good coding pratices, come aboard!

dashboard/statistics_funcs.py

@@ -408,18 +408,3 @@ def create_rev_date(row):
 }
 
 global_exclusions = [{"points": ["<style>", "</style>"], "multiline": True}]
-
-
-# it needs to be here rather than in the main script:
-gdfs_dict = get_gdfs_dict(include_all_data_dummy=True)
-
-# # for the inclusion of all data:
-# gdfs_dict["all_data"] = None
-
-updating_dicts = {}
-for category in paths_dict["data"]:
-
-    if os.path.exists(paths_dict["versioning"].get(category)):
-        updating_dicts[category] = pd.read_json(versioning_dict[category])
-    else:
-        updating_dicts[category] = pd.DataFrame()

dashboard/statistics_generation.py

@@ -12,7 +12,10 @@
 
 single_chart = args.single
 
-# data adaptation
+# loading the data:
+gdfs_dict = get_gdfs_dict(include_all_data_dummy=True)
+
+# data adaptation (preprocessing)
 for category in gdfs_dict:
     # creating the  folder if it does not exist
     create_folder_if_not_exists(os.path.join(statistics_basepath, category))
@@ -23,57 +26,13 @@
 
     # creating a ref to improve readability
     cat_gdf = gdfs_dict[category]
-    update_df = updating_dicts[category]
 
     print("Adaptations for:", category)
 
     # creating additional fields
 
     if "LineString" in geom_type_dict[category]:
         create_length_field(cat_gdf)
-        # create_weblink_field(cat_gdf)
-    # elif "Point" in geom_type_dict[category]:
-    #     create_weblink_field(cat_gdf, "Point")
-
-    # uncertain about polygon cases
-    # elif (:
-    #     geom_type_dict[category] == "Polygon"
-    #     or geom_type_dict[category] == "MultiPolygon"
-    # ):
-    #     create_weblink_field(gdfs_dict[category])
-
-    if "survey:date" in cat_gdf.columns:
-        cat_gdf["Year of Survey"] = cat_gdf["survey:date"].apply(get_year_surveydate)
-
-    # # updating info:
-    # update_df["month_year"] = (
-    #     update_df["rev_month"].map("{:02d}".format)
-    #     + "_"
-    #     + update_df["rev_year"].astype(str)
-    # )
-
-    # update_df["year_month"] = (
-    #     update_df["rev_year"].astype(str)
-    #     + "_"
-    #     + update_df["rev_month"].map("{:02d}".format)
-    # )
-
-    # update_df.sort_values("year_month", inplace=True)
-
-    # # Fill missing values with a default (e.g., 1 for month or day) TODO: move to data adaptation script
-    # update_df["rev_year"] = (
-    #     update_df["rev_year"].fillna(default_missing_year).astype(int)
-    # )
-    # update_df["rev_month"] = (
-    #     update_df["rev_month"].fillna(default_missing_month).astype(int)
-    # )
-    # update_df["rev_day"] = update_df["rev_day"].fillna(default_missing_day).astype(int)
-
-    # update_df["rev_date_obj"] = update_df.apply(create_rev_date, axis=1)
-
-    # update_df["age_years"] = (
-    #     pd.Timestamp(datetime.today()) - update_df["rev_date_obj"]
-    # ).dt.days / 365.25
 
     cat_gdf["category"] = category
 
@@ -86,7 +45,7 @@
 
 # generating the charts by using the specifications
 with open(os.path.join(statistics_basepath, "failed_gen.txt"), "w+") as error_report:
-    charts_specs = get_charts_specs()
+    charts_specs = get_charts_specs(gdfs_dict=gdfs_dict)
 
     for category in charts_specs:
         generated_list_dict[category] = []

dashboard/statistics_specs.py

@@ -2,7 +2,7 @@
 
 
 # to avoid issues with circular imports it was encapsulated in a function:
-def get_charts_specs():
+def get_charts_specs(gdfs_dict):
     return {
         "sidewalks": {
             "sidewalks_smoothness_x_surface": {