{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6821285f",
   "metadata": {},
   "source": [
    "# Multi-Criteria Analysis — Usage Example\n",
    "\n",
    "This notebook demonstrates how to use the `MulticriteriAnalysis` class from `wolfhece.multicriteria.analysis`.\n",
    "\n",
    "## Core concept\n",
    "\n",
    "The analysis works in **two stages**:\n",
    "\n",
    "1. **Binary scoring** — Each input array is evaluated cell-by-cell against a condition (e.g. `≥ threshold`). Every cell receives a binary score: `1` (condition met) or `0` (condition not met).\n",
    "2. **Score combination** — The binary scores from all inputs are combined using a chosen method: `sum`, `average`, or `percentage`.\n",
    "\n",
    "```\n",
    "  Input array 1  ──► [condition + threshold] ──► binary score 1 ─┐\n",
    "  Input array 2  ──► [condition + threshold] ──► binary score 2 ─┼──► combined result\n",
    "  Input array 3  ──► [condition + threshold] ──► binary score 3 ─┘\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e77892",
   "metadata": {},
   "source": [
    "## 1. Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a21d9b24",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "from wolfhece.multicriteria.analysis import (\n",
    "    Input,\n",
    "    MulticriteriAnalysis,\n",
    "    Operator,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02ec6a7e",
   "metadata": {},
   "source": [
    "## 2. Create three synthetic input arrays\n",
    "\n",
    "We simulate three spatially distributed variables on a 6×6 grid:\n",
    "\n",
    "| Variable | Physical meaning | Condition | Threshold |\n",
    "|---|---|---|---|\n",
    "| `water_depth` | Water depth (m) | `≥` | 0.3 m |\n",
    "| `velocity` | Flow velocity (m/s) | `≥` | 0.5 m/s |\n",
    "| `hazard_index` | Hazard index (–) | `≥` | 2.0 |\n",
    "\n",
    "A cell is \"dangerous\" (score = 1) when its value meets the condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c875995",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "water_depth:\n",
      " [[0.7699999809265137 0.4399999976158142 0.8600000143051147\n",
      "  0.699999988079071 0.09000000357627869 0.9800000190734863]\n",
      " [0.7599999904632568 0.7900000214576721 0.12999999523162842\n",
      "  0.44999998807907104 0.3700000047683716 0.9300000071525574]\n",
      " [0.6399999856948853 0.8199999928474426 0.4399999976158142\n",
      "  0.23000000417232513 0.550000011920929 0.05999999865889549]\n",
      " [0.8299999833106995 0.6299999952316284 0.7599999904632568\n",
      "  0.3499999940395355 0.9700000286102295 0.8899999856948853]\n",
      " [0.7799999713897705 0.1899999976158142 0.4699999988079071\n",
      "  0.03999999910593033 0.15000000596046448 0.6800000071525574]\n",
      " [0.7400000095367432 0.9700000286102295 0.33000001311302185\n",
      "  0.3700000047683716 0.4699999988079071 0.1899999976158142]]\n",
      "\n",
      "velocity:\n",
      " [[0.1899999976158142 0.7099999785423279 0.3400000035762787 1.0\n",
      "  0.6600000262260437 1.25]\n",
      " [1.0499999523162842 0.4699999988079071 1.25 1.2100000381469727\n",
      "  0.5799999833106995 0.4300000071525574]\n",
      " [1.0199999809265137 0.20999999344348907 0.30000001192092896\n",
      "  0.009999999776482582 1.1799999475479126 1.0]\n",
      " [1.059999942779541 1.1699999570846558 0.6899999976158142\n",
      "  0.8500000238418579 0.20999999344348907 0.17000000178813934]\n",
      " [1.0 0.7099999785423279 0.8500000238418579 1.149999976158142\n",
      "  0.949999988079071 0.8299999833106995]\n",
      " [0.8399999737739563 0.46000000834465027 0.05000000074505806\n",
      "  0.6600000262260437 0.3199999928474426 0.6100000143051147]]\n",
      "\n",
      "hazard_index:\n",
      " [[3.4100000858306885 0.9399999976158142 0.23000000417232513\n",
      "  1.1299999952316284 1.1699999570846558 2.6500000953674316]\n",
      " [2.2300000190734863 3.140000104904175 2.6600000858306885\n",
      "  1.6299999952316284 3.259999990463257 0.6700000166893005]\n",
      " [0.09000000357627869 0.36000001430511475 2.890000104904175\n",
      "  1.850000023841858 0.6499999761581421 2.0]\n",
      " [0.6100000143051147 2.7899999618530273 1.7799999713897705\n",
      "  1.5199999809265137 1.2100000381469727 2.5199999809265137]\n",
      " [1.4500000476837158 0.3499999940395355 0.4699999988079071\n",
