crs_conversion.py

@@ -2,17 +2,23 @@ import geopandas as gpd
 from shapely.geometry import Polygon, MultiPolygon
 from shapely.geometry.polygon import orient
 
-input_file = 'data/selected_buildings.geojson'
+# Path to the input GeoJSON file
+input_file = 'data/cmm_test.geojson'
 
-output_file = 'data/selected_buildings_corrected.geojson'
+# Path to the output GeoJSON file
+output_file = 'data/cmm_test_corrected.geojson'
 
+# Read the GeoJSON file
 gdf = gpd.read_file(input_file)
 
+# Set the CRS to EPSG:3857 if not already set
 if gdf.crs is None:
     gdf.set_crs(epsg=3857, inplace=True)
 
+# Convert the CRS to EPSG:4326 (longitude and latitude)
 gdf = gdf.to_crs(epsg=4326)
 
+# Function to reorient geometries to follow the right-hand rule
 def reorient_geometry(geom):
     if geom.is_empty:
         return geom
@@ -24,6 +30,10 @@ def reorient_geometry(geom):
     else:
         return geom
 
+# Apply the reorientation to the geometry column
 gdf['geometry'] = gdf['geometry'].apply(reorient_geometry)
 
+# Save the transformed GeoDataFrame to a new GeoJSON file
 gdf.to_file(output_file, driver='GeoJSON')
+
+print(f"Converted GeoJSON with right-hand rule orientation has been saved to {output_file}")

data/cmm_test.geojson

@@ -0,0 +1,7 @@
+{
+"type": "FeatureCollection",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
+"features": [
+{ "type": "Feature", "properties": { "cerc_id": 10000000, "provinc_id": "72015684390246800000000", "matricu_18": "684390246800000000", "feature_id": "905021fe-faed-40cb-a193-8b8b9863eb6e", "contr_year": "1986", "height": 9.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "attached", "lot_name": "1461297", "lot_area": 1247.7, "build_area": 145.3, "build_type": "1", "floor_num": 1, "unit_num": 2, "region": "layer_80", "g_objectid": 1169999.0, "g_co_mrc": "720", "g_code_mun": "72015", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.959074500485556, 45.526439602454957 ], [ -73.95906913536642, 45.52644040687705 ], [ -73.959067332931753, 45.526434277213156 ], [ -73.959072648380882, 45.526433504485176 ], [ -73.959074500485556, 45.526439602454957 ] ] ], [ [ [ -73.959284044103711, 45.526434113367664 ], [ -73.959285089854276, 45.526437239705743 ], [ -73.959323607759771, 45.526430905630932 ], [ -73.959339294217543, 45.526477977063195 ], [ -73.959365136004763, 45.526473721285491 ], [ -73.959384577774543, 45.526470522456577 ], [ -73.95939879848838, 45.526513191459713 ], [ -73.95918505777766, 45.526548375245248 ], [ -73.95915412932338, 45.526455490326235 ], [ -73.959284044103711, 45.526434113367664 ] ] ], [ [ [ -73.959318219290097, 45.526552949174281 ], [ -73.959334062104787, 45.526613722737721 ], [ -73.959216751906709, 45.5266288329885 ], [ -73.959200273841773, 45.52656565039198 ], [ -73.95922525487974, 45.526562432921921 ], [ -73.959220722410464, 45.526545052740744 ], [ -73.959294284368852, 45.526535583122445 ], [ -73.959299435528266, 45.526555364285208 ], [ -73.959318219290097, 45.526552949174281 ] ] ], [ [ [ -73.959361589683013, 45.526663537853317 ], [ -73.959418160975417, 45.526683936473432 ], [ -73.959367699213033, 45.526753086934306 ], [ -73.959311113787507, 45.526732688346264 ], [ -73.959361589683013, 45.526663537853317 ] ] ] ] } }
+]
+}

data/cmm_test_corrected.geojson

@@ -0,0 +1,7 @@
+{
+"type": "FeatureCollection",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
+"features": [
+{ "type": "Feature", "properties": { "cerc_id": 10000000, "provinc_id": "72015684390246800000000", "matricu_18": "684390246800000000", "feature_id": "905021fe-faed-40cb-a193-8b8b9863eb6e", "contr_year": "1986", "height": 9.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "attached", "lot_name": "1461297", "lot_area": 1247.7, "build_area": 145.3, "build_type": "1", "floor_num": 1, "unit_num": 2, "region": "layer_80", "g_objectid": 1169999.0, "g_co_mrc": "720", "g_code_mun": "72015", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.959074500485556, 45.526439602454957 ], [ -73.959072648380882, 45.526433504485176 ], [ -73.959067332931753, 45.526434277213156 ], [ -73.95906913536642, 45.52644040687705 ], [ -73.959074500485556, 45.526439602454957 ] ] ], [ [ [ -73.959284044103711, 45.526434113367664 ], [ -73.95915412932338, 45.526455490326235 ], [ -73.95918505777766, 45.526548375245248 ], [ -73.95939879848838, 45.526513191459713 ], [ -73.959384577774543, 45.526470522456577 ], [ -73.959365136004763, 45.526473721285491 ], [ -73.959339294217543, 45.526477977063195 ], [ -73.959323607759771, 45.526430905630932 ], [ -73.959285089854276, 45.526437239705743 ], [ -73.959284044103711, 45.526434113367664 ] ] ], [ [ [ -73.959318219290097, 45.526552949174281 ], [ -73.959299435528266, 45.526555364285208 ], [ -73.959294284368852, 45.526535583122445 ], [ -73.959220722410464, 45.526545052740744 ], [ -73.95922525487974, 45.526562432921921 ], [ -73.959200273841773, 45.52656565039198 ], [ -73.959216751906709, 45.5266288329885 ], [ -73.959334062104787, 45.526613722737721 ], [ -73.959318219290097, 45.526552949174281 ] ] ], [ [ [ -73.959361589683013, 45.526663537853317 ], [ -73.959311113787507, 45.526732688346264 ], [ -73.959367699213033, 45.526753086934306 ], [ -73.959418160975417, 45.526683936473432 ], [ -73.959361589683013, 45.526663537853317 ] ] ] ] } }
+]
+}

data/selected_buildings.geojson

@@ -1,52 +0,0 @@
-{
-"type": "FeatureCollection",
-"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
-"features": [
-{ "type": "Feature", "properties": { "cerc_id": 10042222, "provinc_id": "66058923687536920000000", "matricu_18": "923687536920000000", "feature_id": "1750228c-42f7-432c-bf99-86164e74e465", "contr_year": "1955", "height": 10.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "detached", "lot_name": "1561273", "lot_area": 421.4, "build_area": 181.0, "build_type": "1", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 1268569.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.652847091532934, 45.470789538971239 ], [ -73.652887196872726, 45.470740637941198 ], [ -73.652726589698347, 45.470675341143156 ], [ -73.652651101233815, 45.470767235562192 ], [ -73.652766698497217, 45.470814235766937 ], [ -73.65280209652326, 45.470771133385092 ], [ -73.652847091532934, 45.470789538971239 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10042223, "provinc_id": "66058923687465550000000", "matricu_18": "923687465550000000", "feature_id": "54fe98c5-2500-425c-981a-736b1a8cfa2a", "contr_year": "1955", "height": 8.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "detached", "lot_name": "1561274", "lot_area": 409.2, "build_area": 184.4, "build_type": "1", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 3069480.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.652747295978074, 45.470643337350687 ], [ -73.652909100761292, 45.470707438418266 ], [ -73.652950294053596, 45.470656535546446 ], [ -73.652904898103273, 45.470638340201873 ], [ -73.652941089903379, 45.470593737565963 ], [ -73.652823598674004, 45.470547139602402 ], [ -73.652747295978074, 45.470643337350687 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10042224, "provinc_id": "66058923687618360000000", "matricu_18": "923687618360000000", "feature_id": "2aa8756a-afd6-42ec-a962-e8c5c996fd7d", "contr_year": "1955", "height": 7.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "detached", "lot_name": "1561272", "lot_area": 421.4, "build_area": 179.7, "build_type": "1", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 3462523.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.652709790904126, 45.47096133445153 ], [ -73.652749501241331, 45.470911939982749 ], [ -73.652702295654493, 45.470893340246995 ], [ -73.652737089123505, 45.470850340376927 ], [ -73.652622996634648, 45.470805534661345 ], [ -73.652549690921319, 45.470900733936617 ], [ -73.652709790904126, 45.47096133445153 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10042330, "provinc_id": "66058923677831460000000", "matricu_18": "923677831460000000", "feature_id": "156062b2-f371-4410-90d6-09a5739307f6", "contr_year": "1955", "height": 7.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "detached", "lot_name": "1561290", "lot_area": 421.4, "build_area": 186.7, "build_type": "1", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 604572.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.653605989751256, 45.470315136029505 ], [ -73.653679197919615, 45.470344039669897 ], [ -73.653768500755149, 45.470231636424167 ], [ -73.653634388908017, 45.470178936397076 ], [ -73.653569990580365, 45.470259932850226 ], [ -73.653631497883566, 45.470284132547583 ], [ -73.653605989751256, 45.470315136029505 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10042331, "provinc_id": "66058923677970620000000", "matricu_18": "923677970620000000", "feature_id": "c37d9815-fc5d-4c33-adc8-106266ee1bfd", "contr_year": "1955", "height": 7.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "detached", "lot_name": "1561291", "lot_area": 421.4, "build_area": 186.7, "build_type": "1", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 3484657.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.653443793018795, 45.470106935668554 ], [ -73.653359900353067, 45.470219640947249 ], [ -73.653429792557731, 45.470245432125495 ], [ -73.653451292051685, 45.470217038228604 ], [ -73.653516390055771, 45.470242036728898 ], [ -73.653581298430908, 45.470157635493926 ], [ -73.653443793018795, 45.470106935668554 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10042635, "provinc_id": "66058923677254770000000", "matricu_18": "923677254770000000", "feature_id": "4ce656c8-ce53-426c-a843-60ab97cdb3b8", "contr_year": "1955", "height": 9.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "detached", "lot_name": "1561286", "lot_area": 421.4, "build_area": 186.7, "build_type": "1", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 575398.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.654309901222945, 45.470545036155421 ], [ -73.654369890444571, 45.470568536522997 ], [ -73.654345291899205, 45.470599538939211 ], [ -73.65441879851538, 45.470628337318551 ], [ -73.654512194744015, 45.47051053517287 ], [ -73.654449200628335, 45.470485835845707 ], [ -73.654462197967533, 45.470469334851281 ], [ -73.654433893608768, 45.470458336483141 ], [ -73.65441859446058, 45.470477738997204 ], [ -73.654376293669245, 45.470461134583637 ], [ -73.654309901222945, 45.470545036155421 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10042636, "provinc_id": "66058923677115430000000", "matricu_18": "923677115430000000", "feature_id": "b4e089b6-49f8-4c42-be7b-e62ea44b40c0", "contr_year": "1955", "height": 7.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "semi-detached", "lot_name": "1561285", "lot_area": 366.8, "build_area": 181.9, "build_type": "2", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 2079549.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.654474602084335, 45.470646733951 ], [ -73.65454600053863, 45.470674835917094 ], [ -73.654567994828625, 45.470646533827093 ], [ -73.654620690452504, 45.470667185113932 ], [ -73.654687518212597, 45.470580770397603 ], [ -73.654565394231369, 45.470532632760083 ], [ -73.654474602084335, 45.470646733951 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10043720, "provinc_id": "66058923668865690000000", "matricu_18": "923668865690000000", "feature_id": "56140dca-5192-43d2-8e8f-8b20c0040103", "contr_year": "1955", "height": 7.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "semi-detached", "lot_name": "1561245", "lot_area": 351.2, "build_area": 188.7, "build_type": "2", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 3072728.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.655000390259502, 45.471538937399444 ], [ -73.654976201996107, 45.471529740477251 ], [ -73.655002101287067, 45.471495940337249 ], [ -73.654833194115412, 45.471432437671524 ], [ -73.654794998416136, 45.471482837737675 ], [ -73.654833892614178, 45.471497434550578 ], [ -73.654808676852682, 45.471530547142002 ], [ -73.65494021136027, 45.471582045277302 ], [ -73.654951290564128, 45.471567940428017 ], [ -73.654971595977315, 45.471575740660001 ], [ -73.655000390259502, 45.471538937399444 ] ] ] ] } },
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-{ "type": "Feature", "properties": { "cerc_id": 10043738, "provinc_id": "66058923687622220000000", "matricu_18": "923687622220000000", "feature_id": "3f5276ec-ad3c-48ee-9168-f427a9b3121d", "contr_year": "1955", "height": 10.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "detached", "lot_name": "1561277", "lot_area": 422.1, "build_area": 181.9, "build_type": "1", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 2072159.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.652682194918924, 45.470397941292759 ], [ -73.652767990333388, 45.470284536588274 ], [ -73.652694395553326, 45.470256238478278 ], [ -73.652669992839861, 45.470288536954968 ], [ -73.652608194216455, 45.470265235177678 ], [ -73.652546193182943, 45.470347037712962 ], [ -73.652682194918924, 45.470397941292759 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10043739, "provinc_id": "66058923687684350000000", "matricu_18": "923687684350000000", "feature_id": "f4d1865c-b04a-4cbd-8e7c-01883cbf8430", "contr_year": "1955", "height": 8.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "detached", "lot_name": "1561278", "lot_area": 407.9, "build_area": 194.3, "build_type": "1", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 2564351.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.65242779386287, 45.470516936839878 ], [ -73.652588789856551, 45.47058253858895 ], [ -73.652665896524738, 45.470488940432411 ], [ -73.6525446903279, 45.470439439736488 ], [ -73.652507391368388, 45.470484738409013 ], [ -73.652467799420805, 45.470468437420209 ], [ -73.65242779386287, 45.470516936839878 ] ] ] ] } },
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-{ "type": "Feature", "properties": { "cerc_id": 10048024, "provinc_id": "66058923686097120000000", "matricu_18": "923686097120000000", "feature_id": "ef766114-0166-4782-b14b-4d688b40e6ba", "contr_year": "1957", "height": 7.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "detached", "lot_name": "1561518", "lot_area": 437.8, "build_area": 239.8, "build_type": "1", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 3414305.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.65320148899599, 45.46987733706186 ], [ -73.653390900036641, 45.469941035988434 ], [ -73.653456795924569, 45.469844041016181 ], [ -73.653267397899739, 45.46978033318188 ], [ -73.65320148899599, 45.46987733706186 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10048056, "provinc_id": "66058923678385510000000", "matricu_18": "923678385510000000", "feature_id": "f9c3f49c-8316-48da-aee8-c694e3100c15", "contr_year": "1955", "height": 7.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "semi-detached", "lot_name": "1561248", "lot_area": 390.2, "build_area": 179.5, "build_type": "2", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 866768.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.654195894862454, 45.471452735553861 ], [ -73.654132940050715, 45.471535693826077 ], [ -73.654255943268993, 45.471583392504201 ], [ -73.65428210112988, 45.471549038772054 ], [ -73.654325100176081, 45.471565337225492 ], [ -73.654362999996195, 45.471515537915721 ], [ -73.654195894862454, 45.471452735553861 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10048057, "provinc_id": "66058923678314240000000", "matricu_18": "923678314240000000", "feature_id": "9b00ca80-cd65-4c1c-9978-8d71137b1f41", "contr_year": "1955", "height": 6.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "semi-detached", "lot_name": "1561250", "lot_area": 390.2, "build_area": 179.5, "build_type": "2", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 1009896.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.654408296870102, 45.471391636003418 ], [ -73.654444985099204, 45.471343619102058 ], [ -73.654319022207275, 45.471294771918565 ], [ -73.654305293248498, 45.47131273883528 ], [ -73.654277197771222, 45.471302036197777 ], [ -73.65426009299928, 45.471324441219934 ], [ -73.654231901150482, 45.471314041050405 ], [ -73.654228998674441, 45.471342436976329 ], [ -73.654266997700333, 45.4713529345835 ], [ -73.654237694155384, 45.471392132843746 ], [ -73.654408193732877, 45.471456438702106 ], [ -73.654446594386556, 45.47140603758811 ], [ -73.654408296870102, 45.471391636003418 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10048058, "provinc_id": "66058923678614390000000", "matricu_18": "923678614390000000", "feature_id": "e1e96159-caf8-4bde-9ed9-2feb13af3f7e", "contr_year": "1955", "height": 7.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "semi-detached", "lot_name": "1561258", "lot_area": 390.2, "build_area": 181.5, "build_type": "2", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 1581180.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.6538797952892, 45.471393333618082 ], [ -73.653852942513012, 45.471427114991805 ], [ -73.653978234423178, 45.471475701736814 ], [ -73.654043796576445, 45.471392532167933 ], [ -73.653876689475865, 45.471327333044968 ], [ -73.653837591744193, 45.471376940340981 ], [ -73.6538797952892, 45.471393333618082 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10048059, "provinc_id": "66058923678466950000000", "matricu_18": "923678466950000000", "feature_id": "f9c3f49c-8316-48da-aee8-c694e3100c15", "contr_year": "1955", "height": 8.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "semi-detached", "lot_name": "1561249", "lot_area": 390.2, "build_area": 184.4, "build_type": "2", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 3545809.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.654223201570275, 45.471697537446317 ], [ -73.65425999115395, 45.471649038151568 ], [ -73.654217991000237, 45.47163323864077 ], [ -73.654255943268993, 45.471583392504201 ], [ -73.654132940050715, 45.471535693826077 ], [ -73.654057401941927, 45.471635236136279 ], [ -73.654223201570275, 45.471697537446317 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10048672, "provinc_id": "66058923676948060000000", "matricu_18": "923676948060000000", "feature_id": "8699acea-8dfb-46cd-87b8-55484ce2ac5d", "contr_year": "1957", "height": 7.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "detached", "lot_name": "1561519", "lot_area": 406.7, "build_area": 216.8, "build_type": "1", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 2395365.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.653415688843594, 45.469952735820264 ], [ -73.653584096878546, 45.470016436536504 ], [ -73.653658691277286, 45.469918837653061 ], [ -73.6534902962361, 45.469855136128402 ], [ -73.653415688843594, 45.469952735820264 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10048785, "provinc_id": "66058923687026990000000", "matricu_18": "923687026990000000", "feature_id": "0052248e-9fd9-4f2e-8852-e4af9d8e5d5c", "contr_year": "1955", "height": 8.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "detached", "lot_name": "1561269", "lot_area": 421.4, "build_area": 179.4, "build_type": "1", "floor_num": 0, "unit_num": 1, "region": "layer_83", "g_objectid": 1578936.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.653331593451014, 45.470769336837492 ], [ -73.653448793775809, 45.470816335999849 ], [ -73.653524298033417, 45.470722232805706 ], [ -73.653363499570148, 45.470658333724451 ], [ -73.653320890341547, 45.470709037979645 ], [ -73.653365898169085, 45.470726938529602 ], [ -73.653331593451014, 45.470769336837492 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10048960, "provinc_id": "66058923669730520000000", "matricu_18": "923669730520000000", "feature_id": "491fc9f1-ef33-4236-96c1-b2899feb8e31", "contr_year": "1955", "height": 7.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "semi-detached", "lot_name": "1561239", "lot_area": 428.3, "build_area": 186.7, "build_type": "2", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 1218879.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.65516999374239, 45.471910534236727 ], [ -73.655035189732317, 45.471859206378547 ], [ -73.654968595623302, 45.471944898383917 ], [ -73.655031000644058, 45.471968638080085 ], [ -73.655008097006856, 45.471998437933863 ], [ -73.655080991705489, 45.47202623877903 ], [ -73.65516999374239, 45.471910534236727 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10049152, "provinc_id": "66058923688311420000000", "matricu_18": "923688311420000000", "feature_id": "e0cd772e-9689-4415-a183-4d223b889336", "contr_year": "1955", "height": 7.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "detached", "lot_name": "1561266", "lot_area": 421.4, "build_area": 181.9, "build_type": "1", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 183656.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.652987701614876, 45.471213539150874 ], [ -73.653056199794548, 45.471240834266737 ], [ -73.65314329542889, 45.471132734859211 ], [ -73.653013892754515, 45.471081136411229 ], [ -73.652948696020133, 45.471162237053228 ], [ -73.653009788929154, 45.471186637402681 ], [ -73.652987701614876, 45.471213539150874 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10049185, "provinc_id": "66058923687189840000000", "matricu_18": "923687189840000000", "feature_id": "dc93eb84-ab30-46b7-9089-19f6578f49b4", "contr_year": "1955", "height": 8.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "detached", "lot_name": "1561267", "lot_area": 413.6, "build_area": 179.5, "build_type": "1", "floor_num": 0, "unit_num": 1, "region": "layer_83", "g_objectid": 320820.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.65312699650984, 45.471027341300875 ], [ -73.653247899419071, 45.471074234020861 ], [ -73.653321499774137, 45.470980037235584 ], [ -73.653156996081108, 45.470916732240283 ], [ -73.653118398067264, 45.470966341323241 ], [ -73.653161796430254, 45.470983034714287 ], [ -73.65312699650984, 45.471027341300875 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10049186, "provinc_id": "66058923687108410000000", "matricu_18": "923687108410000000", "feature_id": "2a861b62-0eed-46c9-b6e8-8fff09dbc748", "contr_year": "1955", "height": 7.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "detached", "lot_name": "1561268", "lot_area": 421.4, "build_area": 179.5, "build_type": "1", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 484029.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.653185993940738, 45.470882533320498 ], [ -73.653343099695078, 45.470945636741547 ], [ -73.653416689761514, 45.470854838633109 ], [ -73.653304091983983, 45.470809632957575 ], [ -73.653270493009515, 45.470851040466357 ], [ -73.65322629667989, 45.47083293632484 ], [ -73.653185993940738, 45.470882533320498 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10049961, "provinc_id": "66058923678090240000000", "matricu_18": "923678090240000000", "feature_id": "b6b13e9c-1aa6-40eb-98b3-cc9d78d3cdd6", "contr_year": "1955", "height": 7.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "semi-detached", "lot_name": "1561253", "lot_area": 387.9, "build_area": 179.5, "build_type": "2", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 1751779.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.65465629144893, 45.471065933427973 ], [ -73.654699391732819, 45.47108333571849 ], [ -73.654738202607007, 45.471033940877646 ], [ -73.654572692090127, 45.470974436644759 ], [ -73.654541794143, 45.471010833216347 ], [ -73.654508492392068, 45.470996439302851 ], [ -73.654481396238566, 45.47102893887228 ], [ -73.654522999627531, 45.471045235540792 ], [ -73.654513780671437, 45.471056783164684 ], [ -73.654628130965548, 45.471101125918594 ], [ -73.65465629144893, 45.471065933427973 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10049962, "provinc_id": "66058923678242960000000", "matricu_18": "923678242960000000", "feature_id": "9b00ca80-cd65-4c1c-9978-8d71137b1f41", "contr_year": "1955", "height": 8.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "semi-detached", "lot_name": "1561251", "lot_area": 390.2, "build_area": 179.5, "build_type": "2", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 2671476.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.654548596710114, 45.471274733269581 ], [ -73.654382392204496, 45.471211839974565 ], [ -73.654319022207275, 45.471294771918565 ], [ -73.654444985099204, 45.471343619102058 ], [ -73.65447110100672, 45.471309439952407 ], [ -73.654510602042322, 45.471324338404166 ], [ -73.654548596710114, 45.471274733269581 ] ] ] ] } },
-{ "type": "Feature", "properties": { "cerc_id": 10049989, "provinc_id": "66058923678543010000000", "matricu_18": "923678543010000000", "feature_id": "13439744-342a-473c-888e-4a438b367918", "contr_year": "1955", "height": 8.0, "function_c": 1000.0, "function_n": "Résidentiel", "adjacency": "semi-detached", "lot_name": "1561257", "lot_area": 390.2, "build_area": 190.9, "build_type": "2", "floor_num": 2, "unit_num": 1, "region": "layer_83", "g_objectid": 3023363.0, "g_co_mrc": "66023", "g_code_mun": "66058", "g_nb_locau": 0.0 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -73.654092192768559, 45.471331936811204 ], [ -73.654163546309576, 45.471234481468947 ], [ -73.654032942866309, 45.471183835136578 ], [ -73.653997595770761, 45.471232239733126 ], [ -73.653961696465771, 45.47121923852815 ], [ -73.653923889899374, 45.471270934461494 ], [ -73.654092192768559, 45.471331936811204 ] ] ] ] } }
-]
-}

hub/city_model_structure/building.py

@@ -27,7 +27,7 @@ class Building(CityObject):
   """
   Building(CityObject) class
   """
-  def __init__(self, name, surfaces, year_of_construction, function, adjacency, lot_area, build_area, terrains=None, city=None):
+  def __init__(self, name, surfaces, year_of_construction, function, adjacency, terrains=None, city=None):
     super().__init__(name, surfaces)
     self._city = city
     self._households = None
@@ -37,8 +37,6 @@ class Building(CityObject):
     self._year_of_construction = year_of_construction
     self._function = function
     self._adjacency = adjacency
-    self._lot_area = lot_area
-    self._build_area = build_area
     self._average_storey_height = None
     self._storeys_above_ground = None
     self._floor_area = None
@@ -58,7 +56,6 @@ class Building(CityObject):
     self._cooling_consumption = {}
     self._domestic_hot_water_consumption = {}
     self._distribution_systems_electrical_consumption = {}
-    self._self_sufficiency = {}
     self._onsite_electrical_production = {}
     self._eave_height = None
     self._energy_systems = None
@@ -282,44 +279,6 @@ class Building(CityObject):
     else:
       self._adjacency = None
 
-  @property
-  def lot_area(self):
-    """
-    Get building lot area
-    :return: float
-    """
-    return self._lot_area
-
-  @lot_area.setter
-  def lot_area(self, value):
-    """
-    Set building lot area
-    :param value: float
-    """
-    if value is not None:
-      self._lot_area = float(value)
-    else:
-      self._lot_area = None
-
-  @property
-  def build_area(self):
-    """
-    Get building build area
-    :return: float
-    """
-    return self._build_area
-
-  @build_area.setter
-  def build_area(self, value):
-    """
-    Set building build area
-    :param value: float
-    """
-    if value is not None:
-      self._build_area = float(value)
-    else:
-      self._build_area = None
-
   @property
   def average_storey_height(self) -> Union[None, float]:
     """
@@ -840,14 +799,6 @@ class Building(CityObject):
 
     return self._distribution_systems_electrical_consumption
 
-  @distribution_systems_electrical_consumption.setter
-  def distribution_systems_electrical_consumption(self, value):
-    """
-    attribute to set the total electricity consumption in kWh
-    :param value: float
-    """
-    self._distribution_systems_electrical_consumption = value
-
   def _calculate_consumption(self, consumption_type, demand):
     # todo: modify when COP depends on the hour
     coefficient_of_performance = 0
@@ -1021,18 +972,3 @@ class Building(CityObject):
     """
     self._pv_generation = value
 
-  @property
-  def self_sufficiency(self):
-    """
-    temporary attribute to get the self-sufficiency in kWh
-    :return: dict
-    """
-    return self._self_sufficiency
-
-  @self_sufficiency.setter
-  def self_sufficiency(self, value):
-    """
-    temporary attribute to set the self-sufficiency in kWh
-    :param value: float
-    """
-    self._self_sufficiency = value

hub/imports/geometry/geojson.py

@@ -36,8 +36,6 @@ class Geojson:
                function_field=None,
                function_to_hub=None,
                adjacency_field=None,
-               lot_area_field=None,
-               build_area_field=None,
                hub_crs=None
                ):
     self._hub_crs = hub_crs
@@ -55,8 +53,6 @@ class Geojson:
     self._year_of_construction_field = year_of_construction_field
     self._function_field = function_field
     self._adjacency_field = adjacency_field
-    self._lot_area_field = lot_area_field
-    self._build_area_field = build_area_field
     self._function_to_hub = function_to_hub
     with open(path, 'r', encoding='utf8') as json_file:
       self._geojson = json.loads(json_file.read())
@@ -133,12 +129,6 @@ class Geojson:
         adjacency = None
         if self._adjacency_field is not None:
           adjacency = str(feature['properties'][self._adjacency_field])
-        lot_area = None
-        if self._lot_area_field is not None:
-          lot_area = float(feature['properties'][self._lot_area_field])
-        build_area = None
-        if self._build_area_field is not None:
-          build_area = float(feature['properties'][self._build_area_field])
         function = None
         if self._function_field is not None:
           function = str(int(feature['properties'][self._function_field]))
@@ -187,8 +177,6 @@ class Geojson:
                                                function,
                                                year_of_construction,
                                                adjacency,
-                                               lot_area,
-                                               build_area,
                                                extrusion_height))
 
         elif str(geometry['type']).lower() == 'multipolygon':
@@ -198,8 +186,6 @@ class Geojson:
                                                      function,
                                                      year_of_construction,
                                                      adjacency,
-                                                     lot_area,
-                                                     build_area,
                                                      extrusion_height))
         else:
           raise NotImplementedError(f'Geojson geometry type [{geometry["type"]}] unknown')
@@ -224,7 +210,7 @@ class Geojson:
       transformed_coordinates = f'{transformed_coordinates} {transformed[self._X]} {transformed[self._Y]} 0.0'
     return transformed_coordinates.lstrip(' ')
 
-  def _parse_polygon(self, coordinates, building_name, building_aliases, function, year_of_construction, adjacency, lot_area, build_area, extrusion_height):
+  def _parse_polygon(self, coordinates, building_name, building_aliases, function, year_of_construction, adjacency, extrusion_height):
     surfaces = []
     for polygon_coordinates in coordinates:
       points = igh.points_from_string(
@@ -257,7 +243,7 @@ class Geojson:
         polygon = Polygon(coordinates)
         polygon.area = igh.ground_area(coordinates)
         surfaces[-1] = Surface(polygon, polygon)
-    building = Building(f'{building_name}', surfaces, year_of_construction, function, adjacency, lot_area, build_area)
+    building = Building(f'{building_name}', surfaces, year_of_construction, function, adjacency)
     for alias in building_aliases:
       building.add_alias(alias)
     if extrusion_height == 0:
@@ -298,7 +284,7 @@ class Geojson:
       building.volume = volume
       return building
 
-  def _parse_multi_polygon(self, polygons_coordinates, building_name, building_aliases, function, year_of_construction, adjacency, lot_area, build_area, extrusion_height):
+  def _parse_multi_polygon(self, polygons_coordinates, building_name, building_aliases, function, year_of_construction, adjacency, extrusion_height):
     surfaces = []
     for coordinates in polygons_coordinates:
       for polygon_coordinates in coordinates:
@@ -331,7 +317,7 @@ class Geojson:
           polygon = Polygon(coordinates)
           polygon.area = igh.ground_area(coordinates)
           surfaces[-1] = Surface(polygon, polygon)
-    building = Building(f'{building_name}', surfaces, year_of_construction, function, adjacency, lot_area, build_area)
+    building = Building(f'{building_name}', surfaces, year_of_construction, function, adjacency)
     for alias in building_aliases:
       building.add_alias(alias)
     if extrusion_height == 0:
@@ -366,7 +352,7 @@ class Geojson:
         polygon = Polygon(wall_coordinates)
         wall = Surface(polygon, polygon)
         surfaces.append(wall)
-      building = Building(f'{building_name}', surfaces, year_of_construction, function, adjacency, lot_area, build_area)
+      building = Building(f'{building_name}', surfaces, year_of_construction, function, adjacency)
       for alias in building_aliases:
         building.add_alias(alias)
       building.volume = volume

hub/imports/geometry_factory.py

@@ -29,16 +29,12 @@ class GeometryFactory:
                function_field=None,
                function_to_hub=None,
                adjacency_field=None,
-               build_area_field=None,
-               lot_area_field=None,
                hub_crs=None,
                total_floor_area_field=None):
     self._file_type = '_' + file_type.lower()
     validate_import_export_type(GeometryFactory, file_type)
     self._path = path
     self._adjacency_field = adjacency_field
-    self._build_area_field = build_area_field
-    self._lot_area_field = lot_area_field
     self._aliases_field = aliases_field
     self._height_field = height_field
     self._centroid_x_field = centroid_x_field
@@ -83,8 +79,6 @@ class GeometryFactory:
                    self._function_field,
                    self._function_to_hub,
                    self._adjacency_field,
-                   self._build_area_field,
-                   self._lot_area_field,
                    self._hub_crs).city
 
   @property

hub/imports/results/archetype_based_demand.py

@@ -49,11 +49,11 @@ class ArchetypeBasedDemand:
     if function in ['residential', 'multifamily house', 'single family house']:
       if height < 6 and adjacency == 'detached':
         usage = 'Single Family'
-      elif height < 6 and ((adjacency == 'attached') | (adjacency == 'semi-detached')):
+      elif height < 6 and adjacency == 'attached':
         usage = 'Row house'
       elif 6 <= height <= 10 and adjacency == 'detached':
         usage = 'Duplex/triplex'
-      elif 6 <= height <= 10 and ((adjacency == 'attached') | (adjacency == 'semi-detached')):
+      elif 6 <= height <= 10 and adjacency == 'attached':
         usage = 'Small MURBs'
       elif 10 < height <= 15:
         usage = 'Medium MURBs'
@@ -65,7 +65,7 @@ class ArchetypeBasedDemand:
       usage = 'Office'
     elif function in ['commercial', 'retail shop without refrigerated food', 'retail shop with refrigerated food',
                       'stand alone retail', 'strip mall']:
-      if (adjacency == 'attached') | (adjacency == 'semi-detached'):
+      if adjacency == 'attached':
         usage = 'Commercial attached'
       else:
         usage = 'Commercial detached'
@@ -117,6 +117,7 @@ class ArchetypeBasedDemand:
   def enrich(self):
     for building in self.city.buildings:
       archetype_key = self._get_archetype_key(building)
+      print(archetype_key)
       if archetype_key and archetype_key in self.archetype_data:
         demand = self.archetype_data[archetype_key]
         area = building.thermal_zones_from_internal_zones[0].total_floor_area

main.py

@@ -4,25 +4,23 @@ from hub.helpers.dictionaries import Dictionaries
 from hub.imports.construction_factory import ConstructionFactory
 from hub.imports.results_factory import ResultFactory
 from hub.exports.exports_factory import ExportsFactory
+import subprocess
+from pathlib import Path
+
 from hub.imports.weather_factory import WeatherFactory
 from pv_assessment.electricity_demand_calculator import HourlyElectricityDemand
 from pv_assessment.pv_system_assessment import PvSystemAssessment
 from pv_assessment.solar_calculator import SolarCalculator
-import random_assignation
-import subprocess
-from pathlib import Path
 
-input_file = "data/selected_buildings.geojson"
+input_file = "data/cmm_test_corrected.geojson"
 demand_file = "data/energy_demand_data.csv"
-
 # Define specific paths for outputs from SRA (Simplified Radiosity Algorith) and PV calculation processes
-output_path = (Path(__file__).parent.parent / 'hub/out_files').resolve()
+output_path = (Path(__file__).parent.parent / 'out_files').resolve()
 output_path.mkdir(parents=True, exist_ok=True)
 sra_output_path = output_path / 'sra_outputs'
 sra_output_path.mkdir(parents=True, exist_ok=True)
 pv_assessment_path = output_path / 'pv_outputs'
 pv_assessment_path.mkdir(parents=True, exist_ok=True)
-
 city = GeometryFactory(
                "geojson",
                input_file,
@@ -30,13 +28,10 @@ city = GeometryFactory(
                year_of_construction_field="contr_year",
                function_field="function_c",
                adjacency_field="adjacency",
-               lot_area_field='lot_area',
-               build_area_field='build_area',
                function_to_hub=Dictionaries().montreal_function_to_hub_function).city
 ConstructionFactory('nrcan', city).enrich()
 WeatherFactory('epw', city).enrich()
 ResultFactory('archetypes', city, demand_file).enrich()
-
 # Export the city data in SRA-compatible format to facilitate solar radiation assessment
 ExportsFactory('sra', city, sra_output_path).export()
 # Run SRA simulation using an external command, passing the generated SRA XML file path as input
@@ -44,7 +39,7 @@ sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
 subprocess.run(['sra', str(sra_path)])
 # Enrich city data with SRA simulation results for subsequent analysis
 ResultFactory('sra', city, sra_output_path).enrich()
-# Initialize solar calculation parameters (e.g., azimuth, altitude) and compute irradiance and solar angles
+# # Initialize solar calculation parameters (e.g., azimuth, altitude) and compute irradiance and solar angles
 tilt_angle = 37
 solar_parameters = SolarCalculator(city=city,
                                    surface_azimuth_angle=180,
@@ -53,13 +48,17 @@ solar_parameters = SolarCalculator(city=city,
 solar_angles = solar_parameters.solar_angles  # Obtain solar angles for further analysis
 solar_parameters.tilted_irradiance_calculator()  # Calculate the solar radiation on a tilted surface
 # Assignation of Energy System Archetypes to Buildings
-random_assignation.call_random(city.buildings, random_assignation.residential_systems_percentage)
+#TODO this needs to be modified. We should either use the existing percentages or assign systems based on building
+# functions
+for building in city.buildings:
+  building.energy_systems_archetype_name = 'Grid Tied PV System'
 EnergySystemsFactory('montreal_future', city).enrich()
 for building in city.buildings:
+  electricity_demand = HourlyElectricityDemand(building).calculate()
   PvSystemAssessment(building=building,
                      pv_system=None,
                      battery=None,
-                     electricity_demand=None,
+                     electricity_demand=electricity_demand,
                      tilt_angle=tilt_angle,
                      solar_angles=solar_angles,
                      pv_installation_type='rooftop',
@@ -72,57 +71,4 @@ for building in city.buildings:
                      csv_output=False,
                      output_path=pv_assessment_path).enrich()
 
-r = []
-for building in city.buildings:
-  r.append((building.build_area - building.lot_area) / building.thermal_zones_from_internal_zones[0].total_floor_area)
-
 print("done")
-
-
-import geopandas as gpd
-import matplotlib.pyplot as plt
-from matplotlib.colors import Normalize
-
-# Load GeoJSON file
-gdf = gpd.read_file(input_file)
-
-# Extract self-sufficiency values
-self_sufficiency_values = [building.self_sufficiency['year'] / 1000 for building in city.buildings]
-
-# Add self-sufficiency values to GeoDataFrame
-gdf['self_sufficiency'] = self_sufficiency_values
-
-# Determine the color normalization range
-vmin = min(0, gdf['self_sufficiency'].min())  # Include 0 if min is positive
-vmax = max(0, gdf['self_sufficiency'].max())  # Include 0 if max is negative
-
-# Set up the figure and axis
-fig, ax = plt.subplots(1, 1, figsize=(14, 10))
-
-# Define a colormap and normalize the values
-cmap = plt.cm.viridis
-norm = Normalize(vmin=vmin, vmax=vmax)
-
-# Plot the GeoDataFrame
-gdf.plot(column='self_sufficiency',
-         cmap=cmap,
-         linewidth=0.8,
-         edgecolor='grey',  # Add edges for better distinction
-         legend=False,      # Turn off the built-in legend
-         ax=ax)
-
-# Add a custom colorbar
-sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
-sm._A = []  # Needed for ScalarMappable with no data
-cbar = fig.colorbar(sm, ax=ax, fraction=0.03, pad=0.04)
-cbar.set_label('Self-Sufficiency (kWh/year)', fontsize=12)
-
-# Add gridlines and axis labels
-ax.grid(color='lightgrey', linestyle='--', linewidth=0.5, alpha=0.7)
-ax.set_title('Building Self-Sufficiency Levels', fontsize=16, fontweight='bold', pad=20)
-ax.set_xlabel('Longitude', fontsize=12)
-ax.set_ylabel('Latitude', fontsize=12)
-
-# Improve layout
-plt.tight_layout()
-plt.show()

pv_assessment/electricity_demand_calculator.py

@@ -5,60 +5,59 @@ class HourlyElectricityDemand:
 
   def calculate(self):
     hourly_electricity_consumption = []
-    # energy_systems = self.building.energy_systems
+    energy_systems = self.building.energy_systems
     appliance = self.building.appliances_electrical_demand[cte.HOUR] if self.building.appliances_electrical_demand else 0
     lighting = self.building.lighting_electrical_demand[cte.HOUR] if self.building.lighting_electrical_demand else 0
-    # elec_heating = 1
+    elec_heating = 0
-    # elec_cooling = 1
+    elec_cooling = 0
-    # elec_dhw = 1
+    elec_dhw = 0
-    # if cte.HEATING in self.building.energy_consumption_breakdown[cte.ELECTRICITY]:
+    if cte.HEATING in self.building.energy_consumption_breakdown[cte.ELECTRICITY]:
-    #   elec_heating = 1
+      elec_heating = 1
-    # if cte.COOLING in self.building.energy_consumption_breakdown[cte.ELECTRICITY]:
+    if cte.COOLING in self.building.energy_consumption_breakdown[cte.ELECTRICITY]:
-    #   elec_cooling = 1
+      elec_cooling = 1
-    # if cte.DOMESTIC_HOT_WATER in self.building.energy_consumption_breakdown[cte.ELECTRICITY]:
+    if cte.DOMESTIC_HOT_WATER in self.building.energy_consumption_breakdown[cte.ELECTRICITY]:
-    #   elec_dhw = 1
+      elec_dhw = 1
-    # heating = None
+    heating = None
-    # cooling = None
+    cooling = None
-    # dhw = None
+    dhw = None
 
-    # if elec_heating == 1:
+    if elec_heating == 1:
-    #   for energy_system in energy_systems:
+      for energy_system in energy_systems:
-    #     if cte.HEATING in energy_system.demand_types:
+        if cte.HEATING in energy_system.demand_types:
-    #       for generation_system in energy_system.generation_systems:
+          for generation_system in energy_system.generation_systems:
-    #         if generation_system.fuel_type == cte.ELECTRICITY:
+            if generation_system.fuel_type == cte.ELECTRICITY:
-    #           if cte.HEATING in generation_system.energy_consumption:
+              if cte.HEATING in generation_system.energy_consumption:
-    #             heating = generation_system.energy_consumption[cte.HEATING][cte.HOUR]
+                heating = generation_system.energy_consumption[cte.HEATING][cte.HOUR]
-    #           else:
+              else:
-    #             if len(energy_system.generation_systems) > 1:
+                if len(energy_system.generation_systems) > 1:
-    #               heating = [x / 2 for x in self.building.heating_consumption[cte.HOUR]]
+                  heating = [x / 2 for x in self.building.heating_consumption[cte.HOUR]]
-    #             else:
+                else:
-    #               heating = self.building.heating_consumption[cte.HOUR]
+                  heating = self.building.heating_consumption[cte.HOUR]
-    heating = self.building.heating_demand[cte.HOUR] * 70
+
-    # if elec_dhw == 1:
+    if elec_dhw == 1:
-    #   for energy_system in energy_systems:
+      for energy_system in energy_systems:
-    #     if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
+        if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
-    #       for generation_system in energy_system.generation_systems:
+          for generation_system in energy_system.generation_systems:
-    #         if generation_system.fuel_type == cte.ELECTRICITY:
+            if generation_system.fuel_type == cte.ELECTRICITY:
-    #           if cte.DOMESTIC_HOT_WATER in generation_system.energy_consumption:
+              if cte.DOMESTIC_HOT_WATER in generation_system.energy_consumption:
-    #             dhw = generation_system.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR]
+                dhw = generation_system.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR]
-    #           else:
+              else:
-    #             if len(energy_system.generation_systems) > 1:
+                if len(energy_system.generation_systems) > 1:
-    #               dhw = [x / 2 for x in self.building.domestic_hot_water_consumption[cte.HOUR]]
+                  dhw = [x / 2 for x in self.building.domestic_hot_water_consumption[cte.HOUR]]
-    #             else:
+                else:
-    #               dhw = self.building.domestic_hot_water_consumption[cte.HOUR]
+                  dhw = self.building.domestic_hot_water_consumption[cte.HOUR]
-    dhw = self.building.domestic_hot_water_heat_demand[cte.HOUR] * 70
+
-    # if elec_cooling == 1:
+    if elec_cooling == 1:
-    #   for energy_system in energy_systems:
+      for energy_system in energy_systems:
-    #     if cte.COOLING in energy_system.demand_types:
+        if cte.COOLING in energy_system.demand_types:
-    #       for generation_system in energy_system.generation_systems:
+          for generation_system in energy_system.generation_systems:
-    #         if cte.COOLING in generation_system.energy_consumption:
+            if cte.COOLING in generation_system.energy_consumption:
-    #           cooling = generation_system.energy_consumption[cte.COOLING][cte.HOUR]
+              cooling = generation_system.energy_consumption[cte.COOLING][cte.HOUR]
-    #         else:
+            else:
-    #           if len(energy_system.generation_systems) > 1:
+              if len(energy_system.generation_systems) > 1:
-    #             cooling = [x / 2 for x in self.building.cooling_consumption[cte.HOUR]]
+                cooling = [x / 2 for x in self.building.cooling_consumption[cte.HOUR]]
-    #           else:
+              else:
-    #             cooling = self.building.cooling_consumption[cte.HOUR]
+                cooling = self.building.cooling_consumption[cte.HOUR]
-    cooling = self.building.cooling_demand[cte.HOUR]
 
     for i in range(8760):
       hourly = 0
@@ -73,5 +72,4 @@ class HourlyElectricityDemand:
       if dhw is not None:
         hourly += dhw[i]
       hourly_electricity_consumption.append(hourly)
-      self.building.distribution_systems_electrical_consumption = hourly_electricity_consumption
     return hourly_electricity_consumption

pv_assessment/pv_system_assessment.py

@@ -135,7 +135,7 @@ class PvSystemAssessment:
     if self.electricity_demand is not None:
       electricity_demand = self.electricity_demand
     else:
-      electricity_demand = [demand*1000 for demand in
+      electricity_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in
                             HourlyElectricityDemand(self.building).calculate()]
     rooftops_pv_output = [0] * len(electricity_demand)
     facades_pv_output = [0] * len(electricity_demand)
@@ -158,15 +158,13 @@ class PvSystemAssessment:
     total_hourly_pv_output = [rooftops_pv_output[i] + facades_pv_output[i] for i in range(8760)]
     imported_electricity = [0] * 8760
     exported_electricity = [0] * 8760
-    self.building.self_sufficiency['hour'] = []
     for i in range(len(electricity_demand)):
       transfer = total_hourly_pv_output[i] - electricity_demand[i]
-      self.building.self_sufficiency['hour'].append(transfer)
       if transfer > 0:
         exported_electricity[i] = transfer
       else:
         imported_electricity[i] = abs(transfer)
-    self.building.self_sufficiency['year'] = sum(self.building.self_sufficiency['hour'])
+
     results = {'building_name': self.building.name,
                'total_floor_area_m2': self.building.thermal_zones_from_internal_zones[0].total_floor_area,
                'roof_area_m2': self.building.roofs[0].perimeter_area, 'rooftop_panels': rooftop_number_of_panels,

random_assignation.py

@@ -16,7 +16,7 @@ energy_systems_format = 'montreal_future'
 
 # parameters:
 residential_systems_percentage = {
-  'Central Hydronic Air and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
+  'Central Hydronic Air and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 100,
   'Central Hydronic Air and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
   'Central Hydronic Ground and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
   'Central Hydronic Ground and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
@@ -30,27 +30,27 @@ residential_systems_percentage = {
   'Central Hydronic Ground and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
   'Central Hydronic Water and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
   'Central Hydronic Water and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
-  'Grid Tied PV System': 10,
+  'Grid Tied PV System': 0,
   'system 1 gas': 0,
-  'system 1 gas grid tied pv': 5,
+  'system 1 gas grid tied pv': 0,
   'system 1 electricity': 0,
-  'system 1 electricity grid tied pv': 10,
+  'system 1 electricity grid tied pv': 0,
   'system 2 gas': 0,
-  'system 2 gas grid tied pv': 10,
+  'system 2 gas grid tied pv': 0,
   'system 2 electricity': 0,
-  'system 2 electricity grid tied pv': 10,
+  'system 2 electricity grid tied pv': 0,
   'system 3 and 4 gas': 0,
-  'system 3 and 4 gas grid tied pv': 10,
+  'system 3 and 4 gas grid tied pv': 0,
   'system 3 and 4 electricity': 0,
-  'system 3 and 4 electricity grid tied pv': 5,
+  'system 3 and 4 electricity grid tied pv': 0,
   'system 6 gas': 0,
-  'system 6 gas grid tied pv': 10,
+  'system 6 gas grid tied pv': 0,
   'system 6 electricity': 0,
-  'system 6 electricity grid tied pv': 10,
+  'system 6 electricity grid tied pv': 0,
   'system 8 gas': 0,
-  'system 8 gas grid tied pv': 10,
+  'system 8 gas grid tied pv': 0,
   'system 8 electricity': 0,
-  'system 8 electricity grid tied pv': 10,
+  'system 8 electricity grid tied pv': 0,
 }
 
 non_residential_systems_percentage = {'system 1 gas': 0,
@@ -102,9 +102,8 @@ def _retrieve_buildings(path, year_of_construction_field=None,
 def call_random(_buildings: [Building], _systems_percentage):
   _buildings_with_systems = []
   _systems_distribution = []
-  _selected_buildings = list(range(len(_buildings)))
+  _selected_buildings = list(range(0, len(_buildings)))
   random.shuffle(_selected_buildings)
-
   total = 0
   maximum = 0
   add_to = 0
@@ -117,24 +116,14 @@ def call_random(_buildings: [Building], _systems_percentage):
         maximum = number_of_buildings
         add_to = len(_systems_distribution) - 1
       total += number_of_buildings
-
+  missing = 0
-  missing = len(_selected_buildings) - total
+  if total != len(_selected_buildings):
-  if missing > 0:
+    missing = len(_selected_buildings) - total
+  if missing != 0:
     _systems_distribution[add_to]['number_of_buildings'] += missing
-  elif missing < 0:
-    for case in sorted(_systems_distribution, key=lambda x: -x['number_of_buildings']):
-      if case['number_of_buildings'] > 0:
-        reduce_by = min(-missing, case['number_of_buildings'])
-        case['number_of_buildings'] -= reduce_by
-        missing += reduce_by
-      if missing == 0:
-        break
-  total = sum(case['number_of_buildings'] for case in _systems_distribution)
-  assert total == len(_selected_buildings), f"Final total {total} does not match available {len(_selected_buildings)}"
-
   _position = 0
   for case in _systems_distribution:
-    for _ in range(case['number_of_buildings']):
+    for i in range(0, case['number_of_buildings']):
       _buildings[_selected_buildings[_position]].energy_systems_archetype_name = case['system']
       _position += 1
   return _buildings

select_features.py

@@ -1,19 +0,0 @@
-import geopandas as gpd
-from shapely.geometry import Polygon
-
-points = [
-  (-73.65522099550455, 45.47216648812992),
-  (-73.65201191054271, 45.47096942503966),
-  (-73.65299060423152, 45.46955428052838),
-  (-73.65532718525228, 45.47041615556808),
-  (-73.65522099550455, 45.47216648812992)
-]
-
-bounding_polygon = Polygon(points)
-
-geojson_path = './data/cerc_cmm_corrected.geojson'
-gdf = gpd.read_file(geojson_path)
-
-filtered_gdf = gdf[gdf.intersects(bounding_polygon)]
-
-filtered_gdf.to_file("./data/selected_buildings.geojson", driver="GeoJSON")