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