feat: add indication pathway processing functions (Task 2.3)
- Add generate_icicle_chart_indication() to pathway_analyzer.py - Variant that uses indication_df instead of directory_df - Groups by Trust → Search_Term → Drug → Pathway - Accepts indication_df mapping UPID → Indication_Group - Add process_indication_pathway_for_date_filter() to pathway_pipeline.py - Processes indication-based pathway for a single date filter - Uses generate_icicle_chart_indication() for hierarchy building - Add extract_indication_fields() to pathway_pipeline.py - Extracts trust_name, search_term, drug_sequence from ids column - Similar to extract_denormalized_fields() but for indication charts - Update convert_to_records() with chart_type parameter - Includes chart_type column in output records - Supports "directory" and "indication" values - Add ChartType type alias (Literal["directory", "indication"]) - Update __all__ exports with new functions
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Andrew Charlwood
@@ -749,3 +749,191 @@ def generate_icicle_chart(
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ice_df = prepare_chart_data(ice_df, minimum_num_patients)
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return ice_df, final_title
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def generate_icicle_chart_indication(
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df: pd.DataFrame,
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indication_df: pd.DataFrame,
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start_date: str,
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end_date: str,
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last_seen_date: str,
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trust_filter: list[str],
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drug_filter: list[str],
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directory_filter: list[str],
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minimum_num_patients: int,
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title: str = "",
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paths: Optional[PathConfig] = None,
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) -> tuple[pd.DataFrame, str]:
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"""
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Generate icicle chart data with indication-based grouping.
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This is a variant of generate_icicle_chart() that groups by Search_Term
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(from GP diagnosis match) instead of Directory. For patients without
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a GP diagnosis match, the fallback directorate is used with a "(no GP dx)"
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suffix to distinguish them.
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Hierarchy: Trust → Indication_Group → Drug → Pathway
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Args:
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df: DataFrame with processed patient intervention data
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indication_df: DataFrame mapping UPID → Indication_Group
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Must have 'UPID' as index and 'Indication_Group' column
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Values are either Search_Term or "Directory (no GP dx)"
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start_date: Start date for patient initiation filter
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end_date: End date for patient initiation filter
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last_seen_date: Filter for patients last seen after this date
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trust_filter: List of trust names to include
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drug_filter: List of drug names to include
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directory_filter: List of directories to include
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minimum_num_patients: Minimum number of patients to include a pathway
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title: Chart title (auto-generated if empty)
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paths: PathConfig for file paths (uses default if None)
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Returns:
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Tuple of (ice_df for chart, final_title) or (None, "") if no data
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"""
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if paths is None:
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paths = default_paths
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# Prepare data - use standard prepare_data function
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result = prepare_data(df, trust_filter, drug_filter, directory_filter, paths)
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if result[0] is None:
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return None, ""
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filtered_df, org_codes, directory_df = result
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# For indication charts, we replace directory_df with indication_df
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# First, ensure indication_df has the correct format (UPID as index)
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if indication_df is not None and not indication_df.empty:
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if 'UPID' in indication_df.columns:
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indication_df = indication_df.set_index('UPID')
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# Rename column for compatibility with build_hierarchy()
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if 'Indication_Group' in indication_df.columns:
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indication_df = indication_df.rename(columns={'Indication_Group': 'Directory'})
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elif 'indication_group' in indication_df.columns:
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indication_df = indication_df.rename(columns={'indication_group': 'Directory'})
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else:
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# Fall back to directory if no indication data provided
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logger.warning("No indication data provided, falling back to directory grouping")
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indication_df = directory_df
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cost_df = filtered_df[["UPID", "Price Actual"]]
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total_costs = pd.DataFrame(cost_df.groupby("UPID").sum())
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total_costs.rename(columns={"Price Actual": "Total cost"}, inplace=True)
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result = calculate_statistics(filtered_df, start_date, end_date, last_seen_date, title)
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if result[0] is None:
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return None, ""
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patient_info, date_df, final_title = result
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df_drug_freq = (
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filtered_df.groupby("UPID")
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.agg({"Drug Name": lambda x: list(x)})
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.reset_index()
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.set_index("UPID")
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)
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df_drug_cost = (
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filtered_df.groupby("UPID")
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.agg({"Price Actual": lambda x: list(x)})
