Hotfix/local antyodaya update

computing/config_loader.py

@@ -3,7 +3,8 @@
 import yaml
 
 _CONFIG_PATH = Path(__file__).resolve().parent / "config.yaml"
-PROJECT_ROOT = Path(__file__).resolve().parents[1]
+# PROJECT_ROOT = Path(__file__).resolve().parents[1]
+PROJECT_ROOT = Path("/home/cfpt-jedi/developer/repos/core-stack-backend")
 
 
 def _load():
@@ -149,7 +150,7 @@ def _output_entry(module: str, index: int = 0) -> dict:
 PAN_INDIA_SLOPE_PERCENTAGE_PATH = PROJECT_ROOT / "data/base_layers/Pan_India_slope_percentage.tif"
 LOCAL_SLOPE_PERCENTAGE_OUTPUT = PROJECT_ROOT / "data/layers/slope_percentage"
 
-PAN_INDIA_MWS_CONNECTIVITY_PATH = PROJECT_ROOT / "data/layers/mws_connectivity/Pan_India_mws_connectivity.geojson"
+PAN_INDIA_MWS_CONNECTIVITY_PATH = PROJECT_ROOT / "data/base_layers/Pan_India_mws_connectivity.geojson"
 LOCAL_MWS_CONNECTIVITY_OUTPUT = PROJECT_ROOT / "data/layers/mws_connectivity/mws_connectivity_local"
 
 LOCAL_MWS_CENTROID_OUTPUT = PROJECT_ROOT / "data/layers/mws_centroid"

computing/misc/antyodaya_local_compute.py

@@ -26,7 +26,7 @@
 from computing.local_compute_helper import (
     PROJECT_ROOT,
     build_output_vector_path,
-    load_precomputed_watersheds,
+    load_precomputed_panchayat,
     read_validated_vector_file,
     write_vector_output,
     validate_geometry,
@@ -39,20 +39,14 @@
 GEOSERVER_WORKSPACE = "antyodaya_2020"
 
 
-def _compute_antyodaya_for_watersheds(watersheds_gdf, antyodaya_gdf):
-    """
-    Spatially filters Antyodaya features with watershed/ROI boundaries.
-    """
+def _compute_antyodaya_for_panchayat(panchayat_gdf, antyodaya_gdf):
     if antyodaya_gdf.empty:
         return antyodaya_gdf
-
-    if watersheds_gdf.crs and antyodaya_gdf.crs and watersheds_gdf.crs != antyodaya_gdf.crs:
-        antyodaya_gdf = antyodaya_gdf.to_crs(watersheds_gdf.crs)
 
-    outer_boundary = watersheds_gdf.geometry.unary_union
+    outer_boundary = panchayat_gdf.geometry.unary_union
 
-    # Precise intersection check
-    antyodaya_in_roi = antyodaya_gdf[antyodaya_gdf.intersects(outer_boundary)].copy()
+    # Clip Antyodaya geometries to the panchayat boundary
+    antyodaya_in_roi = gpd.clip(antyodaya_gdf, outer_boundary).copy()
 
     # Final cleanup
     antyodaya_in_roi = antyodaya_in_roi[~antyodaya_in_roi.geometry.is_empty]
@@ -77,7 +71,7 @@ def generate_antyodaya_data_local(
 ):
     if state and district and block:
         layer_name = f"antyodaya20_{valid_gee_text(district.lower())}_{valid_gee_text(block.lower())}"
-        watersheds_gdf, watershed_source = load_precomputed_watersheds(
+        panchayat_gdf, watershed_source = load_precomputed_panchayat(
             state=state,
             district=district,
             block=block,
@@ -88,22 +82,22 @@ def generate_antyodaya_data_local(
         if not roi_path or not asset_suffix:
             raise ValueError("ROI path and asset_suffix are required for custom runs.")
         layer_name = f"antyodaya20_{valid_gee_text(asset_suffix).lower()}"
-        watersheds_gdf = read_validated_vector_file(roi_path, f"Invalid ROI file: {roi_path}")
+        panchayat_gdf = read_validated_vector_file(roi_path, f"Invalid ROI file: {roi_path}")
         print(f"ROI source: {roi_path}")
 
     if not os.path.exists(PAN_INDIA_ANTYODAYA_2020):
         raise FileNotFoundError(f"PAN INDIA Antyodaya file not found at {PAN_INDIA_ANTYODAYA_2020}")
 
     print("Loading Antyodaya data overlapping ROI...")
-    antyodaya_gdf = gpd.read_file(PAN_INDIA_ANTYODAYA_2020, mask=watersheds_gdf)
+    antyodaya_gdf = gpd.read_file(PAN_INDIA_ANTYODAYA_2020, mask=panchayat_gdf)
     antyodaya_gdf = validate_geometry(antyodaya_gdf)
     if antyodaya_gdf.empty:
         print("Warning: PAN INDIA Antyodaya file has no valid geometries overlapping ROI")
     else:
         print(f"Loaded {len(antyodaya_gdf)} Antyodaya features")
 
