most_used_nodes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln: failed to create symbolic link 'graph.json': File exists\r\n"
     ]
    }
   ],
   "source": [
    "path = Path(\".\")\n",
    "if path.joinpath('../cf-graph-countyfair').is_dir():\n",
    "    !ln -s ../cf-graph-countyfair/graph.json graph.json\n",
    "\n",
    "elif not path.joinpath(\"cf-graph-countyfair\").is_dir():\n",
    "    !git clone https://github.com/regro/cf-graph-countyfair --depth=1\n",
    "    !ln -s cf-graph-countyfair/graph.json graph.json\n",
    "\n",
    "try:\n",
    "    from conda_forge_tick.utils import load_graph\n",
    "except:\n",
    "    if not path.joinpath(\"cf-scripts\").is_dir():\n",
    "        !git clone https://github.com/regro/cf-scripts\n",
    "        !ln -s cf-scripts/conda_forge_tick  conda_forge_tick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/cjwright/dev/conda-forge/cf-graph-countyfair\n"
     ]
    }
   ],
   "source": [
    "cd ../cf-graph-countyfair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "gx = load_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "gx2 = nx.create_empty_copy(gx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "for node, attrs in gx.nodes('payload'):\n",
    "    req = attrs.get('requirements', {}).get('run', [])\n",
    "    gx2.add_edges_from(zip([node]*len(req), req))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'build': {'c_compiler_stub',\n",
       "  'cuda_compiler_stub',\n",
       "  'cxx_compiler_stub',\n",
       "  'sysroot_linux-64'},\n",
       " 'host': {'c_compiler_stub',\n",
       "  'cuda_compiler_stub',\n",
       "  'cudnn',\n",
       "  'cutensor',\n",
       "  'cxx_compiler_stub',\n",
       "  'cython',\n",
       "  'fastrlock',\n",
       "  'linux-sysroot',\n",
       "  'nccl',\n",
       "  'pip',\n",
       "  'python',\n",
       "  'setuptools'},\n",
       " 'run': {'c_compiler_stub',\n",
       "  'cuda_compiler_stub',\n",
       "  'cxx_compiler_stub',\n",
       "  'fastrlock',\n",
       "  'linux-sysroot',\n",
       "  'numpy',\n",
       "  'python',\n",
       "  'setuptools'},\n",
       " 'test': {'c_compiler_stub',\n",
       "  'cuda_compiler_stub',\n",
       "  'cxx_compiler_stub',\n",
       "  'mock',\n",
       "  'pytest'}}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gx.nodes['cupy']['payload']['requirements']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = [(n, gx2.degree[n]) for n in gx2.nodes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>python</td>\n",
       "      <td>7767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c_compiler_stub</td>\n",
       "      <td>3250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>r-base</td>\n",
       "      <td>2539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>numpy</td>\n",
       "      <td>2338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cxx_compiler_stub</td>\n",
       "      <td>2109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>scipy</td>\n",
       "      <td>1171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>six</td>\n",
       "      <td>838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gcc-libs</td>\n",
       "      <td>830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pandas</td>\n",
       "      <td>823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>requests</td>\n",
       "      <td>736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0     1\n",
       "0             python  7767\n",
       "1    c_compiler_stub  3250\n",
       "2             r-base  2539\n",
       "3              numpy  2338\n",
       "4  cxx_compiler_stub  2109\n",
       "5              scipy  1171\n",
       "6                six   838\n",
       "7           gcc-libs   830\n",
       "8             pandas   823\n",
       "9           requests   736"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(sorted(z, key=lambda x: x[1], reverse=True)[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.2887e+04, 4.0000e+00, 1.0000e+00, 2.0000e+00, 1.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00]),\n",
       " array([   0. ,  776.7, 1553.4, 2330.1, 3106.8, 3883.5, 4660.2, 5436.9,\n",
       "        6213.6, 6990.3, 7767. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m/QG8geEVKg8C75jTPg8znMF9i+GfsvuB5zM8Cfi57vPzRr7/HV2+M4ycVQcGDH/zPgj8CctOhk2Y7acY/tXu34D7u4839CEf8BPAp7tsJ4Hf79YXnm1Zzlfz5InchWdjODd/oPs49cTPeR+yddt8EXCi+3X9EPDcvmTrtvtM4GvAZSNrvcgHvJPhgc9J4K8ZXpmz5tl8GwZJash6Hu9IklbJ0pekhlj6ktQQS1+SGmLpS1JDLH1JaoilL0kN+V/3KllfOSzaXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([x[1] for x in z], log=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.769054214483577"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gx2.number_of_edges() / gx.number_of_nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.993114364859517"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gx.number_of_edges() / gx.number_of_nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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}