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https://github.com/Hestia-Homes/Model.git
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HA Analysis finalised
This commit is contained in:
Khalim Conn-Kowlessar
parent
6ccfff0411
commit
3dd30445f9
1 changed files with 225 additions and 32 deletions
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@ -51,6 +51,12 @@ PROPERTY_TYPE_LOOKUP = {
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'MAISONETTE': "Maisonette",
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'HOSTEL': None
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},
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"HA5": {
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"House": "House",
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"Flat": "Flat",
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"Bungalow": "Bungalow",
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"Bedsit": None
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},
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"HA6": {
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"property_type": {
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'HOUSE': "House",
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@ -161,6 +167,21 @@ PROPERTY_TYPE_LOOKUP = {
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"Hostel": None,
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"Block": None,
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},
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"HA20": {
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"House": "House",
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"Flat": "Flat",
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'Sheltered Flat': "Flat",
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'Maisonette': 'Maisonette',
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'Bungalow': 'Bungalow',
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'House. SD': 'House',
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'House. MT': 'House',
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'House. ET': 'House',
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'Sheltered Bungalow': 'Bungalow',
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'Guest Accomodation': None,
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'Sheltered House': 'House',
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'House. MT ': 'House',
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'House. D': 'House'
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},
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"HA24": {
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'01 HOUSE': 'House',
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'02 FLAT': 'Flat',
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@ -3632,6 +3653,9 @@ def get_property_type_and_built_form(property_meta, ha_name):
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elif ha_name == "HA2":
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property_type = PROPERTY_TYPE_LOOKUP[ha_name].get(property_meta["Dwelling Type"].strip())
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built_form = None
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elif ha_name == "HA5":
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property_type = PROPERTY_TYPE_LOOKUP[ha_name].get(property_meta["Asset Type"].strip())
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built_form = None
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elif ha_name == "HA6":
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property_type = PROPERTY_TYPE_LOOKUP[ha_name]["property_type"][property_meta["Dwelling type"]]
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built_form = property_meta["built_form"]
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@ -3694,6 +3718,9 @@ def get_property_type_and_built_form(property_meta, ha_name):
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elif ha_name == "HA19":
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property_type = property_meta["Dwelling Type"]
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built_form = None
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elif ha_name == "HA20":
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property_type = PROPERTY_TYPE_LOOKUP[ha_name].get(property_meta["Asset Type"].strip())
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built_form = None
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elif ha_name == "HA21":
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property_description = property_meta["Property Type"].strip().lower()
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if "house" in property_description:
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@ -5775,6 +5802,7 @@ def forecast_remaining_sales(loader):
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results.append(to_append)
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results = pd.DataFrame(results)
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results.to_csv("pipeline_remaining_raw.csv")
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totals_row = {}
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for col in results.columns:
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@ -6006,9 +6034,7 @@ def fml_data_pull(loader):
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"HA7", "HA14", "HA25", "HA39", "HA16", "HA28", "HA13",
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"HA50", "HA24", "HA15", "HA32", "HA28", "HA6", "HA1", "HA107", "HA41", "HA48", "HA2", "HA63", "HA12",
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"HA117", "HA35", "HA34", "HA56", "HA19", "HA18", "HA9", "HA27", "HA30", "HA31", "HA54", "HA49",
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'HA8', 'HA11', 'HA21', 'HA37', 'HA42', 'HA44', 'HA45', 'HA51', 'HA52',
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# NEW - add property type
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"HA17"
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'HA8', 'HA11', 'HA21', 'HA37', 'HA42', 'HA44', 'HA45', 'HA51', 'HA52', "HA17", "HA5", "HA20",
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]
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# Can't pull from EPC database because it's based in Scotland
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@ -6105,7 +6131,7 @@ def fml_analysis(loader):
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"HA7", "HA14", "HA25", "HA39", "HA16", "HA28", "HA13",
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"HA50", "HA24", "HA15", "HA32", "HA28", "HA6", "HA1", "HA107", "HA41", "HA48", "HA2", "HA63", "HA12",
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"HA117", "HA35", "HA34", "HA56", "HA19", "HA18", "HA9", "HA27", "HA30", "HA31", "HA54", "HA49",
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'HA8', 'HA11', 'HA21', 'HA37', 'HA42', 'HA44', 'HA45', 'HA51', 'HA52'
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'HA8', 'HA11', 'HA21', 'HA37', 'HA42', 'HA44', 'HA45', 'HA51', 'HA52', "HA17", "HA5", "HA20",
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]
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no_ciga_cavity_descriptions = [
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@ -6124,22 +6150,6 @@ def fml_analysis(loader):
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# TODO: There will be some properties that are subject to CIGA that do not look like they ned a CIGA check! pass
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# them! Non-invasices will have checked the wall though
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codes = [
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"HA39", "HA14", "HA24", "HA15", "HA32", "HA28", "HA6", "HA1", "HA7",
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"HA16", "HA107", "HA25", "HA50", "HA41", "HA48", "HA2", "HA63", "HA12",
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"HA117", "HA13", "HA35", "HA34", "HA56", "HA19", "HA18", "HA9", "HA27",
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"HA30", "HA31", "HA54", "HAXX", "HA49", "HAXXX",
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]
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values = [
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706, 2161, 1053, 793, 0, 656, 1200, 1647, 4248, 2703, 1087, 1876, 2135,
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1078, 775, 538, 518, 401, 466, 2627, 98, 1050, 524, 191, 538, 384, 204,
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281, 422, 74, 313, 71, 6
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]
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# Create a dictionary mapping
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remaining_eligible_mapping = dict(zip(codes, values))
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results = []
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wall_descriptions = []
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for ha_name in tqdm(has_bruh):
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@ -6397,9 +6407,13 @@ def fml_analysis(loader):
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without_survey_without_ciga_expected = 0
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else:
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# We apply the same conversion rate as the properties with a survey
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without_survey_without_ciga_expected = np.round(
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without_survey_needing_ciga.shape[0] * (ciga_check_expectation / ciga_check_needed.shape[0])
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)
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if ciga_check_needed.shape[0] == 0 and ciga_check_expectation == 0:
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without_survey_without_ciga_expected = without_survey_needing_ciga.shape[0]
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else:
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without_survey_without_ciga_expected = np.round(
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without_survey_needing_ciga.shape[0] * (ciga_check_expectation / ciga_check_needed.shape[0])
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)
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without_survey_passed_ciga = fuck_this[
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(fuck_this["estimated"] == True) &
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@ -6466,15 +6480,6 @@ def fml_analysis(loader):
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without_survey_identified_as_gbis_qualified
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)
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surveys = loader.data[ha_name]["survey_list"]
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sold_now = 0
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if not surveys.empty:
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sold_now = surveys[
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surveys["installation_status"].str.lower().str.contains("eco4")
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].shape[0]
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sales_since_nov = sold_now - original_figures["No. of Tech surveys complete - Eco 4"].values[0]
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results.append(
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{
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"HA Name": ha_name,
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@ -6498,6 +6503,194 @@ def fml_analysis(loader):
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# TODO: Change the left hand side number for our post CIGA estimates
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def create_final_report():
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"""
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This function will produce the final output for the HA analysis
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:return:
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"""
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epc_validated_results = pd.read_csv("analysis - revised.csv")
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pipeline_results = pd.read_csv("pipeline_remaining_raw.csv")
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####################################
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# Original Warmfront estimates
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####################################
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# Create the volumes result
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all_ha_summary_remaining = pipeline_results[
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[
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"('', '', '', 'HA Name')",
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"('ECO4 original', '', 'Remaining - #', '')",
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"('GBIS original', '', 'Remaining - #', '')",
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]
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].copy().rename(
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columns={
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"('', '', '', 'HA Name')": "HA Name",
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"('ECO4 original', '', 'Remaining - #', '')": "# ECO4 remaining - All HA Summary",
