Github_info.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate size of all github repos associated with a given account ##\n",
    "\n",
    "Uses SO answers http://stackoverflow.com/questions/8646517 & http://stackoverflow.com/questions/1094841"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from github import Github\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import getpass\n",
    "\n",
    "def h_fmt(num, suffix='B'):\n",
    "    \"\"\" Convert bytes to human readable format \"\"\"\n",
    "    for unit in ['','Ki','Mi','Gi','Ti','Pi','Ei','Zi']:\n",
    "        if abs(num) < 1024.0:\n",
    "            return \"%3.1f%s%s\" % (num, unit, suffix)\n",
    "        num /= 1024.0\n",
    "    return \"%.1f%s%s\" % (num, 'Yi', suffix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Username: rc-softdev-admin\n",
      "Enter Password for Ghub user rc-softdev-admin\n",
      "········\n"
     ]
    }
   ],
   "source": [
    "username = input(\"Username: \")  # rc-softdev-admin\n",
    "print(\"Enter Password for Ghub user {0}\".format(username))\n",
    "g = Github(username, getpass.getpass())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of repos: 401 \n",
      "Total size: ~7.5GiB\n"
     ]
    }
   ],
   "source": [
    "repo_info = [[r.name, h_fmt(r.size * 1000), r.size * 1000] for r in g.get_user().get_repos()]\n",
    "df = pd.DataFrame(data=repo_info, columns=['repo','size -h','kb'])\n",
    "print(\"Number of repos: {0} \\nTotal size: ~{1}\".format(len(df), h_fmt(df['kb'].values.sum())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>repo</th>\n",
       "      <th>size -h</th>\n",
       "      <th>kb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>DryBulkABM</td>\n",
       "      <td>668.0MiB</td>\n",
       "      <td>700416000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>NoGradWarp_Unit_Test</td>\n",
       "      <td>525.8MiB</td>\n",
       "      <td>551304000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Neonate2016</td>\n",
       "      <td>325.5MiB</td>\n",
       "      <td>341275000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>cartodb-1</td>\n",
       "      <td>292.6MiB</td>\n",
       "      <td>306806000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>289.8MiB</td>\n",
       "      <td>303868000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>matplotlib</td>\n",
       "      <td>277.3MiB</td>\n",
       "      <td>290756000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>NTS</td>\n",
       "      <td>226.4MiB</td>\n",
       "      <td>237406000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>body-diffusion</td>\n",
       "      <td>224.1MiB</td>\n",
       "      <td>234944000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>ingo_web</td>\n",
       "      <td>215.3MiB</td>\n",
       "      <td>225728000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>PancreaticProject</td>\n",
       "      <td>197.2MiB</td>\n",
       "      <td>206799000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     repo   size -h         kb\n",
       "137            DryBulkABM  668.0MiB  700416000\n",
       "224  NoGradWarp_Unit_Test  525.8MiB  551304000\n",
       "54            Neonate2016  325.5MiB  341275000\n",
       "386             cartodb-1  292.6MiB  306806000\n",
       "83                 Avatar  289.8MiB  303868000\n",
       "81             matplotlib  277.3MiB  290756000\n",
       "315                   NTS  226.4MiB  237406000\n",
       "112        body-diffusion  224.1MiB  234944000\n",
       "184              ingo_web  215.3MiB  225728000\n",
       "80      PancreaticProject  197.2MiB  206799000"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show top 10\n",
    "df.sort_values('kb', ascending=False).head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary histogram ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.special\n",
    "from bokeh.plotting import figure, show, output_notebook, vplot\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "            var render_items = [{\"docid\":\"192a3ee8-1925-4afd-b5b8-ffb6d3daa187\",\"elementid\":\"7cbcba39-1827-4d91-a8ce-f18a08b2f225\",\"modelid\":\"a777bbde-4bf0-49bb-bbff-d0185a467976\",\"notebook_comms_target\":\"08eca111-d5c9-4697-94b8-c59b28cae929\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
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       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
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     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[124]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x11258e080>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1 = figure(title=\"Repo size\")\n",
    "hist, edges = np.histogram(df['kb'], density=False, bins=100)\n",
    "p1.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:])\n",
    "p1.xaxis.axis_label = 'Size (b)'\n",
    "p1.yaxis.axis_label = 'Number'\n",
    "show(p1)"
   ]
  }
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