{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2ec166b9",
   "metadata": {},
   "source": [
    "# KPI Decomposition Deep Dive\n",
    "\n",
    "Break down production, mobilisation, sequencing, and downtime metrics for the tiny7 scenario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f2971e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T12:57:12.650851Z",
     "iopub.status.busy": "2026-10-05T12:57:12.650745Z",
     "iopub.status.idle": "2026-10-05T12:57:15.331066Z",
     "shell.execute_reply": "2026-10-05T12:57:15.329925Z"
    }
   },
   "outputs": [],
   "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",
    "\n",
    "from docs.examples.analytics import utils\n",
    "from fhops.evaluation import playback_summary_metrics\n",
    "from fhops.scenario.io import load_scenario\n",
    "\n",
    "SCENARIO = PROJECT_ROOT / \"examples/tiny7/scenario.yaml\"\n",
    "ASSIGNMENTS = PROJECT_ROOT / \"tests/fixtures/playback/tiny7_assignments.csv\"\n",
    "playback_tables = utils.load_deterministic_playback(SCENARIO, ASSIGNMENTS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7f495ea",
   "metadata": {},
   "source": [
    "## Scenario Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3062e03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T12:57:15.332743Z",
     "iopub.status.busy": "2026-10-05T12:57:15.332586Z",
     "iopub.status.idle": "2026-10-05T12:57:15.353754Z",
     "shell.execute_reply": "2026-10-05T12:57:15.353281Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "samples                    1.000000\n",
       "total_production       17658.811008\n",
       "total_hours              576.000000\n",
       "mobilisation_cost        479.160000\n",
       "average_utilisation        0.380952\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scenario = load_scenario(SCENARIO)\n",
    "summary_metrics = playback_summary_metrics(playback_tables.shift, playback_tables.day)\n",
    "pd.Series(summary_metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1a4b6ec",
   "metadata": {},
   "source": [
    "## Production vs Mobilisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fdf3a10c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T12:57:15.355379Z",
     "iopub.status.busy": "2026-10-05T12:57:15.355241Z",
     "iopub.status.idle": "2026-10-05T12:57:15.361536Z",
     "shell.execute_reply": "2026-10-05T12:57:15.360852Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>day</th>\n",
       "      <th>production_units</th>\n",
       "      <th>mobilisation_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2007.912755</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1438.086529</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3193.795810</td>\n",
       "      <td>53.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4047.344153</td>\n",
       "      <td>106.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2969.193965</td>\n",
       "      <td>106.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1595.687799</td>\n",
       "      <td>53.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2406.789997</td>\n",
       "      <td>159.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   day  production_units  mobilisation_cost\n",
       "0    1       2007.912755               0.00\n",
       "1    2       1438.086529               0.00\n",
       "2    3       3193.795810              53.24\n",
       "3    4       4047.344153             106.48\n",
       "4    5       2969.193965             106.48\n",
       "5    6       1595.687799              53.24\n",
       "6    7       2406.789997             159.72"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_metrics = playback_tables.day[[\"day\", \"production_units\", \"mobilisation_cost\"]]\n",
    "prod_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98676f1a",
   "metadata": {},
   "source": [
    "## Sequencing & Downtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "045fb3b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T12:57:15.362884Z",
     "iopub.status.busy": "2026-10-05T12:57:15.362762Z",
     "iopub.status.idle": "2026-10-05T12:57:15.368138Z",
     "shell.execute_reply": "2026-10-05T12:57:15.367427Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>sequencing_violations</th>\n",
       "      <th>downtime_hours</th>\n",
       "      <th>downtime_events</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   day  sequencing_violations  downtime_hours  downtime_events\n",
       "0    1                      0             0.0                0\n",
       "1    2                      0             0.0                0\n",
       "2    3                      0             0.0                0\n",
       "3    4                      0             0.0                0\n",
       "4    5                      0             0.0                0\n",
       "5    6                      0             0.0                0\n",
       "6    7                      0             0.0                0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq_metrics = playback_tables.day[\n",
    "    [\"day\", \"sequencing_violations\", \"downtime_hours\", \"downtime_events\"]\n",
    "]\n",
    "seq_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d222853",
   "metadata": {},
   "source": [
    "## Visualise Utilisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1566b1e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-05T12:57:15.369590Z",
     "iopub.status.busy": "2026-10-05T12:57:15.369442Z",
     "iopub.status.idle": "2026-10-05T12:57:15.392115Z",
     "shell.execute_reply": "2026-10-05T12:57:15.391443Z"
    }
   },
   "outputs": [
    {
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       "alt.Chart(...)"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart = utils.plot_utilisation_heatmap(playback_tables.shift)\n",
    "chart"
   ]
  }
 ],
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