87 add option in points to h3 to define field containing score

README.md

@@ -116,8 +116,8 @@ We can generate an H3 polygon layer from the point layer. As an overlay it may m
 ### Example
 
 ```bash
-# python -m src.create_webmap path/to/input_file.gpkg path/to/output_file.gpkg cell_resolution 
-python -m src.points_to_h3 data/processed/Three_Rivers_GVI.gpkg data/processed/Three_Rivers_h3_polygons_10.gpkg 10
+# python -m src.create_webmap path/to/input_file.gpkg "score_field" path/to/output_file.gpkg cell_resolution 
+python -m src.points_to_h3 data/processed/Three_Rivers_GVI.gpkg "gvi_score" data/processed/Three_Rivers_h3_polygons_10.gpkg 10
 ```
 
 The larger the number for the [H3 cell resolution](https://h3geo.org/docs/core-library/restable/), the smaller the individual hexagons. 
@@ -131,8 +131,8 @@ To display an OpenStreetMap basemap under the data, you will need an API key fro
 ### Example
 
 ```bash
-# python -m src.create_webmap path/to/input_file.gpkg path/to/output/output_file.html default_zoom_for_webmap 
-python -m src.create_webmap data/processed/Three_Rivers_h3_polygons_10.gpkg data/processed/Three_Rivers_gvi_webmap.html 10
+# python -m src.create_webmap path/to/input_file.gpkg "score_field" path/to/output/output_file.html default_zoom_for_webmap 
+python -m src.create_webmap data/processed/Three_Rivers_h3_polygons_10.gpkg "gvi_score" data/processed/Three_Rivers_gvi_webmap.html 10
 ```
 
 ## Config files

pyproject.toml

@@ -17,6 +17,7 @@ dependencies = [
   "folium",
   "geopandas",
   "geopy",
+  "h3 <4",
   "h3pandas",
   "loguru",
   "mapclassify",

src/create_webmap.py

@@ -29,6 +29,10 @@ def main(
         Path,
         typer.Argument(help="Path to file containing h3 polygons."),
     ],
+    score_field: Annotated[
+        str,
+        typer.Argument(help="Name of field containing GVI score."),
+    ],
     filename: Annotated[
         str, typer.Argument(help="(Optional) Path to file for HTML output.")
     ] = "./data/processed/gvi_webmap.html",
@@ -49,7 +53,7 @@ def main(
         gdf = gdf.to_crs("EPSG:4326")
 
     # Round GVI score to make map labels more readable
-    gdf["gvi_score"] = round(gdf["gvi_score"], 2)
+    gdf[score_field] = round(gdf[score_field], 2)
 
     # get central coordinates of all features
     centre = (
@@ -88,8 +92,8 @@ def main(
 
     # Lookup the colourmap values for each GVI score
     cmap = matplotlib.colormaps["Greens"]
-    gdf["gvi_norm"] = (gdf.gvi_score - np.min(gdf.gvi_score)) / (
-        np.max(gdf.gvi_score) - np.min(gdf.gvi_score)
+    gdf["gvi_norm"] = (gdf[score_field] - np.min(gdf[score_field])) / (
+        np.max(gdf[score_field]) - np.min(gdf[score_field])
     )
     gdf["html_color"] = gdf["gvi_norm"].apply(
         lambda x: matplotlib.colors.rgb2hex(cmap(x))
@@ -99,25 +103,25 @@ def main(
     # Pick 4 evenly-spaced values from the gvi scores to use in the legend
     legend_gvi = list(
         np.arange(
-            gdf.gvi_score.min(),
-            gdf.gvi_score.max(),
-            (gdf.gvi_score.max() - gdf.gvi_score.min()) / 4,
+            gdf[score_field].min(),
+            gdf[score_field].max(),
+            (gdf[score_field].max() - gdf[score_field].min()) / 4,
             dtype=float,
         )
     )
 
     # Generate labels by looking up what the GVI score would be for those values
     legend_label_1 = round(
-        np.linspace(gdf.gvi_score.min(), gdf.gvi_score.max(), 100)[0], 1
+        np.linspace(gdf[score_field].min(), gdf[score_field].max(), 100)[0], 1
     )
     legend_label_2 = round(
-        np.linspace(gdf.gvi_score.min(), gdf.gvi_score.max(), 100)[33], 1
+        np.linspace(gdf[score_field].min(), gdf[score_field].max(), 100)[33], 1
     )
     legend_label_3 = round(
-        np.linspace(gdf.gvi_score.min(), gdf.gvi_score.max(), 100)[66], 1
+        np.linspace(gdf[score_field].min(), gdf[score_field].max(), 100)[66], 1
     )
     legend_label_4 = round(
-        np.linspace(gdf.gvi_score.min(), gdf.gvi_score.max(), 100)[99], 1
+        np.linspace(gdf[score_field].min(), gdf[score_field].max(), 100)[99], 1
     )
 
     # Normalise the label values to lookup against the colourmap

src/points_to_h3.py

@@ -2,6 +2,7 @@
 from pathlib import Path
 
 import geopandas as gpd
+import pandas as pd
 import h3pandas  # noqa: F401
 import typer
 
@@ -20,6 +21,10 @@ def main(
         Path,
         typer.Argument(help="Path to file containing point layer with GVI scores."),
     ],
+    score_field: Annotated[
+        str,
+        typer.Argument(help="Field containing the score"),
+    ],
     output_file: Annotated[
         Path,
         typer.Argument(
@@ -39,6 +44,7 @@ def main(
 
     Args:
             input_file: Path to file containing point layer with GVI scores.
+            score_field: The field name from the input data containing the score
             cell_resolution: H3 cell resolution to aggregate to,
                 between 0 (largest) and 15 (smallest)
             aggregation_operations:
@@ -67,20 +73,31 @@ def main(
     else:
         raise Exception("Expected point data in interim data file but none found")
 
-    # Check data contains numeric gvi_score field
+    # Check data contains score field
+    if score_field in gpd.read_file(input_file).columns:
+        pass
+    else:
+        raise Exception("Specified score field not found in input file")
 
     # Load input data
     gdf = gpd.read_file(input_file)
 
     # Exclude points with no GVI score
-    gdf = gdf[~gdf.gvi_score.isna()]
+    gdf = gdf[~gdf[score_field].isna()]
+
+    # Check score field is numeric - if not, convert
+    if gdf[score_field].dtype == pd.StringDtype:
+        try:
+            gdf[score_field] = pd.to_numeric(gdf[score_field])
+        except Exception as e:
+            raise Exception("Could not convert score field to numeric data type")
 
     # Assign points to h3 cells at the selected resolution
     gdf_h3 = gdf.h3.geo_to_h3(cell_resolution).reset_index()
 
     # Aggregate the points to the assigned h3 cell
     gvi_mean = gdf_h3.groupby("h3_" + f"{cell_resolution:02}").agg(
-        {"gvi_score": "mean"}
+        {score_field: "mean"}
     )
 
     # Convert the h3 cells to polygons