Merge pull request 'Add TIR bar/threshold lines, Dagoverzicht + hypo/hyper Level-1 charts to PDF (#14, #15, #16, #17, #18)' (#23) from dev into main

pdf_export.py

@@ -21,8 +21,13 @@ from fpdf import FPDF
 from fpdf.enums import XPos, YPos
 
 from reporting import (
+    HIGH,
+    LOW,
+    VERY_HIGH,
+    VERY_LOW,
     agp_percentile_bands,
     completeness_warning,
+    detect_events,
     gmi_percent,
     percent_cv,
     sensor_active_percent,
@@ -31,12 +36,46 @@ from reporting import (
 
 SECTION_SUMMARY = "Samenvatting"
 SECTION_AGP = "AGP profiel"
+SECTION_DAGOVERZICHT = "Dagoverzicht"
 SECTION_EVENTS = "Hypo-/hyperevents"
 
-AVAILABLE_SECTIONS = [SECTION_SUMMARY, SECTION_AGP, SECTION_EVENTS]
+AVAILABLE_SECTIONS = [SECTION_SUMMARY, SECTION_AGP, SECTION_DAGOVERZICHT, SECTION_EVENTS]
 
 _LINE_KWARGS = {"new_x": XPos.LMARGIN, "new_y": YPos.NEXT}
 
+# Zelfde kleuren als het PALETTE["light"] in ui_common.py — bewust hier
+# gedupliceerd (5 hex-waarden) in plaats van ui_common.py te importeren, want
+# dat zou een streamlit-dependency in deze module trekken die verder bewust
+# vrij van UI-framework-imports is (los te unit-testen, zie moduledocstring).
+_MUTED = "#898781"
+_TIR_BAND_ORDER = ["very_low", "low", "in_range", "high", "very_high"]
+_TIR_BAND_LABELS = {
+    "very_low": "Zeer laag",
+    "low": "Laag",
+    "in_range": "In bereik",
+    "high": "Hoog",
+    "very_high": "Zeer hoog",
+}
+_TIR_COLORS = {
+    "very_low": "#8b1a1a",
+    "low": "#d03b3b",
+    "in_range": "#0ca30c",
+    "high": "#fab219",
+    "very_high": "#c76f0a",
+}
+_THRESHOLD_LINES = [VERY_LOW, LOW, HIGH, VERY_HIGH]
+DAGOVERZICHT_MAX_DAYS = 14
+
+
+def _add_threshold_lines(ax) -> None:
+    """Teken de 4 klinische drempellijnen (3.0/3.9/10.0/13.9 mmol/L) op een matplotlib-as.
+
+    PDF-tegenhanger van ui_common.add_threshold_lines() voor de Plotly-versie
+    op de dashboardpagina's (gitea #15) — zelfde 4 waarden, zelfde grijstint.
+    """
+    for value in _THRESHOLD_LINES:
+        ax.axhline(value, color=_MUTED, linewidth=0.7, linestyle=":")
+
 
 def _agp_figure_png(glucose: pd.DataFrame) -> bytes:
     bands = agp_percentile_bands(glucose)
@@ -45,6 +84,7 @@ def _agp_figure_png(glucose: pd.DataFrame) -> bytes:
     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")
+    _add_threshold_lines(ax)
     ax.set_xlabel("Uur van de dag")
     ax.set_ylabel("mmol/L")
     ax.set_xlim(0, 24)
@@ -58,6 +98,69 @@ def _agp_figure_png(glucose: pd.DataFrame) -> bytes:
     return buf.getvalue()
 
