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]>

pages/8_Export.py

@@ -0,0 +1,52 @@
+import requests
+import streamlit as st
+
+from auth import require_auth
+from nightscout_client import NightscoutConfigError
+from pdf_export import AVAILABLE_SECTIONS, SECTION_EVENTS, build_report_pdf
+from reporting import HIGH, LOW, VERY_HIGH, VERY_LOW, detect_events, summarize_events
+from ui_common import load_range_data, select_period_days
+
+st.set_page_config(page_title="Glucose dashboard — Export", page_icon="📊", layout="wide")
+require_auth()
+
+st.title("Export")
+st.caption("Stel een PDF-rapport samen om naar het diabetesteam te sturen (FR10).")
+
+start, end, days = select_period_days(default_label="90 dagen")
+sections = st.multiselect("Onderdelen", AVAILABLE_SECTIONS, default=AVAILABLE_SECTIONS)
+
+try:
+    glucose, _treatments = load_range_data(start, end)
+except NightscoutConfigError as exc:
+    st.error(str(exc))
+    st.stop()
+except (requests.RequestException, ValueError) as exc:
+    st.error(f"Kon geen data ophalen van Nightscout: {exc}")
+    st.stop()
+
+if glucose.empty:
+    st.info("Geen glucosemetingen gevonden in deze periode.")
+    st.stop()
+
+events_summary = {}
+if SECTION_EVENTS in sections:
+    levels = {
+        f"Hypo Level 1 (<{LOW})": (LOW, "below"),
+        f"Hypo Level 2 (<{VERY_LOW})": (VERY_LOW, "below"),
+        f"Hyper Level 1 (>{HIGH})": (HIGH, "above"),
+        f"Hyper Level 2 (>{VERY_HIGH})": (VERY_HIGH, "above"),
+    }
+    for label, (threshold, direction) in levels.items():
+        events = detect_events(glucose, threshold, direction)
+        events_summary[label] = summarize_events(events, days)
+
+pdf_bytes = build_report_pdf(sections, glucose, events_summary, start, end)
+
+st.download_button(
+    "Download PDF",
+    data=pdf_bytes,
+    file_name=f"glucoserapport_{start.strftime('%Y%m%d')}_{end.strftime('%Y%m%d')}.pdf",
+    mime="application/pdf",
+)
+st.caption(f"Periode: {start.strftime('%d-%m-%Y')} t/m {end.strftime('%d-%m-%Y')}")

pdf_export.py

@@ -0,0 +1,134 @@
+"""
+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())

requirements.txt

@@ -3,5 +3,7 @@ pandas>=2.2,<3.0
 requests>=2.32,<3.0
 plotly>=5.24,<6.0
 python-dotenv>=1.0,<2.0
+matplotlib>=3.9,<4.0
+fpdf2>=2.7,<3.0
 pytest>=8.0,<9.0
 ruff>=0.6,<1.0

tests/test_pdf_export.py

@@ -0,0 +1,41 @@
+from datetime import date
+
+import pandas as pd
+
+from pdf_export import SECTION_AGP, SECTION_EVENTS, SECTION_SUMMARY, build_report_pdf
+
+
+def _sample_glucose():
+    timestamps = pd.date_range("2026-01-01", periods=48, freq="30min")
+    return pd.DataFrame({"timestamp": timestamps, "mmol": [6.0 + (i % 5) for i in range(48)]})
+
+
+def test_build_report_pdf_produces_valid_pdf_bytes():
+    glucose = _sample_glucose()
+    events_summary = {
+        "Hypo Level 1": {"count": 2, "avg_duration_minutes": 20.0, "events_per_week": 1.0},
+    }
+    pdf_bytes = build_report_pdf(
+        [SECTION_SUMMARY, SECTION_AGP, SECTION_EVENTS],
+        glucose,
+        events_summary,
+        date(2026, 1, 1),
+        date(2026, 1, 2),
+    )
+    assert isinstance(pdf_bytes, bytes)
+    assert pdf_bytes.startswith(b"%PDF")
+    assert len(pdf_bytes) > 500
+
+
+def test_build_report_pdf_handles_empty_glucose():
+    empty = pd.DataFrame(columns=["timestamp", "mmol"])
+    pdf_bytes = build_report_pdf(
+        [SECTION_SUMMARY, SECTION_AGP], empty, {}, date(2026, 1, 1), date(2026, 1, 2)
+    )
+    assert pdf_bytes.startswith(b"%PDF")
+
+
+def test_build_report_pdf_handles_no_sections_selected():
+    glucose = _sample_glucose()
+    pdf_bytes = build_report_pdf([], glucose, {}, date(2026, 1, 1), date(2026, 1, 2))
+    assert pdf_bytes.startswith(b"%PDF")