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eglassman/SATD.ipynb

Last active January 2, 2016 05:48
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Sharing an IPython Notebook attempt number 2
Raw
daylight.csv
Date Sunrise Sunset Daylength
2014-01-01 07:13 16:22 09:09
2014-01-02 07:13 16:23 09:10
2014-01-03 07:13 16:24 09:11
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Raw
description.md

Well, I wanted to

  • make and share an IPython notebook as a gist.
  • make a joke in a graph formatted like an XKCD comic.
  • express my own relationship with the New England winter.

I hope I've managed all three in one fell swoop!

-elg

Display the source blob
Display the rendered blob
Raw
SATD.ipynb
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import csv\n",
"import pandas as pd\n",
"import datetime as dt\n",
"import calendar\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f=open(\"daylight.csv\") #source: http://ptaff.ca/soleil/?l1pays=USA&l1etat=Massachusetts&l1ville=Boston&l2pays=&l2etat=&l1cityname=Boston%2C+Massachusetts%2C+USA&l1ltd=42&l1ltm=21&l1lts=24&l1ltx=N&l1lgd=71&l1lgm=3&l1lgs=24&l1lgx=W&l1tz=-5.0&l1dst=US&l2cityname=&l2ltd=&l2ltm=&l2lts=&l2ltx=N&l2lgd=&l2lgm=&l2lgs=&l2lgx=E&l2tz=0&l2dst=&year=2014&month=01&day=04&lang=en_CA&go=Show+the+graph%21\n",
"maxRowsToPrint = 10\n",
"rowsPrinted = 0\n",
"for row in csv.reader(f):\n",
" print(row)\n",
" rowsPrinted += 1\n",
" if rowsPrinted>=maxRowsToPrint:\n",
" break"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['Date', ' \"Sunrise\"', ' \"Sunset\"', ' \"Daylength\"']\n",
"['2014-01-01', ' \"07:13\"', ' \"16:22\"', ' \"09:09\"']\n",
"['2014-01-02', ' \"07:13\"', ' \"16:23\"', ' \"09:10\"']\n",
"['2014-01-03', ' \"07:13\"', ' \"16:24\"', ' \"09:11\"']\n",
"['2014-01-04', ' \"07:13\"', ' \"16:25\"', ' \"09:12\"']\n",
"['2014-01-05', ' \"07:13\"', ' \"16:26\"', ' \"09:13\"']\n",
"['2014-01-06', ' \"07:13\"', ' \"16:27\"', ' \"09:14\"']\n",
"['2014-01-07', ' \"07:13\"', ' \"16:28\"', ' \"09:15\"']\n",
"['2014-01-08', ' \"07:13\"', ' \"16:29\"', ' \"09:16\"']\n",
"['2014-01-09', ' \"07:13\"', ' \"16:30\"', ' \"09:17\"']\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"daylightdf = pd.read_csv('daylight.csv',names=['Date','Sunrise','Sunset','Daylength'], header=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dayLengthdf = daylightdf['Daylength']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"listOfDayTimeMin = [];\n",
"for dayLen in dayLengthdf:\n",
" #print dayLen\n",
" #print dayLen.split(':')\n",
" x = dt.datetime.strptime(dayLen, ' \\\"%H:%M\\\"')\n",
" numMins = x.hour*60 + x.minute\n",
" listOfDayTimeMin.append(numMins)\n",
"print listOfDayTimeMin"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[550, 551, 552, 553, 554, 555, 556, 557, 559, 560, 562, 563, 565, 566, 568, 570, 571, 573, 575, 577, 579, 581, 583, 585, 587, 589, 592, 594, 596, 598, 601, 603, 605, 608, 610, 613, 615, 618, 620, 623, 626, 628, 631, 633, 636, 639, 641, 644, 647, 650, 652, 655, 658, 661, 663, 666, 669, 672, 675, 678, 680, 683, 686, 689, 692, 695, 698, 700, 703, 706, 709, 712, 715, 718, 721, 723, 726, 729, 732, 735, 738, 741, 744, 746, 749, 752, 755, 758, 761, 764, 766, 769, 772, 775, 778, 781, 783, 786, 789, 792, 795, 797, 800, 803, 806, 808, 811, 814, 816, 819, 822, 824, 827, 829, 832, 835, 837, 840, 842, 845, 847, 849, 852, 854, 857, 859, 861, 863, 866, 868, 870, 872, 874, 876, 878, 880, 882, 884, 886, 888, 890, 891, 893, 895, 896, 898, 899, 901, 902, 903, 905, 906, 907, 908, 909, 910, 911, 912, 913, 913, 914, 915, 915, 916, 916, 916, 917, 917, 917, 917, 917, 917, 917, 917, 917, 916, 916, 915, 915, 914, 914, 913, 912, 911, 911, 910, 909, 908, 906, 905, 904, 903, 901, 900, 899, 897, 896, 894, 892, 891, 889, 887, 885, 883, 882, 880, 878, 876, 