willemjan/glucose-dashboard
1
0
Fork 0
Code Issues 1 Pull Requests Actions Packages Projects Releases Wiki Activity

Redesign the PDF report: landscape pages, headers/footers, icon, daily stats

Browse Source 
Every report section now gets its own landscape A4 page (or, for
Dagoverzicht, one page per day) via a GlucoseReportPDF(FPDF) subclass
with a real header (section title, report version, insulin/pump/sensor
type from new REPORT_* env vars) and footer (page number, generation
timestamp, Nightscout link) on every page, plus an original SVG icon
in the header — inspired by the LibreView AGP report.

New "Insulineprofiel" PDF section mirrors the dashboard page (basal
bar chart, ISF/carb-ratio segments, DIA). Each Dagoverzicht day now
draws the same basal/bolus/carb/target overlays as the interactive
charts (matplotlib port of the Plotly helpers from #30) plus a new
per-day stats block (reporting.daily_stats: avg/min/max glucose,
total/basal/bolus/SMB insulin, hypo/hyper counts and durations).

Builds on #29 (profile data) and #30 (overlay logic/colors).
This commit is contained in:
willemjan
2026-09-05 22:44:14 +02:00
parent d33516e2a1
commit f2c3f97b1f
8 changed files with 718 additions and 100 deletions
Download Patch File Download Diff File Expand all files Collapse all files
pdf_export.py
+363 -96
View File
@@ -2,15 +2,23 @@
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).
matplotlib voor de statische grafieken 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).
Elke sectie is één (of, voor Dagoverzicht, meerdere) liggende A4-pagina('s)
met een vaste kop-/voettekst (device-info, paginanummering, genereerdatum,
Nightscout-link) via de GlucoseReportPDF-subklasse hieronder — geïnspireerd op
de LibreView/AGP-rapportopmaak (zie docs/design-reporting.md).
"""
from __future__ import annotations
import io
import os
from datetime import datetime, timezone
from pathlib import Path
import matplotlib
@@ -20,34 +28,54 @@ import pandas as pd
from fpdf import FPDF
from fpdf.enums import XPos, YPos
from profile_data import segments_table, segments_to_hourly_bars, total_daily_basal
from reporting import (
BOLUS_CATEGORY_CORRECTION,
BOLUS_CATEGORY_FOOD,
BOLUS_CATEGORY_SMB,
HIGH,
LOW,
VERY_HIGH,
VERY_LOW,
agp_percentile_bands,
classify_bolus,
completeness_warning,
daily_stats,
detect_events,
effective_basal_series,
glucose_min_max,
gmi_mmol_per_mol,
gmi_percent,
percent_cv,
sensor_active_percent,
target_glucose_series,
tir_breakdown_5band,
)
SECTION_SUMMARY = "Samenvatting"
SECTION_AGP = "AGP profiel"
SECTION_INSULIN_PROFILE = "Insulineprofiel"
SECTION_DAGOVERZICHT = "Dagoverzicht"
SECTION_EVENTS = "Hypo-/hyperevents"
AVAILABLE_SECTIONS = [SECTION_SUMMARY, SECTION_AGP, SECTION_DAGOVERZICHT, SECTION_EVENTS]
AVAILABLE_SECTIONS = [
SECTION_SUMMARY,
SECTION_AGP,
SECTION_INSULIN_PROFILE,
SECTION_DAGOVERZICHT,
SECTION_EVENTS,
]
REPORT_VERSION = "1.0"
_ICON_PATH = Path(__file__).parent / "assets" / "icon.svg"
