notebooks/UnusuallyHighChurn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ozafl_6Y0eGy"
   },
   "source": [
    "assume notebook in the style of [anl_get_commits.ipynb](https://colab.research.google.com/drive/1KVCVwkmQUdJb2pS2r8p9Gyi0t7CYzHkV?usp=sharing#scrollTo=c8nKzlPmaaKq) by fickas@cs.uoregon.edu has been run to generate appropriate .csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "9e6IoX2Q3WH_",
    "outputId": "8d9b6f76-1327-4f9e-a0f1-4f23598347c0"
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'google'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-b129a852f09a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'google'"
     ]
    }
   ],
   "source": [
    "from google.colab import drive \n",
    "drive.mount('/content/drive', force_remount=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "v8D2-s7P3vRo",
    "outputId": "baa8eff1-d900-497a-b100-6594402a32f5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[01;34mdrive\u001b[0m/  \u001b[01;34msample_data\u001b[0m/\n"
     ]
    }
   ],
   "source": [
    "ls /content/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "in8F_ejS38kB",
    "outputId": "711e4daa-2719-434f-ac70-a649c1ea3341"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[01;34m'My Drive'\u001b[0m/   \u001b[01;34m.shortcut-targets-by-id\u001b[0m/   \u001b[01;34m.Trash\u001b[0m/\n"
     ]
    }
   ],
   "source": [
    "ls -a /content/drive/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "u5gGdgWZ41KH",
    "outputId": "2e104af4-fb47-4d8c-e47b-c56081f88d67"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "moose_sorted_commits.csv  spack_sorted_commits.csv\n"
     ]
    }
   ],
   "source": [
    "ls -a /content/drive/My\\ Drive/SortedCommits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IkOhBl2F5IHG"
   },
   "outputs": [],
   "source": [
    "path = '/content/drive/My Drive/SortedCommits/moose_sorted_commits.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z2dPyg-_5sv4"
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "moose_sorted_commits = pd.read_csv(\"/content/drive/My Drive/SortedCommits/moose_sorted_commits.csv\").drop(columns=['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "K_X8oW6r5yT7",
    "outputId": "9e8d5487-ca45-4f79-ce7c-64238cc9fa89"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>doy</th>\n",
       "      <th>dow</th>\n",
       "      <th>full_date</th>\n",
       "      <th>files</th>\n",
       "      <th>message</th>\n",
       "      <th>person</th>\n",
       "      <th>locc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>162</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2008-06-10 14:45:00+00:00</td>\n",
       "      <td>['a/.gitignore b/.gitignore', 'a/COPYING b/COP...</td>\n",
       "      <td>Starting MOOSE project - adding license, c...</td>\n",
       "      <td>b'Cody Permann &lt;codypermann@gmail.com&gt;'</td>\n",
       "      <td>1243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>162</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2008-06-10 15:01:21+00:00</td>\n",
       "      <td>['a/framework/Makefile b/framework/Makefile']</td>\n",
       "      <td>Fix Makefile\\n    \\n    r111\\n</td>\n",
       "      <td>b'Derek Gaston &lt;derek.gaston@inl.gov&gt;'</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>162</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2008-06-10 14:47:02+00:00</td>\n",
       "      <td>['a/framework/Makefile b/framework/Makefile', ...</td>\n",
       "      <td>Add stroma\\n    \\n    r110\\n</td>\n",
       "      <td>b'Derek Gaston &lt;derek.gaston@inl.gov&gt;'</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>163</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2008-06-11 10:18:20+00:00</td>\n",
       "      <td>['a/framework/Makefile.export b/framework/Make...</td>\n",
       "      <td>Fix Skagia makefile to link against Stroma...</td>\n",
       "      <td>b'Derek Gaston &lt;derek.gaston@inl.gov&gt;'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>163</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2008-06-11 07:36:16+00:00</td>\n",
