Bodemlagen2024 revisited

pyproject.toml

@@ -41,7 +41,7 @@ test = [
 
 [tool.hatch.envs.default]
 installer = "uv"
-python = "3.11"
+python = "3.13"
 
 [tool.hatch.envs.lintformat]
 detached = true

src/nhflodata/data/mockup/bodemlagen_pwn_2024/v2.0.0/Koster_drilling_interpretations/_convert_koster_interpretation_shapes_to_geojson.py

@@ -1,14 +1,25 @@
-from pathlib import Path
+"""Convert Koster drilling interpretation shapefiles (DAW) to GeoJSON.
+
+Reads the Dawaco-exported shapefile archive containing top (TS) and thickness
+(DS) interpretations per aquitard layer from Koster (1997), and converts them
+to GeoJSON format with coordinate precision of 0.01 m.
+
+This is a one-time conversion script. The source shapefiles are archived in
+koster_drilling_interpretations/daw_data_TS_DS.zip within the data directory.
+"""
 
 from nhflotools.geoconverter.geoconverter import GeoConverter, print_results
 
+from nhflodata.get_paths import get_abs_data_path
+
 converter = GeoConverter()
 
-# Convert folder with all files
-input_folder = Path("/Users/bdestombe/Downloads/daw_data_TS_DS")
-output_folder = Path(
-    "/Users/bdestombe/Projects/NHFLO/data/src/nhflodata/data/mockup/bodemlagen_pwn_2024/v2.0.0/Koster_drilling_interpretations_geojson"
-)
+data_path = get_abs_data_path("bodemlagen_pwn_2024", "2.0.0")
+
+# Convert folder with all files.
+# Source shapefiles are archived in koster_drilling_interpretations/daw_data_TS_DS.zip.
+input_folder = data_path / "koster_drilling_interpretations" / "daw_data_TS_DS.zip"
+output_folder = data_path / "koster_drilling_interpretations" / "geojson"
 results = converter.convert_folder(
     input_folder=input_folder, output_folder=output_folder, coordinate_precision=0.01, overwrite_with_target_crs=True
 )

src/nhflodata/data/mockup/bodemlagen_pwn_2024/v2.0.0/botm/botm.py

@@ -363,9 +363,7 @@
 
 # --- Merge and save as a single GeoJSON ---
 gdf_all = gpd.GeoDataFrame(
-    {
-        layer: gdfi["value"] for layer, gdfi in zip(layer_order, gdfs, strict=True)
-    },  # TODO: This creates separate columns per layer, but we want a single "value" column and a "layer" column to identify the layer. This is needed for the inversion step later.
+    {layer: gdfi["value"] for layer, gdfi in zip(layer_order, gdfs, strict=True)},
     geometry=gdfs[0].geometry.copy(),
     crs=crs,
 )

src/nhflodata/data/mockup/bodemlagen_pwn_2024/v2.0.0/botm/interpolation_helper_functions.py

@@ -147,7 +147,7 @@
         -77.50: [-80.00],
         -82.50: [-85.00, -80.00],
         -87.50: [-90.00, -85.00],
-        -92.50: [-95.00 - 90.00],
+        -92.50: [-95.00, -90.00],
         -97.50: [-100.0, -95.00],
         -102.5: [-105.0, -100.00],
         -107.5: [-110.0, -105.00],

src/nhflodata/data/mockup/bodemlagen_pwn_2024/v2.0.0/boundaries/_convert_boundaries.py

@@ -1,27 +1,31 @@
-"""
-Boundaries geven aan waar geldige data voor bodemlage_pwn_2024 aanwezig is.
+"""Convert boundary shapefiles and produce per-layer boundary GeoJSON files.
 
-Binnen de boundaries zou je geldige waarden moeten vinden voor de top en dikte van de aquitards.
-Aan cell nodes buiten de boundaries moeten nan waarden worden toegekend.
+Boundaries indicate where valid data for bodemlagen_pwn_2024 is available.
+Within the boundaries, valid values for the top and thickness of the aquitards
+should be found. Grid cells outside the boundaries are assigned NaN values.
 
-De samenvoeging is gedaan voor de slechtdoorlatende lagen S2.1, S1.3, S1.2 en S1.1. Voor de dieper
-gelegen lagen (S3.2, S3.1 en S2.2) bevatten de shapefiles van het Bergen model geen informatie (zie
-paragraaf 3.3.6 van report).
-"""
+The merge was done for the aquitard layers S2.1, S1.3, S1.2, and S1.1. For the
+deeper layers (S3.2, S3.1, and S2.2), the Bergen model shapefiles contain no
+information (see report section 3.3.6).
 
-from pathlib import Path
+This is a one-time conversion script. The source shapefiles are archived in
+boundaries.zip within the boundaries/ data directory.
+"""
 
 import geopandas as gpd
 import pandas as pd
 from nhflotools.geoconverter.geoconverter import GeoConverter, print_results
 
+from nhflodata.get_paths import get_abs_data_path
+
 converter = GeoConverter()
 
-# Convert folder with all files
-input_folder = Path("/Users/bdestombe/Downloads/boundaries")
-output_folder = Path(
-    "/Users/bdestombe/Projects/NHFLO/data/src/nhflodata/data/mockup/bodemlagen_pwn_2024/v2.0.0/boundaries"
-)
+data_path = get_abs_data_path("bodemlagen_pwn_2024", "2.0.0")
+
+# Convert folder with all files.
+# Source shapefiles are archived in boundaries/boundaries.zip.
+input_folder = data_path / "boundaries" / "boundaries.zip"
+output_folder = data_path / "boundaries"
 results = converter.convert_folder(
     input_folder=input_folder, output_folder=output_folder, coordinate_precision=2, overwrite_with_target_crs=True
 )

src/nhflodata/data/mockup/bodemlagen_pwn_2024/v2.0.0/dikte_aquitard/_merge_masks.py

@@ -12,12 +12,12 @@
   absence according to Koster (1997).
 """
 
-from pathlib import Path
-
 import geopandas as gpd
 import pandas as pd
 
-data_dir = Path("/Users/bdestombe/Projects/NHFLO/data/src/nhflodata/data/mockup/bodemlagen_pwn_2024/v1.0.0")
+from nhflodata.get_paths import get_abs_data_path
+
+data_dir = get_abs_data_path("bodemlagen_pwn_2024", "1.0.0")
 layer_names = ["S11", "S12", "S13", "S21", "S22", "S31", "S32"]
 
 # Load the masks