Merge branch 'master' into fix-download

Author: reyery

cea/datamanagement/surroundings_helper.py

@@ -33,15 +33,15 @@ def generate_empty_surroundings(crs) -> gdf:
     return gdf(columns=["name", "height_ag", "floors_ag"], geometry=[], crs=crs)
 
 
-def calc_surrounding_area(zone_gdf, buffer_m):
+def calc_surrounding_area(zone_gdf: gdf, buffer_m: float):
     """
     Adds buffer to zone to get surroundings area
 
     :param geopandas.GeoDataFrame zone_gdf: Zone GeoDataFrame
     :param float buffer_m: Buffer to add to zone building geometries
     :return: Surrounding area GeoDataFrame
     """
-    merged_zone = zone_gdf.geometry.unary_union
+    merged_zone = zone_gdf.geometry.union_all()
     if isinstance(merged_zone, MultiPolygon):
         merged_zone = merged_zone.convex_hull
 

cea/datamanagement/weather_helper/weather_helper.py

@@ -35,7 +35,7 @@ def fetch_weather_data(weather_file: str, zone_file: str):
     weather_data = gpd.read_file(WEATHER_DATA_LOCATION)
 
     # Find nearest weather data based on centroid of zone
-    centroid = zone_gdf.make_valid().to_crs(weather_data.crs).unary_union.centroid
+    centroid = zone_gdf.make_valid().to_crs(weather_data.crs).union_all().centroid
     index = weather_data.sindex.nearest(centroid)[1][0]
     url = f"https://climate.onebuilding.org/{weather_data.iloc[index]['url']}"
     data_source_url = "https://climate.onebuilding.org/sources/default.html"

cea/datamanagement/zone_helper.py

@@ -10,7 +10,7 @@
 import numpy as np
 import osmnx
 from geopandas import GeoDataFrame as Gdf
-from shapely.geometry import Polygon
+from shapely import Polygon
 import pandas as pd
 
 import cea.config

cea/demand/demand_writers.py

@@ -11,20 +11,19 @@
 import numpy as np
 import pandas as pd
 
-from cea.demand.time_series_data import (EnergyBalanceDashboard, Solar, ElectricalLoads, HeatingLoads, 
+from cea.demand.time_series_data import (ElectricalLoads, HeatingLoads, 
                                        CoolingLoads, FuelSource, HeatingSystemMassFlows, 
                                        CoolingSystemMassFlows, HeatingSystemTemperatures, 
                                        CoolingSystemTemperatures, RCModelTemperatures)
 
+from cea.utilities.reporting import TSD_KEYS_ENERGY_BALANCE_DASHBOARD, TSD_KEYS_SOLAR
+
 if TYPE_CHECKING:
     from cea.demand.building_properties.building_properties_row import BuildingPropertiesRow
     from cea.demand.time_series_data import TimeSeriesData
 
 FLOAT_FORMAT = '%.3f'
 
-TSD_KEYS_ENERGY_BALANCE_DASHBOARD = list(EnergyBalanceDashboard.__dataclass_fields__.keys())
-TSD_KEYS_SOLAR = list(Solar.__dataclass_fields__.keys())
-
 
 def get_all_load_keys():
     """Get all available load keys from time series data classes."""
@@ -66,7 +65,7 @@ def __init__(self, loads=None, massflows=None, temperatures=None):
         self.mass_flow_vars = massflows if massflows else get_all_massflow_keys()
         self.temperature_vars = temperatures if temperatures else get_all_temperature_keys()
 
-        self.load_plotting_vars = TSD_KEYS_ENERGY_BALANCE_DASHBOARD + TSD_KEYS_SOLAR
+        self.load_plotting_vars = TSD_KEYS_ENERGY_BALANCE_DASHBOARD | TSD_KEYS_SOLAR
 
         self.OTHER_VARS = ['name', 'Af_m2', 'Aroof_m2', 'GFA_m2', 'Aocc_m2', 'people0']
 

cea/demand/thermal_loads.py

@@ -204,8 +204,9 @@ def write_results(bpr: BuildingPropertiesRow, building_name, date, locator, reso
         print('Creating instant plotly visualizations of demand variable time series.')
         print('Behavior can be changed in cea.utilities.reporting code.')
         print('Writing detailed demand results of {} to .xls file.'.format(building_name))
-        reporting.quick_visualization_tsd(tsd, locator.get_demand_results_folder(), building_name)
-        reporting.full_report_to_xls(tsd, locator.get_demand_results_folder(), building_name)
+        tsd_df = reporting.calc_full_hourly_dataframe(tsd, date)
+        reporting.quick_visualization_tsd(tsd_df, locator.get_demand_results_folder(), building_name)
+        reporting.full_report_to_xls(tsd_df, locator.get_demand_results_folder(), building_name)
 
     writer.results_to_csv(tsd, bpr, locator, date, building_name)
 

cea/demand/time_series_data.py

@@ -838,4 +838,60 @@ def get_temperature_value(self, temperature_type: str):
             if hasattr(obj, temperature_type):
                 return getattr(obj, temperature_type)
 