-    result_gdf = _compute_antyodaya_for_watersheds(
-        watersheds_gdf=watersheds_gdf,
+    result_gdf = _compute_antyodaya_for_panchayat(
+        panchayat_gdf=panchayat_gdf,
         antyodaya_gdf=antyodaya_gdf,
     )
     print(f"Final valid Antyodaya features after spatial filter: {len(result_gdf)}")

computing/misc/drainage_density_local_compute.py

@@ -185,4 +185,4 @@ def drainage_density(
             update_layer_sync_status(layer_id=layer_id, sync_to_geoserver=True)
             print(f"Sync Data for layer_id: {layer_id}")
 
-    return True
+    return True

computing/misc/facilities_proximity_local_compute.py

@@ -26,27 +26,30 @@
 
 
 def _compute_proximity_for_panchayat(panchayat_gdf, facilities_gdf):
-    """
-    Filters facilities to strictly those intersecting the panchayat boundaries,
-    without altering/clipping their geometries.
-    """
     if facilities_gdf.empty:
         return facilities_gdf
 
-    # Ensure CRS matches
-    if panchayat_gdf.crs and facilities_gdf.crs and panchayat_gdf.crs != facilities_gdf.crs:
-        facilities_gdf = facilities_gdf.to_crs(panchayat_gdf.crs)
-
     outer_boundary = panchayat_gdf.geometry.unary_union
 
     # Keep facilities that intersect the boundary, geometries unchanged
-    facilities_in_roi = facilities_gdf[facilities_gdf.intersects(outer_boundary)].copy()
+    facilities_in_roi = gpd.clip(facilities_gdf, outer_boundary).copy()
 
     # Final cleanup
     facilities_in_roi = facilities_in_roi[~facilities_in_roi.geometry.is_empty]
     facilities_in_roi = facilities_in_roi[facilities_in_roi.geometry.is_valid]
     facilities_in_roi = facilities_in_roi[facilities_in_roi.geometry.notna()]
 
+    # Rename NAME to censusname
+    if "NAME" in facilities_in_roi.columns:
+        facilities_in_roi = facilities_in_roi.rename(columns={"NAME": "censusname"})
+
+    # Add state, district, tehsil from panchayat_gdf
+    for col in ["state", "district", "tehsil"]:
+        if col in panchayat_gdf.columns:
+            # Assign the value for the tehsil (taking the first valid row)
+            first_val = panchayat_gdf[col].dropna().iloc[0] if not panchayat_gdf[col].dropna().empty else None
+            facilities_in_roi[col] = first_val
+
     return facilities_in_roi
 
 

computing/misc/livestocks_local_compute.py

@@ -11,7 +11,7 @@
 from computing.local_compute_helper import (
     PROJECT_ROOT,
     build_output_vector_path,
-    load_precomputed_watersheds,
+    load_precomputed_panchayat,
     read_validated_vector_file,
     write_vector_output,
     validate_geometry,
@@ -54,22 +54,14 @@ def _coerce_nullable_integer_columns(gdf):
             gdf[column] = gdf[column].astype("Int64")
     return gdf
 
-def _compute_livestocks_for_watersheds(watersheds_gdf, livestocks_gdf):
-    """
-    Spatially filters Livestock features with watershed/ROI boundaries.
-    """
+def _compute_livestocks_for_panchayat(panchayat_gdf, livestocks_gdf):
     if livestocks_gdf.empty:
         return livestocks_gdf
-
-    if watersheds_gdf.crs and livestocks_gdf.crs and watersheds_gdf.crs != livestocks_gdf.crs:
-        livestocks_gdf = livestocks_gdf.to_crs(watersheds_gdf.crs)
-
-    outer_boundary = watersheds_gdf.geometry.unary_union
 
-    # Precise intersection check
-    livestocks_in_roi = livestocks_gdf[livestocks_gdf.intersects(outer_boundary)].copy()
+    outer_boundary = panchayat_gdf.geometry.unary_union
 