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"('GBIS original', '', 'Remaining - #', '')": "# GBIS remaining - All HA Summary",
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}
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)
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all_ha_summary_remaining["# Total remaining - All HA Summary"] = (
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all_ha_summary_remaining["# ECO4 remaining - All HA Summary"] +
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all_ha_summary_remaining["# GBIS remaining - All HA Summary"]
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)
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all_ha_summary_remaining = all_ha_summary_remaining.sort_values("HA Name")
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####################################
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# Postcode list - pre-CIGA
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####################################
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postcode_list_pre_ciga_remaining = pipeline_results[
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[
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"('', '', '', 'HA Name')",
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"('ECO4 pre-ciga', '', 'Remaining - #', '')",
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"('GBIS Postcode list', 'Warmfront post code list', 'Remaining - #', 'GBIS total')",
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]
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].copy().rename(
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columns={
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"('', '', '', 'HA Name')": "HA Name",
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"('ECO4 pre-ciga', '', 'Remaining - #', '')": "# ECO4 remaining - Postcode list (pre CIGA)",
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"('GBIS Postcode list', 'Warmfront post code list', 'Remaining - #', 'GBIS total')": (
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"# GBIS remaining - Postcode list (pre CIGA)"
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),
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}
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)
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postcode_list_pre_ciga_remaining["# Total remaining - Postcode list (pre CIGA)"] = (
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postcode_list_pre_ciga_remaining["# ECO4 remaining - Postcode list (pre CIGA)"] +
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postcode_list_pre_ciga_remaining["# GBIS remaining - Postcode list (pre CIGA)"]
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)
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postcode_list_pre_ciga_remaining = postcode_list_pre_ciga_remaining.sort_values("HA Name")
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####################################
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# Postcode list - post-CIGA
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####################################
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postcode_list_post_ciga_remaining = pipeline_results[
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[
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"('', '', '', 'HA Name')",
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"('ECO4 post-ciga', '', 'Estimated remaining eligible - #', '')",
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"('GBIS Postcode list', 'Warmfront post code list', 'Remaining - #', 'GBIS total')",
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]
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].copy().rename(
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columns={
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"('', '', '', 'HA Name')": "HA Name",
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"('ECO4 post-ciga', '', 'Estimated remaining eligible - #', '')":
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"# ECO4 remaining - Postcode list (post CIGA)",
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"('GBIS Postcode list', 'Warmfront post code list', 'Remaining - #', 'GBIS total')": (
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"# GBIS remaining - Postcode list (post CIGA)"
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),
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}
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)
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postcode_list_post_ciga_remaining["# Total remaining - Postcode list (post CIGA)"] = (
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postcode_list_post_ciga_remaining["# ECO4 remaining - Postcode list (post CIGA)"] +
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postcode_list_post_ciga_remaining["# GBIS remaining - Postcode list (post CIGA)"]
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)
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postcode_list_post_ciga_remaining = postcode_list_post_ciga_remaining.sort_values("HA Name")
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####################################
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# From EPC Database
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####################################
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from_epc_database = epc_validated_results[
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[
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"HA Name",
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"EPC verified ECO4 Eligible - Remaining",
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"EPC verified GBIS Eligibile - Remaining"
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]
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].copy().rename(
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columns={
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"EPC verified ECO4 Eligible - Remaining": "# ECO4 remaining - From EPC Database (post CIGA)",
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"EPC verified GBIS Eligibile - Remaining": "# GBIS remaining - From EPC Database (post CIGA)",
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}
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)
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from_epc_database["# Total remaining - From EPC Database (post CIGA)"] = (
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from_epc_database["# ECO4 remaining - From EPC Database (post CIGA)"] +
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from_epc_database["# GBIS remaining - From EPC Database (post CIGA)"]
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)
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from_epc_database = from_epc_database.sort_values("HA Name")
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# Combine the datasets
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volumes = all_ha_summary_remaining.merge(
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postcode_list_pre_ciga_remaining, how="left", on="HA Name"
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).merge(
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postcode_list_post_ciga_remaining, how="left", on="HA Name"
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).merge(
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from_epc_database, how="inner", on="HA Name"
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)
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revenue = volumes.copy()
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# Convert the ECO4 volumes to revenue
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for col in [
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'# ECO4 remaining - All HA Summary',
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'# ECO4 remaining - Postcode list (pre CIGA)',
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'# ECO4 remaining - Postcode list (post CIGA)',
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'# ECO4 remaining - From EPC Database (post CIGA)'
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]:
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revenue[col] = revenue[col] * 1710
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# Convert the GBIS volumes to revenue
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for col in [
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'# GBIS remaining - All HA Summary',
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'# GBIS remaining - Postcode list (pre CIGA)',
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'# GBIS remaining - Postcode list (post CIGA)',
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'# GBIS remaining - From EPC Database (post CIGA)'
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]:
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revenue[col] = revenue[col] * 600
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# Re-calculate the totals
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revenue['# Total remaining - All HA Summary'] = (
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revenue['# ECO4 remaining - All HA Summary'] + revenue['# GBIS remaining - All HA Summary']
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)
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revenue['# Total remaining - Postcode list (pre CIGA)'] = (
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revenue['# ECO4 remaining - Postcode list (pre CIGA)'] + revenue['# GBIS remaining - Postcode list (pre CIGA)']
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)
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revenue['# Total remaining - Postcode list (post CIGA)'] = (
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revenue['# ECO4 remaining - Postcode list (post CIGA)'] + revenue[
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'# GBIS remaining - Postcode list (post CIGA)']
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)
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revenue['# Total remaining - From EPC Database (post CIGA)'] = (
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revenue['# ECO4 remaining - From EPC Database (post CIGA)'] +
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revenue['# GBIS remaining - From EPC Database (post CIGA)']
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)
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# Replace the # with £ in the columns
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revnue_colnames = [col.replace("#", "£") for col in revenue.columns]
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revenue.columns = revnue_colnames
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# We check that each column gets smaller
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decreasing_check1 = all(
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volumes["# ECO4 remaining - Postcode list (pre CIGA)"] >= volumes[
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'# ECO4 remaining - Postcode list (post CIGA)']
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)
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if not decreasing_check1:
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raise ValueError("decreasing_check1 failed")
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# Just HA32 and HA17 should fail this, and it's due to GBIS jobs looking like ECO4
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decreasing_check2 = volumes[volumes["# ECO4 remaining - From EPC Database (post CIGA)"] > volumes[
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"# ECO4 remaining - Postcode list (post CIGA)"]]
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if set(decreasing_check2["HA Name"].tolist()) != {"HA17", "HA32"}:
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raise ValueError("decreasing_check2 failed")
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# Check for GBIS
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decreasing_check3 = all(
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volumes["# GBIS remaining - Postcode list (pre CIGA)"] >= volumes[
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'# GBIS remaining - Postcode list (post CIGA)']
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)
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if not decreasing_check3:
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raise ValueError("decreasing_check3 failed")
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# Don't perform this - this happens for multiple
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# decreasing_check4 = volumes[volumes["# GBIS remaining - From EPC Database (post CIGA)"] > volumes[
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# "# GBIS remaining - Postcode list (post CIGA)"]]
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# Store final outputs
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volumes.to_csv("HA Analysis Final - volumes.csv")
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revenue.to_csv("HA Analysis Final - revenue.csv")
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def app():
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"""
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This app contains the housin association analysis for HAs 1, 6, 14, 39 and 107.
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