diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 1a8b9572e..a67f4d71c 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -16,6 +16,7 @@ updates: - '/documentation' - '/segmenter' - '/controller' + - '/reporter' patterns: ['*'] multi-ecosystem-group: 'deps' diff --git a/.github/workflows/build-reporter.yml b/.github/workflows/build-reporter.yml new file mode 100644 index 000000000..011c01bc3 --- /dev/null +++ b/.github/workflows/build-reporter.yml @@ -0,0 +1,55 @@ +name: Build the reporter + +on: + push: + branches: + - 'main' + - 'software/beta' + - 'software/stable' + tags: + - 'reporter/v*' + paths: + - 'reporter/**' + - '.github/workflows/build-reporter.yml' + pull_request: + paths: + - 'reporter/**' + - '.github/workflows/build-reporter.yml' + merge_group: + workflow_dispatch: + inputs: + git-ref: + description: 'Git ref (optional)' + required: false + +env: + IMAGE_REGISTRY: ghcr.io/${{ github.repository_owner }} + IMAGE_NAME: 'reporter' + MAIN_BRANCH: 'main' # pushing to this branch will update the "edge" tag on the image + BETA_BRANCH: 'software/beta' # pushing to this branch will update the "beta" tag on the image + STABLE_BRANCH: 'software/stable' # pushing to this branch will update the "stable" tag on the image + TAG_PREFIX: 'reporter/v' # pushing tags with this prefix will add a version tag to the image and update the "latest" tag on the image + PUSH_IMAGE: ${{ (github.event_name == 'pull_request' && !github.event.pull_request.head.repo.fork) || github.event_name == 'push' || github.event_name == 'push tag' }} + +jobs: + ci-checks: + runs-on: ubuntu-24.04-arm + steps: + - uses: actions/checkout@v5 + + - name: Install uv + uses: astral-sh/setup-uv@v6 + with: + enable-cache: true + + - name: Set up Python + run: uv python install + + - name: Install dependencies + working-directory: ./reporter + run: | + uv sync --locked --dev + + - name: Run checks + working-directory: ./reporter + run: uv run poe check diff --git a/eslint.config.mjs b/eslint.config.mjs index 8534a54a7..b1777b518 100644 --- a/eslint.config.mjs +++ b/eslint.config.mjs @@ -8,6 +8,7 @@ export default defineConfig([ globalIgnores([ "controller/.venv", "segmenter/.venv", + "reporter/.venv", "frontend/dist", "frontend/public", ]), diff --git a/justfile b/justfile index 326e507f6..8897d818c 100644 --- a/justfile +++ b/justfile @@ -14,6 +14,7 @@ setup: just --justfile node-red/justfile setup just --justfile controller/justfile setup just --justfile segmenter/justfile setup + just --justfile reporter/justfile setup just --justfile os/justfile setup just --justfile backend/justfile setup just --justfile frontend/justfile setup @@ -24,6 +25,7 @@ setup-dev: just --justfile node-red/justfile setup-dev just --justfile controller/justfile setup-dev just --justfile segmenter/justfile setup-dev + just --justfile reporter/justfile setup-dev just --justfile os/justfile setup-dev just --justfile backend/justfile setup-dev just --justfile frontend/justfile setup-dev @@ -41,6 +43,7 @@ test: just --justfile node-red/justfile test just --justfile controller/justfile test just --justfile segmenter/justfile test + just --justfile reporter/justfile test just --justfile os/justfile test just --justfile backend/justfile test just --justfile frontend/justfile test diff --git a/node-red/projects/dashboard b/node-red/projects/dashboard index fb2d7adc4..c47130817 160000 --- a/node-red/projects/dashboard +++ b/node-red/projects/dashboard @@ -1 +1 @@ -Subproject commit fb2d7adc4abb7d33fdf500b6bc60d4bdd4e68e36 +Subproject commit c471308179aacca15116560baf03f067554281ce diff --git a/os/image/rpios.js b/os/image/rpios.js index 54b7962f4..a60f25e45 100644 --- a/os/image/rpios.js +++ b/os/image/rpios.js @@ -3,7 +3,6 @@ import assert from "node:assert" import { basename, join } from "node:path" -import { fileURLToPath } from "node:url" import { access, readFile, readlink } from "node:fs/promises" import { once } from "node:events" diff --git a/reporter/README.md b/reporter/README.md new file mode 100644 index 000000000..c460c5e1f --- /dev/null +++ b/reporter/README.md @@ -0,0 +1,140 @@ +# reporter + +The PlanktoScope's PDF report generator. + +## Introduction + +This service renders a self-contained PDF acquisition report for one or more +segmented acquisitions. It reads each acquisition's `metadata.json`, its EcoTaxa +TSV, the flat-field image and a selection of ROI thumbnails, recomputes the same +quality-control statistics the Validation dashboard shows, draws the charts with +matplotlib, and converts an HTML template to PDF with WeasyPrint. Everything — +fonts, images and charts — is embedded, so the report renders offline in the +field and needs no external assets. + +The report is triggered from the dashboard's "Report export" dialog over MQTT and +written to `/home/pi/data/reports/`, where the dashboard serves it for download. + +## Usage + +### Development + +Install runtime dependencies and (re)install the systemd service: + +```sh +cd reporter +just setup +``` + +Install all dependencies including development tooling: + +```sh +just setup-dev +``` + +Start the reporter for development (stops the service first): + +```sh +just dev +# make changes and restart +``` + +Run the code auto-formatter on the project: + +```sh +just format +``` + +Run all checks (formatting and linting): + +```sh +just test +``` + +### Prerequisites + +To use this project, you'll need: + +- Python >= 3.13.5 +- uv +- Pango and harfbuzz-subset (WeasyPrint's native dependencies — `just setup` + installs them) + +An MQTT broker must be reachable on port 1883 of the host. We recommend +[Mosquitto](https://mosquitto.org/). + +### Rendering without MQTT + +For local iteration you can render straight from a dialog payload, bypassing the +broker: + +```sh +uv run main.py --payload '{"acquisition_paths":["/home/pi/data/img///"]}' +``` + +The output directory defaults to `/home/pi/data/reports` and can be overridden +with the `REPORTER_OUTPUT` environment variable. + +## API + +The reporter subscribes to `actuator/reporter/generate` and publishes progress to +`status/reporter`. + +### Generate a report + +**topic** `actuator/reporter/generate` + +**payload** (combined report — one PDF covering a selection of acquisitions, as +sent by the "Report export" dialog): + +```json +{ + "action": "generate", + "acquisition_paths": [ + "/home/pi/data/img///" + ], + "sections": { "...": "which report sections to include (optional)" }, + "gallery": { "...": "object-gallery options (optional)" } +} +``` + +A legacy single-acquisition payload is also accepted; it produces the full report +plus a one-page summary: + +```json +{ + "action": "generate", + "acquisition_path": "/home/pi/data/img///" +} +``` + +### Status + +**topic** `status/reporter` + +While working the reporter publishes `{"status": "generating", ...}`. On success +it publishes the download location: + +```json +{ + "status": "done", + "filename": "___report.pdf", + "url": "/api/files/reports/___report.pdf" +} +``` + +On failure it publishes `{"status": "error", "error": ""}`. + +## Licensing + +Except where otherwise indicated, source code provided here is covered by the +following information: + +Copyright PlanktoScope project contributors + +SPDX-License-Identifier: GPL-3.0-or-later + +You can use the source code provided here under the [GPL 3.0 License](https://www.gnu.org/licenses/gpl-3.0.en.html). + +Bundled IBM Plex fonts in `fonts/` are licensed under the SIL Open Font License; +see `fonts/LICENSE-IBMPlex.txt`. diff --git a/reporter/fonts/IBMPlexMono-Medium.ttf b/reporter/fonts/IBMPlexMono-Medium.ttf new file mode 100644 index 000000000..f24cbab26 Binary files /dev/null and b/reporter/fonts/IBMPlexMono-Medium.ttf differ diff --git a/reporter/fonts/IBMPlexMono-Regular.ttf b/reporter/fonts/IBMPlexMono-Regular.ttf new file mode 100644 index 000000000..f7300999f Binary files /dev/null and b/reporter/fonts/IBMPlexMono-Regular.ttf differ diff --git a/reporter/fonts/IBMPlexMono-SemiBold.ttf b/reporter/fonts/IBMPlexMono-SemiBold.ttf new file mode 100644 index 000000000..58ef40119 Binary files /dev/null and b/reporter/fonts/IBMPlexMono-SemiBold.ttf differ diff --git a/reporter/fonts/IBMPlexSans-Medium.ttf b/reporter/fonts/IBMPlexSans-Medium.ttf new file mode 100644 index 000000000..a3826c4e9 Binary files /dev/null and b/reporter/fonts/IBMPlexSans-Medium.ttf differ diff --git a/reporter/fonts/IBMPlexSans-Regular.ttf b/reporter/fonts/IBMPlexSans-Regular.ttf new file mode 100644 index 000000000..bd6817d52 Binary files /dev/null and b/reporter/fonts/IBMPlexSans-Regular.ttf differ diff --git a/reporter/fonts/IBMPlexSans-SemiBold.ttf b/reporter/fonts/IBMPlexSans-SemiBold.ttf new file mode 100644 index 000000000..09ec8cdae Binary files /dev/null and b/reporter/fonts/IBMPlexSans-SemiBold.ttf differ diff --git a/reporter/fonts/LICENSE-IBMPlex.txt b/reporter/fonts/LICENSE-IBMPlex.txt new file mode 100755 index 000000000..c35c4c618 --- /dev/null +++ b/reporter/fonts/LICENSE-IBMPlex.txt @@ -0,0 +1,93 @@ +Copyright © 2017 IBM Corp. with Reserved Font Name "Plex" + +This Font Software is licensed under the SIL Open Font License, Version 1.1. + +This license is copied below, and is also available with a FAQ at: http://scripts.sil.org/OFL + + +----------------------------------------------------------- +SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 +----------------------------------------------------------- + +PREAMBLE +The goals of the Open Font License (OFL) are to stimulate worldwide +development of collaborative font projects, to support the font creation +efforts of academic and linguistic communities, and to provide a free and +open framework in which fonts may be shared and improved in partnership +with others. + +The OFL allows the licensed fonts to be used, studied, modified and +redistributed freely as long as they are not sold by themselves. The +fonts, including any derivative works, can be bundled, embedded, +redistributed and/or sold with any software provided that any reserved +names are not used by derivative works. The fonts and derivatives, +however, cannot be released under any other type of license. The +requirement for fonts to remain under this license does not apply +to any document created using the fonts or their derivatives. + +DEFINITIONS +"Font Software" refers to the set of files released by the Copyright +Holder(s) under this license and clearly marked as such. This may +include source files, build scripts and documentation. + +"Reserved Font Name" refers to any names specified as such after the +copyright statement(s). + +"Original Version" refers to the collection of Font Software components as +distributed by the Copyright Holder(s). + +"Modified Version" refers to any derivative made by adding to, deleting, +or substituting -- in part or in whole -- any of the components of the +Original Version, by changing formats or by porting the Font Software to a +new environment. + +"Author" refers to any designer, engineer, programmer, technical +writer or other person who contributed to the Font Software. + +PERMISSION & CONDITIONS +Permission is hereby granted, free of charge, to any person obtaining +a copy of the Font Software, to use, study, copy, merge, embed, modify, +redistribute, and sell modified and unmodified copies of the Font +Software, subject to the following conditions: + +1) Neither the Font Software nor any of its individual components, +in Original or Modified Versions, may be sold by itself. + +2) Original or Modified Versions of the Font Software may be bundled, +redistributed and/or sold with any software, provided that each copy +contains the above copyright notice and this license. These can be +included either as stand-alone text files, human-readable headers or +in the appropriate machine-readable metadata fields within text or +binary files as long as those fields can be easily viewed by the user. + +3) No Modified Version of the Font Software may use the Reserved Font +Name(s) unless explicit written permission is granted by the corresponding +Copyright Holder. This restriction only applies to the primary font name as +presented to the users. + +4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font +Software shall not be used to promote, endorse or advertise any +Modified Version, except to acknowledge the contribution(s) of the +Copyright Holder(s) and the Author(s) or with their explicit written +permission. + +5) The Font Software, modified or unmodified, in part or in whole, +must be distributed entirely under this license, and must not be +distributed under any other license. The requirement for fonts to +remain under this license does not apply to any document created +using the Font Software. + +TERMINATION +This license becomes null and void if any of the above conditions are +not met. + +DISCLAIMER +THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT +OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE +COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, +INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL +DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM +OTHER DEALINGS IN THE FONT SOFTWARE. diff --git a/reporter/justfile b/reporter/justfile new file mode 100644 index 000000000..e824bf381 --- /dev/null +++ b/reporter/justfile @@ -0,0 +1,20 @@ +setup: + # WeasyPrint renders through Pango; harfbuzz-subset embeds subset fonts in the PDF + sudo apt install -y --no-install-recommends libpango-1.0-0 libpangoft2-1.0-0 libharfbuzz-subset0 + uv sync --no-group dev + sudo cp planktoscope-org.reporter.service /etc/systemd/system/ + sudo systemctl reenable planktoscope-org.reporter + sudo systemctl restart planktoscope-org.reporter + +setup-dev: + uv sync --group dev + +dev: + -sudo systemctl stop planktoscope-org.reporter + uv run main.py + +format: + uv run poe fmt + +test: + uv run poe check diff --git a/reporter/logo.png b/reporter/logo.png new file mode 100644 index 000000000..5d449d454 Binary files /dev/null and b/reporter/logo.png differ diff --git a/reporter/main.py b/reporter/main.py new file mode 100644 index 000000000..79bc7320d --- /dev/null +++ b/reporter/main.py @@ -0,0 +1,1280 @@ +#!/usr/bin/env python3 +"""PlanktoScope PDF report generator. + +Subscribes to ``actuator/reporter/generate``. On each message it reads an +acquisition's metadata.json + EcoTaxa TSV + flat field + ROI thumbnails, +computes the same QC statistics the Validation dashboard shows (the JS logic +is ported verbatim so the PDF matches the screen), renders an HTML template, +and converts it to a self-contained PDF with WeasyPrint. Completion (or +failure) is published to ``status/reporter``. + +Trigger payload: {"action": "generate", + "acquisition_path": "/home/pi/data/img///"} +""" + +import asyncio +import base64 +import csv +import io +import json +import math +import os +import re +import statistics +import sys +import traceback +from datetime import datetime, timezone +from pathlib import Path + +import aiomqtt +import numpy as np +from PIL import Image, ImageFilter + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from matplotlib import font_manager +from matplotlib.colors import LinearSegmentedColormap +from matplotlib.ticker import FixedLocator, FuncFormatter +from jinja2 import Environment, FileSystemLoader, select_autoescape +from weasyprint import HTML + +# --------------------------------------------------------------------------- # +# Configuration +# --------------------------------------------------------------------------- # +MQTT_HOST = os.environ.get("REPORTER_MQTT_HOST", "localhost") +MQTT_PORT = int(os.environ.get("REPORTER_MQTT_PORT", "1883")) +TOPIC_IN = "actuator/reporter/generate" +TOPIC_OUT = "status/reporter" + +REPORTS_DIR = Path(os.environ.get("REPORTER_OUTPUT", "/home/pi/data/reports")) +HARDWARE_JSON = Path("/home/pi/PlanktoScope/hardware.json") +HERE = Path(__file__).resolve().parent +FONTS_DIR = HERE / "fonts" + +# Dashboard colors (match the Validation page) +BLUE = "#1565C0" # flowing +AMBER = "#F59E0B" # stuck +GREY = "#9CA3AF" + +# Thresholds — kept identical to the dashboard QC function nodes. +SHARP_LAPLACIAN_MIN = 0.176 # >= this == in focus (~P40 of new ratio metric) +BLURRY_LAPLACIAN_MAX = 0.086 # < this == blurry (~P10 of new ratio metric) +STUCK_BIN_PX = 60 +STUCK_MIN_FRAMES = 8 +SENSOR_ACROSS = 3040 # object_x range +SENSOR_ALONG = 4056 # object_y range +GALLERY_COUNT = 48 + +# Object-gallery plate: the tile grid is chosen to fill the page rather than +# fixed at 6 columns, so a 12-object plate doesn't leave two thirds of it blank. +GALLERY_PLATE_MM = 212.0 # tile area below the section head +GALLERY_WIDTH_MM = 178.0 # A4 minus 16mm margins +GALLERY_GAP_MM = 2.1 +GALLERY_META_MM = 5.2 # index + ESD line under each tile + +FRAME_PREFIX_RE = re.compile(r"^(.+?)_\d+\.(jpe?g|png)$", re.IGNORECASE) + +# All report sections the "Report export" dialog can toggle. Cover, size +# distribution and gallery are always rendered; these are the optional ones. +SECTION_KEYS = ("metadata", "lightness", "saturation", "cleanness", "alignment", + "focus", "clogging", "stability", "spatial") + +# "Objects images" criteria dropdown -> EcoTaxa TSV column (see the mockup). +# Every value is sorted descending (largest / most first). +CRITERIA_COLS = { + "area": ("object_area", "Area"), + "equivalent_diameter": ("object_equivalent_diameter", "Equivalent diameter"), + "major": ("object_major", "Major axis"), + "minor": ("object_minor", "Minor axis"), + "perimeter": ("object_perim.", "Perimeter"), + "circularity": ("object_circ.", "Circularity"), +} +CRITERIA_UNIT = {"area": "px²", "equivalent_diameter": "µm", "major": "px", + "minor": "px", "perimeter": "px", "circularity": ""} + +# --------------------------------------------------------------------------- # +# Tolerance model +# +# Every check is drawn on a track so the reader sees how much headroom a +# measurement has, not just whether it passed. ``lo``/``hi`` bound the track, +# ``good`` is the acceptable band. All bands are the dashboard's thresholds. +# --------------------------------------------------------------------------- # +TOLERANCE = { + "lightness": {"title": "Lightness calibration", "lo": 180, "hi": 255, "good": (220, 240), + "limit": "220–240", "lo_label": "180", "hi_label": "255", "unit": "mean lum", "dec": 0}, + "saturation": {"title": "Saturation calibration", "lo": 0, "hi": 15, "good": (0, 5), + "limit": "< 5%", "lo_label": "0%", "hi_label": "15%", "unit": "chroma", "dec": 1}, + "cleanness": {"title": "Cleanness of optical trail", "lo": 0, "hi": 12, "good": (0, 1), + "limit": "≤ 1", "lo_label": "0", "hi_label": "12", "unit": "spots", "dec": 0}, + "alignment": {"title": "Flowcell alignment", "lo": 50, "hi": 100, "good": (85, 100), + "limit": "≥ 85%", "lo_label": "50%", "hi_label": "100%", "unit": "uniformity", "dec": 0}, + "focus": {"title": "Object focus", "lo": 0, "hi": 50, "good": (0, 30), + "limit": "< 30%", "lo_label": "0%", "hi_label": "50%", "unit": "blurry", "dec": 1}, + "clogging": {"title": "Flowcell clogging", "lo": 0, "hi": 30, "good": (0, 10), + "limit": "< 10%", "lo_label": "0%", "hi_label": "30%", "unit": "stuck", "dec": 1}, + "stability": {"title": "Flow stability", "lo": 0, "hi": 150, "good": (0, 100), + "limit": "CV < 100%", "lo_label": "0", "hi_label": "150%", "unit": "frame CV", "dec": 0}, +} +# Order the conformance panel and the check tables read in. +TOLERANCE_ORDER = ("lightness", "saturation", "cleanness", "alignment", + "focus", "clogging", "stability") + + +def _track(key: str, value: float) -> dict: + """Position ``value`` and its acceptable band on the 0–100% track for ``key``.""" + s = TOLERANCE[key] + span = float(s["hi"] - s["lo"]) or 1.0 + place = lambda v: max(0.0, min(100.0, 100.0 * (v - s["lo"]) / span)) + g0, g1 = s["good"] + return {"pos": place(value), "good_start": place(g0), "good_end": place(g1), + "clipped": value > s["hi"] or value < s["lo"], + "limit": s["limit"], "lo_label": s["lo_label"], "hi_label": s["hi_label"]} + + +# --------------------------------------------------------------------------- # +# Data loading +# --------------------------------------------------------------------------- # +def img_to(path: str, kind: str) -> str: + """Map an /img/ acquisition path to its /objects/ or /clean/ sibling.""" + return path.replace("/img/", f"/{kind}/").rstrip("/") + + +def find_tsv(objects_dir: Path) -> Path | None: + if not objects_dir.is_dir(): + return None + for f in sorted(objects_dir.iterdir()): + if f.name.lower().startswith("ecotaxa_") and f.suffix == ".tsv": + return f + return None + + +def read_tsv(tsv_path: Path) -> tuple[list[str], list[dict]]: + """Return (headers, rows). Skips the EcoTaxa type row ([t]/[f]) and any + bracketed metadata lines, mirroring the dashboard parsers.""" + with open(tsv_path, newline="", encoding="utf-8", errors="replace") as fh: + reader = csv.reader(fh, delimiter="\t") + rows_raw = [r for r in reader if r] + if not rows_raw: + return [], [] + headers = [h.strip() for h in rows_raw[0]] + data = [] + for r in rows_raw[1:]: + if r and r[0].strip().startswith("["): # type row or EcoTaxa meta + continue + data.append(dict(zip(headers, r))) + return headers, data + + +def fnum(v) -> float | None: + try: + f = float(str(v).replace(",", ".")) + return f if math.isfinite(f) else None + except (TypeError, ValueError): + return None + + +def frame_of(file_name: str) -> str | None: + if not file_name: + return None + m = FRAME_PREFIX_RE.match(file_name) + return m.group(1) if m else file_name + + +# --------------------------------------------------------------------------- # +# Display formatting (precomputed so the templates stay declarative) +# --------------------------------------------------------------------------- # +def fmt_coord(value) -> str: + v = fnum(value) + return f"{v:.3f}°" if v is not None else "—" + + +def fmt_position(lat, lon) -> str: + la, lo = fnum(lat), fnum(lon) + if la is None or lo is None: + return "—" + ns = "N" if la >= 0 else "S" + ew = "E" if lo >= 0 else "W" + return f"{abs(la):.3f}°{ns}, {abs(lo):.3f}°{ew}" + + +def fmt_sampled(date, time) -> str: + d = str(date or "").strip() + t = str(time or "").strip() + hm = f"{t[:2]}:{t[2:4]}" if len(t) >= 4 and t.isdigit() else t + return f"{d} {hm}".strip() or "—" + + +# --------------------------------------------------------------------------- # +# QC computations — ported from the Validation dashboard function nodes +# --------------------------------------------------------------------------- # +def compute_sharpness(rows): + blurs = sorted(b for b in (fnum(r.get("object_blur_laplacian")) for r in rows) if b is not None) + total = len(blurs) + q = lambda p: blurs[int(total * p)] if total else 0 + sharp = sum(1 for v in blurs if v >= SHARP_LAPLACIAN_MIN) + blurry = sum(1 for v in blurs if v < BLURRY_LAPLACIAN_MAX) + return { + "total": total, + "median": q(0.5), "p10": q(0.1), "p90": q(0.9), + "sharpPct": 100 * sharp / total if total else 0, + "blurryPct": 100 * blurry / total if total else 0, + "values": blurs, + } + + +def compute_concentration(rows): + frames = set() + for r in rows: + f = frame_of(r.get("img_file_name", "")) + if f: + frames.add(f) + n = len(frames) + return {"totalObjects": len(rows), "nFrames": n, + "perFrame": len(rows) / n if n else 0} + + +def compute_homogeneity(rows): + counts = {} + for r in rows: + f = frame_of(r.get("img_file_name", "")) + if f: + counts[f] = counts.get(f, 0) + 1 + per_frame = [counts[k] for k in sorted(counts)] + n = len(per_frame) + mean = sum(per_frame) / n if n else 0 + std = math.sqrt(sum((c - mean) ** 2 for c in per_frame) / n) if n else 0 + return {"nFrames": n, "mean": mean, "std": std, + "cv": 100 * std / mean if mean else 0, "perFrameCounts": per_frame} + + +def compute_spatial(rows): + t1, t2 = SENSOR_ACROSS / 3, 2 * SENSOR_ACROSS / 3 + left = center = right = 0 + points = [] + for r in rows: + x, y = fnum(r.get("object_x")), fnum(r.get("object_y")) + if x is None or y is None: + continue + if y < t1: + left += 1 + elif y < t2: + center += 1 + else: + right += 1 + points.append((x, y)) + total = left + center + right + return { + "total": total, "points": points, + "leftPct": 100 * left / total if total else 0, + "centerPct": 100 * center / total if total else 0, + "rightPct": 100 * right / total if total else 0, + } + + +def compute_stuck(rows): + """Per-frame spatial-bin stuck detection (dashboard STUCK node). + + Returns a per-row boolean list ``stuck_flags`` plus summary counts.""" + bw = math.ceil(SENSOR_ACROSS / STUCK_BIN_PX) + bh = math.ceil(SENSOR_ALONG / STUCK_BIN_PX) + bin_frames: dict[int, set] = {} + objs = [] # (row_index, bin) + for i, r in enumerate(rows): + x, y = fnum(r.get("object_x")), fnum(r.get("object_y")) + if x is None or y is None: + objs.append((i, None)) + continue + frame = frame_of(r.get("img_file_name", "")) + # heatmap x = object_y, heatmap y = object_x (matches AUDIT binning) + bxi = min(bw - 1, max(0, int(y // STUCK_BIN_PX))) + byi = min(bh - 1, max(0, int(x // STUCK_BIN_PX))) + b = byi * bw + bxi + objs.append((i, b)) + if frame is not None: + bin_frames.setdefault(b, set()).add(frame) + + stuck_flags = [False] * len(rows) + stuck_count = 0 + for i, b in objs: + if b is not None and len(bin_frames.get(b, ())) >= STUCK_MIN_FRAMES: + stuck_flags[i] = True + stuck_count += 1 + counted = sum(1 for _, b in objs if b is not None) + return { + "stuck_flags": stuck_flags, + "stuckCount": stuck_count, + "total": counted, + "stuckPct": 100 * stuck_count / counted if counted else 0, + } + + +def esd_values(rows): + return [d for d in (fnum(r.get("object_equivalent_diameter")) for r in rows) if d is not None] + + +# --------------------------------------------------------------------------- # +# Optics / calibration QC — ported from Validation Panels 1-4 (client-side +# canvas analysis of clean/flat_color.jpg). Each returns a dashboard-style dict: +# {value, measure, state ∈ good|review|alert, label} +# and the pill labels match the dashboard exactly. +# --------------------------------------------------------------------------- # +def _luma(a): + """Rec.709 luminance of an HxWx3 uint8/float array.""" + return 0.2126 * a[..., 0] + 0.7152 * a[..., 1] + 0.0722 * a[..., 2] + + +def _open_rgb(flat_path: Path): + try: + return Image.open(flat_path).convert("RGB") + except (OSError, ValueError): + return None + + +def compute_lightness(flat_path: Path): + """Panel 1 — mean luminance of the flat field (100x100). Good 220-240.""" + im = _open_rgb(flat_path) + if im is None: + return None + a = np.asarray(im.resize((100, 100)), float) + mean = float(_luma(a).mean()) + state = "good" if 220 <= mean <= 240 else ("alert" if mean < 200 else "review") + label = {"good": "Optimal", "review": "Suboptimal", "alert": "Critical"}[state] + return {"label": label, "state": state, "meanLum": mean, "num": mean, + "value": f"{mean:.0f}", "measure": f"{mean:.0f} / 255 mean luminance"} + + +def compute_saturation(flat_path: Path): + """Panel 2 — mean HSV saturation of the flat field (200 wide). Good < 5%.""" + im = _open_rgb(flat_path) + if im is None: + return None + w, h = im.size + W = 200 + H = max(8, round(W * h / max(1, w))) + a = np.asarray(im.resize((W, H)), float) + mx = a.max(axis=2) + mn = a.min(axis=2) + sat = np.where(mx == 0, 0.0, (mx - mn) / np.where(mx == 0, 1, mx)) + saturation = float(sat.mean() * 100) + state = "alert" if saturation > 10 else ("review" if saturation > 5 else "good") + label = {"good": "Good", "review": "Calibration suggested", + "alert": "Calibration needed"}[state] + return {"label": label, "state": state, "saturation": saturation, "num": saturation, + "value": f"{saturation:.1f}%", "measure": f"{saturation:.1f}% mean