mirror of
https://github.com/Hestia-Homes/Model.git
synced 2026-06-08 11:17:27 +00:00
matching ecosurv data to asset list
This commit is contained in:
Khalim Conn-Kowlessar
parent
9e179e7f9b
commit
e99f1506f9
4 changed files with 203 additions and 55 deletions
2
.idea/Model.iml
generated
2
.idea/Model.iml
generated
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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="Fastapi-backend" jdkType="Python SDK" />
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<orderEntry type="jdk" jdkName="AssetList" 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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@ -382,6 +382,8 @@ class AssetList:
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self.outcomes_for_output = pd.DataFrame()
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self.master_surveyed = None
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self.unmatched_submissions = pd.DataFrame()
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self.ecosurv = None
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self.ecosurv_no_match = pd.DataFrame()
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# When this is True, we intend to break the programme into multiple phases. We may need to review
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# how this is structured in the future, as depending on how we get future data, we may need to
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@ -1114,7 +1116,7 @@ class AssetList:
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def identify_worktypes(self, cleaned):
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if self.STANDARD_SAP is not None:
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if self.landlord_sap is not None:
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# We add a SAP category for all work type identification
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self.standardised_asset_list["SAP Category"] = np.where(
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(
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@ -1135,16 +1137,22 @@ class AssetList:
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)
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else:
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# We add a SAP category for all work type identification
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# We break into 4 categories (54 or less, 55-68, 69-74, 75 or more)
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self.standardised_asset_list["SAP Category"] = np.where(
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self.standardised_asset_list[self.EPC_API_DATA_NAMES["current-energy-efficiency"]] <= 68,
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"SAP Rating 68 or less",
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(self.standardised_asset_list[self.EPC_API_DATA_NAMES["current-energy-efficiency"]] <= 54),
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"SAP Rating 54 or less",
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np.where(
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(
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self.standardised_asset_list[self.EPC_API_DATA_NAMES["current-energy-efficiency"]] <=
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self.EMPTY_CAVITY_SAP_THRESHOLD
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(self.standardised_asset_list[self.EPC_API_DATA_NAMES["current-energy-efficiency"]] <= 68),
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"SAP Rating 55-68",
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np.where(
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(
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self.standardised_asset_list[self.EPC_API_DATA_NAMES["current-energy-efficiency"]] <=
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self.EMPTY_CAVITY_SAP_THRESHOLD
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),
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f"SAP Rating 69-{self.EMPTY_CAVITY_SAP_THRESHOLD}",
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f"SAP Rating {self.EMPTY_CAVITY_SAP_THRESHOLD + 1} or more"
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),
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f"SAP Rating 69-{self.EMPTY_CAVITY_SAP_THRESHOLD}",
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f"SAP Rating {self.EMPTY_CAVITY_SAP_THRESHOLD + 1} or more"
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)
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)
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@ -1406,7 +1414,12 @@ class AssetList:
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elif self.old_format_non_intrusives_present:
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self.standardised_asset_list["solar_non_intrusives_walls_insulated"] = (
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self.standardised_asset_list["non-intrusives: WFT Findings"].str.lower().str.strip().isin(
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["retro drilled", "retro filled", "ewi", "retro drilled/ solid"]
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[
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"retro drilled", "retro filled", "ewi", "retro drilled/ solid", "retro drilled and filled",
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]
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) |
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self.standardised_asset_list["non-intrusives: WFT Findings"].str.lower().str.strip().str.contains(
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"retro drilled"
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)
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)
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else:
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@ -1565,13 +1578,6 @@ class AssetList:
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solar_roof_meets_criteria
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)
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# We shouldn't have an overlap
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if (
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self.standardised_asset_list["solar_eligible"] &
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self.standardised_asset_list["solar_eligible_needs_heating_upgrade"]
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).sum():
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raise ValueError("Both heating upgrade and no heating upgrade are true - this should not be possible")
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# We check for a specific sub-set of properties which are uninsulated solid wall properties that are EPC E
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# or below (we'll use 57 as a threshold) - These are for a pilot with Net Zero Renewables
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self.standardised_asset_list["solar_eligible_solid_wall_uninsulated"] = (
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@ -1617,27 +1623,58 @@ class AssetList:
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)
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# We break the cavity reason into a few different categories, when the EPC is different from inspections
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self.standardised_asset_list["cavity_reason"] = np.where(
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(
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self.standardised_asset_list["epc_indicates_empty_cavity"] &
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~self.standardised_asset_list["non_intrusive_indicates_empty_cavity"] &
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(self.standardised_asset_list['non-intrusives: Insulated'] == "RETRO DRILLED") &
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pd.isnull(self.standardised_asset_list["cavity_reason"])
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),
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"EPC Shows Empty Cavity, inspections show retro drilled: " + self.standardised_asset_list["SAP Category"],
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self.standardised_asset_list["cavity_reason"]
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)
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if self.old_format_non_intrusives_present:
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self.standardised_asset_list["cavity_reason"] = np.where(
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(
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self.standardised_asset_list["epc_indicates_empty_cavity"] &
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~self.standardised_asset_list["non_intrusive_indicates_empty_cavity"] &
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(self.standardised_asset_list['non-intrusives: WFT Findings'].str.lower().str.strip().isin(
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[
