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debugging fuel code
This commit is contained in:
Khalim Conn-Kowlessar
parent
bdc4c213ad
commit
c10bf032dc
4 changed files with 147 additions and 53 deletions
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@ -1220,6 +1220,12 @@ class Property:
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else:
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self.heating_energy_source = ['Electricity']
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if set(self.heating_energy_source) == {'Electricity', 'LPG'}:
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if self.main_fuel["clean_description"] in ["Lpg not community", "Lpg community"]:
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self.heating_energy_source = ['LPG']
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else:
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self.heating_energy_source = ['Electricity']
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if set(self.heating_energy_source) == {'Natural Gas', 'Wood Logs'}:
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# It means they have mixed heating so we take the primary one, based on main fuel
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# This will probably happen in the case of an extension
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@ -144,6 +144,11 @@ class SearchEpc:
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"error": None
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}
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# Keys that we check for missing values to determine if the EPC is incomplete
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CHECK_MISSING_KEYS = [
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"lighting-cost-current", "heating-cost-current", "hot-water-cost-current", "energy-consumption-potential"
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]
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def __init__(
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self,
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address1: str,
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@ -217,6 +222,9 @@ class SearchEpc:
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# By default, this is set to false. This flag indicates whether we should overwrite SAP 2005 entires.
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self.overwrite_sap05 = False
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# Be default, this is set to false. This flag indicates whether we should take the existing EPC, but use
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# the estimated EPC to clean missings
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self.clean_missing_on_expired = False
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def set_strict_property_type_search(self):
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"""
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@ -988,20 +996,40 @@ class SearchEpc:
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) = self.extract_epc_data(address=self.full_address)
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# Before we return, we check if we need to overwrite a SAP05 EPC
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# If we have don't have SAP05 in the heating description and overwrite_sap05 is False, we return
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is_sap_o5 = "SAP05:" in self.newest_epc.get("mainheat-description", "")
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good_data = not is_sap_o5 and (response["status"] == 200)
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# ---- SAP 05 overwriting logic ----
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is_sap_05 = "SAP05:" in self.newest_epc.get("mainheat-description", "")
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if good_data or not overwrite_sap05:
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needs_sap_05_overwrite = is_sap_05 and (response["status"] == 200) and overwrite_sap05
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# ---- Cleaning expired EPC logic ----
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epc_is_expired = (pd.Timestamp.now() - pd.Timestamp(
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self.newest_epc.get("lodgement-date", pd.Timestamp.now()))).days > 3650
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epc_has_missing_key_data = any([self.newest_epc.get(k) in [None, ""] for k in self.CHECK_MISSING_KEYS])
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epc_needs_cleaning = epc_is_expired and epc_has_missing_key_data
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# ---- We don't have an epc ----
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no_epc = response["status"] != 200
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# If we don't have to overwrite SAP05, or we don't have missing data on an expired EPC, we return
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if not needs_sap_05_overwrite and not epc_needs_cleaning and not no_epc:
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# If the data is fine, or we're preventing SAP05 overwrites, we just exit here
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return
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# By default, we don't exclude old but we will do, when we are estimating to overwrite a SAP05 EPC
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lmks_to_drop, exclude_old = [], False
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if is_sap_o5:
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self.overwrite_sap05 = True
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if needs_sap_05_overwrite or epc_needs_cleaning:
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self.overwrite_sap05 = needs_sap_05_overwrite
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self.clean_missing_on_expired = epc_needs_cleaning
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lmks_to_drop = [self.newest_epc["lmk-key"]]
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exclude_old = True
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self.heating_system = (
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self.newest_epc["mainheat-description"] if
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self.clean_missing_on_expired and self.heating_system is None else self.heating_system
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)
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self.ordnance_survey_client.property_type = self.newest_epc["property-type"]
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self.ordnance_survey_client.built_form = self.newest_epc["built-form"]
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# Step 2: If we don't have an EPC, we use the ordnance survey api to find the uprn
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if skip_os:
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@ -1016,13 +1044,24 @@ class SearchEpc:
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exclude_old=exclude_old
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)
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# If we have overwritten a SAP05 EPC, we need to update older_epcs too
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if self.overwrite_sap05:
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# We keep a record of the fact that we have performed a SAP05 overwrite
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estimated_epc["sap_05_overwritten"] = True
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self.older_epcs = [self.newest_epc.copy()]
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self.newest_epc = estimated_epc
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elif self.clean_missing_on_expired:
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# We perform the cleaning
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for k in self.CHECK_MISSING_KEYS:
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if self.newest_epc[k] in ["", None]:
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self.newest_epc[k] = estimated_epc[k]
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self.newest_epc["estimated"] = True
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self.older_epcs = []
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else:
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self.older_epcs = []
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self.newest_epc = estimated_epc
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# If we have overwritten a SAP05 EPC, we need to update older_epcs too
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self.older_epcs = [] if not self.overwrite_sap05 else [self.newest_epc.copy()]
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self.newest_epc = estimated_epc
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self.full_sap_epc = {}
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# Finally, set a standardised address 1 and postcode
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@ -1077,7 +1116,9 @@ class SearchEpc:
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if not self.newest_epc:
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raise ValueError("No EPC data available to set UPRN source - run find_property first")
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if self.newest_epc.get("estimated") and file_format == "domna_asset_list" and (self.newest_epc["uprn"] < 0):
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if (self.newest_epc.get("estimated") and
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(file_format == "domna_asset_list") and
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(float(self.newest_epc["uprn"]) < 0)):
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self.newest_epc["uprn-source"] = self.UPRN_SOURCE_SIMULATED
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def check_attribute_variations(self):
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@ -11,6 +11,68 @@ from backend.app.db.models.funding import FundingPackageMeasures, FundingPackage
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from backend.app.db.models.inspections import InspectionModel
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def prepare_plan_data(
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p, body, scenario_id, eco_packages, valuations, new_sap_points, new_epc, default_recommendations
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):
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"""
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Utility function to prepare the data that goes into the production of a plan. Is a fairly rough and unstructured
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function that will need improving in the future
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:param p: Instantiated property
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:param body: request body, PlanTriggerRequest
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:param scenario_id: unique identifier for the scenario
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:param eco_packages: Pre-constructed eco packages for a property
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:param valuations: valuation improvement data
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:param new_sap_points: sap points, post default recommendations
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:param new_epc: new epc rating, post default recommendations
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:param default_recommendations: list of default recommendations for a property
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:return:
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"""
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# Plan carbon savings
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co2_savings = sum([r["co2_equivalent_savings"] for r in default_recommendations])
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post_co2_emissions = p.data["co2-emissions-current"] - co2_savings
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# Plan bill savings
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energy_bill_savings = sum([r["energy_cost_savings"] for r in default_recommendations])
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post_energy_bill = sum(p.current_energy_bill.values()) - energy_bill_savings
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# energy consumption
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energy_consumption_savings = sum([r["kwh_savings"] for r in default_recommendations])
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post_energy_consumption = p.current_energy_consumption - energy_consumption_savings
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valuation_post_retrofit, valuation_increase = None, None
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if valuations["current_value"]:
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valuation_increase = valuations["average_increase"]
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valuation_post_retrofit = valuations["average_increased_value"]
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return {
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"portfolio_id": body.portfolio_id,
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"property_id": p.id,
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"scenario_id": scenario_id,
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"is_default": True if p.is_new else False,
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"name": body.scenario_name,
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"valuation_increase_lower_bound": (
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valuations["lower_bound_increased_value"] - valuations["current_value"]
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),
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"valuation_increase_upper_bound": (
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valuations["upper_bound_increased_value"] - valuations["current_value"]
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),
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"valuation_increase_average": (
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valuations["average_increased_value"] - valuations["current_value"]
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),
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"post_sap_points": new_sap_points,
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"post_epc_rating": new_epc,
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"post_co2_emissions": post_co2_emissions,
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"co2_savings": co2_savings,
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"post_energy_bill": post_energy_bill,
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"energy_bill_savings": energy_bill_savings,
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"post_energy_consumption": post_energy_consumption,
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"energy_consumption_savings": energy_consumption_savings,
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"valuation_post_retrofit": valuation_post_retrofit,
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"valuation_increase": valuation_increase,
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"plan_type": eco_packages.get(p.id, (None, None, None))[2]
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}
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def create_plan(session: Session, plan):
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"""
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This function will create a record for the plan in the database if it does not exist.
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@ -419,10 +419,6 @@ def averages_cleaning(prepared_epc: EPCRecord, cleaning_data: pd.DataFrame):
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"number_habitable_rooms",
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"number_heated_rooms",
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"floor_height",
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"lighting_cost_current",
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"heating_cost_current",
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"hot_water_cost_current",
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"energy_consumption_potential",
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]
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if not any([pd.isnull(prepared_epc.prepared_epc[k]) for k in variables_to_clean]):
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@ -470,29 +466,29 @@ def averages_cleaning(prepared_epc: EPCRecord, cleaning_data: pd.DataFrame):
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prepared_epc.prepared_epc["floor_height"] = clean_floor_height
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prepared_epc.floor_height = clean_floor_height
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if pd.isnull(prepared_epc.lighting_cost_current):
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# This is a basic assumption as an average
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prepared_epc.prepared_epc["lighting_cost_current"] = assumptions.AVERAGE_LIGHTING_COST
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prepared_epc.lighting_cost_current = assumptions.AVERAGE_LIGHTING_COST
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# if pd.isnull(prepared_epc.lighting_cost_current):
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# # This is a basic assumption as an average
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# prepared_epc.prepared_epc["lighting_cost_current"] = assumptions.AVERAGE_LIGHTING_COST
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# prepared_epc.lighting_cost_current = assumptions.AVERAGE_LIGHTING_COST
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if pd.isnull(prepared_epc.heating_cost_current):
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# This is a basic assumption as an average
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appliance_cost = AnnualBillSavings.estimate_appliances_energy_use(
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total_floor_area=prepared_epc.total_floor_area
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) * AnnualBillSavings.ELECTRICITY_PRICE_CAP
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heating_cleaned_value = assumptions.AVERAGE_HEATING_AND_APPLIANCE_COST - appliance_cost
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prepared_epc.prepared_epc["heating_cost_current"] = heating_cleaned_value
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prepared_epc.heating_cost_current = heating_cleaned_value
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if pd.isnull(prepared_epc.hot_water_cost_current):
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# This is a basic assumption as an average
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prepared_epc.prepared_epc["hot_water_cost_current"] = assumptions.AVERAGE_HOT_WATER_COST
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prepared_epc.hot_water_cost_current = assumptions.AVERAGE_HOT_WATER_COST
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if pd.isnull(prepared_epc.energy_consumption_potential):
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# Set to current
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prepared_epc.prepared_epc["energy_consumption_potential"] = prepared_epc.energy_consumption_current
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prepared_epc.energy_consumption_potential = prepared_epc.energy_consumption_current
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# if pd.isnull(prepared_epc.heating_cost_current):
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# # This is a basic assumption as an average
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# appliance_cost = AnnualBillSavings.estimate_appliances_energy_use(
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# total_floor_area=prepared_epc.total_floor_area
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# ) * AnnualBillSavings.ELECTRICITY_PRICE_CAP
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# heating_cleaned_value = assumptions.AVERAGE_HEATING_AND_APPLIANCE_COST - appliance_cost
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# prepared_epc.prepared_epc["heating_cost_current"] = heating_cleaned_value
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# prepared_epc.heating_cost_current = heating_cleaned_value
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#
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# if pd.isnull(prepared_epc.hot_water_cost_current):
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# # This is a basic assumption as an average
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# prepared_epc.prepared_epc["hot_water_cost_current"] = assumptions.AVERAGE_HOT_WATER_COST
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# prepared_epc.hot_water_cost_current = assumptions.AVERAGE_HOT_WATER_COST
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#
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# if pd.isnull(prepared_epc.energy_consumption_potential):
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# # Set to current
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# prepared_epc.prepared_epc["energy_consumption_potential"] = prepared_epc.energy_consumption_current
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# prepared_epc.energy_consumption_potential = prepared_epc.energy_consumption_current
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return prepared_epc
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@ -1281,6 +1277,10 @@ async def model_engine(body: PlanTriggerRequest):
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)
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property_value_increase_ranges[p.id] = valuations
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property_plan_data = db_funcs.recommendations_functions.prepare_plan_data(
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p, body, scenario_id, eco_packages, valuations, new_sap_points, new_epc, default_recommendations
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)
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# TODO - this is not right, especially if the existing run failed
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if p.is_new:
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property_details_epc = p.get_property_details_epc(
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@ -1300,23 +1300,8 @@ async def model_engine(body: PlanTriggerRequest):
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if not recommendations_to_upload:
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continue
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new_plan_id = db_funcs.recommendations_functions.create_plan(session, {
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"portfolio_id": body.portfolio_id,
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"property_id": p.id,
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"scenario_id": scenario_id,
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"is_default": True if p.is_new else False,
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"name": body.scenario_name,
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"valuation_increase_lower_bound": (
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valuations["lower_bound_increased_value"] - valuations["current_value"]
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),
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"valuation_increase_upper_bound": (
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valuations["upper_bound_increased_value"] - valuations["current_value"]
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),
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"valuation_increase_average": (
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valuations["average_increased_value"] - valuations["current_value"]
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),
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"plan_type": eco_packages.get(p.id, (None, None, None))[2]
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})
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new_plan_id = db_funcs.recommendations_functions.create_plan(session, plan=property_plan_data)
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db_funcs.recommendations_functions.upload_recommendations(
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session, recommendations_to_upload, p.id, new_plan_id
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