Skip to content

Instantly share code, notes, and snippets.

@xiangze
Created October 18, 2015 16:07
Show Gist options
  • Save xiangze/81df36aa5caa0753ebfd to your computer and use it in GitHub Desktop.
Save xiangze/81df36aa5caa0753ebfd to your computer and use it in GitHub Desktop.
Display the source blob
Display the rendered blob
Raw
{
"metadata": {
"name": "",
"signature": "sha256:e4ffaa6ab7be60865c17782becce9018a7fb993dbcff3b00831186a535d36329"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import matplotlib.pylab as plt\n",
"%matplotlib inline\n",
"import numpy as np\n",
"from datetime import datetime\n",
"from datetime import timedelta\n",
"import NiconicoSnapshotAPIWrapper as nico\n",
"api = nico.NiconicoSnapshotAPIWrapper('NiconicoSnapshotAPIWrapper')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"\u52d5\u753b\u7dcf\u6570"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"start='2015-01-01 00:00:00'\n",
"end='2015-10-20 23:59:59'\n",
"end=\"{0:%Y-%m-%d %H:%M:%S}\".format(datetime.now())\n",
"size=100\n",
"print end"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2015-10-18 21:57:27\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_s=api.query(u\"\u30b9\u30d7\u30e9\u30c8\u30a5\u30fc\u30f3\", retry = 3, size = size, filters=[api.makeFilterRange('start_time', start,end)])\n",
"data_m=api.query(u\"\u30de\u30ea\u30aa\u30e1\u30fc\u30ab\u30fc\", retry = 3, size = size, filters=[api.makeFilterRange('start_time', start,end)])\n",
"\n",
"print data_s.total\n",
"print data_m.total"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"34428\n",
"3334\n"
]
}
],
"prompt_number": 31
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"\u6642\u7cfb\u5217\u5909\u5316"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"et=datetime(2015,4,1)\n",
"N=201\n",
"daterange=[ ( (et + timedelta(days=t)).strftime(\"%Y-%m-%d %H:%M:%S\"),(et + timedelta(days=t+1)).strftime(\"%Y-%m-%d %H:%M:%S\")) for t in xrange(N)]\n",
"startdate=zip(*daterange)[0]\n",
"print daterange[-1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"('2015-10-18 00:00:00', '2015-10-19 00:00:00')\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"num_s=[api.query(u\"\u30b9\u30d7\u30e9\u30c8\u30a5\u30fc\u30f3\", retry = 3, size = 0, filters=[api.makeFilterRange('start_time', d[0],d[1])]).total for d in daterange]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"num_m=[api.query(u\"\u30de\u30ea\u30aa\u30e1\u30fc\u30ab\u30fc\", retry = 3, size = 0, filters=[api.makeFilterRange('start_time', d[0],d[1])]).total for d in daterange]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(num_s,\".-\")\n",
"plt.title(\"splatoon\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": [
"<matplotlib.text.Text at 0x99b8cd0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEKCAYAAADzQPVvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x9ace930>"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(num_m,\".-\")\n",
"plt.title(\"Mario Maker\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
"<matplotlib.text.Text at 0x9b61430>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEKCAYAAADzQPVvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiIiIiIiIiIiIiIiIiLh/H94Z5weCjRlWgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x9b6e3d0>"
]
}
],
"prompt_number": 30
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"\u30de\u30ea\u30aa\u30e1\u30fc\u30ab\u30fc\u767a\u58f2\u65e5\u4ee5\u964d"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"et=datetime(2015,9,9)\n",
"N=40\n",
"daterange=[ ( (et + timedelta(days=t)).strftime(\"%Y-%m-%d %H:%M:%S\"),(et + timedelta(days=t+1)).strftime(\"%Y-%m-%d %H:%M:%S\")) for t in xrange(N)]\n",
"startdate=zip(*daterange)[0]\n",
"print daterange[-1]\n",
"num_mm=[api.query(u\"\u30de\u30ea\u30aa\u30e1\u30fc\u30ab\u30fc\", retry = 3, size = 0, filters=[api.makeFilterRange('start_time', d[0],d[1])]).total for d in daterange]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"('2015-10-18 00:00:00', '2015-10-19 00:00:00')\n"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(num_mm[:-1],\".-\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
"[<matplotlib.lines.Line2D at 0x8d55bb0>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/w/YcZU1AVGr2wAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x8b745b0>"
]
}
],
"prompt_number": 26
}
],
"metadata": {}
}
]
}
Display the source blob
Display the rendered blob
Raw
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment