Allow running pv plots without running demand

cea/analysis/lca/embodied.py

@@ -15,7 +15,7 @@
 import cea.inputlocator
 from cea.constants import SERVICE_LIFE_OF_BUILDINGS, SERVICE_LIFE_OF_TECHNICAL_SYSTEMS, \
     CONVERSION_AREA_TO_FLOOR_AREA_RATIO, EMISSIONS_EMBODIED_TECHNICAL_SYSTEMS
-from cea.datamanagement.void_deck_migrator import migrate_void_deck_data
+from cea.datamanagement.utils import migrate_void_deck_data
 
 __author__ = "Jimeno A. Fonseca"
 __copyright__ = "Copyright 2015, Architecture and Building Systems - ETH Zurich"

cea/datamanagement/archetypes_mapper.py

@@ -100,6 +100,7 @@ def archetypes_mapper(locator,
         supply_mapper(locator, building_typology_df)
 
 
+
 def indoor_comfort_mapper(list_uses, locator, occupant_densities, building_typology_df):
     comfort_DB = pd.read_csv(locator.get_database_archetypes_use_type())
     # define comfort
@@ -400,7 +401,6 @@ def verify_building_standards(building_typology_df, db_standards):
         diff = typology_standards.difference(db_standards)
         raise ValueError(f'The following standards are not found in the database: {", ".join(diff)}')
 
-
 def main(config):
     """
     Run the properties script with input from the reference case and compare the results. This ensures that changes

cea/datamanagement/format_helper/cea4_migrate.py

@@ -24,6 +24,7 @@
 from cea.datamanagement.format_helper.cea4_verify import cea4_verify, verify_shp, \
     COLUMNS_ZONE_4, print_verification_results_4, path_to_input_file_without_db_4, CSV_BUILDING_PROPERTIES_3_CSV
 from cea.datamanagement.format_helper.cea4_verify_db import check_directory_contains_csv
+from cea.datamanagement.utils import migrate_void_deck_data
 from cea.utilities.dbf import dbf_to_dataframe
 
 COLUMNS_ZONE_3 = ['Name', 'floors_bg', 'floors_ag', 'height_bg', 'height_ag']
@@ -550,9 +551,12 @@ def migrate_cea3_to_cea4(scenario, verbose=False):
                 pass
                 # print('For Scenario: {scenario}, '.format(scenario=scenario_name), 'zone.shp already follows the CEA-4 format.')
             else:
-                raise ValueError('! zone.shp exists but follows neither the CEA-3 nor CEA-4 format. CEA cannot proceed with the data migration.'
-                                 'Check the following column(s) for CEA-3 format: {list_missing_attributes_zone_3}.'.format(list_missing_attributes_zone_3=list_missing_attributes_zone_3),
-                                 'Check the following column(s) for CEA-4 format: {list_missing_attributes_zone_4}.'.format(list_missing_attributes_zone_4=list_missing_attributes_zone_4)
+                if list_missing_attributes_zone_4[0] == 'void_deck' and len(list_missing_attributes_zone_4) == 1:
+                    config = cea.config.Configuration()
+                    locator = cea.inputlocator.InputLocator(config.scenario)
+                    migrate_void_deck_data(locator)
+                else:
+                    raise ValueError('! zone.shp exists but follows neither the CEA-3 nor CEA-4 format. CEA cannot proceed with the data migration. Check the following column(s) for CEA-3 format: {list_missing_attributes_zone_3}.'.format(list_missing_attributes_zone_3=list_missing_attributes_zone_3), 'Check the following column(s) for CEA-4 format: {list_missing_attributes_zone_4}.'.format(list_missing_attributes_zone_4=list_missing_attributes_zone_4)
                                  )
         else:
             print("! Ensure zone.shp (CEA-3 format) is present in building-geometry folder.")
@@ -571,9 +575,12 @@ def migrate_cea3_to_cea4(scenario, verbose=False):
                 pass
                 # print('For Scenario: {scenario}, '.format(scenario=scenario_name), 'surroundings.shp already follows the CEA-4 format.')
             else:
-                raise ValueError('surroundings.shp exists but follows neither the CEA-3 nor CEA-4 format. CEA cannot proceed with the data migration.'
-                                 'Check the following column(s) for CEA-3 format: {list_missing_attributes_surroundings_3}.'.format(list_missing_attributes_surroundings_3=list_missing_attributes_surroundings_3),
-                                 'Check the following column(s) for CEA-4 format: {list_missing_attributes_surroundings_4}.'.format(list_missing_attributes_surroundings_4=list_missing_attributes_surroundings_4)
+                if list_missing_attributes_zone_4[0] == 'void_deck' and len(list_missing_attributes_zone_4) == 1:
+                    config = cea.config.Configuration()
+                    locator = cea.inputlocator.InputLocator(config.scenario)
+                    migrate_void_deck_data(locator)
+                else:
+                    raise ValueError('surroundings.shp exists but follows neither the CEA-3 nor CEA-4 format. CEA cannot proceed with the data migration. Check the following column(s) for CEA-3 format: {list_missing_attributes_surroundings_3}.'.format(list_missing_attributes_surroundings_3=list_missing_attributes_surroundings_3), 'Check the following column(s) for CEA-4 format: {list_missing_attributes_surroundings_4}.'.format(list_missing_attributes_surroundings_4=list_missing_attributes_surroundings_4)
                                  )
         else:
             print('! (optional) Run Surroundings Helper to generate surroundings.shp after the data migration.')

cea/datamanagement/utils/__init__.py

@@ -0,0 +1,121 @@
+from __future__ import annotations
+from typing import TYPE_CHECKING
+import warnings
+
+import pandas as pd
+import geopandas as gpd
+
+if TYPE_CHECKING:
+    from cea.inputlocator import InputLocator
+
+
+def migrate_void_deck_data(locator: InputLocator) -> None:
+    """Check if void_deck exists in zone.shp and copy it from envelope.csv if necessary.
+
+    :param locator: the input locator object.
+    :type locator: cea.inputlocator.InputLocator
+    """
+
+    zone_gdf = gpd.read_file(locator.get_zone_geometry())
+    isin_zone = "void_deck" in zone_gdf.columns
+
+    if not isin_zone:
+        envelope_df = pd.read_csv(locator.get_building_architecture())
+
+        if "void_deck" in envelope_df.columns:
+            # assign void_deck from envelope.csv to zone.shp and remove it from envelope.csv
+            zone_gdf = zone_gdf.merge(
+                envelope_df[["name", "void_deck"]], on="name", how="left"
+            )
+            zone_gdf["void_deck"] = zone_gdf["void_deck"].fillna(0)
+            print("Migrated void_deck data from envelope.csv to zone.shp.")
+            envelope_df.drop(columns=["void_deck"], inplace=True)
+            envelope_df.to_csv(locator.get_building_architecture(), index=False)
+
+        else:  # cannot find void_deck anywhere, just initialize it to 0
+            zone_gdf["void_deck"] = 0
+            warnings.warn(
+                "No void_deck data found in envelope.csv, setting to 0 in zone.shp"
+            )
+
+    # Validate that floors_ag is larger than void_deck for each building
+    actual_floors = zone_gdf["floors_ag"] - zone_gdf["void_deck"]
+    invalid_floors = zone_gdf[actual_floors <= 0]
+    if len(invalid_floors) > 0:
+        invalid_buildings = invalid_floors["name"].tolist()
+        warnings.warn(f"Some buildings have void_deck greater than floors_ag: {invalid_buildings}",
+                      RuntimeWarning)
+
+
+def generate_architecture_csv(locator: InputLocator, building_typology_df: gpd.GeoDataFrame):
+    """
+    Generate an architecture CSV file with geometric properties
+
+    Includes:
+    - Af_m2: Conditioned floor area [m2]
+    - Aroof_m2: Roof area [m2]
+    - GFA_m2: Gross floor area [m2]
+    - Aocc_m2: Occupied floor area [m2]
+    
+    :param locator: InputLocator instance
+    :param building_typology_df: GeoDataFrame containing building geometry data
+    """
+    # Get architecture database to access Hs, Ns, Es, occupied_bg values
+    architecture_DB = pd.read_csv(locator.get_database_archetypes_construction_type())
+    prop_architecture_df = building_typology_df.merge(architecture_DB, left_on='const_type', right_on='const_type',
+                                                    # avoid column name conflicts and keep left ones
+                                                    # possible conflicts: 'void_deck'
+                                                    suffixes=('', '_y'))
+
+    # Calculate architectural properties
+    # Calculate areas based on geometry
+    footprint = prop_architecture_df.geometry.area  # building footprint area
+    floors_ag = prop_architecture_df['floors_ag']  # above-ground floors
+    floors_bg = prop_architecture_df['floors_bg']  # below-ground floors
+    void_deck = prop_architecture_df['void_deck']  # void deck floors
+
+    # Get shares from architecture database
+    Hs = prop_architecture_df['Hs']  # Share of GFA that is conditioned
+    Ns = prop_architecture_df['Ns']  # Share of GFA that is occupied
+    occupied_bg = prop_architecture_df['occupied_bg']  # Whether basement is occupied
+
+    # Calculate GFA components using proper equations
+    gfa_ag_m2 = footprint * (floors_ag - void_deck)  # Above-ground GFA
+    gfa_bg_m2 = footprint * floors_bg  # Below-ground GFA
+    gfa_m2 = gfa_ag_m2 + gfa_bg_m2  # Total GFA
+
+    # Split shares between above and below ground areas
+    # Using the same logic as in useful_areas.py split_above_and_below_ground_shares
+    effective_floors_ag = floors_ag - void_deck
+    denominator = effective_floors_ag + floors_bg * occupied_bg
+    share_ag = effective_floors_ag / denominator
+    # Handle division by zero case
+    share_ag = share_ag.fillna(1.0).where(denominator > 0, 1.0)
+    share_bg = 1 - share_ag
+
+    Hs_ag = Hs * share_ag
+    Hs_bg = Hs * share_bg
+    Ns_ag = Ns * share_ag
+    Ns_bg = Ns * share_bg
+
+    # Calculate areas using proper equations from useful_areas.py
+    af_m2 = gfa_ag_m2 * Hs_ag + gfa_bg_m2 * Hs_bg  # Conditioned floor area
+    aocc_m2 = gfa_ag_m2 * Ns_ag + gfa_bg_m2 * Ns_bg  # Occupied floor area
+    aroof_m2 = footprint  # Roof area equals footprint
+
+    # Create DataFrame directly from vectorized calculations
+    architecture_df = pd.DataFrame({
+        'name': prop_architecture_df['name'],
+        'Af_m2': af_m2,
+        'Aroof_m2': aroof_m2,
+        'GFA_m2': gfa_m2,
+        'Aocc_m2': aocc_m2,
+    })
+
+    # Ensure parent folder exists
+    locator.ensure_parent_folder_exists(locator.get_architecture_csv())
+
+    # Save to CSV file
+    architecture_df.to_csv(locator.get_architecture_csv(), index=False, float_format='%.3f')
+    print(f"Architecture data generated and saved to: {locator.get_architecture_csv()}")
+

cea/demand/demand_main.py

@@ -6,6 +6,8 @@
 import time
 from itertools import repeat
 
+import geopandas as gpd
+
 import cea.config
 import cea.inputlocator
 import cea.utilities.parallel
@@ -15,7 +17,7 @@
 from cea.utilities import epwreader
 from cea.utilities.date import get_date_range_hours_from_year
 from cea.demand import demand_writers
-from cea.datamanagement.void_deck_migrator import migrate_void_deck_data
+from cea.datamanagement.utils import generate_architecture_csv, migrate_void_deck_data
 
 
 __author__ = "Jimeno A. Fonseca"
@@ -110,6 +112,10 @@ def main(config):
 
     if not radiation_files_exist(locator, config):
         raise MissingInputDataException("Missing radiation data in scenario. Consider running radiation script first.")
+
+    # Ensure that the architecture csv is generated
+    zone_df = gpd.read_file(locator.get_zone_geometry())
+    generate_architecture_csv(locator, zone_df)
 
     demand_calculation(locator=locator, config=config)
 

cea/demand/demand_writers.py

@@ -87,7 +87,7 @@ def results_to_hdf5(self, tsd: TimeSeriesData, bpr: BuildingPropertiesRow, locat
         self.write_to_hdf5(building_name, columns, hourly_data, locator)
 
         # save total for the year
-        columns, data = self.calc_yearly_dataframe(bpr, building_name, tsd)
+        columns, data = self.calc_yearly_dataframe(bpr, building_name, tsd, locator)
         # save to disc
         partial_total_data = pd.DataFrame(data, index=[0])
         partial_total_data.drop('name', inplace=True, axis=1)
@@ -101,12 +101,12 @@ def results_to_csv(self, tsd: TimeSeriesData, bpr: BuildingPropertiesRow, locato
         self.write_to_csv(building_name, columns, hourly_data, locator)
 
         # save annual values to a temp file for YearlyDemandWriter
-        columns, data = self.calc_yearly_dataframe(bpr, building_name, tsd)
+        columns, data = self.calc_yearly_dataframe(bpr, building_name, tsd, locator)
         pd.DataFrame(data, index=[0]).to_csv(
             locator.get_temporary_file('%(building_name)sT.csv' % locals()),
             index=False, columns=columns, float_format='%.3f', na_rep='nan')
 
-    def calc_yearly_dataframe(self, bpr: BuildingPropertiesRow, building_name, tsd: TimeSeriesData):
+    def calc_yearly_dataframe(self, bpr: BuildingPropertiesRow, building_name, tsd: TimeSeriesData, locator):
         # if printing total values is necessary
         # treating timeseries data from W to MWh
         data = dict((x + '_MWhyr', np.nan_to_num(tsd.get_load_value(x)).sum() / 1000000) for x in self.load_vars)
@@ -115,9 +115,17 @@ def calc_yearly_dataframe(self, bpr: BuildingPropertiesRow, building_name, tsd:
         keys = data.keys()
         columns = self.OTHER_VARS
         columns.extend(keys)
+
+        # get the architecture data
+        architecture_df = pd.read_csv(locator.get_architecture_csv())
+        Af_m2 = float(architecture_df.loc[architecture_df['name'] == building_name, 'Af_m2'].iloc[0])
+        Aroof_m2 = float(architecture_df.loc[architecture_df['name'] == building_name, 'Aroof_m2'].iloc[0])
+        GFA_m2 = float(architecture_df.loc[architecture_df['name'] == building_name, 'GFA_m2'].iloc[0])
+        Aocc_m2 = float(architecture_df.loc[architecture_df['name'] == building_name, 'Aocc_m2'].iloc[0])
+
         # add other default elements
-        data.update({'name': building_name, 'Af_m2': bpr.rc_model.Af, 'Aroof_m2': bpr.rc_model.Aroof,
-                     'GFA_m2': bpr.rc_model.GFA_m2, 'Aocc_m2': bpr.rc_model.Aocc,
+        data.update({'name': building_name, 'Af_m2': Af_m2, 'Aroof_m2': Aroof_m2,
+                     'GFA_m2': GFA_m2, 'Aocc_m2': Aocc_m2,
                      'people0': tsd.occupancy.people.max()})
         return columns, data
 

cea/demand/occupancy_helper.py

@@ -13,7 +13,7 @@
 import cea.utilities.parallel
 from cea.constants import HOURS_IN_YEAR, MONTHS_IN_YEAR
 from cea.datamanagement.schedule_helper import read_cea_schedule
-from cea.datamanagement.void_deck_migrator import migrate_void_deck_data
+from cea.datamanagement.utils import migrate_void_deck_data
 from cea.demand.building_properties import calc_useful_areas
 from cea.demand.constants import VARIABLE_CEA_SCHEDULE_RELATION
 from cea.utilities import epwreader

cea/import_export/result_summary.py

@@ -178,7 +178,7 @@ def get_results_path(locator, cea_feature, list_buildings):
     list_appendix = []
 
     if cea_feature == 'architecture':
-        path = locator.get_total_demand()
+        path = locator.get_architecture_csv()
         list_paths.append(path)
         list_appendix.append(cea_feature)
 
@@ -2142,7 +2142,12 @@ def normalize_dataframe(df, area_column):
         df_time_path = locator.get_export_results_summary_cea_feature_time_period_file(
             summary_folder, cea_feature, appendix, time_period, hour_start, hour_end
         )
-        df_time_resolution = pd.read_csv(df_time_path)
+
+        if not os.path.exists(df_time_path):
+            print(f"File not found: {df_time_path}.")
+            break
+        else:
+            df_time_resolution = pd.read_csv(df_time_path)
 
         if bool_use_acronym:
             df_time_resolution.columns = map_metrics_and_cea_columns(

cea/inputlocator.py

@@ -1329,6 +1329,15 @@ def get_street_network(self):
 
     # OUTPUTS
 
+    # ARCHITECTURE
+    def get_architecture_folder(self):
+        """scenario/outputs/data/architecture"""
+        return os.path.join(self.scenario, 'outputs', 'data', 'architecture')
+
+    def get_architecture_csv(self):
+        """scenario/outputs/data/architecture/architecture.csv"""
+        return os.path.join(self.get_architecture_folder(), 'architecture.csv')
+
     # SOLAR-RADIATION
     def get_solar_radiation_folder(self):
         """scenario/outputs/data/solar-radiation"""

cea/resources/radiation/main.py

@@ -14,7 +14,7 @@
 import cea.config
 import cea.inputlocator
 from cea.datamanagement.databases_verification import verify_input_geometry_zone, verify_input_geometry_surroundings
-from cea.datamanagement.void_deck_migrator import migrate_void_deck_data
+from cea.datamanagement.utils import migrate_void_deck_data
 from cea.resources.radiation import daysim, geometry_generator
 from cea.resources.radiation.daysim import GridSize
 from cea.resources.radiation.radiance import CEADaySim

cea/resources/radiationCRAX/main.py

@@ -21,7 +21,7 @@
 import cea.config
 import cea.inputlocator
 from cea.datamanagement.databases_verification import verify_input_geometry_zone, verify_input_geometry_surroundings
-from cea.datamanagement.void_deck_migrator import migrate_void_deck_data
+from cea.datamanagement.utils import migrate_void_deck_data
 from cea.resources.radiation import geometry_generator
 from cea.resources.radiation.daysim import GridSize, calc_sensors_building
 from cea.resources.radiation.geometry_generator import BuildingGeometry

cea/tests/test_calc_thermal_loads.py

@@ -8,8 +8,10 @@
 import unittest
 
 import pandas as pd
+import geopandas as gpd
 
 from cea.config import DEFAULT_CONFIG, Configuration
+from cea.datamanagement.utils import generate_architecture_csv, migrate_void_deck_data
 from cea.demand.building_properties import BuildingProperties
 from cea.demand.occupancy_helper import occupancy_helper_main
 from cea.demand.thermal_loads import calc_thermal_loads
@@ -39,6 +41,11 @@ def setUpClass(cls):
         # Extract reference case
         cls.locator = ReferenceCaseOpenLocator()
 
+        # FIXME: Update reference case to have void deck and architecture file
+        # Ensure that the architecture csv is generated
+        migrate_void_deck_data(cls.locator)
+        generate_architecture_csv(cls.locator, gpd.read_file(cls.locator.get_zone_geometry()))
+
         cls.config = Configuration(DEFAULT_CONFIG)
         cls.config.scenario = cls.locator.scenario
         cls.weather_data = epwreader.epw_reader(cls.locator.get_weather('Zug_inducity_2009'))[

cea/visualisation/a_data_loader.py

@@ -6,7 +6,7 @@
 
 import cea.inputlocator
 import os
-import cea.config
+
 from cea.import_export.result_summary import process_building_summary
 import pandas as pd