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fixed variance for HA6
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Khalim Conn-Kowlessar
parent
e966dfdf6e
commit
e2055b3b7d
1 changed files with 129 additions and 6 deletions
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@ -730,6 +730,81 @@ class DataLoader:
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"Post Code"
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] = "ST5 7BY"
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# PERFORM ADDITIONAL DROPS
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# Dropping rows based on multiple conditions
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conditions_to_drop = [
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(survey_list['Street / Block Name'] == "Bedford Crescent") & (survey_list['Post Code'] == "ST5 3EH") & (
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survey_list['NO.'] == 23) & (survey_list['INSTALLED OR CANCELLED'].str.contains("NO UPDATE YET")),
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(survey_list['Street / Block Name'] == "Hereford Avenue") & (survey_list['Post Code'] == "ST5 3EJ") & (
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survey_list['NO.'] == 92) & (survey_list['INSTALLED OR CANCELLED'].str.contains("NO UPDATE YET")),
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(survey_list['Street / Block Name'] == "Seabridge Lane") & (survey_list['Post Code'] == "ST5 3EX") & (
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survey_list['NO.'].isin([16, 18, 42])) & (
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survey_list['INSTALLED OR CANCELLED'].str.contains("NO UPDATE YET")),
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(survey_list['Street / Block Name'] == "ESKDALE PLACE") & (survey_list['Post Code'] == "ST5 3QW") & (
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survey_list['NO.'] == 5) & (survey_list['SUBMISSION DATE'].astype(str) == "2023-03-06 00:00:00"),
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(survey_list['Street / Block Name'] == "Birch House road") & (survey_list['Post Code'] == "ST6 2LS") & (
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survey_list['NO.'].isin([56, 58])),
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(survey_list['Street / Block Name'] == "Blackthorn Place") & (survey_list['Post Code'] == "ST6 2LS") & (
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survey_list['NO.'].isin([37, 39])),
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(survey_list['Street / Block Name'] == "Whitethorn Way") & (survey_list['Post Code'] == "ST5 7BT") & (
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survey_list['NO.'].isin([17, 6])),
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(survey_list['Street / Block Name'] == "Lion Grove") & (survey_list['Post Code'] == "ST5 7HQ") & (
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survey_list['NO.'].isin([10, 12])) & (
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survey_list['INSTALLED OR CANCELLED'].str.contains("NO UPDATE YET")),
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(survey_list['Street / Block Name'] == "DENRY CRESCENT") & (survey_list['Post Code'] == "ST5 8JW") & (
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survey_list['NO.'] == 87) & (survey_list['INSTALLED OR CANCELLED'].str.contains("NO UPDATE YET")),
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(survey_list['Street / Block Name'] == "HOLLINS CRESCENT") & (survey_list['Post Code'] == "ST7 1JW") & (
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survey_list['NO.'] == 19)
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]
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# Combine all conditions with an OR "|"
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combined_condition = np.logical_or.reduce(conditions_to_drop)
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# Drop rows that meet the combined condition
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survey_list = survey_list[~combined_condition]
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# Making replacements using np.where
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survey_list['Post Code'] = np.where(
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(survey_list['Street / Block Name'] == "Whitethorn Way") & (survey_list['Post Code'] == "ST5 3EH") & (
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survey_list['NO.'] == 17),
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"ST5 7BT",
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survey_list['Post Code']
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)
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survey_list['Post Code'] = np.where(
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(survey_list['Street / Block Name'] == "Whitethorn Way") & (survey_list['Post Code'] == "ST5 3ED") & (
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survey_list['NO.'] == 6),
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"ST5 7BT",
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survey_list['Post Code']
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)
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# Maple avenue (stoke on trent, not newcastle) should be st7 1jw
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survey_list["Post Code"] = np.where(
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(survey_list["Street / Block Name"].str.lower().str.contains("maple avenue")) & (
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survey_list["Post Code"].str.lower() == "st7 1jx"
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),
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"st7 1jw",
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survey_list["Post Code"]
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)
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# Hollins Crescent should be st7 1jx
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survey_list["Post Code"] = np.where(
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(survey_list["Street / Block Name"].str.lower().str.contains("hollins crescent")) & (
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survey_list["Post Code"].str.lower() == "st7 1jw"
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),
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"st7 1jx",
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survey_list["Post Code"]
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)
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# Additional drops as the above misses some:
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survey_list = survey_list[
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~((survey_list["NO."].astype(str).isin(["18", "42"])) &
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(survey_list["Street / Block Name"] == "Seabridge Lane") &
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(survey_list["Post Code"] == "ST5 3EY") &
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(survey_list["SUBMISSION DATE"].astype(str) == "24.07.2023") &
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(survey_list["INSTALLED OR CANCELLED"].str.contains("NO UPDATE YET")))
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]
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return survey_list
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@staticmethod
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@ -1176,6 +1251,11 @@ class DataLoader:
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if matching_lookup.shape[0] != survey_list.shape[0]:
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raise ValueError("Mismatch in the number of survey rows and matching lookup rows")
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matching_lookup = matching_lookup[~pd.isnull(matching_lookup["asset_list_row_id"])]
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if matching_lookup["asset_list_row_id"].duplicated().sum():
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raise ValueError("Duplicated matches in survey list")
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# Merge onto the survey list
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survey_list = survey_list.merge(matching_lookup, how='left', on="survey_list_row_id")
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@ -1483,7 +1563,7 @@ class DataLoader:
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# TODO: We might have more indications of partial cancellations
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survey_list["installation_status"] = np.where(
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survey_list["installed_or_cancelled_clean"].isin(["loft cancelled"]),
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"partially cancelled",