-        raise ValueError(f"Temperature type '{temperature_type}' not found in any temperature properties.")
+        raise ValueError(f"Temperature type '{temperature_type}' not found in any temperature properties.")
+
+    def get_moisture_value(self, moisture_type: str):
+        """
+        Get the moisture values for a specific moisture measurement type.
+        """
+        load_objects = [
+            self.moisture
+        ]
+
+        for obj in load_objects:
+            if hasattr(obj, moisture_type):
+                return getattr(obj, moisture_type)
+
+        raise ValueError(f"Moisture type '{moisture_type}' not found in any moisture properties.")
+
+    def get_ventilation_mass_flow_value(self, ventilation_type: str):
+        """
+        Get the ventilation mass flow value for a specific ventilation type.
+        """
+        load_objects = [
+            self.ventilation_mass_flows
+        ]
+
+        for obj in load_objects:
+            if hasattr(obj, ventilation_type):
+                return getattr(obj, ventilation_type)
+
+        raise ValueError(f"Moisture type '{ventilation_type}' not found in any ventilation properties.")
+
+    def get_occupancy_value(self, occupancy_var: str):
+        """
+        Get the value for a specific occupancy-related variable.
+        """
+        load_objects = [
+            self.occupancy
+        ]
+
+        for obj in load_objects:
+            if hasattr(obj, occupancy_var):
+                return getattr(obj, occupancy_var)
+
+        raise ValueError(f"Moisture type '{occupancy_var}' not found in any occupancy-related properties.")
+
+    def get_solar_value(self, solar_irradiation_type: str):
+        """
+        Get the solar irradiation value for a specific solar irradiation type.
+        """
+        load_objects = [
+            self.solar
+        ]
+
+        for obj in load_objects:
+            if hasattr(obj, solar_irradiation_type):
+                return getattr(obj, solar_irradiation_type)
+
+        raise ValueError(f"Moisture type '{solar_irradiation_type}' not found in any occupancy-related properties.")

cea/interfaces/cli/excel_to_shapefile.py

@@ -44,9 +44,9 @@ def excel_to_shapefile(excel_file, shapefile, index, crs, polygon=True):
     """
     df = pd.read_excel(excel_file)
     if polygon:
-        geometry = [shapely.geometry.polygon.Polygon(json.loads(g)) for g in df.geometry]
+        geometry = [shapely.Polygon(json.loads(g)) for g in df.geometry]
     else:
-        geometry = [shapely.geometry.LineString(json.loads(g)) for g in df.geometry]
+        geometry = [shapely.LineString(json.loads(g)) for g in df.geometry]
     df.drop('geometry', axis=1)
 
     gdf = gpd.GeoDataFrame(df, crs=crs, geometry=geometry)

cea/interfaces/cli/shapefile_to_excel.py

@@ -46,14 +46,14 @@ def shapefile_to_excel(shapefile, excel_file, index=None):
 
 
 def serialize_geometry(geometry):
-    """Take a shapely.geometry.polygon.Polygon and represent it as a string of tuples (x, y)
+    """Take a shapely.Polygon and represent it as a string of tuples (x, y)
 
     :param geometry: a polygon or polyline to extract the points from and represent as a json object
-    :type geometry: shapely.geometry.polygon.Polygon
+    :type geometry: shapely.Polygon
     """
-    if isinstance(geometry, shapely.geometry.polygon.Polygon):
+    if isinstance(geometry, shapely.Polygon):
         points = list(geometry.exterior.coords)
-    elif isinstance(geometry, shapely.geometry.LineString):
+    elif isinstance(geometry, shapely.LineString):
         points = list(geometry.coords)
     else:
         raise ValueError("Expected either a Polygon or a LineString, got %s" % type(geometry))

cea/optimization_new/network.py

@@ -25,7 +25,7 @@
 from geopandas import GeoDataFrame as Gdf
 import networkx as nx
 from networkx.algorithms.approximation.steinertree import steiner_tree
-from shapely.geometry import LineString, Point
+from shapely import LineString, Point
 import wntr
 import random
 

cea/technologies/network_layout/connectivity_potential.py

@@ -11,7 +11,7 @@
 
 import pandas as pd
 from geopandas import GeoDataFrame as gdf
-from shapely.geometry import Point, LineString, MultiPoint, box
+from shapely import Point, LineString, MultiPoint, box
 from shapely.ops import split, linemerge, snap
 
 import cea.config
@@ -211,7 +211,7 @@ def snappy_endings(lines, max_distance, crs):
     return df
 
 
-def split_line_by_nearest_points(gdf_line, gdf_points, tolerance_grid_snap, crs):
+def split_line_by_nearest_points(gdf_line: gdf, gdf_points: gdf, tolerance_grid_snap: float, crs: str):
     """
     Split the union of lines with the union of points resulting
 
@@ -225,8 +225,8 @@ def split_line_by_nearest_points(gdf_line, gdf_points, tolerance_grid_snap, crs)
     """
 
     # union all geometries
-    line = gdf_line.geometry.unary_union
-    snap_points = gdf_points.geometry.unary_union
+    line = gdf_line.geometry.union_all()
+    snap_points = gdf_points.geometry.union_all()
 
     # snap and split coords on line
     # returns GeometryCollection