github-package-dependencies.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1279e3e5-d3fa-4b62-88d6-53fd50633dcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "667a8e33-5efa-4ccc-946e-9f7494b9baec",
   "metadata": {},
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>helee</td>\n",
       "      <td>helee/HN_Plotter</td>\n",
       "      <td>2020-03-11 08:11:46</td>\n",
       "      <td>2020-07-15 18:07:49</td>\n",
       "      <td>helee-HN_Plotter/makePlots_muon_eachYear.C</td>\n",
       "      <td>C</td>\n",
       "      <td>iostream</td>\n",
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       "      <th>2</th>\n",
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       "      <td>helee/HN_Plotter</td>\n",
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       "      <td>helee-HN_Plotter/makePlots_DY_eachYear.C</td>\n",
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       "      <th>3</th>\n",
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       "      <td>2020-07-15 18:07:49</td>\n",
       "      <td>helee-HN_Plotter/makePlots_electron_eachYear.C</td>\n",
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       "      <td>2018-10-23 06:32:47</td>\n",
       "      <td>helee-TnP_analysis/treeAnalysis.C</td>\n",
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       "      <th>5224784</th>\n",
       "      <td>hkaushalya</td>\n",
       "      <td>hkaushalya/haddws</td>\n",
       "      <td>2014-01-21 23:24:01</td>\n",
       "      <td>2021-09-09 13:34:32</td>\n",
       "      <td>hkaushalya-haddws/haddws.C</td>\n",
       "      <td>C</td>\n",
       "      <td>TH1.h</td>\n",
       "      <td>cpp_include_quote</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5224785</th>\n",
       "      <td>hkaushalya</td>\n",
       "      <td>hkaushalya/haddws</td>\n",
       "      <td>2014-01-21 23:24:01</td>\n",
       "      <td>2021-09-09 13:34:32</td>\n",
       "      <td>hkaushalya-haddws/haddws.C</td>\n",
       "      <td>C</td>\n",
       "      <td>TTree.h</td>\n",
       "      <td>cpp_include_quote</td>\n",
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       "    <tr>\n",
       "      <th>5224786</th>\n",
       "      <td>hkaushalya</td>\n",
       "      <td>hkaushalya/haddws</td>\n",
       "      <td>2014-01-21 23:24:01</td>\n",
       "      <td>2021-09-09 13:34:32</td>\n",
       "      <td>hkaushalya-haddws/haddws.C</td>\n",
       "      <td>C</td>\n",
       "      <td>TKey.h</td>\n",
       "      <td>cpp_include_quote</td>\n",
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       "    <tr>\n",
       "      <th>5224787</th>\n",
       "      <td>hkaushalya</td>\n",
       "      <td>hkaushalya/haddws</td>\n",
       "      <td>2014-01-21 23:24:01</td>\n",
       "      <td>2021-09-09 13:34:32</td>\n",
       "      <td>hkaushalya-haddws/haddws.C</td>\n",
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       "      <td>2021-09-09 13:34:32</td>\n",
       "      <td>hkaushalya-haddws/haddws.C</td>\n",
       "      <td>C</td>\n",
       "      <td>TProfile.h</td>\n",
       "      <td>cpp_include_quote</td>\n",
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       "  </tbody>\n",
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       "<p>5224789 rows × 8 columns</p>\n",
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      "text/plain": [
       "              owner                repo             created  \\\n",
       "0             helee    helee/HN_Plotter 2020-03-11 08:11:46   \n",
       "1             helee    helee/HN_Plotter 2020-03-11 08:11:46   \n",
       "2             helee    helee/HN_Plotter 2020-03-11 08:11:46   \n",
       "3             helee    helee/HN_Plotter 2020-03-11 08:11:46   \n",
       "4             helee  helee/TnP_analysis 2018-10-23 06:25:20   \n",
       "...             ...                 ...                 ...   \n",
       "5224784  hkaushalya   hkaushalya/haddws 2014-01-21 23:24:01   \n",
       "5224785  hkaushalya   hkaushalya/haddws 2014-01-21 23:24:01   \n",
       "5224786  hkaushalya   hkaushalya/haddws 2014-01-21 23:24:01   \n",
       "5224787  hkaushalya   hkaushalya/haddws 2014-01-21 23:24:01   \n",
       "5224788  hkaushalya   hkaushalya/haddws 2014-01-21 23:24:01   \n",
       "\n",
       "                    updated                                        filename  \\\n",
       "0       2020-07-15 18:07:49      helee-HN_Plotter/makePlots_muon_eachYear.C   \n",
       "1       2020-07-15 18:07:49        helee-HN_Plotter/makePlots_TT_eachYear.C   \n",
