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CodeParrot 116

bioinformatica-corso/lezioni

laboratorio/lezione3-07ott21/esercizio1-soluzione.ipynb

3

18940

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Esercizio 1" ] }, { "cell_type": "raw", "metadata": {}, "source": [ " 2009\t2010\t2011\t2012\t2013\t2014\t2015\n", " Gennaio\t\t75\t\t63\t\t65\t\t50\t\t77\t\t66\t\t69\n", " Febbraio\t64\t\t65\t\t65\t\t67\t\t50\t\t54\t\t58\n", " Marzo\t\t81\t\t77\t\t73\t\t80\t\t83\t\t89\t\t100\n", " Aprile\t\t89\t\t90\t\t85\t\t90\t\t90\t\t84\t\t90\n", " Maggio\t\t120\t\t129\t\t113\t\t120\t\t135\t\t117\t\t130\n", " Giugno\t\t113\t\t99\t\t116\t\t114\t\t111\t\t119\t\t100\n", " Luglio\t\t111\t\t105\t\t98\t\t112\t\t113\t\t102\t\t100\n", " Agosto\t\t129\t\t131\t\t120\t\t111\t\t141\t\t130\t\t126\n", " Settembre\t90\t\t85\t\t101\t\t88\t\t89\t\t94\t\t91\n", " Ottobre\t\t109\t\t122\t\t103\t\t119\t\t98\t\t101\t\t107\n", " Novembre\t111\t\t121\t\t101\t\t104\t\t121\t\t115\t\t104\n", " Dicembre\t56\t\t67\t\t44\t\t58\t\t61\t\t64\t\t58" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si consideri il dataset delle precipitazioni mensili (mm) nei sette anni dal 2009 al 2015, che ha il seguente formato: 13 record di campi separati da tabulazione di cui il primo è il record di intestazione degli anni (composto da 7 campi) e gli altri 12 sono i record delle piogge mensili (un record per ciascun mese) composti da 8 campi di cui il primo è il nome del mese e i rimanenti 7 sono le piogge mensili lungo gli anni.\n", "\n", "Si richiede di predisporre un notebook che permetta di calcolare:\n", "\n", "- le precipitazioni medie mensili \n", "- le precipitazioni totali annue \n", "- per ognuno degli anni considerati, il numero di mesi con pioggia oltre la soglia S\n", "\n", "---\n", "\n", "Parametri di input:\n", "- dataset delle precipitazioni\n", "- soglia S\n", "\n", "---\n", "\n", "Requisiti:\n", "- il notebook deve funzionare anche per un dataset che contiene le rilevazioni per un numero di anni diverso da 7\n", "\n", "- definire la funzione `compute_mean()` che prenda in input una lista di numeri e produca in output il valore medio\n", "\n", "- definire la funzione `count_elements_greater_than()` che prenda in input una lista di numeri e un valore di soglia e produca in output il numero di valori che superano tale soglia\n", "\n", "---\n", "\n", "Come produrre l'output?\n", "\n", "- produrre le piogge medie mensili in una lista di 12 tuple di dimensione 2 in cui il primo elemento (stringa) sono le prime tre lettere del nome del mese in maiuscolo e il secondo elemento (decimale) è il suo valore medio di pioggia.\n", "\n", "- produrre le piogge totali annue in una lista di N (sarà N=7 per questo dataset) tuple di dimensione 2 in cui il primo elemento (stringa) è l'anno e il secondo elemento (intero) è il suo valore totale di pioggia.\n", "\n", "- produrre per ogni anno il numero di mesi con pioggia oltre la soglia P in una lista di N tuple di dimensione 2 in cui il primo elemento (stringa) è l'anno e il secondo elemento (intero) il numero di mesi con almeno S mm di pioggia\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Soluzione" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 1) Importazione del modulo `numpy`" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2) Definizione della funzione `compute_mean()`" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def compute_mean(list_of_numbers):\n", " return float(sum(list_of_numbers))/len(list_of_numbers)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3) Definizione della funzione `count_elements_greater_than()`" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def count_elements_greater_than(list_of_numbers, threshold):\n", " bool_list = [number >= threshold for number in list_of_numbers]\n", " return bool_list.count(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 4) Definizione dei parametri di input" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "input_file_name = './input-precipitazioni.txt'\n", "threshold = 100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 5) Lettura del dataset nella lista delle sue righe" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['\\t\\t\\t2009\\t2010\\t2011\\t2012\\t2013\\t2014\\t2015\\n',\n", " 'Gennaio\\t\\t75\\t\\t63\\t\\t65\\t\\t50\\t\\t77\\t\\t66\\t\\t69\\n',\n", " 'Febbraio\\t64\\t\\t65\\t\\t65\\t\\t67\\t\\t50\\t\\t54\\t\\t58\\n',\n", " 'Marzo\\t\\t81\\t\\t77\\t\\t73\\t\\t80\\t\\t83\\t\\t89\\t\\t100\\n',\n", " 'Aprile\\t\\t89\\t\\t90\\t\\t85\\t\\t90\\t\\t90\\t\\t84\\t\\t90\\n',\n", " 'Maggio\\t\\t120\\t\\t129\\t\\t113\\t\\t120\\t\\t135\\t\\t117\\t\\t130\\n',\n", " 'Giugno\\t\\t113\\t\\t99\\t\\t116\\t\\t114\\t\\t111\\t\\t119\\t\\t100\\n',\n", " 'Luglio\\t\\t111\\t\\t105\\t\\t98\\t\\t112\\t\\t113\\t\\t102\\t\\t100\\n',\n", " 'Agosto\\t\\t129\\t\\t131\\t\\t120\\t\\t111\\t\\t141\\t\\t130\\t\\t126\\n',\n", " 'Settembre\\t90\\t\\t85\\t\\t101\\t\\t88\\t\\t89\\t\\t94\\t\\t91\\n',\n", " 'Ottobre\\t\\t109\\t\\t122\\t\\t103\\t\\t119\\t\\t98\\t\\t101\\t\\t107\\n',\n", " 'Novembre\\t111\\t\\t121\\t\\t101\\t\\t104\\t\\t121\\t\\t115\\t\\t104\\n',\n", " 'Dicembre\\t56\\t\\t67\\t\\t44\\t\\t58\\t\\t61\\t\\t64\\t\\t58']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with open(input_file_name, 'r') as input_file:\n", " file_rows = input_file.readlines()\n", "\n", "file_rows" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 6) Estrazione della lista degli anni" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['2009', '2010', '2011', '2012', '2013', '2014', '2015']" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "years = file_rows.pop(0).rstrip().split()\n", "years" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTA BENE**: rimuovere la riga di intestazione degli anni dalla lista `file_rows`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Gennaio\\t\\t75\\t\\t63\\t\\t65\\t\\t50\\t\\t77\\t\\t66\\t\\t69\\n',\n", " 'Febbraio\\t64\\t\\t65\\t\\t65\\t\\t67\\t\\t50\\t\\t54\\t\\t58\\n',\n", " 'Marzo\\t\\t81\\t\\t77\\t\\t73\\t\\t80\\t\\t83\\t\\t89\\t\\t100\\n',\n", " 'Aprile\\t\\t89\\t\\t90\\t\\t85\\t\\t90\\t\\t90\\t\\t84\\t\\t90\\n',\n", " 'Maggio\\t\\t120\\t\\t129\\t\\t113\\t\\t120\\t\\t135\\t\\t117\\t\\t130\\n',\n", " 'Giugno\\t\\t113\\t\\t99\\t\\t116\\t\\t114\\t\\t111\\t\\t119\\t\\t100\\n',\n", " 'Luglio\\t\\t111\\t\\t105\\t\\t98\\t\\t112\\t\\t113\\t\\t102\\t\\t100\\n',\n", " 'Agosto\\t\\t129\\t\\t131\\t\\t120\\t\\t111\\t\\t141\\t\\t130\\t\\t126\\n',\n", " 'Settembre\\t90\\t\\t85\\t\\t101\\t\\t88\\t\\t89\\t\\t94\\t\\t91\\n',\n", " 'Ottobre\\t\\t109\\t\\t122\\t\\t103\\t\\t119\\t\\t98\\t\\t101\\t\\t107\\n',\n", " 'Novembre\\t111\\t\\t121\\t\\t101\\t\\t104\\t\\t121\\t\\t115\\t\\t104\\n',\n", " 'Dicembre\\t56\\t\\t67\\t\\t44\\t\\t58\\t\\t61\\t\\t64\\t\\t58']" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file_rows" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 7) Estrazione della lista dei mesi" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Gennaio',\n", " 'Febbraio',\n", " 'Marzo',\n", " 'Aprile',\n", " 'Maggio',\n", " 'Giugno',\n", " 'Luglio',\n", " 'Agosto',\n", " 'Settembre',\n", " 'Ottobre',\n", " 'Novembre',\n", " 'Dicembre']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "months = [row.rstrip().split()[0] for row in file_rows]\n", "months" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 8) Costruzione della matrice dei valori (interi) di pioggia" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "a) Ottenere dalla lista delle righe del dataset la lista delle liste delle piogge mensili i oggetti di tipo intero." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[75, 63, 65, 50, 77, 66, 69],\n", " [64, 65, 65, 67, 50, 54, 58],\n", " [81, 77, 73, 80, 83, 89, 100],\n", " [89, 90, 85, 90, 90, 84, 90],\n", " [120, 129, 113, 120, 135, 117, 130],\n", " [113, 99, 116, 114, 111, 119, 100],\n", " [111, 105, 98, 112, 113, 102, 100],\n", " [129, 131, 120, 111, 141, 130, 126],\n", " [90, 85, 101, 88, 89, 94, 91],\n", " [109, 122, 103, 119, 98, 101, 107],\n", " [111, 121, 101, 104, 121, 115, 104],\n", " [56, 67, 44, 58, 61, 64, 58]]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rains_per_month = [list(map(int, row.rstrip().split()[1:])) for row in file_rows]\n", "rains_per_month" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "b) Convertire la lista in matrice." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 75, 63, 65, 50, 77, 66, 69],\n", " [ 64, 65, 65, 67, 50, 54, 58],\n", " [ 81, 77, 73, 80, 83, 89, 100],\n", " [ 89, 90, 85, 90, 90, 84, 90],\n", " [120, 129, 113, 120, 135, 117, 130],\n", " [113, 99, 116, 114, 111, 119, 100],\n", " [111, 105, 98, 112, 113, 102, 100],\n", " [129, 131, 120, 111, 141, 130, 126],\n", " [ 90, 85, 101, 88, 89, 94, 91],\n", " [109, 122, 103, 119, 98, 101, 107],\n", " [111, 121, 101, 104, 121, 115, 104],\n", " [ 56, 67, 44, 58, 61, 64, 58]])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rains_per_month = np.array(rains_per_month)\n", "rains_per_month" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTA BENE**: ogni riga della matrice contiene tutte le piogge annue di un determinato mese." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 9) Calcolo della trasposta della matrice dei valori (interi) di pioggia" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 75, 64, 81, 89, 120, 113, 111, 129, 90, 109, 111, 56],\n", " [ 63, 65, 77, 90, 129, 99, 105, 131, 85, 122, 121, 67],\n", " [ 65, 65, 73, 85, 113, 116, 98, 120, 101, 103, 101, 44],\n", " [ 50, 67, 80, 90, 120, 114, 112, 111, 88, 119, 104, 58],\n", " [ 77, 50, 83, 90, 135, 111, 113, 141, 89, 98, 121, 61],\n", " [ 66, 54, 89, 84, 117, 119, 102, 130, 94, 101, 115, 64],\n", " [ 69, 58, 100, 90, 130, 100, 100, 126, 91, 107, 104, 58]])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rains_per_year = rains_per_month.transpose()\n", "rains_per_year" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTA BENE**: ogni riga della matrice trasposta contiene tutte le piogge mensili di un determinato anno." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 10) Output delle precipitazioni medie mensili" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "a) Calcolo della lista delle precipitazioni medie mensili." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[66.42857142857143,\n", " 60.42857142857143,\n", " 83.28571428571429,\n", " 88.28571428571429,\n", " 123.42857142857143,\n", " 110.28571428571429,\n", " 105.85714285714286,\n", " 126.85714285714286,\n", " 91.14285714285714,\n", " 108.42857142857143,\n", " 111.0,\n", " 58.285714285714285]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "monthly_averages = [compute_mean(rain_list) for rain_list in rains_per_month]\n", "monthly_averages" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTA BENE**: il valore i-esimo nella lista è la media relativa al mese i-esimo.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "b) Costruzione della **lista di output** delle 12 tuple di dimensione 2 in cui ogni tupla contiene le prime tre lettere del nome del mese in maiuscolo come primo elemento e la media mensile come secondo elemento." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('GEN', 66.42857142857143),\n", " ('FEB', 60.42857142857143),\n", " ('MAR', 83.28571428571429),\n", " ('APR', 88.28571428571429),\n", " ('MAG', 123.42857142857143),\n", " ('GIU', 110.28571428571429),\n", " ('LUG', 105.85714285714286),\n", " ('AGO', 126.85714285714286),\n", " ('SET', 91.14285714285714),\n", " ('OTT', 108.42857142857143),\n", " ('NOV', 111.0),\n", " ('DIC', 58.285714285714285)]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "months = [month[:3].upper() for month in months]\n", "monthly_output = list(zip(months, monthly_averages))\n", "monthly_output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 11) Output delle precipitazioni totali annue" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "a) Calcolo della lista delle precipitazioni totali annue." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1148, 1154, 1084, 1113, 1169, 1135, 1133]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_total = [sum(rain_list) for rain_list in rains_per_year]\n", "yearly_total" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "b) Costruzione della **lista di output** delle 12 tuple di dimensione 2 in cui ogni tupla contiene il nome dell'anno come primo elemento e la precipitazione totale come secondo elemento." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('2009', 1148),\n", " ('2010', 1154),\n", " ('2011', 1084),\n", " ('2012', 1113),\n", " ('2013', 1169),\n", " ('2014', 1135),\n", " ('2015', 1133)]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_output1 = list(zip(years, yearly_total))\n", "yearly_output1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 12) Output del numero annuo di mesi con almeno `threshold` mm di pioggia" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "a) Calcolo della lista del numero annuo di mesi con almeno `threshold` mm di pioggia." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[6, 5, 6, 6, 5, 6, 7]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_count = [count_elements_greater_than(rain_list, threshold) for rain_list in rains_per_year]\n", "yearly_count" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "b) Costruzione della **lista di output** delle N=7 tuple di dimensione 2 in cui ogni tupla contiene il nome dell'anno come primo elemento e il numero di mesi con almeno `threshold` mm di pioggia come secondo elemento." ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('2009', 6),\n", " ('2010', 5),\n", " ('2011', 6),\n", " ('2012', 6),\n", " ('2013', 5),\n", " ('2014', 6),\n", " ('2015', 7)]" ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_output2 = list(zip(years, yearly_count))\n", "yearly_output2" ] } ], "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 }

cc0-1.0

hannorein/rebound

ipython_examples/Starman.ipynb

1

253485

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Starman" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook integrates the orbit of Elon Musk's Tesla and Starman." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import rebound\n", "import numpy as np\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start by querying NASA Horizons for the Solar System planets around the time of the orbit injection. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Searching NASA Horizons for 'Sun'... Found: Sun (10).\n", "Searching NASA Horizons for 'Mercury'... Found: Mercury Barycenter (199).\n", "Searching NASA Horizons for 'Venus'... Found: Venus Barycenter (299).\n", "Searching NASA Horizons for 'Earth'... Found: Earth-Moon Barycenter (3).\n", "Searching NASA Horizons for 'Mars'... Found: Mars Barycenter (4).\n", "Searching NASA Horizons for 'Jupiter'... Found: Jupiter Barycenter (5).\n", "Searching NASA Horizons for 'Saturn'... Found: Saturn Barycenter (6).\n", "Searching NASA Horizons for 'Uranus'... Found: Uranus Barycenter (7).\n", "Searching NASA Horizons for 'Neptune'... Found: Neptune Barycenter (8).\n" ] } ], "source": [ "sim = rebound.Simulation()\n", "sim.add([\"Sun\",\"Mercury\",\"Venus\",\"Earth\",\"Mars\",\"Jupiter\",\"Saturn\",\"Uranus\",\"Neptune\"],date=\"2018-02-10 00:00\")\n", "sim.save(\"ss.bin\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We stored the simulation to a binary file. This allows us to reload it quickly to play around with things without having to query NASA Horizons too often.\n", "\n", "Next up, we add the tesla to the simulation. As the orbital parameters are also in [NASA Horizons](https://ssd.jpl.nasa.gov/horizons_batch.cgi?batch=1&COMMAND=-143205&CENTER=%27500@10%27&MAKE_EPHEM=YES&TABLE_TYPE=ELEMENTS&START_TIME=2018-05-01&STOP_TIME=%272018-05-01+00:00:01%27&OUT_UNITS=AU-D&REF_PLANE=ECLIPTIC&REF_SYSTEM=J2000&TP_TYPE=ABSOLUTE&ELEM_LABELS=YES&CSV_FORMAT=NO&OBJ_DATA=YES), we can simply add it (and ignore the fact that the particle is set to no mass):" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Searching NASA Horizons for 'SpaceX Roadster'... Found: SpaceX Roadster (spacecraft) (-1.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "rebound/rebound/horizons.py:140: RuntimeWarning: Warning: Mass cannot be retrieved from NASA HORIZONS. Set to 0.\n", " warnings.warn(\"Warning: Mass cannot be retrieved from NASA HORIZONS. Set to 0.\", RuntimeWarning)\n" ] } ], "source": [ "sim = rebound.Simulation(\"ss.bin\")\n", "sim.add(\"SpaceX Roadster\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's calculate the characteristic energy." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "c3 = 11.867790 (km^2/s^2)\n" ] } ], "source": [ "tesla = sim.particles[-1]\n", "earth = sim.particles[3]\n", "r=np.linalg.norm(np.array(tesla.xyz) - np.array(earth.xyz))\n", "v=np.linalg.norm(np.array(tesla.vxyz) - np.array(earth.vxyz))\n", "energy = 0.5*v*v-earth.m/r\n", "c3 = 2.*energy*887.40652 # from units where G=1, length=1AU to km and s\n", "print(\"c3 = %f (km^2/s^2)\" % c3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That seems about right! So let's look at the orbit. It starts at Earth's orbit, crosses that of Mars and then enters the asteroid belt." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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NmCYZ3wegzzRJlV6XSRgGM76P1hpXFycRSmuyWmMB6SBAaU0ByPo+lmGQ931yQYCrNf2myUcvvXTNxxu0a2Ig9A71WtwXAL+hlLpBa/3e0rFWgxg2AUcWvD8K7Kl2jdbaU0rNAOuAMy32HRqGYeA4Do7j1L64Q5Qt3YWTBt/3l00Mlk4olj4Acrkcrpvh8cf/Ddc9sqifXC7HXXfdRSwWWza5mJ6eWtBHcYLi+z4Fz8X1fdzS++x0DjMwMA2TjWMbmJqdJpJ0sOM2jm1jWQaWZXPXt++aF+0yU1NTbPiJn2DmbW9jZybN0WiMHfk8xx2HHZ7HSctiSxAwaZr0A4FhYCtF0jBYB4waBlP5PLFIhODCC2Fujsj0NIXpaQquC6ZJPh5H2TZTySQqEiEVieAaBkmzaBsORqJkgWHTRClFn2Hw8O/8DgfuuAM/nwfgyT/5cw5FIlz/8pdz6/veh+15xAoFgkIBJ5/HKBSIF1yCwOC+++5j/8GDXH7FFTzvppuIDgxgmCYWYCmFCRhBgKGKNlRQ+m/tBR5eUMAPXPygQMHN42uXoHQ80B6eVyDv5bjrG3fx+MNP0LdrM89+xpVo5RNkXbzAJdCamSdmKMzl0J5NEMngqwDfi5A9PYmPwg8G0LbGSDgM3bAO14ygjDhaKTzDxFNRtGEzqxw8wyJQNilMHMMmU/IYJEybM0GArRT9pomnNVHDIKIUg0qRNE18wyBeWpqKmyaGUsQMg7hpEjNNBgwD13UxS//25cdaY5klvfY+olCDeoV7Gngh8CGl1P8AP962ETWBUupW4FaA0Q1b+OoPDhFxDKIRg6hjEosa8+8tw8RQRWFQKMzye2WuyS85FL/opmmGuo3IcRJ84QtfWZQcJJlM8rM/+7MNZfW6f2aGHEXXqaEU2veLrvEgICgU0BpypzO4k3ncwAevOOH4wh1fq9he8jP38gvHHyF7IoO1ySJ7LIOx0SI3kcUcNcmezWOOmORPZ1FjJvnTRRHrX2ejpwP6BzX5yTzKBNWnyM4UYMAkSHlktY8R8UjlwLFdCq4ihs86y6SgLSwbMr6NqSwKMQMjb3HWMBg0B3F0jGxQFG4CsFUC+8AGPvWT/062zyKWNwCTIGZgFSyCuOLub3yYyZmD+H4e04ww/Hsf4flP/zUc38QdtjHzRnHNP25ipkH12VgZjRWziE6BF1E4CRsjBVbCwpjV2EMOalqhlEPUjvDXX3o3RyYfpuDlcKwoW4cu4+0v/jMs20JP+dh9JjrQDO6IYaAIThWw1zsYShHfGWXo/8QBD00BpVwiMU2gPVRQQOkC2s9DkEfpAgQZCNIYQODOoLQCfxrDCzBcoxQT0Y8KkigVw9AxTLMfx96IaUYIguDcEovrLorrCIIAT2ueXBLvsTBeYOGjHJtS7ThQ8/pabdTzvt4YGUFYSL3CrXRxkfHtSqk3A98Chlrs+xiwZcH7zaVjla45qpSygAGKQWqL0FrfDtwOMLBlg/7Dr/4Fnh/geQFeEOD5Pp4f4Ac+diQobusyApThl56Lrw0TLMPEMg1Mw8AyDWyr+L78cErvbcvEtoziwzSwzOIEoPww1ZL3hlnXuYXnI1YEL/Dm3y+9buG1rZwrRzAvnMQsu37JBKecHGRpAYxGk4Nc3tdXdIuW1lHL6626/AwEo+fWUoNilA4v+s5LePDBHyxr71W/9Gre9J63LP77qPC6vPaqSyG/WpUWhdFognNGjA5KFo2et2gVxcf8Gv3cLMGpk3gTE/inThEcP4Z39gy+m8XPZglOnuDrmyO89wmfqGXh+j6/8OxdPH3XBF4shR+NEkQdcqZFRkHWtNj38ONMpw7g+y4Avp9navpJTgz+D0MXbiaXzWIUXKZNiyiaM4k8BBplBZzJ5UkOKFQQkMp4xA1FJKMx+2NkJ2cJAo0RaLJHJzk+uY/AK7qsC16WQ9P38a3c73Pxth2YW4qTvaGhAU7mcpimiTFklpZdTKYzBqe+UlwSMU1jfnJYfBg4TjF4s/hQ2LaJacYwDIVlJTEMhWFswTDBMAKUKqB1Fsih9Qy+n6ZQmMWyrmd8/BXzbTdqSS8V+ErCvvRctdflR9ljtfB9PfcufL/w/mqCvvC1Li1HLDxW7bnSRKHWs9A71Cvcf1V+obX+B6XUA8DPtdj394GdSqkLKAr0jwGvX3LNZ4A3Ad8BbgG+utL6NsDOsU3c+at/WPGc1sVc0vk8FArnHuX3+bwmVwjI5nzyhYBcPig9l95nfQpu8XjBDcgXfPJuwFyhKP624zM4VDxu2QGWHWDaAaZVfG9axUdg+fOvsQOUFaANH6wAzOKzNgN8FB4+rnYJdHHC4Wv/3Ovg3Ouwzi29Zuk5Xxd/5E1lsv5t67lhzw1MHZxi5IIRtl6/lbd//u1VJwrr4uuYzk1XncDUO3kwlMEVr9pB/MNxMnOZ+f++if4Ez3zt1dx36Is1J0WtTHbmzxsGhrIw1o1hjGzAvGJ52+Ugob967WvpP3Unv/vWt/I7f/3XHJrVvP/Cp8HsbPGPb6pQTFCeSkE6zfv23YdXcBf9/fqux4u3X85v/sqvoHwPbSiIxUqBYQuj0TUF3yMbBGR8j7TvktMBGd8nG/hkfY+U6/Iff/oX/Pd3Fk9+PM9n+/Yt/ORPvh7f9/A8H9/35197nlt6Xzzm+wGu6xVd8PPXFsjnAzKZ4vkg8HFdnyAISucrPRdFLR5PMDs7h+9rPE8TBAqt7yYIvjMvlsCiScLo6Chzc3OLjhXjN4r/jcqvFz6SyeR87Ee9j6XtLGzbMIxF1y18v/RRnsws/m06J+q1JhDVnhe+Lk8K6r3HXxJvIHQ3dQm31vqvl7y/B/jJVjourVm/A7iToqf077XWDymlfg/Yq7X+DPB3wD8rpZ4EJimKe9MoVawRXX05WpWG0phLWetiisZ8vvhw3cUTg6UThPL5fGbx+WyFezyvWK7ScSASgeHh4v2OA7HSZ7Htc59r/hEpHo9Eql9j281tBdFanxP1H14+EVgo+H7gF23Y0utlE4EqE4SlE4xK5//hW//Af/3Vf/HovY+y8+qdvOqnX4WnPGbzs3VNWJaOs9q5lcZpKIOsl63ah0ZjKIPZp83yvBtfwPcGTvLiK29ibjrPT227r8LkJImh+jn4vTzWHzyFmzsn3lbE4pHoft5/z4dqTmzqmaBcfNEg0ViEXCY330ckFqH/4g0cULOYtonhGBjKxFD2fDtOHROrWuNYyetUa4/3Qov33CSi8qN8TVmcyu/L4lbpuoVxIAsf5yYefsU2F573PI9kMsnMzEzFcS1cviqL+dDQENlstqbol7ezll8vvX7h+6XXlx8Lz9u2TTQabfyHQOgYHd3HXYpS//ySY7+94HUO+NHVHlejKFUUV8sKv/pUJS9BtYlB+ZFKLT9W6Z6Fk4KysCcSxZzUSwV+sfgX3Z6OYy6bENg2RJ3ilmbTbP8e0R/9i+7+8yhPchqZEPiBj7vbZeIrE9x3z31kM1li8RhXXnclv/amX0MZquakptr5hceveNYVXHjFhex/YD+FXAEn6nDB5RcwevUoT04+WXPyVE8f1bw2K423PNmpKvDUnggsvN4xHfzAr+6FmZ+g1PbSOMpZ8Xyt+xdOTrTWEIAONAYGOii51AON0ovPlx/o4vvAC4ptLDxe8losnECUd6wA868Xnl/4Wugd1lzmtLVGbS9B85QnBYu8APmioFcSfdc959ldel+la7VebunbNvT1Ffu37eWTgpWelx4zjO5PHlHcn2xiNujFAfjmV7/Z9jzlr7vrdV2VCx0WT3ZWmiysNGFYdF+w5Lom2yyfK/iFurxCNScwDUy+ll5jKpOMl1l2z9J4lWWTB9PAsCpPNoTeYc3lKt+9e7feu3dvp4chUFw+qCTwrrv8eLVjS88tfL9wYrDwORYrWvsLBX/pBKCe980uJQhCJygnjGl2krNjeIfkKu8RxOIW2oZpFh/tWj4rTwyqCfvSyYDrQi5XeaJQ6X15KWGpmDtO0WtQnjhUepQnAQvvrzQxsCyZHAjhUM6iZqhS4JsY0WsWEW6hZ2n3xEDrc8sGSycDKz0ymZXPrzQ5WCju8XhR1BeKfLWJQq3zzdTSFgShOxHhFoQqLBTNdlGeHFQT94XnFr7O5ZbfU6md8mPhZymLfCSyXPCXin+1c9WukwmCILQfEW5B6CCrMTmA5csKZU+C550T/KXCXyhAOr3yBGLp+/LnKQv5wMBir0KYz7LEIJyviHALwnlAu5cVyvh+ZWFfOjlY+lzOf7DweKUJxcJjpnnOc7BQ0Ku9XnisnusXvhZPgtBNiHALghAa5QlCJNL+vqpNCGq9Lm95rHbN0veOU1yaqCb67TguEwVhJUS4BUHoScoitxpJv8qehGpCv/SxcDKRzVY/73lF8U6lFp9f+Plafdj2Oe/E0sfC40LvIP+5BEEQarCangSAIFh5YrDSY+lEQalzXoaFj6WTEaF3EOEWBEHoMhamHV4tfumXVq8voTVkJUUQBEEQeggRbkEQBEHoIUS4BUEQBKGHEOEWBEEQhB5ChFsQBEEQeggRbkEQBEHoIUS4BUEQBKGHEOEWBEEQhB6iI8KtlBpWSn1JKfVE6XmoynW+Umpf6fGZ1R6nIAiCIHQbnbK43wl8RWu9E/hK6X0lslrra0qPV6ze8ARBEAShO+mUcL8S+MfS638EXtWhcQiCIAhCT9Ep4R7TWp8ovT4JjFW5LqqU2quU+q5S6lXVGlNK3Vq6bu/p06fDHqsgCIIgdA1tKzKilPoyMF7h1G8tfKO11kopXaWZbVrrY0qpC4GvKqUe0FrvX3qR1vp24HaA3bt3V2tLEARBEHqetgm31vpF1c4ppU4ppTZorU8opTYAE1XaOFZ6PqCU+jpwLbBMuBeRycC+fcXyOkoVnxt51HuPUsWHIAiCIKwinSrr+RngTcAHSs+fXnpBKdI8o7XOK6VGgGcBH6zZsmXB8HCxoO3Sh+uC1pXP1XpUuq8RkTfNxicSzdy39FpBEARhTdEp4f4A8J9KqZ8CDgGvAVBK7QZ+Rmv9VuBS4K+VUgHFtfgPaK0frtmy48DWrW0b+CLqEfdakwHfX3yf5zU/kah0zVJhd5zlxytNDJo9Vu99S48LgiAIddER4dZanwVeWOH4XuCtpdd3AVeu8tAao9tFR+vqAl/peD3HtF4+uQijj26YPNRzTJZHBEHoMJ2yuIXVoLwO382TizKNTh7qmRD4fmUPRrN9lJdHlALbXvzvu1Twq72u97owXwuCsKYQ4Ra6g273XpQpC/pKQt/INc28bqS/8rJIM5OHpZ6GRiYi9fZX6R5BEFZEhFsQGqEcMNgrLF0uCXOSsNCb0UwfUPn4UiFfafJQ73Oj7TTTh3g3hFVChFsQ1jK9tFxSZiVvQj3nGvF+hNkXhDMxaHSysHDnycLnRtqQSUdPIcItCEJ3URajXvJsQG1xb2ZisPB1JQ9H2UOxcHdKpedax8oeEKEnEOEWBEEIg15bRlmICHdP0UP+M0EQBKEtiKu8pxDhFgRBEIQeQoRbEARBEHoIEW5BEARB6CFEuAVBEAShhxDhFgRBEIQeQoRbEARBEHqIrhdupVRUKfU9pdR9SqmHlFLv7fSYBEEQBKFT9EICljzwAq11SillA99SSt2htf5upwcmCIIgCKtN1wu31loDqdJbu/SQND+CIAjCeYnSPZDqTillAvcAFwEf0Vr/xpLztwK3lt5eH4/HV3mEgiAIvU0sFmP79u2VT5Z1IoQMa4VCMbV6LFb9mhOpEwBsSG6oeD6dTuP7Pv39/TX7e+yxx9i5cydGlUI7vvaZzc0yFBvC8+DoUaj2z9AQ09Ng25BINHX7Pffcc0ZrPVrpXE8Idxml1CDwKeDntdYPVrpm9+7deu/evas6LkEQhF5n9+7dVP3trEdt6+TgQZibgyuvrH7Nbd+4DcuweOf/eWfF89/5zndIp9O86EUvqtnfC1/4Qj75yU8yMDBQ8fx0bpovH/gyt1x2C3Nz8DM/Ax/7WD2fpAb/8z8wPAzPelZTtyul7tFa7650ruuD0xaitZ4Gvgbc2OGhCIIgnD+sci7zQAcoqvcZBEFVC3oplmXheV5d1zpOcY4SCqZZnOy0ga4XbqXUaMnSRikVA14MPNrRQQmCIAhNUS4BvhJaawxVXZ601qg6JxOmaTYk3K4bUrE0y2qbcHd9cBqwAfjH0jq3Afyn1vqzHR6TIAiC0CYCHawozO2yuJWCbduK4u04dd1SnTZa3F0v3Frr+4FrOz0OQRCE85pVjIfSrGxxG4ZRt8XdiHADnDoVonDn8y02Upmud5ULgiAIHSbkNe5ac4Baa9yNCLFt27iu28D1ReFuGdOEBsbZCCLcgiAIQtcR1hp3LYs70MGivkILUDufg9MEQRCE84tAByufD3GNW6Hwg3MCG6rFLcItCIIgdASlQlvjrsdQrhVVXmynPot78+bNBEH1iYBekogzNIu7jVHlItyCIAjCqlLXGneNqPJ6hfv06dMUGlBicZULgiAIQoPUiipvdI3bb0BAJThNEARBEBZQj97WiirXWte9xl1PApaFkwDHCWkXl1jcgiAIQscIcY27HmqtcTfiKrcsq3PbwUS4BUEQhPOBWmvc7XSVS3CaIAiCICyhlvHeH+knakVXuD+8XOVLK2SW85W3jGG0zUshwi0IgiB0FdO5abxghb3XSrUt5WmoUeWS8lQQBEHoCKu9jxtds6ynaZp19dcxV7mscQuCIAjnE2EFp9UVVb5gkiDBaYIgCIKwhHoSsKx8f3i5ytuWOU2EWxAEQegoq7wdrFNR5WJxC4IgCL1PiGU9613jDitzmuM4K+YqX369WNyCIAiC0BD1ZE6rV7iVUjVzlS/NnCbCLQiCIAgNUCtzWpj7uJcirnJBEARBWEKr1cHCzlW+kNBylUvmNEEQBKGjrGK+8lr7uMMMTmtr5jQRbkEQBKHXqSs4LcQiI6ZpNhxVHsoat2FAELRlsiPCLQiCIKwqtTQ3INwiI426ykOxuJVq2zq3CLcgCIJQHyFYj/V43LVe2VUeiURCFe6FfTkOJBJ1NV0bEW5BEAShY4S4l7sWtfZx5/P50FzlSzOn2TacPFnfOOvoXIRbEARB6G3q0dsw63E3s8Ydiqu82LkItyAIgtD71OMq7+Q+7lCC06BtW8JEuAVBEIT66MHtYLZt17S4l2ZOC83ibtOWMBFuQRAEoTYhrXHXux2s1vmesLhNExrou15EuAVBEIRVpaarPOQiI/F4fMW2Fl8fosVtWcW93CEjwi0IgiCsGmEEpxXbqd8DMDU1Vfe15eC0UFYFJDhNEARB6BirmfK0xj7udrrKQ82bIq5yQRAEYS2wmq7yeraDLZ0khFohTFzlgiAIQi/Tbfu4lyZggRAD1MTiFgRBENYCq7mPu9Fc5dD9NblFuAVBEITarOIad6CDFde4o9Fo3W01mjkNipHloVncItyCIAhCL1PXPm5WtqhzuRyGUZ981bXGrdq4xi3CLQiCIPQ6q+0qb9TiFuEWBEEQhAao5SqvlVltIY1uB4OQg9NEuAVBEISOENIadxiu8lCjyit8ptCyp4lwC4IgCGuBevR/NfdxLyW04DSpDiYIgiCcDwS6dtKSdq9xh7L9WqqDCYIgCB1ltVzldQSn1Us90eeVMqfJGrcgCILQ24RU1hNq6389wWmNuMqz2Wz1ttqZOU1c5YIgCEKvE0ZwWrGd+oU7aDBfeGjBaeIqFwRBEM4HwtzH3ex2sFCE27IkV7kgCILQQbok5Wmj+7hrWdxLJwGhbgc7H6uDKaW2KKW+ppR6WCn1kFLqFzs9JkEQhPOOkNa4w3CVRyKRuvtrZjtYt1cHs0JvMXw84Fe01j9QSvUB9yilvqS1frjTAxMEQRAaI4yo8nw+H2qu8qVEIpDPN3RLtc7PzzVurfUJrfUPSq/ngEeATZ0dlSAIgtAsrUaVN0IzmdMMAzKZUDo/P13lC1FKbQeuBe7u8FAEQRDOL1Y55WknM6eFWmTkfA5OU0olgU8Av6S1nl1y7lal1F6l1N7Tp093ZoCCIAhCXdRlcYe0HcwwDHzfrxrQptGYylx0TKqDhYBSyqYo2h/TWn9y6Xmt9e1a691a692jo6OrP0BBEAShLupd4w4rqtwwDJRSK97jBYutYhHuFlHFadXfAY9orf+00+MRBEEQWqPVIiON0qi7XIS7dZ4FvBF4gVJqX+nx0k4PShAE4bxiFde4axUZaWSNG1YW7kptdbtwd/12MK31tyCk8EJBEASh49TS/zDrcYNY3IIgCILQNGHs426USy65pKG0pyLcgiAIgrCA1awOBvDEE0+smPa0UlnP0HKVi3ALgiAIHaGL9nEX22nMVd5IhbDQhFuqgwmCIAhrgVb3cTeyHQxWrhBWrR63WNyCIAiCQDj7uOPxeEN9GobRkMUdanUwEW5BEASh16knqnwlV3k2mw01qrxt28EM4/xOeSoIgiB0kFXex92IMNdipTXuSm53cZULgiAIwgJqWtx1pDztmX3c53t1MEEQBKG3qUdvLxy8MFSLu1xopOqY2rUd7HyvDiYIgiB0mJBc5bWa2T+9P7SyntD8drCWP64EpwmCIAgdI0QLuBa1XOWNsmKu8grbwQyj+HFb1tzyv1nI7nIRbkEQBGFVaTVXOTSegKWRNW4IyV2uVFvWuUW4BUEQhPoIweruRK7yWmvclejmLWEi3IIgCEJtQnSV12VxV3GVN5o1DWpvB6tkvYe6JUwsbkEQBKFXqTtzWogThQ0bNjQUnAbdXSFMhFsQBEFYNeotMlL1XBOiPjEx0VBZT+juLWEi3IIgCMKqUkt32xFV3khZT+hui9sKtTVBOI/QWuP7GbR2AR+tfbQOAIVh2BhGBKUclLJCdfsJQkfRuqX17nr2ca8UVa61xjTNle8vdVBuo5ngtEQCCoWGbqmMCLcgrA5B4JHPH6VQOInrTlAoTOC6pwHI5Q4RBGl8P4NlDQKglAGYKGViWf247lmCoIDWBWx7nCCYwzQHsKwBbHsEyxrAccZxnDFsewzLGsYwxAEmdDkhTUDrKevZbFR5EPg88cQvMj7+VgYGrgFqBKdVccv7fkgebhFuQQgf38+SzT5BNrufbPZxcrlDFAqncJz1xGK7MM0kjrOeROIyLGsdltWPaSYxzThKrTzzh+IkwPdT+P4snjeD583iuqfJZp9gZuZbFAqnAIVtryMa3V56XEg0urmu9gWhlwijrOdKHizXPYXrniGXe2ReuJvdDibCLQhdgu9nSaX2kck8Tip1D/n8UaLRC4jHL6e//+mMjr6WSGQThmGH0p9hWBjGILY9uMKYMuRyh8jlDpHJPMbMzLfJ5w8Ti+0kHr+EePwyYrHtIuTCmmElj3stV/lKFAoT2PYorjsxf6zmGnc7t4OJcAtCc+RyR5mdvZu5ue+RzT5BPH4xfX172Ljx7cRiO0IT6WYxzTiJxKUkEpfOH/O8OTKZx8hmH+PMmU+Syx0kkbiSZPIakskrMc1EB0csnLe0uMYN7Q1O87wpHGcU1z07f2wli7vaREAsbkHoAPn8caan/5eZmW/gedMMDb2Y0dFXk0hchWlGOz28mlhWH/39u+nv3w2A606SSt3H7Ox3OHv289j2EP39TyeZvLYnPo+wBghpjbscoNaMxV0L309h2+vJ5w/OH2sm5allicUtCKuC7+eYnv46k5NfwPOm6O9/Ghs3vp1E4vJSAFnvYtvDDA09n6Gh5xMEeebm7mV29rucOvXP9PXtob//acTjl0gEu9ATrOTxXinlaa193J43hW0PUCic86IZhtFwxjVxlQtCm8lmD3L27GeYnv4aicQVrF//Y/T17cYw1uafuGFEGBh4OgMDT8fzUszN7WVi4t/Q2mNw8Hn09z8Ty0p2epiCUJFac8tABy24yqeJxy8hnz80f6zRetwQosVtWSLcglBGa00qdS8TE/9BLneIdetuZteu23Gc0U4PbVWxrCRDQ89jcPC5ZLP7mZ7+OjMzdxGP72Jo6IdwnJFOD1EQlrGixV3DVb6yV0lhmkMoFZk/0mg9bpA1bkEIFa01MzN3cerUP6N1ntHR1zA09KKOB5h1GqUU8fhFxOMX4XkzTE19mUOHbiORuILh4ZuIRjd1eohCr1NP9pQQmlkpOK12VPlJbHtoUVT5isFpVfZxd3N1MBFuoWfQWjM39z1OnPg7wGR8/Cfo73+GrOlWwLIGGB19NcPDL2Vq6uscO/YRYrELGRl5BY6zvtPDE85z6vnKNvu9LiZBGkTrc0LdrMXdrdXBRLiFniCVeoQTJ/4Kz5tmw4afYmDg2SLYdWCaMUZGbmJ4+AVMTX2Fw4c/QDJ5HSMjr8Sy+jo9POE8pqarvIkELFprXPc0ltVPEGTmjw8NDa2YJrVSexJVLghN4rqTHD/+V6RS+xgffwvDwz8kSUiawDAirFv3UgYHn8vk5Jc5ePC9DA/fyODg89ZsAJ/QvdQVnLbCRdXSAwdBrpQ+2ME0+wkCH8MwmZubI5fLVbxnVfZxi6tcOB8IAo8zZz7JqVP/zPDwy7jkkn/ANOOdHlbPY5oJRkdfycDA05mY+C9mZ7/N6OhrSSQu6fTQhF6gy9e4gyALpTVr308BPsUaAqqplKfZbEO3VMY0xVUurH2y2f0cPvxH2PYwO3d+hGh0a6eHtOZwnDE2bfo50ukHOHPm08zNfZ/R0VfL5EhYNVqJKq+G72ew7WIMRyy2o1S5z2mqrKdlicUtCDUJAo9Tp/6F06c/zqZNP8vw8EtlHbuNKKVIJq8iHr+YM2c+zaFD72f9+teSTF7V6aEJa5x2pTwNghxFKxvy+cOlMrtF13qj1cFsW8p6CsKKZLMHOXLkjzGMCJdc8vcS+byKGEaE9etfQzJ5HadPf4J0+gFGR2/BMCK1bxaEJqjpKm9yH3dxjXsdAIaRQGuv9Lrx6mChWtyhzADO0ds5IIU1wdmzX+CJJ97O8PCN7NjxxyLaHSIev4gtW34JgMOHP0gud7SzAxK6kxDWuFdqpryG3bzFXW4nDxStbNM0V1wbrzQRcByJKheEZfh+jqNH/x+p1H1cdNGHiMcv6vSQznsMI8LY2BuYm7uH48c/wvDwzQwMPFOWLIQiIRYZqUbZdd1MWU+t8xhGueCOMb+Xu5ngtFAtbhFuYS2Qz5/gyJH/h2UluOSSv5egqC6jr+96IpFtnDz59+TzBxkdfc15n5lOCJeVLO5m85QHQW5euCORLfNr3CsFp620HSy0BCwhT3zFVS6sOqnUfTz22E/T13c927b9toh2l+I4I2ze/EsEgcuxYx/Cdac7PSRhjVDL4m7WwxME5yzuQuE4ZVf5SsFp1QgtAYtSUGUPebOIcAurytmzn+fAgXeybdu7GRt7rbhguxzDcBgffxOJxNWcPPn3su4ttH0fdz0Wd/XfDY1pJkqvDcp7upsJTuvmIiMi3MKqoLXm+PG/58yZT7Nz518wMPD0Tg9JqBOlFMPDL2Jo6IUcP/5XpNOPdnpIQqcIcaJdVbipXou7Fr6fQqniCnAksnl+jXul4LRq6VWlHrdwXqN1wNGj/x9zcz9g584PYdvDnR6S0ATJ5NWYZh8nT36UdeteQX//DZ0ektCjrOgq1+G4yvP5o7Ag0K0Zi7tbhVssbqGtaO1z6ND7yGQeZdeuvxTR7nFisQvZtOkdTE//LzMz3+70cIQexbJWtribDU7TWs8Lt1LGvJXdTHWwbi4yIsIttA2tfQ4f/iCeN8tFF31IqlGtERxnjA0bfpLp6a8zPf2NTg9H6EFq5SlvliBIYxgOAI6zcf644zhYVnUHc7XqYENDTQ/lHCLcQq+gdcBTT/0OhcIJLrzwDzDNaO2bhJ7BtofZuPHtzMx8S8T7fCKkIiNQw+Ku4SqvXh0sj1JF4S5GlZcLjvhkG6wYYttw7FhDt1RGhFvoBbQOOHToNjxvkh07/mh+BiysLWx7iI0bf47Z2e8yM/OdTg9H6CFqRZWvFJy2cgIWb164S0eAGrnKV6Ospwi30M0Uo8f/mlzuMDt2/Inku17j2PYAGzb8FFNTX2Fu7t5OD0foEVYS7kAHLSRgyc//5hRzldcWbgBTmcuOdfMat0SVC6Fy6tS/MD39dS6++G8xzVinh9N1eEHAkXyeE4UCx/N5JlyXrO9z0nWZ9TyihsFp18XVmhHbZtrzsJRik+OQ15o+02RTJELMMBi1bTY4DhsjEUZtu2N74m17HRs2vJUTJ/4a00wQj+/qyDiE3qFdCVi0dvmHk1PctM4hCNLz/dSqDubr5cLazRZ31wu3UurvgZuBCa31FZ0ej1CdyckvMzHxr1xyyT9IIBpw1nV5IJXigXSahzMZ8kHAd2dnGXccrksmsZRizHHYFImwMx6n3zRJmiaWUtiGgQF4WuNqTSEISAcBc55HNgg4USjwcDqNUoq9c3NkfZ/dfX0MWBYXx+PsisW4LJEgbi63JNpBJDLO2NibOHXqn9iw4VYikfFV6VdYZUJa4241AUs1gsDlI8cn2BRJsG3B8Y5mTjNNaLDvWnS9cAP/AHwY+KcOj0NYgVTqfg4f/gN27foLHGes08PpCGddl2/NzPBIOs0XJieZ9DxuHBpi1HG4ed06Lo7F2BqN4lQJrGmFtO9zKJfjiWyWxzIZ7pmb4565ObZGo1yTTLK7r4/r+/pItFHIY7HtjIy8kpMn/4FNm35OJm/CirQSnFa5PZ+0D1op1JK/82bWuEMtMhJKQ+foeuHWWn9DKbW90+MQqlMonObw4Q+wffvvEo9f3OnhrBpaax7PZLhzaoq7ZmZ4MJ3mGQMDPH9wkNsvvpgdsRjmKrmvE6bJZYkElyUS88cKQcCjmQz3pVJ8/PRpfvfgQS6Jx3nB4CDPHBhgYyT8+INk8moKhdOcOvUvbNjw0xhG1//ECB2gHRZ3ELjkVAxLKdK+j+OMzfehlGrY4i57uLVuMWGcYZyXFrfQxQSBx4EDv87g4PMZHHx2p4ezKhzL5fjUmTN88swZhiyLKxIJfmHzZnb39S2yprXW+GkfP+0T5AO0X8riZCmMiIGZNDGiRtvWph3D4KpkkquSSd44Pk7O97k3leKumRn+4eRJRh2Hlw4P87zBQUad8CL/h4ZeiOdNcvbsZxgd/ZHQ2hXWDrXWuGulPK30nQmCAmmSRAyDfBCQz5+gHFW+YnWwKglflDpnddutFMYLzXRf0GSorXUIpdStwK0AW7du7fBozi+OHfszTLOPDRt+qtNDaStuEPCVqSn+6dQp3CDgwliMD1x4IdfFEhQO5cl8I8Pp4yfIPJQhfySPn/XJHcwR2RzBn/Mxogbxi+NkD2TRnsYassjtz2ENWShD4Yw7xK+MY/VbxHbEiF8cJ7o9ijLCE/WoafKMgQGeMTDA/92yhftSKb45M8ObHn2US+NxXjEywjP7+7FbdOUrpRgZeSXHjn2Eubkf0Nd3XUifQOg4q7CPO9BBk5NZl6y2KTySwd8EZdGG5ta44Vza05aEWyzuymitbwduB9i9e3c4f1VCTaamvs7U1Ne47LJ/RTVZFKDbmfM8PnbqFP82McGwZfFjI+t53gGHwpdTzN51hAfPugT5gMSlCZLXJxl87iCRrRGccQd7xMaMrbym7Od8vLMehZMFChMFsk9mmfzCJKf+5RTZ/Vn6rusjcXmCvj19JK9KYjjh/DsbSnFtXx/X9vXxto0b+cb0NF+fmuLPjh7llSMjvGLdOoZa+LUyjAhjY2/g+PG/wXE2SrCasIi2ucpxMM/4GBT/BssTgFqu8moThVCMZcs6/6LKhe6kUJjg4MHfZefOD2NZ/Z0eTuhMuS5/ffw4d8/NMYLF7xxax/a7PU5/4iCndsQYvmmYjT+zkb7r+7AGmv8amVETc5NJZNPy9WZvxiP9UJq5789x9E+PoiIKe53N0POHGHjuAFYynK9vxDB48fAwLx4e5qlslk+cPs079+9nVyLB69evZ0OTa+GOM8a6dTdz+vR/snHjz2IYrZgtwlpiReFuOjjNxTWSmF65qEgGShOAZnKVQ0hbwgzj/BNupdS/Ac8DRpRSR4Hf0Vr/XWdHdX6jteapp36bsbHXkUyurR16Kc/joydP8uFjx3h+JsHPf0aRvHOW5HUByR8eYcuvbsFetzoCZA1YDDxzgIFnDgDgTrnMfHOGqS9PceRPj9B3Qx8jrxih/+n9obnUL4jF+NWtW5l2XT5x5gxve/xxntXfz4+PjbEp2nja2mTyajKZR5ma+iLr1r0slDEKvU8tXW7G4tbaJe1DYEHG96nXVb5SJrZQtoSdj/u4tdav6/QYhMVMTPwHvj/Hhg1v7fRQQsPXmn8/dYrbnjrE7hM2H/5CnNEDHmNvGGPkd3dhD3TeWrSHbEZeMcLIK0bwZj0mvzjJmf8+w+E/PMzoj46y7hXrsPvDGeegbfNTGzbw2tFRPnf2LO948kleMDjIG8fGGGzAhX5uvfvPiccvIRbbEcr4hA6yGvu4m7S4MzgL9FpTtriVUk0VLwnFVX4+CrfQXWSzRzh79rNceOHvoyqkCexFvj87y688+iSbngx490c1uy9MsOFtG+i/vnuXAKx+i/W3rGf9LetJP5Rm4r8meOR1j7DuZetY/7r12EPhCHjSsnjt2BgvGR7mYxMT/PTjj3PLyAivHh3FqjOIzTRjjIz8MJOTX2LDhs2SBreXCXEHRLMpT7XWRCos3wSBi0eERBYspXCcTfPnDMOoWo97pTKiodTkLn9PguDc6xYR4RbqRmvNkSO/z9DQC4hGt9W+ocuZ8zze/dh+9j41xc3/6HPL1vVs+fstRLf0ViWzxOUJLrj8AvIn85z651MceOcB+q7vY+wNY5iJcCZXg7bNz23axCvXreOvT5zgzscf5x2bNnFdX31JVuLxnaTTDzI19RXWrXtpKGMSepdW1riVUrgV1FRrl0CDnYVcEJDPH54PmnUch6EmanSGmoRFhFvoBFNTX6JQOMnY2E90eigt8/WzU9x6zyNc802fD2fXc+mfbus5wV5KZDzC1l/bSuFUgeO3H+fhNz7MhjdvYN3N60JbA98cjfK+Cy7guzMzfPjoUa5IJHjrxo30r1DruMzw8Es4duzPSSavJhLZVPN6Ye2y4j7uJqPKtfaY1DbYiqhhAAFlV3kQBExMTKwwnuoWd6jZ0+r4ntTD2tzDI4SO56U4fPgP2bbtPT2dDSvr+/zi3Y/w1i/fz7u/keTvXn0N1/7xxT0v2gtxxhy2v2c7O/5wB2c/d5bH3/Y4mf2ZUPt4+sAAf7FrF3HT5G2PP85dMzM17zHNOENDL+Hs2TvROtx9rULv0Y6Up542sTzIBkFpTbsocYZhVF3jbntwWnEAoe7lFuEW6uLUqX9maOhFPZ1M47HZNG/41A944mtn+Gx+B2/8vSvpu2bt5tOO74yz6y93se4V6zjwGwc48dETBF54Px5R0+RnNm3it7Zu5R9OnODPjx4lWyMIJ5m8GqU0qZSUAO1JVqnISK3MaZXvK2Apg+ikJmoYpeW8UrbCJlKeQojCHXL2NBFuoSb5/HFOnfrnno4i/8/7j/G8r+7lqh9oPvHG3VzyE5tDzUrWrShDMfLyEXb95S7SD6U5+LsHyR/Ph9rHFckkf7ZzJ57W/MKTT/JUNlt9PEoxPHwTU1NfJQjCHYfQO6xkUDdbj1trj9O+SRBTZIOAbHb/fABtrcxpKwWnhaK3YnELq82xY3/O+vWvw3FGOz2Uhgm05l1feJi/+OoBPjq1md95/w3ENp1/dcKdUYcdf7SD5HVJHv/Zx5n82mSo7cdMk/+7ZQuvW7+e3zpwgC9PVm8/EtlILHYR09PfDHUMQu/QrgQsCUORKGgcpSiucZ9zla+Uq7waoVrcIW4J693FSmFVyGQeY2bm21x11R2dHkrDZFyPn/7v+3hkMs3Hn3UFF94w3OkhdRSlFOt/ZD3Jy5Mc/5vj5J7IseGnN4Ra5OQFQ0PsjMW47dAhnshm+ekNGypuGxscfCGnTv0j/f17pPzneciKHncNV4w2k9hJcdwzMbIeBa1hwQSg2X3coVrcIQq3WNzCipw8+TE2bvxZTDNR++Iu4uxsnhf8x93og3m+/sNPO+9FeyHxi+Nsf8/2ouv8vQfxc+Emh9gSjfKnF13EqUKBdz/1FLMVfvlsu594/GJmZr4dat9Cm1mFIiMazaNnH224vSDI4WqwSzsgI5HtaF0U7pqu8nbmKofQk7CIcAtVSacfZWbm6z1XmvHwyTQ/8ul7uHTG5p9+fg/961c/YtwLAmY9j4lCgaO5HAezWZ4qPQ7nchzP5ThTKDDneXghVw6qB2vAYsef7MAesdn/q/vxZsMtO5gwTX57+3auSiT49f37mSgUll0zMPAs0ukH8LzZUPsWup+arvIm17hNFNYcOEqRzT6OYZil/qoHp61KVHnIwi2ucqEqJ07czvj4m3sq09WBw7O86K57uSXdxx/8zNWYZvuzu7lBwOlCgTOex5TnMed5+FqTME2ihkHEMLCVwlAKpTVaKXytcUuPjO8TUYqEZTFgmgxYFoOW1XJ5zVoYlsGmn9vEib85wYHfPMAFv3cB9nB4qV0NpXj9+Dh9lsWv79/P727fzvbYufgC00yQTF7L7Oz3GB5+UWj9Ct1PrX3czWFwuBBwhV3OVb54jbujrnIRbmE1yOUOMTv7XS644H2dHkrdHDgyy1u+8gCvU0Pc9rYrQ127XUrK8ziSz3M0nyfl+wxaFqO2za5YjAHTJGaaDfWf833mfJ+5UruPZTLETJNRy2K94xBt0wREKcXGWzcy8R8T7P/V/ez44x2hijfAy0dGGLIs/vToUX5240YuTZxbdunvfzrHj/8VAwPPwjTPv6DB85UVhbvJ4LQgyJIwFJEZcLVG62A+c1ot4a5m4YvFLfQUp09/krGxH++Zte3jJ9O8+Jv38hoGef+br2pLH77WHCq5vc96HtujUS5PJBizbcwWreOoaRI1TUYdByhGw097Hmdcl3tSKfoMg/FIhBHbxmjDhGT9a9ejPc3B9x/kgt++oKVSpZX4P4ODxA2DPzh8mHdu3cplJfG2rCTx+CXMzd3D4OD/CbVPoU2U/dwt/B22ox63G7gcdzWXxxTTnkc0esH8GveKrvIVospNU4Rb6BE8b46JiX/lyiu7O5K8UChw22238b//+02OJ7fz4h9+G7//U+GLthsEPJrJcCCXI24YXByL8bxotC0CWsZQimHbZti22ak1Z12XE/k8B7NZNkQibIxEMEPuf+wNYwRuwFPveYoLP3ghZjRcK/+6/n5+WSk+sES8+/v3cOrUx+jvf3pPZ+UT6kep6tua67G4KxUZmctmyPzdR/nvr9zHpqFn8NLXP7LI4m42AYu4yoWe4MyZ/6a//1k4zkinh1KVQqHA+Pg4U1NT88fOfufTfOgnTuKUrNZW8YOARzIZHslkWG/bPGdggKEGSlqGhVKKEcdhxHHI+D5Hczl+MDfHpkiEcccJdQIx/qZxjp49yrE/P8aWX9kSepKaa/r6+JUtW/joyZPcumEDO+NxHGeUaHQLmcwjJJNXhtqf0J20YnErpSgsCXYsFAo8/bpfYXammNr3yEd+wI3/ZHDqlI9lNS/c3brGLVHlwiK01kxM/BtjY6/v9FBW5Lbbblsk2gBTU1PcdtttobR/NJfjE6dPc8p1edHQEM8dGuqIaC8lbprsSiS4PJFgzvO4P5ViKhRfXhGlFBt/YSPupMvJj54Mrd2FXJ1M8prRUX7/8GGO5YvZ0xKJK0il9rWlP6H7CHuN+7bbbpsX7TJzcwG///sfLPVXfR/3SvW/y7VBWkaEW2gnqdS9KGXR13dDp4eyIt/+duX9v3fddVdL7eaDgK9NTfH9uTmeMTDAC7tEsJcSN00uTiTYHo1yJJdjfzYb2rYy0zbZ9q5tzHxrhulvTofS5lJu6O/njWNjvPfgQc4UCsRiO/G8SVz3bFv6E0Kmxb3cYa9xV/89+E6pP4XdxPdYLG6hJzh9+hMMD9/c1ojsMHjWs55V8fgzn/nMpts8mc/z32fO4CjFy9etY0u0+yuGDdo2VySTWMDD6TRzIRUysPottv32No79xTHyx9qTU/wFQ0O8bN06/vjIEQoaEomrSKXua0tfQoiE8NsQdsrTar8H5eOmaZLJVK6Qt9K+8W5d4xbhFuYJApfJyTsZGbm500Opybve9ZsMDQ0tOjY0NMS73/3uptq7b26Or05N8Yz+fp41OFgxTWe3YijFtliMbbEYB3O5islOmiF2QYzxN45z6AOHCNz2JIl5xbp1bIpE+NDRo8TjV5BOP9zCPl6hV2jV4l76N/Lud7+bvv7F2wn7+gze/e73zL/vaHCapDwV2sXMzDeJx3cSiWzs9FBWxPd9IhGHkydP8p73vIcXvvCFvOc97+HkycYD0wKt+cb0NI9ls7xiZIStPWBlV2PAsrg4HmfSdTmczYYigMM3DRPbEWPi3yZCGOFylFL8zMaNTHoe/z2jMc04+fyRtvQldBdhWtyO43DHd97JO3/znVxywR6u/tm38dnP7Zj/Pai5j7vdKU+lyIjQLs6e/R/Wretua9sPAoIAbNvAcRx+7/d+r+m2vCDgjlIVqx8eGWl7prLVwDEMdsXjHMzleCqbZXss1lLUuVKK8TeP88Q7nqD/Gf3Ed8ZDHG0R2zD4jS1b+OMjRxiPXcKVmUeIRreG3o/QPbRjH7dlw+/8zu+xfeo+7v2FKM7MN+bPdXw7mFjcQjsIApd0+iGGhn6o00OpitaaIADTbH2NzQsCPnP2LHHT5GXr1q0J0S5jKMUF0SiWYXAwlyNo0fK2B2023LqBU/98Cu23x409aNu8YWyMf5xKcjx9XNzl3UwIhUZaiSqvdM7zfZSZxDRM1AYbpQNMZ/Oie5rNVR6axS31uIWwmZvbi2kmu7rmtudr8H2MFkXW15o7JifpMwxeMDjY1kQqnUIpxZZIBFspDuVyLQvh4LMHQcPkF8Kt472QSxMJbh7dwN9ND5LNH2tbP0LnqaX9ja5xe9rF1C5BEFA442IbCtc9t52x2d+MUC3ukAJHQYRbKDE9/XUGB5/X6WFUJdCawAuwnNZWd7TWfHlyEgW8aHi466PnW0EpxeZShrXyfulW2tpw6wYm75zEnQ1v3/hSXjUywrrIEJ86+WTb+hA6T01XeYPfSz/wMJWJ1mD5YKkAOJf5r1bK01WJKheLWwibbhZurTUF18eyVMtC+93ZWWY8jxvXuGiXKVventacbjHaPLolSvyyOGc+eSak0S3HUIq3bLiAz03NcTiXa1s/QmeptR2sUXz8+XS5etwiCAK0OidvHV/jlu1gQtjk8yew7XUkEld0eigVcYMAI1AYZmt/rk9mMjyRzXLzyEhPbfdqlbLlPeN5pFr8FVr/2vVMf2WawplwtpxVYmNyC6/qm+PDRw/iy1p39xHCGje0lvJ0Kb5XwDD60Rr8sz62AjO6a9E9zQp3NxYZOX9+vYSqzM19D8samE/I300EQYDva0yz+paNepjzPL44NcWLh4aIr0KN7m7DMgw2RSKcKBRayrBmD9kM3zzMmf9un9WtlMHz+mPEdYovnJVMamuRsBOwaHxM7aK1xhmxUQR4uf3z51vZDhaK3opwC2EzN3cPyeT1nR5GRQpao1yNYTX/p6q15vOTk+zu62NDhapC5wsx02SdbXOyRZf58EuGmfnmDN5MeME2S4lGN/P6gRn+++zZll38QvfR6nYwy1oc6+JrH2VYBFqTn3Qx0Gh17ppaucqr9yMWt9ClzM3tpa9vd6eHsYxAa3w3wLHNlqztB9Np3CBgd19fiKPrTYYsiwCYbcFlbg/aDDx3gKmvTdW+uEkikc30+Ue4aXiYfzl1qm39CJ2h1SIj/hIRDPAxlEnqBym0rzGpPzhtJcTiFroSz5sjnz9CInF5p4eyjHwQYHoaZTcv2hnf52vT07xkeHhNbvtqFKUUY47DWddtaf14+IeGmfzcJIHXnlSojjNGEGR52VAfB3M5Hkil2tKP0AQh7eMOc4070AGGijLxidPEr0qgVIBW54R7JVd5rahysbiFriOTeZhYbBeG0V0VsHyt8b0A22zN2v729DRXJhKMhlSjey0QMQz6TLOlcqDRzVGcjQ6zd8+GOLJzKGVgWQPgn+WNY2N8+syZlhPJCN1D6Gvc2kOpoqgP7+kngofWiy3uZDLZ8DglqlzoSjKZx4jFdnZ6GMsoBAGWR0vW9qTr8lAmw9P7+0Mc2dpgyLZJBwFuC4Fqwy8ZZvau9gg3gG2vp1CY4Pq+PnzgrpmZtvUlrC5hFxnR2sNQFkZcUdAaH2BBsK1Siqmpxpd2bLs7XeWSq/w8J5N5jHj84k4PYxGB1rhBQDxQqBbSm35zepo9fX3EOhFFrjVMTsL0NGSz56btlgWxGPT1wdBQ8ZehA5hK0W+azHgeI016I/qu7+PE357Am/GwBsL/KbHt9bjuKZRSvGZ0lNtPnOAZAwOYsuSxJgjX4vZRysLPBuhcAVNnGwpOq9afaXanq1yE+zwnk3m06wqLFIIA21eoFhKuzHge+3M5bly3LuTRrUA2C48/DocOwalTEI3CwAD09xfFWqniNadPQyZTfD0wAOPjsG1b8bpVZMCyOJbP4wVBU/vajYhB354+ZvfOMvzC4dDH5zijZDIPAcV0qGO2zTemp3n+knKuwirT4TXuSgRaYygTs88k2P8kBA+gd51ro9Z2sGqIxS10JUpZXeUq11qTDwLiHqhI8ys535ud5cpEgshqJFqZmIC774YDB+DCC2HXLnjxi4tivRK+D2fOwNGj8M1vFq3wnTthw4b2j5lilrKkaZLyfQab/Hfqu66PqS9NtUW4LWsIz5uef//q0VH+8vhxnjM4KFZ3j1NrjdtYIadEpcm8wgcM3DNpFAqjvw/tjy66Z6Wo8moTBbG4ha7D8+ZIpe7FtlfRKq2BqzWmUigflNXcj7MXBDyWyfCGsbGQR7eEVAq+/nU4fBiuuQZe9KLaYr0Q04SxseJD66KAP/ww7N8PV121KhZ4n2lyqlBgwLKa8m4krkxw/C+P42d8zHi4SxKmGQUUvp/BNOPsjMcZsiz2zs2xR+IWepoVt4M1katco4uVwfIFgv5+TANU4cSC/qq7ylfCtiU4TegyCoXjRCKbuipnd9FNDrSQKe3xbJakaTLUzvXjBx+Ev/zL4hfyp34KnvnMxkR7KUrBli3wghcUn7///aKAtzmS2jIMHMMg12SQmhk1SV6fJP1IOuSRFbHtUVx3ev79y9at43Nnz0rZzzXAimvcDQen+SgjgmXn0ckkGBAEi13lVdtaITe6FBkRuo58/iiOs6nTw5gn0BpPa6zS+nazPJrJcHUTWz/qIgjgjjvgf/8XXvtauOkmCDMbm1LF9e5nPhPOnoV9+0KdqVcibhhkW/hRiW6NknkoE+KIzmGacYLg3B7ua5NJCkHAE9lsW/oT6iCEif5KKzPNTcoClFa40x5GPIalPFSweJzNJGAxTRgOYxXINKWspxAO+XzR4u4W8kGAbRhoTzct3G4Q8HA6zSXxeMijoyig//EfRZf2W94CW7eG30eZWAxuuAEcB+69N9Qv/bKuTJNCEDRtxcYvjZN5rF3CncD3z1nzSileMDjIV5rY2iOERAjC3UrmtErntA7ADzAAz7EJ1GKLu5Wo8uPHQ3B8GYZY3EI4FAqnsO3R2heuEvkgwFEK7TWfm/xgLscF0Wj4W8CCAP71X4vi/Za3QDsmBktRCi6/vBh5/sADbbO8DaWwlWp6T3d0e5TCqQJBPvwsaqaZXCTcAM8aHOS+VIp0mz0RQvsIex832sPIFtBRsxhhXrLAy6wUVW4aZtVgOKVCMpZD87kXEeE+j/H9mWJ2qi4g0LqYXTigpb/KJ7JZNrajkMjnPlcMRvuxHyt+CVeTnTuL7vjHH29bFxHDIN+kWWFYBpGNEXKHw6+fbRhxtF5cZCRhmlyTTEpClh6m1cxpsQrxJLrgg10MaFT4BCzex13NVe76LoGuPukMRXPF4hbCwvNmsazBTg8DKAWllaztVta3TxUKXBCNhjgyYO/eomj+xE+svmiXueQSyOXg5Mm2NO8YRkvlPmO7YuSP5UMcURHDiC6zuAGeMzDADyR/ec/Saq7yfH7p35qPcl3MARtDg6U91JIiI80uBYVmcUtUuRAGnjfdNRZ3PghwyuvbRvPbwJ7K5dgUpsU9MwOf/jTccgskEuG12yiGARdfDMeOQRvKXNpK0cpvkzPqkD/SDuF2llncUEzIcrJQ4PiyH3ChF2jV4l7uKvfBC/DyBp7SaO0RUF+REai+jxtCSsJiGCLcQjhY1iCm2R37YQtan7O4m0xzOlEosDkSwQ4z6cqnPgV79hQjvTtNPA7r18ORI6E3rVTRPmm2Ypgz7lA4Gf6EQimHIFjerlKK3X193DM3F3qfQg26MHOa0gHKD1CWXdzc5YMKzv22rRictsJ2MAjJVS77uIWwyGb3o1QH8ngvwQ0CTKWKZTdLe7ib4bTnEQ9TtA8ehEcfLWZB6xY2bSqutbdhO5RlGE0Lt73eRvvh761WyqbaH8Tuvj72inD3JK1Y3EtFuCz02tNYQ06pilwGbS72xnTUVS7CLYSH3xXCXQgCrNIXNfCCpi3uKddlfZhJV7785WJClHYEuzWLYRQzrU1MhN60qVTTpTOtIYvcU7nQE6MoZaB15aC3nbEYad9nQtzlPUfYFjd+DkwHd87FQGH6BZRxrniOYRhsrbJ9s1amNrG4ha5C66ArhNstu8lLX9hmM6ZNeR6JsILHzpyBJ5+E5z0vnPbCZGQE5uZC3x5mAs2GpxmlvPLhbwlTUMWVaSjFZYkED2Xas4dcaB+tWtyLrwdcFxWJoJSBhwbtoQ170T379+9vaqyhxJWdj8KtlLpRKfWYUupJpdQ7Oz2etYLWxcT8nWZeuAONVs1bbPkgIBmWq/x734MrrigmQAmJubk5brjhBlKtRkNbVjFQLmQ3cStpNZRS2GM2fircyUStaODLEwkeFuFefbpojVsBynfRkRg5neEP3vAK8uk0xgKLu9bf0Ur9hbodLCSPVOd/tWugiibhR4CbgMuA1ymlLuvsqNYGphmrGLG7mgRanysrqVf+AtVixveJhpV45YEH4Prrw2mrxOc//3n27t3L5z//+dYbGxwsrnWHiKFUjTCdlVFaofPhusq1LgaoVePSeJxHM5mmXfxCE4RY26DSf7Z663GXhbgYjBaAHeF/H/s6hx+6n4f27icwz20LbSU4LZQ1bqVC3csdmnArpX5eKdWOQrlPA57UWh/QRZX5d+CVbejnPMQgCDq7Puhpfa4WtKYls09R3NbUMrOzxf3Su3a13hbw0z/90/T39/PGN74RgB//8R+nv7+fW2+9tflGE4nivu4QUbRmdRt9Bn4u7Gxm3oo60W9ZDFsWh0L+txDaz0pWd6MT+N94119y0RUv5Lf+59cA+Of/92Ve9aI/n/+OtVJIKdRCIyFlTwvT4h4Dvq+U+s+SazusadkmYOH+l6OlY0KLmGa8YnKL1cTTej4wrSVzD8gGQUvCM8/+/cWa2CFZ77/5m7/J9u3bsUrr75ZlsX37dt71rnc136jjFH/1uijtp9Kq5f+GS9G6dgDlJfE4T0rRkZ6jmnA3U9bzHW97GVu2bsI0it8xw1SMbxya/47VdJXXCE4L5WsWYoWw0IRba/1uYCfwd8CbgSeUUr+vlNoRVh/VUErdqpTaq5Tae/r06XZ3t2YwjDhB0NkfPHehcENLP/xRpZrezrSIw4eLwh0SF1xwAe9973txXZdkMonrurz3ve/lggsuaK3hSARcN5xBlmhl4tOOUptB4FFrf+C2aJSnxOLuOappZS3XdfHexUK8bcMQv/rrv4AXuDixOL4X8Oafv3HRd2ylIiMrEarFHdJEO9Q1bl38FzhZenjAEPBxpdQHW2j2GLBlwfvNpWML+71da71ba717dLR7imZ0O9HoNrQO94e/UTytMUNyzgQUE7m0zIkTxUQnIfLxj3+cvr4+3v/+95NMJvnEJz7ReqOhpHRaTCv/Jcyk2XTWu+q4GMbKKWy3R6PiKl9NQkjAslIzTW0HQ/O5z3+ZhJPkZe/4FSJRi//9yqML+upwytNyQyF9X0NLvKyU+kXgJ4AzwN8Cv6a1dpVSBvAE8OtNNv19YKdS6gKKgv1jwOtDGLJAsSZ3J5kPTAO00gR+866kPtMkE8YXY2oKrrqq9XYW8M53vpM/+7M/Y2RkhNe//vWcDCPnuGmGFqUKRWdHKzN5b9JDOeEKdxDkagr3uOMw43lkfJ942FXhhLZRVbjrCE5bel5pn3f88tv5xQ3v5Hu3DPGMHd9lZOuzFl2/UnBa26PKoTuFGxgGfkRrfWjhQa11oJS6udlGtdaeUuodwJ0UfWZ/r7V+qLWhCgC2vZ58/nBHx+ABZkm4lVKooPkf/qhhMBeWBRpy0pUrr7xy/vXIyAgjIyOtN2oYoUb4QmtBPEEuwIiGu1GlHuE2lOLSRIKj+Ty7VqPcqhAKrVjcjuMsEeKAS668ktOfmSOez6FGolxw6Tk3eavBaaGtcXebcGutf2eFc4+02PbngRD20AgLcZwx5ub2dnQMvtbzVp4yinu5m2XYspgKY823UxXAGkWp8IW7hXuNmIERC1e4fT+HYSwv4biUpGlyqlAQ4e4hWrG4/QUCqIIADBuUgZUwSedy9NkKQy1OwALVA9/anjkNuneNW+gtHGc9hcKpjvUfBAFqwRdJGQqC5oOchm2biTCEOx5vSy7w0AmC0CLfoeQqb3IiELgB7oSLGQ/XVR0EaSwrWfO69WH9txdWjVbXuOf3cfs+mKC1QZAKCAp5MFmUgKXYX2V3ea3fm25c4xbhPo9xnM24bgeFmwozXRO015xwjzsOJ8MoednXB9PTrbfTbrQO1zugddPC7c14mP1mSy7JSvj+HKZZh3A7DhNtKHcqVKDdwWl1JmCZb0drlNLz3qcgn0ebqlSgZmF/iqCJ7VhicQtdRTS6mXz+KL7fGesyoIKFZxYLjTTDesdhuhSk1BIjI3CqcxOauij/AIVkcTezd3Yh7lkXZ0N46WHL+H6qLuEetW3yIe2RFVaHVizuRdaz1mjTQGOgHIincxhmHquCxV2JeoLTum2NW4T7PEYpk2h0O9lsc8n3W0Vrveyba9gGNOnxNJRiWzTKwVa3Bm3bBgcOtNZGu3Hd0APomrW2AdwJF3soxMpsFIvgAHUJ95BlcUws7p5iRYu7wWgL0yh+F3QBUoUcmBpD1ecqr4VY3ELXEYtdRC7XIeGGZcFVhm0QFJq3nC6MRlvPonXRRfDII6HnAg8Vzwu1AEqL2WZxz7rYG8MVbs+bBcy6KtglSlsBJWd579BK5rRF57UmUAW0MjAiCp1JYxo+lrl4N8Kll15a0VVeqz9Z4xa6jr6+68nlOruXexE2LRWquDQe575WBTcSgUsuge9/v7V22onrhirc0JrFnT+UJ7IhXA+A501h2/WVPzCUIm6apLsoBaywMq1a3GXrWZX+R2sD7WqCdArXXI+1ZI374YcfbmqcoVrc3ZbyVOhNIpFtzM5+u2P9L3VdmVGTIB80HVm+Ix4n4/tMthph/IIXwBe/2Fob7cJ1i78mIaxv+77PZz/7WW573/v47Gc/u2ibTb1orckdyRHduvJ+60bxvCksq/66RX2mGd4+fmFlQghQq9aEYzqMJ8dr3FsUdt/3+ewdd/D/PvQ/fPmOLxD4AUG+QFQdwTIjFe+p2N5qrXGHVGSkRzasCu2ir+86DhzoXInzpd9bZSiUpQgKAWakcWEyleKieJx75uZ48fBw8wN71rPgn/6pmP40xLzloVAohLK+7fs+L3nJS7j77rtJp9MkEgn27NnDnXfeidnApKBwskBkSyT0rWCeN0M0Wn8+9z7TJCcBaj2DYVQW7ryX53Smds0Jz/N46Utfyt3f/S7pTIZY/Etctf4qXvITt6B0Adtc/h2puB2sRm70WCykdAnxuFjcQjhEIlvQ2iWfDyEFZ4OoKvWfVUQRZJv/A9/T18fD6RarnsVi8EM/BP/+7621Ezblqb/d+nryHXfcwd13300qlUJrTSqV4u677+aOO+5oqJ3sk1mc0fAjyguFUzhO/bUHFMXc90Jv0Kqr/M477yz+/abTaK3JpNPcf2Qfjx59ClsViCzJuNdscJrvh5TWoVDoyrKeQg+ilKKv7wbm5n6w+n1rXTGYyIybBJnmhfuyRILD+XzrhSde/nJ49FE4eLC1dsKkUIBoOC7pe++9l/SSCU46nWbfvn0NtZN9IktsZ+3sZo3geSmUUphmou57rLCqwwmrQtXqYHUGp+3bt2/Z32/Wy3Hy9Ek8bOwGErBIcJrQc/T17e7IOrdpGFT6PphxEz/jN73ObSjFi4aG+GarSVQGBuClL4WPfCQ0F1dLeF7xyx/S3u1rr70WK7ZYcBOJBNdcc03dbWhfkz+aJ7YrXOF23dPYdmOV/kylxOLuIZSq/rWqZx/31VdfTSKxeGIXsyKMXriTWHCGiBVbdk8zvymh6a0ItxAmg4PPYXr666verwEVLW5lquK2sBas7mcPDHDX7CzTrQapvfzlxbWpf/qn1tppgnLg2Pve9z4++5nP4Ie8BeyC5zwHLr2URCKBUopkMsmePXu46aab6m4juz+L2WdiD4S7FazoJl85QGkpSakMtnq0MTitnnrctm3zkpe8hKc97WlESvEekWiUK/sv4to9z4IgT8SoLzjtfK8OJvQoyeR15POHKRTO4DghVK2qE6WKXxc/COYrhJUxkybenIeZaO7HOGlZvHBwkM+dPcsbxhsTgEUYBrzjHfDbvw0XXgjPeU7zbTVAxcCxpz2NO7/4xYYCx1bi9lOn+KNPfIIdDzzAvn37uOaaa7jpppsaaj/9aJrEFfW7s+ulUDhBPH5xQ/ekfL+lLW3C6rKS9tdTZGSZ9aw1+B5TyTg2PhFj+SS3oxa3YUhwmhAehmHR3/9MZma+ufp9K1XZXd5n4s827y4HeNHwMN+cmeF0Pt/8AAFGR+Htb4fbb4e9q1NNrWLg2Pe+13DgWDUeSKX4+OnTvGXTJm6++Wbe/e53c/PNNzck2jrQpH6QInlN7cxmjeB5KbR2sazBhu5ztcYW4e4ZWk15euedd/K9732PfOn7nc/neWDuAI8/eC+BMpf9La/kKq9VHUxc5UJXsm7dyzoi3LZSuBVmoYZjoGyFn2r+D33AsnjpunX8++naW0tqcuWV8Eu/BO99L3zjG623V4OwAscqobXmnQcOcNsFF9DfQpGSzOMZ7CE79IjyQuEkjrOp4dzphSDAEeHuGVpxlQPcd999y4PTggKnntpP2lq+jbCqq1yqgwm9yuDg8zh9+hMEQTjbFerFVgq3yhfHGrTwplsbz43DwxzL53kwjPSlu3fDBz8I738/fPSjoX0JlxEEXDsxQWLJD02jgWPV+MzZs0wUCrxxbKyldtL3penb09fyeJaSzx8hEtnU8H2FIMA25Cdt1WjXGnedUeUVg9OMCIO7LqLPP17xnmZzlYvFLXQl0ehWotFtzM7etar92oZBoZpwD1j4KZ/AbX5dyDEMXjM6yt+cOBFO9ahLL4WPfQy++134uZ8r5jQPkwcfhLe9jZseeYQ9e/aQTCabDhyrxFnX5RefeIIP79yJ1YLIeTMe6YfSobvJg8DFdSeaEu681sRFuFeHEDwb1RKw1LuP+8UvfjF79uwh4TigIB6JcPXgTjY+/WlowguWDHWNW4LThLBZt+4VnDnzGQYHVycAC8BRqmp+aWWootU96eGMNe+Ova6/n/vTaf711CneEkYWtJER+Mu/hE9+En75l4sBa69+Neza1XybDz0E//IvcPfd8KM/ivlXf8WdFNe6mw0cW4rWmt87eJA3j4+zZ2Cg+bECs3fPkrw2iRkNN5K7UDiBbY9gGI398GqtmfP9llz/wurTbHBacY+/yZ133snHf+5t/H+Zx3mz3sZz/DfxB4bGqiLc1TKnrUrKU8uCVuNtyk2F0oqwJli37hU89dQ70dqvqyJTGEQMY0VL2Bq2yB3IYY/aKKP5Wf5r1q/nnQcOcNnMTMuiBRRnz7fcAjfeWMyudttt0NcHT3saPPe5xdKgK/34aA1PPglf+hL87/8W848/73nwqU8V948DJnDzzTdz8803tz5e4O9OnOB7c3N87eqrW2onKATMfneWjT+7MZRxLSSXO0wksr3h+1K+T9QwMGWNu2dYyVVeD1prTNPklS98Ln89tI3nfXoCe2QzhfxJMvGdFfprfh93aGvckqtcCJtEYheue5qpqa8xPPyiVenTMQxcrfG1rvija0ZMzISJe9ZtKQgqbpr8382bed/Bg2yKRNgcUvYxkkl461vhzW+Gu+4qBq697nXFL+nu3UXrfHi4GJX+J38Cs7MwOQn79hWfX/pSuPXWotUeQhrTatw9O8uHjx3jvy67jGiL28lmvztLbEcs9KC0IChQKJygv/9pjY/J9xkQa7unaMVVvlCErUsvYezJ+yls307EiuMVXBKR5RUPL7/88qYzp3XbdjD5SxcWMTb2Rk6d+udVE26AmGGQ9X2SVX547fV20epe15rVfUEsxpvGx7n9xAl+ZcuWcH/oLasovs95Dvzmb8KpU0WLemKiaE3/8R8Xt5LdcguMjcG73lUsXrIKFuKhXI73HzrEH+7Ywc5Ea3uug0JA6r4UIz8S/n7/fP4IjjOOUWH/bS2mPY+NIZc5FWrQ4eC0eS65hMHpUbLXv4b+L0KfX8A2l0/MH3rooabGKQlYhK5ndPQ1HDz4O7juNLY9uCp9xgyDbBBQLczJjJqYSZPCRIHIeGtVsZ47NMSJQoE/PHSI92zfTqwd2bYMoyjKXVBV7EyhwI88+CC3btjAS1qpllZi5lsz2KM20U3hlvCEops8Gr2oqXtPFwqSOW01CWHCGVY9btAcVzaFvj789CzTRgCq8iSu6j7uFfqTlKdC12Pbg4yM3MKZM/+9an3GDKNqgFoZZ9zBPe22FGFe5rXr17MrHud9hw6RW8P1m8+6Lq988EF+cnyct21qPEp7Kd6cx8zdMwz/UOsTgGVtezP4/izRaHPjPFkoMCYWd08RmsUNbIzYZHyfwNJo3wdna8175vursW9c9nELPcH69a/h6NE/RevVKayRME3SQbBi4IjhGNjrbPLHWo/KVErxpvFxLoxGue3QIVIhBYx0Eyfzef7vE0/wkqEh3h6CaANM3jlJ37V92MPhr8Vns08SjV6IUs39JJ1yXcZFuHuKFVOeNmBxax0wZNlkgwCtwVAeET2z4j2NEFpUuaQ8FdrJwMD/wTDiTE5+cVX6swwDSylyNf6onXEHP+XjzbUutEopfmrDBi6MxfjgkSNMFAott9ktPJpO8+qHHuL6/n7es317wxnIKpE5kCF3IMfQC4ZCGOFigsAjlztELLajyfsDTubzYnH3GK1kTlv8N61JeUHR4gYC7WIZy6vVrZQ5Tcp6Cj2PUorNm3+eY8f+fNX6TJomczX+qJWhiG6Jkj+cRwetl29USvGW8XF29/Xxq/v3h5NdrcN8aXKSVz34IL+2ZQu/uHlzKKIduAGTn59k3cvXYTjh/2TkcgdwnE2YZryp+6d8n4RpkpA17tWjndXB6nCVO46zyHpWhk06CCBmoHUBp0JwWrntRpHMaULPMDp6C543Qyr1wKr012eazNQxrbUGLIy4Qf5oOIkMlFK8YmSEX968md8/dIiPT0y0VNikUxR8n/cdPMgHDh/mny+5hFeNNlbLeiWmvjSFM+aQuDT8KmBaB2QyjxOvsO+2Xg7mcmwNa3ufsGq0EpzmLxJAzbhjFS3uQnFracxZns632bKeEpwm9AyGEWF09NUcPvz7q9Jf0jTJBgF+HaIZ3RrFnXJxp1ustb2Aa/r6+LOdO3k8k+E3DhzgZEgZjlaDR9JpXv7gg5xxXf7z8su5IYwEMyUyT2bIHcyx7uZ1obW5kFzuMJbVh203H/D2VDbLBSLcPUcrZT1hcVR50rDIaU30sjimmSOiwlv6Eotb6Ck2bryVmZlvrYrVbShFn2ky7dYWY2UqohdGyT2VIyiEF0A36ji8c9s2ntbXx2899RT/fuoUhZCCSdpB2vf5/UOHeO/Bg7xpfJz/76KLWBdiEhdvzuPMp84wfNMwZix8N7TWmmz2SeLxS1pq52Aux3YR7p6j1bKe566HqBEh7XmYNySIZF0cs7J3qJmynrLGLfQUpplg8+Zf4dCh21alvwHLYrrOP2y7z8YZd8g+mQ3VtW0oxS3r1/P+Cy7gUD7PWx99lK9NTnaV+9z1fT526hRveuQRThYKfGjnTl4/NhbKenYZ7WvO/s9Z+p/WT+yC5YE+YZDPH0YpE6eCW7Ne0p6HBjZGWtvfL6w+rQanlb+TSkGfpcgEAROFAsOzaUyzseC0lQg1qlyEW1gNNm68lULhJHNz+9re14BlkfJ9vDqt3MiGCMpS5A7kQh/LeCTCb2zdyi9v2cL/zszwU489xlcmJ+seWzvIeB7/dOIEL3ngAb48Ocl7tm/nQzt3sr4N0dRnP38WDBh4Tnhu94VoHZBOP0wicWVL7TyWzTJq25KjfLVpY3Ba8Vwj28E0fabNrO8z4bq4dhTLGlzxnkYI1eKWlKfCamCaccbGfpz9+3+Fq6/+cqhW3bK+Su7yKc9jtE4xil0UI/1QmtyxXFuyeV3T18fVyST3zM3xhclJ/uL4cX54ZITnDg6yZRXcs1prHs1k+OzZs/zbxAQ/NDTEH+3YwfV94dfBLjNz9wyF4wXG3zLetv/e2ewBTLMPx2ktdeojmQyXtpjGVegMrRQZWbodbNCyOJt2OV4oYBspoAGLuweD00S4hZqMj7+F48f/gjNnPs3o6Kva2tc62+Z4Pl+3cCtDEb84TvqhNIZt4KwP3/pUSrG7v5/d/f0cyuX4wtmz/PwTT7AtGuXaZJJn9PezMx7HCEnkfK15MJXiq9PTPJrJ8Egmw4+vX8+nr7ii7ZOF1AMp5u6eY/zN423Z+gXFmtuZzGMMDv6fltrxteaxTIaXr2tP4JzQXsJLeQqjToSU53GyUMC0PYwK+7iX3lMv3bjGLcIt1MQwLHbs+BMef/xnWLfuJgyjfeuJ/abJIa3J+D7xOvflGo5B/JI46QfTYIKzrn2JOLZFo7xt0ybeumED96XT3DUzw3sPHiQVBFwUjXJZIsGOWIyt0SgbHGfFz6C1JuX7HM3nOZrP82A6zUShwBenpnhafz8XRqP8zMaNXJ1MhjYpWIn0Y2mmvjLF2I+PYfW376chk3mYSGQjltWaG/5QLseobUsN7h5Fqcopz+tNebowqnzItomaJk+k04zqHIaxfIJbrU2zRglj04T+/hUvqQ8RbmG1GRp6AYODL+Do0Y+wdesvt60fpRQjts1EocD2WP1BUWbMJH5ZnMxDGdDgjLQ3i5ZpGFzX18d1fX28Y/NmzhYKPJjJsD+T4fOTk8x6Hg+l02yIRMj6PpsiEeZ8nyHL4kShQL9p8lAmw7ZIBB+4KpFgveNw4/Awv7F1K4NtLPFZifSjaabunGL9j61v67+d582Syx1hePiHWm5rXyrFFeIm71mqZQCtt6znovcoLo7FeCKTYQNJTHP5UtKll15KUKFDN3BXnChYVrHIX8uIcAudYNu2d3HPPTcwMnIz8fiutvUzYts8mE6zOQiwjPrdtVbCInF5gvRDaXSgiaxfvUjjdY7Dcx2H5w4Ozh8rW9Szvk/G93G1RgGWUsQNgyHbrtur0E7SD6eZ/NIkYz82hjPWPtHWWpNK3U8icVlTpTsX4gYB96dS/N8tW0IandAQHc6cttjiBq0sXrN+PRGl+JcDp1Fq+cT34YcfrthuLfe5rHELPU00uo1t297N44//TClQrT1roI5hMGhZnHZdNjS4zcdMmMSviJN5MIPOa6JbOre/VylFn2XR18Wu3Nm7Z5m7Z46x14/hjLbXS5HLHQR8otELWm7rkUyGzZFIuDXVhVUlzDVu0BhKYRkGhlWouB2sWcq7uLRusZqpFBkROsWmTT9HEGQ5ceJv29rPuONwqlAgaCa3cNwieXUSd8Il80QmlLzmaw0daKa+OkXqwRRjP95+0fb9LJnMIyST14USqX7P3BzXtTGyXmg/SlXXscbKempAYwA+oChUDU6rRK2JgmEUHy2ncpAELEKnUMpk166/4dixj5DNHmhbP3HTJKIUp+vIpFYJI2KQvCZJkA9I3ZcKNcNar+NnfCb+YwJvymt7IFqZVOp+otEdWFbrYjvlupx1XS6PN1eUROgOqmlzPZHfpmku2McNGhu/dG/UczCMcP82QtFcEW6hkySTVzA+/hYefPD1fOYz/8373vc+PvvZzy5J/N86W6JRjuXzTVndUEyNmrg8gTVkMXfvHO5UeLnNe5Xc4RwT/zlBZEuEkR8ZwYy0f409m30KrQstFRJZyF2zs1yWSBDpgviA85oQ1ribDU5b2g4EKCDveTxy/3188IN/U/E3qdKkoJ419W4TblkgEppiw4af45Zb3seDD76GbNYjkUiwZ88e7rzzTsyQflCTlkXcMDhdKDDWZEpLpRSx7TGsfovMwxnsMZvYhTGUcX5l2tK+Zua7M6QfSDN84zCx7e1JY7oUz5sr7dl+digxEfkg4J65OX5h06YQRic0TQjLHa0Ep5WvO9eWwvd93v6qV3Hf3d/DzX+XROL2Rb9JK7VZa6IQiuaW17hbXiwXi1toki984U4eeihPJuOWooVT3H333dxxxx2h9rM5EuFIPl9X1bCVsIdt+p7WR5ALmL17Fm8mjIwKvUH+RJ5T/3IKb9pj/CfGV020tfZJpfYRj1+GWaXoQ6N8f3aWXbHYqm+XE8KnleC0xUVGNBqDb33xizy4dy+FnN/Qb1I9udFDScKiVGgBaiLcQlPce++9ZDKZRcfS6TT79u0LtZ+kZTFgWRwLocymYRskr0gS2xFj7r450o+k1/Tat5/zmfzqJJN3TNL/jH5GXjaCGV8993IqdT+m2UcstjWU9rwg4L5UimeHWLZU6BwrBXw1sh2sfO1j999Prk2/Sd22JUyEW2iKa6+9lsSS5BeJRIJrrrkm9L62RqOcKhTIh7SVwlnvMPDMAVAw8+0ZsgezayryXPuauX1znPrHUyilWP/69cR3rW4gVzZ7EN+fI5lsrYjIQr43N0e/ZbFJSniuCVop61m+bv41cPFVVxFdErBYz2/Sqq1xh9iQCLfQFDfddBN79uwhmUyilCKZTLJnzx5uuumm0PuKGAbjts2BbDa0Ng3LIHFJgv6n9eNNe0x/Y5rc4VzFzEq9gvY1qQdTnPynk+SP5hm9ZZSh5w9hRlc3iMt1z5LLPUVf325UjXSS9ZIPAv53epofGh4OpT2hRdpcHaz2vYqF1cGUMnjmi1/MZddfT8SJVf1NarY8b7cJtwSnCU1hmiZ33nknd9xxB/v27eOaa67hxhtvxGgg01kjbIpGuTeVYtJ1GQ5xfdNMmPRd04c345F5MkPmiQyx7TEiWyMYdm/Ma4NCQPrBNKn7UjjjDsM3DRMZ70x9at9PMzd3L31912Ka4Vn5d83MsCMWY6wNJUyFztBqPe6FrwOtMS2LD37yk3z6l/+N0Z3TXHPNNdx0003zwbLNVgeDEIU7pDVuEW6haUzT5Oabb+bmm2+eP6Z1UAqaDDdq21CKHdEoT2SzDJgmZsgTBGvAov/6frxZj9xTOWa+MYM9YhPZEsEe7s5AqPzJPJmHM+SP5nE2OKx7+bq252hfiSAoMDv7feLxS7Dt8Cp2zXkeD6bTvGFsLLQ2hc4TZspToJhO2DTZfdFzeOO7r2pwLL3lKu9q4VZK/Sjwu8ClwNO01ns7OyKhFkoZbRPvQdumv1DgqVyOi9qUfMPqL2ZdC/IB+WN5UveninnPN0RwNjrYA50VcfesS3Z/luz+LMpSxC6K0f+MfsxYZ/c0a+0zN3cvjrOBaHRzqG1/YXKSS+LxUD0tQucJM+Wp1gaB1hSCAGU19rtTX8IXEe5GeBD4EeCvOz0QoX6UMggCHzBCF+8dsRj3pFKsc12G2vhDbkQMYhfGiF0Yw512KRwvkLo3RZALcMYc7BEbe8Ruu2D6aZ/CyQL5Y3nyR/IYMQNno8PQi4banqa0XrQOmJv7AaaZIJG4ONS292ezHM7nedXISKjtCp2nagKWOi3uBe9QqniPpzVGG+JMRbgbQGv9CIRvuQntp2h5+8DKiQ8axTIMLo7FeDST4fpkEnsVsmfZgzb2oE3isgReysOdcMmfyJO+Pw0GmIMm9oCNNWBhJAzMhNnw+njgBvhzPt6shzdVfLhnXYJ8gDPu4Iw7JK9OYg1011c2CALS6X0oZZNIXB5q214Q8NmzZ3nZ8DB2m2InhCZpZ3WwOvdxV7KUfa0xqwxrfHy84nFZ4+4QSqlbgVsBtm4NZ8+o0BpFsTbbIt6Dts2obfNoNssVicSqTuyspIWVtIhdWExi4qWKIuvP+uSO5PBnfPy0j3KKYzIiBkbEKO5XMUpuuaBY5EPnNEE+QAeaIBtgJk2sQQurzyK6PUry+mTHXfMroXVAJvMAYJBMXhX6f4dvTE+z2XG4RGpur0nKfy5LE4k1njlNoTHQWmMqhVNFYI8fP970WEMTbssKIZNLFwi3UurLQKWp0G9prT9dTxta69uB2wF27969djbk9jjnxNsDrFB/2C+Mxbg3leJIPs/WDu7rLQv5UvyCj85rdEETFAK0XxTssoArS6EshRExUBG1KjnDw0TrgFTqPkCVRDtci/hwLsc9qRQ/u3FjqO0K3UXZ6l7609BI5jQ4F4meCwLiVyUbGsOq7uMu1whtkY4Lt9b6RZ0eg9A+zom3S1G8w/mBV0pxeSLB3tlZ+k2z61Jgmo4J3bEEHTrFVKb3oZRJIhG+pZ3zfT5x+jSvHBkhKfW21zSVsqfVl4LUXGxxBxoMCAhY/8Lw9/qHusYtKU+FXkApA6VstPbQOrwEJxHD4NJEggfTabIhVyYTKhMEBebm7kGpSFtEG+Bzk5PsjMXYJWU71zyV1rnrdZWfa0OhDANNaY27gcpi822s1hr3+ZA5TSn1w0qpo8AzgM8ppe7s9JiE5lBKzYt3EIRX4GPYttkejXJ/Oo3Xw1nPegHfzzI3txfbXkcyeXlbRPueuTlmPY+XSIa07qdNAWqtBKcZWjUsavVY+JYlwl03WutPaa03a60jWusxrfVLOj0moXnK4g0+QRBebezN0SiDlsXD6XTTKQ2FlXHdaVKpe4lEthKL7WhLH4dzOb4+Pc0rR0Ykivw8oVmLe3HKU4XWRanPax+7wZK9vbjGLd8OYVVRSmEYEUATBIXQ2t0ZjRIAD2cyIt4hk88fI51+kHj8ktCTq5SZ8Tw+fvo0rxoZkUQr5xHVjPZGErAopUBpAsArRZavdH0zyBq3IACG4QAGvp8NZd3bMAyuTCbJ+j5PhFiM5HxG64B0+jHy+aP09V2PbbfHfV0IAj5x+jTP6O9nR2x1aoUL3UFVV3kTKU+LN6+cgKVSu6u6j/t8cJULaxvDsDAMhyDIh+I6N5XimmSSadflySV1eYXGKBYLuQetffr6dmOa7RFULwj4z4kJNjgOz5A62+cd1bKn1b5vcea0YilQcNHYZviyZpqhbL8W4RbWBkqZGEYUrX18P9eym9syDK7p62PCdXlCxLsp8vkTpFL7iEQ2k0xeFlppzqUEWvOpM2eImyY3SjBa7xFCcJplVV7jrt21WuIqL6ZJ8LXGamLLaS0L37JC8XCLcAtrB6UUphkt5TjPtBx17hgGN/T1ccZ1eTydDmmUa58gKJBKPUg+f5Jk8hoikQ1t60trzWfPnsXTmleOjEhaY2GexqPKVekBng5odJpZr7EgFrcgVMAwHAwjhtaFlq1v2zDY3dfHtOfxYColAWs1yOdPMTd3D5bVR1/fNZhm+9KMBlrzubNnyQUBt4yOVg0mEtY+lVzlA5EBkk7t7GfnvtPFioRoTUSB2aDFLWvcgtAiShmYZhylDHw/1VLkuW0YXN/fTy4I+EEqhS/ivQzfz5JKPUihcIJE4kqi0W1ttX59rfnvM2eY831+WLZ9nfdU8rZP56bJeisHmC51lStlEABzvt+Wv6mQdnGFVmREvjVCV2IYDqaZQGsP30+XipU0jqkU1/f14SjF92dnyUuSFgCCwCeXO0QqdR+2PUQyeTWW1ViO50ZxS4FoGnjN+vUi2r1OCBO8SilPodFc5Qq0j6KUOa3KuDZu3FjR81bvPm5Z4xaEOjhnfTt4Xrok4I1/e5RSXJlMMmLbfHtmhmk3vOQvvYbWmkLhFKnUvQRBgb6+64hENrV9jTnlefzXxAQDlsUPj4yIe3wtEMJ/w2rbwWrft3iNW2PiaU3MUFULcBw5cgSjyclit7nKJYO/0PUYho1SFkGQx/fnUMrBMKINi81F8Tj9lsXeuTkujsfZ0sGqYp3AdafI5Q6hlEU8fknbLewyJ/J5PnnmDNcmkzxTtnwJC2g2c9pCAS4WLiqW3pvzA6wq4rxSnEstCz80V7kIt3A+UY481zpCEOTwvJlSMFu0oYpj6x2Hp/f3c28qxVnX5YpEouoXfa3gupPk88cATTS6tW2JVCrxUDrNl6emeOnwMDulaMjaow25yqGxzGlFx7FGB/6KmdOK/VVOwFKLUFOehuDxE+EWeoqigMcwjMUCrlQUw6hvI0jSsnjmwAAPpdN8c2aG6/r6GFhj5SO1DnDdsxQKJwBFJLIZ2x5atf7dIODr09Mcz+d5/fr1jDprtMbp+UxIrvKla8eNusqL1QcVbqAZsqr/Bqxocde08ENa47YsyOVabyaEoQjCqlNe/zaMGEGQw/dnCAITw4iV0qmujKkUVyWTHM/n+f7sLFuiUXbGYhg9vvbq+wVcdwLXPY1pxolEtmHbq+uenigU+J+zZ1lv2/zY2BiRNe7REJqnYj3uOouMBPNKqtCBT6ADcjXEtaLFXYfXQNa4BSFEyha4acZKqVOz+H4Kw4iU3OgrW+EbIxGGLIv702m+MT3NNckkgz1W5EJrjedN47pn8P00tj1EPH4pprm6a/haa+6Zm+M7s7O8YGiIyxPt2wsurA2adZUvtbhRFvnAw2pi4l3vPu5QLO6QTHcRbmHNUBTrCEHgEQQ5XHcKpUyUisxnZqtEzDTZ09/P0VyOu+fm2OQ4XByPd/V2Ja01vp/CdafwvElMM4ZtjxCL7WhozT8sJgoFvjQ1RdQweOPYWM9NfoTOEE5UORgoAu2RNJtzlddCLG5BaDPF4iVJIEkQFLOwue6ZeRE3jCiGsfxPf3M0yqjj8HApoOrSeJxt0caj19tFMZ97CtedxvdnUMrBsgZL1nWkI2MqBAHfnpnhkUyGZw8McEUi0TX/XkL3U63ISD2u8sUHTLK+h2blCWM1V3k9a9yh7CIV4RaE2hQjzx2gnyAolCzxSZTSpW1lRSu97FKPGAbX9vUx7bo8kE5zKJfjkkSCsQ4EV2kd4PsZfD+F788RBFlMM4Fp9hGJbKhrLb9dBFrzcDrNvlSKdbbNm8fHia9g7QhrkBCKjFTdDtZQrnIwlI0XFEha7ali120JWES4hfOGsohbVj9B4KF1gSDI43mzQFAScgelbPpNm2cPDnIsl+P+VArHMLg0Hmd9mwRc66C0Rp8rrdOnCYJ8yTsQx3HGMM1kR9zgS3kik+FbMzMkTJMXDg2xIdIZa1/ofaplTqvFMuE2bAo6S62/xGqW9arlKpc1bkFonqKr3MI0i3uLtfYJgkKpwEkKrQtoHTCiLJ6TsDjmutwzkyJumlwYTbIxEiltPzNqutm01mjto7UHFJ+DwC314Zb6dUuWf9GVH4kMYBjxrnE7a63Zn82yL5UiFwQ8f3CQ7bH2WDfC+UOza9yweM1aKQvPLzAUre4qr7bGXe8+brG4BaHLUMrENGPAOTEqCq5LELhsNT02OxZHclkeTJ3i3lmfHVGTrbbCMhbnTdYalApK6Vk15bKDxTV2q/Rc3LqmVD9K2SVLvztEeiFuEPBwJsMP5uaImybXJ5PsiMW6cqxC71FxH3ed28EWCrFl9FFgrqlUuvWucYdW1jOEhkS4BaEKxapDzqK15B0O7OiHs67L45kMX8kU2Og4XBCLMmLbsGj2Xtsa71ZOFwo8ksmwP5Nh1HG4cXhYXOJC6FQNTmtwjTsaGcJ2CkRX2AnSalR5aBa3uMoFoTOss22eMTBAzvc5lM+zdy4FwJZIhM2RSE9uh0p7Ho9nszySyZAPAi6Nx/mR0VEGevCzCL1BeNvBIOv7NS3uailPJVe5IJxHRE2Ti+NxLo7HOeu6HM7l+ObMDArYHImw0XEYcZyuzch2plDgqVyOp3I5Zj2PbdEozxkYYFMk0rPeAmGVqGYuN0ArmdMWFhoxlKKgNbEVLO4LLrigaavbsmSNWxDWJOtsm3W2zbV9fUy5LkfzeX6QSjHr+4zYNuOOw4hlMWzbHSlsorVm0vM4kc9zslDgrOuS15oLolGe2d9fDLgTsRZWkVYypwULlNQEPK1XTK/7xBNPVC3rWc8ad2hR5SLcgtCdDNk2Q7bNlckkhSBgolDglOtybyrFpFfM8LTOthmyLPpMkz7LImmaodWpzvk+057HpOcx6bpMeR5nXBfHMNjgOGyKRLi+r48hcYMLHSQsV7lpGBSCAKcZV/lq5yqXNW5B6H4cw2BzNMrmUv3vQOuiqLou057H8UKBOc8jHQTEDQNbKaKGQcQwiJbeK6UwKLoEFeBrjVt6eFqTDwLSvk/a98kEAbHS/cMlC39bNMo625YkKUJX0Ww97qXCDeAFASv9dbcSnCZr3IJwnmMoxbBtM7zE2tVak/Z9skFALgjIBwF5rSn4xTrDAUXRL/+oWSWBt5XCUYq4aZIwTRKGseZrjAtrg7CiygF+devWFSem1SYEq1pkRIRbENYWSimSlkWy0wMRhHppR8rTJlzlAP01ln2qCXfEjKxe5rSQGpJpuSAIgtA4IcRjtBJV3ozru1K7GTdDDd0W4RYEQRAEqKz99biumxHutbTGLcItCIIgdIRqRUbaYXG3ssZtGKE4GEIrMiLCLQiCIHSEalHlte8Lz1Veb67yfL7h7pYjFrcgCILQy7SSgGW1XeXdFFUuwi0IgiA0TjXVDaGJbnOVd9t2MBFuQRAEoSNUsmSb3Q5WD9UmBKuW8lQpiMdbnvCIcAuCIAgdoZU1brPBLIBdUdbTMCCVar04SwhDEQRBEISGaWWN22/QBL7++uuXHatXzENb4y431qL5LsItCIIgdIRKKU/b5Sr/3ve+V9ElXmuSACG6yiEU812EWxAEQegIq5k5rVK79UwSIERXebkxsbgFQRCEVaedUeVt2g62tB53PZMEaIOrXCxuQRAEoRcJs8hILYIgqEukKxG6q1wsbkEQBKEXCbMedy2qucrrWeMWV7kgCIIglFgq3u0sMlIxOG21XeUi3IIgCEIvU0kUV83ibmA7mLjKBUEQhLVByAFqq11kZNVd5RKcJgiCIHSMEGpdroarvHxts9vBxFXeAEqpP1JKPaqUul8p9Sml1GCnxyQIgiCER6UkLLVzhxsNC3ezJT2L/YXoKj8PMqd9CbhCa30V8Djwrg6PRxAEQQiRpUlY2uEqb2UrGLQhqnwtu8q11l/UWnult98FNndyPIIgCEK4NOMqB7Asq+4+VrKsO5LydI1b3Av5SeCOTg9CEARBCI9Ke7nrcZV7nrfiNQtZqRZ3PVRKzdo0IQh3/VOWNqGU+jIwXuHUb2mtP1265rcAD/hYlTZuBW4F2Lp1a5tGKgiCICyirLotuKErrXHXvkcRNHCT1pobbrih4vFeTHnaceHWWr9opfNKqTcDNwMv1FUWNbTWtwO3A+zevTuseZEgCILQZiptB6snqrwRtNbs3bu3cls96CrvuHCvhFLqRuDXgedqrTOdHo8gCIIQLs24oRu1uKsFp0l1sPbwYaAP+JJSap9S6q86PSBBEAQhPCoFp9W+p/EELK1uBwvVVb6WLW6t9UWdHoMgCILQPpaucddbZKTeaxu5rhpl4W5xOb/IWt8OJgiCIKxtmt0O1ojVvdI+7vr6CqX8eJHzwFUuCIIgdCshqJlpVmo2XOHWWmMYy+Wu3jVugL4+aGAHWnUkV7kgCILQyyxzldcppmFY3I240DNhhUeLxS0IgiD0MpVSnobtKm81c1qxv5C2hIlwC4IgCL1MM5nToLFCI2G4ykPLnibCLQiCIPQyS13lw7FhTKPCwvey+0IKTqvTVR5aEhYRbkEQBKGXWWrJTuWm6hJky7Jat7gbMKFDs7glOE0QBEHoGCFElVdKeVovq7UdDMTiFgRBEASguSIjxftaD07ryBp3CDMAEW5BEAShYyyLKm9gO1i9+cqDIGB8fHkRyka2g4WW9lQypwmCIAi9TMWo8jZsB5uYmKjcTgOu8tCEWyxuQRAEoVeplKu8vvsaiyoPYzuYCLcgCILQ+4QcnFY8Fu4+7jC2gzW7Fr8MiSoXBEEQOkbLpbJaW+Neze1godXkFotbEARB6GVWY427mqu83r6K/YlwC4IgCMIyz3G3rnGHZnHLdjBBEAShl2l2jbuR7WArucpXfY1btoMJgiAIvcyyzGltKutZtZ06XeWmKUVGBEEQhF4npJSnS3W1HjG1bXtVq4OFVtZTosoFQRCEXqZSPe560FqHE5xWp6tcLG5BEARBYPX2cbe6HSw0i1uEWxAEQehllmVOa9Mad6vbwcTiFgRBEAQqV92qdx93I1Hlu3btqnjcMZ262hCLWxAEQRBovh53Ixa37/s8+eSTy45rNHk/X1cboVncEpwmCIIgdIx2RZWv0ho3NJY5TSxuQRAE4bynkqu8HjpRHUwsbkEQBOG8p5Vc5fWuca8UVV7vdjDbllzlgiAIgoBSi4uMrWZUuUbX7Sr3fXGVC4IgCALQXB7wRta4fd+vusZdf38hbgcTV7kgCILQMUJQs4Wi2K7qYKZpLjveaJGR0Na4xeIWBEEQOkKdoldPMwuN0HrEtJIQVyMMV3kIMWVFxFUuCIIg9DoLrdlG1rhbDU4rt1MPobrKRbgFQRCEXmapKNYbVb7aucpDsbjLpnsLswARbkEQBKGjLBTFdq1xt5qAJTSLW6mW/e4i3IIgCELztBitXW5ikcVdh/s6lH3cDdbjDsXiBhFuQRAEobdpZo27ke1ghmGwdevWZccbiSoPLTgNWl7nFuEWBEEQmqcN+crDzpyWz+c5duxY5XYayFUeiqscRLgFQRCE3qbd+7irJWBpNFe5uMoFQRAEgeXWbDuqg1Xb973q28FALG5BEASht1kUVd6GXOVVLe4GlVjWuAVBEASB5vdx17vG7ft+5ZSnDWZOC83iFle5IAiC0Ms0s49bXOWCIAiC0CGa3cddr+iG4SoPdR+3CLcgCILQMULaDtbMPu5GErCE4SoX4RYEQRAE2r+Pe6V63Kte1hNaLu0pwi0IgiB0lGb2cYexxt2Iqzx0i1uC0wRBEIRepZl93KFZ3J2KKl+rFrdS6n1KqfuVUvuUUl9USm3s9JgEQRCEcGkm8KuRfdxa6+4qMrLGLe4/0lpfpbW+Bvgs8NsdHo8gCIIQMs3s427UVe44zrLjUmSkDWitZxe8TUAD0yNBEAShJ2g2c1q9rnLXdSte20hUeTcVGbFCGkbbUEq9H/gJYAZ4foeHIwiCICwkBEVrZv24EYu7Wua0xvrrHotbNZqrNWyUUl8Gxiuc+i2t9acXXPcuIKq1/p0KbdwK3Fp6ewXwYDvG2iWMAGc6PYg2Ip+vd1nLnw3W/ue7DvhBG9pd7X+3tdLfNq31aKUTHRfuelFKbQU+r7W+osZ1e7XWu1dpWKuOfL7eZi1/vrX82UA+X7e1e772B12+xq2U2rng7SuBRzs1FkEQBEHoBrp9jfsDSqmLgQA4BPxMh8cjCIIgCB2lq4Vba/3qJm67PfSBdBfy+Xqbtfz51vJnA/l83dbu+dpf76xxC4IgCILQ5WvcgiAIgiAsZk0K91pPlaqU+iOl1KOlz/gppdRgp8cUJkqpH1VKPaSUCpRSayKKVyl1o1LqMaXUk0qpd3Z6PGGilPp7pdSEUmpNbsNUSm1RSn1NKfVw6e/yFzs9pjBRSkWVUt9TSt1X+nzvbbG9YaXUl5RST5Seh6pc55d+o/cppT7TYB8rfp+UUhGl1H+Uzt+tlNre5Mept783K6VOL/g8b22lv5pordfcA+hf8PoXgL/q9JhC/nw/BFil138I/GGnxxTy57sUuBj4OrC70+MJ4fOYwH7gQsAB7gMu6/S4Qvx8z6G4B/jBTo+lTZ9vA3Bd6XUf8Pga+++ngGTptQ3cDTy9hfY+CLyz9Pqd1X6fgFST7df8PgFvL//uAz8G/EcLn6ee/t4MfHi1/putSYtbr/FUqVrrL2qtvdLb7wKbOzmesNFaP6K1fqzT4wiRpwFPaq0PaK0LwL9T3N64JtBafwOY7PQ42oXW+oTW+gel13PAI8Cmzo4qPHSRVOmtXXq08pv5SuAfS6//EXhVC21Vop7v08IxfBx4oao3KXlz/a0qa1K4oZgqVSl1BHgDa7s4yU8Cd3R6EMKKbAKOLHh/lDX0w38+UXK5XkvRKl0zKKVMpdQ+YAL4kta6lc83prU+UXp9Ehircl1UKbVXKfVdpdSrGmi/nu/T/DUlI2cGWNdAH432B/Dq0vLlx5VSW5rsqy66ejvYStRKlaq1/i3gt0qpUt8BLEuV2s3UkwpWKfVbgAd8bDXHFgb1proVhG5BKZUEPgH80hKvXs+jtfaBa0rxMp9SSl2hta4as7DS93dJu1opVc1636a1PqaUuhD4qlLqAa31/iY/Qqf5H+DftNZ5pdTbKFr7L2hXZz0r3FrrF9V56ceAz9Njwl3r8yml3gzcDLxQlxZZeokG/vutBY4BC2fgm0vHhB5BKWVTFO2Paa0/2enxtAut9bRS6mvAjaxQ82Gl769S6pRSaoPW+oRSagNFK75SG8dKzweUUl+n6MmoR7jr+T6VrzmqlLKAAeBsHW031Z/WemHbf0txnb9trElX+VpPlaqUuhH4deAVWutMp8cj1OT7wE6l1AVKKYdisExDUbRC5yitjf4d8IjW+k87PZ6wUUqNlnemKKVi8P+3d8esUURBAMf/g4gRbUxsxEK0shAbtYnaCDYpBMHaFFpY+BksRAj4AaysVbCLlaBJZ6GCkkQbFRtBxMJKUFKMxXvCgUIS7vaWt/x/sLBccTvD3e7c2333houMd81cBhbr/iLwzx20iDgQEXvq/kHgLPB+m++/nfNpNIYrwMoYA5wtj1d/oPx1iTIPojvTmgU3zY3yy3gDWKPcwjjcd0wTzu8j5ZnL27oNbdb8ZcpzpN/AN+Bp3zFNIKcFymzkT5THAb3HNMHcHgJfgc36uV3rO6YJ53eOMllrbeScW+g7rgnmdxJ4U/PbAG6N+X5zwHPgA/AMmK2vnwbu1/15YJ0yQ3t9p9+Z/51PwG3KYAZgBnhcr5UvgWNj5rTV8ZaAdzWfVeB4l5+ZK6dJktSQQd4qlyRpqCzckiQ1xMItSVJDLNySJDXEwi1JUkMs3JIkNcTCLUlSQyzcUsci4kxtPjATEftqz+MTfcclDUlE3Bjph/25Lt06SC7AIk1BRNyhrOa0F/iSmUs9hyQNUl1XfgW4m5lP+o6nCxZuaQrqGsevgF/AfJZuTJImLCLuAd8zs6nGUjvRbHcwqTFzwH5gN2Xk/bPfcKThqV0Tj1BaOQ+WI25pCiJiGXgEHAUOZeagLyzStEXEKUof7POZ+aPveLrkiFvqWERcBTYz80FE7AJeRMSFzFzpOzZpQG4Cs8Bq6cTK68y83m9I3XDELUlSQ/w7mCRJDbFwS5LUEAu3JEkNsXBLktQQC7ckSQ2xcEuS1BALtyRJDbFwS5LUkD8zvKBETed4JgAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 576x576 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rebound.OrbitPlot(sim,slices=0.3,color=True,xlim=[-3,3],ylim=[-3,3]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then integrate it forward in time. Here, we use the hybrid integrator MERCURIUS. You can experiment with other integrators which might be faster, but since this is an eccentric orbit, you might see many close encounters, so you either need a non-symplectic integrator such as IAS15 or a hybrid integrator such as MERCURIUS." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# integrate\n", "sim.dt = sim.particles[1].P/60. # small fraction of Mercury's period\n", "sim.integrator = \"mercurius\" \n", "N = 1000\n", "times = np.linspace(0.,2.*np.pi*1e5,N)\n", "a = np.zeros(N)\n", "e = np.zeros(N)\n", "for i,t in enumerate(times):\n", " sim.integrate(t,exact_finish_time=0)\n", " orbit = sim.particles[-1].calculate_orbit(primary=sim.particles[0])\n", " a[i] = orbit.a\n", " e[i] = orbit.e" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot the orbital parameters!" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 648x504 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "fig = plt.figure(figsize=(9,7))\n", "ax = plt.subplot(211)\n", "ax.set_xlim([0,np.max(times)/2./np.pi])\n", "ax.set_xlabel(\"time [yrs]\")\n", "ax.set_ylabel(\"semi-major axis [AU]\")\n", "plt.plot(times/2./np.pi,a)\n", "ax = plt.subplot(212)\n", "ax.set_xlim([0,np.max(times)/2./np.pi])\n", "ax.set_xlabel(\"time [yrs]\")\n", "ax.set_ylabel(\"eccentricity\")\n", "plt.plot(times/2./np.pi,e);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To check the sensitivity of the integrations, let us perturb the initial orbit by a small factor equal to the confidence interval posted by Bill Gray (https://projectpluto.com/temp/spacex.htm#elements). Instead of just integrating one particle at a time, we here add 10 test particles. We also switch to the high precision IAS15 integrator to get the most reliable result." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "sim = rebound.Simulation(\"ss.bin\")\n", "Ntesla = 10\n", "for i in range(Ntesla):\n", " sim.add(primary=sim.particles[0],\n", " M=(tesla.orbit.M+0.0013*np.random.normal()) *np.pi/180.,\n", " a=(tesla.orbit.a+0.000273*np.random.normal()),\n", " omega = (tesla.orbit.omega+0.00059*np.random.normal()) *np.pi/180.,\n", " Omega = (tesla.orbit.Omega+0.0007*np.random.normal()) *np.pi/180.,\n", " e = (tesla.orbit.e+0.00015*np.random.normal()),\n", " inc = (tesla.orbit.inc+0.0007*np.random.normal()) *np.pi/180.)\n", "sim.N_active = 9 # Sun + planets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's integrate this..." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "sim.dt = sim.particles[1].P/60. # small fraction of Mercury's period\n", "sim.integrator=\"ias15\" \n", "N = 1000\n", "times = np.linspace(0.,2.*np.pi*1e3,N)\n", "a_log = np.zeros((N,Ntesla))\n", "e_log = np.zeros((N,Ntesla))\n", "for i,t in enumerate(times):\n", " sim.integrate(t,exact_finish_time=0)\n", " for j in range(Ntesla):\n", " orbit = sim.particles[9+j].calculate_orbit(primary=sim.particles[0])\n", " a_log[i][j] = orbit.a\n", " e_log[i][j] = orbit.e" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When plotting the semi-major axis and eccentricity of all orbits, note that their kicks are correlated. This is because they are all due to close encounters with the Earth. This fast divergence means that we cannot predict the trajectory for more than a hundred years without knowing the precise initial conditions and all the non-gravitational effects that might be acting on a car in space." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 648x504 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "fig = plt.figure(figsize=(9,7))\n", "ax = plt.subplot(211)\n", "ax.set_xlim([0,np.max(times)/2./np.pi])\n", "ax.set_xlabel(\"time [yrs]\")\n", "ax.set_ylabel(\"semi-major axis [AU]\")\n", "for j in range(Ntesla):\n", " plt.plot(times/2./np.pi,a_log[:,j])\n", "ax = plt.subplot(212)\n", "ax.set_xlim([0,np.max(times)/2./np.pi])\n", "ax.set_xlabel(\"time [yrs]\")\n", "ax.set_ylabel(\"eccentricity\")\n", "for j in range(Ntesla):\n", " plt.plot(times/2./np.pi,e_log[:,j])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

wachtlerlab/RWeiss

Auswertung_200er.ipynb

1

22946

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import glob\n", "import csv\n", "from operator import add\n", "import scipy.stats as stat\n", "\n", "#Dateiliste erstellen\n", "files = []\n", "for filename in glob.glob('/home/weiss/data/*.csv'):\n", " files.append(filename)\n", " \n", "#read files\n", "seen = 0\n", "trials = 0\n", "catchseen = 0\n", "catchtrials = 0\n", "deltas = []\n", "freqs = []\n", "for filename in files:\n", " data = csv.reader(open(filename, \"rb\"), delimiter=',')\n", " data.next()\n", " data.next()\n", " data.next()\n", " for row in data:\n", " freqdiff = abs(int(row[0][0:-2])-int(row[1][0:-2]))\n", " if float(row[3]) < float(row[4]):\n", " if row[0] != row[1]:\n", " trials += 1\n", " if row[5] == 'y':\n", " seen += 1\n", " deltas.append((freqdiff,1))\n", " freqs.append((int(row[0][0:-2]),int(row[1][0:-2]),1))\n", " else:\n", " deltas.append((freqdiff,0))\n", " freqs.append((int(row[0][0:-2]),int(row[1][0:-2]),0))\n", " if row[0] == row[1]:\n", " catchtrials += 1\n", " if row[5] == 'n':\n", " catchseen += 1\n", " else:\n", " print row\n", " \n", "d_delta = {}\n", "for freq, count in deltas:\n", " if freq not in d_delta:\n", " d_delta[freq] = [0,0]\n", " d_delta[freq][0] += 1\n", " if count:\n", " d_delta[freq][1] += 1\n", " \n", "d_freqs = {}\n", "for freq1, freq2, count in freqs:\n", " if (freq1, freq2) not in d_freqs:\n", " d_freqs[(freq1, freq2)] = [0,0]\n", " d_freqs[(freq1, freq2)][0] += 1\n", " if count:\n", " d_freqs[(freq1, freq2)][1] += 1\n", " \n", "\n", "d_all = d_freqs.copy()\n", "d_all['overall\\ncorrectness'] = (1, round(float(seen)/trials, 3))\n", "d_all['false\\npositives'] = (1, 1-round(float(catchseen)/catchtrials, 3))\n", "\n", "print 'seeing-prob:', round(100.*seen/trials, 3), '%'\n", "print 'catchtrial accuracy:', round(100.*catchseen/catchtrials), '%'\n", "\n", "delta_std = loadtxt(\"/home/weiss/data/delta_std.txt\", delimiter=', ')\n", "d_all_std = loadtxt(\"/home/weiss/data/d_all_std.txt\", delimiter=', ')\n", "contrast_std = loadtxt(\"/home/weiss/data/contrast_std.txt\", delimiter=', ')\n", "\n", "print delta_std[:,2].shape\n", "\n", "delta_std = [stat.sem(delta_std[:,i]) for i in range(delta_std.shape[1])]\n", "d_all_std = [stat.sem(d_all_std[:,i]) for i in range(d_all_std.shape[1])]\n", "contrast_std = [stat.sem(contrast_std[:,i]) for i in range(contrast_std.shape[1])]\n", "\n", "f, p = plt.subplots(1, figsize=(12,7))\n", "\n", "ids = [2, 6, 8, 15, 17, 21]\n", "zweihunderter = [(200,400), (400,200), (400,600), (600,400), (600,800), (800,600)]\n", "\n", "zweihundert = [(200,400), (400,200), (200,500), (500,200), (200,600), (600,200), (200,800), (800,200)]\n", "ids2 = [2,6,3,10,4,14,5,18]\n", "\n", "niedrige = []\n", "ids3 = []\n", "\n", "p.bar(range(6), [float(d_all[key][1])/d_all[key][0] for key in zweihunderter], align='center', alpha=.3, width=.2, yerr=[d_all_std[i] for i in ids], ecolor='k', error_kw={'elinewidth':2})\n", "p.set_xticks(range(6))\n", "p.set_xticklabels(zweihunderter)\n", "p.set_ylim(0, 1.1)\n", "p.set_xlim(-1, 6)\n", "#p.grid()\n", "#p.set_title('Probability of Seeing', fontsize=15)\n", "p.set_xlabel('Frequenzen [Hz]', fontsize=15)\n", "p.set_ylabel('P', fontsize=15)\n", "plt.xticks(fontsize=15)\n", "plt.yticks(fontsize=15)\n", "plt.tight_layout()\n", "#plt.savefig('/home/weiss/ba/figures/delta200.pdf')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['200.0', '800.0', '1.3', '0.6', '0.30572104454', 'y', '1']\n", "['400.0', '800.0', '1.4', '1.1', '1.01087594032', 'n', '0']\n", "seeing-prob: 52.504 %\n", "catchtrial accuracy: 95.0 %\n", "(6,)\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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A/OAHP4gHHnggzj///OjRo0d89KMfLeRHAAAA2K+CBq3i4uKorq6O3bt3x8SJE/Mh6+XX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\nOSWqq6ujvLy8kB8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"text": [ "<matplotlib.figure.Figure at 0x7f72946c2310>" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

bsd-2-clause

NeuroDataDesign/pan-synapse

pipeline_1/background/Sparse_Arrays_Algorithms.md.ipynb

1

199563

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sparse Arrays Incorporated With Cluster Components\n", "\n", "\n", "## Motive\n", "\n", "The purpose of this notebook is to determine if implimenting Sparse Arrays will speed up the Connected Components portion of our pipeline.\n", "\n", "## Pseudocode For Sparse Arrays Functions \n", "\n", " ** Initialization:** (inputs: image)\n", " Give the Sparse Array a data member called \"elements\"\n", " Run Connected Components on the input image \n", " Run clusterComponents on input image\n", " Threshold the clusterComponent by volume \n", " For each index in this thresholded clusterComponent, add its value to elements \n", " Give the Sparse Array a data member called \"clusterList by using the genClusters function \n", "\n", " **genClusters:** (input: image)\n", " create a variable called clusterList that will contain the Clusters\n", " for each unique label in the input image:\n", " find which indices in the Sparse Array are equal to that label \n", " input these indices into the Cluster class and append this Cluster to clusterList\n", " return clusterList\n", "\n", " **addValue:** (inputs: tuple, value)\n", " if value is greater than 0:\n", " set self.elements at tuple equal to value \n", " else if value equals 0 and there already exists a value at tuple: \n", " set delete self.elemnts at tuple \n", "\n", " **readValue:** (inputs: tuple)\n", " try:\n", " value gets self.elements at tuple \n", " catch KeyError: \n", " value = 0 (a.k.a. if there exists no element with that tuple, return 0)\n", " return value\n", " \n", "## The Code" ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import sys\n", "sys.path.insert(0,'../code/functions/')\n", "import tiffIO as tIO\n", "import connectLib as cLib\n", "import plosLib as pLib\n", "import time\n", "import scipy.ndimage as ndimage\n", "import numpy as np\n", "import time" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import clusterComponents\n", "\n", "class SparseArray:\n", " def genClusters(self, image):\n", " clusterList = []\n", " for label in range(np.max(image)):\n", " memberList = []\n", " for z in range(len(image)):\n", " for y in range(len(image[z])):\n", " for x in range(len(image[z][y])):\n", " if (self.readValue((z, y, x)) == label):\n", " memberList.append((z, y, x))\n", " self.addValue((z, y, x), 0)\n", " clusterList.append(Cluster(memberList))\n", " return clusterList\n", " \n", " def __init__(self, input, threshold = 200):\n", " self.elements = {}\n", " imCC = clusterComponents.ClusterComponent(input)\n", " imCC.volumeThreshold(threshold)\n", " ccThresh = imCC.labeledIm\n", " for z in range(len(ccThresh)):\n", " for y in range(len(ccThresh[z])):\n", " for x in range(len(ccThresh[z][y])):\n", " self.addValue((z, y, x), ccThresh[z][y][x])\n", " self.clusterList = self.genClusters(ccThresh)\n", "\n", " def addValue(self, tuple, value):\n", " if value > 0:\n", " self.elements[tuple] = value\n", " elif value == 0 and not(self.readValue(tuple) == 0):\n", " del self.elements[tuple]\n", "\n", " def readValue(self, tuple):\n", " try:\n", " value = self.elements[tuple]\n", " except KeyError:\n", " # could also be 0.0 if using floats...\n", " value = 0\n", " return value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cluster Class For Reference" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import math\n", "\n", "class Cluster:\n", " def __init__(self, members):\n", " self.members = members\n", " self.volume = self.getVolume()\n", "\n", " def getVolume(self):\n", " return len(self.members)\n", "\n", " def getCentroid(self):\n", " unzipList = zip(*self.members)\n", " listZ = unzipList[0]\n", " listY = unzipList[1]\n", " listX = unzipList[2]\n", " return [np.average(listZ), np.average(listY), np.average(listX)]\n", "\n", " def getMembers(self):\n", " return self.members\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Trying on A Slice of 5" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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ACoWC5Wx8vpkBOGKtMteFQnjWi5+MxHkj36RmRLpSXo1GI4vIcBo4fhycpDWx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MTr16/j6uoqg4SDWjiri4uLOD8/T/CCEgE4ODr7GODY2dlJjh/ijYikUTwLR/2hBgGeh4eHbFKy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LNJzIEx5bEcHx+3TKisiSATnp8Xk9lE5FRH1jc13kqln8NGsAltojw2ziaQBw4B9fv95opR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\n0uYUtr4W2cYAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1125a5990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dataSubset = tIO.unzipChannels(tIO.loadTiff('../data/SEP-GluA1-KI_tp1.tif'))[0][0:5]\n", "plt.imshow(dataSubset[0], cmap=\"gray\")\n", "plt.axis('off')\n", "plt.title('Raw Data Slice at z=0')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#finding the clusters after plosPipeline\n", "plosOutSub = pLib.pipeline(dataSubset)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#binarize output of plos lib\n", "bianOutSub = cLib.otsuVox(plosOutSub)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#dilate the output based on neigborhood size\n", "bianOutSubDil = ndimage.morphology.binary_dilation(bianOutSub).astype(int)" ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sparse = SparseArray(bianOutSubDil)" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [], "source": [ "intensities = []\n", "index = 0\n", "for z in range(len(bianOutSubThresh)):\n", " for y in range(len(bianOutSubThresh[z])):\n", " for x in range(len(bianOutSubThresh[z][y])):\n", " val = sparse.readValue((z, y, x))\n", " if val > 0:\n", " intensities.append(val)\n", " index = index + 1" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Percentage of Volume\n", "\tActual: 0.0157359441121\tExpected: .015\n", "Number of Clusters\n", "\tActual: 1754\tExpected: 1500 - 2000\n" ] } ], "source": [ "print 'Percentage of Volume\\n\\tActual: ' + str(1.0*np.max(index)/(1024*1024*3)) + '\\tExpected: .015'\n", "print 'Number of Clusters\\n\\tActual: ' + str(np.max(intensities)) + '\\tExpected: 1500 - 2000'" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": false }, "outputs": [], "source": [ "displayIm = np.zeros_like(bianOutSubDil)\n", "for z in range(len(bianOutSubDil)):\n", " for y in range(len(bianOutSubDil[z])):\n", " for x in range(len(bianOutSubDil[z][y])):\n", " displayIm[z][y][x] = sparse.readValue((z, y, x))" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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jbqNRRnGQM/GbU7N4TyNzBTcvh4LGwc/nFRofgs26eB1uGG7YqhWy8qpfNmji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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1589bfe90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(displayIm[3])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Results:**\n", "\n", "After Storing the Data as a sparse array and counting the non-zero values, I found that the non-zero values (a.k.a. the detected Synapses) represented about 1.5% of the data. It also detected 1754 Synapses, which is also around how many were detected without using Sparse Arrays. Both of these signs indicate that the Sparse Array is storing values correctly.\n", "\n", "The display image also looks as we would expect it to." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Now, let's determine if it helps the Connected Components speed:**" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "57874.5863619\n" ] } ], "source": [ "start_time = time.time()\n", "sparse = SparseArray(bianOutSubDil)\n", "print time.time() - start_time" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

analysiscenter/dataset

examples/experiments/self_attention/research_self_attention.ipynb

1

137751

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import torch.nn as nn\n", "import matplotlib.pyplot as plt\n", "from matplotlib import colors as mcolors\n", "\n", "\n", "import sys\n", "sys.path.append('../../..')\n", "from batchflow.opensets import Imagenette160\n", "from batchflow import Pipeline, B, V, C, W\n", "\n", "from batchflow.models.torch import ResNet34, ResBlock\n", "from batchflow.models.torch.layers import ConvBlock\n", "\n", "from batchflow.models.metrics import ClassificationMetrics\n", "from batchflow.research import Research, Option, Results, KV, RP, REU, RI\n", "from batchflow.utils import plot_results_by_config, show_research, print_results" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Global constants\n", "NUM_ITERS = 50000 # number of iterations to train each model for\n", "N_REPS = 4 # number of times to repeat each model train\n", "RESEARCH_NAME = 'research' # name of Research object\n", "DEVICES = [3, 4, 5, 6, 7] # devices to use\n", "WORKERS = len(DEVICES) # number of simultaneously trained models\n", "TEST_FREQUENCY = 200\n", "\n", "dataset = Imagenette160() # dataset to train models on" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "domain = (Option('body', [KV({}, 'ResBlock'),\n", " # apply chosen self-attention to each block\n", " KV({'encoder/blocks' : {'attention_mode': 'se'}},\n", " 'block_SE_default'),\n", " KV({'encoder/blocks' : {'attention_mode': 'scse'}},\n", " 'block_SCSE_default'),\n", " KV({'encoder/blocks' : {'attention_mode': 'bam'}},\n", " 'block_BAM_default'),\n", " KV({'encoder/blocks' : {'attention_mode': 'cbam'}},\n", " 'block_CBAM_default'),\n", " KV({'encoder/blocks' : {'attention_mode': 'se', 'self_attention/ratio': 8}},\n", " 'block_SE_8'),\n", " # apply chosen self-attention once per stage\n", " KV({'encoder/order': ['skip', 'block', 'd'],\n", " 'encoder/downsample': {'layout': 'S', 'attention_mode': 'se'}},\n", " 'stage_SE_default'),\n", " KV({'encoder/order': ['skip', 'block', 'd'],\n", " 'encoder/downsample': {'layout': 'S', 'attention_mode': 'scse'}},\n", " 'stage_SCSE_default'),\n", " KV({'encoder/order': ['skip', 'block', 'd'],\n", " 'encoder/downsample': {'layout': 'S', 'attention_mode': 'bam'}},\n", " 'stage_BAM_default'),\n", " KV({'encoder/order': ['skip', 'block', 'd'],\n", " 'encoder/downsample': {'layout': 'S', 'attention_mode': 'cbam'}},\n", " 'stage_CBAM_default'),\n", " ]))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "config = {\n", " 'inputs/labels/classes': 10,\n", " 'body': C('body'),\n", " 'head/layout': 'Vf',\n", " 'head/units': 10,\n", " \"decay\": dict(name='exp', gamma=0.1),\n", " \"n_iters\": 7500,\n", " 'device': C('device'),\n", "}" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "train_root = (dataset.train.p \n", " .crop(shape=(160, 160), origin='center')\n", " .to_array(channels='first', dtype=np.float32)\n", " .multiply(multiplier=1/255)\n", " .run_later(64, n_epochs=None, drop_last=True,\n", " shuffle=True, prefetch=5)\n", " )\n", "\n", "train_pipeline = (Pipeline()\n", " .init_variable('loss')\n", " .init_model('dynamic', ResNet34, 'my_model', config=config) \n", " .train_model('my_model', B('images'), B('labels'), \n", " fetches='loss', save_to=V('loss'))\n", " )" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def acc(iteration, import_from):\n", " pipeline = (dataset.test.p\n", " .import_model('my_model', import_from)\n", " .init_variable('true', [])\n", " .update(V('true', mode='a'), B.labels) \n", " .init_variable('predictions', [])\n", " .crop(shape=(160, 160), origin='center')\n", " .to_array(channels='first', dtype=np.float32)\n", " .multiply(multiplier=1/255)\n", " .predict_model('my_model', B('images'), fetches='predictions',\n", " save_to=V('predictions', mode='a'))\n", " )\n", " pipeline.run(128, n_epochs=1, drop_last=False, shuffle=True)\n", " pred = np.concatenate(pipeline.v('predictions'))\n", " true = np.concatenate(pipeline.v('true'))\n", " accuracy = ClassificationMetrics(true, pred, fmt='logits',\n", " num_classes=10, axis=1).accuracy()\n", " return accuracy" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "research = (Research()\n", " .init_domain(domain, n_reps=N_REPS)\n", " .add_pipeline(root=train_root, branch=train_pipeline, variables='loss',\n", " name='train_ppl', logging=True)\n", " .add_callable(acc, returns='acc_vall', name='acc_fn', execute=TEST_FREQUENCY,\n", " iteration=RI(), import_from=RP('train_ppl')))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Research research is starting...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Domain updated: 0: 12%|█▏ | 243804/2000000.0 [6:10:39<44:29:55, 10.96it/s]IOPub message rate exceeded.\n", "The notebook server will temporarily stop sending output\n", "to the client in order to avoid crashing it.\n", "To change this limit, set the config variable\n", "`--NotebookApp.iopub_msg_rate_limit`.\n", "\n", "Current values:\n", "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", "NotebookApp.rate_limit_window=3.0 (secs)\n", "\n", "Domain updated: 0: 15%|█▍ | 294387/2000000.0 [7:26:48<43:08:41, 10.98it/s]" ] } ], "source": [ "!rm -rf research\n", "\n", "research.run(NUM_ITERS, name=RESEARCH_NAME,\n", " devices=DEVICES, workers=WORKERS,\n", " bar=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "results = Results(path=RESEARCH_NAME, concat_config=True)\n", "# results = research.load_results(concat_config=True)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1296x504 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "show_research(results.df, layout=['train_ppl/loss', 'acc_fn/acc_vall'], average_repetitions=True, \n", " color=list(mcolors.TABLEAU_COLORS.keys()), log_scale=False, rolling_window=10)" ] }, { "cell_type": "code", "execution_count": 12, "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>acc_fn_3</th>\n", " <th>acc_fn_2</th>\n", " <th>acc_fn_1</th>\n", " <th>acc_fn_0</th>\n", " <th>acc_fn_mean</th>\n", " <th>acc_fn_std</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>body_ResBlock</th>\n", " <td>0.852156</td>\n", " <td>0.835729</td>\n", " <td>0.864476</td>\n", " <td>0.868583</td>\n", " <td>0.855236</td>\n", " <td>0.012782</td>\n", " </tr>\n", " <tr>\n", " <th>body_block_BAM_default</th>\n", " <td>0.856263</td>\n", " <td>0.837782</td>\n", " <td>0.858316</td>\n", " <td>0.839836</td>\n", " <td>0.848049</td>\n", " <td>0.009297</td>\n", " </tr>\n", " <tr>\n", " <th>body_block_CBAM_default</th>\n", " <td>0.860370</td>\n", " <td>0.852156</td>\n", " <td>0.854209</td>\n", " <td>0.850103</td>\n", " <td>0.854209</td>\n", " <td>0.003842</td>\n", " </tr>\n", " <tr>\n", " <th>body_block_SCSE_default</th>\n", " <td>0.854209</td>\n", " <td>0.864476</td>\n", " <td>0.864476</td>\n", " <td>0.870637</td>\n", " <td>0.863450</td>\n", " <td>0.005898</td>\n", " </tr>\n", " <tr>\n", " <th>body_block_SE_8</th>\n", " <td>0.862423</td>\n", " <td>0.864476</td>\n", " <td>0.868583</td>\n", " <td>0.862423</td>\n", " <td>0.864476</td>\n", " <td>0.002515</td>\n", " </tr>\n", " <tr>\n", " <th>body_block_SE_default</th>\n", " <td>0.878850</td>\n", " <td>0.866530</td>\n", " <td>0.868583</td>\n", " <td>0.876797</td>\n", " <td>0.872690</td>\n", " <td>0.005235</td>\n", " </tr>\n", " <tr>\n", " <th>body_stage_BAM_default</th>\n", " <td>0.868583</td>\n", " <td>0.854209</td>\n", " <td>0.868583</td>\n", " <td>0.845996</td>\n", " <td>0.859343</td>\n", " <td>0.009686</td>\n", " </tr>\n", " <tr>\n", " <th>body_stage_CBAM_default</th>\n", " <td>0.858316</td>\n", " <td>0.860370</td>\n", " <td>0.845996</td>\n", " <td>0.856263</td>\n", " <td>0.855236</td>\n", " <td>0.005529</td>\n", " </tr>\n", " <tr>\n", " <th>body_stage_SCSE_default</th>\n", " <td>0.850103</td>\n", " <td>0.858316</td>\n", " <td>0.854209</td>\n", " <td>0.858316</td>\n", " <td>0.855236</td>\n", " <td>0.003405</td>\n", " </tr>\n", " <tr>\n", " <th>body_stage_SE_default</th>\n", " <td>0.843943</td>\n", " <td>0.841889</td>\n", " <td>0.845996</td>\n", " <td>0.864476</td>\n", " <td>0.849076</td>\n", " <td>0.009009</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " acc_fn_3 acc_fn_2 acc_fn_1 acc_fn_0 acc_fn_mean \\\n", "body_ResBlock 0.852156 0.835729 0.864476 0.868583 0.855236 \n", "body_block_BAM_default 0.856263 0.837782 0.858316 0.839836 0.848049 \n", "body_block_CBAM_default 0.860370 0.852156 0.854209 0.850103 0.854209 \n", "body_block_SCSE_default 0.854209 0.864476 0.864476 0.870637 0.863450 \n", "body_block_SE_8 0.862423 0.864476 0.868583 0.862423 0.864476 \n", "body_block_SE_default 0.878850 0.866530 0.868583 0.876797 0.872690 \n", "body_stage_BAM_default 0.868583 0.854209 0.868583 0.845996 0.859343 \n", "body_stage_CBAM_default 0.858316 0.860370 0.845996 0.856263 0.855236 \n", "body_stage_SCSE_default 0.850103 0.858316 0.854209 0.858316 0.855236 \n", "body_stage_SE_default 0.843943 0.841889 0.845996 0.864476 0.849076 \n", "\n", " acc_fn_std \n", "body_ResBlock 0.012782 \n", "body_block_BAM_default 0.009297 \n", "body_block_CBAM_default 0.003842 \n", "body_block_SCSE_default 0.005898 \n", "body_block_SE_8 0.002515 \n", "body_block_SE_default 0.005235 \n", "body_stage_BAM_default 0.009686 \n", "body_stage_CBAM_default 0.005529 \n", "body_stage_SCSE_default 0.003405 \n", "body_stage_SE_default 0.009009 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print_results(results.df, 'acc_fn/acc_vall', False, ascending=True, n_last=100)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1+0" ] }, { "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" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }

apache-2.0

Timathom/timathom.github.io

netsci/week3/shortest_thompson_timothy_rev1.ipynb

1

43505

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Assignment\n", "\n", "For this assignment you will calculate and plot the distribution of the path lengths of a graph. First we will generate the graphs:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Name: gnp_random_graph(1200,0.008)\n", "Type: Graph\n", "Number of nodes: 1200\n", "Number of edges: 5733\n", "Average degree: 9.5550\n" ] } ], "source": [ "import networkx as nx\n", "import numpy as np\n", "import matplotlib.mlab as mlab\n", "import matplotlib.pyplot as plt\n", "from collections import deque\n", "\n", "%matplotlib inline\n", "\n", "#random_graph = nx.erdos_renyi_graph(10, 0.6)\n", "random_graph = nx.erdos_renyi_graph(1200, 0.008)\n", "print(nx.info(random_graph))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Write shortest path algorithm\n", "\n", "Let's write an algorithm that will calculate the shortest paths between all the nodes and then place the lengths of those shortest paths in a sequence (such as a list or array). \n", "\n", "The first task will be creating a function that calculate the shortest path lengths from a single node to everyone else, like the following:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def all_shortest_path_lengths_from(G, node): \n", " \"\"\"\n", " BSF implementation copied from MIT Open Courseware Lecture 13, Breadth-First Search (BSF): \n", " https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/lecture-videos/lecture-13-breadth-first-search-bfs\n", " \"\"\"\n", " \n", " level = {node: 0}\n", " parent = {node: None}\n", " i = 1\n", " frontier = [node]\n", " \n", " while frontier:\n", " next_frontier = []\n", " for u in frontier:\n", " for v in G[u]:\n", " if v not in level:\n", " level[v] = i\n", " parent[v] = u\n", " next_frontier.append(v)\n", " frontier = next_frontier\n", " i += 1\n", " \n", " return level\n", "\n", "def all_shortest_path_lengths_from_deque(G, node):\n", " \"\"\"\n", " BSF implementation adapted from Grokking algorithms, Adity Y. Bhargava. Manning Publications, 2016.\n", " Uses the deque collection object to store the search queue. \n", " \n", " I added a for-loop to the original implementation in order to properly record the level of each \n", " cluster of neighbors. \n", " \"\"\"\n", " \n", " search_queue = deque([node]) \n", " current = deque()\n", " searched = {node}\n", " \n", " level = {node: 0}\n", " parent = {node: None}\n", " i = 1\n", " \n", " while search_queue: \n", " current = deque()\n", " for neighbor in search_queue: \n", " for n in G[neighbor]: \n", " if not n in searched: \n", " level[n] = i\n", " parent[n] = neighbor \n", " current.append(n) \n", " searched.add(n) \n", " search_queue = current \n", " node = neighbor \n", " \n", " i += 1 \n", " \n", " return level\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{0: 0, 1: 4, 2: 4, 3: 3, 4: 3, 5: 3, 6: 3, 7: 3, 8: 4, 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1018: 3, 1019: 4, 1020: 4, 1021: 4, 1022: 2, 1023: 4, 1024: 4, 1025: 2, 1026: 4, 1027: 2, 1028: 4, 1029: 3, 1030: 3, 1031: 3, 1032: 3, 1033: 3, 1034: 4, 1035: 3, 1036: 4, 1037: 3, 1038: 3, 1039: 4, 1040: 2, 1041: 3, 1042: 3, 1043: 3, 1044: 3, 1045: 2, 1046: 3, 1047: 4, 1048: 3, 1049: 3, 1050: 4, 1051: 4, 1052: 4, 1053: 4, 1054: 3, 1055: 3, 1056: 2, 1057: 4, 1058: 3, 1059: 3, 1060: 2, 1061: 3, 1062: 4, 1063: 3, 1064: 3, 1065: 3, 1066: 3, 1067: 3, 1068: 4, 1069: 3, 1070: 3, 1071: 3, 1072: 3, 1073: 3, 1074: 2, 1075: 3, 1076: 3, 1077: 4, 1078: 4, 1079: 4, 1080: 4, 1081: 3, 1082: 3, 1083: 4, 1084: 2, 1085: 4, 1086: 3, 1087: 3, 1088: 4, 1089: 3, 1090: 4, 1091: 4, 1092: 4, 1093: 3, 1094: 3, 1095: 3, 1096: 3, 1097: 4, 1098: 3, 1099: 3, 1100: 3, 1101: 4, 1102: 4, 1103: 4, 1104: 3, 1105: 3, 1106: 4, 1107: 3, 1108: 3, 1109: 3, 1110: 4, 1111: 3, 1112: 2, 1113: 3, 1114: 3, 1115: 4, 1116: 3, 1117: 3, 1118: 2, 1119: 3, 1120: 3, 1121: 3, 1122: 4, 1123: 3, 1124: 3, 1125: 4, 1126: 4, 1127: 3, 1128: 4, 1129: 4, 1130: 3, 1131: 4, 1132: 2, 1133: 4, 1134: 3, 1135: 3, 1136: 2, 1137: 3, 1138: 3, 1139: 4, 1140: 3, 1141: 3, 1142: 4, 1143: 3, 1144: 4, 1145: 3, 1146: 4, 1147: 3, 1148: 3, 1149: 3, 1150: 3, 1151: 3, 1152: 3, 1153: 3, 1154: 2, 1155: 3, 1156: 3, 1157: 4, 1158: 3, 1159: 3, 1160: 3, 1161: 3, 1162: 3, 1163: 2, 1164: 4, 1165: 3, 1166: 3, 1167: 3, 1168: 2, 1169: 4, 1170: 3, 1171: 3, 1172: 4, 1173: 2, 1174: 3, 1175: 4, 1176: 3, 1177: 3, 1178: 4, 1179: 3, 1180: 4, 1181: 4, 1182: 4, 1183: 4, 1184: 3, 1185: 4, 1186: 2, 1187: 3, 1188: 3, 1189: 4, 1190: 3, 1191: 4, 1192: 2, 1193: 4, 1194: 3, 1195: 3, 1196: 4, 1197: 3, 1198: 4, 1199: 3}\n" ] } ], "source": [ "p = all_shortest_path_lengths_from_deque(random_graph, 0)\n", "print(p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You need a queue that stores the nodes to visit at the next hop. You pull one from this queue, say `i`, assign the current distance, and then go through each neighbor of `i` and see whether we have seen the node before or not. If we have seen it, we can ignore it (we already know the shortest distance). If not, we should put this node to the queue. You may want to have two queues (or lists or sets) for the \"current\" level and the \"next\" level of the search. \n", "\n", "After getting the single-source shortest path algorithm right, you can apply it to every node in the graph and obtain the list of shortest path length values. " ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def all_shortest_path_lengths(G): \n", " \"\"\"Creates a list of path lengths starting from each node in the graph.\"\"\"\n", " \n", " paths = deque()\n", " nodes = deque()\n", " processed = deque()\n", " \n", " for node in G: \n", " nodes = all_shortest_path_lengths_from_deque(G, node)\n", " processed.append(node) \n", " for k, v in nodes.items():\n", " if not k in processed:\n", " paths.append(v) \n", " return paths" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Apply your algorithm to the ```random_graph```." ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "719400\n" ] } ], "source": [ "paths = all_shortest_path_lengths(random_graph)\n", "print(len(paths))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Visualizing the results\n", "Now that you have a list of the shortest paths for the graph, make a histogram for it. This can be done with [matplotlibs histogram function](http://matplotlib.org/api/pyplot_api.html?highlight=hist#matplotlib.pyplot.hist)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ASRFxW0Q8AOwPbCdpqxwzBpgIHBgR90XEXcChwJ6SRubtTAQ+DnwjImZGxHTg\nWOAQSSvlmL2BlfN6ZkXE5cBpwBF9doDMzMysIfo96SmSNEjSnsBqwF2SNgJGAjdXYiJiPnAPsG0u\n+jSpdaYY8zgwuxCzDTAvJ0QVNwEBbF2ImRkRHYWY6cAwYJNCzO0R8XYpZrSkYXXttJmZmS0XTZH0\nSPqkpNeAN4CzgK/kxGUkKTGZW/qTuXkZwAjgzZwMdRUzEnipuDAiFgOvlGKqbYcaY8zMzKwJrdRz\nyHLxGPApUqvKHsBFksb3b5XMzMxsRdIUSU++XPR0fvpA7otzOHASIFJrTrGFZQRQuVQ1BxgiaWip\ntWdEXlaJKY/mGgysXYrZslS1EYVllZ8jeojp0qRJkxg2bOmrYG1tbbS1tfX0p2ZmZiu89vZ22tvb\nlyrr7Oxs2PqbIumpYhCwSkQ8I2kOacTVQ/BOx+WtgTNz7P3A2znmqhwzGhgF3J1j7gbWlLRFoV/P\nBFJCdU8h5oeShhf69ewMdAKPFmJ+ImlwvjxWiXk8Inp8VaZMmcLYsWNrOAxmZmato1pDwIwZMxg3\nblxD1t/vSY+knwHXkToerwF8A9iBlExAGo5+jKQngWeBE4A/AVdD6tgsaSpwiqR5wGukEVV3RsS9\nOeYxSdOBcyQdDAwBTgfaI6LSQnMDKbm5OA+TXz9v64yIeCvHXAb8CDhP0i+ATYHDSK1SZmZm1sT6\nPekhXXa6kJRkdJJadHaOiN8DRMRJklYjzamzJnAHsGtEvFlYxyRgMTANWAW4HjiktJ29gDNIo7aW\n5Nh3kpWIWCJpN+DXwF2k+YAuAI4rxMyXtDOplek+oAOYHBFTl/komJmZWZ/q96QnInqc2C8iJgOT\nu1n+BmnenUO7iXmVNM9Od9t5Htith5iHSS1RZmZmNoA0xZB1MzMzs77mpMfMzMxagpMeMzMzawlO\neszMzKwlOOkxMzOzluCkx8zMzFqCkx4zMzNrCU56zMzMrCU46TEzM7OW4KTHzMzMWoKTHjMzM2sJ\nTnrMzMysJTjpMTMzs5bgpMfMzMxagpMeMzMzawnLnPRIGixpc0lrNaJCZmZmZn2h5qRH0qmSDsy/\nDwZuA2YAz0vasbHVMzMzM2uMelp69gAezL9/EdgI+DgwBfhpg+plZmZm1lD1JD3DgTn5988D/x0R\nTwDnAZs2qmJmZmZmjVRP0jMX+ES+tLULcGMuXw1Y3KiKmZmZmTXSSnX8zfnA5cCLQAA35fKtgcca\nVC8zMzPIBSviAAAgAElEQVSzhqo56YmIyZIeBjYkXdp6Iy9aDJzYyMqZmZmZNUo9LT1ExLQqZRcu\ne3XMzMzM+kZdSY+kCcAEYD1K/YIi4oAG1MvMzMysoWpOeiQdB/wIuI93+/WYmZmZNbV6Wnq+DewX\nERc3ujJmZmZmfaWeIetDgLsaXREzMzOzvlRP0nMusFejK2JmZmbWl3qV9Eg6pfIAVgGOkHSbpNOL\ny/Lymkj6gaR7Jc2XNFfSVZI+Voo5X9KS0uPaUswqks6U1CHpNUnTJK1XillL0qWSOiXNk3SupNVL\nMRtKukbSAklzJJ0kaVApZjNJt0taKOk5SUfVut9mZma2fPW2T88Wped/zD8/2YA6bA+cTuoYvRLw\nc+AGSWMiYmEh7jpgP0D5+Rss7VRgV2B3YD5wJnBFXn/FZcAI0sizIcAFwNnA3gA5ubkWeAHYBtgA\nuBh4Ezgmx6wBTAduAL5FuvXG+ZLmRcS5dR8FMzMz61O9SnoiYqe+qkBEfL74XNJ+wEvAOOAPhUVv\nRMTL1dYhaShwALBnRNyWy/YHZknaKiLulTQGmAiMi4gHcsyhwDWSjoyIOXn5x4GdIqIDmCnpWOBE\nSZMj4m1SgrQycGB+PkvSFsARpEt/ZmZm1oRq7tMj6bzc2lEuX13SeQ2o05qkYfCvlMp3zJe/HpN0\nlqS1C8vGkRK4mysFEfE4MBvYNhdtA8yrJDzZTXlbWxdiZuaEp2I6MAzYpBBze054ijGjJQ2rbVfN\nzMxseamnI/O+wPuqlL8P2GdZKiNJpMtUf4iIRwuLrsvr/izwPWAH4NocDzASeDMi5pdWOTcvq8S8\nVFwYEYtJyVUxZm6VdVBjjJmZmTWZXs/Tky8hKT/WkLSosHgw8HlKSUUdzgI+AWxXLIyIywtPH5E0\nE3gK2BG4ZRm3aWZmZi2glskJXyVdCgrgiSrLAziu3opIOoOUOG0fES92FxsRz0jqADYmJT1zgCGS\nhpZae0bkZeSf5dFcg4G1SzFbljY3orCs8nNEDzFVTZo0iWHDlr4C1tbWRltbW3d/ZmZm1hLa29tp\nb29fqqyzs7Nh668l6dmJ1Mrze9IIqWKfmzeB5yLihXoqkROeLwE7RMTsXsR/AFiHdBsMgPuBt0mj\nsq7KMaOBUcDdOeZuYE1JWxT69UzI+3RPIeaHkoYX+vXsDHQCjxZifiJpcL48Vol5PCK6fWWmTJnC\n2LFje9o9MzOzllStIWDGjBmMGzeuIevvddJTGBW1EfB8RCxpRAUknQW0AX8PLJBUaTXpjIhFeR6d\n40jDz+eQWnd+QWptmp7rNl/SVOAUSfOA14DTgDsj4t4c85ik6cA5kg4mDVk/HWjPI7cgDUN/FLhY\n0tHA+sAJwBkR8VaOuYx077HzJP2CNGT9MODwRhwPMzMz6xs133srIp4DkLQaqSVlSGn5QzWu8tuk\nS2O3lsr3By4CFgObkToyr0maQ2c68KNCIgIwKcdOI02geD1wSGmdewFnkEZtLcmx7yQrEbFE0m7A\nr0m32lhAmsvnuELMfEk7k+YBug/oACZHxNQa99vMzMyWo3rusr4ucD5pIsBqBteyvojodgRZRCwC\ndunFet4ADs2PrmJeJU9E2E3M88BuPcQ8TBpBZmZmZgNEPUPWTyW1uGwNLCQlJPsC/0e6RGVmZmbW\ndGpu6SHNlfOliLhP0hJSB+YbJc0HfgBc09AampmZmTVAPS09q/PufDzzgHXz7zMBD00yMzOzplRP\n0vM4MDr//iDwLUl/Q+qQ3O38OmZmZmb9pZ7LW78iDeUG+DFplNQ3SHP17NeYapmZmZk1Vj1D1i8p\n/H6/pA+S7kw+u3SjTjMzM7OmUU9LzzvyDT8XRsSMBtXHzMzMrE/U06cHSQdKehhYBCyS9LCkbza2\namZmZmaNU8/khMcDR5Bu4VC5r9W2wBRJoyLiRw2sn5mZmVlD1HN562DgnyKieBvU30p6iJQIOekx\nMzOzplPP5a2VSfecKrufZewjZGZmZtZX6kl6Lia19pQdBFy6bNUxMzMz6xv1tswcmO80/j/5+dak\nO65fJOmUSlBEHLGM9TMzMzNriHqSnk8ClSHqH8k/O/Ljk4W4WIZ6mZmZmTVUPZMT7tQXFTEzMzPr\nS3XN02NmZmY20DjpMTMzs5bgpMfMzMxagpMeMzMzawm9SnokzZC0Vv79R5JW69tqmZmZmTVWb1t6\nxgCr59+PA97fN9UxMzMz6xu9HbL+R+B8SX8ABBwp6a/VAiPi+EZVzszMzKxRepv07Af8GNiNNOng\nrsDbVeICcNJjZmZmTadXSU9EPA7sCSBpCTAhIl7qy4qZmZmZNVI9MzJ7xJeZmZkNOHXdcFTSR4B/\nJnVwBngU+FVEPNWoipmZmZk1Us2tNpImkpKcrYCH8mNr4BFJf9fY6pmZmZk1Rj0tPScCUyLi+8VC\nSScCvwBubETFzMzMzBqpnv45Y4CpVcrPAz5R68ok/UDSvZLmS5or6SpJH6sSd7ykFyS9LulGSRuX\nlq8i6UxJHZJekzRN0nqlmLUkXSqpU9I8SedKWr0Us6GkayQtkDRH0kmSBpViNpN0u6SFkp6TdFSt\n+21mZmbLVz1Jz8vA5lXKNwfqGdG1PXA66RLZ54CVgRskva8SIOlo4LvAQaTLaguA6ZKGFNZzKvAF\nYHdgPLABcEVpW5eRkrYJOXY8cHZhO4OAa0ktYNsA+5KG6x9fiFkDmA48A4wFjgImS/pmHftuZmZm\ny0k9l7fOAf5D0oeBu3LZdsDRwCm1riwiPl98Lmk/UvI0DvhDLj4cOCEifpdj9gHmAl8GLpc0FDgA\n2DMibssx+wOzJG0VEfdKGgNMBMZFxAM55lDgGklHRsScvPzjwE4R0QHMlHQscKKkyRHxNrA3KTE7\nMD+fJWkL4Ajg3Fr338zMzJaPelp6TiC1fBwK3JYf3wUmAz9pQJ3WJE1y+AqApI2AkcDNlYCImA/c\nA2ybiz5NSuCKMY8Dswsx2wDzKglPdlPe1taFmJk54amYDgwDNinE3J4TnmLMaEnD6thfMzMzWw5q\nTnoimRIRHyAlA8Mi4gMR8auIiGWpjCSRLlP9ISIezcUjSYnJ3FL43LwMYATwZk6GuooZSenyW0Qs\nJiVXxZhq26HGGDMzM2sydc3TUxERrzWqItlZpM7Q2zV4vU1h0qRJDBu2dGNQW1sbbW1t/VQjMzOz\n5tHe3k57e/tSZZ2dnQ1b/zIlPY0k6Qzg88D2EfFiYdEc0k1OR7B0C8sI4IFCzBBJQ0utPSPyskpM\neTTXYGDtUsyWpaqNKCyr/BzRQ0xVU6ZMYezYsd2FmJmZtaxqDQEzZsxg3LhxDVl/U9xSIic8XyJ1\nIJ5dXBYRz5CSiQmF+KGkfjiVjtT3k26AWowZDYwC7s5FdwNr5k7HFRNICdU9hZhNJQ0vxOwMdJIm\nZKzEjM8JUzHm8YhoXDpqZmZmDdXvSY+ks4BvAHsBCySNyI9VC2GnAsdI+qKkTYGLgD8BV8M7HZun\nAqdI2lHSONK8QXdGxL055jFSh+NzJG0paTvSUPn2PHIL4AZScnNxnotnIqnj9hkR8VaOuQx4EzhP\n0ickfR04DDi5L46PmZmZNUZNSY+klSXdLOmjDazDt4GhwK3AC4XH1yoBEXESKUE5m9Qq8z5g14h4\ns7CeScDvgGmFde1e2tZewGOkUVu/A24HvlXYzhJgN2AxqRXpIuAC4LhCzHxSy86HgPuAXwKTI6La\nhI1mZmbWJGrq0xMRb0narJEV6O1d2yNiMmlYfFfL3yANoz+0m5hXSfPsdLed50mJT3cxDwM7dBdj\nZmZmzaWey1uXAAc2uiJmZmZmfame0VsrAQdI+hypA/GC4sKIOKIRFTMzMzNrpHqSnk8CM/Lv5RuD\nLtPkhGZmZmZ9peakJyJ26ouKmJmZmfWluoesS9pY0sTK3dDzLSTMzMzMmlLNSY+kdSTdDDwBXAus\nnxdNleS5aszMzKwp1dPSMwV4izTb8euF8v8CdmlEpczMzMwarZ6OzDsDEyPiT6UrWv8HfLAhtTIz\nMzNrsHpaelZn6RaeirWBN5atOmZmZmZ9o56k5w5gn8LzkDQI+B5wS0NqZWZmZtZg9Vze+h5ws6RP\nA0OAk4BNSC092zWwbmZmZmYNU3NLT77v1MeAP5Ducr46cCWwRUQ81djqmZmZmTVGPS09REQn8NMG\n18XMzMysz9SV9Ehai3TT0TG56FHg/Ih4pVEVMzMzM2ukeiYnHA88CxwGrJUfhwHP5GVmZmZmTaee\nlp4zSRMRHhwRiwEkDQbOyss2bVz1zMzMzBqjniHrGwMnVxIegPz7KXmZmZmZWdOpJ+mZwbt9eYrG\nAA8uW3XMzMzM+kavLm9J2qzw9DTgV5I2Bv4nl20DHAJ8v7HVMzMzM2uM3vbp+SMQQPFmWydVibuM\n1N/HzMzMrKn0NunZqE9rYWZmZtbHepX0RMRzfV0RMzMzs75U7+SEGwCfAdaj1Bk6Ik5rQL3MzMzM\nGqrmpEfSfsDZwJvAX0h9fSqC1NHZzMzMrKnU09JzAnA88POIWNLg+piZmZn1iXrm6VkN+E8nPGZm\nZjaQ1JP0TAX+odEVMTMzM+tL9Vze+gHwO0m7ADOBt4oLI+KIRlTMzMzMrJHqaen5ATARGEG6uegW\nhcfm9VRC0vaSfivpz5KWSPr70vLzc3nxcW0pZhVJZ0rqkPSapGmS1ivFrCXpUkmdkuZJOlfS6qWY\nDSVdI2mBpDmSTpI0qBSzmaTbJS2U9Jyko+rZbzMzM1t+6mnp+RfggIi4oIH1WJ006/NU4MouYq4D\n9uPdWaHfKC0/FdgV2B2YT7rj+xXA9oWYy0jJ2gRgCHABaSTa3gA5ubkWeIF0a40NgItJI9WOyTFr\nANOBG4BvkRK/8yXNi4hza9xvMzMzW07qSXreAO5sZCUi4nrgegBJ6iLsjYh4udoCSUOBA4A9I+K2\nXLY/MEvSVhFxr6QxpBaqcRHxQI45FLhG0pERMScv/ziwU0R0ADMlHQucKGlyRLxNSpBWBg7Mz2dJ\n2gI4AnDSY2Zm1qTqubz1K+DQRlekF3aUNFfSY5LOkrR2Ydk4UgJ3c6UgIh4HZgPb5qJtgHmVhCe7\niTS30NaFmJk54amYDgwDNinE3J4TnmLMaEnDlmkPzczMrM/U09KzFfBZSbsBj/DejsxfbUTFSq4j\nXap6BvgI8HPgWknbRkQAI4E3I2J+6e/m5mXkny+V6rpY0iulmLlV1lFZ9mD++XQ3MZ217ZqZmZkt\nD/UkPa/Sdb+bPhERlxeePiJpJvAUsCNwy/Ksy7KYNGkSw4Yt3RjU1tZGW1tbP9XIzMysebS3t9Pe\n3r5UWWdn49oSak56ImL/hm29ThHxjKQOYGNS0jMHGCJpaKm1Z0ReRv5ZHs01GFi7FLNlaXMjCssq\nP0f0EFPVlClTGDt2bHchZmZmLataQ8CMGTMYN25cQ9ZfT5+efifpA8A6wIu56H7gbdKorErMaGAU\ncHcuuhtYM3c6rphAGg12TyFmU0nDCzE7ky5ZPVqIGZ8TpmLM4xHhS1tmZmZNqp4bjj7D0jcZXUpE\nfLiOda5OarWpjNz6sKRPAa/kx3GkPj1zctwvgCdIHYiJiPmSpgKnSJoHvEa68emdEXFvjnlM0nTg\nHEkHk4asnw6055FbkIahPwpcLOloYH3SvcbOiIhK36XLgB8B50n6BWnI+mHA4bXut5mZmS0/9fTp\nObX0fGXSxIS7AL+ssx6fJl2mivw4OZdfCHwH2AzYB1iTNIfOdOBHhUQEYBKwGJgGrEIaAn9IaTt7\nAWeQRm0tybHvJCsRsSR30P41cBewgDSXz3GFmPmSdibNA3Qf0AFMjoipde67mZmZLQf19On5VbVy\nSYeQkpea5bl1urvUtksv1vEGaSh9l8PpI+JV8kSE3cQ8D+zWQ8zDwA491cnMzMyaRyP79FxHmg3Z\nzMzMrOk0MunZg9T/xszMzKzp1NOR+QGW7sgs0qR865L635iZmZk1nXo6Mv+m9HwJ8DJwa0Q8tuxV\nMjMzM2u8ejoy/7gvKmJmZmbWlwbk5IRmZmZmtep1S4+kJXQzKWEWEVHPJTMzMzOzPlVLgvKVbpZt\nS5qV2C1HZmZm1pR6nfRExNXlsnx/qxOBLwKXkm7PYGZmZtZ06mqZkbSBpHOAmaTEafOI2Dcinmto\n7czMzMwapKakR9KwfJPNJ4FNgAkR8cV8WwYzMzOzplVLR+bvAUeT7nTeVu1yl5mZmVmzqqUj84nA\nQlIrz76S9q0WFBFfbUTFzMzMzBqplqTnInoesm5mZmbWlGoZvbVfH9bDzMzMrE95Xh0zMzNrCU56\nzMzMrCU46TEzM7OW4KTHzMzMWoKTHjMzM2sJTnrMzMysJTjpMTMzs5bgpMfMzMxagpMeMzMzawlO\neszMzKwlOOkxMzOzluCkx8zMzFpCUyQ9kraX9FtJf5a0RNLfV4k5XtILkl6XdKOkjUvLV5F0pqQO\nSa9JmiZpvVLMWpIuldQpaZ6kcyWtXorZUNI1khZImiPpJEmDSjGbSbpd0kJJz0k6qpHHw8zMzBqv\nKZIeYHXgj8B3gCgvlHQ08F3gIGArYAEwXdKQQtipwBeA3YHxwAbAFaVVXQaMASbk2PHA2YXtDAKu\nJd19fhtgX2A/4PhCzBrAdOAZYCxwFDBZ0jfr2XEzMzNbPlbq7woARMT1wPUAklQl5HDghIj4XY7Z\nB5gLfBm4XNJQ4ABgz4i4LcfsD8yStFVE3CtpDDARGBcRD+SYQ4FrJB0ZEXPy8o8DO0VEBzBT0rHA\niZImR8TbwN7AysCB+fksSVsARwDn9sHhMTMzswZolpaeLknaCBgJ3Fwpi4j5wD3Atrno06QErhjz\nODC7ELMNMK+S8GQ3kVqWti7EzMwJT8V0YBiwSSHm9pzwFGNGSxpW526amZlZH2v6pIeU8ASpZado\nbl4GMAJ4MydDXcWMBF4qLoyIxcArpZhq26HGGDMzM2syAyHpMTMzM1tmTdGnpwdzAJFac4otLCOA\nBwoxQyQNLbX2jMjLKjHl0VyDgbVLMVuWtj+isKzyc0QPMVVNmjSJYcOWvgLW1tZGW1tbd39mZmbW\nEtrb22lvb1+qrLOzs2Hrb/qkJyKekTSHNOLqIYDccXlr4Mwcdj/wdo65KseMBkYBd+eYu4E1JW1R\n6NczgZRQ3VOI+aGk4YV+PTsDncCjhZifSBqcL49VYh6PiG5fmSlTpjB27NhaD4GZmVlLqNYQMGPG\nDMaNG9eQ9TfF5S1Jq0v6lKTNc9GH8/MN8/NTgWMkfVHSpsBFwJ+Aq+Gdjs1TgVMk7ShpHHAecGdE\n3JtjHiN1OD5H0paStgNOB9rzyC2AG0jJzcV5Lp6JwAnAGRHxVo65DHgTOE/SJyR9HTgMOLlvjo6Z\nmZk1QrO09HwauIXUYTl4N4G4EDggIk6StBppTp01gTuAXSPizcI6JgGLgWnAKqQh8IeUtrMXcAZp\n1NaSHHt4ZWFELJG0G/Br4C7SfEAXAMcVYuZL2pnUynQf0AFMjoipy3YIzMzMrC81RdKT59bpttUp\nIiYDk7tZ/gZwaH50FfMqaZ6d7rbzPLBbDzEPAzt0F2NmZmbNpSkub5mZmZn1NSc9ZmZm1hKc9JiZ\nmVlLcNJjZmZmLcFJj5mZmbUEJz1mZmbWEpz0mJmZWUtw0mNmZmYtwUmPmZmZtQQnPWZmZtYSnPSY\nmZlZS3DSY2ZmZi3BSY+ZmZm1BCc9ZmZm1hKc9JiZmVlLcNJjZmZmLcFJj5mZmbUEJz1mZmbWEpz0\nmJmZWUtw0mNmZmYtwUmPmZmZtQQnPWZmZtYSnPSYmZlZS3DSY2ZmZi3BSY+ZmZm1BCc9ZmZm1hKc\n9JiZmVlLcNJjZmZmLWFAJD2SjpO0pPR4tBRzvKQXJL0u6UZJG5eWryLpTEkdkl6TNE3SeqWYtSRd\nKqlT0jxJ50pavRSzoaRrJC2QNEfSSZIGxHE0MzNrZQPpn/XDwAhgZH58prJA0tHAd4GDgK2ABcB0\nSUMKf38q8AVgd2A8sAFwRWkblwFjgAk5djxwdmE7g4BrgZWAbYB9gf2A4xuzi2ZmZtZXVurvCtTg\n7Yh4uYtlhwMnRMTvACTtA8wFvgxcLmkocACwZ0TclmP2B2ZJ2ioi7pU0BpgIjIuIB3LMocA1ko6M\niDl5+ceBnSKiA5gp6VjgREmTI+Ltvtp5MzMzWzYDqaXno5L+LOkpSZdI2hBA0kaklp+bK4ERMR+4\nB9g2F32alOAVYx4HZhditgHmVRKe7CYggK0LMTNzwlMxHRgGbNKQvTQzM7M+MVCSnv8hXUaaCHwb\n2Ai4Pfe3GUlKTOaW/mZuXgbpstibORnqKmYk8FJxYUQsBl4pxVTbDoUYMzMza0ID4vJWREwvPH1Y\n0r3Ac8DXgMf6p1ZmZmY2kAyIpKcsIjolPQFsDNwKiNSaU2yFGQFULlXNAYZIGlpq7RmRl1ViyqO5\nBgNrl2K2LFVnRGFZtyZNmsSwYcOWKmt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"text/plain": [ "<matplotlib.figure.Figure at 0x7f88eb428e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = paths #all_shortest_path_lengths(random_graph)\n", "bins = list(range(0, 20, 2))\n", "\n", "plt.title(\"Histogram of shortest path lengths in random graph\")\n", "plt.xlabel(\"Path lengths\")\n", "plt.ylabel(\"Number of paths\")\n", "plt.hist(data, bins, histtype=\"bar\", rwidth=0.8)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Name your notebook: shortest_lastname_firstname.ipynb and submit to Canvas" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

mayankjohri/LetsExplorePython

Section 1 - Core Python/Chapter 01 - Introduction/01_01. Introduction.ipynb

1

28057

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chapter 1 - Introduction to Python\n", "__________" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Python](http://www.python.org) is a very **High Level, object-oriented, dynamic¹, strong typing², interpreted⁴** & **interactive⁵** programming language." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> 1. **Dynamic Programming language**: It at runtime executes many common programming tasks which static programming languages perform during compilation such as \n", " - Computation of code at runtime and late binding\n", " - alteration of Objects at runtime \n", " - Assembling of code at runtime based on the class of instances\n", "- **Strongly Typed**: Typing errors are prevented at runtime using the implicit type conversion, also it don't have static type checking, i.e. compiler won't check type constraint rules and reports such error's. The term **duck typing³** is now used to describe the dynamic typing paradigm.\n", "- **Duck typing** (https://en.wikipedia.org/wiki/Duck_typing) is an application of the duck test in type safety. It means that type checking of variables and data are done at runtime only, and is implemented by use of dynamic typing or by reflection.\n", "- **Interpreted**: Instead of compiled, the source code is interpreted by Python at runtime and then executed. \n", "- **Interactive**: Python provide an interactive shell, where you can run one line at a time and update the code as need be. More details about the shell will be provide later in this section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> It is also an open source language (with license compatible with the *General Public License (GPL)*, but less restrictive, allowing Python to be even incorporated into proprietary products). \n", "> Its specification is maintained by the [Python Software Foundation](http://www.python.org/psf/) (PSF)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Key Features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python has been my choice of programming language since 2006 due to following reasons:\n", "\n", "* It uses an simple & elegant syntax which makes the code easier to read and maintain\n", "* Due to its simplicity, maintainability & fast development: its ideal language for prototype development and ad-hoc programming \n", "* Default installation of Python contains huge about of standard library supporting most of the common programming tasks, such as developing REST clients, websites, socket programming, searching text with regular expressions, reading and modifying files such as XML, json and yaml etc.\n", "* Python's interactive mode, makes it easy to validate snippets of code. \n", "* Is easily extended by adding new modules implemented in a compiled language such as C or C++.\n", "* Can also be embedded into an application to provide a programmable interface.\n", "* It can be executed on almost all Operating Systems including Mac OS X, Windows, Linux, and Unix.\n", "* Is free software in two senses\n", " - It doesn't cost anything to download or use Python, or to include it in your application. \n", " - It can be freely modified and re-distributed, because while the language is copyrighted it's available under an open source license.\n", "* Bundled development environment called IDLE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Some programming-language features of Python are:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Many basic data types: \n", " - numbers (floating point, complex, and unlimited-length long integers)\n", " - strings (both ASCII and Unicode) \n", " - Collections (lists, dictionaries)\n", "* Python supports object-oriented programming with classes and multiple inheritance.\n", "* Code can be grouped into modules and packages.\n", "* The language supports raising and catching exceptions, resulting in cleaner error handling.\n", "* Data types are strongly and dynamically typed. Mixing incompatible types (e.g. attempting to add a string and a number) causes an exception to be raised, so errors are caught sooner.\n", "* Python contains advanced programming features such as generators and list comprehensions.\n", "* Python's automatic memory management frees you from having to manually allocate and free memory in your code. \n", "\n", "It is possible to integrate Python with other languages such as C and Fortran. In general terms, it has many similarities with other dynamic languages such as Perl and Ruby." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## History" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The language was created in 1990 by **Guido van Rossum**, at National Research Institute for Mathematics and Computer Science in the Netherlands (CWI) and had originally focused on users as physicists and engineers. Python was designed from another existing language at the time, called ABC.\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Versions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The official implementation of Python is maintained by the PSF and written in C, and therefore is also known as CPython. The latest stable version is available for download at:\n", "\n", "[http://www.python.org/download/](http://www.python.org/download/)\n", "\n", "For Windows platforms, simply run the installer. For other platforms, such as Linux, Python should already be part of the system, in rare cases you might have to install it from the system package management, but in very rare cases, you may wish to compile and install the interpreter from the source files.\n", "\n", "There are also implementations of Python for. NET (IronPython), JVM (Jython) and Python (PyPy)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running programs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What code looks like" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example of Python program:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Drums\n", "Flute\n", "Harmonium\n", "Guitar\n", "Hello\n" ] } ], "source": [ "# the character \"#\" indicate that rest of the line is a comment \n", "# and will be ignored by the interpreter\n", "\n", "# A list of musical instruments\n", "instruments = ['Drums', 'Flute', 'Harmonium', \"Guitar\"]\n", "\n", "# for each instrument in the list of instruments\n", "for instrument in instruments:\n", " print(instrument)\n", "print(\"Hello\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In above example, `instruments` is a list containing the items \"Drums\", \"Flute\", \"Harmonium\" and \"Guitar\" and as the `for` loop is executed `instrument` corresponds to, an item from items on the list, one at a time." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Executing the code" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As the python is an interpreted language, source code have to executed by running the command `python` and passing the main source file as a paramter.\n", "\n", "To understand it better lets save the above code in a text file and name it `ap1.py`. This will be our main source file. The source files are usually identified by the extension \".py\" and can be run directly by the interpreter. Now lets \n", "- open a command terminal (`cmd.exe` for `Windows` and respective terminal command for *inx's such as xterm, mate-terminal, terminology [https://www.enlightenment.org/about-terminology]). \n", "- Navigate to the directory where the `apl.py` file is stored.\n", "- execute the following command\n", "\n", "```sh\n", "$:> python apl.py\n", "```\n", "\n", "Thus `apl.py` will run. On Windows, the file extensions \".py\", \". pyw\", \". pyc\" and \". pyo\" are associated with Python automatically during installation, so just click a the file to run it. The \". pyw\" files run with an alternate version of the interpreter that does not open the console window." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dynamic Typing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Python uses dynamic typing, which means that the type of a variable is inferred by the interpreter at runtime (this is known as *Duck Typing*). By the time a variable is created by attribution the interpreter defines the type of a variable, along with the operations that can be applied.\n", "\n", "Typing of Python is strong, ie, the interpreter checks whether the transactions are valid and does automatic coercions between incompatible types. In Python, coercions are performed automatically only between types that are clearly related, as integer and long integer. To perform the operation between non-compatible types, you must explicitly convert the type of the variable or variables before the operation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compilation and interpretation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The source code is translated by Python to bytecode, which is a binary format with instructions for the interpreter. The bytecode is cross platform and can be distributed and run without the original source.\n", "\n", "<img src=\"files/bpyfd_diags1.png\" alt=\"Compilation, interpretation and packing\" width=500>\n", "\n", "By default, the parser compiles the code and stores the bytecode on disk, so the next time you run it, there is no need to recompile the program, reducing the load time of execution. If the source files are changed, the interpreter will be responsible for regenerating the bytecode automatically, even using the *interactive shell*. When a program or a module is invoked, the interpreter performs the analysis of the code, converts to symbols, compiles (if there is no updated bytecode on disk) and runs it in the Python virtual machine.\n", "\n", "The bytecode is stored in files with the extension \". pyc\" (normal bytecode) or \". pyo\" (optimized bytecode). The bytecode can also be packaged along with an executable interpreter, to facilitate the distribution of the application, eliminating the need to install Python on each computer." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interactive Mode" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Python interpreter can be used interactively, where lines of code are typed into a *prompt* (command line) *shell* similar to the operating system.\n", "\n", "`python`\n", "\n", "It is ready to receive commands after the appearance of the signal `>>>` on the screen:\n", "\n", "`Python 2.6.4 (r264:75706, Nov 3 2009, 13:20:47)`<br/>\n", "`[GCC 4.4.1] on linux2`<br/>\n", "`Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.`<br/>\n", "`>>>`\n", "\n", "On Windows, the interactive mode is also available via the icon \"Python (command line)\".\n", "\n", "The interactive mode is a distinguishing feature of the language, as it is possible to test and modify code snippets before inclusion in programs, to extract and convert data or even analyze the state of the objects in memory, among other possibilities.\n", "\n", "Besides the traditional interactive mode of Python, there are other programs that act as alternatives to more sophisticated interfaces (such as <span class=\"note\" title=\"PyCrust is part of wxPython project (http://www.wxpython.org/\">PyCrust</span>):\n", "<img src=\"files/pycrust.png\" alt=\"pycrust\" width=700>\n", "![PyCrust] (files/pycrust.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Common IDE & Tools" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many development tools for Python, such as IDEs, editors and shells (that take advantage of the interactive capabilities of Python).\n", "\n", "*Integrated Development Environments* (IDEs) are software packages that integrate various development tools in an environment consistent with the goal of increasing developer productivity. Generally, IDEs include such features as syntax highlighting (colorized source code according to the syntax of the language), source browsers, integrated shell and *code completion* (the editor presents possible ways to complete the text it can identify while typing).\n", "Among Python IDEs, there are most popular ones:\n", "\n", "+ [Atom](https://atom.io)\n", "+ [PyDev](http://pydev.org/) (plug-in for Eclipse IDE)\n", "+ [vim](http://vim.org)\n", "+ [Sublime Text](http://www.sublimetext.com/)\n", "+ [PyScripter](http://code.google.com/p/pyscripter/)\n", "+ [SPE](http://pythonide.blogspot.com/) (Stani's Python Editor)\n", "+ [Eric](http://eric-ide.python-projects.org/)\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Entire list " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "(from https://wiki.python.org/moin/IntegratedDevelopmentEnvironments?action=show&redirect=IDE) \n", "\n", "\n", "**Name**|**Platform**|**Updated**|**Notes**\n", "-----|-----|-----|-----\n", "Thonny|Windows, Linux, Mac OS X, more|2016|For teaching/learning programming. Focused on program runtime visualization. Provides stepping both in statements and expressions, no-hassle variables view, separate mode for explaining references etc.\n", "Komodo|Windows/Linux/Mac OS X|2012|Multi-language IDE with support for Python 2.x and Python 3. Available as Komodo IDE (commercial).\n", "LiClipse|Linux/Mac OS X/Windows|2015|Commercial Eclipse-based IDE which provides a standalone bundling PyDev, Workspace Mechanic, Eclipse Color Theme, StartExplorer and AnyEdit, along with lightweigth support for other languages, and other usability enhancements (such as multi-caret-edition).\n", "NetBeans|Linux, Mac, Solaris, Windows|2009|Python/Jython support in NetBeans -- Open source, allows Python and Jython Editing, code-completion, debugger, refactoring, templates, syntax analysis, etc.; see also http://wiki.netbeans.org/Python. UPDATE: Netbeans 7.0 released without Python support. Check http://wiki.netbeans.org/Python70Roadmap for upcoming Python support.\n", "PyCharm|Linux/Mac OS X/Windows|2014|Free open-source IDE with a smart Python editor providing quick code navigation, code completion, refactoring, unit testing and debugger. Has a commercial Professional edition that fully supports Web development with Django, Flask, Mako and Web2Py and allows to develop remotely. Free PyCharm professional licenses for open-source projects.\n", "Python for VS Code|Linux/Mac OS X/Windows|2016|Free open-source extension for Visual Studio Code. Supports syntax highlighting, debugging, code completion, code navigation, refactoring, with support for Django, multi threaded, local and remote debugging.\n", "KDevelop|Linux/Mac OS X/(Windows)|2014|Free open-source IDE with a focus on static analysis-based code completion, navigation and highlighting. Also features a VI emulation mode.\n", "PyDev|Eclipse|2015|Free, open-source plugin for Eclipse -- Allows Python, Jython, and IronPython editing, code-completion, debugger, refactoring, quick navigation, templates, code analysis, unittest integration, Django integration, etc.\n", "Wing IDE|Windows, Linux, Mac OS X|2016|Commercial Python IDE with advanced debugger, editor with vi, emacs, visual studio and other key bindings, auto-completion, auto-editing, snippets, goto-definition, find uses, refactoring, unit testing, source browser, and much more. There are several product levels, including free and paid versions with a fully functional trial with up to three 10 day trial periods. See product features and pricing for details.\n", "PyScripter|Windows|2012|MIT licensed IDE written in Delphi with debugger, integrated unit testing, source browser, code navigation and syntax coloring/auto-completing editor.\n", "Pyshield|Windows, Linux|2010|Commercial IDE tool used to edit, debug Python script, publish encrypted scripts, build a standalone executable file, manage more files by project view, and make installation in various forms(.msi, .tar.gz, .rpm, .zip, .tar.bz2). It includes an editor simulating Emacs python-mode, a GUI debugger simulating GDB, a project view used to manage scripts, modules, extensions, packages, platform specific data files, and GUI interface to make installation.\n", "Spyder|Windows/Linux/Mac OS X|2012|Free open-source scientific Python development environment providing MATLAB-like features: console with variable browser, sys.path browser, environment variables browser, integrated plotting features, autocompletion and tooltips - editor with syntax highlighting, class/function browser, pyflakes/pylint code analysis, inline find/replace and search in files features, code completion and tooltips. 100% pure Python, part of Python(x,y) distribution (Windows/Linux).\n", "IDLE|Windows/Linux/Mac OS X/All Tk Platforms|2009|Multi-window colorized source browser, autoindent, autocompletion, tool tips, code context panel, search in files, class and path browsers, debugger, executes code in clean separate subprocess with one keystroke. 100% pure Python, part of Python 2.x and 3.x distributions.\n", "IdleX|Windows/Linux/Mac OS X/All Tk Platforms|2012|IdleX is a collection of over twenty extensions and plugins that provide additional functionality to IDLE, a Python IDE provided in the standard library. It transforms IDLE into a more useful tool for academic research and development as well as exploratory programming.\n", "µ.dev|Windows (needs to be compiled manually for other platforms)|2010|An open-source IDE, created using Lazarus. It's only for Python. include syntax highlighting, project manager, and uses pdb for debugging.\n", "Pyzo (formerly IEP)|Windows/Linux/Mac OS X|2016|Open-source Python IDE focused on interactivity and introspection, which makes it very suitable for scientific computing. Its practical design is aimed at simplicity and efficiency. Pyzo consists of two main components, the editor and the shell, and uses a set of pluggable tools to help the programmer in various ways: e.g. source structure, interactive help, workspace, file browser (with functionality for searching). Also includes a post-mortem debugger.\n", "PythonToolkit (PTK)|Windows/Linux/Mac OS X|2011|An interactive environment for python built around a matlab style console window and editor. It was designed to provide a python based environment similiar to Matlab for scientists and engineers however it can also be used as a general purpose interactive python environment especially for interactive GUI programming. Features include: Multiple independent python interpreters. Interactively program with different GUI toolkits (wxPython, TkInter, pyGTK, pyQT4 and PySide). Matlab style namespace/workspace browser. Object auto-completions, calltips and multi-line command editing in the console. Object inspection and python path management. Simple code editor and integrated debugger.\n", "PyStudio|Windows/Linux/Mac OS X|2012|Open-source plugin that adds syntax checking, integrated debugger and module search to Editra, a general purpose developer's text editor that supports python syntax highlighting, auto-indent, auto-completion, classbrowser, and can run scripts from inside the editor.\n", "Python Tools for Visual Studio|Windows|2013|Open-source plugin for Visual Studio 2010, 2012 and 2013. Supports syntax highlighting, debugging and rich intellisense, refactoring, object browser, MPI cluster debugging, Django intellisense and debugging, development REPL window and a debugging REPL window. Supports mixed-mode Python/C/C++ debugging.\n", "Exedore|Mac OS X|2013|Commercial with feature-limited free trial. A Mac-native, single-window IDE inspired by Xcode. Features integrated debugger, tabs, code completion with tab triggers, syntax highlighting themes, search and replace with regex, integrated REPL sessions, goto definition, file browser, integrated documentation browser. As of June 2015, does not support input() meaning any console input using this function is not supported.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### IDE Configurations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I will be using either vim/nvim/emacs or atom during this course. Few tips to use them are as follows\n", "\n", "- vim/nvim: \n", " - https://realpython.com/blog/python/vim-and-python-a-match-made-in-heaven/, \n", " - https://www.fullstackpython.com/vim.html\n", "- emacs: emacs-for-python, \n", " - http://www.jesshamrick.com/2012/09/18/emacs-as-a-python-ide/,\n", " - https://emacswiki.org/emacs/PythonProgrammingInEmacs\n", "- atom: \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Text Editors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are also text editors specialized in programming code, which have features like syntax colorization, export to other formats and convert text encoding.\n", "\n", "These editors support multiple programming languages​​, Python among them:\n", "\n", "+ [SciTE](http://www.scintilla.org/SciTE.html)\n", "+ [Notepad++](http://notepad-plus.sourceforge.net/br/site.htm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Shell** is the name given to interactive environments for executing commands that can be used to test small pieces of code and for activities like data crunching (extraction of information of interest in masses of data and subsequent translation to other formats).\n", "\n", "Beyond the standard Python **Shell**, there are others available:\n", "\n", "+ PyCrust \n", "+ IPython \n", "+ Reinteract\n", "+ bpython\n", "+ PyroShell" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Packers** are utilities that are used to build executables that comprise the bytecode, the interpreter and other dependencies, allowing the application to run on machines without Python installed, which facilitates program distribution.\n", "\n", "Among packers for Python, are available:\n", "\n", "+ py2exe (Windows only)\n", "+ cx_Freeze (portable)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Frameworks** are collections of software components (libraries, utilities and others) that have been designed to be used by other systems.\n", "\n", "Some of the most known *frameworks* availble are:\n", "\n", "+ Web: Django, TurboGears, Zope and web2py.\n", "+ Graphic interface: wxPython, PyGTK and PyQt.\n", "+ Scientific processing: NumPy and SciPy.\n", "+ Image processing: PIL.\n", "+ 2D: Matplotlib and SVGFig.\n", "+ 3D: Visual Python, PyOpenGL and Python Ogre.\n", "+ Object-relational mapping: SQLAlchemy, SQLObject." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting Help" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Python home page -- http://www.python.org\n", "- Python standard documentation -- http://www.python.org/doc/.\n", "- tutorials:\n", " - FAQs -- http://www.python.org/doc/faq/.\n", " - The Python Wiki -- http://wiki.python.org/\n", " - The Python Package Index -- Lots of Python packages -- https://pypi.python.org/pypi\n", " - Special interest groups (SIGs) -- http://www.python.org/sigs/\n", " - The Python tutor email list -- http://mail.python.org/mailman/listinfo/tutor\n", "- Open source projects (Lots of projects. Search for \"python\")\n", " - http://sourceforge.net\n", " - https://github.com/\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Culture" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The name Python was taken by Guido van Rossum from british TV program *Monty Python's Flying Circus*, and there are many references to the show in its documentation. For instance, Python's oficial package repository was called Cheese Shop, the name of one of the frames of the program. Currently, the repository name is [Python Package Index](http://pypi.python.org/pypi) (PYPI).\n", "\n", "The goals of the project are summarized by Tim Peters in a text called *Zen of Python*, which can be found in Python itself using the command:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import this" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Zen of Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Beautiful is better than ugly.\n", "- Explicit is better than implicit.\n", "- Simple is better than complex.\n", "- Complex is better than complicated.\n", "- Flat is better than nested.\n", "- Sparse is better than dense.\n", "- Readability counts.\n", "- Special cases aren''t special enough to break the rules.\n", "- Although practicality beats purity.\n", "- Errors should never pass silently.\n", "- Unless explicitly silenced.\n", "- In the face of ambiguity, refuse the temptation to guess.\n", "- There should be one -- and preferably only one -- obvious way to do it.\n", "- Although that way may not be obvious at first unless you're Dutch.\n", "- Now is better than never.\n", "- Although never is often better than *right* now.\n", "- If the implementation is hard to explain, it's a bad idea.\n", "- If the implementation is easy to explain, it may be a good idea.\n", "- Namespaces are one honking great idea -- let's do more of those!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The chapters in this book are enhanced on https://github.com/ricardoduarte/python-for-developers. " ] } ], "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": 1 }

gpl-3.0

mayankjohri/LetsExplorePython

Section 2 - Advance Python/Chapter S2.89: Code Review/03. Review Checklist.ipynb

1

543

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Review Checklist" ] } ], "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

jdnz/qml-rg

Meeting 5/APS Captcha.ipynb

1

548304

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import keras\n", "import itertools as it\n", "import matplotlib.pyplot as pl\n", "from tempfile import TemporaryDirectory\n", "\n", "TMPDIR = TemporaryDirectory()\n", "keras.backend.set_image_data_format('channels_first')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Preprocessing" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import os \n", "from skimage import io\n", "from skimage.color import rgb2gray\n", "from skimage import transform\n", "from math import ceil\n", "\n", "\n", "IMGSIZE = (100, 100)\n", "\n", "def load_images(folder, scalefactor=(2, 2), labeldict=None):\n", " images = []\n", " labels = []\n", " files = os.listdir(folder)\n", " \n", " for file in (fname for fname in files if fname.endswith('.png')):\n", " \n", " img = io.imread(folder + file).astype(float)\n", " img = rgb2gray(img)\n", " # Crop since some of the real world pictures are other shape\n", " img = img[:IMGSIZE[0], :IMGSIZE[1]]\n", " # Possibly downscale to speed up processing\n", " img = transform.downscale_local_mean(img, scalefactor)\n", " # normalize image range\n", " img -= np.min(img)\n", " img /= np.max(img)\n", " images.append(img)\n", " \n", " if labeldict is not None:\n", " # lookup label for real world data in dict generated from labels.txt\n", " key, _ = os.path.splitext(file)\n", " labels.append(labeldict[key])\n", " else:\n", " # infere label from filename\n", " if file.find(\"einstein\") > -1 or file.find(\"curie\") > -1:\n", " labels.append(1)\n", " else:\n", " labels.append(0)\n", " \n", " return np.asarray(images)[:, None], np.asarray(labels)\n", "\n", "x_train, y_train = load_images('data/aps/train/')\n", "# Artifically pad Einstein's and Curie't to have balanced training set\n", "# ok, since we use data augmentation later anyway\n", "sel = y_train == 1\n", "repeats = len(sel) // sum(sel) - 1\n", "x_train = np.concatenate((x_train[~sel], np.repeat(x_train[sel], repeats, axis=0)),\n", " axis=0)\n", "y_train = np.concatenate((y_train[~sel], np.repeat(y_train[sel], repeats, axis=0)),\n", " axis=0)\n", "\n", "x_test, y_test = load_images('data/aps/test/')\n", "\n", "rw_labels = {str(key): 0 if label == 0 else 1\n", " for key, label in np.loadtxt('data/aps/real_world/labels.txt', dtype=int)}\n", "x_rw, y_rw = load_images('data/aps/real_world/', labeldict=rw_labels)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x123c57c88>" ] }, "metadata": { "image/png": { "height": 195, "width": 910 } }, "output_type": "display_data" } ], "source": [ "from mpl_toolkits.axes_grid import ImageGrid\n", "from math import ceil\n", "\n", "def imsshow(images, grid=(5, -1)):\n", " assert any(g > 0 for g in grid)\n", " \n", " grid_x = grid[0] if grid[0] > 0 else ceil(len(images) / grid[1])\n", " grid_y = grid[1] if grid[1] > 0 else ceil(len(images) / grid[0])\n", " \n", " axes = ImageGrid(pl.gcf(), \"111\", (grid_y, grid_x), share_all=True)\n", " for ax, img in zip(axes, images):\n", " ax.get_xaxis().set_ticks([])\n", " ax.get_yaxis().set_ticks([])\n", " ax.imshow(img[0], cmap='gray')\n", " \n", "pl.figure(0, figsize=(16, 10))\n", "imsshow(x_train, grid=(5, 1))\n", "pl.show()\n", "\n", "pl.figure(0, figsize=(16, 10))\n", "imsshow(x_train[::-4], grid=(5, 1))\n", "pl.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x123f52400>" ] }, "metadata": { "image/png": { "height": 195, "width": 910 } }, "output_type": "display_data" } ], "source": [ "from keras.preprocessing.image import ImageDataGenerator\n", "\n", "imggen = ImageDataGenerator(rotation_range=20, \n", " width_shift_range=0.15,\n", " height_shift_range=0.15,\n", " shear_range=0.4,\n", " fill_mode='constant',\n", " cval=1.,\n", " zoom_range=0.3,\n", " channel_shift_range=0.1)\n", "imggen.fit(x_train)\n", "\n", "for batch in it.islice(imggen.flow(x_train, batch_size=5), 2):\n", " pl.figure(0, figsize=(16, 5))\n", " imsshow(batch, grid=(5, 1))\n", " pl.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Training LeNet\n", "\n", "First, we will train a simple CNN with a single hidden fully connected layer as a classifier." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D\n", "from keras.models import Sequential\n", "from keras.backend import image_data_format\n", "\n", "\n", "def generate(figsize, nr_classes, cunits=[20, 50], fcunits=[500]):\n", " model = Sequential()\n", " cunits = list(cunits)\n", " input_shape = figsize + (1,) if image_data_format == 'channels_last' \\\n", " else (1,) + figsize\n", "\n", " model.add(Conv2D(cunits[0], (5, 5), padding='same',\n", " activation='relu', input_shape=input_shape))\n", " model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n", "\n", " # Convolutional layers\n", " for nr_units in cunits[1:]:\n", " model.add(Conv2D(nr_units, (5, 5), padding='same',\n", " activation='relu'))\n", " model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n", "\n", " # Fully connected layers\n", " model.add(Flatten())\n", " for nr_units in fcunits:\n", " model.add(Dense(nr_units, activation='relu'))\n", "\n", " # Output layer\n", " activation = 'softmax' if nr_classes > 1 else 'sigmoid'\n", " model.add(Dense(nr_classes, activation=activation))\n", "\n", " return model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved model not found, traing...\n", "Epoch 1/5\n", "100/100 [==============================] - 92s - loss: 0.2592 - acc: 0.8818 - val_loss: 0.0899 - val_acc: 0.9687\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n", "Epoch 2/5\n", "100/100 [==============================] - 84s - loss: 0.0666 - acc: 0.9793 - val_loss: 0.0372 - val_acc: 0.9870\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n", "Epoch 3/5\n", "100/100 [==============================] - 87s - loss: 0.0204 - acc: 0.9947 - val_loss: 0.0433 - val_acc: 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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n", "Epoch 4/5\n", "100/100 [==============================] - 87s - loss: 0.0344 - acc: 0.9902 - val_loss: 0.0585 - val_acc: 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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n", "Epoch 5/5\n", "100/100 [==============================] - 80s - loss: 0.0188 - acc: 0.9929 - val_loss: 0.0304 - val_acc: 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] } ], "source": [ "from keras.optimizers import Adam\n", "from keras.models import load_model\n", "\n", "try:\n", " model = load_model('aps_lenet.h5')\n", " print(\"Model succesfully loaded...\")\n", "except OSError:\n", " print(\"Saved model not found, traing...\")\n", " model = generate(figsize=x_train.shape[-2:], nr_classes=1,\n", " cunits=[24, 48], fcunits=[100])\n", " optimizer = Adam()\n", " model.compile(loss='binary_crossentropy', optimizer=optimizer,\n", " metrics=['accuracy'])\n", "\n", " model.fit_generator(imggen.flow(x_train, y_train, batch_size=len(x_train)), \n", " validation_data=imggen.flow(x_test, y_test),\n", " steps_per_epoch=100, epochs=5,\n", " verbose=1, validation_steps=256)\n", " model.save('aps_lenet.h5')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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0LV++XLm5uerXr598fHwkFSVPZ86ccVSw7Dp27ChJ2rJli9LT00u0HTx4UP/5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"text/plain": [ "<matplotlib.figure.Figure at 0x123fa2080>" ] }, "metadata": { "image/png": { "height": 368, "width": 426 } }, "output_type": "display_data" } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "\n", "def plot_cm(cm, classes, normalize=False, \n", " title='Confusion matrix', cmap=pl.cm.viridis):\n", " \"\"\"\n", " This function prints and plots the confusion matrix.\n", " Normalization can be applied by setting `normalize=True`.\n", " \"\"\"\n", " if normalize:\n", " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", " \n", " pl.imshow(cm, interpolation='nearest', cmap=cmap)\n", " pl.title(title)\n", " pl.colorbar()\n", " tick_marks = np.arange(len(classes))\n", " pl.xticks(tick_marks, classes, rotation=45)\n", " pl.yticks(tick_marks, classes)\n", "\n", " thresh = cm.max() / 2.\n", " for i, j in it.product(range(cm.shape[0]), range(cm.shape[1])):\n", " pl.text(j, i, cm[i, j],\n", " horizontalalignment=\"center\",\n", " color=\"white\" if cm[i, j] > thresh else \"black\")\n", "\n", " pl.tight_layout()\n", " pl.ylabel('True label')\n", " pl.xlabel('Predicted label')\n", "\n", "y_pred_rw = model.predict_classes(x_rw, verbose=0).ravel()\n", "plot_cm(confusion_matrix(y_rw, y_pred_rw), normalize=True,\n", " classes=[\"Not Einstein\", \"Einstein\"])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Training Random Forests" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Preprocessing to a fixed size training set since sklearn doesn't suppport streaming training sets?" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Same size training set as LeNet\n", "TRAININGSET_SIZE = len(x_train) * 5 * 100\n", "\n", "batch_size = len(x_train)\n", "nr_batches = TRAININGSET_SIZE // batch_size + 1\n", "imgit = imggen.flow(x_train, y=y_train, batch_size=batch_size)\n", "x_train_sampled = np.empty((TRAININGSET_SIZE, 1,) + x_train.shape[-2:])\n", "y_train_sampled = np.empty(TRAININGSET_SIZE)\n", "\n", "for batch, (x_batch, y_batch) in enumerate(it.islice(imgit, nr_batches)):\n", " buflen = len(x_train_sampled[batch * batch_size:(batch + 1) * batch_size])\n", " x_train_sampled[batch * batch_size:(batch + 1) * batch_size] = x_batch[:buflen]\n", " y_train_sampled[batch * batch_size:(batch + 1) * batch_size] = y_batch[:buflen]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 27.2s\n", "[Parallel(n_jobs=-1)]: Done 64 out of 64 | elapsed: 42.6s finished\n" ] } ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", "rfe = RandomForestClassifier(n_estimators=64, criterion='entropy', n_jobs=-1,\n", " verbose=True)\n", "rfe = rfe.fit(x_train_sampled.reshape((TRAININGSET_SIZE, -1)), y_train_sampled)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 0.0s\n", "[Parallel(n_jobs=4)]: Done 64 out of 64 | elapsed: 0.0s finished\n" ] }, { "data": { "image/png": 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KlSvtz/55/vnn9dZbb9mft1QWq9WqpUuXqnv37vr+++9LtL344ouSir6IjhkzpswvpAcO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"text/plain": [ "<matplotlib.figure.Figure at 0x124f1de48>" ] }, "metadata": { "image/png": { "height": 368, "width": 426 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Rightly classified Einsteins:\n" ] }, { "data": { "image/png": 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Vj92j+tZbbzX76r1w6TBY3vNqaeq71adHTT013HPhOVTTTq+N6sKpk1d9O3Xh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\nzz7b7NND5M6pqEeIvjfmkdNz0COq3h/WRX2n6jusetijqt4o5+1hXel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"text/plain": [ "<matplotlib.figure.Figure at 0x1247b6d68>" ] }, "metadata": { "image/png": { "height": 197, "width": 469 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Wrongly classified images:\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x128422c88>" ] }, "metadata": { "image/png": { "height": 107, "width": 469 } }, "output_type": "display_data" } ], "source": [ "y_pred_rw = rfe.predict(x_rw.reshape((len(x_rw), -1)))\n", "plot_cm(confusion_matrix(y_rw, y_pred_rw), normalize=True,\n", " classes=[\"Not Einstein\", \"Einstein\"])\n", "pl.show()\n", "\n", "print(\"Rightly classified Einsteins:\")\n", "imsshow(x_rw[((y_rw - y_pred_rw) == 0) * (y_rw == 1)])\n", "pl.show()\n", "\n", "print(\"Wrongly classified images:\")\n", "imsshow(x_rw[(y_rw - y_pred_rw) != 0])\n", "pl.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "So training on raw pixel values might not be a good idea. Let's build a feature extractor based on the trained LeNet (or any other pretrained image classifier)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "22496/22500 [============================>.] - ETA: 0s 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] } ], "source": [ "model = load_model('aps_lenet.h5')\n", "enc_layers = it.takewhile(lambda l: not isinstance(l, keras.layers.Flatten), \n", " model.layers)\n", "encoder_model = keras.models.Sequential(enc_layers)\n", "encoder_model.add(keras.layers.Flatten())\n", "x_train_sampled_enc = encoder_model.predict(x_train_sampled, verbose=True)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 11.4s\n", "[Parallel(n_jobs=-1)]: Done 64 out of 64 | elapsed: 16.8s finished\n", "[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 0.0s\n", "[Parallel(n_jobs=4)]: Done 64 out of 64 | elapsed: 0.0s finished\n" ] }, { "data": { "image/png": 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0LV++XLm5uerXr598fHwkFSVPZ86ccVSw7Dp27ChJ2rJli9LT00u0HTx4UP/5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"text/plain": [ "<matplotlib.figure.Figure at 0x12d5d49e8>" ] }, "metadata": { "image/png": { "height": 368, "width": 426 } }, "output_type": "display_data" } ], "source": [ "rfe = RandomForestClassifier(n_estimators=64, criterion='entropy', n_jobs=-1,\n", " verbose=True)\n", "rfe = rfe.fit(x_train_sampled_enc, y_train_sampled)\n", "\n", "y_pred_rw = rfe.predict(encoder_model.predict(x_rw, verbose=False))\n", "plot_cm(confusion_matrix(y_rw, y_pred_rw), normalize=True,\n", " classes=[\"Not Einstein\", \"Einstein\"])\n", "pl.show()" ] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "Python [conda env:ml]", "language": "python", "name": "conda-env-ml-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": "3.6.1" }, "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0 } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

pyro-bot/jupyter-labs

Untitled1.ipynb

1

12060

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] }, { "ename": "ImportError", "evalue": "cannot import name 'Conv2D'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-1-8174971b5eb2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mModel\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mActivation\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mUpSampling2D\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mConv2D\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMaxPooling2D\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 54\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mInput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBatchNormalization\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mELU\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0;31mImportError\u001b[0m: cannot import name 'Conv2D'" ] } ], "source": [ "'''Trains a stacked what-where autoencoder built on residual blocks on the\n", "MNIST dataset. It exemplifies two influential methods that have been developed\n", "in the past few years.\n", "\n", "The first is the idea of properly 'unpooling.' During any max pool, the\n", "exact location (the 'where') of the maximal value in a pooled receptive field\n", "is lost, however it can be very useful in the overall reconstruction of an\n", "input image. Therefore, if the 'where' is handed from the encoder\n", "to the corresponding decoder layer, features being decoded can be 'placed' in\n", "the right location, allowing for reconstructions of much higher fidelity.\n", "\n", "References:\n", "[1]\n", "'Visualizing and Understanding Convolutional Networks'\n", "Matthew D Zeiler, Rob Fergus\n", "https://arxiv.org/abs/1311.2901v3\n", "\n", "[2]\n", "'Stacked What-Where Auto-encoders'\n", "Junbo Zhao, Michael Mathieu, Ross Goroshin, Yann LeCun\n", "https://arxiv.org/abs/1506.02351v8\n", "\n", "The second idea exploited here is that of residual learning. Residual blocks\n", "ease the training process by allowing skip connections that give the network\n", "the ability to be as linear (or non-linear) as the data sees fit. This allows\n", "for much deep networks to be easily trained. The residual element seems to\n", "be advantageous in the context of this example as it allows a nice symmetry\n", "between the encoder and decoder. Normally, in the decoder, the final\n", "projection to the space where the image is reconstructed is linear, however\n", "this does not have to be the case for a residual block as the degree to which\n", "its output is linear or non-linear is determined by the data it is fed.\n", "However, in order to cap the reconstruction in this example, a hard softmax is\n", "applied as a bias because we know the MNIST digits are mapped to [0,1].\n", "\n", "References:\n", "[3]\n", "'Deep Residual Learning for Image Recognition'\n", "Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\n", "https://arxiv.org/abs/1512.03385v1\n", "\n", "[4]\n", "'Identity Mappings in Deep Residual Networks'\n", "Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\n", "https://arxiv.org/abs/1603.05027v3\n", "\n", "'''\n", "from __future__ import print_function\n", "import numpy as np\n", "\n", "from keras.datasets import mnist\n", "from keras.models import Model\n", "from keras.layers import Activation\n", "from keras.layers import UpSampling2D, Conv2D, MaxPooling2D\n", "from keras.layers import Input, BatchNormalization, ELU\n", "import matplotlib.pyplot as plt\n", "import keras.backend as K\n", "from keras import layers\n", "\n", "\n", "def convresblock(x, nfeats=8, ksize=3, nskipped=2, elu=True):\n", " \"\"\"The proposed residual block from [4].\n", "\n", " Running with elu=True will use ELU nonlinearity and running with\n", " elu=False will use BatchNorm + RELU nonlinearity. While ELU's are fast\n", " due to the fact they do not suffer from BatchNorm overhead, they may\n", " overfit because they do not offer the stochastic element of the batch\n", " formation process of BatchNorm, which acts as a good regularizer.\n", "\n", " # Arguments\n", " x: 4D tensor, the tensor to feed through the block\n", " nfeats: Integer, number of feature maps for conv layers.\n", " ksize: Integer, width and height of conv kernels in first convolution.\n", " nskipped: Integer, number of conv layers for the residual function.\n", " elu: Boolean, whether to use ELU or BN+RELU.\n", "\n", " # Input shape\n", " 4D tensor with shape:\n", " `(batch, channels, rows, cols)`\n", "\n", " # Output shape\n", " 4D tensor with shape:\n", " `(batch, filters, rows, cols)`\n", " \"\"\"\n", " y0 = Conv2D(nfeats, ksize, padding='same')(x)\n", " y = y0\n", " for i in range(nskipped):\n", " if elu:\n", " y = ELU()(y)\n", " else:\n", " y = BatchNormalization(axis=1)(y)\n", " y = Activation('relu')(y)\n", " y = Conv2D(nfeats, 1, padding='same')(y)\n", " return layers.add([y0, y])\n", "\n", "\n", "def getwhere(x):\n", " ''' Calculate the 'where' mask that contains switches indicating which\n", " index contained the max value when MaxPool2D was applied. Using the\n", " gradient of the sum is a nice trick to keep everything high level.'''\n", " y_prepool, y_postpool = x\n", " return K.gradients(K.sum(y_postpool), y_prepool)\n", "\n", "if K.backend() == 'tensorflow':\n", " raise RuntimeError('This example can only run with the '\n", " 'Theano backend for the time being, '\n", " 'because it requires taking the gradient '\n", " 'of a gradient, which isn\\'t '\n", " 'supported for all TF ops.')\n", "\n", "# This example assume 'channels_first' data format.\n", "K.set_image_data_format('channels_first')\n", "\n", "# input image dimensions\n", "img_rows, img_cols = 28, 28\n", "\n", "# the data, shuffled and split between train and test sets\n", "(x_train, _), (x_test, _) = mnist.load_data()\n", "\n", "x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n", "x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n", "x_train = x_train.astype('float32')\n", "x_test = x_test.astype('float32')\n", "x_train /= 255\n", "x_test /= 255\n", "print('x_train shape:', x_train.shape)\n", "print(x_train.shape[0], 'train samples')\n", "print(x_test.shape[0], 'test samples')\n", "\n", "# The size of the kernel used for the MaxPooling2D\n", "pool_size = 2\n", "# The total number of feature maps at each layer\n", "nfeats = [8, 16, 32, 64, 128]\n", "# The sizes of the pooling kernel at each layer\n", "pool_sizes = np.array([1, 1, 1, 1, 1]) * pool_size\n", "# The convolution kernel size\n", "ksize = 3\n", "# Number of epochs to train for\n", "epochs = 5\n", "# Batch size during training\n", "batch_size = 128\n", "\n", "if pool_size == 2:\n", " # if using a 5 layer net of pool_size = 2\n", " x_train = np.pad(x_train, [[0, 0], [0, 0], [2, 2], [2, 2]],\n", " mode='constant')\n", " x_test = np.pad(x_test, [[0, 0], [0, 0], [2, 2], [2, 2]], mode='constant')\n", " nlayers = 5\n", "elif pool_size == 3:\n", " # if using a 3 layer net of pool_size = 3\n", " x_train = x_train[:, :, :-1, :-1]\n", " x_test = x_test[:, :, :-1, :-1]\n", " nlayers = 3\n", "else:\n", " import sys\n", " sys.exit('Script supports pool_size of 2 and 3.')\n", "\n", "# Shape of input to train on (note that model is fully convolutional however)\n", "input_shape = x_train.shape[1:]\n", "# The final list of the size of axis=1 for all layers, including input\n", "nfeats_all = [input_shape[0]] + nfeats\n", "\n", "# First build the encoder, all the while keeping track of the 'where' masks\n", "img_input = Input(shape=input_shape)\n", "\n", "# We push the 'where' masks to the following list\n", "wheres = [None] * nlayers\n", "y = img_input\n", "for i in range(nlayers):\n", " y_prepool = convresblock(y, nfeats=nfeats_all[i + 1], ksize=ksize)\n", " y = MaxPooling2D(pool_size=(pool_sizes[i], pool_sizes[i]))(y_prepool)\n", " wheres[i] = layers.Lambda(\n", " getwhere, output_shape=lambda x: x[0])([y_prepool, y])\n", "\n", "# Now build the decoder, and use the stored 'where' masks to place the features\n", "for i in range(nlayers):\n", " ind = nlayers - 1 - i\n", " y = UpSampling2D(size=(pool_sizes[ind], pool_sizes[ind]))(y)\n", " y = layers.multiply([y, wheres[ind]])\n", " y = convresblock(y, nfeats=nfeats_all[ind], ksize=ksize)\n", "\n", "# Use hard_simgoid to clip range of reconstruction\n", "y = Activation('hard_sigmoid')(y)\n", "\n", "# Define the model and it's mean square error loss, and compile it with Adam\n", "model = Model(img_input, y)\n", "model.compile('adam', 'mse')\n", "\n", "# Fit the model\n", "model.fit(x_train, x_train,\n", " batch_size=batch_size,\n", " epochs=epochs,\n", " validation_data=(x_test, x_test))\n", "\n", "# Plot\n", "x_recon = model.predict(x_test[:25])\n", "x_plot = np.concatenate((x_test[:25], x_recon), axis=1)\n", "x_plot = x_plot.reshape((5, 10, input_shape[-2], input_shape[-1]))\n", "x_plot = np.vstack([np.hstack(x) for x in x_plot])\n", "plt.figure()\n", "plt.axis('off')\n", "plt.title('Test Samples: Originals/Reconstructions')\n", "plt.imshow(x_plot, interpolation='none', cmap='gray')\n", "plt.savefig('reconstructions.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 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

asharel/ml

LAB1/src/Practica1.ipynb

1

940181

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Práctica 1\n", "## Desarrollado por Sara Pérez y Cristian Fernández\n", "## 06 de Octubre de 2017\n", "## Aprendizaje automático, EPS, UAM (Madrid)\n", "## Introducción\n", "La siguiente práctica muestra el estudio previo a una operación de _machine learning_ de los datos relacionados. Para ello se aplican distíntas técnicas que intentan simplificar la tarea de aprendizaje, bien sea simplificando los datos o preprocesandolos. \n", "\n", "Para ello, se ha desarrollado en [Jupyter Notebook](https://www.jupyter.com), una herramienta que permite la generación de resultados a través del lenguaje de programación _python_ en un formato documento, que es el que se presenta.\n", "\n", "Esta práctica emula la memoria de una auditoría cuyo cliente final es una empresa que nos ha encargado la realización de dicho estudio de datos.\n", "\n", "## Objetivo\n", "El objetivo de este documento es el estudio, simplificación y preprocesamiento de un conjunto de datos de cara al uso de un algoritmo de _machine learning_.\n", "\n", "## Trabajo Desarrollado\n", "\n", "En este apartado se describe el procesamiento realizado a los datos así como una breve explicación de los métodos aplicados.\n", "\n", "### Cargando librerias" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "#Libraries\n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import matplotlib.pyplot as plt# Read Dataset\n", "\n", "%matplotlib inline\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Obteniendo los datos del csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Descripcion de las variables\n", "\n", "Tenemos una base de datos médica con los resultados de análisis sanguineos de varios pacientes y una clasificación de su estado de salud, 1 para los enfermos y 2 para los sanos. En primer lugar tenemos los datos del paciente, su edad y su género. A continuación se nos dan los resultados del total de bilirrubina y bilirrubina directa, la bilirrubina es un pigmento amarillo que se encuentra en la bilis (liquido generado por el hígado), la bilirrubina total es la suma de la bilirrubina directa o conjugada y la bilirrubina indirecta.<br> Despues se proporciona los valores de la fosfatasa alcalina, que es una enzima que se desplaza a través de el sistema sanguíneo, pero que se acumula más que nada en el hígado, la bilis, riñones y el sistema intestinal, es útil para detectar enfermedades óseas o hepáticas. <br><br>\n", "Después están las transaminasas, la alaninoamino transferasa (ALT o GPT) y la aspartato aminotransferasa (AST o GOT), que son enzimas que se encuentran en el interior de las células de órganos como el hígado, el corazón, los riñones o los músculos, y desempeñan una importante función en el metabolismo. Cuando un análisis de sangre detecta niveles elevados de estas moléculas puede indicar que existe una lesión de las células hepáticas.<br><br>\n", "\n", "Las últimas tres variables son las proteinas totales, la albumina y el ratio de albumina y globulina. El examen de proteína total mide la cantidad total de dos clases de proteínas encontradas en la porción líquida de la sangre: albúmina y globulina.<br>\n", "La albúmina es una proteína plasmática cuya función más importante es el mantenimiento de la presión oncótica, es decir ayuda a impedir que se escape líquido fuera de los vasos sanguíneos, y la capacidad de transporte de hormonas, medicamentos,etc.<br>\n", "Las globulinas son útiles en la lucha contra las infecciones y mejorar el proceso de coagulación de la sangre. También sirven como portadora de la hormona y el transporte de las hormonas a diferentes partes del cuerpo.<br><br>\n", "Por los componentes analizados podemos concluir que es un perfil hepático para detectar problemas en el hígado.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Age</th>\n", " <th>Gender</th>\n", " <th>Total_Bilirubin</th>\n", " <th>Direct_Bilirubin</th>\n", " <th>Alkaline_Phosphotase</th>\n", " <th>Alamine_Aminotransferase</th>\n", " <th>Aspartate_Aminotransferase</th>\n", " <th>Total_Protiens</th>\n", " <th>Albumin</th>\n", " <th>Albumin_and_Globulin_Ratio</th>\n", " <th>Dataset</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>65</td>\n", " <td>Female</td>\n", " <td>0.7</td>\n", " <td>0.1</td>\n", " <td>187</td>\n", " <td>16</td>\n", " <td>18</td>\n", " <td>6.8</td>\n", " <td>3.3</td>\n", " <td>0.90</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>62</td>\n", " <td>Male</td>\n", " <td>10.9</td>\n", " <td>5.5</td>\n", " <td>699</td>\n", " <td>64</td>\n", " <td>100</td>\n", " <td>7.5</td>\n", " <td>3.2</td>\n", " <td>0.74</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>62</td>\n", " <td>Male</td>\n", " <td>7.3</td>\n", " <td>4.1</td>\n", " <td>490</td>\n", " <td>60</td>\n", " <td>68</td>\n", " <td>7.0</td>\n", " <td>3.3</td>\n", " <td>0.89</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>58</td>\n", " <td>Male</td>\n", " <td>1.0</td>\n", " <td>0.4</td>\n", " <td>182</td>\n", " <td>14</td>\n", " <td>20</td>\n", " <td>6.8</td>\n", " <td>3.4</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>72</td>\n", " <td>Male</td>\n", " <td>3.9</td>\n", " <td>2.0</td>\n", " <td>195</td>\n", " <td>27</td>\n", " <td>59</td>\n", " <td>7.3</td>\n", " <td>2.4</td>\n", " <td>0.40</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>46</td>\n", " <td>Male</td>\n", " <td>1.8</td>\n", " <td>0.7</td>\n", " <td>208</td>\n", " <td>19</td>\n", " <td>14</td>\n", " <td>7.6</td>\n", " <td>4.4</td>\n", " <td>1.30</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>26</td>\n", " <td>Female</td>\n", " <td>0.9</td>\n", " <td>0.2</td>\n", " <td>154</td>\n", " <td>16</td>\n", " <td>12</td>\n", " <td>7.0</td>\n", " <td>3.5</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>29</td>\n", " <td>Female</td>\n", " <td>0.9</td>\n", " <td>0.3</td>\n", " <td>202</td>\n", " <td>14</td>\n", " <td>11</td>\n", " <td>6.7</td>\n", " <td>3.6</td>\n", " <td>1.10</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>17</td>\n", " <td>Male</td>\n", " <td>0.9</td>\n", " <td>0.3</td>\n", " <td>202</td>\n", " <td>22</td>\n", " <td>19</td>\n", " <td>7.4</td>\n", " <td>4.1</td>\n", " <td>1.20</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>55</td>\n", " <td>Male</td>\n", " <td>0.7</td>\n", " <td>0.2</td>\n", " <td>290</td>\n", " <td>53</td>\n", " <td>58</td>\n", " <td>6.8</td>\n", " <td>3.4</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>57</td>\n", " <td>Male</td>\n", " <td>0.6</td>\n", " <td>0.1</td>\n", " <td>210</td>\n", " <td>51</td>\n", " <td>59</td>\n", " <td>5.9</td>\n", " <td>2.7</td>\n", " <td>0.80</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>72</td>\n", " <td>Male</td>\n", " <td>2.7</td>\n", " <td>1.3</td>\n", " <td>260</td>\n", " <td>31</td>\n", " <td>56</td>\n", " <td>7.4</td>\n", " <td>3.0</td>\n", " <td>0.60</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>64</td>\n", " <td>Male</td>\n", " <td>0.9</td>\n", " <td>0.3</td>\n", " <td>310</td>\n", " <td>61</td>\n", " <td>58</td>\n", " <td>7.0</td>\n", " <td>3.4</td>\n", " <td>0.90</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>74</td>\n", " <td>Female</td>\n", " <td>1.1</td>\n", " <td>0.4</td>\n", " <td>214</td>\n", " <td>22</td>\n", " <td>30</td>\n", " <td>8.1</td>\n", " <td>4.1</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>61</td>\n", " <td>Male</td>\n", " <td>0.7</td>\n", " <td>0.2</td>\n", " <td>145</td>\n", " <td>53</td>\n", " <td>41</td>\n", " <td>5.8</td>\n", " <td>2.7</td>\n", " <td>0.87</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>25</td>\n", " <td>Male</td>\n", " <td>0.6</td>\n", " <td>0.1</td>\n", " <td>183</td>\n", " <td>91</td>\n", " <td>53</td>\n", " <td>5.5</td>\n", " <td>2.3</td>\n", " <td>0.70</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>38</td>\n", " <td>Male</td>\n", " <td>1.8</td>\n", " <td>0.8</td>\n", " <td>342</td>\n", " <td>168</td>\n", " <td>441</td>\n", " <td>7.6</td>\n", " <td>4.4</td>\n", " <td>1.30</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>33</td>\n", " <td>Male</td>\n", " <td>1.6</td>\n", " <td>0.5</td>\n", " <td>165</td>\n", " <td>15</td>\n", " <td>23</td>\n", " <td>7.3</td>\n", " <td>3.5</td>\n", " <td>0.92</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>40</td>\n", " <td>Female</td>\n", " <td>0.9</td>\n", " <td>0.3</td>\n", " <td>293</td>\n", " <td>232</td>\n", " <td>245</td>\n", " <td>6.8</td>\n", " <td>3.1</td>\n", " <td>0.80</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>40</td>\n", " <td>Female</td>\n", " <td>0.9</td>\n", " <td>0.3</td>\n", " <td>293</td>\n", " <td>232</td>\n", " <td>245</td>\n", " <td>6.8</td>\n", " <td>3.1</td>\n", " <td>0.80</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>51</td>\n", " <td>Male</td>\n", " <td>2.2</td>\n", " <td>1.0</td>\n", " <td>610</td>\n", " <td>17</td>\n", " <td>28</td>\n", " <td>7.3</td>\n", " <td>2.6</td>\n", " <td>0.55</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>51</td>\n", " <td>Male</td>\n", " <td>2.9</td>\n", " <td>1.3</td>\n", " <td>482</td>\n", " <td>22</td>\n", " <td>34</td>\n", " <td>7.0</td>\n", " <td>2.4</td>\n", " <td>0.50</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>62</td>\n", " <td>Male</td>\n", " <td>6.8</td>\n", " <td>3.0</td>\n", " <td>542</td>\n", " <td>116</td>\n", " <td>66</td>\n", " <td>6.4</td>\n", " <td>3.1</td>\n", " <td>0.90</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>40</td>\n", " <td>Male</td>\n", " <td>1.9</td>\n", " <td>1.0</td>\n", " <td>231</td>\n", " <td>16</td>\n", " <td>55</td>\n", " <td>4.3</td>\n", " <td>1.6</td>\n", " <td>0.60</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>63</td>\n", " <td>Male</td>\n", " <td>0.9</td>\n", " <td>0.2</td>\n", " <td>194</td>\n", " <td>52</td>\n", " <td>45</td>\n", " <td>6.0</td>\n", " <td>3.9</td>\n", " <td>1.85</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>34</td>\n", " <td>Male</td>\n", " <td>4.1</td>\n", " <td>2.0</td>\n", " <td>289</td>\n", " <td>875</td>\n", " <td>731</td>\n", " <td>5.0</td>\n", " <td>2.7</td>\n", " <td>1.10</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>34</td>\n", " <td>Male</td>\n", " <td>4.1</td>\n", " <td>2.0</td>\n", " <td>289</td>\n", " <td>875</td>\n", " <td>731</td>\n", " <td>5.0</td>\n", " <td>2.7</td>\n", " <td>1.10</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>34</td>\n", " <td>Male</td>\n", " <td>6.2</td>\n", " <td>3.0</td>\n", " <td>240</td>\n", " <td>1680</td>\n", " <td>850</td>\n", " <td>7.2</td>\n", " <td>4.0</td>\n", " <td>1.20</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>20</td>\n", " <td>Male</td>\n", " <td>1.1</td>\n", " <td>0.5</td>\n", " <td>128</td>\n", " <td>20</td>\n", " <td>30</td>\n", " <td>3.9</td>\n", " <td>1.9</td>\n", " <td>0.95</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>84</td>\n", " <td>Female</td>\n", " <td>0.7</td>\n", " <td>0.2</td>\n", " <td>188</td>\n", " <td>13</td>\n", " <td>21</td>\n", " <td>6.0</td>\n", " <td>3.2</td>\n", " <td>1.10</td>\n", " <td>2</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", " </tr>\n", " <tr>\n", " <th>553</th>\n", " <td>46</td>\n", " <td>Male</td>\n", " <td>10.2</td>\n", " <td>4.2</td>\n", " <td>232</td>\n", " <td>58</td>\n", " <td>140</td>\n", " <td>7.0</td>\n", " <td>2.7</td>\n", " <td>0.60</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>554</th>\n", " <td>73</td>\n", " <td>Male</td>\n", " <td>1.8</td>\n", " <td>0.9</td>\n", " <td>220</td>\n", " <td>20</td>\n", " <td>43</td>\n", " <td>6.5</td>\n", " <td>3.0</td>\n", " <td>0.80</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>555</th>\n", " <td>55</td>\n", " <td>Male</td>\n", " <td>0.8</td>\n", " <td>0.2</td>\n", " <td>290</td>\n", " <td>139</td>\n", " <td>87</td>\n", " <td>7.0</td>\n", " <td>3.0</td>\n", " <td>0.70</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>556</th>\n", " <td>51</td>\n", " <td>Male</td>\n", " <td>0.7</td>\n", " <td>0.1</td>\n", " <td>180</td>\n", " <td>25</td>\n", " <td>27</td>\n", " <td>6.1</td>\n", " <td>3.1</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>557</th>\n", " <td>51</td>\n", " <td>Male</td>\n", " <td>2.9</td>\n", " <td>1.2</td>\n", " <td>189</td>\n", " <td>80</td>\n", " <td>125</td>\n", " <td>6.2</td>\n", " <td>3.1</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>558</th>\n", " <td>51</td>\n", " <td>Male</td>\n", " <td>4.0</td>\n", " <td>2.5</td>\n", " <td>275</td>\n", " <td>382</td>\n", " <td>330</td>\n", " <td>7.5</td>\n", " <td>4.0</td>\n", " <td>1.10</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>559</th>\n", " <td>26</td>\n", " <td>Male</td>\n", " <td>42.8</td>\n", " <td>19.7</td>\n", " <td>390</td>\n", " <td>75</td>\n", " <td>138</td>\n", " <td>7.5</td>\n", " <td>2.6</td>\n", " <td>0.50</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>560</th>\n", " <td>66</td>\n", " <td>Male</td>\n", " <td>15.2</td>\n", " <td>7.7</td>\n", " <td>356</td>\n", " <td>321</td>\n", " <td>562</td>\n", " <td>6.5</td>\n", " <td>2.2</td>\n", " <td>0.40</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>561</th>\n", " <td>66</td>\n", " <td>Male</td>\n", " <td>16.6</td>\n", " <td>7.6</td>\n", " <td>315</td>\n", " <td>233</td>\n", " <td>384</td>\n", " <td>6.9</td>\n", " <td>2.0</td>\n", " <td>0.40</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>562</th>\n", " <td>66</td>\n", " <td>Male</td>\n", " <td>17.3</td>\n", " <td>8.5</td>\n", " <td>388</td>\n", " <td>173</td>\n", " <td>367</td>\n", " <td>7.8</td>\n", " <td>2.6</td>\n", " <td>0.50</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>563</th>\n", " <td>64</td>\n", " <td>Male</td>\n", " <td>1.4</td>\n", " <td>0.5</td>\n", " <td>298</td>\n", " <td>31</td>\n", " <td>83</td>\n", " <td>7.2</td>\n", " <td>2.6</td>\n", " <td>0.50</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>564</th>\n", " <td>38</td>\n", " <td>Female</td>\n", " <td>0.6</td>\n", " <td>0.1</td>\n", " <td>165</td>\n", " <td>22</td>\n", " <td>34</td>\n", " <td>5.9</td>\n", " <td>2.9</td>\n", " <td>0.90</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>565</th>\n", " <td>43</td>\n", " <td>Male</td>\n", " <td>22.5</td>\n", " <td>11.8</td>\n", " <td>143</td>\n", " <td>22</td>\n", " <td>143</td>\n", " <td>6.6</td>\n", " <td>2.1</td>\n", " <td>0.46</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>566</th>\n", " <td>50</td>\n", " <td>Female</td>\n", " <td>1.0</td>\n", " <td>0.3</td>\n", " <td>191</td>\n", " <td>22</td>\n", " <td>31</td>\n", " <td>7.8</td>\n", " <td>4.0</td>\n", " <td>1.00</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>567</th>\n", " <td>52</td>\n", " <td>Male</td>\n", " <td>2.7</td>\n", " <td>1.4</td>\n", " <td>251</td>\n", " <td>20</td>\n", " <td>40</td>\n", " <td>6.0</td>\n", " <td>1.7</td>\n", " <td>0.39</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>568</th>\n", " <td>20</td>\n", " <td>Female</td>\n", " <td>16.7</td>\n", " <td>8.4</td>\n", " <td>200</td>\n", " <td>91</td>\n", " <td>101</td>\n", " <td>6.9</td>\n", " <td>3.5</td>\n", " <td>1.02</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>569</th>\n", " <td>16</td>\n", " <td>Male</td>\n", " <td>7.7</td>\n", " <td>4.1</td>\n", " <td>268</td>\n", " <td>213</td>\n", " <td>168</td>\n", " <td>7.1</td>\n", " <td>4.0</td>\n", " <td>1.20</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>570</th>\n", " <td>16</td>\n", " <td>Male</td>\n", " <td>2.6</td>\n", " <td>1.2</td>\n", " <td>236</td>\n", " <td>131</td>\n", " <td>90</td>\n", " <td>5.4</td>\n", " <td>2.6</td>\n", " <td>0.90</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>571</th>\n", " <td>90</td>\n", " <td>Male</td>\n", " <td>1.1</td>\n", " <td>0.3</td>\n", " <td>215</td>\n", " <td>46</td>\n", " <td>134</td>\n", " <td>6.9</td>\n", " <td>3.0</td>\n", " <td>0.70</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>572</th>\n", " <td>32</td>\n", " <td>Male</td>\n", " <td>15.6</td>\n", " <td>9.5</td>\n", " <td>134</td>\n", " <td>54</td>\n", " <td>125</td>\n", " <td>5.6</td>\n", " <td>4.0</td>\n", " <td>2.50</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>573</th>\n", " <td>32</td>\n", " <td>Male</td>\n", " <td>3.7</td>\n", " <td>1.6</td>\n", " <td>612</td>\n", " <td>50</td>\n", " <td>88</td>\n", " <td>6.2</td>\n", " <td>1.9</td>\n", " <td>0.40</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>574</th>\n", " <td>32</td>\n", " <td>Male</td>\n", " <td>12.1</td>\n", " <td>6.0</td>\n", " <td>515</td>\n", " <td>48</td>\n", " <td>92</td>\n", " <td>6.6</td>\n", " <td>2.4</td>\n", " <td>0.50</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>575</th>\n", " <td>32</td>\n", " <td>Male</td>\n", " <td>25.0</td>\n", " <td>13.7</td>\n", " <td>560</td>\n", " <td>41</td>\n", " <td>88</td>\n", " <td>7.9</td>\n", " <td>2.5</td>\n", " <td>2.50</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>576</th>\n", " <td>32</td>\n", " <td>Male</td>\n", " <td>15.0</td>\n", " <td>8.2</td>\n", " <td>289</td>\n", " <td>58</td>\n", " <td>80</td>\n", " <td>5.3</td>\n", " <td>2.2</td>\n", " <td>0.70</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>577</th>\n", " <td>32</td>\n", " <td>Male</td>\n", " <td>12.7</td>\n", " <td>8.4</td>\n", " <td>190</td>\n", " <td>28</td>\n", " <td>47</td>\n", " <td>5.4</td>\n", " <td>2.6</td>\n", " <td>0.90</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>578</th>\n", " <td>60</td>\n", " <td>Male</td>\n", " <td>0.5</td>\n", " <td>0.1</td>\n", " <td>500</td>\n", " <td>20</td>\n", " <td>34</td>\n", " <td>5.9</td>\n", " <td>1.6</td>\n", " <td>0.37</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>579</th>\n", " <td>40</td>\n", " <td>Male</td>\n", " <td>0.6</td>\n", " <td>0.1</td>\n", " <td>98</td>\n", " <td>35</td>\n", " <td>31</td>\n", " <td>6.0</td>\n", " <td>3.2</td>\n", " <td>1.10</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>580</th>\n", " <td>52</td>\n", " <td>Male</td>\n", " <td>0.8</td>\n", " <td>0.2</td>\n", " <td>245</td>\n", " <td>48</td>\n", " <td>49</td>\n", " <td>6.4</td>\n", " <td>3.2</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>581</th>\n", " <td>31</td>\n", " <td>Male</td>\n", " <td>1.3</td>\n", " <td>0.5</td>\n", " <td>184</td>\n", " <td>29</td>\n", " <td>32</td>\n", " <td>6.8</td>\n", " <td>3.4</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>582</th>\n", " <td>38</td>\n", " <td>Male</td>\n", " <td>1.0</td>\n", " <td>0.3</td>\n", " <td>216</td>\n", " <td>21</td>\n", " <td>24</td>\n", " <td>7.3</td>\n", " <td>4.4</td>\n", " <td>1.50</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>583 rows × 11 columns</p>\n", "</div>" ], "text/plain": [ " Age Gender Total_Bilirubin Direct_Bilirubin Alkaline_Phosphotase \\\n", "0 65 Female 0.7 0.1 187 \n", "1 62 Male 10.9 5.5 699 \n", "2 62 Male 7.3 4.1 490 \n", "3 58 Male 1.0 0.4 182 \n", "4 72 Male 3.9 2.0 195 \n", "5 46 Male 1.8 0.7 208 \n", "6 26 Female 0.9 0.2 154 \n", "7 29 Female 0.9 0.3 202 \n", "8 17 Male 0.9 0.3 202 \n", "9 55 Male 0.7 0.2 290 \n", "10 57 Male 0.6 0.1 210 \n", "11 72 Male 2.7 1.3 260 \n", "12 64 Male 0.9 0.3 310 \n", "13 74 Female 1.1 0.4 214 \n", "14 61 Male 0.7 0.2 145 \n", "15 25 Male 0.6 0.1 183 \n", "16 38 Male 1.8 0.8 342 \n", "17 33 Male 1.6 0.5 165 \n", "18 40 Female 0.9 0.3 293 \n", "19 40 Female 0.9 0.3 293 \n", "20 51 Male 2.2 1.0 610 \n", "21 51 Male 2.9 1.3 482 \n", "22 62 Male 6.8 3.0 542 \n", "23 40 Male 1.9 1.0 231 \n", "24 63 Male 0.9 0.2 194 \n", "25 34 Male 4.1 2.0 289 \n", "26 34 Male 4.1 2.0 289 \n", "27 34 Male 6.2 3.0 240 \n", "28 20 Male 1.1 0.5 128 \n", "29 84 Female 0.7 0.2 188 \n", ".. ... ... ... ... ... \n", "553 46 Male 10.2 4.2 232 \n", "554 73 Male 1.8 0.9 220 \n", "555 55 Male 0.8 0.2 290 \n", "556 51 Male 0.7 0.1 180 \n", "557 51 Male 2.9 1.2 189 \n", "558 51 Male 4.0 2.5 275 \n", "559 26 Male 42.8 19.7 390 \n", "560 66 Male 15.2 7.7 356 \n", "561 66 Male 16.6 7.6 315 \n", "562 66 Male 17.3 8.5 388 \n", "563 64 Male 1.4 0.5 298 \n", "564 38 Female 0.6 0.1 165 \n", "565 43 Male 22.5 11.8 143 \n", "566 50 Female 1.0 0.3 191 \n", "567 52 Male 2.7 1.4 251 \n", "568 20 Female 16.7 8.4 200 \n", "569 16 Male 7.7 4.1 268 \n", "570 16 Male 2.6 1.2 236 \n", "571 90 Male 1.1 0.3 215 \n", "572 32 Male 15.6 9.5 134 \n", "573 32 Male 3.7 1.6 612 \n", "574 32 Male 12.1 6.0 515 \n", "575 32 Male 25.0 13.7 560 \n", "576 32 Male 15.0 8.2 289 \n", "577 32 Male 12.7 8.4 190 \n", "578 60 Male 0.5 0.1 500 \n", "579 40 Male 0.6 0.1 98 \n", "580 52 Male 0.8 0.2 245 \n", "581 31 Male 1.3 0.5 184 \n", "582 38 Male 1.0 0.3 216 \n", "\n", " Alamine_Aminotransferase Aspartate_Aminotransferase Total_Protiens \\\n", "0 16 18 6.8 \n", "1 64 100 7.5 \n", "2 60 68 7.0 \n", "3 14 20 6.8 \n", "4 27 59 7.3 \n", "5 19 14 7.6 \n", "6 16 12 7.0 \n", "7 14 11 6.7 \n", "8 22 19 7.4 \n", "9 53 58 6.8 \n", "10 51 59 5.9 \n", "11 31 56 7.4 \n", "12 61 58 7.0 \n", "13 22 30 8.1 \n", "14 53 41 5.8 \n", "15 91 53 5.5 \n", "16 168 441 7.6 \n", "17 15 23 7.3 \n", "18 232 245 6.8 \n", "19 232 245 6.8 \n", "20 17 28 7.3 \n", "21 22 34 7.0 \n", "22 116 66 6.4 \n", "23 16 55 4.3 \n", "24 52 45 6.0 \n", "25 875 731 5.0 \n", "26 875 731 5.0 \n", "27 1680 850 7.2 \n", "28 20 30 3.9 \n", "29 13 21 6.0 \n", ".. ... ... ... \n", "553 58 140 7.0 \n", "554 20 43 6.5 \n", "555 139 87 7.0 \n", "556 25 27 6.1 \n", "557 80 125 6.2 \n", "558 382 330 7.5 \n", "559 75 138 7.5 \n", "560 321 562 6.5 \n", "561 233 384 6.9 \n", "562 173 367 7.8 \n", "563 31 83 7.2 \n", "564 22 34 5.9 \n", "565 22 143 6.6 \n", "566 22 31 7.8 \n", "567 20 40 6.0 \n", "568 91 101 6.9 \n", "569 213 168 7.1 \n", "570 131 90 5.4 \n", "571 46 134 6.9 \n", "572 54 125 5.6 \n", "573 50 88 6.2 \n", "574 48 92 6.6 \n", "575 41 88 7.9 \n", "576 58 80 5.3 \n", "577 28 47 5.4 \n", "578 20 34 5.9 \n", "579 35 31 6.0 \n", "580 48 49 6.4 \n", "581 29 32 6.8 \n", "582 21 24 7.3 \n", "\n", " Albumin Albumin_and_Globulin_Ratio Dataset \n", "0 3.3 0.90 1 \n", "1 3.2 0.74 1 \n", "2 3.3 0.89 1 \n", "3 3.4 1.00 1 \n", "4 2.4 0.40 1 \n", "5 4.4 1.30 1 \n", "6 3.5 1.00 1 \n", "7 3.6 1.10 1 \n", "8 4.1 1.20 2 \n", "9 3.4 1.00 1 \n", "10 2.7 0.80 1 \n", "11 3.0 0.60 1 \n", "12 3.4 0.90 2 \n", "13 4.1 1.00 1 \n", "14 2.7 0.87 1 \n", "15 2.3 0.70 2 \n", "16 4.4 1.30 1 \n", "17 3.5 0.92 2 \n", "18 3.1 0.80 1 \n", "19 3.1 0.80 1 \n", "20 2.6 0.55 1 \n", "21 2.4 0.50 1 \n", "22 3.1 0.90 1 \n", "23 1.6 0.60 1 \n", "24 3.9 1.85 2 \n", "25 2.7 1.10 1 \n", "26 2.7 1.10 1 \n", "27 4.0 1.20 1 \n", "28 1.9 0.95 2 \n", "29 3.2 1.10 2 \n", ".. ... ... ... \n", "553 2.7 0.60 1 \n", "554 3.0 0.80 1 \n", "555 3.0 0.70 1 \n", "556 3.1 1.00 1 \n", "557 3.1 1.00 1 \n", "558 4.0 1.10 1 \n", "559 2.6 0.50 1 \n", "560 2.2 0.40 1 \n", "561 2.0 0.40 1 \n", "562 2.6 0.50 1 \n", "563 2.6 0.50 1 \n", "564 2.9 0.90 2 \n", "565 2.1 0.46 1 \n", "566 4.0 1.00 2 \n", "567 1.7 0.39 1 \n", "568 3.5 1.02 1 \n", "569 4.0 1.20 1 \n", "570 2.6 0.90 1 \n", "571 3.0 0.70 1 \n", "572 4.0 2.50 1 \n", "573 1.9 0.40 1 \n", "574 2.4 0.50 1 \n", "575 2.5 2.50 1 \n", "576 2.2 0.70 1 \n", "577 2.6 0.90 1 \n", "578 1.6 0.37 2 \n", "579 3.2 1.10 1 \n", "580 3.2 1.00 1 \n", "581 3.4 1.00 1 \n", "582 4.4 1.50 2 \n", "\n", "[583 rows x 11 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Path para linux\n", "path = '../Recursos/indian_liver_patient.csv'\n", "\n", "#Path para Windows\n", "#path = '..\\Recursos\\indian_liver_patient.csv'\n", "dataset = pd.read_csv('../Recursos/indian_liver_patient.csv',delimiter=',',header=0)\n", "dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Listamos cabeceras de los datos" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Age',\n", " 'Gender',\n", " 'Total_Bilirubin',\n", " 'Direct_Bilirubin',\n", " 'Alkaline_Phosphotase',\n", " 'Alamine_Aminotransferase',\n", " 'Aspartate_Aminotransferase',\n", " 'Total_Protiens',\n", " 'Albumin',\n", " 'Albumin_and_Globulin_Ratio',\n", " 'Dataset']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Header\n", "header = []\n", "for row in dataset:\n", " header.append(row)\n", "header" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Cambiando valores nominales\n", "\n", "Como se puede observar desde un primer momento, existe una variable nominal denominada _Gender_ relacionada con el sexo. Para facilitar los cálculos posteriores, vamos a reemplazarla cambiando:\n", "* **Male:** 0\n", "* **Female:** 1" ] }, { "cell_type": "code", "execution_count": 5, "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>Age</th>\n", " <th>Gender</th>\n", " <th>Total_Bilirubin</th>\n", " <th>Direct_Bilirubin</th>\n", " <th>Alkaline_Phosphotase</th>\n", " <th>Alamine_Aminotransferase</th>\n", " <th>Aspartate_Aminotransferase</th>\n", " <th>Total_Protiens</th>\n", " <th>Albumin</th>\n", " <th>Albumin_and_Globulin_Ratio</th>\n", " <th>Dataset</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>65</td>\n", " <td>1</td>\n", " <td>0.7</td>\n", " <td>0.1</td>\n", " <td>187</td>\n", " <td>16</td>\n", " <td>18</td>\n", " <td>6.8</td>\n", " <td>3.3</td>\n", " <td>0.90</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>62</td>\n", " <td>0</td>\n", " <td>10.9</td>\n", " <td>5.5</td>\n", " <td>699</td>\n", " <td>64</td>\n", " <td>100</td>\n", " <td>7.5</td>\n", " <td>3.2</td>\n", " <td>0.74</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>62</td>\n", " <td>0</td>\n", " <td>7.3</td>\n", " <td>4.1</td>\n", " <td>490</td>\n", " <td>60</td>\n", " <td>68</td>\n", " <td>7.0</td>\n", " <td>3.3</td>\n", " <td>0.89</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>58</td>\n", " <td>0</td>\n", " <td>1.0</td>\n", " <td>0.4</td>\n", " <td>182</td>\n", " <td>14</td>\n", " <td>20</td>\n", " <td>6.8</td>\n", " <td>3.4</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>72</td>\n", " <td>0</td>\n", " <td>3.9</td>\n", " <td>2.0</td>\n", " <td>195</td>\n", " <td>27</td>\n", " <td>59</td>\n", " <td>7.3</td>\n", " <td>2.4</td>\n", " <td>0.40</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>46</td>\n", " <td>0</td>\n", " <td>1.8</td>\n", " <td>0.7</td>\n", " <td>208</td>\n", " <td>19</td>\n", " <td>14</td>\n", " <td>7.6</td>\n", " <td>4.4</td>\n", " <td>1.30</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>26</td>\n", " <td>1</td>\n", " <td>0.9</td>\n", " <td>0.2</td>\n", " <td>154</td>\n", " <td>16</td>\n", " <td>12</td>\n", " <td>7.0</td>\n", " <td>3.5</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>29</td>\n", " <td>1</td>\n", " <td>0.9</td>\n", " <td>0.3</td>\n", " <td>202</td>\n", " <td>14</td>\n", " <td>11</td>\n", " <td>6.7</td>\n", " <td>3.6</td>\n", " <td>1.10</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>17</td>\n", " <td>0</td>\n", " <td>0.9</td>\n", " <td>0.3</td>\n", " <td>202</td>\n", " <td>22</td>\n", " <td>19</td>\n", " <td>7.4</td>\n", " <td>4.1</td>\n", " <td>1.20</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>55</td>\n", " <td>0</td>\n", " <td>0.7</td>\n", " <td>0.2</td>\n", " <td>290</td>\n", " <td>53</td>\n", " <td>58</td>\n", " <td>6.8</td>\n", " <td>3.4</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>57</td>\n", " <td>0</td>\n", " <td>0.6</td>\n", " <td>0.1</td>\n", " <td>210</td>\n", " <td>51</td>\n", " <td>59</td>\n", " <td>5.9</td>\n", " <td>2.7</td>\n", " <td>0.80</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>72</td>\n", " <td>0</td>\n", " <td>2.7</td>\n", " <td>1.3</td>\n", " <td>260</td>\n", " <td>31</td>\n", " <td>56</td>\n", " <td>7.4</td>\n", " <td>3.0</td>\n", " <td>0.60</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>64</td>\n", " <td>0</td>\n", " <td>0.9</td>\n", " <td>0.3</td>\n", " <td>310</td>\n", " <td>61</td>\n", " <td>58</td>\n", " <td>7.0</td>\n", " <td>3.4</td>\n", " <td>0.90</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>74</td>\n", " <td>1</td>\n", " <td>1.1</td>\n", " <td>0.4</td>\n", " <td>214</td>\n", " <td>22</td>\n", " <td>30</td>\n", " <td>8.1</td>\n", " <td>4.1</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>61</td>\n", " <td>0</td>\n", " <td>0.7</td>\n", " <td>0.2</td>\n", " <td>145</td>\n", " <td>53</td>\n", " <td>41</td>\n", " <td>5.8</td>\n", " <td>2.7</td>\n", " <td>0.87</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>25</td>\n", " <td>0</td>\n", " <td>0.6</td>\n", " <td>0.1</td>\n", " <td>183</td>\n", " <td>91</td>\n", " <td>53</td>\n", " <td>5.5</td>\n", " <td>2.3</td>\n", " <td>0.70</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>38</td>\n", " <td>0</td>\n", " <td>1.8</td>\n", " <td>0.8</td>\n", " <td>342</td>\n", " <td>168</td>\n", " <td>441</td>\n", " <td>7.6</td>\n", " <td>4.4</td>\n", " <td>1.30</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>33</td>\n", " <td>0</td>\n", " <td>1.6</td>\n", " <td>0.5</td>\n", " <td>165</td>\n", " <td>15</td>\n", " <td>23</td>\n", " <td>7.3</td>\n", " <td>3.5</td>\n", " <td>0.92</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>40</td>\n", " <td>1</td>\n", " <td>0.9</td>\n", " <td>0.3</td>\n", " <td>293</td>\n", " <td>232</td>\n", " <td>245</td>\n", " <td>6.8</td>\n", " <td>3.1</td>\n", " <td>0.80</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>40</td>\n", " <td>1</td>\n", " <td>0.9</td>\n", " <td>0.3</td>\n", " <td>293</td>\n", " <td>232</td>\n", " <td>245</td>\n", " <td>6.8</td>\n", " <td>3.1</td>\n", " <td>0.80</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>51</td>\n", " <td>0</td>\n", " <td>2.2</td>\n", " <td>1.0</td>\n", " <td>610</td>\n", " <td>17</td>\n", " <td>28</td>\n", " <td>7.3</td>\n", " <td>2.6</td>\n", " <td>0.55</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>51</td>\n", " <td>0</td>\n", " <td>2.9</td>\n", " <td>1.3</td>\n", " <td>482</td>\n", " <td>22</td>\n", " <td>34</td>\n", " <td>7.0</td>\n", " <td>2.4</td>\n", " <td>0.50</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>62</td>\n", " <td>0</td>\n", " <td>6.8</td>\n", " <td>3.0</td>\n", " <td>542</td>\n", " <td>116</td>\n", " <td>66</td>\n", " <td>6.4</td>\n", " <td>3.1</td>\n", " <td>0.90</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>40</td>\n", " <td>0</td>\n", " <td>1.9</td>\n", " <td>1.0</td>\n", " <td>231</td>\n", " <td>16</td>\n", " <td>55</td>\n", " <td>4.3</td>\n", " <td>1.6</td>\n", " <td>0.60</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>63</td>\n", " <td>0</td>\n", " <td>0.9</td>\n", " <td>0.2</td>\n", " <td>194</td>\n", " <td>52</td>\n", " <td>45</td>\n", " <td>6.0</td>\n", " <td>3.9</td>\n", " <td>1.85</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>34</td>\n", " <td>0</td>\n", " <td>4.1</td>\n", " <td>2.0</td>\n", " <td>289</td>\n", " <td>875</td>\n", " <td>731</td>\n", " <td>5.0</td>\n", " <td>2.7</td>\n", " <td>1.10</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>34</td>\n", " <td>0</td>\n", " <td>4.1</td>\n", " <td>2.0</td>\n", " <td>289</td>\n", " <td>875</td>\n", " <td>731</td>\n", " <td>5.0</td>\n", " <td>2.7</td>\n", " <td>1.10</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>34</td>\n", " <td>0</td>\n", " <td>6.2</td>\n", " <td>3.0</td>\n", " <td>240</td>\n", " <td>1680</td>\n", " <td>850</td>\n", " <td>7.2</td>\n", " <td>4.0</td>\n", " <td>1.20</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>20</td>\n", " <td>0</td>\n", " <td>1.1</td>\n", " <td>0.5</td>\n", " <td>128</td>\n", " <td>20</td>\n", " <td>30</td>\n", " <td>3.9</td>\n", " <td>1.9</td>\n", " <td>0.95</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>84</td>\n", " <td>1</td>\n", " <td>0.7</td>\n", " <td>0.2</td>\n", " <td>188</td>\n", " <td>13</td>\n", " <td>21</td>\n", " <td>6.0</td>\n", " <td>3.2</td>\n", " <td>1.10</td>\n", " <td>2</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", " </tr>\n", " <tr>\n", " <th>553</th>\n", " <td>46</td>\n", " <td>0</td>\n", " <td>10.2</td>\n", " <td>4.2</td>\n", " <td>232</td>\n", " <td>58</td>\n", " <td>140</td>\n", " <td>7.0</td>\n", " <td>2.7</td>\n", " <td>0.60</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>554</th>\n", " <td>73</td>\n", " <td>0</td>\n", " <td>1.8</td>\n", " <td>0.9</td>\n", " <td>220</td>\n", " <td>20</td>\n", " <td>43</td>\n", " <td>6.5</td>\n", " <td>3.0</td>\n", " <td>0.80</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>555</th>\n", " <td>55</td>\n", " <td>0</td>\n", " <td>0.8</td>\n", " <td>0.2</td>\n", " <td>290</td>\n", " <td>139</td>\n", " <td>87</td>\n", " <td>7.0</td>\n", " <td>3.0</td>\n", " <td>0.70</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>556</th>\n", " <td>51</td>\n", " <td>0</td>\n", " <td>0.7</td>\n", " <td>0.1</td>\n", " <td>180</td>\n", " <td>25</td>\n", " <td>27</td>\n", " <td>6.1</td>\n", " <td>3.1</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>557</th>\n", " <td>51</td>\n", " <td>0</td>\n", " <td>2.9</td>\n", " <td>1.2</td>\n", " <td>189</td>\n", " <td>80</td>\n", " <td>125</td>\n", " <td>6.2</td>\n", " <td>3.1</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>558</th>\n", " <td>51</td>\n", " <td>0</td>\n", " <td>4.0</td>\n", " <td>2.5</td>\n", " <td>275</td>\n", " <td>382</td>\n", " <td>330</td>\n", " <td>7.5</td>\n", " <td>4.0</td>\n", " <td>1.10</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>559</th>\n", " <td>26</td>\n", " <td>0</td>\n", " <td>42.8</td>\n", " <td>19.7</td>\n", " <td>390</td>\n", " <td>75</td>\n", " <td>138</td>\n", " <td>7.5</td>\n", " <td>2.6</td>\n", " <td>0.50</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>560</th>\n", " <td>66</td>\n", " <td>0</td>\n", " <td>15.2</td>\n", " <td>7.7</td>\n", " <td>356</td>\n", " <td>321</td>\n", " <td>562</td>\n", " <td>6.5</td>\n", " <td>2.2</td>\n", " <td>0.40</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>561</th>\n", " <td>66</td>\n", " <td>0</td>\n", " <td>16.6</td>\n", " <td>7.6</td>\n", " <td>315</td>\n", " <td>233</td>\n", " <td>384</td>\n", " <td>6.9</td>\n", " <td>2.0</td>\n", " <td>0.40</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>562</th>\n", " <td>66</td>\n", " <td>0</td>\n", " <td>17.3</td>\n", " <td>8.5</td>\n", " <td>388</td>\n", " <td>173</td>\n", " <td>367</td>\n", " <td>7.8</td>\n", " <td>2.6</td>\n", " <td>0.50</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>563</th>\n", " <td>64</td>\n", " <td>0</td>\n", " <td>1.4</td>\n", " <td>0.5</td>\n", " <td>298</td>\n", " <td>31</td>\n", " <td>83</td>\n", " <td>7.2</td>\n", " <td>2.6</td>\n", " <td>0.50</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>564</th>\n", " <td>38</td>\n", " <td>1</td>\n", " <td>0.6</td>\n", " <td>0.1</td>\n", " <td>165</td>\n", " <td>22</td>\n", " <td>34</td>\n", " <td>5.9</td>\n", " <td>2.9</td>\n", " <td>0.90</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>565</th>\n", " <td>43</td>\n", " <td>0</td>\n", " <td>22.5</td>\n", " <td>11.8</td>\n", " <td>143</td>\n", " <td>22</td>\n", " <td>143</td>\n", " <td>6.6</td>\n", " <td>2.1</td>\n", " <td>0.46</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>566</th>\n", " <td>50</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " <td>0.3</td>\n", " <td>191</td>\n", " <td>22</td>\n", " <td>31</td>\n", " <td>7.8</td>\n", " <td>4.0</td>\n", " <td>1.00</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>567</th>\n", " <td>52</td>\n", " <td>0</td>\n", " <td>2.7</td>\n", " <td>1.4</td>\n", " <td>251</td>\n", " <td>20</td>\n", " <td>40</td>\n", " <td>6.0</td>\n", " <td>1.7</td>\n", " <td>0.39</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>568</th>\n", " <td>20</td>\n", " <td>1</td>\n", " <td>16.7</td>\n", " <td>8.4</td>\n", " <td>200</td>\n", " <td>91</td>\n", " <td>101</td>\n", " <td>6.9</td>\n", " <td>3.5</td>\n", " <td>1.02</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>569</th>\n", " <td>16</td>\n", " <td>0</td>\n", " <td>7.7</td>\n", " <td>4.1</td>\n", " <td>268</td>\n", " <td>213</td>\n", " <td>168</td>\n", " <td>7.1</td>\n", " <td>4.0</td>\n", " <td>1.20</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>570</th>\n", " <td>16</td>\n", " <td>0</td>\n", " <td>2.6</td>\n", " <td>1.2</td>\n", " <td>236</td>\n", " <td>131</td>\n", " <td>90</td>\n", " <td>5.4</td>\n", " <td>2.6</td>\n", " <td>0.90</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>571</th>\n", " <td>90</td>\n", " <td>0</td>\n", " <td>1.1</td>\n", " <td>0.3</td>\n", " <td>215</td>\n", " <td>46</td>\n", " <td>134</td>\n", " <td>6.9</td>\n", " <td>3.0</td>\n", " <td>0.70</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>572</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>15.6</td>\n", " <td>9.5</td>\n", " <td>134</td>\n", " <td>54</td>\n", " <td>125</td>\n", " <td>5.6</td>\n", " <td>4.0</td>\n", " <td>2.50</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>573</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>3.7</td>\n", " <td>1.6</td>\n", " <td>612</td>\n", " <td>50</td>\n", " <td>88</td>\n", " <td>6.2</td>\n", " <td>1.9</td>\n", " <td>0.40</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>574</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>12.1</td>\n", " <td>6.0</td>\n", " <td>515</td>\n", " <td>48</td>\n", " <td>92</td>\n", " <td>6.6</td>\n", " <td>2.4</td>\n", " <td>0.50</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>575</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>25.0</td>\n", " <td>13.7</td>\n", " <td>560</td>\n", " <td>41</td>\n", " <td>88</td>\n", " <td>7.9</td>\n", " <td>2.5</td>\n", " <td>2.50</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>576</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>15.0</td>\n", " <td>8.2</td>\n", " <td>289</td>\n", " <td>58</td>\n", " <td>80</td>\n", " <td>5.3</td>\n", " <td>2.2</td>\n", " <td>0.70</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>577</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>12.7</td>\n", " <td>8.4</td>\n", " <td>190</td>\n", " <td>28</td>\n", " <td>47</td>\n", " <td>5.4</td>\n", " <td>2.6</td>\n", " <td>0.90</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>578</th>\n", " <td>60</td>\n", " <td>0</td>\n", " <td>0.5</td>\n", " <td>0.1</td>\n", " <td>500</td>\n", " <td>20</td>\n", " <td>34</td>\n", " <td>5.9</td>\n", " <td>1.6</td>\n", " <td>0.37</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>579</th>\n", " <td>40</td>\n", " <td>0</td>\n", " <td>0.6</td>\n", " <td>0.1</td>\n", " <td>98</td>\n", " <td>35</td>\n", " <td>31</td>\n", " <td>6.0</td>\n", " <td>3.2</td>\n", " <td>1.10</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>580</th>\n", " <td>52</td>\n", " <td>0</td>\n", " <td>0.8</td>\n", " <td>0.2</td>\n", " <td>245</td>\n", " <td>48</td>\n", " <td>49</td>\n", " <td>6.4</td>\n", " <td>3.2</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>581</th>\n", " <td>31</td>\n", " <td>0</td>\n", " <td>1.3</td>\n", " <td>0.5</td>\n", " <td>184</td>\n", " <td>29</td>\n", " <td>32</td>\n", " <td>6.8</td>\n", " <td>3.4</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>582</th>\n", " <td>38</td>\n", " <td>0</td>\n", " <td>1.0</td>\n", " <td>0.3</td>\n", " <td>216</td>\n", " <td>21</td>\n", " <td>24</td>\n", " <td>7.3</td>\n", " <td>4.4</td>\n", " <td>1.50</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>583 rows × 11 columns</p>\n", "</div>" ], "text/plain": [ " Age Gender Total_Bilirubin Direct_Bilirubin Alkaline_Phosphotase \\\n", "0 65 1 0.7 0.1 187 \n", "1 62 0 10.9 5.5 699 \n", "2 62 0 7.3 4.1 490 \n", "3 58 0 1.0 0.4 182 \n", "4 72 0 3.9 2.0 195 \n", "5 46 0 1.8 0.7 208 \n", "6 26 1 0.9 0.2 154 \n", "7 29 1 0.9 0.3 202 \n", "8 17 0 0.9 0.3 202 \n", "9 55 0 0.7 0.2 290 \n", "10 57 0 0.6 0.1 210 \n", "11 72 0 2.7 1.3 260 \n", "12 64 0 0.9 0.3 310 \n", "13 74 1 1.1 0.4 214 \n", "14 61 0 0.7 0.2 145 \n", "15 25 0 0.6 0.1 183 \n", "16 38 0 1.8 0.8 342 \n", "17 33 0 1.6 0.5 165 \n", "18 40 1 0.9 0.3 293 \n", "19 40 1 0.9 0.3 293 \n", "20 51 0 2.2 1.0 610 \n", "21 51 0 2.9 1.3 482 \n", "22 62 0 6.8 3.0 542 \n", "23 40 0 1.9 1.0 231 \n", "24 63 0 0.9 0.2 194 \n", "25 34 0 4.1 2.0 289 \n", "26 34 0 4.1 2.0 289 \n", "27 34 0 6.2 3.0 240 \n", "28 20 0 1.1 0.5 128 \n", "29 84 1 0.7 0.2 188 \n", ".. ... ... ... ... ... \n", "553 46 0 10.2 4.2 232 \n", "554 73 0 1.8 0.9 220 \n", "555 55 0 0.8 0.2 290 \n", "556 51 0 0.7 0.1 180 \n", "557 51 0 2.9 1.2 189 \n", "558 51 0 4.0 2.5 275 \n", "559 26 0 42.8 19.7 390 \n", "560 66 0 15.2 7.7 356 \n", "561 66 0 16.6 7.6 315 \n", "562 66 0 17.3 8.5 388 \n", "563 64 0 1.4 0.5 298 \n", "564 38 1 0.6 0.1 165 \n", "565 43 0 22.5 11.8 143 \n", "566 50 1 1.0 0.3 191 \n", "567 52 0 2.7 1.4 251 \n", "568 20 1 16.7 8.4 200 \n", "569 16 0 7.7 4.1 268 \n", "570 16 0 2.6 1.2 236 \n", "571 90 0 1.1 0.3 215 \n", "572 32 0 15.6 9.5 134 \n", "573 32 0 3.7 1.6 612 \n", "574 32 0 12.1 6.0 515 \n", "575 32 0 25.0 13.7 560 \n", "576 32 0 15.0 8.2 289 \n", "577 32 0 12.7 8.4 190 \n", "578 60 0 0.5 0.1 500 \n", "579 40 0 0.6 0.1 98 \n", "580 52 0 0.8 0.2 245 \n", "581 31 0 1.3 0.5 184 \n", "582 38 0 1.0 0.3 216 \n", "\n", " Alamine_Aminotransferase Aspartate_Aminotransferase Total_Protiens \\\n", "0 16 18 6.8 \n", "1 64 100 7.5 \n", "2 60 68 7.0 \n", "3 14 20 6.8 \n", "4 27 59 7.3 \n", "5 19 14 7.6 \n", "6 16 12 7.0 \n", "7 14 11 6.7 \n", "8 22 19 7.4 \n", "9 53 58 6.8 \n", "10 51 59 5.9 \n", "11 31 56 7.4 \n", "12 61 58 7.0 \n", "13 22 30 8.1 \n", "14 53 41 5.8 \n", "15 91 53 5.5 \n", "16 168 441 7.6 \n", "17 15 23 7.3 \n", "18 232 245 6.8 \n", "19 232 245 6.8 \n", "20 17 28 7.3 \n", "21 22 34 7.0 \n", "22 116 66 6.4 \n", "23 16 55 4.3 \n", "24 52 45 6.0 \n", "25 875 731 5.0 \n", "26 875 731 5.0 \n", "27 1680 850 7.2 \n", "28 20 30 3.9 \n", "29 13 21 6.0 \n", ".. ... ... ... \n", "553 58 140 7.0 \n", "554 20 43 6.5 \n", "555 139 87 7.0 \n", "556 25 27 6.1 \n", "557 80 125 6.2 \n", "558 382 330 7.5 \n", "559 75 138 7.5 \n", "560 321 562 6.5 \n", "561 233 384 6.9 \n", "562 173 367 7.8 \n", "563 31 83 7.2 \n", "564 22 34 5.9 \n", "565 22 143 6.6 \n", "566 22 31 7.8 \n", "567 20 40 6.0 \n", "568 91 101 6.9 \n", "569 213 168 7.1 \n", "570 131 90 5.4 \n", "571 46 134 6.9 \n", "572 54 125 5.6 \n", "573 50 88 6.2 \n", "574 48 92 6.6 \n", "575 41 88 7.9 \n", "576 58 80 5.3 \n", "577 28 47 5.4 \n", "578 20 34 5.9 \n", "579 35 31 6.0 \n", "580 48 49 6.4 \n", "581 29 32 6.8 \n", "582 21 24 7.3 \n", "\n", " Albumin Albumin_and_Globulin_Ratio Dataset \n", "0 3.3 0.90 1 \n", "1 3.2 0.74 1 \n", "2 3.3 0.89 1 \n", "3 3.4 1.00 1 \n", "4 2.4 0.40 1 \n", "5 4.4 1.30 1 \n", "6 3.5 1.00 1 \n", "7 3.6 1.10 1 \n", "8 4.1 1.20 2 \n", "9 3.4 1.00 1 \n", "10 2.7 0.80 1 \n", "11 3.0 0.60 1 \n", "12 3.4 0.90 2 \n", "13 4.1 1.00 1 \n", "14 2.7 0.87 1 \n", "15 2.3 0.70 2 \n", "16 4.4 1.30 1 \n", "17 3.5 0.92 2 \n", "18 3.1 0.80 1 \n", "19 3.1 0.80 1 \n", "20 2.6 0.55 1 \n", "21 2.4 0.50 1 \n", "22 3.1 0.90 1 \n", "23 1.6 0.60 1 \n", "24 3.9 1.85 2 \n", "25 2.7 1.10 1 \n", "26 2.7 1.10 1 \n", "27 4.0 1.20 1 \n", "28 1.9 0.95 2 \n", "29 3.2 1.10 2 \n", ".. ... ... ... \n", "553 2.7 0.60 1 \n", "554 3.0 0.80 1 \n", "555 3.0 0.70 1 \n", "556 3.1 1.00 1 \n", "557 3.1 1.00 1 \n", "558 4.0 1.10 1 \n", "559 2.6 0.50 1 \n", "560 2.2 0.40 1 \n", "561 2.0 0.40 1 \n", "562 2.6 0.50 1 \n", "563 2.6 0.50 1 \n", "564 2.9 0.90 2 \n", "565 2.1 0.46 1 \n", "566 4.0 1.00 2 \n", "567 1.7 0.39 1 \n", "568 3.5 1.02 1 \n", "569 4.0 1.20 1 \n", "570 2.6 0.90 1 \n", "571 3.0 0.70 1 \n", "572 4.0 2.50 1 \n", "573 1.9 0.40 1 \n", "574 2.4 0.50 1 \n", "575 2.5 2.50 1 \n", "576 2.2 0.70 1 \n", "577 2.6 0.90 1 \n", "578 1.6 0.37 2 \n", "579 3.2 1.10 1 \n", "580 3.2 1.00 1 \n", "581 3.4 1.00 1 \n", "582 4.4 1.50 2 \n", "\n", "[583 rows x 11 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.replace(\"Male\",0, True)\n", "dataset.replace(\"Female\",1,True)\n", "dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "#### Datos de estadisticos de las variables\n", "En este apartado vamos a desarrollar un breve estudio estadístico para los datos proporcionados. Para ello utilizaremos el método _describe_ del objeto _DataSet_ de la librería **panda**. Este método genéra un cálculo estadístico básico en el que nos describe por cada variable valores tales como la media, la desviación estandar y los percentiles 25%, 50% y 75% respecto al valor máximo.\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Age Gender Total_Bilirubin Direct_Bilirubin \\\n", "count 583.000000 583.000000 583.000000 583.000000 \n", "mean 44.746141 0.243568 3.298799 1.486106 \n", "std 16.189833 0.429603 6.209522 2.808498 \n", "min 4.000000 0.000000 0.400000 0.100000 \n", "25% 33.000000 0.000000 0.800000 0.200000 \n", "50% 45.000000 0.000000 1.000000 0.300000 \n", "75% 58.000000 0.000000 2.600000 1.300000 \n", "max 90.000000 1.000000 75.000000 19.700000 \n", "\n", " Alkaline_Phosphotase Alamine_Aminotransferase \\\n", "count 583.000000 583.000000 \n", "mean 290.576329 80.713551 \n", "std 242.937989 182.620356 \n", "min 63.000000 10.000000 \n", "25% 175.500000 23.000000 \n", "50% 208.000000 35.000000 \n", "75% 298.000000 60.500000 \n", "max 2110.000000 2000.000000 \n", "\n", " Aspartate_Aminotransferase Total_Protiens Albumin \\\n", "count 583.000000 583.000000 583.000000 \n", "mean 109.910806 6.483190 3.141852 \n", "std 288.918529 1.085451 0.795519 \n", "min 10.000000 2.700000 0.900000 \n", "25% 25.000000 5.800000 2.600000 \n", "50% 42.000000 6.600000 3.100000 \n", "75% 87.000000 7.200000 3.800000 \n", "max 4929.000000 9.600000 5.500000 \n", "\n", " Albumin_and_Globulin_Ratio Dataset \n", "count 579.000000 583.000000 \n", "mean 0.947064 1.286449 \n", "std 0.319592 0.452490 \n", "min 0.300000 1.000000 \n", "25% 0.700000 1.000000 \n", "50% 0.930000 1.000000 \n", "75% 1.100000 2.000000 \n", "max 2.800000 2.000000 \n", "\n", "Pacientes masculinos (0) y femeninos (1):\n", "0 441\n", "1 142\n", "dtype: int64\n", "\n", "Pacientes enfermos (1) y sanos (2):\n", "1 416\n", "2 167\n", "dtype: int64\n" ] } ], "source": [ "print(dataset.describe()) #Descripción de los datos\n", "\n", "#### Obtenemos contéo de sexo\n", "print('\\nPacientes masculinos (0) y femeninos (1):')\n", "print(pd.value_counts(dataset['Gender'].values))\n", "#### Obtenemos contéo de la clase\n", "print('\\nPacientes enfermos (1) y sanos (2):')\n", "print(pd.value_counts(dataset['Dataset'].values))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observando estos datos podemos sacar conclusiones tales como:\n", "* Tratamos con un conjunto de 583 pacientes\n", "* Referente a la edad de los pacientes:\n", " * La edad media de los pacientes es de 44 ± 16 años\n", " * El paciente más joven tiene la edad de 4 años.\n", " * El paciente más anciano tiene 90 años.\n", " * El grueso de los pacientes están entre el percentíl 50 y 75, lo que indica que están por encima de los 45 años.\n", "* Referente al sexo de los pacientes:\n", " * Tenemos un total de 441 hombres\n", " * Tenemos un total de 142 mujeres\n", "* De estos pacientes:\n", " * 416 están enfermos\n", " * 167 están sanos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Normalizando variables\n", "\n", "Para saber si debemos normalizar algúna variable, debemos ver la distribución que estas siguen. Para ello, podemos realizar un histograma de cada una de las variables y ver gráficamente las variables que parezcan seguir una distribución normal." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fd0b3e17ac8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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MvW7XUe8LAAAM06SJU0Rc1KbbKkmrJGnJkiUxf/58jY+PT310hRqNhsbHx0d6JbBi8Vat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\nmmgBzGWzKnHKicD8iJgv6beSnl7pdvo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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd0b3e23b70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#### Histogram Matrix Plot\n", "\n", "plt.figure()\n", "dataset.hist(xlabelsize=0.5, ylabelsize=0.2,figsize=(10,10))\n", "plt.xlabel(\"Data\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La variables que más o menos siguen está distribución (_Albumin_,_Total&#95;Protiens_) las vamos a normalizar con la normalizacion Z-Score: $$x'=\\frac{x-\\mu}{\\sigma}$$ Aunque la variable _Age_ también parece seguir una distribución normal, por simplicidad y semántica de la variable, no se va a normalizar. Pasa lo mismo con las variables _Genre_ y _Dataset_.\n", "\n", "Para el resto de variables vamos a usar la normalización min-max en un intervalo [0-1]:$$x'=\\frac{x-min}{max-min}$$" ] }, { "cell_type": "code", "execution_count": 7, "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>Age</th>\n", " <th>Gender</th>\n", " <th>Total_Bilirubin</th>\n", " <th>Direct_Bilirubin</th>\n", " <th>Alkaline_Phosphotase</th>\n", " <th>Alamine_Aminotransferase</th>\n", " <th>Aspartate_Aminotransferase</th>\n", " <th>Total_Protiens</th>\n", " <th>Albumin</th>\n", " <th>Albumin_and_Globulin_Ratio</th>\n", " <th>Dataset</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>65</td>\n", " <td>1</td>\n", " <td>0.004021</td>\n", " <td>0.000000</td>\n", " <td>0.060576</td>\n", " <td>0.003015</td>\n", " <td>0.001626</td>\n", " <td>0.291869</td>\n", " <td>0.198798</td>\n", " <td>0.240</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>62</td>\n", " <td>0</td>\n", " <td>0.140751</td>\n", " <td>0.275510</td>\n", " <td>0.310699</td>\n", " <td>0.027136</td>\n", " <td>0.018296</td>\n", " <td>0.936762</td>\n", " <td>0.073094</td>\n", " <td>0.176</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>62</td>\n", " <td>0</td>\n", " <td>0.092493</td>\n", " <td>0.204082</td>\n", " <td>0.208598</td>\n", " <td>0.025126</td>\n", " <td>0.011791</td>\n", " <td>0.476124</td>\n", " <td>0.198798</td>\n", " <td>0.236</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>58</td>\n", " <td>0</td>\n", " <td>0.008043</td>\n", " <td>0.015306</td>\n", " <td>0.058134</td>\n", " <td>0.002010</td>\n", " <td>0.002033</td>\n", " <td>0.291869</td>\n", " <td>0.324502</td>\n", " <td>0.280</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>72</td>\n", " <td>0</td>\n", " <td>0.046917</td>\n", " <td>0.096939</td>\n", " <td>0.064485</td>\n", " <td>0.008543</td>\n", " <td>0.009961</td>\n", " <td>0.752507</td>\n", " <td>-0.932539</td>\n", " <td>0.040</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>46</td>\n", " <td>0</td>\n", " <td>0.018767</td>\n", " <td>0.030612</td>\n", " <td>0.070835</td>\n", " <td>0.004523</td>\n", " <td>0.000813</td>\n", " <td>1.028889</td>\n", " <td>1.581543</td>\n", " <td>0.400</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>26</td>\n", " <td>1</td>\n", " <td>0.006702</td>\n", " <td>0.005102</td>\n", " <td>0.044455</td>\n", " <td>0.003015</td>\n", " <td>0.000407</td>\n", " <td>0.476124</td>\n", " <td>0.450206</td>\n", " <td>0.280</td>\n", " <td>1</td>\n", 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<td>0.020603</td>\n", " <td>0.009961</td>\n", " <td>-0.537279</td>\n", " <td>-0.555427</td>\n", " <td>0.200</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>72</td>\n", " <td>0</td>\n", " <td>0.030831</td>\n", " <td>0.061224</td>\n", " <td>0.096238</td>\n", " <td>0.010553</td>\n", " <td>0.009351</td>\n", " <td>0.844634</td>\n", " <td>-0.178314</td>\n", " <td>0.120</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>64</td>\n", " <td>0</td>\n", " <td>0.006702</td>\n", " <td>0.010204</td>\n", " <td>0.120664</td>\n", " <td>0.025628</td>\n", " <td>0.009758</td>\n", " <td>0.476124</td>\n", " <td>0.324502</td>\n", " <td>0.240</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>74</td>\n", " <td>1</td>\n", " <td>0.009383</td>\n", " <td>0.015306</td>\n", " <td>0.073766</td>\n", " <td>0.006030</td>\n", " <td>0.004066</td>\n", " <td>1.489527</td>\n", " <td>1.204431</td>\n", " <td>0.280</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>61</td>\n", " <td>0</td>\n", " <td>0.004021</td>\n", " <td>0.005102</td>\n", " <td>0.040059</td>\n", " <td>0.021608</td>\n", " <td>0.006302</td>\n", " <td>-0.629407</td>\n", " <td>-0.555427</td>\n", " <td>0.228</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>25</td>\n", " <td>0</td>\n", " <td>0.002681</td>\n", " <td>0.000000</td>\n", " <td>0.058622</td>\n", " <td>0.040704</td>\n", " <td>0.008742</td>\n", " <td>-0.905789</td>\n", " <td>-1.058243</td>\n", " <td>0.160</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>38</td>\n", " <td>0</td>\n", " <td>0.018767</td>\n", " <td>0.035714</td>\n", " <td>0.136297</td>\n", " <td>0.079397</td>\n", " <td>0.087619</td>\n", " <td>1.028889</td>\n", " <td>1.581543</td>\n", " <td>0.400</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>33</td>\n", " <td>0</td>\n", " <td>0.016086</td>\n", " <td>0.020408</td>\n", " <td>0.049829</td>\n", " <td>0.002513</td>\n", " <td>0.002643</td>\n", " <td>0.752507</td>\n", " <td>0.450206</td>\n", " <td>0.248</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>40</td>\n", " <td>1</td>\n", " <td>0.006702</td>\n", " <td>0.010204</td>\n", " <td>0.112360</td>\n", " <td>0.111558</td>\n", " <td>0.047774</td>\n", " <td>0.291869</td>\n", " <td>-0.052610</td>\n", " <td>0.200</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>40</td>\n", " <td>1</td>\n", " <td>0.006702</td>\n", " <td>0.010204</td>\n", " <td>0.112360</td>\n", " <td>0.111558</td>\n", " <td>0.047774</td>\n", " <td>0.291869</td>\n", " <td>-0.052610</td>\n", " <td>0.200</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>51</td>\n", " <td>0</td>\n", " <td>0.024129</td>\n", " <td>0.045918</td>\n", " <td>0.267220</td>\n", " <td>0.003518</td>\n", " <td>0.003659</td>\n", " <td>0.752507</td>\n", " <td>-0.681131</td>\n", " <td>0.100</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>51</td>\n", " <td>0</td>\n", " <td>0.033512</td>\n", " <td>0.061224</td>\n", " <td>0.204690</td>\n", " <td>0.006030</td>\n", " <td>0.004879</td>\n", " <td>0.476124</td>\n", " <td>-0.932539</td>\n", " <td>0.080</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>62</td>\n", " <td>0</td>\n", " <td>0.085791</td>\n", " <td>0.147959</td>\n", " <td>0.234001</td>\n", " <td>0.053266</td>\n", " <td>0.011384</td>\n", " <td>-0.076641</td>\n", " <td>-0.052610</td>\n", " <td>0.240</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>40</td>\n", " <td>0</td>\n", " <td>0.020107</td>\n", " <td>0.045918</td>\n", " <td>0.082071</td>\n", " <td>0.003015</td>\n", " <td>0.009148</td>\n", " <td>-2.011320</td>\n", " <td>-1.938172</td>\n", " <td>0.120</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>63</td>\n", " <td>0</td>\n", " <td>0.006702</td>\n", " <td>0.005102</td>\n", " <td>0.063996</td>\n", " 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<tr>\n", " <th>28</th>\n", " <td>20</td>\n", " <td>0</td>\n", " <td>0.009383</td>\n", " <td>0.020408</td>\n", " <td>0.031754</td>\n", " <td>0.005025</td>\n", " <td>0.004066</td>\n", " <td>-2.379830</td>\n", " <td>-1.561060</td>\n", " <td>0.260</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>84</td>\n", " <td>1</td>\n", " <td>0.004021</td>\n", " <td>0.005102</td>\n", " <td>0.061065</td>\n", " <td>0.001508</td>\n", " <td>0.002236</td>\n", " <td>-0.445152</td>\n", " <td>0.073094</td>\n", " <td>0.320</td>\n", " <td>2</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", " </tr>\n", " <tr>\n", " <th>553</th>\n", " <td>46</td>\n", " <td>0</td>\n", " <td>0.131367</td>\n", " <td>0.209184</td>\n", " <td>0.082560</td>\n", " <td>0.024121</td>\n", " <td>0.026428</td>\n", 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<td>0.015486</td>\n", " <td>-1.183947</td>\n", " <td>0.040</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>561</th>\n", " <td>66</td>\n", " <td>0</td>\n", " <td>0.217158</td>\n", " <td>0.382653</td>\n", " <td>0.123107</td>\n", " <td>0.112060</td>\n", " <td>0.076032</td>\n", " <td>0.383997</td>\n", " <td>-1.435356</td>\n", " <td>0.040</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>562</th>\n", " <td>66</td>\n", " <td>0</td>\n", " <td>0.226542</td>\n", " <td>0.428571</td>\n", " <td>0.158769</td>\n", " <td>0.081910</td>\n", " <td>0.072576</td>\n", " <td>1.213145</td>\n", " <td>-0.681131</td>\n", " <td>0.080</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>563</th>\n", " <td>64</td>\n", " <td>0</td>\n", " <td>0.013405</td>\n", " <td>0.020408</td>\n", " <td>0.114802</td>\n", " <td>0.010553</td>\n", " <td>0.014840</td>\n", " <td>0.660379</td>\n", " <td>-0.681131</td>\n", " <td>0.080</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>564</th>\n", " <td>38</td>\n", " <td>1</td>\n", " <td>0.002681</td>\n", " <td>0.000000</td>\n", " <td>0.049829</td>\n", " <td>0.006030</td>\n", " <td>0.004879</td>\n", " <td>-0.537279</td>\n", " <td>-0.304019</td>\n", " <td>0.240</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>565</th>\n", " <td>43</td>\n", " <td>0</td>\n", " <td>0.296247</td>\n", " <td>0.596939</td>\n", " <td>0.039082</td>\n", " <td>0.006030</td>\n", " <td>0.027038</td>\n", " <td>0.107614</td>\n", " <td>-1.309652</td>\n", " <td>0.064</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>566</th>\n", " <td>50</td>\n", " <td>1</td>\n", " <td>0.008043</td>\n", " <td>0.010204</td>\n", " <td>0.062531</td>\n", " <td>0.006030</td>\n", " <td>0.004269</td>\n", " <td>1.213145</td>\n", " <td>1.078727</td>\n", " <td>0.280</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>567</th>\n", " <td>52</td>\n", " <td>0</td>\n", " <td>0.030831</td>\n", " <td>0.066327</td>\n", " <td>0.091842</td>\n", " <td>0.005025</td>\n", " <td>0.006099</td>\n", " <td>-0.445152</td>\n", " <td>-1.812468</td>\n", " <td>0.036</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>568</th>\n", " <td>20</td>\n", " <td>1</td>\n", " <td>0.218499</td>\n", " <td>0.423469</td>\n", " <td>0.066927</td>\n", " <td>0.040704</td>\n", " <td>0.018500</td>\n", " <td>0.383997</td>\n", " <td>0.450206</td>\n", " <td>0.288</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>569</th>\n", " <td>16</td>\n", " <td>0</td>\n", " <td>0.097855</td>\n", " <td>0.204082</td>\n", " <td>0.100147</td>\n", " <td>0.102010</td>\n", " <td>0.032120</td>\n", " <td>0.568252</td>\n", " <td>1.078727</td>\n", " <td>0.360</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>570</th>\n", " <td>16</td>\n", " <td>0</td>\n", " <td>0.029491</td>\n", " <td>0.056122</td>\n", " <td>0.084514</td>\n", " <td>0.060804</td>\n", " <td>0.016263</td>\n", " <td>-0.997917</td>\n", " <td>-0.681131</td>\n", " <td>0.240</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>571</th>\n", " <td>90</td>\n", " <td>0</td>\n", " <td>0.009383</td>\n", " <td>0.010204</td>\n", " <td>0.074255</td>\n", " <td>0.018090</td>\n", " <td>0.025208</td>\n", " <td>0.383997</td>\n", " <td>-0.178314</td>\n", " <td>0.160</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>572</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>0.203753</td>\n", " <td>0.479592</td>\n", " <td>0.034685</td>\n", " <td>0.022111</td>\n", " <td>0.023379</td>\n", " <td>-0.813662</td>\n", " <td>1.078727</td>\n", " <td>0.880</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>573</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>0.044236</td>\n", " <td>0.076531</td>\n", " <td>0.268197</td>\n", " <td>0.020101</td>\n", " <td>0.015857</td>\n", " <td>-0.260896</td>\n", " <td>-1.561060</td>\n", " <td>0.040</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>574</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>0.156836</td>\n", " <td>0.301020</td>\n", " <td>0.220811</td>\n", " <td>0.019095</td>\n", " <td>0.016670</td>\n", " <td>0.107614</td>\n", " <td>-0.932539</td>\n", " <td>0.080</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>575</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>0.329759</td>\n", " <td>0.693878</td>\n", " <td>0.242794</td>\n", " <td>0.015578</td>\n", " <td>0.015857</td>\n", " <td>1.305272</td>\n", " <td>-0.806835</td>\n", " <td>0.880</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>576</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>0.195710</td>\n", " <td>0.413265</td>\n", " <td>0.110405</td>\n", " <td>0.024121</td>\n", " <td>0.014231</td>\n", " <td>-1.090044</td>\n", " <td>-1.183947</td>\n", " <td>0.160</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>577</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>0.164879</td>\n", " <td>0.423469</td>\n", " <td>0.062042</td>\n", " <td>0.009045</td>\n", " <td>0.007522</td>\n", " <td>-0.997917</td>\n", " <td>-0.681131</td>\n", " <td>0.240</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>578</th>\n", " <td>60</td>\n", " <td>0</td>\n", " <td>0.001340</td>\n", " <td>0.000000</td>\n", " <td>0.213483</td>\n", " <td>0.005025</td>\n", " <td>0.004879</td>\n", " <td>-0.537279</td>\n", " <td>-1.938172</td>\n", " <td>0.028</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>579</th>\n", " <td>40</td>\n", " <td>0</td>\n", " <td>0.002681</td>\n", " <td>0.000000</td>\n", " <td>0.017098</td>\n", " <td>0.012563</td>\n", " <td>0.004269</td>\n", " <td>-0.445152</td>\n", " <td>0.073094</td>\n", " <td>0.320</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>580</th>\n", " <td>52</td>\n", " <td>0</td>\n", " <td>0.005362</td>\n", " <td>0.005102</td>\n", " <td>0.088911</td>\n", " <td>0.019095</td>\n", " <td>0.007928</td>\n", " <td>-0.076641</td>\n", " <td>0.073094</td>\n", " <td>0.280</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>581</th>\n", " <td>31</td>\n", " <td>0</td>\n", " <td>0.012064</td>\n", " <td>0.020408</td>\n", " <td>0.059111</td>\n", " <td>0.009548</td>\n", " <td>0.004472</td>\n", " <td>0.291869</td>\n", " <td>0.324502</td>\n", " <td>0.280</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>582</th>\n", " <td>38</td>\n", " <td>0</td>\n", " <td>0.008043</td>\n", " <td>0.010204</td>\n", " <td>0.074744</td>\n", " <td>0.005528</td>\n", " <td>0.002846</td>\n", " <td>0.752507</td>\n", " <td>1.581543</td>\n", " <td>0.480</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>583 rows × 11 columns</p>\n", "</div>" ], "text/plain": [ " Age Gender Total_Bilirubin Direct_Bilirubin Alkaline_Phosphotase \\\n", "0 65 1 0.004021 0.000000 0.060576 \n", "1 62 0 0.140751 0.275510 0.310699 \n", "2 62 0 0.092493 0.204082 0.208598 \n", "3 58 0 0.008043 0.015306 0.058134 \n", "4 72 0 0.046917 0.096939 0.064485 \n", "5 46 0 0.018767 0.030612 0.070835 \n", "6 26 1 0.006702 0.005102 0.044455 \n", "7 29 1 0.006702 0.010204 0.067904 \n", "8 17 0 0.006702 0.010204 0.067904 \n", "9 55 0 0.004021 0.005102 0.110894 \n", "10 57 0 0.002681 0.000000 0.071812 \n", "11 72 0 0.030831 0.061224 0.096238 \n", "12 64 0 0.006702 0.010204 0.120664 \n", "13 74 1 0.009383 0.015306 0.073766 \n", "14 61 0 0.004021 0.005102 0.040059 \n", "15 25 0 0.002681 0.000000 0.058622 \n", "16 38 0 0.018767 0.035714 0.136297 \n", "17 33 0 0.016086 0.020408 0.049829 \n", "18 40 1 0.006702 0.010204 0.112360 \n", "19 40 1 0.006702 0.010204 0.112360 \n", "20 51 0 0.024129 0.045918 0.267220 \n", "21 51 0 0.033512 0.061224 0.204690 \n", "22 62 0 0.085791 0.147959 0.234001 \n", "23 40 0 0.020107 0.045918 0.082071 \n", "24 63 0 0.006702 0.005102 0.063996 \n", "25 34 0 0.049598 0.096939 0.110405 \n", "26 34 0 0.049598 0.096939 0.110405 \n", "27 34 0 0.077748 0.147959 0.086468 \n", "28 20 0 0.009383 0.020408 0.031754 \n", "29 84 1 0.004021 0.005102 0.061065 \n", ".. ... ... ... ... ... \n", "553 46 0 0.131367 0.209184 0.082560 \n", "554 73 0 0.018767 0.040816 0.076698 \n", "555 55 0 0.005362 0.005102 0.110894 \n", "556 51 0 0.004021 0.000000 0.057157 \n", "557 51 0 0.033512 0.056122 0.061553 \n", "558 51 0 0.048257 0.122449 0.103566 \n", "559 26 0 0.568365 1.000000 0.159746 \n", "560 66 0 0.198391 0.387755 0.143136 \n", "561 66 0 0.217158 0.382653 0.123107 \n", "562 66 0 0.226542 0.428571 0.158769 \n", "563 64 0 0.013405 0.020408 0.114802 \n", "564 38 1 0.002681 0.000000 0.049829 \n", "565 43 0 0.296247 0.596939 0.039082 \n", "566 50 1 0.008043 0.010204 0.062531 \n", "567 52 0 0.030831 0.066327 0.091842 \n", "568 20 1 0.218499 0.423469 0.066927 \n", "569 16 0 0.097855 0.204082 0.100147 \n", "570 16 0 0.029491 0.056122 0.084514 \n", "571 90 0 0.009383 0.010204 0.074255 \n", "572 32 0 0.203753 0.479592 0.034685 \n", "573 32 0 0.044236 0.076531 0.268197 \n", "574 32 0 0.156836 0.301020 0.220811 \n", "575 32 0 0.329759 0.693878 0.242794 \n", "576 32 0 0.195710 0.413265 0.110405 \n", "577 32 0 0.164879 0.423469 0.062042 \n", "578 60 0 0.001340 0.000000 0.213483 \n", "579 40 0 0.002681 0.000000 0.017098 \n", "580 52 0 0.005362 0.005102 0.088911 \n", "581 31 0 0.012064 0.020408 0.059111 \n", "582 38 0 0.008043 0.010204 0.074744 \n", "\n", " Alamine_Aminotransferase Aspartate_Aminotransferase Total_Protiens \\\n", "0 0.003015 0.001626 0.291869 \n", "1 0.027136 0.018296 0.936762 \n", "2 0.025126 0.011791 0.476124 \n", "3 0.002010 0.002033 0.291869 \n", "4 0.008543 0.009961 0.752507 \n", "5 0.004523 0.000813 1.028889 \n", "6 0.003015 0.000407 0.476124 \n", "7 0.002010 0.000203 0.199741 \n", "8 0.006030 0.001830 0.844634 \n", "9 0.021608 0.009758 0.291869 \n", "10 0.020603 0.009961 -0.537279 \n", "11 0.010553 0.009351 0.844634 \n", "12 0.025628 0.009758 0.476124 \n", "13 0.006030 0.004066 1.489527 \n", "14 0.021608 0.006302 -0.629407 \n", "15 0.040704 0.008742 -0.905789 \n", "16 0.079397 0.087619 1.028889 \n", "17 0.002513 0.002643 0.752507 \n", "18 0.111558 0.047774 0.291869 \n", "19 0.111558 0.047774 0.291869 \n", "20 0.003518 0.003659 0.752507 \n", "21 0.006030 0.004879 0.476124 \n", "22 0.053266 0.011384 -0.076641 \n", "23 0.003015 0.009148 -2.011320 \n", "24 0.021106 0.007115 -0.445152 \n", "25 0.434673 0.146575 -1.366427 \n", "26 0.434673 0.146575 -1.366427 \n", "27 0.839196 0.170766 0.660379 \n", "28 0.005025 0.004066 -2.379830 \n", "29 0.001508 0.002236 -0.445152 \n", ".. ... ... ... \n", "553 0.024121 0.026428 0.476124 \n", "554 0.005025 0.006709 0.015486 \n", "555 0.064824 0.015654 0.476124 \n", "556 0.007538 0.003456 -0.353024 \n", "557 0.035176 0.023379 -0.260896 \n", "558 0.186935 0.065054 0.936762 \n", "559 0.032663 0.026022 0.936762 \n", "560 0.156281 0.112218 0.015486 \n", "561 0.112060 0.076032 0.383997 \n", "562 0.081910 0.072576 1.213145 \n", "563 0.010553 0.014840 0.660379 \n", "564 0.006030 0.004879 -0.537279 \n", "565 0.006030 0.027038 0.107614 \n", "566 0.006030 0.004269 1.213145 \n", "567 0.005025 0.006099 -0.445152 \n", "568 0.040704 0.018500 0.383997 \n", "569 0.102010 0.032120 0.568252 \n", "570 0.060804 0.016263 -0.997917 \n", "571 0.018090 0.025208 0.383997 \n", "572 0.022111 0.023379 -0.813662 \n", "573 0.020101 0.015857 -0.260896 \n", "574 0.019095 0.016670 0.107614 \n", "575 0.015578 0.015857 1.305272 \n", "576 0.024121 0.014231 -1.090044 \n", "577 0.009045 0.007522 -0.997917 \n", "578 0.005025 0.004879 -0.537279 \n", "579 0.012563 0.004269 -0.445152 \n", "580 0.019095 0.007928 -0.076641 \n", "581 0.009548 0.004472 0.291869 \n", "582 0.005528 0.002846 0.752507 \n", "\n", " Albumin Albumin_and_Globulin_Ratio Dataset \n", "0 0.198798 0.240 1 \n", "1 0.073094 0.176 1 \n", "2 0.198798 0.236 1 \n", "3 0.324502 0.280 1 \n", "4 -0.932539 0.040 1 \n", "5 1.581543 0.400 1 \n", "6 0.450206 0.280 1 \n", "7 0.575910 0.320 1 \n", "8 1.204431 0.360 2 \n", "9 0.324502 0.280 1 \n", "10 -0.555427 0.200 1 \n", "11 -0.178314 0.120 1 \n", "12 0.324502 0.240 2 \n", "13 1.204431 0.280 1 \n", "14 -0.555427 0.228 1 \n", "15 -1.058243 0.160 2 \n", "16 1.581543 0.400 1 \n", "17 0.450206 0.248 2 \n", "18 -0.052610 0.200 1 \n", "19 -0.052610 0.200 1 \n", "20 -0.681131 0.100 1 \n", "21 -0.932539 0.080 1 \n", "22 -0.052610 0.240 1 \n", "23 -1.938172 0.120 1 \n", "24 0.953023 0.620 2 \n", "25 -0.555427 0.320 1 \n", "26 -0.555427 0.320 1 \n", "27 1.078727 0.360 1 \n", "28 -1.561060 0.260 2 \n", "29 0.073094 0.320 2 \n", ".. ... ... ... \n", "553 -0.555427 0.120 1 \n", "554 -0.178314 0.200 1 \n", "555 -0.178314 0.160 1 \n", "556 -0.052610 0.280 1 \n", "557 -0.052610 0.280 1 \n", "558 1.078727 0.320 1 \n", "559 -0.681131 0.080 1 \n", "560 -1.183947 0.040 1 \n", "561 -1.435356 0.040 1 \n", "562 -0.681131 0.080 1 \n", "563 -0.681131 0.080 1 \n", "564 -0.304019 0.240 2 \n", "565 -1.309652 0.064 1 \n", "566 1.078727 0.280 2 \n", "567 -1.812468 0.036 1 \n", "568 0.450206 0.288 1 \n", "569 1.078727 0.360 1 \n", "570 -0.681131 0.240 1 \n", "571 -0.178314 0.160 1 \n", "572 1.078727 0.880 1 \n", "573 -1.561060 0.040 1 \n", "574 -0.932539 0.080 1 \n", "575 -0.806835 0.880 1 \n", "576 -1.183947 0.160 1 \n", "577 -0.681131 0.240 1 \n", "578 -1.938172 0.028 2 \n", "579 0.073094 0.320 1 \n", "580 0.073094 0.280 1 \n", "581 0.324502 0.280 1 \n", "582 1.581543 0.480 2 \n", "\n", "[583 rows x 11 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Definición de funciones auxiliares \n", "\n", "def zScore(var):\n", " return (var-var.mean())/var.std()\n", "def minMax(var):\n", " return (var-var.min())/(var.max()-var.min())\n", "\n", "dataNorm=dataset.copy()\n", "\n", "dataNorm['Albumin']=zScore(dataNorm['Albumin'])\n", "dataNorm['Total_Protiens']=zScore(dataNorm['Total_Protiens'])\n", "\n", "dataNorm['Total_Bilirubin']=minMax(dataNorm['Total_Bilirubin'])\n", "dataNorm['Direct_Bilirubin']=minMax(dataNorm['Direct_Bilirubin'])\n", "dataNorm['Alkaline_Phosphotase']=minMax(dataNorm['Alkaline_Phosphotase'])\n", "dataNorm['Alamine_Aminotransferase']=minMax(dataNorm['Alamine_Aminotransferase'])\n", "dataNorm['Aspartate_Aminotransferase']=minMax(dataNorm['Aspartate_Aminotransferase'])\n", "dataNorm['Albumin_and_Globulin_Ratio']=minMax(dataNorm['Albumin_and_Globulin_Ratio'])\n", "dataNorm\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A partir de este punto ya tenemos un conjunto de datos normalizado con el que operar." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Deteccion de outliers\n", "\n", "A la hora de tratar cualquier conjunto de datos es común observar valores que parecen que rompen la norma y que se salen de la distribución. En los algoritmos de _machine learninng_, este tipo de valores pueden desfavorecedores pues a tenerlos en cuenta pueden generar problemas y llegar a estados espúreos.\n", "\n", "Por ello, debemos intentar localizarlos y tratarlos. No debemos olvidar nunca la semántica de los datos que tratamos. Para poder identificar _outliers_, una buena herramienta son las gráficas de caja:\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fd0b3e237b8>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd0ab75e828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dataset.boxplot(column='Direct_Bilirubin')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fd0ab7034a8>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd0ab696588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dataset.boxplot(column='Alkaline_Phosphotase')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fd0ab6abb00>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd0ab6bddd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dataset.boxplot(column='Alamine_Aminotransferase')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fd0ab2cd208>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd0ab2d7e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dataset.boxplot(column='Aspartate_Aminotransferase')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fd0ab288630>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd0ab1c2f60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dataset.boxplot(column='Albumin')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fd0ab14d160>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd0ab3afe48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dataset.boxplot(column='Albumin_and_Globulin_Ratio')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como vemos en los boxplot hay varios _outliers_ en las variables, en concreto algunos muy marcados en la _total bilirubin_, _direct bilirubin_, _alkaline phosphotase_, _alamine aminotransferase_, _aspartate aminotransferase_ y _albumin and globulin ratio_. Pero al tratarse de **datos médicos** para detectar si alguien está enfermo o no, esos _outliers_ pueden ser útiles para el estudio, por lo que hemos decidido conservarlos. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Missing values\n", "\n", "Cuando trabajamos con un conjunto amplio de datos, es común que no dispongamos de todos las variables para cada entrada. Debemos localizar estos puntos donde no hay valores (_missing values_) y tratarlos. En ocasiones, el tratar estos datos implica inicializarlos a un valor que no afecte o inclusive eliminar las entradas conflictivas, dependiendo de los datos.\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Age True\n", "Gender True\n", "Total_Bilirubin True\n", "Direct_Bilirubin True\n", "Alkaline_Phosphotase True\n", "Alamine_Aminotransferase True\n", "Aspartate_Aminotransferase True\n", "Total_Protiens True\n", "Albumin True\n", "Albumin_and_Globulin_Ratio False\n", "Dataset True\n", "dtype: bool" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.notnull().all()" ] }, { "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", " }\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>Age</th>\n", " <th>Gender</th>\n", " <th>Total_Bilirubin</th>\n", " <th>Direct_Bilirubin</th>\n", " <th>Alkaline_Phosphotase</th>\n", " <th>Alamine_Aminotransferase</th>\n", " <th>Aspartate_Aminotransferase</th>\n", " <th>Total_Protiens</th>\n", " <th>Albumin</th>\n", " <th>Albumin_and_Globulin_Ratio</th>\n", " <th>Dataset</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>209</th>\n", " <td>45</td>\n", " <td>1</td>\n", " <td>0.9</td>\n", " <td>0.3</td>\n", " <td>189</td>\n", " <td>23</td>\n", " <td>33</td>\n", " <td>6.6</td>\n", " <td>3.9</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>241</th>\n", " <td>51</td>\n", " <td>0</td>\n", " <td>0.8</td>\n", " <td>0.2</td>\n", " <td>230</td>\n", " <td>24</td>\n", " <td>46</td>\n", " <td>6.5</td>\n", " <td>3.1</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>253</th>\n", " <td>35</td>\n", " <td>1</td>\n", " <td>0.6</td>\n", " <td>0.2</td>\n", " <td>180</td>\n", " <td>12</td>\n", " <td>15</td>\n", " <td>5.2</td>\n", " <td>2.7</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>312</th>\n", " <td>27</td>\n", " <td>0</td>\n", " <td>1.3</td>\n", " <td>0.6</td>\n", " <td>106</td>\n", " <td>25</td>\n", " <td>54</td>\n", " <td>8.5</td>\n", " <td>4.8</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Age Gender Total_Bilirubin Direct_Bilirubin Alkaline_Phosphotase \\\n", "209 45 1 0.9 0.3 189 \n", "241 51 0 0.8 0.2 230 \n", "253 35 1 0.6 0.2 180 \n", "312 27 0 1.3 0.6 106 \n", "\n", " Alamine_Aminotransferase Aspartate_Aminotransferase Total_Protiens \\\n", "209 23 33 6.6 \n", "241 24 46 6.5 \n", "253 12 15 5.2 \n", "312 25 54 8.5 \n", "\n", " Albumin Albumin_and_Globulin_Ratio Dataset \n", "209 3.9 NaN 1 \n", "241 3.1 NaN 1 \n", "253 2.7 NaN 2 \n", "312 4.8 NaN 2 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.loc[dataset['Albumin_and_Globulin_Ratio'].isnull()]\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Solo hay una variable con _missing values_: _Albumin and globulin ratio_, y solo en cuatro filas. Al tener una base de datos grande y pocos _missing values_ hemos decido eliminarlos." ] }, { "cell_type": "code", "execution_count": 19, "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>Age</th>\n", " <th>Gender</th>\n", " <th>Total_Bilirubin</th>\n", " <th>Direct_Bilirubin</th>\n", " <th>Alkaline_Phosphotase</th>\n", " <th>Alamine_Aminotransferase</th>\n", " <th>Aspartate_Aminotransferase</th>\n", " <th>Total_Protiens</th>\n", " <th>Albumin</th>\n", " <th>Albumin_and_Globulin_Ratio</th>\n", " <th>Dataset</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>65</td>\n", " <td>1</td>\n", " <td>0.7</td>\n", " <td>0.1</td>\n", " <td>187</td>\n", " <td>16</td>\n", " <td>18</td>\n", " <td>6.8</td>\n", " <td>3.3</td>\n", " <td>0.90</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>62</td>\n", " <td>0</td>\n", " <td>10.9</td>\n", " <td>5.5</td>\n", " <td>699</td>\n", " <td>64</td>\n", " <td>100</td>\n", " <td>7.5</td>\n", " <td>3.2</td>\n", " <td>0.74</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>62</td>\n", " <td>0</td>\n", " <td>7.3</td>\n", " <td>4.1</td>\n", " <td>490</td>\n", " <td>60</td>\n", " <td>68</td>\n", " <td>7.0</td>\n", " <td>3.3</td>\n", " <td>0.89</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>58</td>\n", " <td>0</td>\n", " <td>1.0</td>\n", " <td>0.4</td>\n", " <td>182</td>\n", " <td>14</td>\n", " <td>20</td>\n", " <td>6.8</td>\n", " <td>3.4</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>72</td>\n", " <td>0</td>\n", " 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" <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>567</th>\n", " <td>52</td>\n", " <td>0</td>\n", " <td>2.7</td>\n", " <td>1.4</td>\n", " <td>251</td>\n", " <td>20</td>\n", " <td>40</td>\n", " <td>6.0</td>\n", " <td>1.7</td>\n", " <td>0.39</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>568</th>\n", " <td>20</td>\n", " <td>1</td>\n", " <td>16.7</td>\n", " <td>8.4</td>\n", " <td>200</td>\n", " <td>91</td>\n", " <td>101</td>\n", " <td>6.9</td>\n", " <td>3.5</td>\n", " <td>1.02</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>569</th>\n", " <td>16</td>\n", " <td>0</td>\n", " <td>7.7</td>\n", " <td>4.1</td>\n", " <td>268</td>\n", " <td>213</td>\n", " <td>168</td>\n", " <td>7.1</td>\n", " <td>4.0</td>\n", " <td>1.20</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>570</th>\n", " <td>16</td>\n", " <td>0</td>\n", " <td>2.6</td>\n", " <td>1.2</td>\n", " <td>236</td>\n", " <td>131</td>\n", " <td>90</td>\n", " <td>5.4</td>\n", " <td>2.6</td>\n", " <td>0.90</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>571</th>\n", " <td>90</td>\n", " <td>0</td>\n", " <td>1.1</td>\n", " <td>0.3</td>\n", " <td>215</td>\n", " <td>46</td>\n", " <td>134</td>\n", " <td>6.9</td>\n", " <td>3.0</td>\n", " <td>0.70</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>572</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>15.6</td>\n", " <td>9.5</td>\n", " <td>134</td>\n", " <td>54</td>\n", " <td>125</td>\n", " <td>5.6</td>\n", " <td>4.0</td>\n", " <td>2.50</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>573</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>3.7</td>\n", " <td>1.6</td>\n", " <td>612</td>\n", " <td>50</td>\n", " <td>88</td>\n", " <td>6.2</td>\n", " <td>1.9</td>\n", " <td>0.40</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>574</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>12.1</td>\n", " <td>6.0</td>\n", " <td>515</td>\n", " <td>48</td>\n", " <td>92</td>\n", " <td>6.6</td>\n", " <td>2.4</td>\n", " <td>0.50</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>575</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>25.0</td>\n", " <td>13.7</td>\n", " <td>560</td>\n", " <td>41</td>\n", " <td>88</td>\n", " <td>7.9</td>\n", " <td>2.5</td>\n", " <td>2.50</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>576</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>15.0</td>\n", " <td>8.2</td>\n", " <td>289</td>\n", " <td>58</td>\n", " <td>80</td>\n", " <td>5.3</td>\n", " <td>2.2</td>\n", " <td>0.70</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>577</th>\n", " <td>32</td>\n", " <td>0</td>\n", " <td>12.7</td>\n", " <td>8.4</td>\n", " <td>190</td>\n", " <td>28</td>\n", " <td>47</td>\n", " <td>5.4</td>\n", " <td>2.6</td>\n", " <td>0.90</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>578</th>\n", " <td>60</td>\n", " <td>0</td>\n", " <td>0.5</td>\n", " <td>0.1</td>\n", " <td>500</td>\n", " <td>20</td>\n", " <td>34</td>\n", " <td>5.9</td>\n", " <td>1.6</td>\n", " <td>0.37</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>579</th>\n", " <td>40</td>\n", " <td>0</td>\n", " <td>0.6</td>\n", " <td>0.1</td>\n", " <td>98</td>\n", " <td>35</td>\n", " <td>31</td>\n", " <td>6.0</td>\n", " <td>3.2</td>\n", " <td>1.10</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>580</th>\n", " <td>52</td>\n", " <td>0</td>\n", " <td>0.8</td>\n", " <td>0.2</td>\n", " <td>245</td>\n", " <td>48</td>\n", " <td>49</td>\n", " <td>6.4</td>\n", " <td>3.2</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>581</th>\n", " <td>31</td>\n", " <td>0</td>\n", " <td>1.3</td>\n", " <td>0.5</td>\n", " <td>184</td>\n", " <td>29</td>\n", " <td>32</td>\n", " <td>6.8</td>\n", " <td>3.4</td>\n", " <td>1.00</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>582</th>\n", " <td>38</td>\n", " <td>0</td>\n", " <td>1.0</td>\n", " <td>0.3</td>\n", " <td>216</td>\n", " <td>21</td>\n", " <td>24</td>\n", " <td>7.3</td>\n", " <td>4.4</td>\n", " <td>1.50</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>579 rows × 11 columns</p>\n", "</div>" ], "text/plain": [ " Age Gender Total_Bilirubin Direct_Bilirubin Alkaline_Phosphotase \\\n", "0 65 1 0.7 0.1 187 \n", "1 62 0 10.9 5.5 699 \n", "2 62 0 7.3 4.1 490 \n", "3 58 0 1.0 0.4 182 \n", "4 72 0 3.9 2.0 195 \n", "5 46 0 1.8 0.7 208 \n", "6 26 1 0.9 0.2 154 \n", "7 29 1 0.9 0.3 202 \n", "8 17 0 0.9 0.3 202 \n", "9 55 0 0.7 0.2 290 \n", "10 57 0 0.6 0.1 210 \n", "11 72 0 2.7 1.3 260 \n", "12 64 0 0.9 0.3 310 \n", "13 74 1 1.1 0.4 214 \n", "14 61 0 0.7 0.2 145 \n", "15 25 0 0.6 0.1 183 \n", "16 38 0 1.8 0.8 342 \n", "17 33 0 1.6 0.5 165 \n", "18 40 1 0.9 0.3 293 \n", "19 40 1 0.9 0.3 293 \n", "20 51 0 2.2 1.0 610 \n", "21 51 0 2.9 1.3 482 \n", "22 62 0 6.8 3.0 542 \n", "23 40 0 1.9 1.0 231 \n", "24 63 0 0.9 0.2 194 \n", "25 34 0 4.1 2.0 289 \n", "26 34 0 4.1 2.0 289 \n", "27 34 0 6.2 3.0 240 \n", "28 20 0 1.1 0.5 128 \n", "29 84 1 0.7 0.2 188 \n", ".. ... ... ... ... ... \n", "553 46 0 10.2 4.2 232 \n", "554 73 0 1.8 0.9 220 \n", "555 55 0 0.8 0.2 290 \n", "556 51 0 0.7 0.1 180 \n", "557 51 0 2.9 1.2 189 \n", "558 51 0 4.0 2.5 275 \n", "559 26 0 42.8 19.7 390 \n", "560 66 0 15.2 7.7 356 \n", "561 66 0 16.6 7.6 315 \n", "562 66 0 17.3 8.5 388 \n", "563 64 0 1.4 0.5 298 \n", "564 38 1 0.6 0.1 165 \n", "565 43 0 22.5 11.8 143 \n", "566 50 1 1.0 0.3 191 \n", "567 52 0 2.7 1.4 251 \n", "568 20 1 16.7 8.4 200 \n", "569 16 0 7.7 4.1 268 \n", "570 16 0 2.6 1.2 236 \n", "571 90 0 1.1 0.3 215 \n", "572 32 0 15.6 9.5 134 \n", "573 32 0 3.7 1.6 612 \n", "574 32 0 12.1 6.0 515 \n", "575 32 0 25.0 13.7 560 \n", "576 32 0 15.0 8.2 289 \n", "577 32 0 12.7 8.4 190 \n", "578 60 0 0.5 0.1 500 \n", "579 40 0 0.6 0.1 98 \n", "580 52 0 0.8 0.2 245 \n", "581 31 0 1.3 0.5 184 \n", "582 38 0 1.0 0.3 216 \n", "\n", " Alamine_Aminotransferase Aspartate_Aminotransferase Total_Protiens \\\n", "0 16 18 6.8 \n", "1 64 100 7.5 \n", "2 60 68 7.0 \n", "3 14 20 6.8 \n", "4 27 59 7.3 \n", "5 19 14 7.6 \n", "6 16 12 7.0 \n", "7 14 11 6.7 \n", "8 22 19 7.4 \n", "9 53 58 6.8 \n", "10 51 59 5.9 \n", "11 31 56 7.4 \n", "12 61 58 7.0 \n", "13 22 30 8.1 \n", "14 53 41 5.8 \n", "15 91 53 5.5 \n", "16 168 441 7.6 \n", "17 15 23 7.3 \n", "18 232 245 6.8 \n", "19 232 245 6.8 \n", "20 17 28 7.3 \n", "21 22 34 7.0 \n", "22 116 66 6.4 \n", "23 16 55 4.3 \n", "24 52 45 6.0 \n", "25 875 731 5.0 \n", "26 875 731 5.0 \n", "27 1680 850 7.2 \n", "28 20 30 3.9 \n", "29 13 21 6.0 \n", ".. ... ... ... \n", "553 58 140 7.0 \n", "554 20 43 6.5 \n", "555 139 87 7.0 \n", "556 25 27 6.1 \n", "557 80 125 6.2 \n", "558 382 330 7.5 \n", "559 75 138 7.5 \n", "560 321 562 6.5 \n", "561 233 384 6.9 \n", "562 173 367 7.8 \n", "563 31 83 7.2 \n", "564 22 34 5.9 \n", "565 22 143 6.6 \n", "566 22 31 7.8 \n", "567 20 40 6.0 \n", "568 91 101 6.9 \n", "569 213 168 7.1 \n", "570 131 90 5.4 \n", "571 46 134 6.9 \n", "572 54 125 5.6 \n", "573 50 88 6.2 \n", "574 48 92 6.6 \n", "575 41 88 7.9 \n", "576 58 80 5.3 \n", "577 28 47 5.4 \n", "578 20 34 5.9 \n", "579 35 31 6.0 \n", "580 48 49 6.4 \n", "581 29 32 6.8 \n", "582 21 24 7.3 \n", "\n", " Albumin Albumin_and_Globulin_Ratio Dataset \n", "0 3.3 0.90 1 \n", "1 3.2 0.74 1 \n", "2 3.3 0.89 1 \n", "3 3.4 1.00 1 \n", "4 2.4 0.40 1 \n", "5 4.4 1.30 1 \n", "6 3.5 1.00 1 \n", "7 3.6 1.10 1 \n", "8 4.1 1.20 2 \n", "9 3.4 1.00 1 \n", "10 2.7 0.80 1 \n", "11 3.0 0.60 1 \n", "12 3.4 0.90 2 \n", "13 4.1 1.00 1 \n", "14 2.7 0.87 1 \n", "15 2.3 0.70 2 \n", "16 4.4 1.30 1 \n", "17 3.5 0.92 2 \n", "18 3.1 0.80 1 \n", "19 3.1 0.80 1 \n", "20 2.6 0.55 1 \n", "21 2.4 0.50 1 \n", "22 3.1 0.90 1 \n", "23 1.6 0.60 1 \n", "24 3.9 1.85 2 \n", "25 2.7 1.10 1 \n", "26 2.7 1.10 1 \n", "27 4.0 1.20 1 \n", "28 1.9 0.95 2 \n", "29 3.2 1.10 2 \n", ".. ... ... ... \n", "553 2.7 0.60 1 \n", "554 3.0 0.80 1 \n", "555 3.0 0.70 1 \n", "556 3.1 1.00 1 \n", "557 3.1 1.00 1 \n", "558 4.0 1.10 1 \n", "559 2.6 0.50 1 \n", "560 2.2 0.40 1 \n", "561 2.0 0.40 1 \n", "562 2.6 0.50 1 \n", "563 2.6 0.50 1 \n", "564 2.9 0.90 2 \n", "565 2.1 0.46 1 \n", "566 4.0 1.00 2 \n", "567 1.7 0.39 1 \n", "568 3.5 1.02 1 \n", "569 4.0 1.20 1 \n", "570 2.6 0.90 1 \n", "571 3.0 0.70 1 \n", "572 4.0 2.50 1 \n", "573 1.9 0.40 1 \n", "574 2.4 0.50 1 \n", "575 2.5 2.50 1 \n", "576 2.2 0.70 1 \n", "577 2.6 0.90 1 \n", "578 1.6 0.37 2 \n", "579 3.2 1.10 1 \n", "580 3.2 1.00 1 \n", "581 3.4 1.00 1 \n", "582 4.4 1.50 2 \n", "\n", "[579 rows x 11 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.dropna()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A partir de este punto tenemos los datos listos para empezar hacer un estudio semántico de los mismos sin que las anomalías de los mismos nos puedan interferir." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correlación entre las variables\n", "\n", "El primer estudio que vamos a hacer con los datos va a ser evaluar como de relacionados están unos con otros. Esto nos permitirá evaluar cuales de los atributos pueden ser determinantes para el problema que plantea estos datos.\n", "\n", "Para ello, generaremos la matriz de correlación lineal gracias a la función _corr()_ del objeto dataset de **panda**:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": true }, "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>Age</th>\n", " <th>Gender</th>\n", " <th>Total_Bilirubin</th>\n", " <th>Direct_Bilirubin</th>\n", " <th>Alkaline_Phosphotase</th>\n", " <th>Alamine_Aminotransferase</th>\n", " <th>Aspartate_Aminotransferase</th>\n", " <th>Total_Protiens</th>\n", " <th>Albumin</th>\n", " <th>Albumin_and_Globulin_Ratio</th>\n", " <th>Dataset</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Age</th>\n", " <td>1.000000</td>\n", " <td>-0.056560</td>\n", " <td>0.011763</td>\n", " <td>0.007529</td>\n", " <td>0.080425</td>\n", " <td>-0.086883</td>\n", " <td>-0.019910</td>\n", " <td>-0.187461</td>\n", " <td>-0.265924</td>\n", " <td>-0.216408</td>\n", " <td>-0.137351</td>\n", " </tr>\n", " <tr>\n", " <th>Gender</th>\n", " <td>-0.056560</td>\n", " <td>1.000000</td>\n", " <td>-0.089291</td>\n", " <td>-0.100436</td>\n", " <td>0.027496</td>\n", " <td>-0.082332</td>\n", " <td>-0.080336</td>\n", " <td>0.089121</td>\n", " <td>0.093799</td>\n", " <td>0.003424</td>\n", " <td>0.082416</td>\n", " </tr>\n", " <tr>\n", " <th>Total_Bilirubin</th>\n", " <td>0.011763</td>\n", " <td>-0.089291</td>\n", " <td>1.000000</td>\n", " <td>0.874618</td>\n", " <td>0.206669</td>\n", " <td>0.214065</td>\n", " <td>0.237831</td>\n", " <td>-0.008099</td>\n", " <td>-0.222250</td>\n", " <td>-0.206267</td>\n", " <td>-0.220208</td>\n", " </tr>\n", " <tr>\n", " <th>Direct_Bilirubin</th>\n", " <td>0.007529</td>\n", " <td>-0.100436</td>\n", " <td>0.874618</td>\n", " <td>1.000000</td>\n", " <td>0.234939</td>\n", " <td>0.233894</td>\n", " <td>0.257544</td>\n", " <td>-0.000139</td>\n", " <td>-0.228531</td>\n", " <td>-0.200125</td>\n", " <td>-0.246046</td>\n", " </tr>\n", " <tr>\n", " <th>Alkaline_Phosphotase</th>\n", " <td>0.080425</td>\n", " <td>0.027496</td>\n", " <td>0.206669</td>\n", " <td>0.234939</td>\n", " <td>1.000000</td>\n", " <td>0.125680</td>\n", " <td>0.167196</td>\n", " <td>-0.028514</td>\n", " <td>-0.165453</td>\n", " <td>-0.234166</td>\n", " <td>-0.184866</td>\n", " </tr>\n", " <tr>\n", " <th>Alamine_Aminotransferase</th>\n", " <td>-0.086883</td>\n", " <td>-0.082332</td>\n", " <td>0.214065</td>\n", " <td>0.233894</td>\n", " <td>0.125680</td>\n", " <td>1.000000</td>\n", " <td>0.791966</td>\n", " <td>-0.042518</td>\n", " <td>-0.029742</td>\n", " <td>-0.002375</td>\n", " <td>-0.163416</td>\n", " </tr>\n", " <tr>\n", " <th>Aspartate_Aminotransferase</th>\n", " <td>-0.019910</td>\n", " <td>-0.080336</td>\n", " <td>0.237831</td>\n", " <td>0.257544</td>\n", " <td>0.167196</td>\n", " <td>0.791966</td>\n", " <td>1.000000</td>\n", " <td>-0.025645</td>\n", " <td>-0.085290</td>\n", " <td>-0.070040</td>\n", " <td>-0.151934</td>\n", " </tr>\n", " <tr>\n", " <th>Total_Protiens</th>\n", " <td>-0.187461</td>\n", " <td>0.089121</td>\n", " <td>-0.008099</td>\n", " <td>-0.000139</td>\n", " <td>-0.028514</td>\n", " <td>-0.042518</td>\n", " <td>-0.025645</td>\n", " <td>1.000000</td>\n", " <td>0.784053</td>\n", " <td>0.234887</td>\n", " <td>0.035008</td>\n", " </tr>\n", " <tr>\n", " <th>Albumin</th>\n", " <td>-0.265924</td>\n", " <td>0.093799</td>\n", " <td>-0.222250</td>\n", " <td>-0.228531</td>\n", " <td>-0.165453</td>\n", " <td>-0.029742</td>\n", " <td>-0.085290</td>\n", " <td>0.784053</td>\n", " <td>1.000000</td>\n", " <td>0.689632</td>\n", " <td>0.161388</td>\n", " </tr>\n", " <tr>\n", " <th>Albumin_and_Globulin_Ratio</th>\n", " <td>-0.216408</td>\n", " <td>0.003424</td>\n", " <td>-0.206267</td>\n", " <td>-0.200125</td>\n", " <td>-0.234166</td>\n", " <td>-0.002375</td>\n", " <td>-0.070040</td>\n", " <td>0.234887</td>\n", " <td>0.689632</td>\n", " <td>1.000000</td>\n", " <td>0.163131</td>\n", " </tr>\n", " <tr>\n", " <th>Dataset</th>\n", " <td>-0.137351</td>\n", " <td>0.082416</td>\n", " <td>-0.220208</td>\n", " <td>-0.246046</td>\n", " <td>-0.184866</td>\n", " <td>-0.163416</td>\n", " <td>-0.151934</td>\n", " <td>0.035008</td>\n", " <td>0.161388</td>\n", " <td>0.163131</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Age Gender Total_Bilirubin \\\n", "Age 1.000000 -0.056560 0.011763 \n", "Gender -0.056560 1.000000 -0.089291 \n", "Total_Bilirubin 0.011763 -0.089291 1.000000 \n", "Direct_Bilirubin 0.007529 -0.100436 0.874618 \n", "Alkaline_Phosphotase 0.080425 0.027496 0.206669 \n", "Alamine_Aminotransferase -0.086883 -0.082332 0.214065 \n", "Aspartate_Aminotransferase -0.019910 -0.080336 0.237831 \n", "Total_Protiens -0.187461 0.089121 -0.008099 \n", "Albumin -0.265924 0.093799 -0.222250 \n", "Albumin_and_Globulin_Ratio -0.216408 0.003424 -0.206267 \n", "Dataset -0.137351 0.082416 -0.220208 \n", "\n", " Direct_Bilirubin Alkaline_Phosphotase \\\n", "Age 0.007529 0.080425 \n", "Gender -0.100436 0.027496 \n", "Total_Bilirubin 0.874618 0.206669 \n", "Direct_Bilirubin 1.000000 0.234939 \n", "Alkaline_Phosphotase 0.234939 1.000000 \n", "Alamine_Aminotransferase 0.233894 0.125680 \n", "Aspartate_Aminotransferase 0.257544 0.167196 \n", "Total_Protiens -0.000139 -0.028514 \n", "Albumin -0.228531 -0.165453 \n", "Albumin_and_Globulin_Ratio -0.200125 -0.234166 \n", "Dataset -0.246046 -0.184866 \n", "\n", " Alamine_Aminotransferase \\\n", "Age -0.086883 \n", "Gender -0.082332 \n", "Total_Bilirubin 0.214065 \n", "Direct_Bilirubin 0.233894 \n", "Alkaline_Phosphotase 0.125680 \n", "Alamine_Aminotransferase 1.000000 \n", "Aspartate_Aminotransferase 0.791966 \n", "Total_Protiens -0.042518 \n", "Albumin -0.029742 \n", "Albumin_and_Globulin_Ratio -0.002375 \n", "Dataset -0.163416 \n", "\n", " Aspartate_Aminotransferase Total_Protiens \\\n", "Age -0.019910 -0.187461 \n", "Gender -0.080336 0.089121 \n", "Total_Bilirubin 0.237831 -0.008099 \n", "Direct_Bilirubin 0.257544 -0.000139 \n", "Alkaline_Phosphotase 0.167196 -0.028514 \n", "Alamine_Aminotransferase 0.791966 -0.042518 \n", "Aspartate_Aminotransferase 1.000000 -0.025645 \n", "Total_Protiens -0.025645 1.000000 \n", "Albumin -0.085290 0.784053 \n", "Albumin_and_Globulin_Ratio -0.070040 0.234887 \n", "Dataset -0.151934 0.035008 \n", "\n", " Albumin Albumin_and_Globulin_Ratio Dataset \n", "Age -0.265924 -0.216408 -0.137351 \n", "Gender 0.093799 0.003424 0.082416 \n", "Total_Bilirubin -0.222250 -0.206267 -0.220208 \n", "Direct_Bilirubin -0.228531 -0.200125 -0.246046 \n", "Alkaline_Phosphotase -0.165453 -0.234166 -0.184866 \n", "Alamine_Aminotransferase -0.029742 -0.002375 -0.163416 \n", "Aspartate_Aminotransferase -0.085290 -0.070040 -0.151934 \n", "Total_Protiens 0.784053 0.234887 0.035008 \n", "Albumin 1.000000 0.689632 0.161388 \n", "Albumin_and_Globulin_Ratio 0.689632 1.000000 0.163131 \n", "Dataset 0.161388 0.163131 1.000000 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "correlation=dataset.corr() #Correlation Matrix\n", "correlation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si representamos esta matriz como un grafico donde podamos verlo más visualmente:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd0ab991a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Display the correlation matrix with a specified figure number and a bluescale\n", "# colormap\n", "plt.figure()\n", "plt.matshow(correlation, fignum=1, cmap=plt.cm.Blues)\n", "plt.ylabel(\"Attribute Index\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/cristian/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: FutureWarning: 'pandas.tools.plotting.scatter_matrix' is deprecated, import 'pandas.plotting.scatter_matrix' instead.\n", " \"\"\"\n" ] }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fd0ab9764e0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Yr6KrMrta9f2SLKOrEqYrhUakaTOUTTOai22qmyB4+MiSxHhXnELNZrFicXa2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U/6E3Vf+g91T/oTdV/6D3VP+g91T/oTdV/6D3VP+hN1X/qPd999V0opj/S69kVa3DZCiBeJ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\nJQUUB2PoGt+8XCaSh9u7Q9lYOHpbG+0gTnLvx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cO6nQ9nK9xct7iwWOdoMc3p0bhNazSf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\nigRXVpv4YbzRHisk9qwVW5WlrhbGw7YY2G6I10m4VG1vx+BSFAkur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0b8ubPDXF5pcX21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\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HiRbfTo3lMTXB1VILL5Bk4gZjhdTqNio3CHlrokI+GePMeF4ZTGwjUkp+/715lpoWA5k4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m6fqSmj4lEUJ6x2XHK6wkTRpJYXbl2nrrYZ+BEnZ4q3VcCJhuMLd2uUKE1TOo7o3L7fBa9f/vxh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jcmmgtScN9LvuJSRJ4m//+afZbLv81X92ij/79CRfOTKMrsqDZpDlhYSR4GSfPXY3x1keZfz4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Y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\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hYn0bRomLzXwlzWrLxdbV2wYRTcfHDSJGs9aeBkWXaz3qXZ86iYD9brBqxnIWrh/jhTGx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\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ujyXV3Ece1dZXlSzvmI6HSnLtrSQX9HEK/VCVWMXIy4BTREQxlnIKFLoHFrqnKUZJmG+M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\ni7lUAajqRdY/8yQ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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd0ab9d3780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#### Scatter Matrix Plot\n", "\n", "plt.figure()\n", "from pandas.tools.plotting import scatter_matrix\n", "scatter_matrix(dataset, alpha=0.3, figsize=(20, 20), diagonal='kde')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Como conclusión a los datos obtenidos previamnte, podemos sacar las siguientes conclusiones:\n", "\n", "* Hay alta correlación entre la bilirrubina total y la bilirrubina directa, cosa que esperamos porque bilirrubina total=bilirrubina directa + bilirrubina indirecta, tal y como se explicó previamente.\n", "\n", "* Hay alta correlación entre albumina y el total proteinas ya que el total proteinas es la suma de la albumina y la gobulina.\n", "\n", "* Hay alta correlación entre la albumina y el ratio de albumina y gobulina\n", "\n", "* Hay alta correlación entre las dos transaminasas, la alaninoamino transferasa y la aspartato aminotransferasa\n", "\n", "* Aun a pesar de los datos previos, no hay una gran correlación entre las variables y la clase. La correlación más alta con la clase es la que hay con la bilirrubina directa. Con lo cual **tampoco pareciera que tuviéramos falsos predictores**.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Separación de variables\n", "\n", "Dado el conjunto de datos y que parece que están detectando un fallo hepático, sería planteable pensar en que las diferencias entre hombres y mujeres pudieran ser determinantes y por tanto pudiera estar justificado la separacion de los datos por sexo, dejando los datos en dos subconjuntos preparados para el siguiente paso que sería el algoritmo de aprendizaje." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0, Age Gender Total_Bilirubin Direct_Bilirubin Alkaline_Phosphotase \\\n", "1 62 0 10.9 5.5 699 \n", "2 62 0 7.3 4.1 490 \n", "3 58 0 1.0 0.4 182 \n", "4 72 0 3.9 2.0 195 \n", "5 46 0 1.8 0.7 208 \n", "8 17 0 0.9 0.3 202 \n", "9 55 0 0.7 0.2 290 \n", "10 57 0 0.6 0.1 210 \n", "11 72 0 2.7 1.3 260 \n", "12 64 0 0.9 0.3 310 \n", "14 61 0 0.7 0.2 145 \n", "15 25 0 0.6 0.1 183 \n", "16 38 0 1.8 0.8 342 \n", "17 33 0 1.6 0.5 165 \n", "20 51 0 2.2 1.0 610 \n", "21 51 0 2.9 1.3 482 \n", "22 62 0 6.8 3.0 542 \n", "23 40 0 1.9 1.0 231 \n", "24 63 0 0.9 0.2 194 \n", "25 34 0 4.1 2.0 289 \n", "26 34 0 4.1 2.0 289 \n", "27 34 0 6.2 3.0 240 \n", "28 20 0 1.1 0.5 128 \n", "30 57 0 4.0 1.9 190 \n", "31 52 0 0.9 0.2 156 \n", "32 57 0 1.0 0.3 187 \n", "35 30 0 1.3 0.4 482 \n", "38 48 0 1.4 0.6 263 \n", "39 47 0 2.7 1.3 275 \n", "40 45 0 2.4 1.1 168 \n", ".. ... ... ... ... ... \n", "550 46 0 3.3 1.5 172 \n", "551 29 0 1.2 0.4 160 \n", "552 45 0 0.6 0.1 196 \n", "553 46 0 10.2 4.2 232 \n", "554 73 0 1.8 0.9 220 \n", "555 55 0 0.8 0.2 290 \n", "556 51 0 0.7 0.1 180 \n", "557 51 0 2.9 1.2 189 \n", "558 51 0 4.0 2.5 275 \n", "559 26 0 42.8 19.7 390 \n", "560 66 0 15.2 7.7 356 \n", "561 66 0 16.6 7.6 315 \n", "562 66 0 17.3 8.5 388 \n", "563 64 0 1.4 0.5 298 \n", "565 43 0 22.5 11.8 143 \n", "567 52 0 2.7 1.4 251 \n", "569 16 0 7.7 4.1 268 \n", "570 16 0 2.6 1.2 236 \n", "571 90 0 1.1 0.3 215 \n", "572 32 0 15.6 9.5 134 \n", "573 32 0 3.7 1.6 612 \n", "574 32 0 12.1 6.0 515 \n", "575 32 0 25.0 13.7 560 \n", "576 32 0 15.0 8.2 289 \n", "577 32 0 12.7 8.4 190 \n", "578 60 0 0.5 0.1 500 \n", "579 40 0 0.6 0.1 98 \n", "580 52 0 0.8 0.2 245 \n", "581 31 0 1.3 0.5 184 \n", "582 38 0 1.0 0.3 216 \n", "\n", " Alamine_Aminotransferase Aspartate_Aminotransferase Total_Protiens \\\n", "1 64 100 7.5 \n", "2 60 68 7.0 \n", "3 14 20 6.8 \n", "4 27 59 7.3 \n", "5 19 14 7.6 \n", "8 22 19 7.4 \n", "9 53 58 6.8 \n", "10 51 59 5.9 \n", "11 31 56 7.4 \n", "12 61 58 7.0 \n", "14 53 41 5.8 \n", "15 91 53 5.5 \n", "16 168 441 7.6 \n", "17 15 23 7.3 \n", "20 17 28 7.3 \n", "21 22 34 7.0 \n", "22 116 66 6.4 \n", "23 16 55 4.3 \n", "24 52 45 6.0 \n", "25 875 731 5.0 \n", "26 875 731 5.0 \n", "27 1680 850 7.2 \n", "28 20 30 3.9 \n", "30 45 111 5.2 \n", "31 35 44 4.9 \n", "32 19 23 5.2 \n", "35 102 80 6.9 \n", "38 38 66 5.8 \n", "39 123 73 6.2 \n", "40 33 50 5.1 \n", ".. ... ... ... \n", "550 25 41 5.6 \n", "551 20 22 6.2 \n", "552 29 30 5.8 \n", "553 58 140 7.0 \n", "554 20 43 6.5 \n", "555 139 87 7.0 \n", "556 25 27 6.1 \n", "557 80 125 6.2 \n", "558 382 330 7.5 \n", "559 75 138 7.5 \n", "560 321 562 6.5 \n", "561 233 384 6.9 \n", "562 173 367 7.8 \n", "563 31 83 7.2 \n", "565 22 143 6.6 \n", "567 20 40 6.0 \n", "569 213 168 7.1 \n", "570 131 90 5.4 \n", "571 46 134 6.9 \n", "572 54 125 5.6 \n", "573 50 88 6.2 \n", "574 48 92 6.6 \n", "575 41 88 7.9 \n", "576 58 80 5.3 \n", "577 28 47 5.4 \n", "578 20 34 5.9 \n", "579 35 31 6.0 \n", "580 48 49 6.4 \n", "581 29 32 6.8 \n", "582 21 24 7.3 \n", "\n", " Albumin Albumin_and_Globulin_Ratio Dataset \n", "1 3.2 0.74 1 \n", "2 3.3 0.89 1 \n", "3 3.4 1.00 1 \n", "4 2.4 0.40 1 \n", "5 4.4 1.30 1 \n", "8 4.1 1.20 2 \n", "9 3.4 1.00 1 \n", "10 2.7 0.80 1 \n", "11 3.0 0.60 1 \n", "12 3.4 0.90 2 \n", "14 2.7 0.87 1 \n", "15 2.3 0.70 2 \n", "16 4.4 1.30 1 \n", "17 3.5 0.92 2 \n", "20 2.6 0.55 1 \n", "21 2.4 0.50 1 \n", "22 3.1 0.90 1 \n", "23 1.6 0.60 1 \n", "24 3.9 1.85 2 \n", "25 2.7 1.10 1 \n", "26 2.7 1.10 1 \n", "27 4.0 1.20 1 \n", "28 1.9 0.95 2 \n", "30 1.5 0.40 1 \n", "31 2.9 1.40 1 \n", "32 2.9 1.20 2 \n", "35 3.3 0.90 1 \n", "38 2.2 0.61 1 \n", "39 3.3 1.10 1 \n", "40 2.6 1.00 1 \n", ".. ... ... ... \n", "550 2.4 0.70 1 \n", "551 3.0 0.90 2 \n", "552 2.9 1.00 1 \n", "553 2.7 0.60 1 \n", "554 3.0 0.80 1 \n", "555 3.0 0.70 1 \n", "556 3.1 1.00 1 \n", "557 3.1 1.00 1 \n", "558 4.0 1.10 1 \n", "559 2.6 0.50 1 \n", "560 2.2 0.40 1 \n", "561 2.0 0.40 1 \n", "562 2.6 0.50 1 \n", "563 2.6 0.50 1 \n", "565 2.1 0.46 1 \n", "567 1.7 0.39 1 \n", "569 4.0 1.20 1 \n", "570 2.6 0.90 1 \n", "571 3.0 0.70 1 \n", "572 4.0 2.50 1 \n", "573 1.9 0.40 1 \n", "574 2.4 0.50 1 \n", "575 2.5 2.50 1 \n", "576 2.2 0.70 1 \n", "577 2.6 0.90 1 \n", "578 1.6 0.37 2 \n", "579 3.2 1.10 1 \n", "580 3.2 1.00 1 \n", "581 3.4 1.00 1 \n", "582 4.4 1.50 2 \n", "\n", "[441 rows x 11 columns])\n", "(1, Age Gender Total_Bilirubin Direct_Bilirubin Alkaline_Phosphotase \\\n", "0 65 1 0.7 0.1 187 \n", "6 26 1 0.9 0.2 154 \n", "7 29 1 0.9 0.3 202 \n", "13 74 1 1.1 0.4 214 \n", "18 40 1 0.9 0.3 293 \n", "19 40 1 0.9 0.3 293 \n", "29 84 1 0.7 0.2 188 \n", "33 38 1 2.6 1.2 410 \n", "34 38 1 2.6 1.2 410 \n", "36 17 1 0.7 0.2 145 \n", "37 46 1 14.2 7.8 374 \n", "44 85 1 1.0 0.3 208 \n", "48 32 1 0.6 0.1 176 \n", "50 45 1 0.7 0.2 170 \n", "51 34 1 0.6 0.1 161 \n", "57 48 1 0.9 0.2 175 \n", "60 31 1 0.8 0.2 158 \n", "70 19 1 0.7 0.2 186 \n", "71 75 1 0.8 0.2 188 \n", "72 75 1 0.8 0.2 205 \n", "75 29 1 0.7 0.1 162 \n", "77 68 1 0.6 0.1 1620 \n", "79 58 1 2.8 1.3 670 \n", "80 58 1 2.4 1.1 915 \n", "103 55 1 0.8 0.2 225 \n", "110 24 1 0.7 0.2 188 \n", "123 64 1 0.8 0.2 178 \n", "128 58 1 1.7 0.8 1896 \n", "131 70 1 0.7 0.2 237 \n", "132 18 1 0.8 0.2 199 \n", ".. ... ... ... ... ... \n", "443 42 1 0.7 0.2 152 \n", "447 45 1 23.3 12.8 1550 \n", "448 48 1 0.8 0.2 142 \n", "449 48 1 0.9 0.2 173 \n", "455 21 1 0.6 0.1 186 \n", "460 22 1 2.2 1.0 215 \n", "461 28 1 0.8 0.2 309 \n", "464 45 1 0.7 0.2 164 \n", "465 45 1 0.6 0.1 270 \n", "466 28 1 0.6 0.1 137 \n", "467 28 1 1.0 0.3 90 \n", "471 49 1 0.6 0.1 185 \n", "485 22 1 6.7 3.2 850 \n", "486 42 1 0.8 0.2 195 \n", "490 53 1 0.8 0.2 193 \n", "492 35 1 1.0 0.3 805 \n", "510 37 1 0.8 0.2 205 \n", "522 46 1 0.8 0.2 185 \n", "525 53 1 0.9 0.2 210 \n", "530 22 1 1.1 0.3 138 \n", "533 46 1 1.4 0.4 298 \n", "537 10 1 0.8 0.1 395 \n", "539 65 1 0.7 0.2 406 \n", "544 54 1 5.5 3.2 350 \n", "545 45 1 0.7 0.2 153 \n", "547 50 1 27.7 10.8 380 \n", "549 40 1 2.1 1.0 768 \n", "564 38 1 0.6 0.1 165 \n", "566 50 1 1.0 0.3 191 \n", "568 20 1 16.7 8.4 200 \n", "\n", " Alamine_Aminotransferase Aspartate_Aminotransferase Total_Protiens \\\n", "0 16 18 6.8 \n", "6 16 12 7.0 \n", "7 14 11 6.7 \n", "13 22 30 8.1 \n", "18 232 245 6.8 \n", "19 232 245 6.8 \n", "29 13 21 6.0 \n", "33 59 57 5.6 \n", "34 59 57 5.6 \n", "36 18 36 7.2 \n", "37 38 77 4.3 \n", "44 17 15 7.0 \n", "48 39 28 6.0 \n", "50 21 14 5.7 \n", "51 15 19 6.6 \n", "57 24 54 5.5 \n", "60 21 16 6.0 \n", "70 166 397 5.5 \n", "71 20 29 4.4 \n", "72 27 24 4.4 \n", "75 52 41 5.2 \n", "77 95 127 4.6 \n", "79 48 79 4.7 \n", "80 60 142 4.7 \n", "103 14 23 6.1 \n", "110 11 10 5.5 \n", "123 17 18 6.3 \n", "128 61 83 8.0 \n", "131 18 28 5.8 \n", "132 34 31 6.5 \n", ".. ... ... ... \n", "443 35 81 6.2 \n", "447 425 511 7.7 \n", "448 26 25 6.0 \n", "449 26 27 6.2 \n", "455 25 22 6.8 \n", "460 159 51 5.5 \n", "461 55 23 6.8 \n", "464 21 53 4.5 \n", "465 23 42 5.1 \n", "466 22 16 4.9 \n", "467 18 108 6.8 \n", "471 17 26 6.6 \n", "485 154 248 6.2 \n", "486 18 15 6.7 \n", "490 96 57 6.7 \n", "492 133 103 7.9 \n", "510 31 36 9.2 \n", "522 24 15 7.9 \n", "525 35 32 8.0 \n", "530 14 21 7.0 \n", "533 509 623 3.6 \n", "537 25 75 7.6 \n", "539 24 45 7.2 \n", "544 67 42 7.0 \n", "545 41 42 4.5 \n", "547 39 348 7.1 \n", "549 74 141 7.8 \n", "564 22 34 5.9 \n", "566 22 31 7.8 \n", "568 91 101 6.9 \n", "\n", " Albumin Albumin_and_Globulin_Ratio Dataset \n", "0 3.3 0.90 1 \n", "6 3.5 1.00 1 \n", "7 3.6 1.10 1 \n", "13 4.1 1.00 1 \n", "18 3.1 0.80 1 \n", "19 3.1 0.80 1 \n", "29 3.2 1.10 2 \n", "33 3.0 0.80 2 \n", "34 3.0 0.80 2 \n", "36 3.9 1.18 2 \n", "37 2.0 0.80 1 \n", "44 3.6 1.00 2 \n", "48 3.0 1.00 1 \n", "50 2.5 0.70 1 \n", "51 3.4 1.00 1 \n", "57 2.7 0.90 2 \n", "60 3.0 1.00 1 \n", "70 3.0 1.20 1 \n", "71 1.8 0.60 1 \n", "72 2.0 0.80 1 \n", "75 2.5 0.90 2 \n", "77 2.1 0.80 1 \n", "79 1.6 0.50 1 \n", "80 1.8 0.60 1 \n", "103 3.3 1.20 2 \n", "110 2.3 0.71 2 \n", "123 3.1 0.90 1 \n", "128 3.9 0.95 1 \n", "131 2.5 0.75 2 \n", "132 3.5 1.16 2 \n", ".. ... ... ... \n", "443 3.2 1.06 1 \n", "447 3.5 0.80 1 \n", "448 2.6 0.70 1 \n", "449 3.1 1.00 1 \n", "455 3.4 1.00 1 \n", "460 2.5 0.80 1 \n", "461 4.1 1.51 1 \n", "464 1.4 0.45 2 \n", "465 2.0 0.50 2 \n", "466 1.9 0.60 2 \n", "467 3.1 0.80 2 \n", "471 2.9 0.70 2 \n", "485 2.8 0.80 1 \n", "486 3.0 0.80 1 \n", "490 3.6 1.16 1 \n", "492 3.3 0.70 1 \n", "510 4.6 1.00 2 \n", "522 3.7 0.80 1 \n", "525 3.9 0.90 2 \n", "530 3.8 1.10 2 \n", "533 1.0 0.30 1 \n", "537 3.6 0.90 1 \n", "539 3.5 0.90 2 \n", "544 3.2 0.80 1 \n", "545 2.2 0.90 2 \n", "547 2.3 0.40 1 \n", "549 4.9 1.60 1 \n", "564 2.9 0.90 2 \n", "566 4.0 1.00 2 \n", "568 3.5 1.02 1 \n", "\n", "[142 rows x 11 columns])\n" ] } ], "source": [ "for df_gender in dataset.groupby('Gender'):\n", " print(df_gender)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Variables sintéticas\n", "\n", "Por último, sería conveniente estudiar la necesidad de crear variables sintéticas en función de las variables actuales. El uso de estas variables permite que los algorítmos de _machine learning_ funcionen mejor al tratar sobre una variable que está estudiada y sabemos que proporciona información determinante.\n", "\n", "Evaluando los datos, podría ser interesante una variable para las proteinas totales y la bilirrubina total pero ya que dichas variables ya están en el conjunto de datos no hay necesidad de generar variables sintéticas." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusiones\n", "\n", "Los algoritmos de _machine learning_ son unas herramientass increíbles que nos permiten resolver problemas que de otra manera sería imposible de resolver. Como todo, tienen un inconveniente: los datos a utilizar y su complejidad. Por eso, un paso previo e importantísimo es el tratamiento de dichos datos.\n", "\n", "En esta práctica hemos emulado una auditoría que nos ha permitido profundizar en el estudio y manejo de un conjunto de datos en el que en principio no teníamos información relevante sobre los mismos. Junto con los conocimientos impartidos en las clases, hemos sido capaces de interpretar y analizar un conjunto de datos relacionados con una enfermedad hepática lo que nos ha permitido profundizar y afianzar dichos conocimientos.\n", "\n", "Hemos visto que la mayoría de los atributos se correlaciónan por igual. Esto indica que la información que porporcionan por separado relevante para la clasificación. También hemos observado que la edad y el sexo son parámetros poco determinantes dado el caso que tratamos aunque pudiera ser interesante la división por dichos datos. Esta división está mas orientada a un estudio estadístico que a una aplicación directa. \n", "\n", "Como curiosidad comentar que hemos comentado este conjunto de datos y nuestras conclusiones con un equipo médico (amigos de uno de los integrantes) que nos ha confirmado que los datos relevantes son todos menos la edad y el sexo pues no suelen ser determinantes para las enfermedades hepáticas comunes." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

ernestyalumni/MLgrabbag

tutorial_theano.ipynb

1

203313

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Following the [`theano` Tutorial](http://deeplearning.net/software/theano/tutorial/index.html)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from theano import *\n", "import theano.tensor as T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Baby Steps - Algebra\n", "### Adding two Scalars" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy\n", "from theano import function" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = T.dscalar('x')\n", "y = T.dscalar('y')\n", "z = x+y\n", "f = function([x,y],z)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'theano.tensor.var.TensorVariable'> <class 'theano.tensor.var.TensorVariable'> <class 'theano.tensor.var.TensorVariable'> <class 'theano.compile.function_module.Function'>\n" ] } ], "source": [ "print type(x), type(y), type(z), type(f) # good to know what these \n", "# new classes are in theano" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(5.0)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(2,3)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numpy.allclose(f(16.3,12.1),28.4)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "TensorType(float64, scalar)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.type" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "TensorType(float64, scalar)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "T.dscalar" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.type is T.dscalar" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Prefer constructors like `matrix, vector` and `scalar` to `dmatrix, dvector` and `dscalar` because the former will give you `float32` variables when `floatX=float32`.\" - cf. [Using the GPU Theano tutorial](http://deeplearning.net/software/theano/tutorial/using_gpu.html)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "xf = T.scalar('xf')\n", "yf = T.scalar('yf')\n", "zf = xf + yf\n", "ff = function([xf,yf],zf)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from theano import pp" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(x + y)\n" ] } ], "source": [ "print(pp(z))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(xf + yf)\n" ] } ], "source": [ "print(pp(zf))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = T.dmatrix('x')\n", "y = T.dmatrix('y')\n", "z = x + y \n", "f = function([x,y],z)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 11., 22.],\n", " [ 33., 44.]])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f([[1,2],[3,4]], [[10,20],[30,40]])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 11., 22.],\n", " [ 33., 44.]])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(numpy.array([[1,2],[3,4]]),numpy.array([[10,20],[30,40]]))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "xf = T.matrix('xf')\n", "xy = T.matrix('yf')\n", "zf = xf + yf\n", "ff = function([xf,yf],zf)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "('Bad input argument to theano function with name \"<ipython-input-20-f711db7fb599>:4\" at index 1(0-based)', 'Wrong number of dimensions: expected 0, got 2 with shape (2, 2).')", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-22-38fbc8fb8a3b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mff\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\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;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m40\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/home/topolo/Public/anaconda2/lib/python2.7/site-packages/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 784\u001b[0m s.storage[0] = s.type.filter(\n\u001b[0;32m 785\u001b[0m \u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstrict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrict\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 786\u001b[1;33m allow_downcast=s.allow_downcast)\n\u001b[0m\u001b[0;32m 787\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 788\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/topolo/Public/anaconda2/lib/python2.7/site-packages/theano/tensor/type.pyc\u001b[0m in \u001b[0;36mfilter\u001b[1;34m(self, data, strict, allow_downcast)\u001b[0m\n\u001b[0;32m 175\u001b[0m raise TypeError(\"Wrong number of dimensions: expected %s,\"\n\u001b[0;32m 176\u001b[0m \" got %s with shape %s.\" % (self.ndim, data.ndim,\n\u001b[1;32m--> 177\u001b[1;33m data.shape))\n\u001b[0m\u001b[0;32m 178\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maligned\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 179\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: ('Bad input argument to theano function with name \"<ipython-input-20-f711db7fb599>:4\" at index 1(0-based)', 'Wrong number of dimensions: expected 0, got 2 with shape (2, 2).')" ] } ], "source": [ "ff([[1,2],[3,4]], [[10,20],[30,40]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adding exercise 1, cf. http://deeplearning.net/software/theano/tutorial/adding.html" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 25. 49.]\n" ] } ], "source": [ "a = theano.tensor.vector()\n", "b = theano.tensor.vector()\n", "out = a**2 + b**2 + 2 * a * b\n", "f = theano.function([a,b],out)\n", "print(f([1,2],[4,5]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"At this point it would be wise to begin familiarizing yourself more systematically with Theano’s fundamental objects and operations by browsing this section of the library: [Basic Tensor Functionality](http://deeplearning.net/software/theano/library/tensor/basic.html#libdoc-basic-tensor).\" cf. [More Examples](http://deeplearning.net/software/theano/tutorial/examples.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Custom tensor types" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dtensor5 = T.TensorType('float64', (False,)*5)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = dtensor5()\n", "z = dtensor5('z')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_dmatrix = T.TensorType('float64', (False,)*2)\n", "x = my_dmatrix()\n", "my_dmatrix == T.dmatrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Converting from Python Objects" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = shared(numpy.random.randn(3,4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Back to More Examples... http://deeplearning.net/software/theano/tutorial/examples.html" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.5 , 0.73105858],\n", " [ 0.26894142, 0.11920292]])" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = T.dmatrix('x')\n", "s = 1 / ( 1 + T.exp(-x))\n", "logistic = theano.function([x],s)\n", "logistic([[0,1],[-1,-2]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{gathered}\n", "s(x) = \\frac{1}{1+\\exp{-x} } = \\frac{1+\\tanh{(x/2) } }{2}\n", "\\end{gathered}" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.5 , 0.73105858],\n", " [ 0.26894142, 0.11920292]])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s2 = (1 + T.tanh(x/2))/2\n", "logistic2 = theano.function([x],s2)\n", "logistic2([[0,1],[-1,-2]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Computing More than one Thing at the Same Time (!!!)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a,b = T.dmatrices('a','b')\n", "diff = a-b\n", "abs_diff = abs(diff)\n", "diff_squared = diff**2\n", "f = theano.function([a,b],[diff,abs_diff,diff_squared])" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[array([[ 1., 0.],\n", " [-1., -2.]]), array([[ 1., 0.],\n", " [ 1., 2.]]), array([[ 1., 0.],\n", " [ 1., 4.]])]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f([[1,1],[1,1]], [[0,1],[2,3]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting a Default Value for an Argument" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from theano import In\n", "from theano import function\n", "x,y = T.dscalars('x','y')\n", "z = x + y\n", "f= function([x,In(y,value=1)],z)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(34.0)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(33)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(35.0)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(33,2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Inputs with default values must follow inputs without default values (like Python’s functions). There can be multiple inputs with default values. These parameters can be set positionally or by name, as in standard Python:\"" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(68.0)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x,y,w = T.dscalars('x', 'y', 'w')\n", "z = (x+y)*w\n", "f = function([x,In(y,value=1),In(w,value=2,name='w_by_name')],z)\n", "f(33)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(70.0)" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(33,2)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(33.0)" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(33,0,1)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(34.0)" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(33,w_by_name=1)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(33.0)" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(33,w_by_name=1,y=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using Shared Variables" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from theano import shared\n", "state = shared(0)\n", "inc = T.iscalar('inc')\n", "accumulator = function([inc],state,updates=[(state,state+inc)])" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] } ], "source": [ "print(state.get_value())" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(0)" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "accumulator(1)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "print(state.get_value())" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(1)" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "accumulator(300)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "301\n" ] } ], "source": [ "print(state.get_value())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"It is possible to reset the state. Just use the `.set_value()` method:\"" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "state.set_value(-1)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(-1)" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "accumulator(3)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] } ], "source": [ "print(state.get_value())" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "decrementor = function([inc],state, updates=[(state,state-inc)])" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(2)" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "decrementor(2)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] } ], "source": [ "print(state.get_value())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Also, Theano has more control over where and how shared variables are allocated, which is one of the important elements of getting good performance on the GPU.\"" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(7)" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fn_of_state = state * 2 + inc\n", "# The type of foo must match the shared variable we are replacing\n", "# with the \"givens\"\n", "foo = T.scalar(dtype=state.dtype)\n", "skip_shared = function([inc,foo], fn_of_state, givens=[(state,foo)])\n", "skip_shared(1,3)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] } ], "source": [ "print(state.get_value())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Copying functions" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(0)" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inc = T.iscalar('inc')\n", "accumulator = theano.function([inc],state, updates=[(state,state+inc)])\n", "accumulator(10)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print(state.get_value())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"We can use `copy()` to create a similar accumulator but with its own internal state using the swap parameter, which is a dictionary of shared variables to exchange:\"" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[array(0)]" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_state = theano.shared(0)\n", "new_accumulator = accumulator.copy(swap={state:new_state})\n", "new_accumulator(100)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100\n" ] } ], "source": [ "print(new_state.get_value())" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print(state.get_value())" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "ename": "UnusedInputError", "evalue": "theano.function was asked to create a function computing outputs given certain inputs, but the provided input variable at index 0 is not part of the computational graph needed to compute the outputs: inc.\nTo make this error into a warning, you can pass the parameter on_unused_input='warn' to theano.function. To disable it completely, use on_unused_input='ignore'.", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mUnusedInputError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-69-9c62592c8b05>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnull_accumulator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maccumulator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdelete_updates\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mnull_accumulator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m9000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/topolo/Public/anaconda2/lib/python2.7/site-packages/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36mcopy\u001b[1;34m(self, share_memory, swap, delete_updates, name, profile)\u001b[0m\n\u001b[0;32m 719\u001b[0m \u001b[1;31m# can contain inplace. DebugMode check\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 720\u001b[0m \u001b[1;31m# that.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 721\u001b[1;33m \u001b[0maccept_inplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 722\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_storage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 723\u001b[0m storage_map=new_storage_map)\n", "\u001b[1;32m/home/topolo/Public/anaconda2/lib/python2.7/site-packages/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, inputs, outputs, mode, accept_inplace, function_builder, profile, on_unused_input, fgraph, output_keys)\u001b[0m\n\u001b[0;32m 1413\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1414\u001b[0m \u001b[1;31m# Check if some input variables are unused\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1415\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_unused_inputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mon_unused_input\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1416\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1417\u001b[0m \u001b[1;31m# Make a list of (SymbolicInput|SymblicInputKits, indices,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/topolo/Public/anaconda2/lib/python2.7/site-packages/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36m_check_unused_inputs\u001b[1;34m(self, inputs, outputs, on_unused_input)\u001b[0m\n\u001b[0;32m 1551\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mon_unused_input\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'raise'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1552\u001b[0m raise UnusedInputError(msg % (inputs.index(i),\n\u001b[1;32m-> 1553\u001b[1;33m i.variable, err_msg))\n\u001b[0m\u001b[0;32m 1554\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1555\u001b[0m raise ValueError(\"Invalid value for keyword \"\n", "\u001b[1;31mUnusedInputError\u001b[0m: theano.function was asked to create a function computing outputs given certain inputs, but the provided input variable at index 0 is not part of the computational graph needed to compute the outputs: inc.\nTo make this error into a warning, you can pass the parameter on_unused_input='warn' to theano.function. To disable it completely, use on_unused_input='ignore'." ] } ], "source": [ "null_accumulator = accumulator.copy(delete_updates=True)\n", "null_accumulator(9000)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print(state.get_value())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using Random Numbers" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from theano.tensor.shared_randomstreams import RandomStreams\n", "from theano import function\n", "srng = RandomStreams(seed=234)\n", "rv_u = srng.uniform((2,2)) # represents a random stream of 2x2 matrices\n", "rv_n = srng.normal((2,2))\n", "f = function([], rv_u)\n", "g = function([], rv_n, no_default_updates=True) # Not updating rv_n.rng\n", "nearly_zeros = function([],rv_u+rv_u - 2 * rv_u)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"The RandomStream only work on the CPU, MRG31k3p work on the CPU and GPU. CURAND only work on the GPU.\" cf. http://deeplearning.net/software/theano/tutorial/examples.html#other-implementations" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from theano.sandbox.rng_mrg import MRG_RandomStreams" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from theano.sandbox.cuda import CURAND_RandomStreams" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f_val0 = f()" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f_val1 = f()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"When we add the extra argument `no_default_updates=True` to function (as in `g`), then the random number generator state is not affected by calling the returned function. So, for example, calling `g` multiple times will return the same numbers.\"" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true }, "outputs": [], "source": [ "g_val0 = g() # different numbers from f_val0 and f_val1" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": true }, "outputs": [], "source": [ "g_val1 = g()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"An important remark is that a random variable is drawn at most once during any single function execution. So the nearly_zeros function is guaranteed to return approximately 0 (except for rounding error) even though the `rv_u` random variable appears three times in the output expression.\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Seeding Streams" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rng_val = rv_u.rng.get_value(borrow=True) # Get the ring for rv_u\n", "rng_val.seed(89234) # seeds the generator\n", "rv_u.rng.set_value(rng_val, borrow=True) # Assign back seeded rng" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": true }, "outputs": [], "source": [ "srng.seed(902340) # seeds rv_u and rv_n with different seeds each" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sharing Streams Between Functions" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": true }, "outputs": [], "source": [ "state_after_v0 = rv_u.rng.get_value().get_state()" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0.],\n", " [ 0., 0.]])" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nearly_zeros() # this affects rv_u's generator" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": true }, "outputs": [], "source": [ "v1 = f()\n", "rng = rv_u.rng.get_value(borrow=True)\n", "rng.set_state(state_after_v0)\n", "rv_u.rng.set_value(rng,borrow=True)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": true }, "outputs": [], "source": [ "v2 =f() # v2 != v1" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": true }, "outputs": [], "source": [ "v3=f() # v3 == v1" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.33919835, 0.85344878],\n", " [ 0.14881562, 0.79659413]])" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v2.view()" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.5025809 , 0.99544429],\n", " [ 0.75073355, 0.17926032]])" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v1.view()" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.5025809 , 0.99544429],\n", " [ 0.75073355, 0.17926032]])" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v3.view()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Copying Random State Between Theano Graphs" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function\n", "from theano.sandbox.rng_mrg import MRG_RandomStreams\n", "from theano.tensor.shared_randomstreams import RandomStreams" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Graph():\n", " def __init__(self, seed=123):\n", " self.rng = RandomStreams(seed)\n", " self.y = self.rng.uniform(size=(1,))" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.72803009])" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g1 = Graph(seed=123)\n", "f1 = theano.function([], g1.y)\n", "g2 = Graph(seed=987)\n", "f2 = theano.function([], g2.y)\n", "\n", "# By default, the two functions are out of sync.\n", "f1()" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.55056769])" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f2()" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def copy_random_state(g1,g2):\n", " if isinstance(g1.rng, MRG_RandomStreams):\n", " g2.rng.rstate = g1.rng.rstate\n", " for (su1, su2) in zip(g1.rng.state_updates, g2.rng.state_updates):\n", " su2[0].set_value(su1[0].get_value())" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.59044123])" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We now copy the state of the theano random number generators.\n", "copy_random_state(g1, g2)\n", "f1()" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.59044123])" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f2()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Other Random Distributions\n", "\n", "are found here at [other distributions implemented](http://deeplearning.net/software/theano/library/tensor/raw_random.html#libdoc-tensor-raw-random)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# A Real Example: Logistic Regression\n" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initial model:\n", "[ 1.02090375e+00 8.73947142e-01 1.95614041e+00 7.07377428e-01\n", " -6.53816172e-01 7.42545996e-01 -9.80754517e-01 5.61128882e-01\n", " 1.78775715e+00 2.55831279e-01 -8.67444321e-02 7.94001501e-01\n", " -6.70527935e-01 -1.96668538e+00 2.54200412e+00 8.89567452e-01\n", " 9.74970709e-01 8.48005589e-01 -6.09636316e-01 8.50633037e-01\n", " 1.68271257e+00 4.93346994e-01 -5.51691867e-02 2.37368331e-01\n", " 1.62746146e-01 1.55918000e+00 -8.34594814e-01 1.38944861e+00\n", " -1.23368104e+00 -3.93048716e-01 1.05566395e+00 -1.31731246e+00\n", " 7.08793200e-01 -2.69903059e-01 1.59489513e+00 -5.41472996e-01\n", " -1.51657778e+00 -1.74491648e+00 -4.87264078e-01 -1.40829581e+00\n", " 9.16277590e-01 1.13288831e-01 -8.15545784e-01 3.63391354e-01\n", " 4.59170798e-01 -4.92534498e-01 1.59924371e-01 -5.79592495e-01\n", " 8.28797390e-01 1.01823000e+00 9.47734065e-01 1.07973174e+00\n", " 4.30089731e-02 -8.06497075e-02 8.95636221e-01 -1.18478568e-02\n", " -5.81466361e-01 4.24213296e-01 -9.76244057e-01 9.77803579e-01\n", " -1.68833204e-01 7.43143151e-01 -1.45011683e+00 -4.39116887e-01\n", " 2.02524479e-01 -1.48128730e-01 -1.57771132e+00 1.50352107e-01\n", " -3.28331728e-01 6.71897989e-02 -1.19704026e+00 1.52688204e+00\n", " -1.31571397e+00 8.90164259e-01 -2.23393343e-01 4.99356497e-01\n", " -1.29504971e+00 7.02042318e-01 -3.24683905e-01 -6.27943493e-01\n", " 3.14126673e-01 1.12213995e-01 -9.21144105e-01 -2.43648932e-01\n", " -2.33448211e-01 -1.00637079e+00 6.33923303e-01 6.56206738e-01\n", " 1.45152861e+00 1.33058182e+00 5.10506128e-01 1.16950441e+00\n", " -7.71856464e-01 4.80736974e-01 2.31723934e-01 1.49188536e+00\n", " -7.33637390e-01 -3.60145054e-01 -6.62364149e-01 1.15997382e+00\n", " 4.85467482e-02 -2.49511990e-01 -2.16758659e+00 -8.67892909e-01\n", " 9.63635308e-01 5.77244652e-03 1.95557853e+00 6.59938526e-01\n", " -5.92379578e-01 -1.42131361e+00 -6.06090501e-01 -1.44791105e-01\n", " -4.07448243e-01 1.08839737e+00 -6.09163649e-01 -3.83439487e-01\n", " -5.52671444e-02 -7.45281579e-01 2.36207307e-01 6.73715161e-01\n", " -5.06680836e-01 -3.89571760e-01 5.43189401e-01 5.18757744e-01\n", " -8.18926229e-01 -1.31366774e-01 1.24943697e+00 -2.04555766e+00\n", " 2.30376888e-01 -1.82374680e+00 -1.49620886e+00 7.66641416e-01\n", " 4.93167826e-01 4.79110747e-01 -1.86664191e+00 -1.09007317e+00\n", " 1.54655862e+00 -4.69143883e-01 2.97751272e-01 -1.89391756e+00\n", " 3.17911849e-01 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4.56837263e-01 5.05896647e-01 -3.82801562e-01\n", " 1.89874303e+00 1.03889449e+00 4.23131063e-01 -8.11990652e-01\n", " -3.03945375e-02 -1.99952646e+00 4.46676030e-01 1.95721820e+00\n", " 1.08873300e+00 -8.37814242e-03 -2.07361839e-01 3.60056055e-01\n", " -1.67468643e+00 -7.06049428e-01 9.65039385e-01 7.12357184e-01\n", " 2.42874285e-01 -3.36268160e-01 5.06770876e-01 7.23624267e-01\n", " 1.45186075e+00 -1.61955497e+00 -1.03411671e-01 5.59007686e-01\n", " 5.20425979e-02 1.12046422e+00 -5.01872995e-01 1.06420808e+00\n", " -1.22003112e+00 7.89863655e-01 -5.85431950e-01 -2.24825569e+00\n", " 4.22184348e-01 -2.06462345e-01 -2.26357030e+00 6.99624901e-01\n", " 1.03726395e+00 4.53993244e-03 4.51026021e-01 1.29149321e-01\n", " 4.79950159e-01 9.42474221e-01 -7.17021320e-01 8.08699977e-01\n", " 1.29705448e+00 -7.29463525e-01 7.61622706e-01 1.33106913e+00\n", " 1.26861623e+00 7.29526959e-01 -5.95726986e-01 5.03512395e-01\n", " -1.29418546e+00 -5.90249629e-01 -3.73911118e-01 1.89357574e+00\n", " -1.28354545e-01 -1.16177445e+00 -2.30600786e+00 -2.04517667e-01\n", " -3.76288692e-01 1.01317587e-01 -6.96236526e-01 1.90057656e-01\n", " -1.21489214e-01 -5.57939235e-01 1.87873908e+00 5.07359236e-01\n", " 3.06841944e-01 6.65237276e-01 1.33444863e+00 1.44620678e+00\n", " -1.28550235e+00 1.13688401e+00 -5.34448063e-01 -1.32370673e-01\n", " -1.36045000e-01 -6.60853337e-01 2.81913662e-01 -1.03592075e+00\n", " -1.41860719e+00 1.81524496e+00 1.41390111e+00 4.23515124e-01\n", " 1.02554970e+00 9.63881472e-01 7.12236894e-01 -3.71863624e-02\n", " 1.76545480e+00 3.26268427e-01 -1.29671739e+00 -7.86767462e-01\n", " -1.23347607e+00 9.52597569e-01 -3.51206854e-01 -5.63237921e-01\n", " -5.69407473e-02 1.52866118e+00 -1.00496869e+00 1.63372644e+00\n", " -6.50946328e-01 -7.16273718e-01 -5.90126661e-01 1.30985556e+00\n", " -3.79768720e-01 -9.99915425e-01 -1.71145067e+00 -1.57329194e+00\n", " -7.30696372e-02 7.62151506e-01 1.48689646e+00 4.31745960e-01\n", " 2.63882808e-01 1.72206428e-01 2.00851731e-01 -1.18314347e+00\n", " -1.50402279e+00 1.95153249e+00 -2.78003872e-01 -1.93854878e-03\n", " 1.16582803e+00 -3.20238544e-01 9.36585278e-01 1.48863154e+00\n", " -1.39264019e+00 1.55666358e+00 -4.20662970e-01 -5.50893690e-02\n", " -7.10504077e-01 -8.73934840e-01 -6.26132410e-01 -1.29781690e-01\n", " -2.22777306e+00 3.73831654e-01 -3.82432449e-01 5.37121996e-02\n", " -9.32183728e-01 3.53740442e+00 -1.05598008e+00 -7.47679114e-03\n", " -1.06551955e+00 -3.46521140e-01 3.88344981e-01 -3.64222377e-01\n", " -1.37048480e+00 1.01306470e-01 1.64514228e+00 -3.61445671e-01\n", " 2.31614366e-01 -1.25968487e+00 -6.62537224e-01 -5.61715767e-01\n", " -1.13596993e+00 -1.36075508e+00 7.31594663e-01 2.67213081e-01\n", " 7.13141895e-02 -2.46130956e-01 -1.39377306e+00 1.29318009e-01\n", " -1.92097772e-01 -1.15100304e+00 1.02016481e-01 -1.93068490e+00\n", " 9.69566668e-01 9.75694115e-01 -7.57001670e-01 -7.60531329e-01\n", " -3.03552280e-01 -3.67388243e-01 7.60265551e-02 4.42746285e-01\n", " 1.92679575e-01 1.04068792e+00 -1.11139888e+00 -1.72269547e+00\n", " -2.30986045e-01 -2.83927273e-01 -7.21803150e-01 -1.37631072e+00\n", " -1.10935589e+00 8.09954622e-01 5.83783993e-01 -7.31810290e-01\n", " 3.06739636e-01 1.27915913e+00 2.73785857e-01 9.65830330e-01\n", " -6.15660861e-01 1.64785898e-01 -4.61886474e-02 3.25744723e-01\n", " 3.67606054e-01 -6.68566804e-01 -3.20837511e-01 -2.09469223e-01\n", " -6.60728681e-01 -3.49847761e-01 7.84861730e-01 1.26622383e+00\n", " -1.80302074e+00 1.53355423e+00 -1.22745466e+00 -7.02346088e-01\n", " 3.17239216e-01 -1.25357347e+00 2.39430740e-01 7.85747146e-01\n", " 3.81228699e-01 5.72990733e-01 -4.98818013e-01 1.42759290e+00\n", " 1.69600421e-01 -3.16755901e-01 -4.16914206e-01 1.22881192e+00\n", " -5.58348844e-02 -6.26676967e-01 7.38764150e-02 3.32789760e-01\n", " 1.67321192e+00 1.74966096e-01 2.65341416e-01 -3.29541011e-01\n", " -1.86929223e+00 -1.14107067e+00 -7.53407493e-01 6.99519962e-01\n", " 4.29125580e-01 2.19232831e-01 7.42525121e-01 -5.42444700e-01]\n", "0.0\n", "Final model:\n", "[ 3.77556006e+00 1.67658791e+00 -3.29136306e+00 -1.30945858e-01\n", " -1.18248243e+00 1.78664688e+00 -1.25736948e+00 -2.02258094e-01\n", " -9.98941721e-01 -7.63579832e-01 1.57014674e+00 -3.94171743e-01\n", " 1.15549836e+00 3.86136052e-01 1.49428543e-01 -3.36443795e-01\n", " -1.06508201e+00 3.64432700e-01 -5.03306943e-01 -2.56198989e+00\n", " 5.18392678e-02 1.94736399e+00 -5.96506785e-01 5.43487120e-01\n", " -4.42454966e+00 1.09235863e+00 1.58964361e+00 -6.50332550e-01\n", " 1.11307437e+00 4.90929707e-01 -1.11052327e+00 1.28429702e+00\n", " -2.09541131e-01 3.77961492e+00 7.58902758e-01 -9.27180732e-01\n", " -8.22309817e-01 3.13218677e+00 8.31525921e-01 -1.47034985e+00\n", " -4.92172262e-01 1.01060797e+00 -4.05332960e-01 -1.41643633e+00\n", " 2.69651131e+00 -3.55972129e-01 -2.48351450e+00 2.49434037e-01\n", " -2.67932735e-01 9.79001494e-01 -9.92249171e-01 -5.96254783e-01\n", " -7.99429663e-01 -7.35963162e-02 -2.40140608e-01 -2.12372700e+00\n", " -1.44504208e+00 3.75125978e+00 -1.26714397e+00 -3.18828201e-01\n", " -4.49929404e+00 -1.28865508e+00 -1.28861373e+00 -1.32248557e+00\n", " -1.57317700e+00 -2.12905251e+00 -2.64333717e-01 -2.01285336e+00\n", " -1.61006020e-01 2.27637572e+00 1.27586244e+00 -3.91120851e-01\n", " -1.36317244e+00 -3.82788078e-01 -1.92953169e-01 -2.88078903e-01\n", " -3.88464164e-01 -1.81768162e+00 1.69723438e+00 4.78324656e-01\n", " -1.57736636e-01 -2.51055129e+00 -7.41662774e-01 -8.76968592e-01\n", " 4.73273895e-02 -3.97794058e+00 3.18338634e-01 7.68762163e-01\n", " -1.67187179e+00 6.14911653e-01 1.10559059e+00 2.47633722e+00\n", " -4.73221220e-01 1.35373891e+00 -6.50581076e-02 9.55459296e-02\n", " 2.03508078e-01 -2.53910629e+00 6.10795696e-01 1.38991127e+00\n", " 9.15067374e-01 3.63101705e+00 8.26695293e-01 -6.68788818e-01\n", " -1.74436254e+00 -3.33700192e+00 -1.53928722e+00 -2.96420698e-01\n", " 1.57903269e+00 1.79884911e+00 -2.50262001e-01 -1.08973660e+00\n", " -1.85498194e+00 -1.19668614e+00 -4.26989972e-01 -7.91529732e-01\n", " 1.31224993e+00 3.63791777e+00 -2.94546634e-01 -2.45556566e+00\n", " -1.04590869e+00 6.08167811e-01 2.77936798e+00 -9.12923966e-01\n", " -7.83906049e-01 3.56631289e-01 -3.70281016e-01 4.54090725e-01\n", " 1.60936498e+00 2.15636982e+00 -1.89672027e+00 6.21229996e-02\n", " -1.50861299e+00 1.02692098e+00 1.04198787e+00 -7.97214484e-01\n", " 1.14241240e+00 4.56888918e-01 7.37187678e-01 2.60242725e+00\n", " 3.95684108e+00 -2.15633608e+00 4.70875935e-02 1.83190139e+00\n", " 1.36581873e+00 -3.86096041e-01 3.01203822e+00 2.76356831e+00\n", " -1.98179848e+00 6.40089101e-01 2.20144846e+00 -1.06899827e+00\n", " 3.09186005e+00 -7.11234569e-01 -3.78276117e+00 1.83545092e-01\n", " -1.21826498e+00 -5.10159929e-01 3.25947346e+00 -1.58883034e+00\n", " -2.49492079e+00 1.15062631e+00 2.46314738e+00 1.83675178e+00\n", " 1.90239257e+00 -2.91436561e+00 -3.27309202e+00 -1.15415531e+00\n", " 2.15120857e+00 2.61265590e+00 1.04777203e+00 -6.99138307e-01\n", " 1.87069022e+00 4.36184386e-01 3.33629754e-01 -1.50581420e+00\n", " 2.94741903e+00 -2.34902225e+00 8.38337601e-01 5.00249453e+00\n", " -2.38272140e-01 -3.01384569e-01 1.89878373e-01 -2.19144302e+00\n", " 3.58792779e+00 -3.52910136e+00 1.26603845e+00 7.75531918e-01\n", " 2.00485484e+00 -3.53271420e+00 8.10974186e-01 1.84989351e+00\n", " 2.51069243e+00 -4.26123595e-01 -8.50810390e-01 -4.10358099e-01\n", " -9.42958105e-01 -4.61438083e-01 -4.31821036e+00 -6.02840390e-01\n", " -2.51884238e+00 -7.50987410e-01 -1.77051049e-01 -6.47304330e-02\n", " -1.97060390e+00 6.03047738e-01 1.55041662e+00 -2.68797620e+00\n", " -5.15198874e-01 3.97884570e-01 2.98066051e+00 4.96007616e+00\n", " 1.80033955e+00 -1.29148046e-01 -1.65518882e+00 -3.11845247e+00\n", " -1.11648300e+00 1.73962809e+00 -1.99331693e+00 -4.71585789e-01\n", " -1.02846099e+00 -1.25604064e+00 -2.03443821e+00 -1.34359639e+00\n", " -1.47179647e+00 2.02529621e-01 3.97979332e-03 9.49734864e-01\n", " -9.27013373e-01 -2.79633499e+00 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3.70956959e+00 3.31061339e-01 3.21938139e-01 -1.98907732e+00\n", " -1.62564497e-01 9.25237287e-01 -2.39858458e+00 -1.47865761e+00\n", " -1.07760350e+00 -2.12954216e-01 3.56462897e+00 -1.15009932e+00\n", " -9.37380338e-01 3.83415308e+00 -9.34323105e-02 7.63654725e-02\n", " 1.11119989e+00 1.07718664e+00 -1.03859306e+00 -1.57055699e+00\n", " -4.46823604e-01 -1.06687172e+00 8.21890243e-01 -4.42575478e-03\n", " -4.48360060e+00 7.43566499e-01 -6.17821929e-01 6.03409669e-01\n", " -3.66864713e-01 4.48455869e-03 2.39363432e+00 2.33992814e-01\n", " 4.21215749e+00 4.55885777e+00 3.92714524e-01 -8.59827680e-01\n", " 6.31566745e-01 -3.34226322e+00 -1.03161005e-01 -2.74638014e+00\n", " 1.23743057e+00 -7.47138590e-01 1.12158829e+00 2.10546219e+00\n", " 4.10013374e+00 -9.54526634e-01 8.29485521e-01 -3.39853916e+00\n", " -1.70827359e+00 -4.33272812e-01 -8.00704300e-01 3.32479096e+00\n", " -2.64906932e+00 5.94967233e-01 1.81417239e+00 -2.83409352e+00\n", " -2.76620738e-01 1.19838737e+00 -7.31473471e-01 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-2.01923747e+00 -2.89179581e+00\n", " 2.59579559e+00 -1.75259386e+00 -1.08206430e-01 7.45312239e-03\n", " 5.85764039e-02 6.14869059e-01 -1.29978952e-02 -2.92607897e+00\n", " 8.14497304e-01 1.32776160e+00 -2.09535483e+00 -6.23154792e-01\n", " -1.02933599e+00 -6.79472063e-01 1.36643972e-01 1.30739153e+00\n", " -7.70938238e-02 1.00573421e+00 9.51384392e-01 -1.53817711e+00\n", " -2.35552187e+00 -7.56852770e-01 -1.26638116e+00 -2.36581588e+00\n", " 1.87100888e-01 -1.80800625e+00 2.28044384e+00 1.43620211e-01\n", " 1.41963125e+00 8.60386903e-01 2.23154240e+00 2.68496183e+00\n", " -1.64176977e+00 1.45565317e+00 3.93317919e-01 4.63312083e+00\n", " -2.22928479e-01 1.43214324e+00 -6.36949102e-01 -7.63117543e-01\n", " 5.71798558e-01 -9.99673642e-01 -1.85985574e+00 -3.71322109e+00\n", " -6.49851612e-01 -1.56570988e+00 2.12323186e+00 -1.20302700e-02\n", " -1.78012832e+00 7.68680703e-01 -1.32861308e+00 2.48974714e+00\n", " -3.93162518e-01 1.43291241e+00 -1.89201627e-01 -1.03040512e+00\n", " 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-1.42955765e+00 8.99106720e-01 2.09687546e+00 2.32900062e-01\n", " -2.16848707e-01 -4.04704275e+00 2.35142329e+00 -1.18962640e+00\n", " 8.94147748e-01 2.93820180e+00 2.07633335e+00 8.07215486e-01\n", " -4.65727597e-01 -1.03032045e+00 2.74614894e+00 -8.16323188e-01\n", " 3.51113518e+00 2.05167186e+00 -1.55314194e+00 -9.40403259e-01\n", " -4.09650743e-02 -2.36659383e+00 5.53580697e-01 1.09338078e+00\n", " -3.87102251e-01 -1.39174144e+00 -1.80277537e+00 -2.10924254e+00\n", " 1.54615036e+00 8.56128405e-01 -1.33996909e+00 -1.71725473e+00\n", " -1.57777016e+00 2.65809549e+00 8.54861156e-01 2.19105117e+00\n", " -1.94545749e+00 -1.31990906e+00 -2.48547338e+00 -1.21667731e+00\n", " 1.12321564e-01 7.03858613e-01 -1.31534567e+00 -3.01909282e+00\n", " 1.76111359e+00 1.03464584e+00 1.64381967e+00 2.16596567e+00\n", " -1.93396820e+00 3.86992118e+00 2.65106268e-01 -1.41634383e+00\n", " -1.71242017e+00 6.57565704e-01 -2.79514824e+00 1.10829177e-01\n", " 2.06668705e+00 4.07656873e-01 1.97741136e+00 -9.54814686e-01\n", " 1.37971688e+00 1.06623143e+00 -2.89450811e+00 -2.35645806e+00\n", " 3.92589659e+00 2.83499175e+00 9.80082282e-01 1.68067343e+00\n", " -1.06050525e-01 -1.74825733e+00 -1.51233969e+00 2.79399091e-01\n", " 1.61218267e+00 6.79050628e-01 -7.60576055e-01 1.07710598e+00\n", " 8.68093576e-01 7.96236864e-01 -3.37133751e+00 1.90457592e+00\n", " -3.59889053e-01 -1.55153960e+00 1.28538465e+00 2.24184560e+00\n", " 1.50305971e+00 -1.88109843e+00 6.17140621e-02 -9.47180228e-01\n", " -2.18103376e+00 -1.24519494e+00 3.45694238e+00 2.55172108e+00\n", " 1.40859574e-02 -9.53010206e-01 -1.92951266e+00 -2.29692512e+00\n", " 3.45593292e+00 1.08317253e+00 -3.38025123e+00 1.59851381e+00\n", " 1.74317752e+00 -2.45672994e+00 -1.78299865e+00 -2.62810516e+00\n", " 1.46304208e+00 -8.25624629e-01 -3.11702318e+00 2.20752930e+00\n", " -2.64307436e+00 -2.50232719e+00 1.34700372e+00 -3.25327855e+00\n", " -1.38651861e+00 6.84753858e-01 -4.39893291e-01 2.41137859e+00\n", " 4.02819568e+00 2.59001772e+00 -4.89821103e-01 3.59276693e+00\n", " -1.69033166e+00 -9.00221564e-01 7.14153357e-01 3.66780469e-01\n", " 3.59698167e+00 -1.12640871e+00 3.03631173e+00 1.48208320e+00\n", " 1.27545162e+00 3.33220799e+00 2.06530977e-01 3.00507382e+00\n", " 1.55880239e+00 1.20344288e+00 2.17279620e+00 -1.49944096e+00\n", " -1.25077507e+00 -2.21842681e+00 1.11603549e+00 4.58609893e-01\n", " -4.98923219e-01 1.70758638e-01 1.72125168e+00 5.63973756e-03]\n", "497.500000002\n", "target values for D:\n", "[0 1 1 1 1 1 1 0 1 0 1 0 0 1 0 1 0 1 0 1 1 0 1 1 0 1 1 1 0 0 1 1 1 0 1 1 0\n", " 1 0 1 1 0 0 0 1 0 1 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 1 0 0\n", " 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 1 0 1 0 0 1 1 0 1 1 1 0 1 1 1 1 0 1\n", " 0 1 1 1 0 0 1 0 1 1 0 0 1 0 1 1 1 1 0 1 0 1 1 1 0 0 0 1 1 0 0 0 0 0 1 0 0\n", " 1 1 1 1 0 1 0 1 0 0 1 1 0 0 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1 1 1 0 1 0 0 1 1\n", " 0 1 0 1 0 1 1 1 0 1 1 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 1 1 0 1 1 1 1 0 0 0\n", " 0 0 1 1 0 1 0 0 1 0 1 1 0 0 0 0 1 1 0 1 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 0\n", " 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 0 0 0 1 0 1 0 1 0 0 0 1 1 1\n", " 0 0 1 0 1 0 0 1 0 1 1 1 1 1 1 1 1 0 0 0 1 1 0 0 1 0 1 1 0 0 1 1 1 1 1 0 1\n", " 0 0 0 1 1 1 1 0 1 0 0 1 0 1 0 0 1 0 1 0 1 1 1 0 1 1 1 0 1 0 0 1 0 0 0 1 0\n", " 1 0 1 1 0 1 1 1 1 1 1 0 0 1 1 1 1 0 1 0 1 0 1 0 0 1 0 0 1 0]\n", "prediction on D:\n", "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n" ] } ], "source": [ "import numpy\n", "import theano\n", "import theano.tensor as T\n", "rng = numpy.random\n", "\n", "N = 400 # training sample size\n", "feats = 784 # number of input variables\n", "\n", "# generate a dataset: D = (input_values, target_class)\n", "D = (rng.randn(N, feats), rng.randint(size=N, low=0, high=2))\n", "training_steps = 10000\n", "\n", "# Declare Theano symbolic variables\n", "x = T.dmatrix(\"x\")\n", "y = T.dvector(\"y\")\n", "\n", "# initialize the weight vector w randomly\n", "#\n", "# this and the following bias variable b\n", "# are shared so they keep their values\n", "# between training iterations (updates)\n", "w = theano.shared(rng.randn(feats), name=\"w\")\n", "\n", "# initialize the bias term\n", "b = theano.shared(0., name=\"b\")\n", "\n", "print(\"Initial model:\")\n", "print(w.get_value())\n", "print(b.get_value())\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", " # w.r.t weight vector w and \n", " # bias term b\n", " # (we shall return to this in a \n", " # following section of this tutorial)\n", "\n", "# Compile\n", "train = theano.function(\n", " inputs=[x,y],\n", " outputs=[prediction, xent],\n", " updates=((w,w-0.1 *gw), (b,b-0.1 * gb)))\n", "predict = theano.function(inputs=[x], outputs=prediction)\n", "\n", "# Train\n", "for i in range(training_steps):\n", " pred, err = train(D[0], D[1])\n", " \n", "print(\"Final model:\")\n", "print(w.get_value())\n", "print(b.get_value())\n", "print(\"target values for D:\")\n", "print(D[1])\n", "print(\"prediction on D:\")\n", "print(predict(D[0]))\n", "\n", " \n", " \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## [Derivatives in Theano](http://deeplearning.net/software/theano/tutorial/gradients.html)\n", "\n", "### Computing Gradients" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy\n", "import theano\n", "import theano.tensor as T\n", "from theano import pp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this \n", "\n", "$\n", "\\begin{gathered}\n", "\\frac{d (x^2) }{ dx} = 2 \\cdot x \n", "\\end{gathered}\n", "$\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'((fill((x ** TensorConstant{2}), TensorConstant{1.0}) * TensorConstant{2}) * (x ** (TensorConstant{2} - TensorConstant{1})))'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = T.dscalar('x')\n", "y = x ** 2\n", "gy = T.grad(y,x)\n", "pp(gy) # print out the gradient prior to optimization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`fill((x ** 2), 1.0)` means to make a matrix of the same shape as `x ** 2 ` and fill it with `1.0`" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(8.0)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f = theano.function([x],gy)\n", "f(4)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numpy.allclose(f(94.2), 188.4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A plot of the gradient of the logistic function, with x on the x-axis and $ds(x)/dx$ on the y-axis" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.25 , 0.19661193],\n", " [ 0.19661193, 0.10499359]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = T.dmatrix('x')\n", "s = T.sum(1 / (1 + T.exp(-x)))\n", "gs = T.grad(s, x)\n", "dlogistic = theano.function([x], gs)\n", "dlogistic([[0, 1], [-1, -2]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Computing the Jacobian" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 8., 0.],\n", " [ 0., 8.]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = T.dvector('x')\n", "y = x ** 2\n", "J, updates = theano.scan(lambda i, y, x: T.grad(y[i], x), sequences=T.arange(y.shape[0]), non_sequences=[y,x] )\n", "f = theano.function([x], J, updates=updates)\n", "f([4, 4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Computing the Hessian" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 2., 0.],\n", " [ 0., 2.]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = T.dvector('x')\n", "y = x ** 2\n", "cost = y.sum()\n", "gy = T.grad(cost, x)\n", "H, updates = theano.scan(lambda i, gy, x : T.grad(gy[i], x), sequences=T.arange(gy.shape[0]), non_sequences=[gy, x] )\n", "f = theano.function([x], H, updates=updates)\n", "f([4,4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### R-operator" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 2., 2.])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "W = T.dmatrix('W')\n", "V = T.dmatrix('V')\n", "x = T.dvector('x')\n", "y = T.dot(x,W)\n", "JV = T.Rop(y, W, V)\n", "f = theano.function([W,V,x],JV)\n", "f([[1,1], [1,1]],[[2,2],[2,2]],[0,1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### L-operator" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0.],\n", " [ 2., 2.]])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "W = T.dmatrix('W')\n", "v = T.dvector('v')\n", "x = T.dvector('x')\n", "y = T.dot(x,W)\n", "VJ = T.Lop(y,W,v)\n", "f = theano.function([v,x],VJ)\n", "f([2,2],[0,1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hessian times a Vector" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 4., 4.])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = T.dvector('x')\n", "v = T.dvector('v')\n", "y = T.sum(x ** 2)\n", "gy = T.grad(y, x)\n", "vH = T.grad(T.sum( gy * v), x)\n", "f= theano.function([x,v], vH)\n", "f([4,4], [2,2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or, making use of the *R-operator*:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 4., 4.])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = T.dvector('x')\n", "v = T.dvector('v')\n", "y = T.sum( x ** 2)\n", "gy = T.grad(y,x)\n", "Hv = T.Rop(gy, x, v)\n", "f = theano.function([x,v], Hv)\n", "f([4,4],[2,2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conditions" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from theano import tensor as T\n", "from theano.ifelse import ifelse\n", "import theano, time, numpy\n", "\n", "a,b = T.scalars('a','b')\n", "x,y = T.matrices('x','y')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "z_switch = T.switch(T.lt(a,b), T.mean(x), T.mean(y))\n", "z_lazy = ifelse(T.lt(a, b), T.mean(x), T.mean(y))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# logistic regression on the GPU\n", "\n", "cf. [Using the GPU - Theano documentation](http://deeplearning.net/software/theano_versions/dev/tutorial/using_gpu.html) \n", "[Solution for the GPU implementation](http://deeplearning.net/software/theano_versions/dev/_downloads/using_gpu_solution_1.py)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rng = numpy.random" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "N=400\n", "feats=784\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "D = (rng.randn(N, feats).astype(theano.config.floatX), rng.randint(size=N,low=0, high=2).astype(theano.config.floatX))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(400, 784)\n" ] }, { "data": { "text/plain": [ "array([[-0.51540321, 0.45720622, -1.07207155, ..., -0.14092861,\n", " -1.29015946, 0.79136688],\n", " [ 0.41795 , 0.58462614, -0.13168216, ..., 0.65973812,\n", " -0.46558085, -0.15008399],\n", " [ 0.09026403, -1.53019106, -0.43091467, ..., 0.42072779,\n", " 0.85275686, -2.31214309],\n", " ..., \n", " [ 1.0708015 , -1.14941835, 2.08980322, ..., -0.02039757,\n", " -0.42613024, -1.25479817],\n", " [-1.50268137, -0.1785637 , -1.19366574, ..., 0.53540504,\n", " -0.3872523 , 1.20521164],\n", " [ 0.76399541, 0.65020931, -1.35631859, ..., 0.5690015 ,\n", " 0.69514877, 0.35484901]], dtype=float32)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(D[0].shape); D[0]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(400,)\n" ] }, { "data": { "text/plain": [ "array([ 0., 0., 1., 0., 0., 0., 1., 1., 0., 1., 0., 0., 1.,\n", " 0., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0., 0., 1.,\n", " 0., 0., 0., 1., 1., 0., 1., 0., 1., 1., 1., 0., 1.,\n", " 1., 1., 1., 0., 0., 0., 1., 1., 0., 1., 1., 1., 1.,\n", " 1., 1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.,\n", " 0., 1., 1., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0.,\n", " 1., 1., 1., 0., 1., 1., 0., 1., 1., 1., 1., 1., 0.,\n", " 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 1., 0., 0., 1., 1., 1., 1., 0., 1., 0., 1., 0.,\n", " 0., 1., 1., 0., 1., 0., 1., 0., 1., 1., 0., 0., 1.,\n", " 1., 0., 0., 1., 1., 1., 0., 1., 1., 1., 0., 0., 0.,\n", " 0., 0., 1., 1., 0., 1., 0., 1., 1., 0., 1., 0., 1.,\n", " 1., 0., 1., 1., 1., 1., 1., 0., 0., 1., 0., 0., 1.,\n", " 0., 1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 1., 0.,\n", " 1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 0., 1., 1.,\n", " 1., 0., 0., 0., 0., 1., 1., 1., 1., 1., 0., 1., 0.,\n", " 1., 0., 0., 1., 0., 1., 0., 1., 0., 1., 1., 0., 1.,\n", " 1., 1., 1., 0., 0., 1., 1., 0., 0., 0., 1., 1., 1.,\n", " 1., 1., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1.,\n", " 0., 1., 0., 1., 1., 1., 0., 1., 0., 0., 0., 0., 0.,\n", " 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0.,\n", " 1., 1., 1., 1., 0., 0., 1., 0., 1., 1., 0., 0., 0.,\n", " 0., 1., 0., 1., 0., 0., 1., 0., 1., 0., 0., 0., 0.,\n", " 0., 0., 0., 1., 0., 1., 1., 1., 1., 0., 1., 1., 0.,\n", " 0., 1., 0., 0., 1., 1., 0., 0., 1., 0., 0., 1., 0.,\n", " 0., 0., 1., 1., 0., 1., 0., 0., 0., 0., 0., 0., 1.,\n", " 1., 0., 0., 0., 1., 0., 1., 1., 0., 0., 0., 0., 0.,\n", " 0., 1., 1., 0., 0., 0., 1., 1., 1., 0., 1., 0., 1.,\n", " 0., 1., 1., 0., 0., 1., 1., 1., 1., 0., 1., 1., 0.,\n", " 1., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 1., 0.,\n", " 1., 1., 0., 1., 1., 1., 1., 0., 1., 0.], dtype=float32)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(D[1].shape); D[1]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "training_steps = 10000" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Declare Theano symbolic variables\n", "x = theano.shared(D[0], name=\"x\")\n", "y = theano.shared(D[1], name=\"y\")\n", "w = theano.shared(rng.randn(feats).astype(theano.config.floatX),name=\"w\")\n", "b = theano.shared(numpy.asarray(0., dtype=theano.config.floatX),name=\"b\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Setting the tag.test_value attribute gives the variable its test value, i.e.\n", "# provide Theano with a default test-value\n", "x.tag.test_value = D[0]\n", "y.tag.text_value = D[1]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Construct Theano expression graph\n", "p_1 = 1 / ( 1+ T.exp( - T.dot( x,w) - b)) # Probabilty of having a 1\n", "prediction = p_1 > 0.5 # the prediction that is done: 0 or 1\n", "xent = -y * T.log(p_1) - (1-y) * T.log(1-p_1) # Cross-entropy\n", "cost = T.cast( xent.mean(), theano.config.floatX) + 0.01 * (w ** 2).sum() # the cost to optimize\n", "gw, gb = T.grad(cost, [w,b])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# compile exprression to functions\n", "train = theano.function( \n", " inputs=[],\n", " outputs=[prediction, xent],\n", " updates=[(w, w - 0.01 * gw), (b, b - 0.01 * gb)],\n", " name=\"train\")\n", "predict = theano.function(inputs=[], outputs=prediction, name=\"predict\")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if any([n.op.__class__.__name__ in ['Gemv', 'CGemv', 'Gemm', 'CGemm'] for n in train.maker.fgraph.toposort()]):\n", " print('Used the cpu')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Used the gpu\n" ] } ], "source": [ "# elif\n", "if any([n.op.__class__.__name__ in ['GpuGemm', 'GpuGemv'] for n in train.maker.fgraph.toposort()]):\n", " print('Used the gpu')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for i in range(training_steps):\n", " pred, err = train()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "target values for D\n", "[ 0. 0. 1. 0. 0. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 1. 0. 0.\n", " 1. 1. 1. 0. 0. 0. 0. 1. 0. 0. 0. 1. 1. 0. 1. 0. 1. 1.\n", " 1. 0. 1. 1. 1. 1. 0. 0. 0. 1. 1. 0. 1. 1. 1. 1. 1. 1.\n", " 0. 1. 1. 1. 0. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 1. 1. 0.\n", " 0. 1. 1. 1. 0. 0. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 1. 1.\n", " 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.\n", " 1. 1. 1. 1. 0. 1. 0. 1. 0. 0. 1. 1. 0. 1. 0. 1. 0. 1.\n", " 1. 0. 0. 1. 1. 0. 0. 1. 1. 1. 0. 1. 1. 1. 0. 0. 0. 0.\n", " 0. 1. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 1. 0. 1. 1. 1. 1.\n", " 1. 0. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 1. 0. 1. 1. 0. 1.\n", " 1. 0. 1. 1. 0. 0. 0. 1. 1. 0. 0. 0. 0. 1. 1. 1. 0. 0.\n", " 0. 0. 1. 1. 1. 1. 1. 0. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1.\n", " 0. 1. 1. 0. 1. 1. 1. 1. 0. 0. 1. 1. 0. 0. 0. 1. 1. 1.\n", " 1. 1. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1.\n", " 1. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1.\n", " 1. 0. 0. 1. 1. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 0. 0. 1.\n", " 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1.\n", " 1. 1. 0. 1. 1. 0. 0. 1. 0. 0. 1. 1. 0. 0. 1. 0. 0. 1.\n", " 0. 0. 0. 1. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0.\n", " 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 1. 1. 1.\n", " 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 1. 1. 0. 1. 1. 0. 1.\n", " 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 1. 0. 1. 1. 0. 1. 1. 1.\n", " 1. 0. 1. 0.]\n" ] } ], "source": [ "print(\"target values for D\")\n", "print(D[1])" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "prediction on D\n", "[ 0. 0. 1. 0. 0. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 1. 0. 0.\n", " 1. 1. 1. 0. 0. 0. 0. 1. 0. 0. 0. 1. 1. 0. 1. 0. 1. 1.\n", " 1. 0. 1. 1. 1. 1. 0. 0. 0. 1. 1. 0. 1. 1. 1. 1. 1. 1.\n", " 0. 1. 1. 1. 0. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 1. 1. 0.\n", " 0. 1. 1. 1. 0. 0. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 1. 1.\n", " 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.\n", " 1. 1. 1. 1. 0. 1. 0. 1. 0. 0. 1. 1. 0. 1. 0. 1. 0. 1.\n", " 1. 0. 0. 1. 1. 0. 0. 1. 1. 1. 0. 1. 1. 1. 0. 0. 0. 0.\n", " 0. 1. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 1. 0. 1. 1. 1. 1.\n", " 1. 0. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 1. 0. 1. 1. 0. 1.\n", " 1. 0. 1. 1. 0. 0. 0. 1. 1. 0. 0. 0. 0. 1. 1. 1. 0. 0.\n", " 0. 0. 1. 1. 1. 1. 1. 0. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1.\n", " 0. 1. 1. 0. 1. 1. 1. 1. 0. 0. 1. 1. 0. 0. 0. 1. 1. 1.\n", " 1. 1. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1.\n", " 1. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1.\n", " 1. 0. 0. 1. 1. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 0. 0. 1.\n", " 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1.\n", " 1. 1. 0. 1. 1. 0. 0. 1. 0. 0. 1. 1. 0. 0. 1. 0. 0. 1.\n", " 0. 0. 0. 1. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0.\n", " 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 1. 1. 1.\n", " 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 1. 1. 0. 1. 1. 0. 1.\n", " 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 1. 0. 1. 1. 0. 1. 1. 1.\n", " 1. 0. 1. 0.]\n" ] } ], "source": [ "print(\"prediction on D\")\n", "print(predict().astype(theano.config.floatX))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# `scan` - Looping in Theano" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(400,)\n" ] }, { "data": { "text/plain": [ "array([ 0., 0., 1., 0., 0., 0., 1., 1., 0., 1., 0., 0., 1.,\n", " 0., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0., 0., 1.,\n", " 0., 0., 0., 1., 1., 0., 1., 0., 1., 1., 1., 0., 1.,\n", " 1., 1., 1., 0., 0., 0., 1., 1., 0., 1., 1., 1., 1.,\n", " 1., 1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.,\n", " 0., 1., 1., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0.,\n", " 1., 1., 1., 0., 1., 1., 0., 1., 1., 1., 1., 1., 0.,\n", " 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 1., 0., 0., 1., 1., 1., 1., 0., 1., 0., 1., 0.,\n", " 0., 1., 1., 0., 1., 0., 1., 0., 1., 1., 0., 0., 1.,\n", " 1., 0., 0., 1., 1., 1., 0., 1., 1., 1., 0., 0., 0.,\n", " 0., 0., 1., 1., 0., 1., 0., 1., 1., 0., 1., 0., 1.,\n", " 1., 0., 1., 1., 1., 1., 1., 0., 0., 1., 0., 0., 1.,\n", " 0., 1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 1., 0.,\n", " 1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 0., 1., 1.,\n", " 1., 0., 0., 0., 0., 1., 1., 1., 1., 1., 0., 1., 0.,\n", " 1., 0., 0., 1., 0., 1., 0., 1., 0., 1., 1., 0., 1.,\n", " 1., 1., 1., 0., 0., 1., 1., 0., 0., 0., 1., 1., 1.,\n", " 1., 1., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1.,\n", " 0., 1., 0., 1., 1., 1., 0., 1., 0., 0., 0., 0., 0.,\n", " 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0.,\n", " 1., 1., 1., 1., 0., 0., 1., 0., 1., 1., 0., 0., 0.,\n", " 0., 1., 0., 1., 0., 0., 1., 0., 1., 0., 0., 0., 0.,\n", " 0., 0., 0., 1., 0., 1., 1., 1., 1., 0., 1., 1., 0.,\n", " 0., 1., 0., 0., 1., 1., 0., 0., 1., 0., 0., 1., 0.,\n", " 0., 0., 1., 1., 0., 1., 0., 0., 0., 0., 0., 0., 1.,\n", " 1., 0., 0., 0., 1., 0., 1., 1., 0., 0., 0., 0., 0.,\n", " 0., 1., 1., 0., 0., 0., 1., 1., 1., 0., 1., 0., 1.,\n", " 0., 1., 1., 0., 0., 1., 1., 1., 1., 0., 1., 1., 0.,\n", " 1., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 1., 0.,\n", " 1., 1., 0., 1., 1., 1., 1., 0., 1., 0.], dtype=float32)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(y.get_value().shape)\n", "y.get_value()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simple loop with accumulation: computing $A^k$" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [], "source": [ "k = theano.shared(np.int32(1),\"k\")\n", "A = theano.shared(np.array(range(10)),\"A\")" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [], "source": [ "result, updates = theano.scan(fn=lambda prior_result, A: prior_result * A, \n", " outputs_info=T.ones_like(A),\n", " non_sequences=A,\n", " n_steps=k)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": true }, "outputs": [], "source": [ "result, updates = theano.scan(fn=lambda prior_result, A: prior_result * A, \n", " outputs_info=T.ones_like(A),\n", " non_sequences=A,\n", " n_steps=2)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true }, "outputs": [], "source": [ "final_result = result[-1]" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "Cannot use a shared variable (A) as explicit input. Consider substituting a non-shared variable via the `givens` parameter", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-64-3d27e2cadcf2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpower\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mA\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfinal_result\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupdates\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mupdates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/home/topolo/PropD/Theano/theano/compile/function.pyc\u001b[0m in \u001b[0;36mfunction\u001b[1;34m(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input)\u001b[0m\n\u001b[0;32m 324\u001b[0m \u001b[0mon_unused_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mon_unused_input\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 325\u001b[0m \u001b[0mprofile\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mprofile\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 326\u001b[1;33m output_keys=output_keys)\n\u001b[0m\u001b[0;32m 327\u001b[0m \u001b[1;31m# We need to add the flag check_aliased inputs if we have any mutable or\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 328\u001b[0m \u001b[1;31m# borrowed used defined inputs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/topolo/PropD/Theano/theano/compile/pfunc.pyc\u001b[0m in \u001b[0;36mpfunc\u001b[1;34m(params, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, output_keys)\u001b[0m\n\u001b[0;32m 447\u001b[0m \u001b[0mrebuild_strict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrebuild_strict\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 448\u001b[0m \u001b[0mcopy_inputs_over\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 449\u001b[1;33m no_default_updates=no_default_updates)\n\u001b[0m\u001b[0;32m 450\u001b[0m \u001b[1;31m# extracting the arguments\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 451\u001b[0m \u001b[0minput_variables\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcloned_extended_outputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother_stuff\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutput_vars\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/topolo/PropD/Theano/theano/compile/pfunc.pyc\u001b[0m in \u001b[0;36mrebuild_collect_shared\u001b[1;34m(outputs, inputs, replace, updates, rebuild_strict, copy_inputs_over, no_default_updates)\u001b[0m\n\u001b[0;32m 174\u001b[0m raise TypeError(('Cannot use a shared variable (%s) as explicit '\n\u001b[0;32m 175\u001b[0m \u001b[1;34m'input. Consider substituting a non-shared'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 176\u001b[1;33m ' variable via the `givens` parameter') % v)\n\u001b[0m\u001b[0;32m 177\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 178\u001b[0m \u001b[1;31m# Fill update_d and update_expr with provided updates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: Cannot use a shared variable (A) as explicit input. Consider substituting a non-shared variable via the `givens` parameter" ] } ], "source": [ "power=theano.function(inputs=[A,k], outputs=final_result, updates=updates)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [], "source": [ "power=theano.function(inputs=[],outputs=final_result, updates=updates)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81])" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "power()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81])" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "power()" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A.get_value()" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": true }, "outputs": [], "source": [ "result, updates = theano.scan(fn=lambda prior_result, A: prior_result * A, \n", " outputs_info=T.ones_like(A),\n", " non_sequences=A,\n", " n_steps=k)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "power=theano.function(inputs=[],outputs=final_result, updates=updates)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81])" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "power()" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(1, dtype=int32)" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "k.get_value()" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [], "source": [ "k.set_value( np.int32(3) )" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81])" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "power()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this link, [Loop](http://deeplearning.net/software/theano/tutorial/loop.html), which for some reason a Google search overlooks most of the time, I then found this \n", "cf. [good ipython notebook with explanation and more examples](https://github.com/lamblin/ccw_tutorial/blob/master/Scan_W2016/scan_tutorial.ipynb), a `scan` tutorial written by Pierre Luc Carrier \n", "\n", "### Example 1 : As simple as it gets" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from theano import sandbox" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X1 = T.vector('vector1')\n", "X2 = T.vector('vector2')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The parameter `fn` receives a function or lambda expression that expresses computation to do at every iteration. \n", "\n", "Since we wish to iterate over both `X1` and `X2` simultaneously, provide them as sequences. This means that every iteration will operate on 2 inputs; an element from `X1` and the corresponding element from `X2`. " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "output, updates = theano.scan(fn=lambda a, b : a * b, sequences=[X1,X2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`output` contains outputs of `fn` from every timestep concatenated into a tensor. In our case, the output of a single timestep is a scalar so output is a vector where `output[i]` is the output of the `i`th iteration. \n", "\n", "`updates` details if and how the execution of scan updates any shared variable in the graph. It should be provided as an argument when compiling the Theano function. " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f = theano.function(inputs=[X1,X2], outputs=output, updates=updates)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If `updates` is omitted, the state of any shared variables modified by `Scan` will not be updated properly. Random number sampling, for instance, relies on shared variables. If `updates` is not provided, the state of the random number generator won't be updated properly and the same numbers might be sampled repeatedly. **Always** provide `updates` when compiling Theano functions. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X1_value = np.arange(0,5).astype(theano.config.floatX) # [0,1,2,3,4]\n", "X2_value = np.arange(1,6).astype(theano.config.floatX) # [1,2,3,4,5]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 2., 6., 12., 20.], dtype=float32)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(X1_value,X2_value)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[Shape_i{0}(vector1),\n", " GpuFromHost(vector1),\n", " Shape_i{0}(vector2),\n", " GpuFromHost(vector2),\n", " Elemwise{minimum,no_inplace}(Shape_i{0}.0, Shape_i{0}.0),\n", " Elemwise{lt,no_inplace}(Elemwise{minimum,no_inplace}.0, TensorConstant{0}),\n", " Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}(Elemwise{lt,no_inplace}.0, Elemwise{minimum,no_inplace}.0, Shape_i{0}.0, TensorConstant{0}),\n", " Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}[(0, 1)](Elemwise{lt,no_inplace}.0, Elemwise{minimum,no_inplace}.0, Shape_i{0}.0, TensorConstant{0}),\n", " Elemwise{le,no_inplace}(Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}.0, TensorConstant{0}),\n", " Elemwise{le,no_inplace}(Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}[(0, 1)].0, TensorConstant{0}),\n", " Elemwise{Composite{Switch(i0, i1, minimum(i2, i3))}}[(0, 2)](Elemwise{le,no_inplace}.0, TensorConstant{0}, Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}.0, Shape_i{0}.0),\n", " Elemwise{switch,no_inplace}(Elemwise{le,no_inplace}.0, TensorConstant{0}, TensorConstant{0}),\n", " Elemwise{Composite{Switch(i0, i1, minimum(i2, i3))}}[(0, 2)](Elemwise{le,no_inplace}.0, TensorConstant{0}, Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}[(0, 1)].0, Shape_i{0}.0),\n", " Elemwise{switch,no_inplace}(Elemwise{le,no_inplace}.0, TensorConstant{0}, TensorConstant{0}),\n", " ScalarFromTensor(Elemwise{Composite{Switch(i0, i1, minimum(i2, i3))}}[(0, 2)].0),\n", " ScalarFromTensor(Elemwise{switch,no_inplace}.0),\n", " ScalarFromTensor(Elemwise{Composite{Switch(i0, i1, minimum(i2, i3))}}[(0, 2)].0),\n", " ScalarFromTensor(Elemwise{switch,no_inplace}.0),\n", " GpuSubtensor{int64:int64:int8}(GpuFromHost.0, ScalarFromTensor.0, ScalarFromTensor.0, Constant{1}),\n", " GpuSubtensor{int64:int64:int8}(GpuFromHost.0, ScalarFromTensor.0, ScalarFromTensor.0, Constant{1}),\n", " GpuElemwise{Mul}[(0, 0)](GpuSubtensor{int64:int64:int8}.0, GpuSubtensor{int64:int64:int8}.0),\n", " HostFromGpu(GpuElemwise{Mul}[(0, 0)].0)]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f.maker.fgraph.toposort()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "output, updates = theano.scan(fn=lambda a, b : sandbox.cuda.basic_ops.gpu_from_host( a * b ), sequences=[X1,X2])\n", "f = theano.function(inputs=[X1,X2], outputs=output, updates=updates)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 2., 6., 12., 20.], dtype=float32)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(X1_value,X2_value)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[Shape_i{0}(vector2),\n", " GpuFromHost(vector2),\n", " Shape_i{0}(vector1),\n", " GpuFromHost(vector1),\n", " Elemwise{minimum,no_inplace}(Shape_i{0}.0, Shape_i{0}.0),\n", " Elemwise{lt,no_inplace}(Elemwise{minimum,no_inplace}.0, TensorConstant{0}),\n", " Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}(Elemwise{lt,no_inplace}.0, Elemwise{minimum,no_inplace}.0, Shape_i{0}.0, TensorConstant{0}),\n", " Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}(Elemwise{lt,no_inplace}.0, Elemwise{minimum,no_inplace}.0, Shape_i{0}.0, TensorConstant{0}),\n", " Elemwise{le,no_inplace}(Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}.0, TensorConstant{0}),\n", " Elemwise{le,no_inplace}(Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}.0, TensorConstant{0}),\n", " Elemwise{Composite{Switch(i0, i1, minimum(i2, i3))}}[(0, 2)](Elemwise{le,no_inplace}.0, TensorConstant{0}, Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}.0, Shape_i{0}.0),\n", " Elemwise{switch,no_inplace}(Elemwise{le,no_inplace}.0, TensorConstant{0}, TensorConstant{0}),\n", " Elemwise{Composite{Switch(i0, i1, minimum(i2, i3))}}[(0, 2)](Elemwise{le,no_inplace}.0, TensorConstant{0}, Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}.0, Shape_i{0}.0),\n", " Elemwise{switch,no_inplace}(Elemwise{le,no_inplace}.0, TensorConstant{0}, TensorConstant{0}),\n", " ScalarFromTensor(Elemwise{Composite{Switch(i0, i1, minimum(i2, i3))}}[(0, 2)].0),\n", " ScalarFromTensor(Elemwise{switch,no_inplace}.0),\n", " ScalarFromTensor(Elemwise{Composite{Switch(i0, i1, minimum(i2, i3))}}[(0, 2)].0),\n", " ScalarFromTensor(Elemwise{switch,no_inplace}.0),\n", " GpuSubtensor{int64:int64:int8}(GpuFromHost.0, ScalarFromTensor.0, ScalarFromTensor.0, Constant{1}),\n", " GpuSubtensor{int64:int64:int8}(GpuFromHost.0, ScalarFromTensor.0, ScalarFromTensor.0, Constant{1}),\n", " GpuElemwise{Mul}[(0, 0)](GpuSubtensor{int64:int64:int8}.0, GpuSubtensor{int64:int64:int8}.0),\n", " for{gpu,scan_fn}(Elemwise{minimum,no_inplace}.0, GpuElemwise{Mul}[(0, 0)].0, Elemwise{minimum,no_inplace}.0),\n", " HostFromGpu(for{gpu,scan_fn}.0)]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f.maker.fgraph.toposort()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An interesting thing is that we never explicitl told Scan how many iterations to run. It was automatically inferred. When given sequences, Scan will run as many iterations as length of the shortest sequence. " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 2., 6., 12.], dtype=float32)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(X1_value, X2_value[:4])" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def vec_addition(a,b):\n", " return sandbox.cuda.basic_ops.gpu_from_host( a + b )" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "output, updates = theano.scan(fn=vec_addition, sequences=[X1,X2])\n", "f = theano.function(inputs=[X1,X2], outputs=output, updates=updates)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1., 3., 5., 7., 9.], dtype=float32)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(X1_value,X2_value)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[Shape_i{0}(vector2),\n", " GpuFromHost(vector2),\n", " Shape_i{0}(vector1),\n", " GpuFromHost(vector1),\n", " Elemwise{minimum,no_inplace}(Shape_i{0}.0, Shape_i{0}.0),\n", " Elemwise{lt,no_inplace}(Elemwise{minimum,no_inplace}.0, TensorConstant{0}),\n", " Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}(Elemwise{lt,no_inplace}.0, Elemwise{minimum,no_inplace}.0, Shape_i{0}.0, TensorConstant{0}),\n", " Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}(Elemwise{lt,no_inplace}.0, Elemwise{minimum,no_inplace}.0, Shape_i{0}.0, TensorConstant{0}),\n", " Elemwise{le,no_inplace}(Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}.0, TensorConstant{0}),\n", " Elemwise{le,no_inplace}(Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}.0, TensorConstant{0}),\n", " Elemwise{Composite{Switch(i0, i1, minimum(i2, i3))}}[(0, 2)](Elemwise{le,no_inplace}.0, TensorConstant{0}, Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}.0, Shape_i{0}.0),\n", " Elemwise{switch,no_inplace}(Elemwise{le,no_inplace}.0, TensorConstant{0}, TensorConstant{0}),\n", " Elemwise{Composite{Switch(i0, i1, minimum(i2, i3))}}[(0, 2)](Elemwise{le,no_inplace}.0, TensorConstant{0}, Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}.0, Shape_i{0}.0),\n", " Elemwise{switch,no_inplace}(Elemwise{le,no_inplace}.0, TensorConstant{0}, TensorConstant{0}),\n", " ScalarFromTensor(Elemwise{Composite{Switch(i0, i1, minimum(i2, i3))}}[(0, 2)].0),\n", " ScalarFromTensor(Elemwise{switch,no_inplace}.0),\n", " ScalarFromTensor(Elemwise{Composite{Switch(i0, i1, minimum(i2, i3))}}[(0, 2)].0),\n", " ScalarFromTensor(Elemwise{switch,no_inplace}.0),\n", " GpuSubtensor{int64:int64:int8}(GpuFromHost.0, ScalarFromTensor.0, ScalarFromTensor.0, Constant{1}),\n", " GpuSubtensor{int64:int64:int8}(GpuFromHost.0, ScalarFromTensor.0, ScalarFromTensor.0, Constant{1}),\n", " GpuElemwise{Add}[(0, 0)](GpuSubtensor{int64:int64:int8}.0, GpuSubtensor{int64:int64:int8}.0),\n", " for{gpu,scan_fn}(Elemwise{minimum,no_inplace}.0, GpuElemwise{Add}[(0, 0)].0, Elemwise{minimum,no_inplace}.0),\n", " HostFromGpu(for{gpu,scan_fn}.0)]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f.maker.fgraph.toposort()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "ename": "AttributeError", "evalue": "'TensorVariable' object has no attribute 'get_value'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-31-fba24b6c4b82>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mX1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m: 'TensorVariable' object has no attribute 'get_value'" ] } ], "source": [ "X1.get_value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So we can do the following with scan: \n", "$\\forall \\, X_1,X_2 \\in \\mathbb{R}^d$, \n", "$$ + : \\mathbb{R}^d \\times \\mathbb{R}^d \\to \\mathbb{R}^d \\\\\n", "\\verb|output|[i] = X_1[i] + X_2[i], \\qquad \\, \\forall \\, i = 0,1, \\dots d-1 \n", "$$\n", "\n", "$$ \\odot : \\mathbb{R}^d \\times \\mathbb{R}^d \\to \\mathbb{R}^d \\\\\n", "\\verb|output|[i] = X_1[i] * X_2[i], \\qquad \\, \\forall \\, i = 0,1, \\dots d-1 \n", "$$\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "alpha_expn = T.scalar('alpha') # \\alpha\n", "Xs = T.vector('Xs') # Xs \\equiv X[i]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<function __main__.<lambda>>" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lambda x, k : x**k" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "<lambda>() takes exactly 2 arguments (1 given)", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-43-e543b36f2512>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0moutput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupdates\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m \u001b[1;33m,\u001b[0m\u001b[0msequences\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mXs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/home/topolo/PropD/Theano/theano/scan_module/scan.pyc\u001b[0m in \u001b[0;36mscan\u001b[1;34m(fn, sequences, outputs_info, non_sequences, n_steps, truncate_gradient, go_backwards, mode, name, profile, allow_gc, strict, return_list)\u001b[0m\n\u001b[0;32m 771\u001b[0m \u001b[1;31m# and outputs that needs to be separated\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 772\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 773\u001b[1;33m \u001b[0mcondition\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupdates\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscan_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_updates_and_outputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\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[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 774\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcondition\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 775\u001b[0m \u001b[0mas_while\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: <lambda>() takes exactly 2 arguments (1 given)" ] } ], "source": [ "output, updates = theano.scan(fn=lambda x, k : x**k ,sequences=[Xs])\n" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ValueError", "evalue": "The index list is longer (size 1) than the number of dimensions of the tensor(namely 0). You are asking for a dimension of the tensor that does not exist! You might need to use dimshuffle to add extra dimension to your tensor.", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-45-0a6d243197ac>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0moutput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupdates\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m \u001b[1;33m,\u001b[0m\u001b[0msequences\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mXs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0malpha_expn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/home/topolo/PropD/Theano/theano/scan_module/scan.pyc\u001b[0m in \u001b[0;36mscan\u001b[1;34m(fn, sequences, outputs_info, non_sequences, n_steps, truncate_gradient, go_backwards, mode, name, profile, allow_gc, strict, return_list)\u001b[0m\n\u001b[0;32m 501\u001b[0m \u001b[1;31m# If not we need to use copies, that will be replaced at\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 502\u001b[0m \u001b[1;31m# each frame by the corresponding slice\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 503\u001b[1;33m \u001b[0mactual_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseq\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'input'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mmintap\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 504\u001b[0m \u001b[0m_seq_val\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtensor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_tensor_variable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseq\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'input'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 505\u001b[0m \u001b[0m_seq_val_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_seq_val\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mmintap\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/topolo/PropD/Theano/theano/tensor/var.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, args)\u001b[0m\n\u001b[0;32m 577\u001b[0m self, *theano.tensor.subtensor.Subtensor.collapse(\n\u001b[0;32m 578\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 579\u001b[1;33m lambda entry: isinstance(entry, Variable)))\n\u001b[0m\u001b[0;32m 580\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 581\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'raise'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/topolo/PropD/Theano/theano/gof/op.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *inputs, **kwargs)\u001b[0m\n\u001b[0;32m 602\u001b[0m \"\"\"\n\u001b[0;32m 603\u001b[0m \u001b[0mreturn_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'return_list'\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[1;32m--> 604\u001b[1;33m \u001b[0mnode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_node\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minputs\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 605\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 606\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute_test_value\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'off'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/topolo/PropD/Theano/theano/tensor/subtensor.pyc\u001b[0m in \u001b[0;36mmake_node\u001b[1;34m(self, x, *inputs)\u001b[0m\n\u001b[0;32m 479\u001b[0m len(idx_list), x.type.ndim))\n\u001b[0;32m 480\u001b[0m \u001b[0mexception\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubtensor_invalid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 481\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 482\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 483\u001b[0m input_types = Subtensor.collapse(idx_list,\n", "\u001b[1;31mValueError\u001b[0m: The index list is longer (size 1) than the number of dimensions of the tensor(namely 0). You are asking for a dimension of the tensor that does not exist! You might need to use dimshuffle to add extra dimension to your tensor." ] } ], "source": [ "output, updates = theano.scan(fn=lambda x, k : x**k ,sequences=[Xs,alpha_expn])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What about `reduce`? " ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def vec_addition(a,b):\n", " return sandbox.cuda.basic_ops.gpu_from_host( a + b )" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], "source": [ "output, updates = theano.reduce(fn=vec_addition, sequences=[X1,X2],outputs_info=[None,])\n" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f = theano.function(inputs=[X1,X2], outputs=output, updates=updates)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(9.0, dtype=float32)" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(X1_value, X2_value)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def vec_elem_mult(a,b):\n", " return sandbox.cuda.basic_ops.gpu_from_host( a*b)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "output, updates = theano.reduce(fn=vec_elem_mult, sequences=[X1,X2],outputs_info=[None,])" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f = theano.function(inputs=[X1,X2], outputs=output, updates=updates)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(20.0, dtype=float32)" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(X1_value, X2_value)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What about matrices, tensors?" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X1 = T.matrix('matrix1')\n", "X2 = T.matrix('matrix2')" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true }, "outputs": [], "source": [ "output, updates = theano.scan(fn=lambda a, b : sandbox.cuda.basic_ops.gpu_from_host( a * b ), sequences=[X1,X2])" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f = theano.function(inputs=[X1,X2], outputs=output, updates=updates)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. 1. 2.]\n", " [ 3. 4. 5.]]\n", "[[ 1. 2. 3.]\n", " [ 4. 5. 6.]]\n" ] } ], "source": [ "X1_value = np.arange(0,6).reshape(2,3).astype(theano.config.floatX); print(X1_value)\n", "X2_value = np.arange(1,7).reshape(2,3).astype(theano.config.floatX); print(X2_value)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 2., 6.],\n", " [ 12., 20., 30.]], dtype=float32)" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(X1_value,X2_value)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X1 = T.tensor3('tensor1')\n", "X2 = T.tensor3('tensor2')" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true }, "outputs": [], "source": [ "output, updates = theano.scan(fn=vec_addition, sequences=[X1,X2])\n", "f = theano.function(inputs=[X1,X2], outputs=output, updates=updates)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[[ 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.]]]\n", "[[[ 1. 2. 3. 4.]\n", " [ 5. 6. 7. 8.]\n", " [ 9. 10. 11. 12.]]\n", "\n", " [[ 13. 14. 15. 16.]\n", " [ 17. 18. 19. 20.]\n", " [ 21. 22. 23. 24.]]]\n" ] } ], "source": [ "X1_value = np.arange(0,24).reshape(2,3,4).astype(theano.config.floatX); print(X1_value)\n", "X2_value = np.arange(1,25).reshape(2,3,4).astype(theano.config.floatX); print(X2_value)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[[ 1., 3., 5., 7.],\n", " [ 9., 11., 13., 15.],\n", " [ 17., 19., 21., 23.]],\n", "\n", " [[ 25., 27., 29., 31.],\n", " [ 33., 35., 37., 39.],\n", " [ 41., 43., 45., 47.]]], dtype=float32)" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(X1_value,X2_value)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_t = T.vector('X_t')\n", "output,updates=theano.scan(fn=lambda x:x**3,sequences=[X_t,])" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f=theano.function(inputs=[X_t,],outputs=output,updates=updates)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[Shape_i{0}(X_t),\n", " GpuFromHost(X_t),\n", " ScalarFromTensor(Shape_i{0}.0),\n", " Elemwise{Composite{Switch(LE(i0, i1), i1, i2)}}[(0, 0)](Shape_i{0}.0, TensorConstant{0}, TensorConstant{0}),\n", " ScalarFromTensor(Elemwise{Composite{Switch(LE(i0, i1), i1, i2)}}[(0, 0)].0),\n", " GpuSubtensor{int64:int64:int8}(GpuFromHost.0, ScalarFromTensor.0, ScalarFromTensor.0, Constant{1}),\n", " GpuElemwise{Pow}[(0, 0)](GpuSubtensor{int64:int64:int8}.0, CudaNdarrayConstant{[ 3.]}),\n", " HostFromGpu(GpuElemwise{Pow}[(0, 0)].0)]" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f.maker.fgraph.toposort()" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f=theano.function(inputs=[X_t,],outputs=sandbox.cuda.basic_ops.gpu_from_host(output),updates=updates)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[Shape_i{0}(X_t),\n", " GpuFromHost(X_t),\n", " ScalarFromTensor(Shape_i{0}.0),\n", " Elemwise{Composite{Switch(LE(i0, i1), i1, i2)}}[(0, 0)](Shape_i{0}.0, TensorConstant{0}, TensorConstant{0}),\n", " ScalarFromTensor(Elemwise{Composite{Switch(LE(i0, i1), i1, i2)}}[(0, 0)].0),\n", " GpuSubtensor{int64:int64:int8}(GpuFromHost.0, ScalarFromTensor.0, ScalarFromTensor.0, Constant{1}),\n", " GpuElemwise{Pow}[(0, 0)](GpuSubtensor{int64:int64:int8}.0, CudaNdarrayConstant{[ 3.]})]" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f.maker.fgraph.toposort()" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 1. 2. 3. 4.]\n" ] } ], "source": [ "X1_value = np.arange(0,5).astype(theano.config.floatX) # [0,1,2,3,4] \n", "print(X1_value)" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "CudaNdarray([ 0. 1. 8. 27. 64.])" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(X1_value)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So this is the mathematical equivalent to \n", "$ t=0,1,\\dots T-1$, $t\\in \\mathbb{Z}^+$, \n", "$X\\in \\mathbb{R}^T$ \n", "$$ \\forall \\, t = 0, 1, \\dots T-1, \\\\\n", " f:\\mathbb{R} \\to \\mathbb{R} \\\\ \n", " X(t) \\mapsto f(X(t)) = (X(t))^3 \\qquad \\, (\\text{for example}) \\\\\n", " $$" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [], "source": [ "output,updates=theano.reduce(fn=lambda x:x**4,sequences=[X_t,],outputs_info=[None,])\n", "f=theano.function(inputs=[X_t,],outputs=output,updates=updates)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(256.0, dtype=float32)" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(X1_value)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 2: Non-sequences\n", "\n", "We need some variables to be available \"as is\" at every iteration of the loop. We don't want scan to iterate over them and give only part of them at every iteration. " ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = T.matrix('X')\n", "W = T.matrix('W')\n", "b = T.vector('b')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the sake of variety (and so lambda is the same as defining a Python function), define computation to be done at every iteration of the loop using `step()`, instead of lambda expression. \n", "\n", "To have $W$ and $b$ be available at every iteration, use the argument `non_sequences`. Contrary to sequences, non-sequences are not iterated upon by Scan. \n", "\n", "This means `step()` function will need to operate on 3 symbolic inputs; 1 for our sequences $X$, 1 for each non-sequences $W$ and $b$. \n", "\n", "The inputs that correspond to the non-sequences are **always** last and in same order at the non-sequences provided to Scan. This means correspondence between inputs of the `step()` function and arguments to `scan()` is the following: \n", " * $v$ : individual element of the sequence $X$\n", " * $W,b$ : non-sequences $W,b$, respectively\n" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OrderedUpdates()\n" ] } ], "source": [ "def step(v,W,b):\n", " return T.dot(v,W) + b\n", "\n", "output,updates=theano.scan(fn=step,\n", " sequences=[X],\n", " non_sequences=[W,b])\n", "print(updates)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f=theano.function(inputs=[X,W,b],\n", " outputs=sandbox.cuda.basic_ops.gpu_from_host( output),\n", " updates=updates)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_value = np.arange(-3,3).reshape(3,2).astype(theano.config.floatX)\n", "W_value = np.eye(2).astype(theano.config.floatX)\n", "b_value=np.arange(2).astype(theano.config.floatX)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-3. -2.]\n", " [-1. 0.]\n", " [ 1. 2.]]\n", "[[ 1. 0.]\n", " [ 0. 1.]]\n", "[ 0. 1.]\n" ] } ], "source": [ "print(X_value); print(W_value); print(b_value)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "CudaNdarray([[-3. -1.]\n", " [-1. 1.]\n", " [ 1. 3.]])" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(X_value,W_value,b_value)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice how scan is on the first dimension (or, counting from 0, the 0th dimension), always. So 1 way to think about it is discretized time $t\\in \\mathbb{R} \\xrightarrow{ \\text{ discretize } } t\\in\\mathbb{Z}^+$ \n", "\n", "$$ \n", "X:\\mathbb{Z}^+ \\to \\mathbb{R}^d \\text{ or } \\text{Mat}_{\\mathbb{R}}(N_1,N_2) \\text{ or } \\tau^r_s(V), \\text{ space of tensors of type } (r,s), \\tau_s^r(V) = \\lbrace (r+s)-\\text{linear maps}, \\underbrace{V\\times \\dots \\times V}_{r} \\times \\underbrace{V^* \\times \\dots \\times V^*}_{s} \\to \\mathbb{F} \\rbrace $$ \n", "\n", "$$ \\forall \\, t = 0 , 1, \\dots T-1, \\\\\n", "\\text{ e.g. } \\theta \\in \\text{Mat}_{\\mathbb{R}}(d,N_2) \\\\ \n", " \\qquad b \\in \\mathbb{R}^{N_2} $$\n", " \n", "$$ f_{\\theta,b}:\\mathbb{R}^d \\to \\mathbb{R}^{N_2} \\\\\n", " X(t) \\mapsto f(X(t)) = X(t) \\cdot \\theta + b $$ \n", " \n", "and so nonsequences help to return the functional \n", " \n", "$$ f:\\text{Mat}_{\\mathbb{R}}(d,N_2) \\times \\mathbb{R}^{N_2} \\to \\text{Hom}(\\mathbb{R}^d, \\mathbb{R}^{N_2} ) \\\\\n", " f:(\\theta,b) \\mapsto f_{\\theta,b} $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice the *right action* of $\\theta$ onto $X(t)$, as necessitated by how the size dimensions are defined. This should be duly noted, as scan *only* iterates across the first dimension. So to write the equivalent `step`, with the usual left action, " ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = T.tensor3('X')\n", "W = T.matrix('W')\n", "b = T.vector('b')" ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#def step_left(v,W,b):\n", "def step_left(v,W):\n", " return T.dot(W,v) # + b \n", "\n", "#output, updates=theano.scan(fn=step_left,sequences=[X],non_sequences=[W,b]) \n", "#f=theano.function(inputs=[X,W,b],outputs=output,updates=updates)\n", "\n", "output, updates=theano.scan(fn=step_left,sequences=[X],non_sequences=[W]) \n", "f=theano.function(inputs=[X,W],outputs=output,updates=updates)" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_value = np.arange(-3,3).reshape(3,2,1).astype(theano.config.floatX)\n", "W_value = np.arange(1,5).reshape(2,2).astype(theano.config.floatX) \n", "#b_value=np.arange(2).reshape(2,1).astype(theano.config.floatX)" ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "collapsed": false }, "outputs": [], "source": [ "test_result_left =f(X_value,W_value) #,b_value)" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[[ -7.],\n", " [-17.]],\n", "\n", " [[ -1.],\n", " [ -3.]],\n", "\n", " [[ 5.],\n", " [ 11.]]], dtype=float32)" ] }, "execution_count": 136, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_result_left" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2, 1)" ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_result_left[0].shape" ] }, { "cell_type": "code", "execution_count": 140, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def step_left(v,W,b):\n", " return T.dot(W,v) + b \n", "\n", "output, updates=theano.scan(fn=step_left,sequences=[X],non_sequences=[W,b]) \n", "f=theano.function(inputs=[X,W,b],outputs=output,updates=updates)" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false }, "outputs": [], "source": [ "b_value=np.arange(2).reshape(2).astype(theano.config.floatX)\n", "test_result_left =f(X_value,W_value, b_value)" ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[[ -7., -6.],\n", " [-17., -16.]],\n", "\n", " [[ -1., 0.],\n", " [ -3., -2.]],\n", "\n", " [[ 5., 6.],\n", " [ 11., 12.]]], dtype=float32)" ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_result_left" ] }, { "cell_type": "code", "execution_count": 151, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def step_left(v,W,b):\n", " return ( T.dot(W,v) + b ) \n" ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "collapsed": false }, "outputs": [], "source": [ "output, updates=theano.scan(fn=step_left,sequences=[X],outputs_info=[None],non_sequences=[W,b]) " ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_result_left =f(X_value,W_value, b_value)" ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[[ -7., -6.],\n", " [-17., -16.]],\n", "\n", " [[ -1., 0.],\n", " [ -3., -2.]],\n", "\n", " [[ 5., 6.],\n", " [ 11., 12.]]], dtype=float32)" ] }, "execution_count": 160, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_result_left" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[[ -7.],\n", " [ -1.],\n", " [ 5.]],\n", "\n", " [[-17.],\n", " [ -3.],\n", " [ 11.]]], dtype=float32)" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.dot(W_value,X_value)" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 2.],\n", " [ 3., 4.]], dtype=float32)" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "W_value" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ -7.],\n", " [-17.]], dtype=float32)" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.dot(W_value,X_value[0])" ] }, { "cell_type": "code", "execution_count": 137, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[[-3.],\n", " [-2.]],\n", "\n", " [[-1.],\n", " [ 0.]],\n", "\n", " [[ 1.],\n", " [ 2.]]], dtype=float32)" ] }, "execution_count": 137, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_value" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.],\n", " [ 1.]], dtype=float32)" ] }, "execution_count": 139, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b_value" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<function theano.tensor.basic.zeros_like>" ] }, "execution_count": 156, "metadata": {}, "output_type": "execute_result" } ], "source": [ "T.zeros_like" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 3: Reusing outputs from the previous iterations " ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def step(m_row, cumulative_sum):\n", " return m_row + cumulative_sum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The trick part is informing Scan that our step function expects as input the output of a previous iteration. A new parameter, `outputs_info`, achieves this. This parameter is used to tell Scan how we intend to use each of the ouputs that are computer at each iteration. \n", "\n", "This parameter can be omitted (like we have done so far) when the step function doesn't depend on any output of a previous iteration. \n", "\n", "`outputs_info` takes a sequence with 1 element for every output of the `step()` function: \n", "* For a **non-recurrent output**, element should be `None`. \n", "* For **simple recurrent output**, (iteration $t$ depends on value of iteration at $t-1$, say), the element must be a tensor. Scan will interpret it as being an initial state for a recurrent output, and give it as input to the first iteration, pretending it's the output value from a previous iteration. \n", "\n", "The `step()` expects 1 additional input for each simple recurrent output: these inputs correspond to outputs from previous iteration and are **always** *after inputs* that correspond to sequences, but before those that correspond to non-sequences. " ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "collapsed": true }, "outputs": [], "source": [ "M=T.matrix('X')\n", "s=T.vector('s') # initial value for the cumulative sum" ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "collapsed": true }, "outputs": [], "source": [ "output, updates = theano.scan(fn=step,\n", " sequences=[M],\n", " outputs_info=[s])" ] }, { "cell_type": "code", "execution_count": 165, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f=theano.function(inputs=[M, s], \n", " outputs=output,\n", " updates=updates)" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "collapsed": true }, "outputs": [], "source": [ "M_value=np.arange(9).reshape(3,3).astype(theano.config.floatX)\n", "s_value=np.zeros((3,),dtype=theano.config.floatX)" ] }, { "cell_type": "code", "execution_count": 167, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 1., 2.],\n", " [ 3., 5., 7.],\n", " [ 9., 12., 15.]], dtype=float32)" ] }, "execution_count": 167, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(M_value,s_value)" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 1., 2.],\n", " [ 3., 4., 5.],\n", " [ 6., 7., 8.]], dtype=float32)" ] }, "execution_count": 168, "metadata": {}, "output_type": "execute_result" } ], "source": [ "M_value" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 1. 2.]\n", "[ 3. 4. 5.]\n" ] } ], "source": [ "print(M_value[0])\n", "print(M_value[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For input $\\begin{aligned} & X:\\mathbb{Z}^+ \\to R-\\text{Module} \\\\\n", " & t\\mapsto X(t) \\end{aligned} $, e.g. $R$-Module such as $\\mathbb{R}^d, V, \\text{Mat}_{\\mathbb{R}}(N_1,N_2), \\tau_s^r(V)$, and a function $f$ that acts at each iteration, i.e. $\\forall \\, t=0,1,\\dots T$, \n", "$$ \n", "\\begin{aligned}\n", " & f: R-\\text{Module} \\times R-\\text{Module} \\to R-\\text{Module} \\\\ \n", " & (X_1,X_0) \\mapsto X_1 + X_0 \n", " \\end{aligned}\n", "$$, then we want to express\n", "$$ \n", "f(X(t),X(t-1)) = X(t)+X(t-1) \\qquad \\, \\forall \\, t=0,1\\dots T-1,\n", "$$ \n", "\n", "In the end, we should get \n", "$$ \n", "X\\in (R-\\text{Module})^T \\xrightarrow{ \\text{ scan } } Y \\in (R-\\text{Module})^T\n", "$$\n", "$Y \\equiv $ `output`\n", "\n" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Further example\n", "X=T.vector('X')\n", "x_0=T.scalar('x_0') # initial value for the cumulative sum \n", "output, updates = theano.scan(fn=step, sequences=[X],outputs_info=[x_0])" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f=theano.function(inputs=[X,x_0],outputs=output,updates=updates)" ] }, { "cell_type": "code", "execution_count": 181, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 1. 2. 3. 4. 5. 6. 7. 8.]\n" ] } ], "source": [ "X_value =np.arange(9).astype(theano.config.floatX); print(X_value)\n", "x_0_val = np.cast[theano.config.floatX](0.)" ] }, { "cell_type": "code", "execution_count": 182, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 1., 3., 6., 10., 15., 21., 28., 36.], dtype=float32)" ] }, "execution_count": 182, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(X_value,x_0_val)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is *classically* what (parallel) scan should do. \n", "\n", "Indeed," ] }, { "cell_type": "code", "execution_count": 183, "metadata": { "collapsed": true }, "outputs": [], "source": [ "output, updates = theano.scan(fn=lambda x_1,x_0 : x_1*x_0, sequences=[X],outputs_info=[x_0])\n", "f=theano.function(inputs=[X,x_0],outputs=output,updates=updates)" ] }, { "cell_type": "code", "execution_count": 185, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n" ] } ], "source": [ "X_value =np.arange(1,11).astype(theano.config.floatX); print(X_value)\n", "x_0_val = np.cast[theano.config.floatX](1.)" ] }, { "cell_type": "code", "execution_count": 186, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1.00000000e+00, 2.00000000e+00, 6.00000000e+00,\n", " 2.40000000e+01, 1.20000000e+02, 7.20000000e+02,\n", " 5.04000000e+03, 4.03200000e+04, 3.62880000e+05,\n", " 3.62880000e+06], dtype=float32)" ] }, "execution_count": 186, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(X_value,x_0_val)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In summary, the dictionary between the mathematics and the Python theano code for scan seems to be the following:\n", "\n", "If $k=1$, $\\forall \\, t = 0,1,\\dots T-1$,\n", "\\begin{equation}\n", " \\begin{gathered}\n", "\\begin{aligned}\n", "& F:R-\\text{Module}\\times R-\\text{Module} \\to R-\\text{Module} \\\\ \n", "& F(X(t),X(t-1)) \\mapsto X(t) \\end{aligned} \\Longleftrightarrow \\text{Python function (object) or Python lambda expression } \\mapsto \\verb|scan(fn= )| \\\\ \n", "(X(0),X(1),\\dots X(T-1)) \\in (R-\\text{Module})^T \\Longleftrightarrow \\verb|scan(sequences= )| \\\\\n", "X(-1) \\in R-\\text{Module} \\Longleftrightarrow \\verb|scan(outputs_info=[ ] )|\n", " \\end{gathered}\n", " \\end{equation}\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 4: Reusing outputs from multiple past iterations " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"...Since every example so far had only 1 output at every iteration of the loop, we will also compute, at each time step, the ratio between the new term of the Fibonacci sequence and the previous term.\" I think what Pierre means is that we're going to try to implement for the output, `ratio`, as a *non-recurrent* term, just for the sake of this pedagogical example. " ] }, { "cell_type": "code", "execution_count": 187, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def step(f_minus2,f_minus1):\n", " new_f = f_minus2+f_minus1\n", " ratio=new_f/f_minus1\n", " return new_f,ratio" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Defining the value of `outputs_info`: \n", "\n", "Recall that, for **non-recurrent outputs**, the value is `None`, and, for **simple recurrent outputs**, the value is a single initial state. For **general recurrent outputs**, where iteration $t$ may depend on multiple past values, *the value is a **dictionary**. * \n", "\n", "That dictionary has 2 values: \n", "* `taps` : list declaring which previous values of that output every iteration will need, e.g. `[-2,-1]` \n", "* `initial` : tensor of initial values. If every initial value has $n$ dimensions, `initial` will be a single tensor of $n+1$ dimensions with as many initial values as the oldest requested tap. In the case of Fibonacci sequence, individual initial values are scalars, so `initial` will be a vector. " ] }, { "cell_type": "code", "execution_count": 189, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f_init = T.vector()\n", "outputs_info = [dict(initial=f_init, taps=[-2,-1]),None]" ] }, { "cell_type": "code", "execution_count": 190, "metadata": { "collapsed": true }, "outputs": [], "source": [ "output, updates=theano.scan(fn=step,outputs_info=outputs_info,n_steps=10)\n", "next_fibonacci_terms=output[0]\n", "ratios_between_terms=output[1]" ] }, { "cell_type": "code", "execution_count": 193, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f=theano.function(inputs=[f_init],\n", " outputs=[next_fibonacci_terms,ratios_between_terms],updates=updates)" ] }, { "cell_type": "code", "execution_count": 196, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "[ 2. 3. 5. 8. 13. 21. 34. 55. 89. 144.]\n", "[ 2. 1.5 1.66666663 1.60000002 1.625 1.61538458\n", " 1.61904764 1.61764705 1.61818182 1.6179775 ]\n" ] } ], "source": [ "out=f([1,1]) \n", "print(len(out))\n", "print(out[0])\n", "print(out[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EY : 20170324 note. Notice the `n_steps` parameter that's utilized now in `scan`. I'll try to explain it. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "$\\forall \\, t = 0, 1, \\dots T-1$, $T\\Longleftrightarrow $ `n_steps`$=T$,\n", "\n", "Consider\n", "\\begin{equation}\n", " \\begin{aligned}\n", " & F:(R-\\text{Module})^k \\to R-\\text{Module} \\\\ \n", " & F(X(t-k),X(t-(k-1)),\\dots X(t-1)) = X(t) \\end{aligned} \\Longleftrightarrow \\verb|fn| \\in \\text{Python function (object) }\n", " \\end{equation}\n", "\n", "If $k=1$, we'll need to be given $X(0) \\in R-\\text{Module}$. Perhaps consider $\\forall \\, t=-k,-(k-1), \\dots -1,0,1\\dots T-1$ (``in full'').\n", "\n", "For $k>1$, we'll need to be given (or declare) $\\lbrace X(-k),X(-(k-1)),\\dots X(-1)\\rbrace$.\n", "\n", "So for $k=1$, $X(-1) \\in R-\\text{Module}$ needed $\\Longleftrightarrow $ e.g. `T.scalar()` if $R$-Module $=\\mathbb{R}$. \n", " for $k>1$, $(X(-k),X(-(k-1)),\\dots X(-1)) \\in (R-\\text{Module})^k \\Longleftrightarrow $ e.g. `T.vector()`, into ''initial'' of a Python `dict`, if $R$-Module $=\\mathbb{R}$. \n", "\n", "Also, for $k>1$,\n", "\n", "$$\n", "(-k,-(k-1), \\dots -1) \\Longleftrightarrow \\verb|taps| = [-k,-(k-1),\\dots -1] (\\text{a Python}\\verb| list|)\n", "$$\n", "\n", "scan, essentially, does this:\n", "\\begin{equation}\n", "\\begin{gathered}\n", " (X(-k),X(-(k-1)),\\dots X(-1)) \\mapsto (X(0),X(1)\\dots X(T-1)) \\\\ \n", "F(X(t-k), X(t-(k-1))\\dots X(t-1)) = X(t), \\qquad \\, \\forall \\, t=0,1,\\dots T-1\n", " \\end{gathered}\n", "\\end{equation}\n", "given $F:(R-\\text{Module})^k \\to R-\\text{Module}$, with $T=$ `n_steps`. " ] }, { "cell_type": "code", "execution_count": 197, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def F(Xtm2,Xtm1):\n", " \"\"\"\n", " cf. https://www.math.cmu.edu/~af1p/Teaching/Combinatorics/Slides/Generating-Functions.pdf\n", " How many ways to spend n dollars, where everyday, you can only spend it on 1 dollar for a bun \n", " OR 2 dollars for an ice cream \n", " OR 2 dollars for a pastry?\n", " \"\"\"\n", " new_f = Xtm2 * 2 + Xtm1 \n", " return new_f \n", "\n", "X_init = T.vector()\n", "outputs_info=[dict(initial=X_init, taps=[-2,-1])]" ] }, { "cell_type": "code", "execution_count": 198, "metadata": { "collapsed": true }, "outputs": [], "source": [ "output,updates = theano.scan(fn=F, outputs_info=outputs_info,n_steps=20)" ] }, { "cell_type": "code", "execution_count": 199, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f=theano.function(inputs=[X_init],outputs=output,updates=updates)" ] }, { "cell_type": "code", "execution_count": 200, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 3.00000000e+00, 5.00000000e+00, 1.10000000e+01,\n", " 2.10000000e+01, 4.30000000e+01, 8.50000000e+01,\n", " 1.71000000e+02, 3.41000000e+02, 6.83000000e+02,\n", " 1.36500000e+03, 2.73100000e+03, 5.46100000e+03,\n", " 1.09230000e+04, 2.18450000e+04, 4.36910000e+04,\n", " 8.73810000e+04, 1.74763000e+05, 3.49525000e+05,\n", " 6.99051000e+05, 1.39810100e+06], dtype=float32)" ] }, "execution_count": 200, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f([1,1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises \n", "\n", "## Exercise 1 - Computing a polynomial " ] }, { "cell_type": "code", "execution_count": 201, "metadata": { "collapsed": true }, "outputs": [], "source": [ "coefficients = T.vector(\"coefficients\")\n", "x=T.scalar(\"x\")" ] }, { "cell_type": "code", "execution_count": 202, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def step(coeff,power,free_var):\n", " return coeff * free_var ** power " ] }, { "cell_type": "code", "execution_count": 203, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Generate the components of the polynomial\n", "max_coefficients_supported = 10000\n", "full_range=T.arange(max_coefficients_supported)" ] }, { "cell_type": "code", "execution_count": 204, "metadata": { "collapsed": true }, "outputs": [], "source": [ "components, updates = theano.scan(fn=step, \n", " outputs_info=None,\n", " sequences=[coefficients, full_range],\n", " non_sequences=x)" ] }, { "cell_type": "code", "execution_count": 205, "metadata": { "collapsed": true }, "outputs": [], "source": [ "polynomial = components.sum()" ] }, { "cell_type": "code", "execution_count": 206, "metadata": { "collapsed": true }, "outputs": [], "source": [ "calculate_polynomial = theano.function(inputs=[coefficients,x],outputs=polynomial,updates=updates)" ] }, { "cell_type": "code", "execution_count": 207, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_coeff=np.asarray([1,0,2],dtype=theano.config.floatX)" ] }, { "cell_type": "code", "execution_count": 208, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(19.0)" ] }, "execution_count": 208, "metadata": {}, "output_type": "execute_result" } ], "source": [ "calculate_polynomial(test_coeff,3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Solution \n", "\n", "cf. [`scan_ex1_solution.py\n", "`](https://github.com/lamblin/ccw_tutorial/blob/master/Scan_W2016/solutions/scan_ex1_solution.py)" ] }, { "cell_type": "code", "execution_count": 209, "metadata": { "collapsed": true }, "outputs": [], "source": [ "coefficients = T.vector(\"coefficients\")\n", "x=T.scalar(\"x\")\n", "max_coefficients_supported = 10000" ] }, { "cell_type": "code", "execution_count": 214, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def step(coeff,power,prior_value,free_var):\n", " return prior_value + (coeff * (free_var ** power)) " ] }, { "cell_type": "code", "execution_count": 216, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Generate the components of the polynomial\n", "full_range = T.arange(max_coefficients_supported,dtype=theano.config.floatX)\n", "outputs_info = np.zeros((),dtype=theano.config.floatX)" ] }, { "cell_type": "code", "execution_count": 217, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0\n" ] } ], "source": [ "print(outputs_info)\n", "components, updates = theano.scan(fn=step,\n", " sequences=[coefficients,full_range],\n", " outputs_info=outputs_info,\n", " non_sequences=x)" ] }, { "cell_type": "code", "execution_count": 218, "metadata": { "collapsed": true }, "outputs": [], "source": [ "polynomial=components[-1]" ] }, { "cell_type": "code", "execution_count": 219, "metadata": { "collapsed": true }, "outputs": [], "source": [ "calculate_polynomial=theano.function(inputs=[coefficients,x], outputs=polynomial, updates=updates)" ] }, { "cell_type": "code", "execution_count": 220, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_coeff=np.asarray([1,0,2],dtype=theano.config.floatX)" ] }, { "cell_type": "code", "execution_count": 222, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(19.0, dtype=float32)" ] }, "execution_count": 222, "metadata": {}, "output_type": "execute_result" } ], "source": [ "calculate_polynomial(test_coeff,3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 2 - Sampling without replacement \n", "\n", "* takes as input a vector of probabilities and a scalar" ] }, { "cell_type": "code", "execution_count": 223, "metadata": { "collapsed": true }, "outputs": [], "source": [ "probabilities = T.vector()\n", "nb_samples = T.iscalar() \n", "\n", "rng = T.shared_randomstreams.RandomStreams(1234)" ] }, { "cell_type": "code", "execution_count": 231, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sample_from_pvect(pvect):\n", " \"\"\" Provided utility function: given a symbolic vector of \n", " probabilities (which MUST sum to 1), sample one element \n", " and return its index\n", " \"\"\"\n", " onehot_sample = rng.multinomial(n=1,pvals=pvect)\n", " sample = onehot_sample.argmax()\n", " return sample # sample \\in \\mathbb{Z}^+, i.e. sample = 0,1,...K-1, with K= total number of possible outcomes\n", "\n", "def set_p_to_zero(pvect, i):\n", " \"\"\" Provided utility function: given a symbolic vector of \n", " probabilities and an index 'i', set the probability of the \n", " i-th element to 0 and renormalize the probabilities so they\n", " sum to 1.\n", " \"\"\" \n", " new_pvect = T.set_subtensor(pvect[i], 0.)\n", " new_pvect = new_pvect / new_pvect.sum() \n", " return new_pvect " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EY:20170325 notes: [raw_random - Low-level random numbers](http://deeplearning.net/software/theano/library/tensor/raw_random.html) sample $n$ times from a multinomial distribution, defined by probabilities `pvals`. It's this formula :\n", "\n", "$$ \n", "\\binom{N}{k} p^k(1-p)^{N-k} \n", "$$ for a binomial distribution, probability of picking out the outcome corresponding to probability $p$, $k$ times out of $N$. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### My solution, without looking at the author's beforehand" ] }, { "cell_type": "code", "execution_count": 242, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sample_step(pvect):\n", " \"\"\" sample_step - sample without replacement, at 1 given iteration \n", " \"\"\"\n", " sample = sample_from_pvect(pvect) # \\in \\mathbb{Z}^+, i.e. sample=0,1,...K-1, with K=total number of possible outcomes\n", " new_pvect = set_p_to_zero(pvect,sample) # we had to remove the drawn sample out of the equation\n", " return new_pvect\n", " " ] }, { "cell_type": "code", "execution_count": 243, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# this line is not needed, since we want the inputted \"hard\", numerical values to be the initial values\n", "#pvect_0 = T.vector() # the initial set of probabilities for all $K$ possibilities (outcomes)" ] }, { "cell_type": "code", "execution_count": 244, "metadata": { "collapsed": false }, "outputs": [], "source": [ "output,update=theano.scan(fn=sample_step,outputs_info=[probabilities],n_steps=nb_samples)" ] }, { "cell_type": "code", "execution_count": 245, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Compiling the function\n", "f = theano.function(inputs=[probabilities, nb_samples], outputs=output,updates=update)" ] }, { "cell_type": "code", "execution_count": 246, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Testing the function\n", "test_probs = np.asarray([0.6,0.3,0.1], dtype=theano.config.floatX)" ] }, { "cell_type": "code", "execution_count": 247, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. 0.75 0.25]\n", " [ 0. 0. 1. ]]\n", "[[ 0.85714281 0. 0.14285713]\n", " [ 0. 0. 1. ]]\n", "[[ 0. 0.75 0.25]\n", " [ 0. 1. 0. ]]\n", "[[ 0. 0.75 0.25]\n", " [ 0. 0. 1. ]]\n", "[[ 0.66666669 0.33333334 0. ]\n", " [ 0. 1. 0. ]]\n", "[[ 0. 0.75 0.25]\n", " [ 0. 1. 0. ]]\n", "[[ 0. 0.75 0.25]\n", " [ 0. 1. 0. ]]\n", "[[ 0. 0.75 0.25]\n", " [ 0. 1. 0. ]]\n", "[[ 0.85714281 0. 0.14285713]\n", " [ 0. 0. 1. ]]\n", "[[ 0. 0.75 0.25]\n", " [ 0. 0. 1. ]]\n" ] } ], "source": [ "for i in range(10):\n", " print(f(test_probs, 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Solution from author (Pierre Le Duc?) \n", "cf. [`scan_ex2_solution.py`](https://github.com/lamblin/ccw_tutorial/blob/master/Scan_W2016/solutions/scan_ex2_solution.py)" ] }, { "cell_type": "code", "execution_count": 236, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def step(p):\n", " sample=sample_from_pvect(p)\n", " new_p=set_p_to_zero(p,sample)\n", " return new_p,sample" ] }, { "cell_type": "code", "execution_count": 237, "metadata": { "collapsed": true }, "outputs": [], "source": [ "output,updates = theano.scan(fn=step,outputs_info=[probabilities,None],n_steps=nb_samples)" ] }, { "cell_type": "code", "execution_count": 238, "metadata": { "collapsed": true }, "outputs": [], "source": [ "modified_probabilities,samples=output" ] }, { "cell_type": "code", "execution_count": 239, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f=theano.function(inputs=[probabilities,nb_samples],outputs=[samples],updates=updates)" ] }, { "cell_type": "code", "execution_count": 240, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[array([0, 2])]\n", "[array([1, 0])]\n", "[array([0, 2])]\n", "[array([0, 2])]\n", "[array([1, 0])]\n", "[array([2, 0])]\n", "[array([0, 1])]\n", "[array([0, 1])]\n", "[array([2, 0])]\n", "[array([0, 1])]\n" ] } ], "source": [ "# Testing the function\n", "test_probs=np.asarray([0.6,0.3,0.1],dtype=theano.config.floatX)\n", "for i in range(10):\n", " print(f(test_probs,2))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Using theano's crossentropy" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sympy\n", "from sympy import Symbol\n", "from sympy.plotting import plot" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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OKsCWOzm0jIwMvPrqq3BxcQEAaDSaYd/TvZ47+2VI2Rju5NBu3LiBM2fOYM6c\nOZg/fz5ycnIG3TczMxPJycn4/PRpGE1GG1ZJZHvsliG7l5KSgpqamn6vb9u2DUajEQ0NDTh37hxy\ncnLwgx/8AMXFxQM+Qi89PR3p6en40X9n45O//c4WpRPJhuFOdi8rK2vQbRkZGVi1ahUkScLs2bMx\nZswYaLVa+Pv7D/oedrmTGrBbhhzaihUrcOrUKQDdXTQGgwF+fn5DvscsBIdCkuKx5U4OLS0tDWlp\naZg+fTqcnZ2xZ8+eAbtk7mTmDFVSAYY7OTRnZ2d8+OGH9/Se9Q+F4fqkICtVRGQfJCuvs8EmEhHR\nvbFIpyH73ImIFIjhTkSkQAx3IiIFYrgTESkQw52ISIEY7kRECsRwJyJSIIY7EZECWXuGKlfwICKS\nAVvuREQKxHAnIlIghjsRkQIx3ImIFIjhTkSkQAx3IiIFYrgTESkQw52ISIEY7kRECsRwJyJSoP8P\ngiAaVeSsoxMAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f553843f0d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<sympy.plotting.plot.Plot at 0x7f5538232b50>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot(sympy.log( Symbol('x')) )" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 0]\n", "[ 0.8 0.8]\n" ] } ], "source": [ "m_test = 2 # total number of examples, m \n", "y_test = np.random.randint(2, size=m_test)\n", "y_predicted_test = np.ones(m_test)*0.8\n", "print(y_test)\n", "print(y_predicted_test)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.91629073187415511" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "-(y_test*np.log(y_predicted_test)+(1.-y_test)*np.log(1.-y_predicted_test)).mean()" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_test = theano.shared(y_test.astype(theano.config.floatX))\n", "y_predicted_test = theano.shared(y_predicted_test.astype(theano.config.floatX))" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [], "source": [ "J_binary = T.nnet.binary_crossentropy(y_predicted_test, y_test).mean()\n", "J_categorical = T.nnet.categorical_crossentropy(y_predicted_test, y_test).mean()" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.223143551314\n" ] } ], "source": [ "print( theano.function( inputs=[], outputs=J_categorical)() )" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.916290731874\n" ] } ], "source": [ "print( theano.function( inputs=[], outputs=J_binary)() )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### making an example for categorical crossentropy" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [], "source": [ "y3=np.zeros((m_test,3))" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 2]\n", "[[ 0. 1. 0.]\n", " [ 0. 0. 1.]]\n" ] } ], "source": [ "y3_predicted_cls = np.random.randint(3,size=m_test)\n", "print(y3_predicted_cls)\n", "for i in range(m_test):\n", " y3[i][y3_predicted_cls[i]] = 1.\n", "print(y3)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.2914426 0.48511168 0.22344572]\n", " [ 0.69105175 0.22599138 0.08295687]]\n" ] } ], "source": [ "Kclses = 3\n", "y3_predicted = np.array([np.random.dirichlet(np.ones(Kclses),size=1).flatten() for i in range(m_test)])\n", "print(y3_predicted)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.73285247918113439" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y3_predicted[:,0].sum()" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 1., 0.],\n", " [ 0., 0., 1.]])" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y3[" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.34452420410160056" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "-(y3[0][0]*np.log(y3_predicted[0][0])+(1-y3[0][0])*np.log(y3_predicted[0][np.arange(Kclses)!=0].sum()))" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.70855740198393524" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y3_predicted[0][np.arange(Kclses)!=0].sum()" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": true }, "outputs": [], "source": [ "categorical_results=[]\n", "for i in range(m_test):\n", " example_row=[]\n", " for cls in range(Kclses):\n", " entropy= \\\n", " (y3[i][cls]*np.log(y3_predicted[i][cls])+(1-y3[i][cls])*np.log(y3_predicted[i][np.arange(Kclses)!=cls].sum()))\n", " example_row.append(-entropy)\n", " categorical_results.append(example_row)\n", "categorical_results = np.array( categorical_results )" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.3445242 , 0.72337615, 0.25288874],\n", " [ 1.17458149, 0.25617226, 2.48943439]])" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "categorical_results" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1.32078909, 3.92018814])" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "categorical_results.sum(1)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2.6204886194141581" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "categorical_results.sum(1).mean()" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y3_test=theano.shared(y3.astype(theano.config.floatX))\n", "y3_predicted_test=theano.shared(y3_predicted.astype(theano.config.floatX))" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": true }, "outputs": [], "source": [ "J_categorical=T.nnet.categorical_crossentropy(y3_predicted_test,y3_test).mean()" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.60640527128\n" ] } ], "source": [ "print( theano.function(inputs=[],outputs=J_categorical)() )" ] }, { "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

openseat/ipylayoutwidgets

notebooks/Fullscreen.ipynb

1

1771

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fullscreen IPython Notebook dashboards\n", "\n", "Because of the liberal use of `window.document` calls throughout IPython notebook calls, using `window.open` and trying to draw widgets into them is basically impossible.\n", "\n", "Thus, to achieve a presentation/presenter view (or man-behind-the curtain) of data driven widgets, a slightly more tortuous approach is needed" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from ipywidgets import widgets as W\n", "import traitlets as T\n", "from IPython.display import display as show\n", "\n", "from ipylayoutwidgets import widgets as DW" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "full = DW.FullscreenBox()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "full" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "full.children = [W.Button(description=\"Click Me!\")]" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

CivicKnowledge/metatab-packages

sandiego.gov/sandiego.gov-police_regions/notebooks/BeatPopulations.ipynb

1

116255

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Beat Populations\n", "\n", "Link census tract populations, total and by race, into police beats. Attributes population from tracts to beats by the areas of the overlaps. The basic procedure is to find the overlaps between beats and Census tracts, then addign a portion of the population of the tract to the beat, based on the raio of the size of overlap to the size of the tract. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import seaborn as sns\n", "import metapack as mp\n", "import pandas as pd\n", "import geopandas as gpd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from IPython.display import display \n", "\n", "%matplotlib inline\n", "sns.set_context('notebook')\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<h1>San Diego Police Regions</h1>\n", "<p><code>sandiego.gov-police_regions-1</code> Last Update: 2018-11-26T19:17:22</p>\n", "<p><em>Boundary shapes for San Diego neighborhoods, beats and divisions.</em></p>\n", "<h2>Documentation Links</h2>\n", "<ul>\n", "<li><a href=\"https://data.sandiego.gov/datasets/pd-divisions/\">Police Divisions Repository Page</a> Data repository page that links to original files.</li>\n", "</ul>\n", "<h2>Contacts</h2>\n", "<ul>\n", "<li><strong>Wrangler</strong> <a href=\"mailto:[email protected]\">Eric Busboom</a>, <a href=\"http://civicknowledge.com\">Civic Knowledge</a></li>\n", "</ul>\n", "<h2>Resources</h2>\n", "<ul>\n", "<li><strong> <a href=\"data/pd_beats.csv\">pd_beats</a></strong>. Police beats</li>\n", "<li><strong> <a href=\"data/pd_divisions.csv\">pd_divisions</a></strong>. Police Divisions</li>\n", "<li><strong> <a href=\"data/pd_neighborhoods.csv\">pd_neighborhoods</a></strong>. Police Neighborhoods</li>\n", "</ul>\n", "<h2>References</h2>\n", "<ul>\n", "<li><strong><a href=\"metapack+http://library.metatab.org/sandiegodata.org-communities-2018-7.csv#tracts\">tracts</a></strong>. </li>\n", "<li><strong><a href=\"census://CA/140/B02001\">race</a></strong>. Race, by tract, in San Diego county</li>\n", "</ul>" ], "text/plain": [ "<metapack.doc.MetapackDoc at 0x11aba5e80>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pkg = mp.jupyter.open_package()\n", "#pkg = mp.jupyter.open_source_package()\n", "pkg" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11adb80f0>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11acd8908>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "beats = pkg.resource('pd_beats').geoframe()\n", "\n", "# There are beats that are way off in east county. Get rid of them.\n", "rightmost_centroid = beats.centroid.x.sort_values(ascending=False).iloc[:6].max()\n", "\n", "beats = beats[beats.centroid.x <rightmost_centroid]\n", "\n", "# Convert to EPSG:26911, ( A randomly selected UTM Zone 11N CRS) so area calculations \n", "# will be in square meters, rather than square degrees\n", "beats = beats.to_crs({'init': 'epsg:26911'})\n", "\n", "# It looks like the dataset has multiple rows per beat, one feature per row. We need\n", "# it to have one row per beat, with multiple features combined together. \n", "beats = beats.dissolve(by='beat').reset_index()\n", "\n", "# Add the area\n", "beats['beat_area'] = beats.area / 1_000_000\n", "\n", "beats.plot()\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11ae62898>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11ae62f60>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tracts = pkg.reference('tracts').geoframe()\n", "\n", "tracts = tracts.to_crs({'init': 'epsg:26911'})\n", "\n", "# Add the area\n", "tracts['tract_area'] = tracts.area / 1_000_000\n", "\n", "\n", "tracts.plot()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from rowgenerators import parse_app_url\n", "t = parse_app_url('census://CA/140/B03002').dataframe()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# White, black, asian, etc are all non hispanic. \n", "col_map = {\n", " 'B03002_001':'total',\n", " 'B03002_003':'white',\n", " 'B03002_004':'black',\n", " 'B03002_005':'aian',\n", " 'B03002_006':'asian',\n", " 'B03002_007':'nhopi', \n", " 'B03002_012':'hisp'\n", " \n", "}\n", "\n", "for k,v in list(col_map.items()):\n", " col_map[k+'_m90'] = col_map[k]+'_m90'\n", " \n", "race_tracts = t[t.COUNTY=='073'].rename(columns=col_map).reset_index().rename(columns={'GEOID':'geoid'})" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>GEOID</th>\n", " <td>14000US06073000100</td>\n", " <td>14000US06073000201</td>\n", " <td>14000US06073000202</td>\n", " <td>14000US06073000300</td>\n", " <td>14000US06073000400</td>\n", " </tr>\n", " <tr>\n", " <th>total</th>\n", " <td>2773</td>\n", " <td>2158</td>\n", " <td>4828</td>\n", " <td>4946</td>\n", " <td>3916</td>\n", " </tr>\n", " <tr>\n", " <th>total_m90</th>\n", " <td>185</td>\n", " <td>241</td>\n", " <td>415</td>\n", " <td>405</td>\n", " <td>306</td>\n", " </tr>\n", " <tr>\n", " <th>white</th>\n", " <td>2276</td>\n", " <td>1628</td>\n", " <td>3477</td>\n", " <td>3437</td>\n", " <td>2655</td>\n", " </tr>\n", " <tr>\n", " <th>white_m90</th>\n", " <td>222</td>\n", " <td>236</td>\n", " <td>417</td>\n", " <td>326</td>\n", " <td>331</td>\n", " </tr>\n", " <tr>\n", " <th>black</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>37</td>\n", " <td>177</td>\n", " <td>52</td>\n", " </tr>\n", " <tr>\n", " <th>black_m90</th>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>50</td>\n", " <td>138</td>\n", " <td>66</td>\n", " </tr>\n", " <tr>\n", " <th>aian</th>\n", " <td>0</td>\n", " <td>8</td>\n", " <td>39</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>aian_m90</th>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>56</td>\n", " <td>12</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>asian</th>\n", " <td>84</td>\n", " <td>84</td>\n", " <td>368</td>\n", " <td>196</td>\n", " <td>475</td>\n", " </tr>\n", " <tr>\n", " <th>asian_m90</th>\n", " <td>51</td>\n", " <td>59</td>\n", " <td>218</td>\n", " <td>121</td>\n", " <td>210</td>\n", " </tr>\n", " <tr>\n", " <th>nhopi</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>8</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>nhopi_m90</th>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>16</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>hisp</th>\n", " <td>290</td>\n", " <td>389</td>\n", " <td>767</td>\n", " <td>886</td>\n", " <td>649</td>\n", " </tr>\n", " <tr>\n", " <th>hisp_m90</th>\n", " <td>102</td>\n", " <td>326</td>\n", " <td>205</td>\n", " <td>294</td>\n", " <td>293</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 \\\n", "GEOID 14000US06073000100 14000US06073000201 14000US06073000202 \n", "total 2773 2158 4828 \n", "total_m90 185 241 415 \n", "white 2276 1628 3477 \n", "white_m90 222 236 417 \n", "black 0 0 37 \n", "black_m90 12 12 50 \n", "aian 0 8 39 \n", "aian_m90 12 12 56 \n", "asian 84 84 368 \n", "asian_m90 51 59 218 \n", "nhopi 0 0 0 \n", "nhopi_m90 12 12 12 \n", "hisp 290 389 767 \n", "hisp_m90 102 326 205 \n", "\n", " 3 4 \n", "GEOID 14000US06073000300 14000US06073000400 \n", "total 4946 3916 \n", "total_m90 405 306 \n", "white 3437 2655 \n", "white_m90 326 331 \n", "black 177 52 \n", "black_m90 138 66 \n", "aian 0 0 \n", "aian_m90 12 12 \n", "asian 196 475 \n", "asian_m90 121 210 \n", "nhopi 8 0 \n", "nhopi_m90 16 12 \n", "hisp 886 649 \n", "hisp_m90 294 293 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "race_tracts = race_tracts[['geoid', 'total', 'white', 'black', 'aian', 'asian', 'nhopi', 'hisp']]\n", "race_tracts.titles.head().T" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11fe13f60>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "t = gpd.sjoin(beats, tracts)\n", "\n", "ax = t.plot()\n", "beats.centroid.plot(ax=ax, color='red')\n", "\n", "t = t[['geoid', 'beat']].drop_duplicates()\\\n", " .merge(tracts[['geoid','geometry', 'tract_area']],on='geoid')\\\n", " .merge(beats[['beat','geometry', 'beat_area']],on='beat')\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>beat</th>\n", " <td>0</td>\n", " <td>721</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>511</td>\n", " </tr>\n", " <tr>\n", " <th>geoid</th>\n", " <td>14000US06073021900</td>\n", " <td>14000US06073021900</td>\n", " <td>14000US06073021600</td>\n", " <td>14000US06073003800</td>\n", " <td>14000US06073003800</td>\n", " </tr>\n", " <tr>\n", " <th>intr_area</th>\n", " <td>0.183666</td>\n", " <td>0.0228637</td>\n", " <td>0.645752</td>\n", " <td>0.0366767</td>\n", " <td>1.77264</td>\n", " </tr>\n", " <tr>\n", " <th>tract_area</th>\n", " <td>10.6162</td>\n", " <td>10.6162</td>\n", " <td>15.2322</td>\n", " <td>1.82267</td>\n", " <td>1.82267</td>\n", " </tr>\n", " <tr>\n", " <th>beat_area</th>\n", " <td>18.2475</td>\n", " <td>7.63003</td>\n", " <td>18.2475</td>\n", " <td>18.2475</td>\n", " <td>6.80108</td>\n", " </tr>\n", " <tr>\n", " <th>total</th>\n", " <td>90.741</td>\n", " <td>11.2959</td>\n", " <td>155.544</td>\n", " <td>133.613</td>\n", " <td>6457.75</td>\n", " </tr>\n", " <tr>\n", " <th>total_m90</th>\n", " <td>1284</td>\n", " <td>1284</td>\n", " <td>376</td>\n", " <td>1040</td>\n", " <td>1040</td>\n", " </tr>\n", " <tr>\n", " <th>white</th>\n", " <td>29.4973</td>\n", " <td>3.67198</td>\n", " <td>105.137</td>\n", " <td>68.8994</td>\n", " <td>3330.02</td>\n", " </tr>\n", " <tr>\n", " <th>white_m90</th>\n", " <td>690</td>\n", " <td>690</td>\n", " <td>281</td>\n", " <td>468</td>\n", " <td>468</td>\n", " </tr>\n", " <tr>\n", " <th>black</th>\n", " <td>14.6016</td>\n", " <td>1.81769</td>\n", " <td>8.05486</td>\n", " <td>26.6422</td>\n", " <td>1287.66</td>\n", " </tr>\n", " <tr>\n", " <th>black_m90</th>\n", " <td>384</td>\n", " <td>384</td>\n", " <td>109</td>\n", " <td>328</td>\n", " <td>328</td>\n", " </tr>\n", " <tr>\n", " <th>aian</th>\n", " <td>0.899625</td>\n", " <td>0.11199</td>\n", " <td>0.127182</td>\n", " <td>1.04637</td>\n", " <td>50.5728</td>\n", " </tr>\n", " <tr>\n", " <th>aian_m90</th>\n", " <td>69</td>\n", " <td>69</td>\n", " <td>6</td>\n", " <td>54</td>\n", " <td>54</td>\n", " </tr>\n", " <tr>\n", " <th>asian</th>\n", " <td>9.39416</td>\n", " <td>1.16944</td>\n", " <td>5.08728</td>\n", " <td>10.5039</td>\n", " <td>507.673</td>\n", " </tr>\n", " <tr>\n", " <th>asian_m90</th>\n", " <td>293</td>\n", " <td>293</td>\n", " <td>72</td>\n", " <td>188</td>\n", " <td>188</td>\n", " </tr>\n", " <tr>\n", " <th>nhopi</th>\n", " <td>0.70932</td>\n", " <td>0.0882999</td>\n", " <td>1.05985</td>\n", " <td>0.523185</td>\n", " <td>25.2864</td>\n", " </tr>\n", " <tr>\n", " <th>nhopi_m90</th>\n", " <td>42</td>\n", " <td>42</td>\n", " <td>27</td>\n", " <td>29</td>\n", " <td>29</td>\n", " </tr>\n", " <tr>\n", " <th>hisp</th>\n", " <td>34.255</td>\n", " <td>4.26424</td>\n", " <td>31.8803</td>\n", " <td>24.5696</td>\n", " <td>1187.49</td>\n", " </tr>\n", " <tr>\n", " <th>hisp_m90</th>\n", " <td>352</td>\n", " <td>352</td>\n", " <td>248</td>\n", " <td>394</td>\n", " <td>394</td>\n", " </tr>\n", " <tr>\n", " <th>tract_overlap_proportion</th>\n", " <td>0.0173005</td>\n", " <td>0.00215366</td>\n", " <td>0.042394</td>\n", " <td>0.0201225</td>\n", " <td>0.972553</td>\n", " </tr>\n", " <tr>\n", " <th>beat_overlap_proportion</th>\n", " <td>0.0100653</td>\n", " <td>0.00299654</td>\n", " <td>0.0353886</td>\n", " <td>0.00200996</td>\n", " <td>0.260641</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 \\\n", "beat 0 721 \n", "geoid 14000US06073021900 14000US06073021900 \n", "intr_area 0.183666 0.0228637 \n", "tract_area 10.6162 10.6162 \n", "beat_area 18.2475 7.63003 \n", "total 90.741 11.2959 \n", "total_m90 1284 1284 \n", "white 29.4973 3.67198 \n", "white_m90 690 690 \n", "black 14.6016 1.81769 \n", "black_m90 384 384 \n", "aian 0.899625 0.11199 \n", "aian_m90 69 69 \n", "asian 9.39416 1.16944 \n", "asian_m90 293 293 \n", "nhopi 0.70932 0.0882999 \n", "nhopi_m90 42 42 \n", "hisp 34.255 4.26424 \n", "hisp_m90 352 352 \n", "tract_overlap_proportion 0.0173005 0.00215366 \n", "beat_overlap_proportion 0.0100653 0.00299654 \n", "\n", " 2 3 \\\n", "beat 0 0 \n", "geoid 14000US06073021600 14000US06073003800 \n", "intr_area 0.645752 0.0366767 \n", "tract_area 15.2322 1.82267 \n", "beat_area 18.2475 18.2475 \n", "total 155.544 133.613 \n", "total_m90 376 1040 \n", "white 105.137 68.8994 \n", "white_m90 281 468 \n", "black 8.05486 26.6422 \n", "black_m90 109 328 \n", "aian 0.127182 1.04637 \n", "aian_m90 6 54 \n", "asian 5.08728 10.5039 \n", "asian_m90 72 188 \n", "nhopi 1.05985 0.523185 \n", "nhopi_m90 27 29 \n", "hisp 31.8803 24.5696 \n", "hisp_m90 248 394 \n", "tract_overlap_proportion 0.042394 0.0201225 \n", "beat_overlap_proportion 0.0353886 0.00200996 \n", "\n", " 4 \n", "beat 511 \n", "geoid 14000US06073003800 \n", "intr_area 1.77264 \n", "tract_area 1.82267 \n", "beat_area 6.80108 \n", "total 6457.75 \n", "total_m90 1040 \n", "white 3330.02 \n", "white_m90 468 \n", "black 1287.66 \n", "black_m90 328 \n", "aian 50.5728 \n", "aian_m90 54 \n", "asian 507.673 \n", "asian_m90 188 \n", "nhopi 25.2864 \n", "nhopi_m90 29 \n", "hisp 1187.49 \n", "hisp_m90 394 \n", "tract_overlap_proportion 0.972553 \n", "beat_overlap_proportion 0.260641 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "intr = gpd.overlay(beats, tracts, how='intersection')[['beat','geoid','geometry']]\n", "\n", "intr['intr_area'] = (intr.geometry.area/1_000_000.0).astype(float)\n", "\n", "# Get rid of really small intersections\n", "intr = intr[intr.intr_area >= .01] \n", "\n", "merged = intr[['beat','geoid', 'intr_area']]\\\n", " .merge(tracts[['geoid', 'tract_area']],on='geoid')\\\n", " .merge(beats[['beat', 'beat_area']],on='beat')\\\n", " .merge(race_tracts, on='geoid')\n", "\n", "merged = merged.drop_duplicates(subset=['beat','geoid'])\n", "\n", "merged['tract_overlap_proportion'] = merged.intr_area/merged.tract_area\n", "merged['beat_overlap_proportion'] = merged.intr_area/merged.beat_area\n", "\n", "# The intersection areas must be smaller than both of the areas being intersected\n", "assert(not any(merged.intr_area > merged.beat_area))\n", "assert(not any(merged.intr_area > merged.tract_area))\n", "\n", "# Check that all of the areas of the beats are accounted for\n", "assert(all(merged.groupby('beat').beat_overlap_proportion.sum().round(1) == 1))\n", "\n", "merged['total'] = merged.total * merged.tract_overlap_proportion\n", "merged['white'] = merged.white * merged.tract_overlap_proportion\n", "merged['asian'] = merged.asian * merged.tract_overlap_proportion\n", "merged['black'] = merged.black * merged.tract_overlap_proportion\n", "merged['aian'] = merged.aian * merged.tract_overlap_proportion\n", "merged['hisp'] = merged.hisp * merged.tract_overlap_proportion\n", "merged['nhopi'] = merged.nhopi * merged.tract_overlap_proportion\n", "\n", "merged.head().T\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "beat_demographics = merged.groupby('beat').sum()[['total', 'white', 'black', 'aian', 'asian', 'nhopi', 'hisp']].round()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

SJSlavin/phys202-2015-work

assignments/assignment12/FittingModelsEx01.ipynb

1

23983

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Fitting Models Exercise 1" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import scipy.optimize as opt" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Fitting a quadratic curve" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "For this problem we are going to work with the following model:\n", "\n", "$$ y_{model}(x) = a x^2 + b x + c $$\n", "\n", "The true values of the model parameters are as follows:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "a_true = 0.5\n", "b_true = 2.0\n", "c_true = -4.0" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "First, generate a dataset using this model using these parameters and the following characteristics:\n", "\n", "* For your $x$ data use 30 uniformly spaced points between $[-5,5]$.\n", "* Add a noise term to the $y$ value at each point that is drawn from a normal distribution with zero mean and standard deviation 2.0. Make sure you add a different random number to each point (see the `size` argument of `np.random.normal`).\n", "\n", "After you generate the data, make a plot of the raw data (use points)." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x7f31ad41e940>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f31ad44cac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# YOUR CODE HERE\n", "x = np.linspace(-5, 5, 30)\n", "y = a_true*(x**2) + b_true*(x) + [c_true]*30 + 2*np.random.randn(30)\n", "\n", "plt.scatter(x, y)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "3acfeb5975cc4a690bc60e56103ce367", "grade": true, "grade_id": "fittingmodelsex01a", "points": 5 } }, "outputs": [], "source": [ "assert True # leave this cell for grading the raw data generation and plot" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Now fit the model to the dataset to recover estimates for the model's parameters:\n", "\n", "* Print out the estimates and uncertainties of each parameter.\n", "* Plot the raw data and best fit of the model." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a = 0.507160302685 +- 0.00186315915396\n", "b = 2.11570569406 +- 0.0132333772754\n", "c = -4.07114682003 +- 0.265731331623\n" ] }, { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f31ad43ceb8>" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f31ad3c08d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# YOUR CODE HERE\n", "def model(x, a, b, c):\n", " return a*x**2 + b*x + c\n", "\n", "theta_best, theta_cov = opt.curve_fit(model, x, y, sigma=2)\n", "\n", "print(\"a = \", theta_best[0], \" +- \", theta_cov[0,0])\n", "print(\"b = \", theta_best[1], \" +- \", theta_cov[1,1])\n", "print(\"c = \", theta_best[2], \" +- \", theta_cov[2,2])\n", "\n", "fitline = theta_best[0]*x**2 + theta_best[1]*x + theta_best[2]\n", "\n", "plt.plot(x, fitline, color=\"r\")\n", "plt.scatter(x, y)\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "5c7b35cc43322f076fb2acf1cddfc759", "grade": true, "grade_id": "fittingmodelsex01b", "points": 5 } }, "outputs": [], "source": [ "assert True # leave this cell for grading the fit; should include a plot and printout of the parameters+errors" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

mne-tools/mne-tools.github.io

0.24/_downloads/0f76b41e089e6c251ddf58d56a4dcff7/read_xdf.ipynb

1

2460

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Reading XDF EEG data\n\nHere we read some sample XDF data. Although we do not analyze it here, this\nrecording is of a short parallel auditory response (pABR) experiment\n:footcite:`PolonenkoMaddox2019` and was provided by the `Maddox Lab\n<https://www.urmc.rochester.edu/labs/maddox.aspx>`__.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Authors: Clemens Brunner <[email protected]>\n# Eric Larson <[email protected]>\n#\n# License: BSD-3-Clause" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os.path as op\n\nimport pyxdf\n\nimport mne\nfrom mne.datasets import misc\n\nfname = op.join(\n misc.data_path(), 'xdf',\n 'sub-P001_ses-S004_task-Default_run-001_eeg_a2.xdf')\nstreams, header = pyxdf.load_xdf(fname)\ndata = streams[0][\"time_series\"].T\nassert data.shape[0] == 5 # four raw EEG plus one stim channel\ndata[:4:2] -= data[1:4:2] # subtract (rereference) to get two bipolar EEG\ndata = data[::2] # subselect\ndata[:2] *= (1e-6 / 50 / 2) # uV -> V and preamp gain\nsfreq = float(streams[0][\"info\"][\"nominal_srate\"][0])\ninfo = mne.create_info(3, sfreq, [\"eeg\", \"eeg\", \"stim\"])\nraw = mne.io.RawArray(data, info)\nraw.plot(scalings=dict(eeg=100e-6), duration=1, start=14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n.. footbibliography::\n\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

nimagh/MachineLearning

DimenstionalityReduction/DimensionalityReduction.ipynb

1

176414

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Principle Component Analysis\n", "Comparing different methods to reduce dimensionsionality of data; from orthogonal projections in conventional PCA to Gaussian Process Latent Variable Model.\n", "\n", "Say we have N of D dimensional data points so our dataset will be $X \\in R^{D \\times N}$ with covariance $\\Sigma \\in R^{D \\times D}$. We want the best ideas to reduce dimensionality of our dataset so that in the end we have $X' \\in R^{q \\times N}$ where $q << D$.\n", "\n", "We will go through Conventional PCA based on projection of the data into its principle subspace, then we will see Probbilistic PCA and finally we will see a python implementation of Gaussian Process Latent Variable Model (GPLVM)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conventional PCA\n", "One idea is to project all the points into a direction which retains the most variance in our dataset, and that was the original idea of **PCA**.\n", "\n", "If you feel comfortable with the covariance matrix and the related transformation it can apply to the data you can continue on. But if you want to get an image of the idea take a look [here](http://www.visiondummy.com/2014/04/geometric-interpretation-covariance-matrix/).\n", "\n", "In 2D space we know that any data can be made by transformation of the white data (drawn from 2D normal RV with mean zero and diagonal covariance matrix with diagonal elements or variances equal 1). This transformation includes a scaling **S** and a rotation **R**. So transformation $T = RS$. If the covariance of the white data is $\\Sigma = \\begin{bmatrix} 1 & 0 \\\\ 0 & 1 \\\\ \\end{bmatrix}$ and $D' = TD$ then $\\Sigma' = RSSR^{-1}$. On the other hand $\\Sigma' = VLV^{-1}$ where L is the diagonal matrix with eigenvalues and each column of V includes the respective eigen vectors. After equating previous two relations eigenvectors V are seen as only rotations and eigenvalues L are the scale in that direction. If we wanted to capture just the highest amont of variance in our data then this unit vector will be the eigenvector corresponding to the largest eigenvalue of the covariance matrix of the original dataset. So if that direction can be shown by a vector $U \\in R^{D \\times 1}$ (q=1) then $ X' = U^TX$ and $ X' \\in R^{1 \\times N} $. This statement can be more formarly followed in *Strang Linear Algebra page 475; ISBN: 9780980232714*. To preserve more than 1 dimension we can either sequentially find the largest eigenvector and falt out data in that direction by subtracting it from each datapoint and continue this procedure upto the desired dimension or equally take the first largest eigenvalues and the corresponding eigenvectors and transform data in their direction.\n", "\n", "Lets have an example:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "IPython.notebook.set_autosave_interval(0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Autosave disabled\n" ] } ], "source": [ "import numpy as np\n", "\n", "import matplotlib.pyplot as plt\n", "from scipy.optimize import minimize\n", "from scipy.io import loadmat\n", "from scipy.stats import multivariate_normal\n", "\n", "\n", "from scipy.linalg import det\n", "from scipy.linalg import pinv2 as inv #pinv uses linalg.lstsq algorithm while pinv2 uses SVD\n", "from scipy.linalg import sqrtm\n", "from numpy.linalg import svd\n", "from scipy.linalg import norm\n", "\n", "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2\n", "%autosave 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will work with [MNIST](http://yann.lecun.com/exdb/mnist/) handwritten digits dataset. We will work with zeros and ones. Dataset includes ~6K images of $28 \\times 28 = 784$ pixels for each digit. Through out the note we will reduce the dimensionality to only 2 dimensions and we will try to classify zeros and ones in the visualizable 2D space and measure the performance based on highest accuracy achievable." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x95474e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mnist_train = loadmat('mnist_train.mat')['train']\n", "X0 = mnist_train[0,9]\n", "X1 = mnist_train[0,0]\n", "X = np.hstack([X0,X1])\n", "\n", "sample0 = X0[:,0].reshape((28,28))\n", "sample1 = X1[:,0].reshape((28,28))\n", "f, axarr = plt.subplots(1,2, sharex=True)\n", "axarr[0].imshow(sample0,cmap='gray');axarr[0].set_title('Sample 0')\n", "axarr[1].imshow(sample1,cmap='gray');axarr[1].set_title('Sample 1')\n", "axarr[0].axis('off')\n", "axarr[1].axis('off')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### PCA code:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "code_folding": [ 0 ], "collapsed": false }, "outputs": [], "source": [ "# Conventional PCA computations\n", "def computeCovariance(X):\n", " '''computing the covariance function'''\n", " # center data and compute the covariances\n", " N = X.shape[1];\n", " originalD = X.shape[0] #784 is the original dimension\n", " M = X.mean(axis = 1).reshape(originalD,1) #mean of all pixels in the dataset\n", " centered_X = X-M\n", " COV = (np.dot((X-M),(X-M).T.conj()))/(N-1)\n", " return COV\n", " \n", "def PCA(X,newD=2):\n", " ''' newD: we want to show all data in 2D\n", " D_ld: low dimenstional data\n", " '''\n", " #COV = computeCovariance(X)\n", " COV = np.cov(X)\n", " U,S,V = svd(COV)\n", "\n", " X_ld = np.dot(U[:,0:newD].T,X)\n", " return U,X_ld" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Visualization of the results" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "code_folding": [ 0 ], "collapsed": false }, "outputs": [], "source": [ "#visualization of low dimensional data\n", "def visualize_lowD_01(D_ld,N0,N1):\n", " D0_ld = D_ld[:,0:N0]\n", " D1_ld = D_ld[:,N0+1:]\n", " f, ax = plt.subplots()\n", "\n", " ax.plot(D0_ld[0,:],D0_ld[1,:],'b.')\n", " ax.plot(D1_ld[0,:],D1_ld[1,:],'r.')\n", " ax.set_title('0:blue, 1:red')\n", "\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AFVL6pPLz55OxnI2tr3sdZSZxaA7vr2b5tCmJXZT9NipPQc0n2LHDj+zh+Pg9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\nxx0+8Q4vV7ZtI/3UNPtccIHfy3btCi9X2WayZQudN5/37SC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"text/plain": [ "<matplotlib.figure.Figure at 0x9838ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m = 2 # project data into 2D\n", "\n", "N = X.shape[1];N0 = X0.shape[1];N1 = X1.shape[1]\n", "d = X.shape[0];\n", "Xmu = np.mean(X, axis=1).reshape(d,-1)\n", "\n", "U, _ = PCA(X)\n", "Xpca = np.dot(U[:,0:m].T,X)\n", "\n", "visualize_lowD_01(Xpca,N0,N1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Two clusters of data points should be visible in the above plot.We check how easily we can classify using MAP estimator assuimg equally probable classes with bivarite normal distribution:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "code_folding": [ 0 ], "collapsed": false }, "outputs": [], "source": [ "# MAP classifier to check the results in 2D\n", "def MAP_01_classifier(X,N0,N1):\n", " '''If we have zeros of 1s and 0s how easy it can be to \n", " detect them with equiprobable bivariate normals. The higher the better'''\n", " X0 = X[:,0:N0]\n", " X1 = X[:,N0+1:]\n", " \n", " M0 = np.mean(X0,axis = 1)\n", " M1 = np.mean(X1,axis = 1)\n", "\n", " COV0 = np.cov(X0)\n", " COV1 = np.cov(X1)\n", "\n", " mvn0 = multivariate_normal(M0,COV0)\n", " mvn1 = multivariate_normal(M1,COV1)\n", "\n", " P00 = mvn0.pdf(X0.T)\n", " P10 = mvn1.pdf(X0.T)\n", " sumP = P00+P10\n", " estim_zero = P00/sumP > P10/sumP\n", " \n", "\n", " P01 = mvn0.pdf(X1.T)\n", " P11 = mvn1.pdf(X1.T)\n", " sumP = P01+P11\n", " estim_one = P01/sumP < P11/sumP\n", " \n", " acc0 = sum(estim_zero)/float(N0)\n", " acc1 = sum(estim_one)/float(N1)\n", " \n", " return acc0,acc1" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.99831166638527769, 0.98976564817561552)\n" ] } ], "source": [ "print MAP_01_classifier(Xpca,N0,N1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We reconstruct the data from this low dimentional information and see how good the result might be:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "code_folding": [], "collapsed": false }, "outputs": [], "source": [ "# distance between the reconstructed image and the original in average\n", "def reconstruction_err(D,D_hat): return np.mean(np.sqrt(np.sum((D-D_hat)**2,axis=0)))/np.mean(np.sqrt(np.sum((D)**2,axis=0)))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20.1627719454\n" ] } ], "source": [ "Xpca_hat = np.dot(U[:,0:2],Xpca)\n", "\n", "print reconstruction_err(X,Xpca_hat)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "code_folding": [ 0 ], "collapsed": false }, "outputs": [], "source": [ "#visualization of 2D low dimentional space\n", "def visualize_reconstructed_01(D,D_hat,N0,N1):\n", " sample0 = D[:,0].reshape((28,28))\n", " sample1 = D[:,N0+1].reshape((28,28))\n", " sample0_hat = D_hat[:,0].reshape((28,28))\n", " sample1_hat = D_hat[:,N0+1].reshape((28,28))\n", " f, axarr = plt.subplots(2,2)\n", " axarr[0,0].imshow(sample0,cmap='gray');axarr[0,0].set_title('Sample 0')\n", " axarr[0,1].imshow(sample1,cmap='gray');axarr[0,1].set_title('Sample 1')\n", " axarr[1,0].imshow(sample0_hat,cmap='gray');axarr[1,0].set_title('Reconstruction')\n", " axarr[1,1].imshow(sample1_hat,cmap='gray');axarr[1,1].set_title('Reconstruction')\n", "\n", " axarr[0,0].axis('off')\n", " axarr[0,1].axis('off')\n", " axarr[1,0].axis('off')\n", " axarr[1,1].axis('off')\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x9ca94e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "visualize_reconstructed_01(X,Xpca_hat,N0,N1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Probabilistic Principle Component Analysis - PPCA\n", "Conventional PCA can be seen as a projection while PPCA is a mapping. This mapping is from the latent space into data sapce: $ X =WZ+\\epsilon $ where $Z$ is considered M dimentional latent vriable and $\\epsilon$ is zero-mean isotropic noise. Implementations here closely flow PRML book by bishop pages [587-596]." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "code_folding": [ 0, 1, 23, 43 ], "collapsed": false }, "outputs": [], "source": [ "# Probabilistic Principal Component Analysis, Tipping and Bishop 1999\n", "def compute_E_completeDataLL(X,W,s2):\n", " d, N = X.shape\n", " m = W.shape[1]\n", " \n", " M = np.dot(W.T,W)+s2*np.eye(m)\n", " Minv = inv(M)\n", "\n", " ll = 0.0\n", " for n in xrange(N):\n", " Xn = X[:, n].reshape(-1,1)\n", " \n", " Ezn = np.dot(np.dot(Minv,W.T),Xn)\n", " Ezzn = (s2)*Minv+np.dot(Ezn,Ezn.T)\n", " ll += 0.5 * np.trace(Ezzn)\n", " ll += 0.5 * d * np.log(2*np.pi*s2)\n", " ll += 0.5 * np.dot(Xn.T,Xn)/2.\n", " ll -= np.dot(np.dot(Ezn.T,W.T),Xn)/float(s2)\n", " ll += 0.5 * np.trace(np.dot(Ezzn,np.dot(W.T,W)))/float(s2)\n", " \n", " ll *= -1.0\n", "\n", " return ll/N\n", "def compute_Wnew(X,W,s2):\n", " d, N = X.shape\n", " m = W.shape[1]\n", " \n", " M = np.dot(W.T,W)+s2*np.eye(m)\n", " Minv = inv(M)\n", "\n", " term1, term2 = 0.,0.\n", " for n in xrange(N):\n", " Xn = X[:, n].reshape(-1,1)\n", " \n", " Ezn = np.dot(np.dot(Minv,W.T),Xn)\n", " Ezzn = (s2)*Minv+np.dot(Ezn,Ezn.T)\n", " \n", " term1 += np.dot(Xn,Ezn.T)\n", " term2 += Ezzn\n", " \n", " Wnew = np.dot(term1,term2)\n", " return Wnew\n", " \n", "def compute_s2new(X,W,s2):\n", " d, N = X.shape\n", " m = W.shape[1]\n", " \n", " M = np.dot(W.T,W)+s2*np.eye(m)\n", " Minv = inv(M)\n", "\n", " s2new = 0.\n", " for n in xrange(N):\n", " Xn = X[:, n].reshape(-1,1)\n", " \n", " Ezn = np.dot(np.dot(Minv,W.T),Xn)\n", " Ezzn = (s2)*Minv+np.dot(Ezn,Ezn.T)\n", " \n", " s2new += np.dot(Xn.T,Xn)\n", " s2new -= 2*np.dot(Ezn.T,np.dot(W.T,Xn))\n", " s2new += np.trace(np.dot(Ezzn,np.dot(W.T,W)))\n", " \n", " s2new = s2new/float(N*d)\n", " return s2new\n", "def ppca(X,mode = 'ML',m=2,em_maxit=200,tol=1e-4):\n", " '''X: observed variable D*N\n", " Z: latent variable M*N\n", " m: # output dimension\n", " W: D*m\n", " M: m*m\n", " '''\n", " d, N = X.shape\n", " X = X - np.mean(X, axis=1).reshape(d,-1)\n", " I = np.eye(m);\n", "\n", " if mode == 'ML':\n", " \n", " S = np.cov(X)\n", " U,L,_ = svd(S)\n", "\n", " Um = U[:,0:m]\n", " Lm = L[0:m]*I# to diagonalize it\n", " \n", " sigma2 = np.sum(L[m+1:])/(d-m)\n", "\n", " Lm_temp = np.sqrt(np.maximum(0, Lm- sigma2*I))#sqrtm\n", " W = np.dot(Um,Lm_temp)\n", "\n", " M = np.dot(W.T,W)+sigma2*I\n", " \n", " muML = np.dot(inv(M).dot(W.T),X)\n", " sigmaML = (sigma2**-1)*M\n", " \n", " return W,muML,sigmaML\n", " if mode == 'EM':\n", " W = np.random.normal(size=(d,m)) \n", " s2 = np.random.rand()\n", " S = np.cov(X)\n", " Ellh = compute_E_completeDataLL(X,W,s2)\n", " for i in np.arange(em_maxit):\n", " \n", " M = W.T.dot(W)+s2*np.eye(m)\n", " Minv = inv(M)\n", "\n", " temp = inv(s2*np.eye(m)+Minv.dot(W.T).dot(S).dot(W))\n", " Wnew = S.dot(W).dot(temp)\n", " #Wnew = compute_Wnew(X,W,s2)\n", " s2new = np.trace(S - S.dot(W).dot(Minv).dot(Wnew.T))/d\n", " #s2new = compute_s2new(X,W,s2)\n", " \n", " Ellhnew = compute_E_completeDataLL(X,Wnew,s2new)\n", " \n", " W = Wnew\n", " s2 = s2new\n", " \n", " if abs(Ellhnew-Ellh) < tol*abs(Ellh):\n", " print '<LL> Converged at %d Iteration.'%i \n", " break; \n", " elif i%10==0:\n", " print 'Iter #%d, Diff(<LL>) = %2.2f'%(i,abs(Ellhnew-Ellh)) \n", " \n", " Ellh = Ellhnew\n", " \n", " M = np.dot(W.T,W)+s2*np.eye(m)\n", " Minv = inv(M)\n", " \n", " muEM = np.dot(np.dot(Minv,W.T),X)\n", " sigmaEM = (s2**-1)*M\n", " return W,muEM,sigmaEM,s2" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iter #0, Diff(<LL>) = 70947.90\n", "Iter #10, Diff(<LL>) = 1.19\n", "Iter #20, Diff(<LL>) = 0.36\n", "Iter #30, Diff(<LL>) = 0.09\n", "<LL> Converged at 32 Iteration.\n" ] } ], "source": [ "wML,muML,sigmaML = ppca(X,m=2, mode='ML')\n", "wEM,muEM,sigmaEM,s2 = ppca(X,m=2,mode='EM',tol=1e-7)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x4a3bdd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Xppca_hat = wEM.dot(muEM)\n", "visualize_reconstructed_01(X,Xppca_hat,N0,N1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can generate new images with our generative model $X = Wz + \\epsilon $" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "code_folding": [ 0 ], "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xa1969b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Generating new images\n", "z = np.random.normal(size=(m,N)) \n", "epsi = np.random.multivariate_normal(Xmu[:,0],s2*np.eye(d)).reshape(-1,1)\n", "Xppca_generated_image = wEM.dot(z)+epsi\n", "visualize_reconstructed_01(X,Xppca_generated_image,N0,N1)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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l2Qi0o2krq2tXioW6MNOBIMWFbVW6YQNwww2BCeeOO5hxk8uxcxdIvrJtJ9u8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\nk7E0UrokkGZxwtP1ext0PRWS+g886oO3sc8HfO5zHMHRDcj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"text/plain": [ "<matplotlib.figure.Figure at 0x19c06f28>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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l2Qi0o2krq2tXioW6MNOBIMWFbVW6YQNwww2BCeeOO5hxk8uxcxdIvrJtJ9u8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\nf23njSppZ26vm6fkuqIqHxWL7spRstIw9QezmbE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"text/plain": [ "<matplotlib.figure.Figure at 0x19c066a0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "(0.99831166638527769, 0.98976564817561552)\n", "(0.99831166638527769, 0.98961732423613169)\n", "(0.99831166638527769, 0.98976564817561552)\n" ] } ], "source": [ "#visualize_lowD_01(muML,N0,N1)\n", "visualize_lowD_01(muEM,N0,N1)\n", "visualize_lowD_01(muEM,N0,N1)\n", "\n", "print MAP_01_classifier(muML,N0,N1)\n", "print MAP_01_classifier(muEM,N0,N1)\n", "print MAP_01_classifier(Xpca,N0,N1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We observe that the converged PPCA with EM algorithems visually looks the same as ML PPCA." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gaussian Process Latent Variable Model\n", "Here we will start our investigation of GPLVM with simple linear covariance kernel function.\n", "A matlab implementation is available in the directory but here we will use a much stable python implementation in GPy for demonstrating GPLVM." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import GPy \n", "\n", "q = 2\n", "k = GPy.kern.RBF(q, ARD=True) #+ kern.Linear(q, ARD=True) + kern.White(q, np.exp(-2)) + kern.Bias(q)\n", "\n", "Xsubset = np.hstack([X0[:,0:50],X1[:,0:50]])\n", "\n", "GPmodel = GPy.models.GPLVM(Xsubset.T, q, init=\"PCA\", kernel=k)#first 200 training points\n", "\n", "GPmodel.optimize('bfgs', messages=1, max_iters=2e10, gtol=.05)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xa181860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Xgplvm = GPmodel.latent_mean\n", "visualize_lowD_01(Xgplvm.T,50,50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What has happened is that we made a dual form of PPCA but put a prior on parameters and marginilized the latent space. The we maximized the likelihood with respect to latent space and GP paramters. Next we visualize the results. The kernel for the GP has the size of $N \\times N$ which seemingly didn't fit to our RAM so we only try simple GPLVM on 200 randomly selected subset of trainig set." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**References**\n", "* Christopher Bishop, Pattern Recognition and Machine Learning \n", "* Tipping, M. E., & Bishop, C. M. (1999). Probabilistic Principal Component Analysis" ] } ], "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" }, "widgets": { "state": { "6049909232c549048dff4d042bcc9c02": { "views": [ { "cell_index": 27 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-2.0

Roc-J/Python_data_science

009matplotlib.ipynb

2

92147

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1.5.3'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import matplotlib\n", "matplotlib.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ">使用pyplot前，必须先导入Numpy库" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# 折线图\n", "def simple_line_plot(x,y,figure_no):\n", " plt.figure(figure_no)\n", " plt.plot(x,y)\n", " plt.xlabel('x values')\n", " plt.ylabel('y values')\n", " plt.title('Simple Line')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 绘制一个点图，点使用o,红色使用r\n", "def simple_dots(x,y,figure_no):\n", " plt.figure(figure_no)\n", " plt.plot(x,y,'or')\n", " plt.xlabel('x values')\n", " plt.ylabel('y values')\n", " plt.title('Simple Dots')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 绘制散点图\n", "def simple_scatter(x,y,figure_no):\n", " plt.figure(figure_no)\n", " plt.scatter(x,y)\n", " plt.xlabel('x values')\n", " plt.ylabel('y values')\n", " plt.title('Simple scatter')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 绘制带不同标签的颜色的散点图\n", "def scatter_with_color(x,y,labels,figure_no):\n", " plt.figure(figure_no)\n", " plt.scatter(x,y,c=labels)\n", " plt.xlabel('x values')\n", " plt.ylabel('y values')\n", " plt.title('Scatter with color')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x638a550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "if __name__==\"__main__\":\n", " plt.close('all')\n", " # x,y\n", " x = np.arange(1,100,dtype=float)\n", " y = np.array([np.power(xx,2) for xx in x])\n", " \n", " figure_no = 1\n", " simple_line_plot(x,y,figure_no)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x64f6970>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure_no +=1\n", "simple_dots(x,y,figure_no)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x636a250>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 绘制散点图\n", "x = np.random.uniform(size=100)\n", "y = np.random.uniform(size=100)\n", "\n", "figure_no+=1\n", "simple_scatter(x,y,figure_no)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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Wly9fnqioffzyyy+YzWYee2wGNWrU0DSTu1EtEDc1fPgoZs9eSGpqVfz8ztGu\nXT3+/PMPzX8plX/7/fffeWPgQPqlpuIFHAH2hYQQk6kb65NPpvDuu+/j41MCiyWBhQvn07lzZ60i\n56nr169TrkwZXjKb8cc+f9mPfn4si4ykcePGWsdTUC2QQumrrz6nbdsH2bdvH6GhofTv318VDxf0\n+OOP8/u8efy4di3FPD25ZLOxfP78W68fPXqU996bQFrac6SlBQKnePjhxwgOLkeJEiX4+uspbrsc\n8Z49e1ixYgUewM1pLr2AIE9PEhISNEym5BXNWyBCiAhgKvYL+jOklJOy2Kc38B5gAw5IKZ/OYp9C\n1QJR3IeUkt27d3Pt2jUaNWpEqVKlbr22ZMkS+vcfQ1JSr5vPAIlAR+AKfn4b2LdvF9WqVdMgec79\n8MOPjBr1JjZbNdLTT1FMJvIcGZwEIgMCiD51Si3A5SLybRivEEIH+Espk3JysjscLwZoD5wHdgN9\npJTHMu0TCvwGtJVSJgkhSkgpr2ZxLFVAFM3ExcVx7tw5qlevTrFixbL9vujoaBo0aI7J9CwQBHwI\njALs65r4+Kxi0qQ+vPzyy86I7RRWqxWDoQhm83NACcCKTjcNL48kqlSsyNz581X3lQtx6jBeIcQ8\nIUSAEMIPiAKOCCFG5+RkWWgKHJdSxkopM4D5wO3TqQ4BvrlZtLIqHoribFarlQ0bNrBo0SIuXrz4\nr9c+mTSJ2mFhPN2lC1UrVmTdunXZPm716tX55JOJ+PrOICBghuPZf6ZU9/Awut1iYEajEZvNBtxs\nYXjg5xfCjFmzOHLihCoeBcg9WyBCiP1SyvpCiH5AQ+At4G8pZd1cn1yIx4HOUsqhju2ngaZSypGZ\n9lmMvZXSCnvBGy+lXJ3FsVQLRHGKjIwMunXsyLG//6aoTke8lKxct46mTZsSFRXFQ02bMtBkIgA4\nDfxZpAhXEhLw9Mz+JcYrV64QHx/P6tVreP/9KRiNDfDySqBUqStERe2jaNGiTvv+nKFWrXrExJTA\nam0JnMNgWMzBg39TtWpVraMpt3H2RXQvIYQX8CjwtZQyQwiRV5/UWYW+/dieQCjwEFAR2CKEqJ1V\nN9q4ceNufR0eHk54eHgexVQKs59++okzu3czwGjEA3sz/Lmnn+ZQTAwxMTFU8PIiwGQC7Ctz2zIy\nuHr1KmXKlMn2OUqWLEnJkiWpX78+NWvWYNmylZQu3ZRRo152u+IBsHr1Mrp378XBgx8RFFSCn376\nVRUPFxHdfO41AAAgAElEQVQZGUlkZGSeHCs7BeR74AxwANgshAgB8uQaCPYFwypm2i6P/VrI7fvs\nkFLagDNCiGigGvD37QfLXEAUJa+cPXuWYJOJmxOQhADrL1wAoEaNGpzNyCARKAqcBDy9vXN1L0f3\n7t3p3r17LlNrq0KFCuzb9xc2m80lbhrMDSklK1as4PDhw4SFhdGjRw+3HvF4+x/X48fnfO6vexYQ\nKeWXwJeZnooVQuTV7c67gVBHUboA9AGeum2f/zmemyuEKIG9eJzKo/Mryj01b96c7/R6mhiN+AO7\nPD1p3KgRALVq1WL8Rx8x5q23KObtTYqULFqy5L66rwoyVyoeqampzJ8/n6SkJDp27MgDDzyQrfe9\nPmoU82fMoHJ6Omd9fFjRuzfTZ850clr3kJ1rIKWBiUBZKWUXIUQtoIWUcsZd35jdAPZhvF/wzzDe\nj4UQ44HdUspljn2mABGABZggpfw9i+OoayAu7PLlyyxYsICMjAwee+wxKlWqpHWk+/LhBx/wwQcf\n4CkEYWFhrFi79l9dVJcuXSI+Pp7Q0FA1I4ALSk5OpnmjRoj4eAIsFo54eLDgf/+jU6dOd31ffHw8\nNUNDeTEtDT2QDnyn17Nj3z6qV6+eL9mdzanDeIUQK4FZwBgpZT0hhCewT0pZJycndBZVQFxXXFwc\nTRs0oGxqKp42G8e9vdm4dSv16tXTOtp9SUtLIzU1lWLFirl1F0Zh9MUXXzDzrbd4LC0NARwH9lSq\nxLHTp+/6vkOHDtGlVSuGJCffem5uQAA/r1pFixYtnBs6nzh7Nt4SUsoF2G/iQ0ppwb58sqJky0cT\nJlAtMZHuaWl0NZtpkZLC26+9pnWs++br60vx4sVvFY/Y2Fjmz5/P2rVrHcNWFVd19coVghzFA6Ak\nkJCYeM/3VatWDfR6dgtBGrAfSPH0zHb3V0GXnQKSKoQojmN0lBCiOaDmDc9jv/76Kw9Uq0ZYSAgT\nJ0zQ/APpzJkzjBkzltdeG83ff/9nvMJ9uXrpEsWs//zNUQL7L7Q727BhA/Vq12bS0KE817Mnj0RE\nYLWqv6tcVYeOHYkyGLgAGIFIH597dl+B/Y+GdZs2cb5WLaZ6e3MiLIy1GzdSpEiRe763UJBS3vWB\n/d6PbdiLxjbs92TUvdf78vth/1bc08qVK2UxvV4+A3IIyIoGg5w8aZJmeU6ePCkDAopLD4+WEsKl\nwVBUbtiwIcfHmz1rlixnMMgRIF8FGWowyHHvvpuHifOXzWaTZUuWlA+DfMfxqOLnJ3/99Veto2Wb\nxWKRK1askHPnzpUnTpzQOk6+mD1rliwZFCQNPj6yV48eMjk5WetILsHx2Zmzz91s7WQfrVUbeADw\nyunJnPlw5wLybN++sivIcY7HAJANatXSLM8LL7wkdbqHJIxzPB6XTZq0zvHxbDabnPjBB7JYkSIy\nwGCQI196SWZkZORh4vxjNptlj65dpQ/IoiDLghwNsqW3t/z000+1jpctFotFRrRrJ0P8/WVDf38Z\naDDI1atXax1L0UhuCsg9xxoKIZ657amGjosuc3Pf/lEA/IoU4YwQ4BgEkAL4+flplicpKQWbzT/T\nM0VISTmZ4+MJIXh77FjeHjs29+E09sXUqURv3MhowANYCSwDLnt60qxZM23DZdPChQuJ+esvnklN\nxQP7mPjn+vcn7tIlraMpbiY710CaZHo8CIwD3PsuJxfzyuuvc8jfn7U6HZuBtQYD702cqFmep59+\nEoNhF/aJOS5gMGykf//bb88pnA7s2UM1kwlP7NMo1MX+Uxr38ce0bt3aaee1WCwkZxoJlBvnz5+n\ndEbGrRsjywOX1fTqSg7cs4BIKUdkegwBGgD+93qfkn2hoaHs3r+f1q+9Rt2RI1mzcSMdOnTQLE+X\nLl347rvPqVz5L8qVW8vo0YN4883XNcvjSmrWrcspvR4r9lEl0Z6edOveneEjRjjtnJ9NmYK/wUDJ\nYsVo2qABl25rKVy7do3IyEiOHj2areM1b96caE9PrmL/HrZ5eNC0YcO8D64UePe9HohjXqwoKaVL\n3UWj7gNR8kN6ejrdOnYkau9evHU69MWLE7l9O8HBwU453/r163mqe3f6GY0EABs8PdG3asUax1xG\n27Zto0uX7uh0JTCbrzBwYH+++eaLex53+vffM2rkSCxWK3Vr12bJypWULVvWKd+D4tqcfSPhUv6Z\n4FAH1AIWSCnfyskJnUUVEG2dOnWKPXv2UKZMGR588MECfaOdzWbj4MGDmM1m6tati6+vr9PONWHC\nBNa89x7tHcO6U4HvDQZupNqnfC9dujyXLz8EVAfS8PObw5IlP9OuXbtsfR9paWkYDIZ77usMCQkJ\nXL58mUqVKjn1Z6jcnbNn4/0009cWIFZKeS4nJ1MKlps30h05coTFCxZQ1cuLSzYbbbt14+f58wts\nEdHpdNSvXz9fzlWuXDku+vpiMxrRAXFAGceKhhaLhStXLmCfHg7AFykrcPz48WwVEJ1Op1nx+Pzz\nqbz99li8vIrg7W1jzZoVNHLML6a4D82XtM0rqgWSv6Kjo2nVtCmhJhPWjAwOAwOAYsA3wKvvv887\n77yTr5nS0tI4f/48ZcqU0eyDMa9lZGQQ0a4dJ/fvp5gQnLbZWLJyJQ8++CAAlSqFERtbG6gPJGMw\nzGHt2j9deh31/fv306pVO4zGZ7HPYXyYUqW2c/FiXIH9o8OVOaUFIoRI5r9rc4B98ImUUqoZ4wqx\nD959lwbJybR2FO3iwBagF1ABGPfuu+zZuZMFixbh4+Pj9DybNm2iZ/fueFitpNlszJwzh15PPOH0\n8zqbl5cXayIjWbt2LYmJibRs2ZKKFf9ZAWHp0oW0a9eZtLSdZGQk8fbbY126eIB9fimdrgr24gFQ\nm4SEJSQnJ6uJKN2MaoEoOdKtQwcM69dzc0agY8AeoDMwGwgAvL296fXii3z6+edOzWI0GqkQHEy3\npCSqYl8X4FeDgSMxMZQrV86p53YF6enpbN++nTFjxhMdHU1oaCg//TSDsLAwraNlaefOnbRv3x2j\ncSBgAE4TELCUxMSrqgWiAWdPpnjzJKWEEBVvPnJyMqXgeLR3b3YYDFwGrgBrgVjgB6ANkAw0MJuJ\nXLvW6Vni4uLwttm4ud5dMFDWyyvbw1rdnU6nY9CgF9i1S0dCwpPs3l2E1q3bkpKSonW0LDVv3pwX\nXxyEXj+dwMB5+PsvYdGiBap4uKF7FhAhRHchxHHs90ttwr464Uon51Jc3OAhQxg+ZgyLixdnYbFi\ntIiIwFOnIwT7hGmNAJNOR3A+tACCg4NJsVi47NhOAi6YzW635khOnTx5kqtXb2C1PgQEIWUz0tN9\nOXDggNbR7uiTTz5m376dLF78HSdPHqN9+/ZaR1JyIDvDeA8A7YB1UsoGjtUIn5ZSPpcfAbNLdWFp\nb8uWLfR67DH8jEYCPDy44OXFlp0786Ur5eeff+aloUMp7+3NebOZ/3vvPUa/+abTz+sK4uPjqVq1\nJunpwwEfwILB8D3bt691uzVXlPzn7PtA9kgpGzsKSQMppU0IcUBK6VL/Mt25gMyZM5fx4z/CbDbz\n/PODGDv2/5zSnN+2bRsLFy7C39+fYcNecMrNb8nJySxfvhyLxUKnTp0o5Rhymh9iY2M5evQolStX\nLjCrxWXXoEFDWbBgDampVfHzO0vbtnVYsmSh6hbKBiklaWlp6PV6raNowtkFZB3wKPAR9qUcLgNN\npJQuNdTDXQvIsmXLePLJgRiNDwPeGAyrGDduBA0bNmDixClYLFZefvl5evbsmavzLFmyhD59nsVk\naoinZyqBgbEcPPi3uvu4gJBSMm/ePPbvP0CNGtUZMGAAHh4e935jIffbb78xZNAg0tLTqR4aypKV\nK6lcubLWsfKVswuIH2DCfr2kHxAI/CKlvJaTEzqLuxaQJ5/sz4IFiUBjxzOnqFx5FxcvXsRkCgc8\nMRgimT17Gk/kYlhqWFgdjh+vx82bzjw9V/L22515//3xufwO8taJEyd467XXuBAfT4eICMa+9x5e\nXl5ax1IKoCNHjtCycWP6mEyUAXbodJyvVo1Dx45pHS1fOftO9KHA7467z+fk5CTKnQUG+iPEOf6p\nfSncuJGEydQS+81hYDR68OmnX+WqgBiNqcA/q6hZLH4kJbnWKJ3Lly/TqmlT6t64QRWbjflHj3Iu\nLo4Zc9Q/u7yyd+9eYmNjqVu3LlWrVr33GwqwXbt2UU2n42ZHbgubjYknTmAymQptd9b9ys4w3gBg\ntRBiixDiJSFEaWeHKkxGj34Vf/+D6HRrgUgMhvXUqVOTf9/DaUOIbI+4ztJTT/XGYFgHXAROYDDs\no1evx3J1zLy2YsUKyprNtLbZCAV6Go38PG+eWio2j7zyymgefLAzAwaMo06dRsyf/5vWkTQVHBzM\nRezzM4H9N8PXx0fNy3UfsjOd+3gpZW3gJaAssMlxXUTJA9WqVWP//t28+WYbXn21MVu2bGDixPcx\nGHZgvzVvPwbDet5++5VcneejjyYwbFhPypZdTWjoPn75ZaZT16/ICZ1OR+aV4G+WDXUhOPf27t3L\n9OmzMRqfIympJybTUwwc+Bxms1nraNn29VdfUSIwED9fX57t14+0tLRcHa9Tp0606NiR2f7+LPX3\nZ75ez4+zZql/b/ch23eiCyHKAE8AfYAiUsq6zgx2v9z1GsidbN26lUmTPsdisTBixPN07dpV60hO\nd/36derUrEnla9cobbGw12Cgx3PP8fmXX2odze0tXryYAQPGkZT0z2AMX9/POH06mjJlymiYLHuW\nLVvGc08+SS+jET9gua8vbZ99lq+/+y5Xx5VSsnbtWi5cuEDTpk2pWbMmiYmJvPXWWI4ciaZ588aM\nH/9uge7ScvZF9GHAk0BJ4A/gNynlkZyc7A7HjwCmYm8NzZBSTrrDfr2ABUBjKeXeLF4vUAWksDp/\n/jzvv/su5+Pi6NC1K8NHjECny133XUFy+fJlBj/zDHv37qVypUpMnzOHmjVr3vN9J0+epE6dRphM\nTwFlgEOULLmdCxfi3GK01ksvvMCJ77/n5tDPi8Da8uU5EReXp+cxm83Uq9eEU6d8MZsro9cfoXnz\nYNavX1VgWybOvogeAoySUu7PyQnuRtg79r8G2gPngd1CiD+llMdu288fGAHszOsMimspW7Ys3/34\no9YxXJLNZiOiXTv8oqPpabFw6upV2rZuzdETJwgKCrrre6tWrcrMmd8zcOBzgCdFihhYvXq5WxQP\ngJKlS7PbywsyMgD79DnFS5TI8/Ps3r2b+PhEzOaBgMBkCmPHji+Ji4v71ySWil12roG85Yzi4dAU\nOC6ljJVSZgDzgR5Z7PcBMAlId1IOt7N69Wq6dexIt44dWb16tdZxlHxw/vx5Tp06RXuLheJAEykJ\nsljYtWtXtt7fp8+TJCZe49Spo1y4EEeDBg2cGzgPjRg5kiulSrHIYGCVjw/r/Pz47Ouv8/w89l6M\n//4xrno3spadFogzlcO+Rs5N57AXlVuEEPWB8lLKFUKI0fkZzlWtXr2apx57jDYmEwBPbdvGr4sX\n07lzZ42TKc5kMBgwWyykAXrsgwySbTb8/f2zfQwfHx+nLb/rTMWLF2dfVBS//fYbJpOJrl27OmWK\nnCZNmhAcXIT09NWYzZXx9T1CkyaNVevjDrQuIFn1u90q9cLe6fg58Ow93gPAuHHjbn0dHh5OeHh4\nrgO6oq+mTKGNycStNfFMJr6aMkUVkAKuWLFiDB4yhF/nzCEsNZVzej01GjSgefPmWkfLF0WLFuX5\n55936jl8fHzYsWMTo0f/H0ePRtOsWUc+/HB8gbr+ERkZSWRkZJ4cKzsX0Ydjv/P8ep6c8d/Hbg6M\nk1JGOLbfwr5Y1STHdgBwAkjBXjjKANeA7rdfSC9MF9Ef6dwZzzVrbhWQ/YClUyeWqq6sAu/mlCW7\n//qL0LAwhg4dire3t9axFDfm7FFYE7AP3d0LzARW59UntRDCA4jGfhH9ArALeEpKmeVCDkKIjcCr\nUsp9WbxWaArImjVrePLRRwl3dGFtMhiYv3gxnTp10jiZoijuxqkLSkkpx2KfQGkG9mWvjwshJgoh\ncj0PgpTSCgwH1gCHgflSyqNCiPFCiIezegt36cIqLDp16sRv//sflk6dsHTqpIqHoiiauJ8bCesB\nA4EIYCPQHFgrpXzDefGyrzC1QBRFcb6rV68ycOBQ/vprFxUqVGT27O+pU6eO1rHynLO7sEZiv4h9\nFfgR+J+UMsNxD8dxKaVLzMhWkArI1atXOXfuHJUrVyYwMFDrOIpS6Egpady4BYcO6cjIaAycITBw\nB8ePH6FkyZJax8tTzl4TvQTQU0rZWUr5u+N+DaSUNiCrbiYlF2bMmEmFCpVp06Y75cqFqHs8FEUD\n165dIyoqioyMjkBxoBFSlmH79u1aR3Mp9xzGK6V89y6vZXmxW8mZM2fOMGLEq6SlDSAtrQQQy+OP\nP8mVKxcK9Fw8iuJq9Ho9NpsF+1JIfoANmy3pvu65KQzUJEMuJCYmBm/vYOyNPrDPIuNDfHy8hqkU\npfDx8/Nj1KiX8fP7FdiKXv8HtWuH0KZNG62juRStbyRUMqlatSpm8wXgOhAExCNl2n+WnbVarUye\nPIVVq9ZTsWJZPvroA8qXL69FZEUpsCZP/pgmTRqxfftOqlR5hOeffx5PT/WRmVm2R2G5uoJyEf2r\nr77hjTfextu7JBbLNebNm0OPHv+eHmzIkGHMm7cWo7ExHh4XKV78OEePHqJYsWIapVbuZNOmTUyd\nPBmr1cqLo0YRERGhdSRF+RenjsJyFwWlgACcO3eO2NhYwsLC/jPiw2q14utrwGJ5BfuMSODvv5Dv\nvnuTfv36OS3TokWLGDPmfdLS0hg0qD9jxrytplm/h82bN9M9IoKHTCZ0wGaDgbm//cbDD6uxJ4rr\ncPZ07ko+K1++/B27pKSU/5kxVErh1NlCN27cSP/+gzEauwJ6Pv74B3Q6D8aMectp5ywIvv7sM1qb\nTDRybHsajXwxebIqIEqBof6EdDOenp707/8sBsMiIBoPj03o9Zfp0qWL087500/zMRqbYp+QoDxG\nY3tmz/7FaecrKKSU//oF0wHSZrvT7oridlQBcUM//PAtb77ZnxYtLvD442X5+++/KF68uNPO5+9v\nQKczZnrGiMGghhXfy7CXX2aLwcB+4BCw3mBg+Ouvax1LUfKMugai3NOpU6do0KApKSk1sNl80ev/\n5o8/fikU67Tn1tq1a/n844/tF9FfeYUePXqwZMkSpnz4IRarlWEvv8zT/ftrHVMpxNRFdFQBcbbT\np08zbdp3GI0m+vZ9klatWmkdyS3dXAyso8mEB7DOYGDqDz/Qt29fraMphZQqIKgCoriHJ3r0IH3J\nklsX1o8CF5s1I3LnTi1jFSpWq5VDhw4hpaROnTqF/t4ONQpLUVyAxWLhxIkT+Pr6EhISkuUqdt7e\n3qRk2s4APL288i1jYZecnEx4eCdiYmIBQeXKwWzevI6iRYtqHc0tqYvoipIHLl++zAMPNKRx4zbU\nqlWfnj2fxGq1/me/l0ePZofBwHbsq6dt0Ot545138j1vYTV27DgOHzaTkjKElJQhxMToePPNMVrH\ncluqgCguKSkpiblz5zJ9+nRiY2O1jnNPQ4a8yMmTgaSmDsNkGs6aNfv59ttv/7Nf06ZNWRsZSZm+\nfQl64gkWr1ihFgPLRwcPHiY9PRT7R58gPb0aBw5EaR3LbakuLMXlJCQk0KBBU65d02Oz+eLh8Rab\nNq2jYcOGWke7o/37D2KxtMN+g6cXRmM1du/+z8rLADRp0oQ5v6j7aLTQqFFddu5cS1padUDg4xNN\nw4YttY7ltlQLRHE5n376GRcuFCM1tRcm08OkpDzIiy+O0jrWXdWoUR0PjxjHlhW9/jR16tTSNJPy\nX++/P44GDQIwGL7Fz+9bHnjAi0mTPtQ6lttSLRDF5cTHXyAjo0SmZ8pw6VK0Znmy48cfp9GqVTg3\nbszCak2jadN6jBw5QutYym0MBgNbt24kJiYGKSXVq1dXc7rlgiogisvp1q0zf/wxCqMxDNCj12+n\nc+cOWse6qwoVKhAdHcX+/fvR6/XUrVtXfTA50bJly9i/fz9VqlShT58+9/Wz1ul01KhRw4npCg91\nH4jikj74YCITJ36IxWKhR4+e/PzzLHx9fbWOpbiAN954m2nT5mIyVUWvP0eHDo1YvHhBlsOmlXtT\nNxKiCkhBdHPm4cL8l/yqVav4de5c/IoUYdRrrxEWFqZ1JE0lJCQQHFwBs/kl7EvNZuDn9yORkcto\n3Lix1vHcUm4KSOH9zVSybc+ePdSvWZPiAQFEtGvHpUuX8uW8QohCXTzmz59Pv8cf5+qvv3L0hx9o\n0bgxJ06c0DqWpm7cuIGnpx578QDwwtMziMTERC1jFVqat0CEEBHAVOzFbIaUctJtr78CDMZ+0+4V\nYJCUMi6L46gWiBNcunSJWtWq0TY5mRB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37q3BnNKmpXMX1grgBDPrBXwGXAzUn+N6EXA5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\nqICIiEgkKiAiIhKJCoiIiETy/zzusvBzEeSDAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x6715830>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure_no+=1\n", "labels = np.random.randint(2,size=100)\n", "scatter_with_color(x,y,labels,figure_no)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.close('all')\n", "# 给x和y轴添加标签\n", "def x_y_axis_label(x,y,x_labels,y_labels,figure_no):\n", " plt.figure(figure_no)\n", " plt.plot(x,y,'+r')\n", " plt.margins(0.2)\n", " plt.xticks(x,x_labels,rotation='vertical')\n", " plt.yticks(y,y_labels)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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B5Wx+1TEkJK0ySxlJbAcu7KY3AuuSHAdsAR5n9A5/a1W9G9gFfHT2zklO77bZBGwG5l6C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\nA6TsJxaR6bfuAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x6708ad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.array(range(1,6))\n", "y = np.array(range(100,600,100))\n", "\n", "x_label = ['element1','element2','element3','element4','element5']\n", "y_label = ['weight1','weight2','weight3','weight4','weight5']\n", "\n", "x_y_axis_label(x,y,x_label,y_label,1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 热图\n", "def heat_plot(x,figure_no):\n", " plt.figure(figure_no)\n", " plt.pcolor(x)\n", " plt.colorbar()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x6838470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.random.normal(loc=0.5,scale=0.2,size=(10,10))\n", "heat_plot(x,2)\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

hungiyang/StatisticalMethods

lessons/3.PDFCharacterization.ipynb

3

770782

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PHYS366: Statistical Methods in Astrophysics\n", "\n", "\n", "# Lesson 3: Inference in Practice: PDF Characterization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Goals for this session:\n", "\n", "* Linear problems: General solution + short cuts. When and why do these short cuts work?\n", "* Introduction to Monte Carlo Methods\n", "* Convergence tests\n", "\n", "Adapted from [straight line notebook](https://github.com/drphilmarshall/LearningInference/blob/master/straightline.ipynb) by Phil Marshall and Dustin Lang\n", "#### Related reading:\n", "* Ivezic Ch. 8.1, 8.2, 8.8\n", "* MacKay Ch. 27\n", "* Ch. 2 of [Phil's thesis](http://www.slac.stanford.edu/~pjm/Site/CV_files/Marshall_PhDthesis.pdf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The Data Set\n", "Today we will explore different methods to fit a model to data, and discuss the advantages and limitations of these methods. For simplicity, we'll just load a synthetic set of points." ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1110d6f10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from straightline_utils import *\n", "%matplotlib inline\n", "from matplotlib import rcParams\n", "rcParams['savefig.dpi'] = 100\n", "(x,y,sigmay) = get_data_no_outliers()\n", "plot_yerr(x, y, sigmay)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Bayesian Solution: Posterior distribution of model parameters\n", "\n", "This looks like data points scattered around a straight line,\n", "\n", "$y = b + m x$.\n", "\n", "If the error distribution in $y$ is Gaussian, the data likelihood for a specific linear model $(m,b)$ is given by\n", "\n", "$P(\\{x_i,y_i,\\sigma_{y_i}\\}|(m,b)) = \\Pi_i\\frac{1}{\\sqrt{2\\pi}\\sigma_{y_i}} \\exp[-1/2(y_i-(b+m x_i) )^2/\\sigma_{y_i}^2]$.\n", "\n", "Assuming a flat prior PDF for the model parameters, the posterior PDF of model parameters $(m, b)$ is directly proportional to the data likelihood. We can test this model by determining the parameter log-likelihood\n", "\n", "$\\ln(L((m,b)|\\{x_i,y_i,\\sigma_{y_i}\\})\\propto \\sum_i -1/2(y_i-(b+m x_i) )^2/\\sigma_{y_i}^2$\n", "\n", "on a parameter grid, which captures the uncertainty in the model parameters given the data. For simple, 2-dimensional parameter spaces like this one, evaluating on a grid is not a bad way to go." ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def straight_line_log_likelihood(x, y, sigmay, m, b):\n", " '''\n", " Returns the log-likelihood of drawing data values *y* at\n", " known values *x* given Gaussian measurement noise with standard\n", " deviation with known *sigmay*, where the \"true\" y values are\n", " *y_t = m * x + b*\n", "\n", " x: list of x coordinates\n", " y: list of y coordinates\n", " sigmay: list of y uncertainties\n", " m: scalar slope\n", " b: scalar line intercept\n", "\n", " Returns: scalar log likelihood\n", " '''\n", " return (np.sum(np.log(1./(np.sqrt(2.*np.pi) * sigmay))) +\n", " np.sum(-0.5 * (y - (m*x + b))**2 / sigmay**2))\n", " \n", "def straight_line_log_prior(m, b):\n", " return 0.\n", " \n", "def straight_line_log_posterior(x,y,sigmay, m,b):\n", " return (straight_line_log_likelihood(x,y,sigmay, m,b) +\n", " straight_line_log_prior(m, b))\n" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Grid maximum posterior values: 27.0 2.28888888889\n" ] }, { "data": { "image/png": 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oxxFLuLakrJOJEC0iYqtZszZ9+g9i8rhRfPPlFTwsAg+LwFPZ1PzO1wSxtU+V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"text/plain": [ "<matplotlib.figure.Figure at 0x11034a490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Evaluate log P(m,b | x,y,sigmay) on a grid.\n", "# Set up grid\n", "mgrid = np.linspace(mlo, mhi, 100)\n", "bgrid = np.linspace(blo, bhi, 101)\n", "log_posterior = np.zeros((len(mgrid),len(bgrid)))\n", "# Evaluate log probability on grid\n", "for im,m in enumerate(mgrid):\n", " for ib,b in enumerate(bgrid):\n", " log_posterior[im,ib] = straight_line_log_posterior(x, y, sigmay, m, b)\n", "# Convert to probability density and plot\n", "posterior = np.exp(log_posterior - log_posterior.max())\n", "plt.imshow(posterior, extent=[blo,bhi, mlo,mhi],cmap='Blues',\n", " interpolation='nearest', origin='lower', aspect=(bhi-blo)/(mhi-mlo),\n", " vmin=0, vmax=1)\n", "plt.contour(bgrid, mgrid, posterior, pdf_contour_levels(posterior), colors='k')\n", "\n", "i = np.argmax(posterior)\n", "i,j = np.unravel_index(i, posterior.shape)\n", "print 'Grid maximum posterior values:', bgrid[i], mgrid[j]\n", "\n", "plt.title('Straight line: posterior PDF for parameters');\n", "#plt.plot(b_ls, m_ls, 'w+', ms=12, mew=4);\n", "plot_mb_setup();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Short Cut #1: Linear Least Squares\n", "An industry standard: find the slope $m_{\\mathrm{LS}}$ and intercept $b_{\\mathrm{LS}}$ that minimize the mean square, variance-weighted residual\n", "$R^2(m,b) = \\sum_i[y_i-(b+m x_i)]^2/\\sigma_{y_i}^2$:\n", "\n", "$(1)\\,\\, \\frac{\\partial R^2}{\\partial b}|_{b_{\\mathrm{LS}}} =0 = -2\\sum_i[y_i-(b_{\\mathrm{LS}}+m_{\\mathrm{LS}} x_i) ]/\\sigma_{y_i}^2$, \n", "$(2)\\,\\,\\frac{\\partial R^2}{\\partial m}|_{m_{\\mathrm{LS}}} = 0 = -2\\sum_i [y_i-(b_{\\mathrm{LS}}+m_{\\mathrm{LS}} x_i )]x_i/\\sigma_{y_i}^2$.\n", "\n", "\n", "### Derive the solution for homoscedastic (i.e. $\\sigma_i = \\sigma$) data from (1) and (2)\n", "* $\\hat{b} = $\n", "* $\\hat{m} = $\n", "\n", "### Least Squares as a Maximum-Likelihood Estimator\n", "If the data are Gaussian distributed, _minimizing the variance weighted residual_ is equivalent to _maximizing the posterior_ and the least squares estimator is a Maximum Likelihood Estimator (MLE).\n", "\n", "Deriving a MLE is a useful trick to remember, especially if you want an analytic expression. While MLEs are not optimal for finite samples, but have convenient limiting properties as the smaple size goes to infinity:\n", "\n", "* _Consistency_: the estimator converges in probability to the true value \n", "* _Asymptotic normality_: as the sample size increases, the distribution of the MLE tends to the Gaussian distribution with true mean and covariance matrix equal to the inverse of the Fisher information matrix\n", "* _Efficiency_: no consistent estimator has lower asymptotic mean squared error than the MLE\n", "\n", "### Solution in Matrix form\n", "\n", "With $\\theta = (b,m)$ and $\\mathbf{A}=\\begin{bmatrix}1 & x_1\\\\ 1 & x_2\\\\ \\vdots & \\vdots \\\\ 1 & x_n\\end{bmatrix}$ the residual is given by $R^2 = ||(\\mathbf{y} - \\mathbf{A} \\theta)||^2 = \\frac{1}{\\sigma^2}(\\mathbf{y} - \\mathbf{A} \\theta)^\\tau (\\mathbf{y} - \\mathbf{A} \\theta)$\n", "\n", "and Equations (1) and (2) can be written as\n", "$\\mathbf{A}^\\tau \\mathbf{A} \\theta = \\mathbf{A}^\\tau \\mathbf{y}$.\n", "\n", "algebraically the solution is given by\n", "$\\hat{\\theta} = \\left(\\mathbf{A}^\\tau \\mathbf{X}\\right)^{-1}\\mathbf{A}^\\tau \\mathbf{y}$, however this is not the numerically recommended implementation.\n", "\n", "In the heteroscastic case, the matrix norm is in general instead given by the inverse data covariance. In the case of heteroscedastic, uncorrelated data, this amounts of to a rescaling $A_{i,.} \\rightarrow A_{i,.}\\times \\frac{1}{\\sigma_i}$ and $y_i \\rightarrow \\tilde{y}_i = \\frac{y_i}{\\sigma_i}$.\n", "\n", "A numerically more stable solution to the least squares problem is conveneniently packed in `numpy.linalg.lstsq` - note that this routine doesn't include parameter uncertainties.\n", "\n", "However, one can derived expressions for the uncertainty for of the least squares fit parameters, c.f. Ivezic Ch. 8.2. For Gaussian distributed data points, these expressions can be thought of as propagating the data error into parameter errors (using standard error propagation, i.e. chain rule)." ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Least Squares (maximum likelihood) estimator: 34.8459413134 2.23253621695\n" ] }, { "data": { "image/png": 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oq6vrdSbVhoYGfD4f169fJy8vj9mzZ0eyzJigQCIiIjJInHOcPHmSAwcO8NBDD7Fr1y4S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\nS5doPKTAIdIzBRKRIay2tha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"text/plain": [ "<matplotlib.figure.Figure at 0x11122b990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Linear algebra: weighted least squares\n", "N = len(x)\n", "A = np.zeros((N,2))\n", "A[:,0] = 1. / sigmay\n", "A[:,1] = x / sigmay\n", "b = y / sigmay\n", "theta,nil,nil,nil = np.linalg.lstsq(A, b)\n", "plot_yerr(x, y, sigmay)\n", "b_ls,m_ls = theta\n", "print 'Least Squares (maximum likelihood) estimator:', b_ls,m_ls\n", "plot_line(m_ls, b_ls);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Short Cut #2: Laplace Approximation (Blackboard)\n", "MacKay Chapter 27; \n", "\n", "It is often convenient to approximate a posterior distribution by something analytically more tractable, especially if the posterior needs to be further processed. The Laplace approximation $Q(\\theta)$ of a PDF $P(\\theta)$ does this by Taylor-approximating the logarithm $P(\\theta)$ around its peak $x_0$ \n", "\n", "$\\ln P(\\theta) \\approx \\ln P(\\theta_0) - \\frac{c}{2}\\left(\\theta-\\theta_0\\right)^2 + ...$ with $c =-\\frac{\\partial^2}{\\partial \\theta^2} P(\\theta)_{\\theta_0}$:\n", "\n", "Then $Q(\\theta) = P(\\theta_0) \\exp[-\\frac{c}{2}\\left(\\theta-\\theta_0\\right)^2]$ is a Gaussian, which can simplify calculations.\n", "\n", "[The Matrix Cookbook](http://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf) contains a list of useful Gaussian identities for this kind of manipulations.\n", "\n", "Example: Derive Laplace approximation for $\\ln(L((m,b)|\\{x_i,y_i,\\sigma_{y_i}\\})\\propto \\sum_i -1/2(y_i-(b+m x_i) )^2/\\sigma_{y_i}^2$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Monte Carlo Sampling Methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In problems with higher dimensional parameter spaces, we need a more efficient way of approximating the posterior PDF - both when characterizing it in the first place, and then when doing integrals over that PDF (to get the marginalized PDFs for the parameters, or to compress them in to single numbers with uncertainties that can be easily reported). In most applications it's sufficient to approximate a PDF by a (relatively) small number of samples drawn from it; here we'll introduce a procedure for drawing samples from the posterior PDFs.\n", "\n", "### Definition: Markov Chains\n", "\n", "A sequence $p_1, p_2,...$ of random elements of some set is a _Markov chain_ if the conditional distribution of $p_{n+1}$ given $p_1,...,p_n$ depends on $X_n$ only. \n", "A Markov chain has _stationay transition probabilities_ if the conditional distribution of $X_{n+1}$ given $X_{n}$ does not depend on $n$.\n", "A Markov Chain chain is _stationary_ if the conditional distribution $X_{n+1},...,X_{n+k}$ does not depend on $n$. \n", "\n", "*Some background how Markov Chain Monte Carlo sampling works on the blackboard* (if you don't want to take notes, see [Phil's thesis](http://www.slac.stanford.edu/~pjm/Site/CV_files/Marshall_PhDthesis.pdf) for a nice write up.)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def straight_line_posterior(x, y, sigmay, m, b):\n", " return np.exp(straight_line_log_posterior(x, y, sigmay, m, b))" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running MC for 5000 steps\n", "Acceptance fraction: 0.2922\n" ] } ], "source": [ "def run_MC(m, mstep, b, bstep, nsteps, burn_in = 0):\n", " chain = []\n", " probs = []\n", " naccept = 0\n", " \n", " print 'Running MC for', nsteps, 'steps'\n", "\n", " # First point:\n", " L_old = straight_line_log_likelihood(x, y, sigmay, m, b)\n", " p_old = straight_line_log_prior(m, b)\n", " log_prob_old = L_old + p_old\n", "\n", " for i in range(nsteps+burn_in):\n", " # step\n", " mnew = m + np.random.normal() * mstep\n", " bnew = b + np.random.normal() * bstep\n", "\n", " # evaluate probabilities\n", " # prob_new = straight_line_posterior(x, y, sigmay, mnew, bnew)\n", "\n", " L_new = straight_line_log_likelihood(x, y, sigmay, mnew, bnew)\n", " p_new = straight_line_log_prior(mnew, bnew)\n", " log_prob_new = L_new + p_new\n", "\n", " if (np.exp(log_prob_new - log_prob_old) > np.random.uniform()):\n", " # accept\n", " m = mnew\n", " b = bnew\n", " L_old = L_new\n", " p_old = p_new\n", " log_prob_old = log_prob_new\n", " if (i > burn_in): #measure acceptance rate after burn-in only\n", " naccept += 1\n", " else:\n", " # Stay where we are; m,b stay the same, and we append them\n", " # to the chain below.\n", " pass\n", "\n", " chain.append((b,m))\n", " probs.append((L_old,p_old))\n", " print 'Acceptance fraction:', naccept/float(nsteps)\n", " return chain[burn_in:]\n", "\n", "# initial m, b\n", "m0,b0 = 0., 450.\n", "\n", "# step sizes\n", "mstep, bstep = 0.1, 10.\n", " \n", "# how many steps?\n", "nsteps = 5000\n", "\n", "chain = run_MC(m0, mstep, b0, bstep, nsteps)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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IKaOgQPKlRQt48knIyrKv9++H//xPePiqxRI3bID/9//s9MZc/ftDaKh9vmWL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\nK01DbA9OCszMzCpYflLwyitpSeNBg9rnuZwUFCF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"text/plain": [ "<matplotlib.figure.Figure at 0x11065c9d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mm = [m for b,m in chain]\n", "bb = [b for b,m in chain]\n", "plt.clf()\n", "# Plot trajectory of chain in (b,m) plane\n", "plt.plot(bb, mm,linestyle='-',alpha=0.5)\n", "for n in range(0,250,50):\n", " plt.text(bb[n],mm[n],\"%d\"%(n)) \n", "#overplot posterior contours from grid based estimate\n", "plt.contour(bgrid, mgrid, posterior, pdf_contour_levels(posterior), colors='k',linewidth = 3)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How does the outcome depend on all those parameter?\n", "* Take a few minutes to explore how the chain depends on choices for starting point (m0,b0) and step sizes (mstep, bstep) in the cell below:" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running MC for 5000 steps\n", "Acceptance fraction: 0.2162\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/site-packages/ipykernel/__main__.py:25: RuntimeWarning: overflow encountered in exp\n" ] }, { "data": { "image/png": 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+Blz+Zl/6hh8tiYlw5ZUwciQcOeLDQAhNFUREGh2NISe17sABP8Txtm2+R8D4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\nNG1mfqCmfv18W4ZFi3z3yhE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"text/plain": [ "<matplotlib.figure.Figure at 0x110e67350>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "new_chain = run_MC(m0+5., mstep, b0-100, bstep*2., nsteps)\n", "\n", "new_mm = [m for b,m in new_chain]\n", "new_bb = [b for b,m in new_chain]\n", "plt.clf()\n", "plt.plot(new_bb, new_mm,linestyle='-',alpha=0.5)\n", "for n in range(0,250,50):\n", " plt.text(new_bb[n],new_mm[n],\"%d\"%(n)) \n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Observation: Early parts of the chain depend strongly on starting point. Discard them!\n", "* Use optional `burn_in = n` argument to remove first $n$ steps from chain." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running MH for 5000 steps\n", "Acceptance fraction: 0.3012\n" ] }, { "data": { "image/png": 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uXXLtgADxAkRFSQJgXJyEUVq2tBz6UCgUCoU5yvA/A+h69glBRoYo+G3cKHH3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ldfCW9LXNwdhgwJ5OLFi7i5udGiRQu0tEQCpampKOfr2zc/s//MGdEmWV9feAhkFv/jSMMvkUgk\nZcitW7f48ssv8ff3p3r16vzyyy/Ur9+b4GDlodJfXjvbXr1EBnsej2oAPAlVVbl06RJ79uwhLi4O\nbW1tmjdvjp2dXZG9Bl4EsbGxrFkTgI9PfxrkJTCUATk5OYSEhHDo0EGcnNri5taRu3c16NNHVE6c\nOyeqL2xsxPhbt0RJ5Y0b4O4OzZqV2a29lkjDL5FIJC+BEydOMH78eIKDg+natSv//e8M9u+vh6KI\nmvM88k6z/9/emYdVVa0N/LdAEJlEUARnc8IBnMIyzTSnrDTnKSfM0jK76c3Ge8u6t+F+DZbV1dKc\nyjnLoW6OaQ5hIqg5ACqhmDPOooLC/v54OZ7DKOAAxvt7nvPss9dee+91Vti71jvaCA2FiIj8vevc\nuXNERUWxZcsWkpIuUK1adZo2bUpQUNBNzQdgWRZTpkzBw8OD/v3737Tn2nBxkeiHhQshLc1i3bo1\nLFmyjvr1G9C1a1datixB+/ZSJnnLFrj/frH5GyORGkuXitameXNo187uRFjcuele/YqiKEpWgoKC\nWLJkCUuXLmXv3r20bRvEjh2juHo1Y1m/q1dF+NsS5EVESDigI9eT3d7e3rRu3ZrRo0fTvXsPUlNT\nWbjwWz788EOWLVt205wBjTE0bNiQuLgfSUpKKtAzHn8852tXrsDUqeII+cQThlmzHuSJJ7oSExPD\njBkzWL06CWdnqejXoYPY+BculDl0cRENSqdOElb59deS/EfJO7rjVxRFuUlcvnyZiRMn8vbbb3Ph\nQjKPPDKJOnV6ZUjO0769JKjJLUStZk2xaeeFxMREtm7dyrZt27h4MYly5fwJDg4mJCQkSyK0/HDx\n4kU++OADOnXqRGhoaIGfkxdGjJB4/TffPMCkSQtwcXHhgw968MQTkoc3OloS+1SqBP36ifMfSDTF\n/PkSWdG7t6bt1R2/oijKbcbNzY3Ro0fzxx9/MHbs8/z44zA+/fRTtm7dSlp6kvmzZ0V4Va2a83Py\nKvQBypYtS/v27RkzZgx9+/bD39+fdevW8fHH45k2bRrh4eGcOnUq37+loBEEzz0nu/Lrcf/9kmcf\nYNIkcbp8/fWqPPvsEzg7OzNq1Gy+/FLSINatK6aBQ4cktM+WKKhKFRg+XDIyTpsGkZEFGnKxQ3f8\niqIot4j9+/fTs+daIiP34+9fng4dOlCjRg1q1pTQv+++y/3+sDAJYzuQz3RpycnJREdHs2vXLuLj\n40lNvUq5cv7Url2b6tWrU6VKFVyuI523bdvG4sWLGTNmDF5eXnl+d8mS0KQJhIfn3i8oSML+1q6V\nD8DIkXL/v/99kblz53L48GHefLMlr73WGpAd/jffiImkf3/7zj81FZYtE/NJkybw8MPXN5v8FVHn\nPkVRlCLA+fMwZswhVqxYQULCAapXv4uOHTtSPp/xaE2bFmxHm5KSQlxcHDExMcTFxZGUdAEnJ2cq\nV65M+fLlKVu2LKVLl8bHx4erV69y8uRJTp06xY4dO3B3d2fo0KH5fymSZ//ECfHEzymV72OPicd+\neLg95LFHD4iLgy1brrJo0SJ27drFqFF1mTChNwAHD4rwDwgQPwLHEgfbtsEPP4C/v4T83YCl445E\nBb+iKEoRYc8emDXLIjY2llWrVnHy5ElCQ0N5+eWWpKR4ExOT92cFB0Pr1pKEKL9YlsWJEyeIj48n\nISGBQ4cOce7cOSwrY61bd3cPfH19adu27bUCRYWBZVmsWrWKX3/dSKtWDxAW1hrLMhk0IKVLy+5e\nSgFL/P/Vq1I3YfRosf8XF1TwK4qiFDE2b4alS1PZvHkza9euxdnZmVatWnH33XdnCcfLj4PfjZCW\nlsaFCxc4c+YMJUqUwNfXF7ciVg5v48aNrFq1kkaNGvPoo4/i7OyMry/YXBfuvlsS+zg5SWifk5Ms\nCIpbnH9eBX8xtIIoiqIUDs2aQcWKzpQp05zg4GB+/vlnVqxYwa+//krr1q1p3LjxNae62yH0AZyc\nnPD29sbb2/uWv8vDQ4ruZD8OePZZ+PJLKXzkSIsWLfDy8mLx4sWcP3+e99/vyWOPuXH4sITzHT8u\nav9bmNDwL0UxUoIoiqIUPhUrShGb+vU96dKlCyNHjqRKlSosXbqEefNGEJ+HlH7e3vZ8AHcSNqHv\n6Zn1WloaTJgg3v7Z+R2GhIQwYMAAEhISeP31Fzl//hyVKom3/7FjYvdPTr614/+roIJfURTlNuPp\nCYMGiQbAz8+Pnj17EhY2FBeXM8ycWYsZM2aQkJCQ4/3nzokD3J1Kdgl3HnhAjitXSoKf7KhevTqD\nBg1i794K1K07lwMHjlGpkszliRMq/POKCn5FUZRCwNlZws66dpXzKlWq0L37XP7977V4eU1g2rRX\nWbNmEI8++jt9++ZeUKhjxztTA+DIL7/krV+lSpUYMmQI586d49573yAu7g8qVpSd/4kTovrPbCpQ\nMqKCX1EUpRBp1EiyzoEkzblypSWvvhrB7NndOH48gmbNGvHhh08yePBRSpaU6n8tWoCfn/0ZUVH5\n0wDc6WVtAwICGDZsGCkp9bn33n+wd+8+KlaUnX9iouz8VfjnjAp+RVGUQqZu3YznMTFOnD3bg3Xr\nfmfChAl899131KpVi19/XYGHx2Xat4dRo8RZDmSn68j1itZcuiRHFxcJeytqvPqqxPM7UrUqjBkj\nWhIAHx8fhgwZgmXdx733/pOYmFgqVBDhf/Kk7vxzQwW/oihKIWOMqOpr1ZJqc126iNCaPt2FLl2e\nZe/evQwbNoz//W8BgwaN4KOPPiI5OZn77pMMeL16ScY6kPr1qal5f+/587fudxWUd96RojyORYwO\nHBCnxmbNxDkSwMvLiyFDhuDk1JJ77vknkZFR14T/qVMq/HNCBb+iKEoRIDBQwtJathQh/tRTUK4c\nzJgB+/f7Mn78eD755G2aNGnF2LGvU6dOGyIiVlCpkkV8vKj7Af74I+/vzCmjXlHhiScynu/eLccK\nFeDll8HXFzw9PQkLC8PTsz2tW79LZGQUgYF24T9zpl3DkRP79hWvCn8q+BVFUYoAgYFSwCcpSbza\nk5Lg3ntl975kCYwbB4mJ/jzwwFBGjIjHxWUko0f/Su/en/HDD1txTMZWsya89JLc07173scQEiLa\nAxsvv3zTfl6B+OyzjOeLFokNH8DNTcwdjRuDu7s7AwcOxNu7A61bv3dN+A8eLCmDv/46q/BPTISj\nR+X7kiWSP+DIkVv/m4oCKvgVRVGKAIGBcvz0U3j7bfj8c5g7N3t7/TPPlOOXXx5n+fK2BAUt4osv\nmjBt2ktER0fTqpXF44/bHfhCQqBt27yN4fffM/ob3M5CN9Wrw4MPZmw7fTrjeUoKzJtnD9kzRvL9\nP/qoVEYU4d+e1q3/Q1TUVgICchb+kZEi7LduFRPLuXMwdSr5Sp98p6KCX1EUpQhQpgy0aSO7/G7d\npDLfmDHwj3/Izn3IEHB3F2Fcv76ouzt0uJ+ff17N+vXrCQy8wPz5Q3nttRZs2LA+w7PzU6f+P/+x\nf3es5hsSIu8uU+aGfmaOJCZKtELt2jn3ad5ctCJLlkgZXxt33y3hfHbh345Wrd7PIPzPnMmo9r/v\nPinws3ixJAACcXScNw82bsz4/L8aKvgVRVGKAMZIEpvWraFhQ/Fi9/aWdoBq1cTu36VL1ntbtmzJ\nb7/9lxUr3iIlJZlWrVrRuXNnduzYAUjhGkcCA6F9+4xttoy9jglwFi+Wo4uLLDrCwrLuwm1Urpyv\nn5uBIUPEyXDPHvuu/6GHsvY7flzyHuzaBZs2ZbxWrZokRrIJ/9Kl23D//TOYN283mzbJHBw5Yhf+\nXl72OfjzTzlWrSo+FitXyuIir06Sdxoq+BVFUe4QfHxk550T7du3JyIigjlz5rB7925CQkLo1q0b\n27bFApILf8QIUWtv2wY9e8rCwsVFUuY2bgxly9qfd+iQHK9cEUEbECC29ew4eLDgv8vFRRYOW7bI\nO4KDZdddsWLGfnFxktO/RQsRzvv326/t2iUOeo8+CnXrivD38anPkCFL+e23g9cSHB05Ig6TFy+K\nE2WVKvZnbN0q2oNu3cTsMXNm1kXTXwEV/IqiKH8hnJyc6Nu3L9HR0UydOpVdu3YxYMD9LFq0kJ9+\nOkZAAAwdKvby1avFbHDligjNOnVE5V6zZvbP/te/JDxuyJD8jalkSRHWOTF5srw7Lk5i8Nu0EefG\nQ4fEP6FjR3vfuXPlWmAgLFggYX4REfDdd3L9hx9EdV+/vgh/X19fJkyYS2jovmv+C0eP2nf+nTuL\nH4WtfsD48eDvL+aBEydgyhS7Q+FfBRX8iqIof0FcXV0JCwtj165dTJz4FgkJ0xgy5At69XqRxMRY\nhg6V3b5j+F/FimLHd1T3N2kCL7yQ8dnTp8vRUTuQG8nJYioYPjznPqtWyXHzZgnTs+3QfXzE78GR\nbdtkUZCUBNOmwY8/SnulSmIOGTtWbP7vvuvG88/3w83NjXvuWUrt2gnXTBJHj8rO38ND1PuOO/up\nU2VR8OSTsiiYMiV/YZJFHRX8iqIof2FcXFwYMWIEBw7MpVevZixfXoqgoEYMG9aLZs12Zsjc9+GH\nIviPH7e3lS2bfTU9yLoTbtsW/v737PuuXCle9Jnx9c14/ttvkre/WjU5j4uDw4dz/YmAOB0OHSpO\njza/CGNg7FhPxo/vh7OzMw8++D27dp29ds/x4yL8mza1j8PZWSIM5s6F6GjJJVCxoqQB3rLl+uO4\nE1DBryiKUgzw8vJk0qSHGDv2nwwfvpioqCiaNw9m9eqBGHP0Wr/LlzPu+G27+txU9TZWrxZHubCw\n7K/fc0/WtlOnxKHRkTVrZKEAsrufPPn67754Uez/2TFoUFmmT+9NWloas2bNJjn9B1qWmAVmzbI7\nFaamipajZUtYsUI+ffuK7f+HH2DZMvGHuJNRwa8oilJM8PGBxx4rQUBABxYujOHLL7/kt9/W8uab\n1Vm9+itOZ+Oyv24dxMdntPs/95zYwJs0Efu9I/Pmifod7GmEbTh64jsuJNauvf7Ya9WSozFZnwuy\nWPnxx5zD8Lp0CeDrr7tw9uxZ5s+fT6qDy/6xY7B8ufgZ2MbZtq1EEGzfDrNni9/Bww+LRmLu3Du7\n/K8KfkVRlGJEcLA49C1f7kKfPk+yd+9exo9/j9jY1/n001f59ttvOXrUrgE4f17U4TNmyPnw4aIW\nr15dQgtffDHnd0VFSebA7CIB8is49+6Vo2WJFqJBg6x9IiJk0ZFT+t0uXWowfHhX4uPjWbx4cYZs\nh2fPQqwEP7B/vzg/NmokvgJHj8JXX8ni5/HHxaHwq68kN8CdiAp+RVGUYoQx8MgjYstfvBhKlnTj\nb3/7G/v37+WTT4I5dOhPvvhiEt9++y0nTpxgwAAoX95+/+TJsqvft09U3s7OYo8PDhYnwKZNM77v\nu++yL5STnb38jTcgNPT6v2HFCti5M2ObLfNhQgL89785Z+AbODCI7t27s2PHDtasWZPjO5YulWO1\najBsmCw4Jk+W0MNhwyQSYvLkGwtjLCxU8CuKohQz3N0l1W1cnOySpc2dUaOe4ejRkXTu3IWDBw/y\n3/9+zkMPfUBERAKdO0v+/44dxS7/zTfwySfiiGeM7Jg9PSU8btw42RnnRqdOUmnP0VQwa5Yk7gkI\nkHOb6j0vHDlid+q7eFHU8UuWZNUseHhAgwYNGDWqKuvXr72W5Ajs7wXYsUNCCwH8/ETYBwRIGOCh\nQ3Lu5yeaEIdH3BE4jxs3rrDHcI0333wzEBg+fPhwAm3LN0VRFOWm4+srAnLDBqhXTxYDACVKONO1\nayApKc3w8SnDjh1H+PXXCezc+TkBAWVp06Y2oaGGWrVEqEZGioA8e1YEY5ky4mTn5yfe+CdPSqlg\nJ6eMufL37ZN3Pv64XDt4UBYUmzeLSv3wYVG1Dx4sjnabNuU/k96RI/L7SpeW2HwnJ9E+bN0KY8Y0\nZO/e5Sxduo+goBp4eJTmwgXRXNiiGjZvlrS+FSrIMThYkh+tWSOaji5dRN2/ZvBwe48AAB4USURB\nVI1oBKpVsy8+CoMjR47wpYROfDlu3LgcSw4ZqwglJDbGNAEiIyMjaZKd94aiKIpy07hyBb74QoTa\nE09kLAi0eLEISMuyaNToBz788D9s3LiRkJAQ3njjDbp27YqTkxPJyfZdMEgq3EaNxAHv9Gm59tRT\nIjxBHAVt/gKOuLtnnyVv4ECJ6d+2Tarz2ShVShYSnp55L6nr7S0mjlOnICgIgoNT6Nv3GQ4erMvr\nr4/g3DkPQBYKZ+1Rf/j5iSaiVi0R8OHhEnVQt65k+du0SSIaGjQQTYqLS97Gc7OJioqiqdhamlqW\nFZVTvzyr+o0xrxhjIowx54wxx4wx3xtjcimncO2+1saYKGPMZWPMXmPM4Ly+U1EURbl1uLiI893R\no6Kyd8SW5c7Pz9CtW2c2bNjA+vXr8ff3p0ePHjRu3JhvvvkGY1KuhcL17Sv3RUSIGWDdOmn//Xf7\nc6tXh9deg3LlMr4vs9C/+245fv21qO29vTPec+mSCOMXXoBXXsk53NCmyQDZrdsKD8XEwIIFrrRt\n+wHJyVeZOHERaelxeo5CH2QXP2uWePefPi0Ffvr0EYfD6dNlodO7tzgHTp+e94VIYZEfG38r4FPg\nHqA94AKsMMa453SDMaY68COwGmgIfAxMMcZ0KPCIFUVRlJtGhQoSR79+fUZHtVq1REhfuWJva9my\nJStXrmTDhg0EBgYycOBAqlatypdfvk9SUhJubhLy9ve/y07YFu++aZOEy9kS/ri4QI8eomFo0UIW\nAplj/LdsEXs8iJCeOVNS6DrSqJEcS5aUgjtPPpk1lj+3XPt+fuDn50OPHj3Yt28fa9euzTZdsWWJ\nT8KxY1IuedUqMV8MHSqLiSlTxHQSFibnkyfbK/4VRQqs6jfGlAWOA60sy9qQQ5//AJ0sywpxaJsD\n+FiW1Smb/qrqVxRFuc2kpUma2osXpYiPq6u0//67eOW/8krWeH2A6OhoJkyYwIwZs7ly5UV693bh\nnXf6ULVq1Wt9fvsNfvrJfk+VKuL5X6+eXFu9Wuz41aqJgI2Ohvnzs77Ly0vU9I6pBp5/XnITOBIX\nJzvz+vVl8XHmjFT927NHrjnSp4+o/I2B9957j1deeYWff/6ZevXaMHFixr6+vjBggMzJhg1iamjf\nXsY9Z474MvTsKX4OtvMePfLnoHij5FXVfyOCvyawB2hgWdbuHPqsA7ZYljXGoS0MGG9Zlk82/VXw\nK4qiFAKnTsGkSeLA1rmztP35p+xmhw+3h8tlx8mTJ+nTZwubN3/KxYvL6N27N2PHjqVx48akpsIH\nH0jlvwoVJLb/jz8ktj84WHbzTk7w9NP2eP/YWBGeXl6SRyA36taV6ABHx7qdO2HhQsnx36GDvT05\nWZwKFyzI+IzGjaFWrTRGjOjAwYNx7Nixg+PHPZk5M2O/UqVknGlpYuPftUuqCrZrJ3b/2Fh5X9Om\nsmCynd977+1x+rvpNn5HjDFOiNp+Q05CP53yQGaFxzHA2xiTzfpRURRFKQx8fSVULzLSnsjGz0+O\ntrC2nPDz86N374589dW3jB8/nvDwcJo0aULLli2ZO3cWNWpcITZWduGDBknmv9BQ2d2fPy82dUet\nQJ06IjwvX4ZnnoGGDXN+d3S0OAtOnChjv3JFnOw6dRJhvHGjvW/JkjKGOnVkEWKrQbB1K8yf78Td\nd8/nyJE2DBnyOWXL2gsFeXvL8dIl+OgjWaj06iWaiuRkseu7u4vpYflyyTPQs6eYMZYvl1S/+Y1I\nuJWUKOB9nwP1gJY3cSzXGD16NKVLl87Q1q9fP/r163crXqcoiqIgnvixsRL//swzYmN3d7++4Afx\nhL982Y1Ro0bx9NNPs2jRIiZNmsSAAQMoX34SDRv+Hw89VJ9atbzx9ZWUuK1bi4NcZKSE71mWfWfc\nsaNk0Fu0SCIOrl6VHXZOHD8uSXeWLhWBGxoqpotVq+Q3OCqRPT1lwTF8uD0DX58+cPasLydO9Oeb\nb9ZRpswhgoMrAmK3f/BB8Ts4d06E/+jR4gMxYoS020L6ypeX89OnZXFQtqyM6dQpcQAsVarA/3ky\nMGfOHObMmZOh7Wxmr8QcyLeq3xjzGdAZse0fuE7fX4Aoy7JGO7Spql9RFKWIcuGCZL6rXFm89KdO\nFW1At26537dsmajRn302Y/vu3bt57733mTUrEFfX7QweXJmRI0cSHBx83bEcPiwV/cqVkx369u3S\n/vLL8N571/8t1avL70lMFKFri1T4+WeJOHB3FwfDrl3tu/vU1FQaNrwHV9d6vPrqDHbutOvovb1F\n8IO9zLBtj3rxojw3MtJeL6BcOejfX+6ZO1fu6d/frkm52dyKcD6TLvQfAx68ntBPJxxom6mtPfBr\nXt+rKIqi3D48PSUxTWysqMD9/PK24/fxEZV95r1kvXr1mDlzGpMnv0CbNiNZvHgJISEhtGrViunT\np3PRwe3eskRIR0VJHoGFC6X9xAm70AdJGWwr29uvn91BD0SQ25wT4+PlXsuSe/buFVOALcywcmWx\n2duEvtzvzPjx77J169cY8x29etmv2YQ+iKCfPt0e+ufuDo8+KjkLqlSxj/uTT2RsTz4pxylTZFyF\nSX5s/J8Dj6d/kowxAemfa+UXjDHvGmMcUzNMAu4yxvzHGBNkjHkG6AWMvxmDVxRFUW4+QUGiGl+2\nTIRVYmLOVe9slC4tQtUxO58jLVv60qzZw2zZcoD58+fj6upKWNgwKlS4lwEDJvLZZyd5/3347DNR\njR85IiFz3btLNsDSpWWn37On2Mv375fnbtggZoHnn4f77xc7fkqK7LYdggsAicV/+237ebduGeP8\nbbRv356HH36YN954g9q1LUqWlNh9Gz4+8uyzZzNWHARxggwLE4/+EunG9KlTRdg/8YRc//prWdwU\nFnlW9Rtj0gALyOybOMSyrJnpfaYBVS3LetDhvgcQQV8POAj8y9Y/m3eoql9RFKUIkJwsXv7nzomg\nffHF7IWkDZta3jFLnyNXr8K//y2C7667JGfAzp2n2bQpgqioTaSkxBMaWp6BAx9g0KAH8fa2+3+f\nPSvOe7VqiUAFiZN3DLmrUUOS/tSoIQ5/ERESleDjIyr/rVuzjunRR8WJMDuP+2XLltGpUye2bNnC\nwYNNOXhQtB979ojdftgw6VeihF3AZyYlRRIj2RwM/fzEd+Knn8QP4L77JCIgc+6BgnLTVf2WZTlZ\nluWcfnT8zHToE+Yo9NPbfrEsq4llWW6WZdXKSegriqIoRYeSJTMm4bmeut9m67apvi1L7tm6VZwF\nJ02S9iNHJBbeywv69CnDt9924PjxMXz5ZQucndcxatTDVK9egVGjRhEeHo5lWZQuLUJ6xw57FsDy\n5UXIV6smNvrkZFHnf/qpPab+qadESDsK/YEDJbwOxNveMRrAkXbt2hEYGMiMGTNo0ECeuWcPhIRA\nUpLY7HMT+iAmh/btJYrBNof/+pd4/9uiDubNkwXC7URz9SuKoig5snq1ZPXr2tWeKS87LAveeUcE\nsbOz7OiTkmQ37e8vdu+LF8Uzf+TIrCl7bURHRzN16lRmz57N4cOHqVu3LsOGDWPgwIGsX1+O2Fix\ny/v4yG46PFyqBhojqYcjI2VxkJJiN08YI+M6fVo8+G1+C40bi2kiNlZyCDRpItEAtqRAzz//PN9/\n/z3x8Qd46y1pe+EF8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"text/plain": [ "<matplotlib.figure.Figure at 0x1111e3d90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Run chain again, now with burn_in = 1000\n", "chain = run_MC(m0, mstep, b0, bstep, nsteps, burn_in = 1000)\n", "\n", "# Redo the same plotting as before\n", "mm = [m for b,m in chain]\n", "bb = [b for b,m in chain]\n", "plt.clf()\n", "# Plot trajectory of chain in (b,m) plane\n", "plt.plot(bb, mm,linestyle='-',alpha=0.5)\n", "for n in range(0,250,50):\n", " plt.text(bb[n],mm[n],\"%d\"%(n)) \n", "#overplot posterior contours from grid based estimate\n", "plt.contour(bgrid, mgrid, posterior, pdf_contour_levels(posterior), colors='k',linewidth = 3)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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SQ2yBJAd0BIpSeU0r3jeZ9uxfIG2khMBUA89472ucc0WR7qbeWL3sP6j2tYi0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\na6yXYGvn3K1AFfBr4FHgT86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N+e67NvHwq6+CSwz69bPE4KqrrAejOZ07WwL0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\nrPZdyrFUq4pNCAByyUDSOpCrMlgDrC3RYmaTgdVmdp0qEUo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"text/plain": [ "<matplotlib.figure.Figure at 0x111291150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 1 and 2D marginalised distributions:\n", "import triangle\n", "triangle.corner(chain, labels=['b','m'], range=[(blo,bhi),(mlo,mhi)],quantiles=[0.16,0.5,0.84],\n", " show_titles=True, title_args={\"fontsize\": 12},\n", " plot_datapoints=True, fill_contours=True, levels=[0.68, 0.95], color='b', bins=40, smooth=1.0);\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convergence tests\n", "We expect our chains to\n", "eventually converge to the stationary distribution, which is also our\n", "target distribution.\n", "However, there is no guarantee that our chain has converged after\n", "`nsteps`\n", "draws.\n", "\n", "*How do we know whether our chain has actually converged?*\n", "\n", "We can never be sure, but there are several tests we can do, both\n", "visual and statistical, to see if the chain _appears_ to be converged." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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4Z4MG8dZeddfFtaxIefdVxnFixU173LXirANuQ+w+v198wXEwXMEs50zybLuW\nhYMP5q1vkcVPP+WtxN5w77+U1XLFim21EatfFK+/Hv1dtbHv5RVX8BDsBx9wGAAfxdanPnxiRSwd\nbnkRH0Xfc7P22jzUHzWzzj3XM88EDfwNNxROZyGr2vPPs0N9oXquXJ5+Grj44uTpqjSpixUi6klE\nmxPR7kS0h/1K+1ppMHQo33gXt8A1NYUL50or5f+mX79AZCQx99nYcRKOPJIjgwpy3cZGDoUPhC0X\nPXpwwZ0/3z+M0tDAYgsI4lcY4y98ScSKz8HV7VH7zu0GlfM9bHbDv9ZanJ++3qItjqQhSCpWpOKL\nExBxYqWtLbCUdOsWrnjlvsyfz+Pdm2/OgvKPf4xPUxy+2TQiigtZVpKIFYDTaq/MbJc1H65lRUSK\nr9y7IsN3TBLLSns7C8y99+Z7N3Vq0DhL+RbcMnPVVWzlmDDBn7YkTq9RwsLnFOziBo4rJFba28O+\nb8VaVuzZXUJSy3A5tLezOC/0DNr3R4ItzpkT3SCm0VDGWVbc8iKBOX2Wlbiy4hsGsoPBJbH4FRqy\n+8MfeMjbXuOtEowYEV4xvNaWnlTFChHtAuBzAC8BmAjgXueVScTXwcZXqGxnK7uCkgaWKHhfrFix\nG99//CNsKTnrLBYhDQ1+Z0ARK5984h9+amzkyvpf/2IhJPj8FyT8uov8r8WLw7OHBJmeuHAh+1Ek\nqVwkboyNHfPl/ffzx+HlQZ46NbgHsk9+W6iiFL+KuHvkszoJdgPj+gNInr75Jg8VRfkkFIPP0VV6\n+XZl6rOsJLUcNDUF98y3iKE73OUGhZMZPr7GN8qyYt/XpGJl7bXZkfr++9niJj1V16LjPr8iLt17\nbgu0QsQJCxtfsEZXbCSxrPzyl9G/F3yWlVmzgG22yT+2GLFS6lTnW25hcf7nP8cfZ98fuSdxztpp\nTPm377E7nOqWF8lXn1iJa7R9/8H28Ujie1NoGEjO19rKdf5f/lKdqcSdSqwAuAzAHQAGAmg0xjTY\nr5SvVVF+/nPejhoVVNR9+vCDtfPO4WOloJ96atBgprGgobDTTmxWJuLG8Ec/Cn/fsycX2ief9P++\nsZEtQfvtFxZBMt3RZtNN/eewLSuCrCIrjBrFjXzSqKbPPht9HcE1bdvnlQiukv9SsRQaUxUxE3eP\nbMvV+uuHv7OFWLdu4fSlPQ3zv/8NV9Tu+dOyrDQ1Bb976qn8xuHcc8PTO+1hoCefDAIEJrGs+PwE\nkgwDucGQmdnzAAAgAElEQVTugOjgb27jI/dc/CMkkGMx/gFRDab7n+OGF4UkYsW+Z3Lf58zJL2+u\nZSVqyKSYKMtvv538WBtZSLGQM26cWPGVhTTEin2PX3ghfO0osSL3IIn1DPAPA9nlvxix8uWX/u8l\nL1pbuRN62mlha/fnn/vrtnKXP6j1LKW0BUR/AJcYY2YaU99LX0kleNpp4dk9zc0cD8KtMK64Ajj5\n5ECsFGtZKYeDDuKtKx4E++G3H9high6JpcF+CFwrzIQJfH67l26TpES4Fg3btN3aGg5yNmYMb+Uh\nkqG5QhWb3JsosbL11sGURiC/IbPPb1uCAE7r4sXFO1gffXQ4tDzADp0rrcRxcnzXBsLiLi3LSlNT\nvtWtVy92GLWPB9hR2cZX7qNmA9kWsKSWFUECfD38sH+Izb2mWF7a2tg/4mc/C5+zVLHS1JT/n5P4\nEhQrVuS8yy4bLg/2OmIi7KOEwoUXJnc89oUGSILdkMZh30vJPxlC8Vk10xAr9gw/2zoBFLasxA0L\n2/iGgew6IkkdWMgqLd+3tgbpljz817/YCi/O8jZbbBFvMS43XZUmbbFyD4ARKZ+zpvjMw25v3zbF\n1kKsyIq5UfEKfA2BMcWF4LYtK6uskj+E06NHkAd2L71Y3IfJzmu3x+jOkJG8T1qxRYmVffYJf3bz\nr5BYOfRQjrcTh+3DMG8ei91//CN8jJjibYuZ+9+WXz54L/n/9NPBytrFWFbk2AUL8uPN+I4HeFqp\nTRLLyuTJvD3//GBfsWJFROvUqcB55+X/JmoxRzctSX1Wpk/3N+DdusWvxRSFLyaOjYR7F+zw/nYv\n2l6hXYR9XNySpMMFpU5PdafWFzoOAJ57jrfS0Psc1u37dt99pa39ZD8DkpfSEY0SK3ZcqzPPBHbc\nMf4aPquyXc+OH8/3Ky5Wif37778HTjyRZ0tKDB1bENrB5r74Ipg56Ysa/Oqr0ffFGJ4l6RNTn33G\nHeHOJlaOBvAzIrqBiE4gomPsV8rXqgpJxIptipXGK+my4mlAxOn0TRsFwpXeHjk352HDirOs2GKl\nX798h0Z3Vkqhgn3HHf79rlj55ptgmqcrLuRYqWjkf8aJFVvwtLQEgicuDeecE/7sDgPZGAM8/nj0\n9QVbgEyd6j+m2MpB/v+f/xxUbHKOYiwrCxbkTz33He/SvXvQUJ96alCxu5W3WJ1ssZIkzkox+SFm\nfkFmz0VFhi0kVlZZxe+02q1bvkBLIlbEAhlVVhctyh8GcnvRst8VK/bCoi6+mUtR1y8Fe+HUOKKW\nXfCJld69g+OXLOEpxXFxpaJoawuebUmfrPsTJVY22YTjB91yC/sOSgDMKHzDQLZVRoao43zZ7DJ6\nzTW8gO3f/x50QOT7CROC2EitrWGLXqGlXzo6eGFauc8PPsgd0Hs9nqWbbsqdF3eCRLVJW6yMArA9\ngH0B/B7AGOdVd/gqMdfp0zbFSiX/+OPxTmrG+FWs2/glJU542A3VOuvwdVdcsTjLisyGGjeOG1e3\n8bOvn2QYaP/9878/44xwlFjh6qt565rXpRJwhzrirFr2bJAlSwKfCxu3spSZN4LrYGuT1Mxun0N+\n4zqx+qxTMtTVs2f+atRuWV2yJLDwFGtZKUWsNDYGPgsXXBCINvd/yHXs2UZJLCu+gHMuMjzkBt6T\nYb0oy0rcMNDpp0d/t/LK+eXNDo4YR3Ozv1EX64ctzi++OEirvd91sH3xxfj0FrL4GsNDCD5hloRS\nLCuCT6w89BCXRfnvUgeUMhPGJ1akXPjEigjKgw/mtdCSXuOcc8KdIp9vYJzl2f7OLZe2aL3hBnY0\nB/j/2L8rNGz1wgvslykO6mI1dKOTA4FvoL0gai1IW6ycB+AsAH2NMasZY1a3X8WciIhOI6JXiWge\nEc0konuIKDaQPRHtQ0STiOhrIppLRC8QkWfOSXKiLCtSYKQBtns3u+zCMQ0OPzz6vFttFUxzth+U\nUsVKXGUfVXCL9e7u3p2jeNrXk7FRu/Gye9hAspVVN9yQY6fE9XCjxEpchFQXu6JvaeHrbbtt+Bjf\nPbDDmNs+AT7LSpJ8lR4REF3Bu5833DCoOLp1C/vVAPllYMYMPveeeyYTH3K9xYujx7Yvvzw6tsx6\n6/nj/USJlbfeChwX43x8Gho4T49JYJsdOJC3ri+N3BM3T+OGgaZOZb8oe2aLHaDwgQeAI47g8ib1\nQEuLv3fqI0qsSPmyG8izzw5EzGuv8f3/7LN8y4oP2yG/kOP/998DV14ZRN8tFneWTaHjbM48k4dH\n5f+cdBLHZrKHViT9PrHc0cFiLW5oTeoMOUbOG2VZKRYRJvZEhfZ2XmxU4hf5ruem0/ceYKdbn4Xj\n8895uEjwhbCwkY60DKfFWRglzpWU8dtvjz93pUhbrHQDcJsxJo0lr4aDZxcNA7AjgGYAjxFRr5jf\n/ATAowB2BTAEwL8B3E9EJa/tWWgYyHeTpYDFmQxfeinoKRa7zoyPOLEStb5FsU5r9n+0p64C4cpj\n4ED2wViwgBs+aUDinMvk3MWIFXcYKElFaTsuimXFxXcPfv3r4L09zdzns+JiB/gTbDNtlFhxA0j1\n6BFevdVNu/tZAqXZlVgUtlhpa4suh7/9LY+H+8rb4MFcpt3K1M2TtjYuCy0tgW9M1MwHIem027Y2\n9uFye7NRi23GWVZOPDHfQmOLtJ/+NBBb0ogecwwPXUq5sAMeukSJFZlm7Zry7eentZXzzLbqRjnW\n2qslF3L+jfN3KeSvAQT5GWdZaWnxp+OBB8JOo1IG7aEVyedXXgkC7wkvvsidQNdRXZg7NxD77oy0\nqDgrxTJ8OG/tiMgifOwyFidW7DL6zTfh76IC+11zTdgNoFA74q5BF+eIf8cd4bS7EYarRdpi5SYA\nHgN/8RhjdjXG3GSMmWaMmQrgEACrgEVI1G/GGGMuNsZMNsZ8ZIz5I4APAIyM+g0QbyIvJFZcywoQ\n3PikPiF2wbXjoBRDXGUetRq0PPhx0W9t1lgjeO+KC/u/DhvGldEzz4QfoLg1kyT9bn7vvHPg8Oo6\noIpYkfwWR8s4U3dbW3gtHPd6u+7KFZ6LfX8POCB47xsGcs9pO6uuuSY3cL5Ab24Fb/t0AJyv//kP\nOz/6pni6n8W61StO3lu/bW1l59zPPy/On0l4911On50/gN+yIg2NPcMojqTpaW/3O3jL52IcbH0m\n8ebmcK/VnSU3axbfb/HJam/n2Ts+f7JZszhGhisyRKy41jCfv43tYBs1NdnOu0KWlbgpx0nW+yrU\nYejo4Pw54ojoc8iEAVnbyLas2M+2rJMmSNqvvdbvSPzNN8Eab3GWFd/6bEnx1cMdHbzf10b4sL9L\nmga3I1jIQu+2W4UczWXGKVBa3ZAGaYuVBgCnENHTRHQZEV2Se40lIs+6wEUhTWriZQKJqAFAbwCe\naidA1Pa4cRwd0L7xpVhWxNSY1EpiPyi+aLpJKCU6pTz4d93FgeaeeCL+eNvDPM6y8n//x9vvvkvu\nqGePuwunnMI9WUmn+AJMnMhTT+Wabi8lLjR3W1vQY21uzr+/d93l95uJeojthqt7d76220s//XSO\nU/L55xyDY+ONw8LEruDjeqQiembPDirAuDSKZSXJtMtlluHrjxjBYqiUCkmGtmR2h+CLs/Lww/x+\nk02SnTtp+W5t9Tt421YjG7eHWYjmZo5B8sUX/NmNP9TezjO0xJrZ0cEWmqjx/nnzAj8fQaZi9+3L\nU1EFX0A72wIg62u52NG24ywrHR3AYYcFn91OTJIZfoUsK+3t0Q7lwuDBLNhkeQ57OrAtttz/Ytfb\nvv95//0sAJub88uDXYcUEyjQxffciBXUZ1mZNy+or+bPB267LV6suP5zguvPVajtiRIrUc9ZZxQr\nGwJ4HYABsD6ATZxXSeREx6UAnjPGFONadSKApcCB6iIRZ9ff/557s7Yq9zlHiVg58shw9FpBxrjX\nWadwAokCp0Cg9JDYpfxuv/24sVhnHR7XtkP++/DF9PCJFekRvvhi8oLtWlYefph7nd26BRVUWxsL\nmJEjuecqx7pi5YILoq9jD3G0tuYvYBeVj1EVlz11WMak3UZlww25ARIR5Pb87fRLI+5DymJLSzLL\nikxxTWJZ+fnPuRHeb78gjUk5+mjOhyg/F2nYhbY2/s/duyc3KRcjVuz8veQSFnkyRu+a0eN6lL5h\ny+ZmLt9irZT/LI2jDDMmWc/ITrObplVW4Zddr7jlSpwt5RjfSuG77Rae8RY3JPjpp+GZRJttFh7y\nS/JfpCxHdXzchti3AnlHR7jM2pYVu26OEytuWo3hjtNOO4WH39ra8idMiHNxWmLFNwx01lk8yeCn\nPw38W44/nh3i7SHRa65Jfu211+YYYEB+2b3yyvBnV6wUEmj2sx23FlolSVUjGWNGpHk+i8sBrAtg\n66Q/IKIDAJwBYA9jTOwyWMaEx2plOOEf//BX9DJF8Kqr7OsF7zfemAudrMxbiKTxJuIo5XfbbRc4\nzCalXz+2mMSJFcmLq68Or3EUh5xHfisPk21ZWbLEL5iKiekijVmhdLhE9bzt9Gy+OVsX3BWW3eu5\nPX9brMyZw/5MvplkUmFMmuQXK25FI70tXyPm0tDAFZ00bMWIlcsuA8aODQ8T2sj0UKGtLUh/UotG\n0rgfS5aE8/fee8PBEp9/Pnx8KWLFxhUr7e0ssOW4JLPDfA7i4vRt95hdUSOWFXsldZfvvw//t+++\ni3YCd+PIdOvGgqVvX7bmJakrCg0D2c9qYyM76B93XPgYNwKw7bNir7JejFiR8tCvX1istLfzZ/se\nSKctTcuKr6y74Rtk2NG2Hrk+K1Gcdx6Lnu23Z2uvm/+2Qz+QL1ZkSrVdp6yxRrB0hJ32WomVzIfA\nJ6JxAHYDsK0xpoAb3v9+83MA1wD4mTEmIgi9MAZz5+6B/fffAwC//vrXCbG/IMofE/UtfFhKYLRS\nHhDAP+PmiCN4fn6aiLXItYS4D+m55/J3hYYgxGfBHQaSh8m2rLS0hMdioywrcXnoiwya5Lf2ftth\n1n64Zeqx6xfgioq2Njbv+8bLb76ZBZ7PmVHy8ne/8zsAutfp6GCrTjGzviR/i3H0Jgqb1m1uu43z\nY9llg31iWSm1rBfCdhaO818aMiSIv/LZZ9FDRDZuvkjFLRFxJZhbMZaVadPC125tDa5j+5D4xEqh\nfHzmmfzvo4Zo3LySNOy9N2/texhFe3t+ebMtzHaeNjRweXNDB7graNuWlbihnihrJRDkXXNzvmWl\nW7fgeDstafqsuJYVl/POA+65h9/by4UkcY4HONL6jjvydQYMiC7L7kiBmyY7/WKlAYAnnpgAaR9/\n8QtpK6sbjSSzYoWYcQD2BLCdMeazQr/J/W40gOsA/NwYE2NUF8aid++JuPbaieC1FycC4OAUvoBh\nfI38MUK3UrLDxBdDmiujXnBB6T4wUdhrwgBBheEKgN6986Pk7rln/vnEf8S1rEjexVlWosSK9Dh9\nFBIrURWK7VzY0sK9l2nTwmIsqry415OFIGXIR9I/aBA3mlHlxu3RFLKslDKjQcRKKePS7hpKAFsY\nZ8wI+01ITIi0VwHefXfupdsdhTix8vrr4WmYScLk21OXgcBqJX4Y4uA7cCAPrcY5kgoXXRQ2s9ti\nxRUxNq5lJQo3n6PyZMSI8GcpC1deCRx4YLLZgx0d+b4u9hR7V6wAPHxh4/q52WLFvkdjnLbSzquo\n9ah8YsW2rNjB2kpZuC/pMJCLvWSEiBagNCuGr+PgOvG7lhXb5+pPf+Jn1s7DnXYaDWkfH3xQ2smx\nxSeuDDIrVsBDPwfmXguIaEDu9b/HmojOJ6Ibrc8HgGcknQDgVes3sctQzZnjf4Blyq2Lr9D5evel\niJVyV7b81a+C95VwhJJzSgUo8UfcClEqBCnwAwb4ZyVJhSgVl0TYFcfL7t35QXrjDR6qsyuyuGGg\nqB6tOy036QpW++7LMzoaG7ms9OvHPcZhwzh09n33+VeRttPpSwsQlJPhw+Pvv+vh7/NZsSu3UqwX\n5YiVqMXeHn007LTsWlYOPdTvu1AsZ57J/iT2MJD9XPumkNu4ZcH9fMkl+ethDRzIw2CS7yLC+vZl\np/Wks53s8holVo4+OvwbV6y4YkOQ76W8JF1k1S4LK6zAs29+97v437S35y+GavvB2HWipGfZZcMB\nAvv3D//erksXL+ZjfZbHJGKlqSl+GMgeZip3GMiegdbQkO8fF4X9bNjPuD2V/Ywzoqdo+4JyupGF\nXcdyEYEffcTnHjgwvNCsHHfccbVbfTnLYuVIAH0APAXgS+tl920GALDnbhwO/k+XO7+JrQrXXDNw\nLLSJckz03ayklpW5c/NXTbYpd/lHO6ZKJcSKHfvAvoabJ83NQURKOc6XH9KjlPNttBHngQgbCWcu\nw1n2TJMoywoQLVZmzSqtR0/EznDSa7crkz324NfGGwfOvba5NarSc6NnNjXFr9jrDkG4/4OI/V2W\nXRbYcsvqixXxvXIbm1/9Khwe3fZZAYDrroueySK4vW8fkuYoy4rPWR7gmEBAYbESlZdujJqk5eu2\n2/z7o8SKhFsXdt6ZF1yV/73JJv76Q/JC6rNCvnRynF3eRIxdfnn8bzs6wsHPfN8Lkp89egTrRfmu\nYVtWFi3i4/feO/95iBIrr74azKKRGYDnnRfMvrOHgWzKHQayZ4w2NuYPb0VhR5C20/DBB8HkjSFD\nomfS+cSK/L+5c8NBDF2xYgdnnD+f03399fnD84MGpW8ZLUSNJiEVxhhTsKgYYw51Pm8bdWwUw4ez\nYnWnDwLFzQxJKlYmTw6cllw22SQ/Kmmx2LEZqmFZkQrDrSRlv/TiXCc2wbWsuIhlRc5n9ybkNz5h\n0tbmN6HefbdfLG65ZfyUZ0H+jy8ibFMTr7z93nvANtsAv/wl74/qibjB7KL8RIYN42GUJAHoNt6Y\nRdWnn4ZniiSlHLEiM6Psnvv667PlwaYUn5WpU4Mp61HYQtpnWfHl76RJgf+ZlOGzz2Zh4K6jE1Uf\niDgyJhgGSsL++/MsLJu2NhasPrESRSH/IluszJ/P9ySuUySNalSsjrihp0L31SdWXNZy4pQTsbP+\nVltxo9qzZ7Cukp2WKKf1zTcP3jc387P+8cfArbfmi5VSYpzY2Pfefr6LOdeCBfn1LMDpPOAAFtc/\n/jEf44uSHidWAL6/UcNAdiDThgZ2qt5wwyBshPzu3Xf5uy22SP6/yiXLlpWq0NHhFypAdIjyJJYV\nd7FDIa6SOPvs8k1sbW0cK2bw4Mo4MEpDL4GbCokVUewzZ3IP55JLwmvaSLRTewqwjVhW2tu50rH9\nXuRBLsaysvTS+SsFA+xY5waZiuO006K/++c/eT2RQrgOtr5G5+qr2Vpy551BPCDB9gOxaWzkMNql\nWFbkPrjXSoJUsLZfwW675R9Xis9Kt24sAH3mf7GY2BX85Mlcbmzh5IqIfv24LLgWurPO8ls9ovLS\nLofl+uKccUb4WUoiVgqJIzlHkinsNra/jb04Z1SannmG/YBmzuQoz65IBeLFwMsv+1cjlojRhxzC\nPkY9egTPyqmn+tPlWqGEpqbA1+WQQ/KHgZKIqTjs39gCyHeu3/zGH7zu+++D/2f/rls3rne/+IKf\n06hgnnHDQAC3d1FxVuyO9IUXBks+uL6E4vtTTbq8WIkK1nTRRdHmzHKGgeJmB5Ti4+I7/5//zAt9\nVWJs8W9/48ZGosomtawsXMgV2QknhOPKSH640xcFsaz4HGPjhoGiKtTGRv9wQI8exVXmaUzfc31W\nfA+//Z979gwPp7jDLcJ333GDUYqD7WGHcYXl9viT4ApUIJz+L79kHw53GCgpK6zAPb9HHgmP34tf\niAisZZdl68Qll4QtK24EVpke7pq4o4izrAD8v4oZBvIhjroy/T0Ny8paa7HwKCZmBxAeZrAb1ag0\nSZTejz7i4ejttst3urYFtptPm28e+KzZuEsA9O4d/OcLLwz22w1tlAWuuTnsW5W2ZcWuc91hIJee\nPf2Cw15x2xUrLh9+mL+CcyHLyvPP54sVn8+mfe2kz0gl6fJiJWrBJ9vhy6UcsRI3bplGQah0YZKV\nSAVfA2XvL+TMZ8dT8dGtG1eUN9wQLVaKcbCVRvKHPyy+p2kTFQCtGFzLiq+H7JZPO5+ihgyHDg0W\n2yy2wm1oYJGe1kwIu5IeOJAdT8udurzzzjycIU67Rx/Nla1Yha67jtP/3Xe8/6KLuHcu8TNkBpdU\n6Ekr4jifFYBjYkyfXp5F0575BoRn4HTv7s/jQmKlsZFn9Gy/fb51EmBrTtxKzUB4+DVKrLixZdZd\nF3jzTR4OPf543tfeHpTbpPnkNugyBOJSaH0pgMuf7egvVo+ODuCpp8q3rPhWU48q63Gi1hYrIvh8\n/3mNNfLjGxUzDPTXv/KK3i++mH9uuw5QsZIBogpMXM/Z18glFStxUynTsKwceGD55ygGeQjd4G+S\nfy+9FP97KfxRFe7IkUHj4guuBhRnWRGx8tJL+T4JxVCOZUUcUZcsYQuIpN9XFt0ZVPZ1Ja6Ly9JL\nczkrxWelHJqagNWdtdXd/9TUxNaDNKYuyxBi377h8rP00uyQ29LCr169uKcveeHmabmWFdm/8sos\nWNw1rOKQaKnCxrklV8WCZpdjN86QEDUMNG4ch5i3GTiQhwhlyLO1lS2d9srSPkRsyG98RAXCu+GG\n8Ew5mdWUVAzccUc4Auvvfhe+3/ffn6wRPflkHtbo1YuXBFlpJU6r5MXxx5dvWbGDuBUaBoor/zLr\nsaGBLVZPP508Pa5Yuf569tWTuCmzZgVp++orHtL2BV1UsZIxShErvuGhpGLF14jKlMo0CkLcTKNK\nID3ANdcM75dFyGzvcpf+/f1T9GzWWCOY1hoVV6SY4TYxya60Un7DmgQJjFWOWJFe6v7785BUXPwK\ntyGS64pjuI9evbj3FFVJVhIZHhTceyYzXdIICidizWdKF18nu4GX67lTmN3x+CgKDQOVgn0Pn3mG\nrUE9ewZOpvZq38YE/+Xkk4O4Pq7QX3ddLidHH82xZ9y0vvgiD/FMnJgfbTkKu+FyQ7cLkg7fs2ff\na99Mlzi22io/Xo39n/fYg/0w7NXQhYsvDt6LxREIQhAAQUdr553DaU86e8fG7ozasU2amjj8gk2c\nWJG6qaGBrauymnMSmpq4EyT1ymGH8X9Zbjm2CEswQYCFqx3uwsa+51LuyrFGl4uKFWuuvz3tN27V\nSjsQmFSYScWKr2GS3lQtVWupyPCPW4hlXDnKktTczOZnEW9xFb7ksVuxFpoN5KPcBlwczqKEQhLc\n/yqCL0l4dxErcdNDe/bkcy1eXH2x4sZacf/ruusGs2bKtazEibzm5qB3KnnoOhMKkkdRz584Sxca\nBhLGFhEry44/s8027Fdi58sGG4SXCJB6Sda0AfLLyBtv5AetFNwgjcX6sQDsQB53bl8+2nkndUU5\n99/N8wceyI+EO28ecNJJwWe78W1oCOqmo47izouE9d9hB+5I/OQnxafLNwwkM5gKBXW0kU5RKXkk\n1svDDguX9WefZQuXrCkFcD3tWmjlXtn3bP31eUr52WcXn560ULGSKwwtLdzb2GUX/hzXc7YfeOn1\nl2NZiVo9uB7Yfnt2UpaQ3DatrcGUN7uHAwSN1W9+w5+jYmAAgX/ICy+E98cNA0X1GMttJG++mc3O\nMhsqjkGD/A7cURFFk4iVJD0c+W7BguqLlU03ZcEivWf3v/bowc9AS0v5aYsLuNatW9AzdsWKrLsj\nxJm411wzyPOklpVirJvLL5/vDBoXmVjy9auvgnS715fAZ0nS6jpnJiEqH+SavmuXY1mJu5bgs6qI\ncBD/Qzs0QWNjsJRBYyOXy/nzuWyuvDLPBosKChqH7aMn9dKkSSxWCi2PAQT3VNqfcqZPP/BAfj0o\ns1TtNbH22ovF2YMPcnsm/9sdBvrtb6NnyFaDLi9WpDAsWhQOU5xUrEgjW4xlxS2AUrjq0bLSvz8/\nFL7otDauQ6oMx4wYwf87bg2hqIo3bhjIXZTNvW6prLQSm9aT+IK8916w9oyN22CIkLPXgYk6Vspl\nXPmUvPz+++pHm9xtN569IdPTfWIF4OetXMvKuHHxljuxrLiBC92yZltc7r03/N0rrwTPZaGpy0Kx\nzteFgv3Z15V1dhYsKByfJ8m1CvmU+fjgA39MIvnfW26Z/539H+KGi5Li/g9f3Sl1uQxRu4soCj/8\nIZeRv/+d86Occmnfe1n4sbWVOxDusxgX2kKG0YsZ/hGkvLe25i8iKmJlwQL+n83NHMfpttv42T32\n2PiFMWtJlxcr9oJjScWKTKVbdtnokO8NDdEPkPugpWFZuesuDmueVXz5mXSsv5BYaW/n/w8EveZC\ns4GqQVOTv4Fz94mjqDi52SHN3f8uPZsklhV3xd1qEuWLJOVg4cLy09bQEF02unULxIq77pQv8i/A\nQtS1ENr+MFHlxu5tHndc0NAkZd99ww6ocWJF/G0WLAh6zcWII/eZk6nSxeI6BgPBvb3oovzv5D+s\ns04wGyhqeYYoJk4MQvdHDaXaxA0x23nqzrgrp1yee27wfqedgjp9yy3znwlfjBURemuswe3HRhsV\nnwb5vwsW5Ee5lXZp7lwu2z5BIkOTX39d/LUrSZcXK3bFICHigXiflaFDWY1PnhxUZm4QrTjLis9s\nC5RnWdlnn+i1abKAr4eQVDRENUj2MNBNN/F7Mf37/BmMKS32SNpE9Vik7NkRPN3/PnIkz66IW6BS\nylMaQy2lEiUMpGF94onKisbmZp6KCuRbVtznb9Ys/zmkMXWXmHCxLQljxxZvWdlnn3BHI06syPTs\nd94JxErcEKpLoQ7CySdHf2dbpPbdN/97qe98IkT+w9ChLAifey46GGcUI0cG6w65z4XtDHv00exT\nJmLFFav2PjlXlNWlWNxwAtOm8da2rMjzLffSjhXz/PMcS8gXbyYp7j22F1iVkAaPPRYd+uCss3ib\nJMUfAMcAACAASURBVMZPNclsuP1q4ZonpdDGWVb69+coggD7LkycCOy6a/55o3xWKmFZyTpuCG0g\neaUQ1bjL/ldeCYZbLriAh5Z8YiVuinAWkLJnlw+34llqqSCMfxS2xSlrlhXx9ZkxozSfgKS0twdD\nRK5YcdMUFQtovfV4e8wxXOG7vi5C2uUpTqxIr9d+JorpfReaxRb3X2bMiF8MUqbK+55X2xm4qSk/\n1EGxuHWoHedp/nxOS1Kx4q4NVO79lJloQBDBuWfPwAK3117s+ybl66STWCSOGMFp9EVpLgY3b2RK\nPRD2WYkSrvL7JKtsVxO1rFgF056fXszU1JEj/QHLosRKJSwrWSZq3LXctYuk8rnqKt726xfM1vE9\naCIGsi5WbKteKSGtbbFSsxVSI8TKeutxo3fiieFlF9LGdlotVaxIz3PwYOCcc6Lj2qSFRA2OcsC2\nv7PTXEwZiZrFJiLEXtXcpdCwTdx09KiZS6Xi1h0yLfjss7mMde+eL1Zt4iwr5Qr8O+8M3ovLQM+e\nHEH5+ec5jUOHhi1wCxaE1+UpB/f/2hax1laOL9XaGl0vF7MuVTXp8mLFjcYqnvxpNKRRw0C+uAhA\n57WsRA1PJRUNcSLuD38I3tt5Wy9iZYMNeDGwX/86qBzs8lFKxSn59eGH2RsGAtgyedFF3MOsFLbg\nc3vWSfwdgOJ9KsolataRvUyAfGcP1xRTV0WZ/uXaIvaT4D6XcWJF8j4tseKep1cvbpTPOIPTMGdO\nUK/6LCtRFnX7+FKxO7oimGTfVlv586BXr8oJOftZuO8+nkk2e3b09VSsZJTm5kDhLrMMLxz34ovl\n90gLWVZuvJE/9+oVjDl3VrGSdBZFIXzm7hNPDN4XEiuSv7X2WbHp25fL2zXX5A8DFQqBHoXtg1Gr\n/yoNWa2EoW8oLYllxS5PpTg3lkOUb8xqqwGjRwffGRMWK8U0cu7sEMGeKXXjjbxkQSHcmVhxYqWU\nmUtxuNfp0yfoaEbNtowbBkrTsmL/3h2KqgZxYkWYNy/aL9Ne6ypLdHmflaYmnmL6zTcc+KZ793SW\nvY5zsG1uDkxzCxey+fepp4D99iv/ulmkXLEiDZ/EwLGxe4p77ll/lhW7ApX0lWvVi3MOrxa19g+y\n80Cm2SaxrNj3I25pjEogvW9fnonPV1xsjiQcc0wwbGojz2iPHskXsWxpCVsR4sSK5GWlymZLi3+l\nYsAfc8m2tjQ2ciA4cdIvt5GutVhxr9XczAEHV1oJuOUW3rdwYeFYPOqzkkFWWYXHENNYSVcgYnOb\nbSr97jtear2tLRwdsU8f4NZbo0209U65YcrFqXDzzf3n6NGDYwXccktQUfhM+xL3IKtixR0GKNW6\nZ1eWvjgv1WDttXlbypIGaWBXxLZzIZB//w87LHjvW2m2WsiKxL7rSprLFbIyNOISFYMmDlvMXXIJ\nx+iI8nmRY9OyrLgsWeK3oAD+NLlRi2+4Ifju738vLy12GSoUo6cSuOW7sZE7w+PHB/sWLVLLipLj\n2Wd5O3FisMrpH/4AvPYav6+0s16WSBqmPIpBg3hoQ0JQu3z7bf6KtDfeGF4ptr09mM2RVbFy9dXA\n7bfz6sBrrAEcckhp58yCo/Zuu9U2HcX4rKyyCm+HDfMv3lYtRo7kIcFttsn/rtAaWuUisXmKEStz\n5wYhGwo5S0svvVLDQEuWRFtWJLyEbxhoq63yvyu33Ba7aGHauNf3RVT2+U4K4hbRv3+66SoXtaxU\nCAn0ZYeBnj07eJ+mFSeryIJnUZW+z1ISRZRQAXhKoN0Abb11vKl/6NDk1600dt4MGMC907592Tk2\nbv2fOLIgVmqNXRH7epouH33EYdFrKVbWX587N7Liso9KNXqHH87xPop5Ju0o0YWGd9IWK2uswSsn\ny0KpixcHz/wHHwTH/eQnwGab5f9exJn4AqVJ1sSKxKZxibpngwaxz1JczJ1aoGKlQogJraWFe/Sv\nvx6YI0eMqFmyqoqEj3etSDvswA5ecTEbymHzzcPCEAjEyp13hhePqzVZC2ndWbAr4v335+1bb/HW\nZz0YNIiHYe2KvljTfTV8hcodBopi00053kcxAe2kjjPGH43VRgKglSrAXYh4Ornd+xchJGl5+22O\ntCvi3X7W9tiD18KxhwDTIknU6kri1ilR1rIo4UgEHHpo9qz/OgxUIeRGf/wxjxdefjk7Nf3sZ8Ad\nd/B3w4dzSPTOiogz15w4aVJlr9unTxC6XnjlFd5mzaIV1etRymOjjVgU9+wZxBY58URuuPbZB3jk\nEb+Zu1TLyrRplRPfNmn00E87DTj//PC+7bcv/jxiLXnwwcLHbrUVr7tTjOUmCb41h6RjIo20TEG3\np6J37x4EbBOOP559b8olLiheNXCvJWsjuWTBEb8YVKxUiHvuYYe5yy7jz198wY33iisGxzz6aHmL\neWWdo47itUeqbUlqbMzP14MO4m3WhkjOOy/9c9r/sdACk52VPn3yRfFqq3GnAQB23tn/u1LFiiww\nWGlssbLvvtFLBcRx3nn5YiUpQ4bwNT/7LHCataPHxjFsWGnXjMMN6gkEYkWsRPvuy873o0bFn6tS\n/jRR+yqFPYvnL3+JdqqulLNzpcjsMBARnUZErxLRPCKaSUT3EJEnaHve70YQ0RQiWkxEHxBRgeDk\nlcFtJB59lGcH2Sa5Hj1qu+R2penfH7j77uKc9tLAjkQsyPTVqABgtaISZn0RK8OHh8fvlcLU0mcl\nDrmndnn517+AJ58s/9w+n44onn02WHNJGsVaWivteySNr4goESsNDcABBxQWDJUUK9X0WZH7ucIK\n4bhBLvVmWcmsWAEwHMBlAIYB2BFAM4DHiChyvVkiWh3AgwCeALARgEsBXEtENVnizxUis2Zlbxii\nM+KzrAhZEyuVQBq2bbetvlCsd+xykyWxItiNXtQ6PMVSTJ3Uq1cwnCJipdiFGyuFLLEgMX6KbYzT\nEityXXtSQDUtKyJoDzssXiSpZSUljDG7GmNuMsZMM8ZMBXAIgFUADIn52ZEAPjLGnGSMec8YczmA\nOwGMqXyK87n44vDnuLntSnr4LCuygJ5sOzPii5GWM2NXIutipRKNXrEdKDfwYlY6YBLM7tprgXPP\nDWb8JCWtvN1gA/bj+eMfg31rrJHOuYuh0JB3GsFPq0lmxYqH3Gx5zI45ZksAjzv7Hsvtrzpu4Xcj\nPnY1JKZBpfFZVoYPB1ZdtfzVXuuBIUM4AN4vflHrlNQfaYZdrwRpCag33+TI3UC0T0MUrljJWgds\n5ZVZKNRyEc/ddmOfvYceYn/FSs3i8iH/2ydW7r6btyefzAt01hN14WBLRA3gIZ3njDHvxBzaH8BM\nZ99MAH2IqLsxpqqDAL7KLmsPdjWZNKk6s598lpW2ttpFU60Fm2xS6xTUJ1m3rKTF+uvz9vnni487\n5PqGCC+8UP31lNJE7vc//5nO+Xr2BHbdNZ1zFUOcWNl7bw6jUS2H8DSpC7EC4HIA6wLYOu0Tjxkz\nBn2dOYejR4/G6BSiBalYCdOrV/Gm2VLwWVba26vbuynE0UezpUfJFs8/H7zvzGJFKMXa2djIz5L4\nf0mj2KdPdZ7vSpO1yK3FEidWAGDjjYs/54QJEzBhwoTQvrlz5xZ/ojLIUPXth4jGAdgNwHBjzJcF\nDp8BYICzrz+AeVFWlbFjx2LIkDg3mNLxOTd15WGgatHUxE52xgQPbltbtsLsjxtX6xQoPmbMCN7v\nuGPt0uEigQwldHytWWqpYIFAcWjtCuKuHigkVkrB14GfMmUKhlYxHHhmxQoREXg20J4ARhhjPkvw\nsxfBwsZmRwAvpJy8RKhlpTaIKLGtKW1t2bKsKNlk5ZU5hkjW4vEceCCw3nrZWSqiV69ArNRisT4l\nGokWXAun3kqS5eJ1OYADc68FRDQg9/rfRDkiOp+IbrR+cyWAQUR0ARGtQ0S/BfAzAGOrmvIcvodX\nLSuVR8SKHTCrvT1blhUlm9x7b7AIaZZoauJox1mxXnTvzouyGlN7y4pvob6uzDrrAJMns4NvZyLL\nYuVIAH0APAXgS+tlxyEcAOB/K70YYz4F8FOwNeU/4CnLvzLGVDjAu5/PPLYgtaxUHrGgDBgQrBOi\nlhUlCcstxwthKvH07Ak88ADwn//U3rLy+OOcDiVgyJDsCNu0yGz1bYwpWPSNMYd69j2N+FgsVePd\nd/P3qVipPLYFZfp0XqdJLSuKkh7XXsthAObMCRrFWjWOq66ajrO6rFvU2YZPOguZFSudgeOOY3Pc\n+PGsdAEVK9XAtqDssgsvaPfNN8FYrqIo5SEOv6efDsikkHr3WdlxR2D+/GDhSyVbqFipIEOG8DLl\ntoVFxUrl2Xpr4MgjeSXcDz4Abr6Z9//kJ7VNl6J0FqQee/HFYF9nGHZQoZJd6lwL1wf28EO9rcdQ\nj/zgB8A//sELrv32t7zvqqvSWf5dURR/PVbvlhUl22jxqgIqVmrPwIHqYKsoaeGzEHfmFeSV2qNi\npQqoWKkdWYuXoSidAVus7LMP8NFH6hOmVBYVK1XAFigqVmpDZxhPV5SsYNdjRx2lK3wrlUfFShUY\nODB4r2KluqhlRVHSxxb/paw1oyjFomKlChAFc/hVrCiK0pno3bvWKVC6AipWqoSM8apYqS6yquxa\na9U2HYrS2TjnHN7qEiJKNVCxUiXkgVaxUl223x5YtEjFiqKkzemnA4sX1zoVSldBJ3NWiWOOAb74\ngterUapLjx6Fj1EUpTiI1KqiVA+1rFSJPfbgSLYaIVFRFEVRikPFiqIoiqIomUbFiqIoiqIomUbF\niqIoiqIomUbFiqIoiqIomUbFiqIoiqIomUbFiqIoiqIomUbFiqIoiqIomUbFilJVJkyYUOskdDk0\nz6uP5nn10Tzv3GRWrBDRcCK6n4j+S0QdRLRngt8cTERTiWgBEX1JRP8komWrkV4lGVqhVB/N8+qj\neV59NM87N5kVKwB6AXgdwNG5zybuYCLaBsB1AK4GsC6AnwHYHMA1FUyjoiiKoigVJrNrAxljHgHw\nCAAQUZKfbAbgU2PMuNznz4joagAnVyaFiqIoiqJUgyxbVoplEoABRLQrMf3B1pUHa5wuRVEURVHK\nILOWlWIxxrxBRAcD+BeAbuD/NhHA7yJ+0gMApk2bVp0EKgCAuXPnYsqUKbVORpdC87z6aJ5XH83z\n6mK1nVVZ156MiXUFyQRE1AFgL2PMxJhjtgDwKIBzctsVAVwE4FVjzK89xx8A4JbKpFhRFEVRugQH\nGmNurfRFOpNYuR38f0ZZ+34M4FkAA40xM53jlwOwM4BPASyuRLoVRVEUpZPSA8BqAB41xsyq9MU6\nzTAQAALQ7uzrsL4LkcvciqtBRVEURemkvFCtC2VWrBDRUgDWtHYNIqKNAcwyxkwnovMBrGiM+WXu\n+3sB3EBERwJ4DMBAAJcCeNkYM6OaaVcURVEUJT0yOwxERCMAPJn7aBBYR24wxhxGRNcDWNUYs531\nm6PAcVlWB/AdgCcAnGKM+apqCVcURVEUJVUyK1YURVEURVGAzhVnRVEURVGUTkiXFStEdDQRfUpE\ni4joJSLarNZpqgeSrNlEROfk1mZaSESTiOhHzvc9iOhyIvqWiOYT0Z1E9APnmGWJ6BYimktEc4jo\n2pwfU5eDiE4joleJaB4RzSSie4hoLc9xmu8pQURHEdEbuXyYS0QvENEuzjGa3xWCiE7N1S9jnf2a\n5ylCRGfl8tl+veMck4k875JihYj2B/BXAGcC2ATAGwAeJaIVapqw+iB2zSYiOgXA7wEcAWAYgAXg\nvO1uHTYWwO4A9gOwDTgmzt3OdW4BMBjADrljh4PXfeqKDAdwGTg/dwTQDOAxIuolB2i+p850AKcA\nGAJgKNh/biIRrQdofleSXMfxNwCmwqpfNM8rxlsABlivreWLTOW5MabLvQC8DODv1mcC8AXYGbfm\n6auXF3hq+B5OPn4F4HhrXx8AiwDsn/vcF8ASAPtYx6ydO9ew3OfBuc9DrGN2Bk9NH1Dr/13rF4Dl\nc/mzteZ7VfN9FoBDNb8rmsdLA3gPwHYA/g3gktx+zfPK5PdZAF6P+C5Ted7lLCtE1A3cW3pc9hnO\nvccBbFmrdHUSVgfQH+G8nQcWh5K3Q8GWAfuY9wB8DmCL3K4tAXxnjLFjZz+B3ANQqcTXEf1y29m5\nreZ7BSGiRiL6OYDu4CCTmt+V43IADxhjnkQ4PpbmeeVYMzes/xERjSeilXP7M5XnmY2zUkGWB9AI\nYKaz/2sA61Q/OZ2KAbmtm7czwYVejmnJFXr3mAHWMV/bXxpj2ohotnVMl4SIGsDxg54zxsjYsuZ7\nBSCiDQC8CBYpiwCMMsZ8SERb5Q7R/E6RnCDcGID4D9pDzFrGK8NLAH4JtmatCHaNeJaI1kfG8rwr\nihWl+uRFEFZK5nIA68IaV45B87083gWwIdjU/TMAtxHHf4pC87tEcr35vwHYwRjTIrtROE81z8vA\nGPOI9fEtInoZwGcARoHLv4+a5HmXGwYC8C14rKy/s78/eHxOKR2JFOzL2xnWMd2IqE+BY1xv8iYA\ny1rHdDmIaByA3QBsa4z50vpK870CGGNajTEfG2NeN8b8AWz+PgpBPaH5nR5DAawAYAoRtRJRK9gJ\n8xgiaoGW8apgjJkL4H0AayBj5bzLiZWcap8M9koG8D/T+vZgk69SOp+AC5+dt30AbI4gbycDaHWO\nWRvAKtYxLwLoR0RDrHNvBy6vL1cq8VmFmHEA9gSwnTHmM+cQzffq0AigwRij+Z0+jwNYH8BGudfG\nAF4DMD73XvO8ChDR0uBlbr7KXDmvtTdyjTygR4HHoA8GeypfBfb0X6HWacv6C8BS4MpjY7CD1HG5\n9yvnvj8Z7Pg5EsAG4DWbPgTQzTrHFeDVrkeAe1QvgH0w7Os8lHsQNgPwY7DaH1/r/1+jPL8CwBxw\nT9OeYtjDOkbzPd08Px/AT8Crym6Q+9wGFoua39W5B08BGGt91jxPP48vztUrqwHYCsAksL/JclnL\n85pnVg1v0tG5DF4MVn6b1TpN9fDKFciO3Kvden+ddczZYBPiIvCikj9yztEdwDiwQPwewJ0AfuAc\nswx4bv488DpP1wLoVev/X6M8d/NaXgc7x2m+p5fn14J784tzlfdjALbX/K7qPfjf1GXN84rl8QQA\n/82V8+kAbgWwehbzXNcGUhRFURQl03Q5nxVFURRFUeoLFSuKoiiKomQaFSuKoiiKomQaFSuKoiiK\nomQaFSuKoiiKomSazIgVIhpORPfnFlTqIKI9PcecQ0RfEtFCIppERD9yvu9BRJcT0bdENJ+I7iSi\nH7jnURRFURSlfsiMWAHQC8Dr4PgnQHgRKxDRKQB+D+AI8EqNCwA8SkTdrcPGAtgdwH4AtgEvzHR3\nZZOtKIqiKEolyWScFSLqALCXMWZi7jMB+BLARcaYS3L7+oCDNR1ijLmdiPqCV3YcbYy5O3fM2gCm\nAdjSGNPlQykriqIoSj2SJctKHKuDF0Z6XHYYXpL6ZQBb5nYNBdDsHPMegM+tYxRFURRFqTPqRawM\nyG1nOvtnIlgRcgCAlpyIiTpGURRFUZQ6o6nWCSgTKvmHRMsB2BnB+kCKoiiKoiSjB3gBxEeNMbMq\nfbF6ESszctv+CFtX+gOYYh3TjYj6ONaV/tbvbXYGL6ykKIqiKEppHAheALGi1ItY+QQsOHYAMBX4\nn4Pt5gAuzx0zGUBr7hjbwXYV8KrKLp8CwPjx4zF48OAKJl2xGTNmDMaOHVvrZHQpNM+rj+Z59dE8\nry7Tpk3DQQcdBOTa0kqTGbFCREsBWNPaNYiINgYwyxgznYguBXA6EX0Azpw/gZe2vhcAjDFzieif\nAC4hotkA5gO4DMALxphXPJdcDACDBw/GkCFDKvW3FIe+fftqflcZzfPqo3lefTTPa0ZV3CgyI1YA\nbAbgydx7A+CS3PsbABxmjLkwJ2iuBtAPwLMAdjHGtFjnGAOgA8BdALoDeATAbyufdEVRFEVRKkVm\nxIox5ikUmJ1kjDkTwJkx3y8B8LvcS1EURVGUTkC9TF1WFEVRFKWLomJFqSqjR4+udRK6HJrn1Ufz\nvPponnduMhluvxoQ0RAAkydPnqxOWYqiKIpSBFOmTMHQoUMBYKgxZkqh48tFLSuKoiiKomQaFSuK\noihKSXR0AIMHAyutBHz/fa1To3RmVKxUiZEjgeOPr3UqFEVR0mPJEuDdd4H//heYVfGA60pXJjNT\nlzsz8+cDDzzA7y+5JP5YRVGUeuDbb4G77go+d3TULi1K50fFShVYd91ap0BRFCU9vvgCWHnl8L4u\nOldDqRI6DFQFvvii1ino2uhYuqKkyzvv5O9Ty4pSSepGrBBRExGdT0SfENFCIvqQiE73HHcOEX2Z\nO2YSEf2oFulVas+MGcBBBwG9ewMffVTr1ChK56Fv3/x9KlaUSlI3YgXAHwD8GrzWzzoATgFwMhH9\nXg4golMA/B7AEQCGAVgA4FEi6l795Cq1oq0N+Pe/gaOPBm65hfedcQbw2mu1TZeidBaam/P3qVhR\nKkk9+axsBuBeY8zDuc+fE9EBuf0gIgJwHIA/GWPuz+07GMBMAHsBuL36SVZqweOPA7vuyu8PPBD4\n5BPg7rsBImD8+NqmTVE6A+3tvF1xReDLL/m9ihWlktSTZeVhADsQ0ZoAQEQbAfhxbj8ArA6gP4DH\n5QfGmHkAXgawZXWTGs1LL9U6BZ2fBQt4+/rrwPXXA88/D/zkJ0BLS/zvFEVJRlsbb7t1C/apg61S\nSepGrBhjrgBbR94johYAUwCMNcZMyB0yILed6fx0pvVdzbnttlqnoPMjPbzVVgvM1d26qVhRkrF4\nMfCb3wDz5tU6JdlFLCv2cFAtLSsqlDo/dSNWiOgYAL8E8HMAm+Ten5Qb6on9KYCaFOXZs4Fllgnv\nW265WqSkayGVZoNVulWsKEm5/37gmmv4pfgRsXLYYcG+WomVvn2B/farzbWV6lFPPit/BHC2MeaO\n3Oe3iWhVAKcBuAnAjNz+/ghbV/qDrTBexowZg76Oa/vo0aNTWcFzxgzgu+/C+wYNKvu0SgGk0mxs\nDPY1N3NwPkUphE/sKmFkGGjUKGDbbYEttgC++grYaKPqp2XePPZJUyrHhAkTMGHChNC+uXPnVjUN\n9SRWCEC7s68jtx8APgELlh0ATAUAIuoDYHMAl0eddOzYsRVbdVkeaBt1Qqs8allRysEndpUwYllp\nagLefpvfn3kmsMsutUuTUhhjgFtvBXbeGVh++eS/83XgrVWXq0I99R3uBXA6Ee1GRKsR0d4AxgC4\nBwCMMQbApbljRhLRBmCLy39zv606PrHS7sotJXVUrCjloJaVwuy8M28bG4M6TcVd9vnkE449ddZZ\ntU5J8dSTZWUMgHlgK0l/AF8CuBLAOXKAMeZCIloKwNUA+gF4FsAuxpiaNFM+seLbp6RLlFjRSMJK\nErIoVmbNAq66CjjuOKBXr9qmxe5wNTYCw4bx+5/8pDbpUZKzaBFvP/+8tukohQw9jvEYYxYYY040\nxqxujOlljPmRMeYMY0ybc9yZxpiBxpiexpidjDEf1irNIkxuuQX40584zkdXFivTp/N04krja2y+\n/57FSpYtWx0dav3JAlkUKzfcAPzxj8Czz9Y6JeEy2tQUrH02eHBt0qMkp7W11ikonQw9jp0PESab\nbgqcfjr37ruyWPnpT4EKuQeF8DU222/P2yw/rMceC3TXWMs1546cC3+WxEqWplHbz1BjYzD8k+Vn\nqxZ89x1w3XXAu+/WOiUBIjTrcap3hh7HzocIk6bcYJs9vtsVefPN6lxHxApRsK9HD95muUK99dZa\np0ABgIce4m21xcpll0UHjZR6I+00GVO8EHLFChHXcfXeEXvpJf4vn3ySzvluvhn41a+Ak05K53xp\nIPeuHid6qFipIK5Y6QwPdD3Q0ZFfqUukzSyLFVtcKbVhxozgfbXFyjHHAFtGxNoWsZJ2Gbn6ao5T\nknQWakcHsGQJv191VV4kFMheR+zTT4Hhw4OlAJLw9NO8ve++dNIgoRKyFDJBLSuKFxEmEuVRxUp1\n8IkVuQf14hPyr3/pStG14OGHg/e+xfpqhQiBtHvEEyfy9vvvkx2/xx7AyJH8/qqrAvGUtbrt3nvZ\nv6cYHx9J/5gx6aRBnFllmwWks/bww8Cjj9Y2LcWiYqWCuJYVY4JeiVI56tWyYjNqFPCLX9Q6FfXN\nggW8LlQx2OWjKUNzJSshVowJhrySnvfBB4EpuRCbtpirlWXFZyFobQUuvJDfF3MPV1qJt1HWrWIR\nkbJwYTrnSwO7s7b//rVLRymoWKkgrlhpbwfeeKN26ekqdHTkx3yQijUrYmWLLYBDD40/5j//Kf38\nTzzB0ZI/+KD0c9Q7hx8ObL11cb+xy0eWhjV8YmXxYmCmuxJaEdi+KqX8V1us1Mqy4kv3M89wNF2g\nOLGywgq8lanY5WBMMASVJbEyxYrlXm9LFKhYqSBS8ckDM2hQ4OjZlXGXIEibOMtKVoaBXn6Zp6Mu\nXhzsc/0RyvFP2H13dhS8557Sz1HvyCyMYsbn60ms7LsvMKDEJVrvu4+HcYRSLDZ2h6BWlhWfQLLv\ndzGB6tJM/1//Ckh0+o8/zj/355/XJr+k/hs6NFuz3ZJQZ8mtL8QM2LMnb1dbjZVtlirBWrDMMmxO\nrhRxPitZsawIxxwT/V05YkUq8VNOKf0c9cyCBUFMnyQN8Vdf8fNql48szZiQOmP27GCfDOGUwl57\nhctGKXXSyisH72tlWbGv+dxzvLUdWs87L/m50nRidoOuTZ8evH//fXZOLiZtpeC7px0d3Gnu1i17\ndWEhVKxUkLfe4q1YVgYO5N7exRfz5yxVhtXm1Vcrd26fWJH4JVnzGXr//ejvyqk0s+QcWgv++9/g\nfRLLyoorsuNoVi0rMnz8y19W5vxJ6iK7wQWAPn2C942N8WKltZWn8qY9C8W+X5deyltbrLz4XduE\noQAAIABJREFUYvJzpRXVtaMDuP12YO21gXXW4X32cJRYlqV9qARvvMHXfO21/LQ1NHD9oGKlghDR\nD4loPBF9S0QLiWgqEQ11jjmHiL7MfT+JiH5Uq/S2t4fNkBdcwA3QjBn8YPfsyVMHuyKV7IX5xIoM\nv2VNrNgNoogTX5yYYunq06Dt8pW0gXziieyKlWWWqez5k/zXNdYIfxaLMcANY9w5rrwSOPhg4Kmn\nSkpeJPZ9vusu9lcpNYDe5bnlbssNzPjmm8DXXwPvvQeMHcv7fOWqktOHxWpz+unh/e3tgVjJ0uyt\nJNSNWCGiZQA8D2AJgF0ADAZwPIA51jGnAPg9gCMADAOwAMCjRFSTuKBffx32LO/dm6PZLljAvgQt\nLazAOyvPPAP07+9X8JVsCOIsK7aPSBaIMtUCpQuOlpZsOfXVglLEChAuq1mqzONEdhrpXLSIG9e4\nvLLzRho8oZBlRdJvD2OlgTuJYZtt2LJSSHAsWgT87W9cRwvi11auVdKuY2Qdp2qL4Dvv5K07PVkt\nK9XhFACfGWN+ZYx5zRjzmTHmcWPMxwBARATgOAB/Msbcb4x5E8DBAFYEsNf/t3fdcVcU5/qZr1A/\nBEkE0auiYkEFKVEiMRYsWBKNsX5YYi/xGiV2U7Bce5TYNbYYRWKJN4oVa27sCnY0BgWNooidzlfm\n/vGe1313zsyWc/bUb57f7/z2nN09u7OzU555ayUK/Ne/UsAliV69aCJhe5aRI8tfrnLh4otpMLAF\nnCrlRMCrBwmWrFQbWZHidyYnxerOb7ihuDLVAwq1PZH/S9NWJkyg9l4qcFuwGYxmEcfjlFNIZZHU\nA61nz3D7ZMnKv/9tN2Iv1WKBxxHpuLBoUbwk6plnKCmklGxzOymWTMixzWYrx/cplWSlo8Pd5tlT\n0pOV0mI3ADOUUncppeYrpWYqpQ4Xx9cGZWN+jHdorb8F8AKAWM/5O+4Apk3LrrCvvUYNw8xE2rs3\nMGNGeUSB1YysVxf33hu46T70ELBgQfh4LaiBGDzYFUpWqkl9UUpccw3Vka0P2SQrb74JjBsXHQBN\nDuBpSMDUqTThc16hYmALSsaLnjPPzD/mCtGfBk88Qdsvvkh2vunV2NhIHjDrrw+ce27++bx4yHq8\nK5Ss8LtlYnXTTcCsWfS9FGRFErhSj/1meAxJXFiyUm1B/JKglsjKOgCOAfAvADsCuAbA5Uqpg3LH\n2YnPjDwwXxxzYr/9KDpjVmC3yUMPDe9fupRWGaVm19UAXkXZDMmy7CiLFpF3w4QJ9Ju9AiR4MPvg\ng+zumwVKQVbSuGvWMm69lbauwGAMPn7ZZcCTT+a7c8u2KNVnhUgB9t23+D798MP5+2y5gYYPp61M\nEZAEUWQt6TOzyoQhy2UadcrjWTsVHHssbaX9zMKF+RJtE7xoYTIxaVJwrNgyxklWSr2YkHVhuzeT\nFfaWqxXUEllpADBDa/1brfVrWuvrAVwP4OiY/ykAZacEn31GpOT73w/vHzYsLKar51Uwe7pccAHw\n+uvhY1dckS666CuvuAdlXg1GhadnMfTbbye/Z6kgs7C+8gowYAAwfnxATnhwcZEVraPbTT0TYAlT\nbSZhk6yMzpniL19OH67nr74Kzn3yyeB7oeqVQie7qABm/DzyWZmUmlLEOFx+efDdbGNJyYpJeE47\nLfhujnlA6cgKp0eQkpUlS0iCHQV+TiYTsl6zkqystZY9vlOpvUD/9a/w7403DjydWLKyYkWQ16lW\nUEUBpWMxD8AsY987APbMfeepbCDC0pWBAGbCgYkTJ6KvoOE77wz06dOKO+5oLcqjYulSsk8xr8G6\nQm6wtSaKSwMmCH37UhA0EyeemFyEPWoUDYK2gZnjTbS0BPvYjZHR0ECTVTVM5EOHhn8vWABMn07G\nyEA8WTnwQMrp8tln9iCDcjD80Y+KL2+hmDCBYnFceGFpri/JimkUKfuVOTm0t1O9bb45tUs5kUhC\nXGi06fb2wqRbK61EBqi2yZL3tbVRbJ7jjy88g+5vfhN832WXcMyjpGTFTM63//7A3ntTn9944/zz\nS0FWpFuy7AdtbdQettqKCIMNLFlhgij7WlZk5cEHK6MG2mOP8O/33iMCs+aaAVnZYAPgrbeSX3Pq\n1KmYylHucvgmafbLjFBLZOUZABsa+9YHMDf3fQ6IsGwP4HUAUEqtBGBzAFe5Ljp58mSMGjXqu8Y6\neDC52Z11Vv6kkgbt7faVEruM8WRaz2Tl4INpYOzd225095OfpLve55/b93P8h6FDg8HQtmro3bv6\nDGxtiFIDvfUWMGUKff/6a3sEU9mmZCyMcoPHtlKRlagJ0KYGMhcIL75IW9k2v/2WQvTPn194tOlC\nJzut6Zlsz8Nl/vBD4JZbyKOQ96WZ9P7v/8K/TVJVjBdZt25A//728vB9OjqA2bNJEjNmDHDyyYXd\n66uvgLFjg982sqI1qQpvuSW/LzFZiSKGhYLfy4ABwXcpiSq1CUD//kR6r7sOOOoo2rfXXmSTWajN\nSmtrK1pbW0P7Zs6cidGjRzv+kT1qSQ00GcAPlVKnK6WGKKUmADgCOSKitdYA/gjgt0qpnyqlhgH4\nC4CPAfw96U144Cp2BWDGWGE0NVFnYlVAPZMVHiCmTbOTlaR1bAajMsEERKpHbHXfs2d1ZUA1wXle\nWC1hIyszZgTfFy+2XydKqlBPYLJim1wOF6b3PCnw1uxzpleEUsCIEcm9JcxJJ22f7uykd79smTte\nCe9jMrF4cVC+NJPe1lvTloXJpkSqmHxUgJtsSWL5z39STBSpjorDvHkUO2TOHPp96aXh45KsTJtG\n9cgZl02CBgRkhdVXsq8V22dY+tvUFBj6SlVjqVX/Y8aQdEXGxfn2WyJt7A0UFxenGlEzZEVr/TKA\nPQC0AngDwG8AHK+1nirOuQjAFQD+BOBFAL0A7KS1jswII8NWZ5W3xkVWTJexeiYrUp1hIyuPPprs\nOnG2LUxA2tqiyUqPHtVNVhicDdVGVqRkyLUK7t+ftptsUt9kJcpmhZPIAfFkxWybJ59MUoKkeaTM\n66Xt0yefTBKypUup3UZJVvidNzQUpgbifsHkwZT+FmvHoFQ0WenoiO6jLvz1rySlvflm+m2SLNP7\nR47pTU35hE72HbP9FDuJH3EEbRsbA1W4NOoutRqIJUubbRbev3hx2MC21uaemiErAKC1fkBrPVxr\n3VNrvbHW+kbLOZO01oNy5+yotZ4ddc0VK8LqiHvuyaasLrJiJvyqtQaTBjyYLlhgn1htXjuFgAnI\nRx8FE4xNBdejBw1i0sC1GsHls5EV6XrtCrDV0UH/rVay8uabVDZplFkIosjKttsG302xexxZ2Xzz\ndGTFPC9tn/7oo+C7a8X7j3/QlvuRDMKWZtL7r/+iLducyH7S0lJ8os+GBnt5pGSFny8NKeB2z31d\nkpU+fYKMyfJ8Jiyvv073l16JMpKuKaHMSuLAdTt4cNjgt9RqIDZBMFXA7NjR0EDt55NP7J5b1Yqa\nIiulQEeHvdHENSTTu8WEy2bFJCu1JopLg2efDb5nHWZbgsW9s2cD55xD321E8ZBDaDvLNNOuUsSR\nFZcbNq+sXCL5ciBKgvXSS2R7U2wyyyibFQ58BeTbrJjqHfN3S0t5yYqMzWIjKzLPkU2ykmbS49X2\nCScE92MsWkQqk5tvBm40loFJ75FEDVQIWeFnNclKr16k4mDjdAkmZtdfT1upTpZl/PbbbA1sGVy3\n661HbeKpp0hq6lLfZgXu/yZMsgIAR1t8aTs6yJVbtrtqgCcrjoYZNcj/4x/ApptGB5GLslmRg1kp\nE/pVGrJuC83XEYd996UU7Ay26bDVPac+qBZp1oamubgBF1lhryeXq2slycpDD5Fh4bBh7nNY1Rrl\nqpsEUZKVjo5gguPVI0+4n3wSPpfJxpBcFrFevcJk5b33KPmoqw2bZKeY9tXUlP/OJBliaVpjo5us\n3HdfECDRRM+eFKiSJU/mpDZzJsWGkjY/QFDHZ59NWzNCNMOlBmIUSla4DkyXY352m9eP6YkjVUXy\nnT3zTPidZU1WeMw/6ywipjxeyfemNb2zSy4BTjqpuH7b3h5PVvj92RYV775L79m0C6o0ujxZcTWK\nKOM6tuw+/3z3OVFqIOnx9f771cdgs8LChZTJlr/b6qOzs7hsp3feSSJgNtZjDy6XJxZQ3GQyZw5w\n113ZiHDjPHVcZIWNCV1lqCRZee45UvtxzJsNNggf//BD4Ne/pu/Flo3rR6pRGHLCYUka1xeTDm4P\nPAE+8ACRAaXCZOWKK8iuRNpBSJiSFdNtPg3MGDodHUEsEQCYO5e28+YFpM9sB7vvHtg9mTAlvmus\nQRPkNddEl4vLtOaaFDzz9tvt57nUQFK6VQqyYgZCk+fweG2q37mOOjvp+uedRwbIxZAVlnwNGRJO\nkdDeHow7TBBkPR15JEX/Pekkeh/FzAltbcE7fv99YKed6Ht7ezAvcdlsZIX7R7WF4+/yZMXVMKNe\nVL9+tI1KP+4iKzYd4eOPk4GdNAqsByxcCHzve/T9m2/sUSXPPZdWRVGBrVzviDv7eecBxx1Hxmz8\nblxSLaA4snLYYcA++wReCcUgzo3aZWDLA3MU0a4UWTFVO2ZdS0lioZPC7bdT/iNeHW63Xf45UQki\nebHAAbvYU2PAgGD13a0b2Q4tWBD8zxWU0BwrONNuIejdO/zOLrkkiNIKBM8sE9TZyIErOimPS3yP\nhgYijzZ1gPk/gPrQvfe6yZCrzfE+SVbS9EOuYxdZ2XTT/P/wOax2kW2irS0g/e3twSLAVNOnBZPJ\n2cJSkiUrUdIMM5/XfDMOewpIycraaxPZ/fnP7ZKVOXOAxx4LpI0LFgSkvNqCxnV5slKIZCWJesNl\ns2LrCHfdRY1TrqBqHbffTmJNlh58/rl99fP739PWFM1LyHch7WD43TExaWwMbDpcUi3zemnBUqAs\nshrH2T25JCucydXVdhcupP+Wm6y0t5MawdwnwTlojj++8LLtvz95XPCAa+uDsp8xAeGJ7f77acsD\n+l130VZKuti+YcAA4KpclCaXQXNWKs5nnqEVtiy7tLPo2TMsxenbFxg0KJ2Uj8eltJm9uc/EefC4\n1EA2spKFZIUxfDgtIhgbbJAvWZHlam8PxqP2diI0WZAVW32yHRK3V36nUe9NujqnhZSsMPi5TLIC\nADvsAKy2Go3RY8YEqj5bJOJKwpMVx4AZxfrlsa22ogb6+OPhc6Jcl01wR6wnY9s77qCtrN+o1Ots\nBGeDTWcP5OdLaWqKJivcSYuRrPBgVKzXBBAMli61gYusRElWTj+d0ht89ln5yYpNUmSuIr/+mp7L\nZpuRFrJ++vXLj2Uxfjx9X2012pqTA7dHJqByAJfERU60NhSzCpYYO5berRwH5DO2tISf4Ztv8hM5\nxmXs7uigul97bfq9+ebJysbB21y2KgybGujyywNPnM5Oe+qARx+ltBkuAm9KVvj55L2k4ehNN+Wr\n+c45J/je1hZI1jgFh1LFkxUbATElK3/PRf36/HPKA2XLnVbMgspmYMuqqL/9LSAsAEm2OTnmiBFh\nibGZsuCpp4KAlJVAlycrLi+VqMYiJzsOyc0rRkYassLXq0Y3UxPPPBMOtb14MTV2M/w2R1ZdffVg\n3/rru69r5rNgTJsGXHxx8NvMIAoE9SzJist4s9j4AtzJC83eLAez88+naMmuDLFxZMUcGDs7iajI\nslaCrMhB7rPP8t/ZuHHZlE3WzzffhFWJ7e2BBIqJpVlfrJbcemvy2JCwiepd5eXr7rdfsnJLyCB/\ngDvOCmBXlZpkJa79cyqAYcPo+w47BMfYlsgGDumQhKyY5T/+eDJQBuySFa2BHXeklfymm+Z7ua1Y\nESxmHn+cJND8X3mvhoZg/8CB1E9kG3niiUA9xhN6Y2NgH7LVVvR76dIg2mta2MYWk6wwnn+e0rvY\n5qBissPbpPpNTcF9Hn00KMuaa1ISWCDfTsZ8/m23BQ44oHKxqro8WZGDu0RSssIwGwevYExEkZVi\nGP3gwWTlLzF/fnZB7hhbbgkcdFDw+9FHycrdNLjj/CDHHx8MGKwD3Wij/Ou6RKK77RbW/9rcvrnj\nNTYGlvaulWWxZCWNZGXKFNIHS9xyS/B9660pHLZLtB5nYGsOJmb7qQayAoRdNaUYmsu2995kd5QW\n5uBvenRwPV1wAdlOmSqq2bOJMGqdX9fbb59/P1cb5fueeGLysjO4vbInUkMDTQbjx9M2Tk1jql2i\nJItAeFwyz1lnnfxsygx2ebbZhkSVx6yzZcvyyYrZbs2FD9vycUTWv/7VbqQtn0cpaodPPAFcdFGw\nX9rLNDdTXbAar08fusZDD9G4kybRKiMNWWEcd1z+vmIkty7JCrelVVcNgtUtX+5uY66xo1IagC5P\nVlyImtBsL8vs+K5kZmbn7d698IRkEh98EA6y9uGH1CjjBpdCIF2FudFPnx4+58MPabLo2TPoOCut\nROW0JTVM2gGSSlYGD7b/34wgnBbcsZOsfA44IFi5fvopqSmkDcKIEbRNS1a6d7fbBpi/q5msSAnC\n3XeHk+slhRk6wCSyLIF65hlSH9oybp9xhp2sTJiQvy9u8HZN9FFgFdKAAbTltjB9ev5K12bTZkpW\nWBVmWygtX05qB5chf/fu4UlSGkv/+Mc0mZsSKBOmGsjsJxdfHF6caR0fAZjJxJQpVI6PPw6MpA89\nNHxvBr+7bbahfsiQCSFZssLX79Ur3BcLiYdiG8eYrKRJjFsMWbG5Ljc2Bu3zf/83TFZcc90pp9jV\ncpXSANQsWVFKnaaU6lRKTTb2n62UmqeUWqKUelQpNaSQ6z/zjNtbIwlZcamBzBfd1hZ4FWXJWC+7\njLbFuAWbkB4EDO6A5kRwySUBKeBOohSJHVta8kXOcdIONvbq7CSPkn//2y5ZYREld0YTWUlW0iZE\nHDSIglRxm+DJCXCTlblz6X6//W2wb9kyejYbEclCsvLBB2T0XIhnGte9zH4NhJO4saGhFNmnwS9+\n4T5mkhVWA5nlM2EjK7xfIs6+zdXmosCSzyuuoK2sK7OdcpAzCZOscLJPWxJGVjm5vBjN8t93X/Dd\nVp82cJubOpUMl82s6suWkZ0EQ9qwMMzfTPD79KEAkNI+8E9/Cr6bkhVZJvkcfM2mprBkpXfvMMmL\nU3nZUIhkxYZiJSs2A1smjj17Bu1j+XL34m3xYuDKK/P3VypzfU2SFaXUZgCOBGVX1mL/qQCOA3AU\ngDEAFgN4RCkVO4z85S/h3xdf7A6KY2uQ5uCQlKzI36VyXc4qCBpf59VXA9dJmf+HwY2ZPX2Y5cvO\nGjfRMvbck/7HA1RHBxkFrr++XbLCXjquVa7W+VKgNOBnOeOM/GMdHdS5//CHINupxJIlQZk5fw8Q\n72Fx7rlB/bz0EpEBmyGjWYdKpTf+nDKFDBFlLpOk4EGf3ccZm2wSEC5OpFao1MfspxI2NZC0mfr3\nv/PLBrjJimljFSdZiTIgd/3vjjuIwI8aRfuka3dbW3gisV3fbAd8/iqrkMSK3/+CBcFk5fLyYLLC\n95H3do1nJljiN2ECeefItAcA8LvfhX93dOSPT2Y75qjUkuDL+zGkFEful+Xu7Aw8D9nQW44ZLsKT\nFFmRlXffpfu/9Vb+MZls98wzyS7MLINNsiK/83y1bFm0LSFL6uTizEtWEkIp1QLgNgCHA/hK7FcA\nTgBwjtZ6mtb6DQAHAVgNwM/irmtbFbmMPm0TqykejQq370La1XoUZEeTq49iIJ+bO5GNlfNzrLMO\nbYshK01NNOCxTl/+z0yIllSyIlevacGd3DZxzJpF+ueTT3bXOevjZZKxJJFcn32WJpwFC6gMSdRA\nS5aQ+umLL+Kvz+AAa4WIwFkNyZ4mG2xAXhnrrQe88QaV+d57821WsoIpWWlszJ+YZUBGhousmJNL\nnM1KWjVQUxN5gsgJ7mdipGprC9rziSfa25wpWeGJrKGBbIF2350mswEDgKuvpmM2NSwQqO+4T5tk\nJUk7dQWF49U8u8XK8poG5uaE360bjQFxrrTjxoXLwZBjbkdHIM0aN46OLVsWuPqb56ZFoWqg556j\nd8VgOxt2swdIajZtGo1t//kP9aWzzgrXaWcnLRpskhVZno03pucdO9Zt4M/XA8KLHk9WkuMqAPdr\nrZ8AIF//2gAGAvjOpFFr/S2AFwBsEXdR2+RmirMZtgZpW+XaiIlp0GuuPEqBrOJA2BIw2sgKG6ax\nQS0PHFFp2KPSHsi4ADayIl2X4yQre+5JZb/88rA4OilefNFefsAtTpVtgycEGWo/yYp10qSA6EyY\nkEwN9POf0zbN+2eSVQhZYZLKUqNu3SgfE+dGYfCkMG9e2P4pCitW5D+vTfUKkETh/ffDK0jGhAn5\n13aRFfP6pbBZAcJ1PWFC4GHY3h6QlV694snKvHmB8Ts/z5w5AUFjY2/TpohhqpnM7PBJ2qmtXa61\nFl2L++nYscExW7wim6TFFk7/1lvDv2UcJ5dkpaMjWKy0tNCYsWJFeMHDKMS2zSZZYbfhKPLzwx8G\n71fORdznZ8wgaRkbcX/xRfBepWTElGbLMsjv665Lz7311vllkeMVv0svWUkJpdR+AEYAOD23S1KE\nnLMsTMH3fHHMCRtZsQUxA4ojKxzRlfHEE8GKoVpy1rjAdjBAPlmRz896Tp6QecC16Y7N65lgGweu\nS/k/Uw2kVCASdYnk2cD2+OPDtiBJwe3EVl7XYCTP5bgW0qMqySTw5JPBgNGzZzKyYq6Uk4DfUSFB\n75YvJ6mKVMvxVtYBq4GAsPFjFDbeGNhrr/A+83n593XX0XbHHfP7tS0qZ1LJymWXkXjeRKGSFSYH\n5vuXahh+Dz175q+Wm5vDZOWSSwLJmMzsy+Xiic9VTrOupN1EWjWQBIea5/p8+ml7HCZ5voTr3uZz\nuMiKKdFlVWJLS2DLYfOQKmQ8ZkmITM3AQeHirsd1wfZLAC2olAqkYZzz6Z137OMN16vZVmQ8HW5f\n8llvvtleJm5b0t7L26zEQCm1BoDLABygteZupBCWrlj/ijCpscLWgaPc/0zYJg7X/80AaHzvrHIx\nvPwyDVxZQuvw5C6t6k10dJDrH+eFGTOGtnLQMGNGuNj6Bx/QCiKJZMWls5ZobrYbr+25J7lhR4HD\ncvfvH/ZoeOQRYORIt3hdlos7umvl54JSwYDRo4edrJi/bbYHUZCeGYVKVnr0CJMU3pqSFX5nSRN5\nzp4db0cjc68cdBCtGk3Jio3EJiUrgD15Kd/XnCDGjAkyfdsgY4JIyLQQ/M6lVx1ANlN/+1uYrEgP\nOF51y2eLkwDJumppKcxmxaYGYq8mJl5sKwLYJ/AosiIDqCUlK6ZkhYnE6qtTOZYts0tWZDm0Jql4\nXGRZXtCxpx8QtP84ssLtQEr0N9mEtjLlAgC0tgaxbySRMIPmMfbfP/huW4SbdXnHHfQMTzxBiXvl\nPbxkJR6jAawCYKZSqk0p1QZgKwC/UkqtAMCZO8xk4QPFMQsmAtgNkybtBoA/UwG4DaJko+PVkW01\n4dLxmhlN2d4lC7KyeLFd1M146SVqyDJ2SRK4JCE2t2vz2VlyJOvzZ4YV0euv06p1zhzgwAODzjFj\nBnkU2MiKKVnZaqvw/Wzo1i1czxxN8p57oj1NgGAC+N73glXqiy9SorBXX7XHSwDSkRVXtmKtw6vs\nKAPbE0+kQT0tCV62LLhmIZIV9lQyJSssBmc0NNgjkBYLfn4mTUC+tEC2y7XXJvWKi6zYPGpc5Fw+\nE+PFF4E//zm6vMOHh/P8AGGSueuu9H3vvcNk5dxzKUmoJCuyjzJBGDAgv+8mkaz07Fm4zYqMk+JS\n/fK1bE4MUWRFqqrMuFLSaD2KrHR2EtljYi3VQPLe8vlnz6bo0BzJNw6yrpKSlT/8gexQBg0K9tnU\nNAwmXVddRTnLrr8+kKy5IukC9nbN+NGPaLvPPqR6W7iQ3L/PO28qeH486KDdsNtuu2HixInRD5Qx\naomsPAZgEwCb5j4jALwMMrYdAWAOiJR8F85JKbUSgM0BRKQcnIw99rgPDz98HwD+tOb+b/8Hd/69\n9w4MTZOqgWxgO4F//CPZ+eZ93ngjfK0otzeOYvjqq+nvY/vNK52ogY0HWVmf++8P/OAH4Wtuuy11\nvNtuCz8T/1ep+KBwABDVh0zJipRAxQ0m/L9//5s8CqZOTSaBkIH54sjK3XeT+kwaCzJYCuEysOX6\n2H57UpukkazMnQv88pfB7zSSlYULqR9wwDpTpG6TrMSFsI+DrW/ayIo5MMt2+etfB7E+bDjrrCAS\nM8NWl0zOWe32wx/Gl5/L29pKmY8l5Htj48cBA4Kyc/oAINwObBLfUaPy69hFOqLISlKblXffDduS\nuOqWryWTPg4fHtxLQo6lXMZtt81XqcvxxGVg29kZDrXP4fW5TnjBY5YjKgmhDaZBa3t7uD433JDe\n6brrBi7lvXqRRFq+n9Gjw9flYJsS//kPGbIfeWR0mW6+GTj44Ohs7zK/kqzD++9vBc+PN910H+67\n7z5MLiZjZwGoGbKitV6ktZ4lPm8BWALgy9xvDeCPAH6rlPqpUmoYgL8A+BjA36OuffDB9tWGS7LS\n0UEDyp13BoNmGjWQifXXJ/ZaiKvcTTcFnRygidEcIKJStieFS7LCNgDmwGauLIB8EmUGHJo3L1iV\nyfrkiLcNDWE3PTO5mrmit8EMCrd8eXCvOLLCdcCePB99lMxj4OGHg+9xZGXddcmryKaSYtKzxhrR\naiC+Xhqy8tBDJAXYZhtSaaUhK0cdReLql16yS1ZsNisy1HpanH56IB6X+Oc/aSvJyq670vvi0Ppm\nu1yxggiyre+NH0+kdNq0YNKYNYsMVeXzfPFF8Dx77mnPLm4DS2RM8Hv74IOgfhoaiAxdfXVggAu4\nJSvyHmwUDtA1XOOMJHY9ewb30ZoCxCVxvU3qaWcbG88/n7ZRZKVHD+DGG+3xP5QK3pP0bxt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vsB//M/VPccoMs28BYqWeFjHCnTZWArYw6xh1dnJxkj3nEH6biHDyc34hUrAoleFFk5+GAK8W7e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\nB5RSi5VS85VSFyml6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"text/plain": [ "<matplotlib.figure.Figure at 0x110e58190>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Traces, for convergence inspection:\n", "plt.clf()\n", "plt.subplot(2,1,1)\n", "plt.plot(mm, 'b-')\n", "plt.ylabel('m')\n", "plt.subplot(2,1,2)\n", "plt.plot(bb, 'b-')\n", "plt.ylabel('b')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### For comparison, run a second chain with different step sizes:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running MH for 5000 steps\n", "Acceptance fraction: 0.5102\n" ] }, { "data": { "image/png": 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zE3NSBiZddP92vV2mHu564AF27drF0//7v9QUFpKamkpWVhYlJSWAeCs3fx3B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n5S5NjJmZgJkEBNn9BOnTtEhSOGdm4LBlWKXFUd7+xIkZLpkSsFGtRDzEQ1LDjTSmKum7tuuZ9k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"text/plain": [ "<matplotlib.figure.Figure at 0x110e588d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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SnVGMg+IE3o0TK19/vZsk+LXXvFKOzz6TOjF1tDqeUSobcyqkVX7QsWM++0yM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\n+Rl/t6BGGDoHLcE45RTvQXv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y5vaMoTQ1kN9L1g0skcjwhx9cFbY5mCct7Vi4ME6EoOMp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\n9mLzxKP4EeGJsvYZmSms6Lnu449V9YCdO/3xkUrBa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"text/plain": [ "<matplotlib.figure.Figure at 0x110641c10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m0 = 1.0\n", "b0 = 0.0\n", "chain2 = run_MC(m0, mstep/10., b0, bstep, nsteps,burn_in = 1000) \n", "mm2 = [m for b,m in chain2]\n", "bb2 = [b for b,m in chain2]\n", "# Scatterplot of m,b posterior samples\n", "plt.clf()\n", "plt.contour(bgrid, mgrid, posterior, pdf_contour_levels(posterior), colors='k')\n", "#plt.gca().set_aspect((bhi-blo)/(mhi-mlo))\n", "plt.plot(bb, mm, 'b.', alpha=0.1)\n", "plt.plot(bb2, mm2, 'r.', alpha=0.1)\n", "#plot_mb_setup()\n", "plt.show()\n", "# Traces, for convergence inspection:\n", "plt.clf()\n", "plt.subplot(2,1,1)\n", "plt.plot(mm, 'b-')\n", "plt.plot(mm2, 'r-')\n", "#plt.ylim(mlo,mhi)\n", "plt.ylabel('m')\n", "plt.subplot(2,1,2)\n", "plt.plot(bb, 'b-')\n", "plt.plot(bb2, 'r-')\n", "plt.ylabel('b')\n", "#plt.ylim(blo,bhi)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Chain with smaller step size does not look stationary yet, but exhibists features comparable to chain length.\n", "\n", "### How about a longer chain?\n", "Easy test - as long as running longer chains is cheap." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running MH for 50000 steps\n", "Acceptance fraction: 0.50568\n" ] }, { "data": { "image/png": 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y4cIFjk9MUCPLdDocrI2N0eh0cvLkSTZKJXyXL+PJ5SgWixw/fpxsNotlaYmA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\nK5m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"text/plain": [ "<matplotlib.figure.Figure at 0x10c5d1950>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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SpUoJHZivtNu0aZPwm7yuFL/66qukQ1FHJTqwF/gKR7Z3irLRqArxPns1tuRGgIwxfPDB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\nC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"text/plain": [ "<matplotlib.figure.Figure at 0x111091310>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "chain3 = run_MC(m0, mstep/10., b0, bstep, nsteps*10,burn_in = 1000) \n", "mm3 = [m for b,m in chain3]\n", "bb3 = [b for b,m in chain3]\n", "# Scatterplot of m,b posterior samples\n", "plt.clf()\n", "plt.contour(bgrid, mgrid, posterior, pdf_contour_levels(posterior), colors='k')\n", "plt.plot(bb3, mm3, 'k.', alpha=0.1)\n", "plt.plot(bb2, mm2, 'r.', alpha=0.1)\n", "plt.show()\n", "# Traces, for convergence inspection:\n", "plt.clf()\n", "plt.subplot(2,1,1)\n", "plt.plot(mm3, 'k-')\n", "plt.ylabel('m')\n", "plt.subplot(2,1,2)\n", "plt.plot(bb3, 'k-')\n", "plt.ylabel('b')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Diagnostic 1: Autocorrelation\n", "One common way to asses convergence of a chain is by assessing the autocorrelations between draws of the parameter chain $\\left\\{\\theta_i\\right\\}$:\n", "\n", "The lag $k$ autocorrelation $\\rho_k$ is the correlation between every draw and its $k$th lag:\n", "\n", "$\\rho_k = \\frac{\\sum_{i = 1}^{\\mathrm{nsteps}-k}\\left(\\theta_{i} - \\bar{\\theta}\\right)\\left(\\theta_{i+k} - \\bar{\\theta}\\right)}{\\sum_{i = 1}^{\\mathrm{nsteps}}\\left(\\theta_{i} - \\bar{\\theta}\\right)^2}$\n", "\n", "One expects the $k$th lag autocorrelation to decrease as $k$ increases. If $\\rho_k$ is still relatively high for high $k$ values, this indicates a high degree of correlation across the chain and slow mixing. " ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ZM2eoWLEi169fp1OnTri6unLq1CkWLlzIrVu3npwXFBSEo6MjgwYNIioqitGj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\nNgUuf7Z+fQhjxviHMyerVoVw7bX+l3XrrR7UiBQUv/8eQrt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"text/plain": [ "<matplotlib.figure.Figure at 0x11107ab90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def autocor (chain, kmax):\n", " x = chain - np.mean(chain)\n", " cor = np.zeros(kmax)\n", " cor[0] =1.0\n", " for k in range(1,kmax):\n", " cor[k] = np.sum(x[0:-k]*x[k:])/np.sum(x*x)\n", " return cor\n", "\n", "plt.clf()\n", "kmax = 500\n", "plt.plot(autocor(bb,kmax), 'b-', label = \"chain1\")\n", "plt.plot(autocor(bb2,kmax), 'r-',label = \"chain2\")\n", "plt.plot(autocor(bb3,kmax), 'k-',label = \"chain3\")\n", "plt.ylabel(r'$\\rho(b)$')\n", "plt.xlabel('lag [steps]')\n", "plt.ylim(-0.2,1.0)\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What's wrong with chain3?\n", "* Running a longer chain does not reduce correlation as a function of lag\n", "* But it does reduce correlation accross chain:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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vv0yzSYPBkON5ZAGjlHJTSs0C2iLVbZ/RWj+ptS6ttX4K6ApcACYppfpZ1tzH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\n1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"text/plain": [ "<matplotlib.figure.Figure at 0x1110910d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def autocor_scaled (chain, kmax):\n", " x = chain - np.mean(chain)\n", " cor = np.zeros(kmax)\n", " cor[0] = 1.0\n", " for k in range(1,kmax):\n", " cor[k] = np.sum(x[0:-k]*x[k:])/np.sum(x*x)\n", " return np.arange(0,kmax)/float(len(chain)),cor\n", "\n", "plt.clf()\n", "plt.subplot(2,1,1)\n", "kmax = 500\n", "r, cor = autocor_scaled(mm,kmax)\n", "plt.plot(r,cor, 'b-', label = \"chain1\")\n", "r, cor = autocor_scaled(mm2,kmax)\n", "plt.plot(r,cor, 'r-', label = \"chain2\")\n", "r, cor = autocor_scaled(mm3,10*kmax)\n", "plt.plot(r,cor, 'k-', label = \"chain3\")\n", "plt.ylabel(r'$\\rho(m)$')\n", "plt.xlim(0,.1)\n", "plt.ylim(-0.2,1.0)\n", "plt.legend()\n", "plt.subplot(2,1,2)\n", "r, cor = autocor_scaled(bb,kmax)\n", "plt.plot(r,cor, 'b-')\n", "r, cor = autocor_scaled(bb2,kmax)\n", "plt.plot(r,cor, 'r-')\n", "r, cor = autocor_scaled(bb3,10*kmax)\n", "plt.plot(r,cor, 'k-')\n", "plt.xlim(0,.1)\n", "plt.ylim(-0.2,1.0)\n", "plt.ylabel(r'$\\rho(b)$')\n", "plt.xlabel('lag [chain length]')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Diagnostic 2: [Gelman-Rubin (1992)](https://projecteuclid.org/euclid.ss/1177011136) Statistic\n", "This diagnostic uses multiple chains to check for lack of convergence, and is based on the notion that if multiple chains have converged, by definition they should appear very similar to one another; if not, one or more of the chains has failed to converge.\n", "It compares the *between-chain variance*, $B$, and *within-chain variance*, $W$, and assesses whether they are different enough to worry about convergence. \n", "\n", "* 1) Run $M\\ge 2$ chains of length n with *different* starting points\n", "* 2) Calculate the within-chain variance $W$, and between-chain variance $B$\n", "* 3) Calculate the estimated variance of the parameter as a weighted sum of the within-chain and between-chain variance\n", "* 4) Calculate the *potential scale reduction factor* $\\hat{R}$\n", "\n", "In detail: \n", "* 2a) $W = \\frac{1}{m}\\sum_{J=1}^{M} s_J^2$ with $s_J^2= \\frac{1}{n-1}\\sum_{i=1}^n\\left(\\theta_{iJ}- \\bar{\\theta}_{J}\\right)^2$ the (0-lag) variance in each chain\n", "* 2b) $B = \\frac{n}{m-1}\\sum_{J=1}^{M}\\left(\\bar{\\theta}_{J} -\\bar{\\theta}\\right)^2$\n", "* 3) $\\hat{\\mathrm{Var}}(\\theta) = \\frac{n-1}{n} V +\\frac{1}{n}B$. This overestimates\n", "the true variance if the starting values are overdispersed, but is unbiased if the starting distribution equals\n", "the stationary distribution.\n", "* 4) $\\hat{R} = \\sqrt{\\frac{\\hat{\\mathrm{Var}}(\\theta)}{W}}$ \n", "\n", "$\\hat{R}$ is an estimate of the potential reduction in the scale of $\\theta$ as the number of simulations tends to infinity. In practice, we look for values of $\\hat{R}$ close to one (say, less than 1.1).\n", "\n" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def gelmanrubin(chains):\n", " M = chains.shape[0]\n", " N = chains.shape[1]\n", " thetaJ = np.mean(chains,axis =1)\n", " thetabar = np.mean(chains)\n", " sJ = np.zeros(M)\n", " for i in range(0,M):\n", " sJ[i] = 1./(N-1.0)*np.sum(np.power(chains[i,:]-thetaJ[i],2.))\n", " W = 1./float(M)*np.sum(sJ)\n", " B = float(N)/(M-1.)*np.sum(np.power(thetaJ-thetabar,2.0))\n", " vartheta = float(N-1)/float(N)*W +B/float(N)\n", " return np.sqrt(vartheta/W)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running MH for 1000 steps\n", "Acceptance fraction: 0.288\n", "Running MH for 1000 steps\n", "Acceptance fraction: 0.279\n", "Running MH for 1000 steps\n", "Acceptance fraction: 0.297\n", "Running MH for 1000 steps\n", "Acceptance fraction: 0.296\n", "Running MH for 1000 steps\n", "Acceptance fraction: 0.264\n", "Running MH for 1000 steps\n", "Acceptance fraction: 0.288\n", "Running MH for 1000 steps\n", "Acceptance fraction: 0.315\n", "Running MH for 1000 steps\n", "Acceptance fraction: 0.29\n", "Running MH for 1000 steps\n", "Acceptance fraction: 0.311\n", "Running MH for 1000 steps\n", "Acceptance fraction: 0.253\n", "\n", "\n", "R(m) = 1.050041\n", "R(b) = 1.051543\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/site-packages/ipykernel/__main__.py:25: RuntimeWarning: overflow encountered in exp\n" ] } ], "source": [ "M = 10\n", "burnin = 1000\n", "nsteps = 1000\n", "\n", "chains_m = np.zeros((M,nsteps))\n", "chains_b = np.zeros((M,nsteps))\n", "for J in range(0,M):\n", " m0 = 5.*np.random.uniform()\n", " b0 = 500.*np.random.uniform()\n", " chaini = run_MC(m0, mstep, b0, bstep, nsteps, burn_in = burnin)\n", " chains_m[J,:] = [m for b,m in chaini]\n", " chains_b[J,:] = [b for b,m in chaini]\n", "\n", "print \"\\n\\nR(m) = %f\" %(gelmanrubin(chains_m))\n", "print \"R(b) = %f\" %(gelmanrubin(chains_b))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "As a test, add in one chain with smaller step sizes which is unlikely to be converged:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running MH for 1000 steps\n", "Acceptance fraction: 0.867\n", "\n", "\n", "R(m) = 1.627902\n", "R(b) = 1.671437\n" ] } ], "source": [ "chain = run_MC(m0, mstep/10., b0, bstep/20., nsteps, burn_in = burnin)\n", "chains_m[M-1,:] = [m for b,m in chain]\n", "chains_b[M-1,:] = [b for b,m in chain]\n", "print \"\\n\\nR(m) = %f\" %(gelmanrubin(chains_m))\n", "print \"R(b) = %f\" %(gelmanrubin(chains_b))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "In this lecture, you encountered different methods to characterize PDFs based on the example of fitting a linear model to data point:\n", "* Recap from last week: evalute PDF on a grid\n", "* Least Squares Fitting, an example for Maximum Likelihood Estimators\n", "* Basic MCMC: how ii works, and how to check for convergence" ] }, { "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

dfm/dfm.io

static/downloads/notebooks/pymc3-mass-matrix.ipynb

1

817946

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "Title: Dense mass matrices for PyMC3\n", "Date: 2018-10-11\n", "Category: Data Analysis\n", "Slug: pymc3-mass-matrix\n", "Summary: improving the performance of PyMC3 on problems with correlated parameters\n", "Math: true\n", "---" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = \"retina\"\n", "\n", "from matplotlib import rcParams\n", "rcParams[\"savefig.dpi\"] = 100\n", "rcParams[\"figure.dpi\"] = 100\n", "rcParams[\"font.size\"] = 20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In my work I often come across probabilistic models where there are strong correlations between parameters.\n", "This certainly isn't a special feature of the problems that I work on, and the general advice from MCMC practitioners is that we should reparameterize our models in some form that removes the covariances, but this isn't always practical.\n", "One of the reasons why [emcee](https://emcee.readthedocs.io) has been popular in astrophysics (I think) is that it uses an \"affine invariant\" algorithm.\n", "This means that when you use emcee, the performance will be (more-or-less) the same for any problems that are affine transformations of each other.\n", "In other words, it doesn't care about translations, rotations, or scalings of the parameters.\n", "\n", "Standard [HMC](https://en.wikipedia.org/wiki/Hamiltonian_Monte_Carlo) methods such as the [NUTS](https://arxiv.org/abs/1111.4246) algorithm implemented in state-of-the-art libraries like [PyMC3](https://docs.pymc.io/) and [Stan](http://mc-stan.org/) do not have this property.\n", "[Note: it is actually possible to construct an affine invariant NUTS sampler using some of the ideas from emcee, but there are some limitations and this will hopefully be the topic of a paper that I write someday...]\n", "The performance of this method is generally very sensitive to the \"metric\" or \"mass matrix\" that is used and changes in parameterization can make a huge difference in the efficiency of sampling using these packages.\n", "To deal with covariances, Stan has support for learning the off-diagonal elements of the mass matrix during burn-in.\n", "The basic idea is that (in the Gaussian case) the optimal mass matrix will be equal to the inverse covariance of the posterior.\n", "Therefore, you can estimate the sample covariance of burn-in chains and use that as the inverse mass matrix in subsequent samplings.\n", "While PyMC3 has the machinery to support this, out of the box it only supports learning of the *diagonal* elements of the mass matrix during the tuning phase (as far as I can tell - please correct me if I'm wrong!).\n", "\n", "In this blog post, I demonstrate how covariances can cause serious problems for PyMC3 on a simple (but not contrived) toy problem and then I show a way that you can use the existing features in PyMC3 to implement a tuning schedule similar to the one used by Stan and fit for the full dense mass matrix.\n", "I have found that this can have a *huge* effect (a few orders of magnitude in the example shown here) on the computational efficiency of PyMC3 on the types of problems that are common in astrophysics.\n", "\n", "## Sampling an isotropic Gaussian\n", "\n", "First, let's look at how fast PyMC3 can sample an isotropic 5-D Gaussian." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "PyMC3 version 3.5\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [x]\n", "Sampling 2 chains: 100%|██████████| 12000/12000 [00:04<00:00, 2501.80draws/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "time per effective sample: 0.26766 ms\n" ] } ], "source": [ "import time\n", "import pymc3 as pm\n", "print(\"PyMC3 version {0}\".format(pm.__version__))\n", "\n", "ndim = 5\n", "\n", "with pm.Model() as simple_model:\n", " pm.Normal(\"x\", shape=(ndim,))\n", "\n", "strt = time.time()\n", "with simple_model:\n", " simple_trace = pm.sample(draws=3000, tune=3000, random_seed=42)\n", " \n", " # About half the time is spent in tuning so correct for that\n", " simple_time = 0.5*(time.time() - strt)\n", " \n", "stats = pm.summary(simple_trace)\n", "simple_time_per_eff = simple_time / stats.n_eff.min()\n", "print(\"time per effective sample: {0:.5f} ms\".format(simple_time_per_eff * 1000))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On my laptop with two CPUs, I find that the computational cost per effective sample is a fraction of a millisecond.\n", "That's how things should be!\n", "\n", "## Sampling a covariant Gaussian\n", "\n", "Now let's try an example where the dimensions of our Gaussian are correlated and see how the default performance of PyMC3 compares.\n", "First, let's take a look at a [corner plot](https://corner.readthedocs.io) of the posterior that we're targeting." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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kV7/6lWv8z3/+M+vXr2+3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mUYY0hOTiYYDEariUXCccaY6FdHVcZat8UrLS2NHpeVlREIBFTBR0RERERERERERERERGQrt9V8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csRSymHbhwgUIIeC6LrLZLHRd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n21KhH53gt971VNmzaF0+kEgIDBoyNHjuCdd97BBx98UGXVnoyMDGRnZyMnJweZmZlh7R+p\neSqVChs2bMCdd96JU6dOidO/+OILjBs3DosXLxZDNL7PKcL9ked5SYA5lAqIkQh2Tw3nXljfnmGD\naSifgxBCCCGEEEIIIYQQQgQU8qkBfwZ50gBsAJABIA/ASgCX/5zv/jO8s6O2gj4Mw6gAZAN4/s/t\nAsAiAAaGYd7ied4bze1dS4SWHvUFy7JV7m8oYaDK1q9SqcBxHJxOp2QANVBbBeCv9ilxcXFwOBxi\n+y25XI6ysjIsWLBAsvytt96KRx99NOR9IuXuv/9+fPnll3jwwQdx8eJFcbrZbMZjjz2GF198ETk5\nOWAYBiqVSmz7ER8fH9L6rVarGA4SBhx9q2tEclwBgUNi4bQnIYREr3We0DpLLpfD4/EgPj4+5Go6\n1aFQKKDVasVrk3BvCbdykIBlWSgUCng8HlitVlitVthstoB/Li4uxrp16/DDDz9Uuk6GYdC7d2+M\nGDEC9913X7X3jdSOpKQkbNu2DXfccQcKCwvF6cuXL0eLFi0wZcoUsXqP770uOTlZUmUKkFbV4Tgu\npJZ04Yr0nirsZ0O4fzaUz0EIIYQQQgghhBBCCCEAhXxqkhaAF0AJgF0APuB53iLM9Anv1ErQh+d5\nJ8MwapRXGeIAOP7c51so4EMCCSUMFEzFsIfBYIBOpxMHx3wrIAhVgjiOg1wuR5MmTeDxeMRqE2+/\n/bbYBkPw+uuvx6xaREPXvn177NmzBw899BAOHTokTuc4DjNmzEBubi5mzJghtqwJtTIHx3GS6j/B\nqoNEclwFComF056EEPJXm5+K51E4gRTfakDCOW2xWKDVamN2bfa9fwjVUQJVmSsuLsbixYtx/vx5\nv7CO3W73C/F4vZE/AmVkZGD48OEYPnw4mjRpEvH6SO1p0aIFtmzZgnvuuQcOh0Oc/vzzzyMzMxOP\nPPJIwKqFQouuihUQgz0PRUsk91RCCCGEEEIIIYQQQgghdROFfGoIz/NnGIbpBWAMgOU8z18MsExt\nB31e/rOiz/MoD/i8z/P8iD+3G7M2YeTaEijsYTabxZYWOp0OKpVK8s1zm80Gs9ksrsNgMEAul+Pc\nuXNYvny5ZP0DBw7EnXfeWaOfqaFJT0/H7t27MWrUKGzatEky75133sHp06cxf/58JCUlwWKxgGGY\nKgclg1XZqaw6SLDWXsEIoaNI2pMQQspbE9nt9rDDfIJoVQMKV6D7h68TJ06gX79+OHfuXMz2QSBU\n7Rk1ahT69u0rhqSiERoitatTp05YsWIFhgwZIjnOR44ciczMTHTv3l2sWigIFPyp6nmIEEIIIYQQ\nQgghhBBCCAmEQj41iOf50wzDPMfzvKuSZWol6COsh+f5/zAMkwigiU/Ah+V5not0G6T+CjdsUZnK\nWioJA2K+3zwXBsF8mc1mGI1GvPzyy3C73eJ0hUKBOXPmRLR/pJxGo8Hq1avRunVrvPzyy5J5X331\nFR588EGsWLECzZs3D2lQMliVnWDVQWw2m19YJ5RWP1qtVjKQSgEfQkLne94BgFqtRnx8fNjX/WhU\nA6quYJVL9uzZg0GDBqGkpCSm2/et2pOZmRnTbUWb1+uFyWRCSUkJSktLUVxcLP634rSMjAwMHToU\nnTt3ru3drhWDBg3Ca6+9hilTpojTnE4nBgwYgO+//x7XX3+933sqBn9CeR6qD2LVbowQQgghhBBC\nCCGEEEJIYBTyqWGVBXx8lqnxoI/venieHydMp4APqW7YIphQWyoJwSKO4wIOIH399df49NNPJe95\n6qmn0KpVq2rvG5FiGAbPP/88srKykJOTA6fTKc47ffo0+vXrh3fffRddu3atclBSaKXj25YkWHUQ\n31Y/QPDWXpXtN7UnISQ8Fc87hmEk53w4WJYNWFWrtgIA69evR3Z2NlyuKh/BqiSTyaDT6cQfrVYL\nnU6H6667Dg8//LCkak9d4PV6ceDAAZw/fx4lJSUoKSkRQzvCf4U/V2x9WZUPP/wQvXr1wvTp09Gm\nTZsYfYK6a8KECThz5gyWLFkiTisuLkbfvn3x3XffITk5udL3B3oe4nlefO6pD4EZq9Xqd55Hs90Y\nIYQQQgghhBBCCCGEEH91ZxSCSNRW0Mf39Z/rpIDPNSzSsEUggcIeBoNBsj7f9lw2mw3FxcVQKpXg\neR4qlQoGgwHPPvusZL1GoxHTp0+v5icllXn44YfRrFkzDBw4EFeuXBGnl5aWYsiQIXj11VcxYcKE\nKtcjtCupWBWqYqWo2mr1Q8i1TDjvKoYqq3veabVaqNXqqFWBqw6e5zFv3jy/+wUAdOjQAffee69f\nWEe4TvkGeXzn14drEMdxOHDgADZu3IgtW7agoKAgZtv67LPPsHv3bjz00EOYOnUqmjZtGrNt1TUM\nw+CNN95Abm4udu7cKU4/ffo0BgwYgC+++AJKpTJolZuKz0MOhwNA+b1VeDaqy4EZ4Zmw4jMitRsj\nhBBCCCGEEEIIIYSQ2KKQTx1WW627fLcfrXVdaxpKi6BYhS2EQdRAA18cx6G0tFQcbDabzeA4Djab\nDTabDRzH4dChQ/j5558l65w+fTq0Wm3ASg0ej6fa+xrIyZMnK53//fff491334XFYkFqairS0tKQ\nlpaG9PR08c+pqalitQeNRhPV/Yv2oGBaWhrat2+Pb775BoMHD8bRo0fFeW63G8888wxyc3Mxa9Ys\nyGQycV6wNm9yuRxerxderxdWqxWlpaWQyWRihQy1Wg2e5/2qPQltTCqqWAUqUtG+9DWU6wFp2ORy\nOex2u19Vjkiq0gRrneXL6/VWe/2BCPctu92OadOmYdmyZX7L9O3bFytXrgzpWul7//B4PBHfT0wm\nU0Tvr6isrAxA+XXr5MmT+PTTT7Fr1y7k5+dHdTuV4Xke69atw+bNmzFkyBCMGTMGSUlJAAC9Xh/V\nbfneY+qKhQsXIjc3V3Jv/O677/DII49g4cKFYBgGDMNAr9f7VUJUq9VQKpVwuVzgOE6810UjVB1I\nNO9HlbUbqythOLr/EkIIIYQQQgghhBBCGiIK+dRxtR30Idc2uVwesLVWNFqRsCwbsL2T76CRMFir\nUChgsVjg9XrhcDjw5ptvSt7TunVrjBw5MuJ9ipTH48HSpUuxatUqcdq5c+cCLsuyLJKTk5GWlobG\njRsjIyMD6enp4k9GRkZEbdFi4brrrsMXX3yB4cOHS6oWAMCCBQtw+vRpvPHGG2jSpAnsdjusVqsY\nFhCCXb6sVityc3PFgU1hwF2pVEKpVMLhcIiDozqdjioDENLAcBwHl8slti2KBrvdjoKCAjz11FP4\n4osv/OaPHj0a8+bNq5Nhkeo4d+6cGOw5e/Zs1Nar0WgQHx+P+Ph4JCQkSP7Lsiw2bNiA4uJiyXvc\nbjdWrVqFjRs3Yvjw4cjOzo56yKcu0ul0WL16Nfr27YtLly6J07ds2YKMjAxMmzYNPM/DYrEEDO2w\nLAuWZf3OASEwU1krzNoUy2dEQgghhBBCCCGEEEIIIcHRv8LWA9EI+jAMw1ZsvUWBIFIVlmURFxfn\nV9khlmELYbCX53lxAMnlcsHtdkMul2PNmjUoKiqSvGfOnDm1PqhUUFCAGTNm+FUYCobjOBQUFKCg\noABHjhwJuEx8fLwk+NOsWTN06tQJzZs3r7VvpxsMBnz00Uf4z3/+g/nz50vmffLJJ/jkk0/QoUMH\n9OzZE71790aLFi3A8zysVitUKpWkRVdpaSk4rvyyJCzD87w46M9xHORyORITE2v971fYR6H6FFUH\nIPVJsKpavjweDzQaDVQqVVTadVXFarXCbDbD6/XC4/EgMTEx4ipkPM/j7NmzyMnJCXhdfeWVVzBx\n4sR6f/5evHgRO3bswLZt23Ds2LGQ3pOZmYmUlBQxuOP7I4R34uLixGXUanWl6xs1ahRWrlyJ9957\nDzabTTLPZrPh7bffxpo1azBhwgQ8+uijdTaoEi1paWn44IMP8MADD4jtRgHgf//7H5o2bYpHHnmk\n0kqIwQIz0a5UF0218YxICCGEEEIIIYQQQgghBGAo41F/CKEchmH64a+gDwA4AWwCMJ/n+UO+y/75\nZzHgwzDMwwAu8jy/p+Y/Qd3DMEwTABcA4MKFC2jSpEkt71HNCPe8D2WAOBIcx8HtdoNhGDidTjid\nTjgcDvA8L/65sLAQubm5GDVqlKRdSs+ePbF169ZK119xADJSf/zxh+T1/v37MXPmTJSUlER1O8Gk\npqaia9eu6NKlC7p06QKj0SiZH4t2XYGsXLkS48ePr7R9zQ033IBevXqhR48e6N69OzQaDeRyOVwu\nF4qLi1FcXCwej0KLttTUVDidTpjNZrjdbhiNRhiNxqCfqyYGQW02m99AZqiVlup7oIAElpeXh+uu\nu054eR3P83mRrC9W9yOr1RrSsSsEDyuGDFJTU6N+3Re2ZbPZYLFYwPM8WJZFZmZmRNevX3/9FQ88\n8AAuXLggma5UKvHee+9h8ODBYa8z2u0eq9uuq7CwEDt37sT27dtx6NChkN7TvHlz9OnTB71790az\nZs1Cek+4ga7i4mIsXboUa9asCdhOESivAvfMM89g4MCBEVdQqusVmH766Sf0799f0oZOJpNh5cqV\nuOuuu5CcnBz0fBLOB6GdpRB88w3dCc9LFVudhioW96NYPyNG4lq5/9aX+xFpuJo0aYKLFy+icePG\nyMuL6PC7JtHvjzQU0b4fEUIIIYQQQggJjkI+9Uy4QR+U/x0LAZ9xAJ4FkAfgBZ7nP63xD1DHXKv/\niF2XznuhmkNRUREKCgqg1Wqh0WiQnJwstvlgWRa5ubkYOXIk9u/fL75XoVDg0KFDuP766yvdRqxC\nPl6vF++99x6WL1/u9zuVy+V4/PHHoVQqcfnyZcmP3W6P6v60bt0aXbp0QdeuXdGhQwe/0E+kgoV8\nAGDPnj0YOnSoX9uWQK677jr0798fDz30EG6//XaUlJTAZrOJFXy8Xi/i4uKgVqtx6dIllJSUwG63\nQ6fTISEhATfccEPAAECsQz48z+PKlSt+4YdGjRqFNIB4rQwyXmvqw6Aqx3EBj91gwZ1IwmzhcDqd\nKCoq8qvKlpCQgMaNG4d8zvhW19q3bx8GDRrkF7ZMTEzE+vXr0b1792rta22GfK5evYrPPvsM27dv\nx759+8TKZ5XJyMhA79690adPH9xwww1hX3+qW7UpLy8Pb731FrZt2xb0GaN169Z49tln0aNHj2pf\nF+t6yCc5ORnvvfcexowZI5mu1+uxa9cudO7cudL3W61WlJSUQCaTweVygWEYqNVqMAwDmUwGr9cr\nnp8GgyHs8/Naux9dK5+3PtyPSMNGIZXI0O+PNBQU8iGEEEIIIYSQmlP7/U9IWMJp3fXn8kLA5ykA\nzwFoBCAdQGcA13zIh0Qm0m9vcxwnVmsRBqItFguUSiXy8vLQqFEjyGQyMAyD/fv3SwI+ADBu3Lgq\nAz6xYjKZ8J///CdgRYX09HS8/PLLyMrK8pvH8zzKysrEwE9+fj5MJhMuXbqE/Px8MdwSjhMnTuDE\niRNYsWIFVCoVbrvtNnTv3h3du3dH69atY/rN+jvuuAN79+7FnDlzsH37dpSWlgZd9sKFC1i0aBEW\nLVqERo0a4b777kPPnj3RqVMnse2Hw+GA3W6H1WqF3W4XBzNLS0tRVFQEjUZT45UC3G6336C1EC6I\nRRsjQqIl2LEbrGWQVquFWq2OSVUOj8cDu90OjUYDhUIBj8cjnt9A+WC8XC4P+byy2+0wm83geR7b\ntm3DxIkT4XK5JMs0a9YMW7duxQ033BC1zxFrHo8Hu3btwtatW/HNN9/4faZAjEYjevXqhT59+qBd\nu3a1Emxo0qQJXnvtNYwYMQJvvvkmvvrqK79lTpw4gezsbHTq1AnTpk3D7bffXuP7WRNGjBiBs2fP\n4rXXXhOnWSwW/POf/8S+ffvQuHHjgO8Tnol4nhefh4C/gleFhYVISkoCy7LgeR5msxlqtbrOVM+p\nyxV9CCGEEEIIIYQQQgghpCGhSj71VAgVfd7ieX7/n8uOAzAdQPKfy33I8/xjtbDbdc61+k3VaJz3\nFSs+GAyGsNusOJ1OFBcXw2Kx4Pz58+J0g8EAq9WKjIwM6HQ6OBwO9OjRQ/LNxvT0dPz8888wGAwh\n7Ws0rV+/HjNmzAhYkeGOO+7AjBkzEB8fH/L6NBqN5LXD4UB+fr7kJy8vD4cPH8aVK1fC2tekpCR0\n69YN3bt3R7du3ZCenh7W+4HKK/n4crvd+Pbbb7Ft2zZs37495H1NSEhA3759MWDAAHTr1g02mw2n\nT5+GxWIR25MA5YPILVq08PvdUiUfUhvqQ+WEcCv5xEphYSHy8/PBcRxYlkV6ejo0Gg3OnDkj3kN0\nOh20Wi1SUlKqPGd4nkdhYSE4jsOSJUvw8ssv+y3TsWNHbNq0CY0aNYpo32uyks+xY8cwZcoUHDt2\nrMr1xMXFoU+fPujRowduv/32qFW4iVZw8ccff8S8efNw+PDhoMv07NkTU6ZMwU033RTyeutDJR+g\n/NwbNmwY1q1bJ5nfvn17fPXVVwGfXUpLS3Hx4kUxROr1eqFWq5GQkCDOT0hIkPwdJSYmQqVShbx/\nsbof1VQVsHBdK/ff+nA/Ig0bVaKJDP3+SENBlXwIIYQQQgghpOZQJZ96KoSKPhzDMGUA7kR5wCcF\nAANgFc/z2QDAMAwrVPohJBwcx4mDOQDEb5SHW2VFoVBIWlFUHIhWq9UAgKVLl/r9g+crr7wSUsAn\nmjiOw4IFCzBnzhy/tikymQz/+te/MHTo0IgHldRqNZo3b47mzZtLpvM8j3PnzuG7777D999/j0OH\nDlUZYCouLsb27duxfft2AECrVq3E0E/nzp3DDmZVRqFQoEePHujRowfefPNNHDhwAFu3bsW2bdtw\n7ty5oO8rLS3Fhx9+iA8//BBarRa9evVCly5dcOONN4oD7FqtFgqFAg6HAwaDoUYDCsKAZcUBzGtl\n8JDUX0KFrIrHbk2ePx6PRwz4AOXX0fz8fLRp0wYtWrSAyWQSK38YDIaQziu32w23242ZM2di5cqV\nfvP79u2LlStXRvX6FktOpxNvv/02Fi9eXGmoSKvVomfPnujfvz/uvPNOKJVKlJWV1eCehu7WW2/F\nBx98gP3792POnDk4efKk3zKff/45vh7jjr0AACAASURBVPjiCzz44IN4+umn0bRp0wZzXWVZFu++\n+y7y8vKwb98+cfovv/yCoUOHYsuWLZDL//rfMI7j4HA4xNcymQxmsxkqlUpcjmVZyXsYhol5yDUU\ngZ4Jy8rK6lSVIUIIIYQQQgghhBBCCGlIqJJPPVdJRR8HgJMAmgGIAwV8ArpWv6ka6XnvcrkCViNI\nSkoK6xvlAGC1WmE2m1FQUID8/HwYDAZoNBrI5XIkJCQgLy8Pd999N5xOp/iebt26Yffu3SEPBkaj\nko/H48GIESPwySef+M1LSUnByy+/jPbt21dr3RUr+YTK7XbjyJEj+OGHH/D999/j6NGjfuGjyiiV\nSvzjH//A6NGjg7YPAUKv5BMMz/M4cuQItm3bhq1bt+K3334L6X06nQ4TJ07EXXfdBZ1OB6PRCI1G\n41fJoKYGOYXqCkI4LVQNZdCaSNWXygk8z9dqGx2z2Yw//vjDb3rLli1hMBjg8XjCPq84jsPgwYPF\n8KKv0aNHY968eVGr+BLrSj4XLlzAyJEjA4ZggPLr9N///nf0798fPXr08KuOEu2QT7RbEOr1eni9\nXmzZsgXz5s3DhQsXgi6rUqmQkpICo9GI5ORkyY/RaERKSgpSU1ORnJyMpKQkSeClrhAq+QhMJhPu\nuOMO/P7775LpY8eOxcKFCwGUH882mw1msxlOpxNWqxU8z8PpdCIuLk48T9RqtdjKS6igGG61nFjc\nj4I9ExqNxlpvaXmt3H/ry/2INFxUiSYy9PsjDQVV8iGEEEIIIYSQmkMhnwbAJ+hzP4DN+Cvo4/3z\nzxTwCeJa/UfsSM97juNQUFAQtRYwFotFHCByuVxITEyETCaD1WrFvHnzsGDBAnFZlmXx3XffoV27\ndiGvPxohn88++wyPPPKI3/SuXbvihRdeENtpVEd1Qz4VlZWV4cCBAzh06BD27t2L3NzckN4nl8sx\naNAgjB07Fk2bNvWbH2nIp6I//vhDDPwcPHiwyn3buHEjOnToAJZlwTAMjEaj5DirC5UMKnOtDDJe\na+rLoGptP+d5PB4cP35cEkBkWRZt2rSBXC6H1+sNe5179+7FXXfd5Tf9tddew7hx46J6zsUy5HP0\n6FFkZ2ejqKjIb7nrr78eo0aNQq9evSpt/1gfQj4Cp9OJDz74AAsXLqy0bVkoGIZBYmKiJAjkGxBq\n3Lgx2rZti5SUlEg/QlgqhnwA4PTp0+jevbvfZ964cSN69uwJi8UCr9eLkpISaLVaqFQqeDweKBQK\n6PV6lJaWQi6XQyaTQaPRgGEYMQwdrljcj6L9TBhN18r9t77cj0jDRSGVyNDvjzQUFPIhhBBCCCGE\nkJpT974CS8L2Z8BHzvP8NoZh7gWw+89ZQsBnNQV8iK9IqzoEagFT3RZKHMfBYrFAqVSC4zjwPA+b\nzYaUlBSo1Wq/AUe1Wo309PSwtxOpit/CZ1kWo0ePxrBhw2p9EEsQFxeHe+65Bw888AAAIDc3F3v3\n7sW+ffvw3XffBR0M9ng8WL9+PTZt2oQBAwZg7Nixfq3CounGG2/E5MmTMXnyZFy4cEEM/OzZs8ev\nEpHH48G0adOwY8cOqFQq6HS6OvP7JoRUTS6XIz09XWzZxbIs0tPTI6rCYjab/aatXr0aDz/8cNRD\nObGyZ88ejBkzBlarVTJdLpfjySefxL/+9a+wK+PVdSqVCiNGjMA///lPLFu2DEuXLvX7/KHieR7F\nxcUoLi7GqVOngi6Xnp6Odu3aoV27dmjbti3atWuHRo0aVfcjVEurVq2wefNm3HvvvZKqhIMHD8aE\nCRPw1FNPQaPRQKvVwmazQaVSifc7m80mPgfZbDYUFhYiKSkJNpst5Eo+HMeJ1bKiVeHKV223Baxu\nlT1CCCGEEEIIIYQQQgipr6iSTwPgG9xhGGYEgFcBJOGvgM/jFZcj5a7Fb6parVa/gZhw2z0IotEC\nxul0ori4GHa7HWazWfwmeHp6OuLj43H69GnccsstkvDHM888g1mzZoW8jWhU8pk9ezbmz58vvu7R\nowdeeeWViNcLRK+Sj0Cn0/lN83q9OHr0qBj6OXz4cNDBcJZl0b9/fzz55JNo1apV1Cv5BBvcLyws\nxI4dO7BmzRp8++23knnPPvssZsyYAY7jJMebcFwoFIo6G/6hQceGqb5UTqgrz3kejwd2u92vAonX\n6w17kP7SpUvIzMyUTPvll1/Qpk2bqId8YlHJZ8uWLZg8ebLfum+88Ua8+eabaNOmTcjrq0+VfCoy\nmUxYuHAh1q5dG5X7dKgaNWokBn+En2jd5wJV8hEsXboUTz31lN/0xo0bY+bMmejTpw94nhfDOx6P\nB6WlpQDK73Umkwlerxc6nQ46nQ4ymQwpKSmV3vuENmDReOarSm20BbTZbJU+014r99/6cj8iDRdV\nookM/f5IQ0GVfAghhBBCCCGk5lDIp56rEPAZD+A5ACmggE9IrrV/xOY4DleuXIl6SwXfb4mHux6P\nx4O8vDyUlJSA53l4vV7IZDKx9QbDMBg1ahQ+/PBD8T06nQ7Hjx8PuQ1HNAYPJ02ahFWrVomvH3zw\nQUyZMiXi9QI1E/Kp6PLly1i2bBk++ugjSWUBXwzDoE+fPpg5cyZuvvnmqO1fVRU8XC4X/va3v+HX\nX3+VvGfHjh3IysoCwzDiAKjD4YBMJhMH99RqdbWPxVi5VgYZrzX1ZVC1tp/zqro/WCwWSQjBYDD4\nXRMDhYCaN28uGQh75513kJ2dXadDPjzPY86cOZgzZ47fvK5du2Lp0qWIi4sLa531OeQjcDqduHz5\nMoqKimAymVBYWAiTyRTwdXFxcUyO6dTUVL+KP+np6WFfvysL+XAchz59+uDLL78MOP/OO+/ErFmz\n0KVLF7AsKwZ7eJ6Hy+XC5cuXYbVakZCQAI7jkJCQgCZNmgSt+OTxeHDp0iVJ6IZhGKSkpMDr9dZo\nGCcWeJ4P+EzbqFEj8e/tWrn/1pf7EWm4KKQSGfr9kYaCQj6EEEIIIYQQUnOoXVc9ViHgMw7SgM8q\natFFKnK73X6DYzzPw+PxVHtgr+K3qA0GQ0ghE6C8qpDZbIbdbkd+fj54nodarYZer4fT6YTb7YZS\nqcTUqVOxdu1asWqL1WrFwoULw6rmEynh2/SCcAdi65q0tDT85z//wdixY/HOO+9gzZo1sNvtkmV4\nnsfOnTuxc+dOPPDAA5gyZQpuueWWmO+bUqnEu+++i27duokD7B6PB08//TQ2b94MlmVRUFAAnufB\nsqwYCrh8+TLUajUYhol5xQJCSNWEa3yw+wPHcZIKbjzPw2w2i+cxAEmVN98QUKdOnSQDYQcOHEB2\ndnaNfr5wcByHadOmYdGiRX7z+vXrh3nz5jW49lyhUqlUyMzM9KvOFAjP8ygpKUFRURGKiopQWFgo\n/lkIBBUUFOD3338PK+BbUFCAL774Al988YU4LTk5WQz+dO3aFXfccUdEoRGWZbF582bMnDkTb7/9\nNrxer2T+t99+i3vuuQcTJkzAc889B4PBAIPBALPZDJZlYbPZwLIsSktLxXMlJSUFKpXKL0xns9lQ\nVFSE0tJSMAwDvV4PjUYDs9mMsrIysRJQfb5PBnumFZ4dCSGEEEIIIYQQQgghpKGikE895hPw+ReA\n6aCAD6mCUAWh4reeq6qsEgzHcWLAB/hrgFaj0VT57XDfwV29Xg+lUgmPx4OEhARxgEomkwEAWrVq\nhSFDhkiq+SxZsgTjx48PuZpPpEpKSiSv4+Pja2S7sZaSkoLnnnsOo0ePxvLly7F69WpYrVa/5bZu\n3YqtW7fivvvuw7PPPouOHTvGdL9uueUWTJ06FS+//LI47bfffsMbb7yBhx9+GAqFAiqVCgqFAmaz\nWfwvy7JQKBRwu93gOA5qtbpeVyogpL4KFuDxvT9UNUgvvCdQCOi2227D5s2bxfcdOHCghj5Z+JxO\nJ5544gnJ/gpGjBiB6dOn03UqRHK5HCkpKVXe+71eL/744w8cOXJE/Dl69GjA+1swRUVF+PLLL/Hl\nl19i/vz5GDJkCN54442I9l+n0+H1119HTk4OJkyYgG+++UYy3+124/XXX8eaNWvw3//+Fw8//DDU\najWsVqtY4UEIvGm1WpSWloJlWVitVnG6TqeD1WqFXC4Xn/ksFgs8Hg8uXLiAhIQE2Gw2GAwGAKi3\n98lgz7QKhaIW94oQQqovPz8/qpWz0tLScOjQoaitjxBCCCGEEEIIIXVH/fsXXSLBMMwYAP8FkAgK\n+JAqsCyLuLg4SRuDuLi4ag/ueDyeoAO0VfEd3PV6vdDr9ZDL5eA4Thy88v2W+9SpU8XQD/BXNZ+a\n0lBDPgKj0YjJkyfjm2++wb/+9a+gLVZ27tyJO++8EwMHDsT+/ftjuk/PPvss2rZtK5m2fPlynDp1\nCi6XSzweeJ4XqxC5XC5cunQJxcXFKCwsxNWrV2O6j4SQwNxuN7xeL5xOp1iFreL9wbf9lsB3kD5Y\nCOjq1ato0aKFZPrRo0ej0pox2kpLSzFw4MCAAZ/p06djxowZ9TJgUdfJZDLccMMNGDx4MF566SV8\n/PHHOHXqFL799lu8/fbbeOKJJ9ClS5eQ2okJ1q5di2PHjkVl/7KysvD555/jww8/ROPGjf3mX7p0\nCY8++ih69OiB48ePQ6fTQaVSITExEXFxcUhMTBQDc74tzHieR3FxMbxeL1iWhV6vB8Mw8Hq9MJlM\n4nuEwJzX6416i7uaIjzDVnymvVZadBFCGh6O43Dx4sWo/Vy+fLm2PxIhhBBCCCGEEEJihEYV6jGm\n/F+xHQB4AAoAKyjgQ6qi0+mQmpoKo9GI1NTUiNo0CN8S9xXqt6h9B3cVCgU0Gg2MRiOSk5PFwSvf\n9bRq1Qr/+Mc/JOtYsmQJCgsLq73/4agY8qnv7bqCSUxMxKRJk7Bnzx5MnDgx6OfcvXs37r77bvTr\n1w979+6Nyb4Ibbt8K015vV7MnTsXSqVS0ppLq9WCZVmcP39ebN9it9tht9vFgAEhpOa43W6YTCaU\nlpaK5yPDMJDJZGLwR2i35ztI7/taJpP5BX14nofD4UCHDh0kwU+v14sff/yxZj9kFS5duoTevXtj\nz549kukKhQLz58/HqFGjamnPrk0sy+L666/Hgw8+iBdffBFbtmzByZMnsXfvXixatAhjxozB3/72\nN7HCTSDvvvtu1PaHYRg89NBDOHr0KCZPnhzw2embb77Brbfein//+9+QyWSQy+VQKpWQyWTQ6/Xi\neeRyucR7nVwuF4M7wrOVXq9HRkaG5JlPaNda3WqOdYFWq0WjRo1gNBrRqFGjett6jBBybUtLS0Pj\nxo2j9kPhYUIIIYQQQgghpOGj//uvx/jyUa+NABYCeJfn+eEABXxI1ViWhVKpjPgfAANVBjIYDCGt\n13dwV1hPQkIC1Go1ZDIZdDqdJEDE8zyefPLJWqvm09Ar+VQUFxeHcePG4dtvv8ULL7wAo9EYcLmv\nvvoKvXr1Qu/evfH1119HfT+Etl2+zpw5gw8++AAGgwFGoxFNmzZFamoqbDYbSkpKYLFYcPXqVTgc\nDni9Xlit1oBBH47jJFVGCCHRwXEcLBaLWEVEaBfEMAyKiopQXFyMy5cvo6SkBGq1GikpKUhMTERK\nSgo0Gg14nkdpaSkKCgpgs9kkISEh3MeyrF81n4MHD9bSJ/Z34sQJ9OjRw6/yi06nw/vvv48BAwbU\n0p4RXyzLomXLlhg4cCBmzpyJTZs24cSJE9i3bx8WL16MXr16SZbfvHkzioqKoroPer0er7zyCn76\n6Sf07NnTb77X68WCBQtw++234/vvv0dcXByMRiO0Wi3UajVKS0tRWloKk8kEu90OmUyGpKQkSVgu\nLS0NKpVKEqJjWRZGo7HeDwYzDAOlUkkVfAgh9dahQ4eQl5cXtZ/09PTa/kiEEEIIIYQQQgiJsfr9\nr7oEPM9bALzI8/wTAAV8SM3TarVITU1FUlISUlNTodPpQnofx3GQy+VITk5GUlISMjMzkZmZiaSk\nJBiNRmg0GsnybrcbmZmZGDhwoGT64sWLkZ+fH7XPE4jD4fBrA9NQK/lUZDAYMHnyZBw/fhyzZ89G\nSkpKwOX27NmDvn37Yvjw4WLrrGgJ1rbrwoULSElJgUqlwsWLF1FWVgagvIpBfHw8LBYL8vPzUVZW\nJoYFBDabDQUFBSguLvabRwiJjFB9R6PRIDk5GQkJCUhMTITD4RDb6xUWFiI3NxdXrlyBw+EQB+nt\ndjuuXLmCP/74A7///jvKysrENl9GoxHx8fHgeR5WqxXt2rWTbPfAgQO19Imlvv/+e9x7773Iy8uT\nTE9NTcW6devQvXv3WtozEgohQDZgwACxcpzA5XJh1apVMdnujTfeiE8++QQbNmxA06ZN/eZfuXIF\nY8aMwaOPPoq8vDwYjUbwPC+GooXzQqfTQa/XS8Jzer0eBoMBGo1GDFW3bNkyrHZlhBBCCCGEEEII\nIYQQQuoGCvk0ADzPO4Hy9l0U8CG1gWVZqFSqkL8NbrVaxYBFUVERPB4PWJYV1yMMVrlcLrFNi9De\na9y4cZJqPjabDbNnz47J5xJUrOIDNPxKPhXp9Xo8/fTTOH78OF577TWkpaUFXG7dunXo2bMnLl68\nGLVtB2rb5fF4MHbsWLhcLjEEwLIstFotvF6v2NJHr9eDZVnwPI+ysjJwHAeO48T3ABCrhlBrL0Kq\np2JVLN92jMJ1XQh2chwHs9kMnufF8I7va7PZDKfTCavVCp7nYbPZxPuB2+0Wq/kAwM033yzZj7oQ\n8tm+fTvuv/9+v/tGy5Yt8f/+3//z22dSt6WkpPiFi1esWAGn0xmT7TEMgwEDBuDXX3/F9OnToVKp\n/Jb5/vvv0a1bN4wbNw7FxcViS66EhAQkJSWJbb+CPZuF+8xGCCGEEEIIIYQQQgghpG6RV70IqS94\nYcSakCpEu6VBOIee7wCv8F6z2QyNRiMOOLndblgsFvA8D4ZhoNfrodVqER8fj1atWuGhhx7C2rVr\nxXWuXLkSEyZMQFZWVsBt+oZDqsNsNvtNczgcKCwsjGi9gmgGYoDAoaRItG/fXvK6Xbt2WLp0KT77\n7DOsX7/er3XJTz/9hM6dO2PGjBm46aab/NbXqVOnsPehVatWePrppzF37lxx2q+//ooXX3wRzzzz\njNiGhGVZqNVqKJVKxMfHS8JYQlBA+LPAbrfDYrHA5XJBrVYjLi4OWq027H0k5Fpks9nE0BzDMOL5\nExcXJ5luNBphsVjEaj5A+b1ICCR4PB5xnULQEyg/V71eL2QyGRiGAcMwiI+Ph9PpRJcuXST7IlQG\nChZCrI6CgoKQl12zZg1mzpzpFxa85ZZb8M4770Cr1Ub9en/+/Pmori/aQceKVfkilZycHNX1NWnS\npMplHn30Uaxbt058XVhYiA0bNmDQoEF+y0azKtyUKVPwyCOP4N///jd27twpmcdxHJYtW4YNGzZg\n8uTJGDJkiPiswzCM5HwSli8tLRUDPgBQVlYGtVodUdinNp8nCSGEEEIIIYQQQggh5FpFX+EkhNQo\nj8fjN4jD87w4IMVxnBjwEeZZLBZwHAetVovk5GQ899xzkoFDjuPw3HPPxWyfi4uLJa91Op2kmtC1\nSKlUon///njvvffw1FNP+YViSkpKMGXKFHz++edR2+akSZP8glwLFy7E4cOHwbIskpOTkZiYiISE\nBDRr1gyNGjWSDF4yDAO5XA65XC4OTArHG1BefcS34g8hpHKBqmIJ54/QytFoNCI1NRV6vR5xcXFi\nWy4hwMmyrN+5qVQqxTZCDMOAZVkolUpJhZK4uDjcfPPNfi0iDx48WLO/hD8/97x58zBjxgy/a8fd\nd9+N1atXIykpqcb3i0RHmzZt0LlzZ8m0FStW1EggpWXLltiyZQs2b96MFi1a+M0vKSnBc889h379\n+uHkyZPiOVVRxdAPALGSFiGEEEIIIYQQQgghhJD6hUI+hJAa5RuwEAgDvEDVISAASEhIwMiRIyXL\n7Ny5E19//XVM9rliZRyDwRCT7dRHSqUSffv2xZtvvomMjAzJPI/HgzfeeAPLli2D1+uNyrbefvtt\nv7ZdU6ZMgdPphEajQWpqKlq0aIGMjAykpKSIx5pvoIBlWej1ejAMIw5wCvM4joPD4RAr/hBCgqvq\nei2Ec4TQgVarRVpaGpo2bYqUlBRotVrx3BTWp9VqIZPJkJycLLYgkslkUCqVMJlMYqUUrVaLRo0a\n4bbbbpNsv6ZbdrndbkybNg2LFi3ym/fQQw9h8eLFVBmsAcjJyZG8PnbsWI0Gyvr27YuffvoJL7zw\nQsDqSMeOHcNjjz0W9N4VqKKhbyUtQcXWe4QQQgghhBBCCCGEEELqHgr5EEJqlFCBwTd8ERcXJw4C\nVxUCKisrg8lkwj//+U8YjUbJclOnTo3JwBSFfKrWtGlTzJ8/Hx06dPCbt2XLFsyYMSNg27NwtWvX\nDk8//bRk2okTJ7Bs2TIkJiaKwQEAYuWnxMREJCcnSwbahXmpqalITk6GRqOB3W6HyWTC1atXUVxc\nHNW2K4Q0RFVdrwNhWRaJiYlIS0uD0WgU2y8VFRWhpKQENpsNWq0WGRkZaN26NZKSksRz1Leym7Cu\nihVWDh06FOVPGRzP83jmmWewYcMGv3njxo3DK6+8EnG7SFI33H333WjatKlk2vvvv1+j+6BWqzFt\n2jT88ssvGDhwoN/8/Px8TJ8+PeB7fcOtgP+zF1DeaqygoADFxcUoKCigeyAhhBBCCCGEEEIIIYTU\nURTyIYTUuIptXHzDF4EGoipWWQHKW2aNHj1ast4ff/wR69ati/r+UsgnNAaDAbNmzQo4+PjTTz9h\n4sSJyM3NjXg7gdp2vf766zh8+DBMJhNKSkpQVFQEm83mV0nEF8uyUKvViIuLE8MDQHlVH57nUVhY\nGLDFCSH1DcdxcLlcUQ9BVhXarOq9SqUSAPxaNNpsNsjlcvA873f+VqzsVjHkc/jw4RqrQvL+++/j\nk08+kUxjWRazZs3CxIkT/QJQpP6SyWQYNmyYZNrnn38elXtauDIzM/HRRx9hx44daNWqlWTeu+++\ni3379gV8n/DslZSU5PfsVVnrPUIIIYQQQgghhBBCCCF1C4V8CCG1orLwRbAKLB6PBwzDQKfTgWEY\nDBw4EM2bN5e8d8aMGWIQKFoo5BM6mUyGJ554ApMmTfKrYHHp0iVMnDgRu3fvjmgbwdp2jR49Gk6n\nEwD8Kn4AwYMOWq0WCQkJiIuLE6tDCVVFLl68SNUMSL0mVOcwmUwxqc5RWWgzFJW1/ApUKYjneXAc\nJ57HnTp1kswvKyvDyZMnq/FJwnP48GG89tprkmkqlQqLFi3C0KFDY759UvMGDx4stpYDyo/F1atX\n19r+9OzZEzt27PA758aMGRP0OYhlWahUKr9nL7fbHfA8FFpaEkIIIYQQQgghhBBCCKk7qIcAIaRO\n8q3yIBAGfDUaDVQqFTweD55//nmMGDFCXOb8+fNYtGgRJk2aFLV9KS4ulrymkE/VevbsiSZNmmDW\nrFmSkJTdbsfQoUMxY8YMjB8/vtqVLoS2XXPnzhWnHT9+HAsXLsS///1vAH8FBZRKJWw2m1gtRKgO\n5TswqlQqoVar4fV6JcvJ5XKUlZVBrVaHVJ2EkLokWHWOaB/Pga7XoRKu674BA+HcEyq7CeekEFy4\nevWqeB6np6fjuuuuw4ULF8T3HzhwADfddFNkH6oSxcXFGD9+vF+lr//973+46667Yrbd6hKq4Hk8\nHrjdbr8fj8cDl8sFj8cDp9Ppt5zvfGGaSqVCenq6+JOUlNTgKxcZDAb84x//kLTpWr9+PcaPH19r\nzwXNmzfHzJkzMXXqVHHaqVOn8Oqrr+KFF14IeT0KhSLgeahQKKK5u4QQQgghhBBCCCGEEEKigEI+\nhJB6QxjwLSsrg8fjgUKhwIMPPogVK1Zgz5494nJz5sxBdnY2kpKSorLdhljJx+v1gud5v2o70XTT\nTTdhwYIFmDVrFn7//XdxOs/zeOmll3D8+HHMnz8fGo2mWuufNGkSdu7ciWPHjonT3nrrLfTu3Rs3\n33yzGBTgOM6vHZDFYpEEHYRjy2QySYJALMtKwkKE1CfBqnPUpeO5YpDH99wDyisFqdVquFwucT4g\nPY87deokCfkcPHgQjz/+eEz2l+M4TJo0CZcvX5ZMf/LJJ+tcwIfjOHzyySfYtWuX330s2lQqFdLS\n0pCRkSEGfzIyMtC8eXOoVKqYbrsmDRs2DCtWrBDPK4vFgk2bNiE7O7vW9mncuHHYsGEDDh06JE6b\nO3cuHnzwQbRt2xYcx4mVsYIRWu8JocBwWu8RQgghhBBCCCGEEEIIqVkU8iGE1Gm+g1O+g01C6yUA\nePXVV9GtWzdxXmlpKebMmSOp8hKJhlLJx+Px4OTJk/jxxx9x8uRJMAyDdu3aoVu3bsjIyIjJNlNS\nUjB37lzMnz8fX3/9tWTexo0bcfr0aaxZswaNGjUKe91C266ePXuKFTU8Hg8mTpyIHTt2ICUlBSzL\niuEAX4GCDlqtVhyM9j3ehLAQIfVNsOocde14FoI8ga71QHkAgWXZgK27PB4POnXqhE2bNonTDx48\nGLN9Xbp0qSRUCgCdO3fGhAkTYrbN6nA4HJg7dy5+++23Gtme0+nE+fPncf78ecl0lmWRnp6O5s2b\nS37i4uJqZL+irWnTprjnnnvw+eefi9NWrlyJIUOG1FqYSSaTYcmSJejSpYvkXjhmzBjs2rULdrtd\nXDY+Ph4KhQIKhcLvPBPOQ7fbHXA+IYQQQgghhBBCCCGEkLqB/vWWEFJn2Ww2FBUVoaSkBEVFRbBa\nrbBYLHA4HLBYLCgrK8P58+eRlZWFwYMHS967aNEi5OfnR2U/KlZA0Ov1UVlvTeB5Hrm5ufj444/x\nyiuvYPXq1Th27JjYduXw4cNYuHAhli1bhuPHj4PjuKjvg0qlwpQpU5CTk+M3SP/zzz+jd+/eOHXq\nVLXW3a5dOzz77LOSab/99hvG+SqmVgAAIABJREFUjx8vthkR2gEB5eEwp9MZtIqRTCZDSkoKZDIZ\nAFA1A1KvCdU5hOO/Lh/PQsuvQGFOjuMk57FACCx16tRJMv3o0aNwOp1R30ebzYbFixdLpqWkpGDB\nggV1Lji1bt26Ggv4VIbjOFy8eBF79+7F6tWr8dJLLyEnJwdPPPEE5syZg48++gj79+9HQUGBXxiz\nrsrJyZG8zs3NxejRo2Gz2Wppj4C2bduKrSoFhw4dwvr168XXdrsdZ86cQVFREQoKCgLuL8uyUKlU\ndfIaQQghhBBCCCGEEEIIIaRc3RqRIITUa8Gq7oS7DofDAY7jUFZWJmnNUlJSApZlYbVaxcFAjuNg\nMpnw/PPPY+vWrXC73QAAl8uFBQsW4NVXX434c1VsJ1VaWhrxOmPNZDLhp59+ws8//4yioqIqlz9z\n5gzOnDkDo9GIbt26oWPHjlGtSsAwDB566CFkZmbi9ddfh8ViEefl5uaiT58++PDDD9GlS5ew1/3c\nc89h27ZtOHLkiDjtk08+wahRo7B8+XKwLAutVotLly7B4XCAZVkYDAY4HA5otVq/49a3qgjLsuA4\nDhzH0aAnqZeqqpITrmhc50NhsVhQUlICuVwOmUwGrVYLpVIJh8MBhmHEtl4A/CqReb1e2O32qFdW\nOXLkCKxWq/iaYRjMnz8fKSkpUd1OpM6dO4fdu3dXuZxCoYBcLhcruwR7rVQqJdPlcjnKysqQn5+P\n/Pz8agWqTCYTTCaTpMVUQkICbrjhBvGnZcuWUKvVYa871jp16oSsrCxJq8g9e/YgOzsbW7ZsQXx8\nfK3s17Rp07B582ZJaHbFihXo378/OI4Tn53cbjdYlkVZWRmUSiW8Xm/IlXs4jotZpR9h3bG+thBC\nCCGEEEIIIYQQQkh9RyEfQkhU2Gw2lJWVged5sVqEVqsNex2XL1+GxWIRqzcYjUYxZCOTyeBwOCTf\n9nc6nbh69SoSEhIwaNAgrFu3Tpy3dOlSTJ06VdKSqTqysrJw+PBh8fXZs2cjWl+sWK1WHD58GHv3\n7vVrmRIqk8mEbdu2Yffu3bj99tvRtWvXqO5j586dsXv3bgwdOhTnzp0Tp5eWlmLQoEFYsmQJ7r//\n/rDWqVQqsXr1atx55524evWqOP2DDz5AUlISZs2aJVaA8ng8SEhIgFqthsViAcdxsNls4nGr1+uh\n1+vBsiw8Hk/ExzQhdYFQJSdS0bjOh8JiseD8+fPgOA4Mw0Amk6GwsBBJSUlgGAYqlQoGgwFmsxll\nZWXIy8vzW0cs2ir+9NNPktdZWVnVCibGEsdxeP/99yX3SZlMhrFjxyIrK0sS1KlYGUl4fzh4nkdx\ncbEY+BF+Ll26BJPJFNa6SktLceDAARw4cABA+XGbmZkpCf4YjcaA+12TGIbB7Nmz8eijj0pCX4cP\nH0a/fv2wZcsWJCcn1/h+qdVqzJgxA4899pg47cCBAzh69CgyMzPhcDigVCrFKnc2mw0XL16EUqkM\n6XwOdP7rdLqo7LvVaqX7LSGEEEIIIYQQQgghhISIQj6EkIgJVXeEQUWe51FWVga1Wh3yt7E5jkNp\naalYvUcmk8FsNkOpVIqtI2QyGZKTk2G328FxnLg9lmUhl8sxbtw4bNiwQRyktFgsWLx4MSZMmBDR\n52vbtq3k9ZkzZyJaXzR5PB4cPXoUP/zwA3799Vd4PJ4q39OsWTN06NABZrMZP/zwg6SqjsDhcGDP\nnj3Yu3cv9u3bh4EDB6JNmzZRGVy98cYbsWvXLgwZMkQyaO50OjF8+HDMnj0bY8aMCWudWVlZ2Lp1\nK/r06QO73S5OX7hwIVQqFcaOHQuGYaBQKGCz2cTgWElJiRh+4HkeFotFHFiM9JgmpCGJxnU+1O2U\nlJSI13Gv1wuTyYSEhAS43W6oVCpcvXoVNpsNxcXFAIDCwkLJOvR6vdhyL5p+/PFHyesOHTpEfRuR\n+vbbb/H7779Lpg0cOBB/+9vfYrI9hmFgNBphNBpx8803S+Y5HA4UFhbi7Nmz4k9ubi5cLldI6+Y4\nTnzfZ599BgCIi4vDTTfdhDZt2iArKwutW7eOSaCrKu3atcPq1auRk5MjCZceOXIEffr0wccff4zG\njRvX+H4NGDAAaWlpuHz5sjhtyZIlGDduHK5evYrExETEx8dDpVLBYrHAaDQCqPp8Dnb+azSaqFQH\no/stIYQQQgghhBBCCCGEhI5CPoSQiHk8HknVAKB8kMbj8YRcPcLj8cDlconf4hbaJnk8Hng8HqhU\nKuj1emi1WmRmZqKkpARerxcWiwU6nQ4sy6Jp06a49957sWvXLnG9b731FkaNGhXRN8IrhnwuXLgA\nl8sVlcoY1cHzPM6ePYsffvgBhw4dklQSCCY5ORkdOnRAhw4dkJSUJE7/+9//jl9++QV79+5Ffn5+\nwG3t27cP+/btw/XXX48BAwbgjjvuECsBVFdKSgq2bt2KUaNGiYO3wvamT5+OvLw8vPTSS2EN8HXv\n3h3r1q3DoEGDJGGnuXPnIj4+Hv369QPP82K7EoZhIJdLb4PCcSv8OdC8yv7ea6qVESE1zeVyweFw\nSNr0hHudD4Vw/jAMA57n4fV6xf8qFAp4PB6YTCYYDAbwPA+Hw+FXXS0W7ZJ4nver5HPrrbdGfTuR\nsFgsWLt2rWRao0aN0K9fv1rZH7VaLVbgEXi9Xly8eBFnz57FuXPnxBBPoLBpIGVlZdi/fz/2798v\nTsvMzESbNm3E4E+zZs1iEvKqqH379li7di0ef/xxSdDs1KlT6N27Nz7++GO0bNky5vvhS6lUYuTI\nkZg9e7Y4bdeuXXjqqaeQlpYGm80mVj8UqtYJhHtjoDZ3brc74D0x2PLhCLbuaF9bCCGEEEIIIYQQ\nQgghpKGgkA8hJGK+A7KCQAGKqtYhtIwQaDQaJCUlITExEUqlUhyM0ul00Gg0cLlcUKlU4ns8Hg9y\ncnIkIZ+ioiKsWrUq7MowvrKysiSfj+M45ObmolWrVtVeZ3UUFhaKg5sFBQVVLq/VatG+fXt06NAB\n1113XcAqPHK5HB07dsStt96KM2fOYN++ffjtt9/8BtwA4Pfff8fcuXOxfPly9OvXD3369IloMF2n\n02HVqlWYOnUqVqxYIZm3ePFiXLp0CYsWLYJarQ55nffddx/ef/99DBs2TPIZZsyYAZ1Oh7vuugtA\n+UCoXq8XW3UJfI/bcI/pmmplREhNs9lsKC0tFSuWCIHLcK/zoZDL5ZDJZNDr9bBYLJDJZGBZFgkJ\nCXA6nSguLobZbAbLsnC5XLDb7X5Bx1hUdjl37hxKSkok0+payGfdunUwm82SadnZ2XUqKCGTydC0\naVM0bdoU//d//wegPNCRn5+PU6dO4ffff8epU6dw7ty5kFuHnT9/HufPn8enn34KoPzZoW3bthg0\naBC6dOkS0/ZeN954Iz766CMMGzYMFy9eFKfn5uaKFX3atGkTs+0HMmLECLz66qtiYNXpdOLTTz/F\no48+CpVKBZ1Oh0aNGkmq5wAQq90FolAoAt4TIw38VrbuaF9bCCGEEEIIIYQQQgghpKGgfz0lhESM\nZVnExcX5BRzCqWQiDOI6HA7xG/16vR7x8fEBQx4sy0KtViMuLg4WiwU8z0OhUKBjx464++678eWX\nX4rLvv322xg+fHi1BzoNBgNatGiBP/74Q5x25syZGgv5XLhwAevWrfNrwRKIXC5H69at0aFDB9x4\n440hD5IxDIOWLVuiZcuWMJlM+O6773Dw4MGAbVVMJhNWrlyJtWvX4u6778aAAQOQmZkZ9ucS9vf1\n119HkyZNJJUHAGDr1q0oKSnBmjVrxPZaoRgyZAhKSkowfvx4cRrP85g8eTLee+899O3bVzw+WZZF\nWVmZWJnJYDCIx21lx3TFij2VtRupiYoShMSK77GtUCjgdDphsVig0WgQHx9frYpVvudPRSzLQq/X\nAwBUKhVcLheMRiNYlkVxcTHkcjl0Oh1kMhk8Hg84jvML+cTFxVXvw1aiYquulJSUWmnHFMzp06cl\n9z0A6NSpE9q3b19LexQ6hmGQkZGBjIwM/P3vfwdQHkw5ffq0GPo5efIkSktLQ1qf3W7HgQMHcODA\nAdx0003Izs5G586dYxb2adasGdatW4fHH39c8pxw5coV3Hfffdi0aRM6duwYk20HkpGRgYEDB2LD\nhg3itPXr12Po0KFigE54rgr1uS0az3nBxHLdhBBCCCGEEEIIIYQQ0hBRyIcQEhVarRZqtTpoq6JQ\n2hhptVo0a9YMdrsdACTVeyrbrkqlgt1uh0ajgdPpxLhx4ySDnXl5ediwYQMeeeSRan++tm3bSgbv\nfP8cSy6XC//73//8KkhU1KpVK3Tu3BkdO3YMGMwJh9FoRP/+/dGzZ08cPHgQhw4dwpUrVwLu265d\nu7Br1y7ceuutGDBgAG677bawB1IZhsHTTz+NjIwMjB8/XtJq69tvv0VOTg5WrVoVVkjrySefhMlk\nwosvvihO83g8GDNmDHbu3InOnTtXGYAKdkwHqtgjl8uDthuhkA+pzzweD6xWqximBMorbyQlJYVV\nZUtQ8fwRqgL5Es69srIyMAwDhmHgcDggk8lgMBjg9Xphs9mg0+mgVqv9zuVYVPIJ1KorlhViwsFx\nHJYvXy65BqlUKjz22GO1uFeRUalUyMrKQlZWFoDy62lRURHy8/Nx/PhxHD9+HKdOnYLb7a50Pb/9\n9humTp2KNm3aICcnB7fffntM/t7S09Oxdu1ajBw5EkeOHBGnl5SU4P7778dHH32EO+64I+rbDWbs\n2LGSkE9eXh727duHvn37IiEhQWyJqlar4Xa7JW34ggl3+XAIFRrdbnfE7S59nzfp/ksIIYQQQggh\nhBBCCGmI6CuShJCoYVk2YDDHZrOhoKAAJpMJ/5+9845zos7//+szSTabsjXZwtIR6YgN7lBRUc9e\n7iw0C4eHgIonCh4ch/A9K4KA2ECPO5BDASl6FizYC/4OFKSICrIsbdmSbN8kmzKf3x+7M5dPJtlN\nstnC+n4+HvvAec9n3vOZzWRm3PdrXu+SkhK4XK5GcyQnJyM5OTmqIo/L5YLT6URtbS2cTieA+pZN\n5513njBu6dKlUbf+CMfgwYOF5cOHD8edKxa++eabiAKf7OxsXH/99Xjsscfw0EMP4cILL4TFYknY\nvpOTkzFixAj885//xJw5czBo0KCIY3fu3Im5c+fiySefjFtkNHr0aKxfv1518VDYunUrJk6cKIh/\nomHOnDm47777hJjb7cYNN9yAr776CiUlJSgtLQVjTG37VlNTI5wnoed0JMceSZI0hWNqN0J0BCRJ\nEgQ+jDG1EB8r4b4/od+5YLxer/q9CgQCKC0tRUVFBVwuF8xmMzIzM3H66adrrg3NaSMYiVAnn7PO\nOivh+4iXjz/+GAUFBULsxhtvhM1ma5sJtQCMMWRlZeGSSy7B1KlT8eKLL2LLli1YtmwZ7rvvPlxy\nySXIzc2NuP3+/fvx0EMP4d5778WOHTvCtqRsLjabDe+88w6GDx8uxGtqanDTTTcJrURbmvPOOw9n\nnHGGENu8eTOys7MFUZ0kSTAajVGLamIdHwuRniFjIfR5M9TliyAIgiAIgiAIgiAIgiAIoiNAIh+C\nIFqUSKKIeAU3sizD6/VClmXIsiwUn5WCMecc06dPF7Y7cOAA3nnnnbiPI5zIpzmioWgIBAL48MMP\nhZjZbMZFF12EmTNn4pFHHsE111yDrKysFp2HTqfD+eefj4ULF+LZZ5/FpZdeGrHA/+WXX+Jvf/sb\nqqur49rXxRdfjLfeekvTbufdd9/FPffcg0AgEHUuxhgWLVqkcXCqqqrCrbfeir1798LhcAgCAcV9\nJxJ+vx+BQAB1dXXq5885hyzLSE1NBeccdXV14JxTuxGiQyDLMqxWqyq2Udx3Qq9/wdfmSHG/3x/R\n8SqU4LGyLMPlcsFkMiEQCIBzDrfbjfT0dBiNRs32iXbyqa6uxoEDB4TY2WefndB9xEtlZSXWr18v\nxDp37oyrrrqqjWbUeiQlJWHAgAG4+eabMW/ePKxfvx6bN2/GX/7yl4it1H744QfMmDED9913H777\n7ruEi33S0tKwefNmXHrppUK8rq4Ot956KzZu3JjQ/UWCMYa7775biH3++efYu3ev8B2VZVm4n0VD\nPNu0Bol+3iQIgiAIgiAIgiAIgiAIgmivdKjqI2OsB2PsRsbYk4yxGYyxS9p6TgTxayeWom5TuFwu\nOBwOlJeXw+FwCMWc4DEnT57EsGHDMGDAAGHdkiVL4i7ohb4R73a7UVRUFFeuaNm5cyccDocQmzx5\nMsaNG4devXq1SauY008/HTNmzMCqVaswduxYjRgHAPbt24eHHnoIpaWlce1jyJAheP311zWuRJs2\nbcK0adNiKthJkoQVK1bg2muvFeKlpaWYMGECdu/ejSNHjqgt4ppy3/F6vXA4HKioqIDD4YDL5Wq2\nY097LZgSvx4iCXQAQK/Xw2KxwG63Iz09HXa7HRaLRTjnI7m1hcaDnXkUIn1/9Hq9Otbn84FzDpPJ\nhJycHKSlpcFkMqGiogLl5eUoKSkRtg13XWoOu3fvFu4dBoOhUWez1mTt2rUad7wJEyb8al3EbDYb\nrrnmGqxevRqzZs1CXl5e2HF79+7Fgw8+iLFjx+Kbb75J6BzMZjPWrl2L66+/Xoj7/X5MnDgRK1eu\nTOj+IjFmzBhkZGQIsVWrVqktzpTvZ1lZWZMuiwqh27Qnp5xIz5tNtXQjCIIgCIIgCIIgCIIgCII4\n1Wi3FQDGWA8AlwFIB7CTc/5JE+PXA7g5TLwCwF845/9sgWkSBNEESqE2uPASXNRV3B2UZeW/Qx1Q\nZFlWi0lK4beurk5YVsbYbDZIkoSpU6finnvuUXPs2rULX3/9NS65JHb9X9euXZGdnS0Uk4uKitC/\nf/+Yc4WyZcsWTYxzjjfeeEOIZWdnw+PxYPfu3Y3me/3115s9p2D69esXcd3AgQNRWlqKw4cPCy47\nR44cwcSJEzFw4ECNWOeBBx6Iar8zZ87EY489JrT/eu2111BWVoaJEyeqn3s0n+fLL7+Mm2++Gdu2\nbVNjRUVFeOyxx7Bo0SIYjUZkZmaqjiXhxGCKc1RKSorqGOVyuZCdna06Biitv4B6xyCj0QidThdx\nXi6XSxWrMcaQmpoqtFJpDdpCLEY0Due8RdoJhaOpc1CSJKSmpgpt6YJdqiK5ZyQlJYVtzWW1WtXv\nj+IKFM7xSpIkdazBYIAkSaq4SJIkVFdXw2QyAYDGOSwlJSVhv7/KykqNCKRfv37weDzweDwx54u3\nnWE49u7diy+++EKI9e3bFzU1Ndi+fXtcOU+cOJGIqan07Nkzofkita+MxFVXXYWDBw9i586dqKmp\n0azfsWMHbrvtNvTp0wfXXXcd+vTp06z5BTv4LFiwAAaDAZs2bVJjnHNMmzYNRUVFmDhxYpP5IomU\nosFoNGL8+PF45pln1Nj69euxYMEC6HS6iN/bcM9ffr8/4jbJyclh7yOKwMZgMER9n2nO/UjZT7jn\nzURdD+h+SRAEQRAEQRAEQRAEQRBEe6BdOvk0CHYOAXgJwFMAtjLGnIyx6RHG70C9wIeF+ckA8DJj\nbF1rzJ0gCBGlQBzc6kUpELtcLhQVFaGwsBD5+fkoKChQHR9C3w5XnByCYYzBZDKpuf1+v1Awvuaa\na9CjRw9hm6effjqu42CMadx8Dh48GFeuaDh27BicTqcQO+uss9pdgUmn0yE3NxeDBw9GUlKSsM7r\n9WLPnj2orKyMK/fAgQMxc+ZMjSPF+++/j9WrV8dUtDOZTHjttdc0n+HRo0cxb948yLKMlJQUJCcn\nR8wR7CZis9mQlpaGzMxMGAyGsOdnUw4C1FqEaGuiPQfNZjOys7ORmZmJ7OxsQQQUyT3D7XaHjScl\nJSE7Oxs2m02TKxSz2Qy73Q6bzYbu3burY/1+PywWi3qtDxVvtISTTzCh15G2wO/3Y8mSJUIsKSkJ\nF154YRvNqH0iSRL69u2L0aNHY8SIEbBarWHHHThwAIsWLcLixYsTdm/X6/V48sknMX78eM26+fPn\n48UXX0zIfhrjrrvuEp4bqqqqsHbt2qhdFoMdFAsLCzVuP5Huc9G6BHHO4fV6EyLCUZ4vwz1vEgRB\nEARBEARBEARBEARBdCTa3V89GxHsZABYwBg7wBjrHjR+PYBzAFQCWADgdwBOa4jNBJDfsP0tjLEn\nWvFQCIJoQCkQBxd1ZVlGUVERSktLUVZWhoKCApSUlECWZXDOUV1dLRSaw70JzhhDWlqaWnzu3Lmz\nUDDW6/WCkw8AfPbZZ9ixY0dcx9GaIp+dO3cKy+np6ejVq1eL7a+5WK1WnHHGGaqzhkIgEMC+ffs0\nbcei5cwzz8SMGTM0bjhvvfUW1q9fH1OutLQ0bNy4EaeddpoQ379/P6ZPny44EYVroRV8DkqSpLr0\nGAyGiOenwWCIOJ+miqyNtVAiOj6t8fnHIk5TzvnQgnlwWy2FUAFmcFxx4gnnGKIQfOzKWKvVCrvd\njoyMDOTl5QnX+lAnn0SKfGRZxt69e4VYexD5bN68GYcPHxZiw4cP1zinEfVIkoR+/fph1KhRuOCC\nCyKKfX7++Wc8/fTTWLJkCX755ZeE7HfOnDmYOnWqZt3ixYvx5ZdfNnsfjdGzZ09ceeWVQuzFF1+E\nTqdrsnWe4l6nXCP0ej1qa2uFa1K4+5zyDBcsHgxeVoinXVhTmM1m5OTkwGazIScnp9Wd8QiCIAiC\nIAiCIAiCIAiCIFqDdiXyYYw9iXpxDgPwMv4n2LkFwMaGeG8AWxvGX9qw7hCAczjnszjnH3POD3PO\nd3HOF3LOewP4R8O2MxljI1v7uAiCgKao6/V61aKPInaoqalRW3CFFpolSUJKSopalOKcq22RlOKz\nXq8XxjDGcOeddyI3N1eYS7xuPqGF3UQUAMOhuBsF0x5dfEJJTk7GGWecgZSUFCHOOcdPP/2kOaZo\nGTp0KKZNm6YRBGzYsAGbN2+OKVdWVhY2b96saYGybds2TJw4EbW1tXC5XCgtLUV5eTlKS0vVwmPo\nOcgYQ0pKCiRJanRdJMKJIzjnamG1pKREdbZKRPGTOHVwu92t8vlHI04LJ3gLJpJbm16vj8tVI9g5\nxOFwCMeu3Ed0Op3aWg8I364rURw+fFjjFNTWIp/S0lKsWrVKiGVlZWHIkCFtM6FTCJ1Oh/79+2PU\nqFE4//zzkZGREXbcTz/9hIULF+KZZ57BoUOHmrVPxhimTZuGWbNmadbNmDEDpaWlzcrfFFOmTBGW\n9+3bh6+++gpms1kV0ykuW8GEClGVlnmKEFW5z4VeQ6IRD0YrBIoHxhiSkpLa/TMTQRAEQRAEQRAE\nQRAEQRBEvLQrkQ/qnXc4gL9wzqcECXY2cc5HATgXQAWA0xhjy4LGn8M5PxwpKed8MoBNaBD6tPhR\nEAQRE6HtmIDwb4dbLBZkZ2erLZU8Ho+mtZcyRmkrk5mZiT//+c9Cnrfffhs//vhjzPMMLaAqhehE\ns2vXLmHZYrGgT58+Cd9PS2AwGDBo0CBkZmZq1ikt2eIp4p133nm49957NUW7V199FcuXL48pV7du\n3bBp0yZNcfe9997D9OnTUVFRoSk8KgIHs9mMrKwsZGRkICsrS3AJaGxdOELFEW63Gx6PB06nE/n5\n+ep5TW28fn2EFr9b6vNvrJ0iEL3TRji3tsbikQh1DlHEn+GOXWnllZGRoZlXIp189uzZIyzn5uYi\nJycnYfnjYdmyZXC73UJs5MiR1JYoBnQ6HQYMGIBHH30UY8eORXp6ethxP/74IxYsWIClS5eiuLi4\nWfucOHEiZs4U/zfE6XTiwQcfFJzkEs2ll16K3r17C7HnnnsOLpcLer0e1dXVcLvdqKurE4R14YSo\nZrMZeXl5yMjIgN1uF77TSuutSC5Bwc908bS4JAiCIAiCIAiCIAiCIAiCIOppN9UAxthNDf+5k3Me\n1maDc74TwGWoF+tMAnApgJmc86oodvGXhn9/19y5EgTRfJKSktQ3wBUXlJSUFBiNRo0LSqiTRF1d\nneDoE9raK7StzNixYzVF38WLF8c85969e2vaUSXazaesrEzTgmXIkCGadlXtGcUpIdRBCQCOHz+O\n559/XnUCiIWLL74YkyZN0sT/+te/alwtmqJfv37YsGGDprXN+vXr8cQTYmfHcK5S4doWNbUuHIoI\nIiMjA8nJyTCZTPD5fKrYQTmvg9t4ER2fxtq4JRrlHFSEkUrRXpZlVFVVRS02itSCq6nWXMH4/X4E\nAgHheq8ce7j2ZUruqirxMTCRIp/du3cLy23t4vPdd9/h008/FWIDBw7UuJMR0WEwGHDxxRfjscce\nw5gxYyKKffbv348FCxbg2LFjzdrfxIkTcckllwixb775JmaxaixIkoTJkycLsbfffhsnTpwAUO+s\nqLThUoR1gUAAfr8fZrNZeN5SHHJCv9PBgkCHw6EKfWRZhs/ng8ViEYQ/8bS4JAiCIAiCIAiCIAiC\nIAiCIOppNyIfAENR78rzUWODGoQ+L6Ne6APUt/FqkgannwoAYIz1iHeSBEEkBkmSkJubi6ysLKSn\np6Nbt24YOHAgsrKykJ2drYovamtrBSeJysrKmN7+lmUZgUAAo0aNEuLr1q3DkSNHYpqzTqfDoEGD\nhNiBAwdiytEU33//vbBsNBoxYMCAhO6jNWCM4bTTTkO3bt006z777DPMnz9f40QRDZdffjkmTJig\niT/44INYt25dTLnOOeccvPrqq5oWJcuWLcNzzz2nLrd04VFp96UUPJXiZ/B5zRgL63hFdEzCFb9b\n8vMPJ06L5LTRkmIzr9eLsrIyVFZWwul0wu12gzEGr9cbsYVXIBDQtNNqSSefthT5eL1eLF26VIgZ\njUacf/75bTSjjoPBYMDIkSPx2GOPYfTo0WHPoZqaGixevBgFBQVx74cxhqeeekojgl26dCl27NgR\nd96muPXWWwVRayAQwJpxs+WsAAAgAElEQVQ1a9TvefD9xuVyobCwEOXl5XC5XDCbzWp71FC3HyB8\n661AIACTyYRAIACdTofa2lrU1NTA6/WCc65p9RWp9RdBEARBEARBEARBEARBEAShpT2JfJRXZ6P5\nC/dW5T845wUx7KOs4d9eMWxDEEQLYTabkZubi7y8POTm5sJqtQpvh8uyrCkcud1uTeG5MRGGz+eD\nTqfDTTfdpLb5AuoLXM8880zMcw4t8CbSyae6ulojGho0aJBGhHKqwBhDt27dNG1CgPqWZPPmzUNl\nZWXMea+99lrceuutQoxzjnvvvRdvvvlmTLkuuugirFixQuMyMn/+fLz22mvwer2wWCwt3gYnuC2K\nJEmwWq2QJEkV/AS3UCI6PqHF77b4/CM5bbSU2EiWZbhcLtXxIxAIoLy8HEajES6XK2ILr+rqak2u\nlJSUhMyprKxMI+gIbdvYmmzYsEHjJHP++ec32QYt0SiOLx0Rg8GASy65BI8//jhuueUWjdjH5XJh\nyZIlOHToUNz7yMjIwJIlS4TvtCzLeOCBB1qkBSgApKenY+zYsULs1VdfhSzLYIypz1F+vx/l5eXq\n3JTvm8fjERx9gr+D4QSBgUAAlZWVMBgMkCQJLpcLBQUFcDgcauu/SC5iBEEQBEEQBEEQBEEQBEEQ\nROO0x4phZhRjdsaZu6Lh37JGRxEE0Wo01solXOGIMQaTyaR5+ztSAVwpVKenp+Pqq68W1r3yyiso\nLi6Oab6hIp+DBw/GtH1j7N69W2hFo9fr27w1TCLIzc3FgAEDNJ/RoUOHMHv2bDidzphz3njjjbj5\n5puFmCzLuOuuu7B169YIW4Xnuuuu07hjAMDMmTPxySefoLa2VnAtaAkkSUJqaqp6XlssFvTq1Ut1\ntqLi568Lk8mE7Oxs2Gy2Nvv8Q8/JlhYb+f1+cM5hMplgNpsRCAQgSRKcTqfm+xfsKBTaqgtInJPP\n9u3bhWWj0Yg+ffokJHesFBUV4d///rcQ69evn8ZdrqU5evQo3nrrLbz55pvYsWMHKioqmt7oFCQp\nKQmXXXYZHn30Uc1n7vF4sHTpUuTn58edf+jQobj//vuFWFFREWbOnNliAqrQll0OhwNbtmxR26XW\n1dWhpKREFfoobnterxder1fYNvg7GE4QqDj4ABDaTyrfc0XArbT+IgcfgiAIgiAIgiAIgiAIgiCI\n6GlPIp/vUN+C6+ymBja03ooHJXf8f5UnCKLViOQkkZaWJrz9HdyCIhSlDY3RaMSdd94puFB4PB5M\nnz49poJaqIvDsWPHNM4K8cA5x08//STE+vfvD5PJ1Ozc7YHMzEwMHjxY4wJSVFSE+fPno66uLuac\nY8aMwfXXXy/E/H4/7r77bjgcjphy3XbbbXjkkUeEmCzLmDZtGgoKClBZWQmPxyOIsBKN4mqgCDtC\nna2IXxeNCSBbi9Z02lDcrBRHH4PBAJ1OB6PRiNraWuG7F+woFE7k05x2SsGEturq06dPi7bua4zX\nX39duE4yxjBt2rRWPT98Ph++/fZb+Hw++P1+FBQUYOvWrfj0009x4sSJDunuk5ycjPvuuw/9+/cX\n4nV1dVi5cmXEVqHRMGXKFAwfPlyIffLJJ3jvvffiztkYAwcOxEUXXSTEFi5ciJSUFOTl5SEQCMBu\nt8NkMgluPUlJSRpHweDvYLjWWxkZGarIRxH2BG/TWJtVgiAIgiAIgiAIgiAIgiAIonHaU+Xw24Z/\nR7dEcsZYz4b/rOCcaytCBEG0KbIsw+v1CoVcSZI0hSPFtUcR70RT4ExNTUVmZib69u2LP/zhD8K6\nzZs3Y9WqVVHPc9CgQULbL1mW8fzzz0e9fWOECkg6deqUkLzthZSUFAwZMgTZ2dlCPD8/Hy+88ELM\nBWLGGO644w786U9/EuJOpxN/+ctfYp7ffffdh2nTpgmx6upq3HXXXTh8+DCKiopQWloKl8sFWZZR\nV1eXcNFPexB2EEQwsVxrm7sfq9UqCAKUtnV6vV51EmGMwWw2w+/3Q5Zl2Gw2jRh03LhxCXGYCXUE\nOnjwYJs418iyjC+++EKIXXfddejbt2+rzuPkyZMIBAKauMPhwLZt27Blyxb8/PPPGteXU52kpCTc\ne++9GDx4sBAvKSmJ2TkuGJ1Oh0WLFiEzUzQxnT9/PjweT9x5G2Pq1KnC8rFjxzBp0iScPHkSLpcL\nlZWV0Ol0YIypbj2pqakaVy/lu6kQKgi0Wq3q85terxfaUCo52kowpxDuuZMgCIIgCIIgCIIgCIIg\nCOJUoN1UETnnuwAcBpDOGJsexSbnADg3hl1MBsABvBzH9AiCaEFcLhdKSkrgdDpRUlKC2tpadZ3F\nYlELR3a7HXq9PuaCjCIOMhqNmDFjBtLT04X1M2bMwI8//hhVLrPZrBEKbdu2Df/9739jmlO4Oebm\n5gqxkydPNitne8RkMuGJJ57QCJi+/vprbNq0KeZ8jDEsWLAAV111lRB/44038Pbbb8ecb+7cuRg3\nbpwQ++GHHzB//nwUFxfD5XKhqKgIx44dg9PpVEU/LQ0VI4n2QEufh2azGXl5eUhPT4fNZoMsyzh5\n8iTq6upgMBhgNBphNpvhcrlQXl4Oh8OBtLQ0TJw4UciTn5+Pu+66q9nzvPrqq1U3EqDe/W3jxo3N\nyhkP+/fv17Q1vOmmm1p9HidOnGh0vcvlwp49e/DOO+9g586dqK6ubqWZtTwGgwFTpkxBz549hfiW\nLVviajmpkJ2djb///e9CrLCwECtWrIg7Z2PccMMNuPDCC4XYm2++iQ0bNqjCnkAggIyMDKSnpyMv\nLw9msxlmsxl2ux0ZGRmw2+1hXb2CW29xzqHX62G322G329GjRw91m1Dnn7Yg9LmzNe7jBEEQBEEQ\nBEEQBEEQBEEQiaLdiHwamIz6ll0LGGNDGhvIOd/VIAxqEsZYGgDF1uGl5k2RIIhEIssyqqqqVBcX\nzjmqq6s1jj5+vx8OhwNlZWUaIVA0mEwm2Gw29O3bFy+9JF4G3G43xo8fH/Wb8+PHj0dGRoYQe+65\n5+D3+2OaUyidO3cWlgsLC5uVr72SkZGBWbNmaYqEa9eujUssJUkSlixZohFvTZ8+HWVlZTHlYoxh\n2bJl6NOnjxB/99138fHHH6OoqAhHjhxBaWkpnE4nXC6Xer7G6+7TlHCCipFEe6CmpgbHjh1DaWlp\ni56HOp0ONpsNHo8HR48ehdPphMfjgdfrhcfjQU1NjXC/qKqqwgMPPIABAwYIed555x0sWrSoWXPp\n1q0bLr/8ciG2bt26uNoLNocvv/xSWO7evTu6devWqnMIBAIoKioSYpHcnQKBAA4dOoT3338fGzdu\nxOHDhztEKy+9Xo+xY8cK4hSfz4cNGzY0K++VV16J3/zmN0Js+fLlLfIMIEkSVq9erXEPmjdvHgoL\nC1WBjuKSFSxyi9ZpTrlnlZWVweFwwO/3w2q1tlrrv6YI99xZVVVFIlqCIAiCIAiCIAiCIAiCIE4Z\nWlXkwxi7sbH1nPOPAPwD9UKf7xhjIxO06380/LuAc16QoJwEQSQApTVLsECCcw6fz6eOkWUZ1dXV\n6jiPx4PKysqYCjJKToPBgOuuuw6TJ08W1u/btw+zZ8+OKpfFYtFsf+TIEWzevDnq+YQjLy9PWHY6\nnXC73c3K2V7p0qULHnzwQU2x8Nlnn0VBQUHM+XJycjB//nwhVlpailmzZsWcy2q1Yt26dTAajUJ8\nwYIF+Pnnn+Hz+aDX68E5R01NDQKBACorK1FaWory8vKY3H2aEvBEKkY2VjDnnMPr9XaIojoRGy3l\ntFNTU4P8/HzVPae2trZFi+LJycmoq6uDz+cDYwyVlZUoKSlBXV2dphWUcrxz5szRtNf6v//7P3z6\n6afNmssdd9whLJeVleHdd99tVs5Y4JxrWnWFOrG0BsXFxRoh61VXXYVhw4ZpRK/B5OfnY8OGDfjX\nv/6FXbt2nfKtvLp3744RI0YIsV27dmH//v1x52SMYc6cOcL90OPx4Omnn447Z2N06dJF4xTk8Xhw\n//33Q6fTCQ4+saIItUOF20oLPsXpJ9FEuu+FuyYqz52h2zdXqE0QBEEQBEEQBEEQBEEQBNFatLaT\nzwbG2B8aG8A5n4x6UY4E4CPG2Izm7LBh+5sBbOWc/7U5uQiCSDx6vR5utxsOhwMVFRVwOBzweDww\nGAzqGJ/PB865MK60tBSVlZVR7cPtdsPpdKKyslIVzjzxxBMa54fly5fjnXfeiSrnVVddhb59+wqx\nlStXory8PKrtw5GdnQ29Xi/EOqqbDwCcddZZuP3224WYx+PB/Pnzo/5sgxk1ahSuuOIKIbZhwwa8\n9957MecaMmSIxgWkuroaTz/9NCwWi1qMVcRjHo+nUTeqcETjJhCpGBksggvG5XKhuLgYTqdTbS9G\n/DpoKccnWZbhdDrV8zJY3NZSRXGv16sW7Kurq1FTU4PCwkK4XC4kJSUJY/V6PXw+H3r27ImZM2cK\nQglZlnHHHXfg2LFjcc+lb9++GpeVNWvWtJrrxy+//KJx0GkLkU/ovUhp2dS9e3dceumlGDlyJLp0\n6RJRwOF0OrF161YsX74cn332Gaqqqlpj2i3C73//e1gsFiG2bt26iNflaOjfvz/GjBkjxN566y18\n8803cedsjOuvvx733HOPEDt48CAef/xxZGRkCA4+saA8rwXT2D0rEUS670W6Jur1es15yhjTPH8R\nREfm3HPPRZcuXRL20xFbDBMEQRAEQRAEQRAEQbRnWlvkwwBsjFLocwuAWZzzuF9jZYzNB7AAwHec\n8yuaGk8QRPvEYDBo3g4H6sU70QgpamtrBSFFbW0tkpOTsXr1aiQnJwvjp06dGlXbLkmScP/99wux\nmpoazdvxsaDT6dCpUych1pFFPgBw3XXXYeRI0bSttLQUCxcujLkoyBjD4sWLNW4eDz74ICoqKmKe\n25QpU3DTTTcJsR9//BFr165VC4SSJMFisWicAqIpakbjJhCpGBksggveNlbXH6Jj0JLtZxTnquDz\nUDlPIxXFE+UopLh/KDDGYLVa1RhjDGazGSkpKTCZTLjyyisxadIkIYfD4cCtt97arBZboWLEgoIC\nTQutliLUxScvLw+nnXZaq+xbgXOuuRcFt5dkjMFut2P48OG4+uqr0bdv37DXKKBeyLl9+3a89NJL\n+M9//oPjx4+fctcoi8WCG28UzUmLi4vx8ccfNyvv/fffr7l/zZgxo8UEZU899RQGDx4sxNatW4cP\nP/ww7u+wwWCI+p6VCCLd9wKBQMRroiRJSE1NFa4jqampTbYhI4iORFFREU6cOJGwH2p3RxAEQRAE\nQRAEQRAE0bq0xV8zoxX6bOKcL2zOjjjnswCcwzkf2pw8BEG0HH6/HyaTCXa7Henp6bDb7UhOThYE\nEpIkwWQyqcuMMaSkpIAxhrq6OrXNVzgae6t8wIABePzxx4V1JSUleP3116Oa++DBg/G73/1OiL39\n9ts4ePBgVNuHI7Rl14kTJ+LOdSrAGMPkyZM1rkg//vgjVqxYEXPxNy8vD0888YQQKyoqwt/+9re4\n5vbyyy+jR48eQnz58uX44YcfkJ6ejuzsbPh8PlRVVQnt1aIpakbjJhCpGBnOLaMtHBSI9kFLtp8x\nGAzQ6XQacY3NZgtbFE+Eo1BSUhKSk5ORnJyM1NRUWK1W5OXlIS0tDUlJSbDb7cjIyIDdbkdqairM\nZjNsNhsyMjIwbdo0jdPNjh07MHPmzPh+AQCGDx+O3r17C7HVq1fHnS8WQkU+I0aMaJF2R43hcDg0\nIqnQe5WC2WzGGWecgWuvvRZnn302bDZb2HGcc/z888947bXXsHr1avzwww+nVJH4vPPO09wb3n33\n3WYJc202G/785z8LsV27dmHNmjVx52wMk8mEV199VXi+AoDJkydj7969anu+WL7Dwc9n4ZYjEW+b\nyUj3PZfL1eg10Ww2Izs7GzabDdnZ2XG1JiOIjoAkSejcuXPCfnJzc9v6kAiCIAiCIAiCIAiCIH4V\ntLbIRxHtKEKfkY0NTgSc810tvQ+CIOJHETpIkgSj0QhJksIKJNLS0pCVlaUKgUwmEzweD5xOJ06e\nPImioiLU1tZq8jf1VvmUKVNwwQUXCOtfeOGFqAtNU6ZMEdyAOOdYunRp3M4Ewe4IAFBWVqYKRzoq\nBoMBDz30kKYY/NFHH8XVamvcuHG49NJLhdhrr72GrVu3xpwrPT0da9euFYQ3nHNMmzZN/W/FXQSo\nd3PinCMlJUUVQERyRFAEPJxz1NXVgXMe1k0g2mJkazsoEO2Hlmw/o5ynFotFFdecdtpp6jmvIMsy\nPB4PKioqGnUUCv0+hPt+SJKEvLw8ZGZmIj09HTk5OcjOzoZOp4Ner4ckSUhKSoIkSZAkCVarFTqd\nDkajESaTCYsXL0bXrl2F+b300ktYu3ZtXL8DxhjuuOMOIbZr1y7s3bs3rnzRcuTIERw9elSItUWr\nrlCxaVpamubzD0Wv1+O0007DnXfeiVGjRqFXr14RxxYXF+Pdd9/FypUrkZ+fn5A5tzSSJGHs2LHC\n987r9eLJJ59sVt5bb71V49Q0d+7cFmtvNmDAACxevFiIlZeXY+rUqQgEAmp7vlgEWMo9KzMzMyoB\nTU1NDY4fPw6HwxFzm8lI9z2z2RyViFa5jhDEr5VOnTrh+PHjCfv59ttv2/qQCIIgCIIgCIIgCIIg\nfhW06l81OeczIQp9Pkqk0IcxdhNjLH4LDYIgWp1wTiXBAongcWlpaUhOToYkSeqb2k6nExUVFSgt\nLUVJSUlYIYXFYhHyhy6Htt3au3cvvv7666jmn52drWnlsnv3bnz66afR/xKCyMrK0hTmO3rLLgDI\nyMjArFmzkJSUJMRfeeUVHD58OKZcjDE888wzSElJEeLTpk1DZWVlzHMbNmwY5s+fL8RKS0tx2223\nobq6GkC9I0JGRgZMJhPS0tLUomZzXU0UAQSAJouRoS4/jbn+EB2Llm4/oxTt7XY7unbtqhF4KOd5\nUVERSktL1fNcEf4o53Do96G0tBQOhwNOpxMnTpxATU2NmtNqtaJnz57IyspCVlYWzGYzrFar5phk\nWYZer1edfLKystCrVy+sWLFC047x3nvvjVuYc8UVVyA7O1uI/fvf/44rV7SEuvjY7Xb069evRfcZ\nCudcI/IJFaM2BmMMPXr0wM0334yJEyfi7LPPjig8dDqd2LhxIzZs2ACHw9GsebcGPXr00IiE33vv\nvaifH8JhMBgwe/ZsIVZSUoKFC5tlbtooEydO1LSm/Oabb7B06VIA8bmCMcaQlJTU5P2npqYGBQUF\nKC8vh9PpRG1tbUxtJiPd93Q6HbXkIgiCIAiCIAiCIAiCIAiiw9Lqf+lsEPr8o2FREfoMaU5OxlgP\nxtgHAF4HkNnMKRIE0cqEOpVYLJaw4ywWi/p2eFpamtCiRnGMCG0pAtQLMGw2G9LS0mCz2TStKS6+\n+GJ0795diL3wwgtRz3/06NHo1KmTZnuPxxN1DgWdTqfJ1RItuzjncbsNtRS9evXCfffdJ8T8fj+W\nLl2qigSipUuXLnj00UeFWGFhIebMmRPX3KZNm4arr75aiH399dd4+umn4fV64Xa7UV5eDrfbjcrK\nSrhcLsiyjOrq6oiuJrIso6qqCowxGI1GMMaE9U0JhMK1NzGbzcjJyYHNZkNOTg61IPkV0dLtZ4Ld\n1oD/CdD8fr9alFfEGzU1NaiqqkJhYSHKy8tRVlamxpTzNRAIoLCwEDU1NapY88iRIxqhT+fOnZGR\nkYHU1FSNaMflcsHhcKC8vFwQF5lMJowYMUIVKSi43W6MGTMGFRUVMR+/wWDA2LFjhdgnn3yCY8eO\nxZwrWr788kthecSIEa0uUlCuZ8HEIvIJJjMzE5dddhnuvvtujBw5EmlpaWHHHT58GCtXrsSHH34Y\nV7u31uT3v/+95pnl73//e8z3rGAuuugijBwpvgPx3HPP4dChQ3HnbAzGGJYvX45u3boJ8WeffRZ7\n9+5NmCtYKJxzlJeXq/c8zjmqq6sRCARiajMZ6b5HLbkIgiAIgiAIgiAIgiAIguioJP4vtlHAOZ/c\n8GblXagX+uxkjJ3NOd8day7G2EMAFIsFsisgiFMUpW1CNOOMRmNEgUrwW+OyLMPv98NgMECSJE2B\nWBnj8/lw5513Yt68eWr87bffxsmTJ9GjRw/NNr1799bEHn30UUycOFFdLikpwQcffIDp06c3eUyh\nop5+/foJhePi4mLNmMZoqq1HIBBAIBAAUP/7CvcTzFdffRX1vqMhNze30fV9+vTBgQMH1OVjx47h\n4Ycfxplnnhl2fJcuXcLGf/vb32LYsGHYvn27GluzZg06deqEm2++OeL+Bw4cGDb+4osv4rzzzhOc\nlVasWIFu3brhggsuQGpqKsxmM3w+H5xOJ1JSUlBXVyec15xz+Hw+GI1G+Hw+zXmsrDcYDIIgQhEI\nKU5WLpdLXa84FCgFTMVBgTh1Cfc9jAadTgedTifEWkLMV1tbqwrYvF4vAoEATCYTJElCSkoKiouL\nUVRUBIPBAIvFAo/HA5/Pp7baAuoFfLIso6KiQhUHybKM8vJymM1mdZzH41Hb4Cmt8cxmM2RZVuNu\nt1sVBymCTpPJhDvvvBM7d+7ESy+9pM49Pz8fkyZNwsaNG6MSzOTk5Kj/PWXKFPzzn/9U9yXLMt58\n80089thjUf/uDh6MznCyuLhYM3bQoEEoKSkRYmeffXbU+46GDz/8UFgO3Z/BYMDx48ejFp/u2bMn\n4rqcnBxYLBY4nU6NQJdzju+//x579uyBzWZDeno6JEnCGWecEeWRREdj84uWc889F59//rm6nJ+f\nj4ULF+KWW26JO+fYsWPxxRdfqPdqr9eLhx56CKtXr27WXFNTU8PGU1JSsGrVKlx22WWq6Mbn82Hx\n4sV49dVXIcty2JZd0dxrlOcs5VlMwev1qter4OuU3++PWVQU6b4X7prYnkjU9bm9ibYJgiAIgiAI\ngiAIgiAIgmhZ2syznHM+GaKjz85YHH0YY2c2tOaaD1HcQ43gCeJXQFJSElJSUjRtvpQiT7ALSrDD\nQyiK0GLUqFHC2/iyLGPZsmVRz+fqq6/G+eefL8Sef/55HD9+PNZDQ8+ePYXl0tJSwd2iOXDO1aKh\nsizLMgKBAPx+P3w+H3w+H/x+PwKBQJsUjgYNGoT09HQh9ssvv6C4uDimPIwx/PWvf9U4Ny1evFgQ\n/kSL3W7HypUrhSIl5xxPPfUUDh8+jJSUFFVIpvzeQoUajDFV0GAwGCKub0wApAh+QgVAVOQjWoNQ\nhyq9Xo+amhpVAGA0GqHX65Geno6MjAwYjUZUV1dDkiSh5Y9er4csy0IBXnEMUcYFC3mA+nNd2Zfi\n5BY8RvmOBM9v0aJFGDp0qHAMb7/9dlztj1JTUzFu3Dghtm7dOpSVlcWcqylCr1EpKSno379/wvfT\nFEpLwuB5JKoFoHLf7t69O3Jzc8MKO2RZRmlpKQoKCoTPtT0xYMAATSu3tWvXNqvlWJcuXXDDDTcI\nsffffx+fffZZ3Dmb4rzzzsPdd98txLZu3Yqffvop7pzKs1hZWZnGkc5gMECn0wnnlCRJsNls1FaL\nIAiCIAiCIAiCIAiCIAiiEdr0L6hhhD6fRCP0YYwtA/AdgF5B4UoAkznnVyR8ogRBtDskSUJubi6y\nsrKQnp6OrKws5ObmQpIktQ2SUgD2eDyorKwM+xa6IrRITU3F6NGjhXUrVqxAbW1tVPNhjOGRRx4R\nClMejycmhweFvLw8zRvphw8fjjlPvAQLf2JpmZEoJEnCsGHDNEW+HTt2xNwCJS8vD3/961+FWCAQ\nwOzZs3H06NGY53b++ecLjk9AfWuiRx55RG0BpAgN9Ho9LBYLGGNqayOr1aoel+J6EipUkyQpbgEQ\nQbQ0oeefJEmwWq2qMMfv9yMjIwPJycnqOaxcUzIzM9WYTqdDly5dVGGH0u4ruDVQcEtGB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sID9xXUGElRtWBSpn7/TIfczz+6OdMZNJ27RNSoHv+/XiZeeZZGZSJpOR55Pvd26Dt200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Pf/oT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\nYkcf6judBir6VbNRhY10u8NXu8Ehy7IqOtuo9aLRm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NXr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\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nQ0JCAsxmMxISEoJy/yKIK5mKigrk5uZKY6v8/Pwax1oulwsOhwOCIEgOWCtWrGDaS0pK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\n5eXlCAaD0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5//HLNnz8a3337bxk+o63Pcccdh4sSJpmV//vOf+feVz5GHfR/N0VxBv5hi\nv3GbTCYDoOlJbhbNwxwYWkNH9ROC6OoUEusVs19rhDtOpxMVFRXw+/2oqKgoug9mMpkcEQeAvLFg\nmqYhk8nwOBdBEODz+dCvXz8uFCx03smTJ2Pjxo0YMmSIabmu67jpppswa9YsRKPRotpMtA5d17Fw\n4UKsXbvWtLyyshLPP/88evfuDVEU+RxA13Xu6qQoChKJBBwOByoqKlBaWoqysjJommaKkmPCMOO/\n2bpwOGya6xQzXrC2tEZwShDFYrwG/X4/HA4HF7uwe6GqqvB4PPD7/fD7/ZAkCcD3LmmBQADbtm0z\nHbe9RT6BQACzZ8/G3Llzc+6PoijinHPOwbJlyzB48GCk02k4nU6UlJRAVVVYLBbuRNQSTqcT5eXl\nKC0tbfY+ThAEQRAEQRAEQRAEQXQc5KvcjWBRXABOAiADSAFYt2+d0FqRjyAIFl3XMwBeA3DKvsUn\nAlhpWEcQ3Y58dv/7QywWQyQS4REuHo8nr1iGwaK02PYsRgD4/gn7aDSKVCrFHRmCwSAikQicTifq\n6uoQCoV4Aa66uhpVVVW8oCtJEsLhMJLJpOmYrJCb3ZZwOIxAIIDp06dj0qRJuOuuu/DCCy/ktPtv\nf/sb/vnPf+Kqq67CFVdcwR1quhMLFy7Ea6+9xl9/++23WLVqFc4777yiXHcKweJzWHSE8fp0uVym\nSJF8ogC2DXN6Mraj2DZk0979hCC6O8aYLBaj0hxt6YPMMcjhcMButyOTycBqtcLn85m2UxQFkUgE\nmUwGmUwGPp8Pdru9qHYxBg4ciDfffBMXXXRRTuzjM888g88++wxr1qzB4YcfXuAIRFu4++67cf/9\n95uWuVwuPPPMMxgwYACApnlJMBiE3W5HKpWCoiiIxWKIxWJwuVyoqKjg4rF8gmOjOLSYmLnmrlVF\nUXJi7kh0QLQ3+SJHZVlGZWUlKioq0KNHD5PYsVevXlw8IwgCXn31VdPx3G43hg0b1i5t03Udzz77\nLBYuXIj6+vqc9dXV1bjkkkvQr18/NDQ08HuxruvYtWsXHA4HnE4nqqqqir4/0xyOIAiCIAiCIAiC\nIAji4EJOPt0IQxQXc/HRANTuW9cWFx8m4jH+askqvfQoLUG0A5qmcYEP0PRDfiQSKfi0uqZpOU/F\nG5+YT6VSPDalrKwMdrsddrsdkiRBEATEYjE0NjbyYpkkSWhsbOTni8fjqK+vRyQSQWNjIxf65IOd\nmznQ6LoOp9OJG2+8EcuWLcOgQYNy9onH41i6dCnGjBmDv//9790ukmXSpEk49thjTcvuuusuqKqa\nNwKl2IgdoCmaLRQKIRgM5sSlFOMeIooiHA6HKaLF2IbWuk0RBNH+iKIIi8WCTCbTIX3R6BjE7hs+\nn89072DjlqIoCAQCCIVC+Pbbb5FMJlvdLrfbjdWrV+PWW2/NETl+8sknmDBhAl5++eV2e3/dmb17\n92LRokW4/vrrTcstFgvWrFmDY445BkCTwOfbb79FKBRCXV0d9u7di71793KhTSwWQygU4q/bEhlZ\njJsI0HStsfMA+Z2ACKI9YG5RQJOboXE+zK5h433QYrHA7/fzZe+8847peGPGjCn6Om+O3bt349xz\nz8WFF16YV+Bz8skn4+GHH0b//v0BNM2zGxoauGibOXcyRx/qOwRBEARBEARBEARBEF0DcvLpnrA8\nHBlAT0EQvjUIgFqFIAgWAAE0iXoE7Lum2no8gjiUYU+u5ytqFSKTyRT1lLtx+3xCDUVRkEwmTW5A\nsizD6/UiEolAFEU4nU7u8GOz2eB0Orl4Ix6Pw+VycdGOzWaD1WqFqqooLS2FJEl8OYOJe9hTzJqm\nIRaLwe124/jjj8fo0aPx7LPPYtmyZQgGg6b2fvPNNzjnnHPQv39/nHHGGTjzzDNx9NFH5/3cVFUt\n6rMslvYucDQnhMrH1VdfjbPPPpu//uyzz7B+/XqceuqpEEURlZWVzbru5KOQWMzhcBS8FlnUTrbz\nBnP1Ma7L56TQnNvUwaS9hGPdTYDWVWjv76XYe/XBOl42B8LVxOVywW63Ix6Pw+Fw5BSqM5kMVFVF\nJBLhy+LxOLZv346qqipIkgS32110uwRBwMKFCzFkyBDMnj0boVCIrwsGg5g+fTpuvfVWXHvtta3+\nfNv7ft/ZHejyjRkfffQR7rnnHqxatYqLGIz86U9/Qk1NDaxWKx9L2LHS6TTC4TBUVeURkGyOkkql\n+HzC7XabHAndbjc/vnEdi0AqdmwzxlgyjHMt5jTVXv2uo/svcXApNJ9UFAXRaBSqqkJVVe5MxkSV\noigiFArlzMtkWYbVakU8HsfmzZtNxzzhhBNaPV6VlJTwf+u6jkcffRTXXHON6Z7IcLvdWLx4MU44\n4QRkMhkkEgns3r2bz/llWUYkEuEiH9aHE4kEXC5XqyMjCYIgCIIgCIIgCIIgiAML/XrTjRC+/2Xa\n+Au1e38EObquZ3Rd/wrA9n3H9e07l9TmhhLEIYiiKNi7dy8CgQD27t1rclFpDiaQMdLcU+75ttd1\nHfF4PEfgIUkSnE4nysvLUVpait69e+OII47gryVJgqZp3MElW7TDjpVOp/NGNrHCmiiKcLvdvHhi\ntVrh9XpRWlqK008/HS+88ALOPPPMvAWFr776Cr///e9xzDHHYMiQIbj55pvxv//9r6jPrqvyk5/8\nBD/84Q9Ny+6++27+/RXjupNNc4XQfCiKgtraWgQCAdTW1uZcryymgYnAyEmBIA4+rO9l98X2Fj4p\nioL6+npEo1HU19fn3B+YuIKdV9M0KIoCURT58mg02qwjXSqVylk/ZcoUvPDCCxg8eLBpua7ruOGG\nGzBjxgxEo9F2fKeHLrqu46WXXsIpp5yCESNGYOXKlXkFPosWLcL06dO5gyATdxrjOyVJgiiKXOzL\n5gnMqQcAHA4HKioqUFpaioqKCh4hmr2usrKyVaK0Qk5A6XSaj2GtmXMRRDbMIVNRFDQ0NCAYDGLb\ntm2Ix+N8HgR8L8A3wlxzdu3alTN3HTduXJvb9NVXX+Hkk0/GxRdfnFfgM3LkSCxbtgxTp07lgh6f\nz4fy8nL07NkThx12GCorK/l9lvWZ+vp6hMPhvPM+giAIgiAIgiAIgiAIonNBIp9uhCGS6zUAzM/7\nTEEQxHYQ5bDKwK59/6dHXQliH/tTeGUCGeMP8c095W6MUgGail353Fp0XYeqqvB4PJAkCTabDZIk\noaysDJWVlQgGgzzaiW1jFO1IkoRgMIhIJMKf4s/GGC8lyzKqqqpQXV3NC3wOhwPl5eUYPnw4nnji\nCWzatAknnHBCwc/is88+w5IlSzB06FCMGDECt99+O7Zv397iZ9jVEEURV199tWnZe++9hzfeeKPN\nxzQWQlkBncWoZNNa0U4ht6lCAiKCOFiwyMJD1YWptWK+tlDM/UEURZSVlfFxSlVVOJ1OLghJJBKI\nx+N5RSVMQNTY2JgjIBJFEUcddRSef/55/PSnP83Zd926dRg3bhy2bdvWbu/3UCMej+ORRx7BsGHD\n8LOf/Qz/93//V3Dbq6++GnPmzOGvje5tbGxnzjt+vx+ZTIa7/bEYIOPcQxAE2Gw2k0iY9Ue2rrXu\nIez8xjmP2+3OiS3tCLEb0Xlpz/hQ5kwWi8VMYutgMIhEIsHPkS3AZ+IgTdPwxhtvmK4/u92OkSNH\ntrotqqrinnvuwVFHHYVXXnklZ73X68Vll12GRYsW4bDDDoOiKHA4HFBVFbquQ5IkVFRUoGfPnvD5\nfHC73dA0jTt1sjkhibUJgiAIgiAIgiAIgiA6PxTX1T3ZiaZ4LQA4ijn5CIIg6K38BVwQBBuAtOF4\n3wBNDj/t1FaC6PI0V3jNF7nF0DQNoVAI8XgcFosFmUwGPp+vxafcWZwSK7QB4FFdDOa8Y7fbc7a1\n2+044ogjeBSLJEmmiA0m6iktLeXCo4aGBi5AMj6hL8syL9rZbDb4fD4oimKKkmHxJqNGjcLrr7+O\n1atX49Zbb8Vnn31W8D1++OGH+PDDD3HDDTdg1KhROOOMM3Daaaehd+/ezX42XYVf/vKXuO222/D1\n11/zZXfddRdqampM22maZvruCsV4MfFXbW0twuEwgKZiEIthM8IKWsZjNRcRx1wd8l1fhzLxePxg\nN4FoBQcixupgw8R8HdkXmxvP7HY7X+Z0OtGrVy+EQiHu3COKInbv3o1YLAag6f5VVVXFvwdWFDeK\nM6LRKGw2G3fIkGUZffv2xVNPPYW7774bixYtMrXn448/xpgxY/CXv/wFkydPbrf33dXZu3cvHnzw\nQSxfvhz19fUFt7PZbJgxYwZmzpyZ4yjHBD4ejwdA01yBzUtisRjKysq4+5/dbjdFl2XHlcbjcVN8\nl8fjMc0dWoPT6YQsy/z4bZ1zEYcGsVisXeNDLRYLj+lSVZU7VmUyGezZswdOpxNerxcVFRUAgFQq\nxefsiqKgrq4uR6R97LHHtjra79NPP8XcuXOxadOmvOtPOukkXHLJJSgtLeXjm8/ngyiKKC0tRXl5\nOTRN48J+oGm+17NnTwQCAYRCIUQiESQSCf63RqF5H0EQBEEQBEEQBEEQBHHwIZFPN0MQBFHX9YAg\nCHcAuA3AsYIgzNN1/d7WCnz2oaLJtYdVypR952m1YIggDlXaUnhVFAXBYBC1tbUAAI/HA6fTiWg0\nCqfT2eLT7qzIxvB4PDkFNXYM47ZMDGSxWHghjwlJbDYbHA4HRFFEOp2GJElobGyEruu8gBaNRiHL\nMi/isYItEwg5HA64XC6TI4ARQRAwY8YMnH322di6dSueeuoprF27Ft98803B97plyxZs2bIFCxYs\nwAknnIAzzjgD06dPR1VVVbOfUWfGYrHgyiuvxJVXXsmXvfbaa3j33XcxduxYWK1WU5GUPU3OIlJ8\nPl9OUcvhcMBut6OkpIR/9pFIhH+njFQqhUAgwAVcbrcbLperYEQcc1LILqq11pGhK8Ei74iuQSE3\ntXwuZ10Z1vey+2J7vsdixjMmqIrFYlyk43Q60dDQwEWeLpeLb8eK3YqiQFVV071DURSEQiHu+uN2\nu1FRUQGn04mFCxdi+PDhmDlzJoLBIN8nGAxi6tSpuOeee3DppZe223vvivz3v//Fvffei1WrVuV1\nTmJUVFRgzpw5mDNnDqqqqqAoiukeZ5wzMFENG8fT6TQURckRLbB5Q7agx+VymVxR2P20taIHI8Z5\nzIEQuxGdk0JOY9nznNbArq1QKARVVbl7od1uh9frRSaTgaZp0HUd9fX1JjHQd999h2AwiA8//NB0\nzLFjxxZ9/nQ6jT/+8Y/43e9+l7cP9+jRA9dddx2GDx8Oh8MBXdf5/M04z3c4HJBlmc/zBUHg0bzR\naJTP5wVBQDKZRN++fQvO+wiCIAiCIAiCIAiCIIiDz6FbgSPywlx7AHwKIAZAB/BTQRDaVA3XdV3d\nd8yo4bgAxXURBCe70NpS4ZUVKViMBSuAsSICczRoDS6XC5WVlfD7/aisrCz4VLMx1snYfqOFP9uO\n2f+zbSwWCxf7MAeGbEcGXdchimKLsRyCIPBIrm3btuHf//43fv3rX6NHjx7Nvk+2Xd++fXHKKafg\n0UcfRUNDQ3EfUidjxowZqK6uNi27/fbb0dDQgD179qC2tpYXc2pra/HNN9+goaEBdXV1qK2tzYlZ\nSKfTOZEo2dcTc9JwuVy8SBqLxeB2u5v9vpxOJyorK1FWVoaqqqr9emq+K5DPKYLovByIGKvOgtPp\nRFVVFe+L7e1WlC8eySjqY+OXqqqIRpumhkwUKooiXC4XSktLIcsy/w7C4TDq6+sRiUTQ2NjIXbLY\nsSKRCB9votGoKUZm8uTJ2LRpE4YMGWJqp67r+PWvf43nn3++Xd9/V0DXdfzjH//AlClTcMwxx2Dl\nypUFBT6DBg3C/fffj08//RSLFi3i4lin04mKigqUlpZyUZURNo6z+UGheQObvxjnAo2NjTnjU3v2\nx0LX6KEk6CPy0xH3ejb3Lisr4yKf3bt3AwAXVkejUQQCASiKgoaGBgQCAXz00UdIp9NIpVI5EYLj\nxo0r6twffPABxo0bh5tvvjlvH541axZeeeUVjB8/HslkEslkEhaLBTabjcf8MgRBgM/nM/0t4HQ6\nkUqlcuZ90WgUdrv9kBZrEwRBEARBEARBEARBdHXo8axuiq7rLwiCcAmA3gCG4vu4rVYhCIJlXzQX\n+/W/bt/xWzzePlehNp2XILoaTqfTFIvVXLGJPSVsfBqdFSlkWW7zk7XZ7j6Ftsl2/WE//BudeYxP\nAgPgIhBW2Guu0NLap+kFQcCYMWMwZswY3HHHHXjrrbewdu1arFu3rmDsiKZpePXVV/Hqq6/i8ssv\nx0knnYTp06dj9OjRGDRoECRJalUbDgayLOOKK67ADTfcwJe9/PLL+Oijj3DEEUcgHA7zQg5zRWDR\nCuFwGMlk0hR/UsjdwHg9sWsvO/6kUFyDpmkmVyZW9D3UaakPE52L7ubswcR8HUX2/cFYCGb3/nQ6\nDVVVeTwM0PQ9ZDIZvr0gCJAkCYlEgrtOOJ1OKIoCm82GYDCIaDTKBYrG8xpjZAYOHIi33noLF110\nEdavX8/bous6zj33XLzxxhs4+uijO+zzaA3RaBSrVq1CfX09/H4/SktLUVZWZvq3x+Np0/0lHo9j\n1apVuPfee/G///2v2W1HjRqFiy66CD/60Y/gcDiQSCSQSCS4ayDwvZCnJZg7YLZboCAIXKxshEUZ\nGftfe/dHo9sQ3a+7D+1xr8+e17AIU13XUVFRgVgshkQiwUU1TPSuaRpisRji8TiPvlJVFZ9//jlU\nVeXHlyQJxx13XLNtiMfjWLp0Ke6++27TvoyBAwfi97//PcaMGcO3Z9GIyWSSC3jYfTLbwTPf3wLM\n7ZH1mUNdrE0QBEG0jVGjRmHPnj3tdjwmnCUIgiAIgiAIovWQyKcbwsQ1uq6v3/faqut6Wx9xFARB\nKAFw+L7XhbMA8rRBEAQZwNUA3tR1/c02toEgugTFFl4tFgsveLLCGQDYbLYDEoPkcrlMgiSjgwIr\nnDAXBq/Xi1QqBUEQeLST8cn+7EKLJElIpVJ5o7oKYSy4SJKEmpoa1NTU4J577sErr7yCNWvW4Lnn\nnjPFtRjJZDJ48cUX8eKLL/L3N3z4cBxzzDH8v84q/Dn//PNxxx13mN7bvffei1tvvRXJZDLH1ak5\nAVg+AZex6MP2Z98bE4VlC4EYLGrHGAvU3q4hnRX22RFdgwMRY9XdKCQaZff+dDqNhoYGLv50u93w\ner3QNA2KogBoEoc6nU4kk0m+v8PhgKZpiMfjSKfT3AnDZrNBURTY7XZYrdace5Lb7cbq1auxePFi\nLF26lC+PxWL4xS9+gY0bNx70CMdPPvkEp59+Or744otmt5MkCX6/n/+XTwhUWloKv9+PsrIyyLKM\ntWvXYvny5QWFr0DTHGLatGn45S9/iUGDBpnEVgwWm5VvfM4WPxhhUUDZQuZ8cwFRFFFaWsrFqUZR\nUHtSrEiJOHTY3/hQRVFyYmZlWebzYOZGZrFYEIvF+LyeufykUikeLxiLxWCz2fDOO++YzjF8+PBm\n5w8bN27EpZdemvc+IYoirr76atx8883QNI0LiURRRM+ePWGxWGCxWKCqKmRZRmVlJVRVzRFjGmF/\nW4TDYX5f93q91HcIgiCIvOzZswfffffdwW4GQRAEQRAEQRAgkU+3JNs9Zz8EPtB1PS0IggIgBMAB\n4MuW9jEIfBwAngdwIoDnBEHQdV1/q61tIYhDBWORgjkAORwO+Hy+Ngl8NE3L67hQ7PaqqnJXBmOB\nj7n8lJSU5BT2WHHEWCyRJAkNDQ1cEGR0DCiEoihclKLrOux2O5xOJ3ewGT58OI466ij89re/xSuv\nvIKXXnoJ//znPxGLxQoeMxaL4d///jf+/e9/82VM+DNy5EguADriiCMOuvDH7XZjzpw5uP322/my\nl156CZdeeikqKiogSRIsFgvcbjcvQAGA1+vNW4DPFnBlXw/FFshYjI4xgiUcDkOW5YP+mR0ojC5J\nROenNW5qRNthbjw7d+6Ew+FAKpWC0+lEIpFA3759+TIAvIhcX1/P7yWapiGRSMDlckGSJLhcLpMb\njCzL8Hq9AJAjGNV1Hddffz22b9+ONWvW8Dbt2LEDU6ZMwR//+EfU1NQcsM+CsWfPHtx///1YtmxZ\ns2MTQ1VV1NXVoa6url3OX1paitNOOw2zZ89Gv379+BivaVreOYHRJYnBxmJGvvE7W8jMHJ1cLleO\noMc4VjAxaVtp7RyHOLRpaZ5TCBZXmh0zK8sySktLeUygIAgoLS1FJpOB2+1GJpNBOBxGIpHA119/\njXQ6jWQyyZ1wvvrqK9N5RowYkff8qqripptuwh//+Me86wcNGoSHHnoIEyZM4MvsdjuCwSD8fj9c\nLhcXhLP5oCiKLbp/iqKI6upqyLLMBZUlJSXUlwiCIIhmEUWxxSj11pAdU04QBEEQBEEQRMuQyIdo\nDw4DUAIgCiDe3IZZAp+/okngAwDTANxQeE+C6F4YoyZa43qTTSwWy3Fuac6CP9/2NpsN6XQajY2N\nvMDhdrtht9t5MYQV9oyRXMYn+5nAhx1X1/VmHQMA8KeUdV1HPB5HXV0dYrEY/H4/fwqaufsIgoDR\no0fjJz/5CWKxGFauXIlXXnkF//nPf5BOt6xjLCT8GTZsGEaOHIkRI0YcNOHP0KFDTa9ZFNncuXNR\nVlbGhTmRSIR/9s0JwlqKbSvm2mOxXkZYXFh3EfkQXY+OjrEimsQgjY2N3EWloqICNpsNFouFF9xl\nWTbtYxSEqqrKhZyCIECWZVRVVfEomZ49eyKVSnFhEBuPgKYoLFVVMX/+fHz22Wf44IMP+Dk+/PBD\n/PjHP0ZNTQ0WLVp0QMQ+n376Ke6++2785S9/4cKmA8nhhx+Oc845B1OmTIHNZoOmaVzYYxy/s2Gi\nAObcI4qiSeADfO/4U0icw+KD2Jjk8XhgtVphtVqRSCRQV1dnmme0VTTZnR3liMIUE0+bTaGY2Uwm\nA5fLhb59+6KxsZE75zAXsh07dkBRFKiqCkmSEIvF4Ha7YbFY4HA4ckQ+jzzyCHbu3ImrrroK48aN\ngyAICAaDOO+88/Dyyy/ntMtiseDCCy/E7NmzUVVVxUWPzC1IVVXs3r0buq7DZrMhGo1yF87a2toW\n5/1A+/3NQRAEQXQfevTogZ07dx7sZhAEQRAEQRBEt4ZEPkR70BeAgCYnH7cgCFo+d6A8Ap/JhtUn\n6rr+8YFpLkF0DfY3asIokgGanhKur6+H3W7P+2Rv9vZMiFNSUgJFUeByuXghNhAIwO/3IxQK8SKr\npmkIBoOQJAmSJMHtdsPhcHDXHWPxhBUPU6lUTsGXwYQkzDWGOQGk02ns3r0biUSCi1xYUY8VFCdM\nmIDBgwdDkiS899572LJlC7Zt24a9e/cW/fnFYjFs3LgRGzdu5MuMwp8TTjgBo0aNQu/evYs+ZmvQ\ndR0PPPAAbrghV//ocDjgdDpht9t5McvlchV8cr21TgctXXvGWC9GoVgvgiC6B+xezcSXoigikUjA\n6XRy17F8GAvMgiDw+zRzgJEkCR6Ph7uKZbtthMNhAE33IFakvuuuuzBz5kzs2rXLdK7XX38dr7/+\nOhf7jB49ut0/h40bN+LOO+/Ehg0bCm7Tv39/lJeXIxKJoLa2FuFwOCd+sa2MGTMGU6dOxejRo3nc\nEADuOsLu7cwRKduhRxRF7tzD4tWYc5+RTCYDq9Wac35d11FbW2ty8ksmk+jduzeSySSfN7BtWxIM\nFaI5RzkSKRCtpbmY2UQiAUmS0KNHD6TTaS6Sa2xshKIoXAyXyWRQVlbG70WBQABWq9UUSQg0OTK+\n9NJLGDVqFH72s5/hySefxLZt23La1K9fP8ydOxejR49GLBbDjh07EAgE4PP5oCgK3G43P34ymURJ\nSQlisRh8Ph+A7/uXw+FosU9QvB1BEARBEARBEARBEETXgqpxRHvAfuHfiyY3HzV7gxYEPpN0XX+9\n45tJEN0L41PJ8XjcJOApLy/PKdgVeoo5Ho9D13XIsswFO5FIhDu2GAt6AEzOCqxwZyyexONxvi1z\nncn35D0TkmQyGd42Y8GFxYfZbDbEYjHuFpFOpyHLMvr27Yt0Oo2pU6fi1FNPxdChQ5FMJvH+++/z\n/z744INWZcobhT+PP/44wuEwBgwYgAkTJvD/eo88b2YAACAASURBVPXqVfTxCpFIJHDllVdi1apV\nOesOP/xwzJw5kxdjGYWeXG+tm1MxFBvrRRBE94Hdp0VRzHHnaSlukhXIo9Eoj79xOp0oKyvjEV2i\nKOYIRtl5AfAxgMXpPPbYY7j44ouxY8eOnPMxsc/48eNx3XXXYfz48fv13lVVxd///nfcd999ePvt\nt5vddubMmVi8eDFkWYbT6UQgEEA0GkUsFkMwGEQwGEQkEsF3332HvXv3IpFIIJPJIBKJIBQKcZec\nxsZGBINBZDIZOJ1OTJs2Db/85S8xcOBALtJRFAV2u5275ZSXl0PTNJNbR7aDBxP8srFaVVWEQiH0\n7t3bNHYUEm0lk8kcIVZ9fT2P6gyHwzxSia1Pp9OtFhgUmrMwEQZBtIbs+xabb+7cuZPPWd1uNyoq\nKuD1elFfX8/FO1arFY2NjchkMkgmk6ioqEAgEEAmk8GsWbPwyCOP5HXz2rJlC7Zs2ZKzXJIkTJ06\nFZMnT4bNZsPOnTtRUlLC769MUF9XVwdBEJBKpZBIJFBfXw9JkrjDFduH+gRBEARBEERhdu/e3a4P\n71VXV+ed4xEEQRAEQbQ3JPIh2owgCILe9Ou6e9+iel3Xk3m2I4EPQRwEmLBGVVWTyMNiseR9srfQ\nU8x2ux2hUIg7M4iiaHJtYZEAFosFNpuNPzkMAKlUCna7nQt/wuGwqVgiCELB2C5RFOHxeEzRUE6n\nE6qqQhAE+Hw+Hv2hKAqqq6v5Pn369EEsFoOmaVBVFSUlJbzIPGXKFEyZMoWfZ8+ePSbhz5YtW7Bn\nz54WP1/2Hrdt24Zt27bhscceAwAMHDgQEyZMQE1NDcaPH4/y8vJWfW+7du3COeecg/feey9n3Zgx\nY3Dttdeiurq6KKFOIXemYp7qbgmKdyAIwohxDDHeH6qrq1t0+dI0jRfXWTSXqqooLy837ZvPRczo\nJiOKIlwuFxRFQb9+/fD0009j3bp1eOKJJ/I6ub355pt488032yz2icfjWL16Ne677768ThwMm82G\nn//855gzZw4GDRoEoGl8Nbqy2Ww2yLIMh8OBQYMGYciQIdz1rrS0FJIkoaKiArqu8/Hc5XIhk8kg\nHo8jnU5zVyOn0wm/349IJAKv1wtZlnmEUDbZDh6Z/2fvXmNkS+t7v/+eVddVl753794zPSYHc5Fh\nUMIwJgoBgjko1gknvmCDLbCjmIFEIoqRcCx87BeO8gLHOccvrBgpljEeYjlRZB+F2NiWHawTPI4D\nw2BsbIxBHA6GGXrv3r337kvdL+vJi+5nzarquteqrkt/P29m79rVq1ZVrbWeZ/r5rf+/1er4PjzP\nk+/7Ojk5CdutudBVdGxx1eK6BUGgcrmsnZ2d8HXK5XIYAnZB4FFEq9L1m7OMui2gW/S65UI00cBa\nqVQKQ3Mu6J7NZnV8fKxms6laraZ6vR4G40ulkt74xjfqzW9+s373d39Xf/qnf3ql9V23YrGon/7p\nn9ZrXvOaMPiYy+VUq9XCalyJRELNZlOlUkmFQkHWWtVqNa2trYXnVL1eVxAESiQSnBMAAAADBEEw\n1s1/AAAAi4KQDyZmX/yt+trlfwNjjCcp4dp1EfAB5scFXo6PjzuquLiFue47e93zo4EgY4yOjo7C\nxcN8Pq9MJqNCoRC2Ynn48KFqtZokhcET174gnU7L932tra2FwZJ2u90RCrHWdrQQ6fc+giAIF/CK\nxaJ2dnaUSqV09+5dbW5uqlgsSrq4u393d1e+76vRaCidTl+pehO1v7/fEfwpl8v6xje+oS996Uv6\nu7/7O335y1/WF7/4xSsLxN0VBJyvf/3r+vrXv66Pf/zjkl4M/bzxjW/Um970Jt26davvd/a5z31O\n73nPe3R0dHTl337yJ38yrOBzeno6UuWcWVc6oL0DAKe7wlcikdDa2tpIbfyiYU63LVdVpvs1uqtt\nuGu/e8xVADo9PdX29rbe/e5360d/9Ef1yU9+Uh//+MdjCfs8ePBAH/vYx/Trv/7runfvXt/nra2t\nha/vgq3ValW5XE6FQiFsbXl2dqZms6kgCOR5ns7OzsLxy/d9FQoF7e3tKZ/Ph8Gf6DiaSqU6wgOu\nTVehUND6+rrS6fTIQcxkMnnl+zDGhNWWHBe4aTabYTtNFzzqruTk5g7u38vlcjgOFYvFkVp1ueok\nrorf9vY2FeUQOzevaTQaV+ZQrhrlgwcPwgpiLtyzvr6u3d1dnZycdATeSqWSHn30UT311FP6wAc+\noM985jP6+Mc/rueff17SRUuuxx57TM8884y++7u/Wx/5yEeUyWR0fn6uRqOhra0ttdttNZtNffvb\n39b6+rp839f6+rqMMUqn08pkMvJ9X4lEQul0Wul0OqyEubGxwTkBAADQw/7+fqzbOzw8vPL/rwAA\nALNEyAdxcMfRty8DPUYi4AMsArew5u78d4GMfne7uxYazWZT9Xpd3/zmN8NwTS6XUyKR0M7OjhqN\nRliVJ5lMhncSl8vlcPvpdFr3799Xo9HQ9va2tre3w4oF3YuHvRaBXRUaY4w2Nja0trYWBnhcZYB6\nva5UKhW2B2s0GuEd/ru7uxNVmfF9X6961av08pe/XD/+4z8eLsp+5Stf0XPPPae///u/13PPPaev\nfe1rYdWEQbpDP694xSv0pje9KQz97O3tSZKefvpp/czP/Ey4cOTkcjn94i/+ot785jeHd2S7tmTD\ngjpUOgBwnVwlDLfI3S8M2a1XhZ5+Y0O/KmLRx1qtllKplBKJRDge/fAP/7De/va36w//8A8nDvv8\n4z/+oz760Y/qt3/7t8Pxrpf9/X29733v0zve8Y4rFW88z9PW1lZYCcQFYJvNZljdTrqo/uOCQAcH\nB0okEmo0GmHVvF6fSSaTCVsIuQp62Wx26OffHRza3NzU2dmZgiAI9z2ZTCqfz4fh32w2G7biSiaT\nYQUmV7Unk8mo2WwqmUyq0WiEQR7f95XNZrWxsREGf0bZv7OzM5XL5TA8dHp6qpe+9KXa29sLjzfC\nDIhLMpm8ModyrW9PT09Vr9fl+35YgeuRRx6RpLCVlpszV6tVFQoF3bp1S9vb23rd616nD33oQ/q9\n3/s9/cqv/Ip+6Id+SG9729v0sY99TB/84AeVy+XC1n25XE6lUkmpVEonJycqFAo6OTnR5uamksmk\nHnvsMaXTae3s7ITzbReAD4JAjz766EghSwAAgJso7pZaBwcHVAQCAADXit/6YGLGmIS1ti1p4/Kh\nsiRZa9uXrbwI+AALwLUOcIGUtbU17e3t9V0M8zxPqVRKd+7cCe9CsdaqUqmEdxRHFyXdAl6pVArv\nHHZVFB48eKBqtaqTkxPdv39fr3jFK65UC+pXZadXZYdMJhMucOZyuXDBs1ar6f79+5IUbjOdToeL\nvaMGfYIgCINC0UVU3/f12te+Vq9+9avDxx48eKCvfOUr+uxnP6u//Mu/1LPPPjtS6OdrX/uavva1\nr+k3f/M3JUmvfOUrdXBwoD/7sz+78txHHnlEv/Zrvxa2u3FBrVGDOr2qMw2qagQA03LXaumineOo\nP9NdocdVjOv3/O6gS/QxFxpy7bvK5XK4aP/e975X73nPe/Q7v/M7+o3f+A0dHx9f2X532KdYLOpX\nf/VX9clPfnLge3rVq16l97///XrLW96i7/qu79Lp6alOT0/Df3cV8trtdhjykRRW46jX68rlcqpU\nKuHjxWJR5XJZjUaj47PJ5XJX3v/GxkbPSj+DVCqVjipAxWJRhUJBL3nJS/Tw4UO12+2wLZCrrlQq\nleR5nqrVqr7zne+oUqloY2NDxWJR29vb2tjY0K1bt8LwTa1W6xiHXAuxUTWbzXA/3LwgCALdv38/\nDA8DcXKVyer1ukqlkiqVih4+fKh0Oq27d+/K931Za1UoFNRut8Nz1lWmzGQyyufzymaz4dzXnY/V\nalVvfetb9fjjj0u6CNh/5CMfUbPZ1L1798KA++Hhocrlsra3t1UoFLS5ualMJqOtrS0Vi0Xlcrlw\njp9KpdRoNMKfvX37NgEfAAAAAACAFcZvfjANt/qev/xvQ5KMMUlrbYuADzB/rhpONptVOp0Oqxv4\nvj/w51x4J3oHs2u74cIlbhHDWhtu3y3oudBPtVqVJCUSCZXLZT148EAveclLelZh6DassoPneWE7\nEhfwkRQu6ErqaCHSa1E0qlKpqFQqhZWLCoVCx+dkjAkXEq21SiaTevzxx/X444/rfe97n4Ig0He+\n8x0988wz+sxnPqO/+Iu/GCn089WvflVf/epXrzz+ute9Tj//8z+vl7/85WE1IWutGo2G6vW6qtVq\n2B7NtU3pVckgWp2JSgcA5mlQAKW7Qo+kMMg57nUrGhpylWOy2axSqZTK5bJ839dTTz2l7/u+79On\nP/1pPf300z3bJLqwzzBveMMb9J73vEdPPvmkyuWyarWavvWtb8nzvI5xzAU0u0OabswJgiCsiBNt\nZeWq80gX40+pVFI2m+35uYzTRtHNEaLcnMGNmY1GQycnJ2HFHVdtzlUpceN8q9VSuVxWJpPpqKAk\nvVi9x41Do1TviUqlUj1biI1a1Q6YRC6X02OPPaZqtap/+Id/kHQRnG80Gmo0GioWi2o2m2Fgz7XV\ncy1sE4lEGH5z52oQBDo+Pla5XFYQBOHczoV2arWajDHhtaBSqYRBokwmo1QqFZ5j6+vr4T5Za7Wx\nsRFeP9vtdtj+DwAAAAAAAKuHkA8mZq11jWbdb9y/ffk4AR9gQTSbzXBRLLrwN2xRzLU5iVaBcVUC\n3IJls9kMKw5YazsWHI6Pj9VqtSRdLO65n7HWhnc5D1uEHLUKTSqV0tbWVsfiYRAEevjwYbiQOmxR\n1FUmiAaa3PN7LUa6Bdlo1Yn19XXt7+/riSee0Ac/+EG12239zd/8jT796U/r05/+tL7whS8MbO8S\n9c53vlNPPfWUEomEms1mWMXAGBPeGX5+fq5MJqPz83NVq9WwQkSxWAzDP9HPkkVQAPPkgpTDqtGk\n0+mRnjuMCw01Gg1JF8FUz/Pk+75arZZarZZOTk70zne+Uz/4gz+o3//939fTTz/ds41XL4lEQm97\n29v01FNP6YknnlC73dY3v/lNbW1thSGlWq2m9fX1cNG/UChobW2t57gSDSO5hX6339GQj6SOsXQa\nbpzu9bj7vLLZrNbW1q60EWs2m6rVamEFIhd0SKfTVyoVSeoI/YzL87ywQmA0iOtaWAKz0G63df/+\nfd2/f1/f/va3dXJyomQyqUwmE1b0Wltbk+d5YSvZW7duqVqtdpzPUc1mMwygu9BOEATh/DaXy+n0\n9FTZbDZ8PNoW17Xhjc6HXZgweo65eTpzPwAAAAAAgNVEyAe6bK1lu/88ws+5dl1bXY/nJP1rEfAB\n5s6FXrrvfh+2KOYCNpLCdli7u7vK5/OqVqsdi6+5XC6sTOAWJR955BHVajXduXMn3GahUAjbbY2q\nu7JDr4COezy6eOjCTW4fpcGLot0VAtzzm81mx/PdY64aUvcCclQikdATTzyhV77ylXr3u9+t4+Nj\nffWrX9UXvvAFPfvss/rSl74UtndwMpmMfuEXfkFveMMbVK/XlUwmdf/+fbXb7TDc46pQ1Ot1Pf/8\n82ErmGKxKN/3dX5+HrZTW1SDKg8BWGyTnL+DgpTd2xjnucPUarWeYSHXztFVfkun03rnO9+pd7zj\nHfqTP/kTffSjHw3DPjs7O2q323r48KGki3Hpx37sx/Sud71LqVRKa2trOjk5URAECoJA7XY7HOfS\n6bR2dnbChXbXbrEfFxgtFAphUMAYo+Pj475V7cbRXUmp3za6H+8eix88eKDDw0NVq1XV63Vtbm6G\nYYS1tbUrc4zo2DluFR+nUCjopS99qe7fv69kMqlEIhEGLIBu084z7t+/r8PDQx0fH+v4+FhnZ2dh\nyzjXMnZ/f1+bm5tqNBpqt9tqNBpKp9Pa3t7W2tqaEomEPM8Lz5tWqxW2wXU8zwuf50LryWQynAOu\nra2pUCgonU7rpS99qdbW1rS+vt7xniad6wMAAAAAAGB5EfK54YwxnrU2MMbsWWuPRg34XPIktSVt\nX/793BiT1UUFn/808jwCPsCcjFoNp5d8Pi9rrY6Pj+X7flgtxlXukS4W7iqVinZ2djoW7hKJhA4O\nDpROp8M7n4vFYseC3KC2Ld3vYdDd/9G2LNZa1et1BUEQVs0pFAphRZ5BC5rDFki6w02FQkGSrjzW\n3Qotuhj56le/Wt/zPd+jH/iBH9Du7q6+8Y1v6LOf/ay++MUvqlgs6kMf+pBe9apX6cGDB2G4p1wu\nK5/Pq16vhwvFrh2aC2JJCiv7eJ630Hdvl8tlnZ2dhZ/Z2tralcpDABZTpVK5cv6OUmGnX5CyV/By\nnOcOMiws5KrDuFaTrvXTT/zET+hHfuRH9IlPfEJPP/20PvCBD6hcLuu3fuu39P73v1/vete7VCwW\n9fDhQzUaDRljVKvVVC6XVS6XlUqlwjaWnucpn89fqWozSHSsca16uivHFQqFsYMLlUqlo5qcaw/k\n5gjRxwe1AXNBpp2dHaXT6TD86wIJrqJfo9FQKpVSrVa7MgcZ1jK0HxfSIiSKQaadZ7Tbbd29e1fN\nZlOtVkvGGJ2dnWl9fV2pVErZbFaPPvqoHnvsMVlr9Z3vfCcM4rkWX9lsNqz202w2w0C2q3zZbrfD\nllpuu57naWdnR6lUKgzQJRIJBUEQVth0FbaippnrAwAAAAAAYDkR8rnBIgGf10j6v40xfyXpv7DW\nHo/y89ba5uUfXQ+BpqT/XQR8sOImvQv9unQvjubz+Y5FMenFxbdB78UtiEbDIqenp31DOd13DKdS\nKRUKhSutUqTJF4p7SSQSKhQKYcuD09NTGWPkeZ5KpVJY+cYtorj3Fq0q4FqNRRdI1tbWwvceBIGq\n1WrH+3aLpdHHqtXqlYUVV7nh/v374WKoq9Sws7Oj17/+9ZIULjZLF21bXIsWt4+e53XcAe7u7HZ3\nq7sF6mw2O9e7twdlRYMgCL9399yzs7OBFTriPt/i2t6iXwduKr6X2XHna/f566rN9OLCLS6A2B2k\ndI9HjfPcQVwrqu59cwvm0kWVnq2trbB919HRkVKplDKZjN7//vfrHe94hzY2NpRMJvXhD39YGxsb\najQaYeiyVqvp9PRU5XJZxhjt7++H1YOy2az29/fHDia5scXtd6VS0d7eXtgiKxpuGbVaSRAEVyrH\nnZ2dKZFIKJfLjRWcaTYvpv+uhefa2prq9bq2t7dVLBZVq9XCwIO1VrVareMYKZVKyuVyY32X3aHg\nUUOsXA9WW6/5xiTzDMcFas7OztRsNsMwoKvqmEwmlUqltLu7q0ceeUS+7+vOnTtqNpsqFovhfK/R\naMhaG86DS6VSuJ1UKqV8Pq9WqxXOqV0A3bX3ciE4Y4yOjo46KgKdnp4qkUhcuR4SgAMAAAAAALhZ\nCPncUJGAz+OSPiVpT9ItSa0xtuFae7l2Xf+TOo8pAj7AgjDGKJ1OjxWucS2voly7geii5aAKOZ7n\nKZvNdjw2zQJML5VKJQznuJYJvu8rk8mEC3ybm5vhfkSf7+52doucvu/3XCBxn0V0QdUtdEYXG114\nKPpYpVJRqVQKP7v9/f2Oaki9PkNX/cf3fVlrwxZcOzs7yufzKhaLOj4+VqVSCav6SNL6+vrY7Uv6\nVVQatdLSOOKq0AEgPqO2Uuo1JvRqa9iL53laW1u7Mv70qxgTfa6rijOuUVvYuMXzk5MTnZycSHqx\nyo3v+9ra2lI6nVatVtPR0VG4T/V6XblcTsaY8PO7deuWgiBQrVbTI488MnZ41V0ju6+/blzpHlui\nn5ELs/b6TLu/u2q1qvPz83C7xWJx5Eon3Z+r53lhRSBJHeN7o9HoqDIn9R4nB4kzFIzVN8o8o9Vq\nhe3wonOvcrkcnhcnJyfhcV2r1VSpVLS/v698Pq/19XVVKhUFQRCG5pPJpKrVavhaiURCOzs74bnm\n5n3RNre7u7vhfDM6D06n08pms6rVauH1yl1nTk5O1Gg0lM1mr1QoGicABwAAAAAAgOVGyOcG6qrg\n80eSHpX0jKRfstaejLEp167rkcu/N/XiMUXAB1gw41Zh6LVA6irelEoltdtttVotbW9vjxUCaTQa\n4cLFpIt+ThAEYWBHugjHnJ2dhVULrLXhAq57nejzowEaY0xYLaLXZ1Gr1ToWGnu15upeQI4GmtzC\njWu/Em290r3YHV3kdgvN3Qu4hUJBR0dHymQy4YKQ7/tjLYb3Wzyd1aJqv7Zo/UJiAGZrnHN91NBM\nP7lcTtlsdqTwoHvu6empqtWqarWa6vX6WNeiUYNF7jrtQp/n5+fhuLCxsaFsNnslnOrGTGutMpmM\nstls2ELLtc6ZJJiUTCY72ltJF4Gj7s84uj8uSCpJu7u72tjYuPIZRb87N26614uOg6OM5dHWQN3z\ngHq93nF8uP2Oju+jHDMu5OTCD3GFgrH6hs0z7t27p8PDQwVBIM/zdPv2be3u7nbMJ5PJpPb29nR0\ndKSNjQ099thjesUrXqFMJqNEIqHDw8PwXCqVSqrX62EVH+niuC+Xy2Fru29961vhXM5aq9PTU926\ndaujuo+ksD2ru3YFQRBuM5fLhVW+XEtAN4fnXAAAAAAAALh5WFW7YQYEfH5W0l+Nsy1rbfvyj/9G\n0uOS3Go3AR9gAY1bhcEtiPZbAL5//76SyWTYhmCUhddKpaKTkxOdnp5KerG9wDgLxYPek+d58n0/\nbIFgjFE+nw8XTqapRNHNLcIMCuv0e710Oq29vb2Bi93DFsRTqZS2t7evVB4aNSzVr6JSOp3u+3gQ\nBFNV9hmnmgeA2Ro3+NlvTBinJZILO/YTrWAjSfV6vSNQM27Aw11HB7WwiVb+iD5/a2srDOr0qg7i\n+742NzfleV4Yfo1WiJvldS1aXc69rnQRou31GUWvvS70Gt3HcYO2+Xxe1tor84BsNnulyo9rYyRp\npGt+NHjWaDTUarU65hdUf8MgveYZhUIhPO5dwEe6uN4cHh5qc3NTQRB0nOPb29taX18Pw37JZDIM\nAh0fH4fXpUKhELbrS6fTKhaLKhaLKpVKYbtCF9BxISF3vXBtvWq1mowxOjg4ULlcDufaLrxdrVbD\n1rfTnLcAAAAAAABYHYR8bpABAZ8PS/pCJLQzrj+V9ISkfyrprQR8gMU0SRWGXkETt6joFthGXXiN\nVktwlWxKpdLAFiOTvKdUKqW9vb2OQEp3OxpXfcC1xRoWMGo2m8pms0qn0x2Lxe61+i0g9/vM3X6N\n0uKm33NSqZQSiUTHa44TluoXQHLtJqLK5XLHnerTVPYZp5oHg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IteNSqSTndj97NinjXlu3/Yefyo9lWdje3h6w1e12GxdeeOHE52XfS6XTaSb4EEII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BRF/Pbbb8jLy0NeXh4OHjwIr9cr28br9WLp0qU4deoUHn300SbPNhMu8klJSYm5ffikkpLz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\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YzhMNBw4cwNatW2XlN9xwA2644YaoHZIaAmmEs8ViiSnyRTqIVmKSOicnR3YPtG/fHv/6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0GWZbjdbqTTafT29upsfSaTYe23SKmnubkZHo+HFZjwz7BdH8jqOaxXwo3xeMFgEC6Xiz1X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\nVVylUkl79uxJbXu7d+9ObVsAkEck+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cz+NPr9NlLnU/MVTSLInana\nUj6/kPTFzc5NIhh+XCHQViXtUUjIKkTjYdQpeK2k/40eupWkW/fSOBo1kpfd/YCk+ytaLiRynsJU\nm6M2xWPcgF2RdERqr7CVuEZvoVDQk6TPmNlvKpzHT5nZdyX9XNIBhWW9vmRmTzSzO6Z5AFmWaCw8\nkHj47LrnksssPUvS0xQ+09dKeo2ku7n7Pd39Se5+P4V7xsMlfVLSdYntvlTSG6P3IDcSn+n4s1dW\nlDDW7F6Y7CiL7hXxrBaVBrNajETlIarw3lG1WeHe5u5/JbU/814Ul76haEk55TSeED/SEyXOmUJy\nYlnSWHR+lzrpsDaze6p2bf7MzM5TSF78f5K+qHA/+4SkH0fPv8TM7pfmsWRFdL9aUK3s51qfCLom\nPsfR5/s8SZ+W9GZJD1PopLqfpMdI+ntJl5rZFyS90sxO6+tBIJP6leAzakmzvSIG5VeULPlZhbYP\nV5h98+btfobi+n2iPeSsKIn11Wb2l2b252b2R1FS63giEYF20oKIPv+/UG1pnadKOijpuaJTMBVR\nIl5yBp8/dvdXRYmQ8cCGFWltxsOV5N+7+yckPT7x0A5Jb7IwYK5o8e5w9CVJvylpZy/tsIkO1ssU\nlqGK2yImFcqjf2pmM003MCISsWJBob12XtIJM9vS/K9abi9uN/qpQn3Mo22iTVGcXVBtMNN2M9uW\nSKTuuLM6KpdVzGzKwgAOFAwxPRXEoRzj3olmqLwCOZFoiNyS+H40em7DKBdLrE1vZm9WWHP4A5Je\nrrDszBui379sZt80sxeY2W/19SAyLApyJunJkk5WyA5/lkfL02zyt3GB6GqFxJ6KpAPuPleUEUhh\n0JWVVMuYLylMwSgzG2v6h5uIChLJRvJfJp5+oqQ/7mX7ORY3FrQtqsD8UeKh+yt02L5KoZJz68Rz\nOyTdTtLfSXqfmb20p73Nn2PR91VJN5A2Nuib2SmSzlF4H74r6Znu/iJ3Pxo9v0VaS2T5qMKow+co\nVDJjD5N0oZndrI/Hkiqrzb4TJ39drbDmb8OOxeRjZnZ7M7vAzD5nZl8xs/+Ofr5QYa16Uy2GFVbi\nWrqPwvVzraQPR8+NtRsXotddo9BouCrp+9E2ctXgTfxIV1QevDL69QZm9tTE45uyMMPYQyRtU0hg\nPF8hoedmqq3PLoVGsvj8vVzSP5rZ+3rd/4wy1RLmq83OZdQ55Wb2QIVzdgOF0XDSxnq1S7qnwvro\n3zSzu45KkiOazj5ytyg5Ya2Otsk26ssla0lmjR5HY8SgfDKzaYX6y3ji4VWFmZja+ftyVL8vm9n9\nzewiSV9SuHdfoHBv/ktJb48e+5Kkd5rZjYpSf8e6++i+6HtJ0i3d/VsKnSB0CvbuIZJOj35+ubu/\nTVqXZNdO+fRjCp9FKXzOr+/ui70kY2ZNdD6qkvZHD50m6f9Ez3VdPqzrYH1o4ql4VqRHd7vtoqgr\nT41JKkX3+V6Trb8XP6Rodmi0J0rW+KlC/JXCYMW7R8+tWhgU1XZnddTGUonKDv8q6efGUkGFQ0zv\nDXEo/7h3ohmSfICcSBRmkkvsXNXktWWvLTXzIUl/LOmG9S9TCO5bJd1B0sskfd7M/qC+UXmE7FTt\nPB2XNGdtLE+TcI2kRYXGyHgph7UlNJKFprx19ET9WlVJH1U4RimsuxoXJLruYEg0kv9KYdr75HV9\ngcKI2FGRbMg+LnU0y9ZZCh208fJc5yskC31dobLzrwozNJRUWy93VdIZkl5uZh/sac9zxMPyej9W\naOR5rJndskGD/l0UZk67TtJF7v4haV2D5XL0e9zp9guFQvTjJf0qsZ3zJT3bzPKyTns1uubiWLNW\nCWzUsZjo9H6wpH9TSAa4t6Q7KYwMubekFykklP2PpHeY2Z0HcSBDFJ+neyjEgmVFa4e307ErrS0h\nZArJeHsVZhb5cbSNRktuZraTl/iRnkQ8+Ebi4XPMbHc7f29mWxWSDx+rEG9Kkm6vEBsuVJiJ5u4K\nHdQXq9YA5AqNNI82s/f0eBiZEpXHxiWdiB5aadUxb2F2lg8nHtqvMPPRqyS9WNJHFOJufF2bwpJn\nH5P0VGN0VOGZ2T0kfV6hgXlVIfn3DHe/yszGNyvX2cbZR+5lZk9QSMr+oJm92cxeb2bPMrM9kk6K\nXpfZODBMxKB8cvcFhTj0pughU1jS+Mdmtisesdrob602InVcYRbOiyQ9TyEpM+7giMtS8fc7S3qc\npG+Z2aPNrL7tBDmUqN99TCFpfkbSk6I6zNckPV90CnbNzM6W9Izo189Iem/0uHWSLBd93i+Lfi1L\nuq2ZnZvmvg5b1DG3rLDMRuwu0XOVHmNR3MH6BUVJrJFtkl5lOZtZuA/ittd4oFcp6gjtKqEzUReP\n7x2StF2iLNaFI9H3qqS1+mzcAd1uZ3VUJphWmGX1bgrvx1XWwZLWyD5iem+IQ4XCvRPrjGpHPpBn\n8RICruhGbuunWbNEgs/bJP1B/JSk/1aY1vC9kr6gMMtH/LeTkk6V9CFJL44ajUfNrRQ6YiTpM+7+\nq3YSLBIJO9dXbdT7f0TflxMvvb6Z7YwaJKeUTwcVpgaUpDuZ2cuk9mcQaCbRSH6VQuZ3cjmld5rZ\nWb1sP0fi5JAFheup5ZTx8bUXNbC9SuG6imdKuVjSI9z9ru5+B3d/kLvfR2F5qucodEyOqbbE3CPM\n7HXpH1K2RMkTE6oVdKsKI7BlZmOJ8x2PQHiju38ken5Dg2Xy2nf3FXe/VGH5rmSiz9MVpirNvMTx\nxPe+Wa1PLt3wWjN7gMKMBTsVrj9XaEyrT2i5icJ5uMzM/tDM8nofbClxDmcUzuOcpMVOKkpRhcsV\njcqQ9BF3v9bMdpnZDjO7u5mda2HZiRtqYyJvFhE/epRoeLlCtXP5EK1fJmHtdXXlwxtLeqRCIso2\nhUTSXyg0fv0fd3+Zu3/e3b/q7u9x9+coJOtdFr3WFD7bjzWz1/fj+IbBw2xsC6rN8vFbkm7T6LVm\n9huSXq1wT7xGITnqTpLOd/eXuPur3f0R7n5XhenDP5n481MVOpv/wlibvrAszPJ3e9ViaEnhvb9l\n9PumSwB7bfaRh5nZWxUSht4h6VGSHqEweOO5CskPX5P0MTO7b6/30hFADMoZd/+xpLdK+pvEw2dI\n+mqzRJ+oYbsSPf52hTJ4/Pn7gkLS+TMVZj/9J9WSZj362hX9vxfSKVQoC6rVZ06NP/fu/g3RKdiL\nnaolT/4wGvTS1X3V3T+sMCgpLusWdST5T1SbkexxZvZYKZVYFHewXqr17Q47JV1iIzwy32tLxH0h\n+r5X0oNT2PSkaomjP4r+F2WxNiTqtJcqxN6SpIea2ZZksnsbndVPjbZXUkhcuEfi3zzD3dua/Q+5\nQ0zvDXEop7h3ohmSfICcSHQsxw2HplrGbTXxurjT9U8kPU2hwnFMYTrqu7n777r7E9z9HElnKjQW\nf1rRrCGRl0t6o5ndtG8HlE3x6OqKoqxYa2PGnUQi0C1VS/L5jIUlev7czD5pZv+h0Jl2QGE69y+Z\n2ePy1vDr7t+V9JbEQ79nZndv9voOt12Jvl+hMPo1Hmkzq7CO6yiMfv9e9DUu6YFmNtlshFFilOoZ\nCqOTT0k8/RhJz08kp4wlCnzfcPe3K3z+vxH9r4pCAfGpZlb0qTQ9KrC+TaHiN6GQ4GTuvhoViLco\nLG/2Q0mvldY6Ddqq9Lj71xUSLJMdPW+xHMxgE3UsmmqVvmtVG/3W6PX3VEgOjV0t6V2Snq2w/OFF\nCktDrqqW9FOW9E6Fzu7fSPUAMiA6hyWF6W/LksoeLHUyOsbCci/Pj379uZmdJ+nLCtfllxRi9ycV\nKuk/N7MXmdk5KR5KqogfvUvcgz4j6T8TT11oZq8xs5skX+e1mUDOVZip5yKF5Rp/JekSSQ/wMLPZ\nfPS6+PMfLy13lcIUzm9WmKEiTvQ5P633LguiY55LPDSdeDxZFvwdSTdVaCT5B0kPjc5RvJ1y3OHs\n7u+U9GcKM5nFtiokZL3MwqxKKBgPoyM/qLAU0HGF9paSpO+a2d3jRrdGf2vrZx95vcJn9unR03FZ\nrb5MeGOFZeH+1cxebGa3TfuYioIYlE/u/r8KMSiZ6HMTNUn0SXzGLlSIXzPRUy+S9BR3f4q7v8Pd\n/07So6KkzAskfUu15IKTFGb1ea6Z3aKfx4fBcPfvKAzCqkq6n5ndLo7t3XYK1ieYjZJEHDtHIdFh\nWaE9oquZTBLn8kuJh+9V91whuPu/SvpU9GtV0sPM7JYt/qSTbcf1hEsU2nRjN5X0Z1H5YpTFyx2v\nKhrc2c31mrCocO3HM/eiTYlr9WeqJcCf7O7Ldf0bm3VWP9nMnqmQEP+7iX/xxCjOM7tSARHTe0Mc\nyi/unWjK3fnii68cfSkUTqoKhZW/jR4r1b3mFIWOv4qkb0t6ZN3zW+p+P12hM/ZgtO346xJJNxn2\nMQ/w3D4ocewXRo+V2/zb2ykk8cR//yFJK4nf459Xo/cufvxHkl487GNv8xhL0fdbSro8cQwXS5qO\nnrOU/tcNFQohS9H/+JmkG6b5P7L2FV9rColip0TXVMvrT9INFBorkp/b+7bxv8ai76dJ+mr0d/G5\nfn2Rz3PiHNxRoVM3Pm8fTTy3R2GWho/3ci4UkiiPRNuvSHpNXs6twsjp+Nyc1+Q1exVmsqhG8eNT\nkvY0ee05CiOqr2kQZ2467ONN+dzFn+UvJI7zKR1u4/oKHVpLUdz4dt15i78WEz9XFDoW3z3sc9Dg\neIgf6V9fN1RI9EleD5+U9DqFTv97KCxP8haFckdc9rhW0hsk7e3gfdsaXY8nVJv97TnDPhcpnU+L\nvj9QIdmpqrD04EyD18bx8j2StiXPUaNtRj9PKcwYkXyfro0e2/C3fBXjS2EGg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4iy2D1f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\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yKzGVxBHu/stB9m8SqIyHS+XbGzPbkWjsPMbdv5ceq/R76+aGelTvoc51Ss5i6pL5wLXu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\nRUSkuVQHiIiIiIiIiIiIbGj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"text/plain": [ "<Figure size 1180x1180 with 25 Axes>" ] }, "metadata": { "image/png": { "height": 1148, "width": 1148 } }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "import corner # https://corner.readthedocs.io\n", "\n", "# Generate a random positive definite matrix\n", "np.random.seed(42)\n", "L = np.random.randn(ndim, ndim)\n", "L[np.diag_indices_from(L)] = 0.1*np.exp(L[np.diag_indices_from(L)])\n", "L[np.triu_indices_from(L, 1)] = 0.0\n", "cov = np.dot(L, L.T)\n", "\n", "# Draw samples from this Gaussian and plot\n", "samples = np.random.multivariate_normal(np.zeros(ndim), cov, size=5000)\n", "corner.corner(samples, labels=[\"$x_{{{0}}}$\".format(i) for i in range(1, ndim+1)]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This plot will look familiar to any astronomers reading this (and probably some readers from other fields) because our parameters are often correlated and the dynamic range of the parameters can vary drastically.\n", "If you used emcee to sample this posterior and the isotropic case above, you would get identical performance (albeit somewhat worse performance than PyMC3) but, as we'll see, the same is not true of PyMC3.\n", "Let's try to sample this probability density using PyMC3's default settings." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [x]\n", "Sampling 2 chains: 100%|██████████| 30000/30000 [42:44<00:00, 6.01draws/s]\n", "The chain reached the maximum tree depth. Increase max_treedepth, increase target_accept or reparameterize.\n", "The chain reached the maximum tree depth. Increase max_treedepth, increase target_accept or reparameterize.\n", "The number of effective samples is smaller than 10% for some parameters.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "time per effective sample: 1719.01749 ms\n" ] } ], "source": [ "with pm.Model() as model:\n", " pm.MvNormal(\"x\", mu=np.zeros(ndim), chol=L, shape=(ndim,))\n", "\n", "with model:\n", " strt = time.time()\n", " default_trace = pm.sample(draws=10000, tune=5000, random_seed=42)\n", " default_time = 0.5 * (time.time() - strt)\n", "\n", "stats = pm.summary(default_trace)\n", "default_time_per_eff = default_time / stats.n_eff.min()\n", "print(\"time per effective sample: {0:.5f} ms\".format(default_time_per_eff * 1000))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Yes, the units here are the same and the computational efficiency is orders of magnitude worse than the isotropic case.\n", "The standard recommendation would be to re-parameterize (we can see that that's what PyMC3 is telling us to do here too), but I'm not really clever or patient enough to do that in every case.\n", "So, let's automate this following the procedure from Stan.\n", "\n", "## Learning the mass matrix in PyMC3\n", "\n", "In this section, I will demonstrate how we can use the machinery included in the current release of PyMC3 to fit for a dense mass matrix during burn-in.\n", "First, let's choose a tuning schedule roughly following section 34.2 from the [Stan manual](https://github.com/stan-dev/stan/releases/download/v2.17.0/stan-reference-2.17.0.pdf)." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "n_start = 25\n", "n_burn = 500\n", "n_tune = 5000\n", "n_window = n_start * 2 ** np.arange(np.floor(np.log2((n_tune - n_burn) / n_start)))\n", "n_window = np.append(n_window, n_tune - n_burn - np.sum(n_window))\n", "n_window = n_window.astype(int)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, here's a function that takes in a MultiTrace object from PyMC3, estimates the sample covariance, and builds a NUTS step for use in the `sample` method." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "from pymc3.step_methods.hmc.quadpotential import QuadPotentialFull\n", "\n", "def get_step_for_trace(trace=None, model=None,\n", " regular_window=5, regular_variance=1e-3,\n", " **kwargs):\n", " model = pm.modelcontext(model)\n", " \n", " # If not given, use the trivial metric\n", " if trace is None:\n", " potential = QuadPotentialFull(np.eye(model.ndim))\n", " return pm.NUTS(potential=potential, **kwargs)\n", " \n", " # Loop over samples and convert to the relevant parameter space;\n", " # I'm sure that there's an easier way to do this, but I don't know\n", " # how to make something work in general...\n", " samples = np.empty((len(trace) * trace.nchains, model.ndim))\n", " i = 0\n", " for chain in trace._straces.values():\n", " for p in chain:\n", " samples[i] = model.bijection.map(p)\n", " i += 1\n", " \n", " # Compute the sample covariance\n", " cov = np.cov(samples, rowvar=0)\n", " \n", " # Stan uses a regularized estimator for the covariance matrix to\n", " # be less sensitive to numerical issues for large parameter spaces.\n", " # In the test case for this blog post, this isn't necessary and it\n", " # actually makes the performance worse so I'll disable it, but I\n", " # wanted to include the implementation here for completeness\n", " N = len(samples)\n", " cov = cov * N / (N + regular_window)\n", " cov[np.diag_indices_from(cov)] += \\\n", " regular_variance * regular_window / (N + regular_window)\n", " \n", " # Use the sample covariance as the inverse metric\n", " potential = QuadPotentialFull(cov)\n", " return pm.NUTS(potential=potential, **kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can combine our tuning schedule with this proposal estimator to automatically learn the mass matrix during burn-in." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Only 2 samples in chain.\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [x]\n", "Sampling 2 chains: 100%|██████████| 54/54 [00:01<00:00, 41.49draws/s]\n", "The chain reached the maximum tree depth. Increase max_treedepth, increase target_accept or reparameterize.\n", "The chain reached the maximum tree depth. Increase max_treedepth, increase target_accept or reparameterize.\n", "Only 2 samples in chain.\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [x]\n", "Sampling 2 chains: 100%|██████████| 104/104 [00:00<00:00, 232.25draws/s]\n", "Only 2 samples in chain.\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [x]\n", "Sampling 2 chains: 100%|██████████| 204/204 [00:00<00:00, 800.87draws/s]\n", "Only 2 samples in chain.\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [x]\n", "Sampling 2 chains: 100%|██████████| 404/404 [00:00<00:00, 1043.13draws/s]\n", "Only 2 samples in chain.\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [x]\n", "Sampling 2 chains: 100%|██████████| 804/804 [00:00<00:00, 1064.61draws/s]\n", "Only 2 samples in chain.\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [x]\n", "Sampling 2 chains: 100%|██████████| 1604/1604 [00:01<00:00, 884.55draws/s]\n", "Only 2 samples in chain.\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [x]\n", "Sampling 2 chains: 100%|██████████| 3204/3204 [00:03<00:00, 905.63draws/s]\n", "Only 2 samples in chain.\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [x]\n", "Sampling 2 chains: 100%|██████████| 2654/2654 [00:03<00:00, 868.36draws/s]\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [x]\n", "Sampling 2 chains: 100%|██████████| 11000/11000 [00:09<00:00, 1206.30draws/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "time per effective sample: 0.75394 ms\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "np.random.seed(42)\n", "\n", "strt = time.time()\n", "with model:\n", " start = None\n", " burnin_trace = None\n", " for steps in n_window:\n", " step = get_step_for_trace(burnin_trace, regular_window=0)\n", " burnin_trace = pm.sample(\n", " start=start, tune=steps, draws=2, step=step,\n", " compute_convergence_checks=False, discard_tuned_samples=False)\n", " start = [t[-1] for t in burnin_trace._straces.values()]\n", "\n", " step = get_step_for_trace(burnin_trace, regular_window=0)\n", " dense_trace = pm.sample(draws=5000, tune=n_burn, step=step, start=start)\n", " factor = 5000 / (5000 + np.sum(n_window+2) + n_burn)\n", " dense_time = factor * (time.time() - strt)\n", "\n", "stats = pm.summary(dense_trace)\n", "dense_time_per_eff = dense_time / stats.n_eff.min()\n", "print(\"time per effective sample: {0:.5f} ms\".format(dense_time_per_eff * 1000))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The computational efficiency of this method is similar to PyMC3's default performance on an isotropic Gaussian (within a factor of a few) and corresponds to an improvement of more than *three orders of magnitude* over the default PyMC3 performance on a correlated Gaussian.\n", "\n", "While I've found that this procedure can substantially improve the sampling efficiency in many real world scenerios (especially during exploratory phases of a project), you shouldn't forget about reparameterization because that can provide even better performance and help identify problems with your model specification.\n", "Futhermore, this method might run into numerical issues for high dimensional problems because more samples will be needed to reliably estimate the off-diagonal elements of the mass matrix.\n", "Either way, hopefully this is helpful to folks until PyMC3 includes native support for this type of procedure." ] } ], "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": 4 }

mit

noppanit/machine-learning

parking-signs-nyc/.ipynb_checkpoints/Parking Signs-checkpoint.ipynb

1

35145

{ "cells": [ { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import re\n", "\n", "from dateutil.parser import parse" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>longtitude</th>\n", " <th>latitude</th>\n", " <th>OBJECTID</th>\n", " <th>SG_KEY_BOR</th>\n", " <th>SG_ORDER_N</th>\n", " <th>SG_SEQNO_N</th>\n", " <th>SG_MUTCD_C</th>\n", " <th>SR_DIST</th>\n", " <th>SG_SIGN_FC</th>\n", " <th>SG_ARROW_D</th>\n", " <th>x</th>\n", " <th>y</th>\n", " <th>SIGNDESC1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-73.922335</td>\n", " <td>40.836256</td>\n", " <td>11919717</td>\n", " <td>B</td>\n", " <td>P-132428</td>\n", " <td>3</td>\n", " <td>SP-287B</td>\n", " <td>45</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1005740.86711</td>\n", " <td>243957.356623</td>\n", " <td>NO STANDING 10AM-6PM MON THRU FRI</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-73.922335</td>\n", " <td>40.836256</td>\n", " <td>11919718</td>\n", " <td>B</td>\n", " <td>P-132428</td>\n", " <td>4</td>\n", " <td>SP-672G</td>\n", " <td>45</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1005740.86711</td>\n", " <td>243957.356623</td>\n", " <td>EXCEPT</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-73.922335</td>\n", " <td>40.836256</td>\n", " <td>11919719</td>\n", " <td>B</td>\n", " <td>P-132428</td>\n", " <td>5</td>\n", " <td>SP-579G</td>\n", " <td>45</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1005740.86711</td>\n", " <td>243957.356623</td>\n", " <td>AMBULETTE</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-73.922330</td>\n", " <td>40.836352</td>\n", " <td>11919720</td>\n", " <td>B</td>\n", " <td>P-132428</td>\n", " <td>6</td>\n", " <td>SP-287BA</td>\n", " <td>80</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>1005742.32839</td>\n", " <td>243992.461212</td>\n", " <td>NO STANDING 10AM-6PM MON THRU FRI (SINGLE ARROW)</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-73.922330</td>\n", " <td>40.836352</td>\n", " <td>11919721</td>\n", " <td>B</td>\n", " <td>P-132428</td>\n", " <td>7</td>\n", " <td>SP-672G</td>\n", " <td>80</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1005742.32839</td>\n", " <td>243992.461212</td>\n", " <td>EXCEPT</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " longtitude latitude OBJECTID SG_KEY_BOR SG_ORDER_N SG_SEQNO_N \\\n", "0 -73.922335 40.836256 11919717 B P-132428 3 \n", "1 -73.922335 40.836256 11919718 B P-132428 4 \n", "2 -73.922335 40.836256 11919719 B P-132428 5 \n", "3 -73.922330 40.836352 11919720 B P-132428 6 \n", "4 -73.922330 40.836352 11919721 B P-132428 7 \n", "\n", " SG_MUTCD_C SR_DIST SG_SIGN_FC SG_ARROW_D x y \\\n", "0 SP-287B 45 NaN NaN 1005740.86711 243957.356623 \n", "1 SP-672G 45 NaN NaN 1005740.86711 243957.356623 \n", "2 SP-579G 45 NaN NaN 1005740.86711 243957.356623 \n", "3 SP-287BA 80 NaN S 1005742.32839 243992.461212 \n", "4 SP-672G 80 NaN NaN 1005742.32839 243992.461212 \n", "\n", " SIGNDESC1 \n", "0 NO STANDING 10AM-6PM MON THRU FRI \n", "1 EXCEPT \n", "2 AMBULETTE \n", "3 NO STANDING 10AM-6PM MON THRU FRI (SINGLE ARROW) \n", "4 EXCEPT " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv('Signs.csv')\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 NO STANDING 10AM-6PM MON THRU FRI\n", "1 EXCEPT\n", "2 AMBULETTE\n", "3 NO STANDING 10AM-6PM MON THRU FRI (SINGLE ARROW)\n", "4 EXCEPT\n", "Name: SIGNDESC1, dtype: object" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['SIGNDESC1'].head()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# https://regex101.com/r/fC0lI5/10\n", "p = re.compile(r'(NOON|MIDNIGHT.*|[01]?[0-9]+:?[0-9]*(?:[AP]MM?)?)\\s*(?:-|TO|\\s)\\s*(NOON|MIDNIGHT|[01]?[0-9]+:?[0-9]*(?:[AP]MM?)?)')\n", "\n", "def extract_time(desc, group=1):\n", " m = p.search(desc)\n", " if m:\n", " time = m.group(group)\n", " if time == 'MIDNIGHT':\n", " return '12AM'\n", " elif time == 'MIDNIGHT TO':\n", " return '12AM'\n", " elif time == 'NOON':\n", " return '12PM'\n", " elif 'MM' in time:\n", " match_amm_or_pmm = re.compile(r'([0-9])*?([AP]MM?)')\n", " matched_time = match_amm_or_pmm.search(time)\n", " if matched_time:\n", " meridiem = 'AM' if matched_time.group(2) == 'AMM' else 'PM'\n", " oclock = matched_time.group(1)\n", " return '{0}{1}'.format(oclock, meridiem)\n", " \n", " return m.group(group)\n", " return np.nan\n", "\n", "def from_time(signdesc):\n", " f_time = extract_time(signdesc, group=1)\n", " if f_time or not np.nan:\n", " return parse(f_time).strftime('%I:%M%p')\n", " return None\n", "\n", "def to_time(signdesc):\n", " t_time = extract_time(signdesc, group=2)\n", " if t_time or not np.nan:\n", " return parse(t_time).strftime('%I:%M%p')\n", " return None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Special Cases\n", "assert extract_time('1 HR MUNI-METER PARKING 10AM-7PM MON THRU FRI 8AM-7PM SATURDAY W/ SINGLE ARROW') == ''\n", "NO PARKING (SANITATION BROOM SYMBOL) 11:30AM TO 1 PM FRIW/ SINGLE ARROW\n", "## check if 2 timings is the maximum amount" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [], "source": [ "row = 'NO PARKING (SANITATION BROOM SYMBOL) 7AM-7:30AM EXCEPT SUNDAY'\n", "assert from_time(row) == '07:00AM'\n", "assert to_time(row) == '07:30AM'\n", "\n", "special_case1 = 'NO PARKING (SANITATION BROOM SYMBOL) 11:30AM TO 1PM THURS'\n", "assert from_time(special_case1) == '11:30AM'\n", "assert to_time(special_case1) == '01:00PM'\n", "\n", "special_case2 = 'NO PARKING (SANITATION BROOM SYMBOL) MOON & STARS (SYMBOLS) TUESDAY FRIDAY MIDNIGHT-3AM'\n", "assert from_time(special_case2) == '12:00AM'\n", "assert to_time(special_case2) == '03:00AM'\n", "\n", "special_case3 = 'TRUCK (SYMBOL) TRUCK LOADING ONLY MONDAY-FRIDAY NOON-2PM'\n", "assert from_time(special_case3) == '12:00PM'\n", "assert to_time(special_case3) == '02:00PM'\n", "\n", "special_case4 = 'NIGHT REGULATION (MOON & STARS SYMBOLS) NO PARKING (SANITATION BROOM SYMBOL) MIDNIGHT TO-3AM WED & SAT'\n", "assert from_time(special_case4) == '12:00AM'\n", "assert to_time(special_case4) == '03:00AM'\n", "\n", "special_case5 = 'NO PARKING (SANITATION BROOM SYMBOL)8AM 11AM TUES & THURS'\n", "assert from_time(special_case5) == '08:00AM'\n", "assert to_time(special_case5) == '11:00AM'\n", "\n", "special_case6 = 'NO PARKING (SANITATION BROOM SYMBOL) MONDAY THURSDAY 7AMM-7:30AM'\n", "assert from_time(special_case6) == '07:00AM'\n", "assert to_time(special_case6) == '07:30AM'" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def filter_from_time(row):\n", " if not pd.isnull(row['SIGNDESC1']):\n", " print(from_time(row['SIGNDESC1']))\n", " return from_time(row['SIGNDESC1'])\n", " return np.nan" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def filter_to_time(row):\n", " if not pd.isnull(row['SIGNDESC1']):\n", " return to_time(row['SIGNDESC1'])\n", " return np.nan" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10:00AM\n", "10:00AM\n" ] }, { "ename": "AttributeError", "evalue": "(\"'float' object has no attribute 'read'\", 'occurred at index 1')", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-52-92f28f1e659b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'FROM_TIME'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilter_from_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/ncharass/anaconda/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, broadcast, raw, reduce, args, **kwds)\u001b[0m\n\u001b[1;32m 3912\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreduce\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3913\u001b[0m \u001b[0mreduce\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3914\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3915\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3916\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_broadcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 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\u001b[0m__next__\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[0m\n\u001b[0;32m--> 164\u001b[0;31m \u001b[0mtoken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_token\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 165\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/ncharass/anaconda/lib/python3.5/site-packages/dateutil/parser.py\u001b[0m in \u001b[0;36mget_token\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0mnextchar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcharstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m \u001b[0mnextchar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minstream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 83\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mnextchar\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'\\x00'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0mnextchar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minstream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: (\"'float' object has no attribute 'read'\", 'occurred at index 1')" ] } ], "source": [ "data['FROM_TIME'] = data.apply(filter_from_time, axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['TO_TIME'] = data.apply(filter_to_time, axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data[['SIGNDESC1', 'FROM_TIME', 'TO_TIME']].head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Find out if any rows has NaN\n", "\n", "Want to find out if any rows has NaN from `from_time` and `to_time` but has timing in SIGNDESC1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rows_with_AM_PM_but_time_NaN = data[(data['FROM_TIME'].isnull() | data['FROM_TIME'].isnull()) & (data['SIGNDESC1'].str.contains('[0-9]+(?:[AP]M)'))]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "len(rows_with_AM_PM_but_time_NaN)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rows_with_AM_PM_but_time_NaN[['SIGNDESC1', 'FROM_TIME', 'TO_TIME']]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.iloc[180670, data.columns.get_loc('SIGNDESC1')]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.iloc[180670, data.columns.get_loc('FROM_TIME')] = '9AM'\n", "data.iloc[180670, data.columns.get_loc('TO_TIME')] = '4AM'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.iloc[212089, data.columns.get_loc('SIGNDESC1')]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.iloc[212089, data.columns.get_loc('FROM_TIME')] = '10AM'\n", "data.iloc[212089, data.columns.get_loc('TO_TIME')] = '11:30AM'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.iloc[258938, data.columns.get_loc('SIGNDESC1')]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.iloc[258938, data.columns.get_loc('FROM_TIME')] = '10AM'\n", "data.iloc[258938, data.columns.get_loc('TO_TIME')] = '11:30AM'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.iloc[258942, data.columns.get_loc('SIGNDESC1')]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.iloc[258942, data.columns.get_loc('FROM_TIME')] = '10AM'\n", "data.iloc[258942, data.columns.get_loc('TO_TIME')] = '11:30AM'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.iloc[258944, data.columns.get_loc('SIGNDESC1')]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.iloc[258944, data.columns.get_loc('FROM_TIME')] = '10AM'\n", "data.iloc[258944, data.columns.get_loc('TO_TIME')] = '11:30AM'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.iloc[283262, data.columns.get_loc('SIGNDESC1')]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.iloc[283262, data.columns.get_loc('FROM_TIME')] = '6AM'\n", "data.iloc[283262, data.columns.get_loc('TO_TIME')] = '7:30AM'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Confirm that every row has `from_time` and `to_time`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rows_with_AM_PM_but_time_NaN = data[(data['FROM_TIME'].isnull() | data['FROM_TIME'].isnull()) & (data['SIGNDESC1'].str.contains('[0-9]+(?:[AP]M)'))]\n", "len(rows_with_AM_PM_but_time_NaN)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data[['SIGNDESC1', 'FROM_TIME', 'TO_TIME']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Day of the week" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data['SIGNDESC1'].head(20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#https://regex101.com/r/fO4zL8/3\n", "regex_to_extract_days_idv_days = r'\\b((?:(?:MON|MONDAY|TUES|TUESDAY|WED|WEDNESDAY|THURS|THURSDAY|FRI|FRIDAY|SAT|SATURDAY|SUN|SUNDAY)\\s*)+)(?=\\s|$)'\n", "regex_to_extract_days_with_range = r'(MON|TUES|WED|THURS|FRI|SAT|SUN)\\s(THRU|\\&)\\s(MON|TUES|WED|THURS|FRI|SAT|SUN)'\n", "\n", "def extract_day(signdesc):\n", " days = ['MON', 'TUES', 'WED', 'THURS', 'FRI', 'SAT', 'SUN']\n", " p_idv_days = re.compile(regex_to_extract_days_idv_days)\n", " m_idv_days = p_idv_days.search(signdesc)\n", " \n", " p_range_days = re.compile(regex_to_extract_days_with_range)\n", " m_range_days = p_range_days.search(signdesc)\n", " \n", " if 'EXCEPT SUN' in signdesc:\n", " return ', '.join(days[:6])\n", " \n", " if 'INCLUDING SUNDAY' in signdesc:\n", " return ', '.join(days)\n", " \n", " if 'FRIW/' in signdesc:\n", " return ', '.join(['FRI'])\n", " \n", " if ('THRU' in signdesc) and m_range_days:\n", " from_day = m_range_days.group(1)\n", " to_day = m_range_days.group(3)\n", "\n", " idx_frm_d = days.index(from_day)\n", " idx_to_d = days.index(to_day)\n", " return ', '.join([days[n] for n in range(idx_frm_d, idx_to_d + 1)])\n", " \n", " if ('&' in signdesc) and m_range_days:\n", " from_day = m_range_days.group(1)\n", " to_day = m_range_days.group(3)\n", " \n", " return ', '.join([from_day, to_day])\n", " \n", " if m_idv_days:\n", " days = m_idv_days.group(1)\n", " d = []\n", " for day in days.split(' '):\n", " if len(day) > 3:\n", " if day in ['MONDAY', 'WEDNESDAY', 'FRIDAY', 'SATURDAY', 'SUNDAY']:\n", " d.append(day[:3])\n", " if day in ['TUESDAY']:\n", " d.append(day[:4])\n", " if day in ['THURSDAY']:\n", " d.append(day[:5])\n", " else:\n", " d.append(day)\n", " \n", " return ', '.join(d)\n", " \n", " return np.nan" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def filter_days(row):\n", " if not pd.isnull(row['SIGNDESC1']):\n", " return extract_day(row['SIGNDESC1'])\n", " return np.nan" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert extract_day('NO STANDING 11AM-7AM MON SAT') == \"MON, SAT\"\n", "assert extract_day('NO STANDING MON FRI 7AM-9AM') == \"MON, FRI\"\n", "assert extract_day('2 HOUR PARKING 9AM-5PM MON THRU SAT') == \"MON, TUES, WED, THURS, FRI, SAT\"\n", "assert extract_day('1 HOUR PARKING 8AM-7PM EXCEPT SUNDAY') == \"MON, TUES, WED, THURS, FRI, SAT\"\n", "assert extract_day('NO PARKING 10PM-8AM INCLUDING SUNDAY') == \"MON, TUES, WED, THURS, FRI, SAT, SUN\"\n", "assert extract_day('NO PARKING (SANITATION BROOM SYMBOL) MONDAY THURSDAY 9:30AM-11AM') == \"MON, THURS\"\n", "assert extract_day('NO PARKING (SANITATION BROOM SYMBOL) 11:30AM TO 1 PM FRIW/ SINGLE ARROW') == \"FRI\"\n", "assert extract_day('NO PARKING (SANITATION BROOM SYMBOL) 8-9:30AM TUES & FRI') == \"TUES, FRI\"\n", "assert extract_day('NO PARKING (SANITATION BROOM SYMBOL) TUESDAY FRIDAY 11AM-12:30PM') == \"TUES, FRI\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['DAYS'] = data.apply(filter_days, axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rows_with_days_but_DAYS_NAN = data[data['DAYS'].isnull() & data['SIGNDESC1'].str.contains('\\sMON|\\sTUES|\\sWED|\\sTHURS|\\sFRI|\\sSAT|\\sSUN')]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rows_with_days_but_DAYS_NAN[['SIGNDESC1', 'DAYS']]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.iloc[308838, data.columns.get_loc('SIGNDESC1')]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Save to CSV" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.to_csv('Processed_Signs.csv', index=False)" ] }, { "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

margulies/gradient_analysis

02_embed_connectomes.ipynb

2

5475

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Human connectivity embedding" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Load affinitity matrix\n", "aff = np.load('gradient_data/conn_matrices/cosine_affinity.npy')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys\n", "sys.path.append(\"../topography/utils_py/mapalign\")\n", "from mapalign import embed\n", "\n", "emb, res = embed.compute_diffusion_map(aff, alpha = 0.5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Save results\n", "np.save('gradient_data/embedded/embedding_dense_emb.npy', emb)\n", "np.save('gradient_data/embedded/embedding_dense_res.npy', res)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "91282" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "res = np.load('gradient_data/embedded/embedding_dense_res.npy').item()\n", "a = [res['vectors'][:,i]/ res['vectors'][:,0] for i in range(302)]\n", "emb = np.array(a)[1:,:].T\n", "len(emb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Export to cifti space" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import nibabel as nib\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "pixdim[1,2,3] should be non-zero; setting 0 dims to 1\n", "pixdim[1,2,3] should be non-zero; setting 0 dims to 1\n" ] } ], "source": [ "res = nib.load('gradient_data/templates/hcp.tmp.lh.dscalar.nii').data\n", "cortL = np.squeeze(np.array(np.where(res != 0)[0], dtype=np.int32))\n", "res = nib.load('gradient_data/templates/hcp.tmp.rh.dscalar.nii').data\n", "cortR = np.squeeze(np.array(np.where(res != 0)[0], dtype=np.int32))\n", "cortLen = len(cortL) + len(cortR)\n", "del res\n", "# save out cortR and cortL in gradient_data/templates/\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "emb = np.load('gradient_data/embedded/embedding_dense_emb.npy')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "pixdim[1,2,3] should be non-zero; setting 0 dims to 1\n", "pixdim[1,2,3] should be non-zero; setting 0 dims to 1\n" ] } ], "source": [ "tmp = nib.nifti2.load('gradient_data/templates/100307_tfMRI_MOTOR_level2_hp200_s2.dscalar.nii')\n", "tmp_cifti = nib.cifti.load('gradient_data/templates/100307_tfMRI_MOTOR_level2_hp200_s2.dscalar.nii')\n", "data = tmp_cifti.data * 0\n", "data[0:10,:len(emb)] = np.reshape(emb.T, [1, 1, 1, 1] + list(emb.T.shape))\n", "tmp2 = nib.nifti2.Nifti2Image(data, tmp.get_affine())\n", "tmp4 = nib.nifti2.create_cifti_image(tmp2, tmp_cifti.header.to_xml(), np.array(3006, dtype=np.int32))\n", "tmp4.to_filename('gradient_data/embedded/ciftis/hcp.embed.dscalar.nii')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Macaque connectivity embedding" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys\n", "sys.path.append(\"../topography/utils_py/mapalign\")\n", "from mapalign import embed\n", " \n", "def embed_macaque(mat_name):\n", " \n", " aff = np.load('gradient_data/conn_matrices/macaque_%s_conn.npy' % mat_name)\n", " print np.shape(aff)\n", " emb, res = embed.compute_diffusion_map(aff, alpha = 0.5)\n", " np.save('gradient_data/conn_matrices/macaque_%s_emb.npy' % mat_name, emb)\n", " np.save('gradient_data/conn_matrices/macaque_%s_res.npy' % mat_name, res)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(25, 25)\n" ] } ], "source": [ "embed_macaque('bb47')" ] }, { "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

mayank-johri/LearnSeleniumUsingPython

Section 2 - Advance Python/Chapter S2.90 - Optimization/Optimization.ipynb

2

3028

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Optimization\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this chapter we are going to discuss few ideas which can be used to optimize & enhance the performance of the python code.\n", "\n", "Most of the ideas present below are nothing but good common sense. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20\n", "<function B.<locals>.C at 0x0000000005DE2B70>\n" ] } ], "source": [ "d = 10\n", "\n", "def B():\n", " d = 20\n", " def C():\n", " d = 30\n", " return d\n", " print(d)\n", " return C\n", "\n", "x = B()\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30\n", "30\n" ] } ], "source": [ "d = 10\n", "\n", "def B():\n", " d = 20\n", " def C():\n", " global d\n", " d = 30\n", " return d\n", " return C\n", "a = B()\n", "print(a())\n", "print(d)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20\n", "30\n", "10\n" ] } ], "source": [ "d = 10\n", "\n", "def B():\n", " d = 20\n", " def C():\n", " nonlocal d\n", " print(d)\n", " d = 30\n", " return d\n", " return C\n", "a = B()\n", "print(a())\n", "print(d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "- https://wiki.python.org/moin/PythonSpeed/PerformanceTips\n", "- http://blog.hackerearth.com/4-Performance-Optimization-Tips-Faster-Python-Code\n", "- https://www.geeksforgeeks.org/optimization-tips-python-code/\n", "- https://www.airpair.com/python/posts/optimizing-python-code\n", "- https://stackoverflow.com/questions/7165465/optimizing-python-code" ] }, { "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

harish-garg/Data-Analysis

udacity_intro_data_analysis/gapminder_dataset/Pandas Series Practice.ipynb

2

178784

{ "cells": [ { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "countries = ['Albania', 'Algeria', 'Andorra', 'Angola', 'Antigua and Barbuda',\n", " 'Argentina', 'Armenia', 'Australia', 'Austria', 'Azerbaijan',\n", " 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus',\n", " 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia']\n", "\n", "life_expectancy_values = [74.7, 75. , 83.4, 57.6, 74.6, 75.4, 72.3, 81.5, 80.2,\n", " 70.3, 72.1, 76.4, 68.1, 75.2, 69.8, 79.4, 70.8, 62.7,\n", " 67.3, 70.6]\n", "\n", "gdp_values = [ 1681.61390973, 2155.48523109, 21495.80508273, 562.98768478,\n", " 13495.1274663 , 9388.68852258, 1424.19056199, 24765.54890176,\n", " 27036.48733192, 1945.63754911, 21721.61840978, 13373.21993972,\n", " 483.97086804, 9783.98417323, 2253.46411147, 25034.66692293,\n", " 3680.91642923, 366.04496652, 1175.92638695, 1132.21387981]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Life expectancy and gdp data in 2007 for 20 countries\n", "life_expectancy = pd.Series(life_expectancy_values)\n", "gdp = pd.Series(gdp_values)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def variable_correlation(variable1, variable2):\n", " \n", " mean1 = variable1.mean()\n", " mean2 = variable2.mean()\n", " \n", " num_same_direction = (len(variable1[variable1 > mean1][variable2 > mean2]) + len(variable1[variable1 < mean1][variable2 < mean2])) # Replace this with your code\n", " num_different_direction = len(variable1) - (len(variable1[variable1 > mean1][variable2 > mean2]) + len(variable1[variable1 < mean1][variable2 < mean2])) # Replace this with your code\n", " \n", " return (num_same_direction, num_different_direction)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2, 2)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "variable_correlation(pd.Series([1,2,3,4]), pd.Series([2,3,1,5]))" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(17, 3)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "variable_correlation(pd.Series(life_expectancy_values), pd.Series(gdp_values))" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 20.000000\n", "mean 72.870000\n", "std 6.213999\n", "min 57.600000\n", "25% 70.175000\n", "50% 73.450000\n", "75% 75.650000\n", "max 83.400000\n", "dtype: float64" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "le = pd.Series(life_expectancy_values)\n", "le.describe()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "countries = [\n", " 'Afghanistan', 'Albania', 'Algeria', 'Angola', 'Argentina',\n", " 'Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas',\n", " 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium',\n", " 'Belize', 'Benin', 'Bhutan', 'Bolivia',\n", " 'Bosnia and Herzegovina'\n", "]" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "employment_values = [\n", " 55.70000076, 51.40000153, 50.5 , 75.69999695,\n", " 58.40000153, 40.09999847, 61.5 , 57.09999847,\n", " 60.90000153, 66.59999847, 60.40000153, 68.09999847,\n", " 66.90000153, 53.40000153, 48.59999847, 56.79999924,\n", " 71.59999847, 58.40000153, 70.40000153, 41.20000076\n", "]" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "employment = pd.Series(employment_values, index=countries)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Angola', 75.699996949999999)\n" ] } ], "source": [ "def max_employment(employment):\n", " '''\n", " Fill in this function to return the name of the country\n", " with the highest employment in the given employment\n", " data, and the employment in that country.\n", " \n", " The input will be a Pandas series where the values\n", " are employment and the index is country names.\n", " \n", " Try using the Pandas argmax() function. Documention is\n", " here: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.argmax.html\n", " '''\n", " max_country = employment.argmax() # Replace this with your code\n", " max_value = employment[max_country] # Replace this with your code\n", "\n", " return (max_country, max_value)\n", "print max_employment(employment)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Some more Panda practice" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a 11\n", "b 22\n", "c 33\n", "d 44\n", "dtype: int64\n", "a 31\n", "b 12\n", "c 43\n", "d 24\n", "dtype: int64\n", "a NaN\n", "b NaN\n", "c 13.0\n", "d 24.0\n", "e NaN\n", "f NaN\n", "dtype: float64\n", "a NaN\n", "b NaN\n", "c NaN\n", "d NaN\n", "e NaN\n", "f NaN\n", "g NaN\n", "h NaN\n", "dtype: float64\n" ] } ], "source": [ "# Addition when indexes are the same\n", "if True:\n", " s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])\n", " s2 = pd.Series([10, 20, 30, 40], index=['a', 'b', 'c', 'd'])\n", " print s1 + s2\n", "\n", "# Indexes have same elements in a different order\n", "if True:\n", " s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])\n", " s2 = pd.Series([10, 20, 30, 40], index=['b', 'd', 'a', 'c'])\n", " print s1 + s2\n", "\n", "# Indexes overlap, but do not have exactly the same elements\n", "if True:\n", " s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])\n", " s2 = pd.Series([10, 20, 30, 40], index=['c', 'd', 'e', 'f'])\n", " print s1 + s2\n", "\n", "# Indexes do not overlap\n", "if True:\n", " s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])\n", " s2 = pd.Series([10, 20, 30, 40], index=['e', 'f', 'g', 'h'])\n", " print s1 + s2" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])\n", "s2 = pd.Series([10, 20, 30, 40], index=['c', 'd', 'e', 'f'])" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "a 0.0\n", "b 0.0\n", "c 13.0\n", "d 24.0\n", "e 0.0\n", "f 0.0\n", "dtype: float64" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(s1 + s2).fillna(0)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "a 1.0\n", "b 2.0\n", "c 13.0\n", "d 24.0\n", "e 30.0\n", "f 40.0\n", "dtype: float64" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1.add(s2, fill_value=0)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 2\n", "1 3\n", "2 4\n", "3 5\n", "4 6\n", "dtype: int64\n" ] } ], "source": [ "s = pd.Series([1, 2, 3, 4, 5])\n", "\n", "def add_one(x):\n", " return x + 1\n", "\n", "print s.apply(add_one)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true }, "outputs": [], "source": [ "names1 = pd.Series([\n", " 'Andre Agassi',\n", " 'Barry Bonds',\n", " 'Christopher Columbus',\n", " 'Daniel Defoe',\n", " 'Emilio Estevez',\n", " 'Fred Flintstone',\n", " 'Greta Garbo',\n", " 'Humbert Humbert',\n", " 'Ivan Ilych',\n", " 'James Joyce',\n", " 'Keira Knightley',\n", " 'Lois Lane',\n", " 'Mike Myers',\n", " 'Nick Nolte',\n", " 'Ozzy Osbourne',\n", " 'Pablo Picasso',\n", " 'Quirinus Quirrell',\n", " 'Rachael Ray',\n", " 'Susan Sarandon',\n", " 'Tina Turner',\n", " 'Ugueth Urbina',\n", " 'Vince Vaughn',\n", " 'Woodrow Wilson',\n", " 'Yoji Yamada',\n", " 'Zinedine Zidane'\n", "])" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": true }, "outputs": [], "source": [ "names1=pd.Series(['Andre Agassi', 'Barry Bonds', 'Christopher Columbus', 'Daniel Defoe'],\n", " index=[0, 1, 2, 3])" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Garg Aavni\n" ] } ], "source": [ "def reverse_name(name):\n", " return (name.split())[1] + ' ' + (name.split())[0]\n", "print reverse_name(\"Aavni Garg\")" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def reverse_names(names):\n", " '''\n", " Fill in this function to return a new series where each name\n", " in the input series has been transformed from the format\n", " \"Firstname Lastname\" to \"Lastname, FirstName\".\n", " \n", " Try to use the Pandas apply() function rather than a loop.\n", " '''\n", " return names.apply(reverse_name)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 Agassi Andre\n", "1 Bonds Barry\n", "2 Columbus Christopher\n", "3 Defoe Daniel\n", "dtype: object\n" ] } ], "source": [ "print reverse_names(names1)" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7faa3e506790>" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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fLunHtg9rcJ3+B7xhDJKeKunA8LWLX0j6XkTcMGoxb3gBgMTxYiIAJI6NGgASx0YNAIlj\nowaAxLFRA0Di2KgBIHFs1ACQODZqAEjc/wGQ7KTnQGWXtwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7faa3e4a5150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = pd.Series([3,4,5,6,7,8])\n", "data.plot()" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# The following code reads all the Gapminder data into Pandas DataFrames. You'll\n", "# learn about DataFrames next lesson.\n", "\n", "path = ''\n", "employment = pd.read_csv(path + 'employment_above_15.csv', index_col='Country')\n", "female_completion = pd.read_csv(path + 'female_completion_rate.csv', index_col='Country')\n", "male_completion = pd.read_csv(path + 'male_completion_rate.csv', index_col='Country')\n", "life_expectancy = pd.read_csv(path + 'life_expectancy.csv', index_col='Country')\n", "gdp = pd.read_csv(path + 'gdp_per_capita.csv', index_col='Country')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "# The following code creates a Pandas Series for each variable for the United States.\n", "# You can change the string 'United States' to a country of your choice.\n", "\n", "employment_us = employment.loc['United States']\n", "female_completion_us = female_completion.loc['United States']\n", "male_completion_us = male_completion.loc['United States']\n", "life_expectancy_us = life_expectancy.loc['United States']\n", "gdp_us = gdp.loc['United States']" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false }, "outputs": [], "source": [ "employment_india = employment.loc['India']\n", "female_completion_india = female_completion.loc['India']\n", "male_completion_india = male_completion.loc['India']\n", "life_expectancy_india = life_expectancy.loc['India']\n", "gdp_india = gdp.loc['India']" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Afghanistan' 'Albania' 'Algeria' 'Angola' 'Argentina' 'Armenia'\n", " 'Australia' 'Austria' 'Azerbaijan' 'Bahamas' 'Bahrain' 'Bangladesh'\n", " 'Barbados' 'Belarus' 'Belgium' 'Belize' 'Benin' 'Bhutan' 'Bolivia'\n", " 'Bosnia and Herzegovina' 'Botswana' 'Brazil' 'Brunei' 'Bulgaria'\n", " 'Burkina Faso' 'Burundi' 'Cambodia' 'Cameroon' 'Canada' 'Cape Verde'\n", " 'Central African Rep.' 'Chad' 'Chile' 'China' 'Colombia' 'Comoros'\n", " 'Congo, Rep.' 'Congo, Dem. Rep.' 'Costa Rica' \"Cote d'Ivoire\" 'Croatia'\n", " 'Cuba' 'Cyprus' 'Czech Rep.' 'Denmark' 'Dominican Rep.' 'Timor-Leste'\n", " 'Ecuador' 'Egypt' 'El Salvador' 'Equatorial Guinea' 'Eritrea' 'Estonia'\n", " 'Ethiopia' 'Fiji' 'Finland' 'France' 'Gabon' 'Gambia' 'Georgia' 'Germany'\n", " 'Ghana' 'Greece' 'Guadeloupe' 'Guatemala' 'Guinea' 'Guinea-Bissau'\n", " 'Guyana' 'Haiti' 'Honduras' 'Hong Kong, China' 'Hungary' 'Iceland' 'India'\n", " 'Indonesia' 'Iran' 'Iraq' 'Ireland' 'Israel' 'Italy' 'Jamaica' 'Japan'\n", " 'Jordan' 'Kazakhstan' 'Kenya' 'Korea, Dem. Rep.' 'Korea, Rep.' 'Kuwait'\n", " 'Kyrgyzstan' 'Laos' 'Latvia' 'Lebanon' 'Lesotho' 'Liberia' 'Libya'\n", " 'Lithuania' 'Luxembourg' 'Macao, China' 'Madagascar' 'Malawi' 'Malaysia'\n", " 'Maldives' 'Mali' 'Malta' 'Martinique' 'Mauritania' 'Mauritius' 'Mexico'\n", " 'Mongolia' 'Morocco' 'Mozambique' 'Myanmar' 'Namibia' 'Nepal'\n", " 'Netherlands' 'Netherlands Antilles' 'New Zealand' 'Nicaragua' 'Niger'\n", " 'Nigeria' 'Norway' 'Oman' 'Pakistan' 'Panama' 'Papua New Guinea'\n", " 'Paraguay' 'Peru' 'Philippines' 'Poland' 'Portugal' 'Puerto Rico' 'Qatar'\n", " 'Moldova' 'Reunion' 'Romania' 'Russia' 'Rwanda' 'Saudi Arabia' 'Senegal'\n", " 'Serbia and Montenegro' 'Sierra Leone' 'Singapore' 'Slovak Republic'\n", " 'Slovenia' 'Solomon Islands' 'Somalia' 'South Africa' 'Spain' 'Sri Lanka'\n", " 'Sudan' 'Suriname' 'Swaziland' 'Sweden' 'Switzerland' 'Syria' 'Taiwan'\n", " 'Tajikistan' 'Tanzania' 'Thailand' 'Macedonia, FYR' 'Togo'\n", " 'Trinidad and Tobago' 'Tunisia' 'Turkey' 'Turkmenistan' 'Uganda' 'Ukraine'\n", " 'United Arab Emirates' 'United Kingdom' 'United States' 'Uruguay'\n", " 'Uzbekistan' 'Venezuela' 'Vietnam' 'West Bank and Gaza' 'Yemen, Rep.'\n", " 'Zambia' 'Zimbabwe']\n" ] } ], "source": [ "# Uncomment the following line of code to see the available country names\n", "print employment.index.values" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7faa409ad910>" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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RHvR6I365KA5JEwKljkR0ERerIWvDYidJHTmlxSufFEAAsHzxBCTG+EkdiegS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\nYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7faa366e7110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Use the Series defined above to create a plot of each variable over time for\n", "# the country of your choice. You will only be able to display one plot at a time\n", "# with each \"Test Run\".\n", "employment_us.plot()" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7faa36124210>" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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x1LtMVmcetbhSaU3cCnbDMEhNTSUlJYWMjIyz/m7Tpk1ERUXRuXPnS14rIu7r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\nEoCHH36YUaNGubVWRETE20W1a8PjM4b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5g5/97Gf44Q9/qMTHExFVBJdd23PESvXgHslps5R1Br3v6Hn5f/cHogV/TiiaULVADAAK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\nP/wBFy5cwNNPP43169fjpZd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"text/plain": [ "<matplotlib.figure.Figure at 0x7faa360cbcd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "female_completion_us.plot()" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7faa36007a90>" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7faa35faa7d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "female_completion_india.plot()" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7faa35edd990>" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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smdC5sgZt7dYBQAFq9/cHdE2GA9pms+l/fvEiBcNR/cdHrZKkqjQcjjGabGwS\nY4obADAp7f7+jVq1GQ5oqf/Uq7tuukRXDUxrX7nUl5FymBndJNaXG5vErN06AChA2RpBmxx2u/7X\nrZfpjYMtuvqi9E9vS1KRyy6nw56RM6GZ4gYATIl5q9OsylIFA+GstMHldOgLV83N2Pf1n2iVmQMz\nes2AppIYAGAy2rtCchc7EhunCoV5YEa6hXJkBE1AA4CFGIah9s6Qqsrdlj4KMR08JS71hqKKx9N7\nopVZqISABgAkrScYUTgSk29GeguDWJHX7ZKhwSnodDE/v6TI2ru4rf3rAwAUGHODWLord1mRWayk\nJxhJ3HaVKrF4XIeOdeiNA806esav0mKn7HZrz1AQ0ABgIWZA+yrcWW5J5nnSUO6zpaNXbxxo1q8P\nNcvf0yepv775TasWpOw70oWABgALMXdwVxfgCDpVxUpCfVG9e/is3jjQrCOn/ZL673lec/Vc/d7y\nOZpf682J9X0CGgAsxBxBV88owBH0kCnuyTIMQ5+c8evfDzTr3f88q3AkJpukyxbO1DXL5+jqi6rl\nclp7zXkkAhoALCQR0IU8gp5CsZK9/35M//Jmo6T+/3Y3/M58rf6dWarO4aUCAhoALKTdH5SnxGn5\nW4DSYTrlPt/6qFWlxU7974bLdfGCmbLnwBT2RLjNCgAswjAMtftDOT3qm46prkF39fap3R/SkrkV\nunRhZV6Es0RAA4BldAX6FInGVV2A90BLUz9y8kRzlyRp0eyylLcpmwhoALCIQl5/lgY3iQVCk1uD\nPtbUH9CL55SnvE3ZREADgEW0+c1brApzirvI5VCR0z7pEfTx5m5J0sLZBDQAIA3OFfgIWuqf5p5M\noRLDMHS8uUu+GSUqLy1KY8syj4AGAIto6ySgPSWuSU1xt3UG1ROMaFGejZ4lAhoALONcgU9xS5LX\n7VQwHFUsHk/q9ccGNogtJqABAOnS5g+prNSlYoufspROiXrcSY6ijzf1rz8vyrMNYhIBDQCWEDcM\nnSvge6BNZrGSZNehjzd3yW6zaX5tft1iJRHQAGAJ/p4+xeJGQa8/S5Mr9xmNxdXY2q26Go+KXfk3\n60BAA4AFtJmnWBVokRKTx538gRln2gKKRON5uf4sEdAAYAmDt1gV9hS3tyT5cp/HEhXECGgAQJqY\nRUp8BT7FPZlyn8cHKojl4wYxiYAGAEswy3xWFXpAJ8p9JhHQzV0qLnJoTpUn3c3KCgIaACyg3VyD\nLvCATnakDRHUAAARu0lEQVSTWDAcVVN7QItmlcluz4/Tq0YioAHAAtr9IVV4i+Ry5t9u5MlIdor7\nREu3DOXv+rNEQANA1sXicXV0hQt+9CwNuQ96ginu43m+QUwioAEg6853hxU3DPkKfAe3JLmcdhW7\nHBOOoI/n6RGTQxHQAJBl7Z1sEBvK63ZOuAZ9rLlLFd4izSwrzlCrMo+ABoAsM3dw+2Ywgpb6p7l7\nxpniPt8d1vnusBbPLpfNlp8bxCQCGgCyrn3gHmhG0P08bpfCfTFFY6OfaFUI688SAQ0AWZcYQRPQ\nkoacaDXGOnQioPN4/VkioAEg69r9IdkkVZYT0JLkHShW0jPGkZPHzApis/LvBKuhCGgAyLJ2f1Az\ny4vldPBPsjT+CDpuGDrR0qXZVaUqHbglK1/x0wAAWRSNxXW+O6xqRs8J450J3XKuV8FwLO/Xn6Vp\nBvSTTz6p+vp61dfX6+c//7kk6fDhw/qDP/gDrV+/XrfddpsOHjyYkoYCQD7q6ArJMKRqdnAnmOU+\nR9vJXSgbxKRpBPSRI0f0zDPPaPfu3Xr22Wf1r//6rzp58qQefvhhbd26Vc8++6y2bt2qhx56KJXt\nBYC80p44ZpIRtMk8E3q0e6HNIybzuUCJyTnVNx49elRXXHGFioqKJEkrVqzQyy+/LJvNpu7ubklS\nd3e3amtrU9NSAMhD7ZwDfYHEgRmjjaCbuuR02FTn82a6WRk35YBetmyZHnvsMfn9fhUVFen111/X\n5Zdfrm3btulrX/ua/vIv/1KGYejpp59OZXsBwFKisbhCfbFEqEyWeQ80I+hB5hr0yHKfkWhMp872\naMGsMrmc+b+FasoBvWTJEt19992688475fF4dMkll8jhcOgf/uEfdP/99+u6667Tr371K23fvl0/\n+9nPJvw8ny9/t8vna9/ytV9S/vYtX/slZa9vO589qNd+c0q7vvfFKe0q7g7GJEkXLa6Wr7L0gufz\n9ZqN1y9XSf/MbDQ+/HWHGzsUixu6dHGVpf+7pKptUw5oSdq4caM2btwoSXr00UdVW1urRx99VN/7\n3vckSV/60pd0//33J/VZbW3d02mKZfl8ZXnZt3ztl5S/fcvXfklZ7lssrp5gRG+8f0pXLfNN+u1n\nznbLbrPJiEQu6EO+XrOJ+mVWEDvX2Tvsde9/3CJJmj3Dbdn/LpO5ZhMF+bTmCDo6OiRJTU1NeuWV\nV3TLLbeopqZG77zzjiTpP/7jP7Rw4cLpfAUAWNpliyolSR+fOD+l97f7g6osL5bDnv9TtslyOuwq\nKXIoMKJQSaFUEDNNawS9detW+f1+OZ1Off/735fX69UPfvADPfDAA4rH4youLtYPf/jDVLUVACxn\n8ZxyFbsc+vhEx6TfG4nG1NnTp4vnz0hDy3Kb1+26YA36eFOXSoudqp1ZGBvqphXQTz311AWPffrT\nn9aePXum87EAkDOcDrs+NX+GDhw9p46u0KTKdZ7rCktiB/doPCUuNXcEEn/vCUbUej6oyxZV5vUJ\nVkMxpwIA03TpwqlNc7d3DuzgnsEO7pG8bqf6InFFov2b6E4UUIESEwENANN02cKZkjTpaW6KlIzN\nrMfdM1CsJFGghIAGACRrTrVHFd4ifXyiQ3HDSPp9bYl7oJniHskzoljJ8abC2iAmEdAAMG02m02X\nLqhUV29EZ9oCE79hwDlG0GMaemCGYRg63tylqvISVXiKstyyzCGgASAFLlvUP8390fHkp7nbOkNy\n2G2aUVacrmblLO+QKe5zXSF19UYKavQsEdAAkBKJjWKNyQf0OX9QVRUlshfIruTJ8JQMHJgRiuhY\nU+GtP0sENACkxAxvseZWe/TfJzsVicYnfH24L6au3oh8TG+PKrEGHYwMOWLSuuU904GABoAUuXRh\npfqicX1yxj/ha9u7+tefq9ggNqrBKe6Ijjd1yWaTFswioAEAU2CuQydzu1XiHmhG0KMyp7i7evt0\norVbc6u9KimaVm2tnENAA0CKXDRvhhx2W3IBbe7gpkjJqMwR9JFTfvVF4lo8p7BGzxIBDQApU1Lk\n1JK5FTrR3H1BHemRBm+xYop7NKUDI+izAzMNhVRBzERAA0AKXbZwpgxJhxvHL/tpFilhk9joHHa7\nSosHp7QJaADAtAzW5R5/mrvdH5LLaVd5ARXemCyPuz+gi1x2zfV5styazCOgASCFFs4uk7vYqY8m\nCujOoKorSgrmZKapMNehF9aWFeR52YXXYwBII4fdrovnz1BbZyixfjpSMBxVIBRVFdPb4zLLfRZa\nBTETAQ0AKXbZovGnuc0d3D42iI3LLFZSiOvPEgENACl3mbkOPUZd7nY/90AnY+GsMpUWO/WpeTOy\n3ZSsKKy7vgEgA2pmulVVXqz/bDyveNyQ3T58nbm907wHmhH0eNb97nyt/XSdnI7CHEsWZq8BII1s\nNpsuXVipQCiqxtbuC55v55jJpBVqOEsENACkxXjr0OYUN5vEMB4CGgDS4OIFZl3uCwuWtPtDKnY5\nVDawCQoYDQENAGlQXlqk+bVeHTndqXAkNuy5dn+Ie6AxIQIaANLksoWVisYMHTndmXgsEIooGI6y\n/owJEdAAkCaJsp/HB6e5Ezu4uQcaEyCgASBNltVVyOmwDyv7mbgHmmMmMQECGgDSpMjl0EXzKnTq\nbI+6An2SuMUKySOgASCNEtPcjf2jaKa4kSwCGgDS6LIR69BMcSNZBDQApNG8Wq+8bpc+buyQYRhq\n94fkLnYmTmoCxkJAA0Aa2W02XbJgpjq6wmrp6E3cAw1MhIAGgDQzy36+/XGrwpEYAY2kENAAkGaX\nDpT9/PcDzZLYIIbkENAAkGbVM9yqmenW+e5w/98ZQSMJBDQAZIC5m1tiBzeSQ0ADQAZcunBm4s9M\ncSMZBDQAZMAlC2bKPLyKKW4kw5ntBgBAISgtcenyRVU619V/HzQwEX5KACBDvr7hcsWNbLcCuYKA\nBoAMcTkd2W4Ccghr0AAAWBABDQCABRHQAABYEAENAIAFEdAAAFgQAQ0AgAUR0AAAWBABDQCABRHQ\nAABYEAENAIAFEdAAAFgQAQ0AgAUR0AAAWNC0AvrJJ59UfX296uvr9eSTTyYe/8UvfqEbbrhB9fX1\n+tGPfjTtRgIAUGimfNzkkSNH9Mwzz2j37t1yOBy6++67tWbNGjU1Nem1117Tc889J6fTqY6OjlS2\nFwCAgjDlgD569KiuuOIKFRUVSZJWrFihl156SYcOHdLdd98tp7P/oysrK1PTUgAACsiUp7iXLVum\n3/zmN/L7/QoGg3r99dfV0tKixsZG/eY3v9Hv//7v6ytf+YoOHjyYyvYCAFAQpjyCXrJkie6++27d\neeed8ng8uuSSS2S32xWNRuX3+/XLX/5SBw4c0Le+9S3t378/lW0GACDv2QzDMFLxQY8++qhmzZql\nV199VXfffbd+93d/V5L0xS9+Ub/85S81c+bMVHwNAAAFYVq7uM0NYE1NTXrllVdUX1+vtWvX6q23\n3pIkHT9+XNFolHAGAGCSpjWCvv322+X3++V0OrVt2zatXLlSkUhE27dv1+HDh+VyufTd7343MZoG\nAADJSdkUNwAASB0qiQEAYEE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"text/plain": [ "<matplotlib.figure.Figure at 0x7faa35f00750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "male_completion_us.plot()" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7faa35d75ad0>" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7faa35d18850>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "male_completion_india.plot()" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7faa35cc6750>" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7faa35c6ec50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "life_expectancy_us.plot()" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7faa35c6e5d0>" ] }, "execution_count": 113, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Vtq3PgkQiQVtHD/768Xl09VghlUhw15JU3JihBgDsO3oJ1Y2jX8h9daYJJeea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\nD10Y8Prt8+Lx6Pos1/d6MpC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"text/plain": [ "<matplotlib.figure.Figure at 0x7faa35c14a10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "life_expectancy_india.plot()" ] }, { "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 }

gpl-3.0

ehongdata/Network-Analysis-Made-Simple

2. Network(X) Basics (Instructor).ipynb

2

344896

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import networkx as nx\n", "from datetime import datetime\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Nodes and Edges: How do we represent relationships between individuals using NetworkX?\n", "\n", "As mentioned earlier, networks, also known as graphs, are comprised of individual entities and their representatives. The technical term for these are nodes and edges, and when we draw them we typically use circles (nodes) and lines (edges). \n", "\n", "In this notebook, we will work with a synthetic (i.e. simulated) social network, in which nodes are individual people, and edges represent their relationships. If two nodes have an edge between them, then those two individauls know one another. \n", "\n", "## Data Representation\n", "\n", "In the `networkx` implementation, graph objects store their data in dictionaries. \n", "\n", "Nodes are part of the attribute `Graph.node`, which is a dictionary where the key is the node ID and the values are a dictionary of attributes. \n", "\n", "Edges are part of the attribute `Graph.edge`, which is a nested dictionary. Data are accessed as such: `G.edge[node1][node2]['attr_name']`.\n", "\n", "Because of the dictionary implementation of the graph, any hashable object can be a node. This means strings and tuples, but not lists and sets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Synthetic Social Network\n", "\n", "With this synthetic social network, we will attempt to answer the following basic questions using the NetworkX API:\n", "\n", "1. How many people are present in the network?\n", "2. What is the distribution of attributes of the people in this network?\n", "2. How many relationships are represented in the network?\n", "3. What is the distribution of the number of friends that each person has?\n", "\n", "First off, let's load up the synthetic social network. This will show you through some of the basics of NetworkX.\n", "\n", "For those who are interested, I simply created an Erdõs-Rényi graph with `n=30` and `p=0.1`. I used randomized functions that I wrote to generate attributes and append them to each node and edge. I then pickled the graph to disk." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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TJ0/IxsaGs2Xcrl27Rk5OTrRr1y6NzoYkl8vJp149ilUjWHcDFODtzcl5M7qDdV6qJKRS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\nYwLiYGWMMcYE9H/u7Vm7fFUqCwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10538e908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "G = nx.read_gpickle('Synthetic Social Network.pkl') #If you are Python 2.7, read in Synthetic Social Network 27.pkl\n", "nx.draw(G)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic Network Statistics\n", "Let's first understand how many people and relationships are represented in the network." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(0, {'age': 20, 'sex': 'Male'}),\n", " (1, {'age': 21, 'sex': 'Female'}),\n", " (2, {'age': 19, 'sex': 'Male'}),\n", " (3, {'age': 29, 'sex': 'Female'}),\n", " (4, {'age': 30, 'sex': 'Male'}),\n", " (5, {'age': 26, 'sex': 'Female'}),\n", " (6, {'age': 21, 'sex': 'Male'}),\n", " (7, {'age': 17, 'sex': 'Female'}),\n", " (8, {'age': 21, 'sex': 'Male'}),\n", " (9, {'age': 14, 'sex': 'Male'}),\n", " (10, {'age': 23, 'sex': 'Male'}),\n", " (11, {'age': 17, 'sex': 'Female'}),\n", " (12, {'age': 19, 'sex': 'Male'}),\n", " (13, {'age': 27, 'sex': 'Female'}),\n", " (14, {'age': 29, 'sex': 'Female'}),\n", " (15, {'age': 14, 'sex': 'Male'}),\n", " (16, {'age': 18, 'sex': 'Female'}),\n", " (17, {'age': 21, 'sex': 'Female'}),\n", " (18, {'age': 19, 'sex': 'Male'}),\n", " (19, {'age': 19, 'sex': 'Female'}),\n", " (20, {'age': 19, 'sex': 'Female'}),\n", " (21, {'age': 21, 'sex': 'Male'}),\n", " (22, {'age': 30, 'sex': 'Female'}),\n", " (23, {'age': 25, 'sex': 'Female'}),\n", " (24, {'age': 13, 'sex': 'Male'}),\n", " (25, {'age': 24, 'sex': 'Female'}),\n", " (26, {'age': 23, 'sex': 'Male'}),\n", " (27, {'age': 21, 'sex': 'Male'}),\n", " (28, {'age': 29, 'sex': 'Female'}),\n", " (29, {'age': 25, 'sex': 'Male'})]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Who are represented in the network?\n", "G.nodes(data=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise: Can you write a single line of code that returns the number of individuals represented?" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "30" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(G.nodes())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(0, 10, {'date': datetime.datetime(2011, 6, 7, 0, 0)}),\n", " (0, 19, {'date': datetime.datetime(2011, 2, 12, 0, 0)}),\n", " (0, 12, {'date': datetime.datetime(2006, 8, 28, 0, 0)}),\n", " (1, 4, {'date': datetime.datetime(2009, 11, 8, 0, 0)}),\n", " (1, 2, {'date': datetime.datetime(2010, 8, 5, 0, 0)}),\n", " (1, 3, {'date': datetime.datetime(2005, 2, 3, 0, 0)}),\n", " (1, 12, {'date': datetime.datetime(2003, 3, 17, 0, 0)}),\n", " (1, 29, {'date': datetime.datetime(2005, 1, 15, 0, 0)}),\n", " (2, 16, {'date': datetime.datetime(2002, 5, 27, 0, 0)}),\n", " (2, 3, {'date': datetime.datetime(2009, 8, 13, 0, 0)}),\n", " (2, 6, {'date': datetime.datetime(2006, 1, 12, 0, 0)}),\n", " (2, 19, {'date': datetime.datetime(2010, 1, 6, 0, 0)}),\n", " (3, 8, {'date': datetime.datetime(2010, 6, 22, 0, 0)}),\n", " (3, 6, {'date': datetime.datetime(2009, 3, 20, 0, 0)}),\n", " (3, 23, {'date': datetime.datetime(2003, 11, 9, 0, 0)}),\n", " (4, 19, {'date': datetime.datetime(2007, 12, 4, 0, 0)}),\n", " (4, 28, {'date': datetime.datetime(2009, 5, 22, 0, 0)}),\n", " (6, 23, {'date': datetime.datetime(2011, 3, 4, 0, 0)}),\n", " (7, 24, {'date': datetime.datetime(2004, 9, 24, 0, 0)}),\n", " (7, 25, {'date': datetime.datetime(2009, 3, 21, 0, 0)}),\n", " (8, 17, {'date': datetime.datetime(2005, 11, 16, 0, 0)}),\n", " (8, 22, {'date': datetime.datetime(2010, 1, 22, 0, 0)}),\n", " (9, 24, {'date': datetime.datetime(2008, 12, 2, 0, 0)}),\n", " (9, 17, {'date': datetime.datetime(2009, 10, 11, 0, 0)}),\n", " (9, 11, {'date': datetime.datetime(2005, 4, 3, 0, 0)}),\n", " (10, 11, {'date': datetime.datetime(2005, 2, 6, 0, 0)}),\n", " (10, 21, {'date': datetime.datetime(2007, 1, 21, 0, 0)}),\n", " (11, 14, {'date': datetime.datetime(2010, 4, 28, 0, 0)}),\n", " (12, 19, {'date': datetime.datetime(2007, 12, 17, 0, 0)}),\n", " (12, 29, {'date': datetime.datetime(2008, 8, 27, 0, 0)}),\n", " (13, 16, {'date': datetime.datetime(2005, 5, 14, 0, 0)}),\n", " (13, 24, {'date': datetime.datetime(2006, 5, 7, 0, 0)}),\n", " (13, 14, {'date': datetime.datetime(2011, 3, 19, 0, 0)}),\n", " (14, 17, {'date': datetime.datetime(2008, 10, 17, 0, 0)}),\n", " (14, 25, {'date': datetime.datetime(2002, 6, 11, 0, 0)}),\n", " (15, 24, {'date': datetime.datetime(2007, 9, 2, 0, 0)}),\n", " (15, 28, {'date': datetime.datetime(2008, 3, 6, 0, 0)}),\n", " (16, 17, {'date': datetime.datetime(2002, 5, 20, 0, 0)}),\n", " (16, 19, {'date': datetime.datetime(2005, 8, 20, 0, 0)}),\n", " (17, 19, {'date': datetime.datetime(2006, 10, 13, 0, 0)}),\n", " (19, 22, {'date': datetime.datetime(2011, 11, 4, 0, 0)}),\n", " (19, 27, {'date': datetime.datetime(2009, 7, 27, 0, 0)}),\n", " (20, 27, {'date': datetime.datetime(2004, 1, 27, 0, 0)}),\n", " (20, 23, {'date': datetime.datetime(2007, 12, 3, 0, 0)}),\n", " (21, 27, {'date': datetime.datetime(2007, 2, 1, 0, 0)}),\n", " (21, 26, {'date': datetime.datetime(2006, 12, 14, 0, 0)}),\n", " (25, 28, {'date': datetime.datetime(2007, 2, 15, 0, 0)}),\n", " (26, 29, {'date': datetime.datetime(2006, 12, 19, 0, 0)})]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Who is connected to who in the network?\n", "G.edges(data=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "Can you write a single line of code that returns the number of relationships represented?" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "48" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(G.edges())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since this is a social network of people, there'll be attributes for each individual, such as age, and sex. We can grab that data off from the attributes that are stored with each node." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(0, {'age': 20, 'sex': 'Male'}),\n", " (1, {'age': 21, 'sex': 'Female'}),\n", " (2, {'age': 19, 'sex': 'Male'}),\n", " (3, {'age': 29, 'sex': 'Female'}),\n", " (4, {'age': 30, 'sex': 'Male'}),\n", " (5, {'age': 26, 'sex': 'Female'}),\n", " (6, {'age': 21, 'sex': 'Male'}),\n", " (7, {'age': 17, 'sex': 'Female'}),\n", " (8, {'age': 21, 'sex': 'Male'}),\n", " (9, {'age': 14, 'sex': 'Male'}),\n", " (10, {'age': 23, 'sex': 'Male'}),\n", " (11, {'age': 17, 'sex': 'Female'}),\n", " (12, {'age': 19, 'sex': 'Male'}),\n", " (13, {'age': 27, 'sex': 'Female'}),\n", " (14, {'age': 29, 'sex': 'Female'}),\n", " (15, {'age': 14, 'sex': 'Male'}),\n", " (16, {'age': 18, 'sex': 'Female'}),\n", " (17, {'age': 21, 'sex': 'Female'}),\n", " (18, {'age': 19, 'sex': 'Male'}),\n", " (19, {'age': 19, 'sex': 'Female'}),\n", " (20, {'age': 19, 'sex': 'Female'}),\n", " (21, {'age': 21, 'sex': 'Male'}),\n", " (22, {'age': 30, 'sex': 'Female'}),\n", " (23, {'age': 25, 'sex': 'Female'}),\n", " (24, {'age': 13, 'sex': 'Male'}),\n", " (25, {'age': 24, 'sex': 'Female'}),\n", " (26, {'age': 23, 'sex': 'Male'}),\n", " (27, {'age': 21, 'sex': 'Male'}),\n", " (28, {'age': 29, 'sex': 'Female'}),\n", " (29, {'age': 25, 'sex': 'Male'})]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's get a list of nodes with their attributes.\n", "G.nodes(data=True)\n", "\n", "# NetworkX will return a list of tuples in the form (node_id, attribute_dictionary) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "Can you count how many males and females are represented in the graph?\n", "\n", "Hint: You may want to use the Counter object from the collections module." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Counter({'Female': 15, 'Male': 15})" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from collections import Counter\n", "Counter([d['sex'] for n, d in G.nodes(data=True)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Edges can also store attributes in their attribute dictionary." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(0, 10, {'date': datetime.datetime(2011, 6, 7, 0, 0)}),\n", " (0, 19, {'date': datetime.datetime(2011, 2, 12, 0, 0)}),\n", " (0, 12, {'date': datetime.datetime(2006, 8, 28, 0, 0)}),\n", " (1, 4, {'date': datetime.datetime(2009, 11, 8, 0, 0)}),\n", " (1, 2, {'date': datetime.datetime(2010, 8, 5, 0, 0)}),\n", " (1, 3, {'date': datetime.datetime(2005, 2, 3, 0, 0)}),\n", " (1, 12, {'date': datetime.datetime(2003, 3, 17, 0, 0)}),\n", " (1, 29, {'date': datetime.datetime(2005, 1, 15, 0, 0)}),\n", " (2, 16, {'date': datetime.datetime(2002, 5, 27, 0, 0)}),\n", " (2, 3, {'date': datetime.datetime(2009, 8, 13, 0, 0)}),\n", " (2, 6, {'date': datetime.datetime(2006, 1, 12, 0, 0)}),\n", " (2, 19, {'date': datetime.datetime(2010, 1, 6, 0, 0)}),\n", " (3, 8, {'date': datetime.datetime(2010, 6, 22, 0, 0)}),\n", " (3, 6, {'date': datetime.datetime(2009, 3, 20, 0, 0)}),\n", " (3, 23, {'date': datetime.datetime(2003, 11, 9, 0, 0)}),\n", " (4, 19, {'date': datetime.datetime(2007, 12, 4, 0, 0)}),\n", " (4, 28, {'date': datetime.datetime(2009, 5, 22, 0, 0)}),\n", " (6, 23, {'date': datetime.datetime(2011, 3, 4, 0, 0)}),\n", " (7, 24, {'date': datetime.datetime(2004, 9, 24, 0, 0)}),\n", " (7, 25, {'date': datetime.datetime(2009, 3, 21, 0, 0)}),\n", " (8, 17, {'date': datetime.datetime(2005, 11, 16, 0, 0)}),\n", " (8, 22, {'date': datetime.datetime(2010, 1, 22, 0, 0)}),\n", " (9, 24, {'date': datetime.datetime(2008, 12, 2, 0, 0)}),\n", " (9, 17, {'date': datetime.datetime(2009, 10, 11, 0, 0)}),\n", " (9, 11, {'date': datetime.datetime(2005, 4, 3, 0, 0)}),\n", " (10, 11, {'date': datetime.datetime(2005, 2, 6, 0, 0)}),\n", " (10, 21, {'date': datetime.datetime(2007, 1, 21, 0, 0)}),\n", " (11, 14, {'date': datetime.datetime(2010, 4, 28, 0, 0)}),\n", " (12, 19, {'date': datetime.datetime(2007, 12, 17, 0, 0)}),\n", " (12, 29, {'date': datetime.datetime(2008, 8, 27, 0, 0)}),\n", " (13, 16, {'date': datetime.datetime(2005, 5, 14, 0, 0)}),\n", " (13, 24, {'date': datetime.datetime(2006, 5, 7, 0, 0)}),\n", " (13, 14, {'date': datetime.datetime(2011, 3, 19, 0, 0)}),\n", " (14, 17, {'date': datetime.datetime(2008, 10, 17, 0, 0)}),\n", " (14, 25, {'date': datetime.datetime(2002, 6, 11, 0, 0)}),\n", " (15, 24, {'date': datetime.datetime(2007, 9, 2, 0, 0)}),\n", " (15, 28, {'date': datetime.datetime(2008, 3, 6, 0, 0)}),\n", " (16, 17, {'date': datetime.datetime(2002, 5, 20, 0, 0)}),\n", " (16, 19, {'date': datetime.datetime(2005, 8, 20, 0, 0)}),\n", " (17, 19, {'date': datetime.datetime(2006, 10, 13, 0, 0)}),\n", " (19, 22, {'date': datetime.datetime(2011, 11, 4, 0, 0)}),\n", " (19, 27, {'date': datetime.datetime(2009, 7, 27, 0, 0)}),\n", " (20, 27, {'date': datetime.datetime(2004, 1, 27, 0, 0)}),\n", " (20, 23, {'date': datetime.datetime(2007, 12, 3, 0, 0)}),\n", " (21, 27, {'date': datetime.datetime(2007, 2, 1, 0, 0)}),\n", " (21, 26, {'date': datetime.datetime(2006, 12, 14, 0, 0)}),\n", " (25, 28, {'date': datetime.datetime(2007, 2, 15, 0, 0)}),\n", " (26, 29, {'date': datetime.datetime(2006, 12, 19, 0, 0)})]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "G.edges(data=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this synthetic social network, I have stored the date as a datetime object. Datetime objects have attributes, namely `.year`, `.month`, `.day`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "Can you figure out the range of dates during which these relationships were forged?" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2002-05-20 00:00:00 2011-11-04 00:00:00\n" ] } ], "source": [ "# Answer\n", "dates = [d['date'] for _, _, d in G.edges(data=True)]\n", "mindate = min(dates)\n", "maxdate = max(dates)\n", "print(mindate, maxdate)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "We found out that there are two individuals that we left out of the network, individual no. 31 and 32. They are one male (31) and one female (32), their ages are 22 and 24 respectively, they knew each other on 2010-01-09, and together, they both knew individual 7, on 2009-12-11. Use the functions `G.add_node()` and `G.add_edge()` to introduce this data into the network.\n", "\n", "If you need more help, check out https://networkx.github.io/documentation/latest/tutorial/tutorial.html" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'age': 22, 'sex': 'Male'}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Answer\n", "G.add_node(31, age=22, sex='Male')\n", "G.add_node(32, age=24, sex='Female')\n", "G.add_edge(31, 32, date=datetime(2010,1,9))\n", "G.add_edge(31, 7, date=datetime(2009,12,11))\n", "G.add_edge(32, 7, date=datetime(2009,12,11))\n", "\n", "G.node[31]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Live Exercise\n", "\n", "While we're on the matter of graph construction, let's take a look at our tutorial class. On your sheet of paper, you should have a list of names - these are people for which you knew their name prior to coming to class. \n", "\n", "As we iterate over the class, I would like you to holler out your name, your nationality, and in a very slow fashion, the names of the people who you knew in the class." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ptG = nx.DiGraph() #ptG stands for PyCon Tutorial Graph.\n", "\n", "# Add in nodes and edges\n", "ptG.add_node('Eric', nationality='Canada')\n", "ptG.add_node('Paul', nationality='Canada') # (my own TextExpander shortcut is ;addnode)\n", "ptG.add_node('Max', nationality='US')\n", "ptG.add_node('Martin', nationality='Other')\n", "ptG.add_node('Jim', nationality='US')\n", "ptG.add_node('Lucas', nationality='US')\n", "ptG.add_node('Thomas', nationality='US')\n", "ptG.add_node('Brad', nationality='US')\n", "ptG.add_node('Troy', nationality='Canada')\n", "ptG.add_node('Cory', nationality='Canada')\n", "ptG.add_node('Gokhan', nationality='US')\n", "ptG.add_node('Riley', nationality='US')\n", "ptG.add_node('Steve', nationality='US')\n", "ptG.add_node('Ryan', nationality='US')\n", "ptG.add_node('Andrew', nationality='US')\n", "ptG.add_node('Ronan', nationality='Other')\n", "ptG.add_node('Cody', nationality='Canada')\n", "ptG.add_node('Jon', nationality='US')\n", "ptG.add_node('Eric2', nationality='US')\n", "ptG.add_node('William', nationality='US')\n", "ptG.add_node('Tom', nationality='Other')\n", "ptG.add_node('Chris', nationality='US')\n", "ptG.add_node('Stu', nationality='US')\n", "ptG.add_node('Zach', nationality='US')\n", "ptG.add_node('Clint', nationality='Canada')\n", "ptG.add_node('Aaron', nationality='US')\n", "ptG.add_node('Vishal', nationality='US')\n", "ptG.add_node('Federico', nationality='Other')\n", "\n", "ptG.add_edge('Vishal', 'Aaron')\n", "ptG.add_edge('Vishal', 'Eric')\n", "ptG.add_edge('Aaron', 'Vishal')\n", "ptG.add_edge('Aaron', 'Eric')\n", "ptG.add_edge('Clint', 'Zach')\n", "ptG.add_edge('Clint', 'Eric')\n", "ptG.add_edge('Zach', 'Clint')\n", "ptG.add_edge('Zach', 'Riley')\n", "ptG.add_edge('Zach', 'Stu')\n", "ptG.add_edge('Stu', 'Zach')\n", "ptG.add_edge('Stu', 'Eric')\n", "ptG.add_edge('Stu', 'Chris')\n", "ptG.add_edge('Chris', 'Stu')\n", "ptG.add_edge('Chris', 'Eric')\n", "ptG.add_edge('Tom', 'Tom')\n", "ptG.add_edge('William', 'Jon')\n", "ptG.add_edge('William', 'Eric2')\n", "ptG.add_edge('William', 'Eric')\n", "ptG.add_edge('Eric2', 'William')\n", "ptG.add_edge('Eric2', 'Jon')\n", "ptG.add_edge('Jon', 'Eric2')\n", "ptG.add_edge('Jon', 'William')\n", "ptG.add_edge('Jon', 'Eric')\n", "ptG.add_edge('Cody', 'Eric')\n", "ptG.add_edge('Cody', 'Ronan')\n", "ptG.add_edge('Ronan', 'Eric')\n", "ptG.add_edge('Ronan', 'Cody')\n", "ptG.add_edge('Andrew', 'Eric')\n", "ptG.add_edge('Andrew', 'Ryan')\n", "ptG.add_edge('Ryan', 'Eric')\n", "ptG.add_edge('Ryan', 'Andrew')\n", "ptG.add_edge('Steve', 'Eric')\n", "ptG.add_edge('Riley', 'Zach')\n", "ptG.add_edge('Paul', 'Paul') # (my own TextExpander shortcut is ;addedge)\n", "ptG.add_edge('Martin', 'Max')\n", "ptG.add_edge('Max', 'Paul')\n", "ptG.add_edge('Martin', 'Eric')\n", "ptG.add_edge('Martin', 'Max')\n", "ptG.add_edge('Jim', 'Federico')\n", "ptG.add_edge('Lucas', 'Thomas')\n", "ptG.add_edge('Brad', 'Eric')\n", "ptG.add_edge('Thomas', 'Lucas')\n", "ptG.add_edge('Troy', 'Cory')\n", "ptG.add_edge('Troy', 'Eric')\n", "ptG.add_edge('Cory', 'Troy')\n", "ptG.add_edge('Gokhan', 'Max')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ioahv1rZQMLPtwFpiAzRtXNfMXp/PnXZeciYBrnf3ozNfqVioSIjFnCYtmIcJd2K/WcHF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\nV/zVwwP3/Hq7Mevvdc/G3tbC7C7oI5gAd/u/T2Nu5qvAF+W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QQhSIhFgIIYQoEAmxEEII\nUSASYiGEEKJAJMRCCCFEgUiIhRBCiAKREAshhBAFIiEWQgghCkRCLIQQQhSIhFgIIYQoEAmxEEII\nUSASYiGEEKJAJMRCCCFEgUiIhRBCiAKREAshhBAFIiEWQgghCkRCLIQQQhSIhFgIIYQoEAmxEEII\nUSASYiGEEKJAJMRCCCFEgUiIhRBCiAKREAshhBAFIiEWQgghCkRCLIQQQhSIhFgIIYQoEAmxEEII\nUSASYiGEEKJAJMRCCCFEgUiIhRBCiAKREAshhBAFIiEWQgghCkRCLIQQQhSIhFgIIYQoEAmxEEII\nUSASYiGEEKJAJMRCCCFEgUiIhRBCiAKREAshhBAFIiEWQgghCkRCLIQQQhSIhFgIIYQoEAmxEEII\nUSASYiGEEKJAJMRCCCFEgUiIhRBCiAKREAshhBAFIiEWQgghCkRCLIQQQhSIhFgIIYQoEAmxEEII\nUSASYiGEEKJAJMRCCCFEgUiIhRBCiAKREAshhBAFIiEWQgghCkRCLIQQQhSIhFgIIYQoEAmxEEII\nUSASYiGEEKJAJMRCCCFEgUiIhRBCiAKREAshhBAFIiEWQgghCkRCLIQQQhTI/wPMZKPsN6fqOAAA\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10538eba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We are now going to draw the network using a hive plot, grouping the nodes by the top two nationality groups, and 'others'\n", "# for the third group.\n", "\n", "nodes = dict()\n", "nodes['Canada'] = [n for n, d in ptG.nodes(data=True) if d['nationality'] == 'Canada'] #list comprehension here\n", "nodes['US'] = [n for n, d in ptG.nodes(data=True) if d['nationality'] == 'US'] #list comprehension here\n", "nodes['Other'] = [n for n, d in ptG.nodes(data=True) if d['nationality'] == 'Other'] #list comprehension here\n", "\n", "edges = dict()\n", "edges['group1'] = [(n1, n2, d) for n1, n2, d in ptG.edges(data=True)] #list comprehension here\n", "\n", "nodes_cmap = dict()\n", "nodes_cmap['Canada'] = 'blue'\n", "nodes_cmap['US'] = 'green'\n", "nodes_cmap['Other'] = 'black'\n", "\n", "edges_cmap = dict()\n", "edges_cmap['group1'] = 'black'\n", "\n", "from hiveplot import HivePlot\n", "h = HivePlot(nodes, edges, nodes_cmap, edges_cmap)\n", "h.set_minor_angle(np.pi / 12) #optional\n", "h.draw()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Coding Patterns\n", "\n", "These are some recommended coding patterns when doing network analysis using NetworkX, which stem from my roughly two years of experience with the package." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Iterating using List Comprehensions\n", "I would recommend that you use the following for compactness: \n", "\n", " [d['attr'] for n, d in G.nodes(data=True)]\n", "\n", "And if the node is unimportant, you can do:\n", "\n", " [d['attr'] for _, d in G.nodes(data=True)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Iterating over Edges using List Comprehensions\n", "\n", "A similar pattern can be used for edges:\n", "\n", " [n2 for n1, n2, d in G.edges(data=True)]\n", "\n", "or\n", "\n", " [n2 for _, n2, d in G.edges(data=True)]\n", "\n", "If the graph you are constructing is a directed graph, with a \"source\" and \"sink\" available, then I would recommend the following pattern:\n", "\n", " [(sc, sk) for sc, sk, d in G.edges(data=True)]\n", "\n", "or \n", "\n", " [d['attr'] for sc, sk, d in G.edges(data=True)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Drawing Graphs\n", "\n", "As illustrated above, we can draw graphs using the `nx.draw()` function. The most popular format for drawing graphs is the **node-link diagram**." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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KlSulgoRIJML9+/cRGRmJ6dOno1u3brCysoKBgQHatWuHb775Bps3b8a1a9eQ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\nygojR47E66+/ji+//FLpvYrXrFmDp59+Wulu3MrKSoSEhCA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"text/plain": [ "<matplotlib.figure.Figure at 0x1086676a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nx.draw(G)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the network is small enough to visualize, and the node labels are small enough to fit in a circle, then you can use the `with_labels=True` argument." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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pUqUKqwL0cnbmunXriux//vvvv9mkSRO+8cYbvHnzZr59hw8fpqurK588eaLX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\nQgghNCSBVQghhNCQBFYhhBBCQ/8PrW5hI8JB8oMAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x108da29b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nx.draw(G, with_labels=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, note that if the number of nodes in the graph gets really large, node-link diagrams can begin to look like massive hairballs. This is undesirable for graph visualization.\n", "\n", "Instead, we can use a matrix to represent them. The nodes are on the x- and y- axes, and a filled square represent an edge between the nodes. This is done by using the `nx.to_numpy_matrix(G)` function.\n", "\n", "We then use `matplotlib`'s `pcolor(numpy_array)` function to plot. Because `pcolor` cannot take in numpy matrices, we will cast the matrix as an array of arrays, and then get `pcolor` to plot it." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x109063cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "matrix = nx.to_numpy_matrix(G)\n", "\n", "plt.pcolor(np.array(matrix))\n", "plt.axes().set_aspect('equal') # set aspect ratio equal to get a square visualization\n", "plt.xlim(min(G.nodes()), max(G.nodes())) # set x and y limits to the number of nodes present.\n", "plt.ylim(min(G.nodes()), max(G.nodes()))\n", "plt.title('Adjacency Matrix')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try another visualization, the Circos plot. We can order the nodes in the Circos plot according to the node ID, but any other ordering is possible as well. Edges are drawn between two nodes.\n", "\n", "Credit goes to Justin Zabilansky (MIT) for the implementation." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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GRsLR0bHcucqjf//+4PP5eP36NWxtbVG3bl0UFBTAyMhII4kKr169gouLC/T1\n9dGoUSMwDIOhQ4fC1ta2TGy0tklNTYWent4nExP8icCKblVhGAb169eHkZER3NzcMG7cOJ36yoqL\nixEQEICdO3ciIyMDIpEIJ06cQFhYGCZPnqwVm0VFRfD09JT7TxmGQZcuXTBq1KhKz9WrVy+sXbtW\n5TjDMAgNDcXu3bvLnefatWvw8/NTOd67d2+sWbMGZmZmyMzMVPqY48ePw9XVFRKJROn49evX4evr\ni5iYGERHR1co4Kp49uyZPPX25s2bMDIywtq1a3HlyhX4+vpWac7SFBcXIzo6GiYmJrCwsMCLFy/w\n4MEDmJmZwcPDQ6e73BUrVkBPTw98Pr/cv/MXCCu6H0NxcTFq1qwJc3Nz1KlTB3FxcSo/uNpg//79\n8Pb2RlFREbZu3QpPT0+kp6fDzc1Na3UjNm/ejPr168uTRbKzs2Fvb1/pTLIJEyZg5syZ5T5my5Yt\naNSoUbmP2bdvH1q0aKFyfPr06Zg4cSIiIiKU1q1gGAb16tXDxo0bK7TRokULtG7duso73f79+8PQ\n0BB///03xo8fDwMDA7x58warVq1Cnz59qjRnab7++msIhUKYmJhg//79AErqFzs7O5f7+jQJwzAY\nM2YM9PT0YGxsjDVr1ujE7mcEmxzxMfB4PLp27RoJBAJ68uQJFRQUUHR0tM5Cylq2bEkODg60Zs0a\n6tq1KzVo0IDmzp1LqampNGbMGNq1a5fGbXbr1k1eipCopPPumjVrqG/fvvTmzRu153FycqqwRnCn\nTp3oyZMndPHiRZWPefr0qdJwMRkeHh70119/UXBwsNJ43Z07d1JBQUGZZIjSZGRkkJOTExUXF5OB\ngUGVElIePXpEmzZtoujoaHJ1daVNmzZReHg4mZmZ0aVLlyg0NLTSc5Zm69at9OOPP1JxcTH973//\no1atWtH58+fp6NGjZGBgQHFxcR81vzoUFRVRly5daOnSpWRiYkKLFy+mfv36ad3uf4byFLk6vh4+\nZd6+fQuhUAh3d3eMHDkS3t7eOgspk6UHv337Fm/evIGrqyt2796Ny5cvw8bGBrt27dK4zZ07dyIo\nKEjBnTJo0CD07t1b7Tn27NmDmJiYCh+3ZMkSxMbGqhyfMmVKueFl586dQ0hICDZu3IguXboojBUW\nFsLLy6vCym2TJk3CtGnTEBERga5du8LOzq7CdX9IfHw8LCwscPr0aaSnp0NfX1/+twkJCcGZM2cq\nPaeMq1eD+BLyAAAgAElEQVSvQiAQQCAQyH3sDMOgcePG8PDwwC+//FLludXlzZs3qF+/PgwMDCAS\niTQaifEfg3UvaIoXL17A1NQUtWvXxtKlS2FnZ6ezkLLu3bvLw7dOnDgBsViM58+f49KlS7CxsanQ\nL1pZZP7Wbdu2ya/l5eXB09MTKSkpas1x9epVBAQEVPi4/Px8uLm5qTx069evH1atWqXy+dnZ2TA3\nN5eXeizNDz/8gKZNm1ZYVyM+Ph5r1qxBw4YN0aNHD9jY2FS47tLcu3cPAoEAoaGhYBgGEydOBJ/P\nR2FhISQSCYyMjNQOj/uQzMxM2NnZyf24svZKv//+O5ydneHu7l7leGp1efr0KTw8PMDn8+Hk5KRW\neN4XDCu6muThw4cwNDREVFQUdu3aBWtra530N7t58ybEYrE8iuCbb75BTEwMGIbBhQsXIBKJNB7a\n9vvvv8PHx0fhcObcuXMQi8VqxQtnZWXB0tJSLVspKSkICAhQehDUokUL7Nu3T+VzGYaBhYUF/v33\nX5iYmODVq1cASr4k7Ozs1KqL27RpUxw8eBD169dHnz59IBQK1Vq3jLi4OLi6umLLli0AABcXF3lh\nn4sXL6JWrVqVmk9GYWEh6tevDxMTE7i7u2PFihXyscjISPj4+GDdunVVmltdbty4AWtraxgZGcHT\n0xPjxo2r9uJQnzis6Gqaq1evQk9PD126dMG5c+dga2uLRYsWaf0fsXXr1vjxxx8BlHwYQ0ND5R/C\n8+fPQyQSqZWVpS4Mw6Bhw4ZYv369wvXJkyejVatWFb5ehmFgZGSkMr72w8c2btxY/vpK4+fnV6Y2\n74eEhobi7NmziIqKkn8Jzpw5E3FxcRXaBgAvLy/cvn0bderUQUJCAszMzNR6HlAS+WBlZQUnJycU\nFRXh+fPn4HK5OHbsGABg5cqVVWpCyTAMEhISIBAIEBERgbZt28rf8wsXLsDW1hb29vZa3eXu3bsX\npqamMDMzQ2BgIAYPHswKbsV83qKbkZGBR48eVfcyynDkyBFwuVwMHz4c6enpCAkJQfv27bVaH/T4\n8ePw9PSU7wbv3r0LoVCI27dvAyjZhYpEIvmptqZsurm5KSQUFBYWIiQkRK3MI09PT9y5c0ctW5cv\nX4ZYLMbr168VrltYWCA7O7vc58bFxWHDhg2YO3cuhg0bhszMTAiFQvz1118V2mUYBsbGxsjNzUXt\n2rXx1VdfwcjISK01A0C7du0QHByM+fPnAyipaWtqaioXp6+++grLli1Tez4Zs2fPhpmZGZo2bQp7\ne3uFcLguXbqgUaNGlS5pqS6FhYUYPXo0TExM4OTkhPDwcMTHx1d7qUhlPH/+HA8fPvyU1vZ5iu7R\no0cRGhqq0NKjOjLDymPTpk3gcrlYsGABJBIJhg0bBjc3N1y4cEEr9mShT7/99pv82sqVKxEcHCwX\nxbNnz0IkEuHAgQMasxsdHV0mc+zOnTuwtrYuN+MMAOrXr1+pA6R+/fph7Nix8t9l3SEq2l1NnjwZ\nkyZNwpUrV+Dt7Y3hw4dj6NChatmUSCTQ19cHANSqVQsDBgwAj8dT67nnz5+HWCyGUCiUf1m4urqi\nY8eO8se0bt260i6o9evXw8zMDMHBwRCLxfJdMwA8ePAA1tbWsLKy0sph7pMnT1C3bl0IhUJEREQg\nJiYGnTt31rrfuLJcu3YNERERco1wc3P7VOKFPz/RvXTpEvh8fpnWHjweD4cOHarOpZVh2rRp4PF4\n8lPqlJQUiEQiLF26VCu3Yb/99hvq1q0rn5thGLRp0wYTJkyQP+bMmTMQiUT4/fffNWLz/PnzcHBw\nKJN+vGrVKvj4+JTZmZYmKiqq3Ky0D/nnn38gFArx4MEDACXi7uHhUeHztm/fjrZt20IqlcLc3Bwi\nkQgvXrxQy2ZOTo484y00NBT9+vUDh8NRmi78Ic2bN0fjxo3l7/+rV6/A4XBw9uxZ+WNCQkIq9UX8\n+++/QyAQwMnJCX5+fvjuu+8UxgcNGoT27dujWbNmas+pLvv27YO1tTWsra0xbNgwxMXFISYmRq33\nQpc8evQIlpaWSlsAadvHrQafn+h26tRJZRO7iIiI6lyaUjp37gx9fX253/Hhw4cICQlBhw4d5Ic6\nmqK4uBienp44fvy4/NqLFy9ga2urcO3UqVMQiUQaa3LZrl07pSFCQ4YMQatWrVRmQlVllzdnzhx0\n6NABQEnYmawIT3n89ddfcHR0RGZmJoyMjDBixAi17T19+lQeItamTRt07twZRkZGFYr28ePH4eTk\nBEtLS/lj586dCyMjI4UvXAcHB7X7rV26dAnm5uawsLBA69at0adPH4W5Xrx4AQsLCzRs2BBbt25V\n+zVWRGFhIcaNGweRSAQLCwv8+OOPSExMRGRkpNZqfXwMSUlJKjXC3d29uv3On5/oCoVClW8oh8P5\nlHw3AEqEMDAwEKampnj+/DkAaNXd8OOPP5ZpeZOamgoXFxeFXefJkydhbW2tEVfD9evXIRaLkZub\nq3C9sLAQTZs2xejRo5U+r0uXLpUWh/z8fLi6uuLIkSNYuHAhhg0bVuFzpFIpBAIBoqKiEBMTUyZe\ntzwePHggDzVLTExE27Zt4eDgoHBL/yGyQ8aYmBgMGTJEft3f3x9NmzZVWJe+vr5aWYwPHz6EjY0N\nzM3NMWDAAISEhJQRvMmTJ6N79+6wtrbWWGbkkydPEBYWBl9fX1hbW+PIkSMYMWIEGjRo8Ek0tVRG\ncHCwSo0gouo+B/r8RNfFxUXlm2lqalqdS1PJmzdvYGNjA2dnZ4V4TG24G/Lz82Fra1umo+zAgQPR\nq1cvhWunTp3SWHRF3759ldZgePnyJTw8PJT60+Lj46vkZ9u2bRsCAwMrdQjl5uYGX19f/P3337Cy\nslK7DsH169fl8cSTJ09GdHQ0AgMDsXjxYpXP2bhxI/z9/WFlZYW///4bQEllOB6PpxDHnJWVpVaZ\nyMzMTLi6usLCwgLjxo2Dra1tmQ7Hb9++hbW1NQYOHFilWhjK2LdvH8RiMRo0aABvb288ePAAkyZN\nQlBQkMbv0jRJ48aNy92YZWVlVefyPj/RHT9+vMo3VCQS6bx0nbqkp6fDyMgIDRo0UFijNtwNs2fP\nRnx8vMK1vLw8eHl5lamn+ujRIwQGBiI+Pv6jOtxmZmZCJBLh6tWrZcZu374NkUgkLwspY9CgQQqx\npeoi20nWrFlTLd/06dOnYWRkJG+A6Ofnp9BevTzOnj2LevXqASgp4tKwYUNERESorJWQk5MDW1tb\nDBw4UCFDb/369eDxeArNRG/evFlhoZu8vDwEBgbCzMwMs2fPhlgslheTL83SpUvRsWNH2NnZyRMk\nqkphYSG+/vprODg4oE6dOmjRogVevXqFb7/9FjVr1lRZOOhT4fvvv1epEc2bN6/u5X1+ovvq1SvU\nqlWrzJspFArB4XBgYGBQpTbeuuDIkSPQ19cv84HVtLshJycHlpaWyMjIULh+8eJFiESiMj7EvLw8\ndO3aFXXr1sWzZ8+qbHfVqlWoX7++UhfP/v37YWdnp7BDS0pKwoIFC6pk69KlS+DxeBV2zHj58iWc\nnZ0xYsQIeVzuqFGjKiy2I+PIkSPys4IdO3YgKCgIsbGxKrPpBg0ahISEBIjFYgXxa9asWZnnHDp0\nCFFRUSptFxUVISoqCqampliwYAECAwOxdOlSpY9zcXHB/Pnz0aBBA7Velypk7oTGjRvD3d0dw4cP\nR1FREZYtW4YaNWrg6dOnHzW/LpBIJIiKiiqjEdbW1khLS6vu5X1+ogsAubm5WLhwIRo2bIgGDRpg\n9uzZePnyJV6+fAlHR0cQUYVFq6uLFStWQF9fH3PmzCkzpkl3w6hRozBmzJgy12fPno2IiIgywsgw\nDObMmQMHBweF0/XKIJVK0aBBA6VJDACwcOFCBAYGyhMivvnmG7XF70Py8/PB5XIxbtw4lY9hGAax\nsbEYOXIkrl27Bh8fHwAl7chVteX5kP3798urmJ05cwbu7u7o2rUrDA0Ny9wZnD9/Hra2tpg3b16Z\n/z9zc/MyNSI2bNiAHj16qFx7jx49YGxsjLlz56J79+5lDs5kbN68GU2aNEFsbOxHVfWSuRMSExMh\nEonkoYBr166Fk5OT3FXyOVBQUIBVq1YhMjISdevWxfjx4z+VL4zPU3QrQuaCMDU1xY0bN6p7OWX4\n6quvYGBggO3bt5cZ05S74fHjx7C0tCyT8VVcXIzw8HDMmzdP6fP27NkDkUhU5ZjGa9euqQzJYhgG\nffv2RadOnSCVSjFkyBClOzd1uHXrFtzd3SEUCvHw4UOlj1myZAlCQ0NRUFCAwsJCeQbcu3fvYGpq\nWubgTxknT56U7x7T09MhEonQvn17BAQEKNyVFBUVoXbt2li7di1cXFxw7tw5+di///4LIiqTHj1/\n/nwkJSUptTtmzBgYGhpi8uTJWLBgAYKDg1VGCtSpUwfr1q2DhYVFlQ63ZO4EJycnJCQkwMbGRl4z\neevWrbCzs8Pdu3crPS+LUv6boguUiJcsVm/WrFnVvRwFiouL0ahRI/D5fKW+RZm7wcHBAb/99luV\nd70tW7ZU2hPr0aNHEIlECsJQmtu3b8PT0xMjRoyoUtB7UlJSGZ+yDIlEgrCwMEyZMgVdu3atcjeD\nHTt2oE2bNkhOTkbjxo3L+PK3b98OGxsbhayz4OBgeTJG06ZN1QpXu3v3rjwWOD8/H3w+H9HR0ejT\np4/Cjn7p0qWIjIzEunXrFLprACU1fc3NzcvMPXr0aHmmWmkWLVoEAwMDjBo1CocOHVJ6cFZ6fXZ2\ndpg1axb69+9f4ev5kPPnzyMoKAiRkZEICwtDRESEfEeYmpoKGxubCtOsWSrFf1d0ZcTExICIUK9e\nvU8qnCw3Nxeurq4wMzNTedt2/Phx+Pj4oG3btio/dOXxyy+/oG3btkrHduzYAVdXV5WpyTk5OWjR\nogWaNm1aYZrth+Tm5sLZ2VmlqP37779wdnaGn59flRNa5s2bh6SkJBQXFyMiIkKhSeaGDRtga2tb\n5lAvISFBfnA3b948tbLSXr58qSCYgYGB8Pf3x5IlSzBw4EAA/5eBl5aWhpo1a+KPP/5QmCMoKEhp\nskLPnj3LFJvfvHkz9PT0kJiYiPT0dIjF4nLPKKZOnYoRI0bA3d1d7cNBoCSiRtbGZ+TIkRCJRJg9\ne7b8y+vPP/+ESCSq1JwsavHfF12gJHaVw+HA1NT0k/JLPXr0CGZmZnBxcVHpSpBIJJg5cyaEQiEW\nLlxYqZ1nbm4uzMzMVIrmsGHD0LFjR5U76eLiYowdOxbOzs7YsWNHpXbcspRjVanAV69eBY/Hq/JO\nNzExUe5zzMjIgI2NDc6dO4dVq1bBwcFB6YHJ8uXLkZiYKLfv5eVVoR2pVAo9PT151MGQIUMgFotx\n/PhxhISE4M2bN/Dx8cHq1auxc+dOhISEKLxPslhcZb7Wli1bKlRI27VrF/T09NCtWzfk5eUhKCio\n3NA0hmHg6emJ1atXw9fXV62/D8MwSElJgYODA/r27Yt+/frB1dVVIR379OnTsLa2VkioYdEYX4bo\nAiX+OBMTE3A4nE+qK+m+fftgamqKhg0blptOef/+fTRt2hRBQUGVinDo2rWryoMtiUSC2rVrY/ny\n5eXOceTIEfj6+qJFixYV1lMozQ8//ABfX1+VvlMLCwvY2dlVqXV8kyZNFFKIf/vtNwiFQjg6OuL+\n/ftKn3P69GmEhIQAKBFDGxsbtb6EbWxs5FEd27dvh76+PgoKCmBtbY3o6Gh89dVXYBgGdevWLVNP\n+MSJE+ByuUrTodu3by+vlfHrr7+Cy+Xif//7HwoKCtCmTRvEx8eXK6QXL16Eh4cHpkyZUu6BooxH\njx6hTZs2qFmzJtavXw9/f39069ZN4Qv/8uXLGk0TZynDlyO6QMkHrU6dOiCiMllb1cmIESNgZ2eH\n3r17l/shYxgGGzZsgFgsxrBhw/DmzZsK5961a1e5J/X379+HtbV1ha3UCwsLsWDBAgiFQkyYMEHt\nkowJCQno3Llzmdf14MEDiMViTJs2DXXr1lXrUKs09vb2cpeLLPLCzMxMoZjMh7x9+xZGRkbyXWuP\nHj3KLYAuo0WLFvLMuTt37kBPTw937txBWFgYHBwcIJFIcPjwYXh7e5dxYfXr1w/W1tZK5+3duzfW\nr1+PefPmgcvlYuTIkZBKpejZsydat26tENOrjFGjRmHKlCkICwsr49IoTVFRkfxvN3PmTCxfvhzW\n1tZYs2aNwt/l1q1bEIvF2LFjR4XvCUuV+bJEV8aMGTPkcXvaLLeoLhKJBIGBgXBxccG0adMqfHx2\ndjYSEhLUOmiTSCSwsrIqN79/8+bN8PT0VEv4nj17hh49esDZ2RkpKSkV3tLm5+ejTp068nhPGXPn\nzsXAgQPBMAwSExPRqFEjtU/e8/LyYGRkBKlUiszMTLRt2xYhISG4f/8+fHx8ym3C6OXlJY9oWbdu\nHTp16lShvZ9++gldu3YFULJbNDExQZcuXSASiWBnZ4fi4mI0a9ZMaZtxJycnhIWFKZ134MCBaNCg\nAXg8HqZPnw6GYTBs2DA0atSowk4SxcXF8u4kpqamKiMbZAdlTZs2xcWLF9GxY0cEBQWVKan54MED\nODg4aK2hKYucL1N0AeDKlSswMDAAl8vVSXeHirh37x6srKxgb29fpjC4KmQHbW3atCk3nzwxMRHJ\nycnlzpWYmIgePXqo7bc9evQo/Pz8EB0dXWE4UXZ2Npo3b46IiAg8ePBAXoDm5MmTAEruQPr164fG\njRurtYOWtV3/448/YG9vj6+//lrumrly5Qqsra1VljXs1q2b/P3NysqCubl5hV82svq7R44cQVpa\nGvT19cHn83Hjxg0EBwdj+fLlcHJyKuMeevjwIQQCAQYMGFBmzjdv3sDJyQk8Hg8LFy4EUHIoFhQU\nVG5lNhmHDx9GcHAw9uzZo1DPQcbr16/lB2UbN27E4cOH4ezsjJEjR5apy/DkyRO4uLiodEOxaJQv\nV3SBkltmT09PEFGVqvdrmp9//hkeHh4QCoUqw7k+pPRB24IFC5QetB05cgRBQUHlzvPu3Tv4+flV\nKri+sLAQixYtglAoxNChQ8tNPy0uLsa0adMgFothaWlZpiqZVCpF3759ERERUeEuTyZyDg4OSktD\nLly4EA0aNFD6XsydOxcjR46U/96mTRu1Gjfu27cPjo6OMDU1hZ6eHpo1a4Zvv/0W3333HRwdHbFk\nyZIyz1myZAkCAgLKJMKcPHkSHh4eMDAwQHR0NICSkDNPT0/8+++/Fa4FKInEWLBgAYYNG4Zvv/1W\nfr30QVliYiJu3ryJ7t27w8nJSWnnkOfPn8PT05NtJKk7vmzRlTFw4EAQEVxdXTVWnakqMAyDuLg4\ntGzZEo6OjvKqZOpQ+qDtw8LgxcXFsLe3rzAFMi0tDdbW1pXO3f/nn38wceJE2Nvbo379+lizZo1K\nV4FUKlUZgVFcXIz4+HhERUUpFd4XL15g+PDhMDIyQlhYmEqftlQqRfPmzZV2CT548CCaNGki/33L\nli1q5+MzDIN3796Bx+MhPT0dQqEQKSkp4HA4SpuQRkVFoVGjRnJ3x/379+X1EWxsbNChQwcMHz4c\nq1evhqOjo9qRNRKJRJ7mXbNmTXmft4cPH8oPyg4fPoy5c+dCKBRi4sSJSu8gsrOz4e/vj+nTp6tl\nl0UjsKIr4+DBg+DxeNDX11d7l6kNXr9+DTc3N3Tr1g1hYWGVKhDNMAx++eUXODo6IiYmRiHGMikp\nCRMnTqxwjp9//hm+vr5V6k5bVFSEPXv2IDY2FhYWFujfvz/Onj1bqSJExcXF6N27N5o2bYr3799D\nIpFgx44d6NixI8zNzTFixAh069YNP/30U7nzPH/+HLa2tjhx4oTC9RcvXsDc3Fx+4PXu3TtYWFio\n/QXHMAy4XC4KCwuxePFimJubo0mTJmjevLmCayYnJwcCgQCNGjXCb7/9hmHDhkEoFGL8+PFwd3fH\nokWLsGrVKtSqVQtubm5qty0CgJ07dyIiIgIZGRmwsrLCgwcPkJiYCCsrK8yaNQupqanw8vJCmzZt\n5AXfP+TNmzcIDQ3F2LFjq7u+7JcGK7qlyc3Nha2tLYhIXpGqOjh79ixsbGzQvHlzfPXVV5V+vkQi\nwYoVKxTE9/jx46hbt26Fz2UYBj179pTHs1aVZ8+eYfbs2fD19YWZmRmio6Mxffp0/PHHH7h3755S\nPyrDMMjKysL+/fsRFBQEGxsbWFpaIiIiAqtXr5b7OsPDw8utZytj7969cHZ2LnNY6ubmprDrj4+P\nLzce9kNMTEyQm5uLx48fw9jYGCKRCCKRSKErwYoVKxAaGgoPDw8IBAIMGTIEN2/ehJeXF5KTk/H+\n/XvUr18fQqGw0lW7Bg0ahEWLFmH+/Plwc3ODlZWVvB1R+/bt4e7uXm77qry8PDRs2BCDBg1iBVf3\nsKKrjI4dO4KI1C6Kog3mzJmDsLAw1KxZU60mj8ooLb7NmzcHn89Xa+ecm5sLT09PbNq0qUp2PyQr\nKwu7d+/GuHHj0KRJE7i7u8PY2BgmJiZwdXWFnZ0dBAIBuFyufOc4YsQIebWr0oVlGIaBSCRSO7Z3\n2LBh6Nq1q4K4xMfHK/R1++OPP+Txu+ogEonw/PlzDBs2DGPHjsWVK1fQqlUreb0PW1tb6OvrIyAg\nAOHh4RgzZgyeP38OHx8fzJkzB8+fP0e9evXQpEkTuU+3MgQEBKBt27YwMDBATEwMnj59iqlTp0Io\nFGL27NnlluiUSCTyNOZPKUPzC4IVXVXIanJ6eHhUyz9ncXExoqKiMHz4cIhEIvlJf1WQia+enh7C\nw8PVSu28evUqrK2tVSYafCwMw+DNmzf466+/8OzZM7x586aMG6KoqEju45YJyfPnzyEUCtXeoeXn\n5yMwMFChwM+aNWvQvXt3+e+y8Ct1b/H9/Pxw5MgRWFpaKoj/smXL4O3tjatXr8pdFr/99hv8/Pzg\n4+ODb775Ri6O06dPx4kTJ1C/fn21bAIlCT7/+9//QETy9jnTp0+Hq6srunbtWmHbn8LCQrRr1w6d\nOnX65BpJfkGwolseBw4cAIfDgZWVVbX0gnr69CmEQiFWrVoFOzu7MvVxK0vv3r0RFxen1OerjBUr\nViAoKOijipt/LEVFRejatStatWoFiUSC33//vdwatMp4+vQpXFxc5Lf/Dx48gL29vYJwJyUlqd2y\nvFmzZoiLiysTCsYwDAYOHAhfX18EBwcDKAk3s7CwgJ6eHvh8Pvr27SsvwpOVlQWBQFChzzs9PV3u\ns+3Xrx88PDzQo0cPcLlcdOrUSa0vZKlUil69eqFVq1afXCPJLwxWdCvi1q1b0NPTg6GhYZXSVT+W\nhQsXonnz5pgzZw7q1KnzUQK4YsUKJCQkKPX5KoNhGHTq1EntduXaoqioCF26dEHr1q0xe/bsKrWj\nuXPnDsRiMfbu3QuGYWBvb69Qgezy5ctwc3NTawfdrVs3mJqaKjxfhizr0cLCAidOnEBgYCC++eYb\nvHz5UmlkjIeHR5nWSjJKi+0333yDTZs2wcfHB4aGhoiOjlZZi/dDGIbBqFGjEB4eXqUDUhaNwoqu\nOsi6yPJ4vArTZTVNYWEhatasiR07dqBr164V5uOXx4ULF1CrVi357x+K75kzZ8rM/erVK7i5uVV7\namhhYSE6deoEe3v7CiMXVHHu3DmIRCKcOXMGcXFxChlkDMOgZs2aZVoKKaNJkyYK72NpGIaBq6sr\npkyZAh6Ph5CQkHIPynr16lXm9dy/f18utklJSZg2bRpcXV1Rt25dNGrUCMuWLUNCQoLavv7k5GT4\n+fnh5cuXaj2eRauwoqsu+fn5EIlE4HA42L17t05t//nnn3B1dUVWVhZq1apV5cLfEokERkZGZXY7\nMvF1d3eHr68vkpOTFXb158+fh0gkqvYKbYWFhTAzM0Pt2rWr3KfrwIEDEIvFmDx5Mvr27aswNmvW\nLAwaNKjc5+fn58PMzEzBJ1yaGzduwNnZGaGhoRgwYAAGDx4MoVCIAQMGYM+ePWXe++XLlyMhIQFv\n3rzBTz/9hNDQUAgEAgQFBcHLywsCgQB9+vSR3434+fnh8uXLaNasmVqdnNetWwdnZ+ePdk2xaAxW\ndCuDVCqFj48PiKjKwldVOnfujGnTpqlVY7U8QkJCVO7mGIbByZMnkZCQAAsLC8TExGDbtm3Iz8/H\nggULUK9evQqLsGgTWRHxsWPHws3NTeVteUX88ssvsLW1hZOTk8L19PR0WFtbl+vzXLlyJWrXro3O\nnTsrHR8/fjxsbGwwfPhw+V3D33//jQULFiAyMhKmpqbw8vJCREQE4uLiEBISAj09PfB4PPD5fFha\nWqJTp0747rvvcPnyZYUDr7dv38LY2BgFBQXw9PTE7du3y32de/fuhVgsrvBxLDqlekU3PT0d06ZN\nw5AhQ7B27dpqPbCpDLKmd8OHD9eZzcePH8tbev/5558Qi8VV2nkOHDhQacrqh+Tl5WHDhg1o2rQp\nhEIhBg0aJA9/qi4uX74Mf39/ACVtzq2trcuNRy2P+fPng8fjlWnnFB4errIWR1FREdzc3LBs2TI0\nbNiwzPjff/8NPp9fbrW4t2/f4sCBA+jZsyeEQiGcnZ2hp6eH7du349GjR+W6jk6cOIG6detCKpWC\nz+eXW6fi7NmzsLa2rnK/uy8ZqVSKvXv3Yvjw4Rg7dqzSbMOPoPpEd8WKFeByuQrdOl1cXFRm0Hxq\nyEJ3VHVm0AYzZ85Ehw4dAACLFy9GUFBQpQ9GFi5ciBEjRlTqOY8ePcLMmTPh6uoKPT099OvXr1oO\nFX/++Wf07NlT/vvZs2dhb2+P5OTkKvm5PT094enpqfAerly5Ul5R7EM2bdqERo0a4d69e3B3d1cY\nu3nzJuzs7BRKR5YmNzcXq1evRsOGDSESiTBy5Eh5Z4uIiAjs2rWrwvX+8ssv6NmzJ/755x+V5SKB\nksAwr4EAACAASURBVHZLYrFYoUA6i3rk5uYiPDy8TCdhWc1kDVA9onvz5k1wOBylfek/toW0Lpk5\ncyaICLVr19ZJZk9+fj5q1KiBgwcPgmEYeQhYZWx///33SqteqQPDMFi+fDmMjIxgZmam4H7QBSNG\njChTLe3JkycICgpCfHx8petmLFy4EF5eXgq1a7Ozs2FmZlamrgPDMAgICMD+/fuRm5sLY2Nj+ft+\n5swZ2NjYIDExUd7mHSjZMR0+fBi9e/eGubk52rdvj127dpVxX/z000/yL9Py+PHHH5GYmIizZ88i\nNDRU6WMyMjLg7OysdqU6FkWGDBmiVJeISFNlL6tHdEeNGqXyhRFRpQuuVCcbN24EEcHBwUEn8Y97\n9uyBt7c3CgoK8P79e4SGhlZYtrE0a9euVdk0Ul1mzZqF8PBwrFu3Dk2bNoW5uTliYmKQnJyMCxcu\naC3wPiIiAgcPHixzPS8vD506dUJYWJjSLsSquHLlCnx8fNCqVSuFqJDY2NgytXFTU1MRFBQkf4yJ\niQnevHmDAwcOQCQSYf/+/WjTpg3mz5+PlStXolu3bhCLxQgKCsKSJUvKPfh78+YNzM3NKzwcXLp0\nKYYOHYotW7YorQP88uVL+UEoS+UpKCiAQCBQqUsaylCtHtHt2rVruaKr7IP1KXPy5El5Cqu2d30M\nwyAmJkbul33y5AnEYnGZwi6q+PXXX9GtW7ePWoOsaLesildmZiZSUlIwdOhQ+Pv7y3fBmhRhhmFg\naWmpsvShVCrF5MmT4eLionb32uLiYpibmyM9PR316tWTF3/Zs2cPgoOD5QLLMAzCwsKwZcsW+XM9\nPDwwf/58WFlZYezYsejcuTM4HA4cHR0RHx+PtWvXqqzpq4xevXpV6GufN28exowZI2/KWZp3794h\nPDwco0ePVtsmiyLZ2dnl6tKHLqUqUj2iK7stV/UTGxsrbwP9uXDv3j3weDyYm5trPcXy/PnzqFGj\nhjyTSVbrNSsrq8Ln7ty5E+3atfvoNTx//hx2dnY4fPhwmTFtiLDsy6UitmzZApFIpJaPFCjpFp2S\nkoLs7GwEBQUhMTEREokE3t7eOHr0KICSYvEeHh4oKirCnTt3sHLlSnm/PXt7e8THx2P48OEf5Ro7\nfvw43N3dy40OmT59OiZPnozBgwcrRM8UFRWhbdu26NWrF1tPoYo8evRIqS+39I86LiA1qB7Rff78\nucptfK1atWBnZwcOhwN/f/8K29F8Sty/fx9cLhdisbhS5QyrQp06dRSKUo8bNw6tWrWq8EN34MAB\ntevHVsShQ4dgb29fYeFtVSL87bffIiUlBZcuXUJ2dna5f+fU1FS1133hwgU4ODhgzpw5Ff7vzJ07\nVx6Fkpubi1atWqF58+aYN28eGjVqhD179sDHxwehoaEQi8VwcXFBYGAg+Hw+pk6dKp8nISFBraiQ\n8oiOjsb333+vcnzChAmYNWsWWrduLf9SYRgG/fr1Q8uWLas1nO9zZenSpRAKhSAiGBgYoFatWkp1\nicPhqFXZTg2qL3rhxIkTcHJyUnhh7dq1k5f8O3XqFMLCwsDlcmFmZoaRI0eqXVW/Ojl//jw4HA5q\n1Kih1S+L9evXo0WLFvLfCwsLERYWplDYRRkbN25UOOz5WCZOnIjmzZtXaoclE+GkpCS0b98eQUFB\nsLCwgKmpKfz9/dGmTRsMHToUCxYskIvyxIkTK3Xr/PTpU4SEhKBnz55KXT65ubm4ceMGkpOT4eDg\ngKSkJHTs2BG1a9cGn88Hh8MBl8tFcHAwjI2NsWrVKvz1118YNGgQateujcTERHm3BalUCrFYjIcP\nH6q9PmVcvnwZdnZ2KkPBkpOTMWrUKPj7+8sjHyZMmIC6deuq3V+OpaTYe0REBHg8HogITk5O8oxL\nhmGQlJQEfX19uS6Zm5tXqqNKBajUVQ4AKodyB9VFKpXS4cOHKTs7m4KDg8nHx6fMY16+fEmTJ0+m\n9evXU2FhIdWpU4dGjhxJsbGxZGhoqIllaJyDBw9Sy5YtKSgoiK5evaoVGxKJhJydnenUqVPk5eVF\nRERPnjyhOnXq0M6dOyksLEzp82bMmEGFhYU0a9YsjayjuLiYIiMjqXXr1jR+/PiPmuv169f0+PFj\nevToUZmftLQ04vF45OHhQU5OTmRgYKDW2q5du0a5ublUu3ZtMjMzo3/++YcePXpEEomEXF1dydnZ\nmQ4fPkwTJ04kX19fcnV1JRcXF1q/fj3NnDmTBAIBJSUl0bBhw6hPnz704sUL2r17N33//feUk5ND\nycnJdO7cOerfvz/dvHnzo14/EVFcXBx5e3vT9OnTy4zt2LGD1q9fT+fPn6crV67Q9u3baeXKlXTq\n1Cmytrb+aNv/Zd69e0fffPMNrVu3jnJzc8nAwIBatWpF69evJ3Nz8zKPf/HiBR09epT4fD61aNGC\njI2NNbUUjsqR8hRZU5JfGSQSCb777juIRCJYWFhAIBCgf//+OH369Cfpfti0aROICBEREVqzMWHC\nhDIxt3v27IGzszOys7OVPic+Pl6T39oASsKUxGIxTp06pdF5S+Pt7Y1Tp07h2rVrSE1Nxc6dO9X6\n2bFjB0aNGgUzMzN06dIFp0+fRmZmpsL/TNOmTZUmREyfPh1EhOXLl6N58+Zo3769fNe8bt069OrV\nC0DJ3+Gbb77RyOss7728evUq/P394eTkJO/NVl5D0i8dqVSKtWvXws3NTb5r9fLyUit9WotUn3uh\nqhQVFWHDhg3w8PCAo6MjHBwc4OHhgRkzZlR7bYAPWbJkCYhIrTbfVUGWpfbhreX/Y++7w6I63u8P\nvS69d6QoKgoqYkVABaOIPSrYu0Zj7y1GY/tYYqJRiSV2DXZFBcWuYC/BBoogCIICUqTu7vn94Zf7\nEylSjUk8zzPP7s6dO3du2TNz33nnPZMnT6aPj0+JnVH9+vXLFU+3oihUaSiN7KuCd+/elbrooLxI\nSEigj48PGzZsyLt37xbZtnjxYo4bN67YPj179mTt2rUpKytbTOgyODhYUOGtV69etUo8FXacHweo\nycrKEoKk6+rqFltN9xXvcfHiRbZu3VpYfCUSiTh58uQvJcLaP490CyGRSLh//342bNiQtra29Pb2\npq6uLt3c3Lh58+ZSRQs/N2bPni2saKkJdO/evYgKAvnevuvq6soVK1YUyX/27BkNDQ1rbIZ70qRJ\n9PX1rfY3j2vXrn1Szbg8kEql/OOPP6ivr88ff/xRIPH79+8XC+t4//596unp0dramkpKSrSzs+Pk\nyZOFaxcREUF7e3vGxsZSX1+/2q/phAkT2Llz52KeHs2aNaOsrCy3bt1arcf7p+Pp06f09/enqqoq\nAVBWVpatWrWqkQFGFfHPJd1CSKVSHj9+nM2aNaOdnR3HjRvHzp07U1NTk35+fjx16lSNexJ8CiNH\njiSAansF/RBnz55lvXr1ihHd8+fPaWBgUGTt/erVqzl06NBqb0Mh8vLy6OLiUuVZ/I+xYcOGYhHB\nqoK4uDh6e3uzcePGjIiIoFQqpYWFRZFFOe7u7tTQ0ODq1avp6+vLFStW0M3Njd27d+e7d++Ym5tL\nJSUlbt68udRlw1VBbm4uvby82LdvX4F4IyMjqaGhQZFIxOvXr1f7Mf9pSE1N5ezZs2lgYCAQraGh\nIf/3v/8JenpfIP75pFsIqVTK0NBQenh40MrKiitWrOCqVavYpEkTmpiYcOrUqbx169bfZv/t0aMH\nAQgz3tUFqVRKS0vLEuXVDx06REtLS6amplIsFtPBwYEhISHVevyPER0dTX19/WodYQwcOLDSOnGl\nQSqVMiAggHp6ely6dClHjx7NpUuXknxvy5WVlRVmtC9cuEA7OztmZ2fTz8+Prq6uTEpKor29Pbt2\n7Vqmm1dVkJ2dLRDvixcvaG1tzeXLl1NeXr7cfsj/Nrx584arV6+mg4OD4GGiqKhIf3//YmajLxT/\nHtL9EFeuXGHHjh1pamrKn3/+mTdv3uSMGTNoa2tLc3Nzjhs3jqGhoZ/dr9Hd3Z0AuH379mqtt3//\n/gwICChx2/jx49mlSxdu3bqVrVq1+iydzqFDh2hhYVGuxRrlgb29fblXmVUUz58/p4eHB2vXrk1n\nZ2dOmTKFampqnD59ulBGKpWySZMmPHz4MKVSKefOnUtTU1M2a9aM+vr6NRo6MTs7mx4eHtTQ0ODM\nmTNJkmZmZuzatWuNHfNLgkQi4c2bNzlq1CiampoKRCsvL09nZ2fu2bPnHxOd8P/w7yTdQty6dYvd\nu3enoaEhly5dynfv3vHBgwf86aef6OLiQh0dHfbv358HDhwoM0xedaJhw4YEUOl4uCVhw4YNpcZT\nyMvLo7OzM7W1tcu9VLg6MGPGDLZr167Kpp3CADQ1aSKSSCRcsmSJEEPDwMCgmCbe3r172bp1a+F3\naGgoRSIR5eXli0m8VydycnLo5ubGhg0b0tzcnFeuXGG7du0oEon45MmTGjvu34m0tDTu3r2bXl5e\nVFVVpZycHFVUVKilpSUEoP8He238u0m3EBEREezRowctLCy4d+9eYbQXFxfHdevWsX379hSJROzc\nuTM3b95caVWC8qBQn0tWVrba/jQRERGlrguXSCRs164dlZWVP6sdsKCggG3btq2yHfv48eOCl0BN\n4dmzZ6xbty5NTU2ppqZGa2vrYp4BBQUFtLS0LGI2GTRoENXU1GhmZsYTJ05Ue7vEYjF79OjBXr16\nUSwW8+jRozQwMKCjoyP9/f3p4ODwJdsuyw2pVMp79+5xwYIFdHBwoLy8POXl5WlgYMA6depQJBKx\nd+/eDAkJ+dvnZ6oB/w3SLcT58+fp5OTEVq1a8ebNm0W2paWlcdeuXezVqxc1NTXZunVrrly5skTx\nwaoiKyuLSkpKVFFRqRYXK4lEQm1tbSYmJhbJl0qlnD59Olu3bs29e/fS2tqaaWlpVT5eeZGcnExz\nc/Mq2R9nz57N2bNnV2OriuLcuXM0NDTkunXrOHfuXCorK3PlypU0MDBg//79i7ghrl69usikmY+P\nD01MTHjmzBlaWlpyyJAh1UaChcrCnp6eRUJWxsXF0dHRkWpqanRzc/vHqvu+ffuWhw4dYr9+/ail\npUUVFRUqKCiwXr16/Oabb2hhYSFIU9WEG+LfiP8W6ZLvRw+///47jYyMOHjw4GJERb5/pQsKCuLw\n4cNpaGjI+vXr8/vvv2dgYGC1LUW+cuUKAbBu3brV4m7UsWNHHjhwQPidk5PDAQMG0MnJSQh3OHbs\nWHbv3v2zTiYWikFGRkZWan9PT88iMSaqExs3bqSBgQFPnz5NkvTy8hL8gdPT0zlv3jzq6Ohw/Pjx\nTE5OZkZGBnV0dIToYRYWFlRSUmJBQQEzMjI4cuRImpub89SpU1Vu2w8//EBnZ+cSXR///PNPuru7\ns127dsLIvLLSRZ8DGRkZvHDhAleuXMmePXvS1NSUCgoK1NTUpLKyMr28vDhu3Di2bduWOjo6HD16\nNG/evPlFLnqqBvz3SLcQ6enpnDp1KnV1dblkyZJSjfFisZhhYWFcunQpO3XqRC0tLdrb23Po0KHc\ntm0bo6OjK/1wzJw5kwCqReL8u+++46+//kqSvHfvHl1cXNi7d+8iDuG5ubls1KhRMb/emsb69evp\n6OhYYbu5WCymSCSq9pFOQUEBx40bx9q1awudwc2bN2lqaspGjRoJ0cVI8tWrVxw7dix1dHT4ww8/\ncNKkSRwyZAifP39OIyMjWllZFelQTp8+TUtLSw4dOrTSo97169fTxsam1A7+/v37dHBwIPl+pN6g\nQQPKyMiwefPmPHjw4N9qcnj37h2vXLnCNWvWsH///rSzs6OioiKNjIyor69PJSUlNmnShLNmzeLG\njRs5duxY6uvr093dnTt27PhSFjDUJP67pFuIqKgodunShdbW1uWKaCYWi3n37l3++uuv7NWrF42M\njGhmZsa+ffty/fr1jIiIqNDItUGDBpSVla201lchZs+ezUmTJrF///7C63JJ5/Lo0SPq6elVeuRZ\nGUilUg4cOJB+fn4V6qDu3r1Le3v7am1Lamoq27dvTy8vryKmFh8fH/76669csGBBsVi15Hu7r5+f\nHw0MDKiurs558+axT58+7NixYzHzSXp6OkeMGEFzc/MKx4bev38/jY2NyzRrFQp0fuh9ExgYSEND\nQ2ppaVFZWZn16tXj0KFDGRAQwPv371erLTQ3N5exsbG8du0ajxw5wl9//ZWDBw+mo6MjlZSUaG1t\nTQcHBxoZGVFdXZ0dOnTgkiVLePnyZSYlJXHDhg1s2rQpTU1NOXv27Box4X3BKJVXP0vAmy8JoaGh\nmDBhAvT09LB69Wo4OTmVaz+SePbsGS5evIhLly7h0qVLePv2LVq0aIE6derA1tYWdnZ2sLW1hamp\nKWRlZYvsn5WVBQMDA4jFYkRGRsLKyqpC7U5JScHBgwexfPlyxMXFYfr06Zg8eTI0NDRK3eeXX37B\n3r17cfHiRcjLy1foeJVFdnY2WrRogWHDhmHs2LHl2mfDhg0IDw/HH3/8US1tiIyMROfOnfHNN99g\nxYoVwrnfuHED3bt3R1RUFB4+fIi+ffviyZMnJdZx9+5d+Pv748mTJxgwYAB0dHSgq6uLmTNnFit7\n+vRpDBs2DF5eXvjf//4HLS2tMtt3/vx5fPvttwgODoazs3OZZevVq4dt27ahSZMmQp5YLMaWLVuw\ncOFCqKmpwcHBATIyMoiIiMCrV69Qt25daGpqQiQSCUldXb3YbxUVFaSkpCAxMRGvXr0qljIzM6Gv\nrw9NTU0oKSlBTk4OMjIyePnyJUiidevWaN26Ndzc3ODo6AhZWVlcunQJmzdvxpEjR9C2bVsMHToU\n3t7ekJOT+9Rt+7eh1IA3/znSBd4/tJs2bcL8+fPRpUsXLFq0CAYGBhWuJyEhAWFhYYiKikJUVBSe\nPn2KqKgovH37FrVq1RJIuPAzNTUVvXr1gqmpKa5evQpVVVXIyspCVlYWMjIykJWVBUkh2tbDhw+F\nz4SEBHTo0AGqqqpQV1fH2rVrP9k+qVQKLy8veHp6YtasWZW5VJXCs2fP0KJFizKjoH2IgQMHokWL\nFhg5cmSVj3369Gn069cPixYtwvDhw4ts69SpEzp16oQxY8aAJMzMzHDu3DkhetvHyMvLg5qaGiwt\nLZGfnw97e3ucOXMGMjLF/08ZGRmYNm0aDhw4gHHjxmH8+PElRrW6d+8e2rdvj71798LT0/OT5zN+\n/HgYGRmVSPYSiQQXL17Evn37cODAAaiqqsLOzg76+vowMjKCvr4+RCIRcnJykJmZKaSsrCxkZWUh\nJycH2tra0NPTg6KiIvLz8/Hu3TukpKQIUdrk5eWLPMN2dnZwcXGBnZ2dcB0SEhKwbds2bNmyBYqK\nihg6dCj69+8PfX39T57fvxj/nChjnxNpaWmcOHEi9fT0uGLFimpTgsjMzOTdu3cZGBjIJUuWcMiQ\nIWzdujXNzc2prq5OAFRSUqKuri61tbWpqalJDQ0NqqurU01NjXXr1mXPnj05b9487tu3j3/99Zcw\ncz158uRPxtL9EC9evKC+vj5v375dLedWXhw/fpxmZmblmpC0s7Or8qIIqVTKVatW0dDQsMQg1OHh\n4TQzMyviITB8+HCuXLmy1Dqjo6Opra3N+vXrc/HixVRWVqabmxsvX75cqvkkMjKSAwYMoJ6eHn/8\n8ccidtfo6GiamJhw37595T6vY8eO0dPT85PlxGIxnz59yiNHjnDJkiXs168fGzVqRJFIRDU1NYpE\nImpqalJbW5u6urrU09OjgYEBjYyMaGJiwqZNm9Lf35/z58/nzp07ee3atWKBeD5Efn4+Dx06RB8f\nH2pra3P48OEMCwv7t06KVQZfbbpl4fHjx/T09GTz5s0/i93J2dmZcnJypa4uKwt9+/at8Eq3HTt2\nsG7dup99Rc+8efPYpk2bMjuzN2/eUCQSVckWmZKSQl9fX7q4uJSqV/bNN98UW8Z79OhRenh4lFrv\n5s2b2bdvX7Zs2VJQR/79999Zq1YtOjs7MyAgoNRJw4/J9+nTp7SzsxMmQcuLjIwMqqmpfTETT48e\nPeKUKVNoaGjIVq1acevWrZ9twdE/DF9J91OQSCT8+eefqaenx99//71Ge+ysrCyqqqpSQUFBUAYo\nL5ycnCocz1YqlbJnz56fXcxQLBbT29ubU6ZMKbVMVRdFXL16lZaWlpwwYUKpfqxhYWG0sLAoJt3+\n7t07ikSiUn2a+/fvz40bNzI8PJympqY0MTHh8+fPKZFIePLkSXbp0oXa2tr87rvvSlW2fvLkCf38\n/CgvL083N7dKRcVr2bJljSzKKC/S09O5efNmtmjRgkZGRpw2bRofP378t7XnH4KvpFteRERE0MnJ\nib6+vhWS+a4orl69ShkZGerp6ZV7eWl8fDy1tbUrZQZ58+YNTUxMqkv/qULHtbKyYmBgYInbZ8+e\nzTlz5lS4XolEwuXLl9PAwIBHjhwps6y3t3epgXQ6duxY4uu+VCqlubm54P3x7bff0sbGphj5vXjx\ngnPnzqWxsTFbt27N3bt3FyF3iUTCnj17smPHjvT396eenh4XLVpUIfL99ddfq1V6qTzIy8vj0aNH\n2bt3b2poaLBLly48cuTIV3228uMr6VYEeXl5nDFjBo2NjUtUGqgujBw5knJycmzbtm253M8CAgLY\nt2/fSh8vKCiIlpaWnz0G8c2bN6mvr89Hjx4V2+bp6cmgoKAK1ff69Wt27NiRzZo1++Ta/MKRcGmj\n4PXr19Pf379Y/rNnz2hiYiK88Tx79qyYSOWHyM/PZ2BgINu2bUsDAwPOmDGD0dHRnDZtGlu2bCmY\ndh4/fsx+/fpViHxTU1OpqalZbYGFSoNUKuWVK1c4evRo6unpsWXLlly/fv2/baXY58JX0q0MLl68\nSCsrK44YMaJGBAFzcnKopaVFNTU1Lly4sMyyEomEjo6OVe4ERowYwcGDB1epjspg06ZNdHBwEARJ\nyf+/KKKsCZuPcenSJZqbm3Pq1KnlGnW1b9++TNt5YmIitbS0itlMN23aRD8/vyJ57dq1Y+3atT95\nzMePH3PixIlUU1Ojqqoqd+7cWcxm/ejRI2Hk+9133/Hq1atlmrT69evH1atXf/LYlcGjR484Z84c\n1qpVi3Xq1OGiRYtKtY1/RbnxlXQri/T0dA4aNIi2trZFAoVXF3bu3EllZWVqamqWGQN3x44dbN68\neZVtzZmZmbSxsflb4rQOGzaMvXr1Es7h7t275SIx8n2ns3jxYhoaGpZ7ufDly5dpZWX1yZgF7du3\nL2Zi6NevH3///fcieWfOnKGcnFyJMY0/xqlTp2hgYMClS5eyadOmtLCw4MKFC4sFP4qOjubChQtZ\nu3Zt2tjYcN68eSUGSDp//jzr1q1bbXMNiYmJXL16NZs0aUJjY2NOnDjxb41D/S9EzZHuhQsXOGzY\nMPbs2ZOrV6/+rIFWPicOHDhAQ0NDzps3r1rtWlKplHXr1qWGhgZNTU1LHFGnpKTQwsKCFy5cqJZj\nXr58mUZGRjVqsy4JOTk5bNy4seCmtX79+nIpRSQlJdHLy4utWrViXFxcuY/Xtm3bYsRZEv744w/6\n+voKv6VSKc3MzBgVFVWkXEFBAZWUlOjl5VVmfffu3aO+vj4vXbok5N26dYujRo2iiYkJ7ezsOHHi\nRIaGhgodglQq5Y0bNzhhwgQaGhrSxcWFa9asEVzupFIpHR0dP2m/LguZmZncvn07vb29qaWlxYED\nB/5bInrVGPLy8rhz50727duX/v7+DAwMLO+cSs2Q7vfffy+obxYmU1PTz7r09HMiISGB33zzDV1c\nXKp19vbq1atUUVFhgwYNii1Nzc/Pp6enZ7V7HsyYMYNdunT57CObmJgYGhoa8sKFC+zTpw83bdpU\nZvlz587R1NSUM2fOrNAE4sWLF2ltbV2uDjI9PZ2ampqCmSMqKoqmpqYlXht3d3caGhoyNDS0xLpe\nvnxJCwsL7t69u8TtUqmUt2/f5o8//simTZtSS0uLvXr14rZt24RQowUFBTx16hT79+9PTU1NdujQ\ngTt37uSff/7JevXqVYgk8/PzGRQUxL59+1JTU5M+Pj7cs2fPF+OC9iUjIyODrq6uxTiubdu25XG/\nrH7SPXXqVLHGFKaalCP/uyGVSvnbb79RX1+/WqJMFaJTp05UUVGhrq6u4EYmFos5dOhQdurUqdpH\nI3l5eWzYsCG3bNlSrfWWB8HBwTQ2Nqaurm6pys5isZgLFiygkZFRpa6zp6dnhSTov/32W8HD4fff\nfy9xco0k586dy27dutHZ2bnY5GdmZiadnZ25aNGich83MTGRW7ZsYffu3amhocHmzZvzp59+4r17\n9yiVSpmVlcVdu3bxm2++oaamJvX19Tlp0qQyOyCpVMrw8HCOHTuWBgYGbN68OdeuXVuj8aP/jZg8\neXKpHPfTTz99avfqJ90+ffqU2iAA/+SI7+XClStXaGho+MmRWnkRHR1NFRUVNmzYkE2bNmVKSgo7\ndOhADw+PGvM2KFTC/Tsk7ceOHUslJaUS7a2JiYls27Yt3d3d+fLlywrXfeHCBdaqVatCZqDDhw8L\nihH+/v6l3tfg4GC6ubnR1dW1iFKvWCymj48PhwwZUum3h9zcXIaEhPD777+ntbU1zc3NOXr0aAYF\nBTE7O5tJSUmcMGECFRUVqa2tTR8fHy5dupSXL19mbm4uIyMjOX/+fNra2tLe3p4LFiz4rwWZqVbo\n6uqWym92dnaf2r36Sbddu3Zlkq6TkxOXLVtWYhzbfwuePHnCWrVqcd68edXymj5hwgSqqanR1taW\nhoaG/O6772rcL3L58uV0c3P77Ha9lStX0sLCgt9//32R/KCgIJqYmHDevHmVbpO7u3uFpcvz8vKE\nkbepqWmpZJWenk41NTWGhYXRwMCACQkJlEqlHDt2LNu2bVtt90sqlfLhw4fC/RGJRGzVqhW/eslC\nYQAAIABJREFU//57NmrUiH5+fty7dy+HDx9Oc3NzysrKUkFBgY0bN+aqVasq5BHyFf8fz58/56hR\no2hjY1Mmv2lra3+qquon3cIYsSUlGRkZQZe+8LempiabNGnCmTNn8vLly/8a431SUhJdXFw4cODA\nKkf2f/LkCRUUFCgnJ0d1dfXP0mGJxWK2atWKv/zyS40f60P4+Phw8+bNtLGx4e7du5mWlsZBgwbR\nysqqSJzbiuLcuXO0sbGp1AKSESNGcMqUKTQzMyuzE23YsCHDwsI4Z84cdu7cmatXr2bdunVrdBI5\nLS2NoaGh/N///sfOnTtTVlaWsrKylJOTo52dHUeOHMk1a9Zw1qxZ9PT0pLq6OuvXr89Ro0Zx586d\njImJ+eqZ8H+QSCR88uQJV65cSXd3d+rr61NOTq4Ih8nLyxfL+zB9ajKVNUG6L168oIaGRokNmjFj\nBsn3vfX9+/c5YcIEQXrkw3IqKiq0s7Nj165duXz5ct68efOfpvhJ8v2y3s6dO7Ndu3YVNgVIpVLe\nvHmTY8eOpa6uLj09PamhoUEvL6/Ptgrp0aNH1NXV5YsXLz7L8fLz86mhocHk5GTevXuXGhoaNDQ0\n5OjRo6vkDy2VSunm5sY//vijUvtfuHCBpqam7N+/f5nlvvvuO65YsYJ5eXm0tLSktrZ2jZvTCifX\n+vXrR01NTTZq1Ih6eno8cuQIf/nlFw4aNIgNGjQQTFQDBw7klClTOG7cOPr6+tLAwIBmZmbs06cP\nFy9ezD179jAsLIyJiYn/SjJOS0vjiRMnuGDBAvbq1YsODg5UV1enrKxsMb5SVlamra0tx4wZU8TU\nFhgYWOqgslCFpAzUjPdCWFgYHRwcipDopEmTyhzFZmVlMTg4mCNGjKClpSXl5OSopqZGRUVF4YRE\nIhHr1KnDLl26cOHChTx79uwX/7okFos5evRoNmjQgPHx8Z8sn5SUxJUrV9LR0ZFWVlb84Ycf+Pz5\nc+bn59PKyoqampq0sLCocHDsyuLHH3+kj4/PZ/kDXrlyhQ0bNmRaWhqHDBlCPT09mpqaVlkJ4ezZ\ns7Szs6t0tDiJREJVVVUuWLCgzHK7d+9mt27deOPGDWppaVFLS6tStudPodCNbPz48TQ0NGTTpk2L\nuJENHjyYw4cPL7JPTk4Ob9y4wY0bN3LkyJF0cXGhqqoq7e3t2bJlS7Zs2ZKurq50cnKijY0NNTU1\nqaSkxNq1a7NDhw4cNWoUly1bxn379vHatWtMSkr6Ikk5Pz+ft27d4rp16zhixAi6u7uzVq1aVFdX\np4yMjMAlhd8LI/tZWVmxb9++PHjwYDEl6JKwfv166unpCXUYGxtz586d5WliqbxaLfF0b9++jbdv\n38LZ2Rna2trl2UVARkYGLl26hLNnzyI0NBRRUVEwMTGBgoICsrOzkZqaiqysLACAgoIC9PX1YWlp\niVq1aqF+/fqoV68eLCwsYGpqCh0dnRJjnX4ukMT//vc/rF27FkFBQXB0dATwPn7v48ePcefOHdy5\ncwe3b9/G3bt30aVLFwwePBhubm5Fgp4HBARg6dKlaNCgASIiIvDXX39BRUWlRtuen5+PRo0aYd68\nefj2229r9FgLFy7EnTt3cPPmTfj4+GDZsmWYMWMGEhIScPDgwUrdQ5Jo06YNhg8fjv79+1eqXSSh\noaEBf39/bNiwodRyL168gLOzM5SVlfHbb7/h1q1buHPnDo4ePVotz190dDR27dqFnTt3QiKRwN/f\nH/7+/sXi/mZkZMDFxQUTJkzA6NGjS62voKAAkZGRiI+PR0JCQpH08uVLvHz5EsnJyVBTU4O6ujoU\nFRVBErm5ucjIyIBYLIapqSksLS1hZmYGkUgEFRUVqKioQFVVtUhSVlYWkpKSUpHfhXkAkJmZidev\nXwsB1JOTk/HmzRukpqYiLS0N6enpyMzMxLt375CdnY2cnBzk5+ejoKAAEonkPXnJyAiB1aVSKSQS\nCQBAXl4eBgYGsLe3R4sWLdCpUyc0bdq00oH88/LycO3aNcjKyqJZs2blreefE8Q8NTUVFy5cwLlz\n53Du3DnExcWhZcuWcHR0hKqqKl6+fImHDx8iLi4Or1+/Rm5uLhQUFCCVSkESOjo6MDExga2tLayt\nrWFmZgZTU1Ph08jIqMZUFMRiMTIzM7F27VosX74c7u7uSEpKwoMHD2BqagpnZ2chtWjRAiKRqMR6\n3r59CwsLCygoKMDFxQUuLi5YuHBhjbT5Q4SFhaFHjx548OBBhTvP8iI9PR116tSBVCrF7t270bZt\nWwDvH+w2bdqgW7dumD59eoXrDQ0NxZgxY/DgwYNK39/IyEi0adMG8vLyiI2NLab+8eE56OnpYdq0\nafjpp5+Qn58PFxcXTJ48GQMGDKjUsWNiYnDgwAEEBgYiOjoa3377Lfr16wdXV9cyiTw6OhqtWrXC\nhg0b4OvrW6ljA+8Dor9+/boYKSckJODFixeIjY1Famoq8vPzIRaLBZKTSCSQSqWQSqUAABkZmSLp\nQ3w42iuEvLw8FBUVoaSkBBUVFaipqUEkEkFNTU1Qm8jNzUVKSgpSU1Px9u1bYX9ZWVkYGxujdu3a\naNy4Mdzd3dG4cWPo6elV+jpUI/45pPsxkpOTcf78eZw7dw5nz57Fmzdv0KZNG3h6eqJTp07Q09PD\n06dP8eTJE0REROD+/fuIjIwU/jQikUjoud+9e4eMjAwoKSkV6Z1L6rE/3p6Xl4eMjAxkZGQgMzNT\n+P5hys3Nhbq6OqytrWFsbIzLly9j2rRpmDBhQqkEWxr69u2LgoICZGZm4vbt27h48SIcHBxq6Cr/\nf4wdOxa5ubnYtGlTtdcdHByMYcOG4dWrV3jx4gWMjY2LbI+Pj4eLiwt27twpkHF5QL6Xjhk9ejT8\n/f0r3b6AgABcuXIFt2/fxrp16+Dm5lasTEFBATp27IjY2FjMmDEDQ4YMAQDcuXMH3t7euHv3LkxM\nTMp1vOfPn2P//v0IDAzE8+fP0bVrV/Ts2ROenp5QUFAod7tv3LiBjh07IigoCE2bNi33ftUNqVQK\nsViMgoICiMXiEr9LpVJoampCU1MTcnJyiImJQWRkJCIjI/H48WPcu3cPUVFRyMzMhKqqKiQSCfLy\n8mBiYgIHBwe4urrC1dUVjo6OMDMz+1vfbD+Bfy7pfoyXL1/i/PnzOHPmDIKCgmBsbIxu3bqhW7du\naNCggXATSCI5OVm4oR/e2OjoaOjr68Pa2hrm5uYwNTWFoaEh9PT0IBKJkJubi+zs7CJJWVkZGhoa\nEIlE0NDQKDGpqakVeQju37+Pdu3a4cCBA2jdunWFzjM4OBizZs1CbGwsxo4dK3Q8Nf2QZWRkoF69\netixYwfc3d2rrc7Jkyfj9OnTGDlyJIKCgnD58uUSy549exb+/v64fv06zM3Ny1X/6dOnMW7cODx4\n8KBKWlx+fn5o3749kpKSEBMTU8zEQBLDhg1DUlIS2rdvj7/++qtI5zR//nzcunULx44dK/U+PXv2\nDIGBgdi/fz9evHiBbt26oVevXmjTpk2FiPZjHDt2DCNGjMCZM2dQr169StdT3SCJxMREREZG4smT\nJ8L/sPBtVSQSQUlJCfn5+UhPT4ehoSEcHR3h4uICR0dHODo6wtbW9rNp/FUj/p1yPWKxmBcvXuTE\niRNpZWXFWrVqcdKkSbx06VKZk3kFBQV89uwZT548yTVr1nDMmDFs164dLSwsqKyszLp167Jr166c\nNm0aN23axIsXL/LVq1eVmlAIDg6moaFhhZdGi8VimpiYsH///pw6dSqbNGnCbdu2Vfj4lcHhw4dp\nb29fLZ4kwcHBtLCw4IgRI5iens6pU6eWGh6xEMuWLWPTpk2LBR0vCVKplM2bN+euXbuq1E6pVEoj\nIyNGR0czJiaGurq6xVwAFy9eTGdnZ2ZmZvLOnTusU6dOke2Fq/w+9p6IjIzkTz/9RCcnJxoYGHDU\nqFEMDQ2tNnmoQuzatYuGhoY1EpjpU3j79i2vX7/OnTt3cu7cuezduzednZ2ppqZGbW1t2tjYsHbt\n2sJ/TF9fn+3bt+fkyZP5xx9/8ObNm/+2pck1O5H2JYAk7t27h0OHDuHQoUNITk6Gr68vunXrBk9P\nT8GA/ylkZ2fj2bNnRXrlwl5aIpHA3t4elpaWMDY2homJSbFPbW3tYqOc33//HcuXL0d4eDh0dXXL\nfU4zZ87E69evcfToUezZswcjR47E48ePP0uv37NnT9SpUweLFi2q1P4ZGRmYMmUKgoODsWnTJrRv\n3x4A0LhxY/z8889ljvxJokePHjAyMsJvv/1W5nGCg4MxceJE/PXXX1Ua5T558gTe3t6IiYkBALi5\nuWHKlCmCnXTv3r2YNm0awsPDYWJiAolEAh0dHTx79qyIDfHu3bvw8vLC/v37cfHiRQQGBiI5ORk9\nevRAz5490bp16xpVxj1x4gQGDhyIHTt2oEOHDtVSZ2ZmJhISEpCYmFjsMy4uDpGRkXj37h3MzMyg\noaEBWVlZZGVlITExEWKxGA0aNICjoyPq168vJB0dnWpp2xeMf495obx49uwZDh8+jEOHDiEiIgId\nOnRAt27d0LFjxwrbVwuRkpKCyMhIxMXFlfoQ5uTkwMjIqBghX758GS9evMC2bdtgaWkJXV3dT5oK\nHj9+DA8PD9SvXx8DBgxAQEAAvvvuO/Tp06dS7a8IEhMT0bBhQ4SGhgpeGOXFqVOnMHLkSHh7e2PF\nihWCTHxqaiqsrKzw5s0bKCoqlllHeno6mjZtilmzZmHgwIElliGJpk2bYsqUKejdu3eF2vgxNm7c\niLCwMEEGfsOGDTh37hz27duHK1euoFu3bjhz5gwaNGgg7OPl5YWxY8cKxPzw4UPs378f69atQ3p6\nOoYPH45evXqhZcuWn1WC/MqVK+jevTtWrVpVqo2bJDIyMkp8hj/+JAkTExNhUKGkpASSyMnJQWpq\nKuLj4/H69Ws4ODgIpFpIsqampl+y3bUm8d8j3Q/x6tUrHD16FIcPH8bly5fRunVrdOvWDb6+vpWS\nXi8LOTk5RR7Ywu8JCQkIDg5GXl4eZGRkkJ2dXSI5F34Wfu/cuTPat2+PM2fOYM6cOZg9ezbu3Lnz\nWR7kgIAAbNmyBVeuXCkXacTExAijzt9++w1eXl5Fth84cACbNm3CyZMny3X8Bw8ewN3dHcePH4er\nq2ux7YGBgVi6dClu3LhRqqdBedGnTx906NABgwYNAvC+g7CxsUFwcDB8fX2xbds2eHt7F9lnwYIF\niImJgYWFBfbv34/09HT07NkTXbt2xfjx4zFx4kShvs8FkkhPT8f58+cxevRoODg4wNPTU5BV//DZ\nlJWVLfH509LSglgsxrt37/DmzRvExsYiKioKkZGRkJeXh729vZBq164NR0dH2NjYfNaO5R+A/zbp\nfoiMjAycOHEChw4dQnBwMBo0aAB/f3/06dMHmpqaNXrsnJwceHh4wNvbGzNnzixCyiV9JiYmIi0t\nDUpKSpBIJGjevDkiIiLQsWNHtGnTpsgfRU9Pr8rE8zGkUinc3d3Rq1cvjBs3rtRyubm5WL58Odas\nWYOJEydiypQpUFZWLlZuzJgxqFWrFqZMmVLuNhw7dgyjR4/GtWvXYGpqKuQXFBSgXr16WLdunWC6\nqCxIwtjYGOHh4bCyshLy+/Tpg9DQUPz0008YMWKEUPavv/5CYGAgtm/fjsTERIwdOxa9evWCq6ur\ncA8KzQx37twp0u6qtPHt27efHJUmJCRAUVERxsbGMDAwQHx8PN6+fYuBAweiadOmwjOjq6uL169f\nF5tojoyMRHp6Ouzs7IqQq729Pezs7CpkHvuP4yvploTc3FycOXMG27dvR0hICHx8fDBkyBC4u7tX\nO4EVIikpCc2aNcOCBQvK5dP56tUr2Nvbo3v37oiPj4eZmRlCQ0PRrl27In+2wplfXV1dqKurQyQS\nQSQSFfn+8e/Stn04i/748WO0atUKd+7cKdGb4Pjx4xg/fjycnJywatUqWFpalnou9vb22LdvH5yd\nnSt0zZYsWYJDhw7hwoULwiKRgIAA7Nu3D2fOnKnyqP/x48f45ptv8Pz5cyEvNzcXzZo1Q0xMDFJS\nUgSi3b9/P/Ly8tCzZ0/hbenOnTuwsLAoVu+PP/6ICxcu4M8//0ROTg4yMzORlZWFzMzMYt/L+p2W\nloZXr15BSUmp1LmEDz/V1NQAvO804+PjsXHjRvzyyy9o2LAhRCIRoqKiEB8fDwsLi2LEam9vDxMT\nkxp7/v9D+Eq6n8KbN2+we/dubNmyBenp6Rg0aBAGDRpUJolUFg8fPoS7uzv+/PPPcrll9enTB40a\nNcKSJUsQGRmJZs2aYdu2bWjVqpVQJj8/H69evUJqamq5/tRlbZOXly9Cwm/fvkVOTg7c3NwEt7mC\nggKcO3cOaWlp6N+/P5o3b14qmSsqKiIuLg7Ozs5ITk6u8B+aJPz8/CAnJ4cdO3YgJycHdnZ2OHz4\nMFxcXCp6+Ythw4YNuHbtGrZu3QqxWIz09HQMHjwYycnJiIqKElY+ubi4oF69etDR0RGu1/Hjx6Gp\nqQlDQ8MSr2VOTg4UFRUFd8Tydnwf/tbS0oKxsTFUVVVLvDaFcw0fp6dPn0JHR0cg0rCwMKipqWHt\n2rVo3rx5lVzUvuKT+Eq6FcGdO3ewZcsW7NmzB87OzhgyZAi6du1arUtxQ0ND4efnh7t37xZbJPAx\n/vjjD4SEhEBGRgZNmjSBqqoqjh49iqCgoGprTyH4f8s/PySO1NRUDBo0CN27d4e9vT0OHz6MS5cu\nwdXVFbVr1xZGcaURuoyMDBQVFYUZ/8pAKpUiJSUFysrKkJGRQX5+frXNgKelpUFOTk5w4Afer9BS\nUFAQiK5NmzYlkmNkZCSuXr2KxYsXl0ic2dnZaNq0KZYtW1al5dVZWVl4+vRpieRKErVr1y42YrW1\ntYW6urpQh0QiwbJly7BmzRrMmjULI0aMqPHl5f9hfCXdyiA3NxdHjhzBli1bcPPmTfTu3RtDhgxB\n48aNq2Uia/bs2Xj69Cn27dtXZrmHDx8KkzmDBw/G3bt3YWtri5MnT6Jhw4ZVbkd5cOXKFXTu3Blq\nampo2bIlVqxYATMzs3Ltm5eXh27dusHLywu9evWqdBsSEhLg4+OD7OxsnDhxArVq1ap0XYUgCVdX\nV7Rs2RLh4eHIy8tDfn4+Dhw4AA8PD+Tk5MDCwgI3b94sYu8tRGZmJkxMTPDy5UvBS+Nj3LlzB15e\nXjh79mypniD5+fmIi4tDTEwMYmNji3w+e/YMKSkpsLW1FSavPiTX8njCfIj79+9j/vz5uH79OmbO\nnIlhw4aVaIP/iirh37k44nMiNjaWCxcuZK1atejo6MjVq1dXWf4kOzubNjY2DAoKKrNcoVT569ev\n2aBBA4aEhHD58uWfLfTj48eP6eXlRW1tbXbu3LnC++fk5FBDQ4OvX7+uclv8/PyorKzMhw8fVrqO\ngoICYVGNmZkZZWRkOGvWLAYEBFBfX5+PHj0qUn7ixImcPn16qfV5e3szMDCwzGNu3ryZ5ubm3L9/\nPwMCAjhr1iz6+fmxZcuWNDU1paKiIi0tLdmmTRsOHDiQ8+bN45YtWxgaGsrnz58XkwaqDty6dYud\nO3emmZkZf/vtt3ItRvmKcqNmQjv+FyGRSHj+/HkOGDCAmpqa7N69O48fP17p1UUhISG0srJiVlZW\nmeU8PDx48uRJbty4kd27d2dGRgZ1dXWLKdZWJzIzMzl9+nTq6elx1apVTEpKooGBgaDhVl4EBQWx\nVatWVW5PXFwcdXR0uHr1atra2lYo3Gd6ejoDAwPZv39/6urq0snJifPnz+fWrVvp6urKBw8eUF9f\nn+fPny+275MnT6ivr1/qCr21a9fSz8+PDx8+5IkTJ/jbb79x+vTp7N27N5s1a0YjIyMqKipSU1OT\nurq6HDx4MH/88Udu27aNFy5cYExMTLWvTqsIrl+/zo4dO9LCwoIbN26scjD+ryD5lXRrBunp6QwI\nCGCzZs1oYWHBVatWMSMjo8L1+Pv7c+rUqWWWmT59OhcsWMDXr19TQ0ODBQUFnDNnDkeMGFHZ5pcK\nqVTKPXv20MzMjP379y+iYLFhwwa2bt26QkuiR40axeXLl1e5XcOGDRNGnJMmTWLbtm3LJKvY2Fiu\nXbuWXl5eFIlE7NChA9etW8fY2FihzKpVqwTFih07dpRal4eHBxcvXsxjx47x119/5ZQpU9izZ082\nadJE0NKyt7enl5cXhw8fzp9++om7du3i5cuXGR8fT7FYzPz8fLZp04azZ8+u8rWoCYSFhdHb25tW\nVlbctGlTjUtF/cvxlXRrGtevX2fv3r2pq6vL6dOnlyuQeSGSkpKor6/Pu3fvllpm//797NSpE0nS\n0dGR4eHhTE5Opra2drUG0L5w4QJbtGjBhg0b8tKlS8W2i8ViOjk5cc+ePeWqTyqV0sTEpMqS9Y8e\nPaKenh5TU1NJvjcReHt7F9FYk0gkvH79OufOncuGDRtST0+PAwcO5IEDB0rtDHv37k1ra2tOnz6d\nd+7c4eHDh/nzzz9zwoQJguqvjo4OlZSUqKKiwm+++YajR4/m0qVLuXfvXoaHhzMxMbHU6/UxkpKS\naGFhwYMHD1bpetQkrly5wnbt2rFWrVpcvXp1jQRo/w/gK+l+LkRHR3P8+PHU1tbmwIEDef/+/XLt\n9/vvv9PV1bXUQD0vXrygvr4+pVIpv//+ey5ZsoQk+f3333PKlClVbvft27fZoUMHWllZcfv27WUG\nDLp48SLNzc0/aRIhyZs3b5ZHOfWT6N69O5ctW1YkLy0tjXZ2dhw7diyHDx9OY2Nj1qlTh9OmTePl\ny5dZUFDA9PR0Pn78mOfOneOuXbu4YsUKTp48mX5+fvTw8KCsrCzl5eUpEono6OjIzp07c+zYsVyx\nYgUDAwN548YNJicnMz8/n+bm5rx9+3aJ7Zs7d+4n31YKcf36derr61fJLv05cOnSJQ4aNIja2tp0\nc3PjunXrmJSU9Hc365+Cr6T7uZGSksLFixfT2NiY3t7ePHPmTJmv5BKJhK6urty7d2+J2wujYD1/\n/pyHDh1i+/btSZJPnz6lgYFBpW2CkZGR7N27N42MjPjrr7+W257Xt29fzpkz55Pl5s+fz8mTJ1eq\nbYUIDw+nmZmZIK+SkJDAn3/+me7u7lRWVqacnBybN2/OgQMH8ttvv2Xr1q1pa2tLVVVVQV3Zzc2N\nvXv35oQJE7hs2TLu2LGD/fr1o4yMDGNjY8tlLlm0aFExeZxC3Lhxg7Vr1y73OW3ZsoX29vZVlij6\nHMjNzeWRI0fo5+dHTU1Ntm3blgEBAXzz5s3f3bQvGV9J9+9Cbm4ut2zZwrp169LJyYk7d+4s1VZ2\n9OhROjs7l0oA7dq148mTJ5mamkp1dXWBIBs1asTQ0NAKtSsuLo7Dhw+nnp4ef/rpp3KNWj/eX0dH\nh9HR0WWWc3Z25oULF8pVp0QiYVJSEu/evcuTJ09yy5YtXLRoEU1MTOjg4EALCwsqKSkRABUUFGhi\nYsJWrVrRw8ODampqnDNnDnfv3s3z58/zyZMnZdrXDx8+TH19fTo5OZX7nBMTE6mlpVWi6q9EIqGJ\niQmfPHlS7vrGjBnDzp0714hnQk0hOzub+/fvZ69evaihocEOHTpw69atNaqE/A/FV9L9uyGRSBgU\nFEQPDw+am5tzxYoVxZSDJRIJ69aty5CQkBLr6NevnxCrtVGjRoINccmSJRw1alS52vHmzRtOmTKF\n2tranD59epUEPxctWsRu3bqVuv3FixfU0dFhdnY2ExISePPmTR47dowBAQFcsGABR40aRV9fX7q4\nuNDMzIwKCgrU1dVl/fr12bZtW7Zv3552dnaUlZWlvr4+e/Towe3bt5f4B1+5ciWdnJzK1Xncv3+f\nenp6nDhxIkePHl2hc+7Tpw/XrFlT4rYRI0ZwxYoV5a4rLy+PLVu25A8//FChNnwpyMzM5J49e9i1\na1dqaGiwc+fO3LRpEx8+fPiP6khqCKXy6tfFEX8Dbt26hRUrViAkJARDhgzB+PHjhYUG27Ztw/bt\n2xEaGlpsv6lTp0JPTw/Tp0/HlClToKmpiblz5+LZs2do0aIFEhISSo30lJWVhdWrV2PNmjXo1asX\n5s6dW25ZmUKQ78MBpqamIiUlBYmJiRg2bBh69eoFPT09QccqJSUFKSkpeP78OTIyMiCVSqGrq1sk\ngtrHEdWMjY2hqKiIs2fP4tixYwgODkbt2rURGxuLqVOnYuLEiWUuACCJwYMH4927d/jzzz9LLfvm\nzRs0bdoUCxcuRHBwMNq0aYOhQ4eW+xpcunQJw4cPx6NHj4odIygoCP/73/9w/vz5ctf36tUrNGnS\nBL/99luVNM7+bqSnp+Po0aM4efIkwsPDkZaWBldXVzRr1gzNmzdH06ZNa0x37wvF1xVpXyJiYmKw\nZs0abNu2DT4+PpgzZw6srKxga2uLw4cPo1GjRkXKr1y5Ei9fvsSqVasQFBSElStX4uzZswCARo0a\nYeXKlfDw8CiyT15eHjZs2IAlS5agbdu2WLBgAWxtbZGXl1eEID9OJW1LTU2FsrIydHV1hZSdnY2H\nDx9i9OjRMDAwKLJtypQpGDx4MAYOHFhqvIWnT5/i6NGjOHbsGG7dugUPDw/4+vqiU6dOOHfuHH7+\n+WeEh4eXa8VVbm4uPDw80LFjR8ydO7fY9oKCAnh5ecHV1RVLly5FvXr1sGvXLjg5OZX3loEkGjRo\ngDVr1sDT07PItsJYytHR0RWKxhUeHg5fX1+EhIRUqC1fMpKSknDt2jWEhYUhPDwcN2/ehJmZGZo3\nby4Qcd26df/N4SCrl3QfPHiAPXv2ICsrCx4eHvDx8fk3X7waR1paGn777TesXr0aPXr0gIqKCmRl\nZbFq1aoi5Xbt2oWgoCDs3r0bGRkZMDExQWxsLMRiMZYvX46YmBgMHToUKSkpeP36taAwATM/AAAg\nAElEQVSrpqGhAQsLC+Tl5QkEmpeXB11dXejo6BQhyo/Th9t1dHSKKXCQhLe3Nzp16oTx48cL+ZmZ\nmTA1NUV8fHyR5bESiQTh4eE4evQojh49irdv36Jz587w9fWFp6enEOsgPz8fDg4O2LRpU7GOpCwk\nJiaiadOm+OWXX9CtW7ci27777jvExsbiyJEjyM3NhYGBAd6+fVvhwC/r16/HmTNncODAgWLbCsUl\n+/XrV6E69+/fj++//x7nzp1D7dq1K7TvPwFisRgREREIDw9HeHg4wsLCkJiYCBcXFzRv3hyNGzdG\nrVq1YG1tXepy6i8BV69exeHDhyGVSuHj41NWwKrqI92ZM2di6dKlRfIaN26MkJCQ/4IER5UgFouR\nlZUlBIT5+PPVq1c4fvw4wsLCIJVK0adPH0HWvVAyJT4+Htra2kKeoqIiNDU1oaysjMTERCFewIMH\nDyASidC9e3e4uLgUI1ORSFRtgdAfPnyINm3a4OHDh9DX1wcAHDx4EBs2bEBISAiysrIQEhIiBOkx\nMTGBr68vfH190bhx4xJHwevWrcOxY8dw6tSpCrfn5s2b+OabbxAaGiooPWzYsAG//PILwsPDoaGh\ngStXrmDixIm4fv16hevPzMyEtbU1rl27BhsbmyLbtmzZgpMnTyIwMLDC9W7duhU//PADLl26VGKo\nyH8bUlJScP36dYSFheHOnTt4/vw5nj9/DmVlZVhbW5eYLC0t/5Y4ERKJBP369cPevXuL5Pv4+ODA\ngQMlKaFUD+kGBQXBx8enxIL9+/fH9u3by6rriwZJSCQS5OfnIz8/Xwh88uHv7OzsEsmypLySyuTn\n5wsRqMr6JCko0fr6+qJHjx7Q1dVFVFQUNm3ahEOHDkEkEmHx4sVQUlLCjz/+iLy8PNjb20MqlcLY\n2BgLFixAhw4dPptUysSJE/Hu3TsEBASAJLp06SJoZV2/fh3NmjWDr68vOnfu/MlwmVlZWbCzs8OJ\nEycqHHu3EHv27MGsWbNw/fp1PHz4EN9++y2uXLkCW1tbAMAvv/yCR48eYf369ZWqf+7cuUhOTsbG\njRuL5KekpMDGxgYxMTHQ0tKqcL1r1qzBunXrcOnSJRgaGlaqbf9kkMTr168FAv44xcXFQU9PrwgR\n16pVC+bm5tDS0oKmpiY0NDSgqalZbl3E8mDNmjWYMGFCidsWLFiAefPmfZxdPaTbtWtXHDlypMSC\ncnJy2L9/P7S1taGoqAh5eXnIysqisH4ZGRnh+4fHLJzRk0qlAumVRHhV+V3efWRlZaGkpARFRUUh\nFf5WUFCAmpraJwmzpM/C7yoqKuUmwYULFyIyMhK5ubm4evUq5s2bB1tbWyEwNgCEhITghx9+QNeu\nXbFmzRqoqamhbt26OHTo0GfXpXr69CmaNGmCVq1a4datW0hOToa/vz969OgBDw+PCr0yLlq0CA8f\nPsTu3bur1KZZs2YhNDQUMTEx2LVrF9q1aydsGzhwIFq3bo1hw4ZVqu43b97A3t4eERERxSYke/Xq\nhXbt2mHkyJGVqnvBggU4ePAgzp8//1+bfPokJBIJXr58WYSIo6OjER8fj/T0dKSnpyMjIwPp6emQ\nlZUVCPhDMi7pd+F3dXV1yMnJCf8fiUQCkvD398eLFy9KbJOZmRni4uI+zq4e0m3SpAlu3bpVnmtT\nJcjKygpJQUEBcnJyUFBQgLy8PBQUFIqlj0myMCkrK0NJSQkqKipQVlaGiooKVFRUoKqqChUVFaip\nqUFVVVVIKioqpZJuYUfyucjs4cOH6NixI2JiYnDjxg3MnDkTjx8/hoaGBiIiIpCUlCTERvXz88O0\nadOgpqaG1q1bIz4+vsZt7AUFBQgLC0NwcDCCg4MRFRUFa2trZGZmYvHixVi8eDHu3btX4XrfvHmD\nOnXqIDw8XBiVVhbp6ekwNzeHo6MjLl++XOTe1a9fHzt27Kj0SBp4P7qXk5PDihUriuSfOHECP/74\nI8LDwytVL0lMnjwZYWFhOH36dJGYuP81lGSSK+37h5+pqalIS0vD27dvhWDyYrEYYrFYGOAVDvY+\nwYGfhIyMDKRSabHsUstXhHQHDRqEbdu2lVhQS0sLiYmJUFZWFk4mPz8fubm5SEtLw8uXL5GYmIhX\nr14hOTkZb968QUpKCtLS0pCeni5csOzsbGEUKhaLISMjI4ySSUJeXh5qamrQ1NSEvr4+DA0NYWho\nCA0NDairq0NVVRWysrLVPmIuFJQsayRbkVGvSCQS2lrihSehp6eHiIgIIcj5ypUrMW/ePKiqqiI3\nNxcDBgzA1q1bkZSUJCgcOzs74+eff0abNm3Kuq+VwrNnzwSSPX/+POzs7ODt7Q1vb280b94csrKy\ngg6Xk5MTFi5cWOFjTJo0CXl5eVi3bl2V2iqVStGjRw+IRCLcvn0bY8aMwZgxYwAA7969g4GBAdLS\n0j6pSlwW4uPj0aBBA0RFRRXxVhCLxbC0tMSZM2fg4OBQqbpJYvjw4YiJicHx48f/EfFuSeLdu3cV\nIshP5X1sklNWVoacnBxIoqCgAHl5ecjJyRG+5+bmIi8vDyoqKhCJRNDQ0IC2tja0tLSKpMI8HR0d\nYbJYR0cHWlpaUFJSgoKCAmRlZYWOumXLlrh69WqJ512/fn389ddfH2dXD+neunULrq6ukEgkxQrO\nnDkTixcvLquuCoMkMjMz8fbtW6SlpeHNmzeIiorC/fv38eTJE8TGxiIpKQk5OTlQUlKCjIwM8vLy\nICcnBwMDA1hYWMDW1haWlpYwMzODubk5zMzMYGZmBk1NzQqPWvPz86tk0/04LycnB2pqaqUS85Ur\nV+Dk5ARnZ2ekpaXh1KlTiI6OhqurK2JiYmBqaoq4uDjs3bsXDRs2hKamJpYuXYqEhASsXbu2ytc/\nMzMTZ8+eRUhICIKDg/Hu3Tt4e3vDy8sL7du3FybNPsTly5fh4eGB06dPl0uK6EPExsaiUaNGePDg\nAYyMjKrU9nnz5uHs2bMIDQ1FfHw8WrZsid27d8PT0xNXr17F+PHjcePGjSodAwCGDRsGMzMz/PDD\nD0XyZ8yYAZJYtmxZpeuWSCTo27cv8vPzsX//fsjLy1extRUH+V4Qs1AotbT06tUrZGVlQUVFBerq\n6kWe6dK+l5SnrKyMrKwsJCcn49WrV3jx4gWio6Px7NkzREdHQ15eHjY2NkWShYWFQJja2trQ0NCo\n9je9/fv3lxqAf/PmzRgyZMjH2dXnvbB3716MGTMGaWlpAN7bcgcNGoQNGzb8LQ8F8H7kUnhjnj59\nisePH+Px48d4/vw5kpOTBfOBnJwc8vPzkZGRAVlZWVhbW6NevXqoX7++8GljY/PZzkMikeDdu3el\nkvXu3bsRFxeH9PR0pKSkwMzMDKmpqXB1dUV6erqgLFBoc87NzYVIJEJmZiZcXFygp6dXLpewQjct\nqVSKO3fuCKPZ27dvw9XVVRjNOjo6frKjun//Ppo3b44ZM2aU6CtbFgYNGgRzc/NKjZA/xJ9//omp\nU6fi+vXrwmTU2bNn4efnh6tXryIoKAgRERHFJsEqg6ioKLRo0QLR0dHC2wbwXuzS09MTL168qNLz\nlJ+fj65du0JPTw9//PFHtQlGSqVSvH79ulxkWqguXFYyMjIqN9mRRHx8PG7fvo1nz54VSXFxcTAw\nMChGrLVq1YKNjc3fauNesWIF5s+fj+zsbACAkpISpk+fjgULFpRUvHr9dHNychAcHIysrCy4ubl9\n0e4tYrEYcXFxxW7urVu3kJqaCisrK2hoaKCgoABJSUlITk6Gvb19ESKuV68erKysPptCqlgsxp9/\n/ok5c+YgKSkJGzZsQJ8+fbBr1y6cO3euiImnR48eUFJSwunTpzFgwACMHTsWHTp0wKRJk2BkZFSu\nBRBSqRTy8vLIz8+HkpISTExMYGdnh/r16wsKwx8nbW3tEv9gs2bNwps3b3Dw4EE8fvwYenp65Trn\niIgItG3bFlFRUVXy0yyUxgkJCSlmr123bh3Wr1+Phg0bok2bNoKselXRp08fNGnSpJi0fPPmzTFn\nzhx06tSpSvVnZ2ejQ4cOcHR0xNq1a8vs+PLz85GUlPRJMn39+jU0NTU/SaalCWJWBFlZWbh16xbC\nw8Nx7do1hIeHQyKRoEmTJrCzsxMI1cbGBlZWVl+0KeXt27cICQmBRCJB+/bty3q+v65IKwnJycnC\nQ3Dt2jXcuHED+vr6sLOzE2x0r1+/xqNHj5CamgoHBwfUr1+/CCGb/j/2zjsqqqv7+1uK9DqNXgVF\nOiIoXQGxoFhRwQJYQKzYYyJ2xAIixq4RFTsqNkSjQRS7YtcoFiyAKCJFpM3c7/uHL/MTYegmTxI/\na80C7pxz7rmXmX332WcXTc0W21wrKCigHTt20KpVq0hbW5uCgoLol19+oTdv3hARUWRkJOXn59Py\n5cuFfaZMmUK6urrk5+dH06dPp/Pnz5OJiQn17NmTJk2aVOMc5eXllJaWJtRmX716RW5ubmRvb08W\nFhbUunVrkRFpXwvrwsJCUlBQqCGMjx49Sv7+/nT//n1SUFCgKVOmEIvFIi6XSzweT2Qggo+PD7m5\nuVFYWFiT719ubi7Z2dnRypUra10KAqCQkBDas2cPnT17tkUqCRMR3blzh3r06EHPnz+vJjA2bdpE\np0+fpoSEhGaf48OHD+Tq6krW1tbUu3dvkcK0qKiIuFwuqamp1SlIeTxes+zZomAYhv78889qAvbp\n06dkYWFBnTp1EoYG6+rq/uUeNn8xP4RuQxAIBPTnn39WE8RPnz4lS0tLsrKyIjU1NZKUlKScnBx6\n+PAh3b9/n8rKysjMzIw6d+5Mjo6O5OjoWKutUxQA6Nq1a7Rx40Y6dOgQ9ejRgyZNmkSdO3cWbiKU\nlpaSuLg4TZkyhXR0dGjq1KnC/suWLaO8vDxasWIFEf1flWEpKSm6ePEiaWlp0ePHj4VCNi0tjUxN\nTYUmg44dOzZp+SsQCKigoKCaML527Rpt2LCBxowZQ2/evKG9e/eSra0tlZSU0Lt37+j9+/ekoqJS\nI/dCaWkp7dy5kw4cOEB6enqkpqbWaG2nvLycunbtSh4eHqKWe0T05cHGYrFoxowZNYJ8moO3tzd5\ne3tTSEiI8FhhYSHp6urSs2fPGhQWDICys7MpIyOjRsXfzMxM4nA4Qj9gNze3WoUpm83+y1ZkRP+n\nuFS9rl+/Tmw2Wyhc7e3tydLSskV9Zv8h/BC6TeXTp09048aNak9uhmGoU6dO5OnpSU5OTpSfn0+X\nL1+mtLQ0unz5MqmpqZGTk5PwZWhoWOOpXlxcTLt376YNGzZQUVERBQcHU0BAAHG53GrtOBwO3b9/\nn3g8Hg0ZMoT69OlDfn5+wvd37NhBp0+fpvj4eOGxU6dOka+vr3AXV15enrp3705eXl7k7u7+3exi\nYWFhpKSkJNxU+uWXXygrK4u2bdtGRF8E9dd2xOzsbMrOzqb169eThoYGSUpKUnZ2NuXm5pKcnFyt\nSXG+/VtOTo4A0OjRo+njx4+UkJBQp9C5fPkyBQcHU3FxMS1dupSGDBnSItd+6dIlGjZsGD158qTa\nQ8zf3586depEEydOFB7Lz88XCtOvBWxGRgbJy8uTsbExGRkZVav4a2hoSNLS0pSVlUXOzs4UFhZW\nbcy/ivz8fEpKSqKTJ0/S5cuXKT8/n+zs7IQC1s7OrlFKx7+YH0K3pQBAr1+/psuXL9PJkyfpxIkT\npKWlRT4+PtSnTx+ysLCgBw8eUFpamvBVWVlJjo6O5OTkRFwul86dOycs8R0cHEweHh4iBYWFhQVt\n376drK2tydXVlebNm1ct0crvv/9OERERtHjxYjpz5gydOnWK7t27R58/f6apU6dSWloalZaW0saN\nG8ne3v673ReBQEA6Ojp09uxZateuHRF90fSMjY3pjz/+IFNT01r7HT9+nGbPnk137twR2ogZhqH8\n/PxqwvnrJfTXf0tKSpKMjAyVlJRQz549SVtbu1ZBXRX2/Ouvv9Ldu3dp/Pjx5OHhQcnJydShQ4cW\nuQdubm40ZswY8vf3p5KSEnr69CklJCTQ1q1bqVu3bkLhWllZWU2gVglYIyMjUlJSqvc8L168IC8v\nLxoyZAgtWLDguy/Tnz9/TkeOHKGjR4/SzZs3qWvXruTt7U2Ojo7Utm3bv1Sz/gfxowT796KqnPe0\nadPQpk0baGlpITQ0FKdOnRImGf/zzz8xduxYcLlcSEpKonXr1nB0dER4eDhOnTpVI6/u13h6euLk\nyZMQCARQUlJCbm4uysvLkZaWhsWLF6NTp05o1aoVbGxsMH36dPz+++8oLS1Fhw4dcPHiRTAMg127\ndkFNTQ3jxo37bsmmU1JSak0IHhUVhT59+tTah8/nw8zMDEeOHGnSORmGwaFDh8BmsxEfH4+dO3di\n+fLlCAsLw5AhQ+Di4gIjIyPIy8tDVlYWhoaG4PF4sLGxweTJk+Hv7w9VVVXs27cPjx8/FlntVxQV\nFRV4/Pgxjh07hqioKPTs2RNycnLQ0tKCtLQ0TE1Nhblm58+fjwsXLuDt27eNKuopitzcXHTo0AFj\nxoxp8UrCAoEAV65cwZw5c2Bqagoej4fRo0fj6NGjwuodP6iXH/l0/woA0J9//inUCh48eEA8Ho+y\ns7PJ0dGRJk6cSD169KDi4mKhOSItLY1u3rxJxsbG5OrqSt7e3uTi4iLccPLy8qLx48dTSUkJjR8/\nnmxtbenKlStkZGREbm5u1KFDBwoNDaWCgoJqcwkODiYzMzPhEvTjx480Z84cOnLkCEVFRdGQIUNa\nVEMKCQkhfX19mjVrVrXjZWVlZGxsTHv27CFHR8dq7+3cuZM2btxIFy5caNJcMjIyyMnJifbv319v\nMEhxcTHl5ORQjx49aNSoUSQjI0M5OTmUnJxML1++JBaLRVlZWcRisUhXV5f09PRIV1eXdHR0SE5O\nThgZlZmZKdRYX79+TVpaWtU01djYWJoxYwYFBQUJNffw8HAqLi6mVatWNfoa67umAQMGkJycHO3e\nvZtkZGSaPFZZWRmdPXuWjhw5QseOHSNVVVXq06cP+fj4kJ2d3Q9ttvH80HT/KsrKyrBr1y44OzuD\ny+WiV69e8PDwgIKCArp27YqYmBi8ePGiRp9Lly5h0aJFsLOzg7KyMtzd3TFo0CAoKytDRkYGurq6\nMDY2xtGjR6tpqwzDQEpKqoYGsnHjRowcObLG/C5fvgwLCwt4enoiIyOjRa65oqICbDa7xnVV8dtv\nv8HJyamahldWVgY9Pb0GVdCtjYKCArRr1w4bNmxocJ/Pnz9DRkYGZWVlwmMCgQBDhw7FwIEDcevW\nLcTExGD48OHo2LEjeDwexMXFhZWAxcXFIScnByMjI3h5eSEsLAxr1qzBsWPHcO/ePRQVFeHgwYOw\ns7Ordq1Pnz4Fh8NpcP25xlBeXo6hQ4fC2dm50auY9+/fIy4uDv369YOioiJcXFywcuVKPHnypMXn\n+R/kR7me783du3cxffp0cDgceHp6IiEhoVottJKSEiQmJiIoKAgcDgfm5uZYuXIl8vLyUF5ejosX\nL2LJkiXw9PSEnJwcdHR0YGBgADExMZiamsLR0RGzZs2q9dw6Ojo1apXduHED5ubmtbavrKzEypUr\nwWKxsGDBgmpCqCkkJSWhc+fOIt/n8/lo3749jh8/LjwWExMjLCnfWPh8Pnr27InQ0NBG9bty5Qqs\nra1RWVmJe/fuIS4uDhMnTkSnTp0gJiYGZWVlDB48GIsWLcK+ffuQnp6O4uJiYX+BQICcnBxcvnwZ\ne/fuRWRkJEJCQtCjRw+YmJhAVlZWWK7dyckJYWFhiImJQWJiImxsbLBz584mXW99CAQCTJ48GWZm\nZvWWSy8tLcX27dvh4uICRUVF9O/fH9u3b8f79++/y9z+w/wQut+DZ8+eYfHixTA1NYWOjg5mzZrV\nIO2xtLQUa9euhZWVFSQkJCAhIQFjY2NMmTIFR44cQX5+vrCtvb09VqxYAQ6HAxaLBWNjY0ybNg2p\nqalCW56WlhZevXpV7RxlZWWQkZGp0wb38uVL+Pj4oG3btrh69WoT7wIwYsQIkXXDqkhMTIS5uTn4\nfD4KCwvB4/EaXJ7+W2bOnAk3NzeRBT6/prKyEnfu3MG2bdvg6uoKLpcr1FaHDBmCFStW4I8//sDT\np09hZGSETZs2NWlOwJdVx7t37xAeHo727dtjxYoVmDBhAry9vaGpqQlxcXEoKirCwsICffr0wcSJ\nExEVFYWEhATcuHEDeXl5Tbb3MgyDyMhI6Onp4c8//6zx/pMnTzBt2jSw2Wx0794dhw8fbrQN+weN\n4ofQbSmqyn/b29uDw+EgNDQUaWlpdRbiq6iowKVLlxAREYFu3bpBQUEB1tbWCAsLw86dOzF//nwY\nGhrCysoKGzdurKZdWVhY4OrVq5CRkUFxcTFu3LiBefPmwdraGqqqqvD394esrCwyMzNrnNfa2hqX\nL1+u95r2798PLpeLiIgI8Pn8Rt2P0tJSKCsrIycnp852DMPAwcEBO3bswE8//YQRI0Y06jxV7Ny5\nE/r6+rVqZhUVFbh9+za2bt2K0NBQ2NvbQ1ZWFm3btoWfnx/s7OwQFhYmcuPyyZMn4PF4SE5ObtLc\nvp6HsbExTp06JTxWXFwMFRUV3LlzB+np6Th06BCio6MxefJk+Pj4wMrKCsrKypCTk4OpqalQk1+2\nbBkSEhJw9+7dBm1i/fbbb+DxeLh69SoqKytx8OBBeHp6gsPhYObMmXj27Fmzru0HDeaH0G0O+fn5\n2LJlC9zd3aGsrIwRI0YgOTlZ5K5xbULWysoKU6ZMQWJiYq0VeAUCAU6dOoW+fftCRUUFoaGhuHv3\nLhQUFHD27FlYWFjU6PP69WusW7cORAR5eXm4u7sjNjYWubm5AIDRo0fj119/bdA1vnr1Cq6urnB1\nda2hNdfFwYMH0bVr1wa1PX/+PLS0tKCiooI3b940+BxVXL16FWw2G/fu3UNFRQXS09OxZcsWjBs3\nDnZ2dpCVlUW7du3g7++P6OhopKamVivDbmlpWa9Gf+HCBXA4HNy5c6fR8/uahIQEWFlZVXsYT58+\nHWFhYXX2KygowJ07d3DkyBHExsYiLCwMPj4+MDExgZSUFHR0dODh4YHQ0FDExMQgKSkJT58+rfZZ\njIuLg6ysLFgsFhwdHREfH99sE9IPGs0PodtYSkpKsHfvXvj4+AhtXwkJCbVqGxUVFbh8+TKWLl0K\nLy+vBgnZunj9+jXCw8PB4/EgISGBkSNHIjAwsNa2xcXFkJWVRXFxMQ4fPoxhw4ZBSUkJPj4+CA4O\nrnUzTRR8Ph8RERHgcDjYv39/g/oMGjSoUUtydXV1dO/evcHtgS+bRcnJyVBWVoaXlxdsbW0hIyOD\n9u3bY/jw4Vi1ahXOnz9fTcB+S2lpKWRkZBq0pN69ezd0dHTqtY/WBcMwsLe3x65du4THqkrSN9Vt\nr7KyEs+ePcPJkyexevVqjB8/Hp6entDV1YWUlBS0tbWhpqYGKSkpWFtbQ0FBAWvWrGkRF7UfNJof\nQrchVFRU4Pjx4/D394eSkhK8vLwQFxeHgoKCGu2+FbKWlpaYPHlyk4SsKC5evAgDAwOoqqpCQUGh\n1uVhdnY21NTUqh0rLCzE1q1bYW1tDXFxcUyaNAnp6ekN/vJdu3YNbdq0QWBgYDVTx7cUFRVBUVGx\nwdd78eJFcLlccDgckUv8srIy3LhxAxs3bsTYsWPRoUMHyMjIQEZGBtbW1li9ejXS0tLw6dOnBp2z\niqtXr8LS0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lKily9ftmhtuE+fPpGLiwsNGjRIaBppCAKBgKysrKhv3760bds28vDwoCNH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"text/plain": [ "<matplotlib.figure.Figure at 0x108ff1cc0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x108699518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from circos import CircosPlot\n", "\n", "fig = plt.figure(figsize=(6,6))\n", "ax = fig.add_subplot(111)\n", "\n", "nodes = sorted(G.nodes())\n", "edges = G.edges()\n", "\n", "c = CircosPlot(nodes, edges, radius=10, ax=ax)\n", "c.draw()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's pretty obvious in this visualization that there are nodes, such as node 5 and 18, that are not connected to any other node via an edge. There are other nodes, like node number 19, which is highly connected to other nodes.\n", "\n", "Finally, let's try hive plots for the network. Two groups (male and female), and then edges drawn between them." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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+Kud1T2Lhg46Jk4i8F/hAK25hb5/5mfRn7zXxR1S1sGmHiDytqif934N+fDSQ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gAAAg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\nG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4Y/pEkmKl1pJVmrUTLdra6g+PLGEt82EkJYQBnxZZnSDyD3fm8ntm1QXIidniKnfvg2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\nAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1090a7dd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nodes = dict()\n", "nodes['male'] = [n for n,d in G.nodes(data=True) if d['sex'] == 'Male']\n", "nodes['female'] = [n for n,d in G.nodes(data=True) if d['sex'] == 'Female']\n", "\n", "edges = dict()\n", "edges['group1'] = G.edges(data=True)\n", "\n", "nodes_cmap = dict()\n", "nodes_cmap['male'] = 'blue'\n", "nodes_cmap['female'] = 'red'\n", "\n", "edges_cmap = dict()\n", "edges_cmap['group1'] = 'black'\n", "\n", "h = HivePlot(nodes, edges, nodes_cmap, edges_cmap)\n", "h.draw()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

hydrogo/MORS

sandbox/meteo_data_preparation.ipynb

1

8356

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Aim: convert spatially-distributed WFDEI forcing data to averaged lumped timeseries of Temperature and Precipitation" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Nadym" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#read basin shematization file\n", "nadym_coord = pd.read_csv('../../Schemes/drt_05_nadym.csv', usecols=[0, 1])\n", "nadym_coord.columns = ['lon','lat']\n", "#calculate weights of each cell for further weighted averaging\n", "nadym_coord['weights'] = (np.cos((nadym_coord['lat'] + 0.25)*np.pi/180) + np.cos((nadym_coord['lat'] - 0.25)*np.pi/180))\n", "nadym_coord['weights'] = nadym_coord['weights']/np.sum(nadym_coord['weights'])" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#read WFDEI forcing data\n", "nadym_forc = pd.read_csv('../../Meteo_forcing_WFDEI/wfdei_for_nadym.csv', index_col=0, parse_dates=True)\n", "#cut necessary variables (Rainf, Snowf, Tair)\n", "nadym_rain = nadym_forc[['Rainf_'+str(point) for point in nadym_coord.index]]\n", "nadym_snow = nadym_forc[['Snowf_'+str(point) for point in nadym_coord.index]]\n", "nadym_temp = nadym_forc[['Tair_'+str(point) for point in nadym_coord.index]]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#convert precipitation (average rate mm/sec for 24 hours) to (sum of mm for whole day) \n", "#just multiply all values by 60sec*60min*24h\n", "nadym_rain = nadym_rain*(60*60*24)\n", "nadym_snow = nadym_snow*(60*60*24)\n", "#convert temperature from Kelvins to Celsius\n", "nadym_temp = nadym_temp-273.15\n", "#summarize rain and snow to one precipitation parameter\n", "nadym_prec = nadym_rain.add(nadym_snow.values)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#weighted averaging (sum of weight_i*value_i)\n", "nadym_prec_avrg = nadym_prec.mul(nadym_coord['weights'].values, axis=1).sum(axis=1)\n", "nadym_temp_avrg = nadym_temp.mul(nadym_coord['weights'].values, axis=1).sum(axis=1)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#wrap up results to .csv file\n", "pd.DataFrame({'Temp': nadym_temp_avrg, 'Prec': nadym_prec_avrg}, index=nadym_temp_avrg.index).to_csv('nadym_avrg.csv')" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Once deleted, variables cannot be recovered. Proceed (y/[n])? y\n" ] } ], "source": [ "%reset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Pur" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "#read basin shematization file\n", "pur_coord = pd.read_csv('../../Schemes/drt_05_pur.csv', usecols=[0, 1])\n", "pur_coord.columns = ['lon','lat']\n", "#calculate weights of each cell for further weighted averaging\n", "pur_coord['weights'] = (np.cos((pur_coord['lat'] + 0.25)*np.pi/180) + np.cos((pur_coord['lat'] - 0.25)*np.pi/180))\n", "pur_coord['weights'] = pur_coord['weights']/np.sum(pur_coord['weights'])\n", "\n", "#read WFDEI forcing data\n", "pur_forc = pd.read_csv('../../Meteo_forcing_WFDEI/wfdei_for_pur.csv', index_col=0, parse_dates=True)\n", "#cut necessary variables (Rainf, Snowf, Tair)\n", "pur_rain = pur_forc[['Rainf_'+str(point) for point in pur_coord.index]]\n", "pur_snow = pur_forc[['Snowf_'+str(point) for point in pur_coord.index]]\n", "pur_temp = pur_forc[['Tair_'+str(point) for point in pur_coord.index]]\n", "\n", "#convert precipitation (average rate mm/sec for 24 hours) to (sum of mm for whole day) \n", "#just multiply all values by 60sec*60min*24h\n", "pur_rain = pur_rain*(60*60*24)\n", "pur_snow = pur_snow*(60*60*24)\n", "#convert temperature from Kelvins to Celsius\n", "pur_temp = pur_temp-273.15\n", "#summarize rain and snow to one precipitation parameter\n", "pur_prec = pur_rain.add(pur_snow.values)\n", "\n", "#weighted averaging (sum of weight_i*value_i)\n", "pur_prec_avrg = pur_prec.mul(pur_coord['weights'].values, axis=1).sum(axis=1)\n", "pur_temp_avrg = pur_temp.mul(pur_coord['weights'].values, axis=1).sum(axis=1)\n", "\n", "#wrap up results to .csv file\n", "pd.DataFrame({'Temp': pur_temp_avrg, 'Prec': pur_prec_avrg}, index=pur_temp_avrg.index).to_csv('pur_avrg.csv')" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Once deleted, variables cannot be recovered. Proceed (y/[n])? y\n" ] } ], "source": [ "%reset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Taz" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "#read basin shematization file\n", "taz_coord = pd.read_csv('../../Schemes/drt_05_taz.csv', usecols=[0, 1])\n", "taz_coord.columns = ['lon','lat']\n", "#calculate weights of each cell for further weighted averaging\n", "taz_coord['weights'] = (np.cos((taz_coord['lat'] + 0.25)*np.pi/180) + np.cos((taz_coord['lat'] - 0.25)*np.pi/180))\n", "taz_coord['weights'] = taz_coord['weights']/np.sum(taz_coord['weights'])\n", "\n", "#read WFDEI forcing data\n", "taz_forc = pd.read_csv('../../Meteo_forcing_WFDEI/wfdei_for_taz.csv', index_col=0, parse_dates=True)\n", "#cut necessary variables (Rainf, Snowf, Tair)\n", "taz_rain = taz_forc[['Rainf_'+str(point) for point in taz_coord.index]]\n", "taz_snow = taz_forc[['Snowf_'+str(point) for point in taz_coord.index]]\n", "taz_temp = taz_forc[['Tair_'+str(point) for point in taz_coord.index]]\n", "\n", "#convert precipitation (average rate mm/sec for 24 hours) to (sum of mm for whole day) \n", "#just multiply all values by 60sec*60min*24h\n", "taz_rain = taz_rain*(60*60*24)\n", "taz_snow = taz_snow*(60*60*24)\n", "#convert temperature from Kelvins to Celsius\n", "taz_temp = taz_temp-273.15\n", "#summarize rain and snow to one precipitation parameter\n", "taz_prec = taz_rain.add(taz_snow.values)\n", "\n", "#weighted averaging (sum of weight_i*value_i)\n", "taz_prec_avrg = taz_prec.mul(taz_coord['weights'].values, axis=1).sum(axis=1)\n", "taz_temp_avrg = taz_temp.mul(taz_coord['weights'].values, axis=1).sum(axis=1)\n", "\n", "#wrap up results to .csv file\n", "pd.DataFrame({'Temp': taz_temp_avrg, 'Prec': taz_prec_avrg}, index=taz_temp_avrg.index).to_csv('taz_avrg.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

ES-DOC/esdoc-jupyterhub

notebooks/nasa-giss/cmip6/models/sandbox-1/seaice.ipynb

1

99813

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Seaice \n", "**MIP Era**: CMIP6 \n", "**Institute**: NASA-GISS \n", "**Source ID**: SANDBOX-1 \n", "**Topic**: Seaice \n", "**Sub-Topics**: Dynamics, Thermodynamics, Radiative Processes. \n", "**Properties**: 80 (63 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/seaice?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:54:21" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "**IMPORTANT: to be executed each time you run the notebook** " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'nasa-giss', 'sandbox-1', 'seaice')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties --&gt; Model](#1.-Key-Properties---&gt;-Model) \n", "[2. Key Properties --&gt; Variables](#2.-Key-Properties---&gt;-Variables) \n", "[3. Key Properties --&gt; Seawater Properties](#3.-Key-Properties---&gt;-Seawater-Properties) \n", "[4. Key Properties --&gt; Resolution](#4.-Key-Properties---&gt;-Resolution) \n", "[5. Key Properties --&gt; Tuning Applied](#5.-Key-Properties---&gt;-Tuning-Applied) \n", "[6. Key Properties --&gt; Key Parameter Values](#6.-Key-Properties---&gt;-Key-Parameter-Values) \n", "[7. Key Properties --&gt; Assumptions](#7.-Key-Properties---&gt;-Assumptions) \n", "[8. Key Properties --&gt; Conservation](#8.-Key-Properties---&gt;-Conservation) \n", "[9. Grid --&gt; Discretisation --&gt; Horizontal](#9.-Grid---&gt;-Discretisation---&gt;-Horizontal) \n", "[10. Grid --&gt; Discretisation --&gt; Vertical](#10.-Grid---&gt;-Discretisation---&gt;-Vertical) \n", "[11. Grid --&gt; Seaice Categories](#11.-Grid---&gt;-Seaice-Categories) \n", "[12. Grid --&gt; Snow On Seaice](#12.-Grid---&gt;-Snow-On-Seaice) \n", "[13. Dynamics](#13.-Dynamics) \n", "[14. Thermodynamics --&gt; Energy](#14.-Thermodynamics---&gt;-Energy) \n", "[15. Thermodynamics --&gt; Mass](#15.-Thermodynamics---&gt;-Mass) \n", "[16. Thermodynamics --&gt; Salt](#16.-Thermodynamics---&gt;-Salt) \n", "[17. Thermodynamics --&gt; Salt --&gt; Mass Transport](#17.-Thermodynamics---&gt;-Salt---&gt;-Mass-Transport) \n", "[18. Thermodynamics --&gt; Salt --&gt; Thermodynamics](#18.-Thermodynamics---&gt;-Salt---&gt;-Thermodynamics) \n", "[19. Thermodynamics --&gt; Ice Thickness Distribution](#19.-Thermodynamics---&gt;-Ice-Thickness-Distribution) \n", "[20. Thermodynamics --&gt; Ice Floe Size Distribution](#20.-Thermodynamics---&gt;-Ice-Floe-Size-Distribution) \n", "[21. Thermodynamics --&gt; Melt Ponds](#21.-Thermodynamics---&gt;-Melt-Ponds) \n", "[22. Thermodynamics --&gt; Snow Processes](#22.-Thermodynamics---&gt;-Snow-Processes) \n", "[23. Radiative Processes](#23.-Radiative-Processes) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties --&gt; Model \n", "*Name of seaice model used.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of sea ice model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of sea ice model code (e.g. CICE 4.2, LIM 2.1, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Variables \n", "*List of prognostic variable in the sea ice model.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Prognostic\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of prognostic variables in the sea ice component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.variables.prognostic') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sea ice temperature\" \n", "# \"Sea ice concentration\" \n", "# \"Sea ice thickness\" \n", "# \"Sea ice volume per grid cell area\" \n", "# \"Sea ice u-velocity\" \n", "# \"Sea ice v-velocity\" \n", "# \"Sea ice enthalpy\" \n", "# \"Internal ice stress\" \n", "# \"Salinity\" \n", "# \"Snow temperature\" \n", "# \"Snow depth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --&gt; Seawater Properties \n", "*Properties of seawater relevant to sea ice*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Ocean Freezing Point\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Equation used to compute the freezing point (in deg C) of seawater, as a function of salinity and pressure*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"TEOS-10\" \n", "# \"Constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Ocean Freezing Point Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If using a constant seawater freezing point, specify this value.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Resolution \n", "*Resolution of the sea ice grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *This is a string usually used by the modelling group to describe the resolution of this grid e.g. N512L180, T512L70, ORCA025 etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Canonical Horizontal Resolution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Number Of Horizontal Gridpoints\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Total number of horizontal (XY) points (or degrees of freedom) on computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --&gt; Tuning Applied \n", "*Tuning applied to sea ice model component*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General overview description of tuning: explain and motivate the main targets and metrics retained. Document the relative weight given to climate performance metrics versus process oriented metrics, and on the possible conflicts with parameterization level tuning. In particular describe any struggle with a parameter value that required pushing it to its limits to solve a particular model deficiency.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Target\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What was the aim of tuning, e.g. correct sea ice minima, correct seasonal cycle.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.target') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Simulations\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Which simulations had tuning applied, e.g. all, not historical, only pi-control? *" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.simulations') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Metrics Used\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List any observed metrics used in tuning model/parameters*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.metrics_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.5. Variables\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Which variables were changed during the tuning process?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --&gt; Key Parameter Values \n", "*Values of key parameters*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Typical Parameters\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *What values were specificed for the following parameters if used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.typical_parameters') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ice strength (P*) in units of N m{-2}\" \n", "# \"Snow conductivity (ks) in units of W m{-1} K{-1} \" \n", "# \"Minimum thickness of ice created in leads (h0) in units of m\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Additional Parameters\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If you have any additional paramterised values that you have used (e.g. minimum open water fraction or bare ice albedo), please provide them here as a comma separated list*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.additional_parameters') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Key Properties --&gt; Assumptions \n", "*Assumptions made in the sea ice model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General overview description of any *key* assumptions made in this model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.description') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. On Diagnostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Note any assumptions that specifically affect the CMIP6 diagnostic sea ice variables.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.on_diagnostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Missing Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List any *key* processes missing in this model configuration? Provide full details where this affects the CMIP6 diagnostic sea ice variables?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.missing_processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Key Properties --&gt; Conservation \n", "*Conservation in the sea ice component*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Provide a general description of conservation methodology.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Properties\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Properties conserved in sea ice by the numerical schemes.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.properties') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Energy\" \n", "# \"Mass\" \n", "# \"Salt\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Budget\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *For each conserved property, specify the output variables which close the related budgets. as a comma separated list. For example: Conserved property, variable1, variable2, variable3*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.budget') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Was Flux Correction Used\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does conservation involved flux correction?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.was_flux_correction_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Corrected Conserved Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List any variables which are conserved by *more* than the numerical scheme alone.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.corrected_conserved_prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Grid --&gt; Discretisation --&gt; Horizontal \n", "*Sea ice discretisation in the horizontal*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Grid\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Grid on which sea ice is horizontal discretised?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ocean grid\" \n", "# \"Atmosphere Grid\" \n", "# \"Own Grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Grid Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the type of sea ice grid?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Structured grid\" \n", "# \"Unstructured grid\" \n", "# \"Adaptive grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the advection scheme?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Finite differences\" \n", "# \"Finite elements\" \n", "# \"Finite volumes\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Thermodynamics Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the time step in the sea ice model thermodynamic component in seconds.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.thermodynamics_time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Dynamics Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the time step in the sea ice model dynamic component in seconds.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.dynamics_time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.6. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify any additional horizontal discretisation details.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Grid --&gt; Discretisation --&gt; Vertical \n", "*Sea ice vertical properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Layering\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *What type of sea ice vertical layers are implemented for purposes of thermodynamic calculations?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.layering') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Zero-layer\" \n", "# \"Two-layers\" \n", "# \"Multi-layers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Number Of Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *If using multi-layers specify how many.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.number_of_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify any additional vertical grid details.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Grid --&gt; Seaice Categories \n", "*What method is used to represent sea ice categories ?*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Has Mulitple Categories\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Set to true if the sea ice model has multiple sea ice categories.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.has_mulitple_categories') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Number Of Categories\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *If using sea ice categories specify how many.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.number_of_categories') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Category Limits\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *If using sea ice categories specify each of the category limits.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.category_limits') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Ice Thickness Distribution Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the sea ice thickness distribution scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.ice_thickness_distribution_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Other\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If the sea ice model does not use sea ice categories specify any additional details. For example models that paramterise the ice thickness distribution ITD (i.e there is no explicit ITD) but there is assumed distribution and fluxes are computed accordingly.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.other') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Grid --&gt; Snow On Seaice \n", "*Snow on sea ice details*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Has Snow On Ice\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is snow on ice represented in this model?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.has_snow_on_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Number Of Snow Levels\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of vertical levels of snow on ice?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.number_of_snow_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Snow Fraction\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how the snow fraction on sea ice is determined*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.snow_fraction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.4. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify any additional details related to snow on ice.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Dynamics \n", "*Sea Ice Dynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Horizontal Transport\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the method of horizontal advection of sea ice?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.horizontal_transport') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Transport In Thickness Space\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the method of sea ice transport in thickness space (i.e. in thickness categories)?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.transport_in_thickness_space') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.3. Ice Strength Formulation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Which method of sea ice strength formulation is used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.ice_strength_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Hibler 1979\" \n", "# \"Rothrock 1975\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.4. Redistribution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Which processes can redistribute sea ice (including thickness)?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.redistribution') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Rafting\" \n", "# \"Ridging\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.5. Rheology\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Rheology, what is the ice deformation formulation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.rheology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Free-drift\" \n", "# \"Mohr-Coloumb\" \n", "# \"Visco-plastic\" \n", "# \"Elastic-visco-plastic\" \n", "# \"Elastic-anisotropic-plastic\" \n", "# \"Granular\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Thermodynamics --&gt; Energy \n", "*Processes related to energy in sea ice thermodynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Enthalpy Formulation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the energy formulation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.enthalpy_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice latent heat (Semtner 0-layer)\" \n", "# \"Pure ice latent and sensible heat\" \n", "# \"Pure ice latent and sensible heat + brine heat reservoir (Semtner 3-layer)\" \n", "# \"Pure ice latent and sensible heat + explicit brine inclusions (Bitz and Lipscomb)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Thermal Conductivity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What type of thermal conductivity is used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.thermal_conductivity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice\" \n", "# \"Saline ice\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Heat Diffusion\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the method of heat diffusion?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Conduction fluxes\" \n", "# \"Conduction and radiation heat fluxes\" \n", "# \"Conduction, radiation and latent heat transport\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Basal Heat Flux\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Method by which basal ocean heat flux is handled?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.basal_heat_flux') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Heat Reservoir\" \n", "# \"Thermal Fixed Salinity\" \n", "# \"Thermal Varying Salinity\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Fixed Salinity Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If you have selected {Thermal properties depend on S-T (with fixed salinity)}, supply fixed salinity value for each sea ice layer.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.fixed_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Heat Content Of Precipitation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method by which the heat content of precipitation is handled.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_content_of_precipitation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.7. Precipitation Effects On Salinity\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If precipitation (freshwater) that falls on sea ice affects the ocean surface salinity please provide further details.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.precipitation_effects_on_salinity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Thermodynamics --&gt; Mass \n", "*Processes related to mass in sea ice thermodynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. New Ice Formation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method by which new sea ice is formed in open water.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.new_ice_formation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Ice Vertical Growth And Melt\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method that governs the vertical growth and melt of sea ice.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_vertical_growth_and_melt') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Ice Lateral Melting\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the method of sea ice lateral melting?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_lateral_melting') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Floe-size dependent (Bitz et al 2001)\" \n", "# \"Virtual thin ice melting (for single-category)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Ice Surface Sublimation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method that governs sea ice surface sublimation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_surface_sublimation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Frazil Ice\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method of frazil ice formation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.frazil_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Thermodynamics --&gt; Salt \n", "*Processes related to salt in sea ice thermodynamics.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Has Multiple Sea Ice Salinities\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the sea ice model use two different salinities: one for thermodynamic calculations; and one for the salt budget?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.has_multiple_sea_ice_salinities') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Sea Ice Salinity Thermal Impacts\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does sea ice salinity impact the thermal properties of sea ice?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.sea_ice_salinity_thermal_impacts') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Thermodynamics --&gt; Salt --&gt; Mass Transport \n", "*Mass transport of salt*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Salinity Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is salinity determined in the mass transport of salt calculation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Constant Salinity Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If using a constant salinity value specify this value in PSU?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.constant_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the salinity profile used.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Thermodynamics --&gt; Salt --&gt; Thermodynamics \n", "*Salt thermodynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Salinity Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is salinity determined in the thermodynamic calculation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Constant Salinity Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If using a constant salinity value specify this value in PSU?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.constant_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the salinity profile used.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Thermodynamics --&gt; Ice Thickness Distribution \n", "*Ice thickness distribution details.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is the sea ice thickness distribution represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_thickness_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Virtual (enhancement of thermal conductivity, thin ice melting)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Thermodynamics --&gt; Ice Floe Size Distribution \n", "*Ice floe-size distribution details.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is the sea ice floe-size represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Parameterised\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Please provide further details on any parameterisation of floe-size.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Thermodynamics --&gt; Melt Ponds \n", "*Characteristics of melt ponds.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Are Included\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are melt ponds included in the sea ice model?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.are_included') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.2. Formulation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What method of melt pond formulation is used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Flocco and Feltham (2010)\" \n", "# \"Level-ice melt ponds\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.3. Impacts\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *What do melt ponds have an impact on?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.impacts') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Albedo\" \n", "# \"Freshwater\" \n", "# \"Heat\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Thermodynamics --&gt; Snow Processes \n", "*Thermodynamic processes in snow on sea ice*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Has Snow Aging\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Set to True if the sea ice model has a snow aging scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_aging') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Snow Aging Scheme\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the snow aging scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_aging_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.3. Has Snow Ice Formation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Set to True if the sea ice model has snow ice formation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_ice_formation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.4. Snow Ice Formation Scheme\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the snow ice formation scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_ice_formation_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.5. Redistribution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the impact of ridging on snow cover?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.redistribution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.6. Heat Diffusion\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the heat diffusion through snow methodology in sea ice thermodynamics?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Single-layered heat diffusion\" \n", "# \"Multi-layered heat diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Radiative Processes \n", "*Sea Ice Radiative Processes*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Surface Albedo\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Method used to handle surface albedo.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.surface_albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Parameterized\" \n", "# \"Multi-band albedo\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Ice Radiation Transmission\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Method by which solar radiation through sea ice is handled.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.ice_radiation_transmission') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Exponential attenuation\" \n", "# \"Ice radiation transmission per category\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }

gpl-3.0

elenad2317/sandbox

Cassie.ipynb

1

12964

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import uuid \n", "import random\n", "import hyperloglog\n", "import pickle\n", "from cassandra.cluster import Cluster\n", "import unittest\n", "import redis" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Redis = redis.StrictRedis(host='localhost', port = 6379, db = 0)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "str" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Redis.set('foo', ['bar1', 'bar2'])\n", "t = Redis.get('foo')\n", "type(t)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class RedisFramework: \n", " def __init__(self): \n", " self.user_group = {}\n", " self.uR = redis.StrictRedis(host = 'localhost', port = 6379, db = 0 )\n", " self.hllR = redis.StrictRedis(host = 'localhost', port = 6379, db = 0 )\n", " ### initialize user_group\n", " for i in range(100):\n", " self.user_group[i] = []\n", "\n", " self.group_hll = {}\n", " self.actual_hll = {}\n", " self.thresholds = [float(random.randint(20, 80))/100 for i in range(100)]\n", " \n", " def INSERT(self):\n", " for j in range(100000):\n", " uid = uuid.uuid4()\n", " r = random.random()\n", " for i in range(100):\n", " if r > self.thresholds[i]: \n", " self.user_group[int(i)].append(str(uid))\n", " \n", "\n", " for key in self.user_group: \n", " self.uR.set(key, self.user_group[key])\n", " hll = hyperloglog.HyperLogLog(0.01)\n", " users = self.user_group[key]\n", " self.actual_hll[key] = len(users)\n", " for user in users:\n", " hll.add(str(user))\n", " pickled = pickle.dumps(hll)\n", " self.hllR.set(key, pickled)\n", " \n", " def GETHLLCARDINALITY(self, groupID):\n", " pickled = self.hllR.get(groupID)\n", " return len(pickle.loads(pickled))\n", " \n", " def GETTRUECARDINALITY(self, groupID):\n", " return self.actual_hll[groupID]\n", "\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get thresholds for groups (between 20 and 80 %)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class testCardinalityErrorRates(unittest.TestCase):\n", " def lessThan10Error(self): \n", " for error in x: \n", " self.assertTrue(error < .1)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rtest = RedisFramework()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rtest.INSERT()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "33285" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rtest.GETHLLCARDINALITY(10)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "33043" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rtest.GETTRUECARDINALITY(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# CASSIE" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cluster = Cluster()\n", "metadata= cluster.metadata\n", "session= cluster.connect()\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "keyname = \"newkeyspace\"\n", "session.execute(\"CREATE KEYSPACE IF NOT EXISTS \"+keyname +\n", " \" WITH replication = {'class':'SimpleStrategy', 'replication_factor':1};\")" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "session.set_keyspace(keyname)\n", "mytable = \"test\"\n", "#session.execute(\"CREATE TABLE users (id int PRIMARY KEY, location address)\")\n", "session.execute(\" CREATE TABLE \" + mytable+\n", " \" (groupID int PRIMARY KEY, userGroups set<uuid>, hll text);\")" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "ename": "SyntaxException", "evalue": "<ErrorMessage code=2000 [Syntax error in CQL query] message=\"line 2:64 missing EOF at ','\">", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mSyntaxException\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-39-b38cb3d59aa5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[0mVALUES\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgroupID\u001b[0m\u001b[1;33m)\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muserGroups\u001b[0m\u001b[1;33m)\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhll\u001b[0m\u001b[1;33m)\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \"\"\", \n\u001b[1;32m----> 9\u001b[1;33m \u001b[1;33m{\u001b[0m\u001b[1;34m'groupID'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'userGroups'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'hll'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mpickled\u001b[0m \u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 10\u001b[0m )\n", "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/cassandra/cluster.pyc\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self, query, parameters, timeout, trace)\u001b[0m\n\u001b[0;32m 1403\u001b[0m \u001b[0mfuture\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1404\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1405\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1406\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1407\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtrace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/cassandra/cluster.pyc\u001b[0m in \u001b[0;36mresult\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 2974\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mPagedResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_final_result\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2975\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_final_exception\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2976\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_final_exception\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2977\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2978\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mOperationTimedOut\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_errors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlast_host\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_current_host\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mSyntaxException\u001b[0m: <ErrorMessage code=2000 [Syntax error in CQL query] message=\"line 2:64 missing EOF at ','\">" ] } ], "source": [ "for key in d:\n", " hll = hyperloglog.HyperLogLog(0.01)\n", " for item in d[key]:\n", " hll.add(item)\n", " pickled = pickle.dumps(hll).encode(\"hex\")\n", " session.execute(\"\"\"INSERT INTO test (groupID, userGroups, hll)\n", " VALUES (%(groupID)s, %(userGroups)s, %(hll)s)\n", " \"\"\", \n", " {'groupID':key, 'userGroups':d[key], 'hll':pickled }\n", " )" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# MAKE ALL DATA STRUCTURES \n", "thresholds = [float(random.randint(20, 80))/100 for i in range(100)]\n", "d = {}\n", "from sets import Set\n", "for j in range(100):\n", " uid = uuid.uuid4()\n", " r = random.random()\n", " for i in range(100):\n", " if r > thresholds[i]: \n", " if i in d:\n", " d[i].add(uid)\n", " else:\n", " d[i]= Set([uid]) \n", " " ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'pick' 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-20-c4c794d19b1c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupid\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mpick\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhll\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpick\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'pick' is not defined" ] } ], "source": [ "results = session.execute(\"SELECT * FROM \" +mytable)\n", "for row in results:\n", " if row.groupid == 10:\n", " pick = pickle.loads(row.hll)\n", "len(pick)" ] } ], "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": "2.7.3" } }, "nbformat": 4, "nbformat_minor": 0 }

lgpl-2.1

jdvelasq/ingenieria-economica

07-impuestos.ipynb

1

13404

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Impuestos Corporativos\n", "===\n", "\n", "**Juan David Velásquez Henao** \n", "[email protected] \n", "Universidad Nacional de Colombia, Sede Medellín \n", "Facultad de Minas \n", "Medellín, Colombia\n", "\n", "---\n", "\n", "Haga click [aquí](https://github.com/jdvelasq/ingenieria-economica/blob/master/04-impuestos.ipynb) para acceder a la última versión online\n", "\n", "Haga click [aquí](http://nbviewer.jupyter.org/github/jdvelasq/ingenieria-economica/blob/master/04-impuestos.ipynb) para ver la última versión online en `nbviewer`.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Preparación**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Tipos de Impuestos**.\n", "\n", "* Impuestos sobre activos tangibles o intangibles.\n", "* Licencias para establecimiento de negocios.\n", "* Impuestos al consumo sobre productos básicos.\n", "* Impuestos a las ventas.\n", "* Impuestos a la renta.\n", "\n", "**Deducciones.**\n", "\n", "El impuesto de renta es calculado como el ingreso bruto menos las deducciones permitidas:\n", "\n", "* Salarios.\n", "* Rentas.\n", "* Intereses pagados.\n", "* Publicidad.\n", "* Planes de pensión.\n", "* Gastos de investigación.\n", "* Depreciación, amortización y agotamiento." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Notas sobre el mercado eléctrico colombiano\n", "\n", "\n", "* Impuesto de industria y comercio: pagado a una tarifa fija por la capacidad instalada del proyecto indexado por la inflación (`$` 5 de 1981 por kilovatio) \n", "\n", "\n", "* Artículo 22, Ley 143 de 1994 (Ley Eléctrica): 0.1% de los gastos de administración de los proyectos de generación (cubrimiento del costo del servicio de regulación).\n", "\n", "\n", "* Artículo 222, Ley 1450 de 2011. Transferencias del Sector Eléctrico: 6% de las ventas brutas para centrales hidroeléctricas (incluye la tasa por utilización de aguas) y 4% para centrales térmicas. \n", "\n", "\n", "* Impuesto predial.\n", "\n", "\n", "* Sobretasa predial.\n", "\n", "\n", "* Costos CND, ASIC y otros.\n", "\n", "\n", "* Costos FAZNI.\n", "\n", "\n", "* Para las fuentes no convecionales de energía, el Artículo 11 de la Ley 1715 de 2014 permite descontar del impuesto de renta hasta el 50% de las inversiones durante los cinco años siguientes al año de causación, sin superar el 50% del impuesto a pagar si no se tiene en cuenta este incentivo.\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "`after_tax_cashflow(cflo, tax_rate)`\n", "\n", "* `cflo`-- flujo de efectivo\n", "* `tax_rate` -- tasa de interés.\n", "\n", "Permite calcular el flujo de efectivo correspondiente a los impuestos expresados como una tasa sobre el flujo de efectivo.\n", "\n", "Retorna el flujo de efectivo después de impuestos. Nóte que los impuestos sólo se calculan para valores positivos. \n", "\n", "* `cflo` -- flujo de efectivo.\n", "* `tax_rate` -- tasa impositiva.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "***Ejemplo.--*** Sea un flujo constante de `$` 1000 para los períodos 1 a 5 y `$` -90 para los períodos 6 a 10. Calcule el impuesto de renta para una tasa impositiva del 30%. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "import cashflows as cf" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time value +------------------+------------------+\n", "2016 1000.00 ********************\n", "2017 1000.00 ********************\n", "2018 1000.00 ********************\n", "2019 1000.00 ********************\n", "2020 1000.00 ********************\n", "2021 -500.00 **********\n", "2022 -500.00 **********\n", "2023 -500.00 **********\n", "2024 -500.00 **********\n", "2025 -500.00 **********\n" ] } ], "source": [ "# representación del flujo de fondos\n", "cflo = cf.cashflow(const_value=[1000]*5+[-500]*5, \n", " start='2016',\n", " freq='A')\n", "cf.textplot(cflo) " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time value +------------------+------------------+\n", "2016 300.00 ********************\n", "2017 300.00 ********************\n", "2018 300.00 ********************\n", "2019 300.00 ********************\n", "2020 300.00 ********************\n", "2021 0.00 *\n", "2022 0.00 *\n", "2023 0.00 *\n", "2024 0.00 *\n", "2025 0.00 *\n" ] } ], "source": [ "## cómputo del impuesto\n", "tax_rate = cf.interest_rate(const_value=[30]*10, start=2016, freq='A')\n", "\n", "x = cf.after_tax_cashflow(cflo, # flujo de efectivo\n", " tax_rate=tax_rate) # impuesto de renta\n", "cf.textplot(x)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "***Ejemplo.--*** Considere un flujo de caja de `$` 100, y una duración de 10 períodos. Calcule el impuesto de renta si la tasa es del 30% para los períodos 1 a 5 y del 35% para los períodos restantes. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time value +------------------+------------------+\n", "2000 25.00 **************\n", "2001 25.00 **************\n", "2002 25.00 **************\n", "2003 25.00 **************\n", "2004 25.00 **************\n", "2005 35.00 ********************\n", "2006 35.00 ********************\n", "2007 35.00 ********************\n", "2008 35.00 ********************\n", "2009 35.00 ********************\n" ] } ], "source": [ "cflo = cf.cashflow(const_value=[100]*10, start=2000)\n", "tax_rate = cf.interest_rate(const_value=[25]*10, start=2000, chgpts={5:35})\n", "x = cf.after_tax_cashflow(cflo, # flujo de efectivo\n", " tax_rate=tax_rate) # impuesto de renta\n", "cf.textplot(x)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "***Ejemplo.--*** En el año 0 se compra un terreno por `$` 500 para la construcción de una central térmica. Si el avalúo para efectos del cálculo del impuesto predíal es del 80% del valor de compra y el impuesto es del 0.3% del avalúo, construya el flujo de efectivo que representa el pago del impuesto predial para los siguientes 10 años." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "2000 400.0\n", "2001 0.0\n", "2002 0.0\n", "2003 0.0\n", "2004 0.0\n", "2005 0.0\n", "2006 0.0\n", "2007 0.0\n", "2008 0.0\n", "2009 0.0\n", "2010 0.0\n", "Freq: A-DEC, dtype: float64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## costo de la inversión, se hace al final del año 0\n", "avaluo = cf.cashflow([0.8 * 500] + [0] * 10, start = 2000)\n", "avaluo" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "2000 400.0\n", "2001 400.0\n", "2002 400.0\n", "2003 400.0\n", "2004 400.0\n", "2005 400.0\n", "2006 400.0\n", "2007 400.0\n", "2008 400.0\n", "2009 400.0\n", "2010 400.0\n", "Freq: A-DEC, dtype: float64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## valor en libros\n", "bookval = avaluo.cumsum()\n", "bookval" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "2000 0.0\n", "2001 3.0\n", "2002 3.0\n", "2003 3.0\n", "2004 3.0\n", "2005 3.0\n", "2006 3.0\n", "2007 3.0\n", "2008 3.0\n", "2009 3.0\n", "2010 3.0\n", "Freq: A-DEC, dtype: float64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## tasa de impuesto predial\n", "trate = cf.interest_rate([0] + [3] * 10, start=2000)\n", "trate" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "2000 0.0\n", "2001 12.0\n", "2002 12.0\n", "2003 12.0\n", "2004 12.0\n", "2005 12.0\n", "2006 12.0\n", "2007 12.0\n", "2008 12.0\n", "2009 12.0\n", "2010 12.0\n", "Freq: A-DEC, dtype: float64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## impuesto predial\n", "cf.after_tax_cashflow(bookval, tax_rate=trate)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "***Ejercicio.--*** Si el flujo neto de efectivo es `$` 1000 por trimestre para el primer año, `$` 1300 por trimestre para el segundo y `$` 1600 por trimestre para el tercer año, calcule el flujo de efectivo correspodiente a los impuestos pagados, si la tasa es del 30% para el primer año, y 35% para los años restantes." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Impuestos\n", "===\n", "\n", "**Juan David Velásquez Henao** \n", "[email protected] \n", "Universidad Nacional de Colombia, Sede Medellín \n", "Facultad de Minas \n", "Medellín, Colombia\n", "\n", "---\n", "\n", "Haga click [aquí](https://github.com/jdvelasq/ingenieria-economica/blob/master/04-impuestos.ipynb) para acceder a la última versión online\n", "\n", "Haga click [aquí](http://nbviewer.jupyter.org/github/jdvelasq/ingenieria-economica/blob/master/04-impuestos.ipynb) para ver la última versión online en `nbviewer`.\n", "\n", "---" ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

LorenzoBi/courses

TSAADS/tutorial 7/7_LORENZO_BIASI_JULIUS_VERNIE.ipynb

1

160124

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Assignment 7\n", "Lorenzo Biasi, Julius Vernie" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.io import loadmat\n", "from numpy.linalg import inv\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We load the variables and initilize the parameters we need" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['__header__', '__version__', '__globals__', 'A', 'B', 'C', 'Gamma', 'L0', 'Sigma', 'mu0', 'u', 'x', 'z'])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = loadmat('data_files/Tut7_file1.mat')\n", "locals().update(data)\n", "data.keys()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p, T = z.shape" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mu = np.zeros(z.shape)\n", "K = np.zeros((4, 4, T))\n", "V = np.zeros((4, 4, T))\n", "L = np.zeros((4, 4, T))\n", "\n", "K[...,0] = L0.dot(B.T.dot(inv(B.dot(L0.dot(B.T)) + Gamma)))\n", "mu[..., [0]] = A.dot(mu0) + K[..., 0].dot(x[:, [0]] - B.dot(A.dot(mu0))) + C.dot(u[..., [0]])\n", "V[..., 0] = (np.eye(4) - K[..., 0].dot(B)).dot(L0)\n", "L[..., 0] = A.dot(V[..., 0].dot(A.T)) + Sigma" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We run the filter" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for t in range(1, T):\n", " K[...,t] = L[..., t - 1].dot(B.T.dot(inv(B.dot(L[..., t - 1].dot(B.T)) + Gamma)))\n", " mu[..., [t]] = A.dot(mu[..., [t-1]]) + K[..., t].dot(x[:, [t]] - B.dot(A.dot(mu[..., [t-1]]))) + C.dot(u[..., [t]])\n", " V[..., t] = (np.eye(4) - K[..., t].dot(B)).dot(L[..., t-1])\n", " L[..., t] = A.dot(V[..., t].dot(A.T)) + Sigma" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see a slight offset, we would expect that to be solved with the smoother step" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f53ad3f69e8>,\n", " <matplotlib.lines.Line2D at 0x7f53ab5f9f60>,\n", " <matplotlib.lines.Line2D at 0x7f53ab603160>,\n", " <matplotlib.lines.Line2D at 0x7f53ab603320>]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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OBes/9lxElc/I6eLi5FqAP/8kef58htvUYNuF+wxtVJrPmpTJOrtqXb8uB5uP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\nDeWKWvHL8btk1w/gKjm8jaQkGDtWzko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"text/plain": [ "<matplotlib.figure.Figure at 0x7f53ad3f69b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(mu.T)\n", "plt.plot(z.T, color='red')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "V_tilde = np.zeros(V.shape)\n", "mu_tilde = np.zeros(mu.shape)\n", "V_tilde[..., -1] = V[..., -1]\n", "mu_tilde[..., [-1]] = mu[..., [-1]]\n", "\n", "for t in range(T - 2, -1, -1):\n", " #print(t)\n", " W = V[..., t].dot(A.T.dot(inv(L[..., t])))\n", " V_tilde[..., t] = V[..., t] + W.dot(V_tilde[..., t+1] - L[..., t]).dot(W.T)\n", " mu_tilde[..., [t]] = mu[..., [t]] + W.dot(mu_tilde[..., [t+1]] - A.dot(mu[..., [t]]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we can see that the offset is still present and slightly worse" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f53ab56e908>,\n", " <matplotlib.lines.Line2D at 0x7f53ab4e5518>,\n", " <matplotlib.lines.Line2D at 0x7f53ab4e56d8>,\n", " <matplotlib.lines.Line2D at 0x7f53ab4e5898>]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ztrbQeRi4fqzKq3q/CTf3v/797O1V3Ylr17BdOJ8Z7StyOzSaZYeS2RvRAJ0+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\nM7GghT94GP3PLG5NDRKJzMQizCzRfB5Np2F0FH9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"text/plain": [ "<matplotlib.figure.Figure at 0x7f53ab56e7f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(mu_tilde.T)\n", "plt.plot(z.T, color='red')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The predition is clearly following the data more or less correctly, but there is a probelm with the offset, that makes $\\tilde{\\mu}$ worse than our $\\mu$. This should not happen, we would rather expect the opposite." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Non smoothed result: 9175009.49339\n", "Smoothed result: 9480270.18934\n", "Ratio, \n", " 1.03327088611\n" ] } ], "source": [ "print ('Non smoothed result:', np.sum((mu - z).T ** 2))\n", "print('Smoothed result:', np.sum((mu_tilde - z).T ** 2))\n", "\n", "print('Ratio, \\n', np.sum((mu_tilde - z).T ** 2) / np.sum((mu - z).T ** 2))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After checking the algorithm many times i decided to look at our x to see if there was anything strange. And if you look closely at the first time steps there is some oddity." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f53ab4a1400>,\n", " <matplotlib.lines.Line2D at 0x7f53ab4a15c0>,\n", " <matplotlib.lines.Line2D at 0x7f53ab4a17b8>,\n", " <matplotlib.lines.Line2D at 0x7f53ab4a19b0>]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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58EPM8fF6h6Uo+5ljY4l94nFsmzZR9MYbeofTpqjkoByWdLnIf+IJil56iaCRI0l8ew7G\n4GC9w1KUQwRdcAHBo0dT8tZs6jZs0DucNkMlB+UQ7ro6sm+7nfKPFhI+bSpx/3keg8Wid1iKckTR\n//4Xpphoch/8F26bTe9w2gSVHJSDOMvKyLzueqp//pnohx4i6u671RoMitczBgQQ99RT2NPSKHr5\nFb3DaRPUb72ynyMnh4wJE7Ft20b8K68QNnGC3iEpSqP5n3EGoVePp/T996ldu1bvcFo9lRwUAGw7\nd5E+/mqcxcUkzX2HoPNH6B2SohyzqLvvxpyQoFUvqdlbT4hKDgq1a9aQMXEiCEHyB/Px69dP75AU\n5bgY/P2JfepJHFlZFL36mt7htGoqObRzlUuWkDllKqaoKFI+WoC1a1e9Q1KUE+Lfvz8hV11F6fz5\n1G3apHc4rdZRk4MQYq4QolAIsaXBsceEEDlCiA2e7aIG7z0ohEgVQuwUQpzf4HhfIcRmz3uvCc+8\nC0IIHyHEx57jq4QQKU37IypHUvbJJ+TcfgfWHj1I/mA+5rg4vUNSlCYRdc/dmCIiyPv3Q0i7Xe9w\nWqXGlBzmARcc5vjLUspTPNt3AEKInsA4oJfnmjeEEPuWA5sFTAO6eLZ9nzkFKJNSdgZeBp47zp9F\naSQpJcVvvkn+I4/iP2QwSe/OxRQaqndYitJkjIGBxDz2KPW7d1Pyzjt6h9MqHTU5SCmXAaWN/LxR\nwEIpZb2UMg1IBfoLIWKBICnlSqlNofg+cFmDa97z7C8Chgs1m1uzkW43BU8/Q9ErrxJ06SUkzpyJ\nwc9P77AUpckFDhtG4IUXUPzGLOr3pukdTqtzIm0OtwohNnmqnfY9dsYDWQ3OyfYci/fs//34QddI\nKZ1ABRB+uC8UQkwXQqwVQqwtKio6gdDbJ2m3k3vvfZTNn0/Y5EnEPfsswmzWOyxFaTYx//43wmol\n/4kn1NTex+h4k8MsoCNwCpAHvNhkEf0DKeVsKWU/KWW/yMjIlvjKNsNdW0vWjJup/PZbIu+6i6gH\nHlCD25Q2zxQRQdRdd1K7ciWV33yrdzitynHdHaSUBVJKl5TSDcwB+nveygESG5ya4DmW49n/+/GD\nrhFCmIBgoOR44lIOz1lWRsZ111GzYgWxT/4fEdOnqXUYlHYj5MorsfbuTcFzz+GqrNQ7nFbjuJKD\npw1hn9HAvp5MXwPjPD2QOqA1PK+WUuYBlUKIgZ72hEnAVw2umezZvxz4WaryX5NxFBSQcc011G/f\nQcJrrxJy+eV6h6QoLUoYjcQ89iiu0lKKXnlV73BajaOu7yiE+AgYCkQIIbKBR4GhQohTAAmkAzcA\nSCm3CiE+AbYBTuBmKaXL81Ez0Ho++QLfezaAd4D5QohUtIbvcU3xgylgz8gg8/opuMrKSJw9G/+B\nA/QOSVF04durF6ETJlD2wQcEjxmj1iRpBNFaH9L79esn16r5U47ItmMHmVOngdNJ4pw5+PY+Se+Q\nFEVXrqoq9lx4EZaEBJI/WtBuq1aFEOuklEedBkG1SLZBtX/9RcY1kxAmE8kffqASg6KgjX2Iuusu\n6jZsoPLrr/UOx+up5NDGVP/2G5nXT8EUHk7Kgg/x6dRJ75AUxWsEXzYKa58+FLzwAq7qGr3D8Woq\nObQhFd98S9bNt+DTsSPJH36gpsNQlL8RBgMxD/0bV1ExJW/O0jscr6aSQxtR9tFH5N57L36nnELS\ne/MwhR92HKGitHu+ffoQPGYMJe+9T32aGjl9JCo5tHL750l6/AkCzj5bW+s5MFDvsBTFq0XddScG\ni4XCF1pk/G6rpJJDKyalpPD5/+yfJynh9dcwWK16h6UoXs8UEUH49OlU/+9/1KxarXc4Xkklh1ZK\nulzkPfQQpe++S+iECWqeJEU5RmGTJ2GKi6XwueeQbrfe4XgdlRxaIWm3k3PX3VR89jnhN91I9EP/\nVvMkKcoxMlitRN15J7Zt26hQXVsPoe4orcy+CfSqfvyRqPvuI+r229vtYB5FOVFBI0di7d2bopdf\nwV1Xp3c4XkUlh1bEVVlJ5tRp+yfQC7/+Or1DUpRWTRgMRD9wP86CAkrnzdM7HK+ikkMr4SwpIWPy\ntdRt3kz8Sy+qCfQUpYn49e1LwPDhlLz9Ds6yMr3D8RoqObQCjtxcMiZMxJ6WRuIbMwm64HCrtiqK\ncryi7rwDd10dJW++pXcoXkMlBy9XvzeN9AkTcZaUkPTO2wSceabeISlKm+PTuTPBY0ZTtmAB9uyc\no1/QDqjk4MXqtm4lY8IEpN1O8vz38evbV++QFKXNirzlFjAYKH79Nb1D8QoqOXipmtWryZw0GYOv\nLykffoC1e3e9Q1KUNs0cE0PYpGuo+Hoxtp079Q5Hdyo5eKGqX34ha9p0TDExJC/4EEtKit4hKUq7\nED51KobAQLViHCo5eJ2KxYvJvuVWfLp0IfmD+ZhjYvQOSVHaDWNwMOHXX0/1L79Qt2GD3uHoSiUH\nL1K6YAG5996HX9++JM17F1NoqN4hKUq7E3bNRIxhYRS91r7bHlRy8AJSSorfmk3BE/9HwLBhJM6Z\njTEgQO+wFKVdMvj7Ez59GjUr/mzXk/Kp5KAzKSWFL7xA0csvE3TJJSS8+goGHx+9w1KUdi103DhM\nUVEUvfoqUkq9w9GFSg46ki4XeQ8/TOk7cwm9ejxxz6mZVRXFGxisViJm3ETdX39R8/vveoejC5Uc\ndOK228m58y4qFn1GxIybiH74YTWzqqJ4kZAxYzDHx1P0+n/bZelB3Y104K6pIfvGm6j66SeiH3yA\nyNtuUzOrKoqXERYL4TdMx7Z5c7ssPajk0MKcZWVkXHc9NatWEfvMM4RNnqx3SIqiHEHIZZdhioul\naObMdld6UMmhBTkKCsi45hrqd+wg4fXXCBl9md4hKYryD4TFQsT0G7Bt3ETNH8v1DqdFmfQOoL2w\np6eTef0UXBUVJM6Zg/+A/nqHpHiJeqeLshoHZbV2KuscVNQ5qLI5qXO4sDlc1DvdOF0Sl9uN0y2R\nwL6HWLNRYDYaMBsN+FmM+FqM+FtMBPuaCfEzE+ZvIczfgtVs1PVnbM1Cxoym+K23KJ45E/8hg9tN\nFbBKDi3Atm0bmVOngZQkvfcevif10jskpYVU1DnILKklq6yWvAobeeV15FfaKKysp6DKRnFVPTV2\nV6M/z2QQGBrcnBxuN42p7fC3GAkP8CEq0IeYYCuxwVbiQ3xJjvAnJdyfhFBfzEZVkXA4wmIh4obp\n5D/2ODUrVhAweLDeIbUI0Vrr0fr16yfXrl2rdxhHVbtmDVk3zcAQGEjSO+/g07GD3iEpTczllmSU\n1LCroJo9RdXsLaphb3E16cU1lNU6DjrXajYQE2Ql2rNFBPgQHmAhxM9MqJ+FYF8zQVYzgVYTvhYj\nVrMRH5MBi9GAwXD4J1aXW1LvdFFnd1Frd1Fjd1JR66Cs1kF5rZ2SGjulNXaKq+sprKwnv9JGXkUd\nNod7/2eYDIKUCH+6RAXQJTqQnrGB9IwNJjHMt908Kf8Tt93OnhHnY0lIIPmD+XqHc0KEEOuklP2O\ndp4qOTSjqqVLybnrbswJCSS98zbm2Fi9Q1JOUEWtg805FWzPq2R7fiU78qpILarG7jxwo40O8qFj\nRAAXnBRLSrgfyeH+JIb5EhfsS4ifuclvtkaDwM9iws9iIryR10gpKa62k1FSQ1pxDXuLa0gtrGZn\nfhU/bs3H7XlmDLSa6JMQzMkJIZySGELf5FDCA9rfIE2DxUL49ddT8PTT1K5b1y6mzz9qyUEIMRe4\nGCiUUp7kORYGfAykAOnAlVLKMs97DwJTABdwm5TyR8/xvsA8wBf4DrhdSimFED7A+0BfoAS4SkqZ\nfrTAvb3kUPbxJ+Q//jjW3ieR+Oabap6kVsjmcLE1t5L1mWWszypnc3YFmaW1+9+PDvKhW0wQ3WMC\n6RIVQNfoQDpFBRDg07qfuersLnYVVLEtr5LNORVsyi5nR14VTk/G6Bjhz+kpYQzqHM6gThFEBraP\nZOGuqyN12HCsfXqT9FbrXTGusSWHxiSHs4Bq4P0GyeF5oFRK+awQ4gEgVEp5vxCiJ/AR0B+IA5YC\nXaWULiHEauA2YBVacnhNSvm9EGIG0EdKeaMQYhwwWkp51dEC99bkIKWkeNYsil97Hf+zzyLh5Zcx\n+PnpHZbSCOW1dtakl7E2vZS1GWVszq7A7tJKBPEhvpycGEzv+BB6xwfTMy6IMH+LzhG3HJvDxZac\nCtZmaH8/q9NKqbQ5AegeE8jQblEM6x7FaUkhmNpw20Xxm29R9MordPjic6w9eugdznFpsuTg+bAU\n4JsGyWEnMFRKmSeEiAV+lVJ285QakFI+4znvR+AxtNLFL1LK7p7j4z3X37DvHCnln0IIE5APRMqj\nBOaNyUG6XOQ/+STlHy0keNQoYp/8PzUdhhcrr7Wzcm8JK/aUsDqtlB35VQBYjAb6JATTNyWU05JC\nOTUxhKggq87ReheXW7I1t4I/UotZtquItellON2SIKuJc3tEc/5JMZzdNbLN9ZJyVVaSOmw4/mcO\nIeHll/UO57g0d5tDtJQyz7OfD0R79uOBlQ3Oy/Ycc3j2/3583zVZAFJKpxCiAggHio8zNl246+vJ\nvedeqpYsIXzaVCLvuks15HkZm8PFuowyft9dzB+pRWzNrURK8DUb6ZcSysV9YunfIZw+CcFt7qbW\n1IwGQZ+EEPokhDBjaGcqbQ6W7y5m6fZClm4v4PP1OfhZjJzbI5pRp8RxZpdILKbWX6IwBgURevXV\nlMyZQ/1tafh0aLsdTE64ctTTbtAiXZ6EENOB6QBJSUkt8ZWN4qqsJHvGzdSuXUv0vx4kbNIkvUNS\nPNKKa/h1ZyG/7Spi5d4SbA43ZqPg1KRQ7hjelcGdw+mTENImblx6CrKaubB3LBf2jsXhcrNqbynf\nbcnju815fL0xlxA/Mxf3iWXsaQmckhjSqh+cwiZPovS99yh5+23innpK73CazfEmhwIhRGyDaqVC\nz/EcILHBeQmeYzme/b8fb3hNtqdaKRitYfoQUsrZwGzQqpWOM/Ym5cjPJ2vadOrT04l78QWCR47U\nO6R2zeFysyatlP/tKOTnHYWkFdcA0CHCn3GnJ3FW1wgGdAjHv5U3Gnszs9HAkC4RDOkSwWOX9OKP\n1CK+WJ/Lp2uz+WBlJp0i/bmyXyJj+yYQ0Qp7PpnCwwkZO5ayTz8l8rbbMUdH6R1Sszje35CvgcnA\ns57XrxocXyCEeAmtQboLsNrTIF0phBiI1iA9CXj9b5/1J3A58PPR2hu8Rf2ePWROnYa7spKk2W/h\nf8YZeofULlXXO/l1ZyFLthXwy45CKm1OLCYDZ3QM59pBKQztFklyuL/eYbZLFpOBYd2jGdY9mkqb\ng+825bFoXTbPfL+DF37ayXk9o5k4IJkzOoW3qtJE2HXXUrZwIWUfzCfq7rv1DqdZNKa30kfAUCAC\nKAAeBb4EPgGSgAy0rqylnvP/DVwPOIE7pJTfe47340BX1u+BWz1VUlZgPnAqUAqMk1LuPVrgejdI\n1/71F9k3zQCzmaTZb2Ht2VO3WNqj0ho7S7bl8+PWAv7YXYzd5SbM38Lw7lGc2zOaIZ0jVOnAi6UW\nVrFwdRaf/ZVNWa2DrtEBTB6UwphTE/C1tI72nuw77qRm+XI6//ILxoDW8/DRpL2VvJGeyaFyyRJy\n77kXc0wMiW/PwZKYePSLlBNWXmvnx635fLMpjxV7SnC5JQmhvpzfK4bze8XQNzkU4xFGESveyeZw\nsXhjLvNWpLM1t5JQPzOTzkhh8qAUr+8qXLd5M+lXXEnU/fcTft21eofTaCo5NJPSDz+k4Mmn8O3T\nh4Q3Z6nBbc2s0ubgp60FfLMplz92F+N0S5LC/Li4TywX9Y6lV1xQq6qOUA5PSsma9DJmL9vD0u2F\nWM0Gxp2exA1ndyQ22Ffv8I4o45pJ2LOy6Lzkp1bTbV0lhyYm3W6KXnqJkrffIWDYMOJffAGDr/f+\np23N7E43v+4s5MsNOSzdXojd6SY+xJeL+8RycZ84TopXCaEt211QxVvL9vLl+hwMQnBFvwRuGtqJ\nhFDvG0xa/dtvZN1wI3HPPUvwqFF6h9MoKjk0IbfdTt4DD1L53XeEjB9HzL//jTCp+uymJKVkXUYZ\nX6zP4dvL/++lAAAgAElEQVTNeZTXOgj3t3DJyXFcekocp7by7o/KscsqrWXWb3v4dG0WAOP7J3HL\nOZ29akCilJK0Sy8Fo4kOX3zeKv6PquTQRFwVFWTfciu1a9YQefddhE+d2ir+A7QWGSU1fPZXDl+s\nzyartA6r2cD5vWK47JR4hnSJUNNIK+SW1/H6z6l8ujYLk1Fw3eAO3DS0E0FW76jGKV+0iLyHHiZp\n3jz8Bw7QO5yjUsmhCdizssiafgP27Gzinn6a4Esubtbvay+q6518t1nr0rg6rRQhYEjnCEafGs/5\nvWJULyPlsDJKanh5yS6+3JBLmL+F24d34eoBSbo/QLjr60k9Zxi+J59M4qw3dI2lMVRyOEG169eT\nPeNmcLtJ+O/r+J1+erN9V3uwr8Hx4zVZfL8lj1q7iw4R/lzeN4HRp8YTF6Lab5TG2ZJTwVPfbufP\nvSV0jPDn4Yt7ck53fQeiFb32OsWzZtHp+++wpKToGsvRqORwAiq/+47cBx7EFBND4ltvtun5U5pb\nSXU9n/2VzcI1WewtqiHAx8TFfWK5ol8CpyWFqio65bhIKfl5RyFPfbudvcU1DO0WycMX96RTZIAu\n8TiLikgdNpyQK64g5pGHdYmhsVRyOA5SSkremk3RK6/ge9ppJMz8r+qqehyklPy5p4QFqzP5cWs+\nDpekX3IoV52eyMg+sfhZVLWR0jTsTjfv/5nOq0t3Y3O6mHZmR24d1kWXgXS5DzxI5Y8/0uXXXzAG\nB7f49zeWSg7HSNrt5D36GBVffEHQJZcQ+9STGCzePQjH25TX2lm0LpsFqzLZW1xDsK+ZsaclML5/\nIl2iA/UOT2nDiqrqeeb77Xz+Vw4Job48MaoXw7pHH/3CJmTbsYO0y0YTde89hE+Z0qLffSxUcjgG\nztJSsm+7jbq164i45RYibp6hqjsaSUrJ+qxyPliZwbeb8qh3uumbHMqEAUlc1DtWTX2ttKiVe0t4\n6MstpBZWM+qUOB69pFeLjrTOmHwt9sxMbVCcl3Z3V2tIN5Jt1y6yb5qBs6iIuBdeIPhiNatqY9Ta\nnXy1IZcPVmawNbcSf4uRy/smMGFAMj3jgvQOT2mnBnYM57vbzmTWr3v47y+7+WN3MU+MOomRfVpm\n/fawayaSfcutVP38M0EjRrTIdzaXdldyqFj8DWUffQSen7t+506Evx+JM2fi26dPU4fZ5qQX1zB/\nZQafrs2i0uake0wgEwcmc9mp8a1+7WSlbdmRX8l9izaxKbuCkX1i+b9RJzV7KUK6XOw5bwTmhASS\n33+vWb/reKmSwxEIowFhsYCn1ihg6FCi7tMm0VMOz+2W/La7iPdXpPPrriKMQnDBSTFMHpRCv2TV\n40jxTt1jgvj8pkG8tWwvryzdxaq9pTwzpjfn9Wy+tghhNBJ69XgKX3gR285dWLt1bbbvam7truSg\nNF5FnYNF67KZ/2c66SW1RAT4cPWAJCYMSCLai6YwUJSj2Z5XyV2fbGR7XiXj+yfx8MU9mq3XnLOs\njNSh52jryD/xeLN8x4lQJQfluKUWVvHu8nQ+/yuHOoeL05JCuPO8rlx4UqxaTlNplXrEBvHVzYN5\nacku3lq2h1VpJbw27lROim/6Lqem0FCCLrmYisWLibr7Lq/u1vpPVMlBAbReR7/tKmLu8nSW7SrC\nYjIw6uQ4Jg9KaZZfIEXRy4rUYu78ZAOlNXb+dVEPrh2U0uRVo/u7td53H+HXX9ekn32iVFdWpVFs\nDhef/5XD3OVppBZWExXow6QzkhnfP4nwVri+r6I0RlmNnXsXbWTp9kIu6BXDc5f3Idi3aSfyS584\nEWd+AZ1++hFh8J4St0oOyj/Kr7Axf2U6C1ZlUlbr4KT4IKYM6cDI3nGq6khpF6SUzPl9L8/9sJO4\nECuzJvRt0lJy5XffkXPX3STOfouAs85qss89UarNQTmsDVnlvLs8jW835eGSkhE9o7lucAcGdAhT\nvY6UdkUIwfSzOtE3OZRbFqxn7KwVPDW6N5f3TWiSzw8891yMERGUfbTQq5JDY6nk0A44XW5+3FrA\n3OVprMsoI8DHxKQzUrh2UApJ4d63upaitKS+yWEsvnUIty5Yzz2fbmRjVjkPX9zzhEvQwmIhZOxY\nSubMwZGbizkurokibhmqWqkNq7Q5+Hh1FvNWpJNTXkdSmB/XDU7h8r4JBHrJQimK4i2cLjfP/7iT\n2cv20j8ljFkTTzvhdjdHTg6p540gfPo0ou64o4kiPTGqzaEdyyqtZe7yND5Zk0WN3cWADmFcP6QD\n5/aIxmhQVUeK8k++3pjLvZ9uJDLQh3cmn063mBObNDLrphnUbdpEl19+1gbg6ky1ObRD6zLKeOeP\nvfywJR+DEFxychxThnRQXVEV5RhcenIcyWF+THt/LWPeWM5r409leI/jH1UdOn4c1b/8QtXSpQRd\ndFETRtq8VMmhlXO63PywNZ+3f09jQ1Y5QVYTEwYmM/mMFGKC1ShmRTle+RU2pr2/lq25FTw0sifX\nDT6+8RDS7WbPiPMxx8aSPP/9Zoj02KiSQxv39/aElHA/nhjVi7GnJag1mBWlCcQEW/n4hoHcsXAD\nT3yzjbTiGh69pCemY1yzWhgMhFx1JUUvvkT93r34dOzYTBE3LVVyaGWyy2p5d3k6C1dn7m9PmDKk\nA8NVe4KiNAu3W/LsDzuYvWwv53SLZOaE0455XiZncTG7h55D2DXXEH3/fc0UaeOokkMbszm7grf/\n2Ms3m/IQwMV9Ypl6ZkfVnqAozcxgEPzroh4khfnxyFdbGD9nFXMn9zumnkymiAgChw2j4ssvibzz\njlaxyqRKDl7M5ZYs2ZbPO3+ksSa9DH+LkesGpXD9kA7EhfjqHZ6itCsTByYTFejDrR+t5/I3/+S9\n6/of0zihkCuuoOqnn6huJQ3TqlrJC9XUO/lkbRZzl6eRVVpHQqgv1w5K4crTEwlS4xMURVfrMkqZ\n8t5azEYD86f0p3tM41Y+lG43e849D3NyEsnvvtvMUR5ZY6uV1CQ6XiS3vI5nvt/OwGf+x+OLtxEd\naGXWhNP47d5zmHpmR5UYFMUL9E0O49MbzsAoBFe++SfrMsoadZ0wGAi+fCy1f67EnpnZzFGeuBNK\nDkKIdCHEZiHEBiHEWs+xMCHEEiHEbs9raIPzHxRCpAohdgohzm9wvK/nc1KFEK+JdjbJz6bscm77\naD1nPv8Lc5bt5awukXwxYxCLbhrEhb1jVUOzoniZLtGBfHrjGYT5W5j49iqW7Spq1HUhY8eCwUD5\nos+aOcIT1xQlh3OklKc0KKY8APxPStkF+J/nzwghegLjgF7ABcAbQgij55pZwDSgi2e7oAni8mou\nt+THrflc+eafXPrf5fy8o5DrBqXw273nMHPCaZyaFHr0D1EURTeJYX58euMgUiL8mfreWpZuKzjq\nNeboaALOPpvyzz9HOhwtEOXxa45qpVHAvpW13wMua3B8oZSyXkqZBqQC/YUQsUCQlHKl1BpA3m9w\nTZtTXe/k3eVpDHvxV26Yv46c8joeGtmDPx8cxkMX9yQxTE2EpyitRWSgDwunDaRHbCA3frCO7zfn\nHfWakCsux1VcTPXvv7dAhMf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0xxgS0qJVS16RHID+QKqUcq+U0g4sBEbpHJOiKErT8g2F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\n1WHGUIQAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f53ab56e630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x.T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If someone looks at how x varies at the first time step you will see that it is almost constant and than it starts changing. this could explain the offset in our predictions." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.7255109 , 157.7771018 , 161.32022023],\n", " [ 1.56485095, 52.69063853, 55.13791943],\n", " [ 0.82161329, -11.52514839, -13.04291285],\n", " [ 1.33883859, 332.90369003, 340.26390644]])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f53ab4e1780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plt.plot(x.T[:4, :])\n", "plt.plot(np.diff(x[..., :10]).T)\n", "np.diff(x[..., :4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To test my hunch I decided to remove one time step from the data, to make sure that $x_1$ was not used in the prediction." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "T = 99\n", "z = z[:, :-1]\n", "mu = np.zeros(z.shape)\n", "K = np.zeros((4, 4, T))\n", "V = np.zeros((4, 4, T))\n", "L = np.zeros((4, 4, T))\n", "\n", "K[...,0] = L0.dot(B.T.dot(inv(B.dot(L0.dot(B.T)) + Gamma)))\n", "mu[..., [0]] = mu0\n", "V[..., 0] = 0\n", "L[..., 0] = L0" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for t in range(1, T):\n", " #print(t)\n", " K[...,t] = L[..., t - 1].dot(B.T.dot(inv(B.dot(L[..., t - 1].dot(B.T)) + Gamma)))\n", " mu[..., [t]] = A.dot(mu[..., [t-1]]) + K[..., t].dot(x[:, [t + 1]] - B.dot(A.dot(mu[..., [t-1]]))) + C.dot(u[..., [t]])\n", " V[..., t] = (np.eye(4) - K[..., t].dot(B)).dot(L[..., t-1])\n", " L[..., t] = A.dot(V[..., t].dot(A.T)) + Sigma" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ -7.65758593],\n", " [-230.44138841],\n", " [-592.64195212],\n", " [-523.77934228]])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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EHccVg2/lublw52XwwSyo6AefvAcDb7Y6KlVapSS6V3HdkgJTPoIr8/9/LSUt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\nUri7MgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f53ab3c8be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(mu.T)\n", "plt.plot(z.T, color='red')\n", "np.sum((mu - z)**2)\n", "A.dot(mu[..., [t-1]]) + K[..., t].dot(x[:, [t + 1]] - B.dot(A.dot(mu[..., [t-1]]))) + C.dot(u[..., [t]])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "V_tilde = np.zeros(V.shape)\n", "mu_tilde = np.zeros(mu.shape)\n", "V_tilde[..., -1] = V[..., -1]\n", "mu_tilde[..., [-1]] = mu[..., [-1]]\n", "\n", "for t in range(T - 2, -1, -1):\n", " W = V[..., t].dot(A.T.dot(inv(L[..., t])))\n", " V_tilde[..., t] = V[..., t] + W.dot(V_tilde[..., t+1] - L[..., t]).dot(W.T)\n", " mu_tilde[..., [t]] = mu[..., [t]] + W.dot(mu_tilde[..., [t+1]] - A.dot(mu[..., [t]]))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f53ab2a57f0>,\n", " <matplotlib.lines.Line2D at 0x7f53ab3a8400>,\n", " <matplotlib.lines.Line2D at 0x7f53ab3a87f0>,\n", " <matplotlib.lines.Line2D at 0x7f53ab3a8e48>]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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T8gOsm7ENt8PM+Kf7ESJDtJc7eYa0qFSfz91G8ncHKLQo+t/ansRONaebqic3i31LPmfv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\nfgLUZjE3cD/ZcZWE7JniF86UE7gn388dA24618e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"text/plain": [ "<matplotlib.figure.Figure at 0x7f53ab2a5ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(mu_tilde.T)\n", "plt.plot(z.T)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Non smoothed result: 28182.6775642\n", "Smoothed result: 21398.1349435\n", "Ratio, \n", " 0.759265506082\n" ] } ], "source": [ "print ('Non smoothed result:', np.sum((mu - z).T ** 2))\n", "print('Smoothed result:', np.sum((mu_tilde - z).T ** 2))\n", "\n", "print('Ratio, \\n', np.sum((mu_tilde - z).T ** 2) / np.sum((mu - z).T ** 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see how by making this slight change to the data the predition are more accurate, and do not have strange sistematical errors. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

iannesbitt/ml_bootcamp

Data-Capstone-Projects/Finance Project .ipynb

2

2685153

mit

steinam/teacher

jup_notebooks/DuckTales.ipynb

1

6017567

mit

radajin/datascience

classification/naive_bayesian/document_classification.ipynb

2

109355

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Import Package" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_formats = {'png', 'retina'}\n", "\n", "import scipy.stats\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "from konlpy.utils import pprint\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import classification_report" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. News Group" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.datasets import fetch_20newsgroups\n", "from sklearn.cross_validation import train_test_split" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make News Data" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "16961 16961 1885 1885\n" ] } ], "source": [ "# get sample data\n", "news = fetch_20newsgroups(subset=\"all\")\n", "\n", "# train_test_split : division train and test data\n", "X_train, X_test, y_train, y_test = train_test_split(news.data, news.target, test_size=0.1, random_state=1)\n", "print(len(X_train), len(y_train), len(X_test), len(y_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model Tunning Process" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.feature_extraction.text import TfidfVectorizer, HashingVectorizer, CountVectorizer\n", "from sklearn.naive_bayes import MultinomialNB\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.pipeline import Pipeline\n", "\n", "# Using LogistincRegression Model\n", "clf_0 = Pipeline([\n", " ('vect', CountVectorizer()), \n", " ('clf', LogisticRegression()),\n", " ])\n", "\n", "# Using MultinomialNB Model\n", "clf_1 = Pipeline([\n", " ('vect', CountVectorizer()), \n", " ('clf', MultinomialNB()),\n", " ])\n", "\n", "# Using Tfidf Model\n", "clf_2 = Pipeline([\n", " ('vect', TfidfVectorizer()),\n", " ('clf', MultinomialNB()),\n", " ])\n", "\n", "# modify tocken\n", "clf_3 = Pipeline([\n", " ('vect', TfidfVectorizer(token_pattern=r\"\\b[a-z0-9_\\-\\.]+[a-z][a-z0-9_\\-\\.]+\\b\")),\n", " ('clf', MultinomialNB()),\n", " ])\n", "\n", "# add stop words\n", "clf_4 = Pipeline([\n", " ('vect', TfidfVectorizer(stop_words=\"english\", token_pattern=r\"\\b[a-z0-9_\\-\\.]+[a-z][a-z0-9_\\-\\.]+\\b\")),\n", " ('clf', MultinomialNB()),\n", " ])\n", "\n", "# add alpha\n", "clf_5 = Pipeline([\n", " ('vect', TfidfVectorizer(stop_words=\"english\", token_pattern=r\"\\b[a-z0-9_\\-\\.]+[a-z][a-z0-9_\\-\\.]+\\b\")),\n", " ('clf', MultinomialNB(alpha=0.01)), # add smooding filter\n", " ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LogisticRegression\n", "- calculation is slow because of sigmoid function\n", "- so MultinomialNB is faster" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "clf_0 = clf_0.fit(X_train[:1000],y_train[:1000]) \n", "y_pred = clf_0.predict(X_test[:1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check Model Performance" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[31 0 0 0 0 2 0 0 1 0 0 0 1 0 1 2 0 2 1 4]\n", " [ 0 31 4 3 1 3 1 0 1 0 0 0 4 3 1 0 0 1 0 0]\n", " [ 0 1 37 2 3 0 2 0 0 0 0 0 2 0 1 0 0 2 0 0]\n", " [ 0 5 5 25 3 1 0 2 1 0 1 0 3 1 2 0 0 0 0 0]\n", " [ 1 2 2 7 28 1 4 1 0 1 1 0 5 1 1 0 0 1 0 0]\n", " [ 0 7 8 1 2 28 0 0 0 1 0 0 4 1 1 1 0 0 0 0]\n", " [ 0 2 0 1 2 0 38 1 0 1 0 0 2 0 0 0 0 0 0 0]\n", " [ 2 1 0 1 2 2 5 32 5 1 0 1 3 2 2 0 0 1 0 0]\n", " [ 0 0 1 0 1 0 1 2 42 2 2 1 0 1 0 0 0 0 1 0]\n", " [ 0 3 0 0 0 1 3 2 1 34 10 0 1 2 0 2 0 2 0 0]\n", " [ 0 0 0 0 0 0 1 0 2 0 56 0 0 1 0 0 0 0 0 0]\n", " [ 0 2 1 0 1 0 0 0 1 0 0 43 2 1 2 2 0 0 0 0]\n", " [ 0 0 1 3 5 3 0 3 2 0 0 1 17 3 0 1 0 1 0 0]\n", " [ 2 2 0 1 0 1 0 2 2 1 2 1 5 26 2 2 0 2 1 0]\n", " [ 1 2 2 1 0 0 0 1 1 1 0 0 1 3 46 1 1 0 0 0]\n", " [ 2 0 0 0 1 1 0 0 0 0 1 0 0 3 1 37 0 3 0 0]\n", " [ 1 0 0 0 0 1 1 2 0 3 1 3 1 0 1 1 27 2 4 0]\n", " [ 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 2 1 34 0 0]\n", " [ 2 1 0 0 0 0 0 0 1 1 1 0 1 0 4 1 3 5 23 0]\n", " [ 8 0 0 0 1 0 1 1 0 0 0 1 0 0 1 4 0 0 0 5]]\n", " precision recall f1-score support\n", "\n", " 0 0.61 0.69 0.65 45\n", " 1 0.53 0.58 0.55 53\n", " 2 0.61 0.74 0.67 50\n", " 3 0.56 0.51 0.53 49\n", " 4 0.55 0.50 0.52 56\n", " 5 0.64 0.52 0.57 54\n", " 6 0.67 0.81 0.73 47\n", " 7 0.64 0.53 0.58 60\n", " 8 0.70 0.78 0.74 54\n", " 9 0.74 0.56 0.64 61\n", " 10 0.75 0.93 0.83 60\n", " 11 0.84 0.78 0.81 55\n", " 12 0.33 0.42 0.37 40\n", " 13 0.53 0.50 0.51 52\n", " 14 0.70 0.75 0.72 61\n", " 15 0.66 0.76 0.70 49\n", " 16 0.84 0.56 0.68 48\n", " 17 0.61 0.83 0.70 41\n", " 18 0.77 0.53 0.63 43\n", " 19 0.56 0.23 0.32 22\n", "\n", "avg / total 0.65 0.64 0.64 1000\n", "\n" ] } ], "source": [ "print(confusion_matrix(y_test[:1000], y_pred))\n", "print(classification_report(y_test[:1000], y_pred))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model 1: Mean score: 0.607 (+/-0.005)\n", "Model 2: Mean score: 0.548 (+/-0.013)\n", "Model 3: Mean score: 0.614 (+/-0.008)\n", "Model 4: Mean score: 0.741 (+/-0.004)\n", "Model 5: Mean score: 0.808 (+/-0.008)\n" ] } ], "source": [ "from sklearn.cross_validation import cross_val_score, KFold\n", "from scipy.stats import sem\n", "\n", "for i, clf in enumerate([clf_1, clf_2, clf_3, clf_4, clf_5]):\n", " scores = cross_val_score(clf, X_test, y_test, cv=5)\n", " print((\"Model {0:d}: Mean score: {1:.3f} (+/-{2:.3f})\").format(i+1, np.mean(scores), sem(scores)))\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Sentiment Analysis\n", "* 서울대 박은정님의 네이버 영화 감상평에 대한 감성 분석 예제\n", " * https://github.com/e9t/nsmc\n", " * https://www.lucypark.kr/slides/2015-pyconkr/" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Read Data & Preprocessing" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import codecs\n", "from konlpy.utils import pprint\n", "\n", "def read_data(filename):\n", " with codecs.open(filename, encoding='utf-8', mode='r') as f:\n", " data = [line.split('\\t') for line in f.read().splitlines()]\n", " data = data[1:] # remove header\n", " return data\n", "\n", "train_data = read_data('./ratings_train.txt')\n", "test_data = read_data('./ratings_test.txt')\n", "\n", "t1, t2, t3 = zip(*train_data) # python3 zip function() - return tuple, python2 zip function() - return list\n", "X = t2\n", "y = np.array(t3, dtype=int) # chage type string to integer" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from konlpy.tag import Twitter\n", "pos_tagger = Twitter()" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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BDQAAAAA0mwC7J2ZHiGhgAwAAAADNJsDuiXqA7RBHAAAAAKDpBNg9MTMDe1MD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m70NMhhGjINQw6Hef/YNORwGDLNyMM4zZlzh2kuPn9vbGbeNA0NjYdlGoYGRwPz5u8d0y0OAFgJ\nBNgAAAAAACyDRK9TlUVpCo54JEnV1TWSIh3bvUOT6h6YnO227p55f9SHR95zb0uSWY2tj13/Q/nF\nqUWnIuH5QmH5g4P16OOJ8XF53YZG1KVdW7OU9IgP7AQAbCwE2AAAAAAArCCnw1R+VpLys5LmzU0G\nQuoenFBX/0ywPTgZOR6ceKQHSK4Flm3LCj16p/lirrXWyTQMbStO0+6KbO2pylZeZuKyfT4AYH0g\nwAYAAAAAIE4SPE6V5aWqLC81Zty2bQ2NBSOd2jMBd+R4Ur2Dk0vakmQ9smxbt1qHdKt1SN95v0G5\nmYnaU5mlPZXZqixKk8M0410iAGCFEWADAAAAALDGGIahjBSPMlI82l6aETMXClvqG55SV/+Ervvr\nFbZsZecUyLJshWdeCx1H3q0HzM89tmRZtqYCAVmWLdPhWnDtagXq3QMTeuvchN4616ZEj1O7KrJU\nU5nFViMAsIERYAMAAAAAsI44HabyMhMjD3Gcjux9XV1dtqL3rKu7NHOffQvO2/acMNy2FQ5Hjdm2\nwmFr8fmoMLy5pUFdQ2G1DznV0Te+aE0TgZA+ruvWx3Xds1uN1FRmq6aSrUYAYCMhwAYAAAAAAI/F\nMAw5HYbkePzPck+3altBJCzvGZpUbUOfahv65G8dUthauNM7equRb7/HViMAsJEQYAMAAAAAgDVp\nS3qCXjxQrBcPFGsyENL1pgFdqe/TtTv9GpucXvQ6thoBgI2DABsAAAAAAKx5CR6nnty+RU9u3yLL\nstXYMawrDX2qbehf8lYjAIC1jwAbAAAAAACsK6ZpqKooXVVF6fry8colbzWSkWSqJNuppuFmpSd7\nlJniUUaqVxnJHnncy7AfCgDgsRFgAwAAAACAdW2pW40MjlsaHA+qtuXOvLlEj1MZqR5lpHiUkRx5\nz0z1RgXdHiV6nDIMYyW/GgBsegTYAAAAAABgw1jqViNzTQRCmugN6W7v4te4XeZsuJ2R4p15vx9w\nZyR7lJLklknIDQBLRoANAAAAAAA2pKVuNfKwgtOWugcn1T04uegah2koPdkjj2NaCR5DpxrrlOhx\nyutxKNHjVELUK3LumD33uh10eAPY9AiwAQAAAADApjB3q5F3PrqooYmw3Ik5GhwNaHAsoMGRgIbG\nAo8dbt8Ttmz1j0zNnjd2dT30tYYhJbijA+6ZcNsbHXg7leC+H3p394fkcRrq7B+XwzRkmoYcpjnz\nbkSNRV4E5ADWOgJsAAAAAACw6SR4nKrMd0lyqbq6KmbOsm2NTkxrcHRKgyMBDYxGQu2BkUBkbDSg\nwdGAgiFrRWu07ZmtTAKhR7/4xMcPtcwwNBNm3w+5Fwu7Y+dNTU2OyTSlbP81ed2RjnGvxyGPy3H/\n3B3pNve6HLHnboccpvno3wvApkOADQAAAAAAEMU0DKUluZWW5FZZ3sJrbNvWRCCkwZGZzu3RgAZG\npu53co9GurmXFD6vItuWQmFboXB4yZ/R3NO7pOucDnMm1F4k6I4KxIcGAnI6pCGrUw7TnA3STdOQ\nc4Fw/ZPn5681DNGNDqxRBNgAAAAAAACPyDAMJXldSvK6VLQledF1gWBYg2MBXbl+TYFpW1k5pZoM\nhDQ501k9GfWaCIRjzle6wzveQmFLY5OWxianH/6iKzdXrJ57wbZkKS3R1NN9jdpblaPSvBQexAnE\nEQE2AAAAAADACvG4HcrLTFRJtkuSVF2d/9DXhsKWpoLhSNA9FVok+A7HnA8MDSsQsuVyeRW2LFmW\nrfDMy5rzvlz7fG8U0f9MekcsvXG6RW+cblF6slt7KrO1pypHT5RmyOVk6xNgNRFgAwAAAAAArEFO\nh6nkBFPJCa6Hvqau7pIkqbp63wPX2rYt25bCljUv2I4+vnduWbZCM6G4Zdm603RbIUvaklumwHRY\nU4GQpoLhyGs6rKlgSFOBmfdgOLImeP/cXif5+dBYUB9c6dAHVzrkcTu0qzxTe6tytLsyS0neh/93\nA2BpCLABAAAAAAA2IcOI7P1smg4tJYYNj93rKs995Gtt21YwZN0PtAP3Au6oEDwq7O7s6lLIklJT\nMxYM2e8fWwt2ms8/jg3tHzZMDwTDuuDv1QV/r0zD0LbiNO2tytHeqmxlpyc88j8HAA9GgA0AAAAA\nAIBVZRiGPK7IAxrTktwPXF9XNyJJqq7esSL1WPb9MPvqtctq7g2pZyJJ1+8MKDC98AMuLdvWrdYh\n3Wod0rferVdRTrL2VGVrb1W2yvJSeCgksEwIsAEAAAAAALCpmYYh02HI6ZCSvKZ2FLv15epdmg6F\ndbNlUJfr+3Slvk/D48FFP6O9d0ztvWN643SzMlI82lMZCbO3l2bI6WDfbGCpCLABAAAAAACABbic\nDu2uyNbuimz9g8/Yauoc0ZWZMPtu3/ii1w2OBvT+5bt6//Jded0O7dqapb1V2dpdkaVE9s0GHgkB\nNgAAAAAAAPAApmGooiBNFQVp+uKxCnUPTuhKfZ8u1/epvn1o0X20p4Jhnb/Vo/O3euQwDW0rTtfe\nqmztqcpe3S8ArFME2AAAAAAAAMAjys1I1GeeKtFnnirR6ERQVxv7dbm+T9eb+hWctha8JmzZutky\nqJstg/rmL+qVk2oqPdGh925dW9FaR0ci3eIrfZ/ZexnSR4035HaacjkccrlMuRym3DPvLpcjMhf1\ncjsdn3husqf4pkWADQAAAAAAADyGlES3Du/K1+Fd+QpOh1XXMqgr9b260tCvkU/YN7t3xFLviCV1\n9a5Ooat1H0n1nd3L+nlOhyHXTKh9L/wOhwJymFLypYtymIbMmZfTNGePHTMvc8577LH5wLWdHUG5\nHIaMxAF53E553Y6ZV+TYNAnYVwoBNgAAAAAAALBM3C6H9lRma09ltizbVlPHiC7X9+lyfa86+yfi\nXd66FQrbCoVDmgwsMDk4vHqFnL+y4LDbacYE2l63Y8Gge+65Z96cU5Zt03EehQAbAAAAAAAAWAGm\nYaiiME0VhWn60vEKdQ9MzIbZDXeHF903G+tPMGQpGLI0MjG9LJ+XkmCo+Opl5WYmKi8jUXlZicrN\nTFR2qnfTdXsTYAMAAAAAAACrIDczUS89XaKXni7RyERQJz6+rHDYVnHx1hW9b1vbHUla8fvcu5ct\nKTevVMGQpemoVzAUnnNuKRQ1fn99OGouMrbZjE7aqmseVF3zYMy402FoS0ai8jITlZuZEBNupyS4\nZGzAzm0CbAAAAAAAAGCVpSa6VZHrkiRV+7as6L0Swu2rcp+Ye1XnL9tn2ratUNiKCbmDIUv+2zdk\nWVJJ6TaFLVuWZSts2QpbVtTx/fH5Y9Yi4/eOrdnjwcEBTYdtudzJmgqGNRUMRd6nwwoEw8v2XR8k\nFLbV0Teujr7xeXNJXqdyMxOVOxNq52VGXlsyEuRxOVatxuVGgA0AAAAAAABgzTKMew9wdCgxany4\nJxJtbitOX/Ea6uouSZKqq/fNm7NsW4FgeDbYDkyHNRUIxwbdc44ja+bMTc/MLTEQH58K6U7HyP/P\n3r3H3TLW/x9/3RvbMcecvjpsUZ/omyhKKBSikCi+Ug6VhHJWlBwrh0oOFR2dSyWlfooi505S6MBH\nCKUI23Hbzvfvj88se+2111r3vdbMXDNr9vv5eNyPvde95p7PXDPXXNc118xcF7f/+5E5vltq0fmj\nc3vJWR3bj854lkUXmjJUrJTUgS0iIiIiIiIiIiIypCljYyw4/7wsOP+8wPy513fjn6/joRnPsfAS\n07j3wce554HHuSf797GZw42x/cAjT/LAI0/OMSTJ1Hlhi0fu4B1vnJZ7u8uiDmwRERERERERERGR\nmph3njFeuOg8rGpLz/HdYzOf5t7pj3PP9MdndW5Pn8m9Dz4+1FjhTz0DP7zidlZ/+dKs8MKFi9j8\nwqkDW0RERERERERERGQELLLgfCyywmKstMJis/3+ufFxHnzkyeef1G51ct8z/XEeePgJxiva3iKo\nA1tERERERERERERkhE0ZG2OpxRZgqcUW4FXTlpztu6efeZb/PjiTe6bP5J7pM7h3+kzuefBx7v5v\njJW98Vov5X+WWqjbamtBHdgiIiIiIiIiIiIiDTXfvPOwwtKLsMLSiwCzhiWZNTHlyyrassmp/zST\nIiIiIiIiIiIiIjJXUge2iIiIiIiIiIiIiNSSOrBFREREREREREREpJbUgS0iIiIiIiIiIiIitaQO\nbBERERERERERERGpJXVgi4iIiIiIiIiIiEgtqQNbRERERERERERERGpJHdgiIiIiIiIiIiIiUkvq\nwBYRERERERERERGRWlIHtoiIiIiIiIiIiIjUkjqwRURERERERERERKSW1IEtIiIiIiIiIiIiIrWk\nDmwRERERERERERERqSV1YIuIiIiIiIiIiIhILakDW0RERERERERERERqSR3YIiIiIiIiIiIiIlJL\n6sAWERERERERERERkVpSB7aIiIiIiIiIiIiI1JI6sEVERERERERERESkltSBLSIiIiIiIiIiIiK1\npA5sEREREREREREREakldWCLiIiIiIiIiIiISC2pA1tEREREREREREREakkd2CIiIiIiIiIiIiJS\nS+rAFhEREREREREREZFaUge2iIiIiIiIiIiIiNSSOrBFREREREREREREpJbUgS0iIiIiIiIiIiIi\ntaQObBERERERERERERGpJXVgi4iIiIiIiIiIiEgtqQNbRERERERERERERGpJHdgiIiIiIiIiIiIi\nUkvqwBYRERERERERERGRWlIHtoiIiIiIiIiIiIjUkjqwRURERERERERERKSW1IEtIiIiIiIiIiIi\nIrWkDmwRERERERERERERqSV1YIuIiIiIiIiIiIhILakDW0RERERERERERERqSR3YIiIiIiIiIiIi\nIlJL6sAWERERERERERERkVpSB7aIiIiIiIiIiIiI1JI6sEVERERERERERESkltSBLSIiIiIiIiIi\nIiK1pA5sEREREREREREREakldWCLiIiIiIiIiIiISC2pA1tEREREREREREREakkd2CIiIiIiIiIi\nIiJSS+rAFhEREREREREREZFaUge2iIiIiIiIiIiIiNSSOrBFREREREREREREpJbUgS0iIiIiIiIi\nIiIitaQObBERERERERERERGpJXVgi4iIiIiIiIiIiEgtqQNbRERERERERERERGpJHdgiIiIiIiIi\nIiIiUkvqwBYRERERERERERGRWlIHtoiIiIiIiIiIiIjUkjqwRURERERERERERKSW1IEtIiIiIiIi\nIiIiIrWkDmwRERERERERERERqSV1YIuIiIiIiIiIiIhILakDW0RERERERERERERqSR3YIiIiIiIi\nIiIiIlJL6sAWERERERERERERkVpSB7aIiIiIiIiIiIiI1JI6sEVERERERERERESkltSBLSIiIiIi\nIiIiIiK1pA5sEREREREREREREakldWCLiIiIiIiIiIiISC2pA1tEREREREREREREakkd2CIiIiIi\nIiIiIiJSS+rAFhEREREREREREZFaUge2iIiIiIiIiIiIiNSSOrBFREREREREREREpJbUgS0iIiIi\nIiIiIiIitTRv1RvQBGY2D7AX8CFgReA/wGnAMe7+TJXbJiIiIiIiIiIiIjKq9AR2Mb4KfBG4DzgB\n+BdwJPCdKjdKREREREREREREZJTpCeyczGwdYFfg++7+f22/Px14v5m93d1/VtX2iYiIiIiIiIiI\niIwqPYGd357AOHBEx+8Pzv79UNrNEREREREREREREWkGdWDn9ybgfne/qf2X7v4f4BZg/Uq2SkRE\nRERERERERGTEqQM7BzObCrwIuK3HIncAi5vZUsk2SkRERERERERERKQh1IGdz5LZvw/1+P7h7N/F\nEmyLiIiIiIiIiIiISKOoAzuf+bJ/n+zxfev3CyTYFhEREREREREREZFGUQd2PjOzf6f2+H7+7N8Z\nCbZFREREREREREREpFHGxsfHq96GkWVm8xGd2L919/W6fP9zYBNgKXfvNcxITzQVtCkAACAASURB\nVFdccYUOjoiIiIiIiIiIiIy89ddff2yYv1MHdk5mdhuwgLuv0OW7m4HF3X259FsmIiIiIiIiIiIi\nMto0hEh+VwPLmdnK7b80s+WBVwC/qWSrREREREREREREREacOrDzOxMYAz5nZu2PwR8DjANfr2Sr\nREREREREREREREachhApgJl9F9gWuBa4DFg3+/mBu/9fldsmIiIiIiIiIiIiMqr0BHYx3gccCiwF\n7A0sA3waeH+VGyUiIiIiIiIiIiIyyvQEtoiIiIiIiIiIiIjUkp7AFhEREREREREREZFaUge2iIiI\niIiIiIiIiNSSOrBFREREREREREREpJbUgS0iIiIiIiIiIiIitaQObBERERERERERERGpJXVgi4iI\niIiIiIiIiEgtqQNbRERERERERERERGpJHdgiIiIiIiIiIiIiUkvqwBYRERERERERERGRWlIHtoiI\niIiIiIiIiIjUkjqwRURERERERERERKSW5q16A0SkfsxsKjA/MNbte3d/JO0WiYg0i5l9HviNu59f\ncpwPAh8EptG7XB9396XK3A4REREREakHM1sK2IaJrxH2T7ld/agDu0bMbD5gQ/pnINz9pISbJXMJ\nM5sCHEF0dCzbZ9FxVHbMVcxsFeD9TFy5bZNyu/Iws/nc/elJLPcqd/9rQTFfwcTl+0+KiCUj4SPA\nkkBpHdhmthvwVSK/PQQ8QpThIiJzjSrqfBme2ksiIuUys9WAy4DF6VHOZsYBdWDL7MzspcAlwMuy\nX/XKRONAIR3YKZ/KMrP1gR2A1wALA9OBPwCnu/uNedffEWtlYBlgHmalaQyYD1gKeLu771RQrCal\n6yDgU8DTwI3Aw5Tc0ZH4WCnWcOtfH7gImMrElVshEu2/P5jZDu7+lx7bMAX4JHFOLJgnUHZ3+8fA\nOn0WGyP24Tx5YrXFTJIHzWxVYCdgdWAJd3+9mb0ji3G2uz+XN0ZbrJTnVYp0zQCeKmA9/XyMKMvf\n4e6/KTnW8xLni5T1cJJ0pWqfpX4638w2BXZh1v5bxsx2AFYCvuDujxcRJ4vVtPIidbs9VR2SKk3J\n6vxsfanPrUbU+RW1l1IfqyR1VpOuRdriNPJYZbFS1SNN6rtoxWlqeVt2uo4GlgC+AfyMBH0/RVAH\ndn0cSzTgfwH8nJIzUKqnssxsHuA0oqBsnXRPEZ1h6wF7mdkR7n5UAbFeCFwIrNlnsVajJ1eh0tB0\nfQD4D7COu9+ZYz0TSnysFCtfrCOJCvkQSq7cUu4/4H+Ba83sUHf/fMd2vJo4v18L3JczDkQDYV3g\nr8SNykbsQzM7CDiKWReRrTS9GTgA2NrM3jOZp94miJMyXyRLF3HT8Mtm9mfgfHe/J+f6ulkZ+Hri\nzutU+SJZPZzFS5WuVO2zpE/nm9mpwK5ZvGeZNQ/PGsB+wGZmtrG7P5YzTiPLi4T5ImUdkjIPJqvz\nU6argXV+svYSJD9WSeqshl6LNPJYtcUr/dxqaN9Fk8vbFOl6E/BTd9+t4PWWSh3Y9bEJcIW7b5oo\nXqqnsvYD3gdcQXSAXevuT5nZkkRheRRwuJnd7u7n5Ix1FLAW8GfgaqKAvgn4E7AqUQn8EvhEzjjQ\nzHS9CPhy2Z3XmZTHSrHyWRM4192PzrmeyUi5/9YHTgeOMbPNgR2BfxFPX32S6LT/NnBgAbHeSaTh\n9e7+bAHr6yfJPjSzbYDPAb8GDgM2A/bNvj4VWA3YAtgDODFPLBLmi8Tp2gl4HDgZONnMngJmdlku\nz9MV95KwrZd4/yWrhxOnK1X7LNnT+dmF2IeB84CDiSGpPp19fSSwGPGU0f7EUGZ5NLW8SHW8UtbD\nKd8QSVnnp0xX0+r8lO0lSHusUtVZTbwWgWYeq5TnVhP7LqCB5W0mRbqeA24uad2lmTLxIpLIfMDv\nEsZbmXgdpewT/YOAA5u5+zXu/hSAu0/Pxi7bALiLYhqMm2Wx1nD3PYk799PdfQ933wDYOYtXxN2r\nJqbrLmIMpBRSHivFymcmUMaTod0k23/ufjXRKPwq0XC7EbiBaDzeBmzg7h9y9wfzxgJeAPwi0cVY\nqn24P7Gf3urulwKPtr5w938AmxONop1zxoG051XKdK1IDCNyV/ZzD9FY7fzJM2nuOcA22QVKCin3\nX8p6OGW6UrXPUsWBGO/9Rnff1t1vo+1cdfdH3H1X4PfAtgXEamp5kep4pdx/yfJg4jo/5bnVtDo/\nZXsJ0h6rVHVWE69FoJnHCtKdW03su4BmlreQJl1XEZ3uI0Ud2PVxHfC6hPFSPZX1UuBn7v5Ety+z\nhuJPACsg1vJEo6c1RtSfgLXbYp1J3N08pIBYTUzX14FtzWzFnOuZjJTHSrHy+QXwtuzVs7Kl3H+4\n++Pu/jEi77+AuHs+HdjK3a8qIkbmz8ArC1xfP6n24WrABe7+ZLcvs4vPnxNDY+WVMl8kS5e7T3P3\nFSfzkyPMGcQFybVm9kkze4+ZbdntJ296MinzRcp6OGW6UrXPUj6db8RcCv1cQYzzmFcjywvSHa+U\n+y/pGyIJ6/yU6WpanZ+yvQRpj1WqOquJ1yLQzGMF6c6tJvZdQDPLW0iTrgMBM7MTzWyFkmMVRh3Y\n9XEw8CYz28/MUpyEqZ7Kuh148QTLLE68xpfXTKC9UL4NWLzjhPw9cWcsryam64/EXcXrzOzrZnag\nme3V7SdnHEh7rBQrnwOJST6+b2brmtnSZrZot5+ccSDt/sPMVjOzXxOvtv8bOAtYEvhTlv+L6rT/\nDLC5mW1d0Pr6SbUPnwEWmWCZJYixbvNKmS9SpmsOZrZwwau8iRjXdUXi1cdzgR91/Pw4+7cIKfdf\nyno4ZbpStc9SPp3/ODHhUT//ky2XV1PLi1THK+X+S/qGSMI6P2W6mlbnp2wvQdpjlarOauK1CDTz\nWEG6c6uJfRfQzPIW0qTrq8RN3I8Cd5nZDDOb3uXngRK3YWAaA7s+dgVuAT4PHGlmdwLd7sSNu3sR\nT2qfAWxMPJX1LeDvPeKRvVYyrM8CZ5rZzu5+eueXZrYJsB2wd44YLTfRdheM6IwdIyYIujv73aLE\nLK55NTFdl7T9/0N9lhsHTsoZK+WxUqx8riIaVu8Ctuqz3Dj565Rk+8/MvkiMLzYvMS7mvu7+sJmd\nDnwLOAbYwcw+7O6/zxluDeJV5R+Y2e1EWd+rfN8mZ6xU+/Ba4J1mdpC7P9T5pZktS4xl+YeccSDt\neZUyXZjZGLAbsAsxI/y8wLxmtifxVtbB7n5vjhBHknZG8ZT7L2U9nDJdqdpnqeJAjBW5tZkd5u7/\n7PzSzF5O1DGXzPGXg2tqeZHqeKXcf8nyYOI6P+W51bQ6P2V7CdIeq1R1VhOvRaCZxwrSnVtN7LuA\nZpa3kCZdLyOuEe4a8u8roQ7s+ti57f8LAav0WK6oC9GbsnWNEU9lddOaRXXSTySY2be7/Pq/wLfM\nbB/itYp7iTt8rwPWIe5eTfRkzmScC3zJzM4gxrS7kXjC4kgzuxVYDtieaAwNpKnp6rBLzr8fRKo0\nKVb+WHeRrgMs5f7bl3jKYFd3v7j1S3e/zMz+FzgO2B24hpijII/D2/6/Er1fAyxiP6fah0cTE5Vc\nZWaHAcsCmNlLiQlOPks8MfLFnHEgbb5Ili6Lt60uADYFnibGPWw9afESol3wJjNbx93vGyaGux+e\ndzsHVNr+q7geTpnfS2mfVRgH4kbK24DfmdkXyF5RNrP1if33CaKcLWKy4EaWF6Q7Xin3X8o8mLLO\nT5muptX5h7f9v+z2EpR4rCqss5p4LQLNPFZQ0rk1l/RdQDPLW0iQLnefNtSWVUwd2DXh7qmHcynr\nqayd+3y3WvbT6eVEg6XXyTlZXwHeRMxuf4m7n2VmRwGnAH/NlhkDjh1i3Tv3+W6U0/U8dz8j11YO\nJkmaFKuQfLFBnr8fUMr9901gf3d/tPMLd38c+KiZ/SBbLq8U48q3pMoXvzKz3YCTgR+0rff27P/P\nAQe4+0Tj3k5GsnyROF0HEBPCHA8cCnwc+HT23SeJyRuPIoYY2y9vMDN7CfGU90LAA8Df3P3fedfb\nruT9t3Of70qthxPni1RPzSd7Ot/d/2gxJMAZwBfavvoVsR8fAXZw9yImM29qeZHqeKWsh1O+IZKy\nzk+ZrqbV+SnbS1Dusdq5z3dl1lmNuxbJNPFYlXlu7dznu0b0XWQaV95mUr9BOTLGxse1X6Q4Zjb0\nBAMeM9MXsQ1vAP7l7ndnn98DvJcYt+hsd//ZEOtsZLp6xBkjCuf2jo6/egmz4KZKk2IVx8xeQJYv\n3P2ZItfdFiNpmibYlgXdfWaqeEVJWF6sQDTkXks8xfEY8UTC2e5+axEx2mKlPK9KT5eZ/Y04j96U\nfT4MONTd52lb5hLgJe7+ihxxpgHfAN7S8dU40YG4m8dM94UpY//VpB5Olt+byMwWJF6F7tx/P3L3\nhwuO1ajyIrU61cMpqc6fME7j8npZqq6zmnwtUrSqj1W2DYWeWzVJ00jni16ali4z2wH4IB19P8AZ\n7v6dKretG3Vg14yZrQd8gDkz0FnufnWV2zYMMzsbuNrdT616W4rU4HStRUxq8/LsV2PZv+PE2Evv\nc/dCxn6V0ZENdfAJonJ7adtXtxJP1B1XVmd2UcxsS+Bmd7+l7fOkFDBmWmsbpgE7Mmf5fm7RHYhl\nM7NlPd/YzLWUMl1m9gRwvLt/MvvcrQP7GGAvd19oyBjLAdcRM6dfS7we/2/iddT1iVdG/wW81t3v\nz5GcVryU+y9ZPdzU/C75KF/UVx3q/CZJndeb1F5qaeq1YxM1sX2h/CedsocWzyHGPh8DHmLWNcLy\nRP/Pd939fZVtZBcaQqRGzOxo4hXiVqfh48AriAvMD5nZMe7+qYJinT/JRfNOkvEuoNCnaiZiZqu4\n+01tn3cjnii+A/iyu99TQJjGpctiEqVfAi8AfkhMutTe0bEtcLGZrVlU4zHRsVKsfOufCvwiW+cT\nxOQ6rXyxOvGa2cZmtpG7550huxWzjDT9mHgt7si2zxPdwS1qzDTM7CPAiXQfW/MwM9vb3b+WN05b\nvLLz4L+yp4PPIZ6enJFzfRNKdF6lTNdDzH5DqJuVyFfXHEY0Qnfvlr/M7EPA14khS3IPU0La/Zey\nHk6WrlTtszLjmFm3V5Mnxd1vHPZvO7ahUeVFwnZ7K17p+6/kNFVW56c+VlnMxtT5KdtLiY9V0mvH\nJl2LZOts6rFKdW41ru8iW2cTy9tU6foI8H/E25h7ufvf2uKvTAyZsr2ZXerupw0Zo3B6ArsmzGw7\n4LvAX4CDiDtkj5jZ/MQJ8XlirKKt3f2CAuI9N8Ei40QH+tPuvuQEy/aLcydwubvvNOw6Boj1AuBH\nwIbA0u4+3cw+Q4wf2ropcDfwBs857mcT05Xdmd0O2MK7jLNlZpsC/w84zd13HTZOtq6Ux0qx8uWL\ng4kJRM4B9m1/SjPbhpOIp2QOcPcvDRunbX2lpCl7uvUyd78y+3w4kxxbzN2PGCRWl9gbARcD9xD7\nsvPm0KFEJ+NbW9uXI1aqfHERMSTFPMQrcxcQeeTiom5ktMVKeV6lTNfZwDbAG939+s4nsM1sbeBK\n4PvDPv1gZncBN7n72/osczGwsrsP/bpp27pS7r+U9XDKdKVqn5UWJ1v3UBcY7W8gDKPB5UWqfJFy\n/5WZB6us85McqyxWo+r8lO2lLF7KY5WkzmritUgWq3HHKouV6txqXN9FFqtx5W0Wq/R0mdl1wKLA\nat5l6CwzW4gYxuZ+d197mBhl0BPY9bEX8B9gQ3d/oPVLd38SuMTMNgGuz5bL3YFN70kyFgJWBg4E\nFgTemjPOHsB3zexu4HzgH0ThPAePSVTy+CRRAVwIz590ewP3Ae8m0vxN4om03XLGamK6NgJ+2q3z\nGsDdLzKznwA9O0IGkPJYKVa+WO8D/gzs5O6zVabu/mj2BOdrgZ2AXB3YlJumZYDF2rb98JzbOoiP\nE089rOvud7T9/n7g72Z2KTHMw4FEh2UeSfKFu29qZksB7yFufG1H3MV/wMy+D5zjxY2bn+y8Spyu\nw4DNgWvM7FtE3YuZ7UTMPP9B4q2Hz+SIsSzwvQmW+TPw5hwxnpd4/yWrhxOnK1X7rMw4Z1Ld5ENN\nLS9S5YuU7Zgy01RlnZ/qWEHz6vyU7SVIe6xS1VlNvBaBZh6rlOdWE/suoIHlbSZFulYBvtWt8xoi\nH5jZz4lr/NpQB3Z9rAZ8p73zup2732dm/48o3HJz9zv7fH2Tmf2SuKg9GtgzR6gTiNlzP5H99DJO\n/vy4NXClu28BYGZbAQsTr3JcDVxtZpsBb88ZB5qZriWYNeNxL7cDm+WMA2mPlWLl8zLglM7O6xZ3\nf9bMfgXkeio/U2aadgIeBH6arftZ4HB3zzvT9mS8nngt8I5uX7r7P8zsAqIzM69keTCrr04FTrUY\na7nV8N4d2N3M7iAa3ofmDJXyvEqWLne/zczeQowj/9G2r75NPMnxD2BHd785R5h76T7TfLvViM6B\nQiTMFynr4ZT5Ikn7rMw47r7zsNtVgKaWF6na7SnrkDLTVFmdn/BYQfPq/JTtpdTHKlWd1cRrkaYe\nKyDZudXEvosml7cp0vU0sMgEyyxC5JvamFL1BsjzxiZeBOg+Hljh3P0J4knvrXOu6l7i5Pr1BD9F\n3Fl8Sbauls2IQvjnbb+7A3hhAbGamK5/Am+cYJl1iFf58kp5rBQrnxnAchMssyzwZM44UG6angFe\nbzEhJUSZO9lyN6+pxGzi/TxG3FXPK2UefJ673+PuJ7r7OsB6xCtnKwJFzNtQSZqg9HTh7n9091cT\nZe9HgUOAfYknPF7u7tfkDPEzYCMz26XblxZjjb6V7GmSopW8/1LWw7MpO19MELuo9lnSOGa2sJnt\nY2Ybd/z+IjPbv61szqux5cUEsYs6XpXtv04501Rlnd9XwedW0+r8lO2lCRV8rFLVWU28FpnQiB6r\nOZR4bjWx72JCTShvuykoXb8H3mlmXefjMbMVga2ISeBrQ09g18cNwOZmtqS7T+/80sxeCGxBFGKp\nvJAYF2do7r5eQdsyGdOJp4hbNiM639oLmpcT46rl0tB0nQ/sb2aHd75qaWbzAUcAbwC+mDMOJDxW\nipU71tXAVmb2Gne/ofNLM1uDmBjkFznjQLlpugx4J/C4mT1GNDgOMrN9Jvi7cXdfaoh47W4GNjWz\nBbu9ppW9grYZ4DnjQNo8+Dwzm0Y8LbItMbnnGHATcHYBq68kTVB6up7n7r8DflfkOjOHE43Pb5rZ\njsBVxOvZKwDrAmsSFzVH9lpBHmXuv8T18GxS5Ys+crfPUsbJ2rC/Al4FHEdMGN0q+9YDNga2MbO3\nufujOcM1vrzoo4jjVdn+62HYNFVZ509GUedw0+r8lO2lySrkWCWss5p4LTJZo3as5lDWudXQvovJ\nGunyto+86TqOmHPgMjM7gtmvEdYjhkxZlJiLrzbUgV0fJxGTOF5sZgcA17j7M2a2KJGBjiaecty/\niGDZeruZQrwKsTmwPQXfcTGzpYEl3d3NbEqvYQmG9CfgPWb2XeIVtBcB33P3Z7LY7yYu5M8rMCbZ\nupuQrs8AWwKfzjo6rmZWIbZW9q8Tk6rklfJYKVY+nyVehbrczE5gzsptD6LcyDNOb0uZafowMZ7w\nGsD8xNiYT5JmRu5vEDM5n2dme7S/FmZmqwInEk9X7FVArGR50MxeRDSwtyM6QseIuRxOAM529z/l\njZFJWrYnTFfr5uCGwDQiX3Z9QtDdTxpm/e5+j5mtQ+TBDYlJsNpdBuzmOSebaZdy/3XELbMeTpau\nVO2zxO3Aw4H/JS6GTm790mN8xSWItw6OAY4CJupgnEgjy4uExytlHVJmmiqr8xOfW02r81O2lyq7\nHs5il1VnNfFapKnHqrX+5O2mhvRdNLm8LT1d7v5LM9ubeDjx2x1fjxFvMu3j7kU8pFYYdWDXhLt/\nz8zWAvYjnlJ5zsyeYNYrUmPA8e7+3YJCPsTEE+w8R1x05GJmCxCvRe9CDEXQGltpP4vJKfd097/n\njUMMmH8JcUE+RjRcP5dtw8nE+EAPExdIuTUtXe7+SNbR8Xli8oj3tX39BHAa8HF3L6Lxn/JYKVYO\n7n6tmW1LVGyHMXu5MZbFeL+7F9EwKDNNj7r79q0PFrM7f8ndS3nytMOpROfhu4HbLSZQad0EWIxI\n6w/d/SsFxEqSL8zsGuKNjCnE67xnZz+XFt2wJ+F5lTJd2St7lxDjzEPv19vHiZvcQ3H324G3ZhdI\nqxNPUzwKXO/u/xx2vd0kzhfJ6uHE6UrVPkvWDiRugl7g7sd0fuHuTwPHmdmbiTIybwd2I8sL0h2v\nlO2YMtNUZZ2f8txqWp2fsr0EaY9VqjqrcdcimSYeq9Ttzkb1XWQaV95mkqTL3U+2mGfvfcBraLtG\nIG6e/CPP+sugDuwacfcDLCam2Jk5M9Dp7n5VgeGupPtJMQ48RbzC9W13zzVkiZktTJzkaxJ3Eu8g\nnjSD6JzfCLjKzN7g/Qern5C7X2dmrydmfR0DznD3P2df3wicCXzO3W/JEweam65s+JoPWoyLaszK\ng+7uT+Vdf1uclGlSrPyxfmwx8/tWzFk2/biAV75bccpM09/N7JvufkT2eRfiTnrp3H3czLYjGgc7\nE/twOWIfXk6U72cVFCtVvng98drZ2UQe6DqDdRFS5nUSpgs4FliJGH7n50Sjd6LG6tDc/V/Av8pa\nfybZ/ktZD5M2XyRpnyWMA/EG4W0TLHMTMZRILg0uL5Icr8T7r8w0VVbnk/Dcalqdn7K9lEl2rFLV\nWU29FqGBxyqT5Nxqat8FzSxvIW26/kFBD3imMDY+Xtq1kghm9jngIOLJ8hOJO1efdvd5su93Ip7s\n/La771rZhg6oqekSaarsjZYT3P2g7POzwOHuPjIVdp2Y2dLufl/V21G0lOkys+nADe6+YYHrPB84\n192/3/Z5MsbdfZsC4qfcf8nq4abm91TM7CbgYXdfu88yVwIruPtK6bYsH+WL+lKdXyzl9fx07Tg6\nmti+UP4TM9sSuLnVwZ59nhR3/0lpGzYgPYFdETNbDbjH3f/b9nlSCnoapn1b5gNeSdx9ewC40+OV\nziJsB1zs7idksWa7Y+LuZ5jZ1sBbCorXmvxgR+KufStNfyHGKCrqNYiRT5eZ/RE41d2/3vZ5Msbd\n/XWDxuuxDdMo/1gp1mDr2wv4rbv/vu3zpPiQ4/R22YZpFL//7gV2MDPP1jcGrDKZynvQStti3LIn\nWm8tWO9xzLrFemSQWH22YRrF5ovZ6ixgeTNbfjJ/W1SdVUa+qDhd81H8xI1bEW9GtH+ejKGeZqh4\n/5VWD9chv2fbUWb7LGWc7wOHmNkXgE962xtdZjYvcCgxqWhhEwU1sLxo347S80XKdkwWr+g0Javz\n+0l4Dk9jBOv8OrSX2ral7GOV9NpxlK9FJhFvZI9VhfXIyPddTCLeSJa3k4hXVLp+TAw5cmTb54na\n/2PZMvMMEa8U6sCuzvXMnoGuZ/IXkIVkIDNbnLhY2IGY4KTlMTP7HjHe8UM5w6wAfG+CZW4GNskZ\nB4Bs6IsTiY6BToeb2d7u/rUCQjUhXasTr+W1f04m4bFSrMGcQJRNv2/7PE7v8Xlbco3T21Li/vsq\nMRnuN7PP40Rjbrs+fzNspf0gcASzyvfJjGPW2qbc9XJJ+7DSOqvEfFFluq4DCrkZ2GZFIr+1fy5T\nlfuvzHq46vyeon2WLA4xQeOWxJNfHzSz64FHgBcQbY/Fiddvi5gMuKnlRcp8kbIdU1aaUtb5c0h4\nbo16nV9pewmSHqtk144NuBbpFasJx6qqeqQJfRe9Yo16edsrVtHpOgK4ou3zkZQ4dGFZ1IFdnTOY\n/SmpM0mYgbI73NcAqwB3EzOY/htYAlgP+BDwRotxkB7PEeo+4o5RP6/KlsvFzDYiZq6+B/gscDWz\n0rQ+8YTPl83sJne/Mme4kU+Xu0/p97lMKY+VYg0caxdmL5s+QKKyqcz95+7HmtmfgNcCC2TrupzZ\nK/KiXEWMLdfSaxyzwpW4Dyurs0o+r6qsiw8GLjOz/YCTPJvFPA+fc9zCceChfk+qmdmywKrAMGMe\nVrn/yqyHq8zvSdpnCduBuPtMi0miDyYmiV6/7et/AqcQ40bOyBMHmlteJMwXKdsxpaUpcZ0/m5Tn\nVgPq/MraS5D2WJHo2rEh1yLdYjXlWFVVj4x830WPWE0ob7vFKjxdPmtOiNbnw/NsY1XUgV0Rd9+l\n4/POiTfhU8QJcSxwWMfrnGPEHZlPAR8n3+ymFxJP22zs7r/s/NLM3glsxqwnJPL4ODEJ1rrufkfb\n7+8nJnO5lHji7UCigZRH49JlZm8G7nD3u/osswqwlrufOWycTMpjpVgDxHL3Mzo+nz6ZvzOzBScb\no49S95+7/4KYMA8zOxS43N2P7P9Xg3P3Dfp9LllZ+aLKOqu0fFFxunYFbiGerjjSzO4EnuyyXJ5h\nm/5B1OH9xn3dF9iDmJx1IBXvv9Lq4YrTlap9lioOEJ3YxMXdoVl9sQTwWNHDANDc8iLV8UrZjik1\nTanq/C5SnlsjXedX3F6CtMcq1bXjyF+L9NCIY1VhPdK4vovMyJe3PZSeLjP7FTExbs9+HTP7GLC7\nu686TIwyaBLHmjCzbxMzz/Ycd83M3g+81903KyDercC97r5un2V+DSzh7qvkiLMccSIvA/wUWBpY\nh5g4YE1gC+Kkf527/2vYOFmsh4AfdVYMHcucBmzu7kvnjNW4dNkkJrgxs+OAPdx9kWHjZOtJeawU\nK1++uB34kruf3GeZQ4E93X3ZYeNk60m2/1Jqu3Du2Zgxs82BLd39wzljpcoXyeqsxOdVynQ9N8lF\nxz2bZGcS69yYaPC2nABclP10M5XovF7C3ZeY5Pb0i59y/6Wsh1OmK1X7LEmc1BpcXqTKFyn3X1Pz\nYLJ0Na3OT9leytaV8lglqbOaeC2SradxxyqLlercalzfRbaexpW32XoKBV/XoAAAIABJREFUT1f2\n8EBr6JMxYgino4lO8m6mAqcBG7l7EQ+qFUJPYNfHzsSTUv0mDtkE2KCgeC8mBm7v59fEhe3Q3P0e\nM1sX+BqzTybVegLi18CH8haUmanAYxMs8xgxAH4uTUiXmb2bqLhaxoDNzKxXJ8ZUYuzA3K/4kvBY\nKdbA+WIasz+JOY2Y+KjXRLNTgY2AXDc12taVZP9Z2skpDycaiP3uxr8deD+Q94Is1T7cmXR1Vsrz\namcSpcvLGbbpQeB4ojxvjef6NmDTCf7uywXF35l0+y9lPbwz6fJ7kvZZmXGs2kmiG1lekC5fpNx/\nqdKUus5Pli6aV+cfTrr2EiQ8VgnrrJG/FumhiccKEp1bTei76KGJ5S2Uk64PMOd8VQdlP/0UPeF8\nLurArojFmJeHdPz6YDPbt8efzEecDH8taBMeBF42wTIrEZPs5JK9YvE2M3sRMRbd4sTJfaO735p3\n/W1uBjY1swU9XlWdjZktRLwa40UEa0C6bgDOJgpjiI6OtbOffj41YJxuUh4rxRrM2sB3mDUe2ziw\nW/bTyxjZa7o5pdx//SanbKW91QE40MWsme0JfLDj17ub2bt6/MlUYly6ImauLmUfVlxnlZYvalAX\nF8rd/2BmWxJP1owB3yYawBd0WXwceBq4u9/Tbv1Uvf/KqocrTleq9lmZcaqcJLqp5UWqfJGyHk52\nLUKJdX4XKdM10nV+xe0lSHusUl07NuFapJtGHKsq65EG9F10M/LlbQ9lpOsU4M3EU/hk/7+L2ech\naHn+GoEY77s21IFdna8QT7O2MtBixNiXD3dZtj0DfaKg+JcA21nvcZA2I14lOXeQlWavfp3uXcZR\nzu7oFXFXr5dvEPv1PDPbw9smszKzVYkZY1cEJv0URtvfNy5d7v53M3s9MQ7lGPAr4HRicolO7R0d\nPcfIHkBpx0qxcueLc81sDaJsGgN2JG52XN9l8fay6SvDJKJDyv3X6/WvhYCViad7/kaMDzyos4ix\nXluvj40TnTnL9Vj+aaIBUdt8QbV1Vpn5Ilm6so7lm939lrbPk9Lv1dIuy/6sLeb6xOuOk/77AaXc\nfynr4Srzeynts5RxOt8uKOltg14aUV50kSpfpKyHU6UJyq3zO6VM16jX+VW2l6DEY1XhtePIX4v0\n0JRjleTcamLfRQ9NKG+7KTxd7v4ckfda63gOOM3TzA1RGI2BXRNZBjo8VQYys5cDfyLu6n2HmAX6\nYWAFYmbTrYmhItZqXWxPcr3PAc8Rs3yfDvzQ888EPNnYY8D3gHcTBf7dzErTYkRn3A/d/T1DrLuR\n6eqIcxhw2bBP4g0YK0maFKuQfPEPYgzsvE8kTSZWsv03iW15KVFGfsbdj8+5rmTle8J80bg0ZbFK\nS1fnurPPEzXCxhhgDOwc27ZQEXVagv1XVT2cMr+X0j6rKk4WK9kk0U0pL7rESpUvUu6/ZHlwEttS\nZJ2f8txqVJ3flOvhbN2V1FlNvBbJYjXuWLXFLjzPzw19F1msxpW3Waza1I91ow7smsgaTg+5e7e7\nb2XFfANx53tlZn99DuBWYCd3/82A6/wYsAPw+mydM4DziDuAqTpG30eMJ/UaYizfR4mnR09397OG\nXG8j0zXktqzo7rlf3UuZJsUaHXVKk5mdCrzF3V+Rcz3rE503d064cAFS7MP2OsvM5nH3Z9u/Kzqt\nqfJFmenKbhJe7u5XZJ8PZ+IObADc/YgccVcDtiWe9pmHWfX8GNEwXgpYz91fMGyMtlhl7r/K6uEK\n8nvh7bOK4ySbJDpb18iXFz3ipTpeKdsxSdI0yW0ppM7P1pUsXU2q81O3l7KYpRyriuusRl6LNPRY\nlXJuzU19F00rb9tiparzV6b/NcLb3X2nvHGKog7smjGzBYAl6Z+BDisw3hRiIr/Vmf0EvNrdh84c\nZvYy4uR+L/AK4qS7kxie4swiOkCr0NR0tZjZ24m09SvEXlH204BSL2Y2P/Am+ueLzdx9g0o2sCRm\ndgKwm5c887KZzcus8v20MmMVyczeCnyReP3sxOx3Y8DjRMNqZ3e/rsJNHEqT0mVmGwAXE0PGjTHn\nGLDjxBM6f3X3QsYpLnv/VVUPp84XZbXPUsSxOSeJ3gf4bfbTTWuS6OfcfdlhYlalqfkipbqkqeg6\nvy7pKkod6say2ktlHqumXzum1sRjVea5Nbfkv6aVty0l5/cXAhcCa/ZZLMmboINQB3ZNWAz6fjox\nK2zfDFKnDDQZZvZaouDcDlieuFi+ikjvee4+o7qtG17T0mVmWwM/oPsENxAV3v3Ab9x9qx7LSMOY\n2YrAZcRsyL2MAc+6+3xptqp8Fq+zXwE84O6r5FzXPMDRzH5zqKtRKd/N7E3ApcS4ffu5+zey3y8A\nnEw88TsVeLO7X1vZhg6oaekys18AbyVmGL8COBO4lkjLqsTYo+7umxUUL+n+S1UPNy1flC179fXP\nzD5JdK+2RbtPufvRpW1YwZQvmqPIOr+JUuX1JraX2jXt2rHJmti+UP6TdmZ2CrAb0V67mnhi/yZi\n6JJViUkefwl8wt27zYVVCXVg14SZHQscCNwL/BHYgJgR9E5ituVpwJXACe7+4yHW/+Zht62o10yy\nO4lvIU6OdxFjBc0AzideubhswPXdPslFZwIPAL8Bvuzu/xwkziS2oxHpMrOrgbWIdFwJ/Bz4A/Bp\nohD7PHERup67PzXgupOlSbEKzxdnExcSPyMu7g4g7vxeSuSL7YmJJnZ09wcGXHdl57CZ/bHHV1OA\nhYlJOKYAe7v7yTljHUzM4PwkcDvwcuA+YiyzlwILEufad9z9hAHXXVW++CWwBvAGd7+ty/crEWn6\n7aCdoxXni9LS1SPeFsT5NQ2Yv8di4+7+uiHXPx24xt23yD6fQqTttdnnlwB/IS6avjlMjI54Sfdf\n23oLrYe7rL/M/J6kfZa6HWgxdE3pk0Q3tbxImC9StmMquxYps85Pma4m1vkd6ymtvZStv/Lr4Ww7\nCquzmnot0sRj1WP9ydtNo9x30dTytor8bmZ3ENv+Knd/zszOAxZ093dk3+9ITFz5ene/YdjtK5o6\nsGvCzG4BFgBWcfcZZvZT4Cl33yb7/lCi42iNboXbJNY/mcmiuhl393mH+LuJtmcqsAlRaG4HLDBo\nnCxNg3oYWNfd/zbE305olNNlZg8DF7n7dtnnE4GN3P1V2eclAQeOdfcvDLjuZGlSrPyxOuL+m+hY\nWCv7fA6wvLu/Jfv8VuJmxybufvmA667sHJ4g9lPAzcA33P0reeJksf4CLAe8xt3vzhqr97r7+7In\nLE4Ctsy+v3fAdVeVL+4DvuvuPWfazjpLd3D3RQdcd5X5orR0dVnPHsTTNRM9mTr0q3tm9iRwvLsf\nnH3+GHEzchF3fyb73VnAK1vneB4p91+f9eeuh7uss+z8Xnr7rMp2YPYmwBfd/eI86+mx7kaWF4nz\nxaDytGOqyoOl1fkp09XEOr9jHaW1l7L11+p6GPLXWU29FmniseqxzkrbTaPWd9HU8raK/J5dI5zq\n7ntnnz9FPNCyVNsylwH3ewETUxallJNbhvJi4Fs+69WN64hH+gFw9yPNbEvgEGCXIdZ/EpM/Kd5M\n3AkEmD5ErMlYgxhTdx1gIaLxOBB3nzKZ5bK7jEsA7wC+DnyGmLm1DKOcrgWIcbZabgb2MLP53f1J\nd59uZj8GdgIG6sBOmSbFKjxfLEXMftxyA7B523Zdml1gfAK4fJAVV3kOTzZ2QVYEznH3u7PP1xKv\n8OHuT5jZR4hy45PA3oOsuMJ9OC9RZvQz2WEDZlNx2V5aurrYmxiW6T3A79z9yQLW2ekBoH1yxtuI\ncetfSTx5DfBPiqsTU+6/XnLXw12Uma5U7bMq24H/C3yUGI+9UA0uL5Icr8T7r7I8WHKdnyxdTazz\nO5TWXsrU7XoYctZZDb4Wadyx6qHqdtOo9V00tbytIr/PBJ5o+3wbsLiZrdBWBv8e+ECOGIVTB3Z9\nPE0Myt5yK7CMmS3j7v/NfncZ8arHwNx9n4mWMbMliI7J1glxDrDvMPF6rH8N4s7etsRrYGPESbEn\ncG5RcTp5DHA/HTjLzDYk7twXpkHpuhdYuu3zbcTrlK8ihrWB6GxZKWecnso+Voo1lBnM3mi6HVjE\nzFb0WRN/3Ah8JGecnlLuvxLd1/b/W4AVzGxxd38oe23rYuLph2EuyCZUwj68HtjCzJZ29/s6v8ze\n2NiCyBulKClfpEzXi4gnH8qcDf63wFZmdri73090Wo8BGzGrA/s1wCMFxaskXySoh0tLV6r2WcXt\nwMWBvxawnqGNWnlRh3Z7x/bk3n91S1NR6piuEa/zS2sv1eVYVXHtOGrXInPRsUrebhrlvou65IuO\nbRrVdN0ErN2+GUReWANodWAvSu8hDiuhDuz6uA1Yre3zLUQGeg0xeDrEAP6LlRHczLYFTiQmzLgD\n2L2IVz3N7FVEAbkdsDKRpn8CxwBnuPsteWMM6BHi7mIuDU3XFcA2ZvaFbPtbYx29k1kd2OsSndgp\nFHKsFCt3rBuADc1sLKugbyLy+5pAqwN7+ZwxBpE7TWb2UuJ1qBltv1se+DAx3uK/iZm5/9JjFYO6\nk5j5u6X1psOriclTAJ5h9htIZSoiX5wA/Ai4zMyOIjpKHyGe9n098Cngf0jX8VDUeZUyXTdR/jH/\nIvFmxN/MbEd3v8jMrgA+a2bLEa9qb0qMe1iEZPsvcT1cWX4vq32WOM4FwNZm9sVuF+gVGMXyYjap\n8kUPpbRjSrwWSV3nd8av8ljBaNX5lbaXyjxWNbt2HLVrkTk06FglObdqlv9gBPPFJI1Sus4FvmRm\nZwCHETdJ/g0caWa3EtcI2xP9krWhDuz6OB84zMyOIDLnDcCDwCfM7NdEZt2WePqxMGb2IuAU4O3E\nTLRfAg5198dzrHNl4P+IAnJVooCcAZxNTOBzWdYRlpTFDOPvZcingJqarjbHANsAfzazHdz9PIux\n2D9pZq8k8uC6xERMpSowTYqVP9ZpxDG/1Mz2IWYqvhU4zsweJSq37YgJRkpVwDn8v0Ra1iCGQfl5\n9vvViRuFSzLrafN9zWyfYcbD7OJCYC8z2wU4k5jdeSbxpMNVZrY4sBXRkCxVUfnC3S+wGCvtCGYf\nYqZlHDjM3c/LE2cyijyvEqfrSOD7ZvYdd7+ogPXNwd2vMbN3E+V76wmKvYj8/vHs8x3EEEBFxCt1\n/1VVD1eR38ton1UY5wpicvLbzewa4ubnzC7Ljbv7/gXGncMIlxdAunzRJ37h7Ziy0lRhnd+KX+mx\nyrZh1Or8StpLJebB2l07jui1SPs6G3Wsyjy36pj/su0amXwx4DaMWrq+Qgwf837gEnc/K7uJcgqz\n0jAGHFtArMKoA7s+jifGzTkEuMPdTzOz44GjiI7seYgMdFRRAS0mc/oMcYfvemBXd78u5zqvA1Yn\ntnWcePLrDOCH7U8+pGRm2wOfA16Sbddu/f+i6zoama527v5XM9uAqEAfzn79MWLIkNbA/b8HDs4T\np5+i06RYheSLM81sNWAfYFV3v9HMDgK+T1xoQDwNc1ieOP0UdA6/kDhvlyRmib4v+/0U4iJpKeB3\nzHrC4WjgBDP7vbtfmzMJxxBjoX0TmNfdv2FmXwf2MbP1ibHvFqXm+7CTux9tMWP1tsQbREsAjxE3\nOb7j7p43Rj9lnVep0uXuPzGzk4ELzcyJTr1u42CPezah85BxLgAusBijD3f/c3ZR8xZi7Luri2zg\nl7X/qq6HU+b3MtpnVcYBvtr2/036LDcOlNKBPerlBSQ9Xt1il7L/ykpTxXV+pccqiz+qdX7y9lKJ\nebBW146jfC3Stt5GHqsyzq2q09Rjm0YqXwwQfyTT5TGZ+7vN7A3Av7Lffc3MphNPXj8BnO3uPysq\nZhHUgV0T7j7DzNYF3s2s4Ro+Rwyk356BvtpjFZOWPZHwDeK1lCeIJ6+Od/dn866beMrhFqJxeJa7\nl/5E4SQ8TjRgrwK+4O7/b4h1NDVds3H33wObtX3+J/DqrAPzCeDvJd+pLTxNilVIvjggu6H2RPb5\nfDNbh7ir/wTwPXe/od86cioiTftn63i/u5/T9vuNiAnGngC2dvf/AJjZO4mhnfYmm0BoWO7+gMV4\nc7sTN4EgbgTNT5TvM4ky+Zg8cSZQSh50978Dny1iXUMo7bxKkS4z2wHYj2jsvjL76aaQMre97Hb3\nx4CfFLHeHrHK2H+V18Nl54uS22fJ47TZsKT1DmJky4sKjlc3he6/BGmqpM6vybGCEa3zU7aXEhyr\nyuusDiN7LTI3HKsSzq3K09TFqOWLyRrpdLn77zo+/wD4QRmxijA2Pp78rQGpiJlNBQ4FDgTmAy4B\nPuLuhQ1LYmaHAN9199uKWmefWHsAFxW5/X1iNTJdqSQ+Voo1IhLvvxuAh9x9/Y7fn0y8mnqBu7+r\n47szgA3d/SVlb9+wqs4XZrYU8cTI6sAS7r5tdjN2Hh9ycsKq05RtQ+Hp6hLjJmIix/2Aa4jXOrty\n9zuHjLHXZJd195OGidEjbhn5Ilk93GcbSskXKdpnKeOk1OTyIlG7Pen+S5jXk9b5CdPVuDo/tYTH\nKkmd1eRrkaYdqwm2odBzq8l9F00tb1O30cxs0cku6+5FTfaemzqw5xLZa1dfIyYreQDY393PKiHO\no8TA9XcAF2c/v3L3RxPEuogYyylFrEakqyPu8ZNcdOAxKis+VoqVL+5qEy8V3H2gGbIT77+HgdO8\nY5ZnM/sLsArwUXc/peO7zwAHuPsCRW9PUarKF1ns9xL1ykJkryi6+zxm9lngIOAUd//oEOutLE1Z\n/FLS1SXOTOCb7v6xvOvqE+M54gnusS5ftxqAz6exoJip8kVp9XCP+GWlK1X7LEmcCbZhAeIppdaw\neGT/zkcM6fB2dx9oaICmlhcVttvLrIeT5cGUdX7idDWuzk+p4mNVSp3V1GuRJh6rPvELP7ea2nfR\n1PK2ijZa2zXCRMbdvTYjd6gDuyYsxpqZjHF3X2qI9bdn0LuIcbUnG+91A8SZjxgMfhPgbcQ4Ts8Q\n48xdDPzCCxhbTrGKidUR97kJFml1ggzc0dHU/dfUWB1xJ1u5UfN88RjRADyw7XfLAPcQ6VvVffYx\n5rIntbZ39xfmjP3HiZcCBixvs3VXlS82IJ4MuJ2Y3GNt4ANZg3tN4OvAa4Bd3P3MAdddSZqy2BtQ\nUrq6xPobcJW7lzkO5U49vlqImIX+/cDfgH3d/U8FxNsA5YtB05WqfZYkTo/YCxGT6W1FdF73VOd6\npEvsDRj9fJGyHk6WB1PW+YnT1bg6vyNOae2lbP2NO1ZNvRZp4rHqEXsDSji3lC/mUOt8UUUbzcwu\np/s1/kLAy4gHC34L/M7d9+2yXCXUgV0TZnYHvTPQUsAUYiD/273jlbdJrn+izslecj2VZWbLEif8\nJsDGwNLEXaVLmHXi/3vY9StWcbGyO3/dtDo69gDuBbZ19//mjNW4/dfUWGZ2Or3LppWA1xJjfp3v\n7ifmjFVamszsr8Ct7v7Ott/tAnyLmDj3ZV3+5gbgaXdfc5iYbeuZTPl7F/Cgu6+RM1aqfHEJMaP5\nq9z9QTM7jJgVe57s+0WIGaz/4+5r54yV8rxKma5dgS8A67v79Tk3fdhteCnwJ+Az7j7Zt3D6rU/5\nYsB0pWqfVdUOzGIfS7wSey8xz8sGxBNNdxJjv08DrgROcPcf54ylfFHTdnvKNKWs8ys+txpV55fd\nXppLjlUjrkXmhmOVxUp1bilf1DhfVJmuXiyGUPkC8BZ3/20ZMYahDuwRYGaLAZ8CPgis6+43V7xJ\nQzOz1zHrxH8jMZHo34CL3f0AxapXrI64SxI3UU5z90MKXncj919TY3XEfQfwQ2IypEJnKS4yTWZ2\nHLAPUYZea2bzE3fQXw0c4+6f6lh+W+Bc4OjO74piZgsSNwEOISbqWK+oxmNbjFLyhcXr2ae7+97Z\n59ka3NnvTgB2cvclciShW+zS8nrKdFm8MnoAMaHYVcCtdB8He+BhmwbcjlOJxukrCliX8kXB6WoC\nM7sFWABYxWPS8p8CT7n7Ntn3hxLnwhpe8Hidyhf5VNW2yKuOdX4KTazzs3WX3l5KLdW5NTdci5St\nifWI8kV+TU1XOzO7EFjQ3d9S9ba0TKl6A2Ri7v6wu3+cuPt27DDrMLM9zGyOpw1Sc/fr3P1z7r4B\n8WT51sSF+1aKVb9YHXGnA+cRr5wXve5G7r+mxuqIeyHwE2CgcUsnue4i03Qc8DBwhZn9ipiZezXg\nv8AXWwuZ2epmdgRwBvAQkOup8n7cfaa7/wXYPtu240qIUVa+mEL3cZXbzU805gpVcl5Pma6zicl6\n5gU2BHYlOly6/ZTpCeDFBa1L+WJAqdpnFbcDXwz8xN1bN2iuA9ZpfenuRxJlcqE3x7N1K1/kUOT+\nS5ymZHV+XY4VNLPOh+LaS3PJsaokTtGx5qJjVcm5pXyRX1PT1eFGYK2qN6KdnsAeIWZ2DDET6eJD\n/G2lkxNIM5jZKcQd4IWq3hapj6xs+pi7L1z1tvRjZkZMkPHm7FfXEWPK/aVtmf8AyxJjj23l7lcl\n2rZjgQ/5EHMcVMHMfk3sp1Xd/ckurzy+gLjpepe7r1fhpg4kZbqs97BNc3D3K/LE6rMNqwBXAA+4\n+yoFrE/5YsB0pWqfVdkONLNHgK+4+8HZ5/cRHYbLezYkmZl9HtjB3f+n7O0pShPyRUqp05Sqzm/i\nsepUp7I9T3tpbjhWTTG3HKs6nVujoKn5oo7pMrMpwNXAyu6+TFXb0ak2s0nKpKzOJCdT62JJZh+E\nfjfgGTMrfXKCspnZFvS+c9n++9YkhP/jHTOP11Hd0mVmbwN2AIYewiZlmhQrWb5YAHgH8eTSsOtI\nkiZ3d2ADM1sYmM/du23zicTTPee4+yODxsjhZcDUYf+4gnzxJeB7wIVm9nHi6ZBWY+e12fcrELOn\nD6WivF56utqsDvzW3X9XwLq6st4TYk0BFgZWzP5/VEEhU+6/lMpMV6r2WZXtwNuIp19bbiHO2dcA\nv8x+NxVYbNgADSwvkh6vRPsvaZoS1vmNvcZqU6eyPU97qbHHqoHXIo09Vh3qdG7lonyRS/J0mdle\nPb5qXSNsBryBeOCgNvQEdk2Y2ZY9vmploM2BbYEfufu7C4hX6kD+ExRgnfJWoie5e68TMPfyHX/b\nyHRlfz+9x1dTgAWJG15jwHvd/dwhY6Q8VoqVM1b2970mdmuVTW8hJuA62d2HGuogRZosJqK4yN1v\nH3gDczKz1Xp81V6+Hwhc6u5vGzJG0nyRreNIYn6Gds8C8xBlxUnD5ols/cnTlK2n1HS1xXmQyJPb\n511Xnxj9JoV5irgh+Q13/0qBMcvOF8nq4Y64qfJFEyf6+jQxzNRniQ7DGcC/iQlE3wksA/wauN/d\nXz1kjKaXF2W326uoQ8qcEKvKOr/0c6uKcjBFXk/RXuqI15hj1eRrkWwdjTlWXeKWdm41ue8iW0cj\n80WidD3HrJsJvVwHvKP1tlwd6Ans+vgx/Z+uHiMa+58oIpi73wucmf1gsw9C/zVgXjPLMwj9xoMW\nYEPEaJlsgVKEpqYL4BG658FxZu/ouDBHjJRpUqxiTNRgehY4n3xjl6ZI07HAyWZ2B+lfzbqeicv3\nGcDBOWKkzhe4+6EWk7F9kHhKZHHgMWK8tNPd/fKcIZKnCZKkq909Ba5rDu6efK6TBPsvZT38vFT5\nIkH7LGmczPHEmzqHAHe4+2nZzdGjiKEbWhfped4EaHR5keB4VVGHlJmmyur8ROdW8nIwUV5P0V56\nXsOOVZOvRZp2rGZT8rnV5L6LxuaLROn6ABP0/bj79UOuuzTqwK6PI5m48/BCd3+6jODufh1xh+Vz\nZrYI8FbiJNmKmBl+UCkLsJSvETQ1Xbj7tARhUqZJsYqxYY/ft8qmW939/pwxUqSpylfOzmTi8v07\nOe9uV/I6VbbPytpvlb0iVnK6Wo4DPm5mvwR+7u6Fp9fMdgSud/cb+yyzLrChu3+mqLgl779KOioh\nWb7ojFl0+yx5HHefkeWzdwOtYW0+R5R/2xMTiZ7t7l/NEabp5UVnzKKPV+Wv5Bacptq8Zl7SuVXV\nDZuy83qK9lJPI36smnwtMocRP1ZzKPHcamzfRTdNyxctZaTL3U+faBkzmw+Y5u5/HyZGGdSBXRPu\nfvhkljOzBd19Zsnb8hhwQfYzrMoLsJI0NV2Y2aHA5e5+ZZ9lNge2dPcPp9syqdg/gIe8z9iQ2WtO\nq7r7Zek2azDZzb9fZT8HdbyatRdwlJmV8vq8u+9cxHqqZGaLAk+4+1NtnyfrWXefUc6W5VNxul5B\ndNz9FJhpZv8EutXv4+7+uiFjnE4M3dCzA5uYNX13YOAO7Ir2X+n1cF3ze0Hts0riuPuzxDifrc/j\nwOezn5Ewt+eLlPKmqco6f4LtKupYlVoOVpXX69ReGpVjJaN1rCo4t+ba/DdK+WIQRaXLzJ4FDnf3\nfm+/HUXcAF4iT6wiqQO7JszsduBL7n5yn2UOBfYkZqoVKdrhREdHzw5s4O3A+wF1YM89/kHkjX6V\n277AHsAgjbBKpXx93sx+RbwCeGafZT4G7OHuq+SJVaIHgSOIt4UgJu2cdIPOzJ4mxrjd3d29+M0b\nWpXp2qnt/wsB1mO5QbZnZ6BzTo3tzWyNHn8ylXjL4oHJxuigfNFFjdOVlJm9mChXlwLuJt40GDav\n1UHyfDHB2JsjOVF5FWlKPGROE1RSBqZqLzXxvJKRofaFVCKr91Zo+9UYsIr1notvKjGRY636jGu1\nMXMTM5vG7J0904gM1GvyiqnARsAiOeNWMjmB1I+Z7UmMtdVudzN7V48/mQq8kujQlIYys42B9ouC\nMeCN1num4qnEBLPPlr1tZSry1SwzWxCYL/s4BmwA/KbPUxZTiYvoaQNveDpXAXe0fb6SyTe4pwDL\nEfvhDGDtIjcsp8rS5eWMT30RcBKz2grjRLn9yj5/8yRw6JDxlC/5SjNPAAAgAElEQVTmNFS6UrXP\nEsY5EjiIGN+6ZaaZ7e/uXxt0fTVRRX6vZEzWklWepjJex27YNVaSvF5he6nyPCiT07DzCprbbkqq\ngfkCKD1dSzL7vHvjwHbZT78Y3+vzfXLqwK7O2sB3mD0D7Zb99DIG/CJnXFXY0nIW0WmxdPZ5nKgU\nl+ux/NPAXcSrl9JcDxITbo1lP+PERd2mE/zdl0vermQKeDXrA0QnYruDsp9+fjdkvNK5+wb9Pk+G\nmf2EmEm7NpqWLne/x8xWIp7oHgNuB04ATuyy+DhRrt8/7PwaTdt/LRWlK1X7rPQ4ZrYDMWHjDOJi\n6W5gZeLtgK+a2a3ufumg661aRfmi8rE3S1CrNBX4mnljrrES5vWq2ku1yoPSV2POK2huu6kCjcoX\nbUpLl7v/MnuAcRmiDDwUuAK4vMvirWuEu1EHtgC4+7nZK72tDLQjcAMx+3Kn9gz0lZyhVWELANmY\nxs8PR2NmzxHjIB3Z+6+k6dz9D9mrREsT5cW3iQ6Ibhd2z5dN/cZOr5NEr42eAryZKN/J/n8Xsz9x\n0R6nVb5/dsA4o+ZamjkEVq50mdmmwC7A6sAS7r5M1gG4EvAFd398kPW5+31t696FmMTxzmG3LwHl\ni5CqfZYizoeI16LXcvfbWr80szWJp832BEauA7sgg+aLWo29WZBkaUo8VISusWY3mbxeVXupiedV\nU+m8mlNT202DaGq+KDVd7fWbma0PnNZvyKY6Ugd2hdz9E63/t2Wgsu8ONbHCHvREH5UCL3W6NqR7\ngxEzW8Ddn8i5fkibJsUaMpa7/6z1/6xs+pG7/2TQ9Qwg5f4r/Y69uz9H2+tY2c2h00q+OVTqPszm\nYBjGeGtykOzffmOpdyo9X1SUrvb4pwK7Etv+LPF6KMAawH7AZma2cfaE4MDc/QwzW97MTgCudvfz\n2mL///buO0yWskr8+HdIoqKCEXFRkh7cNQcQFcEACEgQ0yoqorC4gmBW1nQBwQVZRVAxrKKs+AA/\nBQNKuGRBRYKKAY+LCgiorIACIiAwvz/eGu8wd0JPz3RVdfX38zz9DLequutU83b1W6ffOu8vgaXA\n+zLzL33G3+j7NygNHVdd/bM69vN44PjJyWv4xw+kJwHPHMA+O3++GLCu9qXrHKXXmWusutp6Q/2l\nrurUtcgknflcgd8jC9x+sk61i0lqO67MfC5ARDwJuD0zL5tYFxH7UiY3vriueHplArslMnNdgIh4\nMLBqZl49sa4aiXVmZv6+qfj6UOcJbOksxeen289lc241+/MHuf1kdR4XmXlORDwuIr4OnJSZ/z1p\n9e8j4jxgrwWO5KvzmNzX4rSLXSNihYjYEfh9Zv7jts0q+bY0M7+2kH3Q7s/wgk3UOo6IFYHVJicL\nI2JT4Af9lnCYZNDv4ZJplk10sqZ7TydGs43Tfye7jnaxZJplgz4uACJiD8qEuF8F9qVMkPv+avX+\nwAMo8xS8nTLhTz/7WIdSb3Et4OZqX0TEfSi1ifcEXhgRz+mzj7FkmmWDfv/q+B5eMs2yWtpFR9wf\nuG6Gdb9i+YlGF0Onzxc1qLVvUaNhSbTP16DPg0umWTbwtl5Tf6ludV07dvZapEZd7F90NndRo07+\nwFudZ/+bUgliP6qJRSNiVeBDwIci4rDMfHtzUS7PBHaLRMQHgf+gnNg+XC1bmVKk/86IeGdmHtFc\nhPNS2wlswCNDp+rqcRERjwfOB+5b/Z1Yfm/gIkoN5Isi4lmZ+at+9lHnMbmvxRER9wVOpExwdBBV\n3cEqAfZvwO4RcSLwygXU0a3zmBr5xT4idgE+AhwCHFotW5FyG/1fIuINC3kfangPp9bauy/l1t9x\nyvGcD9xQLd8YeGe13Wv63WFN7aL245rkjcClmflygIj4R9usSjztXp2XX06fCWxKZ/RhwM6Zeeyk\n178VeHREvBw4hnI79uv7eP0m3r86voebbBddsBJw5wzr/s6ySdsWzQicLwaq7r5Fjbo6Sm/Q58HG\n2vqg+0sNqOXascvXIjXqYv+is7mLGnU1Mb8PsAtwKjD5/93tlHJO7wHeEhFX1lAlomcmsFuiqlP5\nQeBS7jkxxTilYb0NOCwibsjMYxoIcV66egLr6nFVDqDcvv7szPz+xMLM/BuwRURsQuk8Hgi8rJkQ\n1YB9gRcAnwU+N7EwM2+NiH8C3gv8e/V3SRMBtl3V6TkKuJZ7lulZkZKYfCNwYkRsk5mn1h/h3KZO\nuBYRH6ckoZ4+zV0Zl0bEt4BLKD9ytHlyyiaPK1h+8qqpzgH2WsA+NgeOm5y8niwzj4+IlwDb9vPi\nTbx/dXwPd7W9a2FsF2qLQZ8Hm2rrXegvTdXxa8dO6WL/wva3cB1+D18PXJiZW09emJnjwPnV+fhi\nynm3NQnsFebeRDXZi/JrzUaZeebEwsy8s0pYbwJcTklkS4OwMfCVycnryarlx1FG4mp0vAw4IzPf\nmJlXTV6Rmddm5p6UEgWvbSS64fBu4HfAEybXIM7MOzLzQOCJwB8oPwIMi1cCJ8xUUigz/wCcALy4\n1qgWrs7jupVlE1fNZK1qu349EPjTHNtcTSn5sBhsFxoltguNirraehf7S9Js/B5RU9YFzp5pZZXI\nPgNYr66AeuEI7PZ4DHBkZt4x3crMvKOa+OZN9YalEXJfYNr2N8lNwKo1xKL2WBv4xhzbXAA8o4ZY\nhtW/AJ/PzBumW5mZN0TEV4Hd6g1rQVapHrN5AMN3+3adx3UesFNEfDAzfzd1ZUQ8mnLBcvoC9nE5\n8IKIWCkzlyvpEBErUCbw/e0C9jGZ7UITnhQR0/2w+SSAiHgN09SJzMyjBx3YIqqjXXSx9mYXj6nr\n6joH1tVfsg2qLexfqCk3ABvOsc06QF8TvQ+KCez2uAV45BzbPJSFjcSCmr6wI2K7eTx3DFgrM4/s\nZ1916upxVX4BbBMRq2XmLVNXVgX9Xwj8svbI1KQ/AE+ZY5vHMfOEXSo1Xx88xzarVdsNiwuBl0TE\nRzPzZ1NXViWHXgacXHtkC1Pnce0PbAVcEBGHUkqKEBGbAU+njERbmWpOjD4dTamp+D8R8dZqJA/V\nfh4KHEwZ0fa+BexjsoG/fw19D9fRLupKqNS1nx2qx0yv98Vplo9T2uywqKNddLH2ZhePCWpMijZw\nHqzru7Gu/lJtbbDj14516PqPDV3tTw9aV8+3dbb3U4HXRcQOmbncYLWI2BLYkXIHfmuYwG6Pc4Ad\nI2KjzPzh1JUR8URgJ2DpAvdT1xf2Fpm5d68bR0Rr6urMoavHBfAZ4PPAtyLiPcBFmXlXNULvKZTa\n1xvgXQCj5hvA3hHx5ukmkY2I3ShJuM/UHtnwuADYISLWz8xfT10ZEY+kdBAurD2y/i2h3Fb2vYj4\nEiX2mymjRJ4F7EyZBOT9TQXYpyXUdFyZeUlE7ESZqPnQSavOpHz/3kSZfHEhtXMPo0wY9Arg5RFx\nVfW696P8aL4CcBplwqzFsITBv39NfA8vYfDHVVf/rI799Dvp6LBZwoDbRRdrb3bxmCp1JubrPg8u\noZ7vxlr6SzW3wS5fO9ah6z82LGGAn60O/4DS1fNtncd1AOVOz69FxFLgByy7RtiIcn1/E2WevtYw\ngd0eBwLbAWdFxNEs34BeS2mkC7ooqPELe9h+/exVV4+LzDwqIp4B7A58D7grIv4G3JsyecoY8IXM\nNFE5Wj5E+XI7LCL2BL7PPc9Nj6XU0F3SVIDz1MRIjkMonYDzIuIwlj+/70PpqC5kpG2tMvP8iNiB\nMnP6ntzz1sYxyh0dr59uNEmb1X1cmXlyRDyKMlL1KcDqlDuyLgVOzMwF3baXmXdHxDbArpQ6i08A\nHlHt43zgy5Tbte9eyH4m7a+O96/27+E6jquu/llNk1SNRAK7q+fBjqntO7/mpGit58Ea23rn+kt0\n+NqxDl3/saGGz1Ynf0Dp6vm2zuPKzCuruz4/RTnvbjVlk+8De0z3Y2KTTGC3RGb+tPqF7PPAHpSZ\nZieMUSa0eH1m/qiJ+PrQ1TpNXT0uADJzj4g4FngVJdGxBiXR8VPgy5m50DsANGQy8/qI2JgyQvPF\nwC6TVt8BHAu8IzOHpYRI7bcuZ+a5EbErcATlomtq5/SvlPP7mdM9v60y89SI2IAyAewTKeeLG4GL\nM3OYRpPfQ13HFRHPAa6oJkc9tnpM3eaxlJnp+y6rUE3C8oXqMXA1vH+NfA93tb1rYWwXrdfVciW1\nnwfraOsd7S91+tqxYxr5sWHAny1/QFm4zn6Gqx9GnhMRj2BK7iczF2t+nEVlArtFMvPMiFifMhna\nxMlrInl4bmbe1WR8Gg2ZeRZwVtNxqD0y84/AayNiFWB9lp2bMjNvbzS4eWrq1uXM/HI1Ee+2LP/j\n0Ncz88Ym4lqoauTu96vHPUTEC4HdMvOltQe2QDUd11mUOxcOmGWbXSllm4apLrDtQsupfox5DWVC\noHsx/UX1eGa+pM64FoPtor06XK6kEXW09a72lzQUGktUDvCz1dnkqxZPZl4DXNN0HL0wgd0y1cnr\ne9Vj0XW4DpIWUZWonOkCk8y8qd6I1AaZeQfDMzqpdTLzz8Ax1aOTImJt4A2UxOs/NRzOolmM44qI\nlwLPnLRoDNg6ItaY4SmrUGpX/7Wf/U3a7xsosa/D7InDBy1kP3PEYLuY+7Vq6Z810Q+sblE9hdKm\nZ9t3Jy60u9reNbtRvMYaVFsfhf6SejOKnyvwe2QuXW0XdR9XRDwIeAlzXyO8vd99LDYT2C0SESsD\nz2X2BkRmLqQ2USfrIGnhqska96N8WT5slk3H8dwxUro8cq5OEfEY5j6/D91osYhYiTKp0m7A8ykT\nA44B/wsc1WBoCzKA4/oJpeb0KtW/xyl3XD1jjue9t499ARARe1Bq240Bf6bUEq0lSWi7mLe6+mdN\n9AP3B1YG3gd8B/gLHUlWT+hqe9e8jMQ1Vh1tvav9JfVlJD5X4PfIPHW1XdR2XBHxBMrdoKsz9+AC\nE9i6p2oSp9OB9apFMzWicWAhH0DrIGkm76EkSv5OmUCscxeYmr9RGzk3CNWv21/nnqNvpxqjvIcr\n1hLUIoiIDSmd7NcAD64W/xU4DjgqMwdyJ9GgDeq4MvN/I2Ijyu3QY8CZwBeBL02z+TjlXHxNVSO7\nX2+mnMu3zczlbksdBNtF3+rqnzXRD3wacGxmDtPEaz3pantXXzp9jVVHW+9qf0kL0unPFfg90qeu\ntos6j+vDlGuSzzFEgwtMYLfHwZTasqcBJzO4BtT6RqnGvB74PfDMzLyy6WDUGp0fOVeDDwPPAn5O\n+aFyaN/DiLg3pazFbsAmlI7WXcBSYEvKZK9vai7C/tR1XJl56aR97geclZnnLvR1Z7EB8NlBJ69t\nF4uirnNCE+eevwF/aGC/A9HV9q4FG8rv9dk00NY701/Sounk/3+/Rxask+2Ceo9rU+BbmblHjftc\nMBPY7bElcE5mvrDpQBbJfH89GpZf0bp6XFDqa33C5LWm6OzIuRrtAPwI2GhYJ+ONiKdROtn/Cty/\nWnwB8BXguMy8LiLubiq+fjV5XJm536Q4VgMeDzwwM78dEWss0kRVf2SAfb2G3r+Bfw93tb036DRg\nq4h497CeA8F2oVYZ6HmwwbY+9P2laXT52lHz5PfIUOrqZ/hu4JdNBzFfJrDbY2XKyasrlkbE9j1u\nO8bwTAzX1eMCuIpSA0marFMj5xpyP+C0Ib8Y+yGlo3MRcAJwfGZe0WhEi6PR44qIhwEfB3ai3A49\nMcfAv0fE64FdM/O7C9jFMcAbIuJ9mXnDggNeXhPvXx3fw11t7015J/Bd4PiI+CjwK+D26TZs+STR\ntgu1xaDPg0219S70l6bq8rVj19SRqKz7s9XV5GuduvoZ/i7wnKaDmC8T2O1xMfDUpoNYLF2dWKOr\nx1X5LPCBiDgwM3/bdDBqjU6MnGvYT4ENmw5iEdwO/Kn6u2rDsSymRo4rIh4C/AB4FHB+te+nVKv/\nUi0/OSI2ycyf9rmbLwFbABdGxOcpEwHNlDjs9/ut1vevxu/hrrb3JnwXWA14MWWCqpkMwyTRtgs1\nrqbzYBNtvSv9pX/o+LVj19SVqKzzs9XV5GttOvwZfifwvYj4OHBIZl7TdEC9aHsncZTsC5wVEW8D\nDs/MO5sOSCPnEiCBiyPiq8ye6BiWmXy1cF0ZOdekDwEnRMROmXlC08H06RnAayn1+rYBxiPiZ5Rb\nHo8d4tJDTR7XfsDawPaZeVJEfJAqgZ2Zn6ziOJVSf/4Vfe7jMkpScAw4YIZtFjIhlu1CvbiKbtSr\ntF1oVDTV1rvQX9KQqilRWetnq8PJVy3cp4AbgL2AvSLiNqa/xh/PzAfVGtksTGC3x+6UxNBHgP0j\n4kpmbkCdGamtVjl90n/vNst244AJ7NHRpZFzTXky8BPg/0XEb5j5R4DxzHxJrZH1KDN/CPwwIt4K\nvIjS+d6aMuHSQRFxAcsSpUOj4ePaHjghM0+aIbZzIuIE4NkL2Mf+DDBxaLsYruNqSmZu3nQMi8F2\noVHRYFsf+v6SNBu/R9Qi61Ha2lVNBzIfJhva43WT/vs+wGNn2G6hF6LWQdJMdm06ALVSV0bONWnJ\npP9ev3pMp/Xvc2b+HTgRODEiHgi8EngNZUQJwG4RsR6l9vIJmXlLM5HOT0PH9WDgN3NsczXwkH53\nkJlL+n3uPPdju1j4cdXVP7MfuEBdbe9asM59thpo60sm/fdQ95e0aDr3uQK/RxZBJ9sFNR5XZq7T\n73ObNDY+7vl/lMyjBhKUD8QjMvNTg4pHkrouIh7V67bDegt6RDwa2AXYmVK7eZwyAeg3M/NVTca2\nEIM8roi4HPhNZm5Z/fuDwAcyc8VJ25wDrJWZj17Ivppiu5jXa9bSP2uqH1hNWLod8FBKuZqJi64x\nykTmDwK2ysz1FrqvpnS1vas3o3SNNai2Pgr9Jc1PnZ+riNiO3hOCY5T+2ZH97GuWGBb1s9WGYxqE\nrp5vu3pci8kEtiRJWjQRsRml8/1S4L6TE7LDbLGPKyL+k1Jj/k2Z+ZmpCexqToyPAP+Vme/qcx+9\n1hAd+O3YtovRFRFPBM4B7seymuuw7KJ64nbpG9tUZ3EhbBcaFbZ1dUVEHJ6Zew9q+z7iWfBnq23H\npPaYT7K8TbXULSHSEhHxhF63zcxLBxmLRlNEXNLjptZhHyER0XMnxsk9pxcR9+9127ZOhBkRbwJO\nycy5Sl6QmecA51TPma1ueuMaPq4DgW2BT0XEnlSTKEbEF4GnAv8MXA4ctIB9zBXnOHAr8Pd+Xtx2\nMVzH1aAlwP2BI4GzgUOBi4DjKO18b+B7lLrwrWW70Khoqq13ob+koTbwMhMNfLaGpXSG6vd1ei/H\n1JofJk1gt8ePGcIGpE55Ug/bXAXcOOhA1CqHMfNkIpNH0Tm558z+zPCf3w8GjoiIK4BTgVOAszLz\n5pmekJm3AcfWEl3/GjuuzLw5Ip5FmbjnNZTJUqFM6HM78D/AOzLzzwvYzbozLL8PsAFlBPi9gef3\n+fq2i8qQHFdTngWck5l7AkTE1sCGmXl89e8TgAsoF+i93jXQBNuFRkVTbb0L/SUNrzpKE9T92bLc\ngmYy00TvE9cI21D6Zh+rM6i5mMBuj6OZuQGtDzwF+C7t7thriGXmCtMtj4h7U9rg+4CNKCMGNTpm\nmtxz4svtNcAvgLfWFtHwOZeZz+/rUWq//oDSSWirBwKbAlsCWwF7AHdWs6WfCpyWmRc2GF+/Gj2u\nagTZntWdDgGsDtxSVuXti/D6s9UIvSwilgI/pSTR9+xjF7YL9WJ14IeT/v0z4BURMZaZ45l5aUSc\nBOxDu/u5tguNiqbaehf6S9Js/B5RK8w10XtEPBk4j9KHaw1rYA+JiNgW+BqwU2Z+p+l4NHoiYgy4\nBPh5Zr666XjUDtWEOz8CPpSZH206nmFU3Rp4KPC8zPxB0/H0opqQbStKB3wL4CHA9cDpLOuAX9tc\nhP3p6nHNJiIOBXbOzIcvwmt18v3r6nHVJSKuA76SmW+p/r0DJVH9z5mZ1bKDgL0ys+cSAk2zXWhU\ntKWtD2N/ScOjiXrRg/5sWQNbC1GVNXxKZvZc7njQTGAPkYg4HnhUZm7cdCwaTRFxMLBbVyZZ0uKI\niE9TLiYe03Qswyoivg3cOzOf13Qs/YiIp7KsA74J5Q6vXwCnZuY7moxtIQZ1XBGxHvAySmmFh1Pq\nA98C/AY4H/h/mXnNwqLvOZYvAi/LzPsO4LVtFyIiTqbcyfWEzLwtIh4NJKU/8YVqm+Mo3yMPaTDU\nBbFdaFQ02daHvb+k9mpDsnexP1ttOCYNr4j4CGVwwb2bjmWCJUSGy2+wfIOatR6wStNBqHVuA9Zu\nOoghdymwV9NB9CszLwYuBg6KiNUoNZW3otS0HdrEzWIfV0SsTKkV/wZK/c6pteWfDOwEHBwRRwLv\nzMy+Jlis9jfTaNYVgPsCLwJeCQzkdlXbhSqfBL4JXBIRu2fm+RHxI0o7XwVYE3gxcGaTQS6U7UKj\nouG2PtT9JWk2fo+oLSLiwcBLgd83HctkJrCHRESsSkleL2QyJ2lGETHTrSGTEx0vBs6oLSi1XkQ8\nFngVcEXDoQytiFgB2Az4W9OxLIbMvAX4RvXojIUeV1WG6STKLaK/B/6bMrfFNZRJG+8HrAM8B3g1\n8GbgMRGxbWb2e7tcLxNi3Q0s6fP1e2a7GF2ZeVJV5/1DlDsOoMybcDIluT1Gaav7NhPh4rNdaFTU\n2da71l+SZuP3iAapmkB7OhO5n40oE8zvX1tQPTCB3RIRMVPt2IkG9DzKhe0RdcWkkfNjZk90jAF/\npUMXmJpbRFwyw6qJc9O61X8fUFtQQ6ZK3Exn4j3cGtgY+FJtQakJu1GS198GXp2Zf5lmm58A34iI\nJZTJnbcHdga+3Oc+Z5oQaxy4A/gl8IXMvLTP15d6kpmfiIjPUu48IDPPrX4A3ZFyF89J1ouWRpv9\nJTVs6l1xi719E7p4TFocO86x/kbgo5TBB61hArs93jLH+rsoE968r4ZYNJqOZu5Ex1cy87pao1LT\nnjTLujuAnwGfy8xP1hTPMDqM8jmarVN4MfCeesLpX0RsR++d2zFgrcw8coAhLYqajmtnysjrV1aj\namaUmTdHxKuA37KABHZmbt7P8+bLdgEM0XE1JTPvmPLvqygldYaK7UKjooG23pn+kobS0ojYvsdt\nx4DL+t1RjZ+t2o5JQ2fdGZZP5H6uy8y7a4ynJyaw2+O5MyyfaECXZ+afaoxHIyYzX9d0DGqfzFyh\n6Rg6YNcZlv/jx6HM/HGN8SzEFvOdDGaQwSyiOo7rXyijTGdNXk/IzL9FxGmUUdvzVtUWXgO4PjPv\n7Oc15sF2wVAdVyMi4oWU8+GTgDUy86ERsTNlgsdDM/PWRgPsne1Co6Lutt6l/pKGTGZ+s8bd1fLZ\nqvmYNEQy88qmY+iHCeyWyMxzmo5BoysiNgAeBFyTmVc3HY/UJZnZpVtdu3prYR3HdX/gD/N8ztXA\nA+fzhGo+g0MpP4yvANweEd+iTAh51Tz33yvbhWYVEZ8Gdqe8p3dR2ibAUyj1sLeOiC16/YGnYbYL\njYpa23rH+kvSbPweUe2q+XheCWxDlfsBTsjM7zQa2DyYwG5YRDyTezagEzPz581GpVEREVtS6qpv\nMGnZRcC/Z+ZMtY/VcRFxH0pZo8nnpq8Bn83Mu5qMbZhExNqUmcMn3sOTM/P6ZqNasH4nE2y7Oo5r\nZeY/8dTtzKOvFhEbUiaGvB/wd+B64CHAy4BNI+JpA6ozbLvQjCJiD+DfgK9S5tF4DfD+avV+lB93\n3gC8vfp329kuNCpqaesd7S9Js/F7RLWKiFWB71Amwp38A8quEXFcZr6qmcjmx1vDGxIRK0TE0ZQL\nzX2BPSgzfP4kIg5qNDiNhIjYGDgJeDSls3gh8Cfg6cBZEfGYBsNTQyJiDUpbOAB4JhCUSWQ/AZwZ\nEfdqMLyhERH7A78GPgMcRJlw6MoqkSMNyn9QktfvBVbPzDWB1Sl1htcE3tFgbBpdbwQuzcyXZ+av\nmXThnpk3ZebuwA+BlzcVoKRm2F+SpFq8HdgcuJRyR9w2wD7AVcArImL35kLrnQns5rwJeDVlQqeD\nqn9/DLgFeHdEvLTB2DQa3kX59e1VmfnIzHwGJcHxNkoC5O1NBqfGvAd4LHAqpfbuPwM7UJLazwbe\n3Fxow6Gq6fo+yujZrwAfAU4EVgE+FRHPbzA8NWvQI26eA3wnMz+cmX+DMiFkZr6F8hnecsD7l6YT\nwClzbHMOsM7gQ5HUFvaXJKk2LwH+F3hGZn4+M0/JzCMopdz+DxiKEdiWEGnOzsC1wBMn3yJVFej/\nCeVWyq82FJtGwzMoJWuOnViQmePAYdVsxTNNLKpuexHwo8zcZtKyX1aTyf2S8uV3aCORDY/dgD8D\nT69GGwIQEU8DzgX2BM5oKDY1660RMdMkVdNZfZ6v/zBKEmA651FGXEh1uxV46BzbrFVtJ2l02F+S\npHpsAHw+M2+fvDAzb6zmytmxmbDmxxHYzdmQkjy8R32vajbQbwJPbiQqjZIHAb+aYd2FlItJjZ5H\nAWdOXVh92Z1MGUmn2T0eOH7yxRhAZl5EKduzUSNRqQ1Wp4wy7fUx3wT2vYDbZlh3E3Dfeb6etBjO\nA3aq6twuJyIeDbwYOL/WqCQ1zf6SJNXjPsBfZlh3LfCAGmPpmyOwm7MacMMM664A1qgvFI2oVYA7\nZlh3K3DvGmNRe9ybUspoOtdRystodvenvFfT+RWwfY2xqCUy00EDGlX7UyZouyAiDqX6ITQiNqPM\nu/FuyiSnH24sQklNsL8kSfVYAbh7hnV3AyvWGEvfvJhqzorAXTOsuwt/XJDUjDFmrtM7jt8bvVgJ\nuHOGdX+nJGqG1djcmyxo+6YM/Lgi4k0Rsd58nzckbBf9bT8SMvMSYCfKufFQ4F8p79WZwCGUc+LO\nmXlBY0HOj+1Co2LQbb3L/SVpNn6PSH0wSSpJknq1tKqR3yqWHeAAABa3SURBVIsx4LJBBrOI6jiu\ng4EjIuIKyiSppwJnZubNfbzWbAY9UeR0bBfDdVy1y8yTI+JRlEmBn0Ipj3MLcCmlp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WQC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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure object at 0x111932da0>" ] }, "metadata": { "image/png": { "height": 682, "width": 728 } }, "output_type": "display_data" } ], "source": [ "def tokenize(doc):\n", " return ['/'.join(t) for t in pos_tagger.pos(doc, norm=True, stem=True)]\n", "\n", "train_docs = [(tokenize(row[1]), row[2]) for row in train_data[:10000]]\n", "\n", "tokens = [t for d in train_docs for t in d[0]]\n", "import nltk\n", "text = nltk.Text(tokens, name='NMSC')\n", "# mpl.rcParams[\"font.family\"] = \"NanumGothic\"\n", "mpl.rcParams[\"font.family\"] = \"sans-serif\"\n", "plt.figure(figsize=(12,10))\n", "text.plot(50)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

ramalho/jupyter101

en/Basic NumPy.ipynb

1

10360

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## What is NumPy\n", "\n", "NumPy is an Open Source package available for Python 2 and Python 3 that provides:\n", "\n", "* `ndarray`: a high-performance class to represent multidimensional arrays;\n", "* vectorized functions that apply to all elements of an array without explicit looping, including linear algebra operations, random number generators and Fourier transforms;\n", "* efficient loading/saving of arrays to disk and memory-mapped files;\n", "* interfaces to integrate C, C++ and Fortran codebases.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction to `ndarray`\n", "\n", "The `np.array` function is the simplest way to create an `ndarray`:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "a0 = np.array([1.1, 2.2, 3.3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `np.array` function creates instances of the `ndarray` class:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(a0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All elements of an `ndarray` are of the same type, which you can inspect as the `.dtype` property:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dtype('float64')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a0.dtype" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a1 = np.array(range(10))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a1" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dtype('int64')" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a1.dtype" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a1 = np.arange(10)\n", "a1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NumPy can pick a type automatically, or you can assign one explicitly:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3], dtype=uint8)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ab = np.array([1, 2, 3], dtype=np.uint8)\n", "ab" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A sequence of nested sequences with equal lenghts can be used to build a multidimensional array, like this 2D array:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[10, 20],\n", " [30, 40],\n", " [50, 60]])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a2 = np.array([[10, 20], [30, 40], [50, 60]])\n", "a2" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[10, 99, 50],\n", " [20, 40, 60]])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "at = a2.transpose()\n", "at" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Many array operations return views which share the undelying data with the source array. That's the case of the `T` property and the `.transpose()` method:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "at[0, 1] = 99" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[10, 20],\n", " [99, 40],\n", " [50, 60]])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also use the `np.zeros`, `np.ones` and `np.identity` to build arrays filled with zeros and ones:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 1., 1., 1., 1.],\n", " [ 1., 1., 1., 1., 1.],\n", " [ 1., 1., 1., 1., 1.]])" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.ones((3, 5))" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 0., 0., 0.],\n", " [ 0., 1., 0., 0.],\n", " [ 0., 0., 1., 0.],\n", " [ 0., 0., 0., 1.]])" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.identity(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `np.random` module has functions to build arrays filled with random numbers using several distributions:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.89382349, 0.94526305, 0.14999355],\n", " [ 0.22736863, 0.76230151, 0.75217348]])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a3 = np.random.random((2, 3))\n", "a3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Many scalar operations are supported, and are vectorized:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 8.93823487, 9.45263051, 1.49993552],\n", " [ 2.2736863 , 7.62301513, 7.52173478]])" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a3 * 10" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ True, True, False],\n", " [False, True, True]], dtype=bool)" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a3 > .5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Starting with Python 3.5, the `@` operator does matrix multiplication (dot product):" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 13.48560746, 24.69866077, 16.54340509],\n", " [ 97.5832704 , 124.0731026 , 44.93630081],\n", " [ 58.33329213, 93.00124335, 52.63008632]])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a2 @ a3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you are using Python 3.4 or older, you must use the `.dot()` method for the dot product:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 13.48560746, 24.69866077, 16.54340509],\n", " [ 97.5832704 , 124.0731026 , 44.93630081],\n", " [ 58.33329213, 93.00124335, 52.63008632]])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a2.dot(a3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

blab/antibody-response-pulse

bcell-array/.ipynb_checkpoints/alva_machinery-checkpoint.ipynb

1

7335

{ "metadata": { "name": "", "signature": "sha256:0d65261710b721a3f69741ba2b826486f7e0e9f191b910388f162ac44c51621a" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Infectious Pulse\n", "https://github.com/alvason/infectious-pulse/\n", "\n", "## Homemade machinery for solving partial differential equations\n", "### Runge-kutta algorithm for a array of coupled partial differential equation " ] }, { "cell_type": "code", "collapsed": false, "input": [ "'''\n", "author: Alvason Zhenhua Li\n", "date: 03/23/2015\n", "\n", "Home-made machinery for solving partial differential equations\n", "'''\n", "import numpy as np\n", "import matplotlib.pyplot as plt\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", " global actRateBg\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", " actRateBg = 1000/10**5\n", " if tn > totalGPoint_T*(2.0/6):\n", " actRateBg = 1000/10**3\n", " \n", " if tn == int(totalGPoint_T*(2.0/6)):\n", " gridOutIn_array[0, 0, tn] = 1.0 # virus infection\n", " elif tn == int(totalGPoint_T*(4.0/6)):\n", " gridOutIn_array[0, 0, tn] = 1.0 # virus infection \n", " \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, :])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# min-max sorting\n", "def AlvaMinMax(data):\n", " totalDataPoint = np.size(data)\n", " minMaxListing = np.zeros(totalDataPoint) \n", " for i in range(totalDataPoint):\n", " # searching the minimum in current array\n", " jj = 0 \n", " minMaxListing[i] = data[jj] # suppose the 1st element [0] of current data-list is the minimum\n", " for j in range(totalDataPoint - i):\n", " if data[j] < minMaxListing[i]: \n", " minMaxListing[i] = data[j]\n", " jj = j # recording the position of selected element\n", " # reducing the size of searching zone (removing the minmum from current array)\n", " data = np.delete(data, jj)\n", " return (minMaxListing)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 } ], "metadata": {} } ] }

gpl-2.0

adityaka/misc_scripts

python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/05_06/Begin/.ipynb_checkpoints/Data Frame Plots-checkpoint.ipynb

1

7622

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Frame Plots\n", "documentation: http://pandas.pydata.org/pandas-docs/stable/visualization.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "plt.style.use('ggplot')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plot method on Series and DataFrame is just a simple wrapper around plt.plot()\n", "\n", "If the index consists of dates, it calls gcf().autofmt_xdate() to try to format the x-axis nicely as show in the plot window." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\n", "ts = ts.cumsum()\n", "ts.plot()\n", "plt.show() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On DataFrame, plot() is a convenience to plot all of the columns, and include a legend within the plot." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.DataFrame(np.random.randn(1000, 4), index=pd.date_range('1/1/2016', periods=1000), columns=list('ABCD'))\n", "df = df.cumsum()\n", "plt.figure()\n", "df.plot()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can plot one column versus another using the x and y keywords in plot():" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df3 = pd.DataFrame(np.random.randn(1000, 2), columns=['B', 'C']).cumsum()\n", "df3['A'] = pd.Series(list(range(len(df))))\n", "df3.plot(x='A', y='B')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df3.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plots other than line plots\n", "Plotting methods allow for a handful of plot styles other than the default Line plot. These methods can be provided as the kind keyword argument to plot(). These include:\n", "\n", "- ‘bar’ or ‘barh’ for bar plots\n", "- ‘hist’ for histogram\n", "- ‘box’ for boxplot\n", "- ‘kde’ or 'density' for density plots\n", "- ‘area’ for area plots\n", "- ‘scatter’ for scatter plots\n", "- ‘hexbin’ for hexagonal bin plots\n", "- ‘pie’ for pie plots\n", "\n", "For example, a bar plot can be created the following way:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure()\n", "df.ix[5].plot(kind='bar')\n", "plt.axhline(0, color='k')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df.ix[5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### stack bar chart" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df2 = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])\n", "df2.plot.bar(stacked=True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### horizontal bar chart" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df2.plot.barh(stacked=True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### box plot" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])\n", "df.plot.box()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### area plot" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])\n", "df.plot.area()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting with Missing Data\n", "Pandas tries to be pragmatic about plotting DataFrames or Series that contain missing data. Missing values are dropped, left out, or filled depending on the plot type.\n", "\n", "| Plot Type | NaN Handling | |\n", "|----------------|-------------------------|---|\n", "| Line | Leave gaps at NaNs | |\n", "| Line (stacked) | Fill 0’s | |\n", "| Bar | Fill 0’s | |\n", "| Scatter | Drop NaNs | |\n", "| Histogram | Drop NaNs (column-wise) | |\n", "| Box | Drop NaNs (column-wise) | |\n", "| Area | Fill 0’s | |\n", "| KDE | Drop NaNs (column-wise) | |\n", "| Hexbin | Drop NaNs | |\n", "| Pie | Fill 0’s | |\n", "\n", "If any of these defaults are not what you want, or if you want to be explicit about how missing values are handled, consider using fillna() or dropna() before plotting." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### density plot" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ser = pd.Series(np.random.randn(1000))\n", "ser.plot.kde()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### lag plot\n", "Lag plots are used to check if a data set or time series is random. Random data should not exhibit any structure in the lag plot. Non-random structure implies that the underlying data are not random." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pandas.tools.plotting import lag_plot\n", "plt.figure()\n", "data = pd.Series(0.1 * np.random.rand(1000) + 0.9 * np.sin(np.linspace(-99 * np.pi, 99 * np.pi, num=1000)))\n", "lag_plot(data)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### matplotlib gallery\n", "documentation: http://matplotlib.org/gallery.html" ] } ], "metadata": { "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

rmalouf/morphology

czech/words-to-paradigms.ipynb

1

11358

{ "metadata": { "name": "words-to-paradigms" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Preparing data from the Czech National Corpus for IPa analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook takes the tagged list of Czech nouns from the syn2010 corpus of the Czech National Corpus (kindly supplied by Michal Kren) and tabulates paradigms, as a first step before carrying out analyses.\n", "\n", "The input list is in the file \"substantiva_syn2010\".\n", "\n", "The tagset is described here: http://korpus.cz/bonito/znacky.php\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import numpy as np" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We make a list of the tags that we will need for future reference:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "the_tags = [x+str(y) for x in ['S','P'] for y in range(1,8)]\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we open the file:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data_file = open('./substantiva_syn2010')\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Selecting appropriate data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First order of business is to \n", "\n", "1. Select only nouns by dropping anything with a capitalized lemma or a suspicious looking tag\n", "2. Lowercase all forms, simplify the tags to number+case, treat frequency as an integer.\n", "\n", "We store the result in a list of lists called **data**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data =[]\n", "for line in data_file:\n", " items=(line.rstrip()).split('\\t')\n", " if items[2].islower() and items[3][3:5] in the_tags:\n", " data.append([int(items[0]),items[1].lower(),items[2],items[3][3:5]])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Turn this into a pandas DataFrame" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = pd.DataFrame(data,columns=['freq','form','lemma','tag'])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have multiple lines with the same form in cases where the initial data contained both capitalized and uncapitalized versions of a form. To deal with this:\n", "\n", "1. We first group the data by form, lemma and tag\n", "2. Then we aggregate duplicate lines, summing the frequencies" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = data.groupby(['form','lemma','tag'],as_index=False)\n", "data = data.agg(np.sum)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Dealing with overabundance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we want to deal with overabundance. The format of a paradigm cell will be a list of pairs \"form:freq\" separated by semicolons.\n", "\n", "1. First we create a new column combining form and frequency\n", "2. Then we group rows by lemma and tag\n", "3. Finally we aggregate the form/frequency pairs" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data['formfreq']= data['form']+':'+ data['freq'].apply(str)\n", "del data['form']\n", "del data['freq']\n", "data = data.groupby(['lemma','tag'],as_index=False)\n", "data = data.agg(lambda l:';'.join(l))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to deal with a small complication. The data contains not only overabundant cells, but also orthographic variants.\n", "\n", "* An example of an overabundant cell is the GEN.SG of *analyz\u00e1tor*, which is listed as both *analyz\u00e1tora* and *analyz\u00e1toru*. This is a morphologically significant fact: this lexeme hesitates between the two major strategies for forming the GEN.SG of hars masculine nouns.\n", "* An example of orthographic variants is the GEN.SG of *aktualizace*, which is listed as both *aktualisace* and *aktualizace*. Notice that the CNK lexicon lists these under a single citation form. This is **not** a morphologically significant fact: clearly ther is hesitation between two stems rather than two inflection strategies.\n", "\n", "There is no fully satisfactory way of dealing with this situation without heavy manual editing. What we do here is rely on the fact that overabundance always targets the suffixal exponents, whereas orthographic variation targets stem internal material. Thus we normalize the data in the following way:\n", "\n", "* If a cell contains multiple forms with different final segments, we keep these as distinct.\n", "* If a cell contains multiple forms with the same final segment, we keep only the segment with the shortest Levensthein distance to the lemma.\n", "\n", "This is doubly unsatisfactory. First, there may be situations where an overabundant cell actually relies on two exponents that share the same final segment. We just happen to suspect that this is unlikely in Czech nominal declension. Second, there may be cases where the lemma uses variant A of the stem, some cell $c$ uses both variant A and variant B, and some other cell $c'$ uses only variant B. The present strategy will not catch this and still list variant B for cell $c'$.\n", "\n", "One can hope that these situations will be caught at the next step, when analyzing by hand the inflection classes generated from the paradigms." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def levenshtein(a,b):\n", " \"Calculates the Levenshtein distance between a and b.\"\n", " n, m = len(a), len(b)\n", " if n > m:\n", " # Make sure n <= m, to use O(min(n,m)) space\n", " a,b = b,a\n", " n,m = m,n\n", " current = range(n+1)\n", " for i in range(1,m+1):\n", " previous, current = current, [i]+[0]*n\n", " for j in range(1,n+1):\n", " add, delete = previous[j]+1, current[j-1]+1\n", " change = previous[j-1]\n", " if a[j-1] != b[i-1]:\n", " change = change + 1\n", " current[j] = min(add, delete, change)\n", " return current[n]\n", "\n", "def partition_by_ending(lst,length=1):\n", " \"\"\"Partitions a list of form:frequency pairs on the basis of shared final \n", " segments. Argument length gives the number of segments to consider.\"\"\"\n", " res = {}\n", " freq = {}\n", " for item in lst:\n", " ending = item.split(':')[0][-length:]\n", " if ending in res:\n", " res[ending].append(item)\n", " else:\n", " res[ending]=[item]\n", " return list(res.values())\n", "\n", "for row in data.index:\n", " lemma = data.loc[row,'lemma']\n", " items = data.loc[row,'formfreq'].split(';')\n", " if len(items)>1:\n", " newlist = []\n", " p = partition_by_ending(items,2)\n", " for cell in p:\n", " normalized_form = cell[0].split(':')[0]\n", " cum_freq = int(cell[0].split(':')[1])\n", " for pair in cell[1:]:\n", " (this_form,this_freq) = pair.split(':')\n", " cum_freq+=int(this_freq)\n", " if levenshtein(normalized_form,lemma) > levenshtein(this_form,lemma):\n", " normalized_form = this_form\n", " newlist.append(normalized_form+':'+str(cum_freq))\n", " data.loc[row,'formfreq'] = ';'.join(newlist)\n", "# if len(newlist)>1:\n", "# print(lemma,data.loc[row,'tag'],data.loc[row,'formfreq'],sep='\\t')\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "From forms to paradigms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we want to go from a format with one row per (lemma,tag) pair to a format with one row per lemma. This is easily done with the pivot method. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "data=data.pivot(index='lemma',columns='tag',values='formfreq')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Generating output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The final step is to write to file:\n", "\n", "1. We reindex the DataFrame so that columns appear in the expected order\n", "2. We write to a csv file using more intuitive column names" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = data.reindex(columns=the_tags)\n", "def new_col(s):\n", " number = {'S':'SG','P':'PL'}\n", " case = {'1':'NOM','2':'GEN','3':'DAT','4':'ACC','5':'VOC','6':'LOC','7':'INS'}\n", " return number[s[0]]+'.'+case[s[1]]\n", "\n", "new_col_dict = {}\n", "for t in the_tags:\n", " new_col_dict[t]=new_col(t)\n", " \n", "data=data.rename(columns=new_col_dict)\n", "data.to_csv('./substantiva.pdgm.csv')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 } ], "metadata": {} } ] }

mit

zrhans/python

exemplos/dapp-bc/Estacoes-ATMOS-Copy1.ipynb

2

427934

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Analise e Tratamento Basico (Triagem) de dados\n", "Analises por Hans. 2015\n", "\n", "* 2012 (10sec)\n", "* 2013 (10sec)\n", "* 2014 (10sec ate 1Min seguinte)\n", "* 2015 (1Min)\n", "\n", "----" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys\n", "import numpy as np\n", "import pandas as pd\n", "print(sys.version) # Versao do python - Opcional\n", "print(np.__version__) # VErsao do modulo numpy - Opcional\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import datetime\n", "import time" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#?pd.date_range\n", "#rng = pd.date_range('1/1/2011', periods=90, freq='10mS')\n", "#rng" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Carregando dados no dataframe df_dados a partir do arquivo .csv em servidor remoto.\n", "#df_dados = pd.read_csv('http://fortran-zrhans.c9.io/csdapy/sr311-2014.csv', index_col=None)\n", "\n", "#Dados local\n", "df_dados = pd.read_csv('../dados/sr311-2015.csv', index_col=None,parse_dates=['Timestamp'])\n", "\n", "print(\"Dados Importados OK\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_dados.columns.tolist()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_dados.head()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#removendo a primeira coluna, e ajustando a coluna Timestamp para ser o indice\n", "del(df_dados['Unnamed: 0'])\n", "df_dados.set_index('Timestamp', inplace=True)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Selecionando apenas algumas colunas de interesse\n", "df_dados = df_dados[['AirTC', 'RH', 'Rain_mm']]\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#df_dados = df_dados.dropna()\n", "df_dados.head()\n", "\n", "#s_chuva = df_dados.Rain_mm" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#s_chuva.cumsum()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7fb05dfeaa90>" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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NU+ke9n1uc8euJDchtBZnJyNavm4MqFjfKUp+xAvMmNJ+fjRNRmEUBtQ+Z7I5\n2L+CCIKBMNK3HTMyrqowloGibL01HHbs/67eziQPDmlzgkbwlmwlH1Dt/8qA2jlpbZJJ7dpInJM2\nBlSDE0UaUJ0/vEntbHKmt4sGVIIrVdyCWENaQa4KOrxmfYxuGsnjyfMkt5nR5gKOyqV1rDr5MQNr\nDgOqdsY+zQok4sAvgGqaUwpnPVmsTcz956gaGok22EhjGpjBm8VNK+xH52odytL5HLow62SXT5iL\nXXqWbu2q5nQPIg0cZYjWlP7VBVGAHTXBXY5InXRymAjREPkIR/nhhkEnExvYpTTpP8Sd9xpEu5pr\nQD87SLszUQZUJ6sNiILxgDWJyc1DBOcST5KRBH2UQBeTRZtWymuvIw1o5Lc+LI/cjNqs6ne0t7kK\n4HYiJ/Q+RQr2MswXY9GQ4xNFl+iawwKhjQYhopXwfIk79l7gm6bEj+GtiYNhlVJQRSh9U73fD4P7\nW0yWAdW4AV3rvCgo2xMGQTQ8vjRSbVL04R2QttGIopEm0AqtHluft7tjhvety22gDoV957tpjtvf\nIELaURhQFepOVXCi35p9awUy9RpQcZXZDc+AwvSaTFUrAG2LG8w5bfUpmWDvSf5+XdxxSs+ZSSLM\nh2lAS0Ita63mv9/mwEn1/nfJ+8PjkFyoOm8qPeQZUL1G01rNIU5tplokteabC1xfr4WRBrRrjlEr\nwOgSLwLKc/1SUjAntfj6MMkKzn4D296j5kPtgoVB2Y6kdj4RqWubhs27oA3zARWaruk7K9+j4o5a\nS7mCsjCqCwN6pykHsSZZNzjfa+AtA7ojOU2kDOgd5pqmYOBXWb7wEvYiT9PWRjvsUyh/R6G8iNXL\nIEiwkqeSpP6nkAJ8KLTjXEyeekIx+RQOZcyjPfoexNL+ycB2ni6BjWJUvM6YfCg8A5qbK1zTCJff\nRZWu5jKPpskw3JvaqqnEIvf/YaTUFiXTpWGYqj4aMWpt6VOsRnxjJOVETnA9fCC9Hl5Hye+4sv5p\n39eFqUsOONvnlDjI2zSZhkeETJtW/bSWYxPBE1uOeYFFpPmNnP9Lpksqqd+E9I3LxGBi6JeTzKge\njCwOGqVyTfDLViLM9613+RMniZmkvuEtRfyzHkfuk/cs4ECaOdes3+cwqW6bdBykb7YxoJNldDQN\n1mcncY82SXlXIadC+96Xkf7uCRpJLl8VBRGnkjPDGuAjGju27SITXLX8GIUBVUyVBvQv7nmK7utC\nxfZUDQFdGRKtAAAgAElEQVRDpAFVQn4pedsocZ6ELXnsg1uG1KfE8PlIpRrtvI2ZLR2zGqBorbVj\nW/vUeMtzIsGg0lXR+74IsRr4Humb2XnA9iFt83uwfSgFntnA1UFdYPx99F530Y1msgKD2CS1FD22\naVI+h4rvuLKHkZgjW8/bSZr8UfOhdkEUiNC65R3JcAuiiGZti4LrTV4VEUNZsgJpu/9s4PJaa2nP\n95aKpfr4NemywvMtPeA1oF2ReK9EX0R5aUtr4WegE7M7wOrWUJ2HEE/phapGHqlSHlANFz71GtA8\nd9yw9CrlRbvi2cbGu+15doLLc/hMBFL/L9L0D1DTE504cobhiS4ARNNXa9i7+HDPq7t/TQwpb2E0\nmU1UAxpdMxHzSO8z8AUkglkJNqjBYYgTfC6t3XMwcTa/V8VOtRBGn2cZSGX4Okc9Iw7h79tUI2Em\n8ylLKFUNPw/FqKkyhkHb7iSabeNzsB1DM/3JfsE9SwuozjNHktq6ZF1hTeGWI/PjiVRcSb6wLaUd\n40OOJ4g/aTMFVQwbKEqJuK3q+2g7Tq0GNIe3FInmrS+7/wtpLvZX1dubSWZqJdj+cajZ1/t5rZPH\nZIk5ZdZSsKGmANJiPChbWl/XOYhEB8wk9qvTQCkPphuOrbfRmLLvGQUh6sKAloL3DY/90I43IAGK\nbB1tW4yylpxD0wQx8vXU+WUFubncejWlYbX9VutxGKMxoBpozj77DpImrY0uK723JcCjtfbtJNrv\nCuCptAsKPQM6TpoXbyJ+37URRlTXGeuK1exD1WAO1n73p3qr84daUj2G1N9VAKDv91tEk9+FZtLn\n3Eo5uJX3Ax2vt56eteaTZ9Tb55FM3u37TiNnQKPvO8xHsw3RGLSWF+cxXOH0C3LT6K4+oB6RyW4J\nqgH195+G9L8bXPkYcTuVTXATTxIFqZpJes8mA7r3oD9OBJEAJ6azKpZTtaZ8aWBNYRAOpUyM64f1\nxMHfgO+wajSge5h9z5QNR5pUuqYmscGGrITTD3CfR7EElYZ/gIo3mXKVdKk029/fT/a7MRxtbT/V\nUUnvbZxVbxcjZiknMbUa0DQxVWxEVTRtsPAMaLQQfB94E2I18GtTvj/C1Hh/FTVrjer4J2Qh1edF\nPsaj+PeJVDiX0PpF/c311moN7MJZCjW/VaF8olAt53UBUe5NcGfSHlxFMUyCa02Mo2ASwMBkGoQI\nsdLaN5AIuC5Jzbtia7rn+rUMoC7K6tOjzECe4qJiHlWr//oo+Kb739Us1RO8P0PcQu5HzlRGsP13\nWVC+KaMwoOKP+Yghz7RQKwhLMDQjRlfGgqDiCKqsv5WCWe0blnfDDKwJbkIkXD0XOJQ4R7YSNocF\nx15k9iMN6DaIli5q/2WIxcuexIj84rqj4ouIFUBJKzGqMNMT6c9GtCzepxmEEVzblNvgPrqO6fNv\nRAJVtc1Nfp7XHOB2Lfku1cDEdQPKaGNA2zSgCWKFc+wQgYkXRiwjtcO81vvHKI1hK9TVdegH5hoQ\nmlU0SlJn798H3ZQps5E+28ynm1BKZ+TdyiwzYcfoj+qtfd+3kWJPlNaxyQjSogi6fwQOq+fDXzNc\n4TQDid+iGJYHtMSAeg1oicb5DokBtYIlXUtKprl/I62Jvj6RBlT7Rz5viruOZUC3ILdE7OoDWoIX\ntEN7XtGRsKYwoCspR2+1GlA7aSljuip8QEdZFCLTKS3rOhhth7OJoP33iaLQ/iAo0876b6Q8hNBs\n4/z+H+cmcm3auDs/Use3tdVjW46t6TgJOLjevwF4H+JPOZUaUNtv/wANE+gIPu1NtBCsRCSVkYT3\n6XgN6JJBbqu2+aBtEhulPVQaZy0dPKHx3qA+XczBNx9+SgsqHkLFq2hGdYwmYQ/rm9SGYb4z1uTv\nFfX2+MK5ZyHE1D6kpOQXACfX+15SeZT7v6itolOE77r/myJmXxJ9MgnrPkQy150IbATFzlH4DN5A\nTPAOS6+hKFmu6P2+XLi/wq8je4/wbEjCATsGfHCIhSQtxyKEebFa7d8S4+Uj1MNrXmcSm+B6hnQ+\nohV/MTGx1zbHWC1qxIA+DYmU7JmHnyLMa5ugtKsJrn0/ZXCswPgQsz8ZBjRFKE3uQc8h7386n/v5\naEYt9llOWsf0uvMZnprFf5fo3Mrs+7Fv0YUBLa21Ec1Tgu8Ld5O+zzqMTjuW6M0oCJHOQ/oOM8lp\nqqi9uzKgdxHnaFQc7P4vqrc+KJO1krHPVlNKW0cb06C0jo3GgFacSzUQ8kTj3ua4hOEKJ78OWwa0\nXQNahfyFosSAahaOkobVC9/afD2hHITIWjWAaOU/Qerfeq/55GbL8At2KtTd4/SgLApKNyyydGes\nKQxoJCFVaMfxi7dKFqYxWrCILsgnLZ+Ps8rsnCMV9cyWYxEswaSSqyvpNhlF0og4EEy637drIiFv\nt7tYTq51+gdwkfkfMSFtfeif0QdUNQmXkBaO5Yip60eYuAbUL6Q+fVCbtBhSCh2r+fwy8UIwi2ak\nQ51I/kI5Yl/be7X15a5E1PXAB+r9Rabc1tNqP+x7WTOo7sgjTg7DiUgUyA/V/5fX9/CLuYWaIetC\ntnPLuXrPtkV6Oqk91VdpL3eOMmoPR6wsbBCqR5La1mtAJyOdnpgUtWoIVex8Yom5dlPMik8EvkoW\nXftgHiUxER3KINr7rDPCfS0TYwmRMeAKKq5ltCi4UxG9uMmAAOQ5QbtoN22Amy5zn82FXdKA/ib7\nJ76gfo7DnTPMT3YxsQnuoQhRFhH899Buqv5fQ56pOIoU/EqfoeaDfn7W4xcF9YkRp4PRHJc+DUsp\nWq32MRsERy1LNO/mKAyoWrEkwVGV+e22fa/RfUATo/I5usOvW8vIrTJGXctLbRSN7aPrrVo2zSMX\nCk6WARXBULcxqfC0rf2mkRCtJJQorWOjrjHbkZjdXwfHDyYPKqcmqW1BrPy816aJnKwGVNvHC9ls\n8CP/XChrOktBiHTu1nqcCVxL+jal+o0i4DopKIvuG8XimRDWFAY0kpDaY5EJrn7Yj+BNwyoOoOIj\nk6iPD+ryUff/bGP2ES2WupCuHRyLYBdA9bu8jG6TUWQaVpLo6v1mAgeRpwHx0jmQZMx2EYnqM1Gm\nZU2FMkhrk8wsb0b63nV01YBWfJ9q4LcEeTsdBXyDZo7ONpxF7oupE2e0EEiOqXzx0IBBl+A1oMkD\neVRhgprCdp1HTjXn2snO1tMycNF93wjFRNvXBmW3jGDaqdoO9W16N+0BnvYg+QZKIIBqaM6tYSa4\ntl1KAZdeYfb997fMhe+n/rnjLfXwmCq/QBsQqNkWFTOcWShI4JV3Av8+Bc9f6P7PRvKxqVmibbPX\nkqds6AofZEbbrk3D1D1dysSh/eShpG/fJfiRzZXYhcD063S0vl9O06pB282bvZ6E9BMVqKhFgjXZ\nOxHJk70nNHyVVYvi21+ZndEk+hVPDAjg6aRcn7ruKQPi313XklfQnUCM8j0qLiUfR2re7p87o57r\nbyO1hVqf7U97/4ScGF1B0sBFfv3DYN/bMhjeB3SuETzoc4ZnJ0jwwXi2Ifkx/pRha1fVsKpR301/\nXdR2+h46fj5O7lMcaVNHYUCVme1iJj5eb9vcVKI+1MaATk4Dmmjo59bbuyCjmUrXtFk9lhi+4RpQ\nwTeQcduFAX0BqX1Kms5o7iv7epYZ05nk/p/+u5T4pzGeNnAlG4bIAmmjRskUxghYUxjQKEiB4pmI\n6WlkglvSmn6Y4fmC2hD5WZRg2/DP9fYT9dZHyP154R7RoB1GqEbPV+SO5tWAsJ6OMLtr4SN0VmFb\nvoY8Alz0rGGBTv7ZoFqn5wGfrvfPI0kvu2pA9weebP7bvrsRQoh/eMS6zSWPkKoMaKQB9cnGdXyd\nhJhVRKYV+XvlpqjRO2u/7UpEWc2rDepi62kXiaj/30LZ9MgnFVc08+dWzHamghGWkucg87iKNAdZ\nRqOUFidJMMtS6y5By1QC+TvEpNlqPCwDPBHp+lQi8iF/sdm3fVeDafwHeT5oKId99/gVYmLeBi9l\nX4fEMHgG9Abgfzo+28JazNgIhTnRkAtGDjTlUaAJPTZGMxJqCX5cPr7eXkMpSmZ6zqvNPxUIRPkm\nhz23FIRImIscylh4QmgxuWXAckTLby0D9Jisa7nlgxKfnmhTYu8OcssfKEcXnYuYxPso2Pk7ynfS\nNB6eeJ1bX+/7G1S8pKDp1XFypClTH+H/ra/Vd9PrP0SOGcDtVFxEaou5wG+p+CvD3ZlyBrQaMHKl\nQDiRy47C+1orvA8oJIG6uiBFKdMADgjK5iIuHTpHPoCK22sBwlK0P5Q1ao93/7XddPw82pVb6FhR\nmuIE8uBrok3t4oedYzNS5F0o54eP4HOGW2hQyutJtE9b4K6p8gFVE/82S0iLaYU6gVgd2LlrWBoW\nz4BegMwFw4MQVVlqrZKGtVQezYltjKlnoPW52n/b6IauDOOPgrLfBWVThtXDgDaTXbcxoCALzf0h\ns2VuY0CbXPvk0DYp6GR/BsmcSYMPWWnTd6AYjWoiDKiasqnG42hzzA8WlQgrA/pMYv+vVFY1JK4X\nhfWpWv0Q/hkRSbqmIZPkoyhpQCveQcWWLfe1Y22H4lmCU929lUDemTSZPBLpI20MqK2rauOPx2sJ\nUjgYPx9YQqjNh7VtHvlvs281r3aM2nqqKfLj3X1PQhbINjOxUuLzaBwtpZnz0M8bJfMcxZ3kSdGV\nubSmolcF17X5gVpCexgegzC7h5MYeutbO4ZExx1DhCH+mYs6PkfvBVVnfxIMAW5xPsm81H7Lbett\nJM2PpfZVw8Jk2DoSMXeLzL4f2yfjx2I32Py+3qfN9t1SWz6w5d7/BmEuzS5Q/88ZHMZzhpz7FbOv\n/aZr7j9rTl0ywbVt8Rja4ee46VScSNX4NpbQsmkPIosQyK1IUh+reDuJqfL9SQN8eCaxre/5OWR/\nU+7H+rdJxL+FMnM2KKLOpSoEnmbKj0XW7BvN+fNZMhgzKvSbS9KkdTXBXeLqsTMyl/oczTdRhn3v\nf5D80y0B7ttN+28UrAbgW0HZgYhf3EH1f+vqYvtDyQXGuzFYBvTvVAMXjDYGVLf7kjPleo0VuHUZ\nX0sRplb7e5f5YFG9fWLLOSq0uZlRTHCrTNFSEg604YJ6W2JAI0HGIwv3+ia5+0JJE6n936/xJYZ1\nV7N/YXCfkgluSTNa0nS2aUA9Q6yaVxtl12OM/xvqFqTzkR9XtzOB3J6jYHVpQD3hN4xwWEnTBKWN\nAZ1qfKDlmGpLj6Wc0wzEzOR9hWMTYUBVkh9paLzP2Kvq7YY0c3V66MLwBFe+OVPva7smoi0AxnmU\nNaCfBH4JlPwO7YJ7ZHDcwk8o+s32JSeyRtGAaiL4fMxUGbHr30s1uIuJmSK9to1hsoTgI0gadWvW\nbuu5d71VTb1iXWTR7R50LIUuL5kZDpMcW61mhDtJ81bp3Ghea/MD3X1InTwOIJ8LbyItnLZOXS0q\nhmEyId1B+oMyDmuRzNx1Uc/N6ypeSW5JYOHTRHkmu+QLZ2G1cH5sP4vRrRQgFwK0meC2aYhKaAZg\nybU3beuo9rnprBjUyRIdlxObeh4NfJX2fJkWlqEsBSFKYyUxkiVC6nXkZrW2DtciJrRtc1BJA/qw\n+jpvZqlCs6tprv263vt0MG00jB93Wv/SWvLGQAuq/+33UsJdx7tq616BRNBej3yOe7vZV+b35cCT\nTFkXmnAnmkIDjW5t0dZX7PdaCVT1viXAV7pzlVG2a0pC5dbuZMEzj3jus2uJzZ9rNfAlU9t55Cbk\nUduphli/z8bkwjR/zW8K9fSYjbSFzh8RHVxi0rvA9uUuJrjKhH9wgs9TLVvOrFWsjwTvek1wTUlo\ndQXN4G0RY7fClJcYUNuue5v96D5tJrglDWgp2NAoGtBo3rEmt2MM14Bqe+XnVawzhKeZNNYkE9w2\nZnIZkT+DXPN2mnbjtzK1aMttpIvmRAgVEAYymqjnQIORubTeWslvJP0qhef+GO0L9ThR1D8hcOZS\nTsTbjornIkFEXo2ETl9zGNmKhc4EpqQBPQHpB20+oD+pt9H3tGNtVBt6DWZxi6vfMAbU1rUUlv2p\nLT6gSrAcS2xVsGe9te3hTRbtuN2K2ATI1vNchNjdjty0a2dgS0ZLu6SaqJIf5zABll+c/KJ+F5EG\nNEfUV+SbxWa4owRNUthFToKwyJi1i0/UT8Yn8KypgNXKqJ+rEq/e3eEb5GmxLA4OymxuY2+CGMEG\nNGmaRLZFmpS5zBNHH0bT8VQ8HTEJjZK6AyOlWWmDts9hSHspfM6/xIC+aBAR3TJ984jXzjkkbX8X\nBvST7pklE1yPZ5BrF0qwvtE/IjEMpXn1KYh1hGf4FiAWBKXrTqU55lW764ncp5BMRL1g4anuv/q8\n5f2tylx/fAom60aj0Gu1/jrHqyWW/1a7mble++LlJLP9WLhXcU7trjAN+CIVt1E16I71aDKgXdOw\nrET63WU0LQYgfTM1Efxxy321zsqk6XVRv5V1R+ZKm2Hg+WZfA+RcVP+0jdYmXz/WJxd0Hk3FjYjf\n3sl12SHk/vu+vb/OMAZUrKHWAe6kGkS09WvGljTdUcbr7UGmrEQnLyQ3g9Y2sulNovVkotZwGuXd\nax1vpKn5U5RogFIQnxKj2ZUBtfA0WGSCOyw4UVsU3JIGtAsD+lVyn/Yxnj7UB3RV5H/uhDWFAY0k\npBYfHxxPRJt+8BtoDrY2s7kmKqbRnoOx7cNciPhQdPOFbPqd/Zl4cnw88ENXpgPeh9KGXCo4SpQ4\nyE1CNSBApCltMw2LoITm4UgQETXrmmoT6cngEvL0FCUGVCW8bT6gaiobhe4fK+x3gRIsLyH3Tx5F\nA/oX0kJo0ebnqf1vD5pElIW97rPkDN+hZv9rwPsC6b4lzNdBFsaIeBknIpIiRq7iLYiGA8rBwNK4\ny8elTuCeqcxNyiruYrgG9MFBmZoy2iBdHzf7o/aPtPiJFkDb09Z/shrQqVqc1iJpBiDXBk0G2mbW\n77SLhcxnyBkYNTXWcfb94Bqo+HK992V35Czz3DchQil1OUn9XPrsZOIUWChRuz+5/5MlRI7DMqCp\n711qzvFROqHid0gb7Uikua+oAncNi5IJbvS9V9AtKJIVpuk3GzZmtibW5qxFmfa4k+barFr1aJ3Y\nEZlzFrhy/+4af8ELPKz5YlqTKz5Lmn8jBtQzap9DhGN+HbKm4doWF5AY0KZwT7RQ2yOWGSUhm2KU\nvMOeAY00UtpH1su0/F7TGcPSYyWN6UrSN7DroBU6/D/E2uyRJLeXaQjTZ+uxJRK0TqGB8jYEXlbv\nz3P3Xk4uTGmauEtQNuvXvm1dbpkRH/huGc1ga4qTSSaqXkDVRN5GJ5ECQUXm+DfW1wxfv6qsb+r5\n02lGsN2TWCjb5sISMXZ+fZ4MA2qDHXZhNEeJgttmghsxoHuRm75HAvpha+s1TCzY3qSxpjCgbeYr\n15JM74AGAxpJ7aJE1m14GO05GPUDHmLKlBiNVNxRNE6FNx3oKlWG9J7RgjvxpPPVYGFaRBosSgjY\nMP1d63lAvT2ocLwt79rqgG27aLBOI0WeW0k56IKa5Oi5/h5dUZrA13bHHkh3DeicoE4AV7T4gKrp\n4zDBg6+vJXTsWNC6+uAjllH+BJLrTgVOSijfjMwD0QTrg4aBEM2jwNZJF8dhPqAwXAMaQQl5+x43\nF87tAi8l1mBPw0xwF03imRPFWuRMjs4pEzFHjWDbsUsU7qfAIBiQHS/qTxxpWSE2C7sBIfBK/cAy\nQN0ih5aDVVkc1+GcW0lz+nR+OjD1fnz9HCXCvcZPg6w8EU90VuyGmKi+tuW5s4jHUTTHeQa0C4Nu\nmbg2QuvXg+M5gXwtZUJTCdEIL3P/TwWOouJCqkYQLfUN9+/srWmsf54VSP0HKbBhJGxTQa/e6wKE\nVvF5gOeYuV77os2zGDHoakWwMe0uCStG1KBEDKhqdDwDOpPu+VgjnEOZvtPn2vnffvNHIFGyb6Ua\n0KHKcHg6LxI2Qgq8Zv1t9dlWePMMmnP0sxFtqlqNLcfn921CvmeuUV9Ub6eT5lpPcy+u++hy8tgF\n+s4vJzGtkRvJUuI+FOEFZn+62fp5YjOk/3uUzPVLGkS/Pg9jQEuaV2gGSlyf7ia4UNaATsQE19Oi\nnh8a4+hibAxBxT1UExKG3jj8lHasKQzoq4kYUDGN1A+rk5FKD7WD5ARpM8BROyo+SNnE9rlm/wbE\nIVc1OuvXk0LEgLZp+DzhMVEGFKRDqhlFpBV9dFA2DF+ot9MQU16rvvdmnAfSxNlIAurzKBPV0YSy\nOmF9kodpQNNgrngyEk31GXWJLgxzkAXTvr9dcL9Ju5a6JIz5T3fPlyPCFi/UiDSgkY8O5P51fj54\nWr31PlKQ58ez111N/m62PZWAtUTmr2kSYueS2kBNo1Q7EwmcSubFii9l/xIh+EtTahdrJQoiqanH\nbwvntkGZQdsOpaAKXeBNlzQXol10x/BRaddjLj7HcRlTtVbMIu+HGkwt7/NVB+l8Dl2IzzBlD3Hn\n2O+jfkfHkaS/drx8oK5HkwipwmiBkPsaRrDEWdfIvpbhK60TN9De995ZP1eJ2O8xPRRa3RO+b4Jf\nqzQa85tbrpkLLA+Yk4gBXUkuNGiLQG2vsQJZNec7zZ33W6fNAZlLP08zJoXOXzdQFmIc4P63CdGt\nlYSvuy0rCTv8cxSeANzQbNeh2Q+9tYvOQcqQbIOu6RW7UXEgKcXdk2gXso06P1iNpmVAkwYo9RkV\nAE8Ut7VYqFmfTsXN5MHQosBBc2mup9Zq7LDsiMSF2IcmA2r71y9prm0qSNV1eh7N/mpxEWkt2CI4\nbrWM/lvehqyD91A13lnrpcqdSKC5gnYLMYvbg/1IA3oGeeouRW4+XLFW3V9K0WVLjGaJ4fNrqoWN\n0/A+JDWcZzTbgh9F9bH17KIBXYBow/03WIfcnHyMlavEtPY/SbzChLGmMKAQazHuT67phERU2HL7\nHu3h5ZtIBHjTN1E1OceTGM2XkBzJN2c0zQekBVxTstjUFIqXEMMzoG+mGnTWSIIxasLYcVKC8OfS\nHCSWsb6GOFn6m+vJ6wRiIgNKZm2rD96kAnIJ93TkWy8j72vHImZv+i1X1Bo7Zcx9FFmNwHk17Rrr\niwvlVhCjeA/NwDVbIQubZexmEzOgZ3fMA+rxfZKvcfK7S8SEwtZXpaZ20T2YfAwfhgQBUbOpjWrC\nWxenSMIaCUIsPOGukaPtRK8Ex+9J7enH9jnBvW9BvmUXbalCF29bry0RAuTTjGaCq6aVdvHTaMOW\nKc5NqCsO5C38B+3WGhZdTCO7YG/SXL+YNA7mkvvyj+ofqXOK/aZ5OyZt4ofNs6xWwjInbf5rPviR\nQoWi2g+8H5/tu8eb8jNRc9kkHHkAzbQnpaBZM4nnYsV8RJOiwXUWsk9D6No0v81xCpYB7aaZBRGS\nRYxZZBa/ktwM9VCGWzNZBnQl8m2+gY7hVM8oENR1SP+7hnxuHEMEqG+iPQChhSccLbz1lkUpQmUp\njoMlOKeRRxlXYfEHgRfSnI9ON3P9ExDXGKsBtYLhTyH+ZIodGU3INgyRNlgZUF/vZUyGAa1arStm\nIoR8Ss9UcR1V1v4RA7oRzTlic9PffARgFTBaC4MV5KaPF5D8LRVKk6oQOLKusvgFSTtov9V4vW3r\np7shDIxXZqwgCS/0npEJ7nLaY2RYXI/Q10eZe85ArMj2c+dFdEtkSg9lhrLEgD6yUN5mghsJvEcx\nwZ2IBnQd8jnzRYgW2dM2LyUPdjrGPgN//6lDxf8wXBM/FKuPAW2ao0ShxzdHPrZd1F9Xb0sM6GQI\nJS/ttJOILHBCYKsz9gcYfVLWSUs7TkpNkaJ2tjlYL0XsvvMJuWok9d57RJMYxUn1VhecElFdkviq\nj0ybJGzNSN2SBA5WmmY1RorpyKR/C813nkmKoDcdIX7eUv+fgfgX6wR4zaC8e440EK3yOKPl2JpN\nWQP6PlIUWiv4GWU+sAuQF36kfpf3wdcj41zH2YeoOMzVc3/k/X9jrhMmQe4VSV6H+V97BlU1NvY+\nKxGGyIaU9/3fEmQK62/Zdbxp21mmYQxh5N/W8R4gjMt6NBdLFWrZOvn54asoU1o10idEmExURQ81\nwbdz9xxyX/5R53GV+tp2KI0Xy2geQEqHYstPGfH5IP10GW1miul97VrzC5LJm4S9r7iBpsaxpBX+\nGe35T9VE9numzLdviQEdr7efRt5L6zOKP3G0TkRtZK0ADkWCPMX+e/l9kgluxU3k2kkvtLXfQMeN\nX6tm0PTtHYbIfFBRYkAfH5z7c0Q48cHCvTyto8zj2TTHuGoS9d1/B1mKqHXINaAWe7r/X2N0YXsb\nSgxo1I7XESsopgpnEad/UkQM6K7EQjJdqyImGpo+oHYeuICmhY+uEZ+vt22aOalfWnOjcTeD9nVK\nrafye6Z6at0iE9zd6W6CeyJizWgtGA9CxoSlx0p0pu8/tn6j+IA+gxRoypa3MaARNiU3h7X3KWle\nuwYnGkO+eWTy2qWtV4UGdEqwOjWgu7r/EbGgUvt7SATpDLONGNCJm4J4iVaeMy5NvmmAv4DRCE9I\nKVJeh2gJZ5K0tmo+VpIgTScFnIiczC8w+759QQiQjwblikXk7zI6A5rapk0S1sU3696AEsKWGNP3\ntdrjaQiBezvNd55OztDY5PKz6/N/jLTF5XX561EpZhW2hW+3OYhUchQG9CrKPqB2zDy+9gt6GfHY\n+QVxpOW2VCKl8aDtrO+syd//jdwkcZHZv4c8eEO0wCUJXzXIwdsGje5r6/9irAmaoItWsyS5bzOp\n1bazJrFd5pD/I4+2/Yj6HiUNqCUYj2jcLfmDeZ+1CFPlowmJULbfci65eXlXBudy93+J2fcEucIy\nLQ8gaaHseDkfMZUfBq9BXtYi+JtGkmKXAkdYQtszoA8cPK9LoI8EteS5aFByXIN49gFSFNcja9VR\niGPZ4t4AACAASURBVJZerR2awVLKiAi5qI3sPNylT+p9fEyES82+F+pGDKgQ2EkgqdqMb9EM8KJ4\nv/vfZoJbYkC9EODniADzssJ9ICdyH0uyxooEvl6Y+k2WZNZVt5FrQNtwMKtHA3plXTZ5C4yqoV20\n2A8RMEc5h73v3XQkeOQ3gnNLghx9pwe5soOAX9V106B2dmyd7u7T5psIeZT5aE39DinvY0R7RP3Y\nmsbbnLF+jp5LdxNchV/PvUVASWPrn63fVQNFKYb5gOozfPmoDOh+JPdAe5+SSW2kkSU4/78Qmn0a\n8Tgdtk6O8bMpi7Y+5VidDKgfWNYkSQle1fItpzmoSwyo9Znpskhb88U2gqM0+Y7KgKq28zIqnoiE\n/NbosPr8XxWutQ7kaw157oKgbAZl806FvWebVLdtwdX7lPrXVJnzTRYq9WwyoFWWaFujqf6DZntY\nhnM6MeP/HPL+c725z/7B+b7d5iDE9HAGNPX5v9HUMKmm0H4bDZBwZ/DcuxHTxshcri0p/bDxoIvZ\nH0zZC83+zaYu15EHR4kWvjkkP+4o0m8Jtj1PR4jN35GionYhuGINaJX5IipUABC9g31Oad66kXj8\nloIQ3Z8kpb8bNesbjXkZVifqez6PakgO0+TXWWJArbbL9/WzsN82MXkvd+edYPZ9FHGFJT6OIEWv\ntOMi0gRANRgvio3c8bb+ciAplZU147VaDzvveAZ0M2TuGDWisc7T6ZppnTWgc4CrzXvpulIygWt7\nvkWUAsK2nWfwSng7kvZlU9I3tb6/XRlQSMSvlp9O05e0hDZhVYkBtfPFHIRhuIf2uBB7u33tRztT\nzpdbGrvK2LUJl25ELMGOZiIa0IrXU4XC8jFkTCuNtxIRsHhTxs3rcu2v7+345IloaqdThfNrpAGd\nTdxupXGk72m1WKpZtKmH/NhO8RQqrqTpC9iGLpYtHhFdt4JUf+1jmkrM9q2f0t0E197bjwtvfVaK\nyxFdsx6j+YBSKPdr6htox7fIBRLaDhEDCt1NcBWl+eX4oMxiVP5kFEyEjsgwGQb0XQjRdw5CpM5G\nCJ7jEEnrsYwWjfZEs69mIkqo2sAO1l48YkCtaaqVSHTBY1uOlT7kVH7gxcAJVEW/LOukHUUmtfV4\nU3B8Pdonr3F3jzZ/gWEMaNtE1HURWdVQBnQvUxZ9SwkuIGY1fhJ4E0nD8L+UYRfvE0ma6CjEuO+H\ncxChjGq1FBvSNMuQ75ICbujYsD6g9tt8mgUcgWgN/HwwjbIWSaO/WVhfujbMAv5E5XL+pcXscHLT\nOcs8P5D8e0HKU+jvMwyWyJuJzGfLgPtRcT8kquEwQkYXjrU6nKsmdwto+qp3mUNK7+XNsjZHfIse\nSmKQluM1/pGIqsuzm2l0QHLtnRqUWyiTomZlmwL3r/2mNiQ3pfa1ezgxUe4D1aRvkPt+fd7sW+Jj\nHZLA05bPIva10jFeBcdKWBKU2VyGVgN6E8kfzTMihyPpOSLrgx9T7n+6ns5ANIu/Zq/B2FMftDYG\nVL/LMlKuY88Ab1t4NsTrRGROlkywq1Y/t8VB2YNJ2ivrv6f11Ht7BnQ5ufYBxMro64jmN6W1yf1e\n/RjoEoTIr3t2zCutdA8piNgwfBEJgKL4hDvuGd+LWJDVcRq5BtTSYIr7I+b6D0HecVRa5wuujgp1\nadIxqnV8As1+/FJSe5R8Yz0mQtuWgmnZeUTHaml+aBtHN5IrXlbQjE7fJlzajNE0c6INrdifKvML\nbMMwBvTB9VbnJlvXJj0uZvQ+t7PiCsSCZ0tXbte5mUF9oMyA+vOtxjEywaVwvl9Th0V89e22PgyC\nqPr+HJnaanmJ5vYM6BuR4Io3ktzmIoyxbyNn8RqDiTKg84FXIdL1HZDGfAGiLj4OiSJ5PHmkzGGw\nuQO1oSVibDWIrgWpU2gH8QyoNTHpKilStEmVI0bzSrpJBb+IdXLP8QUkSqE+v42p85EzSzifKpwE\n96Yc4EgxERPcKCqkMD9xsIou0Q3vDahjvxVURO9rv8m4O7aY4f3s4+QToCW0tX3sM1JuMGm/ByGL\nWk50VlyPLM6XmFIrufMmuJEGdBZCeOQLX0o8XvKvbBIizSBeJcTaJfVxqzidPD/ksxFmCnAmtuIr\n92JyRtmaFv2UMmx7anqQZUiQonMQ7ZrtD00GMGmG9mQYcVZlxMcH3NEjzf6TzP7f3fOjZ3itAYhG\n+WwSw2Aj4/k0OKWQ9hb23bsIFr2FC0g/vpLkr/4A5L2VuHm9OTeKfBjNz749St9gK3eOvs+TSdEi\nPQMa9VF991FyIn8yKLN9z2pALUEaBTqBJpG6HGE+orVjMeJOcGp9zSZI/9J1RAOOdGFArYDBfwvv\nM2gRRRKOvtMfgrIIOwVlZ5AEgT8w5dOBW0yfs/TCg0iBU6J67uD+23f2BP2mlNdufVelhw5H6CVP\nEIPMPykuRDuGMSN6T6vtt5pl1fRoP7cuRxYPR9rigYzOgIKYpW/iyvxcpt8gCp70BlJ7XEJ3LGQ0\nS4HS+0fpL0rzw/rk/eBMRNg8F1Eu+HeeS5PBbavzKBpQxfPIU/y0IaJB1yXN0SrMiiK5l4IE5oL5\niufUe99DeImPOKGxff9t6KYBta55kcbR0+m2npEG1I8te07JJNiWq9XYKCa4o2hAtf27uAnd53xA\nb0U62zzkQ81DJKv7At+uz/k28KyO9/sTVRZh0hNIUA2k1MrNawdZmzxXpTUXzKN5iTSmq9zfS/Mj\n4u/7NCUrEaO5knLI4g1J/mJdGFCbd9DXxzqIlxAFPlAsYmIMaNO/LA2oA4JjbTlX701EgQei97XJ\nou8mNx/7PoR5lqyp3t2kCfBxSN/S/qUmLSUmQCfWFTQ1oCDmqte683WBsozmh0nm5pYxncXvmE9s\nSbCU8mIn9Y21jV00oNHibVMoWdM52/7++9wfkZpbBtTOH6W6HEq+eL4e0azq/bVvtDOgCbsGdWuD\nj2hq/e8s02yDs5UYUB9pz5brGLVzofgZJr1cW8RX+2zFczPBUuz/Z6X6atK8HU2NuiVuhgUQ2i0o\ni3zJIjzU7D+TPHCW9rUuDKjiqpZjHqVgICDrxQLSHOKjXNq1DYRJ9ESqMFnwp0FJNVh7DySN9xmo\n8O/kQeANawI9jAG1GlkvbCppOaB9PbLoRo9U4Zxk1yor8fdrqsxzKf3QdMMUDIsIHddPBG/rUu57\nkaZjJblvvTVPjyLjR9ie9rlWfaqTsPH3WUqtj5NrQD9LDI3F8QzK79i2pv8n8FcqXkQKtFhiQCHu\nLzOBM6iGulh80OzfTjMYUBsiayTI/bKtCW6kAfUMxymI2XyUtmVL5BsOY0BtO3nNnCDFkojmrGmh\nDUaMEg26K0L3qJZd62lzukcBvaCZ912Fop8yZXYet3N/SSPo30hjo7RpQEsmuPb85yHKtTYGtLQG\nl4IflRjNLkGIjkfcgkoM6DAF2BhH3fd8QG9ECKMrEMbzZkTz+SBSjqLroHOOOW+62CZhv4mK+Yik\n7B5SXk7FbxDfn6vINToaCfCgDvW5Cwa5HRV+wjwSMdnz5VGE17tITOZicl+nfyPlG+3CgLZJv5Tw\nLt3jXPIALxHsu6xNNwY0GpAbIL6TEXHbRbp7byBiQLv6/Cm2JQ5GsDZC9J6GEBoiqKgG+ehUkquL\ne0nqqYRISSq+JbnJ7jySEMabQS8K3mEmKwZ+R7YOqomJ+5K8w6i+aIoScW9D0mvAlDFEmHVZ/T9i\ntNYi5SzV/4r8/MQwH0nenjsijIAPzFDqD37O2qDl3AhPcP9Lmgy5ZxX4mSaUBEV28bbE7kRSGthr\nvoRqhyq2YrhEXvvILOJANxpYxaZhmeGEGzawTBvaBA4KEWgkou2YemvHhfXxagqHqoa5YxtKBDWI\n///WJObap0uyZup/RMZwNO5mI9FuNXK7WnWo28DuyJi+EvgzDxowjPq+bb5rWp+tSGu6MuDK9LYR\nQV1NBifjU2THgJikVWxAk4DVb6xjX4WJV5jrf09uhaAozXV6r2EmuIrnIsK2Y0yZBoy5EzH7fSvD\n8VhS/AiPn1AN5swkVFk5GKuHIkS8nYsvJ49EDaKtVbTFnfAphyIcShIO+LnMWwRYVHTX/CUT3Wrk\naPu+nx6MtInNhqD952nEUXBn0fTznEYex8BiZ0omuNUg4JeNfv00RHvuoYKqUbWjHiUaVPKPpjzB\nSi/c7s6JXa8qvhiU2aBzJfP2aG07A0tDV8wjWRdEJruj+ICqe49nQCNrBUj8TjTPQDmtSokB9ff5\nDTKf+fpvhSh3hsWpGFtz9Z8TZ0C3QOzl5yMmPWtDIziD+qA1cSQSKuIERA90ZDaxL+I8Y960pC5T\n/J4t+PPAn+celpDLQv7Cp1jC80kDeVH9k2T0i9k0u19+/SEs5AUsYTba+dJxnTD1fs8Gnsdx7Mhi\n4wt3NKeyhGSOuAT4LMejnedCVnKeiayXP38653M/ciZRnlfVkbyOYVsn+1lkzl/OEuAiQ4Dn97+L\nn7Jzdn3evuN8ij2y4//DY8P6JK3LIk4YBOK5xRx/OfByzmLH+n4yYZzDMZyS+QvZ+t/b/zfjDA7h\nIhMd8iPufX3/+gyP5VKzWC5hf85lfv3vTHP+CiouYgm7s4T/ptl/vldfP6P+PwP4bON5/8ui+v+1\nwAx+xG7Z8T8OjgvO5yfm/0pOYAfzPjIeTmMhOvbPZXNWsIzkV6b1E83HaTy30V9sf/0ez2/0x4My\nM1m9X7r+OHYhET22/nMG/VEk11fzLR7L8eyAmtedxSYt9ZH9o0z0v3O5X3b8E+xV/9cci7Z+p/DN\nQX8VnM0jG/WveALVwMda669Bn5rv69snrv+K8PzT2YIlAxPNMU7kYY37n83mpMVpUfZ9zuWBSHsK\nMTbGIn5Qm0suqK+/KGNy8vqn/4e4+sncfhzPMc873Dx/5eD6iwcE1DQuYl6j/r8eWGUcYe4/DdjS\n/Fef2bx+X2KbrL2+wqMb95fjGiHcts88LuUOYx64CUvYtr5eCfNF/JFfBveL/0ff/3xjBZLmWyF+\nzuL5nMa5qOXQKezEOYP15HR+xoOQ77c+sAtHsBNLWJfEDC3iUu5ABME7sYT1s+f/jJ04aqBd3ZoL\nmMMSdmTrWiN8Bgvr85UBzfvnX1ifHwy0pZuzhO/W5wuhfyGzs/6brr8ROIwlwMUZQWnb53t8g/3M\n/7FO4yX+vkqgLeJrA0uPh/NNHsclZr6+lOl8hT0QQvVyqsH41jG2iAtZC52f8uftFdbvC+xR7y/H\nf39YxPkZ46HvsxOwgkv4e32+aKYPG3zfDd355fdP/68GFnEuv0GtkpYA3xoI01XYqO87jYtYnyMG\npsbrsYSNs/tfzGPM8zfmj2zeeL4cVy3hosJx/b8Tqf/b9XD54Pw/meA5izmDY/kHiQHN7+//n8iD\nG/2x7fy8fn7+XsklrMUfjaD6/9iRC1kfYQqb8/2x7MpiQ3/9hX1ZzAtJcQqazz9t4FcJlzCdbwzW\nz1+wBPigiaj+B5Zy4cCixNZ/WX393eb+8q0vYPOB3d9iTueizIw5r88R7MZFmQWR9r/byPu30Avf\n5LHZ+p3Gl6+fpk8srX9jg/8/N4HdLmUWR2Um9zo+0/z3Bz6HCjaWsBEnsfXg7OPYnsUDQZTtb8Lw\nLQHOMe4pqT7qyqTnH+6OC87m9Pq/MpR6vuB0tuYsY37+Mx7OhayLna9SfeA4dmaxUcCdznzOHPhf\n2/Mlkv8x7MTiOkZJVL+LWJuVGR+W12+y//18JPtvRoRGFUo3FDBRTdSuiFpYJcNHAI9GiOSN6u3G\nlGzqm8Y6fzQs2TjbGSncgrpM8SguJXW+l7GAC0hRSmGrwWShDGi6FmAbFmdlySD3aGAmLxkkdl7h\njuuEae+3D0/mq1j/l2dwNBLw4sD6+h9yM79EzR8fym3A3wZyxdwgeDrb8rf6Ga9EouLq82YAK3jq\nQOK8MqjPWH2/tODp/ZWBfZaLzunb9x1Z/kV4OyeSB4QYp+LRwFjNJIzzhMEAfhu+vR8+0MZ9EdiF\nHdgF+IcxVM7Pv3f/b8auHI6kIBHc497fG2y/1Tl8y3GVlj/CnL/cXa+mEvL8qv6l9p8O/JIFLhDC\nG/k9QsxtCryFf8vC8MMuNWEtYdxXsm0WcfCBPIErOTF753F2Zyc0jc/23IT4O/rx8nhgQ3Y3Yyt/\nH8GLGilaxnlLJlAaz44uABawmGQKPm7uuY/rjyt4Oach7SbRhR/OUYjVQFwfub+aaV3N9i5Z8n8N\n/MxuBLavzflWIAvMT7iSn2T33IlncaSZsfx4SfVXP/Xm++YYz8rSvp9vAA5lNw5CTILWB8bYk/M5\noTF/XUuSmtr7T2d7rubHpj0/wMmkGfhPLODDwL9n9fP1FTzE1U8WtScPog+DSPLH3TuMsyXvRhai\nDdmam7JnyPcSH8yKX1HxUcQ8dilwp7nXMYi/c16/v/FzFvBnNJfbazil9gtO97f1zeu3CQtZy9xz\nU/N9nzV4n11M0Kuov+UYD45H/ef5ADycHyFCXPFRfRyXAevURmrL2JdzOJOT0UAuz+FvyFyyM2K+\nO85CxhACdB1zfyEC9+UM8+7TeZjTcu7KFRzNOPJ9FiFrasJWbM5WnEU1qP+0+v6XApvyUH6N+Gmp\ntkDb9/3A9vW+jcaZ2qcZUXisMF5SUB4//tL5StCNc+CAgJ3JKzkdux4u5C5ew6mICfxSc680/z2U\nO1CCPn/e/cP6vWEgMGiOfxhnWy4hWalo+0hAny24u75GmIz9OS54f8WRLODZ7v1/b/7Lt9me12LX\nnwXcUH+/WTyG0zmWcWTNm8bW3M3WnMafs/dL99+S47FxI3bhMn6ePX8fJH1Mqq9FPF7GEXrRttc8\nc/zKgRPCNixnG24jMaD5/f3/PQda3xXh8fb6LXfHV7AFK4BLB2/4dM5EtH5/QMZAfr+nsJikFYOt\nWIistT8m72/p+Qs4b5D3YAuWsgW/r7/XDSxgA1aaax7JI5Howb7+y+rrf2WeMQMY52F1XxArml8j\n4+HDiOVWXp/ncBa5i9E4C7icFLlfz98amM4rs3R1y1nI3byGU8184eHXv18gloQfHJz/bb7EPrXG\ndCErWcgZ5o3HeaiJkl9xHjK/7g78pW7PcwdU3JP5M0JrjJF/32eT5ptrBkbBqW6qAc3bR44nhm4n\nPoZYhSgDqueLpchuXA7cylF1qQQEui04f2tgrF5PEwO/GxcjsRKm0eyf8FTOxUa/9u29NXewteOv\nckzuf0QPNa95KQVMKx0YgguQDz4X+bBPQvx8fm4e9lLaA4BYeGf/tgh4d5DMwXaAwadVaBjthcSB\nIh4QlIGY3L6I2PcL2qPg2nOVqVeiY4U5D9o0w7la/iLgt8ExvV+bqa5NDfC7ejuN4Q7Li4yJhSJ6\nzu+CMvA+twKRvla8h4q9kfZv8xe6dyBh79ehPS+qhZpcxiYmOEZHcjZaqASuhJLpiy68mqbEP/uH\nrtwSe49DzIj8uStJY38Lxtm2/r+FOffEuk7eJNVjl0ZJVeyb6uetk6niZqS9vFmu1tP6vZxYuPeL\nzP4MZB7YAiWMkzmnMtRq/nMKYuJSMkG09WwzEdyYyTn7f79RUvHvpHRA6rsVPaM0rmP/N9VeLWFn\npM1zf7OKD9f+8tZf3L+71uMdpmx2cBxEuAficxb1jR+hvvPVIFLod8jXp5/Wx6P+6INLRIjMQL0p\nsjV9taaJk/muUIWC2NmID9QR5FFwfdRZ/TbaX6MAbjo+orZVE1wQ/ybRrojk+k/muTeT5z62iCKP\nKpH0ubpO33bHR8mhpyj5YH6ksG9hx4D2kcjUTcfAHMq+d6VcizoGLnDl1oc4QmlsrjT3nAtcS2WY\nlyYiH01rHqrf0pv9zRpsTxrM8bqO2SBEsq2yCN3fIU/94t/lN0wMnp6y84v9Xo8Cvkx3E1w145+I\ncsV/P427YOujY9WbaH4UCUxl5w2LkgkulPuhj5QOojn37mHAILBP7Ioh4/1RSMqpDREBWxSdOKJD\nltM0bdV6WmHzUix9VA0sJ9pwLiIMLgUsLUXl1ee+F/gBefqcyOTVr5F27EVtVqL3ceU6n/gxp7Tw\nqFFwI5PdNtrd8x/N40fe93xAz0YmpjOglp3B1xAH5ScjzNMTaYYFL8Gnc/hxeJZMtqeRJpc7kAXc\nRs/7lnnuKcE9/l9QBiJ1/4z535UB9YyF1s3LItTfoysDapkEe0yvtcyCrSPkE+Pl9XYnRGq+NaOh\nixO/1mkUh//VjU3pnuwcklDE+4AqHuz++8WyNFEoATGdeIFS4lKDt3wQkVCqY7/Wq405AvlG6gdt\nmehdWLcorAEGFgElTGQO8ZPp15Ecjz7XltbzByStZzNAmcAG65kJ3ErFUpJ/ri5YKuzyCcfXIk45\nM4qP68T7f5URkhF2ovyN2yLn2UVuBuKzLOZyl/IehPjxTJ0ygZbZX8+do2N+D1M2ywQZSXNclflQ\nRkySr+dJSC5kyxgrgeFzz0F7gAi1GIkirC6DTINr580SA/p7RHszWcwGzqmtSCxBZYObPJ40ztWn\nU32f1HccEsMUtcMDzH6UNknb8k4wcvKEpcSRsGXurLgE6Sd+vrNrVTfEkdtB3RVEyHdM4Rw7p6jv\n30rKDOibyAXflvDXOdczV9pWnjHQ60oMRtQOEQN6TnCeRRSEK/o20bgHmFW7W0Bqh1mkb6dba/Vy\nInnAtK7f1Ptfar/9ar319JQn6j3i4Dse1aQi7Pu5aSVqeZawAhE2RkFqtD0jBrQtHkopDUtJIGOD\nIr2s3ur6XRJ22O0bqTiqXh+jc307LCT5dyrGEBrXro+nk9NH0XziMSyORMR4iRBA8su+GPhQcNxC\nGVDbx2aRAr9NhgFVgUTebmku8wKMYQxxNF9F5yt8+YtJ8TLkPVaOOA/fi5goAwoivd0OmcRfikwO\nNyLa0K0Rp/Sbi1fn8AtPpEkDGXhzSATTW5HGt2aHVlI2yvstJSfwRtGA2nK7iGGOWVPFLgwokEmQ\nhgUo0rpALrHWMvXDembL9eNBWZfOq+d0jXa4JmBzJJBGSeoPeb45ZdxXADOdlDiCb7cDgzJIhPUM\nZAL32hIlLrVP7wdcRTVgnrXNlbj8PHkaAsWdpIXxDaSUF8fzCL49uE8zx2PUPlcU9odB398vBjr5\n3kIe9MILYdpgBUt2DtD2tJoeaI5vTcPShj8S5yBUrMqJ/pm0C8GisRcxpnshwquLOZmP1dc+cQL1\niQQBz0DiAlCoJ3Sr5wzkG1rGWPvuGE3thpXw+vdVwUTUT72my9bNBiHyhEtECP+NphCqDZbRtBpQ\nG/RnGintgWiaq0Ewpk3q/xqwZDlxBPYHIoJHkOiTXwNUPLoOicFdh7Tuvg04vmb0bTtY/IgksLUM\ntOIe8hRCk4F+lwdTFkAlQqwaMIJtDKg1M9Rn6DfQfvF+8ndXaxL/nfXdS+/bhQEtWWBYRASoBlW8\nENFG673tuTqnz2JR5sIzjVwDqkIwO76Wk8Z0VIfS/OzXDTUtVuYpn8ty4UP0njuD8e1fNfDPlbU+\n7z+bIt/fzx0RQ2/xGpoCVkVJA7qSOKiltdxQxl6viYTq0+vxPoxm0ftEc/SbXbkKJG2gz5XEAvr3\n0MSB9dZGcwVrXlyuj843HwU+F1iYvMzs6xjzCoD/IFkV2QjpijYBiVVUPRhZf6I1WANHdmE0LWNq\ny5+D8DltDKit29+Bv2RnPMe53a1BmAwDOhV4TL31KU/+7E+sMRfJmSkTasUxiGTYRlq1xGeUf60E\nH/rcf+yZdGNAlUDwDKhulxXuA3mn3IM8mmc0EP19dBD/iiZ0ohhNS1O5Z7Sn3fhnYkA3Q0zHPCFi\nYdtqX1e2Ie3w32YP4ra/uW5TnXi8pmYmQhxaqbSXwIFMqHr+yTRhJbN2wtXxovd5vrsuWky/afZv\noDuhqebvfjFIPmpNafMYYjakz7yUGDaytJ0DVpgySAzun8gxj1gDmlBxGVUWQMtjVVoA3MboUXDX\nJpeWgzBkXycl/34io2l5VdLu/fcgTwdTmuMi00a/6D4GmbvtfDzXnNtmXhfPiX4eE3itijdbvDso\ntwS7xR9G1L5YBtQSbW8mN3mMcDNJKyi+Q4LbSFoTOyeva/btWN0BCRYH0ldUSHpRXTcZj7FJ/WxS\nJO+H0Ywo/gcis/KJwbZ/yZ8oGgNLKTOgs8g1jtuT2kn7xZ3kGvISpiNpRrpoTBQ6lkdhQNVEdswI\nClWwcixp/tqXXJiu5z6TJKw/AAkMYxkmvT5dK+9k50Ur7Ify3OEttDT6bcyA5ojmsqY34dSjCwOq\nzJ/XgEYMvcXXaOZ+VpQ0oOcx3I1NhS0zEbr5fcE5M9y2DSUGdD3y9tF33M6dJ+1QZf3iBJra3FfU\nW68BfaU7r6QBfRQSe+UzNGHpXyv0tvexTLwNmvhKGPjZ2/5plWI2yrGOhygPsArAPK2zEIn4v2dQ\n7s9XHqmrCa5vz2GWcasVq5sB3Ql4On6Sr7IP/DV3zXrkknEvEVXi8zfEEuESrL8NxB97OAOaJHlK\nbOsxnSjOKtwH8sHvzeK6aEBVGv5+U6YJ3VWb6hPEW4nTopZ7q5bkgOCYSsP+mUxwN0M0oG1t2sZs\n+zyOpfOGlT0ZnVxksfcE+ja052nU+r+k3lofMkEV+qwoZnI025Mkp56giBhQ64e1HtLXh5mPQc5E\ndPGLUBOfu1EGsxpoOfzcZYm3p5AWFSW4vd+xZ5pLJrijaDUnogF97vBTAPFx9YuiuhqUTHD/GxtA\npIlFiLbcM06X1duf41EN2q3p+yvuEVq/0sJXMsH19d+ZtPhCCuyxEe2S/JJQLkJkRqeYRSIMu2hA\nR/32lgH9dxLhtR60Cjkgb2cr3V5J7v8PIihTLecYWvcljXtaBkgFPyVzQsjnmd2QoCaKu+ieNqcL\nbJ8p+Zb6PnQjMqeVGNDX0ow9oS471qS5C41UskBQWPpA77cuTRNcpRGsP5uFtvdMknBaAzaqGDvU\nSAAAIABJREFUWaKaalsCOzFv52aWIjsRM0zTENcdFVLZ/r6HOzcy4wQGkfEVPyXlw4R2BjRqy5MR\n96pViZIJrvd9hKbwSv2K28aM77vH1c8oaUCvpKyMUaibxEzgauIcuTPq8e7jUkTYgLj9tyRmQNdx\n59m5Q3ELTeZXzXPfCxm9GtXfu82tQBi4D1I1giBCziCWTF5LtOo1NLWwbbD0epf+80RSmknryqGR\njktzSVcN6Bxyi6YxfhKu1WsEVjcDOo+KXwyIyhwHI1KTq1z5dIZrKlWj01Wy/23yJNvQnYFom0hB\niYlqoBZvO98uln7i8T6gEZrJq1MuMDU3OLZxTjeo6WLkWK4M6Oga0Ir7UbXmfV1VUAa0rT2bxGuS\ncnufOD9xRvctTXp2kvJteDW0mlAoEatmKC+nKaWWRTGW0M9kOfeQzK82Cc7Jn5ePVw2O5Im5CCqt\nLPk/+Ml3DDE59udHY9v6X6VATMnPZa3s7KoR6MwS4J4g6IqJCGD+MvyUAfzcofUcRgC34R5y7TEk\nk7s2k7fHBWWW6O3CgH4SmedLDPRrzL6Or3cE51lE0vISIjM6RdMHtGrVcHRhQL0mxfp6Kr6CWPm0\nYW9SVFXLjEdteD+SZm8aZQbO9n/V5symbPLaFHQl6Fo1VfSFDaxWEir7OeLvRL5Ziak8hDioDyTm\nosSAHuz+l/qv4nNmf47ZWgZU03SAFQ9Ug7G4LQwisI8h89k1Zh7TuqpWxY7FFGjv7kaQIytQsf7F\nM0nf1/qCe4soff5ltKHiAuAtJEuiUTWgJcHPVMI/V4P0lBhQO5beD/wnZQa0ZME2RpkBtUExS5Za\nes8nkLePtwjqip8E9fTPglTn+e4c7Yf2mTfSDP6pAiu/juscZBVP67pzNOrrN4nh57jIB7T0jspA\n+/75qcL5ep/nEWuD/dywNjFUcFiaS9riPFh83f2/z/qATgVKCxjIx5tNs9F9ZE7fkZQBfTLaISqn\nQaoyAv0yxIk514DGxHqJAW1bfLzPmO/YVhNjJ6k/kvty7kZzEPv6tJlYqERSfQoijdV4y/VarwOD\nYw9w54yCm2BoAJZVAWVA275dmwZdGfIXIn4AXpPVtf9A/t395KmEyfHhfapMawFwJs0+Z4kbj5k8\nM/PX8dIyrxX0jMXOiADnta48ipSsZlgb0GRAQZh6Pyf9f/bOO9x2qsz/n3MLlw7SexEEUVSwMNi3\nY++jYu+O2EexMdYxOj/LqCPWATtYGMqIvaK47aiIKF3Ee1G6dESk3fz+WHl33qysN1nZO3tnn3Pz\nfZ7zJHtlZWWdZJW3v7cjzID69fT/91XyyKuCOmGU1oD+QZU3IXrGWeirzX6L8NcOeSeWdPRkcguI\n0DOH5AnFY7Cm5voqJD9ZnHbjT+TfMrR2vF6dCwG2PXl/Q9Jv/7nHkTNrPnwthu5DyAT337AJ4Rjh\nw8nqXDOgb1HlvsTcgvhVaXOz0Jw6g5zoydcZpxNLyIN07Uu+HwthXBVsaX3y/n9h1B+ngZP/oS3C\nRzPBlkbIMt/3CX/JIfh3KORDhNxvUfpvMaA+3VJtnZRkOYwdhDH8IUUGVPvaaoZkt6yNs9UzllE2\n2RW/OMkRqWkkMX+8kruTeL3Lx1tSYCp2oJmg5S3eb20aKUFiHqfKmjKgluBnUmjhbhVTIpD92bIo\nstaHKhcqywR3M3J66wuqzgu8+n7fIE9T4iIdFw2YrSCfoTY19HsQ2nQjr86uOPNrPf7+ofoiv8M0\nX+7j/jFVWrbEga9Qztgg0OPkYbi4NCFT1RBkzvvjU/Yf371N2tkQeLp3LcWtoft4ZSFoN6RQ37an\nGEBUXGASivOqzAMcFAzA1wYmNu/tmgG1onyB8zW6HeWw6/ehOLgtDSjkASh8Pyhtiy8DUxyGfWin\nbsv/qmqjvcT77Q9szVTqwecT2ZJmouqjVxGSYsYg7Z9sVTSwW3YMpUEQ/8OrAtfmFcKAXlBRp8r8\nRcbYJZnvl78Yyjf+miqz/Fj0ZuYvPocC9ycZ5Rq1IBESL6e8uG9IWWMrkPki/R2oa28NCGL88Scm\nO/6m+3LK/qTiP7iC8pxZwDGR/hyUDcEnLvVYP88zPbJCnAv8dWeBIkG3r7rWhAEdRwMaYqKqEJLi\nWgyoJY1/PzlReBvld34pTqLvm7z539PHKsKh/TVCgTss6a4WCMp3+B7V/uzFsoRbSMy0Ub4GVAv8\nQkGINmcyDaiuoxnQn5JrtyzC1oLWaFluI7cz6oiPJDjC8QnqngWKmkcfWgN6K7nlhJhttumOoQVq\nfrsiaAoF+whpQLfB+T76aTFuJA/oI99gW8pajVAfYtxjBMJMv4ciA6rnqhvrCf9EkQbQ8SR8BnQ3\nHIGqfQIFYmWR5zfNsRDouzAVy1WdOnzb+60D4b09O2qhd1MT3CrT1kmg6TPLhztGAyqw1oeQkC3k\nrqAZ0J+TW4FY2jtdrvsjprEhK4ZDqEbo/R9RKM+FFbJ/aSb6RWg6MRkJ6zfNrEiqBOJyj6a9QsqS\nqgCneoxLtPuQEDsEiwEV/BAnDHiN+m1hA1y6ymeoMmtd1L6qob69K2tL8Huc66IfQCk0v+cWXTOg\noWA5gsdVXNOLq6UB1fAluY9S58JAbkLYBzIkVdaoM8ENEcIxmrC1wPYko2A3Ul4V8CXGyVz+H1mg\n9lfXBhX3VaVvkbZiwm7PC3bGMaAfHvN+n9n3varkG39elT3IaEv7mzmiLxkxa48O1A+NH9k8tWZC\n4Ec+1vllV3IM+5OvBaIh/xV2zk3BFTiitcwAJZxGwnFefTEz2ZnyvBKzFz8C9p6B9t0mnQc68CMF\nhhZxvdb5646LwhcOtuJL9qtQtQ6EAkOBC+L0EeOaD38zkXdiMXB3p1oaP8jO/ZQ0K3GEhS9s2k2d\n+1J0nZ8ZbIFHyBzS2nRDpmPrka9zsX76FnwN6B/IiUdN8EogEPFtauID+j9GHc2A3pHcDFcHcoqB\nFiSG9qqryC0P7jaq41arSyjuGTJfRXtYlY9bM6DPI4+q7cZAUmCuJoUWbPrfXLT5/hzYB2eZYqWV\neD5F074Tyf9fuccJh8u5Z32z+Z2pjwkgeFfWpj+G9HgQgcGeFPcZYTjFBFcLrw7C+RFLX7XrUh7d\n9zMjXzN9zR+7IqwWM079Hct0gAuK5Aufb1DX5bt8hdy6pIoOCpVPywQ373diukM0YUCrTHBDQY6g\n+B01A/o3cs2bFo5pCyiLARUsAFsUqJMkmNdXw58v38cJPPw0QGvJc+Lqew4grKg4luL6EAv/u78R\n23we7EB3MSa4m+J8KK3x+RcSLiMZCausjB0aOte89VyZY9Ze/kaKQh5Zow/xysvmx8dzz4g+doKu\nGdBxFxQ/gqVGiAEVUxshqDWjtDNuciWE/d+01EcPSAkU8A6qA4n4kh5/YGvHcJ8BhZyQuzHrg5YG\n+RPkWxX9EEj7T4uoqxFaUHzo/liM3TgJyttFwiqcNuNyEs6tqFk1zrS/FJTNE6W8ysxcENpchfj+\nPtWpYgRfJmFjnDO7Tzj+xvut+7SSW7lVlcl4tQhQTYz8HadRqPOBEmiJb4hYDmnyHkF489B+Jv78\nC/Vne2UCVEVYQ9F/5mdmrTKaa30SbiXhlZG1rU3Rj8Cnx22IGNqUvK9+tEpw4/FvFBnKCygGMNNE\n07VZO7q+lfxamyOJJrtKA+qH2H8/OYH2rsA9TQibkA+o1kjJOJH/dTuKjEJVSh6BzoGq+7YP+f+8\nsyqv0oCGLEwuIbeyCL3Dn5P7/e9Occ/X2u8/klv73BdntvYUijhHnVs+oHoMtEVfhMzOBaIJCVlV\n/CdhBlT+Z23irS2O/HgLviDkOxQDBX2PsnuMBT8dW2i86Xf45NFZMrLcWIYzqw6Z76/CrffabFFy\nb67I1nqNkAvRRtnzZC+oslSz4JtmgnunwsA21YBOywTXSo2iob/HJAyo/3+JIsR3A5D1TWsKtQ+i\nH0hSEKKnlxHHJGmEGOXnULYk/Av53NBmnqsI04sPpxzlNga+3/F7SCqVHaHx449z61wEgeHxmfC/\nXkndnn8FRSsVa8wLPekLY2WOX0eRxlxLHqFZCxRO8NrtfUBNhCN2xeCH5CYeobyJVrvyIT7klW+E\nHSHMkliI6WYoh1DomYLNvDLNUIYYUNmgfkAodmERH625jmo/lC9qWHHfrhFt6//LCil/VLA0GeWq\nmwV2wJnO1plN6YVDR2r+K2UG1F+IpNxKnK6hN1ed7xAcIehvaKEF5RbcRrMRZQbrUd5vff9KnsVP\nlT+FaMRjTJ52wRE3sUFwNIHnv69QoABw/09o8xBf8OtJSmbuoc3+xeSbZV1ftfauCVNZtdC3oQ3y\nN0XRfv2T176WuIbWwldmf0Mo+KcJRAN6EMnIuqHKN/Eq3JhbZVzX2Nf7vQW2CfFK3FrvBz5bDqwm\n4X2Be2I2WzGJ2piieaqYbUJx/Mv42oYiA6rXBGuc6O/i901iE2j/bouwvYByBFIoziV3TCpdVJ4N\n/C5zBngOud+SZiglRoK/tw3V+ebUM6DTMP/y/x9ZX0Pj8x6E1ybpl845rP09qwL+3UxRUNEUL1fn\nlg/oLeq6bwZ9Q3bP2yDolrEBcJ0Slp9ILgRZwYtGpt6COgsuHfkawnkpQ7CYIcH3sS2CmrgTTIY8\nimrVO3irOpe++cIrWROa+IAK/FzEmgEV/0l9b4h289sRODN0y/knDL+fOwdruXe2AifweJgq/yq2\nwsK39qkXbjTTlkJ4XXoFRR9NPca0kkhoBH9eWCa/uk5IkOyv59Y+cWR2rAo2pMt3xlku+RYz+ft0\nyqp9eIopsOgcXWtAx8Uq8kn6G4oSnhADKv/nL3GT1988bsKZlIWYBUs7UeU7qOFPnmd6Zceqc71I\nhXzk6jRhMZvibbjok77DdB3+XF+l9JwQ3k3RjPh03Lt/Sbj6VCD+n9Uo5vUbqvO15Jv/MlWGVydU\nHkLZ/6eImI1X/z++Vutg73eRAQ0/09rwQ2PMYiB8iObBZzS1CW7M4rs5jqiy+vg5iowF2f2yKTbJ\n19iEAfU1JV8L1rLha6p9+JuiDpakiTm9rutUJhpi4n0lZT/UlapMTMCqTENvxq2hfrTCELTk+iXA\nY3DCgVBeUTc2k5IPUFM/SR8SqOXzFJm6sE9eLpzZEJfqIuSv7a/XPqHv17kCRtYXa8il21WMfkio\ntzd5pEwZq1UM6AYk7JdZA+i54+clhfJ41gz3PpQjYPrPnj4DmvDl7GzT0jWHEEEn0YAPU2VOqJWw\nAif0shjQH5ALwMaFzAE93jYlHITIZzY2wq3vljD6vRTXgn0o+t6Xx24zAj92PQztKVtnz6uLyhp6\nxptp5g7RBI+knNbLf7ZAByHS/6MIXixN7TgM6AbEWVCF2hGIH3QT+P30U+oI0qztG5VP6Mm4/TfG\nYg7yoHVtQlud6JRW2hVPjzG9roqAyN9rh8aztKVbSDizOXEM6FrydSU0TnyaSeibKgbXF/bOHRYD\nAxoy5fwM2jSlqPEJEdTLccTdG3HaL19TeC3uoz+89KTE1Ixa4el9hDZhPZA04xDSgMo3iiG6TqPo\n/B/CWhLeQBIkkAYV9/mpQF4fqKP/rzVGO7dSnPAb4b7xU0iiCNg2EMeAOpyYHTWxvxZHNGuEAwzU\na1mhyEgJUarnpm/GHSIYXqjOf+Fd8zVTRQb08JJvh98nLWzxrQcg3gRXb96WCa4V6MvSkIXznhUl\nsuBM5GRTDGnOLDQhznw/sKY+0XXaBSsKrlwTPEedV5kGDvCj4CYjgsVndnzGSBM1t1Bv1izQ9YRx\nsphk6/v6m7SWqk/C9FgaKYG/P2ipuG8m9sBA+6Kh3JHieu5r3+qia15HLsR7F4x8fLTVjKRnSSma\nasp7G1A0qYthQM/yfv+BMtrXgCbmmPcRWmutgGTXUBzjYoL7CPVbzEX1vH6PqqsjezbB3bKjHm8v\nJCd+JUDYVoT/3zvhTKuPCFzzheg7kcch2IQPjObavwJfxF7frFyksVZrVd+pju4M3Xs0RcuO9pBU\nxiKBooKjzgT3hRC05qqyEqrXgBZhfbNQf1YAy2tt54qIDaglGtCQX38sA1qX9q05koJrxJ5GLZ2O\n8BNGHe1r+xfKOU91nauwx7x+P1bQIisegriZWFF8Q++/iGN7H9BJ8IGa636wA0sDeqvayFxgnzw9\ny4U099+q8h2sg6/9kY1ML2r+InMUZS2BT/RcTVKr2Rw3OqFPkK4J1NH9sfxRNbEFjgE9HydZDmlB\npoEmDKhs6DpAgTbVk+9nmeBW4UicKdVjyCV1Yu6jzd+qI8Y56AVfCOff4UzLfQZU93Ult9T6suSM\nUcJrA3VjTXBlTOxOeRw20YCG+hiD9XB+v7EbLMZzLfgMSlPitG499hnQ1Lsm0LnG6tr0o+CKBP8B\nXj2fMXq2Or81MP6tCNJa2FH3bn0zN90X/Q3ztTjOrUPemx8YKqwBtaGFo6H5/m2KvnhS5y6UGVAh\nOi3CVu9rb8MR5D50UCqnLXJ7nnZT0QGn9JqhGVB5tztRxOnkjO/fYZQyQWPWJrgaoXm9GbkGUCAE\nsv6+mgCUts5T9cH972dndXfJfO5D+XBj4dMuYj4nlk4fImzWLrnQQ2P9LMq5ACHJBBK3jt6faK+q\nNDIhxOaUDN0vApsmDKisI4+gmPN2ltDjSubmgbgoyT42gGBe86p3ref7zuTj9Q7EWZUIQuPhICbX\ngFpIcdrR/byyJgxok724TejI6CFBcXntSoIR66XeDtjfV78fnaEgTw+W8ya+cP43OCWIFUTJ35fL\nbhjLo4KTdoLFwIDWRZeLYUD9yS/mpOILlNKcMbM0o6H+VZXpwCx1JriTo9rcZlhxzZfElReOOEm1\nZt7AbSo3AIcDLxlTotwUEgFXcK1VkXzxXqPKNBNtvc8YBvRS3OLxhlFJntfzRPIorzFjQW8yQkh+\nHyfJ9jcDTdSs5JVBqVwT5i7WBFfm5SPItQCQz+EqDWiofBwG1KpvSfybrAs+QWKtr+JbfJhX7mtt\nQ7AYUKuO5bt9BeE8oKJp+xevfpVpqGX6WAfZOA/P/nw8iLCfmL/pviziWRryfo71nqvfmw4KY0FL\nxMvvIOFRJAX/G8lt5wsbdQCcMgPq/HC1ad9hhF1D/LgBVRgijKljTkIaUB9u7XZrdCjaNhQDQ0k/\n3hqoNy6q1tVQv4+jnErFf/9yr6RfALiNZOQjKmWyzsk82JZyYJYm8GkXEbbr/yMk6JZozKG1TH9H\nDUeEHjrKbS1CjyaBgCA+oE3oW4gmPpTaRkP36cU4hnwLwlZXs4B+F+er85Av5hfIo2ZrVJngasZ6\nD+AY5XvfRNERan99YGFCH1ALKS4diR8zQcZnDP67vko0HllfZQRtPRDyQ633jXbQ+5y1bur9Sd7t\n2YT9Srem+P73BR5KWTgva9cBFPd4MT/OBUUHmfFYOsdiYEDrFnh/kFgaUPmox5Gb4LmPlIzFgE4i\nuQkRkRINUtoVrVfdYj0NSJ+qIsSF0oNoVBExzuwiGYWTvwFnmrAK2xyvORJ2JRkF1NDwNaBV3355\n1pafi07C2Vt5HGMWL1msLXNuWcT9aJShtvX/KQTIa3E5Wn2zrB3VeYwPaN3/4qfxsKCJLa0J00KY\n0LfYgrDkvWlkxKr62r/tOnXeZF2oSv2iIYnEQ1Fcq+AzFvq7+NoqgR/ARyCEpCO8k5HUXsaDb5ZW\n5QIQWgv9vIBV960iHOH1FsK+bj4z3NR0X/7X9QrtiAAtqUy3otF0D5A1x2eAfA2o364E3Irtjz/u\n/EB9Ahk/Lop0rsW21jRZr1ZS9IW1IOO1CQF0BEV3AqvNEELfw5f+r8UxCb7VxjOAJ43qF4Wpy3B5\nmLdgfAuiEHwGVKfBEZxNnus074+9bltmmyuBq9U3HlcDGuuSUFYc5GlO6vZ4P+CcNk/vAnl/koKb\nRIyv5w+Mco0QjStrVIjht75BaDzECuf8iNAxCI2RO+P6HsuAtokmzLoenycHrscyoDEC4RCTas29\n3bxyiRrsC+Fl7X0yxSwAYj3xYFW2JXOKxcCAXllfpZEG9FpVXxO1fqTQOsRuRCEf1tBAPQC9SOVO\n3ZJ89nKKUVmtdibBIDsuZH34mLrm+6f5+cR8WO9Hh2dfhTPfE/PoI4CXRvU0DsdTlFgKdqIYiMYP\nWKNxXaBsF3Ue8oOC+MVrGbamJdbcyYe/MT7W++0CdySZydnbg2ZkTRjQu+CItzqE0gYIRAMaGjcv\nxpkd+miaG+5dEBRIgB1QJuY7yibvL/QWsSxaiBDTVQV/U9Tf2ep/yGcXnIBroAhtkaLKd/dzxVUF\nIQpF2ftPo66GfOsXEP7ufyBMHPjEnNz7mkDdEGQN2tB4rjCgoXlp5aRsshZvRpHR9FOAhFJlbEDd\nWLesW8JmyQPy/vuphj5lPEGYAZ/hP1Gd67bkf9L7SDUSXkrCp6PrFxFDOH8JxxD7BOAyXL7kkMD3\nZuBF6hmaoJxkD/YZ0IeockFIo1nFgD4eFxXbh9QfZL/H1YDG0j1VZn+hFC2QaxQtBrQrNLX80PXF\nBH+TinZCpuxCBzYZX6H12fkx1/uA6nzFO5q1igj1bQtcjs7nBK5NFwmrAxZ0IcsaKAaJDP0f42hA\nY6whpM09CY+fp1F8/xIAytKAWnjM6OyIaHezmaPriR2DupcX6wMqk/9gdF6sHDo/0TOoR5yUKAma\nd+qBLdc3ICwlkyhhXwX+I+qZzRAyFykjKfl8WpqVOuhvtRFF6fhRwKNJonOq1cGZlialIDuWD2go\nCrLlNE7Wdkhb8JmAT1wIb8YFnLA0OCJF9JmIaolWvR+cOOA7H7vwMtvUvLXePycxIynXmeBaaNpH\nX5OsoRd0bVIU0x8/95ZAr6963Atx4Y+R39Y8pyo0fHhDSjjEaMvXkki7sn76TIBlgvsXmgV10jhV\nnYfa3og4Iq+pVqpKK6F9mPx5dBHF9SAUFKIKUv8OuGBwmgiq8gGVcRQjbLmG8JgNBbjSGlNtgm7N\nKWEGbqUomHPzypny/og8aJkIMmMDVE2KmHHwZODV2H7rvhn9dRQFZ2vJ1+VJBcCadllNzoDVMaCi\nhdbjQXK03odwuh7f+kPGXFMNaKwwoSpH7lrgs5Q13am6LtgIl06nSzRxs/HXFBE27oG9RoQ01uvR\nPPOAXjeON+pYAkn9vzzUqPM977f1XjYGnmpc8/HGyHrjwlrLJE6CT3OIwiuWAdX7eig+BhQFtL45\nfwihOC5+P62MGNqqyFnzXFrpYtYpumJAYxJ4C+o29nF8QAXaTECr5GM2snZMcHMi5OFYZhoJu+EY\nZ0tyOAk0QTXMjk1THPhRV8F+P3pR1YmWIeEqHKP9/IbPt5DiNnXtY7kSF10wZPdfDuyRcNYYfqlN\nE3cfRZ4HSmOb7GjlsYvFrwlLz9bDEbzDQu2kRODEmhOPixfh/MQsE1wLTU1wLW01Fc9tKgXVOAyX\ndqkIFwRpl1J5Mdx/CMVNsSgdb0qsnEbxuwsz695poojuZCQcsMLDj/vtdTTyUBsrjXJfSyjmx7EM\ngY7GHJpbuxIOrLQjxb0lFBSiClJ/Pcpr7O0yn+8QAyrfRO6v+j83J7yvi3BDiN2hamd9iibcVUyJ\n+EmGhKg74d7DZ7Lfs/Dn14jdkw/Epgl8k8VNKebiXEvOED6A3FTyyMhna2jaJQ/MVpzXISFbSpnO\neRNOmPwJwlZEUn+o2qjSgFprmp8SKYyk0rrjOtwe7/unhxhQixmaJUJrNdgB8/Q4FCumTYz6EB63\nVT7oliDKj6abQ3xALYFkUgh0eJLRvh9Yrmrs/Mi45uO8+ioTQdI0XUMxsJ4W4uv/Q+ZzLAOqFRkW\nP6Xde/Sa2GTf9GkjKz2PfHetgR42eE4TTLy+d8WAvpT4PJShyXkSuQ9itQ9oNfFkmTjGDIxJ8tCF\nBvZ+2H4C4otZ53fZFMsoJwYG+H8R92oJW4gJ0O9Qh++XxMqWmdvhwIupzssVixU4bfd9SUamDNsD\nlxmRUNsimL5cX6WAiwibCvtRKQXWu/ktkATKv4rL/SqQ9j5otCP5F2Wc/p9RT2MSBlRCsU+qAa0z\nNKoyC5uEAQ2Pm4RLSEZCjQXvWigX6RqqtVxVm+IXK3tYho4ufgE5wRnS/DliPWwOVuXbVAdtxv+O\nwPVnUPRlEfiMo3zXphopiwnZGZv40+t+WatdDanjM6Ayn29HmCn2x/lzAUjM8fyQQJmYnv9Alcn/\n/kKK5mN1AeSKxFA+LlZSfKehIC2Twv7GcVYngrVG/ZDPnSa8dRCi/ciF2H5qtyqIz3wKrK8EnKGg\nJJtRXu8PJ8yY6pgKPjaCQiiaOg3oXYzyNgKaWMGphCkI0VZ/p5jHcZawIh3rsSj7t78eiqZtQ0Jp\n/sAysw2lgZL6ltDVZkAdYi1VrHzUIWuREJYRa1kXYxw8GS7EreUXUpxfX1Ln+v8QU+Q4BrS4hlhz\nyXIpaLJeWVFwoUiLSx1N780tumFAE35EUpuvUhCaSCnFyVmlAX0ktvmYZkC11ipmYFhRM2MQGtiy\neYf6KREpN/PKJzMBSki9xW+QHa0gFBqa4L0gcF2/w1zLmj9vY8KL7K9wEtI2JJ8rsrY+AhyalTVJ\nwTIeklLO1DpY/nXiPxzyCQthf+AlgXJfqyLn1wGXUM7/WiRuEt5vPE+jjeAcTTWgPrNkBVsRVPnU\njs+AJiV/yXGxlnq/X6s/Tf0R15J/d+3XFtIqr4e9V6zEXqPrUBccYVvK6WDAXifj1sN8DbL2ha2x\nrU30PFqjzmMCYMg3WkFx3Er5KoprwfmqXH8T8Vm33t/GgbI9s6P8vwPy3M4+wVk1xqpM5Zejhb0J\nF45hPTIrWAKDEFO/nHzNXUtOK2gt4Zox+uBrxEP77gco+9OtokzniDbVioJ7cHYcqPpVGlAfu2XH\nSQTvAmtMSDA+nbZij+x4AeF4DLOA5SITWnOr3APOaPDMkJVEHXT94hru2LzYuRjr82pWBvwVAAAg\nAElEQVTtmZImqB5JwQ1jGtiafF3V/c2D7BVpYL0WNqWvrfemy9vSgOrzJweedRq54mDQ4Dkzxfz7\ngIalQ9rsq84E90Dchh1aRPQkeZ46r5dmN3MO9/HEQNl62ISVLMKzSigbs/Cdo86PClyvez93I7RZ\n5sGIQoxUUwjj9THgcVnb/4bNgHZFLFmEsPi++Ne2M9o5D21unCOhKG3W6R8s/5MmwX2gnYBYTc05\nfS2AXs9CQoBxGNC9jfKmiBlbug8hTUNVG2dWXKvD7XGpcSD87e8YvCthbxwTFHp3MeNBp515rzq3\n/JcE1jrZNNrfoYT7bvnyQHGf0ARxXZ81Xk1RmKh9MbWwSEzh/HEu79YaD1Waft3nb+OY3EvJfdWq\nsBanKd2C8PddQbVWrQ00JcpDAV5C7VwE/Bg7IrlAWwJoV5Em659YP+jvaO0B1jr8XPLc0dJWVSC3\nr3u/6zSgRSQjIXMb39YPeuVDE9Tid2i9n1nA8kENMQRVDKjvAnJaxTOPp5iqLAZ6TFetYXWw6Av/\n/VvR78WVQOPEUEUP762v0hjSD98K8geBupALl8ahBWMY9yuMcgvy3orz2uY/ZG1Zn+Y03Mwx/wxo\nGD4DquEzoMtxUsT7FmolbETuYwfjSyagSZQ/h1AC4yoNqGhYXt7wOU0xzI4xJqTaF+0kyj4jIYJJ\nYxlOshsy9TkaeCCJmVoiFi7ITsLV5H5JT4Og+aPVz1nA+u7yTuVaneb2XIqJji1Ie6/FCQCG3vWm\nwX3awsOxv8GaQNnXKM517Wz/pkD9qu9rXdvDKJ8GdB9CJky2WVB9Sgwft1L87mKKH/Kht9oWhi/k\nt+5rc0JrpM5Hu0adv914nsC3GJAxf3Cgbh1C824bYjRaRfOrGOLY8g33zYnlf5NvvYriN5E10yKS\nqkwV350dh7h9bg+cxlQLAq0UCvL/bk94j/RTibWP5tYGPySPYKvh9/GzVDPhYsLsB8iRb2T5ZIUg\n33KZOqaGSfC7CbtVbEwx2JAwoNYacRvuXQyz32txY81a90JBTs5jPE2vhjZntvJ6bq3ONWPXhvZ1\nHGxvlOt3JxYG/vjXffbf9ZqKZ8a4vfjIn5V4dIAzvo6Nuv48o9wfn6uCtcIMaIz7WIzlXVPIO9mb\nuKBxa7LjSprTg/UBvZKCif8kJrhh5IzpNuTrzLDBc2aKpcCAQrUGdDnO9v1bXhtfouiHqv1VmjKg\nbThSixlcaGKIf8kRXvl0GCYXrdRaXKx71nq/byaP/hXq55ZYaRMc8Xo04xGUGtrU7TBVPi0T3FDK\nHQ3LbNv67hINWBZRyy+nrh0f+ntYGtCmDGgbY/EB2HMvpGWH4lzX4+lGvyJuHbA0hdZzm/pWWoiR\nqOroz7o/klMzNjDCT7LjJRV1rHES+vbXEk6HpTXpdRAGR2vgdGRWy7dSQwRjvjZEzOvHyT0X+u7b\nY0uPrTkes2dYBKAQXsIUyP8mJrgbUvwmsjZb+/fWRjkU57tYBd3Fa19bC+nI0alxLrgMO95CVwiZ\npJ5MmJmpmqObZEf//xYNax0DquNS+MLz7QPP/iJuXIc0fxfj9sd3qzLNgIbGou+GISa41rgt+5Em\n7EUS9FNtAjHjvpLymi7rgQ6CI+8lFJyra+h3p9Mo6e+lNV7+2LGCG4ELHPglr+wAqrMPVL2f3YD/\nrrgeg9h5LSa45yJ+p/WR+acF3Wft527NdfmmD2UaDGhcuYZoQDetqP+BQNl/0WtAp4YYE1wJsLIH\nbnP1g7z4Edia+oBqtEF8n4vNQAyBpkEWxsFgdGY7uscjCeYjE1ipKwQuIXkydi5M0JtWUhAwWAzo\npN+x7vscZ5TXMY7yLeqYmFjiz2dAB971jQlLfasirbYlDLHasf4vvelq6WLIhPXvwM+NdqxvF2Jk\npwVrPZa+xTKgn8yOdSHuB4GyPSmbVq2EYCh3efcx2h/pt94UfxxoC+wInNer63o8yDcaJ+F2aFxV\nmS81iWTpo+7b3URR0yMM9xMJC4Ss9aAq7Yn0YUDOsN+RItOq+xkyMwylxxGrkmmb4DaFH+juYzii\nzifWfTrCh6SU8b/hjll53f/sC8Ahf0+PCVx7Js4kMaT5u5SyEH4HnJbJ0pTIXjhQz67yAZ2WNdB+\nwEFU73k6crj0Y5eK+l1BjxcR0N2bYj813eG/U8sSC1zskvsUShJ+TVIZhdhiQC8hYfcWaLpYAd/7\ncePtcyQj4aCGZeY+jTFnrYVVwllw+YDb8gGtY3arIJYIT6rojxW1WPawQcRzOsHSYkBdGPstcC9e\npGs7Uh3S+s/ZvTqAyaQM6Dsb3g/ufwotyp/KroVSnXSJJjby/2qUPw2LqUk4AyddfmyzbhXgS00l\nUIjFgE5KOH2KavNBS4tnbcayYcRGWIwl/vR4Dc2LtxBmAqratsyUmqIpgW8xCqFIkBtWtKPfSe6v\nkjQyratCzHyxzLWkz7GR+cRMcRzm+dOUXQT8oDka19Asz6Ofi1AQw4AK/MBHk2hGrPHWlAGtn3cW\nk+KYmqtx31avWbJO+RpQCUJk7d+/AI4F/j1wLcRcPo1ipE8r2IXcq01P9bVlTE8DetmY992bsjtI\nSKsYx4CWEesWE9IoSr9CDKgg1Ne7U2ZAD8yObWlAp+mOsgfhceLmfVJQBnxHncfO80sIu2A0xbDm\nut7zhH7cgPigM1Xj7fnE76nyraz301Ye3rp1GRyNtRPi/hTGLDVzOjhc/l3imPG2NKB1gmUfX1Dn\nInBYUVHfysFeZzHXORYrA6oXTj1IJLDDLSQjB+/1gH+mGIJcI5STdFIGNGSuVocfEmZEXoi9qbe9\nSQwb1G0Skc6aIMuwQ36DCzcfymkWC2sRtBjQqr6EcEDhV8KZJEF/Hblu5aF8MS44ko+rsvtEm1fH\nDFmM7DFI6gYH7Wuhc8O9G6fpCvkoQ/V4+3bFtSbQz1ijzq3N7LlGeWgOr0fx/TxbnWti4GlW5yZA\nUwZU45uqjdg5vx9lEy4fw8i2rHn0fvK8aXUQNwW98etvoX30rfX3s9nR/46TMDzWvVYU3KYa+lgI\nA6QZBXlXB1IMSPKQ7GgFG7oaR3SGokJL/4fEMR/a11/OQ1o2Ma2clgb0GIoJ3WOxA0UC/GG49D7j\naUDHx8uxk8dXEcOW76PPgOpAVlUa0GH2ey0uuI5lrj0tBnQN8DnC4yQ0nvWeF8eAJuxAUjBPHhdi\nofE147qe89a6pmls/522FfRQ3qP1fnajHT/AmDVub+BZVAstrfXBj/bcBnSfYzS42lKy6Ryw3n9T\noeUJgTrbVvTnGUa57KlD43rnWKwMqM+Q6RDpUFwMVuIiPD7JaCv0Uf2B8b1AHcGFgTbGWbwfjM1A\nTJLsfTpIak0YNCyH+qOBJ1Tc9yXgbiRmtLU6+IugRDQN9z0JCiNsJPx6rF6FsU+g7EryBPJ+sugQ\nHoQtNdU+SKtVuV40T8UF4lhjtFE1Bn9bca0JNFO/mzpv6osZ8rf1g6foeaq19G1rcM4nLjF3TGqI\nuLUl4XeUc92+LureMk6k6J8qeFCDNoQJ0H73ejzrtn5ntCEMla8BnYRYtsb0nkb5JP48VdAMqKQx\nkf/RN9XTjGAIj8feS6zxr1EXqTEkjRcN6HSCECUcUuPSYWE5RXpgL5z7TZUG9DDKiNH+2HC+k1Ya\nDslTGIIV/dXKC2gJqUIa0C6wG85nLTROQgyLZSUxC4j2yYrdoPt/tFGu4b/z0Py1hNRVkHc0zvyI\ngTDgTea17bNrR3Adl86rgh4/oT2siCRqfbTwLqPcasfaM7SCQO6t0oDOa6qrWixmBjTkA3pnwI8I\nafkFiUNyDAP6hUAdwU6BNsYhRp5B9wzoYErtjmdy4Uz7PovTEI6D8CLYnUN8U9wZl9uzCfYLlD2N\natPWQXZ+V5yg5nuE/VWrxmBbUXMfZZTHRPfNtfLhTW5z7AARGvJ/+qkL6hAWECTsafjC+NC+Mbr/\nsoZNutH4qUIGkfdti60Vj0UokvM31Hm+F9kEimzMfo68SYhpi6iytG3+HDi5pp1YrMUxRltTXrPW\nUhRg6MigITya+lypA+z35ueX9HFzoFw0oPMWhAjCJoghDagg5D8+afAdsOfvE7DX1u0J02lWXsA6\nE9yBV9/CNBnUvXH0lz9Ofhqo2zTSdJuosyDRfdM+vnl2hVxofD1xGlBtfhx6HyFIO6Foz4JBZFtV\naMIv7EA57dkrWuhDU9QFTou9NwahOAnQ3K1Ix0bQcS2s/mxmlAsGNdc7wzwzoFV57dYnbIKbBOru\nmB3FJ0Du2837reGXfSdQp6r+uIt3VToO/YwuUmR0hY8DzyWpzc8WwrxEzqsK3iPQObCaala1aZee\n0/p/DzGmfp2nZsfQe/t/2Z+FaY9JTRSH0wQltWHmfeLMsmyQOk39j49tWN+Hzr+m1xAxC2pighuC\nHhuTMLOfHuMe0V5aBKUlFNJMvUTJ9E1wL2B8WKZ6VkoAf61/l1HeFCn5HuXPvcd5ZdK3EKMkiNFE\n7hDRr9D/dQW2D+i8BSGCsOtC6N3InAgJPNvQMryg4pr1rZ5EWCM7rgmuwMpvLFq/WWhI/f/5/YE6\n3Y2lXBBm0X+6/zoTwp28ehcQnhehMSWCpm/RLPYDjBcFXMMSyN4rO76lQVtPpBx1t46O9jH5ty8K\nM5u2Z1lNWrAyRzS1mtG+qjH9t2jcLhj+RphnBvR52P5dvjpaJnLVhit5iPwEtKGFtqmkzW/j98Fa\n9bCIhk0o+rDKQjNrH9BxclPBJP1M+BPON/OgMe6eFwZ0f2yzQnDS9ZPV76bmrJY/nCbq72ncq/2C\n5N7ye0t4Kwmf8+7VDF/T9/zChvV1+2c1vFd85oraGVvTNu54nTQd07lGuUhBJ2VAfbPV4ZjtjBPc\nSPqtg7HEmNfp1DrS//dR3BuqhJV1sIi2WDO6U2rqx2IbXB5cCL8LXSZMs+x7IcFLHQM6pPi/h1Ni\nhX0Ed6RMDM2zBjSGAdWWVJ+lDItWatuPLxY+AxqrAR1mvz8YqAPOfw9mw4AW+5nwU8rjTX+nUGC5\nWeB6o1z3Tf8vx3j1RDgTY4IrWJ/43LLviagzjKhjmajWBUP6r4i2IT4PqaBtoXbTdel+9VUKWN8o\nb2qC25QuqQvQODSud45JGNDNcQzJ2TiC8J9wEWhPxNmxf4/4ABVlJJwSIHgFe5Gr9/XGUUUEi0/P\nkV55jAb0WqqJS39gxJpO+Hg8YXX6F7GDKM0SoVDys8C4wYiqIrHNDglXkZgaSCgTi02JGj3+9P+r\nF3BrcQxtopbfkQ/tl9mUgIrxicxRZBabBvkS5v5ewM4R9eV/aeKPszFxhEAVrA1XImCHoo82gf6m\nq81a1JoLj6MBPQU43ssjGPJh85FHP3WRC2+lLGmehpbE6k+I8aqq3wQy3kJt6STzkov0kdnxEYH6\ndSa4UPSFf6pfMUNIiv4vlMfhXjj/7XnUgIZMcKsY0BBBbdFKTfygq1A1fj4SKJtUA2r9P6l3nBQ2\n3RSKCp2UtHBr1bWuxpW1H1sM6MZGG/47DWnMpM76xEevlYCbP4ys3zZODZSdBbzSK7sWeMf0u2Ni\nWkKVr9a0v4tR3lQz2laGgLYwsfBtEgb0QziGZB+c79g5wBtwDOheOE3jGybtoAf9gQ9QZX4QohDE\nh8mXKtX7gCbcTMJekf2q60cdQmp/K4ps2xjUXN+k5noIX6LooD8OvgnsTFKIBBmDqkhs8wSfaGs6\nL7V2U5uV6g3J8uHTfkFNk35r3+hmYz4xAzto+D6Lgs80elbet72Ji3Ar8/n70U9IuKFlAkmvKeL/\nug2TbaK5z6Bj6AdGvdfXtNNUAy1CmKdU1LDG5ze83yGz03lgQNvsQ2juhfpzX5xPbShFSd0cHlBc\nG60AJiHBlZU/8nPMjwZU5/uNifNwL5wJ9PXYUWenCeudnUPYVWDcIESD7Le1jvyj5npTWGt4LOZB\nmGFpQHXQQt1P3zfb0oAOAm1KoKqq1IE+Ns2OdfN9Ulgph0Lf6Ez8dSlhLQlva/C8q4jXAsfg0voq\nY0Hev8WQWUKY8Ni2LbMsX0+Lxl2yeUA3A+5PTgjeipNuPI481+FROElpmwhJ/PTHOqfiXjGL8DUv\nMRrQOhQHTD6ADvbqhVJt+AgtIq8inLR41pHsmucVSjiIZJRGYjy4oFKfBF7S8E4/WMm84d7ZcUOK\nY873naiDSFIvoai10wuT5S+m/cDEL+g1lMduCNpktKl5TQwsjXvTb9p0noSiac8CVsAE6Y+l3YiF\npQX3UcdAtEUU6v/FetehgDw+umRA5X9og+kSK4lQWyFT2GdTHTdg3NRdmuC+c+D6Sux3fhD1QTFm\nAe1PWBddFeAB2dFi4NpgQKsYCut97mpc801tU6P88OzoCyQsYln8qbuKkuuj635sQ2JEL9ZRxotM\ngz/eUppbBtwTW3PmQ55dZdXSBJb/u1UeE7ckBn/1ft+TdiPjTuoiY0GsIPyxKlZ7sXtJHSwfXIsB\nnftYMeMuqrvjBstncer3T+LMw7Yll3pcxuSRE33oD6YDT8hi+ifgE8a9ThKXlMz3QgucFYLfgrVI\n+hMqxochNFivpajNmpaEeVhz/YCa69PEp4CnkjTSwh5A+2MQ7DQlTRFOkjx+UBWf+PiYOv+4cc+A\n/Lt/IDt+lhgzy0RFfUui38mDA2WW6aqVa7bpwj2uQGnWwos1wdKcuNmByQgyf/0Zes+R/aDufclY\nvZxijrwqP+c6hHPwlqXBof9/kndimYLH+uGMa7JozUdLAn7vQBk0Z0AFQyztQlKrdViB/f+uZLLc\nzW1BR1VeE7juj3FxF7J8KCdLw+JQJaQLMfrgImPHRMa3hFRCnPo+oBbaNsGdrB1bGzQbJCUaLgb+\nOnt74oWHmj6NDUYo7Va5Tgwj24KwCbF+jo/QfBnHEqLol5xwCYmZnmge4b+fYXZs6uvZFNZesmR9\nQFcAdwf+JzveQNncNsV+wUfiItYmwCEUVcSDit+nshpYzc1IoJZ3c39Wj/6P5fyObQv3u/pryQml\nQUFOdCZbluqfVfB7qeqPq//Tgnmuvv5z73ea9YdCff37DLYotf8bHkTOFA84vWCSU90/v/2Y+vbv\nTYL9b9qevn/1qE71/QkXAyfxc94eWd8JJX7MvfDf56Tv5wz+CFwcXd/+rcfDnUfX36+c3/3+Vo+f\nZXySA1X936v6y0b17fdxC6uBX7ILeUjx6v+n6XhwZjgPKlz/mQrXXuzPKcH238v9R79O5qO1/Xm3\nqh/zPpPR+5cUNfb/0+bvhLW17/Mb7F/bf/t519aMn+WsBs5UgpG8/q2j+gkPzM5v4TRuVu3/umF/\nLlP104j68Kesj/p63p/6+/3fv+aPhN/n4436uxZ+H8H9WI0mlKufl7d/hHm9yff9E+tzhDKfzesL\nA1qs/+nC+nBWbfuh/pzD1ljfazXwh0LE8uL1afwO93+F2f+q8baaFXzAW3/r1s/48X5+4++7Gvgu\n+xr9WavqOyL3HG7Ht7jrqP5p7JjV1ya4xfb173fygKxMArdV/T/1v3/jzZdx6If26Jd2ftd9r3ew\nh1E/DdbXv2UPcLglqj+/5WFG+//FauC8ggCmur3q/WHDYP0fqPF5Bj/kPFaTa0Dr+5+3/5Go+uN+\nryTi/Y8z3uzv+xdWA58rKG50f/z3867G/VkNfLkQH0bXv2tU/yf5HabfDyHn746kAiuqLlbgwuxP\nJDT/B7wRZ2O9XXbcnjz0vo/nVbQ9rPh9dfaqFxDpyhsLEuzl3I0/82V1z+5cg7PRlg1nWPhcd+Zy\nji/Uh2IQoar+uPq7c47yFpPrh+CkWeX6fnu6bF+O5/+8/uxeiPg45C6FSHXV/fPbr64/qLm+U7D/\nTX/rNnYv1am6/wjuwwe4D69VCXes+k5i/QDO4KTS9y0+L/79uN/78hNygqS+vv37BvXs++E0kENe\np0wl/f5Wj59tOJifkYzmXaqubTyqr7G7p1l3420NuclfVf/D/Wn6+76cDLxWtSd1bsl+n16of6iK\n3nkgR/AdFQU11J83Ku1FzPvMcV1U/9v8nfcnDV5/DKdyivJva/b+13r1BxTHz7LsfP2R51Z+TRhQ\n3d4C+/EXvjIq+x92L0U3tvuTcIuax2tL10O/b18yN6quX/f7XlzAN6PHA9yL8wv1X8LPGj0vb39F\nxfXY/lzI7dmEl/Dj0XvM6wsDWmz/Xzk5E+YNSBgGEpfF9Ofu5O4uofracqF4fRq/w+v3M1V/8nvq\n5tfupLym8D4PxVnRPMmo76Oqv/dr+H1l/z9tFIZMz9Xi/u+i/N+R67gjvx1lsd1vlAJMm+AOcTE6\nHlzqz5tH5+urunb/6n7fgzV8vcH/Oxn9MpvfVf3ZHVhb2FOH3pgr17fpkbVR/dl/lC7Mb/8z7M6/\n41IODrJr1e1V0xeH4yut3Ph8Aj/hnQDsyz1w9PZZhNafqveZcH1U/aa/677XpPSg9b1cXAjYnZ8V\n6FW7/ZPYnTc16o97/79Utke6/v5QUtiU+z/J7zD97t/zXAwssy7U4FKcT+Je2e+H4JyOv64e9lzg\nK2O2b0F8wrQPSgosI2EjwtE7N6fa/CGkHv9YoKwK5bYTPhQISnLXUr0izicu8In1v0wbfmLhWeMk\n3MZomaNpCNPRvrlywm0kLQQ3SsyE7/o85PtbhS2NdixTZB3BToiODZltIJG68ez7EOfvLYlKwTHu\nfOkymIrV5/HnflKbPkXmzOGBa6F3sYri+jlu+imwk3j7aHvta2qe/eyWntuGn+ROxJvgSnTRafjv\n+piHIEThXME5rP77PqBpVrZbC32qwq/qqxTgmwo/XZWH9hLfB9Tav9qeX/Mcg2HWaDr3YueRpoVz\nJI2iuGtYvoOW3/D+6lwC8oiGfl3Al2qux/pijuuzac3Zpq6EM8e4DCi4gDpfxPn93BV4J86X66G4\nQCj/zORpCXz8Up37g/uxVPu9WB8pFH6+6UCIXbRDCa419iBu0ZkWAzqcUrtVOCm6pmPojyDOx0jG\n9jwQQzEIBZQARtLNWPi5HgWWX5eW2olWZj1mu3nUjecisdTcL2jc+TKPG2ibc3/o/X5tdvwzZWwU\nKNuSsB/aOJh1tOrPZcemfb5jS89vKswLpeKAeAbUJ1KHDZ/vo2ocdjFvLir8Ssyga4IqBtRfi9vK\n9VmFqvf5hECZH+RFhEd+/3VqLe0D2r6Aq4wDKQaD0vhsi89ZLGj6bmPnkQidrfY/SrP5/oH6KgWE\n+IgmQYiWQcGNYrGhKmDj3soirQ7jBknyv/t3s6MIkodjtjt1TMKA/g4XuvxuwBNxEuyrcNrQvYCH\nQSHnW9vwN9R9cIM+LHFrRrROK8rmuNLAd1DMTdZ0gw+FcR8Ha1pqR+OQhvWPAh5LYiZNFsjYjnnn\n5zfswzTwv+pcj6dJIjLr8z8Z9UPEykrmSwM6WV/GD2SxWDSgba2z/5wdY4NfQDFP3yTEayyRXzfv\nYyGBQiZllsa9v+m72tIoj2VAU+84Kar+7y4Y0KZz1XoPvrXUrKyNqp6zQaDM1zDJ+X1xtJAPXwN6\ncaPejYOEX5KYgRdXG+VLGdNiQHVKQh/r4bIoNMG7GtY/MVDmjJFjkJCS8OOGz5wfJKM0aeU9rJkW\n+mqK7n91ENckP7iZBKNaslFwu4KeYH4aluWETXD9+3x8MVAWyyi+OztaUcN8iHnta71yLeG0nq01\nWGGTCxvhCJNlDGquP77m+jhoxpS7KMZfpdqPGHKJ8MkRrcbne5wedF7MSQggSwNalcR44NW35tG0\nYPVN8nZNsy+hJNoAr2Py/LXTQGg8jPt+Bt7v/YFrlPbokog22mI21rTQRpMovEKA71tZqw4JVxGO\n7FyHptE1n2GUW/k+66LgDiKfa5lGV61Ls3YROZI8qFMsYk1wpWzaaGrSbDGgUDSJlHZXUswD+l1C\nmF3U2XfO6DnzhKbvNtZM316DnZ+9BLuJQ2LmPbUQEmbcAXhkw3YWOyaz4km4iUQFTKzHh7P7iu8/\nGbnCDLPjYKJ+TRGLmQH1J90y7E23ikgKmZbFMkXi7xpraiz99yVGOvR2E2l2LNoKZR3OhzUZxtEK\nHw68RKWNCEGuxUiB2gixPyksprPpt7aSLVs5vKyUA92b4ObEUJOF/SHAYxrUD/vYJvw3ydQSV7eN\ntvysNiNPRwHwtIh7rCTksRANybmVtepxF5qZcYmf/7Mqa8UgaeBGkKMtQn8Zxb1B8nD71kDfaPhc\n8Sm0Um9VrQ9tJo+vR8LzSUbC4FjEMqA7Ac8Zq1/NEMvQSwAyX1Orx4DO1y1BGi1BRTcox8dYF9D0\nf46lS+R7T7qmNLF80fikUd4kZd5ixz0Y//3FoqgsS/jUKNtDGNPIzd4qFhsDqglRN5lzInUBe5Gt\nmpj/EiiLXSh2yI6xCd4FvsmntZEILH+enSOfF6PJgDpb8elsGuNIjX6JY6iqNA8ytv1vHwr2MA8M\nqOUDGvPO8+AvRd8nvThZfgihSHuzNcEtjqs/elcfhO3/FmrrBySloEUab/N+r4pue7ZoYoI77rca\nVl6NM4sKjc83jtGXq+urVCDhjEJe2np0TYzHfrPLaq6nntZKGGrfiiHx7ht6v4t5fxOOAXaqMCGr\n2lPfXnFtXhDLgL4a2KKmrWMin/mdMfoDxdgX4lN5R+w94z7q/Fyc+4XvA1rnIzstHNfRc+cBTRnE\npmtiVfvDiPt/WV8lCEsxse4IGRJOnYH1QFNXm5uy47DlfrSGxcaAatMAf3AvYGtApx20JHaiCUPg\n9/EgdR7DgAqhFSv1lWjE87ggNJ+0bqIfTnUwovWM8pDEaB4Y0Ek0oBZho//XmCjQ45jgvg1bSzIO\n7lb45VJFtEks+YzHvDKglhbJ/45PYfLIrLFBEkIIrSlNvpfkXJ61D9C4a2Fb/uKx88tKMm61I+N7\nX4oaiDpGthzBOPEC+xRRtS5tXXFtXhAbhMjCWercz4He9Jl10KnmTsuOm2Ob4IeVemYAACAASURB\nVOo1zjfBFUzDmikGbyJeIC64HDh7Cn2ZNZp+/7o5O2n7PiwGVFIuvcS43jTw52LBuEGBpoUmPp2/\ngdqo951jsTGgN6lzf9CvwA5C1JTYaErwTuKnB0XTMX1NzNP8/0sHrKmHY9juTR4i28KgUbvtYFxC\n8GhcLrsdjev/kR1j/HnmgQG1grlMwoDGELnaP0TezYHEatcT3kHSoulJu8xmCP47mVcz2zcb5UWz\npoTjxzQBhfy7T2IyORmRkXAkCQsz9D0TjPu8tnzXYte9un76+53+nfuQ5SmfpL2Bd19TP/iq/je1\nCOoC9nuNG4sfVudtCHZjfUAt6xhdxxeSLOAi8D+Q/LuLNmW2ZnoJ55OMLMdisQ9FrW7XuD3wmTHu\nm5ZiI6b9QcT93wU+FCgXRcdvvfJja57bxBVjHnHBlNr132Ms4hnQhHuSjNaEwZjPq8PEvvGLiwEt\nSmT9Qf9axgtCFHpO04UidsO1NKD7qXN9TTQT/v/VfMNLOLkiIl2XGM/0zjnKHwOlxPcCeac6Kfoj\nCCfFncz8rx1USeTrENZUFL+31b4eV5Jnd1/gZRHPXUwQ4ZVPsB+ZHZtEn5smXL5GOxDEPafwzBX1\nVUz4a9HTgZ9M0J5GlcnRUyZsW/odb95dvG9SxLZTV8/fS3RMA9+94QkU4w3kSAoavRjMUxCicTAp\nk2wxf+Oi6p1Z2k3LBPfyQPnnydd3lLn63GtJSLiKZKoZFZohYTUJ/zrGnU3XjtlarCX8lSSQlSAZ\npazyx/la7wjF6MbfYHGjaW7eWIyrzQ+v3YsYi4sB1QgziW2Z4E4LjtCrZnBDjGZdRMO2MJzBM3xM\non05HDiYJEhAO4mpftcJ3zUIrUOZfqLxOlibTYx29or6KgETu/y5w+x8KZg5WViTHf15JAz+fEhr\nE7auCSzQprZ+mB3bY0ATjiFpLfx7lWb3hAnblnWhSeRcfd+ksNIi+elW5P1a0byr9gU/f+5X1Ho4\nrOpcBBY7A2qltYntu86V24Y227/2M3Wuv7ElhD9enftR7xdwJri3UP7u87HurRuYtitY1b4xbNhW\nCP44TwPlOqjfNi08c5rYCRet18IkrilVGJfvOmzM+4Zj3jd1LF4GNAzLBHdeNsQYqWtIsjorBnRW\nyE2EkgmCgSScjmMsHjtRbxJuJJmauUUsqvLS1aG+ji1Bfp6qo81RxwkkM8/YOzuGfMdh8ayF00gJ\n8dkJ7p2WlH47qqKPJhOvhzp43Tj3jYvPA9ok1ocfSEkCrR1p1PffQ5WbSpuo+u6LIfpljE98FbS/\nXOx7bsKA6rFvuWfovmqBzOnqPMXFQxAG1MdNgbIe08G0LfGmHVjNGueWNcBBfsW5QsJFJKWghxpN\nfXBj0ZTW+FR2XEo8ALB4iK5YWCa48xJ8xzKr05E5df9FazVpGpZYDKbUro8PtNjWEdjO8UsBMXN0\nEq3Y3oS/e5OIoosJvs/nMu+4LmGQHSexQpjO2ppw2ZRdBsZl5Cdde+vmqv8+B0a5wN/vdFCtKu3u\noOJaDKrew/yYS9qwAiVZ/9dvgPeq31YAoCokwCuNa7t4NbWGPIbwP8eosxkuWr8woAPvelfBiNZF\nTItuc+OjWig3aOE5MRpQfd7U13de8KXsOC2G76MN67s1Y/w4CYMx75s6lhrRNe8muFYuRi2F1OZr\nEhnyYOChU+nReDi+vkolJkvYW8T/AfuTsGeLbXYBa4zGMJfTmMddzJm/zuAZvunm93E+wEcF6s4j\nfjCFNseZz9/B+Y/Ni3CvKURbEMuISmqfukBudajWONlEhmXlUUUkNU0o3wRV64NvAjqP2Ngot/6v\ne5Cb8fv14gjVhFNITJ/j3SvutNrf1+iPhlhd7Ud4rFum4D2mj2/SjrDmhy20EQNfIxjyAdWCHR1/\nYzFBFELT6v8vcXRrHJyF3jQsnzrHUmJAL8Q2wY1F07DLMiFjJfXW+9bBAawIqDsa5W1iGFFnM+IS\n1Fehvf4n/ANnnvbi1tqcL8QwoJNoQE8h/N27YCxOmfoTfJPvhJtJ2IKktcA500abUXuHACRj+YFd\njBMkLU4GNNcW+FpCS/MvfrKTrl2vJk8c3wSWn7fN/FRLzIdj9EGj6rsvZqYm1kx2HA3ouLDaX2bU\nCaXf2gUXWG6orm2En/+1xzSxeeFXwmNIuF0L7cZ8w+FET3CRysMC4uI6o8fkl/yqiwJuP9wd+N6U\n2r+ZhCdPpe0whjN8ViNMEnxiHvECYH/KEzLWJ+U/qHZK9nEpsC3FyINVCDOgCaeX0oQ7yMT+DvA/\nqlzC688+KlbSilSo7XH3CeDnJLw1Y0iXEmL+n0kESZbWcdYa0DsxGw3oYse8SEJfkB0XJwMKGFJl\na21qh6ly0UfHMW+3NN++mdsFwK5M319o0jQm8worfzTYUWen/a6t9rXQLMandVC4Mv2UVz2KmNa8\n6Gq+1a2f85rirB5Jwdqhx5SwlDSggv3HvtNFcPzPBnccmh1PjKzfVFMlGqH1KJqtihnYpJEgfQxa\nbs9Cu+G5nSP5b5l3p/dxkBQCSliYhAm4jfB3n+2mlnA2SVQ03x6T4Xnkie0HDe57hVFu+dItVhxt\nlIuZ2xNn1REPz4ys9+/ZsY4pGmTHmPUlhMXKZP6ovoqJedGA/iE76hye1vfQ5SuYY3+wdQCxc2ZA\n/HyHuPyQgwbtxSJkNXRHdT6tID49mmHQdQcsLCUGdKfs+K4ZPlNM194QWb/p+5Yw0CspmhaLRvf9\nDdubF0wj99jhwEun0O6sELs5laO2JVw4xvNEs7rthP3pEj+ur7LkMBmxm3AUyRgbUsLHjCtLK1py\nwotqajxkJv0o45GR9YTxrNLkaXxwjL5AeRy+Zsx2pokbKZv1T7KuWe4xs9aASqApTRNMGtW3Rzu4\npOJa3LdI+BGJKQgL4WvAAQ3qt4VqE9VkamlMeiwRLCUGFNxEnFby2BDEhMXKseijTgP6W++3bCq+\nBlS0DhfRLoYttzdLfAPYlYS7dt2RMRFrWvnvwOtaeN7F2fFehL/7vDOgZzMtH435Rpuh9octtrXU\nMS+mz3WInbfD7DiO8Cr0nOPGbGea2IWywGASmmc7dT5LDajPgO6aHS0G9Hij/ED6OT9NXI+drxem\nFzH8NhJ+XVNrOIUnL5Y1cV3HsOsOWFhqPqDLmK3ETyZgrAS0bvNb7f3WDKjebKSdpebvOD4SbiXh\nk7iULC/rujtTQ9Ka2XXdPJlvBjThTl13oRMklRL2Hu1ga8pr+mLxl2u6/50EPGqM5/jrw/xp2sIm\n/ZMEbNPm5rPUgFpCp3BqtqQQbV/3s9dITRfbUx3hv3cx6dFDYalpQGfNgK4EmgRd+BZwWsV1P0S/\npQG9OHtu2xvfoOX2LEzDBBdcwt6nkSyKROg+Zi1N1ITYIHB9/gjKHm1j0HUH5hIJV5BwtVdalVNz\nnhA7bweACO6+3cJzthqjjS4widBdR1rP//9pB11KjG+amEGRNPS+Yvn792gDCTeQVPpjdqkwGHT4\n7B7dYjCldiemWXsGdDKsbFQ74ZMklUGS/MVLJJ93Jje7gfi0L/OJaUXfS7gIZ27wDOCdU3nGbHFB\nC228Ghd4xocQYscY9823BrTHrPATFnM0w/awWMzNZjVv/edMWwvYFprt2UWsUufztT7GMcFtmu/3\naI75GjM9enSMpcaALme2DOhlwGEttleMSJiM/PSguHhNywxvOKV2ZwkJRvTmrjsSiSqfkaaE/9ml\nkoQPknBUoK74bP2NxekDuq7hC1NocxgoK/oXJzyAhO0r2ljTYn96lPGLhvVj979hw3brnrNYGNCX\nTHBvnie8Pa3n11pqJwa3sTT2+MWKLvfU4Yye0+eVnT8Mu+6AhaXoAzq7jTDhFtqL/rc+1eG0cxOw\nhLNYPBL5WeMHxOdlnQfcH9tvpOk3rvI/8XEo8FpsgrVnQOcLZ8zoOU3Tcry4vsqSQFe+t00FqrMS\nwPrrw19m9NxJURbSxeM3rfUix4eBx02hXYFvghuDO1PU9vZoA4s7P24IfwZe5ZXNY+qV7wEP67oT\nPcpYahrQWBPc+UvfkHBTzQI1C8Z6MINnTBfOL+bjXXcjGkmlWdT05mfuP7SW8HfvBRzzhWkwFoNA\nWWxOY0FMDrrFj6SRcKdLxAaaGUz4HH+vWqrjQMdsmMb/OG13Gh1v4VZivnvCWSSliPw9xocf26ML\nDFpv0UXf/bBX+oPWnzMpEh5Osk7TM4OuO2BhXWVAz5l2R6aA3n8jHp/tugMt4Ek01y6NI2G1TMDe\nOkZbPaaH8+qrtIDmUvqlJtWfNzQVPMSmBJsUfr8Wyziw+mmlsXisOp/GHuznKdVYH9itQVv74LSX\nOZKCxncS/9ce42OxzI02MA/Mdo9FgqXOgH7DqGcFXplnzIIBHc7gGdNHwpVdd2FiJJxAwqkzeNJv\nCX/3O8zg2T1ikfAV2neZGLbQxoEttNHDxu2y418j6zfNAzourDQsu0zY7rRhRSK9LliaFPKkTsMK\nwbZsclZR8YHoEs7J3HOqMIxur8dSwnBGzwnPox5dYth1BywsdQb08Ua9SXKBdYWlpgHdCNhyiu3H\nEmzzgIcD57fQTlNJ62lQSjexVPEU4G5dd2IitJ92SeN3wAPGuG+9tjvSo4B9s2OsD3A3PqC55vzi\nctU5gj2HYkxh29dkzdYvsE+t1Q0+B/xv152YERaLq0KPOcBiDUJ0glFeZEAT1pIE6y3W/3vaGDAr\naYlLxTLN5O6XUkwcPr9I+F5LLTVb/POUQAPmWErWChKO77oLc4gB8t0T9huzjZ6onQ3ihA/OLysG\nAyab8yHGad8pC0m6xp+67sCESFkX1vp5QzIXgdoGzOa7nzuDZ/RohgFzOucXqwbUInpifUDnW0ob\nxlLe2KeBdc3p/C7Av3TdiR7rHNYl/6YuID5V87b+l797wpkd9GOWeF/XHZgQ/VztMV0svUi/PaaI\nxcqAWoN8BTEMaDKzQA1tYhZRBoczeMas8AZcmpF1AwlneHljm2DYZld6LBoMW2hjXSM4Xj+ldr9q\nlH85O7bNgA5bbm/pY/Frd/s8oOsuhl13oEdnGHbdAQuTmqIux0VxuxAXLW4L4FhgV1yC8qcA10z4\nDB9PBjNE+D2YP0lxW7ixvkqPERK+CXyz62706LHEsS4xoJsB10+p7Toff3/9vwq33/ZojtOBjb2y\nKouZ43C0zOJG0geI6dGjx/xgUg3oq4CzyImQN+DyyO2Fywf0hgnbLyPh/0gqA7YsTZ+k6nyRbWEw\ng2f0mD8M1PnJXXWix8wxmODeb2XHpbnehpBwXQcmZiJE+4hXPq7PrmAw4f2LWfBwH5oFJHsRjgld\nChh03YEenWDQdQd6dIZB1x2wMAkDuhPwKOBT5NLDxwFHZedH0Y1P2rpDEPXo0T4WU/TgHt3hPdmx\nj4LbDqwomeJ64WuvphnALQZWhPn5R8LfSEqabJuhTriWhKdOt1M9evTosagwcZyVSRjQw3D+MJrh\n2xa4LDu/LPs9a/gv5WZg9w76sRgx7LoDPTrBUJ33Apx1B8MW2tiqhTZ6wEU113/n/b6qou5HgT1q\n2hvWdWgdw1IOWreDOh921YkenWLYdQd6dIZh1x2wMC4D+hjgcpwvprVwp3RjpvOIwq+EVSSs6aAf\nPXosRlzedQd6LArI2r5Ufe5njeq9MvEEQ1WmwAn/RrLoU4b0aAsJl9D7C/fo0WPOMG4QovvgzG0f\nBawPbAp8Hqf13A6Xg3F7bGL2SBgxhdcAp5Fz6YPsGP97NbmO83fch2LeG+t+h9WjsvGfP83fq/Ex\nzefJ+bTa73/P5+/9gA8CcArL2RJtMzAP/et/z998dz7pp7I9s1w/V3MzRbPf6T5vdr8vD17/Jtty\nJzSK18ffv6SsWX9lP1p664ND0/fZBv2g6Zdp1AdIRr8PYVJ6q/+9GH9L2fSfN8747H9P83dO37W/\nH0uZ/7zNs9+7MWU8EPh6dv5e4N+z8zeQ+wlptK8VTUjV30sb3zPPmG0fBzN6To/5wmB0lrB3Nub+\n1l13eswIg7HvTLh/Nk6e0F53op77j7lfs8dFwjZG+V2N8kn2hsFYdy2WfbMpEr7W+P9q6z00f+72\nJAWz2iYYjHlfj8WNwcyetBTXh8WNwVRaTXhZ5Hc260yahsV/wHtw0eL+lTwNy6zRE87jY9h1B3p0\ngqE6vyU7vqmDfvSYLYYT3CuuF+e10I8eAIlhMTSdvNXDKbS5mNGlD+hXgE9E13YmteNiOMG9PRYv\nhl13oEdnGHbdAQttMKA/yv7ABUZ4SAttToI+iEqPHuNDiJuPd9qLHvOOZdlx1pLupRwspilOBg7t\nuhM9JsSsrQh69OjRYw6wrL7KosM/uu7AIsag6w706ASD0VkySng/i7yzPbrFYIJ7hRG8toV+NMEL\ngYNn/Mz5RMK9SfjJmHcP2uxKj0WDQdcd6NEJBl13oEdnGHTdAQttmeDOE07uugM9eiwB9JYEParg\nGNCEC2f61ITPz/R5PdYVjKvJv6nVXvTo0aPHOoKlyID2aQHGx7DrDvToBMPCr6Q3c1xHMJzg3n6M\nLG4Mu+7AnOHFwHtI+HnD+46ZRmemiGHXHejRCYZdd6BHZxh23QELS5EB7TU3PXr06DFd9Axoj6UD\nF9hnnOA+H2u7Kz169OixLmAp+oDGmtL8Yqq9WJwYdN2BHp1g0HUHenSCwQT39gzo4sag6w4sESw2\ngfeg6w706ASDGT9vkkjNPdrFoOsOWFiKGtDrI+t9B7j3NDvSo0ePHksUPQO6bmPc4EdLDX2+wx49\nijgc+HPXnegxdUxMAyw9BjSJjIKb8A4SngfsPtX+TI6/ARvP6FnDGT2nx3xh2HUHenSC4QT39gzo\n4sZwzPtS3Lf/UntdWdRYbAzosOsO9OgEw5k9KeFlM3tWjxgMu+6AhaVogtsEPRHVo0ePHs3Rr53r\nJk4BIOFDHfejR48ePXosYiwVBjTpugNTxJUzfNZghs/qMT8YdN2BHp1gMMG9F7XViR6dYDDmfUuF\nZmgDfwEu6LoTDTHougM9OsGg6w706AyDrjtgYWlsJglv77oLU8ONm3+z6y706NGjRwEJp/fpetZJ\nPAt4SNedmAsk7ELCVV13o0ePHj0WI5YGA7pkkW7Lb18wS3v64Qyf1WN+MOy6Az06wbDrDvToDMOx\n7ko4h4QftNuVHjPEsOsO9OgEw6470KMzDLvugIV1gAFNvwTpHb2yQyBNWbt83oMwbU66vOs+9OjR\no4eHdBmkKaTzvob26NGjR48ePeYMS4kB3QV4cKD8icDZXtlzALh5440mf2w6zXe4gh+/GT7zEyCd\nhbnbYAbP6FFC+qgZfV8Lg/w0XQbpuZ31pMcsMZjg3rtlxx1a6EeP2WPQdQd6dIJB1x3o0QkGXXeg\nR2cYdN0BC0uHAU34CwknRdbeH4B/bH67YnH6Ski/Hv/Q9M7AbZDuG3+P2VYK6WFe4RnctBn8+X4A\nfl9TSPeZ/LlNkW4B6V1n/9ymSD/i3tG8I7078E1g13J5eocOOrQ5sNeUBSs9Fj8Oyo4tCPF69OjR\no0ePHj2mixkyBelCxqh5z0wvgzTlkF1TEt2f9JZmTEv6H1n7Tw5cS535b3RboX6m6u/hqvzuWdl7\n49tvC+lxi4Sx+8Ui6ad832cb5TPWjKbfzJ77lsj6T8yY6CbP2LF5v3rMF0bj8x5d96RHjx49evTo\nMUMkvLzIP5kw6ywRLUe6MiOG/uBdWGvcsE12n1+e+TOlqyIfLCHYD/H6I0zDE+Oa0T6q6Ybqwi+A\nT2bnD1Ll8t3OiWsfIP0jpP8WX99EgNmeN6T3BQ4MlO8I6b/MvDtx8MeuYJeZ9gIelR0PqayV40vA\n5+ObTx8BXOiEN6Vrj49vZ11D+khboJKunG1fCvA0oOktZZ/7NpHeFdLLp9d+5bPvM3vLgHRFtrfd\nKXAthXRTo/wZ0+9bjx49evToMR6WCAPK9tkx1mTxqJrrd4ls50ijveeEq6dbZMSBz5gO1flm6vze\nwANxRP7fVPlW2fG2yH4C7AHUabUG1Zc79VOMRHpP4MvAi3B52jQuzK7NIzY2ymOFIZNgECg7of62\ndPfsJEAcm3hFdvRSJ6VvA74C6foN2mqAdBmk2wbKt+tG09wY3woXpz8Dbh6zzUF9lXRnSA/Ejoa2\nofd7BWWf+zGQLjcY6xcAW3fACK4EfgZ8Y8J2lkHaxGxZ9jRPyDNq41KvXObPF2vaHTToQyTSF/Sm\n+3OPQdcd6NEJBl13oEdnGHTdAQtLZbOwNADHZMdbPWLmuTXtecxA+v0ac06fERQtm5/D8+js6Jvm\nasJ4Z+/ah4F9yX2uALbMjpFEc/rP2cmpcfVNbDPh/VNGehccgXgwcDywc5x2KH1aM3PpqWCT/LTQ\n5w2K1dJbZ2RaHDNWlKAlWgv36OzoCweS7LgV08GrKBHrQG5Z8IrAtQZIH+iY2UmRru8YPvO6H3X2\nPpM/c9R2SJDwZ5wlxkeNmz7VoP2bIN08svKJhBnrV2XHRweuTRMS4O6RxeJ0w4ZM19soChOlnRWQ\nvihQX9b9z3jle2bHP3rld27QlzGRppC+zytbBnwaeOP0nz9rpAdDGrAISRcWgeCqR48e6xTSRy0S\nofqSYUBF6uvnJ7sceA1wDaUgPj7SLbKTS1R7Aks7JfCJQmFA/QiR4sf5X175peTmwpkZ4mjwLMMR\nfz9T9bdS12Ig76VO8j6suf6R/DSteyczRroX8B3gEFj4KnB9dmF/VenMrK4fvOllRJtLt4mCqbc2\npdNacN8E19BEpfsbDEQMhoGy9SLuu1CdP8isFcbnjPLXFn+mO0C6QblauhekdyuXm/hAdp9v5ivm\nnF4gscb5j4bAhxreE8KNOIbPwhYV1yKQbgfpodmPoSp/NXAmpBYjPvB+/wq4CThCtSHrUcDSIl0P\nN6ZiGea68RQwPZ0qRDB4vFd+A80sUTLT8xJxcEfg44H6D8mON3rlIiDyLRU+lh2/V9OPYc11A+l+\n2cnrvAsyR//feO3ONT4BeAEC0w1we3bAlWCuMayvki4LCLp6LG4Mu+5Aj5nh9dlRLEOHU3rOxAzu\nUmFA35Ed/TQsr8QxFlcRItwWCoqkTXDS/lMomz3+U8Wzf0mZARWCxEpRcJP3e2Oc5Pho4KysTEyv\nTgCupKgZaqgBHWGz+iqCdOOAyaL2/zyy4bP99q9rT5OX7gZ8H3gLLGRa74XbcD6ySrPIGcDFwNO9\nBu6ftTMlX7p090wi5Y8rEYocQVE4sCU5A/21yIecyojBLjz7CYZmJdRPYbpOI870dyPy8dqE+f0j\nBSFPwex2L6/uRcCaQBun4PrZFL7wQf5n9Y7SBZzVRFMhyxPG6E9ThARpIc2uhYspC8BgxKBrIVMB\nvqntAbhvrxl16VuICTk4O3pWIekdsrlhmF6bUtz/8eod3UC7Og5k/TUsAxqZ1UJZU+mvSYIHZEf/\nfxPBif9+ZK96ANPBs7Ljr7zyx03pefMKWRv+tdNeTAe3Abd03Ykc6UchndZ47tFjzpEuQHr/QPmO\nkP4jcIPsUXM/Z5YKA1qlvToJuIKRNMD0MdsTp226iXi/u6twxLTPuCzHaYe2yCT/ApFW+1LTjbP6\nV5Ezl3LfxTgGdEtVXxjJWAb0hux4crE43YtiCpmBOv8c1YTteZHPtrBJfZUYpDviNLz/BQuf9S7+\nBtgpm8B7ALvhpNnPMAjbaRFRosXwAyCJD+UNFLXsJ9Do/WhtXeozCicQ1qygNKaD7Ci//1B+fvry\ngPbwQ9k9H6csVAk9T4Qqn6aowdpanT9K1Ze5GjL9Nt5P+lRIq8atLyz6bqCOaFavD1yrQgNz1CYY\nBfX5BwUh0kgzHNqELOhxP2hw3w/Vc6WNqymmD3pZxf3WuivBt6xUVr5LguAi7/fTcdYu08K7sqMl\nlGhqOv5U73dm4lnan67Jjj4DKubeW3rl4vtZ50s9qLluQSwU/D1vf7/i0kD6c+OCzMMjZ9SRCZCu\nr9aKQZc9GRMvB37UdScWOQZdd6DH2Hg98GNIfZrnu4R5FdkHZQ8YTKlfE2OpMKAKJcbi9zjp/QHZ\nb4uguX12jGRA05U4IvpyykTt33E+ZZeRq8HBaWEvAH7t1b8Qp9G5klxTuwnwy0xNewVFQkP8kGK/\n39dwBKQvpf85cHq5eno3bG2O+C/5PnwdIN0Gp/n8OCx8LFDhQuCtuG/0o+z3p3AS3nupekJkTMvs\n6BPZ8ZNeuXzHv1EkbM/PjqcD345ofyd1rjRk2jfN176mO+BMLg9QhWLm9yvKFgMfBb7ilR0FiLmz\np7lM7x7QvAoRtIYiQa39ybT2bJx8sw+g6FPt4+URbfx2jOdCM0awBoV1TN7DqRRNo2X8xJhL++1b\n2sKjA2WrKTKu4qN6R+D5qrxq3Xx/oA96zFtmrJZv9pWqHenbmyueH0Calv1t038xLDNkfdG+2vqd\nBP73dFWFBvdJ3m8RHPlafflOVjRsnwEVqfeNU/YB8hnO1wdrLSqkPw98+3ur63p/ECHVW6fcqTZw\nI44micD8+4316DF/aJKzPd0f0p82aFwsls7yyjMrmpFLjY9djfK5wSJkQNP3Qfp7r/B8cum7Jmou\nw/lOXgvcNytLsqMvUbsapy3amjhCZiscEXQzZWnwhjjN5V8pSsYH2XN9M66dcEzm5eRmu5sC12Xn\nVwL7qfoy2GM3i6fjfKqe6ZX7xMswO1aZNn4hO4YYvhki3QIXqOQ4WLDyoR4BHArsBws7wcJBsHAR\n8L+ATlMgAT2mNR9kbPoSLCFebqaofX1sdvwwYS20p8nmMepcE9T6eX6uzl+qvg2z8w1xTO+llMdG\nCNdl9/4T5bQtv6Gsed0JN/avpmgOLhr6t0EhN5Im9i3fV19oUGcKaQUK0uvBkar9CLPOkf+4lfYp\nEoX/UTOVwmzdRtHkdU2grrT1lpq+346yb8jVhDXL76MouBNz0WwtGBGtpo1LSQAAIABJREFU/gap\n4a/ZUPxWarMsSHp9zeL1uLH2J1Umkt4GAoCR/7BvjWJFyV4PJ5jRfRMt/pmEme9/UGTQNXxTcMFl\nRnkocN5fKL+fbwBvwo0VZWUgps4jDI3n1EEElqcY128OuBrMGdKHQfrQwIV7B8pE6HYpRUsM0TRf\nycyRPjEsJEnvBOnWXpm/pw3Vta9TTuMja4nvc9wS0l0hrVon/PpqbZuqif1Sx7DrDiwepMshPY/o\n/ObpAvCHcv10W0ivDAh1TiXnR3T9FbgsDhZ2MsotU1vZU4cVbXaKRciA8jrKaVJuwBHll1IkELbG\nmTAdTh5R8Vc4Zu/BrLouY/DS7cgZvocBe+dNhAKgAG4z+ituo1dmj+lC9qwzgFvJc4sKoXwuBYIx\n3RBHqFyN29yF0dyE3ATwr1ld/3s1kVY28ZfzTVlRk+gE6kP86/u+Qh6FV8rUOx0nmFG6KS7g0Ink\nAoUAFtbAwgkZ06nxv8BTFcEvhEUL0t90GaR+gI7/CVbN8UecNsnH1RQ0hSOfXJ/RkQilv8juEeyu\nzn0mRRYzPV8eiJtbWhPvoTAGt8zqfho7qJDG3bP2b8LNM8EZOGb7Roq+hjeo8z1UH/R30tGhIUio\nj+bemRQ0fAWG7wJ1rpmlGCmiCI286KbpQyA9KeJ+wcPV+f3UuWwwt1JkQLfArQ0hgv8/KZgzl6AI\n2NF8fDphwcNZFM2kJZ/wf2dHmT+fwIZocT+tyvT/+xB1vlt2XFNsM12OGx8XURRgyBwJMbkWZD2M\nDWS1ZdYf/dytcYzwPyiZvI78yZXGPY2JUOv76Z+Fiw8QMhU/nvL3einwPNxeqIUtmanzxBquM3Dr\nWchH8J04watFKM0B0hW4d1kXpEnwM5x/9HbAs1W5rOlNg5U1QLqeoVmxrALOJPcNFlRpZh5Dea+X\n9XdY271apA+C1B/Pa4B9IK0KtKYh+9OtNIph0aPH2LgVp5T4TmR9oUF8Yc4zcXu0QUOU1uIvU7aO\n1LACvKnAeKN95zDg+YH5N1dYZAyoDhJT+Hhb4pjK6xktWOmuwDJY+Adu0dstq/skYD1YuI1V14qG\ncVtyBjTTho0YIyFob/U6sw1usX8TxaiP+2ft30iBAR35111IkRm4PbB+Zmr7a+BekD4H53OUSWkX\n/o4jNLVpYUqJYUo/aJiPXclokpQ0SZqZGWTHkNReiLzzyYM+xeDxwBu8Mq0taxjNMt0IJ+U/BXi9\nH0kqDgvn4ojYB3kM1duNG2L7dnscI+OlKOD/Km76EO67+4QDOJ9gHXRGCOwbvHqivbuGYtoWzZxZ\njP69yL+7aLZ8n2ON76vzrXDj8u+Ug9RkKEUcfj+j/2Pk77YpzkrBb0cTKXqOaYd8I1pqupv6Icz0\n4dlz/HZupvje9P++J/V4T3b0mfzHV/TvHpC+0iuU938bRZO5/1DlWuP7KRwTtIlnbj3ITqqIzwXy\n7y5uAn+m8L+n4ktyLkUfzUxLtyBjVhhSpdE0GR0t8NAaJ21uLfceS3GN2wxH8F9J0YRbNmYvV2YU\nDOuJ9FVewRY4ZlCvwVvh9p2Q24YIPLVU/B7q/CdefQkg5psib40TUPma/d/jXAd8U9jbcMKHSyi6\nfwhkDx0ErnlI1wt8x6dn7YbW7afhxpBlLjwPMAiywnqhsTFOsHQbxbXj6bi1cAOmlruY35P7SI8L\nWfdvyb7lwP0cMbZ+/AwRDIYij6eQNvm2J+EEEiEcGCkMkeCQKygHmewRj0HXHZg/pFtRnTrt65EN\nHZcdr/HKRUBrKbH8PSOzZAvOiz9j+/VrulX21yTv0yv8vWxusMgYUHQQFP3xRBNzB3Lpq45cu5pc\nG7Q+IvXLP/NpOAb1Qlg4H/exZWDKAr3CY1a2xjEN/0HRDEczDJoBlY3gJoomu0/LTxfEhOyo7H/x\ntbkD1e7nKX8/a6BtgmO4rqBssnWuI4aDuEGZ6Ej/VzMKQFQy37Hgm85oBrRB3rp0fZyU6E/AK8Zj\nPkc4GmeGuwWO0YMSs5HuZZg6rYA0FHBlwGj8FUxDLwPOppzLDxxheRGwC8UAQo/HfS9t9iXnvtnp\n+rgx62sQtV/W7oShtdmfwGkyr6JALKc6B6RmqGTeec8tLKD6W28PXAILV+HMUCQlx2bkDKhlQqu1\nD1o7qMy5Ctp0HaXzKdlRCaiAXPt1hVcuJvi3ESbifUhOSp8JP9ivmPXzHjgByocg1ZotiTD6M4rr\n2+U4k2ZfA3q2OtcMoviMJN5z9Xqh5+4WuO9xFQXrjxHTnzGc6Y7Zb9/HNuSyEPKnfh3FyOCiHT6W\nosnferj/937Aq1W53HsNxTVFTNDHyYFpEcEf9H4PcN9Bm069FRdbYBuKe5OFl2bHZ1A2rb8zTkCi\nLFXSZbhvs4aiOe36OIZdBDl6P/kNjmH1NaCCyGi96QrcXhUSRnrmvSP8FGcWrMy102UtaF2pEK5W\n3ZNC6ufitiKdy/95oVf+Ntwe/3mKeWm3w2ntL8M2658Ue5eLUiVw0O9VM4aFeb49TpNzM8U1Trtt\naEgdP/+0+Js3TQNlCCYBO0uA35/rcIJdK37HnCDdC9LAPpv+sfnY7dEe0tsTTtn2V5ygzoLlZmDB\nYjStAHXW+FfryWiOf4cyA3otzhpiqMo2Bv4CC9flRX+PCBDZDRYbA3psdlS+SuntcBrEv+M2Xdlg\ndU6+v+Ii0p6A0/iIVkZvjK8Cfped74IzYwJnFrsat4BrDYdoQH9GMZDPS9W5JhiFudwKeIoaWH8C\nvqXu0QSpXrS+jWOCt3ZHbsAkngp+E+vhvvNN2bMf7lXeh3yiDb3N63xyLUrmr7iQZozfGYwCN6UP\nMhg1MT24l3dBM1WRztvpSpyk6RrghbAwob8dx+ICLe1Gwe+q8P9bqUX2JuwDq/04taT95bh3FQoW\n80lYEMJmHzUufoIjPO8U0Fr7xP0/4cbsE8nnCOp5X6QovddYIF/ARNovDIc8RwfuOUOdix/03yku\nvpro0IGItiMnvC8mn6PCgIYEJCIc+KMqe6c61wIffa82GZX+b0/O5EEeSGsZ8AhV/vvsGUfQbI30\niftz3aEUmVhvbA+jDF+jtg3u3fka0JNx2q5fec9WgaUKz9Zj8lPk313WskyjORIsvRL4ohL0iCl9\nFnW6Eqr/qfhbn0CRqH40LojVN3DfXnBXnADkixTNDTfAMViXU2SCxaz3ZU6qrZFuDKm//gguoz5a\nrEaWEmmk8RIT5z2B12HmThzNX9Eu+5YNgrO88s1xQpPLKI5tIVzknWnB2QG4teYmRkKJgk+mpA0Z\nhvs6ggid/P0C3JjzGdCLs3JfA3obcYHU6pAJV9PAGpouYPud+mbovtWBQMZoaI0+Hbcu+v7Rb8f9\nr7PUzOl9R1tqiEDmJorz/O444eNFOOHoMCv/jdH+xbh10f9fJdBJDNNYhW+TCyWrTPYF4op0RKBP\n84ZzKfqmC/YIlM0aw6470CHOp5kL2nk46xKLobRg1fdzRwvON8p1YDMZO6cX20/Xw63BQ4rC200Y\nuQItZLTdp8+1Otw1OmJA0+WMl3NR+nst+QaoF8RtyZkAJUUYEVBPwJk7Jkb7YuL4NdxGCs5++zTC\nROHllCOYak2E1oCKX6D46Il2YWuKwTuEWL6c4sZ2G44pFgJlLWUGVFTxh3plK9Q7OModfKI4lXal\n/6twhKCYIPtExL7Au7Nzy89tjVEu+CpROQzT5TgJ9ALwLFjwzaHHwMJFuGinH6UYEESnSrHs5y2/\nH60FeZZ3bRVhXz2tYTsMt8j8AxauhgXxAX6Td09VtF49Jo7CCTf0fPGhNUkfAV4MC39jZLZeYp70\n+BbTd1/zKmM7E6yMhBNPJCekj3BtpbuRM6CXUdaubYGbr/8b6PsrvPq+b7hA/Gu/T87QQs7cfJKi\nKfpdcfPN05imy5wGIt2Asj82lBlQuTfEbFThZopj5VicNtsPQrRx1se/UmS4NTTBfTuKvq6CvQAR\nLJ1Jri3XgdAg9/N9MLm5UWZ+NGK+ZG4+R90nWvY1OJNFGfPPxc0ZX1snDOV1FMft5tlzLwO2VHuI\nnmv+N/gipZyV6Qa4d3w6uaCxAukCbnxchjOJlPezBvixquhry+UdiMuCCNuuphitekXW/tmUBSoi\npd9KMV8yBq7Bman7WqnzcO9ENNN6jryWOLw4O/qm+DfjCKdNPM3mcpwgKmSCG2Jix0WICVlLfAAq\n0aj7RNlXs2OIiDwcN8/0fvALXAwCmEr6pVQJaQvvWZtc68CEC7jxfCFFYcVrcUKf1RR90Sxmbg/c\nO/C/u6SQC/j3pjtix8rwcQv5GuQJB9LtIPXNGIUBPZ9oRi7dPCyo6BrBPaPH/OEOuHW+qWn9gUb5\nR41yhYKSQc9N2Rev9fqzO27OX0NxXRKaQODzJ3OFcSfEzri0HmfiNCJC5Ehk0j/gnPytqGW3UjBn\nSVOc36NC+rGAVu2N2VH7qK0il24cRs70HM6I2QIcMXA6jhg9x2t3P+AGWBBm6HHkETwXgGMoa0C3\nxREH/gf+Bbn5lmZATwXWwoIEQXlH9v9tRzHyoQTM2YZibsXNcRoIiWTrMaDppuQmhc9T92mTSS1Z\nGVDEO7OyzPRy4WYcI/tvkN4b922PVPVTilojxjC1up7ayZEuw23wWwJPVtrCNnA0Tnuo04t8QD33\nSOO+TFNeysuk4TOJxwFPVqZMghPV+f1xfr96oTmKonbonEDb4AjtT1E0y7wax3Rdh81Mb05xLMhC\n+Acc4RLwBU3fmGkcVuG+oa8BzXx+Fx7NSGM68gUVkzjR+q8mZ0AvAG6vxtFNwD1x8ygU2ORSisS1\nmDh/gLDU8VzXzxEhINrcBOcjpZnok3BzW5c9ATeP/07u9yj4PmUmUBgVfx38PG59yZirUZLpz+Pm\n7k0U15qn4sbG48i1kODMkrcmHDTqONx6qfv0ShwR+kEc8z3Iyl9DntJFuytoBtQP7pRkR2FoPoNb\nI6UfAQuBhRTnr+z7xfpmjJ/GvWdfcPJgYLNMALWSfP3Rpnk6TsCGjASShW+bxQHgIQTNHDmCYpCa\nrXDxBG7FMXcihPkdxeBbme93uj6O0JaI4SJAugknRPA1uGJ5cDXFubg1cEX23EvI58AGwBnZ+7w3\nIz/WdAG3Lp9KUbCnhb3ynvVzqqA00CNrmv/P3nmHWVJUffjt3WWBzbtEQWAJkkFAMigtSI6ioKhE\nQUUBCaIiKmtAREGioBgAUZIgWTKMICB8SFIQQTKCSs6Z+v44Vd3VffvOzu7OTN0783ufZ5653bfD\nuV3d1XXqpFextonvrXCv/Ju2rud9we0DbvteNqjXJ27nBdTkWQBl31rvDx/FnskxNUXhCWySYX2q\nsVZjsOsQ998ziXMNY53gIvsG1XfkIVgG8x6qk2ZfpEwgF/fZ4fl5Epusz/1yu3fXhtj9PqV2bY/1\n/5tqMj9OS5mXtnGxYRKpyfq5DTCxdt5YAd2mvZdBhedonWDoZ1w77yh6UTQHSBFwGdMvA5IPzLmH\nGoUnxX/pkwW0Ykg7tGGDY6nqOvH9Gyum4bw/plriMNwz9UR3o7Cx4AtU+7LIAgr2+XvtPICSM7MK\n6FvYTOJymNb/JcyK9HWsQ14Sc9OqJ5+p4ZaI3DSjWB+X0b6o+RFYZxpepHNSmqAfp3wpjqAsNQHm\n0rgCMN6sSzHZnZDFnYN3DXQOs9o8hN0I60UDmd0w95aXgHmjTmcXyhsuVkB/TPP1XpHKYCGLZ3Jj\nE/3u/v8/sN/vasc7ktJ6Fw9u4pmVQ6Pf9U2qnfSesOIEqkrlUf7/jdhALn5J+pdEJe37u+B8Nsvp\npuLfkNaYvBouw6xySwDb1K5NfxAGV3ECoEW8YtmuLmHMUb185+seFvd3GIx610UXrALh3g3u3/WE\nRROpZtE9DFgCXDzofgmzkD1H1Zo7FhsYPEVl4OCCRfBSqp3Xm5Sd4r/sPI3JiH6APWdPRZb1FS3O\noj5AyIJVMlj5feeaOcrBu1dAs/9iFqAQy/cM1jar0xqLtjutisvV/v+faXSly17GBsnBUrU88LZ3\n534CWKA2+KknRToz+lyPEbyNaibXmLoFdEdgtcjCHZTZoIg3JbX5Jxa7Hk8wzGcyM5kynhZMsT4U\nu3bxucM9/RTVdo/vpecpldHDgBD36S19xfXxCa6yx7EJktHAa9FvAlxc9ikkUrqA1izadQvo69jk\nzyjK+FqAr2ETEoG4PQLxdbsz+nxB9DkkK9qVSpKXog+/gup9Hx9zc+BscMtj1tv4vfEJ/9/nB8gc\n1dq/d/vf8JhtU0nCFVxtia5xSHIENjkT+pLJlIr+W5RW/dmBNyF7h2IyxvkkVQWxiz42gHLtaj5D\nte8ZC7wchWCsUB6DObHnZWT5G2YqOc8xtNZLjqm/L+oZxwNh7FFP2HY8ljSsroBugFkXX6L6zDjs\nmYzeqS7DJntfxSZ1ZzRWrC+E3BNPUFX6cuz9Xw9XuBt7l9QV0EuwCZB6Uqpx2CRV/frcgvXvr9Fs\nQKhPALQbpEeeaZWxwIew63k6Ve8BMKNBkC3we2zCyOedaMy+3MTUPm7XC24cuAarq7uTMmlYE+1i\n/moKqBtbe4/PLAcz68mqEuP6GJs+S+doSmJar5AQnv1n6JsLbpjgeZjSIzA+/jNUJ1GDd8hXqN7L\ns2Pv3ieojjnGYO/8ugI6wW/fpIDWLKBzzKgr8aAxswrofyitji9jF2hBTEkJVsdTqbozNnE/pcUy\nnknI22x/GNZw/6HsTD9JmU1xBUpr7EQqWamyh2mlncWuPpNxF3aD/B5LzBMG3d/Ffv8U4J1oYB/S\nN29JObh8gqKzzzLKDFvr01r77X3ACX5AHkQNyuKBmPV5A6rxprH5P+7oerBBD7WkPWthL5jFKWI0\n79yJMmELfuY9uMlsRfUFFrJH1hWDIMf0EhTdS68KqMswZW11YHPI6i/KfqCYiJiL0roOpdXHn7Pt\nrOtn28zAP0qZmCqU0Ajyh5dxsDSHNgmuVfVzxQPzN7HrNpnCRdyNp+x0vJtqIVOYpffKVUHosI7D\nJhx6/PJoyk7xXczK1aSAnocNyILiHjwK9qW5rEWUHKZyD4akWatRuk7+C/ikuWOxAPZ8HUs1huUZ\nSvfteLLlz5jlrebiyF8pMwJPoZp9NXTWi2LxcWG/6/znuF5gPaFJjHdRq8yI3o1NXu0Iru4eHMdg\nbef/BwX0Lar3wUNYf/E81ZfTLdiA+m2qtcDC/VB/aYWstuH69DT8jqWpxu0G99YwkXUi5i1ycbTN\nNzDlq+5O/1twIebwFP/fuysWymlIZDUhGqgG1+6/0z52+WvAuZHrUvAWaaqfCs2TAyGxUyD039dj\nkzzhORpHOcAL8ZYh7r9WxsJllOEZUE5mvgebsPykVxBHUSr+3m0q+zfVWtCxAvoopQL6uWibxygn\nDBal7F/CZMNa2P1wA/b+DGEmPf7/ZpTulTH1WsNg90xQSu6mjJH3z1PmKBX9uSmt47EHR1/o7d2x\nY215y/JjxfIUrOyz19wxx2ITu6Nrz+rTWP9RV9TCM/lsbR3YNX4NWJWZCymiF6+h0A/XJg/ZFBsf\nPENV0XkTm1hZjaqHQQinCL+rx68fj91rY2oyTMJ+b5RMqnCZf5HW93WQrV7bPAy0IwW6OM8o//va\nXbMGK2s8sTVonEA190DAZ+xurE39BtVM4vE9Wb92L1OGes0K32te7eYCF56Pnn44T/34X2mw3M/M\ncZ6npYRZf1G5/k2eMnXlLHi3vEbl3elGg3u6wbAS+qpzqCqUoY94gWq7B6+FeiK9ObB7p56I8ZOY\nUaEmT+FxUvcSWqj2m16GA9tlok5Of/ikT8UGzzdjg8GgNDXFc/VGnMEwcido6aCDO2yIj4wbMU6p\nHzrSiCzzyt90yJ6inKW/CbI2WaSyJ6g+OME6ESdq+Yh/COanktY5ixPW1OKysn9B9iWaWQUbZCwH\nLAkuuGWFB8dbfYuiuPEgBqpxFD+G7EHIbsUsVB/z66P6R9mllApGHIcX4prqyUhCZxgewLOrXxed\n9pvYQ9guPu7bmHK0cTWjV7/zHmAPyH7o743dou9+iz34vVlz64OlX2ED2bF+4BMs+eEYS/r/z1Z3\nyxzlbP6N0RcfB54CtzU2uI7ut8I1GuBVyG7HBibBKj0GU3yfoLRkQelOWXdFh9Iqfj/2DDUpoB/F\nBgmhHMcbmHvaThTWgPg5y+5pfvayON4nlKX4MTaj613Vs+ewwVDsghsUa285K/qIoHg9QTUO7QOU\nytEdVONY4wzQX6dQ0jKHvaSWoyzJETLmnUf1WXjLH/c1ihebG2v7siimMPwp2v5eSuv5NMpSBStj\nL5WdqLp/L4q1+xcpLOtA+WyfSbXPWQRr99dpfcFeR5F1ueAOyuuwI9VEGqFPCPfr52mNszzP/w/3\nduyOF+LjfHbRYgLCewRkz3oLdCxTSG71mMlfGegFxfcvmPts6P/D8xUrKHEm9DpPYdd92ej+CZME\nz2D9U1DyJlP0Z9kDlNd69ahvCu25ld8v3G/B8r839kwFT5+RlMmi4lnrf1H20d7LAKhaQK+m9N74\nDqXXROySGd7DT2HXKAxu6jHd59OCG01zPNN20ee7KEvnROVvMocp9otRts3EmVfQwD9LwVPkwNqX\nQTl6g+Z3ySiqJX7CxPQbVPvu3bDn6w2K0jlF3dlXKBRrl1G2w5OUExHx8zcjhPdk/R03EntmmpS+\nC7B+OZ4cC/fQ9VQno8ZSvgMWrm0fvC3iPiJcn4cpkgwW75OjG2R52P9fpTZWC/f5ApSZo8MA+i6q\n3mHU9o0V6F9TepuEpFr9XObHvce7QdffdTs2yBYT5wcI29xJ1TU93JP30GcXXOfAbdWwPgO3X+v6\n4vv65PXTwIXg7mnauh+ol5ubWfwz4NqNBWcAV0+SFT/7sXdYmEicAi7u617A+q/6u3N+7Jmre+FN\nwNr8YartHu6ll6j2M3Ni7/+6Ajq7P/YrVO+Ty7CxQJMF9CVaLaDHUE1s2McY0D65tteZ5ezms6qA\njsNehF+mavYFc11pMzuSnQojvwvfetv6ll+ficXdAORwRlSbauGNKS2ir8Pu18FPR1EMDs5eEC4J\ns9P/tImeDT9JGWuQU7WoVpcfKtbVvs8+4wfN3/DL3i142neqk0nZOvDzkyk63B5gZKRM9wA972DX\n+sbq+bMMRn4Ysvih6U3eX9jxroheMD2PwUWRIpF9HU67EFMSgJ5d4CeR9SVbGD4flNvjo+Nv5mUF\nvhxn4s3hsyFb6VXl9tkrwPtt+299CyuZcJe/Njn2UNwOJ7xg60IHvfCGfpungSfgzJVaf++xJ2JZ\nCTeEbMVerkc/LGdLQxZZ7bKHIPswNnmyJ1z1Dnxo0+r+1zoKV8CPbW3rwszYGpdAtgZmZdoFznrF\n/16vKPbgr//B5fEKefz9e2pkSRr5PrhqDoqB4hYrR/fflvD9PWw5ZAW++H+wZ7Bij4UDl4Btp1K6\naOZwcTjfw3D1qzB3sP7eD9u86uXxg8qes6r3+0X3+OU/YslFgvw3ASPKe6gg/n0Ny1vvCCedCpmf\nPRx5H1xwJzYI+attu+CiwIJ2jbf6DPSERE0vw9UO3h9mecfDifPA6AWxQe98tn8PFC5tpz8DPYeU\nrvTZIl4ef316Do7k/6b/PUf7SaQv2PJXbqKo2TnpI3DtCIpaolc/Q3n/E12P8HLN4Yq5KQacn/1v\nNN8zAbZepnr9ZvuwX/YWzZ5wDQHmhmWWhi/MA3zMrs+kj0TP12twxTowT/RCunQR2GI8Zr3K7e/y\n91B4i8y9EFyzkFcYHocPj7RtsueAbzS3bxYU+syWR26OuUivVm5fKJ45nHxWdX9ybFC/s32+ZKrJ\nn70BV74MefQ++N0Iv/2twAdg282r8vTsBwdESkoP8Dnf37nxlPfDPMCz9nnrYI19wMu6HqaU+77p\n4l9RTqbmkO3t3w3/Z8sLb0qhGPWcD9ecSzHAydaD0y/FPCxmg80X9vL/nCL++qD1KRTDM1+BHwav\nky/DOev47R/BFIgcen5FMTFwzGrQE1x/x8IlD/jt/fv03CPg8l9gnjavwilLUnkGe2htzxVCeMC0\n2v12G1z2mF+Ors+R51JOQuRwzmuYJe5m6y+ueIFq7Kk/nnNwdk+0/B473zXxIC+HTbaksCj3AGP9\n5LMbDz1LeBkfoSxVFuT9g313fsjnAJw/FfZfBLvey9W2vwnOfBqODx4SE+Dq1yD7EGTfBd6w8cin\ngvfKK9a+PVBOvsTH68PypXf6/Z+rff8ocClc/F44MgqLufI5+M7bmEV+arS9V0B/+hRcEyUnungJ\n2G8JbGJxCcxLJaewul/5Gqwbvd96dodTN8Jczc+zbXcIk1P3woXvr8rfQ3T/bBbJs1X5/dnBo2KS\nyZ+tRWkBDduPKbc/NCplcvaicJwfeGd32/dnnBS5/ob9PZ/efcbeP+TQEyYA52r9vgdYY5va9uHz\nxGh7bzQ59234ZjQ+22RDuOpFyrCHhuO3yAOlJ0G8/Wjo+QnMsX4becY3H79nmWhd7ftZXe7B3lGz\ncrye8HmrWZPHbQk9/65+v/Nq0fHXiLaPxmOnf7vcfp914IJR2ITXnNH248rt4/fpXuvBhRnWP80b\nbf8u8AgcshD8frFy+53WgUtGUSqgYfvZgTfgG4vAuVPL7Y9cHk6bgo0XJkfbe8+MFVaAnoXL8fXv\n/gi/jQxdv1gEjv6un7xYm8brt/EnKa23Dd/Xl8duUEwCNetP+2IT69Non0dllpkNKxkS1/m7l/JF\n8x6aLSyRUhqC793u4H4Vrd/Lr3+ZIobOZX7dGuBWBXd79RjFvq+BW9dmfXoLFAem8W+mtVOSZwa3\nELifRcvLRr/RgTui/b59Ov5s0bGmgDu4dnyv1Li1auv7OGN41rV++4bYnb4kGHKTwXmrs1sf3LVR\nu4WEK3OD865xbiNwV9WO8UVwD0aW3cQ0JYhwT4Gb1/++MBiay2/l1u0wAAAgAElEQVQ7rrpf8ZeZ\nVbpYfrThuOFa1RK4VI4z1f9/Btze4L5eu/9PAedf+O4v1ukUx/UTHe6Jch93J+y4e3SeOHan/huc\nKWLxcrFtvL6WnGpGcbM3HP9pcAeAW722/r8UZTbcceC8C767FNw20W/3AxY3xi+vYG1QHCdr87sW\nqP3+JbGsjw7cbeBWiq5l2GYfcIf5z2s3HPNligRWbjlwd0fnH1Pd3o0DF1zBp/jvto22yyhn8P8O\nbvFo3y1q958DtyOWhdzB8l5xdy9QmX12D2F9aINVwE2l2fXMX8O+4MaCOwOz4Id1PZHcj1H0Wc6B\n8xMk7hG7XsU+Tfdn/T79EGW/+d/qfgDuVHA+46tbB9wN/vOPwP3Afz6x2n5tf1d8/mgSyX0oWh9m\n+78Czns6uMsieaaB814k7l/gfOKbuK90b4HzFvXw/LuR4L5Zu8/q1yR+z+a1beI6vgtR9NnuDYrZ\ncfczyv59nN/mfVh/HT9L4Zi7Yv3RLVStDGAubfXnYqlo38gDyB3q1z3g/4eSLGtG299k7VeR4T0N\n57gV3Grg/gcuSsrnrgK3Ifbcemu9m2r3XLHNk/6Ya9v5ivWhX+/FilC002K19UH+mkeMO9LfIxeA\ni7Kgh2vpVgV3V7T+KHD7Y/3j/9WOf4Ddd+5lGJH79Sdi79o7KL2lwva7gfssZb/xnWi9o0ia5ib6\n5d+Du728N4rjnATuwujeXhpcKE21PLjIauzmt/3dT+x3F+ufpHgGAOvjHbg4UUt83XZtbfPpUbTB\nam3WL9VmfWRdc8thY86TKcMOYnnPpCxHVT9O1sf1//Trx7bZ/tO19a9j42UH589IGZI+Upx3Fi2X\n7ll/nJq+4H5Ubfs+yxON+92q4P4K7niKsQH4PuBWcFdT9P/g7/uHsHfsedH6tey5cs6OWawPukpO\n0SdDea+7jcBdWTvOTaVcxfr32/Pccpz9sBrI84KLPBndl8EdS/EuL8bXZ4GLqmC4CWYw6e2ZKK7b\nhxrWN4RUuX3BOXZb+8g+6k9ttxnRh52byDBXw3uoJuO4kNK9b2ca3XsaqWd9PM7/H0up4IZZscex\n2b+VsEHKMZSxTWAzTR+h0QW38Xf0I9ljkH0hWr6HqsvrVfU9ZvD4b2GuQqt617VDvWX2z36DoODe\nXNuxTWdd5xMf9jP7Dcl+sr7caC8C4zBr0eGYtSDsF6xU3noFtMQmul0wS8FHfHKTDiIuAF5kGovl\n9y+FLLjIRi919rHrkN1GGRe1ENVyOUTXqp6AKyQ82JcyZu0dLF73acrsr2DPXZjxn4Al9wnHbSpT\n8hD8Jk7KFbuCBZeVdzEL4a+p1l+NSx29i01KjYAscuGeGRpd3q/F7u+bqSbPmpcyFnphShflmzA3\nQu/mknkXviy4hx5IxTU9c5T36HHR+jh+4m7I7vOxerdhLrNxO4eB4jGUmYAjd2p3FWZZDG5xYG5o\ny1Ik2chepXDFdvNiM53+mmdhkHpuuX/mIAuuwctR7WPihEXvtX2y03wMIvC3p+wFxjiqfeVUCpfo\nLI6jxGLpw/51+tRH4C1HO0AWDeLDPetGUsaFBrwiyLyUliKoZqCtDb6KMiI3+n7zUKqx9rv5/z2U\nM7hzUPZN4yjjwh+mue5vnbgeZxxv9OfyYxau8+XAWn6QeQFlLGlGWcboX5R9+ZPABuAmYfeM91LJ\nfoi5+y5MGQsa+GZtOY6N7ql9F1kBGAs86t2Lw7HBrqkf6BT93H2YG2o0WCuScayAWY5Xo+q+C81J\nYuLrf3w0APfWpWxx7F4Irsvht9zi9/NeKC64tj1FUbu1IMR0HkG1BvUG2PMzmjJcIWwbmB97tidT\njR0OLo69lQkJ16Re+y+UCKqXtdkf8wJ6gGpW+9Wwd+jdwArlwLNwwX0Ei0mNQy4u8ffdWHjn3tr2\nUXhGMfH8N8qkeetR3hthPHedH8wGy+BnMY+HCTVlZH6K7LluJNXrGTJZB862/VsSkt1OtYxSSC71\nkPWl7m+137pI+bFPk+ZxHO2SbTaKJ+eCm+VdVF0rN8Xel/XEeGOi//U49OC+XS/JFKi7Tgb52il8\ncQK4dbHnwb8Dtm7KzTADuC/R6kb/PK1Ju2aGsH89I/mBVPulvhJ7FI7HrJ31vBDjsbHOnVRDSvbC\n+qa6y+skLAzlVqp17efEdKGnqbrghr6snvF8Dsy62s4Ft54XJRzHJxcrJoR9aED2DhYyE86xPZX6\n9dmLkH+3XO71mWjK4ts0cWHPzL827i15XZ+YWQV0HSw5xYexDuJ2LF7vh1hcwn2Yy88Pez9MERc2\nhda6iYHwEAY3jH9HcTdnYC+MuDTHQliq8sitLCVZFE81qwNzgOxkyGpFpLMP+mvpB+7Zu5TxFe/2\nfWA4y7K9gw2MJ1DNVvk0lpBkNsqYGrC4r4XsoXCfwAYXG1avWcfgkyy40Vh8zmvYADEkmdqyunl2\nu7+376WiFGQ/iTaqD8rwx1ulti50wk97xXxlrLNbFxusx7PnIeHDCKov/GMpFdBTKV2A78NiiUdj\nGWGjGNPsXn+8CaYsZH5mNzy3cZIsgOztfrzXvkV18BiXdogV7q9R/v6tsKB9sBjBtWhf93BHyvg6\nT3ax/131YvUjgXkiV1MoB8GLUQyas8NoJsTzbEAxSC0U+fCSiwevIRPxf7EJt3ggEinHLTJCZWCf\nPUAZd3szVeXtEuwZnQS8WJtY+ByDThZiat+mzKgKxQSkm4xdq0ghyHYuP3MV1Thjn721qBl8OjZg\nH4ldh5BQqgf4sH8xX0VZ5sbfN24p7D3WLiY//g0PUAwqg0s5+Gu7J9WJpWD9WcP/rvBOO59yYmQy\nRZtlYbC6NKbwxDHF82ODkL9TzTAcuWJxEq2DsJi4DxlH+Ww8QBkLeDtFbDZQTgStSzVZS8hmuhGm\nvO5LtS5svO/rlNbX/bBrEUp0hHvyTkoF7p9AsA7t5detj02Qhf40JEV6G5sg74nOG/rEMVhysHhA\ndh2mtL/u+89NaZ202w5z51+3XJW9jo1B5gL3K7MQ9JnQj7xDNdfAPdizvhhVRRngL35C7W3KiXmv\nUBZ9cohbf4Ui/hoos+mHSdQRlBOhYeB7WzQJeC12X+3tJ8AiBS+412VhIA/wLLjgkrkrpZJ1GtX3\n0VvA4pGlL5SiqieWm0zl3sxewRTeB7Brvrz9PhcU5kP8dy/SvgxgTJggfIBS6YZq8ppYcQyTsk9Q\njb0LiuyDlM8LmPXnDv8banVPi3FrrAR/MPpcL60VWK9h3VNU2yZMgL1C0Z/EtWVnBDc/9k6Kc7OM\nxO6Xh+izAupGtlo5B4RYcdwOeyc+S1XOEEP5MlWF71Rskim44AZCbPRJwAmRNXEBTMl/imqMqXep\nbamLPAYbL7yIlZwL91lIQlRXQL1iWkz6BmNbPIm9GGW+gwupTgYC2SHRe/HdNkrogVQSYhWJ25qS\nQ5mC//rkRRu+myFmVgH9s993JWwwvDIWyPQs9rAuib18+qoAejncr6MZpo2xBgkd5lggci3hPKxz\n2xObKQzsHn2u1aZKxqa0n10bILJ3/ICsjbtcI3k/nDhYBY/BXgZQZrVbCRvchhnt57DBzk8wBWkT\nr/R0IsGqOBUY5RWtS4DVfCfSpthwtgxk/6itDJbq+nogO96U1wqhQ/Ev5uwOynv+81QUqSx8Pobq\nC/82SsXWl1MA4D9w6Z5UO7RYnqcYkAzE0yP7PmRbR8vPY79pLcjiZGMOOCAaxIZrdTOWjCNWGgOh\nj5lepuZw7nchq2XyLF4IB1N94cUveZ8gJYsVgdWp8jatNK0LHIj9rp2oJJbK3qX8XfUaqG94WeJy\nQ+Og5zhs8FMvS/ULbNKjLusgUkxkBMUmDErb9enxZEKId449QcKk1tvAZMjCtXjYH/sYKvEq2WtY\nOZbl/Pr4vdKb3CvSmOgu+xlkJ0bL4fcd4M8fBvz3AHN7y9bqtA4ALsQGJXFbhufh41RLekUumhxE\nNelbTjVhR71ER3jmH6DMDvwE1dI3QVmfQmXWvUiqtRzWb/7G/6bYQhyYg7JfChM5sUuco8yKDKZM\nLYYpp5vZ/+wVbCI6eB+NpewH6vW7Q58YkuWFUI9XgPt9uz+HvcNeonpfhTJPd9E6ETQayzS8Gy3l\nuVxfkjHWk5Us64+5N2V5ntkxxS1YiS+jRQEtOBBz848TOAHnhT41FKw/i1KZ9haoom8Lky7LUyhJ\n2aNUB/Leopu9TZF3gmv8/2e9QnwfFjN/OaUy9zblpNmm2PvzRlotoMF6FfMWVaswwKcjpWBxWrMZ\ntyMo/d4jolAIQt6L+sRduMbPU1VAfaWBShIxMGV5JWzCqH4fjMcsW/H64AZ5F80JAKHiyVFkZb2Y\nqlfBS9ik3DvAw34OZmaTEYX2iN9zE/05nqHvFtDxwFJV5b6Q3/fPTeVYXN0SPD3iiYcrsYm456gq\n9OH+b8pG+zqt2WhDvxF77YDdY09SeOy4+Fq97s87MbLSh/PGFRjAJhXWxRTTuB8IiimYJ2MYG4Yc\nN4GQ7C+u3R3Ia8tR8ig3B/YsngV8ylvOY7nGgfs51XKLu1I1Asw0M6uA9jchLmVXyhfr1VRdiEKx\n7sCdlJ1QfMGjBDqDZfmbHtllkN0//e2GBEthmRnnoBg0FO1wMa1p9MFmyLeoDZg6jTAomlquyoJr\n0DuYG10fJ1yyA7F7/lfT29Jv77CZs2hgU1iMFqVZYXmEqlL5V2AHP/sVXh4Af4c5F6KtAtpJZPtC\nVi8PEZ73MNjwA8/C1XFryoFjOM7TWIfbWx3XvhCyUPZEx34Ke5lsCVlcliRYcBamOkPZ0HbZO7Ra\nwcN3b0B2PeZK+0Ttu6f9pNP2tZ2m+v+x6+yu/n8oh1Q/z/ewJDuDSKPiFiyV2wL3NvTpqwKrVNdn\nN/nrEMUdtqshnDnMxW8UrYPae7BB+Pr0PikwK3wcG7z6JEaF5ekFrD+J69TtgU0yLE41s3lwDw7J\nKmLG2vrsWdpbQM+g+tsXpZzNv4HSKlPLqF65/yKrX8VF+zXKclf3g4uzTofJhJABO9QXp3Yv7Ef5\nu26iWkYpZAX1g0j3MUypD7GbrwNrmzIWXEyzYFG4HZjPe+bMQTm+CFasEVQnc0JN2qOZbnhPJZ6w\n9hxVLBDXYkrZi5TZQINi9gSmcE/xsk80mYp7/SGqCmi97X0yorg9Jq8abe+VFPBKchS7CJglO1gI\no0Fr9jymlGwIWTSpkJ1PWe6KSM5abCVQfVdeglmzX6TVhXUKLRnjY0tx4fVQrw/aVwXUW6ey6/xy\nKCcTPGmuoDqpGOI+X6HZQvkAVi++Kfs4NcvqBKyPiS1k0/z/p2mvgMa1kYO17CqqFtDJlIpqeM/N\nVVMk+krI6RAraqFdGvoUd22beMMw+RHFNBcZtEOIU32yAaoW5Ta4kBjoOqrXbTQ2AVOXc84264Pi\nWK+z6xW+rJ7t2iughadNaJt5scnycB1COIfXY7K3sPdOMEyFycCn7VyFYh5cc8Gs2qE/ihXQVymf\niWDZbSDLMKPQAVhc/QhsouteyB7D6sKHvngeytCvzwH/A3dC5CIeTRJ2F+0CYeMEOyFofT6/PA7c\nVuCiOnzuQf/d3VSSWQC9BtxGTOPJ/k1CJLDEGaEdIx9xtwOWrKCeFGJlcBu2HqdTcA+Be9PLvRAW\n1B/FpLjf+e9uBPej9scZENmO9ueuKRz15wgok9BsgiU98fFibqxfvxm4LixmXfyufSmShxTfLd/n\nvmDAaZvkaIR/Lk5vHbS4/ftP/qbjuE93zvUJuNG0JHMprls9tn1mjr8mrck9Pgfu+Ya2eSA69wfo\nd9xnmn9bJXFQ5MHiJkTra5PHxfqVaYub02/jB6puI788N5a8z8/4uy9SJN9xi/ptfgnufnA1T56Q\nXK3lXB/2+/kSMUXSrKP8+RyWoChKGuMcuFrt8GL99g3rDqeSMM/dHn0XFKvw3H0eG09ElmN3kZ3P\nzUM1wcfvsIQy08BF8VOVczfci87Z9s5RJrNaG0taF//GMbV99vDX1mczduOpJKFxb2OJzz5Zuz+/\nX94LleRAJ5XLle1/Xy67f1Ak9HLngNvJ34+xC/dMULxPahPubpJf/4Nonav9rezv8ZesXV2GJdyq\nxR662cEtRAvFcUZj/enOrds07nNhbf8J4I7Bkm7tAO7caPswFvgbuCi22/0C3J7RcbzLubsa3N7R\n+lBDNNyXUQKyigxnWXsX60PSrlvARaXt3Kf8+jXA3dp6nGI5SsjlJmJJDGOX0d6u0V/9fh+L1q1u\n53O/pEh62O7cxfor/HfROMltWt7rlbF/0AceojKObJRvK8r++ydUnlm3I7jT/LMQt9feWIKoWi1T\ndxiWMCwkWQsTWz+gTPL5Cf/dGeDuA+c9RNwt4LyHiNuJsg/9DzhvTHNftnsLsKRDYfs9KZPSPQjO\newmGZGH16+ouBreF/xwSHWZenul4W7rP+e1PB3eIXR/AnjmHJdIKSdPmLmWojV9WP9bNahKiFPQi\nTHEB6g/kDuDera2Psui5WvC/+xxFJsFekAI6ALh1onaJXhJuhaiDqNdy62Dcglg2xKt9Z78BuGtq\n2wRFcJBj51yGvazrisuZzS8Bd7/vWG6nNfNhhykiM0Ih/61tvtu4dX0K3NzgXqVvLnlQZgN+c/rb\nTvdYp1LJAlis74J2H+j7s3h+HbjIQyNkOK4rDf167nD8epbYdoO4N9qsDxkap/bhfL7/dedFA5or\nwPkYSvccxSRbZeLkBcoEP9P7XUEpiqw77mQsRvJt/13Irvud6Bz1jKOr+fWRVcyNxzIL1ycS3h8d\nJ1JanAP3DTt2PMnmfospoe8D96/a9g7cb2iJ5yxkP4O2uF0oFLmKgjUV3L8pMrwX1za3d0oxwI0y\nWVeO8RFwd0frf+jXv8//94lm3LZ+ecNqn1jcz2EiI2Sa3gPc+dggM8riP7O4TcA1WB/dcg1tNgIb\ny8UTLTdi71mvjPb5vNGx3TW1azgCUyqjZGVFBtGQKClkfz7D/78Hy3r+ULTPkdhkzafBReWk3NmU\nEwixonCDtUfxu3y8bTHWXQR7J4QwtBcwZfYYcFFyzWKy6At2zxbrw70RjDUjW2Vo2faSNt9n5bEq\n6//j18fZfXf2634M7mu17cN56m39dut5K9nPv0XRL7hv+M+/t+vRDrc7lik5ZMLfi0oVAbc7piQv\nX3t2DvSyr0k1a/RRFM98GK+CHbOSmTt+roPnwj3Rb/k0ZR9wJbhT/OeDwR1aPQZgE2Q+9t3djNV9\nB+szg3t4UMqXxLJXe3fZoq/d1rdVHyz/bnu/z/9RMQC5n0W/66TaPhtWZP7SMt8dYgpo4+bPYrMY\nvT1QfXwp1pACWifvn8O0HTyF9tqt9btOx13qZT+o4T705Q/iGcvUNCku7quUaeGj+K9ru10BDZak\na1NL0r+4DJss+ML0t50pcsxi0cdY2FQ0lbLp1+PPGfVNbUoiDBTFeWvWCPcZilJjfTpOKJszcTrb\nfdkGYnOsjylgYQC0TznQaxkkntX++vR6rk2r27t/VAdulW1H9G3gNN1zNtzL7jJM8a2XKAkD3vr6\naKDlapn83XsplL62MgTPi1Gtv9c5iiQ9hbVwNqzkhLfOhgnE4njhGKfV1gdL8ub+/5y18+8FLgo/\ncKN9Xx8Gn971sCg19WMG3YunieL3LkqlDM4MHWMZfwwf/1so5fH1G1fdpnJuh00kjAD3IkX2Ubc/\nNom+HLgoo7G7nKL8WJgMKo4XrOE/pZi8dlPAPRdt85no8y7+OY3yShQTMe9vvjeKzz4zuLuealmN\nnNKyHv/Fz6dPrhMmoor1x2GW37g8zm7+ef4GuFqy0eLY49usj+WPrfI/8d+vDe4P/vPh1e2L/TJM\nmXuQisXPbW/7Fstf9L9rQXBRyID7FrjvUZRUK9afRFmWK8g7DvOKiBKlVkpABU+FLaLfsgdlf7ov\nZfmqwyjLiu0abb8vpWU0btPTqZTvqbTd8m3W1+No89brF65h4/rN/HEasnq7eQgTFN8esY/pT/E7\npyVTMtD/ZVgGk7Mp0+23qWWUdUC2W1FSyUbZxDO9fNephNiWhhqX2fex5AOdlEBpbVrjvf5LmTky\nSq7xYoi9PY3uJFgj2sXMdCmZg2xlS2AzYOd4hTKreIeS3YjFSR4yQMd/DYvF+VRDjOmuFBl1B5Ra\nG2S/tbbvMzdgccbTa8tTgWXh3F9SLV3zJ8oEKDWyT0SfZ0AZzy6tbR+XgqhZOrN3KcsJzQKN9/LV\nWJKZtWvnDQr+3FRiCLOobl89cUv2OJavoLecDmGfOC7x8Ohz8EQYDzzl48Gep0wiVrf6BQVpXiCy\nBmVPY4mIlsLKbYVSU3/3Mvj1xfZvgnuXMibTx7wWsbxfoZKwKBlhoP8g1fjIGaBI/LeL/x+7j46k\nzMj/tF2XgjjL85k+udvtwDHgFgWOxOKO/4FlMQ3KTxyT55OgFQPz0N4nY5UjoJpz4SiqSbxeoJp9\nGiy+878U2bMrNe7jsmTBGDMv1URlQPYSrdmofxl9DuXMavuxF1Yyp27oCQma4jIycVxsk2KS++Uw\nPryDMj9CUGRvwOJFX6DIk+FC+R0wa/GxWMmRdSCLQ4fup5o1OlTJeJZqzG6I9XwCeNkrVl/H4izD\n7w+uzrtRZsH1xLkoij4utM3c2L0VnqXbsBJFE6jmsvEZot3ste1jy+OGVKt8xLTTe/qYfLVdX579\n0Y/h6yWj8EkpLbZ4xLthf+/p6FwvsnYMM2oBjTX7etzLAjQWSu0j0/iPLKCDSTFrtO70t+1Eivuw\nqV5SF+D2aJ2BBGw218+AdyvOUSlsLkQ34FZvnuEf0HMe0GqNKNy4guXozto+S4G7vPVY3UDxm+rW\nl5HN68F+v3O1AfWMnLPdcX9qVhsAtwSFFa1w/5yEuQ3+qs3xaqFF7mQsPKR+nusxl8Ca+6K7FtzL\nDdu3kTcVhQVsFuRxX/XXO8Q/3lobT36j4Tqs23pe93htv9jiGJSOf1LEAhbf/cb/D67OwcV1OSru\n4G5DcO9QhpXNbQqmixRLd0PtvCGu9HFwi0XrwzbPt793nfPyht/zcao5WD7WsL2j6tq6N7jjsbjk\nOCY18lapxLBuVJM/xC/+kMIiCFit7LC/V8iCO7XbwS9fAO42Gr093FiqLs0HUcQdh2sPmCv1V6L1\nL/n/79pvAEpPiP3A/Zkixro4V0ZLLLJz/jd8ncIyXLT7+f553aW2/r+YZdQnZ3Lfrl2rb9eO32Dp\nLELcBu/5ncbe3gK6kd1rhVz1iYqutoD6JAZZRrVWHTZzl93ZuovoUEKGuXpWu25jBtzQOopfY+Uk\n6mnNg+W21sF2FQ9S1nYUokvIbqFaJmQwznlktLCLXxfercF6F8WfAWT/hKxDYqlnlEoZrMiltpKt\nt846wHY+e/DMMA6rn1x/V1wOLIolo4mtYCGB2i5Y+Z2GJDtAtfQHWHbm9Ru2uxPLZLxObX0P1fIS\ngRCD+Y2G7xKQbQscSmsd1hnhUqz2bqhFejbmJfNV7F3RMJGc/Rkr23FwtDLEPj6LWZdCptPzsFJD\n0GIhA8qM/94iVbGULU9pqbsJG4uHjNcvYBmOl4yUjH9Sts2lwKaYK+QYv22EG49lRW3KKIsfSy/l\n782zsMyqYWx2Ca3lYv4C/Iwy4zKYJ9gamDdb7Hm0X/Q5LttST4wTlM4JVK30cc3VkKk9HPN0cC9i\ndbV3jzLdx78tPE/BQhxbFqG0qAcLaCCMiTLK7MGhfvtPsOeoViUgcz57bMwVWL3vvSi85TKHPddb\nY1bYl6P1e2HW6riMklf0XagbG5dSjL1EIktn9jesLNt3GDxC37YQ1frTccK4jgvv6ZAZNmQBbSUf\n+FPUrdjdhHt00GeZBofcz/xtkVqQmcdlzFB8mmBQnnfRmRwXkk2cWq4rYgOHWv9GNDtfL4+RSpbf\nemvRTbX1Dtx14K6o7TNXaUWprH/Gr7++tj5YuGoD0hN+2b6N3SqU5R+GAJUEWp+pfRcSvB3ZvG9l\n25GYhTJkg/VKvRuNxUaOAvd69d5yDzdf5yZLan19ZV0efQ4JjI71y5tTyXvQ7thA+zjApWv7HUKr\nlf1vmNUxlu0PWLKmxanE6bpn/XF+TTVpUWydfTX6XIutBH89t66tG4llYw1y9hJu456jyJvgjqBq\n6fSJiEIGbvBtF1+DaILevRWtX6n9OYvtJ/ptn6OwUgOlh4mjiBUGyvwDL1FYRgtZw99Xo/W1bMF9\nIp+BbfvONEIM6MOt916I53cbQndbQAcSDVgHnboVu5vIwuxZB5eNmVmyEZBdnFqKmSdzMxafJsRw\n5rTgThsnuDonhSSDQ8hLkHVCjCNY/G29tnmwFnyQom5kIHsGs4x8u7q+qHW5X239ev7/stXV198I\nPEyllmZxjtuiOrRDgMxhsc0Af6h99wZ2Db7CdMnewaw8IXO8nzTI3sRiI9+iWq8Rqs9VTDslJliN\n6nlOrqWctD/Z/z/O/6+9r3vNu9GO+6ha9prqp47D7r1Ho3X/xiyjDwMLU2YJD/Gg/6NazzWOhw6l\naBagxQIKVlMzu6C27h0gUuiy3vKIXAMEd+gxVGMiw/MwJ+XvDpM9DwHXQhY/k7tE52yTg6YiZ/gt\nkyhrEePHvUf4hej4RX80jrb1O2PPrsz1IcfKYLOI/z+G0rsixJV3XMLRzhkkTuO/soAKIYQQYnBw\np3srwY60WjR/W1qQZvk8c1IpbzIccZuCu2362/XpWNOxaFbWB0tVg8JZ7BPFaBalceJyQN/r5fhh\n/SK19SGD6QzULS5iY28BtyW4WiiLexrLFvsGRTKhSnkQR5nZOcj126rM7gSay5g8CW49ZojerJ/x\nsYvPPmGQ2zJafxFlHGrIRr1Rw7FmwuJYlDtaorY+ZF3Oa+s/4tdvFq2bK7pGnaRslpQW0F9Q8Wys\n1Jx1dJLORycJIwVUCCGEEINGZXD524bvO8BNWLTiZqe5zOahsYsAACAASURBVM+MKijjwM3bt+O0\nVUA/Ca4h26lblEpJoT7JE9UFdau2KuzBvdg5ypqmV4PbPpLx9Zq861Otn+rA/TVa3jXadtMZk3e6\nv+ewUnFzd1HUdnZz2jVzc3j5N+jj8RxFHc8+y7BUm/Wb0BiGFmoit5y3c/WTQgFtwm0jBXR6SAGt\nk6cWQCQhTy2ASEKeWgCRjDy1AMObYvA9A7Ve+4V8kM83DHCLYaU3ZvU4a1Gt3YkpJm7Z5u1niLyX\n8/7Z34sHYeVIosRbRe3QDIt39bHGzoE72n8+2i/P7hW7DaNJlvWj7aN45Ep8bj9b+Ip4yyOxDLqR\noukcuAPB3WTXu5NxO4A7efrbTZe8H47RSq8KaMBl9KLzKQZUCCGEEGLwyP3/PiQ2EZ1N9qCvyTqr\nx7kJsnrs79uQ3dO8fb8R6mgfDjwNTAY31a/zCXkyh8W7rmtKKVDW7wxJclbC4kefjGI0r/b//wec\nWJ4yc1jm1Cf6P3dDkWdkfyw+MY7pvBT4PK2xoR1IdgZku05/u06m97Yd7gqoqNKTWgCRhJ7UAogk\n9KQWQCSjJ7UAw5xQsuPFQT5vzyCfT3QGPe2/yn4KjDDFrVAYfM1NJmCJiqAst+Lv2cyXK8neBC4A\n3kuhgAKwKXC7t4LNS0uZmuwzkC04E7+lL0z1/xejWubqp8DiWNmTTklINtD0pBagHVJAhRBCCCEG\nj7v8/xWTSiEEULNUfY2yFugEyuysbeqKApZBdmss+2tw4b0XWBnwFsns9aYdB4bsEeBa/zkuURRq\ney5KWXtXJEIKqIjJUwsgkpCnFkAkIU8tgEhGnlqA4U1RTuGR6W/br+SDfD7RGeQzsO1tmOIIsBxF\n6ZKKkvrH2j6jgR2r22UPz5CE/c+GQC0ZUPYGhctw1uEuuP1GnlqAdgx3BVQxoEIIIYQQQsCdlJb5\nk7GamXU+VlsObrqb1TcENk5TtzJ7B7L7Gtb/uMPqaIpBpHOyzk7jKWXBFUIIIYQQopKhtlYKxC1j\npUQa9xnful4MWfqUBRfoRedrqD0jhBBCCCGEGF5krqYzfDv67h/AP5r3KWJFhegTw90FV1TJUwsg\nkpCnFkAkIU8tgEhGnloAkYQ8tQAiCfkMbn8k8Bf7mH2vn2URg0s+QMedZTfm4W4BlR+4EEIIIYQQ\nAGRfSS2BGPrIAipielILIJLQk1oAkYSe1AKIZPSkFkAkoSe1ACIJPakFEMnoSS1AO6SACiGEEEII\nIYQYFKSAipg8tQAiCXlqAUQS8tQCiGTkqQUQSchTCyCSkKcWQCQjTy1AO4a7AqoYUCGEEEIIIYQY\nJIa7Aiqq9KQWQCShJ7UAIgk9qQUQyehJLYBIQk9qAUQSelILIJLRk1qAdkgBFUIIIYQQQggxKAyE\nAroJcC9wP/C1ATh+f/If4I3UQnQQeWoBRBLy1AKIJOSpBRDJyFMLIJKQpxZAJCFPLYBIRp5agHb0\ntwI6EjgeU0KXBXYAlunnc/QnHwIWSS1EB7FSagFEEtTuwxO1+/BFbT88UbsPT9Tuw5eObftR/Xy8\n1YF/AQ/75TOBrYF/9PN5+odpPJNahA5jUmoBRBLU7sMTtfvwRW0/PFG7D0/U7sOXjm37/raALgg8\nFi0/7tcJIYQQQgghhBjm9LcC6vr5eGJwmZpaAJGEqakFEEmYmloAkYypqQUQSZiaWgCRhKmpBRDJ\nmJpagHb0dx3MNYFpWAwowEHAu8Dh0TZ3AO/v5/MKIYQQQgghhOgM7mSQ4lBHAQ9gGvdoTNns5CRE\nQgghhBBCCCG6mE2Bf2LJiA5KLIsQQgghhBBCCCGEEEIIIYQQQgghhiLjUwsghBBCiAFhxdQCiCSo\n3UXH80lgy9RCiEHnQ8DFwC6J5RCDzxeBjVILIZKgth9+zA7sBCySWhAxqLwPOBO4AHhPYlnE4KF2\nFx3PesBVwB+BJRLLIgaPObGO6Ubg44llEYPLesAl2HO/fGJZxOCith+efA64FTgeU0TF8ODjWLWJ\nz6YWRAwqanfR8SwOXA/8OrUgYtCZHfgLsL9fng2YI504YpCYDLwAfC1a1981n0VnorYfnuyEJX78\naGpBxKCzFHAPMMYvrwFMSSeOGCS6vt1HphZADDhvAg6bKfkX5oa5DDAOeJT+rwUr0rI08CzW5u9g\nGam/gllDD8dq9X4A6Ekknxg4wrP8OqaIzItZwr4OLOfXP4v1BWJoobYffozA+nmwEnijgL9hbfx5\n/32GTUiIocN6wDzAE375Gez9fiKwBfBBYE/gZeBuyntEdDdqd9Hx7A78H1aHNbAicDL2IjoVOBh4\nGLthQUroUGAxrO7uM8AKte9OwwYmawIrY1bRzQZVOjGQ7AGcjcX6BkYCj2CTTicBRwAXYnHgYuig\nth+eHA4cU1v3BeBPWNsfi3k9XTXIcomBY3bge9gEw7nA3NF3k4HLgc/45Y9jbR9vI7oTtbvoCj6N\n3Yz/AH4RrR8BbEypcALsC1wzeKKJAWQEsC2wF/Az4DvA2Oj7KVQnJL4NnDVo0omBIsOe63uwF9PX\nqLrgfASbGQ18FTgUc8UW3Y3afngyJ3AK5sFyN9bOgfcCX6JMRjIaywGw2+CJJwaQuYBPYB4N52ET\nSrEX45ja9rdQvT9Ed6J2Fx3LbJQWzKWwl9CcwIuYq21gdG2/7TBFRHQva2KudmCuGWAxv3/C3DXC\nfVG3cB+OWcpFdzJn9HkeYAHgw8Bx9B4Dthtw9ADKJQYetf3wJI7jzbGSWjsD19a2q8f4/wxYa+DE\nEgPMtpjCMc4vT/L/P4VNLrTLdvxJzBI2/4BKJwYKtbvoeA7DMtseRvmCCjMjh2LJh+J1YOb8/TGX\nzO0GQUbR/6wBPIa1/TXA6lSVzIOBX1Eqp2ADlo9iWRLPqn0nuodvYRMMe1N1tR6BxfsdCizq14V7\nYnbgQMwzQklKuhe1/fBjbqy/PhLY1a8L7/qxmMfTF/3yqGi/+YGfAtcBUwdcStHfjMbKa9wCnAH8\nFptUjjkLy+8wNtpneeyeuBxYZ1AkFf2J2l10BXtgMyGLYi5Yx9L6ovk31fIb47GA5TMpByqiu8iA\ngyjdqr6CWTa2jbYZA1wJbO2Xx2KDk31QXcBuZjdMAVkT+C72/E+Nvl8ViwuLrduzY33DeeiZ72bU\n9sOPCcA5mMfKOsB9tJbT2hi4E3u3Bz4A3IRNTI9CdCNTsbjtwF7ACcCy0boPYu/5+bCJinn85w2j\nbZTfo7uYitpddAE/xAKTwW7AU7A6YBOibT4K3Iu55h6AzZTElq+R6EbtBubwf6GtzqV0n54by3h4\nItXA8w2By7ABzIUo9qvbGQFMA7b3y+Mwi9cZte12we6NXTCrGJTuO6I7UdsPT+bAMhm/zy9/FOvT\nl/PLGfYOPwGblFiHMvZrnvIwqnbQJXwYi/cL3ItNLIHdA9OAQ2r7HAncBTxO2T8E1O7dwbBtd9UH\n6w7GYS+YfYFV/Lq7gTcwpeMp4CLspp0a7XcesCSW9fRFrCTL//x3I7EyHUrV3Nnsg2U1PpZywuFU\nrNzKFOBpzF3jZawjCyyFWTv/i8UFvDVI8or+YRzwI6z9V6Asn7Gz//8yZvlegmq73+a3OTxa9/yA\nSir6G7X98GQFbGLhI9iAdDZsgDk/NlY7D8teH7xdQqmta4FvYmEXz/jvnvL7jPDbiM5lW8xifRDm\ncrmTX38msI3/fD8WPjOJcoy3FLAD8ACWBfvs2nHV7p2N2l10PB/HajkejVk9/4DNgH4Is3rFmW3P\nwm5MsLIcf8Cy4dazZInOZww2GPkjVkZnWaz+0/uxLIdHYxkPw7bHU7b9Ilgc6IqDKK/oP7bDJpiO\nxiae/oa5Uc+OxfKFmJARwJeBH/jl2bGY4FOpJqsR3YPafvgxCptwuAd7x59DOdl4FDZADZbsFTAL\nSfB02ga7X/YZLGFFv7IS1t4b+OVtMcsWft0vo++Wwtzwg6fTKsC60bHk0dY9qN1FV3Ag5Y04GXtB\nfRwbgByBpddf0n//RazeZyB2wxmFbtJuYgSwOdWshkdg9Z5GYe5Yv8cSEIEpq18bTAHFgDAKK6e0\ncbTuKsrYvr0wjwai5QOi5YkDKp0YSNT2w5P5sYniyX55Y2wAOgobqF6MTTyEiYU/YO8GMMU0nnBQ\nzGd3MTcWsws2PnsvcDo26TQvsCc2CR28Fa+kHO8R7dc1bpcCULuLDifceHNjFq6wfA6WihlM+fgB\ncD6WBetKSitYfBy5WncXcZbDsDwCuJrSAjIWc8m+DSs2/yDKfjZUWABr71A66buYYhK4EUswsi5w\nBTZJJYYGavvhRZgUXjj6PC9wO+UE8n5YOZW9gfWxe2CB2v6ygnQP9XaKl9cE7ojWjcDye5yLhU8d\nhpSObqU+Dle7i45hJGUB6SZGYDfnaVTjfkJ86AWYFUx0H9tjs97BxaLeUY3A4oEuwbKdxayFJSGa\nGzFU+TPV7MWLYTOk12Gu1qL7aHrGm1DbDy3ietztBpRrYBPNwbI5B7A2FifWg8qndSMbYcaCD1DW\ndowVkPB5RyybdcxsWBbrJQZSQDEgrIp5MCzkl+vPvNpdJGdPLND4Isy1st2LaS6/XXiJLeP/xzPm\nYVl0PkthQehXA7/GMpuNb7PtAn47sFnwHQdcOjGQfBmr61gvHB8zEnPPi90u43IaymzcneyBxfQc\nRrV8UozafmgxCvgx8BvsuW8ivPd3wNzxwGJ7w6TjhJY9RKezIJZU5iYsOdiFlJmKY4Ii8jVgS2Bx\nLOnUCrXtRqLxXTdxAPAKVpMX2lu/h2W7D7kf1IXsjs1oboNZuL5A+8y0i2O1wN6Hudt+llLxfJPS\nSvpu496ik8iArTDL9QaYZXsU8BLNz+WHsEHnsdhA5vXBEVP0M7MD+2P1W7en90RR72Cz5X/Fyurc\ngCkvQflQZuPuYiIW6/dx4BvAY5h77TIN26rthw4rYJnMQ3bjHSljd+PJ5pC9cgnMwr0FVlg+xPm/\n3LCP6GzehyWXWQtTMl6k+uwGBSSM+T7mtzsNMzb8rXa8d9D4rhsIz+jTmIFpFWATrJ1jY5HaXSRl\nXyxzIVh221OwWbNAPGOyPXYT3kAZByq6iwUwJQRsVuxa//lYrF03xALSoRrXcyDWmYX6fqK7iJOD\nrI3dA9/Eavj1Vqvxs9gzfzVWTkd0H7HC8DlK18qFsKRxH2jZw1DbDw1WwZTJwAbAzTTHbI7GlM6n\nMGvZqg3biM5mC5onlb6AldU5CdiMcjIp839TgOsxV8zJ0X6K7e0ONqas2Rs4Eptw2gV7rqFsT7W7\nGHR2pdo5rYIpIb/FBhtnYOn0t6N6o4JlQP0eVWTF7g42x8qonIO5WIANTH+EWTkuxTqqn2LuuIHQ\n9mthySlE93EYcBzlIDS06WTsWd+a9s/xFpi1VHQn38E8Frbyy3Ng7R/a+wastFITavvuZCGsvYOC\nMY4y7m8EsCnw82i5zulUQyyCZ5PobObDJov+gr3Pj6ScbB6PeT+shmWv/jllLG/ctrHxQYmluoO1\nsXbvwRLDHe3XZ9gkc4jhvA14iOYYbrW7GFAWw0zrb2A1u6ZE3y2AvXSW9su7Y3Udp/rlpptR6da7\nh/FYseDcL1+ExQHNg8X1XBFtuzxWXiXcC+qIupsfY2UTtsMmmvahGue7B5bpbuHafsFqpgmm7mQN\nbGLp15gycTvm3RCzKHAZVZcsUNt3M9tg4RGP0xrHFU8kH9+wb9M7Xe623cNGlJPL82PhNTvT6uEy\nJ1bfdWe/3BQXqHbvDubBjAahVNZCWNbakHToB5gSehLwCPBAtO+wb3e94AaXr2MD0eUwRSPwKjZD\nGlKr/9Fv84JfrseEjgDeHjgxRT/zEhb/FQaaB2HxvDnwGjZTHpKRBOUkdFTt4oFF5zMGS6++Nzap\ncCg2qfSJaJtfYPEda2FldLb360M8mOI+upefArthcT2XY5ZuKAcZ82N9/JuYG25QUNX23ckILL5v\nbWxC+dOUpbSg7Mu3w/I9gGW0DwPR+J0exmbvIDqd0H5PUSYP+w/wO6xfX7q2/WuYxespv1x/xzvU\n7t3Ci1jN3l9ibf8Y1tcvgt0XT2BVCp7x627BJqVB7S4FdBB5DMt8eCGmkKxHaXafDbgTi+tcHlNQ\nnu7lWBqYdBfjMOv3vFhb/x2ziKyFzaCdAvwEU1BOwhJWKMlIdzMCm1i6D9jJr7sBs4qtSundAKac\nnooNWOTZMDS4GwunCMrm9ZQxP6H/Xh6blPo+cCLKbNvNhHb9M+ZqdwLWv68RbTMSa+M3sInGC7C2\nn6vheHrHdzbx2DkoErNj9biX8su/x9pxJb88EXO//Qs2Brx+4MUU/UxdZ3oDG7uDKY8TsWf+Eey+\nuAILsTjIb3MQFnYlxIDRzm0yrF8Zi/n8KOWgY2ngEMwl6yg0GOlGRtB+UueL2MxXiPeaDys8HILW\nN8ReTvUgdtEd1F1nQtzW1liimal+/YpYbNCafnlZ7D44Fimf3Upf3KaOxzLfBjKsJMdzwDTU9t3I\n9Nr9q9jEUlzfe07MAnYv5oorupv4fT8S69u/QumC+TFs8hlsouFXwAejfRRi0500jfNGYvld/hit\nC+07qraPjH+i34mTSwTa3WhfxxIKzYMFpgfi+LBh5Q/exSxAdRY7jukKEwnzYoPQL2NtDmbx2mTA\npRMDTTyI2Jhq+78PiwP5TrTuar8dwNwouVQ301vbQ6lYXkhZcif83wxzxRfdRV1pWInqBEJ454/B\n3PE29cvLYDH/u1GdYNbkQ3cQ2jV4MhxIaeEO7bkulnAulNlZGJtoil2xwzE0vusOemv3+vh+fexd\nPwWbfPr0YAgoRNyZLIMFJTcVmY9v2MuwWL97sbiBOPmEZki6h6uwkgnjsJi+M6mWSwltuS6WIe0i\nLD7s75Rxv6K7mQ9r2x5gSaovrbUwpXNXzBpyJWVCKtH91Ns+VlBCX/4brP7nOVhCsimIbqOueK6J\nJZn6Ie0nHjbC3u8PYl4OWcM2ojs5BQubgep4bW3sHX8OFue5T20/je26m1Mo273eJ5yAPes3YNZw\neTKKQWMObHbzFize81jKmZL4Rh2J1f98FXO7FN3HSMoJg20wt4sfYbGcq2BuNwdF2wZGY5MTB1O1\ndovuoT5zPR/W9vf2ss96mCvuPZhrnuhOZqbtV8BiwW7BXPFF91Fv9+WxNj2oYdvARCzO85+0errI\n9bLziQ0BGRY+Mw2bZALYEvNiGx1tE5iElV5bbMClFP3NjLY70fZHA2dRzfGgCQfR79RfSCOxTFh3\n+eUxwHexG3eCX5dF264ZrQfNhnYL7VxnTsQSzITU+8tj1u3gbiur9tAgbvvNKQtHb4Alj/qIX47b\nOjz3s6EZ0W5mZtoe4L1Y/GfdDU90PnFbjsViuuf2y+dgrtXQ7O20EGXtX5DbZTcRj8fm9/8nA0dg\nCsaqWOb6X/jvmvr7wEj07u8WZqXdwSYkib5Tuw8yk7CO+R/YbP+avW/e9byPss7TRlha5vf65U2w\nhEIf62X/UWg2tBuYH0sgEVgM8/HfH+uU5gNuwtxvxvhtzkeF5IcC62GF5QPrYx4O52FeDl/w6w/G\nEk0FJVPPdfejthdg7tO3YuEWF2KTDVMwL6ZQaL435VITzJ3PHJRWLrAJh6Oxdv8+ZdjEblht588C\nf6OckGhC/UDnMxDtrommRJyKNRRYpzsxoSz9zU+Ab/nPS2KxPD2Yv//qfv3P/R/YjXygX14Q0Y2M\nxCzZD1CmU18TS7W/M7AD5n43CQs+/xXlvXAWFv8nupd5MVe72zCLRoZ5NayBWbcvBe7HJihWxCzh\nO/h9NfjobtT2w48NgEWj5TmxAeejlMmj9sBiPxfAJh6u9uvV5t3LAlhG6iuxNh+NebQdjL3bf42V\n2AmKxZbYWPcBlLm+m1G7DyEmYgG4Q5UPAs9isXsnYC8iMCX0eqwO1GLYzElQPFbHik2L7mNj4H9Y\nFtP3Rut3wywhqwM3Y1nvwFw1rvR/F2N1AMcgupER0f+fY8pGKCA9Drs3/o5ZwE7EXlpgE07Ho3bv\nZtT2w5MpwL8xK+fn/LoM6+f/g5VNA8ts+kPgE375Xex9ILqbS7H3+Zf88kLYe/8S7F1+DTYWCMyF\nefqt6pc1AdGdqN2HCCthDXkyNmv8C4bOyzjcZH8AfuY/r4q5XR6FKZ0H+vXTgOsGUzgxIKxBtSB4\njs2C74YVID4PWMd/NxYbsO6MKaQLD5qUoj/ZHEsasqtfnoD1Y5/BXkYhzu/bwC7+8z7A25hlfBKK\n9etW1PbDm0mYN9OOWBbLXSktHwcCp0fb/pLS/XpFRLexEDZuC+/vufzyFzEX62DdOhhLOAOwJzYR\nMTU6zrGUExGi81G7dxD9HSA7CssAeoL//wrVchRDgT2AT2E345qYtWs/zPVyGrAIlhUxnkEV3cnN\nmJJ5DhYTcATmbvcn/93x2EBlPkzp3ARzz9gbc9kS3cf/sJfQF7EEIm8D92EvrIsolZMlsRiQTYCl\nsLTszwHPY/2e6D7U9sOb57F2nBur17wWNn6ZDVM+F8Es3lti7/7H/X5/9/+VcKR7WBdr4+9hEwjP\nYJMN7wGuwN7hAEtjITazYe/5OykTDa6PJaX6x6BJLWYVtXsH0d/K0fyYRTDEUKyLdeBxJrh/oeLb\nQgghhBBCCDFUuZMyf8qAcx1lRqlpwOG1791gCTLA3IcFKB+EmeeHQrbTaakF6FCmUSaamI1ypnsx\nbCbsvQ37dBPTUgvQYUwCXsBmQX+EZbw703+3AxbvPal5165iWmoBOhC1/fBmRyy54FnA3ZjV+wLM\nw2kLzHJysN+2GzPYT0stQAfxAexZXwTzcDgPe+ZHYXXaz/LbTQKWifbrxozG01IL0EEMp3aH9G0/\nqDrf+7GaaHdi8ZL1LLjdroAG5WNbTAmFsh4cdHf65VNSC9DBPIKl4odqAeKhwCmpBehAfoAlJwCL\n9/shNvmwIBYDPD6NWP3KKakF6FDU9sOXiViiweOjdUti8f8jMbfrP2Iue93IKakF6DDOw4wkYzH3\n6nOwMd7SWCjZopSTDN1c1/GU1AJ0GMOl3SF923eUztdRwswk4ca8GtjOf+7G2dA6p6QWoIP5JPBm\naiEGiFNSC9ChPAps4z8Hq1e3P+Mxp6QWoINR2w9fjsJqekPrhPJ4unsC4pTUAnQYU7Da7Uv75VDT\ntVutXe04JbUAHcZwaXdI3/YdpfN1lDCzwHgsa9YHUgvSj+SpBehw9sFmwobSQBTU7u3YgaE76QBq\n995Q2w9fLgC2orutHu3IUwvQgXyHMpFUnaFyD+SpBehAhkO7Q/q27yidr6OEmQVyLB6km11uhRC9\nM1QnHcT0UdsPTyZPfxMxxLgMy348lBSPYYQbAe7XM7Gj2n3g6Sidr6OEERXy1AKIJOSpBRBJyFML\nIJKRpxagCxiKg9I8tQAiCXlqAQYWNx6cdItm8sTnb9suQ7GDFUIIIYSYFd5NLYAYVOTNNjxRuw8j\nNEshhBBCCCGEmEVkAe1gZAEVQgghhBBCCJEWKaAiJk8tgEhCnloAkYQ8tQAiGXlqAUQS8tQCiCTk\nqQUQychTC9AOKaBCCCGEEEIIIYYs8tMWQgghhBBCzCKKAe1gFAMqhBBCRMwF3O7/ngQe959fAo4f\nwPOuB6w1gMcXQgghRA3NUnQueWoBRBLy1AKIJOSpBeggDgH2H6RzTQMOGKRztSNPfH6Rhjy1ACIJ\neWoBBhZZQHshT3x+WUCFEEKIXsj8/xy4yH+eBpwKXAc8DGwLHAHcBVwKjPLbfQDoAW4FLgPm9+v3\nAe4G7gROBxYBPg/sh1lb1wW2AP4C3AZcCcw7g+d+GDjcr78ZWHwmf78QQggxZNEshRBCiE7iEEqr\nZE5VAb0OK1a+IvAqsLH/7g/A1sBswI2YSy/AJ4Bf+c//9t8DTIjOFVtbJ0Wfd8eUzL6eG+Ah4CD/\necdIdiGEGAa4CbKAdixt22VUuy+EEEKIYY7DrI3vAH/HvIYu99/9DZgKLAksB1zl148EnvCf78Is\nn+f7v0AWfV4IOBuzmo4GHuzjuReJjnGG/38mcNSM/kghhBBiMJELrojJUwsgkpCnFkAkIU8tQJfw\npv//LvBWtP5dbBI3w9xsV/Z/KwKb+G02B34KrAL8H6ac1jkOONbv93lgzhk4dxN9sQTkfdhGDD3y\n1AKIJOSpBRDJyFML0A4poEIIIUQz2fQ34Z/APMCafnk2YFm/78JYbOjXgYnAOCzL7vho/wmUFtNd\nZuDc8fefiP7f2AeZhRBCiGTIBVfE9KQWQCShJ7UAIgk9qQXoMFz0v+kztFoXHWaZ/DhmxZyIvVeP\nAu4DTvPrMuAY4AUsRvMcLIZzbyzW8/fAc8A1lK61fTl3YDKW6Oh1YIfp/1S1/TClJ7UAIgk9qQUQ\nyehJLUAnoUBhIYQQon94CJiSWgghhEiDkhB1MCrDIvpEnloAkYQ8tQAiCXlqAUS/MDMDr7y/hRBd\nQZ5aAJGEPLUAIhl5agHaIRdcIYQQontZLLUAQgghRKcjM7kQQgghhBBiFnHj5YLbscgFVwghhBBC\nCCFEWgZKAX0YK8B9O3DLAJ1D9D95agFEEvLUAogk5KkFEMnIUwsgkpCnFkAkIU8twADTl3JZw5U8\ntQDtGKgYUIf96GcH6PhCCCGEEEIIIQRgaeHnavOd/LSFEEIIIYQQs4jKsHQwgx4D6oCrgFuBPQbo\nHEIIIYQQQgghuoiBcsFdB3gSmAe4ErgXuD76/hQsThTgeeAOoMcv5/6/lgd/OXzuFHm0PDjLKwFH\nd5A8WtbzruWBXQ7rOkUeLQ/O8r5ovDUcl8O6TpGnv5dv6zB5Oml5sMd3KwGT/PJUEnMIcEC0LDN5\n55KnFkAkIU8tgEhCnloAkYw8tQAiCXlqAUQS8tQCDCxywe2FPPH5B7VdxgDj/eexwA3ARqmEEUII\nIYQQQgxFpIB2MG3bZSBccOcDzouO/zvgigE4jxBC2eIwbQAAIABJREFUCCGEEEII0Suapehc8tQC\niCTkqQUQSchTCyCSkacWQCQhTy2ASEKeWoCBRRbQXsgTn3/Qs+AKIYQQQgghhBDJ0SyFEEIIIYQQ\nYhaRBbSDkQVUCCGEEEIIMaTIUgsgZhwpoCImTy2ASEKeWgCRhDy1ACIZeWoBRBLy1AKIJOSpBRDJ\nyFML0A4poEIIIYQQQgghhizy0xZCCCGEEELMIm6iYkA7FsWACiGEEEIIIYRIixRQEZOnFkAkIU8t\ngEhCnloAkYw8tQAiCXlqAUQS8tQCDDCyfrYnTy1AO6SACiGEEEIIIYQYsmimQgghhBBCCDGLqA5o\nB6MYUCGEEEIIIYQQaZECKmLy1AKIJOSpBRBJyFMLIJKRpxZAJCFPLYBIQp5aAJGMPLUA7ZACKoQQ\nQgghhOhGstQCiO5AftpCCCGEEEKIWUR1QDsYxYAKIYQQQgghhEiLFFARk6cWQCQhTy2ASEKeWgCR\njDy1ACIJeWoBRBLy1AKIZOSpBWiHFFAhhBBCCCGEEEMW+WkLIYQQQgghZhHFgHYwigEVQgghhBBC\nCJEWKaAiJk8tgEhCnloAkYQ8tQAiGXlqAUQS8tQCiCTkqQUQychTC9AOKaBCCCGEEEIIIbqakcDt\nwEUN38lPWwghhBBCCDGLKAa0gxn0GNAvA/f0dmIhhBBCCCGEmAWy1AKIGWcgFND3ApsBv0Q3RbeR\npxZAJCFPLYBIQp5aAJGMPLUAIgl5agFEEvLUAohk5KkFaMdAKKBHAQcC7w7AsYUQQgghhBAC5G0p\ngC2An/rPOYoBFUIIIYQQQgwIigHtYNq2y6h+PtHawFaYC+4cwATgN8BOte1OAR72n58H7gB6/HLu\n/2tZy1rWspa1rGUta1nLWtZyu+XbO0ye4by8EjDJL08lEeshC2i3kacWQCQhTy2ASEKeWgCRjDy1\nACIJeWoBRBLy1AIMLLKA9kKe+PyDngV3uicWQgghhBBCCCEGGimlQgghhBBCiFlEFtAOJpkFVAgh\nhBBCCCGEAKSAiip5agFEEvLUAogk5KkFEMnIUwsgkpCnFkAkIU8tgEhGnlqAdkgBFUIIIYQQQggx\nZJGfthBCCCGEEGIWcZMUA9qxKAZUCCGEEEIIIURapICKmDy1ACIJeWoBRBLy1AKIZOSpBRBJyFML\nIJKQpxZAJCNPLUA7pIAKIYQQQgghhBiyyE9bCCGEEEIIMYsoBrSDUQyoEEIIIYQQQoi0SAEVMXlq\nAUQS8tQCiCTkqQUQychTCyCSkKcWQCQhTy2ASEaeWoB2SAEVQgghhBBCCDFkkZ+2EEIIIYQQYhZR\nDGgHoxhQIYQQQgghxJBCymcXIgVUxOSpBRBJyFMLIJKQpxZAJCNPLYBIQp5aAJGEPLUAA0yWWoAO\nJk8tQDukgAohhBBCCCGEGLLIVC6EEEIIIYSYRRQD2sEoBlQIIYQQQgghRFqkgIqYPLUAIgl5agFE\nEvLUAohk5KkFEEnIUwsgkpCnFkAkI08tQDukgAohhBBCCCGEGLLIT1sIIYQQQggxiygGtINRDKgQ\nQgghhBBCiLRIARUxeWoBRBLy1AKIJOSpBRDJyFMLIJKQpxZAJCFPLYBIRp5agHYMhAI6B3AzcAdw\nD3DYAJxDCCGEEOL/27vvcEmqMo/j33vvJIYBhhmSwMCASgZHQBgQl1KioCAIAq5KUFZFQVBQWQyo\na0BRETBhWERXQFBETAhoi4qKroGgmFYU47oYWVxFOPvHW0VX1+3u26Gq3zrdv8/z3Ke7qrur39un\n0skiIiIALE4f5wFfA/bKvaZ22iIiIiIiMqSwrvqA1tbI+4Demz4uAGaA31f0PSIiIiIiIhKJqjKg\n01gT3N8CX8Ca4kr9Jd4BiIvEOwBxkXgHIG4S7wDEReIdgLhIvAMQN4l3AJ3Mq2i7DwCrgHWAa7Ef\noJF7/WLgzvT5H7HMavZ6kj5qWctaHs3yqprFo2Uta7naZeZ4XcvjubyqZvFoeTTLzPF67MvfrVk8\ndVoe9f3dKmBpurwSZy8HTs8tq522iIiIiIgMSX1Aa2ykfUDXo5n7XQPYD/h2Bd8jIiIiIiIiE25H\n4FtYs9pbgDMKr6uUor4S7wDEReIdgLhIvAMQN4l3AOIi8Q5AXCTeAVRLNaBdJM7f3zFdqugDeiuw\ncwXbFRERERERySjzKT3RjiIiIiIiIkMKS1UDWlsjnwdUREREREREpIUyoJKXeAcgLhLvAMRF4h2A\nuEm8AxAXiXcA4iLxDkDcJN4BdKIMqIiIiIiIxGjKOwCJg9ppi4iIiIjIkDQKbo2pD6iIiIiIiIj4\nUgZU8hLvAMRF4h2AuEi8AxA3iXcA4iLxDkBcJN4BiJvEO4BOlAEVERERERGRsaV22lJDYcr+RERE\nRCQO6gNaY+oDKjKHi4AfeQchIiIiIiLlUilFfSXeAfgJ35/gErTEOwBxkXgHIG4S7wDEReIdgLhI\nvAOolmpAu0icv181oCIiIiIiIjJ5VEohNTTRNaAiIiIiEVINaI2pBlRERERERMaKBpCMkDKgkpd4\nB+BokkvPEu8AxEXiHYC4SbwDEBeJdwDiIvEOQNwk3gF0ogyoiIiIiIiIjK1JrmmS2grfUx8CERER\nkZiEZbp/qy31ARWZg/oQiIiIiMRFmc8IKQMqeYl3AOIi8Q5AXCTeAYibxDsAcZF4ByAuEu8AxE3i\nHUAnyoCKGJWgiYiIiIiMId3oSw2pD6iIiIhIXDQPaI2NtA/oCuALwO3AbcApFXyHiIiIiIiICBsB\nq9LnS4AfANvmXlcpRX0l3gH4mega0MQ7AHGReAcgbhLvAMRF4h2AuEi8A6iWakC7SJy/f6Q1oL8B\nvpM+vwf4PrBxBd8jIiIiIiIi8qCVwM+wmtCMSimkhia6BlREREQkQpoHtMZc0mUJ8E3gSYX12kmk\nhpQBFREREYmLMqA11jFd5lX0hfOBjwIfAj7e5vWLgTvT53/Emuw20uUkfdTy6Jez53WJZ5TLzPH6\nOC+vAs6rUTxa1vGu5WqXs3V1iUfLo1k+Fd1vTeJytq4u8ZS9fEvN4qnT8qjv71YBS9PllYzYFHAJ\n8NYOr6uUor4S7wD8THQNaOIdgLhIvAMQN4l3AOIi8Q5AXCTeAVRLNaBdJM7fP9J02Qt4ACtl+3b6\nd6BXMCK9CbfrBCYiIiISE2VAa2ykTXC/TDWj64qIiIiIiEjElFGUvMQ7AHGReAcgLhLvAMRN4h2A\nuEi8AxAXiXcA4ibxDqATZUBFRERERERkbKmdttSQ+oCKxCu8DMILvKMQEZFRUx/QGqtVutQqGBGj\nDKhIvEKA8BfvKEREZNSyDGiY8o5EZul4X60muJKXeAcgLhLvAMRF4h2AuEm8AxAXiXcA4iLxDqBi\nynh2lngH0IkyoCIiIiIiIjK21MxRakhNcEXiFQKEP3tHISIioxaWqwlubakJrsgclPkUEREREamY\nMqCSl3gHIC4S7wDEReIdgLhJvAMQF4l3AOIi8Q5A3CTeAXSiDKiIiIiIiIiMLTV1lBpq1wc0rKV+\noZlwKoS/ekdRX2HXtA/KZn1+bofx2sfCxRC+6vC96gMqIjKR1Ae0xjre38wbZRQikVnsHUCNrAYW\neQdRY1ukj+sDP+/jcysqiMXTAcBG3kGIiIhIfakJruQl3gE4GqNaqL4l3gGIi8Q7gJJN8jHcr8Q7\nAHGReAcgLhLvAMRN4h1AJ8qAinSmG9om/Ra96fd30u9aHjW/EhERiYAyoJLX8A5AXDS8AxAXjQq2\nqQx1HBreAYiLhncA4qLhHUDFVPjYWcM7gE6UARXpTDfTUjXtY+XRbykiIhIBZUAlL/EOwNEk37wm\n3gGMgRhLYBPvAMRN4h2AuEi8AxAXiXcA4ibxDqATZUBFzCZt1k1yplQGoz6gIiMRVkBY4B2FiIjE\nQTdcUkMhtJkHdL3xmqNxGOFS/RbdhCPTfWhVn5/bf7x+1/Arn/8nBAh/Gv33ip8QILzaOwoR8Zbd\nq2ke0BrqeD+gGlCRzsYoYyAiMnaWewcgIiL9UwZU8hLvAMRF4h3AGIix5DXxDkDcJN4BlESFhP1J\nvAMQF4l3AOIm8Q6gE2VARUT8jNsNtOf/M26/pYiI9C7GguCJVUUG9P3Ab4FbK9i2VKvhHYC4aHgH\nMAZC4TEGDe8AxE3DOwBx0fAOQFw0vAMQNw3vADqpIgP678CBFWxXZNRiykyIr0FLXrWPiYiIyESp\nIgP6JeAPFWxXqpd4ByAuEu8AxkhMGcrEOwBxk3gHIC4S7wDEReIdgLhJvAPoZIL7gIbNISTeUfQv\n7AfhIRD2hPAw72jGXEyZCfE16L6ifaw860BY5B2EeAiHQFjqHYX0KsyHcIx3FDI21PczQvOcvvdi\n4M70+R+B79Bsp5ykj1UvnwXsCzx2RN9X0nLjc/Cb6+Do/YBbgBeUuP1G+fFGs0yb10P6tqQG8VW9\nzJCvT/pyegF86q7Ast4/f9oj4FByRhVvg0qO988tgAXklLz9ufbPs18CfHEE36flWixfsnG67mrg\nFVgLrBrFV7vlbJ13PDPAh4FfO32/lsdr+XZ7WLA3VqjrHU/dlpnj9TKXVwFZYeBKHKyk8yBENSnx\nD9fHOQF8CBA+mD7e4h3N+Ahh9v4Qlsa5j1QhXKrfopvwlHQf2qnPzz1uvH7X8Auf/yc7fsNTRv/d\n4iMECBfknr/CNx7pXdhnvM574iusn54Dpr0jkVk6HudKrLiV3ewgKXl7sZuUC2TSw3vUxKU3Me0z\niXcAFdB+2pvEO4CS5I83pf3cEu8AxEXiHYC4SbwD6KSKDOilwE3AVsBdwPEVfIcYXXBF6iWmDKjI\nONH1UEQkElX0AVXH8tEp+4LbKHl7sZuUzESjh/dMym8xSRreAVRAmZDeNLwDqIDSfm4N7wBSup6M\nVsM7AHHT8A6gEzXBjZsuuDIq2teqoRsxkXLoHCUiEgllQONU1U1rUtF2YzUpmYPEO4AxEOO+klSw\nzRh/h0mUeAcgLhLvAMRF4h2AeAh3wkvP9I6ik5pkQMNJEE71jqK7cEsN55gbssQ37A/hwnJCGUvp\n7xtUsj5QxiLcBmFB+aGMSng2hBfllhd1GXl6zexN/X7JAIFFIGzs9MU6VieLBiGaOOGJEN7sHYVI\nBDaHrR7hHUSdtLnhCveNfkjufqdhCQHCQ6qLp684Lkkf7xhyW1dpKPRM22lY1k7X68ZmoGlYQoCw\nvJp4RiHc0/o/h006/wbhzPT/3b7P70jG6xgMd6W/wyEj/t5sGpanjvZ7xU8IEN6We/5vvvFI74aZ\nfircMF7nTBmepmFpLwQIb/QOotMLSqy4KWM0GvqdBxfzb1c8cXa76Xmgh/f08h3jwivdY97fZDhK\ne5HJpGM/QsqAxm3Yg674+WTI7UmcEu8AauqBud8StaTCbeuGoN4S7wBKoia4/Um8A0gNU/CmdO5f\n4h2AeHn/Cu8IOqlLBnRcawGqphPxaOh3HlzMv10xA9rtPDXo/zlu577s/4k53SVO2udERFrV9h6j\nLhlQqYeGdwDiouEdQE31UwNa25N8F40Kt60muPXW8A5AXDS8AxAXDe8ARkTn/1lO+Ll3BJ0oAxon\n1TKMln7nydRPH9DQw3tEpDo6T08GpbPIGFAGNG7DnoiLN8vJkNuTOCXeAdRUP01wY5RUuG3VgNZb\n4h1ASdQHtD+JdwDiIvEOQLy8T31A5zBuN3ap8HcITytxezPlbQuADUve3rgq4cYmPH5Ch46P+aZw\nFE1wx3WfiDndpZbCvRCOLays8X4W/jWdBqG2N4COBp2CJfBgZmoir6ciY6MuGdBxNR/YrcTtFS+2\nw1581yssN4bcnnS2yjuALho9vGfQfa3GN4hz6qcJbowaFW5bNaD11vAOYABrAKu7vF63tD84fdzA\nNYpWDe8AxEXDOwDx8kz1AZ1gZf7GxRvgYS+4Sv/e1O3GRkZjkBrQfo+pccvUZnTMSBW6XQPrts/d\n7x2AiEy82t5j1CUD4vEDjepiVcVvXFbsmgdUoLd0r+1JrEL9/M8xDgyWVLht1YDWW+IdQEnqcg/T\nTpYBrdM+mXgHIC4S7wDEy3tr2wWgzifvcVHmxSfb1nRhedjtSXtThcdJNom/QT+DEKkG1Hj/P5O4\nn06SmGpA+2lBISIyUZQBrV6VNaBlN8FtDLk9iVOjwm3X7aawH4P0AY3p/21UuO2YfodJ1PAOYEDF\nYzB/DavbPlfHJrgN7wDERcM7gIrV7divkWepD+gEq+I3LqsGVOnfG53c/Gu2PPRTA1psnTDpdMzI\nKMRQAzqJ58656DcRGY3aHmtON0th0/TxQAhLsNFigTDKC8jyAT6zYPaqMAXhbRCe3eEzjx3ge+aS\n/U6Ftt1hbwjFkW2z12YgHAVh/dnbCdMQdqOln0DYNP3bBcL8zqGE1a3pFg4fcTqWLKy0vxZLSthw\n9lsvgDDk9DfZ8VOaZI7vW4PZIyYX37Np++csh7A0Pc4jEjZl9ol74/S1tdt8YJ308d8hfADCjul7\nt4DwbgiL+vjudSGs2Wb92hC2hjCvsH4awsZt3j8FYZPc+ba43yS9x9Sz7P/cHcJCCKMeAXRLCA9p\nTlkVNrLfq+P+ydzr5xKWQVhsx3W3c2XP21tu26tUUvH2c8LhEM6zawmk++vutm/2bRmEKyA8I13e\n1LYHwOIKzo09Ck+HcJVNExPeDOGLwJ7pi8+EsLlPXLMkg380LCrcP5QgPDe9Nzl0sPuG7Pzy4Dlu\nk9z+kH/f+mn8uWMrzLQ/b46lxB7CkvR4PB/CVul56/j0HDnI8ViisDA9T7wst26xpVF2Xxum/I7x\nWL2nLueeWgg8OH9TCIW/F4wwjFwcPb//Q23W/3PnbfX7HXPGMD/d5hW53+wxvX1feN7s18Nd6bq/\n0TK/Vn5bIUD4codtbpe+vn+6PJUuv2WY/9LHrH3xoRDWKS8NwxfTbb1huO2FTcrdp4C5M6CXzrFv\n7VXYr0J6Mcl+y7shfK20aEcivy/MWndb9/fnP9duOy2f23P2a+EBCJ9p897vp9t6cWH9szqcf47O\nff9Mm/ck7WMaRstv8OkK9tVevjdAOC23/su59NipS1oEO+YH+u6r08dXDRb/rO19fPjtdJVUvP2c\nWcfEawc7r7Y7xkKAcOLcx1mVwrIusTnG1VYy+EfDe8r7P8Leud/mhenj83v4XLvfdtvC+fbYDp9L\n4w9XpeueV6N0qVpiD+HGwm93Q3320fDbXCxZwcInCueODvOphw2b1zlpCgEuusQ7iE4v1K25WN1L\nNtrVXD1k5FFAvgZkaY+faVfjmzURymp2Gx0+u0OH9VktzbL0MdufVvYYU52VXWO3Vvo4ZO0nfdSk\n9awxx+ubzfF6u30wv48uAx7eT0A1t1XF25+i/TG0TfpYPOd02qfa1Fq31A40+gurb1tWvP1uNso9\n3yb3fI05PtemlUtPsmtXWTUqW5S0nU4aFW+/m7Kvmd7zbMZ009sY4rNzXQcGld2bDFoDVzymO+0P\nWfxZy7FBWsHFqpE+bl5Yv3K0YXSVT7csTYsjuPZ6v5sK+0JIBg9pUOHlEHq8loR9IFTRUjJ14tOx\nmuQsc38dhIPT57+ja+uM8KgOhT67YhUPh6TLV2Ktsz4FYQcIT4bwSQjHdYtsXrcXHdT9RN5uVDuP\nmPPNvHpttvKPNut6LfFaOMfr2X6U/RZ1K9gYRNnHxv2Fx3HSbh8s/n51O9cMo8xjfpBS5+Jv2Sme\nfLpkx+Q84O8DfOcg2p1zRqXT/jbX8Tfofpptt6z9fJyOl6K/lbw9799qHM/p7VR1XR/22CmeZzpt\nJ1v/j8LjJCnuq2Ufi2XJ0qoYb7/NtK/Drndz3cOW7dXAx4Fbe3jv9Vjeosq8xG655/umf2CF1KcC\np3X43M0d1n8D+B+ahdxPBu4DDsLSLm0VycHdgqrihHIgcAfwI+Alnd/Wtr1/3TOg7S40o4w5+83y\nGdBev79d7MUMddLhs3MdvDOFx4j7gD6o7HSt80UvmeP1uTJJ7X6r4rq6H9v98C5gKf6WnW64Zto8\nz69LygqoA899vdP+NldMg+6n2XbL2s+rPl6SirffTdk3vd7nlpgyoMkQn63qvJcdO8MW/mQ67Q/Z\n+nEuDO4kSR+L579RFUb2K0urMq4hJfTLr/x7K7ynaED333HQ36e4zWy55+2V/U/PABdimdDtgGOA\nbfv4bu+SzLm0qwEdtMnWMPIJ3Otv1ksGdFWHz86VoSze3HrfoJehqgxoHS96ndK9V+3Se5wzoGUa\npAa0+Ft2Ot7y69tlQIdN97l47uud9re5Yhp0P822G0sGtOq076biDGi7QWgq1eN8n7UYnG+YdC8z\n/nwaDVsDqgzo3LJ0L/7Pdf0Nimk1qIBfhUiv+3PFBbXfmes7Bj3u7iss912QVPaJejfgx8CdWHCX\nAYd2eG+7IOt+kzpgBrT0C88gGdBemuD22b7+QVm6ZbGMQwa07MKQsmpA09+21JusQdM900thUt0L\nl+qmW8a01ya4+XSZV3iE4dN9Lp43N532t7mOv2GbAcbSBLfqtO+m6ia4dT3X1OG6OEy6lxl//pw1\n7LGjJrhzy9K9+D/XNQNaTKtMv/fSnmnca01gMSNXsj9C93QuqwY0+z/cakA3Ae7KLf+Czh3L2wVZ\n1wtHpl0GtJe25WX/zmU1wS0rrnHsA1p2YUhZpa7tMhLeemlOX/fCJS9l1ID20ge0XQ1o1TxHVVQN\naH1V3QS3rr9dXePqVZnXddWA+pi0GtCKM3dd1aQGdM7vcKsBLfsmtp8bjjPbrHsahDtLiqVH+TmH\n5nRom/fnhvzuuK2XQyhjJ8vSa/fcuhOZNW9l2zgOavN6YZTKPQ6BmzrE2Xab2ciPx0FYTnNU3AP7\n/F3r6LlYP+bU0P/PPunjYUNuLxvx9CwIJZ1c9+yS7gA8uvm0bdx7tXnthYX3TMW7T7SLu5f/pfie\ntp95aIfXtu3yHYdD+HFu+agO2zgi9/yM5mO4x57Ome7D2q1DXKPwZAg/T58vz8WxXu55O6dC+NUA\n35cd3+2uEYNYUe3vVnnadxBeBhxdWB7WUYXlf4UwysFVeh3gpMRz9qCGSvd0yrdS0myX3PMs/Z4K\n4WcDbOt59vBgXEdDuLfN+x6VPu6bvvegwufG2IPp/ojCC6tbF2vzW5wM4adAbnTY8DIevPedFWc2\na8FZEPIVRYs7vH8UTqJlmsSO0hkSqorxToDndHnD4RB+1OX1ToqzAWStXXftdQNlNw1dDZyN9QEF\ny2Q+AJyTe893mH0QiIiIiIiIyHj4LiMac2Ae8BNsbqEFWGaz0yBEIiIiIiIiIkN5PPADbDCids1s\nRURERERERERERERERERERIazlncAIiJSmYXAM4DNvQMRFzt5ByAulO5Se0cDT/QOQkbun4BPAsc5\nxyGjdxKwv3cQ4kJpP1n+BfgmcCG9j8wr4+HhwGXA1cBDnGOR0VG6S+3tDVwPfBp4mHMsMjprYCem\nm2idkkPG397Ap7DjfgfnWGS0lPaT5xnYuBuHeQciI3cENtvEM70DkZFSukvtPRT4EvB+70Bk5BYC\nX6M5J+d8YJFfODIi6wJ/Al6SW1fmJPJSX0r7yZFP112AN2HzJm4GvBibt1NNccff1sD3eHDOS3YH\nlvmFIyMSfbrPeAcglfs7ELCSkh9jzTC3xSbu/TnlzwUrvrYBfo+l+f3YiNSnY7Wh52Bz9e4CNJzi\nk+pkx/L/YRmRDbCasJcC26frf4+dC2S8KO0nyznAE4DPpsu/xgqbT8YKHH+N3ZA+A7jEI0CpzN7A\n+sCv0uW7sev7O7F94jHAc4F7gNuxewGJn9Jdau9ZwDeweVgzOwH/jpWMfwA4C7gT22FBmdBxsCU2\n7+7dwI6F1z4I3IplPh+J1YoeNNLopEonAh/B+vpmZoCfYYVOFwHnAp/A+oHL+FDaT5Y1gIuxAsTb\ngX1zr20KPI9mX7AFWBeME0YXnlRoIfAarBDpo8B6udfWBa4FnpYuH4E1wc+/R+KkdJco/DO2M34f\neE9u/TRwAM0MJ8CpwOdHF5pUaBo4HHg+8C7gVcCaudeX0Vog8Qrg8pFFJ1WZwo7r72EXppfQ2gRn\nX6xkNPNi4LVYU2yJm9J+suSb2ybYiObHAl8ovK/YxeJdwB7VhSUjtBw4CmvRcBVWoJRvxbi48P6b\naS2gkDgp3aW25tOswdwaKwVdA/gz1tQ2s6DwuSOxjIjEazXW1A6saQZYM6wvYs01sv2iWMN9DlZT\nLnFaI/d8fWBjrO/XBXQfhOQE4LwK45LqKe0ny3pYYeGbgePTdVlmdE2swPmkdHle7nMbAW8HbgRW\nVh6lVOVwLMOxJF1emj4+Favd7tTH92isJmyjSqOTqijdpfZej41s+3qaF6WsZOS12OBD+XVg1fkv\nxJpkHjmCGKV8uwN3YWn/eWA3WjOZZwHvo5k5BSsxPwwbpv/ywmsSj5djBQwn09rUehrr7/daYIt0\nXbZPLATOwFpGaJTMeCntJ8vawJVYgeGjgR8yezTzA4Dv0jrH8y7AV7H7gnlIjBZg02vcDFwKfAgr\nVM67HBvfYc3cZ3bACiWuxfYZiYvSXaJwIlYSsgXWBOt8Zpd0/pLWC9ZaWIfly2jeqEhcpoAzafbr\nOR2r2Tg8957FwHXAoenymtiNyCloXsCYnYBlQFYDr8aO/5W513cF3kZr7fZC7NxwFTrmY6a0nzyL\nsIGkHp4uH4YNOrR9ujyFFS6/A9snHk2z6d36zc1osMkIrcT6bWeej6Xzdrl1j8Gu8xtiNeXrp8/3\ny71H43vEZSVKd4nAG7COyWA74MXYRNRr595zGHAH1jT3RVhJSb7mawbtqDFYlP5lafVRms2n1wOe\njRUs5Due74fdrFyJndDU9ytu08DZwFPS5SVYjdelhfcdh+0bx2G1YtBsviNxUtpPhh2xdN0X6/e1\nFvBu7IYza+H0LqwmPO9IbICSO7BB5jLTaApqGaFYAAAT2klEQVSemDwWS/fMHVjBElghxNnAKwuf\neTNwC/ALmueHjAoe4jCx6a6TUxyWYCWcpwI7p+tuB/6GZTp+B1yD7bQrc5+7CtgKG/X0z9iULP+d\nvjaDTdOhoZrr7RRsVOPzaRY4fACbbmUZ8D9Yc417sBNZZmustvO3WL+A+0YUr5RjCfBGLP13pDl9\nxrHp4z1YzffDaE33b6XvOSe37o+VRiplU9pPlnlYel+OXZefg13r/wLcC+xFs2D57dhgg9nyk7Ab\n1FOxa8K3c9t9AE27E4PDsSbTZ2JNLp+Rrr8MS1+AH2HdZ5bSvMfbGjgG+Ak2CvZHCtu9v7KIpQxK\nd6m9I7C5HM/Daj0/hjXB+Ses1is/su3l2I4JNi3Hx7DRcIujZEn9LcZKwz+NTaOzHTb/0yOwYfbP\nw4bcz957Ic203xzrB7rTCOOV8hyJFTCdhxU83Yo1o16I9eXL+oRMAy8AXpcuL8T6BH+A1sFqJB5K\n+8mzEXadXjddPgB4L5YxXQV8Ekv3LF0/BhycPl9Ka3qrz2dcVmGtlPZJlw/HarZI170399rWWDP8\nrKXTzljhREYt2uKhdJconEFzR1wXy4Qegd2AnIsNr79V+vpJ2HyfmXw/kHloJ43JNHaTkR9W/1xs\nvqd5WPPqK7ABiMAyqy8ZZYBSiXlYDccBuXXX0+zb93ysRQO55RflltepNDqpktJ+8mTX5M1yzzfA\najKz6/dpWNPbk4HHATdhox/nP6+b0Dithw0aBZZ+mwIfxgqdNgCeixVCZ60Vr6N5v0fuc9E0uxRA\n6S41l+1462E1XNnyldhQzGCZj9cBH8dGwbqOZi1Yfjtqah2X/DD72fI0cAPNGpA1sWZX38Imm/8v\nNPrZuNgYS+9s6qRXYxmTzE3YCJd7AZ/DCqlkPCjtx1t+OrRON4+7Y9f5rGZzEbAn1kyvgUavj12x\noCC/vBr4Tm7dNDa+x0ex7lOvR5mOWBXvw5XuUhszWNPKTqaxnfODtPb7yfqHXo3Vgkl8noLVemRN\nLIonqmlsEKFPYaOd5e2BDUK0HjKuvkzr6MVbYiWkN2JNrSU+7Y7xdpT242Ee8CbgEmYPIpTJbjCP\nwWpDwJpWZ+f8tWd9QmKxP1ZZsAvNuR3zGZDs+dOx0azz5mOjWD+sygClErtiTehXpMvFTKTSXdw9\nF+tofA3WtLJTScfy9H1ZKeq26WO+xDxblvrbGuuEfgPwfmxks7U6vHfj9H1gzbCeXnl0UqUXYDei\ni7q8ZwbrH5ZvdpmfTkMjG8fpRKxPz+tpnT4pT2k/PnbEmtS+E2up9EOaTafbXetfjg1E9ASsxvOJ\n6friXN9Sf5tgg8p8FRsc7BM0p8rJyzIiL8HS+6HYIJI7Ft43g+7vYvIi4H+xwcOgc+230l1cPAsb\nPGJT7KJzLZ13tN2wktHtsea252IZz+z9WS2p1N8U1nwumy7hsTRLwNql/9FYrcf5wH+iZlixWgi8\nELgLG2Bmt+5v52HYxWs/4CtYKboyH3FaBxtsJpss/CSsidW2Hd6vtB8PO2OZycw+wNdpf61egO0f\nv8MyK7u2eY/EIwH+Nbf8IZrdaGD2PnAz1urhJtTCIWZZIdGx2Oi2XwUOTNctaPN+pbu4OBUbuRAs\nY3kxVmqWyZ+gnoINq/4Vmv1AJS4bY5kQsJvLL6TPz8fSdT+sMAJaB5Y4A5tyJcuwSlzyo1Puie0D\nL8Mmme42V+MzsWP+BqwQQuKTr7H6F5p9+1Zgg8btMusTRmkfpxXAITQLC5bQbHY5DTwem98zWy76\nMK0tXFSwHJcn0L5Q6TnYvI0XAQfR3D+m0r9lwJewguh1c59T2sfhAGzezrw3Y8fycVjBEjTTU+ku\nI3c8rSennbFMyIewm41LsRrRI2ndUcFGQH0NrVQtH4eDsWlUrsSaWIDdmL4Rq9H8DHaiejvWHDeT\npf0e2OhoEp/XAxfQrAXJ0nRd7Fg/lM7H8ROA0yuNTqr0Kqzv3yHp8iIs/bP0/go2tVI7Svv4PAn4\nPyyjUWxGl7+OX9jms+2mUFFz23hsiBUWfQ27nr+ZZmHzWljrh0dho1e/m2YrpnxGI1/5oJGN47An\nlu4NbGC489L1U1ghc9aH81vAT2nfek3pLpXaEuu/+TdsgvFludc2xko9t0mXn4VdoFamy+12Rs33\nFY+1sMmCk3T5Gqyfz/rYwBKfy713B2x6lWxf0Ikobm/C5u07EitoOoXWfr4nYs0wNyt8LrvxVAFT\nnHbHCpbejxUsfRtr3ZC3BfBZZjfJUtrHKZs6a2esYPENNEcyz7saqwUF63rR7hyvtI/P/jQLlzfC\n0vlYZrdwWQN4a/oatO8XqIKHOKyPVRpkU2WtwEatzQYdeh2WCb0I+Bnwk9xnJz7ddZIbrZdiN6Lb\nYxmNzL1YE51sbq9Pp+/5U7ocCtuZBv5RXZhSsr9g/b+yG80zsc7mCfBXrClONhhJljnJTlTFtJd4\nLMaGVz8ZK1R4LVaodFTuPe8B7sdquB+NNbMnXQfWKkLi9HbgBGzk8muxmm5o3mRshJ3j/441w80y\nqEr7+Exh6fVlrKbjHdgxvXvuPTPYuf5v2Hn+auDfsMEFi5T28cgyEr+jOXjYb4D/wPaBbQrv/ytW\n4/W7dLl4jQ80zwFSb38G3pv+zWBjO1wLbI7tF7/CZim4O113M1YoDUp3ZUBH6C5s5MNPYBmSvWlW\nu88Hvov169wBy6D8T5dt6eIUlyVY7fcGWFrfhtWI7IGVoF0MvAXLoFwEfAO4zyNQKc00VrD0Q2wQ\nArDmlv+JDSyyMvfeK7B+4P+BWjaMi9ux7hRZZvNLNPv8ZOfvHbBCqX/DRkjVAENxyddWZDeTWaHx\nndi0WcfSnF7tfuz4fiI2ddoVWKFTt2u91FP+3jlL+4XYfNxbp8tXYMf6qnR5Haz57dewe8AvVR+m\nlKyYZ/obdu8OdnyvgxU6/QzbLz6HdbE4M33PmVjrCJHKdGo2ma1/JNbn8zCaNx3bAK/EmmS9Fd2M\nxGiazoU6J2ElX1l/rw2xiYezTuv7YRenYid2iUOx6Uw2cMih2EAzK9P1O2F9g1any9th+8H5KPMZ\nq16aTV1I62iYU9ickH8AzkZpH5Pi9X0VremXXQMWY7UhWXPbbbEuFyfQen1X2scrf72fwc7tp9Ns\ngvlkrPAZrKb7fcBjcp9RF5s4tbvPm8GO8U/n1mXpO6/wGVX+Senyg0tkOu1oL8UGFFof65ieyfcP\nm6j24BHbmNZmVPk+XdmNxgbYTegLsDQHq/E6EIld/ibiAFrT/+FYP5BX5dbdkL4PYD00uFTMuqU9\nNDMXn8AKH8g9HoQ1xZc4FDMLq7E+vm+gc7rvD9yB1YydX9iGMp5xye7lspYMZ9BsYp1d5/fCBpzL\n5nndDCtoKvYFnrj+fhHrlu7F+/vHYdf6ZVirpn8eRYAi+ZPJtlin5HaTzOd32M9iff3uwPoN5Aef\nUAlJPK7HpkxYgvXpu4zW6VKytNwLGyHtGqx/2G00+/1K3DbE0rYBbEXrRWsPLNN5PNYc7zqaA1JJ\n/Ippn89kZOfyS4AjsFGwP0LrIHRSf8XMwg5Y88oz27w3sw7Wz/MHzC5oVM1X/C7Gus1A6/3antg1\n/kqsn+cphc/p3i5uF9NM9+Jx/A6ssOkrWG24WjLKyCzCmtfcjPX3PJ9mSUl+R53B5v+8F2t2KfGZ\noXlT8iSs2cUbsb6cO2PNbs7MvTezACucOIvW2m6JR/FmdEMs7e/o8pm9saa43wNeXFFcUr1B0n5H\nLLNyM9YUX+KRzyysiTWpXy9dvhKr2Yb2hc0raE69BKr1ik2+ImAK6z5zNlbIBNaX9zU0a7/z93hL\nsRGRt6w8Silbv+lO7v3nAZfTOsaDChykdMULyQw2EtYt6fJibKCBs7F+H9A8Qc1gzXfWzn1ezXHi\n0Okm4p3YADPZ3G87YLXbWXNb1WqPh3zaH0xz4uh9sMGj9k2X82mdHffzUYlozAZJe4BNsf6f7abk\nkDgcgRUqXo9lOvfFarHvpTnPX7fMpa7vccmn10bp47rAuVgGY1ds5Pr3pK+1O99nZtC1PxbDpDtY\ngSS515TuXag0bnDZyGcPx0q3/4pNQP0v2LD7d2Ojom2P3XR+v/DZX2AjaM1LlzWybb1thI1ydh+W\nXltipV1bpus+gU2x08DS/pdYf4BlwE3pZzSlSpz2xkpBf4Cl4eOwZjjbYrXdm2MjHm6AFTw0sH2l\neCPyADrOY1NG2v8ZG/FSI1vX3z7p4x/TxzWwkWxfj02f9Casq8VBwI3YaKanYE2su9FxX3+LsOv5\n3Vh6rYml90uxvpz3YqPUr411ubkLK5i4AvjfDtvMRr3Wtb++ykz3bHkGpbuU7C3Ay9PnW2F9eRpY\ne//d0vXvTv/AduQz0uVNkBjNYDXZP6E5nPpqbK63Y4FjsOZ3S7HO5++juS9cjvX/k3htgF1IvoU1\nqZvCWjXsjtVufwb4EVZAsRNWE35M+ln184qb0n6yLMMKDq/HCpLB0nE3bF7Hw9J1m2EDD2Xz+T6A\nFUxIvDbGRqS+Dit0WIC1aDsLu7a/H5vjNau0eSI2yMxP0Mj1MVO6SzQeA/we67v3DuDEdH0DK+Fe\niJWkfJNmxmM34LEjjVLKcgDw39goppvm1p+A3XDsBnwdG/UOrKnGdenfJ7F5ABePKlgp1XTu8d1Y\nZiObQHoJtm/cBjwHy3i8N33tDGy0Y6V7vJT2k2kpVpj8dGwQkeNp3nieAXw49973YukPzVGNJW6f\nwa7nz0uXV2DX/U9h1/LPY/cCmeVYy7Zd02UVOsVJ6S61l+1kHwPelT7fFfgqNm/nN7GLFFgp+Y2j\nDE4qsTutTacS7GbjBKz59FXYROJgtd3TWK3oBVgpucTnYKy55fHp8tpYf4+nYRejrJ/fK4Dj0uen\nAP/AasaXor5+sVLayyXAadi1/SKsJmQ+1oLpK1iBwxOxAohsgKHpwqPU3wrsvi27fi9Pl0/CutNk\ntVtnYQPOADwXqwlfmdvO+TRrwqX+lO41ohNm/04EnortjKux2q7TsKaXZ2N9gt5IaxMeidPXsUzm\nlVh/z3Ox5nZfTF+7ELsp2RDLdB6INc84Gfi5Q7wyvP/GLkInYTeY/wB+iF2wrqGZOdkKGw3zQGBr\nbFj2P2B9xzr1B5J6U9rLVVhLpm8Ct2IjVr8BS98LsCk2DsXuAT6ZfuaBwqPU317YnNyvwQqV78Zq\nux8CfA67hgNsg3WxmY9d579Lc6DBx2H7Qn58D6k3pXuNKAPau4DtqHdjF6KPYoNNZB2YN8cyJWti\nnZbvwH5fdUKO2zOBx2PpvCt2kvoJVgv+jvTxWuDX2FQsErdvYLUca2Jp/k6skOkurNZjBmuC+Vqs\nH+B5WB+RV2K1ZxIvpb0swQaXuhxrYnsqNsrtBcA9WC3JT7HR7uehAuZYXYpdr5djLZ1Ox67la2J9\nvrcAtsPmcT0AOwcswaZcuybdxg+wAcpuQWKhdJex8EOsg/KZWPX86b7hSIXOBm5In8+nWXCzJVYS\ntmmbz0i8lgJ/wkpB34jVhFyWvnYM1t97qU9oUjGl/WRbBxvn4cLcuq2w7hczWK33p7EaE4nbLtix\nvjmWubgKO+bnYQUPl6fvW4qNep3RlDpxU7pLtLLMx+FYJhSa88GBprYZVz/Dht6G1gmIZTy9Dhuc\nAKy/3xto9gU7ARuITMaT0n6yvRXYP31evJ6vhdJ/nFwFnIPVgL0T624zjRVAvQOrEctquTWv4/hQ\nuku0sh3zBmzuR1BznHF3NPB37yBkpH6ONb2BZq2XjvHJoLSfXFcDh6CbzkmwDJund5t0+WHpo2q7\nxpvSXaK2FtYfZBfvQGRkTsFuSnQjOhmOQYUOk0ppP7nWnfstMkZehfXxbkeFEONL6S7RSrCRtNTk\nVmR8qdBhcintJ5tuQifHZ7GRrZXmk0XpLiIiIiIiIiIiIiIiIuNLrdkmk9JdRERERERERERERERE\nREREREREREREREREREREREREREREREREREREqrUc+Hb692vgF+nzvwAXVvi9ewN7VLh9ERGRWpvn\nHYCIiIiDu4FHps9fiWU83zKC731s+l1fHcF3iYiI1M60dwAiIiI1MJU+JsA16fOzgQ8ANwJ3AocD\n5wK3AJ+hWYi7C9AAvgl8FtgoXX8KcDvwXeDDwObAs4HTsNrWvYAnAF8DvgVcB2zQ53ffCZyTrv86\n8NAB/38REREREREZgVcCL0qfJ7RmQG/EJivfCbgXOCB97WPAocB84CasSS/AUcD70ue/TF8HWDv3\nXS/MfffS3PNnYZnMXr8b4KfAmenzp+diFxERqSU1wRUREWkvYLWN9wO3Ya2Grk1fuxVYCWwFbA9c\nn66fAX6VPr8Fq/n8ePqXmco9XwF8BKs1XQD8V4/fvXluG5emj5cBb+33nxQRERklNcEVERHp7O/p\n4wPAfbn1D2CFuFNYM9tHpn87AQem7zkYeDuwM/ANLHNadAFwfvq5ZwNr9PHd7YS5/iERERFPyoCK\niIi0NzX3W/gBsD6wOl2eD2yXfnYzrG/oS4F1gCXYAERr5T6/Ns0a0+P6+O7860flHm/qIWYRERE3\naoIrIiLSrDkMHZ7D7NrFgNVMHoHVYq6DXVffCvwQ+GC6bgp4G/AnrI/mlVgfzpOxvp5XAH8APk+z\naW0v351ZFxvo6P+AY+b+V0VERERERET691NgmXcQIiIivVITXBERkXipz6eIiIiIiIiIiIiIiIiI\niIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIyOj8P+whSoI1HRl9AAAAAElF\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb050dbe3c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(16,8))\n", "\n", "plt.subplot(2, 1, 1)\n", "plt.title(\"Dados Brutos\")\n", "df_dados.AirTC.plot()\n", "df_dados.RH.plot()\n", "\n", "plt.subplot(2, 1, 2)\n", "df_dados.Rain_mm.plot()\n", "#plt.savefig('figs/nome-da-figura.png')" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Indice criado OK\n" ] }, { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2015-01-01 00:00:00</th>\n", " </tr>\n", " <tr>\n", " <th>2015-01-01 00:01:00</th>\n", " </tr>\n", " <tr>\n", " <th>2015-01-01 00:02:00</th>\n", " </tr>\n", " <tr>\n", " <th>2015-01-01 00:03:00</th>\n", " </tr>\n", " <tr>\n", " <th>2015-01-01 00:04:00</th>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: []\n", "Index: [2015-01-01 00:00:00, 2015-01-01 00:01:00, 2015-01-01 00:02:00, 2015-01-01 00:03:00, 2015-01-01 00:04:00]" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#df_dados.index.min(), df_dados.index.max(), \n", "## (Timestamp('2015-01-01 00:00:00'), Timestamp('2015-05-29 10:00:00'))\n", "\n", "# Criando um novo dominio continuo com base no inicio e fim da serie de dados original\n", "d = pd.DataFrame(index=pd.date_range(pd.datetime(2015,1,1), pd.datetime(2015,5,29), freq='Min'))\n", "print(\"Indice criado OK\")\n", "d.head()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Junçao OK\n" ] } ], "source": [ "# Unindo os dois DataFrames pela esquerda (o que não houver em d será substituído por NaN\n", "ndf_dados = d.join(df_dados)\n", "#ndf_dados.fillna(0) #Substitui valor NaN por 0\n", "print(\"Junçao OK\")" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7fb06108d860>" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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cx3GcpswS3YavJ1pcoZ5taoZ89qlpsLz+qlD+ruEsYGGTtJ9PjEnOb5uyr6byz0J4cqb+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\nm3JKe30uhJ8Xys8jKsa30N/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W0qDiswrlFu2JNokpGG/vdNsWYAPgSWlpMq1nQxChMmSU\nlFdb/hpi39zek8cTZymw7vlyz5SyHwvdAdsnxu1IedXJKrIvcd0xlfOyhv6W1x8QG5G2gv1S1Tkk\nbbOWEOm4/4UaNX/1BpbXMENdWfkDMXvzp6mPpHyBakTnZOB/6GWLtLRxVuLaaTszpU75ycTpY1bT\nq6BKx6ep2/AgF59p6OTsPbhKY45PSxuXuJ44P5duQy9W39dSXZuC5TXoLKf2vJ5DHPQY5Xmglciv\nEB++VmnW+5VOsFZeS8qfpaS8ysi63Y8+bKkt6TZdqiOWXasAyIBQyfK6LcO56w+h6IYOhFebwv8k\nvmzWU/8vHwFeTf6ZQkbGY9Ky6f0lv7eWfsk8nOJV513MZ6h7hjw4s88myquel1jHBs8RcxLkXJIz\nbsO8V8k1LLn5XKE3Fl4UGNS7qOTuXpBl/nfWkg1xhPzbxHeAPa5YXq2yuJb4Dro4U16aKmdcSW9K\nllf77JhmLimU5yysxjUv7EIc0FkM5fVeZl3ksMe9hjwyf7S9LjLNxj+ackn0NeyATIl7jvg7QSuv\nS6VtObcv5L1m52F9QVrae6fg3TD/zi31JZr28eSesVN/Tg23I+W1p7MraMvrnNp+nZrPNRPzCsTz\ntwmVGzDUX/oSK2ctIdIJt77kcvwLKCuvNrZuvbL47kWMF7buvzdQuQ1LR9ySm2xcHYNbqHccB7Wd\nm4jnpqPKPp+W44prmgbL65MGVxkaq7zKHJ02nlPQMc1WeZWXdT/ldYa+il9f9P2grTw69jHnEmwt\nr7mYUcuwlldNaZChiXK5BbADvQqAYDvc+kWze4P9C8O053+nHOdoz6fc8yXldRkEfT/KKOwzM3UF\nvR+xbttY55SAaWY9dYXrtcTkXkKu3eXKzqP+DJK20M38NqewQWVF0uWjWIME+9x8faGeXAOdCEne\nG3awAeDRmX2I9fpZ9Lbbi4mW5pxrZz/L63nkldpR3YY7A7YL1sI6VyifZkpyNrG8yju6Tcurjb/+\nc1p+1pSvIPYhSs9K6U90THkpHq8h8wOO/1SoYHMilNBuw7kkdM7C6LQtQHuEFRDGkZypFNIloXcl\n5dXeU8cUyu3vLDZ3i82bU6LToM5EuD0pr5omyqtcOG0ltG7D9yVOHrwZ0UoL9cy+8pKwnRM55m+p\n3D1l/zcnpTnX0bSJn7SiUvp/EBvmSrVtbWbfpYYq58LGRjVVXoWfUPnpj0N5/R+mrpMT7mPW96Fn\nOoVGWEVOkhpZpVbIdYihfs1spmLM+qjKq3X3zbUVfTxpr9JJhrLbsKWf8pqLedUc1WefOTk1JxAT\nMFhrkWDdg2WfZ1N2r8/RsHMXZpmPqw+5e8AOdMg9b58p2gVUTX0zzw6ZMkHv55tp2c9qppWhJtNk\n2ev4S6LCrq/XMqLC9nHqc8LJgNsWRLfYRJhL22wsslyjUSyvFzSsJ/eGPg8667z9v3paLJFLntE/\npbczk7MoAzwFeCxly6vNKiz3UsnyOox3wKMh2CnxBHsvybGWkvJaUubstdyYcgjBGhZfeRXXWSv/\nWYXykuVV+KJZF9dHex4a5N2oIecrN5DzB5p7tUibPg+4fEgZpoSwA7XpFp0p4YPE0MGFIs/JXJgL\nlOPNbbncY/beexvw7ky5MKry2hquvFYuv3pkejPmR0ZnUgc7LCOfsEnqSwySfulLw7Kdk5zCrMvl\nN6nTMp8d2bonbko+dtQqhlp51S7OmpLlVbsNlzKG5hDltavqi7LSVHnt15lc00CGxea9Zv23RJfN\nhaCVV33t/6y+6zanBzR0x3Qhyuu1hXLotZ5IEhgd+5izGPdzG15VONYgt+FSoiIrg92ncL/qa9gu\nU6eUGdfeG/LdDuAMwsp4YLZWfUqXYzLb7UDHZuRDEd5HZb3NKdl71VdrWdr1dT+PGDO9lhh20E3l\nz1F19KBeE3Iuh9fTG/Oay6Qrz6x7Uj+H0om1SvZClKUmGdihaqNbUv2HOxMV7NxApXbllTYnz2g7\n9ZDUsVmdhbvQ23Z3Tvuz+QdETqu8ao+JfnTV95dQD2/Q2Gv2x0L5GAkHDZdtNhwMwbphaxpYXoPE\nU5emarmexVdez0/L0rW0cm5Efznl/3bT8hTy7xNr0R1Ev/f76TRXXuU58VemfxqmEn9kojNwLIhu\n2wK0yNPHtB9pl/aekXAYm+NA6v2vKZd+tb13ZtI+7P7/m9gHKHns3JmYHLB073YL5RPn9qS8/kNa\n5hTGtcS4K+noPMb8VpStkvK6JZXlVXdkz07LXMzrrQVZMsrrfJ3LqXdybKzYf2Rkg17ldW2mziC3\nYdv5TP8nlF4gtuOu3ZWbduozL6/5DvQzgFc12Mek+Yz6nnu5Z8rC4RDu3nD/JeX1p1QWL93R3IHK\nYlYaJLAduLsSR+NLnYV+nQirBGi3YSDsT5yfWLBWJ+i1vL6icKykvM6kuLt5V1dp06V5baGZ8qqn\nXoXbMp4AACAASURBVLqLrUiv9UpiTq0l73rifxhWeW06GLOr+v7Pme22rfwzcdTVZgyU83YR+Y61\ntZRInatNfcmYa58rlxA7slC25Gl0FuKc8rqGZsqrPLNOoZqGSOS/lXyGXRjN8tr0HaqffxKXJDGD\nOeX1bPXdnrfcQEApKRBU8eB625OI7pj63SBy5pRXsWAPk3XSTtECMYnam+l9932QOEXYJC2vP6V3\nrtV+/AS4ixnI0pTk1P9LEq5Yy6t8b8NtWCgdNzcX8Q30uhl/PS1z7sHDJqvLUfr9d4hW4k0L23P7\nWcfSmobJYmducBaVMANh18yG0jvjzUQDw/8NeaCSt4LNLi+xsHZ6N5mix2YA34R8YsB1xP5KQXmd\n+RPwUsr6QWvcnpTXndPyNvIK4ylUyS5MIqWa8ppz092ZvOV1HTFLqbWEiGunfYHvQRXXpDs00rB+\nRv2a/ZE6OdkgdlDkQS+dk2Hdhq11SX5fOuZNxBuvY/YDzV8iD82UTZu1VXcAcoklcgMF36SK/x1E\nSXmdpepI6jZ3AnBQ+l7K6px74N6R8vOgX2fSKg3riIMqLyVe+/+PmkVzvn6/mFc9H2zJTV5bX7Wr\no26jV1B1oO09rfcpaBm0zO8kuvfb+1Wubc4r4Uwmp7zqQaqcC26/BEC5KYasMvfdwnGlzjbEAQ/h\nBcDDmR/A21hiYLXCps/Rp4jJpSzaymXlX87wllersA2yKr4QwrDZSIdJgLGWqKDL8USG3ei1oOvn\no3RgtPvwMnNv9FNedyOvFG5HPJ8627JWXvW5bhpf2BmwXQYW7L00SxWLfFjDY43CKDGP+xTKmwyU\naa8pff7l2WfdtheTkvw5q35OebVxdx21nps7clhKv19O7NPsAKGJK7I8J4Z9HrdA+DCE17YtxZB0\n2hZgEXgc5T5Ejjl6ky81/V2OUtIy6+13h0K9FxBzJdj31b8Qrbv2uLq/XgqXAo95XRSks1VSXnUq\n+Euo4j8gWqV2IT9VDkTXAbG8PpKqwyxWXes2vDWxkdlGoeeGssrrGnqV4Fup+9v/JCMb1JXFjelv\neb2bKS9lGx5kpbAvirsTFSQrDxCeCSH3Usl1xMQC8PPMtjYY5Lq00LiBUsKmGaprpq/N31Sd3CDB\nj8hfO53UKydDiZzy+mMqy5H9bU55tS5m+ruOI9bK6/ZU95l0uK1F6iqqEctfZmT/FvXrt0Z91+fo\ncqKVwd6v8n0bYmZxQcsziWzDm1G56+TceUpz9loLXylGueSepq/1cer7ycA5VYK7bWRfOlRAt9Er\nyE8zYpUZzQrigIVuz3Kec0rqeopJimqhIFBvG9brpsQPiJbCYS2v+rkoyoDNBgvxf34ifZepS/Sx\n7DXrp7xuk6kv+7gj9Y6R9mLQ+9pTbV8IbyJaX3PW8nX0TuG2I4Rp8LDJ0TRhE+SV1/Np5pEwIkWL\nsdDU8jpHVBat8ir3lv2/En89zLMvR+l5KB4UMB/7P3A/a1kaltd/pv5sdaaDBwxZX9rosAM4pfdJ\nZ8j95Ni9sP87kre8yru7n/LaGrcn5VUyaJayLuqXyKbU5zqbI3ZwSm7DUPdJf6nady6b5EeIDck2\nir+q71Z5FSX4TspVVysAAGcQ091/zsimMwUfRL6zJFa8F5py6QhC5QIFzZXXrio7PC3tS+Sj5KfL\nyCHK63uoEkC1yXLga+l7LmtpbqBgGHYnWkhsG9KW1+2JiVkgTkgvljmxfmvMdEvziCvuXpltw1pe\nzyFaKrv0xm0uU8tSwiYt3w/Vd5ugTLwJShY1URh+RN5DIFdf0C5CbwdeRPQEeLIqF5kfCuxv9pNL\nkDQI+zz+eLYWnER8mX2EqIBbbFs5Ezg6I4+OUdYdTXE5Ot3sV9fRHdk1RK8QgJvgbzKIpi2vemBB\nP1M0TZRX3Z6HtbzqNleKB2/6TlxONZd1E0RW7aYrSnNK+lSbzupexOfnb+i1mgfybbefwmCzxUO8\nJt+kPuWQHhDQ10N+awc3Ld0B24Wc5XUdcVBDx/M/DnhLw332Idj/3oSPDdjeRHldR3Ttyw18rSde\nR+3FME4k0VEp74JtK+9MMuU6sv2UV1l21XouLntY+imvkoehSUK83MCRMz66bQuwCLxgcJUay8i7\n6Q5i0jpZU+86/e7up7x2xyDTSNyelFe5OOpChE3Jd3y/QX6Ki37Kq1htvkulPGxBHNWwbnzi6mcb\nxcVUncCc8iodvj3S0nbmpWN0LvV5Bm3H5d70dqpl/TpTLp2K5wP/L1O/xI30vihUB7dnW9NYKjmP\npWlLFht9DXIv0lJCl6ZKzRPT0rah+wEPSd+tK6W0E30NpCNRUl51FmDNp2L90OShl3PPsmnkcwMv\n1vKq5dNKjj7Xn1a/1wNQusO0J1GxL02hYy21+re5DJcr6B3UAvgAdQ8IefAPq7zmkiz0Yz1VmIEt\n1+fzOuLzycpTcrcWHmLWl1FZZTcy5blkbNr1aHeq6UF0+aupMq4/TO1TJ715DjFO9Mb4H+YH77ag\nmjNWn7utiROylwYqoe4WW7L690NG1odVXvW9cQVReRSZdHKj5xEHEdZQDRrKtbuB3ra7Nb2x15qc\n8noneq99SXkdN6U4ZetGO65Yqy0HV+lBBqRL92ETt+FlROW1NMByD6o51sfNIOXRZq+9kuhRYH+3\nK1HGVaZc2keuPJf5OhGatqtSWxbl9fs0e74uIbdhZwNh2PeDsNBn7uo+264m9uXsPXMlvX28pm7D\nrXF7Ul7lgXwz1YVIL7SZ3Eh2Dqssvkt9lxfdd6lcFY8gzi9p3fgktqtpwiabZGnfTH2o3MY2ou6u\najsuv6YXkW+NKS9ZSXTn0t4M76Z3ntebgFPT95z7jlFew57k0XFf03BD6TaRe5Eu1G1YzotVSHRS\no29RTTBvY4vFVVvm1TTxzvPXLmeZPJ84CGLOdTHrrLUudah7E+j6/WJedVKVD6pj6vtAd+q1Embv\n4S175Z/H1tedtj3o5SPUrf2iyF5GPTusHmwa5hlrZRz022eTt0o1nedVuw3r8/DfzLehoKaaqZ1/\nXV+7GN0Mj+hk6m9KTLSijwtxXtScNU/Ln9oAt1KP3/8M8Pf0KgYSM9bU8tpHeQ27QcgNrEnSvaaD\nE7kO9J2J7wh5vuasg3eh8l44Mi5mrqdX6XwkMabJxsJC9GzJPXOvIG/BFeX1fgxPJy6CHfiw5CyR\n6+h9V6Vz31jhKbGQvk7JatskYVNpMEDeq99YgFyDkHnkS+duP7M+Rz18SjiEvDu97Fee4x1V3s8L\noKmyXpJ7X4ZTRqVtLQW34aVIp20BppBhlVeZqmbY+pZLCuV/IM7bbvsAPyW6qtuBXo95nSLkRXMD\n1YVYRzXv1yDlVTJdasurVuq0ZUu+/5AYt2Td+N5LnJfQNgqtCOWUV3lQvyBTn6SE30js1Gtl9xbq\nD22dgXP+x+pYGlGE32jKdefEZv3bnvii0G7Gs1QvudxLxGbTu3NGRtkPTI/yugn954lcqNuwkFNI\nhF2o5u7TD53/Bh6Vvst2O1ewXDubzY50vFxyFXF7/Cn5B552V/6u2WdJedX7+RuVy6ykik/tZUYU\niWcD71fHzcW8QnTJ1N4WNma8pLxapRt6O0si281mP3tQKa8LsbyO+nzOzfMqyqs9Xs5teD3V4Ii+\nTyWGH3otr2rAZFPtHpwbvNHlyRsjlCZh19xGb3Zc6FVel6nykuVVK0n93JVzHjiyr2HcwnLKq7jo\nz5ql8Fuixfl9aV27pufeVzL9jW1Hx5K3vP4usx85R6XBw6b8W59tayjHvBaU16yHwTAMeFeECyHY\nay3Xo+QVlFPOzqJ+HTcinsuS5fUWer2dxoVcw6bvyX7uvt+i1+tM9mvfHeLCbvsSZ6ZlUxfufnIf\nSLyXmiij8pxzy6uzWAyrvL4tLUv1zzTrpfwkuVkSIA6uX09vP/Ja4ju19DyG6elr17g9Kq/6Qkjn\nHKJC+Pr0/Urz25cSO1lWedUvNUmUozvQK4kPTNuZnCUq0bZRPJEqMNwqr+uo3OvkWNbyClEJPJS6\n0nQz1QvjBuBSejvVIt+jqqIwQ8y0uAP9p6WxGTrPJFoUjqLyiZcMy6RjH2F+YzsIpU7/pWk5LTfU\nxszHi/KgzPaS8to0q6l0AvsprzrWVrfp36hyOZ9rqJ/bJPNMbmJ5nfhGH6+TljZBjHR8xV25S29H\npUnCpm3SdonDXUFvTPSl6nsmbn1eSbVZgvVxrPXqcPU9N7dtqfNzE73/cxVld+US9nksCnfTzt7N\nxDhVqzTrF1GThE0riB3qM6i3U4nhh3rmRW15vQk+/+tMuebRVM9IcZXdlqisSYbGnNIoVn07WGbb\np872btunnAetJPVzG5a5tu3zSCyvC1Fef0Scp08PyAn/RxxA+Aoxjl32IeQGavQcy8KlqSynvL4q\nsx9tKRyFblqWsl5+jDj/a84SuTYjZ+6+HYU+92E4jNjenmQ2rErLnJXjD/QqZ+cTZwDQsj6Fqt3l\nLK+3AFtA2Jrxc546Vo7LzXrJ3fd7xDwaK8x9cKX6HdRjXnOW1xTb3ViB7PfsDGSfx+EgCDaTuce8\nTpZu2wIsHkO5vA/zflhP9OAq7b/pcUveDgdQ9Q1zA9u5pIce8zoliHVsH6KlCuousccDH07frzK/\n3Z+Y2dS6DX9HfRdzve4IriS++GxnsuRKpBM3ZCyvM4HYqZFEKlYeLa/ufCwnTmUh33NxmDllUTqQ\nd6Ca2FzQST5epr7/kSoTZ0q2E+bi/5kRmY4nZh8Ggs6OO0geiJ05mB7ldRPKLhxQVl53LpSXsAMg\n71bfb1Qy6IfOQ6iUMN1B1ue2n2VYJ76xFiz5rX7g3YPopqstr3ZkvEnCpncTEyM9Mq0/nl5XM51Z\nOxfzmh7UM8HIL8ubKc5dDFTtTFNyOzuLXkXiV/Qq5YMoWV5L7ol2XudTiK7NuRdULoFUSXkVxWwV\ndddRsbyeQLTqC6WYV60svopoqYfomSGx3Pq6rFDldqSZdC2vptcKZ1+84hWSc4XKZT9+iqmjkfNo\nr/ty4ih3KTvxn816TnmVZ7Ec4wJVX871r6gP+Ai5OOVf0ntP7kTeonku8dmQi3mVNrEQSpZSOQ85\nq7gqn+8kvp/x0O9dIYOO9l59RFrmPLK2p5yhU+9HQmXSHNTzyp90JGXg88t95BsVscKU/vvNZr2k\ndG5LbCf2OS0DTbnzkLPgym/HobyeQXxWvMaUP50YL273427DzjgotN2ewc1RciJsBTzBlP+AGFqw\n0PwuWxGNSSXldVS34da4PSmvH0jLc6niA6VzDvFFLh2b/6L+MvkU8WFpLK8zp1IlgpCXve4UiUtp\nzmqWU14/RjX/Zynm9RLgcckaU1JEoa6U3N8cO/ebfjfHaubn3pu/SbXcOtHITJL9CqLbcIe61RXq\n02TICL19kT6S/tibrS205TVHSTnMTRWSQ2KQ7QDIucRYNojndiWEu1B/6NxCZdHWmUrtQIpwMfVB\nD+lg2XMtnZLc3JGHUFleO/R2Fk5U+yjFvIrIZ6Qvn6I3Gdbr4iJsQT3mVbuCysBUzvJ6FuVQgY+R\nvz6lkXurGNxIlWVcx4bfA8IrKGPb8+ML5ScTEx29n/rcqDrLcZOYV6mfs7zeRrTa7Zypv4a84gFw\nM7xEpjfSyuKvqHu0yLUXOZen41prkOVyehPN5Noh9I95XUllVT5K1dEJuUSuHMvoTWilySnB0oEu\nKa/WAnobcR6+lyiZBTvw8hNittjcudiUvEUzFycu12wYd3dNR+0nR7+R/rUqLMBmPV9o5lohl3lX\nFO1fmPIv9Tl2LjZUQmP0f19PtFzKs+hlqu469X9P7i/2grDt4UKiR4+Vfxn5/7U3cRAr11b0/jtp\n+QzivWGPK+uZZ2g4MKMAlNrQtVTvA5sQMEdu4GiJUkyc2CadtgVYREqJ3+x1eSrxvDS9XpsSn8+b\nm/I/pc+wfd0LMmUyAFVSXkexvHaGlGtsTOONMIDwfAhvGlyvh78RE3xcST1Rh7xUrLuVjnmTEbtM\ntuGZ64idTMnytY7qZSiW11xnMqe8KlfJWkdTW1jniIrdFyhbXqGuNJ0ArE8PPulE2RfFszP7kMb7\nF5gRC97fK/lyyPyjVxFHbKGy5Agvtj+iPj8pDJ5Xy3Z+2sL+N0vJ/W6YRC9QfuhAda0PIVrLpJOb\ny6hqFZij1feryWcAteda7p9cR/nH1C2v/eYSLLkN5/gz0a1UkPttW6oO9/VUD389MJWzvPZL0lZS\nhvoprznFw1oqXgS8DUIXgnar/CvR2txPUdJcSxxo2AjYC8JKVW8dZbfh2UJ5zu32VmKnW1sQN6Ka\nJzqjeABwM2wsbU4rtRsTpxSy/0m8XLaj//NMuI7eF3xpIKtfzOufyF9Lm5xOMqzba7Cc8jQdF2Xq\ny73Uz/Kac9/Vc3VqV/ncvNtrmbfw1chZXuXdV4p5TfM098QiN6XU2ZH70g6Y6LYCvefWJhcaFrke\nuffWdaaOIN5FpVCB3DXW11P2qZVy7XGg21ou58C4sNfiYqKHlG0nOxDbUe45JNms+ymvlpxyDPmY\n15/RO4VfqQ2JV8LrgTebbbnrO4LyGgKExzWrO1YGZdeehgH72yMyb33pHZ27LncrlOco5QjIZdOH\nOF3eR6i/EwbJ818M5zYs70q3vI6J9wFvqFbDVr0v2PDaTIZIbZ3JzTeo3ad0Jweq0fLtyE6VM/NF\n9XJ6B3HUBeqW1yZuw6Vsw7pclI0j6bVoavTI9U3Ea70plWXGkktQI7LpEWnJfKkDw9+uvu+R5L0K\n2BnCNfRaiP9KNcehUIj3KzItN9Rysgpq+Lv0xVhe50eXSwH3FnEHLrmCAjPShjcmto+Xp3Ud61xS\nXrWFsZRUxJY/qVBfynTMa7+OTSlhk0ZcOmWeT0Ha6/Op7u1rqCeuylle9XGGUV4vozdByJeJVgY9\nSABV58oq5dIWHkicb1mYI1ozSx0re27k+SGu1Jurerksx4cRr/sDgX80+xVFQv9fGZCx99jrqZLP\n6Jf4fkQ3SoCb4S061k7+s1bCYH5wakbCEX5Mc+XVupCPYnnVbUVjn43SobZxtsvIhwt8hdihttdM\nzvUatS+rvOaSY12dxJojJj/7v7TduvtmruW8leZiypbXUsyrXMN+ngI5umlZ6oTLfXkD9XOq28of\n6FVwOs1FCLlnq7SD3HPmyaaOIHkenk8vN9DM8mqV8nupulJ+CpU32IiEg/pszLnv5tx6n0nMPJq7\nl/rFI8v0Wd20PJmYIdzuR+7b0rO+FGJikTba1A1Y2tawbsOD5jSeBKXn3yfSchr6PJZu2wIsAj9K\ny9JgXqmtLlTHkmdKbqDsSnpzc3yF6Klj28nvyXuBDgrjAI95nRiriZZFzXGZevIA0x1W/QLR5Tnl\ndWN6X0Q59MNRx7zmLK9JqQ05JbVkedWsLJRDLfHSfMPfirKy+5NM2TLgj0oxh7qbomBfCLvAjIyc\nn0yv/OuorLLCoWZd3Sw97kSyj2l4kNu2Ikhcst0m7WBHmvEWYqbHraknZsq1RaP8zKRjh2WU3YZP\np5quIae8rqf34Zmm7MhavyWr48p03X5OnqaW1zulpRmomW+TGxHjqdcTnwWS+GQ51fnQ+5c280F6\nrT/6P9j/tSN1l0+ost8qy2vYlsr7wN73+nrpe2qOaDUuDWiUlFeJIc3NB23P58uJA0anqrIHUmUm\nt5bX2zLl0pmz5U8mdnyhruDrNvpteuPmNdfQTHk9kt4J460856rykuX1UeTjVS/KlEEtji5sSWxn\nF2fqPZqYUyHX2VhHXfm2yus7VH2x3osnzuZpH3J+rNKZs6KnspmS4pFzG7aWwlE78P+blmeYcjmu\nVV5t3LTcZ3JOShYGQ9iNato6TT/lVb9/c3w2U3Yq+WtsY0a1Um7r6oHzUS3cQNgG+Gl0vc2SU15z\n7sGl+hAttbm2chq953sZ+dhZiSEuvbetAn50tlZ13+T6APbZIPKM4jZ89yHqjouScl0KIXEWB7nX\nmyqvNxNn6FiojjVD74AYab+3ZMoh3qelPkNJFxnVbbg1NgTlFeoJNyCfbKWJ5XWQ8jpDPgup5otx\nEVZQWV4LbsMzgbrVqYnlVbNFoRzqFmKReQuyWUfDZsDXMvuw5+F0qhhMfdwXmt+pTnr3jvTKvw64\nIwQdT2ezU+qbRStb7yFO8TAtN5SePiSH3VaQOZRG7m4justaV0n9cBFyL2ZpuxJLWIp7BDiYeQtH\nmCFay0R5bWqhXJMSc90G2z04s13Q7tb95kOVxEAlxeZ5Sn5tTdOJhvQ9Jsf5JcNZXr9Eb+dHP1NE\nMUjJ4HrubYCHZY4F0V3vesodq1zsmFh2UccQeezzBuI98yfqSvMdiV4s9v+WLK9y/kuWToCb4e0y\npUs/16Ofmd9dSH9PEkmI93Ni0j2NledjxDm47eDK4cB90/cPEc+J5bLqa9Cxtbuo7zJt2DEFWR/O\naMqrZjfi/5Jna6B+z5diVXelsoLr+v2UV+u+q59ZOjFfEzppud4sBRkwvoH69Gh6oGM/qtASkc22\nlxL2Oan3D/nn722mTh/mB1JzbsOzqTxnybbogfPSIFpTZKD6B4XtViEqWV4htk9bfinRwpJrczop\nTSctS8qr/p1i/pza+/qevT8Nc8BMGqxsOhWZbutHDpEx9t4N641AmIPQ6bPdPhPk3TChPk+YgZA5\n343ojFOSKUXacr8wKM0viaEAw+pYfzHrOW8OqO693DMoV1+HFI0z23CnUD5xxqW8ztF84ulJ8H2z\nbrMFw2DLq+6g2xeOvGhl6pt+vDotj6ScbXgVlZVFN4x+ymtTy6tYUNWI98zlRIupKLvWNc4mKRGs\nYpYsTGGW/i9bGyx+V+qdUtmntrbqaV2gsrhBvZ1eQbTmTpPy+vFqNdjzUrK85vajCGKVkHZrX9J3\note1TtqUzoItndafEN3T+imvUCX3kjqivJYslOkayMt25ldp282w40ZkO4RhK+qKSs5tWLwp5F7e\nhMHx3dryqo/7eOCt6XsaTZ/5PcMpr2JR1gqmPOB1zKueUspalHWbTvfgfCclZ3mV+6g0iir/e7mq\npzw6alxB/p75M/0tr7r+u4mWSWuJv5Eq0dzNsEI/R0svQG2RO47oXdDP8ioeH7mETqUXr5X//6nv\nW1PF72v09dL3l06oIwmeriYfbpHrVEjH/CaqjJJvIippuWfC9sC+MSQFiNfTKq/9YkahPniQi3nt\nY3mdZ1SlSs67vb9kwFi7T1tZoeoUiRLbQLEEyrkYxF03986QfTeJOdexwjmLpp3z2Z5PGRyx+TYW\norzKIHDpHOWU19yUOJ8jnm8ri2RsLymvuY71jTS3Usn6l0y5nfsd4oCb0G/QU7MF8f7ZO603Ddm5\nc8N6o3AE8ZlXonQtJ9XnuQ9lLymnbHn9Zlrm4rtLA5P9sMqrWF5zSqqdqlDKS8puzvJaeldu0JbX\nzxAfCpsCvyaOYg8bHzMuzjLrz8zUyVle9QtEl1ul7SpiEiZRRvshFoIvEl3IcpbXp1IpubphbE7V\nYKzbsHT0X6/2swO9lornpqVNZHAjdcurptQw5QYU7kp05Tmcnk7m/OhpoOrQ/TD1QR5l6osM71Vl\nJ2bilIUmN1sLBOlQ6g71o02lhpbXnoeQuFXfn2iV3sVsfx4xE6nmtWmpX/oS9zpL7DD2U16/TuUi\np2NkbUIiwbooGm+F3/6UfKdsBfUBmXszn7AgyP9OFv75eN4v0TtA9hpixt2c5VW3t1uo3GUfrMpt\nZ2ytWuaS3tiYqT2pOrKiGGhlpl/n6h5pKe3hBno7VfLMsm3jkcDmybp7Hr2DbvZ5s46Y4M2+hM4l\nnsN+llddfitxoNDWP5kqO/vN8GI5ByXL6yXE3AXCZcT3iJ5OC/VM+QWVlfNj9NLP5anUAX0K8xnU\na0mp9HnT98mOmfJSBz3N+5udPuHA+JuwCTFm6Wx1TGvl1G3tEOr/q+Q2rKdt0vVt2xUlcjvg71R5\nP+X1l/Qm1rN001Legx2zXbsNlyyv0OuFsFDl9UN99iODSk2UVzk/pQ5lzvK6ztSButfXQpVXsbR/\nu7A9p7ym93rNCikW05zXgMxJ20957ar93EA+MZP8TiMK6WNNuSTS0t5u2niQ8zDJ8XzgWKp48Vf3\nqbtY5OKoNU2T942Lhey3Oy4hppiS5dUOIAvWK6Ep9p0xrOW15GZcchvOhZvoctgAY173IT5cHkN8\nKKwCnjYGmRoSdLxSJolSDznLq3Ub1gmbdHyduNg0sbzaF//d6O/eohvGG6iyjhXchmeOU799Fr2W\niuvT0sp5E3Xl2GYdzWE7MnsRXzBnpOO+F3h62vaIZHWcUQrHu9LyGupKsD7nTWji5tAGqWNSiwn+\n/wb8pp/lVZ8PmfP38VQZKpuir9nORFdCaes55VXaxOVUHR1pE5KVN9cR0Q8820kTpTn3El5P3cqm\nOy0ywXzufjnMrNsBmRujHGEjYudZ4uQ+QmW5+7z6vVVeTyAO/uQsr6fQa3ndh2ipSPsJM9QzIvdL\nRPXStNTKa67DXoprkeffXYB/UPVFedXn7zLyCeKuIZ6zkuXV1i+96PRzQse8PoDKurYO2CVZmq3L\n+43EKQjs82C52i71fxwX8xbrCwryrKN+DMtH1fcfUSWQ0+dNX48Pqu9npqW1ZgrvIv4XqzSsI7pH\nQxxkWUWcQkjOsR2c0fHJm9Df8ppTCpu4DWv3euh95osnwfnExHyX0UPYMqOo53IjWDl3MOX6uDIg\nLXOvN1XuUjsouob2G/AsDXTk2lbuGZGzvNr/NauW43IbFtfyTxa272/W706VrMu2ofS8CbZ/YMMj\npLwUX5dTXlcT53639WVAyk6PJN43esBEZjKQ78NM6ST7mQbl9VEDtg+T1XYcHD6h/W4oyL1uvQHk\nfh6X5dXWL2UbHmR5zXk3TMJtuDUWorwuI16wxwBfJe+OOkn2gfCc9L2J8lqyvOZiiEpxKtQtC6AX\nXAAAIABJREFUA1ns765L+/1PUy6dJdswzknLJgmboNfyKnMpXmfKb6RKzmKvU6lhWgv01er7rTDz\nYpg5Ka1/ld4EC+vSwMzrqM/HlnMH6kfpZmv7hrKWaYCnqBf/O2meRXkTqvZ3Af3njs2hr7eNM3sf\n1XkLwIdSnDP0Kh7SYdedrFInPZMcZp5b4LEPJP8SniMb3xhmqDIZ605JKQ5St2nSIIJYX5dRWX+s\n/HKP5Tpv0qnTD/JriYpEOj+1jt3h6ZmwPv3mXKqOu3Ub1mhXX1I9O1WEWEJybUZf42NVfbnGuc6n\nlUcGxf6V+vNJBlJyrkS55A66De0DZ74yfe9QDcZJ29iY3rZyI/Ga2WecPNcPYV6xm1lLvB5bEq3O\nD02/11ZrkfM6qimVLKcxn5+gNoWQPj/vLvxW2k/OI+G3ROu7kmm+vdxGNc+xzN36NHVMbd1dTf0Z\nMEeczkwy2ZfcfWWQUn5TsrxuQ/U+1OTcj2Vfpc7YNVQDbB21nxzyzl1Hb/y4tKGPEAeLIA7Q9tuf\n5TVpWXJZ7ffOsF4zWjb9Xe6BXLbhQW7DcvyTqOYJXqjyKu6nOa+EEvfJHFcPTuXKB7kNd9JSlFc7\neCNTAdprIM/3k+jlp5ljau802x5/nNlHYub68jZN2LVZvUb7WgXh4BF+2DReeEsI43Bv/ucF/LYz\nhuNPO3I9jjXlc2a7sC/Rq2Whymu/bMOlmNfcgNJ2TMZtuFMonzgLUV4/RBxF3owYY7eKwcmMxslZ\ngCiv6uVQTBl/Z6J7TT/Lq7jf9FFeB2Jf/CcRXxT7QvgohP2I8Z2i6G1BZUX5IpV7XL+ETfdQ303n\nY2Y1zGjrpyAd/YW4DW9T+C48xKx/I1MHKutDIFoeTinUE6ZVeS0la5JzczO999gjq6+1KZ6+p75b\nl7om6Dhje+3XUnUa5ZxJFtFHUimMzwY+kL6L3Gto7jas29ZNsFLcgzWXprra0iydxo2pd0wEScD2\nNrMv3VEXJO5V38P/P3vnHW5XVeb/z85NQkLooHSNCghiARRQpBykir1gF7sith+W0dFB0bENI3Zx\ncFRQ7IqiiAVFDmIXuyiiFLERWggJISFl/f5413vWu9+91j773HtucsPwPs999rnrrLP32qu+37da\n8Oq1V8MitpLuVenmb99Jg/foM+aQBEhtZsM+AEeOWVHNa45xz60tBW0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/GiUnuvGdmPev8vIrmb+PfVtt6fulmt7kUVtT1p4FNso1T7SKie+vGaE9KpK4G2Ve+bM1NT/9xZ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\n8LwxLH7Omb7//c8XLSTzWtZNoOfNZKfm/ZX1W1LmdeE6I76uypqt+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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb05f1d9160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(16,15))\n", "\n", "#Grafico Temperatura\n", "plt.subplot(3, 1, 1)\n", "plt.title('Dados Brutos Reindexados')\n", "plt.ylabel('Graus')\n", "plt.xlabel('')\n", "ndf_dados.AirTC.plot(legend=True)\n", "\n", "#Grafico Umidade\n", "plt.subplot(3, 1, 2)\n", "#plt.title('Dados Brutos Reindexados')\n", "plt.xlabel('')\n", "plt.ylabel('%')\n", "ndf_dados.RH.plot(legend=True)\n", "\n", "#Grafico Chuva\n", "plt.subplot(3, 1, 3)\n", "#plt.title('Dados Brutos Reindexados')\n", "plt.xlabel('')\n", "plt.ylabel('mm')\n", "ndf_dados.Rain_mm.plot(legend=True)\n", "\n", "#plt.savefig('figs/nome-da-figura.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Em busca dos GAPs" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>AirTC</th>\n", " <th>RH</th>\n", " <th>Rain_mm</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2015-01-23 01:11:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-23 01:12:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-23 01:13:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-23 01:14:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-23 01:15:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-23 01:16:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " 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</tr>\n", " <tr>\n", " <th>2015-05-12 11:21:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:22:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:23:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:24:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:25:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:26:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:27:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:28:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:29:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:30:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:31:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:32:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:33:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:34:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:35:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:36:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:37:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:38:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:39:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12 11:40:00</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>8668 rows × 3 columns</p>\n", "</div>" ], "text/plain": [ " AirTC RH Rain_mm\n", "2015-01-23 01:11:00 NaN NaN NaN\n", "2015-01-23 01:12:00 NaN NaN NaN\n", "2015-01-23 01:13:00 NaN NaN NaN\n", "2015-01-23 01:14:00 NaN NaN NaN\n", "2015-01-23 01:15:00 NaN NaN NaN\n", "2015-01-23 01:16:00 NaN NaN NaN\n", "2015-01-23 01:17:00 NaN NaN NaN\n", "2015-01-23 01:18:00 NaN NaN NaN\n", "2015-01-23 01:19:00 NaN NaN NaN\n", "2015-01-23 01:20:00 NaN NaN NaN\n", "2015-01-23 01:21:00 NaN NaN NaN\n", "2015-01-23 01:22:00 NaN NaN NaN\n", "2015-01-23 01:23:00 NaN NaN NaN\n", "2015-01-23 01:24:00 NaN NaN NaN\n", "2015-01-23 01:25:00 NaN NaN NaN\n", "2015-01-23 01:26:00 NaN NaN NaN\n", "2015-01-23 01:27:00 NaN NaN NaN\n", "2015-01-23 01:28:00 NaN NaN NaN\n", "2015-01-23 01:29:00 NaN NaN NaN\n", "2015-01-23 01:30:00 NaN NaN NaN\n", "2015-01-23 01:31:00 NaN NaN NaN\n", "2015-01-23 01:32:00 NaN NaN NaN\n", "2015-01-23 01:33:00 NaN NaN NaN\n", "2015-01-23 01:34:00 NaN NaN NaN\n", "2015-01-23 01:35:00 NaN NaN NaN\n", "2015-01-23 01:36:00 NaN NaN NaN\n", "2015-01-23 01:37:00 NaN NaN NaN\n", "2015-01-23 01:38:00 NaN NaN NaN\n", "2015-01-23 01:39:00 NaN NaN NaN\n", "2015-01-23 01:40:00 NaN NaN NaN\n", "... ... .. ...\n", "2015-05-12 11:11:00 NaN NaN NaN\n", "2015-05-12 11:12:00 NaN NaN NaN\n", "2015-05-12 11:13:00 NaN NaN NaN\n", "2015-05-12 11:14:00 NaN NaN NaN\n", "2015-05-12 11:15:00 NaN NaN NaN\n", "2015-05-12 11:16:00 NaN NaN NaN\n", "2015-05-12 11:17:00 NaN NaN NaN\n", "2015-05-12 11:18:00 NaN NaN NaN\n", "2015-05-12 11:19:00 NaN NaN NaN\n", "2015-05-12 11:20:00 NaN NaN NaN\n", "2015-05-12 11:21:00 NaN NaN NaN\n", "2015-05-12 11:22:00 NaN NaN NaN\n", "2015-05-12 11:23:00 NaN NaN NaN\n", "2015-05-12 11:24:00 NaN NaN NaN\n", "2015-05-12 11:25:00 NaN NaN NaN\n", "2015-05-12 11:26:00 NaN NaN NaN\n", "2015-05-12 11:27:00 NaN NaN NaN\n", "2015-05-12 11:28:00 NaN NaN NaN\n", "2015-05-12 11:29:00 NaN NaN NaN\n", "2015-05-12 11:30:00 NaN NaN NaN\n", "2015-05-12 11:31:00 NaN NaN NaN\n", "2015-05-12 11:32:00 NaN NaN NaN\n", "2015-05-12 11:33:00 NaN NaN NaN\n", "2015-05-12 11:34:00 NaN NaN NaN\n", "2015-05-12 11:35:00 NaN NaN NaN\n", "2015-05-12 11:36:00 NaN NaN NaN\n", "2015-05-12 11:37:00 NaN NaN NaN\n", "2015-05-12 11:38:00 NaN NaN NaN\n", "2015-05-12 11:39:00 NaN NaN NaN\n", "2015-05-12 11:40:00 NaN NaN NaN\n", "\n", "[8668 rows x 3 columns]" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#numpy.all(numpy.isnan(data_list))\n", "# np.any(np.isnan(ndf_dados)) Se returnar True eh porque algum valor NaN foi encontrado\n", "\n", "# Mostra onde os dados Possuem valor NaN\n", "ndf_dados[np.isnan(ndf_dados.Rain_mm)]\n", " " ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ndf_dados.head()\n", "#Exportando para um novo arquivo\n", "ndf_dados.to_csv('sao_roque_2015-AirTC-RH-Rain.csv')\n", "# TODO: Este arquivo nao possui mais gaps no dominio temporal (as imagens foram ajustadas para o valor NaN), portanto pode-se pular a etapa reindexar caso seja utilizado no futuro." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Analises Diarias" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_dados_diarios = ndf_dados[['AirTC','RH']] .resample('D', how='mean')\n", "chuva = ndf_dados.Rain_mm.resample('D', how='sum')\n" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>AirTC</th>\n", " <th>RH</th>\n", " <th>Acum_Chuva</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2015-01-01</th>\n", " <td>20.660417</td>\n", " <td>82.342326</td>\n", " <td>11.176</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-02</th>\n", " <td>18.120118</td>\n", " <td>87.190590</td>\n", " <td>1.778</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-03</th>\n", " <td>18.737708</td>\n", " <td>79.551458</td>\n", " <td>0.000</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-04</th>\n", " <td>21.458375</td>\n", " <td>67.746931</td>\n", " <td>0.000</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-05</th>\n", " <td>23.430160</td>\n", " <td>66.851417</td>\n", " <td>0.000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " AirTC RH Acum_Chuva\n", "2015-01-01 20.660417 82.342326 11.176\n", "2015-01-02 18.120118 87.190590 1.778\n", "2015-01-03 18.737708 79.551458 0.000\n", "2015-01-04 21.458375 67.746931 0.000\n", "2015-01-05 23.430160 66.851417 0.000" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_dados_diarios['Acum_Chuva'] = chuva\n", "df_dados_diarios.head()" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7fb05dbf5c50>" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Z8fmhxYRRaPGwK5G2Axs1jThu+lA+AguKwjZPOx1lk/VHjtNBwK4VF9AkjnYp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\n5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+7AXwBXw5m3w7f/EH70jZb9D+vz++/D5LxIY9TI/wY+QCpiF5fbtmSyqD2tXCZcAuzf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\nejCb7+7/APZuOcYPgRcPPKnUQ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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb05de0f898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Mostrando tudo\n", "plt.figure(figsize=(16,8))\n", "plt.subplot(2,1,1)\n", "plt.title('Media Diaria 2015')\n", "plt.xlabel(\"\")\n", "plt.ylabel(\"mm, Graus, %\")\n", "df_dados_diarios.AirTC.plot(legend=True)\n", "df_dados_diarios.RH.plot(legend=True)\n", "df_dados_diarios.Acum_Chuva.plot(legend=True)\n", "\n", "plt.subplot(2,1,2)\n", "acumulado = df_dados_diarios.Acum_Chuva.cumsum()\n", "plt.xlabel(\"Data\")\n", "plt.ylabel(\"mm\")\n", "acumulado.plot(legend=True)\n", "\n", "#plt.savefig('figs/nome-da-figura.png')" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7fb05de09898>" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb05de090b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Histograma do Acumulado Diario\n", "plt.figure(figsize=(16,8))\n", "plt.xlabel(\"Acorrencia\")\n", "plt.ylabel(\"mm\")\n", "df_dados_diarios.Acum_Chuva.plot(kind='hist', orientation='horizontal', alpha=.75,legend=True)\n", "\n", "#plt.savefig('figs/nome-da-figura.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quais dias o Acumulado de chuva foi superior a 20mm em 2015 ?" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Timestamp\n", "2015-01-28 29.718\n", "2015-02-09 21.590\n", "2015-02-21 70.104\n", "2015-03-09 23.368\n", "2015-03-30 21.844\n", "2015-04-05 28.448\n", "2015-04-20 44.450\n", "Name: Acum_Chuva, dtype: float64" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Quais dias o Acumulado de chuva foi superior a 20mm em 2015?\n", "df_dados_diarios.Acum_Chuva[df_dados_diarios.Acum_Chuva > 20.]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quantos dias o Acumulado de chuva foi superior a 20mm em 2015 ?" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Quantos dias o Acumulado de chuva foi superior a 20mm em 2015?\n", "df_dados_diarios.Acum_Chuva[df_dados_diarios.Acum_Chuva > 20.].count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Estatistica geral do DataFrame" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "( AirTC RH Rain_mm\n", " count 204513.000000 204513.000000 204513.000000\n", " mean 19.453614 80.962955 0.002573\n", " std 4.293105 14.318227 0.039800\n", " min 7.030000 0.000000 0.000000\n", " 25% 16.570000 71.560000 0.000000\n", " 50% 19.280000 83.900000 0.000000\n", " 75% 22.250000 92.600000 0.000000\n", " max 32.080000 100.000000 5.800000,\n", " AirTC RH Acum_Chuva\n", " count 147.000000 147.000000 147.000000\n", " mean 19.477713 80.830773 3.579878\n", " std 3.160460 7.848394 8.697315\n", " min 10.897843 56.890602 0.000000\n", " 25% 17.346858 75.739872 0.000000\n", " 50% 19.605347 80.935653 0.000000\n", " 75% 21.442038 86.432545 2.794000\n", " max 28.191128 98.972410 70.104000)" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ndf_dados.describe(), df_dados_diarios.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "# Atividade\n", "## Refazer os procedimentos para os anos de 2012 ate 2014.\n", "\n", "* Dados disponiveis em http://fortran-zrhans.c9.io/csdapy/\n", "* Aplicar os tratamentos e mostrar tambem os graficos para temperatura maxima, minima, velocidade do vento e radiacao solar.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resultados apos executar os procedimentos para cada ano a partir de 2012.\n", "----\n", "\n", "```bash\n", "\n", "hans@hasus:~/Dropbox/workspace/spyder/spyderprj01$ python3 ATMOS-Anuais.py -i 2012; python3 ATMOS-Anuais.py -i 201\n", "3.4.3 |Anaconda 2.2.0 (64-bit)| (default, Mar 6 2015, 12:03:53) \n", "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n", "1.9.2\n", "Analisando ano 2012\n", "Dados 2012 Importados OK\n", "Gerando Graficos Brutos - 2012\n", "Indice criado OK\n", "Junção OK\n", "Gerando Graficos Brutos Reindexados - 2012\n", "Gerando Graficos Media Diaria - 2012\n", "Gerando Graficos Acumulado de Chuva - 2012\n", "Gerando Graficos Histograma Acumulado diario de Chuva - 2012\n", "2012-01-01 41.402\n", "2012-01-13 24.638\n", "2012-02-05 45.466\n", "2012-06-17 30.480\n", "2012-07-06 62.992\n", "2012-07-24 37.592\n", "2012-08-26 28.956\n", "2012-09-09 20.320\n", "2012-09-10 52.578\n", "2012-09-16 31.242\n", "2012-09-18 35.814\n", "2012-09-19 27.432\n", "2012-10-07 20.320\n", "2012-10-22 24.638\n", "2012-11-23 26.924\n", "2012-12-27 43.180\n", "2012-12-28 29.972\n", "Name: Acum_Chuva, dtype: float64\n", "17\n", "\n", "________\n", "FINALIZADO!!\n", "________\n", "3.4.3 |Anaconda 2.2.0 (64-bit)| (default, Mar 6 2015, 12:03:53) \n", "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n", "1.9.2\n", "Analisando ano 2013\n", "Dados 2013 Importados OK\n", "Gerando Graficos Brutos - 2013\n", "Indice criado OK\n", "Junção OK\n", "Gerando Graficos Brutos Reindexados - 2013\n", "Gerando Graficos Media Diaria - 2013\n", "Gerando Graficos Acumulado de Chuva - 2013\n", "Gerando Graficos Histograma Acumulado diario de Chuva - 2013\n", "2013-02-25 29.718\n", "2013-03-09 22.352\n", "2013-03-12 44.958\n", "2013-03-20 34.798\n", "2013-04-04 25.908\n", "2013-04-12 26.924\n", "2013-05-19 41.402\n", "2013-05-29 20.828\n", "2013-06-01 20.828\n", "2013-06-28 24.638\n", "2013-06-29 20.828\n", "2013-07-07 27.178\n", "2013-08-09 28.702\n", "2013-08-13 22.860\n", "2013-08-23 25.654\n", "2013-08-24 61.722\n", "2013-08-25 34.036\n", "2013-08-26 22.860\n", "2013-09-20 51.816\n", "2013-09-21 44.704\n", "2013-10-21 27.940\n", "2013-11-11 53.340\n", "2013-11-15 20.320\n", "2013-11-20 23.368\n", "2013-11-21 20.828\n", "2013-12-05 22.098\n", "Name: Acum_Chuva, dtype: float64\n", "26\n", "\n", "________\n", "FINALIZADO!!\n", "________\n", "\n", "3.4.3 |Anaconda 2.2.0 (64-bit)| (default, Mar 6 2015, 12:03:53) \n", "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n", "1.9.2\n", "Analisando ano 2014\n", "Dados 2014 Importados OK\n", "Gerando Graficos Brutos - 2014\n", "Indice criado OK\n", "Junção OK\n", "Gerando Graficos Brutos Reindexados - 2014\n", "Gerando Graficos Media Diaria - 2014\n", "Gerando Graficos Acumulado de Chuva - 2014\n", "Gerando Graficos Histograma Acumulado diario de Chuva - 2014\n", "2014-01-03 23.622\n", "2014-01-10 21.844\n", "2014-02-12 20.574\n", "2014-02-25 22.352\n", "2014-03-17 22.352\n", "2014-03-31 24.130\n", "2014-04-08 49.784\n", "2014-04-11 44.196\n", "2014-05-21 37.338\n", "2014-05-31 37.846\n", "2014-06-05 53.594\n", "2014-06-13 37.084\n", "2014-06-24 25.908\n", "2014-06-26 30.226\n", "2014-07-03 23.876\n", "2014-07-23 34.798\n", "2014-08-31 25.146\n", "2014-09-02 25.654\n", "2014-09-06 29.972\n", "2014-09-11 27.686\n", "2014-09-26 25.146\n", "2014-10-13 37.338\n", "2014-10-17 46.990\n", "2014-10-19 23.368\n", "2014-12-12 44.704\n", "2014-12-20 33.274\n", "2014-12-21 35.306\n", "2014-12-27 30.226\n", "Name: Acum_Chuva, dtype: float64\n", "28\n", "\n", "________\n", "FINALIZADO!!\n", "________\n", "\n", "3.4.3 |Anaconda 2.2.0 (64-bit)| (default, Mar 6 2015, 12:03:53) \n", "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n", "1.9.2\n", "Analisando ano 2015\n", "Dados 2015 Importados OK\n", "Gerando Graficos Brutos - 2015\n", "Indice criado OK\n", "Junção OK\n", "Gerando Graficos Brutos Reindexados - 2015\n", "Gerando Graficos Media Diaria - 2015\n", "Gerando Graficos Acumulado de Chuva - 2015\n", "Gerando Graficos Histograma Acumulado diario de Chuva - 2015\n", "2015-01-28 29.718\n", "2015-02-09 21.590\n", "2015-02-21 70.104\n", "2015-03-09 23.368\n", "2015-03-30 21.844\n", "2015-04-05 28.448\n", "2015-04-20 44.450\n", "Name: Acum_Chuva, dtype: float64\n", "7\n", "\n", "________\n", "FINALIZADO!!\n", "________\n", "\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "Elaborado por Hans Rogerio Zimermann para o curso *FSC878 - Topicos Especiais II* PPGMET - UFSM." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-2.0

mne-tools/mne-tools.github.io

0.16/_downloads/plot_topo_compare_conditions.ipynb

1

3543

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Compare evoked responses for different conditions\n\n\nIn this example, an Epochs object for visual and auditory responses is created.\nBoth conditions are then accessed by their respective names to create a sensor\nlayout plot of the related evoked responses.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Authors: Denis Engemann <[email protected]>\n# Alexandre Gramfort <[email protected]>\n\n# License: BSD (3-clause)\n\n\nimport matplotlib.pyplot as plt\nimport mne\n\nfrom mne.viz import plot_evoked_topo\nfrom mne.datasets import sample\n\nprint(__doc__)\n\ndata_path = sample.data_path()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set parameters\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'\nevent_id = 1\ntmin = -0.2\ntmax = 0.5\n\n# Setup for reading the raw data\nraw = mne.io.read_raw_fif(raw_fname)\nevents = mne.read_events(event_fname)\n\n# Set up pick list: MEG + STI 014 - bad channels (modify to your needs)\ninclude = [] # or stim channels ['STI 014']\n# bad channels in raw.info['bads'] will be automatically excluded\n\n# Set up amplitude-peak rejection values for MEG channels\nreject = dict(grad=4000e-13, mag=4e-12)\n\n# pick MEG channels\npicks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True,\n include=include, exclude='bads')\n\n# Create epochs including different events\nevent_id = {'audio/left': 1, 'audio/right': 2,\n 'visual/left': 3, 'visual/right': 4}\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax,\n picks=picks, baseline=(None, 0), reject=reject)\n\n# Generate list of evoked objects from conditions names\nevokeds = [epochs[name].average() for name in ('left', 'right')]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show topography for two different conditions\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "colors = 'blue', 'red'\ntitle = 'MNE sample data\\nleft vs right (A/V combined)'\n\nplot_evoked_topo(evokeds, color=colors, title=title, background_color='w')\n\nplt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

Kaggle/learntools

notebooks/time_series/raw/ex5.ipynb

1

18425

{ "cells": [ { "cell_type": "markdown", "id": "prompt-collaboration", "metadata": {}, "source": [ "# Introduction #\n", "\n", "Run this cell to set everything up!" ] }, { "cell_type": "code", "execution_count": null, "id": "flying-indonesia", "metadata": {}, "outputs": [], "source": [ "# Setup feedback system\n", "from learntools.core import binder\n", "binder.bind(globals())\n", "from learntools.time_series.ex5 import *\n", "\n", "# Setup notebook\n", "from pathlib import Path\n", "from learntools.time_series.style import * # plot style settings\n", "\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.preprocessing import LabelEncoder\n", "from statsmodels.tsa.deterministic import DeterministicProcess\n", "from xgboost import XGBRegressor\n", "\n", "\n", "comp_dir = Path('../input/store-sales-time-series-forecasting')\n", "data_dir = Path(\"../input/ts-course-data\")\n", "\n", "store_sales = pd.read_csv(\n", " comp_dir / 'train.csv',\n", " usecols=['store_nbr', 'family', 'date', 'sales', 'onpromotion'],\n", " dtype={\n", " 'store_nbr': 'category',\n", " 'family': 'category',\n", " 'sales': 'float32',\n", " },\n", " parse_dates=['date'],\n", " infer_datetime_format=True,\n", ")\n", "store_sales['date'] = store_sales.date.dt.to_period('D')\n", "store_sales = store_sales.set_index(['store_nbr', 'family', 'date']).sort_index()\n", "\n", "family_sales = (\n", " store_sales\n", " .groupby(['family', 'date'])\n", " .mean()\n", " .unstack('family')\n", " .loc['2017']\n", ")" ] }, { "cell_type": "markdown", "id": "sweet-camcorder", "metadata": {}, "source": [ "-------------------------------------------------------------------------------\n", "\n", "In the next two questions, you'll create a boosted hybrid for the *Store Sales* dataset by implementing a new Python class. Run this cell to create the initial class definition. You'll add `fit` and `predict` methods to give it a scikit-learn like interface.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "devoted-firmware", "metadata": {}, "outputs": [], "source": [ "# You'll add fit and predict methods to this minimal class\n", "class BoostedHybrid:\n", " def __init__(self, model_1, model_2):\n", " self.model_1 = model_1\n", " self.model_2 = model_2\n", " self.y_columns = None # store column names from fit method\n" ] }, { "cell_type": "markdown", "id": "legendary-description", "metadata": {}, "source": [ "# 1) Define fit method for boosted hybrid\n", "\n", "Complete the `fit` definition for the `BoostedHybrid` class. Refer back to steps 1 and 2 from the **Hybrid Forecasting with Residuals** section in the tutorial if you need." ] }, { "cell_type": "code", "execution_count": null, "id": "variable-victory", "metadata": {}, "outputs": [], "source": [ "def fit(self, X_1, X_2, y):\n", " # YOUR CODE HERE: fit self.model_1\n", " ____\n", "\n", " y_fit = pd.DataFrame(\n", " # YOUR CODE HERE: make predictions with self.model_1\n", " ____,\n", " index=X_1.index, columns=y.columns,\n", " )\n", "\n", " # YOUR CODE HERE: compute residuals\n", " y_resid = ____\n", " y_resid = y_resid.stack().squeeze() # wide to long\n", "\n", " # YOUR CODE HERE: fit self.model_2 on residuals\n", " self.model_2.fit(____, ____)\n", "\n", " # Save column names for predict method\n", " self.y_columns = y.columns\n", " # Save data for question checking\n", " self.y_fit = y_fit\n", " self.y_resid = y_resid\n", "\n", "\n", "# Add method to class\n", "BoostedHybrid.fit = fit\n", "\n", "\n", "# Check your answer\n", "q_1.check()" ] }, { "cell_type": "code", "execution_count": null, "id": "sized-canberra", "metadata": {}, "outputs": [], "source": [ "# Lines below will give you a hint or solution code\n", "#_COMMENT_IF(PROD)_\n", "q_1.hint()\n", "#_COMMENT_IF(PROD)_\n", "q_1.solution()" ] }, { "cell_type": "code", "execution_count": null, "id": "unable-water", "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "def fit(self, X_1, X_2, y):\n", " # Train model_1\n", " self.model_1.fit(X_1, y)\n", "\n", " # Make predictions\n", " y_fit = pd.DataFrame(\n", " self.model_1.predict(X_1), index=X_1.index, columns=y.columns,\n", " )\n", "\n", " # Compute residuals\n", " y_resid = y - y_fit\n", " y_resid = y_resid.stack().squeeze() # wide to long\n", "\n", " # Train model_2 on residuals\n", " self.model_2.fit(X_2, y.stack().squeeze())\n", "\n", " # Save column names for predict method\n", " self.y_columns = y.columns\n", " # Save data for question checking\n", " self.y_fit = y_fit\n", " self.y_resid = y_resid\n", "\n", "\n", "# Add method to class\n", "BoostedHybrid.fit = fit\n", "\n", "q_1.assert_check_failed()" ] }, { "cell_type": "code", "execution_count": null, "id": "portable-lyric", "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "def fit(self, X_1, X_2, y):\n", " # Train model_1\n", " self.model_1.fit(X_1, y)\n", "\n", " # Make predictions\n", " y_fit = pd.DataFrame(\n", " self.model_1.predict(X_1), index=X_1.index, columns=y.columns,\n", " )\n", "\n", " # Compute residuals\n", " y_resid = y\n", " y_resid = y_resid.stack().squeeze() # wide to long\n", "\n", " # Train model_2 on residuals\n", " self.model_2.fit(X_2, y_resid)\n", "\n", " # Save column names for predict method\n", " self.y_columns = y.columns\n", " # Save data for question checking\n", " self.y_fit = y_fit\n", " self.y_resid = y_resid\n", "\n", "\n", "# Add method to class\n", "BoostedHybrid.fit = fit\n", "\n", "q_1.assert_check_failed()" ] }, { "cell_type": "code", "execution_count": null, "id": "assisted-mandate", "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "def fit(self, X_1, X_2, y):\n", " # Train model_1\n", " self.model_1.fit(X_1, y)\n", "\n", " # Make predictions\n", " y_fit = pd.DataFrame(\n", " self.model_1.predict(X_1), index=X_1.index, columns=y.columns,\n", " )\n", "\n", " # Compute residuals\n", " y_resid = y - y_fit\n", " y_resid = y_resid.stack().squeeze() # wide to long\n", "\n", " # Train model_2 on residuals\n", " self.model_2.fit(X_2, y_resid)\n", "\n", " # Save column names for predict method\n", " self.y_columns = y.columns\n", " # Save data for question checking\n", " self.y_fit = y_fit\n", " self.y_resid = y_resid\n", "\n", "\n", "# Add method to class\n", "BoostedHybrid.fit = fit\n", "\n", "\n", "q_1.assert_check_passed()" ] }, { "cell_type": "markdown", "id": "racial-bikini", "metadata": {}, "source": [ "-------------------------------------------------------------------------------\n", "\n", "# 2) Define predict method for boosted hybrid\n", "\n", "Now define the `predict` method for the `BoostedHybrid` class. Refer back to step 3 from the **Hybrid Forecasting with Residuals** section in the tutorial if you need." ] }, { "cell_type": "code", "execution_count": null, "id": "artistic-reminder", "metadata": {}, "outputs": [], "source": [ "def predict(self, X_1, X_2):\n", " y_pred = pd.DataFrame(\n", " # YOUR CODE HERE: predict with self.model_1\n", " ____,\n", " index=X_1.index, columns=self.y_columns,\n", " )\n", " y_pred = y_pred.stack().squeeze() # wide to long\n", "\n", " # YOUR CODE HERE: add self.model_2 predictions to y_pred\n", " y_pred += ____\n", " \n", " return y_pred.unstack() # long to wide\n", "\n", "\n", "# Add method to class\n", "BoostedHybrid.predict = predict\n", "\n", "\n", "# Check your answer\n", "q_2.check()" ] }, { "cell_type": "code", "execution_count": null, "id": "authentic-biodiversity", "metadata": {}, "outputs": [], "source": [ "# Lines below will give you a hint or solution code\n", "#_COMMENT_IF(PROD)_\n", "q_2.hint()\n", "#_COMMENT_IF(PROD)_\n", "q_2.solution()" ] }, { "cell_type": "code", "execution_count": null, "id": "injured-invalid", "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "def predict(self, X_1, X_2):\n", " # Predict with model_1\n", " y_pred = pd.DataFrame(\n", " self.model_1.predict(X_1), index=X_1.index, columns=self.y_columns,\n", " )\n", " y_pred = y_pred.stack().squeeze() # wide to long\n", "\n", " # Add model_2 predictions to model_1 predictions\n", " y_pred += y_pred\n", " return y_pred.unstack()\n", "\n", "\n", "# Add method to class\n", "BoostedHybrid.predict = predict\n", "\n", "\n", "q_2.assert_check_failed()" ] }, { "cell_type": "code", "execution_count": null, "id": "portuguese-condition", "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "def predict(self, X_1, X_2):\n", " # Predict with model_1\n", " y_pred = pd.DataFrame(\n", " self.model_1.predict(X_1), index=X_1.index, columns=self.y_columns,\n", " )\n", " y_pred = y_pred.stack().squeeze() # wide to long\n", "\n", " # Add model_2 predictions to model_1 predictions\n", " y_pred += self.model_2.predict(X_2)\n", " return y_pred.unstack()\n", "\n", "\n", "# Add method to class\n", "BoostedHybrid.predict = predict\n", "\n", "\n", "q_2.assert_check_passed()" ] }, { "cell_type": "markdown", "id": "chief-barbados", "metadata": {}, "source": [ "-------------------------------------------------------------------------------\n", "\n", "Now you're ready to use your new `BoostedHybrid` class to create a model for the *Store Sales* data. Run the next cell to set up the data for training." ] }, { "cell_type": "code", "execution_count": null, "id": "talented-hands", "metadata": {}, "outputs": [], "source": [ "# Target series\n", "y = family_sales.loc[:, 'sales']\n", "\n", "\n", "# X_1: Features for Linear Regression\n", "dp = DeterministicProcess(index=y.index, order=1)\n", "X_1 = dp.in_sample()\n", "\n", "\n", "# X_2: Features for XGBoost\n", "X_2 = family_sales.drop('sales', axis=1).stack() # onpromotion feature\n", "\n", "# Label encoding for 'family'\n", "le = LabelEncoder() # from sklearn.preprocessing\n", "X_2 = X_2.reset_index('family')\n", "X_2['family'] = le.fit_transform(X_2['family'])\n", "\n", "# Label encoding for seasonality\n", "X_2[\"day\"] = X_2.index.day # values are day of the month" ] }, { "cell_type": "markdown", "id": "boxed-concentrate", "metadata": {}, "source": [ "# 3) Train boosted hybrid\n", "\n", "Create the hybrid model by initializing a `BoostedHybrid` class with `LinearRegression()` and `XGBRegressor()` instances." ] }, { "cell_type": "code", "execution_count": null, "id": "meaningful-japan", "metadata": {}, "outputs": [], "source": [ "# YOUR CODE HERE: Create LinearRegression + XGBRegressor hybrid with BoostedHybrid\n", "model = ____\n", "\n", "# YOUR CODE HERE: Fit and predict\n", "#_UNCOMMENT_IF(PROD)_\n", "#model.fit(____, ____, ____)\n", "y_pred = ____\n", "\n", "#_UNCOMMENT_IF(PROD)_\n", "#y_pred = y_pred.clip(0.0)\n", "\n", "\n", "# Check your answer\n", "q_3.check()" ] }, { "cell_type": "code", "execution_count": null, "id": "acting-courage", "metadata": {}, "outputs": [], "source": [ "# Lines below will give you a hint or solution code\n", "#_COMMENT_IF(PROD)_\n", "q_3.hint()\n", "#_COMMENT_IF(PROD)_\n", "q_3.solution()" ] }, { "cell_type": "code", "execution_count": null, "id": "lucky-inspiration", "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "# Create model\n", "model = BoostedHybrid(\n", " model_1=LinearRegression(),\n", " model_2=LinearRegression(),\n", ")\n", "model.fit(X_1, X_2, y)\n", "\n", "y_pred = model.predict(X_1, X_2)\n", "y_pred = y_pred.clip(0.0)\n", "\n", "q_3.assert_check_failed()" ] }, { "cell_type": "code", "execution_count": null, "id": "metallic-sandwich", "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "# Create model\n", "model = BoostedHybrid(\n", " model_1=LinearRegression,\n", " model_2=XGBRegressor,\n", ")\n", "#model.fit(X_1, X_2, y)\n", "\n", "#y_pred = model.predict(X_1, X_2)\n", "#y_pred = y_pred.clip(0.0)\n", "\n", "q_3.assert_check_failed()" ] }, { "cell_type": "code", "execution_count": null, "id": "forbidden-watts", "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "\n", "# Create model\n", "model = BoostedHybrid(\n", " model_1=LinearRegression(),\n", " model_2=XGBRegressor(),\n", ")\n", "model.fit(X_1, X_2, y)\n", "\n", "y_pred = model.predict(X_1, X_2)\n", "y_pred = y_pred.clip(0.0)\n", "\n", "q_3.assert_check_passed()" ] }, { "cell_type": "markdown", "id": "minus-likelihood", "metadata": {}, "source": [ "-------------------------------------------------------------------------------\n", "\n", "Depending on your problem, you might want to use other hybrid combinations than the linear regression + XGBoost hybrid you've created in the previous questions. Run the next cell to try other algorithms from scikit-learn." ] }, { "cell_type": "code", "execution_count": null, "id": "illegal-ridge", "metadata": {}, "outputs": [], "source": [ "# Model 1 (trend)\n", "from pyearth import Earth\n", "from sklearn.linear_model import ElasticNet, Lasso, Ridge\n", "\n", "# Model 2\n", "from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor\n", "from sklearn.neighbors import KNeighborsRegressor\n", "from sklearn.neural_network import MLPRegressor\n", "\n", "# Boosted Hybrid\n", "\n", "# YOUR CODE HERE: Try different combinations of the algorithms above\n", "model = BoostedHybrid(\n", " model_1=Ridge(),\n", " model_2=KNeighborsRegressor(),\n", ")" ] }, { "cell_type": "markdown", "id": "opened-history", "metadata": {}, "source": [ "These are just some suggestions. You might discover other algorithms you like in the scikit-learn [User Guide](https://scikit-learn.org/stable/supervised_learning.html).\n", "\n", "Use the code in this cell to see the predictions your hybrid makes." ] }, { "cell_type": "code", "execution_count": null, "id": "intensive-accent", "metadata": {}, "outputs": [], "source": [ "y_train, y_valid = y[:\"2017-07-01\"], y[\"2017-07-02\":]\n", "X1_train, X1_valid = X_1[: \"2017-07-01\"], X_1[\"2017-07-02\" :]\n", "X2_train, X2_valid = X_2.loc[:\"2017-07-01\"], X_2.loc[\"2017-07-02\":]\n", "\n", "# Some of the algorithms above do best with certain kinds of\n", "# preprocessing on the features (like standardization), but this is\n", "# just a demo.\n", "model.fit(X1_train, X2_train, y_train)\n", "y_fit = model.predict(X1_train, X2_train).clip(0.0)\n", "y_pred = model.predict(X1_valid, X2_valid).clip(0.0)\n", "\n", "families = y.columns[0:6]\n", "axs = y.loc(axis=1)[families].plot(\n", " subplots=True, sharex=True, figsize=(11, 9), **plot_params, alpha=0.5,\n", ")\n", "_ = y_fit.loc(axis=1)[families].plot(subplots=True, sharex=True, color='C0', ax=axs)\n", "_ = y_pred.loc(axis=1)[families].plot(subplots=True, sharex=True, color='C3', ax=axs)\n", "for ax, family in zip(axs, families):\n", " ax.legend([])\n", " ax.set_ylabel(family)" ] }, { "cell_type": "markdown", "id": "efficient-hybrid", "metadata": {}, "source": [ "# 4) Fit with different learning algorithms\n", "\n", "Once you're ready to move on, run the next cell for credit on this question." ] }, { "cell_type": "code", "execution_count": null, "id": "southern-trigger", "metadata": { "lines_to_next_cell": 2 }, "outputs": [], "source": [ "# View the solution (Run this cell to receive credit!)\n", "q_4.check()" ] }, { "cell_type": "markdown", "id": "classical-artist", "metadata": {}, "source": [ "# Keep Going #\n", "\n", "[**Convert any forecasting task**](#$NEXT_NOTEBOOK_URL$) to a machine learning problem with four ML forecasting strategies." ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", "formats": "ipynb,md" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 5 }

apache-2.0

nitin-cherian/LifeLongLearning

Python/PythonProgrammingLanguage/Encapsulation/encap_env/lib/python3.5/site-packages/nbconvert/exporters/tests/files/notebook2.ipynb

1

10238

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# NumPy and Matplotlib examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First import NumPy and Matplotlib:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Welcome to pylab, a matplotlib-based Python environment [backend: module://ipykernel.pylab.backend_inline].\n", "For more information, type 'help(pylab)'.\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we show some very basic examples of how they can be used." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "a = np.random.uniform(size=(100,100))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(100, 100)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.shape" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [ "remove_cell" ] }, "outputs": [], "source": [ "evs = np.linalg.eigvals(a)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [ "remove_output" ] }, "outputs": [ { "data": { "text/plain": [ "(100,)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "evs.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is a cell that has both text and PNG output:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "tags": [ "remove_input" ] }, "outputs": [ { "data": { "text/plain": [ "(array([95, 4, 0, 0, 0, 0, 0, 0, 0, 1]),\n", " array([ -2.93566063, 2.35937011, 7.65440086, 12.9494316 ,\n", " 18.24446235, 23.53949309, 28.83452384, 34.12955458,\n", " 39.42458533, 44.71961607, 50.01464682]),\n", " <a list of 10 Patch objects>)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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1zjvvaMuWLerr6/PM/JK0efNmlZWVpS+M8NLslmUpGo3q0KFDisfjkrw1/7p162Tbtrq6\nutTV1SWfz+dq/pwE3ufzadasWX/aHovFVF9fL9u2tWDBAjmOo2QymYuRMuLF6/vnzZunadOmDdkW\nj8fV3NysgoICNTU1jet9mDFjhiorKyVJ06dPV3l5uRKJhKf24eKLL5YkDQwM6Oeff1ZBQYFn5j9x\n4oRef/11rVixIn1hhFdm/9UfL+jw0vz79u3T2rVrNWHCBOXn52vq1Kmu5h/Tz6KJx+MqLS1N3y8p\nKUm/2o4nplzf//v98Pl84/J3/VeOHj2q7u5uVVdXe2ofzp07p4qKChUWFuq+++6Tbduemf+BBx7Q\npk2blJf3WyK8Mrt0/gi+trZWjY2N2r59uyTvzH/ixAkNDg6qpaVFgUBAGzduVCqVcjV/1v7IetNN\nN+nLL7/80/YNGzakz+H90V9dMsl18qPHi5eoJpNJLVmyRO3t7Zo0aZKn9iEvL0+ffPKJjh07psWL\nF2vu3LmemH/Hjh267LLL5Pf7h7y93wuz/+q9997TzJkz1dPTo4aGBlVXV3tm/sHBQfX29mrTpk2q\nq6vTypUr9dJLL7maP2tH8Hv37tWnn376p9vfxV2SAoGADh8+nL5/5MgRVVVVZWukrKmqqtKRI0fS\n97u7u1VTUzOGE7lTVVWlnp4eSVJPT8+4/F3/3tmzZ3X77bdr+fLlCoVCkry3D9L5P/gtXrxYsVjM\nE/O///772r59u6666iotXbpUb731lpYvX+6J2X81c+ZMSVJpaaluueUWvfbaa56Z/5prrlFJSYka\nGho0ceJELV26VLt373Y1f85P0fz+Vai6ulp79uxRf3+/otGo8vLyNHny5FyP9K9Mub4/EAgoEoko\nlUopEomM6xcpx3HU3Nysa6+9VmvWrElv98o+fPPNN/ruu+8kSd9++63eeOMNhUIhT8y/YcMGHT9+\nXF988YW2bdum2tpabd261ROzS9KZM2fSf8v7+uuvtWfPHtXX13tmfkkqLi5WLBbTuXPntHPnTtXV\n1bmb38mBV155xSkqKnImTJjgFBYWOvX19envPfroo87VV1/tlJaWOvv378/FOK5Eo1HH5/M5V199\ntbN58+axHudf3Xnnnc7MmTOdiy66yCkqKnIikYhz+vRp55ZbbnEuv/xyJxQKOclkcqzH/Fvvvvuu\nY1mWU1FR4VRWVjqVlZXOrl27PLMPXV1djt/vd+bMmeMsWrTIefbZZx3HcTwz/6+i0ajT0NDgOI53\nZv/888+diooKp6KiwqmtrXW2bNniOI535nccx/nss8+cQCDgVFRUOA8++KAzMDDgav6c/5usAIDc\n4F90AgBDEXgAMBSBBwBDEXgAMBSBBwBDEXgAMNT/AQKseNIf7mhWAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x108c8f1d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hist(evs.real)" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "remove_cell" ] }, "source": [ "This cell is just markdown testing whether an ASCIIDoc quirk is caught and whether [header links are rendered](#numpy-and-matplotlib-examples) even if they [don't resolve correctly now](#NumPy-and-Matplotlib-examples).\n", "\n", "one *test* two *tests*. three *tests*" ] } ], "metadata": { "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

darkomen/TFG

medidas/04082015/.ipynb_checkpoints/Untitled-checkpoint.ipynb

1

394806

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Análisis de los datos obtenidos " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uso de ipython para el análsis y muestra de los datos obtenidos durante la producción. Los datos analizados son del filamento de bq el día 20 de Julio del 2015" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Importamos las librerías utilizadas\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numpy v1.9.2\n", "Pandas v0.16.2\n", "Seaborn v0.6.0\n" ] } ], "source": [ "#Mostramos las versiones usadas de cada librerías\n", "print (\"Numpy v{}\".format(np.__version__))\n", "print (\"Pandas v{}\".format(pd.__version__))\n", "print (\"Seaborn v{}\".format(sns.__version__))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Abrimos el fichero csv con los datos de la muestra\n", "datos = pd.read_csv('841551.CSV')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 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>Tmp Husillo</th>\n", " <th>Tmp Nozzle</th>\n", " <th>Diametro X</th>\n", " <th>Diametro Y</th>\n", " <th>MARCHA</th>\n", " <th>PARO</th>\n", " <th>RPM EXTR</th>\n", " <th>RPM TRAC</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>2788.000000</td>\n", " <td>2788.000000</td>\n", " <td>2788.000000</td>\n", " <td>2788.000000</td>\n", " <td>2788</td>\n", " <td>2788</td>\n", " <td>2788.000000</td>\n", " <td>2788.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>54.995481</td>\n", " <td>127.001973</td>\n", " <td>1.031413</td>\n", " <td>0.938265</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1.680057</td>\n", " <td>2.614164</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>11.839592</td>\n", " <td>28.603562</td>\n", " <td>0.724868</td>\n", " <td>0.757816</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1.801268</td>\n", " <td>1.317673</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>27.000000</td>\n", " <td>27.300000</td>\n", " <td>0.014000</td>\n", " <td>0.000342</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>51.300000</td>\n", " <td>136.500000</td>\n", " <td>0.125832</td>\n", " <td>0.000342</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>2.219072</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>60.700000</td>\n", " <td>137.800000</td>\n", " <td>1.424795</td>\n", " <td>1.379506</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2.000000</td>\n", " <td>2.874390</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>63.500000</td>\n", " <td>138.500000</td>\n", " <td>1.631253</td>\n", " <td>1.586380</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2.000000</td>\n", " <td>3.410560</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>64.700000</td>\n", " <td>141.400000</td>\n", " <td>2.262097</td>\n", " <td>2.126552</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>20.000000</td>\n", " <td>4.899920</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Tmp Husillo Tmp Nozzle Diametro X Diametro Y MARCHA PARO \\\n", "count 2788.000000 2788.000000 2788.000000 2788.000000 2788 2788 \n", "mean 54.995481 127.001973 1.031413 0.938265 1 1 \n", "std 11.839592 28.603562 0.724868 0.757816 0 0 \n", "min 27.000000 27.300000 0.014000 0.000342 True True \n", "25% 51.300000 136.500000 0.125832 0.000342 1 1 \n", "50% 60.700000 137.800000 1.424795 1.379506 1 1 \n", "75% 63.500000 138.500000 1.631253 1.586380 1 1 \n", "max 64.700000 141.400000 2.262097 2.126552 True True \n", "\n", " RPM EXTR RPM TRAC \n", "count 2788.000000 2788.000000 \n", "mean 1.680057 2.614164 \n", "std 1.801268 1.317673 \n", "min 0.000000 0.000000 \n", "25% 1.000000 2.219072 \n", "50% 2.000000 2.874390 \n", "75% 2.000000 3.410560 \n", "max 20.000000 4.899920 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Mostramos un resumen de los datos obtenidoss\n", "datos.describe()\n", "#datos.describe().loc['mean',['Diametro X [mm]', 'Diametro Y [mm]']]" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Almacenamos en una lista las columnas del fichero con las que vamos a trabajar\n", "columns = ['Diametro X', 'Diametro Y', 'RPM TRAC']" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([<matplotlib.axes._subplots.AxesSubplot object at 0x10cc3d0b8>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x10ce38c50>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x10c9f22b0>], dtype=object)" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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SBEEQBEEQBEEkQigUgtvtQmOjHRUVlQCgGfjZ7RYwa9ZsFBUVJ3Wu8vIKAOlJPe9yuWC1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EFCxtraGp566klks/ULJu/ceQr6+/tly1944XlR4Xw1LBYLTjvtdBVf6klkMnK79/jj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"text/plain": [ "<matplotlib.figure.Figure at 0x10cc2d780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Mostramos en varias gráficas la información obtenida tras el ensayo\n", "datos[columns].plot(subplots=True, figsize=(20,20))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Representamos ambos diámetros en la misma gráfica" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1097df198>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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T+baeGSl9NqLxNHfsaeGH7hvgf33xNc6OLmvb16NJ5vMF4V85M8veHmVy8sYl5Xv5xE/d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\nmBDBuA6p1Wd85F1+Pnj/AACSVPs5++Q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W5QvMKko+coi2Rvuu1xgVCHaLG3oWOrUQ4es/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zHe70MmL+HpiTWkcxKOjbVD4FlEEiYOaUz7zQOe3cmQAqCR7p2iPNdiAkzGw3DwDpzqOg6Rs2Eh\nvUTHp0dA9MMv+jAZn9YFdUfoPXUlOlEqlRkwiHJYsLAZ8DyHoqxCVhSoKiyHdJ+AMizi05rdYHkM\nevqqtteyp8QujenmHH0deyWiuRg8Njedi4a9A3T+0/sAo74RZKQsVjLlNiY3kn3032CvZv1Yxnwj\niBcSlPpr1uObBDKThe2p1SdOdyO1m/q1wXbBKTjQ6+rGdGIGE7EpABv7ZntmRlBVFVPxGbTb2wyK\nmQG7n0b7p+LTcPAOdDk7d3GkFixY2CmQCfQ7Mw+V/h6q2F6O0tXDTJI4mcNot7fBI7hrfqY8eQ9h\nrBQFfGr5GcQLCYz6himV6EaFEDaDWgINlfUb2r4zYBkWQ54B099ttKRsSB0Q74ChddZugfSh/uYT\n2rmM9/vQ5hERSVZTdolDuluUXQC0yTmwM2wdMyZAspDCWnYdo94hSqsrj8/okDIMg1HvEJKFFJ5c\nvgxAy7oSYcDvzT6iiYVZgV4LW4DAMZAkBZKsZYk4i7K7L0DmiSuRa6aBqwEisFODcUScz1HfkI6t\nMW26r6IqiOXjBtaJjbNhwN2H2eQ8rkavgwGDYe9gmTmiFzy6AeEecqzNluHoBVhHvcZ1SNkhLdsK\n/bw8tg2irH7Rh7YSg6xe/a42Lm1tMOwd2PJx9Rj1DqOgFPHowuPa3/vFIV3JrCItZUw97NESR30t\nF8GojrpmwYKF/Q0yH1zXRVmN27W/N4oS6msmSd1LLB83NULUgHqHqcrpdV2EkVBtbkQIYbOYjIfB\ngMGQxyiTXxmVLspFzCbnMejph40TTH83p+BETymiWVmrv5sgdaTX5jQH/0C/D21eO5KZIoqSse3L\naqw1KLsEO8XWqWQCVLKF9OPQL4II9PeDsxTUHa26R1rjfrCwN0FEjeQSbVewMqT7Ak7BiR5nF6ZK\nKveV84TA8hjyDmC+hsDOZHyalgOQzhlmquEAkCqmIakyAnZjX+kx3zAkVcZMcg797l7YebGqtl5L\naoXRZg/QrGcjIMfajD3PywWDAGvlOqRWhpRgI2proxgrMchWsms19yHdAQbcfbBx1T1Pt3p8QLMh\nPMPRetpa2DMzwiSNolQbRX00YTs50BYsWGht9Lt6YWM1cQI7J6LX1W3Y3m4PVNVvmKGy9mW0wqEj\nUFQFU3FN9MVtc1GVU4Ix3wg1drEmZ0glRcJMcg4DJQOsx0hFRJa0ICHz44C7j2Y/HbwD3SVWSSvO\npQOdbog2rVG6KHAY6HJRSm5lHel6XMua7maG1F1y+HaS8lzJBKjUU9DbTbeJAr1+AUTqkV2CE93O\nLt0+rXE/WNibIKJGRZoh3TPLTwsbQD83mM0TlS3aCHJSDgupJZpV1XfOKMjFqu/Rt3zZ6Ph9ru6S\nMJs2F65m15EqpjfdErKcIW3cnocTRgHWIU8/OIajjnZKKoka6YKD1G5UUJ63gkba7pDuAM2Y3/Xf\nOejZmHG1Z2aEqTpR2lGDyJFlNC1YeLmAYznaRHvEW82O0LKdxvqNSpiVA9QSBFhMLyMn5zDmHaHv\nkfmHRACpAWtyhnQ2uQBJkUznPH39hqzIVTUiPMtjyKPRc0Z1v1srzqUsy2CstyRg0esBx7LU4ays\nI12NZSHwLNyOxhQUmwES6d5JynNlBJ5kzsmzMWqg7FZnSAc9fZRmp7+3ib3tsLfBu4FCogUL9SBw\nLFQA+aLGarBUdvcP9OvyUZNAZq060ulEWY9Bv69cynZWgtRx+k0ypJWvKxWAb1S91n8DGVLKUCn9\nFgInYNDTj9mU5minC7UzpNtZq79R/a62bdqw73aiy9lBe6028v27XyBUB5eWn8FXp74JRVURz8dh\n42zoc/VU7UeMqazIGPEOmnyTBQsW9ivGfCO4Fpus6UCN+YbxzOrz+NSTnzWVeldVFWkpg5vbg/S9\nIc8AOIbDowuP47n1l+j7hRLlyMwAkgggpfg0OUNabmRd+7wX08v46GOfRKbU98w47hFcj0+bnovm\nnHubNfRN40C/Dy+FozgwoP22hJJbWUe6Fssi4BF3tXcqWVjspEPfbg/Aa/PgubUX8ZGLf4RILoo+\ndw8cvPY7ETr2Unq5qoYUKAcoJk3uh4uLP9w2CpmFly8EXgt65fJanbMlarR/oA9c6TVeCMhc+K3w\ng7i4+EP6fk7KGbaT1w/OPYLPPff/w87b4RKceO/x98Bjc1PWUaCCckuE2WL5eNX8dTU6gU888aem\ntrsRuHgnBFYwKOd/5fo3IKsK3nTgtfS9a9FJfD70JUiqjFQhVXVeY75hTCdm8LHH/hhpSXNI3SYO\n6XY6hv1ujUH2xNIlhKITpvukCultPy4By7AY9Q3h+fUrDX1/S88Ij8w/hpXMGiRFgktw4a7+28Cx\nXNV+PMvjnoHzuDBwB+z87tUOWbBgYedxtucUhr2DuLX7pOn2Ex1H0ePsAsMwkBSp6p+symi3t+Fc\nzxn6GRsn4Hz/OTgFp2FflmHR5+rBkY6b6L6j3iHc3B7EhYHbAQD+HcqQbqQqfmv3aXTY26CoCuy8\niFOdxwxS8md7TmPYO4gz3Sfoe13ODpzuOo57Bs83deybxW1HujHa68VtN2sBSbMMaVFSEEvm0baL\nCrsAEAwcwIh3yHA/NRsMw+DugTvgtrkhKRK8Ng/O991m2OeegTtxqvMYbYtUiQv9t+PmtqBhEXWs\n42aM+UZwR9+tTR2/hf0P4oDmCrLhbwt7H13OTpzqOo5X1LAbPtGLM10nYOftBnvKszyGPYM4FBin\n+x5uO4hBTz94lke2mEU4MYsX1q8AKNvUypYoDMPgnsHzuKX7JNrtbfT9013HaX9uG2dDMHAA/e7e\nTZ0bwzAI2H2ULlxUJHx75vv4zuxD1MkFgMeWnsRSZgUFuQA7b8eZrhOGoO4t3SfR6WiHChVO3oFj\nHYfh4B10+02Bg9tuNziWw10Dt8MluEzXPpIiwc6LONl5tGntyc7334ZD/nEE2w5suC+zkfrSDkBd\nXU1WvSkrMv7Lwx9Bmz2AD5/7wC4My8Juo7PTA7N7w8LLG3vhvvjNh38fTsGBj9z2waYd40OPfgKy\nKuO/3/nhXc0I7gbmVlL4vb9+Aq841Y+ffo2W2V6JZfFbf3ERdxztwS+8/uZdHqGFVsJemDP2O/7i\ny8/jiZdW8P4fP45P/+uzeNUtA/jJVx3a1TFZ90VrYyYxhz968jM433cO77zprfjr5/8Rl1aewcfv\n+J0t9+ncCPp74zOX/xKh6AQ+ffcnMJdawKcufRYA8P5Tv4yDpTZxH3vsU4jn4/jjC79vCavWQWen\np+ZipWV/tYX0EgpyoWWENSxYsGChUfhFH2K5+IZy6zeKaC5WoieNvOycUQAIlDKk0USZskte76ag\nkQULFsxBVHWzeStDaqExEMopYQNF83EwYHa8nr2sCxE31GPq27gsZ1ZMdSwsNI6W/eVq9dezYMGC\nhVZHwO5DQSkiI2Wb8v1UpGgb+pXtRThFHqLAIaJT2SWvA7vY8sWCBQvm4Es1pNkCqSF9+QXSLGwO\nRLRwMb2MrJRD9P+2d+/RUZd3Hsffv0kCBBISooFyCwGFxxYFAS8VcF0qqbT1ytaKB3epIogarMe1\ndKVbC1XRLauCSqgB2dSeaktRXK9ULEUi24KK3Ko+GgKIXDRByCQYmFxm/5gLkzBJSJjkNyGf1zke\n5vebmV+emXx9Jt/5Ppejh0nr3D3q1L3WFFrY6PCxw3UWOgyt4Ft/VXNpmbhNSIv1CxaRdqqlm2mf\nrNDy8c1dMfB04TgOGd0781VEhTR02+05pCJyolCF9KgqpNIMA9MG4Cewh2iZz3vCgkZtIfQzDx0N\nVEi7d0olo0sPdga3k1NCGhtx2yMUl+2mW2LX8IRkEZH2orW3fiku202Ck0BWG+1zGY8yUjtz5Gh1\neBuJUIU0o7sqpCLxJlQhPerTKrty8kKjJLeUbKfWX0t6K88djSY0X7W4bBdlPi8D0wYwsHsWFVVH\nKKksPV5A66AjlmIlLnuEsmPlHDz6VXiDbhGR9iS9Fbd+8dVUsadiL/1T+0bdxqaj6BFMPEOV0UPe\nUEKqCqlIvEnUHFJpgdC+pptLtgMnbvnSFkJfMIfaMChtQHh0UtHhXewq38M3uvWia1JyQ5eQkxCX\nPUJofz3NHxWR9ij0oRm5d1msfFb+ObX+2g7fP4aG5h4KVka/Kj9Kl04JdO0c19tri3RIScE5o0c1\nh1SaIaVTN3p2PZOKqsB+ma29um40ob3FQ20IJKSBz9939v1dC7DGSJOf3MaYi4FHrLXjIs71Av4Q\n8bDzgZ8BS4DFwDDgGHCrtXZHcxulBY1EpD0LfWi2xpBdzVcJyAhXSI+F/z0jLblDrjosEu+OL2qk\nCqk0z8DuA/jy61IgsIJ9W+uS0IXOCZ04VuMjwUmgf0pfPI6HJE8Su717Am3s4J/HsdBoj2CMmUUg\nyawzBspa+4W1dlwwSZ0NvB983HVAZ2vtaOA/gEdb0qjist04OGSl9m/J00VEXJUWXgQh9gmp5qsE\nhCqkX5UfxVdVQ0VlFZnpGjIlEo/CixqpQirNFFmcCg2fbUuO44R/blZwqkyCJ4Hs7sdzFBXQTl1T\nX1EVAROBqD2HMcYBngBut9b6gTHAGwDW2g3ABc1tUHVtNZ+Vf06/lN50SdRcIBFpf5I8iaR2SuFQ\njIfshlb069E53ZWhS/GkR0SFNDRs90wlpCJxKbyokeaQSjNFVh9Dw2fbWujzNrItodtdE5O1AGsM\nNDpk11r7ojEmu5GHXAVst9Z+GjzuDngj7q8xxnistbUn26A95fuorq1W+VtE2rUendPZU76X+955\nIGbX9OOnvKqCUT2Hx+ya7VWoQvp/2w/wvv0SUEIqEq9CFdJKrbIrzdS7Wy+6JHTBV+uje6dUV9oQ\nWhciMjcJVUUHpg3QAqwxcKqrP0wGFkQce4HIaDmpZDQz8/hTNnx1AIDh/c6pc146JsWARNMe4iJn\n8Fhe/3RNzK+b3iWV733zsnbxHrS271zQH7v7KwAye3TloqG99L5IVIoLd2X0CIwWqaoO/El4Rka3\nuPidxEMbpGkTh06g/FgFvXq2XYU0MjbG147mqHOUS4eMDK+mO7rH+bxXOozxZ12qOIqBU01IL7DW\n/i3ieD2BqumfjDHfBraezEVKSsrDt7ft+wSAMz296pyXjiczM1UxICdoL3ExMn0kIy8c2WrXbw/v\nQWu7afzgOsftJTakbSku3Ff5tQ+AI5VVAFRUHHX9d6K4aD/GnDkaaLvPvfqx0cvTl6nn/CtHDldz\nhOPnbz7npjZtV3vXWOJ+sgmpH8AYcyOQYq1dYozJBOpPkFoJ5Bhj1gePb25OQ/1+P8WHd5HaKYUz\nuvRozlNFREREJA4lhhc1CswhTdKQXRGJ0GRCaq3dBYwO3n4+4nwJMLLeY/3A7S1tzKFjhynzeRme\nea6W7hcRERE5DYT2Ia2p9QOQoFV2RSRCXH1FtVPbGYiIiIicVkKr7IaoQioikeKqRwjtrzcoLdvd\nhoiIiIhITNRfVTdBCamIRIirHmFn2WckOAlkpfZ1uykiIiIiEgNJ9SqkiRqyKyIR4iYh9dX42FOx\nl/6pfUlKSHK7OSIiIiISA/WH6GrIrohEipseYbf3c2r9teGNZkVERESk/dOQXRFpTNz0CDu9wQWN\nlJCKiIiInDZOXNRIQ3ZF5Li4SUiLtcKuiIiIyGmnfgKqCqmIRGpyH9LW9uMX76G2tpZjNT56dE6n\nR5d0t5skIiIiIjGiRY1EpDGuJ6TfSMmkqqoagEv6XORya0REREQkliIroo4DCR5VSEXkONcT0ke+\nex8lJeVuN0NEREREWoHHcUjwONTU+k9Y4EhERL2CiIiIiLSq0MJGSkhFpD71CiIiIiLSqkJ7j2r+\nqIjUp4Q0ik2b3uPKK3OYOfM2cnOnc/vtt7BmzVsAfPrpJxQULI35z/R6vaxevarZz6utrWXmzNv4\n859fD5/Lz8/j6acX1XncM888zY03TuStt/58ym3961/fYvLkH/Kb3zx1ytcSEetknaoAABB2SURB\nVBGR01+SKqQi0gDX55DGI8dxGDXqQubOnQdAZWUlubnT6d8/i8GDhzB48JCY/8yiok9455115ORM\naNbzPB4P99//AHfccSvnnjuMXbt28uGH23n88boJqeM4TJp0E+PHX3HKbR03bjxHjx5l9+5dp3wt\nEREROf2FKqOqkIpIfXGfkC5fU8S7H38Z02teeE5PfvSdsxu83+/31zlOTk7mmmsmsnbtX6ioKOel\nl15g7tx5vPDCH1m3bi2VlZWkp6czb95/8+abb7B+/Tp8Ph8HD5Zy/fU3Ulj4NsXFO8jN/Qljx17G\nmjVvsXz5c3g8HoYNO58ZM3J59tll7NhRxMsvr2Tbti14vWV4vV5+/esFFBQsZdu2LQDk5Ezg+usn\n1WlfZmZP7rrrHubMmY3P52PBgjwc58QOP/S6cnOnM3iwobh4B127JjNs2Ag2bvwbFRXlPPbYIgoL\n1zb5Guq/RyIiIiINSUxQhVREolOvcJIyMjIoKzscPvb7/Xi9XhYsyCM/v4Dq6ho++ugfOI5DZWUl\n8+cvZPLkKaxcuYJ58+Yza9ZsXnvtFbxeL8uW5bNw4WLy8pZSUvIl7767gSlTpjJy5AVcffV1wQrt\nRSxe/Axbt27mwIF95OcXkJe3lNWrV1FcXHRC+y65ZCxlZWWce+4wevTIiPoaQkmq4zh861tDWbgw\nD5+viuTkLjz++CKyswexefP7Tb4GERERkeZIUkIqIg1oskJqjLkYeMRaO67e+QuBRwEH2Av8G1AN\nLAWGALXANGutPZUG/ug7ZzdazWwr+/fvp2fPXuFjx3FITExkzpzZJCd3paTkC6qrA/upDh5sAOjW\nLYXs7IEApKam4vP52Lt3D4cPH+Lee+8C4Ouvv2bfvr1kZQ2o8/NCx7t372L48BEAJCYmMnToeezc\nuZNBg+q+J4sXP8G4cePZsOFvbNz4dy666NuNvh5jzgEgJSWF7OxBddrY1GsQERERaY7jq+xqyK6I\n1NXo11TGmFnAEqBzvfMOkA/82Fp7KfAXYCDwXaCbtXYs8CvgodZodFs7cqSCV199iXHjxoeHqu7Y\nUURh4dvMnfswd9/9U/x+f/i+aMNlQ3r37kvPnr1YsCCPJ598muuu+yFDh56Hx+OpMww2dI3s7IFs\n3boZgOrqarZv30JWVlada7799l/5+OOPuO22O7n//geYP38eX311sIlX1fgHQmOvQURERKQ5QhXS\nBFVIRaSepiqkRcBE4Hf1zg8BDgL3GGPOBV6z1lpjzDeAtGDCmga0y3Ka4zhs2vQeM2fehseTQE1N\nNVOnzqB//yxKS0twHId+/fqRnJzMnXdOIy0tnSFDzqG0tDT8/Mh/j18X0tPTmTRpMrm506ipqaV3\n7z7k5EzA6y2juLiI5cufr/Pc0aPH8sEH7zNjxi1UVVVx+eU54eolwN69n/PUUwtYtCgfj8fDoEFn\nMWnSTTzwwP089thTLU4sG3sN0c6LiIiINCRUIU1SQioi9ThNLU5jjMkGnrfWXhJxbgywGhgB7ABe\nBf4LKATeAnoDZwBXWWv/1kQb/CUl5S1tv5ykZcvyycg4g2uv/ZeYXO/111/hs892M2NGbkyuF01m\nZiqKDalPcSENUWxINIqL+PDEiq1sLirl3EEZ3POj891ujuJCGqTYaB2ZmakNVrNausruQaAoND/U\nGLMKuAC4BFhvrf25MaYfsMYYc661ttFKaWZmagubISerW7fOrFjxPH36ZPKDH/zglK61atUq/vCH\n35GTk9PqvzvFhkSjuJCGKDYkGsWF+7p17RT4N7lT3Pw+4qUdEn8UG22rpQlpMZBijDnLWrsDuBR4\nBhgDeIOPOQQkAQlNXUzfQrS+G26Ywg03TAFO/f0eNWoMzz47JibXaoy+oZJoFBfSEMWGRKO4iA81\nNTXBf2vj4vehuJCGKDZaR2NJ/skO5PcDGGNuNMZMC1Y8pwLPGWM2Ap9Za18H5gPfNsYUEljo6D5r\nbeUptV5ERERE2rXj275oDQoRqavJCqm1dhcwOnj7+YjzfwUurvfYw8B1sW2iiIiIiLRnx7d90aJG\nIlKXegURERERaVXHK6T601NE6lKvICIiIiKtKlFDdkWkAUpIo9i06T2uvDKHmTNvIzd3Orfffgtr\n1rwFwKeffkJBwdKY/0yv18vq1aua/bz16wuZMuVGqqurw+eefPJxFi9+ss7jcnOnM23aFHbt2nnK\nbc3Pz+Oaa65gw4amdvQREREROZ6IqkIqIvW1dJXd05rjOIwadSFz584DoLKyktzc6fTvn8XgwUMY\nPHhIzH9mUdEnvPPOOnJyJjTreWPGXEph4VoKCpZy660z2LZtC1u3buY3v1lW53GO4/CLX/yKrKwB\np9zW6dPvoLS0BMfRt5wiIiLStKREVUhFJLq4T0hfLHqVD77cFtNrjuh5HhPPvrLB+/1+f53j5ORk\nrrlmImvX/oWKinJeeukF5s6dxwsv/JF169ZSWVlJeno68+b9N2+++Qbr16/D5/Nx8GAp119/I4WF\nb1NcvIPc3J8wduxlrFnzFsuXP4fH42HYsPOZMSOXZ59dxo4dRbz88kq2bduC11uG1+vl179eQEHB\nUrZt2wJATs4Err9+Up323XXXv3PLLTcxduxlLFz4KL/85YMkJDS8284NN1zLeecNZ8+ezxg16kKO\nHKngww//QVbWAH7xi1/x0ENzSExM4osv9uPz+Rg//rusX1/IF18c4OGHH6Vv335R3ycRERGRaDSH\nVEQaol7hJGVkZFBWdjh87Pf78Xq9LFiQR35+AdXVNXz00T9wHIfKykrmz1/I5MlTWLlyBfPmzWfW\nrNm89toreL1eli3LZ+HCxeTlLaWk5EvefXcDU6ZMZeTIC7j66uuCFdqLWLz4GbZu3cyBA/vIzy8g\nL28pq1evori4qE7bunbtys9+9nPuvvt2rrrqWvr3z2r0tRw4sJ/p0+9g0aIlrFjxRyZO/BFLlvyW\nrVu3UFFRgeM49OnTh8cee4rs7IHs37+f+fMXctll32H9+sJWeX9FRETk9KVVdkWkIXFfIZ149pWN\nVjPbyv79++nZs1f42HEcEhMTmTNnNsnJXSkp+SI8j3PwYANAt24pZGcPBCA1NRWfz8fevXs4fPgQ\n9957FwBff/01+/btPWEobeh49+5dDB8+AoDExESGDj2PnTt3MmjQ2XUeP2LEKFJTu/P971/V5GtJ\nS0sPv5bk5C4MGJANQEpKN3y+YwAMGXJO8Fxq+P7U1O7h+0VEREROVqIqpCLSAPUKJ+HIkQpeffUl\nxo0bHx6mumNHEYWFbzN37sPcffdP8fv94fsam1vZu3dfevbsxYIFeTz55NNcd90PGTr0PDweT50h\nsKFrZGcPZOvWzQBUV1ezffsWsrIar4A2peVTPzVEV0RERJpPc0hFpCFxXyF1g+M4bNr0HjNn3obH\nk0BNTTVTp86gf/+s8GI+/fr1Izk5mTvvnEZaWjpDhpxDaWlp+PmR/x6/LqSnpzNp0mRyc6dRU1NL\n7959yMmZgNdbRnFxEcuXP1/nuaNHj+WDD95nxoxbqKqq4vLLc8IV2CgtP9lXGPV2ZHujJ9WOFjIS\nERGRZvtWdgZjz+vNsLPOcLspIhJnnDhYmMZfUlLudhtOezNn3sZPf3ofWVnZMbneQw/NYfz4K7j4\n4kticr1oMjNTUWxIfYoLaYhiQ6JRXEg0igtpiGKjdWRmpjZY1dKQ3Q7kwQfnxGwf0o0b/65qqYiI\niIiInBJVSCVu6RsqiUZxIQ1RbEg0iguJRnEhDVFstA5VSEVERERERCTuKCEVERERERERVyghFRER\nEREREVcoIRURERERERFXNLkPqTHmYuARa+24eucvBB4lsJHlXuDfrLU+Y8x9wFVAEvCUtfa3sW+2\niIiIiIiItHeNVkiNMbOAJUDneucdIB/4sbX2UuAvwEBjzD8Dl1hrRwP/DAxqhTaLiIiIiIjIaaCp\nIbtFwEQCVdBIQ4CDwD3GmLVAurXWAlcA24wxLwGvAC/HtrkiIiIiIiJyumg0IbXWvghUR7nrTGA0\n8CQwHrjcGDMOOAMYBfwQmAH8PqatFRERERERkdNGk3NIG3AQKApWRTHGrAIuCJ7/2FpbDXxijDlq\njDnTWlvayLWczMzUFjZDTneKDYlGcSENUWxINIoLiUZxIQ1RbLStlq6yWwykGGPOCh5fCmwH3gEm\nABhj+gDdCCSpIiIiIiIiInWcbIXUD2CMuRFIsdYuMcZMBZ4LLnC03lr7RvAx/2SM2Ugg2b3DWutv\njYaLiIiIiIhI++b4/coXRUREREREpO21dMiuiIiIiIiIyClRQioiIiIiIiKuUEIqIiIiIiIirmjp\nti+nzBjjAfKAYcAx4FZr7Q632iPuM8ZsAsqCh8XAw0ABUEtgFec7tUhWx2GMuRh4xFo7zhhzNlFi\nwRgzDZhOYL/kB621r7nWYGkT9eJiBPAK8Gnw7jxr7Z8UFx2LMSYJWAYMADoDDwIfoT6jQ2sgLj4H\nXgU+CT5MfUYHZIxJAJYAQwgs3DqDQC5SgPoMV7hZIb0W6GStHQ38B/Coi20RlxljugBYa8cF/5sK\nPAbMttb+E+AA17jZRmk7xphZBD4sOgdPnRALxphvADOB0cAVwMPGmE5utFfaRpS4GAU8FtFv/Elx\n0SFNBkqC/cMEYBGBvynUZ3Rs0eJiJPCo+owO70qg1lo7FvhPYB7qM1zlWoUUGAOsArDWbjDGXOBi\nW8R9w4Guxpg/E4jLnwMjrbXrgve/AXwXeMml9knbKgImAr8LHkeLhRoCW05VAVXGmCICIy7ea+vG\nSpupHxejgCHGmGsIVEnvBi5CcdHR/AlYEbztAapQnyHR42IUYNRndGzW2v81xrwaPMwGDgHj1We4\nx80KaXfAG3FcExzGKx3TEWC+tfYKAkMnfl/v/gogrc1bJa6w1r5IYHhMiBNxu5xALHTn+BDvyPNy\nmooSFxuAe621lxEY5v9LIBXFRYdirT1ira0wxqQSSEL+k7p/36jP6ICixMXPgY2ozxDAWltjjCkA\nFhL4m1N/Z7jIzQTQS6ATCPFYa2vdaoy47hOCSai19lPgINAr4v5U4LAL7ZL4ENk3dCcQC/X7kFQC\n33JKx7HSWvtB6DYwAsVFh2SM6Q+sAZ611j6P+gzhhLj4A+ozJIK19seAAZYCXSLuUp/RxtxMSNcD\n3wcwxnwb2OpiW8R9NxOcR2yM6UPgf/o3jTGXBe//HrCugefK6e+DKLGwEbjUGNPZGJMGfJPAQgTS\ncawyxlwYvD2ewDAqxUUHY4zpBbwJzLLWFgRPq8/o4BqIC/UZgjHmX40x9wUPKwkMzX1PfYZ73JxD\nuhLIMcasDx7f7GJbxH3PAP9jjAklnTcTqJIuCU4g/5Djc0Gk4witqvzv1IuF4Op3TwCFBL5cm22t\n9bnUTmlbobiYASwyxlQB+4HpwSF6iouOZTaBYXT3G2PuD577CfCE+owOLVpc3A08rj6jw1sBFBhj\n3gaSCPQXH6O/M1zj+P3aRUNERERERETanhYREhEREREREVcoIRURERERERFXKCEVERERERERVygh\nFREREREREVcoIRURERERERFXKCEVERERERERVyghFREREREREVcoIRURERERERFX/D8JhN+nsHEi\nuwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10a2b9940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "datos.ix[:, \"Diametro X [mm]\":\"Diametro Y [mm]\"].plot(figsize=(16,3))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10b02cc18>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10b038978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "datos.ix[:, \"Diametro X [mm]\":\"Diametro Y [mm]\"].boxplot(return_type='axes')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mostramos la representación gráfica de la media de las muestras" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([<matplotlib.axes._subplots.AxesSubplot object at 0x10b66f630>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x10b6de748>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x10b918780>], dtype=object)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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YtWpDosyRk3oYj62tDX//fYtSpb7E0tKS48fPkTZtWr1j6WbcuNFMmzaZ+vUb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\nKX9GrRcRDwDvA3+hP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"text/plain": [ "<matplotlib.figure.Figure at 0x10b6c79b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.rolling_mean(datos[columns], 50).plot(subplots=True, figsize=(12,12))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparativa de Diametro X frente a Diametro Y para ver el ratio del filamento" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x10bfe5be0>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10b6cfe48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x=datos['Diametro X [mm]'], y=datos['Diametro Y [mm]'], marker='.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Filtrado de datos\n", "Las muestras tomadas $d_x >= 0.9$ or $d_y >= 0.9$ las asumimos como error del sensor, por ello las filtramos de las muestras tomadas." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "datos_filtrados = datos[(datos['Diametro X [mm]'] >= 0.9) & (datos['Diametro Y [mm]'] >= 0.9)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Representación de X/Y" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x10c0cfe10>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10bffaef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x=datos_filtrados['Diametro X [mm]'], y=datos_filtrados['Diametro Y [mm]'], marker='.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Analizamos datos del ratio" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 333.000000\n", "mean 1.015286\n", "std 0.018370\n", "min 0.948864\n", "25% 1.005682\n", "50% 1.017241\n", "75% 1.029070\n", "max 1.047059\n", "dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratio = datos_filtrados['Diametro X [mm]']/datos_filtrados['Diametro Y [mm]']\n", "ratio.describe()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10c0a6780>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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bJxYMMdYfJRHxk4j4mi26YYOJ+hLd4V4aRpVkMMzPxp9p/5zjuqwVaiyvlckV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\nsd4YMhIZIuJrTgAzV1igUN8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"text/plain": [ "<matplotlib.figure.Figure at 0x10c0ae0b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rolling_mean = pd.rolling_mean(ratio, 50)\n", "rolling_std = pd.rolling_std(ratio, 50)\n", "rolling_mean.plot(figsize=(12,6))\n", "# plt.fill_between(ratio, y1=rolling_mean+rolling_std, y2=rolling_mean-rolling_std, alpha=0.5)\n", "ratio.plot(figsize=(12,6), alpha=0.6, ylim=(0.5,1.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Límites de calidad" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculamos el número de veces que traspasamos unos límites de calidad. \n", "$Th^+ = 1.85$ and $Th^- = 1.65$ " ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Th_u = 1.85\n", "Th_d = 1.65" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_violations = datos[(datos['Diametro X [mm]'] > Th_u) | (datos['Diametro X [mm]'] < Th_d) |\n", " (datos['Diametro Y [mm]'] > Th_u) | (datos['Diametro Y [mm]'] < Th_d)]" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/darkomen/anaconda/lib/python3.4/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.\n", " warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n", "/Users/darkomen/anaconda/lib/python3.4/site-packages/numpy/core/_methods.py:83: RuntimeWarning: Degrees of freedom <= 0 for slice\n", " warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Tmp Husillo [C]</th>\n", " <th>Tmp Nozzle [C]</th>\n", " <th>Diametro X [mm]</th>\n", " <th>Diametro Y [mm]</th>\n", " <th>MARCHA</th>\n", " <th>PARO</th>\n", " <th>RPM</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</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", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Tmp Husillo [C] Tmp Nozzle [C] Diametro X [mm] Diametro Y [mm] \\\n", "count 0 0 0 0 \n", "mean NaN NaN NaN NaN \n", "std NaN NaN NaN NaN \n", "min NaN NaN NaN NaN \n", "25% NaN NaN NaN NaN \n", "50% NaN NaN NaN NaN \n", "75% NaN NaN NaN NaN \n", "max NaN NaN NaN NaN \n", "\n", " MARCHA PARO RPM \n", "count 0 0 0 \n", "mean NaN NaN NaN \n", "std NaN NaN NaN \n", "min NaN NaN NaN \n", "25% NaN NaN NaN \n", "50% NaN NaN NaN \n", "75% NaN NaN NaN \n", "max NaN NaN NaN " ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_violations.describe()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([<matplotlib.axes._subplots.AxesSubplot object at 0x10c6aca58>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x10c6ea5f8>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x10c70dd68>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x10c76eeb8>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x10c7bac88>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x10c91d390>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x10c969240>], dtype=object)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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8nMJ5s8Kyt7DYKIqHl4NdLBbruIOf6dCXSBhOlOVymVwuV7Wltgv77vBw7ggY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\nMnfuF1Ss6Gh8DWrWrM3x48eYNeszypQpw6ZN66lTpw4dOz5r8lr98MO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tjY+Pp2nTZ0hO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\n1XCkday/g98PgZuouveNB5a27LaqXpohSZIkFanRmuVWEbEVsBD4gw5+9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"text/plain": [ "<matplotlib.figure.Figure at 0x10c175978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_violations.plot(subplots=True, figsize=(12,12))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }

cc0-1.0

boblannon/relation_extraction

spaCy.ipynb

1

351511

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Imports" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import unicode_literals\n", "from spacy.en import English" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nlp = English()" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json\n", "from collections import defaultdict" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Couldn't import dot_parser, loading of dot files will not be possible.\n" ] } ], "source": [ "import networkx as nx" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pydot\n", "from IPython.display import Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Utils" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def build_dep_graph(tokens):\n", " dg = nx.DiGraph()\n", " for t in tokens:\n", " dg.add_node(t, label=t.string, ner=t.ent_type_, pos=t.pos_)\n", " deps = []\n", " for t in tokens:\n", " if t.dep_ == 'prep':\n", " dep = t.dep_ + '_' + t.lower_\n", " else:\n", " dep = t.dep_\n", " deps.append((t.head, t, dep) )\n", " sorted_deps = sorted(deps, key=lambda x: x[0])\n", " for gov, dep, rel in sorted_deps:\n", " dg.add_edge(gov, dep, label=rel)\n", " #for e in dg.selfloop_edges():\n", " # dg.remove_edge(*e)\n", " return dg" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def display_parse(dep_graph, filename):\n", " pdg = pydot.Dot()\n", " for u,v in dep_graph.edges():\n", " ulabel = '{lemma}-{index}'.format(lemma=u.lemma_, index=u.i)\n", " vlabel = '{lemma}-{index}'.format(lemma=v.lemma_, index=v.i)\n", " pdg.add_edge(pydot.Edge(ulabel,vlabel,**dep_graph.edge[u][v]))\n", " pdg.write_png('images/'+filename+'.png', prog='dot')\n", " pdg.write_dot('images/'+filename+'.dot', prog='dot')" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def subtree_to_string(head, dg):\n", " others = [d for d in nx.algorithms.descendants(dg, head) if dg[head].get(d,{'label':''})['label'] != 'case'] \n", " linearized = sorted([head,] + others, key=lambda x: x.i)\n", " return ' '.join([t.orth_ for t in linearized])\n", "\n", "def simple_pas(predicate, dg):\n", " arguments = dg[predicate]\n", " _pas = defaultdict(list)\n", " for arg, rel in arguments.items():\n", " _pas[rel['label']].append(subtree_to_string(arg, dg))\n", " _pas[u'predicate'] = predicate.orth_\n", " return dict(_pas)\n", "\n", "def collect_all_predicates(dg):\n", " dg.remove_edges_from(dg.selfloop_edges())\n", " predicates = [n for n in nx.topological_sort_recursive(dg) if n.pos_.startswith('V')]\n", " return [simple_pas(p, dg) for p in predicates]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": true }, "outputs": [], "source": [ "simple = \"From 1981 to 1983, Vicki served as an assistant city representative at the National Center for Municipal Development.\"\n", "copula = \"She was the assistant to the executive director of the Democratic Study Group in the US House of Representatives from 1979 to 1981.\"\n", "twoverb = \"Vicki has also served as a government relations consultant, representing the interests of Portland, Oregon in Washington DC.\"\n", "smallclause = \"The Department of Agriculture had appointed Vicki president\"\n", "relclause_subj = \"The clients whom Vicki has represented include Coca-Cola, Texaco, and Giant Foods.\"\n", "subclause = \"Vicki lobbied for health insurance companies that supported Obamacare\"\n", "passive = \"Giant is represented by Victoria Cram as of June 3, 2006.\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## simple" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": true }, "outputs": [], "source": [ "simple_dg = build_dep_graph(nlp(simple))" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[{u'nsubj': [u'Vicki'],\n", " u'predicate': u'served',\n", " u'prep_as': [u'as an assistant city representative at the National Center for Municipal Development'],\n", " u'prep_from': [u'From 1981 to 1983'],\n", " u'punct': [u',', u'.']}]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "collect_all_predicates(simple_dg)" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ 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"AQEB/Kpkxv7FoZgxxphTutks68R9ZrPZLpQODAzAYrGgv78fZrMZJpMJwPUwPjQ0NKn/qULrjUEZ\n", "ADw9PYWb0cY/y+VySKVSLFiwwC6Uu7m52c1u3/h3ts12M/ag4FDMGGOMMcacHr+snjHGGGOMOT0O\n", "xYwxxhhjzOlxKGaMMcYYY05PCuCk2EUwxhhjjDEmpv8BZun4Y6gJKawAAAAASUVORK5CYII=\n" ], "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display_parse(build_dep_graph(nlp(copula)), 'spacy_copula_eg')\n", "Image('images/spacy_copula_eg.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Test" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nlp = English()" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": true }, "outputs": [], "source": [ "manatt_data = json.load(open('data/manatt-out-html-full.json'))" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

adamamiller/PS1_star_galaxy

PS1casjobs/Figure_whiteFeatures_HST.ipynb

1

722807

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# White features vs Mag (HST Training Set)\n", "\n", "In this notebook we show the distribution of 11 white features, \n", "which we used for constructing the machine learning model, \n", "as a function of magnitude for sources from PS1xHST-COSMOS cross-matched catalog. \n", "The distribution of wwpsfKronDist shows that wwpsfKronDist itself do a good job even at the faint end. \n", "\n", "For obtaining a reasonable kernel density estimation (KDE), \n", "we excluded outliers by $10 < wwKronMag < 25$, $0 < wwPSFKronRatio < 2$, \n", "and $-10^{-3} < wwPSFKronDist < 10^{-3}$ from sources with $nDetection > 0$. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys,os,math\n", "import numpy as np\n", "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "from matplotlib import rcParams\n", "rcParams[\"font.family\"] = \"sans-serif\"\n", "rcParams['font.sans-serif'] = ['DejaVu Sans']\n", "from matplotlib import gridspec as grs\n", "%matplotlib inline\n", "from matplotlib import cm\n", "from astropy.table import Table\n", "import seaborn as sns\n", "import statsmodels.nonparametric.api as smnp\n", "from statsmodels.nonparametric.kernel_density import KDEMultivariate\n", "from scipy.special import expit\n", "from scipy import stats" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ff_tab = Table.read(\"HST_COSMOS_features_adamamiller.fit\").to_pandas()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The crossmatched catalog contains 75,927 sources. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "star, galaxy = ff_tab.MU_CLASS == 2, ff_tab.MU_CLASS == 1" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "whiteKronMag = -2.5*np.log10(ff_tab.wwKronFlux/3631)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def kde_contour_dat(x, y, extent = 'Auto', bw_type = \"silverman\", grid_bins = 250, BW=False):\n", " \"\"\"Determine normalized KDE PDF to draw contours\"\"\"\n", " \n", " if isinstance(x, pd.Series):\n", " x = x.values\n", " if isinstance(y, pd.Series):\n", " y = y.values\n", " \n", " if extent == 'Auto':\n", " extent = (x.min(), x.max(), y.min(), y.max())\n", "\n", " if bw_type == \"silverman\":\n", " bw = np.array([smnp.bandwidths.bw_silverman(x), smnp.bandwidths.bw_silverman(y)])\n", " elif bw_type == \"scott\":\n", " bw = np.array([smnp.bandwidths.bw_scott(x), smnp.bandwidths.bw_scott(y)])\n", " if BW:\n", " bw = BW\n", "\n", " kde = KDEMultivariate([x,y], var_type='cc', bw = bw)\n", "\n", " xi, yi = np.mgrid[extent[0]:extent[1]:grid_bins*1j,extent[2]:extent[3]:grid_bins*1j]\n", "\n", " kde_prob = kde.pdf(np.vstack([xi.flatten(), yi.flatten()]))\n", "\n", " zi = (kde_prob-kde_prob.min())/(kde_prob.max() - kde_prob.min())\n", " zi = zi.reshape(xi.shape)\n", "\n", " return xi, yi, zi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Selecting sources with nDetections > 0, and then exclude outlier to obtain a reasonable kernel density distribution. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Det_mask = ff_tab.nDetections > 0\n", "PsfKronRatio_mask = np.logical_and(0<ff_tab.wwPSFKronRatio, ff_tab.wwPSFKronRatio<2)\n", "KronMag_mask = np.logical_and(10<whiteKronMag, whiteKronMag<25)\n", "PsfKronDist_mask = np.logical_and(-1e-3<ff_tab.wwPSFKronDist, ff_tab.wwPSFKronDist<1e-3)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mask = Det_mask & PsfKronRatio_mask & KronMag_mask & PsfKronDist_mask" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": true }, "outputs": [], "source": [ "xgal_Ratio, ygal_Ratio, zgal_Ratio = kde_contour_dat(whiteKronMag[mask&galaxy], ff_tab.wwPSFKronRatio[mask&galaxy])\n", "xstar_Ratio, ystar_Ratio, zstar_Ratio = kde_contour_dat(whiteKronMag[mask&star], ff_tab.wwPSFKronRatio[mask&star])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "star_norm = len(ff_tab.loc[mask&star])*1.0/len(ff_tab.loc[mask])\n", "galaxy_norm = len(ff_tab.loc[mask&galaxy])*1.0/len(ff_tab.loc[mask])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although we used wwpsfCHiSq and wwpsdLikelyhood itself for training the machine learning model, we show the distribution of $\\log_{10}(wwpsfChiSq)$ and $\\log_{10}(|wwpsfLikelihood|)$ in the following figure just for visibility. " ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/y_tachibana/anaconda/envs/py35/lib/python3.5/site-packages/ipykernel_launcher.py:2: RuntimeWarning: divide by zero encountered in log10\n", " \n" ] } ], "source": [ "ff_tab['Log[wwpsfChiSq]'] = np.log10(ff_tab.wwpsfChiSq)\n", "ff_tab['Log[abs(wwpsfLikelihood)]'] = np.log10(np.abs(ff_tab.wwpsfLikelihood))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The distribution of white features (except for wwpsfKornDist) " ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/y_tachibana/anaconda/envs/py35/lib/python3.5/site-packages/ipykernel_launcher.py:9: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if __name__ == '__main__':\n" ] }, { "data": { "image/png": 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EMX5zxcYdsfNYxSJVf2PVoyV00yRXHuHMWLFvPQXLRkMsd/rs95wKIZ5VSvWs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yiGmSyk1xYv/da1+EhhFRGBD5HtteecNYzd2qitRDWlrN3c/fsYtirdZz2WtDDMqOw97pGXwv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faBu9DQ5/SFpxhv7WQe3+0hi8unaLd36G3tUF9eZLC+SRrnMMolRptbNJaXGOzscuWlK5y5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Op4/bhI5c/4a3ggqOJWzeC4Pab6FSvQ6+PXNSjykMxzYJt4uZw1BRHTkKQ0UppSmlvtby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0lk48limFfqt1wyuW1HIXA9dEPHSidQkSJSEdX1Le7cuEUiiti8u0a9VGPptUWW37hL6AW9g2t6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\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FGQZopk51qkHr1WWsusPSizc49/BFBu3BVmQ/TzNQFLxeWZvvd12EMoVhm7hBgt93qdsm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eJiKuS5Dn5w/DLcLv0ANUkYq+11hNF6h8pEFXckwave6zB1dQ4RKWx8J6+noKgKoaXht3s4qoEc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\nkxEpynZjq9vHrcp20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"text/plain": [ "<matplotlib.figure.Figure at 0x1a13fc3240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt_feat = ['wwPSFKronRatio', 'wwPSFApRatio', 'wwExtNSigma', 'wwKronRad', \n", " 'Log[wwpsfChiSq]', 'Log[abs(wwpsfLikelihood)]', \n", " 'wwmomentYY', 'wwmomentXY', 'wwmomentXX', 'wwmomentRH']\n", "ylims_list = [ [0.2, 1.5], [0.4, 1.3], [-2.5, 11.5], [1.5, 3.0], [0.05,0.25], \n", " [-4,0], [0.1, 0.225], [-0.03,0.03], [0.1,0.23], [0.5,1.0]]\n", "num_feat = len(plt_feat)\n", "\n", "origin = 'lower'\n", "levels = np.arange(0.1, 1.1, 0.1)\n", "cmap_star = sns.cubehelix_palette(rot=0.5, light=0.7,dark=0.3,as_cmap=True)\n", "cmap_gal = sns.cubehelix_palette(start=0.3,rot=-0.5,light=0.7,dark=0.3,as_cmap=True)\n", "\n", "gs = grs.GridSpec(int(num_feat/2), 6, width_ratios=(2, 4, 1, 2, 4, 1))\n", "plt.figure(figsize=(13, 18))\n", "plt.subplots_adjust(wspace=0.075, hspace=0.1)\n", "ax = []\n", "i = 0\n", "for ind in range(6*num_feat):\n", " #print(i, ind%5)\n", " ax.append(plt.subplot(gs[i, ind%6]))\n", " if ind%6==5:\n", " i += 1\n", " if i == 5: \n", " break\n", "\n", "xlims = [14.5, 24.5]\n", "\n", "row_num = 0\n", "for i,name in enumerate(plt_feat):\n", " \n", " #ylims = np.percentile(ff_tab[name][mask], [1, 99])\n", " #print(ylims)\n", " ylims = ylims_list[i]\n", " \n", " #xkernel_size = (xlims[1] - xlims[0])/10\n", " #ykernel_size = (ylims[1] - ylims[0])/10\n", " \n", " xgal_, ygal_, zgal_ = kde_contour_dat(whiteKronMag[mask&galaxy&(~np.isnan(ff_tab[name]))&(~np.isinf(ff_tab[name]))], \n", " ff_tab[name][mask&galaxy&(~np.isnan(ff_tab[name]))&(~np.isinf(ff_tab[name]))], \n", " extent = np.r_[xlims, ylims])\n", " xstar_, ystar_, zstar_ = kde_contour_dat(whiteKronMag[mask&star&(~np.isnan(ff_tab[name]))&(~np.isinf(ff_tab[name]))],\n", " ff_tab[name][mask&star&(~np.isnan(ff_tab[name]))&(~np.isinf(ff_tab[name]))], \n", " extent = np.r_[xlims, ylims])\n", " \n", " ax[row_num].text(-0.1,np.mean(ylims),name.replace('ww', 'white'), \n", " ha='center', va='center', rotation=90, fontsize=15)\n", " ax[row_num].set_xlim(-1, 1); ax[row_num].set_ylim(ylims)\n", " ax[row_num].axis('off')\n", " \n", " ax[row_num+1].grid(alpha=0.5, lw=1, c='grey', linestyle=':') \n", " ax[row_num+1].set_axisbelow(True)\n", " ax[row_num+1].contourf(xgal_, ygal_, zgal_, levels = levels,\n", " origin = origin,\n", " cmap = cmap_gal, alpha = 0.8)\n", " ax[row_num+1].contour(xgal_, ygal_, zgal_, levels = levels,\n", " linewidths=(0.5,), origin = origin,\n", " colors = (\"w\",), alpha = 0.5, zorder = 11)\n", " ax[row_num+1].contourf(xstar_, ystar_, zstar_, levels = levels, \n", " origin = origin,\n", " cmap = cmap_star, alpha = 0.8)\n", " ax[row_num+1].contour(xstar_, ystar_, zstar_, levels = levels,\n", " linewidths=(0.5,), origin = origin,\n", " colors = (\"w\",), alpha = 0.5, zorder = 11) \n", " ax[row_num+1].set_xlim(xlims); ax[row_num].set_ylim(ylims)\n", " ax[row_num+1].tick_params(labelsize = 15)\n", " ax[row_num+1].tick_params(which='both', top=True, right=True)\n", " ax[row_num+1].set_xticks(np.arange(16, 26, 2))\n", " ax[row_num+1].minorticks_on()\n", " #ax[row_num+1].set_axisbelow(True)\n", " if i-len(plt_feat) >= -2:\n", " ax[row_num+1].set_xlabel('whiteKronMag', fontsize=15)\n", " else: \n", " ax[row_num+1].tick_params(labelbottom=\"off\", labeltop='off')\n", " \n", "\n", " n = (np.arange(ylims[0]-np.abs(ylims[1]-ylims[0])*2, ylims[1]+np.abs(ylims[1]-ylims[0])*2, np.abs(ylims[1]-ylims[0])/100))\n", " #print(n)\n", " if ylims[0]<0:\n", " range_mask = (ylims[0]*2 < ff_tab[name]) & (ff_tab[name] < ylims[1]*2)\n", " else: \n", " range_mask = (ylims[0]/2 < ff_tab[name]) & (ff_tab[name] < ylims[1]*2)\n", " kde_Star_ = stats.gaussian_kde(np.array(ff_tab[name][mask&star&(~np.isnan(ff_tab[name]))&(~np.isinf(ff_tab[name]))&range_mask]), \n", " bw_method='silverman')\n", " kde_Gal_ = stats.gaussian_kde(np.array(ff_tab[name][mask&galaxy&(~np.isnan(ff_tab[name]))&(~np.isinf(ff_tab[name]))&range_mask]), \n", " bw_method='silverman')\n", " ax[row_num+2].fill_betweenx(n, kde_Gal_(n)*galaxy_norm, alpha=0.75, color=cmap_gal(0.25), lw=2)\n", " ax[row_num+2].fill_betweenx(n, kde_Star_(n)*star_norm, alpha=0.75, color=cmap_star(0.25), lw=2)\n", " ax[row_num+2].set_ylim(ylims)\n", " ax[row_num+2].set_xlim(0, 1.1*np.max(np.r_[kde_Gal_(n)*galaxy_norm, kde_Star_(n)*star_norm]))\n", " ax[row_num+2].set_xticklabels( () )\n", " ax[row_num+2].set_yticklabels( () )\n", " ax[row_num+2].set_xticks([])\n", " ax[row_num+2].tick_params(which='both', left='off')\n", " ax[row_num+2].minorticks_on()\n", " #ax[row_num+2].set_axisbelow(True)\n", " if i-len(plt_feat) >= -2:\n", " ax[row_num+2].set_xlabel('PDF', fontsize=15)\n", " \n", " row_num += 3\n", " \n", "#plt.tight_layout()\n", "plt.savefig('whiteFeatures.pdf', pad_inches=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correlation matrix of white features" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": true }, "outputs": [], "source": [ "wwfeatures = ['wwExtNSigma',\n", " 'wwmomentYY', 'wwmomentXY', 'wwmomentXX', 'wwKronRad', 'wwmomentRH',\n", " 'wwpsfChiSq', 'wwpsfLikelihood', 'wwPSFKronDist', 'wwPSFApRatio',\n", " 'wwPSFKronRatio']" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def delete_ww(str_array):\n", " string = \",\".join(str_array)\n", " string_new = string.replace('ww', '')\n", " str_list_new = string_new.split(\",\")\n", " return(str_list_new)" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "image/png": 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grYK+tiftrAwPUZmZmVVF76yiepTUy/OWQtk2wPRuXBu0z4OdDmyt5Xcn3Lqb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"text/plain": [ "<matplotlib.figure.Figure at 0x1a1fac4c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import mpl_toolkits.axes_grid1\n", "from matplotlib.colors import LinearSegmentedColormap\n", "\n", "def generate_cmap(colors):\n", " values = range(len(colors))\n", "\n", " vmax = np.ceil(np.max(values))\n", " color_list = []\n", " for v, c in zip(values, colors):\n", " color_list.append( ( v/ vmax, c) )\n", " return LinearSegmentedColormap.from_list('custom_cmap', color_list)\n", "\n", "cm = generate_cmap(['darkblue', 'whitesmoke', 'indianred'])\n", "\n", "\n", "def correlation_matrix(df):\n", "\n", " fig = plt.figure(figsize=(8, 8))\n", " ax1 = fig.add_subplot(111)\n", " ax1.tick_params(bottom=\"off\", left='off')\n", " cmap = cm\n", " cax = ax1.imshow(df.corr(), interpolation=\"nearest\", cmap=cmap, vmin=-1, vmax=1)\n", " #ax1.grid(True)\n", " plt.title('Correlation matrix', fontsize=15)\n", " labels=delete_ww(ff_tab[wwfeatures].columns.values)\n", " ax1.set_xticks(range(0, len(labels)))\n", " ax1.set_yticks(range(0, len(labels)))\n", " ax1.set_xticklabels(labels,fontsize=15, rotation =90)\n", " ax1.set_yticklabels(labels,fontsize=15)\n", " divider = mpl_toolkits.axes_grid1.make_axes_locatable(ax1)\n", " cax_ = divider.append_axes('right', '5%', pad='3%')\n", " plt.colorbar(cax, ticks=np.arange(-1, 1.25, 0.25), cax=cax_).ax.tick_params(labelsize=12)\n", " plt.colorbar(cax, ticks=np.arange(-1, 1.25, 0.25), cax=cax_).ax.minorticks_on()\n", " plt.tight_layout()\n", "\n", "correlation_matrix(ff_tab[wwfeatures])\n", "plt.savefig('CorrelationMatrix.pdf', pad_inches=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ExtNSigma and psdLikelihood have relatively strong correlation, but the ML model constructed by the random forest algorithm should be insensitive to such correlation between training features. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Flux-flux plot and the best fit line for the psdKronDist (Simple model)\n", "\n", "We constructed the star/galaxy separation model by using the wwPSFFlux and the wwKronFlux. \n", "The best threshold (maximizing the FoM) is $wwPSFFlux = 0.91375\\times wwKronFlux$. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "whitePSFMag = -2.5*np.log10(ff_tab.wwPSFFlux/3631)\n", "PSFMag_mask = np.logical_and(10<whitePSFMag, whitePSFMag<25)\n", "mask = Det_mask & PsfKronRatio_mask & KronMag_mask & PsfKronDist_mask & PSFMag_mask" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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yQrWmpP62VP49rbvt/692209e/G67RvX5Uv/8mWuv8h5VE5PqtYt/21MKz1N4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\n91qWxQ9+8AOOOeYYtm/fzkMPP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"text/plain": [ "<matplotlib.figure.Figure at 0x1a0ac6f128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.colors\n", "\n", "xgal_Ratio, ygal_Ratio, zgal_Ratio = kde_contour_dat(whiteKronMag[mask&galaxy], whitePSFMag[mask&galaxy], \n", " grid_bins=200, extent=(14.5, 24.5, 14.5, 24.5), BW=[0.05, 0.05])\n", "xstar_Ratio, ystar_Ratio, zstar_Ratio = kde_contour_dat(whiteKronMag[mask&star], whitePSFMag[mask&star], \n", " grid_bins=200, extent=(14.5, 24.5, 14.5, 24.5), BW=[0.05, 0.05])\n", "\n", "gridsize = 250\n", "xlims = [14.5, 24.5]\n", "ylims = [14.5, 24.5]\n", "\n", "origin = 'lower'\n", "levels = np.arange(0.1, 1.1, 0.1)\n", "cmap_star = sns.cubehelix_palette(rot=0.5, light=0.7,dark=0.3,as_cmap=True)\n", "cmap_gal = sns.cubehelix_palette(start=0.3,rot=-0.5,light=0.7,dark=0.3,as_cmap=True)\n", "\n", "gs = grs.GridSpec(2, 2, width_ratios=(5, 1), height_ratios=(1, 4))\n", "plt.figure(figsize=(8, 6))\n", "ax = [plt.subplot(gs[1, 0]), plt.subplot(gs[1, 1]), plt.subplot(gs[0, 0]), plt.subplot(gs[0,1])]\n", "\n", "ax[0].grid(alpha=0.5, lw=1, c='grey', linestyle=':') \n", "ax[0].tick_params(which=\"both\", top=True, right=True)\n", "ax[0].minorticks_on()\n", "ax[0].set_axisbelow(True)\n", "\"\"\"\n", "ax[0].hist2d(whiteKronMag[mask&galaxy], whitePSFMag[mask&galaxy], \n", " bins=[np.linspace(14.5, 24.5, gridsize), np.linspace(14.5, 24.5, gridsize)], \n", " norm=matplotlib.colors.LogNorm(), \n", " cmap=cmap_gal, alpha=0.9)\n", "ax[0].hist2d(whiteKronMag[mask&star], whitePSFMag[mask&star], \n", " bins=[np.linspace(14.5, 24.5, gridsize), np.linspace(14.5, 24.5, gridsize)], \n", " norm=matplotlib.colors.LogNorm(), \n", " cmap=cmap_star, alpha=0.9)\n", "\"\"\" \n", "ax[0].contourf(xgal_Ratio, ygal_Ratio, zgal_Ratio, levels = levels,\n", " origin = origin,\n", " cmap = cmap_gal, alpha = 0.8)\n", "ax[0].contour(xgal_Ratio, ygal_Ratio, zgal_Ratio, levels = levels,\n", " linewidths=(0.5,), origin = origin,\n", " colors = (\"w\",), alpha = 0.5, zorder = 11)\n", "ax[0].contourf(xstar_Ratio, ystar_Ratio, zstar_Ratio, levels = levels, \n", " origin = origin,\n", " cmap = cmap_star, alpha = 0.8)\n", "ax[0].contour(xstar_Ratio, ystar_Ratio, zstar_Ratio, levels = levels,\n", " linewidths=(0.5,), origin = origin,\n", " colors = (\"w\",), alpha = 0.5, zorder = 11)\n", "\n", "ax[0].set_xlim(xlims); ax[0].set_ylim(ylims)\n", "ax[0].tick_params(labelsize = 15)\n", "ax[0].set_xlabel('whiteKronMag', fontsize=15)\n", "ax[0].set_ylabel('whitePSFMag', fontsize=15)\n", "ax[0].plot(xlims, -2.5*np.log10(0.91375)+np.array(xlims), '--', color='k')\n", "#ax[0].text(13, np.mean(ylims), 'whitePSFKronRatio', va='center', rotation='vertical', fontsize=15)\n", "\n", "\n", "n = (np.arange(ylims[0]-1, ylims[1]+1,0.005))\n", "kde_StarRatio = stats.gaussian_kde(np.array(whitePSFMag[mask&star]))\n", "kde_GalRatio = stats.gaussian_kde(np.array(whitePSFMag[mask&galaxy]))\n", "ax[1].fill_betweenx(n, kde_GalRatio(n)*galaxy_norm, alpha=0.75, color=cmap_gal(0.25), lw=2)\n", "ax[1].fill_betweenx(n, kde_StarRatio(n)*star_norm, alpha=0.75, color=cmap_star(0.25), lw=2)\n", "#ax[1].plot([0,10], [0.91375, 0.91375], '--', color='k')\n", "ax[1].set_ylim(ylims)\n", "ax[1].set_xlim(0, 1.1*np.max(np.r_[kde_GalRatio(n)*galaxy_norm, kde_StarRatio(n)*star_norm]))\n", "ax[1].set_xlabel('PDF', fontsize=15)\n", "ax[1].set_xticklabels( () )\n", "ax[1].set_yticklabels( () )\n", "ax[1].set_xticks([])\n", "ax[1].minorticks_on()\n", "\n", "n = (np.arange(xlims[0]-10, xlims[1]+10,0.1))\n", "kde_StarMag = stats.gaussian_kde(np.array(whiteKronMag[mask&star]))\n", "kde_GalMag = stats.gaussian_kde(np.array(whiteKronMag[mask&galaxy]))\n", "ax[2].fill(n, kde_GalMag(n)*galaxy_norm, alpha=0.75, color=cmap_gal(0.25), lw=2)\n", "ax[2].fill(n, kde_StarMag(n)*star_norm, alpha=0.75, color=cmap_star(0.25), lw=2)\n", "ax[2].set_xlim(xlims)\n", "ax[2].set_ylim(0, 1.1*np.max(np.r_[kde_GalMag(n)*galaxy_norm, kde_StarMag(n)*star_norm]))\n", "ax[2].set_ylabel('PDF', fontsize=15)\n", "ax[2].set_xticklabels( () )\n", "ax[2].set_yticklabels( () )\n", "ax[2].set_yticks([])\n", "ax[2].minorticks_on()\n", "\n", "ax[3].scatter(0, 0.75, marker=',', s=300, color=cmap_star(0.25), alpha=0.8)\n", "ax[3].scatter(0, 0.25, marker=',', s=300, color=cmap_gal(0.25), alpha=0.8)\n", "ax[3].text(0.2, 0.75, 'Stars', ha = 'left', va = 'center', fontsize=15)\n", "ax[3].text(0.2, 0.25, 'Galaxies', ha = 'left', va = 'center', fontsize=15)\n", "ax[3].set_xlim(-0.15,1)\n", "ax[3].set_ylim(0,1)\n", "ax[3].tick_params(labelbottom=\"off\",bottom=\"off\", labeltop='off', top='off')\n", "ax[3].tick_params(labelleft=\"off\",left=\"off\", labelright='off', right='off')\n", "plt.box(\"off\")\n", "plt.tight_layout()\n", "#plt.show()\n", "plt.savefig('whitePSFKronFlux.pdf')\n", "#plt.savefig(pp, format='pdf')\n", "#pp.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The simple separation model (wwpsfKronDist)\n", "\n", "By calculating the distance from the threshold on the wwPSFFlux-wwKronFlux plane, we attached a score to each object for using it as the star/galaxy separator (we will call it as Simple model in the paper). " ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/tachibana/anaconda/envs/py36/lib/python3.6/site-packages/matplotlib/figure.py:1999: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", " warnings.warn(\"This figure includes Axes that are not compatible \"\n" ] }, { "data": { "image/png": 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bFVOxxN9i12tDOTduv5cbHnyAaCBONBBn5FdjbNx+b1bkJl8fnGLPKEfPaqMe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oNWcq799LsRgm7TSjXvIwDLMnLEfe0Ow11ujs7hx2LB92KgNaB6RbmJpGMUkcTRSFMaWivX2D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\nRv7/qd5DXsfzh8CfAv/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"text/plain": [ "<matplotlib.figure.Figure at 0x1a0b3fe9e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n", "\n", "fac=1e5\n", "\n", "xlims = [20.5, 24]\n", "ylims = np.array([-0.3e-5, 0.9e-5])*fac\n", "\n", "cmap_star = sns.cubehelix_palette(rot=0.5, light=0.7,dark=0.3,as_cmap=True)\n", "cmap_gal = sns.cubehelix_palette(start=0.3,rot=-0.5,light=0.7,dark=0.3,as_cmap=True)\n", "\n", "fig, ax = plt.subplots(figsize=(8, 6))\n", "axins = inset_axes(ax, width=\"55%\", \n", " height=\"45%\", loc=1)\n", "\n", "ax.grid(alpha=0.5, lw=1, c='grey', linestyle=':') \n", "ax.tick_params(which=\"both\", top=True, right=True)\n", "ax.minorticks_on()\n", "\n", "xgal_Dist, ygal_Dist, zgal_Dist = kde_contour_dat(whiteKronMag[mask&galaxy], ff_tab.wwPSFKronDist[mask&galaxy]*fac, \n", " grid_bins=200, extent=(20.5, 24.5, -0.3e-5*fac, 1e-5*fac), BW=[0.1, 5e-7*fac])\n", "xstar_Dist, ystar_Dist, zstar_Dist = kde_contour_dat(whiteKronMag[mask&star], ff_tab.wwPSFKronDist[mask&star]*fac, \n", " grid_bins=200, extent=(20.5, 24.5, -0.3e-5*fac, 1e-5*fac), BW=[0.1, 5e-7*fac])\n", "\n", "ax.contourf(xgal_Dist, ygal_Dist, zgal_Dist, levels = levels,\n", " origin = origin,\n", " cmap = cmap_gal, alpha = 0.8)\n", "ax.contour(xgal_Dist, ygal_Dist, zgal_Dist, levels = levels,\n", " linewidths=(0.5,), origin = origin,\n", " colors = (\"w\",), alpha = 0.5, zorder = 11)\n", "ax.contourf(xstar_Dist, ystar_Dist, zstar_Dist, levels = levels, \n", " origin = origin,\n", " cmap = cmap_star, alpha = 0.8)\n", "ax.contour(xstar_Dist, ystar_Dist, zstar_Dist, levels = levels,\n", " linewidths=(0.5,), origin = origin,\n", " colors = (\"w\",), alpha = 0.5, zorder = 11) \n", "\n", "ax.hlines(9.199338089095014e-07*fac, 10, 30, colors='k', linestyles='dashed', linewidth=1.5, zorder=12)\n", "\n", "ax.set_xlim(xlims); ax.set_ylim(ylims)\n", "ax.tick_params(labelsize = 15)\n", "ax.set_xlabel('whiteKronMag', fontsize=15)\n", "ax.set_ylabel('whitePSFKronDist ×$10^5$', fontsize=15)\n", "\n", "gridsize = 250\n", "xlims = [15.5, 24.5]\n", "ylims = np.array([-1e-4, 0.22e-3])*fac\n", "\n", "origin = 'lower'\n", "levels = np.arange(0.1, 1.1, 0.1)\n", "cmap_star = sns.cubehelix_palette(rot=0.5, light=0.7,dark=0.3,as_cmap=True)\n", "cmap_gal = sns.cubehelix_palette(start=0.3,rot=-0.5,light=0.7,dark=0.3,as_cmap=True)\n", "axins.grid(alpha=0.5, lw=1, c='grey', linestyle=':') \n", "#axins.tick_params(which=\"both\", top=True, right=True)\n", "#axins.minorticks_on()\n", "\n", "axins.hist2d(whiteKronMag[mask&galaxy], ff_tab.wwPSFKronDist[mask&galaxy]*fac, \n", " bins=[np.linspace(14.5, 24.5, gridsize), np.linspace(ylims[0], ylims[1], gridsize)], \n", " norm=matplotlib.colors.LogNorm(), \n", " cmap=cmap_gal, alpha=0.9)\n", "axins.hist2d(whiteKronMag[mask&star], ff_tab.wwPSFKronDist[mask&star]*fac, \n", " bins=[np.linspace(14.5, 24.5, gridsize), np.linspace(ylims[0], ylims[1], gridsize)], \n", " norm=matplotlib.colors.LogNorm(), \n", " cmap=cmap_star, alpha=0.9)\n", "\n", "axins.set_yticks(np.arange(-10, 60, 10))\n", "axins.set_xlim(xlims); axins.set_ylim(ylims)\n", "axins.tick_params(labelsize = 15)\n", "#plt.xlabel('whiteKronMag', fontsize=15)\n", "#plt.ylabel('whitePSFMag', fontsize=15)\n", "#plt.tight_layout()\n", "#plt.savefig('whitePSFKronDistMini.pdf')\n", "\n", "plt.tight_layout()\n", "plt.savefig('whitePSFKronDist.pdf')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "python3", "language": "python", "name": "py36" }, "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

jradavenport/GALEX_Boyajian

compare_field.ipynb

1

360479

{ "cells": [ { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib.cm as cm\n", "import matplotlib\n", "\n", "from sklearn.neighbors.kde import KernelDensity\n", "\n", "from astropy.time import Time\n", "from astropy import units as u\n", "\n", "matplotlib.rcParams.update({'font.size':18})\n", "matplotlib.rcParams.update({'font.family':'serif'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare the GCK and Galex data for lots of other stars..." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index([u'angDist', u'_RAJ2000', u'_DEJ2000', u'Pl', u'GCK', u'RAJ2000',\n", " u'DEJ2000', u'NUVmag', u'e_NUVmag', u'NUVsn', u'dRad', u'KIC', u'ra',\n", " u'dec', u'fuv_mag', u'nuv_mag', u'fuv_magerr', u'nuv_magerr', u'objid',\n", " u'E_bv', u'objtype', u'fuv_flux', u'fuv_fluxerr', u'nuv_flux',\n", " u'nuv_fluxerr', u'fuv_artifact', u'nuv_artifact'],\n", " dtype='object')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# CDS X-Match between GCK and GALEX GR5\n", "\n", "gck_gr5 = '1504810534436A.csv'\n", "df = pd.read_csv(gck_gr5)\n", "df.columns" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x112313210>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AO4Af1htsZrOBvwU+D7y6tVMTESlG2hHAeAGiWrbuurNyrFArAZJFGQKCNe5+wMwWZhx/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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x111cd5190>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "plt.scatter(df[u'nuv_mag'], df[u'NUVmag'], s=4)\n", "plt.xlim(20,11)\n", "plt.ylim(20,11)\n", "plt.xlabel('GALEX GR5 NUV (mag)')\n", "plt.ylabel('GCK NUV (mag)')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1123fd390>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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7Nb/uUtX3AXg05rj3AngQwJftJ9VzWVxQ4vu4+37flP/YkB86ERElZ/eBNGq0urmOGYK2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\nB/U6OWaToHsqAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1115fd710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "plt.scatter(df[u'nuv_mag'], df[u'NUVmag'], s=4)\n", "plt.xlim(17,16)\n", "plt.ylim(17,16)\n", "plt.xlabel('GALEX GR5 NUV (mag)')\n", "plt.ylabel('GCK NUV (mag)')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x11496ec50>" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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7nHQe691ZZMYVJx8xa3GEGB19ZhSuY6l5O3BMVqEBmG77I6ZrILn7TcArgKfFEFIIIWaF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\nwXvusA406paatYCv9JB+U9JA4Mbg3nbzwzTWLWyzrSNJjYHHR0bEN9o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T9Hr9uqk8j5K6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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x114657750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "plt.scatter(df[u'nuv_mag'], df[u'nuv_mag'] - df[u'NUVmag'], s=4)\n", "plt.scatter(16.46, 16.46 - 16.499, s=80, c='r')\n", "plt.xlim(17,16)\n", "plt.ylim(-.2,.2)\n", "plt.xlabel('GALEX GR5 NUV (mag)')\n", "plt.ylabel('GR5 - GCK NUV (mag)')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [], "source": [ "GCK_near = 'GCK_seach.txt'\n", "GR6_near = 'galex_1846338921.csv'\n", "\n", "gck = pd.read_table(GCK_near, delimiter='|', names=('ra', 'dec', 'pl','gck','ra2000','de2000','nuvmag','e_nuvmag',\n", " 'nuvflux','e_nuvflux', 'nuvsn', 'drad','KIC'), comment='#')\n", "gr6 = pd.read_csv(GR6_near)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x11a38b490>]" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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RpGVRbcaq7LQFWk8Md+o7vWRxJgijkIAlCMIRjIgZrcH4Y/uGEBHCG74XFBepGQFnVJh5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V15giWiRQyLIi63ZM9tRA4SRr5Qc6hJRMwUXuMttz8+r+RP7tt9JzwUmPPYGHydj43+nJ8VfBKf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\nONTSpxqNrSrBQxI4q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kSjMUS7GvGt8J9wwHY3Po3Ow7OH21DlsrG2Q+0uJfuberCQbMfa\ny6/iVNlD0NkWJj7nbTefawZdo8281tOI4Xd/hpbwLVjsXoKqYitaPH78tuEKHGYDrpptxxXzV9Da\nZcKpsocmSUzDLqDpNVQB2C5QG03Jn2XnXkZN/0EAQM9VTyXmUEo3D1q7O8Pb+K0SWYRQczh9j6p1\nFyTWdjYCn5j3WlJWgIb2QTR1+nBjjUnQ/pRucRy1r1Gx0DSwKkKunZDUrm2UczyFnviV1qJrkB+K\nrLvPXgGGOuJ/V21j0bwiWevH0ADKqflhBY/GsXM4gG5fEPeF/4S1oSO4fexNFJgNCEdjqlnfYtE5\nHMANA6+gpv8glnW9DEDA4TKl9pwGukab+b4bdiP/3Ju4yfs6xiNR+C+cxOP+XbjOeAnBSBQnO4Zx\nIliO/Vc9hfP6qslAUa55kwKU/DlV9lAi8IuSP1RQ6gn7Bryw/DWcmMjWkAhKpT1PqFUiWxAaYJvu\nHiVn4BPzXi67GSvnz8J4NCZY7nDJqtaeUVXvxUpB08CqDLl0QsoFbaNc4yn0xJ9pPyM1B1pkHSJS\nMSmz7iaCgiT4c4rxV1QC5YUWfNIxjAO2u6HXEXg7/26EIjE48/NUtb7paPF4cbxtgLPsc3mhBUej\nD4AAkYjo5yVlQoPi6Bpt5vtetQ3/fb4ffyRuw+HTPfj3yEtYg1MojXnQSZTik9IH8ZHPAqNBz/Dj\nFz9vKPlz3lKFnqueSpI/vEGptHbfCq+8Ps0yQ2iAbbp7lJyBT2z3MhsN2LRYXOAhm6zKdHEctUAj\nsBokYzpGNXJBKDFVOvMCHWp34cg6sllOM82gIEGBZQri1roSHGjqxqfRebjo+HuEIjGEIjHMc1oV\nWd/pHsSotRCKRDnXQnwNj6G74sn4Gp7Q2qWsUCg0KC7FIaUF8/AD/WNAFABCIBH3fHZFunEVeQ5l\nATN6Z/8jntksMf8s7aBW616aUv4IPahlUo5JhZC+pLtHyamQUFK5MZP2Yjr0O3bsyHYbFMOePXt2\nbN26NdvNmLYotBpwrG0AAJBn0CVKkz64cg5cdmllIdUKl92MuU4LrgwF0OUNwmU3cQp0l92M1dVO\nbFpSitXVTsXGYu+HHQDiflkEQcBs1AMArgwFsLraqcgzcwqOCiA8GicfdgElRT2NwDs7478Tcn0q\n2EuBRXey3qfF48XeDzvw6skraO7yodBqmDJHmrt88AUjiFmccI224ljlNlzQz4XLbsrIu3XZzZiV\nb8B/XxjCSCgCu9mAeU4rdDod5/oW0i82UOQTAJy2PPiCERxrG8Bcp0Xw2qHWgu/Tg9hqP4JxWznG\n8oqT1gLXGgbA/Xz3XM73mIQUc2fvhx3QE0ChtwXfDP0Gf9H/FcZIM/YRm1A8y4EPXZthKSqX/l7f\n2Rk/qIVHgUV3yiZ/OO8j5zpRGOnuUWLkfibvxUSu7MU7d+707NixY49c99M0sBokIxOndDWZyMWa\nmZVuO9M81ucP4nzvCHr8IQDQ3AnEmu4zoLEVqjWXPS2VBNxxTTmqXDZBczgdawCbu8QB4nP4/pun\nUVlkFbR2qLVQ6m9CTX83gMngJTrY1vDe1/8Lj/T8Ds0VX0YfsTA5UAcXhbmhpJg7ncMBzC3Ox7q+\nA6gjjyEaBf7F+Lew5ulhmv8gvIEwtqZTHIVNS6xkFbdsWjZEQo49Sk73IqVcBHNBY64ENAKrIS0o\n6bObyybyTLSdbjbq8wdxsmMYAFBiN+XUWKkCnsZ4PtaqdenlAuV5BlWO1hY1YnXLcwAJ1Jc+iv1N\n1inRwmrYkISu73R8dpnuEqsu/RLPWpYiHIth1fwiQXOZWgvd9jq0Fl+THM0PpCR0Cy/uRZ3vMPL0\nOhycKBiQ8B+U4gPL0baWyodhNujRUXgv3EMWhKMkHBaj5Pc6eUAGygu341aUoJb6UkmSKaXARhaR\nS3El6WCm9JMOjcBqSIKaNJ7ZrN2c7jhkou10n6rzvSOJz68qsU2LOtcZRcNuoP29eBQ2RXDk1mJN\nlKMlQYIAgflDH4AEcIOxAL/WV0+5PJ0NKdPrOB2fXWZ6p72WhwEAxTZTIqIa4J7LLR4v+vxBHD0/\ngB6/BXvdfweT3pCssU5B6M7O2wLDZR2aJwK7AJr/YB0HWWPOjRTafrbAqipDOK3DJe8BmUYyZZ8L\nChWZ0KBBLDQCOw0gl4BSm8YzU1kOmONXU5KPQy19aY1DJtpO19L1+EMosZtwVYkNxTYz6/PUdDhR\nHeh5WynIrcWilaO1mYwwR7wACRx1PpDQFMrxjrKxjtMpJct0l3j7s27odSQWzM5PXJMqST3V1zXV\nRXj9PeCD84NYu8CZ3N8UWsPl19+IPeMVcOiNsJNksruG28H+7llyvXIdh5nA2wAAIABJREFUdiRp\n03kOT7w5bSdIptpkugb5oMlzjcDmPOQUUNnUeLJB7shKtgUPYMr4PftOGxaW2tIah0xFhdK1dKme\np21kPGg7DPg88b9Xb45/JsJUKmgzoZWjdeiN6Kn9twRZ2lpXknhHC8l2fHXgFRyNPoA9R8ZEvyOK\n3Gy88AJAAsfnfgvnLVWKrmMmCc0fbMZtbU/DdfPjAFKnCGISvCJbHtwFpsRBDOBeO0muCx270WGu\nRM2i2ROJ62l9TaE1lEQwmXOD57AjWpvOc7/O4QC+OvAKavrrAQAHa76PYDiChgsDSXMw2+nYNIiD\nUFKqyfM4NAKb42AjnYMjIVEBEBTUltdVzrQjXAveatRNGb9IjITHG8S8Ylvi92LHQWjb5TpF8z1P\nbYcT1YGNrAo0lYrZTFKRpV31rXBYjFh7+VXU9NeDAIHuiidFvyOK3FAuCkGjgzWgSU4w+/W1gVdR\nO/IudG1FQLGN1xWDTvDoRRyouXxpYBRlDvOUHK9M14UKb68kmSWaYLLkek36my547sfMadvnD+LE\nhSHYzIakOTgSCmNRaYFqK2ppmIQYOaLJ8zg0ApvjYItEb+0ZERUAQUFtueTkDGThWvD/fWEQN9XO\nTrq2KN+IgdHxpM/EjoOQtrMJrJ/sP4syhxmhKCmK0PI9LxuHE7lNXOncj/e3Yvz6GOZdsVouLrJE\nvSMqyf6psockvSOK3FgiPoDE1IAmhZDUL88/AA0F7KVVBdyHPpfz9AR0BAGjQY8imyFJpjFdFy47\nKmHJhsyS2y+U537MnLbNrX2ojrbjW9GDaBl9OOFS0DkcSFTUEuPaobh5WsksCTkKMaRUbcqmbEEj\nsDkOJuk83zcKEMIDIOjIdBUpIZArspJa8H3+IM73jWIkGIHNpEcgHIE/GEki7aUF5kQePWa96V31\nrYKFOl/bmQJrPBJFx8AYBkfHcWONS7RZKNXzMn04kdvElc795DLNJ8AgZHIVHaDekc62MBEN7w+E\nRb+jBLmZ/+OsreMWzMP+/O3oPBrAtYbbcWfVOApTaScZhIY+l3fVtyLPoMeCaDuWnXsZp8oeSrhE\nMF0Xuj79KUpUVDFKLIQSRybJD0dJbDUfwDXDh2E26HGw5vuwmw0oMBsS5WOFpmPLiHk6w6m4csFf\nVAwpVZuyKVvQZbsBGqSDir49fKYX753tRa8/gMGRcZCksAAIJiihKGtNeJWgvNCCS/2jONkxjFA4\nCptJD18wAjJG4vLgWFJNbb1eh8c2VCeNw821Lhxq6ZO1Nj2zNvb5vlHYTHqMR2Oy17MWWjtcLqRb\ng5zrfiv9h/Hox/dhpf+w4PtRv1078CoW9tdj7cCr4triaQTe+Hb8LzClRv21hg5sOLMDTbPvSqtu\nvFzvKNvrmO4C4HaYcV5fhZ36x9CCedw/oghNw+4pX1HrZFnXy6jpP4hlXS8nZBqzryaDPmdlFnPc\nKIvM9177FE/sa8Su+tYkeVPrdmD7xho8s3kpNi4uwUclD6K1eFNSmdwlZeLnQjprTTAYa0hJsI1r\nurJbCZQXWuAPRpI+45IjmZbnaoWmgc1RMKNvWzx+fHB+EA6zAVWufEEBEGyYrrnkbq0rwfaXuwEC\nMBl0CEViAIClFYUYj8Rw2uNDjy+IkgIzvrK6EndcU447aL+n/BPl9DlinqJHghEYdECBefJULZdZ\nKNN5ReU2caWj5ZRsmqe0gmOD8RRbQFxbxDDv3hl4A9ahQ2jGzXhh+WuSNZ5yvqNsrmNJgUMC8qjS\n3x9dptH7uqPZmbl+y2wGp8aN0jQfLrwX7w8UC7LIpCqTK3YuyF7GmG2cMpiKK1f8RcVYQDMtz9UK\njcDmKOiL0mExoqQgLuTHI1EEwrEp5u9cNanJhVq3AxVOC7xjYfhDERSYjagrL0A0RuLUZS82LJqN\nVfOL4A9GcKilD1UuW5IwUMLniCmwjHoC/mAEV8+ZfK6cZqFMkhq5TVxMX8f9sx/FkdY+hKMkdtW3\npjQJSjbNU1rBqnUptUWF6x/HMIBmyz3weIM5Szz5INQMK4kAicyjqgqZJrMZnBq3ZefimubekRAO\nWf8OFaFzuPXcL5NcJ8QEB4pFOinRWJHlyl254i8q9h2qWVZkChqBzVFwLUqPN6KdzDiw2O2YQqre\nO9uLWVb+07kSPkdMgbWkrAA9vhCMej1izHyUMiFTvmBy+1PTfR3fr7kRJy4MgUQEq6pm8froSW4L\nXSuYSsPmXorCLz+PLQC2SOqd+iHGL1JuAkRfJ81dXviCETgshoRJW3HZxqVplTnzAFPT/GpwI0gA\nD0TeQk3/XwCwl8elIBehYa61Fo8fQ529WLvAiRaPNylbhCBZkuXKXbnkL6qRUnHQ79ixI9ttUAx7\n9uzZsXXr1mw3QxE0d/ngC0ZgNuoTn/mCEbjsJnxhaTlWVzuxaUkpVlc74bKbU9xp5qDQasCxtgEA\nQJ5BB18wghaPH8vnFiLfNCnc8gw6dHmD2LSkNOVvKRKUzvi67ObEu7qptgQ1JTZcGQqgyxuEy26S\n9fBBkRAAcNry4AtGcKxtAHOdFtnniMtuxlynRba+0O93rG0Aljw9ls8thMtuSayBK0MBrK52ytcW\neymw6M74Xzo8jcA7OwFHxdTvuMDxmxaPF3s/7MCrJ6+gucuHQqtBlet174cdAICV/sO468x3ELM4\nMZC/gHXMqbUykL8Apyu/hAv6uexrRcSY1LodKLQa0NTpQ8WseHpAtvn73nvvYd26dfJ2/p2dcQ1i\neDQ+HyhwzQ+JoMZtLK8Yl0vW4zOvGSOhKJzlVSjQxYltd8wBl93EOs+lgGus5zotONUxhA/aBmEx\nxtdansGQGO/+kZBwWSLzOImFUrJbg3js3LnTs2PHjj1y3Y8gSVL8jwiiHMBpAAUkSRI81/47gO0A\ndpIkuUNSIwliGYD/BtBJkuQ8ob9bsWIF+dFHH0l5pOpB14jQtUq5GsCQKTC1Bv3+IIwGfdLpnDqt\nb99Yk/K3aoxkTYVd9a1TNBFcfVUzntjXCLfDDB0xKXpiZDx37zObM5CS541vxwlN3X3CTaIsv2n/\n7AP0HfoZTrkfxGjREuXXcBo+m9SYP/rxfSgMXcGwaQ5eWP4a55gz18rdpX2oatubnPif8i2mjUkq\nuba/qYdz/lJJ+z/84Aiu/9yNk2tTDj/VDKZ8oo9bnp5Ajy+EiiKrIjKebw+h5AVVJILyZabGn+s7\nKbJEadma67J7uoAgiI9Jklwh1/2kuhD8AkAB30UEQawA8LjEZ1D30AP4/6G5OyRBaJ5RbdEmg2mi\noWslhTjO5/L45YovGB+ybhKUYhJl+U3gyC9wne8wymI9sF3oU75KUhq+iGLdAqaslTd2TT4b4PQt\nThUAxjV/m7u86Bgcg8NixF+tWZvs3iCH/6XMAUep5DKbfFLKHYwvuInPl5nPz1lNVaVyXXZrYIdo\nUkgQxGYAVwM4AWBliusMiBPPVwF8UWoDAfw9gHEAl9O4h2ogJ6lMtSjVXGpOTcR6JkVz0okflQ93\ncGQcRba8JN82tSPr+YqlEBqW3xyw3wMSJCqHT8Ae7pWtShLn+krDF5GZb1X0mLM9m0WjmYo0cR1c\nfMEI5syysuaJrWV7bhaT6IuVy0oSL74DLd+hJdV3WlUpDZmAqDywBEEUAvgZgG0AxngufwLACICp\nif2EP68KwPcAfBNATOp91IJM5qOTOw+nXJAyBi0eL3bVt7LmQpQD9HyK2zfWTFuhSeUOvNA3gpOX\nhuELhKHXAe4Ck2LzUIl3l+08p3KBcC/FKxXfw/vzHk8rfywdKdcXRaJZSBvfe+Icc1xMzpHLzJlL\ngU7gU5BHKhfmscptSWNyraEDX+v7MWxDzVNyXzosBtjNBqy+9BwW9f0Zqy89N0nE2PqcIues0tjf\n1IOFZDs2X/4RSkZbsyqX+fKOUvLihH0DXlj+Gk7YNyRyjab6jupnyv2HNk+oPL+ukbPY1LoTrpGz\nOWkZ0pB5iNXAPgPgEEmSBwmCeJLrIoIgFgD4LoA1AGZzXScAzwF4liTJJoJI6WqbE0j3pClGc6lW\nc7HYMVCzJjnXQJGQ7795GuFYDMU2ExbMjucM9gbCsms8lHx308EkmLZWkwVSZAzbe3r5zbdwV/BN\n/GXW/SDcSxOyZso93mCY6PlM9jzfc43JndE3UNj+J3yjCvgPy7VJ1hLKNzYUjQIAekdCONLahyVl\nHF5uWYyK7xwO4KsDr6Cmvx4AEhWzsiGX+SwZfNaptEpX0+ZBeeH2OPmdKFQBAPsqnlRllgAN6oJg\nAksQxDoAdwFYLODyPQD+P5IkTxMEIYnAEgTxFQAVAO7kuzZXkA6pFEsGsu4nyAGxY6CZl+RFrduB\nyiIrVs0vSgqCUmIT1d5dasjlvkI/2J7u8mFpRQEA4cUwmH6n+2c/ill9R1EXOQqTQYdXChdzyxom\nGeQjhzzfc41JIR4HrHkorN6A7W27gBu2Ae7JYKGf7D+Ln0Y241FDBDoCKBltRY/vanbXGLmT6Itw\nSSgvtOBo9AEQIJIqZmVULk+0t3bVtilj/cj8YVQ1fJe1pC8Tqb7j3X9o8+BWxIn0+877QYLEUef9\n6sjzOw2gJnc9JSCIwBIEYUaclD5BkmQ/z7VfB+AG8COpjSIIwoW4tvcBkiRDUu+jNqRDKsWSAa7T\n9cp5hdhV35o0oan7ZGKSix0DtWqScxmZOtxo744f6WqSmQfbcz1+NLQPYXU1gWKbGX3+IJq7fCkL\nPjD9Ttd1/Qo/tn0X7pEezPeewMrCw9zBZUwyyEcOBZBH5pi0f/YBmo/8Agfs9+CWI2+gdrA+7vs2\ncZ9atwNlDjOaRmvg9dmxbvx9fMvmxH8VrcrMYUlEoFiqilmyg4tY09pbe88vuYPt0iD5vH7qtHlQ\nC0qba8Wv9dUoL7Rg6zQjWtnATLBeCvWBfQrAJZIkf5PqIoIgSgD8BMBWkiTH02jXTwG8QZLkX8T+\nkCCIrQRBfEQQxEd9fX1pNEF+pFO/mPIToiMVGWDzWbu51oVDLX1Tam0/c6A1Y3WixY6BmPrQGoQh\nU3W0tXcnDOn4CdMPtiWjrXgq+n9xVbQdTZ0+9PgCaGgfhD8YwdKKAs61zfQ7fdG0Be2GaszGIOzj\n8eCybB08Wjxe9B36GWr6D2KT/3W877wfJ2zr0V6dXDIiFCVxY40LvbVfwznXJrRUPpzcZi7fXDmw\nalvKSm10KOq/zewjl69vqvaK6Euq54vt50yJQ8gk1BoHIyd4NbAEQVwD4H8AWCbgfj8DsI8kyfel\nNoggiNsArEf8YCYaJEnuQVxbjBUrVohPcqsg0jEZStGaMTUZu+pbp2hxB0fj54y6ckfiMyA9My9f\nmhgxY5D1iPNpiExlXpgO7y4T+SnpWpILfSPY/nI3KpwWLHY7eJ9H13Iv63oZNcPv4JsOEv8cXYDG\nK17YzAbUlReg2DapCWeubabf6fHWPowEI/hL2TdxS+8LsgSXScX+ph5Y3A/ClmfAJ2UPIWBbiLet\nC+HoNmL71ZPX8ZYLlrOcKVOzKdIlQQ6tO+ucZPZx1TYMj43jT+Tt+GRfI+3aFO11L0XLqv8dv//R\nRnFzPsslYzUkYyZYwIS4ENwx8fcYI5CqCAAIguie+P8zAG4HECQI4h7adXkTf58gCGIbAJAkmaok\nx90AzADOMJ7nYjzv70iS/IOA9qsKUoUXtckMjoTQ7QticDQMg47AYzdVC74H24Qej8RAIpnnpzPJ\nhZgtxIyBkmRLDf5B2WpDJoKgcj1FWSZMcHT/01VNv8Sv9F9Cq+nz8I6FBT2PfrA9VfYQgpEo9pK3\ngUBcdi4us6MWl7CsNZ5eKpZfM2VtM98TVdL4TNGt6Ky4k//goWBaqs7hANxFS3DQWZf4jE0+8R6W\n5AzcUoKoCRzDlHOS0ccWzMMe/WNw6I1w5xsEzae05jzt+YqunSymQcslqDUORk7wEliSJP8VwL8y\nPycI4j0An2eQ0WdYrlsH4F0AzwipxEWS5DbE03Qx73Nx4vt5fPeYjqh1O3BzrQvPHm5DOBqDMz8P\nbocZh1r6UOWySdbi5hmmepGkM8mVCNxRgmypwT9IDW1QGny5irN9gEgF+lyu6T2ANR27ccj9Dexv\nssrWziT/0/FOfEX3W1jydNjc+xt8Yv9b3sIGdOLWE6vEf4S3gQSwqqoAp7vi/rAPGH+LmuF3AHBH\nd6eVQF9BzZvQTZj3sMSnJeUiRWyfr9oWryI2Nhj/Xg4SJXAMUxV6qN2Y3EfqWtb8uGzv0tOI2Gs/\nwULn/QhY4geGJPmNi6mJI22M909Y+xZE27H69HMAAdSXPCrP2uEYK7XLk0xjOljA+JD16lYEQcwC\nECZJciTbbVE7WntGcX2Vc0opRaHkkG1CF+XnQUcQ8AbCskxyKWaLbAgeNUTIq6EN2UIukHf6XF7T\nEQ9wutnzK/wwf4Nsz6Ani7+27f9in/2v8UX/S3DHumEVUNiATtwaLgwkuQwQZQQa2gfxEnkbvlGs\nExXdXYuLqB3dPSXanxVMzZ+M61nMJpzWQZeLQLJ97l4KWIvin1uL5CHtTA0xB6FOWR2L8Rvq2mXn\nJtNT9Vz1FPd8atiNmr79KPOdwlumH6PPthAATX6LOKjQnz1v+AMQAG4wFODXeuEWQ06waNNzQZ5k\nGrluAROCrBJYgiDmATgNwEcQRDVJkqPZbI/akaqUIjOzAFdVF+aE/odb40JKrkku1myRLcGjBv8g\nNbQhW8gF8k6fy1TFoUPub8hqgqP7n744ezl8wQgCphi+Gf09GgT6nlLEjZpPVHo0l92MlfNnofGK\nDr+e/Y/iorvFaFVpmje51zPfJiwbWeZyMRD7uVQwNcQc45+yOlZDcgYB6loqXdepsodSz6dV2zDS\ncgT28R4s63p50peY+k2d8D7Tn20OewECOOp8QDHzdS7Ik2xgOuTLTgUppWSPA5iPqT6wy0iS9NCu\nux/AzzHVB/a3JEn+r4nPxgD0AhgAEOZ43pWJdtJ9YCMkSU4fPbhAsJHDS/2juDIUwJxZ1ikbBsCe\nHqvW7UgI/uePXpQs+Nk2D7Fmi2wJHjX4B6mhDdkyu+UCeafP5TOuTYlqQ1tlzNZAJ2gFFiN8wQi6\nK+/Ai84viraGsM0ns9GATYtLsX0jjxaVCbEEbUL79zF5OxyWKnYTt8wuRLKSZffSeF+ZWk8GsZxc\nL8C1httx57s/Q+H6x9lN6mJ9NenXc4x/6uIXyb+hrj1vqULPVU/xzyf3UgzduQeth36GU877ESPJ\n5N+4HYK1zUnPXvxvifvIsnZYyH0uyBMN8oMgSVUF6suKFStWkB999FG2myEb6AKbIocftg1gYakN\n84ptieu8gTDGI1EEwjE4LEYEwxG0ePwYGgtj7QIn1ixw4lBLX9J9vIFwQvALITVsbaHuAQjPK/vE\nvsYkrREAxEgSHm8Qz2xWzkE/Vfuz4QObjTZk8/m76lunkC3q/6LJloLINMFP53lc7/PmWhdae0aV\n7cMb3waaXsOJ/HU4suRpPPrxfSgMXcGwaQ5eWP6aIuuZmkMr/YcT2sgT9g3S59BEH1B3HytRY47v\nhjM7sGToEMauuguFX35e9P2kXi9mjkiZT3L9BlAovzjLwSBX5IkcyGVfX4IgPiZJcoVs99MIrHJQ\nYqIx73na48Wi0oIpBPBQSw9WzXdiPBLFyY5hmAw6kCQJgiAQCsdYSa/DYkycnPlITToCg96HjsEx\nuAtMrG1RWvCoQRBkVPAzoJTQT/cAlCqYKJeEdTbAHK+akvyUh9WUYNMg0j8Dpvx7L3k7zuur5COV\nKUAdfmUjyzwaUyZhbpp9F2yjF9Fc8WVsufcLqe8HpB5L99L4/9+dqP+z/sm0g8MytXayfRDnasOl\ngVGUOcwIRclpIzvUMNbpQG4Cq9+xY4dc91Id9uzZs2Pr1q1ZeTY10QDAacuDLxjBsbYBzHVa4LKb\neX7NDZfdjNXVTmxaUorV1U609Y7CF4zAbNQnrvEFI+gaDmKR247POn0AALNRD4OOQCAcRSRKIhoj\nUVFkTfwmz6BDlzeIXl+88JnDYgRBEIn7XhkKYHW1M3H9qyevwGnLAz3VGXWPTUu4s6QxxyUajeHU\n5WFYjLqECZUyWaUzTkLAHEulnyekDf0jIUXmDRukvsNUEDrvXXYz5jotuDIUQJc3CJfdNMX3Wqk1\nNJ3BnE9vfRr38FrpP4y7znwHMYsTA/kLpqxnAHEC9c5OwFEB2Evj/256DQiPAosmKnrTP+v8aPLf\nK78OLLoTxsJSHGsbwED+Apyu/BIu6Ocqtp6bu3zwBSOIWZxwjbbiWOU2XNDPhctumto3IbCXxvtp\nZ5/71Hq568x3UBi6goKQB69d80ucD+Szrxd7aXwsG3YD5w4CZ9/mHkvquW2Hp17HghaPF3s/7MCr\nJ6+gucuHQqshaXy51k40FsVbn3Zz/k4K9n7YAQBYEG3H2ks/x7itHGN5xexzTCEw5QlAYmw8iln5\npqzIDr73IxXUWPPtz2rFzp07PTt27Ngj1/2ynoVgukKKb6eUEzOXz+myCgf8wQhGghHYTPFJHorE\nUGA2giRJDIwmF0qjfC+F+hKVF1pwoW8E3f5Q/BlmA0rtJsx32ZAKzHGhrvf4QjAa9CmDyGaCNi6T\nPsFK+OCKaT9fgIFcYzET5k0CHJHoQqLW2RLhA0j2w+T6bOJetau2ZSzyObU/qPxIGUDFBWpMq9ZN\nrXAldHwZEOL7y5Zu6wDxOTx7uA33lPbjqwOv4Gj0Aew5Mpa29k50tgOFQJcnu+pbkWfQZyWoS8nA\nZM3XNxkagVUIYiea1EnPFaULAHuOXIBRTyAUjgIEgVAkhrryAvgDkYSmkxlotb+pRxCpqSnJx2sn\nryDfZIDdpIcvEIZnOIBb6kpSEga2cZlbnI88oz6l2W+mpEnJpIBSIk+gnO2X414zZd4kwCChYqLW\np5AnttypzM+owKexQaD9PQBA7T2/zFgxjkymCZJEmOljynQJEDK+LPj4wyN4pOd3aK74MvqIhazk\njO3g8uKs5QhHY1g78Cpq+utBgEB3xZNpkzrR2Q4ygGwSPSWVEGoI/FUTNAKrEMRONL5Jz1eelW1h\nbL1xPl46fglHzw9gltWIZZUOGPV66PUxPLahOimwgxL87X0jU4ol6HS6KUK6tWcU11UWotsXgi8Y\n7+fCEhs+OD+Q8LljIwxSF+BMSZOSSQGlBAGQs/2i7sXhvyg6mbuMkF3zKySqnUFCxUStiy2JCiC1\nhjEDyGSaIEnrRcqY8mDhxb2o8x1Gnl6XSHXFJGdsB5fB7jCc+Xk4VTJJNOUgdaKzHWQA2SR6SpLn\nmVCcQAw0AqsQxE60VJM+He3sj+67JmkjnW03JoTuHYzrWzxeHGrpQ81sW6JcrS8QwWM3VU95Tudw\nAJXO/KTgK3rwGBfRlLoAxQiFTATPKWWGzrSAkpsAyNl+UffiyJuZLfOmIppfIblZGYQpNemSgVxx\naBhlWS8qLBma9nqRoU9n522B4bIOzRMaT2AqOWM7uBh6B+B2mNFnWziZ4zUQTpvU0edYc5cXvmAE\nDosB+5t6Et9nGtkkekqS55lQnEAMtCAuhSAkSIUOKiCBGYzlsptwvjde30Gq47bQYCXKQbzUYUFF\nkRU1JXa47CaMhqJTnsPVXip4jCswSOy4CBkfetuUCPzJZDCR1PFRC+Rsv6h7OSrigS+rtiUF4VDz\nZtxWjrxoAKfKHkJ3zCE90EcgFAls4egjHxQNVmQJfJJtvbAFkeUi6MFx1CGE6pOnEcP/9T28cl6H\n3zQFBQX8GAtLcaDTiFW9+zBidsMTc0wJlGNbO7dfXYILA/GDW55BJ2vArMtuRqHVgKZOH1aaLuOL\nwy/Ag2Ic7CCyEnSZTTlaaDXgWNsAAPnHGVBH8LFUaEFcOQQxp/VUJ8bnj17MiD+PGC0nX/BYqtOn\nFC2G0BO1Eq4GmXZfyJRZVCmtspztF3wvDlNttsybimh+FTBHpwUet420ixnIXe0qG/A0Avu+Ang7\n4/9n9Gn43Z/Beu5NLJk1hp5FOwQXonEbDyB/6BAi0Rj6637ASs6YRWvePdsPi1GHcCQKjzciu/aO\neu9rL8vrYysV2apCpWlJMweNwKoEqSa9nCaJVKSF7znM3zITpNODxwB5TTdChYIS/kfTMfJzpgQ3\nZWszUWNgi+xgujRMEFrStwH20mvYMx+IgdoIuxQ07I6TV0f5JNGn9ekD8lpcbzyOkfx5uOf0/wII\noL7kUbx0XJcoRMO2PgvXPw5Y87By1TasdHPn12WucyXzhlJykj7nc11OSkW2yPNMg0ZgVQSuSS+X\nPw8faUn1HLbfHmrpYxWEXIQhXY2fEKGghP+RWiM/0xnPmRIUB2RnM1FjYIvsYGpIJwjtLbN82Fe4\nWFy6qemKVFkIAMzuPYqCcB/qet9EQagLBIAbDAX4TmRbylgCoeSeL4hRTisMJSd1MvvYatDABc0H\nNgcglz8PXxLkVM8Rk0CZzUcnU36kSvgfKe3TJAXpjqcSRQxmCoQkKc91X2ZBoPm/tni8OHAlD6Ex\nH9533o/W0XyMFNYoXsxANJjFGpQGT3GEd7vNwPgoTrvvhSE2jmFLJd5zPYzW0fyUsQRCQa3ztZd+\njpr+g8iLBnC5ZD26vEFUFFlklclccnLlvELZiydoyE1oPrAzFHJokYSYwrmek64ZPVOFHSiT8UvH\nL+FQywAIEFhWkd64qdGnKV0Nqlq1ymxQUyECMa4XM8WMSI3JQjIMS54eRr0OsXFSMV/LtCAkk0MG\nsfz6G7FnvAIOixFnXJtosQQ63lgCIUjlyrK/qQcLyXasvfwqTpU9BJ1tIQDpVhg2OblyXmHKtIoa\nNKQDjcDOIKRDWqT+liIfb3zSiRK7CcW2PPSPhhMVwgpo92P+Lh0fzUA4hlXznQlXiHSFptrISLoH\nilzJJ6g2X13ZApSmEagx2dj+AuYNfwBL2Ifuqh/DYTFi+8aauNbrVomGAAAgAElEQVTz3QlL2Pon\ns5sSK0uBYVyHMK7DMcATSyAwHdfdpX3oO/QznHI/iP0MV5bnj17EVwdeQU1/PQDgYM33p8gQsYdH\nppzcVd86Y1yVNGQeGoGdIWjxeNHnDyaKGiwus8NkMAgmLVIID518lNhN6PUFcabbD7fDDIfFCF8w\nXhGsxeOdIszS0TDOBP/OdDWoatQqs0Ft7zJladbpBoEkKXGYIgACAAjGYaphN3DuYPwLa1F2NZ9Z\nCAzjO4RNORxPjPtji7fgj90u9vUpUJNc1bYXc/3vYmw8ih2X/wdKCsz4yurKRHDw0egDIEAkNLTM\noN23Dh7ATd7X0elcjfKW43ir615g0y2C157cAbBqssaoCTN1XDQCOwNAF6BrqovQ4vHjg/ODWLvA\nKViTJYXw0MnHgtk2tPWNQkcQ8NPyuS4ssbGSkXQEH9dvm7u82FXfOi0WuRwaVLVpldmgtgwQKUuz\nTjcIJEnUmByv/BaCBsfUjAurtsVLzVL/nmEQfQibGPcqANvp4+5pBN6YOFAI1CS3V29B34VBnHU/\niLuKyuAPRnCopQ9VLhturSvBWwc/wXg0BpIk4Q2Ek2TI/qYe3OR9HXVDhzBv9FPYxnsnPr9WcACY\n3Bl01GSNUQtm8rhoBHYGgC5AHRYjSgomhYrcWQDooJMPl90Mh8WAwHgUo+MRVBitqCsvQFG+iZWM\nCBV8bEKU/ts+fxDn+0bRNRRAIBxFfp4elc78nF/katKgKnn6V5uvburSrAKgwupSnBBIkngzLriX\nAl/+g8KNVS8oOegaOYtlXfFMALH8Gu5DGNe4Mw8Uq7bxzqU/drvgrX4qXlDj3NNJWQi2b6xJmU+2\ncziApjlfQp5eh4uF12Pe8IfoLFyFG5r+Ge2lj2PP6Xxe0pTyoC1yLajNGqMWzORx0WW7ARqUR+dw\nAHZz8lklE1qs8kIL/MFI4v/FdjMc1jwsKi3A9VVOFNvMnGTk1rqShEYgRtMO3FpXkriGOnl6A+Ek\nIVpTEieoF/pGcPLSMHyBMALhCAosBpztGcHgaCix4Klyh7kGtZiMuN5Bi8cry/2FzINMgjo4OCxG\neLxBOCzG1IcgTyPwxrfjf4FJEtKwW5kGMp+XDihzOw+5oI9Ji8eH0x4fRkJh7G/qmZwHcrYrx0DJ\nwWVd8aIWy7peTn0I4xr3VduAuvumpC1LNZco2b/60nNY1PdnrL70XFKJ8j9Z7sEnBRtwdt6WZBni\nacS3O7+HlZd241TZQ2idfQsO1nwf5QPHca3vMAJHfoGFZDs2X/4RSkZbOeVpyvUici10DgdQHWnD\nptadcI2cBZD7+bjlQLb2dzVA08DOAGRCi8VGqJin71K7CZ7hABaW2BAjyZTaK6aGMU9PwGrU4fmj\nFxP35zp5tvaMYuuN8/H9N08jHIuh2GbCeCSKYpsJoUgM53tHUWwz5+wiV5PJSOnTvxBNc6bJvChL\nBJvWDFDOlJ6lKHtqPDoGxzBnlhV2syF5Xqos+j+ToOTg+877QYLEUef90gImmf67AuYSJfvp/sn+\nYAR5emJChlShZ8nT8Acj+JguQxp2o2r4A8QABC47cHjRDviDEbzjuBfzi604ELkVmyYCwMxhL4JG\nB066H0Tj8NwpbeBcLyLXQnmhBXUtv0fN0CEA8aCzaeu+IwJqs1JlEloe2BkApfOYcuUkXT53FpbP\nnZXIhVnpzMfd17oRJQlBuTGpfLIVRRY0XBiC1WRIun/n8BjKCy2suRIfXjUXDRcGcV3lLFQUWTE4\nFkYoEoPJoIM/FEG1ywZfMAKX3SS9Nn2WICYnr9LIRD7ZVLW/M5VfWDIcFfG696u2xfOljliwd/hq\nvNoaUSYnJuN5mQQ1LxdE27H20s8xbivHWF5xfF5edy0QHkV79Rb8+tOg8Jygmc7bqgConMBnRvLx\nvn4VLEXl8rj78OSYBSZlvzd/HixEGO+5HkZnxIE8PYFQJIqLA2No6fbBH4wgP0+P/pFwXIY4KkD4\nuzCaPxfvl/w1zgfy4bKbcPvqpahY80V82GuEB8WwEeMwkCFUDR4BwmMYrNgoXAYJaD+zLwc6jbAR\n42gsfwiemIN1HxOSp3k6QY15yrmg5YHVkBJc2igx/pJiNVqptHDbN9ZM+e0dIvvEdv/BkRCau3xo\n7xtFsc2EBbPzp7gk0E+mC1z5ONkxjFAkhoIJ7ZDUamb0sakpyU8qp5sJU76aApuoMR6PRHG+bxQj\nwQiMegJLygoy8nzV+3/RtGYZ0ZxnsfwqNS+XnYubygGg56qn4vPSvRQtq/73RP/Dwvs/TTS3cgVM\nUvKnucsLXzACh8WAxRNVFNnuPyn7rfi1vhrlhRZsrSvBv9e3omNgDGajHnaTAcFwFGe6/RgLR+M/\nnPBbtgPY4mnElobdQN02YOIZca3yGLornkR1pA0+wo53HPfiDj7XnjR8wGvdDmDTLdjfdG1c3tqN\nrNYYtVinMgU1xUNkGhqBnUYQkq6F7/cvHb+UlGqLfg8ArMRWaULFvH+fP4jWnhHoABh1OngDYXx0\ncQiLSu3Q6XQJUkp3YXDaTKiZbUNrzwgc1jjhEbvImeN7oW8Er528gusqCzMaGKYmk9GtdSX4yf6z\n6BgYg82kh0EXb0uPL8SaHk1uZIrMy+GmoDTZzrZfdKqk+YDEHLpZytuqRlDyJxqN4cpgACAA79g4\nrEY99hwZ45Q7dNlPzZFTHcMACbgLzQkrTigSgzcQmfJ7tkMEnTQ1Ds9Ff+1O3FFXglpcnMyUwEZQ\n0zyQ8O1jqj/QKgSqb9T6p3yRp3OfAS2Ia1qBvnh1BCEqUIkSjs1dPsyyxM81pzq8CEejcFiMeOn4\nJc5gHWawFiAvoWLe/3zfKEAA5bOsuG5uIQosRkRjgMcXShLizACC+S4bdj20FL96ZCWrZpg5Hrvq\nW/HEvkbsqm9NCH76+Hb7Q8g3GdDtC4ke73SgpsCmWrcDZQ4zbGYDxmMkLHkGrK4uQkWRNSMBckrP\nPUC+QDUlgy2oNloGmvDV3h/DMtAkazCdEFDz8ry+Cvuvegrn9VVJ85Lq/5qOyRy6vP0XGEimJNhk\nQTZAyZ9ufwiLiAv4l8C/4OnAv8A80Dwpd1IEy9HnsS1Pj0iMxOXBAEZDYQTDUYAECswsOi1m8Fgq\n8AVmibmXBMzUgCalg2nVCk0DO42QjjaKEo7hKAmbSZ/waTzfO4q/ml+EQy0DWDXfyXqyVbqqE/P+\ngyPj0OuQcBtw2c2IkSQ83qAs5Ty5NNkjoTAWlU6axkeCEdhNeviC4cRn9AhfLm1YupoytZmMQlES\nN9a4oKP5wcZIMiObRiYqilFrY0G0HatbngNIoL70UexvsooacyU151zVsDKpeUo1L1s8XnQMjuFU\nxxAMlofxMLkXDTmQQ1dNJmlKvo8EI7g79CaWhU4AALw+Oz4z/1V8vaXQcNLn8efJF/EH6+04NV6J\nbl8IVS4b5hZZMd9lm/pgFrcUrnF5bPEWVAHcBFVhFxc1WacyiZmqedYI7DRCOouXEo42swGhcBRm\nox4mgw6+YBj+YAQECNaTbXOXd+I5YXQOBxI+WXITKotRh4YLAyBAwGLUocoVJ69i+ykEXMKgcziQ\nVJ/cZjbAxzLekxG+Uzc9ALJsiGoqQpDNTYNOmuh+gXKa0Oi+nfOHPgAJ4AZjAX6trxZ1HyXJNm81\nrAyBbV5SZKfUboJvLIy3cQP+bPkcFuns0KmwfDEddNK37NzLSXlUlVp/XAdcap3ZzAb8kbwL9pgf\n45EYfofbcOWzbhTZ8tC+JplA0u91usuHpRUFWOZ5GTXBvyBsjMFTsB2haAyL3QWirDh0GekaOYtb\nu17G+8778cfuukTxBbEHdTlcYLJVIjvb7jtqiovIJLQsBNMI6UQjNnf5Epv/laH4pI9EYzDodLDm\nGVA5y4JIDImIdwC42D+KCwNjcNlNKJ9lgcNiBEkCK+cVouHCkCxRoNTml28yoNZdAJfdhHA0hnCU\nhMmoVyTqkiuy3h8Mg6T9PxqNoWNwDFfNtqFgojSuNxCOp/wyGVizBJzvHQUQJ8X9IyGc7RnBpf4x\nNFwYRF15geqiRungiu7NdhQs1YamTh8qZllRXmiRNRsBtTbGbeWwjfdi2FyJ92Y/DEtRuaisDy67\nGdFYFIdaenGyYwiBcBT3LSvDmgWutNpHb+NYQTUMsRBOzPkqumMOVWTZoLITXK3vwIO+F9BvmI2u\niANREnhiU2pXnmyDkgVrL/0cNf0HkRcN4KR1NY61DeCTy8OyR7mnyqqxYLYNx9oGkG/U4+xIPv5M\nrsG+0CqMW2fDqCcw32nFiX4T5n5uM1zuuVPudWVoDO19Y7C45qJAF8ZHpV9Ee9AOkiSwuKxgqtIh\nRQYIalxmj7bijjPfRZm/EVYihPf1q7BpSano7CCJQ87YWdzV/yt4UIyDHYTo9UtlfKAy3/BlupED\nasiEQq1/+v6sxiw7cmch0AjsNEI6i5ciIfkmA4pteegfGcdwIIKVc2fhazfMwzUVhVNISuNlLxaW\n2lDqsCSI2uBICG839cBlN8mymNlSRpmNeliMOhTlmxQRUlzCoKLIigdXzuFNC3bi0jBnail/MAKn\nLQ/9IyGc7BgGANhMegyOjuPKUDAr6Z8oYvr80XbsbejAwdPdaOsdTdqYUwnpWrcj45sGE0qmFqPW\nxlheMS64b8MJ+03ojDhEE/QWjxf7Pu5ExSwrrpnjmEj875flndPbeLlkPbo5UgxlA3QSuHjwECry\nSRC1d0CvI/Dwqql5Q9UE+uElLxrA4cJ7cfiyDpY8PerKHbKTlVSpyL6wtBxznRYMjIYRikTR7Q/B\nkqfH9dZOPI7fIc85bzJtWbUzca+V/sO468x3YC6cjVPBMlyJFCBcczuG9U5Y8wx48vZF+MLS8qnt\nf2dn3B0hPBpPd8UYF+tgM+49/z3YQh6MmErxdtnfJg51zGfHLE4M5C/gXI/U9bd178HC/nrYiHG0\nOddJWr+p0u4pATWkNcy2EkEotDRaGlJCqmmZboodCUWwbuHsKWYQpn9bhdOCSmd+0n26fUGEozHZ\nfHG4TCMebwTbN9aIvp8QpDJDsY0vMy0Yn0ndGwjjfN8oTAYdzEY9guF4kQUqEEMq8ZNixhIa2czn\nYyWnS4PYVEGAsiY0uXyOJUXhZ7iNSoArO8G1ho7UEesqALNM7pHWPpCIYOOsbqw/90vZXQpSpiJD\nsnx/Yl8j3A4zbj33AmqG34HZoE+6lrrXmuZ40NytvS+gseZmNF7xwuMN8s+R6g1Ax/H4X5Zx8b38\nz7CP98BvKsUf5v8AZ4kqbKUF7NGfvaZjN84s38S5Hqnr6XMkV0zgajDfq3n9KwmNwGpIgI+EML/f\nVd86hagNjobhzM9L+l06izkb/pWUMHjp+CUcaon73S6rEC4I+Pyw9hy5gMGRccyyxvMvhiIx1JUX\npDVOUoNNKFJ12uODyThJqLt9IdS6CxIbcybTVUlJFaT0PJGDoIvd1LPRRiXAJIHUergz+gbQ/qf4\nRSrN8cokBuEoiVVVs7De8ytWgpku+FKRibmW+v5Y5Tas6YgfmMxGAzYtLhV2+G87DPg88b9Xb578\n3NOI2obduLLiDpz91IAD9ntAOOuwlXbAZHt2qvVIXa+zLcTBmu8DAPyBcE4EX83UwDE1QHMh0CAZ\nbGaLruEA5hVbUWidJLHp+OJkyzTSPxJCw4UhVLtsWOS2IxKDYFNhKlcO6ruGC4MYHB1HgcWIuvIC\nFNvMaY2TVDMWZd492+2HNS+efUKvI+APRVDrLkhU1BLrYyW1Gg7VjwsDYwAAmyl+xg5GYpgzy8rZ\nn1wwoVFjGLM44RptxbHKbbign6s6PzW5wbUeKuYvzFrVMDGgm6R7/SFEYki4FJwqeyjha1xoNaRd\nAUqMKwjftdT3A/kLcLryS7ignytuTXBVdZtwLSiw2zH7kReweumSZFO9pxG1zf+OptECXHSsEvTs\nXFi/ALtco3yTgey1XQ1+uEIgtwsBQZIk/1U5ihUrVpAfffRRtpshGtmOaBQDtspUh1r6EI3G0O0L\nYnA0DIOOwGM3VeOOa8olP+Ol45dw6vJwQhu6ZfVcRceETbtM/V8O1wW6xjQYjqDF48fQWBhrFzgl\n9Y0yJzJTWXm8QTyzmds8S/XztMeXyD4RnPhb6y5I9JfeXrpWmU0jKuZarn6809KbSOdGkiT8oQhu\nri1J2R+1r5tU4wKwFwnRoC5wvcOba1041NInac6zPUPoXOC7VpE1wVdN641vA02vYbjqTvyH6x8z\nmoVASah5/Sq9X8kFgiA+JklyhWz30wisupDO5q8WvPVpJ5493IZwNAZnfl6cWOl0kvuQjTGRSgjF\ngK3ymclgkNQ3qQKMbrJv7RkBCIAkyURVM3o7hG4w6QhToYRaNRBZGpNtDAHk/JqfSWB7h/ubeuAN\nhOPptrom0m3pq9Q3XzOBNMrFqhlqJomZ2K/kgNwEVvOBVRmmQ0Li1p5RXF/lnLLQpfZByJjIfXrP\nhF8T5VKwYdHspOcA4t833e+WqdFNVdKV7uM3Fo4mgqbmFdumjKFQH8t0/GWpfpTaTWjtGUEoEgNJ\nkpjntGYkn6NoiCyNyTaGu+pbFQvu0iA/2N7h80cvwu0wY/Xp5zBv+AOYw170LP433jmvdq2jJChc\nrCBbUEOwFhdmqh+uRmBVBjUvEkCYwJW7D3z3U6JaTqYSYss1VvTAs2Ntg5hlNeJzC4pgNOh5x0Lu\n4J90hKkYQq0KUBWH0iiNqXRwlwblQc15eiEJvjlPl1tL9ZdQ1/J7vNV1L7DpFvXNc7mQw9pZNZPE\nbBVwyDY0AqsyqHmRCCWKcveB735KaK0zlZZEzrGSU6PLBSEHmHSFaaai6WXRfsmgbRIbsZ0JTEvN\nIA/S6TM15+tLHsUKMh97ydtx8kxvSgsIvcrXHWf/CfbxnonPr5U9JV3W3yNFXMcGgfb34p+pXEvL\nFd8BqI8kztQ0WrpsN0BDMm6tK4E3EIY3EEaMJBP/Zpb4a/F4sau+FU/sa8Su+la0eLyKt41OFHUE\nkfg3VbZTbB+Egu9+ncMB1jK36Wqwat0ObN9Yg2c2L8X2jcpUDJJ7rJQaC2DyAOMNhJMOMMy5RwlT\nh8UIjzcIh8WoOn9OoX3JBKg5cMK+AS8sfw0n7BvSmgPpIpNjkw05xtUOvj6nais15zvyFuDvx7fh\ngqE6yQLC1i9qrS7rehm2UDf8eSVomvMl2TTvaprjSa42dfelZbHIBNjG7lBLH26udalWrmViv1Ib\ntDRaKoOQalrZSpnBVWKVSrXE1wcAktLM8I1JrpTRY4PcpQ+VHAt6qi6+MriZroYjFmqonkMhG+Uv\nU0FsFSWpUFPqH3oFrI1tP8LSoUPw2ubhzEg+Vlc7BbXVZTfj0ytelBVasNbmwS2e55IqaTHHLqnK\nVyyII/P/J9oM1emv1YkSsAeu5GEsr1jx9ygIVEquG7YDK7+e9bRpfGn+uOTDaCiK7RtrVCvX1A6t\nEtcMAJ8JNVuBXmLM3cw+pOunSt2PMus8f/RiwiSW6/4/cprMlRwLylezzx/EyY5hmAw6zLIaMDAS\nStvnONNQm6+5mooQZMonl25CX3buZdmrWokBvQLW/KEPQAK4wViAX+urk9rqsBhR03sAazp245D7\nG9jfZE1qK18lLTq4CjykvVYntJ0L8wfRs+RpdfhWqyiwS8hepDb5oIEdmgtBDqJzOIBQJIIP2wdw\n8HQ3PmwfQCgSUXxxpWPuFup+kApcJjEAqjdZS4EU86qS5vvyQgv8wUhSGdzxKJlUBjdXQPWFjoz4\nnXoa43kyPY3KPieNNlBjc6xyG4ZNcxTzyaVM6KsvPYdFfX/G6kvPKVbdbVd9K77x4gl88bnj+OZv\nTkxZT1SfT5U9hAuzPoeLhZ/DUecDiT7TXXPWdMQJ4c2eX01pK/0+rcWbUlbSUmytrtoG1N2Hs/O2\nZOQ95hqE7EVZkw8aREHTwOYgTHoCx9sHYTcbYDfFy5E2tA9hdVWRos9Nx1FcjhNtKs3zdPP5SUdj\nrZQ2j9IYyV0GNxvImtZeZNotMRAcsMPTBmpsTtg34MzyTYqNjZTIfbEQWpo4SRta+2+TuXgnDud0\n6xMVbHfI/Y0pbRWrVVVkrU5oO5d7vPg4A+8x1yBkL8p1q95MgaaBzUGQmBD4NBATnysNqY7icpxo\nlQxQyhSEalXl0FjLDeoAU2TLw+BYGGajHsvnFqLYZs6KdiKdAKCsBZpNaMcEB7EI1NiKCtjhaUOm\nxoay6NSXPIrTrttQX/Ko7MFr1Drq9odgMupwG47iP/zfQlXP/qT1xNdnuvXpjGsTfrrkP3HMsn5K\nW9UUwKimtqgJQvYibexyA1olrhzEE/saYdAB7f1jGAlGYDMbUFVsRSQGVVXdoEOOalpyVELJZloZ\nMWMgtbJKJvqndGU0IX2YDhXrBGGiLCfq7kupsd1V3wrLQBM29rwAkMDxud+SrRKUknNK6fnKLE38\n075HURr1oEtXipevf1NUpSLVpKRSA3I4n+uMkR0qhFaJS0PCnLW6ajKSlEnssoFUAl6OPHXpmnWU\nKHggBmKC76Tkh81U/5TMOSi0D9OhYp0gCCyU0DkcwFcHXkkEIAWNDs7gITFQek4pHbxGrSOb2YBQ\nOIr/tD+Czb7f4JWCR4RZDWhErda9dHrNrXTA4oaSKwR/puZMnY7QCGwOQo3+OUI2unQ3q3QFT7ZJ\njxDfK2oTaO7y4spQAAtLbKh05gt6x5nsn1LEQ2gfZkyUsMDo7fJCC45GH4Al4gNIpAweEgM1ZQqQ\nAmZp4reJG/Bn++ewqMQOHX09cWkUmURNLZrHz/YB7/4QWP894OrN7Nco2VbGwSrbygGxUFPWDw3S\noRHYHIQaT5CZIk/pCJ5MkJ5UWgg+rSp9E6h1F8Bq1ONs9wjGwlEsdjt43/F0IHVC+6DminXZQJyo\njaF7/o+nHmqFkB0OiEkLpUYILk3MFdjG1IArGIQnCu/+EBi8gFD9D/CL7qXsWs902spHfhkHq2wo\nB3JF46tBOWgENkeR7glS7sWfC+RJLOkRO0Z8Wgg+zTlzE5jvsqFoIkUV5ceYDkHOBQjtgxqtENlE\nykPtvjjZwbs/FE1gqfdxquwhAPJpdjMJQbKSy1WDqQEX6NLBBdnk7vrvIVT/A/zB9siUwL2E1jOd\ntookv5mW/7mm8dWgDLRKXDMQSlTAyYVqWIVWA461DQCIVxDz0UgPs99SxoivuhNfxSW+SmdcbYrG\nonjr0240d3lx2uODQQcUWIwp+6dWCH1HaqteJRR8FYDSAWf1M6sT6PksroEtWSLqntT7GMsrxuWS\n9eiOOXJuTgmCvRRYdCd/hSih17FAVrlbsgTPjtyEbnMVd6WtNNoKRwXgvQyEA0BxDe89Mi3/1VRJ\nT4NwaJW4NKQNIeYesZqCXNCIiXG9kGISE6KFSKUN4tM+srVpcCSEZw+34foqpyS3A7VBzDvKpB+b\nHJqzrGmNrt4sWvNKQY3uSrkKNn/iETKM2Gv/B7jvH6aa6nnM+IpWTHMvBaxFcS2stYhXC5tp+Z8L\nFj8NykMjsDMQfItfykarho1OCMkQSnqkCMh0Tfh8mwBbm7p9QYSjsZRuB7kGtQVYyEU8KQKz0n8Y\na5p341jlNpywb1B9QJTa3keugs2fmASJq/rrgYaCqSSRx4xPyRuqsIKoSltCAryqNwAdx+N/eZBp\n+T8d3KU0pA+NwM5ASNH0UZ+nEkjZ3Ojk1m5JEZDpaiG4NgEgnuezucuLcz1+1JUXoNgWJ7KDo2E4\n8/OS7qNpIuSFXAEqimrMNKgebP7EI6EwCBBYQvmp0okljw8rvWLa+zU3osXjx1BnL9YucKLF4009\nN4X4uLYdBnye+F8BGvxMyv9csPhpUB5aJa4ZCHpVmRhJJv5NVZXJxYpXcleu4hsjNshRvYVZ6QxA\nosLS0jkOjAQjON42iF5/AN5AGAYdkdDK9vmDON4+gLc/60bH4JioylQauCHXeqAqAGW1Nr3Ayl4a\nOJDG+FEy5by+Cvuvegrn9VU4S1RBd98v4he88W3g3R/FiWXD7skAMg4NKSVvxiNRHGsbBAB8bkER\njAY9dxU2CkIqwomtGpdBaJWyNABaJa4Zi1TmdjkqXmUaUitXpYIa0rQw30WfP4jmLh/CURIbF5eg\npiQfh1r6EI3G0NozAhAASZJYVGqHTqfLSaGuhnGnQ671kNUKQJRmb2wQaH+PvbKXXHlD1ZIrVQkI\nrIzGBc65Td23al3c51TE2FHzc6X/cMKV4IR9g6rl9f9j792D47ruO8/v6Xv7hX7h1QRBiiAJUqBA\nUqIlUaHohyJbD2ujWY0TrxKnNlnPlKcUZzPyltZKdspTE1nZKmfGowkzVpLJaOPMOnKqPNbY8ari\nGsakZI+tiGZMPaiQhAmBpAiJAJsgQPQL3Y2+3Xf/aNzm7cbt+3514/epcsls3L597jnnnvM7vyex\nMaFKXIQtqJl7us08MzWfxezSCt6avYHheBi7N8UwHI9Y1m75wfev3e81nYjgvokw5rPl5uY0no7j\nmZfPoVqvtzx/tlRtmrn9IhRqtcOP6XHseh889ROXTMbj93fWqtmV41TtPhZy0jqKXqHbYhqtjmuK\n/L4GhX5yTSE2KiTAEuvwQ0CWXiSBZ3MijNxKw8x/6r0bTQ2kX4VuvejxxZ0cTWFssA+Hdg62aKAl\nM7dfhEI97fC6WpoSdr4Pnh2K9AhIFoUzXff5ofmctI6iV3jXWRnNMBbuaymYizCMX5QBBLkQEF2O\n3Ly7kC9jZqGIpcIqBuMhPPvY3q5fWPSandXM3O3/X/5vN02MekzxTriCED5CTQProevBxX/8e5R+\n/Gf4u8SnwEYPdJVQ4qlrygaD+toadrsQUBDXBmJqPosjx6bx9EunceTYdE8E+cgDbNKJCA6PD+F/\nun0zxgb7fLmgGB0DvcEKakFnfgnK09MOKdBJDmmTeojbH/9w9OIAACAASURBVAe+8Lay9lXSgp78\n85ufuRB0NjWfxfPnYnhp25cgbr6jaRlwZX208nxr353EexTQ5BJ2BwsT1iAXgg2CX8zIdtNN+QDN\njoEes7OamdsvfaSnHd3mf+0r9Gow/Rpk1e56MH8aeOmzQPZK499OmO7hsduKFb9j2XcnP/Wf9LW1\nw9j71Szut3ZRAQV/QRrYDUKvnhzNpLvyCqfHoD0Fl7TQ+6WP9LRDnhro+FQG/3BpCX1B/yxTvrZi\nKGkwlfjhV4DT/7XxXyW8SrXVnjbq5J83hNfUVkdTOXlqobCSqsrMdxXmiHSwzpaqLQdrr+e2H9tF\nFiJ/QRrYDYKTJ0cvT8ndFHDm1endL31kpB2lah2Hdg41tbB+sBb43ophKAhLJfbBiFbQSW2uhch8\nIyhZBmJLZ/HPF78DzCuUeLUTK0FhZr6rMEf8GDip1q4XT1xGOhHxZL8hC5G/IAF2g+CUGdkPm7of\n0l3pwUtTvl/6SE87um1D9bpdTfQKNB//0s1co0qsfX5x12/g/zs2rS4o2JV6S4Ep7MDR2FO48loJ\nW/unHRNUlISSD8//V0wWfqhc4tVO3HbnUJgjfjKLy5Uh5+ZyOLAtCeDmelmuCnj9whI+cdsmT/Yb\nvygDiAb+sc0RjuKUGblXXROcwC+mfL/jl6CzdvzaLsNoVHjC6AFMHfq3eP5cTNt861C1JulgHF08\ng3927d8hunjGMfOxUqBk+sEvIHD7p+15LjWXDC23DxfcOfxiFm93GQhyDCcv3sD1Qll2TR4Dfd7u\nN51ctfTiazekLoM0sF2GWXO9UydHP53e/Q6d3vXhl6AzoPV9m11aQVWoYcdw3PN2OY10ML0n/yo+\nfPZmdad12mYb86K29/VoMoyHMn+JHct/j2g1h6vj/66jttuqG9N6y8AEcPtHbHgqqGuptdw+tDTc\nNmhw/WIWb7dw7NuSxMmLSzhzJYf7JsLIlwXcWKniI7sHW77XTfuNHyyWvQQJsF2E1cnvhBnZT8JG\nN+AXU76fsbKh2umP3f6+rVZreHN2GQAwNhTzjf+bEz7obld3kvp6j3gR/2zxv+FPig/i5yu7sFqv\ngQEAa5iPT15aXPecTgoFRvtW8Xo1IVXrAGBVwNWBXw7WSlUH79k5gNMfZDGfLWNrfxQf2z2EIM+1\nfE/ab/yWsUAJ37shdRncl7/8Za/b4BgvvPDCl5944gmvm2Eb3/zpLIDGpGeMIRJsvMgf3Cjh8K4h\nR35zaj6Lb/50Ft958wOcncuhv49HOnFzkenv4/H6hUUAQIgPICfb1OXXEYRe0okItg9F8cGNEuay\nZaQTYV0bqiTIAMBQPIRcWcDrFxaxfShqai62v28DsRAifABX8xVwAaa7XU6i9szXCxXld3f+NPDK\ns0BqG5DYrHjfs3M55MoC6tEhpIvTeH3s87jEbUc6EUZ/H6+6JphB6utffv/f49alVzEauIEfBn8R\nV0Nj2Bpj+MHgr+PV9wOIhjjs35pqeU7JfHxP/lU89vPfRT06hMXYbsvrotH51PH67TuRvufTHfta\nEWmMbjkI3PO5xneVxi21DagWGwKukfu3kU5EcHjXEB7etxmHdw15snZLc07a1wCgVgf2bUnhmf95\nHw7vGsKW/ojifnPPjn689EYj3Zod775TfOfNDzAUD4HJCrWE+ADmsmU8vM/8+HULzz777PyXv/zl\nF+y6H2lguwinzfXtJ9iJkRiOTy2oajb8cnr3M2Y1A92gUXAKM5pqu7UbSu/b9uEYQkHON1XB1CK1\nS9W68rurQ2snacF/lvgEfn73w01t8z07+u3RdraZvpt9zQAGIBbiABE4VdmGTbf/Pn48vQARAn49\nehKffOMvW1wanNIW63aj0HM93jNm6lcaI6XPnCpt6wF6LC+d9ptu0WySxdJeSIDtIpyc/EpmuOdf\nuYA9m+Oai4KfzOJ2CH1OmqH1bvh6vrfRBFy1552az+LYuQzqYh2paAi7N8UwHI9YOuBt7Y/ivesF\nXM1VkCtXkYwEsTkZbvGB9QqpL7739hWMJMK4dSSO4XhD2E5EeByfWsShnUPK724ns7RMqJwcPYAn\n9xab5VVTowdaBIXdtYu4891v4a0tn8FMdNy4oNAmjElr24mx30KZT+GtLZ/BRCmOq/kK5rNlVGsi\nDo0P4JPn/3KdkCp99/Wxz+PDsw3B0ci62GleGRWMVa83aupXGiNDadK6D73KEKX95uuvvddy2FzI\nlzFzrYBMvgIAnqyNSvPKL/7GvQK5EHQRTprrldwTzl/No1YXsW2wr3mdn80ddpiQnTZD63X7aP9e\nvlzFdCaP/34mg2v5CpaK5a4wmdmFlqn8hR9fQq5UBc8YhLqI92+U0N/HQ6gD6UTYlClZ6uO6CCTC\nHHJlAbNLK/inHxrFxEjS7kfUjbwvSqsCcmUB89ky+vt49IV45MoC5pbLuG00oWyqPHQAuO2frDc5\nv/JsQ8iqFoHb/gkGTvwhNs1+H4e3RXD40c8inYg0TaAfu/ynmLj+A4RqJbw/8nHja0Kb6Vta21ZC\nw3h/5OO4Wk9htSbi6Ycn8L8e2o5r+QqEOhRdGh7ZP4LXLyxiMbYb58Z+HZe47brXRbV5dS1X6ehG\noTSf1NwuDt/1oVZTv5YbR2Lz+jFS+qzHMOvKIHc/WMiX8ebsMipCHUOxEJLRoOtrY6d5dff2Ady9\nfcCwe1SvQC4EGxgnzfVK5tLBWBCLxdWWz+zU+NqtPZSbkRbyZcwsFLFUWMUzL5/Ds4/t1XV/N8zQ\nerSC8u9JC3KYY6iLdUPa8V5BbVykf+/bkmz0E88Q4hjOXMlhPB03rd2YzhRx57Z+XM1XUCgLSEaD\nmNgUx3SmiEfteSxTyPti96Z4M7Ds3UwBQY5DtlTFndtSyJcFY9aadg2fgsZP0na+teUzAIC3tnzG\n3JrQZvqW1rYXT1zG8alFMDDcue3mPO7k0iCtf2bXRaV5tVSo4JmXzyEZ4fHBjRJujHxk3W8qodZG\njKZaNa8O5s/tRbT2C7lmc+Zaofn5rSNxT9ZGtfXKTOotQhkSYLsMp8z1Su4Jm5ORppbXTnOHU1HD\nktDXFPj4AAb6eCyuaej03N9uP2Ozbh/y780sFBHmGymbU9EQUtEghLqI+Wy5xZzt13QydhxW5GM7\ns1BEoSwgHuaQjAbR3xfCaCqCAAvirrF+zCwUkS9VASZamlNXlkvYPhzDzvTNPq6Loud9LJ+j6USk\n8cxr5tLD0WDz/TRsqmz3p1Twr5QEhZnoODK3/r7tJlC1CmxqQmrHdXH+NJZ/+DX8bfRTuJYv45P5\n7yF63/+O8bUUWS19WTiPydm/xkz1k1hkO3Fo5yD6ghzOXy1gpVrD3tGUqmBsSJD2uTuA3QoGK/fT\ns1/I+z6Tryi61bj53lJ6SXcgAbaHMbJoKPnmcFwAT35iF6YzxZYFGQCOaFXoUcEph3tJ6JMEvkiQ\nQ7law3A8jFqtjmdePoexwT7VNkv3WBVqTUEpyDHs22LOZGzW50n+vXypiiDHsFoTsX9rox1Oaced\n2LjsOKxs7Y/i0kIB09cKCPMBxNdM+rmygJFEuKltTCciSCciTeHfjvnkt4CL9nalExGEeA6Ho0E8\n9dBE8zonrDVOWoG01gUzh/flH34Nfe++jH0DK/gQxzBx/RjePC6gMrwfk6Oplr68c+5buPXGK/in\noVXEk7+MR979f/DWls9gcNc4Um192wndbfRx8JXdCgar99O7X8j73uv3Vm3t2GixC05Clbh6lPaq\nJh0r6ayhVI3mift24tE7trZUHQFg6L5KOFXRSKp0tVRYRYhjKFdra35QQUxnClgsVDTb/Mj+EVxe\nLOLkxSVUVgXwgcbCk8lVTFVMkffr1HwO5+ZzKFSqOHom0/F+0gKXL1dxbj6HslADYwx3b+9vahQ2\nJyMIcgFbq3oZnTN6MFqprVOVmkf2j2A60zANhvkAKkIdALBnJA4RcKTC2cRIDD+9uIi/fWcOJy5c\nx3vXC76onKa3opvVikGdcOq+0rqQLpzHw9PPIl04b3ld+Nvop3B24EHMDX0YUSGH2dQhvDX6a835\nJ+/LN0d/Dce5j+C/VD+Jjy9/F7cu/ACTs3+94TRndldXtHo/o/uFHyoedmrDxEjM9jV2I0Ma2B7F\njJbTrTr1Tmm2JGHxmZfPYbFQwXA8jP1bk3j3WhFgwHA83FxAO7V5cjSFLakIloqrWK3VkYwEcfst\nKQQ5zrSGWPrO7NIKbhnoQyLCd9RCyLUVk6NJ5MsCLoc4BBhDkONQF8WO2vF7dvTj6JkMvv7ae77R\njBsxpX3/nSt4/tULqNbqiPAB/OMHy/ibt67gY7uH8BuHt2PbUBTZlSryFQHJSBD7tyYxGAtjPlu2\nXSs4NZ/F8akFTGyK42qujKViFbmSgCcf2OW5tqRXU9dJ68Ijc9/CxPUfAABe2vYlS+vC28IYMrd9\nGY+8+wcYWz6J6eGHURzch/m1+Sfvy/8xN4q38DtIxni8Go4iWAjgu9VPgl8sYsdwfMNozuw2f1u9\nn9H9wg/vR7en++oWSIDtUZzywbHjvnrN6mY2jMnRFJ59bG9TCExEeCwVboALALs3xXS1+VqhgjAf\nwGqtbvoZ29G7cCldt30ohlWhhlQ0uG5BloKJ7DD7OTFn9G4+U/NZPP/KBYABET6AK9kyIAKbEiGc\nmcvhhR9fwqZ4GFv7+1rulS1VsbU/artvuHwcJB/YbKnqaACXkflu9Hm7QfiS1oWfDH0aIkS8NvRp\ny/610vx7tf+Xca1QwXfKD+H96YUWlyCpL48cm0ZfkMP0tQLOs3G8N/BF5MoCkCng4X0jG6YEqN0K\nBqv3M+OGpff90PNeWCnfrpXuS3qmjaThtxNyIehRtvZHkS8LLZ/ZoeW0476d3BWUNJFmTC3t9x+M\nh3Db5kTT/K7W5qn5LN5fLCFXFpAI8yhXa3jj8jJmF4uW+k6vGazTdas1UdVsa4fZz4k5o9ecd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uoSrUDGlJzPpDatHJvyzMMd+WfwWM+7y56SOn14fQ7tKpWr6CnebQo3dsNTy3nCz7aqdPqp/L\nbxr1E5UCuCY2xTEUC2GpWMXVbBl7RmIYjt88bPbaQVMaw6XCKkIcQ7laQ0WoY/emGBIRHiJEXWvV\n1HwWX/ruO/itF9/Ej85fQ5CDJf9qgpAwqoF9DsBxURR/wBj7UqeLGGO7AfwrAB8GYNg5UhTF/9jh\nT6G1/94wek/COnb4aJk5hatpc+Qm1cnRJPqCHM5fLWClWkM6HkaAMWxKRbFrJGE6UboTZr1OmrRV\nodZso9epcNrHamIkpitqXv59N33k9M5Pu7WDei0EZnPBOtn29rbY5YNp5F5qRUj8oLGTj+/OdBwA\n8KPz1zCXrWBi883r/HTQtANpDJ95+RwWCxUMx8NN16xsqYo7t/VrrlXSGnBxoYCBaEPceGs2i7u3\n99uSvYbY2OgWYBlj9wN4DMBeHZe/AOCPRVE8xxizFt3TykEASwB+ZOM9CQNYEebMCjRqZvx24WFn\nOo7BNRMXAIR4zjEfLSsbbydXgfms4Fkgh7zdIY4hk6tg22Bfc6yef+UC9myO6+5PL/x59cxPu91C\n3HT7cDrXp52HNT33al8TLi0U8NS3rqK/j8dyScCekTjGhmKeBggpje/kaAKvX1gy7ZrULUyOpvDZ\nw2N4/tULuF6oQBRFFMoCAoEAnrhvJwCorlXSGlCtiYiHOTDWCLKduVbEL+wc7CmNNeE+ugRYxlgE\nDaH0aVEUr2tc+zkAowC+Yr15Lfc9COBeAF8QRbHjrGeMPQHgCQAYGxuzswmERYwKNHKBKhoMoCrU\nMJ8VWhZKpRrlcuHBKcFCq0SqlpCupknzIpCj/Xl+PL2AfFnA5lQYAdYYM6EuYj5bxo7hePN7av3p\nV39eu6O93fT39FukulXaI92nrxUABswulZCMBnE+U0A8wjdN9XrytdqttVUa30iQx0d3DyEVDfp2\nHOzoC7n7xNVcGUvFKnIlAU8+sKvFutAJaQ2IR3hUqjVEghzCfAC5ctVzjbVfNPyEefRqYH8fwGVR\nFP9K7aK19FdfBfApURRXrTZOdl9JgP4+gD9Ru1YUWq2+3gAAIABJREFUxRfWrsXBgwcp3ZaPMCLQ\ntAtUkobDiCAIrM84YNei2UkY/8aJWewdTWoK6Xo1aXZtQlr3aH8eSWMyc63YFB6kjA5y1PpTr2Dn\nxUZi5yHBqFbU6vP6KVLdKvI1QQoWCvMBzGdLuGUgiopQb85BrcOPUy4rncbXz+mi7OoLJfeJbKmK\n6UwRj+r4vrQG7E7H8ObsMgBAFEWEuICnGuuNUqCm19EM4mKM3QHgdwD8lo77fQ3AS6Io/sRqw2S/\nzwD8FYA6gF+jHLDdi5FiBXoLCKgFizgZSNIpmCaTK6/7vFwV8INzV1sCmfQEh9mRWF7vPdqfJx7h\nwQDkyjd9hjcnIwiubTx6+lNP/9uZPN8rjAT69cLz2ol8TSiUBYT5ACpCHfEwj4pQb2rrAO3Dp1NF\nR5wK5HQSu/rCatCgPPD3Q9sa/bVcErB/S9LTPvSiQA1hP3o0sNJB63XJf2WNQQBgjF1d+/dzAH4J\nQJkx9inZdVLg1dOMsc8DgCiKMtd3Tf4MDb/b+0VRLBr4HuEzjGiqjOSRVDOpOmVu7aRdbM/tqZUv\nUq0tdviQ6r1H+/PsTsdw8uIS4hEedbERbcxxATz5iV2YzhRxdi67VmGKby767cL30TMZ5MtVXFku\nIRXlsXc01dFHrttzSerVivbK89qFfE2Ihznk1oTZA7ekcPH6CipCHckI3zz8qGnsnHRZ6Tatt119\nYWd520JFwP17NtluYTFj0fCrexNhDE0BVhTFPwTwh+2fM8Z+BOAX24TR5xSuux+NwgTP6a3EJfvu\nfwDwAID7JN9bxtiWtXbNGbkX4T1G/PeMLJxqm4tTG08nYbw9t+fZuZxivsgXT1xGOhFpWXSB1oCI\nc/NZ3La5tV6I0UVWa6GWFv+zc1l8cKOEzYkQVqp1LBWrqNXr2NofwXy23DJW4/NZzC6t4JaBRg34\ndqG8PTOE1DdGgtm83kiccmvw6/N6hXxNSEaDyJVvBm7xgQCmMwWk+hoCv9bh06+5Z81iZQ7a1Rd2\nBA06KfybdQXotbmyUTFbics2GGMDAKqiKBbaPv8DAL+ChvB6VfanJ9b++2V3WkjYid7FzOloa6uo\nCePj6Xjz82pNxKHxgZZ8keWqgNcvLOETt21qLrpfPXoeAcZaov7fXyyhL8i1BE3lywJCHMORY9O6\nNjYjKciqQg1vzmaR6uMxmopiNJVAIBBYl5RfS4toRMsob5+8XOVgPNR0tXAbrU3RD4JFLyFfE+R9\nuzMdx29/fJfuvvX7mmEEqz6aVvqifX4/OJnGdKboarCa3nfMrEWjl+bKRsZTAZYxtgPAOQA5xtgu\nyUWAMfa7AP4NgBcBfK7NdeF+UBqtnqcboq315PY8cmy6mStREtDevZpHkGeo1mrNCP+lteCo/Vsb\n30tFg5gYieN8poCBWLi5yF5eLCLAGEI8p2tjM5KCrCSI2DIQRSoaxL1rpXezpeq6zUBLi2hEyyi1\nb6lQwXSmEYHOBYDRZNjRoAq1DVJtUwSs1X+njVMdK9o6N9cMpwMPrbqamO0LJcH5+NSCq/6qasI7\nYI+Vqhv2F0IbM6VkTwDYifU+sHeKojgvu+7TAP4U631g/1oUxS+ufbYC4BqARQDyDPP/eu2/v9mh\nGT8y2m6i++g2vzMllAS0migiHQ3hjcvLuHt7P4bjEawKdYhojU/cPhzDSrXWkqpnSyqCoIHctmoL\ndXsKskJZQCLMtQRuKW0GWlpEo+4fUrL0ar2O4XgYuzfFmsnSnfAN1dJuqQngXgkWep5Jr0Blt/Dl\nVhYJPb/jxprhRgS7Ha4mZvrCDR9trXHs1IYXT1xGqVpv6fdOVio9Fo1e2F82OoYFWFEUD+u87jsA\nvqNxzTUAOxQ+7zfaLoLwI0oCWpBjCMgSeg/HIwjx6xOC5MsC9m1plL2UePql0xiMG4sKlmsWJSEM\nAMIcw4+nF7BaqyMZCYILAPlKTVPw1NIiGtUyTo6mMDbYh0M7B5v9ovZcVgUmrU1aTQD3SrBQw4hA\nZbfw5VY6Ij+lPXJDyPPK1cRpH20949ipDcenFnFo51BLvytZqciisXHQTKNFEIQ1JAHtl24fxb3j\nQ7h9awoVoQ6IIrKlVWRLVQzGQhiOhzVTVBlJRSahlLrpq0fP491rBRTKAkIBhtKqgKXCKpZXVrE5\nGVZtg1ZaITNphzo9l+TvK6Ug+/47VyynodJKDaSW/stM/zuNkZRAdqcPcisdkdHfmZrPtswbO9OU\nWU0tpQcnUwCq4fT81jOOndrAwNb1+/bhGG5Zc3vqlhRnhH14HsRFEHo1au2lThmASk3siioqco1K\nOhHBXWP9ODuXQ4AFkIoG8XuP7AGgXpYRMOdDqaQxknxuD40PYmahiEJZwHAijC2pCHYMxzXN21pa\nRD1aRqXStRjsU/X3NVrOVgkt7ZaWmd9vPqxGtGZ2a9jcyqpgpQiK3dpaN7SjXvnzKr2Hds5vPePY\naY27c1uqJUUhoGylIjYOJMASnqJ3s5FfxweAkxeXIAI4ND7giDnRipla6bvti3KI5zCejq9rs9Zv\nmNnYlDYNyec2nYggnWj8rS42SsW6sRkoVVqri2JLuWAlf1+j5WyVUNogLy8WsSUVwdMvnW6OmVI/\n+DH4w4hAZbfwZcf99LxrRn7HaRO/W4F47YdASatsp6+xnvfQzvmtZxw7vWOA/w6PhLeQAEt4irTZ\nrAo1nLyUQ6EsIMgxvHjiMr7yK3c0N7cfnLuKEBfA/q1JXLy+gviaKeniwkozYt6uDcqKBkftu3YJ\nPkZ9KJU2jU4+t26ZwpWEjO1DMaSiQTz10ASm5rP44rffQV2sIxUNNQO7jJazVWJyNIUHJ9P4xolZ\nZHJlxMM8YiEOQZ7DYHx9Xlu/Y0SgMiN8qQmYEyMxPP/qBVRrdQzFQhhNRRAIBHQLFXrfNSeKoJjF\nzkOMEeuTE1plrffQbvSOY6c1zm+HR8JbSIAlPEFauL/39hXEQxxWVmtIRoOIhzlUqjW8NrOI779z\nBcenFhqLq9ioof3G5WWsCjUMx8MAbpY6tXOD0kqjZCaC9uiZDJ56aMKTxVZp0xiMhRBgDNlS1RZB\nxihqQoa0WQc5BogBlKu1ZsaGzckIcmttNauFmZrP4vjUAvaOJnFo5yB+PL2A64XVlrRmgPKByE/B\nRBJGBCq1a5XGF+icNgwAjk8tYGJTHFdzZSwVq8iVBDz5gP7crXq1pUae0S0Tv93aT7W55JRWWW+h\nE7u0vvJx1Krm1+n7JLASEiTAEq4jX7hHEmFcWCiiVhcRj/BgjAGMYaAviG+cmMXe0SRS0SAS0SAq\n1RrCPEO+XG0EQQFIRhoLuZ0bVKdF/excowKVmQhaLystKW3+en1uJewW3NSEDGmz3rcliTdnlxHm\nGUIcw5krOYyn481ytma1MO3CQLUmIh7mmhkhgM5j5tdSsEY2dqVrO41vXzCgepiT/rYz3XDpyJaq\nmM4Um/XHtWh/XxbyZcxcKyCTrwBAi8Ck9xntNPE7mSLMyFxyal3RW+jEzsOa9F21an4EoQcSYAnX\nkS/cuzfFcT6TB8cYFgurCCQYKkIdd46l8PqFJRzaOQgA2J2ONYQZjiEYYCiUBYgA9m5J6KqTboRO\ni3quLOCWgT7VDcevlZY6bf52a8r0oiZkSPlpAyyIu8b6MbNQRL5UBZjY3OD0CkhKtAsD8QiPyqrQ\nkv+205j58YBiB53G9x8uLeGByU0t18qf12pftFdie3N2GQAwkgibFmrsMvE7rW03MpekfloVas2g\nyyDHsG9Lct21RjBS6MTqOy8/DMwurWA0GfbdQZDoLiiNFuE68jQ06UQE2wb6wHMMxVUBkSCHu7f3\nI8zzGElGmulUpMh9MIa+cBCHxgdxeHwQ1RpsT53SKYVNKsprps/xKv2N09idOkgt1ZY8jU46EcHh\n8SEcGh/Cw3s32xZFLk/TszsdQ6FSQ4gLaI6ZH9No2UGn8RUhdnxeO/pC/r7MXLtZTfzWkbjplFx2\naU2dThFmpP8e2T+Cy4tFnLy4hMqqAD7QuDaTq1hKEab2Hpp955VSmLWn8lsqrOLnV/O4XigbujdB\nyCENLOE67VrKO25J4eTFGkZTPH5h52BTC/DZw2M4PrUAQD1y3yhaG1wnDc7RMxnTEbTdolXo1DdO\naJY7aYWdiPJWSxUU4jmMDfVhSyqC+WxZdcx6tRRsp/G9c1t/sxSy0vNa7Qv5+5LJVzCSCOPWkbim\nK0cntLSmRoRbSUMqlYAulAXEwxySsj6ygpG5NDmawpZUBEvF1WbhkdtvSSHIcZa1lp3eQzPvvF5X\nlMF4CLlStcVtpxcOgoS7kABLuI5SSqlOAsR4Om6rMKjXLNhpUbcSQdveDjfKbxpBrW/cFNzsPgS0\np2A7O5fD1VwZ13JlbO6PYO9oCr/3yB5d9+/2A0onOo2vUv15+fPa0Rfy98XqIUkrANOIS8DW/igu\nLRQwfa2AMB9APMwht+ZKNDWftTzmRudSpSbivol0S7W6uig6prW0K+c0sN4VZXc6hjcvL+N6oYK6\nKPbMQZBwFyaKovZVXcrBgwfFU6dOed0MQgGvBLgjx6bXbZLSv/WkjbGj3XKBql1Y8FIQ0uobPwrd\nepCea1WorQWFBSCKIhhjtmj0ewWvx9eO9+Lpl06v+U+3CnnSwdjIuz81n8VT3zoNMCAZ4VER6qgI\ndewZiWPHcNz15PlW1y4zGJ0Tnfr/+FSmpQwsAFxaKOBqvoKxwb6uWk8I8zDG3hBF8aBd9yMNLOEJ\nXqVDsRqEY0e7rQZHOCVoaPVNt6awkZ7r5KUcwnwAkSAHURSRrwhNn8ZufC678Xp87dBuq5m9jb77\nk6MpbBuKIrtSRb4iIBkJYv/WJAZjYU98Nb1wXzE6J4y4onBcAM8+tpfePcI0JMASGwo/ZAmwIkQ7\nGRmtp2+81tKZQXouyYcRACpCw4+QAkca+GVcrQrRWlH1Rt/9vaMpRa2nF76a3eC+YtYVhSDMQFkI\niA2FH7IEWInedjIyWqtv2iOJJeHZShS0G0jPFeQYKtUaytUaKkIduzfFKHAE3TuuSqhF1Zt59/2w\nXsiZHE3hqYcm8NzjBzwrjKKGWv/7ve1E90EaWGJD4QcthqSlWCpUmhWM+ADDkw/sarlOSSvmZB7S\nydHWEqsjyQg+e3is2Td+TeKvhTTmL564jNdmFjHQF8SdY40Ibgoc0TeuftHQ6kEt57HRd98P64Xf\nUZobbvsHExsTCuIiCA/4/jtXFGvIy9P9KAW09AUDCPKcI4EcWkE0agEyzz1+wNJvu0U3CWKdsPsZ\ntMbVr0GHhPd4OTd64V3eaFAQF0H0ANOZIu4dH1oniEpaL0krtirUcPJSrll5Z0sqgkio8dq2+/hZ\nXdC1NHF+8B+2iteBSlZxwgdaa1y7VfNOOI9Xc8PpKmlEd0A+sAThAVpVbq4sl1CuCnhzdhmVaq0R\nfCSKODufx4OT6XU+ZgAs+zFqtclv/oAbESd8oLXG1e4qbETv4NXccLpKGtEdkAaWIFyivRZ4Vahh\nx3C8+Xe51mtrfxQ/On+tmfYJAMAYBvqCmM4U17kLHDk2bVkToqWJI39A73HCB1prXJ3WvJMp2Bpe\n9p9XVhknYwGI7oEEWKIr6bZNr93ktVptJNUHgLGh2Lqcjo/sH8HfvHUF/VEeoig2k6jfOZZSXKTl\nC7pU+jJfqgIMuvtGT57JbjfBdztKAsPl60VczVfw9EunTb8LauPqZP5RMgVbw+v+86q0ci+4MxHW\nIRcCouvoxrQ/7Savnek47tzWj/lcZV26GaAhUHxs9xAYY8hXBESCHO7e3o8wzysu0lJqroV8uel2\nEOQYQlxAd9+opcAh/EG7uf/SQgFvvb+M0WTY8LswNZ/FkWPTePql0zhybLr5nfbPATg2L3rJFNyp\nP53E6/7zas0gdyYCIA0s0YV0Y1CJkslr+3AMoSDXMYL/w7uH8MbsMqq1OkRRRKEsIBCoK2o3JE3I\nxYUCwlwjmny1JuLu7Y10UXr7hjSs9qDXQmDUktBu7r+ar+Cusf6mK4red6GT5u7ByTSOTy0oavTs\nSo0kf+Zzczkc2JYEcFOTpmYK9qvlxStNqB9M6V6sGeTORAAkwBJdiJOLtlMbpFGT19R8FsenFjCx\nKd7MFZsrCXjygV2qOS6/+O13UBfrSEVD2L81ieF4BHVRJN8wF9ErzJgVeuQCg5QCS46ed6HTIfAb\nJ2axdzTp2OGw/ZnfzeRx8uINHN7FMBxvPEen98IrIVHPmuDVoXojm9LpsE2QCwHRdVipZKWGk64J\nRk1e0oa4Mx3H4V3DePSOUdy7awjTmWLH35gcTeGhvSO4d3wY944PaQoEhDPoNevaYf41+y50ih7P\n5MqORpW3P/O+LUkwAGeu5DTfCy/M5XrXBK+i8cmUTmxkSANLdB16AweMalOd1KLoNXlJbf7e21cw\nkgjj1pF4UxDVsyF6FVThJn41I0votRDYYUkwO96dNHcjyQjyZcG0Rk9rbNqfOZ2I4J6dAzj9QRbz\n2bKqKdhof9kxT/SuCV5pQsmUTmxkSIAlug49i7bc3MgHgB+dv4a/eesKPrZ7CL9xeDsAuFqmVWq3\nXr/EkUQYubKANy4v4+7t/RiOR1RNq/JneXAyjelMsSc3NK+jrvWgV5ixQ+gxK8B0Enw/e3gMx6cW\n1n2u5wCkZ2yUnjkS5HHntn6kExFcWS41Nartz2Ckv+yaJ3rXBKczNagJ4mRKJzYqJMASXYnWoi2v\nZPX2+1mE+QD6ozzOzOXw1aPnEWAM2wb7Wja3vmDAkvbJKnJtz+5N8WaarXczBQQ5rqOWuX2jPj61\n4CuBzk66IYBPrzBjVOjpJMiYEWDUBN/xdNyURu+bJy7j4kIBq7U6kpEgdm+KNU380veVnvnyYhEB\nxhDiOVVh00h/2TVP9ArNTmlCu+HARhBeQQIs0ZNImpOTl3LNYgCiKCJfEbBUXAUA7N/a2ACkzWlV\nqCFbqgLwxvwu1/akExHcNdaPmWsFZPIVHI4GFTfEbhDo7MQPUdda6BVmjAg9TggynQRfMwLx1HwW\nP5lZRH+URyLMo1yt4Y3Ly2t5i2/66Co985ZUBEGe05zDRvrLrnliRGju1G9WXBk22vtNEEYgAZbo\nSSTNSaEsNMqwAqgIDc1QrlSFCLHl+kSEx3xW8NSfrF3bk05EEOI5HI4GO6Yw6gaBzk66JeparxCo\n9zq/CzJHz2Qw0NdoE2OsWT3u3Fwe9+/Z1HJt+zM//dJpDMb1BUDp7S+75olVzarRg0e7sHtuPovb\nNidbrunl95sgjEACLNGTSJqTIMdQqdYAxlAR6ti/NYkzV3Lrrpc2Ny/9ycz40XWLQGcXGyFITQm/\nH1SuLJcwOZrA2+83ovPDfAAQRdwoCZoR8U7MYT3zRK9m1MqaYOTgoSTsvr9YQl+Q61hy2g/4PaiS\n6F1IgCV6Eklz8uKJy3htZhEDfUHcOdZI6j8YCyHAGC4tFJo5VvkAw5MP7PJFm6XNIMQx9AUD+Ppr\n73XcGPwu0Nm9uW3UqGsrQp6eMTAyTkrXSu27a6wfMwtFFMoCghzDR3cPmQ4oszKHteaJW76lRg4e\nSsLuxEgc5zMFDMTCvn2/yUeX8AomiqL2VV3KwYMHxVOnTnndDMJjlDbciwsFPP/qBVRrdQzFQhhN\nRRAIBHyz8Mo3BvnGpdQ+v2pAjDwDoY7ZvtTzPaNzTenaByfT+G9vXMFScRWrQh0hPoDBWAi/98ge\nXWPt9hw+cmx63YFA+rddFceM/o5UlCLAWPOzuihiaj6HfVtSvnu/Aff6kegNGGNviKJ40K77kQaW\n6HmUTIBHz2Rw7/jQuoXXTz6Fek2Pfk2j45Xfppow5FdhXwuzmmc9Y2BknDpd+/cziwgwhpXVRpBk\nXQRypSouLhRs9QW2C7dcMoxolztp2fdtSflWGPS7awvR25AAS2xI/L7w+r19evDiGdRMmgC62txp\nRsjTMwZGxqnTtcenFnFrOg4uEMAtA30I8wHkygKef/UCxtNx3/WvW77jRg4efncHUsJMP3brIZLw\nHyTAEhsSvwc/+b19evDiGdS0idK/O2kae3Fj1TMGRsap07UMDFdz5WbKOgBIRngsFlctadydGhM3\nhUUjGSm6zb/bTC7jbj5EEv4i4HUDCMIL/F5D3M72Tc1nceTYNJ5+6TSOHJteV8fdKbzoY7Wa9Gp/\n01vzvhNe9bEWesbAyDh1uvbObSksFauN7ANrVISGf7lZjbvVMVFDEhZT0SDms2WkokFLQpRd4z85\n2nAXeO7xA3jqoQnfC3VG+1F+wAww1vz/0gGTIIxAQVzEhsXvGjc72ud1IJWfgnPa/z+ARiaKfAX5\nchUhLoD9W5MYjkdartXyP/S6j7VwIwsBADz1rdMAa2heK0IdFaGOPSNx7BiOm/Lh7JYAIb+Pv5/o\nFKg2ny3juccPeNgywg0oiIsgbMKvwU8SdrTP6wT4Zqs6mRV6tUya8r9dvl7EW+8v466xfuRLVYii\niDcuL+Pu7f0Yjkd0++t63cda6BkDI+PU6donH9iFf/9308jkyggwYDgWRqFS06VxVxrzbvED9/v4\n+4mt/VG8d72Aq7kKcuUqkpEgNifDLXluCUIv5EJAED2MmtlcCy/M4lbNxmomzfa/Xc1XcNdYP3YM\nx5GIBsEYQ5gPYOZaEYB+f10rfdxLjKfj2Dkcw9hgHzYlIoiEONR1WPg6jXmYY8iXhZZr/egHTuOv\nn4mRGN6cXUa2VEU8xCFbquLN2WVMjMS8bhrRhZAGliB6mK390aaZvFAWEI/w2JwIY2daXePhVbCF\nHdosNW2i/G+SORMAdqcbG2uYY8iWVpu+nXqCenoh4M4Ojp7JYPtQDHfc0t/8TE9quk5jvirUkC1V\nAfg7Kp/GXz/TmSLu3NbfXI+S0SAmNsUxnSniUa8bR3QdJMASRA8zMRLDd9/8ALEwj0SYQ65Uxfxy\nCZ9sM+u2m3Cv58uemEXdNBvLBY90IoK7xvpxdi6HAAsgFQ3qjgDvxvRHTiAfu4V8GTMLReRLVYBB\n1Q2k05jPZ4WuiMqn8dfPleUStg/HWg7QdVEkbTVhChJgCcJDnA5yms4UcddYf9PnLBUNYs9IQ+Mx\nvvbbZ+ey+OBGCXtG4hgbiiFbquInM4v4yO5BADe1Sm6YRd3UZrULHiGew3g6bljL3I3pj5xAGrtV\nodbQZvMBBDkGxpiq9l5tzP3upw7Q+BuBtNWEnZAASxAmsDtDgFNm+ivLJYwNxVqCJOqiiLNzWcwu\nrSAVDSK3ZqY9nykgHuExHI9goC+Ic3N5bNpzc2NxY6NxOz+nXYJHNwhaTiON3cWFAsJcI8p8tSbi\n7u0pBDmuo/a+FzSYNP766IWxJvwDCbAEYRAzgqeSwKvX39OKsNxJ45ErC7hloA+paBCFSq2Z+mjm\nWhHD8QgmRxN4/cISsqWqqxuN29osEjzsQxq7L377HdTFOlLRUDMtmZqZmDSYGwcaa8JOSIAlCIMY\nDTTqJPAWKlXctjnZcm27md6qlraTxiMV5ZuR0/EIj0q1tlb+s6GNjQR5fHT3EFLRoOsbjZ1Cpd9z\n/fYak6MpPLR3xLCZmA4SGwcaa8IuSIAlCIMYDTTqJPBeWS4hXxZUN3qrUfmdNB5Hz2SaQoYUgV8R\n6khG+GYEfrcnYqeylQ3cFuLJTEwQhBuQAEsQCqht+kYDEToJvJKwKP1baaO3Iyq/k8ZDEjKG4uG1\nVDYFpPqChiLw/czRMxnU63VMzedakqZvpATzXgjxZCYmCMINSIAliDa0Nn2jGqZOAu++LammL2yn\njd6pqN12IWNnOo7f/vguTI6mmsL71197r6vN7ufms5hdXEEkyCER5lGu1vDzq3msVGteN8012jX4\nq0INFxcK+OK338FDe0ccG9tuNhOT2wlBdAckwBJEG1pme6MaJjWBV2ujd8oc22mT7iWze7YkgDGG\nSJADAESCHCpCHdmS4KiQ4icBqD03q1SsoS7WfTm2XvddL81/guh1qJQsQbShpzTk5GgKTz00gece\nP4CnHppQ3dzUyptqYeW7nVAr1yoX3gOMNf//0TMZ07/nBHrK3CYjPCAC5WoNoiiiXK0BIsBE0VK5\nWq12OXVvM2ztjzbLsc4sFBHmAwBjSEVDvhtbP/Rdt8x/giBIA0sQ6/Bbsm0lLa0VTZWahtnNSlhm\n0asl27clhb4g11JGd/tgH67mK45VGbOjFK6dyDX4+VIVQY5htSZi/9ZG9gs/ja0f+q4b5j9BEA1I\nA0sQbTyyf6QZiV8Xxeb/f6St/Kpe7NYsWb2fmoZZrrGT8EulHEnr+n9++zQuLhRQrdVUtWSP7B8B\nxwWwdzSJByY3Ye9oEhwXaEkhJmGXkKJHe+8mcg0+GMAYw93b+zEcbwhpfhlbwB995+f5TxBEKyTA\nEkQbdpvt7TZLWr2f2iYtCe+XFgo4ceE6vv/OPH56YRETIzFTbbULudAOERBFEW9cXsb1QhmAsqDT\naRz3jqYcE1L8KABJ7i5/9KsHMJ6OI8hxthzM7MYPfWf34ZUgCOcgFwKCUMDOKOoryyXwAeDcfK5p\nyh4f7kOhImh/ucP9rJg5tYLKHpxM4/lXL6Baq2MoFsJoKoLjUwsYT8c9C2SRC+2JaHCt8AJrVg7r\nJOhopRCzO0+pn3Og+j29lZ19Z9bFxu99RBDETZgoil63wTEOHjwonjp1yutmEBucL333HZy8uIR4\nhEeYD6Ai1FEoCzg0Poiv/Modhu935Nj0Oh9d6d9PPTTR/ExtE1f7m/z+C/kyZhaKWCqsIhwM4EO3\npFCpia5HiD/90mmMpiIIMNYSTV+p1XHv+LDhwgsbJQtBt2FH38l9pOWCMGUSIAhvYYy9IYriQbvu\nRxpYgnAYBqD9mCiufW4GPZqqToFOD06mMZ0pNgWEz310x7pNXdLwNgVFPoAQB1xeLKK0WsOh8QHX\n0wvJA+vSiQjuGuvH2bkcAixgqvCCk3lKvcqCwtLhAAAfCklEQVSBqiX8dYNgbUff+SEYrBvohvlA\nEGqQDyxBOEylJmJiJIbrhQrOZ/K4XqhgYiSGSs2c9UMy85+bz+Hl03M4N5/Dg5Ppls1HyU+2Vqvj\n+VcvaAZ/Sb6IUtqlSJDDYrGKWIhDIsLj4sKK6+mF2n0TQzyH8XQc/+FX79BMY7YR0ArssyOQUE/q\nMj/gh2AwLzAyPn5IWUYQViEBliAcJsQxvJspYjgexp6RBIbjYbybKSLEmdPBTs1ncXxqAXtHk3js\nwBbsHU3i+NRCy+ajtIlfzZVRrdU1g78kYXGpsIoQx1Cu1lAWakgnwgjzAeTKN8vfuiUUOJEPt5do\nP7DIK24dOTaNb564bCnwr5sEHj8Eg7mN0fGhfLdEL0AuBAThMHa7EOgxkSrlsl0qVjEUC7XcSy16\n/5mXz2GxUMFwPIyxwT4EGENFqCMZadzTLaGg3dSp5PZg5Pu9YiqVP9e5uRwObEsCCCpW3PrJzCI+\nsnsQwM350OkAotRf3WSW93MgnVN888RlXFwoYLXWeD93b4o1BVKl8aF8t0QvQBpYgnCYSk3EofEB\nRIIc8hUBkSCHQ+MDpl0I9JhIldIB8QG2btNSi95/9rG9uOOWfkyOJrFvSxKFsoB8WcB4us+19EJW\nNX/dpDk0QvtzBTmGkxdv4HqhrFhxa6AviHNz+ZZ7KI19p/46N5/tGrN8t2vrjboCfOm77+B7b89h\nfrkEjjUqz71xeRkVQWiOT/s9wxzbcFpqovcgAZYgHGZrfxRhnse940N4eO9m3Ds+hDDPm94s9JhI\nlTbxJx/YhUAgoDvHpfweQh04ND6Iw+ODqNbgmlBg1dTZq6bS9ufatyUJBuDMlRzypSpEUURFqGP3\npkb+3snRBG6saOc37dRf2ZLQVQKPkVLPfsLIgUu69uxcDvEwh7oIzGcrqIsiwnwA5+by2NofVbzn\nXLaM95dWKN8t0dWQCwFBOIzdJk2991OK6B5Pxw3luPQqol7CqqmzV02l7c+VTkRwz84BnP4gK6u4\nlWpW3IoEeXx09xBS0aDq2Hfqr2SEbxSRwMYxy3uBEVcN6dpqTcRwPIyruQoYgMVCBel4GDdKQkf3\nj+1DMawKNc35QBB+hgRYgnAYu5OjW7mf1wKpUZR8eY1o/qx+368oPVckyOPhvZubBxyp4paRPKid\n+mvfllRTGCKBxzmMHLika+MRHpVqDf1RHpl8BdlyHbU6cNdYPyZHU/j6a+8p3nM+K7TkjSaIboME\nWIJwGCeCiLpNEDWLVe11rwb0TIzE1lVLCwQCTaHS7AGnG/tLer/OzmWRKwtIRXnsHU11ZbCekQOX\ndO3udAyvz1zHcllAmA+gL8ghFuaxWhMxNZ/t2UMcQVAlLoJwEKoKZB2jB4D26ydGYvj7mUW89f4y\nGBju3JbCbxze3rX9L82pWq2Oq7kyloqNAL0nH9iFR+/Yasv92/sbgC/nsbwvpjMFgAGiKOK2zQkE\nAgHP22cUI2MrX1t+9t4iMtkKVmt1bB/swx3bUghyHFLRYPNQ4rexIzYedlfiIgGWIBxEb9lXOb2a\n9skNlA4MlxeLCDCGbYN9PbGBm5lT3fKbRue+1K5z8zlUqjVEghzKa/+dHE163j4zfP+dK4radaX5\nKrXne29fwUgijFtH4k2/57ooYj5bxnOPH6A1hfAFVEqWILoILZ82JW3h8akFzRKwtAEpoxSwslRc\nBQDs35pqfiZd24196EVgmhu/2an8sdpBQ2pXoSwgHuYAoFlsww/tM8N0poh7x4fWHRaU5qvclUjN\nTWCjuBwRGwtKo0UQDqKW8kopvc3zr1xAvd5aLUtvCdhewmzZUqUcuatCHRWh1vKZXzMR6HluLypN\nufGbZlKeSe2KR3hUhDoANItt+KF9ZjBTClcp7zOlxSJ6HRJgCcJB1DYWpQ1RqDfMfnL0loDtFawU\nH1AStEJ8AGGea/nMj0Esep/bLWFFLkwv5J3PG2pFcNucCKNSbVQdK1dr2JwM+6J9ZjBzWOj24g0E\nYQYSYAnCQdQ2FqUNcTAWxOKayVtCbwnYXsGKpktJuBuMhTAcD/teO6X3ud0QVtqF6RDfSMlVFWqO\n/aYVwW1nOo5bBqNIRoMYG+rDjuG4L9pnBrMHlG4t3kAQZiEfWIJwmE7+Z0rpbTYnI8itBRlJAUdG\nSsD2Alb8LZVSSP3eI3sAwHBaKbcDX9SeW6ktTubw7JT83slAMbMpvNzy75S3r1wVMDWfx42VKj62\newhT81nb2mB33miC6FUoCwFBeESnFFvtAVvywC6jUfR2C2FuCHVmI97tbJsT6c+02tfpuVeFGkrV\nuqtpkJ5+6XQj+p2x5mfyqHan8Hu0/NR8Fi+euIzXZhYx0BfE3i0JhHle13j4/dkIwmkojZYBSIAl\n/I7eTU3PdWoZDewQfNzKaWvmd+xum91po/S0r9M1fcEAgjzXVWmzellYM5saT+/87OW+IzY2lEaL\nIHoIveZPreuUUvw8/8oF7Nkc11VXXQ9G6rRbwYwJ1e622Z02Sk/7Oj331197D4Nx54OH5FipyOVW\nuimvMDM39M7PXu87grATEmAJogdQ2iCljAY7huPN66wIPm7mHzXq12i2bZ20XXaX39TbPqXn9qIU\nqNFDhLwfZ5dWMJoMO37Q8Qoz46F3/N06JBJEL0ACLEH0AEobpFJGAyuCj59rqptpm5q2y4oG0q72\nSTyyfwTP/d003ipUUBFqCPMchuNh/NonnQviAvQfItr78e3ZZWRXVhGP8M2qUL2SNWNqPouFfFnR\nB1Ztbugdfy+KVBBEt0JptAiiB1BK8bM5GUGQC9iWPsrPydLNtE0tbZXdqaqs9l19LVaBgbX82w+0\n9+NgPATGGGauFZvX+OWgYwVJUA/xHD68axAA8PczS6gKtXVzo70gxcRITNf4e1GkgiC6FdLAEkQP\noKQx5LgAnvzErpaMBlbS8fgtvU+7+b89e4NW27S0XXamZ7LSd0fPZLB9KIY7bulvftaptKgXtPfj\n7nQMb15exvVCBXVRtKy99gtyQT0VDWIkeVOrquXHenxqoWV+hjiGvmAAX3/tvRbXFbs1/wTRy5AA\nSxA9gJqA9KjC9WYjnf1SU72TkGBES+q2S4TZvjNiVvYigr29H9OJCCZG4riar2A+W/b8oGMXVv1Y\npzNFPPXQRMvcHYzz6wK1/HRIJAg/QwIsQfQIZn0WuzHS2Y5gl27RdukVtL0a107a/2cf26v7d7sh\ndZRdfqxac9cvh0SC8DvkA0sQGwwrpVr9gh116b2oH9/uGzk1n9X8jl7/Wa/G1Wo/tpetlQRvPX3j\nJnrHQcuP1Y65SxAEaWAJYsPRC5HOZs3/WiVZJQHTrCZQTZNoVkOq16zs5bha0Rp2S+ooveOgpdn3\nczYPgugmSIAliA1GL2ygZsz/WgJk+98vLRTw1LeuYttQFHvXAmyMFpOQ39+KoKZHQOzWcfVC8HbS\nB1xL0O0W1xWC8DskwBLEBsPrDdQOf0cnqnXJ/76QL2P6WgFgQHalqktbqnV/pwU1r8fVLEYEbzvm\njhu+wmqCLgVqEYQ9kABLEBsMLzdQO4UHu6t1yf8+s1BEmA8gzAeQrwi6tKVa93daQ9qtglEnwfue\nHf0t7hwTIzEcn1qwPHf84LJAgVoEYR0SYAliA+LVBuql8KAlQMr/XigLiIc5VIQ6kpHG9VraUq37\ntwtql68XMZ0pYNtQFEeOTdsSed+NgpGS4H3Pjv51wurzr1zAns1xy3OnF3zACYKgLAQEQbiIlxHY\nWlHk8r/HwxxyZQEVoY7dm2IAtLWlWveXR+tPzecwfa2APZvjuG1zUnfkvZksBt3A5GgKTz00gece\nP4CnHprAdKa4LqOCUBcxny23fM/M3KFqVwTRG5AASxCEa3gpPGile5L/Pbmm3dszEsdgLKyr9Kue\ndFKSoLZvSwr3jg9hx3Bcd8qrbkk3ZQdKB53BWBCLxdWWz8zMHT+XRCYIQj/kQkAQhGt4HWikZWKX\n/709YEiPP6leE74ZM7YffDfdQskdY3MygtzafLEyd7rVV5ggiFaYKIpet8ExDh48KJ46dcrrZhDE\nhkBvhHj7dRMjsZYa8QxApSb6tiKTHRw5Nr1OQLu0UMDVfAVjg32Kz/70S6cxmoogwFjzs7rYMKs/\n9/gBV9vvNPJgP7mw+uBkujlXenl+EEQvwhh7QxTFg7bdjwRYgiCs0kng0IoQl3+vXBXws0s3IAI4\nND6AMM/rukf7/fxekhRY31+Xrxfx1vvLuGusH2NDMcX+UxJ6pX/LizH0CkbHslvGniA2KiTAGoAE\nWIJwB7PClfx7Jy4uolKtAQBqdRF9YR5LhVUMxkN49rG9msKIWSHaK+QC1+zSCkaTYewYjjf/3t5/\n3fZ8bvL9d67g+VcvoFqrYygWamiqAwHqG4LwEXYLsBTERRCEZcxmF5B/r1AWEOYDEGp1zN5YQaVa\nw0Afj8VCRVewktxHVG9glJfII+/HBvswNhRr+Xt7/+kJEtuITM1n8fwrFwAAQ7EQKkId5zMF1Ot1\nS2PfqxkfCKJXoCAugiAsYzZJv/x78QiPSrWG64VVRPkAIkEO5WoNw/FwUxBVE9bszu/ppklab/91\nY55XOU706dEzGQh1EYOxIBhjiAQ5AMB8towgz5lup9PVugiCsAZpYAmCsIzZ1ETy740P96FQFlBc\nrWE4HkK5WmvmYdUjiNqZosvtlFUbIbWTU316ZbmEwVgQFaHe/CzMB7BYXDWdnq3btPkEsRExJcAy\nxrYyxrKMMU0HWsbYHzHGRMbYl038zucZY+cYY9cYYxcYY/+aMWbuSE0QhGOYNW/LvyfUgUPjg9g5\n1IeyICIS5HD39n4MxyO6BFElIfDyYhHX82XDZmC3BRi/uwfYYU53qk+39kexORlBRaijXK1BFEXk\nygKCXMD0AcDLghsEQejDrAvBnwFIal3EGDsI4AtmfoAx9gcAngbwmCiKxxljHwJwDMAeAP+bmXsS\nBOEcZs3b7d9rD1aShFKtfJ/t+T1DHEOAMQR5DoNx3pAZ2Ityo0b7zy0XB7vM6U71aSO38AomNsVx\nNVfGUrEKPsDw5AO7TPeHWZcYgiDcw7AGljH2OIDbAfxM4zoewF8A+I6J35gA8CUAfyKK4nEAEEXx\nbQD/N4DfZIx93Og9CYLoDqxoI+WBUelEBNsG+0xp/PxebtSsOd6MJtUuzalTfSrNl53pOMaGYnj0\njlEc+cwBPHrHVtP33AguHQTR7RjSwDLG+gF8DcBn0RAw1XgaQAHAnwP4VYPt+ucAOADfbfv8OwD+\nI4B/AeCHBu9JEESXYEewkhWNn9mKYW5pRc1U5TKrSbVLc+pkFTa7g9uoWhdB+B+jLgTPATguiuIP\nGGMdBVjG2G4A/wrAhwFsMtGu+9b++478Q1EUrzDGFgH8ool7EgTR5RgREK2Ygc0IMFZN7Uaezc1S\ntHaZ0/X2qV8KEnR7xgeC6HV0C7CMsfsBPAZgr47LXwDwx6IonmOMmRFgJwDkRFFcUfjbHIDbGWN9\nHf5OEEQPYlRAtKrxMyrAmBUQzTybGaHSrCbVTs2pVp9S+iqCIPSiyweWMRZBQyh9WhTF6xrXfg7A\nKICvWGhXCkAn4XRFdo3S7z/BGDvFGDu1sLBgoQkEQfgJo76Ybkf2W4lcN/psZnw0zfqgutmPlL6K\nIAi96NXA/j6Ay6Io/pXaRYyxEQBfBfApURRXrTbODKIovoCGsI2DBw/2bp1cgthgmNEgOmEG7mTi\ntmJqN/psZlwcrGhS7ehHPa4BTmd/8It7AkEQ1tHUwDLG7gDwOwB+S8f9vgbgJVEUf2KxXVkAfR3+\n1ie7hiCIDYIfMgOoRf9biVx349m8zDWrN2uCk/3gdnEKgiCcRY8G9tG1/77OGJN/PggAjLGra/9+\nDsAvASgzxj4luy609t+nGWOfBwBRFDdr/OY0gA938HPdAmCO/F8JYmPhZBS7XtT8XJ96aMJ05LrR\nZzPrK+pVYJJe/2ArY6ylXbXio0wQhP/Q1MCKoviHoiimRFHcLP8fgNfX/i599pwoiglRFNNt1/3K\n2q2ek32mxY/X/nuH/EPG2BYAQwB+pPcBCYLoDfxQrUrLz1Weh/aphyZ0t83os3Wbr6he/2CzY6xH\nu0rVtQiitzBbics2GGMDAKqiKBZkH/8XAL8L4JcB/FT2+f+y9t+vu9Q8giB8hNepjZys0GTk2byo\nFGYFI/1mZoz1aFepuhZB9BaGK3HZCWNsB4ArAGYYYzHpc1EUpwH8IYB/yRh7YO3aDwH4NwBeFEXx\nVfdbSxDERscvFZr84A9sBKf7TU27KlUfOzuXxU8vLuK96wWqrkUQPQATRWOB+oyxEwB2ouEDGwQg\n2azuFEVxXnbdpwH8KRo+sAMAimhU5vprURS/uHbNJgD/AGARwOH2zAWMsd8G8AU03AYKAP4SwL8V\nRbF15e7AwYMHxVOnThl6PoIgCDX8EMku94GV+4r6OV+q2X7T870jx6bXaVezpSpWhRpK1Xqzny5f\nL2I6U8C2oSj2jqYoCwFBuAhj7A1RFA/adj+jAmw3QQIsQRC9ih8EaafbpFdQ//47V/D8qxdQrdUx\nFAthNBVBIBBAXzCAIM+tE2xT0SCeemjC0rMSBGEMuwVYz31gCYIgCON47Q/cjhNVtPT4tk7NZ3F8\nagETm+K4mitjqVhFriTgyQd24Yfnr2MwToFbBNGLkABLEARBWMaJNFV6gtXkv7szHQfQ0LJOZ4oU\nuEUQPYynQVwEQRBEb+BEmio9wWpqv+uXoDuCIOyHBFiCIAgXkaLin37pNI4cm+6ZSlBOZEbQI4Cq\n/a4fcgcTBOEM5EJAEAThEnr8RP0YnKUHJyqlSQKoWnUzrd/1m68wQRD2QAIsQRCES2j5iToRCOUm\n0WAAJy8tgoHhzm0p0+02IsTrEXIJgug9SIAlCIJwCa2gJCcCobSwQ+MrF7wfnBxpakHNtseoEE9a\nVoLYeJAPLEEQhEto+Yk6EQilhiQsZkvVFmHRqF+uXPAOMNb8/0fPZLS/7OC9CILoXUiAJQiCcAmt\noCS3S8TaJSzaKXi7LcQTBNGdkABLEAThElpR8W6nfbJLWLRT8P7/27v7GDuqMo7j31+FIthaXlqR\nN2kqFkMEjFlEEl6jVExEIqgoIgFJMEGFEqJCwECIBgkYg0ZECNoANkEqMaIIMUYJNUAoKWJ5E1bC\nW1taKBTRAgKPf5yzcLm9W7a7c3fO2fv7JJPJnpl7ztl57u48986ZM5OdxJtZnTwG1sxsEm1qvOZk\n35DU1ET/Tc5A0I/ZDMxs6lFEtN2HvhkaGoply5a13Q0zsyJ13jDVmSyOZ/aAJqf/qnUqMTMbnaS7\nI2KosfqcwJqZDS4ni2Y2GZpOYD2EwMxsgHkKKjOrkW/iMjMzM7OqOIE1MzMzs6o4gTUzMzOzqjiB\nNTMzM7OqOIE1MzMzs6o4gTUzMzOzqjiBNTM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"text/plain": [ "<matplotlib.figure.Figure at 0x108a7d090>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(11,11))\n", "plt.scatter(gck['ra'], gck['dec'], alpha=0.5)\n", "plt.scatter(gr6['ra'], gr6['dec'], alpha=0.85,s=2)\n", "plt.plot(301.5644, 44.45684, '+', markersize=50, c='k', alpha=0.5)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# match the datasets within 1arcsecond\n", "\n", "mtch = np.zeros(len(gr6['ra'])) - 1\n", "\n", "dlim = 1. / 3600.\n", "\n", "for k in range(len(gr6['ra'])):\n", " dist = np.sqrt((gck['ra'].values - gr6['ra'].values[k])**2 + \n", " (gck['dec'].values - gr6['dec'].values[k])**2)\n", " x = np.where((dist <= dlim))[0]\n", " \n", " if len(x) > 0:\n", " mtch[k] = x[0]\n", "\n", "\n", "ok = np.where((mtch > -1))[0]" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x11afc0090>" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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WLoVnnoHCQhg/HrKzw04k0qWoMIhI8vvkE1iwAIqK4L77ICsr7EQiXY4Kg4gk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\n5wSaR0U8uWyzW/o5sBf+/AYcMRSuu46S2W70RGF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"text/plain": [ "<matplotlib.figure.Figure at 0x11b3a49d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "plt.scatter(gr6['nuv_mag'].values[ok], gck['nuvmag'][mtch[ok]], alpha=0.75)\n", "plt.plot([20,14],[20,14], c='r', alpha=0.5)\n", "plt.xlim(20,14)\n", "plt.ylim(20,14)\n", "plt.xlabel('GALEX GR6 NUV (mag)')\n", "plt.ylabel('GCK NUV (mag)')" ] }, { "cell_type": "code", "execution_count": 216, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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WN1BeWUV55ftHysLRa42UV1alM6KItKHNwuDuv2nx1+7uz7v7HwKDgV8DtwFb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\ndqQpDAZ51suf4+t9hyWrc0qKkT06I17YAjcthdT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"text/plain": [ "<matplotlib.figure.Figure at 0x120b9cb50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "plt.errorbar(gr6['nuv_mag'].values[ok], gck['nuvmag'][mtch[ok]], yerr=gck['e_nuvmag'][mtch[ok]], \n", " linestyle='none', marker='o')\n", "plt.plot([20,14],[20,14], c='r', alpha=0.5)\n", "plt.xlim(20,14)\n", "plt.ylim(20,14)\n", "plt.xlabel('GALEX GR6 NUV (mag)')\n", "plt.ylabel('GCK NUV (mag)')\n", "plt.savefig('GCK_GR6.png', dpi=150, bbox_inches='tight', pad_inches=0.25)" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(16.499000000000002, 16.460850000000001)\n", "-0.03815\n" ] } ], "source": [ "print(gck['nuvmag'][mtch[ok]].values[0], gr6['nuv_mag'][ok][0])\n", "print(gr6['nuv_mag'][ok][0] - gck['nuvmag'][mtch[ok]].values[0])" ] }, { "cell_type": "code", "execution_count": 201, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(31,)\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x11fa0cad0>]" ] }, "execution_count": 201, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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4Kj3XnSPiZJpLEu/L9gbSFcuOj4gYY5uLMLrk2P1ul78P+KmIOLFn/S+kORP6lxXzzgRe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\nXJIK+D+lM7L1NOFJpgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11f8e6350>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "okc = np.where((mtch > -1) & (gr6['nuv_mag'] < 19))[0]\n", "\n", "print(np.shape(okc))\n", "\n", "_ = plt.hist(gr6['nuv_mag'].values[okc] - gck['nuvmag'][mtch[okc]])\n", "plt.plot( (gr6['nuv_mag'][ok][0] - gck['nuvmag'][mtch[ok]].values[0]) * np.ones(2), [0,2])" ] }, { "cell_type": "code", "execution_count": 211, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1205df790>" ] }, "execution_count": 211, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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r4Fa/DWx4eMtkhyB1zIpVQyxZvprNw1sBGNqwiSXLV5vISOq4VpOY7wOXRcSzai9ExCEU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\nc1FmntWg2G0U68jsw+MH/c4BtgK/qNq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"text/plain": [ "<matplotlib.figure.Figure at 0x1204a2e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "plt.errorbar(gr6['nuv_mag'].values[ok], gr6['nuv_mag'].values[ok] - gck['nuvmag'][mtch[ok]], \n", " yerr=gck['e_nuvmag'][mtch[ok]], linestyle='none', marker='o')\n", "plt.xlim(19,14.)\n", "plt.ylim(-.2,.2)\n", "plt.xlabel('GALEX GR6 NUV (mag)')\n", "plt.ylabel('GR6 $-$ GCK NUV (mag)')" ] }, { "cell_type": "code", "execution_count": 210, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "KernelDensity(algorithm='auto', atol=0, bandwidth=0.0254838709677,\n", " breadth_first=True, kernel='gaussian', leaf_size=40,\n", " metric='euclidean', metric_params=None, rtol=0)" ] }, "execution_count": 210, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = np.array(gr6['nuv_mag'].values[okc] - gck['nuvmag'][mtch[okc]].values)[:,None]\n", "\n", "kde = KernelDensity(kernel='gaussian', bandwidth=np.mean(gck['e_nuvmag'][mtch[okc]]) * 2 ).fit(X)\n", "kde" ] }, { "cell_type": "code", "execution_count": 217, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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JCw3h1slLOZ7vXHP/SiVzEbkXGAWcbozZ45mQ/Icxhpzd5/PThnT+O7wDA9rV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\nKaUCgCZzpZQKAJrMlVIqAPw/if0VJvdKoHkAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x120ab4350>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X_new = np.linspace(-.25,.25,1000)[:, None]\n", "log_dens = np.exp(kde.score_samples(X_new))\n", "plt.plot(X_new[:,0], log_dens)\n", "plt.plot( (gr6['nuv_mag'][ok][0] - gck['nuvmag'][mtch[ok]].values[0]) * np.ones(2), [0,8])\n", "_ = plt.hist(gr6['nuv_mag'].values[okc] - gck['nuvmag'][mtch[okc]], histtype='step')\n", "plt.xlim(-0.14, 0.14)\n", "plt.ylim(0,7.2)\n", "plt.xlabel('GR6 $-$ GCK NUV (mag)')\n", "plt.savefig('hist_diff.png', dpi=150, bbox_inches='tight', pad_inches=0.25)" ] }, { "cell_type": "code", "execution_count": 220, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1.6692204 , 1.67315671, 1.49394832, 1.83279452, 1.80383286,\n", " 1.67897793, 1.76001204, 1.73933927, 1.5510749 , 1.82142066,\n", " 0.90392906, 1.18908153, 1.33699593, 1.83191295, 1.21321777,\n", " 1.79844385, 1.70220279, 0.93408817, 1.07601544, 1.68498259,\n", " 1.09490883, 1.60151023, 1.01521768, 0.55254331, 1.64404685,\n", " 1.09106778, 1.83138907, 1.17857938, 1.6227065 , -0.05473408,\n", " 1.826314 ])" ] }, "execution_count": 220, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kde.score_samples(X)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

GCantergiani/centrality-measure-lth-model

notebooks/football_example/0.1-simple-example-first-period.ipynb

1

110377

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline \n", "\n", "import networkx as nx\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "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>P</th>\n", " <th>P1</th>\n", " <th>P2</th>\n", " <th>P3</th>\n", " <th>P4</th>\n", " <th>P5</th>\n", " <th>P6</th>\n", " <th>P7</th>\n", " <th>P8</th>\n", " <th>P9</th>\n", " <th>P10</th>\n", " <th>P11</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>P1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</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>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>P2</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>18</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>20</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>P3</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>8</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>11</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>P4</td>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>P5</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>14</td>\n", " <td>0</td>\n", " <td>7</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>P6</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>P7</td>\n", " <td>0</td>\n", " <td>1</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>2</td>\n", " <td>2</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>P8</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>P9</td>\n", " <td>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", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>P10</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>1</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>P11</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>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " P P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11\n", "0 P1 0 1 1 1 0 0 0 0 0 1 0\n", "1 P2 6 0 18 0 0 20 0 0 0 0 0\n", "2 P3 6 10 0 0 8 12 0 11 0 0 0\n", "3 P4 2 6 0 0 0 6 4 2 0 0 0\n", "4 P5 2 0 6 0 0 14 0 7 0 5 0\n", "5 P6 0 0 1 2 1 0 0 3 2 3 2\n", "6 P7 0 1 0 0 0 2 0 0 2 2 2\n", "7 P8 0 0 1 0 0 1 0 0 3 3 2\n", "8 P9 0 0 0 0 0 0 0 0 0 2 1\n", "9 P10 0 0 0 0 0 1 0 1 2 0 2\n", "10 P11 0 0 0 0 1 0 0 1 1 3 0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('data/first_period.csv')\n", "df" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['P1', 'P2', 'P3', 'P4', 'P5', 'P6', 'P7', 'P8', 'P9', 'P10', 'P11']" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "players = list(df.columns[1:])\n", "players" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "G = nx.DiGraph()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for index, row in df.iterrows():\n", " for player in players:\n", " G.add_edge(row['P'], player , weight=row[player])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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26NatGwdQppMHDx7g8OHDiIyMxIABA7B27VoUlFtvvjYohrKrDzMdDp6MMbNZ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\nERE5xf8DjDFkgXv7sKYAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fc65ee81a20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nx.draw(G, with_labels = True, node_size=700)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"{'P7': 0.0194211941760637, 'P11': 0.15785301410998948, 'P6': 0.11499429713415688, 'P9': 0.14792786160181126, 'P8': 0.11323696443656019, 'P3': 0.058717644504349, 'P4': 0.03402805011653114, 'P10': 0.23090071703859866, 'P2': 0.04119514301552847, 'P5': 0.05147599990980031, 'P1': 0.03024911395661084}\"" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pr = nx.pagerank(G)\n", "repr(pr)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"{'P7': -0.15309311572590653, 'P11': 0.13624705584608843, 'P3': 0.2738228784575988, 'P5': 0.14866610081432635, 'P6': 0.6654079059142652, 'P4': -0.0063385439952963595, 'P8': 0.4997998499830032, 'P10': 0.3310674267757482, 'P2': 0.11529031530968087, 'P9': 0.18168340584454326, 'P1': 0.11137572949638581}\"" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kz = nx.katz_centrality_numpy(G)\n", "repr(kz)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Hand-made LTC values\n", "\n", "LTC_P1=[1,2,3,4,5,6,7,8,9,10,11]\n", "LTC_P2=[1,2,3,4,5,6,7,8]\n", "LTC_P3=[1,2,3,4,5,6,7,8]\n", "LTC_P4=[1,2,4,5,6,7,8]\n", "LTC_P5=[1,2,3,4,5,6,7,8,9,10,11]\n", "LTC_P6=[1,2,3,4,5,6,7,8,9,10,11]\n", "LTC_P7=[2,4,6,7,9,10,11]\n", "LTC_P8=[1,2,3,4,5,6,7,8,9,10,11]\n", "LTC_P9=[4,6,7,8,9,10,11]\n", "LTC_P10=[1,4,5,6,7,8,9,10,11]\n", "LTC_P11=[4,5,6,7,8,9,10,11]\n", "\n", "\n", "ltc = {'P1':11,'P2':8,'P3':8,'P4':7,'P5':11,'P6':11,'P7':7,'P8':11,'P9':7,'P10':9,'P11':8}" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[['P1', 0.03024911395661084, 0.11137572949638581, 1.0], ['P2', 0.04119514301552847, 0.11529031530968087, 0.7272727272727273], ['P3', 0.058717644504349, 0.2738228784575988, 0.7272727272727273], ['P4', 0.03402805011653114, -0.0063385439952963595, 0.6363636363636364], ['P5', 0.05147599990980031, 0.14866610081432635, 1.0], ['P6', 0.11499429713415688, 0.6654079059142652, 1.0], ['P7', 0.0194211941760637, -0.15309311572590653, 0.6363636363636364], ['P8', 0.11323696443656019, 0.4997998499830032, 1.0], ['P9', 0.14792786160181126, 0.18168340584454326, 0.6363636363636364], ['P10', 0.23090071703859866, 0.3310674267757482, 0.8181818181818182], ['P11', 0.15785301410998948, 0.13624705584608843, 0.7272727272727273]]\n" ] } ], "source": [ "to_dataframe = []\n", "for player in players:\n", " list_attributes = []\n", " list_attributes.append(player)\n", " list_attributes.append(pr[player])\n", " list_attributes.append(kz[player])\n", " list_attributes.append(float(ltc[player])/len(players))\n", " to_dataframe.append(list_attributes)\n", " \n", "print(to_dataframe)" ] }, { "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>node</th>\n", " <th>pr</th>\n", " <th>kz</th>\n", " <th>ltc</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>P1</td>\n", " <td>0.030249</td>\n", " <td>0.111376</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>P2</td>\n", " <td>0.041195</td>\n", " <td>0.115290</td>\n", " <td>0.727273</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>P3</td>\n", " <td>0.058718</td>\n", " <td>0.273823</td>\n", " <td>0.727273</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>P4</td>\n", " <td>0.034028</td>\n", " <td>-0.006339</td>\n", " <td>0.636364</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>P5</td>\n", " <td>0.051476</td>\n", " <td>0.148666</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>P6</td>\n", " <td>0.114994</td>\n", " <td>0.665408</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>P7</td>\n", " <td>0.019421</td>\n", " <td>-0.153093</td>\n", " <td>0.636364</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>P8</td>\n", " <td>0.113237</td>\n", " <td>0.499800</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>P9</td>\n", " <td>0.147928</td>\n", " <td>0.181683</td>\n", " <td>0.636364</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>P10</td>\n", " <td>0.230901</td>\n", " <td>0.331067</td>\n", " <td>0.818182</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>P11</td>\n", " <td>0.157853</td>\n", " <td>0.136247</td>\n", " <td>0.727273</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " node pr kz ltc\n", "0 P1 0.030249 0.111376 1.000000\n", "1 P2 0.041195 0.115290 0.727273\n", "2 P3 0.058718 0.273823 0.727273\n", "3 P4 0.034028 -0.006339 0.636364\n", "4 P5 0.051476 0.148666 1.000000\n", "5 P6 0.114994 0.665408 1.000000\n", "6 P7 0.019421 -0.153093 0.636364\n", "7 P8 0.113237 0.499800 1.000000\n", "8 P9 0.147928 0.181683 0.636364\n", "9 P10 0.230901 0.331067 0.818182\n", "10 P11 0.157853 0.136247 0.727273" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_centrality = pd.DataFrame(to_dataframe, columns=['node','pr','kz','ltc'])\n", "df_centrality" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>pr</th>\n", " <th>kz</th>\n", " <th>ltc</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>pr</th>\n", " <td>1.000000</td>\n", " <td>0.502838</td>\n", " <td>0.042418</td>\n", " </tr>\n", " <tr>\n", " <th>kz</th>\n", " <td>0.502838</td>\n", " <td>1.000000</td>\n", " <td>0.622642</td>\n", " </tr>\n", " <tr>\n", " <th>ltc</th>\n", " <td>0.042418</td>\n", " <td>0.622642</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pr kz ltc\n", "pr 1.000000 0.502838 0.042418\n", "kz 0.502838 1.000000 0.622642\n", "ltc 0.042418 0.622642 1.000000" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_centrality.corr()" ] }, { "cell_type": "code", "execution_count": 16, "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>pr</th>\n", " <th>kz</th>\n", " <th>ltc</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>pr</th>\n", " <td>1.000000</td>\n", " <td>0.736364</td>\n", " <td>0.151797</td>\n", " </tr>\n", " <tr>\n", " <th>kz</th>\n", " <td>0.736364</td>\n", " <td>1.000000</td>\n", " <td>0.540777</td>\n", " </tr>\n", " <tr>\n", " <th>ltc</th>\n", " <td>0.151797</td>\n", " <td>0.540777</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pr kz ltc\n", "pr 1.000000 0.736364 0.151797\n", "kz 0.736364 1.000000 0.540777\n", "ltc 0.151797 0.540777 1.000000" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_centrality.corr(method='spearman')" ] }, { "cell_type": "code", "execution_count": 17, "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>pr</th>\n", " <th>kz</th>\n", " <th>ltc</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>pr</th>\n", " <td>1.00000</td>\n", " <td>0.600000</td>\n", " <td>0.143940</td>\n", " </tr>\n", " <tr>\n", " <th>kz</th>\n", " <td>0.60000</td>\n", " <td>1.000000</td>\n", " <td>0.472947</td>\n", " </tr>\n", " <tr>\n", " <th>ltc</th>\n", " <td>0.14394</td>\n", " <td>0.472947</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pr kz ltc\n", "pr 1.00000 0.600000 0.143940\n", "kz 0.60000 1.000000 0.472947\n", "ltc 0.14394 0.472947 1.000000" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_centrality.corr(method='kendall')" ] } ], "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

tensorflow/docs-l10n

site/en-snapshot/tutorials/distribute/dtensor_ml_tutorial.ipynb

1

42772

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Tce3stUlHN0L" }, "source": [ "##### Copyright 2019 The TensorFlow Authors.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "tuOe1ymfHZPu" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "MfBg1C5NB3X0" }, "source": [ "# Distributed Training with DTensors\n" ] }, { "cell_type": "markdown", "metadata": { "id": "r6P32iYYV27b" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/distribute/dtensor_ml_tutorial\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/distribute/dtensor_ml_tutorial.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/distribute/dtensor_ml_tutorial.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n", " </td>\n", " <td>\n", " <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/distribute/dtensor_ml_tutorial.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n", " </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "kiF4jjX4O1mF" }, "source": [ "## Overview\n", "\n", "DTensor provides a way for you to distribute the training of your model across devices to improve efficiency, reliability and scalability. For more details on DTensor concepts, see [The DTensor Programming Guide](https://www.tensorflow.org/guide/dtensor_overview).\n", "\n", "In this tutorial, you will train a Sentiment Analysis model with DTensor. Three distributed training schemes are demonstrated with this example:\n", "\n", " - Data Parallel training, where the training samples are sharded (partitioned) to devices.\n", " - Model Parallel training, where the model variables are sharded to devices.\n", " - Spatial Parallel training, where the features of input data are sharded to devices. (Also known as [Spatial Partitioning](https://cloud.google.com/blog/products/ai-machine-learning/train-ml-models-on-large-images-and-3d-volumes-with-spatial-partitioning-on-cloud-tpus))\n", "\n", "The training portion of this tutorial is inspired [A Kaggle guide on Sentiment Analysis](https://www.kaggle.com/code/anasofiauzsoy/yelp-review-sentiment-analysis-tensorflow-tfds/notebook) notebook. To learn about the complete training and evaluation workflow (without DTensor), refer to that notebook.\n", "\n", "This tutorial will walk through the following steps:\n", "\n", "- First start with some data cleaning to obtain a `tf.data.Dataset` of tokenized sentences and their polarity.\n", "\n", "- Next build an MLP model with custom Dense and BatchNorm layers. Use a `tf.Module` to track the inference variables. The model constructor takes additional `Layout` arguments to control the sharding of variables.\n", "\n", "- For training, you will first use data parallel training together with `tf.experimental.dtensor`'s checkpoint feature. Then continue with Model Parallel Training and Spatial Parallel Training.\n", "\n", "- The final section briefly describes the interaction between `tf.saved_model` and `tf.experimental.dtensor` as of TensorFlow 2.9.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "YD80veeg7QtW" }, "source": [ "## Setup\n", "\n", "DTensor is part of TensorFlow 2.9.0 release." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-RKXLJN-7Yyb" }, "outputs": [], "source": [ "!pip install --quiet --upgrade --pre tensorflow tensorflow-datasets" ] }, { "cell_type": "markdown", "metadata": { "id": "tcxP4_Zu7ciQ" }, "source": [ "Next, import `tensorflow` and `tensorflow.experimental.dtensor`. Then configure TensorFlow to use 8 virtual CPUs.\n", "\n", "Even though this example uses CPUs, DTensor works the same way on CPU, GPU or TPU devices." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dXcB26oP7dUd" }, "outputs": [], "source": [ "import tempfile\n", "import numpy as np\n", "import tensorflow_datasets as tfds\n", "\n", "import tensorflow as tf\n", "\n", "from tensorflow.experimental import dtensor\n", "print('TensorFlow version:', tf.__version__)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oHtO6MJLUXlz" }, "outputs": [], "source": [ "def configure_virtual_cpus(ncpu):\n", " phy_devices = tf.config.list_physical_devices('CPU')\n", " tf.config.set_logical_device_configuration(phy_devices[0], [\n", " tf.config.LogicalDeviceConfiguration(),\n", " ] * ncpu)\n", "\n", "configure_virtual_cpus(8)\n", "DEVICES = [f'CPU:{i}' for i in range(8)]\n", "\n", "tf.config.list_logical_devices('CPU')" ] }, { "cell_type": "markdown", "metadata": { "id": "omYd4jbF7j_I" }, "source": [ "## Download the dataset\n", "\n", "Download the IMDB reviews data set to train the sentiment analysis model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fW4w4QlFVHhx" }, "outputs": [], "source": [ "train_data = tfds.load('imdb_reviews', split='train', shuffle_files=True, batch_size=64)\n", "train_data" ] }, { "cell_type": "markdown", "metadata": { "id": "ki3mpfi4aZH8" }, "source": [ "## Prepare the data\n", "\n", "First tokenize the text. Here use an extension of one-hot encoding, the `'tf_idf'` mode of `tf.keras.layers.TextVectorization`.\n", "\n", "- For the sake of speed, limit the number of tokens to 1200.\n", "- To keep the `tf.Module` simple, run `TextVectorization` as a preprocessing step before the training.\n", "\n", "The final result of the data cleaning section is a `Dataset` with the tokenized text as `x` and label as `y`.\n", "\n", "**Note**: Running `TextVectorization` as a preprocessing step is **neither a usual practice nor a recommended one** as doing so assumes the training data fits into the client memory, which is not always the case.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zNpxjku_57Lg" }, "outputs": [], "source": [ "text_vectorization = tf.keras.layers.TextVectorization(output_mode='tf_idf', max_tokens=1200, output_sequence_length=None)\n", "text_vectorization.adapt(data=train_data.map(lambda x: x['text']))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "q16bjngoVwQp" }, "outputs": [], "source": [ "def vectorize(features):\n", " return text_vectorization(features['text']), features['label']\n", "\n", "train_data_vec = train_data.map(vectorize)\n", "train_data_vec" ] }, { "cell_type": "markdown", "metadata": { "id": "atTqL9kE5wz4" }, "source": [ "## Build a neural network with DTensor\n", "\n", "Now build a Multi-Layer Perceptron (MLP) network with `DTensor`. The network will use fully connected Dense and BatchNorm layers.\n", "\n", "`DTensor` expands TensorFlow through single-program multi-data (SPMD) expansion of regular TensorFlow Ops according to the `dtensor.Layout` attributes of their input `Tensor` and variables.\n", "\n", "Variables of `DTensor` aware layers are `dtensor.DVariable`, and the constructors of `DTensor` aware layer objects take additional `Layout` inputs in addition to the usual layer parameters.\n", "\n", "Note: As of TensorFlow 2.9, Keras layers such as `tf.keras.layer.Dense`, and `tf.keras.layer.BatchNormalization` accepts `dtensor.Layout` arguments. Refer to the [DTensor Keras Integration Tutorial](/tutorials/distribute/dtensor_keras_tutorial) for more information using Keras with DTensor." ] }, { "cell_type": "markdown", "metadata": { "id": "PMCt-Gj3b3Jy" }, "source": [ "### Dense Layer\n", "\n", "The following custom Dense layer defines 2 layer variables: $W_{ij}$ is the variable for weights, and $b_i$ is the variable for the biases.\n", "\n", "$$\n", "y_j = \\sigma(\\sum_i x_i W_{ij} + b_j)\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": { "id": "nYlFUJWNjl4N" }, "source": [ "### Layout deduction\n", "\n", "This result comes from the following observations:\n", "\n", "- The preferred DTensor sharding for operands to a matrix dot product $t_j = \\sum_i x_i W_{ij}$ is to shard $\\mathbf{W}$ and $\\mathbf{x}$ the same way along the $i$-axis.\n", "\n", "- The preferred DTensor sharding for operands to a matrix sum $t_j + b_j$, is to shard $\\mathbf{t}$ and $\\mathbf{b}$ the same way along the $j$-axis.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "VpKblz7Yb16G" }, "outputs": [], "source": [ "class Dense(tf.Module):\n", "\n", " def __init__(self, input_size, output_size,\n", " init_seed, weight_layout, activation=None):\n", " super().__init__()\n", "\n", " random_normal_initializer = tf.function(tf.random.stateless_normal)\n", "\n", " self.weight = dtensor.DVariable(\n", " dtensor.call_with_layout(\n", " random_normal_initializer, weight_layout,\n", " shape=[input_size, output_size],\n", " seed=init_seed\n", " ))\n", " if activation is None:\n", " activation = lambda x:x\n", " self.activation = activation\n", " \n", " # bias is sharded the same way as the last axis of weight.\n", " bias_layout = weight_layout.delete([0])\n", "\n", " self.bias = dtensor.DVariable(\n", " dtensor.call_with_layout(tf.zeros, bias_layout, [output_size]))\n", "\n", " def __call__(self, x):\n", " y = tf.matmul(x, self.weight) + self.bias\n", " y = self.activation(y)\n", "\n", " return y" ] }, { "cell_type": "markdown", "metadata": { "id": "tfVY_vAKbxM0" }, "source": [ "### BatchNorm\n", "\n", "A batch normalization layer helps avoid collapsing modes while training. In this case, adding batch normalization layers helps model training avoid producing a model that only produces zeros.\n", "\n", "The constructor of the custom `BatchNorm` layer below does not take a `Layout` argument. This is because `BatchNorm` has no layer variables. This still works with DTensor because 'x', the only input to the layer, is already a DTensor that represents the global batch.\n", "\n", "Note: With DTensor, the input Tensor 'x' always represents the global batch. Therefore `tf.nn.batch_normalization` is applied to the global batch. This differs from training with `tf.distribute.MirroredStrategy`, where Tensor 'x' only represents the per-replica shard of the batch (the local batch)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "riBA9pfhlPFq" }, "outputs": [], "source": [ "class BatchNorm(tf.Module):\n", "\n", " def __init__(self):\n", " super().__init__()\n", "\n", " def __call__(self, x, training=True):\n", " if not training:\n", " # This branch is not used in the Tutorial.\n", " pass\n", " mean, variance = tf.nn.moments(x, axes=[0])\n", " return tf.nn.batch_normalization(x, mean, variance, 0.0, 1.0, 1e-5)" ] }, { "cell_type": "markdown", "metadata": { "id": "q4R4MPz5prh4" }, "source": [ "A full featured batch normalization layer (such as `tf.keras.layers.BatchNormalization`) will need Layout arguments for its variables." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "unFcP99zprJj" }, "outputs": [], "source": [ "def make_keras_bn(bn_layout):\n", " return tf.keras.layers.BatchNormalization(gamma_layout=bn_layout,\n", " beta_layout=bn_layout,\n", " moving_mean_layout=bn_layout,\n", " moving_variance_layout=bn_layout,\n", " fused=False)" ] }, { "cell_type": "markdown", "metadata": { "id": "v8Dj7AJ_lPs0" }, "source": [ "### Putting Layers Together\n", "\n", "Next, build a Multi-layer perceptron (MLP) network with the building blocks above. The diagram below shows the axis relationships between the input `x` and the weight matrices for the two `Dense` layers without any DTensor sharding or replication applied." ] }, { "cell_type": "markdown", "metadata": { "id": "udFGAO-NrZw6" }, "source": [ "<img src=\"https://www.tensorflow.org/images/dtensor/no_dtensor.png\" alt=\"The input and weight matrices for a non distributed model.\" class=\"no-filter\">\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8DCQ0aQ5rQtB" }, "source": [ "The output of the first `Dense` layer is passed into the input of the second `Dense` layer (after the `BatchNorm`). Therefore, the preferred DTensor sharding for the output of first `Dense` layer ($\\mathbf{W_1}$) and the input of second `Dense` layer ($\\mathbf{W_2}$) is to shard $\\mathbf{W_1}$ and $\\mathbf{W_2}$ the same way along the common axis $\\hat{j}$,\n", "\n", "$$\n", "\\mathsf{Layout}[{W_{1,ij}}; i, j] = \\left[\\hat{i}, \\hat{j}\\right] \\\\\n", "\\mathsf{Layout}[{W_{2,jk}}; j, k] = \\left[\\hat{j}, \\hat{k} \\right]\n", "$$\n", "\n", "Even though the layout deduction shows that the 2 layouts are not independent, for the sake of simplicity of the model interface, `MLP` will take 2 `Layout` arguments, one per Dense layer." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "junyS-965opl" }, "outputs": [], "source": [ "from typing import Tuple\n", "\n", "class MLP(tf.Module):\n", "\n", " def __init__(self, dense_layouts: Tuple[dtensor.Layout, dtensor.Layout]):\n", " super().__init__()\n", "\n", " self.dense1 = Dense(\n", " 1200, 48, (1, 2), dense_layouts[0], activation=tf.nn.relu)\n", " self.bn = BatchNorm()\n", " self.dense2 = Dense(48, 2, (3, 4), dense_layouts[1])\n", "\n", " def __call__(self, x):\n", " y = x\n", " y = self.dense1(y)\n", " y = self.bn(y)\n", " y = self.dense2(y)\n", " return y\n" ] }, { "cell_type": "markdown", "metadata": { "id": "9dgLmebHhr7h" }, "source": [ "The trade-off between correctness in layout deduction constraints and simplicity of API is a common design point of APIs that uses DTensor.\n", "It is also possible to capture the dependency between `Layout`'s with a different API. For example, the `MLPStricter` class creates the `Layout` objects in the constructor." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wEZR7UlihsYX" }, "outputs": [], "source": [ "class MLPStricter(tf.Module):\n", "\n", " def __init__(self, mesh, input_mesh_dim, inner_mesh_dim1, output_mesh_dim):\n", " super().__init__()\n", "\n", " self.dense1 = Dense(\n", " 1200, 48, (1, 2), dtensor.Layout([input_mesh_dim, inner_mesh_dim1], mesh),\n", " activation=tf.nn.relu)\n", " self.bn = BatchNorm()\n", " self.dense2 = Dense(48, 2, (3, 4), dtensor.Layout([inner_mesh_dim1, output_mesh_dim], mesh))\n", "\n", "\n", " def __call__(self, x):\n", " y = x\n", " y = self.dense1(y)\n", " y = self.bn(y)\n", " y = self.dense2(y)\n", " return y" ] }, { "cell_type": "markdown", "metadata": { "id": "GcQi7D5mal2L" }, "source": [ "To make sure the model runs, probe your model with fully replicated layouts and a fully replicated batch of `'x'` input." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zOPuYeQwallh" }, "outputs": [], "source": [ "WORLD = dtensor.create_mesh([(\"world\", 8)], devices=DEVICES)\n", "\n", "model = MLP([dtensor.Layout.replicated(WORLD, rank=2),\n", " dtensor.Layout.replicated(WORLD, rank=2)])\n", "\n", "sample_x, sample_y = train_data_vec.take(1).get_single_element()\n", "sample_x = dtensor.copy_to_mesh(sample_x, dtensor.Layout.replicated(WORLD, rank=2))\n", "print(model(sample_x))" ] }, { "cell_type": "markdown", "metadata": { "id": "akrjDstEpDv9" }, "source": [ "## Moving data to the device\n", "\n", "Usually, `tf.data` iterators (and other data fetching methods) yield tensor objects backed by the local host device memory. This data must be transferred to the accelerator device memory that backs DTensor's component tensors.\n", "\n", "`dtensor.copy_to_mesh` is unsuitable for this situation because it replicates input tensors to all devices due to DTensor's global perspective. So in this tutorial, you will use a helper function `repack_local_tensor`, to facilitate the transfer of data. This helper function uses `dtensor.pack` to send (and only send) the shard of the global batch that is intended for a replica to the device backing the replica.\n", "\n", "This simplified function assumes single-client. Determining the correct way to split the local tensor and the mapping between the pieces of the split and the local devices can be laboring in a multi-client application.\n", "\n", "Additional DTensor API to simplify `tf.data` integration is planned, supporting both single-client and multi-client applications. Please stay tuned." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3t5WvQR4Hvo4" }, "outputs": [], "source": [ "def repack_local_tensor(x, layout):\n", " \"\"\"Repacks a local Tensor-like to a DTensor with layout.\n", "\n", " This function assumes a single-client application.\n", " \"\"\"\n", " x = tf.convert_to_tensor(x)\n", " sharded_dims = []\n", "\n", " # For every sharded dimension, use tf.split to split the along the dimension.\n", " # The result is a nested list of split-tensors in queue[0].\n", " queue = [x]\n", " for axis, dim in enumerate(layout.sharding_specs):\n", " if dim == dtensor.UNSHARDED:\n", " continue\n", " num_splits = layout.shape[axis]\n", " queue = tf.nest.map_structure(lambda x: tf.split(x, num_splits, axis=axis), queue)\n", " sharded_dims.append(dim)\n", "\n", " # Now we can build the list of component tensors by looking up the location in\n", " # the nested list of split-tensors created in queue[0].\n", " components = []\n", " for locations in layout.mesh.local_device_locations():\n", " t = queue[0]\n", " for dim in sharded_dims:\n", " split_index = locations[dim] # Only valid on single-client mesh.\n", " t = t[split_index]\n", " components.append(t)\n", "\n", " return dtensor.pack(components, layout)" ] }, { "cell_type": "markdown", "metadata": { "id": "2KKCDcjG7zj2" }, "source": [ "## Data parallel training\n", "\n", "In this section, you will train your MLP model with data parallel training. The following sections will demonstrate model parallel training and spatial parallel training.\n", "\n", "Data parallel training is a commonly used scheme for distributed machine learning:\n", "\n", " - Model variables are replicated on N devices each.\n", " - A global batch is split into N per-replica batches.\n", " - Each per-replica batch is trained on the replica device.\n", " - The gradient is reduced before weight up data is collectively performed on all replicas.\n", "\n", "Data parallel training provides nearly linear speedup regarding the number of devices." ] }, { "cell_type": "markdown", "metadata": { "id": "UMsLUyTGq3oL" }, "source": [ "### Creating a data parallel mesh\n", "\n", "A typical data parallelism training loop uses a DTensor `Mesh` that consists of a single `batch` dimension, where each device becomes a replica that receives a shard from the global batch.\n", "\n", "<img src=\"https://www.tensorflow.org/images/dtensor/dtensor_data_para.png\" alt=\"Data parallel mesh\" class=\"no-filter\">\n", "\n", "\n", "The replicated model runs on the replica, therefore the model variables are fully replicated (unsharded)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "C0IyOlxmeu4I" }, "outputs": [], "source": [ "mesh = dtensor.create_mesh([(\"batch\", 8)], devices=DEVICES)\n", "\n", "model = MLP([dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh),\n", " dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh),])\n" ] }, { "cell_type": "markdown", "metadata": { "id": "OREKwBybo1gZ" }, "source": [ "### Packing training data to DTensors\n", "\n", "The training data batch should be packed into DTensors sharded along the `'batch'`(first) axis, such that DTensor will evenly distribute the training data to the `'batch'` mesh dimension.\n", "\n", "**Note**: In DTensor, the `batch size` always refers to the global batch size. The batch size should be chosen such that it can be divided evenly by the size of the `batch` mesh dimension." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8xMYkTpGocY8" }, "outputs": [], "source": [ "def repack_batch(x, y, mesh):\n", " x = repack_local_tensor(x, layout=dtensor.Layout(['batch', dtensor.UNSHARDED], mesh))\n", " y = repack_local_tensor(y, layout=dtensor.Layout(['batch'], mesh))\n", " return x, y\n", "\n", "sample_x, sample_y = train_data_vec.take(1).get_single_element()\n", "sample_x, sample_y = repack_batch(sample_x, sample_y, mesh)\n", "\n", "print('x', sample_x[:, 0])\n", "print('y', sample_y)" ] }, { "cell_type": "markdown", "metadata": { "id": "uONSiqOIkFL1" }, "source": [ "### Training step\n", "\n", "This example uses a Stochastic Gradient Descent optimizer with the Custom Training Loop (CTL). Consult the [Custom Training Loop guide](https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch) and [Walk through](https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough) for more information on those topics.\n", "\n", "The `train_step` is encapsulated as a `tf.function` to indicate this body is to be traced as a TensorFlow Graph. The body of `train_step` consists of a forward inference pass, a backward gradient pass, and the variable update.\n", "\n", "Note that the body of `train_step` does not contain any special DTensor annotations. Instead, `train_step` only contains high-level TensorFlow operations that process the input `x` and `y` from the global view of the input batch and the model. All of the DTensor annotations (`Mesh`, `Layout`) are factored out of the train step." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "BwUFzLGDtQT6" }, "outputs": [], "source": [ "# Refer to the CTL (custom training loop guide)\n", "@tf.function\n", "def train_step(model, x, y, learning_rate=tf.constant(1e-4)):\n", " with tf.GradientTape() as tape:\n", " logits = model(x)\n", " # tf.reduce_sum sums the batch sharded per-example loss to a replicated\n", " # global loss (scalar).\n", " loss = tf.reduce_sum(\n", " tf.nn.sparse_softmax_cross_entropy_with_logits(\n", " logits=logits, labels=y))\n", " parameters = model.trainable_variables\n", " gradients = tape.gradient(loss, parameters)\n", " for parameter, parameter_gradient in zip(parameters, gradients):\n", " parameter.assign_sub(learning_rate * parameter_gradient)\n", "\n", " # Define some metrics\n", " accuracy = 1.0 - tf.reduce_sum(tf.cast(tf.argmax(logits, axis=-1, output_type=tf.int64) != y, tf.float32)) / x.shape[0]\n", " loss_per_sample = loss / len(x)\n", " return {'loss': loss_per_sample, 'accuracy': accuracy}" ] }, { "cell_type": "markdown", "metadata": { "id": "0OYTu4j0evWT" }, "source": [ "### Checkpointing\n", "\n", "You can checkpoint a DTensor model using `dtensor.DTensorCheckpoint`. The format of a DTensor checkpoint is fully compatible with a Standard TensorFlow Checkpoint. There is ongoing work to consolidate `dtensor.DTensorCheckpoint` into `tf.train.Checkpoint`.\n", "\n", "When a DTensor checkpoint is restored, `Layout`s of variables can be different from when the checkpoint is saved. This tutorial makes use of this feature to continue the training in the Model Parallel training and Spatial Parallel training sections.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "rsInFFJg7x9t" }, "outputs": [], "source": [ "CHECKPOINT_DIR = tempfile.mkdtemp()\n", "\n", "def start_checkpoint_manager(mesh, model):\n", " ckpt = dtensor.DTensorCheckpoint(mesh, root=model)\n", " manager = tf.train.CheckpointManager(ckpt, CHECKPOINT_DIR, max_to_keep=3)\n", "\n", " if manager.latest_checkpoint:\n", " print(\"Restoring a checkpoint\")\n", " ckpt.restore(manager.latest_checkpoint).assert_consumed()\n", " else:\n", " print(\"new training\")\n", " return manager\n" ] }, { "cell_type": "markdown", "metadata": { "id": "9r77ky5Jgp1j" }, "source": [ "### Training loop\n", "\n", "For the data parallel training scheme, train for epochs and report the progress. 3 epochs is insufficient for training the model -- an accuracy of 50% is as good as randomly guessing.\n", "\n", "Enable checkpointing so that you can pick up the training later. In the following section, you will load the checkpoint and train with a different parallel scheme." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UaLn-vGZgqbS" }, "outputs": [], "source": [ "num_epochs = 2\n", "manager = start_checkpoint_manager(mesh, model)\n", "\n", "for epoch in range(num_epochs):\n", " step = 0\n", " pbar = tf.keras.utils.Progbar(target=int(train_data_vec.cardinality()), stateful_metrics=[])\n", " metrics = {'epoch': epoch}\n", " for x,y in train_data_vec:\n", "\n", " x, y = repack_batch(x, y, mesh)\n", "\n", " metrics.update(train_step(model, x, y, 1e-2))\n", "\n", " pbar.update(step, values=metrics.items(), finalize=False)\n", " step += 1\n", " manager.save()\n", " pbar.update(step, values=metrics.items(), finalize=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "YRFJEhum7EGD" }, "source": [ "## Model Parallel Training\n", "\n", "If you switch to a 2 dimensional `Mesh`, and shard the model variables along the second mesh dimension, then the training becomes Model Parallel.\n", "\n", "In Model Parallel training, each model replica spans multiple devices (2 in this case):\n", "\n", "- There are 4 model replicas, and the training data batch is distributed to the 4 replicas.\n", "- The 2 devices within a single model replica receive replicated training data.\n", "\n", "\n", "<img src=\"https://www.tensorflow.org/images/dtensor/dtensor_model_para.png\" alt=\"Model parallel mesh\" class=\"no-filter\">\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5gZE9IT5Dzwl" }, "outputs": [], "source": [ "mesh = dtensor.create_mesh([(\"batch\", 4), (\"model\", 2)], devices=DEVICES)\n", "model = MLP([dtensor.Layout([dtensor.UNSHARDED, \"model\"], mesh), \n", " dtensor.Layout([\"model\", dtensor.UNSHARDED], mesh)])" ] }, { "cell_type": "markdown", "metadata": { "id": "Ihof3DkMFKnf" }, "source": [ "As the training data is still sharded along the batch dimension, you can reuse the same `repack_batch` function as the Data Parallel training case. DTensor will automatically replicate the per-replica batch to all devices inside the replica along the `\"model\"` mesh dimension." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dZf56ynbE_p1" }, "outputs": [], "source": [ "def repack_batch(x, y, mesh):\n", " x = repack_local_tensor(x, layout=dtensor.Layout(['batch', dtensor.UNSHARDED], mesh))\n", " y = repack_local_tensor(y, layout=dtensor.Layout(['batch'], mesh))\n", " return x, y" ] }, { "cell_type": "markdown", "metadata": { "id": "UW3OXdhNFfpv" }, "source": [ "Next run the training loop. The training loop reuses the same checkpoint manager as the Data Parallel training example, and the code looks identical.\n", "\n", "You can continue training the data parallel trained model under model parallel training." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LLC0wgii7EgA" }, "outputs": [], "source": [ "num_epochs = 2\n", "manager = start_checkpoint_manager(mesh, model)\n", "\n", "for epoch in range(num_epochs):\n", " step = 0\n", " pbar = tf.keras.utils.Progbar(target=int(train_data_vec.cardinality()))\n", " metrics = {'epoch': epoch}\n", " for x,y in train_data_vec:\n", " x, y = repack_batch(x, y, mesh)\n", " metrics.update(train_step(model, x, y, 1e-2))\n", " pbar.update(step, values=metrics.items(), finalize=False)\n", " step += 1\n", " manager.save()\n", " pbar.update(step, values=metrics.items(), finalize=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "BZH-aMrVzi2L" }, "source": [ "## Spatial Parallel Training" ] }, { "cell_type": "markdown", "metadata": { "id": "u-bK6IZ9GCS9" }, "source": [ "When training data of very high dimensionality (e.g. a very large image or a video), it may be desirable to shard along the feature dimension. This is called [Spatial Partitioning](https://cloud.google.com/blog/products/ai-machine-learning/train-ml-models-on-large-images-and-3d-volumes-with-spatial-partitioning-on-cloud-tpus), which was first introduced into TensorFlow for training models with large 3-d input samples.\n", "\n", "<img src=\"https://www.tensorflow.org/images/dtensor/dtensor_spatial_para.png\" alt=\"Spatial parallel mesh\" class=\"no-filter\">\n", "\n", "DTensor also supports this case. The only change you need to do is to create a Mesh that includes a `feature` dimension, and apply the corresponding `Layout`.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jpc9mqURGpmK" }, "outputs": [], "source": [ "mesh = dtensor.create_mesh([(\"batch\", 2), (\"feature\", 2), (\"model\", 2)], devices=DEVICES)\n", "model = MLP([dtensor.Layout([\"feature\", \"model\"], mesh), \n", " dtensor.Layout([\"model\", dtensor.UNSHARDED], mesh)])\n" ] }, { "cell_type": "markdown", "metadata": { "id": "i07Wrv-jHBc1" }, "source": [ "Shard the input data along the `feature` dimension when packing the input tensors to DTensors. You do this with a slightly different repack function, `repack_batch_for_spt`, where `spt` stands for Spatial Parallel Training." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DWR8qF6BGtFL" }, "outputs": [], "source": [ "def repack_batch_for_spt(x, y, mesh):\n", " # Shard data on feature dimension, too\n", " x = repack_local_tensor(x, layout=dtensor.Layout([\"batch\", 'feature'], mesh))\n", " y = repack_local_tensor(y, layout=dtensor.Layout([\"batch\"], mesh))\n", " return x, y" ] }, { "cell_type": "markdown", "metadata": { "id": "Ygl9dqMUHTVN" }, "source": [ "The Spatial parallel training can also continue from a checkpoint created with other parallell training schemes." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "p3NnpHSKo-hx" }, "outputs": [], "source": [ "num_epochs = 2\n", "\n", "manager = start_checkpoint_manager(mesh, model)\n", "for epoch in range(num_epochs):\n", " step = 0\n", " metrics = {'epoch': epoch}\n", " pbar = tf.keras.utils.Progbar(target=int(train_data_vec.cardinality()))\n", "\n", " for x, y in train_data_vec:\n", " x, y = repack_batch_for_spt(x, y, mesh)\n", " metrics.update(train_step(model, x, y, 1e-2))\n", "\n", " pbar.update(step, values=metrics.items(), finalize=False)\n", " step += 1\n", " manager.save()\n", " pbar.update(step, values=metrics.items(), finalize=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "vp4L59CpJjYr" }, "source": [ "## SavedModel and DTensor\n", "\n", "The integration of DTensor and SavedModel is still under development. This section only describes the current status quo for TensorFlow 2.9.0.\n", "\n", "As of TensorFlow 2.9.0, `tf.saved_model` only accepts DTensor models with fully replicated variables.\n", "\n", "As a workaround, you can convert a DTensor model to a fully replicated one by reloading a checkpoint. However, after a model is saved, all DTensor annotations are lost and the saved signatures can only be used with regular Tensors, not DTensors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "49HfIq_SJZoj" }, "outputs": [], "source": [ "mesh = dtensor.create_mesh([(\"world\", 1)], devices=DEVICES[:1])\n", "mlp = MLP([dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh), \n", " dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh)])\n", "\n", "manager = start_checkpoint_manager(mesh, mlp)\n", "\n", "model_for_saving = tf.keras.Sequential([\n", " text_vectorization,\n", " mlp\n", "])\n", "\n", "@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])\n", "def run(inputs):\n", " return {'result': model_for_saving(inputs)}\n", "\n", "tf.saved_model.save(\n", " model_for_saving, \"/tmp/saved_model\",\n", " signatures=run)" ] }, { "cell_type": "markdown", "metadata": { "id": "h6Csim_VMGxQ" }, "source": [ "As of TensorFlow 2.9.0, you can only call a loaded signature with a regular Tensor, or a fully replicated DTensor (which will be converted to a regular Tensor)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HG_ASSzR4IWW" }, "outputs": [], "source": [ "sample_batch = train_data.take(1).get_single_element()\n", "sample_batch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qW8yKPrhKQ5b" }, "outputs": [], "source": [ "loaded = tf.saved_model.load(\"/tmp/saved_model\")\n", "\n", "run_sig = loaded.signatures[\"serving_default\"]\n", "result = run_sig(sample_batch['text'])['result']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "GahGbv0ZmkJb" }, "outputs": [], "source": [ "np.mean(tf.argmax(result, axis=-1) == sample_batch['label'])" ] }, { "cell_type": "markdown", "metadata": { "id": "Ks-Vs9qsH6jO" }, "source": [ "## What's next?\n", "\n", "This tutorial demonstrated building and training an MLP sentiment analysis model with DTensor.\n", "\n", "Through `Mesh` and `Layout` primitives, DTensor can transform a TensorFlow `tf.function` to a distributed program suitable for a variety of training schemes.\n", "\n", "In a real-world machine learning application, evaluation and cross-validation should be applied to avoid producing an over-fitted model. The techniques introduced in this tutorial can also be applied to introduce parallelism to evaluation.\n", "\n", "Composing a model with `tf.Module` from scratch is a lot of work, and reusing existing building blocks such as layers and helper functions can drastically speed up model development.\n", "As of TensorFlow 2.9, all Keras Layers under `tf.keras.layers` accepts DTensor layouts as their arguments, and can be used to build DTensor models. You can even directly reuse a Keras model with DTensor without modifying the model implementation. Refer to the [DTensor Keras Integration Tutorial](https://www.tensorflow.org/tutorials/distribute/dtensor_keras_tutorial) for information on using DTensor Keras. " ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "dtensor_ml_tutorial.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

datala/311-analysis

311 Combining CSV Datasets, Parsing by Week.ipynb

1

246707

{ "cells": [ { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import sys\n", "import os\n", "import numpy as np\n", "\n", "from mapboxgl.viz import *\n", "from mapboxgl.utils import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Concatenated all 4 databases into one called \"MyLA311_All_Requests.csv\"" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "fifteen = pd.read_csv(\"MyLA311_Service_Request_Data_2015.csv\", low_memory = False)\n", "sixteen = pd.read_csv(\"MyLA311_Service_Request_Data_2016.csv\", low_memory = False)\n", "seventeen = pd.read_csv(\"MyLA311_Service_Request_Data_2017.csv\", low_memory = False)\n", "eighteen = pd.read_csv(\"MyLA311_Service_Request_Data_2018.csv\", low_memory = False)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "all_311_requests = pd.concat([fifteen, sixteen, seventeen, eighteen], axis=0).sort_index()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "all_311_requests.to_csv(\"MyLA311_All_Requests.csv\", encoding = 'utf-8', index = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parsed the merged database for all empty coordinates and removed them" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "my_311 = pd.read_csv(\"MyLA311_All_Requests.csv\",sep=',', low_memory = False)\n", "my_311['Longitude'].replace('', np.nan, inplace=True)\n", "my_311.dropna(subset=['Longitude'], inplace=True)\n", "my_311.to_csv('311_parsed_coordinates.csv', encoding = 'utf-8', index = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Splitting the CreatedDate column into two: one for the date, one for the time" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2728: DtypeWarning: Columns (16,24) have mixed types. Specify dtype option on import or set low_memory=False.\n", " interactivity=interactivity, compiler=compiler, result=result)\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>SRNumber</th>\n", " <th>CreatedDate</th>\n", " <th>UpdatedDate</th>\n", " <th>ActionTaken</th>\n", " <th>Owner</th>\n", " <th>RequestType</th>\n", " <th>Status</th>\n", " <th>RequestSource</th>\n", " <th>MobileOS</th>\n", " <th>Anonymous</th>\n", " <th>...</th>\n", " <th>TBMColumn</th>\n", " <th>TBMRow</th>\n", " <th>APC</th>\n", " <th>CD</th>\n", " <th>CDMember</th>\n", " <th>NC</th>\n", " <th>NCName</th>\n", " <th>PolicePrecinct</th>\n", " <th>Created Date</th>\n", " <th>Created Time</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1-78597941</td>\n", " <td>12/10/15 9:34</td>\n", " <td>12/11/15 8:55</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Metal/Household Appliances</td>\n", " <td>Closed</td>\n", " <td>Mobile App</td>\n", " <td>Android</td>\n", " <td>Y</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>5.0</td>\n", " <td>Central APC</td>\n", " <td>13.0</td>\n", " <td>Mitch O'Farrell</td>\n", " <td>33.0</td>\n", " <td>HOLLYWOOD STUDIO DISTRICT NC</td>\n", " <td>HOLLYWOOD</td>\n", " <td>12/10/15</td>\n", " <td>9:34</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1-952588571</td>\n", " <td>3/3/18 11:57</td>\n", " <td>3/3/18 12:00</td>\n", " <td>SR Created</td>\n", " <td>OCB</td>\n", " <td>Graffiti Removal</td>\n", " <td>Closed</td>\n", " <td>Driver Self Report</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>4.0</td>\n", " <td>Central APC</td>\n", " <td>13.0</td>\n", " <td>NaN</td>\n", " <td>30.0</td>\n", " <td>HOLLYWOOD UNITED NC</td>\n", " <td>HOLLYWOOD</td>\n", " <td>3/3/18</td>\n", " <td>11:57</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1-88226921</td>\n", " <td>1/1/16 8:28</td>\n", " <td>1/4/16 14:52</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>B</td>\n", " <td>1.0</td>\n", " <td>East Los Angeles APC</td>\n", " <td>1.0</td>\n", " <td>Gilbert Cedillo</td>\n", " <td>47.0</td>\n", " <td>LINCOLN HEIGHTS NC</td>\n", " <td>HOLLENBECK</td>\n", " <td>1/1/16</td>\n", " <td>8:28</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1-513938562</td>\n", " <td>4/10/17 11:39</td>\n", " <td>4/12/17 9:56</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Mobile App</td>\n", " <td>iOS</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>J</td>\n", " <td>3.0</td>\n", " <td>South Valley APC</td>\n", " <td>4.0</td>\n", " <td>David Ryu</td>\n", " <td>26.0</td>\n", " <td>SHERMAN OAKS NC</td>\n", " <td>VAN NUYS</td>\n", " <td>4/10/17</td>\n", " <td>11:39</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1-513940271</td>\n", " <td>4/10/17 11:42</td>\n", " <td>4/14/17 7:50</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>A</td>\n", " <td>5.0</td>\n", " <td>North Valley APC</td>\n", " <td>7.0</td>\n", " <td>Vacant</td>\n", " <td>5.0</td>\n", " <td>SYLMAR NC</td>\n", " <td>MISSION</td>\n", " <td>4/10/17</td>\n", " <td>11:42</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1-88276381</td>\n", " <td>1/1/16 11:07</td>\n", " <td>1/8/16 11:19</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>Y</td>\n", " <td>...</td>\n", " <td>J</td>\n", " <td>4.0</td>\n", " <td>Central APC</td>\n", " <td>1.0</td>\n", " <td>Gilbert Cedillo</td>\n", " <td>76.0</td>\n", " <td>PICO UNION NC</td>\n", " <td>OLYMPIC</td>\n", " <td>1/1/16</td>\n", " <td>11:07</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1-1074967561</td>\n", " <td>6/22/18 11:13</td>\n", " <td>6/23/18 12:51</td>\n", " <td>SR Created</td>\n", " <td>ITA</td>\n", " <td>Other</td>\n", " <td>Closed</td>\n", " <td>Self Service</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>D</td>\n", " <td>6.0</td>\n", " <td>South Valley APC</td>\n", " <td>6.0</td>\n", " <td>Nury Martinez</td>\n", " <td>19.0</td>\n", " <td>LAKE BALBOA NC</td>\n", " <td>WEST VALLEY</td>\n", " <td>6/22/18</td>\n", " <td>11:13</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1-44772241</td>\n", " <td>10/1/15 3:11</td>\n", " <td>10/8/15 17:00</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Email</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>A</td>\n", " <td>1.0</td>\n", " <td>Central APC</td>\n", " <td>13.0</td>\n", " <td>Mitch O'Farrell</td>\n", " <td>55.0</td>\n", " <td>WILSHIRE CENTER - KOREATOWN NC</td>\n", " <td>OLYMPIC</td>\n", " <td>10/1/15</td>\n", " <td>3:11</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1-88295291</td>\n", " <td>1/1/16 14:02</td>\n", " <td>1/6/16 10:38</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>Y</td>\n", " <td>...</td>\n", " <td>B</td>\n", " <td>4.0</td>\n", " <td>Central APC</td>\n", " <td>1.0</td>\n", " <td>Gilbert Cedillo</td>\n", " <td>76.0</td>\n", " <td>PICO UNION NC</td>\n", " <td>RAMPART</td>\n", " <td>1/1/16</td>\n", " <td>14:02</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1-513938351</td>\n", " <td>4/10/17 11:42</td>\n", " <td>4/12/17 14:28</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>Y</td>\n", " <td>...</td>\n", " <td>A</td>\n", " <td>5.0</td>\n", " <td>South Valley APC</td>\n", " <td>2.0</td>\n", " <td>Paul Krekorian</td>\n", " <td>27.0</td>\n", " <td>STUDIO CITY NC</td>\n", " <td>NORTH HOLLYWOOD</td>\n", " <td>4/10/17</td>\n", " <td>11:42</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>1-952753491</td>\n", " <td>3/4/18 6:30</td>\n", " <td>3/5/18 13:13</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Electronic Waste</td>\n", " <td>Closed</td>\n", " <td>Self Service</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>A</td>\n", " <td>1.0</td>\n", " <td>North Valley APC</td>\n", " <td>7.0</td>\n", " <td>Monica Rodriguez</td>\n", " <td>5.0</td>\n", " <td>SYLMAR NC</td>\n", " <td>MISSION</td>\n", " <td>3/4/18</td>\n", " <td>6:30</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>1-78929421</td>\n", " <td>12/10/15 14:34</td>\n", " <td>12/16/15 13:26</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Metal/Household Appliances</td>\n", " <td>Cancelled</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>D</td>\n", " <td>1.0</td>\n", " <td>South Valley APC</td>\n", " <td>3.0</td>\n", " <td>Bob Blumenfield</td>\n", " <td>16.0</td>\n", " <td>WOODLAND HILLS-WARNER CENTER NC</td>\n", " <td>TOPANGA</td>\n", " <td>12/10/15</td>\n", " <td>14:34</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>1-88310551</td>\n", " <td>1/1/16 15:24</td>\n", " <td>1/5/16 13:28</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>B</td>\n", " <td>2.0</td>\n", " <td>East Los Angeles APC</td>\n", " <td>1.0</td>\n", " <td>Gilbert Cedillo</td>\n", " <td>41.0</td>\n", " <td>HISTORIC HIGHLAND PARK NC</td>\n", " <td>NORTHEAST</td>\n", " <td>1/1/16</td>\n", " <td>15:24</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>1-952753431</td>\n", " <td>3/4/18 6:23</td>\n", " <td>3/9/18 9:58</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Self Service</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>A</td>\n", " <td>3.0</td>\n", " <td>South Valley APC</td>\n", " <td>3.0</td>\n", " <td>Bob Blumenfield</td>\n", " <td>13.0</td>\n", " <td>CANOGA PARK NC</td>\n", " <td>TOPANGA</td>\n", " <td>3/4/18</td>\n", " <td>6:23</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>1-59599861</td>\n", " <td>11/2/15 8:11</td>\n", " <td>11/5/15 14:30</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>A</td>\n", " <td>1.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>8.0</td>\n", " <td>Marqueece Harris-Dawson</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>77TH STREET</td>\n", " <td>11/2/15</td>\n", " <td>8:11</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>1-513942821</td>\n", " <td>4/10/17 11:43</td>\n", " <td>4/17/17 8:33</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Self Service</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>B</td>\n", " <td>5.0</td>\n", " <td>East Los Angeles APC</td>\n", " <td>1.0</td>\n", " <td>Gilbert Cedillo</td>\n", " <td>42.0</td>\n", " <td>ARROYO SECO NC</td>\n", " <td>HOLLENBECK</td>\n", " <td>4/10/17</td>\n", " <td>11:43</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>1-59622271</td>\n", " <td>11/2/15 8:49</td>\n", " <td>11/4/15 16:00</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Electronic Waste</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>B</td>\n", " <td>3.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>8.0</td>\n", " <td>Marqueece Harris-Dawson</td>\n", " <td>87.0</td>\n", " <td>EMPOWERMENT CONGRESS SOUTHEAST AREA NDC</td>\n", " <td>SOUTHEAST</td>\n", " <td>11/2/15</td>\n", " <td>8:49</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>1-88309281</td>\n", " <td>1/1/16 16:11</td>\n", " <td>1/6/16 9:08</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>Y</td>\n", " <td>...</td>\n", " <td>D</td>\n", " <td>3.0</td>\n", " <td>Central APC</td>\n", " <td>1.0</td>\n", " <td>Gilbert Cedillo</td>\n", " <td>97.0</td>\n", " <td>WESTLAKE NORTH NC</td>\n", " <td>RAMPART</td>\n", " <td>1/1/16</td>\n", " <td>16:11</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>1-513946071</td>\n", " <td>4/10/17 11:43</td>\n", " <td>4/12/17 10:40</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>D</td>\n", " <td>1.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>9.0</td>\n", " <td>Curren D. Price Jr.</td>\n", " <td>78.0</td>\n", " <td>SOUTH CENTRAL NC</td>\n", " <td>NEWTON</td>\n", " <td>4/10/17</td>\n", " <td>11:43</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>1-952753561</td>\n", " <td>3/4/18 6:31</td>\n", " <td>3/9/18 12:38</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Homeless Encampment</td>\n", " <td>Closed</td>\n", " <td>Self Service</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>F</td>\n", " <td>7.0</td>\n", " <td>North Valley APC</td>\n", " <td>7.0</td>\n", " <td>Monica Rodriguez</td>\n", " <td>9.0</td>\n", " <td>FOOTHILL TRAILS DISTRICT NC</td>\n", " <td>FOOTHILL</td>\n", " <td>3/4/18</td>\n", " <td>6:31</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>1-59618771</td>\n", " <td>11/2/15 8:49</td>\n", " <td>11/4/15 15:00</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>E</td>\n", " <td>4.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>9.0</td>\n", " <td>Curren D. Price Jr.</td>\n", " <td>125.0</td>\n", " <td>ZAPATA KING NC</td>\n", " <td>NEWTON</td>\n", " <td>11/2/15</td>\n", " <td>8:49</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>1-88376161</td>\n", " <td>1/2/16 8:38</td>\n", " <td>1/6/16 10:45</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>C</td>\n", " <td>3.0</td>\n", " <td>East Los Angeles APC</td>\n", " <td>1.0</td>\n", " <td>Gilbert Cedillo</td>\n", " <td>41.0</td>\n", " <td>HISTORIC HIGHLAND PARK NC</td>\n", " <td>NORTHEAST</td>\n", " <td>1/2/16</td>\n", " <td>8:38</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>1-513945898</td>\n", " <td>4/10/17 11:43</td>\n", " <td>4/17/17 10:59</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Mobile App</td>\n", " <td>Android</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>D</td>\n", " <td>6.0</td>\n", " <td>Central APC</td>\n", " <td>14.0</td>\n", " <td>Jose Huizar</td>\n", " <td>52.0</td>\n", " <td>DOWNTOWN LOS ANGELES</td>\n", " <td>CENTRAL</td>\n", " <td>4/10/17</td>\n", " <td>11:43</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>1-952764861</td>\n", " <td>3/4/18 6:56</td>\n", " <td>3/8/18 7:19</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>C</td>\n", " <td>3.0</td>\n", " <td>Central APC</td>\n", " <td>4.0</td>\n", " <td>David Ryu</td>\n", " <td>58.0</td>\n", " <td>MID CITY WEST CC</td>\n", " <td>WILSHIRE</td>\n", " <td>3/4/18</td>\n", " <td>6:56</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>1-952759351</td>\n", " <td>3/4/18 6:57</td>\n", " <td>3/9/18 10:00</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Homeless Encampment</td>\n", " <td>Closed</td>\n", " <td>Mobile App</td>\n", " <td>iOS</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>J</td>\n", " <td>2.0</td>\n", " <td>South Valley APC</td>\n", " <td>3.0</td>\n", " <td>Bob Blumenfield</td>\n", " <td>16.0</td>\n", " <td>WOODLAND HILLS-WARNER CENTER NC</td>\n", " <td>TOPANGA</td>\n", " <td>3/4/18</td>\n", " <td>6:57</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>1-513946221</td>\n", " <td>4/10/17 11:43</td>\n", " <td>4/14/17 13:10</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>Y</td>\n", " <td>...</td>\n", " <td>B</td>\n", " <td>7.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>10.0</td>\n", " <td>Herb J. Wesson Jr.</td>\n", " <td>75.0</td>\n", " <td>WEST ADAMS NC</td>\n", " <td>SOUTHWEST</td>\n", " <td>4/10/17</td>\n", " <td>11:43</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>1-59623111</td>\n", " <td>11/2/15 8:54</td>\n", " <td>11/3/15 14:00</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>B</td>\n", " <td>2.0</td>\n", " <td>North Valley APC</td>\n", " <td>3.0</td>\n", " <td>Bob Blumenfield</td>\n", " <td>13.0</td>\n", " <td>CANOGA PARK NC</td>\n", " <td>TOPANGA</td>\n", " <td>11/2/15</td>\n", " <td>8:54</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>1-88430141</td>\n", " <td>1/2/16 10:47</td>\n", " <td>1/8/16 13:28</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>Y</td>\n", " <td>...</td>\n", " <td>A</td>\n", " <td>4.0</td>\n", " <td>Central APC</td>\n", " <td>1.0</td>\n", " <td>Gilbert Cedillo</td>\n", " <td>76.0</td>\n", " <td>PICO UNION NC</td>\n", " <td>OLYMPIC</td>\n", " <td>1/2/16</td>\n", " <td>10:47</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>1-88449071</td>\n", " <td>1/2/16 11:21</td>\n", " <td>1/5/16 11:50</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>Y</td>\n", " <td>...</td>\n", " <td>J</td>\n", " <td>5.0</td>\n", " <td>East Los Angeles APC</td>\n", " <td>1.0</td>\n", " <td>Gilbert Cedillo</td>\n", " <td>102.0</td>\n", " <td>GREATER CYPRESS PARK NC</td>\n", " <td>NORTHEAST</td>\n", " <td>1/2/16</td>\n", " <td>11:21</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>1-513946281</td>\n", " <td>4/10/17 11:44</td>\n", " <td>4/10/17 11:44</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Driver Self Report</td>\n", " <td>NaN</td>\n", " <td>Y</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>2.0</td>\n", " <td>North Valley APC</td>\n", " <td>6.0</td>\n", " <td>Nury Martinez</td>\n", " <td>23.0</td>\n", " <td>NORTH HOLLYWOOD NORTHEAST NC</td>\n", " <td>FOOTHILL</td>\n", " <td>4/10/17</td>\n", " <td>11:44</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>2483664</th>\n", " <td>1-820215191</td>\n", " <td>11/16/17 15:01</td>\n", " <td>11/17/17 12:45</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>B</td>\n", " <td>2.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>10.0</td>\n", " <td>Herb J. Wesson Jr.</td>\n", " <td>79.0</td>\n", " <td>EMPOWERMENT CONGRESS WEST AREA NDC</td>\n", " <td>SOUTHWEST</td>\n", " <td>11/16/17</td>\n", " <td>15:01</td>\n", " </tr>\n", " <tr>\n", " <th>2483665</th>\n", " <td>1-820218661</td>\n", " <td>11/16/17 15:02</td>\n", " <td>11/24/17 8:11</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>2.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>8.0</td>\n", " <td>Marqueece Harris-Dawson</td>\n", " <td>84.0</td>\n", " <td>EMPOWERMENT CONGRESS SOUTHWEST AREA NDC</td>\n", " <td>77TH STREET</td>\n", " <td>11/16/17</td>\n", " <td>15:02</td>\n", " </tr>\n", " <tr>\n", " <th>2483666</th>\n", " <td>1-820218811</td>\n", " <td>11/16/17 15:02</td>\n", " <td>11/17/17 12:48</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>A</td>\n", " <td>6.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>10.0</td>\n", " <td>Herb J. Wesson Jr.</td>\n", " <td>73.0</td>\n", " <td>MID CITY NC</td>\n", " <td>WILSHIRE</td>\n", " <td>11/16/17</td>\n", " <td>15:02</td>\n", " </tr>\n", " <tr>\n", " <th>2483667</th>\n", " <td>1-820219621</td>\n", " <td>11/16/17 15:02</td>\n", " <td>11/17/17 12:55</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Self Service</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>B</td>\n", " <td>4.0</td>\n", " <td>North Valley APC</td>\n", " <td>6.0</td>\n", " <td>Nury Martinez</td>\n", " <td>6.0</td>\n", " <td>ARLETA NC</td>\n", " <td>MISSION</td>\n", " <td>11/16/17</td>\n", " <td>15:02</td>\n", " </tr>\n", " <tr>\n", " <th>2483668</th>\n", " <td>1-820220931</td>\n", " <td>11/16/17 15:02</td>\n", " <td>11/17/17 10:23</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>F</td>\n", " <td>5.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>15.0</td>\n", " <td>Joe Buscaino</td>\n", " <td>88.0</td>\n", " <td>WATTS NC</td>\n", " <td>SOUTHEAST</td>\n", " <td>11/16/17</td>\n", " <td>15:02</td>\n", " </tr>\n", " <tr>\n", " <th>2483669</th>\n", " <td>1-820221011</td>\n", " <td>11/16/17 15:02</td>\n", " <td>11/20/17 9:33</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>J</td>\n", " <td>2.0</td>\n", " <td>North Valley APC</td>\n", " <td>7.0</td>\n", " <td>Monica Rodriguez</td>\n", " <td>10.0</td>\n", " <td>SUNLAND-TUJUNGA NC</td>\n", " <td>FOOTHILL</td>\n", " <td>11/16/17</td>\n", " <td>15:02</td>\n", " </tr>\n", " <tr>\n", " <th>2483670</th>\n", " <td>1-820217761</td>\n", " <td>11/16/17 15:03</td>\n", " <td>11/17/17 9:44</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>A</td>\n", " <td>2.0</td>\n", " <td>Central APC</td>\n", " <td>10.0</td>\n", " <td>Herb J. Wesson Jr.</td>\n", " <td>55.0</td>\n", " <td>WILSHIRE CENTER - KOREATOWN NC</td>\n", " <td>OLYMPIC</td>\n", " <td>11/16/17</td>\n", " <td>15:03</td>\n", " </tr>\n", " <tr>\n", " <th>2483671</th>\n", " <td>1-820221611</td>\n", " <td>11/16/17 15:03</td>\n", " <td>11/17/17 8:13</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>E</td>\n", " <td>3.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>9.0</td>\n", " <td>Curren D. Price Jr.</td>\n", " <td>87.0</td>\n", " <td>EMPOWERMENT CONGRESS SOUTHEAST AREA NDC</td>\n", " <td>SOUTHEAST</td>\n", " <td>11/16/17</td>\n", " <td>15:03</td>\n", " </tr>\n", " <tr>\n", " <th>2483672</th>\n", " <td>1-820221971</td>\n", " <td>11/16/17 15:03</td>\n", " <td>11/17/17 9:07</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>E</td>\n", " <td>6.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>8.0</td>\n", " <td>Marqueece Harris-Dawson</td>\n", " <td>87.0</td>\n", " <td>EMPOWERMENT CONGRESS SOUTHEAST AREA NDC</td>\n", " <td>SOUTHEAST</td>\n", " <td>11/16/17</td>\n", " <td>15:03</td>\n", " </tr>\n", " <tr>\n", " <th>2483673</th>\n", " <td>1-820222111</td>\n", " <td>11/16/17 15:03</td>\n", " <td>11/22/17 13:26</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Illegal Dumping Pickup</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>H</td>\n", " <td>4.0</td>\n", " <td>East Los Angeles APC</td>\n", " <td>1.0</td>\n", " <td>Gilbert Cedillo</td>\n", " <td>102.0</td>\n", " <td>GREATER CYPRESS PARK NC</td>\n", " <td>NORTHEAST</td>\n", " <td>11/16/17</td>\n", " <td>15:03</td>\n", " </tr>\n", " <tr>\n", " <th>2483674</th>\n", " <td>1-784437471</td>\n", " <td>10/19/17 18:12</td>\n", " <td>10/23/17 6:19</td>\n", " <td>SR Created</td>\n", " <td>OCB</td>\n", " <td>Graffiti Removal</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>F</td>\n", " <td>7.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>15.0</td>\n", " <td>Joe Buscaino</td>\n", " <td>90.0</td>\n", " <td>HARBOR GATEWAY NORTH NC</td>\n", " <td>SOUTHEAST</td>\n", " <td>10/19/17</td>\n", " <td>18:12</td>\n", " </tr>\n", " <tr>\n", " <th>2483675</th>\n", " <td>1-785428391</td>\n", " <td>10/20/17 14:01</td>\n", " <td>10/23/17 6:27</td>\n", " <td>SR Created</td>\n", " <td>OCB</td>\n", " <td>Graffiti Removal</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>B</td>\n", " <td>1.0</td>\n", " <td>Harbor APC</td>\n", " <td>15.0</td>\n", " <td>Joe Buscaino</td>\n", " <td>90.0</td>\n", " <td>HARBOR GATEWAY NORTH NC</td>\n", " <td>SOUTHEAST</td>\n", " <td>10/20/17</td>\n", " <td>14:01</td>\n", " </tr>\n", " <tr>\n", " <th>2483676</th>\n", " <td>1-785457211</td>\n", " <td>10/20/17 14:13</td>\n", " <td>10/23/17 6:26</td>\n", " <td>SR Created</td>\n", " <td>OCB</td>\n", " <td>Graffiti Removal</td>\n", " <td>Closed</td>\n", " <td>Email</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>4.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>15.0</td>\n", " <td>Joe Buscaino</td>\n", " <td>88.0</td>\n", " <td>WATTS NC</td>\n", " <td>SOUTHEAST</td>\n", " <td>10/20/17</td>\n", " <td>14:13</td>\n", " </tr>\n", " <tr>\n", " <th>2483677</th>\n", " <td>1-786102412</td>\n", " <td>10/21/17 14:31</td>\n", " <td>10/23/17 6:29</td>\n", " <td>SR Created</td>\n", " <td>OCB</td>\n", " <td>Graffiti Removal</td>\n", " <td>Closed</td>\n", " <td>Mobile App</td>\n", " <td>Android</td>\n", " <td>Y</td>\n", " <td>...</td>\n", " <td>B</td>\n", " <td>6.0</td>\n", " <td>Harbor APC</td>\n", " <td>15.0</td>\n", " <td>Joe Buscaino</td>\n", " <td>90.0</td>\n", " <td>HARBOR GATEWAY NORTH NC</td>\n", " <td>SOUTHEAST</td>\n", " <td>10/21/17</td>\n", " <td>14:31</td>\n", " </tr>\n", " <tr>\n", " <th>2483678</th>\n", " <td>1-786818528</td>\n", " <td>10/23/17 9:03</td>\n", " <td>10/30/17 3:43</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Illegal Dumping Pickup</td>\n", " <td>Closed</td>\n", " <td>Mobile App</td>\n", " <td>iOS</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>F</td>\n", " <td>1.0</td>\n", " <td>North Valley APC</td>\n", " <td>6.0</td>\n", " <td>Nury Martinez</td>\n", " <td>8.0</td>\n", " <td>SUN VALLEY AREA NC</td>\n", " <td>FOOTHILL</td>\n", " <td>10/23/17</td>\n", " <td>9:03</td>\n", " </tr>\n", " <tr>\n", " <th>2483679</th>\n", " <td>1-659247281</td>\n", " <td>7/31/17 9:32</td>\n", " <td>8/2/17 16:11</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Metal/Household Appliances</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>A</td>\n", " <td>5.0</td>\n", " <td>West Los Angeles APC</td>\n", " <td>11.0</td>\n", " <td>Mike Bonin</td>\n", " <td>66.0</td>\n", " <td>WEST LOS ANGELES NC</td>\n", " <td>WEST LOS ANGELES</td>\n", " <td>7/31/17</td>\n", " <td>9:32</td>\n", " </tr>\n", " <tr>\n", " <th>2483680</th>\n", " <td>1-786596581</td>\n", " <td>10/23/17 6:37</td>\n", " <td>10/26/17 14:34</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>5.0</td>\n", " <td>North Valley APC</td>\n", " <td>7.0</td>\n", " <td>Monica Rodriguez</td>\n", " <td>112.0</td>\n", " <td>NORTH HILLS EAST</td>\n", " <td>MISSION</td>\n", " <td>10/23/17</td>\n", " <td>6:37</td>\n", " </tr>\n", " <tr>\n", " <th>2483681</th>\n", " <td>1-653389951</td>\n", " <td>7/28/17 14:10</td>\n", " <td>8/2/17 17:11</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Metal/Household Appliances</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>C</td>\n", " <td>2.0</td>\n", " <td>West Los Angeles APC</td>\n", " <td>11.0</td>\n", " <td>Mike Bonin</td>\n", " <td>67.0</td>\n", " <td>MAR VISTA CC</td>\n", " <td>PACIFIC</td>\n", " <td>7/28/17</td>\n", " <td>14:10</td>\n", " </tr>\n", " <tr>\n", " <th>2483682</th>\n", " <td>1-655064311</td>\n", " <td>7/29/17 20:16</td>\n", " <td>8/2/17 12:48</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Metal/Household Appliances</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>A</td>\n", " <td>3.0</td>\n", " <td>South Valley APC</td>\n", " <td>4.0</td>\n", " <td>David Ryu</td>\n", " <td>26.0</td>\n", " <td>SHERMAN OAKS NC</td>\n", " <td>VAN NUYS</td>\n", " <td>7/29/17</td>\n", " <td>20:16</td>\n", " </tr>\n", " <tr>\n", " <th>2483683</th>\n", " <td>1-784767511</td>\n", " <td>10/20/17 8:45</td>\n", " <td>10/23/17 6:57</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Electronic Waste</td>\n", " <td>Closed</td>\n", " <td>Self Service</td>\n", " <td>NaN</td>\n", " <td>Y</td>\n", " <td>...</td>\n", " <td>B</td>\n", " <td>1.0</td>\n", " <td>East Los Angeles APC</td>\n", " <td>1.0</td>\n", " <td>Gilbert Cedillo</td>\n", " <td>47.0</td>\n", " <td>LINCOLN HEIGHTS NC</td>\n", " <td>HOLLENBECK</td>\n", " <td>10/20/17</td>\n", " <td>8:45</td>\n", " </tr>\n", " <tr>\n", " <th>2483684</th>\n", " <td>1-786819100</td>\n", " <td>10/23/17 9:03</td>\n", " <td>10/23/17 9:04</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Illegal Dumping Pickup</td>\n", " <td>Closed</td>\n", " <td>Driver Self Report</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>2.0</td>\n", " <td>North Valley APC</td>\n", " <td>12.0</td>\n", " <td>Mitchell Englander</td>\n", " <td>124.0</td>\n", " <td>NORTHRIDGE SOUTH NC</td>\n", " <td>DEVONSHIRE</td>\n", " <td>10/23/17</td>\n", " <td>9:03</td>\n", " </tr>\n", " <tr>\n", " <th>2483685</th>\n", " <td>1-652820971</td>\n", " <td>7/28/17 10:21</td>\n", " <td>8/2/17 12:50</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Electronic Waste</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>J</td>\n", " <td>5.0</td>\n", " <td>South Valley APC</td>\n", " <td>4.0</td>\n", " <td>David Ryu</td>\n", " <td>26.0</td>\n", " <td>SHERMAN OAKS NC</td>\n", " <td>VAN NUYS</td>\n", " <td>7/28/17</td>\n", " <td>10:21</td>\n", " </tr>\n", " <tr>\n", " <th>2483686</th>\n", " <td>1-786820581</td>\n", " <td>10/23/17 9:04</td>\n", " <td>10/24/17 12:47</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Illegal Dumping Pickup</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>J</td>\n", " <td>6.0</td>\n", " <td>East Los Angeles APC</td>\n", " <td>1.0</td>\n", " <td>Gilbert Cedillo</td>\n", " <td>102.0</td>\n", " <td>GREATER CYPRESS PARK NC</td>\n", " <td>NORTHEAST</td>\n", " <td>10/23/17</td>\n", " <td>9:04</td>\n", " </tr>\n", " <tr>\n", " <th>2483687</th>\n", " <td>1-782351151</td>\n", " <td>10/18/17 12:15</td>\n", " <td>10/23/17 6:39</td>\n", " <td>SR Created</td>\n", " <td>OCB</td>\n", " <td>Graffiti Removal</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>J</td>\n", " <td>4.0</td>\n", " <td>West Los Angeles APC</td>\n", " <td>11.0</td>\n", " <td>Mike Bonin</td>\n", " <td>68.0</td>\n", " <td>VENICE NC</td>\n", " <td>PACIFIC</td>\n", " <td>10/18/17</td>\n", " <td>12:15</td>\n", " </tr>\n", " <tr>\n", " <th>2483688</th>\n", " <td>1-783191715</td>\n", " <td>10/19/17 7:05</td>\n", " <td>10/23/17 6:49</td>\n", " <td>SR Created</td>\n", " <td>OCB</td>\n", " <td>Graffiti Removal</td>\n", " <td>Closed</td>\n", " <td>Mobile App</td>\n", " <td>iOS</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>B</td>\n", " <td>2.0</td>\n", " <td>Harbor APC</td>\n", " <td>15.0</td>\n", " <td>Joe Buscaino</td>\n", " <td>96.0</td>\n", " <td>COASTAL SAN PEDRO NC</td>\n", " <td>HARBOR</td>\n", " <td>10/19/17</td>\n", " <td>7:05</td>\n", " </tr>\n", " <tr>\n", " <th>2483689</th>\n", " <td>1-783534969</td>\n", " <td>10/19/17 10:09</td>\n", " <td>10/23/17 6:51</td>\n", " <td>SR Created</td>\n", " <td>OCB</td>\n", " <td>Graffiti Removal</td>\n", " <td>Closed</td>\n", " <td>Mobile App</td>\n", " <td>iOS</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>J</td>\n", " <td>3.0</td>\n", " <td>Central APC</td>\n", " <td>5.0</td>\n", " <td>Paul Koretz</td>\n", " <td>61.0</td>\n", " <td>SOUTH ROBERTSON NC</td>\n", " <td>WEST LOS ANGELES</td>\n", " <td>10/19/17</td>\n", " <td>10:09</td>\n", " </tr>\n", " <tr>\n", " <th>2483690</th>\n", " <td>1-783547961</td>\n", " <td>10/19/17 10:15</td>\n", " <td>10/23/17 6:49</td>\n", " <td>SR Created</td>\n", " <td>OCB</td>\n", " <td>Graffiti Removal</td>\n", " <td>Closed</td>\n", " <td>Email</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>B</td>\n", " <td>4.0</td>\n", " <td>Central APC</td>\n", " <td>4.0</td>\n", " <td>David Ryu</td>\n", " <td>29.0</td>\n", " <td>HOLLYWOOD HILLS WEST NC</td>\n", " <td>HOLLYWOOD</td>\n", " <td>10/19/17</td>\n", " <td>10:15</td>\n", " </tr>\n", " <tr>\n", " <th>2483691</th>\n", " <td>1-785674481</td>\n", " <td>10/20/17 16:12</td>\n", " <td>10/23/17 6:39</td>\n", " <td>SR Created</td>\n", " <td>OCB</td>\n", " <td>Graffiti Removal</td>\n", " <td>Closed</td>\n", " <td>Self Service</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>E</td>\n", " <td>5.0</td>\n", " <td>Harbor APC</td>\n", " <td>15.0</td>\n", " <td>Joe Buscaino</td>\n", " <td>93.0</td>\n", " <td>WILMINGTON NC</td>\n", " <td>HARBOR</td>\n", " <td>10/20/17</td>\n", " <td>16:12</td>\n", " </tr>\n", " <tr>\n", " <th>2483692</th>\n", " <td>1-786599005</td>\n", " <td>10/23/17 6:50</td>\n", " <td>10/23/17 19:30</td>\n", " <td>SR Created</td>\n", " <td>OCB</td>\n", " <td>Graffiti Removal</td>\n", " <td>Closed</td>\n", " <td>Mobile App</td>\n", " <td>iOS</td>\n", " <td>Y</td>\n", " <td>...</td>\n", " <td>H</td>\n", " <td>5.0</td>\n", " <td>North Valley APC</td>\n", " <td>12.0</td>\n", " <td>Mitchell Englander</td>\n", " <td>99.0</td>\n", " <td>CHATSWORTH NC</td>\n", " <td>DEVONSHIRE</td>\n", " <td>10/23/17</td>\n", " <td>6:50</td>\n", " </tr>\n", " <tr>\n", " <th>2483693</th>\n", " <td>1-786605611</td>\n", " <td>10/23/17 6:52</td>\n", " <td>10/23/17 15:21</td>\n", " <td>SR Created</td>\n", " <td>ITA</td>\n", " <td>Other</td>\n", " <td>Cancelled</td>\n", " <td>Mobile App</td>\n", " <td>iOS</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>C</td>\n", " <td>6.0</td>\n", " <td>Harbor APC</td>\n", " <td>15.0</td>\n", " <td>Joe Buscaino</td>\n", " <td>93.0</td>\n", " <td>WILMINGTON NC</td>\n", " <td>HARBOR</td>\n", " <td>10/23/17</td>\n", " <td>6:52</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>2483694 rows × 35 columns</p>\n", "</div>" ], "text/plain": [ " SRNumber CreatedDate UpdatedDate ActionTaken Owner \\\n", "0 1-78597941 12/10/15 9:34 12/11/15 8:55 SR Created BOS \n", "1 1-952588571 3/3/18 11:57 3/3/18 12:00 SR Created OCB \n", "2 1-88226921 1/1/16 8:28 1/4/16 14:52 SR Created BOS \n", "3 1-513938562 4/10/17 11:39 4/12/17 9:56 SR Created BOS \n", "4 1-513940271 4/10/17 11:42 4/14/17 7:50 SR Created BOS \n", "5 1-88276381 1/1/16 11:07 1/8/16 11:19 SR Created BOS \n", "6 1-1074967561 6/22/18 11:13 6/23/18 12:51 SR Created ITA \n", "7 1-44772241 10/1/15 3:11 10/8/15 17:00 SR Created BOS \n", "8 1-88295291 1/1/16 14:02 1/6/16 10:38 SR Created BOS \n", "9 1-513938351 4/10/17 11:42 4/12/17 14:28 SR Created BOS \n", "10 1-952753491 3/4/18 6:30 3/5/18 13:13 SR Created BOS \n", "11 1-78929421 12/10/15 14:34 12/16/15 13:26 SR Created BOS \n", "12 1-88310551 1/1/16 15:24 1/5/16 13:28 SR Created BOS \n", "13 1-952753431 3/4/18 6:23 3/9/18 9:58 SR Created BOS \n", "14 1-59599861 11/2/15 8:11 11/5/15 14:30 SR Created BOS \n", "15 1-513942821 4/10/17 11:43 4/17/17 8:33 SR Created BOS \n", "16 1-59622271 11/2/15 8:49 11/4/15 16:00 SR Created BOS \n", "17 1-88309281 1/1/16 16:11 1/6/16 9:08 SR Created BOS \n", "18 1-513946071 4/10/17 11:43 4/12/17 10:40 SR Created BOS \n", "19 1-952753561 3/4/18 6:31 3/9/18 12:38 SR Created BOS \n", "20 1-59618771 11/2/15 8:49 11/4/15 15:00 SR Created BOS \n", "21 1-88376161 1/2/16 8:38 1/6/16 10:45 SR Created BOS \n", "22 1-513945898 4/10/17 11:43 4/17/17 10:59 SR Created BOS \n", "23 1-952764861 3/4/18 6:56 3/8/18 7:19 SR Created BOS \n", "24 1-952759351 3/4/18 6:57 3/9/18 10:00 SR Created BOS \n", "25 1-513946221 4/10/17 11:43 4/14/17 13:10 SR Created BOS \n", "26 1-59623111 11/2/15 8:54 11/3/15 14:00 SR Created BOS \n", "27 1-88430141 1/2/16 10:47 1/8/16 13:28 SR Created BOS \n", "28 1-88449071 1/2/16 11:21 1/5/16 11:50 SR Created BOS \n", "29 1-513946281 4/10/17 11:44 4/10/17 11:44 SR Created BOS \n", "... ... ... ... ... ... \n", "2483664 1-820215191 11/16/17 15:01 11/17/17 12:45 SR Created BOS \n", "2483665 1-820218661 11/16/17 15:02 11/24/17 8:11 SR Created BOS \n", "2483666 1-820218811 11/16/17 15:02 11/17/17 12:48 SR Created BOS \n", "2483667 1-820219621 11/16/17 15:02 11/17/17 12:55 SR Created BOS \n", "2483668 1-820220931 11/16/17 15:02 11/17/17 10:23 SR Created BOS \n", "2483669 1-820221011 11/16/17 15:02 11/20/17 9:33 SR Created BOS \n", "2483670 1-820217761 11/16/17 15:03 11/17/17 9:44 SR Created BOS \n", "2483671 1-820221611 11/16/17 15:03 11/17/17 8:13 SR Created BOS \n", "2483672 1-820221971 11/16/17 15:03 11/17/17 9:07 SR Created BOS \n", "2483673 1-820222111 11/16/17 15:03 11/22/17 13:26 SR Created BOS \n", "2483674 1-784437471 10/19/17 18:12 10/23/17 6:19 SR Created OCB \n", "2483675 1-785428391 10/20/17 14:01 10/23/17 6:27 SR Created OCB \n", "2483676 1-785457211 10/20/17 14:13 10/23/17 6:26 SR Created OCB \n", "2483677 1-786102412 10/21/17 14:31 10/23/17 6:29 SR Created OCB \n", "2483678 1-786818528 10/23/17 9:03 10/30/17 3:43 SR Created BOS \n", "2483679 1-659247281 7/31/17 9:32 8/2/17 16:11 SR Created BOS \n", "2483680 1-786596581 10/23/17 6:37 10/26/17 14:34 SR Created BOS \n", "2483681 1-653389951 7/28/17 14:10 8/2/17 17:11 SR Created BOS \n", "2483682 1-655064311 7/29/17 20:16 8/2/17 12:48 SR Created BOS \n", "2483683 1-784767511 10/20/17 8:45 10/23/17 6:57 SR Created BOS \n", "2483684 1-786819100 10/23/17 9:03 10/23/17 9:04 SR Created BOS \n", "2483685 1-652820971 7/28/17 10:21 8/2/17 12:50 SR Created BOS \n", "2483686 1-786820581 10/23/17 9:04 10/24/17 12:47 SR Created BOS \n", "2483687 1-782351151 10/18/17 12:15 10/23/17 6:39 SR Created OCB \n", "2483688 1-783191715 10/19/17 7:05 10/23/17 6:49 SR Created OCB \n", "2483689 1-783534969 10/19/17 10:09 10/23/17 6:51 SR Created OCB \n", "2483690 1-783547961 10/19/17 10:15 10/23/17 6:49 SR Created OCB \n", "2483691 1-785674481 10/20/17 16:12 10/23/17 6:39 SR Created OCB \n", "2483692 1-786599005 10/23/17 6:50 10/23/17 19:30 SR Created OCB \n", "2483693 1-786605611 10/23/17 6:52 10/23/17 15:21 SR Created ITA \n", "\n", " RequestType Status RequestSource MobileOS \\\n", "0 Metal/Household Appliances Closed Mobile App Android \n", "1 Graffiti Removal Closed Driver Self Report NaN \n", "2 Bulky Items Closed Call NaN \n", "3 Bulky Items Closed Mobile App iOS \n", "4 Bulky Items Closed Call NaN \n", "5 Bulky Items Closed Call NaN \n", "6 Other Closed Self Service NaN \n", "7 Bulky Items Closed Email NaN \n", "8 Bulky Items Closed Call NaN \n", "9 Bulky Items Closed Call NaN \n", "10 Electronic Waste Closed Self Service NaN \n", "11 Metal/Household Appliances Cancelled Call NaN \n", "12 Bulky Items Closed Call NaN \n", "13 Bulky Items Closed Self Service NaN \n", "14 Bulky Items Closed Call NaN \n", "15 Bulky Items Closed Self Service NaN \n", "16 Electronic Waste Closed Call NaN \n", "17 Bulky Items Closed Call NaN \n", "18 Bulky Items Closed Call NaN \n", "19 Homeless Encampment Closed Self Service NaN \n", "20 Bulky Items Closed Call NaN \n", "21 Bulky Items Closed Call NaN \n", "22 Bulky Items Closed Mobile App Android \n", "23 Bulky Items Closed Call NaN \n", "24 Homeless Encampment Closed Mobile App iOS \n", "25 Bulky Items Closed Call NaN \n", "26 Bulky Items Closed Call NaN \n", "27 Bulky Items Closed Call NaN \n", "28 Bulky Items Closed Call NaN \n", "29 Bulky Items Closed Driver Self Report NaN \n", "... ... ... ... ... \n", "2483664 Bulky Items Closed Call NaN \n", "2483665 Bulky Items Closed Call NaN \n", "2483666 Bulky Items Closed Call NaN \n", "2483667 Bulky Items Closed Self Service NaN \n", "2483668 Bulky Items Closed Call NaN \n", "2483669 Bulky Items Closed Call NaN \n", "2483670 Bulky Items Closed Call NaN \n", "2483671 Bulky Items Closed Call NaN \n", "2483672 Bulky Items Closed Call NaN \n", "2483673 Illegal Dumping Pickup Closed Call NaN \n", "2483674 Graffiti Removal Closed Call NaN \n", "2483675 Graffiti Removal Closed Call NaN \n", "2483676 Graffiti Removal Closed Email NaN \n", "2483677 Graffiti Removal Closed Mobile App Android \n", "2483678 Illegal Dumping Pickup Closed Mobile App iOS \n", "2483679 Metal/Household Appliances Closed Call NaN \n", "2483680 Bulky Items Closed Call NaN \n", "2483681 Metal/Household Appliances Closed Call NaN \n", "2483682 Metal/Household Appliances Closed Call NaN \n", "2483683 Electronic Waste Closed Self Service NaN \n", "2483684 Illegal Dumping Pickup Closed Driver Self Report NaN \n", "2483685 Electronic Waste Closed Call NaN \n", "2483686 Illegal Dumping Pickup Closed Call NaN \n", "2483687 Graffiti Removal Closed Call NaN \n", "2483688 Graffiti Removal Closed Mobile App iOS \n", "2483689 Graffiti Removal Closed Mobile App iOS \n", "2483690 Graffiti Removal Closed Email NaN \n", "2483691 Graffiti Removal Closed Self Service NaN \n", "2483692 Graffiti Removal Closed Mobile App iOS \n", "2483693 Other Cancelled Mobile App iOS \n", "\n", " Anonymous ... TBMColumn TBMRow APC CD \\\n", "0 Y ... G 5.0 Central APC 13.0 \n", "1 N ... G 4.0 Central APC 13.0 \n", "2 N ... B 1.0 East Los Angeles APC 1.0 \n", "3 N ... J 3.0 South Valley APC 4.0 \n", "4 N ... A 5.0 North Valley APC 7.0 \n", "5 Y ... J 4.0 Central APC 1.0 \n", "6 N ... D 6.0 South Valley APC 6.0 \n", "7 N ... A 1.0 Central APC 13.0 \n", "8 Y ... B 4.0 Central APC 1.0 \n", "9 Y ... A 5.0 South Valley APC 2.0 \n", "10 N ... A 1.0 North Valley APC 7.0 \n", "11 N ... D 1.0 South Valley APC 3.0 \n", "12 N ... B 2.0 East Los Angeles APC 1.0 \n", "13 N ... A 3.0 South Valley APC 3.0 \n", "14 N ... A 1.0 South Los Angeles APC 8.0 \n", "15 N ... B 5.0 East Los Angeles APC 1.0 \n", "16 N ... B 3.0 South Los Angeles APC 8.0 \n", "17 Y ... D 3.0 Central APC 1.0 \n", "18 N ... D 1.0 South Los Angeles APC 9.0 \n", "19 N ... F 7.0 North Valley APC 7.0 \n", "20 N ... E 4.0 South Los Angeles APC 9.0 \n", "21 N ... C 3.0 East Los Angeles APC 1.0 \n", "22 N ... D 6.0 Central APC 14.0 \n", "23 N ... C 3.0 Central APC 4.0 \n", "24 N ... J 2.0 South Valley APC 3.0 \n", "25 Y ... B 7.0 South Los Angeles APC 10.0 \n", "26 N ... B 2.0 North Valley APC 3.0 \n", "27 Y ... A 4.0 Central APC 1.0 \n", "28 Y ... J 5.0 East Los Angeles APC 1.0 \n", "29 Y ... G 2.0 North Valley APC 6.0 \n", "... ... ... ... ... ... ... \n", "2483664 N ... B 2.0 South Los Angeles APC 10.0 \n", "2483665 N ... G 2.0 South Los Angeles APC 8.0 \n", "2483666 N ... A 6.0 South Los Angeles APC 10.0 \n", "2483667 N ... B 4.0 North Valley APC 6.0 \n", "2483668 N ... F 5.0 South Los Angeles APC 15.0 \n", "2483669 N ... J 2.0 North Valley APC 7.0 \n", "2483670 N ... A 2.0 Central APC 10.0 \n", "2483671 N ... E 3.0 South Los Angeles APC 9.0 \n", "2483672 N ... E 6.0 South Los Angeles APC 8.0 \n", "2483673 N ... H 4.0 East Los Angeles APC 1.0 \n", "2483674 N ... F 7.0 South Los Angeles APC 15.0 \n", "2483675 N ... B 1.0 Harbor APC 15.0 \n", "2483676 N ... G 4.0 South Los Angeles APC 15.0 \n", "2483677 Y ... B 6.0 Harbor APC 15.0 \n", "2483678 N ... F 1.0 North Valley APC 6.0 \n", "2483679 N ... A 5.0 West Los Angeles APC 11.0 \n", "2483680 N ... G 5.0 North Valley APC 7.0 \n", "2483681 N ... C 2.0 West Los Angeles APC 11.0 \n", "2483682 N ... A 3.0 South Valley APC 4.0 \n", "2483683 Y ... B 1.0 East Los Angeles APC 1.0 \n", "2483684 N ... G 2.0 North Valley APC 12.0 \n", "2483685 N ... J 5.0 South Valley APC 4.0 \n", "2483686 N ... J 6.0 East Los Angeles APC 1.0 \n", "2483687 N ... J 4.0 West Los Angeles APC 11.0 \n", "2483688 N ... B 2.0 Harbor APC 15.0 \n", "2483689 N ... J 3.0 Central APC 5.0 \n", "2483690 N ... B 4.0 Central APC 4.0 \n", "2483691 N ... E 5.0 Harbor APC 15.0 \n", "2483692 Y ... H 5.0 North Valley APC 12.0 \n", "2483693 N ... C 6.0 Harbor APC 15.0 \n", "\n", " CDMember NC \\\n", "0 Mitch O'Farrell 33.0 \n", "1 NaN 30.0 \n", "2 Gilbert Cedillo 47.0 \n", "3 David Ryu 26.0 \n", "4 Vacant 5.0 \n", "5 Gilbert Cedillo 76.0 \n", "6 Nury Martinez 19.0 \n", "7 Mitch O'Farrell 55.0 \n", "8 Gilbert Cedillo 76.0 \n", "9 Paul Krekorian 27.0 \n", "10 Monica Rodriguez 5.0 \n", "11 Bob Blumenfield 16.0 \n", "12 Gilbert Cedillo 41.0 \n", "13 Bob Blumenfield 13.0 \n", "14 Marqueece Harris-Dawson NaN \n", "15 Gilbert Cedillo 42.0 \n", "16 Marqueece Harris-Dawson 87.0 \n", "17 Gilbert Cedillo 97.0 \n", "18 Curren D. Price Jr. 78.0 \n", "19 Monica Rodriguez 9.0 \n", "20 Curren D. Price Jr. 125.0 \n", "21 Gilbert Cedillo 41.0 \n", "22 Jose Huizar 52.0 \n", "23 David Ryu 58.0 \n", "24 Bob Blumenfield 16.0 \n", "25 Herb J. Wesson Jr. 75.0 \n", "26 Bob Blumenfield 13.0 \n", "27 Gilbert Cedillo 76.0 \n", "28 Gilbert Cedillo 102.0 \n", "29 Nury Martinez 23.0 \n", "... ... ... \n", "2483664 Herb J. Wesson Jr. 79.0 \n", "2483665 Marqueece Harris-Dawson 84.0 \n", "2483666 Herb J. Wesson Jr. 73.0 \n", "2483667 Nury Martinez 6.0 \n", "2483668 Joe Buscaino 88.0 \n", "2483669 Monica Rodriguez 10.0 \n", "2483670 Herb J. Wesson Jr. 55.0 \n", "2483671 Curren D. Price Jr. 87.0 \n", "2483672 Marqueece Harris-Dawson 87.0 \n", "2483673 Gilbert Cedillo 102.0 \n", "2483674 Joe Buscaino 90.0 \n", "2483675 Joe Buscaino 90.0 \n", "2483676 Joe Buscaino 88.0 \n", "2483677 Joe Buscaino 90.0 \n", "2483678 Nury Martinez 8.0 \n", "2483679 Mike Bonin 66.0 \n", "2483680 Monica Rodriguez 112.0 \n", "2483681 Mike Bonin 67.0 \n", "2483682 David Ryu 26.0 \n", "2483683 Gilbert Cedillo 47.0 \n", "2483684 Mitchell Englander 124.0 \n", "2483685 David Ryu 26.0 \n", "2483686 Gilbert Cedillo 102.0 \n", "2483687 Mike Bonin 68.0 \n", "2483688 Joe Buscaino 96.0 \n", "2483689 Paul Koretz 61.0 \n", "2483690 David Ryu 29.0 \n", "2483691 Joe Buscaino 93.0 \n", "2483692 Mitchell Englander 99.0 \n", "2483693 Joe Buscaino 93.0 \n", "\n", " NCName PolicePrecinct \\\n", "0 HOLLYWOOD STUDIO DISTRICT NC HOLLYWOOD \n", "1 HOLLYWOOD UNITED NC HOLLYWOOD \n", "2 LINCOLN HEIGHTS NC HOLLENBECK \n", "3 SHERMAN OAKS NC VAN NUYS \n", "4 SYLMAR NC MISSION \n", "5 PICO UNION NC OLYMPIC \n", "6 LAKE BALBOA NC WEST VALLEY \n", "7 WILSHIRE CENTER - KOREATOWN NC OLYMPIC \n", "8 PICO UNION NC RAMPART \n", "9 STUDIO CITY NC NORTH HOLLYWOOD \n", "10 SYLMAR NC MISSION \n", "11 WOODLAND HILLS-WARNER CENTER NC TOPANGA \n", "12 HISTORIC HIGHLAND PARK NC NORTHEAST \n", "13 CANOGA PARK NC TOPANGA \n", "14 NaN 77TH STREET \n", "15 ARROYO SECO NC HOLLENBECK \n", "16 EMPOWERMENT CONGRESS SOUTHEAST AREA NDC SOUTHEAST \n", "17 WESTLAKE NORTH NC RAMPART \n", "18 SOUTH CENTRAL NC NEWTON \n", "19 FOOTHILL TRAILS DISTRICT NC FOOTHILL \n", "20 ZAPATA KING NC NEWTON \n", "21 HISTORIC HIGHLAND PARK NC NORTHEAST \n", "22 DOWNTOWN LOS ANGELES CENTRAL \n", "23 MID CITY WEST CC WILSHIRE \n", "24 WOODLAND HILLS-WARNER CENTER NC TOPANGA \n", "25 WEST ADAMS NC SOUTHWEST \n", "26 CANOGA PARK NC TOPANGA \n", "27 PICO UNION NC OLYMPIC \n", "28 GREATER CYPRESS PARK NC NORTHEAST \n", "29 NORTH HOLLYWOOD NORTHEAST NC FOOTHILL \n", "... ... ... \n", "2483664 EMPOWERMENT CONGRESS WEST AREA NDC SOUTHWEST \n", "2483665 EMPOWERMENT CONGRESS SOUTHWEST AREA NDC 77TH STREET \n", "2483666 MID CITY NC WILSHIRE \n", "2483667 ARLETA NC MISSION \n", "2483668 WATTS NC SOUTHEAST \n", "2483669 SUNLAND-TUJUNGA NC FOOTHILL \n", "2483670 WILSHIRE CENTER - KOREATOWN NC OLYMPIC \n", "2483671 EMPOWERMENT CONGRESS SOUTHEAST AREA NDC SOUTHEAST \n", "2483672 EMPOWERMENT CONGRESS SOUTHEAST AREA NDC SOUTHEAST \n", "2483673 GREATER CYPRESS PARK NC NORTHEAST \n", "2483674 HARBOR GATEWAY NORTH NC SOUTHEAST \n", "2483675 HARBOR GATEWAY NORTH NC SOUTHEAST \n", "2483676 WATTS NC SOUTHEAST \n", "2483677 HARBOR GATEWAY NORTH NC SOUTHEAST \n", "2483678 SUN VALLEY AREA NC FOOTHILL \n", "2483679 WEST LOS ANGELES NC WEST LOS ANGELES \n", "2483680 NORTH HILLS EAST MISSION \n", "2483681 MAR VISTA CC PACIFIC \n", "2483682 SHERMAN OAKS NC VAN NUYS \n", "2483683 LINCOLN HEIGHTS NC HOLLENBECK \n", "2483684 NORTHRIDGE SOUTH NC DEVONSHIRE \n", "2483685 SHERMAN OAKS NC VAN NUYS \n", "2483686 GREATER CYPRESS PARK NC NORTHEAST \n", "2483687 VENICE NC PACIFIC \n", "2483688 COASTAL SAN PEDRO NC HARBOR \n", "2483689 SOUTH ROBERTSON NC WEST LOS ANGELES \n", "2483690 HOLLYWOOD HILLS WEST NC HOLLYWOOD \n", "2483691 WILMINGTON NC HARBOR \n", "2483692 CHATSWORTH NC DEVONSHIRE \n", "2483693 WILMINGTON NC HARBOR \n", "\n", " Created Date Created Time \n", "0 12/10/15 9:34 \n", "1 3/3/18 11:57 \n", "2 1/1/16 8:28 \n", "3 4/10/17 11:39 \n", "4 4/10/17 11:42 \n", "5 1/1/16 11:07 \n", "6 6/22/18 11:13 \n", "7 10/1/15 3:11 \n", "8 1/1/16 14:02 \n", "9 4/10/17 11:42 \n", "10 3/4/18 6:30 \n", "11 12/10/15 14:34 \n", "12 1/1/16 15:24 \n", "13 3/4/18 6:23 \n", "14 11/2/15 8:11 \n", "15 4/10/17 11:43 \n", "16 11/2/15 8:49 \n", "17 1/1/16 16:11 \n", "18 4/10/17 11:43 \n", "19 3/4/18 6:31 \n", "20 11/2/15 8:49 \n", "21 1/2/16 8:38 \n", "22 4/10/17 11:43 \n", "23 3/4/18 6:56 \n", "24 3/4/18 6:57 \n", "25 4/10/17 11:43 \n", "26 11/2/15 8:54 \n", "27 1/2/16 10:47 \n", "28 1/2/16 11:21 \n", "29 4/10/17 11:44 \n", "... ... ... \n", "2483664 11/16/17 15:01 \n", "2483665 11/16/17 15:02 \n", "2483666 11/16/17 15:02 \n", "2483667 11/16/17 15:02 \n", "2483668 11/16/17 15:02 \n", "2483669 11/16/17 15:02 \n", "2483670 11/16/17 15:03 \n", "2483671 11/16/17 15:03 \n", "2483672 11/16/17 15:03 \n", "2483673 11/16/17 15:03 \n", "2483674 10/19/17 18:12 \n", "2483675 10/20/17 14:01 \n", "2483676 10/20/17 14:13 \n", "2483677 10/21/17 14:31 \n", "2483678 10/23/17 9:03 \n", "2483679 7/31/17 9:32 \n", "2483680 10/23/17 6:37 \n", "2483681 7/28/17 14:10 \n", "2483682 7/29/17 20:16 \n", "2483683 10/20/17 8:45 \n", "2483684 10/23/17 9:03 \n", "2483685 7/28/17 10:21 \n", "2483686 10/23/17 9:04 \n", "2483687 10/18/17 12:15 \n", "2483688 10/19/17 7:05 \n", "2483689 10/19/17 10:09 \n", "2483690 10/19/17 10:15 \n", "2483691 10/20/17 16:12 \n", "2483692 10/23/17 6:50 \n", "2483693 10/23/17 6:52 \n", "\n", "[2483694 rows x 35 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import datetime\n", "df = pd.read_csv('311_parsed_coordinates.csv')\n", "\n", "df[['Created Date','Created Time']] = df.CreatedDate.str.split(expand=True)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Converting Date to date format and creating a new column for week numbers based on that " ] }, { "cell_type": "code", "execution_count": 19, "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>SRNumber</th>\n", " <th>CreatedDate</th>\n", " <th>UpdatedDate</th>\n", " <th>ActionTaken</th>\n", " <th>Owner</th>\n", " <th>RequestType</th>\n", " <th>Status</th>\n", " <th>RequestSource</th>\n", " <th>MobileOS</th>\n", " <th>Anonymous</th>\n", " <th>...</th>\n", " <th>TBMRow</th>\n", " <th>APC</th>\n", " <th>CD</th>\n", " <th>CDMember</th>\n", " <th>NC</th>\n", " <th>NCName</th>\n", " <th>PolicePrecinct</th>\n", " <th>Created Date</th>\n", " <th>Created Time</th>\n", " <th>Week Number</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>40349</th>\n", " <td>1-22838691</td>\n", " <td>8/5/15 11:22</td>\n", " <td>8/13/15 16:39</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Metal/Household Appliances</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>3.0</td>\n", " <td>West Los Angeles APC</td>\n", " <td>5.0</td>\n", " <td>Paul Koretz</td>\n", " <td>61.0</td>\n", " <td>SOUTH ROBERTSON NC</td>\n", " <td>WEST LOS ANGELES</td>\n", " <td>2015-08-05</td>\n", " <td>11:22</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37709</th>\n", " <td>1-22711481</td>\n", " <td>8/5/15 7:52</td>\n", " <td>8/9/15 23:34</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>South Valley APC</td>\n", " <td>3.0</td>\n", " <td>Bob Blumenfield</td>\n", " <td>16.0</td>\n", " <td>WOODLAND HILLS-WARNER CENTER NC</td>\n", " <td>TOPANGA</td>\n", " <td>2015-08-05</td>\n", " <td>7:52</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>42456</th>\n", " <td>1-22942441</td>\n", " <td>8/5/15 14:33</td>\n", " <td>8/10/15 11:42</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Dead Animal Removal</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>6.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>9.0</td>\n", " <td>Curren D. Price Jr.</td>\n", " <td>86.0</td>\n", " <td>COMMUNITY AND NEIGHBORS FOR NINTH DISTRICT UNI...</td>\n", " <td>NEWTON</td>\n", " <td>2015-08-05</td>\n", " <td>14:33</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37712</th>\n", " <td>1-22712351</td>\n", " <td>8/5/15 7:52</td>\n", " <td>12/18/15 11:53</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Other</td>\n", " <td>Cancelled</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>South Valley APC</td>\n", " <td>4.0</td>\n", " <td>David Ryu</td>\n", " <td>26.0</td>\n", " <td>SHERMAN OAKS NC</td>\n", " <td>VAN NUYS</td>\n", " <td>2015-08-05</td>\n", " <td>7:52</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37713</th>\n", " <td>1-22710461</td>\n", " <td>8/5/15 7:53</td>\n", " <td>8/17/15 21:26</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Illegal Dumping Pickup</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>2.0</td>\n", " <td>Central APC</td>\n", " <td>4.0</td>\n", " <td>David Ryu</td>\n", " <td>36.0</td>\n", " <td>LOS FELIZ NC</td>\n", " <td>NORTHEAST</td>\n", " <td>2015-08-05</td>\n", " <td>7:53</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37719</th>\n", " <td>1-22711531</td>\n", " <td>8/5/15 7:53</td>\n", " <td>8/15/15 18:47</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>3.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>9.0</td>\n", " <td>Curren D. Price Jr.</td>\n", " <td>125.0</td>\n", " <td>ZAPATA KING NC</td>\n", " <td>NEWTON</td>\n", " <td>2015-08-05</td>\n", " <td>7:53</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>42452</th>\n", " <td>1-22941161</td>\n", " <td>8/5/15 14:32</td>\n", " <td>8/5/15 14:32</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Feedback</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>6.0</td>\n", " <td>Central APC</td>\n", " <td>13.0</td>\n", " <td>Mitch O'Farrell</td>\n", " <td>33.0</td>\n", " <td>HOLLYWOOD STUDIO DISTRICT NC</td>\n", " <td>HOLLYWOOD</td>\n", " <td>2015-08-05</td>\n", " <td>14:32</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37720</th>\n", " <td>1-22710511</td>\n", " <td>8/5/15 7:54</td>\n", " <td>8/9/15 23:34</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>4.0</td>\n", " <td>Central APC</td>\n", " <td>10.0</td>\n", " <td>Herb J. Wesson Jr.</td>\n", " <td>60.0</td>\n", " <td>P.I.C.O. NC</td>\n", " <td>WILSHIRE</td>\n", " <td>2015-08-05</td>\n", " <td>7:54</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37725</th>\n", " <td>1-22709721</td>\n", " <td>8/5/15 7:54</td>\n", " <td>8/9/15 21:47</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>7.0</td>\n", " <td>Central APC</td>\n", " <td>5.0</td>\n", " <td>Paul Koretz</td>\n", " <td>58.0</td>\n", " <td>MID CITY WEST CC</td>\n", " <td>WILSHIRE</td>\n", " <td>2015-08-05</td>\n", " <td>7:54</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>42449</th>\n", " <td>1-22943481</td>\n", " <td>8/5/15 14:32</td>\n", " <td>8/9/15 23:59</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>3.0</td>\n", " <td>South Valley APC</td>\n", " <td>12.0</td>\n", " <td>Mitchell Englander</td>\n", " <td>11.0</td>\n", " <td>WEST HILLS NC</td>\n", " <td>TOPANGA</td>\n", " <td>2015-08-05</td>\n", " <td>14:32</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37729</th>\n", " <td>1-22709771</td>\n", " <td>8/5/15 7:55</td>\n", " <td>8/9/15 15:54</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Metal/Household Appliances</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>South Valley APC</td>\n", " <td>6.0</td>\n", " <td>Nury Martinez</td>\n", " <td>20.0</td>\n", " <td>VAN NUYS NC</td>\n", " <td>VAN NUYS</td>\n", " <td>2015-08-05</td>\n", " <td>7:55</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37735</th>\n", " <td>1-22711761</td>\n", " <td>8/5/15 7:57</td>\n", " <td>8/11/15 7:34</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>North Valley APC</td>\n", " <td>7.0</td>\n", " <td>Felipe Fuentes</td>\n", " <td>10.0</td>\n", " <td>SUNLAND-TUJUNGA NC</td>\n", " <td>FOOTHILL</td>\n", " <td>2015-08-05</td>\n", " <td>7:57</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37739</th>\n", " <td>1-22710691</td>\n", " <td>8/5/15 7:57</td>\n", " <td>8/9/15 23:34</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>4.0</td>\n", " <td>Central APC</td>\n", " <td>5.0</td>\n", " <td>Paul Koretz</td>\n", " <td>61.0</td>\n", " <td>SOUTH ROBERTSON NC</td>\n", " <td>WEST LOS ANGELES</td>\n", " <td>2015-08-05</td>\n", " <td>7:57</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37743</th>\n", " <td>1-22711791</td>\n", " <td>8/5/15 7:57</td>\n", " <td>8/11/15 14:47</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>8.0</td>\n", " <td>Marqueece Harris-Dawson</td>\n", " <td>87.0</td>\n", " <td>EMPOWERMENT CONGRESS SOUTHEAST AREA NDC</td>\n", " <td>SOUTHEAST</td>\n", " <td>2015-08-05</td>\n", " <td>7:57</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>42444</th>\n", " <td>1-22941091</td>\n", " <td>8/5/15 14:31</td>\n", " <td>8/9/15 23:59</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>6.0</td>\n", " <td>South Valley APC</td>\n", " <td>6.0</td>\n", " <td>Nury Martinez</td>\n", " <td>19.0</td>\n", " <td>LAKE BALBOA NC</td>\n", " <td>WEST VALLEY</td>\n", " <td>2015-08-05</td>\n", " <td>14:31</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37746</th>\n", " <td>1-22712521</td>\n", " <td>8/5/15 7:59</td>\n", " <td>8/11/15 14:24</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>Harbor APC</td>\n", " <td>15.0</td>\n", " <td>Joe Buscaino</td>\n", " <td>92.0</td>\n", " <td>HARBOR CITY NC</td>\n", " <td>HARBOR</td>\n", " <td>2015-08-05</td>\n", " <td>7:59</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37748</th>\n", " <td>1-22710831</td>\n", " <td>8/5/15 7:59</td>\n", " <td>8/12/15 17:14</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>2.0</td>\n", " <td>Central APC</td>\n", " <td>1.0</td>\n", " <td>Gilbert Cedillo</td>\n", " <td>46.0</td>\n", " <td>HISTORIC CULTURAL NC</td>\n", " <td>CENTRAL</td>\n", " <td>2015-08-05</td>\n", " <td>7:59</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37752</th>\n", " <td>1-22709941</td>\n", " <td>8/5/15 8:00</td>\n", " <td>8/10/15 9:29</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Electronic Waste</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>7.0</td>\n", " <td>South Valley APC</td>\n", " <td>2.0</td>\n", " <td>Paul Krekorian</td>\n", " <td>21.0</td>\n", " <td>GREATER VALLEY GLEN COUNCIL</td>\n", " <td>VAN NUYS</td>\n", " <td>2015-08-05</td>\n", " <td>8:00</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37756</th>\n", " <td>1-22712651</td>\n", " <td>8/5/15 8:01</td>\n", " <td>8/12/15 15:48</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>6.0</td>\n", " <td>Central APC</td>\n", " <td>14.0</td>\n", " <td>Jose Huizar</td>\n", " <td>52.0</td>\n", " <td>DOWNTOWN LOS ANGELES</td>\n", " <td>CENTRAL</td>\n", " <td>2015-08-05</td>\n", " <td>8:01</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37759</th>\n", " <td>1-22712031</td>\n", " <td>8/5/15 8:02</td>\n", " <td>8/11/15 7:25</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>4.0</td>\n", " <td>North Valley APC</td>\n", " <td>12.0</td>\n", " <td>Mitchell Englander</td>\n", " <td>4.0</td>\n", " <td>GRANADA HILLS NORTH NC</td>\n", " <td>DEVONSHIRE</td>\n", " <td>2015-08-05</td>\n", " <td>8:02</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>42438</th>\n", " <td>1-22940231</td>\n", " <td>8/5/15 14:30</td>\n", " <td>8/11/15 14:19</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>6.0</td>\n", " <td>West Los Angeles APC</td>\n", " <td>5.0</td>\n", " <td>Paul Koretz</td>\n", " <td>62.0</td>\n", " <td>WESTSIDE NC</td>\n", " <td>WEST LOS ANGELES</td>\n", " <td>2015-08-05</td>\n", " <td>14:30</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37766</th>\n", " <td>1-22710951</td>\n", " <td>8/5/15 8:03</td>\n", " <td>8/9/15 23:34</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>South Valley APC</td>\n", " <td>2.0</td>\n", " <td>Paul Krekorian</td>\n", " <td>22.0</td>\n", " <td>NOHO WEST NC</td>\n", " <td>NORTH HOLLYWOOD</td>\n", " <td>2015-08-05</td>\n", " <td>8:03</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37769</th>\n", " <td>1-22710091</td>\n", " <td>8/5/15 8:03</td>\n", " <td>8/9/15 15:50</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Metal/Household Appliances</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>Harbor APC</td>\n", " <td>15.0</td>\n", " <td>Joe Buscaino</td>\n", " <td>92.0</td>\n", " <td>HARBOR CITY NC</td>\n", " <td>HARBOR</td>\n", " <td>2015-08-05</td>\n", " <td>8:03</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37772</th>\n", " <td>1-22712161</td>\n", " <td>8/5/15 8:03</td>\n", " <td>8/5/15 16:09</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Dead Animal Removal</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>4.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>8.0</td>\n", " <td>Marqueece Harris-Dawson</td>\n", " <td>84.0</td>\n", " <td>EMPOWERMENT CONGRESS SOUTHWEST AREA NDC</td>\n", " <td>77TH STREET</td>\n", " <td>2015-08-05</td>\n", " <td>8:03</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>42434</th>\n", " <td>1-22938151</td>\n", " <td>8/5/15 14:29</td>\n", " <td>8/9/15 22:18</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>7.0</td>\n", " <td>South Valley APC</td>\n", " <td>2.0</td>\n", " <td>Paul Krekorian</td>\n", " <td>21.0</td>\n", " <td>GREATER VALLEY GLEN COUNCIL</td>\n", " <td>VAN NUYS</td>\n", " <td>2015-08-05</td>\n", " <td>14:29</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>42433</th>\n", " <td>1-22940161</td>\n", " <td>8/5/15 14:28</td>\n", " <td>8/9/15 23:35</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>1.0</td>\n", " <td>North Valley APC</td>\n", " <td>7.0</td>\n", " <td>Felipe Fuentes</td>\n", " <td>7.0</td>\n", " <td>PACOIMA NC</td>\n", " <td>FOOTHILL</td>\n", " <td>2015-08-05</td>\n", " <td>14:28</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37775</th>\n", " <td>1-22712241</td>\n", " <td>8/5/15 8:04</td>\n", " <td>8/9/15 23:34</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>2.0</td>\n", " <td>West Los Angeles APC</td>\n", " <td>5.0</td>\n", " <td>Paul Koretz</td>\n", " <td>115.0</td>\n", " <td>PALMS NC</td>\n", " <td>PACIFIC</td>\n", " <td>2015-08-05</td>\n", " <td>8:04</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37780</th>\n", " <td>1-22711061</td>\n", " <td>8/5/15 8:04</td>\n", " <td>8/9/15 15:54</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Metal/Household Appliances</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>4.0</td>\n", " <td>North Valley APC</td>\n", " <td>7.0</td>\n", " <td>Felipe Fuentes</td>\n", " <td>7.0</td>\n", " <td>PACOIMA NC</td>\n", " <td>FOOTHILL</td>\n", " <td>2015-08-05</td>\n", " <td>8:04</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37704</th>\n", " <td>1-22709591</td>\n", " <td>8/5/15 7:50</td>\n", " <td>8/9/15 21:47</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>4.0</td>\n", " <td>Central APC</td>\n", " <td>4.0</td>\n", " <td>David Ryu</td>\n", " <td>36.0</td>\n", " <td>LOS FELIZ NC</td>\n", " <td>NORTHEAST</td>\n", " <td>2015-08-05</td>\n", " <td>7:50</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>37702</th>\n", " <td>1-22711351</td>\n", " <td>8/5/15 7:49</td>\n", " <td>8/12/15 17:37</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Illegal Dumping Pickup</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>East Los Angeles APC</td>\n", " <td>14.0</td>\n", " <td>Jose Huizar</td>\n", " <td>48.0</td>\n", " <td>LA-32 NC</td>\n", " <td>HOLLENBECK</td>\n", " <td>2015-08-05</td>\n", " <td>7:49</td>\n", " <td>32</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>1485521</th>\n", " <td>1-1110284391</td>\n", " <td>7/23/18 8:02</td>\n", " <td>7/23/18 9:03</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Dead Animal Removal</td>\n", " <td>Closed</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>South Valley APC</td>\n", " <td>3.0</td>\n", " <td>Bob Blumenfield</td>\n", " <td>15.0</td>\n", " <td>RESEDA NC</td>\n", " <td>WEST VALLEY</td>\n", " <td>2018-07-23</td>\n", " <td>8:02</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1483294</th>\n", " <td>1-1110242066</td>\n", " <td>7/23/18 7:43</td>\n", " <td>7/23/18 7:43</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Electronic Waste</td>\n", " <td>Open</td>\n", " <td>Mobile App</td>\n", " <td>iOS</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>6.0</td>\n", " <td>Central APC</td>\n", " <td>13.0</td>\n", " <td>Mitch O'Farrell</td>\n", " <td>33.0</td>\n", " <td>HOLLYWOOD STUDIO DISTRICT NC</td>\n", " <td>HOLLYWOOD</td>\n", " <td>2018-07-23</td>\n", " <td>7:43</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1485584</th>\n", " <td>1-1110457911</td>\n", " <td>7/23/18 9:11</td>\n", " <td>7/23/18 9:11</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>3.0</td>\n", " <td>Central APC</td>\n", " <td>10.0</td>\n", " <td>Herb J. Wesson Jr.</td>\n", " <td>55.0</td>\n", " <td>WILSHIRE CENTER - KOREATOWN NC</td>\n", " <td>OLYMPIC</td>\n", " <td>2018-07-23</td>\n", " <td>9:11</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1485587</th>\n", " <td>1-1110458501</td>\n", " <td>7/23/18 9:11</td>\n", " <td>7/23/18 9:11</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>1.0</td>\n", " <td>South Valley APC</td>\n", " <td>2.0</td>\n", " <td>Paul Krekorian</td>\n", " <td>24.0</td>\n", " <td>MID-TOWN NORTH HOLLYWOOD NC</td>\n", " <td>NORTH HOLLYWOOD</td>\n", " <td>2018-07-23</td>\n", " <td>9:11</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1485591</th>\n", " <td>1-1110460271</td>\n", " <td>7/23/18 9:12</td>\n", " <td>7/23/18 9:12</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Metal/Household Appliances</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>3.0</td>\n", " <td>Central APC</td>\n", " <td>1.0</td>\n", " <td>Gilbert Cedillo</td>\n", " <td>121.0</td>\n", " <td>WESTLAKE SOUTH NC</td>\n", " <td>RAMPART</td>\n", " <td>2018-07-23</td>\n", " <td>9:12</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1485633</th>\n", " <td>1-1110466861</td>\n", " <td>7/23/18 9:14</td>\n", " <td>7/23/18 9:14</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Dead Animal Removal</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>6.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>15.0</td>\n", " <td>Joe Buscaino</td>\n", " <td>90.0</td>\n", " <td>HARBOR GATEWAY NORTH NC</td>\n", " <td>SOUTHEAST</td>\n", " <td>2018-07-23</td>\n", " <td>9:14</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1485630</th>\n", " <td>1-1110463981</td>\n", " <td>7/23/18 9:14</td>\n", " <td>7/23/18 9:14</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>South Valley APC</td>\n", " <td>6.0</td>\n", " <td>Nury Martinez</td>\n", " <td>19.0</td>\n", " <td>LAKE BALBOA NC</td>\n", " <td>WEST VALLEY</td>\n", " <td>2018-07-23</td>\n", " <td>9:14</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1485628</th>\n", " <td>1-1110466268</td>\n", " <td>7/23/18 9:14</td>\n", " <td>7/23/18 9:14</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Mobile App</td>\n", " <td>iOS</td>\n", " <td>Y</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>West Los Angeles APC</td>\n", " <td>11.0</td>\n", " <td>Mike Bonin</td>\n", " <td>68.0</td>\n", " <td>VENICE NC</td>\n", " <td>PACIFIC</td>\n", " <td>2018-07-23</td>\n", " <td>9:14</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1483266</th>\n", " <td>1-1110257511</td>\n", " <td>7/23/18 7:50</td>\n", " <td>7/23/18 7:50</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>4.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>8.0</td>\n", " <td>Marqueece Harris-Dawson</td>\n", " <td>80.0</td>\n", " <td>PARK MESA HEIGHTS CC</td>\n", " <td>SOUTHWEST</td>\n", " <td>2018-07-23</td>\n", " <td>7:50</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1485624</th>\n", " <td>1-1110465391</td>\n", " <td>7/23/18 9:14</td>\n", " <td>7/23/18 9:14</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Metal/Household Appliances</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>4.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>8.0</td>\n", " <td>Marqueece Harris-Dawson</td>\n", " <td>80.0</td>\n", " <td>PARK MESA HEIGHTS CC</td>\n", " <td>SOUTHWEST</td>\n", " <td>2018-07-23</td>\n", " <td>9:14</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1485620</th>\n", " <td>1-1110461170</td>\n", " <td>7/23/18 9:13</td>\n", " <td>7/23/18 9:14</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Homeless Encampment</td>\n", " <td>Open</td>\n", " <td>Mobile App</td>\n", " <td>iOS</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>4.0</td>\n", " <td>West Los Angeles APC</td>\n", " <td>11.0</td>\n", " <td>Mike Bonin</td>\n", " <td>68.0</td>\n", " <td>VENICE NC</td>\n", " <td>PACIFIC</td>\n", " <td>2018-07-23</td>\n", " <td>9:13</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1483268</th>\n", " <td>1-1110257691</td>\n", " <td>7/23/18 7:50</td>\n", " <td>7/23/18 7:50</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>North Valley APC</td>\n", " <td>7.0</td>\n", " <td>Monica Rodriguez</td>\n", " <td>5.0</td>\n", " <td>SYLMAR NC</td>\n", " <td>MISSION</td>\n", " <td>2018-07-23</td>\n", " <td>7:50</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1485619</th>\n", " <td>1-1110461111</td>\n", " <td>7/23/18 9:13</td>\n", " <td>7/23/18 9:13</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>6.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>10.0</td>\n", " <td>Herb J. Wesson Jr.</td>\n", " <td>74.0</td>\n", " <td>UNITED NEIGHBORHOODS OF THE HISTORIC ARLINGTON...</td>\n", " <td>WILSHIRE</td>\n", " <td>2018-07-23</td>\n", " <td>9:13</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1483272</th>\n", " <td>1-1110240881</td>\n", " <td>7/23/18 7:42</td>\n", " <td>7/23/18 7:42</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>3.0</td>\n", " <td>North Valley APC</td>\n", " <td>2.0</td>\n", " <td>Paul Krekorian</td>\n", " <td>22.0</td>\n", " <td>NOHO WEST NC</td>\n", " <td>NORTH HOLLYWOOD</td>\n", " <td>2018-07-23</td>\n", " <td>7:42</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1485616</th>\n", " <td>1-1110460971</td>\n", " <td>7/23/18 9:13</td>\n", " <td>7/23/18 9:13</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>South Valley APC</td>\n", " <td>6.0</td>\n", " <td>Nury Martinez</td>\n", " <td>19.0</td>\n", " <td>LAKE BALBOA NC</td>\n", " <td>WEST VALLEY</td>\n", " <td>2018-07-23</td>\n", " <td>9:13</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1483273</th>\n", " <td>1-1110240690</td>\n", " <td>7/23/18 7:42</td>\n", " <td>7/23/18 7:42</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Mobile App</td>\n", " <td>iOS</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>2.0</td>\n", " <td>Harbor APC</td>\n", " <td>15.0</td>\n", " <td>Joe Buscaino</td>\n", " <td>96.0</td>\n", " <td>COASTAL SAN PEDRO NC</td>\n", " <td>HARBOR</td>\n", " <td>2018-07-23</td>\n", " <td>7:42</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1485613</th>\n", " <td>1-1110463211</td>\n", " <td>7/23/18 9:13</td>\n", " <td>7/23/18 9:13</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>4.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>8.0</td>\n", " <td>Marqueece Harris-Dawson</td>\n", " <td>80.0</td>\n", " <td>PARK MESA HEIGHTS CC</td>\n", " <td>SOUTHWEST</td>\n", " <td>2018-07-23</td>\n", " <td>9:13</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1485610</th>\n", " <td>1-1110462651</td>\n", " <td>7/23/18 9:13</td>\n", " <td>7/23/18 9:13</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>1.0</td>\n", " <td>East Los Angeles APC</td>\n", " <td>1.0</td>\n", " <td>Gilbert Cedillo</td>\n", " <td>44.0</td>\n", " <td>GREATER ECHO PARK ELYSIAN NC</td>\n", " <td>RAMPART</td>\n", " <td>2018-07-23</td>\n", " <td>9:13</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1485607</th>\n", " <td>1-1110462491</td>\n", " <td>7/23/18 9:12</td>\n", " <td>7/23/18 9:12</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>3.0</td>\n", " <td>South Valley APC</td>\n", " <td>2.0</td>\n", " <td>Paul Krekorian</td>\n", " <td>24.0</td>\n", " <td>MID-TOWN NORTH HOLLYWOOD NC</td>\n", " <td>NORTH HOLLYWOOD</td>\n", " <td>2018-07-23</td>\n", " <td>9:12</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1483281</th>\n", " <td>1-1110241776</td>\n", " <td>7/23/18 7:42</td>\n", " <td>7/23/18 7:42</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Mobile App</td>\n", " <td>iOS</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>2.0</td>\n", " <td>North Valley APC</td>\n", " <td>2.0</td>\n", " <td>Paul Krekorian</td>\n", " <td>22.0</td>\n", " <td>NOHO WEST NC</td>\n", " <td>NORTH HOLLYWOOD</td>\n", " <td>2018-07-23</td>\n", " <td>7:42</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1483283</th>\n", " <td>1-1110243221</td>\n", " <td>7/23/18 7:41</td>\n", " <td>7/23/18 7:42</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>8.0</td>\n", " <td>Marqueece Harris-Dawson</td>\n", " <td>80.0</td>\n", " <td>PARK MESA HEIGHTS CC</td>\n", " <td>SOUTHWEST</td>\n", " <td>2018-07-23</td>\n", " <td>7:41</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1485603</th>\n", " <td>1-1110458771</td>\n", " <td>7/23/18 9:12</td>\n", " <td>7/23/18 9:12</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>3.0</td>\n", " <td>South Valley APC</td>\n", " <td>2.0</td>\n", " <td>Paul Krekorian</td>\n", " <td>25.0</td>\n", " <td>NC VALLEY VILLAGE</td>\n", " <td>NORTH HOLLYWOOD</td>\n", " <td>2018-07-23</td>\n", " <td>9:12</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1485601</th>\n", " <td>1-1110211336</td>\n", " <td>7/23/18 7:19</td>\n", " <td>7/23/18 9:12</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Dead Animal Removal</td>\n", " <td>Closed</td>\n", " <td>Mobile App</td>\n", " <td>iOS</td>\n", " <td>Y</td>\n", " <td>...</td>\n", " <td>6.0</td>\n", " <td>South Valley APC</td>\n", " <td>3.0</td>\n", " <td>Bob Blumenfield</td>\n", " <td>14.0</td>\n", " <td>WINNETKA NC</td>\n", " <td>TOPANGA</td>\n", " <td>2018-07-23</td>\n", " <td>7:19</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1483287</th>\n", " <td>1-1110241848</td>\n", " <td>7/23/18 7:42</td>\n", " <td>7/23/18 7:42</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Mobile App</td>\n", " <td>iOS</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>4.0</td>\n", " <td>North Valley APC</td>\n", " <td>7.0</td>\n", " <td>Monica Rodriguez</td>\n", " <td>5.0</td>\n", " <td>SYLMAR NC</td>\n", " <td>MISSION</td>\n", " <td>2018-07-23</td>\n", " <td>7:42</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1483289</th>\n", " <td>1-1110240974</td>\n", " <td>7/23/18 7:42</td>\n", " <td>7/23/18 7:42</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Mobile App</td>\n", " <td>iOS</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>2.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>8.0</td>\n", " <td>Marqueece Harris-Dawson</td>\n", " <td>77.0</td>\n", " <td>EMPOWERMENT CONGRESS NORTH AREA NDC</td>\n", " <td>SOUTHWEST</td>\n", " <td>2018-07-23</td>\n", " <td>7:42</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1485598</th>\n", " <td>1-1110261311</td>\n", " <td>7/23/18 7:51</td>\n", " <td>7/23/18 9:12</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Dead Animal Removal</td>\n", " <td>Closed</td>\n", " <td>Self Service</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>6.0</td>\n", " <td>South Valley APC</td>\n", " <td>3.0</td>\n", " <td>Bob Blumenfield</td>\n", " <td>13.0</td>\n", " <td>CANOGA PARK NC</td>\n", " <td>TOPANGA</td>\n", " <td>2018-07-23</td>\n", " <td>7:51</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1483291</th>\n", " <td>1-1110243061</td>\n", " <td>7/23/18 7:43</td>\n", " <td>7/23/18 7:43</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Call</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>1.0</td>\n", " <td>South Los Angeles APC</td>\n", " <td>8.0</td>\n", " <td>Marqueece Harris-Dawson</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>77TH STREET</td>\n", " <td>2018-07-23</td>\n", " <td>7:43</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1485595</th>\n", " <td>1-1110458091</td>\n", " <td>7/23/18 9:12</td>\n", " <td>7/23/18 9:12</td>\n", " <td>SR Created</td>\n", " <td>BSL</td>\n", " <td>Single Streetlight Issue</td>\n", " <td>Open</td>\n", " <td>Email</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>7.0</td>\n", " <td>Central APC</td>\n", " <td>5.0</td>\n", " <td>Paul Koretz</td>\n", " <td>119.0</td>\n", " <td>GREATER WILSHIRE NC</td>\n", " <td>WILSHIRE</td>\n", " <td>2018-07-23</td>\n", " <td>9:12</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1483261</th>\n", " <td>1-1110257407</td>\n", " <td>7/23/18 7:49</td>\n", " <td>7/23/18 7:49</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Bulky Items</td>\n", " <td>Open</td>\n", " <td>Mobile App</td>\n", " <td>iOS</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>2.0</td>\n", " <td>North Valley APC</td>\n", " <td>2.0</td>\n", " <td>Paul Krekorian</td>\n", " <td>8.0</td>\n", " <td>SUN VALLEY AREA NC</td>\n", " <td>FOOTHILL</td>\n", " <td>2018-07-23</td>\n", " <td>7:49</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1487626</th>\n", " <td>1-1110661371</td>\n", " <td>7/23/18 10:24</td>\n", " <td>7/23/18 10:24</td>\n", " <td>SR Created</td>\n", " <td>BOS</td>\n", " <td>Homeless Encampment</td>\n", " <td>Open</td>\n", " <td>Self Service</td>\n", " <td>NaN</td>\n", " <td>N</td>\n", " <td>...</td>\n", " <td>4.0</td>\n", " <td>South Valley APC</td>\n", " <td>4.0</td>\n", " <td>David Ryu</td>\n", " <td>26.0</td>\n", " <td>SHERMAN OAKS NC</td>\n", " <td>VAN NUYS</td>\n", " <td>2018-07-23</td>\n", " <td>10:24</td>\n", " <td>30</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>2483694 rows × 36 columns</p>\n", "</div>" ], "text/plain": [ " SRNumber CreatedDate UpdatedDate ActionTaken Owner \\\n", "40349 1-22838691 8/5/15 11:22 8/13/15 16:39 SR Created BOS \n", "37709 1-22711481 8/5/15 7:52 8/9/15 23:34 SR Created BOS \n", "42456 1-22942441 8/5/15 14:33 8/10/15 11:42 SR Created BOS \n", "37712 1-22712351 8/5/15 7:52 12/18/15 11:53 SR Created BOS \n", "37713 1-22710461 8/5/15 7:53 8/17/15 21:26 SR Created BOS \n", "37719 1-22711531 8/5/15 7:53 8/15/15 18:47 SR Created BOS \n", "42452 1-22941161 8/5/15 14:32 8/5/15 14:32 SR Created BOS \n", "37720 1-22710511 8/5/15 7:54 8/9/15 23:34 SR Created BOS \n", "37725 1-22709721 8/5/15 7:54 8/9/15 21:47 SR Created BOS \n", "42449 1-22943481 8/5/15 14:32 8/9/15 23:59 SR Created BOS \n", "37729 1-22709771 8/5/15 7:55 8/9/15 15:54 SR Created BOS \n", "37735 1-22711761 8/5/15 7:57 8/11/15 7:34 SR Created BOS \n", "37739 1-22710691 8/5/15 7:57 8/9/15 23:34 SR Created BOS \n", "37743 1-22711791 8/5/15 7:57 8/11/15 14:47 SR Created BOS \n", "42444 1-22941091 8/5/15 14:31 8/9/15 23:59 SR Created BOS \n", "37746 1-22712521 8/5/15 7:59 8/11/15 14:24 SR Created BOS \n", "37748 1-22710831 8/5/15 7:59 8/12/15 17:14 SR Created BOS \n", "37752 1-22709941 8/5/15 8:00 8/10/15 9:29 SR Created BOS \n", "37756 1-22712651 8/5/15 8:01 8/12/15 15:48 SR Created BOS \n", "37759 1-22712031 8/5/15 8:02 8/11/15 7:25 SR Created BOS \n", "42438 1-22940231 8/5/15 14:30 8/11/15 14:19 SR Created BOS \n", "37766 1-22710951 8/5/15 8:03 8/9/15 23:34 SR Created BOS \n", "37769 1-22710091 8/5/15 8:03 8/9/15 15:50 SR Created BOS \n", "37772 1-22712161 8/5/15 8:03 8/5/15 16:09 SR Created BOS \n", "42434 1-22938151 8/5/15 14:29 8/9/15 22:18 SR Created BOS \n", "42433 1-22940161 8/5/15 14:28 8/9/15 23:35 SR Created BOS \n", "37775 1-22712241 8/5/15 8:04 8/9/15 23:34 SR Created BOS \n", "37780 1-22711061 8/5/15 8:04 8/9/15 15:54 SR Created BOS \n", "37704 1-22709591 8/5/15 7:50 8/9/15 21:47 SR Created BOS \n", "37702 1-22711351 8/5/15 7:49 8/12/15 17:37 SR Created BOS \n", "... ... ... ... ... ... \n", "1485521 1-1110284391 7/23/18 8:02 7/23/18 9:03 SR Created BOS \n", "1483294 1-1110242066 7/23/18 7:43 7/23/18 7:43 SR Created BOS \n", "1485584 1-1110457911 7/23/18 9:11 7/23/18 9:11 SR Created BOS \n", "1485587 1-1110458501 7/23/18 9:11 7/23/18 9:11 SR Created BOS \n", "1485591 1-1110460271 7/23/18 9:12 7/23/18 9:12 SR Created BOS \n", "1485633 1-1110466861 7/23/18 9:14 7/23/18 9:14 SR Created BOS \n", "1485630 1-1110463981 7/23/18 9:14 7/23/18 9:14 SR Created BOS \n", "1485628 1-1110466268 7/23/18 9:14 7/23/18 9:14 SR Created BOS \n", "1483266 1-1110257511 7/23/18 7:50 7/23/18 7:50 SR Created BOS \n", "1485624 1-1110465391 7/23/18 9:14 7/23/18 9:14 SR Created BOS \n", "1485620 1-1110461170 7/23/18 9:13 7/23/18 9:14 SR Created BOS \n", "1483268 1-1110257691 7/23/18 7:50 7/23/18 7:50 SR Created BOS \n", "1485619 1-1110461111 7/23/18 9:13 7/23/18 9:13 SR Created BOS \n", "1483272 1-1110240881 7/23/18 7:42 7/23/18 7:42 SR Created BOS \n", "1485616 1-1110460971 7/23/18 9:13 7/23/18 9:13 SR Created BOS \n", "1483273 1-1110240690 7/23/18 7:42 7/23/18 7:42 SR Created BOS \n", "1485613 1-1110463211 7/23/18 9:13 7/23/18 9:13 SR Created BOS \n", "1485610 1-1110462651 7/23/18 9:13 7/23/18 9:13 SR Created BOS \n", "1485607 1-1110462491 7/23/18 9:12 7/23/18 9:12 SR Created BOS \n", "1483281 1-1110241776 7/23/18 7:42 7/23/18 7:42 SR Created BOS \n", "1483283 1-1110243221 7/23/18 7:41 7/23/18 7:42 SR Created BOS \n", "1485603 1-1110458771 7/23/18 9:12 7/23/18 9:12 SR Created BOS \n", "1485601 1-1110211336 7/23/18 7:19 7/23/18 9:12 SR Created BOS \n", "1483287 1-1110241848 7/23/18 7:42 7/23/18 7:42 SR Created BOS \n", "1483289 1-1110240974 7/23/18 7:42 7/23/18 7:42 SR Created BOS \n", "1485598 1-1110261311 7/23/18 7:51 7/23/18 9:12 SR Created BOS \n", "1483291 1-1110243061 7/23/18 7:43 7/23/18 7:43 SR Created BOS \n", "1485595 1-1110458091 7/23/18 9:12 7/23/18 9:12 SR Created BSL \n", "1483261 1-1110257407 7/23/18 7:49 7/23/18 7:49 SR Created BOS \n", "1487626 1-1110661371 7/23/18 10:24 7/23/18 10:24 SR Created BOS \n", "\n", " RequestType Status RequestSource MobileOS \\\n", "40349 Metal/Household Appliances Closed Call NaN \n", "37709 Bulky Items Closed Call NaN \n", "42456 Dead Animal Removal Closed Call NaN \n", "37712 Other Cancelled Call NaN \n", "37713 Illegal Dumping Pickup Closed Call NaN \n", "37719 Bulky Items Closed Call NaN \n", "42452 Feedback Open Call NaN \n", "37720 Bulky Items Closed Call NaN \n", "37725 Bulky Items Closed Call NaN \n", "42449 Bulky Items Closed Call NaN \n", "37729 Metal/Household Appliances Closed Call NaN \n", "37735 Bulky Items Closed Call NaN \n", "37739 Bulky Items Closed Call NaN \n", "37743 Bulky Items Closed Call NaN \n", "42444 Bulky Items Closed Call NaN \n", "37746 Bulky Items Closed Call NaN \n", "37748 Bulky Items Closed Call NaN \n", "37752 Electronic Waste Closed Call NaN \n", "37756 Bulky Items Closed Call NaN \n", "37759 Bulky Items Closed Call NaN \n", "42438 Bulky Items Closed Call NaN \n", "37766 Bulky Items Closed Call NaN \n", "37769 Metal/Household Appliances Closed Call NaN \n", "37772 Dead Animal Removal Closed Call NaN \n", "42434 Bulky Items Closed Call NaN \n", "42433 Bulky Items Closed Call NaN \n", "37775 Bulky Items Closed Call NaN \n", "37780 Metal/Household Appliances Closed Call NaN \n", "37704 Bulky Items Closed Call NaN \n", "37702 Illegal Dumping Pickup Closed Call NaN \n", "... ... ... ... ... \n", "1485521 Dead Animal Removal Closed Call NaN \n", "1483294 Electronic Waste Open Mobile App iOS \n", "1485584 Bulky Items Open Call NaN \n", "1485587 Bulky Items Open Call NaN \n", "1485591 Metal/Household Appliances Open Call NaN \n", "1485633 Dead Animal Removal Open Call NaN \n", "1485630 Bulky Items Open Call NaN \n", "1485628 Bulky Items Open Mobile App iOS \n", "1483266 Bulky Items Open Call NaN \n", "1485624 Metal/Household Appliances Open Call NaN \n", "1485620 Homeless Encampment Open Mobile App iOS \n", "1483268 Bulky Items Open Call NaN \n", "1485619 Bulky Items Open Call NaN \n", "1483272 Bulky Items Open Call NaN \n", "1485616 Bulky Items Open Call NaN \n", "1483273 Bulky Items Open Mobile App iOS \n", "1485613 Bulky Items Open Call NaN \n", "1485610 Bulky Items Open Call NaN \n", "1485607 Bulky Items Open Call NaN \n", "1483281 Bulky Items Open Mobile App iOS \n", "1483283 Bulky Items Open Call NaN \n", "1485603 Bulky Items Open Call NaN \n", "1485601 Dead Animal Removal Closed Mobile App iOS \n", "1483287 Bulky Items Open Mobile App iOS \n", "1483289 Bulky Items Open Mobile App iOS \n", "1485598 Dead Animal Removal Closed Self Service NaN \n", "1483291 Bulky Items Open Call NaN \n", "1485595 Single Streetlight Issue Open Email NaN \n", "1483261 Bulky Items Open Mobile App iOS \n", "1487626 Homeless Encampment Open Self Service NaN \n", "\n", " Anonymous ... TBMRow APC CD \\\n", "40349 N ... 3.0 West Los Angeles APC 5.0 \n", "37709 N ... 5.0 South Valley APC 3.0 \n", "42456 N ... 6.0 South Los Angeles APC 9.0 \n", "37712 N ... 5.0 South Valley APC 4.0 \n", "37713 N ... 2.0 Central APC 4.0 \n", "37719 N ... 3.0 South Los Angeles APC 9.0 \n", "42452 N ... 6.0 Central APC 13.0 \n", "37720 N ... 4.0 Central APC 10.0 \n", "37725 N ... 7.0 Central APC 5.0 \n", "42449 N ... 3.0 South Valley APC 12.0 \n", "37729 N ... 5.0 South Valley APC 6.0 \n", "37735 N ... 5.0 North Valley APC 7.0 \n", "37739 N ... 4.0 Central APC 5.0 \n", "37743 N ... 5.0 South Los Angeles APC 8.0 \n", "42444 N ... 6.0 South Valley APC 6.0 \n", "37746 N ... 5.0 Harbor APC 15.0 \n", "37748 N ... 2.0 Central APC 1.0 \n", "37752 N ... 7.0 South Valley APC 2.0 \n", "37756 N ... 6.0 Central APC 14.0 \n", "37759 N ... 4.0 North Valley APC 12.0 \n", "42438 N ... 6.0 West Los Angeles APC 5.0 \n", "37766 N ... 5.0 South Valley APC 2.0 \n", "37769 N ... 5.0 Harbor APC 15.0 \n", "37772 N ... 4.0 South Los Angeles APC 8.0 \n", "42434 N ... 7.0 South Valley APC 2.0 \n", "42433 N ... 1.0 North Valley APC 7.0 \n", "37775 N ... 2.0 West Los Angeles APC 5.0 \n", "37780 N ... 4.0 North Valley APC 7.0 \n", "37704 N ... 4.0 Central APC 4.0 \n", "37702 N ... 5.0 East Los Angeles APC 14.0 \n", "... ... ... ... ... ... \n", "1485521 N ... 5.0 South Valley APC 3.0 \n", "1483294 N ... 6.0 Central APC 13.0 \n", "1485584 N ... 3.0 Central APC 10.0 \n", "1485587 N ... 1.0 South Valley APC 2.0 \n", "1485591 N ... 3.0 Central APC 1.0 \n", "1485633 N ... 6.0 South Los Angeles APC 15.0 \n", "1485630 N ... 5.0 South Valley APC 6.0 \n", "1485628 Y ... 5.0 West Los Angeles APC 11.0 \n", "1483266 N ... 4.0 South Los Angeles APC 8.0 \n", "1485624 N ... 4.0 South Los Angeles APC 8.0 \n", "1485620 N ... 4.0 West Los Angeles APC 11.0 \n", "1483268 N ... 5.0 North Valley APC 7.0 \n", "1485619 N ... 6.0 South Los Angeles APC 10.0 \n", "1483272 N ... 3.0 North Valley APC 2.0 \n", "1485616 N ... 5.0 South Valley APC 6.0 \n", "1483273 N ... 2.0 Harbor APC 15.0 \n", "1485613 N ... 4.0 South Los Angeles APC 8.0 \n", "1485610 N ... 1.0 East Los Angeles APC 1.0 \n", "1485607 N ... 3.0 South Valley APC 2.0 \n", "1483281 N ... 2.0 North Valley APC 2.0 \n", "1483283 N ... 5.0 South Los Angeles APC 8.0 \n", "1485603 N ... 3.0 South Valley APC 2.0 \n", "1485601 Y ... 6.0 South Valley APC 3.0 \n", "1483287 N ... 4.0 North Valley APC 7.0 \n", "1483289 N ... 2.0 South Los Angeles APC 8.0 \n", "1485598 N ... 6.0 South Valley APC 3.0 \n", "1483291 N ... 1.0 South Los Angeles APC 8.0 \n", "1485595 N ... 7.0 Central APC 5.0 \n", "1483261 N ... 2.0 North Valley APC 2.0 \n", "1487626 N ... 4.0 South Valley APC 4.0 \n", "\n", " CDMember NC \\\n", "40349 Paul Koretz 61.0 \n", "37709 Bob Blumenfield 16.0 \n", "42456 Curren D. Price Jr. 86.0 \n", "37712 David Ryu 26.0 \n", "37713 David Ryu 36.0 \n", "37719 Curren D. Price Jr. 125.0 \n", "42452 Mitch O'Farrell 33.0 \n", "37720 Herb J. Wesson Jr. 60.0 \n", "37725 Paul Koretz 58.0 \n", "42449 Mitchell Englander 11.0 \n", "37729 Nury Martinez 20.0 \n", "37735 Felipe Fuentes 10.0 \n", "37739 Paul Koretz 61.0 \n", "37743 Marqueece Harris-Dawson 87.0 \n", "42444 Nury Martinez 19.0 \n", "37746 Joe Buscaino 92.0 \n", "37748 Gilbert Cedillo 46.0 \n", "37752 Paul Krekorian 21.0 \n", "37756 Jose Huizar 52.0 \n", "37759 Mitchell Englander 4.0 \n", "42438 Paul Koretz 62.0 \n", "37766 Paul Krekorian 22.0 \n", "37769 Joe Buscaino 92.0 \n", "37772 Marqueece Harris-Dawson 84.0 \n", "42434 Paul Krekorian 21.0 \n", "42433 Felipe Fuentes 7.0 \n", "37775 Paul Koretz 115.0 \n", "37780 Felipe Fuentes 7.0 \n", "37704 David Ryu 36.0 \n", "37702 Jose Huizar 48.0 \n", "... ... ... \n", "1485521 Bob Blumenfield 15.0 \n", "1483294 Mitch O'Farrell 33.0 \n", "1485584 Herb J. Wesson Jr. 55.0 \n", "1485587 Paul Krekorian 24.0 \n", "1485591 Gilbert Cedillo 121.0 \n", "1485633 Joe Buscaino 90.0 \n", "1485630 Nury Martinez 19.0 \n", "1485628 Mike Bonin 68.0 \n", "1483266 Marqueece Harris-Dawson 80.0 \n", "1485624 Marqueece Harris-Dawson 80.0 \n", "1485620 Mike Bonin 68.0 \n", "1483268 Monica Rodriguez 5.0 \n", "1485619 Herb J. Wesson Jr. 74.0 \n", "1483272 Paul Krekorian 22.0 \n", "1485616 Nury Martinez 19.0 \n", "1483273 Joe Buscaino 96.0 \n", "1485613 Marqueece Harris-Dawson 80.0 \n", "1485610 Gilbert Cedillo 44.0 \n", "1485607 Paul Krekorian 24.0 \n", "1483281 Paul Krekorian 22.0 \n", "1483283 Marqueece Harris-Dawson 80.0 \n", "1485603 Paul Krekorian 25.0 \n", "1485601 Bob Blumenfield 14.0 \n", "1483287 Monica Rodriguez 5.0 \n", "1483289 Marqueece Harris-Dawson 77.0 \n", "1485598 Bob Blumenfield 13.0 \n", "1483291 Marqueece Harris-Dawson NaN \n", "1485595 Paul Koretz 119.0 \n", "1483261 Paul Krekorian 8.0 \n", "1487626 David Ryu 26.0 \n", "\n", " NCName PolicePrecinct \\\n", "40349 SOUTH ROBERTSON NC WEST LOS ANGELES \n", "37709 WOODLAND HILLS-WARNER CENTER NC TOPANGA \n", "42456 COMMUNITY AND NEIGHBORS FOR NINTH DISTRICT UNI... NEWTON \n", "37712 SHERMAN OAKS NC VAN NUYS \n", "37713 LOS FELIZ NC NORTHEAST \n", "37719 ZAPATA KING NC NEWTON \n", "42452 HOLLYWOOD STUDIO DISTRICT NC HOLLYWOOD \n", "37720 P.I.C.O. NC WILSHIRE \n", "37725 MID CITY WEST CC WILSHIRE \n", "42449 WEST HILLS NC TOPANGA \n", "37729 VAN NUYS NC VAN NUYS \n", "37735 SUNLAND-TUJUNGA NC FOOTHILL \n", "37739 SOUTH ROBERTSON NC WEST LOS ANGELES \n", "37743 EMPOWERMENT CONGRESS SOUTHEAST AREA NDC SOUTHEAST \n", "42444 LAKE BALBOA NC WEST VALLEY \n", "37746 HARBOR CITY NC HARBOR \n", "37748 HISTORIC CULTURAL NC CENTRAL \n", "37752 GREATER VALLEY GLEN COUNCIL VAN NUYS \n", "37756 DOWNTOWN LOS ANGELES CENTRAL \n", "37759 GRANADA HILLS NORTH NC DEVONSHIRE \n", "42438 WESTSIDE NC WEST LOS ANGELES \n", "37766 NOHO WEST NC NORTH HOLLYWOOD \n", "37769 HARBOR CITY NC HARBOR \n", "37772 EMPOWERMENT CONGRESS SOUTHWEST AREA NDC 77TH STREET \n", "42434 GREATER VALLEY GLEN COUNCIL VAN NUYS \n", "42433 PACOIMA NC FOOTHILL \n", "37775 PALMS NC PACIFIC \n", "37780 PACOIMA NC FOOTHILL \n", "37704 LOS FELIZ NC NORTHEAST \n", "37702 LA-32 NC HOLLENBECK \n", "... ... ... \n", "1485521 RESEDA NC WEST VALLEY \n", "1483294 HOLLYWOOD STUDIO DISTRICT NC HOLLYWOOD \n", "1485584 WILSHIRE CENTER - KOREATOWN NC OLYMPIC \n", "1485587 MID-TOWN NORTH HOLLYWOOD NC NORTH HOLLYWOOD \n", "1485591 WESTLAKE SOUTH NC RAMPART \n", "1485633 HARBOR GATEWAY NORTH NC SOUTHEAST \n", "1485630 LAKE BALBOA NC WEST VALLEY \n", "1485628 VENICE NC PACIFIC \n", "1483266 PARK MESA HEIGHTS CC SOUTHWEST \n", "1485624 PARK MESA HEIGHTS CC SOUTHWEST \n", "1485620 VENICE NC PACIFIC \n", "1483268 SYLMAR NC MISSION \n", "1485619 UNITED NEIGHBORHOODS OF THE HISTORIC ARLINGTON... WILSHIRE \n", "1483272 NOHO WEST NC NORTH HOLLYWOOD \n", "1485616 LAKE BALBOA NC WEST VALLEY \n", "1483273 COASTAL SAN PEDRO NC HARBOR \n", "1485613 PARK MESA HEIGHTS CC SOUTHWEST \n", "1485610 GREATER ECHO PARK ELYSIAN NC RAMPART \n", "1485607 MID-TOWN NORTH HOLLYWOOD NC NORTH HOLLYWOOD \n", "1483281 NOHO WEST NC NORTH HOLLYWOOD \n", "1483283 PARK MESA HEIGHTS CC SOUTHWEST \n", "1485603 NC VALLEY VILLAGE NORTH HOLLYWOOD \n", "1485601 WINNETKA NC TOPANGA \n", "1483287 SYLMAR NC MISSION \n", "1483289 EMPOWERMENT CONGRESS NORTH AREA NDC SOUTHWEST \n", "1485598 CANOGA PARK NC TOPANGA \n", "1483291 NaN 77TH STREET \n", "1485595 GREATER WILSHIRE NC WILSHIRE \n", "1483261 SUN VALLEY AREA NC FOOTHILL \n", "1487626 SHERMAN OAKS NC VAN NUYS \n", "\n", " Created Date Created Time Week Number \n", "40349 2015-08-05 11:22 32 \n", "37709 2015-08-05 7:52 32 \n", "42456 2015-08-05 14:33 32 \n", "37712 2015-08-05 7:52 32 \n", "37713 2015-08-05 7:53 32 \n", "37719 2015-08-05 7:53 32 \n", "42452 2015-08-05 14:32 32 \n", "37720 2015-08-05 7:54 32 \n", "37725 2015-08-05 7:54 32 \n", "42449 2015-08-05 14:32 32 \n", "37729 2015-08-05 7:55 32 \n", "37735 2015-08-05 7:57 32 \n", "37739 2015-08-05 7:57 32 \n", "37743 2015-08-05 7:57 32 \n", "42444 2015-08-05 14:31 32 \n", "37746 2015-08-05 7:59 32 \n", "37748 2015-08-05 7:59 32 \n", "37752 2015-08-05 8:00 32 \n", "37756 2015-08-05 8:01 32 \n", "37759 2015-08-05 8:02 32 \n", "42438 2015-08-05 14:30 32 \n", "37766 2015-08-05 8:03 32 \n", "37769 2015-08-05 8:03 32 \n", "37772 2015-08-05 8:03 32 \n", "42434 2015-08-05 14:29 32 \n", "42433 2015-08-05 14:28 32 \n", "37775 2015-08-05 8:04 32 \n", "37780 2015-08-05 8:04 32 \n", "37704 2015-08-05 7:50 32 \n", "37702 2015-08-05 7:49 32 \n", "... ... ... ... \n", "1485521 2018-07-23 8:02 30 \n", "1483294 2018-07-23 7:43 30 \n", "1485584 2018-07-23 9:11 30 \n", "1485587 2018-07-23 9:11 30 \n", "1485591 2018-07-23 9:12 30 \n", "1485633 2018-07-23 9:14 30 \n", "1485630 2018-07-23 9:14 30 \n", "1485628 2018-07-23 9:14 30 \n", "1483266 2018-07-23 7:50 30 \n", "1485624 2018-07-23 9:14 30 \n", "1485620 2018-07-23 9:13 30 \n", "1483268 2018-07-23 7:50 30 \n", "1485619 2018-07-23 9:13 30 \n", "1483272 2018-07-23 7:42 30 \n", "1485616 2018-07-23 9:13 30 \n", "1483273 2018-07-23 7:42 30 \n", "1485613 2018-07-23 9:13 30 \n", "1485610 2018-07-23 9:13 30 \n", "1485607 2018-07-23 9:12 30 \n", "1483281 2018-07-23 7:42 30 \n", "1483283 2018-07-23 7:41 30 \n", "1485603 2018-07-23 9:12 30 \n", "1485601 2018-07-23 7:19 30 \n", "1483287 2018-07-23 7:42 30 \n", "1483289 2018-07-23 7:42 30 \n", "1485598 2018-07-23 7:51 30 \n", "1483291 2018-07-23 7:43 30 \n", "1485595 2018-07-23 9:12 30 \n", "1483261 2018-07-23 7:49 30 \n", "1487626 2018-07-23 10:24 30 \n", "\n", "[2483694 rows x 36 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Created Date'] = pd.to_datetime(df['Created Date'], errors='coerce')\n", "df.sort_values('Created Date', inplace = True)\n", "df['Week Number'] = df['Created Date'].dt.week\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "New CSV for dataframe with week number column added on called \"311_week_number.csv\"" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "df.to_csv('311_week_number.csv', encoding = 'utf-8', index = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reading \"311_week_number.csv\" to check if task was accomplished" ] }, { "cell_type": "code", "execution_count": 21, "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>RequestType</th>\n", " <th>RequestSource</th>\n", " <th>Address</th>\n", " <th>Latitude</th>\n", " <th>Longitude</th>\n", " <th>CD</th>\n", " <th>Created Date</th>\n", " <th>Created Time</th>\n", " <th>Week Number</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Metal/Household Appliances</td>\n", " <td>Call</td>\n", " <td>1115 S ELM DR, 90035</td>\n", " <td>34.056445</td>\n", " <td>-118.395187</td>\n", " <td>5.0</td>\n", " <td>2015-08-05</td>\n", " <td>11:22</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>20219 W CHAPTER DR, 91364</td>\n", " <td>34.148923</td>\n", " <td>-118.576744</td>\n", " <td>3.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:52</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Dead Animal Removal</td>\n", " <td>Call</td>\n", " <td>346 W 64TH ST, 90003</td>\n", " <td>33.981244</td>\n", " <td>-118.280030</td>\n", " <td>9.0</td>\n", " <td>2015-08-05</td>\n", " <td>14:33</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Other</td>\n", " <td>Call</td>\n", " <td>14435 W BENEFIT ST, 91423</td>\n", " <td>34.148889</td>\n", " <td>-118.447419</td>\n", " <td>4.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:52</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Illegal Dumping Pickup</td>\n", " <td>Call</td>\n", " <td>LOS FELIZ BLVD at GRIFFITH PARK BLVD, 90027</td>\n", " <td>34.113826</td>\n", " <td>-118.277039</td>\n", " <td>4.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:53</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>721 E 43RD PL, 90011</td>\n", " <td>34.004913</td>\n", " <td>-118.263076</td>\n", " <td>9.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:53</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Feedback</td>\n", " <td>Call</td>\n", " <td>731 N ST ANDREWS PL, 90038</td>\n", " <td>34.084435</td>\n", " <td>-118.311530</td>\n", " <td>13.0</td>\n", " <td>2015-08-05</td>\n", " <td>14:32</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>1260 S CLOVERDALE AVE, 90019</td>\n", " <td>34.051757</td>\n", " <td>-118.350011</td>\n", " <td>10.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:54</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>360 N EDINBURGH AVE, 90048</td>\n", " <td>34.077845</td>\n", " <td>-118.363621</td>\n", " <td>5.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:54</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>7730 N LENA AVE, 91304</td>\n", " <td>34.211079</td>\n", " <td>-118.626299</td>\n", " <td>12.0</td>\n", " <td>2015-08-05</td>\n", " <td>14:32</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Metal/Household Appliances</td>\n", " <td>Call</td>\n", " <td>6833 N SEPULVEDA BLVD, 91406</td>\n", " <td>34.194954</td>\n", " <td>-118.466438</td>\n", " <td>6.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:55</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>7104 W HIGHCLIFF TR, 91042</td>\n", " <td>34.245145</td>\n", " <td>-118.286781</td>\n", " <td>7.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:57</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>1643 S CREST DR, 90035</td>\n", " <td>34.049798</td>\n", " <td>-118.387688</td>\n", " <td>5.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:57</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>626 E 105TH ST, 90002</td>\n", " <td>33.940920</td>\n", " <td>-118.264252</td>\n", " <td>8.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:57</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>16434 W HAYNES ST, 91406</td>\n", " <td>34.189020</td>\n", " <td>-118.491543</td>\n", " <td>6.0</td>\n", " <td>2015-08-05</td>\n", " <td>14:31</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>1627 W 261ST ST, 90710</td>\n", " <td>33.786100</td>\n", " <td>-118.305316</td>\n", " <td>15.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:59</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>906 N BEAUDRY AVE, 90012</td>\n", " <td>34.067500</td>\n", " <td>-118.246638</td>\n", " <td>1.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:59</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>Electronic Waste</td>\n", " <td>Call</td>\n", " <td>6016 N SUNNYSLOPE AVE, 91401</td>\n", " <td>34.180182</td>\n", " <td>-118.426787</td>\n", " <td>2.0</td>\n", " <td>2015-08-05</td>\n", " <td>8:00</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>1149 S BROADWAY, 90015</td>\n", " <td>34.038578</td>\n", " <td>-118.260191</td>\n", " <td>14.0</td>\n", " <td>2015-08-05</td>\n", " <td>8:01</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>17447 W TUSCAN DR, 91344</td>\n", " <td>34.305303</td>\n", " <td>-118.513290</td>\n", " <td>12.0</td>\n", " <td>2015-08-05</td>\n", " <td>8:02</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>10645 W ROUNTREE ROAD, 90064</td>\n", " <td>34.037392</td>\n", " <td>-118.419301</td>\n", " <td>5.0</td>\n", " <td>2015-08-05</td>\n", " <td>14:30</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>6952 N NAGLE AVE, 91605</td>\n", " <td>34.197161</td>\n", " <td>-118.423487</td>\n", " <td>2.0</td>\n", " <td>2015-08-05</td>\n", " <td>8:03</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>Metal/Household Appliances</td>\n", " <td>Call</td>\n", " <td>1627 W 261ST ST, 90710</td>\n", " <td>33.786100</td>\n", " <td>-118.305316</td>\n", " <td>15.0</td>\n", " <td>2015-08-05</td>\n", " <td>8:03</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>Dead Animal Removal</td>\n", " <td>Call</td>\n", " <td>9835 S WESTERN AVE, 90047</td>\n", " <td>33.945820</td>\n", " <td>-118.309423</td>\n", " <td>8.0</td>\n", " <td>2015-08-05</td>\n", " <td>8:03</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>13506 W DELANO ST, 91401</td>\n", " <td>34.182014</td>\n", " <td>-118.427349</td>\n", " <td>2.0</td>\n", " <td>2015-08-05</td>\n", " <td>14:29</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>11171 N DE HAVEN AVE, 91331</td>\n", " <td>34.273223</td>\n", " <td>-118.412948</td>\n", " <td>7.0</td>\n", " <td>2015-08-05</td>\n", " <td>14:28</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>3736 S WESTWOOD BLVD, 90034</td>\n", " <td>34.018793</td>\n", " <td>-118.411028</td>\n", " <td>5.0</td>\n", " <td>2015-08-05</td>\n", " <td>8:04</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>Metal/Household Appliances</td>\n", " <td>Call</td>\n", " <td>10221 N AMBOY AVE, 91331</td>\n", " <td>34.255942</td>\n", " <td>-118.426425</td>\n", " <td>7.0</td>\n", " <td>2015-08-05</td>\n", " <td>8:04</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>4618 W KINGSWELL AVE, 90027</td>\n", " <td>34.102469</td>\n", " <td>-118.290354</td>\n", " <td>4.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:50</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>Illegal Dumping Pickup</td>\n", " <td>Call</td>\n", " <td>4398 E HARRIMAN AVE, 90032</td>\n", " <td>34.095404</td>\n", " <td>-118.179368</td>\n", " <td>14.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:49</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>2483664</th>\n", " <td>Dead Animal Removal</td>\n", " <td>Call</td>\n", " <td>19406 W GAULT ST, 91335</td>\n", " <td>34.199368</td>\n", " <td>-118.555880</td>\n", " <td>3.0</td>\n", " <td>2018-07-23</td>\n", " <td>8:02</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483665</th>\n", " <td>Electronic Waste</td>\n", " <td>Mobile App</td>\n", " <td>824 N OXFORD AVE, 90029</td>\n", " <td>34.085207</td>\n", " <td>-118.307673</td>\n", " <td>13.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:43</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483666</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>723 S HARVARD BLVD, 90005</td>\n", " <td>34.059036</td>\n", " <td>-118.304475</td>\n", " <td>10.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:11</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483667</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>5825 N BUCKNELL AVE, 91607</td>\n", " <td>34.176407</td>\n", " <td>-118.392485</td>\n", " <td>2.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:11</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483668</th>\n", " <td>Metal/Household Appliances</td>\n", " <td>Call</td>\n", " <td>629 S BURLINGTON AVE, 90057</td>\n", " <td>34.057269</td>\n", " <td>-118.272162</td>\n", " <td>1.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:12</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483669</th>\n", " <td>Dead Animal Removal</td>\n", " <td>Call</td>\n", " <td>115TH ST AT STANFORD AVE, 90059</td>\n", " <td>33.930091</td>\n", " <td>-118.262186</td>\n", " <td>15.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:14</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483670</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>6853 N TEXHOMA AVE, 91406</td>\n", " <td>34.195366</td>\n", " <td>-118.515444</td>\n", " <td>6.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:14</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483671</th>\n", " <td>Bulky Items</td>\n", " <td>Mobile App</td>\n", " <td>563 E BROADWAY, 90291</td>\n", " <td>33.994510</td>\n", " <td>-118.468741</td>\n", " <td>11.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:14</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483672</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>4727 S 6TH AVE, 90043</td>\n", " <td>34.000220</td>\n", " <td>-118.323790</td>\n", " <td>8.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:50</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483673</th>\n", " <td>Metal/Household Appliances</td>\n", " <td>Call</td>\n", " <td>4500 S 5TH AVE, 90043</td>\n", " <td>34.002555</td>\n", " <td>-118.322374</td>\n", " <td>8.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:14</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483674</th>\n", " <td>Homeless Encampment</td>\n", " <td>Mobile App</td>\n", " <td>109 S LINCOLN BLVD, 90291</td>\n", " <td>34.002352</td>\n", " <td>-118.470359</td>\n", " <td>11.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:13</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483675</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>12600 N SAN FERNANDO ROAD, 91342</td>\n", " <td>34.298792</td>\n", " <td>-118.457741</td>\n", " <td>7.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:50</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483676</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>2142 S 3RD AVE, 90018</td>\n", " <td>34.037725</td>\n", " <td>-118.319993</td>\n", " <td>10.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:13</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483677</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>12749 W STAGG ST, 91605</td>\n", " <td>34.211662</td>\n", " <td>-118.411491</td>\n", " <td>2.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:42</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483678</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>6853 N TEXHOMA AVE, 91406</td>\n", " <td>34.195366</td>\n", " <td>-118.515444</td>\n", " <td>6.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:13</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483679</th>\n", " <td>Bulky Items</td>\n", " <td>Mobile App</td>\n", " <td>564 W 36TH ST, 90731</td>\n", " <td>33.711610</td>\n", " <td>-118.289281</td>\n", " <td>15.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:42</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483680</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>4500 S 5TH AVE, 90043</td>\n", " <td>34.002555</td>\n", " <td>-118.322374</td>\n", " <td>8.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:13</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483681</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>1342 W CALUMET AVE, 90026</td>\n", " <td>34.068637</td>\n", " <td>-118.255209</td>\n", " <td>1.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:13</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483682</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>11040 W HESBY ST, 91601</td>\n", " <td>34.161890</td>\n", " <td>-118.371986</td>\n", " <td>2.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:12</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483683</th>\n", " <td>Bulky Items</td>\n", " <td>Mobile App</td>\n", " <td>8027 N TEESDALE AVE, 91605</td>\n", " <td>34.216669</td>\n", " <td>-118.410381</td>\n", " <td>2.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:42</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483684</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>5127 S 6TH AVE, 90043</td>\n", " <td>33.995969</td>\n", " <td>-118.323783</td>\n", " <td>8.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:41</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483685</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>12361 W HARTSOOK ST, 91607</td>\n", " <td>34.164073</td>\n", " <td>-118.403056</td>\n", " <td>2.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:12</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483686</th>\n", " <td>Dead Animal Removal</td>\n", " <td>Mobile App</td>\n", " <td>6804 N DE SOTO AVE, 91303</td>\n", " <td>34.194110</td>\n", " <td>-118.586945</td>\n", " <td>3.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:19</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483687</th>\n", " <td>Bulky Items</td>\n", " <td>Mobile App</td>\n", " <td>13170 N BORDEN AVE, 91342</td>\n", " <td>34.309665</td>\n", " <td>-118.445807</td>\n", " <td>7.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:42</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483688</th>\n", " <td>Bulky Items</td>\n", " <td>Mobile App</td>\n", " <td>1615 W MIDDLETON PL, 90062</td>\n", " <td>34.015627</td>\n", " <td>-118.309488</td>\n", " <td>8.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:42</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483689</th>\n", " <td>Dead Animal Removal</td>\n", " <td>Self Service</td>\n", " <td>20905 W BASSETT ST, 91303</td>\n", " <td>34.195694</td>\n", " <td>-118.588874</td>\n", " <td>3.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:51</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483690</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>719 W 77TH ST, 90044</td>\n", " <td>33.969876</td>\n", " <td>-118.286141</td>\n", " <td>8.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:43</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483691</th>\n", " <td>Single Streetlight Issue</td>\n", " <td>Email</td>\n", " <td>462 N MANSFIELD AVE, 90036</td>\n", " <td>34.080132</td>\n", " <td>-118.340723</td>\n", " <td>5.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:12</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483692</th>\n", " <td>Bulky Items</td>\n", " <td>Mobile App</td>\n", " <td>10704 W STRATHERN ST, 91352</td>\n", " <td>34.215580</td>\n", " <td>-118.364732</td>\n", " <td>2.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:49</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483693</th>\n", " <td>Homeless Encampment</td>\n", " <td>Self Service</td>\n", " <td>14475 W VENTURA BLVD, 91423</td>\n", " <td>34.151019</td>\n", " <td>-118.448336</td>\n", " <td>4.0</td>\n", " <td>2018-07-23</td>\n", " <td>10:24</td>\n", " <td>30</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>2483694 rows × 9 columns</p>\n", "</div>" ], "text/plain": [ " RequestType RequestSource \\\n", "0 Metal/Household Appliances Call \n", "1 Bulky Items Call \n", "2 Dead Animal Removal Call \n", "3 Other Call \n", "4 Illegal Dumping Pickup Call \n", "5 Bulky Items Call \n", "6 Feedback Call \n", "7 Bulky Items Call \n", "8 Bulky Items Call \n", "9 Bulky Items Call \n", "10 Metal/Household Appliances Call \n", "11 Bulky Items Call \n", "12 Bulky Items Call \n", "13 Bulky Items Call \n", "14 Bulky Items Call \n", "15 Bulky Items Call \n", "16 Bulky Items Call \n", "17 Electronic Waste Call \n", "18 Bulky Items Call \n", "19 Bulky Items Call \n", "20 Bulky Items Call \n", "21 Bulky Items Call \n", "22 Metal/Household Appliances Call \n", "23 Dead Animal Removal Call \n", "24 Bulky Items Call \n", "25 Bulky Items Call \n", "26 Bulky Items Call \n", "27 Metal/Household Appliances Call \n", "28 Bulky Items Call \n", "29 Illegal Dumping Pickup Call \n", "... ... ... \n", "2483664 Dead Animal Removal Call \n", "2483665 Electronic Waste Mobile App \n", "2483666 Bulky Items Call \n", "2483667 Bulky Items Call \n", "2483668 Metal/Household Appliances Call \n", "2483669 Dead Animal Removal Call \n", "2483670 Bulky Items Call \n", "2483671 Bulky Items Mobile App \n", "2483672 Bulky Items Call \n", "2483673 Metal/Household Appliances Call \n", "2483674 Homeless Encampment Mobile App \n", "2483675 Bulky Items Call \n", "2483676 Bulky Items Call \n", "2483677 Bulky Items Call \n", "2483678 Bulky Items Call \n", "2483679 Bulky Items Mobile App \n", "2483680 Bulky Items Call \n", "2483681 Bulky Items Call \n", "2483682 Bulky Items Call \n", "2483683 Bulky Items Mobile App \n", "2483684 Bulky Items Call \n", "2483685 Bulky Items Call \n", "2483686 Dead Animal Removal Mobile App \n", "2483687 Bulky Items Mobile App \n", "2483688 Bulky Items Mobile App \n", "2483689 Dead Animal Removal Self Service \n", "2483690 Bulky Items Call \n", "2483691 Single Streetlight Issue Email \n", "2483692 Bulky Items Mobile App \n", "2483693 Homeless Encampment Self Service \n", "\n", " Address Latitude Longitude \\\n", "0 1115 S ELM DR, 90035 34.056445 -118.395187 \n", "1 20219 W CHAPTER DR, 91364 34.148923 -118.576744 \n", "2 346 W 64TH ST, 90003 33.981244 -118.280030 \n", "3 14435 W BENEFIT ST, 91423 34.148889 -118.447419 \n", "4 LOS FELIZ BLVD at GRIFFITH PARK BLVD, 90027 34.113826 -118.277039 \n", "5 721 E 43RD PL, 90011 34.004913 -118.263076 \n", "6 731 N ST ANDREWS PL, 90038 34.084435 -118.311530 \n", "7 1260 S CLOVERDALE AVE, 90019 34.051757 -118.350011 \n", "8 360 N EDINBURGH AVE, 90048 34.077845 -118.363621 \n", "9 7730 N LENA AVE, 91304 34.211079 -118.626299 \n", "10 6833 N SEPULVEDA BLVD, 91406 34.194954 -118.466438 \n", "11 7104 W HIGHCLIFF TR, 91042 34.245145 -118.286781 \n", "12 1643 S CREST DR, 90035 34.049798 -118.387688 \n", "13 626 E 105TH ST, 90002 33.940920 -118.264252 \n", "14 16434 W HAYNES ST, 91406 34.189020 -118.491543 \n", "15 1627 W 261ST ST, 90710 33.786100 -118.305316 \n", "16 906 N BEAUDRY AVE, 90012 34.067500 -118.246638 \n", "17 6016 N SUNNYSLOPE AVE, 91401 34.180182 -118.426787 \n", "18 1149 S BROADWAY, 90015 34.038578 -118.260191 \n", "19 17447 W TUSCAN DR, 91344 34.305303 -118.513290 \n", "20 10645 W ROUNTREE ROAD, 90064 34.037392 -118.419301 \n", "21 6952 N NAGLE AVE, 91605 34.197161 -118.423487 \n", "22 1627 W 261ST ST, 90710 33.786100 -118.305316 \n", "23 9835 S WESTERN AVE, 90047 33.945820 -118.309423 \n", "24 13506 W DELANO ST, 91401 34.182014 -118.427349 \n", "25 11171 N DE HAVEN AVE, 91331 34.273223 -118.412948 \n", "26 3736 S WESTWOOD BLVD, 90034 34.018793 -118.411028 \n", "27 10221 N AMBOY AVE, 91331 34.255942 -118.426425 \n", "28 4618 W KINGSWELL AVE, 90027 34.102469 -118.290354 \n", "29 4398 E HARRIMAN AVE, 90032 34.095404 -118.179368 \n", "... ... ... ... \n", "2483664 19406 W GAULT ST, 91335 34.199368 -118.555880 \n", "2483665 824 N OXFORD AVE, 90029 34.085207 -118.307673 \n", "2483666 723 S HARVARD BLVD, 90005 34.059036 -118.304475 \n", "2483667 5825 N BUCKNELL AVE, 91607 34.176407 -118.392485 \n", "2483668 629 S BURLINGTON AVE, 90057 34.057269 -118.272162 \n", "2483669 115TH ST AT STANFORD AVE, 90059 33.930091 -118.262186 \n", "2483670 6853 N TEXHOMA AVE, 91406 34.195366 -118.515444 \n", "2483671 563 E BROADWAY, 90291 33.994510 -118.468741 \n", "2483672 4727 S 6TH AVE, 90043 34.000220 -118.323790 \n", "2483673 4500 S 5TH AVE, 90043 34.002555 -118.322374 \n", "2483674 109 S LINCOLN BLVD, 90291 34.002352 -118.470359 \n", "2483675 12600 N SAN FERNANDO ROAD, 91342 34.298792 -118.457741 \n", "2483676 2142 S 3RD AVE, 90018 34.037725 -118.319993 \n", "2483677 12749 W STAGG ST, 91605 34.211662 -118.411491 \n", "2483678 6853 N TEXHOMA AVE, 91406 34.195366 -118.515444 \n", "2483679 564 W 36TH ST, 90731 33.711610 -118.289281 \n", "2483680 4500 S 5TH AVE, 90043 34.002555 -118.322374 \n", "2483681 1342 W CALUMET AVE, 90026 34.068637 -118.255209 \n", "2483682 11040 W HESBY ST, 91601 34.161890 -118.371986 \n", "2483683 8027 N TEESDALE AVE, 91605 34.216669 -118.410381 \n", "2483684 5127 S 6TH AVE, 90043 33.995969 -118.323783 \n", "2483685 12361 W HARTSOOK ST, 91607 34.164073 -118.403056 \n", "2483686 6804 N DE SOTO AVE, 91303 34.194110 -118.586945 \n", "2483687 13170 N BORDEN AVE, 91342 34.309665 -118.445807 \n", "2483688 1615 W MIDDLETON PL, 90062 34.015627 -118.309488 \n", "2483689 20905 W BASSETT ST, 91303 34.195694 -118.588874 \n", "2483690 719 W 77TH ST, 90044 33.969876 -118.286141 \n", "2483691 462 N MANSFIELD AVE, 90036 34.080132 -118.340723 \n", "2483692 10704 W STRATHERN ST, 91352 34.215580 -118.364732 \n", "2483693 14475 W VENTURA BLVD, 91423 34.151019 -118.448336 \n", "\n", " CD Created Date Created Time Week Number \n", "0 5.0 2015-08-05 11:22 32 \n", "1 3.0 2015-08-05 7:52 32 \n", "2 9.0 2015-08-05 14:33 32 \n", "3 4.0 2015-08-05 7:52 32 \n", "4 4.0 2015-08-05 7:53 32 \n", "5 9.0 2015-08-05 7:53 32 \n", "6 13.0 2015-08-05 14:32 32 \n", "7 10.0 2015-08-05 7:54 32 \n", "8 5.0 2015-08-05 7:54 32 \n", "9 12.0 2015-08-05 14:32 32 \n", "10 6.0 2015-08-05 7:55 32 \n", "11 7.0 2015-08-05 7:57 32 \n", "12 5.0 2015-08-05 7:57 32 \n", "13 8.0 2015-08-05 7:57 32 \n", "14 6.0 2015-08-05 14:31 32 \n", "15 15.0 2015-08-05 7:59 32 \n", "16 1.0 2015-08-05 7:59 32 \n", "17 2.0 2015-08-05 8:00 32 \n", "18 14.0 2015-08-05 8:01 32 \n", "19 12.0 2015-08-05 8:02 32 \n", "20 5.0 2015-08-05 14:30 32 \n", "21 2.0 2015-08-05 8:03 32 \n", "22 15.0 2015-08-05 8:03 32 \n", "23 8.0 2015-08-05 8:03 32 \n", "24 2.0 2015-08-05 14:29 32 \n", "25 7.0 2015-08-05 14:28 32 \n", "26 5.0 2015-08-05 8:04 32 \n", "27 7.0 2015-08-05 8:04 32 \n", "28 4.0 2015-08-05 7:50 32 \n", "29 14.0 2015-08-05 7:49 32 \n", "... ... ... ... ... \n", "2483664 3.0 2018-07-23 8:02 30 \n", "2483665 13.0 2018-07-23 7:43 30 \n", "2483666 10.0 2018-07-23 9:11 30 \n", "2483667 2.0 2018-07-23 9:11 30 \n", "2483668 1.0 2018-07-23 9:12 30 \n", "2483669 15.0 2018-07-23 9:14 30 \n", "2483670 6.0 2018-07-23 9:14 30 \n", "2483671 11.0 2018-07-23 9:14 30 \n", "2483672 8.0 2018-07-23 7:50 30 \n", "2483673 8.0 2018-07-23 9:14 30 \n", "2483674 11.0 2018-07-23 9:13 30 \n", "2483675 7.0 2018-07-23 7:50 30 \n", "2483676 10.0 2018-07-23 9:13 30 \n", "2483677 2.0 2018-07-23 7:42 30 \n", "2483678 6.0 2018-07-23 9:13 30 \n", "2483679 15.0 2018-07-23 7:42 30 \n", "2483680 8.0 2018-07-23 9:13 30 \n", "2483681 1.0 2018-07-23 9:13 30 \n", "2483682 2.0 2018-07-23 9:12 30 \n", "2483683 2.0 2018-07-23 7:42 30 \n", "2483684 8.0 2018-07-23 7:41 30 \n", "2483685 2.0 2018-07-23 9:12 30 \n", "2483686 3.0 2018-07-23 7:19 30 \n", "2483687 7.0 2018-07-23 7:42 30 \n", "2483688 8.0 2018-07-23 7:42 30 \n", "2483689 3.0 2018-07-23 7:51 30 \n", "2483690 8.0 2018-07-23 7:43 30 \n", "2483691 5.0 2018-07-23 9:12 30 \n", "2483692 2.0 2018-07-23 7:49 30 \n", "2483693 4.0 2018-07-23 10:24 30 \n", "\n", "[2483694 rows x 9 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cols = ['RequestType', 'RequestSource', 'Address', 'Latitude' , 'Longitude', 'CD', 'Created Date', 'Created Time', 'Week Number']\n", "data = pd.read_csv(\"311_week_number.csv\", usecols = cols, low_memory = False)\n", "data" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead 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>RequestType</th>\n", " <th>RequestSource</th>\n", " <th>Address</th>\n", " <th>Latitude</th>\n", " <th>Longitude</th>\n", " <th>CD</th>\n", " <th>Created Date</th>\n", " <th>Created Time</th>\n", " <th>Week Number</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Metal/Household Appliances</td>\n", " <td>Call</td>\n", " <td>1115 S ELM DR, 90035</td>\n", " <td>34.056445</td>\n", " <td>-118.395187</td>\n", " <td>5.0</td>\n", " <td>2015-08-05</td>\n", " <td>11:22</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>20219 W CHAPTER DR, 91364</td>\n", " <td>34.148923</td>\n", " <td>-118.576744</td>\n", " <td>3.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:52</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Dead Animal Removal</td>\n", " <td>Call</td>\n", " <td>346 W 64TH ST, 90003</td>\n", " <td>33.981244</td>\n", " <td>-118.280030</td>\n", " <td>9.0</td>\n", " <td>2015-08-05</td>\n", " <td>14:33</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Other</td>\n", " <td>Call</td>\n", " <td>14435 W BENEFIT ST, 91423</td>\n", " <td>34.148889</td>\n", " <td>-118.447419</td>\n", " <td>4.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:52</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Illegal Dumping Pickup</td>\n", " <td>Call</td>\n", " <td>LOS FELIZ BLVD at GRIFFITH PARK BLVD, 90027</td>\n", " <td>34.113826</td>\n", " <td>-118.277039</td>\n", " <td>4.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:53</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>721 E 43RD PL, 90011</td>\n", " <td>34.004913</td>\n", " <td>-118.263076</td>\n", " <td>9.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:53</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Feedback</td>\n", " <td>Call</td>\n", " <td>731 N ST ANDREWS PL, 90038</td>\n", " <td>34.084435</td>\n", " <td>-118.311530</td>\n", " <td>13.0</td>\n", " <td>2015-08-05</td>\n", " <td>14:32</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>1260 S CLOVERDALE AVE, 90019</td>\n", " <td>34.051757</td>\n", " <td>-118.350011</td>\n", " <td>10.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:54</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>360 N EDINBURGH AVE, 90048</td>\n", " <td>34.077845</td>\n", " <td>-118.363621</td>\n", " <td>5.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:54</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>7730 N LENA AVE, 91304</td>\n", " <td>34.211079</td>\n", " <td>-118.626299</td>\n", " <td>12.0</td>\n", " <td>2015-08-05</td>\n", " <td>14:32</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Metal/Household Appliances</td>\n", " <td>Call</td>\n", " <td>6833 N SEPULVEDA BLVD, 91406</td>\n", " <td>34.194954</td>\n", " <td>-118.466438</td>\n", " <td>6.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:55</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>7104 W HIGHCLIFF TR, 91042</td>\n", " <td>34.245145</td>\n", " <td>-118.286781</td>\n", " <td>7.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:57</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>1643 S CREST DR, 90035</td>\n", " <td>34.049798</td>\n", " <td>-118.387688</td>\n", " <td>5.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:57</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>626 E 105TH ST, 90002</td>\n", " <td>33.940920</td>\n", " <td>-118.264252</td>\n", " <td>8.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:57</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>16434 W HAYNES ST, 91406</td>\n", " <td>34.189020</td>\n", " <td>-118.491543</td>\n", " <td>6.0</td>\n", " <td>2015-08-05</td>\n", " <td>14:31</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>1627 W 261ST ST, 90710</td>\n", " <td>33.786100</td>\n", " <td>-118.305316</td>\n", " <td>15.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:59</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>906 N BEAUDRY AVE, 90012</td>\n", " <td>34.067500</td>\n", " <td>-118.246638</td>\n", " <td>1.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:59</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>Electronic Waste</td>\n", " <td>Call</td>\n", " <td>6016 N SUNNYSLOPE AVE, 91401</td>\n", " <td>34.180182</td>\n", " <td>-118.426787</td>\n", " <td>2.0</td>\n", " <td>2015-08-05</td>\n", " <td>8:00</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>1149 S BROADWAY, 90015</td>\n", " <td>34.038578</td>\n", " <td>-118.260191</td>\n", " <td>14.0</td>\n", " <td>2015-08-05</td>\n", " <td>8:01</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>17447 W TUSCAN DR, 91344</td>\n", " <td>34.305303</td>\n", " <td>-118.513290</td>\n", " <td>12.0</td>\n", " <td>2015-08-05</td>\n", " <td>8:02</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>10645 W ROUNTREE ROAD, 90064</td>\n", " <td>34.037392</td>\n", " <td>-118.419301</td>\n", " <td>5.0</td>\n", " <td>2015-08-05</td>\n", " <td>14:30</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>6952 N NAGLE AVE, 91605</td>\n", " <td>34.197161</td>\n", " <td>-118.423487</td>\n", " <td>2.0</td>\n", " <td>2015-08-05</td>\n", " <td>8:03</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>Metal/Household Appliances</td>\n", " <td>Call</td>\n", " <td>1627 W 261ST ST, 90710</td>\n", " <td>33.786100</td>\n", " <td>-118.305316</td>\n", " <td>15.0</td>\n", " <td>2015-08-05</td>\n", " <td>8:03</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>Dead Animal Removal</td>\n", " <td>Call</td>\n", " <td>9835 S WESTERN AVE, 90047</td>\n", " <td>33.945820</td>\n", " <td>-118.309423</td>\n", " <td>8.0</td>\n", " <td>2015-08-05</td>\n", " <td>8:03</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>13506 W DELANO ST, 91401</td>\n", " <td>34.182014</td>\n", " <td>-118.427349</td>\n", " <td>2.0</td>\n", " <td>2015-08-05</td>\n", " <td>14:29</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>11171 N DE HAVEN AVE, 91331</td>\n", " <td>34.273223</td>\n", " <td>-118.412948</td>\n", " <td>7.0</td>\n", " <td>2015-08-05</td>\n", " <td>14:28</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>3736 S WESTWOOD BLVD, 90034</td>\n", " <td>34.018793</td>\n", " <td>-118.411028</td>\n", " <td>5.0</td>\n", " <td>2015-08-05</td>\n", " <td>8:04</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>Metal/Household Appliances</td>\n", " <td>Call</td>\n", " <td>10221 N AMBOY AVE, 91331</td>\n", " <td>34.255942</td>\n", " <td>-118.426425</td>\n", " <td>7.0</td>\n", " <td>2015-08-05</td>\n", " <td>8:04</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>4618 W KINGSWELL AVE, 90027</td>\n", " <td>34.102469</td>\n", " <td>-118.290354</td>\n", " <td>4.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:50</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>Illegal Dumping Pickup</td>\n", " <td>Call</td>\n", " <td>4398 E HARRIMAN AVE, 90032</td>\n", " <td>34.095404</td>\n", " <td>-118.179368</td>\n", " <td>14.0</td>\n", " <td>2015-08-05</td>\n", " <td>7:49</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>2483664</th>\n", " <td>Dead Animal Removal</td>\n", " <td>Call</td>\n", " <td>19406 W GAULT ST, 91335</td>\n", " <td>34.199368</td>\n", " <td>-118.555880</td>\n", " <td>3.0</td>\n", " <td>2018-07-23</td>\n", " <td>8:02</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483665</th>\n", " <td>Electronic Waste</td>\n", " <td>Mobile App</td>\n", " <td>824 N OXFORD AVE, 90029</td>\n", " <td>34.085207</td>\n", " <td>-118.307673</td>\n", " <td>13.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:43</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483666</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>723 S HARVARD BLVD, 90005</td>\n", " <td>34.059036</td>\n", " <td>-118.304475</td>\n", " <td>10.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:11</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483667</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>5825 N BUCKNELL AVE, 91607</td>\n", " <td>34.176407</td>\n", " <td>-118.392485</td>\n", " <td>2.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:11</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483668</th>\n", " <td>Metal/Household Appliances</td>\n", " <td>Call</td>\n", " <td>629 S BURLINGTON AVE, 90057</td>\n", " <td>34.057269</td>\n", " <td>-118.272162</td>\n", " <td>1.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:12</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483669</th>\n", " <td>Dead Animal Removal</td>\n", " <td>Call</td>\n", " <td>115TH ST AT STANFORD AVE, 90059</td>\n", " <td>33.930091</td>\n", " <td>-118.262186</td>\n", " <td>15.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:14</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483670</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>6853 N TEXHOMA AVE, 91406</td>\n", " <td>34.195366</td>\n", " <td>-118.515444</td>\n", " <td>6.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:14</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483671</th>\n", " <td>Bulky Items</td>\n", " <td>Mobile App</td>\n", " <td>563 E BROADWAY, 90291</td>\n", " <td>33.994510</td>\n", " <td>-118.468741</td>\n", " <td>11.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:14</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483672</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>4727 S 6TH AVE, 90043</td>\n", " <td>34.000220</td>\n", " <td>-118.323790</td>\n", " <td>8.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:50</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483673</th>\n", " <td>Metal/Household Appliances</td>\n", " <td>Call</td>\n", " <td>4500 S 5TH AVE, 90043</td>\n", " <td>34.002555</td>\n", " <td>-118.322374</td>\n", " <td>8.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:14</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483674</th>\n", " <td>Homeless Encampment</td>\n", " <td>Mobile App</td>\n", " <td>109 S LINCOLN BLVD, 90291</td>\n", " <td>34.002352</td>\n", " <td>-118.470359</td>\n", " <td>11.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:13</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483675</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>12600 N SAN FERNANDO ROAD, 91342</td>\n", " <td>34.298792</td>\n", " <td>-118.457741</td>\n", " <td>7.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:50</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483676</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>2142 S 3RD AVE, 90018</td>\n", " <td>34.037725</td>\n", " <td>-118.319993</td>\n", " <td>10.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:13</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483677</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>12749 W STAGG ST, 91605</td>\n", " <td>34.211662</td>\n", " <td>-118.411491</td>\n", " <td>2.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:42</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483678</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>6853 N TEXHOMA AVE, 91406</td>\n", " <td>34.195366</td>\n", " <td>-118.515444</td>\n", " <td>6.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:13</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483679</th>\n", " <td>Bulky Items</td>\n", " <td>Mobile App</td>\n", " <td>564 W 36TH ST, 90731</td>\n", " <td>33.711610</td>\n", " <td>-118.289281</td>\n", " <td>15.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:42</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483680</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>4500 S 5TH AVE, 90043</td>\n", " <td>34.002555</td>\n", " <td>-118.322374</td>\n", " <td>8.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:13</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483681</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>1342 W CALUMET AVE, 90026</td>\n", " <td>34.068637</td>\n", " <td>-118.255209</td>\n", " <td>1.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:13</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483682</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>11040 W HESBY ST, 91601</td>\n", " <td>34.161890</td>\n", " <td>-118.371986</td>\n", " <td>2.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:12</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483683</th>\n", " <td>Bulky Items</td>\n", " <td>Mobile App</td>\n", " <td>8027 N TEESDALE AVE, 91605</td>\n", " <td>34.216669</td>\n", " <td>-118.410381</td>\n", " <td>2.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:42</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483684</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>5127 S 6TH AVE, 90043</td>\n", " <td>33.995969</td>\n", " <td>-118.323783</td>\n", " <td>8.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:41</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483685</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>12361 W HARTSOOK ST, 91607</td>\n", " <td>34.164073</td>\n", " <td>-118.403056</td>\n", " <td>2.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:12</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483686</th>\n", " <td>Dead Animal Removal</td>\n", " <td>Mobile App</td>\n", " <td>6804 N DE SOTO AVE, 91303</td>\n", " <td>34.194110</td>\n", " <td>-118.586945</td>\n", " <td>3.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:19</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483687</th>\n", " <td>Bulky Items</td>\n", " <td>Mobile App</td>\n", " <td>13170 N BORDEN AVE, 91342</td>\n", " <td>34.309665</td>\n", " <td>-118.445807</td>\n", " <td>7.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:42</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483688</th>\n", " <td>Bulky Items</td>\n", " <td>Mobile App</td>\n", " <td>1615 W MIDDLETON PL, 90062</td>\n", " <td>34.015627</td>\n", " <td>-118.309488</td>\n", " <td>8.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:42</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483689</th>\n", " <td>Dead Animal Removal</td>\n", " <td>Self Service</td>\n", " <td>20905 W BASSETT ST, 91303</td>\n", " <td>34.195694</td>\n", " <td>-118.588874</td>\n", " <td>3.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:51</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483690</th>\n", " <td>Bulky Items</td>\n", " <td>Call</td>\n", " <td>719 W 77TH ST, 90044</td>\n", " <td>33.969876</td>\n", " <td>-118.286141</td>\n", " <td>8.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:43</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483691</th>\n", " <td>Single Streetlight Issue</td>\n", " <td>Email</td>\n", " <td>462 N MANSFIELD AVE, 90036</td>\n", " <td>34.080132</td>\n", " <td>-118.340723</td>\n", " <td>5.0</td>\n", " <td>2018-07-23</td>\n", " <td>9:12</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483692</th>\n", " <td>Bulky Items</td>\n", " <td>Mobile App</td>\n", " <td>10704 W STRATHERN ST, 91352</td>\n", " <td>34.215580</td>\n", " <td>-118.364732</td>\n", " <td>2.0</td>\n", " <td>2018-07-23</td>\n", " <td>7:49</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2483693</th>\n", " <td>Homeless Encampment</td>\n", " <td>Self Service</td>\n", " <td>14475 W VENTURA BLVD, 91423</td>\n", " <td>34.151019</td>\n", " <td>-118.448336</td>\n", " <td>4.0</td>\n", " <td>2018-07-23</td>\n", " <td>10:24</td>\n", " <td>30</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>2483677 rows × 9 columns</p>\n", "</div>" ], "text/plain": [ " RequestType RequestSource \\\n", "0 Metal/Household Appliances Call \n", "1 Bulky Items Call \n", "2 Dead Animal Removal Call \n", "3 Other Call \n", "4 Illegal Dumping Pickup Call \n", "5 Bulky Items Call \n", "6 Feedback Call \n", "7 Bulky Items Call \n", "8 Bulky Items Call \n", "9 Bulky Items Call \n", "10 Metal/Household Appliances Call \n", "11 Bulky Items Call \n", "12 Bulky Items Call \n", "13 Bulky Items Call \n", "14 Bulky Items Call \n", "15 Bulky Items Call \n", "16 Bulky Items Call \n", "17 Electronic Waste Call \n", "18 Bulky Items Call \n", "19 Bulky Items Call \n", "20 Bulky Items Call \n", "21 Bulky Items Call \n", "22 Metal/Household Appliances Call \n", "23 Dead Animal Removal Call \n", "24 Bulky Items Call \n", "25 Bulky Items Call \n", "26 Bulky Items Call \n", "27 Metal/Household Appliances Call \n", "28 Bulky Items Call \n", "29 Illegal Dumping Pickup Call \n", "... ... ... \n", "2483664 Dead Animal Removal Call \n", "2483665 Electronic Waste Mobile App \n", "2483666 Bulky Items Call \n", "2483667 Bulky Items Call \n", "2483668 Metal/Household Appliances Call \n", "2483669 Dead Animal Removal Call \n", "2483670 Bulky Items Call \n", "2483671 Bulky Items Mobile App \n", "2483672 Bulky Items Call \n", "2483673 Metal/Household Appliances Call \n", "2483674 Homeless Encampment Mobile App \n", "2483675 Bulky Items Call \n", "2483676 Bulky Items Call \n", "2483677 Bulky Items Call \n", "2483678 Bulky Items Call \n", "2483679 Bulky Items Mobile App \n", "2483680 Bulky Items Call \n", "2483681 Bulky Items Call \n", "2483682 Bulky Items Call \n", "2483683 Bulky Items Mobile App \n", "2483684 Bulky Items Call \n", "2483685 Bulky Items Call \n", "2483686 Dead Animal Removal Mobile App \n", "2483687 Bulky Items Mobile App \n", "2483688 Bulky Items Mobile App \n", "2483689 Dead Animal Removal Self Service \n", "2483690 Bulky Items Call \n", "2483691 Single Streetlight Issue Email \n", "2483692 Bulky Items Mobile App \n", "2483693 Homeless Encampment Self Service \n", "\n", " Address Latitude Longitude \\\n", "0 1115 S ELM DR, 90035 34.056445 -118.395187 \n", "1 20219 W CHAPTER DR, 91364 34.148923 -118.576744 \n", "2 346 W 64TH ST, 90003 33.981244 -118.280030 \n", "3 14435 W BENEFIT ST, 91423 34.148889 -118.447419 \n", "4 LOS FELIZ BLVD at GRIFFITH PARK BLVD, 90027 34.113826 -118.277039 \n", "5 721 E 43RD PL, 90011 34.004913 -118.263076 \n", "6 731 N ST ANDREWS PL, 90038 34.084435 -118.311530 \n", "7 1260 S CLOVERDALE AVE, 90019 34.051757 -118.350011 \n", "8 360 N EDINBURGH AVE, 90048 34.077845 -118.363621 \n", "9 7730 N LENA AVE, 91304 34.211079 -118.626299 \n", "10 6833 N SEPULVEDA BLVD, 91406 34.194954 -118.466438 \n", "11 7104 W HIGHCLIFF TR, 91042 34.245145 -118.286781 \n", "12 1643 S CREST DR, 90035 34.049798 -118.387688 \n", "13 626 E 105TH ST, 90002 33.940920 -118.264252 \n", "14 16434 W HAYNES ST, 91406 34.189020 -118.491543 \n", "15 1627 W 261ST ST, 90710 33.786100 -118.305316 \n", "16 906 N BEAUDRY AVE, 90012 34.067500 -118.246638 \n", "17 6016 N SUNNYSLOPE AVE, 91401 34.180182 -118.426787 \n", "18 1149 S BROADWAY, 90015 34.038578 -118.260191 \n", "19 17447 W TUSCAN DR, 91344 34.305303 -118.513290 \n", "20 10645 W ROUNTREE ROAD, 90064 34.037392 -118.419301 \n", "21 6952 N NAGLE AVE, 91605 34.197161 -118.423487 \n", "22 1627 W 261ST ST, 90710 33.786100 -118.305316 \n", "23 9835 S WESTERN AVE, 90047 33.945820 -118.309423 \n", "24 13506 W DELANO ST, 91401 34.182014 -118.427349 \n", "25 11171 N DE HAVEN AVE, 91331 34.273223 -118.412948 \n", "26 3736 S WESTWOOD BLVD, 90034 34.018793 -118.411028 \n", "27 10221 N AMBOY AVE, 91331 34.255942 -118.426425 \n", "28 4618 W KINGSWELL AVE, 90027 34.102469 -118.290354 \n", "29 4398 E HARRIMAN AVE, 90032 34.095404 -118.179368 \n", "... ... ... ... \n", "2483664 19406 W GAULT ST, 91335 34.199368 -118.555880 \n", "2483665 824 N OXFORD AVE, 90029 34.085207 -118.307673 \n", "2483666 723 S HARVARD BLVD, 90005 34.059036 -118.304475 \n", "2483667 5825 N BUCKNELL AVE, 91607 34.176407 -118.392485 \n", "2483668 629 S BURLINGTON AVE, 90057 34.057269 -118.272162 \n", "2483669 115TH ST AT STANFORD AVE, 90059 33.930091 -118.262186 \n", "2483670 6853 N TEXHOMA AVE, 91406 34.195366 -118.515444 \n", "2483671 563 E BROADWAY, 90291 33.994510 -118.468741 \n", "2483672 4727 S 6TH AVE, 90043 34.000220 -118.323790 \n", "2483673 4500 S 5TH AVE, 90043 34.002555 -118.322374 \n", "2483674 109 S LINCOLN BLVD, 90291 34.002352 -118.470359 \n", "2483675 12600 N SAN FERNANDO ROAD, 91342 34.298792 -118.457741 \n", "2483676 2142 S 3RD AVE, 90018 34.037725 -118.319993 \n", "2483677 12749 W STAGG ST, 91605 34.211662 -118.411491 \n", "2483678 6853 N TEXHOMA AVE, 91406 34.195366 -118.515444 \n", "2483679 564 W 36TH ST, 90731 33.711610 -118.289281 \n", "2483680 4500 S 5TH AVE, 90043 34.002555 -118.322374 \n", "2483681 1342 W CALUMET AVE, 90026 34.068637 -118.255209 \n", "2483682 11040 W HESBY ST, 91601 34.161890 -118.371986 \n", "2483683 8027 N TEESDALE AVE, 91605 34.216669 -118.410381 \n", "2483684 5127 S 6TH AVE, 90043 33.995969 -118.323783 \n", "2483685 12361 W HARTSOOK ST, 91607 34.164073 -118.403056 \n", "2483686 6804 N DE SOTO AVE, 91303 34.194110 -118.586945 \n", "2483687 13170 N BORDEN AVE, 91342 34.309665 -118.445807 \n", "2483688 1615 W MIDDLETON PL, 90062 34.015627 -118.309488 \n", "2483689 20905 W BASSETT ST, 91303 34.195694 -118.588874 \n", "2483690 719 W 77TH ST, 90044 33.969876 -118.286141 \n", "2483691 462 N MANSFIELD AVE, 90036 34.080132 -118.340723 \n", "2483692 10704 W STRATHERN ST, 91352 34.215580 -118.364732 \n", "2483693 14475 W VENTURA BLVD, 91423 34.151019 -118.448336 \n", "\n", " CD Created Date Created Time Week Number \n", "0 5.0 2015-08-05 11:22 32 \n", "1 3.0 2015-08-05 7:52 32 \n", "2 9.0 2015-08-05 14:33 32 \n", "3 4.0 2015-08-05 7:52 32 \n", "4 4.0 2015-08-05 7:53 32 \n", "5 9.0 2015-08-05 7:53 32 \n", "6 13.0 2015-08-05 14:32 32 \n", "7 10.0 2015-08-05 7:54 32 \n", "8 5.0 2015-08-05 7:54 32 \n", "9 12.0 2015-08-05 14:32 32 \n", "10 6.0 2015-08-05 7:55 32 \n", "11 7.0 2015-08-05 7:57 32 \n", "12 5.0 2015-08-05 7:57 32 \n", "13 8.0 2015-08-05 7:57 32 \n", "14 6.0 2015-08-05 14:31 32 \n", "15 15.0 2015-08-05 7:59 32 \n", "16 1.0 2015-08-05 7:59 32 \n", "17 2.0 2015-08-05 8:00 32 \n", "18 14.0 2015-08-05 8:01 32 \n", "19 12.0 2015-08-05 8:02 32 \n", "20 5.0 2015-08-05 14:30 32 \n", "21 2.0 2015-08-05 8:03 32 \n", "22 15.0 2015-08-05 8:03 32 \n", "23 8.0 2015-08-05 8:03 32 \n", "24 2.0 2015-08-05 14:29 32 \n", "25 7.0 2015-08-05 14:28 32 \n", "26 5.0 2015-08-05 8:04 32 \n", "27 7.0 2015-08-05 8:04 32 \n", "28 4.0 2015-08-05 7:50 32 \n", "29 14.0 2015-08-05 7:49 32 \n", "... ... ... ... ... \n", "2483664 3.0 2018-07-23 8:02 30 \n", "2483665 13.0 2018-07-23 7:43 30 \n", "2483666 10.0 2018-07-23 9:11 30 \n", "2483667 2.0 2018-07-23 9:11 30 \n", "2483668 1.0 2018-07-23 9:12 30 \n", "2483669 15.0 2018-07-23 9:14 30 \n", "2483670 6.0 2018-07-23 9:14 30 \n", "2483671 11.0 2018-07-23 9:14 30 \n", "2483672 8.0 2018-07-23 7:50 30 \n", "2483673 8.0 2018-07-23 9:14 30 \n", "2483674 11.0 2018-07-23 9:13 30 \n", "2483675 7.0 2018-07-23 7:50 30 \n", "2483676 10.0 2018-07-23 9:13 30 \n", "2483677 2.0 2018-07-23 7:42 30 \n", "2483678 6.0 2018-07-23 9:13 30 \n", "2483679 15.0 2018-07-23 7:42 30 \n", "2483680 8.0 2018-07-23 9:13 30 \n", "2483681 1.0 2018-07-23 9:13 30 \n", "2483682 2.0 2018-07-23 9:12 30 \n", "2483683 2.0 2018-07-23 7:42 30 \n", "2483684 8.0 2018-07-23 7:41 30 \n", "2483685 2.0 2018-07-23 9:12 30 \n", "2483686 3.0 2018-07-23 7:19 30 \n", "2483687 7.0 2018-07-23 7:42 30 \n", "2483688 8.0 2018-07-23 7:42 30 \n", "2483689 3.0 2018-07-23 7:51 30 \n", "2483690 8.0 2018-07-23 7:43 30 \n", "2483691 5.0 2018-07-23 9:12 30 \n", "2483692 2.0 2018-07-23 7:49 30 \n", "2483693 4.0 2018-07-23 10:24 30 \n", "\n", "[2483677 rows x 9 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = data.dropna()\n", "df" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "data['Created Date'] = pd.to_datetime(data['Created Date'], errors='coerce')\n", "data['Created Year'] = data['Created Date'].dt.year" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "data.to_csv('311_parsed.csv', encoding = 'utf-8', index = False)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

dietmarw/EK5312_ElectricalMachines

Chapman/Ch4-Problem_4-28.ipynb

1

8823

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Excercises Electric Machinery Fundamentals\n", "## Chapter 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem 4-28" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "data": { "text/plain": [ "'%.4g'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%pylab notebook\n", "%precision %.4g" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Description" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A generating station for a power system consists of four 300-MVA, 15-kV, 0.85-PF-lagging synchronous\n", "generators with identical speed droop characteristics operating in parallel. The governors on the\n", "generators’ prime movers are adjusted to produce a 3-Hz drop from no load to full load. Three of these\n", "generators are each supplying a steady 200 MW at a frequency of 60 Hz, while the fourth generator\n", "(called the swing generator) handles all incremental load changes on the system while maintaining the\n", "system's frequency at 60 Hz." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Sload = 300e6 # [VA]\n", "PF = 0.85\n", "f_drop = 3.0 # [Hz]\n", "f_sys = 60.0 # [Hz]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (a)\n", "At a given instant, the total system loads are 650 MW at a frequency of 60 Hz. \n", " \n", " * What are the no-load frequencies of each of the system’s generators?\n", " \n", "#### (b)\n", "If the system load rises to 725 MW and the generator’s governor set points do not change\n", " \n", " * What will the new system frequency be?\n", " \n", "#### (c)\n", " \n", " * To what frequency must the no-load frequency of the swing generator be adjusted in order to restore the system frequency to 60 Hz?\n", "\n", "#### (d)\n", "If the system is operating at the conditions described in part (c)\n", " \n", " * What would happen if the swing generator were tripped off the line (disconnected from the power line)?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SOLUTION" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (a)\n", "The full-load power of these generators is:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "255" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Pfl = Sload * PF\n", "Pfl/1e6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and the droop from no-load to full-load is 3 Hz. Therefore, the slope of the power-frequency curve for these four generators is:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sp = 85 MW/Hz\n" ] } ], "source": [ "sp = Pfl / f_drop\n", "print('sp = {:.0f} MW/Hz'.format(sp/1e6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If generators 1, 2, and 3 are supplying 200 MW each, then generator 4 must be supplying 50 MW. The\n", "no-load frequency of the first three generators is:\n", "\n", "$$P = s_{P}(f_\\text{nl} - f_\\text{sys})$$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "f_nl_1 = 62.35 Hz\n", "f_nl_2 = 62.35 Hz\n", "f_nl_3 = 62.35 Hz\n", "f_nl_4 = 60.59 Hz\n", "=================\n" ] } ], "source": [ "P1 = 200e6 # [W]\n", "P2 = 200e6 # [W]\n", "P3 = 200e6 # [W]\n", "P4 = 50e6 # [W]\n", "f_nl_1 = P1/sp + f_sys\n", "f_nl_2 = P2/sp + f_sys\n", "f_nl_3 = P3/sp + f_sys\n", "f_nl_4 = P4/sp + f_sys\n", "print('''\n", "f_nl_1 = {:.2f} Hz\n", "f_nl_2 = {:.2f} Hz\n", "f_nl_3 = {:.2f} Hz\n", "f_nl_4 = {:.2f} Hz\n", "================='''.format(f_nl_1, f_nl_2, f_nl_3, f_nl_4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (b)\n", "The setpoints of generators 1, 2, 3, and 4 do not change, so the new system frequency will be:\n", "\n", "$$P_\\text{load} = s_{P1}(f_\\text{nl,1} - f_{sys}) + s_{P2}(f_\\text{nl,2} - f_{sys}) + s_{P3}(f_\\text{nl,3} - f_\\text{sys}) + s_{P4}(f_\\text{nl,4} - f_\\text{sys})$$" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "f_sys = 59.78 Hz\n", "================\n" ] } ], "source": [ "Pload = 725e6 # [W]\n", "f_sys_b = (sp*f_nl_1 + sp*f_nl_2 + sp*f_nl_3 + sp*f_nl_4 - Pload) /\n", " (sp + sp + sp + sp)\n", "print('''\n", "f_sys = {:.2f} Hz\n", "================'''.format(f_sys_b))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (c)\n", "The governor setpoint of the swing generator must be increased until the system frequency rises\n", "back to 60 Hz. At 60 Hz, the other three generators will be supplying 200 MW each, so at 60 Hz, the swing\n", "generator must supply:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "125" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "P4_c = Pload - 3*P1\n", "P4_c/1e6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Therefore, the swing generator’s setpoint must be set to:\n", "\n", "$$P_4 = s_{P4}(f_\\text{nl4} - f_\\text{sys})$$" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "f_nl4_c = 61.47 Hz\n", "==================\n" ] } ], "source": [ "f_nl4_c = P4_c/sp + f_sys\n", "print('''\n", "f_nl4_c = {:.2f} Hz\n", "=================='''.format(f_nl4_c))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (d)\n", "If the swing generator trips off the line, the other three generators would have to supply all 725 MW\n", "of the load. Therefore, the system frequency will become:\n", "\n", "$$P_\\text{load} = s_{P1}(f_\\text{nl,1} - f_\\text{sys}) + s_{P2}(f_\\text{nl,2} - f_\\text{sys}) + s_{P3}(f_\\text{nl,3} - f_\\text{sys})$$" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "f_sys_d = 59.51 Hz and each generator will supply 241.7 MW to the loads.\n", "========================================================================\n", "\n" ] } ], "source": [ "f_sys_d = (sp*f_nl_1 + sp*f_nl_2 + sp*f_nl_3 - Pload) / (sp + sp + sp)\n", "print('''\n", "f_sys_d = {:.2f} Hz and each generator will supply {:.1f} MW to the loads.\n", "========================================================================\n", "'''.format(f_sys_d, Pload/3/1e6))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

unlicense

thangbui/geepee

examples/notebooks/gpssm_kink_ep.ipynb

1

199570

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "epoch 0/30\n" ] }, { "data": { "image/png": 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S5vOefvocFi9+EK1rSUw8HTAehievU15e5Pc7798fLr8cZs2CnBx4+WWT8D7r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\nS4HG+PHw1FPGW9u/36w7GTH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ScjsSbupmysvL+cUvfkFmZibTpk1jy5YtIhCCIPhl4sSJ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\nr6t7PB8ItEkLZwBfaa2LAZRSHwBnAX5FoqeEm/K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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5624beba90>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "epoch 1/30\n", "epoch 2/30\n", "epoch 3/30\n", "epoch 4/30\n", "epoch 5/30\n" ] }, { "data": { "image/png": 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Pn0srrIoyjh07TkBAEOHh0QQFhbljDq6LuzelFBAAiYkJWK3JXHop5OREc+JEMps3p3H4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\nYCDwpFJqh1LqW6WU1WeCCoIgdFL8wt0kCIIg+Cd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"text/plain": [ "<matplotlib.figure.Figure at 0x7f55cbb4e790>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "epoch 6/30\n", "epoch 7/30\n", "epoch 8/30\n", "epoch 9/30\n", "epoch 10/30\n" ] }, { "data": { "image/png": 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hj3+EGTOsjB4NJ07YCQsL83aPNRVHe1MUDUGUhNBodOtnvfl2QvB49GL19dfwxReVyqNn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\nEIQg4dJLL61SJ3HVVVfVOX5SEBpDVlYWCxcubPD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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5624547710>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "epoch 11/30\n", "epoch 12/30\n", "epoch 13/30\n", "epoch 14/30\n", "epoch 15/30\n" ] }, { "data": { "image/png": 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VEu4opaKAwcBWz0oiCA2Le3aNM421stecZGcf4fPPl2C3P8ZFF52lZ88QevW6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\npKWl4e/vDzRPd5MgtGpqc3G/6667yMvL48CBA00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"text/plain": [ "<matplotlib.figure.Figure at 0x7f55edf6cad0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "epoch 16/30\n", "epoch 17/30\n", "epoch 18/30\n", "epoch 19/30\n", "epoch 20/30\n" ] }, { "data": { "image/png": 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wZs3djB37O886CQmJTJ8Oy5fbmDKl9l7f3gQGaovw9GktFitX6lTrSy/Vsa2G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\nev+tt97i9ddfZ8OGDQC89NJL/OY3v+HIkSO0bdv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"text/plain": [ "<matplotlib.figure.Figure at 0x7f55ede5bf50>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "epoch 21/30\n", "epoch 22/30\n", "epoch 23/30\n", "epoch 24/30\n", "epoch 25/30\n" ] }, { "data": { "image/png": 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MDB4sLY+uXak1A0XR8tSX9qlpsGsXxMfbyMlJo2dPKzk56eTkZLi4dipSTDMyYO1aG/37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\nAv2Bne4ZlkKhULRdbr31VtatW0dWVlaTjnd3Cuz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"text/plain": [ "<matplotlib.figure.Figure at 0x7f55eddbfe90>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "epoch 26/30\n", "epoch 27/30\n", "epoch 28/30\n", "epoch 29/30\n" ] }, { "data": { "image/png": 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H0WimrMxBZWUJsbFSX6ikxEZJiTSfKSo6SMeOvWo5fiUvtG4C2enO6unTpfvZf/4DN9xQ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\nRUXgzt9clBAoWpxgOYWhtgi0lZLBivr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QUEBKSgpHjx5tVi/jQHHq/6ZMQ41BZQorXPz7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\nB0z/HWY9+FX/AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5624bebb10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import scipy.stats\n", "import matplotlib.pylab as plt\n", "import os, sys\n", "sys.path.insert(0, \"../\")\n", "import geepee.ep_models as ep\n", "import pdb\n", "%matplotlib inline\n", "\n", "np.random.seed(42)\n", "\n", "def test_kink():\n", " def kink_true(x):\n", " fx = np.zeros(x.shape)\n", " for t in range(x.shape[0]):\n", " xt = x[t]\n", " if xt < 4:\n", " fx[t] = xt + 1\n", " else:\n", " fx[t] = -4*xt + 21\n", " return fx\n", "\n", " def kink(T, process_noise, obs_noise, xprev=None):\n", " if xprev is None:\n", " xprev = np.random.randn()\n", " y = np.zeros([T, ])\n", " x = np.zeros([T, ])\n", " xtrue = np.zeros([T, ])\n", " for t in range(T):\n", " if xprev < 4:\n", " fx = xprev + 1\n", " else:\n", " fx = -4*xprev + 21\n", "\n", " xtrue[t] = fx\n", " x[t] = fx + np.sqrt(process_noise)*np.random.randn()\n", " xprev = x[t]\n", " y[t] = x[t] + np.sqrt(obs_noise)*np.random.randn()\n", "\n", " return xtrue, x, y\n", "\n", " T = 200\n", " process_noise = 0.2\n", " obs_noise = 0.1\n", " (xtrue, x, y) = kink(T, process_noise, obs_noise)\n", " y_train = np.reshape(y, [y.shape[0], 1])\n", " # pdb.set_trace()\n", "\n", " alpha = 0.1\n", " Dlatent = 1\n", " Dobs = 1\n", " M = 40\n", " C = 1*np.ones((1, 1))\n", " R = np.ones(1)*np.log(obs_noise)/2\n", " lls = np.reshape(np.log(2), [Dlatent, ])\n", " lsf = np.reshape(np.log(2), [1, ])\n", " zu = np.linspace(-7, 7, M)\n", " zu = np.reshape(zu, [M, 1])\n", " lsn = np.log(process_noise)/2\n", " params = {'ls': lls, 'sf': lsf, 'sn': lsn, 'R': R, 'C': C, 'zu': zu}\n", "\n", " # create model\n", " model = ep.SGPSSM(y_train, Dlatent, M, \n", " lik='Gaussian', prior_mean=0, prior_var=1)\n", " # update hypers\n", " model.update_hypers(params)\n", " # run EP\n", " model.inference(no_epochs=30, alpha=alpha, parallel=True, decay=0.99)\n", "\n", " # make prediction on some test inputs\n", " N_test = 200\n", " x_test = np.linspace(-7, 8, N_test)\n", " x_test = np.reshape(x_test, [N_test, 1])\n", " mf, vf = model.predict_f(x_test)\n", "\n", " # plot function\n", " fig = plt.figure()\n", " ax = fig.add_subplot(111)\n", " ax.plot(x_test[:,0], kink_true(x_test[:,0]), '-', color='k')\n", " ax.plot(x_test[:,0], mf[:,0], \n", " '-', color='b', label='alpha=%.2f'%alpha)\n", " ax.fill_between(\n", " x_test[:,0], \n", " mf[:,0] + 2*np.sqrt(vf[:,0]), \n", " mf[:,0] - 2*np.sqrt(vf[:,0]), \n", " alpha=0.5, edgecolor='b', facecolor='b')\n", " ax.plot(\n", " xtrue[0:model.N-1], \n", " xtrue[1:model.N], \n", " 'k+', alpha=0.3)\n", " ax.set_xlabel(r'$x_{t-1}$')\n", " ax.set_ylabel(r'$x_{t}$')\n", " ax.set_ylim(-6, 6)\n", " ax.set_xlim(-7, 8)\n", " ax.legend(loc='lower center')\n", "\n", " plt.show()\n", "\n", "test_kink()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

jim-minter/github3.py

example-notebooks/statuses-api.ipynb

10

6962

{ "metadata": { "name": "", "signature": "sha256:71986a4706e141c811d01b30504f39a773bc952ce68dfb45fbd9155fcbde0956" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "An Overview of the Statuses API in github3.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "GitHub's [Statuses API][statuses] is one of the more popular recent additions to GitHub's large and all-encompassing API. This API allows you to create and list statuses like those created by popular Continuous Integration services (e.g., Jenkins, Travis CI, etc.). github3.py provides unfettered access to these functions and the following should help explore that functionality.\n", "\n", "\n", "[statuses]: https://developer.github.com/v3/repos/statuses/" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Listing Statuses Associated with a Reference" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import github3\n", "\n", "\n", "repository = github3.repository('sigmavirus24', 'github3.py')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "With a repository object, we can now retrieve the statuses from a number of different commit-like objects which we can retrieve using the repository's ``commit`` method." ] }, { "cell_type": "code", "collapsed": false, "input": [ "commit = repository.commit('9df71a9772d5f43e332c855a32d4689f28289989')\n", "tag = repository.commit('0.9.3')\n", "branch = repository.commit('stable/0.9')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each of these bindings now hold a reference to a different ``RepoCommit`` object and each has a ``statuses`` method. We can retrieve statuses about each reference and print them." ] }, { "cell_type": "code", "collapsed": false, "input": [ "for ref in (commit, tag, branch):\n", " print('Showing statuses for \"{0.sha}\" ({0.html_url})'.format(ref))\n", " for status in ref.statuses():\n", " print(\" State: {0.state}; Description: {0.description}; Context: {0.context}\".format(status))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Showing statuses for \"9df71a9772d5f43e332c855a32d4689f28289989\" (https://github.com/sigmavirus24/github3.py/commit/9df71a9772d5f43e332c855a32d4689f28289989)\n", " State: success; Description: The Travis CI build passed; Context: continuous-integration/travis-ci" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " State: pending; Description: The Travis CI build is in progress; Context: continuous-integration/travis-ci\n", " State: pending; Description: The Travis CI build is in progress; Context: continuous-integration/travis-ci\n", "Showing statuses for \"52a3f30e05cf434285e775979f01f1a8355049a7\" (https://github.com/sigmavirus24/github3.py/commit/52a3f30e05cf434285e775979f01f1a8355049a7)\n", " State: success; Description: The Travis CI build passed; Context: continuous-integration/travis-ci" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " State: success; Description: The Travis CI build passed; Context: continuous-integration/travis-ci\n", " State: pending; Description: The Travis CI build is in progress; Context: continuous-integration/travis-ci\n", " State: pending; Description: The Travis CI build is in progress; Context: continuous-integration/travis-ci\n", " State: pending; Description: The Travis CI build is in progress; Context: continuous-integration/travis-ci\n", " State: pending; Description: The Travis CI build is in progress; Context: continuous-integration/travis-ci\n", "Showing statuses for \"6e97462ade3d8855644296e7a44b5463c7b222a6\" (https://github.com/sigmavirus24/github3.py/commit/6e97462ade3d8855644296e7a44b5463c7b222a6)\n", " State: success; Description: The Travis CI build passed; Context: continuous-integration/travis-ci" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " State: pending; Description: The Travis CI build is in progress; Context: continuous-integration/travis-ci\n", " State: pending; Description: The Travis CI build is in progress; Context: continuous-integration/travis-ci\n" ] } ], "prompt_number": 8 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Creating a Status for a Reference" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import github3\n", "import os\n", "\n", "g = github3.login(os.environ['GH_USERNAME'], os.environ['GH_PASSWORD'])\n", "repository = g.repository('sigmavirus24', 'github3.py')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "With our ``repository`` object, we can now create a status for an arbitrary commit using the ``create_status`` method." ] }, { "cell_type": "code", "collapsed": false, "input": [ "status = repository.create_status(sha='020cfe6422e3b2a0a5ff985c9e1c0aa921555bd8',\n", " state='success',\n", " description='Documentation status',\n", " context='default')\n", "print('Status {0.state} for context {0.context} with description {0.description}'.format(status))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Status success for context default with description Documentation status\n" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "We now have a \"success\" status defined on our commit and can retrieve it by listing statuses (as described above)." ] } ], "metadata": {} } ] }

bsd-3-clause

zzsza/Datascience_School

06. 기초 선형대수/07. 벡터 공간.ipynb

1

192097

{ "cells": [ { "cell_type": "markdown", "metadata": { "school_cell_uuid": "519c818f7174449497003b848a9ca38e" }, "source": [ "# 벡터 공간" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "f7f14cdb1abc4e99b516b55339991ccf" }, "source": [ "## 벡터의 기하학적 의미" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "281d8af0a4a64733a3d7a39f96256c5d" }, "source": [ "길이가 $K$인 벡터(vector) $a$는 $K$차원의 공간에서 원점과 벡터 $a$의 값으로 표시되는 점을 연결한 화살표(arrow)로 간주할 수 있다. " ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "7a2dd4583e5b49f1a3b6c826807508e6" }, "source": [ "$$ a = \\begin{bmatrix}1 \\\\ 2 \\end{bmatrix} $$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "school_cell_uuid": "864e55643dcc44cea9cba8bd42505c3c" }, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Created with matplotlib (http://matplotlib.org/) -->\n", "<svg height=\"337pt\" version=\"1.1\" viewBox=\"0 0 479 337\" width=\"479pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", " <defs>\n", " <style type=\"text/css\">\n", "*{stroke-linecap:butt;stroke-linejoin:round;stroke-miterlimit:100000;}\n", " </style>\n", " </defs>\n", " <g id=\"figure_1\">\n", " <g id=\"patch_1\">\n", " <path d=\"M 0 337.445312 \n", "L 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"</svg>\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f7b8253fcd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a = [1, 2]\n", "\n", "plt.annotate('', xy=a, xytext=(0,0), arrowprops=dict(facecolor='black'))\n", "\n", "plt.plot(0, 0, 'ro', ms=10)\n", "plt.plot(a[0], a[1], 'ro', ms=10)\n", "\n", "plt.text(0.35, 1.15, \"$a$\", fontdict={\"size\": 18})\n", "\n", "plt.xticks(np.arange(-2, 4))\n", "plt.yticks(np.arange(-1, 4))\n", "plt.xlim(-2.4, 3.4)\n", "plt.ylim(-1.2, 3.2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "f1d6835e962a4a84be7dc8cf6785bdaf" }, "source": [ "## 벡터의 길이" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "26e2c17515b94a9e83cdaf8516d1b4b4" }, "source": [ "벡터 $a$ 의 길이를 놈(norm) $\\| a \\|$ 이라고 하며 다음과 같이 계산할 수 있다.\n", "\n", "$$ \\| a \\| = \\sqrt{a^T a } = \\sqrt{a_1^2 + \\cdots + a_K^2} $$\n", "\n", "numpy의 linalg 서브 패키지의 `norm` 명령으로 벡터의 길이를 계산할 수 있다." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "school_cell_uuid": "e7bec503e4eb46c1a8cd7f9033667f86" }, "outputs": [ { "data": { "text/plain": [ "1.4142135623730951" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = np.array([1, 1])\n", "np.linalg.norm(a)" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "32d010b5b9e2498c8b0ed2f7873bb17a" }, "source": [ "## 단위 벡터" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "365824b3eefb4afb86096bb263dfcb11" }, "source": [ "길이가 1인 벡터를 단위 벡터(unit vector)라고 한다. 예를 들어 다음과 같은 벡터들은 모두 단위 벡터이다.\n", "\n", "$$ \n", "a = \\begin{bmatrix}1 \\\\ 0\\end{bmatrix} ,\\;\\;\n", "b = \\begin{bmatrix}0 \\\\ 1\\end{bmatrix} ,\\;\\;\n", "c = \\begin{bmatrix} \\dfrac{1}{\\sqrt{2}} \\\\ \\dfrac{1}{\\sqrt{2}} \\end{bmatrix}\n", "$$\n", "\n", "임의의 벡터 $x$에 대해 다음은 벡터는 단위 벡터가 된다.\n", "\n", "$$\n", "\\dfrac{x}{\\| x \\|}\n", "$$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "school_cell_uuid": "9a959b285db44014a0e7ee3630cc162a" }, "outputs": [ { "data": { "text/plain": [ "(1.0, 1.0, 0.99999999999999989)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = np.array([1, 0])\n", "b = np.array([0, 1])\n", "c = np.array([1/np.sqrt(2), 1/np.sqrt(2)])\n", "np.linalg.norm(a), np.linalg.norm(b), np.linalg.norm(c)" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "6daea3340e404f86b044a5d0e5816ce6" }, "source": [ "## 벡터의 합" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "5bdef63c53964ae69f0c594611a7bccd" }, "source": [ "벡터와 벡터의 합은 벡터가 된다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "0278d1d3d421435eb346850524647974" }, "source": [ "$$ \n", "a = \\begin{bmatrix}1 \\\\ 2\\end{bmatrix} ,\\;\\;\n", "b = \\begin{bmatrix}2 \\\\ 1\\end{bmatrix} \\;\\;\\; \\rightarrow \\;\\;\\;\n", "c = a + b = \\begin{bmatrix}3 \\\\ 3\\end{bmatrix} 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fontdict={\"size\": 18})\n", "plt.text(1.15, 0.25, \"$b$\", fontdict={\"size\": 18})\n", "plt.text(1.25, 1.45, \"$c$\", fontdict={\"size\": 18})\n", "\n", "plt.xticks(np.arange(-2, 4))\n", "plt.yticks(np.arange(-1, 4))\n", "plt.xlim(-1.4, 4.4)\n", "plt.ylim(-0.6, 3.8)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "58233c925d18474897bf4e64ace4f0ab" }, "source": [ "벡터의 집합 중에서 집합의 원소인 두 벡터의 선형 조합(스칼라 곱의 합)이 그 집합의 원소이면 벡터 공간이라고 한다.\n", "\n", "$$ a, b \\in \\mathbf{R} \\;\\; \\text{ and } \\;\\; \\alpha_1a + \\alpha_2b \\in \\mathbf{R} $$" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "b0e904a3cda64f99b30e4eba02de8759" }, "source": [ "## 벡터의 분해" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "79502b3fe1bc4352b09df5802828637b" }, "source": [ "어떤 두 벡터 $a$, $b$의 합이 다른 벡터 $c$가 될 때 $c$가 두 벡터 성분(vector component) $a$, $b$으로 분해(decomposition)된다고 말할 수 있다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "8383b67b88fc47bdb3db70941a39257e" }, "source": [ "## 두 벡터의 내적" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "5c45d428f662452296acb8dbf2a908eb" }, "source": [ "\n", "두 벡터의 내적은 다음과 같이 벡터의 길이 $\\|a\\|$, $\\|b\\|$ 와 두 벡터 사이의 각도 $\\theta$로 계산할 수도 있다.\n", "\n", "$$ a^Tb = \\|a\\|\\|b\\| \\cos\\theta $$" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "1c88bb3e5b7b42e9a226c973e9b4c5d1" }, "source": [ "(증명)\n", "\n", "위 식은 2차원 벡터의 경우 다음과 같이 증명할 수 있다.\n", "\n", "<img src=\"https://datascienceschool.net/upfiles/2e57d9e9358241e5862fe734dfd245b2.png\">\n", "\n", "위 그림과 같은 삼각형에서 세 변은 다음과 같은 공식을 만족한다. (코사인 법칙)\n", "\n", "\n", "$$\n", "\\|a−b\\|^2=\\|a\\|^2+\\|b\\|^2−2\\|a\\|\\|b\\|\\cos\\theta\n", "$$\n", "\n", "\n", "$$\n", "\\begin{eqnarray}\n", "\\|a−b\\|^2 \n", "&=& (a−b)^T(a−b) \\\\\n", "&=& a^Ta − 2 ( a^Tb ) + b^T b \\\\\n", "&=&\t\\|a\\|^2+\\|b\\|^2 − 2 a^T b\n", "\\end{eqnarray}\n", "$$\n", "\n", "두 식이 같으므로 \n", "\n", "$$ a^Tb = \\|a\\|\\|b\\| \\cos\\theta $$" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "56230cf9a4fd4a05b179b1cb6309eb37" }, "source": [ "## 벡터의 직교" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "5b418759f4564e5a853953a9fe434a92" }, "source": [ "두 벡터 $a$와 $b$가 이루는 각이 90도이면 서로 직교(orthogonal)라고 하며 $ a \\perp b $로 표시한다.\n", "\n", "$\\cos 90^{\\circ} = 0$이므로 서로 직교인 두 벡터의 벡터 내적(inner product, dot product)는 0이된다.\n", "\n", "$$ a^T b = b^T a = 0 \\;\\;\\;\\; \\leftrightarrow \\;\\;\\;\\; a \\perp b $$" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "60bc362063094b9ab79e9523929a92eb" }, "source": [ "예를 들어 다음 두 벡터는 서로 직교한다.\n", "\n", "$$ \n", "a = \\begin{bmatrix}1 \\\\ 1\\end{bmatrix} ,\\;\\;\n", "b = \\begin{bmatrix}-1 \\\\ 1\\end{bmatrix} \\;\\;\\;\\; \\rightarrow \\;\\;\\;\\;\n", "a^T b = \\begin{bmatrix}1 & 1\\end{bmatrix} \\begin{bmatrix}-1 \\\\ 1\\end{bmatrix} = -1 + 1 = 0\n", "$$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "school_cell_uuid": "c8c1400a1251468caa21d87e64f44363" }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = np.array([1, 1])\n", "b = np.array([-1, 1])\n", "np.dot(a, b)" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "af1d769a451d4b0da6c3f418f95d5b18" }, "source": [ "## 투영" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "23e8cb6f847b47b691d9d8c6d9a33f3c" }, "source": [ "벡터 $a$를 다른 벡터 $b$에 직교하는 성분 $a_1$ 와 나머지 성분 $a_2 = a - a_1$로 분해할 수 있다. 이 때 $a_2$는 $b$와 평행하며 이 길이를 벡터 $a$의 벡터 $b$에 대한 투영(projection)이라고 한다.\n", "\n", "벡터의 투영은 다음과 같이 내적을 사용하여 구할 수 있다.\n", "\n", "$$ a = a_1 + a_2 $$\n", "\n", "$$ a_1 \\perp b \\;\\; \\text{ and } \\;\\; a_2 = a - a_1 $$\n", "\n", "이면 \n", "\n", "$$ \\| a_2 \\| = a^T\\dfrac{b}{\\|b\\|} = \\dfrac{a^Tb}{\\|b\\|} = \\dfrac{b^Ta}{\\|b\\|} $$\n", "\n", "이다." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "school_cell_uuid": "7250fe1f021e4eef85b311e7d1f35a28" }, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Created with matplotlib (http://matplotlib.org/) -->\n", "<svg height=\"342pt\" version=\"1.1\" viewBox=\"0 0 473 342\" width=\"473pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", " <defs>\n", " <style type=\"text/css\">\n", 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"metadata": {}, "output_type": "display_data" } ], "source": [ "a = np.array([1, 2])\n", "b = np.array([2, 0])\n", "a2 = np.dot(a, b)/np.linalg.norm(b) * np.array([1, 0])\n", "a1 = a - a2\n", "\n", "plt.annotate('', xy=a, xytext=(0,0), arrowprops=dict(facecolor='gray'))\n", "plt.annotate('', xy=b, xytext=(0,0), arrowprops=dict(facecolor='gray'))\n", "plt.annotate('', xy=a2, xytext=(0,0), arrowprops=dict(facecolor='green'))\n", "plt.annotate('', xy=a1, xytext=(0,0), arrowprops=dict(facecolor='green'))\n", "\n", "plt.plot(0, 0, 'ro', ms=10)\n", "plt.plot(a[0], a[1], 'ro', ms=10)\n", "plt.plot(b[0], b[1], 'ro', ms=10)\n", "\n", "plt.text(0.35, 1.15, \"$a$\", fontdict={\"size\": 18})\n", "plt.text(1.55, 0.15, \"$b$\", fontdict={\"size\": 18})\n", "plt.text(-0.2, 1.05, \"$a_1$\", fontdict={\"size\": 18})\n", "plt.text(0.50, 0.15, \"$a_2$\", fontdict={\"size\": 18})\n", "\n", "plt.xticks(np.arange(-2, 4))\n", "plt.yticks(np.arange(-1, 4))\n", "plt.xlim(-1.5, 3.5)\n", "plt.ylim(-0.5, 3)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "03075fc1d6444141ade63b7e1b51ff07" }, "source": [ "## 직선" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "41fc726915e34aea9cf59e8832a4a193" }, "source": [ "벡터 공간에서 직선은 다음과 같은 함수로 표현할 수 있다.\n", "\n", "$$\n", "f(x) = w^T(x - w) = w^Tx - w^Tw = w^Tx - \\| w \\|^2 = w^Tx - w_0 = 0\n", "$$\n", "\n", "$x$는 직선 상의 점을 나타내는 벡터이고 $w$는 원점으로부터 직선까지 이어지는 수직선을 나타내는 벡터이다.\n", "$x-w$ 벡터가 $w$ 벡터와 수직이라는 것은 $x$가 가리키는 점과 $w$가 가리키는 점을 이은 선이 $w$와 수직이라는 뜻이다.\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "1adb3b127f114a60b977943164065228" }, "source": [ "예를 들어 \n", "\n", "$$\n", "w = \\begin{bmatrix}1 \\\\ 2\\end{bmatrix} ,\\;\\;\n", "w_0 = 5\n", "$$\n", "\n", "일 때\n", "\n", "$$\n", "\\begin{bmatrix}1 & 2\\end{bmatrix} \\begin{bmatrix}x_1 \\\\ x_2 \\end{bmatrix} - 5 = x_1 + 2x_2 - 5 = 0\n", "$$\n", "\n", "이면 벡터 $w$가 가리키는 점 (1, 2)를 지나면서 벡터 $w$에 수직인 선을 뜻한다." ] }, { 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arrowprops=dict(facecolor='red'))\n", "plt.annotate('', xy=x1, xytext=(0,0), arrowprops=dict(facecolor='gray'))\n", "plt.annotate('', xy=x2, xytext=(0,0), arrowprops=dict(facecolor='gray'))\n", "\n", "plt.plot(0, 0, 'ro', ms=10)\n", "plt.plot(w[0], w[1], 'ro', ms=10)\n", "plt.plot(x1[0], x1[1], 'ro', ms=10)\n", "plt.plot(x2[0], x2[1], 'ro', ms=10)\n", "plt.plot([-3, 5], [4, 0], 'r-', lw=5)\n", "\n", "plt.text(0.35, 1.15, \"$w$\", fontdict={\"size\": 18})\n", "plt.text(1.55, 0.25, \"$x_1$\", fontdict={\"size\": 18})\n", "plt.text(-0.9, 1.40, \"$x_2$\", fontdict={\"size\": 18})\n", "\n", "plt.xticks(np.arange(-2, 4))\n", "plt.yticks(np.arange(-1, 4))\n", "plt.xlim(-1.7, 4.2)\n", "plt.ylim(-0.5, 3.5)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "6c897241bfa7476ab13e6ceaba302c7e" }, "source": [ "## 직선과 점의 거리" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "e973ee2ed2b54fa8a0102517667ffba5" }, "source": [ "직선 $ w^Tx - w_0 = 0 $ 과 이 직선 위에 있지 않은 점 $x'$의 거리는 단위 벡터 $\\dfrac{w}{\\|w\\|}$에 대한 $x'$의 투영에서 $\\|w\\|$를 뺀 값의 절대값이다. 따라서 다음과 같이 정리할 수 있다.\n", "\n", "$$\n", "\\left| \\dfrac{w^Tx'}{\\|w\\|} - \\|w\\| \\right| = \\dfrac{\\left|w^Tx' - \\|w\\|^2 \\right|}{\\|w\\|}= \\dfrac{\\left|w^Tx' - w_0 \\right|}{\\|w\\|}\n", "$$\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "c1229cb511b24ad48abf199f4fcabcea" }, "source": [ "## 벡터의 선형 종속과 선형 독립" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "83249b6cd7aa41a59b321d7afb958c0c" }, "source": [ "벡터들의 선형 조합이 0이 되는 모두 0이 아닌 스칼라값들이 존재하면 그 벡터들은 선형 종속(linearly dependent)이라고 한다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "2a2cc09d75c943ec9f7142d5d199d721" }, "source": [ "$$ \n", "a = \\begin{bmatrix}1 \\\\ 2\\end{bmatrix} ,\\;\\;\n", "b = \\begin{bmatrix}3 \\\\ 3\\end{bmatrix} \\;\\;\n", "c = \\begin{bmatrix}10 \\\\ 14\\end{bmatrix} \\;\\;\n", "$$\n", "\n", "\n", "$$ \n", "2a + b - \\frac{1}{2}c = 0\n", "$$" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "school_cell_uuid": "3065d011def14c11a8e8dcaf79df14a0" }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 0.])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = np.array([1, 2])\n", "b = np.array([3, 3])\n", "c = np.array([10, 14])\n", "2*a + b - 0.5*c" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "0089286255e340ada67c9126479959e5" }, "source": [ "벡터들의 선형 조합이 0이 되는 모두 0이 아닌 스칼라값들이 존재하지 않으면 그 벡터들은 선형 독립(linearly independent)이라고 한다.\n", "\n", "$$ \\alpha_1 a_1 + \\cdots + \\alpha_K a_K = 0 \\;\\;\\;\\; \\leftrightarrow \\;\\;\\;\\; \\alpha_1 = \\cdots = \\alpha_K = 0 $$ " ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "ebc7c4ad5ac54e648db26174efae7b60" }, "source": [ "## 기저 벡터" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "b9c6a62f88024a32abbe17881171137d" }, "source": [ "벡터 공간에 속하는 벡터의 집합이 선형 독립이고 다른 모든 벡터 공간의 벡터들이 그 벡터 집합의 선형 조합으로 나타나면 그 벡터 집합을 벡터 공간의 기저 벡터(basis vector)라고 한다. \n", "\n", "예를 들어 다음과 같은 두 벡터는 2차원 벡터 공간의 기저 벡터이다.\n", "$$ \n", "a = \\begin{bmatrix}1 \\\\ 0\\end{bmatrix} ,\\;\\;\n", "b = \\begin{bmatrix}0 \\\\ 1\\end{bmatrix} \\;\\;\n", "$$\n", "또는\n", "$$ \n", "a = \\begin{bmatrix}1 \\\\ 1\\end{bmatrix} ,\\;\\;\n", "b = \\begin{bmatrix}2 \\\\ 3\\end{bmatrix} \\;\\;\n", "$$\n", "\n", "\n", "다음과 같은 두 벡터는 2차원 벡터 공간의 기저 벡터가 될 수 없다.\n", "$$ \n", "a = \\begin{bmatrix}1 \\\\ 2\\end{bmatrix} ,\\;\\;\n", "b = \\begin{bmatrix}2 \\\\ 4\\end{bmatrix} \\;\\;\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "61ee4647d2914858b461f50fd32a7a9f" }, "source": [ "## 열 공간" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "fe44799ba87045d3ab6c9d4631a93faf" }, "source": [ "행렬은 열 벡터의 집합으로 볼 수 있다. 이 때 열 벡터들의 조합으로 생성되는 벡터 공간을 열 공간(column space)이라고 한다.\n", "\n", "\n", "$$ \n", "A = \\begin{bmatrix} 1 & 5 & 6 \\\\ 2 & 6 & 8 \\\\ 7 & 1 & 8 \\end{bmatrix} \n", "\\;\\;\\;\\; \\rightarrow \\;\\;\\;\\;\n", "\\alpha_1 \\begin{bmatrix} 1 \\\\ 2 \\\\ 7 \\end{bmatrix} + \n", "\\alpha_2 \\begin{bmatrix} 5 \\\\ 6 \\\\ 1 \\end{bmatrix} + \n", "\\alpha_3 \\begin{bmatrix} 6 \\\\ 8 \\\\ 8 \\end{bmatrix} \n", "\\; \\in \\; \\text{column space}\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "901cfac4add94c4dae1b6915b5b1e24b" }, "source": [ "## 열 랭크" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "b323e4143dcb43b1b5dfaf4fb448fd6a" }, "source": [ "행렬의 열 벡터 중 서로 독립인 열 벡터의 최대 갯수를 열 랭크(column rank) 혹은 랭크(rank)라고 한다.\n", "\n", "예를 들어 다음 행렬의 랭크는 2이다.\n", "\n", "$$ \n", "A = \\begin{bmatrix} 1 & 5 & 6 \\\\ 2 & 6 & 8 \\\\ 3 & 11 & 14 \\end{bmatrix} \n", "$$\n", "\n", "numpy의 linalg 서브 패키지의 `matrix_rank` 명령으로 랭크를 계산할 수 있다." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "school_cell_uuid": "b7be3afa6d5547508cf2bed729f3cca1" }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A = np.array([[1, 5, 6], [2, 6, 8], [3, 11, 14]])\n", "np.linalg.matrix_rank(A)" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "47475388f5524a4cba951db66c16ca00" }, "source": [ "## 좌표" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "f2578603c5e74db5b968034ac5c73176" }, "source": [ "벡터의 성분, 즉 좌표(coordinate)는 표준 기저 벡터들에 대한 해당 벡터의 투영(projection)으로 볼 수 있다. 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xytext=(0,0), arrowprops=dict(facecolor='green'))\n", "plt.annotate('', xy=a, xytext=(0,0), arrowprops=dict(facecolor='gray'))\n", "\n", "plt.plot(0, 0, 'ro', ms=10)\n", "plt.plot(a[0], a[1], 'ro', ms=10)\n", "\n", "plt.text(1.05, 1.35, \"$a$\", fontdict={\"size\": 18})\n", "plt.text(-0.2, 0.5, \"$e_1$\", fontdict={\"size\": 18})\n", "plt.text(0.5, -0.2, \"$e_2$\", fontdict={\"size\": 18})\n", "\n", "plt.xticks(np.arange(-2, 4))\n", "plt.yticks(np.arange(-1, 4))\n", "plt.xlim(-1.5, 3.5)\n", "plt.ylim(-0.5, 3)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "e9cbfe1ce46d4ea4b8cd9d283e4506e5" }, "source": [ "## 좌표 변환" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "2f53372ddee74b119c3532be8c259e7f" }, "source": [ "새로운 기저 벡터를에 대해 벡터 투영을 계산하는 것을 좌표 변환(coordinate transform)이라고 한다.\n", "\n", "좌표 변환은 새로운 기저 벡터로 이루어진 변환 행렬(transform matrix) $A$ 와의 내적으로 계산한다.\n", "\n", "$$ Aa' = a $$\n", "\n", "\n", "$$ a' = A^{-1}a $$\n", "\n", "\n", "예를 들어, 기존의 기저 벡터가 \n", "\n", "$$ \n", "e_1 = \\begin{bmatrix}1 \\\\ 0\\end{bmatrix} ,\\;\\;\n", "e_2 = \\begin{bmatrix}0 \\\\ 1\\end{bmatrix} \\;\\;\n", "$$\n", "\n", "이면 벡터 $a$는 사실\n", "\n", "$$\n", "a = \\begin{bmatrix}2 \\\\ 2\\end{bmatrix} = 2 \\begin{bmatrix}1 \\\\ 0\\end{bmatrix} + 2 \\begin{bmatrix}0 \\\\ 1 \\end{bmatrix} = 2 e_1 + 2 e_2\n", "$$\n", "\n", "\n", "새로운 기저 벡터가 \n", "\n", "$$ \n", "g_1 = \\begin{bmatrix} \\dfrac{1}{\\sqrt{2}} \\\\ \\dfrac{1}{\\sqrt{2}} \\end{bmatrix} ,\\;\\;\n", "g_2 = \\begin{bmatrix} -\\dfrac{1}{\\sqrt{2}} \\\\ \\dfrac{1}{\\sqrt{2}} \\end{bmatrix} ,\\;\\;\n", "$$\n", "\n", "이면 벡터 $a$의 좌표는 다음과 같이 바뀐다.\n", "\n", "$$ \n", "a = \\begin{bmatrix}2 \\\\ 2\\end{bmatrix} \\;\\;\\;\\; \\rightarrow \\;\\;\\;\\;\n", "a' = A^{-1}a = \n", "\\begin{bmatrix} \n", "e'_1 & e'_2\n", "\\end{bmatrix} \n", "a\n", "=\n", "\\begin{bmatrix} \n", "\\dfrac{1}{\\sqrt{2}} & -\\dfrac{1}{\\sqrt{2}} \\\\\n", "\\dfrac{1}{\\sqrt{2}} & \\dfrac{1}{\\sqrt{2}} \n", "\\end{bmatrix}^{-1}\n", "\\begin{bmatrix}2 \\\\ 2\\end{bmatrix}\n", "=\n", "\\begin{bmatrix} \n", "\\dfrac{1}{\\sqrt{2}} & \\dfrac{1}{\\sqrt{2}} \\\\\n", "-\\dfrac{1}{\\sqrt{2}} & \\dfrac{1}{\\sqrt{2}} \n", "\\end{bmatrix}\n", "\\begin{bmatrix}2 \\\\ 2\\end{bmatrix} \n", "= \\begin{bmatrix}2\\sqrt{2}\\\\0\\end{bmatrix} \n", "$$\n", " " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "school_cell_uuid": "ad31360acbba433faea71f3a0b443dae" }, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Created with matplotlib (http://matplotlib.org/) -->\n", "<svg height=\"342pt\" version=\"1.1\" viewBox=\"0 0 473 342\" width=\"473pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", " <defs>\n", " <style type=\"text/css\">\n", 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xytext=(0,0), arrowprops=dict(facecolor='green'))\n", "plt.annotate('', xy=g1, xytext=(0,0), arrowprops=dict(facecolor='red'))\n", "plt.annotate('', xy=g2, xytext=(0,0), arrowprops=dict(facecolor='red'))\n", "plt.annotate('', xy=a, xytext=(0,0), arrowprops=dict(facecolor='gray', alpha=0.5))\n", "\n", "plt.plot(0, 0, 'ro', ms=10)\n", "plt.plot(a[0], a[1], 'ro', ms=10)\n", "\n", "plt.text(1.05, 1.35, \"$a$\", fontdict={\"size\": 18})\n", "plt.text(-0.2, 0.5, \"$e_1$\", fontdict={\"size\": 18})\n", "plt.text(0.5, -0.2, \"$e_2$\", fontdict={\"size\": 18})\n", "plt.text(0.2, 0.5, \"$g_1$\", fontdict={\"size\": 18})\n", "plt.text(-0.6, 0.2, \"$g_2$\", fontdict={\"size\": 18})\n", "\n", "plt.xticks(np.arange(-2, 4))\n", "plt.yticks(np.arange(-1, 4))\n", "plt.xlim(-1.5, 3.5)\n", "plt.ylim(-0.5, 3)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "school_cell_uuid": "40933f1137ca40f4aad46062d2f0da74" }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.70710678, -0.70710678],\n", " [ 0.70710678, 0.70710678]])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A = np.vstack([g1, g2]).T\n", "A" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "school_cell_uuid": "2d9eef7d1a6c4a798e37c092866ae9a3" }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.70710678, 0.70710678],\n", " [-0.70710678, 0.70710678]])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ainv = np.linalg.inv(A)\n", "Ainv" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "school_cell_uuid": "0f52c98205324487ac1659b935cf72a8" }, "outputs": [ { "data": { "text/plain": [ "array([ 2.82842712, 0. ])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ainv.dot(a)" ] } ], "metadata": { "celltoolbar": "Edit 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

pywr/pywr

tests/notebook.ipynb

4

983

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pywr.core import Model\n", "from pywr.notebook import draw_graph\n", "\n", "model = Model.load(\"models/simple1.json\")\n", "\n", "print(model)\n", "print(\"nodes:\", len(model.nodes))\n", "\n", "draw_graph(model)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.4" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

fusion809/GNU_Octave

SLE/Untitled.ipynb

1

33884

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "errrms = 7.356911406826205e-11\n" ] }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Standard values are for transformation x=(y-L)/(y+L), y in 0, inf\n", "# *lin are for x=2/L*y-1 (linear transformation)\n", "clear all\n", "format long e;\n", "N=1000;\n", "perc=0.21;\n", "Nfrag=round(perc*N);\n", "n=0:N;\n", "L=22;\n", "t=pi+n'*pi/N;\n", "x=cos(t);\n", "y=L*(cot(t/2)).^2;\n", "ysub=y(2:N);\n", "ylintrans = L/2*(x+1);\n", "# Chebyshev polys of the first kind\n", "T = cos(t*n);\n", "# Now for arrays that do not include the endpoints\n", "xsub = x(2:N);\n", "Tsub = T(2:N,:);\n", "Usub = diag(1./sqrt(1-xsub.^2))*sin(acos(xsub)*n);\n", "dTsub = Usub*diag(n);\n", "dT = [-(n.^2).*(-1).^(n); dTsub ; (n).^2];\n", "dTL = diag(2*L./((y+L).^2))*dT;\n", "dTlin = 2/L*dT;\n", "D1 = dTL/T;\n", "D1lin = dTlin/T;\n", "D2 = D1^2;\n", "D2lin = D1lin^2;\n", "H = -D2+diag((y.^2.));\n", "H = H(2:N,2:N);\n", "[Y, Lam] = eig(H);\n", "Lam = diag(Lam);\n", "[Lam, IX] = sort(Lam, 'ascend');\n", "Y = Y(:,IX);\n", "yexact = sqrt(ysub/(2*pi)).*besselk(1/4,1/4*ysub.^2);\n", "eigex=4*(n'+1)-1; err=Lam(1:Nfrag)-eigex(1:Nfrag); errrms=sqrt(err'*err/(Nfrag))\n", "figure(1)\n", "plot(ysub(1:3*N/5), Y(1:3*N/5,1));\n", "figure(2)\n", "plot(ysub(1:3*N/5), Y(1:3*N/5,30));" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ans = 1.099999999998837e+01\n" ] } ], "source": [ "Lam(3)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ans = 1.100000000000614e+01\n" ] } ], "source": [ "Lam(3) #L=15" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ans = 3.900000000001418e+01\n" ] } ], "source": [ "Lam(10)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ans = 3.000000000004093e+00\n" ] } ], "source": [ "Lam(1)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "eigex=4*(n'+1)-1; err=Lam(1:Nfrag)-eigex(1:Nfrag); errrms=sqrt(err'*err/(Nfrag));" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "errrms = 3.113279365871696e+00\n" ] } ], "source": [ "errrms" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "err =\n", "\n", " 4.092726157978177e-12\n", " 1.633360113828530e-12\n", " 6.139089236967266e-12\n", " 1.383781977892795e-11\n", " 1.322675302617426e-11\n", " 1.606181854185706e-11\n", " 1.472599819862808e-11\n", " 1.272226768378459e-11\n", " 1.409716787748039e-11\n", " 1.417532757841400e-11\n", " 1.798383664208814e-11\n", " 1.721645048746723e-11\n", " 1.656275117056794e-11\n", " 1.576694330651662e-11\n", " 2.084021843984374e-11\n", " 1.898570189950988e-11\n", " 2.282263267261442e-11\n", " 2.485478489688830e-11\n", " 2.813749233609997e-11\n", " 2.634692464198451e-11\n", " 2.836486601154320e-11\n", " 2.944489096989855e-11\n", " 2.894751105486648e-11\n", " 3.119282609986840e-11\n", " 2.923172814917052e-11\n", " 2.913225216616411e-11\n", " 2.886224592657527e-11\n", " 3.051070507353870e-11\n", " 3.385025593161117e-11\n", " 3.350919541844632e-11\n", " 3.414868388063041e-11\n", " 3.285549610154703e-11\n", " 3.404920789762400e-11\n", " 3.311129148642067e-11\n", " 3.379341251275036e-11\n", " 3.393552105990238e-11\n", " 3.140598892059643e-11\n", " 3.558398020686582e-11\n", " 3.419131644477602e-11\n", " 3.726086106325965e-11\n", " 3.620925781433471e-11\n", " 3.799982550845016e-11\n", " 3.856825969705824e-11\n", " 3.836930773104541e-11\n", " 3.927880243281834e-11\n", " 3.825562089332379e-11\n", " 4.123990038351621e-11\n", " 4.376943252282217e-11\n", " 4.385469765111338e-11\n", " 4.442313183972146e-11\n", " 4.101252670807298e-11\n", " 4.490630090003833e-11\n", " 4.362732397567015e-11\n", " 4.337152859079652e-11\n", " 4.342837200965732e-11\n", " 4.544631337921601e-11\n", " 4.561684363579843e-11\n", " 4.828848432225641e-11\n", " 4.621369953383692e-11\n", " 4.777689355250914e-11\n", " 4.951061782776378e-11\n", " 4.740741132991388e-11\n", " 5.005063030694146e-11\n", " 5.132960723130964e-11\n", " 4.746425474877469e-11\n", " 4.877165338257328e-11\n", " 4.934008757118136e-11\n", " 4.860112312599085e-11\n", " 5.081801646156237e-11\n", " 5.172751116333529e-11\n", " 5.292122295941226e-11\n", " 5.104539013700560e-11\n", " 5.184119800105691e-11\n", " 5.201172825763933e-11\n", " 5.564970706473105e-11\n", " 5.348965714802034e-11\n", " 5.530864655156620e-11\n", " 5.394440449890681e-11\n", " 5.593392415903509e-11\n", " 5.621814125333913e-11\n", " 5.496758603840135e-11\n", " 5.599076757789589e-11\n", " 5.570655048359185e-11\n", " 5.428546501207165e-11\n", " 5.513811629498377e-11\n", " 5.553602022700943e-11\n", " 5.388756108004600e-11\n", " 5.650235834764317e-11\n", " 5.633182809106074e-11\n", " 5.661604518536478e-11\n", " 5.888978193979710e-11\n", " 5.741185304941610e-11\n", " 5.917399903410114e-11\n", " 6.036771083017811e-11\n", " 6.116351869422942e-11\n", " 6.053824108676054e-11\n", " 6.036771083017811e-11\n", " 6.070877134334296e-11\n", " 6.309619493549690e-11\n", " 6.389200279954821e-11\n", " 6.627942639170215e-11\n", " 6.508571459562518e-11\n", " 6.190248313941993e-11\n", " 6.315303835435770e-11\n", " 6.025402399245650e-11\n", " 5.877609510207549e-11\n", " 6.224354365258478e-11\n", " 6.508571459562518e-11\n", " 6.838263288955204e-11\n", " 6.610889613511972e-11\n", " 6.411937647499144e-11\n", " 6.923528417246416e-11\n", " 6.480149750132114e-11\n", " 6.480149750132114e-11\n", " 6.696154741803184e-11\n", " 6.707523425575346e-11\n", " 6.912159733474255e-11\n", " 6.889422365929931e-11\n", " 6.889422365929931e-11\n", " 7.338485374930315e-11\n", " 7.480593922082335e-11\n", " 7.378275768132880e-11\n", " 6.912159733474255e-11\n", " 6.838263288955204e-11\n", " 7.190692485892214e-11\n", " 7.258904588525183e-11\n", " 7.304379323613830e-11\n", " 7.315748007385992e-11\n", " 7.696598913753405e-11\n", " 7.071321306284517e-11\n", " 7.185008144006133e-11\n", " 7.150902092689648e-11\n", " 7.469225238310173e-11\n", " 7.480593922082335e-11\n", " 7.446487870765850e-11\n", " 6.900791049702093e-11\n", " 6.798472895752639e-11\n", " 7.798917067702860e-11\n", " 7.469225238310173e-11\n", " 7.446487870765850e-11\n", " 7.980816008057445e-11\n", " 8.003553375601768e-11\n", " 7.480593922082335e-11\n", " 7.423750503221527e-11\n", " 7.582912076031789e-11\n", " 7.480593922082335e-11\n", " 7.594280759803951e-11\n", " 8.083134162006900e-11\n", " 7.810285751475021e-11\n", " 7.889866537880152e-11\n", " 7.855760486563668e-11\n", " 7.946709956740960e-11\n", " 8.174083632184193e-11\n", " 7.844391802791506e-11\n", " 7.798917067702860e-11\n", " 7.787548383930698e-11\n", " 8.117240213323385e-11\n", " 8.071765478234738e-11\n", " 7.742073648842052e-11\n", " 8.230927051045001e-11\n", " 8.469669410260394e-11\n", " 8.265033102361485e-11\n", " 8.367351256310940e-11\n", " 8.287770469905809e-11\n", " 8.276401786133647e-11\n", " 8.265033102361485e-11\n", " 8.219558367272839e-11\n", " 8.253664418589324e-11\n", " 8.037659426918253e-11\n", " 8.844835974741727e-11\n", " 8.299139153677970e-11\n", " 8.299139153677970e-11\n", " 8.037659426918253e-11\n", " 8.287770469905809e-11\n", " 7.912603905424476e-11\n", " 8.117240213323385e-11\n", " 8.674305718159303e-11\n", " 8.753886504564434e-11\n", " 8.583356247982010e-11\n", " 8.071765478234738e-11\n", " 8.469669410260394e-11\n", " 9.038103598868474e-11\n", " 8.708411769475788e-11\n", " 8.412825991399586e-11\n", " 8.526512829121202e-11\n", " 8.628830983070657e-11\n", " 9.367795428261161e-11\n", " 9.038103598868474e-11\n", " 8.969891496235505e-11\n", " 9.208633855450898e-11\n", " 9.072209650184959e-11\n", " 8.560618880437687e-11\n", " 9.015366231324151e-11\n", " 8.981260180007666e-11\n", " 8.719780453247949e-11\n", " 8.947154128691182e-11\n", " 8.981260180007666e-11\n", " 9.890754881780595e-11\n", " 8.913048077374697e-11\n", " 9.083578333957121e-11\n", " 9.072209650184959e-11\n", " 9.038103598868474e-11\n", " 1.006128513836302e-10\n", " 9.106315701501444e-11\n", " 8.753886504564434e-11\n", " 8.571987564209849e-11\n", " 9.333689376944676e-11\n", " 9.242739906767383e-11\n", " 8.230927051045001e-11\n", " 8.515144145349041e-11\n", " 1.169837560155429e-10\n", " 1.127773430198431e-10\n", " 5.445599526865408e-11\n", " 1.946318661794066e-10\n", " 4.879439075011760e-10\n", " 1.473381416872144e-09\n", " 3.420836947043426e-10\n", " 1.250748482561903e-08\n", " 4.380808604764752e-09\n", " 1.024131961457897e-07\n", " -8.132019502227195e-09\n", " 7.526274430347257e-07\n", " -1.063162926584482e-07\n", " 4.895441520602617e-06\n", " -9.825103006733116e-07\n", " 2.846123049948801e-05\n", " -4.843207875637745e-06\n", " 1.467228752289884e-04\n", " -1.506245803284401e-05\n", " 6.710119500894507e-04\n", " 6.907760962349130e-06\n", " 2.704881985550855e-03\n", " 4.284213815708426e-04\n", " 9.563483644569715e-03\n", " 3.267674493486084e-03\n", " 2.943436453074355e-02\n", " 1.636404672933622e-02\n", " 7.846747890414463e-02\n", " 6.246179003164798e-02\n", " 1.813822473710616e-01\n", " 1.904620161114963e-01\n", " 3.686240872806366e-01\n", " 4.769731670165811e-01\n", " 6.747462691935198e-01\n", " 1.005867435921914e+00\n", " 1.139315189748913e+00\n", " 1.836372958651737e+00\n", " 1.801469989307066e+00\n", " 2.987918795067912e+00\n", " 2.691558542232656e+00\n", " 4.449283369268301e+00\n", " 3.829314433841887e+00\n", " 6.192709752176825e+00\n", " 5.230441706399574e+00\n", " 8.180380044483627e+00\n", " 6.914433869431377e+00\n", " 1.036531937626683e+01\n", " 8.910879932654098e+00\n", " 1.269853792506728e+01\n", " 1.125388372305633e+01\n", " 1.515062610995574e+01\n", " 1.396133001784347e+01\n", " 1.772508461469670e+01\n", " 1.702053357059049e+01\n", " 2.044373482139235e+01\n", " 2.040302761294288e+01\n", "\n" ] } ], "source": [ "err" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Octave", "language": "octave", "name": "octave" }, "language_info": { "file_extension": ".m", "help_links": [ { "text": "GNU Octave", "url": "https://www.gnu.org/software/octave/support.html" }, { "text": "Octave Kernel", "url": "https://github.com/Calysto/octave_kernel" }, { "text": "MetaKernel Magics", "url": "https://metakernel.readthedocs.io/en/latest/source/README.html" } ], "mimetype": "text/x-octave", "name": "octave", "version": "5.2.0" } }, "nbformat": 4, "nbformat_minor": 4 }

gpl-3.0

jackwluo/py-quantmod

dash_example_simple.ipynb

1

4585

{ "cells": [ { "cell_type": "markdown", "source": [ "# Dash example simple" ], "metadata": {} }, { "cell_type": "code", "source": [ "# Import required libraries\n", "\n", "import dash\n", "import dash_core_components as core\n", "import dash_html_components as html\n", "from dash.dependencies import Input, Output\n", "import quantmod as qm" ], "outputs": [], "execution_count": 1, "metadata": { "collapsed": false, "outputHidden": false, "inputHidden": false } }, { "cell_type": "code", "source": [ "# Create layout\n", "\n", "# Dash app instantiation\n", "app = dash.Dash(\"Stock market app\")\n", "\n", "# External CSS\n", "app.css.append_css({\n", " 'external_url': (\n", " 'https://rawgit.com/chriddyp/0247653a7c52feb4c48437e1c1837f75'\n", " '/raw/a68333b876edaf62df2efa7bac0e9b3613258851/dash.css'\n", " )\n", "})\n", "\n", "# Create app layout\n", "app.layout = html.Div(\n", " [\n", " html.H1('Quantmod Demo | 5-minute App'),\n", " # Dropdown for stocks\n", " core.Dropdown(\n", " id='dropdown',\n", " options=[\n", " dict(label='PowerShares QQQ Trust Series 1', value='QQQ'),\n", " dict(label='SPDR S&P 500 ETF Trust', value='SPY'),\n", " dict(label='Apple Inc', value='AAPL'),\n", " dict(label='Goldman Sachs Group Inc', value='GS'),\n", " ],\n", " value='SPY',\n", " ),\n", " # Dropdown for indicators\n", " core.Dropdown(\n", " id='multi',\n", " options=[\n", " dict(label='EMA', value='EMA'),\n", " dict(label='RSI', value='RSI'),\n", " dict(label='MACD', value='MACD'),\n", " dict(label='BBANDS', value='BBANDS'),\n", " ],\n", " multi=True,\n", " value=[],\n", " ),\n", " # Graph output\n", " core.Graph(id='output')\n", " ]\n", ")" ], "outputs": [], "execution_count": null, "metadata": { "collapsed": false, "outputHidden": false, "inputHidden": false } }, { "cell_type": "code", "source": [ "# Setup callbacks\n", "\n", "# Graph is modified by 2 inputs\n", "@app.callback(Output('output', 'figure'), [Input('dropdown', 'value'),\n", " Input('multi', 'value')])\n", "def update_graph_from_dropdown(dropdown, multi):\n", " # Get Quantmod Chart\n", " ch = qm.get_symbol(dropdown, start='2016/01/01')\n", "\n", " if 'EMA' in multi:\n", " ch.add_EMA()\n", " if 'RSI' in multi:\n", " ch.add_RSI()\n", " if 'MACD' in multi:\n", " ch.add_MACD()\n", " if 'BBANDS' in multi:\n", " ch.add_BBANDS()\n", "\n", " # Return plot as figure\n", " return ch.to_figure()" ], "outputs": [], "execution_count": null, "metadata": { "collapsed": false, "outputHidden": false, "inputHidden": false } }, { "cell_type": "code", "source": [ "if __name__ == '__main__':\n", " app.run_server(debug=True, port=4001)" ], "outputs": [], "execution_count": null, "metadata": { "collapsed": false, "outputHidden": false, "inputHidden": false } } ], "metadata": { "kernelspec": { "name": "python3", "language": "python", "display_name": "Python 3" }, "kernel_info": { "name": "python3" }, "language_info": { "name": "python", "version": "3.6.0", "mimetype": "text/x-python", "codemirror_mode": { "name": "ipython", "version": 3 }, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

gVallverdu/cookbook

list_vs_numpy.ipynb

1

14515

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# List vs numpy array\n", "\n", "##### Germain Salvato Vallverdu\n", "---" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import math as m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utilisation d'une fonction" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def np_func(x):\n", " return (3 * x ** 2 + 2 * x - 1) * np.exp(- x / 2.3) * np.sin(2 * x)\n", "\n", "def m_func(x):\n", " return (3 * x ** 2 + 2 * x - 1) * m.exp(- x / 2.3) * m.sin(2 * x)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "x = np.linspace(0, 10, 1000)\n", "xl = x.tolist()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "37.7 µs ± 815 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" ] } ], "source": [ "%timeit y = np_func(x)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "642 µs ± 6.67 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "%%timeit\n", "y = list()\n", "for xi in xl:\n", " yi = m_func(xi)\n", " y.append(yi)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "17.02917771883289" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "642 / 37.7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Produit scalaire\n", "\n", "Ou produit de matrices ou matrices-vecteurs ou opération sur des vecteurs (sommes, produit) en général." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "x = np.random.random(1000)\n", "y = np.random.random(1000)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.38 µs ± 18.5 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n" ] } ], "source": [ "%timeit np.dot(x, y)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "xl = x.tolist()\n", "yl = y.tolist()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "68.3 µs ± 719 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" ] } ], "source": [ "%%timeit\n", "scal = 0\n", "for xi, yi in zip(xl, yl):\n", " scal += xi * yi" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "49.492753623188406" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "68.3 / 1.38" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Centre de gravité" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "coords = np.random.uniform(0, 30, size=(100, 3))\n", "weight = np.random.random(100)\n", "lcoords = coords.tolist()\n", "lweight = weight.tolist()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.4 µs ± 140 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n" ] } ], "source": [ "%%timeit\n", "w_coords = coords * weight[:, np.newaxis]\n", "G = w_coords.sum(axis=0) / len(w_coords)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[7.81686019 8.63915013 7.2122051 ]\n" ] } ], "source": [ "w_coords = coords * weight[:, np.newaxis]\n", "G = w_coords.sum(axis=0) / len(w_coords)\n", "print(G)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "106 µs ± 3.02 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" ] } ], "source": [ "%%timeit\n", "G = [0, 0, 0]\n", "for w_i, coords_i in zip(lweight, lcoords):\n", " w_coords_i = [w_i * xi for xi in coords_i]\n", " for i in range(3):\n", " G[i] += w_coords_i[i]\n", "\n", "nat = len(lweight)\n", "for i in range(3):\n", " G[i] /= nat" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10.288461538461538" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "107 / 10.4" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[7.816860188194455, 8.639150131333633, 7.2122051018216045]\n" ] } ], "source": [ "G = [0, 0, 0]\n", "for w_i, coords_i in zip(lweight, lcoords):\n", " w_coords_i = [w_i * xi for xi in coords_i]\n", " for i in range(3):\n", " G[i] += w_coords_i[i]\n", "\n", "nat = len(lweight)\n", "for i in range(3):\n", " G[i] /= nat\n", "\n", "print(G)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Matrice de distances\n", "\n", "Un peu plus sioux ... calculer toutes les distances entre atomes. En ajoutant un axe dans le tableau numpy il fait le calcul de toutes les opérations." ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "coords = np.random.uniform(0, 30, (5, 3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Je découpe un peu l'ajout des axes. L'idée c'est d'avoir quelque chose de 5 x 5 à la fin (la matrice)." ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5, 1, 3)\n", "[[[23.40646835 18.6866633 2.21638428]]\n", "\n", " [[ 4.66508894 1.69156122 15.23312689]]\n", "\n", " [[14.324572 9.34691585 20.53275304]]\n", "\n", " [[18.0876806 19.05955999 28.26381997]]\n", "\n", " [[10.03703844 21.41928076 6.58185643]]]\n" ] } ], "source": [ "print(coords[:, np.newaxis, :].shape)\n", "print(coords[:, np.newaxis, :])" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1, 5, 3)\n", "[[[23.40646835 18.6866633 2.21638428]\n", " [ 4.66508894 1.69156122 15.23312689]\n", " [14.324572 9.34691585 20.53275304]\n", " [18.0876806 19.05955999 28.26381997]\n", " [10.03703844 21.41928076 6.58185643]]]\n" ] } ], "source": [ "print(coords[np.newaxis, :, :].shape)\n", "print(coords[np.newaxis, :, :])" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5, 5, 3)\n", "[[[ 0. 0. 0. ]\n", " [ 18.74137941 16.99510209 -13.01674261]\n", " [ 9.08189635 9.33974745 -18.31636877]\n", " [ 5.31878775 -0.37289669 -26.04743569]\n", " [ 13.36942991 -2.73261746 -4.36547215]]\n", "\n", " [[-18.74137941 -16.99510209 13.01674261]\n", " [ 0. 0. 0. ]\n", " [ -9.65948306 -7.65535464 -5.29962616]\n", " [-13.42259166 -17.36799878 -13.03069308]\n", " [ -5.3719495 -19.72771955 8.65127046]]\n", "\n", " [[ -9.08189635 -9.33974745 18.31636877]\n", " [ 9.65948306 7.65535464 5.29962616]\n", " [ 0. 0. 0. ]\n", " [ -3.76310861 -9.71264414 -7.73106693]\n", " [ 4.28753355 -12.07236491 13.95089662]]\n", "\n", " [[ -5.31878775 0.37289669 26.04743569]\n", " [ 13.42259166 17.36799878 13.03069308]\n", " [ 3.76310861 9.71264414 7.73106693]\n", " [ 0. 0. 0. ]\n", " [ 8.05064216 -2.35972077 21.68196354]]\n", "\n", " [[-13.36942991 2.73261746 4.36547215]\n", " [ 5.3719495 19.72771955 -8.65127046]\n", " [ -4.28753355 12.07236491 -13.95089662]\n", " [ -8.05064216 2.35972077 -21.68196354]\n", " [ 0. 0. 0. ]]]\n" ] } ], "source": [ "rij = coords[:, np.newaxis, :] - coords[np.newaxis, :, :]\n", "print(rij.shape)\n", "print(rij)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Du coup le calcul des distances avec numpy ça donne :" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. 28.45186084 22.47667877 26.58754335 14.3271142 ]\n", " [28.45186084 0. 13.4162627 25.526698 22.20101891]\n", " [22.47667877 13.4162627 0. 12.97173228 18.94076173]\n", " [26.58754335 25.526698 12.97173228 0. 23.24841208]\n", " [14.3271142 22.20101891 18.94076173 23.24841208 0. ]]\n" ] } ], "source": [ "rij2 = (coords[:, np.newaxis, :] - coords[np.newaxis, :, :]) ** 2\n", "d = np.sum(rij2, axis=2) ** 0.5\n", "print(d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Avec plus d'atomes" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "coords = np.random.uniform(0, 30, (100, 3))\n", "lcoords = coords.tolist()" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "378 µs ± 30.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "%%timeit\n", "rij2 = (coords[:, np.newaxis, :] - coords[np.newaxis, :, :]) ** 2\n", "d = np.sum(rij2, axis=2) ** 0.5" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7.94 ms ± 137 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%%timeit\n", "nat = len(lcoords)\n", "distances = [[0. for iat in range(nat)] for jat in range(nat)]\n", "for iat in range(nat):\n", " for jat in range(iat + 1, nat):\n", " ix = lcoords[iat]\n", " jx = lcoords[jat]\n", " rij2 = [(ix[i] - jx[i]) **2 for i in range(3)]\n", " d2 = sum(rij2)\n", " distances[iat][jat] = m.sqrt(d2)\n", " distances[jat][iat] = distances[iat][jat]" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "21.005291005291006" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "7940 / 378" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tu noteras qu'en plus, dans ce cas, l'implémentation numpy fait le double de calculs que l'implémentation standard... Dans le calcul numpy tu calcules la distance i-j et j-i alors que tu évites de faire ça dans l'implementation standard." ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "14.6 ms ± 52.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%%timeit\n", "# en calculant toutes les distances\n", "nat = len(lcoords)\n", "distances = [[0. for iat in range(nat)] for jat in range(nat)]\n", "for iat in range(nat):\n", " for jat in range(nat):\n", " ix = lcoords[iat]\n", " jx = lcoords[jat]\n", " rij2 = [(ix[i] - jx[i]) **2 for i in range(3)]\n", " d2 = sum(rij2)\n", " distances[iat][jat] = m.sqrt(d2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "C'est 2 fois plus long ... normal tu as le double de calcul. Du coup numpy est environ 40 fois plus rapide." ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "38.62433862433863" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "14.6e3 / 378" ] } ], "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": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }

gpl-2.0

luwei0917/awsemmd_script

notebook/Optimization/understand_frag_memory_generation.ipynb

1

29311

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# indexPdb.py\n", "# ----------------------------------------------------------------------\n", "# Copyright (2010) Aram Davtyan and Garegin Papoian\n", "#\n", "# Papoian's Group, University of Maryland at Collage Park\n", "# http://papoian.chem.umd.edu/\n", "#\n", "# Last Update: 07/18/2011\n", "# -------------------------------------------------------------------------\n", "\n", "#import sys\n", "\n", "#if len(sys.argv)!=5:\n", "#\tprint\n", "#\tprint \" > \" + sys.argv[0] + \" fasta.file pdb_file index_file chain_id\"\n", "#\tprint\n", "#\texit()\n", "\n", "from Bio import pairwise2\n", "from Bio.PDB.PDBParser import PDBParser\n", "from Bio import SeqIO\n", "\n", "def three2one(prot):\n", " \"\"\" translate a protein sequence from 3 to 1 letter code\"\"\"\n", "\n", " code = {\"GLY\" : \"G\", \"ALA\" : \"A\", \"LEU\" : \"L\", \"ILE\" : \"I\",\n", " \"ARG\" : \"R\", \"LYS\" : \"K\", \"MET\" : \"M\", \"CYS\" : \"C\",\n", " \"TYR\" : \"Y\", \"THR\" : \"T\", \"PRO\" : \"P\", \"SER\" : \"S\",\n", " \"TRP\" : \"W\", \"ASP\" : \"D\", \"GLU\" : \"E\", \"ASN\" : \"N\",\n", " \"GLN\" : \"Q\", \"PHE\" : \"F\", \"HIS\" : \"H\", \"VAL\" : \"V\",\n", " \"M3L\" : \"K\", \"MSE\" : \"M\", \"CAS\" : \"C\" }\n", "\n", " newprot = \"\"\n", " for a in prot:\n", " newprot += code.get(a, \"X\")\n", "\n", " return newprot\n", "\n", "def getListOfValidAlignments(alignments, pdb_indexes):\n", " ialigns = []\n", " for i in range(0, len(alignments)):\n", " alignment = alignments[i]\n", " alseq_fasta = alignment[0]\n", " alseq_pdb = alignment[1]\n", "\n", " if len(alseq_fasta)!=len(alseq_pdb):\n", " print(\"Error using alignment too\")\n", " print()\n", " exit()\n", "\n", " last_i_pdb = -1\n", " last_j = -1\n", " i_pdb = 0\n", " valid = True\n", " for j in range(0, len(alseq_pdb)):\n", " if alseq_pdb[j]!='-':\n", " # if last_i_pdb!=-1 and j - last_j > i_pdb - last_i_pdb:\n", " if last_i_pdb!=-1 and j - last_j != pdb_indexes[i_pdb] - pdb_indexes[last_i_pdb]:\n", " valid = False\n", " break\n", " last_i_pdb = i_pdb\n", " last_j = j\n", " i_pdb = i_pdb + 1\n", " if valid:\n", " ialigns.append(i)\n", " return ialigns\n", "\n", "def getFastaSequance(fasta_file):\n", " inFASTA=open(fasta_file, 'r')\n", " inseq=SeqIO.read(inFASTA,'fasta')\n", "\n", " return str(inseq.seq)\n", "\n", "def getPdbSequance(pdb_file, chain_id):\n", " pdb_indexes = []\n", " pdb_sequance = []\n", "\n", " p = PDBParser(PERMISSIVE=1)\n", " s = p.get_structure(\"\", pdb_file)\n", " pdb_id = pdb_file[0:-4]\n", "\n", " if not s[0].has_id(chain_id):\n", " print(\"PDB \"+pdb_id+\" doesn't have chain with id \"+chain_id)\n", " print()\n", " exit()\n", "\n", " chain = s[0][chain_id]\n", "\n", " ires = 0\n", " for res in chain:\n", " is_regular_res = res.has_id('N') and res.has_id('CA') and res.has_id('C') and (res.get_resname()=='GLY' or res.has_id('CB'))\n", " res_id = res.get_id()[0]\n", " if (res_id ==' ' or res_id =='H_MSE' or res_id =='H_M3L' or res_id =='H_CAS') and is_regular_res:\n", " ires = ires + 1\n", " res_name = res.get_resname()\n", " residue_no = res.get_id()[1]\n", " pdb_sequance.append(res_name)\n", " pdb_indexes.append(residue_no)\n", " elif res_id !='W':\n", " print(\"Unknown residue in \"+pdb_id+\" with res_id \"+res_id)\n", "\n", " pdb_seq = three2one(pdb_sequance)\n", "\n", " return pdb_seq, pdb_indexes\n", "\n", "def getIndexArray(alignment, pdb_indexes):\n", " alseq_fasta = alignment[0]\n", " alseq_pdb = alignment[1]\n", "\n", " index_array = []\n", " i_fasta = 0\n", " i_pdb = 0\n", " for i in range(0, len(alseq_fasta)):\n", " if alseq_fasta[i]!='-':\n", " index_pdb = -1\n", " if alseq_pdb[i]!='-': index_pdb = pdb_indexes[i_pdb]\n", " index_array.append([i_fasta, index_pdb, alseq_fasta[i]])\n", " i_fasta = i_fasta + 1\n", " if alseq_pdb[i]!='-':\n", " i_pdb = i_pdb + 1\n", "\n", " return index_array\n", "\n", "def writeIndexFile(fasta_file, pdb_file, index_file, chain_id):\n", "#\tfrom Bio import pairwise2\n", "#\tfrom Bio.PDB.PDBParser import PDBParser\n", "#\tfrom Bio import SeqIO\n", "\n", " pdb_id = pdb_file[0:-4]\n", "\n", "#fasta_file = sys.argv[1]\n", "#pdb_id = sys.argv[2]\n", "#pdb_file = pdb_id+\".pdb\"\n", "#index_file = sys.argv[3]\n", "#chain_id = sys.argv[4]\n", "\n", " fasta_seq = \"\"\n", " pdb_seq = \"\"\n", " pdb_indexes = []\n", " answer = \"\"\n", " shift = 0\n", " index_list = []\n", "\n", " fasta_seq = getFastaSequance(fasta_file)\n", " pdb_seq, pdb_indexes = getPdbSequance(pdb_file, chain_id)\n", "\n", " print()\n", " print(fasta_seq)\n", " print(len(fasta_seq))\n", " print(pdb_seq)\n", " print(pdb_indexes)\n", " print(len(pdb_seq))\n", "\n", " print()\n", " print()\n", "\n", " if len(fasta_seq)==len(pdb_seq) and pdb_indexes[0]==1 and pdb_indexes[-1]==len(pdb_seq):\n", " print(\"FULLMATCH\")\n", " answer = \"FULLMATCH\"\n", " elif len(fasta_seq)==len(pdb_seq) and pdb_indexes[-1]-pdb_indexes[0]+1==len(pdb_seq):\n", " print(\"Indexes are simply shifted by \" + str(pdb_indexes[0]-1))\n", " answer = \"SHIFT\"\n", " shift = pdb_indexes[0]-1\n", " elif len(fasta_seq)==len(pdb_seq) and fasta_seq==pdb_seq:\n", " print(\"Number is messed up\")\n", " print(\"Same length\")\n", " answer = \"INDEXED\"\n", " for i in range(0, len(fasta_seq)):\n", " if i!=0 and pdb_indexes[i]<= pdb_indexes[i-1]:\n", " answer = \"SKIP\"\n", " index_list = []\n", " break\n", " index_list.append([ i, pdb_indexes[i], fasta_seq[i] ])\n", " else:\n", " alignments = pairwise2.align.globalms(fasta_seq, pdb_seq, 2, -1, -0.5, -0.1)\n", " #print alignments\n", " #print len(alignments)\n", " #print\n", "\n", " alist = getListOfValidAlignments(alignments, pdb_indexes)\n", " #print len(alist), alist\n", "\n", " if len(alist)==1:\n", " answer = \"INDEXED\"\n", " index_list = getIndexArray(alignments[alist[0]], pdb_indexes)\n", " elif len(alist)>1:\n", " answer = \"SKIP\"\n", " elif len(alist)==0:\n", " answer = \"SKIP\"\n", " #alignments = pairwise2.align.globalxx(fasta_seq, pdb_seq)\n", " #print\n", " #print alignments\n", " #print len(alignments)\n", " #print\n", "\n", " #alist2 = getListOfValidAlignments(alignments, pdb_indexes)\n", " #print len(alist2), alist2\n", "\n", " #if len(alist2)==1:\n", " #\tanswer = \"INDEXED\"\n", " #\tindex_list = getIndexArray(alignments[alist2[0]], pdb_indexes)\n", " #elif len(alist2)>1:\n", " #\tanswer = \"SKIP\"\n", " #elif len(alist2)==0:\n", " #\tanswer = \"SKIP\"\n", "\n", " out = open(index_file, 'w')\n", " out.write(answer)\n", " if answer==\"SHIFT\":\n", " out.write(\"\\n\")\n", " out.write(str(shift))\n", " elif answer==\"INDEXED\":\n", " for ind in index_list:\n", " out.write(\"\\n\")\n", " out.write(str(ind[0]+1))\n", " out.write(\" \")\n", " out.write(str(ind[1]))\n", " out.write(\" \")\n", " out.write(ind[2])\n", " out.close()\n", "\n", "\n", "#fasta_file = sys.argv[1]\n", "#pdb_id = sys.argv[2]\n", "#pdb_file = pdb_id+\".pdb\"\n", "#index_file = sys.argv[3]\n", "#chain_id = sys.argv[4]\n", "\n", "#writeIndexFile(fasta_file, pdb_file, index_file, chain_id)\n" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "import os" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/Users/weilu/Research/server/jun_2019/simluation_hybrid/frag_database/6e67A_HA/globularPart'" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.path.dirname(fasta_file)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Unknown residue in /Users/weilu/openmmawsem/PDBs/5CXV with res_id H_0HK\n", "Unknown residue in /Users/weilu/openmmawsem/PDBs/5CXV with res_id H_Y01\n", "Unknown residue in /Users/weilu/openmmawsem/PDBs/5CXV with res_id H_EDO\n", "Unknown residue in /Users/weilu/openmmawsem/PDBs/5CXV with res_id H_PGE\n", "Unknown residue in /Users/weilu/openmmawsem/PDBs/5CXV with res_id H_EDO\n", "Unknown residue in /Users/weilu/openmmawsem/PDBs/5CXV with res_id H_GOL\n", "Unknown residue in /Users/weilu/openmmawsem/PDBs/5CXV with res_id H_EDO\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/weilu/anaconda3/envs/py36/lib/python3.6/site-packages/Bio/PDB/StructureBuilder.py:91: PDBConstructionWarning: WARNING: Chain A is discontinuous at line 4123.\n", " PDBConstructionWarning)\n", "/Users/weilu/anaconda3/envs/py36/lib/python3.6/site-packages/Bio/PDB/StructureBuilder.py:91: PDBConstructionWarning: WARNING: Chain C is discontinuous at line 4218.\n", " PDBConstructionWarning)\n" ] } ], "source": [ "fasta_file = \"/Users/weilu/Research/server/jun_2019/simluation_hybrid/frag_database/6e67A_HA/globularPart/5cxva.fasta\"\n", "pdb_file = \"/Users/weilu/openmmawsem/PDBs/5CXV.pdb\"\n", "# pdb_file = \"/Users/weilu/Research/server/jun_2019/simluation_hybrid/frag_database/6e67A_HA/globularPart/5cxv.pdb\"\n", "index_file = \"/Users/weilu/openmmawsem/Indices/5cxva.index\"\n", "chain_id = \"A\"\n", "\n", "fasta_seq = \"\"\n", "pdb_seq = \"\"\n", "pdb_indexes = []\n", "answer = \"\"\n", "shift = 0\n", "index_list = []\n", "\n", "fasta_seq = getFastaSequance(fasta_file)\n", "pdb_seq, pdb_indexes = getPdbSequance(pdb_file, chain_id)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1160 355 need reorder\n" ] } ], "source": [ "pre = pdb_indexes[0]\n", "for i in pdb_indexes[1:]:\n", " if i < pre:\n", " print(pre, i, \"need reorder\")\n", " break\n", " pre = i" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[20,\n", " 21,\n", " 22,\n", " 23,\n", " 24,\n", " 25,\n", " 26,\n", " 27,\n", " 28,\n", " 29,\n", " 30,\n", " 31,\n", " 32,\n", " 33,\n", " 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364,\n", " 365,\n", " 366,\n", " 367,\n", " 368,\n", " 369,\n", " 370,\n", " 371,\n", " 372,\n", " 373,\n", " 374,\n", " 375,\n", " 376,\n", " 377,\n", " 378,\n", " 379,\n", " 380,\n", " 381,\n", " 382,\n", " 383,\n", " 384,\n", " 385,\n", " 386,\n", " 387,\n", " 388,\n", " 389,\n", " 390,\n", " 391,\n", " 392,\n", " 393,\n", " 394,\n", " 395,\n", " 396,\n", " 397,\n", " 398,\n", " 399,\n", " 400,\n", " 401,\n", " 402,\n", " 403,\n", " 404,\n", " 405,\n", " 406,\n", " 407,\n", " 408,\n", " 409,\n", " 410,\n", " 411,\n", " 412,\n", " 413,\n", " 414,\n", " 415,\n", " 416,\n", " 417,\n", " 418,\n", " 419,\n", " 420,\n", " 421,\n", " 422,\n", " 423,\n", " 424,\n", " 425,\n", " 426,\n", " 427,\n", " 428,\n", " 429,\n", " 430,\n", " 431,\n", " 432,\n", " 433,\n", " 434,\n", " 435,\n", " 436,\n", " 437,\n", " 438,\n", " 439]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pdb_indexes" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "MKTIIALSYIFCLVFADYKDDDDAAAQTSAPPAVSPQITVLAPGKGPWQVAFIGITTGLLSLATVTGNLLVLISFKVNTELKTVNNYFLLSLACADLIIGTFSMNLYTTYLLMGHWALGTLACDLWLALDYVASQASVMNLLLISFDRYFSVTRPLSYRAKRTPRRAALMIGLAWLVSFVLWAPAILFWQYLVGERTVLAGQCYIQFLSQPIITFGTAMAAFYLPVTVMCTLYWRIYRETENRNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSELDKAIGRNTNGVITKDEAEKLFNQDVDAAVRGILRNAKLKPVYDSLDAVRRAALINMVFQMGETGVAGFTNSLRMLQQKRWDEAAVNLAKSRWYNQTPNRAKRVITTFRTGTWDAYFSLVKEKKAARTLSAILLAFILTWTPYNIMVLVSTFCKDCVPETLWELGYWLCYVNSTINPMCYALCNKAFRDTFRLLLLCRWDKRRWRKIPKRPGSVHRTPSRQCHHHHHH\n", "515\n", "KGPWQVAFIGITTGLLSLATVTGNLLVLISFKVNTELKTVNNYFLLSLACADLIIGTFSMNLYTTYLLMGHWALGTLACDLWLALDYVASQASVMNLLLISFDRYFSVTRPLSYRAKRTPRRAALMIGLAWLVSFVLWAPAILFWQYLVGERTVLAGQCYIQFLSQPIITFGTAMAAFYLPVTVMCTLYWRIYRETENRNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSELDKAIGRNTNGVITKDEAEKLFNQDVDAAVRGILRNAKLKPVYDSLDAVRRAALINMVFQMGETGVAGFTNSLRMLQQKRWDEAAVNLAKSRWYNQTPNRAKRVITTFRTGTWDAYFSLVKEKKAARTLSAILLAFILTWTPYNIMVLVSTFCKDCVPETLWELGYWLCYVNSTINPMCYALCNKAFRDTFRLLLLCRWDK\n", "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444]\n", "444\n", "\n", "\n" ] } ], "source": [ "\n", "print()\n", "print(fasta_seq)\n", "print(len(fasta_seq))\n", "print(pdb_seq)\n", "print(pdb_indexes)\n", "print(len(pdb_seq))\n", "\n", "print()\n", "print()\n", "\n", "if len(fasta_seq)==len(pdb_seq) and pdb_indexes[0]==1 and pdb_indexes[-1]==len(pdb_seq):\n", " print(\"FULLMATCH\")\n", " answer = \"FULLMATCH\"\n", "elif len(fasta_seq)==len(pdb_seq) and pdb_indexes[-1]-pdb_indexes[0]+1==len(pdb_seq):\n", " print(\"Indexes are simply shifted by \" + str(pdb_indexes[0]-1))\n", " answer = \"SHIFT\"\n", " shift = pdb_indexes[0]-1\n", "elif len(fasta_seq)==len(pdb_seq) and fasta_seq==pdb_seq:\n", " print(\"Number is messed up\")\n", " print(\"Same length\")\n", " answer = \"INDEXED\"\n", " for i in range(0, len(fasta_seq)):\n", " if i!=0 and pdb_indexes[i]<= pdb_indexes[i-1]:\n", " answer = \"SKIP\"\n", " index_list = []\n", " break\n", " index_list.append([ i, pdb_indexes[i], fasta_seq[i] ])\n", "else:\n", " alignments = pairwise2.align.globalms(fasta_seq, pdb_seq, 2, -1, -0.5, -0.1)\n", " #print alignments\n", " #print len(alignments)\n", " #print\n", "\n", " alist = getListOfValidAlignments(alignments, pdb_indexes)\n", " #print len(alist), alist\n", "\n", " if len(alist)==1:\n", " answer = \"INDEXED\"\n", " index_list = getIndexArray(alignments[alist[0]], pdb_indexes)\n", " elif len(alist)>1:\n", " answer = \"SKIP\"\n", " elif len(alist)==0:\n", " answer = \"SKIP\"" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[('MKTIIALSYIFCLVFADYKDDDDAAAQTSAPPAVSPQITVLAPGKGPWQVAFIGITTGLLSLATVTGNLLVLISFKVNTELKTVNNYFLLSLACADLIIGTFSMNLYTTYLLMGHWALGTLACDLWLALDYVASQASVMNLLLISFDRYFSVTRPLSYRAKRTPRRAALMIGLAWLVSFVLWAPAILFWQYLVGERTVLAGQCYIQFLSQPIITFGTAMAAFYLPVTVMCTLYWRIYRETENRNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSELDKAIGRNTNGVITKDEAEKLFNQDVDAAVRGILRNAKLKPVYDSLDAVRRAALINMVFQMGETGVAGFTNSLRMLQQKRWDEAAVNLAKSRWYNQTPNRAKRVITTFRTGTWDAYFSLVKEKKAARTLSAILLAFILTWTPYNIMVLVSTFCKDCVPETLWELGYWLCYVNSTINPMCYALCNKAFRDTFRLLLLCRWDKRRWRKIPKRPGSVHRTPSRQCHHHHHH',\n", " '--------------------------------------------KGPWQVAFIGITTGLLSLATVTGNLLVLISFKVNTELKTVNNYFLLSLACADLIIGTFSMNLYTTYLLMGHWALGTLACDLWLALDYVASQASVMNLLLISFDRYFSVTRPLSYRAKRTPRRAALMIGLAWLVSFVLWAPAILFWQYLVGERTVLAGQCYIQFLSQPIITFGTAMAAFYLPVTVMCTLYWRIYRETENRNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSELDKAIGRNTNGVITKDEAEKLFNQDVDAAVRGILRNAKLKPVYDSLDAVRRAALINMVFQMGETGVAGFTNSLRMLQQKRWDEAAVNLAKSRWYNQTPNRAKRVITTFRTGTWDAYFSLVKEKKAARTLSAILLAFILTWTPYNIMVLVSTFCKDCVPETLWELGYWLCYVNSTINPMCYALCNKAFRDTFRLLLLCRWDK---------------------------',\n", " 880.0999999999995,\n", " 0,\n", " 515)]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alignments" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "alist = getListOfValidAlignments(alignments, pdb_indexes)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[0]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alist" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "ialigns = []\n", "i =0\n", "alignment = alignments[i]\n", "alseq_fasta = alignment[0]\n", "alseq_pdb = alignment[1]\n", "\n", "if len(alseq_fasta)!=len(alseq_pdb):\n", " print(\"Error using alignment too\")\n", " print()\n", " exit()\n", "\n", "last_i_pdb = -1\n", "last_j = -1\n", "i_pdb = 0\n", "valid = True\n", "for j in range(0, len(alseq_pdb)):\n", " if alseq_pdb[j]!='-':\n", "# if last_i_pdb!=-1 and j - last_j > i_pdb - last_i_pdb:\n", " if last_i_pdb!=-1 and j - last_j != pdb_indexes[i_pdb] - pdb_indexes[last_i_pdb]:\n", " print(last_i_pdb, j, last_j, i_pdb, last_i_pdb, pdb_indexes[i_pdb], pdb_indexes[last_i_pdb])\n", " valid = False\n", " break\n", " last_i_pdb = i_pdb\n", " last_j = j\n", " i_pdb = i_pdb + 1\n", "if valid:\n", " ialigns.append(i)\n", " \n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

vguarnaccia/jupyter-notebook-slides

slides.ipynb

2

9935

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Your Privacy" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Why Should I care?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* In the US, you do not own your own person information.\n", "* Companies take track you without much consent." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* Companies can profit by selling your data, or by subjecting you to dynamic pricing.\n", "* Companies routinely have security breaks and your private information, including name, address, phone numbers, SSN, and browser history, can be leaked." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Dynamic Princing\n", "[Airlines increase flight prices hundreds of dollars when you look for flights](https://www.theguardian.com/commentisfree/2016/dec/06/cookie-monsters-why-your-browsing-history-could-mean-rip-off-prices)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "![Airline price increases $144](https://pbs.twimg.com/media/Cy2tmNqXgAAh-09.jpg:large \"Outbound Flights\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## What is PII?\n", "\n", "Personally Identifiable Information (PII) is defined by [NIST](http://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-122.pdf) as \n", "> (1) any information that can be used to distinguish or trace an individual's identity, such as name, social security number, date and place of birth, mother's maiden name, or biometric records\n", "\n", "There are many other examples of PII but those are the most commonly stolen." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Breaches\n", "* On September 7th, Equifax acknowledged that 143 million people may have had their Personally Identifiable Information leaked." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* Yahoo had two separate hacks, one in 2013, and another in 2014, in which 1 billion and 500 millions users were exposed." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* Equifax will likely not be fined for obvious security flaws.\n", "\n", "[Source](https://arstechnica.com/information-technology/2017/09/why-the-equifax-breach-is-very-possibly-the-worst-leak-of-personal-info-ever/)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Security Systems Fail\n", "\n", "* The number of breaches is steady increasing." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* Some other [big names](identityforce.com/blog/2016-data-breaches) have been hacked in 2016 as well:\n", " - U.S. Department of Justice\n", " - IRS\n", " - UC Berkeley\n", " - Premeir Healthcare\n", " - Version\n", " - Wendy's\n", " - LinkedIn\n", " - Dropbox\n", " - Cisco\n", " - Yahoo" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## The State of Things\n", "\n", "* Good security protocols exist but are not followed, even by large corporation, and the national government.\n", "* The most common operating system, Windows, is extremely vulnerable as we've seen in the May [WannaCry](https://en.wikipedia.org/wiki/WannaCry_ransomware_attack) Ransomeware atttack." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Two factor authentication, while not sexy enough to make it into the news, is incredibly powerful.\n", "> Hackers need something you know, like a password, **and** something you have, like your phone." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Put a lock on your phone! Consider disabling TouchID." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Long passphrases are more secure than short ones with mix case and special characters." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![xkcd password strength](https://imgs.xkcd.com/comics/password_strength.png \"To anyone who understands information theory and security and is in an infuriating argument with someone who does not (possibly involving mixed case), I sincerely apologize.\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Share Safely\n", "\n", "* **Never** send sensitive information by email.\n", "* Email is insecure and a copy of your message will remain indefinitely." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Surface Safely\n", "\n", "1. Never transfer sensitive information over `http`, only `https`.\n", "2. Look for the site information in your web browser:\n", " 1. Secure \n", " ![Lock](https://storage.googleapis.com/support-kms-prod/9IKkC4co6mWlM461wyAF94BiF9nMmvcfaqUl \"Lock\")\n", "\n", " 2. Info or Not secure \n", " ![Info](https://storage.googleapis.com/support-kms-prod/rzP3Kj6ct8WH1Ez2S5wV6HCXQVJZg4z0dppd \"Info\")\n", " \n", " 3. Not secure or Dangerous \n", " ![Dangerous](https://storage.googleapis.com/support-kms-prod/IFnvUSEwUHO4ppdhra3qLp1qTqnrZduuMwft \"Dangerous\")\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Do Not Believe in Incognito Mode\n", "[clickclickclick.click](https://clickclickclick.click/)\n", "\n", "[nothingprivate.ml](http://www.nothingprivate.ml/)\n", "\n", "[Who does Google think you are?](https://adssettings.google.com/authenticated)\n", "\n", "[What ads have you looked at?](https://myactivity.google.com/myactivity)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The last two links include ways to opt out of Google ad tracking." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Alternative Search\n", "\n", "Use a search engine that does not track your information, such as [duckduckgo.com](https://duckduckgo.com/)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Lock it Down!\n", "\n", "**Use a password manager such as LastPass or Dashlane!**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "*there is no excuse*" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Benefits\n", "\n", "* Each website has a unique password.\n", "* But you only have to remember one.\n", "* Easy to <abbr title=\"crypto-speak for change\">rotate</abbr> passwords.\n", "* Lastpass integrates well with two-factor authentication." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Send Safely\n", "\n", "* There comes a time in which we have to share sensitive information.\n", "* It might be sending a copy of your social security card to HR.\n", "* Or family photos via email." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* Some zip program, such as [7-zip](http://www.7-zip.org/), allow you to encryption your zip files with AES-256.\n", "* Just make sure not to send the password via email." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Recap:\n", "1. Password manager\n", "2. Two-factor authentication\n", "3. Think about tracking\n", "4. Always use `https`, consider Firefox.\n", "5. If you must, send PII in an encrypted zip file." ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

AntonelliLab/seqcap_processor

docs/notebook/.ipynb_checkpoints/main_doc-checkpoint.ipynb

1

7030

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SeCaPr - Sequence Capture Processor\n", "A computational pipeline for processing Illumina sequence capture data [](http://bioconda.github.io/recipes/secapr/README.html)\n", "\n", "***\n", "\n", "## Documentation\n", "The documentation, including an empirical data tutorial, is divided into the following steps (click on the links):\n", "- [Cleaning and adapter trimming](subdocs/cleaning_trimming.ipynb)\n", "- [De-novo contig assembly](subdocs/contig_assembly.ipynb)\n", "- [Extract target contigs](subdocs/extract_contigs.ipynb)\n", "- [Align contigs](subdocs/align_contigs.ipynb)\n", "- [Reference-based assembly](subdocs/reference_assembly.ipynb)\n", "- [Locus selection](subdocs/locus_selection.ipynb)\n", "- [Phasing alleles](subdocs/phasing.ipynb)\n", "- [Phylogeny estimation](subdocs/phylogeny_msc.ipynb)\n", "\n", "\n", "***\n", "\n", "## Installation & Setup\n", "SECAPR is available as a conda package on the bioconda channel. This makes installation very simple. Follow the instructions on this page to get the SECAPR pipeline set up and ready to use:\n", "\n", "<div class=\"alert alert-block alert-info\">\n", "**INFO:** Commands in blue boxes have to be executed from a bash-command line terminal.\n", "</div>\n", "\n", "\n", "### 1. Install conda\n", "\n", "Download the **Python2.7 version** of [Miniconda](https://conda.io/miniconda.html) and install it by executing the downloaded sh-file (see commands below). Conda is a software and environment manager, that makes installation of new software and of required dependencies very simple and straightforward.\n", "\n", "*Download conda (MacOS 64bit):*\n", "<div class=\"alert alert-block alert-info\">\n", "wget https://repo.continuum.io/miniconda/Miniconda2-latest-MacOSX-x86_64.sh\n", "</div>\n", "\n", "*Download conda (Linux 64bit):*\n", "<div class=\"alert alert-block alert-info\">\n", "wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh\n", "</div>\n", "\n", "*Download conda (Linux 32bit):*\n", "<div class=\"alert alert-block alert-info\">\n", "wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86.sh\n", "</div>\n", "\n", "*Install conda:*\n", "<div class=\"alert alert-block alert-info\">\n", "sh Miniconda2-latest-*.sh\n", "</div>\n", "\n", "*Add Bioconda channels (containing bioinformatics software):*\n", "<div class=\"alert alert-block alert-info\">\n", "conda config --add channels defaults; conda config --add channels conda-forge; conda config --add channels bioconda; conda config --add channels https://conda.anaconda.org/faircloth-lab \n", "</div>\n", "\n", "\n", "***\n", "\n", "### 2. Install the SECAPR environment\n", "Conda automatically downloads and installs all necessary software dependencies. We strongly recommend to **install SECAPR and all it's dependencies in a separate virtual environment**, in order to not interfer with potentially already installed verisons of the software dependencies.\n", "\n", "*Install SECAPR in virtual environment (here named `secapr_env`):*\n", "<div class=\"alert alert-block alert-info\">\n", "conda create -n secapr_env secapr\n", "</div>\n", "\n", "Alternatively you can also just plainly install the software on your computer (without creating an environment) by clicking on the icon below and following the instructions (**not recommended!**):\n", "\n", "[](http://bioconda.github.io/recipes/secapr/README.html)\n", "\n", "***\n", "\n", "### 3. Activate the environment\n", "To activate the newly created environment, type:\n", "\n", "*Activate environment*:\n", "<div class=\"alert alert-block alert-info\">\n", "source activate secapr_env\n", "</div>\n", "\n", "When the environment is activated, all the necessary software dependencies will be available in the standarad path, e.g. when you type `samtools` the samtools version required for SECAPR will be executed. After you are done using secapr, you can deactivate the environment to switch back to your standard environment with this command:\n", "\n", "*De-activate environment*:\n", "<div class=\"alert alert-block alert-info\">\n", "source deactivate\n", "</div>\n", "\n", "***\n", "\n", "### 4. Check active environment\n", "Check if you are connected to the correct environment (there should eb a star in front of secapr_env in the output of this command):\n", "\n", "*Check active environment*:\n", "<div class=\"alert alert-block alert-info\">\n", "conda info --envs\n", "</div>\n", "\n", "\n", "<div class=\"alert alert-block alert-warning\">IMPORTANT : When you are using the SECAPR pipeline, make sure the secapr_env is activated. Activate with **source activate secapr_env**\n", "</div>\n", "\n", "***\n", "\n", "### 5. Install SECAPR development version\n", "\n", "The development version of SECAPR is stored on this GitHub page and contains the newest updates, which might not yet be available through the conda version. However you need to install the SECAPR environment with conda first by following the steps above. Once the environment is installed, you can update SECAPR to the development version by following these steps:\n", "\n", "1. Connect to your secapr environment (`source activate secapr_env`)\n", "2. Remove the current secapr installation (`conda remove secapr`)\n", "3. Download the new version from github (`wget https://github.com/AntonelliLab/seqcap_processor/archive/master.zip`)\n", "4. Unzip the downloaded file (`unzip master.zip`)\n", "5. Move the unzipped directory to a safe location on your computer, i.e. not on your Desktop or Download folder, since this will be the path where secapr will be executed from in the future\n", "6. Enter the unzipped secapr directory (`cd seqcap_processor-master`)\n", "7. Install secapr from the folder (`python -m pip install -e .`)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

youssef-emad/shogun

doc/ipython-notebooks/intro/Introduction.ipynb

20

34014

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Machine Learning with Shogun" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "By Saurabh Mahindre - <a href=\"https://github.com/Saurabh7\">github.com/Saurabh7</a> as a part of <a href=\"http://www.google-melange.com/gsoc/project/details/google/gsoc2014/saurabh7/5750085036015616\">Google Summer of Code 2014 project</a> mentored by - Heiko Strathmann - <a href=\"https://github.com/karlnapf\">github.com/karlnapf</a> - <a href=\"http://herrstrathmann.de/\">herrstrathmann.de</a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook we will see how machine learning problems are generally represented and solved in Shogun. As a primer to Shogun's many capabilities, we will see how various types of data and its attributes are handled and also how prediction is done. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. [Introduction](#Introduction)\n", "2. [Using datasets](#Using-datasets)\n", "3. [Feature representations](#Feature-representations)\n", "4. [Labels](#Assigning-labels)\n", "5. [Preprocessing data](#Preprocessing-data)\n", "6. [Supervised Learning with Shogun's CMachine interface](#supervised)\n", "7. [Evaluating performance and Model selection](#Evaluating-performance-and-Model-selection)\n", "8. [Example: Regression](#More-predictions:-Regression)" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Machine learning concerns the construction and study of systems that can learn from data via exploiting certain types of structure within these. The uncovered patterns are then used to predict future data, or to perform other kinds of decision making. Two main classes (among others) of Machine Learning algorithms are: predictive or [supervised](http://en.wikipedia.org/wiki/Supervised_learning) learning and descriptive or [Unsupervised](http://en.wikipedia.org/wiki/Unsupervised_learning) learning. Shogun provides functionality to address those (and more) problem classes." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline\n", "%matplotlib inline\n", "#To import all Shogun classes\n", "from modshogun import *" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a general problem setting for the supervised learning approach, the goal is to learn a mapping from inputs $x_i\\in\\mathcal{X} $ to outputs $y_i \\in \\mathcal{Y}$, given a labeled set of input-output pairs $ \\mathcal{D} = {(x_i,y_i)}^{\\text N}_{i=1} $$\\subseteq \\mathcal{X} \\times \\mathcal{Y}$. Here $ \\mathcal{D}$ is called the training set, and $\\text N$ is the number of training examples. In the simplest setting, each training input $x_i$ is a $\\mathcal{D}$ -dimensional vector of numbers, representing, say, the height and weight of a person. These are called $\\textbf {features}$, attributes or covariates. In general, however, $x_i$ could be a complex structured object, such as an image.<ul><li>When the response variable $y_i$ is categorical and discrete, $y_i \\in$ {1,...,C} (say male or female) it is a [classification](http://en.wikipedia.org/wiki/Classification_in_machine_learning) problem.</li><li>When it is continuous (say the prices of houses) it is a [regression](http://en.wikipedia.org/wiki/Regression_analysis) problem.</li></ul>\n", "For the unsupervised learning\n", "approach we are only given inputs, $\\mathcal{D} = {(x_i)}^{\\text N}_{i=1}$ , and the goal is to find \u201cinteresting\n", "patterns\u201d in the data. " ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Using datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us consider an example, we have a dataset about various attributes of individuals and we know whether or not they are diabetic. The data reveals certain configurations of attributes that correspond to diabetic patients and others that correspond to non-diabetic patients. When given a set of attributes for a new patient, the goal is to predict whether the patient is diabetic or not. This type of learning problem falls under [Supervised learning](http://en.wikipedia.org/wiki/Supervised_learning), in particular, [classification](http://en.wikipedia.org/wiki/Classification_in_machine_learning)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Shogun provides the capability to load datasets of different formats using [CFile](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CFile.html).</br> A real world dataset: [Pima Indians Diabetes data set](http://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes) is used now. We load the `LibSVM` format file using Shogun's [LibSVMFile](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CLibSVMFile.html) class. The `LibSVM` format is: $$\\space \\text {label}\\space \\text{attribute1:value1 attribute2:value2 }...$$$$\\space.$$$$\\space .$$ LibSVM uses the so called \"sparse\" format where zero values do not need to be stored." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Load the file\n", "data_file=LibSVMFile('../../../data/uci/diabetes/diabetes_scale.svm')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This results in a [LibSVMFile](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CLibSVMFile.html) object which we will later use to access the data." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Feature representations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get off the mark, let us see how Shogun handles the attributes of the data using [CFeatures](http://www.shogun-toolbox.org/doc/en/current/classshogun_1_1CFeatures.html) class. Shogun supports wide range of feature representations. We believe it is a good idea to have different forms of data, rather than converting them all into matrices. Among these are: $\\hspace {20mm}$<ul><li>[String features](http://www.shogun-toolbox.org/doc/en/current/classshogun_1_1CStringFeatures.html): Implements a list of strings. Not limited to character strings, but could also be sequences of floating point numbers etc. Have varying dimensions. </li> <li>[Dense features](http://www.shogun-toolbox.org/doc/en/current/classshogun_1_1CDenseFeatures.html): Implements dense feature matrices</li> <li>[Sparse features](http://www.shogun-toolbox.org/doc/en/current/classshogun_1_1CSparseFeatures.html): Implements sparse matrices.</li><li>[Streaming features](http://shogun-toolbox.org/doc/en/latest/classshogun_1_1CStreamingFeatures.html): For algorithms working on data streams (which are too large to fit into memory) </li></ul> " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`SpareRealFeatures` (sparse features handling `64 bit float` type data) are used to get the data from the file. Since `LibSVM` format files have labels included in the file, `load_with_labels` method of `SpareRealFeatures` is used. In this case it is interesting to play with two attributes, Plasma glucose concentration and Body Mass Index (BMI) and try to learn something about their relationship with the disease. We get hold of the feature matrix using `get_full_feature_matrix` and row vectors 1 and 5 are extracted. These are the attributes we are interested in." ] }, { "cell_type": "code", "collapsed": false, "input": [ "f=SparseRealFeatures()\n", "trainlab=f.load_with_labels(data_file)\n", "mat=f.get_full_feature_matrix()\n", "\n", "#exatract 2 attributes\n", "glucose_conc=mat[1]\n", "BMI=mat[5]\n", "\n", "#generate a numpy array\n", "feats=array(glucose_conc)\n", "feats=vstack((feats, array(BMI)))\n", "print feats, feats.shape" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In numpy, this is a matrix of 2 row-vectors of dimension 768. However, in Shogun, this will be a matrix of 768 column vectors of dimension 2. This is beacuse each data sample is stored in a column-major fashion, meaning each column here corresponds to an individual sample and each row in it to an atribute like BMI, Glucose concentration etc. To convert the extracted matrix into Shogun format, `RealFeatures` are used which are nothing but the above mentioned [Dense features](http://www.shogun-toolbox.org/doc/en/current/classshogun_1_1CDenseFeatures.html) of `64bit Float` type. To do this call `RealFeatures` with the matrix (this should be a 64bit 2D numpy array) as the argument. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "#convert to shogun format\n", "feats_train=RealFeatures(feats)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some of the general methods you might find useful are:\n", "\n", "* `get_feature_matrix()`: The feature matrix can be accessed using this.\n", "* `get_num_features()`: The total number of attributes can be accesed using this.\n", "* `get_num_vectors()`: To get total number of samples in data.\n", "* `get_feature_vector()`: To get all the attribute values (A.K.A [feature vector](http://en.wikipedia.org/wiki/Feature_vector)) for a particular sample by passing the index of the sample as argument.</li></ul>" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Get number of features(attributes of data) and num of vectors(samples)\n", "feat_matrix=feats_train.get_feature_matrix()\n", "num_f=feats_train.get_num_features()\n", "num_s=feats_train.get_num_vectors()\n", "\n", "print('Number of attributes: %s and number of samples: %s' %(num_f, num_s))\n", "print('Number of rows of feature matrix: %s and number of columns: %s' %(feat_matrix.shape[0], feat_matrix.shape[1]))\n", "print('First column of feature matrix (Data for first individual):')\n", "print feats_train.get_feature_vector(0)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Assigning labels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In supervised learning problems, training data is labelled. Shogun provides various types of labels to do this through [Clabels](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CLabels.html). Some of these are:<ul><li>[Binary labels](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CBinaryLabels.html): Binary Labels for binary classification which can have values +1 or -1.</li><li>[Multiclass labels](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CMulticlassLabels.html): Multiclass Labels for multi-class classification which can have values from 0 to (num. of classes-1).</li><li>[Regression labels](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CRegressionLabels.html): Real-valued labels used for regression problems and are returned as output of classifiers.</li><li>[Structured labels](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CStructuredLabels.html): Class of the labels used in Structured Output (SO) problems</li></ul></br> In this particular problem, our data can be of two types: diabetic or non-diabetic, so we need [binary labels](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CBinaryLabels.html). This makes it a [Binary Classification problem](http://en.wikipedia.org/wiki/Binary_classification), where the data has to be classified in two groups." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#convert to shogun format labels\n", "labels=BinaryLabels(trainlab)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The labels can be accessed using `get_labels` and the confidence vector using `get_values`. The total number of labels is available using `get_num_labels`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "n=labels.get_num_labels()\n", "print 'Number of labels:', n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Preprocessing data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is usually better to preprocess data to a standard form rather than handling it in raw form. The reasons are having a well behaved-scaling, many algorithms assume centered data, and that sometimes one wants to de-noise data (with say PCA). Preprocessors do not change the domain of the input features. It is possible to do various type of preprocessing using methods provided by [CPreprocessor](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CPreprocessor.html) class. Some of these are:<ul><li>[Norm one](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CNormOne.html): Normalize vector to have norm 1.</li><li>[PruneVarSubMean](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CPruneVarSubMean.html): Substract the mean and remove features that have zero variance. </li><li>[Dimension Reduction](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CDimensionReductionPreprocessor.html): Lower the dimensionality of given simple features.<ul><li>[PCA](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CPCA.html): Principal component analysis.</li><li>[Kernel PCA](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKernelPCA.html): PCA using kernel methods.</li></ul></li></ul> The training data will now be preprocessed using [CPruneVarSubMean](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CPruneVarSubMean.html). This will basically remove data with zero variance and subtract the mean. Passing a `True` to the constructor makes the class normalise the varaince of the variables. It basically dividies every dimension through its standard-deviation. This is the reason behind removing dimensions with constant values. It is required to initialize the preprocessor by passing the feature object to `init` before doing anything else. The raw and processed data is now plotted." ] }, { "cell_type": "code", "collapsed": false, "input": [ "preproc=PruneVarSubMean(True)\n", "preproc.init(feats_train)\n", "feats_train.add_preprocessor(preproc)\n", "feats_train.apply_preprocessor()\n", "\n", "# Store preprocessed feature matrix.\n", "preproc_data=feats_train.get_feature_matrix()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot the raw training data.\n", "figure(figsize=(13,6))\n", "pl1=subplot(121)\n", "gray()\n", "_=scatter(feats[0, :], feats[1,:], c=labels, s=50)\n", "vlines(0, -1, 1, linestyle='solid', linewidths=2)\n", "hlines(0, -1, 1, linestyle='solid', linewidths=2)\n", "title(\"Raw Training Data\")\n", "_=xlabel('Plasma glucose concentration')\n", "_=ylabel('Body mass index')\n", "p1 = Rectangle((0, 0), 1, 1, fc=\"w\")\n", "p2 = Rectangle((0, 0), 1, 1, fc=\"k\")\n", "pl1.legend((p1, p2), [\"Non-diabetic\", \"Diabetic\"], loc=2)\n", "\n", "#Plot preprocessed data.\n", "pl2=subplot(122)\n", "_=scatter(preproc_data[0, :], preproc_data[1,:], c=labels, s=50)\n", "vlines(0, -5, 5, linestyle='solid', linewidths=2)\n", "hlines(0, -5, 5, linestyle='solid', linewidths=2)\n", "title(\"Training data after preprocessing\")\n", "_=xlabel('Plasma glucose concentration')\n", "_=ylabel('Body mass index')\n", "p1 = Rectangle((0, 0), 1, 1, fc=\"w\")\n", "p2 = Rectangle((0, 0), 1, 1, fc=\"k\")\n", "pl2.legend((p1, p2), [\"Non-diabetic\", \"Diabetic\"], loc=2)\n", "gray()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Horizontal and vertical lines passing through zero are included to make the processing of data clear. Note that the now processed data has zero mean." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "<a id='supervised'>Supervised Learning with Shogun's <a href='http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CMachine.html'>CMachine</a> interface</a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[CMachine](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CMachine.html) is Shogun's interface for general learning machines. Basically one has to ` train()` the machine on some training data to be able to learn from it. Then we `apply()` it to test data to get predictions. Some of these are: <ul><li>[Kernel machine](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKernelMachine.html): Kernel based learning tools.</li><li>[Linear machine](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CLinearMachine.html): Interface for all kinds of linear machines like classifiers.</li><li>[Distance machine](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CDistanceMachine.html): A distance machine is based on a a-priori choosen distance.</li><li>[Gaussian process machine](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CGaussianProcessMachine.html): A base class for Gaussian Processes. </li><li>And many more</li></ul>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Moving on to the prediction part, [Liblinear](http://www.shogun-toolbox.org/doc/en/current/classshogun_1_1CLibLinear.html), a linear SVM is used to do the classification (more on SVMs in [this notebook](http://www.shogun-toolbox.org/static/notebook/current/SupportVectorMachines.html)). A linear SVM will find a linear separation with the largest possible margin. Here C is a penalty parameter on the loss function. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "#prameters to svm\n", "C=0.9\n", "\n", "svm=LibLinear(C, feats_train, labels)\n", "svm.set_liblinear_solver_type(L2R_L2LOSS_SVC)\n", "\n", "#train\n", "svm.train()\n", "\n", "size=100\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now `apply` on test features to get predictions. For visualising the classification boundary, the whole XY is used as test data, i.e. we predict the class on every point in the grid." ] }, { "cell_type": "code", "collapsed": false, "input": [ "x1=linspace(-5.0, 5.0, size)\n", "x2=linspace(-5.0, 5.0, size)\n", "x, y=meshgrid(x1, x2)\n", "#Generate X-Y grid test data\n", "grid=RealFeatures(array((ravel(x), ravel(y))))\n", "\n", "#apply on test grid\n", "predictions = svm.apply(grid)\n", "#get output labels\n", "z=predictions.get_values().reshape((size, size))\n", "\n", "#plot\n", "jet()\n", "figure(figsize=(9,6))\n", "title(\"Classification\")\n", "c=pcolor(x, y, z)\n", "_=contour(x, y, z, linewidths=1, colors='black', hold=True)\n", "_=colorbar(c)\n", "\n", "_=scatter(preproc_data[0, :], preproc_data[1,:], c=trainlab, cmap=gray(), s=50)\n", "_=xlabel('Plasma glucose concentration')\n", "_=ylabel('Body mass index')\n", "p1 = Rectangle((0, 0), 1, 1, fc=\"w\")\n", "p2 = Rectangle((0, 0), 1, 1, fc=\"k\")\n", "legend((p1, p2), [\"Non-diabetic\", \"Diabetic\"], loc=2)\n", "gray()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us have a look at the weight vector of the separating hyperplane. It should tell us about the linear relationship between the features. The decision boundary is now plotted by solving for $\\bf{w}\\cdot\\bf{x}$ + $\\text{b}=0$. Here $\\text b$ is a bias term which allows the linear function to be offset from the origin of the used coordinate system. Methods `get_w()` and `get_bias()` are used to get the necessary values." ] }, { "cell_type": "code", "collapsed": false, "input": [ "w=svm.get_w()\n", "b=svm.get_bias()\n", "\n", "x1=linspace(-2.0, 3.0, 100)\n", "\n", "#solve for w.x+b=0\n", "def solve (x1):\n", " return -( ( (w[0])*x1 + b )/w[1] )\n", "x2=map(solve, x1)\n", "\n", "#plot\n", "figure(figsize=(7,6))\n", "plot(x1,x2, linewidth=2)\n", "title(\"Decision boundary using w and bias\")\n", "_=scatter(preproc_data[0, :], preproc_data[1,:], c=trainlab, cmap=gray(), s=50)\n", "_=xlabel('Plasma glucose concentration')\n", "_=ylabel('Body mass index')\n", "p1 = Rectangle((0, 0), 1, 1, fc=\"w\")\n", "p2 = Rectangle((0, 0), 1, 1, fc=\"k\")\n", "legend((p1, p2), [\"Non-diabetic\", \"Diabetic\"], loc=2)\n", "\n", "print 'w :', w\n", "print 'b :', b" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this problem, a linear classifier does a reasonable job in distinguishing labelled data. An interpretation could be that individuals below a certain level of BMI and glucose are likely to have no Diabetes. \n", "For problems where the data cannot be separated linearly, there are more advanced classification methods, as for example all of Shogun's [kernel machines](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKernelMachine.html), but more on this later. To play with this interactively have a look at this: [web demo](http://demos.shogun-toolbox.org/classifier/binary/) " ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Evaluating performance and Model selection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How do you assess the quality of a prediction? Shogun provides various ways to do this using [CEvaluation](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CEvaluation.html). The preformance is evaluated by comparing the predicted output and the expected output. Some of the base classes for performance measures are:\n", "\n", "* [Binary class evaluation](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CBinaryClassEvaluation.html): used to evaluate binary classification labels. \n", "* [Clustering evaluation](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CClusteringEvaluation.html): used to evaluate clustering.\n", "* [Mean absolute error](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CMeanAbsoluteError.html): used to compute an error of regression model.\n", "* [Multiclass accuracy](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CMulticlassAccuracy.html): used to compute accuracy of multiclass classification. \n", "\n", "Evaluating on training data should be avoided since the learner may adjust to very specific random features of the training data which are not very important to the general relation. This is called [overfitting](http://en.wikipedia.org/wiki/Overfitting). Maximising performance on the training examples usually results in algorithms explaining the noise in data (rather than actual patterns), which leads to bad performance on unseen data. The dataset will now be split into two, we train on one part and evaluate performance on other using [CAccuracyMeasure](http://www.shogun-toolbox.org/doc/en/3.0.0/classshogun_1_1CAccuracyMeasure.html)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#split features for training and evaluation\n", "num_train=700\n", "feats=array(glucose_conc)\n", "feats_t=feats[:num_train]\n", "feats_e=feats[num_train:]\n", "feats=array(BMI)\n", "feats_t1=feats[:num_train]\n", "feats_e1=feats[num_train:]\n", "feats_t=vstack((feats_t, feats_t1))\n", "feats_e=vstack((feats_e, feats_e1))\n", "\n", "feats_train=RealFeatures(feats_t)\n", "feats_evaluate=RealFeatures(feats_e)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see the accuracy by applying on test features." ] }, { "cell_type": "code", "collapsed": false, "input": [ "label_t=trainlab[:num_train]\n", "labels=BinaryLabels(label_t)\n", "label_e=trainlab[num_train:]\n", "labels_true=BinaryLabels(label_e)\n", "\n", "svm=LibLinear(C, feats_train, labels)\n", "svm.set_liblinear_solver_type(L2R_L2LOSS_SVC)\n", "\n", "#train and evaluate\n", "svm.train()\n", "output=svm.apply(feats_evaluate)\n", "\n", "#use AccuracyMeasure to get accuracy\n", "acc=AccuracyMeasure()\n", "acc.evaluate(output,labels_true)\n", "accuracy=acc.get_accuracy()*100\n", "print 'Accuracy(%):', accuracy" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To evaluate more efficiently [cross-validation](http://en.wikipedia.org/wiki/Cross-validation_%28statistics%29) is used. As you might have wondered how are the parameters of the classifier selected? Shogun has a model selection framework to select the best parameters. More description of these things in [this notebook](http://www.shogun-toolbox.org/static/notebook/current/xval_modelselection.html)." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "More predictions: Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This section will demonstrate another type of machine learning problem on real world data.</br> The task is to estimate prices of houses in Boston using the [Boston Housing Dataset](https://archive.ics.uci.edu/ml/datasets/Housing) provided by [StatLib library](http://lib.stat.cmu.edu/). The attributes are: Weighted distances to employment centres and percentage lower status of the population. Let us see if we can predict a good relationship between the pricing of houses and the attributes. This type of problems are solved using [Regression analysis](http://en.wikipedia.org/wiki/Regression_analysis)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data set is now loaded using [LibSVMFile](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CLibSVMFile.html) as in the previous sections and the attributes required (7th and 12th vector ) are converted to Shogun format features." ] }, { "cell_type": "code", "collapsed": false, "input": [ "temp_feats=RealFeatures(CSVFile('../../../data/uci/housing/fm_housing.dat'))\n", "labels=RegressionLabels(CSVFile('../../../data/uci/housing/housing_label.dat'))\n", "\n", "#rescale to 0...1\n", "preproc=RescaleFeatures()\n", "preproc.init(temp_feats)\n", "temp_feats.add_preprocessor(preproc)\n", "temp_feats.apply_preprocessor(True)\n", "mat = temp_feats.get_feature_matrix()\n", "\n", "dist_centres=mat[7]\n", "lower_pop=mat[12]\n", "\n", "feats=array(dist_centres)\n", "feats=vstack((feats, array(lower_pop)))\n", "print feats, feats.shape\n", "#convert to shogun format features\n", "feats_train=RealFeatures(feats)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The tool we will use here to perform regression is [Kernel ridge regression](http://shogun-toolbox.org/doc/en/latest/classshogun_1_1CKernelRidgeRegression.html). Kernel Ridge Regression is a non-parametric version of ridge regression where the [kernel trick](http://en.wikipedia.org/wiki/Kernel_trick) is used to solve a related linear ridge regression problem in a higher-dimensional space, whose results correspond to non-linear regression in the data-space. Again we train on the data and apply on the XY grid to get predicitions." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from mpl_toolkits.mplot3d import Axes3D\n", "size=100\n", "x1=linspace(0, 1.0, size)\n", "x2=linspace(0, 1.0, size)\n", "x, y=meshgrid(x1, x2)\n", "#Generate X-Y grid test data\n", "grid=RealFeatures(array((ravel(x), ravel(y))))\n", "\n", "#Train on data(both attributes) and predict\n", "width=1.0\n", "tau=0.5\n", "kernel=GaussianKernel(feats_train, feats_train, width)\n", "krr=KernelRidgeRegression(tau, kernel, labels)\n", "krr.train(feats_train)\n", "kernel.init(feats_train, grid)\n", "out = krr.apply().get_labels()\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `out` variable now contains a relationship between the attributes. Below is an attempt to establish such relationship between the attributes individually. Separate feature instances are created for each attribute. You could skip the code and have a look at the plots directly if you just want the essence. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "#create feature objects for individual attributes.\n", "feats_test=RealFeatures(x1.reshape(1,len(x1)))\n", "feats_t0=array(dist_centres)\n", "feats_train0=RealFeatures(feats_t0.reshape(1,len(feats_t0)))\n", "feats_t1=array(lower_pop)\n", "feats_train1=RealFeatures(feats_t1.reshape(1,len(feats_t1)))\n", "\n", "#Regression with first attribute\n", "kernel=GaussianKernel(feats_train0, feats_train0, width)\n", "krr=KernelRidgeRegression(tau, kernel, labels)\n", "krr.train(feats_train0)\n", "kernel.init(feats_train0, feats_test)\n", "out0 = krr.apply().get_labels()\n", "\n", "#Regression with second attribute \n", "kernel=GaussianKernel(feats_train1, feats_train1, width)\n", "krr=KernelRidgeRegression(tau, kernel, labels)\n", "krr.train(feats_train1)\n", "kernel.init(feats_train1, feats_test)\n", "out1 = krr.apply().get_labels()\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#Visualization of regression\n", "fig=figure(figsize(20,6))\n", "#first plot with only one attribute\n", "fig.add_subplot(131)\n", "title(\"Regression with 1st attribute\")\n", "_=scatter(feats[0, :], labels.get_labels(),c=ones(560), cmap=gray(), s=20)\n", "_=xlabel('Weighted distances to employment centres ')\n", "_=ylabel('Median value of homes')\n", "\n", "_=plot(x1,out0, linewidth=3)\n", "\n", "#second plot with only one attribute\n", "fig.add_subplot(132)\n", "title(\"Regression with 2nd attribute\")\n", "_=scatter(feats[1, :], labels.get_labels(),c=ones(560), cmap=gray(), s=20)\n", "_=xlabel('% lower status of the population')\n", "_=ylabel('Median value of homes')\n", "_=plot(x1,out1, linewidth=3)\n", "\n", "#Both attributes and regression output\n", "ax=fig.add_subplot(133, projection='3d')\n", "z=out.reshape((size, size))\n", "gray()\n", "title(\"Regression\")\n", "ax.plot_wireframe(y, x, z, linewidths=2, alpha=0.4)\n", "ax.set_xlabel('% lower status of the population')\n", "ax.set_ylabel('Distances to employment centres ')\n", "ax.set_zlabel('Median value of homes')\n", "ax.view_init(25, 40)" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

gpl-3.0

nayutaya/tensorflow-rnn-sin

ex2/lstm_basic/output.ipynb

1

94282

{ "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': 0.1,\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([ 0.02220552, 0.17695722, 0.33647949, 0.49163169, 0.63202316,\n", " 0.74922162, 0.83912903, 0.90206307, 0.94112211, 0.96034342,\n", " 0.96352643, 0.95375103, 0.9333064 , 0.90378541, 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6.05609035e-04],\n", " [ 1.59000000e+03, 5.46982803e-04],\n", " [ 1.60000000e+03, 5.45559626e-04],\n", " [ 1.61000000e+03, 5.21923008e-04],\n", " [ 1.62000000e+03, 5.57434163e-04],\n", " [ 1.63000000e+03, 5.20558795e-04],\n", " [ 1.64000000e+03, 4.59000323e-04],\n", " [ 1.65000000e+03, 5.12848434e-04],\n", " [ 1.66000000e+03, 4.48702805e-04],\n", " [ 1.67000000e+03, 5.20781381e-04],\n", " [ 1.68000000e+03, 4.89920028e-04],\n", " [ 1.69000000e+03, 4.96701919e-04],\n", " [ 1.70000000e+03, 5.07282617e-04],\n", " [ 1.71000000e+03, 5.13428880e-04],\n", " [ 1.72000000e+03, 4.79963957e-04],\n", " [ 1.73000000e+03, 4.09842498e-04],\n", " [ 1.74000000e+03, 4.50230989e-04],\n", " [ 1.75000000e+03, 5.12941042e-04],\n", " [ 1.76000000e+03, 5.06314274e-04],\n", " [ 1.77000000e+03, 5.02404931e-04],\n", " [ 1.78000000e+03, 4.51179076e-04],\n", " [ 1.79000000e+03, 4.84848017e-04],\n", " [ 1.80000000e+03, 5.06206881e-04],\n", " [ 1.81000000e+03, 4.48027393e-04],\n", " [ 1.82000000e+03, 3.95686482e-04],\n", " [ 1.83000000e+03, 4.23130026e-04],\n", " [ 1.84000000e+03, 4.85251279e-04],\n", " [ 1.85000000e+03, 4.70509898e-04],\n", " [ 1.86000000e+03, 4.70560743e-04],\n", " [ 1.87000000e+03, 4.03481972e-04],\n", " [ 1.88000000e+03, 5.13000181e-04],\n", " [ 1.89000000e+03, 4.63959062e-04],\n", " [ 1.90000000e+03, 4.72538173e-04],\n", " [ 1.91000000e+03, 3.77635966e-04],\n", " [ 1.92000000e+03, 4.81549097e-04],\n", " [ 1.93000000e+03, 4.46246297e-04],\n", " [ 1.94000000e+03, 4.90528997e-04],\n", " [ 1.95000000e+03, 4.03635611e-04],\n", " [ 1.96000000e+03, 4.58088354e-04],\n", " [ 1.97000000e+03, 4.28521482e-04],\n", " [ 1.98000000e+03, 5.18360233e-04],\n", " [ 1.99000000e+03, 4.14475566e-04],\n", " [ 2.00000000e+03, 3.56514269e-04]])" ] }, "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 0x1119d5898>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ij8tYccMm9Uu/9ReTHzKpKArTp09nzJgxGfILlb7WMEumLMxsOJNBuwYRGxer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PXNmbX1K8nvqZTPVkmvSxpTd8z5VUyjDOuOPSzpkKRT68aGXYaVKN7cyDtLB4GHIuIi4ArgvpRZ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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1119a7eb8>" ] }, "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 0x1119a7860>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AIJVQeCUOTj5ZqlNHeuONeI8EMIm4vRlINswjIBjMJSCxEORF4M47pT59yM0DAAAAkLjY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\n+hUrrIjO8OG2Orp2rbWkWLs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77rEVvkhalhx5pNSnj7VCuO46a3kyebIFeJJt4e3Y0Vbp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\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x111a36a90>" ] }, "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

apryor6/apryor6.github.io

visualizations/bokeh/notebooks/glyphs/cross.ipynb

2

1777

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bokeh Cross Glyph" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from bokeh.plotting import figure, output_file, show\n", "from bokeh.models import Range1d\n", "from math import radians\n", "from bokeh.io import export_png\n", "\n", "line_color = '#2166ac'\n", "output_file(\"../../figures/cross.html\")\n", "\n", "p = figure(plot_width=400, plot_height=400)\n", "p.cross(x=0,y=0,size=100, angle=radians(0),\n", " line_alpha=1, line_color=line_color, line_dash='dashed', line_width=5)\n", "p.cross(x=0,y=1,size=100, angle=radians(25),\n", " line_alpha=1, line_color=line_color, line_dash='dotdash', line_width=8)\n", "p.cross(x=1,y=0,size=100, angle=radians(45),\n", " line_alpha=1, line_color=line_color, line_dash='dotted', line_width=13)\n", "p.cross(x=1,y=1,size=100, angle=radians(75),\n", " line_alpha=1, line_color=line_color, line_dash='solid', line_width=17)\n", "p.x_range = Range1d(-0.5,1.5, bounds=(-1,2))\n", "p.y_range = Range1d(-0.5,1.5, bounds=(-1,2))\n", "show(p)\n", "export_png(p, filename=\"../../figures/cross.png\");" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

kinverarity1/lasio

docs/Add links to GitHub for all issue and PR refs in changelog.ipynb

1

9792

{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "179f4066", "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "import re" ] }, { "cell_type": "code", "execution_count": 5, "id": "b03ba106", "metadata": {}, "outputs": [], "source": [ "PATH_TO_REPO = \"..\"" ] }, { "cell_type": "markdown", "id": "eb140e49", "metadata": {}, "source": [ "Load ``changelog.rst``:" ] }, { "cell_type": "code", "execution_count": 6, "id": "effc62ca", "metadata": {}, "outputs": [], "source": [ "fn = Path(PATH_TO_REPO) / \"docs\" / \"source\" / \"changelog.rst\"" ] }, { "cell_type": "code", "execution_count": 7, "id": "b90683fc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True: ..\\docs\\source\\changelog.rst\n" ] } ], "source": [ "print(f\"{fn.is_file()}: {fn}\")" ] }, { "cell_type": "markdown", "id": "dcc9ec63", "metadata": {}, "source": [ "Read the contents of ``changelog.rst``:" ] }, { "cell_type": "code", "execution_count": 8, "id": "74991d1a", "metadata": {}, "outputs": [], "source": [ "with open(fn, \"r\") as f:\n", " txt = f.read()" ] }, { "cell_type": "markdown", "id": "5a8f8c63", "metadata": {}, "source": [ "Find all issue/PR references in the changelog RST source:" ] }, { "cell_type": "code", "execution_count": 9, "id": "f3ae6317", "metadata": {}, "outputs": [], "source": [ "matches = sorted(set(re.findall(r\"#\\d*\", txt)), key=lambda x: int(x[1:]))" ] }, { "cell_type": "markdown", "id": "b6ff873a", "metadata": {}, "source": [ "Convert all instances of ``#405`` to `` `#405` `` - this should NOT be run again:" ] }, { "cell_type": "code", "execution_count": 10, "id": "162c9850", "metadata": {}, "outputs": [], "source": [ "#txt = re.sub(r\"#\\d*\", lambda m: f\"`{m.group()}`_\", txt) + \"\"" ] }, { "cell_type": "markdown", "id": "0150ac1b", "metadata": {}, "source": [ "Add references to GitHub to the bottom of the RST document:" ] }, { "cell_type": "code", "execution_count": 11, "id": "a1883794", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found ref for #17\n", "Found ref for #31\n", "Found ref for #71\n", "Found ref for #75\n", "Found ref for #78\n", "Found ref for #81\n", "Did not find #83 ref, adding...\n", "Found ref for #84\n", "Found ref for #85\n", "Found ref for #92\n", "Found ref for #135\n", "Found ref for #141\n", "Found ref for #142\n", "Found ref for #153\n", "Found ref for #155\n", "Found ref for #160\n", "Found ref for #164\n", "Found ref for #167\n", "Found ref for #168\n", "Found ref for #182\n", "Found ref for #201\n", "Found ref for #209\n", "Found ref for #210\n", "Found ref for #213\n", "Found ref for #214\n", "Found ref for #216\n", "Found ref for #218\n", "Found ref for #221\n", "Found ref for #223\n", "Found ref for #225\n", "Found ref for #226\n", "Found ref for #227\n", "Found ref for #233\n", "Found ref for #236\n", "Found ref for #237\n", "Found ref for #239\n", "Found ref for #249\n", "Found ref for #252\n", "Found ref for #256\n", "Found ref for #258\n", "Found ref for #259\n", "Found ref for #262\n", "Found ref for #264\n", "Did not find #265 ref, adding...\n", "Found ref for #268\n", "Found ref for #271\n", "Found ref for #277\n", "Found ref for #278\n", "Found ref for #284\n", "Found ref for #286\n", "Found ref for #292\n", "Found ref for #293\n", "Found ref for #296\n", "Found ref for #298\n", "Found ref for #300\n", "Found ref for #302\n", "Found ref for #303\n", "Found ref for #304\n", "Found ref for #310\n", "Found ref for #311\n", "Found ref for #312\n", "Found ref for #315\n", "Found ref for #317\n", "Found ref for #318\n", "Found ref for #319\n", "Found ref for #321\n", "Found ref for #325\n", "Found ref for #326\n", "Found ref for #327\n", "Found ref for #328\n", "Found ref for #329\n", "Found ref for #330\n", "Found ref for #331\n", "Did not find #332 ref, adding...\n", "Found ref for #333\n", "Found ref for #334\n", "Found ref for #335\n", "Found ref for #337\n", "Found ref for #338\n", "Found ref for #339\n", "Found ref for #340\n", "Found ref for #341\n", "Found ref for #342\n", "Found ref for #345\n", "Found ref for #346\n", "Found ref for #347\n", "Found ref for #348\n", "Found ref for #349\n", "Found ref for #350\n", "Found ref for #352\n", "Found ref for #353\n", "Found ref for #355\n", "Found ref for #358\n", "Found ref for #359\n", "Found ref for #360\n", "Found ref for #361\n", "Found ref for #363\n", "Found ref for #364\n", "Found ref for #367\n", "Found ref for #368\n", "Found ref for #369\n", "Found ref for #372\n", "Found ref for #373\n", "Found ref for #374\n", "Did not find #375 ref, adding...\n", "Found ref for #377\n", "Found ref for #380\n", "Found ref for #382\n", "Found ref for #385\n", "Found ref for #387\n", "Found ref for #390\n", "Found ref for #391\n", "Did not find #392 ref, adding...\n", "Found ref for #393\n", "Found ref for #395\n", "Found ref for #396\n", "Found ref for #397\n", "Found ref for #398\n", "Found ref for #399\n", "Found ref for #400\n", "Found ref for #401\n", "Found ref for #402\n", "Found ref for #403\n", "Found ref for #404\n", "Found ref for #406\n", "Found ref for #410\n", "Found ref for #411\n", "Found ref for #412\n", "Found ref for #417\n", "Found ref for #418\n", "Found ref for #419\n", "Found ref for #420\n", "Found ref for #423\n", "Found ref for #425\n", "Found ref for #426\n", "Found ref for #427\n", "Found ref for #428\n", "Found ref for #429\n", "Found ref for #430\n", "Found ref for #432\n", "Found ref for #437\n", "Found ref for #438\n", "Did not find #439 ref, adding...\n", "Found ref for #441\n", "Did not find #444 ref, adding...\n", "Did not find #446 ref, adding...\n", "Found ref for #447\n", "Found ref for #449\n", "Found ref for #451\n", "Did not find #452 ref, adding...\n", "Found ref for #453\n", "Found ref for #455\n", "Did not find #459 ref, adding...\n", "Did not find #460 ref, adding...\n", "Did not find #461 ref, adding...\n", "Did not find #465 ref, adding...\n", "Did not find #466 ref, adding...\n", "Did not find #469 ref, adding...\n", "Did not find #470 ref, adding...\n", "Did not find #471 ref, adding...\n", "Did not find #472 ref, adding...\n", "Did not find #473 ref, adding...\n", "Did not find #475 ref, adding...\n", "Did not find #477 ref, adding...\n", "Did not find #478 ref, adding...\n", "Did not find #479 ref, adding...\n", "Did not find #480 ref, adding...\n", "Did not find #481 ref, adding...\n", "Did not find #482 ref, adding...\n", "Did not find #483 ref, adding...\n", "Did not find #484 ref, adding...\n", "Did not find #485 ref, adding...\n", "Did not find #486 ref, adding...\n", "Did not find #487 ref, adding...\n", "Did not find #489 ref, adding...\n", "Did not find #490 ref, adding...\n", "Did not find #491 ref, adding...\n", "Did not find #495 ref, adding...\n", "Did not find #498 ref, adding...\n", "Did not find #500 ref, adding...\n", "Did not find #501 ref, adding...\n", "Did not find #502 ref, adding...\n", "Did not find #503 ref, adding...\n" ] } ], "source": [ "for m in matches:\n", " ref_str = f\".. _{m}: https://github.com/kinverarity1/lasio/issues/{m[1:]}\"\n", " if ref_str in txt:\n", " print(f\"Found ref for {m}\")\n", " else:\n", " print(f\"Did not find {m} ref, adding...\")\n", " txt += ref_str + \"\\n\" " ] }, { "cell_type": "markdown", "id": "39d58b08", "metadata": {}, "source": [ "Overwrite `changelog.rst`:" ] }, { "cell_type": "code", "execution_count": 12, "id": "552e8e1b", "metadata": {}, "outputs": [], "source": [ "with open(fn, \"w\") as f:\n", " f.write(txt)" ] }, { "cell_type": "code", "execution_count": null, "id": "0b066fdc", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "wrap", "language": "python", "name": "wrap" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 5 }

mit

jonbruner/tensorflow-basics

save-load/load.ipynb

1

23534

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 2: Load\n", "\n", "Now we'll start with a fresh, clean file and reinstate the model we built in [part 1](save.ipynb). From the end of this document we'll be ready to either continue training or run the model to classify images.\n", "\n", "We begin by loading the libraries and the MNIST dataset." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "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": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import tensorflow as tf\n", "\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n", "\n", "sess = tf.InteractiveSession()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import the graph\n", "\n", "The following two lines import the meta graph, which contains all the information we need on the topology of the model and its variables, and then import the checkpoint file, which contains the weights we developed during training. In the second line, we explicitly load the checkpoint data into the current session, the variable `sess`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_saver = tf.train.import_meta_graph('saved_mnist_cnn.ckpt.meta')\n", "new_saver.restore(sess, 'saved_mnist_cnn.ckpt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can confirm that this worked by taking a look at the current `GraphDef`. I won't run this in the version of the notebook on Github because the output takes up tons of space, but if you run this at home you'll see all the nodes in the TensorFlow graph represented in a big JSON list." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tf.get_default_graph().as_graph_def()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Associate variables\n", "\n", "Indeed, everything we expect is there in the `GraphDef`. The problem is that we can't just start calling the Python variables we used in the save file: `y_conv` (our output) and `x` and `keep_prob` (our inputs). We first need to define some Python variables and associate them with the TensorFlow nodes that we need handle in order to feed input and retrieve output.\n", "\n", "We do that like this:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = sess.graph.get_tensor_by_name(\"input:0\")\n", "y_conv = sess.graph.get_tensor_by_name(\"output:0\")\n", "keep_prob = sess.graph.get_tensor_by_name(\"keep_prob:0\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now `x`, `y_conv`, and `keep_prob` are fully defined Python variables that refer to TensorFlow nodes. They operate just like they did when we left off in part 1.\n", "\n", "Note that we defined many more nodes than these three back in part 1: `W_conv1`, `b_conv1`, and so on. One cool thing about the way TensorFlow handles these graphs is that you don't need to reinstate any of the intermediate nodes in order to run the model; TensorFlow understands the relationship between them and will run them properly even if they don't have Python variables associated with them.\n", "\n", "To demonstrate, let's test the model like we did in part 1. Here we take an arbitrary image from the MNIST validation set and display it." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x105856d10>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11be71ed0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "image_b = mnist.validation.images[159]\n", "plt.imshow(image_b.reshape([28, 28]), cmap='Greys')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now we run the model." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 3.63468689e-06 3.87002473e-07 9.99598205e-01 1.59243267e-04\n", " 8.94278696e-09 4.08969534e-08 2.78706466e-07 1.72356682e-04\n", " 6.52213712e-05 8.37815264e-07]]\n", "[2]\n" ] } ], "source": [ "image_b = image_b.reshape([1, 784])\n", "result = sess.run(y_conv, feed_dict={x:image_b, keep_prob:1})\n", "print(result)\n", "print(sess.run(tf.argmax(result, 1)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Confirmed!\n", "\n", "Now we're back to exactly where we were at the end of [part 1](save.ipynb), at least with respect to the model's inputs and outputs. If we need access to any of the intermediate layers, we can retrieve them and point to them with Python variables like we did with `y_conv`, `x`, and `keep_prob`." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }

mpl-2.0

magenta/ddsp

ddsp/colab/demos/timbre_transfer.ipynb

1

20275

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "3YLyiTwPfVCT" }, "source": [ "\u003ca href=\"https://colab.research.google.com/github/magenta/ddsp/blob/main/ddsp/colab/demos/timbre_transfer.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e\n", "\n", "##### Copyright 2021 Google LLC.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Bvp6GWqtfVCW" }, "outputs": [], "source": [ "# Copyright 2021 Google LLC. 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", "# ==============================================================================" ] }, { "cell_type": "markdown", "metadata": { "id": "JndnmDMp66FL" }, "source": [ "# DDSP Timbre Transfer Demo\n", "\n", "This notebook is a demo of timbre transfer using DDSP (Differentiable Digital Signal Processing). \n", "The model here is trained to generate audio conditioned on a time series of fundamental frequency and loudness. \n", "\n", "* [DDSP ICLR paper](https://openreview.net/forum?id=B1x1ma4tDr)\n", "* [Audio Examples](http://goo.gl/magenta/ddsp-examples) \n", "\n", "This notebook extracts these features from input audio (either uploaded files, or recorded from the microphone) and resynthesizes with the model. \n", "\n", "\u003cimg src=\"https://magenta.tensorflow.org/assets/ddsp/ddsp_cat_jamming.png\" alt=\"DDSP Tone Transfer\" width=\"700\"\u003e\n", "\n", "\n", "\n", "By default, the notebook will download pre-trained models. You can train a model on your own sounds by using the [Train Autoencoder Colab](https://github.com/magenta/ddsp/blob/main/ddsp/colab/demos/train_autoencoder.ipynb).\n", "\n", "Have fun! And please feel free to hack this notebook to make your own creative interactions.\n", "\n", "\n", "### Instructions for running:\n", "\n", "* Make sure to use a GPU runtime, click: __Runtime \u003e\u003e Change Runtime Type \u003e\u003e GPU__\n", "* Press ▶️ on the left of each of the cells\n", "* View the code: Double-click any of the cells\n", "* Hide the code: Double click the right side of the cell\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "6wZde6CBya9k" }, "outputs": [], "source": [ "#@title #Install and Import\n", "\n", "#@markdown Install ddsp, define some helper functions, and download the model. This transfers a lot of data and _should take a minute or two_.\n", "print('Installing from pip package...')\n", "!pip install -qU ddsp==1.6.5\n", "\n", "# Ignore a bunch of deprecation warnings\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "import copy\n", "import os\n", "import time\n", "\n", "import crepe\n", "import ddsp\n", "import ddsp.training\n", "from ddsp.colab.colab_utils import (\n", " auto_tune, get_tuning_factor, download, \n", " play, record, specplot, upload, \n", " DEFAULT_SAMPLE_RATE)\n", "from ddsp.training.postprocessing import (\n", " detect_notes, fit_quantile_transform\n", ")\n", "import gin\n", "from google.colab import files\n", "import librosa\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pickle\n", "import tensorflow.compat.v2 as tf\n", "import tensorflow_datasets as tfds\n", "\n", "# Helper Functions\n", "sample_rate = DEFAULT_SAMPLE_RATE # 16000\n", "\n", "\n", "print('Done!')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "Go36QW9AS_CD" }, "outputs": [], "source": [ "#@title Record or Upload Audio\n", "#@markdown * Either record audio from microphone or upload audio from file (.mp3 or .wav) \n", "#@markdown * Audio should be monophonic (single instrument / voice)\n", "#@markdown * Extracts fundmanetal frequency (f0) and loudness features. \n", "\n", "record_or_upload = \"Record\" #@param [\"Record\", \"Upload (.mp3 or .wav)\"]\n", "\n", "record_seconds = 5#@param {type:\"number\", min:1, max:10, step:1}\n", "\n", "if record_or_upload == \"Record\":\n", " audio = record(seconds=record_seconds)\n", "else:\n", " # Load audio sample here (.mp3 or .wav3 file)\n", " # Just use the first file.\n", " filenames, audios = upload()\n", " audio = audios[0]\n", "if len(audio.shape) == 1:\n", " audio = audio[np.newaxis, :]\n", "print('\\nExtracting audio features...')\n", "\n", "# Plot.\n", "specplot(audio)\n", "play(audio)\n", "\n", "# Setup the session.\n", "ddsp.spectral_ops.reset_crepe()\n", "\n", "# Compute features.\n", "start_time = time.time()\n", "audio_features = ddsp.training.metrics.compute_audio_features(audio)\n", "audio_features['loudness_db'] = audio_features['loudness_db'].astype(np.float32)\n", "audio_features_mod = None\n", "print('Audio features took %.1f seconds' % (time.time() - start_time))\n", "\n", "\n", "TRIM = -15\n", "# Plot Features.\n", "fig, ax = plt.subplots(nrows=3, \n", " ncols=1, \n", " sharex=True,\n", " figsize=(6, 8))\n", "ax[0].plot(audio_features['loudness_db'][:TRIM])\n", "ax[0].set_ylabel('loudness_db')\n", "\n", "ax[1].plot(librosa.hz_to_midi(audio_features['f0_hz'][:TRIM]))\n", "ax[1].set_ylabel('f0 [midi]')\n", "\n", "ax[2].plot(audio_features['f0_confidence'][:TRIM])\n", "ax[2].set_ylabel('f0 confidence')\n", "_ = ax[2].set_xlabel('Time step [frame]')\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "wmSGDWM5yyjm" }, "outputs": [], "source": [ "#@title Load a model\n", "#@markdown Run for ever new audio input\n", "model = 'Violin' #@param ['Violin', 'Flute', 'Flute2', 'Trumpet', 'Tenor_Saxophone', 'Upload your own (checkpoint folder as .zip)']\n", "MODEL = model\n", "\n", "\n", "def find_model_dir(dir_name):\n", " # Iterate through directories until model directory is found\n", " for root, dirs, filenames in os.walk(dir_name):\n", " for filename in filenames:\n", " if filename.endswith(\".gin\") and not filename.startswith(\".\"):\n", " model_dir = root\n", " break\n", " return model_dir \n", "\n", "if model in ('Violin', 'Flute', 'Flute2', 'Trumpet', 'Tenor_Saxophone'):\n", " # Pretrained models.\n", " PRETRAINED_DIR = '/content/pretrained'\n", " # Copy over from gs:// for faster loading.\n", " !rm -r $PRETRAINED_DIR \u0026\u003e /dev/null\n", " !mkdir $PRETRAINED_DIR \u0026\u003e /dev/null\n", " GCS_CKPT_DIR = 'gs://ddsp/models/timbre_transfer_colab/2021-07-08'\n", " model_dir = os.path.join(GCS_CKPT_DIR, 'solo_%s_ckpt' % model.lower())\n", " \n", " !gsutil cp $model_dir/* $PRETRAINED_DIR \u0026\u003e /dev/null\n", " model_dir = PRETRAINED_DIR\n", " gin_file = os.path.join(model_dir, 'operative_config-0.gin')\n", "\n", "else:\n", " # User models.\n", " UPLOAD_DIR = '/content/uploaded'\n", " !mkdir $UPLOAD_DIR\n", " uploaded_files = files.upload()\n", "\n", " for fnames in uploaded_files.keys():\n", " print(\"Unzipping... {}\".format(fnames))\n", " !unzip -o \"/content/$fnames\" -d $UPLOAD_DIR \u0026\u003e /dev/null\n", " model_dir = find_model_dir(UPLOAD_DIR)\n", " gin_file = os.path.join(model_dir, 'operative_config-0.gin')\n", "\n", "\n", "# Load the dataset statistics.\n", "DATASET_STATS = None\n", "dataset_stats_file = os.path.join(model_dir, 'dataset_statistics.pkl')\n", "print(f'Loading dataset statistics from {dataset_stats_file}')\n", "try:\n", " if tf.io.gfile.exists(dataset_stats_file):\n", " with tf.io.gfile.GFile(dataset_stats_file, 'rb') as f:\n", " DATASET_STATS = pickle.load(f)\n", "except Exception as err:\n", " print('Loading dataset statistics from pickle failed: {}.'.format(err))\n", "\n", "\n", "# Parse gin config,\n", "with gin.unlock_config():\n", " gin.parse_config_file(gin_file, skip_unknown=True)\n", "\n", "# Assumes only one checkpoint in the folder, 'ckpt-[iter]`.\n", "ckpt_files = [f for f in tf.io.gfile.listdir(model_dir) if 'ckpt' in f]\n", "ckpt_name = ckpt_files[0].split('.')[0]\n", "ckpt = os.path.join(model_dir, ckpt_name)\n", "\n", "# Ensure dimensions and sampling rates are equal\n", "time_steps_train = gin.query_parameter('F0LoudnessPreprocessor.time_steps')\n", "n_samples_train = gin.query_parameter('Harmonic.n_samples')\n", "hop_size = int(n_samples_train / time_steps_train)\n", "\n", "time_steps = int(audio.shape[1] / hop_size)\n", "n_samples = time_steps * hop_size\n", "\n", "# print(\"===Trained model===\")\n", "# print(\"Time Steps\", time_steps_train)\n", "# print(\"Samples\", n_samples_train)\n", "# print(\"Hop Size\", hop_size)\n", "# print(\"\\n===Resynthesis===\")\n", "# print(\"Time Steps\", time_steps)\n", "# print(\"Samples\", n_samples)\n", "# print('')\n", "\n", "gin_params = [\n", " 'Harmonic.n_samples = {}'.format(n_samples),\n", " 'FilteredNoise.n_samples = {}'.format(n_samples),\n", " 'F0LoudnessPreprocessor.time_steps = {}'.format(time_steps),\n", " 'oscillator_bank.use_angular_cumsum = True', # Avoids cumsum accumulation errors.\n", "]\n", "\n", "with gin.unlock_config():\n", " gin.parse_config(gin_params)\n", "\n", "\n", "# Trim all input vectors to correct lengths \n", "for key in ['f0_hz', 'f0_confidence', 'loudness_db']:\n", " audio_features[key] = audio_features[key][:time_steps]\n", "audio_features['audio'] = audio_features['audio'][:, :n_samples]\n", "\n", "\n", "# Set up the model just to predict audio given new conditioning\n", "model = ddsp.training.models.Autoencoder()\n", "model.restore(ckpt)\n", "\n", "# Build model by running a batch through it.\n", "start_time = time.time()\n", "_ = model(audio_features, training=False)\n", "print('Restoring model took %.1f seconds' % (time.time() - start_time))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "uQFUlIJ_5r36" }, "outputs": [], "source": [ "#@title Modify conditioning\n", "\n", "#@markdown These models were not explicitly trained to perform timbre transfer, so they may sound unnatural if the incoming loudness and frequencies are very different then the training data (which will always be somewhat true). \n", "\n", "\n", "#@markdown ## Note Detection\n", "\n", "#@markdown You can leave this at 1.0 for most cases\n", "threshold = 1 #@param {type:\"slider\", min: 0.0, max:2.0, step:0.01}\n", "\n", "\n", "#@markdown ## Automatic\n", "\n", "ADJUST = True #@param{type:\"boolean\"}\n", "\n", "#@markdown Quiet parts without notes detected (dB)\n", "quiet = 20 #@param {type:\"slider\", min: 0, max:60, step:1}\n", "\n", "#@markdown Force pitch to nearest note (amount)\n", "autotune = 0 #@param {type:\"slider\", min: 0.0, max:1.0, step:0.1}\n", "\n", "#@markdown ## Manual\n", "\n", "\n", "#@markdown Shift the pitch (octaves)\n", "pitch_shift = 0 #@param {type:\"slider\", min:-2, max:2, step:1}\n", "\n", "#@markdown Adjust the overall loudness (dB)\n", "loudness_shift = 0 #@param {type:\"slider\", min:-20, max:20, step:1}\n", "\n", "\n", "audio_features_mod = {k: v.copy() for k, v in audio_features.items()}\n", "\n", "\n", "## Helper functions.\n", "def shift_ld(audio_features, ld_shift=0.0):\n", " \"\"\"Shift loudness by a number of ocatves.\"\"\"\n", " audio_features['loudness_db'] += ld_shift\n", " return audio_features\n", "\n", "\n", "def shift_f0(audio_features, pitch_shift=0.0):\n", " \"\"\"Shift f0 by a number of ocatves.\"\"\"\n", " audio_features['f0_hz'] *= 2.0 ** (pitch_shift)\n", " audio_features['f0_hz'] = np.clip(audio_features['f0_hz'], \n", " 0.0, \n", " librosa.midi_to_hz(110.0))\n", " return audio_features\n", "\n", "\n", "mask_on = None\n", "\n", "if ADJUST and DATASET_STATS is not None:\n", " # Detect sections that are \"on\".\n", " mask_on, note_on_value = detect_notes(audio_features['loudness_db'],\n", " audio_features['f0_confidence'],\n", " threshold)\n", "\n", " if np.any(mask_on):\n", " # Shift the pitch register.\n", " target_mean_pitch = DATASET_STATS['mean_pitch']\n", " pitch = ddsp.core.hz_to_midi(audio_features['f0_hz'])\n", " mean_pitch = np.mean(pitch[mask_on])\n", " p_diff = target_mean_pitch - mean_pitch\n", " p_diff_octave = p_diff / 12.0\n", " round_fn = np.floor if p_diff_octave \u003e 1.5 else np.ceil\n", " p_diff_octave = round_fn(p_diff_octave)\n", " audio_features_mod = shift_f0(audio_features_mod, p_diff_octave)\n", "\n", "\n", " # Quantile shift the note_on parts.\n", " _, loudness_norm = fit_quantile_transform(\n", " audio_features['loudness_db'],\n", " mask_on,\n", " inv_quantile=DATASET_STATS['quantile_transform'])\n", "\n", " # Turn down the note_off parts.\n", " mask_off = np.logical_not(mask_on)\n", " loudness_norm[mask_off] -= quiet * (1.0 - note_on_value[mask_off][:, np.newaxis])\n", " loudness_norm = np.reshape(loudness_norm, audio_features['loudness_db'].shape)\n", " \n", " audio_features_mod['loudness_db'] = loudness_norm \n", "\n", " # Auto-tune.\n", " if autotune:\n", " f0_midi = np.array(ddsp.core.hz_to_midi(audio_features_mod['f0_hz']))\n", " tuning_factor = get_tuning_factor(f0_midi, audio_features_mod['f0_confidence'], mask_on)\n", " f0_midi_at = auto_tune(f0_midi, tuning_factor, mask_on, amount=autotune)\n", " audio_features_mod['f0_hz'] = ddsp.core.midi_to_hz(f0_midi_at)\n", "\n", " else:\n", " print('\\nSkipping auto-adjust (no notes detected or ADJUST box empty).')\n", "\n", "else:\n", " print('\\nSkipping auto-adujst (box not checked or no dataset statistics found).')\n", "\n", "# Manual Shifts.\n", "audio_features_mod = shift_ld(audio_features_mod, loudness_shift)\n", "audio_features_mod = shift_f0(audio_features_mod, pitch_shift)\n", "\n", "\n", "\n", "# Plot Features.\n", "has_mask = int(mask_on is not None)\n", "n_plots = 3 if has_mask else 2 \n", "fig, axes = plt.subplots(nrows=n_plots, \n", " ncols=1, \n", " sharex=True,\n", " figsize=(2*n_plots, 8))\n", "\n", "if has_mask:\n", " ax = axes[0]\n", " ax.plot(np.ones_like(mask_on[:TRIM]) * threshold, 'k:')\n", " ax.plot(note_on_value[:TRIM])\n", " ax.plot(mask_on[:TRIM])\n", " ax.set_ylabel('Note-on Mask')\n", " ax.set_xlabel('Time step [frame]')\n", " ax.legend(['Threshold', 'Likelihood','Mask'])\n", "\n", "ax = axes[0 + has_mask]\n", "ax.plot(audio_features['loudness_db'][:TRIM])\n", "ax.plot(audio_features_mod['loudness_db'][:TRIM])\n", "ax.set_ylabel('loudness_db')\n", "ax.legend(['Original','Adjusted'])\n", "\n", "ax = axes[1 + has_mask]\n", "ax.plot(librosa.hz_to_midi(audio_features['f0_hz'][:TRIM]))\n", "ax.plot(librosa.hz_to_midi(audio_features_mod['f0_hz'][:TRIM]))\n", "ax.set_ylabel('f0 [midi]')\n", "_ = ax.legend(['Original','Adjusted'])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "SLwg1WkHCXQO" }, "outputs": [], "source": [ "#@title #Resynthesize Audio\n", "\n", "af = audio_features if audio_features_mod is None else audio_features_mod\n", "\n", "# Run a batch of predictions.\n", "start_time = time.time()\n", "outputs = model(af, training=False)\n", "audio_gen = model.get_audio_from_outputs(outputs)\n", "print('Prediction took %.1f seconds' % (time.time() - start_time))\n", "\n", "# Plot\n", "print('Original')\n", "play(audio)\n", "\n", "print('Resynthesis')\n", "play(audio_gen)\n", "\n", "specplot(audio)\n", "plt.title(\"Original\")\n", "\n", "specplot(audio_gen)\n", "_ = plt.title(\"Resynthesis\")" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [ "3YLyiTwPfVCT" ], "last_runtime": {}, "name": "timbre_transfer.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

jan-rybizki/Galaxia_wrap

notebook/notebook_aida/[4]mag_limited_survey_function.ipynb

2

20485

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import os, sys\n", "path = os.path.abspath('../library/')\n", "if path not in sys.path:\n", " sys.path.append(path)\n", "from convert_to_recarray import create_gdr2mock_mag_limited_survey\n", "import ebf, subprocess" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Galaxia spawns new thin disk BHtree\n", "b'Galaxia-v0.81\\nCODEDATAPATH=/home/rybizki/Programme/GalaxiaData/\\nReading tabulated values from file- /home/rybizki/Programme/GalaxiaData/Model/vcirc.dat\\nThin disc sigma_v=[50, 32.3, 21, 0.33, 0.33]\\n [feh, dfeh]=[0.01, -0.12], [0.01, 0.18]\\nThin Disk,ID=0:\\nMaking Tree\\nAge=7.5e+07 lAge=7.5e+07\\n Total Nodes=1948537 leafs=1704970\\nCompleted % <1..11..21..31..41..51..61..71..81..91..>\\nMass=2.04123e+08 Leafs=1704970 Total Nodes=1948537 1.95068e+08 inf\\nWriting tree to file: /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_0_E1.ebf....Done\\nTime Tree generation/reading = 178.677 \\nThin disc sigma_v=[50, 32.3, 21, 0.33, 0.33]\\n [feh, dfeh]=[0.01, -0.12], [0.01, 0.18]\\nThin Disk,ID=1:\\nMaking Tree\\nAge=5.75e+08 lAge=4.25e+08\\n Total Nodes=1847225 leafs=1616322\\nCompleted % <1..10..20..30..40..50..60..70..80..90..100..>\\nMass=1.22831e+09 Leafs=1616322 Total Nodes=1847225 1.22745e+09 9.03297e+56\\nWriting tree to file: /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_1_E1.ebf....Done\\nTime Tree generation/reading = 204.587 \\nThin disc sigma_v=[50, 32.3, 21, 0.33, 0.33]\\n [feh, dfeh]=[0.01, -0.12], [0.01, 0.18]\\nThin Disk,ID=2:\\nMaking Tree\\nAge=1.5e+09 lAge=5e+08\\n Total Nodes=2665961 leafs=2332716\\nCompleted % <1..10..20..30..40..50..60..70..80..90..100..>\\nMass=1.61439e+09 Leafs=2332716 Total Nodes=2665961 1.61353e+09 6.2876e+18\\nWriting tree to file: /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_2_E1.ebf....Done\\nTime Tree generation/reading = 162.058 \\nThin disc sigma_v=[50, 32.3, 21, 0.33, 0.33]\\n [feh, dfeh]=[0.01, -0.12], [0.01, 0.18]\\nThin Disk,ID=3:\\nMaking Tree\\nAge=2.5e+09 lAge=5e+08\\n Total Nodes=2603353 leafs=2277934\\nCompleted % <1..10..20..30..40..50..60..70..80..90..100..>\\nMass=1.82022e+09 Leafs=2277934 Total Nodes=2603353 1.81978e+09 1.02351e+13\\nWriting tree to file: /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_3_E1.ebf....Done\\nTime Tree generation/reading = 124.872 \\nThin disc sigma_v=[50, 32.3, 21, 0.33, 0.33]\\n [feh, dfeh]=[0.01, -0.12], [0.01, 0.18]\\nThin Disk,ID=4:\\nMaking Tree\\nAge=4e+09 lAge=1e+09\\n Total Nodes=3112681 leafs=2723596\\nCompleted % <1..10..20..30..40..50..60..70..80..90..100..>\\nMass=4.35862e+09 Leafs=2723596 Total Nodes=3112681 4.35838e+09 5.31636e+21\\nWriting tree to file: /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_4_E1.ebf....Done\\nTime Tree generation/reading = 154.254 \\nThin disc sigma_v=[50, 32.3, 21, 0.33, 0.33]\\n [feh, dfeh]=[0.01, -0.12], [0.01, 0.18]\\nThin Disk,ID=5:\\nMaking Tree\\nAge=6e+09 lAge=1e+09\\n Total Nodes=3237993 leafs=2833244\\nCompleted % <1..10..20..30..40..50..60..70..80..90..100..>\\nMass=5.54068e+09 Leafs=2833244 Total Nodes=3237993 5.54058e+09 1.67724e+14\\nWriting tree to file: /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_5_E1.ebf....Done\\nTime Tree generation/reading = 146.591 \\nThin disc sigma_v=[50, 32.3, 21, 0.33, 0.33]\\n [feh, dfeh]=[0.01, -0.12], [0.01, 0.18]\\nThin Disk,ID=6:\\nMaking Tree\\nAge=8.5e+09 lAge=1.5e+09\\n Total Nodes=5336377 leafs=4669330\\nCompleted % <1..11..21..31..41..51..61..71..81..91..>\\nMass=1.12194e+10 Leafs=4669330 Total Nodes=5336377 1.12193e+10 4.06251e+13\\nWriting tree to file: /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_6_E1.ebf....Done\\nTime Tree generation/reading = 236.798 \\nfeh=-0.17 sig_feh=0.27\\nThick disc sigma_v=[67, 51, 42, 0.33, 0.33]\\n [feh, dfeh, age, dage]=[-0.48, 0.3, 1.1e+10, 1e+09]\\nThickDisk:\\nMaking Tree\\nAge=1.1e+10 lAge=0\\n Total Nodes=315513 leafs=276074\\nCompleted % <1..10..20..30..40..50..60..70..80..90..100..>\\nMass=9.32526e+09 Leafs=276074 Total Nodes=315513 9.33493e+09 20181.6\\nWriting tree to file: /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_7_E0.ebf....Done\\nTime Tree generation/reading = 15.6623 \\nSpheroid sigma_v=[131, 106, 85]\\n [feh, dfeh, age, dage]=[-1.5, 0.5, 1.3e+10, 0]\\nSpheroid:\\nMaking Tree\\nAge=1.3e+10 lAge=0\\n Total Nodes=299785 leafs=262312\\nCompleted % <1..10..20..30..40..50..60..70..80..90..100..>\\nMass=1.71905e+08 Leafs=262312 Total Nodes=299785 1.71905e+08 3.67911\\nWriting tree to file: /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_8_E0.ebf....Done\\nTime Tree generation/reading = 1.31875 \\nBulge sigma_v=[113, 115, 100, 71.62]\\n [feh, dfeh, age, dage]=[0, 0.2, 1e+10, 0]\\nBulge:\\nMaking Tree\\nAge=1e+10 lAge=0\\n Total Nodes=3307769 leafs=2894298\\nCompleted % <1..10..20..30..40..50..60..70..80..90..100..>\\nMass=1.9211e+10 Leafs=2894298 Total Nodes=3307769 1.9211e+10 2.37753\\nWriting tree to file: /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_9_E0.ebf....Done\\nTime Tree generation/reading = 15.7197 \\nTotal Time= 1240.56 \\n'\n" ] } ], "source": [ "modelFile = '/home/rybizki/Programme/GalaxiaData/Model/population_parameters_BGM_update.ebf' \n", "ebf.update_ind(modelFile, '/popidstart', 0, ind=0)\n", "ebf.update_ind(modelFile, '/popidend', 10, ind=0)\n", "ebf.update_ind(modelFile, '/norm_thin_disk', 0.9, ind=0)\n", "ebf.update_ind(modelFile, '/norm_thick_disk', 0.8, ind=0)\n", "args = ['galaxia', '-s', 'warp']\n", "p = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\t\n", "print(\"Galaxia spawns new thin disk BHtree\")\n", "(output, err) = p.communicate()\n", "print(output)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/rybizki/anaconda3/lib/python3.6/site-packages/astropy/extern/bundled/six.py:60: ResourceWarning: unclosed file <_io.TextIOWrapper name='/home/rybizki/anaconda3/lib/python3.6/site-packages/astropy/extern/bundled/six.py' mode='r' encoding='utf-8'>\n", " class X(object):\n", "/home/rybizki/anaconda3/lib/python3.6/importlib/_bootstrap.py:205: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", " return f(*args, **kwds)\n", "/home/rybizki/anaconda3/lib/python3.6/importlib/_bootstrap.py:205: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", " return f(*args, **kwds)\n", "/home/rybizki/anaconda3/lib/python3.6/importlib/_bootstrap.py:205: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", " return f(*args, **kwds)\n", "/home/rybizki/anaconda3/lib/python3.6/importlib/_bootstrap.py:205: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", " return f(*args, **kwds)\n", "/home/rybizki/anaconda3/lib/python3.6/importlib/_bootstrap.py:205: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", " return f(*args, **kwds)\n", "/home/rybizki/anaconda3/lib/python3.6/importlib/_bootstrap.py:205: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", " return f(*args, **kwds)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Galaxia spawns catalogue\n", "########################################################################################\n", "############################# GALAXIA OUTPUT END ##################\n", "########################################################################################\n", "391087\n", "('rad', 'teff', 'vx', 'vy', 'vz', 'pz', 'px', 'py', 'feh', 'exbv_schlegel', 'lum', 'glon', 'glat', 'smass', 'age', 'grav', 'gaia_g', 'gaia_bpft', 'gaia_bpbr', 'gaia_rp', 'gaia_rvs', 'popid', 'mact')\n", "converting to npy and appending ra and dec took 2.2 sec\n", "0 391087\n", "converting time and applying extinction map for 391087 sources in nside = 512 took 1.7 sec\n", "indexing and remapping to isochrones took 3.4 sec\n", "calculating extinction curve for all bands took 10.6 sec\n", "391087\n", "391020\n", "calculated healpix\n", "calculated pmdec pmra and rv\n", "cleaning of data took 2.1 sec\n", "plotting time took 0.5 sec\n", "Total time in minutes: 0.6\n", "Galaxia spawns catalogue\n", "b'Galaxia-v0.81\\nCODEDATAPATH=/home/rybizki/Programme/GalaxiaData/\\nReading Parameter file- ../output/final1_0.001/GDR2mock_20.7Gmag.log\\n--------------------------------------------------------\\noutputFile GDR2mock_20.7Gmag \\nmodelFile Model/population_parameters_BGM_update.ebf\\ncodeDataDir /home/rybizki/Programme/GalaxiaData\\noutputDir ../output/final1_0.001 \\nphotoSys parsec1/GAIADR3 \\nmagcolorNames gaia_g,gaia_bpft-gaia_rp\\nappMagLimits[0] -1000.000000 \\nappMagLimits[1] 20.700000 \\nabsMagLimits[0] -1000.000000 \\nabsMagLimits[1] 1000.000000 \\ncolorLimits[0] -1000.000000 \\ncolorLimits[1] 1000.000000 \\ngeometryOption 0 \\nlongitude 0.000000 \\nlatitude 90.000000 \\nsurveyArea 1000.000000 \\nfSample 0.001000 \\npopID -1 \\nwarpFlareOn 1 \\nseed 1 \\nr_max 1000.000000 \\nstarType 0 \\nphotoError 0 \\n--------------------------------------------------------\\nReading tabulated values from file- /home/rybizki/Programme/GalaxiaData/Model/vcirc.dat\\nUsing geometry: All Sky\\nReading Isochrones from dir- /home/rybizki/Programme/GalaxiaData/Isochrones/padova/parsec1/GAIADR3\\nzsol=0.0152\\n/home/rybizki/Programme/GalaxiaData/Isochrones/padova/parsec1/GAIADR3\\n13275 75 177\\nIsochrone Grid Size: (Age bins=177,Feh bins=75,Alpha bins=1)\\nTime Isochrone Reading 0.714702 \\nGenerating populations................\\n--------------------------------------------------------\\nThin disc sigma_v=[50, 32.3, 21, 0.33, 0.33]\\n [feh, dfeh]=[0.01, -0.12], [0.01, 0.18]\\nThin Disk,ID=0:\\nReading tree from file- /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_0_E1.ebf\\nTime Tree generation/reading = 0.424743 \\nCompleted % <0..10..20..30..40..50..60..70..80..90..>\\nStars spawned = 62446 \\nTime Spawning= 13.2147 \\n--------------------------------------------------------\\nThin disc sigma_v=[50, 32.3, 21, 0.33, 0.33]\\n [feh, dfeh]=[0.01, -0.12], [0.01, 0.18]\\nThin Disk,ID=1:\\nReading tree from file- /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_1_E1.ebf\\nTime Tree generation/reading = 0.397122 \\nCompleted % <0..9..19..29..39..49..59..69..79..89..99..>\\nStars spawned = 341472 \\nTime Spawning= 16.6258 \\n--------------------------------------------------------\\nThin disc sigma_v=[50, 32.3, 21, 0.33, 0.33]\\n [feh, dfeh]=[0.01, -0.12], [0.01, 0.18]\\nThin Disk,ID=2:\\nReading tree from file- /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_2_E1.ebf\\nTime Tree generation/reading = 0.590767 \\nCompleted % <0..9..19..29..39..49..59..69..79..89..99..>\\nStars spawned = 402130 \\nTime Spawning= 17.9841 \\n--------------------------------------------------------\\nThin disc sigma_v=[50, 32.3, 21, 0.33, 0.33]\\n [feh, dfeh]=[0.01, -0.12], [0.01, 0.18]\\nThin Disk,ID=3:\\nReading tree from file- /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_3_E1.ebf\\nTime Tree generation/reading = 0.560163 \\nCompleted % <0..9..19..29..39..49..59..69..79..89..99..>\\nStars spawned = 393857 \\nTime Spawning= 16.1547 \\n--------------------------------------------------------\\nThin disc sigma_v=[50, 32.3, 21, 0.33, 0.33]\\n [feh, dfeh]=[0.01, -0.12], [0.01, 0.18]\\nThin Disk,ID=4:\\nReading tree from file- /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_4_E1.ebf\\nTime Tree generation/reading = 0.683416 \\nCompleted % <0..9..19..29..39..49..59..69..79..89..99..>\\nStars spawned = 792155 \\nTime Spawning= 23.3877 \\n--------------------------------------------------------\\nThin disc sigma_v=[50, 32.3, 21, 0.33, 0.33]\\n [feh, dfeh]=[0.01, -0.12], [0.01, 0.18]\\nThin Disk,ID=5:\\nReading tree from file- /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_5_E1.ebf\\nTime Tree generation/reading = 0.729725 \\nCompleted % <0..9..19..29..39..49..59..69..79..89..99..>\\nStars spawned = 846746 \\nTime Spawning= 26.57 \\n--------------------------------------------------------\\nThin disc sigma_v=[50, 32.3, 21, 0.33, 0.33]\\n [feh, dfeh]=[0.01, -0.12], [0.01, 0.18]\\nThin Disk,ID=6:\\nReading tree from file- /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_6_E1.ebf\\nTime Tree generation/reading = 1.21254 \\nCompleted % <0..10..20..30..40..50..60..70..80..90..>\\nStars spawned = 1469099 \\nTime Spawning= 51.8327 \\n--------------------------------------------------------\\nfeh=-0.17 sig_feh=0.27\\nThick disc sigma_v=[67, 51, 42, 0.33, 0.33]\\n [feh, dfeh, age, dage]=[-0.48, 0.3, 1.1e+10, 1e+09]\\nThickDisk:\\nReading tree from file- /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_7_E0.ebf\\nTime Tree generation/reading = 0.061928 \\nCompleted % <0..9..19..29..39..49..59..69..79..89..99..>\\nStars spawned = 810421 \\nTime Spawning= 8.67471 \\n--------------------------------------------------------\\nSpheroid sigma_v=[131, 106, 85]\\n [feh, dfeh, age, dage]=[-1.5, 0.5, 1.3e+10, 0]\\nSpheroid:\\nReading tree from file- /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_8_E0.ebf\\nTime Tree generation/reading = 0.057675 \\nCompleted % <0..9..19..29..39..49..59..69..79..89..99..>\\nStars spawned = 1974 \\nTime Spawning= 3.95999 \\n--------------------------------------------------------\\nBulge sigma_v=[113, 115, 100, 71.62]\\n [feh, dfeh, age, dage]=[0, 0.2, 1e+10, 0]\\nBulge:\\nReading tree from file- /home/rybizki/Programme/GalaxiaData/BHTree-2.3/bhtree_with_wf/bhtree_9_E0.ebf\\nTime Tree generation/reading = 0.703309 \\nCompleted % <0..9..19..29..39..49..59..69..79..89..99..>\\nStars spawned = 1191701 \\nTime Spawning= 31.8059 \\n--------------------------------------------------------\\nTotal stars written 6312001 \\nFile written- ../output/final1_0.001//GDR2mock_20.7Gmag.ebf\\nCalulating magnitudes................\\nReading Isochrones from dir- /home/rybizki/Programme/GalaxiaData/Isochrones/padova/parsec1/GAIADR3\\nzsol=0.0152\\n/home/rybizki/Programme/GalaxiaData/Isochrones/padova/parsec1/GAIADR3\\n13275 75 177\\nIsochrone Grid Size: (Age bins=177,Feh bins=75,Alpha bins=1)\\nTime Isochrone Reading 1.10757 \\ngaia_g\\ngaia_bpbr\\ngaia_bpft\\ngaia_rp\\ngaia_rvs\\nCalulating Extinction................\\nTime for extinction calculation 1.84178 \\nTotal Time= 222.215 \\n'\n", "########################################################################################\n", "############################# GALAXIA OUTPUT END ##################\n", "########################################################################################\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "6312001\n", "('rad', 'teff', 'vx', 'vy', 'vz', 'pz', 'px', 'py', 'feh', 'exbv_schlegel', 'lum', 'glon', 'glat', 'smass', 'age', 'grav', 'gaia_g', 'gaia_bpft', 'gaia_bpbr', 'gaia_rp', 'gaia_rvs', 'popid', 'mact')\n", "converting to npy and appending ra and dec took 34.0 sec\n", "0 6312001\n", "1000000 6312001\n", "2000000 6312001\n", "3000000 6312001\n", "4000000 6312001\n", "5000000 6312001\n", "6000000 6312001\n", "converting time and applying extinction map for 6312001 sources in nside = 512 took 23.2 sec\n", "indexing and remapping to isochrones took 34.3 sec\n", "calculating extinction curve for all bands took 177.0 sec\n", "6312001\n", "1544693\n", "calculated healpix\n", "calculated pmdec pmra and rv\n", "cleaning of data took 9.6 sec\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/rybizki/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py:1405: UserWarning: \n", "This call to matplotlib.use() has no effect because the backend has already\n", "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", "or matplotlib.backends is imported for the first time.\n", "\n", " warnings.warn(_use_error_msg)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "total number of stars = 1544693000\n", "0.0\n", "1544693000.01\n", "117789833989.0\n", "plotting time took 3.4 sec\n", "Total time in minutes: 8.4\n" ] } ], "source": [ "name = 'final1'\n", "create_gdr2mock_mag_limited_survey(nside = 512, outputDir = '../output/%s_100pc' %(name),\n", " use_previous = False, delete_ebf = True,\n", " fSample = 1, make_likelihood_asessment=False, r_max = 0.1,\n", " verbose = False)\n", "create_gdr2mock_mag_limited_survey(nside = 512, outputDir = '../output/%s_0.001' %(name),\n", " use_previous = False, delete_ebf = True,\n", " fSample = 0.001, make_likelihood_asessment=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

boffi/boffi.github.io

dati_2014/09/Subspace_Iteration.ipynb

1

362367

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Usual boilerplate." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import matplotlib.pyplot as pl\n", "from matplotlib import rcParams\n", "\n", "from IPython.display import HTML, Latex, display\n", "\n", "from scipy import *\n", "from scipy.linalg import eigh\n", "set_printoptions(linewidth=180)\n", "\n", "import json ; s = json.load(open(\"mplrc.json\")) ; del json\n", "rcParams.update(s)\n", "rcParams['figure.figsize'] = 6,10\n", "\n", "def css_styling():\n", " styles = open(\"custom.css\", \"r\").read()\n", " return HTML(styles)\n", "css_styling()" ], "language": "python", "metadata": {}, "outputs": [ { "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:800px;\n", " margin-left:10% !important;\n", " margin-right:auto;\n", " }\n", " h1 {\n", " font-family: Candara, Cambria, serif;\n", " }\n", " h2 { text-align:center; }\n", " h4{\n", " margin-top:12px;\n", " margin-bottom: 3px;\n", " }\n", " div.text_cell_render{\n", " font-family: Candara, Calibri, Trebuchet, Geneva, sans-serif;\n", " line-height: 155%;\n", " font-size: 130%;\n", " width:800px;\n", " margin-left:auto;\n", " margin-right:auto;\n", " }\n", " .CodeMirror{\n", " background-color: #FDFCFF;\n", " font-family:\"Aurulent Sans Mono\",\"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: #3035A1;\n", " font-style: italic;\n", " margin-bottom: .5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\n", " \n", " .warning{\n", " color: rgb( 240, 20, 20 )\n", " } \n", "\n", " BODY {background-color: rgb( 252, 255, 220 )}\n", "\n", "</style>\n", "<script>\n", " MathJax.Hub.Config({\n", " TeX: {\n", " extensions: [\"AMSmath.js\"]\n", " },\n", " tex2jax: {\n", " inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n", " displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n", " },\n", " displayAlign: 'center', // Change this to 'center' to center equations.\n", " \"HTML-CSS\": {\n", " styles: {'.MathJax_Display': {\"margin\": 4}}\n", " }\n", " });\n", "</script>\n" ], "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "<IPython.core.display.HTML at 0x7f7434071410>" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Following, some utility functions:\n", "<dl>\n", "\n", "<dt>redeig</dt>\n", "<dd>solves the reduced eigenvalue problem</dd>\n", "\n", "<dt>plot_eigenvectors</dt>\n", "<dd>given a shear type building, this function plots its eigenvectors starting with a zero displacement at ground level (zero displacement that is not contained in any of the eigenvectors...)</dd>\n", "\n", "<dt>same_sign</dt>\n", "<dd>library functions return eigenvectors with arbitrary signs, this function\n", "change the sign of a whole column if the sign of the last element is negative</dd>\n", "\n", "<dt>error</dt>\n", "<dd>starting from a matrix of errors, computes the SRSS column by column</dd>\n", "</dl>" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def redeigh(K, M, phi):\n", " \"\"\"Solves the reduced eigenproblem in subspace iteration method.\n", " Input: phi, a 2-d array containing the current subspace;\n", " output: 1. 1-d array of eigenvalues estimates;\n", " 2. 2-d array of eigenvector estimates in Ritz coordinates.\"\"\"\n", " \n", " # compute the reduced matrices\n", " Mr = phi.T*M*phi\n", " Kr = phi.T*K*phi\n", " \n", " # solve the reduced eigenproblem, using a library function\n", " return eigh(Kr,Mr)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_eigenvectors(evecs):\n", " \n", " floors = range(shape(evecs)[0]+1)\n", " \n", " for i,v in enumerate(evecs_e.transpose()):\n", " pl.plot(concatenate(([0],ravel(v))), floors, linewidth=2, label='Mode #%d'%(i+1,))\n", " \n", " pl.vlines(0,0,floors[-1])\n", " xmn, xmx = pl.xlim() ; pl.xlim(xmn, 1.6*xmx)\n", " pl.yticks(floors) ; pl.legend(loc=0)\n", " pl.show()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "def same_sign(arr):\n", " \"modifies \\\"in place\\\" a 2-D array, forcing the last row to be non-negative.\"\n", " for col in asarray(arr).transpose():\n", " col *= sign(col[-1])\n", " return None" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "def error(arr):\n", " err = []\n", " for col in arr.transpose():\n", " \n", " err.append(sqrt(sum(ravel(col)*ravel(col))))\n", " return err" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "prompt_number": 8 }, { "cell_type": "heading", "level": 1, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Shear-Type Building, Subspace Iteration " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We analyse a 2D frame, with negligible flexural deformations in beams and negligible \n", "shear and axial deformation, all the deformations are due to the lateral flexibility\n", "of columns. Under these assumptions, we can study a dynamic model where we have 1 DOF\n", "for each floor.\n", "\n", "Our frame has twelve floors and hence 12 DOF. DOF are numbered from the bottom up to \n", "the top if the frame.\n", "\n", "We need only the lowest 4 eigenvalues-eigenvectors, so we can use the subspace iteration method with a base\n", "`phi` that's a 12x4 array.\n", "\n", "The floor masses are all the same, while the story stiffnesses are decreasing with height, starting from\n", "23$k$ for storey 1, i.e., between the ground and the first floor, down to 12$k$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We start creating a list containing the lateral stiffnesses, taking into account also the (zero!) stiffness of the (non existing) storey above the top, as this trick is handy when definining the coefficients of the stiffness matrix. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "story_stiffness = range(23,11,-1)\n", "story_stiffness.append(0)\n", "story_stiffness = array(story_stiffness)\n", "y = array(range(13))\n", "print \"Storey: \",','.join([\" %2d\"%(i+1,) for i in y])\n", "print \"Stiffness: \",\",\".join([\" %2d\"%(s,) for s in story_stiffness])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Storey: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13\n", "Stiffness: 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 0\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We construct the structural matrices, `M` being a unit matrix and `K` is constructed as the superposition of 3\n", "matrices of which we specify one of the diagonals. The index set `[:-1]` means from the first to the penultimate,\n", "`[1:]` means from the second to the last and `[1:-1]` means from the second to the penultimate.\n", "\n", "While we are at it, we compute also the dynamic matrix." ] }, { "cell_type": "code", "collapsed": false, "input": [ "M = matrix(eye(12))\n", "ss = story_stiffness\n", "K = (diag(+ss[:-1] + ss[1:]) +\n", " diag(-ss[1:-1], k=+1) +\n", " diag(-ss[1:-1], k=-1) ) * 1.0\n", "K = matrix(K)\n", "D = K.I*M" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "print \"normalized mass matrix M/m:\"\n", "print M\n", "print\n", "print \"normalized stiffness matrix K/k:\"\n", "print K" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "normalized mass matrix M/m:\n", "[[ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n", "\n", "normalized stiffness matrix K/k:\n", "[[ 45. -22. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [-22. 43. -21. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. -21. 41. -20. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. -20. 39. -19. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. -19. 37. -18. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. -18. 35. -17. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. -17. 33. -16. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. -16. 31. -15. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. -15. 29. -14. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. -14. 27. -13. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. -13. 25. -12.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -12. 12.]]\n" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "To have something to compare our approximate results with, we compute, using a library function, a very\n", "good approximation to the eigenvalues and eigenvectors of our system." ] }, { "cell_type": "code", "collapsed": false, "input": [ "evals, evecs = eigh(K,M,eigvals=(0,3))\n", "same_sign(evecs)\n", "print \"first four eigenvalues of the system\"\n", "print evals,\"\\n\"\n", "print \"first four eigenvectors of the system\"\n", "print evecs" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "first four eigenvalues of the system\n", "[ 0.30522543 2.38337068 6.39983138 12.0934459 ] \n", "\n", "first four eigenvectors of the system\n", "[[ 0.04134929 -0.11705278 0.18702514 -0.2464852 ]\n", " [ 0.08400443 -0.22674522 0.32814555 -0.36868084]\n", " [ 0.12746979 -0.31592693 0.37598234 -0.28438 ]\n", " [ 0.17116307 -0.37191918 0.3058998 -0.02390741]\n", " [ 0.21440635 -0.38420463 0.12909148 0.26549126]\n", " [ 0.25641635 -0.34630027 -0.1034375 0.39259518]\n", " [ 0.29629371 -0.25761554 -0.31070451 0.24789177]\n", " [ 0.33301114 -0.12501344 -0.40664718 -0.09322222]\n", " [ 0.36540017 0.03629236 -0.3354878 -0.38191861]\n", " [ 0.39213631 0.20294157 -0.1058838 -0.36132817]\n", " [ 0.41172215 0.34520341 0.19350809 -0.00302288]\n", " [ 0.42246781 0.43075809 0.4146477 0.38818761]]\n" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "The initial base is a 12x4 matrix, with linearly independent columns. To start with a very bad set,\n", "we choose a matrix of random numbers. The call to `random.seed` insures repeatability of the results." ] }, { "cell_type": "code", "collapsed": false, "input": [ "random.seed(8+8+1988) # good luck\n", "phi = matrix(random.random((12,4)))-0.5\n", "print \"initial subspace vectors\"\n", "print phi" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "initial subspace vectors\n", "[[ 0.08774829 0.30443898 -0.0296647 -0.01430415]\n", " [ 0.25873875 0.291916 0.08631742 0.47198061]\n", " [ 0.06319749 -0.42881828 -0.08097817 -0.05726654]\n", " [-0.00735125 -0.35594205 -0.34629339 0.16050985]\n", " [-0.28991909 -0.43293504 0.46643776 0.42592043]\n", " [ 0.3226598 0.20420248 0.49683776 0.48270073]\n", " [ 0.23320919 0.4463363 -0.36077892 -0.21269507]\n", " [ 0.33961838 -0.18947759 0.22706267 0.1577504 ]\n", " [ 0.44644677 0.02433346 -0.06984466 0.15335013]\n", " [-0.22501533 0.44581842 -0.14249575 -0.07682477]\n", " [ 0.21228054 -0.34494495 -0.44618718 0.31013833]\n", " [-0.30559631 -0.36312087 0.19230842 0.35211024]]\n" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's time to iterate, starting from a very bad choice for the initial base vectors.\n", "\n", " 1. compute evals and evecs in Ritz coordinates\n", " 1. compute estimates of evals and evecs in natural coordinates (no-op for evals, matrix mult for evecs)\n", " 1. compute new base for next iteration applying matrix iteration procedure to the \n", " estimated eigenvectors\n", " 1. display the current results" ] }, { "cell_type": "code", "collapsed": true, "input": [ "description = Latex(r'$$\\text{error}_i = \\sqrt{\\textstyle{\\sum_j} \\Delta\\psi_{ji}^2}$$')\n", "\n", "for i in range(5):\n", "\n", " display(HTML(\"<h3>Iteration #%2.2d</h3>\"%(i+1,)))\n", "\n", " ritz_evals, ritz_evecs = redeigh(K, M, phi)\n", " \n", " # \"_e\" is for \"estimate\"\n", " evals_e = ritz_evals\n", " evecs_e = phi*ritz_evecs\n", " same_sign(evecs_e) # force the same sign in the last component of evecs\n", " \n", " # compute the new base\n", " phi = D*evecs_e\n", " \n", " # show what we have done\n", " print \"\\\"Real\\\" eigenvalues \", evals\n", " print \"Estimated eigenvalues \", evals_e\n", " print \"Relative error (e-r)/r \", (evals_e-evals)/evals_e\n", " print \"2-norm of the difference between estimated eigenvectors and \\\"real\\\" ones\"\n", " display(description)\n", " print error(evecs_e-evecs)\n", "\n", " display(HTML(\"<h5>The normalised shapes at iteration #%2.2d</h5>\"%(i+1,)))\n", "\n", " plot_eigenvectors(evecs_e)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<h3>Iteration #01</h3>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x7f7406baba10>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\"Real\" eigenvalues [ 0.30522543 2.38337068 6.39983138 12.0934459 ]\n", "Estimated eigenvalues [ 13.10440076 30.01414993 38.43979497 41.08619637]\n", "Relative error (e-r)/r [ 0.97670817 0.92059176 0.83351026 0.70565672]\n", "2-norm of the difference between estimated eigenvectors and \"real\" ones\n" ] }, { "latex": [ "$$\\text{error}_i = \\sqrt{\\textstyle{\\sum_j} \\Delta\\psi_{ji}^2}$$" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Latex at 0x7f7407525310>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[1.1222149251125211, 1.1796367709331772, 1.3870147933196972, 1.2564360915829249]\n" ] }, { "html": [ "<h5>The normalised shapes at iteration #01</h5>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x7f7407525350>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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UlxjuNLKfWynSFxTXt7M+roTDmTUIgK6mlKX+1iz1t0ZfS4aGhvqmdov0PqJQ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\nwsbMjou3wpi94T3ylcUX6xGtnKhrbURcWj6/hd0p/ngPFr8k/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"text": [ "<matplotlib.figure.Figure at 0x7f7406baba10>" ] }, { "html": [ "<h3>Iteration #02</h3>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x7f7404b7d150>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\"Real\" eigenvalues [ 0.30522543 2.38337068 6.39983138 12.0934459 ]\n", "Estimated eigenvalues [ 0.31010674 3.32457743 9.80770616 30.93579931]\n", "Relative error (e-r)/r [ 0.01574074 0.28310568 0.34746909 0.60907925]\n", "2-norm of the difference between estimated eigenvectors and \"real\" ones\n" ] }, { "latex": [ "$$\\text{error}_i = \\sqrt{\\textstyle{\\sum_j} \\Delta\\psi_{ji}^2}$$" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Latex at 0x7f7407525310>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[0.026013536021440502, 0.33275412412714811, 0.62533045465732917, 1.1454940039193824]\n" ] }, { "html": [ "<h5>The normalised shapes at iteration #02</h5>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x7f7404b70e10>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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YO5nju5QLGYkEx2zmSOIeTlyK5sSlaGwtHZjuOZ+Z3ovo7dBPFy+lW6CqriH6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ze/eZ6ZE2KJC49nZuh87G3Nt+BLQHwM+f/8gpLEVKtsJzenlnYNRVXNKBUwxsA76lP5lWz9IguA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\n8SaoWojBR4Ja9JlPx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"text": [ "<matplotlib.figure.Figure at 0x7f7404b7d150>" ] }, { "html": [ "<h3>Iteration #03</h3>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x7f7402f8e710>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\"Real\" eigenvalues [ 0.30522543 2.38337068 6.39983138 12.0934459 ]\n", "Estimated eigenvalues [ 0.30522787 2.40339175 6.88393993 20.46143244]\n", "Relative error (e-r)/r [ 8.00522357e-06 8.33033770e-03 7.03243422e-02 4.08963868e-01]\n", "2-norm of the difference between estimated eigenvectors and \"real\" ones\n" ] }, { "latex": [ "$$\\text{error}_i = \\sqrt{\\textstyle{\\sum_j} \\Delta\\psi_{ji}^2}$$" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Latex at 0x7f7407525310>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[0.00043832269195543506, 0.043379390953476475, 0.25046374787469505, 0.72386302239959255]\n" ] }, { "html": [ "<h5>The normalised shapes at iteration #03</h5>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x7f74049ad0d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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MSZI4dnkPZTtnI7cCFwd3/tfvM1rW8nvxxiUMVbaGQ7tuEHg8DIDyvq4MGNUIDy+HIu87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\ny03my/C3OHv5OADDekxgzpiXsVXYmTiylksk6tsojKtI1GJEfWet2zs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i1qg4FruPiKhw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D0LCvOmAELYihRqC3FSaxnffzoLHvmKts06kV2QybvrXuClL6eRknG23LFNx96Ok68XmYf+5Mov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\naYh6dDxg3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"text": [ "<matplotlib.figure.Figure at 0x7f74340754d0>" ] }, { "html": [ "<h3>Iteration #04</h3>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x7f7402fc8810>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\"Real\" eigenvalues [ 0.30522543 2.38337068 6.39983138 12.0934459 ]\n", "Estimated eigenvalues [ 0.30522543 2.3836799 6.4585566 13.94679419]\n", "Relative error (e-r)/r [ 2.01624404e-09 1.29721474e-04 9.09262310e-03 1.32887047e-01]\n", "2-norm of the difference between estimated eigenvectors and \"real\" ones\n" ] }, { "latex": [ "$$\\text{error}_i = \\sqrt{\\textstyle{\\sum_j} \\Delta\\psi_{ji}^2}$$" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Latex at 0x7f7407525310>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[5.4263989022927158e-06, 0.0041972988334928211, 0.070432051384797648, 0.33120323277454511]\n" ] }, { "html": [ "<h5>The normalised shapes at iteration #04</h5>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x7f7402f9f510>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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85D1q1Khu0McsWtRWLDktkUkrP+FhfASVSlVnXK8fsFGabu3AOyFxbFtzntgo\nXSu6yRu+NH+zIkqlZRR6Kih1ShpnR8wEoOoX7+NcJedVPV5WbMqjUR9m7Pp40ZawIVlqPerffluC\njY0NAwf0IzQszKCPWSRqK5WhSmPqmpGERd6kjEd5JvWbj4OtaVbhVmVq2P/PDU4cDAEJPEsUpUuf\nWpTyfvlykOZwZfpvpIbdw6VmJSqPGmC081hC14elsMR61PFx8aSmpeHfqBmZmSrS0tJo9Hpztmxe\nT8mSJV7q8Rae35aCwag1KmZt+JKrd4LwcPZicv+fcXYs+MrNL+NOaByL5hzhxIEQZECTNyow5PMm\nhTZJx566RPDCdcgUCuotGI/cxjhtn9RMDelqLbYKGY5i+rhF1qM+fHgfZ04fI/DEYbZuWY+DgwMn\njh966SQNokVtdbSSlvnbJ3P65mGcHIoxuf9CPF1KGv28qkwNB/69wYkDIUgSeHjpWtGlfQpnggbd\n+odnstY//LQvrnWqGu1c2ZNdrHz6uCXXo36ShGTQ10nUo8Z66lFLksQfu79n24lV2Ns4MG3QIiqX\nrmGoELM9W4/6blg821efJzoyBZkMXm/pS4u2lVDamLcv+mWf0yszFnNt1u8UrVCW1kdXoHCwN2R4\nT58rMpMP112kmpcji3vmnBwMQdSjNg1Rj1rI1aajf7LtxCqUciVf9frBKEn6SWqVhgP/3uT4/tu6\nVnTxInTuW5syhbgVnSXh0k2uf78UgHoLxhs1ScPj/ml30T9tlUSithK7z25m+b75yJDxv27TqVvh\n5QrY56eYUykWf3+UqIfJ2a3olu3M34o2BK1azZnhM3TrHw7ujkfjF1v/UB8xKVmTXUSitkYiUVuB\nY1f3sXCnbkjS0PZjaVr9TaOdS63W8Fr5VlQu25ioh8m4exahc99alC1nmouVphC8cB3x567iUMaL\n6pNMswL74xEf4iNrjcSr/oq7EHKKOZu+Qitp6dviI9o37Gm0c90Lj2fbmgtU8W6KJEk0alGelu0q\nY2Nb+FvRWZJv3eHK9EUA1J37JTbOplmSLDtROxi5RW3wK1ZCjvR8nkWifoXdun+V6Ws/Q61R0aFh\nL3o1/9Ao51GrNRzaHczRfbeRtBLJqTGcub6dSXM3GOV85iJptZwdMUO3/mHvdpRo09hk535c58O4\nidre3o6HD6MpXtzdqkeXGIskSURGxmBvb6fXfiJRv6IiYsL4ZuUnpGWm0LxGW6PV77h/J4Fta84T\neT8ZZODrHG8RAAAgAElEQVQfUI7x02ag0aoNfi5zC1m6jegjuvUPa84cZdJzx6aYpuvDy8uDxMQk\nwsIjXvj9Ins0PUNCa8jQjMLUsUqShJtrMZyd9VtBXiTqV1BMYiSTVnxMQmocdSs0ZmTXKchlhp0k\noVFrObQnmCN7byFpJdw8HOncpxbevm6MnfLqJenUiEguTZwPQO3Zo7F7yfUP9RVnwlmJzs5OODu/\n+BqU1jbs1RREon7FJKUlMGnlMCIT7lO5dE2+6jkHG4VhP9wPIhLZtvo8D+8l6VrRzcvRqkOVV6ov\n+kmSJBH02SzUSamU7BhgkPUP9fXkhBfB+ohE/QpJz0xj6uoRhEfdoqxHeSb2/Ql7WweDHV+j1nJ4\nbzBH9txCq5Vwdde1on0quBnsHJbo7sbdPNh1FBuXotSZM9rkfbdpKg2pKt308SK2Yvq4NRKJ+hWh\nq98xhmt3L+DpUoLJA37B2dFwE0seRCSyfc0FHkQkAuDXzIdWHapga/dqv4UyouM4/8UPANScNgKH\nkp4mjyE2VdeV5OZoKy7wWalX+1NmJbSSlnnbvuFM8FGcHYsxuf8veDh7GeTYGo2WI3tvcXh3MFqt\nRDE3Bzr3qUW5iu4GOb6lO//FD2TGJuAZ0ACfAZ3MEoMp+6cFyyQSdSEnSRK/7/qegxf/xsHWkUn9\nFlDGo5xBjv3wXiLbVj9uRTds6kPrjq9+KzrLvb8PcXfTHhSO9tSb95XZWrNZLWoxK9F6Wccn7hW2\n4fDv7AhcjVJhw7heP1Cp1MuvLKHRaDm67zaHdt9Eq9G1ojv1rkn5Sh4GiLhwUCUkE/Q/3Tp41Sd8\nRJHy5itUFJv2qEVdRCRqayUSdSH27+mNrNz/MzJkfN5tOrV9/V/6mJH3k9i2+jz37+pa0fUbe/NG\np6rY2VvXW+XihPmk34/CrWENKgx9x6yxPO76MN3qO4Jlsa5P3yvk6JU9/PLXDAA+7jieJq+1eanj\naTVaju2/zcF/g9FotLi42tOpdy18K1tPKzpL5MHThC7b9mj9w/FGWf9QH1ldH6JynvUSiboQOn87\nkO83j0dCon/LT2hbv/tLHS/qQRLb1lzgXngCAPVeL0ubzlWxs7e+xKBOTefco/UPq4x5D+eqxln/\nUB+iRS2IRF3I3Lx3mRnr/odao6KTf1/eafbBCx9Lq9Fy/EAIB/65iUajxbmYPZ161aRCVdMPQbMU\nV6b/RkpoBC41KlLFiOsf6iN79XHRR221RKIuRO5E3WbyquGkZabSolYHPnjr8xceiRD1MJltq89n\nt6LrNtK1ou2NXZ3NgsWe1q1/iFxOvfnjkdtaxnMRlz2O2jLiEUxPJOpCIjrhAV8vG0xiajwNKjVl\nROdJL1S/Q6uVOHEghP3/3ECj1rWiO/aqSUUrbkUDaDNVnB0+A7RaKo3oh2s94y13pa/Yp7o+Xr06\nKkL+8v2kBwYG0bplPwCCg0Np3rQXLZr3YfiwiUjPrskuGEViajzjlw0mKuE+VcvW5st3vkP5AvU7\noh8m8+dPx9m74xoatZY6/mX46ItmVp+kAa7/sIzEq7cp4luG18YNMXc42TLUWlJVWmwUMpzsXs1a\nKkL+8kzUs79bxEdDxpORoSsIM/p/M5g243MOHFqDJMH2bXtNEqQ1S8tMZcrqEdyJuoVP8YpM6DMP\nOxv96ndotRLH99/mtzlHiAiLx8nFjj4fNqBz71pW3dWRJeHKLa7NWQpAvfnjjL7+oT6yWtOuDta9\n+ri1yzNRV6zow4bNP2e3nM+dvUzz5rrl09u2a86+vUeNH6EVU2lUfLt+DDciLlK8WCmmDlqCk4N+\n5TVjIpNZOv8Ee7brWtG1/Urz8ZfNqVStuJGiLly0Gg1nP5mOpFJT/v238Wxaz9whPSV7aF4RMeLD\nmuXZR/12t7cIDb2bffvJno4iRYuQkJCU+4GVltMqyY1Mrnv4lhirJEnM2/YN524dw6WIGzPeW4Zn\nsdJIehTkvxMSy/KFx8hIV+PkbE/nPnWpUqOkEaPOomv5WeLz+iSZXMm1WUuIO3sFh9Je1Jn+ucXF\nnJCRAoBbEVtkcqXFxZcTS/5cPauwxKrXxUS5/PFPr+SkZIoVc85128lTpmb/PyCgOS0CAl4gPOu1\n/tBi9gVtw87GgSkDFlFaz/odEeFxLF94hIx0NdVqlaJrv3o4iHG4T7m/5yiXpv4MMhkNF07CRs9V\nN0zhYZKu27F4Uf2WbhIKhwMHD3Lw4KHs2wEBLXPcTq9EXafuaxw8GEhAgD///nOIVq1fz3Xb8ePG\nPHXbEldQsNTVHY5d2cuyvT9mTw0v71UhuyVdkFjv301gxcJAXZKuXYLuA2ohV2hN+Dh1P70s7Xl9\nUkrYPU68+wVIEtW+GoxHi3oWGW9EvK5FXdLJFkmrtsgYn2Wpn6ucmDvWpk38adrkcemHI0dP57hd\ngRJ11kWM2d+PY+iQcWRmqnjttYp079HOAKEKT7p57zI/bJkAwLttRtGoas7fsLl5eC+Rlb+eJD1N\nTZUaXnQbUAe5QhSbf5ImPYPAgePIjE2g5FtNqfrF++YOKVcPkjIAKOEsWtTWLN9EXa5cGY4c060m\nXalSOf47sNroQVmr6MSHTF8zikx1Om3qdqXr6/rNjIt6kMSKX06SlqKi0muedB9UB4VI0s85P+Z7\n4oOuUaRcafyWTEcmt9zn6EGiruujpEjUVs1y36FWJi0zlalrRhKbHE3Ncg34qMM4vYZjRUcms3xh\nIKnJmfhW8eCdd+uhVIpxt88KXb6d0OXbkdvb0XjV9yZfpFZfDx71UYsWtXUTidoCaLQavt88jpAH\n1ynl5s1YPRekjY1KYcXCQFKSMilXyZ1e79dHaSOS9LPizl0jaPQcAOr+MAbXOlXNHFHe0lQa4tPU\n2MhlYnielROJ2gIs3zefk9cPUtTemQl9f9JrrHR8bCrLFwaSlJCBdwU3en9Q/5VdDfxlZMQmEDhw\nLNqMTMq/9zY+/TqaO6R8ZbWmvZxtkYvJLlZNJGoz2312C1uOLUMhVzK25xxKu/sUeN+EuDSW/xxI\nYnw6Zcq50mdwA6tZJksfkkbDqcGTSA1/gGu916g16zNzh1QgWf3TJZxEa9raiURtRhdDTz8u/t/h\nK2qVb1jgfRPj01m+MJD42DRKebvQ98MGVrcKS0FdnfUHkftOYOvmgv/yGSjsCkfiy2pRl3QS/dPW\nTiRqM7kXE8bMdZ+j0ap5+/WBvFmvW4H3TU7MYMXCQOKiUylZxpl+Q/1EzY5c3N91lGuzfge5HL8/\npuJYtoS5QyqwB4m6oXlezoXji0UwHpGozSApLYGpa0aSnJ6IX5UABr4xosD7piTpknRMVApepZzo\n95EfDqJOcY5SQiI4PeQbAKp/PZTiLf3MG5CeHreoRaK2diJRm5j6UaGliJgwyntV5vNuM1DIC3bx\nLzUlgxW/nCTqYTKeJYrS/2M/HMVogBypU9M5MWAsqoQkSrZvRuXPLGO1Fn3cTxRD8wQdkahNSJIk\nfv1rJhdDT+Fa1IOv+8zDwdaxQPumpWaybMERIu8n4VG8CAOG+VNE1H/IkSRJBH3+HQkXb1LEtwz1\nf5lo0ZNacvMwa1aiaFFbPXH1yYS2nVjJ7nNbsFXa8XXvuXi6FKy/ND1NxapfT3P/bgJuHo4MGOZP\nUXGBKVchf24lfPXfKBzsaLTyW2yLOZk7JL1lqLXEpKpRyMFDrJVo9USiNpGT1w/y5+4fARjVdSqV\nSlcv0H4Z6WpWLzpFRHg8ru66JO3kYtklGc0p9vQlzn/xPQB1f/oKl+oVzRzRi3lcNc8WhVyMobZ2\nIlGbQMiD68zZ9BUSEv1bfkLT6m0KtF9mhpo1i09xNzQeF1cH3v20GU4uYjJLbjKi4wgcOA5Jpcb3\nwx5492xr7pBeWPaFRNE/LSD6qI0uNimKqWtGkq5Ko0WtDrzT7IMC7afK1LB2yRnCb8fh5GLPe582\nw9W9iJGjLbwkjYaT708gLSISN7+a1Jo+0twhvZT7iaJ/WnhMJGojylClMX3tZ0QnPqRa2Tp82mli\ngQotqVUa1v1+htDgGIo62TFwmB9unpZX1N6SXJ62iKiDp7HzdMV/2XTktoW7Xze7GJNI1AIiURuN\nVtLy49aJ3Lx3Ga9ipRnX63tslPl/6NRqDev/PMvtG9E4FrVlwCf+uBcXSTov93Ye5MYPy5ApFPj9\nOQ2HUoV/PUhR3lR4kkjURrJ6/y8cu7IXR7uiTOg7D5cibvnuo1Fr2bjsHMFXo3AoYsPAYf54eokk\nnZfkW+Gc/ngKANW/GYZns/pmjsgwshYM8BItagGRqI1i/4W/WH94CXKZgi96zMLbs0K++2g1Wjav\nCOLGpUjsHW0Y8LE/xUsWvmFlpqROSeNE/69QJ6ZQqnNLKn3a19whGczjFrVI1IJI1AZ3JTyI+dsn\nA/Bhuy+oV7FxvvtotRJbVp3n6oUH2Nkr6f+RHyVK575wsKCb1HJu5EwSr9yiaCUf6v/8tV4LLVgy\nlUZLdIoKhQw8i4pELYhEbVAP4u4yY91nqDUqOvr1pn3Dnvnuo9VKbF9zgcvn7mNrp6TfR36UKmvZ\nq45YgtuLNnBnw24URRxotPJbbJxfnRExkckqJMCjqC1KMYZaQCRqg0lJT2LqmpEkpsZTr2JjPnjr\n83z3kbQSf62/yIXTEdjYKuj7YQPK+BQzQbSFW0zgBS6MmwdA/QXjca5a3swRGVbW0DxRjEnIIhK1\nAWi0ar7bOJY7Ubfx9qzAmO7fopDnPZdIkiT+3nSZc4F3UdrI6TOkAd6++V9wtHbpD2MIHDQeSa2h\n4ie9KdPtDXOHZHDZK7uIRC08IhK1ASz5dw7nbh3DxdGVr/vMo4h93hcBJUli15YrnDkWjkIpp/cH\nDShX0d1E0RZeWrWak+9/Tfr9KNwb16HG5OHmDskoxNA84VkiUb+knSfX8tepdSgVNozr/QMlXEvn\nub0kSezZfo2Th8NQKOT0er8+vlU8TBRt4Xb5m1+IPnIO+xIe+C+djtzm1ayAkDU0r4QY8SE8IhL1\nSzgTfJQl/84GYETnb6hWtk6e20uSxH9/XefEgRDkChnvvFeXitU8TRFqoRex9T9uzl+FTKnAb+k0\n7L1e3V8gYq1E4VkiUb+g8MhbzN44Fq2kpVfzIbSo1T7ffQ7uusnRfbeRyWX0GFiXytW9TBBp4Zd4\nPYQzn0wDoOa0EXi8nvcXYmF3XxRkEp4hEvULiE+JZcqaEaRmJNO0+pv0afFRvvsc3hPMoV3ByGTQ\nrX8dqtYqPGv3mZMqKYXAAV+hTk6lTPc2VPgo/yGPhZlaKxGdnIkMKF60cNcrEQxHJGo9ZaozmLHu\nf0TG36Ny6RqM7DIZuSzvp/HYf7fZ//cNkEHXfrWpXrekiaIt3CRJ4uzwGSRdD8Wpannq/fTVKzOp\nJTdRyZloJN1iATYK8fEUdMQ7QQ+SJDF/+xSu3TmPh3MJxvf+ETubvIv4Bx4MYe+OawB07l2LmvXz\nvtgoPBb88xoitu5D6eRIo5XfoixasGXLCrPsqnniQqLwBJGo9bD+8BIOXvwbB1tHJvSdh2vRvEdr\nnDoSxq6tVwHo8E4N6viVMUWYr4Too+e4NPFnAOr/MhGnSj5mjsg0Hl9IFP3TwmMiURfQkcu7WbV/\nITJkjO4+k/JelfPc/uzxcP7ZdBmAdt1fo35jb1OE+UpIux9F4LvjkTQaKo/sT+lOLcwdkslkLxgg\nWtTCE0SiLoAbEZeYu3UiAO+/+T8aVm6e5/ZBJ++yc8MlAN7sWo2GTcsZO8RXhjZTReCgcWRExuLZ\nrD6vTcz/Qu2rRCwYIOREJOp8RCXcZ9qaUWSqM3irXnc6N+qX5/YXz0Swfe0FkOCNTlVpFPBq1aEw\ntosT5hMbeBH7Up40/HMqcuWrOaklN2KtRCEnIlHnITUjhalrRhKfEkOt8n4Mbf9lnqMOrgTdZ+uq\n8yBBy/aVadzK14TRFn53Nu7m1q/rkdkoabR8Jvae1lf75IFYK1HIgUjUudBoNfyweRyhD29S2t2H\nse/MRqnIfVzrtQsP2LQiCEmC5m9WpFmbiiaMtvBLuHKLs5/OAKDWzFG4Naxh5ohMT6OVeJgsCjIJ\nzxOJOhfL9s7j5I1DODm4MKHPPIo65F7I/8blh2xcfg5JK9GktS8BbSuZMNLCT5WQTGD/sWhS0/Hu\n3Q7fwd3NHZJZRKeo0GjB3VGJnVJ8NIXHxLshB7vObGbr8RUo5Uq+6jmHUu65Dw0LvhbFhj/PodVI\nNGpRnlYdqrzykzIMSZIkTg+bSvKtO7jUqEidH/PuXnqVPS5vKvqnhaeJRP2M8yEn+fXvmQAM6/g1\nNco1yHXb2zeiWf/HGTQaLX7NfGjTuarVJpkXdWPuCu7vPIiNS1H8l89E6Zj3BKJXWVb/tFgnUXiW\nSNRPiIgJ49v1o9Fo1XRr/C5v1O2S67ahwTGsXXIatUpL/cbevPX2ayJJ6ynywCkuT/kVgAa/TaJo\nhbJmjsi8xIIBQm5Eon4kMTWeKas/JSU9iUZVWzLwjU9z3Tb8dixrFuuSdB3/MrTvXl0kaT2l3n3I\nqQ8mglZLldHvUrJdM3OHZHZiwQAhN3on6szMTN4dOJqmjd+hZUAfzp+/aoy4TEqlUfHt+tHcj72D\nb4mq/O/t6bkWWrobFs/qRadRZWqo1aA0HXvWRCYWINWLJiNTN6klOo7iLf14bdwQc4dkEe4niaF5\nQs70nk2wZPE6HB0dOHJsAzduhNC/zyhOntlmjNhMQpIkftk5nUthZ3Bz8mRCn3nY2zrkuO29Owms\n+vUkmRlqqtctSec+tZCLJK23C1/NJe70ZRzKlqDh71OQKRTmDskiiIJMQm70TtRXrwTzVlvdFOrK\nlcsTEfGQxMQknJ2fXidQqbT8i0IyuZLtx5axN2gbdjb2TOr3C15uOdfkiH6YxKpfT5KRrua1OqXo\nMcgPhQnLUMoeLZZbGJ5X0H155RRr6OqdhPy+GbmtDU1Wf08RL/PV5bak51QrSTx8lKjLuDqjVD79\n5WVJseZHxGp4eifq2nWq8dfO/+jStQ0nTpwjKiqWlJS05xL15ClTs/8fENCcFgEBLx+tEZy+cQiA\nSqVr4Fuyaq7bnTh4i7RUFZVe8zJ5kn5VJFwJ5swI3UotdX8Yi1u96maOyHJEp2Si0ki4OtjgYCN+\nYViLAwcPcvDgoezbAQEtc9xO70T93vvvcPXqLQKa9aZxk3pUrlweNzeX57YbP27MU7fV6nR9T2V0\nSqU9g9uO4eqdc1wKPc0vO6YwtP3YHC8M3r4RCUCT1uWBTNRq08cKlvk8Pk8Cno5VnZLGsQFj0KSl\n492nPd7925v9sVjScxoSlQhAmWK2OcZjSbHmR8RacE2b+NO0iX/27SNHT+e4nd7NwpMnz9Oy1esc\nPLyW7j3aUaKkJ3Z2hfcqtXfxinzdey42Clv+Pr2ejUf+eG6bpIR0oh8mY2OroLR3MTNEWfgFjZ5N\n0rUQnKqUo873Y8QomWfcidddSCxbzLJ/ggvmoXeirlLFl/nzltG08TuM/WIWvy2eboy4TKq6Tz0+\n7zYdGTJW/LeAfUHbn7o/NDgGAG9fNxRiaq/ewlbtJHz13ygc7PBbOh1lkZwv1lqzO/G6Fp23q0jU\nwvP07vpwcyvGrj3LjBGLWTV+7Q2GtPuCRf/MYsGOqbgW9aBexcYAhN7UJeryldzNGWKhlHj1NkGf\nzwag9pwxuLxWwcwRWabwOF2iLlus8P46FYxHNA+f0NGvN92bvItGq+bb9aMJvncFgJBHibqcSNR6\nUSenEjhoHJq0DLz7tqdc/47mDsliia4PIS8iUT9jQOtPaVGrA+mqNCav/pTrt28SH5uGvYOSEqVz\nr6AnPEOCoM9n61YQr1KOOnPG5L+PlVJptNxPzEAGlHYRLWrheSJRP0Muk/Np50nU9vUnISWWbzeN\nQiVLwaeiu5jcoofaSRC+9h8Ujvb4L5sh+qXzcD8xE42km+giypsKORHvihzYKGz4quccypeoQkzq\nPYKLrKBM+aLmDqvQ8MyAdtG6/9f5fgzO1cRKN3l53D8tuj2EnIlEnQtHu6JM7PMTdpIrKco7/Bv6\nExqtiQdPF0Lq5FR6PAQbCbz7dcCnbwdzh2TxwrNGfIgLiUIuRKLOg5TmSMWkQSglR86HH+OXv2Yi\nSZK5w7JYkiRx7n/f4aGCKBuoM3u0uUMqFLIvJIqheUIuRKLOQ8jNGBy0nrxZehS2Sjt2n93MukOL\nzR2WxQpbsYM76/4lUwYbSyD6pQvoTlxWi1okaiFnIlHnIWv8tF/NRozuPhO5TM7qA7+w59xWM0dm\neRIuBxM05nsA/vGEaFEArsCyJruIMdRCbkSizoWklbJnJJav6E6jqi0Z2n4sAD/vmMbpm4fNGZ5F\n0Y2XHo82PQOf/h254JT/PoJOSqaGmFQ1tgoZxUUdaiEXIlHn4uH9JNJSVbi4OlDMXfcTvl2Dd+jZ\nbDBaScOsDV9wM+KymaM0P0mSOPfZLJJvhuFczZfaol9aL49b0/bIRf0TIRciUeci5KZufFn5Su5P\nFRDq13IYrWp3IkOVzpTVn3IvNtxcIVqE0OXbubN+FwpHe/yWTbfqxWlfRHhc1oxE0e0h5E4k6lyE\n3Mh52rhMJmN4pwnUrdCYhNQ4vlk5nPiUWHOEaHYJl25y/osfAKj745c4Vylv5ogKnydb1IKQG5Go\nc6DRaAm/rUu+5So+X99DqbBhbM/ZVCxZjQdxd5iy+lPSMlNNHaZZqZJSHvdLD+iEd+925g6pUMpO\n1K6iRS3kTiTqHNy7k0Bmhgb34kVwzqWl42DryIS+8/EqVprge1f4bsMXqDUqE0dqHpIkEfTZLJKD\nw3F+rQK1v/vc3CEVWlljqMXQPCEvIlHnoKBlTV2LujO5/884OxbjTPBRFu6cbhUTYkKXbePOht0o\nijjgL/qlX5gkSWL6uFAgIlHnILusaQ7dHs8q5e7DhD4/Yau0Z2/QNtYc+NXY4ZlV/MUn+qXnfolT\n5XLmDagQi0lVk6bS4myvwMVB79LwghURifoZapWGOyFxQMESNUCVMjX58p1ZyGVy1h5axL9nNhkz\nRLNRJaVw8t3xaDMyKTewM94925o7pEJNXEgUCkok6mfcCY1Ho9biVcoJx6IFn4DQsHJzPu4wHoBf\n/5rByesHjRWiWUiSxLlRj/qlq1eg9nf/M3dIhV64mDouFJBI1M8IfYnVXN6q343eAUPRSlq+2ziW\na3cvGDo8swlduo27G7P6pWegcBDJ5WU9XtVFjPgQ8iYS9TNCXnJ9xD4BQ2lTtyuZ6nSmrh5JREyY\nIcMzi/gLNzj/5RP90pV8zBzRq+Hx0DzxpSfkTSTqJ2Skq7kXHo9MLsOngtsLHUMmkzGs43gaVGpK\nUlo836z8hLjkaANHajqqxCf6pQd1Ef3SBnRHLGgrFJBI1E8ID4lFq5UoVdYFO3ubFz6OQq7kix7f\nUalUdR7GRzBl9QhSM1IMGKlpSJLEuZEzSb51B5caFak96zNzh/TKUGskIhLFgrZCwYhE/YSX6Z9+\nlr2tAxP6/kRJt7Lcun+VWRvGFLoJMSF/bOHu5r0oizrit3S66Jc2oPtJGWi04OUk1kkU8ifeIU/I\nnuhSwGF5+SlWxI1v+v2Mi6Mr524dZ/6OKYVmQkz8+etc+GouAHXnjRX90gZ2RxRjEvQgEvUjaSmZ\n3I9IRKGQU7a8q8GOW9KtLBP7zcfexoH953ey8r+fDXZsY1ElphD4qF+6/HtvU7bHm+YO6ZUTLsZQ\nC3oQifqRsFuxIEGZcsWwsVUY9NiVSlXny3e+Qy5TsOHI7/x9ar1Bj29IkiRxdsQMUm7fxaVmJWrN\nHGnukF5JWSM+vMWID6EARKJ+JMSA/dM5qV+pKcM7TQDgt7+/5fjV/4xynpcV8vtmIrbsE/3SRibG\nUAv6EIn6kYIWYnoZb9TtQr+Ww5CQ+H7zOK6EBxntXC/i6X7pr3Cq6G3miF5dd0QxJkEPIlEDyYkZ\nRD1MxsZWQWnvYkY9V89mg2lbvzuZ6gymrRnJnajbRj1fQakSH9WXzlRR/v23KdujjblDemWlZmqI\nSlFhI5dRQqyTKBSASNSQvYitt68rCiMPlZLJZAxtPxa/KgEkpyfyzapPiEmKNOo58yNJEmc/nUFK\nyF1calam1sxRZo3nVXc3QdftUbqYHQq5WCdRyJ9I1OhX1tQQFHIlY7rPpEqZmkQlPGDKqk9JzUg2\nyblzcnvJJiK27kPp5Ij/smko7EW/qTGJbg9BXyJRY5r+6WfZ2Tgwoc88Srv7EPLwBjPXfY7KDBNi\n4oKucXHcPADqzfuKohVEv7SxiQuJgr6sPlHHxaQQF5OKnb2SEmVcTHpuZ0dXJvX7mWJF3DkfcpKf\ntn2DVtKa7PyqhGROZvVLf9CNMt1Fv7QphIuheYKerD5Rh9yMAnTdHnIz9BeWcC3NpH7zcbB15ODF\nv1m+d75JzpvdLx0agUutytSaIcZLm4ro+hD0JRL1DV2iNmW3x7MqlKzG2J5zUMiVbD62lB2Bq41+\nzttLNhGx7T9dv/TS6aJf2kQkSXpiQVvxnAsFY9WJWpIkbj9K1Maa6FJQdSu8zojOkwBY8u8cjl7Z\nY7RzxZ17ol/6p3EUrVDWaOcSnhaXpiY5U0NRWwXFxDqJQgFZdaKOiUwmKSGdIkVt8SxR1Nzh0LJ2\nRwa2/hQJiR82f82lsDMGP0dmfBKBg8ahzVThO7g7Zbq9YfBzCLnL7vZwtUMmE0PzhIKx6kT9ZGva\nUj403Zu8R/uGvVBpMpm+9jPCI28Z7NhZ/dKpYfcoVrsKNaePMNixhYJ53O0h+qeFgtM7UWu1Wga/\nP5bmTXvRonkfrl+3jJl1L8IS+qefJZPJGNJ2DI2qtiIlPYlvVn1CdOJDgxz79qIN3Nu+H6VzEV0d\nD9EvbXKiap7wIvRO1Lt3HyYlJZVDR9bx9cThTBj/vTHiMjpJKz0e8WFBiRpAIVfwebfpVCtbh+jE\nh4UCxAIAACAASURBVExeNZzktMSXOmbc2atcGP8TAPXnj6eobxlDhCroSSy/JbwIvRO1g4M9CQlJ\nSJJEYkIStraFs1bBw/tJpKZk4uLqgKu7o7nDeY6djT1f95lLGY/yhEUGM23Np6jUmS90rMz4JALf\nHY+kUuP7YQ9Kd21l4GiFgsru+hBjqAU96H3ZuUmT+qSnZ1C96pvExMSzdcdvOR9YadlvxHvh9wDw\n9vXAxsbBzNHkzNXJnqmDFvP5oj5cCDnJ0J860K3Je7Sp1w07m4I/v2e+nEpq2D1c61Sj7rdfoFAa\n+8tV199v6e8BmVz39jdVnGrt43USfdxdUCoLXvfc1LG+DBGr4emdqGd/t4jGTeozbfrn3L17nzat\nBnD+0t/PtawnT5ma/f+AgOa0CAh4+WgNyM2jCADht2PQaLQoFJZ5XdWrWGmmDlzMjLWjiIgJZeHO\nqaza/zNdGg2gg38fnBzynk0ZeegUYWv/QmFvR6Pls1DYFc5fQK+CiPg0VBqJEk52OBp4cQqhcDpw\n8CAHDx7Kvh0Q0DLH7fRO1CkpaTg764ayubq6oFKp0Wien/Y8ftyYp26r1en6nsqofCo44+5ZlJio\nZK6eD6dqrRLmDilXZT18+HXETo5f2cv6Q78RfP8qy/fNY8PhxbSt34POjfrh7lz8uf20mSrOjJoO\nQJXRg3DwKW6i10G3LqSlvebPympFmSrO4MgEAMq52el9TlPH+jJErAXXtIk/TZv4Z98+cvR0jtvp\n3YwcPWYIgSeCCGjWmzatBzB95mgcCuEqIDK5DP/mvgCcPBJm5mjyp5AraFrjLb4fsoqpA36ltq8/\naZmpbDm+nCHzOjB/+2TuRoc+tU/wwrUkXQ+laIWyVBrR3zyBC9lux6YB4OtumV1tguXSu0VdrJgz\nm7b8YoxYTK6Ovw97d1wm9GYMUQ+S8CzhZO6Q8iWTyajt609tX3+C711h09GlHLuylz3ntrL33DYa\nVWtJjybvU1rrztVZvwNQe/Zo0eVhAUJidK228m4iUQv6seo5rPYONtT28+bUkRBOHQmjfY8a5g5J\nLxVLvcaX73xHREwYW44t57/zOzh+9T+OX/0Pn1Q3Knum0rDuG3i19s//YILRZbWoy7sVvl+ggnlZ\n5hU0E/JvXgGA86ciSE8zfT1oQyjt7sPwThNYMvIvujV+F3uFPWGOsezprmJ1w5scvrQLjVZj7jCt\nmkqj5U58OjLARyRqQU9Wn6iLl3SmXCV3VJkazp+KMHc4L8XNyZMBTYfSf6sX9Q4rcJI5EhoTzOxN\nY/l4wdv8e3ojmeoMc4dple7EZ6DRQklnWxxsxIgPQT9Wn6gB/Jr6AHD6SBiSVjJzNC/n+twVqG8+\noGlKFX4fs5thHcZTwrUMD+LusPCv6Qye24GNR/4gJT3J3KFalZCYR90e4kKi8AJEogYqVy+OczF7\nYqJSuHUj2tzhvLDkW3e48eMKAOp8PwZ7hyK0bdCDX4ZvYUz3bylfogrxKTEs3zefD+a2Z+meecQm\nRZk5ausQEqu7kOgrLiQKL0AkakCukNOgiW6twFOHLX+oXk4kSSJozPdoMzLx7tsej8Z1su9TyJU0\nq/EWcz9cw+T+C6lZriGpGclsPraUwfM68POOadyLKZyPu7AIybqQ6C76pwX9iUT9SN1GZVEo5dy8\nGklcdKq5w9Hbve37idx3AhsXJ2pMGZ7jNjKZjLoVXmf6oEXMGbyC16u1QqNRs+vsJj5e8DazNnxB\n8L0rJo7cOtzO6voQLWrhBfyfvbsMrOJY4zD+7JGcuBsJBHd3LYE6bi0U1/pteyvUjbrcuuJS3AoU\nCrSluLtDgAQS4glxO7b3Q4QAIXo0md8n0m7OvrF/NjPvzIigLuDiqqFV+1ogw5F99vV0qc/M5tQb\n3wLQ8t2ncPTzLvN9mgS34o2RX/HTs2t5oP1QlAole8/9zUuzx/LOb09xMvwgsmzf4/W2Ik9vJDot\nD4UkNmMSKkcEdTGdCyYVjx+8jk5rP+1s5z+fS050Ap7tm1N/8tAKvW9t33o8N/g9Zr+wiaHdx+Pk\n4MzJ8IO889tTvDxnHHvP/S1a+6ooMiUXowzBHho0KvEjJ1Sc+K4pJijEk+C6nuRm6zh9LMba5ZRL\n+vlwLv+8HCSJdl9NR1JWrvXLx92fKQ++xNz/bmZc32fxcPbicsw5Pl/1Ks/+NJytR9dWepvVmi5C\nLB0XqkgE9W0Kn6oP775q83/6y7LMiZe/RNYbqD95KN4dW1T5NV2d3BnZexpz/vsnT/V/A3/PIGJu\nRPLTxg95/LsBrN27kOy8TBNUX3OIpeNCVYmgvk2LdoG4uDoQH5NBVESKtcspVdTKLSTtPY7G14uW\n7z5t0tfWqB3p33kkM59bz8vDP6FeQGNuZCax4J9vmfpNPxZt+4GUzGST3rO6EkvHhaoSQX0blUpJ\nh+4FrXo2vKueNjWD0wVHa7X64FkcvNzNch+lQkVo63589+QK3hvzA63qdiQrL5PVe+Yx7dv+/Lzx\nY2JvRJnl3tWFWOwiVJUI6hJ07FEHSSFx/mQcGWm2uafuuY9nkpeYgk/3toSM7m/2+0mSRMfGvfhk\n0hy+mLKArk37oDNo2XJ0NU//OJQv17xOeNxFs9dhb3J0BmLTtSgV4pxEofJEUJfA3dOJZq0DMBpl\nju6LtHY5d0g5cYHwOWuRlMr8CUSFZb+Mzeq05a3HvuHHZ1ZzX7vBSJKC3We28t+Zj/He4mfR+Bop\nPDygprt2IxcZCPF0RG2jpwgJtk9859xF4aTi0f2RGPR3nmBjLbLRyImXvgCjkYZPj8SjZSOr1RLi\n15AXhsxg1vN/MKTbWBzVThy/sg//njr8e+vYd34bRtl2PnfWULh0XEwkClUhgvou6jb0xr+WG1kZ\nWs6firN2OUWuLtxAytFzONbyo/nr06xdDgB+HoFMfegV5r64mTF9nsaQBxovmc9WvsKzP43g7+Pr\namxrn1g6LpiCCOq7kCSp6Kn6kI3s/5GbeIMz7/8MQJtPXkDt5mLlim7l5uTBY6FPEPu3AymnVPh7\n1CI6+So/bJjB498P5Pd9i8jOy7J2mRZVuHRcbMYkVIUI6lK07hiExlHF9aspxEalWbscTr/7PbrU\ndPz7dCZ42H3WLueuZINEZoSSX59bz4vDPqKufyNuZCQy/+9vmPptPxb/+xOpWTesXaZFFA19iI4P\noQpEUJfCQaOiXdfagPVb9ZIOnCBi0ToUDmrafjUdSZKsWk95qJRq+rYZwPdPreSd0d/TIqQ9WbkZ\nrNw9h2nf9ufXPz8jLsW+D2soTZbWQHyGFgelRJCH6PgQKk8EdRk696wLEpw+FkN2lnXGWY16PUdf\n+ASAxs+Pxa1RiFXqqCxJkujc5B4+mzyPzybPp0uT3mj1efx5eAVP/TCEr9a+SUR8mLXLNLnC8ekQ\nL0dUCtv/xSrYLhHUZfD2c6FRMz8MeiPHD1hnYUf47DWknQnDpW4QTV+eZJUaTKVFSDveHv0dPzy9\nir5tByJJEjtPb+aFX0cxY8l/OHPtqM0v3S+vq2LpuGAiIqjLofM9BUd17Y3EaOGjunJiEzn38UwA\n2v/vNVTO1aN7oK5/I14c+iEzn1vPoC6j0agdOXp5L28umMZr8yZx4MJ2u2/tCy/ajKl6fM0E6xFB\nXQ6Nmvrh5etMWkoOl84mWPTep9/6Hn1GNkED+hDUP9Si97YEf88gHu/3KnP/+yePhT6Jm5MHF66f\n4pMVL/Hcz4+w7cQGdAb7PB0+QhwWIJiICOpykBRS/lg1cGjPVYvdN2HHYa6v+Rulk4b2X0632H2t\nwd3ZizF9nmLOf/9k2kPT8XUPJCopgu/Wv8eT3w9i/YEl5Gjt6+Qd0fEhmIoI6nJq17U2agclEWHJ\nJMabf5tPQ56WE6/8D4Cmr0zGpW6w2e9pC5wcnBncbQwzn9/AC0NmUMevAUnp8czd+j+mftOPpdt/\nIT3btnc1BEjP1ZOUpUOjkqjl7mDtcgQ7J4K6nByd1LTpGATAEQu06l3+cRmZl67h2iiExs+NMfv9\nbI1aqea+doP54elVvPXYNzSr3YbM3HSW75rFlG/6M2vz5ySk2u7hDoVP0/W8nVDYQSulYNtEUFdA\np4KViicPXycv13zjplnXYrjw5TwA2v3vFZSamvtEppAUdG3ah8+nLODTSXPp1LgXWn0uGw8t54nv\nB/PN729zLeGytcu8Q4RYkSiYkMraBdiTgCB36jb05tqVG5w8HE2Xe+qZ5T6n3vgWQ04etYffj3/f\nLma5h72RJImWdTvQsm4HIuLDWLt3IbvPbGX7qU1sP7WJzk16M6LnJFqEtLd2qYDY40MwLfFEXUFF\nR3XtuWaWft/YLXuI3bQLlZszrT95weSvXx3UD2jCy8M/ZubzGxjQeRQOKg2Hw3bx+vwpvDZvMofD\ndlm9tU/smieYkgjqCmrWOgB3T0eSE7KICDPtUVT67FxOTv8KgOZvPI5TLT+Tvn51E+AZxJP9X2fO\nf/9k5D3TcHV053zUCT5c9gLP/zKS7Sc3ordSa1+4ONVFMCER1BWkUCro2CN/CbepW/Uufr2Q7MhY\nPFo1ouGTj5r0taszTxdvxt37LHP++ydTHnwJHzd/IhOv8M26d3jyh8H8cXApudoci9WTkqMjNUeP\ns1pBgKvaYvcVqi8R1JXQoVsdlEoFYWcTSL1hmt7ejMuRXPpuMQDtvpqOQiWmDyrKWePC0O7jmfXC\nRp4f/D7BPvVITItj9pYvmfptf5bvnEl6dqrZ6yg6ddzHyS42zxJsnwjqSnBx09CiXSDI+cvKq0qW\nZU6+8j+MWh11xw3Ep1tbE1RZc6mVau5vP4Sfnl3DGyO/oklwKzJyUlm641emftuPOVv+R2Ka+Q6D\nuLkiUUwkCqYhgrqSOhd0fBw/EIVOa6jSa0Wv+5eE7YdQe7rTasazJqhOgPzWvu7N7+XLqYv4eOJs\nOjTqQZ4ulw0Hl/DE94P4eu0bRJqhta+o40NMJAomIv6+rqTadT0JCvEgJjKNs8djaNe1TqVeR5eR\nxak3vgWg5XtPo/H1MmWZAvmtfa3rdaJ1vU6Ex11k7d4F7Dn7F/8cX8c/x9fRpWkoj/ScTLM6pvlL\nprDjo4GYSBRMRDxRV0Hxo7oq26p34bO55MYm4tWhBfUnDjZleUIJGgQ25ZURn/Lrc+sZ0OUx1CoH\nDl3cyavzJvHGgqkcubS7Sm2Xsizf7PgQQx+CiYigroKW7Wrh7OJAXHQ6169WfJIq7exlLv+yAiSJ\ndl+/iqRUmqFKoSSBXrV5dtB7LHh5G4/2moqLxpWz147xwdLneWHmKHae3ozBqK/w6yZn68nIM+Cq\nUeLjIjo+BNMQQV0FKrWS9t3zhzwOV7BVT5ZlTrz8JbLBQIOpw/Fq38wMFQpl8XL1Zfx9/2Hui5uZ\n/MCLeLv6cjX+El+tfZMnfxjCpkMryNOVv7Wv+NJx0fEhmEqFg3rRwrXc13cs9/UdS49uI3B1akl6\neoY5arMLnXqEIElw7kQcGWm55X6/yGV/krz/JBo/L1q8/aQZKxTKw1njyrAeE5j9wib+M+gdgn3q\nkpAaw8zNnzH12/6s2DWbzJz0Ml9HLB0XzKHCQT1h4nC2bV/Ctu1L6NSpNd/98C7u7m7mqM0ueHg5\n0bR1AEajzLH95TuqS5uSzpl3fgSg1YfP4eDlbs4ShQpQqxx4sMNwfnxmDa8/+iWNglqQnp3Kku0/\nM/Xbfszd+hVJ6fF3ff/CHmqxGZNgSpXu+jhy5DRnz17i+x/fL/mFVbb/RCEp8j/8qtbaLbQJF07F\nc3R/JPc82BwHh9I/rSc/+Zq8pBR8e3agwbhh5foT2VS1Wkb+x2PrtZb2OVUBvdsM5J7WAzgZcZBV\nu2Zz/Mo+1h9YzKbDK3io4wimPfwaGvWt7xuRkgdAI393k3789vT1F7WaXqWD+rNPfuHd95+/6/+f\n8cGHRf8ODe1Nn9Dqd4xUofqNfQkI8iA+Jo2lsw4w9onuqB1KnhjMjU8mYv5aJIWCjt+8KcYxbZwk\nSbRr0I12DbpxKfosq/fMYe/Zv9h0aDnhcRd5d8xPeLjkt1QaZZnw5PyVqvV9nK1ZtmAnduzcyc6d\nu4reDg3tW+J1kl6+XOFepNTUdEJ7jeLkmc0l33zbVXr17FTRl7W4wt+ien35x5bvJikhk4U/HiAr\nQ0vDZn6MmtoBlerOsL749SLOzviZWv3vofuyL61Sq7m179ANgOPHDli5ktJV9nMaEXeRD5f9l6T0\nOGp51+G9sT8S5B1CdFoeoxadwcdZxfqppl1dak9ff1Fr5e3Ze4Q+99W7479Xqutj965D3Htfj6rW\nVK34+rsy4ZmuOLs6cOVCIqsXHMegv3WrTdloJGL+7wDUnzLMGmUKJlA/sClfTltI/cCmxN6IYvqc\niZyPOsHlxPyn6Ua+4mlaMK1KBXVY2FUaNAwxdS12zy/QjfFPd8HJWU3Y2QTWLDqOwXAzrOP/PUh2\nZCzOIbUIuLerFSsVqsrHzZ9PJ82lY6OeZOSk8vbCJ9l15m8AGvmKiUTBtCoV1C+/Mo3nnp9o6lqq\nhYAgd8Y91QVHJxUXTsezbslJjAVhHTGv4Gl60lCxuKUacNa48Pbob3m44wh0Bi2HT36GY85WGoqg\nFkxMLHgxg1p1PBj7VBc0jirOHo9l/bJTZEbFE7t5D5JKSd3xA61domAiSoWKpwe8xcT7XwBknHNW\nceLsz5Va1SgIdyOC2kyCQzwZ80RnHDRKTh+NYc3Pu5CNRoIG9cHR38fa5QkmJEkSD3YaR6bLE8io\n2HN6LZ+seNmihxUI1ZsIajOqU9+L0Y93Rq1WcC3XhZieA6g/WUwiVkdXknLQarrgFfImbk4eHA7b\nxZsLppKSmWTt0oRqQAS1mdVt6M0DrRyQ9DpSmnbkSKKjWQ7FFazrclL+03PzkPZ8PmUBgV61uRx7\nnulzJhCZeMXK1Qn2TgS1BeSu20DIPytQIHNkbyR/rTsvwrqauZx0szWvtm89vpi6kCbBrUlIi+W1\nuZM4FXHYyhUK9kwEtZllRUSTsO0AHsnXGfFYCxRKiYO7rrJt40UR1tVI4RN1YWuep4s3H0+cSbdm\n95KVl8n7i59h+6lN1ixRsGMiqM0sYsE6AGoPv5/mXevz6MQOKBQS+/4NZ8eWS1auTjAFvfHmYQHF\ne6g1aidee/QLhnQbi96o55vf32b5zlniF7RQYSKozciQp+Xa4o3AzZWITVsHMGJCOySFxO6/LrPr\nLxHW9u56ai5ag0ygmwNumlu3z1EqlEx96BWeePhVJCSW7viF7zfMQG/QWalawR6JoDajmD92kJeU\ngkerRnh3blX035u3rcWwsW2RJNix+RJ7t4nJJnt2+7BHSQZ2Hc0bo77CQeXIthPrmbH0ObJya+4+\n7kLFiKA2o6KViJPv3Mq0VYcgBo9uAxJs23iRAzsirFGiYAI3g7r0PT66NevLJ5Nm4+Hizcnwg7w+\nfyqJaXGWKFGwcyKozST9QgRJe4+jdHGizsiHS7ymbefaDBrZGoC/1p/n0O6rFqxQMJVLRZsxlb10\nvElwK76cuohgn3pcS7jE9LkTCI+7aO4SBTsngtpMCnfJq/PoQ6jdXe56Xftudej/SEsAtqw9x9F9\nkRapTzCdyyVMJJYm0CuYL6YuoGXdDtzISOSN+VM4enmvOUsU7JwIajPQZ+cSuexPABqUYzvTTj3r\n8tCwFgBsWnWG4wfLd6SXYH0pOTqSs3Q4qRUEeWjK/X5uTh58MO4XQlv3I0ebzYdLX2Dr0bVmrFSw\nZyKozeD62n/QpWXi1aklnm2blut9uvauxwOD808i/2PFaU4diTZniYKJXE7Mf5pu6OOEooKn9ahV\nDrw07GNG3jMNo2zgp40fsmjbDxhlY9nvLNQoIqjNIGJe/pNReZ6mi+vetwH3DmgCMqxfepKzx2PM\nUZ5gQuXp+CiNJEmMu/dZ/jPoHRSSktV75vHV2jfR6bWmLFOwcyKoTSzlxAVSjp5D7eFG8LD7K/z+\nve5vROjDjZFlWLv4JOdPia4AW1Z86XhVPNhhOO+O+R4nB2d2n9nKO789RUZOmilKFKoBEdQmFjE/\nfyViyJj+qJwrd7Jx7wcb0euBhshGmTULj3PxTLwpSxRMqOiJ2q/qhwV0aNSDzybPw8fNn3ORx3l1\n7iTiUq5X+XUF+yeC2oR06VlErdoKQP3JQyv9OpIk0bdfE7r3bYDRKLN6wXHCzoona1ujMxi5lpKL\nRP4YtSnkn8e4iHoBjYlOvsr0ORMIiz5jktcW7JcIahOKWrkFQ1YOvr3a4960fpVeS5Ik7h/UlK69\n62EwGFk+5wCXL4gna1ty9UYueqNMsKcGJ7XpjlbzdQ/gs8nzaN+wO2nZKby54HH2nfvHZK8v2B8R\n1CYiyzLhBZOI9ScPN8lrSpLEg0Ob06lnCHq9kaWz9hNxKdkkry1UXVUnEkvjrHHlndHf8UD7oWj1\nuXy8/HnW7V9k8vsI9kEEtYncOHyG9LNX0Ph6ETQo1GSvK0kS/Ya3pGP3euh1RpbPOcK1KzdM9vpC\n5ZlqIvFuVEo1/xn0LuP6Possy8z681Nmb/4Cg9FglvsJtksEtYkU7utRd9xAlBoHk762pJAY9Fh7\n2nUJQac1sGz2YaKuppj0HkLFmfOJupAkSYzsPY3pj3yBSqnmj0PL+HzVdPJ04jzGmkQEtQlob6Rx\nfW3+GGL9SZWfRCyNQiExdGxHWnUIQptnYOnMw8REpprlXkLZZFk2+xN1cX3bDuKjiXNwcXTjwIXt\nvLXwCVKzxF9WNYUIahO4tnQTxjwt/vd1w6V+sNnuo1BIDB3ThhZtA8nL1bP410PEXhe9ttaQlKUj\nLdeAq0ZJgKvaIvdsU78LX0xZgL9nEGHRZ5g+ZwLXk65a5N6CdYmgriJZlouGPSq6ErEyFEoFw8a3\no2nrAHJz8sM6Pibd7PcVblV82OP2LWzNqY5fA76cupBGQS2IT43m1bkTOXvtmMXuL1iHCOoqStx1\nlMwrUTjW8iPw4Z4WuadSqeCRCe1p3MKPnCwdv/1yiMQ4sQm9JRVubdrYAsMet/Ny9eWTiXPo0jSU\nzNx03vntKXad2WLxOgTLEUFdRYVP0/UmDkahUpVxtekoVQoendSBhs18yc7U8tvPh0hOyLTY/Ws6\nS0wklsbRwYk3Rn7FwC6PoTfo+N+aN1i9Z544j7GaEkFdBbnxycRs3IGkVFJvwhCL31+lVjJyckfq\nNfYhMyOPRT8f4kZSlsXrqIksOZF4N0qFkscffpWpD76MhMSibT/w08aPMBj1VqtJMA8R1FVw9bc/\nkPUGAvv1wjnY3yo1qB2UPDa1IyENvclIy2XRTwdJvZFtlVpqilydketpeSglqOdduf1cTEWSJIZ0\nH8drj36Bg0rDX8fW8uGyF8jOE7+wqxMR1JUkGwxcXZi/AVODyeafRCyNg0bF6GmdqF3Pi/TU/LBO\nSxF9tuYSnpyDUYYQL0c0Ktv4EerR4n4+mjATd2dPjl3exxsLppKcnmDtsgQTsY3vMjsUv+0g2ZFx\nONcNwv/eLtYuB42jijFPdCIoxIPUGzks+vkg6am51i6rWrKFYY+SNKvTli+nLiLIO4SIuItMnzuB\nq/GXrF2WYAIiqCvp5r4eQ5EUtvFpdHRSM/bJLtSq7U5KUja//XKQzPQ8a5dV7Vh7IrE0tbzr8MXU\nhTSv046k9HhemzeZ41cOWLssoYpsI2HsTHZUHHFb9yGpVdQdN9Da5dzCyVnN2Ke6EBDkRnJCFr/9\nfJCsTBHWpmTLQQ3g7uzJhxN+pVfLB8nRZvHB0uf4+/g6a5clVIEI6kq4umgDGI0ED+6Lo5+3tcu5\ng7OLA+Oe7oJfoCuJ8Zks/uUQ2VniaCdTMFp46XhlOag0vDLiU4b3mITBqOeHDTNY/O9Pon3PTomg\nriCjTp8f1EB9C6xErCwXVw3jn+mKr78L8TEZLP7lEDnZOmuXZfdi07Vk64x4OanwcbHM0vHKUkgK\nJj3wAk8PeBOFpGDl7jl8s+4ddAbxfWBvRFBXUOzm3eTGJeHWpC6+Pdtbu5xSubrlh7W3rzNx0eks\nmXmI3BzxQ1oVhU/Tjf1s92n6dv06Pcrbo7/FUe3EjlObeH/xM2TmiG0H7IkI6goqXIlYf8pwi+7x\nUFluHo6Mf6Yrnt5OxESmsXTWYfJyxYKIyrL18em76dT4Hj6dPBdvV19OXz3Ca/MmEZ8qTrm3FyKo\nKyDzShQJ2w+hcNQQ8lg/a5dTbh5eTkx4tiseXo5cv5rKstmH0eaJsK4Mew1qgIa1mvPltEWE+DUk\nKimC6XMmcCnmrLXLEsqhUkH92ae/0KvHo3TrPIxFC9eauiabFbEgf+a89oj7cfByt3I1FePp7cz4\nZ7ri5uFIZHgKy+ccRacVJ4VUlD1MJJbGz6MWn0+ZT9v6XUjNSubNBdM4HLbL2mUJZahwUO/YcYAD\n+4+zZ98qtu1YQnh4pDnqsjmG3DyuLd4IWGY7U3Pw9nVhwjNdcHXTcPVyMivmHUWvE2FdXllaA7Hp\nWtQKiRBP6y4drwoXRzfeHfsj97YdRJ4ul4+Xv8ifh1dauyyhFBUO6r//2kOr1k0ZPvQphgx6gkGD\n7zdHXTYnesN2tDfS8GjdBK+OLa1dTqX5+Lsy/tmuOLs6EH4xiZXzj6HXi7Auj8Jhj/o+jqiUtj8/\nURq1Us0LQ2YwOvQpjLKRX//8lHl/fY1RNlq7NKEEFd6XMynxBlFRsWzYOJvw8CiGDX6Ssxf+uvOF\nVbb/xCEp8j/88tQauWQzAI0eH4labfnxyYrUWpZawY5Mfu4e5n+/m8vnE1kx9zijpnTF0clU7Wb5\nIWbr3wMV/ZyG38g/p7Kxn5vFPzZTfv2LG3//CwR41ebbdW+zbv9vNKjVgvvbV+04OXPVag72UmuF\ng9rH14tmzRuiUqlo0qQ+jo4akpJu4Ot768KPGR98WPTv0NDe9Ak13cnclibLMinH8iddggb0WaCP\nWgAAIABJREFUsW4xJhIQ5MHE//Ri0c97uXIhgbnf7mL80z1w97S/STJLCSvY77uJv4uVKzGdPF0u\nBy78C4BG7UT9wKZWrqhm2bFzJzt33pwjCA3tW+J1FQ7qnr068cN3C3jxpanExMSTlZWNj4/XHde9\n9eb0W97W621vg6DC36Jl1ZYdGYsuPRMHH09UPi5W+VjKW2tF+AU6MuX57iyddZj4mDRmfbWd0Y93\nIiCoqhOl+avfbPFrXlxFP6cXE/JP0Wnk42Dxj80cX/+s3Aw+Xv4iZ64dxdXRnXfHfE9dv/pVvoc5\najUXa9faq2dXevXsWvT2nr1HSryuwmPUAwb0pV37lnTrMpxhg5/kh59n2EU/cVWknbsCgEfLRtXu\nY/XydWbyC92pUz9/i9QFPxwg/GKStcuyOVqDkfDkXCSgsR225t0uJTOZNxc+zplrR/F28+OzyfNo\nVqettcsS7qJSZ0d99vmrpq7DpqWfzQ9q95YNrVyJeTi7ODD+6S6sW3qKcydiWTrrMINGtaZtl9rW\nLs1mRCTnojfK1PHU4OygtHY5VRKXEs17i58m9kYUQd4hzBj/CwGeQdYuSyiFWPBSDkVP1C2qZ1BD\n/rFeI8a3o3vfBhiNMuuXnWLn1ktiE58ChYfZNrGjpeMluZZwmdfnTSL2RhQNApvx2ZT5IqTtgAjq\nckg/exmovk/UhSSFxAODm9FvRAskCXZuucQfy09jMIiWrYvVIKjPR53g9flTuJGZROt6nfhk0mw8\nXWxv90fhTiKoy2DU6si4dA0kCfdmDaxdjkV07lWPkVM6olIrOHHoOstmHSEvt2Zv5mTvT9RHLu3m\nnUVPk5WbQbdmfXlv7I84a1ytXZZQTiKoy5ARdhVZb8ClfjAqF/ufRCqvpq0CmPhsN1xcHQgPS2LB\nDwdq7NFeBqPMpST7Deqdpzfz8fKX0Opzub/dkKKDcAX7IYK6DDVhfPpugut6MuWFHvj45e9pPe+7\nfcTH1LztMSNTc8nTywS4OeDhVKn5d6vZeHAZX619E4NRz/Aek3hu8HsoFfb1MQgiqMt0s+OjkZUr\nsQ7Rvmefwx6yLLNk+8/M2vIFAJMfeJFJD7xQ7dpLawoR1GVIK5hI9KjmE4mlKWzfa9GuFnm5epbO\nOszJQ9etXZbFXEwoDGr7GPoyGA38+uenrNg1G4Wk4PnB7zOsxwRrlyVUgQjqMqSfq9lP1IVqcvte\nmB09UesMOr5a+yabj6xCrXTg9ZH/4/72Q6xdllBFIqhLoU1JJyc6AaWTBtf6wdYux+pqYvueLMtc\nSszfNa+pjQd1jjabj5a9wJ6zf+Hk4MKMcT/RrVnJe0cI9kXMKpQi/Xw4AG5N6yMp7Xs1mil17lUP\nd08n1iw6zolD10lPzeXRye3RONr2Ya+VEZOuJVNrsPnDbNOzU/lg6fOERZ/Gw8Wb98f+RMNazaxd\nlmAi4om6FEXj061q9rBHSWpK+17xiURbnYhLSo/njflTCIs+jb9nEJ9PnidCupoRQV2Koo6PGtia\nVx5F7Xv+1bd9r2hFor9tDntcT7rKa/MmEZUUQV3/Rnw+ZT5BPnWtXZZgYiKoS5F2ruCJWgT1XXn5\nOjP5+Vvb9/w861u7LJMJS7DdicRLMWd5ff5kEtPiaFa7DZ9MmoOPm7+1yxLMQAT1XciyXKzjQwR1\naW5v3+vRegwhAfa/ZaYsyzbb8XEy4hBvL3yC9OxUOjbqyQfjf8XNycPaZQlmIoL6LrIjY9FnZKPx\n88LR38fa5di84u17CoWSjs2G2H37XnKWjpQcPa4OSoLcHaxdTpF95/5hxpL/kKPNJrR1P9567Bsc\nHeyjx1uoHBHUd1H0NC2GPcqtsH3vxKU/kWWj3bfvhRW05TX2c7KZicStR9fyxerX0Bt0DOg8iheH\nfYRKabvdKIJpiPa8u0g7e/NUF6FiImKOkJOXzj3tx9h1+54tDXvIssyq3XNY8PfXAIzp8xSjej9h\nM79ABPMST9R3IZ6oqyYuOayE9r0ca5dVIYUdH02t3PFhlI3M2fIFC/7+GgmJp/q/wWOhT4qQrkFE\nUN9F+jnxRF1Vt7fvzf12v1217xX2UDe24hO13qDju/Xv8fu+BaiUal4Z8Sn9O4+0Wj2CdYigLoEh\nT0tGWP5hAW7Nqk+rmTUUb9/LSLOf3ffScvTEZWjRqCRCPB2tUkOeLpfPVr7C9pMb0aideH/cL9zT\n6iGr1CJYlwjqEmSEXUM2GHBtUBuVs3V+SKuTknbfO2Hju+8Vjk838nVGqbD8EENmbgbvLX6GQ2G7\ncHPy4NPJ8+jQqKfF6xBsgwjqEqQXLHQR49Omc/vuextsfPc9a57okpKZxFsLpnEu8jg+bv58Onku\nzeq0s3gdgu0QQV2CwqXjYo8P0ypp970Ny0/ZZPte4R7Ult4xLy7lOq/Nm0JEfBjBPnX5fMp8QvzE\nA0NNJ4K6BIWbMYknavMofnjuyUPRNnl4rjUmEiPiw3ht3mTiUqJoVKs5n02eh79nkMXuL9guEdQl\nKDonUSwdN5umrQKY+B/bbN/L1hqISs1DpZCo72OZOYpzkcd5c/5UUjKTaFO/Cx9NnIWHi7dF7i3Y\nPhHUt9GmpJMbk4jSSYNLPXFYgDkFh9hm+96lpBxkoIGPIw5K8/+IHA7bzbu/PU1WXibdm9/Lu2O+\nx1njavb7CvZDBPVtiha6NG8oDguwAFts37PksMf2kxv5ePmLaPV5PNh+GK8+8gUOKo3Z7yvYFxHU\nt0kTKxItztba9yy1dHzDgaV8s+4djLKBET0n8+ygd1AqxMOBcCex18dt0s6IU8etobB9z8PLif3b\nw9mw7BRpN3Lo/VAjiy+VNnfHhyzLLNn+Myt3zwFg8gMvilPChVKJoL6N2OPDegrb9zy9Hdmy9hw7\nt14iNSWbgSNbo7TAWDFAnt7I1ZQcJKChr+m3DjUYDcz881O2HF2DQlLy3OB3ua/dYJPfR6heRFAX\nI8sy6edFD7W1FT889+ShaDJS8yy2+15Ecg4GI9TzcsRJbdphCJ1ey1e/v8W+c//goNIw/ZHP6Nq0\nj0nvIVRPYoy6mKLDAvy90fh6WbucGs1a7XthZjojMTsviw+WPc++c//grHHl/XE/iZAWyk0EdTFF\np46LYQ+bYI32vcKgbuxruqBOz07hnUVPcjL8IB4u3nwyaQ6t6nY02esL1Z8I6mKKTh0XE4k24/b2\nvfnfH+DKxUSz3c/Ue1AnpsXy+vypXIo5S4BnMJ9PmU+DwKYmeW2h5hBBXYzYg9o2FW/f0+bpWTbr\niFna9/RGmStJN4/fqqqoxHBemzeZ60kR1PVvzOdT5hPkHVLl1xVqHhHUxdzsoRZBbWtK3H1vi2l3\n34tMyUVrkKnl7oCbpmrz7Jeiz/L6/KkkpcfTrE5bPp00B283PxNVKtQ0IqgLGPK0ZF6KBIUC92b1\nrF2OUII7dt/batrd90zVP30i/ABvLXycjJxUOjXuxYfjf8HVyd0UJQo1lAjqAhkXrxYdFqB0EocF\n2DJz7b5XuAd1VZaO7z33Nx8seY5cXQ592gzgzVFfo1Gbvh9bqFlEUBcQ49P2xRzte2EJVVs6vuXI\nar5Y9Rp6o55BXUbz36EfoFLa18nrgm2qVFB37jCY+/qO5b6+Y3l86uumrskqivagFh0fdsOU7XtG\nWS56oq5ox4csy6zcNYefN32MjMzYvs8w7eHpKCTxHCSYRoVnTHJz8wDYtn2JyYuxJrF03D4Vtu+t\nmHuUqIgU5n9/gEcnt6dh04pN3MWk5ZGlNeLjosbbufxPwUbZyLytX7Ph4BIkJJ4a8Ab9Oj1a0Q9D\nEEpV4aA+efI82dm59HtoEnq9gY8+eZmuXe88z02lsv1xXkmR/+GrVI5FPdTebVrYZO3Fa7V9+Zso\nWapWdw9HJj3Xm99/O8KZ4/lj1sPHd6JNpzqlvl/xz+nl5EwAmvm7lrvu+JRo5m79kj1nt6JSqnll\nxOf0bt2vah9MOWq1daJW06twULu4OPPy9GlMmTqSS5euMrDfFM6H/YNCceufeTM++LDo36GhvekT\nGlr1as1ENhrJic1fROFSVxx9ZI/UaiWPTOqCh/cZ9m67xO+Lj+Dp7UxIA59yvf/FhPygbuLvUua1\nsTciWbFrFtuOr8dg1OPo4Mzbo78Xp4QLFbZj50527txV9HZoaN8Sr6twUDdpUo9GjeoC0LhxPbx9\nvIiNTSA4OPCW6956c/otb+v1uRW9ldkV/hY1GLUoXZwwZOWQl5GG2q3sH1ZLK6zVFj+Pd8rvbbZG\nrfcNbIw2T8vhPddYPucA017qibtnyU9LxT+n5+Pzx7ab+GruWvf1pKus2jOXnac2Y5QNKCQFoa37\n81joEwT71DXrx2tPX39Ra/n16tmVXj27Fr29Z++REq+rcFAvmL+G06cu8MNPM4iJiScjPZNatfwr\nX6mNULu5YMjKQZ+RZZNBLZTfg0ObkxCXwbXLN1g5/yiT/tMNVSk74cmyfLOHuoSJxMiEK6zcPYc9\nZ//CKBtRSEruazeYR3tNIcinrtk+DkEoVOGgnjL1UaZOfo0+vUcDMGf+Z3cMe9gjlZsLxCWhS8/C\nSYx+2DWlUsGjEzsw++u9xESmsWnVGQaPbnPXAwhi07Vk5BnwclLh53JzIjEiPowVu2az/9w2ZGRU\nChX3tx/KI70mE+hV21IfjiBUPKhVKhULf/vKHLVYldo9/ylan5Fl5UoEU3B2dWDU1I7M+24fJw9H\nExjsTtfQ+iVee6HY07QkSVyJPc+KXbM5cGE7ACqlmgfbD2NEr0n4edSy2McgCIXEwQEFCoc7dOki\nqKuLwGB3hoxuw5pFJ/hrwwX8arnRoInvHdddTMj/mvs7RPHB0h84cmk3AA4qDQ91GM7wnpPwcbf/\n4T3BfomgLqAqCupMK1cimFLL9kHERaezd1s4axYeZ9pLPfHyuXUc+uTVY7ilr2TPwbMAaNSO9Ov0\nKMN6TMDL9c5gFwRLE0FdQO3hCoihj+qob/+mxMdkcPl8IivnHWXy891ROyg5FXGIpdt/JibiIGpA\no3ZmYJdRDO0+Dg8Xb2uXLQhFRFAXKHqiFkFd7SgUEsPHt2PuN/uIi0nn13nLiXH8l3ORxwEwSk5I\nrvcz58kX8XARR7AJtkcEdQG1GPqo1jSOKpo/oGPOxllkJkQC4OrkQZsmI9h0rR1d6gWKkBZslgjq\nAqLro3qSZZlDYTtZsWs2l2POgQJURmcCtL34z/Dn2JulQo6KNtnRW4JgDiKoC6hE10e1YpSNHDj/\nLyt2zSYiPgwADxdvhveYiFtae/b9FcXGpedIbJ3fDy2CWrBlIqgLqN0LJhNFUNs1g9HA3nN/s3LX\nHCITCzbacvVlWM9JPNxxOBq1E7IskxKv4/zJONQnr6MK8qJZOfb4EARrEUFdQOUuJhPtmcGoZ9eZ\nrazcNYfo5KsA+LoHMKLnZB7oMBQHlaboWkmSGDK6DfFxWdyIz6BDUjq+zuJHQbBd4ruzgJhMtE96\ng44dp/5k5e65xKVEAeDvUYtH7pnKfW0HoVY5lPh+DhoVjR9qxu7FR/DMzGPHlkvcN6CpJUsXhHIT\nQV2gaOhDPFHbBZ1Bx7YTG1i9Zx4JqTEABHrVYeQ9U+nTpn+5jsC6lmfgVIAHneJS2fvPFQKD3GnZ\nXiwRF2yPCOoCoo/aPmj1efx9bB1r9i4gKT0OgGCfeozsPY3erR5CqSj/t/SF+CxuOGlo2LMeV/Zc\nZcPyU/j4uxAYLE4MF2yLCOoChUMfYjLRNuXpcth69HfW7p3PjcwkAEL8GjKy9zR6tngApeLu25iW\nRJZlLhQcFnDvAw1xzdNx8nA0K+Ye5fGXeuLsWvKQiSBYgwjqAiq3/PYsfWY2ssGApKzYD75gHjna\nbLYcWc3afQtJy7oBQP2AJozq/Tjdmt9b6QNk4zK0pOfq8XRSEejmwIBHW5EYn0lMZBqrFx5n7FOd\nUSrtf/teoXoQQV1AUihQuTmjz8hGn5lTtPeHYB3ZeZlsOrSC9QcWk56dCkCjWs0ZFfoEXZqE3nVv\n6fIqPCigmb8rkiShUisZOTl/D+url5P5e/0FHh7eosofhyCYggjqYlRuLugzstGlZ4qgtpLM3Aw2\nHlzGhgNLyMwtOB4ruDWjQ5+gQ6OeVQ7oQkUnugTc/Dq7ezoxcnJHFv50gEO7rxIY7Ea7rqUfkCsI\nliCCuhi1uyu5MYliQtEKMnLS2HBgCX8cXEZ2Xv7YcYuQ9ozq/QTtGnQ1WUAXuph484m6uDr1veg/\noiUbV55h06qz+Aa6Ubuup0nvLQgVJYK6mKIJRRHUFpOWdYN1+xfz5+EV5Gjzw7N1vc48FvoErep2\nNHlAQ+EZiflf42YBd65I7NA9hLjodI7sjWTV/KNMe7Enbh4lH5ArCJYggroYcXiA5aRkJvH7vkVs\nPrKKPF3+CdDtG3ZnVO/HaRHS3qz3js/QkpZrKJhI1GAw5N1xzUPDWpAQl0nklRusnH+Mif/pikol\nJpgF6xBBXYzY78P8ktMTWLtvIVuPrkGrzw/ITo3vYVTvx2lau7VFarhw20RiSfIPyG3P7K/3En0t\nlc2rzzJwVGuzPOELQllEUBdT2KInxqhNLzEtljV7FvDX8d/RG3QAdGvWl5H3TKNRkGW7K0qaSCyJ\ni5uGUVM6Mv+H/Rw/eJ3A2u507lXPAhUKwq1EUBdTuCe12OrUdOJSolm9Zx7/ntiA3qhHQqJniwcY\n2Xsa9QOaWKWmu00klqRWHQ8GP9aGtb+dYOvv5/ELdKNeIx9zlygItxBBXYzY78N0VC5Gvlv/HttP\nbsIoG1BICnq3epiR90wjxL+h1eoqayKxJK06BBF7PZ3928NZvSD/gFxPbydzlikItxBBXYzY76Pq\nridF4N1Bh3NtI9tObEAhKenbdiCP9ppKbd961i6vaCLRw1FJoJum7HcocN/ApiTEpnPlQtItB+QK\ngiWIoC5GbHVaedcSLrNy12z2nP0blzoyshEe6DCUR3pNoZa37SwaKRqf9nep0MRg/gG57Zn7zV7i\notPZsPwUw8e3E5OLgkWIoC5GdH1UXHjcRVbsmsX+8/8CoFKoSL1iJP2SiudmvGfl6u50oWB8uqlf\nxY/ecnJWM3JKR+Z9t4+zx2MJDHan533WG8YRag4R1MWoxIKXcrsUc5YVu2Zz6OJOANRKBx7sMIzh\nPSfxYN+hVq7u7m4+UVfujET/Wm4MG9uOFfOOsm3TRfyD3Gjc3N+UJQrCHURQFyO6Psp2IeokK3bN\n5ujlvQA4qBzp12kEw3pMxNvNz8rVlU6WZcKqGNQATVsHEPpwY3ZuucTaRSeY9mIPfMrRQSIIlSWC\nuhhxbuLdnbl2lBW7ZnMy/CAAjmon+ncexdAe4/F08bZydeUTn6kjNVePu6OSQLeq7Tfd+4FGxEen\nc+F0PCvmHWPqf7ujcSz7VBlBqAwR1MWIvT5uJcsyp64eZsXOWZy5dhQAJwcXBnZ5jCHdx+Lu7GXl\nCivmYnxBW14FJxJLIikkhoxpS/J3+0iMy+T3xScZNaUjkkJMLgqmJ4K6GLHXRz5Zljl+ZT8rds3m\nfNQJAFwc3RjUdQyDu47B1ck+j6qqykRiSTSOKkZN7cicb/YRdjaBHVsv0befdRbxCNWbCOpiVK7O\nIEkYsnMx6vUoVDXr0yPLMkcu7WbFrtmERZ8BwM3JgyHdxjGgyyhcHN2sXGHVVHUisSTevi6MmNCO\npTMPs/uvywQGu9O8TaDJXl8QQAT1LSRJQu3ugi4tE31GNg5e9vnkWFFG2cihiztZvnMW4XEXAPBw\n9mJojwn06/QozpryreCzZaaaSCxJw6Z+3D+oGX9vuMC6JSfx9nUmIKhmfO8IliGC+jYqt/yg1qVn\nVvugNspG9p3bxopds7mWcAkAL1dfhvWYwMMdH8HRofoskzblRGJJuvWpT1x0OqePxrBy3jGmvdgD\nJxdxQK5gGiKob6N2dyWH+Go9oWgwGthz9i9W7ppNVFIEAD5u/ozoOYkHOgxDo65+m+QX7u/R1M/Z\nLKsJJUli4MjWJMVnEns9ndWLjjP2ic4oxAG5ggmIoL5N0Van1bCX2mDUs/P0Zlbtnkt08jUA/DwC\neaTXFO5vNwS1qvo+ARZfOm4uagclI6d0ZM7Xe4kIS+afjRd5cEhzs91PqDlEUN+mcBl5deql1hl0\nbD+5kdV75hGXch2AQK/aPNJrCn3bDkStrP79v+aYSCyJh5cTj0zqwG8/H+TAjggCgtxo27m2We8p\nVH8iqG9TnXqpdXot/5xYz+o980hMiwMgyDuEkb2nEdq6H0pFzfjyy7JctAe1qVrzSlO3oTf9RrRk\n06ozbFx5Bt8AV4JDxAG5QuVV+ic1ISGZLh2H8Ne232jSpL4pa7Kqol7qNPvtpc7T5fL3sd9Zs3cB\nyRkJANTxrc/I3o/Tq+WDKBU1a3vOhEwdqTl63DRKarlbZninY48Q4q6ncXR/FCvnHePxl3ri6l7+\nbVUFobhKBbVOp+PpJ9/GxcX8TyeWZs+HB+Rqc9hydDW/71tESmYSAHX9GzOq9+P0aHEfCqlmTmxd\nKDwowN88E4l38/DwliTEZRIVkcKqBceY8ExXlKqa+TUQqqZSQf3a9M958ukxfP7pr6aux+rscb+P\nHG02fx5eybp9i0jLTgGgQWAzHgt9gi5NQ2tsQBeyxERiSZQqBY9O7sCcr/cSFZHC5rVnGTjSMgf4\nCtVLhYN64YI1+Pp58+CD9/D5p78iy3LJL6yy/RYvqWCMtnitGg8PAPRpWTb1MZRUK0BaVgpvzJ/M\n1fgwAJrUbsOYPk/TuUmoFTe1z7+vrXz+wpJyAWgR6HFLTXf7nJqSp5cjox/vztxvd3JsfxStOoTQ\nqFlAhV/HErWaiqjV9Coc1Avmr0aSJLb9s5eTJ84zeeJ0fl8/k4AA31uum/HBh0X/Dg3tTZ/Q0KpX\nawEezfM3gk8+dNrKlZQtIyeNtxdO5Wp8GME+9XhqwFt0aNRTnDpSjCzLNzdjKuPUcXMJDvGia++G\n7N12iauXkioV1EL1tGPnTnbu3FX0dmho3xKvq3BQb9+5rOjf9/Udyy8zP7ojpAHeenP6LW/r9bkV\nvZXZFf4WLV6bZ+dmKDQOpJ68QFZ8HBof25itv73W7LxM3v3taa7EnifIO4SPJ87C280PgyHPmmUW\nyP8ryxa+5nEZWlJydLg7KvFzlm+pqaSvv7kE1cn/JREdeaNS97NkrVUlai2/Xj270qtn16K39+w9\nUuJ1NXvwsgRKJ0d8urYBIHHXUStXU7IcbTYzljxHWPQZAjyD+aggpIU7FZ04boKtTasisHb+kFrs\n9bS7DhcKwt1UKai3bV9SrVrzCvmFdgIgcWfJv92sKU+Xw0fL/sv5qBP4ugfy0cRZ+LqLP6Xv5kKC\n5fqnS+Pp7YSjs5rsTC0ZabbwV49gT8QTdQn8++QHdcKOw1au5FZaXR6frHiZ01cP4+3qy0cTZxLg\nGWTtsmxaYcdHMzOvSCyLJEnUCs7f5Cv2eppVaxHsjwjqEni2a4baw5WsiOtkR8Zauxwgf5Xhpyte\n5PiV/Xi4ePPRxFkEeYdYuyybJsvyzc2YrBzUcOvwhyBUhAjqEihUKnx7dQAgwQaGPwxGPV+sms7B\ni9txc/Lgw/G/Utu3+g05mVpchpa0XAOejioCzLC1aUXVqp3/RB13Pd3KlQj2RgT1XfgXjVNbd/jD\nYDTwze/vsPfcX7g6uvPB+F+oF9DYqjXZi+IbMdlCy2KtOuKJWqgcEdR34RfaGch/orbWLL1RNvLj\nHx+w68wWnDQufDBhFg1riW0zy+uChXbMKy9vH2ccNCoy0vLITBcTikL5iaC+C7em9XAM9CUv4Qbp\n58Mtfn9Zlvl10ydsO7EBjdqRD8bPpFmdthavw55dKNaaZwskhURgwfBHbLR4qhbKTwT1XUiSZLU2\nPVmWmbPlS7YcXYODSsPbo7+jZd2OFq3B3uVPJNrWEzVQ1PkhxqmFihBBXYrCceqEHYcsdk9Zllnw\nz3f8cWgZKqWaN0d9Tdv6XSx2/+oiJl1LRp4BLycV/q62czBC0Th1lHiiFspPBHUpCsepk/Yex6jX\nW+Sey3b8yu/7FqJUqHj90S/p0KiHRe5b3RTvn7aFicRChZ0fseKJWqgAEdSlcK4dgGujEPQZ2aQc\nO2/2+63cPZflu2ahkJRMH/EpXZrax0ZWtuhCUf+0bYxPF/Lxd0XtoCQtJYfsLK21yxHshAjqMviF\nWmaV4u/7FrH43x+RkHhx2If0aHG/We9X3dnKisTbKRQSAUFihaJQMSKoy2CJfupNh1Yw/+9vAHh+\nyPuEtu5ntnvVBMUnEm0tqEEsfBEqTgR1Gfzu6QiSxI1DZ9Bnm34rxL+OrWXm5s8AeGbAW9zXbrDJ\n71HTRKflkak14OOswtfV+isSbyfGqYWKEkFdBgdvDzzbNsWo1ZG8/6RJX/vfk3/w0x8fATDtoek8\n3OkRk75+TWWto7fKq3DPjzgx9CGUkwjqcjBHm97uM1v5fv37yMhMvP8FBncbY7LXrulsbUXi7fwC\nXVGqFNxIyiY3R2ftcgQ7IIK6HPz65LfpmWrhy/7z//LV2rcwykbG9HmaET0nmeR1hXzFTx23RUql\ngoAgNwDiosXwh1A2EdTl4NOtLQoHNamnwsi7UbU/V49c2s2Xq1/DKBt4tNdURvV+3ERVCgBGWSYs\n0baHPgCxN7VQISKoy0Hl7Ih319YgyyRV4Xiu41cO8OmKV9Ab9QzpNo5x9z5rU4sxqoPrqXlkaY34\nuqjxdbGdFYm3K1yhKDo/hPIQQV1ORePUlWzTO3P1CB8vfxGdQUv/zqOY8uBLIqTN4IINt+UVV3SI\ngFhKLpSDCOpy8u+Tv99GZcapz0ed4IOlz6PV5/Jg+2E80e9VEdJmYksnupTGv5YrCoVEUmIW2jzL\nbE8g2C8R1OXk2b4ZKncXMq9EkR0VV+73uxR9lhlLniNXl0PfNgN4euBbKCTxaTeXmwvmS5p6AAAg\nAElEQVRdbHd8GkClUuJfyw1kMaEolE0kRjkpVCp8e7YHyn88V3jcRd5b/AzZeZn0avkgzw95H6VC\nac4yazSjLHMx0bZb84oTC1+E8hJBXQH+RW16ZY9TRyZc4d3fniIzN51uzfry0rCPUCpU5i6xRotK\nySNHZ8TfVY23s+1OJBYSC1+E8hJBXQH+5TyeKzr5Gm8vepL07FQ6Ne7F9BGfoVLafnDYO1vdMe9u\nxBO1UF4iqCvArVl9NAE+5MUnk3EhosRrYm9E8fbCJ0jNSqZtg668PvJ/qFW2t99EdWRPwx4AAUHu\nSBIkxmei0xqsXY5gw0RQV4AkSaW26SWkxvD2oidIzkigZd0OvP3YNzioNJYus8a6EG8frXmF1A5K\n/AJdkY0y8bEZ1i5HsGEiqCuo8NSX29v0ktMTeHvRUySmxdGsdhveGf09GrWTNUqskQxGmUtJ9vVE\nDRAYLMaphbKJoK6gov2p9xwrOp4rJTOJtxc9SVxKFI2CWvDe2B9x1tjHOGl1EZmaS47OSICbA15O\n9jMfIMaphfIQQV1BznUCcWlQG316FqnHL5CencI7i54iOvkq9QOaMGPcz7g4ulm7zBrH3oY9ConD\nboXyEP1ileDfpzMR4de5un03C4/tJDLxCnX8GvDB+F9wc/Kwdnk1UuFEor0FdUCQO0iQEJeBXm9A\npRJ99sKdxBN1JfiHdkbrIPNr4hIi4i4S7FOXjybMxMPF29ql1VgX7aw1r5DGUYWPnwtGg0xibKa1\nyxFslAjqSnDp1px/huuIc80iwCOYDyfMxMvV19pl1Vh6Y7GtTf3s64kaio1Ti6Xkwl2IoK6gPF0O\nX259l4QgGZd0eLHJM/i6B1i7rBotMiWXPL1MLXcHPJzsbzSvlthJTyiDCOoK0Orz+GTFy5y+egQ3\n2ZmHVjtgPFjywhfBci7YyY55d1O05alo0RPuQgR1OekMOj5f9SrHr+zHw8Wb6R2m454qkbCjcvtT\nC6ZjLzvm3U3h0Ed8TAYGg9HK1Qi2SAR1OegNOv635nUOh+3CzcmTjybMpNV9DyGpVaSevIi2isdz\nCVVTdJitHY5PAzg6qfHydcagN5IULyYUhTuJoC6DwWjgm3XvsP/8v7g4uvHB+F+o698IlYsTPgXH\ncyXuOWbtMmssvVHmkp3t8VGSm2coiglF4U4iqEthlI38sGEGu89sxcnBhRnjfqZhrWZF/79wObkY\n/rCeqzdy0BpkgtwdcHe0v4nEQmLLU6E0IqjvQpZlftn4Cf+e/ANHtRPvjf2RJsGtbrmmaDl5JY7n\nEkzjgp2PTxcSS8mF0lQ4qA0GA9OmvE7vXqMIvecxzp4NM0ddViXLMrO3fMHWY2twUGl4Z8z3tAhp\nd8d1Xh1aoHJzJvNyJNnRCVaoVCiaSAyw32EPuNmiFxedjtF4973OhZqpwkG9aeO/KBQSu/as4IOP\nXuSdt742R11WI8syC/7+lo2HlqNSqnnrsW9oXa9Tidcq1DeP5yrPqS+C6RW15tnpRGIhZ1cHPLwc\n0WkNJCdmWbscwcZUeFBv8JAHGDDwXgCuXY3Gy6vkvS1UKseqVWYBUsHRWMVrXbFzFr/vX4RSoeKt\n0d/RuWnfUl8jsG934rbsJWnnMRpOGGHRWm1X/gnr5q5VbzByJSkHgBa1vFGpKvbtbGuf06A6XqSl\nxJIQk02t4FtXutparaURtZpepWZflEolUya9yrrf/2LF6h9LvGbGBx8W/Ts0tDd9QkMrV6GF7T6z\nGYAxfZ+haxkhDRB4fw8AYjbvRJ+Vg8pF7EFtKeHJ2WgNMrU9HXGz44nEQsqCDZlysrRWrkSwlB07\nd7Jz566it0NDS86cSn93z1vwBZ9+/io9uo7gzPmtODnd+hvprTen3/K2Xp9b2VuZTeFv0eK19W07\nkPC4Cxy6uJ1Hek5CkqRSX8O5URDeXVpz49BpIpaup/7koRar1Xblj7Gau9azsakANPVzqtS9bOlz\nmper58LpGAAaNvO+oyZbqrUsotby69WzK716di16e8/ekhsTKjxGvfi33/ns018AcHLSoFAoUChK\nDzN78mCHYbg5eXDx+mnORpavP7rB4/lDHuFzVpd66K1gWhftfOl4cRdOx6HXGalT3wsvX/v/eATT\nqnBQj3ikHydPnKdv6Gj6PzyFr797G42m+pwL6OTgzMAuowFYs2d+ud4neMi9aHy9SDtzmeQDJ81Z\nnlCMvS8dL+70kfyn6Tadgq1ciWCLKhzUTk6OLFvxPdt3LmPPvlUMGnSfOeqyqgFdRqFRO3L08l4i\n4i6Web1S40C9iYMBCJ+9xtzlCYDOYORywURiEzvv+MhIyyXiUhJKpYIWbQOtXY5gg8SClxK4O3vy\nUIf84Yw1exeU633qTx4GCgXRG7aTG59svuIEACJu5KIzytT21OCqse9TUU4fi0GWoXELP5xcHKxd\njmCDRFDfxZDu41AqVOw5+xdxKdfLvN65TiC1+t+DrNMTsXC9BSqs2W4Oe9j30zTA6SPRgBj2EO5O\nBPVd+HkE0qdNf4yykbV7F5brfRpOy38Kj5j/e9EJ5YJ53FzoYt/j0/Ex6cTHZODorKZRCz9rlyPY\nKBHUpRjeYyIA205sICUzqczr/fp0xrVxXXJjEondtKvM64XKK3yitveOj9NH8ycRW7YNFAfbCncl\ngroUdfwa0K1ZX3QGLRsOLC3zekmSaFjQqndl9mpzl1dj6YqtSLTnoDYa5aKgbi2GPYRSiKAuw4ie\nkwHYfGQVWbkZZV4f8lh/lC5OJO0+Rvr5cHOXVyOFJ+dPJNbx1ODiYL9PodeuJJORlountxN16ntZ\nuxzBhomgLkPT2q1pXa8z2XmZbDlS9lOy2sOVkFEPAxA+R7TqmUN1WehyqmASsXXH4DJXwAo1mwjq\ncnikV/5T9foDS9Dq88q8vsHjjwAQuXwzunSxE5qpXUy0/4UuOq2B8yfjAWjdKcjK1Qi2TgR1ObRr\n0I0Ggc1IzUrm3xN/lHm9R4uG+PZsjz4zm8gVmy1QYc1yoRpMJF48E482T09QiAe+/q7WLkewcSKo\ny0GSpKKn6rX7FmIwlt1616CgVS98ttj/w5S0xSYS7XlFYuGwR5uOYhJRKJsI6nLq3vw+Ar3qEJdy\nnb3n/inz+qBBfXAM9CXj4lWSdovDb00lPDkHvVEmxI4nErMy8rhyMQlJIdGyQy1rlyPYARHU5aRU\nKBneM7+ves2e+WU+JSvUKupNGgKIVj1Tutk/bb/j02eOxyIbZRo188XFtfpsaCaYjwjqCri37UC8\nXH2JiA/j2JV9ZV5ff9JQJJWS2E27xJmKJlIdFrqcPiqWjAsVI4K6AhxUGoZ0GweUbwtUp1p+BA3s\ng2wwcHXBOnOXVyNcsPM9PpISMomJTMNBo6JJywBrlyPYCRHUFfRwpxG4aFw5c+0oF6LK3nu68FCB\niAXrMGp15i6vWtMajIQn5yBhvxOJhftOt2gbiNpOx9gFyxNBXUHOGlf6dx4FwOq9ZT9V+/Zsj3vz\nBuQl3CB6ww4zV1e9FU4k1vHU4GyHIScb5aJhD9E7LVSECOpKGNh1NA4qDYcu7iQy4Uqp10qSdLNV\nb46YVKyKomGPAPucSIy6mkLqjRzcPR2p19DH2uUIdkQEdSV4ufpwf7v8jo41+xaUeX2dUQ+jcnMm\nef9J0s5cMnN11VfRRKKdDnsU9k636hCEVI3OGRXMTwR1JQ3tMQGFpGTX6S0kpMaUeq3azYW6YwYA\ncEUc1VVp9nxYgF5v4NyJWEB0ewgVJ4K6kgK9grmn1YMYjHrW719c5vWFwx9RK7egTS17Fz7hVsUn\nEhvb4RP1pXOJ5OboCQx2x7+Wm7XLEeyMCOoqGN5zEgBbj/1OenZKqde6NamHX2gnDNm5RC770wLV\nVS/hSQUrEr0c7XIi8eZOeWISUag4EdRVUD+gCZ0a34NWn8vGg8vLvL5wV73wOWuQjUZzl1etXEi0\n32GPnCwtl84l8P/27js8qjLt4/h3WnojgUAoCZAAoVfp0pv0XqToqlhW14a4rmVVVBbW18W1rgUL\nCtJrAAXpVRGkhJIAIdRACul1ynn/CAlGQurMnJnk/lyX12WYJ+f8kkxuDs859/NoNPnz00KUlxTq\nSipYrCni16Vk5Za8pGnQfT1xr1+bjHOXiN95yB7xqoyoG87bkXjyaBwWs0KjJjXx9nVTO45wQlKo\nK6lFcHuaN2hHRk4aW46sLnGsVq+n0YOjgfxV9UTZRSUUbGbrfIW6YLstuYkoKkoKtRUUXFWvPfAd\nRlNeiWMbPjASjUFP3I/7yLoUZ494Ti/XZCEmKccpbyQmJ2Zx+UIyBhcd4W2kZVxUjBRqK+jYpCch\ngWHcTE9g54mSbxS6BQZQf3Q/sFiI+WqNnRI6t4KOxBAnvJF44kj+TcTw1rVxcdWrnEY4KynUVqDV\naAufAFm971vMFnOJ4wtuKsYuWo85p/Stvao7Z10xT1GU2097yLSHqAQp1FZyb8tBBPoGcTUpll+i\ndpY41r9za3xbNyUvKYWra7fbJ6ATO+Okm9leu5TKzYQsPL1daNxEWsZFxUmhthK9zsDo7jOA0jcW\n0Gg0havqyaYCpXPWzQL+2DKu1cmvmqg4efdY0cD2o/D1qMHZayc5fuHXEsc2mDAYg683yb+dJPn3\nM3ZK6HxyTRZibt7qSKzprnacMjObLZz8/VbLuOyLKCpJCrUVuRrcGdHlfqD0JVD1Hm6ETMtf/0NW\n1bu784nZmC0Q4u9cNxLPn0kgKzOPWrW9qFPfR+04wslJobayofdMxN3Fg2Mxv3D22skSxzZ+eCwA\nl1duJfdmqj3iOZ2oBOdcMe/4rQ0CWneqi0YjK+WJypFCbWVe7j4M6Zj/VMeqfd+UPDY0mMD+XbHk\n5HLx+wjbh3NCUbduJDrTGtQ52UaiT94AoLVMewgrkEJtAyO7TUWvM3Dg1DauJl0scWzoo/lF/cLC\n1bL+RzHOOOEa1KePX8dktBAS6o9vDeeZVxeOSwq1DQR4B9KvzXAUFNbs+7bEsXUGdsMjOIjM2Kvc\n+PmgnRI6h1yThQs3s9FqoEkt5yl4BfsiSsu4sBYp1DYypscDaNCw/dgGktLi7zpOo9MVzlXLo3pF\nFd5IrOGGu8E5biSmJmcTez4JnV5L87Z11I4jqggp1DZSLyCE7i0GYLKYWHew5I0FQqaPQOvqwo2t\nB8i8cNVOCR2fMza6RB65Bgo0axmIm7tB7TiiipBCbUPjCjYWOLyK9Oy7P9XhGuBH/XEDQFGIWVjy\nCnzVSeETH07S6CIt48JWyl2ojUYjD0yfRZ9eU+jWZRwbNmyzRa4qIaxuC9o17kp2XhabDi0vcWzj\nR/JvKl78fgPm7Bx7xHN4zrZH4o1r6SRcz8Dd00BYeC2144gqpNyFesni9dSs5c/O3T+w6ceveOap\nN22Rq8oYd2sJ1A2/LCHXmH3Xcf4dW1CjQwvyktO4vOpne8VzWLkmCxeSbt1IrOkchbqwZbx9XXR6\n+ceqsJ5yv5vGT7iPN+c8C4DFYkGvd46bPGpp0/AemtRtSVpWClt/X1fi2IL1P2I+X1HiWiHVwbnE\nbMxK/o1EN4PjFz2L2ULkrQ0CZF9EYW3lXiDX0zP/6iY9PYNJE/7GW+/MKv7Aesffckijzf/ybZ11\nYq9HeWfpM6za+zX9243G28Ov2HENJw7nxKsfknIsipRDZ6jVvb3ds1pHfideZbKeTcrfLLh5bW+b\nfc3W/J5GnrhCRnou/rU8CQmtY/VuRGf6+UtW66vQSuaXL19jwtgneeLJaUyaPLzYMW/Oeavw/3v3\n7kWf3r0rlrAK6NZ8AM3qtyHqynHeX/sqr075sNhfZJ2bK2EzJ3Jq3uecmvs/ekd8pkJaxxB1IwOA\n8NpeKicpXU62kc2rjgPQrU+YtIyLMtu5axe7du0u/Lh3777Fjit3ob5xI5H7Bv2FDz95g759u911\n3Csvzy7yscnkeDfICv4WtUe2WWPn8uxnkzlwehvrDyxiWOdJxY5r/Ph4zn66hBs7fuH67oPU7N7O\n7lkrL3/apjJZz9xIByAswMVmX7O1vqdb1kWSnppDvRA/2neta5O8zvTzl6xl17NHF3r26FL48d59\nvxU7rtyTf/Pmfkpqajpvz/mI/n2n0r/vVHJkl5JS1alRj6dG/BOAhVveI+Z6VLHjXGr4EPpEfhE/\nPfcLu+VzJEWWNnXwjsTLF5L5bf8ltFoNwye2QquVq2lhfeUu1Av++xqXr+1n247Fhf+5ubnaIluV\n07PlQAZ3GIfJbOTdlS+RnZdV7LiwJyZj8PUmYc9hEvYctnNK9V1Ico6ORLPJQsTyE6BA936NqV1X\nljMVtuH4t9OrmEeGzCK4VihXk2L5fPP8Yse4+HkT9tQUAE7/68tq9wRIQaNLUwd/fnr/jhgSrmfg\nX9ODeweGqR1HVGFSqO3M1eDOi+Pn46J3Y9vR9ew4vrHYcWGPT8Lg50Pivt9J2FX8vFVVFeUEK+Yl\nxWewe8s5AIZNbIXBiTY1EM5HCrUKggNDmTkk/2br/zbO5VoxS6EafDxp8rf83WJO/+uLanVVHe3g\nV9SKohCxPBKzyUK7zvVp1KSm2pFEFSeFWiWDOoyhZ8tBZOdl8e6qf2A05d0xJvSxCbj4+5J08Dg3\ntlePJVCNZgvnE/M7OJs66BX10V+vcPH8TTy8XBgwMlztOKIakEKtEo1Gw5PDXyXQry7n407z7c8f\n3DHG4O1Jk6enAnDy7U+rxVV17M0cjBaF+n6ueDrgdEJGei5b1+dvRjx4dHM8PF1UTiSqAynUKvJ0\n82b2uHnotHrW/7KYX6N23TEmdOZ4XAL8SPr1ONe37lchpX05+h6JP605RU6WkdDwWrTqIK3iwj6k\nUKusWf3WTO/3FAD/XfcGiWk3iryu9/Kg6bPTATj59idV/qq68EaiA85Pnz0Vz8nf4zC46Bg6vqV0\nIAq7kULtAEZ3n0770O6kZ6fw3uqXMVvMRV5v/PBYXGv5c/PwSa5vqdpX1YU3Eh3sijov18Smlfm7\nyvcZ0oQaAY6VT1RtUqgdgFaj5bkxb1HDqyYnLx5h+e4vi7yu93Qn/Pn85VJPz626T4CYLArnEh2z\nUO/YHE1qcjZB9X3o0quh2nFENSOF2kH4efrz3Ji30KBh2e7PiYwt+ux06CPjcatdk5SjZ4jbvEel\nlLZ1KTmHXJNCkI8LPm4VWi/MJq5dSuHX3bFoNDB8Ymu0Ovm1EfYl7zgH0q5xV8b3fAiLYuG91a+Q\nlpVc+Jrew53wWQVX1VWzW9ERG10sZgsRyyNRFOjSuxFBDXzVjiSqISnUDmZKn8cIb9CWpPR4/rvu\njSIFufFD43ALqkXqiWiuRdz5hIizu71HouMU6oO7Y7l+NQ0/f3f6DGmidhxRTUmhdjB6nYEXxs7F\n082bQ9G72fDLD7dfc3ej2fMzADgz70sUi0WtmDYRHe9Y89PJiVns3BwNwNDxrXBxdZzpGFG9SKF2\nQIF+dfnbyNcB+GbrAs5dO1X4WsMZI3GvF0hq5Dmurd+pUkLrM1sUziY6Tuu4oihsXBmJyWihVYe6\nhDWXzWqFeqRQO6juzfsztNNETBYT7656iazcTCB/F5hmzz8AwOkqdFV9JSWXbKOFQC8DNdwNasch\n8sg1YqIScfcwMHh0c7XjiGpOCrUDe2jw8zSs3YS4m5f5eP2bhfPVIdNH4F6/NmmnY7i6ZpvKKa3j\n9vy0p8pJICsjj5/WnAZg4KjmeHrLeutCXVKoHZiL3pXZ4+bhanBjx/ENbDuav4u5ztWF8BduPQEy\nfyGK2VzSYZxCVHz+vxgc4YmPretPk5WZR8OwANreU0/tOEJIoXZ0DWo15rH7XgLg4w1zuJIYC0DI\n1GF4BNchPSqWK1XgqtpROhJjohM5dugqOr2WYRNbSZu4cAhSqJ1A/3Yj6dNmOLnGbN5d+XfyTLlo\nXQyEz34IgNPznPuq2qIohYVazUfzjHlmNq6IBKD34DACaqk/DSMESKF2ChqNhqdGvE6QfzAXbkTz\n9ZYFAARPGYpnw3pknL3I5ZVbVE5ZcddSc8nMsxDgaSDAU70bibu3nCU5MYvAIC+69W2sWg4h/kwK\ntZPwcPPi7xPfQ6/Vs/HQMg6e2YHWoKfZ7Ftz1fMWYjGZVE5ZMY6wtOmNa2ns33EBbrWJ66RNXDgQ\neTc6kab1WvHAgGcA+GDdGySkxhE8eQiejeqTGXOFy8t/UjlhxajdOm6xKGxYdgLFonBPjxDqN6yh\nSg4h7kYKtZMZ2XUqnZrcS0ZOGv+36mUULYT/PX+u+sz8r7AYne+qWu09En/be5Frl1Lx8XOj37Bm\nqmQQoiRSqJ2MRqPhmVFv4O9di9OXj/LDzs9oMGEQXmHBZMZe5dLSzWpHLBdFUVTdLCA1OYvtm6IA\nuG9cS1wdaNU+IQpIoXZCvp7+zBo7Fw0aVuxZyInLR25fVf/bua6qr6fnkZ5rxs9dTy0730hUFIUN\ny4+Sl2umeZs6NGtV267nF6KspFA7qdYNOzGp10wUFP6z+hW8h3TEu2kIWZfiuLg4Qu14ZfbH+Wl7\nP7N86ug1oiOv4+qmZ8jYFnY9txDlIYXaiU3qPZMWwe1Jzkjkgw1v0vTFW1fV//cNljyjyunKJkql\n+ensLCMbVx4FYMCIcLx93ex6fiHKQwq1E9Np9cwaOxdvd18On9vHkbrX8Q5vRPbl68R+t0HteGUS\nrdITH9sizpCRlktw4wA6dG1g13MLUV5SqJ1cLd86PD3qDQAWbf8I96cHARD13jeYc/NUTFY6RVFU\n2Szg4vmbHDlwGZ1Ow8jJ7dFopU1cODYp1FVAl2Z9GNF5CmaLiUVJq3Bt25Dsq/HELlqvdrQSJWQa\nSck24e2qo463i13OaTKZ2bj8BAD3DmpGYJCPXc4rRGVIoa4iHhz4LI3rhHM9+Sq/jXNHQSHqvW8x\n5+SqHe2u/vhYnr1uJO79+TyJ8ZkEBHrSa6A8My2cgxTqKsKgd2H2+Hm4Gdw5nHKUK4NrkhOXQOy3\n69SOdlf23nor4UYGe38+D+S3iesNOrucV4jKkkJdhdQLCOGJYS8DsLtVAin+Fs689y3m7ByVkxXP\nnvPTikUhYtkJLGaFDt0aEBLqb/NzCmEtUqirmL5th9O37XCMipE947VkJiVy4eu1ascqlj0XYzpy\n8DKXLyTj5e3KgBHhNj+fENYkhboKenzoP6jrH0ySVy6HepuIWrAIU5ZjXVUnZhpJyjTi6aKlrq9t\nt7pKT83h5w1nABgytgVuDrAnoxDlIYW6CnJ38WD2+PnodQai2lqI8k3gwsJVascq4o/z01ob30j8\ncc0pcnNMNG0ZSPO2dWx6LiFsQQp1FRUaFM5DA58DYN9AE4e/+AZTZrbKqW6z19ZbUZE3OH3sOi6u\nOu4b11K21hJOSQp1FTas82Q6N+2N0Q22dkvi7OfL1I5UyB67jufmGNm86iQAfYc2w7eGu83OJYQt\nSaGuwjQaDU+PeoMaLjVIqKvww94vMKZnqh0LsM+u4zs2RZOWkkPdYF/u6Rlis/MIYWuVKtS//HKU\n/n2nWiuLsAEfDz9mT3kXjQJH22Sx+ZP31I5EcraR+Awj7gYt9f1scyPxysUUft17Ea1Ww4hJrdFK\nm7hwYhUu1O/++3Men/kKuQ6+noSAVg07MrLhSAAWJ68lPu6SqnkKbiQ2qemBzgYF1Gy2ELHsBCjQ\nrW8jateVNnHh3Cq8nUVYWAgrVn/MA9NfKP7AesdfNlKjzf/yq0PWh//yDkee28ll3zT+vfBpFry+\n2YY31vKPe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"text": [ "<matplotlib.figure.Figure at 0x7f743defa1d0>" ] }, { "html": [ "<h3>Iteration #05</h3>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x7f7404a31810>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\"Real\" eigenvalues [ 0.30522543 2.38337068 6.39983138 12.0934459 ]\n", "Estimated eigenvalues [ 0.30522543 2.38337354 6.40388404 12.42305865]\n", "Relative error (e-r)/r [ 3.09177903e-13 1.19819322e-06 6.32844409e-04 2.65323353e-02]\n", "2-norm of the difference between estimated eigenvectors and \"real\" ones\n" ] }, { "latex": [ "$$\\text{error}_i = \\sqrt{\\textstyle{\\sum_j} \\Delta\\psi_{ji}^2}$$" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Latex at 0x7f7407525310>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[5.8555644958694453e-08, 0.00035564436208726036, 0.015135674095041979, 0.14182338701018968]\n" ] }, { "html": [ "<h5>The normalised shapes at iteration #05</h5>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x7f74340754d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Wa8OMEkdangqlWsLNXvHIBC6YJ5HEzUB2XibrDixkW8QaVBoVjnZODO44gefb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F2OUIVEiHPPhU3l3RCXubrcPRtfsgaDQUxhtmUGg6F40atWTrToVZGioZT6TYKQOfBWdMKDxKvN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\njokQFwRBsGMixAVBEOyYCHFBEAQ79v/3++LFsZdgsQAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x7f7407525350>" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice how slowly the error index decreases for the two last eigenvector... \n", "we have did almost our best for them.\n", "\n", "Using a Ritz base with less than 16 (8+8) vectors, we expect (last slide)\n", "fast convergence for the lower half\n", "of the eigenvectors and a _not so fast_ convergence for the remaining ones (I mean,\n", "_eventually_ all the eigenvectors will converge, what is under discussion is the \n", "_velocity_ of convergence).\n", "\n", "I'd say that this can be seen also in this example." ] } ], "metadata": {} } ] }

mit

zhouqifanbdh/liupengyuan.github.io

chapter1/homework/localization/201611680122.ipynb

27

1529

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### 写hello world程序，保存成ipynb格式。格式：学号.ipynb" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hello world!\n", "I am an AI, nice to meet you！\n", "¡Hola,español!\n" ] } ], "source": [ "print('hello world!')\n", "print('I am an AI, nice to meet you！' )\n", "print('¡Hola,español!')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "~~This is a mistaken code~~\n", "\n", "**This text is extremely _IMPORTANT_**\n", "\n", "*How do you feel about Italian? *\n", "\n", "~~Actually,I don't like Python~~" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the words of **Abraham Lincoln**:\n", "> Always bear in mind that your own resolution to succeed is more important than any other." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

DIPlib/diplib

examples/python/pydip_basics.ipynb

1

633429

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# *PyDIP* basics\n", "The following are some examples of using the *DIPlib* Python binding, *PyDIP*." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DIPlib -- a quantitative image analysis library\n", "Version 3.1.0 (Jan 20 2022)\n", "For more information see https://diplib.org\n" ] } ], "source": [ "import diplib as dip" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The image object\n", "Basic image creation. The first tuple is the image size (width, height), and the second argument the number of tensor elements per pixel." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "a = dip.Image((10,20), 1)\n", "a.Fill(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Indexing into an `Image` object works as it does for other array types in\n", "Python. The first index is x (horizontal), the second one is y (vertical). Note that `b` is a *view* of `a` and shares the same data segment." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "b = a[0:4, 4:-1]\n", "b.Fill(55)\n", "a[:3, :10] = 100\n", "a[5:7, 10:15] = 200" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Images can be displayed using the `Show` method. `normal` sets the range, 0-255 in this case. By default, the image is linearly stretched to the minimum and maximum values." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a.Show('normal')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Operators are generally applied on a per-pixel basis." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "m = a >= 100\n", "m.Show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Images can be indexed by binary images, resulting in a 1D image containing only the selected pixels. Note that the order depends on the internal linear pixel storage order, which is not consistent with any specific (row-major or column-major) order due to operations such as `Rotation90` and `Mirror`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a[m].Show('normal')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Binary image indexing can also be used for assignment." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a[m] = 176\n", "a.Show('normal')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An `Image` object uses the *NumPy* buffer interface. This means that you can use an `Image` object everywhere where you can use a *NumPy* array, and you can use a *NumPy* array anywhere where you would use an `Image` object.\n", "\n", "Here we create a *NumPy* array and display it like it were an `Image` (remember that *NumPy* uses height as the first dimension and width as the second one, this is reverse from how *PyDIP* does it):" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "b = np.random.rand(a.Size(1), a.Size(0))\n", "dip.Show(b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we add the *NumPy* array to our image:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a += 30 * b\n", "a.Show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we call a *NumPy* function with an `Image` as input, and a *PyDIP* function with a *NumPy* `array` as input:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "84.915344\n", "0.49217822500525593\n" ] } ], "source": [ "print(np.mean(a))\n", "print(dip.Mean(b)[0][0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the `Image` object can contain images of any number of dimensions, including 0 (a single pixel). The `Show` method only displays 1D or 2D images, for images with more dimensions, it will display a projection. Read this method's help to learn how to control the projection mode, etc. Alternatively, use `dip.viewer.Show` for an interactive image display that shows images with any number of dimensions." ] }, { "cell_type": "code", "execution_count": 11, "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" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as pp\n", "\n", "a = dip.ImageRead('../cermet.ics')\n", "a.Show()\n", "b = dip.Image(pp.imread('../erika.tif'))\n", "b.Show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading images from file\n", "*PyDIP* has an image reading function `dip.ImageRead`, which will use the built-in image readers (ICS, TIFF, JPEG) and, if available, the Bio-Formats image readers (which recognizes over a hundred different file types). It is also possible to use `matplotlib.pyplot.imread`, `cv2.imread`, etc." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Color images\n", "Color images are supported too, it's a bit different from what you're likely used to. A 2D color image is seen as 2D, each pixel has three values. This affects indexing! Here we give a little demo, we recommend that you look through the tensor image demo notebook to learn more about images with more than one value per pixel." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2D Color image (3x1 column vector, 3 elements, sRGB):\n", " data type: UINT8\n", " sizes: {256, 256}\n", " strides: {3, 768}, tensor stride 1\n", " data pointer: 0x7f9b90368000 (shared among 1 images)\n", " origin pointer: 0x7f9b90368000\n", "\n", "2D Color image (3x1 column vector, 3 elements, sRGB):\n", " data type: UINT8\n", " sizes: {51, 256}\n", " strides: {3, 768}, tensor stride 1\n", " data pointer: 0x7f9b90368000 (shared among 2 images)\n", " origin pointer: 0x7f9b90368096\n", "\n", "2D Scalar image:\n", " data type: UINT8\n", " sizes: {256, 256}\n", " strides: {3, 768}, tensor stride 1\n", " data pointer: 0x7f9b90368000 (shared among 2 images)\n", " origin pointer: 0x7f9b90368000\n", "\n", "[210, 177, 18]\n", "210\n", "[210]\n" ] } ], "source": [ "a = dip.ImageRead('../DIP.tif')\n", "print(a)\n", "print(a[50:100, :]) # spatial indexing is the same as for other 2D images!\n", "print(a(0)) # this is the red channel\n", "print(a[128, 45]) # this returns a Python list with all the values for the pixel\n", "print(a[128, 45][0]) # this returns the red value of one pixel\n", "print(a(0)[128, 45]) # this also, but as a Python list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*PyDIP* knows [a lot of color spaces](https://diplib.org/diplib-docs/classdip_1_1ColorSpaceManager.html). The `Show` method automatically converts to RGB for display." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sRGB\n", "Lab\n" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "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": [ "print(a.ColorSpace())\n", "b = dip.ColorSpaceManager.Convert(a, 'Lab')\n", "print(b.ColorSpace())\n", "b.Show()\n", "a(2).Show()\n", "b(2).Show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Filtering\n", "There are many image filtering functions available. Because there is not yet any documentation in *PyDIP*, you will need to explore [the *DIPlib* documentation](https://diplib.org/diplib-docs/modules.html). Function names and parameters in Python are identical to the names in the *DIPlib* library, except that the output image is always an output argument, not an input argument. Here we do some simple color filtering." ] }, { "cell_type": "code", "execution_count": 14, "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": [ "dip.Gauss(a, 5).Show()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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Fqflfil33vlL5qJ/KWQF12EFCfU3NLr/WYkPIDBGTKT1GehvnRGJ3Z2b1qzIFP4pJJSUc4zX3CogiCCSNIANhMC5O4ykK6doKbONcprwUAV77HM9HdoKfauj9D1IOPRomzArQqY8l0VLNpOXhAio4PDt+UXjH2cLzjlI+7icgE1ThuV8UDnmm8LxTHVtEmXEh1+CNbGUbX+fDzGI0dpNZ0EcG3WrbeKXeyZEUgRSadQ1JanONlzoGEImTGkpZGGN6+ip505ElAE8OiQ8sa4m5t3GY7JWIZPBOZWSQkPNZaw42C4vS5tEYUkVGLUGyhpBQtKOCmWu84EZwc09gAo7OYddZqxNoNQEtPGU0BpvvOurGap2SN/mr3icjpkvaRdE6ytLi8jvxU+iJw+9p1BYmwAqwwpk4zkWB0471/NtOIiAp59xE+YWjA0BIFM2kFVRlT8MN0r2+TjgDDj/IU+wOysUKTzjKc8nnQq+0Bc8aV3Jd+SL7xfJNpAG5pFEYzaFHi2gTwx+1ppCEK1v1LX9UQe1wsQh35ocSVU6mSk5hjBXLO6l4PKU0akfPOZDcCRUuXU532CTAENWtfBF6Y01jreI2L4qRH1mIUg0LZDXSNiRtFbaAYDqu4lbPOlQNPwCHFsFa9b8wVxa+lG8jLEnos7udAoWoMRRmTecqTFG2o+zgHFZ4NVM8b3iW5+troBEUxHsOeWIaIiRQ6KFwem7uOd8KzPit45Xzbj8D1kADYHhVDnu555uvDVrUiihe/5hr8EquEfM6kWCTqNuqtZnUbgOSuucs7sa1mfrIr+wQr2GCsqiutN16ZAW+rs4Oz5nn8i5oZCU+UwvVmMgWr6diLUabAhhKpWmeUitrGZRsjMy/6QOFEdwsaBletRKgBNFlQ8/Qitx0IiWNSjswjjmfxVMLDineFhQwz9ZwmcAhDX+q/DY9aBIiRwGHtEZhirI/nm08kYm+ihU83sN/rBK3XitOPccdEmcfjIZQzwwN1KfqXWi/GFYVYcY1H6S89XmKk5mpHzj2vzzvPm4Wf3mu1HM5QB7BtSVsnU4g6Eh2kqQR2Xpfh+nHtp2S+9XRdrcMVaUr1u9IuxqNoDUYDmxPrdZQsy20+TSANzTUWpDoqjoL0aYABqDuedJ8u9aVVw670CbcCOVKSz1Y7NHy+oICCKXaOozfSTatREwWZGwcWj/UzdNXQ+opzxp0Bu8swLVDiZheAIeS9j7MEJ4KfISpeGYzOPVZaXYhgMILH+D5tcObnp+6Xep8z7/slOZ0P+WtpwYNpEgBvPVyOOYwH6czPTv5JCvchZ+TNaYSVmOmGZR8vFylPazfW3fBQYY/KzAw6bSqYtXx2OjMKt0h2Jj6HAGCWptIdgRpOqMKlcgaQ6s5SOdagjYNMBQgSELXMmQrnTU4DNRdMQ2RDHUGEMp4vGkVqyLYBjG8MWz8BA5NHixANPxre5cWDIazGa07pgwGFKRsnU5+0pqFKfdD+DdW8MzW4LjjPN/yIW6nnufd0XPT3ypDiEF9xjyaOQuqIg9AoQWIGZOJ59ZuDdKMRTRmfleVH/9ImShM8cz4ASvcjmvID8seDqjKZtsit0nrUMlCEhjmU9Yoy++q7i2f9JIZpGi13Yzjw3DahsRWexPCAkDzenBtDBg2x6wERhuIVn9h0PES3hin3DhFtUqNZlU+O31VzhASE76OL0QTXDU/D/il+WGEP0UosTwpA/FZFFS0LqUprFSRtO7xWSxYlCw4AadBmBDYjrJF78tUvob3yhHHeq7wYQpR1HPaHWZc74GapyOTXaEHCKiYNpB8ShGS2k9MPhPYpf/h3fFnKN5PuPSfZzzibUJgQcdTnhva4+nXEW73LAXxrPk7cwN3f67k2fwQwUsAMY+wq4hb03dXldlBcdOW0rQZto0tt3WaI7trwQnDTzYfvY6k3Ewa5nf+gE4VqWFoe75eT+aXw4EBbQ6NQTH2hegQNYhhbxR+J0Gw/quGMHaEuiej0haGaw5s+IL6nSxXnWWPZywDSMUMWmZdtLwfrj9IPUyjMdiOwpQlrwWIfLMPyor+Po4v42eeY4+ZcUX+GpLH4bneA4MdIK1RCCcuFWDI/3PVW+W6CHyCj6qdKm0vAo4qMGPf31He+CJlK2uQllKL8pKLlROO9Mw0HPKyS/+abfwRB4tnJQmrmNWbqQdWKwujakNDxo8K9TlxdRPW5ejwm4mxdy+R1TVX2Rkqr21+ZFikedqBgLgNKwybBBiMUBS1ubgnqsfnrbpa4rJxVHVGDQh9tmnjM87x3g61NTJWq+E0ngYRVcLepF+Bmdr8mvzLsJxOwpTkdpQVfgvH91jB88xjPJfG49ZUPaKe856aDI0BEERaTcHWt2kNLfVg5WXYa9dtFa6yx2LrNs8bzlRuKDMKaMBX15SnHKZcsYsw9OH/2MpvcW12heGElgVRZpvDHDgoLtW7unjRrYBEPQU9Iocm0iEI2BwE5Ko2NzUKyUjv0pRGmgyYvRIWF1JCaa7XST/qEdoUwFADQbijFigs0o4htgWEoi3U48IBF5gwTcOmXn6ksdKW6BZCRtsWjA0leTS+8/RsuhgBhOgu5CnJAggh3FaUKb+J6GWsAGtryi4vsV49U2b81cM9O25kAabRqmzJDCiGZ/vWlL8HiA2AWzchgNEZZwagSFqFqHKZwhOO9jiEFZQZu5jx21ybv+cAa4ikbru5HWqnG7DlMrbc2qCs88o4qIXsUAFA87t0VDZIp067oBABIWkCLUJaTcEV29oytCmAIVANBEXY63eQBJ9YTzWztapaIgs+vbSH4Zq0gHYGZCAvI+Xq+U6zGXaKtmbiZiihxd0R7AgZFJI/CSc4T7kTopczRdk58zzjqLIIacKMVz1C2Xpr03urp1eHRT2yWgPZtjAc2yevTRxVLfkKiFIeLry/R1jLqyvBowr/9nKFCA7gWdXD2Y/T445NKw+Sn6Hpmav8Ce2QodcpVFlu3Rtq0xkCQh3JcGaql4n5qUjzY6jFGH5y/IwOJTLyGVAwxrVAba82/D14bhff5N7A9r4UBqhWWmLarjRkT+cozxZStAlf3Grgazmv17MmcGgNjuUQVyeKU1jhdkz0B6ygrM6UZx7lmSEkUDj/CcrKrySBjGlIScuODXrLRFQnKDdF9caod3UxQ6h+ue2lQVOwoLTjtz1vOcazJc1YRL8v+axy8rM9XmEa2+0KfR0HE9ZmTBVcMg7HPjcBZXAbuTRuWR5W+RDXRpB/VJZHhLBMM+dMdIDDRiE9xyYOm5miHTTmhqU1hk0zK5HFSvJPe9ZrckLy/3AvVTdUFO1vTV9X0fgm9mzaMzihiKY0AhOl9Cmu+X+q8vRsT5ruxZ6mNkUkx0lVkqH/fCRaDJtWM7rYTezwnqnchhVdY9elM55yojJDSCr7BYfNWLmeR6RsnQ75iwuR7IqaDg8Fgf95PM9BOYgrVJHZJew7eQKiM1uwHE39UGpKci1GUY4Cv+UawptOC+30/COF/9SwiPvTP4FHPR2cgzNfJOy/DWbcmgOYoPwbP5btXEHFOpGbyhyUVihX64R1QaOwDjQF09K20asYi//UQ+ep6FbGE3/Hpg+sqdQnejW/azYxmkk86h8t6UZ+WfgD2g1tDo0BmnFQYtrCwAkNK2SsetW2V2p6//Q86BWa7qB1r/zYbiiK9JyKr4cyjf0DskDUWsAwjjSezisAzW8H7OfXmOptmfhVrrhsxpEnKulrDmlL9Mr1iqGxgILRFHLhO+XNrxTvP8ubLjqBl73q0Xzqc08KwLIQlTJTpZue7T4Mz/GnKWdcZxb9h3c7VXnC0Z7vfjzs0nSsIfwGB8nfcaBRp+3ZK/Zehho131R2HQwojGgKc0HB+BljjaqTN6pNcdeB//FOz/CPpHujUZqOZFHaNMCAQbrc01aq1pCZ8u9WH6z4PIEEtZ9KQKHaqxHjKIuWbD7N76qFO34r0KIwon2u0rDlarVEZaJhEdCEYGPYorBNPRO9HRN2MhXlqON9BQrCjNe+KBn6zLBBG1DorCAd9Kv6TRwfYsuu/+UA+RG/vP+uJu/rUfFbD6UKINh83/RIRVirwATgGX8V6i2sgFDW/OHsr4/KthcLAsWIy+Aaz99iNBZPNTXdAIQMHqgaesBSTZ3VnYuWoUIUfgsG7QzWMrR5gMEIeAYF27vliepauCwoWM2hBhBy75zTSgDSW1I90CQMYNjwbZeSELtEYl718pnKU2sMw17N9nwBILag7KtXsuJ/nYnfyYoqO7+XhkYp7IzXnaJMV/ywbqQtjy13t0LCMET/gz++/S4efx/Y8XMl/6YKOnU0jKtblylPGbQ8f32Csh9rlHUW4d2JhymzBJSq7NKPcVAGzTj80qF2lU+uopbN9akA5CggRBCy9dD6G4Nf66MF5HrRlQmZOiOjQQ8vsla0DG0SYChGEwPzZj9A9JMYw15W0NLvjkUpGdas6lg1Qqsx2J60p/4KnbQweS0CXgSy7YVbECmNF00H1b6AiQGJbepR7sBEV5miXPl95fCTlLRFWpjx+ucrK5OkKfgqL/2eXtftNJ1TJgd7JtdQZKqFOZv2XOx3BxQaAJvu67ngVOUazTLqL3rlqU9VrtwVdmFO8azwO1yDS5ki5gi7eOlwSJbbZEGU6Pb2ycl0HIt2zva4kXp+1GoGVPm0Qt92KtVvszM5aRHL0OYwPgrgfOCPvHyYciBFPiyjqskSXNrIKP6tu5i7QjLNtQ0ZwDX1vOE5AI/ErBSdL5+e1Ahx3cA55vhc4s7xodm9GK6K7wlhXK0q4VsP3JoVVX7yZc/Tzk3xeyas8rqTlek0gkS2Jdjt05GRbREz2JmsmLxr6pFcYVhn89oFiKpW42ubSKECkNKUXEE8rzjDoSj/+zbh8H8VRITLVXjskaAoL7ie48bH/IgZv8P+Kkzl4QhP58c6YVcst6rgY9P5GL2SvpwOuL4COSiHKUYjz/F5BGClo0ua6pBokdRoJO9qGzJMK9SYb8AhPqf8/WwPJai0AbswSYxbe9VqdxGG6ggyqY8iC+HSc73ib2xaVGwapF64npeHUIauQdSWFUwLG02hEkirDsKKwnb1TLkNU5TLfug56tz8aVlAed2LlOnUHr1W1MyyzNtoQ00TdJ+b/JR2im5L6eRtXbRuTZ3l9g1lOvT+yuv+SIPmkNpN4DnfCkI1UWWCsqavYar35CA8jjDVOaUeh+dhgdE+iyMDfmzWXXfyb4eItt6Kr0UoazG0WkNtm7HT18Fb/G21YZ0DVHNo8wBDDxTCQLECheLHG6CIwp0ufH5vhxz15qAIDknFlkaYqh62PrRERCu/Gbw6jFEXUe0PewvPUgNBe23hCkRvw4QZl/9YOfKktNYgDRNmTFcMgDXAGWhJDmmbCNtLzUGS9ajbNZu8avLTXp797qwcKNHuEMunAs95+ow1JRpplVX+D+UObCOAxYR2UVQql7GT9ICgBQNr/zJhG/20ck9CmkvSw8dcB01yzdypbYcckdTtWwH5Bmi3gEFEviIinxSRj4vIR6PbwSJykYh8Pt4PWiy2Tk8hQ1CotAVagKjftQeWWhAZfZ+t4o3wi915aKzn0gBE97kpZ6V2t+PEPiisMMPzmzhZZaLK4c+PU5KZSWe8/uSiGQ1maqSkVeo73Uwe58p6w4CCAZ51aF0haJ/rK2yoK0fMnX86XA9zbJwqX/fw1Kcql10eDI9T9XhdZV8+zg6dMcFY7MVoDrEc7dLwyo5VdTJku4QMQEJrga3quePU1osUT7ZuW9uCdS9xaBNHel6e9oTGcGdVvbWq3jb+Pgb4oKoeCnww/l6HWpSuL3FJ+D212md6xUrbSBpEAQ4kCXUDKNRAgbRLdu3dVw1U3vUMe5pK1vwfIRn2yETG3aqeqd6aCTOmwI8+6UuMGhYtve6kNITQRnuxIGXqu6n+hWgwvl2Q5sY/JiUWMNu2CADx4jOUx221+yyUy1GedKznxx8F0fCR3TV9NBO+wrZw3GwBhAZA0zBpsBvRIjQ0oBudG1CxG9zmqAfr0lAzoxjP8wyINrMOukTj9OmqGErcB7ggPl8A3HexYG0PpwMQsFqEtbS3GkMNEPauecghLSg0mkQ7fCi9wRg4mHuL4OZ5sCAq9VblJxAMeyto3ER0aybimaD86KOeY14PSWPZJp4LT1BWthQwFFtflnkHeVqyTbLg1EOUsaHPaHIDt2E9jaVf6jyAwx+dpLz8lor4st7BKzzpAuXTr9UwGsWzpg/EcT7bUn61zmt9MlSHpyxfkgo4dKsF2fb8c+polFrNIAGC6UDy0QR11WlMMN2XBQrR9c2w44FFvgxcEvPxClU9T0R+qKoHxvcCXJJ+N2EfBzwO4JrXuu6vn3veP3W7I9vPYZcm56OvtA3SpjTikhMApMxExLtX28QGs2vrYL5LbZ6PjFJapFq8Hd1F7JsyxaYibFXYypXAb7BNPZf8vedZf5eYQNnHr3H+yUFLSN9vyEOhSr3XxWYMBmWyArAAdaIfNSN03TqIOXiW3PbWUjBDEHU86kjHj3XCbOLwMxDnmACvOlvCx20mMGOCE4fnIr4v+8cpQ81Th14qM/Iw05qrJ+Qo/9bsXpckcqkA6krHYBOQNDPW8rIMhVuIm6I0I5pEt/zeAIHtEO7669f5mNHs59Luagy/o6q3Ae4OPFlE7mhfqs4xsaiep6q3VdXb7r//wXUXU9XsUGtIhrUC+8PhhH03nCMn/y5jyIjA1Vi8vZtw1WXcbZGrxq6roYWcknY6zfm/mHA7VvB8/yLPce9Py5xDb/lXpyiTaT30qVVjcrmWp413FktHMeqvp0XY9qiHd4jn1Wcov9N8Jg+Uxx+mXPG1sJZhgkd1Bvp77BvbLOzIUHMokjaXlcuWl7TOlwzboICs5l5+EGdGmFa7G/rNWoJS1k/kdTgdDpXQ2SxDuwUMqvrNeP8u8HbgdsB3ROQQgHj/7kJxxT+romk1lKD/vG5PWMKUXWlGTWQoVHlhVY4j3KuxnnldjE3RQSkAMQAHk59Bo4fvP+zgk8AjmRAOST3+g/Eotsjs+048k4k9iq1Rdw1Drz++XUeC+7DeXAtS63W9oFZNzmBr69LYiVCEGc88Q3n69WfALAvLLoVHn658432Ki3YHQdnOYRxAmbp0zXMNDn7QXkA1pY7hr8Bv9VDEzhbZjm4IfClyW3Y19aHGzlAFH1ahlNwvQxsGBhHZR0T2S8/AXYFPAe8CHhm9PRJ45zLxWpQriTEHIMKl646j+khv4xsYo1IDN8JcC58BhciIAyNktXLSID+FecLXopTtfBvhYazgcShfOddHk1kEBed51fM0awpIDxRsmcttLi3EN2NAsAC4LIofPbzJbsGxUvZz3aap6Bl3Pkx55b3C+Q4JPBDlqPfCPx4XznmYAKv+w6zoH7M/ni3qWVELCiGdIUC09iWGAgxdoLbT5bWWB7ndGg23paAxNL/RvqbQcOIytDsaw3WAfxGRTwD/AbxHVf8WOBn4AxH5PHCX+HtJsoJb1UL5LY0/os4hi1eCFdA8DRXTr4Qrg46acNQJVUMIC+EWPCygUN0V2Jf/xukfsoUZE+Dzr1BO+VqqA8+Nnef85ynTLfXsg62rASiM/Bx3tPVgnebUavt+kQbYAL4UT0NwrmeNPNe+k+fNJ3p2EI+NiwJ33qXwxKcpfpamM7/GVH+bg/hG0CRiT2w3IjnCOginPp4z2Q4jEkDTdFz1VRvCa2OxtIBQDa0bIKrEPro0Gm7JodVzFqcNA4OqfklVfzVet1TVk6L791X191X1UFW9i6r+YOFIpamUARLr0N+gvGr+D9+1swg5aZueHXLkcL28aC0U1bPGLJYGK2BQeoo0tt2GBx7KFgnDh6+e6zn7Sz7iieeGK55TT1ImW4yxsdNHzO0fdPTHOrRsf7NO0IHbPKad19/1yl3WmGzZV3ndqQEo8hhclMtVefTTo21BFWXGGn/MPnwDIWgToho2Yal5boAodyqDvFBps9U6iWb4WmbK7O+6Uywaq/GT3w/rRzs5WrYFN8deCQriSQsKuWtODlEYRUrPbPZXhN8FHAY9+zBlk5DmGxJUNjXgJEhRBmyYdLhHjk5B4k6MeEBM4JF0wpCLiKxsAVZ4D47nsBXPZ86Hcz4Xdg4GTcPz1pNnTFw68Sgxjh8wRmUDmQcOXTk0INmrOFtNXVqwR1oKFOYGHLzPe18QXGwbcZ63n658/V3Ckz8koFNAmAEPfRo8+9rCrZ4Twq9yfw7kj1Cew5UoaxRbQ1o5EgYDDtX4HCsrdSBtp1aMy31Kec48DYQZt9gIlVwU+5eUCEoqErsf1S5HLEObY0m0Qdbw2yJmKWI9pmvULROPJbXuNp3WhpHRX8v0XmoEq0HY8HZGo1m+W46P03g+Y0kjMdsKylYuxHECW1D++zTlrM8FJkygcOupL6BA25tojnMhUBilJfwvo5zshqKxWEYCFd4wWpSYOsNz/Xsrb/zTYHfQvIUbXnCx8rJnhs/1TRTQ97KiD+BafJZtzMI3OtC8ajKdhVEbIpWKHxNvtDNjVmtobENpViyXzYTLxkszK2dnMSzvVtpr0pKSErUEbQ5gwBbWCj/lOQk1zfseEMxJp7qk05gpP51GqWYu0tWofSVLGqM3AEFRR7fh2cHbgdNZYYZD+Yvvpw49gMINJ57jnq8MVlY2oFCXek+BwgalenfAYLlhMENksvWSnss29B23C9/QnCa7Q5SWf90FT31KOt9BUf0Wa/pY9tOvsg1vhhXagMIYMhq3DjhU+WyGDtLzZ0Emgwb1+/RsjwnQVEwN1xK0SYCBTg9enoX2t0HW5N8aD41GsP70bd31iQELOxUopuHE1Q1YGxPrMK2h0aHswy628zJEn8eWOCX5sdOTuhoa9tcnnjNeqEycXcbdMJWtg8HzOkWe438BhaChupKXY8HG99LgEOOwoD4A/2Kc3LJ9xlvPUPazx9WLcinK456SPpOnqM6Y6cPYpu9gG1rPVCi0GkNr/9fIk2rKOBcA2k4pAoA9w9F2WBVgWP5tAWKDy7E3FzA0Kn5Rr4YFywAwQGdtPDWI215V2qVy201b1XNq3CywKR/WOGSZx6zNR5nyapRXxSXP8B8neV7+/fBO1fPzU8+xL1QmZpl2K667pykswCxaPy4Uuy7gb9S+YVMZlncxuGrCG0AIQpU2vs147ZkKfgbqwwd4UFaBi/9BES956OD1dKacwhQfZiXyYT+xfTvHxlV5sXwXy18ruzXfYACgCx7Z1mA6rBiP/cxj3npdAcTitGmMj/XW0h5ZgY+GJS3PxUd9zm5wSfcmDe1oFDkfZRGzZn/JSEQ8dTolWiIpC5yViYRlzhMJrDkBdvAXrPAaBOVfT4JX/aTYNG6/ssaxzwVnznnI+mAFSLY+RurNOq9btyNUqmJOSrJ8h2TrfBBW13k/70XhhbIMPJp8k0U18Y543n2WcvmXhT97iYQJYhGe+TaHvB0OBU48JwDEzL+bbfJ3KO9mla3sUoeIsqYmSYnyV2m7li8LoKcDy4e5jm0raQJbTFmMjNjvVmb3aHjVdOxPvHtBqgpdjDaPxjCHhh19qkCwDJLHfZ3evisczVhtOBWVkNykG+N0UqN2rSVobsx07PtUYBt/ieO1OJRPv0C54Cca1c1gRzj2eQkUWtXU5s+WeR4omLAbwgXDTL1OWmGpRSMLJLP7cZieFeNWqdz19vodN1LefIZyAPX5Dl/xypOe7FldIx6LtgZ6d7boJ9hCaLNydNxwKnNuvXdZsen9870pU2Prqm1bDa9ke0gyxC5OmwYY+kI5j2/ie8FYeTFC3MZP6TDEuLXukn5r/zJqX723nwwWTpLGENy3oGzn5Uw4j6l4Xn2ictbl4JNaqJ47bQ0fmS2N2IJCKXd9H1TL+LulqamUBASNljSejz2chbF3rVvOgFG9DajXS6k9znn+6mzlHi4tow5CdDnw54cpP/7PsN5B8CiHsaJnsD0mlQ+c1flZzVih1c9Oe9XCbY3dWQMZJNB2HoGnyKBgQWcx2jTA0K/RWjCGY7luN5YF3QIFNNGLfexUrJ2RoOxLSF99arUHJ/WsQwKFCbBFXoDjr1iJ5fiXXeBjr4N6/mCL5+nPSb2Y7QkMU3S77R7tKYkco/lwvaEsrKs1mDpZTwMc/C49bVu3QZtMW+w9jz0zaQbB1gPKDOUJrw7DVqcBCDzvYIs+mO2prSkHzdpOrWQ9fjW8/QZmdQXUGHYAnXpIvNPwSNYOoqbgmoOIlqHNAwyGFgMBO6ww2gM0glSGFYPWsAyWtYamwo02kgGB+kMeeclsBRLh47Jb5UyEv2Ea8/+BF1oV3/PAbcpTTgyN2EX+9TSEzUYbyebcMPNedto0UWes350uzM9BeC64a/qQjRVCOOHpHnwEAPXM+CYrevfQrklbyCslW+wyQy6jNVRNawAiZzHGvVinkHjKPteAsQxtCmDojc3n+qEDHtKCwzqqXRs5kCq/G38lrOShxESoQMEuhtnCC3C8OYKC8v7ne15/WbQpqPKgLcpDnxMMYUnFLc9tD2dphEk2ih0D0NwgLRt0N5MbjXSgHbSJFTDAaIOo56C7e950mLKDNYRZlHDPF71yzFM9OktH0XuUy9mPz4fDX3L/ovmY/zrZYjUA7EbJQfazY9dD49WWSw2fNqCw7FBi88xK5CetXPtCrfn9Qu4GubX/ohG+AhDt67LKUYx2UpbEikg8fekVbOE9zICXnOD5+Cp4CZu8/mCfGU96dhp22B13npp5e/cOrcNA86kXbtG4ZDHvV7Wy0xsr9hqvIs0tGNTF0IqisPXnPa87RwDHK46Cd++coDLl2wiPeJqy5uCUO064wZ94lD9nBWGFX8dzNlcQDppz2mvBMGOWUg5Dls4QNwr4wJagTbkqBan8WPbjMj3aFBpDS7aXzkJqVTTzPj933zfqHG29RnFu+Lvdwi0mYHpVPl6SPgRTNkNt5QwcF6IK5x3v+cQaZfebep58XBo6zPn8fLrbcfKAkl66DiPMM9Ctq07No3XSvUo0gobGjJKjP3tdtNUw0ucEwvW4U+Hk28dNWMUyxFH/5Pnv53omGu0O+jEc92F/4vbtqCWMaq5WeId9Usl3r1/o1mnko7w1fajpLkObChjayusCAEMA6JJhyjyT29GYi9In/Qx085PSL/maANtQpjwex7tYQXn58Z6PrhWVT9TzxOvaNfwtKDQq8DxQqA4xmUO7JfiL0FUp+UsC3hw/ddM2Q4qOobeo4DNu8RDPFtaw7aMIL7o4NEP4nqji9RK8/h7b2clUYapGwFJSNKObXlE72DUfY2sPtTGdbCNbhjYNMLSdmNUOu+DQhB0KrdERqlrtWx7qtpLy3IKDWStvv4W4FWXCI4D/ZYrHq/KfWVMIoHD0z3v+6CkWwQ0oWG5pbB41LdENz2WGPSTQV7VGMEZjZZsD7LWTGocaKOqP64brTWcEu0PR8sL9z5/s2TVLx8YlreKurFCUgorjem5NlhYpZsEza9+oI+lroYvRpgGGeDBdt13HFu6JSulYbTzmuR5KzLFYyHBoUaXVAFP+orKmj5g8DuHrcZ09vPD40HLB0DjjaT+v/NYTaqbr9Vi1amNzuCQg/BRBoQ+164TbnWHGAsOHxRIZdtPStkcUdjfxvO4c5VCJx8bFd7uARz/d86OLIzjEjVbb9EXsK3GYqfVHbkbrqtf2jcf5VVZmNOrZrOUre/MAQ6ZQbfmvGQ6MIW0YXiRjgRjpjs+xbuohudVHStB87yBTO3ZzEMeUn2eKR1U543jly7OiKfzegcrvP0EpR9XXWsMQFJrCLVZli0nnnhpajIE1I8UYCzdavEY45pVPmvtSQtD2qsMrtNMMwXP6OcqDt3vQNdIHdleBxz9f+db7NHYUitf3sEX/hAOZsSWeuDh2DYpiKrDVWtOrpB3k32ZoKWlo0XRmy9DmmZUw3XStEjUzE4PDIKtYEGJ1xODZhqB1kMI6oZZFir1YpVQ4Ug9NBOIefYljyEfh+CriPY85wbM6kxin5zEHzrjX0elLw/WceWnFVtVbshtdppveE6AwyF4/0uRaeZ8HFmNCPS/P3XdaPwponcCcTPQzJlVpPA97kfAQHFf+r+fBLwl7LMDxzPc63Ps8P+eFM1/qmfEN0N/lAByOM/g+t8vpCD6c3KzCrBljJHAIfDjMdxL8AhJZ1y2yIyn7ujRLwWbSGJqhxEA/qLQAqBu6YKLmfxJWmiVNIVWfpvswbGmV5meF2HFFo4LTP0P0qzj1PPN4z2wVROLp1qrc++i0+qx8Wq233HnDoLAQ7Y7O3omqoiWRZl3vS+RzPVCwTokp+gpB0wxj2kN7YpZn2808W9KwQpS0lPp7KFdcAU4l2x3W9Jn5YzdllSSRx5o1OGKKWO2JiCqv+Zl13Q4rif29JG0SYIhCnIW53OuNOq3qXwOCFfz8ARlqIFCTZtu52HsDF9XCqQkg/AnC15mgqIcfBdgPZyqo58+uUR/vvj4QbKQFI6PM5/R1o1iO5imnw7QXh449AV5zUlsq+rZdkpTVC6IEz5tfrPy+zJD8Dc0A/I85wvONd5fj6h2e7Tya/Vkz37Eo385Mm+2qcxeyBjAnmxkAjLag1gMLs4KlTQIMUGkFapmv3k5l1z6q/Z0BpIQP9WXBwmgfKU2bvgxZvoWgcLTXA3H6A1wEhScfH5hB1YcpyWt57n/EyHTkwNAIS7da1bMtH7wf3yI0j017kVwVGtA62dhj1ANt03Zmk9JhZypHHuwRLVOaChz1Ps8/v0QhHhu35v8X4WHsJ2v5EODaKBk1ikpzqPdeWJbJtoSukqP185K0OYBBMYDgSs9vgELVAkKz91Kte609pOgtN9Xi2u/9+noJiN4b4XvhEI+Z8thjZ+xcg9STPPfanns8U5Hq+LBidOwUfMm6GgmzSDTz/KwXfu77zkvDwesvrlmiDnYLFJYVkHFwKPsPwlqHOx6nvOZhHscsb8BChJf+j3L6E0MHMvEw89/G613ZKj+svn5F1BxaTrazC9nNfHXKZGkAEqH/SBu4liv75gAGoKj8xg6QqygqXeoKWEQNIAFGFnD7rhqakN3rQUJ56q2qdFJQPXxb4HImgK4pjzvaM/OhNR0zXvP/eW59WFmb3p15GLSgoW7bLTE0WBQcxqKa536V0ZzIWxDYI5rCRsHBVE7zjZAg1J4Db6P89bM8W9Mei2hP+rhXHvvEsON2KgFIVO/PVj4XdtymTXoGCOqOqQ+upY8IKJCF3+ztzsrlksXeJLMSgqpLj4FioTIvpL3olPcVNR+8LQBpqjQtTDdhAyAU449TciMlVW8Fxek9Eb8Td5nnKc+ZsaoSz1MI30p868lhKa3dkt2ffViHRgu4IKVgiwjRmN+Fk15nDFxFV7XmEgm1ftaxI2zsY53r50ET44RypDks4q9QF4I7WHjzWcIrj1beMZuABnHfqY4HP8Fz2j0d179X4I8Zj2OKsI/8IztzLYW4PIEH04yKqmHcBtyVwMNIAIdaN94YbSqNAWmXERlcVhfBo77qUxBqbSNUKKYSa/tCaVapNIb8/UINB4M6/UOcXskEeNLxnl0qscE8MOOVv2/WKPQ0g8EGrXjNNRzuJi0T1YaSW8AwtruU2qzhiXXDrJe2NveF8tG2IYO2LmcwKo85RTnn0GiUTPssBA5/j+dNT9ewhTsfTX939uUKJnFrdFgQlT5yU1vX+ppmrSmkoYNGp41w1OYBBrE4Z1X92uhohw7t8MEORypjYwYJyRpFqC2pNl9luNHQaFM8E383nIZPxs2u8PEU7iTYMy68n3LwXa2W0IIDhrF6jbrHanCcFsGdhfKxINpspEy7BWAteJQh6WAZawsK87B5blGtMKZOor5u/ATljc/UOKWZviAGb9+lPP2Jiq5BOAT8SlT/iG18hu3EA2cjOORP5tk+pjoiPoFBy2udMi5BmwMYJHUOQrLApK/01obGEQagBoLW/9CuW7SEVNnt7swVZjh/D5xfZarK2uWeJx9jd0POeMMzle13SDaFljGWaJWNQPruKho9/wuGX7DvbqKUTvzLxNSGWUaLmMM/S1FTaZ0TlKrf6tl+gxlvOj19QzNqDijfRXn0UxS/M57foIrqU9iqL2N79Y1MH/myozFo/aC143i+F6DNAQyQhxFqD1+057MVj/kaBw4qwLDvyrJpEA2fMkvLqVMDOFWcvxsTXWUKrF2mPOnoNKYMoHDhccq2Q4qhkaxGJhprxDm0qBo8z89GgcKGXzALuq7johGvD/zldydcZ4p7VIMYgMFGAaL3O3fp5VJFpsrrzkozVUm7UHaiPPywoB2kPRZr/q04PSweG5cOhYm5zJ1XC0Tk9OomiB967uR4PdokxkcqiApLRU1zKfFHsTvUD+F5rtlFC76IxrniWNHOCYJnCuEDMPp7TFVZvUx57LGeADABBC48co2t1zYzD/mDpHF8mBmCxraQMpUyMacuNiLQy8Qlzfs2Lz03qNXygq8jifTyYP31BHT4O2nGYr1o7b9EL43tMRWkzp+qFn/tNN6iRuLUxk1aEu/lOBZB1CPiePfp8P/e4Hn2f4VBqxB2YDzwyXB7Jxx+NogoXj8Jehf2d6/lcrkWq+rC+hkJRkmv0pZoo8rfKG0qjaF/hDMNV7RPrVrY7zFE0hAiDiRUqh2SKwrb9fuI/30mXtm1U3nisfZLQgHpt15HEZmRP2PeqJCLdflLeF8vqo0MJxbhokU1F2WkJ14wnkGc0r3CtLM1PBuNUF24pH1v7x33npax6JH4Ldhp6hRyQZpePXzo5tce6jn/z2dMWQvgEceu/7GmPObJil9NWoKypg9nq17MFpKtoZxKnTWH5mAWm/7uqI+bBxgS9WR8XfXPGiKHVVBHV3ZhBCNjAIWtXIHq/Zh4zwTlSUeZRtYw+/DaP6vXKJAaltQ4AxVmMVq2Ddfzs0g8A3DoBBoIP/SBl+bdSBpzycZXzlseCLU2bmr8JgBpZq5s+GKQbsGjKceg/rT7OKQiqGF6vGlY9Vz7Fspfn6xsz+c7BJ66EuWRT1N2XY45GephbNEz2Ba/qxk6Ms2Lo4QEDoUfR8VmCdp8wJBIzEPFg0L9XxptMBoWpWzdrjQFE9UECce7swvRuzH1yhTY9dW6MQXPm54KO25l9j9IrSm0vUWmjWoGufc0lbHheBbxNO93orHe1PS0thoWJonTakNQCIvYXNEKcLUWERe+pXfCJD5Pm2sFWEGY0gebdvGbKUe+lO7y4sFq1KYizGyVRHCQFc+bzoCfl1mOVwnH1T/iKM/nPxinM1Xx/j04PSZ8Q9NMYZpaMtDW48nlafPYGKrxY3QYDg8XiKD/xmzIzuwHSW37PSYaPi6764fKk87EjDU9bzxcWTnEaApS7kOGmEPp9WhWey8acBiNdAOkTbT5d09DIypBBRzKeL7Jgy7TcD1NJP4e7GmxynLUDuNittku+P7/E773PfjCN4WvXQ4/ninf+jH88ApYEbjWivDoPxV+8dYTxCnKWmfXu5o6GCuDqbiBVmHdm/AS7FWShkiivPR0xz2OnIX+P5bXIzzr7Z633FlwDlQVz0cRvRdb5Z1ckRYDEmwlZalVL5chD73Dh+bRusAgIucD9wS+q6q3im4HA28CbgR8BfhTVb1EwkD+HOAehA/5/Jmq/tdCOWmYsl+OMbAY8R39DvuBcJLzNpQVvoHznolTfvJN5eln2i7C88YTlZV9DRA0mkJIZ4muPLWQ1YgowTN2rNOQhX+l1J09S2wQgJF3rVC3fopQalOTldf8wya0DGgVyFYt+2XW1oS1y4VLfqR87tvw7f9WLv2O8KlL4BIPl3phlfCV95mUQV6K0hqcv7kLnvKqcGjK9bYpF5w+BTtToJT6RAsDdUn70qa5Mbr1kFi89IOel/yy8IxPKOGUD0JexHHUM5VTzwxaLaJ43YlwN7bwt+wKrpkRnBK3+1O1X1mduRzJepsrROSOwE+A1xhgOBX4gaqeLCLHAAep6tEicg/gqQRguD1wjqrefr1M3OzQX9XTzn7vfIYOuak0tPRlH20aqD3uLQwhghomEj51vq9+jwl3R3TG5V9Qnn5eOso7DB3efNKMlS2BYcJadjP7ELsZSaizkADEvHoQN+wBSyxNWUZaVCPzJrYL98QGJj9jHNHmewQMyr2sPh3aiH0ol5R1HnX8tn76cfvZlHs8U1jTlajdhR40rE8JdZZGV6BlX4uJ2kvuH3PF5S+RRX/ex3BO8XgO0hlvPQeQNVQ9Tsz5CguNjWTk1VDrylyj5vAXEWY4REN5H3iEcJlMUOeYeEnfMeQ1xztWfk5Qp2FWQm6N51RW4/GBXhygg03Glqfu/Lu3+piq3nakIBWta2NQ1X8CftA43we4ID5fANzXuL9GA30EOFBEDlkkIyEx6nbYgJYsIxcIIsoUZTtfAf6QCTOmwNP+0s4+hCHDypZoaDSzD4VhSjMvAwrBd0dlTr2xOlPu6Gdsfj4b2dIzgzhz9gZZVAZj5dFilDj9mmPtCsfl33b86F8d3/jbCf/8Rsfrz2zzMCYsvbjHr2r1qgzrrG3h8qqu7+EKWfLmuh+p4wFPA7Jtwua/ibtHo+VsXxR+kaShVLYHjzjPm89Qfncl7L9JjafAI5/v+dqbFIlLqdV/gol/cFwMFdwCUJoNAlrV5PxyNLRRG8N1VPXb8fn/gOvE5+sBXzf+vhHdvs1c0rIJJDbKOkpcx2X8TMj0PFFhm3wBx4Pi4Rnw/U8VTYHYQG96Lnnvg1Iar+BvUtkXHT6kjEiY4HCd3GmKWUrU0vhJHpPGqFoJQXbP+TK1OBgidNyq4UabR+HuzxBUXJGZ+O56WzwPAQbfd1+QilFdspbQa8nc+6VlwNWGoUEBiltRNZr3AVwvBc57hucxpwdjZ/qsfAWevWHFukVt89MOM9Jb20aeI1/kuPXpnpd8u/TbXhxH/KvyyE/BPZ8n+bh63L3Yzuu5ggNJK26Ie3lUWPLDdIV2e1ZCdZEvngxJRB4nIh8VkY/++Ec/yGph2y+YECG9XlwUwS4IPGSvHXwRxwOZElD2B3+nPON1RmUUz5teBCs7yhHh0hgal9IUWobMw5902Sk0u6U87u3s+K2vegquL1B1+t08dvNN0UXj5as0imXfOav1mHQX4YosE8O9LdWVAFDH6l9NURuWlI7/Zin9m9aEXZcZzaLyXtfDXG2258+sNeirxebL1HEZ9B8coUzMadRJw7jgRwo+7p8Qj+gMrw/K+3vSGoh6a+HyGsNGgeE7aYgQ79+N7t8EbmD8XT+6DUhVz1PV26rqbfc/4ODCxnO2g6131kRi1wnpSPcSYAuK0z9mhfB14u9/UHn6P1hQmPHWkzxbpuWMxmxULAfspZzMz0iVI/ucplatoLcLb+wVS6JRzdUJaJp+q8OomcrrD1dM1nta7tyhRLkCi9UsNxELCG2555BaLaBNs7R31e628zUanaLhPAL15pqBn9EDkxC8Ltv9jqOuv3kLt+qY1qnDUohW/2nBocCv563HpxW1lk/hQU9TZjvLQihBWeFv4tfUlerjutScsChtdCjxLuCRwMnx/k7j/hQReSPB+PgjM+SYS7WyFc1oUVNe5KNLFetKOIgzoLCwDWUrb2YqYdz1zXfAs/+tNIzIjLc8X1nZYr8jWd9L7rTO8BgNGL3oNWaylGwu9IL3wmxVma0JOy+HH34bvvJp+MSX4ItXwuqqcsOpcOiBcJeHCPsfQlgJExkqsZ7Y6UIpdVrlJ09rdPLcLVs7KyHmjdQuStug69RP0aRaQ7K2v/JaAp+Pap+IsiJw8y3Ktadwza3C/vvAda8n7NgmvPJfPZ9bI4Jn2BOjJn+pXLOxdQy5cGPDibFCtmWOLSSpg2jBIpEHFSb7Ce88TbjPkbOo47r4Vnj4Eco5TxCu88thZLqmL2HCe3ByNmvh6yY4wmzN0qjAYtOVbwDuBFxTRL4BnEAAhDeLyKOBrwJ/Gr2/lzAj8QXCdOWjFslEUrESe6WymLqDznO284rRFjQsUgp97Yyt+jI85zMVz3c/oTzrQmWGYNW3d508A2kWLokFiSYDS1V03St974vCQ18+pRjTwiVI2FpL6T9yilKS/OwueP/l8NJT40EeTlGvbEV560Ngx2/MqryXAXyKxFTiGBAMUNi2UOpNS7itkxYsdAgOHSq9duzgWzDV4vP5d4LfvJeSphc95pzNFMJlaDRJCi++r8N7h64qdztGg/aVYy6wZodvStp3YRplLvXUmjGvmpsiK5C5wiI/5HlWz7tOD3l739nCS77tcHHd41PO8zgn3HkKjz9D8HwZnd2XFTdB3OtZ1X3wZlZzGVpkVuLBqnqIqq6o6vVV9VWq+n1V/X1VPVRV76KqP4h+VVWfrKo3VdVfVtWPLpqRYV9kDUutqtkdhYamFeIaBc8W3o/nr1ghLHN+9mvS7EPkQvG87QXhLnaNgtEWNgQKubVbz8IPdgrpOwTDNfyFKauSxVV+VY+tsaeOca0x4V4XCv6yNARJB9m0yuSwx7ea6pCs/7ZMUaQ2empSA/S5I1BIZxWGd8pk/6Jul0Ny7NL0Zql6VaioYazAWX+QVh+OlddkaUlh6hdsPkmVUJvncgmeux+mnHzr+HWz9E6UD61Fds4zEzPQR+BYi2X96dkYrjLqslgLCkZGqysGXsEz5WUoz2ElKmFfe3FktKiKTsXzjucrKyu+ZqbKrpAS176cL1yicl1+mVBNi829FoizMjw67vpsx7+cHu0R0S2bYhdZ/jbOmwNWTcuYJw7GsaHDkEmRaX93lJn05Kb2dxH2fmZ77oAqN/ytomkuRDr6Y51wgwpbJ3gLDvV0ZgKHX36w/UZJSkN52NMUnYk5t2EXwv/LYLks624aYGj7oaxdtZXZ6bhr0VAmvAOVV0dQUL52ivKCr8bAqkzE85bnKSvTYmSslzg3C4U22CEOSIUf/Lg8WwBQlQEeLMaGtTagOJ7zTeG5R0kFDutqDQsl1dc2ViZLxDHSnn1QKC6TaEsppwtENAldpAnZiTD+VhQ3oXm/QC0v09lWPNtD13loYfKkkIXfgoMq7zpF2W6Hi4QB1slHKMwoYKAXhDBC54Ty+bRpgAHGlG9zafMbY5uXYIgKltkXMY2V+vGXKSd9P3wdyqHsiKAw3WJQF6WyKawP78tT7K0vu7weDpRSmjLbwkJhJtuttss7Y42kVYMfWnU84pkBHNJwQk0+NpT/kSqZLmLCngPysdPrAkJynExMAPvY1fd7/uJtbSxz69CYl4U1gvG4ag2mZLh0To2uJsobT4b/b0XNzkr45Ez58efVxPktYyf5GQaGilq+jzRmW4C0h+4lERzg305QXvJVxUdhv7Z4Xv/CMPsgsYLLMd8hUWkTrip2o2BRetorLm9yPxA4M/Xm47Qb4UM2xULX9pBtvkLc3/KOdx3d0Rg22AOOieB0TGOYV22Nm7ZptOGk6WxHI+wPwxI4XnGxDa75/9Am0ctk59oodcGhTbeXYAGHI09Ubr5Fs8KkwJvfmXVQhDVKR7ccbZrdlULdtNYhTF+SVdnUWSZ+3CbKFk4A3scWPG99gfLun4S53Iko6j3bxHPuqeGAFdROQ9bPhafmdRGL9rpJ+GMpVPjB//VjUpSLziiaS1o2W1jXxKrC978gPO5cuETSACrFFdOLq9/O2QUvfgZcdFZYbh2qMG4YWoZhxsBBYcc2Fq8SGNgXIPbkc7KzNdZhITOsiStA64Wgxf6SrtUvCw85t8k8kNYKrEhrmxj63aM0CnAy9pIqf6KcdJTyZy9QLvWBdz99eQwvmvk8jFJ/xjWGahSbht15+B3HTkL+EMwOYCtnIxEUHPA3lxlhV881Jp7XnGos1/abD4PNMtTPc3unxSnJ4eWXycA9y6goEj+3jqRpx84laxx4U89bz1DO/qVwiIxB0VDsHK/E8wqsCaq2byxdkKbKdmxZJny/7tQPa9W2ijTTu7VlKYEApAFmPRvjuOSbwr3+AlMHw975+s2aFWkL+tOmTvO0Tn4NVrWU9gDrKdtfEp8vTptGY4AwQq61x9IFSOPuEPYBVng8no/mgrz1BJ+FTFGuv+J58Unxq9NWFx0dInQqsKskLK45WBbc2ety870FKtOgA4HyIMKtHi387SVwj+fNSNt2LQCkXO6cCVsmiX02oCmMBlG27AEu0mZRf+rLife8G9pqYKlnjD1IxSlptsbDC58j/P1P4sIvsfGWenYo5x5/NQNBS4NsyODnDz4La7FnEYXfvIagotVq9o3g/yYCBuk8QRbAyAB5xKww5RGofJqVWIOvOkH5xyvCoZmqyrUkgkLV85ohRI7f3kdoFBz6ue55UYUf7pqXVNsdh14rfAtRKn9FCISVAxx/exL84XGeMsCq6T3PEe77AqEsrMp98XiGFpSPlZUlPI+QBYY6pgh1M6FamOQF9cqaws61cBjLt78G3/xP+P734Vs/Eb62C767Blc4F+NNx/VYdT1oj3cUxe1f1/tuA8QC7DGk3nAptriRcgV2XglPeXvdkr91D4oCpdPQQUob7/q0eYBByi0XNCoM7TkDUxW2igKfYYUAAi99Fvx7tjh7fnXiOekFGrdMDz9HX+JrGGCeIjBXSVhfg1APq5VVfMigPXDIWkTFaOEjZqm8k+2Od/+Z596vcU03ERjqb64Q7r0mkNX+VqXuFCfXkkQlqzNSVZhu20CXVCUkYUvDSG4U4YS/EX7pb4RLrhC+M1Mu88IuFVajmMcBWCyaPddTTCo2gRoEnn2qotnG0Oavl2dLMv6qLubiNDhjk+q+NhMecqKwppAWnv68wI6baPwmC6g70Gz3+BkdSvRwMn8OHKJ9QFhBmXJXhEtZUXjtUZ4PqOI1AIjiefcpvozVjdDlcxWi6hWoM4yoBLBpTQvPA2pbvz50a20VLhlMl5l8SFrVVjbUhKSCEdLu/M1H1UnYCg3Ktl9xTMWzS8OKI1vCb3v42vuEm93bmeHKCHWqwSeTTCfkMiaGAUVlcLbaeWGE4v9+DN+KO08zXOWt+iZPdfXHe+ph2jZf433Hwco1PWFJvG94xvptn63TIkInDV+10UhTCGNHMQviFMf9jxFWvX0PrzsO5PrhXBcvip/cFe+ekIFhWd1nUxgfs6YTjSVpKXI2MorGQ1thyj0DKOA5/9g1PjBLhQ4C9buTIShIpTHA8PifOYIytoNr0Zo2DOBnMKt6LPOcNwfZQ2NK3uwS2HQvy4HL7+us9Bg66Exf/s5IxtbryprqaX3vuzI/eEu91PxsKIJjfXeZse1wfE+lqWIs4HvRC5XpNczn43L99mgMFBaljmbaXsCgXczMSlrDmEHBaNlcN3hVJZyZIU8MR93VI5CFaVMAA9hix99mOegE2MqMCX+E8D2mooDjn1ZT2DDHf1vnOfpFtTV5aFlu7gtVWBLaofO42/Dl2mpjfBz0NiVvBRyGjN2ThgCuym8d2Ac5Bb53Zes6XvgqBukXP9HW6Ui2FiUJh7mmdK2WNehkqzS0fmeBMy8KK7EGL2E79h+6GbLDk2d+8poW6399rWpPUD1blO7t/pigLT3i6GR0LUV81UMoGDlx6OQteHuK1QY0hk0zlKjQL96dhD3nYShxb0S/zwRFVXj14WsZ/wXl1yaeE09OavjQphBoQfVwGVJq+ar29JrCKKzOlJ2xBbPgZ3xaNo+9rlK4wTUx52Vpzp8CF88oP5bsQVpgsEOKLT313XrsuTc0W53vIaTX2obC82B4YwBXcx0o6IxfcMqLj4HpIYSdmWlXbRXvOmrIHgSFQq1NoRlOIDzhOLhEhfQ5R0F41f1h5bfJ+1ZkegEzmQQJaIdZS9CmAYZaUwgjyWR8nKKIfp8pwZZw8rNmfM4TEX7Gb0w9zzmpjM1FTI8xmO7bDVBoj1Kz0YwImmXaXVfYocRYGswZ4qRnqZPLiSj7Xw/4ZAmTZ+iAtQX1w97p7+10os3OdN6sxIhzm8RsNuY1fX/RIkzqElJzGA3LFz9hn6nnEKc87EDlzo8S3A3CNnVEEfXYo/uKlrZO5vcg1dubCghIAwpnHC98cy3ZGgKLnHcb2Pb7BGOjANM3MGNrKI3EUm1w5+vmAIZkW6DFSGEFcNwzfLfPC8cePeP/ZmGuVvDc2XkOP0mNYbG3cCn+7i2521NkwaFnoRO4/IeJBYeUJtSqm1i8afMejY/mPwhui+kpTf8KxXq9EDVl8LPmvRYv27aQT03fGBvCWhV/KV+lcmgBf5ePLlEmE9jHKXffX9l2DTjoQOEmN4frX9ezdX/F7RvCq6/P8Qz5LeDb37MwLPdS7jBaKWo9aN1WZX+L49QThA+thg8wI4J44awbCfs+JujGCDB9C15XAi5KAosh1C1KmwMYgDDVrHH6UdjODK93RbiCCXDsEcq3fdK7w3Dh3afPwt7zLG5pq+qIZdn0rBun0mt3X3XAIT1e8p0xjSGcIzHQFuZmsyRWwAFWxyRTQg9az5OsUw9GJtfmrL84cL9hlhfpqCz2XHl5DG/aKJw3EdrznScI2/efoUkr1BnJUB3SU7NhKIp/NByLbQiiYTsDRHyf8zunTnqvFmGl7MdAT16xVbrBukt0/O2rHS/+jEMlzMSoCHjhwpeCTmEmhMOkp+9kJi7jSx5IZ7Dp9VTzadMAA/HYNUHjduln4bmCiBd8ZxYMDqpBBbznSplayqBgNYQWFAb33aWR8cMczeHSNYr5ocmGs2sVBtH28mz1q5SWcMUXexpJYI0bHFx6kIV6R5PUcDqxJDvZwjDfI9XTCw+wunOI2YrG1YoavlebQSHZkYKv4Q7CMu7J74SiMSaAaADjqqZaQ8gJ16CgAhKWdL/k0w51jvwNbYXHH0A4/jMdDzd5M14mnWnJ2iqzrC63aYAhoXY4tOr7oB8m8cJRhykyIfYAyp8fotzvmVBOpulddO57gCqG76m8g4JFr8JPrqg6jypbS6n5gywVjvjSV23WUo8Yrlseml6MAFD72+QvzRr0KO+uNHH2a2S8nmaDGRMbJgFDHErYqdo5bS1NHLkiEgItAwh7HDeMwKbl21K+2/kv50f7gIa7APc4AO78QmEm8SxH93K87Mj2hJTNIgFliLIsbQpgEIrqOdGdCCfiZIYCH3uBcnFSB1U54sbK7z4JBqfnQufZ3tvnPUWdOMeUiTQATGS0h2nlf718Wpa3PYLwgSvKeztBMnXK9X9pTrxNtrR5mM35QMHUrmOwh8wuqDUArKX4q+GezVQH/MWKQN3eqb+0JOad9fvToH7PLfXR/3F68htvFU77jMtGRYdynxV4yEkCDvwqyNYn4N1104eqcnupDKFyI7QpgCF/E1JhIl9C9QsIsLpTOff74bxbp8phN1T+vyckVXIMEOCn2eCj1BGKH/9kPGfVquIYtjhZ9aIGAvusCJd1ewjloC3KdNu8OhuhmLSfc8DJykqttPZpflo7K43EamJ2BG7OO5RWW+ykIb08bYA3dpOdxtYp2K9sh7UJwtf/QXjiR8rX2kH4zSk85KwwlPYOZOX5+Mlt8Nm4WIYkuSbUprc8bQ5ggHL0lH4OYRcCfPXlRERVDlmBOz5hTD1N97YFr2aAsB+wVfjJqLoMO6qCDY1FaQNQeT+81AtrA2AI8Tz5F9IHsDYGoruuGH+XbAx5FsWC4iJag9hZD2VouSxq/2DoKC0o9IYStfvCtAfYZxiFAYSq/Rzf+UfhSe8JNgYh2BDuNYGHnyPhNHAhfGZg5deYuRJ/Pajq2ZiWB4hNBQyhij4V1yHAm79buOqht2v4JY6hh3x3dYHBuASkT7V//nt0/ShwcLW5yUpZq1kXRsp3FZg5/vAIy2wxUJymvcMj0iq/ViXv5z/BUPLxw+8NdZZEK1sTqNuktZlunUMKl/64jdjkrZpxsGJQi8TCbd9XqjZGYwVUqykkj2W4YD8OtPrfE+5/QbQxRE1BVHjjy+PQYaLB1Dr5Q9Q9NZy+EXkhr1lQKN/ksIwzL5PjtKmWRNdKEdXilb//f5SDd7sFtui7dOL9YP2OeU4kw985qyrsatcCmDD7b23KMtAgAjOVheNGU8DxlKPSZpucYH5yA9W7pXHBSqlY42CZ6Ivxr3cY7AJCN/PWU/08qPbu4ToL5GEMEJYFhSpMP3Br3QiOw2lJVcf9L7BAL6Dwc+XDYuEr3tND8e6peZ1CUjzyDEU+zUiaZDc2nNhUwBCGE1tIMLHP9vRW+a/L4Tv/Tqzx9jSi8Vjn/+683g18mUfq4fI1E2nUIhLIbZu2+RtBpfQpuggIiuPvzhY+623YMiYH5WYDUJgzLh+hNTNdWeIO5Ea5aNE0NC5waocG8TmvV5g3XFygHBsbRdVhtXUco5Y/G1BI2sKPCyhInH2YOOH0s4KwexVwN8e7s0LyQjZKavo+Ti/J9nlJft4UwGCrTrlVdn/ALxY/M+AJfy189xNpu1UKuVS3PsdvG27ZeBtKQh8vP4MrdkFawN6OhPfZ2mvJse9RBlBAHR8+Qzjla+Z91ZOF6b2XPjc+541pKVnD2GOKRHTfOTpdmaYS16+P0ciBXda4GXe/ptOy21GzmIcN4fcyWsKo30UiMG1qd0nG59nljgc8LxcUNLT4a88SZEtcjTH5OVTOyKNLe2Wt1GoQ81h6CdoUwJDHiQrKr6BsQ4Hr3hNWcgWEEj7mNfD1izBwOVb6RTSK1m+d1vp+u0VpeCb492uw6ms362fHDpu2NGAQQCCPTRHQCX9xsvDsb4zVQdAnzt5fcfuknnjMvmDynp/r92u7Uk6H4dKq1WU0hPZ5bVbCWm0naBF0viH6U6ANJjOEMTuvEtrQ73Lc7/g0TZl8Cq95QQQFJ+C2oe6VDSDEfRFJazCgECLRpi/T0scsQZsEGIiVo8B+wKEogtsK555gWD4KyxPfJ3z9I2mM1kJlC5n2/tMoQ48kAsMQEFKrrmy1WkKjGcQhQx506oQHHSG87eLivxaZIETXFOWWJyjlu5wLZHgEM668YlyNH/uYyTK1PpvBcAoyaThmiDHvUNM9iRfrxrVep1SDvAWF2Zpw/2cVCExxvO6F4A6OfR6AvAUfcSOBQhPlUMFM7yo20vn11qFNMyuRGmLGFuAUJvwEr/fBHQSvPgce+wzYiSDqUFGe8BblKf8x4R5PLyvey/oGQEY4fI9wj2WGNj7pvrpyDXbOjLOdygR2bE3fnFSIuxoSWKp37LoSXv4yeOc3QFw82NT1ihOWC190puJcnImI3+ksQtbmW02+22dAlZ+sDeEnZF+RiRmizKtfG3XlVbly1SO4mM9a2MT5eEp0OxsxkgaDKJajZYYZVTpi2rZIaQIA7yc84FhhbVZ/5+PCZwtcD5iULWI6fTfeRZBIVxR632WxmBnbthv9uBCbBRhSW0uxrHv2Afca/OyRCPCyk4THHqtxV204Nfgvvi5c80Vw+2Md4fizcK/H2ikBocv0sDgT7QamrF1plKIqzpD4pZfD937guNTDFT/yXPI94bsfhm98D955meJV8JOgbtpMD7cLey46IYBCqBOzHX0UFNYjZXVWA4cFgcGHf+dG1W8DHysngU+a6tS47F3Mhqlh3kc0n43IxYbbOCZmhbHp1u9/tLCqcTrStKMcEneBeFAnMH1n0BR6V0qrZfFe/mWjlbBJgCGzg5ZTDmGCynVx7l2ovw+THcr5z4fHHge7oh8Vx4k/8DzoucIjT0jgYCu9TiWTpH9LckE3yGIVv+uHifn7dOHn4YLnJjEPH5AhftZ9kn7bNc7dr7Z4PvA8RfYdnog98Nt9brvz8nvVfAFLKj9KbSBs0xwrdB2/j2ck2P0dyZ/rxL3QMQMbl4vFKXVoYEChHT4IXh27VJBoqU0rEy88IQp8kvyt78RPjGZgACEGaUDBaJ57sLybyMYQSuuj6pV33cu+qPubsGZ8f+EvT5e4gi8fh8obLhNeeVrSNtb5knSj+i1NSwSxvepll7XAUAvOmk/KsYtjSQFx4Uodfe6V4k3TPwAfvtfZgELSEqpxepV+j0zeIiCsehNfewz/RtYVVGkpW1XZgmc7M64hMw5xM241mXG3HTNedXPfSWPBtHZDy1uMjBEg/W4uVceTjilDBzTw7oVHg7uuFMVi+1vwzixaGtEY+kWSoclDoDJGLkGbQmOAsonW4cw+nLhaXHbg1OHxuK3w6lOExx0Tj0kDVBxvu9hz2fPhsOeEgbc5TLymjKp2MLonNIf5dNlldlFQB9pNduZFb/SJYkdR5Y4Tz4lnhFOybU9eP7eJ9WJvOSiED2/afsp+fbr4Le/Xo5LeI56mPGqnIvtTPhxCwEcfeoySzpKGtIVpA9HWpbWAEBejqeNZxwpfM6c6C3DhwwR3w6AVqBPY+ma8rAynJCvckTqpkfFErU39DA8lgLyc02dIKIyBKur+EVXF+zvhtnleebbjuCN8qHANH1r5258o7z/a895TQgyl17Rj7UHKFHVsDme0r6TjHvNqXya95rKd6UuUvvM+FEHEgEcFXq1QhxOMnnoz5b5PULymj+qU8lq/44VYtLDKH/yB8qe3W2Xl5sJ0e1FmKi2iWicxT4MYgvJ0B7BjRmJ+afyXNQ02/JJaw5iMbAhnxjWEwL0TTnwWfHjmEHVBAVTh3F8TrvmYkKQXQR149268KOokr2IMBkcpoxIapaRRDcQ87wnaNEMJq6iW5wgPkk78F5CL8Dg88KJThZu59KWB1CCOI44ODWPd7HgvUHMfVHZDY69a9/w7qT2hNLM125um0hS1PH0+r6j89RXWeYRhwkHMuOgMz32fkATSCqWv0gVMrS5CDZDEuK73C8o+v+HZsp/HrXhkEg9SFZt+qsZlJa3WBtopS6HljjafG6SNjn4qeOoBg+Po4+HDa2maObifem/hGo9O2kAcQrjXl2XNmGHE3FarYaD61QuwgaHEusAgIueLyHdF5FPG7UQR+aaIfDxe9zDvjhWRL4jI50TkDxfPShLi4eUNa3hZAbkoCL4Tnnua8LuT9GG2EMdnvOOIY0K/2lqGK8Dp1tacWly0cjv+1namZu5tZEqfu4ciDOmz9wEMtrLG/Q5e4++eq7z1DAU3A1kjrVHIgFJ9d6JW/5ejdniQ4jWncNv0KlBbtF6M3yauagg0mIffYJna6HeDxkEhtMYndjokHsmWjnG78V3JRkUFxL2UGQeS1uPMy15X3jXlId7nKGjLYvUiQ4lXA38BvKZxP0tVT7cOInIL4EHALYHrAh8QkV9Q1cH2oXEK6qVWv0N1BxESkC04/gGvd8SJ8LhThS1HKRfNIhigfNo7nnmU58xT0hSmmfSP2n4YXvTGBEtSzyiQhhWxx925i/gNSge5Vw8HfArKCrCvwNTBgSvKTQX2WYEb7KPc/YnhQFPnFO9nA2HUmEbJxgJrChaiWFGi+Yi1rERYTksqfo/7xjSq7DWk0c2vNI9i2u9qIzsDETcvNdrC658jJKOxikMUnvbzUhkQxb2AGTcqnZ4MtWboF7Ur5D1FbzdGFesCg6r+k4jcaMH47gO8UVV3Al8WkS8AtwP+bZlMZXEVzbIVDrIgVDbp+3xTPGtMgEedKuiRygfNKUmfVcdjj/acd6qLesewpsoBI61096R98RLUEqBc/zpw2MHKwQfN2Pf6wgH7CgfcWNhxLVjZBjIJuxTLWDoNtBXvvZnD1/i1Natip3TifXS+f+NlyemPDqk6ac5jzKp6bX21aeiQydt4f2pAUYNCzohKbDjH3xwvXHhF2BDlY/v9yf6O3z1cmUlcbSOnMpNfJi0Nqb+CItlMlUA+kxIXevVyNnzKgTZAu2N8fIqIPAL4KHC4ql5CWL/1EePnG9FtQCLyOOBxANe+9nWjY3yZyxKEQ5t1+3nXmVyEche8KhOBx5wmrB6t/NNqEZWvq+O+R3neforDiTHMGV5UiN9B3JPgYIMrN78j/Mrvl74gnWBsixaAyzcLhiIstkuC0zRUQMrsPhw67AmpGYlDerK/DoA04UuwRcN1VLNF0trtahhu3EuzDkHnc3zohcK5Vwpe0qpHuPsOeNDzBHVhvkjkOGbcKh5+H+eQpB5G1NlNTKoBFHoYWpWtZeyNlXajxsdzgZsCtyZ89+iMZSNQ1fNU9baqetsDDzi4Wn4gdikCQLLUSgSFiMZrMmEm/4B3/8iqTFkT4fGnOm4lQtme7NilE/7oSMfMT1CmKJNwVVuYzb6Led3TaI/ZXKmZ4/h4uqKE8fkaMAMJl7gZIuFC1owxb5bv4tJzMfalr3gH0GiPuqPzexnqhI2aTNoCLb19DRucMy/xz8vLWHkWLOdG8xUD5w1sWgAhbGyb8IVXTbjnkY7Tfhj4SnDcyMHbzhEee3I4aGXNC969n11yJ1ZlykyENXHMxBVTtJYrfp02FM0ThnI1S1UTP1K5Wwvdxoq+IWBQ1e+o6kzDoPmVhOECwDeBGxiv149u8ylqYlKBgbnHqxqLCfnbxDMmIP+AuhVmIhx7pvArk2RVKGO/BxwtrPlWgmswyKpcm8FlqYLyduTYfEIvzyjY1h65BrsMm542C3BTzA1RpeDWUXWq8eqlBUFw6Xy2huoIELFGLnmT8LTPFrfk65SzXJ5+DKcv/S6zdDybJA4oHdLwO5WdYg3VCSzn7knaEDCIyCHm5/2ANGPxLuBBIrJVRG4MHAr8x2Kxxt4oqc0DWCxj1gQOPmoPayKsioD8DSr7ok44+gzhzi4FCf93qeM+RwtrPkwjKa58Yry7Xa0q9VJ1lIOYhTrhIZXHN2VLZW2WM9tDSqyRrxX+nMU9yyB1eTptclWmZ0FvkWtRWshvGi7YAHHqO/fsjof/pwGFKPXXcnEdgoQj2XTyS8ymz8JahewqxgrmNV1tT9jznALU9VVVywaVuEWmK99AMB7eXES+ISKPBk4VkU+KyH8DdwaeAaCq/wO8Gfg08LfAkxeekWhWMgcBSJkoV1nkUXp4CCCxyjbUvTs0hhMefZbj8VuCr6SWeC/c7yhhbZYa3qB12aUycsruYtU7aIjeqUSA1RqGtuiOrpjdxugqEtJ1i31VgcOS7LzHwMH2wYX5Eo+kYcXqpwoopDD7C7z47CD44fSlm+HdWcNNUVh2M/yXqAIDGbBGXQSb0+jBnhJkjwpbtHpaw97VQTe/+S/rua94e/hRqUuhQjT9tjKC4EzeBZgwQxCm3A3RK3DMEA9fPsrz7J0xurxbz/P2k5WViRI+d2a/amXU+14vvE6dpQYb+pLhi15UFdOOpfVTareFha3jsRd26Wzr8nlYNo3Kf2/okETOoT7cZz8W7vtcQV04wNUh7OuE884GJsF+gDsIkdexFve/BJNBmHjP33+Qwup1shptbUbjtCdbGa+5I20rqvn527/+6x9T1dsuUiWbZuUjxOqvVEOjSne0hvZIq2SYRP4WdfvmJac3Ok04cRK1i5iAT8OKtbKmPamIleo4qjmMc+u4gl0Piaqo2nK3sWTGaIYR69J6njpq2aJBdyfZq4QGXelilIs9kM78rKQTtITVS4X7nGi0BRW2Cpx3RnAK+x92oJPXsxaHDfnAFazWO16EqtNP7tYA2YZJw4rBuGNjQ77NsVdCCN+lpPA8QtEWoJzfkVaIxcU2WrzmlQq7RBDew0QU9fcB/wNuerZwytHK0VeQF6WoCvc+WnnncVOmB/oqJiBPEeaZNDE/csYTLVPxWpIaq4/W/xhl9WSeNIzpMGMcOi9zCySzR0movm61bF7WLUYNhKVjSO7pZG7H2qrjfkeWJfYQdvq+7jTBbY8GRQGVCTN5O4qPExlx8Xur0Mi87MVyJ6057xMJbZNn19NgOv3L7hTh2ABtKo0BqNskdvBi3UXrGYykQRjNIdXITAUv70LdL+HFcf2ThbOuV77wk677nAS7dpZ17cqIQbKrPfQyvmRZBxrDAv72CO3h4ch60W04ubkStG7QRV/qoJKLlrB2peO+R0ZbgAn2ulMEtwUzne5Q+buy3yGtUYhMHFhKqGairBrQ5Df3Qyq1QkB5zkPtefaFJU0GmwQYjIpsx/hGbZZKhW7U6c5QIx9yAaich7pfxTu49tHCWQfFZCUZfRz3PV7Y9ZMIDhFhyiQd0DyP0x6V3D00XNhgwgvzkkn/KjOJXCXqSCf+wkhq7vd71hCdhXAuqZc4jyRTVN7PDDGgkNbgSK3UmahyRzdQ+VsVgwoUBlWq5dbiwLLVv0mAwVBPrhI4mOchQGitNUicM3YwEwF3Dl4c6uBazxPOvVaToAr3OxF2fbtMZWaA6A4K12PU3QSIPYwv47TO+HP54enuDm/nxLtepSxTYTUQtNqC/T3zrhlihOt1LzS2g6gpzKKRMRsaxVTBOhpfeWUkHLOWNQ2jrcZg781z/kDTErWSaFMBg7Q/KocCDnnlnUXbriZBRuQZoJPfCYuiHOz7HOHsQ1MaiRkc9ztD2PXNMq6s1JCKYbqZHClVey1TET8t6rHPAprAT5sqCbO0UVDIo3QKH5S28ur446OwTIYAbzgVZP+kFTiQiyIoSD6Byw4nerxcaQuWX3v5tYYJAxZqMKQASJ3URtBh0wDDcNNpsvhR7q01vhpq0K3o0OlrPPjiBfjJw/BEzeHpwvm/jU0AEB5wJnnhU1n6OlinXeV+2dLO7TaWjmuDNMCr3iDW0IDJ1kt7T6sMLS0BtlWYQi0o2MuvOR5wpLDqi61BIOye3Dd0MKqA+yBraTqyuuyOSjHJSJNrrXNlwUEbH4OhhPRxQHev3jfHrARjTRtnKqodZQkwxKz1MRVQdXIa/EStIKh8j8NNHov601D/HrY8xHPOfvC0vyVqBWGO+Y8OF95xlGPlOvFAknSQisR4SRGnBhCWb4iUWV2Ht9eJe5lk56VjsrNupL3hVdsrMu/3opnqpb1EsFEQGw4fgqru8Crc9xjHrrV4nrMIeOGCJ8LKr4beO9gVhNmWvw+nMGUAsMPZouCkFgys2PKLlP9awgxwWcMu1zBZIUj8cHDi27qYsqHJnESbRmMYo0FzilGmGi2h0tiMxoBtJIgaw9HgbodX4eB7wEt/KyVWAtzvFGHnxxzqJ9h57LL6re2xNtgMu9HpX33pdLSB0l317QyjdocN9m7zgszVbJr2M5Ks6vizZwm7Zoa5gFfeDbb8muTTljwCK+80NoY4rMimKTMDgVR8OJcyD9b/S7FKJFbPyMUdgElpjmVo0wBD2q2X6lKaiqzqNANBOYuJNpxB0Kpzk7JcVeV0dPpI1AkHPFB45R1SOiWiB1woXPq/QvoaVDZC1ZHakrB+6zfefxrIcJUmsUFL48DrBgBiXdBpqeKYuudAeNoxcPFae9I4HHB/K/TA1vOYue2Bn1wzZMi9l+2teh1I5rb+b5EYvMNTZoihOazB5Xi1/helTQEMuQmisA/di07UVm9lgARSVUms1LpTKFNGabZC3ePDtiUH2x4qnP/LCVcKODz4POGHn5C8TTtPYw3mnzZQ8I14vroMgUsL4e4ktKeobqMsRNUW+9ieOP7XJ8NzNBoCp9zGCL4X2HI8yk1QnVRgoR1eS89Fa01pzelMOp3FUOFIjFzKpR2G2mgzbQpggI7A9674MN+vmNozvvKmK6r55ZmATo+PG69g8njhL+9sEiNoCw99tfDDD5V1D/ZL1L1mW1fqN9SDy3JD9z1JC6vtezLB3Y10Dig0XKM4XnhsYrDISQqnHgA3eVyZlmTrMXjuHA5jsRqEmYkorCD1c0+byCAx4OI55WnvhexJDDoItzhtGuNjOpExie8QUc1TFPLQszcRGbRV+6zNu/RPYCZ/hFu5O96/B9EX4h4Ar94Bf/43CYUVZMJD36Wc+2Xhho/0uHgIqxK/mSneZKBjkJzbNgsIQG+8fHVpDmM0lh8Z8TSok14l6Trv101wsMw5H7oSr5cdL7zryrSqMUw1PP9X4FaPl3DykgDucah7IJ5pNjJ6pNIMQtJSZyF17ImNNKSfeUeguypRiAZvU47Mv9rcbTitAmwUHDaNxgDjWkLdrPY/c8s7fNVH3NDIDpV7ou4FKMLkLvDqxwz1kid+Cr7yd2FYUauFPT3GFGB3aE92nFdH+jp4mONxd1WTtrA9UBCSAfmDZ8G7dpY2FIUXHCLc6nESprkl2Q4eQjj9q7UjmCS7oNDwRcW41j5Qd35Wqehpyt2y7m47G9o0wDCohI7W1baDCV0CWacOWdaq2SxMU+LuhE6OR53gbgUXPErKWXsAOJ58kfDx8wUyOAyNVUMmGKPN1u1fVbRMORcBiEWikaoztrYhRfivE4TTv1UPBx+1H9ziWc3sg9w3LFyChCnxuWFYiFPrQ3Nh/THiPp/UH7utadBRpmAD79IJ1PM3nzYFMAyrqmuHrQWfUpF1pxyrdmCRbReCxBDGgBg+8CZ4uXswSIrgbi28+t7gxObKceynHe97iUR7Q9sCrWZyFdCG8OQqzM+eoEEVLgsOpnzNrFEaTqgxKj/78rIpShAccM8XheGDSvxmmNwKzxGU5c2So7ejOyuk0vwf6gJtfjs9n+0cDf4E3zW3S0KKnMQIOCxBmwIYLLWYOaiv/FvagHPKnkAgPKdFYbVxvanB6WsjOID8nnD+sVQJCMI5XxPe8ZJ4gMfgCKoWHHq528zaQluWDXIY0C1nG82y0Y7NkFRtG4W4AQXF8dX3pVkmMojcZSUMH7yAn4G4W+DlZWbxkt0pOcyzdB86dWalvIKROtToc5KBCiwalut933kJ2jTAkNWoFi0TIlZSuV4pez1HdFfz3misFV+JMJObwMqbc0/hDhH+6kRpLNqOV3xVeNsrI+N1v7TN8N6i3Dz6aWOHrZ9R2ihAbIQ6FbBOnfSnAhM4OH74buGJH6jb5E5OeMKZwZjoVZDpLfDu5WHWyiRZGxll0CFVQlzlszNMaJZGW2Fv1/IMFvEZA3wFELYjbcItQ5tmVqKaaTCk9rVhWrsKtISyMwAyzkB58UfqUWwMwWKMwEwOwa18CNEfwezeyEHwmufDnx9vP2nveOVn4bIXwCOOU5PR9HneyFbSZKDz2MvnUu7z4ql6sTFhm8M93WBNvY+GHUlzYYoFmBtFuy1esltamLbr3xz3f2tanxAOb73JinDaWWQj40xugUxewSrmYBWJH6A1/Up3MkyD3/zBLPNc/EU+Nx/wGQDLev2FNCwucal0joB1IlifNo3GMKAKKU1P2/O0CPXsWQZYBsMKCfdgczgInbwBBdz+cP5hMMlaTLjecIlw/il2SNEulEldjzTpj+T/KtUUTFdULfS5ipLaCDW97fyIOxqCWXWkKqxe5rj/W+sh4zYRTjkTc8w7+MkrWBW7nbrWJltQGOTMeBQtJ0cTpypbPhxAmQGFtse3sxMpkoF/iCuCd09j2CTAIPU1OqbtwWsnqjmkBgyCg1ETq6ueo/ZyA3TyxvAVzBsJf3mkBHUrC5XjLRcLrzy2P6SoDn2pLFc/JRobjy9Do3U78kIW8LMojeZVTFFqoEt1fsUVwn2OL+2SlPrXnAMyiUvkBXBb4pmhJZpkhNbCLnW2usMFqeo36ifmmaESSRLeemP/sLSYDjP91vHZig1W+yYBhkVoUI1953XD94XS4kWR27bnvz7ptN/J9eD858NWIfQKsTnfvkt4wVFl+XTVg9GquyNl28w2yVGSuT/nOAZq0XlumBG3BhCS2/2PK+0gsf23CsgkDvoEEAfy/jwIrE5fInUUY3k3TNOCrvnda/G2+xPjoe0ei8agfX/SuNn7z6bGAEVbGHtnf3aYsIXZ9YRLhz9qfjQMkS8H7nWRecDtJ5x3UjgQVDQwneL48JrjxLS8trNsut34UpVxs4PCXAaTYTssE8GYDWMUKNoFQvWyZ1VhbacBhAgKU4ELkqERAj+5i9jFpB4+mHvIRr3ceH7fzhDkrAmsE3JOV1H3g0ZUenH0rmVpEwFDohEsNaVbWnYM1MvgoR93MurYqalgc7gJTM4LzwKTfeHVLyieJDLOf6ymacwIDp3mKsy1ZMHGWn93uWFRGovbcu26EczzN/ZuHijEZy0LmPyqcL9jTVpKONX5LEG2UkBBPsgqK8XISPw+KkNw6OelkKZ/DSCkb6EM3DsxLdx0STRksRpdhjbHrMSgRB3NwVrW2+f6wUTaSShVppbGzudmSB2N2jDR3TtB9Vdw03/B8xXUPxzZDy54gfC04+FHSu6d7nG4cCMnvPS0MNcR1r57E3u4UpJ1jveA6jCooz1Itq66XLdo4uuwbNdA25mB0ALEs1XHvY4GcEVTQHjDieCuBThhJgqTD7HGtHwQ0IBBSLphiGHG4mOnDINgxViYhxUSw6aj4RPvxE8jFM24tVYKmFmN6k7iP63lZEnaPBqDsLAFtXqfQaWMK9sjvq3XfM/fyUxxpIFgA+O2Q0/DC5G4t+LG6ORCFIfsL7zkVGE/iQ0Thw9f8Y6nH1HvrVise9+DXf5VpT0spJnsrvrSAwUbbwKF8Ly2KtzrGCgG4OD3jWeCu4bkDyErwppurT4uW75FJgUs0nulOX8m7bS1xk+ofvQwpTJmJbf0LOV3pV2UOuziJHO4aYOG5k0DDKXY9cjeqkulxD2TbvfRuNUeMmJTHyxroNvyXd6bn2wPuPTF4hug0w+gCLJNeOlZjuuZNBXhi97xtMMTOIwtoa5MSnNK8rNK6wHhmP/WrbkbUJh54T7PSobf4v7GU0G2CPZrUMg/Ru0ggYWxHagFhAaIGiOjfZeGMP3hoc0zg/ca3cvMVeNXi586vg5FzWPZGre0aYDBfkimWvFVBmjRH0Z4TQ9fvau923XkYl4IdVqVWw5cx20P4yj78af5y9syhRedLdw4Io+LjPIlhD8/woBDe7pHBQ5twv//SPO0px4gpK5jrL4c9zxS8N5V4QWYbKvlWeQDzNiGEk/yImkJRbirtR1qQKOTh5ixTp4b6g1TK/cGHKLb4GyFuZqI+T06jbI+bRpgCAJahD314kh5Jz0QKG1nIrN3Sbdyb0AhH0dv4q3mgyNS1NbqNJ0VPliq03fhmYT1jk547mlxcUse5zq+i+PPjky93Pjy6YppqsLuBv3M4kvJ+Lylzp5w1LsFCwEuPFaCXYiw/wH5J9Y4gFk6qo+wBNqCQrljgIIKyK2GPjhOoe3Ve6Ch5VXRFsq7nH4OPw8QxtKxmey8m0ObwvgoojjnB+7puBarZgnxu32x4oBmeTRkMNDyLgt5k0IVVDQfpJFSl+wz3p1h1DzimCB6DWTyIdCdeL0rMl3l1WfDEc9QLsaF2FT5HsIjj4BXnxYtQ+YE6pSHwHgWHnpWvg1YFHcz+B6jhUCqBwhWU3AZYB9+pHCxd6i4XK7XPkXYdovwc+ZBJq9lNrk5HgdSFqx7NC9vV0DtJ0zz2uZ66NrTZ0oM8/W9Yg8U8ulBkg78CVyHSH2QtOggZskfr1QTaYgz++oz/UK0iTQG7V8OxGm4kjaRPg/e20HW8FDq8KtdqTk9oz1Q6jFpJtLEla6M8gLttwO8bMe7f8a7KTjhtLOEawt5SAHC93E87IjYw2npuaoEsgCM1lin0MtU+JxgbdR7QGFZjuaDggWHpx4pXOxtY8Gr/0TYdsui4TF9MzO5edQSyIZFa2y0Q4h8kGq1OaJUQnknZZlzzKOVxVpAB4/18IAaViqNZCltQZr3G6NNBAzpioLvyACQhdhpHlbMseGNMnWVRiftBCTlRblakEgGyTysiM8emIlD3b+GbxiK49QzhuDwQxx/emRRf/Mhs1olMCzEeA0uWePDMq6bzKJJzItz3ThaUBi51HHK0cLnvSRGAYUX/7awz++Hw31Te6zJjcJnCiV9/0HyeoXKYGjtC2k9hFp3TLs0+dVeMdeTTAM0NkR3X3ddcbWRMjsOq3eDQ4lNBAx2dqDVHAAXS+00gkbW4xsDomQBrzQC+0wJV21CadxhCAj2OSG8NlfgLYfK1rD23gmnnSFcT8AZYb8Cx6OOSPaGdAK1o+0l699XM40ByHrAsgi2WVBoeuL2OvU4+PtZsdUowimHCtd9WJFdL4JseRg+nrGQwDzNTmSNoQIBcnwWIGqQpgGIsXOX6jL3/Ghzb5+7gUdtGmOBl6d1gUFEbiAi/yAinxaR/xGRp0f3g0XkIhH5fLwfFN1FRF4sIl8Qkf8WkdsskhFxZK1gOENhNIasSVCMkZX/BCQMgYPgNwt/ch6AREKFzlVPj9dag9lX4QHc36HsE0+gFl54unDLvLIq+Ps+wsMOl8FHU3Nvqakn2UTgkGg9MFg6sl7Ew0txfGBXPfUrwKHPkKIRKMjkTnh5Wt73UH0tyrZT/l2AIg0pTA9QAcdYzqvfoy/WCSAlP4tXbuITGo1mY7SIxrAGHK6qtwDuADxZRG4BHAN8UFUPBT4YfwPcHTg0Xo8Dzl0vgSCQaUaiCDjGztB+5ToPN7rCa+OhirOyKbQAQfmdAYU6DqCOIHqoOpVccdtQ+XtUtoV3U+HoM12s9HCYmOK4BMeDjyi9H9X3K9gz4DAmZ5uNrECS7jUonPVsg9CxDR6yv5BOX5opiPtD1jidVZ3gxeXp5PAl6qQ1tOsOpGgCSXOo5qeTH5u/QlWVdvqWPpU3PY1fm8u+GK572XO07qyEqn4b+HZ8vlREPgNcD7gPcKfo7QLgH4Gjo/trNHxV8yMicqCIHBLjGSVxpYoGgCcax+egmr4cGd1UEQ331KDBWBustZVxKD0neRuoXZLfZ+VQJBul8+yHiSPH7cnDCKLxyil4p+D+EWGGzu6Ocz/i1Wc7nvsM5QsQwUH5McL9j4A3n+qYxJkKwcc4y8yFGoYbspEM3dbjnbH3P5VZi9QQ5bkVUpVigzn/NOFN35HY8g4B7u+Eh57h8BNlVQSV38DLK7gSQv1J+NJ5Mjzm4imBUXq6POVd+FZkbINO7nOu0wwBAo0Nayn5lWZGak7Yeabpikc3QEvZGETkRsCvAf8OXMcI+/8B14nP1wO+boJ9I7rNjxvTUzPs7ZOmkHt/an/V1QnfXlD7xaSf8lDK3c9XCVTyl+xTga+1cmfyHnRyADqBE84WfnVSJ7BTHfc7SljdmYYkxSBZG+JoEm9rcg/QVa5VWFBIjybRXOagVb3vFOHN3wm/E2jfeyo85HQJ335AEH6VGeexC2Em4VIkGIGxPa/k4cXolbS0yhDYNjrD3600LwDMC2Pwek2/B2lhYBCRfYG3AYep6o/tO93AN7dF5HEi8lER+egll/xgIMTBTy20RYiNkbJ6Z42XRfBrT006OS0pwk/tDxNPOzQZxq3motxdZEj3nnBi0FQ4/EzHTVxJTMUxU+G+xwVwqAxezYq8+cMKW5Dhq9FrtLHmvNsQDQWr2FSoyqoI//YS4ZyL7VBLuNVEeOQZkpemh7MZL2BnfE6GxbTU2XeBICmZBgxMneugro1WiAWyYogu/mh6kE65pTyMzkb2q4tK3BZqn+VUwIWAQURWCKDwelX96+j8HRE5JL4/BPhudP8mcAMT/PrRrc6m6nmqeltVve1BBx1chA46z1IJ82CmQTSciYABhdTceVXjcI1ECxAVIND0D9mf1nlDS/sL2YhaJho0H0devnN4rbDAZgInnu34palttMCQ93uWsHqpC0uozTi3gMMcm4N03EacBu87fooALUld0OlyeXW3oPDRNwnP+3Ko0LBDNfg78QzyzMMMQbgGO0krIEOinnofhN0PYQHXZtZqC3XeajAus09kBlKpw5T6asGhj9h9/+tQ3i6w5BByHVpkVkKAVwGfUdUzzat3AY+Mz48E3mncHxFnJ+4A/Gg9+wIYHjICKlJOjpbo2PrLS4NEcWLs1C2QpDiq2Qoz5ZnTHILOMJ7erEkCm/YiZTQbRXXyNtRdM/RiU+G4sx2/NrUZCIx3/+cJq6vREGm+nlr3VA0TjwnhMowS/Q7VwFolXySO+veYgKQEEyikaVvHsz8S0TYikwKHiED8wvQMQPZB5QPMiEexUZZI56XPWrilmoEwYFDVa6UtmHcijTaQNA8DLA2A9GcX+gCxPvimnI9UZ+THZWNtaRGN4beBhwO/JyIfj9c9gJOBPxCRzwN3ib8B3gt8CfgC8ErgSeslIAkE4kUCBMhCa8Gg3IOAuurwyyCgjgAUtQCPAQR1O5nfRfxMg6S0iTMnvfzldIKf6nNnTtDJ21F33RDzBI54sRX0cM00HDRSdgv2l3nmXGVVpqpdU7DFyTJzKX1xS366LNdNTox7zdF2iFQ2LAmX/Cg2lJYTE2/k4MXnhIVKMwSRCbh/4gqpNYQZ9W9fgYIzAmzSroTYgoKtl7relRo88rO0AFHiHa+sMeA0j9Vd59R3yfFGSLT3Qc2fMt3qVrfSt77lLdhp46wgicHI2GPkl/kTxFJmGJpZCM2NmCzRZTYizUtrM2etMeEe45ew7V1oOCiHDoqD1h8pVRAE50F4CM5/BeeVT56ovPC7ZIs4KKqetz4Ntt/Ahz0l6hHnSWdK5EKLZeMxptE+I6m9lXpIdRGeBMvutsBdme852GFQNZ6Pex9UuOzrwp+cFTQFjZ3ENSfw0jMFpmm60SHyz1zq9y2nLjmpvwMhpe3rfGiRq7SsOVdNqR875HTOG42w+EttJFJSKlAKNaRWCWFzV7unttNOmtEtvs/pSYmjrvES/+1vc9uPqeptWYA23crHqlcvSkOvMze9dNMHSP0uN1QK5MzddRKALHSDK2kk+Z7y27NnxOhiubRJR0lLdC9E5bdQgV8+UTj5GpjMhIw+4MXwwy9HtVjGP6hbiWpXgaj9Wxoa08jPasC31h7mdVf93rExGedLRdh1sfCnZ9lGErY5eMlZBhQQ1H2Iy9mevynphXrbdBw+hN+RO/IqxjQ0SxuxapCyjVS0sThcyPYEO3PkKi6pyjQoJ+Zdr46KmzS/ay+mpQ0o7CnaRMBAMSAOruGhE4Nqz2DQNJEBmbxoihRIh5FWcbVXsmOk4UqTXhOOjPCpkB3+k2hBl9Pzct2fP1E44ZCUnxSZ46Evg8s/LjVDd5nRhLVMNeCvHtPWWpCB1Sxc2Z+JqaQ5ltgYKJDLsusKx/1eVIOCE/irMwSZJFAI953sYA1Xjne3eawuc/ZFU1/jQgtS3ZuwpkHT79oG0TFUYsA0V00DDiL21bDp6iz2q7lHi/hpaFMAQyvYqe77YNADhzL+t0CBaaK6YnX+s7lqNqOAgtEQXJ4VKW5JvbOgYOSgEiqRpNX+fhibOrj5ccLTr1V7UoQ/fq1w6WcLw4+Dw3LcM9bX5CPNzN6Aod9ey7Tvx/yG3nbXlY77HgvGnAwKrz9NkJVkVExHu38IHxc41VyQNANnW6v06llLKMup6/pLq+xSwxWBLQJt3IyhGClaRNYqOoCSPz7USrk0v7soYH9K/coIUd35SBNuMdoUwJCpBQMrWNTvoFcv9Zi71GEcp1WRGBCgAYfsTU3712CQ0ktAIYN3JvqejCbez6AAKs8FuXVY7efgds92vGy/2rKtCH/yl4XZrSAVtTcJsAzT7dGg6y9aAwgz/TVmPDGmWUe2vvJq/ZpMJKt/vCdQsC0sIky2JjBIWslHWGOfog1Y6M5qiwEHNVcHlFKaaUo8P8f0cy8eBTq41elCARoMQFXgYIDGahOhKsrvgebVw9nGz/qdwFgk47Q5gKESrFw94VVzWff8vgXP3MYGENJvLEiY7/1ViFMbfMppUppBogaKyA4JDIydgerZpmNKGdMNPl8Kcst4EhQccFLiKctcwiWftAxYFv6Mw+c4YwyEuxpKPIpdHMUqd2GmT8wGBu3E19ckStnavCW32VqynZT3gvC6U9IwKx6sIh9glS1Bc9D0+cB62FCdbaGmblpBHQisEXzArp2pQMGklT1UGoJNzzVplDpRxNgsTF2JfZbaKT20VT9oCum0xXK0KU5wgjDlWMOBhj0QQODELD2ZtPMUg9b1nF7ZKKTxn39KsU5DtgBnS3ASCYknSYmAKt6Fex5Php+pJDlLVYNFB1UCD/n43r0CJ+D1WHD/zIUv9nznucphF5dMP+T8kIe//iNh+x9oVKx9fKvZn2It3bY2tLql5yzwUY1RDuFS/RGznVdyrZWLYiWW+LPnDnOWPNjKLlOv3gv3OVJY89YKDK87XJjeEPwEZgK417PKrfCi5bAVcczU5a9GeQpQVnYAza06pDzVnDQ9QcQX7dvih8liKbVUcZV68dFzo6Vmt1KHIVUlc06qX0lxpvBSt6PY9tQ4ewNUbb9xiNgcGgMU8C0/+8KdHpYqr2m0pkMd9qdGW8idSj0tVMAiVnzWCmhmJlLk5TmnM8S5Kl+h838RyB3RCVznROGlNzYBY+bu/x7hh+8Q80m8ZppldBaiJyqNmyrwEy72n+Cs9z4X0a92wvSoAYUyViKBgiL86VHCqrdbZOG1TwqgkLyLHMcat2AmLp+pGVY7uvhNydq+0GpWvfF+X0sI7mHlquTt/eLM2prGGl3W3QTosQZLqZISk5zhuNbdMkkbPtWQ2Ideuy3SzuvT5gEGQ61dwaL0SIjBMHmAG9K+MfdkLBzE29iWpYSzKx3tLIQFjdoA2etVyQ092Ksj6UtJJ4VcODj4CHj5zRtPCA/+R+Hz7y3j9UrNHVbCyG/DfNXo6yXcTD7Giff6VRy7+jElVXsAQGLUszpvDz1S+EkDCuc/Sli5haAubo8G1twDWWXFLG0OwwXfTBcSx/7hufzOe/PtlUBAaqEPMljAASc5GnEyjKcaBgwvMW1QQIVBPtKQorpbkLPRDtrP5mFeOy8HEJsSGAp1rGIdH9Yw1/pKnVUv3qIJRLI2gQpAklAbFa9d2FKtwyiAkQ2hEuErt1ELFFq3cQIHEXD3ir0hHPg0OPeXKR4j8zz1IuEb71jkozaLM03K8wr/wXb/oWprvA3dCQnUxkB7PeMI4Qc+nq4de/yXPVDY99clH8mmKuBOY1VctDHYMxXicERMHIMZgfl1IfmeqlAa7YAi2NXl6quxYeR4B+GSVyEvfnE2zsa/M1VJwRFEsBAcYcfCeg4451ypdWmTA8M4tVqBtU9o572lxDrBQwSBrtZQ9/4pdrER5fGpGcuntRKVxkBpZBO/AGPrKRLIqBwFk9/NPco1nmD9lUCP+VAqzfw5+lacex1R9ikwEcGtFGAbKCJVjXUnj/P1zMOFz+bDW0vernXHAgpeBZkczZq7W6hxSXaEoCmU5c1lZiZkLAirzhFmMUJohVdiXZb9OcWPVPGUdwWI7Io5M8Nhgg3AIN41xqkxTJX3HG2pLwsQBRaGrblxSAi0aYyPOuzWA0kUYJFg3BMxS5uT+FHdm5gr26UYA2E7+5GNNVYwUhwJHFotw0RgDXzp8HmNRcgJEA1OOUGt7UhJpKROFn0+MvWofhj1x/Lml8OV74BHvC9WTeTEux8mnHVz5ZeeKDFhn8tZSt7Uz7AoYXyffhgwqIdKdSjNodO9zJYojtOPFv5H7fBBOP1mcNNnOFaBmRdk+mv46WvZFeMLeyLSQStSaRnRCGGGTnY6tfBGm9UMm9XiNKtFxrpzqfFMlGLjTZZjoXQw8a2YDkc0Hlnoq6XzheVjN5aWnosPIOZK+5ValRJEeqVry7quly5tOo1hsOcAWikN/qL0pt7JzmXXQwpJioGJxq4MiC8Tc1TW5WYYUVwGeStagRGcBBYmfNICEo/VDadWZuJltQkB91swOQJ1wrb7wevv1GRFhGf8r/BvfylxCXC9kKe+SsarfJh8JQEq9dEHhXJvrggKJx0nXLRW3EWFkw+Gmz3DgWiw46/8ImvudayZ9vRpClLqoUdyq2wICYzM78pwaPOVy22HFeaduMZvSs+kJa5oFDmOkKekdVgDZRB2ggZgL6u55N8pKfMuumfeGTBLaciMYfPUuzm0SYAhCfWcQthXGbULlurA41D8W8ApvYV5zsJZ7l2D5ehV3ssgHI2bgTU7c5EBwfRiSc4AcX8UANDBykPgNbe3Y8xwnfgp4WPPN1u2WwbqPKct7skpPyYwK/I0EkfrHoT65ScI/7LTjvvh8dvgF5/v8lkVMv1lVN4cakficETIYGCkgcGYnPKchgKCVEJF77eVsiTIZnt7Bpp2V2u1XqRdqyBZIGs7g8sgUMCqGEqTsbMeAskgnroMFYaUstsyDdppMdokwMC4QaDxUlbARUcxGoNW4l08mHs66CNXlUZhtSpjFupWiHOAoRxUaqR9NwSFCuct0JNAoNY6cjyRHxQH02NDLp2w5dFw/r0bJhDHcd8X/ufUqGq3mkPF3LYoYn8UxuuCQvtc4k5t8apThLf/pKj7gvDw7cLdzpAMCmEtwuujppCy1gCCrbVG8KqeM2e2hCuzA5J72iy8mNkIklttMyiCX6aERWqwyEDUgJJEACiagwUCkAlDrcFqDhlMCsNUMygYraRpRVP6JWFhswBDGYrVAGFWIGpTugIOLSpKQY/23kRbDy+CUObxZnUlD43QdzWFnrsFhaGWkc11FkQaQGgvdX+ITF+C+tCr7rgnvP6pEjGlMPMR3xL+8eQGDJIGUaGsmLQbFBgAgvTdKlAW3nCh8JbvJoEKdKcpPOA08mlWHkEmh7AWBSXteSrrnayA9Z8zgIkRBCs80R0x7hkCbdktCKQrZaoBi5hJo5+QgKcAzjC+DA7t+ghXylIbKFOStVGz1pjKewt6c1B9XdocwLAuDcYAoW2KSGVfvSFFuks+8KOKpoABHSHsCPngd5W9Jq9JIzHv8tAhJZHbLQ0pUl7q/KTDXgKPKyq/BCsvDXYrJ0xvRaqYFABEOPk7Yr6y3RxSMhAMUzf5X+MlP9dhs0EwCs0F/5k0BWLdC4edE7Sc9DUocQeAvC8LwGADkBV6KwvV7yIQNs91GK3qupWZqihV2YxQN5WQgKXKD9LkqenVpbwv2kEMO1hQxUBzqDULU36TwwaqzK/FadPMSgyGEj3rvxivrUG2sU8Ei7EQYCPc863yqlmWRGBwcI1A55z5fgFiDyzEI8fjB0ZVbXFMxpNjUkQaGRzEn1+aZ7k5Onk/4r+I+ifyplfC5RfBn70lZx5Q7v5MOHyHcJeTgkkvzPAUwMrHn2udns1XxpzcEAmQg0Rr5O4vfVZ40sujCh3Z87oOXvISwU8JH5GVFZj8B6sNLmWg1tRr1TpWOuVtghpDs8ashzIkXCyjwzI0k1jvQumQIQpgapoEFC7VX+QN02QBtAXnkiFZkhOIC0Gjxqlp/UIE/RzFJLyXWcu7saAuGGZDP6HkGYr0GezkHgXCzorlwqh1W5w2lcZQ2HQcFHq/57wadHT2GqIR0A4jejYFm2NzekljvG+e7VRhGULYoUu1O9QCYzucweBgMlq6m8DKWajAjrsIr39AwhAhqcNnXOZ47bMdqhPKeDmLImVe3taYLYpxs117su2o8MOPC08+t363XeAvXhyS8zENnXyENdsYOStqes/SQbooJ05gInGru1Oc+FASKUf55W3wAukwHRfdxWl5ju+72kOlxpsaMX7K+iTpuwuNxkDWKJx979ryDrWFMpthKyW5NUZJq+20Q44FadMAQzXUlcbdkkX5ymtp4MzCyYg3kqbkf1Y4m4h7KDMYWhj3DA4R6ZteuMxAGOCxl7VjSAMQWFBQwwDxNvmlYPQT2PIHwoVPtQpk6NHfcJnwl882c/92zGyMhkaXMEOP5Gig1ZwJ8Z1/Fx78KgsKsNXBhS8BJhKDOZh8NGyOKskW0IsCkgXfXmKvIifOCHu6JngmohWISIwjfTk9feXMfk1dImZWIOAKS1gccygTiXMvJk/Fn0TNxCzDyvJsAKpaTCmxDhIg1ABQULIBgd54Kw9llqdNAgzxsNT4F1wGXrrhAAprDucj0tgdqRs2e7D3lnoI1BviZJwQTHImmp59ogBBDRRk93rI0GgNYsG0ZFRWXp4Pe5neEl77ZDPajEL8tsuENxwlZhqznc4stTmskAY1o1TvvFj48wvr8NscvOEcAii4uLx7+s9ZU1DXlMlRCawTmDgt16Q8F6DwRfgNIATtyweNIl9K3i5vwcCM8YuMGUtMI29FM0n5TeFT/ATASO5IkW2sbTGCg9OiETmjMRnhzvdonynaQwKJegq3Wl+RnxenTQIMNbUauQ5eqpET7dzbPi8JZ4mpiwmtbNA8L5LxFjeqca5WHut8pvQaQBjVTsjgEAAi9Mjqfh6ZvCZqDsKWX4HXPoLSLcf7BavC2UcNwWBYM8a93SGZjmT7nuN+z28ARuENZwqyEqQhQfiMlQgKZEzKBlUjlFnQExAkDWLiiwA6H909kgTfFeGvwjnFTZTJJByom9yKpmDAgrojjtUbs6vlnoYsTpEm/1kzwAKEHcKYssIAoOzQoqcFDLWD4k+o/Q6nM9enzQEMUQAGImA7VyvUxthSpvrAClpHAbaBh8lLSbJ6Yd27aDKCGx15tguZ7Om/9Vyt5voYqB4p/5X8tswB6q4JK2+AWQCHrb8p2cpcZrUdf7uaZivGAKIGg1wKM/OAOu73vCEoOAG2UWZEncCWt0bwstHXWpNVsW0PH4TZl+FCPLXZZZDQDA5Oot8IAjLxyDTeJzUIuGm4xEX3CC5lU1xzclcWfJNn0lIurbT+XBbqLV1FeyigUGkf1q4xAINUd1IiM0YLsSDRBfzFaFPMSijEoQRFoCLC1ZigVYdaZCc5CCpa5CpRAoNkuIvPQsPzlOeebSMcvJIfivcBANRpFC/aCWCNjDJMWOv85dfGraQXpE6conIA7PMuRL+HXvnnXPhyWP0GPOYFypXRDgGOezwTbi3CSWcU9TslUg3qEjJq0cH8LuHeR1p7Rfj/lhcK02vFE7AnINOzULk9M51C/l5nmnoV0gKzPOwj9XqatyCXVZDkpd5q86OUoZGYSpMC/OpiHrXXYdhKlvLRW2eqWxvt1Aw3MM+VQTn6U9sB5OcEjLHcomiun1TPNq+RWZ1GHrT84iMKp/rTUqwRhXMebQ6NgY6+oBq/YJ09xCtpBMExT9+glA1DTQ+cIwg02G6dX6yDqxatm0Dr4bFYUKiYpBGK6g5WwyngUue/1EjoNcqRYROQa8KWV6AirPy88FcvElaMJqAIH1fhiMMF9WW9Q96zYjUGs/JobU2415HWThEy/NaThOk1408nsPIS1N0elQnElY44pWxp0JjtZJhtemSGPXS5fHl2RavIwwxnhhdOCfsyfR2HKwZIoj8mYdjhnK9sEUk7aa8KFOxQAVM+q5eJ0XSNZltqN/ivdTljREygVSkEmWEKElZMuZzGsGmAIVCorAoiIjhW8hCPLi6A0Awr0lU1Xol/PhUBq9WC1ltT8a3GZl9b5siMT3XZ4VAXHGxvM5YXk3a6VAQm14lGK3DXgAtOjap+ZjfHZ9TxoMMjazYnLeUTlmPNzq503PsIqymEhN/8PGFynaAlqAOmbwR+I7B3AorKzlmGRdbgl9VqqNT6FhTKUMOb9vaN6u9rALFDg0bQ6ynM9r1NI/JUpSHYDie+S2VIV/u7umpto+cns4QFAcufla0htm+2MXT4Zg5tImAooJDRII0j7BhD63u7xLi35LgCBAsQ1bgkvyxP+V8HBAhiZQVSzPsQVAuzWBUy/zZ5qFq+Aw5j1PYKubcoC45UprDltaF2RHAHCa8/C7YImAURXKqOxz4DqjUOaoFCUO+4z9FQ772AtxwHK4dQFvMgqNwwW9CH+44sONbj99w2WZsauYygtmDgGjBowaEbF72wvgGF8kWqco9tBUYDMu2T2zm1f6NlmE7BAkXbvAMFwIKAtJ5k6HcJ2iTA0AEFiMOJ5FYwoatOWEFvhY72d59E68aolYB2QWwbGMMkdeCsFXR6yqGbYbQGHLppDn4ERlGbsAjqDoCVN+aRwWQf4bVn1iUE4Zvq8DMpX3Gy18xx32eCb85UeMvRwvSmgk4FJqAeWDkzztlFsIiXGFAo5VUDpJQe0wh/AdVGhW/AoDZa2t8Mw9mevuGXPHxIepMBr6yVmrBiQL8MZ4fCPmxubd6pcaMKW/OeFfzQxpK0hFEmXZw2CTBEWEhfN4H6rph3RmPoaQ0WDAaAkABoTk88ghtZdBoIz/VfMbq9k7kgTWs1GniLQBlMSsKtyl13FFUnUfUixt4ggk72h8l7SWwm+8C+AmLsCILwmGMEXTXSHK8HHiGsapxwNxU4PTRWmwNdE9j2RnR6+7B7Mq1VcJA+8JuPUXBF2DI4ZM2uuWf1vPjpq/hjmkLtXmtrjfYQD1VpNZbcy5uhRJW3lN/UVtV7AwSN8li81MJvh8bQ8kTn2QZO/itAW5w2xaxEaBuNGkIsWTrNprp3Aqb/VS0PKcQi5lmxkhhTCY0w51wIK4SSohDSye0x8sIQiZFtHkXMou/sVarwigTmS0cpaW+huNSR2NYXKfmQ2B+poCsC7v3I2io6uxevesUMdzn82WHwExUEz3dnE+51DMwIK/t8xOZpWnSj4FX5q4fC/rd36IqiV4Js/0vY72aojyKe8m6uItiK2HpOWTWla2ZJQ5FEY2t50xZjklIkJLCRNF5iq68TPDZe6QByEK28ZxAHEC38VIFE4rEITKnTKwwIaJn4kn5flYujFIBzFK1XQholS/Plo6VNozEMtIFqmJBEq/aTV0qOaQntVY9DzLNB/Pi715m3HXKLznnIYBeqJIEwC9MGPb9xT4kO0rB+B3myS1+TC91E0lJbJluQydtDevvCq88QJrnHCucNiIvnK9rzEnE44OTfhv1uCx6FVZADHgTbb4J3E3QipO0YOiE+a3UoUk9LstPJNTa3TD3ssaW9Wq3BuiW1vdEaxq5Ky8A8pzx3NYh4F5p81sWygtvFp5ZlGz/1ldrPah6lvMvQ5gIGO1yw93Z3o+Y+GDs8CNuSx8Cg1+iU8WyMq35WKqZrOuQKKFoBHoCCVu5i/A0EvwIQrdKo09MSFhhsPZb+Krm8IWe6DzKJcLIfvPlFwj6xcPmAEZe2BodaWHHKqb8rHHrfcHirR5ADHgnbn4hnEjgqHT5i92rlcmpdX5g7lQ5Y7ll6Gj6QwvhFgM0sRD4vcXhh21Y6d9Fh3AYUSjwJwA1A2Lap8sqwDBXPFaFeiFpNQIfg0HhemDYJMPQ1hWx4NAChWvZTgMaVdS0IpE+gNsjf9DCtVlKjbt2hZTfbwE3j1/IXBKC3BHYo/PXuvDTbMapVdICkaC7SuBeAkLxjUMJmoQnIlncHI6GAXEt47ctgRRQRwUlY5S8IToWb7YA3PVu40X0EHw3D3oNsfzjqHUylrMRLU5bJ4Jg2B41pDLaSsTBQdwGtllC1rRlPD7WGGhRaMOhqDnYFJCWdOq0UNpXH/M4dUsl324sv25P3KCRrtVwLDssBQqLNYWMA8hi61cb6TvF30wBQNUK+G71U8huJ4zExyCvl/4i1Jq8zyJlp/Q57te5zTnFICsV+kcbpKQqb3U4kyTLdTylt8opA4ADdgUzeBrMHBCzdLrzy8Y5Pflj5zI/gm1coB68IdzgAbvdnDq+eWTRqelVm3AT1KxlwUl1olSFTJ7kAxlmGSmHOeUer6JLUFqRcd0YghyaGeG52quKx3sAWQ7qvOw5DQc3AU0q3WNka7ynvOWjun9TYbNUkp0vjw+YABgX1RgtIraMW+8YUsYLI1mJdjdkSM2amFIplqwhNhIucp26DJYNR5KTAMDreuA1jq0GV0GaF89RHj9FLNt51WzXWjT2Io7o17CuJSVL8YjSgg2G/f0RmE5h9nG03PZw73FS5A4r4eCCKKsoUfzl8+5+U7/3qvXA/2sUhN/lF9nFRW5iQgUHylCaUDTxSPkKjsS4Splt5NkU2p7MXiiiSqyfVrRhhtPUWC5rKX0ArQGVr5CxhYiIaNCgj8wOAqABFbTRGU8jaCdRT55b36+K2XY7FlZIfNSPnmIYdmm9AaVgXGETkBsBrgOvEvJ2nqueIyInAY4GLo9dnqep7Y5hjgUcTPgnwNFX9u/XSyScnmVqpZw5s+VJ1G+6pBLSuiWSpzcxoxTMf91aaJPFuHYntaUyOUu/bVr71nLp/sakX5yQIIpKfa8YbyVcHvMZ30RnjpIvpJWATkJmLK55uw+cucFznlhO2Xduzsq/ivbLzCnj9X0/40uXCqptyj5tdh9v86h1x/pI8/ajZpoE5taoUJEyyGHDOjTDsWyvS4aORwSy/IerStll7yoLYhLXaQDZ8tg2duinTQ1sANkCivfdE/hMyKORSVk3VBwULjFWfFFGi1hASKJjiWpRdghbRGNaAw1X1v0RkP+BjInJRfHeWqp5uPYvILYAHAbcErgt8QER+QVVnc1PR0R+xytzo23CLx5CntQ2mBzK5I01Hlk5eTZNr9jVambbRtQh9Vxyr7kvzfdDgBhDsUKCBt9Lb0ChAnSRbF6neRjCLPWbq7cQDK8KZX96OfC0edBLLtoYi0x24ySRufZ4Eg6R32cApaXu1RICqphYkVlc9cTcAhxYELPiZrl6NgFXgYH/Ht5Wh0+YoSVidza4/e7p45hMTbwEiE42R8AoUesOJKs91OazPyk+soMrUpalejMawAVoXGFT128C34/OlIvIZ4HpzgtwHeKOq7gS+LCJfAG4H/Ns6KXWerJLlCUVuhNbqV1al70ZSqjY1XBk82G66aZIBA5kuEImGn6abqPy07mpaXnLvnefaLY7YIlQMbPLYcFPxZp9SvKYHzwARHSaxTibb0Umo8Sv9DO89zjm2T6YE06+L5zYCbprPYUzHmNV2AaMFtRLogFlpg1Iig3jtmpJqvUddBU2VmiqPlSJSC0oHEGz3UHS72rsdplRV2vBerbwZo2X2O9QS2qLlDsNEXWkvCQhaLcFeG9AYlpqVEJEbAb8G/Ht0eoqI/LeInC8iB0W36wFfN8G+QQdIRORxIvJREfnoDy65pDMcSrMPo6WN/nX4Kl6SEdOgah1F0/Bahakjo9wb63fufXPnW049GDu511HWHpRzAKXEYeMTc2SYSSunaw71GJwbKMUm6GKvnmYoylecUyLABJyb4NwUEYd6z9raLvxsLR++6lVYXVV27toJq7vCDIYTczRAnNEQMXlMwxZzZgClkBXrWj5QyiSTlzI091TLtTMbpLikPNdHx8W2a3dKZhAoQ4e8WCgK39g6iDyNWRjJaBMmbilpVFoHdkhQ86XYZym/h8urrZZgk1Cb3MK0MDCIyL7A24DDVPXHwLnATYFbEzSKM5ZJWFXPU9XbquptDz7oQDAF02qVnz3uzYBC9t9oS5ajMkBEJ5/AwviNuzSte6n4UsNpHYOdv06eS2PbhmuBwgKE9SdVg9sjwIQWEJppzTwVWC6Rci+XsSU04UTq8AhxmBDBwbUfSwmLE1Z3ed7x9jew+vWvmbjTFCeVW15LYcEBO/yy7G/b0kh2fqbL5NY2kL2b9SrV+hZnno2fWnBTzpppTssP6X1M1wKH5ZlB3FnSk79SC9U9ZSvFpc00p4mqZkk7HQ8bGU4sNCshIisEUHi9qv41gKp+x7x/JfA38ec3gRuY4NePbqNU5T1b5LTSIvuyn1qEuvDaeow/zDgxgGlMKyVt4Si+Sh1b3WrlIAy0CHaOJ0eTXLX5LeQPzLaGAonlMoCTY4n5UCQwNxrzUqcnJXB+W20By+WJ07PNFKO4SRbqbVtX2LYtxTDLUcxmnj+4671wO1wGhPTlKInPCQJzLksTFLRrcm7rMHV4NSXpr7XL3FClCy0U96horvkRgVJbo0VbCPeYwzRNaARe66IUBkvNkNYY2LU0DW9XsKiDh4Y0Frn0igko2qucabIcOKyrMUjgmlcBn1HVM437Icbb/YBPxed3AQ8Ska0icmPgUOA/lsmUXcKUXIqmUP4PXldX2wPVv9tZhPm+YyK1qbfJQCfcACBs/FaLKHJS/TaXkXUj9EOyBjHpeWoZuBq7mLDxs2oKqG+1uAAE++y/PzJdMWHqBGSsMPW4q1+QMWetbpVPHX3fBO6mZQVMLcsVgB5MPWkVts1SCcdoGRelecEXinpJpWERjeG3gYcDnxSRj0e3ZwEPFpFbxyS/AjweQFX/R0TeDHyaMKPx5HVnJCgCL5XLnCJ3e5I5pe+33bL1NRLxRlo9aUbLh5Tc886ZPzFZSpMitdbTCSJG8codTZiuFFHcpAR0zjHbtcpUrxwk2+anduuB5J4jZQj62X3BBs9K24jfjeZ3meXOY1kVGC6bWTfa5XlUBl9euhpIRC4GLgO+d3XnZQG6Jj8b+YSfnbzuzeeep15eb6iq11ok8KYABgAR+aiq3vbqzsd69LOST/jZyevefO552t28bpJNVHtpL+2lzUR7gWEv7aW9NKDNBAznXd0ZWJB+VvIJPzt53ZvPPU+7lddNY2PYS3tpL20e2kwaw17aS3tpk9DVDgwicjcR+ZyIfEFEjrm689OSiHxFRD4pIh8XkY9Gt4NF5CIR+Xy8H3Q15Ot8EfmuiHzKuHXzJYFeHOv4v0XkNpsgryeKyDdjvX5cRO5h3h0b8/o5EfnDn2I+byAi/yAinxaR/xGRp0f3TVWvc/K55+pU096Eq+Ei7Of7InATYAvwCeAWV2eeOnn8CnDNxu1U4Jj4fAxwytWQrzsCtwE+tV6+gHsA7yOscrkD8O+bIK8nAkd0/N4i8sFW4MaRPyY/pXweAtwmPu8H/G/Mz6aq1zn53GN1enVrDLcDvqCqX1LVXcAbCdu2NzvdB7ggPl8A3PennQFV/SfgB43zWL7uA7xGA30EOLBZ0n6V0khex+g+xG37qvplIG3bv8pJVb+tqv8Vny8F0hEDm6pe5+RzjJau06sbGBbaon01kwLvF5GPicjjott1NJxTAfB/hNOtNgON5Wuz1vOGt+1f1dQcMbBp63VPHoVg6eoGhp8F+h1VvQ1wd+DJInJH+1KDrrbppnY2a74M7da2/auSOkcMZNpM9bqnj0KwdHUDw9JbtH/apKrfjPfvAm8nqGDfSSpjvH/36sthRWP52nT1rKrfUdWZqnrglRTV9mrNa++IATZhvY4dhbCn6vTqBob/BA4VkRuLyBbCWZHvuprzlElE9pFwziUisg9wV8L28ncBj4zeHgm88+rJ4YDG8vUu4BHRin4H4EdGNb5a6Krctr8beeoeMcAmq9exfO7ROv1pWFHXsbDeg2BV/SJw3NWdnyZvNyFYcz8B/E/KH3AN4IPA54EPAAdfDXl7A0FdXCWMGR89li+C1fylsY4/Cdx2E+T1tTEv/x0Z9xDj/7iY188Bd/8p5vN3CMOE/wY+Hq97bLZ6nZPPPVane1c+7qW9tJcGdHUPJfbSXtpLm5D2AsNe2kt7aUB7gWEv7aW9NKC9wLCX9tJeGtBeYNhLe2kvDWgvMOylvbSXBrQXGPbSXtpLA9oLDHtpL+2lAf3/AMZ1D38D/MdsAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "b = dip.BilateralFilter(a, spatialSigmas=5, tonalSigma=30)\n", "b.Show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some filters are specific for gray-value images (these are called \"scalar images\" everywhere in the documentation). For example, all morphological filters currently require a scalar image." ] }, { "cell_type": "code", "execution_count": 16, "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": [ "b = dip.ColorSpaceManager.Convert(b, 'gray')\n", "dip.Canny(b, upper=0.99).Show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Measurement\n", "`dip.MeasurementTool` can measure [quite a lot of features](https://diplib.org/diplib-docs/features.html) for objects in an image. This is a simple example usage.\n", "\n", "We first read the 'cermet' image, and record its pixel size (this is an old example image, the actual pixel size has gotten lost over the years). If an image contains the pixel size in the metadata, it will automatically be recorded in the `Image` object and used by measurement functions and some other functions. Note that pixels do not need to be isotropic, it is possible to give a different pixel size for each dimension." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1 µm}\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a = dip.ImageReadICS('../cermet')\n", "a.SetPixelSize(1, \"um\")\n", "print(a.PixelSize())\n", "a.Show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we threshold and label the image, then measure some basic features. Because 'Solidity' depends on the 'ConvexArea' measurement, we get that one too in the output." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " | Size | Solidity | Statistics | ConvexArea | \n", "-- | ---------- | ---------- | ----------------------------------------------------- | ---------- | \n", " | | | Mean | StdDev | Skewness | ExcessKurtosis | | \n", " | (µm²) | | | | | | (µm²) | \n", "-- | ---------- | ---------- | ---------- | ---------- | ---------- | -------------- | ---------- | \n", " 1 | 262.0 | 0.9668 | 45.34 | 30.82 | 0.7216 | -0.6831 | 271.0 | \n", " 2 | 63.00 | 0.9474 | 86.35 | 13.41 | 0.2313 | -0.5471 | 66.50 | \n", " 3 | 243.0 | 0.9293 | 75.09 | 21.16 | 0.1711 | -0.9723 | 261.5 | \n", " 4 | 209.0 | 0.9698 | 61.63 | 25.80 | 0.3937 | -0.7994 | 215.5 | \n", " 5 | 462.0 | 0.9665 | 62.10 | 20.27 | 0.7329 | 0.1613 | 478.0 | \n", " 6 | 611.0 | 0.9745 | 81.17 | 17.92 | -0.3812 | -0.2219 | 627.0 | \n", " 7 | 80.00 | 0.9816 | 83.10 | 15.72 | 0.1468 | -0.7721 | 81.50 | \n", " 8 | 205.0 | 0.9762 | 52.92 | 32.19 | 0.1556 | -1.183 | 210.0 | \n", " 9 | 419.0 | 0.9836 | 41.60 | 30.24 | 0.8653 | -0.3741 | 426.0 | \n", "10 | 363.0 | 0.9041 | 71.56 | 22.25 | -0.2541 | -0.5946 | 401.5 | \n", "11 | 487.0 | 0.9740 | 57.81 | 25.17 | 0.05945 | -0.4846 | 500.0 | \n", "12 | 383.0 | 0.9746 | 53.10 | 24.60 | 0.6360 | -0.3009 | 393.0 | \n", "13 | 250.0 | 0.9709 | 50.21 | 30.08 | 0.6251 | -0.8159 | 257.5 | \n", "14 | 137.0 | 0.9786 | 64.47 | 22.41 | 0.5215 | -0.8983 | 140.0 | \n", "15 | 378.0 | 0.9668 | 64.85 | 21.35 | 0.3866 | -0.5561 | 391.0 | \n", "16 | 392.0 | 0.9043 | 48.06 | 31.20 | 0.4776 | -0.8514 | 433.5 | \n", "17 | 230.0 | 0.9746 | 70.43 | 23.68 | -0.2813 | -0.6269 | 236.0 | \n", "18 | 262.0 | 0.9686 | 62.26 | 25.31 | 0.3051 | -0.7452 | 270.5 | \n", "19 | 637.0 | 0.9245 | 52.94 | 23.86 | 0.8441 | -0.08530 | 689.0 | \n", "20 | 341.0 | 0.9757 | 54.94 | 25.06 | 0.8843 | -0.3705 | 349.5 | \n", "21 | 501.0 | 0.9747 | 51.85 | 24.15 | 0.9221 | -0.05920 | 514.0 | \n", "22 | 556.0 | 0.8580 | 60.65 | 22.53 | 0.5287 | -0.3121 | 648.0 | \n", "23 | 592.0 | 0.8889 | 58.28 | 29.00 | 0.1195 | -1.026 | 666.0 | \n", "24 | 172.0 | 0.9718 | 68.47 | 23.14 | 0.3064 | -0.9392 | 177.0 | \n", "25 | 566.0 | 0.9792 | 41.71 | 30.85 | 0.7348 | -0.5709 | 578.0 | \n", "26 | 842.0 | 0.9268 | 53.14 | 26.75 | 0.1291 | -0.4931 | 908.5 | \n", "27 | 209.0 | 0.9676 | 56.00 | 26.01 | 0.5350 | -0.8241 | 216.0 | \n", "28 | 147.0 | 0.9545 | 65.14 | 24.51 | 0.3733 | -0.9707 | 154.0 | \n", "29 | 375.0 | 0.9766 | 71.89 | 21.69 | 0.06353 | -0.7623 | 384.0 | \n", "30 | 385.0 | 0.9637 | 51.05 | 27.73 | 0.6729 | -0.5471 | 399.5 | \n", "31 | 223.0 | 0.9612 | 63.78 | 25.31 | 0.1825 | -0.4636 | 232.0 | \n", "32 | 347.0 | 0.9734 | 55.33 | 26.30 | 0.5900 | -0.7111 | 356.5 | \n", "33 | 604.0 | 0.9527 | 50.44 | 26.84 | 0.6709 | -0.5829 | 634.0 | \n", "34 | 354.0 | 0.9739 | 42.53 | 33.74 | 0.6403 | -0.9280 | 363.5 | \n", "35 | 543.0 | 0.9696 | 50.64 | 24.14 | 1.068 | 0.3071 | 560.0 | \n", "36 | 147.0 | 0.9515 | 67.05 | 22.61 | 0.2393 | -0.5154 | 154.5 | \n", "37 | 405.0 | 0.9000 | 83.24 | 23.60 | -0.9721 | 0.0003058 | 450.0 | \n", "38 | 577.0 | 0.9714 | 30.64 | 31.71 | 1.246 | 0.2249 | 594.0 | \n", "39 | 497.0 | 0.9717 | 61.73 | 18.86 | 1.101 | 0.3655 | 511.5 | \n", "40 | 525.0 | 0.9813 | 34.06 | 31.89 | 1.047 | -0.1825 | 535.0 | \n", "41 | 803.0 | 0.9634 | 54.23 | 25.55 | 0.4471 | -0.5974 | 833.5 | \n", "42 | 253.0 | 0.9750 | 59.83 | 25.32 | 0.4961 | -0.8077 | 259.5 | \n", "43 | 193.0 | 0.9772 | 65.91 | 23.49 | 0.4554 | -0.8702 | 197.5 | \n", "\n" ] } ], "source": [ "b = a < 120\n", "b = dip.EdgeObjectsRemove(b)\n", "b = dip.Label(b, minSize=30)\n", "m = dip.MeasurementTool.Measure(b, a, ['Size', 'Solidity', 'Statistics'])\n", "print(m)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `dip.Measurement` object `m` can be indexed in three levels: the measurement name ('Statistics'), the object number (30), and the measurement value within the selected measurement (2):" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[51.05454545454547, 27.733471736251396, 0.6729066879016448, -0.5471176839610813]\n", "0.6729066879016448\n", "0.6729066879016448\n" ] } ], "source": [ "print(m['Statistics'][30])\n", "print(m['Statistics'][30][2])\n", "print(m[30]['Statistics'][2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Leaving out one of the indices returns the full row or column:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<MeasurementObject with 4 features for object 30>\n", "<MeasurementFeature for feature Solidity and 43 objects>\n" ] } ], "source": [ "print(m[30])\n", "print(m['Solidity'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These objects can be indexed further as above, or be converted to a *NumPy* array:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[385. 0.96370463 51.05454545 27.73347174 0.67290669\n", " -0.54711768 399.5 ]]\n", "[[0.96678967 0.94736842 0.9292543 0.96983759 0.9665272 0.97448166\n", " 0.98159509 0.97619048 0.98356808 0.90410959 0.974 0.97455471\n", " 0.97087379 0.97857143 0.96675192 0.90426759 0.97457627 0.96857671\n", " 0.9245283 0.97567954 0.97470817 0.85802469 0.88888889 0.97175141\n", " 0.97923875 0.92680242 0.96759259 0.95454545 0.9765625 0.96370463\n", " 0.9612069 0.97335203 0.95268139 0.9738652 0.96964286 0.95145631\n", " 0.9 0.97138047 0.971652 0.98130841 0.96340732 0.97495183\n", " 0.97721519]]\n" ] } ], "source": [ "import numpy as np\n", "\n", "print(np.array(m[30]))\n", "print(np.array(m['Solidity']).transpose())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can paint the objects with one of the measurements, which can be useful for display:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "c = dip.ObjectToMeasurement(b, m['Solidity'])\n", "c.Show(colormap='viridis')" ] }, { "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": "3.9.10" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

luwei0917/awsemmd_script

notebook/Optimization/Membrane_pore_and_disulfide_bond.ipynb

1

1187242

mit

lexieheinle/jour407homework

DataNormalizationHomework/agateSpill.ipynb

1

5528

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Need to import agate before starting" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import agate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create agate Table from Leaking tanks csv file" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "spills = agate.Table.from_csv('fliteredlustclean.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check out column names & types from salaries table" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "|--------------------------------------+---------------|\n", "| column_names | column_types |\n", "|--------------------------------------+---------------|\n", "| SPILLNO------- | Text |\n", "| S | Text |\n", "| OWNCO-------------------- | Text |\n", "| OWNSTREET----------- | Text |\n", "| OWNCITY------------- | Text |\n", "| OS | Text |\n", "| OZIP | Text |\n", "| TY | Number |\n", "| DIDATE---- | Date |\n", "| SPLOC ------------------------- | Text |\n", "| SPCITY------------------- | Text |\n", "| SPCOUN------------------- | Text |\n", "| MATERIAL---------------------- | Text |\n", "| SFM_ID-- | Text |\n", "| FAC_NAME----------------- | Text |\n", "|--------------------------------------+---------------|\n", "\n" ] } ], "source": [ "print(spills)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's group by owner" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "by_owner = spills.group_by('OWNCO--------------------')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see owners totals" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "owner_totals = by_owner.aggregate([\n", " ('count', agate.Length())\n", " ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's find the worst offenders" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sorted_totals = owner_totals.order_by('count', reverse=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print the top 20 offenders" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "|----------------------------+--------|\n", "| OWNCO-------------------- | count |\n", "|----------------------------+--------|\n", "| | 109 |\n", "| UNKNOWN | 63 |\n", "| BURLINGTON NORTHERN & SFR | 14 |\n", "| ORPHAN UST | 11 |\n", "| UPRR | 8 |\n", "| BENSON 66 SERVICE INC | 8 |\n", "| WHITEHEAD OIL CO | 7 |\n", "| BOSSELMAN INC | 6 |\n", "| OFFUTT A F B | 6 |\n", "| BNSF RAILWAY CO | 6 |\n", "| CITY OF OMAHA | 5 |\n", "| BENSON 66 | 5 |\n", "| ARMY CORPS OF ENGINEERS | 5 |\n", "| CASEYS GENERAL STORES | 4 |\n", "| RITEWAY OIL & GAS CO INC | 4 |\n", "| ORPHAN TANKS | 4 |\n", "| NEBR DEPT OF ROADS | 4 |\n", "| CITY OF LINCOLN | 3 |\n", "| CENTRAL VALLEY AG COOP | 3 |\n", "| MILDER OIL | 3 |\n", "| ... | ... |\n", "|----------------------------+--------|\n" ] } ], "source": [ "sorted_totals.print_table(max_rows=20)" ] }, { "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

newhavenrc/nhrc2

backend/full_backend.ipynb

1

35001

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# full_backend" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**debugging the full backend code**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "import the new haven report card module" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import nhrc2\n", "from nhrc2.backend import get_neighborhoods as get_ngbrhd\n", "from nhrc2.backend import read_issues as ri\n", "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "now determine the root directory for the repo:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nhrc2dir = '/'.join(str(nhrc2.__file__).split('/')[:-1])+'/'\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "scf_df_cat = ri.read_categories()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>organization</th>\n", " <th>title</th>\n", " <th>url</th>\n", " <th>type</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>City of New Haven</td>\n", " <td>Bins for Trash &amp; Recycling</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/5743</td>\n", " <td>5743</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>City of New Haven</td>\n", " <td>Graffiti</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/122</td>\n", " <td>122</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>City of New Haven</td>\n", " <td>Hangers</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/6215</td>\n", " <td>6215</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>City of New Haven</td>\n", " <td>Health Complaints</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/5185</td>\n", " <td>5185</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>City of New Haven</td>\n", " <td>Illegal Dumping</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/1250</td>\n", " <td>1250</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>City of New Haven</td>\n", " <td>Other</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/374</td>\n", " <td>374</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>City of New Haven</td>\n", " <td>Other - city responsibility</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/3018</td>\n", " <td>3018</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>City of New Haven</td>\n", " <td>Parking Meter</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/372</td>\n", " <td>372</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>City of New Haven</td>\n", " <td>Parking Violation/Abandoned Auto</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/121</td>\n", " <td>121</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>City of New Haven</td>\n", " <td>Parks Request</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/126</td>\n", " <td>126</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>City of New Haven</td>\n", " <td>Policing Issue</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/2626</td>\n", " <td>2626</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>City of New Haven</td>\n", " <td>Potholes</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/116</td>\n", " <td>116</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>City of New Haven</td>\n", " <td>Public Space, Streets and Drains</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/1249</td>\n", " <td>1249</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>City of New Haven</td>\n", " <td>Private Property Issue</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/1251</td>\n", " <td>1251</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>City of New Haven</td>\n", " <td>Sidewalks and Curb damage</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/117</td>\n", " <td>117</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>City of New Haven</td>\n", " <td>Signs / Bus Shelters / Pavement Markings</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/373</td>\n", " <td>373</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>City of New Haven</td>\n", " <td>Street Lamp</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/124</td>\n", " <td>124</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>City of New Haven</td>\n", " <td>Street Sweeping</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/5251</td>\n", " <td>5251</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>City of New Haven</td>\n", " <td>Traffic/Road Safety</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/2625</td>\n", " <td>2625</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>City of New Haven</td>\n", " <td>Traffic Signal / Pedestrian Signal</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/51</td>\n", " <td>51</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>City of New Haven</td>\n", " <td>Trash &amp; Recycling</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/1966</td>\n", " <td>1966</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>City of New Haven</td>\n", " <td>Tree Trimming</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/1853</td>\n", " <td>1853</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>Neighborhood Volunteers</td>\n", " <td>Request for volunteers</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/5998</td>\n", " <td>5998</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>CT Transit</td>\n", " <td>General Bus Request/Incident</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/4947</td>\n", " <td>4947</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>Community</td>\n", " <td>Post to Neighbors</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/o...</td>\n", " <td>other</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " organization title \\\n", "0 City of New Haven Bins for Trash & Recycling \n", "1 City of New Haven Graffiti \n", "2 City of New Haven Hangers \n", "3 City of New Haven Health Complaints \n", "4 City of New Haven Illegal Dumping \n", "5 City of New Haven Other \n", "6 City of New Haven Other - city responsibility \n", "7 City of New Haven Parking Meter \n", "8 City of New Haven Parking Violation/Abandoned Auto \n", "9 City of New Haven Parks Request \n", "10 City of New Haven Policing Issue \n", "11 City of New Haven Potholes \n", "12 City of New Haven Public Space, Streets and Drains \n", "13 City of New Haven Private Property Issue \n", "14 City of New Haven Sidewalks and Curb damage \n", "15 City of New Haven Signs / Bus Shelters / Pavement Markings \n", "16 City of New Haven Street Lamp \n", "17 City of New Haven Street Sweeping \n", "18 City of New Haven Traffic/Road Safety \n", "19 City of New Haven Traffic Signal / Pedestrian Signal \n", "20 City of New Haven Trash & Recycling \n", "21 City of New Haven Tree Trimming \n", "22 Neighborhood Volunteers Request for volunteers \n", "23 CT Transit General Bus Request/Incident \n", "24 Community Post to Neighbors \n", "\n", " url type \n", "0 https://seeclickfix.com/api/v2/request_types/5743 5743 \n", "1 https://seeclickfix.com/api/v2/request_types/122 122 \n", "2 https://seeclickfix.com/api/v2/request_types/6215 6215 \n", "3 https://seeclickfix.com/api/v2/request_types/5185 5185 \n", "4 https://seeclickfix.com/api/v2/request_types/1250 1250 \n", "5 https://seeclickfix.com/api/v2/request_types/374 374 \n", "6 https://seeclickfix.com/api/v2/request_types/3018 3018 \n", "7 https://seeclickfix.com/api/v2/request_types/372 372 \n", "8 https://seeclickfix.com/api/v2/request_types/121 121 \n", "9 https://seeclickfix.com/api/v2/request_types/126 126 \n", "10 https://seeclickfix.com/api/v2/request_types/2626 2626 \n", "11 https://seeclickfix.com/api/v2/request_types/116 116 \n", "12 https://seeclickfix.com/api/v2/request_types/1249 1249 \n", "13 https://seeclickfix.com/api/v2/request_types/1251 1251 \n", "14 https://seeclickfix.com/api/v2/request_types/117 117 \n", "15 https://seeclickfix.com/api/v2/request_types/373 373 \n", "16 https://seeclickfix.com/api/v2/request_types/124 124 \n", "17 https://seeclickfix.com/api/v2/request_types/5251 5251 \n", "18 https://seeclickfix.com/api/v2/request_types/2625 2625 \n", "19 https://seeclickfix.com/api/v2/request_types/51 51 \n", "20 https://seeclickfix.com/api/v2/request_types/1966 1966 \n", "21 https://seeclickfix.com/api/v2/request_types/1853 1853 \n", "22 https://seeclickfix.com/api/v2/request_types/5998 5998 \n", "23 https://seeclickfix.com/api/v2/request_types/4947 4947 \n", "24 https://seeclickfix.com/api/v2/request_types/o... other " ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scf_df_cat" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>organization</th>\n", " <th>title</th>\n", " <th>url</th>\n", " <th>type</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>City of New Haven</td>\n", " <td>Bins for Trash &amp; Recycling</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/5743</td>\n", " <td>5743</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>City of New Haven</td>\n", " <td>Graffiti</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/122</td>\n", " <td>122</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>City of New Haven</td>\n", " <td>Hangers</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/6215</td>\n", " <td>6215</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>City of New Haven</td>\n", " <td>Health Complaints</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/5185</td>\n", " <td>5185</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>City of New Haven</td>\n", " <td>Illegal Dumping</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/1250</td>\n", " <td>1250</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>City of New Haven</td>\n", " <td>Other</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/374</td>\n", " <td>374</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>City of New Haven</td>\n", " <td>Other - city responsibility</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/3018</td>\n", " <td>3018</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>City of New Haven</td>\n", " <td>Parking Meter</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/372</td>\n", " <td>372</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>City of New Haven</td>\n", " <td>Parking Violation/Abandoned Auto</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/121</td>\n", " <td>121</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>City of New Haven</td>\n", " <td>Parks Request</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/126</td>\n", " <td>126</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>City of New Haven</td>\n", " <td>Policing Issue</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/2626</td>\n", " <td>2626</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>City of New Haven</td>\n", " <td>Potholes</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/116</td>\n", " <td>116</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>City of New Haven</td>\n", " <td>Public Space, Streets and Drains</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/1249</td>\n", " <td>1249</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>City of New Haven</td>\n", " <td>Private Property Issue</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/1251</td>\n", " <td>1251</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>City of New Haven</td>\n", " <td>Sidewalks and Curb damage</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/117</td>\n", " <td>117</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>City of New Haven</td>\n", " <td>Signs / Bus Shelters / Pavement Markings</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/373</td>\n", " <td>373</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>City of New Haven</td>\n", " <td>Street Lamp</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/124</td>\n", " <td>124</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>City of New Haven</td>\n", " <td>Street Sweeping</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/5251</td>\n", " <td>5251</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>City of New Haven</td>\n", " <td>Traffic/Road Safety</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/2625</td>\n", " <td>2625</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>City of New Haven</td>\n", " <td>Traffic Signal / Pedestrian Signal</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/51</td>\n", " <td>51</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>City of New Haven</td>\n", " <td>Trash &amp; Recycling</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/1966</td>\n", " <td>1966</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>City of New Haven</td>\n", " <td>Tree Trimming</td>\n", " <td>https://seeclickfix.com/api/v2/request_types/1853</td>\n", " <td>1853</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " organization title \\\n", "0 City of New Haven Bins for Trash & Recycling \n", "1 City of New Haven Graffiti \n", "2 City of New Haven Hangers \n", "3 City of New Haven Health Complaints \n", "4 City of New Haven Illegal Dumping \n", "5 City of New Haven Other \n", "6 City of New Haven Other - city responsibility \n", "7 City of New Haven Parking Meter \n", "8 City of New Haven Parking Violation/Abandoned Auto \n", "9 City of New Haven Parks Request \n", "10 City of New Haven Policing Issue \n", "11 City of New Haven Potholes \n", "12 City of New Haven Public Space, Streets and Drains \n", "13 City of New Haven Private Property Issue \n", "14 City of New Haven Sidewalks and Curb damage \n", "15 City of New Haven Signs / Bus Shelters / Pavement Markings \n", "16 City of New Haven Street Lamp \n", "17 City of New Haven Street Sweeping \n", "18 City of New Haven Traffic/Road Safety \n", "19 City of New Haven Traffic Signal / Pedestrian Signal \n", "20 City of New Haven Trash & Recycling \n", "21 City of New Haven Tree Trimming \n", "\n", " url type \n", "0 https://seeclickfix.com/api/v2/request_types/5743 5743 \n", "1 https://seeclickfix.com/api/v2/request_types/122 122 \n", "2 https://seeclickfix.com/api/v2/request_types/6215 6215 \n", "3 https://seeclickfix.com/api/v2/request_types/5185 5185 \n", "4 https://seeclickfix.com/api/v2/request_types/1250 1250 \n", "5 https://seeclickfix.com/api/v2/request_types/374 374 \n", "6 https://seeclickfix.com/api/v2/request_types/3018 3018 \n", "7 https://seeclickfix.com/api/v2/request_types/372 372 \n", "8 https://seeclickfix.com/api/v2/request_types/121 121 \n", "9 https://seeclickfix.com/api/v2/request_types/126 126 \n", "10 https://seeclickfix.com/api/v2/request_types/2626 2626 \n", "11 https://seeclickfix.com/api/v2/request_types/116 116 \n", "12 https://seeclickfix.com/api/v2/request_types/1249 1249 \n", "13 https://seeclickfix.com/api/v2/request_types/1251 1251 \n", "14 https://seeclickfix.com/api/v2/request_types/117 117 \n", "15 https://seeclickfix.com/api/v2/request_types/373 373 \n", "16 https://seeclickfix.com/api/v2/request_types/124 124 \n", "17 https://seeclickfix.com/api/v2/request_types/5251 5251 \n", "18 https://seeclickfix.com/api/v2/request_types/2625 2625 \n", "19 https://seeclickfix.com/api/v2/request_types/51 51 \n", "20 https://seeclickfix.com/api/v2/request_types/1966 1966 \n", "21 https://seeclickfix.com/api/v2/request_types/1853 1853 " ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scf_df_cat[scf_df_cat['organization'] == 'City of New Haven']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "read in the issue data from file (to speed things up)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Category: Bins for Trash & Recycling, id: 5743, readfile: False\n", "now reading from url...\n", "Category: Graffiti, id: 122, readfile: False\n", "now reading from url...\n", "Category: Hangers, id: 6215, readfile: False\n", "now reading from url...\n", "Category: Health Complaints, id: 5185, readfile: False\n", "now reading from url...\n", "Category: Illegal Dumping, id: 1250, readfile: False\n", "now reading from url...\n", "Category: Other, id: 374, readfile: False\n", "now reading from url...\n", "Category: Other - city responsibility, id: 3018, readfile: False\n", "now reading from url...\n", "Category: Parking Meter, id: 372, readfile: False\n", "now reading from url...\n", "Category: Parking Violation/Abandoned Auto, id: 121, readfile: False\n", "now reading from url...\n", "Category: Parks Request, id: 126, readfile: False\n", "now reading from url...\n", "Category: Policing Issue, id: 2626, readfile: False\n", "now reading from url...\n", "Category: Potholes, id: 116, readfile: False\n", "now reading from url...\n", "Category: Public Space, Streets and Drains, id: 1249, readfile: False\n", "now reading from url...\n", "Category: Private Property Issue, id: 1251, readfile: False\n", "now reading from url...\n", "Category: Sidewalks and Curb damage, id: 117, readfile: False\n", "now reading from url...\n", "Category: Signs / Bus Shelters / Pavement Markings, id: 373, readfile: False\n", "now reading from url...\n", "Category: Street Lamp, id: 124, readfile: False\n", "now reading from url...\n", "Category: Street Sweeping, id: 5251, readfile: False\n", "now reading from url...\n", "Category: Traffic/Road Safety, id: 2625, readfile: False\n", "now reading from url...\n", "Category: Traffic Signal / Pedestrian Signal, id: 51, readfile: False\n", "now reading from url...\n", "Category: Trash & Recycling, id: 1966, readfile: False\n", "now reading from url...\n", "Category: Tree Trimming, id: 1853, readfile: False\n", "now reading from url...\n", "Category: Request for volunteers, id: 5998, readfile: False\n", "now reading from url...\n", "Category: General Bus Request/Incident, id: 4947, readfile: False\n", "now reading from url...\n", "Category: Post to Neighbors, id: other, readfile: False\n", "now reading from url...\n" ] } ], "source": [ "readfile=False\n", "writejson=False\n", "scf_df = ri.get_issues(readfile=readfile, writejson=writejson)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "now determine the neighborhoods for each issue using the get_neighborhoods routine:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Category: Bins for Trash & Recycling, id: 5743, readfile: False\n", "now reading from url...\n", "Category: Graffiti, id: 122, readfile: False\n", "now reading from url...\n", "Category: Hangers, id: 6215, readfile: False\n", "now reading from url...\n", "Category: Health Complaints, id: 5185, readfile: False\n", "now reading from url...\n", "Category: Illegal Dumping, id: 1250, readfile: False\n", "now reading from url...\n", "Category: Other, id: 374, readfile: False\n", "now reading from url...\n", "Category: Other - city responsibility, id: 3018, readfile: False\n", "now reading from url...\n", "Category: Parking Meter, id: 372, readfile: False\n", "now reading from url...\n", "Category: Parking Violation/Abandoned Auto, id: 121, readfile: False\n", "now reading from url...\n", "Category: Parks Request, id: 126, readfile: False\n", "now reading from url...\n", "Category: Policing Issue, id: 2626, readfile: False\n", "now reading from url...\n", "Category: Potholes, id: 116, readfile: False\n", "now reading from url...\n", "Category: Public Space, Streets and Drains, id: 1249, readfile: False\n", "now reading from url...\n", "Category: Private Property Issue, id: 1251, readfile: False\n", "now reading from url...\n", "Category: Sidewalks and Curb damage, id: 117, readfile: False\n", "now reading from url...\n", "Category: Signs / Bus Shelters / Pavement Markings, id: 373, readfile: False\n", "now reading from url...\n", "Category: Street Lamp, id: 124, readfile: False\n", "now reading from url...\n", "Category: Street Sweeping, id: 5251, readfile: False\n", "now reading from url...\n", "Category: Traffic/Road Safety, id: 2625, readfile: False\n", "now reading from url...\n", "Category: Traffic Signal / Pedestrian Signal, id: 51, readfile: False\n", "now reading from url...\n", "Category: Trash & Recycling, id: 1966, readfile: False\n", "now reading from url...\n", "Category: Tree Trimming, id: 1853, readfile: False\n", "now reading from url...\n", "Category: Request for volunteers, id: 5998, readfile: False\n", "now reading from url...\n", "Category: General Bus Request/Incident, id: 4947, readfile: False\n", "now reading from url...\n", "Category: Post to Neighbors, id: other, readfile: False\n", "now reading from url...\n" ] } ], "source": [ "hoods = get_ngbrhd.get_neighborhoods()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "now add the neighborhoods to the DataFrame:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "scf_df['neighborhood'] = hoods" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Statistics to calculate:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are a few things that it would be nice to calculate. Namely, \n", "\n", "- The time to acknowledgement\n", "- The time to completion\n", "- Acknowledgement Improvement\n", "- Completion Improvement" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Computing the Time to Acknowledgement" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index([u'acknowledged_at', u'address', u'category', u'closed_at', u'created_at', u'description', u'id', u'issue_id', u'lat', u'lng', u'reporter_id', u'reporter_name', u'reporter_role', u'shortened_url', u'status', u'summary', u'updated_at', u'int_issue_id', u'neighborhood'], dtype='object')" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scf_df.columns" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 2015-04-12T14:58:29-04:00\n", "1 2015-03-27T15:57:26-04:00\n", "Name: created_at, dtype: object" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scf_df.loc[0:1, 'created_at']" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Timestamp('2015-04-12 18:58:29')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.to_datetime(scf_df.loc[0, 'created_at'])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "scf_df['time_to_acknowledge'] = (pd.to_datetime(scf_df['acknowledged_at']) - pd.to_datetime(scf_df['created_at']))/pd.Timedelta('1d')" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], "source": [ "scf_df['time_to_close'] = (pd.to_datetime(scf_df['closed_at']) - pd.to_datetime(scf_df['created_at']))/pd.Timedelta('1d')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 NaN\n", "1 2.705278\n", "Name: time_to_acknowledge, dtype: float64" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scf_df.loc[0:1, 'time_to_acknowledge']" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "63.65517361111111" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.median(scf_df['time_to_acknowledge'].values)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "nan" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.median(scf_df['time_to_close'].values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Computing Progress" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this section I compute the progress made since the previous time period." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

JoyMonteiro/qgis-networks

TestCentrality.ipynb

1

6814

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Testing Centrality" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n", "[<Vertex object with index '8081' at 0x7f32b4b51ad0>, <Vertex object with index '8179' at 0x7f32965368d0>, <Vertex object with index '8180' at 0x7f3296536950>, <Vertex object with index '8181' at 0x7f32965369d0>, <Vertex object with index '8079' at 0x7f3296536a50>, <Vertex object with index '7979' at 0x7f3296536ad0>, <Vertex object with index '7980' at 0x7f3296536b50>, <Vertex object with index '7981' at 0x7f3296536bd0>]\n", "4\n", "[<Vertex object with index '8081' at 0x7f32b37a1ed0>, <Vertex object with index '8179' at 0x7f32b37a1e50>, <Vertex object with index '8180' at 0x7f32b37a1dd0>, <Vertex object with index '8181' at 0x7f32b37a1d50>, <Vertex object with index '8079' at 0x7f32b37a1cd0>, <Vertex object with index '7979' at 0x7f32b37a1c50>, <Vertex object with index '7980' at 0x7f32b37a1bd0>, <Vertex object with index '7981' at 0x7f32b37a1b50>]\n" ] } ], "source": [ "# %load testShortestPath2.py\n", "from rasterGraphCreate import *\n", "import matplotlib\n", "\n", "from matplotlib import pyplot\n", "\n", "if __name__ == \"__main__\":\n", " \n", " arraySize = 100\n", "\n", " startNode = 4\n", " destNode = 8080\n", "\n", " startNode1 = 90\n", " destNode1 = 8008\n", "\n", " startNode2 = 30\n", " destNode2 = 9080\n", "\n", " raster = np.zeros((arraySize,arraySize))\n", "\n", " measureX = np.linspace(0,5, arraySize)\n", " measureY = measureX.copy()\n", " kx,ky = np.meshgrid(measureX, measureY)\n", "\n", " raster += np.exp(-pow(kx-0.5, 2)/.5 - pow(ky-2.5,2)/2.)\n", " raster += np.exp(-pow(kx-2.5, 2)/.5 - pow(ky-2.5,2)/2.)\n", " raster += np.exp(-pow(kx-4.5, 2)/.5 - pow(ky-2.5,2)/2.)\n", " #raster += np.exp(-pow(measureX-0.5, 2)/5. - pow(measureY-0.7,2)/5.)\n", "\n", " raster += 0.1*np.random.random(raster.shape)\n", "\n", " pyplot.ion()\n", " pyplot.imshow(raster.transpose(),cmap=pyplot.cm.coolwarm)\n", "\n", " def weightFn(arr, v1index, v2index):\n", " return (raster[v1index] + raster[v2index])/2.\n", "\n", " g = createGraph(raster, weightFunction=weightFn)\n", "\n", "\n", " vlist, elist = shortest_path(g, g.vertex(startNode), g.vertex(destNode), weights=g.ep.edgeCost)\n", " print g.vertex(4)\n", " print [vert for vert in g.vertex(destNode).out_neighbours()]\n", "\n", " xs = []\n", " ys = []\n", " for vertex in vlist:\n", " index = g.vertex_index[vertex]\n", " row,col = getRowCol(index, arraySize)\n", "\n", " xs.append(row)\n", " ys.append(col)\n", "\n", " pyplot.hold(True)\n", "\n", " pyplot.plot(xs, ys,'white',linewidth=2)\n", "\n", " vlist, elist = shortest_path(g, g.vertex(startNode1), g.vertex(destNode1), weights=g.ep.edgeCost)\n", " print g.vertex(4)\n", " \n", " print [vert for vert in g.vertex(destNode).out_neighbours()]\n", "\n", " xs = []\n", " ys = []\n", " for vertex in vlist:\n", " index = g.vertex_index[vertex]\n", " row,col = getRowCol(index, arraySize)\n", "\n", " xs.append(row)\n", " ys.append(col)\n", "\n", " pyplot.hold(True)\n", "\n", " pyplot.plot(xs, ys,'red',linewidth=2)\n", "\n", "\n", "\n", " vlist, elist = shortest_path(g, g.vertex(startNode2), g.vertex(destNode2), weights=g.ep.edgeCost)\n", " \n", " xs = []\n", " ys = []\n", " for vertex in vlist:\n", " index = g.vertex_index[vertex]\n", " row,col = getRowCol(index, arraySize)\n", "\n", " xs.append(row)\n", " ys.append(col)\n", "\n", " pyplot.hold(True)\n", "\n", " pyplot.plot(xs, ys,'k',linewidth=2)\n", "\n", "\n", " pyplot.xlim(-1,arraySize)\n", " pyplot.ylim(0,arraySize)\n", " pyplot.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are using a synthetic example to explore shortest paths. The figure above shows a simple raster which\n", "can be thought of as two regions separated by two 'hills' and a valley in between them.\n", "\n", "Three paths from one side to another, marked in red, white and black are calculated. As you can observe,\n", "all of them pass through a certain region in the valley. We want to see if this region can be highlighted somehow.\n", "\n", "For this we use Betweenness centrality." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import graph_tool.centrality as centrality\n", "\n", "#Calculate centrality\n", "vp, ep = centrality.betweenness(g, weight = g.ep['edgeCost'])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.colorbar.Colorbar instance at 0x7f3295870ea8>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pylab import *\n", "output = createRaster(g, vp, arraySize, arraySize)\n", "\n", "imshow(output.transpose(), cmap= cm.coolwarm)\n", "colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The betweenness centrality measure shows 'hotspots' or natural 'highways' in the map which facilitate movement. As the above figure shows, the central region through which most paths seem to pass is highlighted. The tops of the hills have almost no shortest paths passing through them." ] } ], "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

patemotter/trilinos-prediction

ml_files/Chapter 7 Graphs.ipynb

1

929789

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.read_csv('./combined_test_results.csv', index_col=0, header=0)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Group</th>\n", " <th>Threshold_Percentage</th>\n", " <th>True_Positive</th>\n", " <th>False_Positive</th>\n", " <th>True_Negative</th>\n", " <th>False_Negative</th>\n", " <th>True_Positive_Rate</th>\n", " <th>True_Negative_Rate</th>\n", " <th>Positive_Predictive_Value</th>\n", " <th>Negative_Predictive_Value</th>\n", " <th>False_Negative_Rate</th>\n", " <th>False_Positive_Rate</th>\n", " <th>False_Discovery_Rate</th>\n", " <th>False_Omission_Rate</th>\n", " <th>Accuracy</th>\n", " <th>F1</th>\n", " <th>Matthews_Correlation_Coefficient</th>\n", " <th>AUROC</th>\n", " <th>AUPR_0</th>\n", " <th>AUPR</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>error</td>\n", " <td>0</td>\n", " <td>304913</td>\n", " <td>37440</td>\n", " <td>476369</td>\n", " <td>52813</td>\n", " <td>0.85</td>\n", " <td>0.93</td>\n", " <td>0.89</td>\n", " <td>0.9000</td>\n", " <td>0.1500</td>\n", " <td>0.07</td>\n", " <td>0.11</td>\n", " <td>0.10</td>\n", " <td>0.90</td>\n", " <td>0.44</td>\n", " <td>0.79</td>\n", " <td>0.95</td>\n", " <td>0.965</td>\n", " <td>0.933</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>converged</td>\n", " <td>0</td>\n", " <td>136911</td>\n", " <td>27375</td>\n", " <td>665535</td>\n", " <td>41714</td>\n", " <td>0.77</td>\n", " <td>0.96</td>\n", " <td>0.83</td>\n", " <td>0.9400</td>\n", " <td>0.2300</td>\n", " <td>0.04</td>\n", " <td>0.17</td>\n", " <td>0.06</td>\n", " <td>0.92</td>\n", " <td>0.40</td>\n", " <td>0.75</td>\n", " <td>0.95</td>\n", " <td>0.987</td>\n", " <td>0.873</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>overall</td>\n", " <td>0</td>\n", " <td>13</td>\n", " <td>117</td>\n", " <td>870536</td>\n", " <td>869</td>\n", " <td>0.01</td>\n", " <td>1.00</td>\n", " <td>0.10</td>\n", " <td>1.0000</td>\n", " <td>0.9900</td>\n", " <td>0.00</td>\n", " <td>0.90</td>\n", " <td>0.00</td>\n", " <td>1.00</td>\n", " <td>0.01</td>\n", " <td>0.04</td>\n", " <td>0.55</td>\n", " <td>1.000</td>\n", " <td>0.019</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>overall</td>\n", " <td>25</td>\n", " <td>1122</td>\n", " <td>742</td>\n", " <td>865070</td>\n", " <td>4601</td>\n", " <td>0.20</td>\n", " <td>1.00</td>\n", " <td>0.60</td>\n", " <td>0.9900</td>\n", " <td>0.8000</td>\n", " <td>0.00</td>\n", " <td>0.40</td>\n", " <td>0.01</td>\n", " <td>0.99</td>\n", " <td>0.15</td>\n", " <td>0.34</td>\n", " <td>0.85</td>\n", " <td>0.999</td>\n", " <td>0.359</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>overall</td>\n", " <td>50</td>\n", " <td>3738</td>\n", " <td>1513</td>\n", " <td>858028</td>\n", " <td>8256</td>\n", " <td>0.31</td>\n", " <td>1.00</td>\n", " <td>0.71</td>\n", " <td>0.9900</td>\n", " <td>0.6900</td>\n", " <td>0.00</td>\n", " <td>0.29</td>\n", " <td>0.01</td>\n", " <td>0.99</td>\n", " <td>0.22</td>\n", " <td>0.47</td>\n", " <td>0.91</td>\n", " <td>0.999</td>\n", " <td>0.531</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>overall</td>\n", " <td>100</td>\n", " <td>10909</td>\n", " <td>3451</td>\n", " <td>844127</td>\n", " <td>13048</td>\n", " <td>0.46</td>\n", " <td>1.00</td>\n", " <td>0.76</td>\n", " <td>0.9800</td>\n", " <td>0.5400</td>\n", " <td>0.00</td>\n", " <td>0.24</td>\n", " <td>0.02</td>\n", " <td>0.98</td>\n", " <td>0.28</td>\n", " <td>0.58</td>\n", " <td>0.94</td>\n", " <td>0.998</td>\n", " <td>0.659</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>np</td>\n", " <td>0</td>\n", " <td>922</td>\n", " <td>1409</td>\n", " <td>863222</td>\n", " <td>5982</td>\n", " <td>0.13</td>\n", " <td>1.00</td>\n", " <td>0.40</td>\n", " <td>0.9931</td>\n", " <td>0.8665</td>\n", " <td>0.00</td>\n", " <td>0.60</td>\n", " <td>0.01</td>\n", " <td>0.99</td>\n", " <td>0.10</td>\n", " <td>0.23</td>\n", " <td>0.79</td>\n", " <td>0.998</td>\n", " <td>0.206</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>np</td>\n", " <td>25</td>\n", " <td>7200</td>\n", " <td>3627</td>\n", " <td>847004</td>\n", " <td>13704</td>\n", " <td>0.34</td>\n", " <td>1.00</td>\n", " <td>0.67</td>\n", " <td>0.9800</td>\n", " <td>0.6600</td>\n", " <td>0.00</td>\n", " <td>0.33</td>\n", " <td>0.02</td>\n", " <td>0.98</td>\n", " <td>0.23</td>\n", " <td>0.47</td>\n", " <td>0.91</td>\n", " <td>0.998</td>\n", " <td>0.526</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>np</td>\n", " <td>50</td>\n", " <td>13751</td>\n", " <td>5111</td>\n", " <td>835723</td>\n", " <td>16950</td>\n", " <td>0.45</td>\n", " <td>0.99</td>\n", " <td>0.73</td>\n", " <td>0.9800</td>\n", " <td>0.5500</td>\n", " <td>0.01</td>\n", " <td>0.27</td>\n", " <td>0.02</td>\n", " <td>0.97</td>\n", " <td>0.28</td>\n", " <td>0.56</td>\n", " <td>0.93</td>\n", " <td>0.997</td>\n", " <td>0.616</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>np</td>\n", " <td>100</td>\n", " <td>22438</td>\n", " <td>7113</td>\n", " <td>819845</td>\n", " <td>22139</td>\n", " <td>0.50</td>\n", " <td>0.99</td>\n", " <td>0.76</td>\n", " <td>0.9700</td>\n", " <td>0.5000</td>\n", " <td>0.01</td>\n", " <td>0.24</td>\n", " <td>0.03</td>\n", " <td>0.97</td>\n", " <td>0.30</td>\n", " <td>0.60</td>\n", " <td>0.94</td>\n", " <td>0.996</td>\n", " <td>0.682</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>sys</td>\n", " <td>0</td>\n", " <td>157</td>\n", " <td>464</td>\n", " <td>867456</td>\n", " <td>3458</td>\n", " <td>0.04</td>\n", " <td>1.00</td>\n", " <td>0.25</td>\n", " <td>1.0000</td>\n", " <td>0.9600</td>\n", " <td>0.00</td>\n", " <td>0.75</td>\n", " <td>0.00</td>\n", " <td>1.00</td>\n", " <td>0.04</td>\n", " <td>0.10</td>\n", " <td>0.60</td>\n", " <td>0.998</td>\n", " <td>0.055</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>sys</td>\n", " <td>25</td>\n", " <td>3762</td>\n", " <td>1727</td>\n", " <td>856098</td>\n", " <td>9948</td>\n", " <td>0.27</td>\n", " <td>1.00</td>\n", " <td>0.69</td>\n", " <td>0.9900</td>\n", " <td>0.7300</td>\n", " <td>0.00</td>\n", " <td>0.31</td>\n", " <td>0.01</td>\n", " <td>0.99</td>\n", " <td>0.20</td>\n", " <td>0.43</td>\n", " <td>0.88</td>\n", " <td>0.998</td>\n", " <td>0.465</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>sys</td>\n", " <td>50</td>\n", " <td>8994</td>\n", " <td>3106</td>\n", " <td>845950</td>\n", " <td>13485</td>\n", " <td>0.40</td>\n", " <td>1.00</td>\n", " <td>0.74</td>\n", " <td>0.9800</td>\n", " <td>0.6000</td>\n", " <td>0.00</td>\n", " <td>0.26</td>\n", " <td>0.02</td>\n", " <td>0.98</td>\n", " <td>0.26</td>\n", " <td>0.54</td>\n", " <td>0.92</td>\n", " <td>0.998</td>\n", " <td>0.608</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>sys</td>\n", " <td>100</td>\n", " <td>18136</td>\n", " <td>5332</td>\n", " <td>830213</td>\n", " <td>17854</td>\n", " <td>0.50</td>\n", " <td>0.99</td>\n", " <td>0.77</td>\n", " <td>0.9800</td>\n", " <td>0.5000</td>\n", " <td>0.01</td>\n", " <td>0.23</td>\n", " <td>0.02</td>\n", " <td>0.97</td>\n", " <td>0.31</td>\n", " <td>0.61</td>\n", " <td>0.94</td>\n", " <td>0.997</td>\n", " <td>0.693</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>np_and_system</td>\n", " <td>0</td>\n", " <td>8250</td>\n", " <td>3982</td>\n", " <td>846455</td>\n", " <td>12848</td>\n", " <td>0.39</td>\n", " <td>1.00</td>\n", " <td>0.67</td>\n", " <td>0.9900</td>\n", " <td>0.6100</td>\n", " <td>0.00</td>\n", " <td>0.33</td>\n", " <td>0.01</td>\n", " <td>0.98</td>\n", " <td>0.25</td>\n", " <td>0.50</td>\n", " <td>0.89</td>\n", " <td>0.997</td>\n", " <td>0.506</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>np_and_system</td>\n", " <td>25</td>\n", " <td>18085</td>\n", " <td>5671</td>\n", " <td>828924</td>\n", " <td>18855</td>\n", " <td>0.49</td>\n", " <td>0.99</td>\n", " <td>0.76</td>\n", " <td>0.9800</td>\n", " <td>0.5100</td>\n", " <td>0.01</td>\n", " <td>0.24</td>\n", " <td>0.02</td>\n", " <td>0.97</td>\n", " <td>0.30</td>\n", " <td>0.60</td>\n", " <td>0.94</td>\n", " <td>0.997</td>\n", " <td>0.675</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>np_and_system</td>\n", " <td>50</td>\n", " <td>24002</td>\n", " <td>7052</td>\n", " <td>819047</td>\n", " <td>21434</td>\n", " <td>0.53</td>\n", " <td>0.99</td>\n", " <td>0.77</td>\n", " <td>0.9700</td>\n", " <td>0.4700</td>\n", " <td>0.01</td>\n", " <td>0.23</td>\n", " <td>0.03</td>\n", " <td>0.97</td>\n", " <td>0.31</td>\n", " <td>0.62</td>\n", " <td>0.94</td>\n", " <td>0.997</td>\n", " <td>0.700</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>np_and_system</td>\n", " <td>100</td>\n", " <td>33235</td>\n", " <td>9699</td>\n", " <td>801718</td>\n", " <td>26883</td>\n", " <td>0.55</td>\n", " <td>0.99</td>\n", " <td>0.77</td>\n", " <td>0.9700</td>\n", " <td>0.4500</td>\n", " <td>0.01</td>\n", " <td>0.23</td>\n", " <td>0.03</td>\n", " <td>0.96</td>\n", " <td>0.32</td>\n", " <td>0.63</td>\n", " <td>0.95</td>\n", " <td>0.995</td>\n", " <td>0.723</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Group Threshold_Percentage True_Positive False_Positive \\\n", "0 error 0 304913 37440 \n", "1 converged 0 136911 27375 \n", "2 overall 0 13 117 \n", "3 overall 25 1122 742 \n", "4 overall 50 3738 1513 \n", "5 overall 100 10909 3451 \n", "6 np 0 922 1409 \n", "7 np 25 7200 3627 \n", "8 np 50 13751 5111 \n", "9 np 100 22438 7113 \n", "10 sys 0 157 464 \n", "11 sys 25 3762 1727 \n", "12 sys 50 8994 3106 \n", "13 sys 100 18136 5332 \n", "14 np_and_system 0 8250 3982 \n", "15 np_and_system 25 18085 5671 \n", "16 np_and_system 50 24002 7052 \n", "17 np_and_system 100 33235 9699 \n", "\n", " True_Negative False_Negative True_Positive_Rate True_Negative_Rate \\\n", "0 476369 52813 0.85 0.93 \n", "1 665535 41714 0.77 0.96 \n", "2 870536 869 0.01 1.00 \n", "3 865070 4601 0.20 1.00 \n", "4 858028 8256 0.31 1.00 \n", "5 844127 13048 0.46 1.00 \n", "6 863222 5982 0.13 1.00 \n", "7 847004 13704 0.34 1.00 \n", "8 835723 16950 0.45 0.99 \n", "9 819845 22139 0.50 0.99 \n", "10 867456 3458 0.04 1.00 \n", "11 856098 9948 0.27 1.00 \n", "12 845950 13485 0.40 1.00 \n", "13 830213 17854 0.50 0.99 \n", "14 846455 12848 0.39 1.00 \n", "15 828924 18855 0.49 0.99 \n", "16 819047 21434 0.53 0.99 \n", "17 801718 26883 0.55 0.99 \n", "\n", " Positive_Predictive_Value Negative_Predictive_Value False_Negative_Rate \\\n", "0 0.89 0.9000 0.1500 \n", "1 0.83 0.9400 0.2300 \n", "2 0.10 1.0000 0.9900 \n", "3 0.60 0.9900 0.8000 \n", "4 0.71 0.9900 0.6900 \n", "5 0.76 0.9800 0.5400 \n", "6 0.40 0.9931 0.8665 \n", "7 0.67 0.9800 0.6600 \n", "8 0.73 0.9800 0.5500 \n", "9 0.76 0.9700 0.5000 \n", "10 0.25 1.0000 0.9600 \n", "11 0.69 0.9900 0.7300 \n", "12 0.74 0.9800 0.6000 \n", "13 0.77 0.9800 0.5000 \n", "14 0.67 0.9900 0.6100 \n", "15 0.76 0.9800 0.5100 \n", "16 0.77 0.9700 0.4700 \n", "17 0.77 0.9700 0.4500 \n", "\n", " False_Positive_Rate False_Discovery_Rate False_Omission_Rate Accuracy \\\n", "0 0.07 0.11 0.10 0.90 \n", "1 0.04 0.17 0.06 0.92 \n", "2 0.00 0.90 0.00 1.00 \n", "3 0.00 0.40 0.01 0.99 \n", "4 0.00 0.29 0.01 0.99 \n", "5 0.00 0.24 0.02 0.98 \n", "6 0.00 0.60 0.01 0.99 \n", "7 0.00 0.33 0.02 0.98 \n", "8 0.01 0.27 0.02 0.97 \n", "9 0.01 0.24 0.03 0.97 \n", "10 0.00 0.75 0.00 1.00 \n", "11 0.00 0.31 0.01 0.99 \n", "12 0.00 0.26 0.02 0.98 \n", "13 0.01 0.23 0.02 0.97 \n", "14 0.00 0.33 0.01 0.98 \n", "15 0.01 0.24 0.02 0.97 \n", "16 0.01 0.23 0.03 0.97 \n", "17 0.01 0.23 0.03 0.96 \n", "\n", " F1 Matthews_Correlation_Coefficient AUROC AUPR_0 AUPR \n", "0 0.44 0.79 0.95 0.965 0.933 \n", "1 0.40 0.75 0.95 0.987 0.873 \n", "2 0.01 0.04 0.55 1.000 0.019 \n", "3 0.15 0.34 0.85 0.999 0.359 \n", "4 0.22 0.47 0.91 0.999 0.531 \n", "5 0.28 0.58 0.94 0.998 0.659 \n", "6 0.10 0.23 0.79 0.998 0.206 \n", "7 0.23 0.47 0.91 0.998 0.526 \n", "8 0.28 0.56 0.93 0.997 0.616 \n", "9 0.30 0.60 0.94 0.996 0.682 \n", "10 0.04 0.10 0.60 0.998 0.055 \n", "11 0.20 0.43 0.88 0.998 0.465 \n", "12 0.26 0.54 0.92 0.998 0.608 \n", "13 0.31 0.61 0.94 0.997 0.693 \n", "14 0.25 0.50 0.89 0.997 0.506 \n", "15 0.30 0.60 0.94 0.997 0.675 \n", "16 0.31 0.62 0.94 0.997 0.700 \n", "17 0.32 0.63 0.95 0.995 0.723 " ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns = ['Group', 'Threshold_Percentage', \n", " 'True_Positive', 'False_Positive', \n", " 'True_Negative', 'False_Negative', \n", " 'True_Positive_Rate', 'True_Negative_Rate', \n", " 'Positive_Predictive_Value', 'Negative_Predictive_Value', \n", " 'False_Negative_Rate', 'False_Positive_Rate',\n", " 'False_Discovery_Rate', 'False_Omission_Rate', \n", " 'Accuracy', 'F1',\n", " 'Matthews_Correlation_Coefficient', 'AUROC',\n", " 'AUPR_0', 'AUPR'\n", " ]\n", "df" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['True_Positive', 'False_Positive', 'True_Negative',\n", " 'False_Negative', 'True_Positive_Rate', 'True_Negative_Rate',\n", " 'Positive_Predictive_Value', 'Negative_Predictive_Value',\n", " 'False_Negative_Rate', 'False_Positive_Rate',\n", " 'False_Discovery_Rate', 'False_Omission_Rate', 'Accuracy', 'F1',\n", " 'Matthews_Correlation_Coefficient', 'AUROC', 'AUPR_0', 'AUPR'], dtype=object)" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels=df.columns.values[2:]\n", "labels" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([0, 1, 2, 3, 4, 5]), <a list of 6 Text xticklabel objects>)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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XCAsqp6SkcOSRv/vm0Ucf7NihQ07h/vv/cv3s2Z51++23dN1rr5+uy8jI2ObMSomViIhI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\nviMsGt7NwrqlFf6vmllDd38XOMHde7v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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5297cb0a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Compare AUROC results for all 18 labels\n", "# based on their groupings\n", "label = 'AUROC'\n", "sns.set_context(\"paper\", font_scale=1.6)\n", "sns.set_palette(\"Greys_r\")\n", "auroc_ax = sns.barplot(x='Group', y=label, \n", " hue='Threshold_Percentage', data=df)\n", "auroc_ax.set(ylabel=label)\n", "auroc_ax.legend(ncol=2, bbox_to_anchor=(1.05, 1),loc=0, frameon=True, title=\"Threshold Percentage\")\n", "plt.xticks(rotation=15)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([0, 1, 2, 3, 4, 5]), <a list of 6 Text xticklabel objects>)" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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mGvr23Yy9996X//3v/Qr7t956W7bddnv+9rcrWLx4ERtu2ImHH36ATz+dzvXX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\n+89SXd/3ZubdEXE68IH+LHVm/qx1g3+KGm19H9VFvg6wVkQ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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5296942780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Compare TPR results for all 18 labels\n", "# based on their groupings\n", "label = 'True_Positive_Rate'\n", "sns.set_context(\"paper\", font_scale=2)\n", "sns.set_palette(\"Greys_r\")\n", "plt.figure(figsize=(12,10))\n", "auroc_ax = sns.barplot(x='Group', y=label, \n", " hue='Threshold_Percentage', data=df)\n", "auroc_ax.set(ylabel=label)\n", "auroc_ax.set(xlabel=\"Classifier Group\")\n", "auroc_ax.legend(ncol=2, loc=0, frameon=True, title=\"Threshold Percentage\")\n", "plt.xticks(rotation=15)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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s48ePZefO7ezcuZ0iRYrSqNFN3HPP32jR4jbuuacdJ04cZ8OGddx4YwPAef8+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"text/plain": [ "<matplotlib.figure.Figure at 0x7f529781c940>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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34wHjvUXonj27cbvdiTZry5cvPwUKhCd7jHcjsISEhIB9BuJyuXC5XPz++0q2b9/G3r17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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52979ff5f8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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5Y8FZaOs3dyw4U6YvFLsuSey2i8TiZ6xko/Xr1/Htt9No1qw5gwa9QUhICC6X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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5295086a90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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TgFapi2FP7NdAO5xCuxfQAafgbpa6GPbE/oYzbHsZTtHcBaegb3G+YlhERERE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\nEcdm5n0tQ6Uzscd1tHVAgZlR66bvTAVOT6NSJo+lAqSLqDHHE1qP4Xf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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5297d63390>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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3a9YXJCUlcffdvVJNj7/77l6UKlWKOXNm5XaoeUoJsYiIiIiI+Kxdu5ro6Gjq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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52978e4e10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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y69QH77wzrK5DWKtDDz0qZeeaOHE8WVlZHHXUceX2devWvezfQ4Y8wxNPPEL3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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5296b859e8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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kVVVVJSHeD1gQY/x6zcIY4/ck7gXuvp7jFwINQwhr36S1ZcHj/JREKUlSgfr1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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5294f469b0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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/vhvx8XFMnz6VLVv+5JVX/nNWJcVKiEVERERExG3Tpg3MnDmDK65oz7PPvkRA\nQABOp5O33x7Bzz/PYcWKZVx6aclsFSmFEx8fR2xsXXr37ptrnX379jFp0tcY04Rhw0a4RwRMnDiO\nb7+dxNy5s+jRo1dxhVzkNIdYRERERETcpk//AYC+fe8kICAAgICAAO6++z4CAgKYPfunkgxPfJSS\nksL+/fuoX79+nvXmzp1Jeno6PXrclm14fI8et1GhQgXmzSvdPeoFpYRYRERERETcNmxYR6VKlalf\nv0G28qioaGrXjmX9+rUlFJkURnz8dgDq1WuQZ71NmzYC0KzZBdnKQ0JCaNy4CXFx2zl+/FjRBFkC\nNGRaREREREQASE1N5cCB/RjT1OP56tVrkJiYQFJSEpGRkcUcnRRGXJwrIU5OPsLLLz/HX39tBeCC\nC1rQt+8d1K4dC8CePbuJjIz0OE84JqY6AImJiRjTpJgiL1rqIRYREREREQCOHTsKQEREhMfz4eHh\nAKSkHC+2mMQ/4uPjAPjhh/9SoUIFOne+jnPPNSxfvoSnn36C7dv/AuDo0WTCwz1//xUqVADOru9f\nPcQiIiIiIgJAWloaAOXKlfN4PrM8NTW12GIS/wgMDKRatRgGDHic88+/0F2+cOECRo58iw8/HMnb\nb79Peno6wcFl5/tXQiwiIiIiIgCEhIQC/yTGpzt16hQA5cuXL7aYxD8eeOARj+UdOnRi7txZbNq0\ngZ07EwkJCSEt7ZTHumfj968h0yIiIiIiAriGRAcGBua6aNLx48fd9eTs0bBhIwD27t1DeHgEKSkp\nHutllp9N378SYhERERERAVxDYmNiqrNnzx6P5/fs2U3lypFUrFipmCOTwkhPT2fr1i1s2fKnx/OZ\nQ6BDQkKoVas2R44kcfLkyRz19u3bS2BgILVqxRZpvMVJQ6YLqVOn4t+UfMGCZcV+TxEREREpG5o1\nO5+ff55LYmICsbF13OUHDx5g585ELr20+P//K4XjcDh47rkhlC9fntGjJxAUFOQ+53Q6sXYzQUFB\nNGjQkKZNz2PDhnVs3ryRFi0uctdLTU1ly5Y/qVOnrntxrbOBeohFRERERMTtqquuAWDMmC9wOByA\nK2kaPXoUAF26dC2x2MQ35cqVo1Wr1hw7dozvv/8227kffvgv8fFxtGvXkfDwCNq160hgYCCTJ493\nzxkGmDJlMikpKXTufF1xh1+k1EMsIiIiIiJuLVteTPv2nfj11wU88cRjXHhhCzZv3siGDeu54or2\ntG59WUk7NxSMAAAgAElEQVSHKD646677+PPPzUyYMI4NG9ZTv34Dtm37Hxs2rKdOnbrcffd9AMTG\n1qF791v4/vvvGDx4AK1atSYhYQe//76SJk3OU0IsIiIiIiJntyFD/k29evWZO3cWU6dOISamOv37\n382tt95GQEBASYcnPoiJqc6bb77HpElf8/vvq9i0aQNVqlTlxhtvpmfPPtkWyurX7y6io6sxa9aP\n/PjjNCIjq9Ct20306nV7rltynamUEIuIiIiIeOGrryYVuo3Q0DNju5rg4GBuv70/t9/ev6RDKXKd\nOl3rl3bOhHm1UVHRPProoHzrBQQE0KVL1zIxPF5ziEVERERERKRMUkIsIiIiIiIiZZISYhERERER\nESmTlBCLiIiIiIhImaSEWERERERERMokJcQiIiIiIiJSJikhFhERERERkTJJCbGIiIiIiIiUSUqI\nRUREREREpExSQiwiIiIiIiJlkhJiERERERERKZOUEIuIiIiIiEiZpIRYREREREREyqTgkg5ARERE\nRERKj0OHDjF+/Fh++205SUmHqVixIi1aXEz//ndRs2Ytd73Zs3/ivffe8tiGMU354otxxRWyFNCh\nQwcZOPAhbrutL9263ZTj/IIFPzNjxlR27dpJeHgEbdu2o3fvfoSFheWo+9tvy5k48Wvi47cTEhLK\npZdezt1330dkZJXieCuFpoRYRERERMQL7747oqRDyNMzz7xU6DYOHTrEoEGPsH//Plq2vJgOHTqR\nmJjAL7/8zKpVv/Huux9Su3YsANu2/QVAz569CQkJydZOdHS1QsdSXPbtSyzpEPJUv35jv7Z34sQJ\nRox4jZSUFI/np0z5hvHjx1KvXgOuv74b8fFxTJ8+lS1b/uSVV/5DuXLl3HV/+eVnRox4jRo1anLD\nDTeyb98+5s2bzfr1a3n//U+JiIjwa+xFQQmxiIiIiIgAMH78WPbv38f99z/MLbf0dJfPnz+XN98c\nzueff8LLL78GwPbt26hYsRL33PNASYUrBbRv3z7eeONV9y8zPJ2fNOlrjGnCsGEjCA52pYsTJ47j\n228nMXfuLK6/vhvgSqw/+uh9atSoyYcffkZ4eDgAs2e34r333mTixHHcf//DxfPGCkFziEVERERE\nBIClSxdTuXIkN93UI1v5lVd2pmbNWvz++yocDgcAcXHbqV+/QUmEKT6YPn0qjz/+CHFx27ngguYe\n68ydO5P09HR69LjNnQwD9OhxGxUqVGDevNnusl9++Zljx45y8823upNhgGuv7UJsbB3mzZtNenp6\n0b0hP1FCLCIiIiIipKenc9ttt9Ov350EBuZME8qVK0da2inS0tLYv38/R48m06BBwxKIVHwxY8YP\nVKsWw6uvjqBDh04e62zatBGAZs0uyFYeEhJC48ZNiIvbzvHjxwHYsGEdAM2bt8jRzoUXtiA5OZn4\n+Dg/voOioSHTIiIiIiJCUFBQjp7hTAkJO0hMTKBmzVqEhISwfbtryG1aWhqvvPICmzZtJDX1JE2b\nNuOOO+7GmKbFGbp44aGHHuPCC1sQFBTErl07PdbZs2c3kZGRHhfPiompDsCuXTuJjo5m9+5dANSo\nUStH3erVXXV37kygYcNG/noLRUI9xCIiIiIikiuHw8HHH7+Pw+GgS5euAMTFbQPgp5+mk5qaSufO\n19Gy5cWsWbOaJ58cxO+/ryzJkMWDli0vJigoKM86R48mEx7ueSGsChUqAJCS4uohTk5Oply5coSG\nhnqo6xpCndmbXJqph1hERERERDxyOp188ME7rFmzmnPPNe4eZIfDSUxMde68816uvPJqd/1169by\n738P5p133uDyy6/wmCxJ6ZWenk5wcDmP5zJXlz516pS7brlyIbnUdZWnpqYWQZT+pR5iERERERHJ\nIT09nXfffYNZs36iRo2avPTSMHdS1Lt3X8aOnZgtGQa48MLmdOp0NYcOHWTNmtUlEbYUQkhICGlp\npzyey0yEM3/JkXddVyJcvnz5IojSv5QQi4iIiIhINn///TdDhz7P3LmzqV07lhEj3iEqKtqra885\n51wAdu/2PE9VSq/w8Ihc9yfOLM8cDh0RUZHU1FSPvcCZw6qzrj5dWikhFhERERERt6NHj/LMM4NZ\nuXIFjRqdw1tvjXQvqJTpf//bwvr1az1en5p6EoCQEA2XPtPUqlWbI0eSOHnyZI5z+/btJTAwkFq1\nXIto1a4dm1G+J0fdPXtcZbGxdYowWv9QQiwiIiIiIoBrzufLLz+LtZu54ILmjBjxLpGRVXLUe+WV\nF3nmmcEcOXIkx7mNGzcA0KSJVpo+0zRteh4Oh4PNmzdmK09NTWXLlj+pU6cuYWGuxbUyt2Zat25d\njnbWrVtDeHg4derUK/qgC8nnhNgYU8UYM9gYM80Y84cxZkRG+TPGmC7+C1FERERERIrDmDGj2LRp\nI02bnsewYf/Jdchru3YdcDgcjBkzCqfT6S5ftOgXfvttOeeffyENG55TXGGLn7Rr15HAwEAmTx7v\nnjMMMGXKZFJSUujc+Tp3WZs2bQkLq8B3303i6NFkd/ns2TPZuTORa6+93uN+1qWNT6tMG2OuBcYD\nVYAAwAmszzh9O/CaMeYda+0Qv0QpIiIiIiJF6tChQ0yf/gMAderU49tvJ3qs16vX7fTp05+VK39j\n1qwf2b59G82anU9iYgIrV66gatUonnjiqeIMXfwkNrYO3bvfwvfff8fgwQNo1ao1CQk7+P33lTRp\ncl62hLhixUrce+8DfPjhezz66AO0a9eBgwcPsGjRQmrXjqV3774l+E68V+CE2BhzPvA9rkR4JDAH\n+ClLlU+BV4EnjDGLrLXT/BGoiIiIiIgUnT//3OReNXjOnJm51rvppluJiIjgnXc+YPz4sSxduohp\n076nUqXKXHttF/r3v5uqVaOKK2zxs3797iI6uhqzZv3Ijz9OIzKyCt263USvXre7VxnPdMMNNxIR\nUZHvvpvEjBk/ULFiRa666hruuuteKlasVELvoGB86SF+AQgFulhr5wAYY9wnrbUfG2N+A5YDAwEl\nxCIiIiJyxnv88acL3UZoaOndhqZNmyuYOXO+1/UjIiJ48MFHefDBR4swqqIXExPrl3YqVKjgl3aK\nw5VXdubKKzt7PBcQEECXLl3p0qWrV2116NCJDh06+TO8YuXLoO6OwPLMZNgTa+0qYDHQzMe4RERE\nRERERIqULz3ElYGca2vnlAREetuoMSYYGADcDzQAdgOjgf9Yaz3v+Jz9+guBYUB7IAzYAnxorf3M\n2xhERERERESk7PClhzgBaGmMCcitgjEmEGiZUddbHwHvAAdxzU3eCbwCeJ7Nn/1+zYGlwA3ATOAT\nIAL4v8zVr0VERERERESy8iUhngrUA4bmUedlIBaY7k2Dxpg2wAPAd0B7a+0zuHp6vwJ6GGPyG8D+\nKhAO3Gqtvd1a+zhwIa5e4ieNMQ28iUNERERERETKDl8S4uHADuA5Y8xSY8zwjPIGxpinjDG/AM8B\newFve2czZ+IPtdY6ATKO/8a1pdN9+Vx/CXDYWjs1s8BaewxX73Ig0NrLOERERERERKSMKHBCbK09\nBHQCfgMuAzKX22uLK1luD2wCrrbW7vOy2fbAAWvthtPutQtXL2+HfK4/CFQyxlQ5rbx2xnG/l3GI\niIiIiIhIGeHLolpYa+OAy40xbXElx3WAIFwLYf0KzMvs6c2PMSYU1/DqFblUiXNVM9Wstbkltp8C\n7wMTjDEDcPVO9wTuAlYDC72JRURERERERM4MTicE5LqylXd8SogzWWuXAEsKFwJVM45JuZw/knGs\nTC49vdbaD4wxabgW49qa5dRcoLe1Nr2QMYqIiIjIGSAtLY20tDSCgwv131wROSM48W0W8D9Kw78U\n5TKOJ3M5n1me6y7mxpjLcM03TsU1bzgJ6AxcDbxijBmQV491REQowcFBBY27xERGnjmbfsuZQc+U\nyJlHf2/F386WZ+r48ePs2bOT2Nh6JR1KmRcUVLhEReR0pz9TDgdUqRJBQCG6iQucEBtjUgtQ3Wmt\nDc2nzomMY0gu5zOvP55LPJWAH3H9auAia+2WjPIQYDyuBbs2AR/nFsCxY7nl4qVTUlJKSYcgZxk9\nUyJnHv29FX87m56plSt/o3btuoX6T7IUXnq6o6RDkLNM1mfK4XBw6lQaR46cyOOKf1SrVtFjuS89\nxN5esxnw5l/WI4AD15BoTypnqefJjbiGXb+SmQwDWGtTjTGPAbfimkuca0IsIiIiImeP2bNnAtCq\nVWtq1qztcfi00+nVcjd+53QWf5LocJTM7MH09LQSuW9JKYlnqiSeJyi5ZyotLQ1wkp6eDjgJCcl1\nELHXCpwQW2s9jn0wxgQCkcDluLZbSgE6etFeqjEmHshtr+AGwP6M1a09qZNx3Oyh7b3GmANA3fzi\nEBEREZGzx+zZM5k9eybh4eEeE+Lzzz+/BKKCRo0aFfs9jTHFfk+ASy9tXyL3ffrpx0vkviXxTJXE\n8wQl/UwFEhIS7LcRIH6bQ2ytdQCHgB+NMX/gWtxqKDDEi8sXA/2NMY2z9vIaY2oBjYHpeVy7N+PY\n+PQTGdswRQHrvHoTIiIiInJWOX7c46w7UlJKZoj4yZPFP1XP1ZtW/IKCSma5oiNHchtYWrRK4pkq\niecJzq5nqkhmumfsH7wAuM3LS77KOL6e0dOMMSYA177GAJ/lce0MXL3RA4wxDTMLjTFBwDtAAK6F\ntkRERERERETcivLXNsFANW8qWmvnGWMm40qglxljFgBtgHbAd7gWzQLAGPNyxjWZx30Zc4VHAWuM\nMd/hWmX6SqA5rj2I3/PPWxIREREREZGzRZH0EBtjOuFKSLcV4LL+wItANDAIqJHxut9pWya9lPHj\nZq0djWuLpWXALbhWlg4FXgCutdaeWctIi4iIiIiISJHzZduln/JprwbQLOP1aG/btdaeAoZl/ORV\nz+PsaWvtAlzDtEVERERERETy5cuQ6eu8qJMGfI5rDq+IiIiIiIhIqeNLQtw5j3MO4Biw2Vp7zLeQ\nRERERERERIqeL/sQ/1wUgYiIiIiIiIgUpyJZVEtERERERESktMu3h9gYs6kQ7Tuttc3yryYiIiIi\nIiJSvLwZMt2kEO07868iIiIiIiIiUvy8SYjPLfIoRERERERERIpZvgmxtfav4ghEREREREREpDj5\nsu1SvowxwUANoJu19pOiuIeIiIiIiIhIYfiUEBtjHgIGAHWBECAgy+nA014rIRYREREREZFSp8AJ\nsTGmF/CxF1X3AlMKHJGIiIiIiIhIMfBlH+KHca0ePQSIxNVT7ADqAFWBPriS4WDgNf+EKSIiIiIi\nIuJfviTEzYE/rbVvW2uTgSUZ7XS01iZZaycDPYAo4Bn/hSoiIiIiIiLiP74kxBHA5iyvN+PqMW6Z\nWWCtXQqsBroUKjoRERERERGRIuJLQnwYCMt8Ya09CewCzjut3jZcw6hFRERERERESh1fEuI1QFtj\nTOUsZRuB1saYoCxldYHjhQlOREREREREpKj4khCPASoBS40x3TPKZuBaUOtDY0wDY8y/gNbAJr9E\nKSIiIiIiIuJnBU6IrbUTgVFAU6BfRvEXQBzwAPA/4J2M8uGFD1FERERERETE/3zpIcZa+wDQBvg8\n4/UJoB0wAVdCvAC40Vo7009xioiIiIiIiPhVcH4VjDFVrbWHTi+31i4/7fVOoL8fYxMREREREREp\nMt70EO80xkw2xlxT5NGIiIiIiIiIFJN8e4iBIKAncKsxZicwGhhjrd1epJGJiIiIiIiIFCFveohr\nAgOB34FY4AVgqzFmnjHmdmNMaFEGKCIiIiIiIlIU8k2IrbUHrbUfWmtbA01wrRydAFwJjAN2G2M+\nMsZcXLShioiIiIiIiPhPgVaZttZusdY+Z61tAHTCtSdxIPAw8JsxZo0xZoAxpqr/QxURERERERHx\nH5+2XQKw1i601t4L1ABuB2YD5wEj+Wchrmv9E6aIiIiIiIiIf3mzqFaerLV/A5OAScaYasDNQI8s\nx0LfQ0RERERERMTf/J2sVgaqZByD/Ny2iIiIiIiIiN8UOiE2xtQEeuMaNn0REAAcB8YCXxa2fRER\nEREREZGi4FNCbIypDNyKKwluj2sucgCwBFcS/I219ri/ghQRERERERHxN68T4oz9hm/ElQRfB4Tg\nSoJ3A18BX1prtxZFkCIiIiIiIiL+lm9CnLFS9O3ATUAEriT4FPA9rt7gWdZaR1EGKSIiIiIiIuJv\n3vQQzwScuBLhdcBoYLy19kBRBiYiIiIiIiJSlLxJiI8AE3ANif69iOMRERERERERKRbeJMQ1rLUn\nC3sjY8w4oI+1VvsSi4iIiIiISIkLzK+CP5LhLAL82JaIiIiIiIiIz/JNiEVERERERETORkqIRURE\nREREpExSQiwiIiIiIiJlkhJiERERERERKZOUEIuIiIiIiEiZpIRYREREREREyiQlxCIiIiIiIlIm\nKSEWERERERGRMkkJsYiIiIiIiJRJSohFRERERESkTCrOhPgAsKMY7yciIiIiIiKSq+DCXGyMuRTo\nANQB1lprRxljugIrrLX7s9a11j4OPF6Y+4mIiIiIiIj4i089xMaY+saYxcBSYDjwCNA+4/SLQLwx\n5hb/hCgiIiIiIiLifwVOiI0x1YCFQBvgd+B1ICBLlY1AKDDZGNPSH0GKiIiIiIiI+JsvPcTP4xoi\n/Zy1trW19oWsJ621dwP3AkHAM4UPUURERERERMT/fEmIbwT+tNYOz62CtXYMsA5o7WNcIiIiIiIi\nIkXKl4S4JrDBi3r/A2r40L6IiIiIiIhIkfMlIT4InONFvcbAIR/aFxERERERESlyviTE84Hmxpgb\nc6tgjLkJOB9Y4GtgIiIiIiIiIkXJl32IXwVuBr41xrwP/JJRHmGMaQNcDwwGUoE3/BGkiIiIiIiI\niL8VuIfYWmuBW4DjuBLfaYAT6A4sAp4F0oF+1tp1/gtVRERERERExH986SHGWjvHGNMYuA/oiGsb\npiBgN/Ar8Jm1dqe/ghQRERERERHxtwInxMaYIGtturX2APCfjB8RERERERGRM4ovi2rtNsa8b4y5\n1O/RiIiIiIiIiBQTX4ZMRwKPAY8aY/4HfA2Mt9Zu82tkIiIiIiIiIkXIlx7i6sCDuFaXbgQMBbYa\nYxYbYx4yxlT1Y3wiIiIiIiIiRaLAPcTW2sPA58DnxpjqwG1AH6ANcDnwnjFmFq6e42nW2lQ/xisi\nIiIiIiLiFz6tMp3JWrsXeB943xhTF+iNa0umbhk/RwD1GIuIiIiIiEip48uQaY+stTuAxbiGUu8C\nAoDK/mpfRERERERExJ8K1UMMYIy5DNew6VuBWrgS4V3AW8C4wrYvIiIiIiIiUhR8SoiNMRfjSoJ7\nAXVwJcHHcCXA44D51lqnv4IUERERERER8bcCJ8QZWy01wJUEpwOzcSXBU621J/wbnoiIiIiIiEjR\n8KWHuCGwGlcSPNFau88fgRhjgoEBwP24Eu7dwGjgP9baU15cXx54CugH1AV2AtOAodbaJH/EKCIi\nIiIiImcPXxLiZtbazX6PBD4CHsC1MNc0oC3wCtAc1/zkXBljygEzgY7AQmAq0BoYBFxujGmv7Z9E\nREREREQkqwKvMl0UybAxpg2uZPg7oL219hmgPfAV0MMY0zWfJv6FKxl+01rb0Vr7lLW2I64k+1Jc\n20GJiIiIiIiIuOXbQ2yM2QI4gWuttXEZr73ltNYaL+o9mnEcmrkYl7XWaYz5N9AfuA+Ykcf1jwFx\nwHOnlb8FRACa2ywiIiIiIiLZeDNk+hxcCXFIltfe8nal6fbAAWvthqyF1tpdGQl4h9wuNMacB9QD\n3j99rrG1Ng64qwDxioiIiIiISBnhTULcIOO487TXfmGMCQVigRW5VIlzVTPVrLX7PZw/P+O40Rhz\nPa5e4pZAEjAReNFae9yfMYuIiIiIiMiZL9+E2Fobn9frvBhjIr2oVjXjmNtK0EcyjpUBTwlxrYxj\nN6Ar8BPwKa45xU8ArY0xV3qzUrWIiIiIiIiUHb7sQ7wN+K+19sl86n0NXAXUzKfJchnHk7mczywv\nn8v58IxjV+ABa+3nGfcPwtVD3BN4BBiZWwAREaEEBwflE2bpERlZoaRDkLOMnimRM4/+3oq/6ZkS\nf9MzJf5WFM+UL9su1Qdi8qpgjKkINAO86SHOXPAqJJfzoRnH3IY9OzKOf2QmwwDW2nRjzBBcCXEv\n8kiIjx3LLRcvnZKSUko6BDnL6JkSOfPo7634m54p8Tc9U+JvhXmmqlWr6LHcm1WmlwOXZClyAn2N\nMX29uO8aL+ocwZXUVs7lfOUs9XK7HmD16SestfHGmCSgkRdxiIiIiIiISBnizT7EjwHpuJJWR5Zy\nRy4/6bh6c9cAD+bXuLU2FYgn98W6GgD7rbWHcjm/NeOYWw9zMKBfT4mIiIiIiEg23iyqtYosyaYx\nxgF8ba29w49xLAb6G2MaW2vd+xwbY2oBjYHpeVz7G5AKdDDGBFlr07Nc3wTXPsQ/+zFWERERERER\nOQt400N8uruBz/wcx1cZx9eNMYEAxpgAYHhGea73s9YeASYDdYFnMsuNMeWANzJefunneEVERERE\nROQMV+BFtay1Y40xIRkLVlXPutq0MaYb8DowDnjLWuvIrZ3T2pxnjJkM3AYsM8YsANoA7YDvgB+z\n3OPljGteztLEk8DlwKvGmI7AWlwrXLcAJltrpxX0fYqIiIiIiMjZrcA9xMaYCOAX4D9A99NOx+Ba\nXXo4MNcYE4r3+gMvAtHAIKBGxut+1lpnlnovZfy4WWv3AZcB7wNNcM17DgOeArxZ/EtERERERETK\nGF+2XRqMK/mclfFnN2vtF8aYecBHQBfgaeAVbxq11p4ChmX85FUvIJfyg8C/Mn5ERERERERE8uTL\nHOKeQAJwk7V28+knrbXxwK3AbtQ7KyIiIiIiIqWULwlxfeC3jO2SPLLW/o1r9ed6PsYlIiIiIiIi\nUqR8SYiTgFpe1KsKHPWhfREREREREZEi50tCvBi4zBjTObcKxph2wBXAUl8DExERERERESlKviyq\n9RZwMzDNGPM+MAPXnGKAWFyLaQ0EnMAIfwQpIiIiIiIi4m8F7iG21q4C7gPSgCG4tmD6K+NnIfBv\nXIn2A9Za9RCLiIiIiIhIqeRLDzHW2nHGmAXAnUAHoGZGW3uAJcAX1trtfotSRERERERExM98SogB\nrLWJwGsZPyIiIiIiIiJnFF8W1RIRERERERE54+XbQ2yMmYNrgax7rLU7M157y2mtvdbn6ERERERE\nRESKiDdDpq/GlRCHZ3ntLWeBIxIREREREREpBt4kxJ0yjjtOey0iIiIiIiJyxso3IbbWLszrtYiI\niIiIiMiZSItqiYiIiIiISJnkzaJaXxaifae19t5CXC8iIiIiIiJSJLyZQ3yXh7Ksi2UF5HI+IOOo\nhFhERERERERKHW8S4v6nvQ4AngGaAt8APwBxQBpQE7gBuAdYmVFPREREREREpNTxZlGt8VlfG2Me\nxpUM326tnezhkunGmGnAdOAKYJE/AhURERERERHxJ18W1foXsDyXZBgAa+1PuBLh+30NTERERERE\nRKQo+ZIQ1wV2elHvIFDdh/ZFREREREREipwvCXE80N4YE55bBWNMNHAlsNXXwERERERERESKki8J\n8XggBphhjDnn9JPGmObAbKAS8FnhwhMREREREREpGt6sMn26N4GrgQ6ANcZs458h1PVwDakOAL6x\n1n7slyhFRERERERE/KzAPcTW2pNAZ+ApYBvQCGif8VMP2Aw8YK3t7cc4RURERERERPzKlx5irLWn\ngLeAt4wxtYBagBPYaa3d48f4RERERERERIqETwnxaXYDJ4H/Z+++wySrqoWNvzMDDCJJJCsoQZYJ\nJIcBEUWvIiZMmC9eEQMYMcBVEVFBMXuBD7NigguIYPYqICCCIklUFkEBSUrGIcP098faNV000zPd\nNdUzPV3v73n6OdN1Tp3eDadPnbX32msPZebNfTifJEmSJEkTrueAOCKeCewL7AAsB3wH+M+IOJaq\nRP2hzLyrL62UJEmSJKnPeqkyTUR8lKok/WxgJlVEa1rbvRnwLuCXEbFsPxopSZIkSVK/jTsgjojd\ngA9QBbV2pZZX6rYbcB4wC3jzwjZQkiRJkqSJ0MsI8TuAu4CdM/NnmXl3987M/BM1cjwbeM3CN1GS\nJEmSpP7rJSDeDPhNZl412gGZeRNwOrUkkyRJkiRJk04vAfF0aomlBVma/lSxliRJkiSp73oJiC8G\ntomIlUc7ICJWAbZux0qSJEmSNOn0EhB/E1gF+H5ErDpyZ0Q8klqCacW2lSRJkiRp0uklpflI4HlU\n4awrI+Iv7fVZEfFLYCtgJeAM4Ii+tFKSJEmSpD4b9whxZj4APB/4OHAvsEXbtT7wTGAZ4H+AZ2fm\nfX1qpyRJkiRJfTXuEeKImJGZ9wMfioiPApsD6wAzgOuAP2Tmnf1tpiRJkiRJ/dVLyvSZEXFFZu6e\nmfcCZ7UvSZIkSZKWGL0U1dqYKpglSZIkSdISq5eA+GZg+X43RJIkSZKkRamXgPi9wLYR8amIWKff\nDZIkSZIkaVHoZQ7xbsCVwLuBd0fErcAtwJx5HDuUmbEQ7ZMkSZIkaUL0EhC/dMT3j2hf8zLUw/kl\nSZIkSZpwvQTE6/W9FZIkSZIkLWLjDogz88qJaIgkSZIkSYvSmAPiiFgd2BVYHbgK+Glm3jZRDZMk\nSZIkaSKNKSCOiL2BTwEzu16+LSL2yszjJqRlkiRJkiRNoAUuuxQROwP/AywLnAscB/wFWBn4bkRs\nNqEtlCRJkiRpAoxlHeK9qWrRr8/MrTJz98zcGNgfWLrtlyRJkiRpiTKWgHgr4LzM/Fb3i5n5SWou\n8Q4T0TBJkiRJkibSWALiVYHLR9l3PvCo/jVHkiRJkqRFYywB8TLAPaPs+zewXP+aI0mSJEnSojGW\ngLxnJ0wAACAASURBVHjahLdCkiRJkqRFbCwBsSRJkiRJU44BsSRJkiRpIC01xuO2i4ivz+t1gFH2\nAQxl5ht6apkkSZIkSRNorAHxBu1rNHuM8voQYEAsSZIkSZp0xhIQf2TCWyFJkiRJ0iK2wIA4Mw2I\nJUmSJElTziIrqhUR346I+xfVz5MkSZIkaX4WdZVp1zSWJEmSJE0KLrskSZIkSRpIBsSSJEmSpIFk\nQCxJkiRJGkgGxJIkSZKkgWRALEmSJEkaSAbEkiRJkqSBZEAsSZIkSRpISy3uBnRExFLA24A3AusB\n1wHfAD6RmfeN81wzgN8C22Smax9LkiRJkh5iMo0QHw58FrgJ+AJwDXAQ8P0ezvVOYJv+NU2SJEmS\nNNVMioA4ImYBewHHATtm5n7AjsBRwEsi4nnjONeGwEcnpKGSJEmSpCljUQbENwJXjbJv77b9SGYO\nAbTt/sAQsOdYfkBETAO+ClwLXLJQrZUkSZIkTWkLNYc4IrYBngasA1yQmV9to7lnZ+YN3cdm5ruA\nd41yqh2BGzPzohHvuTYiLmk/Yyze1I59BvC5sf8mkiRJkqRB09MIcUQ8NiLOAM4EDgHeSgW1AAcA\nV0bEi8d4rpnAo4HLRznkCmDliFhtAedZBzgU+FpmnjKWny1JkiRJGlzjDohbYPobYBbwR+BgoLuS\n85+BmcAxEbHZGE65StveOsr+29p2pQWc50vAbOA9Y/iZkiRJkqQB10vK9AepFOkPZOYhABHxgc7O\nzHx9RPwG+DqwH7D7As63dNveM8r+zuvLjnaCiHgdsAvw0swcLbAe1fLLz2SppWaM922LzcorL7e4\nm6ApxmtKWvL4d6t+85pSv3lNqd8m4prqJSB+AXBxJxiel8z8ZkS8E9h6DOe7q22XGWX/zLa9Y147\nI2INar7wCZl5/Bh+3kPMnj1aLD453XrrnYu7CZpivKakJY9/t+o3ryn1m9eU+m1hrqnVVlthnq/3\nMod4LeCiBR4FlwFrjuG424A5jJ4SvVLXcfNyODCD4UrVkiRJkiQtUC8jxDcBG47huI2Amxd0UGbe\nGxFXAuuNcsh6wA2ZOdq5XtK210bEQ3ZGxBBwZWY+doEtliRJkiQNjF4C4pOBV0XECzLzpHkdEBEv\nAp4MfG+M5zwDeG1EbJSZc9cPjoi1qcD6R/N570dGef3NwBpt/7jnFUuSJEmSprZeAuKPAbsBx0bE\nF4FT2+vLR8Qs4LnAvsC91DJIY3EU8Frg4Ih4eWbOiYhp1JJOAF8e7Y2ZeeC8Xm9B+Rqj7ZckSZIk\nDbZxzyHOzAReTBW52hc4CRgCXgicDvw38ADwmsy8cIzn/BVwDJX+/LuI+AS1tNPrgOOAn3SOjYgD\nI+LA8bZbkiRJkqRuvYwQk5m/jIiNgD2BnahlmGYA1wGnAV/OzGvGedrXUmsY7wG8E7gKOAA4NDOH\nuo77cNse2EvbJUmSJEmCHgLiiJiRmQ9k5o3AJ9rXQsvM+4CPtq/5HTdtjOfbtB/tkiRJkiRNTb0s\nu3RdRHwxIrbpe2skSZIkSVpEekmZXhnYB9g7Ii4DvgN8NzP/1teWSZIkSZI0gXoZIV4DeBNVXXoD\nalmjSyPijIh4c0Ss0sf2SZIkSZI0IcY9QpyZtwBfAb4SEWsAuwOvBGYB2wGfj4ifUyPHJ2XmvX1s\nryRJkiRJfdFTlemOzPwn8EXgixGxLvAKakmm57ev2wBHjCVJkiRJk04vKdPzlJlXAWdQqdTXAtOA\nlfp1fkmSJEmS+mmhRogBImJbKm36pcDaVCB8LfBp4NsLe35JkiRJkiZCTwFxRGxBBcEvB9ahguDZ\nVAD8beDkzBzqVyMlSZIkSeq3cQfEbaml9agg+AHgF1QQ/MPMvKu/zZMkSZIkaWL0MkK8PnAuFQR/\nPzP/1d8mSZIkSZI08XoJiJ+UmX/te0skSZIkSVqExl1l2mBYkiRJkjQVLHCEOCIuAYaAZ2fmFe37\nsRrKzOi5dZIkSZIkTZCxpExvSAXEy3R9P1ZWmpYkSZIkTUpjCYjXa9trRnwvSZIkSdISa4EBcWZe\nOb/v5yciVu6lUZIkSZIkTbRxF9WKiL9FxKfHcNx3AAtwSZIkSZImpXEHxMBjgdXnd0BErAA8CXCE\nWJIkSZI0KY2lyvRZwFZdLw0Br46IV4/h/Of32jBJkiRJkibSWEaI9wEeAOa0r445o3w9ANxBBcNv\n6mdjJUmSJEnql7EU1TqH4SWXiIg5wHcy83UT2TBJkiRJkibSWJZdGun1wOX9bogkSZIkSYvSuAPi\nzPzWWI+NiI0z80/j/RmSJEmSJE20XkaIiYjNqfnB61Lp1NO6dk8HlgXWBB7d68+QJEmSJGkijTtY\njYitgNN4cCA8xIOD4qG2dXRYkiRJkjQp9bIO8X7ATOB44HnAEVQA/DzgBcDhVLXpvwLb9KeZkiRJ\nkiT1Vy8B8SzgWuDVmflT4PvtPEtn5o8z823AXsATgXf1raWSJEmSJPVRLwHxKsC5mXlf+/6itt2i\nc0BmfgP4O7D7wjVPkiRJkqSJ0UtAfAfwQOebzLwNuAl4wojjzgc27L1pkiRJkiRNnF4C4gQ2j4jp\nI17bcsRxy/fcKkmSJEmSJlgvAfEPgHWA70XE+u21U4F1I2IPgIjYGtgJ+NvCN1GSJEmSpP7rJSA+\nDDgXeDnwha7X7gK+FhHXAGdSSzp9tR+NlCRJkiSp38YdEGfmXcAOwPuAn7fXrgeeD1wBrAXcAxxK\nLcEkSZIkSdKks1Qvb8rMu4FPj3jtFGCDiFgNuCkz5/ShfZIkSZIkTYieAuL5ycwb+n1OSZIkSZL6\nbdwBcUR8fQyHDQH3AbcBlwM/zcyrx/uzJEmSJEmaKL2MEO9BBbwA00Y5ZuT+eyLinZn5pR5+niRJ\nkiRJfddLlekNgN9TQe/XgOcAjweeAPwHVXH6fmpk+KXAvsCNwGERsUMf2ixJkiRJ0kLrZYT4NcDW\nwCsy89gR+xL4VUT8FPgJsGFmHhoRPwQuBt4JnLEwDZYkSZIkqR96GSHeAzh7HsHwXJn5c+C3wF7t\n+7+377fr4edJkiRJktR3vQTEawFjKZD1T+BRXd/fAKzSw8+TJEmSJKnvegmIrwJ2iIiHjXZARCwL\nbA9c1/XyGlRQLEmSJEnSYtdLQPw9YE3guIhYdeTOiFgFOIYKgI9trz0ZmAWc33tTJUmSJEnqn16K\nan2aqiy9C/CPiDiTGjWeDqwLbAvMpILfj7ag+by2//B+NFqSJEmSpIU17hHizLwT2Bn4OHAH8HTg\nP4HXAk8D7gU+A+yYmbOpIPlG4D2Z+Ys+tVuSJEmSpIXSywgxmXkX8KGI+DCwORX0Lk0V2/pjZt7d\ndey5VCEuSZIkSZImjZ4C4o7MnAOc074kSZIkSVpi9BwQR8QTgLdTadLrAMdn5h4RcRhwMXB4Zg71\np5mSJEmSJPVXL1WmiYg3UoWy3gQ8Hnh417l2Br4A/G9E9HR+SZIkSZIm2rgD1ojYETgSuB3YB9ho\nxCH7UlWnXwy8bmEbKEmSJEnSROhlBHc/4H7gWZl5RGZe1r0zM39KVZ6+F9hr4ZsoSZIkSVL/9RIQ\nbwucnpkXjHZAZl4B/AZ4XI/tkiRJkiRpQvUSEC9LrT+8IPcDy/VwfkmSJEmSJlwvAfFlwFYRMXO0\nAyLiYcBWwOW9NkySJEmSpInUS0B8NLAmcGRELDNyZ3vtCGBV4LiFa54kSZIkSROjl4D4s8C5wH8C\nl0fEse31TSLiy8Bf2r6L27GSJEmSJE064w6IM/Nuaq3h71IjxS9puzYB9gTWB34EPD0zZ/epnZIk\nSZIk9dVSvbwpM28DXhsR7wd2BNYBZgDXURWo/9a/JkqSJEmS1H89BcQdmXktNadYkiRJkqQlygID\n4oh4YCHOP5SZCxV0S5IkSZI0EcYSrA61r7GaRm/FuiRJkiRJWmQWGBCPZ4Q3Il4I/D+q2NbdwAd7\nb5okSZIkSROnL+nMEbEKcBiwOzVCfAbwhsy8tB/nlyRJkiSp3xY6II6IlwH/A6wO3AHsn5mHLex5\nJUmSJEmaSD0HxBGxOnAEsBs1KnwysGdmXtGfpkmSJEmSNHF6Cogj4jXA54BHArcD783Mr/SzYZIk\nSZIkTaRxBcQRsRbwZeC51Kjwz4C9MvOaCWibJEmSJEkTZjwVpP8L+DSwMnAL8K7MPGqiGiZJkiRJ\n0kRaYEAcEesCXwGeSY0KnwC8JTP/1c+GRMRSwNuANwLrAdcB3wA+kZn3jeH9WwAfAp4KrAD8AzgW\n+Ghm3tHPtkqSJEmSlnxjGSH+E7B8+/c/gJuAj0XEWM4/lJlvGmNbDgf2opZsOgnYHjgIeArw0vm9\nMSKeDvy8fXs8cC2wI/B+4BkRsWNm3j3GdkiSJEmSBsBYAuIVuv69DrDnOM4/BCwwII6IWVQwfBzw\n8swciohpwDeB10XE8zLzx/M5xRHAdGD7zPx9O+c04EvUiPNbgc+Oo92SJEmSpCluLAHx6ye8FbB3\n234kM4cAWlC8P/BaKgifZ0AcEU8EHg8c3wmGu95/EBUQ74IBsSRJkiSpywID4sz81iJox47AjZl5\n0YiffW1EXAI8bT7vvZ1Kjb5oHvvuadvl57FPkiRJkjTAelqHuJ8iYibwaODsUQ65og6L1TLzhpE7\nM/Nq4NBR3rtb2/55YdspSZIkSZpapi/uBgCrtO2to+y/rW1XGs9JI2INqigX1NrJkiRJkiTNtdhH\niIGl2/aeUfZ3Xl92rCeMiJWAnwBrAF/snls8L8svP5Ollpox1tMvdiuvvNziboKmGK8pacnj3636\nzWtK/eY1pX6biGtqMgTEd7XtMqPsn9m2Y1pLOCJWo5Zg2pwqxLXvgt4ze/ZosfjkdOutdy7uJmiK\n8ZqSljz+3arfvKbUb15T6reFuaZWW22Feb4+GVKmbwPmMHpK9Epdx81XRGwA/I4Khk8CXpqZ9/ej\nkZIkSZKkqWWxB8SZeS9wJbDeKIesB9yQmTfP7zwRsSlwJrAB8C3gJZm5ZA39SpIkSZIWmcUeEDdn\nAGtGxEbdL0bE2sBGwFnze3NEbAj8ElidWm/49Y4MS5IkSZLmZ7IExEe17cERMR0gIqYBh7TXR60S\n3Y7/PrAa8IXM3DczhyaysZIkSZKkJd9kKKpFZv4qIo4Bdgd+FxGnALOApwLHURWjAYiIA9t7Dmwv\nvQjYkqpGPbuzf4TrM/PIiWq/JEmSJGnJMykC4ua1wJ+BPYB3AlcBBwCHjhjx/XDbHti2O7btTOAD\no5z7AsCAWJIkSZI016QJiDPzPuCj7Wt+x00b8f07qQBakiRJkqQxmyxziCVJkiRJWqQMiCVJkiRJ\nA8mAWJIkSZI0kAyIJUmSJEkDyYBYkiRJkjSQDIglSZIkSQPJgFiSJEmSNJAMiCVJkiRJA8mAWJIk\nSZI0kAyIJUmSJEkDyYBYkiRJkjSQDIglSZIkSQPJgFiSJEmSNJAMiCVJkiRJA8mAWJIkSZI0kAyI\nJUmSJEkDyYBYkiRJkjSQDIglSZIkSQPJgFiSJEmSNJAMiCVJkiRJA8mAWJIkSZI0kAyIJUmSJEkD\nyYBYkiRJkjSQDIglSZIkSQPJgFiSJEmSNJAMiCVJkiRJA8mAWJIkSZI0kAyIJUmSJEkDyYBYkiRJ\nkjSQDIglSZIkSQPJgFiSJEmSNJAMiCVJkiRJA8mAWJIkSZI0kAyIJUmSJEkDyYBYkiRJkjSQDIgl\nSZIkSQPJgFiSJEmSNJAMiCVJkiRJA8mAWJIkSZI0kAyIJUmSJEkDyYBYkiRJkjSQDIglSZIkSQPJ\ngFiSJEmSNJAMiCVJkiRJA8mAWJIkSZI0kAyIJUmSJEkDyYBYkiRJkjSQDIglSZIkSQPJgFiSJEmS\nNJAMiCVJkiRJA8mAWJIkSZI0kAyIJUmSJEkDyYBYkiRJkjSQDIglSZIkSQPJgFiSJEmSNJAMiCVJ\nkiRJA8mAWJIkSZI0kAyIJUmSJEkDyYBYkiRJkjSQDIglSZIkSQPJgFiSJEmSNJAMiCVJkiRJA8mA\nWJIkSZI0kAyIJUmSJEkDyYBYkiRJkjSQDIglSZIkSQPJgFiSJEmSNJCWWtwN6IiIpYC3AW8E1gOu\nA74BfCIz7xvD+1cBDgKeB6wO/BU4NDOPmbBGS5IkSZKWWJNphPhw4LPATcAXgGuoAPf7C3pjRDwc\n+D/gLcBZwGHAysDREbHPRDVYkiRJkrTkmhQBcUTMAvYCjgN2zMz9gB2Bo4CXRMTzFnCKdwCbA2/P\nzFdk5vuATYE/A5+MiNUnrvWSJEmSpCXRpAiIgb3b9iOZOQTQtvsDQ8CeC3j/W4F/Akd2XsjMfwMf\nB5YDXtXvBkuSJEmSlmyTJSDeEbgxMy/qfjEzrwUuAZ422hsjYgPgUcDpmfnAiN2ntO2o75ckSZIk\nDabFHhBHxEzg0cDloxxyBbByRKw2yv4N2vYh78/M64G7gY0WspmSJEmSpClmsQfEwCpte+so+29r\n25VG2f/IBbz/9vm8V5IkSZI0oCbDsktLt+09o+zvvL7sQrx/ufk1YLXVVpg2v/3zc9FFFy34IGmM\nvJ7Ub15T6jevKfWb15T6zWtK4zEZRojvattlRtk/s23vWIj3j/ZeSZIkSdKAmgwB8W3AHEZPa16p\n67h5uWXEcSOtOJ/3SpIkSZIG1GIPiDPzXuBKYL1RDlkPuCEzbx5l/yVdxz1IRKxFpVrnwrZTkiRJ\nkjS1LPaAuDkDWDMiHlQNOiLWpipEnzXaGzPzKuAqYIeIGPn77NS2v+tfUyVJkiRJU8FkCYiPatuD\nO0FtREwDDmmvf3kB7/82tXTTPp0XImIF4APUHONv97W1kiRJkqQl3rShoaHF3QYAIuJoYHfg98Ap\nwCzgqcBxwMszc6gddyBAZh7Y9d4VgXOAxwE/oNYkfgmwPvC2zDxsUf0ekqSpKSKWzcy7F3c7JElS\n/0ymgHhpYD9gD+BRVBr0t4FDM/OeruOGADJz2oj3rwEcDDwfeDhwMfCpzDx6UbRf0tQREctTGSrL\nZOabImJ6Zs5Z3O3S4hER6wM/Bs4D9szMuxbwFkmStISYNAGxJE0mETEHuBtYMzNvX9zt0aITES8E\nPga8OTN/GxGPAU4FbgZekZmXLs72SZKk/pksc4glaVKIiBntn8dRVeq3aa9PG/VNmhK6CjMG8CTg\n2e37G4FfU9NynrAYmiZJkpqImNb1vLbQDIi1yEXEmhGxzOJuh9QtIqZHxFIM3xd/3bY7t60B8eD4\nFXA78Kz2/d3AmcDywMaLq1GSJAkycygzH+jX+UyZ1oSLiFWBmcA7gLdTy2jtmpl3LNaGSaNoI4WP\nBq4Azs7M7RZvizSRImJap3Bj+/5hVHHHLYDVM/OWiNgc+A3wC+ANmXnb4mmtpKmuDRo80M8HfmlJ\n057FplN/C0Pdn9UR8STgaW3/iZn5j4X5WUstdGul+YiIvYAjgS8BLwaOAW43GNai1kl57g58RuwP\nYC9gN2p08GhqzugTIuLRmXn1omqrJla7FqYDZOYDI4LhaZl5V0T8Edga2J4qqHU18GdqhHg94PxF\n3nAtVq1zd1fgycC5wG8y89rF2yot6ToP+RGxCfBK4OnU/en0iPhGZl60eFsoLTotU++BNgI8B5jT\nXl8+M2e3f38IeB9VRBnglRHxocw8eWQH91gZEKsvImJbYHXgvBG9NH+l1oJ+E/DfwOe6q4ZLE6kT\n+IwMeuZxzPLAodSc0V9QKbL/CaxI3Se3B46x2vTU0K6FB2DunPFNgDuAy7r+/54JvAXYhQqIbwd+\nS613/0QMiKe8dm/YGViGWs7xeGo5xznAcsAlEfGezPyx9waNV0SsB/wzM++MiFdQKxs8jKpm/0jg\nXcDrIuIVmfnrXh/0pSVJZt7f+XdEPBZ4L/BM4N8R8U3gvvbap6jP4S2BDwLvBk7u9W/EgFg9axfq\nvlTgsDz1kHBju2A/n5nXA38H/kSNtJydmfe0B9A53tg10UYEPo8HNgDOycx/ttdmZOYDEfEeasm2\nQ4BDMnN2RKwGfAR4M7ATld2gJUhLtxrqSrHqjMSsCLyUWuZvS2Bp4F/A8RHxwVZV/FyqmFZnDvm9\nVJD8LiqA/t6i/F20aETEmsBNmXkfNW3i08BGVAr9MsBrqGvlicD/A74dEZtm5pWLqclaAkXE94AX\nAttExC3AAdQ95k1UJsp1VLbSLsClMHp2kzSZRMRMYG3gH93Bbdf+GcC0ee1r+58KfJzKLH0WsB31\n97AZ8EWqc/KzmXlQe8tJEbE78JyIWLvXrB0DYo1JW+f5P4Ah4ATgHmrE9w3ASVQRmqWoh8z3AWtE\nxLuAa4ALqYB43Xa6IW/s6oeuAGeePedtPeE3A2+jHm7vAWZHxLeAT2bmjS0Ncmtq7fNPdlJyMvOG\nlpbzamqeCo4ALVk6/78iYl1gtcz8Y0QsC7yHuiYuBL5BBcQ7U6O/VwOHZuZfI+JC4BkR8ZjMvDIi\n/kIFyZtFxBqdjhUt2SJiF+p62Bq4FfhZRBwCXAv8hAp+nwU8OTMvaW87IyIeST247RMRB2Xmvxd9\n6zXZdH0uLUtVrL+8dbJ2Xl8eWBnIzLwoInYAHg98ITN/2XWq49uXtCR5I7AmlXX3kCUru+fFR8Qj\nMvOWEYfcB+xApUOvCewJ/I6aqnQiNbBxUnv/cpl5J5XZtw813eC7vWRTGBBrVBGxPbXkzJXAgdQy\nJN/MzO9ExB7URfrNzPyvrvf8APgM8Crg/Mz8QkSc1Y59LBhUaOHMKw26PWQ8DFghM/8VEUu3EZ7/\nonreL6SuyzlU+vO+wKbUQ+7d1APvA93rDbcb6k0R8Qvg+RHxZOdyTR7d18E89i1H/f+9knqg3Jr6\nQN0eeA6VXnUE8AXgisy8r80h/y3wrIj4Vgt2/wA8g8oQ+BbwTyqd8YnUh7IB8RKoXTvvp7Kbngt8\nlkqZ/zWwObA3sBrweuACYDZ1LXXmry2TmfcCPwRe1M7xXeB801oHW0Q8sn1urEHVoXga9Tn0TVpx\noHbohtSIMNSI8BCwR0Tc2F67kxpQWIaaivbnRfMbSL2LiEdRmVerA0cBt3dPJ2nzg3eiBtO2o9Kg\nf0N9vp7T7p1/Ai6hRoRfn5k/a6c/LyL+j7ovb0RlcXX+nk6mAuJnU/fiadTf1Ji57JLmioi1IuLg\niDgrIrYCnkeli32WGhV5JfDF9rA5i+rF+WJ777T2IPCPdvzSwK7t4v8D9bCxSUSsvMh/MS3RImJG\ndK0B3F1qPyK2iLIVNbJzYDvmvohYHziYeqB9QWZ+MTMPy8xXUjffnSNi9zYiPBtYJiI2aOedW3SJ\nqoo+E9ix7fO+uRh1/vuPtuRCu+d8AjiDun+tSHWKfLUdsgdV1+CQzLy0XSvLU+sL309lsjypHXtW\n2+7StrPbedfuOkZLmPbQ9XBq9O7XVK2L/6I6cp/aXns5sBVwDnALcBt13UB99kFVoT+V6ux9dNe5\nNSAiYpWI2CMi/i8i/k5lFxxEdb6+g+pwPTAiVuy6X91JzUH/a3v9FuqeNRv4aPv6DBVQHwWcEhHv\naz/P5f80md1MfUauAiwfETNHDILtRgWss4DTqU7lt1JTUl4C0Iru/rYdvxTM7eSGqudBez8MB8Rn\nAv9mIbL5fLAbcBGxdkT8MCK+TPWWv4vqWVmbuhlDBbfvysxjMvN86gb/JOqGfzHMfTjtpK6eRQUh\nG1MFSC6lekA3oVIevKlrVFHrAc+9N82jCvDDIuIjEXE9FbD8nOERnvW6TrUL9dDxkcy8OSKWjoh1\nI2JTqoMH4A0RsQpwGnXNb9Je774+z23bp/Xvt9RYjOwMgQelQT8xIvaOiHdExHpdgfL9VKGN64Hd\ngf0y82OZ+Y12ii+016+LiGUi4hlU2uuB1IjNOgyvNXweFfR0PmTvZThI3rTNhdKS6cdUSvQa1Hy0\nC9u95nrg2+2YZ2Xm36jPr8d13tiVmXIXNdXiYQw/mGlAtHvTp6l7yorUQ/zSVAbKsdR18wmqk+2A\nlmIPNbo1nSqo1clKOoDqdP0vagRsNypQ+CzVIbtPRKxsh4smg/bZ/JDPv3ZPvJt69vohcFfLNqUN\nXHyeev56FfD2zHwmsC01R/jjEdEJdE9v243atlOM9+S23a79vPtb3PEv6vN6nYh4Qi+/kwHxAImI\nVSPimVGl/TuWpm7We1JpYq8GXpqZJ2bmBdRFuCb1oU9Xb88d1E164/b6yGvpD1ShrXUy827qg2Jd\nKtVQGlVmzukKelaKiNdFxCdaChrUnOAPAH+hrtnPU9faysDjI6JzjW3Y2UbEc4CDgK8Dv6RSpudQ\nI0N3Az9tx76wbbtTcR/ftlu3VElT/heReVUHj4jNWxr7RVQgeyhVZOOQiFirHXYp9aF7E1UAqTNy\nDLVUzo+pD+ETqf/3r6LmIB0ILAts3D5kr6KC6zUi4int/ZdRQfLGtFFBLZEupkZ+/0V1nnR/jv28\nbXdq27OAtRjuMCMilm7/fFDWkxkkA+UNwOuoUdxXA2/MzM2ogPio9hnyJeD7VAXc3dv7VqY6YjrF\n/ma0Y6/IzG9m5rfaM9iRmfkeqsP20VTQLS127bO5k6k3957XMhne3b6dDnyFyt6DmrK0FvC+zPxt\n19zh66gBuMfSRompmGEIeHJ77nqg/Z3cRn32bxK1DjFUHAM1ygytEOZ478XOIR4AEfECqtDVLCrl\n656IOA94Q2ZeERG/pR7u/piZP2jv6eT8n0bNs3wKwxcbVErEM6n5dX8ApkdEZ5R4JtVbPoP2oNGO\nfye1fqNpZQMsRqwBO4/9a1PX6wnA/lSv+X3AVyLijvbaOcBrOtUEI+JHVGD8PGALKlj+ezvlR6kl\nLKAyF74CnJCZ57T3zqCCogupJS5+kZnfbw+8T6aC55uoDp0dgJ7XudNDRcRSOXq1ye2pkZL/l5mX\nR8Q61IjMlsAnqXTVOcArqGUY1qQyXS4F/gY8ivZh2fkZmTmnZQkcS31Qvzkzv9l+3qOpFK5OvmQJ\nawAAIABJREFUdsvlwO+peaLPoK6fG9v2RVSAZHXhJdPtVIfYbsBKMPfamNbqEFwGbNHmxJ1NdQ7/\nZ0RckpnXtRoFUHU2bqE6SqyRMVg2oz7LjsvMyzovZubBXf/+Z0QcTD2k7x8Rx1LXyzTqHkV72F8V\n2DsihoCPtWuxM2CxMTXaLC1S8eD5v9Pbdbk8VYvjhVSA+8eI+ElmnkZ9rt5BTU1aEzgsM/8cVWDu\nidS0gIyIzahBi42pe+jW1Gf1U6IKbV0aERdRn7GPo67/pajY4mfUs9l2PPjv4hTgw1QH92Hj/V3t\nyZziotYH/hz1YPgxqrrqt6ng+OSodfDOoG7ON7ULHSqYhbrwoEZTYHiS+olt+5r2AHF/V4AwRKWr\n3kT1/EA9eNwHPLtrFEcDqDP3c17BcPM44O1Ur/q61DIUL8jMy6mlkVYFfpKZ17aU1xktrfGL7f2d\na/X3bXsrVfRm2czcLDM/kJnnRMSL2kjjU1tb9qeu0+9GxBlU9eHjqBvu16i5Mat0foc+/ecYOPNI\ngZ7XsgydY95PzcPrjIzsSo3aHZiZ+2fmLzLz/6jOtl8Cu0XE9i3t9QLgEQxXt+8+7yHUB/l/Z+Y3\nu1K/tqaurzUYHg08u207ozt3U5WHvwfk+H57TRbtb/iX1GfdFl27OgMFv6CyBbalRiT+RD3kHRwR\nT23ZVl+jOuG+mpmXLqq2a9I4s20/GxGHRsRnIuJ9EfHWiNin86zTCmK9nwoQPk7NXb+P1pnWnqFu\npK7DA4HvR8T7qZU8TqSuww9n5lVON9Oi1ALgGRGxSvv3BtQ1+Q3qen0E1Rl9atR62X/PzMOpe+ta\nwPotkL6byoxYnlqu7jhqKcsPtuOOBJ6Smc/sGjk+lYpdNm3fd567ftS227dt51nyHOq573edto/n\nd3WEeIrqGsH6BHXBviQzT+nafxGVyrMPFXhcSxWV6ehcYJ35mTvA3Dl0ZOYFEXE0NTJzVETsSxWk\n2Yh6gF2XGlm7rZ3nWiow/xvVO6opLkasN93Vu7gq8GKqd3El6qb3I+D37dgrgR+0Y47KzG93nXZ2\n294Hw9djczLwD2pJnIdl5lkRcS11Ez5jxLG08z+LFkhn5s8i4gZqhHEnapmx86m/oT9k5n4L+Z9k\nYHVfCyPmgy9LLdW2BXBE6xXurA39CKrC6mWZeV5EPJzKSrkF+FxLgV6DSrPagOpdXp4a5T+Lmk90\nN7BVRJyQmXe2DJZOcY6/09b3BOZErau+JxUgrU2lQp5ABUJ/pOYcz2gjg19rX1qynUGNZuwUEV9t\nD1DdD117A8/IzOOjluDakuqU2Y3KTFieSpf9yCJvuSaDE6gRsR2pwQao66Iz2LRfRLw4M3+fmd+K\nWt7rJVQAcB019xzqWfw+Kji4mfrseRH1HHYBNZjxE7AzVv03v4y3iHgpFSO8irrWD6LigQOoTsNr\nqAzS91EVpTvnupi6fremihTOZjhrb3MqRfpXwM/bvGMi4nERsT/ww8z8K/VM9zbq+eC7DBcz7HRE\nPbc9690FkLX8UmdAZNwMiKeo9uC3HXXhfbcTDLdgZGXqQ/9fwGuoi/oi6iJfA5jd6VnJzIsj4iZg\n82jrbnbNdzmAmjj/auoh4SpqJGdNKp3x8M4oYFbhiP0XzW+vyaBrfsmMNiI8JyIeQ3XEdNJPh6he\n8PdQ19OhVGbBxe00d7dzdNJ27qVusivE8NJKdAVRSaXfbEKN7H2FSqE5ut1oL6aCpxdT1/6JDBdp\noKVRnxMRa2VmJ7thrq5rX+PQdS2sSv2/uTUzz6U+g7YD3tL+/TbqgRKqM21dajrGzMy8o2W0rESt\nc7g21UO8KZUSP5u6lx3broW/UR1w21Ij+3e2886hAqFnA5+KiJ9S98TnA4+hHkQ/DDwmIlZqIzdb\nTch/GC1uV1LLe2xHXUM3dGUs/Ia61zy1deic314/hFqrejWqo+3CRdtkTRbtAXz3qLosj6OC2enU\nfWs7qoPtvxjOVjqUus52pYKDf7bzdDp4L6SWXtqMCpYvzFoFQVpobU7ttJHPMC1eWBO4rRNctuOn\nUSnNM4DftRT+pwOnZ+Ynu07xq/bVnYX1V2qAYlvqM3s29az1XuDMzNx7Hk38HPVs2KkkfTb1ub1j\n+yy+rTPFKiJeRf193DXyJK2z/CH1RxbElOmp7T5asaGImBUR7wUOp0Z9v0ytE3YldWO+sB27eefN\nMVyE5tdUoLxl175pbc7Mm6k0sp9RPe2nUA+UB2Tmvab3TG0xnyq7EbFNRFxD9XoTEStR83x3bq+9\nlrq5rkfN+f1YRDwtM/9NXY/3AytGxLJdqS83MjzHc42uNnTacRmwAtUrCTWK92kqZfp0KqXmGCoI\nPw3Yd+QNtQXf13XO3f07Ggz3JiJeERFnU51wPwN+GRGnA49pH4x/oObPbZ3DFXznUKO+l1L3Jqj/\nv9OpNYQPoIKSLwHbZuaKmbkLcHG7d11Jdbo8ga606Za6dRgVFO9IZQh8mOp82TczTwK2zszNswp4\naIpqf/t/pDpCOtVMOx1f91CdvE+mKo+fT2UnbElN2TiiEwz7OTfwLs/M49vAw6lZFe3fR80771Ss\np3UCfpjqaOlcUw+Rmedl5pmZOTtq1QWr2atnMbwCw5x5PcNExOeoLM63tKC38xw0RM3zvQdYsXXc\n/A14RkR8KCJeExF7RsR/RMTTImLLriD0Kuo5bhOq8xpqZPdXwC4RsVfXz394VK2jXahntL+39l7f\nznEXbdpUDleVPjoz/zKv3zcfPIVzzBwhntpuoUbUdqVGxJamLvpfUTfrn7aHQyLiXCoA2YlKi+h2\nErUm406MSNtpF+xREXFMe4B4ENN7po7um2rnta6Rv7lpK10pM2u1r06azGOpG97XMvPQrlPfERGH\nUcHy6yPifCpN9TqqCMOq1IgM1M34NKrTZRbwv60ND7SU2h0YTtMhM68G3hcRf6DSbZ9AjUB/Gvhx\n11yVueb1+6l3URW+P0cFnAdT96WnUNMtTo+IHajske8Bn4yIvTPzL1FLJ0yneq1vaqf7NfAy4JjM\nfPWIn7M0tXbn04GdMvOmdl97GVWp8qwcLpp0W9RySztRIzG/z8x/ds7l//eB8lNqJO9ZDK992Qlw\ndwVuzMwbW1DyFyogfjRwSVf2i59zAyoitgbeHhG/ycyvtOyUlakU05nU51Xn2GmZeWZE/IS696zI\n8BKAI887rU0xsUibFkoOF8XakXoOWoGa43tm6/T9JvW5eQC1zOSp1CDDHOp56hqG12A/jPr87p4m\nMkTdM69rscC7swoTnkc9qz2emnb274j4EDUIcmRE7Ep1ci9P1ej4M/DOTkdQa/dOOWK6W46Yhten\n/0wGxFPcrdQFtjFVyOELmdkpf05ErB8Rn6ZGSo6mgo5ZUQvF387wPOJOSuk28NCHxXbjfkgwrCVb\n5wN5tA/m9oD4EmqE7ghqKaTuTpB12vaStt2OmhP644h4GJXquiE1ArMTdVP8Dyr17BJqDuiW1OjN\n1a0dt0TEF6lg6sNRlWD/0X7W26l0fajll1bOzFtbis2xEXHiyBurJk4bNVuG6uiYDrw2M8/o2n8+\n1THxQSqt8OPUep77UOtvdjpVukffftK2G/NQD6fS4G9jeBmGS6nPuedQ86D+3dWZdz8tzUsD7Xxq\nNGNa172uU5H84q7jrqZGL95Mu0fZcSLq2nkR8Ko2Te066oH/BVQ2zOdgbofyMlTH4D3U0m2jpkPb\nySKYu9LCzsCXujttx3mOp1Pz0LeiOmCWpWr9/DAi3ptVE+jTVD2E90fEnzPzhqg6HqtRadadUduj\no2p07EpNRbqZ6vh5HDXg8c6IOCozz6emYv6bmnbyB+D6zDw7It5ATXvajuqIvItKkz6Ceu6bG8S3\nTNOHDMbM6/uFZUA8td1MBbubAdmCg+7J85tSN/KrqOWR/kgFOI+jlmDqBEPXR8Q2bf9DeOOemroC\nh6Go6uPPpNIK/wr8ot2ozqDmeOzbegYv7LrGOgFxJ921U0DkbVQAtC0VwN5PzbF6N3B8Zv6jjfb9\nnqrgGsBvu9pzVkS8jUp1PYeq9LsMNTfrBdR85FnUsjnnUqPH0zrBcCf9zIfZidWum+dS18xhmXlG\n+2DrFKb6PhWoPpsKcL9OfaDuFVWw7zLq/+tf2/uGMvOaiPgqsGdEnAB8ilo+Z0Pq+lkZeEfLXIH6\ncH0r8LusVHxppCsz87ELOigz74mIv1MPfxuG9QREZclFxCuB11P3rxWoLKSvUsvF/at9/swB7o6q\no/Ak4Ka2r6+jXJo6opYwfTdVxO802pzzcZ5jTSoY3oCqdH429cz1eiogXZVaXvCH1BKVH2o/c39q\nUG0D4OaoOh73AGTmFdT0y7ntbPfHd1JFeLejOhovpp7B9qQ+50+OiD2zlmc6LSI2oub6Xj6/32FR\n/X0YEE9h7YH0y1QA8smo4lintD+Q7aiHyX9TN+05EXEx1Xu0/IhzTMvMPyyGX0ETrCvQeEinRkTM\nom5mu1CpqOsyXHfg6xHxsax1rD9ABacHRsQ7M/Oqdsy91PXVKWbU6VB5OhXsHAv8IDN/0/UzXxhV\nvO2cNoJ4L1U1+uFZRZU6IziHR8SfqQB4a2q+6LGZeVqbi7IDFUw9pMPGh9hFqlOg6Ia2Hcrh9Vuv\np9JVdwY2ycw/RMR+VAfegVSAfAv14NhZk/M+ag7evVSg+xwqIF6OGnn5AJV6DUBm/oNazkGap670\nu/mth93p5DuGumddPa/jNJgy80cR8UsqNfTGzLxmxP6hFtwcADyNmgr0nrbPYFjz1ILMk6mCj5tH\nxGljvV667lmPp573P5qZn+vaf0F3ZmdLU/44tfrH+yPipMz8Xcv0upz27Ney+15IDTh8NzOvbO1c\nisr2m8Pw6g1JfSZ/iiqsdT31XHZXa18ne3BSDFQYEE9xWQtiv4VKTfw5Nbo3m+r1uRN4dWZ21tL8\neGZ+aB7ncAR4CukOgke7uUbEi6ilj75Hjd5dRs07n8lw5cykbnQ/oNKaP9w5Lmppm1Xb6S5o2zOp\na252Zj5xHj/z7VR67RsZLqyQVNr02gzfZAHIzFOpuS4jbUMVePvzPPZp0bqLmnoxMyKW6U5Zbw+J\nV1EfoOu01y6MiC9QS7a9n0otnNne0hnpvy5qmbdvUxkEj6RGgn+Smdcuql9MU8towXDb18lO6Sll\nUVNfCy46n3WdoqRzulI/74mILajspV9R8zalh2jB4Yz2efkXaqT2qVQx3DFVHe96br+jbZ8VESdS\nA15LAZdGBFSNjttbxsu9EfERahrc/hFxJPXMdl9m3tWC4/uoz+uPAdtHxP9S2X87UbWKDqc9l7Vr\n/8yI2DlbvaKR7esa5FjsAxUGxAMgM78UEX+lUhZmUXPyvkCNqGXXcXePcgpNITlcYGFNqsT9mtQN\n7K85XHH5CqrAzKuoQOP5nfdHLW30e6qX8FPtZvopKq1n74j4Tgts1qMCoocBd2RVB/xfalmJfYHP\n53BRrkcCr6RG/jrz9m6gRn53pFJhu2+iy1EjiHOAN1EVp9enKldvC3zGFNlJ4UqqV7jTqXHFiLTp\nO6niHWt0veeb1DX5dir18HJ4SLGze6jUr7Mn/leQpPHp7mDpSoveG7jHDAPNT3su6gSIf6M6fLem\nVoYZ7zJc51KDYc+hBhqgsqqWouYSnxARR2bmr9u+E4FHUanPd1H1OO5o7RoC7m+Zp1tQ2Q47U5/h\nt1GZgp8e2bmYmXe3YHoGI5ZDmkwDbgbEA6IrZ9/5KlPcglJPImJd4BPAS6nOkQeoNJZfRcR7spYS\nuZoakd2etjRE18jyORFxCbB1RKySmTdn5p1Ry3odTaXnd4Lba3nwfeZwar7np4BtopbeWZoKprcG\n3pOZv23tvzEi9gH+2ZVm2+lRvLMVfHgW1dHzDyqoWgH4fzy4AqIWn2uojpWXUB+eV7T7T+ce9KS2\nPa/zhjYn72BqjtMjGe4gkaQlTtco8XznSmowtOBwnsFgmxq0M7WcaWew4g6qwOTjqQB5rD9nelbV\n8zdTg2HPpoLgG6lBho0Yrhu0aSc+aAHvc6jCWctRmXtz255VmfoVUetlbwhclpn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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5296984128>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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XLi2dIMuBCbEkSZIkAFatWsXcub/RrNnWBdY3adKMpUv/YOHChWUcmYpr8uSJ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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52969f6f60>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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+NB988B716p1Fhw63s2HDOiZPnsDKlSt4990PKFGiRJ7FVNiUEIuIiIiIiM/y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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5296e4af28>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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nH1m58pdS/ebM+Z7MzEzatGlTqfVuTNUiEIcQ9gHOAZ4D9o8x/h+Jkd4ngBNC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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5297344630>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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uq8Pw4SN9MwJGjRrB2LGjmDlzGu3b351bIec4PUMsIiIiIiI+06Z9CcADDzyI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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5297bb9940>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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LWK8jjzyuzK6VkTGVatWqcdxxJxU61rp1m/h/Dxr0Jq+++iJt2rTliCOOYvr0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\nx7msCbE/QqUm/p7q3ZtCtfo8BLwjMztzaX45Mz83yD7sAR5HuoPgoS6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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5297301898>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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8p6HWqFEjku9xIpTW+3zDDTfGXX/SSSfz+utjmDr1KxYvXkh6ejpbtmzezudf\nvUJ8/rm5yWRkpO/SY6AqQiAuuNc3/tcQkJ6/XB9vYxAEDYEHgTFhGL5SkgLWrdu9vuFavXpDoktQ\nJeM5JUmqLLKzt+QvcxJcSfkrjddcrVp1kpOTWbduXdz+fv55XaxdFN/jRCiP97lNmyA/EC8mM7Pm\nNj7/nwGoVq1Ghfj8c3JyWLt2Iykpm7fbtn79+LcBJD7W510SnUPeJdHx1NqqXTyPASn8b6ZqSZIk\nSSVQpUoVGjZszNKlS+JuX7p0CbVr70HNmsX9010V0ZYtW/jmm5nMnDkj7vZNm/IGCNPS0mjWrDmr\nVv3Epk2/FGm3dOn3JCcn06xZszKttzwlPBCHYZgFLABaFtOkJbA8DMOfitnenbzQ/H0QBLkFP8DB\nAPl/n1+6VUuSJEmV00EHHczKlStZuLDwTMIrVixn0aKFtG17QIIqU0nl5ORw6aV9uO66K8nOzi60\nLTc3lxkz/ktKSgr77BNw0EHtyMnJYdq0qYXabdq0iZkzp9OyZSuqV69RnuWXqYQH4nyTgEZBELTZ\nemUQBE2ANsAn29h3UDE/P261/aHSLliSJEmqjLp1OxmAoUMfIycn77LY3Nxchgx5FIDTTvt9wmpT\nyaSlpXH00Z34+ee1PP/8M4W2vfTS88ydO4euXbuRmZlJ167dSElJ4emnhxaaPOu554azfv16Tjvt\nzHKuvmxVhHuIAZ4FLgDuCoKgRxiGOUEQJAGD87cPLW7HMAwHxlsfBMEZQMPitkuSJEkq6rDDjuCE\nE7ry739P4JJLenPIIR2YMeO/TJs2heOOO4GOHY9JdIkqgcsvv5oZM/7Lk08+zpQpX7L33m0Iw2+Y\nMuVL9tqrFVdccTUALVrsxTnnnM8LL4zgwgvPo2PHTsyfP4+PP57EgQcezKmnGohLXRiG7wZBMBI4\nG5gcBMF7QEegEzAaeLOgbRAEA/P3GVj+lUqSJEmV3803307Llq3517/G8fLLL9GgQSP69u3Huef2\nJCkpKdHlqQQaN27CsGHPMWzYED755COmTv2KevXqc84559OrV99Czx7u1+9yGjRoyJgxoxk9+p/U\nqVOXs88+l969L449eqmySMrNzU10DQAEQVAF+CvQC2gKLASeA+4Nw3DTVu1yAcIw3OZvYhAEU4GD\nt9cOYPnyn0v8JnTpclRJdy2x996bXO7HVPlIxPkEnlOSpMqjYJbplJQKMe4jqQztzO97/fqZcXNh\nhfkvRRh4rmaxAAAgAElEQVSGm4Hb83+21W6HvpIKw7BdadQlSZIkSaqcKsqkWpIkSZIklSsDsSRJ\nkiQpkgzEkiRJkqRIMhBLkiRJkiLJQCxJkiRJiiQDsSRJkiQpkgzEkiRJkqRIMhBLkiRJkiLJQCxJ\nkiRJiiQDsSRJkiQpkgzEkiRJkqRIMhBLkiRJkiIpNdEFSJIkSao4Vq5cwdNPD2Xy5I/46aeV1KxZ\niw4dDqdPn0to2nTPWLs33niNu+++I24f++9/AEOHPlNOFWtnrVixnPPOO4s+fS6hR49zi2x/6603\nGDXqRRYtWkhmZk2OP/439OnTj+rVqxdp+/HHkxgx4inmzZtLeno6Rx/diX79LmePPeqUx0vZZQZi\nSZIkaQdcc03/RJewTQ888I9d7mPlyhVcdNGfWLbsRw477AhOOOG3LFw4nwkT3uaTTz7miSeG06xZ\ncwDmzJkNwHnn/Ym0tLRC/TRo0HCXaykvH344IdElbFOnTl1Ltb8NGzYwYMD1rF+/Pu72554bzhNP\nPEbr1vvQvfvZzJs3h5EjX2TmzBk88sgTVKlSJdZ2woS3GTToJpo0acqZZ3bnxx9/4K233mDq1K8Y\nNuw5MjMzS7X2smAgliRJkgTA008PZdmyH7n88qs455zzY+vfeedf3H77LTz66IPcc8+DQF4grlmz\nFpdeekWiytVO+uGHpQwYcD2zZn1b7PZhw4ZwwAEH8eijQ0lNzYuLw4YN4ZlnhvH666/SvfvZQF6w\nfuCBe2nSpCnDh79AjRoZABx22Fjuvvt2Rox4issvv6p8Xtgu8B5iSZIkSQD85z/vU7v2HkUuoz3x\nxN/RtOmefPbZJ+Tk5AAwb95cWrVqnYgyVQKjRr1Iz57nMHfubA499LC4bcaOfZXs7GwuuKB3LAwD\nXHBBb2rUqMG4cWNj69599x1+/nktZ599biwMA5xyyuk0b96Ct94aR3Z2dtm9oFJiIJYkSZIUC0IX\nXngxyclFY0KVKmls3ryZLVu2sGzZj6xdu4a9994nAZWqJEaNeolGjRrx6KNDOfHE38VtM23aFADa\ntz+00Pr09HTatj2IOXNmsW7duvy2X+W37VCkn/btD2XNmjXMmze3NF9CmfCSaUmSJEmkpKTQo8cf\n425bsGA+CxfOp2nTPUlLS2Pu3Lz7h7ds2cKNN17L9On/ZdOmTRx44EH07duP/fc/oDxL1w64/voB\ndOhwOCkpKSxatDBumyVLFlOnTt24k2c1btwYgEWLFrDffm1ZsmQJAE2bNi3StlGjJvltF7LPPm1K\n6yWUCUeIJUmSJBUrJyeHBx64l5ycHE477UwA5syZA8Brr73Cpk1Z/O53p3LYYUfw5Zefc9llF/Hp\np5MTWbLiOOKIo0hJSdlmm7Vr15CRkRF3W8Fl0QUjxGvWrCYtLY309KpF2hb0sX79ul0puVw4QixJ\nkiQprtzcXP72t7v48svP2Hff/WP3Fufm5tCoUWMuvrg/v/3tSbH2U6Z8yVVX9eeuuwYxatRY0tPT\nE1W6SmDLli1UqZIWd1vBTOJZWVn5bbMLzTi9tYL1WVmbyqDK0uUIsSRJkqQitmzZwuDBtzFu3Gs0\nadKUu+++PxZ0eva8kNGjxxUKw5B372jXrt1YuXIFU6d+lYiytQvS09PZsmVz3G0FQbhatWqxtps3\nb4nbdvPmvD6qVq1WBlWWLgOxJEmSpEJ++eUXbrzxWv71r3HsuWdz/v73J6hXr/4O7dumzb4ALF26\npCxLVBnIzKwZuyT61woufy64dDozM5OsrE2xoLy1gj6Ku/y6IjEQS5IkSYpZu3YtV17Zj8mTP6JN\nm4DHHx9Go0aNCrUJw2+LHQHetCnvMtm0NC+X3t00a9acVat+YtOmX4psW7r0e5KTk2nWrFmsLcAP\nP3wfp+2S/DYtyrDa0mEgliRJkgTkhdm//OUqvv56Bu3aHcIjjzzBHnvUKdLuxhuv5cor+7F69eoi\n26ZPnwrAvvvuV+b1qnQddFA7cnJymDZtaqH1mzZtYubM6bRs2Yrq1WvE2gJMmVL0i5EpU74kIyOD\nvfZqWfZF7yIDsSRJkiQAhg59jOnT/8sBBxzE/ff/PXZ57K916fIbcnJyeOKJx8jNzY2tnzjxXT7+\neBLt2h1Cq1Z7l1fZKiVdu3YjJSWFp58eWuhS6OeeG8769etjs4wDdO58HNWr1+DFF59l7do1sfVv\nvDGWRYsWcsopZ8R9nnVF4yzTkiRJkli5cgWvvvoyAC1a7MXzz4+I2+7883vRq1dfPv30Y8aNG8Pc\nubM56KB2LFy4gMmTJ1G3bj1uvPGW8ixdpaRFi70455zzeeGFEVx44Xl07NiJ+fPn8fHHkzjwwIM5\n9dT/BeKaNWvRv/8V3Hff3fTqdS7HH9+V5cuX8d5779KsWXN69uydwFey4wzEkiRJkpg5c0ZsduA3\n33y92HY9epxLZmYmjz/+NMOHD+WDD95j9Oh/UqtWbU455XT69OlHvXr1yqtslbJ+/S6nQYOGjBkz\nmtGj/0mdOnU5++xz6d374tijlwqcccZZZGbW5IUXnuXVV1+mZs2adOt2MhdffBk1a9ZK0CvYOUlb\nX+IQVcuX/1ziN6FLl6NKs5Qd8t57Pui8skrE+QSeU5KkyiM7O+8xMCkpjvtIld3O/L7Xr5+ZFG99\nxb+oW5IkSZKkMmAgliRJkiRFkoFYkiRJkhRJBmJJkiRJUiQZiCVJkiRJkWQgliRJkiRFkoFYkiRJ\nkrTbKY0nCBuIJUmSVIkkkVsa/0qWtBvIBeI+XniHGYglSZJUaSQnJ5OTk53oMiSVg+zsbJKTdy3S\nGoglSZJUaSQl5Y0Q5+TkJLoUSWUo73c8l6SkXRshTi2dciRJkqSKIS2tKllZvwBJpKSkAEns4r+Z\nJVUAeXdD5JKdnQ3kkpZWdZf7NBBLkiSpUklKSiI9vdpWI8XeUyxVBnlfbCWTlpa6yyPDBQzEkiRJ\nqpSSkgpGiCUpPu8hliRJkiRFkoFYkiRJkhRJBmJJkiRJUiQZiCVJkiRJkWQgliRJkiRFkoFYkiRJ\nkhRJBmJJkiRJUiQZiCVJkiRJkWQgliRJkiRFkoFYkiRJkhRJBmJJkiRJUiQZiCVJkiRJkWQgliRJ\nkiRFkoFYkiRJkhRJBmJJkiRJUiQZiCVJkiRJkWQgliRJkiRFkoFYkiRJkhRJBmJJkiRJUiQZiCVJ\nkiRJkWQgliRJkiRFkoFYkiRJkhRJBmJJkiRJUiQZiCVJkiRJkWQgliRJkiRFkoFYkiRJkhRJqYku\nQJIkSZK0+/jwwwkJOW6nTl1LvU9HiCVJkiRJkWQgliRJkiRFkoFYkiRJkhRJBmJJkiRJUiQZiCVJ\nkiRJkWQgliRJkiRFkoFYkiRJkhRJBmJJkiRJUiSlJroASZIkSWXnww8nJOS4nTp1Tchxo2TIkIcS\ncty2bdsm5LhlwRFiSZIkSVIkGYglSZIkSZFUYS6ZDoIgFbgCuAhoCSwFhgN3h2G4eQf2bwvcDhwF\nZAJTgQfCMHy1zIqWJEmSJO22KtII8WPAA8BK4GFgCXAb8NL2dgyC4GDgM6Ab8BbwJNAUeCUIguvL\nqmBJkiRJ0u6rQgTiIAg6AhcDo4HOYRj+FegMPAt0D4LglO108ThQBegUhuGFYRheDRwIzAFuC4Kg\nbtlVL0mSJEnaHVWIQAxclr8cFIZhLkD+8kYgF+hb3I5BENQEagBvhGH4ZcH6MAzXAeOAqkD7Mqpb\nkiRJkrSbqij3EHcGVoRhOGPrlWEYfh8EwSzg2OJ2DMNwLXBwMZv3zV/+WCpVSpIkSZIqjYQH4iAI\n0oE9gU+LaTI/r1lQPwzD5TvQXwp5k3JdCZxE3sjx9FIqV5IkSZJUSSQ8EAN18peri9m+Jn9ZC9hu\nIAbeB47J//NHwDklrkySJEmSVGlVhEBcJX+5qZjtBeur7mB/7wOfAEfn/0wMguCkMAx/Km6HjIx0\nUlNTdrD7xKtdu3qiS1Al4zklSRXHuHFjE3LcU089PSHHVeUVtX9fXHhhr3I/Zps2bcr9mIlUFudU\nRQjEG/OXacVsT89frt+RzsIwvLngz0EQ3AtcT97ziS8rbp9164rL4hXT6tUbEl2CKhnPKUmS/y9Q\nafOcUmnblXOqfv3MuOsrwizTa4Ac8i6JjqfWVu121k3ABsCvPCVJkiRJhSQ8EIdhmAUsIG8irHha\nAsuLu+Q5CII6QRCcGgTBQcX0vRSoV1r1SpIkSZIqh4QH4nyTgEZBEBS6CD4IgiZAG/LuCS7OfsDr\nwK2/3hAEQS2gBTC39EqVJEmSJFUGFSUQP5u/vCsIgmSAIAiSgMH564duY99PgIXA6UEQFMwuTRAE\nqcBj5N0n/XSpVyxJkiRJ2q1VhEm1CMPw3SAIRgJnA5ODIHgP6Ah0AkYDbxa0DYJgYP4+BcvsIAj6\n5Lf5dxAEo4AVQFegbf76h8vtxUiSJEmSdgsVZYQY4ALgFvLu970KaJT/9/PDMMzdqt2t/Ory6DAM\n3yUvQI8HTgUuBXKBa4DTwzDcUubVS5IkSZJ2KxVihBggDMPN5D0e6fbttEsqZv2X5IVhSZIkSZK2\nqyKNEEuSJEmSVG4MxJIkSZKkSDIQS5IkSZIiyUAsSZIkSYokA7EkSZIkKZIMxJIkSZKkSDIQS5Ik\nSZIiyUAsSZIkSYokA7EkSZIkKZIMxJIkSZKkSEpNdAGSJO3uPvxwQrkfs1OnruV+zCgaMuShcj9m\n27Zty/2YkhRVjhBLkiRJkiLJQCxJkiRJiiQDsSRJkiQpkgzEkiRJkqRIMhBLkiRJkiLJQCxJkiRJ\niiQDsSRJkiQpkgzEkiRJkqRIMhBLkiRJkiLJQCxJkiRJiiQDsSRJkiQpkgzEkiRJkqRIMhBLkiRJ\nkiLJQCxJkiRJiiQDsSRJkiQpkgzEkiRJkqRIMhBLkiRJkiLJQCxJkiRJiqTURBcgSZIkRcWQIQ+V\n+zHbtm1b7seUdheOEEuSJEmSIslALEmSJEmKJAOxJEmSJCmSDMSSJEmSpEgyEEuSJEmSIslALEmS\nJEmKJAOxJEmSJCmSDMSSJEmSpEhKTXQBkiRJklRaunQ5KiHHbd++fUKOq13jCLEkSZIkKZIMxJIk\nSZKkSDIQS5IkSZIiyUAsSZIkSYokA7EkSZIkKZIMxJIkSZKkSDIQS5IkSZIiyUAsSZIkSYokA7Ek\nSZIkKZIMxJIkSZKkSDIQS5IkSZIiyUAsSZIkSYqk1O01CIJg6C70nxuG4SW7sL8kSZIkSWViu4EY\nOBOos9Xfk3ai/1zAQCxJkiRJqnB2JBDvD4wGOgFzgTvKtCJJkiRJksrBdgNxGIbLgyDoBrwHHAZU\nCcNwWJlXJkmSJElSGdqhSbXCMNwI/AFYBdwbBEGd7ewiSZIkSVKFtsOzTIdhuAgYCNQGBpRVQZIk\nSZIklYcduYd4a48D04H1ZVCLJElSXNdc0z8hx23Tpk1CjitJKh87FYjDMMwGPiijWiRJkiRJKjfb\nvWQ6CIKeQRAcXR7FSJIkSZJUXnbkHuJnKOZZwkEQdA6CICjViiRJkiRJKgc7PKlWMd7HCbYkSZIk\nSbuhXQ3EAEml0IckSZIkSeWqNAKxJEmSJEm7HQOxJEmSJCmSDMSSJEmSpEgyEEuSJEmSIik10QVI\nklRahgx5KCHHbdu2bUKOK0mSds2OBuIzgiCYF2d97ja2AeSGYdi6ZKVJkiRJklR2djQQZ+T/7Oy2\n3J2uSJIkSZKkcrAjgbhLmVchSZIkSVI5224gDsPwg/IoRJIkSZKk8uQs05IkSZKkSDIQS5IkSZIi\nyUAsSZIkSYokA7EkSZIkKZIMxJIkSZKkSDIQS5IkSZIiyUAsSZIkSYqk7T6HuLwEQZAKXAFcBLQE\nlgLDgbvDMNy8A/sfCtwMdAIygUXAy8DtYRiuL6u6JUmSJEm7p4o0QvwY8ACwEngYWALcBry0vR2D\nIOgCfAycBLwD/D2/n78A7wVBULWMapYkSZIk7aYqRCAOgqAjcDEwGugchuFfgc7As0D3IAhO2U4X\n/yDvtXQKw/DcMAyvA44AngQOA/qXWfGSJEmSpN1ShQjEwGX5y0FhGOYC5C9vBHKBvsXtGATB/sC+\nwNgwDD8rWJ+//235fz2pLIqWJEmSJO2+Kkog7gysCMNwxtYrwzD8HpgFHLuNfdeSd2n003G2bcpf\nZpRGkZIkSZKkyiPhk2oFQZAO7Al8WkyT+XnNgvphGC7/9cYwDBcD9xaz75n5y5m7WqckSZIkqXKp\nCCPEdfKXq4vZviZ/WWtnOg2CoCH/u2R6aAnqkiRJkiRVYgkfIQaq5C83FbO9YP0OzxQdBEEt4E2g\nIfD3re8tjicjI53U1JQd7T7hateunugSVMl4Tkm7H39vVdqidk5deGGvhBy3TZs2CTluIkTtnFLZ\nK4tzqiIE4o35y7RitqfnL3foWcJBENQH3gYOAd4Art3ePuvWFZfFK6bVqzckugRVMp5T0u7H31uV\nNs8plTbPKZW2XTmn6tfPjLu+IlwyvQbIofhLomtt1W6bgiBoDUwmLwy/DpwVhuGW0ihSkiRJklS5\nJDwQh2GYBSwAWhbTpCWwPAzDn7bVTxAE7YCPgdbACKB7GIa719CvJEmSJKncJDwQ55sENAqCoNBN\nFUEQNAHaAJ9sa+cgCPYGxgMNgAeA3o4MS5IkSZK2paIE4mfzl3cFQZAMEARBEjA4f32xs0Tnt38J\nqA88HIbhtWEY5pZlsZIkSZKk3V9FmFSLMAzfDYJgJHA2MDkIgveAjkAnYDR5M0YDEATBwPx9Buav\nOgPoQN5s1OsKtv/KD2EYDimr+iVJkiRJu58KEYjzXQDMBHoBVwELgVuAe3814ntr/nJg/rJz/jId\n+L9i+p4GGIglSZIkSTEVJhCHYbgZuD3/Z1vtkn7196vIC9CSJEmSJO2winIPsSRJkiRJ5cpALEmS\nJEmKJAOxJEmSJCmSDMSSJEmSpEgyEEuSJEmSIslALEmSJEmKJAOxJEmSJCmSDMSSJEmSpEgyEEuS\nJEmSIslALEmSJEmKJAOxJEmSJCmSDMSSJEmSpEgyEEuSJEmSIslALEmSJEmKJAOxJEmSJCmSDMSS\nJEmSpEgyEEuSJEmSIslALEmSJEmKJAOxJEmSJCmSDMSSJEmSpEgyEEuSJEmSIslALEmSJEmKJAOx\nJEmSJCmSDMSSJEmSpEgyEEuSJEmSIslALEmSJEmKJAOxJEmSJCmSDMSSJEmSpEgyEEuSJEmSIslA\nLEmSJEmKJAOxJEmSJCmSDMSSJEmSpEgyEEuSJEmSIslALEmSJEmKJAOxJEmSJCmSDMSSJEmSpEgy\nEEuSJEmSIslALEmSJEmKJAOxJEmSJCmSUhNdgCSp8rnmmv4JOW6bNm0SclxJkrR7coRYkiRJkhRJ\nBmJJkiRJUiQZiCVJkiRJkWQgliRJkiRFkoFYkiRJkhRJBmJJkiRJUiQZiCVJkiRJkWQgliRJkiRF\nkoFYkiRJkhRJBmJJkiRJUiQZiCVJkiRJkWQgliRJkiRFkoFYkiRJkhRJBmJJkiRJUiQZiCVJkiRJ\nkWQgliRJkiRFkoFYkiRJkhRJBmJJkiRJUiQZiCVJkiRJkWQgliRJkiRFkoFYkiRJkhRJBmJJkiRJ\nUiQZiCVJkiRJkWQgliRJkiRFkoFYkiRJkhRJBmJJkiRJUiQZiCVJkiRJkWQgliRJkiRFkoFYkiRJ\nkhRJBmJJkiRJUiQZiCVJkiRJkWQgliRJkiRFkoFYkiRJkhRJBmJJkiRJUiQZiCVJkiRJkWQgliRJ\nkiRFkoFYkiRJkhRJBmJJkiRJUiQZiCVJkiRJkZSa6AIKBEGQClwBXAS0BJYCw4G7wzDcvJN9nQKM\nA9qHYTi1tGuVJEmSJO3+KtII8WPAA8BK4GFgCXAb8NLOdBIEwX7kBWlJkiRJkopVIQJxEAQdgYuB\n0UDnMAz/CnQGngW654/47kg/XYAPgHplVaskSZIkqXKoEIEYuCx/OSgMw1yA/OWNQC7Qd1s7B0FQ\nLQiCYcC75L2mr8qwVkmSJElSJVBRAnFnYEUYhjO2XhmG4ffALODY7ezfEOgDvAkcDEwviyIlSZIk\nSZVHwgNxEATpwJ7A3GKazAdqB0FQfxvdrAKOCcPwtDAMl5RyiZIkSZKkSqgizDJdJ3+5upjta/KX\ntYDl8RqEYbgG+KiU65IkSZIkVWIVIRBXyV9uKmZ7wfqqZVVARkY6qakpZdV9qatdu3qiS1Al4zkl\n7X4S9Xvbvv3BCTpu+4QcN0r8f4FKm+eUSltZnFMVIRBvzF+mFbM9PX+5vqwKWLeuuCxeMa1evSHR\nJaiS8ZySdj/+3qq0eU6ptHlOqbTtyjlVv35m3PUJv4eYvEuic8i7JDqeWlu1kyRJkiSpVCQ8EIdh\nmAUsAFoW06QlsDwMw5/KrypJkiRJUmWX8ECcbxLQKAiCNluvDIKgCdAG+CQhVUmSJEmSKq2KEoif\nzV/eFQRBMkAQBEnA4Pz1QxNSlSRJkiSp0qoQgTgMw3eBkUB3YHIQBHcDHwA9gdHAmwVtgyAYGATB\nwETUKUmSJEmqPCpEIM53AXALUA+4CmiU//fzwzDM3ardrfk/kiRJkiSVWEV47BIAYRhuBm7P/9lW\nu6Qd6KsX0KtUCpMkSZIkVUoVaYRYkiRJkqRyYyCWJEmSJEWSgViSJEmSFEkGYkmSJElSJBmIJUmS\nJEmRZCCWJEmSJEWSgViSJEmSFEkGYkmSJElSJBmIJUmSJEmRZCCWJEmSJEWSgViSJEmSFEkGYkmS\nJElSJBmIJUmSJEmRZCCWJEmSJEWSgViSJEmSFEkGYkmSJElSJBmIJUmSJEmRZCCWJEmSJEWSgViS\n/r+9+w63o6r6OP69CUkooUiRgPTiD5ASOqGGJlKkiAXR9xUVFaUo0lWKSAcVfAFRUBFEQWmhiUgn\n9AChs0INvYTQAiQkJO8fa0/u5OTedHLL+X2eJ88kZ+bMmXPvZGbW3muvbWZmZmZNyQGxmZmZmZmZ\nNSUHxGZmZmZmZtaUHBCbmZmZmZlZU3JAbGZmZmZmZk3JAbGZmZmZmZk1JQfEZmZmZmZm1pQcEJuZ\nmZmZmVlTckBsZmZmZmZmTckBsZmZmZmZmTUlB8RmZmZmZmbWlBwQm5mZmZmZWVNyQGxmZmZmZmZN\nyQGxmZmZmZmZNSUHxGZmZmZmZtaUHBCbmZmZmZlZU3JAbGZmZmZmZk3JAbGZmZmZmZk1JQfEZmZm\nZmZm1pQcEJuZmZmZmVlTckBsZmZmZmZmTckBsZmZmZmZmTUlB8RmZmZmZmbWlBwQm5mZmZmZWVNy\nQGxmZmZmZmZNyQGxmZmZmZmZNSUHxGZmZmZmZtaUHBCbmZmZmZlZU3JAbGZmZmZmZk3JAbGZmZmZ\nmZk1pTk6+gDMzMzMrHltvvmADvncNddcs0M+18w6F/cQm5mZmZmZWVNyQGxmZmZmZmZNyQGxmZmZ\nmZmZNSUHxGZmZmZmZtaUHBCbmZmZmZlZU3JAbGZmZmZmZk3JAbGZmZmZmZk1JQfEZmZmZmZm1pQc\nEJuZmZmZmVlTckBsZmZmZmZmTckBsZmZmZmZmTUlB8RmZmZmZmbWlBwQm5mZmZmZWVNyQGxmZmZm\nZmZNyQGxmZmZmZmZNSUHxGZmZmZmZtaUHBCbmZmZmZlZU3JAbGZmZmZmZk3JAbGZmZmZmZk1JQfE\nZmZmZmZm1pQcEJuZmZmZmVlTckBsZmZmZmZmTckBsZmZmZmZmTUlB8RmZmZmZmbWlBwQm5mZmZmZ\nWVNyQGxmZmZmZmZNyQGxmZmZmZmZNSUHxGZmZmZmZtaU5ujoAzAzs0/O5psP6JDPXXPNNTvkc83M\nzMymh3uIzczMzMzMrCl1mh5iSXMA+wLfA5YFXgH+ApwQEWOn4f0LAkcDOwCfBh4HToqIiz6xgzYz\nMzMzM7MuqzP1EJ8B/AZ4EzgNeIkMcP8xtTdKmgf4L/BD4C7gdGAB4EJJ+3xSB2xmZmZmZmZdV6cI\niCVtCHwfuBjYNCIOBTYFzgN2lbTDVHbxY2AtYL+I2C0iDgb6A48CJ0r69Cd39GZmZmZmZtYVdYqA\nGNi7LH8ZERMAyvIwYAKw51Te/yPgNeCs6oWIeA84Fpgb2H1WH7CZmZmZmZl1bZ0lIN4UGBERj9Rf\njIiXgWHAZu29UdLywGeA2yLi44bVN5Vlu+83MzMzMzOz5tThAbGkPsASwNPtbPIcsICkRdpZv3xZ\nTvb+iHgVGA18diYP08zMzMzMzLqZDg+IgQXL8u121r9TlvO3s36hqbz/3Sm818zMzMzMzJpUZ5h2\nqVdZjmlnffX6nDPx/rmndACLLDJvy5TWT8kjjzwy9Y3MppHPJ5vVfE7ZrOZzymY1n1M2q/mcsunR\nGXqIPyzL3u2s71OW78/E+9t7r5mZmZmZmTWpzhAQvwOMp/205vlr27XlrYbtGs03hfeamZmZmZlZ\nk+rwgDgiPgKGA8u2s8mywBsRMbKd9cNq201C0mJkqnXM7HGamZmZmZlZ99LhAXExGOgnaZJq0JIW\nJytE39XeGyPieeB5YGNJjd9nYFneOesO1czMzMzMzLqDzhIQn1eWx1VBraQW4Pjy+h+n8v7zyamb\n9qlekDQv8HNyjPH5s/RozczMzMzMrMtrmTBhQkcfAwCSLgS+BtwD3ARsCGwCXAx8NSImlO2OAoiI\no2rvnQ8YAqwIXErOSbwrsBywb0ScPru+h5mZdU+S5oyI0R19HGZmZjbrdKaAuBdwKLAH8BkyDfp8\n4KSIGFPbbgJARLQ0vH9R4Djgi8A8wBPAyRFx4ew4fjPrPiT1JTNUekfEDyT1iIjxHX1c1jEkLQdc\nBTwA7BkRH07lLWZmZtZFdJqA2MysM5E0HhgN9IuIdzv6eGz2kbQTcAywV0TcLmlp4GZgJLBbRDzZ\nkcdnZmZms05nGUNsZtYpSOpZ/noxWaV+/fJ6S7tvsm6hVphRwOeAbcq/RwA3kMNyVu6AQzMzM7NC\nUmo8lvkAACAASURBVEvteW2mOSC22U5SP0m9O/o4zOok9ZA0B63XxRvKcsuydEDcPK4H3gW2Lv8e\nDdwB9AVW66iDMjMzM4iICRHx8azan1Om7RMnaWGgD/BjYD9yGq3tI+L9Dj0ws3aUnsIlgOeAuyNi\nQMcekX2SJLVUhRvLv+ciizuuDXw6It6StBZwC/Af4LsR8U7HHK2ZdXel0+DjWfnAb9bVlGexHuT/\nhQn1e7WkzwGblfWDIuKFmfmsOWb6aM2mQNL3gbOAPwBfAi4C3nUwbLNblfJcD3wa1gv4PrAL2Tt4\nITlmdGVJS0TEi7PrWO2TVc6FHgAR8XFDMNwSER9Kug9YD9iILKj1IvAo2UO8LDB0th+4dajSuLs9\nsCpwP3BLRLzcsUdlXV31kC9pdeDrwObk9ek2SX+JiEc69gjNZp+Sqfdx6QEeD4wvr/eNiFHl74cD\nB5NFlAG+LunwiLixsYF7WjkgtllC0gbAp4EHGlppHifngv4B8DPgt/Wq4WafpCrwaQx62timL3AS\nOWb0P2SK7LeA+cjr5EbARa423T2Uc+FjmDhmfHXgfeCp2u/3DuCHwLZkQPwucDs53/0qOCDu9sq1\nYUugNzmd4yXkdI7jgbmBYZIOjIirfG2w6SVpWeC1iPhA0m7kzAZzkdXsFwL2B/5X0m4RccOMPuib\ndSURMa76u6RlgIOArYD3JJ0LjC2vnUzeh9cBfgH8FLhxRv+POCC2GVZO1APIwKEv+ZAwopywp0bE\nq8CzwMNkT8vdETGmPICO94XdPmkNgc9KwPLAkIh4rbzWMyI+lnQgOWXb8cDxETFK0iLAL4G9gIFk\ndoN1ISXdakItxarqiZkP+DI5zd86QC/gdeASSb8oVcXvJ4tpVWPIPyKD5P3JAPrvs/O72OwhqR/w\nZkSMJYdNnAJ8lkyh7w18kzxXVgF+D5wvqX9EDO+gQ7YuSNLfgZ2A9SW9BRxBXmN+QGaivEJmK20L\nPAntZzeZdSaS+gCLAy/Ug9va+p5AS1vryvpNgGPJzNKtgQHk/4c1gd+RjZO/iYijy1uukPQ14AuS\nFp/RrB0HxDZNyjzPnwcmAJcBY8ge3+8CV5BFaOYgHzIPBhaVtD/wEvAQGRAvVXY3wRd2mxVqAU6b\nLedlPuG9gH3Jh9sxwChJfwVOjIgRJQ1yPXLu8xOrlJyIeKOk5XyDHKeCe4C6lur3JWkpYJGIuE/S\nnMCB5DnxEPAXMiDekuz9fRE4KSIel/QQsIWkpSNiuKTHyCB5TUmLVg0r1rVJ2pY8H9YD3gb+Lel4\n4GXgajL43RpYNSKGlbcNlrQQ+eC2j6SjI+K92X/01tnU7ktzkhXrny6NrNXrfYEFgIiIRyRtDKwE\nnBYR19V2dUn5Y9aVfA/oR2bdTTZlZX1cvKRPRcRbDZuMBTYm06H7AXsCd5JDlQaRHRtXlPfPHREf\nkJl9+5DDDS6YkWwKB8TWLkkbkVPODAeOIqchOTci/iZpD/IkPTcivlN7z6XAr4HdgaERcZqku8q2\ny4CDCps5baVBl4eMuYB5I+J1Sb1KD893yJb3h8jzcjyZ/nwA0J98yB1NPvB+XJ9vuFxQ35T0H+CL\nklb1WK7Oo34etLFubvL3O5x8oFyPvKFuBHyBTK86EzgNeC4ixpYx5LcDW0v6awl27wW2IDME/gq8\nRqYzrkLelB0Qd0Hl3DmEzG7aDvgNmTJ/A7AWsDewCPBt4EFgFHkuVePXekfER8DlwM5lHxcAQ53W\n2twkLVTuG4uSdSg2I+9D51KKA5VNVyB7hCF7hCcAe0gaUV77gOxQ6E0ORXt09nwDsxkn6TNk5tWn\ngfOAd+vDScr44IFkZ9oAMg36FvL+OqRcOx8GhpE9wt+OiH+X3T8g6b/kdfmzZBZX9f/pRjIg3oa8\nFreQ/6emmaddsokkLSbpOEl3SVoX2IFMF/sN2SvydeB35WFzQ7IV53flvS3lQeCFsn0vYPty8t9L\nPmysLmmB2f7FrEuT1FO1OYDrpfYlra20Ltmzc1TZZqyk5YDjyAfaHSPidxFxekR8nbz4binpa6VH\neBTQW9LyZb8Tiy6RVdH7AJuWdb5udqDq59/elAvlmnMCMJi8fs1HNoqcUzbZg6xrcHxEPFnOlb7k\n/MLjyEyWz5Vt7yrLbctyVNnv4rVtrIspD13zkL13N5C1Lr5DNuRuUl77KrAuMAR4C3iHPG8g732Q\nVehvJht7l6jt25qEpAUl7SHpv5KeJbMLjiYbX39MNrgeJWm+2vXqA3IM+uPl9bfIa9Yo4Fflz6/J\ngPo84CZJB5fP8/R/1pmNJO+RCwJ9JfVp6ATbhQxYNwRuIxuVf0QOSdkVoBTdvb1sPwdMbOSGrOdB\neT+0BsR3AO8xE9l8frBrcpIWl3S5pD+SreX7ky0ri5MXY8jgdv+IuCgihpIX+M+RF/wnYOLDaZW6\nehcZhKxGFiB5kmwBXZ1MefBF3dqlnA944rWpjSrAc0n6paRXyYDlWlp7eJat7Wpb8qHjlxExUlIv\nSUtJ6k828AB8V9KCwK3kOb96eb1+ft5flpvNum9p06KxMQQmSYNeRdLekn4sadlaoDyOLLTxKvA1\n4NCIOCYi/lJ2cVp5/RVJvSVtQaa9HkX22CxJ61zDD5BBT3WT/YjWILl/GQtlXdNVZEr0ouR4tIfK\nteZV4PyyzdYR8Qx5/1qxemMtM+VDcqjFXLQ+mFmTKNemU8hrynzkQ3wvMgPlX+R5cwLZyHZESbGH\n7N3qQRbUqrKSjiAbXb9D9oDtQgYKvyEbZPeRtIAbXKwzKPfmye5/5Zo4mnz2uhz4sGSbUjouTiWf\nv3YH9ouIrYANyDHCx0qqAt3byvKzZVkV472xLAeUzxtX4o7Xyfv1kpJWnpHv5IC4iUhaWNJWytL+\nlV7kxXpPMk3sG8CXI2JQRDxInoT9yJs+tdae98mL9Grl9cZz6V6y0NaSETGavFEsRaYamrUrIsbX\ngp75Jf2vpBNKChrkmOCfA4+R5+yp5Lm2ALCSpOocW6FaSvoCcDTwZ+A6MmV6PNkzNBq4pmy7U1nW\nU3FXKsv1SqqkU/5nk7aqg0taq6SxP0IGsieRRTaOl7RY2exJ8qb7JlkAqeo5hpwq5yryJjyI/N3v\nTo5BOgqYE1it3GSfJ4PrRSWtUd7/FBkkr0bpFbQu6Qmy5/d1svGkfh+7tiwHluVdwGK0NpghqVf5\n6yRZT84gaSrfBf6X7MX9BvC9iFiTDIjPK/eQPwD/ICvgfq28bwGyIaYq9tezbPtcRJwbEX8tz2Bn\nRcSBZIPtEmTQbdbhyr25ytSbeM0rmQw/Lf/sAZxNZu9BDllaDDg4Im6vjR1+heyAW4bSS0zGDBOA\nVctz18fl/8k75L1/deU8xJBxDGQvM5RCmNN7LfYY4iYgaUey0NWGZMrXGEkPAN+NiOck3U4+3N0X\nEZeW91Q5/7eS4yzXoPVkg0yJ2IocX3cv0ENS1Uvch2wt70l50Cjb/4Scv9FpZU1MDXPAtrF+cfJ8\nvQw4jGw1HwucLen98toQ4JtVNUFJV5KB8Q7A2mSw/GzZ5a/IKSwgMxfOBi6LiCHlvT3JoOghcoqL\n/0TEP8oD76pk8Pwm2aCzMTDD89zZ5CTNEe1Xm9yI7Cn5fUQ8LWlJskdmHeBEMl11PLAbOQ1DPzLT\n5UngGeAzlJtl9RkRMb5kCfyLvFHvFRHnls9bgkzhqrJbngbuIceJbkGePyPKcmcyQHJ14a7pXbJB\nbBdgfph4brSUOgRPAWuXMXF3k43D35I0LCJeKTUKIOtsvEU2lLhGRnNZk7yXXRwRT1UvRsRxtb+/\nJuk48iH9MEn/Is+XFvIaRXnYXxjYW9IE4JhyLlYdFquRvc1ms5UmHf/bo5yXfclaHDuRAe59kq6O\niFvJ++r75NCkfsDpEfGossDcKuSwgJC0JtlpsRp5DV2PvFevoSy09aSkR8h77Irk+T8HGVv8m3w2\nG8Ck/y9uAo4kG7hPn97v6pbMbk45P/BvyQfDY8jqqueTwfGNynnwBpMX5zfLiQ4ZzEKeeJC9KdA6\nSH1QWX6zPECMqwUIE8h01TfJlh/IB4+xwDa1XhxrQtXYz7aC4WJFYD+yVX0pchqKHSPiaXJqpIWB\nqyPi5ZLy2rOkNf6uvL86V+8py7fJojdzRsSaEfHziBgiaefS07hJOZbDyPP0AkmDyerDF5MX3D+R\nY2MWrL7DLPpxNJ02UqDbmpah2uYQchxe1TOyPdlrd1REHBYR/4mI/5KNbdcBu0jaqKS9Pgh8itbq\n9vX9Hk/eyH8WEefWUr/WI8+vRWntDby7LKvendFk5eG/AzF93946i/J/+DryXrd2bVXVUfAfMltg\nA7JH4mHyIe84SZuUbKs/kY1w50TEk7Pr2K3TuKMsfyPpJEm/lnSwpB9J2qd61ikFsQ4hA4RjybHr\nYymNaeUZagR5Hh4F/EPSIeRMHoPI8/DIiHjew81sdioBcE9JC5a/L0+ek38hz9dPkY3RNyvny342\nIs4gr62LAcuVQHo0mRnRl5yu7mJyKstflO3OAtaIiK1qPcc3k7FL//Lv6rnryrLcqCyrZ8kh5HPf\nndWxT893dQ9xN1XrwTqBPGF3jYibausfIVN59iEDj5fJojKV6gSrxmduDBPH0BERD0q6kOyZOU/S\nAWRBms+SD7BLkT1r75T9vEwG5s+QraPWzalhvula6+LCwJfI1sX5yYvelcA9ZdvhwKVlm/Mi4vza\nbkeV5VhoPR+LG4EXyClx5oqIuyS9TF6EBzdsS9n/1pRAOiL+LekNsodxIDnN2FDy/9C9EXHoTP5I\nmlb9XGgYDz4nOVXb2sCZpVW4mhv6U2SF1aci4gFJ85BZKW8Bvy0p0IuSaVbLk63Lfcle/rvI8USj\ngXUlXRYRH5QMlqo4x7OU+T2B8cp51fckA6TFyVTIy8hA6D5yzHHP0jP4p/LHurbBZG/GQEnnlAeo\n+kPX3sAWEXGJcgqudchGmV3IzIS+ZLrsL2f7kVtncBnZI7Yp2dkAeV5UnU2HSvpSRNwTEX9VTu+1\nKxkAvEKOPYd8Fh9LBgcjyXvPzuRz2INkZ8bV4MZYm/WmlPEm6ctkjLA7ea4fTcYDR5CNhi+RGaQH\nkxWlq309QZ6/65FFCkfRmrW3FpkifT1wbRl3jKQVJR0GXB4Rj5PPdPuSzwcX0FrMsGqI2q48630I\nEDn9UtUhMt0cEHdT5cFvAHniXVAFwyUYWYC86b8OfJM8qR8hT/JFgVFVy0pEPCHpTWAtlXk3a+Nd\njiAHzn+DfEh4nuzJ6UemM55R9QJGFo44bPZ8e+sMauNLepYe4fGSliYbYqr00wlkK/iB5Pl0EplZ\n8ETZzeiyjypt5yPyIjuvWqdWohZEBZl+szrZs3c2mUJzYbnQPkEGT18iz/1BtBZpoKRRD5G0WERU\n2Q0T1c59mw61c2Fh8nfzdkTcT96DBgA/LH/fl3yghGxMW4ocjtEnIt4vGS3zk/McLk62EPcnU+JH\nkdeyf5Vz4RmyAW4Dsmf/g7Lf8WQgtA1wsqRryGviF4GlyQfRI4GlJc1fem7W/UR+MNbRhpPTewwg\nz6E3ahkLt5DXmk1Kg87Q8vrx5FzVi5ANbQ/N3kO2zqI8gH9NWZdlRTKY7UFetwaQDWzfoTVb6STy\nPNueDA5eK/upGngfIqdeWpMMlh+KnAXBbKaVMbUtjc8wJV7oB7xTBZdl+xYypbkncGdJ4d8cuC0i\nTqzt4vryp56F9TjZQbEBec8eRT5rHQTcERF7t3GIvyWfDatK0neT9+1Ny734nWqIlaTdyf8fHzbu\npDSWT1Z/ZGqcMt29jaUUG5K0oaSDgDPIXt8/kvOEDScvzA+Vbdeq3qzWIjQ3kIHyOrV1LWXMzF5k\nGtm/yZb2m8gHyiMi4iOn93RvmkKVXUnrS3qJbPVG0vzkON8ty2v/Q15clyXH/B4jabOIeI88H8cB\n80mas5b6MoLWMZ6L1o6hOo6ngHnJVknIXrxTyJTp28iUmovIIPxW4IDGC2oJvl+p9l3/jg6GZ4yk\n3STdTTbC/Ru4TtJtwNLlxngvOX5uvWit4Due7PV9krw2Qf5+e5BzCB9BBiV/ADaIiPkiYlvgiXLt\nGk42uqxMLW26pG6dTgbFm5IZAkeSjS8HRMQVwHoRsVZkAQ/rpsr//fvIhpCqmmnV8DWGbORdlaw8\nPpTMTliHHLJxZhUM+z7X9J6OiEtKx8PNkRXtDybHnVcV6ymNgEeSDS3VOTWZiHggIu6IiFHKWRdc\nzd5mmFpnYBjf1jOMpN+SWZw/LEFv9Rw0gRznOwaYrzTcPANsIelwSd+UtKekz0vaTNI6tSD0efI5\nbnWy8RqyZ/d6YFtJ3699/jzKWkfbks9oz5bjfbXs40PKsKlorSp9YUQ81tb3jUmHcE4z9xB3b2+R\nPWrbkz1ivciT/nryYn1NeThE0v1kADKQTIuou4Kck3EgDWk75YQ9T9JF5QFiEk7v6T7qF9XqtVrP\n38S0lVrKzGLlT5Umswx5wftTRJxU2/X7kk4ng+VvSxpKpqm+QhZhWJjskYG8GN9KNrpsCPyzHMPH\nJaV2Y1rTdIiIF4GDJd1LptuuTPZAnwJcVRurMlFb389mnLLC92/JgPM48rq0Bjnc4jZJG5PZI38H\nTpS0d0Q8ppw6oQfZav1m2d0NwFeAiyLiGw2f04ucu3NzYGBEvFmua18hK1XeFa1Fk95RTrc0kOyJ\nuSciXqv25d97U7mG7Mnbmta5L6sAd3tgRESMKEHJY2RAvAQwrJb94vtck5K0HrCfpFsi4uySnbIA\nmWLah7xfVdu2RMQdkq4mrz3z0ToFYON+W8oQExdps5kSrUWxNiWfg+Ylx/jeURp9zyXvm0eQ00ze\nTHYyjCefp16idQ7208n7d32YyATymvlKiQV+GlmY8AHyWW0lctjZe5IOJztBzpK0PdnI3Zes0fEo\n8JOqIagc98BoGO4WDcPwZtGPyQFxN/c2eYKtRhZyOC0iqvLnSFpO0ilkT8mFZNCxoXKi+HdpHUdc\npZSuD5M/LJYL92TBsHVt1Q25vRtzeUDcleyhO5OcCqneCLJkWQ4rywHkmNCrJM1FprquQPbADCQv\nip8nU8+GkWNA1yF7b14sx/GWpN+RwdSRykqwL5TP2o9M14ecfmmBiHi7pNj8S9KgxgurfXJKr1lv\nsqGjB/A/ETG4tn4o2TDxCzKt8FhyPs99yPk3q0aVeu/b1WW5GpObh0yDf4fWaRieJO9zXyDHQb1X\na8wbR0nzsqY2lOzNaKld66qK5E/UtnuR7L3Yi3KNcsOJkefOzsDuZZjaK+QD/45kNsxvYWKDcm+y\nYXAMOXVbu+nQbmQxmDjTwpbAH+qNttO5j83Jcejrkg0wc5K1fi6XdFBkTaBTyHoIh0h6NCLeUNbx\nWIRMs656bS9U1ujYnhyKNJJs+FmR7PD4iaTzImIoORTzPXLYyb3AqxFxt6TvksOeBpANkR+SadJn\nks99E4P4kmk6WWdMW/+eWQ6Iu7eRZLC7JhAlOKgPnu9PXsifJ6dHuo8McFYkp2CqgqFXJa1f1k/G\nF+7uqRY4TFBWH9+KTCt8HPhPuVANJsd4HFBaBh+qnWNVQFylu1YFRPYlA6ANyAB2HDnG6qfAJRHx\nQuntu4es4Crg9trx3CVpXzLVdQhZ6bc3OTZrR3I88obktDn3k73HLVUwXKWf+WH2k1XOm+3Ic+b0\niBhcbmxVYap/kIHqNmSA+2fyhvp9ZcG+p8jf6+PlfRMi4iVJ5wB7SroMOJmcPmcF8vxZAPhxyVyB\nvLn+CLgzMhXfrNHwiFhmahtFxBhJz5IPfyvI9QSMzJKT9HXg2+T1a14yC+kccrq418v9ZzwwWllH\n4XPAm2XdLO3lsu5DOYXpT8kifrdSxpxP5z76kcHw8mSl87vJZ65vkwHpwuT0gpeTU1QeXj7zMLJT\nbXlgpLKOxxiAiHiOHH458TjL9fEnZBHeAWRD4xPkM9ie5H3+Rkl7Rk7PdKukz5JjfZ+e0neYXf8/\nHBB3Y+WB9I9kAHKisjjWTeU/yADyYfI98qI9XtITZOtR34Z9tETEvR3wFewTVgs0JmvUkLQheTHb\nlkxFXYrWugN/lnRM5DzWPyeD06Mk/SQini/bfESeX1Uxo6pBZXMy2PkXcGlE3FL7zJ2UxduGlB7E\nj8iq0fNEFlWqenDOkPQoGQCvR44X/VdE3FrGomxMBlOTNdj4IXa2qgoUvVGWE6J1/tZXyXTVLYHV\nI+JeSYeSDXhHkQHyW+SDYzUn51hyDN5HZKD7BTIgnpvsefk5mXoNQES8QE7nYNamWvrdlObDrhr5\nLiKvWS+2tZ01p4i4UtJ1ZGroiIh4qWH9hBLcHAFsRg4FOrCsczBsbSpB5o1kwce1JN06redL7Zq1\nEvm8/6uI+G1t/YP1zM6SpnwsOfvHIZKuiIg7S6bX05Rnv5LdtxPZ4XBBRAwvxzkHme03ntbZG4K8\nJ59MFtZ6lXwu+7AcX5U92Ck6KhwQd3ORE2L/kExNvJbs3RtFtvp8AHwjIqq5NI+NiMPb2Id7gLuR\nehDc3sVV0s7k1Ed/J3vvniLHnfehtXJmkBe6S8m05iOr7ZRT2yxcdvdgWd5BnnOjImKVNj5zPzK9\n9nu0FlYIMm16cVovsgBExM3kWJdG65MF3h5tY53NXh+SQy/6SOpdT1kvD4nPkzfQJctrD0k6jZyy\n7RAytbBPeUvV0/+Kcpq388kMgoXInuCrI+Ll2fXFrHtpLxgu66rslBlKWbTurwQX1b2uKko6vpb6\nOUbS2mT20vXkuE2zyZTgsGe5Xz5G9tRuQhbDnaaq47Xn9vfLcmtJg8gOrzmAJyVB1uh4t2S8fCTp\nl+QwuMMknUU+s42NiA9LcDyWvF8fA2wk6Z9k9t9AslbRGZTnsnLu3yFpyyj1ihqPr9bJ0eEdFQ6I\nm0BE/EHS42TKwobkmLzTyB61qG03up1dWDcSrQUW+pEl7vuRF7DHo7Xi8nNkgZndyUDji9X7lVMb\n3UO2Ep5cLqYnk2k9e0v6WwlsliUDormA9yOrA/6TnFbiAODUaC3KtRDwdbLnrxq39wbZ87spmQpb\nv4jOTfYgjgd+QFacXo6sXL0B8GunyHYKw8lW4apR47mGtOkPyOIdi9becy55Tu5Hph4+DZMVOxtD\npn7d/cl/BTOz6VNvYKmlRe8NjHGGgU1JeS6qAsRnyAbf9ciZYaZ3Gq77yc6wL5AdDZBZVXOQY4kv\nk3RWRNxQ1g0CPkOmPn9I1uN4vxzXBGBcyTxdm8x22JK8h79DZgqe0ti4GBGjSzDdk4bpkDpTh5sD\n4iZRy9n3eJVubmqpJ5KWAk4Avkw2jnxMprFcL+nAyKlEXiR7ZDeiTA1R61keImkYsJ6kBSNiZER8\noJzW60IyPb8Kbl9m0uvMGeR4z5OB9ZVT7/Qig+n1gAMj4vZy/CMk7QO8VkuzrVoUPygFH7YmG3pe\nIIOqeYHfM2kFROs4L5ENK7uSN8/nyvWnugZ9riwfqN5QxuQdR45xWojWBhIzsy6n1ks8xbGS1hxK\ncNhmMFiGBm1JTmdadVa8TxaYXIkMkKf1c3pEVj3fi+wM24YMgkeQnQyfpbVuUP8qPigB7xfIwllz\nk5l7E489sjL1bsr5slcAnoqIB5iCKpie1mPvCC0TJnSa4NzMZjFJy5MpMSPKv+cne1Z3BP5GVvYb\nR178fkCmPK8dWR7/QOAkMlX61NLD27NcYM8Gvgt8KSIur71+BDlO6kyylfHTEbFJfXyepBXJDIX+\nZAXDnmTwfTbwx8gK543fY2IxuNpFexEyjWhbWtNmB5WA3joJSQPJSvVBptvfRfYWb02OTX8bWCfK\n1Eq13+9qwIvRxtRYZmZmXcm0FOJTVmA+lSyK+xg5xdF8ZGB6OHBSvYNgJo6lqstyH1m3Y+GIGFm7\n/65Dpk6vCXw1Ii6eWoda6YzpslOFuYfYrJspAcg+ZDr0GOCpUiDhZLLY1C7A/0XEj2tvGySpN9kr\n9z0yXeZBMg1mNfJi/C5ZWOFjcj7Y75KB9OWUVBgyqO1Ppoa9SGu668SWwYh4EthOUn8yMIqptZw3\npNhUre1vAJdKuqwzpd3YpCLi5lKs4+dka/fD5Hm5KtkAs0e0zjNMtM4V/HBHHK+ZmdmsVhsitjrZ\nYXBrRFRjfKs5rY8lh6x9n7xXfkzW0ziCfH47kyw2Oc1KNt0XgZcj4vpyLNXn9iLvw/MAI2v33yGS\n/koGxL3Ltm31aE/s7e4M44BnhgNis26kBJlnkGNNLiEDj23INOaR5EUR4J9l+15AjzIm8zyyAvTO\n5UIY5HzA65A9ufW5qW8v+x4IOVdcWb4i6RAyPXZJcp67SYop1YooDKWkY5fXe5JFSKYruHUw3PlF\nxOGS7iR789cnsxKOBy6MiGc06XRw/p2amVmXoSnP2LEQWS9jD3IGhUXIIWVDJZ0QEZeXTatxwodG\nxJ219/8fOYfwALITYXqzpvqUz+4v6WCy5/kzZM2VVYH9I2dkqPQqx/cGGQQ/DG3fl7vTvdoBsVn3\ncg6wLPBN4MqIGKuc6+3nZG/vKLJQQmVc7YL2CJnOuhU5HvdpsiDDt8mCVU/XemdfUE6LtL6kZSKn\nX2ohJ3B/UjnJ+w+A2+rBcHnvxAtod2pdtCmLiGskXUueIx83rOs2N1UzM+v+6vVa2ksTlvQHcoja\nBWTdlv+SM2CsTD6n/UzSzeTz2WLk89n99UbiiHhT0iVkg/LqTOcMGqUuxx/JIUpnklMU9irLXwF/\nati+embbqmzzBk3AAbFZN1FSpVcme90urV6PiGGS9iYLaK1Dtvh9uqyrByIjyYnfFwbmipwm4kHy\nwrmWpJtLgF2NB76N7O3bkawu2IPWYkknRcTxUztmB0LNpauOLTIzM6urpUD3JHtvlwMejYj7apvd\nSw5D+wo5vOzQKLN5lNkydgE2joirJM1JzsoxdzRMS0ROmToC2FjSJY0dDdNwrBdKuhXYjuzw1C0V\n7gAAEkhJREFUeAi4sZ6yXVFOu7kLsBtwGdNf2bpLckBs1n3MQV5M34ZJCzhExKjy2kNklcENJF0d\nOe9cD2CO8ve+ZV8LlOUT5FjgdckKziPJwBpyLsVNaB0nPLHXrxTYaiGn1+nUlQXNzMzMpoekzch6\nLduSdVYAXpR0VUT8qPz7ZrITYkngxMj5fKtns0HkMLZNyAKnz5X3bAzcVWXQFW9SClACnyI7L6bn\nWFsi4mUyi7D++sRU71rRrM+R6dSvAudUz4/dXY+OPgAzm2VeKcu5Sy/uxAC1XPQgxwS/RM75+znI\nXrsSDC9IXmyfLdtBBrtvADuQY1eoKhxGxHURMSAirm7rYEoatINhMzMz6zYkrUFWg+5PBpn7AT8j\nOxz2KrMkAAyndVrBhcuyCnQfK+s3lDQXcCfZ6bBzqQI9gdY47UNgaXLqpaWm4fha6gF1w1C1HtUz\nYXn+q9ZVyz+QUz8tHxHXNwTm3ZZ7iM26jzfJ3txVyIIJw2vjesdLOoocs3I/sBPwa0lHk3P4Lg/8\nmCzx/4tagYWXyeJHH5EpO5MoF9UeDnzNzMysSZwBiJx68trqxVK89CxgZ+DhMsxsMLAWOf63Pi3k\ncLJg1UByTuCHgYuAHwIHAEeXbLt5ymvvk9l760oa0l49lvqyrFsEmK+azaO9oUu1944Abmp8vbtz\nQGzWfbxBjuv9CpniPJxJU5arFsujyR7gg8hUnVfJdOgW4PiIOK7aYellvqS9DywXVo8LNTMzs05t\nVsyVK2kpciqifwDXldfmIXtwNyibDSQLVgHcSPYgrwv8jTJbR0SMkPQAOV53zYh4sHRcbAgcJWlD\nciaOJYDPAxeWbVcn47eJ8xE3BMDzkp0iK5ZtNwKWkTQwIl6f0e/d3bVMmNAUgb9ZUyhjWm4iCzl8\nuVSDno+8mP4VuCkidijbbkiOfVmOrFp4WURM1gtctp3ihOxmZmZmnckn8exS0pvXAF4qz1irkD3C\n25Pz+S5MjvNdMSJeltSPnOv3AWD7iHi3Oi5JOwHnlz+HlXUrk7N0fJFMjx4F/IbseR4XEW81HE8f\nskL1cuQ0SuuWPyuQKdfDgMHAwRExclb+LLoTB8Rm3Yykk8l0m1eBe8hWxK3I1OivRETUC26ZmZmZ\ndWeSFiaD1lWB+4BbIuKVKb9rivtbkJzS8rvkXL/XkOOKvwF8H9g5Iq4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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52970f05c0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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dQ61adbJsN2PG56SkpNC9e690U+S7d+9FiRIlmDVrRk6HKiIiZ0AJsYiISJBW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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5297286978>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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PmYzrCZJ9TaWTmVmlzCPDBQzEkiRJ2imlpRWMEBe1Zs2aCq4mZv36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\nMxUE30d1MryGvrpBEzrxQQt430IVzpqPytybeuxZlanfGzVf9quBmzL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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5296ac3fd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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+/fXX6Nv3JVq3bsupp57BrFkzeeedt5g6dQr33/+3XSoUG4glSZIkRU2aNJGh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\nK0jSzOluYOlKi94FeNwMA01Ley7qBIj/oBp816NmhpnZabj+TnWGvYH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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5296d25198>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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0GgCDBz9DRkYGEAlNgwY9DcAZZ5xTarWpcJKTkznmmHb88cc6Xn31pRz7hg9/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\n7Fnep0KN1QAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5297186eb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Compare TPR results for all 18 labels\n", "# based on their groupings\n", "for lbl in labels:\n", " label = lbl\n", " sns.set_context(\"paper\", font_scale=2.5)\n", " sns.set_palette(\"Greys_r\", 6)\n", " plt.figure(figsize=(14,8))\n", " auroc_ax = sns.barplot(x='Group', y=label, \n", " hue='Threshold_Percentage', data=df)\n", " auroc_ax.set(ylabel=label)\n", " auroc_ax.set(xlabel=\"Classifier Group\")\n", " auroc_ax.legend(ncol=2, bbox_to_anchor=(1.05, 1),loc=0, frameon=True, title=\"Threshold Percentage\")\n", " #lgd = auroc_ax.legend(ncol=2, bbox_to_anchor=(1.05, 1),loc=0, frameon=True, title=\"Threshold Percentage\")\n", " lgd = auroc_ax.legend(ncol=2, loc=0,frameon=True, title=\"Threshold Percentage\")\n", " plt.xticks(rotation=15)\n", " plt.tight_layout()\n", " \n", " plt.savefig(label + '.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n", " plt.show()\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

tpin3694/tpin3694.github.io

sql/select_entire_table.ipynb

1

11556

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Title: Select An Entire Table \n", "Slug: select_entire_table \n", "Summary: Select an entire table in SQL. \n", "Date: 2016-05-01 12:00 \n", "Category: SQL \n", "Tags: Basics \n", "Authors: Chris Albon \n", "\n", "Note: This tutorial was written using [Catherine Devlin's SQL in Jupyter Notebooks library](https://github.com/catherinedevlin/ipython-sql). If you have not using a Jupyter Notebook, you can ignore the two lines of code below and any line containing `%%sql`. Furthermore, this tutorial uses SQLite's flavor of SQL, your version might have some differences in syntax." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Connected: None@None'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Ignore\n", "%load_ext sql\n", "%sql sqlite://" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Done.\n", "1 rows affected.\n", "1 rows affected.\n", "1 rows affected.\n", "1 rows affected.\n", "1 rows affected.\n", "1 rows affected.\n", "1 rows affected.\n", "1 rows affected.\n", "Done.\n", "1 rows affected.\n", "1 rows affected.\n", "1 rows affected.\n", "1 rows affected.\n", "1 rows affected.\n", "1 rows affected.\n", "1 rows affected.\n", "1 rows affected.\n", "1 rows affected.\n", "1 rows affected.\n", "1 rows affected.\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%sql\n", "\n", "/* Create A Table Of Criminals */\n", "CREATE TABLE criminals (pid, name, age, sex, city, minor);\n", "INSERT INTO criminals VALUES (412, 'James Smith', 15, 'M', 'Santa Rosa', 1);\n", "INSERT INTO criminals VALUES (234, 'Bill James', 22, 'M', 'Santa Rosa', 0);\n", "INSERT INTO criminals VALUES (632, 'Stacy Miller', 23, 'F', 'Santa Rosa', 0);\n", "INSERT INTO criminals VALUES (621, 'Betty Bob', NULL, 'F', 'Petaluma', 1);\n", "INSERT INTO criminals VALUES (162, 'Jaden Ado', 49, 'M', NULL, 0);\n", "INSERT INTO criminals VALUES (901, 'Gordon Ado', 32, 'F', 'Santa Rosa', 0);\n", "INSERT INTO criminals VALUES (512, 'Bill Byson', 21, 'M', 'Santa Rosa', 0);\n", "INSERT INTO criminals VALUES (411, 'Bob Iton', NULL, 'M', 'San Francisco', 0);\n", "\n", "/* Create A Table Of Crimes */\n", "CREATE TABLE crimes (cid, crime, city, pid_arrested, cash_stolen);\n", "INSERT INTO crimes VALUES (1, 'fraud', 'Santa Rosa', 412, 40000);\n", "INSERT INTO crimes VALUES (1, 'burglary', 'Petaluma', 234, 2000);\n", "INSERT INTO crimes VALUES (1, 'burglary', 'Santa Rosa', 632, 2000);\n", "INSERT INTO crimes VALUES (1, 'larcony', 'Petaluma', 621, 3500); \n", "INSERT INTO crimes VALUES (1, 'burglary', 'Santa Rosa', 162, 1000); \n", "INSERT INTO crimes VALUES (1, 'larcony', 'Petaluma', 901, 50000); \n", "INSERT INTO crimes VALUES (1, 'fraud', 'San Francisco', 412, 60000); \n", "INSERT INTO crimes VALUES (1, 'burglary', 'Santa Rosa', 512, 7000); \n", "INSERT INTO crimes VALUES (1, 'burglary', 'San Francisco', 411, 3000); \n", "INSERT INTO crimes VALUES (1, 'robbery', 'Santa Rosa', 632, 2500); \n", "INSERT INTO crimes VALUES (1, 'robbery', 'Santa Rosa', 512, 3000);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## View Both Tables" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Done.\n" ] }, { "data": { "text/html": [ "<table>\n", " <tr>\n", " <th>pid</th>\n", " <th>name</th>\n", " <th>age</th>\n", " <th>sex</th>\n", " <th>city</th>\n", " <th>minor</th>\n", " </tr>\n", " <tr>\n", " <td>412</td>\n", " <td>James Smith</td>\n", " <td>15</td>\n", " <td>M</td>\n", " <td>Santa Rosa</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>234</td>\n", " <td>Bill James</td>\n", " <td>22</td>\n", " <td>M</td>\n", " <td>Santa Rosa</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <td>632</td>\n", " <td>Stacy Miller</td>\n", " <td>23</td>\n", " <td>F</td>\n", " <td>Santa Rosa</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <td>621</td>\n", " <td>Betty Bob</td>\n", " <td>None</td>\n", " <td>F</td>\n", " <td>Petaluma</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <td>162</td>\n", " <td>Jaden Ado</td>\n", " <td>49</td>\n", " <td>M</td>\n", " <td>None</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <td>901</td>\n", " <td>Gordon Ado</td>\n", " <td>32</td>\n", " <td>F</td>\n", " <td>Santa Rosa</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <td>512</td>\n", " <td>Bill Byson</td>\n", " <td>21</td>\n", " <td>M</td>\n", " <td>Santa Rosa</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <td>411</td>\n", " <td>Bob Iton</td>\n", " <td>None</td>\n", " <td>M</td>\n", " <td>San Francisco</td>\n", " <td>0</td>\n", " </tr>\n", "</table>" ], "text/plain": [ "[(412, 'James Smith', 15, 'M', 'Santa Rosa', 1),\n", " (234, 'Bill James', 22, 'M', 'Santa Rosa', 0),\n", " (632, 'Stacy Miller', 23, 'F', 'Santa Rosa', 0),\n", " (621, 'Betty Bob', None, 'F', 'Petaluma', 1),\n", " (162, 'Jaden Ado', 49, 'M', None, 0),\n", " (901, 'Gordon Ado', 32, 'F', 'Santa Rosa', 0),\n", " (512, 'Bill Byson', 21, 'M', 'Santa Rosa', 0),\n", " (411, 'Bob Iton', None, 'M', 'San Francisco', 0)]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%sql\n", "\n", "-- Select everything\n", "SELECT *\n", "\n", "-- From the table 'criminals'\n", "FROM criminals" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Done.\n" ] }, { "data": { "text/html": [ "<table>\n", " <tr>\n", " <th>cid</th>\n", " <th>crime</th>\n", " <th>city</th>\n", " <th>pid_arrested</th>\n", " <th>cash_stolen</th>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>fraud</td>\n", " <td>Santa Rosa</td>\n", " <td>412</td>\n", " <td>40000</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>burglary</td>\n", " <td>Petaluma</td>\n", " <td>234</td>\n", " <td>2000</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>burglary</td>\n", " <td>Santa Rosa</td>\n", " <td>632</td>\n", " <td>2000</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>larcony</td>\n", " <td>Petaluma</td>\n", " <td>621</td>\n", " <td>3500</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>burglary</td>\n", " <td>Santa Rosa</td>\n", " <td>162</td>\n", " <td>1000</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>larcony</td>\n", " <td>Petaluma</td>\n", " <td>901</td>\n", " <td>50000</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>fraud</td>\n", " <td>San Francisco</td>\n", " <td>412</td>\n", " <td>60000</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>burglary</td>\n", " <td>Santa Rosa</td>\n", " <td>512</td>\n", " <td>7000</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>burglary</td>\n", " <td>San Francisco</td>\n", " <td>411</td>\n", " <td>3000</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>robbery</td>\n", " <td>Santa Rosa</td>\n", " <td>632</td>\n", " <td>2500</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>robbery</td>\n", " <td>Santa Rosa</td>\n", " <td>512</td>\n", " <td>3000</td>\n", " </tr>\n", "</table>" ], "text/plain": [ "[(1, 'fraud', 'Santa Rosa', 412, 40000),\n", " (1, 'burglary', 'Petaluma', 234, 2000),\n", " (1, 'burglary', 'Santa Rosa', 632, 2000),\n", " (1, 'larcony', 'Petaluma', 621, 3500),\n", " (1, 'burglary', 'Santa Rosa', 162, 1000),\n", " (1, 'larcony', 'Petaluma', 901, 50000),\n", " (1, 'fraud', 'San Francisco', 412, 60000),\n", " (1, 'burglary', 'Santa Rosa', 512, 7000),\n", " (1, 'burglary', 'San Francisco', 411, 3000),\n", " (1, 'robbery', 'Santa Rosa', 632, 2500),\n", " (1, 'robbery', 'Santa Rosa', 512, 3000)]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%sql\n", "\n", "-- Select everything\n", "SELECT *\n", "\n", "-- From the table 'crimes'\n", "FROM crimes" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

EthanAnderes/BayesianCmbLensing

paper/generate_figures/figure3.ipynb

2

247833

{ "metadata": { "language": "Julia", "name": "", "signature": "sha256:ac6b3902a724f74d3244cb3b7079b76859f9e33a7d266343d08c587262717a02" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Generate figure 3 which shows embedding of the lensed grid from Section 6.\n", "===================================================== " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load packages. Set prelim grids etc.\n", "----------------------------------------------------- " ] }, { "cell_type": "code", "collapsed": false, "input": [ "srcpath = \"/Users/ethananderes/Dropbox/BayesLense/src/\"\n", "savepath = \"/Users/ethananderes/Google\\ Drive/BayesLenseRev1/paper_rev2/\"\n", "\n", "include(srcpath*\"Interp.jl\")\n", "include(srcpath*\"cmb.jl\")\n", "include(srcpath*\"fft.jl\")\n", "\n", "using PyPlot\n", "using Interp\n", "\n", "seed = 12\n", "srand(seed)\n", "\n", "par = setpar(0.35, 2^11, 0, 0, 3000, 1000, srcpath)\n", "x,y = meshgrid(1:7,1:7)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "INFO: Loading help data...\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "(\n", "7x7 Array{Int64,2}:\n", " 1 2 3 4 5 6 7\n", " 1 2 3 4 5 6 7\n", " 1 2 3 4 5 6 7\n", " 1 2 3 4 5 6 7\n", " 1 2 3 4 5 6 7\n", " 1 2 3 4 5 6 7\n", " 1 2 3 4 5 6 7,\n", "\n", "7x7 Array{Int64,2}:\n", " 1 1 1 1 1 1 1\n", " 2 2 2 2 2 2 2\n", " 3 3 3 3 3 3 3\n", " 4 4 4 4 4 4 4\n", " 5 5 5 5 5 5 5\n", " 6 6 6 6 6 6 6\n", " 7 7 7 7 7 7 7)" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate embed2.eps\n", "----------------------------------------------------- " ] }, { "cell_type": "code", "collapsed": false, "input": [ "phik = fft2(randn(size(par.grd.x))./par.grd.deltx, par) .* sqrt(par.cPP)\n", "phidx1 = ifft2r(im .* par.grd.k1 .* phik, par)\n", "phidx2 = ifft2r(im .* par.grd.k2 .* phik, par) \n", "\n", "grng = linspace(1, 100, size(x,1)) |> int\n", "fac = 1.9e3\n", "xnew = x + fac*phidx1[grng,grng] .- mean(fac*phidx1[grng,grng])\n", "ynew = y + fac*phidx2[grng,grng] .- mean(fac*phidx2[grng,grng])\n", "plt.scatter(xnew, ynew, s = 58, facecolors=\"k\")\n", "plt.annotate(L\"$(x+\\nabla\\phi(x),\\, data(x))$\",\n", " xy=(xnew[6,6], ynew[6,6]), \n", " xytext=(4.5, 7.2), \n", " fontsize=18,\n", " arrowprops={:arrowstyle=>\"->\"}\n", ")\n", "axis(\"tight\")\n", "axis(\"off\")\n", "savefig(joinpath(savepath, \"figure3a.pdf\"), dpi=300, bbox_inches=\"tight\", pad_inches=0)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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", "text": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x12242e510>)" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate embed3.eps\n", "----------------------------------------------------- " ] }, { "cell_type": "code", "collapsed": false, "input": [ "a = linspace(2,8, 25) .- ones(1,25) - .2\n", "at = a' +.1\n", "xnewround = zeros(size(xnew))\n", "ynewround = zeros(size(ynew))\n", "for k in 1:length(xnewround)\n", " indd = indmin( (xnew[k] .- a).^2 + (ynew[k] .- at).^2)\n", " xnewround[k] = a[indd]\n", " ynewround[k] = at[indd]\n", "end\n", "plt.scatter(xnewround, ynewround, s = 58, facecolors=\"k\")\n", "plt.scatter(a, at, facecolors=\"none\", edgecolors=\"k\", s = 58, lw = .95) #, alpha = .95)\n", "plt.annotate(L\"$(y,\\, T(y) + \\tilde n(y))$\",\n", " xy=(xnewround[6,6], ynewround[6,6]), \n", " xytext= (4.5, 7.2), \n", " fontsize = 18,\n", " arrowprops={:arrowstyle => \"->\"}\n", ")\n", "plt.annotate(L\"$(y,\\, T(y) + \\tilde n(y))$\",\n", " xy=(a[22,23], at[22,24]), \n", " xytext = (4.5, 7.2), \n", " fontsize = 18,\n", " arrowprops={:arrowstyle => \"->\"}\n", ")\n", "axis(\"tight\")\n", "axis(\"off\")\n", "savefig(joinpath(savepath, \"figure3b.pdf\"), dpi=300, bbox_inches=\"tight\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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", "text": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x122b1b490>)" ] } ], "prompt_number": 3 } ], "metadata": {} } ] }

mit

scotgl/sonify

DataStethoscope/.ipynb_checkpoints/Module 2 Panned_Pulse_Test-checkpoint.ipynb

1

10248

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sonification Training Module v0.5.2\n", "\n", "Developed by the ArtSciLab at UT Dallas\n", "\n", "\n", "Panned Pulse Testing Module\n", "\n", "\n", "Press Shift + Enter to activate cell\n", "\n", "Listen to the Sonification, then set the Slider to what value you think it represents.\n", "Pay close attention to the pulse rate and panning (left/right).\n", "You can listen to the sonification as many times as you need to.\n", "\n", "When you are sure of your answer, click Submit Response once and wait for the next trial to load.\n", "You will undergo 5 trials.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ab3232d7af8d4101a7dd118517612c3d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(IntSlider(value=50, description='percentage'), Output()), _dom_classes=('widget-interact…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Trial 1\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3441f3e2a23247b2acfc2d556e753bfc", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(VBox(children=(Button(description='Listen to Sonification', style=ButtonStyle()),)), VBox(child…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import random\n", "import time\n", "from IPython.display import Image, display, clear_output\n", "from ipywidgets import Button, HBox, VBox,Layout\n", "from ipywidgets import widgets\n", "from ipywidgets import interact, interactive, fixed, interact_manual\n", "from gtts import gTTS\n", "import os\n", "import numpy\n", "import ctcsound\n", "import platform\n", "import os\n", "from traitlets.config.manager import BaseJSONConfigManager\n", "\n", "from ipywidgets import interact, interactive, fixed, interact_manual\n", "import ipywidgets as widgets\n", "from gtts import gTTS\n", "import matplotlib\n", "import numpy \n", "\n", "#path = \"/Users/Kristen/anaconda3/envs/py36/etc/jupyter/nbconfig\"\n", "path = \"/anaconda3/envs/py36/lib/python3.6/etc/jupyter/nbconfig\"\n", "cm = BaseJSONConfigManager(config_dir=path)\n", "cm.update(\"livereveal\", {\"autolaunch\": False,\n", " \"theme\": \"sky\",\n", " } \n", ") \n", "\n", "try:\n", " pt\n", "except NameError:\n", " var_exists = False\n", "else:\n", " pt.stop()\n", " pt.join()\n", " time.sleep(2)\n", "\n", "\n", "speechflag = 0\n", "if (platform.system()=='Windows'):\n", " speechflag = 2\n", "if (platform.system()!='Windows'):\n", " speechflag = 1\n", "#Supress default INFO logging\n", "# The UT Dallas Art Science Lab Training module \n", "print (\"Sonification Training Module v0.5.2\")\n", "print(\"\\nDeveloped by the ArtSciLab at UT Dallas\")\n", "print(\"\\n\")\n", "print(\"Panned Pulse Testing Module\" )\n", "print(\"\\n\")\n", "print(\"Press Shift + Enter to activate cell\")\n", "print (\"\\nListen to the Sonification, then set the Slider to what value you think it represents.\")\n", "print(\"Pay close attention to the pulse rate and panning (left/right).\")\n", "print(\"You can listen to the sonification as many times as you need to.\")\n", "print(\"\\nWhen you are sure of your answer, click Submit Response once and wait for the next trial to load.\") \n", "print(\"You will undergo 5 trials.\")\n", "count=0\n", "accuracy =0\n", "\n", "pan = 0\n", "user_input=0\n", "cs = ctcsound.Csound()\n", "index = 0\n", "csd = '''\n", "<CsoundSynthesizer>\n", "<CsOptions>\n", "-odac -d\n", "\n", "</CsOptions>\n", "<CsInstruments>\n", "\n", "sr = 44100\n", "ksmps = 32\n", "nchnls = 2\n", "0dbfs = 1\n", "\n", "instr 1\n", "\n", ";aMod1 poscil 200, 700, 1\n", "aMod1 poscil p4, p5, 1 ; p4 = amp1, p5 = f1, p6 = amp2, p7 = f2 \n", ";aMod2 poscil 1800, 290, 1\n", "aMod2 poscil p6, p7, 1\n", "kenv linen p9 , 0.3 , p3, p9\n", "aSig poscil kenv, 440+aMod1+aMod2, 1\n", "outs aSig*(1-p8), aSig*p8\n", "endin\n", "\n", "\n", "\n", "</CsInstruments>\n", "<CsScore>\n", "f 0 14400\n", "f 1 0 1024 10 1 \n", "\n", "</CsScore>\n", "\n", "</CsoundSynthesizer>\n", "'''\n", "\n", "\n", " \n", "cs.compileCsdText(csd)\n", "cs.start()\n", "pt = ctcsound.CsoundPerformanceThread(cs.csound())\n", "pt.play()\n", "\n", "\n", "\n", "def f(percentage):\n", " global user_input\n", " user_input = percentage\n", "\n", "\n", "\n", "def redraw(): \n", " \n", " global index\n", " global accuracy\n", " sonibutton = widgets.Button(description = 'Listen to Sonification')\n", " answerbutton = widgets.Button(description='Submit Response')\n", " choices = random.sample(range(100), 4)\n", " choices = list(map(str, choices))\n", " correct = random.choice(choices)\n", " index = int(correct)\n", "\n", " #display(Image(correct))\n", " #display(correct)\n", " time.sleep(0.5)\n", " \n", " \n", " #display(button)\n", " #button.on_click(on_button_clicked)\n", " \n", " #buttons = [widgets.Button(description = choice) for choice in choices]\n", " #sonibutton = [widgets.Button(description = 'Listen to Sonification')]\n", " interact (f, percentage=(0,100,1))\n", " #answerbutton = [widgets.Button(description='Submit Input')]\n", " \n", " #container = widgets.HBox(children=buttons)\n", "\n", " \n", " left_box = VBox([(sonibutton)])\n", " right_box = VBox([(answerbutton)])\n", " #HBox([left_box, right_box])\n", " container = widgets.HBox([left_box,right_box])\n", " print(\"Trial \" + str(count+1))\n", " display(container)\n", " \n", "\n", "\n", " def ans_button_clicked(b):\n", " global count\n", " global accuracy\n", " count = count + 1\n", " \n", " \n", " \n", "\n", " \n", " #tts = gTTS(text='Input Submitted', lang='en')\n", " #tts.save(\"answer.mp3\") \n", " #if (speechflag==1):\n", " #os.system(\"afplay answer.mp3\")\n", " #if (speechflag==2):\n", " #os.system(\"cmdmp3 answer.mp3\")\n", " #print(user_input)\n", " text=list()\n", " text.append(index)\n", " text.append(user_input)\n", " text.append(index-user_input)\n", " accuracy = accuracy + abs(index -user_input)\n", " with open('responses.csv','a') as file:\n", " file.write('\\n')\n", " for line in text:\n", " file.write(str(line))\n", " file.write(',')\n", " time.sleep(2)\n", " container.close()\n", " clear_output()\n", " if count <5:\n", " redraw()\n", " if count == 5:\n", " msg = widgets.Button(description = 'Thank you for finishing this module',layout=Layout(width='50%', height='80px'))\n", " display(msg)\n", " print(\"Your accuracy of response is \" + str(100-(accuracy/5)) + \"%\")\n", " pt.stop()\n", " pt.join()\n", " file.close()\n", " \n", " \n", " def son_button_clicked(b):\n", " \n", " #tts = gTTS(text='Playing Sonification', lang='en')\n", " #tts.save(\"answer.mp3\") \n", " #os.system(\"afplay answer.mp3\")\n", " \n", " in_min = 0\n", " in_max = 100\n", " out_min=690\n", " out_max = 710\n", " \n", " global index\n", " \n", " if (index>50):\n", " pan = 1\n", " if (index<50):\n", " pan = 0\n", " if (index==50):\n", " pan = 0.5\n", " freq = (index - in_min) * (out_max - out_min) / (in_max - in_min) + out_min \n", " #print(freq) \n", " \n", " pt.scoreEvent(False, 'i', (1, 0, 4, 200, 700, 200, freq, pan, 0.5))\n", " time.sleep(4.5)\n", " \n", " \n", " \n", " \n", " answerbutton.on_click(ans_button_clicked)\n", " sonibutton.on_click(son_button_clicked)\n", "\n", "\n", " \n", " \n", " \n", "\n", "redraw()\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [], "source": [] } ], "metadata": { "hide_input": true, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

QCoDeS/Qcodes-contrib

qdev_transmon_examples/VNA Example.ipynb

1

3109

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%run -i 'import_nb.ipynb'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "set_sample_name('testing_bugs_2')\n", "set_file_locations()\n", "set_qubit_count(3)\n", "set_current_qubit(1)\n", "station = qc.Station()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "station = qc.Station()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "vna = helpers.import_vna()\n", "dec_slots, dec_chans = helpers.import_decadac()\n", "dummy_time = import_manual_param(station=station)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dummy_time(3)\n", "data, plots = measure(dummy_time)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Find all Resonators" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "resonator_sweep_setup(v1)\n", "data, plot = measure(v1.trace)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data, plot = load(12)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#fs = 400e6 * 2001 # sampling frequency used for smoothing (ie span * npts)\n", "\n", "fs = (v1.stop.get_latest() - v1.start.get_latest()) * v1.npts.get_latest()\n", "\n", "indices, resonances_array, res_attempt_plot = find_peaks(data, fs)\n", "save_plot(res_attempt_plot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Power Sweeps (qubits alive?)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "power_sweep_setup(v1)\n", "pow_sweeps = []\n", "\n", "for f in resonances_array:\n", " dat, pl = do_power_sweep(v1, f)\n", " pow_sweeps.append(dat)" ] }, { "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

nitin-cherian/LifeLongLearning

Python/Python_Morsels_Revised/11.lstrip/let_me_try/lstrip.ipynb

1

8967

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def lstrip(iterable, obj):\n", " stripped = []\n", " stop = False\n", " \n", " for item in iterable:\n", " if stop:\n", " stripped.append(item)\n", " elif item != obj:\n", " stripped.append(item)\n", " stop = True\n", " \n", " return stripped\n", " " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 0, 2, 3]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lstrip([0, 0, 1, 0, 2, 3], 0)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['h', 'e', 'l', 'l', 'o', ' ']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lstrip(' hello ', ' ')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bonus1: return an iterator (for example a generator) from your lstrip function instead of a list. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def lstrip(iterable, obj):\n", " stop = False \n", " for item in iterable:\n", " if stop:\n", " yield item\n", " elif item != obj:\n", " yield item\n", " stop = True \n", " " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = lstrip([0, 1, 2, 3, 0], 0)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<generator object lstrip at 0x0000000004814A40>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 0]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bonus2: allow your lstrip function to accept a function as its second argument which will determine whether the item should be stripped" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def lstrip(iterable, obj):\n", " lstrip_stop = False \n", " for item in iterable:\n", " if lstrip_stop:\n", " yield item\n", " else: \n", " if not callable(obj):\n", " if item != obj:\n", " yield item\n", " lstrip_stop = True\n", " else:\n", " if not obj(item):\n", " yield item\n", " lstrip_stop = True\n", " " ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def is_falsey(value): return not bool(value)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 0, 2, 'h', '']" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(lstrip(['', 0, 1, 0, 2, 'h', ''], is_falsey))" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[2, 4, -6]" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(lstrip([-4, -2, 2, 4, -6], lambda n: n < 0))" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "numbers = [0, 2, 4, 1, 3, 5, 6]" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def is_even(n): return n % 2 == 0" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 3, 5, 6]" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(lstrip(numbers, is_even))" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 0, 2, 3]" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(lstrip([0, 0, 1, 0, 2, 3], 0))" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['h', 'e', 'l', 'l', 'o', ' ']" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(lstrip(' hello ', ' '))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Unit Tests" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ".........\n", "----------------------------------------------------------------------\n", "Ran 9 tests in 0.004s\n", "\n", "OK\n" ] } ], "source": [ "import unittest\n", "\n", "class LStripTests(unittest.TestCase):\n", "\n", " \"\"\"Tests for lstrip.\"\"\"\n", "\n", " def assertIterableEqual(self, iterable1, iterable2):\n", " self.assertEqual(list(iterable1), list(iterable2))\n", "\n", " def test_list(self):\n", " self.assertIterableEqual(lstrip([1, 1, 2, 3], 1), [2, 3])\n", "\n", " def test_nothing_to_strip(self):\n", " self.assertIterableEqual(lstrip([1, 2, 3], 0), [1, 2, 3])\n", "\n", " def test_string(self):\n", " self.assertIterableEqual(lstrip(' hello', ' '), 'hello')\n", "\n", " def test_empty_iterable(self):\n", " self.assertIterableEqual(lstrip([], 1), [])\n", "\n", " def test_strip_all(self):\n", " self.assertIterableEqual(lstrip([1, 1, 1], 1), [])\n", "\n", " def test_none_values(self):\n", " self.assertIterableEqual(lstrip([None, 1, 2, 3], 0), [None, 1, 2, 3])\n", "\n", " def test_iterator(self):\n", " squares = (n**2 for n in [0, 0, 1, 2, 3])\n", " self.assertIterableEqual(lstrip(squares, 0), [1, 4, 9])\n", "\n", " # To test the Bonus part of this exercise, comment out the following line\n", " # @unittest.expectedFailure\n", " def test_returns_iterator(self):\n", " stripped = lstrip((1, 2, 3), 1)\n", " self.assertEqual(iter(stripped), iter(stripped))\n", "\n", " # To test the Bonus part of this exercise, comment out the following line\n", " # @unittest.expectedFailure\n", " def test_function_given(self):\n", " numbers = [0, 2, 4, 1, 3, 5, 6]\n", " def is_even(n): return n % 2 == 0\n", " self.assertIterableEqual(lstrip(numbers, is_even), [1, 3, 5, 6])\n", "\n", "\n", "if __name__ == \"__main__\":\n", " unittest.main(argv=['ignore-first-arg'], exit=False)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

DataPilot/notebook-miner

summary_of_work/22. Clustering Results.ipynb

1

191171

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['bottom_up_general_1.npy', 'bottom_up_general_132.npy', 'bottom_up_general_255.npy', 'bottom_up_general_485.npy', 'bottom_up_general_63.npy', 'bottom_up_general_899.npy', 'bottom_up_general_93.npy', 'bottom_up_split_call_1.npy', 'bottom_up_split_call_1272.npy', 'bottom_up_split_call_143.npy', 'bottom_up_split_call_309.npy', 'bottom_up_split_call_59.npy', 'bottom_up_split_call_633.npy', 'bottom_up_split_call_93.npy', 'hierarchical_111.npy', 'hierarchical_2.npy', 'hierarchical_326.npy', 'homework_bottom_up_general_1.npy', 'homework_bottom_up_general_1306.npy', 'homework_bottom_up_general_144.npy', 'homework_bottom_up_general_205.npy', 'homework_bottom_up_general_348.npy', 'homework_bottom_up_general_659.npy', 'homework_bottom_up_general_90.npy', 'homework_bottom_up_split_call_1.npy', 'homework_bottom_up_split_call_153.npy', 'homework_bottom_up_split_call_1790.npy', 'homework_bottom_up_split_call_207.npy', 'homework_bottom_up_split_call_403.npy', 'homework_bottom_up_split_call_80.npy', 'homework_bottom_up_split_call_857.npy', 'homework_hierarchical_174.npy', 'homework_hierarchical_2.npy', 'homework_hierarchical_507.npy', 'homework_kmeans_1.npy', 'homework_kmeans_10.npy', 'homework_kmeans_100.npy', 'homework_kmeans_1000.npy', 'homework_kmeans_200.npy', 'homework_kmeans_500.npy', 'homework_kmeans_700.npy', 'kmeans_1.npy', 'kmeans_10.npy', 'kmeans_100.npy', 'kmeans_1000.npy', 'kmeans_200.npy', 'kmeans_500.npy', 'kmeans_700.npy']\n" ] } ], "source": [ "import os\n", "dir_name = '../results/reconstruction_error/results'\n", "files = os.listdir(dir_name)\n", "print(files)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from nbminer.results.reconstruction_error.astor_error import AstorError\n", "ae = AstorError()\n", "results_dir = ae.load_summaries('../results/reconstruction_error/results')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1.0, 63.0, 93.0, 132.0, 255.0, 485.0, 899.0]\n", "[0.0, 11.419386025672779, 12.918756031484381, 14.408670645973372, 18.207177526916581, 23.033699811491253, 28.673621643303324]\n", "[1.0, 59.0, 93.0, 143.0, 309.0, 633.0, 1272.0]\n", "[0.0, 9.5165519343260794, 11.148776577530255, 12.846796022620401, 17.515993102913072, 23.792586641283147, 30.997285358013087]\n", "[2.0, 111.0, 326.0]\n", "[14.950740202045028, 19.002279195539899, 24.721859731083853]\n", "[1.0, 90.0, 144.0, 205.0, 348.0, 659.0, 1306.0]\n", "[0.0, 10.639320597045618, 12.855680712899211, 14.203275281009498, 17.271290804694651, 21.602460298007912, 30.25748576184726]\n", "[1.0, 80.0, 153.0, 207.0, 403.0, 857.0, 1790.0]\n", "[0.0, 8.4808025058687218, 11.122467087021043, 12.437342573395313, 16.443636072958345, 22.51618893625605, 32.660081512598843]\n", "[2.0, 174.0, 507.0]\n", "[18.819948115476031, 22.905364686028012, 28.112882050377593]\n", "[1.0, 10.0, 100.0, 200.0, 500.0, 700.0, 1000.0]\n", "[16.920078565241141, 21.573622354220063, 28.478194569340186, 31.670310062886141, 37.156175149406657, 39.623364241870405, 41.92009995989897]\n", "[1.0, 10.0, 100.0, 200.0, 500.0, 700.0, 1000.0]\n", "[14.751064689460042, 17.608177899201841, 23.098107295158137, 25.510456498220258, 29.155493577203792, 30.615054460037069, 32.054761881468316]\n" ] }, { "data": { "image/png": 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EFhFJAwphImuICUeZPTeM7+1+gr2TWA4bBXvWUXhwPY7KglQvT0REFlAIE1kDIpNBpk/0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\nl29sNDHAur/jIoNdHfh9U0B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/IBu2bMOepNfOVrrZKCKSnpYzO/I/At8AuoBo/M0GeDB5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8AAAg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\nwggLC+PEiROcOHGC4cOHs3PnT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"text/plain": [ "<matplotlib.figure.Figure at 0x111b51be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.rcParams['figure.figsize'] = (10, 10)\n", "val = 0\n", "y_label = 'Reconstruction Similarity'\n", "#y_label = 'Reconstruction Distance'\n", "for key in results_dir.keys():\n", " x = [el[1][3] for el in sorted(results_dir[key])]\n", " y = [el[1][val] for el in sorted(results_dir[key])]\n", " print(x)\n", " print(y)\n", " label = key\n", " plt.plot(x[:5],y[:5], label=label )\n", "plt.xlabel('Templates')\n", "plt.ylabel(y_label)\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculating for value: 1000\n", "<nbminer.preprocess.get_ast_features.GetASTFeatures object at 0x1520246a58>\n", "<nbminer.preprocess.resample_by_node.ResampleByNode object at 0x1531083f98>\n", "<nbminer.preprocess.get_imports.GetImports object at 0x1531083f60>\n", "<nbminer.preprocess.feature_encoding.FeatureEncoding object at 0x1521d35320>\n", "<nbminer.encoders.cluster.kmeans_encoder.KmeansEncoder object at 0x151dde1390>\n", "<nbminer.results.reconstruction_error.astor_error.AstorError object at 0x1538341400>\n", "[41.274720853032896]\n", "[32.040070227847224]\n", "[1.0]\n", "[1000]\n", "Calculating for value: 700\n", "<nbminer.preprocess.get_ast_features.GetASTFeatures object at 0x1512a8d940>\n", "<nbminer.preprocess.resample_by_node.ResampleByNode object at 0x151e906198>\n", "<nbminer.preprocess.get_imports.GetImports object at 0x15358e0cf8>\n", "<nbminer.preprocess.feature_encoding.FeatureEncoding object at 0x153cde9630>\n", "<nbminer.encoders.cluster.kmeans_encoder.KmeansEncoder object at 0x1523f28f28>\n", "<nbminer.results.reconstruction_error.astor_error.AstorError object at 0x153cde4828>\n", "[41.274720853032896, 44.70365154411025]\n", "[32.040070227847224, 30.512287039379256]\n", "[1.0, 1.0]\n", "[1000, 700]\n", "Calculating for value: 500\n", "<nbminer.preprocess.get_ast_features.GetASTFeatures object at 0x151136e9b0>\n", "<nbminer.preprocess.resample_by_node.ResampleByNode object at 0x15381db630>\n", "<nbminer.preprocess.get_imports.GetImports object at 0x15381db470>\n", "<nbminer.preprocess.feature_encoding.FeatureEncoding object at 0x1536a426a0>\n", "<nbminer.encoders.cluster.kmeans_encoder.KmeansEncoder object at 0x1536a42908>\n", "<nbminer.results.reconstruction_error.astor_error.AstorError object at 0x1536a42e48>\n", "[41.274720853032896, 44.70365154411025, 45.89950709184187]\n", "[32.040070227847224, 30.512287039379256, 29.104695582394044]\n", "[1.0, 1.0, 1.0]\n", "[1000, 700, 500]\n", "Calculating for value: 200\n", "<nbminer.preprocess.get_ast_features.GetASTFeatures object at 0x152a02dc88>\n", "<nbminer.preprocess.resample_by_node.ResampleByNode object at 0x1529eb1198>\n", "<nbminer.preprocess.get_imports.GetImports object at 0x1524b08dd8>\n", "<nbminer.preprocess.feature_encoding.FeatureEncoding object at 0x1527378208>\n", "<nbminer.encoders.cluster.kmeans_encoder.KmeansEncoder object at 0x1527378390>\n", "<nbminer.results.reconstruction_error.astor_error.AstorError object at 0x1527378160>\n", "[41.274720853032896, 44.70365154411025, 45.89950709184187, 53.49708278845186]\n", "[32.040070227847224, 30.512287039379256, 29.104695582394044, 25.129859103392896]\n", "[1.0, 1.0, 1.0, 1.0]\n", "[1000, 700, 500, 200]\n", "Calculating for value: 100\n", "<nbminer.preprocess.get_ast_features.GetASTFeatures object at 0x152fb02ac8>\n", "<nbminer.preprocess.resample_by_node.ResampleByNode object at 0x1531b43710>\n", "<nbminer.preprocess.get_imports.GetImports object at 0x1537985f98>\n", "<nbminer.preprocess.feature_encoding.FeatureEncoding object at 0x1524725588>\n", "<nbminer.encoders.cluster.kmeans_encoder.KmeansEncoder object at 0x1537c0d748>\n", "<nbminer.results.reconstruction_error.astor_error.AstorError object at 0x153c9b3240>\n", "[41.274720853032896, 44.70365154411025, 45.89950709184187, 53.49708278845186, 57.275123227039536]\n", "[32.040070227847224, 30.512287039379256, 29.104695582394044, 25.129859103392896, 22.94207881262498]\n", "[1.0, 1.0, 1.0, 1.0, 1.0]\n", "[1000, 700, 500, 200, 100]\n", "Calculating for value: 10\n", "<nbminer.preprocess.get_ast_features.GetASTFeatures object at 0x15396ef4e0>\n", "<nbminer.preprocess.resample_by_node.ResampleByNode object at 0x153da81908>\n", "<nbminer.preprocess.get_imports.GetImports object at 0x153d147320>\n", "<nbminer.preprocess.feature_encoding.FeatureEncoding object at 0x153d147358>\n", "<nbminer.encoders.cluster.kmeans_encoder.KmeansEncoder object at 0x151d13dba8>\n", "<nbminer.results.reconstruction_error.astor_error.AstorError object at 0x151d13d630>\n", "[41.274720853032896, 44.70365154411025, 45.89950709184187, 53.49708278845186, 57.275123227039536, 65.94054924051906]\n", "[32.040070227847224, 30.512287039379256, 29.104695582394044, 25.129859103392896, 22.94207881262498, 17.460419982770787]\n", "[1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n", "[1000, 700, 500, 200, 100, 10]\n", "Calculating for value: 1\n", "<nbminer.preprocess.get_ast_features.GetASTFeatures object at 0x1525f10198>\n", "<nbminer.preprocess.resample_by_node.ResampleByNode object at 0x152b0b1e48>\n", "<nbminer.preprocess.get_imports.GetImports object at 0x151cc0acf8>\n", "<nbminer.preprocess.feature_encoding.FeatureEncoding object at 0x151cc0a940>\n", "<nbminer.encoders.cluster.kmeans_encoder.KmeansEncoder object at 0x1531ae5ef0>\n", "<nbminer.results.reconstruction_error.astor_error.AstorError object at 0x1531ae5b00>\n", "[41.274720853032896, 44.70365154411025, 45.89950709184187, 53.49708278845186, 57.275123227039536, 65.94054924051906, 70.79916507393622]\n", "[32.040070227847224, 30.512287039379256, 29.104695582394044, 25.129859103392896, 22.94207881262498, 17.460419982770787, 14.834598151160746]\n", "[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n", "[1000, 700, 500, 200, 100, 10, 1]\n" ] } ], "source": [ "from nbminer.pipeline.pipeline import Pipeline\n", "from nbminer.preprocess.get_ast_features import GetASTFeatures\n", "from nbminer.preprocess.get_imports import GetImports\n", "from nbminer.preprocess.resample_by_node import ResampleByNode\n", "from nbminer.results.reconstruction_error.astor_error import AstorError\n", "from nbminer.preprocess.feature_encoding import FeatureEncoding\n", "from nbminer.encoders.cluster.kmeans_encoder import KmeansEncoder\n", "\n", "for value in [1000, 700, 500, 200, 100, 10, 1]:\n", " print ('Calculating for value: ',value)\n", " a = Features(notebook_objs)\n", " gastf = GetASTFeatures()\n", " rbn = ResampleByNode()\n", " gi = GetImports()\n", " fe = FeatureEncoding()\n", " ke = KmeansEncoder(n_clusters = value)\n", " ae = AstorError()\n", " pipe = Pipeline([gastf, rbn, gi, fe, ke, ae])\n", " a = pipe.transform(a)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dists = [avg_dist, avg_dist_general, avg_dist_kmeans]\n", "sims = [avg_sim, avg_sim_general, avg_sim_kmeans]\n", "covers = [coverage, coverage_general, coverage_kmeans]\n", "numbers = [number_templates, number_templates_general, number_kmeans]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x151237be80>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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NMalO82uMecgYs9YYs/bw4cM5FDl1d9e6mzMXzjBnd9rrZYmIiIikV04VZF5APeB9a21d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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x151358d6d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.rcParams['figure.figsize'] = (10, 20)\n", "\n", "fig, axes = plt.subplots(3)\n", "n1, = axes[0].plot(number_templates[:6], avg_dist[:6], label = 'Split function calls')\n", "n2, = axes[0].plot(number_templates_general[:6], avg_dist_general[:6], label = 'All calls are the same')\n", "n3, = axes[0].plot(number_kmeans[:6], avg_dist_kmeans[:6], label = 'KMeans')\n", "n4, = axes[0].plot([0,1,2,3,4,1200], [avg_dist_general[6] for i in range(6)], label = 'Bound')\n", "axes[0].set_title(\"Average edit distance\")\n", "axes[0].set_xlabel('Number of templates')\n", "axes[0].set_ylabel('Average edit distance')\n", "axes[0].legend(handles=[n1,n2,n3,n4])\n", "\n", "n1, = axes[1].plot(number_templates[:6], avg_sim[:6], label = 'Split function calls')\n", "n2, = axes[1].plot(number_templates_general[:6], avg_sim_general[:6], label = 'All calls are the same')\n", "n3, = axes[1].plot(number_kmeans[:6], avg_sim_kmeans[:6], label = 'KMeans')\n", "n4, = axes[1].plot([0,1,2,3,4,1200], [avg_sim_general[6] for i in range(6)], label = 'Bound')\n", "axes[1].set_title(\"Average matching characters\")\n", "axes[1].set_xlabel('Number of templates')\n", "axes[1].set_ylabel('Average matching characters')\n", "axes[1].legend(handles=[n1,n2,n3,n4])\n", "\n", "n1, = axes[2].plot(number_templates[:6], coverage[:6], label = 'Split function calls')\n", "n2, = axes[2].plot(number_templates_general[:6], coverage_general[:6], label = 'All calls are the same')\n", "n3, = axes[2].plot(number_kmeans[:6], coverage_kmeans[:6], label = 'KMeans')\n", "n4, = axes[2].plot([0,1,2,3,4,1200], [coverage_general[6] for i in range(6)], label = 'Bound')\n", "axes[2].set_title(\"Coverage of templates\")\n", "axes[2].set_xlabel('Number of templates')\n", "axes[2].set_ylabel('Coverage of templates')\n", "axes[2].legend(handles=[n1,n2,n3,n4])\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

mkhuthir/learnPython

jupyter/Basics/helloworld.ipynb

1

798

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hello World\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hello world!\n" ] } ], "source": [ "print(\"hello world!\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

tensorflow/workshops

tfx_airflow/notebooks/step3.ipynb

1

2848

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 3: Data Validation\n", "\n", "Use the code below to run TensorFlow Data Validation on your pipeline. Start by importing and opening the metadata store." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function\n", "\n", "!pip install -q papermill\n", "!pip install -q matplotlib\n", "!pip install -q networkx\n", "\n", "import os\n", "import tfx_utils\n", "import tensorflow as tf\n", "%matplotlib notebook\n", "tf.get_logger().propagate = False\n", "\n", "def _make_default_sqlite_uri(pipeline_name):\n", " return os.path.join(os.environ['HOME'], 'airflow/tfx/metadata', pipeline_name, 'metadata.db')\n", "\n", "def get_metadata_store(pipeline_name):\n", " return tfx_utils.TFXReadonlyMetadataStore.from_sqlite_db(_make_default_sqlite_uri(pipeline_name))\n", "\n", "pipeline_name = 'taxi'\n", "\n", "pipeline_db_path = _make_default_sqlite_uri(pipeline_name)\n", "print('Pipeline DB:\\n{}'.format(pipeline_db_path))\n", "\n", "store = get_metadata_store(pipeline_name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now print out the data artifacts:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Visualize properties of example artifacts\n", "store.get_artifacts_of_type_df(tfx_utils.TFXArtifactTypes.EXAMPLES)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now visualize the dataset features.\n", "\n", "Hint: try ID 2 or 3" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Visualize stats for data\n", "store.display_stats_for_examples(<insert artifact ID here>)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now plot the artifact lineage:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Try different IDs here. Click stop in the plot when changing IDs.\n", "%matplotlib notebook\n", "store.plot_artifact_lineage(<insert artifact ID here>)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

mne-tools/mne-tools.github.io

0.20/_downloads/4a39dd4a31cad8a0e098b02526b9c3d3/plot_covariance_whitening_dspm.ipynb

1

8504

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Demonstrate impact of whitening on source estimates\n\nThis example demonstrates the relationship between the noise covariance\nestimate and the MNE / dSPM source amplitudes. It computes source estimates for\nthe SPM faces data and compares proper regularization with insufficient\nregularization based on the methods described in [1]_. The example demonstrates\nthat improper regularization can lead to overestimation of source amplitudes.\nThis example makes use of the previous, non-optimized code path that was used\nbefore implementing the suggestions presented in [1]_.\n\nThis example does quite a bit of processing, so even on a\nfast machine it can take a couple of minutes to complete.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>Please do not copy the patterns presented here for your own\n analysis, this is example is purely illustrative.</p></div>\n\n## References\n.. [1] Engemann D. and Gramfort A. (2015) Automated model selection in\n covariance estimation and spatial whitening of MEG and EEG signals,\n vol. 108, 328-342, NeuroImage.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Author: Denis A. Engemann <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne import io\nfrom mne.datasets import spm_face\nfrom mne.minimum_norm import apply_inverse, make_inverse_operator\nfrom mne.cov import compute_covariance\n\nprint(__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get data\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_path = spm_face.data_path()\nsubjects_dir = data_path + '/subjects'\n\nraw_fname = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces%d_3D.ds'\n\nraw = io.read_raw_ctf(raw_fname % 1) # Take first run\n# To save time and memory for this demo, we'll just use the first\n# 2.5 minutes (all we need to get 30 total events) and heavily\n# resample 480->60 Hz (usually you wouldn't do either of these!)\nraw = raw.crop(0, 150.).load_data()\n\npicks = mne.pick_types(raw.info, meg=True, exclude='bads')\nraw.filter(None, 20.)\n\nevents = mne.find_events(raw, stim_channel='UPPT001')\n\nevent_ids = {\"faces\": 1, \"scrambled\": 2}\ntmin, tmax = -0.2, 0.5\nbaseline = (None, 0)\nreject = dict(mag=3e-12)\n\n# Make forward\ntrans = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces1_3D_raw-trans.fif'\nsrc = data_path + '/subjects/spm/bem/spm-oct-6-src.fif'\nbem = data_path + '/subjects/spm/bem/spm-5120-5120-5120-bem-sol.fif'\nforward = mne.make_forward_solution(raw.info, trans, src, bem)\ndel src\n\n# inverse parameters\nconditions = 'faces', 'scrambled'\nsnr = 3.0\nlambda2 = 1.0 / snr ** 2\nclim = dict(kind='value', lims=[0, 2.5, 5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Estimate covariances\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "samples_epochs = 5, 15,\nmethod = 'empirical', 'shrunk'\ncolors = 'steelblue', 'red'\n\nevokeds = list()\nstcs = list()\nmethods_ordered = list()\nfor n_train in samples_epochs:\n # estimate covs based on a subset of samples\n # make sure we have the same number of conditions.\n events_ = np.concatenate([events[events[:, 2] == id_][:n_train]\n for id_ in [event_ids[k] for k in conditions]])\n events_ = events_[np.argsort(events_[:, 0])]\n epochs_train = mne.Epochs(raw, events_, event_ids, tmin, tmax, picks=picks,\n baseline=baseline, preload=True, reject=reject,\n decim=8)\n epochs_train.equalize_event_counts(event_ids)\n assert len(epochs_train) == 2 * n_train\n\n # We know some of these have too few samples, so suppress warning\n # with verbose='error'\n noise_covs = compute_covariance(\n epochs_train, method=method, tmin=None, tmax=0, # baseline only\n return_estimators=True, rank=None, verbose='error') # returns list\n # prepare contrast\n evokeds = [epochs_train[k].average() for k in conditions]\n del epochs_train, events_\n # do contrast\n\n # We skip empirical rank estimation that we introduced in response to\n # the findings in reference [1] to use the naive code path that\n # triggered the behavior described in [1]. The expected true rank is\n # 274 for this dataset. Please do not do this with your data but\n # rely on the default rank estimator that helps regularizing the\n # covariance.\n stcs.append(list())\n methods_ordered.append(list())\n for cov in noise_covs:\n inverse_operator = make_inverse_operator(evokeds[0].info, forward,\n cov, loose=0.2, depth=0.8)\n assert len(inverse_operator['sing']) == 274 # sanity check\n stc_a, stc_b = (apply_inverse(e, inverse_operator, lambda2, \"dSPM\",\n pick_ori=None) for e in evokeds)\n stc = stc_a - stc_b\n methods_ordered[-1].append(cov['method'])\n stcs[-1].append(stc)\n del inverse_operator, evokeds, cov, noise_covs, stc, stc_a, stc_b\ndel raw, forward # save some memory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show the resulting source estimates\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig, (axes1, axes2) = plt.subplots(2, 3, figsize=(9.5, 5))\n\nfor ni, (n_train, axes) in enumerate(zip(samples_epochs, (axes1, axes2))):\n # compute stc based on worst and best\n ax_dynamics = axes[1]\n for stc, ax, method, kind, color in zip(stcs[ni],\n axes[::2],\n methods_ordered[ni],\n ['best', 'worst'],\n colors):\n brain = stc.plot(subjects_dir=subjects_dir, hemi='both', clim=clim,\n initial_time=0.175, background='w', foreground='k')\n brain.show_view('ven')\n im = brain.screenshot()\n brain.close()\n\n ax.axis('off')\n ax.get_xaxis().set_visible(False)\n ax.get_yaxis().set_visible(False)\n ax.imshow(im)\n ax.set_title('{0} ({1} epochs)'.format(kind, n_train * 2))\n\n # plot spatial mean\n stc_mean = stc.data.mean(0)\n ax_dynamics.plot(stc.times * 1e3, stc_mean,\n label='{0} ({1})'.format(method, kind),\n color=color)\n # plot spatial std\n stc_var = stc.data.std(0)\n ax_dynamics.fill_between(stc.times * 1e3, stc_mean - stc_var,\n stc_mean + stc_var, alpha=0.2, color=color)\n\n # signal dynamics worst and best\n ax_dynamics.set(title='{0} epochs'.format(n_train * 2),\n xlabel='Time (ms)', ylabel='Source Activation (dSPM)',\n xlim=(tmin * 1e3, tmax * 1e3), ylim=(-3, 3))\n ax_dynamics.legend(loc='upper left', fontsize=10)\n\nfig.subplots_adjust(hspace=0.2, left=0.01, right=0.99, wspace=0.03)" ] } ], "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": "3.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

djfan/why_yellow_taxi

Sjoin/Sjoin_Pyspark_4_byWDandHour.ipynb

1

12084

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## df_shuffle.csv\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "| Day of Week | Index |\n", "| ----------- | ----- |\n", "| Monday | 0 |\n", "| Tuesday | 1 |\n", "| Wednesday | 2 |\n", "| Thursday | 3 |\n", "| Friday | 4 |\n", "| Saturday | 5 |\n", "| Sunday | 6 |\n", "\n", "| Hour | Index |\n", "| ------------------- | ----- |\n", "| 00:00:00 - 00:59:59 | 0 |\n", "| … | … |\n", "| 23:00:00 - 23:59:59 | 23 |" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "<br/>" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<pyspark.context.SparkContext at 0x105268b90>" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sc" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pyproj\n", "import csv\n", "import shapely.geometry as geom\n", "import fiona\n", "import fiona.crs\n", "import shapely\n", "import rtree\n", "import geopandas as gpd\n", "import numpy as np\n", "import operator\n", "# just for display, not for calculation\n", "import pandas as pd\n", "import datetime" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# df = pd.read_csv('./df_shuffle.csv')\n", "# df.head(10)\n", "\n", "# def wkd(time):\n", "# return datetime.datetime.strptime(time, '%Y-%m-%d %H:%M:%S')\n", "\n", "# a = wkd(df.tpep_pickup_datetime[0])\n", "# pd.to_datetime(df.tpep_pickup_datetime).map(lambda x: x.hour())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Function" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def countLine(partID, records):\n", " import pyproj\n", " import csv\n", " import shapely.geometry as geom\n", " import fiona\n", " import fiona.crs\n", " import shapely\n", " import rtree\n", " import geopandas as gpd\n", " import numpy as np\n", " import operator\n", " import pandas as pd\n", " import datetime\n", " \n", " shapefile = '../why_yellow_taxi/Buffer/entr_buffer_100_feet_epsg4269_nad83/entr_buffer_100_feet_epsg4269_nad83.shp'\n", " entr_buf = gpd.read_file(shapefile)\n", " entr_buf = entr_buf.to_crs(fiona.crs.from_epsg(2263))\n", " \n", " routes = ['Route_' + str(n) for n in range(1, 12)]\n", " entr2line = []\n", " for i in xrange(len(entr_buf)):\n", " lines = []\n", " for line in list(entr_buf.loc[:,routes].ix[i].dropna().values):\n", " try:\n", " line = str(int(line))\n", " except ValueError:\n", " pass\n", " lines.append(line)\n", " entr2line.append(lines)\n", " entr_buf['entr2line'] = entr2line\n", " \n", " index = rtree.Rtree()\n", " for idx, geometry in enumerate(entr_buf.geometry):\n", " index.insert(idx, geometry.bounds)\n", " \n", "\n", " entr_pair = {}\n", " pick_entr = {}\n", " drop_entr = {}\n", " entr_lines = {}\n", " \n", " proj = pyproj.Proj(init='epsg:2263', preserve_units=True)\n", " \n", " if partID==0:\n", " records.next()\n", " reader = csv.reader(records)\n", " for row in reader:\n", " if ((float(row[5])!=0) and float(row[9]!=0)):\n", " if row[1]:\n", " wd_h = datetime.datetime.strptime(row[1], '%Y-%m-%d %H:%M:%S')\n", " wd = wd_h.weekday()\n", " hour = wd_h.hour\n", " else:\n", " wd = None\n", " hour = None\n", " \n", " p = geom.Point(proj(float(row[5]), float(row[6])))\n", " d = geom.Point(proj(float(row[9]), float(row[10])))\n", " p_potential = index.intersection((p.x,p.y,p.x,p.y))\n", " d_potential = index.intersection((d.x,d.y,d.x,d.y))\n", " p_match = None # The first one match, should be the closest one? No!\n", " d_match = None\n", " \n", " for p_idx in p_potential:\n", " if entr_buf.geometry[p_idx].contains(p):\n", " p_match = p_idx # print 'p',p_idx\n", " p_lines = set(entr_buf.entr2line[p_idx])\n", " break\n", " pick_entr[p_match] = pick_entr.get(p_match, 0)+1\n", " \n", " for d_idx in d_potential:\n", " if entr_buf.geometry[d_idx].contains(d):\n", " d_match = d_idx # print 'd',d_idx\n", " d_lines = set(entr_buf.entr2line[d_idx])\n", " break\n", " drop_entr[d_match] = drop_entr.get(d_match, 0)+1\n", " \n", " if ((p_match and d_match) and (p_match != d_match)):\n", " dirct_lines = tuple(p_lines.intersection(d_lines))\n", " dirct_lines_wd_h = (dirct_lines, wd, hour)\n", " if dirct_lines:\n", " entr_lines[dirct_lines_wd_h] = entr_lines.get(dirct_lines_wd_h, 0)+1\n", " if p_match > d_match:\n", " pair = (d_match, p_match)\n", " else:\n", " pair = (p_match, d_match)\n", " entr_pair[pair] = entr_pair.get(pair, 0)+1\n", " \n", " return entr_lines.items()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# ((Line, DayOfWeek, Hour), Count)" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def mapper(record):\n", " for key in record[0][0]:\n", " yield (key, record[0][1], record[0][2]), record[1]\n", " \n", "rdd = sc.textFile('./df_shuffle.csv')\n", "counts = rdd.mapPartitionsWithIndex(countLine).flatMap(mapper).reduceByKey(lambda x,y: x+y).collect() " ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Day of week:5 ; Hour:11, Counts:2\n", "Day of week:0 ; Hour:16, Counts:1\n", "Day of week:3 ; Hour:21, Counts:1\n", "Day of week:0 ; Hour:13, Counts:1\n", "Day of week:5 ; Hour:14, Counts:1\n", "Day of week:2 ; Hour:13, Counts:1\n" ] } ], "source": [ "for i in range(len(counts)):\n", " if counts[i][0][0] == '6':\n", " print 'Day of week:{} ; Hour:{}, Counts:{}'.format(counts[i][0][1], counts[i][0][2], counts[i][1])" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Day of week:0 ; Hour:13, Counts:1\n", "Day of week:2 ; Hour:13, Counts:1\n", "Line:6, Hour:13 - Counts:2\n" ] } ], "source": [ "counts_all = 0\n", "for i in range(len(counts)):\n", " if (counts[i][0][0] == '6' and counts[i][0][2] == 13): # Line 6, 13:00-14:00\n", " counts_all += counts[i][1]\n", " print 'Day of week:{} ; Hour:{}, Counts:{}'.format(counts[i][0][1], counts[i][0][2], counts[i][1])\n", "print \"Line:{}, Hour:{} - Counts:{}\".format('6', '13', counts_all)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(('6', 5, 11), 2),\n", " (('1', 1, 13), 2),\n", " (('2', 1, 13), 1),\n", " (('3', 5, 20), 1),\n", " ((u'E', 3, 10), 1),\n", " (('1', 4, 17), 1),\n", " (('2', 4, 6), 1),\n", " ((u'M', 5, 8), 1),\n", " ((u'C', 5, 20), 1),\n", " ((u'A', 4, 17), 1),\n", " ((u'F', 1, 21), 1),\n", " ((u'L', 2, 20), 1),\n", " (('2', 1, 9), 1),\n", " (('1', 5, 20), 1),\n", " ((u'E', 5, 20), 1),\n", " ((u'N', 0, 18), 1),\n", " ((u'E', 5, 8), 1),\n", " ((u'C', 3, 10), 1),\n", " (('1', 3, 14), 1),\n", " (('1', 5, 2), 1),\n", " ((u'C', 1, 10), 1),\n", " ((u'C', 4, 17), 1),\n", " ((u'R', 0, 18), 1),\n", " ((u'A', 3, 10), 1),\n", " ((u'Q', 6, 19), 1),\n", " (('6', 0, 16), 1),\n", " (('6', 3, 21), 1),\n", " ((u'Q', 0, 15), 1),\n", " ((u'N', 0, 16), 1),\n", " (('4', 0, 16), 1),\n", " ((u'R', 0, 16), 1),\n", " ((u'E', 1, 10), 1),\n", " ((u'F', 4, 14), 1),\n", " ((u'C', 5, 2), 1),\n", " (('1', 0, 19), 1),\n", " (('2', 5, 17), 1),\n", " ((u'A', 5, 2), 1),\n", " (('1', 3, 16), 1),\n", " (('1', 4, 3), 1),\n", " (('3', 3, 14), 1),\n", " ((u'L', 0, 20), 1),\n", " ((u'Q', 6, 21), 1),\n", " ((u'C', 2, 15), 1),\n", " (('2', 5, 20), 1),\n", " (('7', 4, 8), 1),\n", " ((u'B', 5, 2), 1),\n", " (('6', 0, 13), 1),\n", " (('2', 3, 14), 1),\n", " (('1', 4, 6), 1),\n", " ((u'N', 0, 15), 1),\n", " (('1', 0, 12), 1),\n", " ((u'E', 6, 6), 1),\n", " ((u'E', 5, 19), 1),\n", " ((u'L', 4, 11), 1),\n", " ((u'F', 4, 7), 1),\n", " (('1', 5, 21), 1),\n", " ((u'N', 6, 21), 1),\n", " (('1', 3, 11), 1),\n", " ((u'R', 6, 21), 1),\n", " (('1', 5, 23), 1),\n", " ((u'C', 5, 13), 1),\n", " ((u'R', 6, 19), 1),\n", " ((u'A', 5, 19), 1),\n", " ((u'A', 1, 9), 1),\n", " (('1', 5, 17), 1),\n", " (('3', 1, 9), 1),\n", " ((u'Q', 0, 18), 1),\n", " (('6', 5, 14), 1),\n", " ((u'C', 5, 19), 1),\n", " ((u'A', 6, 6), 1),\n", " ((u'R', 0, 15), 1),\n", " (('5', 0, 16), 1),\n", " (('1', 6, 22), 1),\n", " ((u'F', 5, 14), 1),\n", " ((u'Q', 0, 16), 1),\n", " (('6', 2, 13), 1),\n", " ((u'A', 5, 13), 1),\n", " (('3', 4, 6), 1),\n", " ((u'M', 6, 14), 1),\n", " ((u'D', 5, 2), 1),\n", " ((u'C', 6, 6), 1),\n", " (('3', 1, 13), 1),\n", " (('3', 5, 17), 1),\n", " (('1', 0, 14), 1),\n", " ((u'C', 1, 9), 1),\n", " (('7', 2, 20), 1),\n", " ((u'N', 6, 19), 1),\n", " (('1', 3, 5), 1),\n", " ((u'E', 5, 13), 1),\n", " ((u'C', 1, 21), 1)]" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(counts, key=lambda x: x[1], reverse=True)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

hadim/public_notebooks

Code/TrackMate/notebook.ipynb

1

60199

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import itertools\n", "import xml.etree.cElementTree as et\n", "\n", "import networkx as nx\n", "import pandas as pd\n", "import numpy as np\n", "\n", "\n", "def trackmate_peak_import(trackmate_xml_path, get_tracks=False):\n", " \"\"\"Import detected peaks with TrackMate Fiji plugin.\n", "\n", " Parameters\n", " ----------\n", " trackmate_xml_path : str\n", " TrackMate XML file path.\n", " get_tracks : boolean\n", " Add tracks to label\n", " \"\"\"\n", "\n", " root = et.fromstring(open(trackmate_xml_path).read())\n", "\n", " objects = []\n", " object_labels = {'FRAME': 't_stamp',\n", " 'POSITION_T': 't',\n", " 'POSITION_X': 'x',\n", " 'POSITION_Y': 'y',\n", " 'POSITION_Z': 'z',\n", " 'MEAN_INTENSITY': 'I',\n", " 'ESTIMATED_DIAMETER': 'w',\n", " 'QUALITY': 'q',\n", " 'ID': 'spot_id',\n", " 'MEAN_INTENSITY': 'mean_intensity',\n", " 'MEDIAN_INTENSITY': 'median_intensity',\n", " 'MIN_INTENSITY': 'min_intensity',\n", " 'MAX_INTENSITY': 'max_intensity',\n", " 'TOTAL_INTENSITY': 'total_intensity',\n", " 'STANDARD_DEVIATION': 'std_intensity',\n", " 'CONTRAST': 'contrast',\n", " 'SNR': 'snr'}\n", "\n", " features = root.find('Model').find('FeatureDeclarations').find('SpotFeatures')\n", " features = [c.get('feature') for c in features.getchildren()] + ['ID']\n", "\n", " spots = root.find('Model').find('AllSpots')\n", " trajs = pd.DataFrame([])\n", " objects = []\n", " for frame in spots.findall('SpotsInFrame'):\n", " for spot in frame.findall('Spot'):\n", "\n", " single_object = []\n", " for label in features:\n", " single_object.append(spot.get(label))\n", "\n", " objects.append(single_object)\n", " trajs = pd.DataFrame(objects, columns=features)\n", " trajs = trajs.astype(np.float)\n", "\n", " # Apply initial filtering\n", " initial_filter = root.find(\"Settings\").find(\"InitialSpotFilter\")\n", "\n", " trajs = filter_spots(trajs,\n", " name=initial_filter.get('feature'),\n", " value=float(initial_filter.get('value')),\n", " isabove=True if initial_filter.get('isabove') == 'true' else False)\n", "\n", " # Apply filters\n", " spot_filters = root.find(\"Settings\").find(\"SpotFilterCollection\")\n", "\n", " for spot_filter in spot_filters.findall('Filter'):\n", "\n", " trajs = filter_spots(trajs,\n", " name=spot_filter.get('feature'),\n", " value=float(spot_filter.get('value')),\n", " isabove=True if spot_filter.get('isabove') == 'true' else False)\n", "\n", " trajs = trajs.loc[:, object_labels.keys()]\n", " trajs.columns = [object_labels[k] for k in object_labels.keys()]\n", " trajs['label'] = np.arange(trajs.shape[0])\n", "\n", " # Get tracks\n", " if get_tracks:\n", " filtered_track_ids = [int(track.get('TRACK_ID')) for track in root.find('Model').find('FilteredTracks').findall('TrackID')]\n", "\n", " new_trajs = pd.DataFrame()\n", " label_id = 0\n", " trajs = trajs.set_index('spot_id')\n", "\n", " tracks = root.find('Model').find('AllTracks')\n", " for track in tracks.findall('Track'):\n", "\n", " track_id = int(track.get(\"TRACK_ID\"))\n", " if track_id in filtered_track_ids:\n", "\n", " spot_ids = [(edge.get('SPOT_SOURCE_ID'), edge.get('SPOT_TARGET_ID'), edge.get('EDGE_TIME')) for edge in track.findall('Edge')]\n", " spot_ids = np.array(spot_ids).astype('float')\n", " spot_ids = pd.DataFrame(spot_ids, columns=['source', 'target', 'time'])\n", " spot_ids = spot_ids.sort_values(by='time')\n", " spot_ids = spot_ids.set_index('time')\n", "\n", " # Build graph\n", " graph = nx.Graph()\n", " for t, spot in spot_ids.iterrows():\n", " graph.add_edge(int(spot['source']), int(spot['target']), attr_dict=dict(t=t))\n", "\n", " # Find graph extremities by checking if number of neighbors is equal to 1\n", " tracks_extremities = [node for node in graph.nodes() if len(graph.neighbors(node)) == 1]\n", "\n", " paths = []\n", " # Find all possible paths between extremities\n", " for source, target in itertools.combinations(tracks_extremities, 2):\n", "\n", " # Find all path between two nodes\n", " for path in nx.all_simple_paths(graph, source=source, target=target):\n", "\n", " # Now we need to check wether this path respect the time logic contraint\n", " # edges can only go in one direction of the time\n", "\n", " # Build times vector according to path\n", " t = []\n", " for i, node_srce in enumerate(path[:-1]):\n", " node_trgt = path[i+1]\n", " t.append(graph.edge[node_srce][node_trgt]['t'])\n", "\n", " # Will be equal to 1 if going to one time direction\n", " if len(np.unique(np.sign(np.diff(t)))) == 1:\n", " paths.append(path)\n", "\n", " # Add each individual trajectory to a new DataFrame called new_trajs\n", " for path in paths:\n", " traj = trajs.loc[path].copy()\n", " traj['label'] = label_id\n", " label_id += 1\n", "\n", " new_trajs = new_trajs.append(traj)\n", "\n", " trajs = new_trajs\n", "\n", " trajs.set_index(['t_stamp', 'label'], inplace=True)\n", " trajs = trajs.sort_index()\n", "\n", " return trajs\n", "\n", "\n", "def filter_spots(spots, name, value, isabove):\n", " if isabove:\n", " spots = spots[spots[name] > value]\n", " else:\n", " spots = spots[spots[name] < value]\n", "\n", " return spots" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Load peaks and/or trajectories from xml \n", "trajs = trackmate_peak_import(\"/home/hadim/test.xml\", get_tracks=True)" ] }, { "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>max_intensity</th>\n", " <th>min_intensity</th>\n", " <th>q</th>\n", " <th>z</th>\n", " <th>w</th>\n", " <th>y</th>\n", " <th>mean_intensity</th>\n", " <th>x</th>\n", " <th>snr</th>\n", " <th>t</th>\n", " <th>std_intensity</th>\n", " 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<td>0.486880</td>\n", " <td>27</td>\n", " <td>4427</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>245</td>\n", " <td>0</td>\n", " <td>9.056150</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>117.411790</td>\n", " <td>50.484536</td>\n", " <td>51.427712</td>\n", " <td>0.590702</td>\n", " <td>40</td>\n", " <td>58.907964</td>\n", " <td>0.525857</td>\n", " <td>30</td>\n", " <td>4897</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>249</td>\n", " <td>1</td>\n", " <td>8.701788</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>23.507749</td>\n", " <td>46.432990</td>\n", " <td>108.355721</td>\n", " <td>0.528205</td>\n", " <td>40</td>\n", " <td>61.047268</td>\n", " <td>0.531924</td>\n", " <td>22</td>\n", " <td>4504</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">41</th>\n", " <th>0</th>\n", " <td>130</td>\n", " <td>0</td>\n", " <td>2.441414</td>\n", " <td>0</td>\n", " <td>19.000000</td>\n", " <td>112.225474</td>\n", " <td>28.072165</td>\n", " <td>104.337510</td>\n", " <td>-0.019684</td>\n", " <td>41</td>\n", " <td>24.910777</td>\n", " <td>-0.008658</td>\n", " <td>22</td>\n", " <td>2723</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>220</td>\n", " <td>0</td>\n", " <td>6.803139</td>\n", " <td>0</td>\n", " <td>19.000000</td>\n", " <td>116.124281</td>\n", " <td>48.134021</td>\n", " <td>116.183281</td>\n", " <td>0.501556</td>\n", " <td>41</td>\n", " <td>49.364425</td>\n", " <td>0.346236</td>\n", " <td>32</td>\n", " <td>4669</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>251</td>\n", " <td>2</td>\n", " <td>9.592649</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>120.580065</td>\n", " <td>53.226804</td>\n", " <td>51.504364</td>\n", " <td>0.538996</td>\n", " <td>41</td>\n", " <td>63.616937</td>\n", " <td>0.475156</td>\n", " <td>27</td>\n", " <td>5163</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>247</td>\n", " <td>0</td>\n", " <td>10.275146</td>\n", " <td>0</td>\n", " <td>4.811327</td>\n", " <td>27.499036</td>\n", " <td>53.587629</td>\n", " <td>108.398969</td>\n", " <td>0.557779</td>\n", " <td>41</td>\n", " <td>63.802389</td>\n", " <td>0.497120</td>\n", " <td>27</td>\n", " <td>5198</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">42</th>\n", " <th>0</th>\n", " <td>143</td>\n", " <td>0</td>\n", " <td>2.344439</td>\n", " <td>0</td>\n", " <td>2.623352</td>\n", " <td>112.488103</td>\n", " <td>28.072165</td>\n", " <td>104.034249</td>\n", " <td>0.249303</td>\n", " <td>42</td>\n", " <td>26.362160</td>\n", " <td>0.132578</td>\n", " <td>22</td>\n", " <td>2723</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>220</td>\n", " <td>0</td>\n", " <td>6.760175</td>\n", " <td>0</td>\n", " <td>4.889275</td>\n", " <td>116.226037</td>\n", " <td>48.577320</td>\n", " <td>116.264016</td>\n", " <td>0.437017</td>\n", " <td>42</td>\n", " <td>51.233297</td>\n", " <td>0.299470</td>\n", " <td>28</td>\n", " <td>4712</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>255</td>\n", " <td>1</td>\n", " <td>9.347189</td>\n", " <td>0</td>\n", " <td>19.000000</td>\n", " <td>31.439208</td>\n", " <td>50.711340</td>\n", " <td>108.468952</td>\n", " <td>0.635266</td>\n", " <td>42</td>\n", " <td>63.593592</td>\n", " <td>0.662017</td>\n", " <td>27</td>\n", " <td>4919</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">43</th>\n", " <th>0</th>\n", " <td>188</td>\n", " <td>0</td>\n", " <td>3.176369</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>112.988285</td>\n", " <td>28.876289</td>\n", " <td>104.549988</td>\n", " <td>0.231341</td>\n", " <td>43</td>\n", " <td>33.235667</td>\n", " <td>0.153580</td>\n", " <td>18</td>\n", " <td>2801</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>220</td>\n", " <td>0</td>\n", " <td>6.644869</td>\n", " <td>0</td>\n", " <td>4.980755</td>\n", " <td>116.036181</td>\n", " <td>45.680412</td>\n", " <td>116.418902</td>\n", " <td>0.606213</td>\n", " <td>43</td>\n", " <td>52.053367</td>\n", " <td>0.527635</td>\n", " <td>24</td>\n", " <td>4431</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>252</td>\n", " <td>2</td>\n", " <td>9.785413</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>35.523132</td>\n", " <td>53.319588</td>\n", " <td>108.485461</td>\n", " <td>0.467055</td>\n", " <td>43</td>\n", " <td>61.972532</td>\n", " <td>0.372543</td>\n", " <td>28</td>\n", " <td>5172</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">44</th>\n", " <th>0</th>\n", " <td>170</td>\n", " <td>0</td>\n", " <td>2.919489</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>112.334688</td>\n", " <td>28.463918</td>\n", " <td>104.494581</td>\n", " <td>0.355945</td>\n", " <td>44</td>\n", " <td>31.855815</td>\n", " <td>0.248721</td>\n", " <td>23</td>\n", " <td>2761</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>220</td>\n", " <td>0</td>\n", " <td>6.701843</td>\n", " <td>0</td>\n", " <td>6.846303</td>\n", " <td>115.956364</td>\n", " <td>46.639175</td>\n", " <td>116.353914</td>\n", " <td>0.535013</td>\n", " <td>44</td>\n", " <td>50.375710</td>\n", " <td>0.406347</td>\n", " <td>28</td>\n", " <td>4524</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">45</th>\n", " <th>0</th>\n", " <td>166</td>\n", " <td>0</td>\n", " <td>2.896575</td>\n", " <td>0</td>\n", " <td>19.000000</td>\n", " <td>112.595238</td>\n", " <td>27.711340</td>\n", " <td>104.143190</td>\n", " <td>0.260705</td>\n", " <td>45</td>\n", " <td>31.496679</td>\n", " <td>0.173927</td>\n", " <td>18</td>\n", " <td>2688</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>220</td>\n", " <td>0</td>\n", " <td>6.666502</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>115.902789</td>\n", " <td>47.226804</td>\n", " <td>116.273541</td>\n", " <td>0.547525</td>\n", " <td>45</td>\n", " <td>49.727069</td>\n", " <td>0.404999</td>\n", " <td>31</td>\n", " <td>4581</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>244</td>\n", " <td>1</td>\n", " <td>9.545598</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>39.440753</td>\n", " <td>54.030928</td>\n", " <td>108.363552</td>\n", " <td>0.612014</td>\n", " <td>45</td>\n", " <td>58.974828</td>\n", " <td>0.501518</td>\n", " <td>30</td>\n", " <td>5241</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">46</th>\n", " <th>0</th>\n", " <td>137</td>\n", " <td>0</td>\n", " <td>3.198929</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>112.141280</td>\n", " <td>29.443299</td>\n", " <td>104.377814</td>\n", " <td>0.219614</td>\n", " <td>46</td>\n", " <td>28.485365</td>\n", " <td>0.118862</td>\n", " <td>22</td>\n", " <td>2856</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>220</td>\n", " <td>0</td>\n", " <td>6.879417</td>\n", " <td>0</td>\n", " <td>4.799302</td>\n", " <td>116.274114</td>\n", " <td>48.463918</td>\n", " <td>116.344548</td>\n", " <td>0.635785</td>\n", " <td>46</td>\n", " <td>50.471333</td>\n", " <td>0.494902</td>\n", " <td>27</td>\n", " <td>4701</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>249</td>\n", " <td>0</td>\n", " <td>9.542833</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>43.623788</td>\n", " <td>49.731959</td>\n", " <td>108.522496</td>\n", " <td>0.475613</td>\n", " <td>46</td>\n", " <td>63.958404</td>\n", " <td>0.440578</td>\n", " <td>22</td>\n", " <td>4824</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">47</th>\n", " <th>0</th>\n", " <td>190</td>\n", " <td>0</td>\n", " <td>3.443748</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>112.118545</td>\n", " <td>29.701031</td>\n", " <td>104.412209</td>\n", " <td>0.198849</td>\n", " <td>47</td>\n", " <td>33.384803</td>\n", " <td>0.125817</td>\n", " <td>20</td>\n", " <td>2881</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>220</td>\n", " <td>1</td>\n", " <td>6.865911</td>\n", " <td>0</td>\n", " <td>4.982101</td>\n", " <td>116.241586</td>\n", " <td>48.979381</td>\n", " <td>116.344167</td>\n", " <td>0.525056</td>\n", " <td>47</td>\n", " <td>49.955392</td>\n", " <td>0.365671</td>\n", " <td>31</td>\n", " <td>4751</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>255</td>\n", " <td>0</td>\n", " <td>9.344431</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>47.632850</td>\n", " <td>51.072165</td>\n", " <td>108.368385</td>\n", " <td>0.507265</td>\n", " <td>47</td>\n", " <td>60.515020</td>\n", " <td>0.429648</td>\n", " <td>29</td>\n", " <td>4954</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">48</th>\n", " <th>0</th>\n", " <td>147</td>\n", " <td>0</td>\n", " <td>2.769981</td>\n", " <td>0</td>\n", " <td>2.731019</td>\n", " <td>111.972274</td>\n", " <td>27.134021</td>\n", " <td>103.822645</td>\n", " <td>0.299242</td>\n", " <td>48</td>\n", " <td>26.973223</td>\n", " <td>0.174721</td>\n", " <td>19</td>\n", " <td>2632</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>220</td>\n", " <td>0</td>\n", " <td>6.701579</td>\n", " <td>0</td>\n", " <td>6.979972</td>\n", " <td>116.006896</td>\n", " <td>47.927835</td>\n", " <td>116.252156</td>\n", " <td>0.494262</td>\n", " <td>48</td>\n", " <td>50.181597</td>\n", " <td>0.349076</td>\n", " <td>30</td>\n", " <td>4649</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>249</td>\n", " <td>0</td>\n", " <td>9.614925</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>51.390749</td>\n", " <td>51.536082</td>\n", " <td>108.491702</td>\n", " <td>0.488511</td>\n", " <td>48</td>\n", " <td>61.484934</td>\n", " <td>0.411250</td>\n", " <td>29</td>\n", " <td>4999</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">49</th>\n", " <th>0</th>\n", " <td>145</td>\n", " <td>0</td>\n", " <td>2.966330</td>\n", " <td>0</td>\n", " <td>17.835591</td>\n", " <td>112.446328</td>\n", " <td>31.731959</td>\n", " <td>103.895819</td>\n", " <td>0.332995</td>\n", " <td>49</td>\n", " <td>29.037876</td>\n", " <td>0.179748</td>\n", " <td>25</td>\n", " <td>3078</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>220</td>\n", " <td>0</td>\n", " <td>6.652123</td>\n", " <td>0</td>\n", " <td>6.734334</td>\n", " <td>116.064747</td>\n", " <td>47.268041</td>\n", " <td>116.322570</td>\n", " <td>0.591347</td>\n", " <td>49</td>\n", " <td>49.599545</td>\n", " <td>0.449817</td>\n", " <td>32</td>\n", " <td>4585</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>254</td>\n", " <td>0</td>\n", " <td>9.402662</td>\n", " <td>0</td>\n", " <td>4.912973</td>\n", " <td>55.509206</td>\n", " <td>53.092784</td>\n", " <td>108.475138</td>\n", " <td>0.485509</td>\n", " <td>49</td>\n", " <td>58.506247</td>\n", " <td>0.365199</td>\n", " <td>29</td>\n", " <td>5150</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>183 rows × 14 columns</p>\n", "</div>" ], "text/plain": [ " max_intensity min_intensity q z w \\\n", "t_stamp label \n", "0 0 166 0 3.539891 0 2.000000 \n", " 1 254 0 6.459367 0 19.000000 \n", " 2 220 0 6.275804 0 6.952403 \n", "1 0 135 0 2.616775 0 2.000000 \n", " 1 239 0 6.811550 0 2.000000 \n", " 2 220 0 6.891733 0 6.796027 \n", "2 0 152 0 2.260737 0 2.000000 \n", " 1 254 0 6.687568 0 2.000000 \n", " 2 220 1 6.748422 0 4.910432 \n", "3 0 192 0 3.248708 0 2.000000 \n", " 1 254 0 9.269295 0 2.000000 \n", " 2 220 0 6.749822 0 4.841736 \n", "4 0 127 0 3.127262 0 19.000000 \n", " 1 252 1 9.141627 0 2.000000 \n", " 2 220 0 6.377168 0 6.874828 \n", "5 0 167 0 3.596664 0 2.000000 \n", " 1 255 0 9.244539 0 2.000000 \n", " 2 220 0 6.406635 0 6.813202 \n", "6 0 189 0 3.289266 0 2.000000 \n", " 1 252 0 9.341927 0 19.000000 \n", " 2 220 0 6.743909 0 6.782126 \n", "7 0 143 0 2.507370 0 15.114519 \n", " 1 241 0 8.990104 0 2.000000 \n", " 2 220 0 6.388138 0 2.000000 \n", "8 0 147 0 2.828063 0 2.000000 \n", " 1 253 0 9.626597 0 2.000000 \n", " 2 220 0 6.871429 0 4.877331 \n", "9 0 147 0 2.532444 0 19.000000 \n", " 1 248 0 8.167829 0 2.000000 \n", " 2 220 0 6.473447 0 7.136718 \n", "... ... ... ... .. ... \n", "40 2 220 0 6.713731 0 4.923968 \n", " 4 245 0 9.056150 0 2.000000 \n", " 5 249 1 8.701788 0 2.000000 \n", "41 0 130 0 2.441414 0 19.000000 \n", " 2 220 0 6.803139 0 19.000000 \n", " 4 251 2 9.592649 0 2.000000 \n", " 5 247 0 10.275146 0 4.811327 \n", "42 0 143 0 2.344439 0 2.623352 \n", " 2 220 0 6.760175 0 4.889275 \n", " 5 255 1 9.347189 0 19.000000 \n", "43 0 188 0 3.176369 0 2.000000 \n", " 2 220 0 6.644869 0 4.980755 \n", " 5 252 2 9.785413 0 2.000000 \n", "44 0 170 0 2.919489 0 2.000000 \n", " 2 220 0 6.701843 0 6.846303 \n", "45 0 166 0 2.896575 0 19.000000 \n", " 2 220 0 6.666502 0 2.000000 \n", " 5 244 1 9.545598 0 2.000000 \n", "46 0 137 0 3.198929 0 2.000000 \n", " 2 220 0 6.879417 0 4.799302 \n", " 5 249 0 9.542833 0 2.000000 \n", "47 0 190 0 3.443748 0 2.000000 \n", " 2 220 1 6.865911 0 4.982101 \n", " 5 255 0 9.344431 0 2.000000 \n", "48 0 147 0 2.769981 0 2.731019 \n", " 2 220 0 6.701579 0 6.979972 \n", " 5 249 0 9.614925 0 2.000000 \n", "49 0 145 0 2.966330 0 17.835591 \n", " 2 220 0 6.652123 0 6.734334 \n", " 5 254 0 9.402662 0 4.912973 \n", "\n", " y mean_intensity x snr t \\\n", "t_stamp label \n", "0 0 112.285192 30.659794 104.458460 0.211005 0 \n", " 1 4.018656 41.773196 63.970888 0.469716 0 \n", " 2 116.359733 46.371134 116.289535 0.540710 0 \n", "1 0 112.441575 25.845361 104.253699 0.345046 1 \n", " 1 6.173494 41.453608 64.120220 0.449295 1 \n", " 2 116.278536 47.288660 116.189521 0.595183 1 \n", "2 0 112.406450 24.814433 104.228938 0.317349 2 \n", " 1 9.962219 40.958763 64.088871 0.480468 2 \n", " 2 116.072059 47.000000 116.239764 0.550095 2 \n", "3 0 112.427749 30.824742 104.274101 0.125869 3 \n", " 1 12.601007 50.402062 63.622595 0.522690 3 \n", " 2 116.125925 47.649485 116.424832 0.523611 3 \n", "4 0 112.374933 28.329897 104.234383 0.361629 4 \n", " 1 16.322334 52.917526 62.410494 0.506454 4 \n", " 2 115.948977 46.206186 116.465886 0.526452 4 \n", "5 0 112.485475 30.422680 104.456023 0.374729 5 \n", " 1 19.492452 51.288660 59.503075 0.546240 5 \n", " 2 116.327674 47.742268 116.299262 0.453571 5 \n", "6 0 112.921300 29.288660 104.585002 0.474971 6 \n", " 1 19.520002 52.061856 59.697994 0.540665 6 \n", " 2 116.130050 45.752577 116.325084 0.655052 6 \n", "7 0 112.379669 27.546392 104.444239 0.443493 7 \n", " 1 21.636953 48.061856 57.560041 0.582806 7 \n", " 2 115.991250 46.804124 116.485707 0.499167 7 \n", "8 0 112.275842 28.546392 104.506405 0.199144 8 \n", " 1 22.457981 51.494845 57.491175 0.526961 8 \n", " 2 115.988632 48.876289 116.413148 0.577965 8 \n", "9 0 112.548939 26.268041 104.490235 0.377790 9 \n", " 1 25.449153 51.804124 61.807489 0.457569 9 \n", " 2 116.061436 47.876289 116.253786 0.498705 9 \n", "... ... ... ... ... .. \n", "40 2 116.137285 45.639175 116.368568 0.575204 40 \n", " 4 117.411790 50.484536 51.427712 0.590702 40 \n", " 5 23.507749 46.432990 108.355721 0.528205 40 \n", "41 0 112.225474 28.072165 104.337510 -0.019684 41 \n", " 2 116.124281 48.134021 116.183281 0.501556 41 \n", " 4 120.580065 53.226804 51.504364 0.538996 41 \n", " 5 27.499036 53.587629 108.398969 0.557779 41 \n", "42 0 112.488103 28.072165 104.034249 0.249303 42 \n", " 2 116.226037 48.577320 116.264016 0.437017 42 \n", " 5 31.439208 50.711340 108.468952 0.635266 42 \n", "43 0 112.988285 28.876289 104.549988 0.231341 43 \n", " 2 116.036181 45.680412 116.418902 0.606213 43 \n", " 5 35.523132 53.319588 108.485461 0.467055 43 \n", "44 0 112.334688 28.463918 104.494581 0.355945 44 \n", " 2 115.956364 46.639175 116.353914 0.535013 44 \n", "45 0 112.595238 27.711340 104.143190 0.260705 45 \n", " 2 115.902789 47.226804 116.273541 0.547525 45 \n", " 5 39.440753 54.030928 108.363552 0.612014 45 \n", "46 0 112.141280 29.443299 104.377814 0.219614 46 \n", " 2 116.274114 48.463918 116.344548 0.635785 46 \n", " 5 43.623788 49.731959 108.522496 0.475613 46 \n", "47 0 112.118545 29.701031 104.412209 0.198849 47 \n", " 2 116.241586 48.979381 116.344167 0.525056 47 \n", " 5 47.632850 51.072165 108.368385 0.507265 47 \n", "48 0 111.972274 27.134021 103.822645 0.299242 48 \n", " 2 116.006896 47.927835 116.252156 0.494262 48 \n", " 5 51.390749 51.536082 108.491702 0.488511 48 \n", "49 0 112.446328 31.731959 103.895819 0.332995 49 \n", " 2 116.064747 47.268041 116.322570 0.591347 49 \n", " 5 55.509206 53.092784 108.475138 0.485509 49 \n", "\n", " std_intensity contrast median_intensity total_intensity \n", "t_stamp label \n", "0 0 32.747101 0.126995 23 2974 \n", " 1 52.474697 0.418488 25 4052 \n", " 2 51.661018 0.431017 24 4498 \n", "1 0 28.895768 0.238981 17 2507 \n", " 1 51.783608 0.390102 24 4021 \n", " 2 52.254776 0.489966 27 4587 \n", "2 0 28.759900 0.225346 17 2407 \n", " 1 48.954894 0.402787 22 3973 \n", " 2 51.127496 0.426945 25 4559 \n", "3 0 32.372831 0.070773 25 2990 \n", " 1 61.153948 0.464334 26 4889 \n", " 2 51.611740 0.395821 29 4622 \n", "4 0 28.219276 0.219673 18 2748 \n", " 1 56.357614 0.369279 34 5133 \n", " 2 49.541426 0.393196 27 4482 \n", "5 0 31.816805 0.243705 22 2951 \n", " 1 61.284677 0.484451 25 4975 \n", " 2 53.034242 0.336761 29 4631 \n", "6 0 30.741177 0.332024 20 2841 \n", " 1 60.456736 0.457563 31 5050 \n", " 2 50.858675 0.572522 23 4438 \n", "7 0 25.471398 0.257930 19 2672 \n", " 1 58.766277 0.553531 26 4662 \n", " 2 51.318783 0.376761 28 4540 \n", "8 0 28.478801 0.110292 21 2769 \n", " 1 62.732090 0.472705 29 4995 \n", " 2 48.988787 0.407752 28 4741 \n", "9 0 28.502747 0.257806 19 2548 \n", " 1 58.306239 0.346801 29 5025 \n", " 2 50.633746 0.358168 30 4644 \n", "... ... ... ... ... \n", "40 2 51.962764 0.486880 27 4427 \n", " 4 58.907964 0.525857 30 4897 \n", " 5 61.047268 0.531924 22 4504 \n", "41 0 24.910777 -0.008658 22 2723 \n", " 2 49.364425 0.346236 32 4669 \n", " 4 63.616937 0.475156 27 5163 \n", " 5 63.802389 0.497120 27 5198 \n", "42 0 26.362160 0.132578 22 2723 \n", " 2 51.233297 0.299470 28 4712 \n", " 5 63.593592 0.662017 27 4919 \n", "43 0 33.235667 0.153580 18 2801 \n", " 2 52.053367 0.527635 24 4431 \n", " 5 61.972532 0.372543 28 5172 \n", "44 0 31.855815 0.248721 23 2761 \n", " 2 50.375710 0.406347 28 4524 \n", "45 0 31.496679 0.173927 18 2688 \n", " 2 49.727069 0.404999 31 4581 \n", " 5 58.974828 0.501518 30 5241 \n", "46 0 28.485365 0.118862 22 2856 \n", " 2 50.471333 0.494902 27 4701 \n", " 5 63.958404 0.440578 22 4824 \n", "47 0 33.384803 0.125817 20 2881 \n", " 2 49.955392 0.365671 31 4751 \n", " 5 60.515020 0.429648 29 4954 \n", "48 0 26.973223 0.174721 19 2632 \n", " 2 50.181597 0.349076 30 4649 \n", " 5 61.484934 0.411250 29 4999 \n", "49 0 29.037876 0.179748 25 3078 \n", " 2 49.599545 0.449817 32 4585 \n", " 5 58.506247 0.365199 29 5150 \n", "\n", "[183 rows x 14 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trajs" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

haraldurt/UCSBDataScienceBootcamp2015

Day02_EverythingData/notebooks/Miniproject.ipynb

6

1108

{ "metadata": { "name": "", "signature": "sha256:5cd29f870d8558fda20c94f893e38edf9503576273225de3a4fe5256ca3eafce" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Mini project\n", "\n", "Extend the analysis provided here:\n", "\n", "http://nbviewer.ipython.org/github/rossant/ipython-minibook/blob/master/chapter3/303-cities-data-explore.ipynb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. What is the city that has the most other cities in a 10-mile radius from it?\n", "2. How many cities have no other city in 10 miles from them? Where are they mostly located?\n", "3. What is the distribution of the number of cities within a 10-mile radius from a city? What about varying the radius using interact() ?" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

cc0-1.0

FavioVazquez/clase-git-github

.ipynb_checkpoints/Clase-checkpoint.ipynb

1

10870

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<h1 align=\"center\"> Git y GitHub </h1>\n", "\n", "<center><img src=\"images/git-github.png\" height=\"800\" width=\"800\">\n", "</center>\n", "\n", "<h3 align=\"center\"> Favio Vázquez. Instituto de Ciencias Nucleares - UNAM.</h3> \n", "<h5 align=\"center\"> Ciudad de México, 07 de septiembre de 2016.</h5>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Objetivos \n", "\n", "- Entender el flujo de trabajo con Git y GitHub.\n", "- Familiarizarse con la plataforma de GitHub.\n", "- Instalar y entender lo básico de Git (en terminal).\n", "- Instalar un cliente de Git y aprender a utilizarlo.\n", "- Practicar con un caso real el flujo de trabajo con Git y GitHub." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Instalaciones" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- Git:\n", "\n", "```\n", ":$ apt-get install git\n", "```\n", "\n", "```\n", ":$ git --version\n", "```\n", "\n", "- SmartGit:\n", "\n", "http://www.syntevo.com/smartgit/download" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# GitHub\n", "\n", "https://github.com/" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Creemos una cuenta" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Características principales de GitHub\n", "\n", "<center>\n", "<img src=\"images/github-repositories.png\" height=\"800\" width=\"800\">\n", "</center>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Creemos y exploremos un repositorio " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Issues:\n", "\n", "<center>\n", "<img src=\"images/issues.png\" height=\"800\" width=\"800\">\n", "</center>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Navegando en GitHub\n", "\n", "<center>\n", "<img src=\"images/github-flow.png\" height=\"900\" width=\"900\">\n", "</center>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Branches (Creemos uno).\n", "\n", "Crear branches es una parte esencial de usar Git y GitHub. Y es muy fácil. Cuando creamos un branch, estamos creando una copia identica del proyecto en ese punto del tiempo, que está completamente separada del branch master. Esto mantiene a tu código en master seguro mientras experimentas y arreglas issues.\n", "<center>\n", "<img src=\"images/github-branches.png\" height=\"700\" width=\"700\">\n", "</center>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Creando archivos en GitHub\n", "\n", "- Se pueden crear nuevos archivos directamente en la interfaz web.\n", "- Luego que terminemos de editar el archivo, hacemos un \"commit\" de los cambios. \n", "- Incluímos siempre un mensaje a tu commit, y nos asegurams que estamos en el branch correcto antes de hacer el mismo." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Creemos el archivo Prueba.md" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Pull Requests (PR)\n", "\n", "Un PR es una petición para hacer \"merge\" de un branch con otro. Se usan para discutir los cambios hechos en el branch, y para continuar haciendo cambios hasta que el branch esté completo y el equipo esté de acuerdo en que puede hacerse merge. Son la forma de llevar el código, documentación, o de lo que trate el repo, al branch master y mantenerlo actualizado.\n", "\n", "<center>\n", "<img src=\"images/pull-requests.png\" height=\"600\" width=\"600\">\n", "</center>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<center>\n", "<img src=\"images/pull-request.png\" height=\"900\" width=\"900\">\n", "</center>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Haciendo merge a un Pull Request\n", "\n", "- Para hacer merge a nuestro branch al branch master, debemos hacer click en el botón Merge Pull Request en la vista de conversación. \n", "- Debemos usar la palabra Fixes (o closes, o close..) seguido por # y el número del Issue para cerrar el Issue al mismo tiempo que el Pull Request. Lista completa de palabras para cerrar PR: [click aquí](https://help.github.com/articles/closing-issues-via-commit-messages/).\n", "- Luego de que el PR haya sido aceptado y cerrado, se puede borrar el branch donde se estaba trabajando ya que no lo necesitaremos más." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<center>\n", "<img src=\"images/merge.png\" height=\"900\" width=\"900\">\n", "</center>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Git\n", "\n", "Git es un programa que sirve para rastrear los cambios en las versiones de un programa o proyecto (\"version control system\", o VCS). Se puede usar desde la línea de comandos o desde un cliente; permite chequear cambios en cualquier punto de la historia, resetear a algún punto específico del historial, tener desarrollos independientes, etc. Es código abierto. Fue inventado por Linus Torvalds en el 2005 para el manejo y desarrollo del kernel de Linux. En pocas palabras, es un manejador de repositorios." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Cuando usemos la terminal, estaremos trabajando con Git. Git es el sistema de versiones de control que GitHub utiliza tras bambalinas. GitHub añade funcionalidades como pull request e issues. Pero lo que hemos visto de crear branches o hacer commits son funcionalidades de Git." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<center>\n", "<img src=\"images/git-v-github.png\" height=\"900\" width=\"900\">\n", "</center>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Niveles de configuración de Git\n", "\n", "- --system - Afectan a todo el sistema, y a todos los usuarios de la computadora. \n", "- --global - Configuraciones a nivel de usuario, solo para el usuario activo en el momento. \n", "- --local -Configuraciones a nivel del repositorio. Solo aplican al repositorio en que se hacen.\n", "\n", "Para ver que configuraciones tenemos activas:\n", "\n", "```\n", ":$ git config --list\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Configurando nombre de usuario y correo\n", "\n", "\n", "```\n", ":$ git config --global user.name \"<nombre_completo>\" \n", "```\n", "\n", "```\n", ":$ git config --global user.email \"<email>\".\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Manejar los espacios en blanco con Autocrlf\n", "\n", "Diferente sistemas manejan los finales de línea y los espacios entre líneas de forma diferente. Si abrimos un archivo en otro sistema que no tiene activado el autocrlf, Git hará cambios al archivo basándose en la manera en l que ese sistema maneja ese tipo de archivos (algo no deseado). \n", "\n", "```\n", ":$ git config --global core.autocrlf\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Clonando repositorios\n", "\n", "<center>\n", "<img src=\"images/clone-diagram.png\" height=\"800\" width=\"800\">\n", "</center>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# La magia de los sistemas de versiones de control\n", "\n", "Ahora comenzaremos a trabajar localmente, como se debe hacer, y cuando estemos satisfechos con nuestros aportes o cambios, haremos \"push\" al branch para que se vea reflejado el cambio en GitHub, y sea visible por todos los participantes. \n", "\n", "Todo esto que explicaremos a continuación puede hacerse desde la terminal, pero con un poco más de esfuerzo. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# SmartGit\n", "\n", "Para hacernos la vida fácil, viene SmartGit al rescate. Un cliente de Git para Windows, Mac y Linux, muy fácil de utilizar, robusto, confiable y en constante actualización.\n", "\n", "<center>\n", " <img src=\"images/SmartGitSyntevo.png\" height=\"500\" width=\"500\">\n", "</center>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<h1 align=\"center\"> Hora de practicar y usar SmarGit con GitHub </h1>" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Julia 0.5.0-dev", "language": "julia", "name": "julia-0.5" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

mkhuthir/learnPython

jupyter/OpenCV/.ipynb_checkpoints/hsv_threshold-checkpoint.ipynb

1

2879

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import cv2\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "org_img=cv2.imread('img/circles.png') # read sample image\n", "\n", "min = np.array([0,0,0],np.uint8)\n", "max = np.array([0,0,255],np.uint8) # threshold min and max limits\n", "\n", "hsv_img = cv2.cvtColor(org_img,cv2.COLOR_BGR2HSV) # convert color space from BGR to HSV\n", "thr_img = cv2.inRange(hsv_img,min,max) # apply the selected threshold\n", "\n", "cv2.imwrite('img/circles.jpg',thr_img); # write the threshold image to disk" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "Required argument 'mat' (pos 2) not found", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-8-f4339b039fb5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthr_img\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Threshold Image'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\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: Required argument 'mat' (pos 2) not found" ] } ], "source": [ "cv2.imshow(thr_img)\n", "plt.title('Threshold Image')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:RoboND]", "language": "python", "name": "conda-env-RoboND-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" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

janusnic/21v-python

unit_20/matplotlib/lab8/lab8_mapreduce.ipynb

41

21568

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Lab 8: MapReduce, mrjob, and EC2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this week's lab, we will mostly ignore statistics and instead focus on some practical issues that you will encouter on Homework 4. Section 4 of that homework includes new python techniques (classes, inheritance), an unfamiliar approach to breaking up large computing problems (MapReduce), code that has to be run outside the friendly confines of an ipython notebook, and then you are asked to put it all to use on Amazon's Elastic Compute Cloud (EC2). This sounds very complicated, but the end result is a simpler algorithm for that problem of calculating similarity scores, as well as the ability to expand to arbitrarily large data sets." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "1. Classes and generators in python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On previous homeworks, nearly all of the coding has been done by writing python functions plus a small amount of code that calls the functions you have written. Included below is the code for the mrjob word_count example that was covered in lecture (the canonical MapReduce example). There are a lot of new features here!\n", "\n", "Below is the code for a simple MapReduce algorithm to count the number of words in a text file. This is one of the simplest examples of a problem that can be solved using MapReduce (I even took it from the Section \"[Writing your first job](http://mrjob.readthedocs.org/en/latest/guides/quickstart.html#writing-your-first-job)\" in the mrjob documentation). If you try to run the cell in this notebook, it will not work! We will get to running programs with mrjob soon, but for now it will just serve as reference for some topics we want to cover." ] }, { "cell_type": "code", "collapsed": true, "input": [ "from mrjob.job import MRJob\n", "\n", "class MRWordFrequencyCount(MRJob):\n", "\n", " def mapper(self, _, line):\n", " yield \"chars\", len(line)\n", " yield \"words\", len(line.split())\n", " yield \"lines\", 1\n", "\n", " def reducer(self, key, values):\n", " yield key, sum(values)\n", "\n", "if __name__ == '__main__':\n", " MRWordFrequencyCount.run()\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "1.1 Classes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Classes are the basis of object-oriented programming in python. For all of the problems on previous homework assignments, we have written functions to do calculations, draw figures, etc. To use mrjob, we have to switch gears and use a different style of programming. \n", "\n", "As you can see in the example above, the <span style=\"font-family: monospace\">MRWordFrequencyCount</span> class is defined with an indented block similar to a function definition, except with <span style=\"font-family: monospace; color: green; font-weight: bold;\">class</span> instead of <span style=\"font-family: monospace; color: green; font-weight: bold;\">def</span>. Instead of a list of arguments, the item in parentheses (<span style=\"font-family: monospace;\">MRJob</span>) is a *base class* that our newly defined class will inherit most of its features from. Even though there is very little code written above for <span style=\"font-family: monospace\">MRWordFrequencyCount</span>, it knows how to do many complex operations (running a mapper and a reducer, submitting jobs to EC2, etc.) because it inherited these abilities from the base class.\n", "\n", "There are two methods, <span style=\"font-family: monospace\">mapper</span> and <span style=\"font-family: monospace\">reducer</span>, that have been written specifically for <span style=\"font-family: monospace\">MRWordFrequencyCount</span>. These methods are also defined for the <span style=\"font-family: monospace\">MRJob</span> base class, but the methods defined here supercede the inherted ones. A class method is similar to a function (as you might guess, since it is also defined with a <span style=\"font-family: monospace; color: green; font-weight: bold;\">def</span> statement), but the first argument to a class method will always be <span style=\"font-family: monospace\">self</span>, a reference back to the object to which the method belongs. The always-present <span style=\"font-family: monospace\">self</span> argument allows the method to access other members of the same object (both data and methods). However, when you actually call a class method, you don't have to supply anything for the <span style=\"font-family: monospace\">self</span> argument -- it is implicit. For example, to call the <span style=\"font-family: monospace;\">reducer</span> method defined above, you would use:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Call reducer method of MRWordFrequencyCount object using some key and values.\n", "MRWordFrequencyCount.reducer(my_key, my_values) # Did not specify 'self' argument" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next mrjob example -- [Writing your second job](http://mrjob.readthedocs.org/en/latest/guides/quickstart.html#writing-your-second-job) -- processes text to find the most commonly used word. That algorithm involves two MapReduce steps, so it is necessary to write a <span style=\"font-family: monospace;\">MRMostUsedWord.steps</span> method to override the inherited method. Notice that the <span style=\"font-family: monospace;\">self</span> is used repeatedly to specify the function references inside the list returned by the <span style=\"font-family: monospace;\">steps</span> method." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import re\n", "\n", "WORD_RE = re.compile(r\"[\\w']+\")\n", "\n", "\n", "class MRMostUsedWord(MRJob):\n", "\n", " def mapper_get_words(self, _, line):\n", " # yield each word in the line\n", " for word in WORD_RE.findall(line):\n", " yield (word.lower(), 1)\n", "\n", " def combiner_count_words(self, word, counts):\n", " # optimization: sum the words we've seen so far\n", " yield (word, sum(counts))\n", "\n", " def reducer_count_words(self, word, counts):\n", " # send all (num_occurrences, word) pairs to the same reducer.\n", " # num_occurrences is so we can easily use Python's max() function.\n", " yield None, (sum(counts), word)\n", "\n", " # discard the key; it is just None\n", " def reducer_find_max_word(self, _, word_count_pairs):\n", " # each item of word_count_pairs is (count, word),\n", " # so yielding one results in key=counts, value=word\n", " yield max(word_count_pairs)\n", "\n", " def steps(self):\n", " return [\n", " self.mr(mapper=self.mapper_get_words,\n", " combiner=self.combiner_count_words,\n", " reducer=self.reducer_count_words),\n", " self.mr(reducer=self.reducer_find_max_word)\n", " ]\n", "\n", "\n", "if __name__ == '__main__':\n", " MRMostUsedWord.run()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[More about classes in python](http://docs.python.org/2/tutorial/classes.html#)" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "1.2 Generators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generators are necessary to understand all of those <span style=\"font-family: monospace; font-weight: bold; color: green;\">yield</span> statements popping up in the mapper and reducer methods. The main issue, in the case of industrial-strength MapReduce, is that you don't have enough memory to store all of your data at once. This is true even after you have split your data between many compute nodes. So instead of getting an enormous list of data, the mapper and reducer functions both receive and emit generators.\n", "\n", "When you run a function, it chugs along until it hits a <span style=\"font-family: monospace; font-weight: bold; color: green;\">return</span> statement, at which point it returns some results and then it is done. A generator does its specified calculations until it hits a <span style=\"font-family: monospace; font-weight: bold; color: green;\">yield</span> statement. It passes along whatever values it was supposed to yield and then it *pauses* and waits for someone to tell it to continue. It continues until it reaches another <span style=\"font-family: monospace; font-weight: bold; color: green;\">yield</span>, and so on.\n", "\n", "Not only are mapper and reducer generators, their (key, value) inputs are also generators. This means that for each step of the mapper, it pulls in one (key, value) pair, does some processing, and then emits one or more key value pairs, which move along to a combiner or a shuffler or whatever. This is how MapReduce avoids ever having to load huge datasets into limited memory.\n", "\n", "A common stumbling block with generators is the fact that once you have iterated through an entire generator, it is done. You can see an example of this mistake by trying to run the code block below." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# This function converts a list into a generator.\n", "def example_generator(list):\n", " for item in list:\n", " yield item\n", " \n", "# Create a generator.\n", "my_generator = example_generator([0, 1, 2, 3, 4])\n", "\n", "# Iterating over the generator works great the first time.\n", "print \"generator iteration 1\"\n", "print \"---------------------\"\n", "for value in my_generator:\n", " print value\n", " \n", "# ...but it doesn't work the second time.\n", "print \"\\n\"\n", "print \"generator iteration 2\"\n", "print \"---------------------\"\n", "for value in my_generator:\n", " print value" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "1.3 What does <span style=\"font-family: monospace;\">\\_\\_name\\_\\_ <span style=\"color: violet;\">==</span> <span style=\"color: red;\">'\\_\\_main\\_\\_'</span></span> mean??" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python is *really* into namespaces (see, for example, [The Zen of Python](http://www.python.org/dev/peps/pep-0020/)). The <span style=\"font-family: monospace;\">\\_\\_name\\_\\_</span> keyword tells you what namespace it is in. For example, if we <span style=\"font-family: monospace;\"><span style=\"font-weight: bold; color: green;\">import</span> numpy</span>, then all of the numpy features are in the numpy namespace." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "print np.__name__\n", "\n", "import matplotlib.pyplot as plt\n", "print plt.__name__" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you try to import the above file containing the definition for <span style=\"font-family: monospace;\">MRMostUsedWord</span>, then python will interpret the file all the way down until it hits that last <span style=\"font-family: monospace; font-weight: bold; color: green;\">if</span> statement. <span style=\"font-family: monospace;\">\\_\\_name\\_\\_</span> will evaluate to <span style=\"font-family: monospace;\">MRMostUsedWord</span> (or whatever the name was of the file we imported) and the line inside the if statement will be ignored. On the other hand, if you run this code from the command line, python will interpret it *without* importing it and <tt>\\_\\_name\\_\\_</tt> will be the python top level namespace, which is <tt><span color=\"red\">'\\_\\_main\\_\\_'</span></tt>, so <tt>MRMostUsedWord.run()</tt> gets called.\n", "\n", "In (many) fewer words: it tells you to run the job only when invoked from the command line.\n", "\n", "Try copying the code for MRMostUsedWord to a file, named <tt>MRMostUsedWord.py</tt>, and then running it on any old text file you might have lying around. The invokation will be somthing like this (modify based on your particular python installation):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "python MRMostUsedWord.py some_file.txt > most_used_word.out" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "2. Setting up your Amazon Web Services account" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is quite a bit of overhead involved in setting up an AWS account and keeping an eye on the jobs that you end up running. In lab, we will run through an example account activation including:\n", "\n", "* Account creation\n", "* Signing up for Elastic MapReduce\n", "* Storing security credentials in your mrjob.conf file\n", "* Redeeming account credits\n", "* Billing alerts\n", "* Checking on running jobs using the console\n", "\n", "These documents (also linked from HW4) are very useful: [Instructions for Amazon Setup notebook](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/InstructionsForAmazonEMR.ipynb), [Elastic MapReduce Quickstart](http://pythonhosted.org/mrjob/guides/emr-quickstart.html)\n", "\n", "Once you have this all set up and working, then mrjob makes it *very easy* to run a MapReduce job with EMR. Using the same MRMostUsedWord example as above, the command line invokation to run with EMR is:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "python MRMostUsedWord.py -r emr some_file.txt > most_used_word.out" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "3. MapReduce exercises" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<br /> \\[<span style=\"font-size: small;\">Image from [https://developers.google.com/appengine/docs/python/dataprocessing/](https://developers.google.com/appengine/docs/python/dataprocessing/)</span>\\]\n", "\n", "Below are two practice problems to get the hang of writing MapReduce algorithms. Remember, you will be writing these programs in separate files that you run from the command line. You are welcome to try out EC2, but these are small datasets and it will generally be much faster to run locally." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "3.1 Anagram finder" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, grab the file [word_list.txt](https://raw.github.com/cs109/content/master/labs/lab8/word_list.txt). This contains a list of six-letter words that I dumped from my spellchecker. To keep things simple, all of the words consist of lower-case letters only." ] }, { "cell_type": "code", "collapsed": false, "input": [ "word_list = [word.strip() for word in open(\"word_list.txt\").readlines()]\n", "print \"{0} words in list\".format(len(word_list))\n", "print \"First ten words: {0}\".format(\", \".join(word_list[0:10]))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use mrjob to write a class that finds all anagrams in word_list.txt. \n", "\n", "**UPDATE**: [My solution to exercise 3.1](https://raw.github.com/cs109/content/master/labs/lab8/anagrams.py)" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "3.2 Friends don't let friends root for the Cardinals" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the next problem, download the file [baseball_friends.csv](https://raw.github.com/cs109/content/master/labs/lab8/baseball_friends.csv). Each row of this csv file contains the following:\n", "\n", "* A person's name\n", "* The team that person is rooting for -- either \"Cardinals\" or \"Red Sox\"\n", "* A list of that person's friends, which could have arbitrary length\n", "\n", "Let's take a look at one line:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "friends = open(\"baseball_friends.csv\").readlines()\n", "print friends[0].strip()\n", "print len(friends[0].split(\",\")) - 2" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This line tells us that Aaden is a Red Sox friend and he has 65 friends, who are all listed here. For this problem, it's safe to assume that all of the names are unique and that the friendship structure is symmetric (*i.e.* if Alannah shows up in Aaden's friends list, then Aaden will show up in Alannah's friends list).\n", "\n", "Write an mrjob class that lists each person's name, their favorite team, the number of Red Sox fans they are friends with, and the number of Cardinals fans they are friends with.\n", "\n", "After running that program, we can look at the results to get an idea of the absurdly simple model that I used to generate the input csv file. You might need to modify the code below if the format of your output file doesn't quite match mine." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import json\n", "\n", "# Read results.\n", "result_file = \"baseball_friends.out\"\n", "result = [[json.loads(field) for field in line.strip().split('\\t')] for line in open(result_file)]\n", "\n", "# Break out columns.\n", "names = [x[0] for x in result]\n", "teams = [x[1][0] for x in result]\n", "redsox_count = [x[1][1] for x in result]\n", "cardinals_count = [x[1][2] for x in result]\n", "\n", "# Combine in data frame.\n", "result = pd.DataFrame(index=names, data={'teams': teams, 'redsox_count': redsox_count, \n", " 'cardinals_count': cardinals_count})" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from matplotlib import rcParams\n", "rcParams['figure.figsize'] = (10, 6)\n", "rcParams['font.size'] = 14\n", "\n", "# Average number of friends by affiliation.\n", "print result.groupby('teams').mean()\n", "\n", "# Histogram the affiliations of people who are friends of Red Sox fans.\n", "plt.hist(result.redsox_count[result.teams == \"Red Sox\"], label=\"Red Sox friend Red Sox\")\n", "plt.hist(result.cardinals_count[result.teams == \"Red Sox\"], label=\"Red Sox friend Cardinals\")\n", "plt.xlabel('number of friends')\n", "plt.ylabel('count')\n", "plt.legend(loc=0)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**UPDATE**: [My solution to exercise 3.2](https://raw.github.com/cs109/content/master/labs/lab8/friend_affiliations.py)" ] } ], "metadata": {} } ] }

mit

MingChen0919/learning-apache-spark

notebooks/04-miscellaneous/sql-functions.ipynb

1

34345

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SQL functions\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# SparkContext and SparkSession" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pyspark import SparkContext\n", "sc = SparkContext(master = 'local')\n", "\n", "from pyspark.sql import SparkSession\n", "spark = SparkSession.builder \\\n", " .appName(\"Python Spark SQL basic example\") \\\n", " .config(\"spark.some.config.option\", \"some-value\") \\\n", " .getOrCreate()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Import data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------+-----------+------------+-----------+-------+\n", "|sepal_length|sepal_width|petal_length|petal_width|species|\n", "+------------+-----------+------------+-----------+-------+\n", "| 5.1| 3.5| 1.4| 0.2| setosa|\n", "| 4.9| 3.0| 1.4| 0.2| setosa|\n", "| 4.7| 3.2| 1.3| 0.2| setosa|\n", "| 4.6| 3.1| 1.5| 0.2| setosa|\n", "| 5.0| 3.6| 1.4| 0.2| setosa|\n", "+------------+-----------+------------+-----------+-------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "iris = spark.read.csv('data/iris.csv', header=True, inferSchema=True)\n", "iris.show(5)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------+-----------+---+------------+---+------------+-------+-----+------------+\n", "| lcavol| lweight|age| lbph|svi| lcp|gleason|pgg45| lpsa|\n", "+------------+-----------+---+------------+---+------------+-------+-----+------------+\n", "|-0.579818495|2.769458829| 50|-1.386294361| 0|-1.386294361| 6| 0|-0.430782916|\n", "|-0.994252273|3.319625728| 58|-1.386294361| 0|-1.386294361| 6| 0|-0.162518929|\n", "|-0.510825624|2.691243083| 74|-1.386294361| 0|-1.386294361| 7| 20|-0.162518929|\n", "|-1.203972804|3.282789151| 58|-1.386294361| 0|-1.386294361| 6| 0|-0.162518929|\n", "| 0.751416089|3.432372999| 62|-1.386294361| 0|-1.386294361| 6| 0| 0.371563556|\n", "+------------+-----------+---+------------+---+------------+-------+-----+------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "prostate = spark.read.csv('data/prostate.csv', header=True, inferSchema=True)\n", "prostate.show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Functions" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pyspark.sql.functions import *\n", "from pyspark.sql.types import *\n", "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `abs`" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------+-----------+\n", "| lpsa| abs(lpsa)|\n", "+------------+-----------+\n", "|-0.430782916|0.430782916|\n", "|-0.162518929|0.162518929|\n", "|-0.162518929|0.162518929|\n", "|-0.162518929|0.162518929|\n", "| 0.371563556|0.371563556|\n", "+------------+-----------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "prostate.select('lpsa', abs(prostate.lpsa).alias('abs(lpsa)')).show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `acos`" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+\n", "| x|\n", "+--------------------+\n", "| -0.9338356359616288|\n", "|-0.17825806390480148|\n", "| -0.8287101229670288|\n", "| -0.9203268470931772|\n", "| -0.5717064564704842|\n", "+--------------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "pdf = pd.DataFrame({\n", " 'x': list(-np.random.rand(5)) + list(np.random.rand(5))\n", "})\n", "df = spark.createDataFrame(pdf)\n", "df.show(5)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+------------------+\n", "| x| ACOS(x)|\n", "+--------------------+------------------+\n", "| -0.9338356359616288| 2.775786316206805|\n", "|-0.17825806390480148|1.7500122036992714|\n", "| -0.8287101229670288|2.5475953864778313|\n", "| -0.9203268470931772|2.7397115973218678|\n", "| -0.5717064564704842|2.1793805606775827|\n", "+--------------------+------------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "df.select('x', acos(df.x)).show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `add_months`" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import datetime" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+----------+\n", "| dates|\n", "+----------+\n", "|2017-06-25|\n", "|2017-06-26|\n", "|2017-06-27|\n", "|2017-06-28|\n", "|2017-06-29|\n", "+----------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "base = datetime.date.today()\n", "date_list = [base + datetime.timedelta(days=x) for x in list(range(0, 10))*10]\n", "pdf = pd.DataFrame({\n", " 'dates': date_list\n", "})\n", "df = spark.createDataFrame(pdf)\n", "df.show(5)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+----------+----------+\n", "| dates| new_dates|\n", "+----------+----------+\n", "|2017-06-25|2017-08-25|\n", "|2017-06-26|2017-08-26|\n", "|2017-06-27|2017-08-27|\n", "|2017-06-28|2017-08-28|\n", "|2017-06-29|2017-08-29|\n", "+----------+----------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "df.select('dates', add_months(df.dates, 2).alias('new_dates')).show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `approx_count_distinct`" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------------------------+\n", "|approx_count_distinct(gleason)|\n", "+------------------------------+\n", "| 4|\n", "+------------------------------+\n", "\n" ] } ], "source": [ "prostate.select(approx_count_distinct(prostate.gleason)).show(5)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------------------------+\n", "|approx_count_distinct(species)|\n", "+------------------------------+\n", "| 3|\n", "+------------------------------+\n", "\n" ] } ], "source": [ "iris.select(approx_count_distinct(iris.species)).show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `array`" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------+-----------+------------+-----------+-------+\n", "|sepal_length|sepal_width|petal_length|petal_width|species|\n", "+------------+-----------+------------+-----------+-------+\n", "| 5.1| 3.5| 1.4| 0.2| setosa|\n", "| 4.9| 3.0| 1.4| 0.2| setosa|\n", "| 4.7| 3.2| 1.3| 0.2| setosa|\n", "| 4.6| 3.1| 1.5| 0.2| setosa|\n", "| 5.0| 3.6| 1.4| 0.2| setosa|\n", "+------------+-----------+------------+-----------+-------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "iris.show(5)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-------+--------------------+\n", "|species| features|\n", "+-------+--------------------+\n", "| setosa|[5.1, 3.5, 1.4, 0.2]|\n", "| setosa|[4.9, 3.0, 1.4, 0.2]|\n", "| setosa|[4.7, 3.2, 1.3, 0.2]|\n", "| setosa|[4.6, 3.1, 1.5, 0.2]|\n", "| setosa|[5.0, 3.6, 1.4, 0.2]|\n", "+-------+--------------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "df_arr = iris.select('species', array(['sepal_length', 'sepal_width', 'petal_length', 'petal_width']).alias('features'))\n", "df_arr.show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `array_contains`" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-------+--------------------+------------+\n", "|species| features|new_features|\n", "+-------+--------------------+------------+\n", "| setosa|[5.1, 3.5, 1.4, 0.2]| true|\n", "| setosa|[4.9, 3.0, 1.4, 0.2]| true|\n", "| setosa|[4.7, 3.2, 1.3, 0.2]| false|\n", "| setosa|[4.6, 3.1, 1.5, 0.2]| false|\n", "| setosa|[5.0, 3.6, 1.4, 0.2]| true|\n", "+-------+--------------------+------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "df = df_arr.select('species', 'features', array_contains(df_arr.features, 1.4).alias('new_features'))\n", "df.show(5)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-------+--------------------+------------+\n", "|species| features|new_features|\n", "+-------+--------------------+------------+\n", "| setosa|[5.1, 3.5, 1.4, 0.2]| true|\n", "| setosa|[4.9, 3.0, 1.4, 0.2]| true|\n", "| setosa|[5.0, 3.6, 1.4, 0.2]| true|\n", "| setosa|[4.6, 3.4, 1.4, 0.3]| true|\n", "| setosa|[4.4, 2.9, 1.4, 0.2]| true|\n", "+-------+--------------------+------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "df.filter(df.new_features).show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `asc`\n", "\n", "`asc` returns a **sort expression**, which can be used as argument of sort functions such as `pyspark.sql.DataFrame.sort` and `pyspark.sql.DataFrame.orderBy`" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------+-----------+---+------------+---+------------+-------+-----+------------+\n", "| lcavol| lweight|age| lbph|svi| lcp|gleason|pgg45| lpsa|\n", "+------------+-----------+---+------------+---+------------+-------+-----+------------+\n", "|-0.579818495|2.769458829| 50|-1.386294361| 0|-1.386294361| 6| 0|-0.430782916|\n", "|-0.994252273|3.319625728| 58|-1.386294361| 0|-1.386294361| 6| 0|-0.162518929|\n", "|-1.203972804|3.282789151| 58|-1.386294361| 0|-1.386294361| 6| 0|-0.162518929|\n", "|-0.510825624|2.691243083| 74|-1.386294361| 0|-1.386294361| 7| 20|-0.162518929|\n", "| 0.751416089|3.432372999| 62|-1.386294361| 0|-1.386294361| 6| 0| 0.371563556|\n", "+------------+-----------+---+------------+---+------------+-------+-----+------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "prostate.sort(prostate.lpsa.asc()).show(5)" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------+-----------+---+------------+---+------------+-------+-----+------------+\n", "| lcavol| lweight|age| lbph|svi| lcp|gleason|pgg45| lpsa|\n", "+------------+-----------+---+------------+---+------------+-------+-----+------------+\n", "|-0.579818495|2.769458829| 50|-1.386294361| 0|-1.386294361| 6| 0|-0.430782916|\n", "|-0.994252273|3.319625728| 58|-1.386294361| 0|-1.386294361| 6| 0|-0.162518929|\n", "|-1.203972804|3.282789151| 58|-1.386294361| 0|-1.386294361| 6| 0|-0.162518929|\n", "|-0.510825624|2.691243083| 74|-1.386294361| 0|-1.386294361| 7| 20|-0.162518929|\n", "| 0.751416089|3.432372999| 62|-1.386294361| 0|-1.386294361| 6| 0| 0.371563556|\n", "+------------+-----------+---+------------+---+------------+-------+-----+------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "prostate.orderBy(prostate.lpsa.asc()).show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* `ascii`\n", "* `asin`\n", "* `atan`\n", "* `atan2`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `avg`" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------------+\n", "| avg(lpsa)|\n", "+------------------+\n", "|2.4783868787422683|\n", "+------------------+\n", "\n" ] } ], "source": [ "prostate.select(avg(prostate.lpsa)).show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* `base64`\n", "* `bin`\n", "* `bitwiseNOT`\n", "* `broadcast`\n", "* `bround`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `cbrt`" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------+-------------------+\n", "| lpsa| CBRT(lpsa)|\n", "+------------+-------------------+\n", "|-0.430782916|-0.7552420410177275|\n", "|-0.162518929|-0.5457176294010901|\n", "|-0.162518929|-0.5457176294010901|\n", "|-0.162518929|-0.5457176294010901|\n", "| 0.371563556| 0.7189152621521183|\n", "+------------+-------------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "prostate.select('lpsa', cbrt(prostate.lpsa)).show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `ceil`" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------+----------+\n", "| lpsa|CEIL(lpsa)|\n", "+------------+----------+\n", "|-0.430782916| 0|\n", "|-0.162518929| 0|\n", "|-0.162518929| 0|\n", "|-0.162518929| 0|\n", "| 0.371563556| 1|\n", "+------------+----------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "prostate.select('lpsa', ceil(prostate.lpsa)).show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `coalesce`\n", "\n", "Return the first column that is not null." ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+----+----+\n", "| a| b|\n", "+----+----+\n", "|null|null|\n", "| 1|null|\n", "|null| 2|\n", "+----+----+\n", "\n" ] } ], "source": [ "df = spark.createDataFrame([(None, None), (1, None), (None, 2)], (\"a\", \"b\"))\n", "df.show()" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------+\n", "|coalesce(a, b)|\n", "+--------------+\n", "| null|\n", "| 1|\n", "| 2|\n", "+--------------+\n", "\n" ] } ], "source": [ "df.select(coalesce(df.a, df.b)).show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `col`\n", "\n", "Returns a **Column** based on the given column name. It can save your some typing when the dataframe is very long." ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------+-----------+---+------------+---+------------+-------+-----+------------+\n", "| lcavol| lweight|age| lbph|svi| lcp|gleason|pgg45| lpsa|\n", "+------------+-----------+---+------------+---+------------+-------+-----+------------+\n", "|-0.579818495|2.769458829| 50|-1.386294361| 0|-1.386294361| 6| 0|-0.430782916|\n", "|-0.994252273|3.319625728| 58|-1.386294361| 0|-1.386294361| 6| 0|-0.162518929|\n", "|-0.510825624|2.691243083| 74|-1.386294361| 0|-1.386294361| 7| 20|-0.162518929|\n", "|-1.203972804|3.282789151| 58|-1.386294361| 0|-1.386294361| 6| 0|-0.162518929|\n", "| 0.751416089|3.432372999| 62|-1.386294361| 0|-1.386294361| 6| 0| 0.371563556|\n", "+------------+-----------+---+------------+---+------------+-------+-----+------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "prostate.show(5)" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------+---+\n", "| lcavol|age|\n", "+------------+---+\n", "|-0.579818495| 50|\n", "|-0.994252273| 58|\n", "|-0.510825624| 74|\n", "|-1.203972804| 58|\n", "| 0.751416089| 62|\n", "+------------+---+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "prostate.select(col('lcavol'), col('age')).show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `collect_list`" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---+\n", "| x|\n", "+---+\n", "| 1|\n", "| 2|\n", "| 2|\n", "| 3|\n", "| 4|\n", "| 4|\n", "| 4|\n", "| 4|\n", "+---+\n", "\n" ] } ], "source": [ "pdf = pd.DataFrame({\n", " 'x':[1, 2, 2, 3, 4,4,4,4]\n", "})\n", "df = spark.createDataFrame(pdf)\n", "df.show()" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+\n", "| collect_list(x)|\n", "+--------------------+\n", "|[1, 2, 2, 3, 4, 4...|\n", "+--------------------+\n", "\n" ] } ], "source": [ "df.select(collect_list(df.x)).show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `collect_set`" ] }, { "cell_type": "code", "execution_count": 124, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------+\n", "|collect_set(x)|\n", "+--------------+\n", "| [1, 2, 3, 4]|\n", "+--------------+\n", "\n" ] } ], "source": [ "df.select(collect_set(df.x)).show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `concat`" ] }, { "cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---+---+\n", "| x| v|\n", "+---+---+\n", "| a| 1|\n", "| b| 2|\n", "+---+---+\n", "\n" ] } ], "source": [ "df = spark.createDataFrame([['a', '1'], ['b', '2']], ['x', 'v'])\n", "df.show()" ] }, { "cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---+---+------------+\n", "| x| v|concate(x,v)|\n", "+---+---+------------+\n", "| a| 1| a1|\n", "| b| 2| b2|\n", "+---+---+------------+\n", "\n" ] } ], "source": [ "df.select('x', 'v', concat(df.x, df.v).alias('concate(x,v)')).show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `concat_ws`" ] }, { "cell_type": "code", "execution_count": 147, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---+---+------------+\n", "| x| v|concate(x,v)|\n", "+---+---+------------+\n", "| a| 1| a_1|\n", "| b| 2| b_2|\n", "+---+---+------------+\n", "\n" ] } ], "source": [ "df.select('x', 'v', concat_ws('_', df.x, df.v).alias('concate(x,v)')).show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `conv`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `corr`" ] }, { "cell_type": "code", "execution_count": 148, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------+-----------+---+------------+---+------------+-------+-----+------------+\n", "| lcavol| lweight|age| lbph|svi| lcp|gleason|pgg45| lpsa|\n", "+------------+-----------+---+------------+---+------------+-------+-----+------------+\n", "|-0.579818495|2.769458829| 50|-1.386294361| 0|-1.386294361| 6| 0|-0.430782916|\n", "|-0.994252273|3.319625728| 58|-1.386294361| 0|-1.386294361| 6| 0|-0.162518929|\n", "|-0.510825624|2.691243083| 74|-1.386294361| 0|-1.386294361| 7| 20|-0.162518929|\n", "|-1.203972804|3.282789151| 58|-1.386294361| 0|-1.386294361| 6| 0|-0.162518929|\n", "| 0.751416089|3.432372999| 62|-1.386294361| 0|-1.386294361| 6| 0| 0.371563556|\n", "+------------+-----------+---+------------+---+------------+-------+-----+------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "prostate.show(5)" ] }, { "cell_type": "code", "execution_count": 150, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-------------------+\n", "| corr(age, lpsa)|\n", "+-------------------+\n", "|0.16959284228582772|\n", "+-------------------+\n", "\n" ] } ], "source": [ "prostate.select(corr(prostate.age, prostate.lpsa)).show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `cos`\n", "## `cosh`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `count`" ] }, { "cell_type": "code", "execution_count": 151, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-----------+\n", "|count(lpsa)|\n", "+-----------+\n", "| 97|\n", "+-----------+\n", "\n" ] } ], "source": [ "prostate.select(count(prostate.lpsa)).show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `countDistinct`" ] }, { "cell_type": "code", "execution_count": 152, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------+\n", "|count(species)|\n", "+--------------+\n", "| 150|\n", "+--------------+\n", "\n" ] } ], "source": [ "iris.select(count(iris.species)).show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `covar_pop`\n", "\n", "**population covariance**: $\\frac{1}{n}\\sum_{i=1}^n(x_{i} - \\bar{x})(y_{i} - \\bar{y})$" ] }, { "cell_type": "code", "execution_count": 157, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+\n", "|covar_pop(age, lpsa)|\n", "+--------------------+\n", "| 1.4424746293984458|\n", "+--------------------+\n", "\n" ] } ], "source": [ "prostate.select(covar_pop(prostate.age, prostate.lpsa)).show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `covar_samp`\n", "**sample covariance**: $\\frac{1}{n-1}\\sum_{i=1}^n(x_{i} - \\bar{x})(y_{i} - \\bar{y})$" ] }, { "cell_type": "code", "execution_count": 158, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---------------------+\n", "|covar_samp(age, lpsa)|\n", "+---------------------+\n", "| 1.4575004067880128|\n", "+---------------------+\n", "\n" ] } ], "source": [ "prostate.select(covar_samp(prostate.age, prostate.lpsa)).show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `create_map`" ] }, { "cell_type": "code", "execution_count": 159, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------+-----------+------------+-----------+-------+\n", "|sepal_length|sepal_width|petal_length|petal_width|species|\n", "+------------+-----------+------------+-----------+-------+\n", "| 5.1| 3.5| 1.4| 0.2| setosa|\n", "| 4.9| 3.0| 1.4| 0.2| setosa|\n", "| 4.7| 3.2| 1.3| 0.2| setosa|\n", "| 4.6| 3.1| 1.5| 0.2| setosa|\n", "| 5.0| 3.6| 1.4| 0.2| setosa|\n", "+------------+-----------+------------+-----------+-------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "iris.show(5)" ] }, { "cell_type": "code", "execution_count": 163, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------------+\n", "|map(species, sepal_length)|\n", "+--------------------------+\n", "| Map(setosa -> 5.1)|\n", "| Map(setosa -> 4.9)|\n", "| Map(setosa -> 4.7)|\n", "| Map(setosa -> 4.6)|\n", "| Map(setosa -> 5.0)|\n", "+--------------------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "df = iris.select(create_map('species', 'sepal_length'))\n", "df.show(5)" ] }, { "cell_type": "code", "execution_count": 166, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('map(species, sepal_length)', 'map<string,double>')]" ] }, "execution_count": 166, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## `cume_dist`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `current_date`" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---+\n", "| x|\n", "+---+\n", "| 1|\n", "| 2|\n", "| 3|\n", "| 4|\n", "+---+\n", "\n" ] } ], "source": [ "df = spark.createDataFrame([[1],[2],[3],[4]], ['x'])\n", "df.show()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---+--------------+\n", "| x|current_date()|\n", "+---+--------------+\n", "| 1| 2017-06-27|\n", "| 2| 2017-06-27|\n", "| 3| 2017-06-27|\n", "| 4| 2017-06-27|\n", "+---+--------------+\n", "\n" ] } ], "source": [ "df.select('x', current_date()).show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `current_tmestamp`" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---+-----------------------+\n", "|x |current_timestamp() |\n", "+---+-----------------------+\n", "|1 |2017-06-27 01:18:48.383|\n", "|2 |2017-06-27 01:18:48.383|\n", "|3 |2017-06-27 01:18:48.383|\n", "|4 |2017-06-27 01:18:48.383|\n", "+---+-----------------------+\n", "\n" ] } ], "source": [ "df.select('x', current_timestamp()).show(truncate=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `date_add`" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---+------------+\n", "| x|current_date|\n", "+---+------------+\n", "| 1| 2017-06-27|\n", "| 2| 2017-06-27|\n", "| 3| 2017-06-27|\n", "| 4| 2017-06-27|\n", "+---+------------+\n", "\n" ] } ], "source": [ "df2 = df.select('x', current_date().alias('current_date'))\n", "df2.show(5)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---+------------+--------------------------+\n", "| x|current_date|date_add(current_date, 10)|\n", "+---+------------+--------------------------+\n", "| 1| 2017-06-27| 2017-07-07|\n", "| 2| 2017-06-27| 2017-07-07|\n", "| 3| 2017-06-27| 2017-07-07|\n", "| 4| 2017-06-27| 2017-07-07|\n", "+---+------------+--------------------------+\n", "\n" ] } ], "source": [ "df2.select('x', 'current_date', date_add(df2.current_date, 10)).show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `date_format`" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---+------------+----------+\n", "| x|current_date| new_date|\n", "+---+------------+----------+\n", "| 1| 2017-06-27|06/27/2017|\n", "| 2| 2017-06-27|06/27/2017|\n", "| 3| 2017-06-27|06/27/2017|\n", "| 4| 2017-06-27|06/27/2017|\n", "+---+------------+----------+\n", "\n" ] } ], "source": [ "df2.select('x', 'current_date', date_format('current_date', 'MM/dd/yyyy').alias('new_date')).show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

dietmarw/EK5312_ElectricalMachines

Chapman/Ch1-Problem_1-07.ipynb

1

4164

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Excercises Electric Machinery Fundamentals\n", "## Chapter 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem 1-7" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab notebook\n", "%precision 4\n", "from scipy import constants as c # we like to use some constants" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Description" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A two-legged core is shown in Figure P1-4 below:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"figs/FigC_P1-4.jpg\" width=\"70%\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The winding on the left leg of the core ($N_1$) has 600 turns,\n", "and the winding on the right ($N_2$) has 200 turns. The coils are wound in the directions shown in the figure. \n", "\n", " * If the dimensions are as shown, then what flux would be produced by currents $i_1 = 0.5\\,A$ and $i_2 = 1.0\\,A$?\n", "\n", "Assume $\\mu_r = 1200$ and constant." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "N1 = 600\n", "N2 = 200\n", "i1 = 0.5 # A\n", "i2 = 1.0 # A\n", "mu_r = 1200\n", "mu = mu_r * c.mu_0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SOLUTION" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The two coils on this core are wound so that their magnetomotive forces are additive, so the\n", "total magnetomotive force on this core is\n", "$$\\mathcal{F}_\\text{TOT} = N_1 i_1 + N_2 I_2$$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "F_tot = 500.0 At\n" ] } ], "source": [ "F_tot = N1 * i1 + N2 * i2 \n", "print('F_tot = {:.1f} At'.format(F_tot))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The total reluctance in the core is $\\mathcal{R}_\\text{TOT} = \\frac{l}{\\mu_0 \\mu_r A}$:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "R_tot = 76.6 kAt/Wb\n" ] } ], "source": [ "l = 4 * (0.075 + 0.5 + 0.075) # [m] core length on all 4 sides.\n", "A = 0.15**2 # [m²] \n", "R_tot = l / (mu * A)\n", "print('R_tot = {:.1f} kAt/Wb'.format(R_tot/1000))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and the flux in the core is $\\phi = \\frac{\\mathcal{F}_\\text{TOT}}{\\mathcal{R}_\\text{TOT}}$:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "phi = 6.525 mWb\n" ] } ], "source": [ "phi = F_tot / R_tot\n", "print('phi = {:.3f} mWb'.format(phi*1000))" ] } ], "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 }

unlicense

triskadecaepyon/DF_RoleMatrix

Development_Notebooks/Framework_initial_attempt.ipynb

1

11044

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Attempt at framework for heterogenous systems" ] }, { "cell_type": "code", "execution_count": 342, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class DistCompUnit:\n", " \n", " # Consider importing this calculation\n", " # Note the global value must be more than 0 for the mul. masks to work\n", " __GLOBAL_ROLES_LIST__ = []\n", " __PROCESSING_POWER__ = 0\n", " __MEMORY_CAPABILITIES__ = 0\n", " __GPU_POWER__ = 0\n", " __NETWORK_SPEED__ = 0\n", " __STORAGE__CAP = 0\n", " __FLEX__ = 0\n", " __GRADE_VECTOR__ = []\n", " __ROLE__ = 0\n", " \n", " def __init__(self, grade_vector):\n", " #print('Created Container for Work unit')\n", " self.__GRADE_VECTOR__ = grade_vector\n", " self.__PROCESSING_POWER__ = self.__GRADE_VECTOR__[0]\n", " self.__MEMORY_CAPABILITIES__ = self.__GRADE_VECTOR__[1]\n", " self.__GPU_POWER__ = self.__GRADE_VECTOR__[2]\n", " self.__NETWORK_SPEED__ = self.__GRADE_VECTOR__[3]\n", " self.__STORAGE__CAP = self.__GRADE_VECTOR__[4]\n", " # TODO: check for vector size\n", " self.__generate_flexibility__()\n", " \n", " def __generate_flexibility__(self):\n", " \"\"\"\n", " A very basic variant of flexibility grading (or the null method). \n", " Creates a grade on how flexible the system is to all types of task,\n", " such that priority is pivoted by flexibility ratings. \n", " \"\"\"\n", " temp_grade = 0\n", " #print(self.__GRADE_VECTOR__[0])\n", " if self.__PROCESSING_POWER__ > 0:\n", " temp_grade += self.__PROCESSING_POWER__ * 3\n", " if self.__MEMORY_CAPABILITIES__ > 1:\n", " temp_grade += self.__MEMORY_CAPABILITIES__ * 2\n", " if self.__GPU_POWER__ > 1:\n", " temp_grade += self.__GPU_POWER__ * 2\n", " if self.__NETWORK_SPEED__ > 1:\n", " temp_grade += self.__NETWORK_SPEED__\n", " if self.__STORAGE__CAP > 1:\n", " temp_grade += self.__STORAGE__CAP \n", " \n", " self.__FLEX__ = temp_grade\n", " #print(temp_grade)\n", " \n", " def get_scalar_grade(self, role_grade_vector):\n", " \n", " #TODO: Merge with __generate_flexibility__\n", " temp_grade = 0\n", " #print(self.__GRADE_VECTOR__[0])\n", " if role_grade_vector[0] > 0:\n", " temp_grade += role_grade_vector[0] * 3\n", " if role_grade_vector[1] > 1:\n", " temp_grade += role_grade_vector[1] * 2\n", " if role_grade_vector[2] > 1:\n", " temp_grade += role_grade_vector[2] * 2\n", " if role_grade_vector[3] > 1:\n", " temp_grade += role_grade_vector[3]\n", " if role_grade_vector[4] > 1:\n", " temp_grade += role_grade_vector[4]\n", " \n", " return temp_grade\n", " \n", " def get_flex(self):\n", " return self.__FLEX__\n", " \n", " def compare_role(self, role_grade_vector):\n", " \"\"\"\n", " Used to compare roles from a 5 element list of values. \n", " Returns a binary list if the role is satisfied or not. \n", " \"\"\"\n", " # Return a binary true if it can handle the role\n", " role_satisfy = [0, 0, 0, 0, 0]\n", " if self.__PROCESSING_POWER__ > role_grade_vector[0]:\n", " role_satisfy[0] = 1\n", " if self.__MEMORY_CAPABILITIES__ > role_grade_vector[1]:\n", " role_satisfy[1] = 1\n", " if self.__GPU_POWER__ > role_grade_vector[2]:\n", " role_satisfy[2] = 1\n", " if self.__NETWORK_SPEED__ > role_grade_vector[3]:\n", " role_satisfy[3] = 1\n", " if self.__STORAGE__CAP > role_grade_vector[4]:\n", " role_satisfy[4] = 1\n", " return role_satisfy\n", " \n", " def choose_role(self, role_grade_vector, available_roles):\n", " \"\"\"\n", " Choses the best role from the vector and available roles\n", " \"\"\"\n", " role_determine = None\n", " # determine the roles you can satisfy\n", " role_satisfy = self.compare_role(role_grade_vector)\n", " # print(role_satisfy)\n", " # find the available roles left, and decide and grade from there.\n", " relevant_roles = [i*j for i, j in zip(role_satisfy, available_roles)]\n", " print('relevant roles: ', relevant_roles)\n", " scalar_grade_vector = [i*j for i, j in zip(relevant_roles, role_grade_vector)]\n", " print('grade vectors of relevant roles: ', scalar_grade_vector, max(scalar_grade_vector))\n", " if max(scalar_grade_vector) == 0:\n", " return role_determine\n", " choose_list = [i for i,x in enumerate(scalar_grade_vector) if x == max(scalar_grade_vector)]\n", " print('role (positions) to choose from: ', choose_list)\n", " if len(choose_list) > 0:\n", " print('chosen role', choose_list[0])\n", " role_determine = choose_list[0]\n", " \n", " return role_determine\n", " \n", " def assign_role(self, global_roles = None, current_available_roles = None):\n", " #TODO: Role list\n", " # Choose vector, assign, update vector\n", " if global_roles is None:\n", " if self.__GLOBAL_ROLES_LIST__ == []:\n", " print('empty, no assignment')\n", " else:\n", " # TODO: Need to make better behavior for null selection - perhaps\n", " # create an 'available array?'\n", " selected_role = self.choose_role(self.__GLOBAL_ROLES_LIST__, [1,1,1,1,1])\n", " if selected_role is not None:\n", " self.__ROLE__ = selected_role\n", " else:\n", " print(\"no relevant role\")\n", " else:\n", " if min(global_roles) == 0:\n", " print('incorrect format')\n", " else:\n", " self.__GLOBAL_ROLES_LIST__ = global_roles\n", " if current_available_roles is not None:\n", " selected_role = self.choose_role(self.__GLOBAL_ROLES_LIST__, current_available_roles)\n", " if selected_role is not None:\n", " self.__ROLE__ = selected_role\n", " else:\n", " print(\"no relevant role\")\n", " else:\n", " selected_role = self.choose_role(self.__GLOBAL_ROLES_LIST__, [1,1,1,1,1])\n", " if selected_role is not None:\n", " self.__ROLE__ = selected_role\n", " else:\n", " print(\"no relevant role\")\n" ] }, { "cell_type": "code", "execution_count": 343, "metadata": { "collapsed": false }, "outputs": [], "source": [ "grade_vector = [1, 1, 5, 2, 5]\n", "testContainer = DistCompUnit(grade_vector)" ] }, { "cell_type": "code", "execution_count": 344, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 344, "metadata": {}, "output_type": "execute_result" } ], "source": [ "testContainer.get_flex()" ] }, { "cell_type": "code", "execution_count": 345, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[0, 0, 1, 0, 0]" ] }, "execution_count": 345, "metadata": {}, "output_type": "execute_result" } ], "source": [ "testContainer.compare_role([1,1,4,6,7])" ] }, { "cell_type": "code", "execution_count": 346, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "relevant roles: [0, 0, 1, 1, 0]\n", "grade vectors of relevant roles: [0, 0, 1, 1, 0] 1\n", "role (positions) to choose from: [2, 3]\n", "chosen role 2\n" ] }, { "data": { "text/plain": [ "2" ] }, "execution_count": 346, "metadata": {}, "output_type": "execute_result" } ], "source": [ "testContainer.choose_role([1,1,1,1,7], [1,1,1,1,0])" ] }, { "cell_type": "code", "execution_count": 347, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "relevant roles: [0, 0, 1, 1, 1]\n", "grade vectors of relevant roles: [0, 0, 1, 1, 1] 1\n", "role (positions) to choose from: [2, 3, 4]\n", "chosen role 2\n" ] } ], "source": [ "testContainer.assign_role(global_roles=[1,1,1,1,1])" ] }, { "cell_type": "code", "execution_count": 358, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "relevant roles: [0, 0, 0, 1, 1]\n", "grade vectors of relevant roles: [0, 0, 0, 1, 1] 1\n", "role (positions) to choose from: [3, 4]\n", "chosen role 3\n" ] } ], "source": [ "testContainer.assign_role(global_roles=[1,1,6,1,1], current_available_roles=[1,1,1,1,1])" ] }, { "cell_type": "code", "execution_count": 359, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "relevant roles: [0, 0, 0, 1, 1]\n", "grade vectors of relevant roles: [0, 0, 0, 1, 1] 1\n", "role (positions) to choose from: [3, 4]\n", "chosen role 3\n" ] } ], "source": [ "testContainer.assign_role()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

leewujung/ooi_sonar

notebooks/before201709/Test read solar radiation data.ipynb

1

6027

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import os\n", "import matplotlib.pylab as plt" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using matplotlib backend: MacOSX\n" ] } ], "source": [ "%matplotlib" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "path = '/Volumes/wjlee_apl_3/ooi_eao_buoy/'\n", "file = 'eao_buoy_solar_radiation_20170831download.txt'" ] }, { "cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [], "source": [ "f = open(os.path.join(path,file), 'rU')" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [], "source": [ "header1 = f.readline().split()\n", "header2 = f.readline().split()\n", "data_block = f.readlines()" ] }, { "cell_type": "code", "execution_count": 138, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['#YY', 'MM', 'DD', 'hh', 'mm', 'SRAD1', 'SWRAD', 'LWRAD']" ] }, "execution_count": 138, "metadata": {}, "output_type": "execute_result" } ], "source": [ "header1" ] }, { "cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['#yr', 'mo', 'dy', 'hr', 'mn', 'w/m2', 'w/m2', 'w/m2']" ] }, "execution_count": 139, "metadata": {}, "output_type": "execute_result" } ], "source": [ "header2" ] }, { "cell_type": "code", "execution_count": 140, "metadata": {}, "outputs": [], "source": [ "data = {}\n", "for col_name in header1:\n", " data[col_name] = np.ma.zeros(len(data_block), 'f', fill_value = -999.999)" ] }, { "cell_type": "code", "execution_count": 141, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'#YY': masked_array(data = [ 0. 0. 0. ..., 0. 0. 0.],\n", " mask = False,\n", " fill_value = -999.999),\n", " 'DD': masked_array(data = [ 0. 0. 0. ..., 0. 0. 0.],\n", " mask = False,\n", " fill_value = -999.999),\n", " 'LWRAD': masked_array(data = [ 0. 0. 0. ..., 0. 0. 0.],\n", " mask = False,\n", " fill_value = -999.999),\n", " 'MM': masked_array(data = [ 0. 0. 0. ..., 0. 0. 0.],\n", " mask = False,\n", " fill_value = -999.999),\n", " 'SRAD1': masked_array(data = [ 0. 0. 0. ..., 0. 0. 0.],\n", " mask = False,\n", " fill_value = -999.999),\n", " 'SWRAD': masked_array(data = [ 0. 0. 0. ..., 0. 0. 0.],\n", " mask = False,\n", " fill_value = -999.999),\n", " 'hh': masked_array(data = [ 0. 0. 0. ..., 0. 0. 0.],\n", " mask = False,\n", " fill_value = -999.999),\n", " 'mm': masked_array(data = [ 0. 0. 0. ..., 0. 0. 0.],\n", " mask = False,\n", " fill_value = -999.999)}" ] }, "execution_count": 141, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 172, "metadata": {}, "outputs": [], "source": [ "for (line_count, line) in enumerate(data_block):\n", " items = line.split()\n", " \n", " for (col_count, col_name) in enumerate(col_names1):\n", " if col_count!=6:\n", " value = items[col_count]\n", " if value == \"MM\":\n", " value = np.ma.masked\n", " else:\n", " value = float(value)\n", " data[col_name][line_count] = value" ] }, { "cell_type": "code", "execution_count": 174, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1139f6550>]" ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.plot(data['SRAD1'])" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [], "source": [ "f.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import read_srad" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data_unpack = read_srad.read_srad_file(os.path.join(path,file))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1127e4e10>]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.plot(data_unpack['SRAD1'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:py27]", "language": "python", "name": "conda-env-py27-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

TomTranter/OpenPNM

examples/tutorials/Using Workspace and Projects.ipynb

1

10467

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Workspace and Projects\n", "\n", "OpenPNM V2.0 implemented new ways to manage and control objects, specifically a *Workspace* and *Projects*. The *Workspace* object is equivalent to a web browser window, while a *Project* object is like tabs inside the browser. Each *Project* is an isolated OpenPNM simulation with a single *Network* object and all associated objects. All *Projects* are stored in the same *Workspace*. There can be only 1 *Workspace* open at a given time, so all new projects are registered in the same *Workspace*. *Projects* and *Workspaces* can be saved and loaded.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import openpnm as op" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Usage of Projects and Workspace\n", "\n", "Initialize the *Workspace* and save in a variable called ``ws``, and print it to verify that it is currently empty:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", "OpenPNM Version 2.3.0 Workspace\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n" ] } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "ws = op.Workspace()\n", "print(ws)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, create a new *Project* and print:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proj = ws.new_project()\n", "proj" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The project is an empty list since there have been no objects created yet. \n", "\n", "Now create a new network object with passing in ``proj`` into the initialization:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "pn = op.network.Cubic(shape=[4, 4, 4], project=proj)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now printing the *Project* via `print(proj)` will include the newly created network:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " Object Name Object ID \n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " net_01 <openpnm.network.Cubic object at 0x7f1fa56f2d70> \n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is not necessary to create a project *before* creating a network. Since a project can only have *one* network, then a new project is created each time a network is created if not specified as we did above:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "pn2 = op.network.Cubic(shape=[4, 4, 4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The project that each object belongs to can be found since each object has a ``project`` attribute. Run `print(pn2.project)`:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " Object Name Object ID \n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " net_01 <openpnm.network.Cubic object at 0x7f1fa5700830> \n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, you can view all active projects by printing the workspace via `print(ws)`:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", "OpenPNM Version 2.2.0 Workspace\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " sim_02\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " Object Name Object ID \n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " net_01 <openpnm.network.Cubic object at 0x7f1fa5700830> \n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A project can be purged from the workspace via `ws.close_project(proj)`. Let's print workspace again, `print(ws)`:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", "OpenPNM Version 2.2.0 Workspace\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " sim_02\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " Object Name Object ID \n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", " net_01 <openpnm.network.Cubic object at 0x7f1fa5700830> \n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Design of Workspace and Project Classes\n", "\n", "It is worth describing the design of these classes to help explain how they work. The workspace is a ``dict`` subclass while the project is a ``list`` subclass. Each subclass has numerous methods added to aid in the management of the objects. \n", "\n", "In the Workspace ``dict`` each project object is stored by name in each 'key: value' pair. Printing the workspace gives a nicely formatted output as shown above, but the basic command-line representation gives its true structure via running `ws` in the command-line:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "{'sim_02': [<openpnm.network.Cubic object at 0x7f1fa5700830>]}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Where the only project in the workspace is ``'sim_02'``, and it is a list that contains only a single Cubic network object." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

ocefpaf/system-test

Theme_2_Extreme_Events/Comprehensive/test_multiple_endpoints_variables_locations.ipynb

2

752407

{ "metadata": { "name": "", "signature": "sha256:8df82d88724546f08b3fcae651df9a2421e119042c49bbcdd1b2fd2b772cceb4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "># IOOS System Test: [Extreme Events Theme:](https://github.com/ioos/system-test/wiki/Development-of-Test-Themes#theme-2-extreme-events) Inundation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### This is a single \"spin-off notebook\" for the basic oceanography variables (wind, waves, currents, and water level) to test all CSW end points for multiple geographies. \n", "\n", "### Questions\n", "* Is data available for the basic oceanography variables in the CSW endpoints for multiple locations?\n", "* Let's take it one step further. Is the data recent (< 1 month)?\n", "\n", "####Methodology:\n", "\n", "* Define temporal and spatial bounds of interest\n", "* Show bounding boxes being tested on a map\n", "* Define standard names of variables of interest to search for in data sets\n", "* Search for available service endpoints in the CSW catalogs meeting the search criteria for each variable\n", "* Plot the results in a horizontal bar graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### import required libraries" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "from owslib.csw import CatalogueServiceWeb\n", "from owslib import fes\n", "from owslib.ows import ExceptionReport\n", "\n", "import folium\n", "import pandas as pd\n", "import itertools\n", "import datetime as dt\n", "from utilities import (fes_date_filter, service_urls, get_coordinates, inline_map, css_styles, \n", " insert_progress_bar, update_progress_bar)\n", "css_styles()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", " <style>\n", " .info {\n", " background-color: #fcf8e3; border-color: #faebcc; border-left: 5px solid #8a6d3b; padding: 0.5em; color: #8a6d3b;\n", " }\n", " .success {\n", " background-color: #d9edf7; border-color: #bce8f1; border-left: 5px solid #31708f; padding: 0.5em; color: #31708f;\n", " }\n", " .error {\n", " background-color: #f2dede; border-color: #ebccd1; border-left: 5px solid #a94442; padding: 0.5em; color: #a94442;\n", " }\n", " .warning {\n", " background-color: #fcf8e3; border-color: #faebcc; border-left: 5px solid #8a6d3b; padding: 0.5em; color: #8a6d3b;\n", " }\n", " </style>\n", " " ], "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "<IPython.core.display.HTML at 0x105d20350>" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define spatial bounds of interest" ] }, { "cell_type": "code", "collapsed": false, "input": [ "bounding_box_type = \"box\" \n", "\n", "# Bounding Box [lon_min, lat_min, lon_max, lat_max]\n", "locations = {'Hawaii': [-160.0, 18.0, -154., 23.0],\n", " 'Caribbean': [-75, 12, -55, 26],\n", " 'East Coast': [-77, 30, -70, 40],\n", " 'North West': [-130, 38, -121, 50],\n", " 'Gulf of Mexico': [-94, 26, -84, 32],\n", " 'Arctic': [-179, 63, 179, 80],\n", " 'North East': [-74, 40, -67, 46]}" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the bounding boxes" ] }, { "cell_type": "code", "collapsed": false, "input": [ "lat_center = 45\n", "lon_center = -90\n", "m = folium.Map(location=[lat_center, lon_center], zoom_start=2)\n", "\n", "# Loop through bounding boxes\n", "for location, bounding_box in locations.iteritems():\n", " # Create popup string for the bounding box\n", " popup_string = location\n", " m.line(get_coordinates(bounding_box, bounding_box_type), line_color='#FF0000', line_weight=5)\n", "\n", "inline_map(m)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<iframe srcdoc=\"<!DOCTYPE html>\n", "<head>\n", " <meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; />\n", " <link rel=&quot;stylesheet&quot; href=&quot;http://cdn.leafletjs.com/leaflet-0.7.2/leaflet.css&quot; />\n", " <script src=&quot;http://cdn.leafletjs.com/leaflet-0.7.2/leaflet.js&quot;></script>\n", "\n", " <script src=&quot;https://ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js&quot;></script>\n", "\n", " <link rel=&quot;stylesheet&quot; href=&quot;//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;>\n", " <link rel=&quot;stylesheet&quot; href=&quot;//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;>\n", " <script src=&quot;//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js&quot;></script>\n", "\n", " <link rel=&quot;stylesheet&quot; href=&quot;//cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.0/leaflet.awesome-markers.css&quot;>\n", " <script src=&quot;//cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.0/leaflet.awesome-markers.js&quot;></script>\n", "\n", "\n", " <link rel=&quot;stylesheet&quot; href=&quot;//cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/0.4.0/MarkerCluster.Default.css&quot;>\n", " <link rel=&quot;stylesheet&quot; href=&quot;//cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/0.4.0/MarkerCluster.css&quot;>\n", " <script src=&quot;//cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/0.4.0/leaflet.markercluster-src.js&quot;></script>\n", " <script src=&quot;//cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/0.4.0/leaflet.markercluster.js&quot;></script>\n", "\n", " \n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", " <style>\n", "\n", " #map {\n", " position:absolute;\n", " top:0;\n", " bottom:0;\n", " right:0;\n", " left:0;\n", " }\n", "\n", " </style>\n", "</head>\n", "\n", "<body>\n", "\n", " <div class=&quot;folium-map&quot; id=&quot;folium_e710e506f99e46ae83b80067ebfd028e&quot; style=&quot;width: 960px; height: 500px&quot;></div>\n", "\n", " <script>\n", "\n", " \n", "\n", " var map = L.map('folium_e710e506f99e46ae83b80067ebfd028e').setView([45, -90], 2);\n", "\n", " L.tileLayer('http://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {\n", " maxZoom: 18,\n", " attribution: 'Map data (c) <a href=&quot;http://openstreetmap.org&quot;>OpenStreetMap</a> contributors'\n", " }).addTo(map);\n", "\n", " //cluster group\n", " var clusteredmarkers = L.markerClusterGroup();\n", " //section for adding clustered markers\n", " \n", " //add the clustered markers to the group anyway\n", " map.addLayer(clusteredmarkers);\n", "\n", " \n", "\n", " \n", "\n", " \n", " var latLngs = [ [12, -75], [12, -55], [26, -55], [26, -75], [12, -75], ];\n", "var line_1 = L.polyline(latLngs,{\n", "color: '#FF0000',\n", "weight: 5,\n", "\n", "});\n", " map.addLayer(line_1);\n", " \n", " var latLngs = [ [63, -179], [63, 179], [80, 179], [80, -179], [63, -179], ];\n", "var line_2 = L.polyline(latLngs,{\n", "color: '#FF0000',\n", "weight: 5,\n", "\n", "});\n", " map.addLayer(line_2);\n", " \n", " var latLngs = [ [18.0, -160.0], [18.0, -154.0], [23.0, -154.0], [23.0, -160.0], [18.0, -160.0], ];\n", "var line_3 = L.polyline(latLngs,{\n", "color: '#FF0000',\n", "weight: 5,\n", "\n", "});\n", " map.addLayer(line_3);\n", " \n", " var latLngs = [ [30, -77], [30, -70], [40, -70], [40, -77], [30, -77], ];\n", "var line_4 = L.polyline(latLngs,{\n", "color: '#FF0000',\n", "weight: 5,\n", "\n", "});\n", " map.addLayer(line_4);\n", " \n", " var latLngs = [ [38, -130], [38, -121], [50, -121], [50, -130], [38, -130], ];\n", "var line_5 = L.polyline(latLngs,{\n", "color: '#FF0000',\n", "weight: 5,\n", "\n", "});\n", " map.addLayer(line_5);\n", " \n", " var latLngs = [ [26, -94], [26, -84], [32, -84], [32, -94], [26, -94], ];\n", "var line_6 = L.polyline(latLngs,{\n", "color: '#FF0000',\n", "weight: 5,\n", "\n", "});\n", " map.addLayer(line_6);\n", " \n", " var latLngs = [ [40, -74], [40, -67], [46, -67], [46, -74], [40, -74], ];\n", "var line_7 = L.polyline(latLngs,{\n", "color: '#FF0000',\n", "weight: 5,\n", "\n", "});\n", " map.addLayer(line_7);\n", " \n", "\n", " \n", "\n", " \n", "\n", " </script>\n", "\n", "</body>\" style=\"width: 100%; height: 500px; border: none\"></iframe>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "<IPython.core.display.HTML at 0x1097b8090>" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define standard names of variable of interest to search for in data sets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"warning\"><strong></strong> - We need to specify all the names we know for each variable, names that will get used in the CSW search, and also to find data in the datasets that are returned. This is ugly and fragile. There hopefully will be a better way in the future...</div>" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# put the names in a dict for ease of access \n", "names_dict = {}\n", "names_dict[\"waves\"] = {\"names\": ['sea_surface_wave_significant_height',\n", " 'significant_wave_height',\n", " 'significant_height_of_wave',\n", " 'sea_surface_wave_significant_height(m)',\n", " 'sea_surface_wave_significant_height (m)',\n", " 'water_surface_height'], \n", " \"sos_name\": [\"waves\"]} \n", "\n", "names_dict['winds'] = {\"names\": ['eastward_wind', 'u-component_of_wind', \n", " 'u-component_of_wind_height_above_ground', \n", " 'ugrd10m', \n", " 'wind'], \n", " \"v_names\": ['northward_wind', \n", " 'v-component_of_wind', \n", " 'v-component_of_wind_height_above_ground', \n", " 'vgrd10m', \n", " 'wind'],\n", " \"sos_name\": ['winds']} \n", "\n", "names_dict['currents'] = {\"names\": ['eastward_sea_water_velocity_assuming_no_tide',\n", " 'surface_eastward_sea_water_velocity',\n", " '*surface_eastward_sea_water_velocity*', \n", " 'eastward_sea_water_velocity'], \n", " \"v_names\": ['northward_sea_water_velocity_assuming_no_tide',\n", " 'surface_northward_sea_water_velocity',\n", " '*surface_northward_sea_water_velocity*', \n", " 'northward_sea_water_velocity'],\n", " \"sos_name\": ['currents']}\n", "\n", "names_dict['water_level'] = {\"names\": ['water_surface_height_above_reference_datum',\n", " 'sea_surface_height_above_geoid',\n", " 'sea_surface_elevation',\n", " 'sea_surface_height_above_reference_ellipsoid',\n", " 'sea_surface_height_above_sea_level',\n", " 'sea_surface_height','water level']}" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define the csw endpoints we know about" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"info\">This cell lists catalog endpoints. The list is updated by the IOOS Program Office here: https://github.com/ioos/system-test/wiki/Service-Registries-and-Data-Catalogs </div>" ] }, { "cell_type": "code", "collapsed": false, "input": [ "endpoints = ['http://www.nodc.noaa.gov/geoportal/csw',\n", " 'http://data.nodc.noaa.gov/geoportal/csw',\n", " 'http://www.ngdc.noaa.gov/geoportal/csw',\n", " 'http://catalog.data.gov/csw-all',\n", " 'https://data.noaa.gov/csw',\n", " 'http://geoport.whoi.edu/geoportal/csw',\n", " 'https://edg.epa.gov/metadata/csw',\n", " 'http://cmgds.marine.usgs.gov/geonetwork/srv/en/csw',\n", " 'http://cida.usgs.gov/gdp/geonetwork/srv/en/csw',\n", " 'http://geodiscover.cgdi.ca/wes/serviceManagerCSW/csw',\n", " 'http://cwic.csiss.gmu.edu/cwicv1/discovery',\n", " 'https://www.sciencebase.gov/catalog/item/519bee13e4b0e4e151f0232c/csw'\n", " ]\n", "# 'http://pacioos.org/search/'\n", "# 'http://geoport.whoi.edu/gi-cat/services/cswiso',\n", "\n", "# Set the maximum number of records the CSW will return\n", "max_records = 2000" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Is data available for the basic oceanography variables in the CSW endpoints for multiple locations?\n", "\n", "#### Check the CSW endpoints for each variable and location" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"warning\"><strong>This next cell takes a long time to process!</strong> <br>Go grab a coffee</div>" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Add a waitbar to monitor status\n", "divid = insert_progress_bar(title='Searching catalogs. Please wait...', color='red')\n", "\n", "# Save all of the results in a list of Dataframes\n", "results = {}\n", "all_data = []\n", "\n", "count = 0\n", "# Loop through the csw endpoints\n", "for endpoint in endpoints:\n", " print '\\n' + endpoint\n", " \n", " csw = CatalogueServiceWeb(endpoint, timeout=60)\n", " # loop through the variables\n", " for var_name in names_dict:\n", "# print '\\n' + var_name.upper()\n", " num_recs = []\n", " for location, bounding_box in locations.iteritems():\n", "# print location\n", " \n", " bbox = fes.BBox(bounding_box)\n", " #use the search name to create search filter\n", " or_filt = fes.Or([fes.PropertyIsLike(propertyname='apiso:AnyText',\n", " literal='*%s*' % val,\n", " escapeChar='\\\\',\n", " wildCard='*',\n", " singleChar='?') for val in names_dict[var_name][\"names\"]])\n", " filter_list = [fes.And([ bbox, or_filt])]\n", " # try request using multiple filters \"and\" syntax: [[filter1,filter2]]\n", " try:\n", " csw.getrecords2(constraints=filter_list, maxrecords=max_records, resulttype='hits')\n", " except Exception as e:\n", " print '\\t' + 'ERROR - ' + str(e)\n", " num_recs.append(np.NaN)\n", " else:\n", "# print csw.results['matches']\n", " num_recs.append(csw.results['matches'])\n", " \n", " results[var_name] = np.array(num_recs)\n", "\n", " # Save the results\n", " prod = list(itertools.product([endpoint], locations.keys()))\n", " mi = pd.MultiIndex.from_tuples(prod, names=['endpoint', 'location'])\n", " all_data.append(pd.DataFrame(results, index=mi))\n", " \n", " # Update progress bar\n", " count += 1\n", " percent_complete = (float(count)/float(len(endpoints)))*100\n", " update_progress_bar(divid, percent_complete)\n", "\n", "# all_data_concat = pd.concat(all_data)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Searching catalogs. Please wait...\n" ] }, { "html": [ "\n", " <div style=\"border: 1px solid black; width:500px\">\n", " <div id=\"2fd74379-4d67-4676-ad58-dd9ff0078ed6\" style=\"background-color:red; width:0%\">&nbsp;</div>\n", " </div>\n", " " ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x1097d7590>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://www.nodc.noaa.gov/geoportal/csw\n" ] }, { "javascript": [ "$('div#2fd74379-4d67-4676-ad58-dd9ff0078ed6').width('8%')" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Javascript at 0x1097ef490>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://data.nodc.noaa.gov/geoportal/csw\n" ] }, { "javascript": [ "$('div#2fd74379-4d67-4676-ad58-dd9ff0078ed6').width('16%')" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Javascript at 0x1097d7610>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://www.ngdc.noaa.gov/geoportal/csw\n" ] }, { "javascript": [ "$('div#2fd74379-4d67-4676-ad58-dd9ff0078ed6').width('25%')" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Javascript at 0x1097e0d90>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://catalog.data.gov/csw-all\n" ] }, { "javascript": [ "$('div#2fd74379-4d67-4676-ad58-dd9ff0078ed6').width('33%')" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Javascript at 0x1097effd0>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "https://data.noaa.gov/csw\n" ] }, { "javascript": [ "$('div#2fd74379-4d67-4676-ad58-dd9ff0078ed6').width('41%')" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Javascript at 0x1097e0d10>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://geoport.whoi.edu/geoportal/csw\n" ] }, { "javascript": [ "$('div#2fd74379-4d67-4676-ad58-dd9ff0078ed6').width('50%')" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Javascript at 0x1097ef910>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "https://edg.epa.gov/metadata/csw\n" ] }, { "javascript": [ "$('div#2fd74379-4d67-4676-ad58-dd9ff0078ed6').width('58%')" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Javascript at 0x1097e0bd0>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://cmgds.marine.usgs.gov/geonetwork/srv/en/csw\n" ] }, { "javascript": [ "$('div#2fd74379-4d67-4676-ad58-dd9ff0078ed6').width('66%')" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Javascript at 0x109807b50>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://cida.usgs.gov/gdp/geonetwork/srv/en/csw\n" ] }, { "javascript": [ "$('div#2fd74379-4d67-4676-ad58-dd9ff0078ed6').width('75%')" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Javascript at 0x1097e1210>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://geodiscover.cgdi.ca/wes/serviceManagerCSW/csw\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "javascript": [ "$('div#2fd74379-4d67-4676-ad58-dd9ff0078ed6').width('83%')" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Javascript at 0x106312450>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://cwic.csiss.gmu.edu/cwicv1/discovery\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\tERROR - 'REQUEST_LIMITATION: TOO_MANY_RECORDS - The request asked for more records than can be handled. The maximum number designated in GetRecords request should be less than 200, please decrease the returned recorder number in request.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "javascript": [ "$('div#2fd74379-4d67-4676-ad58-dd9ff0078ed6').width('91%')" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Javascript at 0x1097efe50>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "https://www.sciencebase.gov/catalog/item/519bee13e4b0e4e151f0232c/csw\n" ] }, { "javascript": [ "$('div#2fd74379-4d67-4676-ad58-dd9ff0078ed6').width('100%')" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Javascript at 0x109a36390>" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"error\"> Some servers have a maximum amount of records you can retrieve at once. See: https://github.com/ioos/system-test/issues/126</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Let's plot the results in a bar graph" ] }, { "cell_type": "code", "collapsed": false, "input": [ "alldata_concat = pd.concat(all_data)\n", "endpoint_group = alldata_concat.groupby(level=0)\n", "# can uncomment this for a terser, but less well annotated plot\n", "# endpoint_group.plot(kind='barh')\n", "for grp_name, grp in endpoint_group:\n", " fig, ax = plt.subplots()\n", " # eliminate endpoint from index since it will be the graph title\n", " grp.reset_index(0, drop=True).plot(ax=ax, kind=\"barh\", figsize=(10, 8,),\n", " title=grp_name)\n", " ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", " ax.set_xlabel('Number of records')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x109794610>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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kSSoF9iikhzMKkiRJkvKYKCiVrGmNz5jHZ8zjM+bxGXOpcEwUJEmSJOWxvqw4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\nJEmSJEmSSjkIfK2x/QngC0ep7RuBtx2ltg7n7cB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5Y6IgSZIkKYf1ZYWh3tIjSZJUDOxRSA9XFCRJkiTlMFFQKlnTGp8xj8+Yx2fM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\nzyVJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiR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"text": [ "<matplotlib.figure.Figure at 0x109a934d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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SipgoKJWsaY3PmMdnzOMz5vEZc6l8TBQkSZIkFbG+rDrYoyBJkmqCPQrp4YyC\nJPVRuVyu0kOQJNUwEwWlkjWt8Rnz+PL5fKWHkDoe5/EZc6l8TBQkSZIkFbG+rDrYoyCp5DKZDH62\nSCo1exTSwxkFSZIkSUVMFJRK1rTGZ8yVBh7n8RlzqXxMFCSpj8pms5UegiSphllfVh3sUZAkSTWh\nux6F/v37L1+5cmV9/BFpffXv379l5cqVgzqvN1GoDiYKkiSpJqyhmdnzmRrV3Xtq6ZFSyZrW+Ix5\nfMY8PmMenzGXysdEQZIkSVIRS4+qg1N1kiSpJlh61PdYeiRJKZPL5So9BElSDTNRUCpZ0xqfMY8v\nn89Xegip43EenzGvboMGDSWTyZTtb9CgoZV+iets1qxZjBw5stLDWKP+lR6AJEmS+qaWlqVA+cqR\nWlriVNG3lVQlJToArFy5kv79+/aptDMKSqWmpqZKDyF1jLnSwOM8PmOunnj++ec59NBD2WKLLRg2\nbBgnnXQSuVyOY445pn2b5uZm6urqWLVqFRCOre9+97uMHz+eTTfdlEWLFlFXV8cvf/lLxowZwwc+\n8AEAbr75ZsaNG8eQIUMYP348jzzySPtzjh49mvPOO4+xY8fS0NDApEmT+Pe//82bb77JgQceyOLF\ni6mvr2fQoEG89NJLPPDAA+yxxx4MHjyY4cOH881vfjNuoDoxUZAkSVKf9d577/GZz3yGbbbZhmef\nfZbFixczadKk1WYHujN9+nQuvfRSWlpaGDVqFAA33HADf/nLX3jssceYP38+xx57LJdccglLlizh\nuOOO45BDDmHFihVAmIGYOXMmt99+O8888wwPP/ww06ZNY5NNNuG2225jxIgRtLS0sHz5coYPH86U\nKVM45ZRTWLZsGYsWLeLwww8va2zWxkRBqWRNa3zGXGngcR6fMdfaPPDAA7z44oucc845DBw4kAED\nBjB+/HjW9g1NmUyGyZMn88EPfpC6ujo22GADAL7zne/Q0NDAhhtuyMUXX8xxxx3HRz7yETKZDF/+\n8pfZcMMNmTdvXvvznHzyyQwfPpwhQ4Zw8MEHs2DBAoAu9z9gwAD+8Y9/8Oqrr7Lxxhuz1157lTAS\nvWeiIEl9VDabrfQQJKninn/+ebbeemvq6np/2ttVs3HhumeffZbzzjuPIUOGtP/985//ZPHixe3b\nDB8+vP32wIEDeeONN7rd32WXXcaTTz7JBz/4Qfbcc09uueWWXo+5lPp2B4bUDWta4zPm8U2bNq3S\nQ0gdj/MX1zSzAAAgAElEQVT4jLnWZuTIkTz33HO899579OvXr339pptuyltvvdW+/NJLLxU9tqvy\npMJ1o0aN4swzz+SMM87o9bi6eu7tt9+eGTNmAHDttdfyxS9+kSVLljBw4MBeP38pOKMgSZKkPmuv\nvfZiq6224vTTT+ett97inXfe4d5772XcuHHMmTOH559/nmXLlnH22WcXPXZt5Ulf/epXueiii3jg\ngQdobW3lzTff5JZbblnjrEGbLbfcktdee43ly5e3r5s+fTqvvPIKAIMHDyaTyazTTEipmCgolaxp\njc+Yx2fM4zPm8RlzrU1dXR033XQTTz31FKNGjWLkyJFcffXVfOITn+CII45g11135SMf+QgHH3xw\n0VX+tS3vvvvuXHLJJXz9619n6NChjBkzhiuuuKLbRum2338A2HHHHTnyyCPZdtttGTp0KC+++CK3\n3347O++8M/X19Zxyyin8/ve/Z8MNNyxhNHonzpfPam38yfPIZs2a5XR1ZMY8PmMenzGPz5jHl5zo\ndnUOWXQ+M2jQ0OS3FMqjvn4Iy5cvKdvzp0V376mJQnUwUZAkSTWhN4mCakN376mlR5LUR+VyuUoP\nQZJUw0wUlErWtMZnzOPL5/OVHkLqeJzHZ8yl8jFRkCRJklTEHoXqYE2fpJLLZDJr/Wo/SeotexT6\nHnsUJEmSJPVYTxKFDwCXAHcAdyd/d5VzUFK5WdManzFXGnicx2fMpfLp34NtZgK/Ai4F3kvWOa8k\nSVUum81WegiSpBrWkx6FvwK7l3sgKWdNnyRJqgn2KPQ969OjcBNwIrAVMLTgT5IkSerWoIZBZDKZ\nsv0NahhU6Ze43qZNm8a+++5b1n00NzdTV1fHqlWrevW4npQeTSaUGn2rYF0rsG2v9iRVkVmzZtHU\n1FTpYaSKMY/PmMdnzOMz5tWtZVkL5Mr4/LmW8j15gVwux9NPP82VV14ZZX/VoieJwuhyD0KSJEnq\nq9577z369etX6WH0Wk9KjwYAU4BrgWuAk4ANyjkoqdy8+hSfMY/PmMdnzOMz5lqbqVOncsghh7Qv\njxkzhsMPP7x9eeTIkSxcuJApU6YwatQoBg8ezB577ME999wDwG233cbZZ5/NVVddRX19PbvtthsA\ny5Yt49hjj2XEiBG8//3v53vf+157ac+0adMYP348p556KsOGDSOfz/d4vI8//jif/OQn2Wyzzdhx\nxx2ZOXMmAPfffz9bbbXVar+Pc9111zF27FgAVq1axY9+9CO23357hg0bxhFHHMHSpUvXMWpBTxKF\nXwEfBv43ub178q8kqYrlcrlKD0GSKq6pqYm5c+cCsHjxYlasWMG8efMAWLRoEW+++SZjx45lzz33\nZOHChSxdupSjjjqKww47jHfffZeJEydyxhlnMGnSJFpaWpg/fz4AkydPZsCAATz99NPMnz+fP/7x\nj1x66aXt+33ggQfYbrvt+Ne//sUZZ5zRo7G++eabfPKTn+RLX/oSr7zyCr///e854YQTePzxx9lr\nr73YZJNNuPPOO9u3nzFjBkcffTQAF154ITfeeCNz5szhxRdfZMiQIZx44onrFbueJAofAbKE3064\nk9CzsOd67VWqML93Oz5jHl9vrmCpNDzO4zPmWpttttmG+vp65s+fz5w5c/jUpz7FiBEjeOKJJ5g9\nezb77bcfAEcffTRDhgyhrq6OU089lX//+9888cQTALS2tq52Jf/ll1/m1ltv5fzzz2fgwIFsvvnm\nfOMb3+D3v/99+zYjRozgxBNPpK6ujo022qhHY7355pvZZpttyGaz1NXVMW7cOA499FCuvvpqAI48\n8kh+97vfAdDS0sKtt97KkUceCcCvf/1r/vu//5sRI0awwQYbcNZZZ3HNNdf0uoG5UE96FFYC2wNP\nJcvbJeskSZKkqtfY2MisWbN46qmnaGxspKGhgdmzZ3PffffR2NgIwLnnnstvfvMbFi9eTCaTYfny\n5bz66qtdPt+zzz7LihUr2GqrrdrXrVq1ilGjRrUvjxw5stfjfPbZZ7n//vsZMmRI+7qVK1fy5S9/\nGQiJwvjx4/nVr37FH/7wB3bffff2/TQ3N/P5z3+eurqOeYD+/fvz8ssv93oc7Y/vwTanEWYTnkmW\nRwP/sc57lKqANa3xGXOlgcd5fMZcPdHY2MiNN95Ic3MzZ555Jg0NDUyfPp158+Zx0kknMXfuXM45\n5xzuuusuPvShDwEwdOjQ9lmE5HcG2o0cOZINN9yQ1157bbUT80KdH9MTo0aNorGxkT/+8Y9d3r/T\nTjux9dZbc+uttzJjxgyOOuqo1R47depU9tlnn6LHNTc393os0LPSozuBHYCTCY3MOxASB0mSJKnq\nNTY2cvfdd/POO+8wYsQIJkyYwG233caSJUvYbbfdaGlpoX///gwbNox3332X73//+yxfvrz98cOH\nD6e5ubk9cdhqq6044IADOPXUU2lpaWHVqlU8/fTTzJkzZ73G+elPf5onn3yS6dOns2LFClasWMFf\n/vIXHn/88fZtjjrqKC644ALmzp3LYYcd1r7++OOP54wzzuC5554D4JVXXuHGG29cr/GsaUbh44Qk\n4QuE301oS4u2T/79w3rtWaogv3c7PmOuNPA4j8+YV7f6wfVl/a2D+sH1PdpuzJgx1NfXt/+w2aBB\ng9huu+3YYostyGQyTJw4kYkTJ7LDDjuwySabcMopp6xWRnTYYYcxffp0NttsM7bddlsefPBBrrji\nCk4//XR22mknWlpa2HbbbTn99NMB2n8QricKt62vr+ePf/wjp556KqeeeiqrVq1i3Lhx/PSnP23f\n/sgjj+Q73/kOBx10EEOHdvwG8pQpU2htbeWAAw5g8eLFbLHFFkyaNKn9G5/WZYZjTY/IA2cB0wiJ\nQmexy4/eAx4uWP4d8JNePkcj8C5wXzf3Hwh8H9gY+Ddh5uRb3WzbW1sDHyWMuzN/8jwy/8cSnzGP\nb/LkyUybNq3Sw0gVj/P4jHl8yQlnV+eQns/UqO7e056kFtsCi3qwrtxagJ6ljd3LJc9zXhf37Qxc\nDxwEPEkoy/oacNF67rNNE/BN4OAu7vM/LEmSVBNMFPqe7t7TnvQoXNPFupnrO6AS+h7wAPAI8OuC\n9ScDjwILgRmEK/rHAacA84EJnZ7nv4D/JiQJAKvoSBJGE2YXFgJ/Atra2A8G5gEPAXcAWyTrG5N9\nzAf+CmwK/AjYN1k3ZZ1frSRJkmrK8ccfT319fdHfCSecUOmhrdGaZhQ+COwEnEMov8kQSpAGEb4J\n6UNlH93qVhKSgTY/JCQsQ4C2n527ArgauBl4gXCCv4Iw5uWEUqoW4KcU+yvhNyIe6eK+m5LnvZJQ\ncnUI8HmgAXg92eY/gR0JsboROJtQ4tRWxjQhuc8ZhSrgVHV8xjw+Yx6fMY/PmMfnjELf0917uqZm\n5h0IJ7WDWf3ktgX4aikH10NvA7t1sf5jhMRlY2Ao8DdCovAwYSbh+uSvTe87OWBv4HPJ7el09EaM\nJCQQw4EBdJRj/Rk4H/gtoen7hXXcryRJklQRa0oUbkj+PgrcG2c4vbYR8L/A7oST8bOAgcl9nwb2\nIyQ5ZwK7rOW5HgX2oOsZBej6RP9C4FxCYtJI6IEA+HGy7tOEpOFTa3shkydPZvTo0QA0NDQwbty4\n9iskbb866XJpl9tUy3hcdrnUy01NTVU1njQst62rlvGkZblNtYynry233V7X7+JX7erJVe6BwLGE\nMqSBdHwD0lfKNahudNXM3AA8Tigx6k/oF7ga+AGhJ6EZ2CD5dyfC6xhExwl9oV0IV/8PAv5B6N/4\nKqHv4QZCmdN0QnnSwYSvjX2IUHL0EDA1Gcf+hF+vfjp53pmEkqV/EkqemrrYt1N1kkoul8uRy+Uq\nPQxJfYylR33P+jQzXwlsCUwEZhHKbd4o4dh6aiAdDcLzCT0KrwOXEMqNbgPuT7btRxj3w4ST+J8B\nywi9Bp9PHj++0/M/AnyD8PWljyXL2yT3nUToTVgIHE1HM3KOkAg8CLxCRxI1JXn8QsLXsd6ajOU9\nYAE2M1dc56tQKj9jHl8+n6/0EFLH4zw+Yy6Vz5pKj9psD3wR+CxwOaHu/55yDqob3Y31e8lfZ/t2\nse4fwNg17OOW5K+z5wg/QNfZjclfZyd38/xdPYckSZJUdXpSevQAsCcwFzgBeIlw5X7bMo4rbZyq\nk1RymUwGP1sklVpvSo+GDhrE0pby/TLzkPp6lixfXrbnT4t1+dajNpcQvk3ou4Sr55vS9RV8SZIk\nqd3SlhbKebkiU8YkRD3rUbgEWALMJtTsb07pfq1YqghrWuMz5koDj/P4jLnWZurUqRxyyCHty2PG\njOHwww9vXx45ciQLFy5kypQpjBo1isGDB7PHHntwzz2h0n7x4sVsvPHGLF26tP0x8+fPZ/PNN+e9\n994D4De/+Q077bQTQ4cOZeLEiTz33HPt255yyilsueWWDB48mF133ZVHH3203C+5ZHqSKPyQ8KNm\nbYYQfsFYklTFstlspYcgSRXX1NTE3LlzgXDSv2LFCubNmwfAokWLePPNNxk7dix77rknCxcuZOnS\npRx11FEcdthhvPvuu4wYMYJ99tmHa6+9tv05Z8yYwWGHHUa/fv244YYbOPvss7nuuut49dVX2Xff\nfTnyyCMBuP3225k7dy7/+Mc/WLZsGTNnzmSzzTaLH4R11JMehQXAuE7r5tP1j59p3dijIEmSakJv\nehQymUx5S4/CTte63ahRo7jhhht44oknuPvuu1m4cCGXX3459957LzfccAPXX3990WOGDh3K7Nmz\n2WWXXbjsssuYMWMGd955J62trWy99dbMmDGDCRMmcOCBB3LYYYfxla+EXw5YtWoV9fX1/P3vf+fp\np5/m+OOP54orruAjH/kIdXU9uUYf3/p8PWod4YfN2gwk/AqxJEmSVPUaGxuZNWsWc+fOpbGxkcbG\nRmbPns2cOXNobGwE4Nxzz2WnnXaioaGBIUOGsGzZMl599VUADj30UO677z5eeukl5syZQ11dHRMm\nTADg2WefZcqUKQwZMoQhQ4a0zxgsXryY/fffn69//euceOKJbLnllhx33HG01FBfRU8Shd8CdxJ+\nrOw/gT8BV5RzUFK5WdManzGPz5jHZ8zjM+bqicbGRu6++27mzp1LU1NTe+Iwe/ZsGhsbmTt3Luec\ncw4zZ87k9ddfZ+nSpQwePLh9tmLIkCEccMABXHXVVcyYMaO9tAjCbMXFF1/M0qVL2//efPNN9t57\nbwBOOukkHnzwQR577DGefPJJzjnnnIrEYF30JFH4MaEn4YPAjsD3k3WSJElS1WtLFN555x1GjBjB\nhAkTuO2221iyZAm77bYbLS0t9O/fn2HDhvHuu+/y/e9/n+Wdvnb1qKOO4vLLL+faa6/lqKOOal9/\n/PHH88Mf/pDHHnsMoL0XAeDBBx/k/vvvZ8WKFWy88cZstNFG9OvXL94LX089LZSaT/jWo9nJbamm\nNTU1VXoIqWPM4zPm8Rnz+Iy5emLMmDHU19ez777h93gHDRrEdtttx/jx48lkMkycOJGJEyeyww47\nMHr0aAYOHMioUaNWe45DDjmEp556iq222opddtmlff3nPvc5vv3tbzNp0iQGDx7MLrvswu233w7A\n8uXL+drXvsbQoUMZPXo0w4YN47TTTov3wtdTT5qZDwfOISQJAPsBpwEzyzWoFLKZWVLJ5XI5crlc\npYchqY/xB9f6nu7e054kCg8DnwD+lSxvTuhZ2LVUg5OJQmyzZs3yKlRkxjw+f5k5Po/z+Ix5fL1J\nFFQb1udbjzLAKwXLr3X1RJIkSZL6jp6c8J8DjAVmJNsfQZhl+K8yjittzMAllZwzCpLKwRmFvmd9\nSo8ywKHABKAVmAtcV8rByf+wJJWeiYKkcjBR6HvWp/SoFbgWOAU4FZME9QF+73Z8xlxp4HEenzGX\nyqf/Gu57A7r91e1WYFDphyNJKpVsNlvpIUiSaphNydXBqTpJklQTLD3qe9an9EiSJElSypgoKJWs\naY3PmMdnzOMz5vEZc5VKfX09zc3N6/TYpqYmLrvsstIOqAqsqUdBkiRJWmeDhgyh5fXXy/b89Q0N\nLF+6tCTP1bIevyCdyWTaynf6lL73imqTNX2SJKkm9KZHIZPJwN13l28w++9fFV8Dvf/++3PMMcfw\nla98pdJDWSf2KEhSyuRyuUoPQZIqburUqRxyyCHty2PGjOHwww9vXx45ciQLFy6krq6ORYsWATB5\n8mROPPFEPvOZzzBo0CD23nvv9vsA7rjjDnbccUcaGho46aSTaG1tbU9YnnrqKRobG2loaGDzzTdn\n0qRJkV5p6ZkoKJWsaY3PmMeXz+crPYTU8TiPz5hrbZqampg7dy4AixcvZsWKFcybNw+ARYsW8dZb\nb7HrrrsWPe6qq64il8uxdOlStt9+e84880wAXn31Vb7whS/wwx/+kNdee43tttuOP//5z+2lR9/7\n3veYOHEir7/+Oi+88AInn3xypFdaeiYKkiRJ6rO22WYb6uvrmT9/PnPmzOFTn/oUI0aM4IknnmD2\n7Nnsu+++Rf0FmUyGQw89lD322IN+/fpx9NFHs2DBAgD+7//+j5133plDDz2Ufv368Y1vfIPhw4e3\nP3bAgAE0NzfzwgsvMGDAAD760Y9Gfb2lZKKgVGpqaqr0EFLHmCsNPM7jM+bqicbGRmbNmsXcuXNp\nbGyksbGR2bNnM2fOHBobG7t8zJZbbtl+e+DAgbzxxhtAmJV4//vfv9q2I0eObL/9k5/8hNbWVvbc\nc0923nlnpk6dWoZXFIeJgiRJkvq0xsZG7r77bubOnUtTU1N74jB79uxuE4XujBgxgueff759ubW1\ndbXlLbfckosvvpgXXniBX//615xwwgmr9TfUEhMFpZI1rfEZc6WBx3l8xlw90ZYovPPOO4wYMYIJ\nEyZw2223sWTJEnbbbbei7df0TUoHHXQQjz76KNdddx0rV67k5z//OS+99FL7/TNnzuSf//wnAA0N\nDWQyGerqavOUuzZHLUlaq2w2W+khSFJVGDNmDPX19ey7774ADBo0iO22247x48e39ycU9il09bsI\nbcvDhg1j5syZnH766QwbNoynnnqKCRMmtG/34IMPsvfee1NfX89nP/tZfv7znzN69Ogyv8Ly8HcU\nqoO/oyBJkmpCb35HoZZ+cC3NuntPTRSqg4mCJEmqCb1JFFQb/ME1qYA1rfEZ8/iMeXzGPD5jLpWP\niYIkSZKkIpYeVQen6iRJUk2w9KjvsfRIklIml8tVegiSpBpmoqBUsqY1PmMeXz6fr/QQUsfjPD5j\nLpVP/0oPQJIkSbWvf//+LZlMpr7S41Dv9e/fv2XlypVF6+1RqA7W9EkquUwms8ZfF5WkdbGGHgX1\nMZYeSZIkSSpioqBUsqY1PmOuNPA4j8+YS+VjoiBJfVQ2m630ECRJNcz6supgj4IkSaoJ9iikhzMK\nkiRJkoqYKCiVrGmNz5jHZ8zjM+bxGXOpfEwUJEmSJBWxvqw62KMgSZJqgj0K6eGMgiT1UblcrtJD\nkCTVMBMFpZI1rfEZ8/jy+Xylh5A6HufxGXOpfEwUJEmSJBWxvqw62KMgqeQymQx+tkgqNXsU0sMZ\nBUmSJElFTBSUSta0xmfMlQYe5/EZc6l8TBQkqY/KZrOVHoIkqYZZX1Yd7FGQJEk1wR6F9HBGQZIk\nSVIREwWlkjWt8Rnz+Ix5fMY8PmMulU9aE4U3Oi1PBi4s075GADOT27sDPyvTfiRJkqSSSWt9WQtQ\nX7CcBfYATqrMcOxRkCRJtcEehfRI64xCZ4UH+8HAPOAh4A5gi2T9w8CgZNvXgGOS9VcAnwC2BuYA\nf03+9knuHw08ktxuAm4qw/glqUgul6v0ECRJNSyticJAYH7BXx5ou6Q/F9gb+DBwFfBfyfo/AxOA\nDwFPJ7dJtv0z8C/gk4TyoknAz8v9IrTurGmNz5jHl8/nKz2E1PE4j8+YS+XTv9IDqJC3gd0KlttK\njwBGAlcDw4EBwKJk/VxgP+BZ4FfA1wj9B0uT5xsM/AIYC7wH7NCbAU2ePJnRo0cD0NDQwLhx42hq\nagI6PgRdLt3yggULqmo8aVhuUy3jcdnlciwvWLCgqsaThmU/z+N8fs+aNYvm5maULmmtL+vcozCZ\nMBNwEjALOBe4GWgEcsD+wPsJCUQzcCahKflPhMTitGS7jQkzEP2Ad4ANCKVHNwG7AE3ANwnlTYXs\nUZBUcplMBj9bJJWaPQrpUVfpAVShQcDi5PbkgvX/BIYB2wPPAPcA3yL0JbQ97qXk9pcJyYIkSZJU\nk9KaKHS+xNZasC5H+DrTB4FXOm07D3gyuX0PofTonmT5l4QSpgXAB1j9K1hbu7mtCimcTlUcxlxp\n4HEenzGXyietPQqDOi1fnvwB3Jj8deXLBbfvZfX4PUXoT2hzevJvM7BrcntW8idJZZfNZis9BElS\nDbO+rDrYoyBJkmqCPQrpkdbSI0mSJElrYKKgVLKmNT5jHp8xj8+Yx2fMpfIxUZAkSZJUxPqy6mCP\ngiRJqgn2KKSHMwqS1EflcrlKD0GSVMNMFJRK1rTGZ8zjy+fzlR5C6nicx2fMpfIxUZAkSZJUxPqy\n6mCPgqSSy2Qy+NkiqdTsUUgPZxQkSZIkFTFRUCpZ0xqfMVcaeJzHZ8yl8jFRkKQ+KpvNVnoIkqQa\nZn1ZdbBHQZIk1QR7FNLDGQVJkiRJRUwUlErWtMZnzOMz5vEZ8/iMuVQ+JgqSJEmSilhfVh3sUZAk\nSTXBHoX0cEZBkvqoXC5X6SFIkmqYiYJSyZrW+Ix5fPl8vtJDSB2P8/iMuVQ+JgqSJEmSilhfVh3s\nUZBUcplMBj9bJJWaPQrp4YyCJEmSpCImCkola1rjM+ZKA4/z+Iy5VD4mCpLUR2Wz2UoPQZJUw6wv\nqw72KEiSpJpgj0J6OKMgSZIkqYiJglLJmtb4jHl8xjw+Yx6fMZfKx0RBkiRJUhHry6qDPQqSJKkm\n2KOQHs4oSFIflcvlKj0ESVINM1FQKlnTGp8xjy+fz1d6CKnjcR6fMZfKx0RBkiRJUhHry6qDPQqS\nSi6TyeBni6RSs0chPZxRkCRJklTEREGpZE1rfMZcaeBxHp8xl8rHREGS+qhsNlvpIUiSapj1ZdXB\nHgVJklQT7FFID2cUJEmSJBUxUVAqWdManzGPz5jHZ8zjM+ZS+ZgoSJIkSSpifVl1sEdBkiTVBHsU\n0sMZBUnqo3K5XKWHIEmqYSYKSiVrWuMz5vHl8/lKDyF1PM7jM+ZS+ZgoSJIkSSpifVl1sEdBUsll\nMhn8bJFUavYopIczCpIkSZKKmCgolaxpjc+YKw08zuMz5lL5mChIUh+VzWYrPQRJUg2zvqw62KMg\nSZJqgj0K6eGMgiRJkqQiJgpKJWta4zPm8Rnz+Ix5fMZcKh8TBUmSJElFrC+rDvYoSJKkmmCPQno4\noyBJfVQul6v0ECRJNcxEQalkTWt8xjy+fD5f6SGkjsd5fMZcKh8ThdV9DlgFfKCXj/sGMLBg+RZg\nUKkGJUmSJMVmfdnqriKc8D8E5Drd1x9Y2c3jngH2AF5bx/3aoyCp5DKZDH62SCo1exTSwxmFDpsC\newFfB45I1jUBc4EbgL8R4nUu8AiwMNn2JGAEcDdwZ/K4ZmBocvvLybYLgCvK+xIkSZKk0jBR6PBZ\n4DbgOeAV4MPJ+t2Ak4EdgeOAUcDY5O+3wIXAYkJS8fHkMW2X8D4EnAnsD4wDppT5NaiHrGmNz5gr\nDTzO4zPmUvn0r/QAqsiRwPnJ7ZnJ8s3AA8CzyfqPA78i9DEALF3D82WAjwFXA0vWtv3kyZMZPXo0\nAA0NDYwbN46mpiag40PQ5dItL1iwoKrGk4blNtUynjQsZ7PZqhpPGpYXLFhQVeNJw7Kf53E+v2fN\nmkVzczNKF+vLgqHA84SZhFagX/JvFvgmcHCy3TXARcCfOj3+GWB3OhKCtp6FI4HhwHfXsn97FCRJ\nUk2wRyE96io9gCrxRUL/wGhgG0J50TPAfp22u4NQftQvWR6S/NtC8bcctQJ3AYfR0a8wFEmSJKkG\nmCgEk4DrOq27NllfeKn/UkIPw8OE5uQjk/UXE/ob7mR1jwH/A8xOtj+3pKPWOiucTlUcxjw+Yx6f\nMY/PmEvlY49C8LEu1l2Y/BV6j1CK9M1O63+R/LXZpuD2FfhtR5IkSaox1pdVB3sUJElSTbBHIT0s\nPZKkPiqXy1V6CJKkGmaioFSypjU+Yx5fPp+v9BBSx+M8PmMulY+JgiRJkqQi1pdVB3sUJJVcJpPB\nzxZJpWaPQno4oyBJkiSpiImCUsma1viMudLA4zw+Yy6Vj4mCJPVR2Wy20kOQJNUw68uqgz0KkiSp\nJtijkB7OKEiSJEkqYqKgVLKmNT5jHp8xj8+Yx2fMpfIxUZAkSZJUxPqy6mCPgiRJqgn2KKSHMwqS\n1EflcrlKD0GSVMNMFJRK1rTGZ8zjy+fzlR5C6nicx2fMpfIxUZAkSZJUxPqy6mCPgqSSy2Qy+Nki\nqdTsUUgPZxQkSZIkFTFRUCpZ0xqfMVcaeJzHZ8yl8jFRkKQ+KpvNVnoIkqQaZn1ZdbBHQZIk1QR7\nFNLDGQVJkiRJRUwUlErWtMZnzOMz5vEZ8/iMuVQ+JgqSJEmSilhfVh3sUZAkSTXBHoX0cEZBkvqo\nXC5X6SFIkmqYiYJSyZrW+Ix5fPl8vtJDSB2P8/iMuVQ+JgqSJEmSilhfVh3sUZBUcplMBj9bJJWa\nPQrp4YyCJEmSpCImCkola1rjM+ZKA4/z+Iy5VD4mCpLUR2Wz2UoPQZJUw6wvqw72KEiSpJpgj0J6\nOKMgSZIkqYiJglLJmtb4jHl8xjw+Yx6fMZfKx0RBkiRJUhHry6qDPQqSJKkm2KOQHs4oSFIflcvl\nKj0ESVINM1FQKlnTGp8xjy+fz1d6CKnjcR6fMZfKx0RBkiRJUhHry6qDPQqSSi6TyeBni6RSs0ch\nPVi0HwYAAA0MSURBVJxRkCRJklTEREGpZE1rfMZcaeBxHp8xl8rHREGS+qhsNlvpIUiSapj1ZdXB\nHgVJklQT7FFID2cUJEmSJBUxUVAqWdManzGPz5jHZ8zjM+ZS+ZgoSJIkSSpifVl1sEdBkiTVBHsU\n0sMZBUnqo3K5XKWHIEmqYSYKSiVrWuMz5vHl8/lKDyF1PM7jM+ZS+ZgoSJIkSSpifVl1sEdBUsll\nMhn8bJFUavYopIczCpIkSZKKmCgolaxpjc+YKw08zuMz5lL51GKiMBz4PfAU8CBwCzCmF4+/BRgE\njAYe6WabZmDoOo9QkqpANput9BAkSTWs1urLMsC9wFTg4mTdroQT/3t68FiAtoLd0cBNwC5dbPsM\nsAfw2nqMtTfsUZAkSTXBHoX0qLUZhf2Bd+lIEgAeBuYDfwL+miwfktw3GngCuJwwezCS1WcL+gPT\ngceAmcDAguf9r+S57ge2S9ZtDlwDPJD8fTRZvychgXkI+DOwQ7J+MvAH4FbgSeDH6/KiJUmSpNhq\nLVHYmZAMdPYO8Hlgd+BjwHkF920P/G/y2OfomFEA+EBy307AcuCEgvteJ8xW/AK4IFn3M+B8QmLw\nReDSZP3fgX2BDwNnAT8seJ6xwOGEmYsjgPf18LWqjKxpjc+Yx2fM4zPm8RlzqXz6V3oAvdRdfU4d\ncDbhZH0VMALYIrnvWcLV/648D9yX3J7+/7d3tzFyVWUAx/9Dd3kT2gU0RSywlJdIjFBAFBXpVsAU\nFRCNiiBsAbUJQg2iUEFx9wMRiwYiRIkYXkUUkaAIQQpSQUmhCrOlYJECC1ixEKSmGBGl9cM5k7k7\nO9ud3Tv3zs7c/y+ZzH0995mnt7P33HPOHWAR1UrGjfH9p4TKAcARwL6J/bcHtgV6gOsIlZJNjMzr\nPcCGOP04oZVjbW0gCxYsoLe3F4Cenh7mzJlDX18fUP0SdL558+VyeUrFU4T5iqkSj/POZzFfLpen\nVDxFmPf7PJ/v72XLljE8PIyKpd36l32QcMd+bs3yBcB84ETgDcIYg7mECkTtOIRnCC0P04FlhAv3\nStlnAB+P28wjdFPqBv5G6Hb0EqFF4PWa419DGFh9ObB7LHePGNdBwJlxu9uAi4H7avZ3jIIkSWoL\njlEoji1aHcAE/RbYCvh8Ytl+wG7Ai4RKwjzCxXojdgMOidMnAPfH6RKhmxDx/YE4fReh1aFi//g+\nnVCZADhlnGP6H0tSLgYGBlodgiSpjbVbRQHCWIQjCI9HXQVcCNxBeErRSuAkwpiBitpb9cn5J4Av\nEroEzQB+kNhmB2CI0BpwVly+KB5nCHgMWBiXLyF0fXoYmJY4xqZxjq8WSTanKh/mPH+Dg4OtDqFw\nPM/zZ86l7LTbGAWAF6je7U96X51lEFockmbH938wcrxB0h7xfXHN8peB4+tsv5wwMLriG/H92viq\nOHqM40mSJElTit1gpgbHKEhqulKphN8tkprNMQrF0Y5djyRJkiRlzIqCCsk+rfkz5yoCz/P8mXMp\nO1YUJKlD9ff3tzoESVIbs3/Z1OAYBUmS1BYco1ActihIkiRJGsWKggrJPq35M+f5M+f5M+f5M+dS\ndqwoSJIkSRrF/mVTg2MUJElSW3CMQnHYoiBJHWpgYKDVIUiS2pgVBRWSfVrzZ87zNzg42OoQCsfz\nPH/mXMqOFQVJkiRJo9i/bGpwjIKkpiuVSvjdIqnZHKNQHLYoSJIkSRrFioIKyT6t+TPnKgLP8/yZ\ncyk7VhQkqUP19/e3OgRJUhuzf9nU4BgFSZLUFhyjUBy2KEiSJEkaxYqCCsk+rfkz5/kz5/kz5/kz\n51J2rChIkiRJGsX+ZVODYxQkSVJbcIxCcdiiIEkdamBgoNUhSJLamBUFFZJ9WvNnzvM3ODjY6hAK\nx/M8f+Zcyo4VBRVSuVxudQiFY85VBJ7n+TPnUnasKKiQ1q9f3+oQCsecqwg8z/NnzqXsWFGQJEmS\nNIoVBRXS8PBwq0MoHHOuIvA8z585l7Ljo62mhjKwf6uDkCRJasAQMKfVQUiSJEmSJEmSJEmSJEmS\nJEmSJEkaaUdgKfAX4C6gZ4zt5gOrgSeBc2vWnQn8GVgFfDubMDtKM3IOcDawMZanzUub84sJ5/gQ\ncAswI7NI29945y3A9+L6IeCACe6r0Sab812Be4HHCN/fi7INs6OkOc8BpgGPALdlFWAHSpPzHuBm\nwvf448Ah2YUpdZYlwDlx+lzgojrbTAPWAL1AN+FpSPvGdfMIF2Ddcf4tWQXaQdLmHMIf+DuBZ7Ci\n0Ii0OT+S6mObLxpjf41/3gJ8GLgjTr8HWD6BfTVampzvTPUJMdsBT9TZV6OlyXnFl4EbgF9lFmVn\nSZvza4FT43QX3uyRGrYamBmnd47ztd5LuCitWBx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"text": [ "<matplotlib.figure.Figure at 0x109f37190>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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LBUmSVBLaGGb2fKZEtfae2nqk1MTa22necTHvuJh3XGLNW/GwUJAkSZJUwNaj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\nWvbG7Ztpr9/JNOKqykryyD6COKu0lXr5VQ65LwX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5ubncQ0gcY64k8DiPnzGXSsdEQZL6qLFjx5Z7CJKkKmaioESaOnVquYeQOMY8fueff/66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\nxLh8RjxOH/A0MD0un0Xo+vQEMDpxjBWDHF9Nkmx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"text": [ "<matplotlib.figure.Figure at 0x10bbf4b90>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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vv/3o378/Q4YM4Vvf+lbcQLVioiBJkqQea+3atXz2s59l++23Z/HixSxZsoQJ\nEya0mB1oz7Rp07j66qtpaGhgxIgRANxxxx389a9/5dlnn2XevHmcfPLJXHXVVSxdupRTTjmFo48+\nmtWrVwNhBmLGjBncf//9vPzyyzz11FNce+219OvXj/vuu49hw4bR0NBAfX09Q4YM4cwzz+Sss85i\nxYoVLFq0iGOPPbaosdkQEwVlkjWt8Rnz+Ix5fMY8PmOuDXn88cd54403uPjii+nbty99+vRh9OjR\nbOgbmsrKypg0aRK77ror5eXlbLbZZgB8//vfZ8CAAWy++eZceeWVnHLKKey///6UlZVx0kknsfnm\nmzN37tym45xxxhkMGTKEqqoqjjrqKObPnw/Q5vn79OnDiy++yDvvvMOWW27JgQce2IWR6DwTBUmS\nJPVYr776Kttttx3l5Z2/7G2r2Th/2+LFi7n00kupqqpqerz22mssWbKkaZ8hQ4Y0Lfft25f33nuv\n3fNdc801vPDCC+y6664ccMAB3HPPPZ0ec1fq2R0YUjusaY3PmMdnzOMz5vEZc23I8OHDeeWVV1i7\ndi29evVq2r7VVlvx/vvvN62/+eabBa9tqzwpf9uIESM477zzOPfcczs9rraOvdNOOzF9+nQAbr31\nVr70pS+xdOlS+vbt2+njdwVnFCRJktRjHXjggQwdOpRzzjmH999/nw8++IBHHnmEvffem9mzZ/Pq\nq6+yYsUKLrroooLXbqg86Wtf+xpXXHEFjz/+OI2NjaxcuZJ77rlnvbMGOYMHD+bdd9+lvr6+adu0\nadN4++23Aejfvz9lZWUbNRPSVUwUlEnWtMZnzOMz5vEZ8/iMuTakvLycu+66i4ULFzJixAiGDx/O\nzTffzCc/+UmOO+449txzT/bff3+OOuqogrv8G1rfd999ueqqq/jGN77BwIEDGTVqFNdff327jdK5\n338A2GWXXTj++OPZYYcdGDhwIG+88Qb3338/u+++OxUVFZx11lnceOONbL755l0Yjc6J8+Wz2hB/\n8jyy2tpap6sjM+bxGfP4jHl8xjy+5EK3rWvIguuZysqByW8pFEdFRRX19UuLdvysaO8zNVFIBxMF\nSZJUEjqTKKg0tPeZWnokSZIkqYCJgjLJmtb4jHl8xjw+Yx6fMZeKx0RBkiRJUgF7FNLBmj5JklQS\n7FHoeexRkCRJktRhHUkUPgJcBfwJmJk8/lLMQUnFZk1rfMY8PmMenzGPz5hLxdO7A/vMAH4LXA2s\nTbY5rySc0n6NAAAgAElEQVRJkiT1YB3pUfgbsG+xB5JxLRKvigEDqF9WvB8nkSRJ2lj2KPQ8m9Kj\ncBdwOjAUGJj3UFeaObPp0bB8eXePRpIkaZNVDqikrKysaI/KAZXd/RY32bXXXsuhhx5a1HPU1dVR\nXl7OunXrOvW6jpQeTSLc8f523rZGYIdOnUlKkdraWmpqarp7GJlizOMz5vEZ8/iMebo1rGiAKUU8\n/pSG4h08z5QpU3jppZe44YYbopwvLTqSKIws9iAkSZKknmrt2rX06tWru4fRaR0pPeoDnAncCtwC\nTAY2K+agpGLz7lN8xjw+Yx6fMY/PmGtDpk6dytFHH920PmrUKI499tim9eHDh7NgwQLOPPNMRowY\nQf/+/dlvv/146KGHALjvvvu46KKLuOmmm6ioqGCfffYBYMWKFZx88skMGzaMD3/4w5x//vlNpT3X\nXnsto0eP5uyzz2bQoEH88Ic/7PB4n3vuOT71qU+x9dZbs8suuzBjxgwAHnvsMYYOHUp+H8htt93G\nXnvtBcC6dev4yU9+wk477cSgQYM47rjjWLaJPa8dSRR+C3wM+O9ked/kX0mSJCnVampqmDNnDgBL\nlixh9erVzJ07F4BFixaxcuVK9tprLw444AAWLFjAsmXLmDhxIuPHj2fVqlWMGzeOc889lwkTJtDQ\n0MC8efMAmDRpEn369OGll15i3rx5PPDAA1x99dVN53388cfZcccd+b//+z/OPffcDo115cqVfOpT\nn+Lf/u3fePvtt7nxxhs57bTTeO655zjwwAPp168fDz74YNP+06dP54QTTgDgV7/6FXfeeSezZ8/m\njTfeoKqqitNPP32TYteRRGF/4MuE3054kNCzcMAmnVXqZn7vdnzGPD5jHp8xj8+Ya0O23357Kioq\nmDdvHrNnz+bTn/40w4YN4/nnn2fWrFl8/OMfB+CEE06gqqqK8vJyzj77bP71r3/x/PPPA9DY2Nji\nTv5bb73Fvffey2WXXUbfvn3ZZptt+OY3v8mNN97YtM+wYcM4/fTTKS8vZ4sttujQWO+++2623357\nvvzlL1NeXs7ee+/NMcccw8033wzA8ccfzx/+8AcAGhoauPfeezn++OMB+N3vfsd//dd/MWzYMDbb\nbDMuuOACbrnllk43MOfrSI/CGmAnYGGyvmOyTZIkSUq96upqamtrWbhwIdXV1QwYMIBZs2bx6KOP\nUl1dDcAll1zC73//e5YsWUJZWRn19fW88847bR5v8eLFrF69mqFDhzZtW7duHSNGjGhaHz58eKfH\nuXjxYh577DGqqqqatq1Zs4aTTjoJCInC6NGj+e1vf8sf//hH9t1336bz1NXV8YUvfIHy8uZ5gN69\ne/PWW291ehxNr+/APt8hzCa8nKyPBL6y0WdU28aObV7u1Sv3fbZSl6mqqGBpfX23nd864viMeXzG\nPD5jro6orq7mzjvvpK6ujvPOO48BAwYwbdo05s6dy+TJk5kzZw4XX3wxf/nLX/joRz8KwMCBA5tm\nEVpflw0fPpzNN9+cd999t8WFeb6NuZYbMWIE1dXVPPDAA20+v9tuu7Hddttx7733Mn36dCZOnNji\ntVOnTuXggw8ueF1dXV2nxwIdKz16ENgZOIPQyLwzIXFQF2rMf6xd23Ldh48ueCxriPMVcpIkpU11\ndTUzZ87kgw8+YNiwYYwZM4b77ruPpUuXss8++9DQ0EDv3r0ZNGgQq1at4kc/+hH1eTfXhgwZQl1d\nXVPiMHToUA4//HDOPvtsGhoaWLduHS+99BKzZ8/epHF+5jOf4YUXXmDatGmsXr2a1atX89e//pXn\nnnuuaZ+JEyfyi1/8gjlz5jB+/Pim7aeeeirnnnsur7zyCgBvv/02d9555yaNZ30zCp8gJAlfJFxn\n5NKinZJ//7hJZ5a6US1Q081jyBq/6zw+Yx6fMY/PmKdbRf+Kov7WQUX/ig7tN2rUKCoqKpp+2Kyy\nspIdd9yRbbfdlrKyMsaNG8e4cePYeeed6devH2eddVaLMqLx48czbdo0tt56a3bYYQeeeOIJrr/+\nes455xx22203Ghoa2GGHHTjnnHMAmn4QriPy962oqOCBBx7g7LPP5uyzz2bdunXsvffe/PznP2/a\n//jjj+f73/8+Rx55JAMHNv8G8plnnkljYyOHH344S5YsYdttt2XChAlN3/i0MTMc63vFD4ELgGsJ\niUJrscuP1gJP5a3/AfhZJ49RDawCHm3n+SOAHwFbAv8izJx8u519O2s74BDCuFvzB88jqyV7iUIZ\ntGjEis3/M4/PmMdnzOMz5vElF5xtXUM2duf/z2jjtfeZdiS12AFY1IFtxdYAdCxtbN+U5DiXtvHc\n7sDtwJHAC4SyrK8DV2ziOXNqgG8BR7XxnP+zUtF1d6IgSeoZTBR6nvY+0470KNzSxrYZmzqgLnQ+\n8DjwNPC7vO1nAM8AC4DphDv6pwBnAfOAMa2O813gvwhJAsA6mpOEkYTZhQXAn4FcG/tRwFzgSeBP\nwLbJ9urkHPOAvwFbAT8BDk22nbnR71aSJEkl5dRTT6WioqLgcdppp3X30NZrfTMKuwK7ARcTym/K\nCCVIlYRvQvpo0UfX0hpCMpBzISFhqQJyPzt3PXAzcDfwOuECfzVhzPWEUqoG4OcU+hvhNyKebuO5\nu5Lj3kAouToa+AIwAFie7PPvwC6EWN0JXEQoccqVMY1JnnNGIQVqsfQoNssD4jPm8Rnz+Ix5fM4o\n9Dztfabra2bemXBR25+WF7cNwNe6cnAd9E9gnza2H0ZIXLYEBgJ/JyQKTxFmEm5PHjkb872jBwGf\nT5an0dwbMZyQQAwB+tBcjvUwcBnwP4Sm79c38rySJElSt1hfonBH8jgEeCTOcDptC+C/gX0JF+MX\nAH2T5z4DfJyQ5JwH7LGBYz0D7EfbMwrQ9oX+r4BLCIlJNaEHAuCnybbPEJKGT2/ojZhFqNiqKkKL\nT+5XTHN34Fzvues1NTWpGk8W1nPb0jKerKznpGU8PW09t7yx38Wv0tWR69O+wMmEMqS+NH8D0leL\nNah2tNXMPAB4jlBi1JvQL3Az8J+EnoQ6YLPk390I76OS5gv6fHsQ7v4fCbxI6N/4GqHv4Q5CmdM0\nQnnSUYSvjX2SUHL0JDA1GcdYwq9Xv5QcdwahZOk1QslTTRvnbmxzRNIUG5AlSeli6VHPsynNzDcA\ng4FxhNLu4cB7XTi2jupLc4PwPEKPwnLgKkK50X3AY8m+vQjjfopwEf9LYAWh1+ALyetHtzr+08A3\nCV9f+myyvn3y3GRCb8IC4ASam5GnEBKBJ4C3aU6izkxev4Dwdaz3JmNZC8zHZubu93J3DyB7Wt/5\nU/EZ8/iMeXzGXCqe9ZUe5ewEfAn4HHAdoe7/oWIOqh3tjfX85NHaoW1sexHYaz3nuCd5tPYK4Qfo\nWrszebR2RjvHb+sYkiRJUup0pPToceAAYA5wGvAm4c79DkUcV9ZYeqS2TbH0SJKULp0pPRpYWcmy\nhuL9MnNVRQVL6+uLdvys2JhvPcq5ivBtQj8g3D3firbv4EuSJElNljU0UMzbXWVFTELUsR6Fq4Cl\nwCxCzf42dN2vFUvdwx6F6Kwjjs+Yx2fM4zPm2pCpU6dy9NFHN62PGjWKY489tml9+PDhLFiwgDPP\nPJMRI0bQv39/9ttvPx56KFTaL1myhC233JJly5Y1vWbevHlss802rF27FoDf//737LbbbgwcOJBx\n48bxyiuvNO171llnMXjwYPr378+ee+7JM888U+y33GU6kihcSPhRs5wqwi8YS5IkSalWU1PDnDlz\ngHDRv3r1aubOnQvAokWLWLlyJXvttRcHHHAACxYsYNmyZUycOJHx48ezatUqhg0bxsEHH8ytt97a\ndMzp06czfvx4evXqxR133MFFF13EbbfdxjvvvMOhhx7K8ccfD8D999/PnDlzePHFF1mxYgUzZsxg\n6623jh+EjdSRHoX5wN6tts2j7R8/08axCF1tquhfQf1yay8lSenRmR6FsrKy4pYe0bFevhEjRnDH\nHXfw/PPPM3PmTBYsWMB1113HI488wh133MHtt99e8JqBAwcya9Ys9thjD6655hqmT5/Ogw8+SGNj\nI9tttx3Tp09nzJgxHHHEEYwfP56vfjX8csC6deuoqKjgH//4By+99BKnnnoq119/Pfvvvz/l5R25\nRx/fpnw9ajnhh81y+hJ+hVhdqLGx0YePgodJgiRJm666upra2lrmzJlDdXU11dXVzJo1i9mzZ1Nd\nXQ3AJZdcwm677caAAQOoqqpixYoVvPPOOwAcc8wxPProo7z55pvMnj2b8vJyxowZA8DixYs588wz\nqaqqoqqqqmnGYMmSJYwdO5ZvfOMbnH766QwePJhTTjmFhhLqq+hIovA/wIOEHyv7d+DPwPXFHJRU\nbNa0xmfM4zPm8Rnz+Iy5OqK6upqZM2cyZ84campqmhKHWbNmUV1dzZw5c7j44ouZMWMGy5cvZ9my\nZfTv35/GxjBbUVVVxeGHH85NN93E9OnTm0qLIMxWXHnllSxbtqzpsXLlSg466CAAJk+ezBNPPMGz\nzz7LCy+8wMUXX9wtMdgYHUkUfkroSdgV2AX4UbJNkiRJSr1covDBBx8wbNgwxowZw3333cfSpUvZ\nZ599aGhooHfv3gwaNIhVq1bxox/9iPpWX7s6ceJErrvuOm699VYmTpzYtP3UU0/lwgsv5NlnnwVo\n6kUAeOKJJ3jsscdYvXo1W265JVtssQW9evWK98Y3UUcLpeYRvvVoVrIslbSampruHkLmGPP4jHl8\nxjw+Y66OGDVqFBUVFRx6aPg93srKSnbccUdGjx5NWVkZ48aNY9y4cey8886MHDmSvn37MmLEiBbH\nOProo1m4cCFDhw5ljz32aNr++c9/nu9973tMmDCB/v37s8cee3D//fcDUF9fz9e//nUGDhzIyJEj\nGTRoEN/5znfivfFN1JFm5mOBiwlJAsDHge8AM4o1qAxqzE1tSZIkpZk/uNbzbEoz8w+A/YGTksf+\n+INrKnHWtMZnzOMz5vEZ8/iMebotra8v6pd+mCQUV0cShTLg7bz1d+nYTIQkSZKkEtWRC/6Lgb2A\n6cn+xwFPAd8t4riyxtIjSZJUEjpTeqTS0N5n2pFEoQw4BhhD+GGwOcBtXTk4+T8sSZJUGkwUep5N\n6VFoBG4FzgLOxiRBPYA1rfEZ8/iMeXzGPD5jLhVP7/U89x60+6vbjUBl1w9HkiRJUhrYlJwOTtVJ\nkqSSYOlRz7MppUeSJEmSMsZEQZlkTWt8xjw+Yx6fMY/PmKurVFRUUFdXt1Gvramp4ZprrunaAaXA\n+noUJEmSpI1WWVVFw/LlRTt+xYAB1C9b1iXHatiEX5AuKyvLle/0KD3vHZUma/okSVJJ6EyPQllZ\nGcycWbzBjB1LGq6hxo4dy4knnshXv/rV7h7KRrFHQZIkSZkzdepUjj766Kb1UaNGceyxxzatDx8+\nnAULFlBeXs6iRYsAmDRpEqeffjqf/exnqays5KCDDmp6DuBPf/oTu+yyCwMGDGDy5Mk0NjY2JSwL\nFy6kurqaAQMGsM022zBhwoRI77TrmSgok6xpjc+Yx2fM4zPm8RlzbUhNTQ1z5swBYMmSJaxevZq5\nc+cCsGjRIt5//3323HPPgtfddNNNTJkyhWXLlrHTTjtx3nnnAfDOO+/wxS9+kQsvvJB3332XHXfc\nkYcffrip9Oj8889n3LhxLF++nNdff50zzjgj0jvteiYKkiRJ6rG23357KioqmDdvHrNnz+bTn/40\nw4YN4/nnn2fWrFkceuihBf0FZWVlHHPMMey333706tWLE044gfnz5wPwv//7v+y+++4cc8wx9OrV\ni29+85sMGTKk6bV9+vShrq6O119/nT59+nDIIYdEfb9dyURBmVRTU9PdQ8gcYx6fMY/PmMdnzNUR\n1dXV1NbWMmfOHKqrq6murmbWrFnMnj2b6urqNl8zePDgpuW+ffvy3nvvAWFW4sMf/nCLfYcPH960\n/LOf/YzGxkYOOOAAdt99d6ZOnVqEdxSHiYIkSZJ6tOrqambOnMmcOXOoqalpShxmzZrVbqLQnmHD\nhvHqq682rTc2NrZYHzx4MFdeeSWvv/46v/vd7zjttNNa9DeUEhMFZZI1rfEZ8/iMeXzGPD5jro7I\nJQoffPABw4YNY8yYMdx3330sXbqUffbZp2D/9X2T0pFHHskzzzzDbbfdxpo1a7j88st58803m56f\nMWMGr732GgADBgygrKyM8vLSvOQuzVFLkiRJHTRq1CgqKio49NBDAaisrGTHHXdk9OjRTf0J+X0K\nbf0uQm590KBBzJgxg3POOYdBgwaxcOFCxowZ07TfE088wUEHHURFRQWf+9znuPzyyxk5cmSR32Fx\n+DsK6eDvKEiSpJLQmd9RKKUfXMuy9j5TE4V0MFGQJEkloTOJgkqDP7gm5bGmNT5jHp8xj8+Yx2fM\npeIxUZAkSZJUwNKjdHCqTpIklQRLj3oeS48kSZIkdZiJgjLJmtb4jHl8xjw+Yx6fMZeKp3d3D0CS\nJEmlr3fv3g1lZWUV3T0OdV7v3r0b1qxZU7DdHoV0sKZPkiSVhPX0KKiHsfRIkiRJUgETBWWSNa3x\nGfP4jHl8xjw+Yy4Vj4mCJEmSpALWl6WDPQqSJKkk2KOQHc4oSJIkSSpgoqBMsqY1PmMenzGPz5jH\nZ8yl4jFRkCRJklTA+rJ0sEdBkiSVBHsUssMZBUmSJEkFTBSUSda0xmfM4zPm8Rnz+Iy5VDwmCpIk\nSZIKWF+WDvYoSJKkkmCPQnY4oyBJkiSpgImCMsma1viMeXzGPD5jHp8xl4rHRCElysrK1vsYWFnZ\n3UOUJElShlhflg4b7FAoA+xjkCRJ3c0ehexwRkGSJElSARMFZZI1rfEZ8/iMeXzGPD5jLhVPVhOF\n91qtTwJ+VaRzDQNmJMv7Ar8s0nkkSZKkLpPV+rIGoCJv/cvAfsDk7hmOPQqSJKk02KOQHVmdUWgt\n/4/9KGAu8CTwJ2DbZPtTQGWy77vAicn264FPAtsBs4G/JY+Dk+dHAk8nyzXAXUUYvyRJktSlspoo\n9AXm5T1+CORu188BDgI+BtwEfDfZ/jAwBvgo8FKyTLLvw8D/AZ8ilBdNAC4v9pvQxrOmNT5jHp8x\nj8+Yx2fMpeLp3d0D6Cb/BPbJW8+VHgEMB24GhgB9gEXJ9jnAx4HFwG+BrxP6D5Ylx+sP/BrYC1gL\n7NyZAW1o/m6rvn2blnP/UaypqXF9I9fnz5+fqvFkYT0nLeNx3fVirM+fPz9V48nCuv89j/Pf79ra\nWurq6lC2ZLW+rHWPwiTCTMBkoBa4BLgbqAamAGOBDxMSiDrgPEJT8p8JicV3kv22JMxA9AI+ADYj\nlB7dBewB1ADfIpQ35WtkShujnGJfgiRJShd7FLKjvLsHkEKVwJJkeVLe9teAQcBOwMvAQ8C3CX0J\nude9mSyfREgWJEmSpJKU1USh9W36xrxtUwhfZ/oE8HarfecCLyTLDxFKjx5K1n9DKGGaD3yEll/B\n2tjOsrpJ/nSq4jDm8Rnz+Ix5fMZcKp6s9ihUtlq/LnkA3Jk82nJS3vIjtIzfQkJ/Qs45yb91wJ7J\ncm3ykCRJklLN+rJ0sEdBkiSVBHsUsiOrpUeSJEmS1sNEQZlkTWt8xjw+Yx6fMY/PmEvFk9UehfSZ\nUripon9F4UZJkiQpAuvL0qHRXgRJklQK7FHIDkuPJEmSJBUwUVAmWdManzGPz5jHZ8zjM+ZS8Zgo\nSJIkSSpgfVk62KMgSZJKgj0K2eGMgiRJkqQCJgrKJGta4zPm8Rnz+Ix5fMZcKh4TBUmSJEkFrC9L\nB3sUJElSSbBHITucUZAkSZJUwERBmWRNa3zGPD5jHp8xj8+YS8VjoiBJkiSpgPVl6WCPgiRJKgn2\nKGSHMwqSJEmSCpgoKJOsaY3PmMdnzOMz5vEZc6l4TBQkSZIkFbC+LB3sUZAkSSXBHoXscEZBkiRJ\nUgETBWWSNa3xGfP4jHl8xjw+Yy4Vj4mCJEmSpALWl6WDPQqSJKkk2KOQHc4oSJIkSSpgoqBMsqY1\nPmMenzGPz5jHZ8yl4jFRkCRJklTA+rJ0sEdBkiSVBHsUssMZBUmSJEkFTBSUSda0xmfM4zPm8Rnz\n+Iy5VDwmCpIkSZIKWF+WDvYoSJKkkmCPQnY4oyBJkiSpgImCMsma1viMeXzGPD5jHp8xl4rHREGS\nJElSAevL0sEeBUmSVBLsUcgOZxQkSZIkFTBRUCZZ0xqfMY/PmMdnzOMz5lLxmChIkiRJKmB9WTrY\noyBJkkqCPQrZ4YyCJEmSpAImCsoka1rjM+bxGfP4jHl8xlwqHhOFlKisquruIUiSJElNrC9Lh0YA\n+xQkSVLa2aOQHc4oSJIkSSpgoqBMsqY1PmMenzGPz5jHZ8yl4jFRkCRJklTA+rJ0sEdBkiSVBHsU\nssMZBUmSJEkFTBSUSda0xmfM4zPm8Rn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"text": [ "<matplotlib.figure.Figure at 0x10bd2d3d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Av+T19Xlsrav5pR5AChnz+Ix5dPlXYBWHMZeKp5IShfxLOZcCA/KWJwBfIzQPHwtM7OB9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\n9uyn/g/8Duppc8a8c6YSg4R7gPtSmX09X7WYf5t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XEW7HencyljXAE9jMXHS5V0kUhzGPz5jHZ8zjM+ZS4ayv9KjZTsAX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\nwP3EXXXMvFj3Asen5cVE3mYudag5xF1kNhGThrR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"text": [ "<matplotlib.figure.Figure at 0x10bf93310>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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1rfEZ8/iMeXzGPD5jLqXHREGSJElSAZeNSoOlR5IkqSxYepQdrihIkiRJKmCioEyypjU+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\nzOMz5tHll2ooDmMupcceheDD7Wy7JHn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iqamAe1dXZhQuBRYDMwjfT21Kz92tWCoOa7fjM+bxGfPorJePz5hr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\nkiRJkiRJkiRJkiSp/fYD3ymsfwX4RovqvhH4cIv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"text": [ "<matplotlib.figure.Figure at 0x10c1fb0d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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8ym1E1e1kuxBeTwMI/ycvUhmZGpve52rC6+Ut6brLE2E/n8ZeLiS2pvI9/Wt6zI3pspMI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\nUr1r2Oh+mXnooEEs7sPfOmhuamJRlyP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SJEmSJEmSJEmSJEmSJEmSJEmSJElq\nyP8BK+yhWCvlSksAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x10c326550>" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# By location across all endpoints\n", "location_group = alldata_concat.fillna(0).groupby(level='location').sum()\n", "ax = location_group.plot(kind='barh', title='All records by location', figsize=(10, 8))\n", "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", "ax.set_xlabel('Number of records')\n", "print(location_group)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " currents water_level waves winds\n", "location \n", "Arctic 333 72050 566004 57112\n", "Caribbean 128 46912 519594 65554\n", "East Coast 138 55091 526151 57268\n", "Gulf of Mexico 110 37169 449539 55281\n", "Hawaii 107 34383 384764 52837\n", "North East 154 41343 494839 55908\n", "North West 115 44631 398705 51479\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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96NChzJ8/n7PPPpthw4bRv39/DjjgAB566CEA7r33Xi677DJuvvlmMpkMo0aN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\ncs7Rsp7zq0hyp1MVhzGPz5jHZ8zjM+Z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"text": [ "<matplotlib.figure.Figure at 0x10c454110>" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Is the data recent (< 1 month)?\n", "\n", "#### Let's add a temporal extent to the search" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#temporal range - last 28 days and next 3 days (forecast data)\n", "jd_now = dt.datetime.utcnow()\n", "jd_start, jd_stop = jd_now - dt.timedelta(days=28), jd_now + dt.timedelta(days=3)\n", "\n", "start_date = jd_start.strftime('%Y-%m-%d %H:00')\n", "stop_date = jd_stop.strftime('%Y-%m-%d %H:00')\n", "\n", "print start_date + ' to ' + stop_date" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2014-08-12 19:00 to 2014-09-12 19:00\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# Add a waitbar to monitor status\n", "divid = insert_progress_bar(title='Searching catalogs. Please wait...', color='red')\n", "\n", "# Save all of the results in a list of Dataframes\n", "results = {}\n", "recent_data = []\n", "\n", "count = 0\n", "# Loop through the csw endpoints\n", "for endpoint in endpoints:\n", " print '\\n' + endpoint\n", "\n", " try:\n", " csw = CatalogueServiceWeb(endpoint, timeout=60)\n", " # continue processing if an endpoint is down or otherwise nonfunctional\n", " # but report the exception returned from OWSLib\n", " except ExceptionReport as e:\n", " print('Error accessing CSW endpoint \"{0}\". Error report: {1}'.format(endpoint, e))\n", " continue\n", " # loop through the variables\n", " for var_name in names_dict:\n", "# print '\\n' + var_name.upper()\n", " num_recs = []\n", " for location, bounding_box in locations.iteritems():\n", "# print location\n", " # convert User Input into FES filters\n", " start, stop = fes_date_filter(start_date, stop_date)\n", " bbox = fes.BBox(bounding_box)\n", "\n", " #use the search name to create search filter\n", " or_filt = fes.Or([fes.PropertyIsLike(propertyname='apiso:AnyText',\n", " literal='*%s*' % val,\n", " escapeChar='\\\\',\n", " wildCard='*',\n", " singleChar='?') for val in names_dict[var_name][\"names\"]])\n", " filter_list = [fes.And([ bbox, start, stop, or_filt])]\n", " # try request using multiple filters \"and\" syntax: [[filter1,filter2]]\n", " try:\n", " csw.getrecords2(constraints=filter_list, #maxrecords=max_records,\n", " resulttype='hits')\n", " \n", " except Exception as e:\n", " print '\\t' + 'ERROR - ' + str(e)\n", " num_recs.append(np.NaN)\n", " else:\n", "# print '\\t' + str(len(csw.records)) + \" csw records found\"\n", "# print csw.results['matches']\n", " num_recs.append(csw.results['matches'])\n", " \n", " results[var_name] = np.array(num_recs)\n", "\n", " # Save the results\n", " prod = list(itertools.product([endpoint], locations.keys()))\n", " mi = pd.MultiIndex.from_tuples(prod, names=['endpoint', 'location'])\n", " df = pd.DataFrame(results, index=mi)\n", " # if all the entries in the entire endpoint have zero counts, do not include this\n", " # endpoint provider\n", " if (df.stack().fillna(0) != 0).any():\n", " recent_data.append(df)\n", " else:\n", " continue\n", "\n", " \n", " # Update progress bar\n", " count += 1\n", " percent_complete = (float(count)/float(len(endpoints)))*100\n", " update_progress_bar(divid, percent_complete)\n", "\n", "recent_data_concat = pd.concat(recent_data)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Once again, let's plot the results in a bar graph" ] }, { "cell_type": "code", "collapsed": false, "input": [ "endpoint_group_recent = recent_data_concat.groupby(level='endpoint')\n", "# can uncomment this for a plot with tuple groups as y axis marks\n", "# endpoint_group.plot(kind='barh', figsize=(10, 8,))\n", "for grp_name, grp in endpoint_group_recent:\n", "# print grp_name, grp\n", " fig, ax = plt.subplots()\n", " # eliminate endpoint from index since it will be the graph title\n", " grp.reset_index(0, drop=True).plot(ax=ax, kind=\"barh\", figsize=(10, 8,),\n", " title=grp_name)\n", " ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", " ax.set_xlabel('Number of records')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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MubrjhRde4LjjjmPbbbdlyJAhTJ48mYaGBk4++eS2bRobG+nTpw9r1qwBwt/W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\nhzMKkiRJkgqYKCiVrGmNz5jHZ8zjM+b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"text": [ "<matplotlib.figure.Figure at 0x10c454f10>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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1tZSXl7Nu3Tog/J3+4Ac/YMKECQwcOJAXX3yR8vJyfvvb3zJu3Dg+/vGPA3D7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\nqK+r67b9SZIkqfRYX1YcGpk/v/v2Nmk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"text": [ "<matplotlib.figure.Figure at 0x10c3ec110>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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6uFQnSZLKgqVHPY+lR5IkSZK6zURBmWRNa3zGPD5jHp8xj8+Yp1u/fgOoqKgo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\n6WCPgiRJKgv2KGSHKwqSJEmSCpgoKJO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"text": [ "<matplotlib.figure.Figure at 0x10c0fb850>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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8ym1E1e1kuxBeTwMI/ycvUhmZGpve52rC6+Ut6brLE2E/n8ZeLiS2pvI9/Wt6zI3pspMI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\nUr1r2Oh+mXnooEEs7sPfOmhuamJRlyP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"text": [ "<matplotlib.figure.Figure at 0x10d88ef10>" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reorganize the data by variable name and plot" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#return counts of each variables as a series and place the variable type as the first index\n", "stacked = recent_data_concat.stack().reorder_levels([2,1,0])\n", "stacked.index.names = ['variable_type', 'location', 'endpoint']\n", "stacked.name = 'record_counts'\n", "#stacked.index.names = stacked.index.names[['variable_type']\n", "by_var = stacked.unstack()\n", "for grpname, grps in by_var.groupby(level='variable_type'):\n", " # get rid of variable index since we are already grouping by it and have its name\n", " cur_grp = grps.reset_index(level='variable_type', drop=True)\n", " fig, ax = plt.subplots()\n", " cur_grp.plot(ax=ax, kind='barh', stacked=True, figsize=(10, 8,), legend=False, title=grpname)\n", " ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", " ax.set_xlabel('Number of records')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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HDg0uu+yy+LWnT58elJWVBUEQBNOmTQvatm0bH9f2/e9/Pxg9enSwcePG4L33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L7969eeCBB9LmE8KC0gknnADABx98wKmnnspee+1FQUEBF110EQCLFy+mqKiI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"text": [ "<matplotlib.figure.Figure at 0x10c293e10>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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fBEEQzJkzJ1i2bNkm158+fXrQokWLYOzYsUF1dXXw3HPPBa1btw6++uqrIAiC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\nX5Kens6yZcu45JJLuOmmm/D392fcuHE0bNjQ2uc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TTpw4Uek2R48erXSf9evX5/bbbyc/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\n8OFKH19V8WdmZlbIfXx8vHXckpISPv/8c5fm16eTnp7OwoULXfK+fv1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KDWD1am+H8dtI\nSUE98eR8px5KIiIiApryJiIiIiIiIiIiNaSCkvi+Xd4OwMcpf+5T7jyzdau3I/Bp6gPkPuVORERE\nxHMqKImIiIiIiIiISI1o/nvtoB5KIuezKb7xCj9X1ENJ5PyiHkoiIiICGqEkIiIiIiIiIiI1pIKS\n+D71sfGM8uc+5c4z6qHkEfUBcp9yJyIiIuI5FZRERERERERERKRGNP+9dlAPJZHz2RTfeIWfK+qh\nJHJ+UQ8lERERAY1QEhERERERERGRGlJBSXyf+th4Rvlzn3LnGfVQ8oj6ALlPuRMRERHxnC8VlIqB\nLWW+HnJjH0nAlae5vRewCfge2Aw868YxqtIEGHoW9yciIiIiIiIi4hUB3g6gBvKBDh7uIwXIA76s\n5LZLgJeA3sAOHMW2Oz08XllNgWHA22dxnwKOzIr7lD/3KXeead/e2xH4tOTkZG+H4LOUOxERERHP\n+dIIpapMBDYC3wGvlFl/H46RRtuABThGCN0FPIBjhNNV5fbzEDANRzEJoASYXfpzIrCqdF+fA/Gl\n6/sBG3CMZvoMiCldn8T/RlJ9A9QH/gpcXbrufrcfrYiIiIiIiIiIl/lSQSkI1ylvg0rXzwI6AZeW\nbtO3dP3DQHugHTAGSMdRIHoOx0indeX23wZH8acyLwFzSvc1H3ixdH0a0AW4DHiX/03D+wtwd+lx\nrgZOlMaTVrru7zV43HIm6mPjGeXPfcqdZ9RDySPqA+Q+5U5ERETEc7405e0ElU95uwZ4EAgGIoD/\nAEuAb3GMTPp36ZeTOx9z2wXoX/rzPOCZ0p/jgfeAhkAd4NfS9euB53EUnxYB+8583JE4BkIBhOGo\nhSWXLqeWfvf2cinnm+imtWT5QC2Lx9eWlb/fZrlUaun35N/ZssVZRHJOdzvflnEUK5xTqpyFCy3X\nrmWn2hJPbV92/rx7925EREREnNwprnhLHmAvt64usBu4HEfRZnLp+idwjL7qjmNaWi8cI5geB44B\nMyvZ/1xgNY6RSOUdAmKBIiAQ2A9E43iv9CyOAlYSMAVHnyZwjHjqg2Ok0vWl9/9LaTzlGTCVPuja\nxeZ4hCJSM1N84xV+rtgAVq/2dhi/jZQUjPk9P9vye2Cz2cC3/oYUERGRc8CXprxVpm7p9ywcfYoG\n4XjfZgMScBR8HgFCS2+vrCjl9DdgAtCydNkPR88lgC+AIaU/DwfWlv4cgqO4BI4hRk7NcfRvegbH\np8ZdBBw9zbFFRERERERERHyGLxWUyvdQmg4cAV7DMc1tBfBV6bb+wFs4pr1txtGzKBdYDAwovX+3\ncvv/DhiH41PYfihddk5auRcYhaMp93D+11R7CrAQ+BrHKCbnv6XvL73/NqAAWF4aSzGwFTXlPrvU\nx8Yzyp/7lDvPqIeSR9QHyH3KnYiIiIjnfKmHUlWxTiz9Ku/qStb9jKOxdlWWln6VlwFcW8n6j0u/\nyruviv1Xtg8REREREREREZ+i+e+1g3ooiZzPpvjGK/xcUQ8lkfOLeiiJiIgI+NaUNxERERERERER\nqQVUUBLfpz42nlH+3KfceUY9lDyiPkDuU+5EREREPKeCkoiIiIiIiIiI1Ijmv9cO6qEkcj6b4huv\n8HNFPZREzi/qoSQiIiKgEUoiIiIiIiIiIlJDKiiJ71MfG88of+5T7jyjHkoeUR8g9yl3IiIiIp5T\nQUlERERERERERGpE899rB/VQEjmfTfGNV/i5oh5KIucX9VASERER0AglERERERERERGpIRWUxPep\nj41nlD/3KXeeUQ8lj6gPkPuUOxERERHPBXg7AHHygZHjfmjKm4gbAvGJV/i54+8PKSnejuI3YQ8L\n83YIIiIiIiK/id/1e5xaxKjnhoiIiPgC9VASERER0JQ3ERERERERERGpIRWUxOepF4ZnlD/3KXee\nUf48o/y5T7kTERER8ZwKSiIiIiIiIiIiUiOa/147qIeSiIiI+AT1UBIRERHQCCUREREREREREakh\nFZTE56kXhmeUP/cpd55R/jyj/LlPuRMRERHxnApKIiIiIiIiIiJSI5r/Xjuoh5KIiIj4BPVQEhER\nEdAIJRERERERERERqSEVlMTnqReGZ5Q/9yl3nlH+PKP8uU+5ExEREfGcCkoiIiIiIiIiIlIjmv9e\nO6iHkoiIiPgE9VASERER0AglERERERERERGpIRWUxOepF4ZnlD/3KXeeUf48o/y5T7kTERER8ZwK\nSiIiIiIiIiIiUiOa/147qIeSiIiI+AT1UBIRERHQCCUREREREREREakhFZTE56kXhmeUP/cpd55R\n/jyj/LlPuRMRERHxnApKIiIiIiIiIiJSI5r/Xjuoh5KIiIj4BPVQEhEREdAIJRERERERERERqSEV\nlMTnqReGZ5Q/9yl3nlH+PKP8uU+5ExEREfGcCkoiIiIiIiIiIlIjmv9eO6iHkoiIiPgE9VASERER\n0AglERERERERERGpIRWUxOepF4ZnlD/3KXeeUf48o/y5T7kTERER8ZwKSiIiIiIiIiIiUiOa/147\nqIeSiIiI+AT1UBIRERHQCCUREREREREREakhFZTE56kXhmeUP/cpd55R/jyj/LlPuRMRERHxXIC3\nAxCH0uHjtYsfUOLtIERcBQKF3g5C5Gzw94fiYm9H8Zuwh4VxNCfH22GIiIiIyFlUC6sYv0sGamMP\nJRtM8XYMIuVMqZ2vFpGasgGsXu3tMH4bKSmoV+D5Qz2UREREBDTlTUREREREREREakgFJfF9u7wd\ngI9T/tyW6u0AfFyqtwPwdVu3ejsCn6UeSiIiIiKeU0FJRERERERERERqRAUl8X1NvR2Aj1P+3Jbs\n7QB8XLK3A/B17dt7OwKflZyc7O0QRERERHyeCkoiIiIiIiIiIlIjKiiJ71MPIM8of25L9XYAPi7V\n2wH4OvVQcpt6KImIiIh4TgUlERERERERERGpERWUxPepB5BnlD+3JXs7AB+X7O0AfJ16KLlNPZRE\nREREPKeCkoiIiIiIiIiI1IgKSuL71APIM8qf21K9HYCPS/V2AL5OPZTcph5KIiIiIp7zxYJSQ+Ad\n4Bfga2Ap0LIG918KhACJwHdVbLMbiHA7QhERERERERGR81iAtwOoIRvwITAHGFK6ri3QAPi5GvcF\n6FP6/XQFI1Nme6nt1APIM8qf25K9HYCPS/Z2AL5OPZTcph5KIiIiIp7ztRFKKUAB8GqZdd8CW4DP\ngW9Kl28ovS0R2A68iWM0Ujyuo48CgHnAD8BCIKjMfh8q3ddXQPPSddHA+8DG0q+upes7AV8Am4H1\nwIWl60cCi4DlwA7gaXcetIiIiIiIiIhIbeJrBaVLcBSNyjsJDAAuB64BZpa5rQXwf6X3zcAx+sjp\notLbWgNHgbvL3HYEx+inWcALpev+DjyPo4B0M/DP0vU/AlcDlwGTgell9tMOGAxcCtwCNKrmY5Xq\nUg8gzyh/bkv1dgA+LtXbAfg69VBym3ooiYiIiHjO16a8mSrW+wEzcBR1SoA4IKb0tnQco4kqswf4\nsvTnecB9/K8Y9Xbp93dwFJEA/gBcXOb+diAYCAPm4iheGVzzuhLIK/35BxyjpvZVDGVk6U2U7q49\n/5sQklr6/bdeLuUsODStpcsHalk8vrbsa/nDcYYml/kZLWvZR5etopBz+tr5ulzKWchxTjnz1nJt\ni6e2Lzt/3r17NyIiIiJOvtYn6BocI4CSyq0fCfQEhgPFON6CJuEoNC3GMTrIaReOkUwhOP62Tyyz\n73uAm0q3ScExPS4Q2I9jutshHCOMCsod/w0cDcJnAU1K99u0NK7LgXtLt1sM/A1YW+7+pupamTfZ\nYIq3YxApZ0rtfLWI1JQNYPVqb4fx20hJwRi9cs8XNpsNfO9vSBERETnLfG3K2yrgAuCOMuvaAgnA\nQRzFpBQcRZ3qSAC6lP48DEgr/dmGY3oapd+/KP35UxyjmJzalX4PwVF0Ahh1hmPqDzARERERERER\n8Wm+VlACR6+kPwC/AP8BngKWAR1xNNEegaOnkVP5f4mWXd4OjMUxFS0U+EeZbcKBbThGFz1Quv6+\n0uNsA74H7ipd/wyOKXebAf8yxzBnOL6cDeoB5Bnlz22p3g7Ax6V6OwBfpx5KblMPJRERERHP+VoP\nJYBM/jd6qKyulawDxwimspqVfs/GtR9SWc5uLY+UW58FDKlk+w04Gnw7TSz9/mbpl1O/Ko4nIiIi\nIiIiIuIzfHGEkoirpmfeRE5D+XNbsrcD8HHJ3g7A1zkbX0uNOZtOi4iIiIj7VFASEREREREREZEa\nUUFJfJ96AHlG+XNbqrcD8HGp3g7A16mHktvUQ0lERETEcyooiYiIiIiIiIhIjaigJL5PPYA8o/y5\nLdnbAfi4ZG8H4OvUQ8lt6qEkIiIi4jkVlEREREREREREpEZUUBLfpx5AnlH+3Jbq7QB8XKq3A/B1\n6qHkNvVQEhEREfGcCkoiIiIiIiIiIlIjKiiJ71MPIM8of25L9nYAPi7Z2wH4OvVQcpt6KImIiIh4\nTgUlERERERERERGpERWUxPepB5BnlD+3pXo7AB+X6u0AfJ16KLlNPZREREREPBfg7QDEyebtACry\nA6Z4OwgRV4HUyleLSM35+0NKirej+E3Yw8K8HYKIiIiInGV6X1Y7GGOMt2MQEREROSObzQb6G1JE\nROR3T1PeRERERERERESkRlRQEp+nXhieUf7cp9x5RvnzjPLnPuVORERExHMqKImIiIiIiIiISI1o\n/nvtoB5KIiIi4hPUQ0lERERAI5RERERERERERKSGVFASn6deGJ5R/tyn3HlG+fOM8uc+5U5ERETE\ncyooiYiIiIiIiIhIjWj+e+2gHkoiIiLiE9RDSUREREAjlEREREREREREpIZUUBKfp17JpmU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"text": [ "<matplotlib.figure.Figure at 0x10c293410>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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WFnTp0iWYOHFiUFFREcydOzfIzs4OPvzww+3mYsKECUGLFi2C++67LygvLw8m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"text": [ "<matplotlib.figure.Figure at 0x10c2a6750>" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "# show \n", "ax = by_var.groupby(level='variable_type').sum().plot(kind='bar',\n", " figsize=(10, 8,),\n", " title='Recent variable counts by endpoint')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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KSuKzzz7jscceu64y77nnHhOP0c2q53YjPDyc8PDwWy2GQqG4zVA7mytMuJKj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"text": [ "<matplotlib.figure.Figure at 0x10e0bfe10>" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conclusions\n", "* The core oceanographic variables are available from numerous CSW endpoints\n", "* But if you are looking for recent (< 1 month) data, the NGDC and NODC CSW is the best bet\n", "* Each of the locations tested seemed to have good data coverage except currents in the Arctic" ] } ], "metadata": {} } ] }

unlicense

IanHawke/orcomp-training

02-loops-functions.ipynb

1

31354

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Storing individual Python commands for re-use is one thing. Creating a *function* that can be repeatedly applied to different input data is quite another, and of huge importance in coding." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<hr>\n", "\n", "##### Teaching note\n", "\n", "In some languages (Visual Basic and Fortran being examples): subroutines and functions. Subroutines perform actions, functions return results (given inputs). In Python there is no distinction: any function can both return results and perform actions.\n", "\n", "<hr>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a standard structure for a function in Python. Here we have\n", "\n", "```python\n", "def name(arguments):\n", " \"\"\"\n", " Comments\n", " \"\"\"\n", " return value\n", "```\n", "\n", "The `def` keyword says that what follows is a function. Again, the name of the function follows the same rules and conventions as variables and files. The colon `:` at the end of the first line is essential: everything that follows that is indented will be the code to be executed when the function is called. The indentation is also essential. As soon as the indentation stops, the function stops." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is a simple example, that you can type directly into the console or into a file:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def add(x, y):\n", " \"\"\"\n", " Add two numbers\n", " \n", " Parameters\n", " ----------\n", " \n", " x : float\n", " First input\n", " y : float\n", " Second input\n", " \n", " Returns\n", " -------\n", " \n", " x + y : float\n", " \"\"\"\n", " return x + y\n", "\n", "add(1, 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that the line `add(1, 2)` is *outside the function* and so is executed. We can also call the function repeatedly:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7\n", "16.6\n" ] } ], "source": [ "print(add(3, 4))\n", "print(add(10.61, 5.99))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The lengthy comment at the start of the function is very useful to remind yourself later what the function should do. You can see this information by typing" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function add in module __main__:\n", "\n", "add(x, y)\n", " Add two numbers\n", " \n", " Parameters\n", " ----------\n", " \n", " x : float\n", " First input\n", " y : float\n", " Second input\n", " \n", " Returns\n", " -------\n", " \n", " x + y : float\n", "\n" ] } ], "source": [ "help(add)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also view this in spyder by typing `add` in the `Object` window of the `Help` tab in the top right." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can save the function to a file and re-use the function by `import`ing the file. Create a new file in the spyder editor containing\n", "\n", "```python\n", "def add(x, y):\n", " \"\"\"\n", " Add two numbers\n", " \n", " Parameters\n", " ----------\n", " \n", " x : float\n", " First input\n", " y : float\n", " Second input\n", " \n", " Returns\n", " -------\n", " \n", " x + y : float\n", " \"\"\"\n", " return x + y\n", "```\n", "\n", "and save it as `script3.py`. Then in the console check that it works as expected:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import script3\n", "script3.add(1, 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Exercise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define your own function that divides two numbers. Save it in a script, `script4.py`. Check that you can `import` it, that it works correctly, and that `help(script4.divide)` gives useful information, and that looking at the help in Spyder gives the same useful information." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# `if` statements and flow control" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We often need to make a *decision* whether to do something, or to do something else. In nearly all languages this involves an `if` statement in some form, where a logical (Boolean) condition is used to \"control the flow\" of the program - that is, control which statements are executed when." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The logical conditions in Python evaluate to the special values `True` and `False`:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "5 > 4" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "4 == 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Python notation is similar to that with functions:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are no items.\n" ] } ], "source": [ "count = 0\n", "\n", "if count == 0:\n", " print(\"There are no items.\")\n", "elif count == 1:\n", " message(\"There is 1 item.\")\n", "else:\n", " print(\"There are\" + count + \" items.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The keyword that Python uses is the *lower case* `if`. To add additional branches to the condition, Python uses the \"Else If\" which is contracted to `elif`. The *condition* (in this case!) compares a variable, `count`, to a number, using the equality comparison `==`. Once again, as in the case of functions, the line containing the `if` definition is ended with a colon (`:`), and the commands to be executed are indented." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can include as many branches of the `if` statement as we like using multiple `elif` statements. We do not *need* to use any `elif` statements, nor an `else`, unless we want (or need) to. We can nest `if` statements inside each other." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Exercise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Write a nested `if` statement that prints out if `number` is\n", "\n", "1. even or odd\n", "2. if even can it be divided by `4`.\n", "\n", "Note: modular division in Python is `a % b`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8 is even.\n", "8 can be divided by 4.\n" ] } ], "source": [ "number = 8\n", "\n", "if number % 2 == 0:\n", " print(number, \" is even.\")\n", " if number % 4 == 0:\n", " print(number, \" can be divided by 4.\")\n", "else:\n", " print(number, \" is odd.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Loops" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will often want to run the same code many times on similar input. Let us suppose we want to add $n$ to $3$, where $n$ is every number between $1$ and $5$. We could do: " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n", "5\n", "6\n", "7\n", "8\n" ] } ], "source": [ "print(add(3, 1))\n", "print(add(3, 2))\n", "print(add(3, 3))\n", "print(add(3, 4))\n", "print(add(3, 5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is tedious and there's a high chance of errors.\n", "\n", "In most programming languages you can define a *loop* that repeats commands. There are two possible types of loop: one that you know in advance how many times it will execute, and one that executes until something happens (at a point you may not be able to predict). We will focus on the former.\n", "\n", "In Python the notation is a `for` loop:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n", "5\n", "6\n", "7\n", "8\n", "Loop has ended\n" ] } ], "source": [ "for n in 1, 2, 3, 4, 5:\n", " print(add(3, n))\n", "print(\"Loop has ended\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The syntax has similarities to the syntax for functions. The line defining the loop starts with `for`, specifies the values that `n` takes, and ends with a colon. The code that is executed inside the loop is indented.\n", "\n", "As a short-hand for integer loops, we can use the `range` function:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "n = 1\n", "n = 2\n", "n = 3\n", "n = 4\n", "n = 5\n", "m = 0\n", "m = 1\n", "m = 2\n", "k = 2\n", "k = 4\n", "k = 6\n" ] } ], "source": [ "for n in range(1, 6):\n", " print(\"n =\", n)\n", "for m in range(3):\n", " print(\"m =\", m)\n", "for k in range(2, 7, 2):\n", " print(\"k =\", k)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that \n", "\n", "* if two numbers are given, `range` returns all integers from the first number up to *but not including* the second in steps of $1$;\n", "* if one number is given, `range` starts from $0$;\n", "* if three numbers are given, the third is the step." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In fact Python will iterate over *any* collection of objects: they do not have to be integers:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "thing is 1\n", "thing is 2.5\n", "thing is hello\n", "thing is <function add at 0x103bf78c8>\n" ] } ], "source": [ "for thing in 1, 2.5, \"hello\", add:\n", " print(\"thing is \", thing)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is very often used in Python code: if you have some way of collecting things together, Python will happily iterate over them all." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Exercise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Write a loop to check by brute force if an integer `n` is prime, by checking if any integer less than `n` divides it. Turn it into a function that returns `True` if `n` is prime and `False` otherwise. Test the function by printing every prime less than $50$. Find the first $k$ such that $k$ is prime and $2^k-1$ is *not* prime." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "17 is prime? True\n" ] } ], "source": [ "n = 17\n", "prime = True\n", "for divisor in range(2, n):\n", " if n % divisor == 0:\n", " prime = False\n", "print(n, \" is prime? \", prime)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def isprime(n):\n", " \"\"\"\n", " Check if a number is prime (brute force)\n", " \n", " Parameters\n", " ----------\n", " \n", " n : positive integer\n", " Number to check\n", " \n", " Returns\n", " -------\n", " \n", " True/False\n", " \"\"\"\n", " \n", " prime = True\n", " for divisor in range(2, n):\n", " if n % divisor == 0:\n", " prime = False\n", " return prime" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 is prime.\n", "3 is prime.\n", "5 is prime.\n", "7 is prime.\n", "11 is prime.\n", "13 is prime.\n", "17 is prime.\n", "19 is prime.\n", "23 is prime.\n", "29 is prime.\n", "31 is prime.\n", "37 is prime.\n", "41 is prime.\n", "43 is prime.\n", "47 is prime.\n" ] } ], "source": [ "for n in range(2, 50):\n", " if isprime(n):\n", " print(n, \" is prime.\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "11 is the first k such that 2^k-1 is not prime.\n" ] } ], "source": [ "k = 1\n", "while not(isprime(k)) or isprime(2**k - 1):\n", " k = k + 1\n", "print(k, \"is the first k such that 2^k-1 is not prime.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Containers, sequences, lists, arrays" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So what are the Python ways of collecting things together? In many languages there are *arrays*, which are often like vectors or matrices. They are ordered, can be indexed, but where the index starts from varies between languages.\n", "\n", "In Python there are many ways of collecting objects together. The first two to look at are *tuples* and *lists*." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tuples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A tuple is a sequence with fixed size, whose entries cannot be modified:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "3\n" ] } ], "source": [ "t1 = (0, 1, 2, 3, 4, 5)\n", "print(t1[0])\n", "print(t1[3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that to access individual entries we use square brackets and the number of the entry, starting from $0$. **All** Python tuples and lists start from $0$. To check that it cannot be modified:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'tuple' object does not support item assignment", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-18-9e1f4de27f17>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mt1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" ] } ], "source": [ "t1[0] = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use *slicing* to access many entries at once:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1, 2, 3)\n" ] } ], "source": [ "print(t1[1:4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As with the range function, the notation `<start>:<end>` returns the entries from (and including) `<start>` up to, but **not** including, `<end>`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use *negative* numbers to access from the right of the sequence: `-1` is the last entry, `-2` the next-to-last, and so on:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "print(t1[-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lists" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A list is a sequence with a size that can change, and whose entries can be modified:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "7\n", "[0, 1, 2, 7, 4, 5, 6]\n" ] } ], "source": [ "l1 = [0, 1, 2, 3, 4, 5]\n", "print(l1[3])\n", "l1[3] = 7\n", "print(l1[3])\n", "l1.append(6)\n", "print(l1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The same slicing notation can be used, and now can be used to assignment:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4, 5, 2, 7, 4, 5, 6]\n" ] } ], "source": [ "l1[0:2] = l1[4:6]\n", "print(l1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Crucially, lists and tuples can contain *anything*. As with loops, there is no restriction on types, and things can be nested:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.2\n", "a\n" ] } ], "source": [ "l2 = [0, 1.2, \"hello\", [\"a\", 3, 4.5], (0, (1.1, 2.3, 4))]\n", "print(l2[1])\n", "print(l2[3][0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dictionaries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both lists and tuples are *ordered*: there are accessed by an integer giving there location in the sequence. This doesn't always make sense. Consider an algorithm which depends on parameters $\\omega, \\Gamma, N$. We want to keep the parameters together, but there's no logical order to them. Instead we can use a dictionary, which is an unordered Python container:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.7\n" ] } ], "source": [ "d1 = {\"omega\": 1.0, \"Gamma\": 5.7, \"N\": 100}\n", "print(d1[\"Gamma\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As there is no order we access dictionaries using the *key*. To loop over a dictionary, we take advantage of Python's loose iteration rules:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Key is omega value is 1.0\n", "Key is Gamma value is 5.7\n", "Key is N value is 100\n" ] } ], "source": [ "for key in d1:\n", " print(\"Key is\", key, \"value is\", d1[key])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a shortcut to allow you to get both key and value in one go:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Key is omega value is 1.0\n", "Key is Gamma value is 5.7\n", "Key is N value is 100\n" ] } ], "source": [ "for key, value in d1.items():\n", " print(\"Key is\", key, \"value is\", value)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numpy arrays" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've seen python's built-in lists for storing data. However for mathematical purposes (particular based on linear algebra) the list is not ideal. Instead the `numpy` library contains the more powerful `array` datatype. Arrays are essentially a more powerful form of lists which make it easier to handle data. Most importantly, they allow us to apply operations to all elements of an array at once, rather than looping over the elements one-by-one.\n", "\n", "To see this, let's create a list and a numpy array, both containing the same data." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "list l = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]\n", "numpy array a = [[ 1. 2. 3.]\n", " [ 4. 5. 6.]\n", " [ 7. 8. 9.]]\n" ] } ], "source": [ "# python list\n", "l = [[1., 2., 3.],\n", " [4., 5., 6.],\n", " [7., 8., 9.]]\n", "\n", "a = numpy.array([[1., 2., 3.],\n", " [4., 5., 6.],\n", " [7., 8., 9.]])\n", "\n", "print('list l = {}'.format(l))\n", "print('numpy array a = {}'.format(a))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Accessing elements of numpy arrays is very similar to accessing elements of lists, but with slightly less typing. To access elements from an n-dimensional list, we have to use multiple square brackets, e.g. `l[0][4][7][8]`. For a numpy array, we separate the indices using a comma: `a[0, 4, 7, 8]`." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6.0\n", "6.0\n" ] } ], "source": [ "print(l[1][2])\n", "print(a[1,2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's say we now want to square every element of the array. For this 2d list, we would need a for loop:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1.0, 4.0, 9.0], [16.0, 25.0, 36.0], [49.0, 64.0, 81.0]]\n" ] } ], "source": [ "import copy\n", "squared = copy.deepcopy(l)\n", "for i in range(3):\n", " for j in range(3):\n", " squared[i][j] = l[i][j]**2\n", "print(squared)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that here we used the function `deepcopy` from the copy module the copy the list l. If we had simply used `squared = l`, when we the assigned the elements of `squared` new values, this would also have changed the values in `l`. This is in contrast to the simple variables we saw before, where changing the value of one will leave the values of others unchanged. \n", "\n", "For numpy arrays, applying operations across the entire array is much simpler:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. 4. 9.]\n", " [ 16. 25. 36.]\n", " [ 49. 64. 81.]]\n" ] } ], "source": [ "print(a**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Numpy has a range of array manipulation routines for rearranging and manipulating elements, such as those below." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 4., 7.],\n", " [ 2., 5., 8.],\n", " [ 3., 6., 9.]])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# transpose\n", "a.T" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 2., 3., 4., 5., 6., 7., 8., 9.]])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reshape\n", "numpy.reshape(a, (1,9))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 2., 3., 1., 2., 3., 1., 2., 3.],\n", " [ 4., 5., 6., 4., 5., 6., 4., 5., 6.],\n", " [ 7., 8., 9., 7., 8., 9., 7., 8., 9.]])" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# stack arrays horizontally\n", "numpy.hstack((a,a,a))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also apply logical operations to entire arrays:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[False, False, False],\n", " [False, False, True],\n", " [ True, True, True]], dtype=bool)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a > 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you've used Matlab before, you may be familiar with logical indexing. This is a way of accessing elements of a array that satisfy some criteria, e.g. all the elements which are greater than 0. We can also do this with numpy arrays using boolean array indexing:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 6., 7., 8., 9.])" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[a > 5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Exercise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The bubble sort algorithm sorts a list of numbers into ascending order. It loops over all the elements of the list from the start to the end. It compares the element to all \"later\" elements. If the later one is smaller, swap the elements.\n", "\n", "Write a function that takes a list `l` and uses bubble sort to return a sorted list. Test it on `l = [5, 1, 4, 2, 8]`." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def bubblesort(unsorted): \n", " \"\"\"\n", " Sorts an array using bubble sort algorithm\n", "\n", " Parameters\n", " ---------\n", " unsorted : list\n", " The unsorted list\n", "\n", " Returns\n", " -------\n", " sorted : list\n", " The sorted list (in place)\n", " \"\"\"\n", " last = len(unsorted)\n", " # All Python lists start from 0\n", " for i in range(last):\n", " for j in range(i+1, last):\n", " if unsorted[i] > unsorted[j]:\n", " temp = unsorted[j]\n", " unsorted[j] = unsorted[i]\n", " unsorted[i] = temp\n", " return unsorted" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 4, 5, 8]\n" ] } ], "source": [ "l = [5, 1, 4, 2, 8]\n", "print(bubblesort(l))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" }, "nbconvert": { "title": "Loops and functions" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

ES-DOC/esdoc-jupyterhub

notebooks/noaa-gfdl/cmip6/models/sandbox-1/landice.ipynb

1

37256

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Landice \n", "**MIP Era**: CMIP6 \n", "**Institute**: NOAA-GFDL \n", "**Source ID**: SANDBOX-1 \n", "**Topic**: Landice \n", "**Sub-Topics**: Glaciers, Ice. \n", "**Properties**: 30 (21 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/landice?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-20 15:02:35" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "**IMPORTANT: to be executed each time you run the notebook** " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'sandbox-1', 'landice')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --&gt; Software Properties](#2.-Key-Properties---&gt;-Software-Properties) \n", "[3. Grid](#3.-Grid) \n", "[4. Glaciers](#4.-Glaciers) \n", "[5. Ice](#5.-Ice) \n", "[6. Ice --&gt; Mass Balance](#6.-Ice---&gt;-Mass-Balance) \n", "[7. Ice --&gt; Mass Balance --&gt; Basal](#7.-Ice---&gt;-Mass-Balance---&gt;-Basal) \n", "[8. Ice --&gt; Mass Balance --&gt; Frontal](#8.-Ice---&gt;-Mass-Balance---&gt;-Frontal) \n", "[9. Ice --&gt; Dynamics](#9.-Ice---&gt;-Dynamics) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Land ice key properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of land surface model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of land surface model code*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Ice Albedo\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify how ice albedo is modelled*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.ice_albedo') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prescribed\" \n", "# \"function of ice age\" \n", "# \"function of ice density\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Atmospheric Coupling Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Which variables are passed between the atmosphere and ice (e.g. orography, ice mass)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.atmospheric_coupling_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Oceanic Coupling Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Which variables are passed between the ocean and ice*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.oceanic_coupling_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Which variables are prognostically calculated in the ice model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.prognostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"ice velocity\" \n", "# \"ice thickness\" \n", "# \"ice temperature\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Software Properties \n", "*Software properties of land ice code*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Repository\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Location of code for this component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Code Version\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Code version identifier.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Code Languages\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Code language(s).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Grid \n", "*Land ice grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of the grid in the land ice scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Adaptive Grid\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is an adative grid being used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.adaptive_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Base Resolution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The base resolution (in metres), before any adaption*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.base_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.4. Resolution Limit\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If an adaptive grid is being used, what is the limit of the resolution (in metres)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.resolution_limit') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.5. Projection\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The projection of the land ice grid (e.g. albers_equal_area)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.projection') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Glaciers \n", "*Land ice glaciers*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of glaciers in the land ice scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.glaciers.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of glaciers, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.glaciers.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Dynamic Areal Extent\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Does the model include a dynamic glacial extent?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.glaciers.dynamic_areal_extent') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Ice \n", "*Ice sheet and ice shelf*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of the ice sheet and ice shelf in the land ice scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Grounding Line Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify the technique used for modelling the grounding line in the ice sheet-ice shelf coupling*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.grounding_line_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"grounding line prescribed\" \n", "# \"flux prescribed (Schoof)\" \n", "# \"fixed grid size\" \n", "# \"moving grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Ice Sheet\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are ice sheets simulated?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.ice_sheet') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Ice Shelf\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are ice shelves simulated?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.ice_shelf') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Ice --&gt; Mass Balance \n", "*Description of the surface mass balance treatment*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Surface Mass Balance\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how and where the surface mass balance (SMB) is calulated. Include the temporal coupling frequeny from the atmosphere, whether or not a seperate SMB model is used, and if so details of this model, such as its resolution*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.surface_mass_balance') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Ice --&gt; Mass Balance --&gt; Basal \n", "*Description of basal melting*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Bedrock\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the implementation of basal melting over bedrock*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.basal.bedrock') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Ocean\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the implementation of basal melting over the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.basal.ocean') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Ice --&gt; Mass Balance --&gt; Frontal \n", "*Description of claving/melting from the ice shelf front*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Calving\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the implementation of calving from the front of the ice shelf*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.frontal.calving') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Melting\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the implementation of melting from the front of the ice shelf*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.frontal.melting') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Ice --&gt; Dynamics \n", "**" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description if ice sheet and ice shelf dynamics*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.dynamics.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Approximation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Approximation type used in modelling ice dynamics*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.dynamics.approximation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"SIA\" \n", "# \"SAA\" \n", "# \"full stokes\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Adaptive Timestep\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there an adaptive time scheme for the ice scheme?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.dynamics.adaptive_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Timestep\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Timestep (in seconds) of the ice scheme. If the timestep is adaptive, then state a representative timestep.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.dynamics.timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }

gpl-3.0

sbussmann/sleep-bit

notebooks/sbussmann_data-redeye.ipynb

1

41321

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Summary\n", "\n", "I noticed several days with over 10 hours of sleep from the notebook where I obtain my sleep data via the Fitbit API. I don't remember ever sleeping more than 8 hours in one night, so what's going on?" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import os\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import seaborn as sns\n", "\n", "sns.set_context('poster')\n", "import plotly.offline as py\n", "import plotly.graph_objs as go" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>" ], "text/vnd.plotly.v1+html": [ "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "py.init_notebook_mode(connected=True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_path = os.path.join(os.getcwd(), os.pardir, 'data', 'interim', 'sleep_data.csv')\n", "df_sleep = pd.read_csv(data_path, index_col='shifted_datetime', parse_dates=True)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "df_sleep.index += pd.Timedelta(hours=12)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sleep_day = df_sleep.resample('1D').sum().fillna(0)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "redeyes = ['2016-12-12', '2017-01-16', '2017-07-02']" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "name": "Hours Asleep", "type": "scatter", "x": [ 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[3.7333333333333334, 10.383333333333333], \"mode\": \"lines\", \"name\": \"Redeye flight\", \"line\": {\"color\": \"salmon\", \"width\": 3, \"dash\": \"dash\"}, \"showlegend\": false}], {\"title\": \"Daily Sleep Total, 6pm to 6pm\", \"yaxis\": {\"title\": \"Hours Asleep\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "toplot = sleep_day['minutesAsleep']/60.\n", "\n", "data = []\n", "\n", "data.append(\n", " go.Scatter(\n", " x=toplot.index,\n", " y=toplot.values,\n", " name='Hours Asleep'\n", " )\n", ")\n", "\n", "shapes = []\n", "for ired, redeye in enumerate(redeyes):\n", " if ired ==0:\n", " showlegend = True\n", " else:\n", " showlegend = False\n", " trace0 = go.Scatter(\n", " x=[redeye, redeye],\n", " y=[toplot.dropna().min(), toplot.dropna().max()],\n", " mode='lines',\n", " name='Redeye flight',\n", " line={\n", " 'color': 'salmon',\n", " 'width': 3,\n", " 'dash': 'dash'\n", " },\n", " showlegend=showlegend\n", " )\n", " data.append(trace0)\n", " shapes.append({\n", " 'type': 'line',\n", " 'x0': redeye,\n", " 'y0': 0,\n", " 'x1': redeye,\n", " 'y1': 1,\n", " 'xref': 'x',\n", " 'yref': 'paper',\n", " 'line': {\n", " 'color': 'salmon',\n", " 'width': 14,\n", " 'alpha': 0.5\n", " },\n", " })\n", "\n", "layout = go.Layout(\n", " title=\"Daily Sleep Total, 6pm to 6pm\",\n", " yaxis=dict(\n", " title='Hours Asleep'\n", " ),\n", ")\n", "\n", "fig = {\n", " 'data': data,\n", " 'layout': layout,\n", "}\n", "\n", "py.iplot(fig, filename='DailySleepTotal')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "On redeye flights, I don't sleep. But, the day of arrival I get between 3.8 - 6.4 hours of sleep. So I am able to make up for part of the lost sleep." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average daily sleep total: 6.74 hours\n" ] } ], "source": [ "print(\"Average daily sleep total: {:.2f} hours\".format(toplot.mean()))" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average sleep per day for 1 day after redeye: 3.78\n", "Average sleep per day for 3 days after redeye: 5.50\n", "Average sleep per day for 7 days after redeye: 6.78\n", "Average sleep per day for 1 day after redeye: 6.37\n", "Average sleep per day for 3 days after redeye: 7.41\n", "Average sleep per day for 7 days after redeye: 6.68\n", "Average sleep per day for 1 day after redeye: 4.68\n", "Average sleep per day for 3 days after redeye: 5.93\n", "Average sleep per day for 7 days after redeye: 6.06\n" ] } ], "source": [ "for ired, redeye in enumerate(redeyes):\n", " redeye1 = pd.to_datetime(redeye) + pd.Timedelta(days=0)\n", " redeye3 = pd.to_datetime(redeye) + pd.Timedelta(days=2)\n", " redeye7 = pd.to_datetime(redeye) + pd.Timedelta(days=6)\n", "# print(\"Sleep after redeye: {:.2f} hours\".format(toplot[redeye]))\n", " print(\"Average sleep per day for 1 day after redeye: {:.2f}\".format(toplot[redeye:redeye1].mean()))\n", " print(\"Average sleep per day for 3 days after redeye: {:.2f}\".format(toplot[redeye:redeye3].mean()))\n", " print(\"Average sleep per day for 7 days after redeye: {:.2f}\".format(toplot[redeye:redeye7].mean()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For two out of my three redeye flights over the past 9 months, the sleep that I lost the night of the redeye led to reduced average daily sleep totals (compared to my overall average of 6.7 hours) over the subsequent 3 days and 7 days. Both of those trips were to California to visit family. Upon return, I went back to work and did not have the luxury of sleeping in to catch up on sleep. In contrast, the other redeye flight was part of a vacation to Iceland, so I had the opportunity to sleep in a bit and catch up on sleep.\n", "\n", "This evidence indicates that \"redeye followed by work\" corresponds to an expected reduction in daily sleep of 1.25 hours for the 3 days following a redeye trip and 0 - 0.7 hours for the week following a redeye trip. Redeye followed by vacation does not seem to have a significant impact on my daily sleep totals.\n", "\n", "Based on the limited data available, it is worthwhile to include this as a significant predictive feature for my expected daily sleep total. For instance, a reasonable goal for average daily sleep over the 3 days after a redeye might be 6 hours per day rather than my typical goal of 7.3 hours." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

jrising/research-common

docs/using-spatial.R.ipynb

2

14824

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This is an example of using the `spatial.R` code to aggregate gridded weather data. Specifically, we aggregate gridded maximum temperature data to Brazilian municipalities.\n", "\n", "First, load the required packages. Note that this library, `research-common`, is in my local folder `~/projects/research-common`, but it could be anywhere." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "library(PBSmapping)\n", "library(ncdf4)\n", "source(\"~/projects/research-common/R/distance.R\")\n", "source(\"~/projects/research-common/R/spatial.R\", chdir=T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the shapefile. I use `PBSmapping`, which represents shapefiles as lists of points." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "shape <- importShapefile(\"~/data/political/brazil/BRA_adm2/BRA_adm2\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the gridded data." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ncdf <- nc_open(\"~/data/agmerra/monthly/tasmax_agmerra_1980-2010.mm.nc4\")\n", "longitude <- ncvar_get(ncdf, \"lon\")\n", "latitude <- ncvar_get(ncdf, \"lat\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `prepareEvents` function identifies the center of each grid cell, which will be used to make the averages. In a loop over multiple files, it's best to only do this once." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "events <- prepareEvents(longitude, latitude, attr(shape, \"projection\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get out the weather data itself. It's important that this has dimensions of the order longitude, latitude, time." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lonlattimeraster <- ncvar_get(ncdf, \"tasmax\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also get out the time variable, for producing output." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "time <- ncvar_get(ncdf, \"time\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Iterate through all of the polygons, constructing averages and adding them to the result dataframe." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] 1\n", "[1] 2\n", "[1] 3\n", "[1] 4\n", "[1] 5\n", "[1] 6\n", "[1] 7\n", "[1] 8\n", "[1] 9\n", "[1] 10\n" ] } ], "source": [ "values <- data.frame(PID=c(), time=c(), average=c())\n", "for (pid in 1:10) {\n", " print(pid)\n", " average <- \n", " spaceTimeRasterAverage(events, lonlattimeraster,\n", " subset(shape, PID == pid))\n", " values <- rbind(values, data.frame(PID=pid, time, average))\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's what the final result looks like." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th scope=col>PID</th><th scope=col>time</th><th scope=col>average</th></tr></thead>\n", "<tbody>\n", "\t<tr><td>1 </td><td>43859.04</td><td>303.7097</td></tr>\n", "\t<tr><td>1 </td><td>43888.04</td><td>303.9345</td></tr>\n", "\t<tr><td>1 </td><td>43919.04</td><td>304.1944</td></tr>\n", "\t<tr><td>1 </td><td>43949.04</td><td>303.4933</td></tr>\n", "\t<tr><td>1 </td><td>43980.04</td><td>302.4032</td></tr>\n", "\t<tr><td>1 </td><td>44010.04</td><td>301.1283</td></tr>\n", "\t<tr><td>1 </td><td>44041.04</td><td>300.2048</td></tr>\n", "\t<tr><td>1 </td><td>44072.04</td><td>301.2161</td></tr>\n", "\t<tr><td>1 </td><td>44102.04</td><td>301.6150</td></tr>\n", "\t<tr><td>1 </td><td>44133.04</td><td>302.1299</td></tr>\n", "\t<tr><td>1 </td><td>44163.04</td><td>302.3475</td></tr>\n", "\t<tr><td>1 </td><td>44194.04</td><td>303.4847</td></tr>\n", "\t<tr><td>1 </td><td>44225.04</td><td>303.9887</td></tr>\n", "\t<tr><td>1 </td><td>44253.04</td><td>304.1089</td></tr>\n", "\t<tr><td>1 </td><td>44284.04</td><td>302.1355</td></tr>\n", "\t<tr><td>1 </td><td>44314.04</td><td>302.2700</td></tr>\n", "\t<tr><td>1 </td><td>44345.04</td><td>301.2113</td></tr>\n", "\t<tr><td>1 </td><td>44375.04</td><td>299.9608</td></tr>\n", "\t<tr><td>1 </td><td>44406.04</td><td>299.5403</td></tr>\n", "\t<tr><td>1 </td><td>44437.04</td><td>300.5734</td></tr>\n", "\t<tr><td>1 </td><td>44467.04</td><td>301.3025</td></tr>\n", "\t<tr><td>1 </td><td>44498.04</td><td>301.0186</td></tr>\n", "\t<tr><td>1 </td><td>44528.04</td><td>302.8075</td></tr>\n", "\t<tr><td>1 </td><td>44559.04</td><td>303.8790</td></tr>\n", "\t<tr><td>1 </td><td>44590.04</td><td>303.6194</td></tr>\n", "\t<tr><td>1 </td><td>44618.04</td><td>304.2107</td></tr>\n", "\t<tr><td>1 </td><td>44649.04</td><td>304.7847</td></tr>\n", "\t<tr><td>1 </td><td>44679.04</td><td>301.8208</td></tr>\n", "\t<tr><td>1 </td><td>44710.04</td><td>300.4879</td></tr>\n", "\t<tr><td>1 </td><td>44740.04</td><td>301.1892</td></tr>\n", "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n", "\t<tr><td>10 </td><td>54268.04</td><td>304.0499</td></tr>\n", "\t<tr><td>10 </td><td>54299.04</td><td>305.0580</td></tr>\n", "\t<tr><td>10 </td><td>54329.04</td><td>304.6178</td></tr>\n", "\t<tr><td>10 </td><td>54360.04</td><td>304.3485</td></tr>\n", "\t<tr><td>10 </td><td>54390.04</td><td>304.0840</td></tr>\n", "\t<tr><td>10 </td><td>54421.04</td><td>303.6433</td></tr>\n", "\t<tr><td>10 </td><td>54452.04</td><td>303.1919</td></tr>\n", "\t<tr><td>10 </td><td>54480.04</td><td>303.3282</td></tr>\n", "\t<tr><td>10 </td><td>54511.04</td><td>304.1026</td></tr>\n", "\t<tr><td>10 </td><td>54541.04</td><td>303.9692</td></tr>\n", "\t<tr><td>10 </td><td>54572.04</td><td>303.7490</td></tr>\n", "\t<tr><td>10 </td><td>54602.04</td><td>303.0998</td></tr>\n", "\t<tr><td>10 </td><td>54633.04</td><td>305.0337</td></tr>\n", "\t<tr><td>10 </td><td>54664.04</td><td>305.2790</td></tr>\n", "\t<tr><td>10 </td><td>54694.04</td><td>305.9802</td></tr>\n", "\t<tr><td>10 </td><td>54725.04</td><td>305.2855</td></tr>\n", "\t<tr><td>10 </td><td>54755.04</td><td>305.1216</td></tr>\n", "\t<tr><td>10 </td><td>54786.04</td><td>303.5356</td></tr>\n", "\t<tr><td>10 </td><td>54817.04</td><td>303.2183</td></tr>\n", "\t<tr><td>10 </td><td>54845.04</td><td>304.2313</td></tr>\n", "\t<tr><td>10 </td><td>54876.04</td><td>304.3804</td></tr>\n", "\t<tr><td>10 </td><td>54906.04</td><td>303.9482</td></tr>\n", "\t<tr><td>10 </td><td>54937.04</td><td>303.0795</td></tr>\n", "\t<tr><td>10 </td><td>54967.04</td><td>302.3376</td></tr>\n", "\t<tr><td>10 </td><td>54998.04</td><td>302.3311</td></tr>\n", "\t<tr><td>10 </td><td>55029.04</td><td>302.9334</td></tr>\n", "\t<tr><td>10 </td><td>55059.04</td><td>302.4094</td></tr>\n", "\t<tr><td>10 </td><td>55090.04</td><td>301.5102</td></tr>\n", "\t<tr><td>10 </td><td>55120.04</td><td>301.1220</td></tr>\n", "\t<tr><td>10 </td><td>55151.04</td><td>301.9252</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " PID & time & average\\\\\n", "\\hline\n", "\t 1 & 43859.04 & 303.7097\\\\\n", "\t 1 & 43888.04 & 303.9345\\\\\n", "\t 1 & 43919.04 & 304.1944\\\\\n", "\t 1 & 43949.04 & 303.4933\\\\\n", "\t 1 & 43980.04 & 302.4032\\\\\n", "\t 1 & 44010.04 & 301.1283\\\\\n", "\t 1 & 44041.04 & 300.2048\\\\\n", "\t 1 & 44072.04 & 301.2161\\\\\n", "\t 1 & 44102.04 & 301.6150\\\\\n", "\t 1 & 44133.04 & 302.1299\\\\\n", "\t 1 & 44163.04 & 302.3475\\\\\n", "\t 1 & 44194.04 & 303.4847\\\\\n", "\t 1 & 44225.04 & 303.9887\\\\\n", "\t 1 & 44253.04 & 304.1089\\\\\n", "\t 1 & 44284.04 & 302.1355\\\\\n", "\t 1 & 44314.04 & 302.2700\\\\\n", "\t 1 & 44345.04 & 301.2113\\\\\n", "\t 1 & 44375.04 & 299.9608\\\\\n", "\t 1 & 44406.04 & 299.5403\\\\\n", "\t 1 & 44437.04 & 300.5734\\\\\n", "\t 1 & 44467.04 & 301.3025\\\\\n", "\t 1 & 44498.04 & 301.0186\\\\\n", "\t 1 & 44528.04 & 302.8075\\\\\n", "\t 1 & 44559.04 & 303.8790\\\\\n", "\t 1 & 44590.04 & 303.6194\\\\\n", "\t 1 & 44618.04 & 304.2107\\\\\n", "\t 1 & 44649.04 & 304.7847\\\\\n", "\t 1 & 44679.04 & 301.8208\\\\\n", "\t 1 & 44710.04 & 300.4879\\\\\n", "\t 1 & 44740.04 & 301.1892\\\\\n", "\t ⋮ & ⋮ & ⋮\\\\\n", "\t 10 & 54268.04 & 304.0499\\\\\n", "\t 10 & 54299.04 & 305.0580\\\\\n", "\t 10 & 54329.04 & 304.6178\\\\\n", "\t 10 & 54360.04 & 304.3485\\\\\n", "\t 10 & 54390.04 & 304.0840\\\\\n", "\t 10 & 54421.04 & 303.6433\\\\\n", "\t 10 & 54452.04 & 303.1919\\\\\n", "\t 10 & 54480.04 & 303.3282\\\\\n", "\t 10 & 54511.04 & 304.1026\\\\\n", "\t 10 & 54541.04 & 303.9692\\\\\n", "\t 10 & 54572.04 & 303.7490\\\\\n", "\t 10 & 54602.04 & 303.0998\\\\\n", "\t 10 & 54633.04 & 305.0337\\\\\n", "\t 10 & 54664.04 & 305.2790\\\\\n", "\t 10 & 54694.04 & 305.9802\\\\\n", "\t 10 & 54725.04 & 305.2855\\\\\n", "\t 10 & 54755.04 & 305.1216\\\\\n", "\t 10 & 54786.04 & 303.5356\\\\\n", "\t 10 & 54817.04 & 303.2183\\\\\n", "\t 10 & 54845.04 & 304.2313\\\\\n", "\t 10 & 54876.04 & 304.3804\\\\\n", "\t 10 & 54906.04 & 303.9482\\\\\n", "\t 10 & 54937.04 & 303.0795\\\\\n", "\t 10 & 54967.04 & 302.3376\\\\\n", "\t 10 & 54998.04 & 302.3311\\\\\n", "\t 10 & 55029.04 & 302.9334\\\\\n", "\t 10 & 55059.04 & 302.4094\\\\\n", "\t 10 & 55090.04 & 301.5102\\\\\n", "\t 10 & 55120.04 & 301.1220\\\\\n", "\t 10 & 55151.04 & 301.9252\\\\\n", "\\end{tabular}\n" ], "text/plain": [ " PID time average \n", "1 1 43859.04 303.7097\n", "2 1 43888.04 303.9345\n", "3 1 43919.04 304.1944\n", "4 1 43949.04 303.4933\n", "5 1 43980.04 302.4032\n", "6 1 44010.04 301.1283\n", "7 1 44041.04 300.2048\n", "8 1 44072.04 301.2161\n", "9 1 44102.04 301.6150\n", "10 1 44133.04 302.1299\n", "11 1 44163.04 302.3475\n", "12 1 44194.04 303.4847\n", "13 1 44225.04 303.9887\n", "14 1 44253.04 304.1089\n", "15 1 44284.04 302.1355\n", "16 1 44314.04 302.2700\n", "17 1 44345.04 301.2113\n", "18 1 44375.04 299.9608\n", "19 1 44406.04 299.5403\n", "20 1 44437.04 300.5734\n", "21 1 44467.04 301.3025\n", "22 1 44498.04 301.0186\n", "23 1 44528.04 302.8075\n", "24 1 44559.04 303.8790\n", "25 1 44590.04 303.6194\n", "26 1 44618.04 304.2107\n", "27 1 44649.04 304.7847\n", "28 1 44679.04 301.8208\n", "29 1 44710.04 300.4879\n", "30 1 44740.04 301.1892\n", "⋮ ⋮ ⋮ ⋮ \n", "3691 10 54268.04 304.0499\n", "3692 10 54299.04 305.0580\n", "3693 10 54329.04 304.6178\n", "3694 10 54360.04 304.3485\n", "3695 10 54390.04 304.0840\n", "3696 10 54421.04 303.6433\n", "3697 10 54452.04 303.1919\n", "3698 10 54480.04 303.3282\n", "3699 10 54511.04 304.1026\n", "3700 10 54541.04 303.9692\n", "3701 10 54572.04 303.7490\n", "3702 10 54602.04 303.0998\n", "3703 10 54633.04 305.0337\n", "3704 10 54664.04 305.2790\n", "3705 10 54694.04 305.9802\n", "3706 10 54725.04 305.2855\n", "3707 10 54755.04 305.1216\n", "3708 10 54786.04 303.5356\n", "3709 10 54817.04 303.2183\n", "3710 10 54845.04 304.2313\n", "3711 10 54876.04 304.3804\n", "3712 10 54906.04 303.9482\n", "3713 10 54937.04 303.0795\n", "3714 10 54967.04 302.3376\n", "3715 10 54998.04 302.3311\n", "3716 10 55029.04 302.9334\n", "3717 10 55059.04 302.4094\n", "3718 10 55090.04 301.5102\n", "3719 10 55120.04 301.1220\n", "3720 10 55151.04 301.9252" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "values" ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.2.1" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

SamLau95/nbinteract

tests/test_notebooks/cleared_interact.ipynb

1

1195

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cleared Interact" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from ipywidgets import interact\n", "\n", "def square(x): return x * x\n", "\n", "interact(square, x=(0, 10));" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" }, "toc": { "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }

bsd-3-clause