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Fixing some formatting bugs

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Khalim Conn-Kowlessar 2024-03-01 22:17:11 +00:00
parent 721bfb19fc
commit f9d1a90689
1 changed files with 48 additions and 38 deletions
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86
etl/eligibility/ha_15_32/ha_analysis_batch_3.py
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@ -2820,6 +2820,8 @@ def forecast_remaining_sales(loader):
results = []
for ha_name, input_data in loader.data.items():
if ha_name == "HA16":
dew
# Original warmfront figures - ECO4
original_warmfront_estimates = december_figures[december_figures["HA Name"] == ha_name]
@ -2991,8 +2993,9 @@ def forecast_remaining_sales(loader):
("ECO4 pre-ciga", "", "Total - £", ""): eco4_pre_ciga_revenue,
("ECO4 pre-ciga", "", "Remaining - £", ""): eco4_pre_ciga_remaining_revenue,
# ECO4 - asset list, post ciga, total
("ECO4 post-ciga", "", "Estimated total eligible - #", ""): eco4_post_ciga_total_results[
"ECO4 - post CIGA - #"],
("ECO4 post-ciga", "", "Estimated total eligible - #", "ECO4 total (post-ciga)"):
eco4_post_ciga_total_results[
"ECO4 - post CIGA - #"],
("ECO4 post-ciga", "", "Estimated total eligible - £", ""): eco4_post_ciga_total_results[
"ECO4 - post CIGA - £"],
("ECO4 post-ciga", "", "Delta vs original estimate - %", ""): eco4_delta_vs_original_estimate,
@ -3059,7 +3062,7 @@ def forecast_remaining_sales(loader):
# For the delta columns, we calculate the delta on the totals
totals_row[("ECO4 post-ciga", "", "Delta vs original estimate - %", "")] = round(
100 * (
totals_row[("ECO4 post-ciga", "", "Estimated total eligible - #", "")] -
totals_row[("ECO4 post-ciga", "", "Estimated total eligible - #", "ECO4 total (post-ciga)")] -
totals_row[("", "Original Warmfront estimate", "Total - #", "ECO4 - November")]
) / totals_row[("", "Original Warmfront estimate", "Total - #", "ECO4 - November")],
1
@ -3093,9 +3096,11 @@ def forecast_remaining_sales(loader):
# GBIS Headlines
headline_gbis_original_remaining = totals_row[("GBIS original", "", "Remaining - #", "")]
headline_gbis_original_remaining_revenue = totals_row[("GBIS original", "", "Remaining - £", "")]
headline_gbis_postcode_list_remaining = totals_row[("", "Warmfront post code list", "Remaining - #", "GBIS total")]
headline_gbis_postcode_list_remaining = totals_row[
("GBIS Postcode list", "Warmfront post code list", "Remaining - #", "GBIS total")
]
headline_gbis_postcode_list_remaining_revenue = totals_row[
("", "Warmfront post code list", "Remaining - £", "GBIS total")
("GBIS Postcode list", "Warmfront post code list", "Remaining - £", "GBIS total")
]
headline_gbis_delta = 100 * (
(headline_gbis_postcode_list_remaining - headline_gbis_original_remaining) /
@ -3205,29 +3210,33 @@ def forecast_remaining_sales(loader):
},
{
("", "", "", "HA Name"): "GBIS rate",
("ECO4 original", "", "Remaining - #", ""): "£" + str(gbis_rate)
("", "Original Warmfront estimate", "Total - #", "ECO4 - November"): "£" + str(gbis_rate)
},
{
("", "", "", "HA Name"): "Median CIGA pass rate",
("ECO4 original", "", "Total - £", ""): str(round(median_ciga_success_rate * 100, 1)) + "%",
("", "Original Warmfront estimate", "Total - #", "ECO4 - November"): str(
round(median_ciga_success_rate * 100, 1)) + "%",
},
{
("", "", "", "HA Name"): "Maximum allowed CIGA pass rate",
("ECO4 original", "", "Total - £", ""): str(round(maximum_ciga_conversion * 100, 1)) + "%",
("ECO4 original", "", "Remaining - £",
("", "Original Warmfront estimate", "Total - #", "ECO4 - November"): str(
round(maximum_ciga_conversion * 100, 1)) + "%",
("ECO4 original", "", "Remaining - #",
""): "- Maximum allowed CIGA conversion for HAs without CIGA checks We do not allow above this to be "
"conservative"
},
{
("", "", "", "HA Name"): "Median ECO4 (no CIGA) sales conversion rate",
("ECO4 original", "", "Total - £", ""): str(round(median_eco4_to_install * 100, 1)) + "%",
("ECO4 original", "", "Remaining - £",
("", "Original Warmfront estimate", "Total - #", "ECO4 - November"): str(
round(median_eco4_to_install * 100, 1)) + "%",
("ECO4 original", "", "Remaining - #",
""): " - Sales conversion rate for a ECO4 property that didn't need a CIGA check. Job must not cancel"
},
{
("", "", "", "HA Name"): "Median ECO4 (subect to CIGA) sales conversion rate",
("ECO4 original", "", "Total - £", ""): str(round(median_ciga_pass_to_install * 100, 1)) + "%",
("ECO4 original", "", "Remaining - £",
("", "Original Warmfront estimate", "Total - #", "ECO4 - November"): str(
round(median_ciga_pass_to_install * 100, 1)) + "%",
("ECO4 original", "", "Remaining - #",
""): " - Sales conversion rate for a ECO4 property that passed a CIGA check. Job must not cancel"
}
]
@ -3236,6 +3245,7 @@ def forecast_remaining_sales(loader):
[
results,
pd.DataFrame([totals_row]),
blank_row,
pd.DataFrame(headlines),
blank_row,
blank_row,
@ -3291,32 +3301,32 @@ def app():
loader.load()
loader.ha_facts_and_figures()
forecast_remaining_sales(loader)
# We load in the additional data required to perform the analysis
cleaned = read_from_s3(
s3_file_name="cleaned_epc_data/cleaned.bson",
bucket_name="retrofit-data-dev"
)
cleaned = msgpack.unpackb(cleaned, raw=False)
cleaned = patch_cleaned(cleaned)
cleaning_data = read_dataframe_from_s3_parquet(
bucket_name="retrofit-data-dev", file_key="sap_change_model/cleaning_dataset.parquet",
)
created_at = datetime.now().isoformat()
photo_supply_lookup, floor_area_decile_thresholds = SolarPhotoSupply.load(bucket="retrofit-data-dev")
outputs = get_epc_data(
loader=loader,
cleaned=cleaned,
cleaning_data=cleaning_data,
created_at=created_at,
photo_supply_lookup=photo_supply_lookup,
floor_area_decile_thresholds=floor_area_decile_thresholds,
pull_data=pull_data
)
analyse_ha_data(outputs, loader)
# cleaned = read_from_s3(
# s3_file_name="cleaned_epc_data/cleaned.bson",
# bucket_name="retrofit-data-dev"
# )
# cleaned = msgpack.unpackb(cleaned, raw=False)
# cleaned = patch_cleaned(cleaned)
#
# cleaning_data = read_dataframe_from_s3_parquet(
# bucket_name="retrofit-data-dev", file_key="sap_change_model/cleaning_dataset.parquet",
# )
# created_at = datetime.now().isoformat()
#
# photo_supply_lookup, floor_area_decile_thresholds = SolarPhotoSupply.load(bucket="retrofit-data-dev")
#
# outputs = get_epc_data(
# loader=loader,
# cleaned=cleaned,
# cleaning_data=cleaning_data,
# created_at=created_at,
# photo_supply_lookup=photo_supply_lookup,
# floor_area_decile_thresholds=floor_area_decile_thresholds,
# pull_data=pull_data
# )
# import pickle
# with open("ha_analysis.pickle", "wb") as f: