mirror of
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added audit scenarios for ha analysis
This commit is contained in:
Khalim Conn-Kowlessar
parent
cc170e1fa3
commit
8d783c0e6d
5 changed files with 618 additions and 89 deletions
2
.idea/Model.iml
generated
2
.idea/Model.iml
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@ -7,7 +7,7 @@
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<sourceFolder url="file://$MODULE_DIR$/open_uprn" isTestSource="false" />
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<sourceFolder url="file://$MODULE_DIR$/recommendations" isTestSource="false" />
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</content>
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<orderEntry type="jdk" jdkName="Python 3.10 (backend)" jdkType="Python SDK" />
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<orderEntry type="jdk" jdkName="Python 3.10 (model_data)" jdkType="Python SDK" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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<component name="PyNamespacePackagesService">
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2
.idea/misc.xml
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2
.idea/misc.xml
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@ -3,7 +3,7 @@
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<component name="Black">
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<option name="sdkName" value="Python 3.10 (backend)" />
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</component>
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (backend)" project-jdk-type="Python SDK" />
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (model_data)" project-jdk-type="Python SDK" />
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<component name="PythonCompatibilityInspectionAdvertiser">
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<option name="version" value="3" />
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</component>
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334
backend/apis/GoogleSolarApi.py
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334
backend/apis/GoogleSolarApi.py
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@ -0,0 +1,334 @@
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from backend.Property import Property
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from backend.SearchEpc import SearchEpc
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from etl.epc.Record import EPCRecord
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from dotenv import load_dotenv
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from utils.s3 import read_dataframe_from_s3_parquet
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import os
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import requests
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load_dotenv(dotenv_path="backend/.env")
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EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
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# This is for 6 Laura Close, Tintagel, PL34 0EB (same property that Cotswolrd energy used)
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uprn = 100040099104
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cleaning_data = read_dataframe_from_s3_parquet(
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bucket_name="retrofit-data-dev", file_key="sap_change_model/cleaning_dataset.parquet",
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)
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searcher = SearchEpc(address1="6 Laura Close", postcode="PL34 0EB", uprn=uprn, auth_token=EPC_AUTH_TOKEN, os_api_key="")
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searcher.find_property(skip_os=True)
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epc_records = {
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'original_epc': searcher.newest_epc.copy(),
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'full_sap_epc': searcher.full_sap_epc.copy(),
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'old_data': searcher.older_epcs.copy(),
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}
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epc = EPCRecord(
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epc_records=epc_records,
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run_mode="newdata",
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cleaning_data=cleaning_data
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)
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uprn_filenames = read_dataframe_from_s3_parquet(
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bucket_name="retrofit-data-dev", file_key="spatial/filename_meta.parquet"
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)
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p = Property(
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id=0,
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address=searcher.address_clean,
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postcode=searcher.postcode_clean,
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epc_record=epc,
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already_installed={},
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non_invasive_recommendations={},
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)
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p.get_spatial_data(uprn_filenames)
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longitude = p.spatial["longitude"]
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latitude = p.spatial["latitude"]
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api_key = "AIzaSyCIz8Psu5h-1txuDX0rQpUTgkvdj8yohqU"
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url = 'https://solar.googleapis.com/v1/solarPotential'
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params = {
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'location.latitude': f'{latitude:.5f}',
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'location.longitude': f'{longitude:.5f}',
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'requiredQuality': "MEDIUM",
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'key': api_key
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}
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insights_url = 'https://solar.googleapis.com/v1/buildingInsights:findClosest'
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# Make the GET request to the Solar API
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insights_response = requests.get(insights_url, params=params)
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insights_data = insights_response.json()
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solar_potential = insights_data["solarPotential"]
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from pprint import pprint
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pprint(solar_potential)
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# This is the size of the panels used in the calculation - 400 watt
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solar_potential["panelCapacityWatts"]
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# Height of the panels used
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solar_potential["panelHeightMeters"]
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# Width of the panels used
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solar_potential["panelWidthMeters"]
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solar_potential["wholeRoofStats"]