874, 871, 869, 867, 865, 863, 861, 858, 856, 854, 851, 849, 847, 844, 842, 839, 837, 834, 832, 829, 827, 824, 821, 819, 816, 813, 811, 808, 805, 803, 800, 797, 795, 792, 789, 786, 784, 781, 778, 775, 773, 770, 767, 764, 761, 759, 756, 753, 750, 747, 744, 742, 739, 736, 733, 730, 727, 725, 722, 719, 716, 713, 710, 708, 705, 702, 699, 696, 693, 691, 688, 685, 682, 679, 677, 674, 671, 668, 665, 663, 660, 657, 655, 652, 649, 646, 644, 641, 638, 636, 633, 630, 628, 625, 623, 620, 618, 615, 613, 610, 608, 605, 603, 601, 598, 596, 594, 592, 590, 587, 585, 583, 581, 579, 577, 575, 573, 572, 570, 568, 567, 565, 563, 562, 560, 559, 558, 556, 555, 554, 553, 552, 551, 550, 549, 548, 548, 547, 547, 546, 546, 545, 545, 545, 545, 545, 545, 545, 545, 545, 545, 546, 546, 547, 547, 548]\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"copyOfMonths = calendar.month_name[1:13]\n",
"print copyOfMonths\n",
"monthLabels = []\n",
"for day in np.arange(364):\n",
" #print day%30\n",
" if day%30==0:\n",
" #print copyOfMonths\n",
" if copyOfMonths==[]: #if its an empty list\n",
" monthLabels.append('')\n",
" else:\n",
" monthLabels.append(' '+copyOfMonths.pop(0)[0:3])\n",
" else:\n",
" monthLabels.append('')\n",
"print monthLabels"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n",
"[' Jan', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ' Feb', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ' Mar', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ' Apr', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ' May', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ' Jun', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ' Jul', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ' Aug', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ' Sep', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ' Oct', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ' Nov', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ' Dec', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline\n",
"plt.xkcd() # Yes...\n",
"\n",
"myconfidence = []\n",
"for daytime in listOfDayTimeMin:\n",
" myconfidence.append( (0-daytime)/2+1100)\n",
"\n",
"#plt.plot(listOfDayTimeMin)\n",
"#plt.plot(listOfDayTimeMin,'r')\n",
"\n",
"fig, ax1 = plt.subplots(figsize=(9,3))\n",
"\n",
"ax2 = ax1.twinx()\n",
"#ax1.plot(x, y1, 'g-')\n",
"#ax2.plot(x, y2, 'b-')\n",
"\n",
"x = arange(364)\n",
"ax2.plot(x, myconfidence, 'red', alpha=0.4)\n",
"ax1.plot(x, listOfDayTimeMin, 'blue', alpha=0.4)\n",
"\n",
"#ax1.set_xlabel('X data')\n",
"ax1.set_ylabel('Minutes of Daylight', color='b')\n",
"ax2.set_ylabel('Sneaking Suspicion that \\nI\\'m doing it all wrong', color='r')\n",
"#ax2.ylim([500,950])\n",
"ax2.set_autoscaley_on(False)\n",
"ax2.set_ylim([600,1050])\n",
"\n",
"plt.title('Seasonal Affective Thesis Disorder (SATD)')\n",
"#plt.xticks( 30*[0,1,2,3,4,5,6,7,8,9,10,11], ('Jan', 'Feb', 'Mar', 'Apr', 'May','Jun','Jul','Aug','Sep','Oct','Nov','Dec') )\n",
"#xticks( arange(364), calendar.month_name[1:13], rotation=17 )\n",
"plt.xticks( x, monthLabels, rotation=40 )\n",
"ax2.set_yticks([])\n",
"#ylabel('Minutes of Daylight')\n",
"plt.show()\n",
"#savefig('satd.png',dpi=300)\n",
"#plt.savefig('foo.png', bbox_inches=0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"WARNING: pylab import has clobbered these variables: ['f']\n",
"`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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MTEyh59u5cycBsLBGCPz+++8EgL7++msiYgJ26tSpJJPJRBFFRBQTE0P+/v4WdanX6638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\nSEggT09POnbsmMv55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"text": [
"<matplotlib.figure.Figure at 0x1068cce90>"
]
}
],
"prompt_number": 8
}
],
"metadata": {}
}
]
}
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