{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b4150bf9",
   "metadata": {},
   "source": [
    "# Ensemble Resilience Comparison\n",
    "\n",
    "Compare stochastic playback statistics across synthetic small, medium, and large scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "62390b55",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T12:57:20.082691Z",
     "iopub.status.busy": "2026-10-05T12:57:20.082577Z",
     "iopub.status.idle": "2026-10-05T12:57:23.429304Z",
     "shell.execute_reply": "2026-10-05T12:57:23.428554Z"
    }
   },
   "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>tier</th>\n",
       "      <th>mean_production</th>\n",
       "      <th>std_production</th>\n",
       "      <th>mean_downtime_hours</th>\n",
       "      <th>samples</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>small</td>\n",
       "      <td>28.699000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>medium</td>\n",
       "      <td>20.356423</td>\n",
       "      <td>5.607148</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>large</td>\n",
       "      <td>15.579618</td>\n",
       "      <td>3.308869</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     tier  mean_production  std_production  mean_downtime_hours  samples\n",
       "0   small        28.699000        0.000000                  0.0        6\n",
       "1  medium        20.356423        5.607148                  0.0       12\n",
       "2   large        15.579618        3.308869                  0.0       18"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "from pathlib import Path\n",
    "\n",
    "PROJECT_ROOT = Path.cwd().resolve()\n",
    "while PROJECT_ROOT != PROJECT_ROOT.parent and not (PROJECT_ROOT / \"pyproject.toml\").exists():\n",
    "    PROJECT_ROOT = PROJECT_ROOT.parent\n",
    "if not (PROJECT_ROOT / \"pyproject.toml\").exists():\n",
    "    raise RuntimeError(\n",
    "        \"Notebook must be executed within a FHOPS checkout (pyproject.toml not found).\"\n",
    "    )\n",
    "if str(PROJECT_ROOT) not in sys.path:\n",
    "    sys.path.insert(0, str(PROJECT_ROOT))\n",
    "\n",
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "\n",
    "from docs.examples.analytics import utils\n",
    "from fhops.scenario.synthetic import SyntheticDatasetConfig\n",
    "\n",
    "SCENARIOS = [\n",
    "    (\n",
    "        \"small\",\n",
    "        PROJECT_ROOT / \"examples/synthetic/small/scenario.yaml\",\n",
    "        PROJECT_ROOT / \"docs/examples/analytics/data/synthetic_small_sa_assignments.csv\",\n",
    "    ),\n",
    "    (\n",
    "        \"medium\",\n",
    "        PROJECT_ROOT / \"examples/synthetic/medium/scenario.yaml\",\n",
    "        PROJECT_ROOT / \"docs/examples/analytics/data/synthetic_medium_sa_assignments.csv\",\n",
    "    ),\n",
    "    (\n",
    "        \"large\",\n",
    "        PROJECT_ROOT / \"examples/synthetic/large/scenario.yaml\",\n",
    "        PROJECT_ROOT / \"docs/examples/analytics/data/synthetic_large_sa_assignments.csv\",\n",
    "    ),\n",
    "]\n",
    "\n",
    "summary_rows = []\n",
    "ensemble_tables = {}\n",
    "for tier, scenario_path, assign_path in SCENARIOS:\n",
    "    config = SyntheticDatasetConfig(\n",
    "        name=f\"synthetic-{tier}\", tier=tier, num_blocks=8, num_days=12, num_machines=4\n",
    "    )\n",
    "    tables, sampling = utils.run_stochastic_summary(scenario_path, assign_path, tier=tier)\n",
    "    ensemble_tables[tier] = tables\n",
    "    production = tables.shift.groupby(\"sample_id\")[\"production_units\"].sum()\n",
    "    downtime = tables.shift.groupby(\"sample_id\")[\"downtime_hours\"].sum()\n",
    "    summary_rows.append(\n",
    "        {\n",
    "            \"tier\": tier,\n",
    "            \"mean_production\": production.mean(),\n",
    "            \"std_production\": production.std(),\n",
    "            \"mean_downtime_hours\": downtime.mean(),\n",
    "            \"samples\": tables.shift[\"sample_id\"].nunique(),\n",
    "        }\n",
    "    )\n",
    "summary_df = pd.DataFrame(summary_rows)\n",
    "summary_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6173e42",
   "metadata": {},
   "source": [
    "## Production Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2911a00e",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2026-10-05T12:57:23.455082Z",
     "shell.execute_reply": "2026-10-05T12:57:23.454486Z"
    }
   },
   "outputs": [
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       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      let deps = [\"vega-embed\"];\n",
       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"6\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"6.4.1\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"7\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-bb382956447c08f90aaaafdc5371c158\"}, \"mark\": {\"type\": \"bar\", \"opacity\": 0.75}, \"encoding\": {\"x\": {\"bin\": {\"maxbins\": 20}, \"field\": \"Production Units\", \"title\": \"Production Units\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"title\": \"Frequency\", \"type\": \"quantitative\"}}, \"height\": 240, \"title\": \"Small Production Distribution\", \"width\": 400, \"$schema\": \"https://vega.github.io/schema/vega-lite/v6.4.1.json\", \"datasets\": {\"data-bb382956447c08f90aaaafdc5371c158\": [{\"Production Units\": 28.699}, {\"Production Units\": 28.699}, {\"Production Units\": 28.699}, {\"Production Units\": 28.699}, {\"Production Units\": 28.699}, {\"Production Units\": 28.699}]}}, {\"mode\": \"vega-lite\"});\n",
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       "alt.Chart(...)"