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"cancelled",
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survey_list["installation_status"]
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)
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else:
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@ -3174,6 +3254,8 @@ def forecast_remaining_sales(loader):
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if survey_list.empty:
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asset_list_remaining = asset_list.copy()
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else:
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# For HA6, there are a small number of postcodes that do not match to any item in the asset list
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survey_list = survey_list[~pd.isnull(survey_list["asset_list_row_id"])]
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asset_list_remaining = asset_list.merge(
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survey_list[["asset_list_row_id", "installation_status"]],
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how="left",
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@ -3183,6 +3265,47 @@ def forecast_remaining_sales(loader):
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asset_list_remaining = asset_list_remaining[pd.isnull(asset_list_remaining["installation_status"])]
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asset_list_remaining = asset_list_remaining.drop(columns=["installation_status"])
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# # TODO: TEMP
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# n_pre_ciga = asset_list[
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# asset_list["ECO Eligibility"].isin(
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# [
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# "eco4 - passed ciga",
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# "eco4 (subject to ciga)",
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# "failed ciga",
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# "eco4"
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# ]
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# )
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# ].shape[0]
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#
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# n_pre_ciga_remaining = asset_list_remaining[
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# asset_list_remaining["ECO Eligibility"].isin(
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# [
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# "eco4 - passed ciga",
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# "eco4 (subject to ciga)",
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# "failed ciga",
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# "eco4"
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# ]
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# )
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# ].shape[0]
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#
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# compare_to_ids = asset_list_remaining["asset_list_row_id"].values
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# assets_diff_ids = [x for x in asset_list["asset_list_row_id"].values if x not in compare_to_ids]
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# diff = asset_list[asset_list["asset_list_row_id"].isin(assets_diff_ids)]
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#
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# n_sold = survey_list[survey_list["installation_status"].str.contains("ECO4")].shape[0]
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# # cancellations = survey_list[]
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# asset_list["ECO Eligibility"].value_counts()
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#
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# # Revenenue
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# pre_ciga_revenue = n_pre_ciga * eco4_rate
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# pre_ciga_remaining_revenue = n_pre_ciga_remaining * eco4_rate
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# sold_revenue = n_sold * eco4_rate
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#
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# pre_ciga_revenue - (pre_ciga_remaining_revenue + sold_revenue)
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# # MISSING 1 SALE from sold
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# cancelled = survey_list[survey_list["installation_status"].str.contains("ECO4")].shape[0]
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# # TODO: END TEMP
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eligiblity_counts = pd.DataFrame(asset_list["ECO Eligibility"].value_counts()).reset_index()
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eligiblity_counts_remaining = pd.DataFrame(asset_list_remaining["ECO Eligibility"].value_counts()).reset_index()
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@ -3402,13 +3525,13 @@ def forecast_remaining_sales(loader):
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("", "Warmfront post code list", "Total #", "ECO4 total (pre-ciga)"): eco4_pre_ciga,
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("ECO4 pre-ciga", "", "Remaining - #", ""): eco4_pre_ciga_remaining,
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("ECO4 pre-ciga", "", "Total - £", ""): eco4_pre_ciga_revenue,
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("ECO4 pre-ciga", "", "Remaining - £", ""): eco4_pre_ciga_remaining_revenue,
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("ECO4 pre-ciga", "", "VARIANCE - TOTAL", ""): variance_total,
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("ECO4 pre-ciga", "", "VARIANCE - REMAINING", ""): variance_remaining,
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("ECO4 pre-ciga", "", "Sold - £", ""): eco4_actually_sold,
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("ECO4 pre-ciga", "", "Confirmed cancellations - £", ""): eco4_confirmed_cancellations,
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("ECO4 pre-ciga", "", "Confirmed cancellations - £", ""): eco4_confirmed_cancellations * eco4_rate,
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# This is for jobs that are in-progress and could still cancel
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("ECO4 pre-ciga", "", "Unconfirmed cancellations - £", ""): eco4_expected_cancellations,
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("ECO4 pre-ciga", "", "Remaining - £", ""): eco4_pre_ciga_remaining_revenue,
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("ECO4 pre-ciga", "", "Unconfirmed cancellations - £", ""): eco4_expected_cancellations * eco4_rate,
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# ECO4 - asset list, post ciga, total
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("ECO4 post-ciga", "", "Estimated total eligible - #", "ECO4 total"):
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eco4_post_ciga_total_results[
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@ -3460,9 +3583,9 @@ def forecast_remaining_sales(loader):
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("GBIS Postcode list", "Warmfront post code list", "Total - #", "GBIS total"): gbis_total,
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("GBIS Postcode list", "Warmfront post code list", "Total - £", "GBIS total"): gbis_total_revenue,
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("GBIS Postcode list", "Warmfront post code list", "Sold - £", "GBIS total"): gbis_actually_sold,
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("GBIS Postcode list", "", "Confirmed cancellations - £", ""): gbis_confirmed_cancellations,
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("GBIS Postcode list", "", "Confirmed cancellations - £", ""): gbis_confirmed_cancellations * gbis_rate,
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# This is for jobs that are in-progress and could still cancel
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("GBIS Postcode list", "", "Unconfirmed cancellations - £", ""): gbis_expected_cancellations,
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("GBIS Postcode list", "", "Unconfirmed cancellations - £", ""): gbis_expected_cancellations * gbis_rate,
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("GBIS Postcode list", "Warmfront post code list", "Remaining - #", "GBIS total"): gbis_remaining,
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("GBIS Postcode list", "Warmfront post code list", "Remaining - £", "GBIS total"): gbis_remaining_revenue,
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("GBIS Postcode list", "", "Delta vs original estimate, remaining - %", ""):
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