{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rock Glacier Dataset: Pre-Training Data Validation & EDA\n",
    "\n",
    "This notebook validates the alignment between our 12-band Earth Engine features and the binary masks, and checks data quality before deep learning training.\n",
    "\n",
    "**Dataset Specs:**\n",
    "- CRS: `EPSG:32645` (UTM Zone 45N)\n",
    "- Resolution: 10m/pixel\n",
    "- Features: 12-band GeoTIFF (Float32/Float64)\n",
    "- Masks: 1-band GeoTIFF (Binary: 0=Background, 1=Rock Glacier)\n",
    "\n",
    "**12-Band Feature Mapping:**\n",
    "\n",
    "| Band (1-indexed) | Name        | Description                     |\n",
    "|:-----------------:|:------------|:--------------------------------|\n",
    "| 1                | B2          | Blue                            |\n",
    "| 2                | B3          | Green                           |\n",
    "| 3                | B4          | Red                             |\n",
    "| 4                | B8          | Near-Infrared (NIR)             |\n",
    "| 5                | B11         | SWIR-1                          |\n",
    "| 6                | B12         | SWIR-2                          |\n",
    "| 7                | NDVI        | Vegetation Index                |\n",
    "| 8                | NDWI        | Water Index                     |\n",
    "| 9                | NDSI        | Snow Index                      |\n",
    "| 10               | Elevation   | Copernicus DEM GLO-30 (meters)  |\n",
    "| 11               | Slope       | Terrain slope (degrees)         |\n",
    "| 12               | Aspect      | Terrain aspect (degrees)        |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Environment Setup & Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 607 feature files.\n",
      "Found 610 mask files.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import rasterio\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import seaborn as sns\n",
    "from rasterio.plot import show\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# ── Directory Configuration ──────────────────────────────────────────────────\n",
    "FEATURE_DIR  = r'D:\\RockGlacier_Project\\features'\n",
    "MASK_DIR     = r'D:\\RockGlacier_Project\\masks'\n",
    "POLYGON_DIR  = r'D:\\RockGlacier_Project\\EDA\\individual_polygons'\n",
    "\n",
    "# ── Band name lookup (1-indexed, matching rasterio .read(band)) ───────────────\n",
    "BAND_NAMES = {\n",
    "    1:  'B2 (Blue)',\n",
    "    2:  'B3 (Green)',\n",
    "    3:  'B4 (Red)',\n",
    "    4:  'B8 (NIR)',\n",
    "    5:  'B11 (SWIR-1)',\n",
    "    6:  'B12 (SWIR-2)',\n",
    "    7:  'NDVI',\n",
    "    8:  'NDWI',\n",
    "    9:  'NDSI',\n",
    "    10: 'Elevation (m)',\n",
    "    11: 'Slope (°)',\n",
    "    12: 'Aspect (°)',\n",
    "}\n",
    "\n",
    "# ── Discover files ────────────────────────────────────────────────────────────\n",
    "feature_files = sorted(glob.glob(os.path.join(FEATURE_DIR, 'feature_*.tif')))\n",
    "mask_files    = sorted(glob.glob(os.path.join(MASK_DIR,    'mask_*.tif')))\n",
    "\n",
    "print(f\"Found {len(feature_files)} feature files.\")\n",
    "print(f\"Found {len(mask_files)} mask files.\")\n",
    "\n",
    "# ── Helper: extract zero-padded index from filename ───────────────────────────\n",
    "def get_idx(filepath):\n",
    "    \"\"\"Return the XXXX index string from 'feature_XXXX.tif' or 'mask_XXXX.tif'.\"\"\"\n",
    "    return os.path.basename(filepath).split('_')[1].split('.')[0]\n",
    "\n",
    "# Build a lookup: idx -> mask_path\n",
    "mask_lookup = {get_idx(p): p for p in mask_files}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Spatial Alignment Validation\n",
    "\n",
    "Checks every `feature_XXXX.tif` / `mask_XXXX.tif` pair for identical `.shape`, `.crs`, and `.transform`.\n",
    "A mismatch here will cause silent tensor corruption in a PyTorch `Dataset` — catch it now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validating spatial alignment across all downloaded pairs...\n",
      "\n",
      "✅  Aligned pairs    : 0\n",
      "❌  Mismatched pairs : 607\n",
      "⚠️  Missing masks    : 0\n",
      "\n",
      "Mismatch details:\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file</th>\n",
       "      <th>feature_shape</th>\n",
       "      <th>mask_shape</th>\n",
       "      <th>feature_crs</th>\n",
       "      <th>mask_crs</th>\n",
       "      <th>shape_match</th>\n",
       "      <th>crs_match</th>\n",
       "      <th>transform_match</th>\n",
       "      <th>feature_bands</th>\n",
       "      <th>mask_bands</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>feature_0000.tif</td>\n",
       "      <td>(95, 84)</td>\n",
       "      <td>(92, 81)</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>feature_0001.tif</td>\n",
       "      <td>(87, 137)</td>\n",
       "      <td>(83, 134)</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>feature_0002.tif</td>\n",
       "      <td>(56, 52)</td>\n",
       "      <td>(53, 49)</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>feature_0003.tif</td>\n",
       "      <td>(74, 76)</td>\n",
       "      <td>(72, 73)</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>feature_0004.tif</td>\n",
       "      <td>(140, 137)</td>\n",
       "      <td>(136, 133)</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
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       "      <th>...</th>\n",
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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>602</th>\n",
       "      <td>feature_0620.tif</td>\n",
       "      <td>(62, 61)</td>\n",
       "      <td>(59, 58)</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>feature_0621.tif</td>\n",
       "      <td>(62, 67)</td>\n",
       "      <td>(59, 64)</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>feature_0622.tif</td>\n",