      "  3.8499999046325684 3.630000114440918 2.799999952316284]\n",
      " [1.059999942779541 3.880000114440918 3.119999885559082 2.869999885559082\n",
      "  1.7999999523162842 1.090000033378601]]\n"
     ]
    }
   ],
   "source": [
    "# Reproducibility\n",
    "rng = np.random.default_rng(seed=42)\n",
    "\n",
    "shape = (6, 6)\n",
    "\n",
    "# --- Water depth (m) ---\n",
    "water_depth = np.ma.MaskedArray(data=rng.uniform(0.0, 1.0, shape).astype(np.float32), mask=False)\n",
    "\n",
    "# --- Flow velocity (m/s) ---\n",
    "velocity = np.ma.MaskedArray(data=rng.uniform(0.0, 1.5, shape).astype(np.float32), mask=False)\n",
    "\n",
    "# --- Hazard index (dimensionless) ---\n",
    "hazard_index = np.ma.MaskedArray(data=rng.uniform(0.0, 4.0, shape).astype(np.float32), mask=False)\n",
    "\n",
    "print(\"water_depth:\\n\", np.round(water_depth, 2))\n",
    "print(\"\\nvelocity:\\n\", np.round(velocity, 2))\n",
    "print(\"\\nhazard_index:\\n\", np.round(hazard_index, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14458865",
   "metadata": {},
   "source": [
    "## 3. Wrap each array in an `Input` object\n",
    "\n",
    "An `Input` bundles together:\n",
    "- the data array,\n",
    "- the condition used to score it (`Operator.*`),\n",
    "- the threshold value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4de9d520",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_depth = Input(name=\"water_depth\",\n",
    "                    array=water_depth,\n",
    "                    condition=Operator.SUPERIOR_OR_EQUAL,\n",
    "                    threshold=0.3)\n",
    "\n",
    "input_velocity = Input(name=\"velocity\",\n",
    "                       array=velocity,\n",
    "                       condition=Operator.SUPERIOR_OR_EQUAL,\n",
    "                       threshold=0.5)\n",
    "\n",
    "input_hazard = Input(name=\"hazard_index\",\n",
    "                     array=hazard_index,\n",
    "                     condition=Operator.SUPERIOR_OR_EQUAL,\n",
    "                     threshold=2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d84d81b",
   "metadata": {},
   "source": [
    "## 4. Visualise the binary scores\n",
    "\n",
    "Before combining, inspect the individual binary score of each input.\n",
    "A cell is white (0 — condition not met) or coloured (1 — condition met)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ebeb3044",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_binary(ax, data, title, threshold, cmap=\"Blues\"):\n",
    "    im = ax.imshow(data.T, origin=\"lower\", cmap=cmap, vmin=0, vmax=1)\n",
    "    ax.set_title(title, fontsize=11)\n",
    "    for i in range(data.shape[0]):\n",
    "        for j in range(data.shape[1]):\n",
    "            ax.text(i, j, str(int(data[i, j])), ha=\"center\", va=\"center\", fontsize=9)\n",
    "    return im\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n",
    "\n",
    "score_depth   = input_depth.score.score\n",
    "score_vel     = input_velocity.score.score\n",
    "score_hazard  = input_hazard.score.score\n",
    "\n",
    "plot_binary(axes[0], score_depth,  \"Score: water_depth ≥ 0.3 m\",   0.3, cmap=\"Blues\")\n",
    "plot_binary(axes[1], score_vel,    \"Score: velocity ≥ 0.5 m/s\",    0.5, cmap=\"Oranges\")\n",
    "plot_binary(axes[2], score_hazard, \"Score: hazard_index ≥ 2.0\",    2.0, cmap=\"Reds\")\n",
    "\n",
    "fig.suptitle(\"Binary scores (0 = condition not met, 1 = condition met)\", fontsize=13)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d60194",
   "metadata": {},
   "source": [
    "## 5. Run the `MulticriteriAnalysis`\n",
    "\n",
    "Three combination methods are available:\n",
    "\n",
    "| Method | Formula | Range |\n",
    "|---|---|---|\n",
    "| `SUM` | $\\sum_i s_i$ | $[0, N]$ |\n",
    "| `AVERAGE` | $\\frac{1}{N}\\sum_i s_i$ | $[0, 1]$ |\n",
    "| `PERCENTAGE` | $\\frac{100}{N}\\sum_i s_i$ | $[0, 100]$ |\n",
    "\n",
    "where $N$ is the number of inputs and $s_i \\in \\{0,1\\}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9487eafb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:No weight provided. Setting weight to equal distribution.\n",
      "WARNING:root:No weight provided. Setting weight to equal distribution.\n",
      "WARNING:root:No weight provided. Setting weight to equal distribution.\n",
      "WARNING:root:No weight provided. Setting weight to equal distribution.\n",
