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examining results on colchester
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1 changed files with 27 additions and 2 deletions
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@ -233,7 +233,8 @@ class AssetList:
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"secondheat-description": "epc_secondary_heating",
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"transaction-type": "epc_reason",
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"energy-consumption-current": "epc_heat_demand",
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"photo-supply": "epc_photo_supply"
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"photo-supply": "epc_photo_supply",
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"estimated": "estimated"
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}
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FIND_EPC_DATA_NAMES = {
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"heating_text": "epc_estiamted_heating_kwh",
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@ -714,6 +715,22 @@ class AssetList:
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columns=self.rename_map
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)
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# We fill any standard columns that are not in the data because they were not provided by the landlord
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missing_variables = [
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v for v in [
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self.STANDARD_EXISTING_PV,
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self.STANDARD_HEATING_SYSTEM,
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self.STANDARD_UPRN,
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self.STANDARD_PROPERTY_TYPE,
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self.STANDARD_YEAR_BUILT,
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self.STANDARD_WALL_CONSTRUCTION,
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self.STANDARD_HEATING_SYSTEM,
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self.STANDARD_EXISTING_PV
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] if v not in self.standardised_asset_list.columns
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]
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for v in missing_variables:
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self.standardised_asset_list[v] = None
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def merge_data(self, df: pd.DataFrame):
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"""
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Used to insert data into the standardised asset list, based on the domna property id
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@ -963,7 +980,6 @@ class AssetList:
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# Extraction
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######################################################
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# TODO When filterting like this, 627 properties are flagged as not needing a CIGA check and 582 are flagged
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# as needing a CIGA check. What is the logic we should be applying here?
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self.standardised_asset_list["non_intrusive_indicates_cavity_extraction"] = (
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(self.standardised_asset_list["non-intrusives: Construction"] == "CAVITY") &
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@ -974,6 +990,15 @@ class AssetList:
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)
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)
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z = self.standardised_asset_list[
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self.standardised_asset_list["non-intrusives: CIGA Check Required"] == "YES"
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]
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z["non-intrusives: Insulated"].value_counts()
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z["non-intrusives: Material"].value_counts()
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z[self.ATTRIBUTE_SAP_THRESHOLD_AND_BELOW].value_counts()
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z[self.EPC_API_DATA_NAMES["current-energy-efficiency"]].max()
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zz = z[z[self.EPC_API_DATA_NAMES["current-energy-efficiency"]] == 105]
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######################################################
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# Solar
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######################################################
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