       "      <td>(53, 65)</td>\n",
       "      <td>(50, 62)</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>feature_0623.tif</td>\n",
       "      <td>(71, 64)</td>\n",
       "      <td>(68, 61)</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>feature_0624.tif</td>\n",
       "      <td>(59, 59)</td>\n",
       "      <td>(56, 55)</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>EPSG:32645</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>607 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 file feature_shape  mask_shape feature_crs    mask_crs  \\\n",
       "0    feature_0000.tif      (95, 84)    (92, 81)  EPSG:32645  EPSG:32645   \n",
       "1    feature_0001.tif     (87, 137)   (83, 134)  EPSG:32645  EPSG:32645   \n",
       "2    feature_0002.tif      (56, 52)    (53, 49)  EPSG:32645  EPSG:32645   \n",
       "3    feature_0003.tif      (74, 76)    (72, 73)  EPSG:32645  EPSG:32645   \n",
       "4    feature_0004.tif    (140, 137)  (136, 133)  EPSG:32645  EPSG:32645   \n",
       "..                ...           ...         ...         ...         ...   \n",
       "602  feature_0620.tif      (62, 61)    (59, 58)  EPSG:32645  EPSG:32645   \n",
       "603  feature_0621.tif      (62, 67)    (59, 64)  EPSG:32645  EPSG:32645   \n",
       "604  feature_0622.tif      (53, 65)    (50, 62)  EPSG:32645  EPSG:32645   \n",
       "605  feature_0623.tif      (71, 64)    (68, 61)  EPSG:32645  EPSG:32645   \n",
       "606  feature_0624.tif      (59, 59)    (56, 55)  EPSG:32645  EPSG:32645   \n",
       "\n",
       "     shape_match  crs_match  transform_match  feature_bands  mask_bands  \n",
       "0          False       True            False             12           1  \n",
       "1          False       True            False             12           1  \n",
       "2          False       True            False             12           1  \n",
       "3          False       True            False             12           1  \n",
       "4          False       True            False             12           1  \n",
       "..           ...        ...              ...            ...         ...  \n",
       "602        False       True            False             12           1  \n",
       "603        False       True            False             12           1  \n",
       "604        False       True            False             12           1  \n",
       "605        False       True            False             12           1  \n",
       "606        False       True            False             12           1  \n",
       "\n",
       "[607 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def check_alignment(feature_path, mask_path):\n",
    "    \"\"\"\n",
    "    Validate spatial alignment between a feature tile and its corresponding mask.\n",
    "    Returns (is_aligned: bool, details: dict).\n",
    "    \"\"\"\n",
    "    with rasterio.open(feature_path) as f_src, rasterio.open(mask_path) as m_src:\n",
    "        details = {\n",
    "            'feature_shape'    : f_src.shape,\n",
    "            'mask_shape'       : m_src.shape,\n",
    "            'feature_crs'      : str(f_src.crs),\n",
    "            'mask_crs'         : str(m_src.crs),\n",
    "            'shape_match'      : f_src.shape     == m_src.shape,\n",
    "            'crs_match'        : f_src.crs        == m_src.crs,\n",
    "            'transform_match'  : f_src.transform  == m_src.transform,\n",
    "            'feature_bands'    : f_src.count,\n",
    "            'mask_bands'       : m_src.count,\n",
    "        }\n",
    "        is_aligned = details['shape_match'] and details['crs_match'] and details['transform_match']\n",
    "    return is_aligned, details\n",
    "\n",
    "\n",
    "print(\"Validating spatial alignment across all downloaded pairs...\\n\")\n",
    "\n",
    "mismatches   = []\n",
    "missing_masks = []\n",
    "aligned_count = 0\n",
    "\n",
    "for f_path in feature_files:\n",
    "    idx    = get_idx(f_path)\n",
    "    m_path = mask_lookup.get(idx)\n",
    "\n",
    "    if m_path is None:\n",
    "        missing_masks.append(os.path.basename(f_path))\n",
    "        continue\n",
    "\n",
    "    is_ok, details = check_alignment(f_path, m_path)\n",
    "    if is_ok:\n",
    "        aligned_count += 1\n",
    "    else:\n",
    "        mismatches.append({'file': f'feature_{idx}.tif', **details})\n",
    "\n",
    "# ── Summary ───────────────────────────────────────────────────────────────────\n",
    "print(f\"✅  Aligned pairs    : {aligned_count}\")\n",
    "print(f\"❌  Mismatched pairs : {len(mismatches)}\")\n",
    "print(f\"⚠️  Missing masks    : {len(missing_masks)}\")\n",
    "\n",
    "if missing_masks:\n",
    "    print(\"\\nFiles with no corresponding mask:\")\n",
    "    for fname in missing_masks:\n",
    "        print(f\"  • {fname}\")\n",
    "\n",
    "if mismatches:\n",
    "    print(\"\\nMismatch details:\")\n",
    "    df_mismatch = pd.DataFrame(mismatches)\n",
    "    display(df_mismatch)\n",
    "else:\n",
    "    print(\"\\n✅  ALL feature/mask pairs are perfectly aligned — safe to build PyTorch Dataset.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Dataset Metadata Summary\n",
    "\n",
    "Quick scan of tile dimensions, dtype, pixel sizes and number of bands — useful for verifying consistent chip sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Feature Tile Metadata Summary ===\n",
      "           height       width  bands  res_x  res_y\n",
      "count  607.000000  607.000000  607.0  607.0  607.0\n",
      "mean    86.682043   89.031301   12.0   10.0   10.0\n",
      "std     30.378732   34.119050    0.0    0.0    0.0\n",
      "min     49.000000   49.000000   12.0   10.0   10.0\n",
      "25%     66.000000   67.000000   12.0   10.0   10.0\n",
      "50%     78.000000   78.000000   12.0   10.0   10.0\n",
      "75%     98.000000  102.000000   12.0   10.0   10.0\n",
      "max    296.000000  332.000000   12.0   10.0   10.0\n",
      "\n",
      "Unique tile shapes (H × W):\n",
      " height  width  count\n",
      "     49     53      1\n",
      "     50     54      1\n",
      "     51     57      1\n",
      "     52     56      1\n",
      "     52     58      1\n",
      "     52     61      1\n",
      "     52     66      1\n",
      "     52     75      1\n",
      "     53     52      1\n",
      "     53     53      1\n",
      "     53     54      1\n",
      "     53     55      1\n",
      "     53     56      1\n",
      "     53     60      1\n",
      "     53     64      1\n",
      "     53     65      1\n",
      "     54     57      1\n",
      "     54     64      1\n",
      "     54     65      1\n",
      "     55     52      1\n",
      "     55     56      1\n",
      "     55     63      1\n",
      "     55     65      1\n",
      "     55     70      1\n",
      "     56     49      1\n",
      "     56     52      1\n",
      "     56     53      1\n",
      "     56     54      1\n",
      "     56     60      1\n",
      "     56     62      2\n",
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      "     58     61      2\n",
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      "     58     63      2\n",
      "     58     68      1\n",
      "     58     69      1\n",
      "     58     70      2\n",
      "     58     71      1\n",
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      "     59     59      2\n",
      "     59     60      1\n",
      "     59     61      1\n",
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      "     59     69      2\n",
      "     59     83      1\n",
      "     59     98      1\n",
      "     60     52      1\n",
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      "     60     59      1\n",
      "     60     60      2\n",
      "     60     62      1\n",
      "     60     63      1\n",
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      "     60     67      2\n",
      "     60     68      1\n",
      "     60     71      1\n",
      "     60     75      1\n",
      "     61     51      1\n",
      "     61     54      1\n",
      "     61     60      1\n",
      "     61     61      2\n",
      "     61     62      1\n",
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      "     61     77      1\n",
      "     61     80      1\n",
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      "     62     78      1\n",
      "     62     81      2\n",
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      "     63     66      2\n",
      "     63     67      2\n",
      "     63     68      1\n",
      "     63     75      2\n",
      "     63     76      1\n",
      "     63     79      1\n",
      "     63     83      1\n",
      "     64     53      1\n",
      "     64     54      1\n",
      "     64     60      1\n",
      "     64     61      1\n",
      "     64     63      2\n",
      "     64     65      1\n",
      "     64     66      1\n",
      "     64     70      1\n",
      "     64     72      1\n",
      "     64     76      2\n",
      "     64     78      1\n",
      "     64     80      1\n",
      "     64     99      1\n",
      "     64    132      1\n",
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      "     65     67      2\n",
      "     65     69      2\n",
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      "    254    124      1\n",
      "    296    186      1\n",
      "\n",
      "Unique dtypes: <ArrowStringArray>\n",
      "['float32']\n",
      "Length: 1, dtype: str\n",
      "Unique CRS:    <ArrowStringArray>\n",
      "['EPSG:32645']\n",
      "Length: 1, dtype: str\n"
     ]
    }
   ],
   "source": [
    "records = []\n",
    "for f_path in feature_files:\n",
    "    with rasterio.open(f_path) as src:\n",
    "        records.append({\n",
    "            'file'        : os.path.basename(f_path),\n",
    "            'height'      : src.height,\n",
    "            'width'       : src.width,\n",
    "            'bands'       : src.count,\n",
    "            'dtype'       : src.dtypes[0],\n",
    "            'res_x'       : round(src.res[0], 4),\n",
    "            'res_y'       : round(src.res[1], 4),\n",
    "            'crs'         : str(src.crs),\n",
    "        })\n",
    "\n",
    "df_meta = pd.DataFrame(records)\n",
    "print(\"=== Feature Tile Metadata Summary ===\")\n",
    "print(df_meta.describe())\n",
    "\n",
    "# Check for unexpected shapes\n",
    "print(\"\\nUnique tile shapes (H × W):\")\n",
    "print(df_meta.groupby(['height', 'width']).size().reset_index(name='count').to_string(index=False))\n",
    "\n",
    "print(\"\\nUnique dtypes:\", df_meta['dtype'].unique())\n",
    "print(\"Unique CRS:   \", df_meta['crs'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Visual Overlay Inspection\n",
    "\n",
    "Three-panel figure for any sample index:\n",
    "1. **True Color RGB** (B4/B3/B2) with red polygon overlay\n",
    "2. **Ground Truth Binary Mask**\n",
    "3. **Derived Feature Heatmap** (NDVI by default — change `HEATMAP_BAND` as needed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "37eec951",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HEATMAP_BAND  = 7   # 1-indexed rasterio band  (7 = NDVI)\n",
    "HEATMAP_LABEL = BAND_NAMES[HEATMAP_BAND]\n",
    "HEATMAP_CMAP  = 'RdYlGn'\n",
    "HEATMAP_VMIN  = -0.2\n",
    "HEATMAP_VMAX  =  0.6\n",
    "\n",
    "def visualize_sample(idx_str, heatmap_band=HEATMAP_BAND):\n",
    "    \"\"\"\n",
    "    Plot a 3-panel EDA figure for tile `idx_str`.\n",
    "    Panel A: True Color RGB with GeoJSON polygon overlay\n",
    "    Panel B: Ground Truth Binary Mask\n",
    "    Panel C: Heatmap of `heatmap_band` (default: NDVI)\n",
    "    \"\"\"\n",
    "    f_path = os.path.join(FEATURE_DIR, f'feature_{idx_str}.tif')\n",
    "    m_path = os.path.join(MASK_DIR,    f'mask_{idx_str}.tif')\n",
    "    p_path = os.path.join(POLYGON_DIR, f'polygon_{int(idx_str)}.geojson')\n",
    "\n",
    "    if not os.path.exists(f_path):\n",
    "        print(f\"[SKIP] Feature not found: {f_path}\")\n",
    "        return\n",
    "    if not os.path.exists(m_path):\n",
    "        print(f\"[SKIP] Mask not found: {m_path}\")\n",
    "        return\n",
    "\n",
    "    # ── Read data ─────────────────────────────────────────────────────────────\n",
    "    with rasterio.open(f_path) as src:\n",
    "        r         = src.read(3).astype(float)   # B4 Red\n",
    "        g         = src.read(2).astype(float)   # B3 Green\n",
    "        b         = src.read(1).astype(float)   # B2 Blue\n",
    "        heatmap   = src.read(heatmap_band).astype(float)\n",
    "        raster_crs = src.crs\n",
    "\n",
    "    with rasterio.open(m_path) as src:\n",
    "        mask = src.read(1)\n",
    "\n",
    "    min_h = min(r.shape[0], mask.shape[0])\n",
    "    min_w = min(r.shape[1], mask.shape[1])\n",
    "    r, g, b = r[:min_h, :min_w], g[:min_h, :min_w], b[:min_h, :min_w]\n",
    "    heatmap = heatmap[:min_h, :min_w]\n",
    "    mask = mask[:min_h, :min_w]\n",
    "\n",
    "    # ── Normalize RGB (clip top 2% brightness) ────────────────────────────────\n",
    "    def norm_band(band):\n",
    "        p2, p98 = np.percentile(band[np.isfinite(band)], [2, 98])\n",
    "        return np.clip((band - p2) / (p98 - p2 + 1e-9), 0, 1)\n",
    "\n",
    "    rgb_norm = np.dstack([norm_band(r), norm_band(g), norm_band(b)])\n",
    "\n",
    "    # ── Mask nodata in heatmap for display ────────────────────────────────────\n",
    "    heatmap_display = np.where((heatmap == -9999) | ~np.isfinite(heatmap), np.nan, heatmap)\n",
    "\n",
    "    # ── Plot ──────────────────────────────────────────────────────────────────\n",
    "    fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
    "    fig.suptitle(f'Sample Index: {idx_str}', fontsize=14, fontweight='bold')\n",
    "\n",
    "    # Panel A – RGB\n",
    "    axes[0].imshow(rgb_norm)\n",
    "    axes[0].set_title('True Color RGB (B4/B3/B2)', fontsize=11)\n",
    "    axes[0].axis('off')\n",
    "\n",
    "    if os.path.exists(p_path):\n",
    "        try:\n",
    "            gdf = gpd.read_file(p_path).to_crs(raster_crs)\n",
    "            gdf.plot(ax=axes[0], facecolor='none', edgecolor='red', linewidth=2)\n",
    "            axes[0].set_title('True Color RGB + Polygon', fontsize=11)\n",
    "        except Exception as e:\n",
    "            print(f\"  [WARN] Could not overlay polygon for {idx_str}: {e}\")\n",
    "\n",
    "    # Panel B – Binary Mask\n",
    "    axes[1].imshow(mask, cmap='gray', vmin=0, vmax=1, interpolation='nearest')\n",
    "    axes[1].set_title('Ground Truth Binary Mask', fontsize=11)\n",
    "    axes[1].axis('off')\n",
    "    rock_pct = 100 * mask.sum() / mask.size\n",
    "    axes[1].set_xlabel(f'Rock Glacier: {rock_pct:.1f}%  |  Background: {100-rock_pct:.1f}%', fontsize=9)\n",
    "    legend_patches = [\n",
    "        mpatches.Patch(color='white', label='Rock Glacier (1)'),\n",
    "        mpatches.Patch(color='black', label='Background (0)'),\n",
    "    ]\n",
    "    axes[1].legend(handles=legend_patches, loc='lower right', fontsize=8)\n",
    "\n",
    "    # Panel C – Heatmap\n",
    "    h_label = BAND_NAMES.get(heatmap_band, f'Band {heatmap_band}')\n",
    "    im = axes[2].imshow(heatmap_display, cmap=HEATMAP_CMAP, vmin=HEATMAP_VMIN, vmax=HEATMAP_VMAX)\n",
    "    axes[2].set_title(f'{h_label} Heatmap', fontsize=11)\n",
    "    axes[2].axis('off')\n",
    "    plt.colorbar(im, ax=axes[2], shrink=0.75, label=h_label)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# ── Visualize first 3 samples ─────────────────────────────────────────────────\n",
    "for f in feature_files[:3]:\n",
    "    visualize_sample(get_idx(f))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aba36cde",
   "metadata": {},
   "source": [
    "## 5. Data Health & NoData Assessment\n",
    "\n",
    "Scan every feature tile for `NaN`, `±Inf`, or `-9999` sentinel values.\n",
    "Files flagged here should be inspected and potentially excluded from training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7fd11357",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scanning all feature tiles for data quality issues...\n",
      "\n",
      "➡ Clean files (no issues)      : 607\n",
      "➡ Files with NaN values        : 0\n",
      "➡ Files with Infinite values   : 0\n",
      "➡ Files with -9999 (NoData)    : 0\n",
      "\n",
      "✅  No data-quality issues found across the full dataset.\n"
     ]
    }
   ],
   "source": [
    "def check_data_quality(f_path, nodata_flag=-9999):\n",
    "    \"\"\"\n",
    "    Read all bands and check for NaN, Inf, or nodata sentinel values.\n",
    "    Returns dict with per-band and global diagnostics.\n",
    "    \"\"\"\n",
    "    with rasterio.open(f_path) as src:\n",
    "        data = src.read().astype(np.float64)   # shape: (bands, H, W)\n",
    "\n",
    "    diagnostics = {\n",
    "        'file'        : os.path.basename(f_path),\n",
    "        'has_nan'     : bool(np.isnan(data).any()),\n",
    "        'has_inf'     : bool(np.isinf(data).any()),\n",
    "        'has_nodata'  : bool((data == nodata_flag).any()),\n",
    "        'nan_pct'     : 100 * np.isnan(data).sum() / data.size,\n",
    "        'nodata_pct'  : 100 * (data == nodata_flag).sum() / data.size,\n",
    "    }\n",
    "    return diagnostics\n",
    "\n",
    "\n",
    "print(\"Scanning all feature tiles for data quality issues...\\n\")\n",
    "quality_records = [check_data_quality(f) for f in feature_files]\n",
    "df_quality = pd.DataFrame(quality_records)\n",
    "\n",
    "# ── Summary counts ────────────────────────────────────────────────────────────\n",
    "nan_count     = df_quality['has_nan'].sum()\n",
    "inf_count     = df_quality['has_inf'].sum()\n",
    "nodata_count  = df_quality['has_nodata'].sum()\n",
    "clean_count   = (~df_quality[['has_nan', 'has_inf', 'has_nodata']].any(axis=1)).sum()\n",
    "\n",
    "print(f\"➡ Clean files (no issues)      : {clean_count}\")\n",
    "print(f\"➡ Files with NaN values        : {nan_count}\")\n",
    "print(f\"➡ Files with Infinite values   : {inf_count}\")\n",
    "print(f\"➡ Files with -9999 (NoData)    : {nodata_count}\")\n",
    "\n",
    "# ── Show problematic files ────────────────────────────────────────────────────\n",
    "problematic = df_quality[\n",
    "    df_quality['has_nan'] | df_quality['has_inf'] | df_quality['has_nodata']\n",
    "].reset_index(drop=True)\n",
    "\n",
    "if not problematic.empty:\n",
    "    print(\"\\n⚠️  Problematic files (recommend inspection / exclusion):\")\n",
    "    display(problematic[['file', 'has_nan', 'has_inf', 'has_nodata', 'nan_pct', 'nodata_pct']])\n",
    "else:\n",
    "    print(\"\\n✅  No data-quality issues found across the full dataset.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5146cafa",
   "metadata": {},
   "source": [
    "## 6. Per-Band Statistics\n",
    "\n",
    "Compute global min / max / mean / std for all 12 bands — useful for deciding normalisation strategy (per-band z-score vs. global min-max)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "38326719",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Per-Band Statistics (sampled from 50 tiles):\n",
      "\n"
     ]
    },
    {
     "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>name</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>p5</th>\n",
       "      <th>p95</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>band</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B2 (Blue)</td>\n",
       "      <td>0.0342</td>\n",
       "      <td>1.0108</td>\n",
       "      <td>0.2902</td>\n",
       "      <td>0.1748</td>\n",
       "      <td>0.1088</td>\n",
       "      <td>0.6385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B3 (Green)</td>\n",
       "      <td>0.0497</td>\n",
       "      <td>0.9814</td>\n",
       "      <td>0.3038</td>\n",
       "      <td>0.1619</td>\n",
       "      <td>0.1299</td>\n",
       "      <td>0.6245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B4 (Red)</td>\n",
       "      <td>0.0649</td>\n",
       "      <td>0.9576</td>\n",
       "      <td>0.3145</td>\n",
       "      <td>0.1528</td>\n",
       "      <td>0.1522</td>\n",
       "      <td>0.6198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B8 (NIR)</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.8865</td>\n",
       "      <td>0.3347</td>\n",
       "      <td>0.1261</td>\n",
       "      <td>0.2055</td>\n",
       "      <td>0.5964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B11 (SWIR-1)</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>0.4996</td>\n",
       "      <td>0.2761</td>\n",
       "      <td>0.0492</td>\n",
       "      <td>0.1935</td>\n",
       "      <td>0.3477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B12 (SWIR-2)</td>\n",
       "      <td>0.0644</td>\n",
       "      <td>0.4637</td>\n",
       "      <td>0.2518</td>\n",
       "      <td>0.0434</td>\n",
       "      <td>0.1818</td>\n",
       "      <td>0.3202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NDVI</td>\n",
       "      <td>-0.2525</td>\n",
       "      <td>0.5095</td>\n",
       "      <td>0.0523</td>\n",
       "      <td>0.0760</td>\n",
       "      <td>-0.0286</td>\n",
       "      <td>0.1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NDWI</td>\n",
       "      <td>-0.5650</td>\n",
       "      <td>0.3116</td>\n",
       "      <td>-0.0799</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>-0.2638</td>\n",
       "      <td>0.0345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NDSI</td>\n",
       "      <td>-0.6708</td>\n",
       "      <td>0.8516</td>\n",
       "      <td>-0.0115</td>\n",
       "      <td>0.2808</td>\n",
       "      <td>-0.3916</td>\n",
       "      <td>0.4613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Elevation (m)</td>\n",
       "      <td>4478.2305</td>\n",
       "      <td>5918.7007</td>\n",
       "      <td>5144.2652</td>\n",
       "      <td>331.2679</td>\n",
       "      <td>4658.2959</td>\n",
       "      <td>5647.1550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Slope (°)</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>68.9936</td>\n",
       "      <td>17.9009</td>\n",
       "      <td>10.4299</td>\n",
       "      <td>3.2059</td>\n",
       "      <td>35.9434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Aspect (°)</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>359.9875</td>\n",
       "      <td>161.0213</td>\n",
       "      <td>126.3876</td>\n",
       "      <td>8.8786</td>\n",
       "      <td>351.8176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               name        min        max       mean       std         p5  \\\n",
       "band                                                                        \n",
       "1         B2 (Blue)     0.0342     1.0108     0.2902    0.1748     0.1088   \n",
       "2        B3 (Green)     0.0497     0.9814     0.3038    0.1619     0.1299   \n",
       "3          B4 (Red)     0.0649     0.9576     0.3145    0.1528     0.1522   \n",
       "4          B8 (NIR)     0.0976     0.8865     0.3347    0.1261     0.2055   \n",
       "5      B11 (SWIR-1)     0.0603     0.4996     0.2761    0.0492     0.1935   \n",
       "6      B12 (SWIR-2)     0.0644     0.4637     0.2518    0.0434     0.1818   \n",
       "7              NDVI    -0.2525     0.5095     0.0523    0.0760    -0.0286   \n",
       "8              NDWI    -0.5650     0.3116    -0.0799    0.1000    -0.2638   \n",
       "9              NDSI    -0.6708     0.8516    -0.0115    0.2808    -0.3916   \n",
       "10    Elevation (m)  4478.2305  5918.7007  5144.2652  331.2679  4658.2959   \n",
       "11        Slope (°)     0.0000    68.9936    17.9009   10.4299     3.2059   \n",
       "12       Aspect (°)    -0.0000   359.9875   161.0213  126.3876     8.8786   \n",
       "\n",
       "            p95  \n",
       "band             \n",
       "1        0.6385  \n",
       "2        0.6245  \n",
       "3        0.6198  \n",
       "4        0.5964  \n",
       "5        0.3477  \n",
       "6        0.3202  \n",
       "7        0.1987  \n",
       "8        0.0345  \n",
       "9        0.4613  \n",
       "10    5647.1550  \n",
       "11      35.9434  \n",
       "12     351.8176  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_BANDS     = 12\n",
    "SAMPLE_LIMIT = 50    # cap for speed — set to None to use full dataset\n",
    "\n",
    "sample_files = feature_files[:SAMPLE_LIMIT] if SAMPLE_LIMIT else feature_files\n",
    "\n",
    "band_pixels = {b: [] for b in range(1, N_BANDS + 1)}\n",
    "\n",
    "for f_path in sample_files:\n",
    "    with rasterio.open(f_path) as src:\n",
    "        for b in range(1, N_BANDS + 1):\n",
    "            arr = src.read(b).astype(np.float32).flatten()\n",
    "            valid = arr[(arr != -9999) & np.isfinite(arr)]\n",
    "            band_pixels[b].extend(valid.tolist())\n",
    "\n",
    "stats_records = []\n",
    "for b in range(1, N_BANDS + 1):\n",
    "    vals = np.array(band_pixels[b])\n",
    "    stats_records.append({\n",
    "        'band'  : b,\n",
    "        'name'  : BAND_NAMES[b],\n",
    "        'min'   : vals.min(),\n",
    "        'max'   : vals.max(),\n",
    "        'mean'  : vals.mean(),\n",
    "        'std'   : vals.std(),\n",
    "        'p5'    : np.percentile(vals, 5),\n",
    "        'p95'   : np.percentile(vals, 95),\n",
    "    })\n",
    "\n",
    "df_stats = pd.DataFrame(stats_records).set_index('band')\n",
    "print(f\"Per-Band Statistics (sampled from {len(sample_files)} tiles):\\n\")\n",
    "display(df_stats.round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42965fad",
   "metadata": {},
   "source": [
    "## 7. Class Separability Analysis\n",
    "\n",
    "Pixel-level KDE plots comparing feature distributions for **Rock Glacier (Class 1)** vs **Background (Class 0)**.\n",
    "Good separation → that feature is highly discriminative for the segmentation model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "29c45002",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SKIP] Shape mismatch for idx=0000: feature=(95, 84) mask=(92, 81)\n",
      "[SKIP] Shape mismatch for idx=0001: feature=(87, 137) mask=(83, 134)\n",
      "[SKIP] Shape mismatch for idx=0002: feature=(56, 52) mask=(53, 49)\n",
      "[SKIP] Shape mismatch for idx=0003: feature=(74, 76) mask=(72, 73)\n",
      "[SKIP] Shape mismatch for idx=0004: feature=(140, 137) mask=(136, 133)\n",
      "[SKIP] Shape mismatch for idx=0005: feature=(77, 127) mask=(72, 124)\n",
      "[SKIP] Shape mismatch for idx=0006: feature=(109, 118) mask=(105, 114)\n",
      "[SKIP] Shape mismatch for idx=0007: feature=(84, 107) mask=(81, 104)\n",
      "[SKIP] Shape mismatch for idx=0008: feature=(69, 59) mask=(67, 56)\n",
      "[SKIP] Shape mismatch for idx=0009: feature=(71, 67) mask=(68, 64)\n",
      "[SKIP] Shape mismatch for idx=0010: feature=(66, 60) mask=(63, 57)\n",
      "[SKIP] Shape mismatch for idx=0011: feature=(95, 67) mask=(92, 64)\n",
      "[SKIP] Shape mismatch for idx=0012: feature=(92, 89) mask=(89, 85)\n",
      "[SKIP] Shape mismatch for idx=0013: feature=(103, 117) mask=(99, 114)\n",
      "[SKIP] Shape mismatch for idx=0014: feature=(128, 125) mask=(124, 121)\n",
      "[SKIP] Shape mismatch for idx=0015: feature=(87, 117) mask=(83, 114)\n",
      "[SKIP] Shape mismatch for idx=0016: feature=(98, 108) mask=(95, 105)\n",
      "[SKIP] Shape mismatch for idx=0017: feature=(84, 103) mask=(80, 99)\n",
      "[SKIP] Shape mismatch for idx=0018: feature=(88, 82) mask=(84, 79)\n",
      "[SKIP] Shape mismatch for idx=0019: feature=(95, 83) mask=(92, 80)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Kolmogorov-Smirnov Statistics (0→1, higher is better separation):\n",
      "  Band  7 (NDVI              ): skipped (no valid pixels)\n",
      "  Band 10 (Elevation (m)     ): skipped (no valid pixels)\n",
      "  Band 11 (Slope (°)         ): skipped (no valid pixels)\n",
      "  Band  9 (NDSI              ): skipped (no valid pixels)\n"
     ]
    }
   ],
   "source": [
    "# Bands to compare (1-indexed)\n",
    "COMPARE_BANDS = [7, 10, 11, 9]   # NDVI, Elevation, Slope, NDSI\n",
    "MAX_SAMPLES   = 20               # number of tiles to sample (memory control)\n",
    "\n",
    "pixel_data = {b: {'rock': [], 'bg': []} for b in COMPARE_BANDS}\n",
    "\n",
    "for f_path in feature_files[:MAX_SAMPLES]:\n",
    "    idx    = get_idx(f_path)\n",
    "    m_path = mask_lookup.get(idx)\n",
    "    if m_path is None:\n",
    "        continue\n",
    "\n",
    "    with rasterio.open(f_path) as f_src, rasterio.open(m_path) as m_src:\n",
    "        # ── Guard: skip pairs whose pixel grids don't match ──────────────────\n",
    "        min_h = min(f_src.shape[0], m_src.shape[0])\n",
    "        min_w = min(f_src.shape[1], m_src.shape[1])\n",
    "\n",
    "        mask_flat = m_src.read(1)[:min_h, :min_w].flatten()\n",
    "\n",
    "        for b in COMPARE_BANDS:\n",
    "            arr   = f_src.read(b)[:min_h, :min_w].astype(np.float32).flatten()\n",
    "            valid = (arr != -9999) & np.isfinite(arr)\n",
    "            pixel_data[b]['rock'].extend(arr[valid & (mask_flat == 1)].tolist())\n",
    "            pixel_data[b]['bg'  ].extend(arr[valid & (mask_flat == 0)].tolist())\n",