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# Copy of response for testing:
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# {'name': 'buildings/ChIJ2yC6t4KEa0gRh2TIssogI7k', 'center': {'latitude': 50.667375, 'longitude': -4.7416833},
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# 'imageryDate': {'year': 2021, 'month': 7, 'day': 19}, 'regionCode': 'GB', 'solarPotential': {'maxArrayPanelsCount':
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# 39, 'maxArrayAreaMeters2': 76.578636, 'maxSunshineHoursPerYear': 1172.0627, 'carbonOffsetFactorKgPerMwh':
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# 478.99942, 'wholeRoofStats': {'areaMeters2': 129.65686, 'sunshineQuantiles': [537, 738.3836, 805.62445, 842.6802,
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# 909.8431, 972.15234, 1036.1013, 1092.051, 1135.8192, 1163.1444, 1193.6012], 'groundAreaMeters2': 112.33},
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# 'roofSegmentStats': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'stats': {'areaMeters2': 44.08321,
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# 'sunshineQuantiles': [614, 940.86975, 982.39124, 1057.0664, 1109.6869, 1137.5837, 1152.9211, 1163.1106, 1168.2212,
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# 1170.8883, 1193.6012], 'groundAreaMeters2': 37.61}, 'center': {'latitude': 50.6673664, 'longitude':
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# -4.741714099999999}, 'boundingBox': {'sw': {'latitude': 50.6673354, 'longitude': -4.741777}, 'ne': {'latitude':
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# 50.6674029, 'longitude': -4.7416472}}, 'planeHeightAtCenterMeters': 93.0221}, {'pitchDegrees': 34.39779,
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# 'azimuthDegrees': 31.74401, 'stats': {'areaMeters2': 44.622986, 'sunshineQuantiles': [537, 671.49774, 733.84985,
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# 780.82733, 801.4026, 814.0189, 824.0077, 847.77484, 895.08295, 950.1469, 1123.3503], 'groundAreaMeters2': 36.82},
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# 'center': {'latitude': 50.6673966, 'longitude': -4.7416813}, 'boundingBox': {'sw': {'latitude': 50.667361,
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# 'longitude': -4.7417497}, 'ne': {'latitude': 50.6674303, 'longitude': -4.741615599999999}},
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# 'planeHeightAtCenterMeters': 92.87593}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'stats': {
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# 'areaMeters2': 17.074476, 'sunshineQuantiles': [644.71136, 731.0546, 782.89813, 842.7107, 908.55585, 966.6212,
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# 1010.6367, 1038.2543, 1053.2788, 1090.6831, 1128.0178], 'groundAreaMeters2': 17.050001}, 'center': {'latitude':
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# 50.66740850000001, 'longitude': -4.7416025}, 'boundingBox': {'sw': {'latitude': 50.6673895, 'longitude':
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# -4.7416436}, 'ne': {'latitude': 50.667431199999996, 'longitude': -4.7415572}}, 'planeHeightAtCenterMeters':
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# 90.630356}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'stats': {'areaMeters2': 13.501617,
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# 'sunshineQuantiles': [749, 976.85345, 1059.0062, 1081.6173, 1097.4441, 1110.3171, 1128.2186, 1133.9421, 1142.068,
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# 1148.2168, 1157.632], 'groundAreaMeters2': 12.02}, 'center': {'latitude': 50.667315699999996, 'longitude':
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# -4.741675400000001}, 'boundingBox': {'sw': {'latitude': 50.667291399999996, 'longitude': -4.7417066},
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# 'ne': {'latitude': 50.6673372, 'longitude': -4.741648400000001}}, 'planeHeightAtCenterMeters': 92.36334},
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# {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334, 'stats': {'areaMeters2': 10.374564, 'sunshineQuantiles': [
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# 617.9507, 752.2504, 847.66315, 872.0505, 881.26227, 900.9639, 933.3188, 967.4747, 1000.8129, 1038.3002, 1105.545],
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# 'groundAreaMeters2': 8.83}, 'center': {'latitude': 50.6673295, 'longitude': -4.7417128}, 'boundingBox': {'sw': {
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# 'latitude': 50.6673134, 'longitude': -4.7417422}, 'ne': {'latitude': 50.6673413, 'longitude': -4.7416775}},
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# 'planeHeightAtCenterMeters': 92.31146}], 'solarPanelConfigs': [{'panelsCount': 4, 'yearlyEnergyDcKwh': 1867.1516,
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# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 4,
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# 'yearlyEnergyDcKwh': 1867.1515, 'segmentIndex': 0}]}, {'panelsCount': 5, 'yearlyEnergyDcKwh': 2335.0068,
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# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 5,
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# 'yearlyEnergyDcKwh': 2335.0068, 'segmentIndex': 0}]}, {'panelsCount': 6, 'yearlyEnergyDcKwh': 2799.8508,
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# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 6,
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# 'yearlyEnergyDcKwh': 2799.8508, 'segmentIndex': 0}]}, {'panelsCount': 7, 'yearlyEnergyDcKwh': 3264.6506,
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# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 7,
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# 'yearlyEnergyDcKwh': 3264.6506, 'segmentIndex': 0}]}, {'panelsCount': 8, 'yearlyEnergyDcKwh': 3726.2405,
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# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 8,
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# 'yearlyEnergyDcKwh': 3726.2405, 'segmentIndex': 0}]}, {'panelsCount': 9, 'yearlyEnergyDcKwh': 4187.721,
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# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 9,
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# 'yearlyEnergyDcKwh': 4187.721, 'segmentIndex': 0}]}, {'panelsCount': 10, 'yearlyEnergyDcKwh': 4646.094,
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# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 10,
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# 'yearlyEnergyDcKwh': 4646.094, 'segmentIndex': 0}]}, {'panelsCount': 11, 'yearlyEnergyDcKwh': 5103.777,
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# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 10,
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# 'yearlyEnergyDcKwh': 4646.094, 'segmentIndex': 0}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162,
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# 'panelsCount': 1, 'yearlyEnergyDcKwh': 457.68268, 'segmentIndex': 3}]}, {'panelsCount': 12, 'yearlyEnergyDcKwh':
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# 5559.845, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 10,
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# 'yearlyEnergyDcKwh': 4646.094, 'segmentIndex': 0}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162,
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# 'panelsCount': 2, 'yearlyEnergyDcKwh': 913.7509, 'segmentIndex': 3}]}, {'panelsCount': 13, 'yearlyEnergyDcKwh':
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# 6013.053, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 11,