 
+def _tir_bar_figure_png(tir: dict) -> bytes:
+    """Gestapelde staafgrafiek van de 5 TIR-banden — PDF-tegenhanger van
+    ui_common.build_tir_stacked_bar_figure() (gitea #14)."""
+    fig, ax = plt.subplots(figsize=(7, 1.1))
+    left = 0.0
+    for band in _TIR_BAND_ORDER:
+        value = tir[band]
+        ax.barh(0, value, left=left, color=_TIR_COLORS[band], label=_TIR_BAND_LABELS[band])
+        left += value
+    ax.set_xlim(0, 100)
+    ax.set_yticks([])
+    ax.set_xlabel("%")
+    ax.legend(loc="upper center", bbox_to_anchor=(0.5, -0.75), ncol=5, fontsize=7, frameon=False)
+
+    buf = io.BytesIO()
+    # bbox_inches="tight" i.p.v. fig.tight_layout(): de legend zit buiten de
+    # axes (bbox_to_anchor met negatieve y), waar tight_layout() niet voor
+    # kan compenseren — savefig's eigen bbox-berekening kan dat wel.
+    fig.savefig(buf, format="png", dpi=150, bbox_inches="tight")
+    plt.close(fig)
+    buf.seek(0)
+    return buf.getvalue()
+
+
+def _daily_figure_png(day_df: pd.DataFrame, day_label: str) -> bytes:
+    """Eén dag als kleine lijngrafiek — PDF-tegenhanger van de Dagoverzicht-pagina (gitea #16)."""
+    hours = day_df["timestamp"].dt.hour + day_df["timestamp"].dt.minute / 60
+    fig, ax = plt.subplots(figsize=(7, 1.4))
+    ax.plot(hours, day_df["mmol"], color="#2a78d6", linewidth=1.2)
+    _add_threshold_lines(ax)
+    ax.set_xlim(0, 24)
+    ax.set_xticks(range(0, 25, 4))
+    ax.set_ylabel("mmol/L", fontsize=8)
+    ax.set_title(day_label, fontsize=9, loc="left")
+    ax.tick_params(labelsize=7)
+    fig.tight_layout()
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png", dpi=150)
+    plt.close(fig)
+    buf.seek(0)
+    return buf.getvalue()
+
+
+def _event_hour_histogram_png(events: pd.DataFrame, color: str) -> bytes:
+    """Aantal events per uur-van-de-dag — PDF-tegenhanger van render_event_level()'s
+    histogram op de Hypo/Hyper Events-pagina's (gitea #17, #18)."""
+    hour_counts = events["start"].dt.hour.value_counts().reindex(range(24), fill_value=0).sort_index()
+    fig, ax = plt.subplots(figsize=(7, 2))
+    ax.bar(hour_counts.index, hour_counts.to_numpy(), color=color)
+    ax.set_xlabel("Uur van de dag")
+    ax.set_ylabel("Aantal events")
+    ax.set_xlim(-0.5, 23.5)
+    ax.set_xticks(range(0, 24, 2))
+    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)
@@ -83,6 +186,9 @@ def _add_summary_section(pdf: FPDF, glucose: pd.DataFrame, start, end) -> None:
     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(2)
+    png_bytes = _tir_bar_figure_png(tir)
+    pdf.image(io.BytesIO(png_bytes), w=160)
     pdf.ln(4)
 
 
@@ -94,7 +200,29 @@ def _add_agp_section(pdf: FPDF, glucose: pd.DataFrame) -> None:
     pdf.ln(4)
 
 
-def _add_events_section(pdf: FPDF, events_summary: dict[str, dict]) -> None:
+def _add_dagoverzicht_section(pdf: FPDF, glucose: pd.DataFrame) -> None:
+    """Eén dagelijkse mini-grafiek per kalenderdag (gitea #16), max. de meest
+    recente DAGOVERZICHT_MAX_DAYS dagen — voor langere periodes zou de PDF
+    anders onwerkbaar lang worden."""
+    pdf.set_font("Helvetica", "B", 13)
+    pdf.cell(0, 8, "Dagoverzicht", **_LINE_KWARGS)
+
+    df = glucose.copy()
+    df["date"] = df["timestamp"].dt.date
+    dates = sorted(df["date"].unique())
+    if len(dates) > DAGOVERZICHT_MAX_DAYS:
+        dates = dates[-DAGOVERZICHT_MAX_DAYS:]
+        pdf.set_font("Helvetica", "I", 9)
+        pdf.cell(0, 5, f"Beperkt tot de meest recente {DAGOVERZICHT_MAX_DAYS} dagen.", **_LINE_KWARGS)
+
+    for day in dates:
+        day_df = df[df["date"] == day]
+        png_bytes = _daily_figure_png(day_df, day.strftime("%A %d-%m-%Y"))
+        pdf.image(io.BytesIO(png_bytes), w=180)
+    pdf.ln(4)
+
+
+def _add_events_section(pdf: FPDF, glucose: pd.DataFrame, 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)
@@ -106,6 +234,18 @@ def _add_events_section(pdf: FPDF, events_summary: dict[str, dict]) -> None:
             f"{summary['events_per_week']:.1f}/week",
             **_LINE_KWARGS,
         )
+    pdf.ln(2)
+
+    # Alleen Level 1 krijgt een grafiek in het PDF-rapport (gitea #17, #18) —
+    # geen tabel met losse events, de samenvattende cijfers staan al hierboven.
+    if not glucose.empty:
+        hypo_events = detect_events(glucose, LOW, "below")
+        if not hypo_events.empty:
+            pdf.image(io.BytesIO(_event_hour_histogram_png(hypo_events, _TIR_COLORS["low"])), w=180)
+        hyper_events = detect_events(glucose, HIGH, "above")
+        if not hyper_events.empty:
+            pdf.image(io.BytesIO(_event_hour_histogram_png(hyper_events, _TIR_COLORS["high"])), w=180)
+
     pdf.ln(4)
 
