polygon_mask.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d2dd8a4a-f52c-479c-a4fa-142f228d065f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Masked raster saved to masked_NEON_D13_NIWO_DP1_20200801_155424_reflectance.dat\n"
     ]
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "import rasterio\n",
    "from rasterio.features import geometry_mask\n",
    "import numpy as np\n",
    "\n",
    "# Input file paths\n",
    "geojson_path = \"aop_macrosystems_data_1_7_25.geojson\"\n",
    "envi_raster_path = \"non_provisional_flightlines/NEON_D13_NIWO_DP1_20200801_155424_reflectance/NEON_D13_NIWO_DP1_20200801_155424_reflectance\"\n",
    "output_masked_raster_path = \"masked_NEON_D13_NIWO_DP1_20200801_155424_reflectance.dat\"\n",
    "\n",
    "# Load the GeoJSON file with polygons\n",
    "polygons = gpd.read_file(geojson_path)\n",
    "\n",
    "# Open the ENVI raster file\n",
    "with rasterio.open(envi_raster_path) as src:\n",
    "    # Read the metadata and data\n",
    "    meta = src.meta.copy()\n",
    "    raster_data = src.read(1)  # Assuming single-band raster, adjust if multi-band\n",
    "    \n",
    "    # Create a mask based on the GeoJSON polygons\n",
    "    mask = geometry_mask(\n",
    "        [geom for geom in polygons.geometry],\n",
    "        transform=src.transform,\n",
    "        invert=True,  # Keep pixels inside the polygons\n",
    "        out_shape=(src.height, src.width),\n",
    "    )\n",
    "    \n",
    "    # Apply the mask to the raster data\n",
    "    masked_raster = np.where(mask, raster_data, np.nan)  # Set masked areas to NaN\n",
    "\n",
    "# Update metadata for the output raster\n",
    "meta.update(dtype='float32', nodata=np.nan)\n",
    "\n",
    "# Save the masked raster to a new ENVI file\n",
    "with rasterio.open(output_masked_raster_path, 'w', **meta) as dst:\n",
    "    dst.write(masked_raster.astype('float32'), 1)\n",
    "\n",
    "print(f\"Masked raster saved to {output_masked_raster_path}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0b5de4a3-6e67-4716-9e24-2f8f048f360a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "AttributeError",
     "evalue": "'MultiPolygon' object has no attribute 'exterior'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[14], line 26\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m geom \u001b[38;5;129;01min\u001b[39;00m polygons\u001b[38;5;241m.\u001b[39mgeometry:\n\u001b[1;32m     25\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m geom\u001b[38;5;241m.\u001b[39mis_empty:\n\u001b[0;32m---> 26\u001b[0m         x, y \u001b[38;5;241m=\u001b[39m \u001b[43mgeom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexterior\u001b[49m\u001b[38;5;241m.\u001b[39mxy  \u001b[38;5;66;03m# Extract exterior boundary of the polygon\u001b[39;00m\n\u001b[1;32m     27\u001b[0m         plt\u001b[38;5;241m.\u001b[39mplot(x, y, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mred\u001b[39m\u001b[38;5;124m\"\u001b[39m, linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPolygon Overlay\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     29\u001b[0m \u001b[38;5;66;03m# Add legend and display\u001b[39;00m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'MultiPolygon' object has no attribute 'exterior'"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import rasterio\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "from rasterio.plot import show\n",
    "\n",
    "# File paths\n",
    "geojson_path = \"aop_macrosystems_data_1_7_25.geojson\"\n",
    "masked_raster_path = \"non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi_resample_MicaSense-to-match_OLI_and_OLI-2_masked\"\n",
    "\n",
    "# Load the GeoJSON file\n",
    "polygons = gpd.read_file(geojson_path)\n",
    "\n",
    "# Open the masked raster file\n",
    "with rasterio.open(masked_raster_path) as src:\n",
    "    # Read the first band for visualization (adjust if needed)\n",
    "    raster_data = src.read(1)\n",
    "    \n",
    "    # Plot the raster data\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    show(raster_data, transform=src.transform, cmap=\"viridis\")\n",
    "    plt.title(\"Masked Raster with GeoJSON Polygons\")\n",
    "    \n",
    "    # Overlay the polygons\n",
    "    for geom in polygons.geometry:\n",
    "        if not geom.is_empty:\n",
    "            x, y = geom.exterior.xy  # Extract exterior boundary of the polygon\n",
    "            plt.plot(x, y, color=\"red\", linewidth=1, label=\"Polygon Overlay\")\n",
    "    \n",
    "    # Add legend and display\n",
    "    plt.legend([\"GeoJSON Polygons\"])\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d5a6fe27-a5f4-48ce-ace9-373eceea9b5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 8 file(s) to process in non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance\n",
      "Processing file: non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi\n",
      "Masked file saved to: non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi_masked\n",
      "Processing file: non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi_resample_Landsat_5_TM.img\n",
      "Adopting georeference from non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/macrosystems/lib/python3.10/site-packages/rasterio/__init__.py:304: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n",
      "  dataset = DatasetReader(path, driver=driver, sharing=sharing, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Masked file saved to: non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi_resample_Landsat_5_TM_masked\n",
      "Processing file: non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi_resample_Landsat_7_ETMplus.img\n",
      "Adopting georeference from non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/macrosystems/lib/python3.10/site-packages/rasterio/__init__.py:304: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n",
      "  dataset = DatasetReader(path, driver=driver, sharing=sharing, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Masked file saved to: non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi_resample_Landsat_7_ETMplus_masked\n",
      "Processing file: non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi_resample_Landsat_8_OLI.img\n",
      "Adopting georeference from non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/macrosystems/lib/python3.10/site-packages/rasterio/__init__.py:304: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n",
      "  dataset = DatasetReader(path, driver=driver, sharing=sharing, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Masked file saved to: non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi_resample_Landsat_8_OLI_masked\n",
      "Processing file: non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi_resample_Landsat_9_OLI-2.img\n",
      "Adopting georeference from non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/macrosystems/lib/python3.10/site-packages/rasterio/__init__.py:304: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n",
      "  dataset = DatasetReader(path, driver=driver, sharing=sharing, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Masked file saved to: non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi_resample_Landsat_9_OLI-2_masked\n",
      "Processing file: non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi_resample_MicaSense.img\n",
      "Adopting georeference from non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/macrosystems/lib/python3.10/site-packages/rasterio/__init__.py:304: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n",
      "  dataset = DatasetReader(path, driver=driver, sharing=sharing, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Masked file saved to: non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi_resample_MicaSense_masked\n",
      "Processing file: non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi_resample_MicaSense-to-match_TM_and_ETMplus.img\n",
      "Adopting georeference from non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/macrosystems/lib/python3.10/site-packages/rasterio/__init__.py:304: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n",
      "  dataset = DatasetReader(path, driver=driver, sharing=sharing, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Masked file saved to: non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi_resample_MicaSense-to-match_TM_and_ETMplus_masked\n",
      "Processing file: non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi_resample_MicaSense-to-match_OLI_and_OLI-2.img\n",
      "Adopting georeference from non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/macrosystems/lib/python3.10/site-packages/rasterio/__init__.py:304: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n",
      "  dataset = DatasetReader(path, driver=driver, sharing=sharing, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Masked file saved to: non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance/NEON_D16_WREF_DP1_20210719_174212_reflectance__envi_resample_MicaSense-to-match_OLI_and_OLI-2_masked\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import rasterio\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "from rasterio.features import geometry_mask\n",
    "\n",
    "def find_reference_envi_file(envi_files, derived_file):\n",
    "    \"\"\"\n",
    "    Finds the reference __envi file for a given derived file.\n",
    "\n",
    "    Parameters:\n",
    "    - envi_files (list): List of all __envi files in the folder.\n",
    "    - derived_file (str): The derived file (e.g., .img) for which to find the reference.\n",
    "\n",
    "    Returns:\n",
    "    - str: Path to the reference __envi file, or None if not found.\n",
    "    \"\"\"\n",
    "    base_name = os.path.basename(derived_file).split(\"_resample\")[0]\n",
    "    for envi_file in envi_files:\n",
    "        if base_name in envi_file:\n",
    "            return envi_file\n",
    "    return None\n",
    "\n",
    "def apply_mask_to_envi_files(folder_path, geojson_path, suffix='_masked'):\n",
    "    \"\"\"\n",
    "    Applies a mask to all ENVI files (including .img files) in a folder and saves\n",
    "    the masked output next to the original files, excluding the .img extension.\n",
    "\n",
    "    Parameters:\n",
    "    - folder_path (str): Path to the folder containing ENVI files.\n",
    "    - geojson_path (str): Path to the GeoJSON file with polygons for masking.\n",
    "    - suffix (str): Suffix to append to the masked file names.\n",
    "    \"\"\"\n",
    "    # Load polygons from GeoJSON\n",
    "    polygons = gpd.read_file(geojson_path)\n",
    "\n",
    "    # Find ENVI files including those with .img extensions\n",
    "    envi_files = glob.glob(os.path.join(folder_path, '*_envi'))\n",
    "    derived_files = glob.glob(os.path.join(folder_path, '*.img'))\n",
    "    all_files = envi_files + derived_files\n",
    "\n",
    "    if not all_files:\n",
    "        print(f\"No ENVI or .img files found in folder: {folder_path}\")\n",
    "        return\n",
    "\n",
    "    print(f\"Found {len(all_files)} file(s) to process in {folder_path}\")\n",
    "\n",
    "    for envi_file in all_files:\n",
    "        try:\n",
    "            print(f\"Processing file: {envi_file}\")\n",
    "\n",
    "            # Open the ENVI file\n",
    "            with rasterio.open(envi_file) as src:\n",
    "                if envi_file.endswith('.img'):\n",
    "                    # Find corresponding __envi file for georeferencing\n",
    "                    reference_envi_file = find_reference_envi_file(envi_files, envi_file)\n",
    "                    if reference_envi_file:\n",
    "                        print(f\"Adopting georeference from {reference_envi_file}\")\n",
    "                        with rasterio.open(reference_envi_file) as ref_src:\n",
    "                            transform = ref_src.transform\n",
    "                            crs = ref_src.crs\n",
    "                    else:\n",
    "                        print(f\"Warning: No reference ENVI file found for {envi_file}. Using default georeference.\")\n",
    "                        transform = src.transform\n",
    "                        crs = src.crs\n",
    "                else:\n",
    "                    transform = src.transform\n",
    "                    crs = src.crs\n",
    "\n",
    "                # Read metadata and data\n",
    "                meta = src.meta.copy()\n",
    "                meta.update(transform=transform, crs=crs)\n",
    "                raster_data = src.read(1)  # Assuming single-band raster, adjust if multi-band\n",
    "\n",
    "                # Create a mask based on GeoJSON polygons\n",
    "                mask = geometry_mask(\n",
    "                    [geom for geom in polygons.geometry],\n",
    "                    transform=transform,\n",
    "                    invert=True,  # Keep pixels inside the polygons\n",
    "                    out_shape=(src.height, src.width),\n",
    "                )\n",
    "\n",
    "                # Apply the mask\n",
    "                masked_data = np.where(mask, raster_data, np.nan)\n",
    "\n",
    "            # Update metadata for the output file\n",
    "            meta.update(dtype='float32', nodata=np.nan)\n",
    "\n",
    "            # Define the output path, removing the .img extension if present\n",
    "            base_name = os.path.splitext(envi_file)[0]\n",
    "            output_file = f\"{base_name}{suffix}\"\n",
    "\n",
    "            # Save the masked raster\n",
    "            with rasterio.open(output_file, 'w', **meta) as dst:\n",
    "                dst.write(masked_data.astype('float32'), 1)\n",
    "\n",
    "            print(f\"Masked file saved to: {output_file}\")\n",
    "\n",
    "        except Exception as e:\n",
    "            print(f\"Error processing {envi_file}: {e}\")\n",
    "\n",
    "\n",
    "# Example Usage\n",
    "folder_path = \"non_provisional_flightlines/NEON_D16_WREF_DP1_20210719_174212_reflectance\"\n",
    "geojson_path = \"aop_macrosystems_data_1_7_25.geojson\"\n",
    "apply_mask_to_envi_files(folder_path, geojson_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ffde5f6d-0e15-45d3-9112-d18051ed8b66",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input shapes do not overlap raster.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mWindowError\u001b[0m                               Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/macrosystems/lib/python3.10/site-packages/rasterio/mask.py:80\u001b[0m, in \u001b[0;36mraster_geometry_mask\u001b[0;34m(dataset, shapes, all_touched, invert, crop, pad, pad_width)\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 80\u001b[0m     window \u001b[38;5;241m=\u001b[39m \u001b[43mgeometry_window\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshapes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpad_x\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpad_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpad_y\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpad_y\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     82\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m WindowError:\n\u001b[1;32m     83\u001b[0m     \u001b[38;5;66;03m# If shapes do not overlap raster, raise Exception or UserWarning\u001b[39;00m\n\u001b[1;32m     84\u001b[0m     \u001b[38;5;66;03m# depending on value of crop\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/macrosystems/lib/python3.10/site-packages/rasterio/features.py:477\u001b[0m, in \u001b[0;36mgeometry_window\u001b[0;34m(dataset, shapes, pad_x, pad_y, north_up, rotated, pixel_precision, boundless)\u001b[0m\n\u001b[1;32m    476\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m boundless:\n\u001b[0;32m--> 477\u001b[0m     window \u001b[38;5;241m=\u001b[39m \u001b[43mwindow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintersection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mraster_window\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    479\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m window\n",