       "2       2020-07-15 18:07:49        helee-HN_Plotter/makePlots_DY_eachYear.C   \n",
       "3       2020-07-15 18:07:49  helee-HN_Plotter/makePlots_electron_eachYear.C   \n",
       "4       2018-10-23 06:32:47               helee-TnP_analysis/treeAnalysis.C   \n",
       "...                     ...                                             ...   \n",
       "5224784 2021-09-09 13:34:32                      hkaushalya-haddws/haddws.C   \n",
       "5224785 2021-09-09 13:34:32                      hkaushalya-haddws/haddws.C   \n",
       "5224786 2021-09-09 13:34:32                      hkaushalya-haddws/haddws.C   \n",
       "5224787 2021-09-09 13:34:32                      hkaushalya-haddws/haddws.C   \n",
       "5224788 2021-09-09 13:34:32                      hkaushalya-haddws/haddws.C   \n",
       "\n",
       "        suffix      package                  how  \n",
       "0            C     iostream  cpp_include_bracket  \n",
       "1            C     iostream  cpp_include_bracket  \n",
       "2            C     iostream  cpp_include_bracket  \n",
       "3            C     iostream  cpp_include_bracket  \n",
       "4            C        TH2.h  cpp_include_bracket  \n",
       "...        ...          ...                  ...  \n",
       "5224784      C        TH1.h    cpp_include_quote  \n",
       "5224785      C      TTree.h    cpp_include_quote  \n",
       "5224786      C       TKey.h    cpp_include_quote  \n",
       "5224787      C  Riostream.h    cpp_include_quote  \n",
       "5224788      C   TProfile.h    cpp_include_quote  \n",
       "\n",
       "[5224789 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches_df = pd.read_csv(\"~/storage/data/physicists/forks-of-cmssw-2022-03-04/GitHub-CMSSW-user-nonfork-dependencies-2022-03-04.csv\", parse_dates=[\"created\", \"updated\"])\n",
    "matches_df"
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  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb405ef2-0a6f-448f-a350-21879b4a6cab",
   "metadata": {},
   "outputs": [],
   "source": [
    "matches_df[\"date\"] = matches_df[\"created\"] + 0.5 * (matches_df[\"updated\"] - matches_df[\"created\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "78ce0f9a-533e-4884-a351-4f5fb38b86c5",
   "metadata": {},
   "outputs": [
    {
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       "      <th>3wolf3</th>\n",
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       "                 repo  created  updated  filename  suffix  package  how  date\n",
       "owner                                                                        \n",
       "3wolf3            172      172      172       172     172      172  172   172\n",
       "4quarks           505      505      505       505     505      505  505   505\n",
       "7quantumphysics    31       31       31        31      31       31   31    31\n",
       "936-BCruz          26       26       26        26      26       26   26    26\n",
       "99bbarton         464      464      464       464     464      464  464   464\n",
       "...               ...      ...      ...       ...     ...      ...  ...   ...\n",
       "zuranski          964      964      964       964     964      964  964   964\n",
       "zwarner           731      731      731       731     731      731  731   731\n",
       "zwimpee           209      209      209       209     209      209  209   209\n",
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       "\n",
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches_df.groupby(\"owner\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "82b6daf1-4174-4f3b-9df9-171edf5611a0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zxmei1/2020Research</th>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zygisx/BattleCity</th>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zygisx/irc-client</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zygisx/numeral-methods</th>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zygisx/qr-service</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11635 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          owner  created  updated  filename  suffix  package  \\\n",
       "repo                                                                           \n",
       "3wolf3/Ragnarok              84       84       84        84      84       84   \n",
       "3wolf3/SUSYggAnalysis        88       88       88        88      88       88   \n",
       "4quarks/CMS_Grid_Support    103      103      103       103     103      103   \n",
