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Adding more ha analysis
This commit is contained in:
Khalim Conn-Kowlessar
parent
df989ba918
commit
707724cdb1
1 changed files with 85 additions and 10 deletions
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@ -472,6 +472,8 @@ def get_ha_32data(ha_data, cleaned, cleaning_data, created_at):
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"message": "No EPC found",
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"gbis_eligible_future": None,
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"gbis_eligible_future_message": None,
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"eco4_eligible_future": None,
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"eco4_eligible_future_message": None,
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"tenure": None,
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"heating_description": None,
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}
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@ -529,6 +531,8 @@ def get_ha_32data(ha_data, cleaned, cleaning_data, created_at):
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"message": "eco4 conditional on post sap",
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"gbis_eligible_future": eligibility.gbis["eligible"],
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"gbis_eligible_future_message": eligibility.gbis["message"],
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"eco4_eligible_future": eligibility.eco4["eligible"],
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"eco4_eligible_future_message": eligibility.eco4["message"],
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"tenure": eligibility.tenure,
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"heating_description": eligibility.epc["mainheat-description"],
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}
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@ -549,6 +553,8 @@ def get_ha_32data(ha_data, cleaned, cleaning_data, created_at):
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"message": None,
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"gbis_eligible_future": eligibility.gbis["eligible"],
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"gbis_eligible_future_message": eligibility.gbis["message"],
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"eco4_eligible_future": eligibility.eco4["eligible"],
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"eco4_eligible_future_message": eligibility.eco4["message"],
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"tenure": eligibility.tenure,
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"heating_description": eligibility.epc["mainheat-description"],
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}
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@ -613,6 +619,10 @@ def get_ha_15data(ha_data, cleaned, cleaning_data, created_at):
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"walls": None,
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"date_epc": None,
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"message": "No EPC found",
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"eco4_eligible_future": None,
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"eco4_eligible_future_message": None,
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"tenure": None,
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"heating_description": None,
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}
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)
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continue
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@ -641,6 +651,11 @@ def get_ha_15data(ha_data, cleaned, cleaning_data, created_at):
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eligibility.check_gbis_warmfront()
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eligibility.check_eco4_warmfront()
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# If the house is not identified, we do a full gbis and eco4 check
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# TODO: Add in ECO4 check
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eligibility.check_gbis()
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eligibility.check_eco4()
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if eligibility.eco4_warmfront["eligible"]:
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scoring_dictionary = prepare_model_data_row(
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property_id=house["row_id"],
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@ -661,6 +676,12 @@ def get_ha_15data(ha_data, cleaned, cleaning_data, created_at):
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"walls": eligibility.walls["clean_description"],
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"date_epc": eligibility.epc["lodgement-date"],
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"message": "eco4 conditional on post sap",
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"gbis_eligible_future": eligibility.gbis["eligible"],
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"gbis_eligible_future_message": eligibility.gbis["message"],
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"eco4_eligible_future": eligibility.eco4["eligible"],
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"eco4_eligible_future_message": eligibility.eco4["message"],
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"tenure": eligibility.tenure,
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"heating_description": eligibility.epc["mainheat-description"],
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}
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)
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continue
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@ -676,7 +697,13 @@ def get_ha_15data(ha_data, cleaned, cleaning_data, created_at):
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"roof": eligibility.roof["clean_description"],
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"walls": eligibility.walls["clean_description"],
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"date_epc": eligibility.epc["lodgement-date"],
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"message": None
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"message": None,
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"gbis_eligible_future": eligibility.gbis["eligible"],
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"gbis_eligible_future_message": eligibility.gbis["message"],
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"eco4_eligible_future": eligibility.eco4["eligible"],
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"eco4_eligible_future_message": eligibility.eco4["message"],
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"tenure": eligibility.tenure,
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"heating_description": eligibility.epc["mainheat-description"],
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}
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)
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@ -794,6 +821,7 @@ def analyse_ha_32_results(results, ha32, no_house_numbers):
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"""
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results_df = pd.DataFrame(results)
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results_df["tenure"] = results_df["tenure"].fillna("Unknown - probably new build")
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# What proportio
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warmfront_identified = results_df[
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@ -857,21 +885,32 @@ def analyse_ha_32_results(results, ha32, no_house_numbers):
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new_possibilities = results_df[
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(~results_df["warmfront_identified"]) &
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(results_df["gbis_eligible"] | results_df["eco4_eligible"])
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(results_df["gbis_eligible"] | results_df["eco4_eligible"]) &
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(results_df["tenure"] == "Rented (social)")
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].copy()
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new_possibilities_full_gbis = results_df[
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future_possibilities_eco = results_df[
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(~results_df["warmfront_identified"]) &
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(results_df["gbis_eligible_future"] == True)
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(results_df["eco4_eligible_future"] == True) & (~(results_df["gbis_eligible"] | results_df["eco4_eligible"]))
