Find potential matches faster

methods/matching/calculate_k.py

@@ -7,7 +7,7 @@
 
 import pandas as pd
 from geopandas import gpd  # type: ignore
-from yirgacheffe.layers import TiledGroupLayer, RasterLayer, VectorLayer  # type: ignore
+from yirgacheffe.layers import GroupLayer, RasterLayer, VectorLayer  # type: ignore
 from yirgacheffe.window import PixelScale  # type: ignore
 
 from methods.common import LandUseClass
@@ -47,14 +47,14 @@ def build_layer_collection(
     outline_layer = VectorLayer.layer_from_file(boundary_filename, None, pixel_scale, projection)
 
     lucs = [
-        TiledGroupLayer([
+        GroupLayer([
             RasterLayer.layer_from_file(os.path.join(jrc_directory_path, filename)) for filename in
                 glob.glob(f"*{year}*.tif", root_dir=jrc_directory_path)
         ], name=f"luc_{year}") for year in luc_years
     ]
 
     cpcs = [
-        TiledGroupLayer([
+        GroupLayer([
             RasterLayer.layer_from_file(
                 os.path.join(cpc_directory_path, filename)
             ) for filename in
@@ -65,21 +65,21 @@ def build_layer_collection(
 
     # ecoregions is such a heavy layer it pays to just rasterize it once - we should possibly do this once
     # as part of import of the ecoregions data
-    ecoregions = TiledGroupLayer([
+    ecoregions = GroupLayer([
         RasterLayer.layer_from_file(os.path.join(ecoregions_directory_path, filename)) for filename in
             glob.glob("*.tif", root_dir=ecoregions_directory_path)
     ], name="ecoregions")
 
-    elevation = TiledGroupLayer([
+    elevation = GroupLayer([
         RasterLayer.layer_from_file(os.path.join(elevation_directory_path, filename)) for filename in
             glob.glob("srtm*.tif", root_dir=elevation_directory_path)
     ], name="elevation")
-    slopes = TiledGroupLayer([
+    slopes = GroupLayer([
         RasterLayer.layer_from_file(os.path.join(slope_directory_path, filename)) for filename in
             glob.glob("slope*.tif", root_dir=slope_directory_path)
     ], name="slopes")
 
-    access = TiledGroupLayer([
+    access = GroupLayer([
         RasterLayer.layer_from_file(os.path.join(access_directory_path, filename)) for filename in
             glob.glob("*.tif", root_dir=access_directory_path)
     ], name="access")

methods/matching/find_potential_matches.py

@@ -58,7 +58,7 @@ def worker(
         example_jrc_layer.pixel_scale,
         example_jrc_layer.projection,
         [start_year, start_year - 5, start_year - 10],
-        [], # CPC not needed at this stage
+        [start_year, start_year - 5, start_year - 10],
         matching_zone_filename,
         jrc_directory_path,
         cpc_directory_path,
@@ -107,10 +107,49 @@ def worker(
         filtered_luc5 = matching_collection.lucs[1].numpy_apply(lambda chunk: chunk == matching[luc5])
         filtered_luc10 = matching_collection.lucs[2].numpy_apply(lambda chunk: chunk == matching[luc10])
 
+        # +/- 10% of CPC_U and CPC_D for t0, t-5 and t-10
+        filtered_cpc_u0 = matching_collection.cpcs[0].numpy_apply(
+            lambda chunk: np.logical_and(
+                chunk >= (matching["cpc0_u"] - matching["cpc0_u"] * 0.1),
+                chunk <= (matching["cpc0_u"] + matching["cpc0_u"] * 0.1)
+            )
+        )
+        filtered_cpc_d0 = matching_collection.cpcs[1].numpy_apply(
+            lambda chunk: np.logical_and(
+                chunk >= (matching["cpc0_d"] - matching["cpc0_d"] * 0.1),
+                chunk <= (matching["cpc0_d"] + matching["cpc0_d"] * 0.1)
+            )
+        )
+        filtered_cpc_u5 = matching_collection.cpcs[2].numpy_apply(
+            lambda chunk: np.logical_and(
+                chunk >= (matching["cpc5_u"] - matching["cpc5_u"] * 0.1),
+                chunk <= (matching["cpc5_u"] + matching["cpc5_u"] * 0.1)
+            )
+        )
+        filtered_cpc_d5 = matching_collection.cpcs[3].numpy_apply(
+            lambda chunk: np.logical_and(
+                chunk >= (matching["cpc5_d"] - matching["cpc5_d"] * 0.1),
+                chunk <= (matching["cpc5_d"] + matching["cpc5_d"] * 0.1)
+            )
+        )
+        filtered_cpc_u10 = matching_collection.cpcs[4].numpy_apply(
+            lambda chunk: np.logical_and(
+                chunk >= (matching["cpc10_u"] - matching["cpc10_u"] * 0.1),
+                chunk <= (matching["cpc10_u"] + matching["cpc10_u"] * 0.1)
+            )
+        )
+        filtered_cpc_d10 = matching_collection.cpcs[5].numpy_apply(
+            lambda chunk: np.logical_and(
+                chunk >= (matching["cpc10_d"] - matching["cpc10_d"] * 0.1),
+                chunk <= (matching["cpc10_d"] + matching["cpc10_d"] * 0.1)
+            )
+        )
+
         filtered_countries = matching_collection.countries.numpy_apply(lambda chunk: chunk == matching.country)
 
         calc = matching_collection.boundary * filtered_ecoregions * filtered_elevation * filtered_countries * \
-            filtered_luc0 * filtered_luc5 * filtered_luc10 * filtered_slopes * filtered_access
+            filtered_luc0 * filtered_luc5 * filtered_luc10 * filtered_slopes * filtered_access * filtered_cpc_u0 * \
+            filtered_cpc_u5 * filtered_cpc_u10 * filtered_cpc_d0 * filtered_cpc_d5 * filtered_cpc_d10
         calc.save(matching_pixels)