       "4quarks/FlashWit             27       27       27        27      27       27   \n",
       "4quarks/GlacierAnalysis       5        5        5         5       5        5   \n",
       "...                         ...      ...      ...       ...     ...      ...   \n",
       "zxmei1/2020Research         168      168      168       168     168      168   \n",
       "zygisx/BattleCity            36       36       36        36      36       36   \n",
       "zygisx/irc-client             7        7        7         7       7        7   \n",
       "zygisx/numeral-methods      139      139      139       139     139      139   \n",
       "zygisx/qr-service            31       31       31        31      31       31   \n",
       "\n",
       "                          how  date  \n",
       "repo                                 \n",
       "3wolf3/Ragnarok            84    84  \n",
       "3wolf3/SUSYggAnalysis      88    88  \n",
       "4quarks/CMS_Grid_Support  103   103  \n",
       "4quarks/FlashWit           27    27  \n",
       "4quarks/GlacierAnalysis     5     5  \n",
       "...                       ...   ...  \n",
       "zxmei1/2020Research       168   168  \n",
       "zygisx/BattleCity          36    36  \n",
       "zygisx/irc-client           7     7  \n",
       "zygisx/numeral-methods    139   139  \n",
       "zygisx/qr-service          31    31  \n",
       "\n",
       "[11635 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches_df.groupby(\"repo\").count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efb2922f-070d-4437-a9e9-3f56f40dac2c",
   "metadata": {},
   "source": [
    "### Top file suffixes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "68cdacad-03e3-424e-94b4-3cf7aafe554d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "suffix\n",
       "py       327740\n",
       "h        168174\n",
       "cc       120165\n",
       "C         65537\n",
       "cpp       48980\n",
       "c         34114\n",
       "cxx       28209\n",
       "hpp       27206\n",
       "hh        19873\n",
       "ipynb      8303\n",
       "H           667\n",
       "c++          73\n",
       "hh~          20\n",
       "json         15\n",
       "CPP          10\n",
       "CC            9\n",
       "Name: how, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches_df[[\"filename\", \"suffix\", \"how\"]].groupby(\"filename\").first().groupby(\"suffix\").count()[\"how\"].sort_values(ascending=False).head(16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "234e44b4-d01c-420c-b636-2400a884f98f",
   "metadata": {},
   "source": [
    "### Identify language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d1d01772-bc0b-4047-989a-bb3c68a4471e",
   "metadata": {},
   "outputs": [],
   "source": [
    "is_python = (matches_df[\"suffix\"] == \"py\") & ((matches_df[\"how\"] == \"python_import\") | (matches_df[\"how\"] == \"python_fromimport\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1917e913-a6e6-4369-8474-997a676744e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "is_python_jupyter = (matches_df[\"suffix\"] == \"ipynb\") & ((matches_df[\"how\"] == \"python_import\") | (matches_df[\"how\"] == \"python_fromimport\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e29ee694-b124-4e38-86ff-d2153e71f542",
   "metadata": {},
   "outputs": [],
   "source": [
    "is_cpp = (matches_df[\"suffix\"].isin([\"h\", \"cc\", \"C\", \"cpp\", \"c\", \"cxx\", \"hpp\", \"hh\", \"H\", \"c++\", \"CPP\", \"CC\"])) & \\\n",
    "         ((matches_df[\"how\"] == \"cpp_include_bracket\") | (matches_df[\"how\"] == \"cpp_include_quote\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d849e08e-5c42-4aee-8644-9370580d956f",
   "metadata": {},
   "outputs": [],
   "source": [
    "is_cpp_jupyter = (matches_df[\"suffix\"] == \"ipynb\") & ((matches_df[\"how\"] == \"cpp_include_bracket\") | (matches_df[\"how\"] == \"ipynb_include_quotedquote\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "773bd305-6d0e-46f8-b1d2-37d0e9da2991",
   "metadata": {},
   "source": [
    "### Cut against framework Python files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d5f4365c-fa58-4193-9c39-377f5ddb22d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "by_filename = matches_df.set_index(\"filename\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "829751e4-89ce-4a14-8f23-f3fbb7be1cae",
   "metadata": {},
   "outputs": [],
   "source": [
    "any_FWCore = (by_filename[\"package\"] == \"FWCore\").groupby(level=0).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "992c7853-f3a4-4f5c-9d16-98c57da4f161",
   "metadata": {},
