMZVcbMcxJLFnLHGUmJBZ0WjZT9SchZYU2cwCp8SqqSqMFY4bSp2L9TpkgJGgbGK29sb7m9uefv2Lb5Uum8qqFjoXcNpHHCNwj+JLXrOGas11mr6VozLSvY0TuC+zWZdl9Wa9aqvU1CDLgN3uxalMn0Hy7tnFDIKK/dfTkxzYBxOGKNkkqxqYmEfFKIGo2QKMcoxzoEpzZCLECEKLKooyhSNU0LHLEVC7tVfIKTil6LQa6UwVhFL5Hg8ioFYSdfRW+nKwAmCVx32p+pY19E3HTEIxS7neF12tp0jJcFKL5bGV8+ckPFJBAqpKMY51OWeMClWqxWlFMZxFMpdEMxdEp1qQrzKNI08XBfvnVQdJiOxcpcNShmcVmgjUIpCM46elMKVpdI0Dev1msPhgDaF7W4DZE5FghdkiSmjtbKuUkILOZdrJ6WKpjEN2+2WlAPHw5lxnOnahiF5Ht6+4Xw8iDq4GMbxXN/PMU0Tu92G1XJJzpHNckHjNCaFemPNAtnkQgmKGTC2AxqKNmjVorQEPcxeAmIe3jzUG1hfp5TFYoHSWbq2Cj1csOrL68Iq2O1ucO0SfxgpRgt0RPWXz+857IvVgvM4kGO87gHCJIf9hQWiUfIeuS7Cqw9OLhIikXOmUZddj1yTlDJGG3Kl412KYimaC3My1ynDoFBGM8YguHya0OlMnJ8oaSRMgs9nMjkFUrrsK4QvLwEpilKCBGCEyg4rQqkLPjKnCe8HrNMM84BShjnOtFbunRgyw1CNAL9gaaG1xmo5zJ1z4gQ5jvK5aEuptEdrHMvlksPjga+8+oCvfPjhlY8+jmca26C1ZRxnFr2jaRzOSadPEXV4TuJVc3HlvIRlXFi+OWecE6x8miZKkdzVcTxTinDtQTFnKrVXPoe+7YSZVMqVztlUSNV7yZe4eF7lEOn7ntWy5+nxHY3ryElUu1pvub/Z8dnnrzE6Sw6u1mhE19EvOvq+JwdP10ic6W63pW17IRsYVUV8oI1id7PmdDoxDAMx1RAUpXGN+FEpVXjzkz8RV1CjiFHYWn3Xk1IkhlAniomcZYIJs+fVy+fSXGmBqF1V9q7Xa968eXOddP7SdfQxJZ6Oe168elm722OVPUPbGu53N5QisE5MgfVyeZX99/0SssLHcB3bc4lsNmu0CozzdE2nUUrsB+a50gQRfMxPk3TFxjFP0iEJ2wCCz8zzjNYGZ2UfkHMGU9DWMAd/5fJiZIk3TROHxwPO9jSNON01jeXd4xPBF/HpqRdKNviazWbBh69ekrPHe8HgFosOceErmJiF1pcVp9OZpunomrZaNweskQdgngM5DjRW023Fq78kR4qZHL0wLFRiOD2yfzqx3C7lBpsKh/hEjJ5hb1gserrW1eWqLHOdteKNEjJpHshYilIU5YipoIxQ04Kf0EWTQqBoTZo7hnighB6rxVvnYi09TZIXemHBHI9nKJrHd5/hmp7l6oZmsZbuV1m0UldbiwKch5E5xitbp6gvKHZLoWRB7SMZoxBv74sZ3lXwJNYDwU91cW/RdTdCTqiSMRphaxlhdqmsrk6VWQnTQlWPGV0mnJooeMbxRPaBoFVd5mZipV9emDmpKr1TKu+nQ9tibUspiZQ9xhR2N+urUjmGTNs1ONXg5yj6DF9I0WNRoomwClM/67btmZqWYRrJURTd2/WOzrU8u72vE2tit9rivWe72oopWIhoNMkn2kVLLpEcI7pb1MV1Q99Ti1FDjp5SEt77yjqB5XJ5Pdi0rjsCqwk+0Cy6Sp7waKWJBJzRkmpVRNjUNRZVCq5xzCnRtjIh5hJZLbekFAQuTYm20XznD/4Zfbtgu94QY+T27hnOKr7y4QuOxyMqB252a7S29H3Pbr0C8vV+P54iXbfBINes63pilILb9y0+TLKvKYUQDKU4jNGEMOOMBCE1jRA4XGMIcaZtNDkL9ppzump9nDa4VqDZXJvU4/4dH3/yNZRSbDc3Altbw3a94nw8sN/vJWXq50Rw/qzXL0WhBxFBLRYLjsdjNRaKLJcN292a5aInxNqVm4bVZnPtsI7H4xUXvL3dEWPktN/z8uVzMIm2ceTcAoqcuYoUnGuY50CcPSKhl45dFquezWZN1zX4MmOtqxv8TIoJuHDL43UBGWMmhkRKEyoLBapteqE5YglBPKiH476yKMR4LedMLjBNlsWyv3Jl4YtLTlHxDdMR7wdyBu9HhjJd/UacKYxVYLJcGm5ubnDOoruerml5eDxeRToxCvvGuMz5cGC56glKoIWUAiMQysThmHjx4gVFibPf6RSwtsFoh6IQ/MgcPPM4YZuW5WLNolNkazgdjqgcxU5Vz1W+Ltx6yMyx1MLmiSFzShOqbDju313tF5R1+DDyqv+IvunQxoDSlHihXcr4rnMWilsWDL5Unx2B0TQlpxr9FiEJDCaMocQllcqHsYqgNCVLNB2AqjTaoqWD18WhSpEtxFUTpQhJ8hAanTAl0FoY5xE/S2EtpcJDtSucZymIFxaHkAtUpU9qjHH03VImtgyBcoXYABpn+fzzNxQtrJNhmAQ60pdwEUOcRpwzNFrhup7VjegNnLvw4Tvm08T5dL6KxGzXQcqEWSifF8FcSVXDEgPKNeI7k4ss7jW0zmC1JItlQKtM18ph/vjwWmiIpWD0Eq0ybWPwM+xWHfM8k6xhu13ia5jI23eP9KsFYfYs2o5l14tBGNIYvXhxKwKsd29o2w0hBIZZnG+f3+6+0K0rdjcrnCmUJHz4D17coYzDR4GyjM70rUPri/guU8KEck74+QZKzhidoQhMpJDdgLOKXP1uYpCFrdVgNTRa9BlG6esiOybRQWzXS1bV8uCqxM6J7GemYeSDFy/r4VVonaNrLDlqtuslJUl9uL+//YUr7C9FoVdKBuzP/+Q1wzQJrUtbjGlZLrbMccSYhq5r0Frz9PREzIll3zPOc01znyrUIio515jK9Mj0XUdBcT6PHA4H+r5nsVjhUsau1mKGdBZOtIzniWn07G42dURtruo7pUT2naBi/hfb2AJKKF+Ns/RtR4yZpo59qMxC9ahbRfSFN28fmKa50jZTXfZ6lpslvXWyyIviIEjJhFxYr9eczzOH/ZGcK+XQSIe8vdvQNJqcE34uHPYD2shkoDA0TUvOkqgTQsI4WzFVxWa1RNVOqyRTbcAyYQqcTke6rpPdQuOIPjIFORx9lOXi4bAn5sJmM7Hb7bDWcnOz5Xw+I0HTEWMtVkuAd4wShxZzIYaCDxkIGN2QE5zPB0n7sZrVasFwesd6syNnT+OWhKTElMtQH5JCyjW8PWuuES1F4ki0jng/8fmPf4zTwsfZbrcYKzRXWbZBUQnnOpRqUbGylIoc5rrI4apyUw/v5tpR5ZRxdelnSiaMA0tbeT/FoBvh4xdkQd9bkdPPo0drOI/DdcLTdSGYUuY8HCkk2taR54w/ew5TVTgbTaNE3GS1I44enzO7rSzx7u7usFbRt455HlksFqSUWK9vpJHIAYdmCBGrjVhK5ILVBl8yKXqcNRgjylI/y/J5vZIp1SlVxWgJlQONaXFGkVW1FcgQfEQRUSVzPu2l628tjQGcpr/d8uL5LU8PD5RiuL/ZVqGdZv/wiM0J6wyrrqFpDDkkljcCq3ZWsVg4Dg+ZZac5p0hjJWRFO42z0BhhpjS6sFiID9RZKZquQ1mLnuE4HzFNL2MaGhRYLfBt3/dX2CSEhnEcAY21CjWPWBTL9Qa9kebsMT9WIoY43Ta94ysfvpK/84Hnd/fC6guBkt6zk6DQdwumaeLFixd861vfZtEtcUZguRACfp6wRrNeLSVdqm3p+/4XrrG/FIVe17G2FMVqsUYbwziO9N2GeYoY1zGMA7O/KBQN+/2B/dORvmu4vb0V3r1zlKJZLpdVxh3ec9a1WCjc39/jrEVpzVzl7jEmgXGQcGmlDCEkHh/2PHt+exVkaQ3WiU9IQjH7wMUzH5Bxu1K95lkgoHEcUToxjoFU3e3Imd1ux+PjY+Xii0Vr09xxPo2EELB1M1/q1++XW7S2HA5D7QKkmzKVn6KVLIFi8jRNdeychbXR9wuUEXXsarXEOFtzegdS9sL3XbQ0ymK1xhlDzgrXt/h5ZJ4GNpsd1jqmMBG97DmUkbQdqw3744lhmvBv37Lb7WisJRWxJrjg4kULjhqyWCMEn5nGSFKyvF5bR0Jhq6OoqaIUXRkXTbskhVzZREehnlVjJyULHYSQp96zNVIhx8hp/4CfDsw1P3y9cBjqkrnYuujz5BxYb1raVozuYp4rNzyiZ/FVN8bRVhuOyc901e7aGcv9s1ecnwzD6TVGS0jGFDzzMNBV07mcpSDJJBgrNdgzTVN1N6x8bntZsBqW/YowP3E6HOnbBdnAzfaW53c96/WG9WIpOacV0nRGcTrvxUoCw/k4sN1uaYxFZblvShKrDmOMROlpjbOazXqNAVxzYXwpNEtc55jngDNiZW1UwXUNo/esFl31qCo41xFiS1uDMd68eYNRsgPYrpZ0fcPju7ekEFm2LXm9ADKrZYtShnEKPLsR5so4jiz7VmARJ7TokjOUFdvNPfP9jRgRloKpWhENOK1Yr2V6MLY6smZZhra2Jdf78jLhXz67nERTYoxhvV5f4am27a+7pFIK8/iGpmlYLVbi2zQMfPLVr2OdZr/fSwM3e1FrrzfX3ZM1lr7SUJ02lCiQb86JD16+rPTSRg5ZDOez0F0vYi4hNYjR436//4Vr7C9FoV+tNvzd//y/5NNPP+UnP/kJ4ygLvbZf8M1vfpP/4dd/nW9/+1flA66Y+Hk4kepi6TSM+NLyeDyxu1mxWu04jQMoMR4zxTCPI64RRZ8sFuWCPbx9ZBw9uS7yrDa4xrDebQk5cJomlkpDZeGYLIpNihIXyqq2XC6XEkycC0YZ8b9JHu0s2hSejgf6pqPkxDwmcjHvjZS0o+mFTupcU6EHodRRBL8bhoF5TkxjoBRDzp6iDArRARzOAxjxC9nZDbZozueRaR7YFvHjN8bgSiL6xGEY5HAl07a9FO6UyDbj7EUtKD5mpWj2h7Ng4FkgrjlMHJ4GdrGw2a1ZG8NhfxJYI3gmH9AUEUAlUQ/PccZYRQngVMdi1ZLLkclHhuGM4Rlx9lhtsEUT5kyYxTHxcBrQ2vLRR1/l+Hjk4eGBU2N5fr/DOUNCsHejW2ISjrwsIi3H4yPz6YAtMyEVVEicnt5y/+xWLLHrFIjOGNUwHs4ckwLd8Ob1W7brNev1mkY7msZSVKLRM8oUGp1JeaZ1BvA8fH7EGc12c8P9/V29lpfFWSLEkdef/4gwnugazRAz0YsH0mq1omsaTC1AShms0nR2wbJfsFtu+Rt/9W9Ua4/C4XDAYHj14Ud8+umndM5BRvYFSgR8pf7y84w1oEvGIL5GIg5y4j4aNYVE0wj8JQpxyWw9nSaWS5kIDLDbrmiMk3ttt+N8znRO0TSiwlguW0LQbBayrL3d9ByPR4FucmDplpwqyaCzDt2tMU6z6lZyMA6f8/L5DdY1TFOPqWZ41tY92jxzs95AjDx/9kxEiiXSJYkeXSwWrFZLlsuOWGNAxUZDi2YAYeXFGLnf7WSBXBureZ5pmx23mx22WDrb1esHbrEGZCq1+qtM88CqX4jQKwNR8n6tkgmwINTpy8FxSZpqjEVZ8ehyxgk0mkRNK4QQzzRJI5AVjON7P6RSEuM8yX4rzPyirz+30CulOuD/ANr67/9BKeU/VUp9Avx94A74beBvl1K8UqoF/nvgXwfeAX+zlPLpn/UeTdPwb/zav8nHn3yN3/iN3+DxsGe/3zP7wPe+930mH/nhjz9DGY0179VobbekXWwYxxFjQKmRtjNoFdGmME1Z3BorSyPFgm5NXaiWqpakWty+52DvdjvBRofEsipyLz4tIQsPnKLRyoqQqSQOp/NVJt87A0UM02LMmLoUnHzEaSOe9LEuUoym5Mhms7l26lJQxfNFLEwzxhSsaUjpIqbSXHx7jDFM43xlHM1ToLQWYxr8fGSeAl0vHaj3kYt7ZKm0SOsc4k5fyKkQcqQoSOnCihE6na/im8v728rYaecGMvStu2K/43iusngxTMslkKMmZzE9y1kOLa2EiaC1JpE4Ho+slyuc67l4E5UCh+MTMYgFcymJnGZOhyee360kTCUVcsy4LottWo5oZdA5QppQWQrdfB4Js2dwmeR7mSyiUGaNKhitq6o5cjw9En2k75/R125cnC813g90TcOia6rbpmOx6JjnwDSMoDyl5Oob41G6kFLkeHiLn85XVkZjDXa5YB8DrdEsOgmXgUVlj2xomwanHY1r5f4pYg+y6hcMw8R62XN3sxU4rPpBUQLWUDtUzWLZsOwdWmVsYyhJmqDlqqtGWSIMg8zoZ1oj0F7f97xWIrSafIKoud0sK7EhsOgbzoNHaUNO8p6KQNfKrqTvloQQOB0e+fijjzAourZhuWhZ1TCN3c3mWghnP9M3js5Zum5BXq1QqtJpk3jZXwzSJMwb1ouezr3iOIllg3MOZ4VR9N5iWJ7fbiFYf4lyj/d9L8pzrYUeXS6ulVEatyi+91oLfTPnWLUTCtP17/n41Xjx4t8EF9ZR1WJoyDFJJFAlD+QoNFcJiklXkZ6gDFHsWbS6WjsIC+m9rbX9uZnRf/r1i3T0M/DXSyknpZQDflMp9Q+B/wT4L0opf18p9d8C/zHw39T/fSylfEMp9beAvwv8zT/rDZ6e9vyHf/s/opTCfr+XxSdwPJ7xs9Dm9qdR7FJL9R8HUObqP6J0oTDhrGLRyTbdh8Q8nll0LdpUqXqSZQto/BwJodLytKZtO+5v7wDhrIcQ6hJWDgrIWCc3jTUNwcvY3VtH8IkUBf/OwaMQBdtiLWkym42u2K4hJ2HHMIKKsri9mKvt9yKsiEHgnxjF0+TVqw8Zh+m6hIaLVe4lqEChZ41zib6XG3AYJqzp0NoxT/F6s8jSKdOsO2LOHI8j1mpSmBmGAaXy1SpYKYUylpLBzwVXf/6iLTc3G/wcmaZ4tRhwRtNYzc3LZ/R9z7t3jxzGgRQhpUDOmuAjJclDVyps17Zt1VC0xORRpROFonXSrXUd2cEwnDAlo1WgECjxTFGKMMmkp5tEpxS++MrRVvjznjRPDKdRWDjR05sVu3VXIZKVYNhFGoPVakMuhnGOQMvpdMJ7gQaNEvFW37Y01pFi5DgeSXkGRGRViqFbtADEmJjnKJRHlVl3G94dz5AVMQpn+pu/8g3++NMf8OLFC3a3NxIlmFUNx6hLTGXp+w6jNNZW0Vbj6LTizWef0ltLZxQPD0dKSnR9i1PQOctqsSDnJff3NwA4I3qQ3W6HMeK10zSWx/0Ti8US71v6+gwtFgsUYr8wzzPT5Hn+fIcucLORe+Tm7mPB961Af5fit1gtmQax0fjah89pmo7j/hFrYf3RBzTVWbJpLAbDWB1e+67BNjLVBD/RNQ5FpG2ledIGVAmUCHOKqGrxcbvbsl4u3hdYH0hUCLQK5Iw3FReHphVWU1FCY4yVMaRKYRiFwr3oenIKYq8QEkrJ18lRmEXL1YIQPa1tyCGSipjUmYulRqVCqmrLYpSquRjlGkuolb4yxS5dPVQTuULdDbxXnqcYSZcp9Bd8/bmFvkiFO9U/uvqrAH8d+A/q3/868J8hhf7frb8H+AfAf6WUUuVLvq0/9R7Aw8MTQMUwpbA62+KnIMvaIstHVcMtLv4ql+zYXDIoxxw0Ri+od4MwL6yFJL4xKQXIstnv+56vfvWGw35gfzyw22yvOGkIkWEUfPb2boMxwp11jSj3pvMRazpICYWm0YZmc8PxeMJqoXKu19Lhno4j1poqO5fFoJ+jGE+lwOn8hGsKfd/RNQKz9E3LMJ5QSrNeL3n37o1YQ1RKqFi/F3JW79Wq1dP78eHActkTQmL2E6UI3a5pGmLIxDRXqljC+8TxeKbvO87HI1prbu5vayi7fLbDKTIM8vm5KlxzToRf210HSAGe/ZmcMjFkjocD8SJei4mcNKWYWsBaTCN7khg9CTnMXtzd8ni7lcNrnMjkK92173ui8liKBDK3DaYzzNPAaX++RrZtVi2Na1jVSWyzu+PjDz9GksDEOnqzWgvTxnuZLiicTiem40jXLBhOIjiaQqIwcz5PnE4n7p89Q0hXhVM4Q56QPIIj93cb9g8HvvWNf5nTOfCwf5LuLCeWTcc3fuUT/vC7v0dKiRc3d0AhpYGvfOUrPH/+nMfPPmPdtLRKM9dGwWlZeCqtsKrQ6oRS4nWec8LYxO5uI4Wy5qAuGikA9/e3UET8teqF3jhNEmPnjLiP3my3GKPIaksBbu+2V8qnrs9iSYm7mxsUhlinSWOETrlctaTgKaq5mrDlLMy02U+M54OIhRphuO3DA9M0sVltgBlv5etNw3Cluqby3k8n1sS4EKTBsclealKFwqq9dK7Ok0lCh87DEVJtgJI0PuM4XimYosFpxEsoesllzsKWcUZU7K7+G+cMWtva9MWrYZ3UraqWNYZYP7fLDuai8r9g6zlK01bqPk++RqVYX/OVpUReePJfbORKSVUzUyrBAtL/3zx6JS30bwPfAP5r4A+Bp1LK5Uj5EfBh/f2HwA/rBYlKqT0C77z9qa/5d4C/A+BqVuI19QiqE6B8SKQokWYl0bSiMitFckqzEjWaVqJmTMUwp8zN9hnROyiWaXqgc9KNDvNI19ja1b53GnRGczg84aunuuB6F1GJwjSa7WZBLokpJVrZutI6jQ8CiaSUiLMHq1Eqk6M4LBpnGcYzN7c7SJnONVDkvWPyLF3LzVa4vJt+I4rcAB9/5ZXAIMOEdcLTHUcvB5/QSri47n3xBnvPD5cO/uITMs+JzWbF/s2evhINrOlQauD16zeQxfyq7wTH9D7w9HRg9pJfuVwuKcryydd/le9+7w+IYcZaxfF45KOPXvH1r3+D3/3d3+Lt2wc++ugZjw97QoiCrxqH0o5YPNY2pJAZjidCEpdMi7gK3GzXNE3D69eveXp4pGsXhAKn41E69lRYtB3WNXSN5eWLO7re0Ri5Zs+e35GzJE/lnDnsJ/aHCR8TKYrD5Kf+h3VMtnSLDSEkUWWvNjwexQqgVHvjS3d1d3/PeThyc3MjqtlZKKNWF27uOl69XKAZmKcHxiHhpwN921HCiM8T3/n9R1adLMsVha5padodt+uW4k+8fL7h/tlKJoq+JZXMYtVTing2rfpWqHpWlMuNFYGXaxSLrhdMWCtePV9XP6ZW4DP9BSuH1VKmqhJYtIrhJN7nPgbCNUy9erznC8VXGi5hjslnUeJwLUwpRXIUY7lLNsCFNtqYBqUs8zxfP8ucC+dxkCyIuWC0k8AaldGXAkcmVAhETE9r0lgWuKkUCbbJQpXCOHu1NbkICy9+U1ZpVEk0VoOyhCDfsw/yMypV3U5dg65eVMZotBH3ytlnSYyrP5PAahCDR1WfGkC84rN06ZdnUaZxMY4jS30IIVQvfjGzA64eWF+0O5HJ+5KD6yjFXj/zXPMhuq77Rco38AsW+iLJE39FKbUD/ifg27/wO/z8r/n3gL8HsFitS8oTtnRoU4iIC6GeRI0WUyDMgTlCG8ZK96uRcrrB1Yc8KUXJiVg8sWRM0+HSmnEaud21bJYWbUVc8e5hT0qBd49P7B8PtG1XD5pLqowYWrWuwXaGxaaFJqNmz7LVmG6FkDoywWvevZ2gOBpniSFdIwldtb69u3vJ6Xyk73uOxzPH/Z5hONE3DmvAjyfubjay2TeiVIy54EPi6XCQxZt1PH9+y/E0MQwjKcmUYattg0irxdflotJVaLQy+CA49KWruTh8/uq3v8n9/b92DVH4w+99n9//7vcgC/QzjpE4BxY3K/wsUYf/12//LhTRJZQiU88fVihDIQAAIABJREFUffoZN3e3fPLNb/P97/4z3rx+YJo8IJYNk5+AiQK4acZpQ9c62qhZNJYXz56B93z88gNc17LqF0wvXvJ0Htmutugs4+1ut2OxWNB2juNxT0wz/9a/82/jlDQH0j3KcrNpGrabO/63f/Kb/NN/+lt88Or/o+7dgy1LrvLOX2bux3nd96ve/aBbr5bQtCQkFGBZAiQGJEBGtEDWSNgDkoAAhwkc44kJ8HjChMPMRGCPJ2wEYzOGQRYDDNiMQMYyFg9LatFSaySQWq2u7q5+VFd11a1b93nOPnvvzJw/VmbufaoFiH8m1CeioqruPWefvfOxcq1vfetb52hdRTFYxqO56+6X8OlPf0aqL4dj1jdOk2WaWXXE2bNn+OxnP8tnP/8FVpeW2dvb40d/7EdCXkbxyT/5L/i2xuM43m8pzo45Pr7I/t4M11o2NrbIMlgZDFhb3cS1NUXmyc0S4+GQcpBTlnlgbDluP/9a0ALfNQHXPZlJ0s2WA3LtKUtLoRW2qRlmitx4tDLQnEjVslZgMrx1osMepI29FgnsxnbOQB08TFH0FNnrUKMrMhOqE3azTvoShH2LUp1mvzYGpx1ZFmin4eceoZoKbKGF4ZMXgS4rlbkogw4VaNY6EdwzmtxklIM8Na2PcGVUqPVO/mhlMJkJ4mWWVnVNSAZ5IQ4hirZuGJYlpclo8gKVGXzAwRWOIjcY40IurJMnid+dqRbrO6cqz3NcMMgRPwcwWkGQnhBevKN2VaLLZr3Wf1pnmIFKB2usAm7bFkdUezU9Vdcw3ghRw3r/V2o+8ldi3Xjv95VSHwVeC6wqpbLg1Z8DLoe3XQbOA08rqSdfQZKyf+7LKM16OV6glZ3Mp9S6xYTJy52ltY55E4o3Qnm1Q6QQlldWJJRRopdtrWVpeYLLC2jneOcoyhGNFU8r08egNRvry5za2ubo+DjJo1rrceG0ndcnrFBSlgWDQmPKkgxPdVLR4CmyAqUaLly4wOUre0xnUw72j0Gb0ApPpHzrZg7Osnv9msA3zQnO1hhj2NzY5tTpLYalSWNQt3NcSBKvry5Lte3hEVtbW+R5ibONZPRRSY2wbWOzjC6kix6SdLqZ07aKyWgkGueN4M7Xr1/n6acuc/72O3jxPffyhS8+Jv1d9w9xoWADJFw/f/42Hnr4UVobwt2sCNocc06OD7m5v8vyZIm/8dbv5KGHvsRDX3iE1nqWlgZSlag8k8mIlckKRZZzemOLRx7+PO6o5tmbl9nY3uTy/tOMliZsrG2zuooYqFooqDcP9qnmU+q6ZjgsMUaztrbB1ctPc3h4yNHRUTL21lry7Ble+cqv45MPfAaVGWwlkr+j8YR3v/vdHB+Llvyjjz7Ks1eusnfzGvfd9z3ccedtXHz0S7zxjd8snZ+uXGE+n/Oxj/1njo6O2NnZxqBFa8e2PHbxEhtr24zLiUQWm5sMBiWDIqOZV2RGUU2PUFbaXdpmxqytxLNVgbJnW+q5NPGQhGOOU9K3VHmYHc1Qg5LJeIgx0p9AZzq100RLrwFp9h4qgXODsx6nhS0VjXeEFZpG2FFFkQtDTItwm3TCCkVRUZffdN5q04qEr3Ogwu+FltwkeMEGCYZ0KIREd0qAmozGtqgmR6kisV6yXFOWRdiLNnm7IAdMEWCVlBfKhYmTIRz6wWBAYbJwqDm0ydI9KIdQGh0MArVUBYjIhgZCx8fH4BVlWbKxtkbbOLxtaWxL3dYibphrIS7U8/SM0eAnEUKvKDOXDqqOMivyKU3bpGdIFO08p267Dmjxel3T+A66itHEV/L6Slg3W0ATjPwQeCOSYP0o8D0I8+b7gX8fPvLb4f+fCL//z38RPg9QZBk/9L738MUvPcwDf/Qx5rOKZZ1hFZzojKPG0rQOrzwqU1JSHvBCZQyta7GDkmwwJMszaueYHh6wtbGGRzMsV3nyyS9RVctsbg85OblGPZ8yHA4Zjw0rkxEXbttKieDNzU329w955JFH8Hj2b16nbk64cP40xmjqeo4xirpuKUajJJZ24cIFnr12g+WlDS4+9ijb29uAo8g1/+J/++ds72xydHDM2+/7PgaDHLMGGxsb5IVmaTRG2htIeKelgp+mrrm5f4htGpZGY4xRjMYFa26J44NjrJdCqpv7B3hv0UFVUpqfOEymOX16h6aas7e3R93OKTLD2vY6P/MzP8P6xgb7+/v8k//5Z3nyias8/PBTvOLe1/DQQw9hW8EmR2XB+XMXqJqKz3zmM7zspS9H64z5vObw8JBLly6xc+Eszz5zhbX1ZQbZgFe/+rW0jWL/5jGnTp3iiccvcfbUDsMiDzKzoBrL9Mo1zg7WOLN9hv3DAw6vHfBDf+tvcfHSo/zeH/wX7njhC7l8+QmWJ2NMJsyfp5++CogQ3qlTp1iarLPy4k1id6Cjo6OEZ05PKk6mNW/9G2/j059+kKXVNZQ35MWAn/7H/4SyHHB0MqUcDnniyhMcHx/xf//Wb3Db7ef5Bz/1D/mVD/4qWytb7O7u8qlPfYrJZMLS0oS6mnP2/Fm219c4vblKblqadobCimCXb1DtCU1ryYvQeSlvUbnG+xxrtbAwnHiFVonAmWunlFkeCtJEOtdoTWkKVKalEtPVIZqyFMqgjEEpjfUt83mLbRzWevmsyUXD33tq22v6Elo0FkVB6wSBNUoqjm1jcYHxlBeFcMyR6ymCzISSaMIrzdy11G2DUcIuy7RUd+gsE2gneMPCPBINqzbIbRN0oETsDYZ5VwSkHAzyMhnJeEDFAyCyaeIh4kPC0oVotWkanFLSbCQXNcvcGLxSFKHPhBxaNVobvGtRKmM8Wg71DLKfdC7P5BFm2Hw+h0YOmKyQKNk7gY4jqQK0CCrKRJEPSqJAXhaamnttoW0DA0gIIXXQbAISGSImauPB6/FhbLt+tX/Z6yvx6E8DvxRweg38mvf+Q0qpLwC/qpT6aeAzwL8O7//XwP+plLoI7AHf95d9wfb6Bu/8nvvAOn5r51c43t/n2vXr7O8fsTc95ulnn+VgOmPqW8pyEMJHaUYdEyOttXjv8K00HlDeUc+mHN48pDo4pp5bqpmlmWuuXtnl9jvO0VSCfWvtQTWMxjnzuQPVoHTL6tqELB8G7L6hrh0mlxL54WDA8XQfyKjmLbadMp6MGZQjVlZH3NjbZ2lpSbDf8ZAnn3iCtplzeHgsmHSWcXBwIJo1eYa1UBQD5tUJTQ3j8YT9wynzylJNJY+wvbMj0rvesr2xyagULZz9w2NGgyEzP6ex0gvXBamBceiUtb1+ChW0RlbXljm1vcPaygrPPvssTz75tEAN+YAVJ52E7rjjayiKAaNyEGQVxjSu4eMfv5/qa2Z80zd9E7Oq5sEHH+TxRx/l3OkzHBzfpK7mtH7ORz7yH/nc5z5HW7dcfrrCNjOODywnrgGvsXgGXnPy1LOsZEMeub7Lq1/9atz2Nh/6td/gaD7DzmsuPvIwrYamzZgeHpOHQqbJZFnw3cbxHz78Hzk4OOAd73gH3ilu7h2wcdcWs9mM3/r3v83u9T3WNjaYVTWrq6tY67l8+QrHU5Fw8FYkgXdOn+KMOcPysjSZ/smf/EmuXLuOVobd3V22N7Z51de9gmeffZayMBwfH5Irx9HN62SqCQl3x9JoRGY8eQbDMqNtg5yyCYJq1jIYDjEhx1K3jVS6WpkrFdhk0qlLQvXCZGRGMPDUEjOkyJwFlwlFcDQ0MJBkqwtSxypQcfueZ99LLk0hTB8vbTq1NoFmGiqMCYlN132m16GXLCuYzyosDu0VLhhQAs6fFZmItiGOiw/d1XCeYijkCmcDHIRDaekRG1klEe+OfHjo2lBGDxdIh5jv5apQHT0R71P3OW8t80BpdFaKraCjYc4CEaNtpU8xSoHRVNVMDjGVoTAYneGdjNlwNALl8DM5GGLuLHrykfUWm7VHFk18xbqaKFbWb1DfPV9PivuvYOjVX+Js///yOru07j/2/g/w4P1/wkP3f5rVpWWuH+0zU5qrbs6ub5niqcLpTej44gjYmDGCuylQOoPMBFqelw4tzjIZDaR4Y3uNleUMoxtOTm4wPT5kfW0Tsq7DUdu2XLn6LJub28wrx5NPP0VmCrY2N1leGlGdHDMaTSgGQ5ZXltg/OGRewXQqXsbS0ooo1mVatE5cy8H+HnmeMZtVrK6u0zYwHg/DZg0NiZuWOnDXAaGFBSYNqhVVzdibFI0NiTofDEmUVABwzuJsg9aO4aiU1oYhhEw8XRD1yroBn1G3FocnD5rgQhv3KO2pqorLV58mUxnD4Yhv+eY3cdttt/GBX/2geDjWUbs5o9GA1fGA2+84LYngk2moMBT4p1QqSCMoqFuml6/zotO3470ocWaDARefvETlWliZcGN2zMrWFqury6kxhWCZQgH9lm/5Fl7/+ten68uzx0RYiVaKz3z2z/j1X/91nNWgM+bTisOTY6yPVFMRoludDDl37hzHs2Mefvhh9vb2RMOobSkKSaC96AV38bKX3cP+wXUuPXqRyWjEuMwwRpMZFTpiOVaWRlSzI9bWpe3bC+66M3RDatlcW5Wip1bki1vb4L3IUiQGie6YVEopCA3HRX+mDS3qpBo3ywr6jSikWMrQNg7bSJvKON+RdWLyTocdAr0wNItJXn/PmEQcPH6vJDsFYprXUU3VodELsIpSKugG+bSuXWC5wKJxk8SoFBCJkW0XyAWRKRU/p5RiMplQx7kORjDeX+tF81RIETYlXmN+KjVlbyVvZ4w4Ed6p9P54v1VTd1XEuRSLRUNsjJGGKgFOTlWwuSiHSvVzaAoTpE6s9WjddSaz1vYqb1XSY4rPEufDt/K+2gqU+d3v++lPe+9f9ZfZ2K8KQ39+vOr/8be9g5uXn2FwLA02ptpxs6658w2v5QotTzx7lSvP7vL45adE/yVQCo2Ryr/hYILDi868liSRUiZ0n7FsbGzglSLLS3RWMBkrJhPNZDyQZhBGoZQsXrRiOptjTEnsbiU9WBHNdGvRWQZeEyn9JzOLbXUoGCowCoaDAuUdrRNVwr29XUaDMdEfapqGssyTtG9TW1rXYLQWCWQ8g1JkeKNWeNNYwcWVogmbhVCwoXxHzRqUOVU1RRvHzs42bTUL9DWYVhVKacrhgIO9Y2bzmiwvReNGi6Fv25bCFEmP43D/gCeeuUQ9q3GtZzyeoLMQSpOhcLSupRzlzI+PeMub3xA4woIF4wJLoxaDkWcZzbTikQc/hz+eB08LGutxhaE1hlY77KBg49Rpbr/9jrRehqOCw0PpqvTa176G17/+deQhxE/UvLChvFf8p9//KNeuXWfvxiF19K7znLzMmYYqwzM7pxgPR1y7fjV5vbFzmHdigLIsk85FrmFjdYUXv+gF3P+JjzMLekB5YchNgfeWMgs6/7kiKzNmx0csT0omg4K7v+YOKc9HYTTC345a5YGBoXyAJQJEpWNRmWtC841QLOckeSqeYeftSeJOJZw6cr9jQrGq54lhkuc5rQ20QS+GWkU8vu0ZWtdBKF65QE9tqauGppZr2+C99g+L2ENVjKQ4EVELvihKqeS1QeyfcFC7Fq07XN5amyiSxhhWV1fTPHovgnHOW4l4Y4Gkl9aRnUhgNz6tlZaHIEWQ0p0uC152nu4DpAGOxSciQ1EUrK2tSSP16ZSDg4MuWgr8eq01g2GR8hsRfhEWXMW8ahiPikS/jM+ZnLjAQozJ84THW/m/RTj37/6Jf/YVGfqvCgmEosx510/9fT7wP/4jdHFC6yx1PaOqpjAssIcnDI2hbmYMjKKZzZlbhzcZw3yMzgwn0/2Fkz9ywLNMU5QZzzx1E2UMg9GE0WQN3ITxeJubN084PrmZDo7IRHABA9ShYi96FyALwwTqoneBpmmleEqFLkxZloGbs721Ea5b411L3UyJIaJSinktUr0esDSi3a29FJtUM7Jc0R5WzJqWPCsph6L2J/0zZNPkJqMspDlzWWScTKeC22rL5voqS6OMyzcPuHDhNg4OjhiWyylxJhrfIVGnNcYjTWKtw6lWsFjv2Npc5/FLjzIajKnrmtFoyObmJqPRiNm8oq4bbtzYxdVyCDe1JPhar2mdCNfNqophnqHyjLn1VI3iyf0j6sMTioFgndoEOQMUfpAzWV5iNBMee+zde3AocssKxyMXH+N3Pvy7XL36jEhWQ/AWZbNU84bt7R3K4ZiyFDG7ppEim6qas7O1zfb2NtefvcaVK1eSkZeoR8Y4Nq6VzQreafZu3OD++/+EF734JRRFzifvv5+qqSlyWc/zuSXLDGauMLZFZ2OmtWY2a3jiDx/gwtmzzNs5h/v7DIcla2srbJ/ZwSA5l2hklPLMXENuMgahxZ/3LRhpkN5S03qLn0uTjUhxFF36cKiVOYWSw3E6mybvMsuyUAFtUTpDewdaOpaFzjyo0KQFRWCDBK/Zi7dqfUtZmiANIXATwYC6VlhaxhvKXPRZNNJ0JkYPgLT4zEkHU9u2AeZSvXoOifii0YverzERRHK4OG/WSYTvPViVMH4CiyfLJI8XDwB5ToVD4F+hzpp0KLYEJp4ShlLrLCczacpS1xXWNmhjRIcr5Bqca7E2yEUr6bYWRRONMdJGUBtMJji+aN9oabwSIblW6gratg3ifKLG6RTMpjNOQivPr+T1VWHoZ/M5T/2/n+YLTz2GqSS7PTdwQ1nOt1O2NlfZ3Fxn/2iPZn4SqiNBZ5rlyYCVtTVuXH9WNCeyKGolTS+KwpAX4JWhtR7fzrhyZZ/BYIQxVhIgTUttpSijKAomw7EIMfXwM+EHd9jmvGnwWjx9G7qxS09PTVEotGtxzZxZdYQGrGswmeD/WZZRT60sDi/Vix4HlIGVIkJJymYURrO5uiKnuA8h/ajE1g1OSdLVO8VwkDEqC2lCnmty49ADg1aWanokjJ6mEnkAranrhqZpyQc52iHCTYMRKwGfLsuS2CKuqio0iqcvX+HUqVMcHx9zcHDAyoooVeahkcT+/j6TyYTZbMqVazcZjUYsL68yGk24sbdPUZSUoxU2NzeZzSqeqp/EFSV6PWf7zGnZyF4ak7ROdEueeOpJ8tERp860LK+ucHx4xEteeg9FUfDQFx7m4sXH+fSnPodS8KIXbYYNKYa5yAcsLRd86eGLzGZzXvl1r0GrjDwvqKuKe1/5Ci5dusTDD38RH2hs0XsUg1MnLyt6VrESG2doWsdnP/dnTMZD7nv723ngU5/kkS89SutEtz02/YgMFwNkpkAXazx7YxYaTw85njkOjvd49Mlr1LYN5fcNw+GQzc0NTp8+zcp4RJ7B0nCI8k3CgG0P05XiHo1xiraywgsPXrjB47xnMBqhtaII8+u9F62gtsUZEcgzgaIr616KFa1taFsfmDuif6R1mbDmVMfR+hBJhaKmAI1FPNo58VRT2X+MQpUkgwWLz0BLt7docOM1hEfeojBJLgAVpASsw6uMaX1M7kVEzWvx9Od1zcnJCbVtw+ERmrgHKEsrJQV+PqqIChnCBbiwLHLUPEI5LVevXuXSpUusra3hbEOpx9hAVXWuTaJoe7s3pNjPCpwjiqAm5SSk+52IstWBfRMPYmstPkBu8cBrmkbyId79ldQrvyqgm41i4P+H77yPhx56iJXRhJvHh/yd/+4n+N9/6VdY39rkS49eRJU5k611Do+P2bt5wLW9A7zJKErpCnP+/Dl2rz8bmn0LA2BQyGLOcoXKMzyacjjkmWs3uLa7z97Nfba2T7G5s5lwtTzPWVlZ4cb16wEbE483y+Mi80k9zikRR/JejKdvQ+lylpMZi7MVa6vL4GqsbdFaJkwabEwXhI4iY2RpPBCd8Lng7aORyJdqrcmKAdLhSjTvW1Qo71aiM55pjo+PRU9kPMJ7y7AoGZSxkYZGZ6VUiKKwVmGVMDSaJuCYXqSRXeA0J+/WioRvxDdF8bAUpc0sY2Njg/X1daSlYs6jlx4Jpeaa0WgiZdxeh96+Eg1JS0aC5n+W4JLYeOP8+fOcPXeaRx99lP0buylMn7cNZTmkqW2qTEQ5aeicxySXSlFdVVXBSGfMmwaTKV5w94u4tnudqqoC1c+kg7xpFrHhPo0tXt97JwJu3jKvTzBGc++99/LSl76UD37wA4AkBQG0ykMxTBYMmw7RojBZYuGOMYomcM8BslxjrUR808MjtLLsrK/zspfew5lT2xRFQTOPOH+d8g06JGuNFtVPE35eZiYlA7WRtSgt+DSZIrRb9HjrkverQ8FO27ahFD+OQYazsUF5L+cT5EiU8zi6PFqc8w76ES/ZhrGODCClFF65hUOs30lJ5knWatSRidcVzznowBi51tzJfe8fHNAE8kaMitO9hKRtpvo0SZU8+rjuZjNRljVZxrSaUVdz6e7VtixNJnKAWWFKaa3JggovkAqgpLGN/NzkBX37q1ToeZuZBe8fkL4DvSgk5jV+8O/9i+cPRr9eDPwbztyByTIJ+jLDtJ4zUDmnT5+GYcFedcI8A+s8B8cnXL66S14OuL67i7WWl7zspRglvRZn00ORUDWFTL5vQEkTa6UN3hRc393n4OSEydIKL3rxC2gaMaaj0UgG08dFJNSsPM8lVG5rTC60w6IcUoVEVOssTW3RSrR2ykLT1CcMRwVY4Ryb4CWtra0xnVZsbKwFnRoxnFVVsbEmnX289yEMlk0yGAwoR+NAn+zwvLIcAFpoXOG93urAxxWermxcz3ze4J2mboN0RNiI8b6UUklKwBgjLd8Cluu9p6klQTSZTAJmGjZLLtWPZVnSVKL5EvFW8ZIDg8N5sgSDiX5NVc+pnQUca2uixbK3t89kssRoNOLk5LjDmgOLo2lFLC4W0GRZ1oW2WpPFzQupqUdkQLzuda/j9OnT/F+/8eup4lMYMUIVjNBBNB6xJiUa++hZaRWgFW/xXhJweSGe2Lve9d/w0Y/+PhcvXiSWzxdFiW3a5AHnuWDqIpEb9qBW4CLsGA7bmPxEkr20DcdBnrZpGpaXxpw9e5pz586wvbMV7i2sUxWqVINXnwf6nw9tOvPcoPAioaCkqYjrURnjuBRFkfJIbdtSzWqBD0PS2DlHNZ1hjESCgyL0OsjLZNDj/SYJAS3OiVEddh2TxLEK1YZ5SMbXy3zbthICQJiXePB4FVp+Wpmn2awCDUfTE4FHlBAc0Cop5EYM3TqRlpDoq6Nwxn7JWosTlcTHArZeVRUXzp/n+vVrgqtr0lqzrTB9pDBLeg1Mp1OqqmI0mlC3M8qyJDdZIgRIe1OJjuIYK6USFNXeUlfw3/74P3/+YPRea8hL2uDRZQ6cU9IYwsHu9Rtcr45oNKysr6VN11oLYcCvPPssF86dY3Z0JEwJ65gzQ2vpDUoII1EmKDAqRqOJJFe9QyvP0mQknmXbUA4GTKdTLDAoRNIAJTii9S15rhmUokBYVTXWOkqj0UbU9PIC6sySFxrjNdYucmxXVlaCYJWiKAYp+aIwjIbC483zMulyeKWT0XFONDo8UM0t0n/SJo8FK3iqc10CyhE1MjRgUCHRpb1UQ64si2b27XfcRVFmoRNRzsMPP8zxVDTws7JAGcPxrEqeRSy6iUYgyzKwnWqgMQbto1InWG8xRmMtNFYabxgveQ3vHEU+YDKZELuHaZRQIL3HEYyvdenwiJFAPwnZBiMdjZaMmeP0aYkOHnjgAQqT4SEZK+9ETiMeatHQxz60wIJBAjBKgRIqqzFCc5xNGz7wgX/L29/+Nl7ykpfw27/9Ibwn5FVEqrjD30WFkVj84gBErROlUUYnGqFWmRQ+Wc/KxhZ5nnPz5k1mTc1nP/9FPvv5L7C+usy5c+c4f+4029vb5GWJaxvqaipaLRpQoYk5skeMCbiyknJT7XVA2I2Un7aeaXWIibDBfC49k8OhGsfc5BkucOOdyjg6OsJyiM466AVg78YNHF4aZwwKyDLKPMcTqIZaoXVJpk3yun1wuiTZ3uJQFIGXHue3CX2hjRFG1tHJSSrAGg6HIjLmndCzQVprWs8g6MwrpShDRAHiNBhCwVagu7qRzL2w4UT3yWjNtWvPiqCZtRAkvuu6lmhTy7WHwxHeO/JSBNuqqpJoRpuUmI7Rsewdg8vzrm4ATxNhnCJfGNOv5PVVYeiH4zEvfvnLaeaS1W7rYDiqGVd3r3Pt8CZzo3FGURO0pJ3FNlAOxUjePDhgbW2Nk+OZhKDWgTZo5zFZLgJnVlgBtpXGGd5I+68806jMMCgNZVmQhw1fZCIxPR4JRIHyaKMYmIxiMKAcjphNGxTSq3M0mXBqZ4fN9RXqesrJ9BCVKUqjExRQZoJrZkZKvNUoJJxC8wFvfaqObWopFDs+mdHYVrTug5hbpuI1FTbIETjnKcqBJHQ9mEx0XIWW1+lodPxjlTbLcDBmMISj6YyDKzcXZBLiZorJ6LjIvFOpd+94PE6MBK0yEaqyFuc986YJDboLdi5sc+ftd7Cxusalp57m4YcfJrfi7c2rhllV45xPmK8Om80Y6VyEzkRgTkuzlejxKroG3lFTBUSpMeZeqmrKdDqlaQTGUUaSxOLXdsY9/i0slsXNFENwhSSvQcrZlTLBW7Uc7B/xr/7VL/KCF9zFD733ffz2//O7XLlyBW/BtpbMaNragrZY68iNzH+ENGKyU+ZAo5UO+DOYrGDeWOaNJSsGlGVJGRpZzNuaL37pIo986fFQCDRnNBiS54atjU3OXzjF2bNnpZcsPnW9Oj46ZDLOWF5aoZk3eGtRgeJYNXNsiOpsIwdnkQ9Q2jMeS2J+HiqsIROtFysFWfQojdHJGY1GUuuRi7HLsoxWN7hAFY7Yc5kXZFmeIB0gwSd5Pgly0V1hVMS1m8YynR2FCvGc8VBkwbNCagVqKw7JcDBgXlWhdemA1dXVNMexhiH2unWQ1uPxyaF8fjgMWjOeTJWJgWTbCo1Pe2V5ZcKgHImz6XVQCi2l4LLqpMa99yyNx6xcQaleAAAgAElEQVSurqJzw8nJCSMzStH1SVWhjeFkNqUJvRb0XwGM+aqAbjZGE/+WF78q4W3WWmzwEqu24biuOWzn1AacDmpyraV2ok7plKZpZpza3qatG3IdWsOFSkDpcSCekvee6byiCSJQqIbXvuZelJeJ2tjYYn9/H5ynmc8ZDAp2QkhMpKKFAhfvCbQyQ56LF6K1CGeKMZJQ0OlYpi7cZtcq6lpofa0TyqN4oMJ5jp640R3Ny/lINZPwWPq/hmRaJp15ZDGSKiHl/SLO9HWveQ0PPvjgws8hlsv7kEiOCefOoEe9ETGqYiC1D+yL4NlaJ/S6yAf23jPMC/HEw0bPQoJ3MB4I1lnbcGAZGjpPOhrafjHIrQUkMRGntE987Oj1iXSASbDH6173Ov76N/41/ugP/pCPfexjmDzD+oi/9oSiXAcx9DngcSyANN4Ru5WkbGzyHqpOA0zStjXlQPqQvvNd72Z3d5d/95u/KQdhSDDG7+jnAJSK4lZd9Wd8du+lpiG93wvs4213GHvtkwFo25a2EfGv2WxGphStlehoWEpntttuP8fp0ztMxkM211aFAeZciPQsmVbYthYac5qj2FtV1Ffr+SzlBXA+VVMpH4u0SMbMWof3nbaLCVW99Ixjn86plEJnkg+Kc2OCVn7TiChg9LJdWDvTacXxTBqQ4OWAmVYzsixjZX2NPM95/NKlNI7r6+uin6VU0KiSudEeRqMJ4/GYqqo4OLgpFbONdBaLB5DWXcK+mk9xrmVvb4/ZbMbm5jZ1XbOzfZqVFYFl27ZlXi1KjivnGY1GoaBPIOOoRY9W3Dw54uBAROicJel9vf0H/vHzB6NfKQb+6zbPYYKhiCF/5Vqq1jK1lhNn8TERhnRksQ5UltNai/M1G2ub+NZy+4XbODg44PhgCrgk0OS9RALT6TE+02SFAV/zza//Bur5IcZkTCaisb48kd6UQo8KRjHgZHmeg1LJk9Nah8bg4sE0bU3b2EDrUsKkSPZC47zC27AxVc/oelAmhmQdLOECxViw3gHHx8fs7OxgMqHMzWbCUVaB/uZ1p98R51frjrrWhzOicY6JYe990hS33kuzhXjvqg3GWXj2jQvFG02DUZ0kq/Kisf70008zb2psL+mlTahqpKuwrNteQs0sVgPeChH0k6MRu3fOJdGtqOznvShxnjt3jmvXrnFyIrroFo9WeY/F0XlU8RUNfUrUxZmLB41SEAxugtejAqGS5hTOOdpQDOdxvPKVr+TN//W38Wu/9mt86YsPJwG9TrNE92ChYDTpyuq1lhaW0B3UAunEuex6FCTlQ6SVo6ybIkV08/mc3GTs7+9j3ZzxYMh0dkSRGUajEbfffjsXzp9hY2ONPNSX4H26pvce7b3AQMZgtEOF5LQK79NGjJeMrczJyckJ+FgY1K3BfoPsoigSbh+jSbmGwF9aa5rWcXJyEtpmFunQFb0fGZejaehohaEcDTk8PCQPfQ+m80oa5pRlkHTeTN8lMI5EeTGiRBvaukHrKFmSpTU1HA4pyjK19XNeDp79/X0ODg5YX18Pchwd51+wfBMa8EjlbUz6jgdDYjrCWslpVPWMaT1PRV6D0ThFSO94z//y/DH0S0Xp7906mxbSYDDg1KlTPPbU00zrhkYpTlqLU4SScC/aN1oKoKy1NLbmzOnTtHXD2vKaJE8OjxKeFXGu6XTK3r5orA2HJW0z5d3v+l6cnYkCHxIxxNM2/u2cJEKkNZiiidbPSgIISNgwaPD9ykaBUMR4ysf61E0VZFg1iiZ4KNGrWV5e5s6776KeVUwmy6wE2YKmaZg3lmvXrnF4eATECjthBjnfpmsYY3C9ZiXRuNxqROPvkyaJEu9ecGI5PHAOo3N2dnbYO9iXBC9dNaYO14gJXhuTcVql0ve+506ES2Qk5H5MP5HbxyI7jW4fPcMeMwHE4G1ubnL33XfzZ3/2Z8znTYoyTCyuUbH0vAudQTD2Lokbo8Evvz+U6XjNcq/xsA4RCTE6bTDBIK6srPC933sfWZbxC7/wC8RgoSxDbYTqku8yJz79DC/GU2vhtsfxcU6a1ZsA5YEk2rvxcGluY4/UtB9C8lAYHHDz5k0ULhjWjsmyPBmxvb3N7bedYWNjS4qVfJT59Wg83rbkxojBD5ozSkeWTTD6XhrOSAVpk6KAtnG0XuDI0EYC7z1VNUsUzPl8zqw6oSxLJksrqaLWGEGfZ7NZeCbp2FRV04B350IqCG1Hl5aWmFWVqMrmss8jw2XeNCyNx7SNQytFnhWp0KssJScQaZJNJaJ587ahqio2tzeCvbChWK8KFNkxxphUeRtpk4OiTHDfeDxOjX6m06m0B/Uyl62TxLozErkfHx8LhXow4ubBPu/64X/6/DH0w7zwd6/vANAEnuurX/1qPvXgZ2isZ24djXO0jlQ5aBGBoEjJquo5S+NJorQBUgACNK7DXZtmLgVPxjAYFmjgbd/9VppWFhUWKYHGR+KIeH1EFowSj5zgOQYvXjaT0PmE+xsbikdjZXBhrOuwWIjMDRWaNXtFlsGrXvUqkWG49izT6VSqM33H5Y6sEK2isJF6DtSRogTVC/vVc/VOfMC2I6PBOYenCZtGCrhWl5bZ2dnhnpd9LWsrK6L2ePFxLl26xNFUQuL4eRkvj/FBwrkXjvc95/j/PEIg8b6DmiOQtL3DVZ8D7cRDRWsRgOrnExItzSviFbQOjc/phKL6+iaufwDFz6huf/TsZ3dIpoM78usdbWAIxWtlSnoJxPZ3d999F+961zt5/LHH+Df/xy+HhLZo/vtQQZwgGi9y3N6L/IDWt0JZnafdh7Jkjrt5lrGw6WCLNEUT/u+I8985N8pLpXhUAnW2SolL5T0bG2ucOXOKc+fPsrI0oSwyci1QqUUK7bTWoSlKG6IuKSCKh0HMwUik7oKYnxXGTdiTWYhqiiynbiq06TRvuoS8GPvZTHohD0Ylo9EoRQnOdZW8VVNT5sJyq0M0Ee8hz3Nu3LiJ94qykKIynQkd8uqVZ0QZMzDPxEMXW7J/eJAkOrJMh6LCkWgOOVhdXeXJJ5/k1KlTIuPgPEtLK5hAwcwy0QMSxVVhaq2treGV4/j4UBwHL6y4GzdukGWy3t/7937++WPoC5P5jdEY67sNdu+993Jjb5+qaWlax42b+5JEC/gwRlOUZSoTHk3GrC6voJCMN14zHpYUxYDV9Q12dna4cWOXj3zkIzgt2hjD4ZClpSW+8Ru+gcbWIfvdJRsj2CkJGQnztcpCg4QgqhRC00xp0JEXq9Im11pjQvVhZvJQGOVCsVVnOQzC/CCF4hL6J8jplrZhsVxdMOlYut2VjPc99PizPuc4/s6o+O/uXlrfsrGxwVvf+p20bc3utWs89thjXL22i2tr8U60SBboXMbfuw4CiYagDbBK+rn3kiDvGahMa/EClREMPCY/A9UwGvVOptXiY9Vm2GTWWsbj2D5xmiIykEK5+IqGPkY13nVJNgCrusMuQUV0B6j30idBKYWJ0EnPozfGBOne7prOOWj7DSNaRmNJnr7mNa/iTW96E7/3ex/hD//wjyjygeiz9+5XwC3JJXWQTpxjgXkiZJEFBcbF39Nh9vEQ0IAN79Mer6R/MKqnkkgfLw9smuBg1EGYrG1rEfEKc5MbqQTd2Fjjjrvu5NTWDiury2RaIiORU7CBDhvyD4FGmusAWUGIFlqyqJippNNWZHjFe4yRWjzobCvUSWNMOpSzTCeSgDCGRIpcGxnLkxNpel+WZXCihBZpUQyHQ5wXhMF7z97eDaERD6IcRslsNku1HJFWKdGF3FdZDtnf3ydSMZVSLC+tYrRmPB4zmUy4ceMGuheVkui6A27evElra/LwnVrrVLsD8O4f/V+fP/RKkxu2L2yEggPL9KTi4cceYmX9LIfTk9TQWilF6z3OhwbQWJQ22Lbh9tvOc+7shTTRJvBZ67loZs/mM5ZWlmn9nIwiFFsollc3mTYO7SL2q1EqE769t7jogSglOKwT2EBl0oxaJheshbtuv53Dw0N2d3fR4f3gcaikmqeUQiEdsqJpFaMemCZ0iasEWHgPvjPgSnnph6pimXaG8lEgCRqh1AA9YSrnsD2PNVER8YH2RzJemRK+78f/+OPs7+8HETKLU6IDYvLY+SvSOUWTJRlM77vCKKQVno5NI3xgZERZV+KhqpBWKSFv4US6NsIIcZziIaoNFEa8p9e//lt51Wtewf33/wkf+chHRLMH6UugAsSmtcjX2jCuLmBoToFPAmLdy4UOZn3OjRihsE6MEc57miMXWEEavJYG3XK3eKNxzuNbcQLmVUOe53zyk5/Ce89bvustrK5N+NCHPgS6lE85haZAMH4LTgvNL0CXyrsUcVrXgnPkRZmixi6RrLFp7OLPSVGNdUJxFZGtmOx2ZAEW8ni0MVKbUAzw3pKXJfP5nEJJ1XRMQNfW0jrP1WsHPP3MA+RlztbWBmfPnGJtbYXTp08xzIc45XFkZFpJk3LvqLynMCJMhrcob7CqFVEy5zDeC1POS5JXhQIvGwy+MqIAK2wGS90KG8hoyQacBLqlymKkLWOS5RoV8naEa1kHipy2rclzgWxGowllOcI5T9PUaJUlGCxCM5PxMtZL+8I8z3FIf1jrPcf7R4EyHSCnsmTeVMxvVuwf7jOZjDp7aIxoThWKuZ2RZ2UQnwvaO1pyjYOy+8xf9vqq8OjPnj3jf/SH38NsdpLkQQ8PjphWjt///Y/KYopt/YJB8d6HLj1lSiaOhpPUxIEQpgpNzYdGxDXHJ4cykWGCzp49z/LqKs084H+TZVZX19PAWr+I03ZaHt3PisB9jVjb9evX5fpavF2nOjhAeY9tHaiuQjQiBeIxRkU8nYxM3IC3euzxUFAhEpC5lHuOsEzy+L0YtRgJpJeWxtPiNfGc78lzk0JbF8kevTUTE6HW91kZpPGJyeaUuAzQUWyMrLyT5JuSPdrH5Jum64YVvzceXBrH2toKP/pjP4JSij/4oz/igQc+nSomJddiOuZVL8pBd307re2eJf7Le59gtS/HVJZkrMAOJuq6BE81xqTKd3BAYkO5KFAl2Lc2MBwOqG3Ne97zA9x+4QK//Eu/wsMPP4IxGRrTwUg+HIaBiRLXt/wh8OM7SKefOO1j//FP93up6+gn6KNj019r0QmSn/mFNZAblbzlmEStqiqsa5e8W5RnNMxZX19na2uDc+fOcWp7KzQ8l1aBUqErn9NhNL1raeu5HNBB9wVckGYIa9NBVc9xbSMMIe04mc1EQT7Ah1mW4bV41vH/zrUQdGi0EgizdcIuM1rsS1YKXJOZnOPpcRo/pRSz6kQiUyPJbhEebFhaWgpruAkdrUJ7xcDUWV5e5sbe9RQBjEajlJuTPIxo9BwfT8N1Z6ysrGDxbG6uc3IyI8tz3vI3/6fnD3QzmUz8S17yIo6OjlKn+bqusa3qbY6wyNKCFwMWM9lCp5OQXcrzO2hnMBiJXkVZMpmMyHNJfIzHY1wbKINZNMZSwZYwTJMnbDMybAB0oDP6HjVQ51LgIrBG1+RcEFDV86q6jjG3bigdtFEEbu3RHHvYcQf5dAakewW8NRgqYxRfe89Lcb7lTz/3eflZUSY5XGABD9ZyCZz3mEwaUUBnJPpKegn/jTBFuAPvPZkKWurB0Eectf9Z3SuPl893YxR/H6OPWMwUr3Hh3Cne+9738Oijj/Ibv/WbtI0PUgc+lb9HCCldL2LCKmqWROrqLVIHqhvP+Pn+PMkCkGtFaCE2vfDhwIyRl2ttqnR0rntWGUuh6OlCDPidd97JD7/3fezu7vKbv/nvePzxS711IxGJ13K4pLFMa6S3BFSn2yMwTDf20UlKGu2qJ0vQOwh8b0z6B3U8iOOekKHQib3VNDZdu67bxHOfz2fyHKpN32WMItdSEDgoM26/cIGzZ09z5vQ2ZZ7RtJII9W2DVqAJjDHb0tYVKkCPMRcRYaX5fCZwEx5cgw2qnlnAvrXW5FkhY28U2FaiVieKp3XbcfPzPEchHH2CqmtkCsXktlAzzQJbazQSbztJLWQmNTNJieO2SUlemcOOutu2In1xdHSEzkRP67bbbsPkOiRkJbL6jnc9jwx9URR+Y3O7t9mCMQ0LOlZcOudQPhRQaMVgMJLiijBYS8vLiaoUD4FuUZmOs9oz2lHMSmcBEgiUSen4EjnJi5sAQAV1xGiMASySKM1MEZ4lfE75pGcj3y9UsAUvM7yUg8hB6W+m6Nkq1YcZXBqv/jyKsZHcgdaa5eVlslyzf/NQNiRi1JP3rDpes3cqVbI6J7kE70WsKTWM6L2ikYjJ0WQIQtQUQ+X4HFnEwXuHmAi2CawRv1e6GcWoIBr7jqFUZJrZbIrKjHQ68j1uOt2B7L0PSqKd8fa9svuFuVX9AzMOsVswaHGNurA+o9GNDawdoWI2vB/naV0jh4qNDkmE54JscGHwypFngkG/4+338fVf//U88cRT/ML7f56qEmOhjE41AHHsjHfBGZFoLjxht55666tvuNP4BCxeq67COEaC/fcDKQqL1/K+T0TQXfMS52QPBJppuldjmFU181oks+tKMPW2FTw+ev94J+0mV1Y4vbPF6dM7bG1uMBxKC03cnHk1pQjJ56YVpppximZeUVVTWUO5JsuFfx9twa3j4Hwr9StWWpRWVRWSz0HZUgnLLAoNVk1LE9parq6usr29zbVruyilUme4aH8kIs6DpPAsEQamUync297cFGfTxS5Z3V6XQyZEngoylTGfz5jV83RgmKzgr3/3Tzx/DP3Gxqb/9m9/c4BcOpaK6nlLINocUtAQMGLVGbiyLDmZnTCZTJhMJqyvb7CxscHZs2dZX1/nqaee4plnnuGpp55KBk8+q4OX5NP/bWC4RG8nMin7nnPczG0P777VcMdmwFFsCkj4sI9dgnrJs0XDYwN2uuhBd2CCJjM+VKfqhXA8epN9AxW9O4ESwvPEa8bndK5HC43f91wvPr6671TSnzSNQSyq0SkvIOJt0s1H6UUWjnMOryJrqWuZpr2XOD6wTWIBifI92CvQV/ssSJVyFyZFJPH5w0TK98RxUBCZLf1nC//6cw298l2iU4WcUH/+o6NivZPcTrxnHwXTmsQeU0oJfRIlvQjahr/9t7+fl7/85Xzk9/4Tv/M7v4NXgkmLDm4wvgj0luAE3Tf4cTziulyEwGKuA38LzVY5Os0d/5y5X1wDEhFHeETktUV2mziXnsSpJxSdRZ56653oHVVzor4PPfhIKJKKtqnZ3NxkfX2VyahgY32NQWnItUKHYq5CGUZD6SExr2fB4PbgLBSDgdShxL3ivKVp5rhGcmZtWyeapDGG8XAojlMYH/G0a5xrWVpaYX19k93d3eC5WwbFohZ/bDbSdzZjtIDtCu8i1BqJB2VZivpm6JmN90HCWGiqWTFgb2+Pb3/388ij39zc8t/1Hd+14NELkyNMkIkLVZozrKyssba2xuqaGPNTp05T13O+8MXPM51O2d3d5fDwWHTHVZcsUUoq30aDcRIBs3icVxhnQ29LQxvhEO3xwUOM4xQ3k3Ii5hXxbmVMj4GxyPtWSlgaEfoQPMMuHByddx5FnWzIsNedgcGk8QFJmiml0KroKIkh879g6DEL3YMSRzwYDvHqXNDSMSIfwSKcobzH9eigXXEPKDKUkuIceW/IO2gVDDjp+X0o5Y8v0RKJhqQ7TLWHTEXA1pGXWcgxtGQxglAZ1hta5yh0h0f3PTfie7MOp9au8zz7N3PrIQYyvX3jHf8dDb0hsDxwCwetcy55wM53xtU5YZTIAe+C9n6M9oR1kmUaXEuWKc6fP897fvAHKfIBP/vP/ilPP3MFtEnMo4xQUIdJyVmT9fe0TnsAOkXO+IxKd+J5fYfA6O5wTFGs6udhevUYXnqpeu/Z3N4CHNdv7OKd7ImMONYSNXkf6yxCghxPxP2j2mg0iLJnRGwtdrQSOMWSG0+ZZ2S5FHqtra3wgrvu5vTOFlpD6x0FkueZz+dkwajGLmxKKXASlc9ms1ATEwTlgvZUWebM51IvQIjjBN4laNOI5IdUHFuKUGAZ9Z+ilLC3LnWoitG5bbtanf7eta6RZu0h2o64vkBGNbN5xWwqOj73/ejPPn8M/cbGpr/vvrcznU6ZTCZJe2J76xRLS0vc+TW3k+c5Dz74IIf7BxwdnbB3c1/w/LoKJ3dsw9V5tvNQ3BQNbNxonffXNQCJn+0bUnlLF65q03l12vckTXueHnQbPXLUtZdNvIh7mhD6ymaLhryazQLml/XwahU6WXVSpwIvmQSXxFe3AftYa0jc+jbwlKUTl0cn3nX/0FGqS3hHGuRz8NpweHHL9/df0fNdMCx+0djEefCOwJYhaM84iszwhje8gaXlMZ/4xCe4sbcXvMDeR6M32jNG/Xnwt4yN9z40mOh7qWF+bTDiIaHvgsG79brhal/2++Rpek+mdQeohOgpwTZOHIgY9XglORWFeMpG+RQNvvMdf5NXv/rVfOniI/zcz72faiZyzkVuenMVHaO8u5eFxOpipNa/55QDUx2j5VZPPq69LjIM3xskDCLsI79r0cFBinmM+Ps+hJr+DnImcT+1rVSnS3+DGdeDbPh0OqVuXVDnDHUFKuY8Gso8F+w/N6ytrZGbjPMXzrG5vsbWxhp1XZGHJj3Kkxyp+XyGt6LJpJEDIB54OgoL0km0xC5WEQaLMt+DvGBQlAEJaGitTY3qm1ZqKWJrQ0+WKKrxOrK3BWoyWXSoFDg5PKy1VPUcpTVZVvCG7/vvnz/0yvX1NV7xilewvr6+kIi4euUaTz/9NJ+4/2Mhi+9EJ0Tr1FuyKEratlmsagziS7cmKl2k6aUNu2h8uwXdhWpxAmJEESeDntiVAiwEfXKfwlaFFy9GJMQ6xgSCe5tM/tYa2tZx7yvu5RX/1b088sgjfOITn0wQi3jnIvfb3YvuIJsYYksJUXh28eAjHBQ9LoKh9zrWACwmWzvYJz6dQDOL4xC8Px+NV7zP5xpVrYSRnQ4d91ycH4QyG7MfPlRb6sxw/fp1jo6OUpn8X3SoxL9vfc8irhzvt19B2hn7+D6lVPhJd8D1D/OFu+8fZErh1GISuDeYvfuLkhW94iytKbI8HAaGdl6hC6FmfuAD/5amafiGv/aNvPe97+F3f/fDPPLIIyEiEwqrrL0eF155nO8dQLr//XFMIhYf3yesKXWL85IKxFTXsSmNa38cVM9hit/dGwvnu0Rx38kK1jocBvLe4+mUcjiksZallRXpFdG2HB8fMz0+DsVILtQ2gCdn3iq0g9m85mS2izGKpy5fpSxzNjZX2dnaZGNjI3R+sygNo9Lg0SgjfaYVSobHeUwZekbkOW2r8L7BK3FEnHc46wLELIdBkRfds+mMXAc6ZZ5hsoJ6LobdZAa8Is+HmEzRNHVPcLAVlIE8XAu8jRr8VmSYlSY3X7n5/qrw6He2d/yb3/xdQOcxR9pkDLvj4vI24La+t6C1x1m94NkopRI3uL+g+oYgFhlF/Cx6KgtYbMLgFw+DyKBYDPcjPGLEo+l51rGwRGtYWlrh1Kltzp07x7nzZ5LY0aXHn+Sxxx7jmWeu4lzMV8RkZtws0aM2tK73fH3PPLY5DDxqAB/gJggqlDoDFC56LgnyudVDVV/mZ/Lq49Nyb+EHXpKT3bh0apSwWPgVrtS9N7BKjJISdSC1pLPWPqfQLM1jr9Cof7/+FrjBOYfVXaTUx/a11SnaEY65W/Do+2vrz0t49v+foq1bkoBJnsJ1OaZ4bzKfYR2F9ZTnJnl9W1ub/N2/+3dYWlriT//0T/nFX/w3vSKiQI31PSPdW9NxTfYra713ae77z2dioZla9Pz79SAx6oldp4AUxSrV1WfEz2vPQnPx+Hd/Dzm6iNi6JiU2I9NFaw29nFkbPOyqqpg3kvy1TZ3uT8fHCMlyE3rhFsYzGBSsrCxzemeLlZUlTp/aFhXWpiU3sndtWwmFVrkEaQrZoE1zFiEfY3KMkqI8sV1NN04q2huFCx3pTJA0btuWNjRqF5XcLhE+HA7lub3p+gEEGPrk5ITv+bHnkdbN5uaW/67vfNvCz7yXpg++hyt6pVAubrhFz8X75270W41TxCo7eOPLv6djuIgX32cgdN8ZlBttkzZPnkFdSxm09yK3kOc5ynjuuvtOXvjCF3Lu3DkmkwmXn77CH//xHwdRslmipcl30YNuIvAqhTj9CMX7zkB47xOnu0/5TH+CoY/Pp3WG9RK9JFy8ZwC6Mek82S6BHcYy/I4+JBC8ZBuK6p97GHZ9OruXTvMYN4zo/nc4d8wxeK1uSbyq58xlfw35wFKKzxgNfTIE0Rg5j3LB04wQglKpQvS5B92ilx6ZRxKN9CmJPh02KQGYILnnJji171Xrql51dag6BdDG88K7X8D7fug9oBQf/OAH+eQnH0D12ETpPl0/CukisqhGGSOWvtMTZTVudY4iCyq+16V57Q4+T2gSbhs0HR7dzdGfFxXJz5aWljg+Pg5j5J4zZiD5HmutIObpEHPkuVSqVtWUaTUL+0KFDm3hsz2uulQRixOmvaMsMiaTCVvrG9xx5+2MBiWbW+uMi0IOk/kMb1vR41HdfBkdEvNaJ5IFfYaakToBFeCe6LQWudxLbILSNHPJP4X9q1zXllH5bp/idToA3/yef/j8MvTf8Za3pUWWjBM6FenEZGWcvFT0EwSj9K3MAbrkEjw3tF80Pj7RAVHdPURopx/696+TDhknfSJbJ8mXM2fO8uY3fzvr6+tMp5Lhf+SRR/jsZz/L1avXwrUDJmc77DPLMtoUyuuFiZXb1b376vjmYSCSZy9j1+/M03nYMcLQOguMIb0wTl0iuR/KLzIwkpG8ZR6jPLIcLIteqkr3uejRx2t2CoQaaxU4QgTUHQKRk784D13Ooz9P8W/XM27RgEaP3qASe0UFDxvo1E4hGfr+2pF7WJ4XuRoAACAASURBVHz2lGQL49svXopj0k92yv11n4+slZhDES+w136PrrtVWWiRQS5L3vX97+See+7h+vUbvP/9P8/h4SFNU3fRme+v1140lg71mNfp75kuAlk42HXn6NBbLyIsFt6nu8I9FSKLuJ9kjT93T4Y76jkhkUW2mCDv1owkTGMkGu2C8TrFWdZKIx4VPPjYCCRpRXmftIEi8y3X3WGskYPVZIoyy9nYWOPc2TNsrK2yurZMUcRos0HjyTTgLU6HymvncM4m0bIsdOLqoEtHYbKkYCn4e5NyHPP5jGZec+PGDWn3qIX3H/V7skz0e978Az/1/DH06+tb/lu/9a0Liyp60/EVfyaLpQAfKZZWwiG3aNjlM4F33pUrysQSIA2vet5vE97SY2yAMBwi7c5bqeDzAdPUntF4zIXbzvG1X/u1nD9/nrqu2d/f51MPPMgXv/jFFH1IcUWb+Mp9xcUFz1vlz1nc3vuAFamQsPQLmi/9+/XeQ6AwihcWGTpetIR6MEKmBedfYKkAprf/lFIpnI7nhfLdZnaKTogrHcQ9mCuoVup43cCiic0TMpWFysgM6yU0jY+lnPmyRiGuBZ3opx6lOp70QtSjOvgt3ZvukrH0k+q2G8MEZfWhm969SGOT/jz18f4+Lt41E4nFT6kCOdYUEBRSAdPz8p3zCa+OdQexA1HaD77irru+hve+972MRgPuv/9+PvzhD3NwcBAShyI1IQJ4i/tLmEv9vEw/2d41rYlj0hd4i7BP/LcxWdibXSFW/71KKWzT4vC9KKObl3gQpXvpRUJf8Uur1BzlVpJCfAYgqE6SpKubdo51Du/Ee86DFIv3XhQ1dXheJ5RnpT1ryysMhyUbGxusrkxYXhkxGpesrIzRShQxcy0yGVGfy7omRajxQNNa0waoqaqmUrBVt4wHIqHsXcvq6ipN03ByMutkmb2wft7yg//g+WPoNza2/Jve9Nb0/xhKxtO/KyiImt15MmAu8M2jRxAHUV6horCfAHQ2Zff7zAulgkfVw3rlB50+eFFkrK6s8PKXv4w3vOH1VPWcGzeui2H/1Ke4ePEiQJBb6KiIrQsLj5gPyBdK7+M9AImBET+bfudcalASw82IxQKBzheNl1jk9HkX0hpKDF88ZDSuOzD793ELLutuuReBZGS8LD7dR784RtMd0863UljkEU686a6Z6zyNufUi6xsNvfbZQr6kvz6ioe885kVvvps/ndZDMloxMR3mJF3XdXULca1p0/OIFwzPIk5/q6Hv32f/T9/Q91/pcOo3Uun9O0aa8TpRRAta8sKQZYZ3vvMd3HPPPSil+Ogf/D6/+zv/IbR8lDC/bVhYW33j3Te80ejGvqgJVlL9zy5y9XVsTakjnNclXZMD5nwy9NGxiYVJMo+9pL/vaLfd/bEwj7e+ovHcXF/n8PCQtm6S5HXfBohDZQLLqWt1uLe3KwnetqM+x7mK4yP3baUraTiUslzj2jnDYc7q2jIXzp0Liq6WlcmKdKryLjXgiXsnVunaNrZBFN5/zEeAkBnq2VSq2Xv7YDabkWvD23/kHz2/DP23fdvbFjyVPoQAfQ+pK12HGHbeKgOw6CXH/wPiTWhPJ/8aKvxMNFDd4lZKBQ9UXlmmOXNGkqePX7qYmnjneZHuUSmVej+2TUiuKdJ3ZUoKlvp2/tY56BvM+HsR+IobbfEZzS0bov/c4okQ2uXqQL3ouOpJ73xhrG+BteJhEp4haZD0ft735OVp+7RV29H1tMcYTW1bjM4YliX1LMB14QCOht6weAj1x7hbI3GsnptQdM6hehXRaXx9x11W/e/oefRfztAD/RiTRUMvP+sbwFuN/CKEdYvRtYtjKrBTP78Ru251xXESdUjVeF4oZrMpL3zh3bz9vrdx7tw5Tk5O+Lmf+zmeuHRZHCAX50OliCWJ4veeLkXUevHZte5kEPrP2M9X9KPd/thqrdFKSZSQsqkCm8SoN6412Ycyd/2D5s97pbUe/n/hwgWUUjx28dGF+on40uIhLHj4SinWNtYpc8mvXb9+HRf6Fs8ry3RepXGXa3TFfuLIeKwTXX8TdPULk5FlGZPRmJWVJU7tbLO8PKHMuzxBnudoJQqZeZBO9ljauYjNKWTLNq2jtZKwVV4M/fHxEe/7+1+ZeuVXbOiVWIRPAZe9929RSt0B/CqwAXwaeJf3vlZKlcAvA68EbgDf672/9Bdde3Nz23/Hd9wXv2eh8Ya+xSNLGy7phnRYdP/38Xfx3x3eHSbLd5CFc66TQLjVuGjV25C9hWgEwfVeFrTvbYpuTDtv1PvO81342a3jfIseSrw/u7DpeyXcwZNPh5OP9xyTYBkKLXs2FjBFHRxZRl9mrrv76h+oLFSPdlW8fUGzdFgHGVzJoXQHZzkseeMb38ja5gaXL1/mkx+/n+nRPBl651ynMOg7BlR3b4s0z/443foSY/nnH6Txeullu5qB5NGZ586rA7RXfYZt+MKuirt/7VhZueAdBt54/H8ccu8RGCF4j0lrqEdKiFGIUioxPLSGLJe8RllofvzHf5ytrQ2cg3/5L9/Poxcfx+Q5TW0XtPr/v/bePdy2qroT/I251t77nHM5PC73gpSomGCb2J1SE42PaBtLHtH4RCtqd1f5faJoaUUFE5BoopVOmfaReEVB1CiF4Bcrvokk2rbmoYkVGoOCkSgQEbAEL6CXe889Z++11hz9x5hjzjHnWvvcw9eF9wJ7nG9/Z+/1nI8xxxzvEduKVAnKEnrL9MS0HZzbujTuIt2bCL1lOEYh57oYmn2QXlKxHtFrB8YAHlRpKc22N+clAwAkTM7ohDlm2wKIt0sVJEOZp1HcAJumAbddzJG17fBVXHfddTEPl7ZbooIbkVTgoyrMcXJBlv8tqpqwbVkSpB155OH4Vw88FitLy1jZtoSua1A7CimcOaTT7gCNO6kcmo1QvpA7dF2L2XSKF/2HrXH0d8eP/rUArgVwePj9NgDvYuaPEdGFAE4H8L7w/8fMfCIRvShc98LNHqzEHUgilhrOkltemAxOumD9bZ9TbgqKeHperIT6PmuQ8tl9FpIOuwOFqvYEL7nOWYzG8nxRISR6LLpcphAGTqkuZjJaFX3whqCYPvrMzVF8zT178Z8GSXAPUWSubAZKqNqEaDifS9FvNQirl06SjkLErGNIGK0Y3ByFWAEC4FuQC5WCKKSyINFDP/WpT8Fpp50GANizby+++MUvxqIqaQ4rZNk1DaT+lxvq5mA3f2Ue7L36vXIuufANjIuCA4J6q09shghQvEevCXOhiBKVPsxAzCcjUcspmE5tAgQ2Kp1G3+kqzNoWRIyq9XjHO/4IT3zi4/G85z0Pr3vda3DVVVfh0o/+V2As/W9mmuNfsrQyNA5Dq0sFvDGEXu1ggOa9SYyHBWFqGOqjDwRcZo4F1auaZJq9C0OhUc2yhhz76NVj1XJ2/nKmzsTHEKHzyZCvDEg5n85JZC7C5oJQA5eIUBHDV6q/b7Ax3Y8d24/ENJTza1uPjY0NrK+Lfl/SV6fSn54II9RouYVvWjhXAxst9u5fQ0VruO32vfjODTdiNBphMqpw2GErOObYnVjdtoJtE0m4uG1lCYDDbH0DUz/D6uo2dJ6DobiKEv5WYEscPREdD+BiAP8ZwFkAngVgN4AHMHNLRE8A8BZmPpWIvhC+f42IagC3AtjJm7xIOXrLFSQ/8ASKDACMj2pKqpRfJzo1MXiYe33y0rDXMwkH6kxCLED02pHzp/z5lktIkkbylNHf0e7X+YwQlAQdQDR0WkJGRDGFr3K+gKQkIAqJtYBgOOqrvLTfcByjTwHh6FVjnC0cU3TCeoHIgvNg14kKQpK5Z5sWkHvuOAccccThOP3lL8VDT/xZcNvhm9/8Jv70zz6O9f0bUtFLbd1BxSUslhSbGOLiLDepv30xjnZ87e+SMbCb7dAeaI2x5dwpjoWeZu+c1241xpWESvFEbU8MqR+gPtXMTWAiApMTjMggD0daLlMKtXfNOlZWlsHc4YgjV/Gyl70UJ5xwAsajFVx66aX4+tevwvp6KE7d1VEV50LpSCHcVUa+rQpKXSgd+tKV9Ed1/XlEtQPBG/VQZAaMZBmJOftQJ0ALtXP2LNsuG60bn6E6dM7nIM6RUUNF54Kw0YtknNxOazeSYjsjhybE9lQkyRWXt62iqka49bbbsL6+jmnToJnORD0b7A9ECdcIoioaOc2OK8VqKARQAgyHFktLSzjssBUcfdR2HHXkEaiXxlhZWsZoVEmOn8phtrGO/+0Vv/M/lKPfBeBsAKvh99EAfsLMqvy8BcADw/cHArg5DGhLRHvC9bfbBxLRGQDOAIBt2w6D55lERxqOVqdGgz2AxDvIpLroFpYvnDC4XIPAqKiChyQqIscAjaKIrVyUc6pnZxMVyCKOh2dXIT84QfyrmQldIOidqk2iC6QhKjG4yhAbMgm0YKQU5WbtJgKgglOGTkcQ5MbJfVGwKSvErQMWtDaBkqoOPfKHYfEicpie0zh2kXjqvBC4FcnBkUTxMbuULyX4GDPEFvCiF/8GnvzkJ6Oua9x000343Of+AldffU30GJLSi+rSJu5xpJs3+TjjMt924w+SDZMkDFM3weIKVX1YiNcEzhOAlKBkimPhgj+zioN2fCrKSxEKfiRVkb5b4wgqJlCVu3mWoMxAZ1Q/aqAUDFGOns1GylKUlzqRokiK5lSjFbEPVYQ9P9mLd+86Hw9/+MPxijNehuef9lw859nPxMUXX4Jrrv42JmPGbNagdqPAIYYgP+KIg6qCcagCPtXwYFAlsQ7Oi4olprtGIvK6DiKDEnZTCRYLGRudrCcpgK7jkTZRSQCWVLStbzMOXaELG4xIJynfUHmdTIIQCQ7Ewhe4wwEPRKMwBYKHl4NHR+KW3LYz7N3zYzAzJjUwOWwb2FWx3KLGyMxmMzTBTtS2LerROASayVpzJFH+3Ib0ERjjrrUW+9b34dbdd8FVhDFVqEcST3H4YdtwzM4d2LY86eHRPDggoSeiZwL4ETN/nYh+dctPPgAw8wcAfACQXDdd1wUi30+Pajl5mJ3ZmRznlvuPkXo+LBK2i0Oqv5r+ASg54SQy2uCTvNKRILE3aockapZqgZyb0AVgoTTA6vdMvUO6KVluTj0mlQhQ9uh5qoTy3ZkOWttScMDh6sg9yvNT5K7cRxhNxlhbW8PZZ/8WTjjhBDjncPnll+Mv//IL8Fq/1xvf96jzRSBoB25z5Gxj2oeAL5tIqJHgzDmvuCQGti4RguL85u3ixCXqsYqCii20IRBrsnio3wfaX6qPhiQVmwtdg/xACOX3PK655tt4w7m/i9/8zVfjwQ9+MM54xSvw/e/fjHe/axeWxuOQADBaEUJ70gbWey/peKZoZ4ruvh6avdS0UtalRruV0hUkFsVmc8zWhFG31JTIlq2VbMdIU/3G+fQavZoC6Erpd0hNF7oawfu+lKnr0XeyUW9s7AcRYTIZYXk55L0JgVsbGxshP78UCxfGMWxGJFXWZFzG8KEf5AkNN6BZCwfC3rUZ/vutu+G2sE4UtsLR/wqAZxPRMwAsQXT07wZwJBHVgas/HsAPwvU/APAgALcE1c0REKPspqAEMQ4ieUBd30IkmOgiE3Ik+2puiI1iZq3VoOSf7vbwJQEsRc8ymlMJSXqPcj9QH+3ivvwZOQKKdJBHmer/kgO1xEUr3icReiiAqx/MI5vEwKAD2YLS+4ciWgXsZpRHy0rZxRajeoJHP/qReP4Lnofl5WWs7VvHrl278L3vfV+4PK+6XhvJzEiFZQC4EDI+3GTomTw3fz5mw3O4uZrSSkIiyjPm2Qs2e4Y1jgsHmrtUaibPKC3IcCZpoNhQ8ijUnODa96QI54BLXQv2gbh5YO9d+/GOd/wxjjrqSJx99tk47rhjcf4F78Ell1yCv/2br4prJAGl04C8SFxxh8Cm2rb/kzuo7VN/DqLniknkV1VWLQZQTWHBczbvdiNKSM4xZ3vTNtEVVTdE20Z9fzmmNjqczPjKsxx847PrgsEBo0qKmGv/nQNmswagESbjGstLh4OZJetlVWHfvn1YW9sv2TOZIFmegutyJ8WLJGGjSLsdScYAdO6A+GzhbrlXBo7+t1i8bj4O4JOcjLFXM/MFRPRqAL/AzK8kMcaexsy/sdlzjz56Bz/9154FV6UMlCCP2o2y3dJ7H4KdtD12wvuclxSjKM6Th9RsTUQ26fkt96LvyXX/JfEoDcXxeD5y2Xl56rAutxTq9Xztku67Y1W/9A3I8oySOFF8n25I1kPBfgAhtjywMWjq5EjwoQSlwcrKMh77uMfg1FNPxY6d27G+vo7rrrsOl17yMayt7TeqKZM7PUhTzF2mS2USl83KzKl9r1YUSgQ/jZtNJibqglxdwpz0r7FNcUxE7eCZJce5SeFroczaacfORmfr8SZoOCuXcrHn16UNfshzJMOrwh+cjJQZccml/mmZPUCDreSatpvitNOei2c/69extraGu+7ah7e//e1Y35iByGFj/7oE+XUpgVmFccIFgswH9SVwHRvrnSPHEHPP2ApngKh92BPYcNpa2MT64ysBL8dEcCAdyzzqnGEiDFhVWmKSzNiGd1YZgycBfZo6I86RqpwHDD3CNIyh6cfVuKzeRKPRJKZSb9sWTTfDXXftQ9u2mE3b0K6QJ8f76IpLRLj885+9x7NXngPgY0T0BwCuAvChcPxDAC4housB3AngRVt5mBo9xJFFRLyuacOkGvfEop5l+t/3X/bB+GNVDUDIcx5AJYlSPC05miHxdTOO0V5fxehUA+aH3iu5Mkw7a1PEAGmBq8hGpK51eS3ZHktfgFVfJAQuNixORDc2OebkDgQtvHM8XsLxD34gfuOFL4jusDfeeCM+cvGlaJqQV78RtY5ydwghZKUEEnk+KYQ73HZ22TnbH0J/LnRshlQe9jxY4w6sR1dxzSbP67ezv5mXz5LrxK1QVRQW94ak1bLtQ22TqF6VkrVymib6k6I4l132OYxHIzz1qU/B6uo2vP71Z+Jzn/sLfP3Kq1DXFZpGiD4G2p+9O+juY54XbV+cpzQ7Q1IukAi/1kwW4utjaokSR3OD/zB3G5lElXSMP2y2MSJx8WU8iM25I+f7m7CjOu4inXlenP/gEAJU6DpvhlPaM52uY+RGYO8xchXqegnj7SMwS0zO3r1raBvJqa+pUjJpawtwSARM7dx5DD/rmc9H13L0Z+/QwQWdr+6AhAo2vseFGrEKdvAFaYJFO9sIPBwnFYgsJAo8rzcLVCe8Qkk45hH0GPln3sYE1EPifyGBRC5kwIsBABw3glAAuCjpVvbf8hS6kVVBxI0VlwYkk/heo4fMOftUsARALOTw3NOeiSc9+YmSQ7uqcNGHL8YVV1whnGrnY2lFWSg5xxufC4A4JRWT8mlp3OR6XUZ1bHfcqHSczHHl6Huct7FJZAvGc6hKlerUZpGzRPEd9redB0uQ9dmtLn/Oc7OLiqWKaSR0XnJpM1+fPbdik446nzMfjaOaT8l6tjgHVLVs3kfvOApvOPucWJLzttt247xd78Zd+/aCPaUqYXM4+lK9FfXwnjJ8ATxcFbj/gWA4xVUa4MxzwuajwdOOhUdOoIkI5Bw6LbQTmuUYWVyKEvkyZkfBcvTidUY5R2/6z64vFZjZyzZwD4qR0JUYPYL3ldiwUnBWDVQu2Fuk8Pr+/fuxvn8Dn77s4/eeyNgdO3fys571vD5XHYmCcYV0+YKKk4VRvCYuxNi1UAQEEFHTV/HKNOgGYbKo24F6sUSRUA659JWcwmbn4/MK0demjpU2pOyNCjUm0JzalryLoVLCqjsw2Feg2sf88Y4BhP6I7ldHTMfDqsdsnzWq1qP1LR784OPxqle9CkccuYq6rnHVVVfhzy+7HLfeuluIemf7n1Q1JThxeAmbcx6dqa6xwtVVIt25FLmaOq1Ru/ki7ZDnsGdPUbq2hF5VKHEcKd8ELAdJRNlmOAR2g/TBlU/al9Jec3CLBPkQvObh/RwvkeKYJfSbreFskzNjmwz6KYnYv33BaXjc4x6H8biGczU++alP4YorrsS+fWtR7RZemr3DDWyGlpOmuGbz2JahdeJMpSkyAYea0kO5ez2uUq9uXulZDuh8LCqfrd1COtDxsL9tBlvFQVGWytqoKFeGEGlaCsJspsGC4T/XIOMmGucj1L/WPEexfaGtNhmcMgJVVcXyhNPpFBd+6P33HkJ/9A4h9D0ulfvEQQl9j4MKhN7qaGO0YSBOckGf0HvvUwTiHO69JApDHin2vP4eiuzU//Y6m+TMPkuvVe7UQgUHH6pU5fcSxO+aQ4GRCqi6yD0ooRfx3oqjGnI+h9BDjGTj8Rgvf8XL8ZjHPAr718WQ9NGPfhRX/eM3JY9Pi0zEzQlirtsEENtFxDEas3CIMIRe+lQGNQ1xxEBO6KM6ocvntydhmNoD/fYn/bOdm/K7JfRs8cGoNzyES7OEXtNtl8SpTO+QCH0hlSDptu21wszkxA4AGE0gmFJCsqoJp59+Oh75yF/AaDTC7Xf8GOeffz5+9KPb0YQ86tl4KwMQIDNoozR89usd6CkprIOYUTI8DdbF0nrY6Hf10lFnDV1zztUgzxKwVEhB5bq1hH7IMKtrQD1iylz7+SYhG4KoYUNKbq7BXWOem8deAHntaRQeQeWmpL+993jfBz9w76kwBcDo85L2riPKxHcg4QCb/zI4QY83QIDBspOKiAxQnMykn7ah0va/XSj2t+fcqKdgESb910lM4qol7KVeUJ9TbmagXJSPft6cFxkZMiCzOO2mqEKkBa/FGIZ04gpEhLqSYscvetEL8bCH/SzuuPMObGxsYNeuXbjj9h+DqI5E3nvxEpJ+malIqURT37S6hyEEREmlJBuyifQdIK7RNbCY+1JPK/f3GQVmjukOsrYVYxD7Eb7GiODw3VVpfON96QFAwf16HwJleFivbxkMq8rQc3DUs//1pF0zpiVUbozON8G9EvAt4YMf+BAe+jMPwplnnonJuMab3/xmXHnllfjIxRejbSTnzqydYTQSP25viFRWBSy+zwZCIsS+6DEbKIUU0Agvol6A1ncmMM+lHDZhLppOSheK8ZVRVaIOIVe6eapvvO+t9fI6O84y3ykyPb/UStPSqrZtwUHVCRbGMK3pKtyT8NIWlmfK/fotLUi2goHovk3g0OHon/n83sLymtPbNNEXRAxI4k6PGJMvrtUCHVVEmMTtYPC5JYdfLiIHu5CGr8+RQvVu85Eq9qngymTLUB07kKImu1h0RPpEUN9m2dwqeJJ6mEQklab0PTEoB9EgmMTLLra1qglagJrZgyoxFs+aDYzqCabTVtXFcUNLY5XrKe042v4BugGm2Ui+8nqPj3hh753H0bNLm7kQ4AqOy+IyYeSqAUo4ACWeltJXxnFvAj5c7wJREo+MfjZRfVbp+63v9WETz5/cb1tuWNf2mvtIiHhdVxiFdNwvfvGL8cu//MvwzFiaTPD3f//3uPTSS9E2PhHpUPu2tzG6ftGWlIq4NK4mlYuWsszG1XgSeRM3p2mb27ZF13RRGnPOSeS3qcdabp5x7Q8wWtpeIoJHExgNUzx9DqpoHwCg81O53leoCtVTlOqUIUR/w2FO6ZYzBs/ECJx3wfn3Ho6ekOvhk1jJPR5zSF0SvsSFEEU14zGhXB9TBx8zBfb165v9zxBBQ6gH7s98cIkgKZCV0x/o/wCrVW4eCPlI8v4bnXKvHSo+K7cqCZa893FTSBtT6JJTIuPjb+dcXGQOKULZd4y23QAAbLSzIFlp/iA1ogOyOPJ41SHROd9ch4macDb9vto+qKQSzxdeM/pnbS9bgVJyK8/pf82VM+/aIRD9tyV2/WdnBHto/ByMOmlYz19uIOFo8DHv4L3ovAGgmYnB8+N/9kl85W//Dq94xStQVxV+6Zd+CY973ONw3rvfi+985zshkjMvGj7EGKUXyxxqtti0WYlqLoRGhzFMEjt7itHB9pne+0yl46iCD/pwRy7WidYUxqVEzczRi8Vu/pZpI2MXE81DnprFQqZic8Mbx9CcVmbcPDiu1UhLjCToiOAoV2UdCO5eNMg9CHZgI3fHUjFJ9L0i9lFQmVijWynexp26Q/ywD5xAVwliBi909SjQZzqEEHcgHJMrK2KpOq+/GTEwpvz0ufIagBsk8nrdEFjkiznaiSTdKwDnOzhO3inCnbSwVZwqEGonvrfkg9dHuLYihzFqOAYm9QiV8yBzb4pclUCnrhXupOsYbduB2aHrAN9RsJEk43lIvBcKlYhLqBokY1vJo3PJkye5tylXJLpWq35hJpAPXg/hw54iXlh8YOZ4jaM6LhbhKpVDq+K7BueGvfiq+y5+d2BUaFChCx8JcyEGqBoHUT3o8b1KlkGFp89gD8cVyAPOSxuYq6ztQxtRj3hzIvAybBTSUVPvo+2BZ3AnOOG8zJXDSLzYfCVxGo7QdC2mTYPv33wz3nvBBdi7dy+ICE0zxW++5j/gib/yeAAEz1Msr4xEBUUeoBagJluPdl4AJYhdGpcY+W7q0fou+ww9J+PEKw9PrXitkEdLHRgOPuYISnSk6UIRlDA2rhamxhZ2if77GKGiWnAYPuTsSZ+OQy2FmAxO7RgVfJeiXrsBqSGby2IjsBuCJ48u/LGTAkQ2b9WB4JBQ3dikZqW6QsANTq6lmzYZmD7HRplaDi5Pa5BKf+l1FspArNSG4UkpOT8rotkNyXIXJadl79FdnYvEYRRSvA7BEPeXcRHab+egVY+qqoqeL55LY6fmDxfOj9HBw3A/0VXOlHL0bMbC6GGtOi3o5FSVpuOifR/mQBHfo/PrTeRpudH64l4HkUZie8y5qh7wCCoWU1yMVj1iNgk12GmsAzNnukerKWGTbljVRvPW4zzpQ5+XG1qHN4lcvaP9MbaPTM2VdOLK9RIzHnj8cXjdFUKIqQAAIABJREFU614D71ssLa3gjjt+jAvOvxB33vnjiK+2zVFFo0Z2DkGR0MpgyeOpLwkk1U+5biznnW5KdjEbsao4r/dLyUefla+0OGvVOZJ/J2XUjeuoR6+DSrKsUGf6w9xF196y7b64NkpGKiFkrp9yvm1bvP+DF25JdXPIcPQ6sIos1rVoCIHsZJTHrLHlQMcVsgkHMqSy71WwXGhJ5EvO3r5jSAVg+6BjYfsmv0NSNi+fUg87tInZc7bdVvpR5Gl9E7LtSSIo5cDlv4s+0XGzQpU+lNRLpQ6+7NsQktv29fudj4Ud2yHkLTc2C5EZcJCYCfMhh0FcGwLm/O6cA5e50XoAZTRm7zle8uoMcfJb5ertuJTn5z1Does6NE0TSthJrdmlpSXxqUcVCaXi1Q//+20488zX45vfvAaj0Qirq9vwB//59/Hv/t3/gSOPPKIgvFVIbid4wl6KvOtGYtdl2Ub5TtlH6F5RV3qgf5txzUpbtHas5J2ZomkaNF2Lpmul5CZVICf58/V58+xqAiIVboZ/ycU49bFk6uxYZA4EyDUXdxcOCR09MEwQpJ+S6Ky8hlk8X3TncwPPKVF7SCpg5mwy+0QyX/RDk26PWY4A6HOmQ/rbDFGNuxwBUG8MUoKS3euy55eIIc+meG0koEyqwgdBysl572PAk4Zz237I9waeU16TOGaORfx3ShRa5CUZjf7d6sxjcdhhZM42luJ7fAaLysZGJMp7crABYrEtnEs+dm4isS8Wt9PUu97OgyFQoR4xzfFg0ipaoVHQrti5G1rI8wh1vNaqkTkn8LGvNpo4pqJOOMudx6ybomtaTJZGGNV1IDyhGIcaEl2NT33qM/jKV76Cs846C23b4lGP/td4zGN/EX/z11/BJz7xSelTkRRQ28vwUf2Y+pa3V9YXxf6obUfsD12UQq3XiurjlXvPiG5gaiynL0bdUNyIOTP46lgRqapUbE/xnSr9lm6vJC7LssGnDZI1wAriTrsZwfYsjg1kUkV3vk+HNt94cjgkOHrlBq3Ozu74es0Qh2clgHmcfwmWiAxxQf22Cai/ri15aGEeR6hEpCQkpRQz7yPPSHpG37J8CuJQEke7aJSLsa5waTwqjMdLKbMft/Dcou1maLsZukDgy/dJf4JeGd1ge+wY2g1xiLCVG7G9T8fvQMit45mNQTG3pUubHbeSu/Tm0zGb3xxy8nDR1ioY0iB6cC7Pb07It8LRl/cd6NrNuV/xC5egbAmU8h5Y27eOjfVZjEC1hIUZ+MmP78LNN/8AZ531W/jqV78aA3+e/L/+Cs477934uZ97uOiziUXNRR7WD1TGza7VbnD9D40PEWXuiAqj0WjTNWTvH5J6vPfoWkbXinukcv2ao9771I7KjaI0a71k4rMGclnFa3z/WAadF1sad2g6+ZScfubGugU4JAg9gEiEssGaQ9iB/sJQmKe2KJ+92aKwYPV48943RKzscRvlau8Zel/GKVPfO2RIFTAkjdj3zRsXbZsuZu+BruO4KSQwAWf2aNg04ubhhCsaGivmXOQeajMg+bpLYltuZAeCjGAbBqD8lF4LW3n+EPHxlqMPG7LijT53SMVX4uKBVEbz2rMZTg/2aU6VMSuRAsBsNsPeu9bgnMN4PM6wYDyeoGtFhfexP/0zvOlNb8L3vncDvG9BjvHKV74Sr33ta+CcBGPVzvXSU4QeZGNpx2Kob1b10jRNxshM2ybio+0/kMeqMOcVp0qm0eJH13VoG3mXZSjj8wOxPxCdKMdY77OMl37q2sXnWVwp+3R3CP0hYYw9+uidfOopp6UDlHPato3dQIct8bDXavbDoV3VGg7ld+IwraGV1He2IHSVBnNsmsY23Es2ZatMbuYGOmcOMk6YG3Mmj6zrIQPlxi0nK6z3bK06r2qdmBXQjGu2IRUbWGwNsyFqisBpXnK3wb7Ky75jiNOX70oINPtfYaD36ZleryWAOt8b36GNWr73ibiGunvfBmN1FRJvcVR/2Pw1eVSnfiNooW0PDeFPWSXzsaiy8SjntpyTiIaEIGEg80jL8MInA7rMc6EbN7jccZJEAfFXH1VmzI13jHirOHRdi//p4SfirLPOwv79+0DsMJlM8OlPfxaf/8svYHl5GRuzJhDmBjl+97XIQ/0HAHJdgRu6rrr+tXqd+e2cE8+cAi98J8/IDb4OzMm4mwh1P5+9MmUpFXKYM3Sxf2QM4uzy/kUtq09qREsj9LeqWZumwfu3GBl7yHD0Qzt3yT0N/daPlQgOBENcTkKYXCQcMpYBCB69iK5cQ7VYhwjMZgbhofEY6mvpoTD8HJ/9V/FcF6Z+dOwst32gcVOEG+K4h/o97xl3B5IInvLfH2iuNzOEKuTj3KGvQuiPc/TEKTb5eXOi6p1cOtV4A6uq6DIV21ZACbwSia0saEtA50E5z13XYdZsBKe+DtGtFVpMu0PXeVx/3b/gHW//IxBVgCOsTzfw/Be8AG/83TdiNBmDSNJai7/+gfENwOBYDEkuVoqy182jLSUuUrCfqBpy6B7L6ZdGYTu29t0lntiNZOhjn6OqYo1v0L5m9oYtwCFB6JkRuTAYzm0I4YcIPoCMeFmwA6q/S04yEfjcf1YmvS9aCYc/n9DoNcq1Ww+itIPLwi7z1FgViV2I/b71p64cG/tsDy2coOJhDbDbdIMc4nyHxMZSXB0SnQ8EB9pkdHPbbEOKuFAct/aRu7PBpA1FOLTaVRluAIhZDC0xGCKkzByyK1aDdpzsugFvi3nQGSJvYd5cWfzubbpzVDqWyDVNkwVFEVHUa4sNifDd716Hs878bVx11VVYXl7GdLaOBx5/HN7+jj/EM5/5dFSVcP8yjn2vqth+VQVWSDp+kvTFeo3iRNv6qPabB3bjUB18ZgdDJ0XLA7HXd7EIs2CWYiDyEXVOqdLJ56rvhaO2haWlpR7OlHit9gHtl83pMx6Pt8TUxr7fnYvvKdi+fQc/7eTnJASKSZ6SV0nijhJkC66IyAMQw477RDYl8IpWdCazydjdXhAYSGoj8T9vUYK6m1s1htzX1zmX3GLvWUxZ/0hrzBbEtwTZhMKl6GKQEDlR1TiqwZlhp8XcYKHs/TlR8Jzr2XV8NXW0N0heGVWcIn9JxOwiVKmjJJTltRYbvE9GXjZGM6qH84LYTSH1LaWoje9QLp7qrM1db+rUj1p/u17KAWbOPEWI8jzrco115+tnSCw3347zMPnyvB0z6/JJRNCUB0N46LlfvcuZOseECsvLy2hDCuBo78hSDngce+yxOPd3zsFoNMLGxgZGoxH27NmDD3/4w7j22msxGS8DkDz5dtxlDQ17vBGpfUjUjkP4oM+IBt9yI3GpELgGSmXGYsUZjeSlQvefFUDKjdVRPVQwXSQNj8fa0nFkYMMeYlB1nNq2xYc//OF7UfbKo3fyqU9/fgpmgJbbqyIiAToYNGcgE6Lrgq9omKAkDl4427Zt0bWmuACn1K0p+EQWVV3X6NijYs3kZzjboAlMiyfo2QZF1LTx2D6iMm6QXSJeEsvjC0KfuADt/2g0gvgAm/eVKobM2OnzPhTcWvndLhRA0iCTT+og7wMBIhcjYXUeZLGYETCbr/bTesTYeRsav5J7mkfoy02kYx/z/lTZ4kmEPhrBQxvBLmNEPOfFXnQsFQc8yPQ5dTr3OOLsvBLjlCsoz2haEvpScig3tHLOdMyYQ0AXJYN0Ob5ceF7LBpH0xS6kMBiPxzH3jI6NgujtO1SVwy/86/8ZZ5xxBrquiQ4AV1xxJS7+L5ekWI7AvSZvqJwpSP1QnA9ZKilEvVPOfDnnAN2cfM4osGFErJpELghBil7npj++VKRBsFKtbWvOMHKGbxrMF7n6yFtsTpMt43rxxRffewj99qN38smnPBdArooZ4lAUkZJaIoihxmVJfc0rouzehCgtVIRWAqHv1kWuv6nWxacc2Qht47Myd6ndfc5YkkTVMc2qbAJ5YiPl9JxzaJqm1167oCejcRyHjfVZQixnuODg8jgajTAajTCdTjFr9idE9Tnx1rY750K1nJD8ra7RtipRiVevUw+ioM+UAiGIfdcEUzq+zrlIIJxz0UOlZLJ1fGxfdSH0Nuu4QG3gWB2JqG4ctj6oJageKXqyP2PaX22LiRw1c51HDucbjr4j9s04Beh7pW3zJakDHbP9KdszD5TYAJCUGESpyMqA2sFuLCoJAMleUtd19NjS13e+MYQoJ6B1XePlL38ZTnzYz2AyEbycTJaxa9cu3HDDDairMbR4fNs2QEw+mObUrlGVeGJqi5AWOGfqhsdCnzNEmNO9ind63BhEuTVxIx7jehS+91Vlkv8orYH0X6QKABkzWFG+CUkxnDabn/F4jKZpcNFFF92LCH1Q3QBpAsrB0nOpiEGB+KYGpQ/ns1Ra2URabxdD1AvCCiBklTXJhahC20i+m7Kd1khaEmoLltBbbkw/erzMUU9EqUpOBcTwbqLcrTFwK6om8N6j81NDKPV5KVOkEjSVIpTDsIuL2KhHyIqoXbyfg9SjXjdVVUFZPiIyXEtOREruFEDMDZJvSDB+8ToHDKJRHMf4LsP5WFAibHFEua7+POXppLXPJXed7kk4qOAGVBBCWPPNQ8/Z6/rcbMJPO35DRsjynRneh3D/rghGtP0opRFlEJg5ZlDUZ8uYBJwLRKms+Sq4UeHnf/7n8apXvxLet5HBqesab3vbO3DT92/BdDoNBDLZ6KxaRH9H1RYHKYlSMF8+nsPEXPsy5Ollf1tCHyXPQOiBkBs/pvyoevfL89tsjmRcZKNcW1uT/ETFOGv7iChLowFIzMD6+vq9i6M/avsOPvmU5x5QZAFyfilNRL4LaxJ/y9FbsNLAAcUklyrX6D2+A4jzPBzySUg4Ty+sBMUSoFKKya/NCYoLmRdTicSAhJx8b60IyCzBH6DEEbisjJvPFrU+y3tvkC9wv3bzMm3UlpXG4qQmMJso5ec3A004F/sYIOU1shLfZsS3IGKGsy4JvT4jfe/jUElo842kz9GXhF6fMUToSxgyIpcbpF6nMNe4afM2cdILl8E3ygBZQm+ZDOs9lEtRiehXVRW5e/UYEc4WWFlZhquA173uNXjAAx6AqpKxnEyWceONN+Gtb31rYDjy+BO7oaXNI2ysHWJaZDs/+WY5TOxLL5w0Xn2OXplNy6jpGiMiwOfjmFSQ+