conda-forge-ci-timing.ipynb

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  {
   "cell_type": "markdown",
   "id": "9148ab11-0300-44de-9625-7d8737abc0b7",
   "metadata": {},
   "source": [
    "# collect timing of github status checks\n",
    "\n",
    "Used to compute things like [this](https://github.com/conda-forge/petsc4py-feedstock/pull/112#issuecomment-2586817022)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "96eef607-7395-4b06-b3cd-13c219f36109",
   "metadata": {},
   "outputs": [],
   "source": [
    "from subprocess import run\n",
    "try:\n",
    "    token = run([\"gh\", \"auth\", \"token\"], check=True, text=True, capture_output=True).stdout.strip()\n",
    "except Exception:\n",
    "    token = None\n",
    "    \n",
    "from github import Github as GitHub\n",
    "gh = GitHub(token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "365f50ab-ce20-403f-82e7-1839eafdc319",
   "metadata": {},
   "outputs": [],
   "source": [
    "repo = gh.get_repo(\"conda-forge/fenics-dolfinx-feedstock\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "54197c47-cba6-4fcb-b5b2-d5355221d566",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "bb6a0483-433c-488d-8978-5a26b283c8a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "name_pat = re.compile(r\"\\(Build\\s+\\w+\\s+([\\w\\.]+)\\)?\")\n",
    "\n",
    "def extract_name(name):\n",
    "    m = name_pat.search(name)\n",
    "    if m:\n",
    "        return m.group(1)\n",
    "    else:\n",
    "        return name\n",
    "\n",
    "def get_checks_df(repo, pr):\n",
    "    records = []\n",
    "    if isinstance(pr, int):\n",
    "        pr = repo.get_pull(pr)\n",
    "    for check in repo.get_commit(pr.head.sha).get_check_runs():\n",
    "        # exclude meta tasks, like the overall job and Check Skip\n",
    "        if \"Build\" not in check.name:\n",
    "            continue\n",
    "        name = extract_name(check.name)\n",
    "        records.append(\n",
    "            {\n",
    "                \"name\": name,\n",
    "                \"started\": check.started_at,\n",
    "                \"completed\": check.completed_at,\n",
    "                \"duration\": (check.completed_at - check.started_at),\n",
    "                \"status\": check.conclusion,\n",
    "            }\n",
    "        )\n",
    "    return pd.DataFrame.from_records(records).sort_values(\"started\").reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "938f0f7b-3a46-4d9b-9240-3805afea11bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "flat_df = get_checks_df(repo, 99)\n",
    "split_df = get_checks_df(repo, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "229a5e17-dab2-4536-82d1-c0fe24c017b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "before_df = get_checks_df(repo, 98)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "d2a6f3e5-51bc-4e22-8841-1f5cd4718aaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('1 days 23:11:17')"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "before_df.duration.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "28ecc8a2-8ebb-480b-8524-58db9f558126",
   "metadata": {},
   "outputs": [],
   "source": [
    "petsc4py = gh.get_repo(\"conda-forge/petsc4py-feedstock\")\n",
    "before_df = get_checks_df(petsc4py, 111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "7b108ada-a6e1-47d6-8d23-612b9dce84aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('0 days 00:51:03')"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "before_df.completed.max() - before_df.started.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "628e7fc4-cbb3-4137-a92a-8e3a13d03af8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('1 days 12:24:58')"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "before_df.duration.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "1f73052c-466c-42d2-a4d2-9ce5665c9d1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "after_df = get_checks_df(petsc4py, 112)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "a13e7016-2362-49b4-8fb4-e28858453591",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/qr/3vxfnp1x2t1fw55dr288mphc0000gn/T/ipykernel_76070/2980723383.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  last_split_df.started -= offset\n",
      "/var/folders/qr/3vxfnp1x2t1fw55dr288mphc0000gn/T/ipykernel_76070/2980723383.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  last_split_df.completed -= offset\n"
     ]
    }
   ],
   "source": [
    "# artificial: split_df had one retry, which skews things\n",
    "# set start of last job to start of second to last job\n",
    "last_split_df = after_df.iloc[-1]\n",
    "next_to_last = after_df.iloc[-2]\n",
    "offset = last_split_df.started - next_to_last.started\n",
    "last_split_df.started -= offset\n",
    "last_split_df.completed -= offset\n",
    "after_df.iloc[-1] = last_split_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "d6345dc6-e5fd-4039-b3ed-fbd7a44dace7",
   "metadata": {},
   "outputs": [],
   "source": [
    "after_df[\"tool\"] = \"rattler-build\"\n",
    "before_df[\"tool\"] = \"conda-build\"\n",
    "df = pd.concat([after_df, before_df])\n",
    "df[\"duration\"] = df[\"duration\"].dt.total_seconds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "12a0dc41-27a7-4c72-8bae-55bc3f9d1cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "220"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(before_df)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 296,