      "File \u001b[0;32m/opt/conda/envs/macrosystems/lib/python3.10/site-packages/rasterio/windows.py:775\u001b[0m, in \u001b[0;36mWindow.intersection\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    763\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return the intersection of this window and another\u001b[39;00m\n\u001b[1;32m    764\u001b[0m \n\u001b[1;32m    765\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    773\u001b[0m \u001b[38;5;124;03mWindow\u001b[39;00m\n\u001b[1;32m    774\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 775\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mintersection\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/macrosystems/lib/python3.10/site-packages/rasterio/windows.py:125\u001b[0m, in \u001b[0;36miter_args.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(args[\u001b[38;5;241m0\u001b[39m], Iterable):\n\u001b[0;32m--> 125\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    126\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/macrosystems/lib/python3.10/site-packages/rasterio/windows.py:239\u001b[0m, in \u001b[0;36mintersection\u001b[0;34m(*windows)\u001b[0m\n\u001b[1;32m    226\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Innermost extent of window intersections.\u001b[39;00m\n\u001b[1;32m    227\u001b[0m \n\u001b[1;32m    228\u001b[0m \u001b[38;5;124;03mWill raise WindowError if windows do not intersect.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    237\u001b[0m \u001b[38;5;124;03mWindow\u001b[39;00m\n\u001b[1;32m    238\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 239\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunctools\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_intersection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwindows\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/macrosystems/lib/python3.10/site-packages/rasterio/windows.py:257\u001b[0m, in \u001b[0;36m_intersection\u001b[0;34m(w1, w2)\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 257\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m WindowError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIntersection is empty \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mw1\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mw2\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mWindowError\u001b[0m: Intersection is empty Window(col_off=8038, row_off=5638, width=13, height=28) Window(col_off=0, row_off=0, width=10992, height=1400)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[25], line 71\u001b[0m\n\u001b[1;32m     68\u001b[0m geojson_file \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maop_macrosystems_data_1_7_25.geojson\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     69\u001b[0m output_file \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtester_output_plot.png\u001b[39m\u001b[38;5;124m\"\u001b[39m  \u001b[38;5;66;03m# Optional\u001b[39;00m\n\u001b[0;32m---> 71\u001b[0m \u001b[43mplot_preserved_polygons\u001b[49m\u001b[43m(\u001b[49m\u001b[43menvi_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgeojson_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_file\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[16], line 37\u001b[0m, in \u001b[0;36mplot_preserved_polygons\u001b[0;34m(envi_file, geojson_file, output_file)\u001b[0m\n\u001b[1;32m     35\u001b[0m preserved_polygons \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m geom \u001b[38;5;129;01min\u001b[39;00m polygons\u001b[38;5;241m.\u001b[39mgeometry:\n\u001b[0;32m---> 37\u001b[0m     geom_mask, _ \u001b[38;5;241m=\u001b[39m \u001b[43mmask\u001b[49m\u001b[43m(\u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mgeom\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcrop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_touched\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnodata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnan\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     38\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39many(\u001b[38;5;241m~\u001b[39mnp\u001b[38;5;241m.\u001b[39misnan(geom_mask)):\n\u001b[1;32m     39\u001b[0m         preserved_polygons\u001b[38;5;241m.\u001b[39mappend(geom)\n",
      "File \u001b[0;32m/opt/conda/envs/macrosystems/lib/python3.10/site-packages/rasterio/mask.py:178\u001b[0m, in \u001b[0;36mmask\u001b[0;34m(dataset, shapes, all_touched, invert, nodata, filled, crop, pad, pad_width, indexes)\u001b[0m\n\u001b[1;32m    175\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    176\u001b[0m         nodata \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m--> 178\u001b[0m shape_mask, transform, window \u001b[38;5;241m=\u001b[39m \u001b[43mraster_geometry_mask\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    179\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshapes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_touched\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mall_touched\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minvert\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcrop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcrop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    180\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpad_width\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpad_width\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m indexes \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    183\u001b[0m     out_shape \u001b[38;5;241m=\u001b[39m (dataset\u001b[38;5;241m.\u001b[39mcount, ) \u001b[38;5;241m+\u001b[39m shape_mask\u001b[38;5;241m.\u001b[39mshape\n",
      "File \u001b[0;32m/opt/conda/envs/macrosystems/lib/python3.10/site-packages/rasterio/mask.py:86\u001b[0m, in \u001b[0;36mraster_geometry_mask\u001b[0;34m(dataset, shapes, all_touched, invert, crop, pad, pad_width)\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m WindowError:\n\u001b[1;32m     83\u001b[0m     \u001b[38;5;66;03m# If shapes do not overlap raster, raise Exception or UserWarning\u001b[39;00m\n\u001b[1;32m     84\u001b[0m     \u001b[38;5;66;03m# depending on value of crop\u001b[39;00m\n\u001b[1;32m     85\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m crop:\n\u001b[0;32m---> 86\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInput shapes do not overlap raster.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     87\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     88\u001b[0m         warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mshapes are outside bounds of raster. \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     89\u001b[0m                       \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAre they in different coordinate reference systems?\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mValueError\u001b[0m: Input shapes do not overlap raster."