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.reset_index()
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.set_index("UPID")
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)
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df_drug_freq["Price Actual"] = df_drug_freq.index.map(df_drug_cost["Price Actual"])
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df_drug_freq["Drug Name"] = df_drug_freq["Drug Name"].apply(_count_list_values)
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df_drug_freq["Drug cost total"] = df_drug_freq.apply(lambda x: _sum_list_values(x), axis=1)
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df1_unique = _drop_duplicate_treatments(filtered_df, True)
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df_drugs = (
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df1_unique.groupby("UPID")
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.agg({"Drug Name": lambda x: list(x)})
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.reset_index()
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.set_index("UPID")
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)
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df_dates = (
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df1_unique.groupby("UPID")
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.agg({"Intervention Date": lambda x: list(x)})
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.reset_index()
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.set_index("UPID")
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)
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df_dates_unwrapped = pd.DataFrame(
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df_dates["Intervention Date"].values.tolist(), index=df_dates.index
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).add_prefix("date_")
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df_drugs_unwrapped = pd.DataFrame(
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df_drugs["Drug Name"].values.tolist(), index=df_drugs.index
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).add_prefix("drug_")
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start_dates = (
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filtered_df[["UPIDTreatment", "Intervention Date"]]
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.sort_values(by=["Intervention Date"], ascending=True)
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.drop_duplicates(subset="UPIDTreatment")
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.set_index("UPIDTreatment")
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)
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end_dates = (
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filtered_df[["UPIDTreatment", "Intervention Date"]]
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.sort_values(by=["Intervention Date"], ascending=False)
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.drop_duplicates(subset="UPIDTreatment")
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.set_index("UPIDTreatment")
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)
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df_drugs_unwrapped["start_dates"] = df_drugs_unwrapped.apply(
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lambda x: _start_date_drug(start_dates, x), axis=1
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)
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df_start_dates_unwrapped = pd.DataFrame(
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df_drugs_unwrapped["start_dates"].values.tolist(), index=df_drugs_unwrapped.index
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).add_prefix("start_date_")
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df_drugs_unwrapped.drop(["start_dates"], inplace=True, axis=1)
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df_drugs_unwrapped["end_dates"] = df_drugs_unwrapped.apply(
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lambda x: _start_date_drug(end_dates, x), axis=1
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)
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df_end_dates_unwrapped_2 = pd.DataFrame(
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df_drugs_unwrapped["end_dates"].values.tolist(), index=df_drugs_unwrapped.index
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).add_prefix("end_date_")
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df_drugs_unwrapped.drop(["end_dates"], inplace=True, axis=1)
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df_drugs_unwrapped = pd.merge(
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df_drugs_unwrapped, df_start_dates_unwrapped, left_index=True, right_index=True
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)
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df_drugs_unwrapped = pd.merge(
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df_drugs_unwrapped, df_end_dates_unwrapped_2, left_index=True, right_index=True
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)
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df_freq_for_merge = pd.DataFrame(
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df_drug_freq["Drug Name"].values.tolist(), index=df_drugs_unwrapped.index
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).add_prefix("freq_")
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df_drugs_unwrapped = pd.merge(
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df_drugs_unwrapped, df_freq_for_merge, left_index=True, right_index=True
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)
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df_drugs_unwrapped["frequency"] = df_drugs_unwrapped.apply(
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lambda x: _drug_frequency_average(x), axis=1
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)
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df_spacing_unwrapped = pd.DataFrame(
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df_drugs_unwrapped["frequency"].values.tolist(), index=df_drugs_unwrapped.index
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).add_prefix("spacing_")
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df_drugs_unwrapped = pd.merge(
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df_drugs_unwrapped, df_spacing_unwrapped, left_index=True, right_index=True
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)
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df_cost_unwrapped = pd.DataFrame(
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df_drug_freq["Drug cost total"].values.tolist(), index=df_drugs_unwrapped.index
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).add_prefix("total_cost_drug_")
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df_drugs_unwrapped = pd.merge(
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df_drugs_unwrapped, df_cost_unwrapped, left_index=True, right_index=True
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)
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df_drugs_unwrapped.drop(["frequency"], inplace=True, axis=1)
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# Build hierarchy with indication_df instead of directory_df
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ice_df = build_hierarchy(
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patient_info,
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date_df,
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filtered_df,
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org_codes,
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indication_df, # Use indication mapping instead of directory
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total_costs,
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df_drugs_unwrapped,
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)
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ice_df = prepare_chart_data(ice_df, minimum_num_patients)
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return ice_df, final_title
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