73017081ef
marimo's data-table viewer formats integer columns with thousands separators, so int32/int64 year columns (DuckDB SELECT year, registry- built frames) displayed as "2,027". New conf.display.plain_years casts year-like integer columns (year, *_year, *_period; autodetected or explicit, polars + pandas) to strings at the display boundary only — analysis frames keep integer dtypes, chart encodings (already :O) are untouched. Applied at every affected display site: pfs_calcs carrier/SQL result tables, pfs_reconciliation delta table, cy2026/cy2027 APM-threshold tables, cms_quality_measures pipeline-result accordions. All five notebooks re-executed headlessly in the notebooks container (nb_integration ci-smoke set): pass=5, displayed year values now serialize as strings.
116 lines
2.8 KiB
Python
116 lines
2.8 KiB
Python
import marimo
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__generated_with = "0.21.1"
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app = marimo.App(width="medium")
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@app.cell(hide_code=True)
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def _():
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import marimo as mo
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return (mo,)
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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# PFS Reconciliation
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Runs `rec.pricers.pfs.PfsPricer` against `pfs.carrier_locality`
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for a chosen year and reports the per-row delta. Goal is perfect
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1:1 concordance. Tracks **homelab/stack#340**.
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""")
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return
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@app.cell(hide_code=True)
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def _():
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from conf import connect
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from conf.display import plain_years
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from rec.engine import reconcile
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from rec.pricers.pfs import PfsPricer
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# PFS reference data lives in the DuckLake lakehouse (M5, #514);
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# queries are unchanged — the lake is the default database.
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con = connect.ducklake()
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pricer = PfsPricer()
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return con, plain_years, pricer, reconcile
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@app.cell(hide_code=True)
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def _(con, mo, pricer):
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_years = pricer.years_available(con)
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if not _years:
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year_picker = mo.ui.dropdown(
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options={"(no data loaded)": 0}, value="(no data loaded)", label="Year"
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)
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else:
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year_picker = mo.ui.dropdown(
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options={str(y): y for y in _years},
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value=str(_years[-1]),
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label="Year",
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)
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tolerance = mo.ui.slider(
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start=0, stop=10, step=1, value=0, label="Tolerance (cents)"
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)
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mo.hstack([year_picker, tolerance], justify="start", gap=1)
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return tolerance, year_picker
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@app.cell(hide_code=True)
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def _(con, mo, pricer, reconcile, tolerance, year_picker):
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_year = int(year_picker.value) if year_picker.value else 0
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if _year == 0:
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result = None
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mo.md("*Load `pfs.rvu`, `pfs.gpci`, and `pfs.carrier_locality` first.*")
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else:
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result = reconcile(pricer, con, _year, tolerance_cents=int(tolerance.value))
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mo.md(result.summary_md(top_n=25))
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return (result,)
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Delta table
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Every row in the outer join, sorted by the largest absolute delta.
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``is_exact`` uses the tolerance above; ``is_near`` is always a 1¢
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window. Null ``fee_gt`` means the row is calculated-only; null
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``fee_calc`` means ground-truth-only.
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""")
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return
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@app.cell(hide_code=True)
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def _(plain_years, result):
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_deltas = plain_years(result.deltas) if result is not None else None
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_deltas
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Warnings
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Non-fatal issues surfaced by the engine — duplicate join keys,
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missing columns, rule lookup failures, etc.
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""")
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return
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@app.cell(hide_code=True)
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def _(mo, result):
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if result is None or not result.warnings:
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mo.md("*(none)*")
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else:
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mo.md("\n".join(f"- {w}" for w in result.warnings))
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return
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if __name__ == "__main__":
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app.run()
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