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"retro drilled and filled", "retro drilled", "retro filled", "retro drilled & filled",
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]
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)) &
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pd.isnull(self.standardised_asset_list["cavity_reason"])
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),
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"EPC Shows Empty Cavity, inspections show retro drilled: " + self.standardised_asset_list[
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"SAP Category"],
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self.standardised_asset_list["cavity_reason"]
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)
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self.standardised_asset_list["cavity_reason"] = np.where(
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(
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self.standardised_asset_list["epc_indicates_empty_cavity"] &
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~self.standardised_asset_list["non_intrusive_indicates_empty_cavity"] &
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(self.standardised_asset_list['non-intrusives: Insulated'] == "FILLED AT BUILD") &
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pd.isnull(self.standardised_asset_list["cavity_reason"])
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),
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"EPC Shows Empty Cavity, inspections show filled at build: " + self.standardised_asset_list["SAP Category"],
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self.standardised_asset_list["cavity_reason"]
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)
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self.standardised_asset_list["cavity_reason"] = np.where(
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(
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self.standardised_asset_list["epc_indicates_empty_cavity"] &
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~self.standardised_asset_list["non_intrusive_indicates_empty_cavity"] &
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self.standardised_asset_list['non_intrusive_indicates_cavity_extraction'] &
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pd.isnull(self.standardised_asset_list["cavity_reason"])
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),
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"EPC Shows Empty Cavity, inspections show filled or other: " + self.standardised_asset_list[
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"SAP Category"],
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self.standardised_asset_list["cavity_reason"]
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)
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else:
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self.standardised_asset_list["cavity_reason"] = np.where(
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(
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self.standardised_asset_list["epc_indicates_empty_cavity"] &
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~self.standardised_asset_list["non_intrusive_indicates_empty_cavity"] &
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(self.standardised_asset_list['non-intrusives: Insulated'] == "RETRO DRILLED") &
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pd.isnull(self.standardised_asset_list["cavity_reason"])
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),
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"EPC Shows Empty Cavity, inspections show retro drilled: " + self.standardised_asset_list[
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"SAP Category"],
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self.standardised_asset_list["cavity_reason"]
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)
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self.standardised_asset_list["cavity_reason"] = np.where(
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(
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self.standardised_asset_list["epc_indicates_empty_cavity"] &
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~self.standardised_asset_list["non_intrusive_indicates_empty_cavity"] &
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(self.standardised_asset_list['non-intrusives: Insulated'] == "FILLED AT BUILD") &
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pd.isnull(self.standardised_asset_list["cavity_reason"])
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),
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"EPC Shows Empty Cavity, inspections show filled at build: " + self.standardised_asset_list[
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"SAP Category"],
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self.standardised_asset_list["cavity_reason"]
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)
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self.standardised_asset_list["cavity_reason"] = np.where(
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(
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@ -1695,34 +1732,46 @@ class AssetList:
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)
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# Flag anything that has existing outcomes
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if (self.outcomes is not None) and ("Surveyed" in self.standardised_asset_list.columns):
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if (self.outcomes is not None) and ("surveyed" in self.standardised_asset_list.columns):
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if "Installer Refusal" not in self.standardised_asset_list.columns:
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if "installer refusal" not in self.standardised_asset_list.columns:
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self.standardised_asset_list["cavity_reason"] = np.where(
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(
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(self.standardised_asset_list["Surveyed"] > 0)
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(self.standardised_asset_list["surveyed"] > 0)
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),
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None,
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self.standardised_asset_list["cavity_reason"]
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)
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else:
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self.standardised_asset_list["cavity_reason"] = np.where(
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(
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(self.standardised_asset_list["Surveyed"] > 0) |
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(self.standardised_asset_list["Installer Refusal"] > 0)
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),
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None,
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self.standardised_asset_list["cavity_reason"]
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)
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for col in ["cavity_reason", "solar_reason"]:
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self.standardised_asset_list[col] = np.where(
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(
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(self.standardised_asset_list["surveyed"] > 0) |
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(self.standardised_asset_list["installer refusal"] > 0)
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),
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None,
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self.standardised_asset_list[col]
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)
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if self.master_surveyed is not None:
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self.standardised_asset_list["cavity_reason"] = np.where(
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(
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(~pd.isnull(self.standardised_asset_list["submission_date"]))
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),
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None,
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self.standardised_asset_list["cavity_reason"]
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)
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for col in ["cavity_reason", "solar_reason"]:
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self.standardised_asset_list[col] = np.where(
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(
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(~pd.isnull(self.standardised_asset_list["submission_date"]))
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),