_LINE_KWARGS = {"new_x": XPos.LMARGIN, "new_y": YPos.NEXT}
_SAME_LINE_KWARGS = {"new_x": XPos.RIGHT, "new_y": YPos.TOP}
# 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).
# gedupliceerd 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 = {
@@ -65,33 +93,83 @@ _TIR_COLORS = {
"very_high": "#c76f0a",
}
_THRESHOLD_LINES = [VERY_LOW, LOW, HIGH, VERY_HIGH]
_BASAL_COLOR = "#7b5ea3"
_CARBS_COLOR = "#eb6834"
_TARGET_COLOR = "#52514e"
_BOLUS_CATEGORY_STYLE = {
BOLUS_CATEGORY_FOOD: ("#1baf7a", "Bolus (voeding)"),
BOLUS_CATEGORY_CORRECTION: ("#2a78d6", "Bolus (correctie)"),
BOLUS_CATEGORY_SMB: ("#898781", "SMB"),
}
_EMPTY_TREATMENT_COLUMNS = ["timestamp", "eventType", "insulin", "carbs", "type", "absolute", "duration"]
DAGOVERZICHT_MAX_DAYS = 14
_HOUR_OF_DAY_LABEL = "Uur van de dag"
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 _report_config() -> dict:
"""Rapportmetadata uit environment variables (insuline/pomp/sensor-type,
Nightscout-URL) — zelfde os.environ-patroon als nightscout_client.py.
Alles heeft een fallback zodat een rapport ook zonder configuratie werkt."""
return {
"insulin_type": os.environ.get("REPORT_INSULIN_TYPE", "").strip() or "Onbekend insulinetype",
"pump_type": os.environ.get("REPORT_PUMP_TYPE", "").strip() or "Onbekende pomp",
"sensor_type": os.environ.get("REPORT_SENSOR_TYPE", "").strip() or "Onbekende sensor",
"nightscout_url": os.environ.get("NIGHTSCOUT_URL", "").strip().rstrip("/"),
}
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")
_add_threshold_lines(ax)
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()
class GlucoseReportPDF(FPDF):
"""FPDF-subklasse met een vaste kop-/voettekst op elke pagina en één sectie
per liggende pagina (zie begin_report_section())."""
def __init__(self, config: dict, generated_at: datetime) -> None:
super().__init__(orientation="L", unit="mm", format="A4")
self._config = config
self._generated_at = generated_at
self.section_title = ""
self.set_auto_page_break(auto=True, margin=18)
self.alias_nb_pages()
def begin_report_section(self, title: str) -> None:
"""Begin een nieuwe pagina voor sectie `title`. Voor secties die over
meerdere pagina's lopen (Dagoverzicht: één pagina per dag) wordt dit
per pagina aangeroepen zodat de kop op elke pagina klopt."""
self.section_title = title
self.add_page()
def header(self) -> None:
if _ICON_PATH.exists():
self.image(str(_ICON_PATH), x=10, y=8, w=8)
self.set_xy(20, 8)
self.set_font("Helvetica", "B", 14)
self.cell(0, 6, self.section_title, **_LINE_KWARGS)
self.set_x(20)
self.set_font("Helvetica", "", 8)
self.set_text_color(120, 120, 120)
device_line = (
f"Glucoserapport v{REPORT_VERSION} · Insuline: {self._config['insulin_type']} · "
f"Pomp: {self._config['pump_type']} · Sensor: {self._config['sensor_type']}"
)
self.cell(0, 4, device_line, **_LINE_KWARGS)
self.set_text_color(0, 0, 0)
self.ln(3)
self.set_draw_color(200, 200, 200)
self.line(10, self.get_y(), self.w - 10, self.get_y())
self.ln(4)
def footer(self) -> None:
self.set_y(-14)
self.set_font("Helvetica", "I", 8)
self.set_text_color(120, 120, 120)
left = f"Gegenereerd: {self._generated_at.strftime('%d-%m-%Y %H:%M')}"
if self._config["nightscout_url"]:
left += f" · {self._config['nightscout_url']}"
self.cell(200, 8, left, **_SAME_LINE_KWARGS)
self.cell(0, 8, f"Pagina {self.page_no()}/{{nb}}", align="R")
self.set_text_color(0, 0, 0)
def _fig_to_png(fig) -> bytes:
buf = io.BytesIO()
fig.savefig(buf, format="png", dpi=150)
plt.close(fig)
@@ -99,10 +177,33 @@ def _agp_figure_png(glucose: pd.DataFrame) -> bytes:
return buf.getvalue()
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."""