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "import rasterio\n",
    "import numpy as np\n",
    "from rasterio.plot import show\n",
    "from rasterio.mask import mask\n",
    "from shapely.geometry import box\n",
    "\n",
    "def plot_preserved_polygons_with_extent_fix(envi_file, geojson_file, output_file=None):\n",
    "    \"\"\"\n",
    "    Plots the flight line ENVI raster with only the polygons preserved after masking.\n",
    "    Handles cases where polygon extent is larger than the raster extent.\n",
    "    \n",
    "    Parameters:\n",
    "    - envi_file (str): Path to the ENVI raster file.\n",
    "    - geojson_file (str): Path to the GeoJSON file containing polygons.\n",
    "    - output_file (str): Optional. Path to save the plot as an image.\n",
    "    \"\"\"\n",
    "    # Load polygons\n",
    "    polygons = gpd.read_file(geojson_file)\n",
    "\n",
    "    # Open the ENVI file\n",
    "    with rasterio.open(envi_file) as src:\n",
    "        raster_data = src.read(1)  # Assuming single-band raster\n",
    "        raster_transform = src.transform\n",
    "        raster_crs = src.crs\n",
    "        raster_bounds = box(*src.bounds)\n",
    "\n",
    "        # Ensure polygons are in the same CRS as the raster\n",
    "        if polygons.crs != raster_crs:\n",
    "            polygons = polygons.to_crs(raster_crs)\n",
    "        \n",
    "        # Clip polygons to the raster extent\n",
    "        polygons = gpd.clip(polygons, raster_bounds)\n",
    "\n",
    "        # Check for intersecting polygons\n",
    "        preserved_polygons = polygons[polygons.geometry.apply(lambda geom: geom.intersects(raster_bounds))]\n",
    "        if preserved_polygons.empty:\n",
    "            print(\"No polygons intersect with the raster extent.\")\n",
    "            return\n",
    "\n",
    "        # Mask the raster with the polygons\n",
    "        masked_data, masked_transform = mask(src, preserved_polygons.geometry, crop=True, all_touched=True, nodata=np.nan)\n",
    "        masked_data = masked_data[0]  # Extract the first (and likely only) band\n",
    "\n",
    "    # Plotting\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "    \n",
    "    # Plot the masked raster\n",
    "    show(masked_data, transform=masked_transform, ax=ax, cmap='gray', title=\"Preserved Polygons on ENVI Raster\")\n",
    "    \n",
    "    # Plot preserved polygons\n",
    "    preserved_polygons.boundary.plot(ax=ax, color='red', linewidth=1.5, label='Preserved Polygons')\n",
    "    \n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # Save or show the plot\n",
    "    if output_file:\n",
    "        plt.savefig(output_file, dpi=300)\n",
    "        print(f\"Plot saved to {output_file}\")\n",
    "    else:\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "\n",
    "envi_file = \"non_provisional_flightlines_NIWO/NEON_D13_NIWO_DP1_20200731_151902_reflectance/NEON_D13_NIWO_DP1_20200731_151902_reflectance__envi\"\n",
    "geojson_file = \"aop_macrosystems_data_1_7_25.geojson\"\n",
    "output_file = \"tester_output_plot.png\"  # Optional\n",
    "\n",
    "plot_preserved_polygons(envi_file, geojson_file, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "80c95436-2724-416d-8108-66e60336f646",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 4395 polygons from aop_macrosystems_data_1_7_25.geojson.\n",
      "Raster CRS: EPSG:32613\n",
      "Raster Bounds: BoundingBox(left=457474.0, bottom=4425279.0, right=458350.0, top=4436477.0)\n",
      "Reprojecting polygons to match raster CRS...\n",
      "Reprojection complete.\n",
      "Clipping polygons to raster bounds...\n",
      "Number of polygons after clipping: 556\n",
      "Set plot limits to raster bounds to zoom in.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6031/1987207302.py:72: UserWarning: Legend does not support handles for PatchCollection instances.\n",
      "See: https://matplotlib.org/stable/tutorials/intermediate/legend_guide.html#implementing-a-custom-legend-handler\n",
      "  ax.legend(loc='upper right')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot saved to tester_output_plot_zoomed.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "import rasterio\n",
    "from shapely.geometry import box\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "def plot_polygons_with_flight_line_border(envi_file, geojson_file, output_file=None, clip_polygons=True):\n",
    "    \"\"\"\n",
    "    Plots the polygon layer with the flight line raster extent as a border overlaid.\n",
    "    The plot is zoomed to the extent of the raster.\n",
    "\n",
    "    Parameters:\n",
    "    - envi_file (str): Path to the ENVI raster file.\n",
    "    - geojson_file (str): Path to the GeoJSON file containing polygons.\n",
    "    - output_file (str): Optional. Path to save the plot as an image.\n",
    "    - clip_polygons (bool): Optional. If True, clips polygons to raster bounds before plotting.\n",
    "    \"\"\"\n",
    "    # Load the polygon layer\n",
    "    polygons = gpd.read_file(geojson_file)\n",
    "    print(f\"Loaded {len(polygons)} polygons from {geojson_file}.\")\n",
    "\n",
    "    # Get the raster extent and CRS\n",
    "    with rasterio.open(envi_file) as src:\n",
    "        raster_crs = src.crs\n",
    "        raster_bounds = box(*src.bounds)  # Convert raster bounds to a Shapely polygon\n",
    "        raster_bounds_coords = src.bounds  # (left, bottom, right, top)\n",
    "        print(f\"Raster CRS: {raster_crs}\")\n",