   "id": "d0305ef0-7ead-4c13-8531-26c0ad546f8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "220"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(after_df)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 308,
   "id": "15446c85-c387-4296-acd8-f34f67f75c9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.416111111111114"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "before_df.duration.sum().total_seconds() / 3600"
   ]
  },
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   "cell_type": "code",
   "execution_count": 309,
   "id": "51ec4bdd-4ac2-4c72-bbaf-13d9c044b163",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.595"
      ]
     },
     "execution_count": 309,
     "metadata": {},
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   "source": [
    "after_df.duration.sum().total_seconds() / 3600"
   ]
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   "cell_type": "code",
   "execution_count": 299,
   "id": "23b45150-de3b-4676-9e6c-9afa425a8d2a",
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    {
     "data": {
      "text/plain": [
       "(Timedelta('0 days 00:06:13'),\n",
       " Timedelta('0 days 00:09:53'),\n",
       " Timedelta('0 days 00:16:13'))"
      ]
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     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "before_df.duration.min(), before_df.duration.median(), before_df.duration.max()"
   ]
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   "cell_type": "code",
   "execution_count": 300,
   "id": "32bf48c9-dabd-42db-a519-dcaefe0948fd",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timedelta('0 days 00:03:42'),\n",
       " Timedelta('0 days 00:06:58'),\n",
       " Timedelta('0 days 00:10:59'))"
      ]
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     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
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    "after_df.duration.min(), after_df.duration.median(), after_df.duration.max()"
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   "id": "df7d0295-ec68-459a-b0f9-0b292314c44f",
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   "outputs": [
    {
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      "text/plain": [
       "Timedelta('0 days 00:51:03')"
      ]
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     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "before_df.completed.max() - before_df.started.min()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "b69170af-ca61-4493-a533-bff4aab572ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('0 days 00:36:40')"
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     },
     "execution_count": 306,
     "metadata": {},
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    "after_df.completed.max() - after_df.started.min()"
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   "execution_count": 280,
   "id": "0a233345-53e8-4ad0-a458-8f2e428a6f24",
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\"linux_ppc64le_devicehostmpiopenmpinumpy2.0python3.9.____cpythonscalarreal\", \"started\": \"2025-01-03T15:35:10+00:00\", \"completed\": \"2025-01-03T15:46:09+00:00\", \"duration\": 659.0, \"status\": \"success\", \"label\": \"conda-build\", \"tool\": \"conda-build\", \"seconds\": 659.0}, {\"name\": \"linux_ppc64le_devicehostmpiopenmpinumpy2python3.13.____cp313scalarcomplex\", \"started\": \"2025-01-03T15:35:11+00:00\", \"completed\": \"2025-01-03T15:44:48+00:00\", \"duration\": 577.0, \"status\": \"success\", \"label\": \"conda-build\", \"tool\": \"conda-build\", \"seconds\": 577.0}, {\"name\": \"linux_ppc64le_devicehostmpiopenmpinumpy2python3.13.____cp313scalarreal\", \"started\": \"2025-01-03T15:35:17+00:00\", \"completed\": \"2025-01-03T15:44:46+00:00\", \"duration\": 569.0, \"status\": \"success\", \"label\": \"conda-build\", \"tool\": \"conda-build\", \"seconds\": 569.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import altair as alt\n",
    "\n",
    "# before_df[\"seconds\"] = before_df.duration.dt.total_seconds()\n",
    "\n",
    "alt.Chart(df).mark_bar().encode(\n",
    "    alt.X(\"duration:Q\", bin=True),\n",
    "    alt.Y(\"count()\"),\n",
    "    alt.Color(\"tool\"),\n",
    "    alt.Facet(\"tool\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d11bae50-8704-421d-9db2-97f12b28f7b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair as alt\n",
    "before_df[\"seconds\"] = before_df.duration.dt.total_seconds()\n",
    "\n",
    "alt.Chart(before_df[[\"seconds\"]]).mark_bar().encode(\n",
    "    alt.X(\"seconds:Q\", bin=True),\n",
    "    y=\"count()\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "d89829b8-1dfa-42af-a98f-21fd70c76a7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x618.034 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 519,
       "width": 841
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "minutes = before_df.duration.dt.total_seconds() / 60\n",
    "minutes.plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "0c9de8d8-8cae-4cee-8825-b1278ad0f343",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        osx_64_mpimpichpython3.9.____cpythonscalarreal\n",
       "1     osx_64_mpiopenmpipython3.11.____cpythonscalarreal\n",
       "2     osx_64_mpiopenmpipython3.10.____cpythonscalarc...\n",
       "3     osx_64_mpiopenmpipython3.12.____cpythonscalarc...\n",
       "4     osx_64_mpiopenmpipython3.11.____cpythonscalarc...\n",
       "                            ...                        \n",
       "95    linux_ppc64le_mpiopenmpipython3.13.____cp313sc...\n",
       "96    linux_ppc64le_mpiopenmpipython3.13.____cp313sc...\n",
       "97    linux_ppc64le_mpiopenmpipython3.9.____cpythons...\n",
       "98    linux_ppc64le_mpiopenmpipython3.9.____cpythons...\n",
       "99    osx_arm64_mpiopenmpipython3.10.____cpythonscal...\n",
       "Name: name, Length: 100, dtype: object"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split_df.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "b451505e-0751-435e-849b-4720126c65d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# artificial: split_df had one retry, which skews things\n",
    "# set start of last job to start of second to last job\n",
    "last_split_df = split_df.iloc[-1]\n",
    "next_to_last = split_df.iloc[-2]\n",
    "offset = last_split_df.started - next_to_last.started\n",
    "last_split_df.started -= offset\n",
    "last_split_df.completed -= offset\n",
    "split_df.iloc[-1] = last_split_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "b98728b9-9e4b-470c-9268-62fd9d6f776c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('0 days 01:33:20')"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat_df.completed.max() - flat_df.started.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "18a7be47-ea7c-40ad-8567-58b8fbc4a992",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('0 days 01:03:48')"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split_df.completed.max() - split_df.started.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "1274220b-7a90-45c2-9e21-3d9f6659d73d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "flat    0 days 18:14:53\n",
       "split   1 days 08:19:22\n",
       "Name: duration, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat_df['label'] = 'flat'\n",
    "split_df['label'] = 'split'\n",
    "merged = pd.concat([flat_df, split_df])\n",
    "merged.groupby('label').duration.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "573393e1-1099-47fa-8d45-e4e3c7992961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "flat    0 days 00:18:39\n",
       "split   0 days 00:06:32\n",
       "Name: duration, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = merged.groupby('label')\n",
    "gb.duration.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "7c2011a0-33df-4567-b887-1dc2fe0074a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "flat    0 days 01:33:13\n",
       "split   0 days 00:41:30\n",
       "Name: duration, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.duration.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "d37e0c29-8cd5-447b-99f4-575e473e7b5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            osx_64\n",
       "1         osx_arm64\n",
       "2            osx_64\n",
       "3            osx_64\n",
       "4          linux_64\n",
       "5          linux_64\n",
       "6         osx_arm64\n",
       "7         osx_arm64\n",
       "8     linux_aarch64\n",
       "9     linux_aarch64\n",
       "10    linux_aarch64\n",
       "11         linux_64\n",
       "12    linux_aarch64\n",
       "13    linux_ppc64le\n",
       "14           osx_64\n",
       "15    linux_ppc64le\n",
       "16    linux_ppc64le\n",
       "17        osx_arm64\n",
       "18    linux_ppc64le\n",
       "19         linux_64\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat_df.name.str.split(\"_\", n=2).str[:2].str.join(\"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "828d1b74-66b6-4176-902d-425ac4a4e242",
   "metadata": {},
   "outputs": [],
   "source": [
    "flat_df['plat'] = flat_df.name.str.split(\"_\", n=2).str[:2].str.join(\"_\")\n",
    "split_df['plat'] = split_df.name.str.split(\"_\", n=2).str[:2].str.join(\"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "d2243573-4ec8-4640-82a0-f9a29f9ebc7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "plat\n",
       "linux_64        0 days 03:04:59\n",
       "linux_aarch64   0 days 05:05:18\n",
       "linux_ppc64le   0 days 05:14:42\n",
       "osx_64          0 days 03:27:21\n",
       "osx_arm64       0 days 01:22:33\n",
       "Name: duration, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat_df.groupby('plat').duration.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "94a5f961-f059-449d-a01c-788383290356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "plat\n",
       "linux_64        0 days 04:24:43\n",
       "linux_aarch64   0 days 09:33:57\n",
       "linux_ppc64le   0 days 10:01:46\n",
       "osx_64          0 days 05:16:08\n",
       "osx_arm64       0 days 03:02:48\n",
       "Name: duration, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split_df.groupby('plat').duration.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "64a563e4-9b95-4968-9608-88d6bba37afb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2916666666666667"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5.25 / 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "2f5f5061-9b44-4d7b-ad51-ed3118009d6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3125"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10 / 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "0c65f47b-5361-4bb0-9515-4389333ab7e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('0 days 18:14:53')"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat_df.duration.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "da44053d-4ca5-4597-accc-b90f76753026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(split_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "8583725a-6ad9-4312-852f-cbb4c5bc6f14",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "41f82b96-2f13-4eb3-a347-a0fd5ce1a24a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2025-01-09 14:18:33+00:00\n",
       "1    2025-01-09 14:18:34+00:00\n",
       "2    2025-01-09 14:18:34+00:00\n",
       "3    2025-01-09 14:18:34+00:00\n",
       "4    2025-01-09 14:18:34+00:00\n",
       "                ...           \n",
       "95   2025-01-09 14:46:07+00:00\n",
       "96   2025-01-09 14:46:09+00:00\n",
       "97   2025-01-09 14:49:11+00:00\n",
       "98   2025-01-09 14:54:42+00:00\n",
       "99   2025-01-09 14:54:42+00:00\n",
       "Name: started, Length: 100, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split_df.started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "2336ae14-92bd-4ffc-8c94-0ba32600493f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1736441406000000000\n",
       "1     1736441407000000000\n",
       "2     1736441408000000000\n",
       "3     1736441409000000000\n",
       "4     1736441410000000000\n",
       "5     1736441410000000000\n",
       "6     1736441410000000000\n",
       "7     1736441410000000000\n",
       "8     1736441410000000000\n",
       "9     1736441410000000000\n",
       "10    1736441411000000000\n",
       "11    1736441411000000000\n",
       "12    1736441411000000000\n",
       "13    1736441411000000000\n",
       "14    1736441412000000000\n",
       "15    1736441412000000000\n",
       "16    1736441412000000000\n",
       "17    1736441413000000000\n",
       "18    1736441413000000000\n",
       "19    1736441422000000000\n",
       "Name: started, dtype: int64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat_df.started.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "a8cb069d-e3c8-4928-bb72-ea710540c018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0      0.0\n",
       " 1      1.0\n",
       " 2      2.0\n",
       " 3      3.0\n",
       " 4      4.0\n",
       " 5      4.0\n",
       " 6      4.0\n",
       " 7      4.0\n",
       " 8      4.0\n",
       " 9      4.0\n",
       " 10     5.0\n",
       " 11     5.0\n",
       " 12     5.0\n",
       " 13     5.0\n",
       " 14     6.0\n",
       " 15     6.0\n",
       " 16     6.0\n",
       " 17     7.0\n",
       " 18     7.0\n",
       " 19    16.0\n",
       " Name: started, dtype: float64,)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(flat_df.started.astype(int).astype(float) - flat_df.started.astype(int).astype(float).min()) * 1e-9,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "a635c5aa-f2c9-42ef-bbd0-8328b0a27dc8",
   "metadata": {},
   "outputs": [
    {
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      "image/png": 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      "text/plain": [
       "<Figure size 1000x618.034 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 504,
       "width": 839
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(\n",
    "    x=flat_df.name,\n",
    "    height=flat_df.duration.astype(int).astype(float) * 1e-9,\n",
    "    bottom=(flat_df.started.astype(int).astype(float) - flat_df.started.astype(int).astype(float).min()) * 1e-9,\n",
    ")\n",
    "plt.grid(False)\n",
    "plt.tick_params(labelbottom=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "0c911a63-c4f7-4c92-9c60-bcc45207489c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x618.034 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 507,
       "width": 839
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(\n",
    "    x=split_df.name,\n",
    "    height=split_df.duration.astype(int).astype(float) * 1e-9,\n",
    "    bottom=(split_df.started.astype(int).astype(float) - split_df.started.astype(int).astype(float).min()) * 1e-9,\n",
    ")\n",
    "plt.grid(True, axis='y')\n",
    "plt.grid(False, axis='x')\n",
    "plt.tick_params(labelbottom=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "23e4644d-9fee-4e2f-b759-e80f7d09e527",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x618.034 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 504,
       "width": 839
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(\n",
    "    x=after_df.name,\n",
    "    height=after_df.duration.astype(int).astype(float) * 1e-9,\n",
    "    bottom=(after_df.started.astype(int).astype(float) - after_df.started.astype(int).astype(float).min()) * 1e-9,\n",
    ")\n",
    "plt.grid(False)\n",
    "plt.tick_params(labelbottom=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "87743c2c-a179-4b29-b14c-e44f5cc57d2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x618.034 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 504,
       "width": 839
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(\n",
    "    x=before_df.name,\n",
    "    height=before_df.duration.astype(int).astype(float) * 1e-9,\n",
    "    bottom=(before_df.started.astype(int).astype(float) - before_df.started.astype(int).astype(float).min()) * 1e-9,\n",
    ")\n",
    "plt.grid(False)\n",
    "plt.tick_params(labelbottom=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c8a8862-c0f4-4acb-9373-08721c4e887e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.10"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}