diff --git a/.env.example b/.env.example index 564bcfd..1129896 100644 --- a/.env.example +++ b/.env.example @@ -15,3 +15,9 @@ NIGHTSCOUT_TOKEN= # het diabetesteam). Zet hier een sha256-hash, NIET het wachtwoord zelf: # python -c "import hashlib; print(hashlib.sha256(b'jouw-wachtwoord').hexdigest())" DASHBOARD_PASSWORD_HASH= + +# Optioneel: apparatuurinfo voor de kop van het PDF-rapport (pages/8_Export.py). +# Puur informatief, geen effect op de berekeningen — leeg = "Onbekend ...". +REPORT_INSULIN_TYPE=Lyumjev 100U +REPORT_PUMP_TYPE= +REPORT_SENSOR_TYPE= diff --git a/README.md b/README.md index 5d4b30f..63b54ee 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,8 @@ klinische rapportagepagina's (zie `docs/requirements-reporting.md` en diabetesteam, dus elke pagina zit achter een wachtwoord). `app.py` laadt `.env` zelf in (via `python-dotenv`), dus dit werkt hetzelfde onder PowerShell, cmd of bash — je hoeft de variabelen niet zelf in je shell te - zetten. + zetten. `REPORT_INSULIN_TYPE`/`REPORT_PUMP_TYPE`/`REPORT_SENSOR_TYPE` zijn + optioneel en puur informatief (getoond in de kop van het PDF-rapport). 3. Lokaal draaien: ```powershell diff --git a/assets/icon.svg b/assets/icon.svg new file mode 100644 index 0000000..d6658c2 --- /dev/null +++ b/assets/icon.svg @@ -0,0 +1,4 @@ + + + + diff --git a/pages/8_Export.py b/pages/8_Export.py index 07b2a14..44c494a 100644 --- a/pages/8_Export.py +++ b/pages/8_Export.py @@ -5,7 +5,12 @@ 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, render_completeness_warning, select_period_days +from ui_common import ( + load_profile, + load_range_data, + render_completeness_warning, + select_period_days, +) st.set_page_config(page_title="Glucose dashboard — Export", page_icon="📊", layout="wide") require_auth() @@ -17,7 +22,8 @@ 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) + glucose, treatments = load_range_data(start, end) + profile = load_profile() except NightscoutConfigError as exc: st.error(str(exc)) st.stop() @@ -43,7 +49,7 @@ if SECTION_EVENTS in sections: events = detect_events(glucose, threshold, direction) events_summary[label] = summarize_events(events, days) -pdf_bytes = build_report_pdf(sections, glucose, events_summary, start, end) +pdf_bytes = build_report_pdf(sections, glucose, events_summary, start, end, treatments=treatments, profile=profile) st.download_button( "Download PDF", diff --git a/pdf_export.py b/pdf_export.py index 06a78f4..8b7f9cd 100644 --- a/pdf_export.py +++ b/pdf_export.py @@ -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) diff --git a/reporting.py b/reporting.py index 1c881d7..7c6c23f 100644 --- a/reporting.py +++ b/reporting.py @@ -601,6 +601,73 @@ def target_glucose_series(profile: dict | None, day_start, day_end) -> pd.DataFr return pd.DataFrame(rows, columns=["timestamp", "target"]) +def _integrate_step_series(series: pd.DataFrame, value_col: str, day_end) -> float: + """Oppervlakte onder een stapfunctie-DataFrame (zoals effective_basal_series()): + som van elke rij se waarde x duur (in uur) tot de volgende rij, of tot + `day_end` voor de laatste rij. Gebruikt om een E/uur-stapfunctie om te + rekenen naar een totaal aantal eenheden.""" + if series.empty: + return 0.0 + rows = series.reset_index(drop=True) + total = 0.0 + for i in range(len(rows)): + start = rows.loc[i, "timestamp"] + end = rows.loc[i + 1, "timestamp"] if i + 1 < len(rows) else day_end + total += rows.loc[i, value_col] * (end - start).total_seconds() / 3600 + return total + + +DAILY_STATS_KEYS = [ + "mean_mmol", + "min_mmol", + "max_mmol", + "total_insulin", + "basal_insulin", + "bolus_insulin", + "smb_insulin", + "hypo_count", + "hypo_duration_minutes", + "hyper_count", + "hyper_duration_minutes", +] + + +def daily_stats(day_glucose: pd.DataFrame, day_treatments: pd.DataFrame, profile: dict | None, day_start, day_end) -> dict: + """Samenvattende statistieken voor één kalenderdag, voor naast de dagelijkse + grafiek in het PDF-rapport: gemiddelde/min/max glucose, totale/basaal-/bolus-/ + SMB-insuline (SMB apart gerapporteerd omdat dit zowel als basaal- als + bolus-vervanging kan gelden, zie classify_bolus()), en het aantal + de totale + duur van hypo- en hyperevents die dag. + """ + min_mmol, max_mmol = glucose_min_max(day_glucose) + mean_mmol = round(day_glucose["mmol"].mean(), 1) if not day_glucose.empty else 0.0 + + bolus = classify_bolus(day_treatments) + by_category = bolus.groupby("category")["insulin"].sum().to_dict() if not bolus.empty else {} + bolus_units = by_category.get(BOLUS_CATEGORY_FOOD, 0.0) + by_category.get(BOLUS_CATEGORY_CORRECTION, 0.0) + smb_units = by_category.get(BOLUS_CATEGORY_SMB, 0.0) + + basal_series = effective_basal_series(profile, day_treatments, day_start, day_end) + basal_units = _integrate_step_series(basal_series, "rate", day_end) + + hypo_summary = summarize_events(detect_events(day_glucose, LOW, "below"), period_days=1) + hyper_summary = summarize_events(detect_events(day_glucose, HIGH, "above"), period_days=1) + + return { + "mean_mmol": mean_mmol, + "min_mmol": min_mmol, + "max_mmol": max_mmol, + "total_insulin": round(bolus_units + smb_units + basal_units, 1), + "basal_insulin": round(basal_units, 1), + "bolus_insulin": round(bolus_units, 1), + "smb_insulin": round(smb_units, 1), + "hypo_count": hypo_summary["count"], + "hypo_duration_minutes": hypo_summary["total_minutes"], + "hyper_count": hyper_summary["count"], + "hyper_duration_minutes": hyper_summary["total_minutes"], + } + + def basal_stats(treatments: pd.DataFrame, period_days: float) -> dict: """Totale basaalinsuline uit Temp Basal-treatments (rate x duur), zonder pompprofiel. diff --git a/tests/test_pdf_export.py b/tests/test_pdf_export.py index 96b147b..75d46ac 100644 --- a/tests/test_pdf_export.py +++ b/tests/test_pdf_export.py @@ -1,17 +1,32 @@ +import re from datetime import date +from pathlib import Path import pandas as pd +import pytest from pdf_export import ( DAGOVERZICHT_MAX_DAYS, SECTION_AGP, SECTION_DAGOVERZICHT, SECTION_EVENTS, + SECTION_INSULIN_PROFILE, SECTION_SUMMARY, + _report_config, build_report_pdf, ) +def _page_count(pdf_bytes: bytes) -> int: + """Grof-maar-betrouwbaar paginatelling op de ruwe PDF-bytes: elke + paginaobject bevat een '/Type /Page' (zonder de 's' van '/Type /Pages') + in zijn objectdictionary, wat altijd onleesbaar-platte tekst blijft ook + al staat contentstream-compressie aan — geen losse PDF-parser nodig voor + deze structurele check (zelfde black-box-filosofie als de rest van deze + testset: geen pixel-vergelijkingen, wel structuur).""" + return len(re.findall(rb"/Type\s*/Page[^s]", pdf_bytes)) + + 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)]}) @@ -90,3 +105,185 @@ def test_build_report_pdf_draws_hypo_and_hyper_charts_when_events_present(): ) assert pdf_bytes.startswith(b"%PDF") assert len(pdf_bytes) > 500 + + +def test_build_report_pdf_always_has_at_least_a_cover_page(): + empty = pd.DataFrame(columns=["timestamp", "mmol"]) + pdf_bytes = build_report_pdf([], empty, {}, date(2026, 1, 1), date(2026, 1, 2)) + assert _page_count(pdf_bytes) == 1 + + +def test_build_report_pdf_one_page_per_single_page_section(): + glucose = _sample_glucose() + pdf_bytes = build_report_pdf( + [SECTION_SUMMARY, SECTION_AGP], glucose, {}, date(2026, 1, 1), date(2026, 1, 2) + ) + # cover + summary + agp = 3 + assert _page_count(pdf_bytes) == 3 + + +def test_build_report_pdf_one_page_per_day_for_dagoverzicht(): + timestamps = pd.date_range("2026-01-01", periods=3 * 24 * 12, freq="5min") # 3 dagen + glucose = pd.DataFrame({"timestamp": timestamps, "mmol": [7.0] * len(timestamps)}) + pdf_bytes = build_report_pdf( + [SECTION_DAGOVERZICHT], glucose, {}, date(2026, 1, 1), date(2026, 1, 3) + ) + # cover + 3 dagen + assert _page_count(pdf_bytes) == 4 + + +def test_build_report_pdf_caps_dagoverzicht_page_count_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() + ) + # cover + max. DAGOVERZICHT_MAX_DAYS dagpagina's, ondanks (DAGOVERZICHT_MAX_DAYS + 10) dagen aan data + assert _page_count(pdf_bytes) == 1 + DAGOVERZICHT_MAX_DAYS + + +def test_build_report_pdf_includes_insulin_profile_section_page(): + glucose = _sample_glucose() + profile = { + "name": "Default", + "dia": 5.0, + "basal": [{"minutes": 0, "value": 0.8}, {"minutes": 720, "value": 1.1}], + "sens": [{"minutes": 0, "value": 2.8}], + "carbratio": [{"minutes": 