{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "35b8f4c5-adb3-45ee-879c-4785673617e1", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 1, "id": "340ecc63-271b-499c-82b0-4a81da89015d", "metadata": {}, "outputs": [ { "ename": "FileNotFoundError", "evalue": "Cannot find 'Assam_X_Relative_Humidity.tif' in /home/aparajita/Desktop/Weather Analytics/Weather_Analytics/GridData/Assam", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[1], line 20\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m path \u001b[38;5;129;01min\u001b[39;00m (rh_tiff_path, precip_raw_tiff, precip_cat_tiff, india_shapefile):\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misfile(path):\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot find \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mos\u001b[38;5;241m.\u001b[39mgetcwd()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# -----------------------------------------------------------------------------\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m# 2) READ & MASK THE 20-BAND RELATIVE HUMIDITY TIFF\u001b[39;00m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;66;03m# -----------------------------------------------------------------------------\u001b[39;00m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m rasterio\u001b[38;5;241m.\u001b[39mopen(rh_tiff_path) \u001b[38;5;28;01mas\u001b[39;00m src_rh:\n", "\u001b[0;31mFileNotFoundError\u001b[0m: Cannot find 'Assam_X_Relative_Humidity.tif' in /home/aparajita/Desktop/Weather Analytics/Weather_Analytics/GridData/Assam" ] } ], "source": [ "import os\n", "import rasterio\n", "import numpy as np\n", "import geopandas as gpd\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import ListedColormap\n", "from matplotlib.patches import Patch\n", "from rasterio.features import geometry_mask\n", "\n", "# -----------------------------------------------------------------------------\n", "# 1) FILE PATHS (for Assam)\n", "# -----------------------------------------------------------------------------\n", "rh_tiff_path = \"Assam_X_Relative_Humidity.tif\" # 20 bands (June–Sept 2020–2024)\n", "precip_raw_tiff = \"Assam_Y_Precipitation_CHIRPS.tif\" # 25 bands (2020–2024 × 5)\n", "precip_cat_tiff = \"Assam_Y_Precipitation_GT_geotif.tif\" # 25 bands (2020–2024 × 5)\n", "india_shapefile = \"SateMask/gadm41_IND_1.shp\" # contains all Indian states\n", "\n", "for path in (rh_tiff_path, precip_raw_tiff, precip_cat_tiff, india_shapefile):\n", " if not os.path.isfile(path):\n", " raise FileNotFoundError(f\"Cannot find {path!r} in {os.getcwd()}\")\n", "\n", "# -----------------------------------------------------------------------------\n", "# 2) READ & MASK THE 20-BAND RELATIVE HUMIDITY TIFF\n", "# -----------------------------------------------------------------------------\n", "with rasterio.open(rh_tiff_path) as src_rh:\n", " rh_bands = src_rh.read().astype(np.float32) # (20, H, W)\n", " rh_transform = src_rh.transform\n", " rh_crs = src_rh.crs\n", " height, width = src_rh.height, src_rh.width\n", " rh_bounds = src_rh.bounds\n", "\n", "if rh_bands.shape[0] != 20:\n", " raise ValueError(f\"Expected 20 bands in {rh_tiff_path}, but found {rh_bands.shape[0]}\")\n", "\n", "# 2a) Load Assam polygon and reproject if needed\n", "gdf = gpd.read_file(india_shapefile)\n", "gdf_assam = gdf[gdf[\"NAME_1\"].str.lower() == \"assam\"].copy()\n", "if gdf_assam.empty:\n", " raise ValueError(\"No feature named 'Assam' found in shapefile.