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).
_entries_since/_treatments_since requested a single page via `count`,
but Nightscout's /api/v1/entries and /api/v1/treatments ignore a large
`count` and cap the response around ~1150/~700 documents regardless of
how far back the requested period goes. Every report page relying on
fetch_entries_range/fetch_treatments_range (7/14/30/90-day selectors)
was silently only getting the most recent ~3-4 days, no matter the
selected period.
Fixed by paginating with `find[date][$lte]` cursors in a new
_paginated_get(), walking back until the requested `since` boundary is
reached or the server returns a short/empty page (= genuinely no more
data, confirmed by manually paginating against the real instance:
entries go back 50+ days, treatments only ~4 days on this install).
Also added reporting.completeness_warning(), shared by the interactive
pages (via ui_common.render_completeness_warning) and the PDF export,
that distinguishes "a structural gap at the start of the period" (data
wasn't fetched/available) from "scattered gaps below the clinical AGP
coverage threshold" (Danne et al. 2017) — the previous single generic
"X% sensor coverage" message read as a CGM/sensor problem even when
the real cause was this fetch bug.
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]>
willemjan