       "      <td>['a/framework/Makefile.export b/framework/Make...</td>\n",
       "      <td>Intial Skagia commit\\n    \\n    r112\\n</td>\n",
       "      <td>b'Derek Gaston &lt;derek.gaston@inl.gov&gt;'</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  ...                                   person  locc\n",
       "0  2008      6  ...  b'Cody Permann <codypermann@gmail.com>'  1243\n",
       "1  2008      6  ...   b'Derek Gaston <derek.gaston@inl.gov>'     5\n",
       "2  2008      6  ...   b'Derek Gaston <derek.gaston@inl.gov>'    72\n",
       "3  2008      6  ...   b'Derek Gaston <derek.gaston@inl.gov>'     1\n",
       "4  2008      6  ...   b'Derek Gaston <derek.gaston@inl.gov>'    11\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moose_sorted_commits.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "P4KzASgl768G"
   },
   "source": [
    "\n",
    "Given a time window (say a year)\n",
    "\n",
    "*   Precompute repository contributors\n",
    "*   Precompute list of files in repo \n",
    "*   Step through list commits, increment user entry in vector with number of contributors as length if user modified file in current commit \n",
    "*   Visualize data\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2N79SayNdgqX"
   },
   "source": [
    "compute list of contributors along with their commits as panda dfs in given year. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wtDpPrm6C--q"
   },
   "outputs": [],
   "source": [
    "import sys \n",
    "def sorted_people_yearly_commits(year):\n",
    "  \"\"\"return list of commits per user as panda dataframes (slices of the original) sorted in descending order by rows. Filter out users with no commits\n",
    "\n",
    "  param  : year as time window from which to search \n",
    "  \n",
    "  \"\"\"\n",
    "  people_commits = []\n",
    "  seen = {}\n",
    "  for i in range(len(moose_sorted_commits['person'].index)):\n",
    "    try:\n",
    "      throw_away = seen[moose_sorted_commits['person'][i]]\n",
    "      if throw_away == 1:\n",
    "        continue \n",
    "    except KeyError:\n",
    "      seen[moose_sorted_commits['person'][i]] = 1\n",
    "      commits_by_p = moose_sorted_commits[(moose_sorted_commits['year'] == year) & (moose_sorted_commits['person'] == moose_sorted_commits['person'][i])]\n",
    "      people_commits.append(commits_by_p)\n",
    "  people_commits = list(filter(lambda x: len(x['full_date']) > 0, people_commits))\n",
    "  people_commits.sort(key=lambda x: len(x['full_date']), reverse=True) #this doesn't work for some reason\n",
    "  return people_commits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "UQle5sePf3MO",
    "outputId": "67eb2fe6-6251-4015-af6c-161720f2416f"
   },
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>doy</th>\n",
       "      <th>dow</th>\n",
       "      <th>full_date</th>\n",
       "      <th>files</th>\n",
       "      <th>message</th>\n",
       "      <th>person</th>\n",
       "      <th>locc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2008</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>249</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2008-09-05 11:50:05+00:00</td>\n",
       "      <td>['a/framework/include/VacuumBC.h b/framework/i...</td>\n",
       "      <td>trivial change in comment of Vacuum BC\\n  ...</td>\n",
       "      <td>b'Ryosuke Park &lt;ryosuke.park@inl.gov&gt;'</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2008</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>249</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2008-09-05 07:24:42+00:00</td>\n",
       "      <td>['a/framework/include/VacuumBC.h b/framework/i...</td>\n",
       "      <td>Adding Vacuum BC and Cross-section definit...</td>\n",
       "      <td>b'Ryosuke Park &lt;ryosuke.park@inl.gov&gt;'</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2008</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>252</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2008-09-08 21:28:10+00:00</td>\n",
       "      <td>['a/framework/include/materials/Constant.h b/f...</td>\n",
       "      <td>adding neutron velocity parameter into mat...</td>\n",
