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paused for the moment
This commit is contained in:
Khalim Conn-Kowlessar
parent
3ccc5eae89
commit
fe193305e6
3 changed files with 120 additions and 29 deletions
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@ -12,6 +12,8 @@ class Funding:
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and flag any tenant specific requirements that need to be considered to the funding to be attained
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"""
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SCHEMES = ["eco4", "gbis", "whlg"]
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ECO_SAP_SCORE_THREHOLDS = [
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{'Band': 'High_A', 'From': 96.0, 'Up to': 100.0, 'Mid-point': 98.0},
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{'Band': 'Low_A', 'From': 92.0, 'Up to': 96.0, 'Mid-point': 94.0},
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@ -34,10 +36,12 @@ class Funding:
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tenure: HousingType,
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starting_epc,
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starting_sap,
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postcode,
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floor_area,
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council_tax_band,
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property_recommendations,
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project_scores_matrix,
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whlg_eligible_postcodes,
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gbis_abs_rate: int,
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eco4_abs_rate: int,
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):
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@ -47,6 +51,10 @@ class Funding:
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:param starting_epc: The current EPC rating of the property
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:param starting_sap: The current SAP score for the property
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:param floor_area: The total floor area of the property
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:param council_tax_band: The council tax band of the property
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:param property_recommendations: The recommendations for the property
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:param project_scores_matrix: The matrix of project scores for ECO4
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:param whlg_eligible_postcodes: The postcodes eligible for WHLG
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:param gbis_abs_rate: The assumed £/abs achieved by the installer for GBIS
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:param eco4_abs_rate: The assumed £/abs achieved by the installer for ECO4
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"""
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@ -58,6 +66,7 @@ class Funding:
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self.tenure = tenure
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self.starting_epc = starting_epc
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self.starting_sap = starting_sap
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self.postcode = postcode
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self.starting_eco_band = self.sap_to_eco_band(self.starting_sap)
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self.floor_area_segment = self.classify_floor_area(floor_area)
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self.gbis_abs_rate = gbis_abs_rate
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@ -75,6 +84,11 @@ class Funding:
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(project_scores_matrix["Starting Band"] == self.starting_eco_band)
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]
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# The postcode column is already lower case
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self.whlg_eligible_postcodes = whlg_eligible_postcodes[
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whlg_eligible_postcodes["Postcode"] == self.postcode.lower()
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]
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# Store the final outputs
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self.gbis_eligibiltiy = {}
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self.eco4_eligibility = {}
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@ -82,6 +96,8 @@ class Funding:
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def output(
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self,
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scheme: str,
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eligible: bool,
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measure_types: List[str],
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estimated_funding: float,
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notify_tenant_benefits_requirements: bool,
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@ -90,12 +106,18 @@ class Funding:
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):
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""""
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"""
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if scheme not in self.SCHEMES:
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raise ValueError("Scheme not recognised")
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return {
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"scheme": scheme,
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"eligible": eligible,
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"measure_types": measure_types,
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"estimated_funding": estimated_funding,
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"notify_tenant_benefits_requirements": notify_tenant_benefits_requirements,
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"notify_council_tax_band_requirements": notify_council_tax_band_requirements,
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"notify_tenant_low_income_requirements": notify_tenant_low_income_requirements
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"requires_benefits": notify_tenant_benefits_requirements,
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"requires_council_tax_band": notify_council_tax_band_requirements,
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"requires_low_income": notify_tenant_low_income_requirements
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}
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@staticmethod
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@ -234,6 +256,8 @@ class Funding:
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# If the council tax band is missing, we nofify the customer that this is a requirement that
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# should be checked
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return self.output(
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scheme="gbis",
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eligible=True,
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measure_types=[recommended_measure["measure_type"]],
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estimated_funding=recommended_measure["estimated_funding"],
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notify_tenant_benefits_requirements=False,
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@ -251,6 +275,8 @@ class Funding:
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# We find the best measure for GBIS
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recommended_measure = self.find_best_gbis_measure(measures=valid_measures)
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return self.output(
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scheme="gbis",
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eligible=True,
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measure_types=[recommended_measure["measure_type"]],
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estimated_funding=recommended_measure["estimated_funding"],
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notify_tenant_benefits_requirements=True,
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@ -260,6 +286,8 @@ class Funding:
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# Otherwise, no funding availability
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return self.output(
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scheme="gbis",
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eligible=False,
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measure_types=[],
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estimated_funding=0,
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notify_tenant_benefits_requirements=False,
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@ -279,6 +307,23 @@ class Funding:
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raise NotImplementedError("Implement social/oo")
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def whlg(self):
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if self.tenure == "Social":
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# We can't do anything for social housing
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self.whlg_eligibility = self.output(
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scheme="whlg",
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eligible=False,
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measure_types=[],
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estimated_funding=0,
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notify_tenant_benefits_requirements=False,
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notify_council_tax_band_requirements=False,
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notify_tenant_low_income_requirements=False
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)
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return
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if not self.whlg_eligible_postcodes.empty:
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print("Eligible implement me!")
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def eco4(self):
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if self.tenure == "Private":
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self.eco4_eligibiltiy = self.eco4_prs()
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@ -292,4 +337,4 @@ class Funding:
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self.gbis()
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# self.eco4()
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# self.whlg()
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self.whlg()
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@ -388,15 +388,26 @@ def extract_property_request_data(
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return patch, property_already_installed, property_non_invasive_recommendations, property_valution
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def get_eco_project_scores_matrix():
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data = read_csv_from_s3(
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def get_funding_data():
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"""
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This function retrieves the eco project scores matrix and the warm homes local grant funding data
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:return:
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"""
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project_scores_matrix = read_csv_from_s3(
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bucket_name=get_settings().DATA_BUCKET,
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filepath="funding/ECO4 Full Project Scores Matrix.csv",
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)
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df = pd.DataFrame(data)
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df.columns = ['Floor Area Segment', 'Starting Band', 'Finishing Band', 'Cost Savings']
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df["Cost Savings"] = df["Cost Savings"].astype(float)
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return df
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project_scores_matrix = pd.DataFrame(project_scores_matrix)
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project_scores_matrix.columns = ['Floor Area Segment', 'Starting Band', 'Finishing Band', 'Cost Savings']
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project_scores_matrix["Cost Savings"] = project_scores_matrix["Cost Savings"].astype(float)
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whlg_eligible_postcodes = read_csv_from_s3(
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bucket_name=get_settings().DATA_BUCKET,
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filepath="funding/whlg eligible postcodes.csv",
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)
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whlg_eligible_postcodes = pd.DataFrame(whlg_eligible_postcodes)
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return project_scores_matrix, whlg_eligible_postcodes
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router = APIRouter(
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@ -544,7 +555,7 @@ async def trigger_plan(body: PlanTriggerRequest):
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logger.info("Reading in materials and cleaned datasets")
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materials = get_materials(session)
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cleaned = get_cleaned()
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eco_project_scores_matrix = get_eco_project_scores_matrix()
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eco_project_scores_matrix, whlg_eligible_postcodes = get_funding_data()
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kwh_client = KwhData(bucket=get_settings().DATA_BUCKET, read_consumption_data=True)
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@ -688,9 +699,7 @@ async def trigger_plan(body: PlanTriggerRequest):
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# Insert the predictions into the recommendations and run the optimiser
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# TODO: If a recommendation has a negative impact on SAP, we should remove it - this seems to have become a
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# possibility with heating system
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# TODO: After optimising, if there are any cheap, quick win measures (e.g. insulate water tank with hot water
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# cylinder jacket), we should add these to the recommendations as default
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# possibility with heating system?
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for p in input_properties:
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if not recommendations.get(p.id):
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@ -802,21 +811,23 @@ async def trigger_plan(body: PlanTriggerRequest):
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# Funding
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# ~~~~~~~~~~~~~~~~
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# for p in input_properties:
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# funding_calulator = Funding(
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# tenure=body.housing_type,
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# starting_epc=p.data["current-energy-rating"],
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# starting_sap=int(p.data["current-energy-efficiency"]),
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# floor_area=p.floor_area,
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# council_tax_band=None, # This is seemingly always None at the moment
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# property_recommendations=recommendations[p.id],
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# project_scores_matrix=eco_project_scores_matrix,
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# gbis_abs_rate=20,
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# eco4_abs_rate=20,
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# )
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# funding_calulator.check_eligibiltiy()
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# # Insert finding
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# p.insert_funding(funding_calulator)
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for p in input_properties:
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funding_calulator = Funding(
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tenure=body.housing_type,
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starting_epc=p.data["current-energy-rating"],
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starting_sap=int(p.data["current-energy-efficiency"]),
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postcode=p.postcode,
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floor_area=p.floor_area,
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council_tax_band=None, # This is seemingly always None at the moment
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property_recommendations=recommendations[p.id],
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project_scores_matrix=eco_project_scores_matrix,
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whlg_eligible_postcodes=whlg_eligible_postcodes,
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gbis_abs_rate=20,
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eco4_abs_rate=15,
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)
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funding_calulator.check_eligibiltiy()
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# Insert finding
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p.insert_funding(funding_calulator)
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logger.info("Uploading recommendations to the database")
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# If we have any work to do, we create a new scenario
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35
etl/funding/app.py
Normal file
35
etl/funding/app.py
Normal file
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@ -0,0 +1,35 @@
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"""
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This scipt prepares the data, required for us to perform funding calculations. The starting data should be stored
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on the machine this is being run on, and this will prepare the information and upload if
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"""
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import pandas as pd
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from utils.s3 import save_csv_to_s3
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STAGE = "dev"
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DATA_BUCKET = "retrofit-data-{stage}"
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PROJECTS_SCORES_MATRIX_LOCATION = "/Users/khalimconn-kowlessar/Downloads/ECO4 Full Project Scores Matrix.csv"
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WHLG_ELIGIBLE_POSTCODES = "/Users/khalimconn-kowlessar/Downloads/WHLG-eligible-postcodes.xlsx"
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def app():
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# Read in the project scores matrix
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project_scores_matrix = pd.read_csv(PROJECTS_SCORES_MATRIX_LOCATION)
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# Store in AWS S3
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save_csv_to_s3(
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dataframe=project_scores_matrix,
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bucket_name=DATA_BUCKET.format(stage=STAGE),
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file_name="funding/ECO4 Full Project Scores Matrix.csv"
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)
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# Read in the Warm Homes Local Grant eligible postcodes data
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whlg_eligible_postcodes = pd.read_excel(WHLG_ELIGIBLE_POSTCODES, sheet_name="Eligible postcodes", header=1)
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# We tidy up the data before we store
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whlg_eligible_postcodes = whlg_eligible_postcodes[["Postcode"]]
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whlg_eligible_postcodes["Postcode"] = whlg_eligible_postcodes["Postcode"].str.lower()
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save_csv_to_s3(
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dataframe=whlg_eligible_postcodes,
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bucket_name=DATA_BUCKET.format(stage=STAGE),
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file_name="funding/whlg eligible postcodes.csv"
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)
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