    "\n",
    "# ── KDE Plots ─────────────────────────────────────────────────────────────────\n",
    "n_cols = 2\n",
    "n_rows = int(np.ceil(len(COMPARE_BANDS) / n_cols))\n",
    "fig, axes = plt.subplots(n_rows, n_cols, figsize=(14, 5 * n_rows))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, b in enumerate(COMPARE_BANDS):\n",
    "    ax = axes[i]\n",
    "    rock_arr = np.array(pixel_data[b]['rock'])\n",
    "    bg_arr   = np.array(pixel_data[b]['bg'])\n",
    "\n",
    "    if len(rock_arr) == 0 or len(bg_arr) == 0:\n",
    "        ax.set_title(f'{BAND_NAMES[b]}  |  No valid pixels found')\n",
    "        ax.axis('off')\n",
    "        continue\n",
    "\n",
    "    # Clip extreme outliers for readability\n",
    "    p1, p99  = np.percentile(np.concatenate([rock_arr, bg_arr]), [1, 99])\n",
    "    rock_arr = rock_arr[(rock_arr >= p1) & (rock_arr <= p99)]\n",
    "    bg_arr   = bg_arr  [(bg_arr   >= p1) & (bg_arr   <= p99)]\n",
    "\n",
    "    sns.kdeplot(rock_arr, fill=True, color='tomato',    label=f'Rock Glacier (n={len(rock_arr):,})', ax=ax, alpha=0.6)\n",
    "    sns.kdeplot(bg_arr,   fill=True, color='steelblue', label=f'Background  (n={len(bg_arr):,})',   ax=ax, alpha=0.6)\n",
    "\n",
    "    ks_stat, _ = stats.ks_2samp(rock_arr[:5000], bg_arr[:5000])\n",
    "    ax.set_title(f'{BAND_NAMES[b]}  |  KS stat = {ks_stat:.3f}', fontsize=11)\n",
    "    ax.set_xlabel(BAND_NAMES[b])\n",
    "    ax.set_ylabel('Density')\n",
    "    ax.legend(fontsize=8)\n",
    "    ax.grid(True, alpha=0.3)\n",
    "\n",
    "for j in range(len(COMPARE_BANDS), len(axes)):\n",
    "    axes[j].set_visible(False)\n",
    "\n",
    "plt.suptitle('Class Separability: Rock Glacier vs Background\\n(higher KS stat = more discriminative feature)',\n",
    "             fontsize=13, fontweight='bold', y=1.01)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nKolmogorov-Smirnov Statistics (0→1, higher is better separation):\")\n",
    "for b in COMPARE_BANDS:\n",
    "    rock_arr = np.array(pixel_data[b]['rock'])[:5000]\n",
    "    bg_arr   = np.array(pixel_data[b]['bg'])[:5000]\n",
    "    if len(rock_arr) == 0 or len(bg_arr) == 0:\n",
    "        print(f\"  Band {b:>2} ({BAND_NAMES[b]:<18}): skipped (no valid pixels)\")\n",
    "        continue\n",
    "    ks, _ = stats.ks_2samp(rock_arr, bg_arr)\n",
    "    print(f\"  Band {b:>2} ({BAND_NAMES[b]:<18}): KS = {ks:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Class Imbalance Calculation & Loss Recommendation\n",
    "\n",
    "Global pixel-level class ratio across the **entire** mask dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======================================================\n",
      "       GLOBAL CLASS IMBALANCE REPORT\n",
      "=======================================================\n",
      "  Rock Glacier pixels (Class 1) :      890,744  (18.31%)\n",
      "  Background pixels   (Class 0) :    3,974,590  (81.69%)\n",
      "  Imbalance ratio  (BG / Rock)  : 4.5:1\n",
      "=======================================================\n",
      "\n",
      "📋  Training Recommendation:\n",
      "  🟢 ACCEPTABLE balance (>15% positive pixels).\n",
      "     → Standard BCE Loss is viable; Focal/Dice may still improve boundary precision.\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_rock_pixels = 0\n",
    "total_bg_pixels   = 0\n",
    "per_tile_ratios   = []\n",
    "\n",
    "for m_path in mask_files:\n",
    "    with rasterio.open(m_path) as src:\n",
    "        mask = src.read(1)\n",
    "    n_rock = int(np.sum(mask == 1))\n",
    "    n_bg   = int(np.sum(mask == 0))\n",
    "    total_rock_pixels += n_rock\n",
    "    total_bg_pixels   += n_bg\n",
    "    total = n_rock + n_bg\n",
    "    per_tile_ratios.append(100 * n_rock / total if total > 0 else 0)\n",
    "\n",
    "overall_total = total_rock_pixels + total_bg_pixels\n",
    "rock_ratio    = 100 * total_rock_pixels / overall_total if overall_total > 0 else 0\n",
    "bg_ratio      = 100 * total_bg_pixels   / overall_total if overall_total > 0 else 0\n",
    "imbalance_ratio = total_bg_pixels / total_rock_pixels if total_rock_pixels > 0 else float('inf')\n",
    "\n",
    "print(\"=\" * 55)\n",
    "print(\"       GLOBAL CLASS IMBALANCE REPORT\")\n",
    "print(\"=\" * 55)\n",
    "print(f\"  Rock Glacier pixels (Class 1) : {total_rock_pixels:>12,}  ({rock_ratio:.2f}%)\")\n",
    "print(f\"  Background pixels   (Class 0) : {total_bg_pixels:>12,}  ({bg_ratio:.2f}%)\")\n",
    "print(f\"  Imbalance ratio  (BG / Rock)  : {imbalance_ratio:.1f}:1\")\n",
    "print(\"=\" * 55)\n",
    "\n",
    "# ── Recommendation ────────────────────────────────────────────────────────────\n",
    "print(\"\\n📋  Training Recommendation:\")\n",
    "if rock_ratio < 5:\n",
    "    print(\"  🔴 SEVERE imbalance (<5% positive pixels).\")\n",
    "    print(\"     → Use Focal Loss (γ=2–4) + Dice Loss combined (e.g. 0.5/0.5).\")\n",
    "    print(\"     → Consider patch-level oversampling: oversample tiles where mask=1.\")\n",
    "    print(\"     → Set pos_weight in BCEWithLogitsLoss to imbalance_ratio as fallback.\")\n",
    "elif rock_ratio < 15:\n",
    "    print(\"  🟠 MODERATE imbalance (<15% positive pixels).\")\n",
    "    print(\"     → Focal Loss (γ=2) OR Dice Loss is recommended.\")\n",
    "    print(\"     → Standard BCE Loss will under-predict rock glaciers.\")\n",
    "else:\n",
    "    print(\"  🟢 ACCEPTABLE balance (>15% positive pixels).\")\n",
    "    print(\"     → Standard BCE Loss is viable; Focal/Dice may still improve boundary precision.\")\n",
    "\n",
    "# ── Histogram of per-tile rock glacier % ─────────────────────────────────────\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "axes[0].bar(['Rock Glacier\\n(Class 1)', 'Background\\n(Class 0)'],\n",
    "            [rock_ratio, bg_ratio], color=['tomato', 'steelblue'], edgecolor='white')\n",
    "axes[0].set_ylabel('% of total pixels')\n",
    "axes[0].set_title('Global Class Distribution')\n",
    "axes[0].set_ylim(0, 100)\n",
    "for rect, val in zip(axes[0].patches, [rock_ratio, bg_ratio]):\n",
    "    axes[0].text(rect.get_x() + rect.get_width()/2, rect.get_height() + 1,\n",
    "                 f'{val:.2f}%', ha='center', fontsize=11)\n",
    "\n",
    "axes[1].hist(per_tile_ratios, bins=30, color='tomato', edgecolor='white', alpha=0.85)\n",
    "axes[1].axvline(np.median(per_tile_ratios), color='black', linestyle='--',\n",
    "                label=f'Median = {np.median(per_tile_ratios):.1f}%')\n",
    "axes[1].set_xlabel('Rock Glacier % per tile')\n",
    "axes[1].set_ylabel('Number of tiles')\n",
    "axes[1].set_title('Per-Tile Rock Glacier Pixel Fraction')\n",
    "axes[1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Inter-Band Correlation Heatmap\n",
    "\n",
    "Pearson correlation between all 12 bands — helps identify redundant features (e.g. B11/B12 are often highly correlated) and informs potential feature reduction or ablation strategies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CORR_SAMPLE   = 10    # number of tiles\n",
    "MAX_PIXELS    = 5000  # pixels per tile to keep memory low\n",
    "\n",
    "pixel_matrix = []\n",
    "\n",
    "for f_path in feature_files[:CORR_SAMPLE]:\n",
    "    with rasterio.open(f_path) as src:\n",
    "        bands = [src.read(b).astype(np.float32).flatten() for b in range(1, N_BANDS + 1)]\n",
    "\n",
    "    tile_mat = np.stack(bands, axis=1)  # shape: (H*W, 12)\n",
    "    # Filter out rows with nodata / nan in any band\n",
    "    valid_mask = np.all((tile_mat != -9999) & np.isfinite(tile_mat), axis=1)\n",
    "    tile_mat   = tile_mat[valid_mask]\n",
    "    if len(tile_mat) > MAX_PIXELS:\n",
    "        tile_mat = tile_mat[np.random.choice(len(tile_mat), MAX_PIXELS, replace=False)]\n",
    "    pixel_matrix.append(tile_mat)\n",
    "\n",
    "all_pixels = np.vstack(pixel_matrix)   # shape: (N, 12)\n",
    "corr_matrix = np.corrcoef(all_pixels.T)\n",
    "\n",
    "df_corr = pd.DataFrame(corr_matrix,\n",
    "                        index  =[BAND_NAMES[b] for b in range(1, N_BANDS + 1)],\n",
    "                        columns=[BAND_NAMES[b] for b in range(1, N_BANDS + 1)])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 10))\n",
    "mask_tri = np.triu(np.ones_like(corr_matrix, dtype=bool), k=1)\n",
    "sns.heatmap(df_corr, mask=mask_tri, annot=True, fmt='.2f', cmap='coolwarm',\n",
    "            center=0, vmin=-1, vmax=1, linewidths=0.5, ax=ax,\n",
    "            annot_kws={'size': 8})\n",
    "ax.set_title('Inter-Band Pearson Correlation (lower triangle)', fontsize=13, fontweight='bold')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. EDA Summary & Pre-Training Checklist\n",
    "\n",
    "Final readiness check before building the PyTorch `Dataset`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╔══════════════════════════════════════════════════════╗\n",
      "║         PRE-TRAINING EDA CHECKLIST                  ║\n",
      "╠══════════════════════════════════════════════════════╣\n",
      "║  ❌  Spatial alignment (shape/CRS/transform)      ║\n",
      "║  ✅  No missing mask files                         ║\n",
      "║  ✅  No NaN / Inf / NoData in feature tiles      ║\n",
      "║  ✅  Class imbalance quantified                    ║\n",
      "╠══════════════════════════════════════════════════════╣\n",
      "║  Total valid pairs  : 0                              ║\n",
      "║  Rock Glacier ratio : 18.31%                            ║\n",
      "║  Imbalance ratio    : 4.5:1 (BG:Rock)                    ║\n",
      "╠══════════════════════════════════════════════════════╣\n",
      "║  RECOMMENDED LOSS FUNCTIONS:                        ║\n",
      "║  → BCE Loss viable; Focal/Dice for better boundaries║\n",
      "╚══════════════════════════════════════════════════════╝\n"
     ]
    }
   ],
   "source": [
    "# Re-compute flags from previous cells\n",
    "has_mismatches   = len(mismatches) > 0\n",
    "has_missing      = len(missing_masks) > 0\n",
    "has_quality_flag = not problematic.empty if 'problematic' in dir() else False\n",
    "\n",
    "print(\"╔══════════════════════════════════════════════════════╗\")\n",
    "print(\"║         PRE-TRAINING EDA CHECKLIST                  ║\")\n",
    "print(\"╠══════════════════════════════════════════════════════╣\")\n",
    "print(f\"║  {'✅' if not has_mismatches else '❌'}  Spatial alignment (shape/CRS/transform)      ║\")\n",
    "print(f\"║  {'✅' if not has_missing   else '❌'}  No missing mask files                         ║\")\n",
    "print(f\"║  {'✅' if not has_quality_flag else '⚠️ '}  No NaN / Inf / NoData in feature tiles      ║\")\n",
    "print(f\"║  {'✅' if rock_ratio > 0    else '❌'}  Class imbalance quantified                    ║\")\n",
    "print(\"╠══════════════════════════════════════════════════════╣\")\n",
    "print(f\"║  Total valid pairs  : {aligned_count:<30} ║\")\n",
    "print(f\"║  Rock Glacier ratio : {rock_ratio:.2f}%{'':<27} ║\")\n",
    "print(f\"║  Imbalance ratio    : {imbalance_ratio:.1f}:1 (BG:Rock){'':<19} ║\")\n",
    "print(\"╠══════════════════════════════════════════════════════╣\")\n",
    "print(\"║  RECOMMENDED LOSS FUNCTIONS:                        ║\")\n",
    "if rock_ratio < 5:\n",
    "    print(\"║  → Focal Loss (γ=2–4) + Dice Loss (0.5/0.5)        ║\")\n",
    "elif rock_ratio < 15:\n",
    "    print(\"║  → Focal Loss (γ=2) or Dice Loss                    ║\")\n",
    "else:\n",
    "    print(\"║  → BCE Loss viable; Focal/Dice for better boundaries║\")\n",
    "print(\"╚══════════════════════════════════════════════════════╝\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a19fde7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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