fix merge issues

urbanopt_des/urbanopt_analysis.py

@@ -1034,7 +1034,7 @@ def create_summary_results(self) -> None:
             # only save off the useful columns for the summary table
             year_end = f"{self.year_of_data}-12-31"
             summary_data_columns = ["Metric", "Units"]
-            for analysis_name in ["Non-Connected"], *list(self.modelica.keys()):
+            for analysis_name in ["Non-Connected", *list(self.modelica.keys())]:
                 summary_data_columns.append(analysis_name)
                 if analysis_name == "Non-Connected":
                     self.urbanopt.grid_metrics_daily

urbanopt_des/urbanopt_geojson.py

@@ -1,7 +1,11 @@
 import json
+import tempfile
 from pathlib import Path
 from typing import Union
 
+from geopandas import GeoDataFrame
+from shapely.geometry import box
+
 
 class URBANoptGeoJSON:
     def __init__(self, filename: Path):
@@ -138,23 +142,68 @@ def create_aggregated_representation(self, building_names: list[str]) -> None:
         """Go through the GeoJSON file and if it is of type Building, then aggregate the characteristics.
 
         #TODO: This is a work in progress and can more easily be accomplished with GeoPandas."""
-        # fields that are summed in the aggregations
-        sum_fields = ["floor_area", "footprint_area"]
-        avg_fields = [
-            "year_built",
-            "number_of_stories",
-            "window_wall_ratio",
-        ]
-
-        # create a dictionary to hold the aggregated values
-        aggregated_values = {}
-        for field in sum_fields:
-            aggregated_values[field] = 0
-        for field in avg_fields:
-            aggregated_values[field] = 0
-
-        for building_id in building_names:
-            pass
+
+        # pull out the project data, because it will need to be stitched into
+        # the geojson file at the end
+        project_data = self.data["project"]
+
+        # read into geodataframe
+        gdf = GeoDataFrame.from_features(self.data)
+
+        # related is a list typically, which isn't supported in geodataframes
+        gdf = gdf.drop(columns=["related"])
+
+        # init new obj, can delete this once the if statement below is
+        # fully fleshed out.
+        gdf_2 = None
+
+        # add a new field to "enable/disable"
+        gdf["enabled"] = True
+        if len(building_names) == 1 and building_names[0] == "all":
+            # dissolve
+            gdf_2 = gdf.dissolve(
+                by="type",
+                aggfunc={
+                    "footprint_area": "sum",
+                    "Footprint Area (m2)": "sum",
+                    "Footprint Area (ft2)": "sum",
+                    "Height": "mean",
+                    "floor_area": "sum",
+                    "Gross Floor Area": "sum",
+                    "number_of_stories": "mean",
+                    "number_of_stories_above_ground": "mean",
+                    "Building Levels": "mean",
+                    "attic_type": "any",
+                    "foundation_type": "any",
+                    "number_of_bedrooms": "mean",
+                    "number_of_residential_units": "mean",
+                    "enabled": "any",
+                    "id": "first",
+                },
+            )
+
+            # splat the total_bounds into a box (total_bounds returns minx, miny, maxx, maxy)
+            gdf_2["geometry"] = box(*gdf_2.total_bounds)
+
+            # Set the id on the first element to "all""
+            gdf_2.loc[gdf_2.index == "Building", "id"] = "all_buildings"
+        else:
+            print("This still needs to be implemented")
+
+        # save the gdf_2 to a temp geojson file
+        temp_dir = Path(tempfile.mkdtemp())
+
+        if gdf_2 is not None:
+            gdf_2.to_file(temp_dir / "temp.geojson", driver="GeoJSON")
+
+        # read it back in as a dict
+        with open(temp_dir / "temp.geojson") as f:
+            gdf_2 = json.load(f)
+
+            # update the project data
+            gdf_2["project"] = project_data
+
+        return gdf_2
 
     def save(self) -> None:
         """Save the GeoJSON file"""

urbanopt_des/urbanopt_results.py

@@ -629,7 +629,7 @@ def process_load_results(self, building_names: list[str], year_of_data: int = 20
                 # skip if Datetime
                 if column == "Datetime":
                     continue
-                load_report.rename(columns={column: f"{column} Building {building_id}"})
+                load_report = load_report.rename(columns={column: f"{column} Building {building_id}"})
 
             # convert Datetime column in data frame to be datetime from the string. The year
             # should be set to a year that has the day of week starting correctly for the real data
@@ -902,10 +902,14 @@ def create_abstract_run(self, run_id: str, load_dataframe: pd.DataFrame) -> None
         # duplicate the load_dataframe so that we can calculate the seconds in the year
         tmp_dataframe = load_dataframe.copy()
         # time column is seconds from the start of the year, as integers
-        tmp_dataframe["time"] = (tmp_dataframe.index - tmp_dataframe.index[0]).total_seconds() + 3600
+        tmp_dataframe["time"] = (tmp_dataframe.index - tmp_dataframe.index[0]).total_seconds()
+        # the last timestamp is weird as it will be negative. Take the second to last value and add 3600
+        tmp_dataframe["time"].iloc[-1] = tmp_dataframe["time"].iloc[-2] + 3600
+        # the first value of the hot water must be zero, else there will be an error
+        tmp_dataframe["TotalWaterHeating"].iloc[0] = 0
+
         # coerce time into int
         tmp_dataframe["time"] = tmp_dataframe["time"].astype(int)
-        print(tmp_dataframe["time"])
         header = "#1\n"
         header += "#Created from results of URBANopt\n\n"
         header += "#First column: Seconds in the year (loads are hourly)\n"