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)
fig, ax = plt.subplots(figsize=(11, 4.2))
hours = bands["minute_of_day"] / 60
ax.axhspan(LOW, HIGH, color="#0ca30c", alpha=0.08, label="Doelbereik")
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=2.5, label="Mediaan")
_add_threshold_lines(ax)
ax.set_xlabel(_HOUR_OF_DAY_LABEL)
ax.set_ylabel("mmol/L")
ax.set_xlim(0, 24)
ax.set_xticks(range(0, 25, 3))
ax.legend(loc="upper right", fontsize=9, ncol=4, frameon=False)
fig.tight_layout()
return _fig_to_png(fig)
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))
"""Gestapelde staafgrafiek van de 5 TIR-banden."""
fig, ax = plt.subplots(figsize=(9, 1.1))
left = 0.0
for band in _TIR_BAND_ORDER:
value = tir[band]
@@ -123,52 +224,127 @@ def _tir_bar_figure_png(tir: dict) -> bytes:
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)."""
def _insulin_profile_figure_png(profile: dict) -> bytes:
bars = segments_to_hourly_bars(profile.get("basal", []))
fig, ax = plt.subplots(figsize=(11, 3.2))
if bars:
hours = [b["hour"] for b in bars]
values = [b["value"] for b in bars]
ax.step(hours, values, where="post", color=_BASAL_COLOR, linewidth=1.5)
ax.fill_between(hours, values, step="post", color=_BASAL_COLOR, alpha=0.3)
ax.set_xlim(0, 24)
ax.set_xticks(range(0, 25, 2))
ax.set_ylim(bottom=0)
ax.set_xlabel(_HOUR_OF_DAY_LABEL)
ax.set_ylabel("E/uur")
fig.tight_layout()
return _fig_to_png(fig)
def _add_basal_overlay(ax2, basal: pd.DataFrame, day_start) -> None:
if basal.empty:
return
hours = (basal["timestamp"] - day_start).dt.total_seconds() / 3600
hours = pd.concat([hours, pd.Series([24.0])], ignore_index=True)
values = pd.concat([basal["rate"], basal["rate"].iloc[-1:]], ignore_index=True)
ax2.fill_between(hours, values, step="post", color=_BASAL_COLOR, alpha=0.3, label="Basaal")
ax2.step(hours, values, where="post", color=_BASAL_COLOR, linewidth=1, alpha=0.8)
def _add_carbs_overlay(ax2, day_treatments: pd.DataFrame, day_start) -> None:
if day_treatments.empty:
return
carbs = day_treatments[day_treatments["carbs"].fillna(0) > 0]
if carbs.empty:
return
hours = (carbs["timestamp"] - day_start).dt.total_seconds() / 3600
ax2.bar(hours, carbs["carbs"], width=0.25, color=_CARBS_COLOR, alpha=0.55, label="Koolhydraten")
def _add_bolus_overlay(ax, day_treatments: pd.DataFrame, day_start, lane_y: float) -> None:
bolus = classify_bolus(day_treatments)
for category in (BOLUS_CATEGORY_SMB, BOLUS_CATEGORY_CORRECTION, BOLUS_CATEGORY_FOOD):
subset = bolus[bolus["category"] == category] if not bolus.empty else bolus
if subset.empty:
continue
color, label = _BOLUS_CATEGORY_STYLE[category]
hours = (subset["timestamp"] - day_start).dt.total_seconds() / 3600
sizes = (subset["insulin"] * 25).clip(lower=15, upper=200)
ax.scatter(hours, [lane_y] * len(subset), s=sizes, color=color, alpha=0.75, label=label, zorder=5)
if category != BOLUS_CATEGORY_SMB:
for x, units in zip(hours, subset["insulin"], strict=True):
ax.annotate(
f"{units:.1f}E", (x, lane_y), textcoords="offset points", xytext=(0, 8), fontsize=6, ha="center"
)
def _add_target_overlay(ax, profile: dict | None, day_start, day_end) -> None:
target = target_glucose_series(profile, day_start, day_end)
if target.empty:
return
hours = (target["timestamp"] - day_start).dt.total_seconds() / 3600
hours = pd.concat([hours, pd.Series([24.0])], ignore_index=True)
values = pd.concat([target["target"], target["target"].iloc[-1:]], ignore_index=True)
ax.step(hours, values, where="post", color=_TARGET_COLOR, linewidth=1.2, linestyle="-.", label="Streefwaarde (pomp)")
def _daily_figure_png(
day_df: pd.DataFrame, day_treatments: pd.DataFrame, profile: dict | None, day_label: str, day_start, day_end
) -> bytes:
"""Eén dag als grafiek: glucoselijn + basaal/bolus/koolhydraten/streefwaarde-overlay."""
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)
fig, ax = plt.subplots(figsize=(11, 3.8))
ax2 = ax.twinx()
basal = effective_basal_series(profile, day_treatments, day_start, day_end)
_add_basal_overlay(ax2, basal, day_start)
_add_carbs_overlay(ax2, day_treatments, day_start)
lane_y = (day_df["mmol"].min() - 1.0) if not day_df.empty else LOW - 1.0
_add_bolus_overlay(ax, day_treatments, day_start, lane_y)
_add_target_overlay(ax, profile, day_start, day_end)
ax.plot(hours, day_df["mmol"], color="#2a78d6", linewidth=1.4, label="Glucose", zorder=6)
_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()
ax.set_xticks(range(0, 25, 2))
ax.set_ylabel("mmol/L", fontsize=9)
ax2.set_ylabel("Basaal (E/uur) · KH (g)", fontsize=9)
ax.set_title(day_label, fontsize=11, loc="left")
ax.tick_params(labelsize=8)
ax2.tick_params(labelsize=8)
buf = io.BytesIO()
fig.savefig(buf, format="png", dpi=150)
plt.close(fig)
buf.seek(0)
return buf.getvalue()
lines1, labels1 = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax.legend(
lines1 + lines2, labels1 + labels2, loc="upper center", bbox_to_anchor=(0.5, -0.2), ncol=6, fontsize=7, frameon=False
)
fig.tight_layout()
return _fig_to_png(fig)
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)."""
"""Aantal events per uur-van-de-dag."""
hour_counts = events["start"].dt.hour.value_counts().reindex(range(24), fill_value=0).sort_index()
fig, ax = plt.subplots(figsize=(7, 2))
fig, ax = plt.subplots(figsize=(11, 2.6))
ax.bar(hour_counts.index, hour_counts.to_numpy(), color=color)
ax.set_xlabel("Uur van de dag")
ax.set_xlabel(_HOUR_OF_DAY_LABEL)
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()
return _fig_to_png(fig)
def _add_summary_section(pdf: FPDF, glucose: pd.DataFrame, start, end) -> None:
def _add_summary_section(pdf: GlucoseReportPDF, glucose: pd.DataFrame, start, end) -> None:
pdf.begin_report_section(SECTION_SUMMARY)
mean_mmol = glucose["mmol"].mean()
min_mmol, max_mmol = glucose_min_max(glucose)
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"Periode: {start.strftime('%d-%m-%Y')} t/m {end.strftime('%d-%m-%Y')}", **_LINE_KWARGS)
warning = completeness_warning(glucose, start, end)
if warning:
@@ -178,7 +354,12 @@ def _add_summary_section(pdf: FPDF, glucose: pd.DataFrame, start, end) -> None:
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"Gemiddelde glucose: {mean_mmol:.1f} mmol/L (min {min_mmol:.1f} · max {max_mmol:.1f})",
**_LINE_KWARGS,
)
gmi = gmi_percent(mean_mmol)
pdf.cell(0, 6, f"Geschat HbA1c (GMI): {gmi:.1f}% ({gmi_mmol_per_mol(gmi):.0f} mmol/mol)", **_LINE_KWARGS)
pdf.cell(0, 6, f"Variabiliteit (%CV): {percent_cv(glucose):.0f}%", **_LINE_KWARGS)
@@ -190,43 +371,117 @@ def _add_summary_section(pdf: FPDF, glucose: pd.DataFrame, start, end) -> None:
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)
pdf.image(io.BytesIO(png_bytes), w=220)
def _add_agp_section(pdf: FPDF, glucose: pd.DataFrame) -> None:
pdf.set_font("Helvetica", "B", 13)
pdf.cell(0, 8, "AGP profiel", **_LINE_KWARGS)
def _add_agp_section(pdf: GlucoseReportPDF, glucose: pd.DataFrame) -> None:
pdf.begin_report_section(SECTION_AGP)
png_bytes = _agp_figure_png(glucose)
pdf.image(io.BytesIO(png_bytes), w=180)
pdf.image(io.BytesIO(png_bytes), w=270)
def _add_insulin_profile_section(pdf: GlucoseReportPDF, profile: dict | None) -> None:
pdf.begin_report_section(SECTION_INSULIN_PROFILE)
if not profile:
pdf.set_font("Helvetica", "", 11)
pdf.cell(0, 6, "Geen pompprofiel gevonden op deze Nightscout-instantie.", **_LINE_KWARGS)
return
pdf.set_font("Helvetica", "", 11)
pdf.cell(0, 6, f"Profielnaam: {profile.get('name') or '-'}", **_LINE_KWARGS)
pdf.cell(0, 6, f"DIA (werkingsduur insuline): {profile['dia']:.1f} uur", **_LINE_KWARGS)
pdf.cell(0, 6, f"Totale dagelijkse basaal: {total_daily_basal(profile['basal']):.1f} E", **_LINE_KWARGS)
pdf.ln(2)
png_bytes = _insulin_profile_figure_png(profile)
pdf.image(io.BytesIO(png_bytes), w=270)
pdf.ln(4)
pdf.set_font("Helvetica", "B", 11)
pdf.cell(90, 6, "Insulinegevoeligheid (ISF)", **_SAME_LINE_KWARGS)
pdf.cell(0, 6, "Koolhydraatratio", **_LINE_KWARGS)
pdf.set_font("Helvetica", "", 10)
isf_rows = segments_table(profile["sens"])
ratio_rows = segments_table(profile["carbratio"])
for i in range(max(len(isf_rows), len(ratio_rows))):
isf_text = (
f"{isf_rows[i]['start']}-{isf_rows[i]['end']}: {isf_rows[i]['value']:.1f} mmol/L per E"
if i < len(isf_rows)
else ""
)
ratio_text = (
f"{ratio_rows[i]['start']}-{ratio_rows[i]['end']}: {ratio_rows[i]['value']:.0f} g per E"
if i < len(ratio_rows)
else ""
)
pdf.cell(90, 5, isf_text, **_SAME_LINE_KWARGS)
pdf.cell(0, 5, ratio_text, **_LINE_KWARGS)
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)
def _add_daily_stats_lines(pdf: GlucoseReportPDF, stats: dict) -> None:
pdf.set_font("Helvetica", "", 10)
pdf.cell(
0,
6,
f"Glucose: gem. {stats['mean_mmol']:.1f} · min {stats['min_mmol']:.1f} · "
f"max {stats['max_mmol']:.1f} mmol/L",
**_LINE_KWARGS,
)
pdf.cell(
0,
6,
f"Insuline: totaal {stats['total_insulin']:.1f} E · basaal {stats['basal_insulin']:.1f} E · "
f"bolus {stats['bolus_insulin']:.1f} E · SMB {stats['smb_insulin']:.1f} E",
**_LINE_KWARGS,
)
pdf.cell(
0,
6,
f"Hypo's: {stats['hypo_count']} ({stats['hypo_duration_minutes']:.0f} min) · "
f"Hyper's: {stats['hyper_count']} ({stats['hyper_duration_minutes']:.0f} min)",
**_LINE_KWARGS,
)
def _add_dagoverzicht_section(
pdf: GlucoseReportPDF, glucose: pd.DataFrame, treatments: pd.DataFrame, profile: dict | None
) -> None:
"""Eén liggende pagina per kalenderdag, max. de meest recente
DAGOVERZICHT_MAX_DAYS dagen — voor langere periodes zou de PDF anders
onwerkbaar lang worden."""
df = glucose.copy()
df["date"] = df["timestamp"].dt.date
dates = sorted(df["date"].unique())
if len(dates) > DAGOVERZICHT_MAX_DAYS:
show_limited_note = len(dates) > DAGOVERZICHT_MAX_DAYS
if show_limited_note:
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:
pdf.begin_report_section(SECTION_DAGOVERZICHT)
if show_limited_note:
pdf.set_font("Helvetica", "I", 9)
pdf.cell(0, 5, f"Beperkt tot de meest recente {DAGOVERZICHT_MAX_DAYS} dagen.", **_LINE_KWARGS)
show_limited_note = False # alleen op de eerste paginas van deze sectie
day_start = pd.Timestamp(day)
day_end = day_start + pd.Timedelta(1, unit="D")
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)
day_treatments = (
treatments[(treatments["timestamp"] >= day_start) & (treatments["timestamp"] < day_end)]
if not treatments.empty
else treatments
)
png_bytes = _daily_figure_png(day_df, day_treatments, profile, day.strftime("%A %d-%m-%Y"), day_start, day_end)
pdf.image(io.BytesIO(png_bytes), w=270)
pdf.ln(2)
stats = daily_stats(day_df, day_treatments, profile, day_start, day_end)
_add_daily_stats_lines(pdf, stats)
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)
def _add_events_section(pdf: GlucoseReportPDF, glucose: pd.DataFrame, events_summary: dict[str, dict]) -> None:
pdf.begin_report_section(SECTION_EVENTS)
pdf.set_font("Helvetica", "", 11)
for label, summary in events_summary.items():
pdf.cell(
@@ -238,17 +493,16 @@ def _add_events_section(pdf: FPDF, glucose: pd.DataFrame, events_summary: dict[s
)
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.
# Alleen Level 1 krijgt een grafiek in het PDF-rapport — 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)
pdf.image(io.BytesIO(_event_hour_histogram_png(hypo_events, _TIR_COLORS["low"])), w=260)
pdf.ln(2)
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)
pdf.image(io.BytesIO(_event_hour_histogram_png(hyper_events, _TIR_COLORS["high"])), w=260)
def build_report_pdf(
@@ -257,20 +511,33 @@ def build_report_pdf(
events_summary: dict[str, dict],
start,
end,
treatments: pd.DataFrame | None = None,
profile: dict | None = None,
) -> bytes:
"""Bouw een PDF met de geselecteerde onderdelen.
"""Bouw een PDF met de geselecteerde onderdelen: één liggende voorpagina,
daarna één (of voor Dagoverzicht: meerdere) liggende pagina('s) per sectie.
`events_summary` bevat vooraf berekende hypo/hyper-samenvattingen (label ->
resultaat van reporting.summarize_events), zodat deze module geen
event-detectie hoeft te herhalen.
event-detectie hoeft te herhalen. `treatments`/`profile` zijn optioneel
(leeg/None) zodat oudere aanroepen zonder insulinedata blijven werken.
"""
pdf = FPDF()
pdf.add_page()
pdf.set_font("Helvetica", "B", 16)
pdf.cell(0, 10, "Glucoserapport", **_LINE_KWARGS)
pdf.set_font("Helvetica", "", 11)
if treatments is None:
treatments = pd.DataFrame(columns=_EMPTY_TREATMENT_COLUMNS)
config = _report_config()
pdf = GlucoseReportPDF(config, datetime.now(timezone.utc))
pdf.begin_report_section("Glucoserapport")
pdf.set_font("Helvetica", "B", 22)
pdf.cell(0, 14, "Glucoserapport", **_LINE_KWARGS)
pdf.set_font("Helvetica", "", 13)
pdf.cell(0, 8, f"Periode: {start.strftime('%d-%m-%Y')} t/m {end.strftime('%d-%m-%Y')}", **_LINE_KWARGS)
pdf.ln(4)
if sections:
pdf.cell(0, 8, f"Onderdelen: {', '.join(sections)}", **_LINE_KWARGS)
if SECTION_INSULIN_PROFILE in sections:
_add_insulin_profile_section(pdf, profile)
if not glucose.empty:
if SECTION_SUMMARY in sections:
@@ -278,7 +545,7 @@ def build_report_pdf(
if SECTION_AGP in sections:
_add_agp_section(pdf, glucose)
if SECTION_DAGOVERZICHT in sections:
_add_dagoverzicht_section(pdf, glucose)
_add_dagoverzicht_section(pdf, glucose, treatments, profile)
if SECTION_EVENTS in sections and events_summary:
_add_events_section(pdf, glucose, events_summary)