\")\n", "if gdf_assam.crs != rh_crs:\n", " gdf_assam = gdf_assam.to_crs(rh_crs)\n", "\n", "# 2b) Build mask and count pixels\n", "assam_geom = [gdf_assam.geometry.union_all()]\n", "assam_mask = geometry_mask(\n", " assam_geom,\n", " transform=rh_transform,\n", " invert=True,\n", " out_shape=(height, width)\n", ")\n", "total_inside = np.count_nonzero(assam_mask)\n", "print(f\"Total pixels inside Assam boundary: {total_inside}\")\n", "\n", "# 2c) Mask RH outside Assam\n", "for i in range(rh_bands.shape[0]):\n", " band = rh_bands[i]\n", " band[~assam_mask] = np.nan\n", " rh_bands[i] = band\n", "\n", "# -----------------------------------------------------------------------------\n", "# 3) READ & MASK RAW PRECIPITATION (25 bands)\n", "# -----------------------------------------------------------------------------\n", "with rasterio.open(precip_raw_tiff) as src_pr:\n", " pr_raw_full = src_pr.read().astype(np.float32) # (25, H, W)\n", "\n", "if pr_raw_full.shape[0] != 25:\n", " raise ValueError(f\"Expected 25 bands in {precip_raw_tiff}, but found {pr_raw_full.shape[0]}\")\n", "\n", "for i in range(25):\n", " arr = pr_raw_full[i]\n", " arr[~assam_mask] = np.nan\n", " pr_raw_full[i] = arr\n", "\n", "# -----------------------------------------------------------------------------\n", "# 4) READ & MASK PRECIPITATION CATEGORY (25 bands)\n", "# -----------------------------------------------------------------------------\n", "with rasterio.open(precip_cat_tiff) as src_pc:\n", " pr_cat_full = src_pc.read().astype(np.int8) # (25, H, W)\n", "\n", "if pr_cat_full.shape[0] != 25:\n", " raise ValueError(f\"Expected 25 bands in {precip_cat_tiff}, but found {pr_cat_full.shape[0]}\")\n", "\n", "for i in range(25):\n", " arr = pr_cat_full[i]\n", " arr[~assam_mask] = -1\n", " pr_cat_full[i] = arr\n", "\n", "# -----------------------------------------------------------------------------\n", "# 5) EXTRACT 20 MONTHLY BANDS (drop each year’s “Total”)\n", "# -----------------------------------------------------------------------------\n", "monthly_indices = []\n", "for yr in range(5):\n", " base = yr * 5\n", " monthly_indices += [base + m for m in range(4)]\n", "\n", "pr_raw_bands = pr_raw_full[monthly_indices] # (20, H, W)\n", "pr_cat_bands = pr_cat_full[monthly_indices] # (20, H, W)\n", "\n", "# -----------------------------------------------------------------------------\n", "# 6) SET UP YEARS & MONTHS\n", "# -----------------------------------------------------------------------------\n", "years = [2020, 2021, 2022, 2023, 2024]\n", "month_names = [\"June\", \"July\", \"August\", \"September\"]\n", "n_years, n_months = len(years), len(month_names)\n", "n_total = n_years * n_months # 20\n", "\n", "# -----------------------------------------------------------------------------\n", "# 7) COLORMAPS & STYLE\n", "# -----------------------------------------------------------------------------\n", "rh_palette = [\"red\",\"orange\",\"yellow\",\"white\",\"green\",\"cyan\",\"lightblue\",\"blue\"]\n", "rh_cmap, rh_vmin, rh_vmax = ListedColormap(rh_palette), 0, 100\n", "\n", "cluster_colors = [\"#ffffff\",\"#79d151\",\"#22a784\",\"#29788e\",\"#404387\",\"#440154\"]\n", "cluster_cmap = ListedColormap(cluster_colors)\n", "cluster_labels = {\n", " 0: \"NoData/Outside\",\n", " 1: \"Scarcity (<0.4×normal)\",\n", " 2: \"Deficit (0.4–0.8×normal)\",\n", " 3: \"Normal (0.8–1.2×normal)\",\n", " 4: \"Excess (1.2–1.6×normal)\",\n", " 5: \"LargeExcess (≥1.6×normal)\"\n", "}\n", "cluster_handles = [\n", " Patch(facecolor=cluster_colors[i], edgecolor=\"black\", label=cluster_labels[i])\n", " for i in range(len(cluster_colors))\n", "]\n", "\n", "# -----------------------------------------------------------------------------\n", "# 8) BUILD FIGURE\n", "# -----------------------------------------------------------------------------\n", "fig, axes = plt.subplots(n_total, 3, figsize=(20, n_total * 2.0), constrained_layout=True)\n", "fig.suptitle(\"Assam 2020–2024: Monthly RH | Precip Category | RH Distribution\", fontsize=18, y=0.98)\n", "if n_total == 1:\n", " axes = axes[np.newaxis, :]\n", "\n", "# -----------------------------------------------------------------------------\n", "# 9) LOOP & PLOT\n", "# -----------------------------------------------------------------------------\n", "for i in range(n_total):\n", " yr_idx, mo_idx = divmod(i, n_months)\n", " year, month = years[yr_idx], month_names[mo_idx]\n", "\n", " # RH stats + missing\n", " rh_layer = rh_bands[i]\n", " valid_rh = rh_layer[~np.isnan(rh_layer)]\n", " rh_min, rh_max = (float(np.nanmin(valid_rh)), float(np.nanmax(valid_rh))) if valid_rh.size else (None,None)\n", " miss_rh = np.count_nonzero(np.isnan(rh_layer[assam_mask]))\n", " pct_rh = miss_rh / total_inside * 100\n", " print(f\"{year} {month} → Missing RH: {miss_rh}/{total_inside} ({pct_rh:.2f}%)\")\n", "\n", " # precip stats\n", " pr_layer = pr_raw_bands[i]\n", " valid_pr = pr_layer[~np.isnan(pr_layer)]\n", " pr_min, pr_max = (float(np.nanmin(valid_pr)), float(np.nanmax(valid_pr))) if valid_pr.size else (None,None)\n", "\n", " # category prep\n", " cat = pr_cat_bands[i]\n", " cat_plot = np.zeros_like(cat, dtype=np.int8)\n", " mask_ok = (cat != -1)\n", " cat_plot[mask_ok] = cat[mask_ok] + 1\n", "\n", " # --- Column 0: RH Map ---\n", " ax0 = axes[i, 0]\n", " im0 = ax0.imshow(rh_layer, cmap=rh_cmap, vmin=rh_vmin, vmax=rh_vmax,\n", " extent=[rh_bounds.left, rh_bounds.right, rh_bounds.bottom, rh_bounds.top],\n", " origin=\"upper\")\n", " title0 = f\"{year} {month} RH (%)\"\n", " if rh_min is not None:\n", " title0 += f\"\\nmin={rh_min:.1f}%, max={rh_max:.1f}%\"\n", " title0 += f\"\\nmissing={miss_rh}/{total_inside} ({pct_rh:.1f}%)\"\n", " ax0.set_title(title0, fontsize=10, loc=\"left\")\n", " ax0.axis(\"off\")\n", " c0 = fig.colorbar(im0, ax=ax0, fraction=0.045, pad=0.02)\n", " c0.set_label(\"RH (%)\", fontsize=8); c0.ax.tick_params(labelsize=6)\n", "\n", " # --- Column 1: Precip Category Map ---\n", " ax1 = axes[i, 1]\n", " im1 = ax1.imshow(cat_plot, cmap=cluster_cmap, vmin=0, vmax=5,\n", " extent=[rh_bounds.left, rh_bounds.right, rh_bounds.bottom, rh_bounds.top],\n", " origin=\"upper\")\n", " title1 = f\"{year} {month} Precip Cat\\nmin={pr_min:.1f} mm, max={pr_max:.1f} mm\"\n", " ax1.set_title(title1, fontsize=10, loc=\"left\")\n", " ax1.axis(\"off\")\n", " c1 = fig.colorbar(im1, ax=ax1, fraction=0.045, pad=0.02)\n", " c1.set_ticks(list(range(6)))\n", " c1.set_ticklabels([cluster_labels[j] for j in range(6)], fontsize=6)\n", " c1.ax.tick_params(labelsize=6); c1.set_label(\"Precip Category\", fontsize=8)\n", "\n", " # --- Column 2: RH Distribution ---\n", " ax2 = axes[i, 2]\n", " if valid_rh.size:\n", " ax2.hist(valid_rh.flatten(), bins=30, density=True, edgecolor=\"black\")\n", " ax2.set_xlim(rh_vmin, rh_vmax)\n", " else:\n", " ax2.text(0.5, 0.5, \"No Data\", ha=\"center\", va=\"center\", fontsize=8, color=\"gray\")\n", " ax2.set_title(f\"RH Dist\\n({year} {month})\", fontsize=10)\n", " ax2.set_xlabel(\"RH (%)\", fontsize=8); ax2.set_ylabel(\"Density\", fontsize=8)\n", " ax2.tick_params(labelsize=7)\n", "\n", "# -----------------------------------------------------------------------------\n", "# 10) Legend\n", "# -----------------------------------------------------------------------------\n", "fig.legend(handles=cluster_handles, loc=\"lower center\", ncol=3,\n", " frameon=True, title=\"Precip Category Definitions\",\n", " bbox_to_anchor=(0.5, -0.005), fontsize=8, title_fontsize=9)\n", "\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "aff1cdf1-12a4-476f-8393-6a4ebb6fa2d6", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:base] *", "language": "python", "name": "conda-base-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 5 }