-    # Final cleanup
+    # Clip Antyodaya geometries to the panchayat boundary
+    livestocks_in_roi = gpd.clip(livestocks_gdf, outer_boundary).copy()
     livestocks_in_roi = livestocks_in_roi[~livestocks_in_roi.geometry.is_empty]
     livestocks_in_roi = livestocks_in_roi[livestocks_in_roi.geometry.is_valid]
     livestocks_in_roi = livestocks_in_roi[livestocks_in_roi.geometry.notna()]
@@ -92,33 +84,32 @@ def generate_livestocks_data_local(
 ):
     if state and district and block:
         layer_name = f"livestocks_{valid_gee_text(district.lower())}_{valid_gee_text(block.lower())}"
-        watersheds_gdf, watershed_source = load_precomputed_watersheds(
+        panchayat_gdf, watershed_source = load_precomputed_panchayat(
             state=state,
             district=district,
             block=block,
             precomputed_roi_dir=precomputed_roi_dir,
         )
-        print(f"Watershed boundary source: {watershed_source}")
     else:
         if not roi_path or not asset_suffix:
             raise ValueError("ROI path and asset_suffix are required for custom runs.")
         layer_name = f"livestocks_{valid_gee_text(asset_suffix).lower()}"
-        watersheds_gdf = read_validated_vector_file(roi_path, f"Invalid ROI file: {roi_path}")
+        panchayat_gdf = read_validated_vector_file(roi_path, f"Invalid ROI file: {roi_path}")
         print(f"ROI source: {roi_path}")
 
     if not os.path.exists(PAN_INDIA_LIVESTOCKS):
         raise FileNotFoundError(f"PAN INDIA Livestocks file not found at {PAN_INDIA_LIVESTOCKS}")
 
     print("Loading Livestocks data overlapping ROI...")
-    livestocks_gdf = gpd.read_file(PAN_INDIA_LIVESTOCKS, mask=watersheds_gdf)
+    livestocks_gdf = gpd.read_file(PAN_INDIA_LIVESTOCKS, mask=panchayat_gdf)
     livestocks_gdf = validate_geometry(livestocks_gdf)
     if livestocks_gdf.empty:
         print("Warning: PAN INDIA Livestocks file has no valid geometries overlapping ROI")
     else:
         print(f"Loaded {len(livestocks_gdf)} Livestock features")
 
-    result_gdf = _compute_livestocks_for_watersheds(
-        watersheds_gdf=watersheds_gdf,
+    result_gdf = _compute_livestocks_for_panchayat(
+        panchayat_gdf=panchayat_gdf,
         livestocks_gdf=livestocks_gdf,
     )
     print(f"Final valid Livestock features after spatial filter: {len(result_gdf)}")
@@ -158,7 +149,7 @@ def generate_livestocks_data_local(
             block=block,
             layer_name=layer_name,
             asset_id=asset_id,
-            dataset_name="Livestock Census",
+            dataset_name="Livestock Census 2019",
             misc={"is_generated_locally": True},
         )
         if layer_id:

computing/mws/mws_connectivity_local_compute.py

@@ -32,23 +32,15 @@
 
 
 def _compute_mws_connectivity_for_watersheds(watersheds_gdf, mws_gdf):
-    mws_in_roi = mws_gdf.copy()
-
-    if mws_in_roi.empty:
+    if mws_gdf.empty:
         print("No MWS connectivity found within the outer boundary.")
-        return mws_in_roi
-
-    print(f"MWS connectivity within outer boundary: {len(mws_in_roi)}")
+        return mws_gdf
 
-    # Step 2: Spatial join to clip results to individual watersheds
-    mws_in_roi = gpd.sjoin(
-        mws_in_roi,
-        watersheds_gdf[["geometry"]],  # no uid, no collision
-        how="inner",
-        predicate="intersects",
-    ).drop(columns=["index_right"], errors="ignore")
+    # Step 1: Clip to the outer boundary of watersheds
+    outer_boundary = watersheds_gdf.geometry.unary_union
+    mws_in_roi = gpd.clip(mws_gdf, outer_boundary).copy()
 
-    # Step 3: Drop empty/invalid geometries
+    # Step 2: Drop empty/invalid geometries
     mws_in_roi = fix_invalid_geometry_in_gdf(mws_in_roi)
     mws_in_roi = mws_in_roi[
         mws_in_roi.geometry.notna()