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       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@6?noext\",\n",
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       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
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       "\n",
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       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      let deps = [\"vega-embed\"];\n",
       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"6\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"6.4.1\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"7\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-0218652fe36584125832d624d48cf762\"}, \"mark\": {\"type\": \"bar\", \"opacity\": 0.75}, \"encoding\": {\"x\": {\"bin\": {\"maxbins\": 20}, \"field\": \"Production Units\", \"title\": \"Production Units\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"title\": \"Frequency\", \"type\": \"quantitative\"}}, \"height\": 240, \"title\": \"Medium Production Distribution\", \"width\": 400, \"$schema\": \"https://vega.github.io/schema/vega-lite/v6.4.1.json\", \"datasets\": {\"data-0218652fe36584125832d624d48cf762\": [{\"Production Units\": 20.1898}, {\"Production Units\": 21.6804}, {\"Production Units\": 20.47441855702875}, {\"Production Units\": 11.738800000000001}, {\"Production Units\": 15.883663096851128}, {\"Production Units\": 27.694000000000003}, {\"Production Units\": 14.179}, {\"Production Units\": 14.179}, {\"Production Units\": 23.627000000000002}, {\"Production Units\": 19.243000000000002}, {\"Production Units\": 27.694000000000003}, {\"Production Units\": 27.694000000000003}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
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      "text/plain": [
       "alt.Chart(...)"
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     "metadata": {},
     "output_type": "display_data"
    },
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       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@6?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@6.4.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@7?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
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       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
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       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
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       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"6\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"6.4.1\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"7\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-6851e39ac2704cec4eb4a05fb3af55d8\"}, \"mark\": {\"type\": \"bar\", \"opacity\": 0.75}, \"encoding\": {\"x\": {\"bin\": {\"maxbins\": 20}, \"field\": \"Production Units\", \"title\": \"Production Units\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"title\": \"Frequency\", \"type\": \"quantitative\"}}, \"height\": 240, \"title\": \"Large Production Distribution\", \"width\": 400, \"$schema\": \"https://vega.github.io/schema/vega-lite/v6.4.1.json\", \"datasets\": {\"data-6851e39ac2704cec4eb4a05fb3af55d8\": [{\"Production Units\": 9.822}, {\"Production Units\": 13.466691342351432}, {\"Production Units\": 19.295}, {\"Production Units\": 8.9634}, {\"Production Units\": 15.383600000000001}, {\"Production Units\": 10.472277388944411}, {\"Production Units\": 16.907}, {\"Production Units\": 19.089}, {\"Production Units\": 15.476}, {\"Production Units\": 12.941851758681231}, {\"Production Units\": 17.082}, {\"Production Units\": 17.6628}, {\"Production Units\": 16.242}, {\"Production Units\": 17.7606}, {\"Production Units\": 16.907}, {\"Production Units\": 19.4192}, {\"Production Units\": 14.258700000000001}, {\"Production Units\": 19.284}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
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      "text/plain": [
       "alt.Chart(...)"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for tier, tables in ensemble_tables.items():\n",
    "    prod = tables.shift.groupby(\"sample_id\")[\"production_units\"].sum()\n",
    "    display(\n",
    "        utils.plot_distribution(\n",
    "            prod, title=f\"{tier.title()} Production Distribution\", xlabel=\"Production Units\"\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01ac8336",
   "metadata": {},
   "source": [
    "## Downtime Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01ef7a7f",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-10-05T12:57:23.465351Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>small</th>\n",
       "      <th>medium</th>\n",
       "      <th>large</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       small  medium  large\n",
       "count    6.0    12.0   18.0\n",
       "mean     0.0     0.0    0.0\n",
       "std      0.0     0.0    0.0\n",
       "min      0.0     0.0    0.0\n",
       "25%      0.0     0.0    0.0\n",
       "50%      0.0     0.0    0.0\n",
       "75%      0.0     0.0    0.0\n",
       "max      0.0     0.0    0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "downtime_summary = pd.DataFrame(\n",
    "    {\n",
    "        tier: tables.shift.groupby(\"sample_id\")[\"downtime_hours\"].sum()\n",
    "        for tier, tables in ensemble_tables.items()\n",
    "    }\n",
    ")\n",
    "downtime_summary.describe()"
   ]
  }
 ],
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