   "outputs": [],
   "source": [
    "is_framework = any_FWCore.reindex(by_filename.index).values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b40ab8cf-0a15-4b08-bf13-09529e97e88f",
   "metadata": {},
   "source": [
    "### How many Python/Jupyter/C++ files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4217ec3d-9083-4034-8c7d-ac6744fc3d34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "filename\n",
       "samhiggie-PUAnalysis/Condor/SUB_Data.py                                                                        69\n",
       "saxenapooja-HTauTau/desy_setup/CMSSW_5_3_9_patch3/python/RecoTauTag/Configuration/updateHPSPFTaus_cff.py       67\n",
       "tmrhombus-UWAnalysis/Wbb8TeV/farmoutAJ_analyzer/text/python/RecoTauTag/Configuration/updateHPSPFTaus_cff.py    67\n",
       "scooperstein-UWHiggsZHBackup/python/RecoTauTag/Configuration/updateHPSPFTaus_cff.py                            67\n",
       "rmanzoni-HTT/RecoTauTag/Configuration/python/updateHPSPFTaus_cff.py                                            65\n",
       "                                                                                                               ..\n",
       "ku-cms-TprimeAna/python/QCDHT2000v2-2_cfi.py                                                                    1\n",
       "ku-cms-TprimeAna/python/QCDHT2000v2-1_cfi.py                                                                    1\n",
       "ku-cms-TprimeAna/python/QCDHT2000_cfi.py                                                                        1\n",
       "ku-cms-TprimeAna/python/QCDHT2000-3_cfi.py                                                                      1\n",
       "jniedzie-lightbylight/RecoEgamma/EgammaElectronProducers/python/ecalDrivenElectronSeeds_cfi.py                  1\n",
       "Name: how, Length: 181697, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches_df[is_python & is_framework].groupby(\"filename\").count()[\"how\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "452fa117-e617-42e6-af24-1609069dc86e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "filename\n",
       "Dr15Jones-CID-App/PythonistaAppTemplate/PythonistaKit.framework/pylib_ext/sympy/polys/polytools.py        571\n",
       "PerilousApricot-CRAB2/external/DBSAPI/DBSAPI/dbsApi.py                                                    230\n",
       "tklijnsma-Archive/MEAnalysis/gc/grid-control/packages/grid_control_cms/DBSAPI/dbsApi_DEPRECATED.py        228\n",
       "Dr15Jones-CID-App/PythonistaAppTemplate/PythonistaKit.framework/pylib_ext/sympy/polys/compatibility.py    207\n",
       "Dr15Jones-CID-App/PythonistaAppTemplate/PythonistaKit.framework/pylib_ext/sympy/matrices/matrices.py      189\n",
       "                                                                                                         ... \n",
       "sergojin-L1TMuonSimulations/Analyzers/test6/utils.py                                                        1\n",
       "cms-tau-pog-TauPerformanceNANO/cmsstyle.py                                                                  1\n",
       "cms-tau-pog-TauReleaseValidation/Var.py                                                                     1\n",
       "sergojin-L1TMuonSimulations/Analyzers/test4/incrementalstats.py                                             1\n",
       "3wolf3-Ragnarok/submitMainToCondor_Data.py                                                                  1\n",
       "Name: how, Length: 146043, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches_df[is_python & ~is_framework].groupby(\"filename\").count()[\"how\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2455c4a8-efd6-4b0c-b0b1-4bcedcb2a002",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "filename\n",
       "NJManganelli-FourTopNAOD/test/tagVarAdd.ipynb                                 7\n",
       "cms-btv-pog-CMSPOS-BTaggingExercise/notebooks/Exercise2_reclustering.ipynb    7\n",
       "cms-btv-pog-CMSPOS-BTaggingExercise/notebooks/Exercise2.ipynb                 6\n",
       "Name: how, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing FWCore in a Jupyter Notebook would be a strange thing to do...\n",
    "matches_df[is_python_jupyter & is_framework].groupby(\"filename\").count()[\"how\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "72b6f61c-3d3c-42d1-ba43-517af4dbb25d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "filename\n",
       "ocerri-BPH_RD_Analysis/Combine/deprecated/RD-MC_side_v0.ipynb                                                                               3228\n",
       "ocerri-BPH_RD_Analysis/Combine/deprecated/CreateCombineCard_v1.ipynb                                                                        1179\n",
       "thaarres-gof_tests/gof.ipynb                                                                                                                 319\n",
       "kingusiu-cofitan/Simple_combine_na3.5TeV_s5_q90.ipynb                                                                                        313\n",
       "kingusiu-cofitan/Simple_combine_na3.5TeV_s5_q30.ipynb                                                                                        313\n",
       "                                                                                                                                            ... \n",
       "mattbellis-air_monitoring/python/sample_calculations.ipynb                                                                                     1\n",
       "ssanders50-pPb_2016_v0/pPb_2016_v0/tmp/src_bak/TrackingCode/HIRun2015Ana/notebook/Performance_PbPb_MB_NTT.ipynb                                1\n",
       "ssanders50-pPb_2016_v0/pPb_2016_v0/tmp/src_bak/TrackingCode/HIRun2015Ana/notebook/Performance_PbPb_MB_TT.ipynb                                 1\n",
       "ssanders50-pPb_2016_v0/pPb_2016_v0/tmp/src_bak/TrackingCode/HIRun2015Ana/notebook/Performance_PbPb_MB_TT_CentralityDependenceStudy.ipynb       1\n",
       "ssanders50-pPb_2016/CMSSW_8_0_24/src/TrackingCode/HIRun2015Ana/notebook/Performance_PbPb_dijet_TT.ipynb                                        1\n",
       "Name: how, Length: 8281, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches_df[is_python_jupyter & ~is_framework].groupby(\"filename\").count()[\"how\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2d034b70-4d4b-4bef-ab05-c1709e947ab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "filename\n",
       "VinInn-ctest/stringLess/Configuration.cc                                                                                                         2000\n",
       "VinInn-ctest/stringLess/ConfigurationOld.cc                                                                                                      2000\n",
       "caleb-james-smith-LearningTensorflow/rnn/data/kernel.c                                                                                            902\n",
       "pjurgielewicz-ROOT_LITE_6/test/stressRooFit_tests.cxx                                                                                             563\n",
       "pjurgielewicz-ROOT_LITE/test/stressRooFit_tests.cxx                                                                                               563\n",
       "                                                                                                                                                 ... \n",
       "giorgiopizz-progetti_elettronica/arm_progetto_1/I2C_display_termometro/Drivers/CMSIS/DSP/Source/FilteringFunctions/arm_fir_lattice_init_f32.c       1\n",
       "carlosperezlara-MTDcharacterization/SiPMonly/BreakdownVoltage/temperatureTrendHDR5e13_2.C                                                           1\n",
       "carlosperezlara-MTDcharacterization/SiPMonly/annealing/currentTrendHDR2s.C                                                                          1\n",
       "sixie-UserCode/patches/RecoEcal/EgammaCoreTools/plugins/EcalClusterEnergyCorrection.h                                                               1\n",
       "zwarner-Mueller-Navalet-Jet/Kinematics100.cpp                                                                                                       1\n",
       "Name: how, Length: 513017, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches_df[is_cpp].groupby(\"filename\").count()[\"how\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a532b9e3-16eb-469d-ace5-7fc279e370a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "filename\n",
       "xiezhen-notebooks/brilsimulator.ipynb                               78\n",
       "xiezhen-notebooks/brildata.ipynb                                    64\n",
       "xiezhen-notebooks/brilmonitoring.ipynb                              35\n",
       "ocerri-PID_timing_studies/plot_script/Delphes_tree_studies.ipynb    33\n",
       "hevjinyarar-ranbox/ranbox22-MODIFIED.ipynb                          31\n",
       "                                                                    ..\n",
       "shchablo-CRO/jump/archive/MUX.ipynb                                  1\n",
       "shchablo-CRO/jump/archive/MUX_1170.ipynb                             1\n",
       "shchablo-CRO/jump/archive/ev2plot.ipynb                              1\n",
       "shchablo-CRO/jump/archive/ped2overview.ipynb                         1\n",
       "shchablo-CRO/jump/archive/13-05-21_MUX_doCode.ipynb                  1\n",
       "Name: how, Length: 86, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches_df[is_cpp_jupyter].groupby(\"filename\").count()[\"how\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "198622c4-3b74-418f-95cd-b7912f9d26ff",
   "metadata": {},
   "source": [
    "### Top Python packages (in files _without_ FWCore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c0896ed6-d250-4661-91ae-88d355d3492e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package\n",
       "ROOT               59681\n",
       "os                 57313\n",
       "sys                40646\n",
       "numpy              29545\n",
       "matplotlib         28224\n",
       "array              21573\n",
       "math               21382\n",
       "__future__         16585\n",
       "re                 14113\n",
       "time               11685\n",
       "argparse           11557\n",
       "optparse           11384\n",
       "HiggsAnalysis       9854\n",
       "pandas              9349\n",
       "madgraph            9248\n",
       "glob                9187\n",
       "OptionParser        8617\n",
       "subprocess          7816\n",
       "json                7797\n",
       "logging             7682\n",
       "Configuration       7598\n",
       "copy                7578\n",
       "of                  6954\n",
       "datetime            6833\n",
       "config              6503\n",
       "print_function      6350\n",
       "collections         5509\n",
       "division            5056\n",
       "random              4698\n",
       "itertools           4435\n",
       "pickle              4363\n",
       "absolute_import     4351\n",
       "tensorflow          4253\n",
       "shutil              3977\n",
       "keras               3797\n",
       "internal            3544\n",
       "pprint              3420\n",
       "scipy               3329\n",
       "multiprocessing     3124\n",
       "TFile               3072\n",
       "torch               2994\n",
       "sqrt                2821\n",
       "object_library      2819\n",
       "string              2636\n",
       "import              2624\n",
       "pdb                 2497\n",
       "argv                2312\n",
       "operator            2295\n",
       "commands            2292\n",
       "httplib             2238\n",
       "Name: how, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches_df[(is_python | is_python_jupyter) & ~is_framework].groupby(\"package\").count()[\"how\"].sort_values(ascending=False).head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af7abcaf-5e31-4389-aea6-b5345ee8452d",
   "metadata": {},
   "source": [
    "### Top C++ packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f6c27595-3c77-4daa-bd9e-21215df45048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package\n",
       "iostream                                          108647\n",
       "vector                                             85492\n",
       "string                                             73077\n",
       "TFile.h                                            64713\n",
       "TCanvas.h                                          42748\n",
       "TTree.h                                            38961\n",
       "TROOT.h                                            37035\n",
       "TMath.h                                            36096\n",
       "fstream                                            36019\n",
       "FWCore/ParameterSet/interface/ParameterSet.h       31641\n",
       "sstream                                            29742\n",
       "FWCore/Framework/interface/Event.h                 29383\n",
       "TStyle.h                                           28689\n",
       "map                                                28513\n",
       "TString.h                                          28122\n",
       "FWCore/Framework/interface/MakerMacros.h           27985\n",
       "TH1.h                                              26767\n",
       "memory                                             25875\n",
       "algorithm                                          25460\n",
       "cmath                                              25177\n",
       "stdio.h                                            23602\n",
       "iomanip                                            23220\n",
       "TH1F.h                                             22891\n",
       "TLegend.h                                          22341\n",
       "stdlib.h                                           21004\n",
       "TSystem.h                                          20424\n",
       "TChain.h                                           20194\n",
       "TLorentzVector.h                                   20155\n",
       "TH2.h                                              19903\n",
       "TF1.h                                              19787\n",
       "FWCore/Framework/interface/Frameworkfwd.h          19136\n",
       "math.h                                             17261\n",
       "TH1D.h                                             15099\n",
       "string.h                                           14360\n",
       "TLatex.h                                           13154\n",
       "FWCore/MessageLogger/interface/MessageLogger.h     12555\n",
       "FWCore/Framework/interface/ESHandle.h              12305\n",
       "TH2F.h                                             12246\n",
       "TGraph.h                                           11842\n",
       "FWCore/Framework/interface/EventSetup.h            11123\n",
       "FWCore/ServiceRegistry/interface/Service.h         11052\n",
       "FWCore/Utilities/interface/InputTag.h              11049\n",
       "cstdlib                                            10701\n",
       "utility                                            10452\n",
       "TObject.h                                          10176\n",
       "FWCore/Framework/interface/EDAnalyzer.h             9821\n",
       "TGraphErrors.h                                      9787\n",
       "Riostream.h                                         9627\n",
       "RooRealVar.h                                        9208\n",
       "TGraphAsymmErrors.h                                 8783\n",
       "Name: how, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches_df[(is_cpp | is_cpp_jupyter)].groupby(\"package\").count()[\"how\"].sort_values(ascending=False).head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65bb1c7e-7e8c-4f73-83b8-5e6db66bebe0",
   "metadata": {},
   "source": [
    "### Timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6c3f3303-7a6c-45ab-93a2-52b9784eb2ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "matches_root = matches_df[\n",
    "    ((is_python | is_python_jupyter) & ~is_framework & (matches_df[\"package\"] == \"ROOT\") |\n",
    "     (is_cpp | is_cpp_jupyter) & matches_df[\"package\"].isin([\n",
    "         \"TFile.h\", \"TCanvas.h\", \"TTree.h\", \"TROOT.h\", \"TMath.h\", \"TStyle.h\", \"TString.h\", \"TH1.h\", \"TH1F.h\", \"TLegend.h\", \"TSystem.h\",\n",
    "         \"TChain.h\", \"TLorentzVector.h\", \"TH2.h\", \"TF1.h\", \"TH1D.h\", \"TLatex.h\", \"TH2F.h\", \"TGraph.h\", \"TObject.h\", \"TGraphErrors.h\", \"TGraphAsymmErrors.h\"\n",
    "     ]))\n",
    "].groupby([\"repo\", \"date\"]).any().droplevel(0)[\"how\"].resample(\n",
    "    \"Q\", origin=pd.to_datetime(\"2013-01-01\")\n",
    ").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "30d42dae-bb37-4ed7-b7f1-45dc180c4f98",
   "metadata": {},
   "outputs": [],
   "source": [
    "matches_uproot = matches_df[\n",
    "    (is_python | is_python_jupyter) & ~is_framework & (matches_df[\"package\"].isin([\"uproot\", \"uproot3\", \"uproot4\"]))\n",
    "].groupby([\"repo\", \"date\"]).any().droplevel(0)[\"how\"].resample(\n",
    "    \"Q\", origin=pd.to_datetime(\"2013-01-01\")\n",
    ").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a8bd32e3-35c9-416e-a861-f45077eef839",
   "metadata": {},
   "outputs": [],
   "source": [
    "matches_awkward = matches_df[\n",
    "    (is_python | is_python_jupyter) & ~is_framework & (matches_df[\"package\"].isin([\"awkward\", \"awkward0\", \"awkward1\"]))\n",
    "].groupby([\"repo\", \"date\"]).any().droplevel(0)[\"how\"].resample(\n",
    "    \"Q\", origin=pd.to_datetime(\"2013-01-01\")\n",
    ").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d80c2246-eda8-4b56-acf7-aa718e0d0e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "matches_framework = matches_df[\n",
    "    is_framework\n",
    "].groupby([\"repo\", \"date\"]).any().droplevel(0)[\"how\"].resample(\n",
    "    \"Q\", origin=pd.to_datetime(\"2013-01-01\")\n",
    ").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "92f97b91-f879-4186-be09-63fa82e42eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "def package_timeseries(package):\n",
    "    return matches_df[\n",