    "        print(f\"Raster Bounds: {raster_bounds_coords}\")\n",
    "\n",
    "    # Ensure CRS alignment\n",
    "    if polygons.crs != raster_crs:\n",
    "        print(\"Reprojecting polygons to match raster CRS...\")\n",
    "        polygons = polygons.to_crs(raster_crs)\n",
    "        print(\"Reprojection complete.\")\n",
    "    else:\n",
    "        print(\"Polygons CRS matches raster CRS. No reprojection needed.\")\n",
    "\n",
    "    # Optionally clip polygons to raster bounds\n",
    "    if clip_polygons:\n",
    "        print(\"Clipping polygons to raster bounds...\")\n",
    "        # Create a GeoDataFrame for the raster bounds\n",
    "        raster_bounds_gdf = gpd.GeoDataFrame({'geometry': [raster_bounds]}, crs=raster_crs)\n",
    "        # Perform spatial overlay (intersection)\n",
    "        clipped_polygons = gpd.overlay(polygons, raster_bounds_gdf, how='intersection')\n",
    "        print(f\"Number of polygons after clipping: {len(clipped_polygons)}\")\n",
    "    else:\n",
    "        clipped_polygons = polygons\n",
    "        print(\"Clipping skipped. All polygons will be plotted.\")\n",
    "\n",
    "    # Check if there are any polygons to plot\n",
    "    if clipped_polygons.empty:\n",
    "        print(\"No polygons overlap the raster extent. Exiting plot function.\")\n",
    "        return\n",
    "\n",
    "    # Plot the polygons\n",
    "    fig, ax = plt.subplots(figsize=(12, 12))\n",
    "    clipped_polygons.plot(ax=ax, color='lightgrey', edgecolor='black', alpha=0.5, label='Polygons')\n",
    "\n",
    "    # Plot the flight line extent as a red border\n",
    "    flight_line_patch = Rectangle(\n",
    "        (raster_bounds_coords.left, raster_bounds_coords.bottom),  # Lower-left corner\n",
    "        raster_bounds_coords.right - raster_bounds_coords.left,  # Width\n",
    "        raster_bounds_coords.top - raster_bounds_coords.bottom,    # Height\n",
    "        linewidth=2, edgecolor='red', facecolor='none', label='Flight Line Extent', alpha=1\n",
    "    )\n",
    "    ax.add_patch(flight_line_patch)\n",
    "\n",
    "    # Customize the plot\n",
    "    ax.set_title(\"Polygon Layer with Flight Line Border (Zoomed to Raster Extent)\", fontsize=16)\n",
    "    ax.set_xlabel(\"Longitude (or X-coordinate)\")\n",
    "    ax.set_ylabel(\"Latitude (or Y-coordinate)\")\n",
    "    ax.legend(loc='upper right')\n",
    "    ax.set_aspect('equal', adjustable='datalim')\n",
    "\n",
    "    # Set the axis limits to raster bounds to zoom in\n",
    "    ax.set_xlim(raster_bounds_coords.left, raster_bounds_coords.right)\n",
    "    ax.set_ylim(raster_bounds_coords.bottom, raster_bounds_coords.top)\n",
    "    print(\"Set plot limits to raster bounds to zoom in.\")\n",
    "\n",
    "    # Optional: Add grid for better spatial reference\n",
    "    ax.grid(True, which='both', linestyle='--', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "    # Save or show the plot\n",
    "    plt.savefig(output_file, dpi=300, bbox_inches='tight')\n",
    "    print(f\"Plot saved to {output_file}\")\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "    plt.close(fig)  # Close the figure to free memory\n",
    "\n",
    "# Example usage\n",
    "if __name__ == \"__main__\":\n",
    "    envi_file = \"non_provisional_flightlines_NIWO/NEON_D13_NIWO_DP1_20200731_154336_reflectance/NEON_D13_NIWO_DP1_20200731_154336_reflectance__envi\"\n",
    "    geojson_file = \"aop_macrosystems_data_1_7_25.geojson\"\n",
    "    output_file = \"tester_output_plot_zoomed.png\"  # Optional\n",
    "\n",
    "    plot_polygons_with_flight_line_border(envi_file, geojson_file, output_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43090f11-c8c5-47f6-8c21-d8ebca7d126e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raster CRS: EPSG:32613\n",
      "Polygons CRS: EPSG:4326\n",
      "Reprojecting polygons to match raster CRS...\n",
      "Reprojection complete.\n",
      "Raster Bounds: BoundingBox(left=449118.0, bottom=4433223.0, right=460110.0, top=4434623.0)\n",
      "Polygons Bounds After Alignment: [-822547.87347454 4426034.20421104  458049.26777551 5221581.72101893]\n",
      "Clipping polygons to raster bounds...\n",
      "Number of Clipped Polygons: 366\n",
      "Clipped Polygons Bounds: [ 455004.13772163 4433223.          458049.26777551 4434268.09674941]\n",
      "Creating mask from polygons...\n",
      "Mask created with shape (1400, 10992). Inside pixels: 8771\n",
      "Unique values in mask: [False  True]\n",
      "Using nodata value: -9999.0\n",
      "Masked data shape: (426, 1400, 10992)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import geopandas as gpd\n",
    "import rasterio\n",
    "from rasterio.features import rasterize\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from rasterio.crs import CRS\n",
    "from rasterio.warp import calculate_default_transform, reproject, Resampling\n",
    "\n",
    "def load_data(envi_path, geojson_path):\n",
    "    \"\"\"\n",
    "    Loads the ENVI raster and GeoJSON polygons.\n",
    "    \"\"\"\n",
    "    if not os.path.exists(envi_path):\n",
    "        raise FileNotFoundError(f\"ENVI raster file not found at: {envi_path}\")\n",
    "    if not os.path.exists(geojson_path):\n",
    "        raise FileNotFoundError(f\"GeoJSON file not found at: {geojson_path}\")\n",
    "\n",
    "    raster = rasterio.open(envi_path)\n",
    "    polygons = gpd.read_file(geojson_path)\n",
    "    return raster, polygons\n",
    "\n",
    "\n",
    "def assign_crs(raster, polygons, raster_crs_override=None, polygons_crs_override=None):\n",
    "    \"\"\"\n",
    "    Assigns CRS to raster and polygons if undefined.\n",
    "    \"\"\"\n",
    "    if raster.crs is None:\n",
    "        if raster_crs_override is not None:\n",
    "            raster.crs = CRS.from_string(raster_crs_override)\n",
    "            print(f\"Assigned CRS {raster_crs_override} to raster.\")\n",
    "        else:\n",
    "            raise ValueError(\"Raster CRS is undefined and no override provided.\")\n",
    "\n",
    "    if polygons.crs is None:\n",
    "        if polygons_crs_override is not None:\n",
    "            polygons.crs = polygons_crs_override\n",
    "            print(f\"Assigned CRS {polygons_crs_override} to polygons.\")\n",
    "        else:\n",
    "            raise ValueError(\"Polygons CRS is undefined and no override provided.\")\n",
    "\n",
    "    return raster, polygons\n",
    "\n",
    "\n",
    "def align_crs_debug(raster, polygons):\n",
    "    \"\"\"\n",
    "    Reprojects polygons to match raster CRS with debug outputs.\n",
    "    \"\"\"\n",
    "    print(\"Raster CRS:\", raster.crs)\n",
    "    print(\"Polygons CRS:\", polygons.crs)\n",
    "\n",
    "    if polygons.crs != raster.crs:\n",
    "        print(\"Reprojecting polygons to match raster CRS...\")\n",
    "        polygons_aligned = polygons.to_crs(raster.crs)\n",
    "        print(\"Reprojection complete.\")\n",
    "    else:\n",
    "        print(\"CRS of raster and polygons already match.\")\n",
    "        polygons_aligned = polygons\n",
    "\n",
    "    print(\"Raster Bounds:\", raster.bounds)\n",
    "    print(\"Polygons Bounds After Alignment:\", polygons_aligned.total_bounds)\n",
    "    return polygons_aligned\n",
    "\n",
    "\n",
    "def clip_polygons_debug(raster, polygons_aligned):\n",
    "    \"\"\"\n",
    "    Clips polygons to raster bounds with debug outputs.\n",
    "    \"\"\"\n",
    "    print(\"Clipping polygons to raster bounds...\")\n",
    "    raster_bounds_geom = gpd.GeoDataFrame({'geometry': [box(*raster.bounds)]}, crs=raster.crs)\n",
    "    clipped_polygons = gpd.overlay(polygons_aligned, raster_bounds_geom, how='intersection')\n",
    "    if clipped_polygons.empty:\n",
    "        print(\"No polygons overlap the raster extent.\")\n",
    "    else:\n",
    "        print(f\"Number of Clipped Polygons: {len(clipped_polygons)}\")\n",
    "        print(\"Clipped Polygons Bounds:\", clipped_polygons.total_bounds)\n",
    "    return clipped_polygons\n",
    "\n",
    "\n",
    "def create_mask(raster, polygons):\n",
    "    \"\"\"\n",
    "    Creates a mask where pixels inside polygons are True and outside are False.\n",
    "    \"\"\"\n",
    "    print(\"Creating mask from polygons...\")\n",
    "    mask = rasterize(\n",
    "        [(geom, 1) for geom in polygons.geometry],\n",
    "        out_shape=(raster.height, raster.width),\n",
    "        transform=raster.transform,\n",
    "        fill=0,  # Background value\n",
    "        dtype='uint8',\n",
    "        all_touched=True  # Include all touched pixels\n",
    "    )\n",
    "    mask = mask.astype(bool)\n",
    "    print(f\"Mask created with shape {mask.shape}. Inside pixels: {np.sum(mask)}\")\n",
    "    \n",
    "    # Additional debug: Check unique values\n",
    "    unique_values = np.unique(mask)\n",
    "    print(f\"Unique values in mask: {unique_values}\")\n",
    "    \n",
    "    return mask\n",
    "\n",
    "\n",
    "def apply_mask(raster, mask):\n",
    "    \"\"\"\n",
    "    Applies the mask to raster data, setting areas outside polygons to nodata.\n",
    "    \"\"\"\n",
    "    raster_data = raster.read()\n",
    "    nodata_value = raster.nodata if raster.nodata is not None else -9999\n",
    "    print(f\"Using nodata value: {nodata_value}\")\n",
    "\n",
    "    if raster.count > 1:  # For multi-band rasters\n",
    "        if mask.shape != raster.read(1).shape:\n",
    "            raise ValueError(\"Mask shape does not match raster band shape.\")\n",
    "        mask_expanded = np.repeat(mask[np.newaxis, :, :], raster.count, axis=0)\n",
    "        masked_data = np.where(mask_expanded, raster_data, nodata_value)\n",
    "    else:  # For single-band rasters\n",
    "        masked_data = np.where(mask, raster_data[0], nodata_value)\n",
    "\n",
    "    print(f\"Masked data shape: {masked_data.shape}\")\n",
    "    return masked_data\n",
    "\n",
    "\n",
    "def save_masked_raster(envi_path, masked_data, nodata, raster):\n",
    "    \"\"\"\n",
    "    Saves the masked raster to a new file.\n",
    "    \"\"\"\n",
    "    base_name, ext = os.path.splitext(envi_path)\n",
    "    output_file = f\"{base_name}_masked{ext}\"\n",
    "    meta = raster.meta.copy()\n",
    "    meta.update({\n",
    "        'dtype': masked_data.dtype,\n",
    "        'nodata': nodata,\n",
    "        'count': raster.count if raster.count > 1 else 1\n",
    "    })\n",
    "\n",
    "    with rasterio.open(output_file, 'w', **meta) as dst:\n",
    "        if raster.count > 1:\n",
    "            for i in range(raster.count):\n",
    "                dst.write(masked_data[i], i + 1)\n",
    "        else:\n",
    "            dst.write(masked_data, 1)\n",
    "    print(f\"Masked raster saved to: {output_file}\")\n",
    "\n",
    "\n",
    "def plot_results_debug(raster, masked_data, nodata, polygons_aligned, clipped_polygons, masked_raster_path):\n",
    "    \"\"\"\n",
    "    Plots the original raster, clipped polygons, clipped polygons on raster, and masked raster.\n",
    "    Includes debug output for alignment issues, saves the result as a high-resolution PNG,\n",
    "    and displays the plot.\n",
    "    \"\"\"\n",
    "    output_folder = os.path.dirname(masked_raster_path)\n",
    "    output_path = os.path.join(output_folder, \"plot_results.png\")\n",
    "\n",
    "    print(f\"Masked data stats: Min={masked_data.min()}, Max={masked_data.max()}, Unique={np.unique(masked_data)}\")\n",
    "\n",
    "    fig, axes = plt.subplots(2, 2, figsize=(18, 12))\n",
    "\n",
    "    # Original Raster\n",
    "    original = raster.read(1)\n",
    "    original = np.ma.masked_equal(original, nodata)\n",
    "    axes[0, 0].imshow(original, cmap='gray', extent=(raster.bounds.left, raster.bounds.right, raster.bounds.bottom, raster.bounds.top))\n",
    "    axes[0, 0].set_title('Original Raster')\n",
    "    axes[0, 0].axis('off')\n",
    "\n",
    "    # Clipped Polygons\n",
    "    clipped_polygons.plot(ax=axes[0, 1], facecolor='none', edgecolor='red')\n",
    "    axes[0, 1].set_title('Clipped Polygons')\n",
    "    axes[0, 1].set_xlim(raster.bounds.left, raster.bounds.right)\n",
    "    axes[0, 1].set_ylim(raster.bounds.bottom, raster.bounds.top)\n",
    "    axes[0, 1].axis('off')\n",
    "\n",
    "    # Clipped Polygons on Raster\n",
    "    axes[1, 0].imshow(original, cmap='gray', extent=(raster.bounds.left, raster.bounds.right, raster.bounds.bottom, raster.bounds.top))\n",
    "    clipped_polygons.plot(ax=axes[1, 0], facecolor='none', edgecolor='blue', linewidth=0.5)\n",
    "    axes[1, 0].set_title('Clipped Polygons on Raster')\n",
    "    axes[1, 0].set_xlim(raster.bounds.left, raster.bounds.right)\n",
    "    axes[1, 0].set_ylim(raster.bounds.bottom, raster.bounds.top)\n",
    "    axes[1, 0].axis('off')\n",
    "\n",
    "    # Masked Raster\n",
    "    # Find the first valid band to plot\n",
    "    valid_band_found = False\n",
    "    for band_index in range(masked_data.shape[0]):\n",
    "        band = np.ma.masked_equal(masked_data[band_index], nodata)\n",
    "        if np.any(band):  # Check if the band has non-nodata values\n",
    "            print(f\"Plotting band {band_index + 1} (valid band found).\")\n",
    "            valid_band_found = True\n",
    "            axes[1, 1].imshow(band, cmap='Reds', extent=(raster.bounds.left, raster.bounds.right, raster.bounds.bottom, raster.bounds.top))\n",
    "            axes[1, 1].set_title(f'Masked Raster (Band {band_index + 1})')\n",
    "            axes[1, 1].axis('off')\n",
    "            break\n",
    "\n",
    "    if not valid_band_found:\n",
    "        print(\"No valid band found to plot.\")\n",
    "        axes[1, 1].text(0.5, 0.5, \"No valid band found\", ha='center', va='center', transform=axes[1, 1].transAxes)\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # Save the figure\n",
    "    print(f\"Saving plot to {output_path}\")\n",
    "    plt.savefig(output_path, dpi=300)  # Save as high-resolution PNG\n",
    "\n",
    "    # Display the plot\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def main_debug(envi_path, geojson_path):\n",
    "    \"\"\"\n",
    "    Main function with additional debug outputs.\n",
    "    \"\"\"\n",
    "    raster, polygons = load_data(envi_path, geojson_path)\n",
    "\n",
    "    # Handle undefined CRS\n",
    "    raster, polygons = assign_crs(raster, polygons)\n",
    "\n",
    "    # Align polygons to raster CRS with debug\n",
    "    polygons_aligned = align_crs_debug(raster, polygons)\n",
    "\n",
    "    # Clip polygons to raster bounds with debug\n",
    "    clipped_polygons = clip_polygons_debug(raster, polygons_aligned)\n",
    "\n",
    "    if clipped_polygons.empty:\n",
    "        print(\"No polygons overlap the raster extent. Exiting...\")\n",
    "        return\n",
    "\n",
    "    # Create mask\n",
    "    mask = create_mask(raster, clipped_polygons)\n",
    "\n",
    "    # Apply mask to raster\n",
    "    masked_data = apply_mask(raster, mask)\n",
    "\n",
    "    # Get the masked raster path\n",
    "    masked_raster_path = f\"{envi_path}_masked\"\n",
    "\n",
    "    # Plot results\n",
    "    plot_results_debug(raster, masked_data, raster.nodata, polygons_aligned, clipped_polygons, masked_raster_path)\n",
    "\n",
    "    # Save masked raster\n",
    "    save_masked_raster(envi_path, masked_data, raster.nodata, raster)\n",
    "\n",
    "\n",
    "# Example usage\n",
    "if __name__ == \"__main__\":\n",
    "    from shapely.geometry import box\n",
    "\n",
    "    envi_path = 'non_provisional_flightlines_NIWO/NEON_D13_NIWO_DP1_20200731_151902_reflectance/NEON_D13_NIWO_DP1_20200731_151902_reflectance__envi'\n",
    "    geojson_path = 'aop_macrosystems_data_1_7_25.geojson'\n",
    "    main_debug(envi_path, geojson_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "44d8aa65-89b0-435e-8966-0e542b665519",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import rasterio\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_envi_raster(file_path):\n",
    "    \"\"\"\n",
    "    Reads and plots an ENVI raster file.\n",
    "\n",
    "    Parameters:\n",
    "    - file_path: Path to the ENVI raster file.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        with rasterio.open(file_path) as src:\n",
    "            # Read the first band (or modify as needed for specific bands)\n",
    "            data = src.read(1)  # Read the first band\n",
    "            data = np.ma.masked_equal(data, src.nodata)  # Mask nodata values\n",
    "\n",
    "            # Plot the raster\n",
    "            plt.figure(figsize=(10, 8))\n",
    "            plt.imshow(data, cmap='viridis', extent=src.bounds, interpolation='nearest')\n",
    "            plt.colorbar(label='Pixel Values')\n",
    "            plt.title('ENVI Raster')\n",
    "            plt.xlabel('Longitude')\n",
    "            plt.ylabel('Latitude')\n",
    "            plt.grid(False)\n",
    "            plt.show()\n",
    "\n",
    "    except FileNotFoundError:\n",
    "        print(f\"File not found: {file_path}\")\n",
    "    except Exception as e:\n",
    "        print(f\"An error occurred: {e}\")\n",
    "\n",
    "# Replace with your ENVI raster file path\n",
    "envi_path = 'non_provisional_flightlines_NIWO/NEON_D13_NIWO_DP1_20200731_154336_reflectance/NEON_D13_NIWO_DP1_20200731_154336_reflectance__envi_masked'\n",
    "plot_envi_raster(envi_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6a46c874-03bd-4575-b0cd-f8db292e9780",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.colors import Normalize\n",
    "\n",
    "def plot_masked_raster(masked_data, transform, nodata=None, bands_to_plot=None, output_path=None):\n",
    "    \"\"\"\n",
    "    Plots the masked raster data.\n",
    "\n",
    "    Parameters:\n",
    "    - masked_data: numpy array\n",
    "        Masked raster data. Shape should be (bands, height, width) for multi-band or (height, width) for single-band.\n",
    "    - transform: affine.Affine\n",
    "        The affine transform of the raster, used to calculate the spatial extent.\n",
    "    - nodata: float or int, optional\n",
    "        The nodata value used in the raster. Areas with this value will be masked.\n",
    "    - bands_to_plot: list of int, optional\n",
    "        List of band indices to plot (1-based). \n",
    "        - If None, the first band is plotted for single-band rasters.\n",
    "        - For RGB visualization in multi-band rasters, provide a list like [1, 2, 3].\n",
    "    - output_path: str, optional\n",
    "        If provided, the plot will be saved to this path.\n",
    "    \n",
    "    Returns:\n",
    "    - None\n",
    "    \"\"\"\n",
    "    # Determine if raster is multi-band or single-band\n",
    "    if masked_data.ndim == 3:\n",
    "        is_multiband = True\n",
    "        num_bands, height, width = masked_data.shape\n",
    "    elif masked_data.ndim == 2:\n",
    "        is_multiband = False\n",
    "        height, width = masked_data.shape\n",
    "    else:\n",
    "        raise ValueError(\"masked_data must be a 2D or 3D numpy array.\")\n",
    "\n",
    "    # Calculate spatial extent from transform\n",
    "    left, top = transform * (0, 0)\n",
    "    right, bottom = transform * (width, height)\n",
    "    extent = (left, right, bottom, top)\n",
    "\n",
    "    plt.figure(figsize=(10, 10))\n",
    "\n",
    "    if is_multiband:\n",
    "        if bands_to_plot is None:\n",
    "            # Default to plotting the first band\n",
    "            bands_to_plot = [1]\n",
    "        \n",
    "        if len(bands_to_plot) == 1:\n",
    "            # Single band plot\n",
    "            band_index = bands_to_plot[0] - 1  # Convert to 0-based index\n",
    "            band = masked_data[band_index]\n",
    "            if nodata is not None:\n",
    "                band = np.ma.masked_equal(band, nodata)\n",
    "            plt.imshow(band, cmap='gray', extent=extent)\n",
    "            plt.title(f'Masked Raster - Band {bands_to_plot[0]}')\n",
    "            plt.axis('off')\n",
    "        \n",
    "        elif len(bands_to_plot) == 3:\n",
    "            # RGB Composite\n",
    "            rgb = []\n",
    "            for band_num in bands_to_plot:\n",
    "                band = masked_data[band_num - 1].astype(float)\n",
    "                if nodata is not None:\n",
    "                    band = np.ma.masked_equal(band, nodata)\n",
    "                # Normalize each band for display\n",
    "                band_min, band_max = np.nanmin(band), np.nanmax(band)\n",
    "                if band_max > band_min:\n",
    "                    band = (band - band_min) / (band_max - band_min)\n",
    "                else:\n",
    "                    band = band  # Avoid division by zero if band_max == band_min\n",
    "                rgb.append(band)\n",
    "            rgb = np.dstack(rgb)\n",
    "            plt.imshow(rgb, extent=extent)\n",
    "            plt.title('Masked Raster - RGB Composite')\n",
    "            plt.axis('off')\n",
    "        \n",
    "        else:\n",
    "            raise ValueError(\"For multi-band rasters, bands_to_plot must have 1 or 3 band indices.\")\n",
    "    else:\n",
    "        # Single-band raster\n",
    "        band = masked_data\n",
    "        if nodata is not None:\n",
    "            band = np.ma.masked_equal(band, nodata)\n",
    "        plt.imshow(band, cmap='gray', extent=extent)\n",
    "        plt.title('Masked Raster')\n",
    "        plt.axis('off')\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    if output_path:\n",
    "        plt.savefig(output_path, dpi=300, bbox_inches='tight')\n",
    "        print(f\"Masked raster plot saved to: {output_path}\")\n",
    "\n",
    "    plt.show()\n"
   ]
  }
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