      "WARNING:root:No weight provided. Setting weight to equal distribution.\n",
      "WARNING:root:No weight provided. Setting weight to equal distribution.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SUM result (integer, range [0, 3]):\n",
      " [[2 2 1 2 1 3]\n",
      " [3 2 2 2 3 1]\n",
      " [2 1 2 0 2 2]\n",
      " [2 3 2 2 1 2]\n",
      " [2 1 2 2 2 3]\n",
      " [2 2 2 3 1 1]]\n",
      "\n",
      "AVERAGE result (float, range [0, 1]):\n",
      " [[0.6700000166893005 0.6700000166893005 0.33000001311302185\n",
      "  0.6700000166893005 0.33000001311302185 1.0]\n",
      " [1.0 0.6700000166893005 0.6700000166893005 0.6700000166893005 1.0\n",
      "  0.33000001311302185]\n",
      " [0.6700000166893005 0.33000001311302185 0.6700000166893005 0.0\n",
      "  0.6700000166893005 0.6700000166893005]\n",
      " [0.6700000166893005 1.0 0.6700000166893005 0.6700000166893005\n",
      "  0.33000001311302185 0.6700000166893005]\n",
      " [0.6700000166893005 0.33000001311302185 0.6700000166893005\n",
      "  0.6700000166893005 0.6700000166893005 1.0]\n",
      " [0.6700000166893005 0.6700000166893005 0.6700000166893005 1.0\n",
      "  0.33000001311302185 0.33000001311302185]]\n",
      "\n",
      "PERCENTAGE result (%, range [0, 100]):\n",
      " [[66.7 66.7 33.3 66.7 33.3 100.0]\n",
      " [100.0 66.7 66.7 66.7 100.0 33.3]\n",
      " [66.7 33.3 66.7 0.0 66.7 66.7]\n",
      " [66.7 100.0 66.7 66.7 33.3 66.7]\n",
      " [66.7 33.3 66.7 66.7 66.7 100.0]\n",
      " [66.7 66.7 66.7 100.0 33.3 33.3]]\n"
     ]
    }
   ],
   "source": [
    "inputs = [input_depth, input_velocity, input_hazard]\n",
    "\n",
    "mca_sum = MulticriteriAnalysis(inputs=inputs, method=Operator.SUM)\n",
    "mca_avg = MulticriteriAnalysis(inputs=inputs, method=Operator.AVERAGE)\n",
    "mca_pct = MulticriteriAnalysis(inputs=inputs, method=Operator.PERCENTAGE)\n",
    "\n",
    "result_sum = mca_sum.results.array\n",
    "result_avg = mca_avg.results.array\n",
    "result_pct = mca_pct.results.array\n",
    "\n",
    "print(\"SUM result (integer, range [0, 3]):\\n\",    result_sum)\n",
    "print(\"\\nAVERAGE result (float, range [0, 1]):\\n\", np.round(result_avg, 2))\n",
    "print(\"\\nPERCENTAGE result (%, range [0, 100]):\\n\", np.round(result_pct, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70882560",
   "metadata": {},
   "source": [
    "## 6. Visualise the combined results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e8fc1e8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n",
    "\n",
    "def plot_result(ax, data, title, vmin, vmax, fmt=\"{:.0f}\", cmap=\"YlOrRd\"):\n",
    "    im = ax.imshow(data.T, origin=\"lower\", cmap=cmap, vmin=vmin, vmax=vmax)\n",
    "    ax.set_title(title, fontsize=11)\n",
    "    plt.colorbar(im, ax=ax, shrink=0.75)\n",
    "    for i in range(data.shape[0]):\n",
    "        for j in range(data.shape[1]):\n",
    "            ax.text(i, j, fmt.format(data[i, j]), ha=\"center\", va=\"center\", fontsize=8)\n",
    "\n",
    "plot_result(axes[0], result_sum, \"SUM  [0 – 3]\",   vmin=0, vmax=3,   fmt=\"{:.0f}\")\n",
    "plot_result(axes[1], result_avg, \"AVERAGE  [0 – 1]\", vmin=0, vmax=1, fmt=\"{:.2f}\")\n",
    "plot_result(axes[2], result_pct, \"PERCENTAGE  [0 – 100 %]\", vmin=0, vmax=100, fmt=\"{:.0f}\")\n",
    "\n",
    "fig.suptitle(\n",
    "    \"Combined multi-criteria results\\n\"\n",
    "    \"(higher value = more criteria simultaneously satisfied)\",\n",
    "    fontsize=13,\n",
    ")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
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    "## 7. Key takeaways\n",
    "\n",
    "- Each input is scored **independently and binaryly**: `1` if the condition is met, `0` otherwise.\n",
    "- The final result reflects **how many criteria are simultaneously satisfied** at each spatial location.\n",
    "- The `PERCENTAGE` method is the most intuitive: `100 %` means all criteria are met, `0 %` means none.\n",
    "- Any `Operator` condition can be used per input (`SUPERIOR`, `INFERIOR`, `BETWEEN`, `OUTSIDE`, etc.).\n",
    "- A `mold` (`WolfArray`) can be supplied to transfer geo-spatial metadata (origin, resolution, CRS) onto the result.\n",
    "- 'results' is a WolfArray object, so it can be easily added to a Wolf viewer"
   ]
  }
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