    "        (is_python | is_python_jupyter) & ~is_framework & (matches_df[\"package\"] == package)\n",
    "    ].groupby([\"repo\", \"date\"]).any().droplevel(0)[\"how\"].resample(\n",
    "        \"Q\", origin=pd.to_datetime(\"2013-01-01\")\n",
    "    ).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "67e6b741-7cba-46dd-abea-4994ebd5540c",
   "metadata": {},
   "outputs": [],
   "source": [
    "comparison = pd.DataFrame({\n",
    "    \"ROOT (C++ and Python)\": matches_root,\n",
    "    \"PyROOT (Python only)\": package_timeseries(\"ROOT\"),\n",
    "    \"CMSSW configuration\": matches_framework,\n",
    "    \"numpy\": package_timeseries(\"numpy\"),\n",
    "    \"matplotlib\": package_timeseries(\"matplotlib\"),\n",
    "    \"pandas\": package_timeseries(\"pandas\"),\n",
    "    \"tensorflow\": package_timeseries(\"tensorflow\"),\n",
    "    \"uproot\": matches_uproot,\n",
    "    \"awkward\": matches_awkward,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "87ac4867-0f21-491d-8b52-fc00eee7389f",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "486ec439-33b4-456d-b9f4-8fdf42bc912c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "comparison.plot(ax=ax,\n",
    "                style=[\"-\", \"--\", \":\", \"-\", \"--\", \"-.\", \":\", \"-\", \"--\"],\n",
    "                color=[c[0], c[0], c[2], c[3], c[3], c[3], c[3], c[1], c[4]], figsize=(13, 6))\n",
    "\n",
    "ax.axvline(x=pd.to_datetime(\"2013-01-01\"), ls=\"--\", c=\"gray\")\n",
    "ax.text(pd.to_datetime(\"2013-01-01\"), 0.3, \"CMSSW moved to GitHub\",\n",
    "        transform=ax.get_xaxis_transform(), rotation=90, ha=\"right\", va=\"center\", c=\"gray\")\n",
    "\n",
    "ax.set_xlim(pd.to_datetime(\"2011-06-01\"), pd.to_datetime(\"2021-09-01\"))\n",
    "ax.set_ylim(0, 260)\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Number of repos matching, quarterly\")\n",
    "ax.get_figure().savefig(\"PLOTS/gihub-package-fullstudy.svg\")\n",
    "ax.get_figure().savefig(\"PLOTS/gihub-package-fullstudy.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f4bec4bb-8e5c-46b2-bfdc-ff09716ca184",
   "metadata": {},
   "outputs": [],
   "source": [
    "comparison2 = pd.DataFrame({\n",
    "    \"total (all CMS user GitHub repos)\": matches_df.groupby([\"repo\", \"date\"]).any().droplevel(0)[\"how\"].resample(\n",
    "        \"Q\", origin=pd.to_datetime(\"2013-01-01\")\n",
    "    ).sum(),\n",
    "    \"with C++ files\": matches_df[is_cpp].groupby([\"repo\", \"date\"]).any().droplevel(0)[\"how\"].resample(\n",
    "        \"Q\", origin=pd.to_datetime(\"2013-01-01\")\n",
    "    ).sum(),\n",
    "    \"with Python files (not CMSSW)\": matches_df[is_python & ~is_framework].groupby([\"repo\", \"date\"]).any().droplevel(0)[\"how\"].resample(\n",
    "        \"Q\", origin=pd.to_datetime(\"2013-01-01\")\n",
    "    ).sum(),\n",
    "    \"with CMSSW configuration\": matches_df[is_python & is_framework].groupby([\"repo\", \"date\"]).any().droplevel(0)[\"how\"].resample(\n",
    "        \"Q\", origin=pd.to_datetime(\"2013-01-01\")\n",
    "    ).sum(),\n",
    "    \"with Python in Jupyter\": matches_df[is_python_jupyter].groupby([\"repo\", \"date\"]).any().droplevel(0)[\"how\"].resample(\n",
    "        \"Q\", origin=pd.to_datetime(\"2013-01-01\")\n",
    "    ).sum(),\n",
    "    \"with C++ in Jupyter\": matches_df[is_cpp_jupyter].groupby([\"repo\", \"date\"]).any().droplevel(0)[\"how\"].resample(\n",
    "        \"Q\", origin=pd.to_datetime(\"2013-01-01\")\n",
    "    ).sum(),\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3177a891-1a9c-423f-8677-e1eb716d5d2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "comparison2.plot(ax=ax,\n",
    "                 style=[\":\", \"-\", \"--\", \":\", \"-\", \"-\"],\n",
    "                 color=[\"gray\", c[0], c[3], c[2], c[1], c[4]],\n",
    "                 figsize=(13, 6))\n",
    "\n",
    "ax.axvline(x=pd.to_datetime(\"2013-01-01\"), ls=\"--\", c=\"gray\")\n",
    "ax.text(pd.to_datetime(\"2013-01-01\"), 0.3, \"CMSSW moved to GitHub\",\n",
    "        transform=ax.get_xaxis_transform(), rotation=90, ha=\"right\", va=\"center\", c=\"gray\")\n",
    "\n",
    "ax.set_xlim(pd.to_datetime(\"2011-06-01\"), pd.to_datetime(\"2021-09-01\"))\n",
    "ax.set_ylim(0, 500)\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Number of repos matching, quarterly\")\n",
    "ax.legend(loc=\"upper left\")\n",
    "ax.get_figure().savefig(\"PLOTS/gihub-language-fullstudy.svg\")\n",
    "ax.get_figure().savefig(\"PLOTS/gihub-language-fullstudy.pdf\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}