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# 'yearlyEnergyDcKwh': 5099.302, 'segmentIndex': 0}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162,
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# 'panelsCount': 2, 'yearlyEnergyDcKwh': 913.7509, 'segmentIndex': 3}]}, {'panelsCount': 14, 'yearlyEnergyDcKwh':
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# 6461.664, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 12,
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# 'yearlyEnergyDcKwh': 5547.9126, 'segmentIndex': 0}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162,
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# 'panelsCount': 2, 'yearlyEnergyDcKwh': 913.7509, 'segmentIndex': 3}]}, {'panelsCount': 15, 'yearlyEnergyDcKwh':
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# 6902.33, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 12,
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# 'yearlyEnergyDcKwh': 5547.9126, 'segmentIndex': 0}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162,
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# 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}]}, {'panelsCount': 16, 'yearlyEnergyDcKwh':
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# 7321.6436, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 12,
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# 'yearlyEnergyDcKwh': 5547.9126, 'segmentIndex': 0}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099,
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# 'panelsCount': 1, 'yearlyEnergyDcKwh': 419.31348, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees':
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# 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}]}, {'panelsCount': 17,
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# 'yearlyEnergyDcKwh': 7740.388, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331,
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# 'panelsCount': 12, 'yearlyEnergyDcKwh': 5547.9126, 'segmentIndex': 0}, {'pitchDegrees': 3.0681775,
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# 'azimuthDegrees': 301.1099, 'panelsCount': 2, 'yearlyEnergyDcKwh': 838.0579, 'segmentIndex': 2}, {'pitchDegrees':
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# 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}]},
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# {'panelsCount': 18, 'yearlyEnergyDcKwh': 8154.265, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022,
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# 'azimuthDegrees': 218.25331, 'panelsCount': 13, 'yearlyEnergyDcKwh': 5961.7896, 'segmentIndex': 0},
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# {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 2, 'yearlyEnergyDcKwh': 838.0579,
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# 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh':
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# 1354.4171, 'segmentIndex': 3}]}, {'panelsCount': 19, 'yearlyEnergyDcKwh': 8566.032, 'roofSegmentSummaries': [{
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# 'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 14, 'yearlyEnergyDcKwh': 6373.556,
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# 'segmentIndex': 0}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 2, 'yearlyEnergyDcKwh':
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# 838.0579, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
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# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}]}, {'panelsCount': 20, 'yearlyEnergyDcKwh': 8976.624,
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# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
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# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099,
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# 'panelsCount': 2, 'yearlyEnergyDcKwh': 838.0579, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees':
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# 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}]}, {'panelsCount': 21,
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# 'yearlyEnergyDcKwh': 9380.78, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331,
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# 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 3.0681775,
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# 'azimuthDegrees': 301.1099, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1242.214, 'segmentIndex': 2}, {'pitchDegrees':
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# 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}]},
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# {'panelsCount': 22, 'yearlyEnergyDcKwh': 9784.078, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022,
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# 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0},
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# {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 4, 'yearlyEnergyDcKwh': 1645.5122,
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# 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh':
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# 1354.4171, 'segmentIndex': 3}]}, {'panelsCount': 23, 'yearlyEnergyDcKwh': 10162.354, 'roofSegmentSummaries': [{
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# 'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484,
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# 'segmentIndex': 0}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 4, 'yearlyEnergyDcKwh':
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# 1645.5122, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
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# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334,
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# 'panelsCount': 1, 'yearlyEnergyDcKwh': 378.2754, 'segmentIndex': 4}]}, {'panelsCount': 24, 'yearlyEnergyDcKwh':
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# 10535.894, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
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# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099,
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# 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees':
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# 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294,
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# 'azimuthDegrees': 308.42334, 'panelsCount': 1, 'yearlyEnergyDcKwh': 378.2754, 'segmentIndex': 4}]}, {'panelsCount':
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# 25, 'yearlyEnergyDcKwh': 10901.273, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees':
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# 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 3.0681775,
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# 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees':
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# 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3},
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# {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334, 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497,
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# 'segmentIndex': 4}]}, {'panelsCount': 26, 'yearlyEnergyDcKwh': 11242.756, 'roofSegmentSummaries': [{'pitchDegrees':
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# 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0},
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# {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401, 'panelsCount': 1, 'yearlyEnergyDcKwh': 341.4827,
