add PDF export (FR10)

pdf_export.py builds a PDF report (summary stats, AGP chart, hypo/hyper event summaries) via fpdf2 + matplotlib rather than plotly's kaleido, to avoid bundling a Chromium build into the image just for this. The Export page lets the user pick which sections to include and a date range, then download the result to send to the hospital team. This completes all ten functional requirements in docs/requirements-reporting.md. Co-Authored-By: Claude Sonnet 5 <[email protected]>
2026-07-26 21:10:46 +02:00
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commit fe6a471f17
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+"""
+PDF-rapportgeneratie voor geselecteerde rapportageonderdelen (FR10).
+
+Gebruikt fpdf2 (puur Python, geen systeemdependencies) voor de PDF-opbouw en
+matplotlib voor een eenvoudige statische AGP-grafiek specifiek voor de PDF —
+bewust niet dezelfde Plotly-figuren als op de pagina's zelf (die zouden
+kaleido vereisen, met een ingebakken Chromium-build: een groter image en meer
+Trivy-oppervlak, voor dit doel niet nodig).
+"""
+
+from __future__ import annotations
+
+import io
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import pandas as pd
+from fpdf import FPDF
+from fpdf.enums import XPos, YPos
+
+from reporting import (
+    agp_percentile_bands,
+    gmi_percent,
+    percent_cv,
+    sensor_active_percent,
+    tir_breakdown_5band,
+)
+
+SECTION_SUMMARY = "Samenvatting"
+SECTION_AGP = "AGP profiel"
+SECTION_EVENTS = "Hypo-/hyperevents"
+
+AVAILABLE_SECTIONS = [SECTION_SUMMARY, SECTION_AGP, SECTION_EVENTS]
+
+_LINE_KWARGS = {"new_x": XPos.LMARGIN, "new_y": YPos.NEXT}
+
+
+def _agp_figure_png(glucose: pd.DataFrame) -> bytes:
+    bands = agp_percentile_bands(glucose)
+    fig, ax = plt.subplots(figsize=(7, 3))
+    hours = bands["minute_of_day"] / 60
+    ax.fill_between(hours, bands["p5"], bands["p95"], alpha=0.15, color="#2a78d6", label="5e-95e pct")
+    ax.fill_between(hours, bands["p25"], bands["p75"], alpha=0.3, color="#2a78d6", label="25e-75e pct")
+    ax.plot(hours, bands["p50"], color="#2a78d6", linewidth=2, label="Mediaan")
+    ax.set_xlabel("Uur van de dag")
+    ax.set_ylabel("mmol/L")
+    ax.set_xlim(0, 24)
+    ax.legend(loc="upper right", fontsize=8)
+    fig.tight_layout()
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png", dpi=150)
+    plt.close(fig)
+    buf.seek(0)
+    return buf.getvalue()
+
+
+def _add_summary_section(pdf: FPDF, glucose: pd.DataFrame, start, end) -> None:
+    mean_mmol = glucose["mmol"].mean()
+    tir = tir_breakdown_5band(glucose)
+    active_pct = sensor_active_percent(glucose, start, end)
+
+    pdf.set_font("Helvetica", "B", 13)
+    pdf.cell(0, 8, "Samenvatting", **_LINE_KWARGS)
+    pdf.set_font("Helvetica", "", 11)
+    pdf.cell(0, 6, f"Sensordekking: {active_pct:.0f}%", **_LINE_KWARGS)
+    pdf.cell(0, 6, f"Gemiddelde glucose: {mean_mmol:.1f} mmol/L", **_LINE_KWARGS)
+    pdf.cell(0, 6, f"Geschat HbA1c (GMI): {gmi_percent(mean_mmol):.1f}%", **_LINE_KWARGS)
+    pdf.cell(0, 6, f"Variabiliteit (%CV): {percent_cv(glucose):.0f}%", **_LINE_KWARGS)
+    pdf.ln(2)
+    pdf.cell(0, 6, f"Zeer laag (<3.0 mmol/L): {tir['very_low']:.0f}%", **_LINE_KWARGS)
+    pdf.cell(0, 6, f"Laag (3.0-3.9 mmol/L): {tir['low']:.0f}%", **_LINE_KWARGS)
+    pdf.cell(0, 6, f"In bereik (3.9-10.0 mmol/L): {tir['in_range']:.0f}%", **_LINE_KWARGS)
+    pdf.cell(0, 6, f"Hoog (10.0-13.9 mmol/L): {tir['high']:.0f}%", **_LINE_KWARGS)
+    pdf.cell(0, 6, f"Zeer hoog (>13.9 mmol/L): {tir['very_high']:.0f}%", **_LINE_KWARGS)
+    pdf.ln(4)
+
+
+def _add_agp_section(pdf: FPDF, glucose: pd.DataFrame) -> None:
+    pdf.set_font("Helvetica", "B", 13)
+    pdf.cell(0, 8, "AGP profiel", **_LINE_KWARGS)
+    png_bytes = _agp_figure_png(glucose)
+    pdf.image(io.BytesIO(png_bytes), w=180)
+    pdf.ln(4)
+
+
+def _add_events_section(pdf: FPDF, events_summary: dict[str, dict]) -> None:
+    pdf.set_font("Helvetica", "B", 13)
+    pdf.cell(0, 8, "Hypo-/hyperevents", **_LINE_KWARGS)
+    pdf.set_font("Helvetica", "", 11)
+    for label, summary in events_summary.items():
+        pdf.cell(
+            0,
+            6,
+            f"{label}: {summary['count']} events, gemiddeld {summary['avg_duration_minutes']:.0f} min, "
+            f"{summary['events_per_week']:.1f}/week",
+            **_LINE_KWARGS,
+        )
+    pdf.ln(4)
+
+
+def build_report_pdf(
+    sections: list[str],
+    glucose: pd.DataFrame,
+    events_summary: dict[str, dict],
+    start,
+    end,
+) -> bytes:
+    """Bouw een PDF met de geselecteerde onderdelen.
+
+    `events_summary` bevat vooraf berekende hypo/hyper-samenvattingen (label ->
+    resultaat van reporting.summarize_events), zodat deze module geen
+    event-detectie hoeft te herhalen.
+    """
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.set_font("Helvetica", "B", 16)
+    pdf.cell(0, 10, "Glucoserapport", **_LINE_KWARGS)
+    pdf.set_font("Helvetica", "", 11)
+    pdf.cell(0, 8, f"Periode: {start.strftime('%d-%m-%Y')} t/m {end.strftime('%d-%m-%Y')}", **_LINE_KWARGS)
+    pdf.ln(4)
+
+    if not glucose.empty:
+        if SECTION_SUMMARY in sections:
+            _add_summary_section(pdf, glucose, start, end)
+        if SECTION_AGP in sections:
+            _add_agp_section(pdf, glucose)
+
+    if SECTION_EVENTS in sections and events_summary:
+        _add_events_section(pdf, events_summary)
+
+    return bytes(pdf.output())