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12 changed files with 157 additions and 161 deletions
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@ -257,10 +257,11 @@ pub fn compute_feature_stats(
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/// Compute property-weighted per-year crime means across the selection.
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///
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/// Each matching property contributes its LSOA's per-year counts; this is the
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/// same property-weighted-LSOA-average shape used elsewhere in the right pane.
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/// LSOAs with no series for a given crime type contribute 0 for that type
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/// (matching how the existing `(avg/yr)` columns treat missing crime types).
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/// Each matching property contributes its postcode's per-year counts (incidents
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/// within 50m of that postcode); this is the same property-weighted-average
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/// shape used elsewhere in the right pane. Postcodes with no series for a given
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/// crime type contribute 0 for that type (matching how the `(avg/yr)` columns
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/// treat missing crime types).
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pub fn compute_crime_by_year(
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matching_rows: &[usize],
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data: &PropertyData,
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@ -273,19 +274,19 @@ pub fn compute_crime_by_year(
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}
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// For each crime type, accumulate per-year sums and the count of rows whose
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// LSOA exists in the crime side table.
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// postcode exists in the crime side table.
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let num_types = crime_by_year.crime_types.len();
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let mut per_type_year_sums: Vec<FxHashMap<i32, f64>> =
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(0..num_types).map(|_| FxHashMap::default()).collect();
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let mut per_type_row_counts: Vec<u32> = vec![0; num_types];
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for &row in matching_rows {
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let lsoa = data.lsoa(row);
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let Some(series_list) = crime_by_year.series_by_lsoa.get(lsoa) else {
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let postcode = data.postcode(row);
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let Some(series_list) = crime_by_year.series_by_postcode.get(postcode) else {
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continue;
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};
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// For every type the LSOA reports, add its per-year counts.
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// For every type the postcode reports, add its per-year counts.
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// For types it doesn't report, treat the row as contributing 0 — so we
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// bump the row count for *every* known type below.
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for series in series_list {
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