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None,
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self.standardised_asset_list[col]
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)
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if self.ecosurv is not None:
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for col in ["cavity_reason", "solar_reason"]:
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self.standardised_asset_list[col] = np.where(
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(
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(~pd.isnull(self.standardised_asset_list["ecosurv_reference"]))
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),
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None,
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self.standardised_asset_list[col]
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)
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blocks_of_flats = self.standardised_asset_list[
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self.standardised_asset_list[self.STANDARD_PROPERTY_TYPE] == "block of flats"
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@ -2081,6 +2130,103 @@ class AssetList:
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self.hubspot_data = programme_data
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def flag_ecosurv(self, ecosurv_landlords=None):
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"""
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This class will match ecosurv data to the asset list
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:return:
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"""
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if ecosurv_landlords is None:
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return
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# TODO: Fetch from Sharepoint
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ecosurv_filepath = "/Users/khalimconn-kowlessar/Documents/hestia/Ecosurv/07.csv"
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logger.info(
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"Getting Ecosurv data from %s", ecosurv_filepath
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)
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self.ecosurv = pd.read_csv(
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ecosurv_filepath,
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encoding="cp437"
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)
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landlords = self.ecosurv["Landlord"].value_counts().reset_index(drop=False)
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landlord_references = landlords[
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landlords["Landlord"].str.lower().str.contains(ecosurv_landlords)
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]
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landlord_ecosurv_data = self.ecosurv[
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self.ecosurv["Landlord"].isin(landlord_references["Landlord"].values)
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]
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# Try and match to asset list
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matched = []
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unmatched = []
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for _, row in tqdm(landlord_ecosurv_data.iterrows(), total=landlord_ecosurv_data.shape[0]):
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postcode = row["Postcode"].lower()
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df = self.standardised_asset_list[
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(
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self.standardised_asset_list[self.STANDARD_POSTCODE].str.replace(" ", "").str.lower() ==
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postcode
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)
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].copy()
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if df.empty:
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unmatched.append(row["Reference"])
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continue
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if df.shape[0] > 1:
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house_no = SearchEpc.get_house_number(row["Address Line 1"], row["Postcode"])
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df["house_no"] = df.apply(
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lambda x: SearchEpc.get_house_number(
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str(x[self.STANDARD_ADDRESS_1]), x[self.STANDARD_POSTCODE]
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),
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axis=1
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)
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df = df[df["house_no"] == house_no]
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if df.shape[0] > 1:
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# We compare address line 1 to full address
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if any(
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df[self.STANDARD_FULL_ADDRESS].str.lower().str.contains(
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row["Address Line 1"].lower(), na=False)
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):
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df = df[
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df[self.STANDARD_FULL_ADDRESS].str.lower().str.contains(
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row["Address Line 1"].lower(), na=False
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)
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]
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if df.shape[0] > 1:
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df = df[df[self.STANDARD_PROPERTY_TYPE] != "other"]
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if df.shape[0] == 1:
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matched.append(
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{
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self.STANDARD_LANDLORD_PROPERTY_ID: df[self.STANDARD_LANDLORD_PROPERTY_ID].values[0],
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"ecosurv_reference": row["Reference"],
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"ecosurv_address1": row["Address Line 1"],
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"ecosurv_postcode": row["Postcode"],
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}
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)
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continue
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if df.shape[0] > 1:
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unmatched.append(row["Reference"])
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continue
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# We now match
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matched = pd.DataFrame(matched)
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self.standardised_asset_list = self.standardised_asset_list.merge(
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matched,
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how="left",
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on=self.STANDARD_LANDLORD_PROPERTY_ID,
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)
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# We keep a record of submissions that were NOT matches
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self.ecosurv_no_match = self.ecosurv[
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self.ecosurv["Reference"].isin(unmatched)
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].copy()
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def flag_outcomes(
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self,
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outcomes_filepath,
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@ -124,6 +124,7 @@ def app():
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]
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master_to_asset_list_filepath = None
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phase = False
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ecosuv_landlords = "paul butler|bromford"
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# Torus
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data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Torus/Phase 1"
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@ -1,4 +1,5 @@
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import numpy as np
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import pandas as pd
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class PropertyValuation:
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