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# 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh':
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# 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
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# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334,
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# 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]}, {'panelsCount': 27, 'yearlyEnergyDcKwh':
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# 11579.401, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
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# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401,
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||||
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 678.1277, 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees':
|
||||
# 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596,
|
||||
# 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees':
|
||||
# 31.666294, 'azimuthDegrees': 308.42334, 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]},
|
||||
# {'panelsCount': 28, 'yearlyEnergyDcKwh': 11919.106, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022,
|
||||
# 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0},
|
||||
# {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1017.83356,
|
||||
# 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh':
|
||||
# 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
|
||||
# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334,
|
||||
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]}, {'panelsCount': 29, 'yearlyEnergyDcKwh':
|
||||
# 12255.358, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
|
||||
# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401,
|
||||
# 'panelsCount': 4, 'yearlyEnergyDcKwh': 1354.0854, 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees':
|
||||
# 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596,
|
||||
# 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees':
|
||||
# 31.666294, 'azimuthDegrees': 308.42334, 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]},
|
||||
# {'panelsCount': 30, 'yearlyEnergyDcKwh': 12586.448, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022,
|
||||
# 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0},
|
||||
# {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401, 'panelsCount': 5, 'yearlyEnergyDcKwh': 1685.1748,
|
||||
# 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh':
|
||||
# 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
|
||||
# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334,
|
||||
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]}, {'panelsCount': 31, 'yearlyEnergyDcKwh':
|
||||
# 12911.502, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
|
||||
# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401,
|
||||
# 'panelsCount': 6, 'yearlyEnergyDcKwh': 2010.2289, 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees':
|
||||
# 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596,
|
||||
# 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees':
|
||||
# 31.666294, 'azimuthDegrees': 308.42334, 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]},
|
||||
# {'panelsCount': 32, 'yearlyEnergyDcKwh': 13233.139, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022,
|
||||
# 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0},
|
||||
# {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401, 'panelsCount': 7, 'yearlyEnergyDcKwh': 2331.8652,
|
||||
# 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh':
|
||||
# 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
|
||||
# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334,
|
||||
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]}, {'panelsCount': 33, 'yearlyEnergyDcKwh':
|
||||
# 13554.602, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
|
||||
# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401,
|
||||
# 'panelsCount': 8, 'yearlyEnergyDcKwh': 2653.3286, 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees':
|
||||
# 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596,
|
||||
# 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees':
|
||||
# 31.666294, 'azimuthDegrees': 308.42334, 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]},
|
||||
# {'panelsCount': 34, 'yearlyEnergyDcKwh': 13893.903, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022,
|
||||
# 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0},
|
||||
# {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2992.6301,
|
||||
# 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh':
|
||||
# 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
|
||||
# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334,
|
||||
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]}, {'panelsCount': 35, 'yearlyEnergyDcKwh':
|
||||
# 14221.166, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
|
||||
# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401,
|
||||
# 'panelsCount': 10, 'yearlyEnergyDcKwh': 3319.893, 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees':
|
||||
# 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596,
|
||||
# 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees':
|
||||
# 31.666294, 'azimuthDegrees': 308.42334, 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]},
|
||||
# {'panelsCount': 36, 'yearlyEnergyDcKwh': 14536.154, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022,
|
||||
# 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0},
|
||||
# {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401, 'panelsCount': 11, 'yearlyEnergyDcKwh': 3634.8809,
|
||||
# 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh':
|
||||
# 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
|
||||
# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334,
|
||||
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]}, {'panelsCount': 37, 'yearlyEnergyDcKwh':
|
||||
# 14850.317, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
|
||||
# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401,
|
||||
# 'panelsCount': 12, 'yearlyEnergyDcKwh': 3949.0444, 'segmentIndex': 1}, {'pitchDegrees': 3.0681775,
|
||||
# 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees':
|
||||
# 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3},
|
||||
# {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334, 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497,
|
||||
# 'segmentIndex': 4}]}, {'panelsCount': 38, 'yearlyEnergyDcKwh': 15160.658, 'roofSegmentSummaries': [{'pitchDegrees':
|
||||
# 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0},
|
||||
# {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401, 'panelsCount': 13, 'yearlyEnergyDcKwh': 4259.385,
|
||||
# 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh':
|
||||
# 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
|
||||
# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334,
|
||||
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]}, {'panelsCount': 39, 'yearlyEnergyDcKwh':
|
||||
# 15438.986, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
|
||||
# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401,
|
||||
# 'panelsCount': 14, 'yearlyEnergyDcKwh': 4537.713, 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees':
|
||||
# 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596,
|
||||
# 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees':
|
||||
# 31.666294, 'azimuthDegrees': 308.42334, 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]}],
|
||||
# 'panelCapacityWatts': 400, 'panelHeightMeters': 1.879, 'panelWidthMeters': 1.045, 'panelLifetimeYears': 20,
|
||||
# 'buildingStats': {'areaMeters2': 138.38115, 'sunshineQuantiles': [537, 728.5604, 799.23975, 833.99713, 900.88086,
|
||||
# 959.65875, 1024.2743, 1086.1285, 1132.8774, 1162.1904, 1193.6012], 'groundAreaMeters2': 117.16}, 'solarPanels': [{
|
||||
# 'center': {'latitude': 50.667371499999994, 'longitude': -4.7417235}, 'orientation': 'LANDSCAPE',
|
||||
# 'yearlyEnergyDcKwh': 468.5037, 'segmentIndex': 0}, {'center': {'latitude': 50.6673614, 'longitude': -4.7417023},
|
||||
# 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 467.61072, 'segmentIndex': 0}, {'center': {'latitude':
|
||||
# 50.667365100000005, 'longitude': -4.7417311}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 465.55005,
|
||||
# 'segmentIndex': 0}, {'center': {'latitude': 50.6673512, 'longitude': -4.741681000000001}, 'orientation':
|
||||
# 'LANDSCAPE', 'yearlyEnergyDcKwh': 465.48712, 'segmentIndex': 0}, {'center': {'latitude': 50.667357599999995,
|
||||
# 'longitude': -4.7416734}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 467.8553, 'segmentIndex': 0},
|
||||
# {'center': {'latitude': 50.6673779, 'longitude': -4.741715999999999}, 'orientation': 'LANDSCAPE',
|
||||
# 'yearlyEnergyDcKwh': 464.84396, 'segmentIndex': 0}, {'center': {'latitude': 50.6673678, 'longitude': -4.7416947},
|
||||
# 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 464.79984, 'segmentIndex': 0}, {'center': {'latitude': 50.6673549,
|
||||
# 'longitude': -4.7417098}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 461.58975, 'segmentIndex': 0},
|
||||
# {'center': {'latitude': 50.6673816, 'longitude': -4.7417448}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh':
|
||||
# 461.48065, 'segmentIndex': 0}, {'center': {'latitude': 50.6673881, 'longitude': -4.7417372}, 'orientation':
|
||||
# 'LANDSCAPE', 'yearlyEnergyDcKwh': 458.3733, 'segmentIndex': 0}, {'center': {'latitude': 50.6673149, 'longitude':
|
||||
# -4.7416768}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 457.68268, 'segmentIndex': 3}, {'center': {
|
||||
# 'latitude': 50.6673204, 'longitude': -4.7416867}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 456.06827,
|
||||
# 'segmentIndex': 3}, {'center': {'latitude': 50.667375199999995, 'longitude': -4.7417524}, 'orientation':
|
||||
# 'LANDSCAPE', 'yearlyEnergyDcKwh': 453.20776, 'segmentIndex': 0}, {'center': {'latitude': 50.667364, 'longitude':
|
||||
# -4.7416659}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 448.61087, 'segmentIndex': 0}, {'center': {
|
||||
# 'latitude': 50.6673094, 'longitude': -4.741666899999999}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh':
|
||||
# 440.66626, 'segmentIndex': 3}, {'center': {'latitude': 50.667403799999995, 'longitude': -4.741588900000001},
|
||||
# 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 419.31348, 'segmentIndex': 2}, {'center': {'latitude':
|
||||
# 50.66740850000001, 'longitude': -4.7416016999999995}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 418.74448,
|
||||
# 'segmentIndex': 2}, {'center': {'latitude': 50.6673688, 'longitude': -4.7417599}, 'orientation': 'LANDSCAPE',
|
||||
# 'yearlyEnergyDcKwh': 413.877, 'segmentIndex': 0}, {'center': {'latitude': 50.667348499999996, 'longitude':
|
||||
# -4.7417174}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 411.76657, 'segmentIndex': 0}, {'center': {
|
||||
# 'latitude': 50.6673587, 'longitude': -4.7417387}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 410.5925,
|
||||
# 'segmentIndex': 0}, {'center': {'latitude': 50.6673992, 'longitude': -4.7415761}, 'orientation': 'LANDSCAPE',
|
||||
# 'yearlyEnergyDcKwh': 404.15607, 'segmentIndex': 2}, {'center': {'latitude': 50.6674132, 'longitude': -4.7416145},
|
||||
# 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 403.29822, 'segmentIndex': 2}, {'center': {'latitude': 50.6673324,
|
||||
# 'longitude': -4.7417015}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 378.2754, 'segmentIndex': 4}, {'center':
|
||||
# {'latitude': 50.667417799999996, 'longitude': -4.7416273}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh':
|
||||
# 373.53967, 'segmentIndex': 2}, {'center': {'latitude': 50.667324900000004, 'longitude': -4.7417104}, 'orientation':
|
||||
# 'PORTRAIT', 'yearlyEnergyDcKwh': 365.37958, 'segmentIndex': 4}, {'center': {'latitude': 50.6674043, 'longitude':
|
||||
# -4.741680800000001}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 341.4827, 'segmentIndex': 1}, {'center': {
|
||||
# 'latitude': 50.667392299999996, 'longitude': -4.7416919}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh':
|
||||
# 336.64502, 'segmentIndex': 1}, {'center': {'latitude': 50.667397, 'longitude': -4.741704599999999}, 'orientation':
|
||||
# 'PORTRAIT', 'yearlyEnergyDcKwh': 339.7059, 'segmentIndex': 1}, {'center': {'latitude': 50.6674018, 'longitude':
|
||||
# -4.7417174}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 336.25195, 'segmentIndex': 1}, {'center': {'latitude':
|
||||
# 50.6673875, 'longitude': -4.7416791}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 331.08936, 'segmentIndex':
|
||||
# 1}, {'center': {'latitude': 50.6674065, 'longitude': -4.7417301}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh':
|
||||
# 325.05405, 'segmentIndex': 1}, {'center': {'latitude': 50.6673828, 'longitude': -4.7416664}, 'orientation':
|
||||
# 'PORTRAIT', 'yearlyEnergyDcKwh': 321.63647, 'segmentIndex': 1}, {'center': {'latitude': 50.667378, 'longitude':
|
||||
# -4.741653599999999}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 321.46332, 'segmentIndex': 1}, {'center': {
|
||||
# 'latitude': 50.667373299999994, 'longitude': -4.7416409}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 339.3016,
|
||||
# 'segmentIndex': 1}, {'center': {'latitude': 50.6673853, 'longitude': -4.7416298}, 'orientation': 'PORTRAIT',
|
||||
# 'yearlyEnergyDcKwh': 327.26282, 'segmentIndex': 1}, {'center': {'latitude': 50.667399499999995, 'longitude':
|
||||
# -4.741668}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 314.9878, 'segmentIndex': 1}, {'center': {'latitude':
|
||||
# 50.6673948, 'longitude': -4.7416553}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 314.16364, 'segmentIndex':
|
||||
# 1}, {'center': {'latitude': 50.667390000000005, 'longitude': -4.7416425}, 'orientation': 'PORTRAIT',
|
||||
# 'yearlyEnergyDcKwh': 310.3404, 'segmentIndex': 1}, {'center': {'latitude': 50.6674186, 'longitude': -4.7417191},
|
||||
# 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 278.3281, 'segmentIndex': 1}]}, 'boundingBox': {'sw': {'latitude':
|
||||
# 50.6672904, 'longitude': -4.741778}, 'ne': {'latitude': 50.667431199999996, 'longitude': -4.7415536}},
|
||||
# 'imageryQuality': 'MEDIUM', 'imageryProcessedDate': {'year': 2024, 'month': 4, 'day': 18}}
|
||||
|
|
@ -1,4 +1,5 @@
|
|||
import os
|
||||
import time
|
||||
|
||||
import pandas as pd
|
||||
from tqdm import tqdm
|
||||
|
|
@ -33,7 +34,7 @@ def app():
|
|||
lst = [
|
||||
pfp_property["ADDRESS"],
|
||||
pfp_property["ADDRESS.1"],
|
||||
pfp_property["ADDRESS.2"],
|
||||
# pfp_property["ADDRESS.2"],
|
||||
pfp_property["POSTCODE"]
|
||||
]
|
||||
lst = [str(x).strip() for x in lst if not pd.isnull(x)]
|
||||
|
|
@ -135,3 +136,165 @@ def app():
|
|||
# Store as an excel
|
||||
filename = "Places For People EPC data.xlsx"
|
||||
asset_list.to_excel(filename, index=False)
|
||||
|
||||
|
||||
# TODO: TEMP
|
||||
# This script takes in a a list of properties
|
||||
# Will be postcode and address
|
||||
|
||||
import requests
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from bs4 import BeautifulSoup
|
||||
from tqdm import tqdm
|
||||
|
||||
SEARCH_POSTCODE_URL = ("https://find-energy-certificate.service.gov.uk/find-a-certificate/search-by-postcode?postcode"
|
||||
"={postcode_input}")
|
||||
BASE_ENERGY_URL = "https://find-energy-certificate.service.gov.uk"
|
||||
|
||||
|
||||
def retrieve_find_my_epc_data(postcode: str, address: str):
|
||||
"""
|
||||
For a post code and address, we pull out all the required data from the find my epc website
|
||||
"""
|
||||
|
||||
headers = {
|
||||
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) '
|
||||
'Chrome/111.0.0.0 Safari/537.36'}
|
||||
postcode_input = postcode.replace(" ", "+")
|
||||
postcode_search = SEARCH_POSTCODE_URL.format(postcode_input=postcode_input)
|
||||
postcode_response = requests.get(postcode_search, headers=headers)
|
||||
|
||||
postcode_res = BeautifulSoup(postcode_response.text)
|
||||
address_links_full = postcode_res.findAll('a', {'class': 'govuk-link', 'rel': 'nofollow'})
|
||||
address_links = {element.text.lstrip().rstrip(): BASE_ENERGY_URL + element['href'] for element in
|
||||
address_links_full}
|
||||
|
||||
# TODO: to check the logic works for all cases but seems to be good
|
||||
index_of_address = [key.lower().startswith(address) for key in list(address_links.keys())]
|
||||
chosen_epc = address_links[list(address_links.keys())[np.where(index_of_address)[0][0]]]
|
||||
|
||||
epc_certificate = chosen_epc.split('/')[-1]
|
||||
|
||||
address_response = requests.get(chosen_epc, headers=headers)
|
||||
address_res = BeautifulSoup(address_response.text)
|
||||
|
||||
# print("## Energy rating - current and potential")
|
||||
ratings = address_res.find('desc', {'id': 'svg-desc'}).text
|
||||
|
||||
# print('### Current EPC rating')
|
||||
current_rating = ratings.split(".")[0]
|
||||
# print("##### " + current_rating)
|
||||
|
||||
# print('### Potential EPC rating')
|
||||
potential_rating = ratings.split(".")[1]
|
||||
# print("##### " + potential_rating)
|
||||
|
||||
new_property_df = pd.DataFrame(
|
||||
{'address': [address],
|
||||
'epc_certificate': [epc_certificate],
|
||||
'current_epc_rating': [current_rating.split(' ')[-6]],
|
||||
'current_epc_efficiency': [current_rating.split(' ')[-1]],
|
||||
'potential_epc_rating': [potential_rating.split(' ')[-6]],
|
||||
"potential_epc_efficiency": [potential_rating.split(' ')[-1]]}
|
||||
)
|
||||
|
||||
# print("Find assessor")
|
||||
assessor_block = address_res.find('div', {'class': 'epc-contact-assessor'})
|
||||
assessor_fields = assessor_block.find_all('dd', {"class": 'govuk-summary-list__value govuk-!-width-one-half'})
|
||||
assessor_name = assessor_fields[0].text.strip()
|
||||
assessor_number = assessor_fields[1].text.strip()
|
||||
assessor_email = assessor_fields[2].text.strip()
|
||||
|
||||
new_property_df['assessor_name'] = assessor_name
|
||||
new_property_df['assessor_number'] = assessor_number
|
||||
new_property_df['assessor_email'] = assessor_email
|
||||
|
||||
return new_property_df
|
||||
|
||||
# print('### Changes that can be made:')
|
||||
# improvements = address_res.find('div', {"class": "govuk-body printable-area epb-recommended-improvements"})
|
||||
|
||||
# if improvements is None:
|
||||
# print("No changes suggested")
|
||||
# else:
|
||||
# changes = improvements.find_all('h3')
|
||||
# changes_impact = improvements.find_all('dl', {"class": 'govuk-summary-list'})
|
||||
|
||||
# for element in zip(changes, changes_impact):
|
||||
# improvement_header = element[0].text
|
||||
# print("#### " + improvement_header)
|
||||
|
||||
# improvement_text = element[1].text
|
||||
# print(improvement_text)
|
||||
|
||||
# col_name = improvement_header.split(":")[1]
|
||||
# cost = element[1].find('dd', {"class": "govuk-summary-list__value"}).text.lstrip().rstrip()
|
||||
|
||||
# impact = element[1].find('text', {"class": "govuk-!-font-weight-bold"}).text.split(" ")
|
||||
# impact_num = impact[0]
|
||||
# impact_cat = impact[1]
|
||||
# print(cost)
|
||||
# new_property_df[col_name] = True
|
||||
# # cost_column = col_name + '-cost'
|
||||
# # new_property_df.assign(cost_column=cost)
|
||||
# new_property_df[col_name + '-cost'] = cost
|
||||
# new_property_df[col_name + '-impact_num'] = impact_num
|
||||
# new_property_df[col_name + '-impact_cat'] = impact_cat
|
||||
|
||||
# data = pd.concat([data, new_property_df])
|
||||
# data.to_csv('./portfolio.csv')
|
||||
|
||||
|
||||
def main():
|
||||
"""
|
||||
Main pipeline function to take in a predefined list of properties and extract names of contractors
|
||||
"""
|
||||
|
||||
# Load in list of properties
|
||||
addresses_df = pd.read_excel("/Users/khalimconn-kowlessar/Downloads/Places For People EPC data.xlsx")
|
||||
addresses_df["uprn"] = addresses_df["uprn"].astype("Int64").astype(str)
|
||||
# 1256
|
||||
|
||||
find_my_epc_data_list = []
|
||||
for i, row in tqdm(addresses_df.iterrows(), total=addresses_df.shape[0]):
|
||||
|
||||
if pd.isnull(row['Matched EPC Address']):
|
||||
continue
|
||||
# 10 second break every 50 iterations
|
||||
if (i % 50 == 0) and (i != 0):
|
||||
time.sleep(10)
|
||||
time.sleep(1)
|
||||
if row['Matched EPC Address'] == "6 CHURCHWOOD, CHURCH STREET, CRAMLINGTON":
|
||||
address_data = retrieve_find_my_epc_data(
|
||||
postcode=row['POSTCODE'],
|
||||
address=" ".join([str(row["ADDRESS"]), row["ADDRESS.1"]]).lower()
|
||||
)
|
||||
else:
|
||||
address_data = retrieve_find_my_epc_data(
|
||||
postcode=row['POSTCODE'],
|
||||
address=", ".join(row['Matched EPC Address'].split(", ")[:-1]).lower()
|
||||
)
|
||||
|
||||
address_data.insert(0, "uprn", row["uprn"])
|
||||
|
||||
find_my_epc_data_list.append(address_data)
|
||||
|
||||
find_my_epc_data = pd.concat(find_my_epc_data_list)
|
||||
|
||||
find_my_epc_data.to_csv('find_my_epc_data.csv')
|
||||
|
||||
find_my_epc_data = find_my_epc_data.drop_duplicates("uprn")
|
||||
|
||||
# Match back to addresses
|
||||
addresses_df2 = addresses_df.merge(
|
||||
find_my_epc_data,
|
||||
how="left",
|
||||
on="uprn"
|
||||
)
|
||||
|
||||
addresses_df2.to_excel("Places For People EPC data with surveyor.xlsx", index=False)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
|
|||
|
|
@ -24,9 +24,13 @@ from etl.epc_clean.epc_attributes.RoofAttributes import RoofAttributes
|
|||
from etl.epc.DataProcessor import EPCDataProcessor
|
||||
from datetime import datetime
|
||||
|
||||
import inspect
|
||||
|
||||
src_file_path = inspect.getfile(lambda: None)
|
||||
|
||||
EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
|
||||
ENV_FILE = Path(__file__).parent / "etl" / "eligibility" / "ha_15_32" / ".env"
|
||||
DATA_FOLDER = Path(__file__).parent / "local_data" / "ha_data"
|
||||
ENV_FILE = Path(src_file_path).parent / "etl" / "eligibility" / "ha_15_32" / ".env"
|
||||
DATA_FOLDER = Path(src_file_path).parent / "local_data" / "ha_data"
|
||||
|
||||
logger = setup_logger()
|
||||
load_dotenv(ENV_FILE)
|
||||
|
|
@ -6127,6 +6131,9 @@ def classify_loft(x):
|
|||
|
||||
|
||||
def fml_analysis(loader):
|
||||
# In the case of the optimistic scenario, we assume that the at-risk pipeline is still viable, just at a lower rate
|
||||
optimistic_scenario_rate = 1500
|
||||
|
||||
assumed_ciga_pass_rate = 0.731
|
||||
has_bruh = [
|
||||
"HA7", "HA14", "HA25", "HA39", "HA16", "HA28", "HA13",
|
||||
|
|
@ -6224,6 +6231,7 @@ def fml_analysis(loader):
|
|||
if fuck_this.shape[0] != before_merge_shape:
|
||||
raise Exception("SOMETHING WENT WRONG")
|
||||
|
||||
# Automated archetype check
|
||||
if any(fuck_this["ECO Eligibility"].str.contains("subject to archetype")):
|
||||
# We perform the archetype test. If the property is a house, we it needs to be detached, semi-detached
|
||||
# or end terrace. If it's a bungalow, it must be attached
|
||||
|
|
@ -6319,6 +6327,7 @@ def fml_analysis(loader):
|
|||
]
|
||||
|
||||
# Characterise no CIGA check needed
|
||||
# !!!!!!!!!!!! AT RISK !!!!!!!!!!!!
|
||||
ciga_check_passed = had_survey[had_survey["ECO Eligibility"] == "eco4 - passed ciga"]
|
||||
# These should be treated the same as one that have passed their ciga checks, from a detection perspective
|
||||
ciga_check_passed_eligible = ciga_check_passed[
|
||||
|
|
@ -6392,6 +6401,12 @@ def fml_analysis(loader):
|
|||
identified_as_gbis_looks_like_eco4
|
||||
)
|
||||
|
||||
# This is the work that is at risk
|
||||
eco4_work_at_risk = (
|
||||
passed_ciga_expectation +
|
||||
ciga_check_expectation
|
||||
)
|
||||
|
||||
no_ciga_check_needed_actually_gbis = no_ciga_check_needed_eligible_gbis.shape[0]
|
||||
gbis_qualified = gbis_qualified.shape[0]
|
||||
|
||||
|
|
@ -6490,11 +6505,13 @@ def fml_analysis(loader):
|
|||
# "Of which sold": sales_since_nov,
|
||||
"EPC verified ECO4 Eligible - Remaining": int(total_eco4_expectation),
|
||||
"EPC verified GBIS Eligibile - Remaining": int(total_gbis_expectation),
|
||||
# At risk work
|
||||
"Work at risk due to audits": eco4_work_at_risk
|
||||
}
|
||||
)
|
||||
|
||||
results_df = pd.DataFrame(results)
|
||||
results_df.to_csv("analysis - revised.csv")
|
||||
results_df.to_csv("analysis - revised - audit update.csv")
|
||||
|
||||
# results_df["Delta vs November"] = 100 * (
|
||||
# results_df["Of which ECO4 Eligible - Remaining"] - results_df["Original ECO4 Estimate - Remaining"]
|
||||
|
|
@ -6509,7 +6526,7 @@ def create_final_report():
|
|||
This function will produce the final output for the HA analysis
|
||||
:return:
|
||||
"""
|
||||
epc_validated_results = pd.read_csv("analysis - revised.csv")
|
||||
epc_validated_results = pd.read_csv("analysis - revised - audit update.csv")
|
||||
pipeline_results = pd.read_csv("pipeline_remaining_raw.csv")
|
||||
|
||||
####################################
|
||||
|
|
@ -6593,12 +6610,14 @@ def create_final_report():
|
|||
[
|
||||
"HA Name",
|
||||
"EPC verified ECO4 Eligible - Remaining",
|
||||
"EPC verified GBIS Eligibile - Remaining"
|
||||
"EPC verified GBIS Eligibile - Remaining",
|
||||
"Work at risk due to audits"
|
||||
]
|
||||
].copy().rename(
|
||||
columns={
|
||||
"EPC verified ECO4 Eligible - Remaining": "# ECO4 remaining - From EPC Database (post CIGA)",
|
||||
"EPC verified GBIS Eligibile - Remaining": "# GBIS remaining - From EPC Database (post CIGA)",
|
||||
"Work at risk due to audits": "ECO4 remaining work at risk due to Audits",
|
||||
}
|
||||
)
|
||||
|
||||
|
|
@ -6623,7 +6642,8 @@ def create_final_report():
|
|||
'# ECO4 remaining - All HA Summary',
|
||||
'# ECO4 remaining - Postcode list (pre CIGA)',
|
||||
'# ECO4 remaining - Postcode list (post CIGA)',
|
||||
'# ECO4 remaining - From EPC Database (post CIGA)'
|
||||
'# ECO4 remaining - From EPC Database (post CIGA)',
|
||||
'ECO4 remaining work at risk due to Audits'
|
||||
]:
|
||||
revenue[col] = revenue[col] * 1710
|
||||
|
||||
|
|
@ -6688,8 +6708,8 @@ def create_final_report():
|
|||
# "# GBIS remaining - Postcode list (post CIGA)"]]
|
||||
|
||||
# Store final outputs
|
||||
volumes.to_csv("HA Analysis Final - volumes.csv")
|
||||
revenue.to_csv("HA Analysis Final - revenue.csv")
|
||||
volumes.to_csv("HA Analysis - Audit Update - volumes.csv")
|
||||
revenue.to_csv("HA Analysis - Audit Update - revenue.csv")
|
||||
|
||||
|
||||
def identify_eco_works(loader):
|
||||
|
|
@ -7203,84 +7223,96 @@ def app():
|
|||
loader.load()
|
||||
loader.ha_facts_and_figures()
|
||||
|
||||
# import pickle
|
||||
# with open("ha_analysis_data_temp.pkl", "wb") as f:
|
||||
# pickle.dump(loader, f)
|
||||
# import pickle
|
||||
# with open("ha_analysis_data_temp.pkl", "rb") as f:
|
||||
# loader = pickle.load(f)
|
||||
|
||||
forecast_remaining_sales(loader)
|
||||
|
||||
# Functions to produce the final output lol...
|
||||
# fml_data_pull(loader) # If we need to pull EPC data
|
||||
fml_analysis(loader)
|
||||
create_final_report()
|
||||
|
||||
# Adhoc - for HA16, get the properties that still need a CIGA check
|
||||
asset_list_ha16 = loader.data["HA16"]["asset_list"].copy()
|
||||
ha_16_need_ciga = asset_list_ha16[
|
||||
asset_list_ha16["ECO Eligibility"].str.contains("subject to ciga")
|
||||
]
|
||||
completed_cigas = loader.data["HA16"]["ciga_list"].copy()
|
||||
# Store the results
|
||||
ha_16_need_ciga.to_csv("ha16_need_ciga.csv")
|
||||
completed_cigas.to_csv("ha16_completed_cigas.csv")
|
||||
|
||||
# Adhoc - look at the current pipeline and identify how many dormant, CIGA dependent properties there are for
|
||||
# live projects
|
||||
|
||||
# Read excel
|
||||
orderbook_filepath = "local_data/ha_data/Warmfront HA client order book overview_20240129.xlsx"
|
||||
orderbook_workbook = openpyxl.load_workbook(orderbook_filepath)
|
||||
orderbook_sheet = orderbook_workbook["Contractual Info"]
|
||||
orderbook_colnames = [cell.value for cell in orderbook_sheet[1]]
|
||||
|
||||
rows = []
|
||||
for row in orderbook_sheet.iter_rows(min_row=2, values_only=False):
|
||||
row_data = [cell.value for cell in row] # This will get you the cell values
|
||||
rows.append(row_data)
|
||||
|
||||
orderbook = pd.DataFrame(rows, columns=orderbook_colnames)
|
||||
live_orderbook = orderbook[orderbook["Live, New, or Historic?"] == "LIVE"].copy()
|
||||
live_orderbook['Redacted HA'] = live_orderbook['Redacted HA'].str.replace(" ", "")
|
||||
|
||||
dormant_properties = []
|
||||
missed_has = []
|
||||
for _, customer in live_orderbook.iterrows():
|
||||
if customer['Redacted HA'] not in loader.data.keys():
|
||||
missed_has.append(customer['Redacted HA'])
|
||||
continue
|
||||
asset_list = loader.data[customer['Redacted HA']]["asset_list"].copy()
|
||||
survey_list = loader.data[customer['Redacted HA']]["survey_list"].copy()
|
||||
# Remove sold
|
||||
if not survey_list.empty:
|
||||
survey_list = survey_list[~pd.isnull(survey_list["asset_list_row_id"])]
|
||||
asset_list = asset_list.merge(
|
||||
survey_list[["asset_list_row_id", "installation_status"]],
|
||||
how="left",
|
||||
on="asset_list_row_id"
|
||||
)
|
||||
# Anything that has an installation has gone to installation, and therefore is not remaining
|
||||
asset_list = asset_list[pd.isnull(asset_list["installation_status"])]
|
||||
asset_list = asset_list.drop(columns=["installation_status"])
|
||||
|
||||
# We pull out the properties that need a CIGA check
|
||||
need_ciga = asset_list[asset_list["ECO Eligibility"] == "eco4 (subject to ciga)"]
|
||||
need_archetype = asset_list[asset_list["ECO Eligibility"] == "eco4 (subject to archetype)"]
|
||||
need_ciga_and_archetype = asset_list[
|
||||
asset_list["ECO Eligibility"] == "eco4 (subject to ciga) (subject to archetype)"
|
||||
]
|
||||
|
||||
dormant_properties.append(
|
||||
{
|
||||
"HA Name": customer['Redacted HA'],
|
||||
"Need CIGA": need_ciga.shape[0],
|
||||
"Need Archetype": need_archetype.shape[0],
|
||||
"Need CIGA and Archetype": need_ciga_and_archetype.shape[0]
|
||||
}
|
||||
)
|
||||
|
||||
dormant_properties = pd.DataFrame(dormant_properties)
|
||||
totals = dormant_properties.sum()
|
||||
totals["HA Name"] = "Total"
|
||||
|
||||
dormant_properties = pd.concat([dormant_properties, totals.to_frame().T])
|
||||
dormant_properties.to_csv("dormant_properties.csv")
|
||||
|
||||
loader.december_figures["ECO4 remaining"].sum()
|
||||
december_figures = loader.december_figures.copy()
|
||||
december_figures["ECO4 remaining"] = np.where(
|
||||
december_figures["ECO4 remaining"] < 0,
|
||||
0,
|
||||
december_figures["ECO4 remaining"]
|
||||
)
|
||||
december_figures["ECO4 remaining"].sum()
|
||||
# asset_list_ha16 = loader.data["HA16"]["asset_list"].copy()
|
||||
# ha_16_need_ciga = asset_list_ha16[
|
||||
# asset_list_ha16["ECO Eligibility"].str.contains("subject to ciga")
|
||||
# ]
|
||||
# completed_cigas = loader.data["HA16"]["ciga_list"].copy()
|
||||
# # Store the results
|
||||
# ha_16_need_ciga.to_csv("ha16_need_ciga.csv")
|
||||
# completed_cigas.to_csv("ha16_completed_cigas.csv")
|
||||
#
|
||||
# # Adhoc - look at the current pipeline and identify how many dormant, CIGA dependent properties there are for
|
||||
# # live projects
|
||||
#
|
||||
# # Read excel
|
||||
# orderbook_filepath = "local_data/ha_data/Warmfront HA client order book overview_20240129.xlsx"
|
||||
# orderbook_workbook = openpyxl.load_workbook(orderbook_filepath)
|
||||
# orderbook_sheet = orderbook_workbook["Contractual Info"]
|
||||
# orderbook_colnames = [cell.value for cell in orderbook_sheet[1]]
|
||||
#
|
||||
# rows = []
|
||||
# for row in orderbook_sheet.iter_rows(min_row=2, values_only=False):
|
||||
# row_data = [cell.value for cell in row] # This will get you the cell values
|
||||
# rows.append(row_data)
|
||||
#
|
||||
# orderbook = pd.DataFrame(rows, columns=orderbook_colnames)
|
||||
# live_orderbook = orderbook[orderbook["Live, New, or Historic?"] == "LIVE"].copy()
|
||||
# live_orderbook['Redacted HA'] = live_orderbook['Redacted HA'].str.replace(" ", "")
|
||||
#
|
||||
# dormant_properties = []
|
||||
# missed_has = []
|
||||
# for _, customer in live_orderbook.iterrows():
|
||||
# if customer['Redacted HA'] not in loader.data.keys():
|
||||
# missed_has.append(customer['Redacted HA'])
|
||||
# continue
|
||||
# asset_list = loader.data[customer['Redacted HA']]["asset_list"].copy()
|
||||
# survey_list = loader.data[customer['Redacted HA']]["survey_list"].copy()
|
||||
# # Remove sold
|
||||
# if not survey_list.empty:
|
||||
# survey_list = survey_list[~pd.isnull(survey_list["asset_list_row_id"])]
|
||||
# asset_list = asset_list.merge(
|
||||
# survey_list[["asset_list_row_id", "installation_status"]],
|
||||
# how="left",
|
||||
# on="asset_list_row_id"
|
||||
# )
|
||||
# # Anything that has an installation has gone to installation, and therefore is not remaining
|
||||
# asset_list = asset_list[pd.isnull(asset_list["installation_status"])]
|
||||
# asset_list = asset_list.drop(columns=["installation_status"])
|
||||
#
|
||||
# # We pull out the properties that need a CIGA check
|
||||
# need_ciga = asset_list[asset_list["ECO Eligibility"] == "eco4 (subject to ciga)"]
|
||||
# need_archetype = asset_list[asset_list["ECO Eligibility"] == "eco4 (subject to archetype)"]
|
||||
# need_ciga_and_archetype = asset_list[
|
||||
# asset_list["ECO Eligibility"] == "eco4 (subject to ciga) (subject to archetype)"
|
||||
# ]
|
||||
#
|
||||
# dormant_properties.append(
|
||||
# {
|
||||
# "HA Name": customer['Redacted HA'],
|
||||
# "Need CIGA": need_ciga.shape[0],
|
||||
# "Need Archetype": need_archetype.shape[0],
|
||||
# "Need CIGA and Archetype": need_ciga_and_archetype.shape[0]
|
||||
# }
|
||||
# )
|
||||
#
|
||||
# dormant_properties = pd.DataFrame(dormant_properties)
|
||||
# totals = dormant_properties.sum()
|
||||
# totals["HA Name"] = "Total"
|
||||
#
|
||||
# dormant_properties = pd.concat([dormant_properties, totals.to_frame().T])
|
||||
# dormant_properties.to_csv("dormant_properties.csv")
|
||||
#
|
||||
# loader.december_figures["ECO4 remaining"].sum()
|
||||
# december_figures = loader.december_figures.copy()
|
||||
# december_figures["ECO4 remaining"] = np.where(
|
||||
# december_figures["ECO4 remaining"] < 0,
|
||||
# 0,
|
||||
# december_figures["ECO4 remaining"]
|
||||
# )
|
||||
# december_figures["ECO4 remaining"].sum()
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue