make staff-tier category labels reflect the actual --senior-threshold/--support-threshold values

classifier.py

@@ -85,6 +85,24 @@ SENIOR_STAFF_THRESHOLD = 100_000
 SUPPORT_STAFF_THRESHOLD = 40_000
 
 
+def _fmt_threshold(v: float) -> str:
+    if v == int(v) and v % 1000 == 0:
+        return f"${int(v) // 1000}k"
+    return f"${v:,.0f}"
+
+
+def category_labels(senior_threshold: float = SENIOR_STAFF_THRESHOLD,
+                    support_threshold: float = SUPPORT_STAFF_THRESHOLD) -> dict:
+    """CATEGORY_LABELS with the staff-tier labels reflecting the actual
+    thresholds passed to classify_title(), instead of the defaults."""
+    labels = dict(CATEGORY_LABELS)
+    labels["SENIOR_PROF_STAFF"] = f"Senior Prof/Staff (>={_fmt_threshold(senior_threshold)})"
+    labels["PROF_STAFF"] = (f"Professional Staff ({_fmt_threshold(support_threshold)}"
+                            f"-{_fmt_threshold(senior_threshold)})")
+    labels["SUPPORT_STAFF"] = f"Support Staff (<{_fmt_threshold(support_threshold)})"
+    return labels
+
+
 def _rx(*patterns):
     return [re.compile(p, re.IGNORECASE) for p in patterns]
 

uc_salary_pipeline.py

@@ -119,7 +119,7 @@ def _group_sort_key(key: tuple) -> tuple:
 
 
 def process_file(path: Path, campus: str, year: int, writer: csv.writer,
-                 senior_threshold: float, support_threshold: float,
+                 senior_threshold: float, support_threshold: float, labels: dict,
                  agg: dict | None = None, group_dims: tuple = (),
                  top_earners: dict | None = None, top_n: int = 10,
                  tie_breaker: itertools.count | None = None,
@@ -157,7 +157,7 @@ def process_file(path: Path, campus: str, year: int, writer: csv.writer,
         )
         writer.writerow([
             year, campus, parsed["title"], category,
-            classifier.CATEGORY_LABELS[category],
+            labels[category],
             round(parsed["gross_pay"], 2), round(parsed["regular_pay"], 2),
             round(parsed["overtime_pay"], 2), round(parsed["other_pay"], 2),
             parsed["first_name"], parsed["last_name"],
@@ -180,7 +180,7 @@ def process_file(path: Path, campus: str, year: int, writer: csv.writer,
     return count
 
 
-def write_aggregate_csv(agg: dict, group_dims: tuple, outpath: Path) -> None:
+def write_aggregate_csv(agg: dict, group_dims: tuple, labels: dict, outpath: Path) -> None:
     """Write one row per (group_dims..., category) with employee count,
     total/mean/median gross pay."""
     header = list(group_dims) + [
@@ -196,14 +196,14 @@ def write_aggregate_csv(agg: dict, group_dims: tuple, outpath: Path) -> None:
             values = agg[key]
             total = sum(values)
             writer.writerow([
-                *prefix, category, classifier.CATEGORY_LABELS[category],
+                *prefix, category, labels[category],
                 len(values), round(total, 2), round(total / len(values), 2),
                 round(statistics.median(values), 2),
             ])
     log.info("Aggregate CSV -> %s", outpath)
 
 
-def write_top_earners_csv(top_earners: dict, outpath: Path) -> None:
+def write_top_earners_csv(top_earners: dict, labels: dict, outpath: Path) -> None:
     """Write the top-N earners per (group_dims..., category) key, ranked
     highest gross_pay first within each group."""
     header = ["category", "category_label", "rank", "year", "campus", "title",
@@ -219,7 +219,7 @@ def write_top_earners_csv(top_earners: dict, outpath: Path) -> None:
             for rank, (gross_pay, _, row) in enumerate(ranked, start=1):
                 year, campus, title, gross, regular, overtime, other, first, last = row
                 writer.writerow([
-                    category, classifier.CATEGORY_LABELS[category], rank,
+                    category, labels[category], rank,
                     year, campus, title, round(gross, 2), round(regular, 2),
                     round(overtime, 2), round(other, 2), first, last,
                 ])
@@ -232,7 +232,7 @@ def _money(v: float) -> str:
     return f"${v:,.0f}"
 
 
-def print_console_summary(console_stats: dict) -> None:
+def print_console_summary(console_stats: dict, labels: dict) -> None:
     """Print one formatted category-by-category summary table per year,
     keyed off (year, category) -> [gross_pay, ...] entries in console_stats."""
     label_w, col_w = 34, 16
@@ -255,7 +255,7 @@ def print_console_summary(console_stats: dict) -> None:
             median = statistics.median(values) if values else 0.0
             total_count += count
             total_pay += total
-            label = classifier.CATEGORY_LABELS[category]
+            label = labels[category]
             print(f"{label:<{label_w}}{count:>{col_w},}"
                  f"{_money(total):>{col_w}}{_money(median):>{col_w}}")
 
@@ -331,6 +331,7 @@ def main():
     else:
         log.info("--skip-download set, classifying cached data only.")
 
+    labels = classifier.category_labels(args.senior_threshold, args.support_threshold)
     group_dims = tuple(dim for dim in ("year", "campus") if dim in args.group_by)
     agg = {} if args.aggregate_output else None
     top_earners = {} if args.top_earners_output else None
@@ -350,7 +351,7 @@ def main():
                     log.info("No data for %s %s, skipping.", campus, year)
                     continue
                 count = process_file(path, campus, year, writer,
-                                    args.senior_threshold, args.support_threshold,
+                                    args.senior_threshold, args.support_threshold, labels,
                                     agg=agg, group_dims=group_dims,
                                     top_earners=top_earners, top_n=args.top_n,
                                     tie_breaker=tie_breaker,
@@ -365,13 +366,13 @@ def main():
     log.info("Total rows written: %d -> %s", total_rows, output_path)
 
     if agg is not None:
-        write_aggregate_csv(agg, group_dims, Path(args.aggregate_output))
+        write_aggregate_csv(agg, group_dims, labels, Path(args.aggregate_output))
 
     if top_earners is not None:
-        write_top_earners_csv(top_earners, Path(args.top_earners_output))
+        write_top_earners_csv(top_earners, labels, Path(args.top_earners_output))
 
     if console_stats is not None:
-        print_console_summary(console_stats)
+        print_console_summary(console_stats, labels)
 
 
 if __name__ == "__main__":