Minor refactor to accommodate sample weighting csv generation.

main.py

@@ -1,11 +1,11 @@
 from utils.forecast import fetch_and_clean_data
-from utils.weights import apply_weights
+from utils.weights import apply_weights_simple
 from utils.zones import get_ventilation_index_zones
 
 
 def main():
     forecast_data = fetch_and_clean_data()
-    weighted_forecast_data = apply_weights(forecast_data)
+    weighted_forecast_data = apply_weights_simple(forecast_data)
     ventilation_index_zones = get_ventilation_index_zones()
 
 

tests/test_weights.py

@@ -3,7 +3,7 @@
 from numpy.testing import assert_equal
 from pandas import DataFrame
 
-from utils.weights import _create_weight_column, _get_weights
+from utils.weights import _create_weight_column, _apply_weight_of_1
 
 
 def test_create_weights_column():
@@ -77,7 +77,7 @@ def test_get_weights():
         "024": test_geo_data_frame_2,
     }
 
-    result = _get_weights(test_data)
+    result = _apply_weight_of_1(test_data)
 
     assert "012" in result
     geo_data_frame_with_weights_1 = result["012"]

utils/csv_sample.py

@@ -0,0 +1,18 @@
+from forecast import fetch_and_clean_data
+from weights import apply_weights_simple
+
+
+OUTPUT_FILEPATH = "./tmp/sample.csv"
+
+
+def generate_csv_sample() -> None:
+    # Generate a sample csv file that can be ingested by the weighting algorithm.
+    forecasts = fetch_and_clean_data()
+    single_forecast = {"012": forecasts["012"]}
+    weighted_forecast = apply_weights_simple(single_forecast)
+
+    weighted_forecast["012"].to_csv(OUTPUT_FILEPATH, columns=["y", "x", "latitude", "longitude", "weight"], index=False)
+
+
+if __name__ == "__main__":
+    generate_csv_sample()

utils/weights.py

@@ -10,7 +10,7 @@ def _create_weight_column(forecast_data: dict[str, GeoDataFrame]) -> dict[str, G
     return forecasts_with_weight_column
 
 
-def _get_weights(forecast_data: dict[str, GeoDataFrame]) -> dict[str, GeoDataFrame]:
+def _apply_weight_of_1(forecast_data: dict[str, GeoDataFrame]) -> dict[str, GeoDataFrame]:
     # For now, fill the weights with 1. At some point in the future we will have
     # a weights configuration file generated by data scientists.
     forecasts_with_weights = {}
@@ -21,7 +21,7 @@ def _get_weights(forecast_data: dict[str, GeoDataFrame]) -> dict[str, GeoDataFra
     return forecasts_with_weights
 
 
-def apply_weights(forecast_data: dict[str, GeoDataFrame]) -> dict[str, GeoDataFrame]:
+def apply_weights_simple(forecast_data: dict[str, GeoDataFrame]) -> dict[str, GeoDataFrame]:
     forecast_data_with_column = _create_weight_column(forecast_data)
-    weighted_data = _get_weights(forecast_data_with_column)
+    weighted_data = _apply_weight_of_1(forecast_data_with_column)
     return weighted_data