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].copy()
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future_possibilities_eco["eco4_eligible_future_message"].value_counts()
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future_possibilities_gbis = results_df[
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(~results_df["warmfront_identified"]) &
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(results_df["gbis_eligible_future"] == True) & (results_df["eco4_eligible_future"] == False) & (
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~(results_df["gbis_eligible"] | results_df["eco4_eligible"]))
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].copy()
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future_possibilities_gbis["gbis_eligible_future_message"].value_counts()
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# We deem that Any EPC that is produced in the last 3 years gives us good confidence
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cutoff_date = datetime.now() - timedelta(days=3 * 365)
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new_possibilities["high_confidence"] = pd.to_datetime(new_possibilities["date_epc"]) >= cutoff_date
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new_possibilities_full_gbis["high_confidence"] = pd.to_datetime(
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new_possibilities_full_gbis["date_epc"]) >= cutoff_date
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future_possibilities_eco["high_confidence"] = pd.to_datetime(
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future_possibilities_eco["date_epc"]) >= cutoff_date
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# We do a quick check on properties that didn't have a house number:
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no_house_numbers_ha32 = ha32[ha32["row_id"].isin(no_house_numbers)]["identified"].sum()
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@ -881,8 +920,10 @@ def analyse_ha_32_results(results, ha32, no_house_numbers):
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new = {
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"n_new_possibilities": new_possibilities.shape[0],
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"new_possibilities_confidence": new_possibilities["high_confidence"].value_counts(),
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"new_possibilities_full_gbis": new_possibilities_full_gbis.shape[0],
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"new_possibilities_full_gbis_confidence": new_possibilities_full_gbis["high_confidence"].value_counts()
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"future_possibilities_gbis": future_possibilities_gbis.shape[0],
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"future_possibilities_gbis_confidence": future_possibilities_gbis["high_confidence"].value_counts(),
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"future_possibilities_eco": future_possibilities_eco.shape[0],
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"future_possibilities_eco_confidence": future_possibilities_eco["high_confidence"].value_counts(),
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}
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return success_rate, new
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@ -898,6 +939,8 @@ def analyse_ha_15_results(results_df, ha15, no_house_numbers):
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For HA32, most of these (if not all) properties were identified under gbis
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"""
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results_df["tenure"] = results_df["tenure"].fillna("Unknown - probably new build")
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# What proportio
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warmfront_identified = results_df[
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results_df["warmfront_identified"]
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@ -930,6 +973,7 @@ def analyse_ha_15_results(results_df, ha15, no_house_numbers):
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]
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sap_low_enough["walls"].value_counts()
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z = ha15[ha15["row_id"].isin(sap_too_high["row_id"].values)]
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investigate_1 = ha15[ha15["row_id"].isin(sap_too_high["row_id"])][
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["row_id", "Postcode", "Address Line 1", "Address Line 2", "Address Line 3"]]
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@ -943,7 +987,29 @@ def analyse_ha_15_results(results_df, ha15, no_house_numbers):
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new_possibilities = results_df[
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(~results_df["warmfront_identified"]) &
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((results_df["gbis_eligible"] == True) | (results_df["eco4_eligible"] == True))
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((results_df["gbis_eligible"] == True) | (results_df["eco4_eligible"] == True)) &
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(results_df["tenure"] == "Rented (social)")
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].copy()
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# These are future possibilityies
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new_possibilities_eco = results_df[
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(~results_df["warmfront_identified"]) &
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(results_df["eco4_eligible_future"] == True) & (~(results_df["gbis_eligible"] | results_df["eco4_eligible"]))
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].copy()
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rids = new_possibilities_eco[new_possibilities_eco["sap"] == 54]["row_id"]
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z = ha15[ha15["row_id"].isin(rids)]
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new_possibilities_gbis = results_df[
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(~results_df["warmfront_identified"]) &
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(results_df["gbis_eligible_future"] == True) & (results_df["eco4_eligible_future"] == False) & (
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~(results_df["gbis_eligible"] | results_df["eco4_eligible"]))
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].copy()
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not_new = results_df[
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(~results_df["warmfront_identified"]) &
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(results_df["gbis_eligible_future"] != True) & (results_df["eco4_eligible_future"] != True) & (
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~(results_df["gbis_eligible"] | results_df["eco4_eligible"]))
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].copy()
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# We deem that Any EPC that is produced in the last 3 years gives us good confidence for GBIS
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@ -1026,6 +1092,13 @@ def app():
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# f
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# )
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# with open("ha32.pickle", "rb") as f:
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# ha32_dict = pickle.load(f)
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# ha32_results = ha32_dict["ha32_results"]
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# ha32_scoring_data = ha32_dict["ha32_scoring_data"]
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# ha32_no_house_numbers = ha32_dict["ha32_no_house_numbers"]
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ha32_success_rate, ha32_new_possibilities = analyse_ha_32_results(
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results=ha32_results, ha32=ha32, no_house_numbers=ha32_no_house_numbers
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)
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@ -1044,5 +1117,7 @@ def app():
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# )
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ha15_success_rate, ha15_new, ha15_identified_results, ha15_missed_results = analyse_ha_15_results(
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ha15_results_df, ha15, ha15_no_house_numbers
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results_df=ha15_results_df,
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ha15=ha15,
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no_house_numbers=ha15_no_house_numbers
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)
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