All notebooks in Scipy folder.
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Scipy Notebooks
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| ipython/ | |
| nbconvert/ | |
| pandas/ | |
| scipy-sphinx-theme-master | |
| scipy.org-new | |
| spyder-2.1.11/ | |
| spyderlib/ |
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| { | |
| "metadata": { | |
| "name": "" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "%matplotlib\n", | |
| "import numpy as np\n", | |
| "import matplotlib\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import datetime as dt\n", | |
| "\n", | |
| "import pandas as pd\n", | |
| "from pandas import Timestamp\n", | |
| "\n", | |
| "from IPython.display import display" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "Using matplotlib backend: Qt4Agg\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 22 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "print \"pandas\", pd.__version__\n", | |
| "print \"numpy\", np.__version__\n", | |
| "print \"matplotlib\", matplotlib.__version__" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "pandas 0.12.0.dev\n", | |
| "numpy 1.7.1\n", | |
| "matplotlib 1.2.1\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 2 | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 1, | |
| "metadata": {}, | |
| "source": [ | |
| "Some data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "d1 = { Timestamp('2012-02-13 07:00:00', tz=None): 11275.1171815089,\n", | |
| " Timestamp('2012-02-13 07:05:00', tz=None): 9716.1975112658201,\n", | |
| " Timestamp('2012-02-13 07:10:00', tz=None): 19025.623839698899,\n", | |
| " Timestamp('2012-02-13 07:15:00', tz=None): 7796.3502853025902,\n", | |
| " Timestamp('2012-02-13 07:20:00', tz=None): 6544.3192784759103,\n", | |
| " Timestamp('2012-02-13 07:25:00', tz=None): 9216.7045087268107,\n", | |
| " Timestamp('2012-02-13 07:30:00', tz=None): 10641.0014973668,\n", | |
| " Timestamp('2012-02-13 07:35:00', tz=None): 9265.5572036521007,\n", | |
| " Timestamp('2012-02-13 07:40:00', tz=None): 10170.9778086931,\n", | |
| " Timestamp('2012-02-13 07:45:00', tz=None): 14619.520279233901,\n", | |
| " Timestamp('2012-02-13 07:50:00', tz=None): 16247.8026636124,\n", | |
| " Timestamp('2012-02-13 07:55:00', tz=None): 14689.2077640114,\n", | |
| " Timestamp('2012-02-13 08:00:00', tz=None): 16864.147144495601,\n", | |
| " Timestamp('2012-02-13 08:05:00', tz=None): 20333.935197651001,\n", | |
| " Timestamp('2012-02-13 08:10:00', tz=None): 15841.608848156,\n", | |
| " Timestamp('2012-02-13 08:15:00', tz=None): 6437.3359980843898,\n", | |
| " Timestamp('2012-02-13 08:20:00', tz=None): 12665.2097513764,\n", | |
| " Timestamp('2012-02-13 08:25:00', tz=None): 20457.090124926199,\n", | |
| " Timestamp('2012-02-13 08:30:00', tz=None): 14574.8467994937,\n", | |
| " Timestamp('2012-02-13 08:35:00', tz=None): 14053.4282778774,\n", | |
| " Timestamp('2012-02-13 08:40:00', tz=None): 24437.793871070699,\n", | |
| " Timestamp('2012-02-13 08:45:00', tz=None): 34235.184037594699,\n", | |
| " Timestamp('2012-02-13 08:50:00', tz=None): 31602.091134013299,\n", | |
| " Timestamp('2012-02-13 08:55:00', tz=None): 22762.4434839891,\n", | |
| " Timestamp('2012-02-13 09:00:00', tz=None): 17191.559276551201,\n", | |
| " Timestamp('2012-02-13 09:05:00', tz=None): 16579.131885726099,\n", | |
| " Timestamp('2012-02-13 09:10:00', tz=None): 22286.171397656999,\n", | |
| " Timestamp('2012-02-13 09:15:00', tz=None): 11130.4330253935,\n", | |
| " Timestamp('2012-02-13 09:20:00', tz=None): 12737.1755230291,\n", | |
| " Timestamp('2012-02-13 09:25:00', tz=None): 15411.276398121499,\n", | |
| " Timestamp('2012-02-13 09:30:00', tz=None): 10824.908275376099,\n", | |
| " Timestamp('2012-02-13 09:35:00', tz=None): 14564.8608923051,\n", | |
| " Timestamp('2012-02-13 09:40:00', tz=None): 13871.813261955,\n", | |
| " Timestamp('2012-02-13 09:45:00', tz=None): 17515.15271821,\n", | |
| " Timestamp('2012-02-13 09:50:00', tz=None): 10896.650143172301,\n", | |
| " Timestamp('2012-02-13 09:55:00', tz=None): 15165.789864091101,\n", | |
| " Timestamp('2012-02-13 10:00:00', tz=None): 15604.4178076958,\n", | |
| " Timestamp('2012-02-13 10:05:00', tz=None): 11069.426251438101,\n", | |
| " Timestamp('2012-02-13 10:10:00', tz=None): 13271.7504875585,\n", | |
| " Timestamp('2012-02-13 10:15:00', tz=None): 18083.089451693701,\n", | |
| " Timestamp('2012-02-13 10:20:00', tz=None): 17383.005997707201,\n", | |
| " Timestamp('2012-02-13 10:25:00', tz=None): 9693.2884842924104,\n", | |
| " Timestamp('2012-02-13 10:30:00', tz=None): 7624.1942330553102,\n", | |
| " Timestamp('2012-02-13 10:35:00', tz=None): 14974.6765113441,\n", | |
| " Timestamp('2012-02-13 10:40:00', tz=None): 10880.8774297666,\n", | |
| " Timestamp('2012-02-13 10:45:00', tz=None): 14925.031108065899,\n", | |
| " Timestamp('2012-02-13 10:50:00', tz=None): 7790.7239986914001,\n", | |
| " Timestamp('2012-02-13 10:55:00', tz=None): 16585.190885459499,\n", | |
| " Timestamp('2012-02-13 11:00:00', tz=None): 30116.171596340599,\n", | |
| " Timestamp('2012-02-13 11:05:00', tz=None): 9044.2243671698106,\n", | |
| " Timestamp('2012-02-13 11:10:00', tz=None): 9032.8891190616505,\n", | |
| " Timestamp('2012-02-13 11:15:00', tz=None): 7746.1659898560501,\n", | |
| " Timestamp('2012-02-13 11:20:00', tz=None): 8674.3468175275993,\n", | |
| " Timestamp('2012-02-13 11:25:00', tz=None): 11730.6137529968,\n", | |
| " Timestamp('2012-02-13 11:30:00', tz=None): 14458.4798661402,\n", | |
| " Timestamp('2012-02-13 11:35:00', tz=None): 9677.03765294406,\n", | |
| " Timestamp('2012-02-13 11:40:00', tz=None): 15697.50783029,\n", | |
| " Timestamp('2012-02-13 11:45:00', tz=None): 11797.7706081939,\n", | |
| " Timestamp('2012-02-13 11:50:00', tz=None): 17381.614453856699,\n", | |
| " Timestamp('2012-02-13 11:55:00', tz=None): 10246.3283131631,\n", | |
| " Timestamp('2012-02-13 12:00:00', tz=None): 8565.9721169700606,\n", | |
| " Timestamp('2012-02-13 12:05:00', tz=None): 7945.8354571313203,\n", | |
| " Timestamp('2012-02-13 12:10:00', tz=None): 14301.8476386163,\n", | |
| " Timestamp('2012-02-13 12:15:00', tz=None): 14134.3943508918,\n", | |
| " Timestamp('2012-02-13 12:20:00', tz=None): 16513.0340654592,\n", | |
| " Timestamp('2012-02-13 12:25:00', tz=None): 13421.556986187299,\n", | |
| " Timestamp('2012-02-13 12:30:00', tz=None): 13802.3940849097,\n", | |
| " Timestamp('2012-02-13 12:35:00', tz=None): 11348.398008644201,\n", | |
| " Timestamp('2012-02-13 12:40:00', tz=None): 14959.364640707499,\n", | |
| " Timestamp('2012-02-13 12:45:00', tz=None): 8262.4433353507193,\n", | |
| " Timestamp('2012-02-13 12:50:00', tz=None): 8163.2341018036504,\n", | |
| " Timestamp('2012-02-13 12:55:00', tz=None): 9289.2420647674699}" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 95 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "df1 = pd.Series(d1)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 96 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "d2 = {Timestamp('2012-02-13 08:13:29', tz=None): 8943.8935927950006,\n", | |
| " Timestamp('2012-02-13 08:46:17', tz=None): 26106.3979595001,\n", | |
| " Timestamp('2012-02-13 09:33:02', tz=None): 12956.625308615899,\n", | |
| " Timestamp('2012-02-13 10:46:21', tz=None): 7501.7440938269401,\n", | |
| " Timestamp('2012-02-13 11:15:29', tz=None): 11522.637284778901,\n", | |
| " Timestamp('2012-02-13 12:17:26', tz=None): 15309.5431869974}" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 38 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "df2 = pd.Series(d2)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 39 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "**Samenvatting tot nu toe**:\n", | |
| "\n", | |
| "- heeft te maken met irregular timeseries vs period timeseries die anders geplot worden\n", | |
| "- irregular wordt geplot als een gewone figuur, waarbij de index wordt omgezet naar datetimes met ``_mpl_repr()``\n", | |
| "- period timeseries wordt geplot met de pandas tseries plotting machinery => geen omzetting naar datetimes, of verkeerde omzetting, want als je daar de ``_mpl_repr()`` opvraagt geeft die gewoon de Periods in een array terug. Als je die dan als int bekijkt krijg je de getallen waar matplotlib niet mee om kan, omdat het geen valid getallen zijn voor zijn num2date\n", | |
| "\n", | |
| "=> pas als je dat gaat combineren komen er problemen van\n", | |
| "\n", | |
| "\n", | |
| "zie op nbviewer voor de eerste versie van deze notebook\n", | |
| "\n", | |
| "Nog een fout is dat hier `fromtimestamp` wordt gebruikt (dat is wat je in de figure window onderaan als cursor-waardne ziet), terwijl t in de frequentie wordt uitgedrukt, bv voor `freq='T'` in minuten, terwijl `fromtimestamp` seconden? verwacht (niet helemaal zeker van, in ieder geval door de minuten in dit voorbeeld x60 te doen, was het wel goed)\n", | |
| "\n", | |
| " > c:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py(86)tsplot()\n", | |
| " 85 # x and y coord info\n", | |
| " ---> 86 tz = series.index.to_datetime().tz\n", | |
| " 87 ax.format_coord = lambda t, y : \"t = {} y = {:8f}\".format(datetime.fromtimestamp(t, tz), y)\n", | |
| "\n", | |
| "=> pandas werkt met timestamp, terwijl matplotlib in ordinals werkt => die zijn niet verenigbaar\n", | |
| "\n", | |
| "**TO DO**:\n", | |
| "\n", | |
| "- de bug met tijd die wordt weergegeven kan ik alvast nagaan door met andere PeriodIndex frequencies te testen\n", | |
| "- voor eerste bug => blijkt iets mis te lopen als er al een ax.freq is (de twee om draaien in volgorden gaf geen problemen? => nagaan) => dat eens debuggen\n", | |
| "\n", | |
| " - \"convert irregular if axes has freq info\" (L1238) => in de conversion loopt mss iets mis, andere freq?\n", | |
| " - \n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 1, | |
| "metadata": {}, | |
| "source": [ | |
| "Making a plot" | |
| ] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 2, | |
| "metadata": {}, | |
| "source": [ | |
| "First of both series seperately" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "fig, ax = plt.subplots()\n", | |
| "ax.plot(df1.index, df1.values)\n", | |
| "display(fig)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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LCi6Rdmw27/5boLeCyxYFosAFdZOZ2+1Ga2sr0vpdXZ1OJ0pKSrBw4UIsWrQI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\n+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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0xe552438>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 80 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "df1.index.asi8" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 123, | |
| "text": [ | |
| "array([1329116400000000000, 1329116700000000000, 1329117000000000000,\n", | |
| " 1329117300000000000, 1329117600000000000, 1329117900000000000,\n", | |
| " 1329118200000000000, 1329118500000000000, 1329118800000000000,\n", | |
| " 1329119100000000000, 1329119400000000000, 1329119700000000000,\n", | |
| " 1329120000000000000, 1329120300000000000, 1329120600000000000,\n", | |
| " 1329120900000000000, 1329121200000000000, 1329121500000000000,\n", | |
| " 1329121800000000000, 1329122100000000000, 1329122400000000000,\n", | |
| " 1329122700000000000, 1329123000000000000, 1329123300000000000,\n", | |
| " 1329123600000000000, 1329123900000000000, 1329124200000000000,\n", | |
| " 1329124500000000000, 1329124800000000000, 1329125100000000000,\n", | |
| " 1329125400000000000, 1329125700000000000, 1329126000000000000,\n", | |
| " 1329126300000000000, 1329126600000000000, 1329126900000000000,\n", | |
| " 1329127200000000000, 1329127500000000000, 1329127800000000000,\n", | |
| " 1329128100000000000, 1329128400000000000, 1329128700000000000,\n", | |
| " 1329129000000000000, 1329129300000000000, 1329129600000000000,\n", | |
| " 1329129900000000000, 1329130200000000000, 1329130500000000000,\n", | |
| " 1329130800000000000, 1329131100000000000, 1329131400000000000,\n", | |
| " 1329131700000000000, 1329132000000000000, 1329132300000000000,\n", | |
| " 1329132600000000000, 1329132900000000000, 1329133200000000000,\n", | |
| " 1329133500000000000, 1329133800000000000, 1329134100000000000,\n", | |
| " 1329134400000000000, 1329134700000000000, 1329135000000000000,\n", | |
| " 1329135300000000000, 1329135600000000000, 1329135900000000000,\n", | |
| " 1329136200000000000, 1329136500000000000, 1329136800000000000,\n", | |
| " 1329137100000000000, 1329137400000000000, 1329137700000000000], dtype=int64)" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 123 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "df1.index.to_period(freq='T').asi8" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 124, | |
| "text": [ | |
| "array([22151940, 22151945, 22151950, 22151955, 22151960, 22151965,\n", | |
| " 22151970, 22151975, 22151980, 22151985, 22151990, 22151995,\n", | |
| " 22152000, 22152005, 22152010, 22152015, 22152020, 22152025,\n", | |
| " 22152030, 22152035, 22152040, 22152045, 22152050, 22152055,\n", | |
| " 22152060, 22152065, 22152070, 22152075, 22152080, 22152085,\n", | |
| " 22152090, 22152095, 22152100, 22152105, 22152110, 22152115,\n", | |
| " 22152120, 22152125, 22152130, 22152135, 22152140, 22152145,\n", | |
| " 22152150, 22152155, 22152160, 22152165, 22152170, 22152175,\n", | |
| " 22152180, 22152185, 22152190, 22152195, 22152200, 22152205,\n", | |
| " 22152210, 22152215, 22152220, 22152225, 22152230, 22152235,\n", | |
| " 22152240, 22152245, 22152250, 22152255, 22152260, 22152265,\n", | |
| " 22152270, 22152275, 22152280, 22152285, 22152290, 22152295], dtype=int64)" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 124 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "fig, ax = plt.subplots()\n", | |
| "#ax.xaxis.freq = 'T'\n", | |
| "ax.plot(df1.index.to_period(freq='T')._mpl_repr(), df1.values)\n", | |
| "display(fig)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "ename": "TypeError", | |
| "evalue": "Axis must have `freq` set to convert to Periods", | |
| "output_type": "pyerr", | |
| "traceback": [ | |
| "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", | |
| "\u001b[1;32m<ipython-input-119-38bdce2d324f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m#ax.xaxis.freq = 'T'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_period\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'T'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mpl_repr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[1;32mC:\\Anaconda\\envs\\devel\\lib\\site-packages\\matplotlib\\axes.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 3994\u001b[0m \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3995\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3996\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3997\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3998\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[1;32mC:\\Anaconda\\envs\\devel\\lib\\site-packages\\matplotlib\\axes.pyc\u001b[0m in \u001b[0;36m_grab_next_args\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 328\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 329\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 330\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mseg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\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 331\u001b[0m \u001b[1;32myield\u001b[0m \u001b[0mseg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 332\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[1;32mC:\\Anaconda\\envs\\devel\\lib\\site-packages\\matplotlib\\axes.pyc\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m 306\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 307\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 308\u001b[1;33m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 309\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 310\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'plot'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[1;32mC:\\Anaconda\\envs\\devel\\lib\\site-packages\\matplotlib\\axes.pyc\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[1;34m(self, x, y)\u001b[0m\n\u001b[0;32m 222\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_xy_from_xy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 223\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myaxis\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[1;32m--> 224\u001b[1;33m \u001b[0mbx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_units\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 225\u001b[0m \u001b[0mby\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_units\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[1;32mC:\\Anaconda\\envs\\devel\\lib\\site-packages\\matplotlib\\axis.pyc\u001b[0m in \u001b[0;36mupdate_units\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 1305\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1306\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mneednew\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1307\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_axisinfo\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 1308\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1309\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[1;32mC:\\Anaconda\\envs\\devel\\lib\\site-packages\\matplotlib\\axis.pyc\u001b[0m in \u001b[0;36m_update_axisinfo\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1340\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misDefault_label\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1341\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1342\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_default_intervals\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 1343\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1344\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mhave_units\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[1;32mC:\\Anaconda\\envs\\devel\\lib\\site-packages\\matplotlib\\axis.pyc\u001b[0m in \u001b[0;36mset_default_intervals\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1801\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0minfo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdefault_limits\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 1802\u001b[0m \u001b[0mvalmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalmax\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minfo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdefault_limits\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1803\u001b[1;33m \u001b[0mxmin\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconverter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1804\u001b[0m \u001b[0mxmax\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconverter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalmax\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1805\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mdataMutated\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\converter.pyc\u001b[0m in \u001b[0;36mconvert\u001b[1;34m(values, units, axis)\u001b[0m\n\u001b[0;32m 107\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 108\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'freq'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 109\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Axis must have `freq` set to convert to Periods'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 110\u001b[0m \u001b[0mvalid_types\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mPeriod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpydt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpydt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 111\u001b[0m if (isinstance(values, valid_types) or com.is_integer(values) or\n", | |
| "\u001b[1;31mTypeError\u001b[0m: Axis must have `freq` set to convert to Periods" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 119 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "df1_2 = df1.copy()\n", | |
| "df1_2.index = df1_2.index.to_period(freq='T')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 113 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "fig, ax = plt.subplots()\n", | |
| "df1_2.plot(ax=ax)\n", | |
| "display(fig)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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0KM6ePYv169djypQpzvt+4okn8Oabb+Kll15y7nfXrl1ISkrCtm3bnBXb4cOH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\nnVxeq6urA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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0xe800b38>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 115 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "fig, ax = plt.subplots()\n", | |
| "ax.plot(df1_2)\n", | |
| "display(fig)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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Pa926dbrqqqt06623evYvXbrUZ1xFRYVKSkr061//Wnv27FFFRYUkUy3OzMz0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\nDwcOHEBzc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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0xe822908>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 125 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "df1.index.to_period(freq='T')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 105, | |
| "text": [ | |
| "<class 'pandas.tseries.period.PeriodIndex'>\n", | |
| "freq: T\n", | |
| "[2012-02-13 07:00, ..., 2012-02-13 12:55]\n", | |
| "length: 72" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 105 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "pd.PeriodIndex(df1.index.values, freq='T')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 106, | |
| "text": [ | |
| "<class 'pandas.tseries.period.PeriodIndex'>\n", | |
| "freq: T\n", | |
| "[2012-02-13 07:00, ..., 2012-02-13 12:55]\n", | |
| "length: 72" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 106 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "df1.index.to_period(freq='T').astype('int64')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 128, | |
| "text": [ | |
| "array([22151940, 22151945, 22151950, 22151955, 22151960, 22151965,\n", | |
| " 22151970, 22151975, 22151980, 22151985, 22151990, 22151995,\n", | |
| " 22152000, 22152005, 22152010, 22152015, 22152020, 22152025,\n", | |
| " 22152030, 22152035, 22152040, 22152045, 22152050, 22152055,\n", | |
| " 22152060, 22152065, 22152070, 22152075, 22152080, 22152085,\n", | |
| " 22152090, 22152095, 22152100, 22152105, 22152110, 22152115,\n", | |
| " 22152120, 22152125, 22152130, 22152135, 22152140, 22152145,\n", | |
| " 22152150, 22152155, 22152160, 22152165, 22152170, 22152175,\n", | |
| " 22152180, 22152185, 22152190, 22152195, 22152200, 22152205,\n", | |
| " 22152210, 22152215, 22152220, 22152225, 22152230, 22152235,\n", | |
| " 22152240, 22152245, 22152250, 22152255, 22152260, 22152265,\n", | |
| " 22152270, 22152275, 22152280, 22152285, 22152290, 22152295], dtype=int64)" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 128 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "df1.index.to_period(freq='T').astype('int64')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "fig, ax = plt.subplots()\n", | |
| "df1.plot(ax=ax)\n", | |
| "display(fig)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "%%debug --breakpoint ./pandas-joris/pandas/tseries/plotting.py:49\n", | |
| "fig, ax = plt.subplots()\n", | |
| "df1.plot(ax=ax)\n", | |
| "display(fig)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "*** Blank or comment\n", | |
| "*** Blank or comment\n", | |
| "NOTE: Enter 'c' at the ipdb> prompt to continue execution.\n", | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(44)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 43 \u001b[1;33m \u001b[1;31m# Used inferred freq is possible, need a test case for inferred\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 44 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;34m'ax'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 45 \u001b[1;33m \u001b[0max\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'ax'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(45)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 44 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;34m'ax'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 45 \u001b[1;33m \u001b[0max\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'ax'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 46 \u001b[1;33m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(50)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 49 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 50 \u001b[1;33m \u001b[0mfreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_freq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 51 \u001b[1;33m \u001b[1;31m# resample against axes freq if necessary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(52)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 51 \u001b[1;33m \u001b[1;31m# resample against axes freq if necessary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 52 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mfreq\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# pragma: no cover\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 53 \u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Cannot use dynamic axis without frequency info'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(56)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 55 \u001b[1;33m \u001b[1;31m# Convert DatetimeIndex to PeriodIndex\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 56 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDatetimeIndex\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 57 \u001b[1;33m \u001b[0mseries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_period\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(57)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 56 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDatetimeIndex\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---> 57 \u001b[1;33m \u001b[0mseries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_period\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 58 \u001b[1;33m freq, ax_freq, series = _maybe_resample(series, ax, freq, plotf,\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(58)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 57 \u001b[1;33m \u001b[0mseries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_period\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 58 \u001b[1;33m freq, ax_freq, series = _maybe_resample(series, ax, freq, plotf,\n", | |
| "\u001b[0m\u001b[1;32m 59 \u001b[1;33m kwargs)\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(59)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 58 \u001b[1;33m freq, ax_freq, series = _maybe_resample(series, ax, freq, plotf,\n", | |
| "\u001b[0m\u001b[1;32m---> 59 \u001b[1;33m kwargs)\n", | |
| "\u001b[0m\u001b[1;32m 60 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(62)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 61 \u001b[1;33m \u001b[1;31m# Set ax with freq info\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 62 \u001b[1;33m \u001b[0m_decorate_axes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfreq\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[1;32m 63 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(65)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 64 \u001b[1;33m \u001b[1;31m# mask missing values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 65 \u001b[1;33m \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_maybe_mask\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 66 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(68)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 67 \u001b[1;33m \u001b[1;31m# how to make sure ax.clear() flows through?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 68 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'_plot_data'\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 69 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_data\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\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(69)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 68 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'_plot_data'\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---> 69 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_data\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 70 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(70)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 69 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_data\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---> 70 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\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 71 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(73)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 72 \u001b[1;33m \u001b[1;31m# styles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 73 \u001b[1;33m \u001b[0mstyle\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'style'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 74 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mstyle\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[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(74)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 73 \u001b[1;33m \u001b[0mstyle\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'style'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 74 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mstyle\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[0m\u001b[1;32m 75 \u001b[1;33m \u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstyle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(77)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 76 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 77 \u001b[1;33m \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplotf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 78 \u001b[1;33m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'label'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> series\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "2012-02-13 07:00 11275.117182\n", | |
| "2012-02-13 07:05 9716.197511\n", | |
| "2012-02-13 07:10 19025.623840\n", | |
| "2012-02-13 07:15 7796.350285\n", | |
| "2012-02-13 07:20 6544.319278\n", | |
| "2012-02-13 07:25 9216.704509\n", | |
| "2012-02-13 07:30 10641.001497\n", | |
| "2012-02-13 07:35 9265.557204\n", | |
| "2012-02-13 07:40 10170.977809\n", | |
| "2012-02-13 07:45 14619.520279\n", | |
| "2012-02-13 07:50 16247.802664\n", | |
| "2012-02-13 07:55 14689.207764\n", | |
| "2012-02-13 08:00 16864.147144\n", | |
| "2012-02-13 08:05 20333.935198\n", | |
| "2012-02-13 08:10 15841.608848\n", | |
| "...\n", | |
| "2012-02-13 11:45 11797.770608\n", | |
| "2012-02-13 11:50 17381.614454\n", | |
| "2012-02-13 11:55 10246.328313\n", | |
| "2012-02-13 12:00 8565.972117\n", | |
| "2012-02-13 12:05 7945.835457\n", | |
| "2012-02-13 12:10 14301.847639\n", | |
| "2012-02-13 12:15 14134.394351\n", | |
| "2012-02-13 12:20 16513.034065\n", | |
| "2012-02-13 12:25 13421.556986\n", | |
| "2012-02-13 12:30 13802.394085\n", | |
| "2012-02-13 12:35 11348.398009\n", | |
| "2012-02-13 12:40 14959.364641\n", | |
| "2012-02-13 12:45 8262.443335\n", | |
| "2012-02-13 12:50 8163.234102\n", | |
| "2012-02-13 12:55 9289.242065\n", | |
| "Freq: T, Length: 72, dtype: float64\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> args\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "series = 2012-02-13 07:00 11275.117182\n", | |
| "2012-02-13 07:05 9716.197511\n", | |
| "2012-02-13 07:10 19025.623840\n", | |
| "2012-02-13 07:15 7796.350285\n", | |
| "2012-02-13 07:20 6544.319278\n", | |
| "2012-02-13 07:25 9216.704509\n", | |
| "2012-02-13 07:30 10641.001497\n", | |
| "2012-02-13 07:35 9265.557204\n", | |
| "2012-02-13 07:40 10170.977809\n", | |
| "2012-02-13 07:45 14619.520279\n", | |
| "2012-02-13 07:50 16247.802664\n", | |
| "2012-02-13 07:55 14689.207764\n", | |
| "2012-02-13 08:00 16864.147144\n", | |
| "2012-02-13 08:05 20333.935198\n", | |
| "2012-02-13 08:10 15841.608848\n", | |
| "...\n", | |
| "2012-02-13 11:45 11797.770608\n", | |
| "2012-02-13 11:50 17381.614454\n", | |
| "2012-02-13 11:55 10246.328313\n", | |
| "2012-02-13 12:00 8565.972117\n", | |
| "2012-02-13 12:05 7945.835457\n", | |
| "2012-02-13 12:10 14301.847639\n", | |
| "2012-02-13 12:15 14134.394351\n", | |
| "2012-02-13 12:20 16513.034065\n", | |
| "2012-02-13 12:25 13421.556986\n", | |
| "2012-02-13 12:30 13802.394085\n", | |
| "2012-02-13 12:35 11348.398009\n", | |
| "2012-02-13 12:40 14959.364641\n", | |
| "2012-02-13 12:45 8262.443335\n", | |
| "2012-02-13 12:50 8163.234102\n", | |
| "2012-02-13 12:55 9289.242065\n", | |
| "Freq: T, Length: 72, dtype: float64\n", | |
| "plotf = <unbound method Axes.plot>\n", | |
| "kwargs = {'label': u'None'}\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(78)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 77 \u001b[1;33m \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplotf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 78 \u001b[1;33m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'label'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 79 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(81)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 80 \u001b[1;33m \u001b[1;31m# set date formatter, locators and rescale limits\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 81 \u001b[1;33m \u001b[0mformat_dateaxis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 82 \u001b[1;33m \u001b[0mleft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_xlim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_lines\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\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> lines[0].get_axes().get_xlim()\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "(22151940.0, 22152295.0)\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> lines[0].get_xdata()\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "<class 'pandas.tseries.period.PeriodIndex'>\n", | |
| "freq: T\n", | |
| "[2012-02-13 07:00, ..., 2012-02-13 12:55]\n", | |
| "length: 72\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(82)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 81 \u001b[1;33m \u001b[0mformat_dateaxis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 82 \u001b[1;33m \u001b[0mleft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_xlim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_lines\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 83 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_xlim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(83)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 82 \u001b[1;33m \u001b[0mleft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_xlim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_lines\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---> 83 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_xlim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 84 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> left\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "22151940\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> right\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "22152295\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(86)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 85 \u001b[1;33m \u001b[1;31m# x and y coord info\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 86 \u001b[1;33m \u001b[0mtz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtz\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 87 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat_coord\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m:\u001b[0m \u001b[1;34m\"t = {} y = {:8f}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromtimestamp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> ax.format_coord\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "<bound method AxesSubplot.format_coord of <matplotlib.axes.AxesSubplot object at 0x000000000EDC1BA8>>\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> lines[0].get_xdata()[0]\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "Period('2012-02-13 07:00', 'T')\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> float(lines[0].get_xdata()[0])\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "*** TypeError: float() argument must be a string or a number\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> int(lines[0].get_xdata()[0])\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "*** TypeError: int() argument must be a string or a number, not 'Period'\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(87)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 86 \u001b[1;33m \u001b[0mtz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtz\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 87 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat_coord\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m:\u001b[0m \u001b[1;34m\"t = {} y = {:8f}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromtimestamp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 88 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(89)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 88 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 89 \u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 90 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "--Return--\n", | |
| "[<matplot...0E134940>]\n", | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(89)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 88 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 89 \u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 90 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1306)\u001b[0;36m_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1305 \u001b[1;33m style=style, **kwds)\n", | |
| "\u001b[0m\u001b[1;32m-> 1306 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1307 \u001b[1;33m \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnewlines\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> c\n" | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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7778f999/v8u6OXPmYMmSJXj//fedAfePf/wjbDYbXn/9dQwZMgQAkJ2djVWrVuEvf/kL\nfvKTnwAAbDYbdu3ahdzcXKxbtw4AMHPmTDgcDuzevRuzZs1CwndNhEePHsXZs2exfv16TJkyBQAw\nbdo0PPHEE3jzzTfx0ksvOY9p165dSEpKwrZt25wV2+HDh2PLli04deoUcnJy/PpBEWmF2ex7BTch\nQbQ0MOASEZEeBNSDGxUVhSFDhiA6Otq57tixY8jNzXWGWwAYP3480tPTUVZW5lz36aefoqmpCbNn\nz3bZ55w5c9Da2oqTJ0+67DMlJcUZbgEgOTkZU6dORXl5OTo6OgAAFosFp0+fxvTp013aEWbMmIH4\n+HiX+yfSK38CrsHA2cyIwsF3b2dEFCCfA25raytMJhOuXr2Kffv24ZNPPsHChQsBiPaEmzdvYsyY\nMW63Gzt2LGpqapxf19bWAoDbtllZWTAYDM7vA0BNTY3HXtuxY8fCZrPh8uXLAIALFy6gs7PTbZ9G\noxGjR492uX8ivfKnBxfgbGZEoXbmDD9oEinF6xYFaceOHXj//fcBANHR0Xjuuecwa9YsAEBjYyMA\nIDU11e12qampaGpqQkdHB4xGIxoaGhAVFeV24VlMTAySk5Nx/fp157qGhgbce++9bvtMSUlxfj8z\nM9N5/3J9z22//PJLXx8ukeaYzcDtt/t+OzmbGRGFxsWLgMUCNDZ29cUTkX98ruA+9thj2LJlC9as\nWYPx48ejqKgIH3zwAQDRVwuIkNpTbGysyzY2m83jdvL2bW1tzq/b2tq83mf39T23ld/vzcyZM1FQ\nUODyLzc3F++9957LdocPH0ZBQYHb7ZcvX4433njDZd2ZM2dQUFDgEtgBYN26ddi0aZPLukuXLqGg\noABVVVUu67dv347Vq1e7rLNarSgoKMBHH33ksn7Pnj146qmn3I5twYIFfBwR8jguXNjkUsH19nEM\nGgRcvx4+j0MvzwcfBx+Ht4/j0CHxOORblVYfh16eDz6O0D6OPXv2OLNYZmYm7r33XqxYscJtP70x\nHDlyJKDZ51evXo2qqiq88847+Oqrr7B06VKsXbvWbeiw4uJilJSU4PDhwzAajSgqKsLBgwdRWlrq\nts+5c+diwoQJzovHZs6ciQcffBCrVq1y2e7kyZNYu3YtNm/ejJycHBw9ehQbNmxAUVERxo0b57Lt\nhg0bUFFRgX379rndX3V1NZYsWYLTp09j/Pjxgfw4iEIuPR0oLATWr/ftdvPmAS0twJ//rMphEVE/\niouB//k/gc8+A37wg1AfDVH4OXPmDCZMmIDi4mLceeedfW4b8EQPU6ZMgcViwaVLl1xaBnpqaGhA\nUlKSc5iw1NRU2O12mHqcE21vb4fZbEZaWppzXWpqqtsnDcC9JULev1zf8/49tU4Q6Y3J5PswYQB7\n/4hCzWoVy35ONhKRFwIOuLKVwGAwYMiQIRg0aJBbKRsAqqqqMHr0aOfX8v89tz137hwcDofbtjU1\nNXA4XIvNlZWViIuLw8iRIwEAmZmZiI6Odttne3s7amtrXfZJpEednUBzMy8yI9Iii0UsGXCJAud1\nwL3p4Z2vo6MDhw8fRlJSEjIyMgCIiu7JkydRX1/v3O7MmTO4cuUK8vLynOvGjx+PxMREHDhwwGWf\nBw4cQFxcHCZNmuRcN2XKFNy4cQPHjx93rjOZTCgrK8PkyZOdVeGEhARMmDABpaWlaGlpcW774Ycf\norW11eX+ifSouVks/Qm4vMiMKLRkBbfbJShE5CevR1H4zW9+A6vVinvuuQdpaWlobGxEaWkprly5\ngl/+8pfOsXB//vOfo6ysDCtXrsSjjz4Kq9WKkpIS3HHHHXjkkUec+4uNjcXTTz+NoqIibNiwATk5\nOfjiiy9QWlqKwsJC5yQPAJCXl4d33nkHmzZtwsWLF50zmTkcDjz55JMux7l48WI8++yzWLFiBfLz\n81FfX499+/Zh4sSJmDhxYoA/LqLwZjaLpb8BlxVcotBhiwKRcrwOuA8++CAOHTqEAwcOwGw2Y+DA\ngcjOzsbzzz/vcmHWkCFDsG3bNuzYsQM7d+5EbGwscnNzsXTpUpdpegFg9uzZMBqN2Lt3L8rLyzFs\n2DAsX74cjz76qMt2UVFR2LhxI1577TW8++67sNlsyM7Oxpo1a5Cenu6ybVZWFrZs2YKdO3dix44d\nGDhwIPLz81FYWOjPz4dIU2QF1t8e3JYW8eY6YICyx0VE/ZMtCqzgEgXO64A7bdo0TJs2zattMzIy\nsHnzZq+2zc/PR35+fr/bJSQkYNWqVW4jKXgybtw4bN++3av7J9KTQCu4gAjJQ4cqd0xE5B1WcImU\nE/BFZkQUPgIJuLLqyzYFotBgwCVSDgMukY4oVcElouBjiwKRchhwiXTEZAIMBqDbNZpekwGXFVyi\n0GAFl0g5DLhEOmI2A4mJQJQfr2wGXKLQYsAlUg4DLpGOmM3+tScA4nYGAwMuUaiwRYFIOQy4RDoS\nSMCNihLVXwZcotBgBZdIOQy4RDpiMvk3Bq7E2cyIQoczmREphwGXSEcCqeACnM2MKJRkiwIruESB\nY8Al0hEGXCJt6uzsCrYMuESBY8Al0hEGXCJtamnp+j9bFIgCx4BLpCNK9OAy4BIFn2xPAFjBJVIC\nAy6RjgRawU1OZsAlCgV5gRnAgEukBAZcIh1RokWBoygQBZ8MuElJbFEgUgIDLpFOdHYCzc3swSXS\nItmikJLCCi6REhhwiXSiqUksA+3BbW4GOjqUOSYi8o6s4A4ezIBLpAQGXCKdMJvFMtAKLsA2BaJg\nkwF30CC2KBApgQGXSCeUCLiy+ss2BaLgkgGXLQpEymDAJdIJWXVlBZdIe2QP7uDBrOASKYEBl0gn\nZAU30B5cgBVcomCzWgGjEbjlFlZwiZTAgEukE0r24DLgEgWX1QoMHAgMGMCAS6QEBlwinTCbAYNB\nVID8xR5cotCwWMRrNzaWLQpESmDAJdIJkwlITASiAnhVy1OkDLhEwcUKLpGyGHCJdMJsDqz/VuJs\nZkTBx4BLpCwGXCKdCHSaXomzmREFH1sUiJTFgEukEwy4RNrFCi6RshhwiXTCZGLAJdKq7gHXbgc6\nO0N9RETaxoBLpBNK9eAmJzPgEgVb9xYFgFVcokAx4BLphJItCrzIjCi4uldwAQZcokAx4BLpBHtw\nibRLBlxZweWFZkSBYcAl0gn24BJpl8XCCi6RkhhwiXRCyXFwzWZxoQsRBYfVKnpwGXCJlMGAS6QD\nnZ2iAqREBTc5GXA4RMglouBgiwKRshhwiXSgqUkslWpRAHihGVEwsUWBSFkMuEQ6IMOokgGXfbhE\nwdHeLv51b1FgBZcoMAy4RDog2wmU6sEFGHCJgqWlRSy7tyiwgksUGAZcIh2QAZcVXCLtsVjEki0K\nRMphwCXSASUDrqwCM+ASBYfVKpZsUSBSDgMukQ4o2YM7YAAQF8eLzIiCRQZctigQKYcBl0gHzGbA\nYAASEpTZHyd7IAoetigQKY8Bl0gH5DS9BoMy+2PAJQqe7i0KHAeXSBkMuEQ6IAOuUhhwiYKne4uC\n0Sg+qLKCSxQYBlwiHTCZGHCJtKp7i4LBINoUGHCJAsOAS6QDZrMyY+BKycm8yIwoWLpXcAHRpsAW\nBaLAMOAS6QBbFIi0y2oVodZoFF+zgksUOAZcIh1gwCXSLoulq3oLiIDLCi5RYBhwiXTAZFK2RYEB\nlyh4rFYxgoIUG8sKLlGgGHCJdECNCq7JBDgcyu2TiDyzWt0ruAy4RIFhwCXSAaUDbnIy0NnZdXU3\nEamHLQpEymPAJdIBNYYJA9imQBQMbFEgUh4DLpHGdXSIN0ile3ABBlyiYGCLApHyGHCJNK6pSSxZ\nwSXSpp4Bl+PgEgWOAZdI48xmsWTAJdImi8W1RYEVXKLAMeASaZyccUzpi8y675uI1OOpRYEVXKLA\nMOASaZys4CrZgxsfD8TEsIJLFAyeWhRYwSUKDAMukcap0aJgMHCyB6JgYYsCkfIYcIk0To2ACzDg\nEgULWxSIlMeAS6RxJhMQFeVaAVICAy5RcLBFgUh5DLhEGidnMTMYlN1vcjIvMiNSm8PBFgUiNTDg\nEmmc0tP0SqzgEqmvvV1Mi81xcImUxYBLpHEMuETaZbWKJWcyI1IWAy6RxplMDLhEWmWxiGXPFgVW\ncIkCw4BLpHFms7Jj4EoMuETq81TB5UVmRIFjwCXSOLVaFJKTRcB1OJTfNxEJbFEgUgcDLpHGqdmD\n294OtLYqv28iEvpqUeCHSyL/MeASaZyaPbgA2xSI1NRbiwIgPmASkX8YcIk0Ts0eXIABl0hNvbUo\nAGxTIAqE0dsNq6qq8MEHH+Czzz5DXV0dkpOTkZ2djcWLFyM9Pd253caNG3H48GG3248cORJvvfWW\n2/pDhw6hpKQEdXV1GDp0KObNm4e5c+e6bdfc3Izi4mIcP34cNpsN2dnZWLp0KbKysty2raioQHFx\nMWprazFw4EBMnToVhYWFiI+P9/bhEmmGmi0KAAMukZp6a1EAOJICUSC8Drh79uzB3//+d+Tl5eGO\nO+5AY2Mj/vSnP+EXv/gFXnnlFWRmZjq3jYmJwerVq11un5CQ4LbPgwcPYuvWrcjLy8OCBQvw+eef\nY/v27WhtbcWiRYuc29ntdqxZswbnz5/HwoULkZSUhP3792PlypUoLi7GiBEjnNvW1tbihRdeQEZG\nBpYtW4b6+nqUlJTg6tWr2Lhxo08/HKJw19EhKkBqXWQGMOASqUlWcLvXX2SLAiu4RP7zOuDOnz8f\nY8eORXR0tHPdtGnT8PTTT2PPnj1Yu3Zt106NRjz00EN97s9ms2HXrl3Izc3FunXrAAAzZ86Ew+HA\n7t27MWvWLGcoPnr0KM6ePYv169djypQpzvt+4okn8Oabb+Kll15y7nfXrl1ISkrCtm3bnBXb4cOH\nY8uWLTh16hRycnK8fchEYc9sFks1K7icrpdIPVYrEBcHRHVrGGSLAlHgvO7Bvfvuu13CLQCMGDEC\nGRkZuHTpkst6h8MBu90Oizz34sGnn36KpqYmzJ4922X9nDlz0NraipMnTzrXHTt2DCkpKc5wCwDJ\nycmYOnUqysvL0dHRAQCwWCw4ffo0pk+f7tKOMGPGDMTHx6OsrMzbh0ukCTLgqtGDm5Ag3nRZwSVS\nj8Xi2n8LdFVw2aJA5L+ALjJzOBxobGxEco93V5vNhvz8fMyaNQuzZ89GUVERWlpaXLapra0FAIwZ\nM8ZlfVZWFgwGg/P7AFBTU+Ox13bs2LGw2Wy4fPkyAODChQvo7Ox026fRaMTo0aNRU1Pj/4MlCkNq\nVnANBk72QKQ2q9W1/xZgBZdICV63KHhSWlqKhoYGLF682LkuLS0NixYtQlZWFux2Oz7++GPs378f\n58+fx9atW51V4IaGBkRFRbmF45iYGCQnJ+P69evOdQ0NDbj33nvd7j8lJcX5/czMTDQ2Nrqs77nt\nl19+GcjDJQo7agZcgAGXSG1Wq3sFlxeZEQXO7wrupUuXUFRUhLvvvhsPP/ywc31hYSEKCwuRl5eH\nadOm4cUXX8TixYtRUVGBY8eOObez2WyIiYnxuO+YmBi0dXtlt7W1edw29rvzOLbvPubKpVzfc1tb\nPx+HZ86ciYKCApd/ubm5eO+991y2O3z4MAoKCtxuv3z5crzxxhsu686cOYOCggKXwA4A69atw6ZN\nm1zWXbp0CQUFBaiqqnJZv337dreL9qxWKwoKCvDRRx+5rN+zZw+eeuopt2NbsGABH4cOH8elS+Jx\nyICr9OOQAZfPBx8HH4c6j0O2KHR/HPIt7IsvtPM4JK0/H3wc4fM49uzZ48ximZmZuPfee7FixQq3\n/fTGcOTIEZ/nSmlsbMRzzz2Hzs5O7Nixw2PFtLu2tjY88sgjmDlzJl544QUAQFFREQ4ePIjS0lK3\n7efOnYsJEyY4Lx6bOXMmHnzwQaxatcplu5MnT2Lt2rXYvHkzcnJycPToUWzYsAFFRUUYN26cy7Yb\nNmxARUUF9u3b53Z/1dXVWLJkCU6fPo3x48f79LMgCqU9e4Cf/QxobnY/zamEBx8Ehg0T90NEyvsf\n/wO4cAE4frxr3fnzwOjRwF//Kl6DRCScOXMGEyZMQHFxMe68884+t/W5gtvc3IwXX3wRFosFmzdv\n7jfcAqJofbNQAAAgAElEQVR6mpSUBLM8nwogNTUVdrsdph6XaLe3t8NsNiMtLc1l256fNAA4WxJS\nU1MBdLUmyPXdNTQ0OLcj0guzWVwI1vMUp1LYokCkLrYoEKnDp4Db1taGX//617h69Spefvll3H77\n7V7dzmq1wmQyYZAcdwjA6NGjAcCt7H3u3Dk4HA7n9+W2NTU1cPSYmLuyshJxcXEYOXIkACAzMxPR\n0dFu+2xvb0dtba3LPon0QE7yYDCos38GXCJ19TWKAi8yI/Kf1wG3s7MTGzZsQGVlJdavX4+77rrL\nbZu2tjZY5ajV3ezevRsAcN999znXjR8/HomJiThw4IDLtgcOHEBcXBwmTZrkXDdlyhTcuHEDx7ud\nwzGZTCgrK8PkyZNhNIpr5RISEjBhwgSUlpa6jNrw4YcforW1FXl5ed4+XCJNMJnUu8AMYMAlUhtH\nUSBSh9ejKLz66qs4ceIEcnNzYTKZ8OGHH7p8f/r06WhsbMQzzzyDH//4x86q6ieffIKPP/4Y9913\nHx544AHn9rGxsXj66adRVFSEDRs2ICcnB1988QVKS0tRWFjoMvNZXl4e3nnnHWzatAkXL150zmTm\ncDjw5JNPuhzH4sWL8eyzz2LFihXIz89HfX099u3bh4kTJ2LixIn+/IyIwpbZrM4YuBIDLpG6PLUo\ncBxcosB5HXDPnz8Pg8GAEydO4MSJEy7fMxgMmD59OhISEjB58mScPn0aH3zwAex2O9LT0/HMM89g\n/vz5bvucPXs2jEYj9u7di/LycgwbNgzLly/Ho48+6rJdVFQUNm7ciNdeew3vvvsubDYbsrOzsWbN\nGqSnp7tsm5WVhS1btmDnzp3YsWMHBg4ciPz8fBQWFvrycyHSBNmioJbkZM5kRqQmTy0KrOASBc7r\ngLt169Z+t0lISMCaNWt8OoD8/Hzk5+d7te9Vq1a5jaTgybhx47B9+3afjoNIi4LRotDSIt5o5Zsu\nESnHU4tCVBRgNLKCSxSIgGYyI6LQUruCK68LZRWXSB2eWhQA0abACi6R/xhwiTQsWAGXfbhE6vDU\nogCIMyYMuET+Y8Al0jAGXCLtcjg8tygAIuCyRYHIfwy4RBrW1AQkJqq3fzlCA1sUiJRns4mQyxYF\nIuUx4BJpWFMTK7hEWmWxiCVbFIiUx4BLpFF2O9DcrG4FV86SxoBLpDw5LxJbFIiUx4BLpFHNzWKp\nZgU3KkrsnwGXSHky4LJFgUh5DLhEGmU2i6WaFVyAs5kRqaWvgMsWBaLAMOASaVRTk1iqWcEFOJsZ\nkVpkD66nFoXYWLYoEAWCAZdIo1jBJdI2VnCJ1MOAS6RRwargMuASqaO/gMsKLpH/GHCJNIoVXCJt\n669FgRVcIv8x4BJplKzgMuASaZOs4MbFuX+PLQpEgWHAJdIos1m8CcbGqns/vMiMSB1Wq2hPMBjc\nv8cWBaLAMOASaZTa0/RKrOASqcNi8dyeALBFgShQDLhEGmU2q3+BGSACbnMz0NGh/n0RRRJZwfWE\nLQpEgWHAJdKoYFZwAbYpECmtr4DLcXCJAsOAS6RRTU3Bq+ACbFMgUlpfLQqs4BIFhgGXSKPM5uBU\ncJOTxZIVXCJl9deiwAoukf8YcIk0ihVcIm3rr0WBFVwi/zHgEmlUsCq4DLhE6mCLApF6GHCJNCpY\nFVzZosCAS6QstigQqYcBl0ijglXBNRqBhAQGXCKl9dei0NEB2O3BPSYivWDAJdKoYFVwAdGmwIvM\niJTVX4sCwDYFIn8x4BJpkN0uJl8IRgUXEG0KrOASKau/FgWAbQpE/mLAJdKg5maxDGYFlwGXSFn9\ntSgArOAS+YsBl0iDzGaxDFYFlwGXSHkWCyu4RGphwCXSoKYmsWTAJdImux1oaem9B5cVXKLAMOAS\naZAMuMFqUUhJARoagnNfRJGgtVUs+6vgMuAS+YcBl0iDgt2iMHw4cO1acO6LKBJYLGLJFgUidTDg\nEmlQsCu4t94KNDaymkSkFKtVLNmiQKQOBlwiDQp2BffWW8Wyri4490ekdzLgskWBSB0MuEQa1NQk\n3gBllUdtMuCyTYFIGWxRIFIXAy6RBgVrml6JAZdIWWxRIFIXAy6RBgVzml4ASEsDjEYGXCKlsEWB\nSF0MuEQaFOwKblRUcEZSOHMGePxxwOFQ936IQq2/FgVZwWWLApF/GHCJNCjYFVxAtCmoHXBLS4E/\n/pFv6qR//bUosIJLFBgGXCINCnYFFwhOwJWjNMhh0Ij0ymoVZ0Z6u1A0JkYs+WGPyD8MuEQa1NSk\nz4Ar99/crO79EIWaxSLaEwwGz983GET4ZQWXyD8MuEQaZDaHpkXh66/VvQ9WcClSWK29tydIAwYw\n4BL5iwGXSINCVcH99lugo0O9+2AFlyKF1dr7BWbSgAFsUSDyFwMukQaF6iIzh0OEXLWwgkuRwpuA\nyxYFIv8x4BJpUKguMgPU68NtaQFMJvF/VnBJ7ywWtigQqYkBl0hj7HYRAENRwQXUC7iyeguwgkv6\nxxYFInUx4BJpjKxuBruCO2yYuLJbrYDbfb+s4JLesUWBSF0MuEQaI6ubwa7gGo3AkCHqV3Cjo1nB\nJf3ztkWBFVwi/zDgEmmM2SyWwa7gAsBtt6lbwY2JEffBCi7pHSu4ROpiwCXSmFBVcAF1J3uoqxNt\nEElJrOCS/nnbg8uAS+QfBlwijQllBVfNgHvtmth/YiIruKR/bFEgUhcDLpHG6LmCO3w4kJDACi7p\nH1sUiNTFgEukMaGu4NbViQkflMYKLkUStigQqYsBl0hjmppEZSc2Nvj3feutQHs70NCg/L5ZwaVI\nwhYFInUx4BJpjNkcmvYEQL3JHjo7gW++YQWXIkNnp6jMskWBSD0MuEQa09QUmvYEoCvgfv21svtt\naBBv+qzgUiRoaRFLzmRGpB4GXCKNaWoKXQV3+HCxVLqCK/fHCi5FAotFLPtrUWAFl8h/DLhEGmM2\nh66CGxcHDB6sfMCVs5ixgkuRwGoVS15kRqQeBlwijQllBRdQZ6gwub/hw0V4t1pFywKRHvkScNmi\nQOQfBlwijQllBRdQJ+DW1YnK8IABXY9NnsYl0hu2KBCpjwGXSGNCXcG97TZ1KriyvzchQSzZpkB6\nxRYFIvUx4BJpjF4ruHKEBvnYeKEZ6RVbFIjUx4BLpDGhruDKgKvkbGas4FIk8bVFQY2ZA4n0jgGX\nSGPCoYLb0tI1ZbASWMGlSOJLBdfhADo61D8mIr1hwCXSELtdBL9QB1xA2TYFOU0vwAou6Z/VChiN\nQExM39sNGCCWbFMg8h0DLpGGyKpmqFsUAOUCrsUiwiwruBQpLJb+2xMA0aIA8EIzIn8w4BKp4He/\nU/5CLKCrqqmnCm73SR4AcdrWYGAFl/TLau2/PQFgBZcoEAy4RAprbweeegp4+23l9y37XkNZwU1I\nEP+UCrjdp+kFRLhNSGAFl/TL24DLCi6R/4zeblhVVYUPPvgAn332Gerq6pCcnIzs7GwsXrwY6enp\nLtv+4x//wCuvvIKKigrExMRg0qRJWLZsGZKTk932e+jQIZSUlKCurg5Dhw7FvHnzMHfuXLftmpub\nUVxcjOPHj8NmsyE7OxtLly5FVlaW27YVFRUoLi5GbW0tBg4ciKlTp6KwsBDx8fHePlzSoN/9Dnjg\nAcDDr0RQ3bghlrIyqaRwqOACIox+/bUy++pZwQU4XS/pm8XiWwWXAZfId15XcPfs2YOPPvoIEyZM\nwHPPPYef/OQn+OKLL/CLX/wCX331lXO7+vp6PP/887h27RqeeeYZzJ8/HydPnsSqVavQ0eNS0IMH\nD2LLli2444478Pzzz+Ouu+7C9u3bsWfPHpft7HY71qxZg7/+9a+YN28elixZghs3bmDlypW4evWq\ny7a1tbV44YUX0NbWhmXLliE/Px/vv/8+NmzY4M/PhzSiowP4xS+A3/421EcCNDaK5TffKL9vGfpC\nWcEFlB0L99o1UakaPLhrXWIiK7ikX1ardz24bFEg8p/XFdz58+dj7NixiI6Odq6bNm0ann76aezZ\nswdr164FAPzxj3+EzWbD66+/jiFDhgAAsrOzsWrVKvzlL3/BT37yEwCAzWbDrl27kJubi3Xr1gEA\nZs6cCYfDgd27d2PWrFlI+O5y6qNHj+Ls2bNYv349pkyZ4rzvJ554Am+++SZeeukl5zHt2rULSUlJ\n2LZtm7NiO3z4cGzZsgWnTp1CTk6O3z8sCl9ffSVaA/7xj1AfSVfAVaOCK1sUwqGCq2QP7vDhojVB\nYgWX9IwtCkTq87qCe/fdd7uEWwAYMWIEMjIycOnSJee6Y8eOITc31xluAWD8+PFIT09HWVmZc92n\nn36KpqYmzJ4922Wfc+bMQWtrK06ePOmyz5SUFGe4BYDk5GRMnToV5eXlzsqwxWLB6dOnMX36dJd2\nhBkzZiA+Pt7l/klfKivFUu8BN5xaFJSs4Mr+W4kVXNIztigQqS+gi8wcDgcaGxudvbX19fW4efMm\nxowZ47bt2LFjUVNT4/y6trYWANy2zcrKgsFgcH4fAGpqajz22o4dOxY2mw2XL18GAFy4cAGdnZ1u\n+zQajRg9erTL/ZO+VFWJpd4DrtksqjryjS9UbrtN+Qpud6zgkp6xRYFIfQEF3NLSUjQ0NGDatGkA\ngMbv3tlTU1Pdtk1NTUVTU5Oz2trQ0ICoqCi3C89iYmKQnJyM69evO9c1NDR43GdKSorz+93vX67v\nua3cjvRHBtyvvw79m4EMuPX1QGensvsO9TS90q23irAtZ2QKBCu4FGnYokCkPr8D7qVLl1BUVIS7\n774bDz/8MADRVwuIkNpT7HevVLmNzWbzuJ28fVu3lNLW1ub1Pruv77mtrZ+/EjNnzkRBQYHLv9zc\nXLz33nsu2x0+fBgFBQVut1++fDneeOMNl3VnzpxBQUGBS2AHgHXr1mHTpk0u6y5duoSCggJUybT2\nne3bt2P16tUu66xWKwoKCvDRRx+5rN+zZw+eeuopt2NbsGCBrh/He+8V4NZbxbSWV66E9nHIgGu3\nA6+/ruzzIafpDfXzIQPpT38a+O9VZWUBkpJcH8e5c+tQXR363yu9vD74OMLrcZw9W+AWcD09jnPn\nzgAI38ehl+eDjyM8H8eePXucWSwzMxP33nsvVqxY4baf3hiOHDni8Hrr7zQ2NuK5555DZ2cnduzY\n4ayYnjt3DkuXLsXatWvx0EMPudymuLgYJSUlOHz4MIxGI4qKinDw4EGUlpa67X/u3LmYMGGC8+Kx\nmTNn4sEHH8SqVatctjt58iTWrl2LzZs3IycnB0ePHsWGDRtQVFSEcePGuWy7YcMGVFRUYN++fW73\nV11djSVLluD06dMYP368rz8OCjGHA0hJAebMEUOF/dd/Ad+dVAiJ554DXnlFHNdnnwE/+IFy+372\nWeCjj8R+Q+nvfwfuvhs4fhz44Q/9309np6hSvfqqGAVD+ud/Bv7yF3E/RHqTlQXMmwf0yBJubt4U\no4vs2wc89lhwjo0onJ05cwYTJkxAcXEx7rzzzj639bmC29zcjBdffBEWiwWbN292aQfo2TLQXUND\nA5KSkmA0ioEbUlNTYbfbYTKZXLZrb2+H2WxGWlqac11qaqrbJw3AvSVC3r9c3/P+PbU5kPZ9+614\nI5gxQ3x98WJIDweNjUBGhvi/0n24soIbakrNZlZfLyrd7MGlSMIWBSL1+RRw29ra8Otf/xpXr17F\nyy+/jNtvv93l+0OGDMGgQYPcStmAmChi9OjRzq/l/3tue+7cOTgcDrdta2pq4HC4FpsrKysRFxeH\nkSNHAgAyMzMRHR3tts/29nbU1ta67JP0Qz7d994rglKoLzRrbASys8X/lR4Lt6kpPALuoEHiAphA\nA27PWcwk9uCSnnGqXiL1eR1wOzs7sWHDBlRWVmL9+vW46667PG43ZcoUnDx5EvX19c51Z86cwZUr\nV5CXl+dcN378eCQmJuLAgQMutz9w4ADi4uIwadIkl33euHEDx48fd64zmUwoKyvD5MmTnVXhhIQE\nTJgwAaWlpWhpaXFu++GHH6K1tdXl/kk/qqqA6Gjge98DRo0Kj4B7221AcrI6FdxwuMjMYFBmqDBP\ns5gBXRVch88NVEThz2LxbhSF6GggKooVXCJ/eD3Rw6uvvooTJ04gNzcXJpMJH374ocv3p0+fDgD4\n+c9/jrKyMqxcuRKPPvoorFYrSkpKcMcdd+CRRx5xbh8bG4unn34aRUVF2LBhA3JycvDFF1+gtLQU\nhYWFzkkeACAvLw/vvPMONm3ahIsXLyIpKQn79++Hw+HAk08+6XIcixcvxrPPPosVK1YgPz8f9fX1\n2LdvHyZOnIiJEyf68zOiMFdZKcJtbGz4BNyUFBHalA64TU3AHXcou09/KRFw5e2HDXNdn5go+nNt\nNiAuLrD7IAon7e3inzcVXEBUcRlwiXzndcA9f/48DAYDTpw4gRMnTrh8z2AwOAPukCFDsG3bNuzY\nsQM7d+5EbGwscnNzsXTpUmelVZo9ezaMRiP27t2L8vJyDBs2DMuXL8ejjz7qsl1UVBQ2btyI1157\nDe+++y5sNhuys7OxZs0apKenu2yblZWFLVu2YOfOndixYwcGDhyI/Px8FBYW+vSDIe2oqupqCRg1\nCjh1KrTHo3bADYcKLqBcBTc1tavXUJKfb5uaGHBJX+TJRV8CLlsUiHzndcDdunWr1zvNyMjA5s2b\nvdo2Pz8f+fn5/W6XkJCAVatWuY2k4Mm4ceOwfft2r+6ftK+qCli4UPx/1Cjg8mVx4VJUQKM8+8du\nB27cEAF32DDle3DD5SIzQATco0cD24enMXCBrsfY3Ax0mxSRSPMsFrH0pkUBEB/+WMEl8l0IIgCR\ncqxW0ZIwdqz4etQocfpPqVm2fGUydQ1bxgpu/zzNYgZ0BVyOpEB6IydHYYsCkboYcEnTzp0Ty+4t\nCkDo+nDlCHWDBysfcO328BlFARABt6EhsNOn1655DriyRYEjKZDe+BNw2aJA5DsGXNI0OUTYmDFi\nGS4BV1ZwGxuVe3OSpzbDpYJ7221iGUiIr6vru0WBFVzSG7YoEAUHAy5pWlWVCJKDBomvk5JE9TQc\nAq4cGeDbb5XZt9ksluFUwQX8b1NwOPqv4DLgkt6wgksUHAy4pGmVlV39t9KoUaGbzaxnBRdQrk1B\nhr1wqeAGGnCbm8WbvacKLlsUSK98Dbis4BL5hwGXNK37EGFSKMfCbWwEYmLE6UelA264VXDT0gCj\n0f+AK2/nqYIbEyMqV6zgkt742qLAi8yI/MOAS5rV2QlUV3uu4IYy4KakiJm+hgwRS6UruOEScKOi\nRBuGvwFX/lw8VXABTtdL+iQruPHx3m3PFgUi/zDgKujf/x2orQ31UUSOf/xDVDZ6C7ihmOZVjoEL\niCpkaqpyY+HKCm64tCgAgQ0V1lcFF+iarpdIT6xW0XZg9HIUerYoEPmHAVchnZ3AL38JHDwY6iOJ\nHJWVYukp4FqtYgirYJMVXEnJocLCrYILBBZw6+rELGXJyZ6/zwou6ZHF4n17AsAWBSJ/MeAqRIYP\nviEHT1WVeKPoMVtzSIcKUzPgms2imjNggDL7U0KgFdzhw0Ubhyes4JIeWa3eX2AGsEWByF8MuAox\nmcSSb8jBU1Ulxr/tOSWvXgNuOE3yIAVawe2t/xZgBZf0ydeAyxYFIv8w4CpEBly+IQdPVZV7ewIg\nru6Pjw+PgDtsmHI9uOE0Ta90663i8XV2+n7b3sbAlVjBJT3yp0WBFVwi3zHgKoQV3ODzNAYuIE55\nZ2SER8BVukUhHCu4djtQX+/7bVnBpUjkT4sCK7hEvmPAVQgruMF1/bq4iKznGLhSKIYKczg8B9ym\npq6xLwMRrhVcAPj6a99vywouRSK2KBAFBwOuQljBDa6qKrH0VMEFQjObmcUCtLe7B1xAmTaFcKzg\n3nabWPrah9veLj6ksIJLkYYtCkTBwYCrEFZwg6uqSlxcNnq05++HooLbfZpeadgwsVQi4IZjBXfY\nMNES4mvA/fZbUfFmBZciDSu4RMHBgKsQBtzgqqwEMjPFOKqejBolJl0IZkCSAXfw4K51Sk7XG44V\nXKNRzNjma8DtbxYzgBVc0if24BIFBwOuQtiiEFxVVb333wKhGSrMUwU3NRWIjlYm4IbjMGGAf0OF\nyZ9HfxXclhago8P/YyMKN2xRIAoOBlyFsIIbXL0NESaFS8CNihKn8ZWq4IZbiwLgX8C9dk20Ngwd\n2vs2MswrcYEeUbhgiwJRcDDgKoQV3OBpbQW++qrvgHvrreL0ebADrsHgPvWsUmPh6q2Cm5YGxMT0\nvk1CgljyNUV64k+LQnu76FknIu8x4CpEBtz2dp5OUlt1tfhj31fAjY4GRo4MfsAdPNh9ZjUlxsK1\n28PzIjPA/wpuX/23QFeY51kR0hN/WhQAvq8Q+YoBVyEmEytOwdLfEGFSsEdSuHHDtT1BUiLgytP0\n4VrBravzrcJUV9d3/y3Q9Vj5eiK9cDj8a1EA2KZA5CsGXIWYTMCIEeL/rDipq6pKXLmfmtr3dsGe\nzaznJA+SEgHXbBbLcK3gtrV19SB7w5sKrvzAyNeT9uzeDZw5E+qjCD/t7WJaa19bFABWcIl8xYCr\nEJMJSE8X/2fFSV39XWAmBbuC21fA/eabwHro5O9UuFZwAd/aFFjB1beVK4GZM/2b4U7P5JkYVnCJ\n1MeAqxBWcIOnsrLvIcKkUaNE6GptVf+YgN4D7rBh4hhkFdYfMuSFawUX8D7gOhz9T9MLsIKrVVar\nmEb7m2+A+fNF1ZIEq1Us/enBZcAl8g0DrgLkBUCygss3ZPXY7cC5c95XcAHg8mV1j0nqq4ILBNam\nIMOxHiq4ZrMI/P21KMTHiwv2WMHVFvl6e/ll4P/9P2D16tAeTziRAZctCkTqY8BVQFOTqEqxRUF9\nly+Lwf99CbjBalNQM+CGcwU3Lk6MHuHt6WgZhPur4BoMoorLD4zaIgPu/PnA1q1AURHw9tuhPaZw\nwRYFouBhwFWAHCKMFVz1VVaKpTcBd+RIEZLCJeAGMhZuOFdwAd+GCvNmml4pMZEfGLVGBtz0dGD5\ncuBnPwMWLwbOng3tcYUDtigQBQ8DrgJkwB0yRAxczzdk9VRViYqhrM72JTZWhKhgBNzWVvHm5Sng\nJiWJN6lAK7ixsV1vduHGl4DrbQUXEBVcvp605fJlMUPdgAHiA+bOncAddwDz5gXWh64HbFEgCh4G\nXAXIgJuczFOqaquqAsaMcZ9MoTfBGknhxg2x9BRwDYbAhwozm8O3egv4XsEdONC7x5OYyNeT1ly+\nLM6eSLfcArz7rnjen3oqsmfkYosCUfAw4CqgZ8BlxUk93g4RJgUr4MoxYD0FXCDwgBuu0/RKvlZw\nhw8Xwb8/fD1pT8+ACwBZWcDvfy+C7pYtoTmucBBIiwIruES+YcBVQPeAy4qTuiorwzvgDh7s+fty\nLFx/mc3heYGZJAOuN9W5ujrv+m+ByHg92WxiYgS9VDY9BVwAmD0b+NWvxL8jR4J/XOFABtz4eO9v\nwx5cIv8w4CrAZAKio8VpJ1ac1NPYCHz7rXdj4EoZGcCVK2L2IDX1V8EdNkz/FVyr1bvffW/GwJUi\n4fV06BDw3/87UFsb6iNRRm8BFwD+9V+BadOABQvE6zLSWK3iGgJvW6wAtigQ+YsBVwEmk6jeGgyR\nUXEKlXPnxNLXCm5Hh/ozKnlTwQ20BzfcK7iAd20KrOC6kr/XgVT4w4XJJD6Q9BZwjUZgzx5Rlfzp\nTyPvtLvF4lt7AsAWBSV99pn+/55QFwZcBciAC/AiMzVVVYkPEVlZ3t9GjrZw8aIqh+TU2CjCWEyM\n5+/LFgW73b/9a6GCC3gXcFnBdSUDbn19aI9DCXKIsN4CLiBGm/n974GTJ4Hy8uAcV7iwWn27wAzo\n+pvCCm5gHA7ghz8Efve7UB8JBQsDrgK6B1yO26meykoRWH15gwjWZA83bvTengCIQNfZ2VXp9VVT\nkz4quG1tYhpXVnC7VFeLZaQEXAD4p38Sy+vX1T2ecONPwI2KEiGXATcwDQ2igu7txbCkfQy4CmAF\nNziqqnzrvwXE6cDUVPUDbm+TPEjDhomlv20K4T5MWGKi+Fn39+YhT8OzgttFbxXcqCjgttv63i4p\nSWzn7wc+rfKnRQEQbQpsUQiM/NsUaR+qIhkDrgJ6Bly9vyGHiq9DhEnBGEmhv4Ab6HS94V7BBcRz\n87vfdY0q4okvs5gBXRVcvYww0FNDg/gH6Cfg3nqr6LXtS1SUeL1EWsD1p4ILiAvNWMENDANu5GHA\nVUDPFgVWcJVnswEXLmg34Oq9ggsAb70lAs7cub2/GfsyixkgPjDa7UBLizLHGG5qasRy0CD9BNz+\n2hOklJSucB8p/A24rOAGTv7tibTfuUjGgKsAVnDVV1sreli1GnBvuUUEVH+ulLfbxYemcK/g3n03\ncOAA8Le/iRmrPF1QV1cnqndDh3q3Txnq9fqhUbYn5OZGZsCNtApuIC0KrOAGhhXcyMOAq4CeFVyL\nxf+r5cmzqiqx9LUHFxAB99IldU9z9xdwAf/HwrVYxLGHewUXAH70I+APfwDefhv45S/dv3/tmriK\nPjrau/0lJIilXj80VlcD6enid5QBV//YohA6rOBGHgZcBfSs4DocXTPWkDKqqsQbYlqa77fNyBCn\nuNUMEN4EXH/HwpXhTgsBFwAeewwoKgJ+8xtg61bX7/kyBi6g/wpudTUwZowI/VoPuA4HA25/2KIQ\nOt0ruHrt6SdXDLgBsttFf2T3Ci6g3zfkUKmoEO0JBoPvt1V7qLCODvEhR62AazaLZbi3KHT33HPA\niy8C//zPQElJ13pfxsAFul5Peq3gnjsH3HmnCLjffqvtN96GBqC1Fbj9du+2j8SA62+LAiu4gbt2\nTcwi19HR9TeV9I0BN0DyCu/uFVy5npThcADHjwOTJ/t3e7UD7s2bYulNwPWnB1drFVzp5ZeBxx8X\n02GJA2cAACAASURBVNCWlYl1vlZw9fx6stvFRWaygtveru03Xm/HwJVSUyMv4AZSwWXADcy1a8Bd\nd4n/s00hMjDgBkgOidSzgqvXilMonD8PXL0KTJ3q3+0HDxZBSa3ZzOSbtFo9uFqs4ALiYrI33gCm\nTAHmzAG+/JIV3O6uXBEVzzvv7LroTsttCr4GXDmKgpar1r5ii0JoOBzib8+4ceJrXmgWGRhwA9Qz\n4Oq54hQqR46IsPSjH/l3e4NB3ZEUZMAdPLjv7YYPF39Y29t9279WK7iAOLX6zjtAZibwyCPiTcaX\nCq48navH15McQUG2KADaD7gxMd6PkJGSIkJbJF2vwBaF0GhqEr9nMuCyghsZGHAD1FvA1WPFKVTK\nyoAJEwKrYAYj4HrTouBw+B5itFrBlZKSgEOHxOD/7e2+VXCNRiA+Xp+vp+pqEQgzMvQTcNPTxYdR\nb8jXS6S0KciLj9miEHzyArPvf18sWcGNDAy4AeqtRUGPFadQcDhEwPW3PUEKl4AL+N6H29QkgtCA\nAb4fW7i49Vbggw+ABx4QH1Z8odfpr6urgdGjxZBpqalindYDrrftCUDkBVyrVfw9k0UQX7BFITAy\n4GZmig8YeqngfvUVUF4e6qMIXwy4AeoZcOXpJz1WnEKhthb4+uvwD7hxcaLS2Bd/ZzPTwjS93hgz\nBvjoI+COO3y7XWKiPl9PcgQFQHyAGTyYAVfP5MWogwb5flu2KARGBtxbbxVDTeqlgvvSS8CiRaE+\nivDFgBsgk0lUYGSwjY4WnxD1WHEKhbIy8TP94Q8D28+oUeK5kh9IlHTjRv/VW6CrN9HXgKuFaXrV\npOcK7pgxXV9rfSxcXwOurFoz4PaPFdzAXLvWNZukngJuebl43d24EeojCU8MuAEymUR1rfv4rJyu\nVzlHjgTefwuoO1SYN5M8AOJNKiUlciu4/tJjBddmE6N6yAouoO2A29kpRjrxJeAmJ4u/m3o5Xdwf\n+eHa34DLCq7/ul/cmpqqj9+5q1e73s8qKkJ7LOGKATdA3WcxkxIT9Vlx8tfly/4NBaRU/y0gLuQB\nQhtwAf/GwmUFV3+vp9pa8futl4D7zTfiAkJfAm5UlGjLYAW3f2xRCEz3gKuXCq7svY2KEkMwkjsG\n3AB5Crh6fEP217lzIlweOOD7bWtqxB8mJQLusGHiTSLUAdefsXBZwdVfBbe6Wix7tih8+21ojidQ\nvo6BK0XSbGZsUQgdPVZw//Y3cT3DXXcx4PaGATdAvQVcvb0h+6ukRMzY9Pvf+35bpfpvAfEp9/bb\nQx9w/ZmulxVc/b2ezp0Tfzfk8GCA6NHWagWXAbd/N2+Kiwn7uxjVE1ZwA6PXCu7kyWJsXwZczxhw\nA8QWhb7t3Sua+//v//X9Aq8jR4CcHOXC3ahR6sxmpnbAbWqK7ICrx9dTdbVoT+jeuy9bFLQ4s9fl\nyyK4efs6kCIt4A4a5Pqce4s9uIHpWcG9fl2brzPJYgE+/VQMuygDrpYfj1oYcAPECm7vzp4V//79\n38XptT/9yfvbKtl/K6k1VFgwenAjuUVBj6+nniMoACLgtraKNy+tkSMo+Bre9HK62Bsy4PqDLQr+\na2kRP/vuFdz2dm1/aP7kE3Fh5wMPAPfcI94jLl0K9VGFHwbcALGC27t9+0Qwe/ppIC8P+M//9P62\n1dWi0hnuAddu970H9+ZNEWS8xQqu/l5P3cfAlbQ8m5mvQ4RJkVjB9QdbFPwnz5h1D7iAttsUysvF\ne+tdd3VNP8w2BXcMuAFiBbd3+/YBs2eL6sOiRcBf/+p99VLJ/ltp1ChxEU9Li3L7bGoSIdeXCi7g\nWxWXFVx9vZ4aG8WbKwNu5AXcnu8V3mIF13/dJ3kAusZf1vKZg/JyIDdXvEeOHCl+rxhw3THgBogV\nXM/OngX+/ndg/nzx9aOPihfj3r3e3f7IEWDiRP+mteyNHAtXyVM53k7TK8mA620frt0ufpcivYJr\ns4nTinpQUyOWnloUAAZcvQq0RaGzU/wj3/QMuFqv4NrtwIkToj0BEG1B3/8+A64nDLgBcDhEdY0V\nXHd794qfy/Tp4uvUVOC//Tfv2hTU6L8FusKlkkMx+Rtwva3gWizi5xHpFVxAPx8az50Ty9GjXdfL\nN16tBdz2dhEi/A24ra3KnlUJVyZTYC0KANsU/HHtmvj5yb/RWq/gVlaKD0sy4AIcSaE3DLgBaG4W\nn6ZYwXXlcIiAO2eOqDxIixYBJ08CFy70fftz50QAVDrgyufJbFZunzLgDh7s3fZpaWLIMm8ruPKD\nUqRXcAH9vKaqq4ERI9zPTgwYID7IaC3gfv21eM37G3CByKjiBlrBBSK3TcFkAj76yL/bXrsmCgvy\nAsiBA8WIH1qt4JaXi7Oh993Xte6ee4Cqqsj9/egNA24A5LBXniq4ejql6quKCvFik+0JUkGB+OPy\n9tt9376sDDAaXT+hKkE+T74OV9YXXyu40dHiVLSvAZcVXP2cFfE0goKkxdnM/B0DF9B+Nc0XgV5k\nBkRuBfd3vwMefNC/ANd9iDBJy2PhlpcDP/iB6wfkceOAjg7xvktdGHAD0FfABfRTcfLV3r3iD/lD\nD7muv+UWUdXtr02hrEz5/ltAfGqPjlY24N64IfbpS4XVl7FwZbWZFVz9vJ48jaAgDR2q3mxmx4+L\n19XZs8ruN5CAGykVXIdDmQpupAbcq1dFwcifUXA8BVwtD09XXu5e/Pn+98WSbQquGHAD0FvA1dsb\nsi8cDjF6wpw5XVWH7hYtEm+wvb0Q1eq/BcQpquRk5Su4KSm+jf/py1i4rODqq4Jrt4uLzHoLuGpU\ncB0O4NVXRQXs1Cng2DFl93/5svj99Od3NFICbmurqD6yRcE/8u/l+fO+31ZPFdxvvhE/g54Bd9Ag\n8QGTAdcVA24A+qvg6uEN2VdffikqVD3bE6QZM8SbWm9V3KoqdfpvJbUCri+GDWMF1xd6+sB49aq4\noCpYLQo2G/DMM8CyZeKfGtNV+zuCAtAV+PQecG/eFEu2KPhHBtzaWt9ve+0acNttruu0WsH929/E\ncvJk9+/xQjN3DLgBYAXX3d694oKrH//Y8/djY4HHHgP27PE8taBa/bdSOARcX1oUeJFZ12NX8gNj\nfT0wc2bwg5UcQSEYFdyvvxYfFP/wB9HDWFQEZGYqP+NRIAE3OlqEPgbcvrGCK5a+BtyODvF60ksF\nt7xcvNY8vd7uuQf44ovgH1M4M/qycUtLC95++21UVlaiqqoKzc3NePHFF/Hwww+7bLdx40YcPnzY\n7fYjR47EW2+95bb+0KFDKCkpQV1dHYYOHYp58+Zh7ty5bts1NzejuLgYx48fh81mQ3Z2NpYuXYqs\nrCy3bSsqKlBcXIza2loMHDgQU6dORWFhIeLj4315yH0ymcQV8T17RSO1gitHT5g713N7gvSznwE7\nd4qx/Hp+Ei0rE1eH3nKLOseotYBrNgMxMa6jUUSaAQNEEFLyA+Nf/wr8+c/ilP2MGcrttz/V1eL5\nzMjw/H2lAu6JE8C8eeLnJntvATEWtD9VsL5cvgxMmOD/7VNTIyfgBjLRA8AKrq+/u998I96XPPXg\najHg/u1vvRd/xo0DrlwR14V4O6qP3vkUcE0mE3bv3o1hw4Zh9OjR+Oyzz3rdNiYmBqtXr3ZZl+Dh\nqqGDBw9i69atyMvLw4IFC/D5559j+/btaG1txaJFi5zb2e12rFmzBufPn8fChQuRlJSE/fv3Y+XK\nlSguLsaIESOc29bW1uKFF15ARkYGli1bhvr6epSUlODq1avYuHGjLw+5TyaT6Dvr2X8ZqRXczz8X\n/YX/+3/3vd2PfiSGSfrP/3QNuLL/trBQvWNMTlZ+mLDewkpvhg8HrFbx+9HfhXRyml5fenz1xmAQ\nPwMlPzCeOiWWwZ6//dw54HvfE2cpPBkyRIx93NIiLor0x+uvA8uXA/ffD/yf/yNaYqRRo0S4V9Ll\ny6Ln3l8pKdo8XewLtij4z24XF17ecovvAffrr8XSUwW3oUG852jlb2trK3D6tLiOxRM5ZW9FhXiP\nJR8DbmpqKt555x0MHjwY586dw9KlS3vfsdGIh3peRt+DzWbDrl27kJubi3Xr1gEAZs6cCYfDgd27\nd2PWrFnOUHz06FGcPXsW69evx5QpUwAA06ZNwxNPPIE333wTL730knO/u3btQlJSErZt2+as2A4f\nPhxbtmzBqVOnkJOT48vD7pWnWcyAyK3g7tvXd3uCFBUFLFwI/P73wLZtXW/2lZXiD9m0aeodY3Iy\ncPGicvtrbATGj/ftNt1nM+s52H9PkT5Nr5SQoOwHxk8+EUul+1H7U13de3sC4Dqb2e23+7bvtjbg\nf/0voLhY9Ntu3ep+JmXUKPGm39bW91kWb7W2imP1t0UBiIzZzORZI7Yo+K6hQczgNmmSuECys1Oc\nmfBGz1nMpLQ08WHBYlF+tB61nDolnv/eKrhjxoj30i+/ZMCVfOrBjYmJwWAva98OhwN2ux0Wi6XX\nbT799FM0NTVh9uzZLuvnzJmD1tZWnDx50rnu2LFjSElJcYZbAEhOTsbUqVNRXl6Ojo4OAIDFYsHp\n06cxffp0l3aEGTNmID4+HmVlZV4dvzd6C7hxccqfUg13sj1h3jxxCrY/P/uZeGPsXk0qKxO3zc1V\n7TCRlBT6FgVZUfOmTUFWcCOdkhXczk7gzBnx/2BXcH0JuL566ingzTdFBfeVVzwH2FGjxGv1yhXf\n9++J3A8Dbt9u3hTvCf62XkVyi4JsT3jgATFUmC+/u9euiYKKfF1JWhx/ubxc/P7cc4/n78fGAmPH\n8kKz7lS7yMxmsyE/Px+zZs3C7NmzUVRUhJYe8zHWfne+YUyPS4qzsrJgMBic3weAmpoaj722Y8eO\nhc1mw+XvBmO8cOECOjs73fZpNBoxevRo1MiJ4BXQW8A1GJSvOIW7zz4Tp496Gz2hp3/6J/GJs/to\nCmr33wLK9uA6HP734ALeBVxWcAUlX0/V1WJfw4YFN+DabOLsQW8jKABiHFzAv4D7X/8FrFrVd4uP\nrAorVbkOZAxcKVIC7qBB/p8Oj+QWBRlwZTubL20K166J13nPiq+cFltLfbh/+5uoYvfW3gSINgVe\naNZFlYCblpaGRYsW4Ve/+hX+5V/+BZMnT8b+/fvx4osvorOz07ldQ0MDoqKikNwjJcbExCA5ORnX\nu/32NTQ0IFV+7Oom5bt00fDdR7HG7/5SpnhIHSkpKc7tlNBbwAXEG3IktSjs3Ss+FXvbXmAwiF6i\nd98V/YZqjn/bnZIBt6VFvOH4GnAHDxaVam/GwmUFV1Cygiv7b+fMCW6Lwvnzop/Qmwqur5M9tLSI\nD0weagAuZMBVKtjLgJue7v8+Iing+iuSWxTk38n77xdB1deA27M9AdBeBdfhEAHX0/Bg3d1zj+jB\n9TRCUSRSJeAWFhaisLAQeXl5mDZtGl588UUsXrwYFRUVONZtlHGbzYaYXs5nx8TEoK3bq7mtrc3j\ntrHffbS1fffRVi5jPZyfi/3/7Z15eFTl+f7vCUkI2SEJomyyKQhUZJOggghoBQQFBQWtilBF/VVA\nrMXaKm3VYv2qFEURFVsqGBBBRAsSJYAolbAYEEhCIGGRkBjIRiALmd8fj29mnznnzFlnns91cQ2Z\nOXOWeWfOuc/9Pkt0dNPr3hg1ahTGjh3r8i89PR1r1651We7LL7/E2LFjPQTuY489hvfeew8AXZCr\nq4Hdu3dj7NixLmIdAJ577jnMnz/f5bljx45h7NixOOTWb2/hwoUeCXs1NTUYO3YsvnFr0L1ixQo8\n+OCDHsc2adIkn8fhjvNxCPwdx9//Pr+pekJUlPTjuOceoLq6BkOGjMV//vMNSksdAlmr46io2I3y\n8rEoLQ1+PJzb9MoZD5sNiIychC1bAh9HVRWQny9vPELle+V8HPHxQGmpOsfx0ksPols3uhicOEEh\nC3ocR14ePRcb6/s4/vSnp3451sDH4TweQqj/+9/+jyMmhhytoqLgxgOg79VLL41FcvIhxMa6Hoec\n8di48UEPgavmeDzwALBli//j0Pr34SxwlRxHZSWNh7h8herv3NtxFBfTrN6yZQsRH/+Ui8ANdBzu\nAlcch7ODq9dxCJSMR14e7auIv/U1HmvWjEVlpesNrF7H8c03wOrV6n6vVqxY0aTFOnXqhD59+mDm\nzJke6/GFbfPmzYq0vkgy81YmzBt1dXW49dZbMWrUKDz55JMAgAULFuCzzz5DZmamx/J33HEH+vXr\n15Q8NmrUKNx0002YM2eOy3I7duzAM888g5dffhn9+/fHli1bMG/ePCxYsAC9RVrhL8ybNw/79+/H\nqlWrXJ7Py8vDww8/jF27dqGvjIyhPn3oC/fmm56vDRhA0/DvvCN5dZZl1y6gf3/gyy+BkSPlvbd/\nf3KVhg8HZs0ip8P5Yqk2H31EwrqyMnhnNCeHeoL/738UWiGHAQPo+7Nkif/lrr2Wpp3efVf5foYC\n995LYlSNEPrBg6ke7D33ALfdRut1KsKiGfPnAy++SN9xf1PVnTtTqI+cgi///S/V9S0qCpycdu21\n1NrT7ZqniEceAb7/3hHTrIRly4Df/IZc6JiY4PfJmcZGuul+8kng5ZeVr+eDD4BFi+hYlXD33SRQ\nvFzqJGG3k3v51lvAww8rW4dVefppqgZSUAD8+tdUXWTNGmnv9XWetdtpPS+/TImZZmfpUuChh6gE\nmL9Sc8eOUZz9Z58BY8bot38Abe/0aUcCr1bs3r0b/fr1w+LFi3GFv+kw6NjoITo6GomJiah0qtGU\nkpKCxsZGVLjNGdfX16OyshKp4jbrl2Xd7zYAR0iCCF8QoQlnvMx5+QpzUIq/EAXh4IYDq1bJC09w\nZvJk4IsvgLVr6cKrpbgFHOOlRpiC+IopqTkotRZuVRXH4ALqxeA2NAB79tCNVceO9JxecbgiwSxQ\nHKaSWrhHj1JsnhSh3rGjujG4wcTfAtq26y0vJ5F75Ehw69m+nb43Sqd+gw1RsNkoTCFcQxREYm7X\nruqEKNhsjlJhVmD7dropDVRHuX17WsaIRLPcXPPFNOsmcGtqalBRUYFkp195119qJLlb37m5ubDb\n7U2vi2Xz8/NhdzvDHDx4EDExMWj/y1m2U6dOaNasmcc66+vrcfjwYZd1BgvH4DqqJ0yY4D/43ReT\nJtFJOzNT+/hbwDFeatTCdQ5RkEubNtJicNVwmkMBtWJwDxyg0lZi5gDQLw43UAUFgVKB26GDtPJJ\narbrNbvAFQImWIF75AjdHCn9DpaXK2/yIIiODt8kMyFwu3QhJ1fKjUZjI73Xm8AFrNXNbPt2ad09\nbTYSwnonmtXV0TlIzTbjaqC6wK2rq0NNTY3H88uWLQMADHSay+3bty8SEhKwbt06l2XXrVuHmJgY\nDBo0qOm5IUOG4OzZs9i2bVvTcxUVFcjKysLgwYMR+Yu6io+PR79+/ZCZmelStWHTpk24cOEChg4d\nqspx2u0kPsLdwd21i77YUqsnuNO2LSCGRMv6twLhhqrh4J49S49KnBl2cOWhloObnU1lg665hn67\niYn6Obi5uf4rKAiUCNzCQgq7kELHjiRMGxvlbcMbVhG4UkWRLwoK6FGpIArWwQXC28EVlWe6dqVQ\nFlHf1h8//0w3Jb4EbkqKNRzcsjLg0CHp7et/9Sv9HdyCAsplEE1qzIJsz23NmjWorq5uqkbw7bff\nouSXlN/x48ejqqoK06dPx/Dhw5tc1Z07d+L777/HwIEDcZ3TKEVHR2Pq1KlYsGAB5s2bh/79+yMn\nJweZmZmYNm2aS+ezoUOHYvXq1Zg/fz4KCwubOpnZ7XY88MADLvv40EMP4fHHH8fMmTMxevRolJaW\nYtWqVRgwYAAGiJ6VQXLuHA2oPwc3HATuypV0QQ7mvuGhh6gLmtP9jGaoHaKQnCy96Lgzl1xCAtdf\nJx27nasoCNRycHfuBHr0cBR3V3O63h9nz5JolergOt3HS+LoURLtUujYkZzAkhKHcFBCdTUJNysI\n3MpKGgMlsy11dY5qEWVlFCMtl4oKdQRuODq4xcWuIQoAhSlcdpn/9/lq8iCwioP73Xf0KFXg9u5N\nMcdqNXORgkigBZQ1qdEK2QJ35cqVOP3L3KrNZsM333yDbdu2wWaz4eabb0Z8fDwGDx6MXbt2YePG\njWhsbES7du0wffp0TPRi840bNw6RkZFYuXIltm/fjksuuQSPPfYYJkyY4LJcREQE/v73v+Ptt9/G\nJ598gtraWvTo0QNz585FO7caNd26dcMrr7yCd955B4sWLUJsbCxGjx6NaSr2gBUCKdxDFD7/HBg3\nTll4gmDKFODOO9VPMPGG2gJXyQUTIGFRX0/r8BUWfu4ciVwWuI4bxmBba2ZnU3iCoEOH4Bzcn36i\n776oX+sLUX5byxCFO+6QtqyIPS4qCk7gqlEDF3DEsGvhpjmvs6BA2e+1qMjhdhvp4IZjiIJo0ysE\nbqdO9Ps/fBhw6vnklUACNyWFZlXMzvbt9DuV2hK+d29yrg8d8t0UQm2cP0dLC9wVK1YEXGbu3Lmy\n1jl69GiMHj064HLx8fGYM2eORyUFb/Tu3RsLFy6UtR9yCCRwwyFEobGRLhrBZvXabPqIW4CEks1m\nvMD91a8ou3vaNKrsIOpcOiPihDlEgX5PdjtQU6O8EUhtLc0U3H+/47mOHeW7pc785jcUHpCd7V/A\niAuAFIHbujWNfW2t9++FO5WV9F2UE6IAkHC79lpp7/GGuDEIVuBGRtJ5VCsHt1kzmm07coSy6uXi\nHL+rRIRfuED/OERBPmfO0NgJgRsTQ983KYlmQuD6uomzSpKZiL+VemPfqxc97tunr8Bt3ZpuRswU\nh6tbklmowQ4ufZlrax0XTCsQEaFeu95gBO4VV1DliP/+lxxwL2HrTd8fdnAdIQXB/Kb27yfXXE0H\n98ABusm77z7/Ma15eTSlKqXvvWj2INUtLCykR6kCNymJvlPBhmYcP04XXTVKrGnV7KGsjPavZUvl\niWYFBSTCY2KUObjiXBOuIQoffkhuohJEIq4QuIAj0SwQp06RS+trmt4KIQp1dRRWJTU8AaDvWfv2\n+sbh5uU59pEFbggg1cEN5Y4i4sIqderELKjVzSwYgQtQ3dIvvqAC2bfe6ine2MF1IER+MLMiO3eS\nULn6asdzHTvSd0HJ96Gqii6i995LoTp/+5vvZaUmmAHyu5kdPUqPUn+HNhsdd7DJdcePkzvmo1eP\nLLQUuCkpFDerVOAeOUKfV+vWygRReTk9hmOIQkMDzVIFqvftC5GI6+zCSi0V5qtEmCAlhZx1b+aC\nWdizh/ZRjsAFyLnVs5JCbi7VG46LM9dNAwtchUhxcBsbzZVRqDbCAbKSgwvQmKlVJiwYgQsAN91E\nDTJ++AEYMcJRmQFgB9cZNRzc7GyavmvRwvFcMK1rxUX28ceBefOA55+nGxZvSC0RBjgErlQn5OhR\nchflxNOqkVynRgUFgZkFbkEBuYZKs+7VErhWDFEQZflEvLZcvDm4QuAGMo8CCVznbmZG0NAQ+Bi2\nb6ffdp8+8tbdu7d+Dq5zAq2S/AEtYYGrkIoKckJ8TTmq4TiZnaIicheDPXHrjRlCFJwZPBj4+mu6\nkA4b5nDu2MF1oMbvyT3BDHCNR5WLyBzu1g344x+pk8+UKZ7Tp42NlGSmlcAtLCT3Vk7yHQtc6Rw5\nQgJX6ZR2ODu4u3bRYzACNzbW9TrbtSudGwONhRQHFzAmDreujm5Iu3UDZs+mVtINDZ7Lbd9OXTLl\nVkPo3Zs6NDobJlohzoNXXskCN2SoqCDhEeHjE1TDcTI74sJqNcwSouBM3750kjt9mkqunTzJDq4z\nwf6ezp+nGFx3gdumDYUtKHFw8/PpItmqFZ0H/v1vEkF33EEVMAQnT9I0qNQQhbg4cpnlOLhS428F\nZhO4WtUkdRa4x45RDLYc7Ha6YencWXlSkhC4wTZ6sKKDm51Nj8EIXGf3FnAtFeYPMzu4paX0Xbr8\nckoyvvFGOs7f/AZYvZrOc3Y78O238sMTABK4AJ3ztEYk0HbrxgI3ZPDXxQwIHwfXauEJgDoCt66O\nxlYtgQsAPXtSRn9NDXDDDRRDFRUlLZM+1An297R3L2Vju2fRN2tGIk2pwO3WzfF3cjKwZg0Jot/+\n1jH9KBwOqQ4uIO9CcfSo/BvNDh2Uxx4DdGxWcnC7dCEnXe44l5TQzYoIUVDq4EZESEsw9IcVk8x2\n7SL38dQp+TcXgGuTB0GXLvToT+Da7dIdXCMErpilmz+fnNb//Q+YMYNibu+8k8T3iBEUgzx4sPz1\nX3kl3bjrEaaQm0uJnPHxtN8scEOAQAJXnMxCWeCGs4Mrpn7UFLgAuRNbt5LweuUVEnbB1H0NFURp\nMKUObnY2XWhFCR1nlLau9RZX26sX8P77wPLlgKhSmJdHFxs5vxWpAtduV+7gAspd3LNn6UbMKgJX\nNGeQkn3vjAhrCNbBTUryPdsnFauFKDQ0UG7B8OF0c/HTT/LX4dzkQRAXR6LX31hWVFDsrz+BGx9P\nn6kRIQpC4LZuTd+LgQMpSXXfPjqu+fPpt92xI3D99fLXHx1NDW30SDTLy3PMTrGDGyJIFbihGqJg\nt4e3gysuxmoLXIA+061bgauuckyjhTvNmlEsntIbxuxsqp7gLZZNaUUBdwdXMGkSxdU9+SQ58rm5\n5DrJqTYg9UJx5gx9JkoFrtJKCmo1eRC0akVOqZoC7vx5+peSQvvZrJn8OFwhojp3dji4civjqNHF\nDLBeiIJIMLv9dvpbSZiCtxAFIHAlhUBNHgAyDowqFSYEroi3d6ZzZ2DmTMrLKCxU/t3RK9HMuUIM\nC9wQQdyV+yLUQxTOnKELEgvc4PfHG5deCuzY4TsrPxwJpl2vtwQzgRIH98wZcn68CVyAHJjrrgPu\nuovKwMkJTwDI2ZFyoRAlwuQK3DZtSOwrdXC1ELiAukkxwplLSSEHvWNH+QL3yBG6aCckkBgS8Vqo\nrQAAIABJREFUoUlyUKOLGWC9EIVdu0hE3nYb/W02gQtoF/sdiJISyuHRssFR794Ug6tlqVKRQOss\ncMvLlYWjaAELXIWEu4Nr1Rq4gKNMWDA/fCFwRZtRLUhIcMSbMY52vXKpqgIOHvTdxapDB5o+lXNS\nDtR6NzISyMigx1275AtcqU6I3Bq4gogIEqfBCNzIyOBa/TojBK6aYQrOAhdQVklBlAhzXo9cQaSW\nwLVaiEJ2NtC9O4nMpCT5Ate9Ta8zaglcIx3cQO29g6V3b7rOBVvv2h/HjpFLL85vwpE2S4c4FrgK\nCSRwReebUHVwrVoDF6A75/p6+mEqRThNWgpcxhWlDu6ePXQz48vB7diRXj9xQvo6hcAVGd3euOQS\nyoiOjnZtLiGFtDRpjR4KC+lzUTKToDT2GCCxctllNO2vBlqUbBLCJRiBe+SII35Xada9mg6ulUIU\ndu0C+vWj/yvpGHj2LMXxeruJ6tqVviu+HP9Tp+h3Eaitt9LEwWDRS+AC0sIU/HVh9IdziTBAfolD\nrWGBq5BAAhcI7Xa9hYUUE2nFGFExbsGEKZw5QydPrnCgH0od3OxsKrvVo4f315U0e8jPJ3coUAm3\na68l4Tx5svR1A9Kn+kSCmZJExGBKhalZQQHQz8EtKJA3c+Ps4IpzHTu4gREJZkLgtm8v38EVXcy8\nObhiTHwlmgWqoCBQmjgYLHoI3Pbt6VrnL9GsoQF44QW6ln/7rfxt5ObS91IYXSxwQwQpAle06w1F\nRIKZFTP81RK4WsXfMt5R6uBmZwPXXEOzKt4QAleO2MvL8x1/605amnynU1woArlLSiooCIJp16u2\nwBUzIWoL3GbNHL/3zp1pylZqnG9NDQkl9xAFdnADIxLMghG43rqYCdQUuKHq4Nps/hPNCgqo5vqf\n/0x/b9wofxu5ueSmi/ObuAlkgWth7HY6UYa7g2vF8ASABa5VUerg7tzpO/4WcMxEyHVwpQpcJUh1\nQpTUwBV07EhCQIkrqLbAjYqiGxi1BW6rVo6bcCGKpIYpiPhmEaIQG0szAUoc3GCbPADWSjLLzqbP\n/Zpr6G+1BW7LlnTD4SsOV6rANTLJTGuBC3gXuHY78N571P731Cmq2HPzzcB338lfv3OJMIDC/6Ki\nWOBamnPnqGi8FIEbyg6uFRPMABa4VkWJg3v2LF0EfcXfCuRM19vt3mvgqokUgStK9QXj4ALKkn9O\nnFBX4ALq18IVNXAFcmvhCiHsnOipJGYzHEMUdu2iBDORbN2hA31uNTXS13H6NN1Q+GqQ4S/RTI6D\nW1ND5eT0wm7XV+Dm5jqc/5IS6rQ4bRowcSKFkVx3HZCeTs0m5MbiOpcIA+imxkylwljgKkAIIw5R\nMHovlMEC15oouWHcvZseAwlcOUkwJSUktI12cIuLaRpYqcBVEpoh9qmuTn2Bq7ab5i5wk5PJ+ZPq\n4BYUUKKwc5KT3CntujoST+EWouCcYAY4vityEjlFFzNfYXBqCFwtkhsDUV1Nv1u9BG5DA3DoEPD5\n5/T39u3UcfG99xw5BOnpNCt94ID0ddfU0DnT/UY/Lc2YsA9vsMBVgFSBG6ohChUV5EpY1cFNTKTH\nykrl62CBqz9KHNzsbHpfILdVjoMrKihoKXATEsix8ydwlZYIEwjRIVfgql0DV6C1gwvIq6RQUEDL\nO3cgk5uUJK4VatbB1bKuqRq4J5gBju+KnNkCb13MnOnSxbvAramhc7tUBxfQV5A5dzHTGtG5cepU\nYMwYCtXat8/RfEPQvz99z+WEKYjP3tnBBdjBtTzh7uBauUQYQAHxcXHs4FoNJQ7uzp10oQ3UJlU4\nuFLEQ14euUpa1ii22QI3e1Da5EHQvDmJALmJZuEicJ1LhAnkhiiUl9OjWiEKgHmK6PvCPcEMANq1\no0c5AtdXkwdB164kgt3PCVJr4ALKEweDQU+Bm5xMN8AHDwJvvw189pn3smvx8cCvfiVP4Obm0iML\n3BAj3B1c0eTBqgIXCL6bGQtc/VHq4AYKTwDou3z+vLQLXX4+ibsWLeTti1wCXSgKC+kCHahUmT+U\nlAo7fpzEsbc2o8FgNoHrXCJMINfBVVPgipKEZg9TcE8wA2jfW7dWX+ACnuMpR+AqLf0WDHoKXABY\nt446mj38sP+qR+np8gVuSornb4wFrsVhB5fcBLW6GBlBMAL34kW6cLHA1Zf4eLq4S73Al5bSd1WK\nwJVTC1frCgqCQBeKYEqECZQK3Hbt1C8RqJfAPXYssAva2Eifr5kcXCFwzZ5o5p5gJpDb7EHE4PpC\nCFz3MAU5AjchgbL+9XZwIyL0u3707i3tPJGeTrG6Un+DvhJtU1NZ4Fqaigo6uQdyTkLVwS0qopNV\noGlfMxOMwK2ooKlsFrj6In5vUm8ad+2iR6kOLiDtAqx1BQVBoG5mwZQIEyjpZvbDDyRg1EZNgStu\nQr0J3MbGwON88iTdSHlzcH/+WXocrBYhClYQuM7hCQI5pcIaGwM7uKmplE/hTeA2by7tM7fZ9C8V\nVlJC+65WF0C1SE+nx//9T9ry7hUUBGlp9Hkq7Y6mJhaWKMZRUUEX20ACL1Qd3MJC6yaYCYIRuOIi\nzAJXX4QjJPU3tXMnZc27u3DeSE2ljPlAYq+xkS6oZnBwCwvVcXCPH5d+MaqvpyzsoUOD2643UlLI\nEFBjCv7sWRKh7gJXai1cbyXCxD7W1kovd1VeLs0MkYIVQhTq6z0TzARyBK5o0+tP4Nps3ispiAoK\nUmcY9G72oFeJMLl06UKfhZQwBbvdv8C9eFF6QxUtYYGrACldzAC6IJ8/Tz/UUMLKJcIELHCthxAJ\nUmdFRPytlAudzSZtCvWnn+g3rZeD60vgXrxI+6qGwK2vd0zrBiI7m8SdFgJX/J7UuDC6t+kVtG9P\nzlkggVtQQN8J9xt5uVn35eXkMqox22UFB1ckmHmbNWnfXnoip78mD854q6QgtUSYQElt42Awq8C1\n2aTH4ZaU0PXT23lQahdGPWCBqwA5AhegxhChhJW7mAmSkpSXCWOBawxyHVypCWYCKfGoeXn0qJeD\ne+YMiVl3TpygG2c1BC4gPTYyK4vGoW/f4LbrDfF7UiNMwZfAjYykYw7U7OHIEaBtW3L1nZGblKRW\nkwfAGg7url0klPr08XytQwf67UoxFqQKXH8OrlTkJg4Gi1kFLuBo+ODtnOOMOA/6cnABc8ThssBV\ngFSBKzdm0AqcO0d3ZlYPUUhMVO7gCoepZUv19ocJjBwH96ef6J8cgSvFwc3PJwcwWGEphbQ0cru8\nXXyDrYErEAJXahzuli3A9deTUFQbPQQuIK2SgqiB647cslJaCFwzO7i+EswAebVwhcANlMjctSvd\n7F244HhOicBlB5dIT6fza6CGD7m5jhARd1jgWhy5Dm4oJZoJARAKDm4wIQrR0dSbntEPOb+n7Gx6\nHDBA+vqlOridOlHmtdaIi6C3C4Uo1ReswE1MpN+CFIEr4m9vvDG4bfrCTAL3yBHvdY7lOrgVFeoJ\nXCuEKPhKMAPkCdziYv9tegVdu9JNoLjhA5SFKOjt4KpdYk8tBgygG/hAYQq5uXTuETddzrRsSSE5\nLHAtSjg7uGpdWI0mWIHbqpX6ZZIY/8j5PWVnk0AUBeal0KEDnZT99aXXq0QY4N8JOXqULuLuU+hK\nkFoqbPdu+uy1iL8F1Be48fEOUehM587k0PqLBfXl4MbG0kXdSAfXrCEK/hLMAPq+Nmsm3cG95JLA\n51j3UmF1dTQ2ZnVwL16kbZnVwY2Lk9bwIS/Pe3gCQGPcqhULXMsSzg5uURF9gS+7zOg9CY6kJJrW\nUnKx4CYPxhAdTVPjUh1cqQlmAinxqGYSuGqFSUgVuFu20AXQl4AJluhoOmeq4aZ5q4Er6NyZ4u99\nJbNVVND7vTm4Nps8QRROIQr+EswAx3VDSrx3oBJhgksvJadXCFwR2iDXwT13zjXMQSvOnKGKJWYV\nuIC0RDNfFRQEZmn2wAJXAVKnnULVwW3XTpsYPD0RNyhKXFwWuMYgyi0F+j01NADffy8v/hYI3Oyh\noYGcPT0qKAD0HY2K8i1w1ZpF6dhRmujIygKuu07b8Ay1auH6E7iBSoWJ532Vl5Mzpa2mwDV7iIK/\nBDNBhw7yHNxAiJbZQuCKaiByDBg9u5np3cVMCenpJGB9/Q7r6+k8yAI3RAl3B9fq4QlA8AKXE8yM\nQUq73i1b6GI1Zoy8dYvuXL7cTNEBSy8HV7iF3po9qFEDVyAcXH9T9g0NwDffaBeeINBD4Arh6kvg\nigoL3hxcwHgH16whCv4SzARSa+EG6mLmTNeujjGT08VMIDdxMBisInAB3w0fCgvpfODvRj8tjcuE\nWRK7XbrAjY2li1SoObhWTzADghO4Zs6CDXXi4wP/njIySMTIdXCjo+nC6MvNzM+nR70ELuDdCamt\npU5bagrcqipH1y1v7NlDy2iVYCbQQ+AmJ9MNqj8HNzHR9/vllJUKNwc3UPiKVIFbXCzNwQVcS4Wd\nOkWzi8KVlQI7uK507kznHV9hCrm59MgObghSU0OB4lIErs0Weu16Q8XBTUykRyW1cFngGkcgB7e+\nHli9Gpg4UVkSoL941Lw8EhkilEEPvF0oRLF8tQSuOB5/cbhbtlCso9ybBrnoIXABR6KZN0SCma/v\nj9TGAPX1FNsp5VohBTM7uPX1wN69gb8f7dtTWS9/nfPsdjrHyhG4hYW0Dz/9RO+T01hDbvOOYCgp\noXFUo7OdVgRq+JCbS+Zd27a+18EC16IIx0/qSUuK42QVamvpDpkdXOknX0ZdAv2evvqKBNLEicrW\n768Wbn4+TVvr2UPe24VC7UomUmrhivhbb1UJ1ERPgevPwfUVngBId3DFuUUtBzcigtxJMzq4Bw7Q\nfgVycDt0oOX8iZ+zZ0msyhG4Fy/S91duiTCAzI7ISP0c3NatzV+Bx1/Dh7w8Ck/wdwzivCWla52W\nsMCViVyBKyUpxiqESg1cQLnAra4mF58dXGMI5OBmZFAIgb9EF3/4c3D1rKAg8CZwjx4lsSPqigZL\n69bkKvk67osXgW3btI+/BdSpSSqaYygVuL5KhDnvoxS3T4R8qCVwAbrBMKPAlZJgBkirhSu1yYNA\n3IwcPqxM4Nps+rXrtcrs36BBvhs+BKqgANB5q7bWeO3DAlcmShzcUAlREBfAUAhRiI6mGqJyBa4V\nYqhCGX8Obm0tsGYNMGmScoekQweaQvXnXOhJ69beBW779upVM4iI8O9c791LoTxax98C6ji4NTX0\nXQgkcEXSoDP19fR8IAf3/Hnajj/UdnABuhExY4iClAQzQJrALS6mR6kOrvgtKBW4AAtcd/w1fJAi\ncEXYh9FhCixwZRLODm5hIQkHtZwjo1HS7EFqj3RGG/w5uF9+SeM5aZLy9XfoQCJHjLOgro6+/0Y4\nuGVlrjGLapYIE/hzrrdsoZtBOV3hlNKqFYlpd+EpB39dzASdO9Nn6i7qjx2jmxt/Dq7UpCQtHNzm\nzc3r4Eqpj5yaSt8lKQ6u1HNss2aOmGqlAldO4mAwWEXg+mr4UFlJNyCBbvTN0q6XBa5Mwt3Bvewy\n7ePw9EKJwGUH11j8ObgZGcBVVwG9eilfv6941CNHSBDp7eCmpZHgcm5KoGaJMEGHDv4F7uDB3tty\nqo2oL+2vokMgpAhcX7Vwxd/+HFypZaXCJURBaoIZQAZJu3b+6y6fPk0iWE4iVteu5CyePq1c4LKD\n64q3RLO8PHqUEqIAsMC1HBUVjoLzUgglB7eoKDTibwVKHdyICP8XT0Y7fDm4588Dn34anHsL+G72\nYESJMMD7hULNLmYCXw7uxYvA1q36xN8C6rTrlSJw27cn589d4BYU0PP+KmXIdXBFxRY1MGOIgtQE\nM0GgZg9S2/Q607UrJUU1NioPUWAH1xVvDR9EibBAN/p6VqbwBwtcmVRU0EVWahmSUHJwQ6UGriAx\nUX6ZsJIS+vHqmUnPOPDl4P73v/R8sAI3OZm+F+5iLz+fSuPo3aLaXeCeO0ffQS0EbkkJ3Sg4s28f\nCTU94m8Bh8ANRmxIEbiRkXTM3gRux47+OzXKcXATE9U9V5gxREFqgpkgUC1cOU0eBF26OISYWR3c\nCxfoemMlgQsAO3Y4nsvNpbEJdNMWHU0GEju4FkNqkwdBKJUJC5UauAKlDi7H3xqHmBFxr6OZkQFc\nfXXgqTMpeEu4yssj91bv8j5C4IrQGLVLhAnEjau78MjKIlE1cKC62/OFEI/BOriRkYEvwt5q4QYq\nEQbQOT06WpqDq2Z4AmDOEAWpCWaCQAJXTpMHQdeujv+b1cEVYs8qAtdbw4e8POnnWDPUwmWBKxO5\nAldKa1ErUF9P2eWh5OAqjcG1ygkqFBEXUecM9nPngPXrg3dvBd6m640oEQZQx61mzRwXCiFwtXBw\nAc/j3rKFSgbFxKi7PV+IFtjBCtxWrQLfjHgrFRaoRBggvaxUebl6TR4EZgxRyM6W1wCkfXtqyNDQ\n4P11JSaCELg2mzIDIjWVrtNa3jxYLX/DW8MHKRUUBCxwLUi4OrgnT5JrFu4OLgtcYxGx7843jevX\nk+BVS+B6c3CNErgi3ltcKI4eJRdP7VCJtm3pguYscBsbKf5Wr/AEgIR0bGzwAldKjLxwcEUxertd\nmoMLSJvSDgcHt74e+OEH6fG3AP2+GhtJ5HpDicDt2JFuBFNTlZXPE98XLV1cqwlcwLXhQ2OjPAc3\nNZUFruVQ4uBWVxvf0SNYxIUv3B1cDlEwFuHgOt80rlxJDlIg500q7g5uTQ1NqepdQUHg7IQcPUr7\nJ6cVqRSEaHY+7v37SWjqlWAmCLYWrhyBW1npqFDx88904yTleySlrJQWAtdsDq7cBDPAfy1cuW16\nBdHR9LtQEp4ASE8cDAYhcEXYkRVIT6dz7Y8/0g1JTY308yA7uBZEiYPb0GCuu24liKlRf9nFVoMd\nXOvh7uBWVQFffKGeewvQd7yiwvHdEHGaRji4gGuzh8JC7WZR3IV9VhYJh0GDtNmeL/QUuIAjTEFK\niTCB1BAFLQSuma4lchPMAP8Ct7ycBLzcJDOAnEWlNdr1yPovKaFrjh7l9tSif39HwwdRQYFDFEIY\nJQ4uYP0whaIiutDGxhq9J+qRlETxm75iwdypr6eLJzu4xuHu4K5bR9nJEyeqtw0xSyHCFIwqESZw\nd3DVjr8VdOzoGpqxZQtw7bVAixbabM8XwSb8SBW47rVwxY2MWg5uRUXohyjITTADKPkvMdG7wJXb\nxcyZRYuAhQvlvw/QL0TBauZIXBwl7wqBGxkp/fyTlsZlwiyHEgcXsH6iWaiVCAMcWdZSx0b8WK12\nkgol3B3cjAyaRlNzZsG9Fm5eHv3mjZpa1FPgCge3sZEErp7xtwK9HNzkZEpqc3ZwU1Ol1a010sE1\nU4jCDz8A11wj/33t23tv9hBMp8jLL1f+20hKIqdSawfXitcOkWiWl0c3hf5K6DmTlqZ94l4gWODK\nJJwd3FBKMAMc4yg1TIHb9BqPs4NbXg5s2KCuewtQHF9kpEPsiQQzvUuECYTALS+nf1oJ3A4dqFLK\nxYsUW1lWpn/8LaCfwAVcKykUFEgLTwCMjcE1i4Nrt1Od5N695b/XV7MHo86xERH0vWMH15P0dBK3\n334rrwyjGbqZscCVgd3ODm4oIVfgWjELNtSIi6PHqipg7VoKL7nrLnW30ayZazvRvDzjEswAh8A9\nepT+1jIGt6GBkkm2bKFsdFHsXU+CEbgNDSQs5QhcEZpw5Ij0RMXUVApvcm+M4bwfVVWhHaJw7Bgl\n6f3qV/Lf66sWrmjTq2b3N6lo3ezBygIXAHbuZIEb0pw/TycuJQLXyg5uYyOdjMLdwWWBazwRESRy\nq6spPOH666nEldo4lwozqkSYIC2Nzjt799LfWoYoAORcZ2VRcwcjYu6DEbiiIoLWDm6gmE3RITGU\nQxRycuhRbYErt02vWrDA9U6nTo79lnOjLxL3WOBaBCGElIQoWNnBPXWKEqzC3cE9fZrGU++kG8aV\nhASaUcjMVLd6gjMiHrWyksbdaIELAN9/T4JTq1hg8fsuLDQu/hYggVteTqEScpHSpteZzp0dTuTJ\nk/IcXOftuVNeTo9qN3owk4Obk0MCXskNZvv2JHzcHXAlXczUQstuZqL8mRUFrmj4ALCDG9IoEbih\n4OCKEmHhLnCteoIKNRISgBUraGbhzju12YZwcEUFBaNDFAASuJdfrp27FR9P4nLjRrooGRF/CzjE\nqXBj5aBE4IqGFoB8B9eX4ycEbig7uPv2kXur5PsoEjlPnHB93sg641o6uFVVNG5WvX6IUoFyBG5c\nHJlBLHAtghKBGx1N/6wscEOxyQNAsV5RUfIcXE4wM574eLrZuPFG7cajY0eKRT1wgP420sEVF8Wc\nHO3CEwQdOgCffEJJdoMHa7stX7RqRY9KwhTkClwhaDdtoke1HdxQTjLLyVEWngD4roVr5DlWSwfX\n6uFtDz0EvPWW/P03ulQYC1wnhFPpCyUCF6ALspVDFAoLqZyOEYH/WmKz0TGJeLlAsINrDkTYj1bh\nCYCjnWhWFokZtYWKHFq1ou9qQ4P2ArdjR+pWNGCAI6FPb9QQuGIdgWjfnpIKMzNJPEptgZyQQDcB\neju4ZglRuHCBki+VCtx27ejRm8BV0uRBDbR0cK0ucNPSgEceUfY+dnBNwoYN/l9XKnBFu16rEool\nwgRyupmxg2sO4uNJlIwfr902xGxFZqax4QkAHatwJPUQuIBx4QlA8AI3IYGEoBQiI+mYDxygz1Zq\nC2Sbzb8gUnqtCIRZQhQOHqQYaSUlwgCaPUtLcxW4drvxDm5lpTafr9UFrlJY4JqIDRvoR+YLcdIS\nDpJUrO7gFhWFXniCQI7AZQfXHPToAUyY4Jgm1gIxhXrsmLHhCQIRh6v1jab4nRuVYAYEL3ClhicI\nRFiC1Phbgb9auOXldN6XWhRfKmYJURAVFHr1Ur4O92YPok2vkTG4QHA1mH1RUuKotRtOsMA1EceP\nU+tBX1RUkLht1kzeeuPjre3ghmINXIFUgWvlLNhQ45VXgI8+0nYbcXGOC56ZBK7WDu7AgSQ8rrtO\n2+34o0ULcvjMLnD9dTPToskDQM50QwOFzxhJTg59XnJa9Lrj3uzB6EY64veuRZhCSQn9hqXOEIQK\nqakscE1Dq1bA8uW+X5fb5EGQkGBdB9du5xAFgJYx0l1gXNGjTqbI9DY6RAHQT+Befz25asEIFzVQ\nmvATjMCVmmAmCOTgaiFwmzenR6PDFHJylIcnCNxr4QqBa1QMbqDaxsEQruYIO7gmYuRIKh7vq/6i\nUoFrZQe3pIQSCsLdwQ3XGKpwRnznzeLgJicbm+ymJ0qbPYS6gysErtFhCqJEWDD4Erih6uCG47Uj\nLY1+xw0NxmyfBa4Tt95KpYFETUR3gnFwrSpwRYmwcHdwjT75MvojHNyuXY3dDwAYNw54/HGj90I/\n9BS4/frRe66+Wt77jHBwRfKckQ7u6dP0Tw2BW1npOP8WF5OAN6paT3IyhRCwg6seYuZJi7hmKbDA\ndaJXL5oCXLHC++vBOLhWDVEI1Rq4AqllwtjBDT+GDQNGjTJ+uh4Afv1r4K9/NXov9ENvB/fnnx2J\nhVLxV0WhvFz9CgqAORzcffvoMViBK24ghYtrZJtewJEExg6uehjdzYwFrhM2G3DPPcDHH3s/gYSj\ng1tYSBf4li2N3hNtkOPgRkaG7ufAeDJuHPD550bvRXiiRODa7coErlJSUui87u1aobWDa6TAzcmh\nREC5McvuuDd7MEMZRq1q4bLANWb7LHDdmDyZWkRu3Oj5mlUcXLsd+OADZa0u3REJZkbdVWtNUhI5\nuIGyksUJKlQ/B4YxE61ayZ8qPneOpu71Erj+upmFcpLZvn002ym3mpA7l15KrqmzwDUqwUygRTez\nixdJNLPA1R8WuG707EnZod7CFKzi4O7cCTz4IJVTCpZQLhEG0Hja7YHHJ1zvwBnGCFJS5Du4ctv0\nBovYjjfHr6IidJPMgmnR60xkJHWOC3UHt6yMrjHheP1ISqJxZoFrIiZPBj791FP0BOPgnjunX+3C\n9993PNbXB7euUC4RBjjGM1CYghlOvgwTLrRqRS6or4o23tBb4PpycBsbaVYoFEMUGhqAH38MvkSY\nwLnZQ3Gx8edYLRzccM7fEB3/WOCaiLvvBs6fJ5ErsNuDE7gAiVytqakh93ncODphrF+vfF2iBm6o\nO7hAYIHLDi7D6EerVo5zrlTM4uBWVtK+h2KIQn4+iWs1HFzA0ezB6Da9Ai0c3HAWuICxtXBZ4Hrh\n8suBwYNdwxQuXCA3VGmIAqBPHO7q1XSCffVV6kq0ZInydZ09S/scygJXlKRhB5dhzIOSdr16C9yk\nJIpDdXf8ysvpMRRDFEQFBTUd3OPHzdNIx19tY6WwwNUmcU8Ksjplnz9/Hh999BEOHjyIQ4cOobq6\nGk8//TRuueUWj2WLiorw5ptvYv/+/YiKisKgQYPw6KOPIsmLQvziiy+QkZGB4uJitG7dGuPHj8cd\nd9zhsVx1dTUWL16Mbdu2oba2Fj169MCMGTPQzUsl9v3792Px4sU4fPgwYmNjceONN2LatGlo0aKF\npGOdPBmYOdORlSsEUDAOrh5xuO+/T+WNOncGfvtbYPp05S5sqNfABdjBZRgzolTgRkY6DAWtEdOv\n7hdvLQWu0SEKOTmUHCbCM4JFCNziYvrb6CSz1FS6FtTXA1FR6qyzpISqTsTFqbM+q2EZB7eiogLL\nli3D8ePH0dVP9fPS0lI88cQTOHXqFKZPn46JEydix44dmDNnDhrcWlp89tlneOWVV9C5c2c88cQT\nuOqqq7Bw4UKscMvyamxsxNy5c/HVV19h/PjxePjhh3H27FnMmjULJ0+edFn28OHDePLJJ1FXV4dH\nH30Uo0ePxvr16zFv3jzJx3rXXTRt8vHH4tjpMRgHV2uBW1AAZGUBU6fS35Mm0Y9KxOTuC9UKAAAg\nAElEQVTKpbCQHkPZwRXj6a8W7oULNP5GuwsMEy4IgSsnHlKYEXpWOvHm+Onh4BoVoqBWgpmgfXsS\n6/v3099Gn2OFcFezMUG4V+AxUuDKcnBTUlKwevVqtGzZErm5uZgxY4bX5T788EPU1tZiyZIlSPul\nTkSPHj0wZ84cbNiwAWPGjAEA1NbW4t1330V6ejqee+45AMCoUaNgt9uxbNky3HbbbYj/xf7csmUL\nfvzxRzz//PMYMmQIAGDYsGG47777sHTpUjz77LNN23/33XeRmJiI119/vcmxbdOmDV555RVkZ2ej\nf//+AY+1dWtgxAhg+XLg4YfVcXC1DlH44AOach8/3rHdKVOA994D/vQncjfkUFQExMSEtnMZH0+l\navw5uOLHGcqfA8OYCRFmINfB1Ss8QeCtm5kQuFo0ejDawd23j8wftRDNHrKz6dFogSsc5MJC9fYl\n3Gf/LOPgRkVFoaWESvdbt25Fenp6k7gFgL59+6Jdu3bIyspqem7Pnj2oqqrCuHHjXN5/++2348KF\nC9ixY4fLOlu1atUkbgEgKSkJN954I7Zv397kDJ87dw67du3CyJEjXcIRbr75ZrRo0cJl+4GYPJna\n9ooYIdqm5Lc3oYeDe/EiCdx77gFiYx3P//a3wMmTwH//K3+de/eGdg1cgI4tMdG/wA33GCqG0ZsW\nLcitNLvA9efgatnJzAgHt6KChJ/aDi5AAjc6WpvPTA7XXEOzB84J5sHCApd+I3a7/ttWPcmstLQU\n5eXluPLKKz1e6969O/Lz85v+Pnz4MAB4LNutWzfYbLam1wEgPz/fa6xt9+7dUVtbi+O/FNM7cuQI\nLl686LHOyMhIdO3a1WX7gbj9dnIwMzLM7+BmZgInTjjCEwR9+9I/uclme/cCy5YB06apt49mJVA3\ns9On6dFod4FhwgWbTX43MzM5uLGxDrdVTURcqBEOrggjUCvBDCDx07w5Cdw2bYw3U6KiqALR6tXq\nCbJwF7ipqVReTtz46YnqAvfML2ekFC9nmpSUFFRVVTW5rWVlZYiIiPBIPIuKikJSUhJ+dro1Lisr\n87rOVr8Ea5X9cpYR2xfPuy9bJiOoKzERGDOGwhSEABJZ93IQweVaOrjvv09NKgYM8Hztt7+llqMn\nTkhbV2Mj8OijQI8ewO9+p+5+mpFAAlc4uE4TEgzDaIxVBK67g6tVkweABGB0tDECNyeHwty6d1dv\nnTYb0K6duXIcJkwA8vKo3q8ahLvANbKbmeoCt/aXX16UlxTE6F9uacUytbW1XpcT769zmoepq6uT\nvE7n592XrfVzZhg1ahTGjh3r8i8nJx179qzF99+TE9usGfDll19i7NixHu9/7LHH8N5777k8t3v3\nbtx++1jExv7s4uA+99xzmD9/vsuyx44dw9ixY3Ho0CGX5xcuXIinnnrK5bmamhqMHTsW33zzDcrK\ngLVryb396KMVePDBB12WvecewGabhKeeWuvyvK/jGDnyMXz33Xt4802HY7B7926MHTvW5aZD7eNw\nZsUKz+MAgEmTJmHtWmnH4Ws83I8jMRHYutX3cezbdwgtWzocGbMeBxAa48HHwccxadIkNDaudRG4\ngY7DWeDqdRyiMYDzcTi36dViPJo3B9at03881q1bi+7dHWESan2v7HY6DiFwjf59jBhB14SpU9X5\nfZw6dchF4Ibb75wE7grMmiX/OFasWNGkxTp16oQ+ffpg5syZHuvxhW3z5s2KjHiRZOZeJkw8/8wz\nz2DEiBEu71m8eDEyMjLw5ZdfIjIyEgsWLMBnn32GzMxMj/Xfcccd6NevX1Py2KhRo3DTTTdhzpw5\nLsvt2LEDzzzzDF5++WX0798fW7Zswbx587BgwQL0dptLmTdvHvbv349Vq1a5PJ+Xl4eHH34Yu3bt\nQt++fV1eu3CBfngXL9JJS6oL6k6bNsBjj1Gyl9osXAjMnk2xtr7uFKdNAzZtAo4c8d9D/OxZ4Ior\ngFtuAf7zH/X31YyMGUOJZuvWeX/9ySfJAXf7fTMMoyG3306xpl98IW355GTgmWeA3/9e2/1y5l//\nAh54gBxVcQP84IPkAG7frs0209LofD93rjbr98X111NS2PLl6q73/vuBf/8beOgh4N131V23Uu69\nF/jhB0fdX6WcP0/hKsuW0TrDkZIS0lBr11L4R7Ds3r0b/fr1w+LFi3HFFVf4XVZ1B9c9ZMCZsrIy\nJCYmIvKXdP6UlBQ0Njaiwm1+uL6+HpWVlUh1KraXkpLicbcBeIZEiO2f8TK35SvMwR8xMTRlce5c\ncAHwCQnaxeC+/z4wdqz/aZDp06kl4pdf+l/XH/9IF5V//EPdfTQzSUn+y4RxkweG0Z+UFOkhCg0N\nNM1tRJIZ4BqH6+zgaoERIQp2u/olwgQi0cxM59gJEyjmOC8vuPVwBR5H6b6QCFFIS0tDcnKyh50N\nAIcOHXKpnyv+775sbm4u7Ha7x7L5+fmwu0V+Hzx4EDExMWj/y6+kU6dOaNasmcc66+vrcfjwYb/1\ne30xeTI9BiNw4+O1icHds4cSwtyTy9wZOJBOTv6SzXbtAt5+G/jLX6iYd7ggJQY3nE9QDGMEcmJw\nxXJGxOAC+grc5s31r6JQVEQGjZYC1+gmD87ccgs5r6tXB7cersBDM8atWoWIwAWAIUOGYMeOHSh1\nOqLdu3fjxIkTGDp0aNNzffv2RUJCAta5zQ2vW7cOMTExGDRokMs6z549i23btjU9V1FRgaysLAwe\nPLjJFY6Pj0e/fv2QmZmJ8+fPNy27adMmXLhwwWX7Uhk2jO4ug3VwtRC4779PYtRLMzkXbDZKNlu3\nDjh1yvN1kVjWqxeFUoQTLHAZxnzIEbh6t+kViO05Ty7qIXD1dnDFVH24OLixscCoUSxw1cKoWrgy\nS/8Da9asQXV1dVMIwrfffouSX0Zx/PjxiIuLw5QpU5CVlYVZs2ZhwoQJqKmpQUZGBjp37oxbb721\naV3R0dGYOnUqFixYgHnz5qF///7IyclBZmYmpk2b1tTkAQCGDh2K1atXY/78+SgsLERiYiI+/fRT\n2O12PPDAAy77+NBDD+Hxxx/HzJkzMXr0aJSWlmLVqlUYMGAABngrMxCAZs2ABQscwfVKiI9XP0Th\nwgXgww+pEYWUJg5TpgBPPQUsXUqxas68/z7w/fdU91duQwirI6VMmJlOvgwTDlx6KQlcKYLRKIHr\ny8HVsp5rdLT+Dm5ODo1B27bqr1tMqoqmD2ZhwgRK0C4sVN6uXghctVobW5XUVIsI3JUrV+L0L4VB\nbTYbvvnmG2zbtg02mw0333wz4uLikJaWhtdffx2LFi3CO++8g+joaKSnp2PGjBlNTqtg3LhxiIyM\nxMqVK7F9+3ZccskleOyxxzBhwgSX5SIiIvD3v/8db7/9Nj755BPU1taiR48emDt3Ltq1a+eybLdu\n3fDKK6/gnXfewaJFixAbG4vRo0djWhBFXSdNUvxWAOTgqtn+D6Bi1GfPUlKDFJKTgYkTKZD/D3+g\nxCqATs5/+APwm98AN9yg7j5aASFw7XbPOoyNjfTDDPc7cIbRm6FD6Te5ZUvg5BSjBG5SEhkgoe7g\nivhbLerUXnEFhdlp4Q4Hw+jR9FmvXk2JxkooKYFLBZ5wxTIO7ooVKyQtd/nll+Pll1+WtOzo0aMx\nevTogMvFx8djzpw5HpUUvNG7d28sXLhQ0vb1ID6e4pjU5L33KLM1QCKhC9OnU+bvV18BI0fSc888\nQ0kaEocr5EhMpCoZNTWOmsWCM2foNXZwGUZfOnWif199JV3geil/rikREbRNIXAbG7WtgwsYk2SW\nk0Pls7Ti6qu1W7dSEhIo9C9YgcvmCAncXbv0364mMbiMJ2onmRUVUfeyQMll7gweDFx1FfDOO/T3\n999T4tnf/ha+Ik5MJ3oLU+AYKoYxjhEj6DwXiLIyulH1UVZdU5y7mVVXk+scSklmFy5QNQE1O5hZ\nhQkTgO++oxKcSmCBSxjl4LLA1Qm1y4T9618UCH/XXfLeZ7ORi7t2LSWbPfYY3T0/8oh6+2Y1hMD1\nViqM2/QyjHEMHw4cPAj89JP/5crKjItzTElxOLiiHWkohSgcOEDOtNlCCPTgttsoJ2XNGmXvZ4FL\nsMANcdR0cBsbKVFs0iRar1zuu4/ixsaMoR7gixaFX2KZM+zgMow5uekmevzqK//LGdGmV+Ds4Ooh\ncPUOUcjJocdevfTbpllo2ZJuspRWU2CBS6SlUdOLc+eCX9fFi9KXZYGrE2o6uFlZlNkpNzxBkJJC\nUy+7d9M60tPV2S+r4k/gnj5NjklCgr77xDAMXRivvtr8AldvB1fPEIV9+4AuXZSZKaHAnXdSdSFh\ndsiBBS5B7XrVcXE3bpS+LAtcnYiPB+rr1TkxLV0KXHklxdMq5amn6M70738Pfn+sTiAH95JLtMke\nZhgmMCIO1+6nqbyRAjclRV8HV+8QhZyc8Iy/FYgEx7Vr5b3PbmeBKxDhQ8EK3MZGSq6XCgtcnRAO\noBphCl9/DdxxR3Ciq08fumiIO6twRoyNL4HLJyiGMY4RIyjJx1/bVLM5uFrXwdVb4IZj/K0gLY1K\n1skNU6ioIFOLrx/qObiffEKz11JhgasTYnon2DCFn36ifwMHBr9PDNGsGY2PrxAFTjBjGOO44Qaq\njuCvmoLRDq4QM+XlQExMcE2BAqFniMLp03STH84CF6CQvq+/prrzUuH8DQdC4DrXi5aL3U7Vnq69\nVvp7WODqhFoObnY2PfbvH9x6GFd8dTNjB5dhjCUujvIEfMXh2u3GO7iA9K5rwdKiBfDDD5QA1bo1\ndRfr1InqoffsCVxzDYmAjz4KfluiRW84hygANGPa0ECt7qXCAteByGMJxsFdv56+93L6dYVx7ry+\nqOXgZmfTD8ateRsTJElJvsuE3Xij7rvDMIwTw4cDr71GGdTNmrm+Vl1N7qmRDi5A7pQeAvd3vwPa\ntycXV+R11Ne7/n/HDmDuXCoj6f55ySEnhwR1ly7q7b8VuewyynlZvRq4/35p72GB60owpcKEe3vD\nDUDfvtLfxwJXJ4TAVcPBHTCAk57Uhh1chjEvI0YAzz1HlV8GDHB9zag2vQLh4JaV6SNwO3QAnnjC\n/zI7d1IY2+efA2PHKt9WTg6VBwtGJIcKd95JNw1VVdKq6pSUUPlNrb8PViEYgZuZSU2p5FRQADhE\nQTfEDyIYB9dupxMXhyeojzeBe+4c/WOByzDGMmAAmQTe4nDNInB//ln7Nr1SGTCABO4bbwS3nnBP\nMHNm/HhK7vv8c2nLl5SQqItglQUgOIH7t7/Rd3rkSHnv449eJ9RwcI8do5MoC1z18SZwxRQTJ5kx\njLFERVGokLc4XKMFbnIyiRi9HFyp/L//B2zaBBw6pOz9DQ3UxSzc428FHTvStffjj6Utz7N/rqSm\nKhO4W7fSv2eflT9zzQJXJ5o3p+mKYBxcTjDTDn8Cl09SDGM8w4cD33xDHZGcMVrgRkQArVrpF4Mr\nlbvuonPXm28qe39+PjmW7OA6mDAB+O9/gZqawMuywHVFqYP7wgv0HRwzRv57WeDqhM0WfLve7GxK\nLmvTRr39YojERE+Be/o0PbKDyzDGM2IECa5vv3V9vqyMasPGxRmzXwCJa7MJ3ObNgd/+FvjgA+8J\ntIHIyqJHdnAdTJhA4nbDhsDLssB1RYnA/f574MsvgT/+UVmoBwtcHUlICE7gcvytdvhycG0245wh\nhmEc9OxJN5vucbiiRJiRibepqeYLUQCARx4hx/tf/5L3vooK4PnngbvvdsQYM0C3biT4pTR9EDG4\nDJGWRjdacmo4v/ACdW2dMEHZNlng6kh8vPIQBbudHFwWuNrgTeCePk0n90iuNcIwhmOzUZiCexyu\nkTVwBSkp5E6Vl2vbxUwubdtSctQbb1CbU6n85S9kxvzjH9rtm1W55x5g1Spg82b/y7GD64rcZg8/\n/EB1h595RnkVDxa4OhJMiEJBAQkw9xI5jDokJdGdpXMLTD5BMYy5GD6cbvSdO0qZQeCmpgJFRSQi\nzeTgApRslpfnvxOcMwcPAv/8J00Lc711T558klr33nEHsH+/92UaGuh7ydcPB3Lb9b74IjUwuece\n5dtkgasjCQnKHdydO+mxXz/19odxIFwXZxf39Gk+QTGMmRgxgmaznN0zswjcggL6v9kE7vXXA1df\nLa1kmN1OjSQ6dgRmz9Z+36xIdDSFKHTsCIwaBZw86bmMcCn5+uFAjsA9dIhc8j/8gSqoKIUFro4E\n4+BmZ9PdjNEn8lDFm8AtKeEEM4YxEx06AF27uoYpmEHgpqQAFy7Q/80mcG024PHHqdXpkSP+l127\nlpze114DYmL02T8rkpgIfPEF3RCMGuWZxMcVeDyRI3BffJG6x0ntGucLFrg6EoyDKzqYMdrgS+Dy\nCYphzMWIEa7T7WYQuM6JWGYTuAAweTLt11tv+V7m/HlybW+9VVlJpnCjbVsqGVZURF3O6usdr7HA\n9SQujip7bN0K5OZS221vFBQAy5cDv/89LR8MLHB1RKmDe/EitajkBDPtSEykR/cQBXZwGcZcDB9O\nMaXHj9PfZhC4zts3o8CNjQUeegh47z3fNVz/8Q+abn/9dW4FL5VevYA1a6ik2vTp5OgCLHC9YbNR\nuMzbbwPdu5PhN3AgMG0asHAhsGULxdbPn0+/p2nTgt8mC1wdUerg5uaSMGaBqx3uDi4nCTCMORk2\njC6WX31FrlllpfEC1+wOLgA8+ihVefjwQ8/XioqAl14CZs0CrrhC/32zMsOGUa3hf/2LSqsBJHBj\nY42tzWxGMjPps8nMpBJgV11F5t2cOdSpsFUrYMkSSuSLjQ1+e1wASUeUOriig1nfvuruD+PAXeD+\n/DPdjbODyzDmIiUFuOYaEri33up4zkiEwG3e3Lyxq506AbfdRslm06a5urRz5gAtW1I7VEY+kycD\nx44Bc+cC7dtzeJs/0tJoFmb4cMdz9fU0K/PDDzQz8/jj6myLBa6OKG30kJ1NxY7NVF8x1IiKAlq0\ncCQL8BQTw5iXESOAf//b+Da9ArF9s7q3gscfB26+Gdi2DRgyhJ77+mvg44+BZcvoGsUo4+mnSeQ+\n8ghNwfO1QzpRUdTIpWdPddfLIQo6IhxcEacjFe5gpg/OzR5Em14+STGM+Rg+HCguJqEGGC9wW7Yk\nR9TsJsSIEWSWiJJh9fVUFmzwYGDKFGP3zerYbFQ/eNQo4Mcf+dphBljg6kh8PIlbX0H+3qivB/bu\nZYGrB84Clx1chjEv119P9UhXrqS/jRa4zZqRyDW7gytKhn3yCXDiBFVVOHCAknw4sSx4IiOBFSvI\nHeea9cbDIQo6IqZ/qqqkB58fOED1FblEmPa4C9y4OE4SYBgzEhtLrmNWFv3dsqWhuwOA4nDNLnAB\nqi36zDPAX/8KZGRQ9j/nd6hHXBx9L/mGwXjYwdURUYpKTH9LITsbiIgA+vTRZp8YB4mJriEKnGDG\nMOZlxAhHa9xIE1g1aWnmENqBSEggkfvOOyTCXnjB6D0KPVjcmgMWuDoycCBNpS1dKv09O3dSKQ12\nErXH3cHl8ASGMS8iC9vo8ATBa68Bf/6z0XshjccfpxCPF190LXHGMKEEC1wdadECePhh4P33PVv7\n+YI7mOmHe5IZO7gMY17696dZF7MI3AEDyIywAldeSUl6M2YYvScMox0scHXm0UepJaIUF7e2FsjJ\n4QQzvUhKci0Txg4uw5iXyEhg9GigSxej98SaWCGcgmGCgQWuzrRtC9x1F2Wt+urFLMjJoSoKLHD1\nwd3BZYHLMOZm6VJ5IV8Mw4QPLHANYOZMoKAA+Pxz/8tlZ5NL8atf6bNf4Y4QuHY7ObgcosAw5qZ5\nc/rHMAzjDgtcAxg4EBg0CHj9df/LZWeTuDVr68dQIymJahSfOUPhIezgMgzDMIw1YYFrEDNnAps3\nUxiCL7KzOTxBT0QZt8OH6ZEdXIZhGIaxJixwDWL8eIrHXbDA++s1NdTujwWufog2m3l59MgOLsMw\nDMNYExa4BhEVRbUIP/wQKC31fH3vXkpC4xJh+iEEbn4+PbKDyzAMwzDWhAWugUyfTl3KFi/2fC07\nm5InevbUf7/CFWeBK3rLMwzDMAxjPVjgGkhKCnDffcCbbwJ1da6v7dxJ7XmjoozZt3DEWeCmpdHN\nB8MwDMMw1oMv4QbzxBPUUWbVKtfnOcFMf5xjcDk8gWEYhmGsCwtcg7nqKmDkSCoZZrfTc5WVQG4u\nx9/qTUwM9WevquIEM4ZhGIaxMixwTcDMmeTYfvcd/b1nD4lddnD1R5QKYweXYRiGYawLC1wT8Otf\nA1dc4Wj8sHMnEBcHdO9u7H6FIyJMgR1chmEYhrEuLHBNQEQE8LvfAZ98Ahw7Rm5u376Uyc/oixC4\n7OAyDMMwjHVhgWsS7r8fiI+nigqcYGYc7OAyDMMwjPWJNHoHGCI+Hpg2DXj7bUoyY4FrDCxwGYZh\nGMb6sINrIh5/HKiupv+zwDUGDlFgGIZhGOvDAtdEXH45cPvtJLK6djV6b8ITdnAZhmEYxvpwiILJ\neOMN4PBh7qJlFKJMGAtchmEYhrEuLHBNxqWX0j/GGDp3Bjp1Apo3N3pPGIZhGIZRCvuEDOPE/fcD\nP/5o9F4wDMMwDBMMLHAZxomICKBFC6P3gmEYhmGYYGCByzAMwzAMw4QULHAZhmEYhmGYkIIFLsMw\nDMMwDBNSsMBlGIZhGIZhQgoWuAzDMAzDMExIwQKXYRiGYRiGCSlY4DIMwzAMwzAhBQtchmEYhmEY\nJqRggcswDMMwDMOEFCxwGYZhGIZhmJCCBS7DMAzDMAwTUrDAZRiGYRiGYUIKFrgMwzAMwzBMSMEC\nl2EYhmEYhgkpIrVY6d69ezF79myvr7355pvo0aNH099FRUV48803sX//fkRFRWHQoEF49NFHkZSU\n5PHeL774AhkZGSguLkbr1q0xfvx43HHHHR7LVVdXY/Hixdi2bRtqa2vRo0cPzJgxA926dVPvIBmG\nYRiGYRhToonAFUyYMAFXXnmly3OXXXZZ0/9LS0vxxBNPICEhAdOnT0dNTQ1WrlyJI0eO4K233kJk\npGP3PvvsM7z22msYOnQoJk2ahB9++AELFy7EhQsXcM899zQt19jYiLlz56KgoAB33303EhMT8emn\nn2LWrFlYvHgx2rZtq+UhMwzDMAzDMAajqcDt3bs3hgwZ4vP1Dz/8ELW1tViyZAnS0tIAAD169MCc\nOXOwYcMGjBkzBgBQW1uLd999F+np6XjuuecAAKNGjYLdbseyZctw2223IT4+HgCwZcsW/Pjjj3j+\n+eebtj1s2DDcd999WLp0KZ599lktD5lhGIZhGIYxGM1jcGtqanDx4kWvr23duhXp6elN4hYA+vbt\ni3bt2iErK6vpuT179qCqqgrjxo1zef/tt9+OCxcuYMeOHS7rbNWqlYuwTkpKwo033ojt27ejoaFB\npSNj9GbFihVG7wITAB4j88NjZG54fMwPj5E10FTgzp8/H2PGjMEtt9yC2bNnIzc3t+m10tJSlJeX\ne4QwAED37t2Rn5/f9Pfhw4cBwGPZbt26wWazNb0OAPn5+V5jbbt3747a2locP3486ONijIFPKuaH\nx8j88BiZGx4f88NjZA00CVGIiorC0KFDce211yIpKQmFhYXIyMjAE088gTfeeANdu3bFmTNnAAAp\nKSke709JSUFVVRUaGhoQGRmJsrIyREREeCSeRUVFISkpCT///HPTc2VlZejTp4/HOlu1atX0eqdO\nnVxeq6urAwAcPHgwuANnNKW8vBy7d+82ejcYP/AYmR8eI3PD42N+eIyMQ+g0odv8oYnA7dmzJ3r2\n7Nn0d3p6OoYMGYJp06ZhyZIlmD9/PmprawGQSHUnOjoaAMXeRkZGora21uty4v3OB1pXVxdwne4U\nFxcDAO69916ph8gYRL9+/YzeBSYAPEbmh8fI3PD4mB8eI2MpLi5Gr169/C6jaZKZM23btsXgwYOx\nbds22O12NG/eHABQX1/vsawQrGKZ5s2be11OLCvEq79l3dfpTP/+/fHHP/4Rbdq0cVkXwzAMwzAM\nYw7q6upQXFyM/v37B1xWN4ELAK1bt0ZDQwMuXLjgEjLgTllZGRITE5vKhKWkpKCxsREVFRUuYQr1\n9fWorKxEampq03MpKSkuIQsCfyERycnJGDFiRHAHxzAMwzAMw2hKIOdWoGsns59++gnNmzdHixYt\nkJaWhuTkZBw6dMhjuUOHDqFr165Nf4v/uy+bm5sLu93usWx+fj7sdrvLsgcPHkRMTAzat2+v5iEx\nDMMwDMMwJkMTgVteXu7x3OHDh/Htt9+62MpDhgzBjh07UFpa2vTc7t27ceLECQwdOrTpub59+yIh\nIQHr1q1zWee6desQExODQYMGuazz7Nmz2LZtW9NzFRUVyMrKwuDBg12aRzAMwzAMwzChh23z5s32\nwIvJY/bs2WjevDl69uyJ5ORkFBUVYf369YiKisIbb7yBDh06AKBSYdOnT0d8fDwmTJiAmpoaZGRk\noHXr1nj77bddxOinn36KBQsWYOjQoejfvz9ycnKwadMmTJs2DZMnT25arrGxEb/73e9w9OhRTJo0\nqamTWWlpKd5++220a9dO7cNlGIZhGIZhTIQmAveTTz5BZmYmTp48iZqaGiQnJ6Nv3764//77XVr1\nAkBhYSEWLVqEffv2ITo6GoMGDcKMGTOQnJzssd7PP/8cGRkZOHnyJO28zYYrrrgCDz30kEtG47Bh\nw7zul81mQ79+/fCPf/wj4DHU1dVh6dKl2LRpE6qrq9G5c2eP7QiKiorw5ptvYv/+/YiKisKgQYPw\n6KOPepQ1CxekfHZ2ux2fffYZ1q9fj59++gnNmjVDp06dcPfdd7s48sFuR8Bj5IrUz27NmjVYu3Yt\nTp06haSkJAwbNgxTp05FTEyMqtsBeIycOX/+PD766CMcPHgQhw4dQnV1NZ5++rD66oMAAAlySURB\nVGnccsstHssG+7nxGClD6hgdPHgQGzduxMGDB1FQUIDGxkZ8/fXXsrbFY6QMKWNkt9uxceNGbNu2\nDYcPH0ZlZSUuvfRS3HTTTZg4caLkxHMeI/OhicDVkr/+9a/YunUr7rzzTrRr1w4bNmzAoUOH8Oqr\nr6J3794AgMzMTI/35ebmYvXq1XjkkUcwceJEVbYDOFzohIQEjB8/HjU1NVi5ciVat26Nt956KyxD\nIqR8du+++y6WL1+O9PR0DBo0CHV1ddiwYQMKCgowb9483HDDDapsB+Ax8oaUz27x4sXIyMjA0KFD\n0bdvXxQWFmLdunW45ppr8PLLL6u2HYDHyJ3i4mJMnjwZl1xyCS699FLs3bvXq3hS43PjMVKG1DH6\n4IMPsHz5cnTp0gXnzp3DyZMn8dVXX8naFo+RMqSM0fnz5zF69Gj07NkTgwYNQsuWLbF//35s3LgR\nV199NV599VVJ2+IxMh+W+iQPHjyIzZs3u4jUkSNHYurUqXjnnXewcOFCAPBaEWHPnj2w2Wy46aab\nVNsOAHz44Yeora3FkiVLmloO9+jRA3PmzMGGDRswZsyYoI/bSkj97DZu3Iju3bvjhRdeaHrvrbfe\nirvuugsbN24MKHB5jJQj5bMrKyvDqlWrcPPNN+MPf/hD03vbt2+Pf/7zn/juu++Qnp4e9HYEPEau\npKSkYPXq1WjZsiVyc3MxY8YMr8sF+7nxGClH6hiNGzcOkydPRnR0NBYsWIATJ07I2g6PkXKkjJEI\nnbzqqquanhs1ahTatGmDDz74ALt370bfvn39bofHyJzoWkUhWLZu3YpmzZrhtttua3ouOjoao0aN\nwo8//uiSrOZMXV0dtm7diquvvtqlpBgAnD59GseOHVO8na1btyI9Pb3piwpQUly7du2QlZUVzOFa\nEqmfXXR0tEcYSmxsLGJiYjymv3mM1EXKZ/fjjz+isbHR44ZQhP+4T7HyGKlLVFQUWrZsGXA5OZ8b\nj5G6SB2jli1bSp7m5jFSFyljFBkZ6SJuBddffz0AeIwHj5F1sJTAzc/PR7t27dCiRQuX57t37w4A\nKCgo8Pq+//3vfzh37pxXZ/ell17CAw88oGg7paWlKC8vx5VXXumx3u7duyM/P1/agYUQUj+7yZMn\nY+fOnVizZg2Ki4tx7NgxvP7666ipqcH48eNd3stjpC6BPrvDhw83NUtxvzCLRinunxuPkf7I/dx4\njMwPj5F5ELXz3eNieYysg6VCFMrKyrw2ahBNI7w1eAAoJjc6Otql9JjAZrPBZrMp2o6/5hEpKSmo\nqqpCQ0NDWMXUSP3sRo8ejYiICLz66qtN0zdJSUn4v//7P4+7aR4jdQn02ZWVlTWdgPfv348+ffo0\nLbNv3z4Anr81HiP9kfu58RiZHx4j8/DRRx8hLi4OAwcOdHmex8g6WOpTrKurQ1RUlMfzwmWqra31\neO3cuXPYsWMHBg0ahLi4OI/XX3vtNcXbEY+Blg2nL6vUzy4rKwv/93//hxtvvBFDhgxBTU0NPv74\nY/z5z3/GggUL0LZt26b38hipi5TPrlu3bujRowdWrFiB1NRU9OnTB0VFRXjttdcQGRnp8VvjMdIf\nuZ8bj5H54TEyB//5z3+we/duzJo1y0M38BhZB0t9itHR0U1Tp87U1dUBcEyfOrN161bU19fLasUr\ndTviUe4+hTJSPru6ujq8/vrruPbaa/Hss882LXPdddfh3nvvxXvvvYc///nPQW/H+ZHHyIHUz27e\nvHn4y1/+0lQxISIiAhMnTsTevXslJcrwGGmLGp8bj5H54THSl82bN2Pp0qUYPXq0S0ytP3iMzIml\nBG5KSgrKyso8nhe2v3sCGUDhCfHx8QEzvpVsx3lK152ysjIkJiaG3Z2YlM/u2LFjqKysxHXXXeey\nTEJCAnr16oX9+/ersh2Ax8gbUj+71NRU/POf/8TJkydx5swZtGvXDi1btmwqg6PWdniMlKHG58Zj\nZH54jPQjOzsbL730EgYNGoRZs2ZJfh+PkTmxVJJZt27dcPz4cdTU1Lg8f+DAAQBAly5dXJ4vKyvD\n3r17MWTIEFlfGqnbSUtLQ3JyMg4dOuSxjkOHDqFr166StxkqSPnsGhoaAAAXL170eP/FixfR2Nio\nynYAHiNvyP0dtW3bFr1790bLli1RWFiIM2fOeC1ernQ7PEbKUONz4zEyPzxG+nDgwAH86U9/Qvfu\n3fHcc88hIkK6POIxMieWErhDhgxBY2Mj1q9f3/ScaBBw1VVXuZTdAKiUkd1u9xue4K3kh5ztDBky\nBDt27HApA7J7926cOHHCa1JbqCPls+vYsSOioqKwefNml/eWlpYiJyfH40fOY6Qucn9HgsbGRixe\nvBgxMTEYO3asy2s8RsYg53PjMTI/PEbGUFRUhLlz5+Kyyy7Diy++6LesG4+RdbCUF96jRw8MHToU\nS5YswdmzZ3HZZZfhyy+/RElJCZ5++mmP5TMzM5sSZHzx0ksvIScnx6Wup5ztTJkyBVlZWZg1axYm\nTJiAmpoaZGRkoHPnzrj11lvVO3iLIOWza9GiBe666y4sX74cs2fPxg033ICamhp8+umnqK+vx5Qp\nU1zWyWOkLlI/u4ULF6K+vr7Jdf/qq6+Qm5uLuXPneohgHiP1WbNmDaqrq5umM7/99luUlJQAAMaP\nH4+4uDhZnxuPkfpIGaPi4mJs2rQJAHXUBCiJyW63o02bNhg5cmTT+niM1CfQGNlsNvz+979HdXU1\n7r77bnz33Xcu72/btq1LZR8eI+tguVa9zv2eq6qq0KVLF0ydOhX9+/d3We7YsWN44IEHMHHiRDzy\nyCM+1zdr1izk5OR4tE6Uuh0AKCwsxKJFi7Bv3z5ER0dj0KBBmDFjhkcjg3BB6me3evVqrF+/HidP\nnkRUVBS6d++O++67z+OGhMdIfaR8dhs3bsTHH3+MkydPIiIiAj169MCUKVO83jDyGKnPPffcg9On\nTwNAU1kiu90Om82G5cuX45JLLgEg/XPjMVIfKWO0d+9ezJ4922MZAOjTp49LK1geI/UJNEZ2ux2T\nJ0+GzWZrGhdnfv3rX+P3v/990988RtbBcgKXYRiGYRiGYfxhqRhchmEYhmEYhgkEC1yGYRiGYRgm\npGCByzAMwzAMw4QULHAZhmEYhmGYkIIFLsMwDMMwDBNSsMBlGIZhGIZhQgoWuAzDMAzDMExIwQKX\nYRiGYRiGCSlY4DIMwzAMwzAhxf8HJ/0AyJSA+DkAAAAASUVORK5CYII=\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0xe526828>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 126 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "%%debug --breakpoint ./pandas-joris/pandas/tools/plotting.py:1320\n", | |
| "fig, ax = plt.subplots()\n", | |
| "df1.plot(ax=ax)\n", | |
| "display(fig)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "*** Blank or comment\n", | |
| "*** Blank or comment\n", | |
| "NOTE: Enter 'c' at the ipdb> prompt to continue execution.\n", | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1698)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1697 \u001b[1;33m \"\"\"\n", | |
| "\u001b[0m\u001b[1;32m-> 1698 \u001b[1;33m \u001b[0mkind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_standard_kind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrip\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 1699 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'line'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1699)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1698 \u001b[1;33m \u001b[0mkind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_standard_kind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrip\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-> 1699 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'line'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1700 \u001b[1;33m \u001b[0mklass\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLinePlot\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1700)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1699 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'line'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1700 \u001b[1;33m \u001b[0mklass\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLinePlot\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1701 \u001b[1;33m \u001b[1;32melif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'bar'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'barh'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1718)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1717 \u001b[1;33m \"\"\"\n", | |
| "\u001b[0m\u001b[1;32m-> 1718 \u001b[1;33m \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[0m\u001b[1;32m 1719 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_fignums\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1719)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1718 \u001b[1;33m \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[0m\u001b[1;32m-> 1719 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_fignums\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1720 \u001b[1;33m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_gca\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1731)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1730 \u001b[1;33m \u001b[1;31m# is there harm in this?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1731 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1732 \u001b[1;33m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1732)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1731 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1732 \u001b[1;33m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1733 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n;;n;;n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1734)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1733 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1734 \u001b[1;33m plot_obj = klass(series, kind=kind, rot=rot, logx=logx, logy=logy,\n", | |
| "\u001b[0m\u001b[1;32m 1735 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muse_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstyle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n", | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1735)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1734 \u001b[1;33m plot_obj = klass(series, kind=kind, rot=rot, logx=logx, logy=logy,\n", | |
| "\u001b[0m\u001b[1;32m-> 1735 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muse_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstyle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1736 \u001b[1;33m \u001b[0mxticks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxticks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myticks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myticks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxlim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxlim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mylim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mylim\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n", | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1736)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1735 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muse_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstyle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1736 \u001b[1;33m \u001b[0mxticks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxticks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myticks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myticks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxlim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxlim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mylim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mylim\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1737 \u001b[1;33m \u001b[0mlegend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1737)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1736 \u001b[1;33m \u001b[0mxticks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxticks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myticks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myticks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxlim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxlim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mylim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mylim\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1737 \u001b[1;33m \u001b[0mlegend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1738 \u001b[1;33m secondary_y=secondary_y, **kwds)\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1738)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1737 \u001b[1;33m \u001b[0mlegend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1738 \u001b[1;33m secondary_y=secondary_y, **kwds)\n", | |
| "\u001b[0m\u001b[1;32m 1739 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1740)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1739 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1740 \u001b[1;33m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\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 1741 \u001b[1;33m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "--Call--\n", | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(862)\u001b[0;36mgenerate\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 861 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m--> 862 \u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 863 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(863)\u001b[0;36mgenerate\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 862 \u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m--> 863 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_args_adjust\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 864 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(864)\u001b[0;36mgenerate\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 863 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_args_adjust\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--> 864 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\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 865 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(865)\u001b[0;36mgenerate\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 864 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\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--> 865 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\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 866 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(866)\u001b[0;36mgenerate\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 865 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\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--> 866 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_plot\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 867 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_post_plot_logic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "--Call--\n", | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1246)\u001b[0;36m_make_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1245 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1246 \u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0m_make_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1247 \u001b[1;33m \u001b[1;31m# this is slightly deceptive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1248)\u001b[0;36m_make_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1247 \u001b[1;33m \u001b[1;31m# this is slightly deceptive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1248 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx_compat\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_index\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_use_dynamic_x\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 1249 \u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_convert_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1249)\u001b[0;36m_make_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1248 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx_compat\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_index\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_use_dynamic_x\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-> 1249 \u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_convert_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1250 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_ts_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1250)\u001b[0;36m_make_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1249 \u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_convert_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1250 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_ts_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1251 \u001b[1;33m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "--Call--\n", | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1294)\u001b[0;36m_make_ts_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1293 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1294 \u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0m_make_ts_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\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[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1295 \u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplotting\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtsplot\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1295)\u001b[0;36m_make_ts_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1294 \u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0m_make_ts_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\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[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1295 \u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplotting\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtsplot\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1296 \u001b[1;33m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1296)\u001b[0;36m_make_ts_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1295 \u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplotting\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtsplot\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1296 \u001b[1;33m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\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 1297 \u001b[1;33m \u001b[0mcolors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_colors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1297)\u001b[0;36m_make_ts_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1296 \u001b[1;33m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\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-> 1297 \u001b[1;33m \u001b[0mcolors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_colors\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 1298 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1299)\u001b[0;36m_make_ts_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1298 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1299 \u001b[1;33m \u001b[0mplotf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_plot_function\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 1300 \u001b[1;33m \u001b[0mlines\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\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1300)\u001b[0;36m_make_ts_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1299 \u001b[1;33m \u001b[0mplotf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_plot_function\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-> 1300 \u001b[1;33m \u001b[0mlines\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 1301 \u001b[1;33m \u001b[0mlabels\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\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1301)\u001b[0;36m_make_ts_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1300 \u001b[1;33m \u001b[0mlines\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-> 1301 \u001b[1;33m \u001b[0mlabels\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 1302 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1303)\u001b[0;36m_make_ts_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1302 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1303 \u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0m_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol_num\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\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 1304 \u001b[1;33m newlines = tsplot(data, plotf, ax=ax, label=label,\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1314)\u001b[0;36m_make_ts_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1313 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1314 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSeries\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 1315 \u001b[1;33m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_ax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# self.axes[0]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1315)\u001b[0;36m_make_ts_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1314 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSeries\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-> 1315 \u001b[1;33m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_ax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# self.axes[0]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1316 \u001b[1;33m \u001b[0mstyle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstyle\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1316)\u001b[0;36m_make_ts_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1315 \u001b[1;33m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_ax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# self.axes[0]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1316 \u001b[1;33m \u001b[0mstyle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstyle\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1317 \u001b[1;33m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpprint_thing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1317)\u001b[0;36m_make_ts_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1316 \u001b[1;33m \u001b[0mstyle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstyle\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1317 \u001b[1;33m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpprint_thing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1318 \u001b[1;33m \u001b[0mkwds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1318)\u001b[0;36m_make_ts_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1317 \u001b[1;33m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpprint_thing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1318 \u001b[1;33m \u001b[0mkwds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\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 1319 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_add_color\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1319)\u001b[0;36m_make_ts_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1318 \u001b[1;33m \u001b[0mkwds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\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-> 1319 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_add_color\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1320 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1321)\u001b[0;36m_make_ts_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1320 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1321 \u001b[1;33m \u001b[0m_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstyle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1322 \u001b[1;33m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "--Call--\n", | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1303)\u001b[0;36m_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1302 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1303 \u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0m_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol_num\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\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 1304 \u001b[1;33m newlines = tsplot(data, plotf, ax=ax, label=label,\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1304)\u001b[0;36m_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1303 \u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0m_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol_num\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\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-> 1304 \u001b[1;33m newlines = tsplot(data, plotf, ax=ax, label=label,\n", | |
| "\u001b[0m\u001b[1;32m 1305 \u001b[1;33m style=style, **kwds)\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1305)\u001b[0;36m_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1304 \u001b[1;33m newlines = tsplot(data, plotf, ax=ax, label=label,\n", | |
| "\u001b[0m\u001b[1;32m-> 1305 \u001b[1;33m style=style, **kwds)\n", | |
| "\u001b[0m\u001b[1;32m 1306 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "--Call--\n", | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(29)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 28 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 29 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mtsplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplotf\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[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 30 \u001b[1;33m \"\"\"\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(44)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 43 \u001b[1;33m \u001b[1;31m# Used inferred freq is possible, need a test case for inferred\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 44 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;34m'ax'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 45 \u001b[1;33m \u001b[0max\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'ax'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> kwargs\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "{'ax': <matplotlib.axes.AxesSubplot object at 0x000000000AF37F60>, 'style': None, 'label': u'None'}\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(45)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 44 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;34m'ax'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 45 \u001b[1;33m \u001b[0max\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'ax'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 46 \u001b[1;33m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(50)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 49 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 50 \u001b[1;33m \u001b[0mfreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_freq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 51 \u001b[1;33m \u001b[1;31m# resample against axes freq if necessary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(52)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 51 \u001b[1;33m \u001b[1;31m# resample against axes freq if necessary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 52 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mfreq\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# pragma: no cover\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 53 \u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Cannot use dynamic axis without frequency info'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> freq\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "'T'\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(56)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 55 \u001b[1;33m \u001b[1;31m# Convert DatetimeIndex to PeriodIndex\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 56 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDatetimeIndex\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 57 \u001b[1;33m \u001b[0mseries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_period\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> isinstance(series.index, DatetimeIndex)\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "True\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(57)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 56 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDatetimeIndex\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---> 57 \u001b[1;33m \u001b[0mseries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_period\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 58 \u001b[1;33m freq, ax_freq, series = _maybe_resample(series, ax, freq, plotf,\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(58)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 57 \u001b[1;33m \u001b[0mseries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_period\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 58 \u001b[1;33m freq, ax_freq, series = _maybe_resample(series, ax, freq, plotf,\n", | |
| "\u001b[0m\u001b[1;32m 59 \u001b[1;33m kwargs)\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> series\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "2012-02-13 07:00 11275.117182\n", | |
| "2012-02-13 07:05 9716.197511\n", | |
| "2012-02-13 07:10 19025.623840\n", | |
| "2012-02-13 07:15 7796.350285\n", | |
| "2012-02-13 07:20 6544.319278\n", | |
| "2012-02-13 07:25 9216.704509\n", | |
| "2012-02-13 07:30 10641.001497\n", | |
| "2012-02-13 07:35 9265.557204\n", | |
| "2012-02-13 07:40 10170.977809\n", | |
| "2012-02-13 07:45 14619.520279\n", | |
| "2012-02-13 07:50 16247.802664\n", | |
| "2012-02-13 07:55 14689.207764\n", | |
| "2012-02-13 08:00 16864.147144\n", | |
| "2012-02-13 08:05 20333.935198\n", | |
| "2012-02-13 08:10 15841.608848\n", | |
| "...\n", | |
| "2012-02-13 11:45 11797.770608\n", | |
| "2012-02-13 11:50 17381.614454\n", | |
| "2012-02-13 11:55 10246.328313\n", | |
| "2012-02-13 12:00 8565.972117\n", | |
| "2012-02-13 12:05 7945.835457\n", | |
| "2012-02-13 12:10 14301.847639\n", | |
| "2012-02-13 12:15 14134.394351\n", | |
| "2012-02-13 12:20 16513.034065\n", | |
| "2012-02-13 12:25 13421.556986\n", | |
| "2012-02-13 12:30 13802.394085\n", | |
| "2012-02-13 12:35 11348.398009\n", | |
| "2012-02-13 12:40 14959.364641\n", | |
| "2012-02-13 12:45 8262.443335\n", | |
| "2012-02-13 12:50 8163.234102\n", | |
| "2012-02-13 12:55 9289.242065\n", | |
| "Freq: T, Length: 72, dtype: float64\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(59)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 58 \u001b[1;33m freq, ax_freq, series = _maybe_resample(series, ax, freq, plotf,\n", | |
| "\u001b[0m\u001b[1;32m---> 59 \u001b[1;33m kwargs)\n", | |
| "\u001b[0m\u001b[1;32m 60 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(62)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 61 \u001b[1;33m \u001b[1;31m# Set ax with freq info\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 62 \u001b[1;33m \u001b[0m_decorate_axes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfreq\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[1;32m 63 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> freq\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "'T'\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> ax_freq\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> series\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "2012-02-13 07:00 11275.117182\n", | |
| "2012-02-13 07:05 9716.197511\n", | |
| "2012-02-13 07:10 19025.623840\n", | |
| "2012-02-13 07:15 7796.350285\n", | |
| "2012-02-13 07:20 6544.319278\n", | |
| "2012-02-13 07:25 9216.704509\n", | |
| "2012-02-13 07:30 10641.001497\n", | |
| "2012-02-13 07:35 9265.557204\n", | |
| "2012-02-13 07:40 10170.977809\n", | |
| "2012-02-13 07:45 14619.520279\n", | |
| "2012-02-13 07:50 16247.802664\n", | |
| "2012-02-13 07:55 14689.207764\n", | |
| "2012-02-13 08:00 16864.147144\n", | |
| "2012-02-13 08:05 20333.935198\n", | |
| "2012-02-13 08:10 15841.608848\n", | |
| "...\n", | |
| "2012-02-13 11:45 11797.770608\n", | |
| "2012-02-13 11:50 17381.614454\n", | |
| "2012-02-13 11:55 10246.328313\n", | |
| "2012-02-13 12:00 8565.972117\n", | |
| "2012-02-13 12:05 7945.835457\n", | |
| "2012-02-13 12:10 14301.847639\n", | |
| "2012-02-13 12:15 14134.394351\n", | |
| "2012-02-13 12:20 16513.034065\n", | |
| "2012-02-13 12:25 13421.556986\n", | |
| "2012-02-13 12:30 13802.394085\n", | |
| "2012-02-13 12:35 11348.398009\n", | |
| "2012-02-13 12:40 14959.364641\n", | |
| "2012-02-13 12:45 8262.443335\n", | |
| "2012-02-13 12:50 8163.234102\n", | |
| "2012-02-13 12:55 9289.242065\n", | |
| "Freq: T, Length: 72, dtype: float64\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(65)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 64 \u001b[1;33m \u001b[1;31m# mask missing values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 65 \u001b[1;33m \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_maybe_mask\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 66 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(68)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 67 \u001b[1;33m \u001b[1;31m# how to make sure ax.clear() flows through?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 68 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'_plot_data'\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 69 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_data\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\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> args\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "series = 2012-02-13 07:00 11275.117182\n", | |
| "2012-02-13 07:05 9716.197511\n", | |
| "2012-02-13 07:10 19025.623840\n", | |
| "2012-02-13 07:15 7796.350285\n", | |
| "2012-02-13 07:20 6544.319278\n", | |
| "2012-02-13 07:25 9216.704509\n", | |
| "2012-02-13 07:30 10641.001497\n", | |
| "2012-02-13 07:35 9265.557204\n", | |
| "2012-02-13 07:40 10170.977809\n", | |
| "2012-02-13 07:45 14619.520279\n", | |
| "2012-02-13 07:50 16247.802664\n", | |
| "2012-02-13 07:55 14689.207764\n", | |
| "2012-02-13 08:00 16864.147144\n", | |
| "2012-02-13 08:05 20333.935198\n", | |
| "2012-02-13 08:10 15841.608848\n", | |
| "...\n", | |
| "2012-02-13 11:45 11797.770608\n", | |
| "2012-02-13 11:50 17381.614454\n", | |
| "2012-02-13 11:55 10246.328313\n", | |
| "2012-02-13 12:00 8565.972117\n", | |
| "2012-02-13 12:05 7945.835457\n", | |
| "2012-02-13 12:10 14301.847639\n", | |
| "2012-02-13 12:15 14134.394351\n", | |
| "2012-02-13 12:20 16513.034065\n", | |
| "2012-02-13 12:25 13421.556986\n", | |
| "2012-02-13 12:30 13802.394085\n", | |
| "2012-02-13 12:35 11348.398009\n", | |
| "2012-02-13 12:40 14959.364641\n", | |
| "2012-02-13 12:45 8262.443335\n", | |
| "2012-02-13 12:50 8163.234102\n", | |
| "2012-02-13 12:55 9289.242065\n", | |
| "Freq: T, Length: 72, dtype: float64\n", | |
| "plotf = <unbound method Axes.plot>\n", | |
| "kwargs = {'style': None, 'label': u'None'}\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> args[0]\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "series = 2012-02-13 07:00 11275.117182\n", | |
| "2012-02-13 07:05 9716.197511\n", | |
| "2012-02-13 07:10 19025.623840\n", | |
| "2012-02-13 07:15 7796.350285\n", | |
| "2012-02-13 07:20 6544.319278\n", | |
| "2012-02-13 07:25 9216.704509\n", | |
| "2012-02-13 07:30 10641.001497\n", | |
| "2012-02-13 07:35 9265.557204\n", | |
| "2012-02-13 07:40 10170.977809\n", | |
| "2012-02-13 07:45 14619.520279\n", | |
| "2012-02-13 07:50 16247.802664\n", | |
| "2012-02-13 07:55 14689.207764\n", | |
| "2012-02-13 08:00 16864.147144\n", | |
| "2012-02-13 08:05 20333.935198\n", | |
| "2012-02-13 08:10 15841.608848\n", | |
| "...\n", | |
| "2012-02-13 11:45 11797.770608\n", | |
| "2012-02-13 11:50 17381.614454\n", | |
| "2012-02-13 11:55 10246.328313\n", | |
| "2012-02-13 12:00 8565.972117\n", | |
| "2012-02-13 12:05 7945.835457\n", | |
| "2012-02-13 12:10 14301.847639\n", | |
| "2012-02-13 12:15 14134.394351\n", | |
| "2012-02-13 12:20 16513.034065\n", | |
| "2012-02-13 12:25 13421.556986\n", | |
| "2012-02-13 12:30 13802.394085\n", | |
| "2012-02-13 12:35 11348.398009\n", | |
| "2012-02-13 12:40 14959.364641\n", | |
| "2012-02-13 12:45 8262.443335\n", | |
| "2012-02-13 12:50 8163.234102\n", | |
| "2012-02-13 12:55 9289.242065\n", | |
| "Freq: T, Length: 72, dtype: float64\n", | |
| "plotf = <unbound method Axes.plot>\n", | |
| "kwargs = {'style': None, 'label': u'None'}\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> args[1]\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "series = 2012-02-13 07:00 11275.117182\n", | |
| "2012-02-13 07:05 9716.197511\n", | |
| "2012-02-13 07:10 19025.623840\n", | |
| "2012-02-13 07:15 7796.350285\n", | |
| "2012-02-13 07:20 6544.319278\n", | |
| "2012-02-13 07:25 9216.704509\n", | |
| "2012-02-13 07:30 10641.001497\n", | |
| "2012-02-13 07:35 9265.557204\n", | |
| "2012-02-13 07:40 10170.977809\n", | |
| "2012-02-13 07:45 14619.520279\n", | |
| "2012-02-13 07:50 16247.802664\n", | |
| "2012-02-13 07:55 14689.207764\n", | |
| "2012-02-13 08:00 16864.147144\n", | |
| "2012-02-13 08:05 20333.935198\n", | |
| "2012-02-13 08:10 15841.608848\n", | |
| "...\n", | |
| "2012-02-13 11:45 11797.770608\n", | |
| "2012-02-13 11:50 17381.614454\n", | |
| "2012-02-13 11:55 10246.328313\n", | |
| "2012-02-13 12:00 8565.972117\n", | |
| "2012-02-13 12:05 7945.835457\n", | |
| "2012-02-13 12:10 14301.847639\n", | |
| "2012-02-13 12:15 14134.394351\n", | |
| "2012-02-13 12:20 16513.034065\n", | |
| "2012-02-13 12:25 13421.556986\n", | |
| "2012-02-13 12:30 13802.394085\n", | |
| "2012-02-13 12:35 11348.398009\n", | |
| "2012-02-13 12:40 14959.364641\n", | |
| "2012-02-13 12:45 8262.443335\n", | |
| "2012-02-13 12:50 8163.234102\n", | |
| "2012-02-13 12:55 9289.242065\n", | |
| "Freq: T, Length: 72, dtype: float64\n", | |
| "plotf = <unbound method Axes.plot>\n", | |
| "kwargs = {'style': None, 'label': u'None'}\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> type(args)\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "<type 'list'>\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> series\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "2012-02-13 07:00 11275.117182\n", | |
| "2012-02-13 07:05 9716.197511\n", | |
| "2012-02-13 07:10 19025.623840\n", | |
| "2012-02-13 07:15 7796.350285\n", | |
| "2012-02-13 07:20 6544.319278\n", | |
| "2012-02-13 07:25 9216.704509\n", | |
| "2012-02-13 07:30 10641.001497\n", | |
| "2012-02-13 07:35 9265.557204\n", | |
| "2012-02-13 07:40 10170.977809\n", | |
| "2012-02-13 07:45 14619.520279\n", | |
| "2012-02-13 07:50 16247.802664\n", | |
| "2012-02-13 07:55 14689.207764\n", | |
| "2012-02-13 08:00 16864.147144\n", | |
| "2012-02-13 08:05 20333.935198\n", | |
| "2012-02-13 08:10 15841.608848\n", | |
| "...\n", | |
| "2012-02-13 11:45 11797.770608\n", | |
| "2012-02-13 11:50 17381.614454\n", | |
| "2012-02-13 11:55 10246.328313\n", | |
| "2012-02-13 12:00 8565.972117\n", | |
| "2012-02-13 12:05 7945.835457\n", | |
| "2012-02-13 12:10 14301.847639\n", | |
| "2012-02-13 12:15 14134.394351\n", | |
| "2012-02-13 12:20 16513.034065\n", | |
| "2012-02-13 12:25 13421.556986\n", | |
| "2012-02-13 12:30 13802.394085\n", | |
| "2012-02-13 12:35 11348.398009\n", | |
| "2012-02-13 12:40 14959.364641\n", | |
| "2012-02-13 12:45 8262.443335\n", | |
| "2012-02-13 12:50 8163.234102\n", | |
| "2012-02-13 12:55 9289.242065\n", | |
| "Freq: T, Length: 72, dtype: float64\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> *args\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "*** SyntaxError: invalid syntax (<stdin>, line 1)\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(69)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 68 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'_plot_data'\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---> 69 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_data\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 70 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(70)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 69 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_data\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---> 70 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\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 71 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(73)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 72 \u001b[1;33m \u001b[1;31m# styles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 73 \u001b[1;33m \u001b[0mstyle\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'style'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 74 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mstyle\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[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> ax._plot_data\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "[(2012-02-13 07:00 11275.117182\n", | |
| "2012-02-13 07:05 9716.197511\n", | |
| "2012-02-13 07:10 19025.623840\n", | |
| "2012-02-13 07:15 7796.350285\n", | |
| "2012-02-13 07:20 6544.319278\n", | |
| "2012-02-13 07:25 9216.704509\n", | |
| "2012-02-13 07:30 10641.001497\n", | |
| "2012-02-13 07:35 9265.557204\n", | |
| "2012-02-13 07:40 10170.977809\n", | |
| "2012-02-13 07:45 14619.520279\n", | |
| "2012-02-13 07:50 16247.802664\n", | |
| "2012-02-13 07:55 14689.207764\n", | |
| "2012-02-13 08:00 16864.147144\n", | |
| "2012-02-13 08:05 20333.935198\n", | |
| "2012-02-13 08:10 15841.608848\n", | |
| "...\n", | |
| "2012-02-13 11:45 11797.770608\n", | |
| "2012-02-13 11:50 17381.614454\n", | |
| "2012-02-13 11:55 10246.328313\n", | |
| "2012-02-13 12:00 8565.972117\n", | |
| "2012-02-13 12:05 7945.835457\n", | |
| "2012-02-13 12:10 14301.847639\n", | |
| "2012-02-13 12:15 14134.394351\n", | |
| "2012-02-13 12:20 16513.034065\n", | |
| "2012-02-13 12:25 13421.556986\n", | |
| "2012-02-13 12:30 13802.394085\n", | |
| "2012-02-13 12:35 11348.398009\n", | |
| "2012-02-13 12:40 14959.364641\n", | |
| "2012-02-13 12:45 8262.443335\n", | |
| "2012-02-13 12:50 8163.234102\n", | |
| "2012-02-13 12:55 9289.242065\n", | |
| "Freq: T, Length: 72, dtype: float64, {'style': None, 'label': u'None'})]\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(74)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 73 \u001b[1;33m \u001b[0mstyle\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'style'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 74 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mstyle\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[0m\u001b[1;32m 75 \u001b[1;33m \u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstyle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(77)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 76 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 77 \u001b[1;33m \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplotf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 78 \u001b[1;33m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'label'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "--Call--\n", | |
| "> \u001b[1;32mc:\\anaconda\\envs\\devel\\lib\\site-packages\\matplotlib\\axes.py\u001b[0m(3865)\u001b[0;36mplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 3864 \u001b[1;33m \u001b[1;31m#### Basic plotting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 3865 \u001b[1;33m \u001b[1;33m@\u001b[0m\u001b[0mdocstring\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdedent_interpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 3866 \u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\anaconda\\envs\\devel\\lib\\site-packages\\matplotlib\\axes.py\u001b[0m(3990)\u001b[0;36mplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 3989 \u001b[1;33m \"\"\"\n", | |
| "\u001b[0m\u001b[1;32m-> 3990 \u001b[1;33m \u001b[0mscalex\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'scalex'\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[1;32m 3991 \u001b[1;33m \u001b[0mscaley\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'scaley'\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\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\anaconda\\envs\\devel\\lib\\site-packages\\matplotlib\\axes.py\u001b[0m(3991)\u001b[0;36mplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 3990 \u001b[1;33m \u001b[0mscalex\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'scalex'\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[1;32m-> 3991 \u001b[1;33m \u001b[0mscaley\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'scaley'\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[1;32m 3992 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\anaconda\\envs\\devel\\lib\\site-packages\\matplotlib\\axes.py\u001b[0m(3993)\u001b[0;36mplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 3992 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 3993 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hold\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcla\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 3994 \u001b[1;33m \u001b[0mlines\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\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> args\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "self = Axes(0.125,0.1;0.775x0.8)\n", | |
| "args = (<class 'pandas.tseries.period.PeriodIndex'>\n", | |
| "freq: T\n", | |
| "[2012-02-13 07:00, ..., 2012-02-13 12:55]\n", | |
| "length: 72, array([ 11275.11718151, 9716.19751127, 19025.6238397 , 7796.3502853 ,\n", | |
| " 6544.31927848, 9216.70450873, 10641.00149737, 9265.55720365,\n", | |
| " 10170.97780869, 14619.52027923, 16247.80266361, 14689.20776401,\n", | |
| " 16864.1471445 , 20333.93519765, 15841.60884816, 6437.33599808,\n", | |
| " 12665.20975138, 20457.09012493, 14574.84679949, 14053.42827788,\n", | |
| " 24437.79387107, 34235.18403759, 31602.09113401, 22762.44348399,\n", | |
| " 17191.55927655, 16579.13188573, 22286.17139766, 11130.43302539,\n", | |
| " 12737.17552303, 15411.27639812, 10824.90827538, 14564.86089231,\n", | |
| " 13871.81326195, 17515.15271821, 10896.65014317, 15165.78986409,\n", | |
| " 15604.4178077 , 11069.42625144, 13271.75048756, 18083.08945169,\n", | |
| " 17383.00599771, 9693.28848429, 7624.19423306, 14974.67651134,\n", | |
| " 10880.87742977, 14925.03110807, 7790.72399869, 16585.19088546,\n", | |
| " 30116.17159634, 9044.22436717, 9032.88911906, 7746.16598986,\n", | |
| " 8674.34681753, 11730.613753 , 14458.47986614, 9677.03765294,\n", | |
| " 15697.50783029, 11797.77060819, 17381.61445386, 10246.32831316,\n", | |
| " 8565.97211697, 7945.83545713, 14301.84763862, 14134.39435089,\n", | |
| " 16513.03406546, 13421.55698619, 13802.39408491, 11348.39800864,\n", | |
| " 14959.36464071, 8262.44333535, 8163.2341018 , 9289.24206477]))\n", | |
| "kwargs = {'label': u'None'}\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> r\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "--Return--\n", | |
| "[<matplot...0AF2BE48>]\n", | |
| "> \u001b[1;32mc:\\anaconda\\envs\\devel\\lib\\site-packages\\matplotlib\\axes.py\u001b[0m(4001)\u001b[0;36mplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 4000 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautoscale_view\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscalex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 4001 \u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 4002 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> r\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tseries\\plotting.py\u001b[0m(78)\u001b[0;36mtsplot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 77 \u001b[1;33m \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplotf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m---> 78 \u001b[1;33m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'label'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 79 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> lines.get_axes()\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "*** AttributeError: 'list' object has no attribute 'get_axes'\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> lines[0].get_axes()\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot object at 0x000000000AF37F60>\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> lines[0].get_axes().get_xlim()\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "(22151940.0, 22152295.0)\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> lines[0].get_data()\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "(<class 'pandas.tseries.period.PeriodIndex'>\n", | |
| "freq: T\n", | |
| "[2012-02-13 07:00, ..., 2012-02-13 12:55]\n", | |
| "length: 72, array([ 11275.11718151, 9716.19751127, 19025.6238397 , 7796.3502853 ,\n", | |
| " 6544.31927848, 9216.70450873, 10641.00149737, 9265.55720365,\n", | |
| " 10170.97780869, 14619.52027923, 16247.80266361, 14689.20776401,\n", | |
| " 16864.1471445 , 20333.93519765, 15841.60884816, 6437.33599808,\n", | |
| " 12665.20975138, 20457.09012493, 14574.84679949, 14053.42827788,\n", | |
| " 24437.79387107, 34235.18403759, 31602.09113401, 22762.44348399,\n", | |
| " 17191.55927655, 16579.13188573, 22286.17139766, 11130.43302539,\n", | |
| " 12737.17552303, 15411.27639812, 10824.90827538, 14564.86089231,\n", | |
| " 13871.81326195, 17515.15271821, 10896.65014317, 15165.78986409,\n", | |
| " 15604.4178077 , 11069.42625144, 13271.75048756, 18083.08945169,\n", | |
| " 17383.00599771, 9693.28848429, 7624.19423306, 14974.67651134,\n", | |
| " 10880.87742977, 14925.03110807, 7790.72399869, 16585.19088546,\n", | |
| " 30116.17159634, 9044.22436717, 9032.88911906, 7746.16598986,\n", | |
| " 8674.34681753, 11730.613753 , 14458.47986614, 9677.03765294,\n", | |
| " 15697.50783029, 11797.77060819, 17381.61445386, 10246.32831316,\n", | |
| " 8565.97211697, 7945.83545713, 14301.84763862, 14134.39435089,\n", | |
| " 16513.03406546, 13421.55698619, 13802.39408491, 11348.39800864,\n", | |
| " 14959.36464071, 8262.44333535, 8163.2341018 , 9289.24206477]))\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> lines[0].get_xdata()\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "<class 'pandas.tseries.period.PeriodIndex'>\n", | |
| "freq: T\n", | |
| "[2012-02-13 07:00, ..., 2012-02-13 12:55]\n", | |
| "length: 72\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> plt.draw()\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "*** NameError: name 'plt' is not defined\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> draw()\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "*** NameError: name 'draw' is not defined\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> pylab.draw()\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "*** ValueError: year is out of range\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> c\n" | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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7778f999/v8u6OXPmYMmSJXj//fedAfePf/wjbDYbXn/9dQwZMgQAkJ2djVWrVuEvf/kL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\nq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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0xeda2c50>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 98 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "lines" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "ax.get_xlim()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 61, | |
| "text": [ | |
| "(22151940.0, 22152295.0)" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 61 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "np.datetime64(\"2012-02-13\").astype(int)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 72, | |
| "text": [ | |
| "15383" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 72 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "pd.to_datetime(22151940.0)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 68, | |
| "text": [ | |
| "Timestamp('1970-01-01 00:00:00.022151940', tz=None)" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 68 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import matplotlib.dates as mdates" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 62 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "mdates.julian2num(22151940.0)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 66, | |
| "text": [ | |
| "20430515.5" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 66 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "2013*365" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 74, | |
| "text": [ | |
| "734745" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 74 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "22151940.0/2000" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "mdates.num2date(734546.34269675927)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 75, | |
| "text": [ | |
| "datetime.datetime(2012, 2, 13, 8, 13, 29, tzinfo=<matplotlib.dates._UTC object at 0x000000000710EBA8>)" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 75 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "%%debug\n", | |
| "fig, ax = plt.subplots()\n", | |
| "df2.plot(ax=ax, style='ro')\n", | |
| "display(fig)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "NOTE: Enter 'c' at the ipdb> prompt to continue execution.\n", | |
| "> \u001b[1;32m<string>\u001b[0m(2)\u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32m<string>\u001b[0m(3)\u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "--Call--\n", | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1654)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1653 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1654 \u001b[1;33mdef plot_series(series, label=None, kind='line', use_index=True, rot=None,\n", | |
| "\u001b[0m\u001b[1;32m 1655 \u001b[1;33m \u001b[0mxticks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myticks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxlim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mylim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1698)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1697 \u001b[1;33m \"\"\"\n", | |
| "\u001b[0m\u001b[1;32m-> 1698 \u001b[1;33m \u001b[0mkind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_standard_kind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrip\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 1699 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'line'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1699)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1698 \u001b[1;33m \u001b[0mkind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_standard_kind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrip\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-> 1699 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'line'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1700 \u001b[1;33m \u001b[0mklass\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLinePlot\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1700)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1699 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'line'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1700 \u001b[1;33m \u001b[0mklass\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLinePlot\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1701 \u001b[1;33m \u001b[1;32melif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'bar'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'barh'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1718)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1717 \u001b[1;33m \"\"\"\n", | |
| "\u001b[0m\u001b[1;32m-> 1718 \u001b[1;33m \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[0m\u001b[1;32m 1719 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_fignums\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1719)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1718 \u001b[1;33m \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[0m\u001b[1;32m-> 1719 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_fignums\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1720 \u001b[1;33m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_gca\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1731)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1730 \u001b[1;33m \u001b[1;31m# is there harm in this?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1731 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1732 \u001b[1;33m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1732)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1731 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1732 \u001b[1;33m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1733 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1734)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1733 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1734 \u001b[1;33m plot_obj = klass(series, kind=kind, rot=rot, logx=logx, logy=logy,\n", | |
| "\u001b[0m\u001b[1;32m 1735 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muse_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstyle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1735)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1734 \u001b[1;33m plot_obj = klass(series, kind=kind, rot=rot, logx=logx, logy=logy,\n", | |
| "\u001b[0m\u001b[1;32m-> 1735 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muse_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstyle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1736 \u001b[1;33m \u001b[0mxticks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxticks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myticks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myticks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxlim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxlim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mylim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mylim\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1736)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1735 \u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muse_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstyle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1736 \u001b[1;33m \u001b[0mxticks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxticks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myticks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myticks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxlim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxlim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mylim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mylim\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1737 \u001b[1;33m \u001b[0mlegend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1737)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1736 \u001b[1;33m \u001b[0mxticks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxticks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myticks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myticks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxlim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxlim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mylim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mylim\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1737 \u001b[1;33m \u001b[0mlegend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1738 \u001b[1;33m secondary_y=secondary_y, **kwds)\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1738)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1737 \u001b[1;33m \u001b[0mlegend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1738 \u001b[1;33m secondary_y=secondary_y, **kwds)\n", | |
| "\u001b[0m\u001b[1;32m 1739 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "--Call--\n", | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1192)\u001b[0;36m__init__\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1191 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1192 \u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\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[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1193 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmark_right\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'mark_right'\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\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1193)\u001b[0;36m__init__\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1192 \u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\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[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1193 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmark_right\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'mark_right'\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[1;32m 1194 \u001b[1;33m \u001b[0mMPLPlot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\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\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1194)\u001b[0;36m__init__\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1193 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmark_right\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'mark_right'\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[1;32m-> 1194 \u001b[1;33m \u001b[0mMPLPlot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\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[1;32m 1195 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx_compat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot_params\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'x_compat'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1195)\u001b[0;36m__init__\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1194 \u001b[1;33m \u001b[0mMPLPlot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\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[1;32m-> 1195 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx_compat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot_params\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'x_compat'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1196 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;34m'x_compat'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1196)\u001b[0;36m__init__\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1195 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx_compat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot_params\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'x_compat'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1196 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;34m'x_compat'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1197 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx_compat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbool\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'x_compat'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> self.x_compat\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "False\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "--Return--\n", | |
| "None\n", | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1196)\u001b[0;36m__init__\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1195 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx_compat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot_params\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'x_compat'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1196 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;34m'x_compat'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1197 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx_compat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbool\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'x_compat'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1740)\u001b[0;36mplot_series\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1739 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1740 \u001b[1;33m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\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 1741 \u001b[1;33m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "--Call--\n", | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(862)\u001b[0;36mgenerate\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 861 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m--> 862 \u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 863 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(863)\u001b[0;36mgenerate\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 862 \u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m--> 863 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_args_adjust\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 864 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(864)\u001b[0;36mgenerate\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 863 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_args_adjust\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--> 864 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\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 865 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(865)\u001b[0;36mgenerate\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 864 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\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--> 865 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\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 866 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> self.data\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "2012-02-13 08:13:29 8943.893593\n", | |
| "2012-02-13 08:46:17 26106.397960\n", | |
| "2012-02-13 09:33:02 12956.625309\n", | |
| "2012-02-13 10:46:21 7501.744094\n", | |
| "2012-02-13 11:15:29 11522.637285\n", | |
| "2012-02-13 12:17:26 15309.543187\n", | |
| "dtype: float64\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(866)\u001b[0;36mgenerate\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 865 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\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--> 866 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_plot\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 867 \u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_post_plot_logic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "--Call--\n", | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1246)\u001b[0;36m_make_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1245 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1246 \u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0m_make_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1247 \u001b[1;33m \u001b[1;31m# this is slightly deceptive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1248)\u001b[0;36m_make_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1247 \u001b[1;33m \u001b[1;31m# this is slightly deceptive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1248 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx_compat\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_index\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_use_dynamic_x\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 1249 \u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_convert_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> s\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "--Call--\n", | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1233)\u001b[0;36m_use_dynamic_x\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1232 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1233 \u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0m_use_dynamic_x\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1234 \u001b[1;33m \u001b[0mfreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_index_freq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1234)\u001b[0;36m_use_dynamic_x\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1233 \u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0m_use_dynamic_x\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1234 \u001b[1;33m \u001b[0mfreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_index_freq\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 1235 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1236)\u001b[0;36m_use_dynamic_x\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1235 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1236 \u001b[1;33m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_ax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1237 \u001b[1;33m \u001b[0max_freq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'freq'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> freq\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1237)\u001b[0;36m_use_dynamic_x\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1236 \u001b[1;33m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_ax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1237 \u001b[1;33m \u001b[0max_freq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'freq'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1238 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mfreq\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# convert irregular if axes has freq info\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1238)\u001b[0;36m_use_dynamic_x\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1237 \u001b[1;33m \u001b[0max_freq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'freq'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1238 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mfreq\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# convert irregular if axes has freq info\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1239 \u001b[1;33m \u001b[0mfreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max_freq\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> ax_freq\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1239)\u001b[0;36m_use_dynamic_x\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1238 \u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mfreq\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# convert irregular if axes has freq info\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1239 \u001b[1;33m \u001b[0mfreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max_freq\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1240 \u001b[1;33m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# do not use tsplot if irregular was plotted first\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1244)\u001b[0;36m_use_dynamic_x\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1243 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1244 \u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_dynamic_freq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1245 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> freq is not None\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "False\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> self.use_dynamic_freq\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "*** AttributeError: 'LinePlot' object has no attribute 'use_dynamic_freq'\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> self._is_dynamic_freq(freq)\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "*** TypeError: expected string or buffer\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "--Return--\n", | |
| "False\n", | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1244)\u001b[0;36m_use_dynamic_x\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1243 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1244 \u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_dynamic_freq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m 1245 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1252)\u001b[0;36m_make_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1251 \u001b[1;33m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1252 \u001b[1;33m \u001b[0mlines\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 1253 \u001b[1;33m \u001b[0mlabels\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\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> x\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "*** NameError: name 'x' is not defined\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> y\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "*** NameError: name 'y' is not defined\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1253)\u001b[0;36m_make_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1252 \u001b[1;33m \u001b[0mlines\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-> 1253 \u001b[1;33m \u001b[0mlabels\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 1254 \u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_xticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconvert_period\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\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1254)\u001b[0;36m_make_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1253 \u001b[1;33m \u001b[0mlabels\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-> 1254 \u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_xticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconvert_period\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[1;32m 1255 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32mc:\\users\\vdbosscj\\scipy\\pandas-joris\\pandas\\tools\\plotting.py\u001b[0m(1256)\u001b[0;36m_make_plot\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;32m 1255 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\u001b[1;32m-> 1256 \u001b[1;33m \u001b[0mplotf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_plot_function\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 1257 \u001b[1;33m \u001b[0mcolors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_colors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | |
| "\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> x\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "array([datetime.datetime(2012, 2, 13, 8, 13, 29),\n", | |
| " datetime.datetime(2012, 2, 13, 8, 46, 17),\n", | |
| " datetime.datetime(2012, 2, 13, 9, 33, 2),\n", | |
| " datetime.datetime(2012, 2, 13, 10, 46, 21),\n", | |
| " datetime.datetime(2012, 2, 13, 11, 15, 29),\n", | |
| " datetime.datetime(2012, 2, 13, 12, 17, 26)], dtype=object)\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> self.data\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "2012-02-13 08:13:29 8943.893593\n", | |
| "2012-02-13 08:46:17 26106.397960\n", | |
| "2012-02-13 09:33:02 12956.625309\n", | |
| "2012-02-13 10:46:21 7501.744094\n", | |
| "2012-02-13 11:15:29 11522.637285\n", | |
| "2012-02-13 12:17:26 15309.543187\n", | |
| "dtype: float64\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> self.data.index\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "<class 'pandas.tseries.index.DatetimeIndex'>\n", | |
| "[2012-02-13 08:13:29, ..., 2012-02-13 12:17:26]\n", | |
| "Length: 6, Freq: None, Timezone: None\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> self.data.index._mpl_repr()\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "array([datetime.datetime(2012, 2, 13, 8, 13, 29),\n", | |
| " datetime.datetime(2012, 2, 13, 8, 46, 17),\n", | |
| " datetime.datetime(2012, 2, 13, 9, 33, 2),\n", | |
| " datetime.datetime(2012, 2, 13, 10, 46, 21),\n", | |
| " datetime.datetime(2012, 2, 13, 11, 15, 29),\n", | |
| " datetime.datetime(2012, 2, 13, 12, 17, 26)], dtype=object)\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> c\n" | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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77WrTpk3QsRtvvFE33XSTDh486B3btm2bUlJSlJiY6B3r3bu3OnfurC1btnjH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\ntCjkFnYHDx5URkaGSktLg44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NmzcrKipKeXl52rRpkwoLC0Nef+zYserTp4/35bG0tDTd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\nICAggC5duihbqn333Xfk5+czdOhQ3n77bfz9/VuySc3u0sz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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0xe4e9ef0>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 91 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 2, | |
| "metadata": {}, | |
| "source": [ | |
| "And then together" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "fig, ax = plt.subplots()\n", | |
| "df2.plot(ax=ax, style='ro')\n", | |
| "df1.plot(ax=ax)\n", | |
| "display(fig)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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5MSkmXSsuLtZ6CNJSkmJ/9hQDnleKGSP5MUbyaWwUt0ql+Omni1kplhyvI/kx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HmsL1ygELvXX1e2HwcP0sOIr4tigBLba2uBlSvp/+6uI4uFi2Jb4uPpgUJOLLzZTOGe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\n0igWQgghhBA93v8DgKcyhSEiBMsAAAAASUVORK5CYII=\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0xe4862e8>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 78 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "%%debug\n", | |
| "fig, ax = plt.subplots()\n", | |
| "df1.plot(ax=ax)\n", | |
| "df2.plot(ax=ax, style='ro', )\n", | |
| "display(fig)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "NOTE: Enter 'c' at the ipdb> prompt to continue execution.\n", | |
| "> \u001b[1;32m<string>\u001b[0m(2)\u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32m<string>\u001b[0m(3)\u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> fig\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "<matplotlib.figure.Figure object at 0x000000000E10B198>\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> ax\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot object at 0x000000000B6AA278>\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> ax.get_xlim()\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "(0.0, 1.0)\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32m<string>\u001b[0m(4)\u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> ax.get_xlim()\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "(22151940.0, 22152295.0)\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> n\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "> \u001b[1;32m<string>\u001b[0m(5)\u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> ax.get_xlim()\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "(734546.34269675927, 734546.51210648147)\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "ipdb> c\n" | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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l5eWmMQEAAGBNnQ64N2/edGprbm7WwYMH5e3trcGDB0u6t6JbWFioqqoqo664uFjffPON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\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0xe10b198>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 59 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# zooming out a little bit => seems data are located in May 1971 ??\n", | |
| "x, y = ax.get_xlim()\n", | |
| "ax.set_xlim(0.9*x, 1.1*y)\n", | |
| "display(fig)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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Xr5ck9ezZ078tISFBzZo1C3o48rZt29S4cWMlJSX5t/Xu3VuXL19Wfn6+f1tF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\n4OBgEhIS0Ol0uLu7Y7VasVqtvU5uq6urefLJJyksLGTcuHH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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0xe10b320>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 55 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "d2 = {Timestamp('2012-02-13 08:13:29', tz=None): 8943.8935927950006,\n", | |
| " Timestamp('2012-02-13 08:46:17', tz=None): 26106.3979595001,\n", | |
| " Timestamp('2012-02-14 09:33:02', tz=None): 12956.625308615899,\n", | |
| " Timestamp('2012-02-14 10:46:21', tz=None): 7501.7440938269401,\n", | |
| " Timestamp('2012-02-15 11:15:29', tz=None): 11522.637284778901,\n", | |
| " Timestamp('2012-02-15 12:17:26', tz=None): 15309.5431869974,\n", | |
| " Timestamp('2012-02-20 07:49:08', tz=None): 2941.1370421294801,\n", | |
| " Timestamp('2012-02-20 08:18:04', tz=None): 5387.8481647497156,\n", | |
| " Timestamp('2012-02-21 08:44:22', tz=None): 12076.6244433867,\n", | |
| " Timestamp('2012-02-21 09:38:19', tz=None): 4860.0232096378304}" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "d1 = {Timestamp('2012-02-13 08:00:00', tz=None): 1000,\n", | |
| " Timestamp('2012-02-13 08:05:00', tz=None): 2000,\n", | |
| " Timestamp('2012-02-13 08:10:00', tz=None): 3000,\n", | |
| " Timestamp('2012-02-14 08:15:00', tz=None): 4000,\n", | |
| " Timestamp('2012-02-14 08:20:00', tz=None): 5000,\n", | |
| " Timestamp('2012-02-14 08:25:00', tz=None): 6000,\n", | |
| " Timestamp('2012-02-15 08:30:00', tz=None): 7000,\n", | |
| " Timestamp('2012-02-15 08:35:00', tz=None): 8000,\n", | |
| " Timestamp('2012-02-15 08:40:00', tz=None): 9000,\n", | |
| " Timestamp('2012-02-16 08:45:00', tz=None): 10000}" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [] | |
| } | |
| ], | |
| "metadata": {} | |
| } | |
| ] | |
| } |
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