Rjrd1Udw6X8NZbQ6zGrxbPM4VZ19IeE1w1BFm9FlGWRtKBZJYEcwYc46PS9zJCnC7jCUL1Z+1Gds2Z0QecBzQBZtKFEnpKT1N9DRHyIU9Nn6X1q1U+coKo/krVfz5Xfy3faMVNiY9un35MXkxlPouhWatuoMC/oTa/N1R3zvZbscTvOCiqxdd38wBr7KYlySSjT7+ReqV5YQ20batMw0Q/tzdd/vG4owtrOO5AToRIsrlpcKc8NSSClpFTiv+1Pqdq01+UeVnkMgHXfteO2vr6B2bTF29/+Tlx77bURZ6fTdRx33LF44xvfiHrkUFUSr0AEVFXaeIgQPYAsztu5sBJyUnEB3ms7+jaOclPN5zlsqKrGp77xNfyI6wQkBUQ8M+AoZhfV7/r80WgU1bZlG2zf7JwOSaqbwSGR68a5CocddlgP2SIKWf2hKTtnCZXlYlW3q8kIyzqTqlYoDY96f47o+SIW/9U6I6gWGUoiJf3r63PtArEqmnIDsTt/0zQYjUZisKqT8VG47SRGl4TbPtt3iHU5bXss0rigrpL3J8Qr3SZLAlQiniKljqH3vsiy2H+GJWxsxq6UkEwG8sBxYUtQEjues2DKAhH2nnKTjrYIkmLoJbFMbWZTIKaUiNLzR6NRbIPOofWb3myBl+/V+0tjoXpNEVHMx24JX1WldymxdKaWwJANTeeornWTJCxNRJdMcBjVVQx2YgbqaowPvP/DeOSjfgHPe95zsLq6iroGduzYjgsvvADve9/7cfXVV4uq1EldA3nHECPBGpMW+6PqMeZys0wqGW2+pv+YN7bee3ES4WC/wUjGMJyPm23X30QBwLeJnui4Vi65h3acsmlatLHrOK2nu5fnBjhECL33Hvv37+/tsMq5qU5WvifXK0FgMcK1dmHGgU4eHPmi7YcpKxFXjwC9D5DMkcqB1fU45GOfZcRvPB5D3UEVaabTaRboMJ2qntxniGDrXWbIYYjb+vo6jjrqKOy7ay8mk0nIPdPGTaeqkxpLslAieTExox7VgoQVZ2UEUwKzPmHT713HcK5vb8gJWnmfQXZvpBHS2E3AOeGsssIRBoEtJ2o3w459qLA1rOe37bTckIW6roWY2z6RB2LqYE2glYMl8pbwpnFIm1RiWNKYW7Hc4kodEohZDnQ8Hh+Qc7Ob8BBjMUR0ACQXS+TqlzSOVbaBOeei54plkmz7ckmwijai6DlW12B0EZ82NmYYjSr807euxT9961o84YmPw4tf/EIQSSbNl770pdizZw8+85nP4BtXXV3M63yirG6T2g/x3km6emUQdV0TlXM4PMbOCT1gEzvReR9VmtLvQtUa1Fiz2SxrP4ddJo6fSx54NjJW7y919PdKQg9w9CklIG2z5CWLXMz8Jioe73WSJWSZu1byqocJSwkCcuOM5Wz0ilJXrKlLE8eeCiITUUz2X1M9aMQR4psMlEp8dTGm96cFosS4bdvo9pYZYtlhMhpjur6BqqqwsrKCpmnA6gYW2j8ajTCbzdBNPZaXl7C6uoo777wzuJlV6FyH0Ui8A8bjsWw8gcYmZPKoR5LbhL24z62vT7GxsYFth6/COYef3HEnAOCI1cOxvLyMffvXozGqbVuMJmNMp+vZBtZ1HSaTCQCPjY2N+M7JZBJzlIyrcRznpunigpVxTWM9ZP+QZyPc28SNW4/ppjedTrEUCOjyZILJZGJy+0wKjp3RdRw9gkS8Vm8cG2kMeK/FqpXBCAu1rjDi5EJbjVI1o7pWIpOM6uPxEtbX1wd18wrlZmaP2TFiZlBVRamFjc85kZA9go+eH0r8CYDnFAEa105RSzcyJipNk9R2bWcdgGncxMbjMVxdYWM2w2QsTJoMlwvFbAiMBn//d/+Aa665Bq95zX/Ejh3bUVUOhx++gle/+hX47nevxwUXXIi1fesSrNioy6LiQp67ZkiyZkbacLwYTmXcJRDKuVxSs4SfWdJhLC0tYe++PTH7JrEL9Ajg8F+hcg5ahMa5Ii7Bi3MDOwJDvAa9ZxBcDMZURkCuT8xqWvdbrzB1SBhjt2/fwf/mpGfHgY0LTXcxNrowbK4PBWDcK9NA9Dm8kKnR5Eix15X/lTDHzLo+N2CmzSTluUjBLy67Vp+nz88WtGdo0IleXzlxYRuNRolDr+ssX4cVZZ0ZRz3W+cQhK3jvQZyMqFQlo1HakFz0be4Q4ghmTRjfoD6SjECxH+vTDYxGuVtibGeXxiB6BVVhjE3cgNpQLLeozyt9v+0GHg1aQA+fhp6n18n5ZDBTqdH2X8cjnet70yhetQavHHyxyfeN7WlDZINLCWeth4vFF0vYymPK9KjtRLltABhpplKfuF9bLIWpX6inyiqZGU5+oNKR3aDHY3EJrusahFRbONP9o4kcsauAJz3pSXjhC/8tdN2J2nKCz3zmMlx++eWo3CjMYT6vuqEn3f2wHUSchdOmXeJQSVuU82+aBq5CDKAr5yl7D5ByZVHCOWYGvNSd9SLqyfsL2pC/PyVN03QrzIz3v/999x5jLNBHYD02JIYOcTRAWnoW+SPxcybgxahu9LkWSo5AoJ+MyyKYfV6pHx/aTEsEKResEvbxeJyVN9NFr9faMGkbpWgNo6XkMKRyUOOQbDAIdowkhTATHEtpQntP00lBFCYfizdYoqTjH0fRzGeSZDza1m58VQz7tn3R30Pjp33tBdix+mW3cW5KrxQdM71WDX12TPVeO5/lMywu1M6hNoSs1Gk75+JGaYPDRIpooIVmNE3GEHdf4rg+2+Z8iUTaZEO0OGrxuQzsS2oY13vfvPVabjoqRRERDjvssOArn2eSzDc6j2bm8Xdf/W943evOws03/wC69tp2hmc84xTs2rULD37I8ahHDnVdRUNt13WYzWZREiz7rKD69oTvuQOD4kvKpCrMmtpfKtd35R1iOu3clCo4KzUMwVwbm/eAH1YxbQaHiOpmeKAcAzCcBjFHt74eF1PcX1VVQlwiIHJKSXevert5ol7fmo9Y0s8dwD1Q2pYIarkplNfGRYe0wenEVkHsU6IT24c2FZAIf533iYvI+pUWezZupnOih5eNMOnlk7uhc7n6qZQm4DjaDQDucd66QaXxmT8e884B+QIr79PzdiGVhLlsQ+JQE+FTjyPdo7Tot75bhiDXV8txytpAnERwe51yo8R55LQmsdL26b2lumqIUOjzrcrLbiySK4bAPhWyKImUxkeo7SLbDHQsKdkaynVTbrJKaNfW1jAajXD00Ufh1ltvBQNxM62qEeAlD5OqyWazGYiAd73r3fC+xTnn/DaOOeaYICGM8Hu/9yZ84xtX46KLLoLv0rMsIzM0VtabKbU/zyTbY+A8wQc3ZHH5FFuMhaF14YiCOqcvacnKQs9NWa5J+Dvk8VRVVTTubhW2xNET0Y1EdA0RfYOIrgzHthPRF4nouvD/qHCciOg8IrqeiK4mol+8Wy0yMESA7UeP9To1YKjIuR41zgyD1e32HzRsoNP/vYXDfYQY0jEPQbnQLDem3G05Jj3iW7RR7ys5Orux6LGy7XYMbV89FZwuEZJ8JR8hmv38QCV435/n8jOPgxoav824YPt7SHK0363UsNmYlO+wUmkpEUSuzaRStpx87u6ZS32lN4a16TgntgLnIOrPouiH/V7iiW3fENjxLCOP532YReW3Z88e7N69G6urq0bCISOx9d+9sT7DbNbij/7oXdi9e7dsCgD27duHRzziETj99NOxc+dO1HWN1dVVTCaTrCjJVvBE2imeQsKkKH1wcaO3+DIksdu5H4J561Tfn9On/L7yGUPc/oHg7qhunsrMj+KkD3oDgC8x88MAfCn8BoCnA3hY+JwB4H134x0ZzCP0QC5WDi1KIppbI/1ARFG9bhSGCIsu4JKoH4gQ2XZbddI8UdguFp3k0l9dn2u5xnKTGCJ6Q+1kZjEgzVFVlcQq3ddXqWh61clkMrdv5fehednKBl/OvR5LHLjrXTfvfnv90HvtRms3W7uBlxvp0ALXe+ZKSAPf8zHnrI9lf+x1oVWD1w0RjSFi0jHHILZynIeeW+Kucw4/2XsX9u7dix07dmBlZQUrKyvYtrwSvY5SZacmy167traG3/u9t+DTn/50kC4lp/2JJ56IN5x7Nv7jb74KzIylpaUeXRhq4xCulFKXjl3XdfCd2jry51mGqxwz68lk+zI03paelOvdPncIj7YC/3909M8BcHH4fjGA55rjH2GB/wbgSCI6bisNcUi6zaqYCO1YxwwmkuhGBJ18sXC9D/VP2aFCFVLJJi6e5SaAKX73qCQjoUbZMkQ89Sxhd150pZr6oORsZfAbJEu4i5/NyqSV/zWVAbNkTKyrsSAJt+h8g843kKRarWmfJHViX6HjSow1TlQpmit/XsyAQsY1wsNzE9urxipCDXAF9k4+geupqYY1FqnxEXDYtm0V4/ESxK/aoWPAg+BB8btjF3IVubkERI+L8VdapqX9mDQqkwFoKmb5Xs5zmSJwM0Jur0lzqscIQA2iUbRnyL1twAu7UJOkEjBUPo4k+thVqKhGXmdXdfs1lOvVYzoO1vc7ZzKqMJYpQysDQCfj7NjBQYyB8wiGLRCvReJrEKrwu8R9BSvH6bzVdR2J2MhVWF9fx+7duzGZTFDXDq1vTFk+zRIqXijeM7oWANcA1/jyl/8ab37z/4k9e/YY9VqLE044AX/4h2/FM57xdEyCN5XFmTR2WjYxSVm6NlWaUtyJEhU6eJ7FtUBEAI9AGAOo4IPHDDPHoCgPUXF13MEbDzuLb23w73dE4qFDBHIuPkNiyh08uxio6FnUswygaQ8sIStsldAzgP+biL5ORGeEY8cy8w/D91sBHBu+PxDAzebeW8KxDIjoDCK6koiunE7XBxdbSZjKSbPcrHkuiAjr6+vxOnvO/s/PFVwkWULjsnuiM1Cx45abzbyd1+7W885rtJx1sRri6BIEQt5B1Evh4/X3JqDtkSChlP9GjnfpPzzIASAGOSuylpHH0q7pdB133nk79u27K6soNI8zmce1z+Pyy7G0hrQhjulAHNE8ope4vyHbQRqrIbVcSYjlHX3p4kBc8Twuz15XjpWeA5LtZzOuUq+30o9KZbZUXrkBl2q/sv16nz7De4/19XXccccdGI1GWFlZyWwTNm9S6jODGWhmHrfvvhN/8AdvxWc/++fxnU0zxWQywcmnPA2///v/CUcddSR8YM5043cOvbEuq4qV67icS924ZzPx9Ve3Ww3oOhDnPW+eyus3ww29ZqsqYGDrxtgnMfMPiOgYAF8kon8uXszUr6CxKTDzBwB8ABD3Sj0ejViUDKWwSYkKIqD3mOfG77bsIFR94cRA4tmDxYNVTgfTSAdGpZw/IZ5nJhAYSR+UW9JDa2Mb8gnKdXi6aK1HhPaPXIWqriVtciV+uW03g+ccQUoEUIM1yIGqNI66sLTMWl89IcYfhdzwl0TPnDvyZl5y41Ya7n7Epejo654NZWhR2LaWxtUh90ofXE2zsn4yS+Ga9FxrILNzoufTsxk234q9JhF9Z3C2huYxsnibRHgrVbnsPdDnUwW72K26RZ+tPuOq9rH4ZCU3ZkbtasF17iQUPxyPeYvMuNix1wym+Tvy8YlzU7kizz7FtWc3F/09mUyEUHYz7N69G845HLa6AoaLEckiyaR04ZLuIsRqeI/prMOXv/TX+NrXvoZf//Wn4ylPeYp4ybTAyrYx3vS7v4Ovfe1r+PSnPovZTLK8quulBjHqBmLntSS+8n7E9ohrzRE/AAAVdklEQVT7a4cUH+gymjVEl5J6Lk8tUVWpMFE4KP803UPnYR06LNgNeSuwpSuZ+Qfh/48AfBrALwO4jYJKJvz/Ubj8BwAeZG4/PhzbWoPmBIrM43hCu+J/+7Elv8ryX0Mwj7NSKA24OthDk2zbpUad1DZB4NFoFA1HdV2jruvoTtn6rmc0BeW+6UPtE47Wh+8IBR2GOcO8jQKlkQ9AFHWTSCt9KH3JSylG31dVI6iapySwJZQcjyXq1j5hn5+1n5DdX7oIlu+fx3np+7Sf6V1J55+uObCBzOKCjoUet1zvfA7fQ3XTJShel/pli5NJJTLUpn6/FUqbT2nz0DZbl07mPB+Ucuuli3DcjL3HbDbDjh07sHPnTiwtLfWKYwP9Tc0D2L+2gcs++xc499w3omu1kI6oIp/whCfgveefh6c/41RIMFSKktf5KrUBQ3PgQnBk1w0zmcwcSinOpythdOeOf5/eSSlGe27IJrlVOCChJ6JtRLSq3wGcAuBbAC4D8JJw2UsAfDZ8vwzAvyeBxwPYw0nFM+8tmWFSO1ES0M0GZt6CLfqSPXvejljq/PP3zN9whjYpJVa2X6pbrCrCaFTFj3NAyz5GdloC0oaSa+ByY8kTVtm+2TG0H4sssumgZ4gqkUnekWwN2fyEsmuuQvTyYBYCf9RRR2N1dTV779DGPG8uy77ZPtpjPOd++1u/2//zCKu9zuJKIk5tJLxlv4Y2k953o7PPN3TXu5dZxjSN4bCKs7zP9muov+W5su8KkXMvxsjquW077Lv1XnuNlRL0+v379+Omm25C0zToWkblEhOU+hNsHy4YQVvGxsYUa2v7sbZvHa997WvxV3/1N1kbNzY2cPLJJ+O89+zCMccck6VFHor3GOqL3RSICOC+wXZo3Oz4mdGN34be471H41NU+BAzodf/j+bojwXwVSL6JoArAFzOzJ8H8H8BOJmIrgNwUvgNAH8B4F8AXA/ggwBetdXGlMjHjqTikhPR3JOPJk5ijh8HSA5sdPDh0zmPQYLgc31pIhRCrBQ8IO9EEz9q5JTSgGL0UkOccGoVVBuWuD8PKRkn+sLxuMby8nIgiB3IeUyWalQ1iYG1a0OuGg/PBM9ilIl2AUI8ri2NhMClGrQlAcgJvWwyOZeUOHQlXsK8JSN2TvQTEaaY4zN9AI+VlSXs3Hl0TFhnud5cRdfnroc2TEv0Kg4fCtWO0Cditl/WwDY0Jt6LidgRwxEHc7Eq86yro9otEldux0OlACv1SNGJFkALMdg3YMzAfgZwB2If8FaM+UnXb+Yw5lfp4v9oJC+YFjVM241ZU3Oka8PziKJh246xNZp7EOBCXEowFNosjPp8K+Fp+7Ud+r2iGsRORC9XAa4CB/VE13W44447QBUw3jbJVH8Zo6SpibkFVVLMpuk6jEZjfPzPPoUvf+lvsbS0EudfN4hz3vDbeMADjg3415mi22K4Zl9FrlxwzwEs5zVISvFviEHx3qNrWhBX4M5urKL+SZcz2HUx+FD6ouPuQzoExYGkAiyJ/d3h6A+ZFAinnPLcXsM79P3ZHecRhhERiCLXA73e5IbtEXxDbOSEOsya6EFuQd62wYqmVUYoAJjAl1RFSDmRyGE7xvLyMg4/fBV17bC8vIzl5WV0HeOGG25A26R2WbV3yUEQEShUyrLHwKmKkwUrdShHXj635NwsESk5QvvMIRuJ5ThEhdTFRVUSc0Vq+66yfVZd48nDqZjsUv4hBZv/SZ9vf6sqq9RnE1LVoLiRaUWzWO0ob599biIsudQyFChGROBQr5e95MSRzSiXtsp3SdvSXJa1GLQNNrgrjWNSt3AnhNi7oEIxhuQSJ8q5tf2Qa/oGSLv5ZZBVgjPPYo+2m+lLsby8jG0rS8HVMuWfkpxMKXAvU+OF5i0vL+Oww7bhDeeejZUVcbeczWZBPTrGt675Ni688EIQUSz0rQWFhIgnzznNiSMq9uEYmJzwdnAhPQOH4iTO2fQVaVyH+HyisIHqMw0uD0m2H77oA1tKgXBIR8YOidPlThqJBQBmB4YH+xABaFKG6gSpuGYzJoanh5ZIlF1VVSBXWugN58YcE7DlU5baGomW6wKXBTzkISfgzDPPBKjJiObq6ir+/LLL8YmPfza8I8/tPbwhDwhk5GFdCMuFWlWEpsmNm/a/Fcctl2gXrz1vCQ/iCEr+8NIlUO5Tl7Dh1BZlX+2GETceRqzlzsxiVB/AlVLy0HYrMS03gHIeZe6UGqF3rnzXqKrhLZNAhKpWLya9ngURYn/TZiTd7CLnV+rcFUQak2clYsfhWW6w38J49J+lzEhWdpMRiU1O0Pv4KAyMvJ8IgXPN50vbIapH9DZYhVE9QecbeBZVjiPGyopw5nVdYzabydqksQwjKjMvFDectbU1zGZTvPF3fhc/e+JD8frXv97Ydzbwsyc+BOdf8B586E8uwhVXXBHGv0VVV+g626YO4mZWxb4CiCpU5ewtHgjDov/1mI6dzpE+g2OSQTtfXVvmm5q/yW4VDhmO/tRTn9M7rpya5Rxzbq1fQFpFRlQuS540ROgzP2RNJeApcZ/kQd5WhrG5S+reIlSipPMTz5PkzTj88MPxgAc8ACeffBJmzX7JDdM02L9/P9bW1vA3f/0VzKbJUKVSQOll0ucmiwWZnQtj1wVJueCChhYzD7yjBDsnzP1c3s4klNNNwVGNtpsJd0xp/vQa+2zbV7vBC5HoF7RmwwXlGV31uZZoDgdPEVI+nEiMTGK9kiu2bdDMj1rYPiW3A1As2MgAsLSFUKH1KaeNPtOm+bVjUW7GpfHUjqv69yseqYQh7/ZoGGBbFpDTeNm5EWZqng46r7amnijlJu29B7yJFLbr2xt84TbeX1UVxuMxlpaWAIjOnUObqqqK9iyZP58ZfgF1GSac/rKX4JGPfCQ02aCohQjT6RQf+pOL8O1vfxtdJ4xI7gAAOBqLtEWqAhROP84/WQO1B7gOG33yzLKStDObqAaKWVzWOr7OOcCna4bW5EX/5YP3nlKCmxH6UozPF3HO0dqkXUSMpssJY1zU5HvEJCJzZxYjOlQGuVPaYQZTLo4zi61AkCy1dzQawVUep5xyCq699lp8//vfx8rKCnwoswYSjwOpOA907XCKBStWl+oNK66HiwbHueyzVVNk1xWEJY62SwKg3idjnnN8gBD6kgO0bXZUxwyJparDwjB+9lUMntJvLe4hz7b3J2I9KEGa+qmRE3UhE6ZR3chYmGd4kzrCBLzFTcald+m4C9ELbUEViJ7P1AOO6vguZSxKdY4tA5gM8AlPlZu0hN45Fwm9h81f5NOGxakubFRBVmn+c5fCLjP+W+NhNr5Byk7TYWw0XQr+kQCvDtONJrt3eXlZjKl1hf3798diPFGXD5HEte5xPZKgr66TYMMTTngwzjnnnDjHWqC9rmvs/tEdeNvb3o7pdBoJfTTMetlwqzpt4GzSBuvGJNx5B9+FdUgtJpNJTNgn79Wx6ktfcc5M7iPifj0NK51+5JIP3buyVw6BNd4lguR713Rtv4QacU5EFBEtwbRIWYqjQ2CJkeoO7Xtje0oXQFT4wue/iFtuuQXMjPX1dcxmLabTBtONBs2sw2zW9nLX2OeWkzxvvMrFVW5GW9nYhQvsf6xXQumdMO99CmleLKfne/doP+z/rbS5dKMcusculrLNpdRU4sDQuJYbWOmqFyNUrZGaHbqWMRTE5lwKKrKb+dBY62/1clKPJyns0fTwU718SjywRAZFfIHF4TJNsvU0sqoZeafgUOmNpPfbMdR7yrH2nRReUam76zrcdddd8d3btm2Dcy5GwMZ7jeol2cgc6mqMH/7wNpxzzrm4/vp/wbZtYdMIgX9HbT8C73znO3HSSSfFFCg6D1VVoR65GPRXEl1CFX3zdZPVe5umyeba4su8zbB0oWyalA5iKJPrVuCQ4ehPOumZmRjKzGCXSqhpxyv01TVq/FSQ4AqA6sIApcTTUw/RK0htRw+rL05eLEBObD1B6sgi99HW1LMcKtyPx2MwdWjbLhJLsAu5341eLnJmfdetktgLDEfFyfV5KL3c1zeYCuFVLq0odmLujQQYwiF774O0lQzflV34PnE+Vv2kkpAej+2ds1nY0nUW/ICRvuRZInceq0QlLtOqbKxITLbugUN2znLzEsRSSEE6Ti5IVuQHible671HRSlwR1U+MMzHkI7ezkfPGKnnSNQHKmkJRyhrwhLOkgO34+/QT/NhvdKU07eqpZKgS0GRXAJRlce8DSVKzMxxjQAIBJOxNFnBYYdvw7Zt27C+vo61/XsjIRQVi3GO8B6usl5kKUBxx/bt+O2zX48jjlgNleDGaFq5dt9de/Gud+3C7bffDu48XC2c+mw2hQZYzUuKqEFp4KSasRKPBCdqqpHkBVVK2OW8uypJb7Yvl15yLyoOLuDRNFPY7H3A/Hqq8jv5dOsitt4bQM5pb+aa1A1wpyXYSehJEETRN7eqKlCVUsZ2Xe4HK1yMfDTQQrlmIHESQ/1W8X1Y2smJeMkZDG0YmgtH83OUn459/CDL36OeP4DTiM1QPL0URTW9sqgMOjj47P98UBfDPJNjHnSU2hMJBCf9v83Vot8tgRvChXKu+3Oez3suKaj6zMXfytmnOZY2ey950bXgtFUBAaV/fWpXjssO5XhQ4RHmXF/i1O+Wwyz7UhJem1qjbYTrViml/EQdtk/q0JK7nydhJlzOI8gZDk3nsXfvXvzkJz8RvArvV8lAr7fSsfZVpH+p63rrj27De977Xlz59atQjWrM2ml0S11dXcW5574BJ530NFBlHQQocu4iNTWwbqW6LmEZqRjkpnYVneO8NoDl1odwbkhCHvIAmgeHBkd/9A4+6WnPAJBzsOz67n4a4q6/lfMBEmegAyvumbkeuqoqILOsC6QgFOsW5uMiKQfaEzByFdRVj5lROymN55zDrNkw16sqQrk3xHblbZB+2Tqzpb5zSOWh/VKOIL7HjA85k13S241QvEKGiF0favPuLhpA1W5Shao7kru97s2J9z66K1pIlbjSWEejerwmt5VYPClBhTBLWDIDrss9jOzz9Z7INZkpkuNaWajL2qzXqyFNDHFVlPL0On2OEvnYD5+kJpvbqMTxklh6g6/JaBcMhjEiWu0puXRoC45nUh4RfNsVY07ROJxJeQVHn63f4JWiuv6hdTQE+gyb/tqx4IlI6+K2OB5LauK2bbGxsYGmyfvjnIOrUj7/rpVxGY1G6PwU4/EIo1GF7Ucfibe85S0xp706Dqinz65d78GNN94Y+9o24gpa9kOkijoyQNa47nUuHGexPIMbq5FYk0ZDsurqZqY4d+klF917jLHHHHMs/+qvngotSAyEHdklhFeRUwh9ISJySgMqwTOhTBoBQ37Pzrh/xWpNgVq5zDWM4yKxwMxA5VCh8MnufMqjkYmsWqUGUdVhRb/cM6HNOPG0oPr6TSWEJadeVSMzLmr07ZDlzYjxAqraSpvKPLcu5UKYpfas9z4WPZbFEQyErRnXQoJKw0CDOkZL6PWeuUZjw3ESapFBBgiWeupEbxpKBKrkdFUnq8+wqp4hCcBeY0EM8sFAaaSIbFxdKAZtfO+tNFp6Ig0BW+nKjIcdg0Ts+2rPIS7bzktJ7MsxKedX75G1JcfadmbuzTdGvV915Yp/uibbthXXT7ZSS7JnjcdjAMDKyko0pNp8NmDdZKqIV/LChOt17eC5xctffjoe+9jHYjZrI344qjGZLOMf//Ef8ZGPXIKNjY1o+9A+aY1fYWQqoVE2C62xDyYpy8XEi4lBC/VsOamYdC6cc1haWsLKygrW1tZARDj88MPxnvP++N5D6IloL4DvHOx2HIKwA8DtB7sRhyAsxqUPizEZhvv6uDyEmXce6KJDJGAK39nKrnR/AyK6cjEufViMSx8WYzIMi3EROISMsQtYwAIWsIB7AhaEfgELWMAC7uNwqBD6DxzsBhyisBiXYViMSx8WYzIMi3HBIWKMXcACFrCABdxzcKhw9AtYwAIWsIB7CA46oSeiXyOi7xDR9UT0hoPdnp8WENGDiOiviOjbRPRPRPTacHw7EX2RiK4L/48Kx4mIzgvjdDUR/eLB7cE9C0RUEdFVRPS58PuhRPQPof//lYjG4fgk/L4+nD/hYLb7ngQiOpKIPkFE/0xE1xLRE+7v+EJEZ4b18y0i+lMiWlrgSh8OKqEnCcs8H8DTATwCwIuJ6BEHs00/RWgBvJ6ZHwHg8QBeHfr+BgBfYuaHAfhS+A3IGD0sfM4A8L6ffpN/qvBaANea328D8C5mPhHAjwGcHo6fDuDH4fi7wnX3VXg3gM8z888BeCRkfO63+EJEDwTwGgCPYeb/BZI4/kVY4EofbJj1T/sD4AkAvmB+nwvg3IPZpoM4Fp8FcDIkcOy4cOw4SIwBALwfwIvN9fG6+9oHUlD+SwD+DYDPASBI0Etd4g2ALwB4Qvheh+voYPfhHhiTIwB8r+zb/RlfADwQwM0Atoe5/xyAU+/vuDL0OdiqG50ohVvCsfsVBBHy0QD+AcCxnIqp3wqp2Qvcv8ZqF4CzkZLdHA3gJ8ys+Wxt3+O4hPN7wvX3NXgogN0ALgoqrT8hom24H+MLM/8AwDsB3ATgh5C5/zoWuNKDg03o7/dARIcB+CSA1zHzXfYcC+txv3KLIqJnAvgRM3/9YLflEIMawC8CeB8zPxrAGpKaBsD9D1+CPeI5kE3wXwHYBuDXDmqjDlE42IT+BwAeZH4fH47dL4CIRhAi/1Fm/lQ4fBsRHRfOHwfgR+H4/WWsfgXAs4noRgAfg6hv3g3gSCLSlB2273FcwvkjANzx02zwTwluAXALM/9D+P0JCOG/P+PLSQC+x8y7mbkB8CkI/tzfcaUHB5vQ/78AHhas5GOIIeWyg9ymnwqQpPv7EIBrmfmPzanLALwkfH8JRHevx/998KZ4PIA9RmS/zwAzn8vMxzPzCRB8+DIz/+8A/grAC8Jl5bjoeL0gXH+f42qZ+VYANxPRw8OhpwH4Nu7f+HITgMcT0UpYTzom92tcGYSDbSQA8AwA3wVwA4A3Huz2/BT7/SSImH01gG+EzzMgOsMvAbgOwP8DYHu4niAeSjcAuAbiaXDQ+3EPj9GvAvhc+P4zAK4AcD2AjwOYhONL4ff14fzPHOx234Pj8SgAVwac+QyAo+7v+ALgPwH4ZwDfAnAJgMkCV/qfRWTsAhawgAXcx+Fgq24WsIAFLGAB9zAsCP0CFrCABdzHYUHoF7CABSzgPg4LQr+ABSxgAfdxWBD6BSxgAQu4j8OC0C9gAQtYwH0cFoR+AQtYwALu47Ag9AtYwAIWcB+H/w9qca62UZ0BqAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#reading in an image\n",
"image = mpimg.imread('test_images/solidWhiteRight.jpg')\n",
"\n",
"#printing out some stats and plotting\n",
"print('This image is:', type(image), 'with dimensions:', image.shape)\n",
"plt.imshow(image) # if you wanted to show a single color channel image called 'gray', for example, call as plt.imshow(gray, cmap='gray')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Ideas for Lane Detection Pipeline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Some OpenCV functions (beyond those introduced in the lesson) that might be useful for this project are:**\n",
"\n",
"`cv2.inRange()` for color selection \n",
"`cv2.fillPoly()` for regions selection \n",
"`cv2.line()` to draw lines on an image given endpoints \n",
"`cv2.addWeighted()` to coadd / overlay two images \n",
"`cv2.cvtColor()` to grayscale or change color \n",
"`cv2.imwrite()` to output images to file \n",
"`cv2.bitwise_and()` to apply a mask to an image\n",
"\n",
"**Check out the OpenCV documentation to learn about these and discover even more awesome functionality!**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Helper Functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below are some helper functions to help get you started. They should look familiar from the lesson!"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"\n",
"\n",
"def grayscale(img):\n",
" \"\"\"Applies the Grayscale transform\n",
" This will return an image with only one color channel\n",
" but NOTE: to see the returned image as grayscale\n",
" (assuming your grayscaled image is called 'gray')\n",
" you should call plt.imshow(gray, cmap='gray')\"\"\"\n",
" return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
" # Or use BGR2GRAY if you read an image with cv2.imread()\n",
" # return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
" \n",
"\n",
"def canny(img, low_threshold, high_threshold):\n",
" \"\"\"Applies the Canny transform\"\"\"\n",
" return cv2.Canny(img, low_threshold, high_threshold)\n",
"\n",
"\n",
"def gaussian_blur(img, kernel_size):\n",
" \"\"\"Applies a Gaussian Noise kernel\"\"\"\n",
" return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)\n",
"\n",
"\n",
"def region_of_interest(img, vertices):\n",
" \"\"\"\n",
" Applies an image mask.\n",
" \n",
" Only keeps the region of the image defined by the polygon\n",
" formed from `vertices`. The rest of the image is set to black.\n",
" \"\"\"\n",
" #defining a blank mask to start with\n",
" mask = np.zeros_like(img) \n",
" \n",
" #defining a 3 channel or 1 channel color to fill the mask with depending on the input image\n",
" if len(img.shape) > 2:\n",
" channel_count = img.shape[2] # i.e. 3 or 4 depending on your image\n",
" ignore_mask_color = (255,) * channel_count\n",
" else:\n",
" ignore_mask_color = 255\n",
" \n",
" #filling pixels inside the polygon defined by \"vertices\" with the fill color\n",
" cv2.fillPoly(mask, vertices, ignore_mask_color)\n",
" \n",
" #returning the image only where mask pixels are nonzero\n",
" masked_image = cv2.bitwise_and(img, mask)\n",
" return masked_image\n",
"\n",
"\n",
"def draw_lines(img, lines, color=[255, 0, 0], thickness=2):\n",
" \"\"\"\n",
" NOTE: this is the function you might want to use as a starting point once you want to \n",
" average/extrapolate the line segments you detect to map out the full\n",
" extent of the lane (going from the result shown in raw-lines-example.mp4\n",
" to that shown in P1_example.mp4). \n",
" \n",
" Think about things like separating line segments by their \n",
" slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left\n",
" line vs. the right line. Then, you can average the position of each of \n",
" the lines and extrapolate to the top and bottom of the lane.\n",
" \n",
" This function draws `lines` with `color` and `thickness`. \n",
" Lines are drawn on the image inplace (mutates the image).\n",
" If you want to make the lines semi-transparent, think about combining\n",
" this function with the weighted_img() function below\n",
" \"\"\"\n",
"\n",
" # Right/Left Slope\n",
" lslope = []\n",
" rslope = []\n",
" \n",
" # Right/Left Centers\n",
" lcenter = []\n",
" rcenter = []\n",
"\n",
" for line in lines:\n",
" for x1, y1, x2, y2 in line:\n",
" slope = (y2 - y1) / (x2 - x1)\n",
" center = [(x1 + x2) / 2, (y1 + y2) / 2]\n",
" if slope > 0.5 and slope < 1.0: # Right Lane\n",
" rslope.append(slope)\n",
" rcenter.append(center)\n",
" if slope < -0.5 and slope > -1.0: # Left Lane\n",
" lslope.append(slope)\n",
" lcenter.append(center)\n",
"\n",
" lslope_avg = np.sum(lslope) / len(lslope)\n",
" rslope_avg = np.sum(rslope) / len(rslope)\n",
" lcenter_avg = np.divide(np.sum(lcenter, axis=0), len(lcenter))\n",
" rcenter_avg = np.divide(np.sum(rcenter, axis=0), len(rcenter))\n",
" \n",
" ly1 = int(img.shape[0])\n",
" lx1 = int((ly1 - lcenter_avg[1]) / lslope_avg + lcenter_avg[0])\n",
" ly2 = int(img.shape[0] * 0.6)\n",
" lx2 = int((ly2 - lcenter_avg[1]) / lslope_avg + lcenter_avg[0])\n",
"\n",
" ry1 = int(img.shape[0])\n",
" rx1 = int((ry1 - rcenter_avg[1]) / rslope_avg + rcenter_avg[0])\n",
" ry2 = int(img.shape[0] * 0.6)\n",
" rx2 = int((ry2 - rcenter_avg[1]) / rslope_avg + rcenter_avg[0])\n",
" \n",
" cv2.line(img, (lx1, ly1), (lx2, ly2), color, thickness)\n",
" cv2.line(img, (rx1, ry1), (rx2, ry2), color, thickness)\n",
" \n",
" \n",
"def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):\n",
" \"\"\"\n",
" `img` should be the output of a Canny transform.\n",
" \n",
" Returns an image with hough lines drawn.\n",
" \"\"\"\n",
" lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]),\n",
" minLineLength=min_line_len,\n",
" maxLineGap=max_line_gap)\n",
" line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)\n",
" draw_lines(line_img, lines, thickness=10)\n",
" return line_img\n",
"\n",
"\n",
"# Python 3 has support for cool math symbols.\n",
"def weighted_img(img, initial_img, α=0.8, β=1., γ=0.):\n",
" \"\"\"\n",
" `img` is the output of the hough_lines(), An image with lines drawn on it.\n",
" Should be a blank image (all black) with lines drawn on it.\n",
" \n",
" `initial_img` should be the image before any processing.\n",
" \n",
" The result image is computed as follows:\n",
" \n",
" initial_img * α + img * β + γ\n",
" NOTE: initial_img and img must be the same shape!\n",
" \"\"\"\n",
" return cv2.addWeighted(initial_img, α, img, β, γ)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test Images\n",
"\n",
"Build your pipeline to work on the images in the directory \"test_images\" \n",
"**You should make sure your pipeline works well on these images before you try the videos.**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['solidYellowCurve.jpg',\n",
" 'solidYellowLeft.jpg',\n",
" 'solidYellowCurve2.jpg',\n",
" 'solidWhiteRight.jpg',\n",
" 'whiteCarLaneSwitch.jpg',\n",
" 'solidWhiteCurve.jpg']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"os.listdir(\"test_images/\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build a Lane Finding Pipeline\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Build the pipeline and run your solution on all test_images. Make copies into the `test_images_output` directory, and you can use the images in your writeup report.\n",
"\n",
"Try tuning the various parameters, especially the low and high Canny thresholds as well as the Hough lines parameters."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# TODO: Build your pipeline that will draw lane lines on the test_images\n",
"# then save them to the test_images_output directory.\n",
"low_threshold = 50 # Canny edge detection\n",
"high_threshold = 150 # Canny edge detection\n",
"kernel_size = 5 # Gaussian blurring\n",
"rho = 2 # Hough Tranform, distance resolution in pixels of the Hough grid\n",
"theta = np.pi / 180 # Hough Tranform, angular resolution in radians of Hough grid\n",
"threshold = 15 # Hough Tranform, minimum number of votes (intersections in Hough grid cell)\n",
"min_line_len = 40 # Hough Tranform, minimum number of pixels making up a line\n",
"max_line_gap = 20 # Hough Tranform, maximum gap in pixels between connectable line segments\n",
"\n",
"\n",
"original_images = os.listdir('test_images/')\n",
"for image in original_images:\n",
" img = mpimg.imread('test_images/' + image)\n",
" vertices = np.array([[(0, img.shape[0]), (450, 320),\n",
" (510, 320), (img.shape[1], img.shape[0])]],\n",
" dtype=np.int32) # Image mask polygon\n",
" gray_img = grayscale(img) # Greyed out image\n",
" edge_img = canny(gray_img, low_threshold, high_threshold) # Canny edges\n",
" mask_img = region_of_interest(edge_img, vertices) # Region of interest\n",
" line_img = hough_lines(mask_img, rho, theta, threshold,\n",
" min_line_len, max_line_gap)\n",
" lane_line_img = weighted_img(img, line_img)\n",
" #cv2.imwrite('test_images_output/' + image, lane_line_img)\n",
" mpimg.imsave('test_images_output/' + image, lane_line_img)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This image is: <class 'numpy.ndarray'> with dimensions: (540, 960, 3)\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x11abeccc0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#reading in an image\n",
"image = mpimg.imread('test_images_output/whiteCarLaneSwitch.jpg')\n",
"\n",
"#printing out some stats and plotting\n",
"print('This image is:', type(image), 'with dimensions:', image.shape)\n",
"plt.imshow(image)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test on Videos\n",
"\n",
"You know what's cooler than drawing lanes over images? Drawing lanes over video!\n",
"\n",
"We can test our solution on two provided videos:\n",
"\n",
"`solidWhiteRight.mp4`\n",
"\n",
"`solidYellowLeft.mp4`\n",
"\n",
"**Note: if you get an import error when you run the next cell, try changing your kernel (select the Kernel menu above --> Change Kernel). Still have problems? Try relaunching Jupyter Notebook from the terminal prompt. Also, consult the forums for more troubleshooting tips.**\n",
"\n",
"**If you get an error that looks like this:**\n",
"```\n",
"NeedDownloadError: Need ffmpeg exe. \n",
"You can download it by calling: \n",
"imageio.plugins.ffmpeg.download()\n",
"```\n",
"**Follow the instructions in the error message and check out [this forum post](https://discussions.udacity.com/t/project-error-of-test-on-videos/274082) for more troubleshooting tips across operating systems.**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Import everything needed to edit/save/watch video clips\n",
"from moviepy.editor import VideoFileClip\n",
"from IPython.display import HTML"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def process_image(image):\n",
" # NOTE: The output you return should be a color image (3 channel) for processing video below\n",
" # TODO: put your pipeline here,\n",