 
@@ -135,8 +275,10 @@ def build_report_pdf(
             _add_summary_section(pdf, glucose, start, end)
         if SECTION_AGP in sections:
             _add_agp_section(pdf, glucose)
+        if SECTION_DAGOVERZICHT in sections:
+            _add_dagoverzicht_section(pdf, glucose)
 
     if SECTION_EVENTS in sections and events_summary:
-        _add_events_section(pdf, events_summary)
+        _add_events_section(pdf, glucose, events_summary)
 
     return bytes(pdf.output())

tests/test_pdf_export.py

@@ -2,7 +2,14 @@ from datetime import date
 
 import pandas as pd
 
-from pdf_export import SECTION_AGP, SECTION_EVENTS, SECTION_SUMMARY, build_report_pdf
+from pdf_export import (
+    DAGOVERZICHT_MAX_DAYS,
+    SECTION_AGP,
+    SECTION_DAGOVERZICHT,
+    SECTION_EVENTS,
+    SECTION_SUMMARY,
+    build_report_pdf,
+)
 
 
 def _sample_glucose():
@@ -10,6 +17,15 @@ def _sample_glucose():
     return pd.DataFrame({"timestamp": timestamps, "mmol": [6.0 + (i % 5) for i in range(48)]})
 
 
+def _glucose_with_hypo_and_hyper_events():
+    """3 dagen data met een echte hypo- (<3.9, >=15 min) en hyperepisode (>10.0)."""
+    timestamps = pd.date_range("2026-01-01", periods=3 * 24 * 12, freq="5min")
+    values = [7.0] * len(timestamps)
+    values[10:15] = [3.0] * 5  # 25 min hypo
+    values[100:106] = [12.0] * 6  # 30 min hyper
+    return pd.DataFrame({"timestamp": timestamps, "mmol": values})
+
+
 def test_build_report_pdf_produces_valid_pdf_bytes():
     glucose = _sample_glucose()
     events_summary = {
@@ -39,3 +55,38 @@ 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")
+
+
+def test_build_report_pdf_includes_dagoverzicht_section():
+    glucose = _sample_glucose()
+    pdf_bytes = build_report_pdf(
+        [SECTION_DAGOVERZICHT], glucose, {}, date(2026, 1, 1), date(2026, 1, 2)
+    )
+    assert pdf_bytes.startswith(b"%PDF")
+    assert len(pdf_bytes) > 500
+
+
+def test_build_report_pdf_caps_dagoverzicht_at_max_days():
+    timestamps = pd.date_range("2026-01-01", periods=(DAGOVERZICHT_MAX_DAYS + 10) * 24, freq="1h")
+    glucose = pd.DataFrame({"timestamp": timestamps, "mmol": [7.0] * len(timestamps)})
+    pdf_bytes = build_report_pdf(
+        [SECTION_DAGOVERZICHT],
+        glucose,
+        {},
+        timestamps[0].date(),
+        timestamps[-1].date(),
+    )
+    assert pdf_bytes.startswith(b"%PDF")
+
+
+def test_build_report_pdf_draws_hypo_and_hyper_charts_when_events_present():
+    glucose = _glucose_with_hypo_and_hyper_events()
+    events_summary = {
+        "Hypo Level 1": {"count": 1, "avg_duration_minutes": 25.0, "events_per_week": 2.3},
+        "Hyper Level 1": {"count": 1, "avg_duration_minutes": 30.0, "events_per_week": 2.3},
+    }
+    pdf_bytes = build_report_pdf(
+        [SECTION_EVENTS], glucose, events_summary, date(2026, 1, 1), date(2026, 1, 4)
+    )
+    assert pdf_bytes.startswith(b"%PDF")
+    assert len(pdf_bytes) > 500