       "      <td>b'Ryosuke Park &lt;ryosuke.park@inl.gov&gt;'</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2008</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>252</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2008-09-08 14:23:27+00:00</td>\n",
       "      <td>['a/framework/include/Reaction.h b/framework/i...</td>\n",
       "      <td>fixing Reaction kernel\\n    \\n    r270\\n</td>\n",
       "      <td>b'Ryosuke Park &lt;ryosuke.park@inl.gov&gt;'</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2008</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>252</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2008-09-08 14:06:09+00:00</td>\n",
       "      <td>['a/framework/include/Reaction.h b/framework/i...</td>\n",
       "      <td>adding generic reaction kernel\\n    \\n    ...</td>\n",
       "      <td>b'Ryosuke Park &lt;ryosuke.park@inl.gov&gt;'</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  month  ...                                  person  locc\n",
       "57  2008      9  ...  b'Ryosuke Park <ryosuke.park@inl.gov>'     2\n",
       "58  2008      9  ...  b'Ryosuke Park <ryosuke.park@inl.gov>'    46\n",
       "59  2008      9  ...  b'Ryosuke Park <ryosuke.park@inl.gov>'     3\n",
       "60  2008      9  ...  b'Ryosuke Park <ryosuke.park@inl.gov>'     4\n",
       "61  2008      9  ...  b'Ryosuke Park <ryosuke.park@inl.gov>'    31\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_people_yearly_commits(2008)[1].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dnyMTiPZdvfz"
   },
   "source": [
    "compute list of unique files in given year along with how many commits they appear in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "l7-f_v6HXgkY"
   },
   "outputs": [],
   "source": [
    "def files_in_year(year):\n",
    "  \"\"\"return list of (file, count) tuples sorted by count\n",
    "\n",
    "  param  : year as time window \n",
    "  \"\"\"\n",
    "  ppl_commits = sorted_people_yearly_commits(year)\n",
    "  seen = {}\n",
    "  for i in range(len(ppl_commits)):\n",
    "    df = ppl_commits[i]\n",
    "    file_lists = [eval(l) for l in list(df['files'])]\n",
    "    for l_file in file_lists:\n",
    "      for file in l_file:\n",
    "        try:\n",
    "          seen[file] += 1\n",
    "        except KeyError:\n",
    "          seen[file] = 1 \n",
    "  seen_sorted = sorted(seen.items(), key=lambda x : x[1], reverse=True)\n",
    "  return seen_sorted "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "id": "rqnbnxK9fNzi",
    "outputId": "7df344ff-2951-4ecc-be02-c4bb51e189a0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('a/framework/include/Kernel.h b/framework/include/Kernel.h', 34),\n",
       " ('a/framework/Makefile b/framework/Makefile', 11),\n",
       " ('a/framework/src/Kernel.C b/framework/src/Kernel.C', 10),\n",
       " ('a/framework/include/materials/Material.h b/framework/include/materials/Material.h',\n",
       "  8),\n",
       " ('a/framework/src/materials/PorousMedia.C b/framework/src/materials/PorousMedia.C',\n",
       "  8),\n",
       " ('a/framework/include/BCFactory.h b/framework/include/BCFactory.h', 7),\n",
       " ('a/framework/src/Moose.C b/framework/src/Moose.C', 7),\n",
       " ('a/framework/include/materials/PorousMedia.h b/framework/include/materials/PorousMedia.h',\n",
       "  5),\n",
       " ('a/framework/include/materials/Constant.h b/framework/include/materials/Constant.h',\n",
       "  4),\n",
       " ('a/framework/Makefile.export b/framework/Makefile.export', 3)]"
      ]
     },
     "execution_count": 92,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files_in_year(2008)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kWJCBvUxgqBY"
   },
   "source": [
    "start computing 2d array of (file_count, user_count) pairs as you step through commit history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VoBL55U8kSJj"
   },
   "outputs": [],
   "source": [
    "def make_indices(year):\n",
    "  \"\"\"return {file : file_rank}\n",
    "     , {person : person_rank}\n",
    "     , list of (file, count) tuples sorted by count\n",
    "     , list of commits per user as panda dataframes (slices of the original) sorted in descending order by rows.\n",
    "\n",
    "  param  : year as time window  \n",
    "  \"\"\"\n",
    "  files   = files_in_year(year)\n",
    "  commits = sorted_people_yearly_commits(year)\n",
    "  f_index_year = {u[0] : files.index(u) for u in files}\n",
    "  ppl_index_year = {list(commits[i]['person'])[0] : i for i in range(len(commits))}\n",
    "  return f_index_year, ppl_index_year, files, commits "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6Z0WEy1lm1cl"
   },
   "outputs": [],
   "source": [
    "def make_user_file_mat(year):\n",
    "  \"\"\"return {file : file_rank}\n",
    "     , {person : person_rank}\n",
    "\n",
    "  param : year as time window\n",
    "  \"\"\"\n",
    "  f_index, ppl_index, files, commits = make_indices(year)\n",
    "  file_user_mat = [[0 for i in ppl_index] for f in f_index] \n",
    "  for i in range(len(commits)):\n",
    "    df = commits[i]\n",
    "    file_lists = [eval(l) for l in list(df['files'])]\n",
    "    for file_l in file_lists:\n",
    "      for f in file_l:\n",
    "        if f in f_index:\n",
    "          file_user_mat[f_index[f]][ppl_index[list(df['person'])[0]]] += 1\n",
    "  return f_index, ppl_index, file_user_mat "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8i7_MMSTTh5U"
   },
   "outputs": [],
   "source": [
    "f_index, ppl_index, mat = make_user_file_mat(2008)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "SIFIlXVbjUIN",
    "outputId": "26f3977c-0a70-429e-99da-b89eaf786a5f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[27, 6, 0, 1, 0, 0], [10, 0, 1, 0, 0, 0], [8, 2, 0, 0, 0, 0], [1, 7, 0, 0, 0, 0], [0, 6, 0, 0, 2, 0], [6, 1, 0, 0, 0, 0], [2, 4, 1, 0, 0, 0], [1, 4, 0, 0, 0, 0], [2, 2, 0, 0, 0, 0], [3, 0, 0, 0, 0, 0]]\n"
     ]
    }
   ],
   "source": [
    "print(mat[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "b64sie2Xj_8b"
   },
   "source": [
    "Turn above data into pandas dataframe and use [seaborn](https://seaborn.pydata.org/#:~:text=Seaborn%20is%20a%20Python%20data,attractive%20and%20informative%20statistical%20graphics.) and [matplotlib's pyplot](https://matplotlib.org/users/pyplot_tutorial.html) to plot heatmap of top rows and values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "czZTKKCzSWh1"
   },
   "outputs": [],
   "source": [
    "def make_pandas_df(year):\n",
    "  f_index, ppl_index, file_user_mat = make_user_file_mat(year)\n",
    "  index_labels  = list(f_index.keys()) \n",
    "  column_labels = list(ppl_index.keys()) \n",
    "  to_return = pd.DataFrame.from_records(file_user_mat, index=index_labels, columns=column_labels)\n",
    "  return to_return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1vbtoANgTrRr"
   },
   "outputs": [],
   "source": [
    "mat_df = make_pandas_df(2009)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 549
    },
    "colab_type": "code",
    "id": "URJj-feaUnBr",
    "outputId": "644c8a4a-438e-4f57-d45c-571eb126689c"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b'Cody Permann &lt;cody.permann@inl.gov&gt;'</th>\n",
       "      <th>b'Derek Gaston &lt;derek.gaston@inl.gov&gt;'</th>\n",
       "      <th>b'Ryosuke Park &lt;ryosuke.park@inl.gov&gt;'</th>\n",
       "      <th>b'Vanessa Gertman &lt;vanessa.gertman@inl.gov&gt;'</th>\n",
       "      <th>b'William Taitano &lt;william.taitano@inl.gov&gt;'</th>\n",
       "      <th>b'Samet Kadioglu &lt;samet.kadioglu@inl.gov&gt;'</th>\n",
       "      <th>b'John Peterson &lt;jw.peterson@inl.gov&gt;'</th>\n",
       "      <th>b'Richard Williamson &lt;richard.williamson@inl.gov&gt;'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a/framework/include/Moose.h b/framework/include/Moose.h</th>\n",
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       "      <th>a/framework/src/ComputeJacobian.C b/framework/src/ComputeJacobian.C</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>a/framework/include/materials/Material.h b/framework/include/materials/Material.h</th>\n",
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      ],
      "text/plain": [
       "                                                    b'Cody Permann <cody.permann@inl.gov>'  ...  b'Richard Williamson <richard.williamson@inl.gov>'\n",
       "a/framework/include/Moose.h b/framework/include...                                       6  ...                                                  0 \n",
       "a/framework/Makefile b/framework/Makefile                                                3  ...                                                  0 \n",
       "a/framework/include/Kernel.h b/framework/includ...                                       0  ...                                                  0 \n",
       "a/framework/src/Moose.C b/framework/src/Moose.C                                          4  ...                                                  0 \n",
       "a/framework/src/ComputeJacobian.C b/framework/s...                                       1  ...                                                  0 \n",
       "a/framework/include/AuxFactory.h b/framework/in...                                       0  ...                                                  0 \n",
       "a/framework/src/Kernel.C b/framework/src/Kernel.C                                        0  ...                                                  0 \n",
       "a/framework/include/materials/Material.h b/fram...                                       3  ...                                                  0 \n",
       "a/modules/combined/src/base/Elk.C b/modules/com...                                       2  ...                                                  0 \n",
       "a/framework/include/utils/ColumnMajorMatrix.h b...                                       3  ...                                                  0 \n",
       "\n",
       "[10 rows x 8 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heat_df_example = mat_df.head(n=10) \n",
    "heat_df_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "75OaAwVlMYtq"
   },
   "outputs": [],
   "source": [
    "import seaborn as sb \n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 957
    },
    "colab_type": "code",
    "id": "mDX-3WL-UohS",
    "outputId": "4a3f179a-1590-4946-fb42-76df03f1bdba"
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1584x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(22,12))\n",
    "sb.heatmap(heat_df_example, annot=True, linewidths=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_kH6C8BVadhQ"
   },
   "source": [
    "I suspect that a visualization such as the above can inform us of something like a hiearchy of who can do what in a given project. For example, this codypermann person (in 2009) must have been  important for moose as he seems to be able to heavily touch files that very few (if any) other users ever touch---The Makefile for example (could also probably be interesting to look at how individuals access to \"important\" files have changed over time).\n",
    "\n",
    "**side note: ** something fishy about the number of commits per user must be going on. github differentiates between the \"author\" of a commit, vs its \"committer\" vs its \"pusher\". I suspect this data must be using \"committer\"s. I wonder how different the heatmap would be were we using \"authors\" instead. \n",
    "\n",
    "(*i.e*): the differences between the different types of people being as described by [this github staff answer](https://github.community/t/why-is-my-commit-associated-with-the-wrong-person/10199). I also think that one can probably, with high accuracy, infer teams, and team leaders by analysing the disparities between authors, committers, and pushers.\n",
    "\n",
    "**N.B** it seems to me that patterns such as: [\"bit-twiddling\", \"in the zone\", or \"busy body\"](https://docs.google.com/document/d/1IbkpdkU0mWhCLMp19q0yuhwH4ixwFWRhTlTdU8Okg1s/edit) could probably be infered from above 2d array especially if time dimension was incorporated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RL4_ONR1nLZm"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "UnusuallyHighChurn.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}