" # you should return the final output (image where lines are drawn on lanes)\n",
" \n",
" vertices = np.array([[(0, image.shape[0]), (450, 320),\n",
" (510, 320), (image.shape[1], image.shape[0])]],\n",
" dtype=np.int32) # Image mask polygon\n",
" gray_img = grayscale(image) # Greyed out image\n",
" edge_img = canny(gray_img, low_threshold, high_threshold) # Canny edges\n",
" mask_img = region_of_interest(edge_img, vertices) # Region of interest\n",
" line_img = hough_lines(mask_img, rho, theta, threshold,\n",
" min_line_len, max_line_gap)\n",
" lane_line_img = weighted_img(image, line_img)\n",
" \n",
" return lane_line_img"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's try the one with the solid white lane on the right first ..."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[MoviePy] >>>> Building video test_videos_output/solidWhiteRight.mp4\n",
"[MoviePy] Writing video test_videos_output/solidWhiteRight.mp4\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████▉| 221/222 [00:02<00:00, 101.67it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[MoviePy] Done.\n",
"[MoviePy] >>>> Video ready: test_videos_output/solidWhiteRight.mp4 \n",
"\n",
"CPU times: user 1.68 s, sys: 202 ms, total: 1.89 s\n",
"Wall time: 2.58 s\n"
]
}
],
"source": [
"white_output = 'test_videos_output/solidWhiteRight.mp4'\n",
"## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video\n",
"## To do so add .subclip(start_second,end_second) to the end of the line below\n",
"## Where start_second and end_second are integer values representing the start and end of the subclip\n",
"## You may also uncomment the following line for a subclip of the first 5 seconds\n",
"##clip1 = VideoFileClip(\"test_videos/solidWhiteRight.mp4\").subclip(0,5)\n",
"clip1 = VideoFileClip(\"test_videos/solidWhiteRight.mp4\")\n",
"white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!\n",
"%time white_clip.write_videofile(white_output, audio=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Play the video inline, or if you prefer find the video in your filesystem (should be in the same directory) and play it in your video player of choice."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<video width=\"960\" height=\"540\" controls>\n",
" <source src=\"test_videos_output/solidWhiteRight.mp4\">\n",
"</video>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML(\"\"\"\n",
"<video width=\"960\" height=\"540\" controls>\n",
" <source src=\"{0}\">\n",
"</video>\n",
"\"\"\".format(white_output))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Improve the draw_lines() function\n",
"\n",
"**At this point, if you were successful with making the pipeline and tuning parameters, you probably have the Hough line segments drawn onto the road, but what about identifying the full extent of the lane and marking it clearly as in the example video (P1_example.mp4)? Think about defining a line to run the full length of the visible lane based on the line segments you identified with the Hough Transform. As mentioned previously, try to average and/or extrapolate the line segments you've detected to map out the full extent of the lane lines. You can see an example of the result you're going for in the video \"P1_example.mp4\".**\n",
"\n",
"**Go back and modify your draw_lines function accordingly and try re-running your pipeline. The new output should draw a single, solid line over the left lane line and a single, solid line over the right lane line. The lines should start from the bottom of the image and extend out to the top of the region of interest.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now for the one with the solid yellow lane on the left. This one's more tricky!"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[MoviePy] >>>> Building video test_videos_output/solidYellowLeft.mp4\n",
"[MoviePy] Writing video test_videos_output/solidYellowLeft.mp4\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████▉| 681/682 [00:07<00:00, 85.35it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[MoviePy] Done.\n",
"[MoviePy] >>>> Video ready: test_videos_output/solidYellowLeft.mp4 \n",
"\n",
"CPU times: user 5.91 s, sys: 627 ms, total: 6.54 s\n",
"Wall time: 8.44 s\n"
]
}
],
"source": [
"yellow_output = 'test_videos_output/solidYellowLeft.mp4'\n",
"## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video\n",
"## To do so add .subclip(start_second,end_second) to the end of the line below\n",
"## Where start_second and end_second are integer values representing the start and end of the subclip\n",
"## You may also uncomment the following line for a subclip of the first 5 seconds\n",
"##clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4').subclip(0,5)\n",
"clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4')\n",
"yellow_clip = clip2.fl_image(process_image)\n",
"%time yellow_clip.write_videofile(yellow_output, audio=False)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<video width=\"960\" height=\"540\" controls>\n",
" <source src=\"test_videos_output/solidYellowLeft.mp4\">\n",
"</video>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML(\"\"\"\n",
"<video width=\"960\" height=\"540\" controls>\n",
" <source src=\"{0}\">\n",
"</video>\n",
"\"\"\".format(yellow_output))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Writeup and Submission\n",
"\n",
"If you're satisfied with your video outputs, it's time to make the report writeup in a pdf or markdown file. Once you have this Ipython notebook ready along with the writeup, it's time to submit for review! Here is a [link](https://github.com/udacity/CarND-LaneLines-P1/blob/master/writeup_template.md) to the writeup template file.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Optional Challenge\n",
"\n",
"Try your lane finding pipeline on the video below. Does it still work? Can you figure out a way to make it more robust? If you're up for the challenge, modify your pipeline so it works with this video and submit it along with the rest of your project!"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[MoviePy] >>>> Building video test_videos_output/challenge.mp4\n",
"[MoviePy] Writing video test_videos_output/challenge.mp4\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 251/251 [00:06<00:00, 38.21it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[MoviePy] Done.\n",
"[MoviePy] >>>> Video ready: test_videos_output/challenge.mp4 \n",
"\n",
"CPU times: user 6.31 s, sys: 487 ms, total: 6.79 s\n",
"Wall time: 7.65 s\n"
]
}
],
"source": [
"challenge_output = 'test_videos_output/challenge.mp4'\n",
"## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video\n",
"## To do so add .subclip(start_second,end_second) to the end of the line below\n",
"## Where start_second and end_second are integer values representing the start and end of the subclip\n",
"## You may also uncomment the following line for a subclip of the first 5 seconds\n",
"##clip3 = VideoFileClip('test_videos/challenge.mp4').subclip(0,5)\n",
"clip3 = VideoFileClip('test_videos/challenge.mp4')\n",
"challenge_clip = clip3.fl_image(process_image)\n",
"%time challenge_clip.write_videofile(challenge_output, audio=False)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<video width=\"960\" height=\"540\" controls>\n",
" <source src=\"test_videos_output/challenge.mp4\">\n",
"</video>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML(\"\"\"\n",
"<video width=\"960\" height=\"540\" controls>\n",
" <source src=\"{0}\">\n",
"</video>\n",
"\"\"\".format(challenge_output))"
]
}
],
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
atulsingh0/MachineLearning | scikit-learn/Numpy_02.ipynb | 1 | 37504 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 2, 3, 4])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.arange(5)\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 2 4 6 8]\n",
"[3 4 5 6 7]\n",
"[-2 -1 0 1 2]\n"
]
}
],
"source": [
"print(a*2)\n",
"print(a+3)\n",
"print(a-2)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1., 1., 1., 1., 1.])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b = np.ones(5)\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-2. -1. 0. 1. 2.]\n",
"[ 1. 2. 3. 4. 5.]\n"
]
}
],
"source": [
"print(a-b*2)\n",
"print(a+b)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The slowest run took 6.27 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
"100000 loops, best of 3: 11.4 µs per loop\n"
]
}
],
"source": [
"# these operations are faster in numpy than pure python\n",
"\n",
"a = np.arange(10000)\n",
"%timeit a+2"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1000 loops, best of 3: 1.03 ms per loop\n"
]
}
],
"source": [
"# in native python\n",
"a = range(10000)\n",
"%timeit [i+2 for i in a]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Array Multiplication"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1-D array"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 8, 3, 4, 2])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.random.randint(1, 10, 5)\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([6, 3, 9, 3, 4])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b =np.random.randint(1, 10, 5)\n",
"b "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([12, 24, 27, 12, 8])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a * b # elementwise operations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2-D array"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[9, 5, 5],\n",
" [7, 2, 4]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.random.randint(1, 10, (2,3))\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[2, 1, 5],\n",
" [5, 2, 7]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b = np.random.randint(1, 10, (2,3))\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[18, 5, 25],\n",
" [35, 4, 28]])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a * b # element wise multiplication"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# a.dot(b) this will throw an error as Matix multiplication is not possible here"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[48, 90],\n",
" [36, 67]])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.dot(b.T) # b.T will transpose the 2,3 matrix to 3,2 matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Comparison"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 3 7 6 5] [1 2 7 8 5]\n"
]
}
],
"source": [
"a = np.array([1, 3, 7, 6, 5])\n",
"b = np.array([1, 2, 7, 8, 5])\n",
"\n",
"print(a,b)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, False, True, False, True], dtype=bool)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Comparison\n",
"a == b"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([False, True, False, False, False], dtype=bool)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a > b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Arraywise Comparison"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = np.array([1,2,3,4])\n",
"b = np.array([3,4, 9, 8])\n",
"c = np.array([1,2,3,4])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array_equal(a, b)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array_equal(a, c)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Logical Operations"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ True True False False] [ True False True False]\n"
]
}
],
"source": [
"a = np.array([1,1,0,0], dtype='bool')\n",
"b = np.array([1,0,1,0], dtype='bool')\n",
"\n",
"print(a, b)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, True, True, False], dtype=bool)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.logical_or(a,b)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, False, False, False], dtype=bool)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.logical_and(a,b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Adv mathematics func"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1. , 0.5, 0. , 3. ])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([1, 0.5, 0, 3])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.84147098, 0.47942554, 0. , 0.14112001])"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sin(a)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\tools\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: RuntimeWarning: divide by zero encountered in log\n",
" if __name__ == '__main__':\n"
]
},
{
"data": {
"text/plain": [
"array([ 0. , -0.69314718, -inf, 1.09861229])"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.log(a)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 2.71828183, 1.64872127, 1. , 20.08553692])"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.exp(a)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 1., 1.],\n",
" [ 0., 0., 1.],\n",
" [ 0., 0., 0.]])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.triu(np.ones((3, 3)), 1)\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 0.],\n",
" [ 1., 0., 0.],\n",
" [ 1., 1., 0.]])"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.T # transposition of array is just a view and stored in memory"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Counting Sums"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = np.array([1,2,3,4])\n",
"x.sum()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3],\n",
" [3, 4, 5]])"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = np.array([[1,2,3],[3,4,5]])\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([4, 6, 8])"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.sum(axis=0) # column sum"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6, 12])"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.sum(axis=1) # row sum"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Other functions"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.min()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 3])"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.min(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([3, 4, 5])"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.max(axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.argmin() # index of min element"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.argmax() # index of max element"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Logical Operations"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.all([True, False, True])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.any([True, False, True])"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.zeros((10,10))\n",
"np.any(a)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.all(a)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.all(a==0)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.any(a!=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Stats"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 2 3 1] \n",
"\n",
" [[1 2 3]\n",
" [5 6 1]]\n"
]
}
],
"source": [
"x = np.array([1, 2, 3, 1])\n",
"y = np.array([[1, 2, 3], [5, 6, 1]])\n",
"\n",
"print(x, \"\\n\\n\",y)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.75"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.mean()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.5"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.median(x)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 2., 5.])"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.median(y, axis=-1) # last axis"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.82915619758884995"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.std()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### broadcasing"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1., 1., 1., 1.],\n",
" [ 1., 1., 1., 1.],\n",
" [ 1., 1., 1., 1.]])"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.ones((3,4))\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1., 1., 1., 1.],\n",
" [ 1., 1., 1., 1.],\n",
" [ 1., 1., 0., 0.]])"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[2:,2:]=0\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(4,)"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.arange(0, 40, 10)\n",
"a.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### newaxis"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0],\n",
" [10],\n",
" [20],\n",
" [30]])"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = a[:, np.newaxis] # newaxis is a nice feature supported by numpy\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(4, 1)"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.shape"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mileposts = np.array([0, 198, 303, 736, 871, 1175, 1475, 1544,1913, 2448])"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0, 198, 303, 736, 871, 1175, 1475, 1544, 1913, 2448],\n",
" [ 198, 0, 105, 538, 673, 977, 1277, 1346, 1715, 2250],\n",
" [ 303, 105, 0, 433, 568, 872, 1172, 1241, 1610, 2145],\n",
" [ 736, 538, 433, 0, 135, 439, 739, 808, 1177, 1712],\n",
" [ 871, 673, 568, 135, 0, 304, 604, 673, 1042, 1577],\n",
" [1175, 977, 872, 439, 304, 0, 300, 369, 738, 1273],\n",
" [1475, 1277, 1172, 739, 604, 300, 0, 69, 438, 973],\n",
" [1544, 1346, 1241, 808, 673, 369, 69, 0, 369, 904],\n",
" [1913, 1715, 1610, 1177, 1042, 738, 438, 369, 0, 535],\n",
" [2448, 2250, 2145, 1712, 1577, 1273, 973, 904, 535, 0]])"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"distance = np.abs(mileposts - mileposts[:, np.newaxis])\n",
"distance"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2 3 4] \n",
"\n",
" [[0]\n",
" [1]\n",
" [2]\n",
" [3]\n",
" [4]]\n"
]
}
],
"source": [
"x, y = np.arange(5), np.arange(5)[:, np.newaxis]\n",
"\n",
"print(x, \"\\n\\n\", y)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0. , 1. , 2. , 3. , 4. ],\n",
" [ 1. , 1.41421356, 2.23606798, 3.16227766, 4.12310563],\n",
" [ 2. , 2.23606798, 2.82842712, 3.60555128, 4.47213595],\n",
" [ 3. , 3.16227766, 3.60555128, 4.24264069, 5. ],\n",
" [ 4. , 4.12310563, 4.47213595, 5. , 5.65685425]])"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"distance = np.sqrt(x ** 2 + y ** 2)\n",
"distance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"the numpy.ogrid function allows to directly create vectors x and y of the previous example, with two\n",
"\"significant dimensions\":"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0]\n",
" [1]\n",
" [2]\n",
" [3]\n",
" [4]] \n",
"\n",
" [[0 1 2 3 4]]\n"
]
}
],
"source": [
"x, y = np.ogrid[0:5, 0:5]\n",
"\n",
"print(x, \"\\n\\n\", y)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(5, 1) (1, 5)\n"
]
}
],
"source": [
"print(x.shape, y.shape)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0. , 1. , 2. , 3. , 4. ],\n",
" [ 1. , 1.41421356, 2.23606798, 3.16227766, 4.12310563],\n",
" [ 2. , 2.23606798, 2.82842712, 3.60555128, 4.47213595],\n",
" [ 3. , 3.16227766, 3.60555128, 4.24264069, 5. ],\n",
" [ 4. , 4.12310563, 4.47213595, 5. , 5.65685425]])"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"distance = np.sqrt(x ** 2 + y ** 2)\n",
"distance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"np.mgrid\n",
"directly providesmatrices full of indices for cases where we can’t (or don’t want to) benefit frombroadcasting"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0 0 0 0]\n",
" [1 1 1 1]\n",
" [2 2 2 2]\n",
" [3 3 3 3]] \n",
"\n",
" [[0 1 2 3]\n",
" [0 1 2 3]\n",
" [0 1 2 3]\n",
" [0 1 2 3]]\n"
]
}
],
"source": [
"x, y = np.mgrid[0:4, 0:4]\n",
"print(x, \"\\n\\n\", y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Flattening the array\n",
"Higher dimensions: last dimensions ravel out “first”."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3],\n",
" [4, 5, 6]])"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([[1, 2, 3], [4, 5, 6]])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3, 4, 5, 6])"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.ravel()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 4, 2, 5, 3, 6])"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.T.ravel()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Reshaping\n",
"reshaping may return view or copy, so use caustiously"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2, 3)"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.shape"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3, 4, 5, 6])"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.ravel()"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2],\n",
" [3, 4],\n",
" [5, 6]])"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.ravel().reshape(3,2)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3],\n",
" [4, 5, 6]])"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.reshape((2, -1)) # # unspecified (-1) value is inferred"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2],\n",
" [3, 4],\n",
" [5, 6]])"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.reshape((3, -1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Adding a dimention"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 2, 3])"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.arange(4)\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[0],\n",
" [1],\n",
" [2],\n",
" [3]])"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[:, np.newaxis]"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[0, 1, 2, 3]])"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[np.newaxis,:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Dimension shuffling"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[[ 0, 1],\n",
" [ 2, 3],\n",
" [ 4, 5]],\n",
"\n",
" [[ 6, 7],\n",
" [ 8, 9],\n",
" [10, 11]],\n",
"\n",
" [[12, 13],\n",
" [14, 15],\n",
" [16, 17]],\n",
"\n",
" [[18, 19],\n",
" [20, 21],\n",
" [22, 23]]])"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.arange(4*3*2).reshape(4, 3, 2)\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(4, 3, 2)"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.shape"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[0,1,0]"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[[ 0, 1, 2, 3],\n",
" [ 4, 5, 6, 7],\n",
" [ 8, 9, 10, 11]],\n",
"\n",
" [[12, 13, 14, 15],\n",
" [16, 17, 18, 19],\n",
" [20, 21, 22, 23]]])"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.ravel().reshape(2,3,4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Resizing"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 2, 3, 0, 0, 0, 0])"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"o = np.arange(4)\n",
"o.resize((8,))\n",
"o\n",
"\n",
"# It will throw an error \n",
"# ValueError: cannot resize an array that references or is referenced\n",
"# by another array in this way. Use the resize function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Sorting data"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[4, 3, 5],\n",
" [1, 2, 1]])"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([[4, 3, 5], [1, 2, 1]])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[3, 4, 5],\n",
" [1, 1, 2]])"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b = np.sort(a, axis=1) # Sorts each row separately!\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[3, 4, 5],\n",
" [1, 1, 2]])"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.sort(axis=1) # inplace sort\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 3, 1, 0], dtype=int64)"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Sorting with fancy indexing:\n",
"a = np.array([4, 3, 1, 2])\n",
"j = np.argsort(a)\n",
"j"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3, 4])"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[j]"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 2\n",
"4 1\n"
]
}
],
"source": [
"# Finding minima and maxima\n",
"j_max = np.argmax(a)\n",
"j_min = np.argmin(a)\n",
"\n",
"print(j_max, j_min) # indexes\n",
"print(a[j_max], a[j_min])"
]
},
{
"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 |
google/or-tools | examples/notebook/contrib/secret_santa.ipynb | 1 | 6890 | {
"cells": [
{
"cell_type": "markdown",
"id": "google",
"metadata": {},
"source": [
"##### Copyright 2021 Google LLC."
]
},
{
"cell_type": "markdown",
"id": "apache",
"metadata": {},
"source": [
"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"you may not use this file except in compliance with the License.\n",
"You may obtain a copy of the License at\n",
"\n",
" http://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software\n",
"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"See the License for the specific language governing permissions and\n",
"limitations under the License.\n"
]
},
{
"cell_type": "markdown",
"id": "basename",
"metadata": {},
"source": [
"# secret_santa"
]
},
{
"cell_type": "markdown",
"id": "link",
"metadata": {},
"source": [
"<table align=\"left\">\n",
"<td>\n",
"<a href=\"https://colab.research.google.com/github/google/or-tools/blob/master/examples/notebook/contrib/secret_santa.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/colab_32px.png\"/>Run in Google Colab</a>\n",
"</td>\n",
"<td>\n",
"<a href=\"https://github.com/google/or-tools/blob/master/examples/contrib/secret_santa.py\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/github_32px.png\"/>View source on GitHub</a>\n",
"</td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "doc",
"metadata": {},
"source": [
"First, you must install [ortools](https://pypi.org/project/ortools/) package in this colab."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "install",
"metadata": {},
"outputs": [],
"source": [
"!pip install ortools"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "code",
"metadata": {},
"outputs": [],
"source": [
"# Copyright 2010 Hakan Kjellerstrand [email protected]\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# http://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License.\n",
"\"\"\"\n",
"\n",
" Secret Santa problem in Google CP Solver.\n",
"\n",
" From Ruby Quiz Secret Santa\n",
" http://www.rubyquiz.com/quiz2.html\n",
" '''\n",
" Honoring a long standing tradition started by my wife's dad, my friends\n",
" all play a Secret Santa game around Christmas time. We draw names and\n",
" spend a week sneaking that person gifts and clues to our identity. On the\n",
" last night of the game, we get together, have dinner, share stories, and,\n",
" most importantly, try to guess who our Secret Santa was. It's a crazily\n",
" fun way to enjoy each other's company during the holidays.\n",
"\n",
" To choose Santas, we use to draw names out of a hat. This system was\n",
" tedious, prone to many 'Wait, I got myself...' problems. This year, we\n",
" made a change to the rules that further complicated picking and we knew\n",
" the hat draw would not stand up to the challenge. Naturally, to solve\n",
" this problem, I scripted the process. Since that turned out to be more\n",
" interesting than I had expected, I decided to share.\n",
"\n",
" This weeks Ruby Quiz is to implement a Secret Santa selection script.\n",
"\n",
" Your script will be fed a list of names on STDIN.\n",
" ...\n",
" Your script should then choose a Secret Santa for every name in the list.\n",
" Obviously, a person cannot be their own Secret Santa. In addition, my friends\n",
" no longer allow people in the same family to be Santas for each other and your\n",
" script should take this into account.\n",
" '''\n",
"\n",
" Comment: This model skips the file input and mail parts. We\n",
" assume that the friends are identified with a number from 1..n,\n",
" and the families is identified with a number 1..num_families.\n",
"\n",
" Compare with the following model:\n",
" * MiniZinc: http://www.hakank.org/minizinc/secret_santa.mzn\n",
"\n",
"\n",
" This model gives 4089600 solutions and the following statistics:\n",
" - failures: 31264\n",
" - branches: 8241726\n",
" - WallTime: 23735 ms (note: without any printing of the solutions)\n",
"\n",
" This model was created by Hakan Kjellerstrand ([email protected])\n",
" Also see my other Google CP Solver models:\n",
" http://www.hakank.org/google_or_tools/\n",
"\"\"\"\n",
"import sys\n",
"from ortools.constraint_solver import pywrapcp\n",
"\n",
"\n",
"\n",
"# Create the solver.\n",
"solver = pywrapcp.Solver('Secret Santa problem')\n",
"\n",
"#\n",
"# data\n",
"#\n",
"family = [1, 1, 1, 1, 2, 3, 3, 3, 3, 3, 4, 4]\n",
"num_families = max(family)\n",
"n = len(family)\n",
"\n",
"#\n",
"# declare variables\n",
"#\n",
"x = [solver.IntVar(0, n - 1, 'x[%i]' % i) for i in range(n)]\n",
"\n",
"#\n",
"# constraints\n",
"#\n",
"solver.Add(solver.AllDifferent(x))\n",
"\n",
"# Can't be one own's Secret Santa\n",
"# Ensure that there are no fix-point in the array\n",
"for i in range(n):\n",
" solver.Add(x[i] != i)\n",
"\n",
"# No Secret Santa to a person in the same family\n",
"for i in range(n):\n",
" solver.Add(family[i] != solver.Element(family, x[i]))\n",
"\n",
"#\n",
"# solution and search\n",
"#\n",
"db = solver.Phase(x, solver.INT_VAR_SIMPLE, solver.INT_VALUE_SIMPLE)\n",
"\n",
"solver.NewSearch(db)\n",
"num_solutions = 0\n",
"while solver.NextSolution():\n",
" num_solutions += 1\n",
" print('x:', [x[i].Value() for i in range(n)])\n",
" print()\n",
"\n",
"print('num_solutions:', num_solutions)\n",
"print('failures:', solver.Failures())\n",
"print('branches:', solver.Branches())\n",
"print('WallTime:', solver.WallTime(), 'ms')\n",
"\n"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
| apache-2.0 |
mohanprasath/Course-Work | coursera/data_visualization_with_python/DV0101EN-3-5-1-Generating-Maps-in-Python-py-v2.0.ipynb | 1 | 46559 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\" width = 400> </a>\n",
"\n",
"<h1 align=center><font size = 5>Generating Maps with Python</font></h1>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Introduction\n",
"\n",
"In this lab, we will learn how to create maps for different objectives. To do that, we will part ways with Matplotlib and work with another Python visualization library, namely **Folium**. What is nice about **Folium** is that it was developed for the sole purpose of visualizing geospatial data. While other libraries are available to visualize geospatial data, such as **plotly**, they might have a cap on how many API calls you can make within a defined time frame. **Folium**, on the other hand, is completely free."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Table of Contents\n",
"\n",
"<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
"\n",
"1. [Exploring Datasets with *p*andas](#0)<br>\n",
"2. [Downloading and Prepping Data](#2)<br>\n",
"3. [Introduction to Folium](#4) <br>\n",
"4. [Map with Markers](#6) <br>\n",
"5. [Choropleth Maps](#8) <br>\n",
"</div>\n",
"<hr>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Exploring Datasets with *pandas* and Matplotlib<a id=\"0\"></a>\n",
"\n",
"Toolkits: This lab heavily relies on [*pandas*](http://pandas.pydata.org/) and [**Numpy**](http://www.numpy.org/) for data wrangling, analysis, and visualization. The primary plotting library we will explore in this lab is [**Folium**](https://github.com/python-visualization/folium/).\n",
"\n",
"Datasets: \n",
"\n",
"1. San Francisco Police Department Incidents for the year 2016 - [Police Department Incidents](https://data.sfgov.org/Public-Safety/Police-Department-Incidents-Previous-Year-2016-/ritf-b9ki) from San Francisco public data portal. Incidents derived from San Francisco Police Department (SFPD) Crime Incident Reporting system. Updated daily, showing data for the entire year of 2016. Address and location has been anonymized by moving to mid-block or to an intersection. \n",
"\n",
"2. Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml) from United Nation's website. The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. For this lesson, we will focus on the Canadian Immigration data"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Downloading and Prepping Data <a id=\"2\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Import Primary Modules:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"import numpy as np # useful for many scientific computing in Python\n",
"import pandas as pd # primary data structure library"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Introduction to Folium <a id=\"4\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Folium is a powerful Python library that helps you create several types of Leaflet maps. The fact that the Folium results are interactive makes this library very useful for dashboard building.\n",
"\n",
"From the official Folium documentation page:\n",
"\n",
"> Folium builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the Leaflet.js library. Manipulate your data in Python, then visualize it in on a Leaflet map via Folium.\n",
"\n",
"> Folium makes it easy to visualize data that's been manipulated in Python on an interactive Leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing Vincent/Vega visualizations as markers on the map.\n",
"\n",
"> The library has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys. Folium supports both GeoJSON and TopoJSON overlays, as well as the binding of data to those overlays to create choropleth maps with color-brewer color schemes."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Let's install **Folium**"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Folium** is not available by default. So, we first need to install it before we are able to import it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"!conda install -c conda-forge folium=0.5.0 --yes\n",
"import folium\n",
"\n",
"print('Folium installed and imported!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Generating the world map is straigtforward in **Folium**. You simply create a **Folium** *Map* object and then you display it. What is attactive about **Folium** maps is that they are interactive, so you can zoom into any region of interest despite the initial zoom level. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [],
"source": [
"# define the world map\n",
"world_map = folium.Map()\n",
"\n",
"# display world map\n",
"world_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Go ahead. Try zooming in and out of the rendered map above."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"You can customize this default definition of the world map by specifying the centre of your map and the intial zoom level. \n",
"\n",
"All locations on a map are defined by their respective *Latitude* and *Longitude* values. So you can create a map and pass in a center of *Latitude* and *Longitude* values of **[0, 0]**. \n",
"\n",
"For a defined center, you can also define the intial zoom level into that location when the map is rendered. **The higher the zoom level the more the map is zoomed into the center**.\n",
"\n",
"Let's create a map centered around Canada and play with the zoom level to see how it affects the rendered map."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [],
"source": [
"# define the world map centered around Canada with a low zoom level\n",
"world_map = folium.Map(location=[56.130, -106.35], zoom_start=4)\n",
"\n",
"# display world map\n",
"world_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's create the map again with a higher zoom level"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [],
"source": [
"# define the world map centered around Canada with a higher zoom level\n",
"world_map = folium.Map(location=[56.130, -106.35], zoom_start=8)\n",
"\n",
"# display world map\n",
"world_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"As you can see, the higher the zoom level the more the map is zoomed into the given center."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Question**: Create a map of Mexico with a zoom level of 4."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"### type your answer here\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"\\\\ # define Mexico's geolocation coordinates\n",
"mexico_latitude = 23.6345 \n",
"mexico_longitude = -102.5528\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # define the world map centered around Canada with a higher zoom level\n",
"mexico_map = folium.Map(location=[mexico_latitude, mexico_longitude], zoom_start=4)\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # display world map\n",
"mexico_map\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Another cool feature of **Folium** is that you can generate different map styles."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### A. Stamen Toner Maps\n",
"\n",
"These are high-contrast B+W (black and white) maps. They are perfect for data mashups and exploring river meanders and coastal zones. "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's create a Stamen Toner map of canada with a zoom level of 4."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [],
"source": [
"# create a Stamen Toner map of the world centered around Canada\n",
"world_map = folium.Map(location=[56.130, -106.35], zoom_start=4, tiles='Stamen Toner')\n",
"\n",
"# display map\n",
"world_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Feel free to zoom in and out to see how this style compares to the default one."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### B. Stamen Terrain Maps\n",
"\n",
"These are maps that feature hill shading and natural vegetation colors. They showcase advanced labeling and linework generalization of dual-carriageway roads."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's create a Stamen Terrain map of Canada with zoom level 4."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [],
"source": [
"# create a Stamen Toner map of the world centered around Canada\n",
"world_map = folium.Map(location=[56.130, -106.35], zoom_start=4, tiles='Stamen Terrain')\n",
"\n",
"# display map\n",
"world_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Feel free to zoom in and out to see how this style compares to Stamen Toner and the default style."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### C. Mapbox Bright Maps\n",
"\n",
"These are maps that quite similar to the default style, except that the borders are not visible with a low zoom level. Furthermore, unlike the default style where country names are displayed in each country's native language, *Mapbox Bright* style displays all country names in English."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's create a world map with this style."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# create a world map with a Mapbox Bright style.\n",
"world_map = folium.Map(tiles='Mapbox Bright')\n",
"\n",
"# display the map\n",
"world_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Zoom in and notice how the borders start showing as you zoom in, and the displayed country names are in English."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Question**: Create a map of Mexico to visualize its hill shading and natural vegetation. Use a zoom level of 6."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"### type your answer here\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"\\\\ # define Mexico's geolocation coordinates\n",
"mexico_latitude = 23.6345 \n",
"mexico_longitude = -102.5528\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # define the world map centered around Canada with a higher zoom level\n",
"mexico_map = folium.Map(location=[mexico_latitude, mexico_longitude], zoom_start=6, tiles='Stamen Terrain')\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # display world map\n",
"mexico_map\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Maps with Markers <a id=\"6\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's download and import the data on police department incidents using *pandas* `read_csv()` method."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Download the dataset and read it into a *pandas* dataframe:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_incidents = pd.read_csv('https://ibm.box.com/shared/static/nmcltjmocdi8sd5tk93uembzdec8zyaq.csv')\n",
"\n",
"print('Dataset downloaded and read into a pandas dataframe!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's take a look at the first five items in our dataset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_incidents.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"So each row consists of 13 features:\n",
"> 1. **IncidntNum**: Incident Number\n",
"> 2. **Category**: Category of crime or incident\n",
"> 3. **Descript**: Description of the crime or incident\n",
"> 4. **DayOfWeek**: The day of week on which the incident occurred\n",
"> 5. **Date**: The Date on which the incident occurred\n",
"> 6. **Time**: The time of day on which the incident occurred\n",
"> 7. **PdDistrict**: The police department district\n",
"> 8. **Resolution**: The resolution of the crime in terms whether the perpetrator was arrested or not\n",
"> 9. **Address**: The closest address to where the incident took place\n",
"> 10. **X**: The longitude value of the crime location \n",
"> 11. **Y**: The latitude value of the crime location\n",
"> 12. **Location**: A tuple of the latitude and the longitude values\n",
"> 13. **PdId**: The police department ID"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's find out how many entries there are in our dataset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_incidents.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"So the dataframe consists of 150,500 crimes, which took place in the year 2016. In order to reduce computational cost, let's just work with the first 100 incidents in this dataset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# get the first 100 crimes in the df_incidents dataframe\n",
"limit = 100\n",
"df_incidents = df_incidents.iloc[0:limit, :]"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's confirm that our dataframe now consists only of 100 crimes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_incidents.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now that we reduced the data a little bit, let's visualize where these crimes took place in the city of San Francisco. We will use the default style and we will initialize the zoom level to 12. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# San Francisco latitude and longitude values\n",
"latitude = 37.77\n",
"longitude = -122.42"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# create map and display it\n",
"sanfran_map = folium.Map(location=[latitude, longitude], zoom_start=12)\n",
"\n",
"# display the map of San Francisco\n",
"sanfran_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now let's superimpose the locations of the crimes onto the map. The way to do that in **Folium** is to create a *feature group* with its own features and style and then add it to the sanfran_map."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [],
"source": [
"# instantiate a feature group for the incidents in the dataframe\n",
"incidents = folium.map.FeatureGroup()\n",
"\n",
"# loop through the 100 crimes and add each to the incidents feature group\n",
"for lat, lng, in zip(df_incidents.Y, df_incidents.X):\n",
" incidents.add_child(\n",
" folium.features.CircleMarker(\n",
" [lat, lng],\n",
" radius=5, # define how big you want the circle markers to be\n",
" color='yellow',\n",
" fill=True,\n",
" fill_color='blue',\n",
" fill_opacity=0.6\n",
" )\n",
" )\n",
"\n",
"# add incidents to map\n",
"sanfran_map.add_child(incidents)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"You can also add some pop-up text that would get displayed when you hover over a marker. Let's make each marker display the category of the crime when hovered over."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [],
"source": [
"# instantiate a feature group for the incidents in the dataframe\n",
"incidents = folium.map.FeatureGroup()\n",
"\n",
"# loop through the 100 crimes and add each to the incidents feature group\n",
"for lat, lng, in zip(df_incidents.Y, df_incidents.X):\n",
" incidents.add_child(\n",
" folium.features.CircleMarker(\n",
" [lat, lng],\n",
" radius=5, # define how big you want the circle markers to be\n",
" color='yellow',\n",
" fill=True,\n",
" fill_color='blue',\n",
" fill_opacity=0.6\n",
" )\n",
" )\n",
"\n",
"# add pop-up text to each marker on the map\n",
"latitudes = list(df_incidents.Y)\n",
"longitudes = list(df_incidents.X)\n",
"labels = list(df_incidents.Category)\n",
"\n",
"for lat, lng, label in zip(latitudes, longitudes, labels):\n",
" folium.Marker([lat, lng], popup=label).add_to(sanfran_map) \n",
" \n",
"# add incidents to map\n",
"sanfran_map.add_child(incidents)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Isn't this really cool? Now you are able to know what crime category occurred at each marker.\n",
"\n",
"If you find the map to be so congested will all these markers, there are two remedies to this problem. The simpler solution is to remove these location markers and just add the text to the circle markers themselves as follows:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# create map and display it\n",
"sanfran_map = folium.Map(location=[latitude, longitude], zoom_start=12)\n",
"\n",
"# loop through the 100 crimes and add each to the map\n",
"for lat, lng, label in zip(df_incidents.Y, df_incidents.X, df_incidents.Category):\n",
" folium.features.CircleMarker(\n",
" [lat, lng],\n",
" radius=5, # define how big you want the circle markers to be\n",
" color='yellow',\n",
" fill=True,\n",
" popup=label,\n",
" fill_color='blue',\n",
" fill_opacity=0.6\n",
" ).add_to(sanfran_map)\n",
"\n",
"# show map\n",
"sanfran_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"The other proper remedy is to group the markers into different clusters. Each cluster is then represented by the number of crimes in each neighborhood. These clusters can be thought of as pockets of San Francisco which you can then analyze separately.\n",
"\n",
"To implement this, we start off by instantiating a *MarkerCluster* object and adding all the data points in the dataframe to this object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"from folium import plugins\n",
"\n",
"# let's start again with a clean copy of the map of San Francisco\n",
"sanfran_map = folium.Map(location = [latitude, longitude], zoom_start = 12)\n",
"\n",
"# instantiate a mark cluster object for the incidents in the dataframe\n",
"incidents = plugins.MarkerCluster().add_to(sanfran_map)\n",
"\n",
"# loop through the dataframe and add each data point to the mark cluster\n",
"for lat, lng, label, in zip(df_incidents.Y, df_incidents.X, df_incidents.Category):\n",
" folium.Marker(\n",
" location=[lat, lng],\n",
" icon=None,\n",
" popup=label,\n",
" ).add_to(incidents)\n",
"\n",
"# display map\n",
"sanfran_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Notice how when you zoom out all the way, all markers are grouped into one cluster, *the global cluster*, of 100 markers or crimes, which is the total number of crimes in our dataframe. Once you start zooming in, the *global cluster* will start breaking up into smaller clusters. Zooming in all the way will result in individual markers."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Choropleth Maps <a id=\"8\"></a>\n",
"\n",
"A `Choropleth` map is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map, such as population density or per-capita income. The choropleth map provides an easy way to visualize how a measurement varies across a geographic area or it shows the level of variability within a region. Below is a `Choropleth` map of the US depicting the population by square mile per state.\n",
"\n",
"<img src = \"https://ibm.box.com/shared/static/2kzaknzdf6crt3n5rx6haskg3wiaklxl.png\" width = 600> "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now, let's create our own `Choropleth` map of the world depicting immigration from various countries to Canada.\n",
"\n",
"Let's first download and import our primary Canadian immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
"```\n",
"!conda install -c anaconda xlrd --yes\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Download the dataset and read it into a *pandas* dataframe:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_can = pd.read_excel('https://ibm.box.com/shared/static/lw190pt9zpy5bd1ptyg2aw15awomz9pu.xlsx',\n",
" sheet_name='Canada by Citizenship',\n",
" skiprows=range(20),\n",
" skipfooter=2)\n",
"\n",
"print('Data downloaded and read into a dataframe!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's take a look at the first five items in our dataset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [],
"source": [
"df_can.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's find out how many entries there are in our dataset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# print the dimensions of the dataframe\n",
"print(df_can.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to *Introduction to Matplotlib and Line Plots* and *Area Plots, Histograms, and Bar Plots* notebooks for a detailed description of this preprocessing."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [],
"source": [
"# clean up the dataset to remove unnecessary columns (eg. REG) \n",
"df_can.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\n",
"\n",
"# let's rename the columns so that they make sense\n",
"df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\n",
"\n",
"# for sake of consistency, let's also make all column labels of type string\n",
"df_can.columns = list(map(str, df_can.columns))\n",
"\n",
"# add total column\n",
"df_can['Total'] = df_can.sum(axis=1)\n",
"\n",
"# years that we will be using in this lesson - useful for plotting later on\n",
"years = list(map(str, range(1980, 2014)))\n",
"print ('data dimensions:', df_can.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's take a look at the first five items of our cleaned dataframe."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_can.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"In order to create a `Choropleth` map, we need a GeoJSON file that defines the areas/boundaries of the state, county, or country that we are interested in. In our case, since we are endeavoring to create a world map, we want a GeoJSON that defines the boundaries of all world countries. For your convenience, we will be providing you with this file, so let's go ahead and download it. Let's name it **world_countries.json**."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# download countries geojson file\n",
"!wget --quiet https://ibm.box.com/shared/static/cto2qv7nx6yq19logfcissyy4euo8lho.json -O world_countries.json\n",
" \n",
"print('GeoJSON file downloaded!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now that we have the GeoJSON file, let's create a world map, centered around **[0, 0]** *latitude* and *longitude* values, with an intial zoom level of 2, and using *Mapbox Bright* style."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"world_geo = r'world_countries.json' # geojson file\n",
"\n",
"# create a plain world map\n",
"world_map = folium.Map(location=[0, 0], zoom_start=2, tiles='Mapbox Bright')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And now to create a `Choropleth` map, we will use the *choropleth* method with the following main parameters:\n",
"\n",
"1. geo_data, which is the GeoJSON file.\n",
"2. data, which is the dataframe containing the data.\n",
"3. columns, which represents the columns in the dataframe that will be used to create the `Choropleth` map.\n",
"4. key_on, which is the key or variable in the GeoJSON file that contains the name of the variable of interest. To determine that, you will need to open the GeoJSON file using any text editor and note the name of the key or variable that contains the name of the countries, since the countries are our variable of interest. In this case, **name** is the key in the GeoJSON file that contains the name of the countries. Note that this key is case_sensitive, so you need to pass exactly as it exists in the GeoJSON file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# generate choropleth map using the total immigration of each country to Canada from 1980 to 2013\n",
"world_map.choropleth(\n",
" geo_data=world_geo,\n",
" data=df_can,\n",
" columns=['Country', 'Total'],\n",
" key_on='feature.properties.name',\n",
" fill_color='YlOrRd', \n",
" fill_opacity=0.7, \n",
" line_opacity=0.2,\n",
" legend_name='Immigration to Canada'\n",
")\n",
"\n",
"# display map\n",
"world_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"As per our `Choropleth` map legend, the darker the color of a country and the closer the color to red, the higher the number of immigrants from that country. Accordingly, the highest immigration over the course of 33 years (from 1980 to 2013) was from China, India, and the Philippines, followed by Poland, Pakistan, and interestingly, the US."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Notice how the legend is displaying a negative boundary or threshold. Let's fix that by defining our own thresholds and starting with 0 instead of -6,918!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [],
"source": [
"world_geo = r'world_countries.json'\n",
"\n",
"# create a numpy array of length 6 and has linear spacing from the minium total immigration to the maximum total immigration\n",
"threshold_scale = np.linspace(df_can['Total'].min(),\n",
" df_can['Total'].max(),\n",
" 6, dtype=int)\n",
"threshold_scale = threshold_scale.tolist() # change the numpy array to a list\n",
"threshold_scale[-1] = threshold_scale[-1] + 1 # make sure that the last value of the list is greater than the maximum immigration\n",
"\n",
"# let Folium determine the scale.\n",
"world_map = folium.Map(location=[0, 0], zoom_start=2, tiles='Mapbox Bright')\n",
"world_map.choropleth(\n",
" geo_data=world_geo,\n",
" data=df_can,\n",
" columns=['Country', 'Total'],\n",
" key_on='feature.properties.name',\n",
" threshold_scale=threshold_scale,\n",
" fill_color='YlOrRd', \n",
" fill_opacity=0.7, \n",
" line_opacity=0.2,\n",
" legend_name='Immigration to Canada',\n",
" reset=True\n",
")\n",
"world_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Much better now! Feel free to play around with the data and perhaps create `Choropleth` maps for individuals years, or perhaps decades, and see how they compare with the entire period from 1980 to 2013."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Thank you for completing this lab!\n",
"\n",
"This notebook was created by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab interesting and educational. Feel free to contact me if you have any questions!"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"This notebook is part of a course on **Coursera** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DV0101EN_Coursera_Week3_LAB2)."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<hr>\n",
"\n",
"Copyright © 2018 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
crdguez/mat4ac | notebooks/matematicas con python/.ipynb_checkpoints/ttm1819-checkpoint.ipynb | 1 | 22932 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"### Instrucciones\n",
"\n",
"requires a copy of reveal.js (version 3.x)\n",
"\n",
"```\n",
"jupyter nbconvert --to slides --SlidesExporter.reveal_theme=sky ttm1819.ipynb\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[NbConvertApp] Converting notebook ttm1819.ipynb to slides\n",
"[NbConvertApp] Writing 283046 bytes to ttm1819.slides.html\n"
]
}
],
"source": [
"!jupyter nbconvert --to slides --SlidesExporter.reveal_theme=white --SlidesExporter.reveal_scroll=True ttm1819.ipynb"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Taller de Talento Matemático \n",
"\n",
"<img src=\"files/img/ttm_logo.jpg\" align=\"center\" width=\"300\" height=\"300\">\n",
"\n",
"## Programando la Geometría con BlocksCAD\n",
"\n",
"<img src=\"files/img/attribution-share-alike-creative-commons-license.png\" align=\"left\" style=\"padding-right:30px;\" width=\"180\" height=\"180\">\n",
"\n",
"\n",
"**Página Web:** [https://mat3d.github.io/](https://mat3d.github.io/)\n",
"\n",
"**Documentación:** [https://github.com/mat3d](https://github.com/mat3d)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Introducción\n",
"\n",
"\n",
"\n",
"* Saludo y presentación\n",
" - **Pablo Beltrán Pellicer** *[@pbeltranp](https://twitter.com/pbeltranp)*\n",
" - **Carlos Rodríguez Jaso** *[@es3a10](https://twitter.com/es3a10)*\n",
"* Objetivo de la sesión:\n",
" - Iniciarse en las posibilidades de [BlocksCAD](https://www.blockscad3d.com/) desde un punto de vista matemático\n",
" \n",
"<video controls autoplay data-autoplay loop src=\"./img/timelapse.mp4\" align=\"center\" width=\"300\" height=\"300\"></video>\n",
"\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Introducción a [BlocksCAD](https://www.blockscad3d.com/)\n",
"\n",
"**BlocksCAD** es un programa para modelar objetos en 3D sin necesidad de tener elevados conocimientos de programación.\n",
"\n",
"La forma más sencilla de trabajar es de manera online a través de su web: [https://www.blockscad3d.com/](https://www.blockscad3d.com/)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## El entorno\n",
"\n",
"El entorno de trabajo de BlocksCAD lo podemos dividir en tres partes:\n",
"\n",
"<img src=\"files/img/zonas_trabajo.png\" width=\"600px\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Aprendiendo lo básico\n",
"\n",
"### Formas 3D: \n",
"\n",
"Son los objetos primitivos que podemos utilizar:\n",
"\n",
"#### Cubos: Mi primer objeto en 3D\n",
"\n",
"Dentro del bloque *Formas 3d*, arrastramos el bloque \n",
"<img src=\"./img/bloque_cubo.png\" width=\"40%\"> y lo llevamos a la área de programa. Al renderizar (botón **Hacer**) ya tenemos nuestro primer objeto con BlocksCAD.\n",
"\n",
"<img src=\"./img/cube10x10x10c.png\" width=\"30%\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Cubos: Mi primer objeto en 3D\n",
"\n",
"Observa qué pasa cuando modificas los parámetros:\n",
"\n",
"<img src=\"./img/cubo_bs.gif\" width=\"70%\">\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Formas 3D: Esferas\n",
"\n",
"Dentro del bloque *Formas 3D* podemos arrastrar el bloque <img src=\"./img/bloque_esfera.png\" width=\"20%\">\n",
"\n",
"Al renderizarlo obtendremos una esfera de *10mm* de radio:\n",
"\n",
"<img src=\"./img/esfera10.png\" width=\"30%\">\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Formas 3D: Cilindros y mucho más\n",
"\n",
"Dentro del bloque *Formas 3D*, tenemos el bloque <img src=\"./img/bloque_cilindro.png\" width=\"60%\">\n",
"\n",
"Explicación de algunos parámetros:\n",
"\n",
"- **radio1**, es el radio de la base inferior de la figura a modelar\n",
"- **candado**, por defecto aparece cerrado, y esto hace que el parámetro **radio2** herede el valor de **radio1**\n",
"- **radio2**, radio de la base superior de la figura. Cuando coincida con **radio1** tendremos un cilindro, y cuando no, tendremos un **tronco de cono** o un **cono** si ponemos que el radio es cero\n",
"\n",
"**Ejemplos:**\n",
"\n",
"| Bloque | Renderizado |\n",
"| :---------------------------------------: | :---------------------------------------------: |\n",
"| <img src=\"./img/bloque11.png\" width=100%> | <img src=\"./img/cilindro5_5_10c.png\" width=60%> |\n",
"| <img src=\"./img/bloque12.png\"> | <img src=\"./img/tronco10_5_20nc.png\" width=60%> |\n",
"| <img src=\"./img/bloque13.png\"> | <img src=\"./img/cono10_20nc.png\" width=60%> |\n",
"\n",
"\n",
"\n",
"BlocksCAD interpreta la base del cilindro como un polígono regular de **\"muchos\"** lados. Podemos generar un **prisma** de base regular modificando ese \"muchos\" con el bloque <img src=\"./img/bloque_aristas.png\" width=\"10%\"> que aparece en *Transformaciones*. Mira este ejemplo que compara un cilindro con un prisma de base triangular:\n",
"\n",
"\n",
"\n",
"<img src=\"./img/cilindro_nlados.png\" width=\"100%\">\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Transformaciones\n",
"\n",
"#### Traslaciones \n",
"Dentro del bloque *Formas 3D*, arrastramos el bloque <img src=\"./img/bloque_traslacion.png\" width=\"20%\">\n",
"En este caso, los parámetros **X**, **Y** y **Z**, son las coordenadas del vector de traslación.\n",
"\n",
"#### Rotaciones\n",
"Bloque <img src=\"./img/bloque_rotacion.png\" width=\"30%\"> \n",
"Los parámetros **X**, **Y** y **Z**, son los grados a rotar en los diferentes ejes.\n",
"\n",
"**Ejemplos:**\n",
"\n",
"| Ejemplo | Bloque | Renderizado |\n",
"| :---------------------------------------------------------: | :----------------------------- | :-------------------------------: |\n",
"| Prisma de 10x20x30 centrado y trasladado 30 en la dirección X, 30 en la Y y 40 en la Z | <img src=\"./img/bloque21.png\"> | <img src=\"./img/traslacion1.png\"> |\n",
"| Ejercicio anterior rotado 45º en el eje X | <img src=\"./img/bloque22.png\"> | <img src=\"./img/rotacion1.png\"> |"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Ops de Conjuntos: Operaciones lógicas \n",
"\n",
"#### Unión: Pegando objetos\n",
"\n",
"Para unir dos o más objetos tenemos que seleccionar el bloque <img src=\"./img/bloque_union.png\" width=\"10%\">\n",
"\n",
"#### Diferencia: Haciendo agujeros\n",
"\n",
"Con la diferencia lo que hacemos es hacer \"agujeros\". La forma de trabajar con el bloque es similar a la unión.\n",
"\n",
"**Ejemplos:**\n",
"\n",
"| Bloque | Renderizado |\n",
"| :----------------------------: | :-----------------------------: |\n",
"| <img src=\"./img/bloque31.png\"> | <img src=\"./img/ejemplo31.png\"> |\n",
"| <img src=\"./img/bloque32.png\"> | <img src=\"./img/ejemplo32.png\"> |\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# ¡Ya conoces lo básico para poder trabajar!\n",
"\n",
"<img src=\"./img/18614-NRQ2AH.jpg\" width=\"40%\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Desafío 1: \n",
"\n",
"Entrénate con BlocksCASD intentando modelar la siguiente figura. No tiene que ser exactamente igual pero sí parecida:\n",
"\n",
"| Vista 1 | Vista 2 |\n",
"| :--------------------------: | :--------------------------: |\n",
"| <img src=\"./img/des1_1.png\"> | <img src=\"./img/des1_2.png\"> |\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Tienes una posible solución en https://www.blockscad3d.com/community/projects/82576"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Desafío 2: Estrella Mudéjar\n",
"\n",
"| Actividad |\n",
"| -------------------------------------------- |\n",
"| Estrella mudéjar sencilla |\n",
"| \"Estrella mudéjar\" sobre un hexágono regular |\n",
"| \"Estrella mudéjar\" paramétrica sencilla |\n",
"| \"Estrella mudéjar\" paramétrica avanzada |\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Estrella mudéjar sencilla\n",
"\n",
"#### Modelo\n",
"Para empezar, vamos a realizar **una sencilla estrella mudejar** con dos prismas cuadrados de dimensiones 10x10x5 (eje_x, eje_y, eje_z o para entendernos, ancho por largo por alto):\n",
"\n",
"<img src=\"files/img/r_est1.png\" width=\"50%\" align=\"center\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Cuestiones iniciales\n",
"\n",
"* ¿Existe alguna **relación** entre el **lado de un cuadrado y su apotema**?\n",
"* ¿Crees que para otros polígonos regulares va a seguir existiendo una **relación entre el lado y la apotema**?\n",
"* ¿Sabes algo de **trigonometría**? En caso afirmativo, las preguntas anteriores deberían resultarte sencillas. En caso contrario, **no te preocupes** la parte teórica la facilitaremos para que puedas hacer el modelado si te ves desbordado.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Respuestas a las preguntas\n",
"\n",
"* La **apotema** es la mitad del lado. <img src=\"./img/apotema_cuadrado.png\" width=\"80%\">\n",
"\n",
"* Sí, siempre va a haber relación. La respuesta está en la **semejanza de triángulos rectángulos** y **la trigonometría**. Esto nos permitirá calcular bien el radio, la apotema o el lado del polígono regular siempre que nos den uno de ellos. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Modelado en BlocksCAD\n",
"\n",
"| Paso | Código | Renderizado |\n",
"| ---------------------------------| --------------------------------------------------| ------------------------------------------ |\n",
"| Insertamos un cubo de 10x10x5 centrado en origen | <img src=\"./img/cubo.png\" width=\"100%\"> | <img src=\"./img/r_cubo.png\" width=\"80%\"> |\n",
"| Insertamos otro igual pero girado 45º sobre el plano XY (girar el eje Z) | <img src=\"./img/cubo45.png\" width=\"110%\"> | <img src=\"./img/r_cubo45.png\" width=\"80%\"> |\n",
"| Juntamos los dos objetos para formar un único objeto | <img src=\"./img/est1.png\" width=\"110%\"> | <img src=\"./img/r_est1.png\" width=\"80%\"> |"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Tienes la solución en https://www.blockscad3d.com/community/projects/266387"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## \"Estrella mudéjar\" sobre un hexágono regular\n",
"\n",
"#### Modelo\n",
"Ahora se propone realizar una especie de estrella mudéjar modificada a partir de un hexágono regular de radio y altura que quieras\n",
"\n",
"<img src=\"./img/est_hex.png\" width=\"50%\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Cuestiones previas\n",
"\n",
"* ¿Hacia qué objeto tiende un polígono regular cuando aumentamos el **número de lados** al polígono?\n",
"* ¿Cuánto tiene que girar el prisma hexagonal superpuesto para generar las puntas en mitad de las aristas del prisma original?¿Puedes dar una **fórmula general** que vaya en función del número de lados y que por tanto sirva para el cuadrado o el hexágono?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Consideraciones a las cuestiones\n",
"\n",
"* Al aumentar el número de lados, el **polígono** se va acercando a un **círculo** como se puede ver en la siguiente animación:\n",
"\n",
" <img src=\"./img/lim_circulo.gif\" width=\"50%\">\n",
"\n",
"* **BlocksCAD** interpreta los cilindros de esta manera, como un **prisma** de base regular **con un número de lados elevado**. Podemos convertir un cilindrode BlocksCAD en un prisma modificando el número de lados:<img src=\"./img/cilindro_nlados.png\" width=\"100%\">\n",
"\n",
"\n",
"* La **rotación** que hay que hacer es la mitad del ángulo central: $\\alpha=\\frac{180}{nlados}$\n",
"\n",
"* **Importante:** Al usar el bloque cilindro modificado por el número de aristas, el parámetro que le damos es el radio del polígono regular y no la longitud de la arista (o lado del polígono regular)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Modelado en BlocksCAD\n",
"\n",
"* Se propone como ejercicio su modelado. \n",
"\n",
" <img src=\"./img/18614-NRQ2AH.jpg\" width=\"40%\" style=\"padding-right:30px;\" align=\"left\"> <img src=\"./img/est_hex.png\" width=\"50%\">\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Tienes una posible solución en https://www.blockscad3d.com/community/projects/272240"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## \"Estrella mudéjar\" paramétrica sencilla\n",
"#### Modelo\n",
"Se pide modificar la estrella anterior para que acepte los siguientes parámetros o variables:\n",
"* **Número de lados** del polígono regular que genera la estrella\n",
"* **Longitud** del radio del polígono\n",
"* Longitud del radio del polígono que genera el **hueco**\n",
"\n",
"**Ejemplo de modelo:** Estrella de David generada con triángulos de radios 10 y 8, exterior e interior respectivamente. \n",
"\n",
"<img src=\"./img/est_david.png\" width=\"30%\">\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Modelado en BlocksCAD\n",
"\n",
"* Se propone como ejercicio su modelado. \n",
"\n",
" <img src=\"./img/18614-NRQ2AH.jpg\" width=\"40%\" style=\"padding-right:30px;\" align=\"left\"> <img src=\"./img/est_david.png\" width=\"40%\">\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Tienes una posible solución en https://www.blockscad3d.com/community/projects/278006"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## \"Estrella mudéjar\" paramétrica avanzada\n",
"\n",
"#### Modelo\n",
"Vamos a añadir un poco de complejidad al programa. En las estrellas anteriores todas las puntas son iguales. Ahora en lugar de que la mitad de las puntas las genere el prisma poligonal girado se pide que sean los generados por un prisma cuadrado con la diagonal superpuesta sobre la arista (el lado del cuadrado que sea 1/3 de la arista).\n",
"\n",
"**Ejemplo:** Para 8 lados tiene que quedar algo así\n",
"<img src=\"./img/estpar.png\" width=\"50%\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Explicación del Modelo\n",
"Como una imagen vale más que mil palabras:\n",
"\n",
"<img src=\"./img/hex_cuad.png\" width=\"60%\">\n",
"\n",
"Se pide por tanto modelar una estrella que tenga como parámetros:\n",
"\n",
"* **Número de lados**\n",
"* Longitud del **lado**\n",
"* **Grosor** de la estrella: Altura y anchura del contorno. Opcional: Si se quiere se pueden hacer dos parámetros."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Cuestiones previas\n",
"\n",
"* Dado un polígono regular: ¿Existe alguna **fórmula general** que **relacione** la longitud del **lado** con el **radio**?¿Y alguna relación entre la **apotema** y el **lado**?\n",
"* El cuadrado que genera las puntas, **¿Cuánto hay que trasladarlo?**\n",
"* ¿Cuántas **rotaciones** hay que hacer del cuadrado para generar todas las puntas?¿Qué ángulo entre ellos?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Respuestas a las cuestiones previas\n",
" <img src=\"./img/semi_angulo_central.gif\" width=\"60%\">\n",
"\n",
" * El **triángulo** formado por el centro del polígono, el punto medio de un lado y un vértice adyacente es **siempre rectángulo**. El ángulo correspondiente al centro del polígono es **la mitad del ángulo central**: $$\\alpha=\\dfrac{\\frac{360}{n_{lados}}}{2}$$\n",
"\n",
" * **Fijado el número de lados** del polígono regular, aunque varíe el tamaño del lado, los triángulos que salgan serán semejantes. **Al ser semejantes los lados son proporcionales**, o dicho de otra forma, **la razón entre lados se mantiene constante y dependen exclusivamente del ángulo** que se apoya en el centro del polígono: Son las **razones trigonométricas**. La principales son:\n",
"\n",
"| Razón trigonométrica | Aplicación en el polígono regular |\n",
"| ------------------------------------------- | --------------------------------------- |\n",
"| **seno** = lado opuesto / hipotenusa | $\\sin{\\alpha}=\\frac{semilado}{radio}$ |\n",
"| **coseno** = lado contiguo / hipotenusa | $\\cos{\\alpha}=\\frac{apotema}{radio}$ |\n",
"| **tangente** = lado opuesto / lado contiguo | $\\tan{\\alpha}=\\frac{semilado}{apotema}$ |\n",
" \n",
"\n",
"* El cuadrado hay que trasladarlo **la apotema del polígono**\n",
"* Habrá que hacer **tantas puntas como lados** y habrá que **rotar la mitad del ángulo central**: $$\\alpha=\\frac{180}{nlados}$$\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"**Ejemplo de aplicación:** Determina el radio y la apotema para un **pentágono regular de lado 6**:\n",
"\n",
"<img src=\"./img/pent.png\" width=\"30%\">\n",
"\n",
"\n",
"* **radio = (6/2)/seno(180/5)**\n",
"* **apotema = (6/2)/tan(180/5)**"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Modelado en BlocksCAD\n",
"\n",
"* Se propone como ejercicio su modelado. \n",
"\n",
" <img src=\"./img/18614-NRQ2AH.jpg\" width=\"40%\" style=\"padding-right:30px;\" align=\"left\"> <img src=\"./img/estpar.png\" width=\"40%\">\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Tienes una posible solución en https://www.blockscad3d.com/community/projects/267149"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## ¡Gracias por vuestra presencia y a seguir modelando con BlocksCAD!\n",
"\n",
"Podéis seguir trabajando en vuestra casa y difundir vuestros progresos en:\n",
"\n",
"\n",
"[#ttmestrellamudejar](https://twitter.com/search?f=tweets&q=%23ttmestrellamudejar&src=typd)\n",
"\n",
"\n",
"**Otros enlaces que pueden ser interesantes:**\n",
"\n",
"* [Tutoriales de BlocksCAD](https://www.youtube.com/channel/UCovK2cRIjoaZNzRwpQP2sFg/playlists) oficiales\n",
"* [Proyecto RepRap](https://reprap.org/wiki/RepRap) sobre impresoras 3D libres\n",
"* [Proyecto Clone Wars](https://www.reprap.org/wiki/Proyecto_Clone_Wars) , subproyecto de RepRap en castellano"
]
}
],
"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": "3.6.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
maojrs/riemann_book | FV_compare.ipynb | 3 | 22022 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Finite volume discretizations with approximate Riemann solvers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the previous few chapters we introduced several approximate Riemann solvers for different nonlinear conservation laws and showed some comparisons of the approximate solution to a single Riemann problem with the true solution, where there often appear to be significant differences. However, their intended use is as a building block for finite volume methods such as Godunov's method or high-resolution extensions. \n",
"\n",
"How do these solvers impact the solution accuracy when used within a finite volume discretization? To investigate, we will use them within [PyClaw](http://www.clawpack.org/pyclaw/) to solve several test problems.\n",
"\n",
"In particular, for both the shallow water equations and the Euler equations, we will apply the numerical method to solve dam break and shock tube problems, which are themselves Riemann problems and so we can compute the exact solution, but once discretized require the solution of a different set of Riemann problems at each cell interface every time step. \n",
"\n",
"For the Euler equations we will also consider the Woodward-Colella blast wave problem. The initial data consists of two Riemann problems, with resulting shock waves of differing strengths. These shock waves later interact with each other.\n",
" \n",
"In this chapter we include extensive sections of code in the notebook. This is meant to more easily allow the reader to use these as templates for setting up other problems in PyClaw. For the code in this chapter we use approximate Riemann solvers from PyClaw that can be found in these files:\n",
"\n",
"- [euler_1D_py.py,](https://github.com/clawpack/riemann/blob/FA16/riemann/euler_1D_py.py)\n",
"- [shallow_roe_with_efix_1D.](https://github.com/clawpack/riemann/blob/FA16/riemann/shallow_1D_py.py)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"hide"
]
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"hide"
]
},
"outputs": [],
"source": [
"%config InlineBackend.figure_format = 'svg'\n",
"import numpy as np\n",
"from exact_solvers import euler\n",
"from clawpack import riemann\n",
"import matplotlib.pyplot as plt\n",
"from ipywidgets import interact\n",
"from ipywidgets import widgets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Shallow water equations\n",
"\n",
"We compare results obtained with using the high-resolution wave propagation method implemented in PyClaw (with limiters to avoid nonphysical oscillations). We compare the results obtained when combined with two different approximate Riemann solvers: the Roe solver and HLLE."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from exact_solvers import shallow_water as sw\n",
"from clawpack import riemann\n",
"from clawpack.riemann.shallow_roe_with_efix_1D_constants import depth, momentum, num_eqn\n",
"\n",
"def setup(riemann_solver='roe',N=20,IC='dam-break'):\n",
"\n",
" from clawpack import pyclaw\n",
"\n",
" if riemann_solver.lower() == 'roe':\n",
" rs = riemann.shallow_roe_with_efix_1D\n",
" elif riemann_solver.lower() == 'hlle':\n",
" rs = riemann.shallow_hlle_1D\n",
" \n",
" solver = pyclaw.ClawSolver1D(rs)\n",
" \n",
" solver.bc_lower[0] = pyclaw.BC.extrap\n",
" solver.bc_upper[0] = pyclaw.BC.extrap\n",
"\n",
" xlower = -5.0\n",
" xupper = 5.0\n",
" x = pyclaw.Dimension(xlower,xupper,N,name='x')\n",
" domain = pyclaw.Domain(x)\n",
" state = pyclaw.State(domain,num_eqn)\n",
"\n",
" # Gravitational constant\n",
" state.problem_data['grav'] = 1.0\n",
" state.problem_data['dry_tolerance'] = 1e-3\n",
" state.problem_data['sea_level'] = 0.0\n",
" \n",
" xc = state.grid.x.centers\n",
"\n",
" x0=0.\n",
"\n",
" hl = 10.\n",
" ul = 0.\n",
" hr = 0.5\n",
" ur = 0.\n",
" state.q[depth,:] = hl * (xc <= x0) + hr * (xc > x0)\n",
" state.q[momentum,:] = hl*ul * (xc <= x0) + hr*ur * (xc > x0)\n",
"\n",
" claw = pyclaw.Controller()\n",
" claw.keep_copy = True\n",
" claw.output_format = None\n",
" claw.tfinal = 1.0\n",
" claw.solution = pyclaw.Solution(state,domain)\n",
" claw.solver = solver\n",
"\n",
" return claw"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"N = 40 # number of grid cells to use\n",
"q_l = [10,0]\n",
"q_r = [0.5,0]\n",
"\n",
"# Roe solution\n",
"roe_sw = setup(riemann_solver='roe',N=N)\n",
"roe_sw.verbosity = 0\n",
"status = roe_sw.run()\n",
"xc_sw = roe_sw.grid.x.centers\n",
"\n",
"# HLLE solution\n",
"hlle_sw = setup(riemann_solver='hlle',N=N)\n",
"hlle_sw.verbosity = 0\n",
"status = hlle_sw.run()\n",
"\n",
"# Exact solution\n",
"xc_exact_sw = np.linspace(-5,5,2000)\n",
"states_sw, speeds_sw, reval_sw, wave_types_sw = \\\n",
" sw.exact_riemann_solution(q_l, q_r)\n",
"def plot_frame(i):\n",
" t = roe_sw.frames[i].t+1.e-13\n",
" fig, ax = plt.subplots(2,1, sharex=True, figsize=(8,5))\n",
" variablenames = [\"Depth\", \"Momentum\"]\n",
" variables = [depth, momentum]\n",
" ylims = [[-1,12], [-5,12]]\n",
" plt.subplots_adjust(hspace=0)\n",
" ax[0].title.set_text('Solutions at t={:.2f}'.format(t))\n",
" ax[0].set_xlim((-5,5))\n",
" ax[1].set(xlabel = 'x')\n",
" for j, variable in enumerate(variables):\n",
" ax[j].set_ylim(ylims[j])\n",
" ax[j].plot(xc_exact_sw,reval_sw(xc_exact_sw/(t+1.e-16))[j],'-k',lw=1)\n",
" ax[j].plot(xc_sw, hlle_sw.frames[i].q[variable,:],'-ob',lw=2,markersize=4)\n",
" ax[j].plot(xc_sw, roe_sw.frames[i].q[variable,:],'-or',lw=0.5,markersize=3)\n",
" ax[j].legend(['Exact','HLLE','Roe'],loc='best')\n",
" ax[j].set(ylabel=variablenames[j])\n",
" plt.show()\n",
" \n",
"interact(plot_frame, i=widgets.IntSlider(min=0,max=10,value=5));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clearly, both solvers give very similar results for this problem. They converge to the same solution, as you can verify by increasing the value of $N$ above in the live notebook. You can also try varying the left and right states."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Euler equations\n",
"\n",
"We perform a similar test for the Euler equations with shock tube initial data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sod shock tube problem"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We first consider the classic shocktube problem proposed by Sod and already discussed in [Euler](Euler.ipynb). This is a particular Riemann problem in which the initial velocity is zero on both sides of a discontinuity in pressure and/or density of a gas, and so the exact Riemann solver for the Euler equations would provide the exact solution for all time, consisting of a right-going shock, a left-going rarefaction, and an intermediate contact discontinuity.\n",
"\n",
"In the numerical experiments done in this notebook, we use this initial data for a more general finite volume method that could be used to approximate the solution for any initial data. In the first time step there is a single cell interface with nontrivial Riemann data, but as the solution evolves on the grid the Riemann problems that arise in subsequent time steps are very different from the single problem we started with. Depending on the accuracy of the numerical method, the resolution of the grid, and the choice of approximate Riemann solver to use at each grid cell every time step, the numerical solution may deviate significantly from the exact solution to the original shocktube problem. This makes a good initial test problem for numerical methods because the exact solution can be computed for comparison purposes, and because it clearly shows whether the method introduces oscillations around discontinuities and/or smears them out. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from clawpack.riemann.euler_with_efix_1D_constants \\\n",
" import density, momentum, energy, num_eqn\n",
"\n",
"def shocktube(q_l, q_r, N=50, riemann_solver='HLL', \n",
" solver_type='classic'):\n",
"\n",
" from clawpack import pyclaw\n",
" from clawpack import riemann\n",
"\n",
" if riemann_solver == 'Roe':\n",
" rs = riemann.euler_1D_py.euler_roe_1D\n",
" elif riemann_solver == 'HLL':\n",
" rs = riemann.euler_1D_py.euler_hll_1D\n",
"\n",
" if solver_type == 'classic':\n",
" solver = pyclaw.ClawSolver1D(rs) \n",
" solver.limiters = pyclaw.limiters.tvd.MC\n",
" else:\n",
" solver = pyclaw.SharpClawSolver1D(rs)\n",
"\n",
" solver.kernel_language = 'Python'\n",
" \n",
" solver.bc_lower[0]=pyclaw.BC.extrap\n",
" solver.bc_upper[0]=pyclaw.BC.extrap\n",
"\n",
" x = pyclaw.Dimension(-1.0,1.0,N,name='x')\n",
" domain = pyclaw.Domain([x])\n",
" state = pyclaw.State(domain,num_eqn)\n",
"\n",
" gamma = 1.4\n",
" state.problem_data['gamma']= gamma\n",
" state.problem_data['gamma1']= gamma-1.\n",
"\n",
" state.problem_data['efix'] = False\n",
"\n",
" xc = state.grid.p_centers[0]\n",
" \n",
" velocity = (xc<=0)*q_l[1] + (xc>0)*q_r[1]\n",
" pressure = (xc<=0)*q_l[2] + (xc>0)*q_r[2]\n",
"\n",
" state.q[density ,:] = (xc<=0)*q_l[0] + (xc>0)*q_r[0]\n",
" state.q[momentum,:] = velocity * state.q[density,:]\n",
" state.q[energy ,:] = pressure/(gamma - 1.) + \\\n",
" 0.5 * state.q[density,:] * velocity**2\n",
"\n",
" claw = pyclaw.Controller()\n",
" claw.tfinal = 0.5\n",
" claw.solution = pyclaw.Solution(state,domain)\n",
" claw.solver = solver\n",
" claw.num_output_times = 10\n",
" claw.keep_copy = True\n",
" claw.verbosity=0\n",
"\n",
" return claw"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"N = 80 # number of grid cells to use\n",
"\n",
"prim_l = [1.,0.,1.]\n",
"prim_r = [1./8,0.,1./10]\n",
"q_l = euler.conservative_to_primitive(*prim_l)\n",
"q_r = euler.conservative_to_primitive(*prim_r)\n",
"\n",
"# Roe-based solution\n",
"roe_st = shocktube(q_l,q_r,N=N,riemann_solver='Roe')\n",
"roe_st.run()\n",
"xc_st = roe_st.solution.state.grid.p_centers[0]\n",
"\n",
"# HLL-based solution\n",
"hll_st = shocktube(q_l,q_r,N=N,riemann_solver='HLL')\n",
"hll_st.run()\n",
"\n",
"# Exact solution\n",
"xc_exact_st = np.linspace(-1,1,2000)\n",
"states_st, speeds_st, reval_st, wave_types_st = euler.exact_riemann_solution(prim_l, prim_r)\n",
"\n",
"def plot_frame(i):\n",
" t = roe_st.frames[i].t\n",
" fig, ax = plt.subplots(3,1, sharex=True, figsize=(8,6))\n",
" variablenames = [\"Density\", \"Momentum\", \"Energy\"]\n",
" variables = [density, momentum, energy]\n",
" ylims = [[0,1.1], [-0.05,0.35], [0,1.1]]\n",
" plt.subplots_adjust(hspace=0)\n",
" ax[0].title.set_text('Solutions at t={:.2f}'.format(t))\n",
" ax[0].set_xlim((-1,1))\n",
" ax[2].set(xlabel = 'x')\n",
" for j, variable in enumerate(variables):\n",
" ax[j].set_ylim(ylims[j])\n",
" ax[j].plot(xc_exact_st,reval_st(xc_exact_st/(t+1.e-16))[j],'-k',lw=1)\n",
" ax[j].plot(xc_st,hll_st.frames[i].q[variable,:],'-ob',lw=2,markersize=4)\n",
" ax[j].plot(xc_st,roe_st.frames[i].q[variable,:],'-or',lw=0.5,markersize=3)\n",
" ax[j].legend(['Exact','HLL','Roe'],loc='best')\n",
" ax[j].set(ylabel=variablenames[j])\n",
" plt.show()\n",
" \n",
"interact(plot_frame, i=widgets.IntSlider(min=0, max=10, value=5, description='Frame'));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you might expect, the HLL solver smears the middle wave (contact discontinuity) significantly more than the Roe solver does. Perhaps surprisingly, it captures the shock just as accurately as the Roe solver does. In the live notebook you can refine the grid and observe the convergence."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### High-order WENO + Runge-Kutta\n",
"\n",
"Next we look at the difference between the HLL and Roe solution when these solvers are employed within a higher-order method of lines discretization using fifth-order WENO and a 4th-order Runge-Kutta scheme."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"N = 80 # number of grid cells to use\n",
"\n",
"prim_l = [1.,0.,1.]\n",
"prim_r = [1./8,0.,1./10]\n",
"q_l = euler.conservative_to_primitive(*prim_l)\n",
"q_r = euler.conservative_to_primitive(*prim_r)\n",
"\n",
"roe_weno = shocktube(q_l,q_r,N=N,riemann_solver='Roe',solver_type='sharpclaw')\n",
"roe_weno.run()\n",
"hll_weno = shocktube(q_l,q_r,N=N,riemann_solver='HLL',solver_type='sharpclaw')\n",
"hll_weno.run()\n",
"\n",
"xc_weno = roe_weno.solution.state.grid.p_centers[0]\n",
"\n",
"# Exact solution\n",
"xc_exact_weno = np.linspace(-1,1,2000)\n",
"states_weno, speeds_weno, reval_weno, wave_types_weno = euler.exact_riemann_solution(prim_l, prim_r)\n",
"\n",
"def plot_frame(i):\n",
" t = roe_weno.frames[i].t\n",
" fig, ax = plt.subplots(3,1, sharex=True, figsize=(8,6))\n",
" variablenames = [\"Density\", \"Momentum\", \"Energy\"]\n",
" variables = [density, momentum, energy]\n",
" ylims = [[0,1.1], [-0.05,0.35], [0,1.1]]\n",
" plt.subplots_adjust(hspace=0)\n",
" ax[0].title.set_text('Solutions at t={:.2f}'.format(t))\n",
" ax[0].set_xlim((-1,1))\n",
" ax[2].set(xlabel = 'x')\n",
" for j, variable in enumerate(variables):\n",
" ax[j].set_ylim(ylims[j])\n",
" ax[j].plot(xc_exact_weno,reval_weno(xc_exact_weno/(t+1.e-16))[j],'-k',lw=1)\n",
" ax[j].plot(xc_weno,hll_weno.frames[i].q[variable,:],'-ob',lw=2,markersize=4)\n",
" ax[j].plot(xc_weno,roe_weno.frames[i].q[variable,:],'-or',lw=0.5,markersize=3)\n",
" ax[j].legend(['Exact','HLL','Roe'],loc='best')\n",
" ax[j].set(ylabel=variablenames[j])\n",
" plt.show()\n",
" \n",
"interact(plot_frame, i=widgets.IntSlider(min=0, max=10, value=5, description='Frame'));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With higher-order discretizations, the difference in solutions due to using different Riemann solvers is less significant. This is partly because these high-order schemes use more accurate values as inputs to the Riemann problem, so that in smooth regions the jump between most cells is very small."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Woodward-Colella blast wave"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we consider the Woodward-Colella blast wave problem, which is discussed for example in <cite data-cite=\"fvmhp\"><a href=\"riemann.html#fvmhp\">(LeVeque 2002)</a></cite>. Here the initial velocity is zero and the density is one everywhere. The pressure is\n",
"\\begin{align}\n",
" p_0(x) = \\begin{cases} 1000 & 0 \\le x \\le 0.1 \\\\\n",
" 0.01 & 0.1 \\le x \\le 0.9 \\\\\n",
" 100 & 0.9 \\le x \\le 1\n",
" \\end{cases}\n",
"\\end{align}\n",
"The boundaries at $x=0$ and $x=1$ are solid walls. The solution involves a Riemann problem at $x=0.1$ and another at $x=0.9$. Later, the waves resulting from these Riemann problems interact with each other."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from clawpack.riemann.euler_with_efix_1D_constants \\\n",
" import density, momentum, energy, num_eqn\n",
"\n",
"def blastwave(N=400, riemann_solver='HLL', solver_type='classic'):\n",
"\n",
" from clawpack import pyclaw\n",
" from clawpack import riemann\n",
"\n",
" if riemann_solver == 'Roe':\n",
" kernel_language = 'Fortran'\n",
" rs = riemann.euler_with_efix_1D\n",
" elif riemann_solver == 'HLL':\n",
" kernel_language = 'Python'\n",
" rs = riemann.euler_1D_py.euler_hll_1D\n",
"\n",
" if solver_type == 'classic':\n",
" solver = pyclaw.ClawSolver1D(rs)\n",
" solver.limiters = pyclaw.limiters.tvd.MC\n",
" else:\n",
" solver = pyclaw.SharpClawSolver1D(rs)\n",
"\n",
" solver.kernel_language = kernel_language\n",
" \n",
" solver.bc_lower[0]=pyclaw.BC.wall\n",
" solver.bc_upper[0]=pyclaw.BC.wall\n",
"\n",
" x = pyclaw.Dimension(0.0,1.0,N,name='x')\n",
" domain = pyclaw.Domain([x])\n",
" state = pyclaw.State(domain,num_eqn)\n",
"\n",
" gamma = 1.4\n",
" state.problem_data['gamma']= gamma\n",
" state.problem_data['gamma1']= gamma-1.\n",
"\n",
" state.problem_data['efix'] = False\n",
"\n",
" xc = state.grid.p_centers[0]\n",
"\n",
" pressure = (xc<0.1)*1.e3 + (0.1<=xc)*(xc<0.9)*1.e-2 + (0.9<=xc)*1.e2\n",
" \n",
" state.q[density ,:] = 1.\n",
" state.q[momentum,:] = 0.\n",
" state.q[energy ,:] = pressure / (gamma - 1.)\n",
"\n",
" claw = pyclaw.Controller()\n",
" claw.tfinal = 0.038\n",
" claw.solution = pyclaw.Solution(state,domain)\n",
" claw.solver = solver\n",
" claw.num_output_times = 30\n",
" claw.keep_copy = True\n",
" claw.verbosity=0\n",
"\n",
" return claw"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"N = 400 # number of grid cells to use\n",
"\n",
"roe_bw = blastwave(N=N, riemann_solver='Roe')\n",
"roe_bw.run()\n",
"hll_bw = blastwave(N=N, riemann_solver='HLL')\n",
"hll_bw.run()\n",
"fine_bw = blastwave(N=4000,riemann_solver='Roe')\n",
"fine_bw.run();\n",
"xc_bw = roe_bw.solution.state.grid.p_centers[0]\n",
"xc_fine_bw = fine_bw.solution.state.grid.p_centers[0]\n",
"\n",
"def plot_frame(i):\n",
" t = roe_bw.frames[i].t\n",
" fig, ax = plt.subplots(3,1, sharex=True, figsize=(8,6))\n",
" variablenames = [\"Density\", \"Momentum\", \"Energy\"]\n",
" variables = [density, momentum, energy]\n",
" ylims = [[-1,10], [-50,120], [-300,3000]]\n",
" plt.subplots_adjust(hspace=0)\n",
" ax[0].title.set_text('Solutions at t={:.3f}'.format(t))\n",
" ax[0].set_xlim((0,1))\n",
" ax[2].set(xlabel = 'x')\n",
" for j, variable in enumerate(variables):\n",
" ax[j].set_ylim(ylims[j])\n",
" ax[j].plot(xc_fine_bw,fine_bw.frames[i].q[variable,:],'-k',lw=1)\n",
" ax[j].plot(xc_bw,hll_bw.frames[i].q[variable,:],'-b',lw=2)\n",
" ax[j].plot(xc_bw,roe_bw.frames[i].q[variable,:],'--r',lw=2)\n",
" ax[j].legend(['Fine','HLL','Roe'],loc='best')\n",
" ax[j].set(ylabel=variablenames[j])\n",
" plt.show()\n",
" \n",
"interact(plot_frame, i=widgets.IntSlider(min=0, max=30, value=15, description='Frame'));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here no exact solution is available, so we compare with a solution computed on a finer grid.\n",
"Again the solutions are fairly similar, though the HLL solution is a bit more smeared.\n",
"\n",
"One should not conclude from these tests that, for instance, the Roe solver is always *better* than the HLL solver. Many factors besides accuracy should be considered, including cost and robustness. As we have seen the HLL solver is more robust in the presence of near-vacuum states."
]
}
],
"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"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": "block",
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| bsd-3-clause |
DSSG2017/florence | dev/notebooks/EDA-FC-plots-MM.ipynb | 1 | 48783 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#### Notebook to generate FirenzeCard analysis\n",
"#### Timeseries, and summary statistics\n",
"import sys\n",
"sys.path.append('../src/')\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline \n",
"import psycopg2\n",
"from features.firenzecard import *\n",
"from IPython.core.debugger import Tracer\n",
"\n",
"from scipy.stats import norm\n",
"from sklearn.neighbors import KernelDensity"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def frequency(dataframe,columnname):\n",
" \"\"\"\n",
" :param dataframe: a pandas dataframe\n",
" :param columnname: a single column, with discrete (including integer) values\n",
" :return: a frequency table, suitable for plotting the empirical PMF, empirical CDF, or empirical CCDF\n",
" \"\"\"\n",
" out = dataframe[columnname].value_counts().to_frame()\n",
" out.columns = ['frequency']\n",
" out.index.name = columnname\n",
" out.reset_index(inplace=True)\n",
" out.sort_values('frequency',inplace=True,ascending=False)\n",
" out['cumulative'] = out['frequency'].cumsum()/out['frequency'].sum()\n",
" out['ccdf'] = 1 - out['cumulative']\n",
" return out\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'user_id', u'museum_id', u'entrances_per_card_per_museum',\n",
" u'museum_name', u'longitude', u'latitude', u'short_name', u'string',\n",
" u'entry_time', u'adults_first_use', u'adults_reuse', u'total_adults',\n",
" u'minors', u'time', u'date', u'hour', u'day_of_week', u'total_people',\n",
" u'time_since_previous_museum', u'total_duration_card_use',\n",
" u'entry_is_adult', u'is_card_with_minors', u'is_in_museum_1',\n",
" u'is_in_museum_2', u'is_in_museum_3', u'is_in_museum_4',\n",
" u'is_in_museum_5', u'is_in_museum_6', u'is_in_museum_7',\n",
" u'is_in_museum_8', u'is_in_museum_9', u'is_in_museum_10',\n",
" u'is_in_museum_11', u'is_in_museum_12', u'is_in_museum_13',\n",
" u'is_in_museum_14', u'is_in_museum_15', u'is_in_museum_16',\n",
" u'is_in_museum_17', u'is_in_museum_18', u'is_in_museum_19',\n",
" u'is_in_museum_20', u'is_in_museum_21', u'is_in_museum_22',\n",
" u'is_in_museum_23', u'is_in_museum_24', u'is_in_museum_25',\n",
" u'is_in_museum_26', u'is_in_museum_27', u'is_in_museum_28',\n",
" u'is_in_museum_29', u'is_in_museum_30', u'is_in_museum_31',\n",
" u'is_in_museum_32', u'is_in_museum_33', u'is_in_museum_34',\n",
" u'is_in_museum_35', u'is_in_museum_36', u'is_in_museum_37',\n",
" u'is_in_museum_38', u'is_in_museum_39'],\n",
" dtype='object')"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../src/output/firenzedata_feature_extracted.csv')\n",
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df1 = df.groupby(['user_id','entry_time']).agg({'time_since_previous_museum':'max', 'total_adults':'sum', 'total_people':'sum', 'museum_id':'max'})\n",
"# df1 = df.groupby(['user_id','entry_time','museum_id']).agg({'time_until_next_museum':['min','max'], 'total_adults':['sum'], 'total_people':['sum']})\n",
"df1.dropna(how=\"any\",inplace=True)\n",
"df1 = df1[df1['time_since_previous_museum']>0]\n",
"df1.reset_index(inplace=True)\n",
"# df1[df1.time_until_next_museum['min']!=df1.time_until_next_museum['max']] # All have minors"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
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" text-align: left;\n",
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"\n",
" .dataframe tbody tr th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user_id</th>\n",
" <th>entry_time</th>\n",
" <th>total_people</th>\n",
" <th>time_since_previous_museum</th>\n",
" <th>total_adults</th>\n",
" <th>museum_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1459702</td>\n",
" <td>2016-06-22 14:26:00</td>\n",
" <td>1</td>\n",
" <td>4.36667</td>\n",
" <td>1</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1459702</td>\n",
" <td>2016-06-22 15:49:00</td>\n",
" <td>1</td>\n",
" <td>1.38333</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1459702</td>\n",
" <td>2016-06-23 09:43:00</td>\n",
" <td>1</td>\n",
" <td>17.9</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1459702</td>\n",
" <td>2016-06-23 11:14:00</td>\n",
" <td>1</td>\n",
" <td>1.51667</td>\n",
" <td>1</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1459702</td>\n",
" <td>2016-06-23 12:57:00</td>\n",
" <td>1</td>\n",
" <td>1.71667</td>\n",
" <td>1</td>\n",
" <td>23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user_id entry_time total_people time_since_previous_museum \\\n",
"0 1459702 2016-06-22 14:26:00 1 4.36667 \n",
"1 1459702 2016-06-22 15:49:00 1 1.38333 \n",
"2 1459702 2016-06-23 09:43:00 1 17.9 \n",
"3 1459702 2016-06-23 11:14:00 1 1.51667 \n",
"4 1459702 2016-06-23 12:57:00 1 1.71667 \n",
"\n",
" total_adults museum_id \n",
"0 1 15 \n",
"1 1 11 \n",
"2 1 3 \n",
"3 1 29 \n",
"4 1 23 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": false
},
"outputs": [
{
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" <th>total_people</th>\n",
" <th>time_since_previous_museum</th>\n",
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" <th>museum_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>1473906</td>\n",
" <td>2016-07-24 11:58:00</td>\n",
" <td>2</td>\n",
" <td>1.16667</td>\n",
" <td>1</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>2017453</td>\n",
" <td>2016-06-17 20:04:00</td>\n",
" <td>2</td>\n",
" <td>0.783333</td>\n",
" <td>1</td>\n",
" <td>41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>189</th>\n",
" <td>2017468</td>\n",
" <td>2016-06-16 12:00:00</td>\n",
" <td>3</td>\n",
" <td>1.23333</td>\n",
" <td>1</td>\n",
" <td>37</td>\n",
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" <tr>\n",
" <th>190</th>\n",
" <td>2017468</td>\n",
" <td>2016-06-16 12:56:00</td>\n",
" <td>3</td>\n",
" <td>0.933333</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>2017468</td>\n",
" <td>2016-06-17 12:34:00</td>\n",
" <td>3</td>\n",
" <td>23.6333</td>\n",
" <td>1</td>\n",
" <td>38</td>\n",
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" <tr>\n",
" <th>192</th>\n",
" <td>2017468</td>\n",
" <td>2016-06-18 10:45:00</td>\n",
" <td>3</td>\n",
" <td>22.1833</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>2017468</td>\n",
" <td>2016-06-18 13:17:00</td>\n",
" <td>3</td>\n",
" <td>2.53333</td>\n",
" <td>1</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>2017468</td>\n",
" <td>2016-06-18 14:58:00</td>\n",
" <td>3</td>\n",
" <td>1.68333</td>\n",
" <td>1</td>\n",
" <td>41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>195</th>\n",
" <td>2017468</td>\n",
" <td>2016-06-18 15:56:00</td>\n",
" <td>3</td>\n",
" <td>0.966667</td>\n",
" <td>1</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>198</th>\n",
" <td>2017470</td>\n",
" <td>2016-06-16 12:07:00</td>\n",
" <td>3</td>\n",
" <td>2.43333</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>199</th>\n",
" <td>2017470</td>\n",
" <td>2016-06-16 14:19:00</td>\n",
" <td>3</td>\n",
" <td>2.2</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>260</th>\n",
" <td>2017487</td>\n",
" <td>2016-06-14 14:04:00</td>\n",
" <td>3</td>\n",
" <td>1.83333</td>\n",
" <td>1</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>261</th>\n",
" <td>2017487</td>\n",
" <td>2016-06-14 21:35:00</td>\n",
" <td>3</td>\n",
" <td>7.51667</td>\n",
" <td>1</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>262</th>\n",
" <td>2017487</td>\n",
" <td>2016-06-15 11:26:00</td>\n",
" <td>3</td>\n",
" <td>13.85</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>263</th>\n",
" <td>2017487</td>\n",
" <td>2016-06-15 13:13:00</td>\n",
" <td>3</td>\n",
" <td>1.78333</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>264</th>\n",
" <td>2017487</td>\n",
" <td>2016-06-15 16:10:00</td>\n",
" <td>3</td>\n",
" <td>2.95</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td>2017489</td>\n",
" <td>2016-06-15 13:46:00</td>\n",
" <td>2</td>\n",
" <td>25.7167</td>\n",
" <td>1</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>317</th>\n",
" <td>2017800</td>\n",
" <td>2016-06-20 14:22:00</td>\n",
" <td>2</td>\n",
" <td>21.5</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>433</th>\n",
" <td>2017818</td>\n",
" <td>2016-06-24 10:58:00</td>\n",
" <td>3</td>\n",
" <td>22.6667</td>\n",
" <td>1</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>453</th>\n",
" <td>2017821</td>\n",
" <td>2016-06-22 14:33:00</td>\n",
" <td>2</td>\n",
" <td>2.23333</td>\n",
" <td>1</td>\n",
" <td>29</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user_id entry_time total_people time_since_previous_museum \\\n",
"26 1473906 2016-07-24 11:58:00 2 1.16667 \n",
"76 2017453 2016-06-17 20:04:00 2 0.783333 \n",
"189 2017468 2016-06-16 12:00:00 3 1.23333 \n",
"190 2017468 2016-06-16 12:56:00 3 0.933333 \n",
"191 2017468 2016-06-17 12:34:00 3 23.6333 \n",
"192 2017468 2016-06-18 10:45:00 3 22.1833 \n",
"193 2017468 2016-06-18 13:17:00 3 2.53333 \n",
"194 2017468 2016-06-18 14:58:00 3 1.68333 \n",
"195 2017468 2016-06-18 15:56:00 3 0.966667 \n",
"198 2017470 2016-06-16 12:07:00 3 2.43333 \n",
"199 2017470 2016-06-16 14:19:00 3 2.2 \n",
"260 2017487 2016-06-14 14:04:00 3 1.83333 \n",
"261 2017487 2016-06-14 21:35:00 3 7.51667 \n",
"262 2017487 2016-06-15 11:26:00 3 13.85 \n",
"263 2017487 2016-06-15 13:13:00 3 1.78333 \n",
"264 2017487 2016-06-15 16:10:00 3 2.95 \n",
"270 2017489 2016-06-15 13:46:00 2 25.7167 \n",
"317 2017800 2016-06-20 14:22:00 2 21.5 \n",
"433 2017818 2016-06-24 10:58:00 3 22.6667 \n",
"453 2017821 2016-06-22 14:33:00 2 2.23333 \n",
"\n",
" total_adults museum_id \n",
"26 1 25 \n",
"76 1 41 \n",
"189 1 37 \n",
"190 1 11 \n",
"191 1 38 \n",
"192 1 10 \n",
"193 1 23 \n",
"194 1 41 \n",
"195 1 29 \n",
"198 1 3 \n",
"199 1 11 \n",
"260 1 32 \n",
"261 1 23 \n",
"262 1 10 \n",
"263 1 3 \n",
"264 1 11 \n",
"270 1 29 \n",
"317 1 3 \n",
"433 1 38 \n",
"453 1 29 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1[df1.total_people>1].head(20)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# df[(df['user_id']==2017470)].sort_values('entry_time')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 4.36667\n",
"1 1.38333\n",
"2 17.9\n",
"3 1.51667\n",
"4 1.71667\n",
"Name: time_since_previous_museum, dtype: object"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x1 = df1.loc[np.repeat(df1.index.values,df1['total_people'])]['time_since_previous_museum']\n",
"x1.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x2 = df1['time_since_previous_museum']"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(309330, 6)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.shape"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(340810,)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x1.shape"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(309330,)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x2.shape"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~qiweihan/122.embed\" height=\"800px\" width=\"1200px\"></iframe>"
],
"text/plain": [
"<plotly.tools.PlotlyDisplay object>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# time_until_next_museum\n",
"trace1 = go.Histogram(x=x1, \n",
" xbins=dict(start=np.min(x1), \n",
" size=0.25, end=np.max(x1)), \n",
" marker=dict(color='#CC171D'), \n",
" name='Firenze Cards'\n",
" )\n",
"trace2 = go.Histogram(x=x2, \n",
" xbins=dict(start=np.min(x2), \n",
" size=0.25, end=np.max(x2)), \n",
" marker=dict(color='#1789CC'), \n",
" name='Total People on Visit'\n",
" )\n",
"\n",
"layout = go.Layout(\n",
" title=\"Time Between Museum Visits\",\n",
" titlefont=dict(size=28),\n",
" barmode='stack',\n",
" legend=dict(\n",
" x=0.8,\n",
" y=0.9,\n",
" traceorder='normal',\n",
" font=dict(\n",
" family='sans-serif',\n",
" size=16,\n",
" color='#000'\n",
" ),\n",
" bgcolor='#FFFFFF',\n",
" bordercolor='#E2E2E2',\n",
" borderwidth=2\n",
" ),\n",
" width=1200,\n",
" height=800,\n",
" xaxis=dict(\n",
" title='Time Gap in Hours (15 minute bins)',\n",
" titlefont=dict(size=20),\n",
" nticks=32,\n",
" ticks='outside',\n",
" tickfont=dict(size=16)\n",
" ),\n",
" yaxis=dict(\n",
" title='Number of People-Visits with Given Time Gap',\n",
" titlefont=dict(size=20),\n",
" ticks='outside',\n",
" tickfont=dict(size=16)\n",
" )\n",
")\n",
"fig = go.Figure(data=go.Data([trace1,trace2]), layout=layout)\n",
"py.iplot(fig, filename='TUNME', sharing='private', auto_open=False)"
]
},
{
"cell_type": "code",
"execution_count": 185,
"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>total_people</th>\n",
" <th>frequency</th>\n",
" <th>cumulative</th>\n",
" <th>ccdf</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>333331</td>\n",
" <td>0.924974</td>\n",
" <td>0.075026</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>19464</td>\n",
" <td>0.978985</td>\n",
" <td>0.021015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>5909</td>\n",
" <td>0.995382</td>\n",
" <td>0.004618</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1512</td>\n",
" <td>0.999578</td>\n",
" <td>0.000422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>111</td>\n",
" <td>0.999886</td>\n",
" <td>0.000114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>19</td>\n",
" <td>0.999939</td>\n",
" <td>0.000061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>0.999958</td>\n",
" <td>0.000042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>10</td>\n",
" <td>6</td>\n",
" <td>0.999975</td>\n",
" <td>0.000025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>14</td>\n",
" <td>2</td>\n",
" <td>0.999981</td>\n",
" <td>0.000019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>12</td>\n",
" <td>2</td>\n",
" <td>0.999986</td>\n",
" <td>0.000014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>0.999992</td>\n",
" <td>0.000008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>20</td>\n",
" <td>1</td>\n",
" <td>0.999994</td>\n",
" <td>0.000006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>0.999997</td>\n",
" <td>0.000003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_people frequency cumulative ccdf\n",
"0 1 333331 0.924974 0.075026\n",
"1 2 19464 0.978985 0.021015\n",
"2 3 5909 0.995382 0.004618\n",
"3 4 1512 0.999578 0.000422\n",
"4 5 111 0.999886 0.000114\n",
"5 6 19 0.999939 0.000061\n",
"6 7 7 0.999958 0.000042\n",
"7 10 6 0.999975 0.000025\n",
"8 14 2 0.999981 0.000019\n",
"9 12 2 0.999986 0.000014\n",
"10 8 2 0.999992 0.000008\n",
"11 20 1 0.999994 0.000006\n",
"12 15 1 0.999997 0.000003\n",
"13 13 1 1.000000 0.000000"
]
},
"execution_count": 185,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fr = frequency(df.groupby(['user_id','entry_time','museum_id']).sum()['total_people'].to_frame(),'total_people')\n",
"fr"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"92.497391555299018"
]
},
"execution_count": 192,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"100-(fr['cumulative'].sub(fr['cumulative'].shift())*100).sum()"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 NaN\n",
"1 5.401145\n",
"2 1.639713\n",
"3 0.419571\n",
"4 0.030802\n",
"5 0.005272\n",
"6 0.001942\n",
"7 0.001665\n",
"8 0.000555\n",
"9 0.000555\n",
"10 0.000555\n",
"11 0.000277\n",
"12 0.000277\n",
"13 0.000277\n",
"Name: cumulative, dtype: float64"
]
},
"execution_count": 193,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fr['cumulative'].sub(fr['cumulative'].shift())*100"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# # Card use count\n",
"# total_card_use_count = pd.DataFrame(df.groupby('user_id', as_index=True).size().rename('total_card_use_count'))\n",
"# df = pd.merge(total_card_use_count.reset_index(), df, on=['user_id'], how='inner')\n",
"\n",
"# trace = go.Histogram(x=df['total_card_use_count'], xbins=dict(start=np.min(df['total_card_use_count']), size=1, end=np.max(df['total_card_use_count'])),\n",
"# marker=dict(color='rgb(0, 0, 100)'))\n",
"# layout = go.Layout(\n",
"# title=\"Card use count\",\n",
"# legend=dict(\n",
"# x=1,\n",
"# y=1,\n",
"# traceorder='normal',\n",
"# font=dict(\n",
"# family='sans-serif',\n",
"# size=12,\n",
"# color='#000'\n",
"# ),\n",
"# bgcolor='#E2E2E2',\n",
"# bordercolor='#FFFFFF',\n",
"# borderwidth=2\n",
"# )\n",
"# )\n",
"# fig = go.Figure(data=go.Data([trace]), layout=layout)\n",
"# py.iplot(fig, filename='CUC', sharing='private', auto_open=False)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"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>user_id</th>\n",
" <th>entry_time</th>\n",
" <th>museum_id</th>\n",
" <th>total_people</th>\n",
" <th>total_adults</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1459702</td>\n",
" <td>2016-06-22 10:04:00</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1459702</td>\n",
" <td>2016-06-22 14:26:00</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1459702</td>\n",
" <td>2016-06-22 15:49:00</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1459702</td>\n",
" <td>2016-06-23 09:43:00</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1459702</td>\n",
" <td>2016-06-23 11:14:00</td>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user_id entry_time museum_id total_people total_adults\n",
"0 1459702 2016-06-22 10:04:00 10 1 1\n",
"1 1459702 2016-06-22 14:26:00 15 1 1\n",
"2 1459702 2016-06-22 15:49:00 11 1 1\n",
"3 1459702 2016-06-23 09:43:00 3 1 1\n",
"4 1459702 2016-06-23 11:14:00 29 1 1"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = df.groupby(['user_id','entry_time','museum_id']).sum()[['total_people','total_adults']].reset_index()\n",
"df2.head()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"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>museums_visited</th>\n",
" <th>total_entries</th>\n",
" <th>adult_entries</th>\n",
" </tr>\n",
" <tr>\n",
" <th>user_id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1459702</th>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1473903</th>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1473904</th>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1473905</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1473906</th>\n",
" <td>11</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" museums_visited total_entries adult_entries\n",
"user_id \n",
"1459702 8 8 8\n",
"1473903 6 6 6\n",
"1473904 6 6 6\n",
"1473905 5 5 5\n",
"1473906 11 12 11"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3 = df2[['user_id','museum_id']].groupby('user_id').count().join(df2[['user_id','total_people','total_adults']].groupby('user_id').sum())\n",
"df3.columns = ['museums_visited','total_entries','adult_entries']\n",
"df3.head()"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~qiweihan/148.embed\" height=\"800px\" width=\"1200px\"></iframe>"
],
"text/plain": [
"<plotly.tools.PlotlyDisplay object>"
]
},
"execution_count": 201,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Frequency plot of number of unique museums visited per card\n",
"x = df3['museums_visited']\n",
"trace1 = go.Histogram(x=x, xbins=dict(start=np.min(x)-.25, size=.5, end=np.max(x)+.25),\n",
" marker=dict(color='#CC171D'),\n",
" name = 'Museums visited')\n",
"\n",
"# trace2 = go.Histogram(x=adult, xbins=dict(start=np.min(adult), size=1, end=np.max(adult)),\n",
"# marker=dict(color='rgb(0, 100, 0)'),\n",
"# name = 'Adults')\n",
"\n",
"layout = go.Layout(\n",
" title=\"Number of Museums Visited per Card\",\n",
" titlefont=dict(size=28),\n",
"# legend=dict(\n",
"# traceorder='normal',\n",
"# font=dict(\n",
"# family='sans-serif',\n",
"# size=12,\n",
"# color='#000'\n",
"# ),\n",
"# bgcolor='#CC171D',\n",
"# bordercolor='#FFFFFF',\n",
"# borderwidth=0\n",
"# ),\n",
" width=1200,\n",
" height=800,\n",
" xaxis=dict(\n",
" title='Number of Museums Visited',\n",
" titlefont=dict(size=20),\n",
" range=[0.75,32.25],\n",
" nticks=32,\n",
" ticks='outside',\n",
" tickfont=dict(size=16)\n",
" ), \n",
" yaxis=dict(\n",
" title='Number of Cards',\n",
" titlefont=dict(size=20),\n",
" ticks='outside',\n",
" tickfont=dict(size=16)\n",
" )\n",
")\n",
"fig = go.Figure(data=go.Data([trace1]), layout=layout)\n",
"py.iplot(fig, filename='MPC_2', sharing='private', auto_open=False)"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"user_id\n",
"1459702 29.016667\n",
"1473903 52.183333\n",
"1473904 52.183333\n",
"1473905 25.166667\n",
"1473906 48.466667\n",
"Name: total_duration_card_use, dtype: float64"
]
},
"execution_count": 204,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = df.groupby('user_id')['total_duration_card_use'].max()/pd.Timedelta('1 hour')\n",
"x.head()"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"KernelDensity(algorithm='auto', atol=0, bandwidth=0.75, breadth_first=True,\n",
" kernel='gaussian', leaf_size=40, metric='euclidean',\n",
" metric_params=None, rtol=0)"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# kde = KernelDensity(kernel='gaussian', bandwidth=0.75).fit(x.as_matrix()c\n",
"# kde"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~qiweihan/150.embed\" height=\"800px\" width=\"1200px\"></iframe>"
],
"text/plain": [
"<plotly.tools.PlotlyDisplay object>"
]
},
"execution_count": 208,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trace = go.Histogram(x=x[x>0],\n",
" xbins=dict(start=np.min(x), size=1.0/4.0,\n",
" end=np.max(x)),\n",
" marker=dict(color='#CC171D'))\n",
"\n",
"layout = go.Layout(\n",
" title=\"Duration of Card Usage\",\n",
" titlefont=dict(size=28),\n",
" width=1200,\n",
" height=800,\n",
" xaxis=dict(\n",
" title='Hours Between First and Last Use of Card (bins of 15 minutes)',\n",
" titlefont=dict(size=20),\n",
" range=[0,72],\n",
" nticks=32,\n",
" ticks='outside',\n",
" tickfont=dict(size=16)\n",
" ),\n",
" yaxis=dict(\n",
" title='Number of Cards',\n",
" titlefont=dict(size=20),\n",
" ticks='outside',\n",
" tickfont=dict(size=16)\n",
" )\n",
")\n",
"fig = go.Figure(data=go.Data([trace]), layout=layout)\n",
"py.iplot(fig, filename='DOU_2', sharing='private', auto_open=False)"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# df['time_until_next_museum'] = df['time_until_next_museum'].apply(\n",
"# lambda x: pd.Timedelta(x) / pd.Timedelta('1 hour'))\n",
"# trace = go.Histogram(x=df['time_until_next_museum'], xbins=dict(start=np.min(x), size=0.25, end=np.max(x)),\n",
"# marker=dict(color='rgb(0, 0, 100)'))\n",
"# layout = go.Layout(\n",
"# title=\"\"\n",
"# )\n",
"# fig = go.Figure(data=go.Data([trace]), layout=layout)\n",
"# py.iplot(fig, filename='TUNM', sharing='private', auto_open=False)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Histogram of Monthly total museum entry data - from florence card \n",
"#https://plot.ly/~qiweihan/110"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The sql extension is already loaded. To reload it, use:\n",
" %reload_ext sql\n"
]
}
],
"source": [
"# Histogram of Monthly total museum entry data - from National Museums\n",
"# Comparison of PROPORTION of Firenze card entries with museum totals (pie chart?)\n",
"%load_ext sql\n",
"#TODO: connect with dbutils\n",
"conn_str = \"\"\n",
"conn = psycopg2.connect(conn_str)\n",
"c = conn.cursor()\n",
"\n",
"test = get_national_museums(conn, export_to_csv=True, export_path='../src/output/')\n",
"test = test[(test['visit_month'] == 'June') | (test['visit_month'] == 'July') | \n",
" (test['visit_month'] == 'August') | (test['visit_month'] == 'September')]"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~qiweihan/124.embed\" height=\"800px\" width=\"900px\"></iframe>"
],
"text/plain": [
"<plotly.tools.PlotlyDisplay object>"
]
},
"execution_count": 195,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trace = Bar(\n",
" x=test['place'],\n",
" y=test['total_visitors'],\n",
" )\n",
"\n",
"fig = go.Figure(data=go.Data([trace]))\n",
"fig['layout'].update(height=800, width=900, title='Stacked subplots')\n",
"\n",
"py.iplot(fig, filename='State Museum Entries', sharing='private')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# # Which museums are most popular? number of entries per museum, per date\n",
"# trace = Bar(\n",
"# x=df[],\n",
"# y=df[],\n",
"# )\n",
"\n",
"# fig = go.Figure(data=go.Data([trace]))\n",
"# fig['layout'].update(height=800, width=900, title='Stacked subplots')\n",
"\n",
"# py.iplot(fig, filename='', sharing='private')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Timeline of usage(per avg hour, calendar day, calendar month, weekday) - segment per museum\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# # Daytype of activation of card\n",
"# # get day of week for first use for every user\n",
"# trace = go.Histogram(x=df[''], xbins=dict(start=np.min(x), size=0.25, end=np.max(x)),\n",
"# marker=dict(color='rgb(0, 0, 100)'))\n",
"# layout = go.Layout(\n",
"# title=\"\"\n",
"# )\n",
"# fig = go.Figure(data=go.Data([trace]), layout=layout)\n",
"# py.iplot(fig, filename='daytype of activation', sharing='private', auto_open=False)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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>day_of_week</th>\n",
" <th>frequency</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Tuesday</td>\n",
" <td>9959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Wednesday</td>\n",
" <td>8150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Friday</td>\n",
" <td>7601</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Thursday</td>\n",
" <td>7592</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Monday</td>\n",
" <td>6439</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Saturday</td>\n",
" <td>5648</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Sunday</td>\n",
" <td>4919</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" day_of_week frequency\n",
"0 Tuesday 9959\n",
"1 Wednesday 8150\n",
"2 Friday 7601\n",
"3 Thursday 7592\n",
"4 Monday 6439\n",
"5 Saturday 5648\n",
"6 Sunday 4919"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dotw = {0:'Monday',\n",
" 1:'Tuesday',\n",
" 2:'Wednesday',\n",
" 3:'Thursday',\n",
" 4:'Friday',\n",
" 5:'Saturday',\n",
" 6:'Sunday'}\n",
"x = df[df['adults_first_use']==1][['user_id','day_of_week']].groupby('user_id').mean()['day_of_week'].map(dotw).to_frame()\n",
"fr2 = frequency(x,'day_of_week')[['day_of_week','frequency']]\n",
"fr2"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~qiweihan/160.embed\" height=\"800px\" width=\"1200px\"></iframe>"
],
"text/plain": [
"<plotly.tools.PlotlyDisplay object>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Frequency plot of number of unique museums visited per card\n",
"trace = go.Bar(x=['Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Satuday'], \n",
" y=[4919,6439,9959,8150,8150,7601,5648],\n",
" marker=dict(color='#CC171D'))\n",
"\n",
"layout = go.Layout(\n",
" title=\"Day of Firenze Card Activation\",\n",
" titlefont=dict(size=28),\n",
" width=1200,\n",
" height=800,\n",
" xaxis=dict(\n",
" title='Day of the Week',\n",
" titlefont=dict(size=20),\n",
" nticks=7,\n",
" ticks='outside',\n",
" tickfont=dict(size=16)\n",
" ),\n",
" yaxis=dict(\n",
" title='Number of Cards Activated',\n",
" titlefont=dict(size=20),\n",
" ticks='outside',\n",
" tickfont=dict(size=16)\n",
" )\n",
")\n",
"fig = go.Figure(data=go.Data([trace]), layout=layout)\n",
"py.iplot(fig, filename='daytype of activation', sharing='private', auto_open=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
mattmcd/PyBayes | scripts/indexed_expressions_20211107.ipynb | 1 | 18999 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sympy as sp\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"outputs": [],
"source": [
"x, y = [sp.IndexedBase(e) for e in ['x', 'y']]\n",
"m = sp.symbols('m', integer=True)\n",
"a, b = sp.symbols('a b', real=True)\n",
"i = sp.Idx('i', m)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 14,
"outputs": [],
"source": [
"loss = (y[i] - (a*x[i] + b))**2"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 15,
"outputs": [
{
"data": {
"text/plain": "(-a*x[i] - b + y[i])**2",
"text/latex": "$\\displaystyle \\left(- a {x}_{Idx\\left(i, \\left( 0, \\ m - 1\\right)\\right)} - b + {y}_{Idx\\left(i, \\left( 0, \\ m - 1\\right)\\right)}\\right)^{2}$"
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loss"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"Having defined the loss function using indexed variables we might hope that the\n",
"implicit summation of repeated indexes might fall through to derivative\n",
"but it looks like this isn't the case.\n",
"\n",
"Below we see taking derivative wrt to fit parameters is only applied\n",
"to each point rather than the whole sum, which is incorrect."
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "code",
"execution_count": 11,
"outputs": [
{
"data": {
"text/plain": "[(-b + y[i])/x[i]]"
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sp.solve(loss.diff(a), a)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 12,
"outputs": [
{
"data": {
"text/plain": "[-a*x[i] + y[i]]"
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sp.solve(loss.diff(b), b)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"Try adding explicit summation around the loss expression. This gives the\n",
"correct set of equations for derivatives but a solution can't be found."
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "code",
"execution_count": 17,
"outputs": [
{
"data": {
"text/plain": "Sum(-2*(-a*x[i] - b + y[i])*x[i], (i, 0, m - 1))",
"text/latex": "$\\displaystyle \\sum_{Idx\\left(i, \\left( 0, \\ m - 1\\right)\\right)=0}^{m - 1} - 2 \\left(- a {x}_{Idx\\left(i, \\left( 0, \\ m - 1\\right)\\right)} - b + {y}_{Idx\\left(i, \\left( 0, \\ m - 1\\right)\\right)}\\right) {x}_{Idx\\left(i, \\left( 0, \\ m - 1\\right)\\right)}$"
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sp.diff(sp.Sum(loss, i),a)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 18,
"outputs": [
{
"data": {
"text/plain": "Sum(2*a*x[i] + 2*b - 2*y[i], (i, 0, m - 1))",
"text/latex": "$\\displaystyle \\sum_{Idx\\left(i, \\left( 0, \\ m - 1\\right)\\right)=0}^{m - 1} \\left(2 a {x}_{Idx\\left(i, \\left( 0, \\ m - 1\\right)\\right)} + 2 b - 2 {y}_{Idx\\left(i, \\left( 0, \\ m - 1\\right)\\right)}\\right)$"
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sp.diff(sp.Sum(loss, i), b)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 19,
"outputs": [
{
"data": {
"text/plain": "[]"
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sp.solve([sp.diff(sp.Sum(loss, i),a), sp.diff(sp.Sum(loss, i),b)], [a, b])"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 22,
"outputs": [
{
"data": {
"text/plain": "{a: -b/x[i] + y[i]/x[i]}"
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sp.solve([loss.expand().diff(a), loss.expand().diff(b)], [a,b])"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"MatrixSymbol seems to be the trick"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "code",
"execution_count": 84,
"outputs": [],
"source": [
"x_2 = sp.MatrixSymbol('x', m, 1)\n",
"y_2 = sp.MatrixSymbol('y', m, 1)\n",
"a_2 = sp.MatrixSymbol('a', 1, 1)\n",
"b_2 = b*sp.OneMatrix(m, 1)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 87,
"outputs": [
{
"data": {
"text/plain": "(-b)*1 - x*a + y",
"text/latex": "$\\displaystyle - b \\mathbb{1} - x a + y$"
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"err = y_2 - (x_2*a_2 + b_2)\n",
"err"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 91,
"outputs": [
{
"data": {
"text/plain": "((-b)*1 - a.T*x.T + y.T)*((-b)*1 - x*a + y)",
"text/latex": "$\\displaystyle \\left(- b \\mathbb{1} - a^{T} x^{T} + y^{T}\\right) \\left(- b \\mathbb{1} - x a + y\\right)$"
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"objective = (err.T * err)\n",
"objective"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 93,
"outputs": [
{
"data": {
"text/plain": "-2*x.T*((-b)*1 - x*a + y)",
"text/latex": "$\\displaystyle - 2 x^{T} \\left(- b \\mathbb{1} - x a + y\\right)$"
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"objective.diff(a_2)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 94,
"outputs": [
{
"data": {
"text/plain": "-((-b)*1 - a.T*x.T + y.T)*1 - 1*((-b)*1 - x*a + y)",
"text/latex": "$\\displaystyle - \\left(- b \\mathbb{1} - a^{T} x^{T} + y^{T}\\right) \\mathbb{1} - \\mathbb{1} \\left(- b \\mathbb{1} - x a + y\\right)$"
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"objective.diff(b)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"Functions of Matrices e.g. generator of rotations"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"t = sp.symbols('t', real=True)\n",
"g = sp.Matrix([[0, -t], [t, 0]])"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 40,
"outputs": [
{
"ename": "TypeError",
"evalue": "cannot determine truth value of Relational",
"output_type": "error",
"traceback": [
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)",
"\u001B[0;32m<ipython-input-40-b0a9bf93039b>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0msp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0meye\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mm\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 2\u001B[0m \u001B[0mg\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
"\u001B[0;32m~/Work/Projects/PyBayes/venv/lib/python3.8/site-packages/sympy/matrices/dense.py\u001B[0m in \u001B[0;36meye\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 916\u001B[0m \"\"\"\n\u001B[1;32m 917\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 918\u001B[0;31m \u001B[0;32mreturn\u001B[0m \u001B[0mMatrix\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0meye\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 919\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 920\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
"\u001B[0;32m~/Work/Projects/PyBayes/venv/lib/python3.8/site-packages/sympy/matrices/common.py\u001B[0m in \u001B[0;36meye\u001B[0;34m(kls, rows, cols, **kwargs)\u001B[0m\n\u001B[1;32m 975\u001B[0m \u001B[0;32mif\u001B[0m \u001B[0mcols\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 976\u001B[0m \u001B[0mcols\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mrows\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 977\u001B[0;31m \u001B[0;32mif\u001B[0m \u001B[0mrows\u001B[0m \u001B[0;34m<\u001B[0m \u001B[0;36m0\u001B[0m \u001B[0;32mor\u001B[0m \u001B[0mcols\u001B[0m \u001B[0;34m<\u001B[0m \u001B[0;36m0\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 978\u001B[0m raise ValueError(\"Cannot create a {} x {} matrix. \"\n\u001B[1;32m 979\u001B[0m \"Both dimensions must be positive\".format(rows, cols))\n",
"\u001B[0;32m~/Work/Projects/PyBayes/venv/lib/python3.8/site-packages/sympy/core/relational.py\u001B[0m in \u001B[0;36m__bool__\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 396\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 397\u001B[0m \u001B[0;32mdef\u001B[0m \u001B[0m__bool__\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\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--> 398\u001B[0;31m \u001B[0;32mraise\u001B[0m \u001B[0mTypeError\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\"cannot determine truth value of Relational\"\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 399\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 400\u001B[0m \u001B[0;32mdef\u001B[0m \u001B[0m_eval_as_set\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\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;31mTypeError\u001B[0m: cannot determine truth value of Relational"
]
}
],
"source": [
"g"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 37,
"outputs": [
{
"data": {
"text/plain": "Matrix([\n[cos(t), -sin(t)],\n[sin(t), cos(t)]])",
"text/latex": "$\\displaystyle \\left[\\begin{matrix}\\cos{\\left(t \\right)} & - \\sin{\\left(t \\right)}\\\\\\sin{\\left(t \\right)} & \\cos{\\left(t \\right)}\\end{matrix}\\right]$"
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sp.exp(g)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 51,
"outputs": [
{
"ename": "ValueError",
"evalue": "m is not an integer",
"output_type": "error",
"traceback": [
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)",
"\u001B[0;32m~/Work/Projects/PyBayes/venv/lib/python3.8/site-packages/sympy/core/compatibility.py\u001B[0m in \u001B[0;36mas_int\u001B[0;34m(n, strict)\u001B[0m\n\u001B[1;32m 300\u001B[0m \u001B[0;32mraise\u001B[0m \u001B[0mTypeError\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 301\u001B[0;31m \u001B[0;32mreturn\u001B[0m \u001B[0moperator\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mindex\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mn\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 302\u001B[0m \u001B[0;32mexcept\u001B[0m \u001B[0mTypeError\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
"\u001B[0;31mTypeError\u001B[0m: 'Symbol' object cannot be interpreted as an integer",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001B[0;31mValueError\u001B[0m Traceback (most recent call last)",
"\u001B[0;32m<ipython-input-51-acebb601e1a1>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0ma\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0msp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mones\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mm\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 2\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
"\u001B[0;32m~/Work/Projects/PyBayes/venv/lib/python3.8/site-packages/sympy/matrices/dense.py\u001B[0m in \u001B[0;36mones\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 1129\u001B[0m \u001B[0mkwargs\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'cols'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mkwargs\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mpop\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m'c'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 1130\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1131\u001B[0;31m \u001B[0;32mreturn\u001B[0m \u001B[0mMatrix\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mones\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 1132\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 1133\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
"\u001B[0;32m~/Work/Projects/PyBayes/venv/lib/python3.8/site-packages/sympy/matrices/common.py\u001B[0m in \u001B[0;36mones\u001B[0;34m(kls, rows, cols, **kwargs)\u001B[0m\n\u001B[1;32m 1155\u001B[0m \u001B[0mcols\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mrows\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 1156\u001B[0m \u001B[0mklass\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mkwargs\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mget\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m'cls'\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mkls\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1157\u001B[0;31m \u001B[0mrows\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcols\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mas_int\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mrows\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mas_int\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mcols\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 1158\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 1159\u001B[0m \u001B[0;32mreturn\u001B[0m \u001B[0mklass\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_eval_ones\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mrows\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcols\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
"\u001B[0;32m~/Work/Projects/PyBayes/venv/lib/python3.8/site-packages/sympy/core/compatibility.py\u001B[0m in \u001B[0;36mas_int\u001B[0;34m(n, strict)\u001B[0m\n\u001B[1;32m 301\u001B[0m \u001B[0;32mreturn\u001B[0m \u001B[0moperator\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mindex\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mn\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 302\u001B[0m \u001B[0;32mexcept\u001B[0m \u001B[0mTypeError\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 303\u001B[0;31m \u001B[0;32mraise\u001B[0m \u001B[0mValueError\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m'%s is not an integer'\u001B[0m \u001B[0;34m%\u001B[0m \u001B[0;34m(\u001B[0m\u001B[0mn\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 304\u001B[0m \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 305\u001B[0m \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
"\u001B[0;31mValueError\u001B[0m: m is not an integer"
]
}
],
"source": [],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"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"
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"nbformat_minor": 0
} | apache-2.0 |
Patri-meteocat/Meteocat_ANL_collaboration | notebooks/.ipynb_checkpoints/maconvolution_fn-checkpoint.ipynb | 2 | 2047 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pylab as plb\n",
"import numpy as np\n",
"import scipy as sp\n",
"import numpy.ma as ma\n",
"\n",
"from pylab import *\n",
"from scipy import ndimage"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def maconvolve(inp, weights, norm=False, output=None, mode='reflect', cval=0.0, origin=0):\n",
" \n",
" k = weights\n",
" data = inp.data\n",
" msk = inp.mask\n",
" \n",
" # Invert the mask and create ones-and-zeros array\n",
" mask_arr = np.logical_not(msk).astype(int)\n",
" # Mask data for convolution\n",
" data_msk = data*mask_arr\n",
" # Convolve masked data with kernel\n",
" data_conv = ndimage.convolve(data_msk, k, mode=mode, output=output, cval=cval, origin=origin)\n",
" \n",
" if norm:\n",
" # normalisation kernel\n",
" k_norm = np.ones(shape(k))\n",
" # Convolve mask with normalisation kernel\n",
" mask_conv = ndimage.convolve(mask_arr, k_norm, mode=mode, output=output, cval=cval, origin=origin)\n",
" # Normalisation factor (depending on number of non-masked values)\n",
" w = 1./mask_conv\n",
" data_conv = data_conv*w\n",
" \n",
" data_out = ma.masked_array(data_conv, msk)\n",
" return data_out\n",
" "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
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"nbformat": 4,
"nbformat_minor": 0
}
| bsd-2-clause |
KshitijT/fundamentals_of_interferometry | 7_Observing_Systems/7_3_analogue.ipynb | 2 | 60705 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***\n",
"\n",
"* [Outline](../0_Introduction/0_introduction.ipynb)\n",
"* [Glossary](../0_Introduction/1_glossary.ipynb)\n",
"* [7. Observing Systems](0_introduction.ipynb) \n",
" * Previous: [7.2 The Radio Interferometer Measurement Equation (RIME)](7_2_rime.ipynb) \n",
" * Next: [7.4 Digital Correlators](7_4_digital.ipynb)\n",
"\n",
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import standard modules:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"from IPython.display import HTML \n",
"HTML('../style/course.css') #apply general CSS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import section specific modules:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from IPython.display import Image\n",
"import scipy.signal"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"HTML('../style/code_toggle.html')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7.3 Analogue Electronics (G- and B-Jones)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the first sections of this chapter, we introduced Jones notation and the Radio Interferometric Measurement Equation (RIME). These ideas have been presented abstractly, as a way to lay out the mathematics of how we describe observing systems. For the remainder of the chapter, we will explore the details of different instrumental effects, their corresponding Jones matrices, and how these effects alter the sky signal. Our ultimate goal - recovering the original sky signal - requires knowledge of the observation system. How well we understand an observing system affects how accurate we can recover the true sky signal. We will discuss the process of calibration in the next chapter.\n",
"\n",
"In [$\\S$ 7.2.3 ➞](./7_2_rime.ipynb) we introduced the concepts of direction-dependent and direction-independent effects (DDE's and DIE's). Historically, direction-independent effects were the primary concern during observations. This was mainly due to simplicity, hardware and array limitations, and because DIE's are the dominant effects on the sky signal. These effects occur upon conversion of the sky's electric field to a voltage - i.e. at the feed - and propagates through the telescope and array electronics. Typically, all the DIE's are wrapped up into a single 'gain' Jones matrix called the $\\mathbf{G}$-Jones. This term is made up of numerous effect, primarily due to the analogue electronic components such as filters, amplifiers, mixers, cables, etc. This will be discussed in this section.\n",
"\n",
"Often, there is a second DIE Jones matrix, the *bandpass* or $\\mathbf{B}$-Jones which is related to the $\\mathbf{G}$-Jones. As we have discussed in the [$\\S$ 7.1 ➞](.s/7_1_jones_notation.ipynb) a Jones matrix is a function of time and frequency. But, it is often a good approximation to think of the analogue electronics effects as separable into a frequency-variable, time-stable function (the bandpass) and a time-variable, frequency-stable function (gain), such that the total, time- and frequency-dependent gain matrix $\\mathbf{G}'$ is\n",
"\n",
"$$\n",
"\\mathbf{G}'(t, \\nu) \\approx \\mathbf{G}(t) \\cdot \\mathbf{B}(\\nu) \n",
"$$\n",
"\n",
"Confusingly, in the literature and software, $\\mathbf{G}$ can be either a time-dependent or time- and frequency-dependent term, depending the author's personal definition. Keep this in mind! The most important point here is that the $\\mathbf{G}$ term has the biggest impact on the observed signal, out of all the Jones terms. This is why we will start with it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 7.3.1 An Idealized Source<a id='instrum:sec:7_3_1'></a>\n",
"\n",
"Let us ignore interferometry for now, and consider a single radio telescope. For a simple sky containing only a single source, what would we expect to observe? The source should have some intrinsic flux $I$ (measured in Janskys) and some spectral index $\\alpha$ of the form\n",
"\n",
"$$\n",
"I(\\nu) = I_0 \\left( \\frac{\\nu}{\\nu_0} \\right)^{-\\alpha}\n",
"$$\n",
"\n",
"where $I_0$ is the source's flux at some reference frequency $\\nu_0$. We think of most radio sources as being stable over long lifetimes. The source flux will thus not change noticably over short periods of time. Though the signal we are measuring is a time-domain signal, we will look at the frequency spectrum throughout this sectionL frequency is what we are ultimately interested in for now. If we were to plot the source's flux as a function of time and observing frequency in a *waterfall plot*, it would look like the figure below:"
]
},
{
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"metadata": {
"collapsed": false
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"source": [
"# Setup an ideal source coherency spectrum\n",
"nchans = 512 # number of frequency channels\n",
"freq0 = 1.1e9 # start frequency (Hz) (centre of first channel)\n",
"freq1 = 1.5e9 # stop frequency (Hz) (centre of last channel)\n",
"freqs = np.linspace(freq0, freq1, nchans)\n",
"ntime = 500\n",
"\n",
"I0 = 1. # flux at reference frequency (Jy)\n",
"nu0 = 1.421e9 # reference frequency (Jy)\n",
"alpha = -0.25 # source spectral index\n",
"Inu = lambda nu: I0 * (nu/nu0)**alpha # Stokes I spectrum\n",
"\n",
"# Stokes Parameters\n",
"I = Inu(freqs)\n",
"Q = np.zeros_like(I)\n",
"U = np.zeros_like(I)\n",
"V = np.zeros_like(I)\n",
"\n",
"# Compute the polarisation values\n",
"brightness = np.empty(shape=(nchans, 2, 2), dtype=np.complex128)\n",
"brightness[:,0,0] = I + Q\n",
"brightness[:,0,1] = U + V*1j\n",
"brightness[:,1,0] = U - V*1j\n",
"brightness[:,1,1] = I - Q\n",
"\n",
"brightness = np.tile(brightness[np.newaxis], [ntime, 1, 1, 1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
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"source": [
"fig, axes = plt.subplots(figsize=(8,8))\n",
"\n",
"ax1 = plt.subplot2grid((5,5), (0,0), colspan=4, rowspan=4)\n",
"plt.imshow(np.abs(brightness[:,:,0,0]+brightness[:,:,1,1]), aspect='auto')\n",
"ax1.get_xaxis().set_visible(False)\n",
"ax1.get_yaxis().set_visible(False)\n",
"\n",
"ax2 = plt.subplot2grid((5,5), (4,0), colspan=4, rowspan=1)\n",
"plt.plot(freqs/1e9, brightness[10,:,0,0]+brightness[10,:,1,1])\n",
"plt.ylabel('flux (Jy)')\n",
"plt.xlabel('Frequency (GHz)')\n",
"\n",
"ax3 = plt.subplot2grid((5,5), (0,4), colspan=1, rowspan=4)\n",
"plt.plot(brightness[:,10,0,0]+brightness[:,10,1,1], np.arange(brightness.shape[0]))\n",
"ax3.get_xaxis().set_visible(False)\n",
"ax3.invert_yaxis()\n",
"ax3.yaxis.tick_right()\n",
"plt.ylabel('Time (s)')\n",
"ax3.yaxis.set_label_position('right')\n",
"\n",
"plt.suptitle('Idealized Source Spectrum')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The bottom plot shows the spectrum for a single moment in time, and the plot on the right shows the flux at a single frequency. The source is stable in time and frequency. This is what we would like to detect. Of course, things are not so simple in practice...any system we build will introduce noise into the measurement. Furthermore, as we have hinted at, our measurement systems alters the original signal! Instead of this nice, ideal measurement we then see something like the waterfall plot at the end of this section. We will step through the various stages of an example analogue front-end to understand why the observed spectrum differs from the original source spectrum."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 7.3.2 System Noise and Sensitivity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All measurement systems have noise. In fact, anything which is not at absolute zero (i.e. everything) has some energy, which is radiated as heat; another term for heat is noise. We are interested in measuring some signal. If our system introduces too much noise into the measurement, then we can not recover the original signal. When we discuss the *sensitivity* of a telescope, we are primarily interested in how much noise the system adds during measurement. This *system noise* is, among other things, a result of electronic components, the telescope's engineering design (antenna feed, dish design, etc.), and sky signals. Newer electronic components tend to have lower noise characteristics than older systems. Telescopes are thus often upgraded with newer systems.\n",
"\n",
"#### Noise as Temperature\n",
"\n",
"It is very common to discuss noise in terms of temperature. Noise is a consequence of heat, and heat is measured as a temperature. From an electronics perspective, we can consider a signal as a voltage in an electrical circuit. The signal is then a Johnson-Nyquist noise source, and the power dissapated by that noise source is:\n",
"\n",
"$$P = k_B T \\Delta \\nu$$\n",
"\n",
"Where $P$ is the power of the voltage signal, $k_B$ is the Boltzmann constant, $T$ is temperature in Kelvin, and $\\Delta \\nu$ is the bandwidth of the signal in Hertz. The noise power of a radio telescope (or a thereof) is thus often described in the literature in terms of a temperature\n",
"\n",
"$$T = \\frac{P}{k_B \\Delta \\nu}$$\n",
"\n",
"#### System Temperature\n",
"\n",
"If we want to determine a telescope's sensitivity, we must first understand its noise characteristics. We can then decide if it is possible to detect the signal we are after. We start by assuming that there is some temperature which defines our telescope's noise characteristics. As you might expect, this is the *system temperature*, and is made up of multiple components\n",
"\n",
"$$ T_{\\textrm{sys}} = T_{\\textrm{sky}} + T_{\\textrm{atmosphere}} + T_{\\textrm{spillover}} + T_{\\textrm{rx}} + \\ldots$$\n",
"\n",
"where $T_{\\textrm{sky}}$ is the stochastic signal from the sky. In the L-band (1.4 GHz) the sky temperature is approximately 10 K, 2.73 K of which are from the Cosmic Microwave Background (CMB). The sky temperature varies depending on the observing frequency,: at low frequency - say 100 MHz -, for example, the sky is close to 1000 K . This is primarily due to synchrotron radiation. We can approximate a 'room temperature' on Earth as 300 K. If the sky noise is significantly 'hotter' than room temperature, then there is clearly little advantage to building instruments with low noise characteristics.\n",
"\n",
"Depending on the observing freqency, a component of noise is also due to the atmosphere and ionosphere: $T_{\\textrm{atmosphere}}$. Though this noise is \"natural\", we nevertheless treat it as part of the observing system. If our telescope was placed on the moon, then there would be essentially no atmospheric noise. Further details of various propagation effects are given in [$\\S$ 7.7 ➞](7_7_propagation_effects.ipynb). In the L-band this is a small noise effect ($\\sim 1$ K); the phase rotation effects discussed later in the chapter are a much more serious effect.\n",
"\n",
"The Earth is a 300 K black-body radiation source, i.e. a very hot noise source. We would like to block the noise from the earth (known as *ground noise*) before it enters our measurement system. This can be done in two ways: cooling the electronics, and/or isolating the system. One way the ground noise enters the system is from the primary beam's illumination pattern. We will discuss primary beams later [$\\S$ 7.5 ➞](7_5_primary_beam.ipynb). For now, what matters is that the telescope optics produce a 'beam', which is sensitive to all locations on the sky. The optics are designed such that the beam is most sensitive to the direction towards which the dish is pointed, but there is always some sensitivity in other directions. Thus, no matter where we point the telescope, it is always sensitive - to some extent - to signal from the ground. If the telescope is pointed low on the horizon, an even more sensitive section of the beam will be pointed at the ground. This is called *spillover noise* $T_{\\textrm{spillover}}$, and is a pointing direction-dependent noise term. If the integrated spillover is on the order of a few precent, this can result in noise of the order of 10's of Kelvin.\n",
"\n",
"The final term of note in the system temperature is the receiver noise: $T_{\\textrm{rx}}$. This is the noise introduced into the measurement by the analogue electronics used to convert the weak sky signal into a digital signal which can be recorded. A KAT-7 or MeerKAT telescope produce approximately 20 K in receiver noise. We will discuss the analogue receiver system further in the rest of this section."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Radiometer Equation\n",
"\n",
"For a single radio telescope, the measurement of the average power over some bandwidth for a direction in the sky is done with a *radiometer*. Given that the noise signal we are measuring is approximately Gaussian, the error of the measured signal can be described by the *ideal radiometer equation*\n",
"\n",
"$$\n",
"\\sigma_{T} = \\frac{T_{\\textrm{sys}}}{\\sqrt{\\Delta\\nu \\tau}}\n",
"$$\n",
"\n",
"where $\\sigma_{T}$ is the standard deviation of the measured noise temperature about the true noise temperature. The radiometer equation describes the necessary bandwidth ($\\Delta \\nu$) and integration time ($\\tau$) required to reach a desired noise level ($\\sigma_{T}$) for an ideal system with a given system temperature ($T_{\\textrm{sys}}$) when observing a broadband source.\n",
"\n",
"From the radiometer equation we can see that a linear decrease in system temperature will lead to a linear reduction in $\\sigma_{T}$. A linear increase in the bandwidth or intergration time only leads to square-root improvement in $\\sigma_{T}$. For example, If there are two systems, with the same bandwidth $\\Delta \\nu$, in which one has a system temperature twice that of the other, i.e. $T_{\\textrm{sys,0}} = 2 T_{\\textrm{sys,1}}$, then $T_{\\textrm{sys,0}}$ will take 4 times longer to reach the same sensitivity as $T_{\\textrm{sys,1}}$.\n",
"\n",
"The radiometer equation above holds for an ideal system, but real-life analogue systems are unstable and introduce 'gain fluctuations'. These fluctuations are due to a number of effects: the physical temperature of the electronics, cross-coupling leakage, RF shielding, etc. The system stability over an observation will depend on how well these effects are managed. These gain fluctuations depend on both time and frequency.\n",
"\n",
"#### System Equivalent Flux Density (SEFD)\n",
"\n",
"The overall sensitivity of a radio telescope is described by the *system equivalent flux density* (SEFD) which is a ratio of the noisiness of the instrument ($T_{\\textrm{sys}}$) to the effective signal *gain* $G_{eff}$. Here, \"gain\" is a catch-all term for all the various amplifications and attenuations of the sky signal we are interested in. Formally the SEFD is defined as\n",
"\n",
"$$\n",
"\\textrm{SEFD} = \\frac{T_{\\textrm{sys}}}{G_{eff}} = \\frac{2 k_B \\eta T_{\\textrm{sys}}}{A_{eff}}\n",
"$$\n",
"\n",
"where $k_B$ is the Boltzman constant, $\\eta$ is an generic efficiency factor (always *greater* than 1 - not a typo!) used to represent the signal loss in various system temperature components of the telescope (e.g. dish surface inefficiencies, feed under-illumination, quantization, etc...), and $A_{eff}$ is the effective collecting area of the telescope. For a dish telescope $A_{eff}$ can be approximated, to first order, as the geometric surface area of the dish.\n",
"\n",
"A decrease in the SEFD is equivalent to an improvement in the sensitivity of the telescope."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 7.3.3 Analogue Receiver Front-End"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To understand the details of the system's temperature and gain, we need to start with the *analogue receiver front-end*, also known as the analogue chain. This is the set of analogue electronic components which are used to condition the weak sky signal in order to do digital computations (like correlation). Ideally, we prefer a digital signal for a number of reasons. Digital signals are predictable, and any computation is deterministic. It is easier to perform operations such as Fourier transforms and correlations on digital signals than to analogue signals. An advantage of analogue signals is that they are continuous: they are free of issues due to Nyquist sampling or aliasing. Analogue components, however, are affected by feedback, cross-talk, temperature, and power supplies. Worst of all, analogue components introduce noise into the signal! In general, analogue components produce variable response in time and frequency, whereas a digital system has a stable response in time and frequency.\n",
"\n",
"We would like to digitize our sky signal as soon as possible. Unfortunately, the sky signal is very weak, and needs to be amplified and conditioned before it can be converted to digital. The *analogue-to-digital converter* (ADC) transforms an analogue signal to a digital signal, but if the signal is too weak then the ADC cannot sample the analogue signal. Below is a block diagram of an example analogue front-end.\n",
"\n",
"*Amplification* is the effect of increasing the strength of a signal absolutely or relative to another signal. In electronics an amplifier might be used for this, but other forms of amplification are possible, e.g. building a larger dish to collect more signal. The opposite effect is called *attenuation*. All Jones matrices can be thought of as performing an amplification or attenuation on the input signal to produce the output signal. This amplification (attenuation can also be seen as an amplification less than 1) can be complex or real-valued, and a function of time, frequency, or direction. Some Jones matrices will have unity gain, e.g. a rotation transform. Signal *conditioning* is the application of different filters to select out a limited signal of interest."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src='figures/analogue_chain.svg' width=800>\n",
"\n",
"**Figure 7.3.1**: Block diagram example of an analogue receiver front-end for a radio telescope. A current is induced in the receptor by the electric field of the sky source. The resistance of the components creates a voltage which is propotional to the amplitude of the electric field. This voltage is amplified by a low noise amplifier (LNA) in a cryostat at 20 K. A second stage amplifier further amplifies the signal in a 70 K intermediate cryostat. The signal is bandpass filtered to select the band of interest. A bandstop filter is used to filter out frequencies with radio frequency interference (RFI). A local oscillator (LO) is mixed with the signal to down mix the original radio frequency (RF) to an intermediate frequency (IF) which is then digitized by an analogue-to-digital converter (ADC). <a id='instrum:fig:analogue_chain'></a><!--\\label{instrum:fig:analogue_chain}-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The electric field of the sky signal induces a current on the *receptor* (also known as a *feed*). We will discuss feeds in [$\\S$ 7.4 ➞](7_6_polarization.ipynb). This is a very weak signal: we need to use an amplifier to increase the strength of the signal. The process of amplification introduces noise, which we would like to minimize. As technology improves, the noise introduced by amplifiers has decreased. The common method to reduce the noise of an amplifier is by cooling it using a *cryostat*. This is a chamber placed under vacuum, which can be cooled down to very low temepratures (20 K if using liquid helium, 70 K for liquid nitrogen). Remember - noise and temperature are the same, so the lower the temperature, the lower the noise. This special amplifier is called a *low noise amplifier* (LNA), and is usually the main component of the system noise. When building a telescope system, we focus on making sure the LNA has the best possible noise performance (i.e. the LNA is a significant cost to the analogue front end).\n",
"\n",
"The idea of the LNA is that we have a weak signal which needs to be amplified while adding the smallest possible amount of noise into the signal. Once the signal has been amplified with the LNA, we can use normal amplifiers and apply filters, because the signal to noise ratio is now sufficiently large that the added noise from these components will not affect the original signal very much. This can be seen in the receiver temperature equation.\n",
"\n",
"$$ T_{\\textrm{rx}} = T_{\\textrm{feed}} + \\frac{T_{\\textrm{passive}}}{G_{\\textrm{feed}}} + \\frac{T_{\\textrm{LNA}}}{G_{\\textrm{feed}} G_{\\textrm{passive}}} + \\frac{T_{\\textrm{amp}}}{G_{\\textrm{feed}} G_{\\textrm{passive}} G_{\\textrm{LNA}}} + \\ldots$$\n",
"\n",
"where the $T$ terms are the additional noise temperatures introduced by each component, and $G$ is the 'gain' term - which can be less than 1 (attenuation) or greater than 1 (amplification). The gain from the feed and passive components will be less than 1, and are similar to an efficiency term in when discussing the primary beam. These will cause the temperature from these components to increase in the system temperature <span style=\"background-color:cyan\">etienne: what does this mean, what \"these\"? Efficiencies?</span>. By placing the LNA as soon as possible, we see that the weak sky signal is amplified and noisier components - such as filters and second stage amplifiers - can be introduced without drowning the sky signal in their noise.\n",
"\n",
"From the receiver temperature equation above, we can see that each additional temperature term includes the gain of the LNA in the denominator. Thus, the more gain is introduced by the LNA, the smaller each additional noise term. There are some noise terms which come before the LNA stage. There is always a feed temperature $T_{\\textrm{feed}}$ (any physical feed will have some resistance - else, there would be no current!). There are passive components such as cables, orthomode transducers (OMTs), waveguides etc. which all add a noise component $T_{\\textrm{passive}}$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Decibel Units\n",
"\n",
"As a quick aside, it is common to discuss electronic and system gain using logarithmic decibel units. This is worth a quick review. We usually describe a signal in linear units. It is often more intuitive, however, to use logarithmic units when discussing values which cover many orders of magnitude, such as in electronics. The standard unit to use is the *decibel*, which is defined (for a power $P$) as:\n",
"\n",
"$$\n",
"P_{dB} = 10 \\log_{10} \\left ( \\frac{P}{P_0} \\right )\n",
"$$\n",
"\n",
"where $P_0$ is the reference power, typically set to unity. Important values to remember are: 3 dB is a factor of 2 increase, 10 dB is a factor of 10, -3 dB is a factor of 0.5. Every factor of 10 increase in decibels is an order of magnitude increase. Note that if the signal is not a power (e.g. it is a voltage or electric field) the definition of decibel is\n",
"\n",
"$$\n",
"P_{dB} = 20 \\log_{10} \\left ( \\frac{V}{V_0} \\right )\n",
"$$\n",
"\n",
"as we need to compute the power of the voltage in the process of converting to decibels. This is useful for describing the response of Jones matrices in decibels."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Returning to the LNA in our analogue front-end example, we typically amplify the sky power signal by 20 to 30 decibels with the LNA. That is, the LNA amplifies the sky signal by a factor of 100 to 1000. We can then use other components without worrying about introducing significant noise relative to the signal strength.\n",
"\n",
"Below is an example of the LNA response in time and frequency. This is a simple model, where the LNA bandpass response is stable in time but the overall system gain varies in time. The bandpass has a frequency-dependent response that ranges between 30 dB and 20 dB. There is a'ripple' across the band due to the LNA design. The LNA peaks around 1.2 GHz and drops off at higher frequencies. The overall gain varies by a few precent over time, which can be due to a number of effects. The heating and cooling of the cryostat is never perfect, so there is variation in the gain. Depending on the observing source, we should expect the gain to change: a bright source will introduce more noise than a dimmer source. Feedback and cross-talk between analogue components will always exist even with good isolation. This is thus a fairly typical LNA response, such as one could expect to see in real life."
]
},
{
"cell_type": "code",
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"collapsed": false
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"source": [
"#LNA bandpass\n",
"#xrange: 0,1\n",
"#yrange: 20-30 dB\n",
"#a made up LNA response spectrum (in dB)\n",
"nfreqVals = np.array([-0.5, 0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45,\n",
" 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0, 1.05])\n",
"lnaVals = np.array([27, 27, 28.1, 28, 27.5, 29, 30.1, 30, 28.5, 28.5, 30,\n",
" 27, 27, 25.5, 23, 24, 25, 22.5, 21, 21, 22, 19.5, 19.5])\n",
"\n",
"#add a little noise for randomness\n",
"lnaStd = 0.5 #standard deviation of noise in dB\n",
"noiseVals = np.random.normal(loc=0.0, scale=lnaStd, size=lnaVals.shape)\n",
"\n",
"nfreqs = np.linspace(0, 1, num=nchans) #normalized frequencies\n",
"coeffs = np.polyfit(nfreqVals, lnaVals + noiseVals, deg=15)\n",
"lnaGain = 10.**(np.polyval(coeffs, nfreqs)/10) #convert to linear\n",
"\n",
"#Gain variation\n",
"#xrange: 0,1\n",
"#yrange: 1 + std\n",
"tVals = np.linspace(-0.5, 1.5, num=60)\n",
"gainStd = 0.10\n",
"gVals = np.random.normal(loc=1.0, scale=gainStd, size=tVals.shape)\n",
"\n",
"ts = np.linspace(0, 1, num=ntime)\n",
"gCoeffs = np.polyfit(tVals, gVals, deg=20)\n",
"gain = np.polyval(gCoeffs, ts)\n",
"\n",
"gainSpectrum = np.outer(gain, 10. * np.log10(lnaGain))\n",
"\n",
"fig, axes = plt.subplots(figsize=(8,8))\n",
"\n",
"ax1 = plt.subplot2grid((5,5), (0,0), colspan=4, rowspan=4)\n",
"plt.imshow(np.abs(gainSpectrum), aspect='auto')\n",
"ax1.get_xaxis().set_visible(False)\n",
"ax1.get_yaxis().set_visible(False)\n",
"\n",
"ax2 = plt.subplot2grid((5,5), (4,0), colspan=4, rowspan=1)\n",
"plt.plot(freqs/1e9, gainSpectrum[100, :])\n",
"plt.ylabel('Gain (dB)')\n",
"plt.xlabel('Frequency (GHz)')\n",
"\n",
"ax3 = plt.subplot2grid((5,5), (0,4), colspan=1, rowspan=4)\n",
"plt.plot(gainSpectrum[:, 100], np.arange(gainSpectrum.shape[0]))\n",
"ax3.get_xaxis().set_visible(False)\n",
"ax3.invert_yaxis()\n",
"ax3.yaxis.tick_right()\n",
"plt.ylabel('Time (s)')\n",
"ax3.yaxis.set_label_position('right')\n",
"\n",
"plt.suptitle('LNA Response')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Filters\n",
"\n",
"Applying *signal filters* to the observed signal is necessary for a number of reasons. We are limited to a finite observing bandwdith, because our antenna feed is only sensitive to a limited frequency range. The LNA is only well-characterized for a limited bandwidth, human-made radio interference limits the usable observing band, the ADC only Nyquist samples up to a specific bandwidth before aliasing occurs, etc. All these factors define the usable bandwidth of the instrument. There will be a *start frequency* $\\nu_0$ - which is the lowest frequency the system records data at - and a *stop frequency* $\\nu_f$ - which is the highest frequency. The range within these frequencies is the *bandwidth* $\\Delta \\nu = \\nu_f - \\nu_0$. We can further define two types of bandwidth: the *analogue bandwidth* is the bandwidth of the system based on the analogue filters, and the *digital bandwidth* is defined by half the sampling rate of the ADC (i.e. the digital bandwidth is set by the Nyquist rate). \n",
"\n",
"The two building blocks of signal filtering are the *high-pass filter*, which supresses low-frequency signals while leaving high-frequency signals untouched, and the *low-pass filter* which does the opposite. Filter design is an active field of study, and a lifetime can be spent on the topic - we will not go into any depth, but merely present the basics needed to understand a radio telescope analogue front end.\n",
"\n",
"A filter works by applying a weighted average in the time domain using some number of samples (called *taps*). The weights within the filter are defined by a window function. A simple window function is a boxcar or truncated Gaussian, but there are many types depending on the desired response. When the time domain signal is transformed into the frequency domain, the window function's Fourier transform is called the *window response function*. The window response of a boxcar is the sinc function, and the response of a Gaussian filter is another Gaussian.\n",
"\n",
"To set the band of the analogue system we use a *bandpass filter* which consists of a low-pass and high-pass filter to created a limted band response. An example of a bandpass filter using a low-pass and high-pass Hann window with 33 taps is shown below. These are *finite-impulse response filters* (FIRs) which are typically used for bandpass filters. Another type of filter is the *infinite-impulse response filter* (IIRs) which we will not discuss further."
]
},
{
"cell_type": "code",
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"metadata": {
"collapsed": false
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"source": [
"# Modified from: https://scipy.github.io/old-wiki/pages/Cookbook/FIRFilter.html\n",
"# The Nyquist rate of the signal.\n",
"sample_rate = 800.0 # ADC sampling rate (MHz)\n",
"nyq_rate = sample_rate / 2.0 # Digital Bandwidth\n",
"\n",
"# Low-Pass Filter\n",
"# The cutoff frequency of the filter.\n",
"lpf_cutoff_hz = 360. # MHz\n",
"# Use firwin with a Hann window to create a low-pass FIR filter.\n",
"lpf_taps = scipy.signal.firwin(33, lpf_cutoff_hz/nyq_rate, window='hann')\n",
"\n",
"# High-Pass Filter\n",
"# The cutoff frequency of the filter.\n",
"hpf_cutoff_hz = 40. # MHz\n",
"# Use firwin with a Hann window to create a high-pass FIR filter.\n",
"hpf_taps = scipy.signal.firwin(33, hpf_cutoff_hz/nyq_rate, window='hann', pass_zero=False)\n",
"\n",
"lpf_w, lpf_h = scipy.signal.freqz(lpf_taps, worN=512)\n",
"hpf_w, hpf_h = scipy.signal.freqz(hpf_taps, worN=512)\n",
"lpfAmp = np.absolute(lpf_h)\n",
"hpfAmp = np.absolute(hpf_h)\n",
"bandpassAmp = lpfAmp*hpfAmp\n",
"\n",
"fig, axes = plt.subplots(figsize=(16,6))\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, bandpassAmp, linewidth=2)\n",
"plt.xlabel('Frequency (MHz)')\n",
"plt.ylabel('Gain')\n",
"plt.title('Bandpass Filter Response')\n",
"plt.ylim(-0.05, 1.05)\n",
"plt.grid(True)\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, 10.*np.log10(bandpassAmp), linewidth=2)\n",
"plt.xlabel('Frequency (MHz)')\n",
"plt.ylabel('Gain (dB)')\n",
"plt.title('Bandpass Filter Response')\n",
"plt.ylim(-10, 0.2)\n",
"plt.grid(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Designing the bandpass filter depends on a number of factors, including desired bandwidth, out-of-band suppression, and financial cost. Filter design is an engineering challenge, set by the project specifications. We will not attempt to cover the topic; see [<cite data-cite='Lyons:2004:UDS:993484'>Understanding Digital Signal Processing</cite> ⤴](https://books.google.co.za/books?id=UBU7Y2tpwWUC) for an introduction. As far as we are concerned, there are a few important characteristics. *Usable band*: this is the amount of band which can be used for observation, in the example above this is approximately 1175 MHz to 1425 MHz. *Roll-off*: this is the rate at which the filter drops in response. There is a transtion region in our example from 1100 MHz to 1175 MHz between the bandpass and bandstop regions. *Bandpass ripple*: we see that the bandpass is not perfectly flat;there is variation, especially when the filter begins to roll-off. We would like a nearly flat response across the band. The filter is continuous in frequency, so even though we would like to put a hard stop to the filter at 1100 MHz and 1500 MHz, the response is non-zero. We will be sampling the analogue signal at some Nyquist rate, which will create aliasing effects. We need to have a low enough filter response at the start and stop frequencies that signals from out of the band do not alias into the band. The cost of supressing aliasing is to reduce the overall usable band, or require sharper filters.\n",
"\n",
"The opposite of a bandpass filter is a *bandstop filter* which supresses signals within a limited frequency range. These are useful in radio astronomy to supress *radio frequency interference* (RFI) which is strong human-made radio signals. We discuss RFI in [$\\S$ 7.8 ➞](7_8_rfi.ipynb); for now, we simply need to kow that RFI can be orders of magnitude stronger than astronomical signals. Signals such as those of mobile telephones at 800 MHz and FM radio aroun 100 MHz (among many others) are present in most populated regions of the Earth.\n",
"\n",
"We can partially seperate out the RFI from astronomical signals when the observed signal is channelized, but before that, RFI can cause issues with the analogue front-end due to the dynamic range of the electronic components. Any analogue electronic component is designed with a limited input amplitude range, kown as the device's *dynamic range*. Within this dynamic range, the device has a *linear response*, i.e. the output can be directly mapped to the input based on a linear scaling relation. Input signals out of the dynamic range produce a non-linear response, along with feedback which can affect other components. We would like to build analogue electronics which are optimized to detect astronomical signals, and so we filter out strong RFI signals before these components are used. In the case of strong, persistent, narrow-band RFI (such as FM radio and mobile telephones) a solution is to use a bandstop filter (or *notch filter*) to supress signals within a limited band around the RFI. Below is a simple spectrum which has three strong, narrow-band RFI source."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Spectrum for strong, narrow-band RFI sources\n",
"rfiSpectrum = np.ones_like(freqs)\n",
"# Add RFI sources\n",
"rscale = 20.\n",
"idx = np.abs(freqs - 1.33e9).argmin()\n",
"rfiSpectrum[idx] += 1. * rscale\n",
"idx = np.abs(freqs - 1.34e9).argmin()\n",
"rfiSpectrum[idx] += 0.3 * rscale\n",
"idx = np.abs(freqs - 1.335e9).argmin()\n",
"rfiSpectrum[idx] += 0.5 * rscale\n",
"\n",
"fig, axes = plt.subplots(figsize=(8,6))\n",
"\n",
"plt.plot(freqs/1e9, rfiSpectrum-1)\n",
"plt.title('Narrow Band RFI')\n",
"plt.xlabel('Frequency (GHz)')\n",
"plt.ylabel('Flux (Jy)')\n",
"plt.grid(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we do nothing to supress the RFI the resulting observed spectrum dynamic range will be dominated by the RFI as shown below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, axes = plt.subplots(figsize=(8,6))\n",
"\n",
"plt.plot(freqs, bandpassAmp * rfiSpectrum, linewidth=2)\n",
"plt.xlabel('Frequency (GHz)')\n",
"plt.ylabel('Gain')\n",
"plt.title('Bandpass with RFI')\n",
"plt.grid(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To mitigate this, we can design a notch filter to supress 40 MHz of band around these sources. In the figure below, the filter is shown in linear and decibel scales. We can see the notch filter will supress signals up to -30 dB in amplitude."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Bandstop/Notch Filter\n",
"# The cutoff frequency of the filter.\n",
"bsf_cutoff_hz = np.array([215., 255.])\n",
"# Use firwin with a Hann window to create a low-pass FIR filter.\n",
"bsf_taps = scipy.signal.firwin(73, bsf_cutoff_hz/nyq_rate, window='hann')\n",
"\n",
"bsf_w, bsf_h = scipy.signal.freqz(bsf_taps, worN=512)\n",
"bsfAmp = np.absolute(bsf_h)\n",
"\n",
"fig, axes = plt.subplots(figsize=(16,6))\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, bsfAmp, linewidth=2)\n",
"plt.xlabel('Frequency (MHz)')\n",
"plt.ylabel('Gain')\n",
"plt.title('Notch Filter Response')\n",
"plt.ylim(-0.05, 1.05)\n",
"plt.grid(True)\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, 10.*np.log10(bsfAmp), linewidth=2)\n",
"plt.xlabel('Frequency (MHz)')\n",
"plt.ylabel('Gain (dB)')\n",
"plt.title('Notch Filter Response')\n",
"plt.ylim(-35, 1.)\n",
"plt.grid(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can then combine the bandstop filter with the bandpass filter, shown below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, axes = plt.subplots(figsize=(16,6))\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, bandpassAmp*bsfAmp, linewidth=2)\n",
"plt.xlabel('Frequency (MHz)')\n",
"plt.ylabel('Gain')\n",
"plt.title('Bandpass Filter w/ Notch Filter Response')\n",
"plt.ylim(-0.05, 1.05)\n",
"plt.grid(True)\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.plot((freq0/1e6)+(lpf_w/np.pi)*nyq_rate, 10.*np.log10(bandpassAmp*bsfAmp), linewidth=2)\n",
"plt.xlabel('Frequency (MHz)')\n",
"plt.ylabel('Gain (dB)')\n",
"plt.title('Bandpass Filter w/ Notch Filter Response')\n",
"plt.ylim(-10, 0.2)\n",
"plt.grid(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The resulting observed RFI signal (shown below) is significantly suppressed by the notch filter, at the cost of losing usable bandwidth. This trade-off that is often necessary."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, axes = plt.subplots(figsize=(8,6))\n",
"\n",
"plt.plot(freqs, bandpassAmp * bsfAmp * rfiSpectrum, linewidth=2)\n",
"plt.xlabel('Frequency (GHz)')\n",
"plt.ylabel('Gain')\n",
"plt.title('Bandpass with Notch Filter for RFI')\n",
"plt.grid(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Hetrodyne Mixing\n",
"\n",
"After the filters in our block diagram analogue front-end example, there is a mixer with a local oscillator. This is know as a *hetrodyne detection*, which is the process of 'mixing' down the original radio frequencies (RF) $\\nu_{\\textrm{RF}}$ to an intermediate frequency (IF) $\\nu_{\\textrm{IF}}$ using a local oscillator (LO) $\\nu_{\\textrm{LO}}$ such that the signal can be sampled digitally. A *local oscillator* is a pure, single-frequency tone. Mixing of the RF and LO results in $\\nu_{\\textrm{IF}} = \\nu_{\\textrm{RF}} - \\nu_{\\textrm{LO}}$. Consider two pure sine waves $\\sin(2 \\pi \\nu_{\\textrm{RF}})$, $\\sin(2 \\pi \\nu_{\\textrm{LO}})$, mixing is the process of multiplying the signals together, using trigonomic identities this results in\n",
"\n",
"$$\\sin(2 \\pi \\nu_{\\textrm{RF}}) \\cdot \\sin(2 \\pi \\nu_{\\textrm{LO}}) = \\frac{1}{2} \\cos(2 \\pi (\\nu_{\\textrm{RF}} - \\nu_{\\textrm{LO}})t) + \\frac{1}{2} \\cos(2 \\pi (\\nu_{\\textrm{RF}} + \\nu_{\\textrm{LO}})t) $$\n",
"\n",
"Because we have used a bandpass filter, any signal above the stop frequency will be suppressed, so the $\\cos(2 \\pi (\\nu_{\\textrm{RF}} + \\nu_{\\textrm{LO}})t)$ term can be dropped, resulting in a mixer output of $\\frac{1}{2} \\cos(2 \\pi (\\nu_{\\textrm{RF}} - \\nu_{\\textrm{LO}})t)$. The mixer has shifted the original signal at frequency $\\nu_{\\textrm{RF}}$ down to $\\nu_{\\textrm{IF}} = \\nu_{\\textrm{RF}} - \\nu_{\\textrm{LO}}$.\n",
"\n",
"The process of hetrodyne mixing is very useful. From an engineering perspective, it is much easier to build electronics at lower frequencies than higher frequencies. Using a mixer a high frequency signal is mixed down to lower frequencies, and cheaper electronics can be used. Without heterodyne mixers, the alternative is to to use *direct RF sampling*. This is common is very low frequency radio astronomy (< 200 MHz). As technology improves, direct RF sampling will see more use at higher frequencies, but for now at least, most radio telescopes uses hetrodyne mixing.\n",
"\n",
"With nearly all the analogue components in place (we will come to the digitization process soon) we are ready to construct the observed spectrum of our idealized source (where we started the section) while including the effects due to the various analogue electronics. As we can see in the figure below, it is quite different from the idealized spectrum we started with! The bottom figure shows the time-dependent bandpass change, and the figure to the right shows the frequency-dependent gain change."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Observed Source Spectrum\n",
"# noise spectrum\n",
"noiseSpectrum = np.random.normal(loc=0., scale=2., size=gainSpectrum.shape)\n",
"\n",
"# bandpass spectrum\n",
"bpSpectrum = np.repeat(bandpassAmp * bsfAmp * rfiSpectrum, ntime).reshape(nchans, ntime)\n",
"\n",
"# observed spectrum\n",
"obsSpectrum = bpSpectrum.T * ( (gainSpectrum * np.abs(brightness[:,:,0,0]+brightness[:,:,1,1])) + noiseSpectrum)\n",
"\n",
"# local oscillator frequencies\n",
"lo = 1.1e9\n",
"ifFreqs = freqs - lo\n",
"\n",
"# save spectrum for use in the next section\n",
"# np.savez('data/analogue_spectrum.npz', obsSpectrum, ifFreqs)\n",
"\n",
"fig, axes = plt.subplots(figsize=(8,8))\n",
"\n",
"ax1 = plt.subplot2grid((5,5), (0,0), colspan=4, rowspan=4)\n",
"plt.imshow(np.abs(obsSpectrum), aspect='auto')\n",
"ax1.get_xaxis().set_visible(False)\n",
"ax1.get_yaxis().set_visible(False)\n",
"\n",
"ax2 = plt.subplot2grid((5,5), (4,0), colspan=4, rowspan=1)\n",
"plt.plot(ifFreqs/1e9, obsSpectrum[50, :])\n",
"plt.plot(ifFreqs/1e9, obsSpectrum[250, :])\n",
"plt.plot(ifFreqs/1e9, obsSpectrum[450, :])\n",
"plt.xlim(ifFreqs[0]/1e9, ifFreqs[-1]/1e9)\n",
"plt.ylabel('Flux')\n",
"plt.xlabel('Frequency (GHz)')\n",
"\n",
"ax3 = plt.subplot2grid((5,5), (0,4), colspan=1, rowspan=4)\n",
"plt.plot(obsSpectrum[:, 130], np.arange(obsSpectrum.shape[0]))\n",
"plt.plot(obsSpectrum[:, 260], np.arange(obsSpectrum.shape[0]))\n",
"plt.plot(obsSpectrum[:, 390], np.arange(obsSpectrum.shape[0]))\n",
"ax3.get_xaxis().set_visible(False)\n",
"ax3.invert_yaxis()\n",
"ax3.yaxis.tick_right()\n",
"plt.ylabel('Time (s)')\n",
"ax3.yaxis.set_label_position('right')\n",
"\n",
"plt.suptitle('Observed Spectrum')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"System noise has hidden the smooth source structure. The LNA response has scaled the flux values and added a bit of ripple to the source. The bandpass filter has supressed the source response at the edges of the band. The notch filter has supressed the RFI signal and part of the source signal. The signals range from 0 MHz (DC) to 400 MHz due to the hetrodyne mixer.\n",
"\n",
"We can see that the analogue front-end transforms the original astronomical signal into the observed signal: this is what the $\\mathbf{G}$-Jones describes. It is generally a time- and frequency-dependent Jones matrix. The time and frequency dependence is due to the variation in response from the analogue electronic components used to filter, condition, and amplify the input voltage in order to digitally sample the signal. The generic $\\mathbf{G}$-Jones is often broken into two components: a time-stable bandpass $\\mathbf{B}$-Jones matrix which represents the filter responses and the general LNA response, and a frequency-stable $\\mathbf{G}$-Jones matrix which accounts for the time-dependent variation of the overall analogue system."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 7.3.4 Digitisation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After signal conditioning in the analogue front-end, we are ready to capture the signal into a digital form so that we can perform the correlation. The term digitisation is worth reflecting upon: it is the process of taking a time-continuous, analogue signal, and converting it to a time-discrete voltage signal (which are 'digits' described by integers). The last analogue component is the *analogue to digital converter (ADC)*. A simple form of analogue-to-digital conversion is a cascading resistor ladder, which measures discrete steps in an analogue signal. Each output of the ladder triggers a bit value of either 0 or 1 - a digital signal is thus created. See Chapter 13 of [<cite data-cite='horowitz2015art'>The Art of Electronics</cite> ⤴](http://artofelectronics.net/).\n",
"\n",
"The dynamic range of an ADC is determined by the number of bits an input analogue signal can be digitized into. For an $n$-bit ADC there are $2^n$ possible digital values, by convention this range is $[-2^{n-1}, 2^{n-1}-1]$. Just as in other electronic components, an ADC has input range in which the resulting digitisation produces linear results. An astronomical signal needs to be amplified such that the analogue signal is within this linear range. The signal is then digitized: an example of a 4-bit (256 values) ADC is shown in the figure below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"adcBits = 4 # number of bits in the ADC\n",
"adcMin = -1 * 2**(adcBits-1) # minimum ADC value\n",
"adcMax = 2**(adcBits-1) - 1 # maximumx ADC value\n",
"\n",
"nsamp = 256 # number of time samples\n",
"phi0 = .1*np.pi # starting phase\n",
"dphi = 6.*np.pi # length\n",
"phis = np.linspace(0, dphi, nsamp)\n",
"amp = 6.5 # signal amplitude\n",
"\n",
"analogueSignal = amp*np.sin(phis+phi0)\n",
"digitalSignal = analogueSignal.astype(int)\n",
"\n",
"fig, axes = plt.subplots(figsize=(16,8))\n",
"\n",
"plt.subplot(2, 1, 1)\n",
"plt.plot(analogueSignal, c='k', label='analogue', linewidth=3, alpha=0.3)\n",
"plt.plot(digitalSignal, '.', label='digital', linewidth=3)\n",
"plt.ylim(adcMin-1, adcMax+1)\n",
"plt.xlim(0, nsamp-1)\n",
"plt.axhline(adcMin, ls='--', c='k')\n",
"plt.axhline(adcMax, ls='--', c='k')\n",
"plt.title('Signal Digitization (4-bit)')\n",
"plt.ylabel('Amplitude')\n",
"plt.legend()\n",
"\n",
"plt.subplot(2, 1, 2)\n",
"plt.plot(analogueSignal-digitalSignal)\n",
"plt.xlim(0, nsamp-1)\n",
"plt.ylim(-1.2, 1.2)\n",
"plt.title('Digitization Error')\n",
"plt.ylabel('Amplitude')\n",
"plt.xlabel('Time Sample')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The digitization results in time-discrete samples. The amplitude of each sample is the closest step in the ADC to the analogue amplitude. This results in *digitization error* as the conversion to digital results in information loss. To reduce the digitisation error, the maximum number of steps in the ADC should be used. This requires knowledge of the input signal amplitude. If the signal is too weak, then only a few bits will be used in digitisation and the dynamic range is not being used optimally; this is shown in the figure below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"adcBits = 4 # number of bits in the ADC\n",
"adcMin = -1 * 2**(adcBits-1) # minimum ADC value\n",
"adcMax = 2**(adcBits-1) - 1 # maximumx ADC value\n",
"\n",
"nsamp = 256 # number of time samples\n",
"phi0 = .1*np.pi # starting phase\n",
"dphi = 6.*np.pi # length\n",
"phis = np.linspace(0, dphi, nsamp)\n",
"amp = 3. # signal amplitude\n",
"\n",
"analogueSignal = amp*np.sin(phis+phi0)\n",
"digitalSignal = analogueSignal.astype(int)\n",
"\n",
"fig, axes = plt.subplots(figsize=(16,8))\n",
"\n",
"plt.subplot(2, 1, 1)\n",
"plt.plot(analogueSignal, c='k', label='analogue', linewidth=3, alpha=0.3)\n",
"plt.plot(digitalSignal, '.', label='digital', linewidth=3)\n",
"plt.ylim(adcMin-1, adcMax+1)\n",
"plt.xlim(0, nsamp-1)\n",
"plt.axhline(adcMin, ls='--', c='k')\n",
"plt.axhline(adcMax, ls='--', c='k')\n",
"plt.title('Signal Digitization (4-bit)')\n",
"plt.ylabel('Amplitude')\n",
"plt.legend()\n",
"\n",
"plt.subplot(2, 1, 2)\n",
"plt.plot(analogueSignal-digitalSignal)\n",
"plt.xlim(0, nsamp-1)\n",
"plt.ylim(-1.2, 1.2)\n",
"plt.title('Digitization Error')\n",
"plt.ylabel('Amplitude')\n",
"plt.xlabel('Time Sample')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another solution to reduce digitization error is to use an ADC with more bits, such as the 6-bit ADC figure below. As the number of bits increases, digitisation will eventually approach the true analogue signal amplitude."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"adcBits = 6 # number of bits in the ADC\n",
"adcMin = -1 * 2**(adcBits-1) # minimum ADC value\n",
"adcMax = 2**(adcBits-1) - 1 # maximumx ADC value\n",
"\n",
"nsamp = 256 # number of time samples\n",
"phi0 = .1*np.pi # starting phase\n",
"dphi = 6.*np.pi # length\n",
"phis = np.linspace(0, dphi, nsamp)\n",
"amp = 28 # signal amplitude\n",
"\n",
"analogueSignal = amp*np.sin(phis+phi0)\n",
"digitalSignal = analogueSignal.astype(int)\n",
"\n",
"fig, axes = plt.subplots(figsize=(16,8))\n",
"\n",
"plt.subplot(2, 1, 1)\n",
"plt.plot(analogueSignal, c='k', label='analogue', linewidth=3, alpha=0.3)\n",
"plt.plot(digitalSignal, '.', label='digital', linewidth=3)\n",
"plt.ylim(adcMin-1, adcMax+1)\n",
"plt.xlim(0, nsamp-1)\n",
"plt.axhline(adcMin, ls='--', c='k')\n",
"plt.axhline(adcMax, ls='--', c='k')\n",
"plt.title('Signal Digitization (6-bit)')\n",
"plt.ylabel('Amplitude')\n",
"plt.legend()\n",
"\n",
"plt.subplot(2, 1, 2)\n",
"plt.plot(analogueSignal-digitalSignal)\n",
"plt.xlim(0, nsamp-1)\n",
"plt.ylim(-1.2, 1.2)\n",
"plt.title('Digitization Error')\n",
"plt.ylabel('Amplitude')\n",
"plt.xlabel('Time Sample')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A problem in radio astronomy is that we wish to maximize the dynamic range of the ADC for a weak astronomical signal, but observe in an environment with strong RFI which dominate the time-domain telescope signal. We need to sample the full dynamic range of the RFI signal. If the maximum/minimum ADC value is smaller than the RFI signal then the RFI will *saturate* the ADC, causing the output of the ADC to sit at the highest or lowest value and no sky signal to be captured: this is shown in the figure below. This results in *spectral leakage*, where a signal which is located at a specific frequency 'leaks' into nearby frequencies due to instrumental effects. A strong RFI source is usually a strong sinusoidal wave. When we take the Fourier transform of that signal, a strong $\\delta$-function is produced in the resulting spectrum, isolated to a single frequency channel. If the ADC is saturated, then the RFI sinusoid will be clipped and turn into a square wave, the Fourier transform of which is similar to the Fourier transform of a boxcar function: a sinc function. A sinc function has longer extended wings - these will leak into the other channels. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"adcBits = 4 # number of bits in the ADC\n",
"adcMin = -1 * 2**(adcBits-1) # minimum ADC value\n",
"adcMax = 2**(adcBits-1) - 1 # maximumx ADC value\n",
"\n",
"amp = 2. # signal amplitude\n",
"rfiAmp = 8.5 # RFI signal amplitude\n",
"rfiPhi0 = 1.35*np.pi\n",
"rfiFreq = 3.37 # frequecy relative to signal frequency\n",
"\n",
"analogueSignal = rfiAmp*np.sin(rfiFreq*(phis+rfiPhi0)) + amp*np.sin(phis+phi0)\n",
"digitalSignal = analogueSignal.astype(int).clip(adcMin, adcMax)\n",
"\n",
"fig, axes = plt.subplots(figsize=(16,8))\n",
"\n",
"plt.subplot(2, 1, 1)\n",
"plt.plot(amp*np.sin(phis+phi0), c='r', label='signal', linewidth=3, alpha=0.3)\n",
"plt.plot(analogueSignal, c='k', label='RFI + signal', linewidth=3, alpha=0.3)\n",
"plt.plot(digitalSignal, '.', label='digital', linewidth=3)\n",
"plt.ylim(adcMin-4, adcMax+4)\n",
"plt.xlim(0, nsamp-1)\n",
"plt.axhline(adcMin, ls='--', c='k')\n",
"plt.axhline(adcMax, ls='--', c='k')\n",
"plt.title('Signal + RFI Digitization (4-bit)')\n",
"plt.ylabel('Amplitude')\n",
"plt.legend()\n",
"\n",
"plt.subplot(2, 1, 2)\n",
"plt.plot(analogueSignal-digitalSignal)\n",
"plt.xlim(0, nsamp-1)\n",
"plt.title('Digitization Error')\n",
"plt.ylabel('Amplitude')\n",
"plt.xlabel('Time Sample')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Not only does saturation cause spectral leakage, but there is significant digitisation error. The naive solution is to attenuate the in-out signal such that the RFI does not cause the ADC to saturate. If we turn off the RFI, however, we can see in the figure below that the sky signal is not well-sampled by the ADC. We are not using the full dynamic range of the ADC. Again, the solution is to use an ADC with more bits to balance RFI from saturating and sampling the sky signal to a sufficient level."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"analogueSignal = amp*np.sin(phis+phi0)\n",
"digitalSignal = analogueSignal.astype(int).clip(adcMin, adcMax)\n",
"\n",
"fig, axes = plt.subplots(figsize=(16,8))\n",
"\n",
"plt.subplot(2, 1, 1)\n",
"plt.plot(analogueSignal, c='r', label='signal', linewidth=3, alpha=0.3)\n",
"plt.plot(digitalSignal, '.', label='digital', linewidth=3)\n",
"plt.ylim(adcMin-4, adcMax+4)\n",
"plt.xlim(0, nsamp-1)\n",
"plt.axhline(adcMin, ls='--', c='k')\n",
"plt.axhline(adcMax, ls='--', c='k')\n",
"plt.title('Signal Digitization (4-bit, Undersampled)')\n",
"plt.ylabel('Amplitude')\n",
"plt.legend()\n",
"\n",
"plt.subplot(2, 1, 2)\n",
"plt.plot(analogueSignal-digitalSignal)\n",
"plt.xlim(0, nsamp-1)\n",
"plt.title('Digitization Error')\n",
"plt.ylabel('Amplitude')\n",
"plt.xlabel('Time Sample')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A typical, modern ADC used for radio astronomy will have 8-12 bits. This is necessary for the strong -RFI environment many telescopes operate in. There is an additional issue for *wide-band system*, which are telescopes which cover significant fractional bandwidth. Most astronomical radio sources are continuum sources, i.e. the spectrum of the source is smooth and continuous in frequency. One way to improve the sensitivity of the telescope, as per the radiometer equation, is to increase the observing bandwidth. This is a fine idea, but means that there is necessarily more power being observed by the telescope, and thus requires an ADC with increased dynamic range compared to a narrow-band system.\n",
"\n",
"In the past, all the electronic components of a radio telescope were analogue. As we have seen, analogue components add noise and distort signals. What we really want is to digitise the observed signal. This has the advantage of allowing us to perfectly control the signal, at the cost of turning a continuous signal into a discrete one. Analogue components are still required to amplify and filter the signal, but modern radio telescope design aims to convert the signal to digital as soon as possible. Once we have a digital signal we are free to do what we want with the signal - in our case, correlate it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***\n",
"\n",
"* [Outline](../0_Introduction/0_introduction.ipynb)\n",
"* [Glossary](../0_Introduction/1_glossary.ipynb)\n",
"* [7. Observing Systems](0_introduction.ipynb) \n",
" * Previous: [7.2 The Radio Interferometer Measurement Equation (RIME)](7_2_rime.ipynb) \n",
" * Next: [7.4 Digital Correlators](7_4_digital.ipynb)\n",
"\n",
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=warn><b>Future Additions:</b></div>\n",
"\n",
"* images: receiver/feed, balun, wave guide, OMT, cryostat, cables, lna\n",
"* sampling rate in ADC section\n",
"* expand radiometer equation discussion\n",
"* expand SEFD discussion, example of observing time for KAT-7, MeerKAT, VLA\n",
"* meerkat or kat-7 analogue receiver chain block diagram\n",
"* full simulation of time-domain voltage signal to auto-correlation spectrum: complex LNA response with phase slope\n",
"* example: KAT-7, MeerKAT waterfall response"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
pcmagic/stokes_flow | head_Force/do_calculate_table_loc/phase_map_v4_ecoB01B05_w0.015.ipynb | 2 | 2480578 | null | mit |
d-k-b/udacity-deep-learning | semi-supervised/semi-supervised_learning_2.ipynb | 1 | 28335 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook, we'll learn how to use GANs to do semi-supervised learning.\n",
"\n",
"In supervised learning, we have a training set of inputs $x$ and class labels $y$. We train a model that takes $x$ as input and gives $y$ as output.\n",
"\n",
"In semi-supervised learning, our goal is still to train a model that takes $x$ as input and generates $y$ as output. However, not all of our training examples have a label $y$. We need to develop an algorithm that is able to get better at classification by studying both labeled $(x, y)$ pairs and unlabeled $x$ examples.\n",
"\n",
"To do this for the SVHN dataset, we'll turn the GAN discriminator into an 11 class discriminator. It will recognize the 10 different classes of real SVHN digits, as well as an 11th class of fake images that come from the generator. The discriminator will get to train on real labeled images, real unlabeled images, and fake images. By drawing on three sources of data instead of just one, it will generalize to the test set much better than a traditional classifier trained on only one source of data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import pickle as pkl\n",
"import time\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from scipy.io import loadmat\n",
"import tensorflow as tf\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"!mkdir data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from urllib.request import urlretrieve\n",
"from os.path import isfile, isdir\n",
"from tqdm import tqdm\n",
"\n",
"data_dir = 'data/'\n",
"\n",
"if not isdir(data_dir):\n",
" raise Exception(\"Data directory doesn't exist!\")\n",
"\n",
"class DLProgress(tqdm):\n",
" last_block = 0\n",
"\n",
" def hook(self, block_num=1, block_size=1, total_size=None):\n",
" self.total = total_size\n",
" self.update((block_num - self.last_block) * block_size)\n",
" self.last_block = block_num\n",
"\n",
"if not isfile(data_dir + \"train_32x32.mat\"):\n",
" with DLProgress(unit='B', unit_scale=True, miniters=1, desc='SVHN Training Set') as pbar:\n",
" urlretrieve(\n",
" 'http://ufldl.stanford.edu/housenumbers/train_32x32.mat',\n",
" data_dir + 'train_32x32.mat',\n",
" pbar.hook)\n",
"\n",
"if not isfile(data_dir + \"test_32x32.mat\"):\n",
" with DLProgress(unit='B', unit_scale=True, miniters=1, desc='SVHN Training Set') as pbar:\n",
" urlretrieve(\n",
" 'http://ufldl.stanford.edu/housenumbers/test_32x32.mat',\n",
" data_dir + 'test_32x32.mat',\n",
" pbar.hook)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trainset = loadmat(data_dir + 'train_32x32.mat')\n",
"testset = loadmat(data_dir + 'test_32x32.mat')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"idx = np.random.randint(0, trainset['X'].shape[3], size=36)\n",
"fig, axes = plt.subplots(6, 6, sharex=True, sharey=True, figsize=(5,5),)\n",
"for ii, ax in zip(idx, axes.flatten()):\n",
" ax.imshow(trainset['X'][:,:,:,ii], aspect='equal')\n",
" ax.xaxis.set_visible(False)\n",
" ax.yaxis.set_visible(False)\n",
"plt.subplots_adjust(wspace=0, hspace=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def scale(x, feature_range=(-1, 1)):\n",
" # scale to (0, 1)\n",
" x = ((x - x.min())/(255 - x.min()))\n",
" \n",
" # scale to feature_range\n",
" min, max = feature_range\n",
" x = x * (max - min) + min\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Dataset:\n",
" def __init__(self, train, test, val_frac=0.5, shuffle=True, scale_func=None):\n",
" split_idx = int(len(test['y'])*(1 - val_frac))\n",
" self.test_x, self.valid_x = test['X'][:,:,:,:split_idx], test['X'][:,:,:,split_idx:]\n",
" self.test_y, self.valid_y = test['y'][:split_idx], test['y'][split_idx:]\n",
" self.train_x, self.train_y = train['X'], train['y']\n",
" # The SVHN dataset comes with lots of labels, but for the purpose of this exercise,\n",
" # we will pretend that there are only 1000.\n",
" # We use this mask to say which labels we will allow ourselves to use.\n",
" self.label_mask = np.zeros_like(self.train_y)\n",
" self.label_mask[0:1000] = 1\n",
" \n",
" self.train_x = np.rollaxis(self.train_x, 3)\n",
" self.valid_x = np.rollaxis(self.valid_x, 3)\n",
" self.test_x = np.rollaxis(self.test_x, 3)\n",
" \n",
" if scale_func is None:\n",
" self.scaler = scale\n",
" else:\n",
" self.scaler = scale_func\n",
" self.train_x = self.scaler(self.train_x)\n",
" self.valid_x = self.scaler(self.valid_x)\n",
" self.test_x = self.scaler(self.test_x)\n",
" self.shuffle = shuffle\n",
" \n",
" def batches(self, batch_size, which_set=\"train\"):\n",
" x_name = which_set + \"_x\"\n",
" y_name = which_set + \"_y\"\n",
" \n",
" num_examples = len(getattr(dataset, y_name))\n",
" if self.shuffle:\n",
" idx = np.arange(num_examples)\n",
" np.random.shuffle(idx)\n",
" setattr(dataset, x_name, getattr(dataset, x_name)[idx])\n",
" setattr(dataset, y_name, getattr(dataset, y_name)[idx])\n",
" if which_set == \"train\":\n",
" dataset.label_mask = dataset.label_mask[idx]\n",
" \n",
" dataset_x = getattr(dataset, x_name)\n",
" dataset_y = getattr(dataset, y_name)\n",
" for ii in range(0, num_examples, batch_size):\n",
" x = dataset_x[ii:ii+batch_size]\n",
" y = dataset_y[ii:ii+batch_size]\n",
" \n",
" if which_set == \"train\":\n",
" # When we use the data for training, we need to include\n",
" # the label mask, so we can pretend we don't have access\n",
" # to some of the labels, as an exercise of our semi-supervised\n",
" # learning ability\n",
" yield x, y, self.label_mask[ii:ii+batch_size]\n",
" else:\n",
" yield x, y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": 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",
" y = tf.placeholder(tf.int32, (None), name='y')\n",
" label_mask = tf.placeholder(tf.int32, (None), name='label_mask')\n",
" \n",
" return inputs_real, inputs_z, y, label_mask"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def generator(z, output_dim, reuse=False, alpha=0.2, training=True, size_mult=128):\n",
" with tf.variable_scope('generator', reuse=reuse):\n",
" # First fully connected layer\n",
" x1 = tf.layers.dense(z, 4 * 4 * size_mult * 4)\n",
" # Reshape it to start the convolutional stack\n",
" x1 = tf.reshape(x1, (-1, 4, 4, size_mult * 4))\n",
" x1 = tf.layers.batch_normalization(x1, training=training)\n",
" x1 = tf.maximum(alpha * x1, x1)\n",
" \n",
" x2 = tf.layers.conv2d_transpose(x1, size_mult * 2, 5, strides=2, padding='same')\n",
" x2 = tf.layers.batch_normalization(x2, training=training)\n",
" x2 = tf.maximum(alpha * x2, x2)\n",
" \n",
" x3 = tf.layers.conv2d_transpose(x2, size_mult, 5, strides=2, padding='same')\n",
" x3 = tf.layers.batch_normalization(x3, training=training)\n",
" x3 = tf.maximum(alpha * x3, x3)\n",
" \n",
" # Output layer\n",
" logits = tf.layers.conv2d_transpose(x3, output_dim, 5, strides=2, padding='same')\n",
" \n",
" out = tf.tanh(logits)\n",
" \n",
" return out"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def discriminator(x, reuse=False, alpha=0.2, drop_rate=0., num_classes=10, size_mult=64):\n",
" with tf.variable_scope('discriminator', reuse=reuse):\n",
" x = tf.layers.dropout(x, rate=drop_rate/2.5)\n",
" \n",
" # Input layer is 32x32x3\n",
" x1 = tf.layers.conv2d(x, size_mult, 3, strides=2, padding='same')\n",
" relu1 = tf.maximum(alpha * x1, x1)\n",
" relu1 = tf.layers.dropout(relu1, rate=drop_rate)\n",
" \n",
" x2 = tf.layers.conv2d(relu1, size_mult, 3, strides=2, padding='same')\n",
" bn2 = tf.layers.batch_normalization(x2, training=True)\n",
" relu2 = tf.maximum(alpha * x2, x2)\n",
" \n",
" \n",
" x3 = tf.layers.conv2d(relu2, size_mult, 3, strides=2, padding='same')\n",
" bn3 = tf.layers.batch_normalization(x3, training=True)\n",
" relu3 = tf.maximum(alpha * bn3, bn3)\n",
" relu3 = tf.layers.dropout(relu3, rate=drop_rate)\n",
" \n",
" x4 = tf.layers.conv2d(relu3, 2 * size_mult, 3, strides=1, padding='same')\n",
" bn4 = tf.layers.batch_normalization(x4, training=True)\n",
" relu4 = tf.maximum(alpha * bn4, bn4)\n",
" \n",
" x5 = tf.layers.conv2d(relu4, 2 * size_mult, 3, strides=1, padding='same')\n",
" bn5 = tf.layers.batch_normalization(x5, training=True)\n",
" relu5 = tf.maximum(alpha * bn5, bn5)\n",
" \n",
" x6 = tf.layers.conv2d(relu5, 2 * size_mult, 3, strides=2, padding='same')\n",
" bn6 = tf.layers.batch_normalization(x6, training=True)\n",
" relu6 = tf.maximum(alpha * bn6, bn6)\n",
" relu6 = tf.layers.dropout(relu6, rate=drop_rate)\n",
" \n",
" x7 = tf.layers.conv2d(relu5, 2 * size_mult, 3, strides=1, padding='valid')\n",
" # Don't use bn on this layer, because bn would set the mean of each feature\n",
" # to the bn mu parameter.\n",
" # This layer is used for the feature matching loss, which only works if\n",
" # the means can be different when the discriminator is run on the data than\n",
" # when the discriminator is run on the generator samples.\n",
" relu7 = tf.maximum(alpha * x7, x7)\n",
" \n",
" # Flatten it by global average pooling\n",
" features = raise NotImplementedError()\n",
" \n",
" # Set class_logits to be the inputs to a softmax distribution over the different classes\n",
" raise NotImplementedError()\n",
" \n",
" \n",
" # Set gan_logits such that P(input is real | input) = sigmoid(gan_logits).\n",
" # Keep in mind that class_logits gives you the probability distribution over all the real\n",
" # classes and the fake class. You need to work out how to transform this multiclass softmax\n",
" # distribution into a binary real-vs-fake decision that can be described with a sigmoid.\n",
" # Numerical stability is very important.\n",
" # You'll probably need to use this numerical stability trick:\n",
" # log sum_i exp a_i = m + log sum_i exp(a_i - m).\n",
" # This is numerically stable when m = max_i a_i.\n",
" # (It helps to think about what goes wrong when...\n",
" # 1. One value of a_i is very large\n",
" # 2. All the values of a_i are very negative\n",
" # This trick and this value of m fix both those cases, but the naive implementation and\n",
" # other values of m encounter various problems)\n",
" \n",
" raise NotImplementedError()\n",
" \n",
" return out, class_logits, gan_logits, features"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def model_loss(input_real, input_z, output_dim, y, num_classes, label_mask, alpha=0.2, drop_rate=0.):\n",
" \"\"\"\n",
" Get the loss for the discriminator and generator\n",
" :param input_real: Images from the real dataset\n",
" :param input_z: Z input\n",
" :param output_dim: The number of channels in the output image\n",
" :param y: Integer class labels\n",
" :param num_classes: The number of classes\n",
" :param alpha: The slope of the left half of leaky ReLU activation\n",
" :param drop_rate: The probability of dropping a hidden unit\n",
" :return: A tuple of (discriminator loss, generator loss)\n",
" \"\"\"\n",
" \n",
" \n",
" # These numbers multiply the size of each layer of the generator and the discriminator,\n",
" # respectively. You can reduce them to run your code faster for debugging purposes.\n",
" g_size_mult = 32\n",
" d_size_mult = 64\n",
" \n",
" # Here we run the generator and the discriminator\n",
" g_model = generator(input_z, output_dim, alpha=alpha, size_mult=g_size_mult)\n",
" d_on_data = discriminator(input_real, alpha=alpha, drop_rate=drop_rate, size_mult=d_size_mult)\n",
" d_model_real, class_logits_on_data, gan_logits_on_data, data_features = d_on_data\n",
" d_on_samples = discriminator(g_model, reuse=True, alpha=alpha, drop_rate=drop_rate, size_mult=d_size_mult)\n",
" d_model_fake, class_logits_on_samples, gan_logits_on_samples, sample_features = d_on_samples\n",
" \n",
" \n",
" # Here we compute `d_loss`, the loss for the discriminator.\n",
" # This should combine two different losses:\n",
" # 1. The loss for the GAN problem, where we minimize the cross-entropy for the binary\n",
" # real-vs-fake classification problem.\n",
" # 2. The loss for the SVHN digit classification problem, where we minimize the cross-entropy\n",
" # for the multi-class softmax. For this one we use the labels. Don't forget to ignore\n",
" # use `label_mask` to ignore the examples that we are pretending are unlabeled for the\n",
" # semi-supervised learning problem.\n",
" raise NotImplementedError()\n",
" \n",
" # Here we set `g_loss` to the \"feature matching\" loss invented by Tim Salimans at OpenAI.\n",
" # This loss consists of minimizing the absolute difference between the expected features\n",
" # on the data and the expected features on the generated samples.\n",
" # This loss works better for semi-supervised learning than the tradition GAN losses.\n",
" raise NotImplementedError()\n",
"\n",
" pred_class = tf.cast(tf.argmax(class_logits_on_data, 1), tf.int32)\n",
" eq = tf.equal(tf.squeeze(y), pred_class)\n",
" correct = tf.reduce_sum(tf.to_float(eq))\n",
" masked_correct = tf.reduce_sum(label_mask * tf.to_float(eq))\n",
" \n",
" return d_loss, g_loss, correct, masked_correct, g_model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def model_opt(d_loss, g_loss, learning_rate, beta1):\n",
" \"\"\"\n",
" Get optimization operations\n",
" :param d_loss: Discriminator loss Tensor\n",
" :param g_loss: Generator loss Tensor\n",
" :param learning_rate: Learning Rate Placeholder\n",
" :param beta1: The exponential decay rate for the 1st moment in the optimizer\n",
" :return: A tuple of (discriminator training operation, generator training operation)\n",
" \"\"\"\n",
" # Get weights and biases to update. Get them separately for the discriminator and the generator\n",
" raise NotImplementedError()\n",
"\n",
" # Minimize both players' costs simultaneously\n",
" raise NotImplementedError()\n",
" shrink_lr = tf.assign(learning_rate, learning_rate * 0.9)\n",
" \n",
" return d_train_opt, g_train_opt, shrink_lr"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class GAN:\n",
" \"\"\"\n",
" A GAN model.\n",
" :param real_size: The shape of the real data.\n",
" :param z_size: The number of entries in the z code vector.\n",
" :param learnin_rate: The learning rate to use for Adam.\n",
" :param num_classes: The number of classes to recognize.\n",
" :param alpha: The slope of the left half of the leaky ReLU activation\n",
" :param beta1: The beta1 parameter for Adam.\n",
" \"\"\"\n",
" def __init__(self, real_size, z_size, learning_rate, num_classes=10, alpha=0.2, beta1=0.5):\n",
" tf.reset_default_graph()\n",
" \n",
" self.learning_rate = tf.Variable(learning_rate, trainable=False)\n",
" inputs = model_inputs(real_size, z_size)\n",
" self.input_real, self.input_z, self.y, self.label_mask = inputs\n",
" self.drop_rate = tf.placeholder_with_default(.5, (), \"drop_rate\")\n",
" \n",
" loss_results = model_loss(self.input_real, self.input_z,\n",
" real_size[2], self.y, num_classes,\n",
" label_mask=self.label_mask,\n",
" alpha=0.2,\n",
" drop_rate=self.drop_rate)\n",
" self.d_loss, self.g_loss, self.correct, self.masked_correct, self.samples = loss_results\n",
" \n",
" self.d_opt, self.g_opt, self.shrink_lr = model_opt(self.d_loss, self.g_loss, self.learning_rate, beta1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def view_samples(epoch, samples, nrows, ncols, figsize=(5,5)):\n",
" fig, axes = plt.subplots(figsize=figsize, nrows=nrows, ncols=ncols, \n",
" sharey=True, sharex=True)\n",
" for ax, img in zip(axes.flatten(), samples[epoch]):\n",
" ax.axis('off')\n",
" img = ((img - img.min())*255 / (img.max() - img.min())).astype(np.uint8)\n",
" ax.set_adjustable('box-forced')\n",
" im = ax.imshow(img)\n",
" \n",
" plt.subplots_adjust(wspace=0, hspace=0)\n",
" return fig, axes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def train(net, dataset, epochs, batch_size, figsize=(5,5)):\n",
" \n",
" saver = tf.train.Saver()\n",
" sample_z = np.random.normal(0, 1, size=(50, z_size))\n",
"\n",
" samples, train_accuracies, test_accuracies = [], [], []\n",
" steps = 0\n",
"\n",
" with tf.Session() as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
" for e in range(epochs):\n",
" print(\"Epoch\",e)\n",
" \n",
" t1e = time.time()\n",
" num_examples = 0\n",
" num_correct = 0\n",
" for x, y, label_mask in dataset.batches(batch_size):\n",
" assert 'int' in str(y.dtype)\n",
" steps += 1\n",
" num_examples += label_mask.sum()\n",
"\n",
" # Sample random noise for G\n",
" batch_z = np.random.normal(0, 1, size=(batch_size, z_size))\n",
"\n",
" # Run optimizers\n",
" t1 = time.time()\n",
" _, _, correct = sess.run([net.d_opt, net.g_opt, net.masked_correct],\n",
" feed_dict={net.input_real: x, net.input_z: batch_z,\n",
" net.y : y, net.label_mask : label_mask})\n",
" t2 = time.time()\n",
" num_correct += correct\n",
"\n",
" sess.run([net.shrink_lr])\n",
" \n",
" \n",
" train_accuracy = num_correct / float(num_examples)\n",
" \n",
" print(\"\\t\\tClassifier train accuracy: \", train_accuracy)\n",
" \n",
" num_examples = 0\n",
" num_correct = 0\n",
" for x, y in dataset.batches(batch_size, which_set=\"test\"):\n",
" assert 'int' in str(y.dtype)\n",
" num_examples += x.shape[0]\n",
"\n",
" correct, = sess.run([net.correct], feed_dict={net.input_real: x,\n",
" net.y : y,\n",
" net.drop_rate: 0.})\n",
" num_correct += correct\n",
" \n",
" test_accuracy = num_correct / float(num_examples)\n",
" print(\"\\t\\tClassifier test accuracy\", test_accuracy)\n",
" print(\"\\t\\tStep time: \", t2 - t1)\n",
" t2e = time.time()\n",
" print(\"\\t\\tEpoch time: \", t2e - t1e)\n",
" \n",
" \n",
" gen_samples = sess.run(\n",
" net.samples,\n",
" feed_dict={net.input_z: sample_z})\n",
" samples.append(gen_samples)\n",
" _ = view_samples(-1, samples, 5, 10, figsize=figsize)\n",
" plt.show()\n",
" \n",
" \n",
" # Save history of accuracies to view after training\n",
" train_accuracies.append(train_accuracy)\n",
" test_accuracies.append(test_accuracy)\n",
" \n",
"\n",
" saver.save(sess, './checkpoints/generator.ckpt')\n",
"\n",
" with open('samples.pkl', 'wb') as f:\n",
" pkl.dump(samples, f)\n",
" \n",
" return train_accuracies, test_accuracies, samples"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"!mkdir checkpoints"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"real_size = (32,32,3)\n",
"z_size = 100\n",
"learning_rate = 0.0003\n",
"\n",
"net = GAN(real_size, z_size, learning_rate)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"scrolled": false
},
"outputs": [],
"source": [
"dataset = Dataset(trainset, testset)\n",
"\n",
"batch_size = 128\n",
"epochs = 25\n",
"train_accuracies, test_accuracies, samples = train(net,\n",
" dataset,\n",
" epochs,\n",
" batch_size,\n",
" figsize=(10,5))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fig, ax = plt.subplots()\n",
"plt.plot(train_accuracies, label='Train', alpha=0.5)\n",
"plt.plot(test_accuracies, label='Test', alpha=0.5)\n",
"plt.title(\"Accuracy\")\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When you run the fully implemented semi-supervised GAN, you should usually find that the test accuracy peaks at 69-71%. It should definitely stay above 68% fairly consistently throughout the last several epochs of training.\n",
"\n",
"This is a little bit better than a [NIPS 2014 paper](https://arxiv.org/pdf/1406.5298.pdf) that got 64% accuracy on 1000-label SVHN with variational methods. However, we still have lost something by not using all the labels. If you re-run with all the labels included, you should obtain over 80% accuracy using this architecture (and other architectures that take longer to run can do much better)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"_ = view_samples(-1, samples, 5, 10, figsize=(10,5))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"!mkdir images"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for ii in range(len(samples)):\n",
" fig, ax = view_samples(ii, samples, 5, 10, figsize=(10,5))\n",
" fig.savefig('images/samples_{:03d}.png'.format(ii))\n",
" plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Congratulations! You now know how to train a semi-supervised GAN. This exercise is stripped down to make it run faster and to make it simpler to implement. In the original work by Tim Salimans at OpenAI, a GAN using [more tricks and more runtime](https://arxiv.org/pdf/1606.03498.pdf) reaches over 94% accuracy using only 1,000 labeled examples."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
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