0, "value": 10.0}], + "target_low": [{"minutes": 0, "value": 4.5}], + "target_high": [{"minutes": 0, "value": 8.0}], + } + pdf_bytes = build_report_pdf( + [SECTION_INSULIN_PROFILE], glucose, {}, date(2026, 1, 1), date(2026, 1, 2), profile=profile + ) + # cover + insulineprofiel + assert _page_count(pdf_bytes) == 2 + assert pdf_bytes.startswith(b"%PDF") + + +def test_build_report_pdf_insulin_profile_section_handles_missing_profile(): + glucose = _sample_glucose() + pdf_bytes = build_report_pdf( + [SECTION_INSULIN_PROFILE], glucose, {}, date(2026, 1, 1), date(2026, 1, 2), profile=None + ) + assert _page_count(pdf_bytes) == 2 + assert pdf_bytes.startswith(b"%PDF") + + +def test_build_report_pdf_insulin_profile_section_renders_without_glucose(): + """De Insulineprofiel-sectie gaat niet over glucosedata en moet dus ook + renderen als er geen glucosemetingen zijn (in tegenstelling tot + Samenvatting/AGP/Dagoverzicht, die wel glucosedata vereisen).""" + empty = pd.DataFrame(columns=["timestamp", "mmol"]) + profile = { + "name": "Default", + "dia": 5.0, + "basal": [{"minutes": 0, "value": 0.8}], + "sens": [{"minutes": 0, "value": 2.8}], + "carbratio": [{"minutes": 0, "value": 10.0}], + "target_low": [], + "target_high": [], + } + pdf_bytes = build_report_pdf( + [SECTION_INSULIN_PROFILE], empty, {}, date(2026, 1, 1), date(2026, 1, 2), profile=profile + ) + assert _page_count(pdf_bytes) == 2 + + +def test_build_report_pdf_dagoverzicht_uses_treatments_and_profile_without_error(): + """Regressietest: de dagpagina moet basaal/bolus/koolhydraten/streefwaarde- + overlays tekenen zonder te crashen als er echte treatments/profile worden + meegegeven (in plaats van de lege defaults).""" + timestamps = pd.date_range("2026-01-01", periods=24 * 12, freq="5min") # 1 dag + glucose = pd.DataFrame({"timestamp": timestamps, "mmol": [7.0] * len(timestamps)}) + treatments = pd.DataFrame( + [ + { + "timestamp": pd.Timestamp("2026-01-01 08:00"), + "eventType": "Meal Bolus", + "insulin": 4.0, + "carbs": 40.0, + "type": "", + "absolute": None, + "duration": None, + }, + { + "timestamp": pd.Timestamp("2026-01-01 12:00"), + "eventType": "Correction Bolus", + "insulin": 0.3, + "carbs": None, + "type": "SMB", + "absolute": None, + "duration": None, + }, + { + "timestamp": pd.Timestamp("2026-01-01 06:00"), + "eventType": "Temp Basal", + "insulin": None, + "carbs": None, + "type": "", + "absolute": 2.0, + "duration": 60, + }, + ] + ) + profile = { + "name": "Default", + "dia": 5.0, + "basal": [{"minutes": 0, "value": 0.8}], + "sens": [{"minutes": 0, "value": 2.8}], + "carbratio": [{"minutes": 0, "value": 10.0}], + "target_low": [{"minutes": 0, "value": 4.5}], + "target_high": [{"minutes": 0, "value": 8.0}], + } + pdf_bytes = build_report_pdf( + [SECTION_DAGOVERZICHT], + glucose, + {}, + date(2026, 1, 1), + date(2026, 1, 1), + treatments=treatments, + profile=profile, + ) + assert pdf_bytes.startswith(b"%PDF") + assert _page_count(pdf_bytes) == 2 # cover + 1 dag + + +def test_report_config_uses_defaults_when_env_vars_unset(monkeypatch): + for var in ("REPORT_INSULIN_TYPE", "REPORT_PUMP_TYPE", "REPORT_SENSOR_TYPE", "NIGHTSCOUT_URL"): + monkeypatch.delenv(var, raising=False) + config = _report_config() + assert config["insulin_type"] == "Onbekend insulinetype" + assert config["pump_type"] == "Onbekende pomp" + assert config["sensor_type"] == "Onbekende sensor" + assert config["nightscout_url"] == "" + + +def test_report_config_reads_env_vars_when_set(monkeypatch): + monkeypatch.setenv("REPORT_INSULIN_TYPE", "Lyumjev 100U") + monkeypatch.setenv("REPORT_PUMP_TYPE", "Omnipod DASH") + monkeypatch.setenv("REPORT_SENSOR_TYPE", "Dexcom G7") + monkeypatch.setenv("NIGHTSCOUT_URL", "https://example.com/") + config = _report_config() + assert config["insulin_type"] == "Lyumjev 100U" + assert config["pump_type"] == "Omnipod DASH" + assert config["sensor_type"] == "Dexcom G7" + assert config["nightscout_url"] == "https://example.com" # trailing slash gestript + + +def test_icon_asset_exists(): + icon_path = Path(__file__).parent.parent / "assets" / "icon.svg" + assert icon_path.exists() + assert icon_path.read_text(encoding="utf-8").strip().startswith("