chroma"} + + +def _count_blobs(binary: np.ndarray, y0: int, y1: int, min_size: int = 8) -> int: + """8-connected components with size >= min_size in rows [y0, y1). Iterative + flood fill (no scipy on the Pi).""" + H, W = binary.shape + visited = np.zeros_like(binary, dtype=bool) + flagged = 0 + ys, xs = np.nonzero(binary[y0:y1]) + for sy, sx in zip(ys + y0, xs): + if visited[sy, sx]: + continue + stack = [(sy, sx)] + visited[sy, sx] = True + size = 0 + while stack: + cy, cx = stack.pop() + size += 1 + for dy in (-1, 0, 1): + for dx in (-1, 0, 1): + if dy == 0 and dx == 0: + continue + ny, nx = cy + dy, cx + dx + if y0 <= ny < y1 and 0 <= nx < W and binary[ny, nx] and not visited[ny, nx]: + visited[ny, nx] = True + stack.append((ny, nx)) + if size >= min_size: + flagged += 1 + return flagged + + +def compute_cleanness(flat_path: Path): + """Panel 3 — debris on the optical path: count dark spots in the flat field + (320 wide) that stand out from a 12px-blurred background by >12 luma.""" + im = _open_rgb(flat_path) + if im is None: + return None + w, h = im.size + W = 320 + H = max(8, round(W * h / max(1, w))) + im = im.resize((W, H)) + orig = _luma(np.asarray(im, float)) + blur = _luma(np.asarray(im.filter(ImageFilter.GaussianBlur(12)), float)) + margin = int(H * 0.05) + binary = orig < (blur - 12) + flagged = _count_blobs(binary, margin, H - margin, min_size=8) + state = "good" if flagged <= 1 else ("review" if flagged <= 6 else "alert") + label = {"good": "Good", "review": "Review needed", "alert": "Alert"}[state] + unit = "spot" if flagged == 1 else "spots" + return {"label": label, "state": state, "flagged": flagged, "num": float(flagged), + "value": str(flagged), "measure": f"{flagged} debris {unit}"} + + +def compute_alignment(flat_path: Path): + """Panel 4 — flowcell alignment from the edge-vs-centre luminance profile + (80 rows). Good quality score >= 85.""" + im = _open_rgb(flat_path) + if im is None: + return None + w, h = im.size + H = 80 + W = max(8, round(H * w / max(1, h))) + a = np.asarray(im.resize((W, H)), float) + profile = _luma(a).mean(axis=1) # per-row mean luminance + edge = max(1, round(H * 0.10)) + top = float(profile[:edge].mean()) + bottom = float(profile[H - edge:].mean()) + center = float(profile[edge:H - edge].mean()) + diff_l = abs(bottom - center) / center if center > 0 else 0 + diff_r = abs(top - center) / center if center > 0 else 0 + score = 100 - max(diff_l, diff_r) * 400 + max_edge = max(top, bottom) + if max_edge > 250: + score -= (max_edge - 250) * 10 + q = max(0, min(100, round(score))) + state = "good" if q >= 85 else ("review" if q >= 70 else "alert") + label = {"good": "Aligned", "review": "Slight misalignment", + "alert": "Misaligned"}[state] + return {"label": label, "state": state, "qualityScore": q, "num": float(q), + "value": f"{q}%", "measure": f"{q}% edge uniformity"} + + +CALIBRATION_SPECS = [ + ("lightness", "Lightness calibration", compute_lightness), + ("saturation", "Saturation calibration", compute_saturation), + ("cleanness", "Cleanness of optical trail", compute_cleanness), + ("alignment", "Flowcell alignment", compute_alignment), +] + + +def compute_calibration(flat_path: Path, sections: dict): + """Run only the calibration panels toggled on. Returns a list of rows for + the template, each {key, title, value, measure, state, label, track}.""" + rows = [] + for key, title, fn in CALIBRATION_SPECS: + if not sections.get(key): + continue + res = fn(flat_path) + if res is None: + rows.append({"key": key, "title": title, "value": "—", + "measure": "flat_color.jpg unavailable", "unit": "no data", + "state": "review", "label": "No data", "track": None}) + else: + rows.append({"key": key, "title": title, "unit": TOLERANCE[key]["unit"], + "track": _track(key, res["num"]), **res}) + return rows + + +# --------------------------------------------------------------------------- # +# Charts -> base64 PNG +# +# Themed to match the templates: hairline axes, mono tick labels, blue/amber as +# the only ink. Each function returns a base64 PNG data-URI. ``panel=True`` +# renders the compact ~3.8x2.65 aspect used by the one-pager's 2x2 grid; the +# default wide aspect is for the full report. +# +# The reporter ships IBM Plex (fonts/) so the charts and the PDF text share one +# typeface; the Pi otherwise only has DejaVu, which we fall back to. +# --------------------------------------------------------------------------- # +def _register_fonts() -> str: + """Load the bundled TTFs into matplotlib. Returns the mono family to use.""" + if FONTS_DIR.is_dir(): + for ttf in sorted(FONTS_DIR.glob("*.ttf")): + try: + font_manager.fontManager.addfont(str(ttf)) + except Exception: # noqa: BLE001 — a bad font must not kill a report + pass + have = {f.name for f in font_manager.fontManager.ttflist} + return "IBM Plex Mono" if "IBM Plex Mono" in have else "DejaVu Sans Mono" + + +CHART_INK = "#15181F" +CHART_ACCENT = "#125A9C" +CHART_AMBER = "#C97A04" +CHART_BLURRY = "#C7CCD4" +CHART_LINEMID = "#BBC1CC" +CHART_GRID = "#EEF1F5" +CHART_MUTED = "#6E7785" +CHART_FAINT = "#A6ACB7" +CHART_PAPER = "#FFFFFF" +CHART_MONO = _register_fonts() +CHART_DPI = 200 + + +def _base_style(): + plt.rcParams.update({ + "figure.dpi": CHART_DPI, + "savefig.dpi": CHART_DPI, + "font.family": CHART_MONO, + "font.size": 8.5, + "text.color": CHART_MUTED, + "axes.edgecolor": CHART_LINEMID, + "axes.linewidth": 0.8, + "axes.facecolor": CHART_PAPER, + "figure.facecolor": CHART_PAPER, + "axes.grid": False, + "xtick.color": CHART_MUTED, + "ytick.color": CHART_FAINT, + "xtick.labelsize": 8, + "ytick.labelsize": 8, + }) + + +def _strip(ax, left=True): + for s in ("top", "right"): + ax.spines[s].set_visible(False) + ax.spines["bottom"].set_color(CHART_LINEMID) + if left: + ax.spines["left"].set_visible(False) + ax.tick_params(length=3, width=0.8, colors=CHART_MUTED) + + +def _hgrid(ax, ys): + for i, y in enumerate(ys): + ax.axhline(y, color=(CHART_LINEMID if i == 0 else CHART_GRID), + lw=(1.0 if i == 0 else 0.8), zorder=0) + + +def _to_datauri(fig): + buf = io.BytesIO() + fig.savefig(buf, format="png", bbox_inches="tight", pad_inches=0.06, + facecolor=CHART_PAPER) + plt.close(fig) + return "data:image/png;base64," + base64.b64encode(buf.getvalue()).decode() + + +def _mono_label(ax, text, x=0.0, y=-0.20, size=7.5, color=CHART_FAINT): + ax.text(x, y, text, transform=ax.transAxes, ha="left", va="top", + fontsize=size, color=color, family=CHART_MONO) + + +def _yticks(ymax, steps): + """Pick from ``steps`` the tick values that fit under ymax (deduped).""" + ticks = sorted({0.0} | {t for t in steps if t <= ymax}) + return ticks + + +def chart_esd(esds, stuck_flags, median, panel=False): + if not esds: + return None + _base_style() + esds = np.asarray(esds, float) + stuck = np.asarray(stuck_flags, bool) + lo = max(1.0, min(20.0, float(esds.min()))) + hi = max(500.0, float(esds.max())) + edges = np.logspace(math.log10(lo), math.log10(hi), 23) + + fig, ax = plt.subplots(figsize=(3.8, 2.65) if panel else (7.6, 2.05)) + cf, _ = np.histogram(esds[~stuck], bins=edges) + cs, _ = np.histogram(esds[stuck], bins=edges) + + ax.bar(edges[:-1], cf, width=np.diff(edges), align="edge", + color=CHART_ACCENT, linewidth=0, zorder=3) + ax.bar(edges[:-1], cs, width=np.diff(edges), align="edge", bottom=cf, + color=CHART_AMBER, linewidth=0, zorder=3) + + ax.set_xscale("log") + ax.set_xlim(lo, hi) + ymax = float((cf + cs).max()) * 1.10 if (cf + cs).size else 1.0 + ax.set_ylim(0, ymax) + _strip(ax) + yt = _yticks(ymax, [round(ymax / 3, -2), round(2 * ymax / 3, -2)]) + ax.set_yticks(yt) + _hgrid(ax, yt) + + ticks = [t for t in (20, 30, 50, 100, 200, 500) if lo <= t <= hi] + ax.xaxis.set_major_locator(FixedLocator(ticks)) + ax.xaxis.set_major_formatter(FuncFormatter(lambda v, _: f"{int(v)}")) + ax.minorticks_off() + + ax.axvline(median, color=CHART_INK, lw=1.0, ls=(0, (3, 3)), zorder=4) + ax.text(median * 1.03, ymax * 0.97, f"median {median:.0f} µm", + color=CHART_INK, fontsize=8, va="top", ha="left", family=CHART_MONO) + + if stuck.any(): + ax.scatter([], [], marker="s", s=42, color=CHART_ACCENT, label="flowing") + ax.scatter([], [], marker="s", s=42, color=CHART_AMBER, label="stuck") + ax.legend(loc="upper right", frameon=False, handletextpad=0.5, + labelcolor=CHART_MUTED, fontsize=8, borderaxespad=0.2) + + _mono_label(ax, "EQUIVALENT SPHERICAL DIAMETER · µm") + return _to_datauri(fig) + + +def chart_sharpness(values, median, cutoff, panel=False, duo=False): + if not values: + return None + _base_style() + v = np.asarray(values, float) + # Data-driven x-axis: the focus measure is a scale/contrast-invariant ratio + # (~0.04–1.4), so a fixed range would squash everything against zero. Cap at + # the 99.5th percentile and always keep the cutoffs comfortably in view. + hi = float(np.quantile(v, 0.995)) if v.size else 1.0 + hi = max(hi, cutoff * 3, SHARP_LAPLACIAN_MIN * 1.5, 0.3) + edges = np.linspace(0, hi, 31) + counts, _ = np.histogram(np.clip(v, 0, hi), bins=edges) + centers = (edges[:-1] + edges[1:]) / 2 + colors = np.where(centers < cutoff, CHART_BLURRY, CHART_ACCENT) + + fig, ax = plt.subplots(figsize=(3.8, 2.65) if panel else ((4.6, 2.51) if duo else (7.6, 2.8))) + ax.bar(edges[:-1], counts, width=np.diff(edges) * 0.94, align="edge", + color=colors, linewidth=0, zorder=3) + + ax.set_xlim(0, hi) + ymax = float(counts.max()) * 1.12 if counts.size else 1.0 + ax.set_ylim(0, ymax) + _strip(ax) + yt = _yticks(ymax, [round(ymax / 2, -2)]) + ax.set_yticks(yt) + _hgrid(ax, yt) + xt = np.round(np.linspace(0, hi, 4 if duo else 6), 2) + ax.set_xticks(xt) + ax.xaxis.set_major_formatter(FuncFormatter(lambda x, _: f"{x:.2f}")) + lab = 9.5 if duo else 8 + ax.tick_params(labelsize=lab) + + ax.axvline(cutoff, color=CHART_INK, lw=1.0, ls=(0, (3, 3)), zorder=4) + if not duo: # no room beside the legend in the narrow column; the caption says it + ax.text(cutoff + hi * 0.01, ymax * 0.97, "blurry cutoff", color=CHART_INK, + fontsize=lab, va="top", ha="left", family=CHART_MONO) + ax.axvline(median, color=CHART_LINEMID, lw=1.0, zorder=2) + flip = median > hi * 0.55 # keep the label inside the axes + ax.text(median + (-1 if flip else 1) * hi * 0.015, ymax * (0.97 if duo else 0.86), + f"median {median:.2f}", color=CHART_MUTED, fontsize=lab, + va="top", ha="right" if flip else "left", family=CHART_MONO) + + ax.scatter([], [], marker="s", s=42, color=CHART_BLURRY, label="blurry") + ax.scatter([], [], marker="s", s=42, color=CHART_ACCENT, label="sharp") + # The tall in-focus bar sits at the right edge in the wide chart, so the + # legend goes upper-center there; in the duo column the peak is left of centre. + ax.legend(loc="upper right" if duo else "upper center", ncol=1 if duo else 2, + frameon=False, handletextpad=0.5, labelcolor=CHART_MUTED, + fontsize=lab, borderaxespad=0.2) + + _mono_label(ax, "FOCUS MEASURE" if duo else "FOCUS MEASURE · SHARPNESS RATIO", + size=(9 if duo else 7.5)) + return _to_datauri(fig) + + +def chart_heatmap(points, thirds, panel=False, gamma=0.72): + """``points`` are (object_x, object_y); thirds = (leftPct, centerPct, + rightPct). Matches the dashboard SPATIAL convention: object_y is the + *across*-flowcell axis (short, 0..ACROSS) and carries the left/center/right + thirds — drawn horizontally; object_x is the *along*-flowcell axis (the long + one) — drawn vertically, so the plot is portrait like the physical flowcell.""" + if not points: + return None + _base_style() + pts = np.asarray(points, float) + x, y = pts[:, 1], pts[:, 0] # x = object_y (across, thirds); y = object_x (along, long) + if x.min() == x.max() or y.min() == y.max(): + return None + + cmap = LinearSegmentedColormap.from_list( + "psblue", ["#FBFCFE", "#9CC3E6", "#16548C"]) + + # finer binning on the long (along / vertical) axis + H, _, _ = np.histogram2d( + x, y, bins=(24, 32), + range=[[x.min(), x.max()], [y.min(), y.max()]]) + k = np.array([[0.5, 1, 0.5], [1, 2, 1], [0.5, 1, 0.5]]); k /= k.sum() + Hs = np.copy(H) + Hs[1:-1, 1:-1] = sum( + k[a + 1, b + 1] * H[1 + a:H.shape[0] - 1 + a, 1 + b:H.shape[1] - 1 + b] + for a in (-1, 0, 1) for b in (-1, 0, 1)) + + lo = Hs.min(); p96 = np.quantile(Hs, 0.96) + norm = np.clip((Hs - lo) / max(1e-9, (p96 - lo)), 0, 1) ** gamma + + # portrait figure: the long (vertical) axis is taller than the short one + fig, ax = plt.subplots(figsize=(2.6, 3.2) if panel else (4.0, 4.4)) + im = ax.imshow(norm.T, origin="lower", aspect="auto", cmap=cmap, + interpolation="nearest") + ax.set_xticks([]); ax.set_yticks([]) + for s in ax.spines.values(): + s.set_color(CHART_LINEMID); s.set_linewidth(0.8) + + if panel: + left, center, right = thirds + for frac, txt in zip((1 / 6, 1 / 2, 5 / 6), + (f"LEFT {left:.0f}%", f"CENTER {center:.0f}%", + f"RIGHT {right:.0f}%")): + ax.text(frac, -0.05, txt, transform=ax.transAxes, ha="center", + va="top", fontsize=8, color=CHART_MUTED, family=CHART_MONO) + _mono_label(ax, "FLOWCELL · CAMERA ORIENTATION", y=-0.12) + else: + _mono_label(ax, "ACROSS FLOWCELL", y=-0.035, size=11) + + cb = fig.colorbar(im, ax=ax, fraction=0.035, pad=0.03) + cb.outline.set_edgecolor(CHART_LINEMID); cb.outline.set_linewidth(0.8) + cb.set_ticks([0, 1]); cb.set_ticklabels(["LOW", "HIGH"]) + cb.ax.tick_params(length=0, labelsize=(7.5 if panel else 10), colors=CHART_MUTED) + if panel: + cb.set_label("DENSITY", color=CHART_FAINT, fontsize=7, family=CHART_MONO, labelpad=6) + return _to_datauri(fig) + + +def chart_concentration(per_frame_counts, mean, panel=True): + if not per_frame_counts: + return None + _base_style() + v = np.asarray(per_frame_counts, float) + n = len(v) + fig, ax = plt.subplots(figsize=(3.8, 2.65) if panel else (7.6, 2.8)) + xs = np.arange(n) + ax.fill_between(xs, v, color=CHART_ACCENT, alpha=0.12, linewidth=0) + ax.plot(xs, v, color=CHART_ACCENT, lw=1.0) + + ymax = float(v.max()) * 1.10 if v.size else 1.0 + ax.set_xlim(0, max(1, n - 1)); ax.set_ylim(0, ymax) + _strip(ax) + yt = _yticks(ymax, [15, 30, 45, 60]) + ax.set_yticks(yt); _hgrid(ax, yt) + ax.set_xticks([t for t in (0, 100, 200, 300) if t <= n]) + + ax.axhline(mean, color=CHART_AMBER, lw=1.0, ls=(0, (4, 3)), zorder=4) + ax.text(n - 1, mean + ymax * 0.04, f"mean {mean:.1f}", color=CHART_AMBER, + fontsize=8, va="bottom", ha="right", family=CHART_MONO) + _mono_label(ax, f"OBJECTS PER FRAME · N = {n}") + return _to_datauri(fig) + + +# --------------------------------------------------------------------------- # +# Images -> base64 +# --------------------------------------------------------------------------- # +def img_b64(path: Path) -> str | None: + try: + data = path.read_bytes() + except OSError: + return None + mime = "image/jpeg" if path.suffix.lower() in (".jpg", ".jpeg") else "image/png" + return f"data:{mime};base64," + base64.b64encode(data).decode() + + +def gallery_layout(n: int) -> tuple[int, float]: + """Columns and tile height (mm) giving the squarest tiles that fill a plate. + + Falls back to a dense 8-column grid when the selection is too large for one + page; the plate then flows across pages at a fixed tile size.""" + best = None + for cols in range(2, 9): + rows = math.ceil(n / cols) + tile_w = (GALLERY_WIDTH_MM - (cols - 1) * GALLERY_GAP_MM) / cols + h = (GALLERY_PLATE_MM - rows * GALLERY_META_MM - (rows - 1) * GALLERY_GAP_MM) / rows + if h < 11: # this many rows can't share one page + continue + h = min(h, tile_w * 1.35, 60.0) # never let a sparse plate become posters + used = rows * (h + GALLERY_META_MM) + (rows - 1) * GALLERY_GAP_MM + # Fill the plate first, squareness second. + score = (GALLERY_PLATE_MM - used) + 2.0 * abs(h - tile_w) + if best is None or score < best[0]: + best = (score, cols, h) + if best is None: + return 8, 12.0 + return best[1], round(best[2], 1) + + +def gallery_tiles(rows, objects_dir: Path, criteria="equivalent_diameter", + mode="top", count=GALLERY_COUNT): + """Pick ROI thumbnails for the plate. + + ``criteria`` selects the ranking column (see CRITERIA_COLS); ``mode`` is + "top" (largest by criteria) or "random"; ``count`` caps the tiles. Every + tile still shows its ESD label so the gallery stays comparable.""" + col, _ = CRITERIA_COLS.get(criteria, CRITERIA_COLS["equivalent_diameter"]) + try: + count = max(1, min(200, int(count))) + except (TypeError, ValueError): + count = GALLERY_COUNT + + enriched = [] + for r in rows: + oid = r.get("object_id") + val = fnum(r.get(col)) + esd = fnum(r.get("object_equivalent_diameter")) + if oid is None or val is None: + continue + enriched.append((val, esd, oid)) + + if mode == "random": + import random + rng = random.Random(1234) # deterministic so re-runs are reproducible + rng.shuffle(enriched) + chosen = enriched[:count] + chosen.sort(key=lambda t: t[0], reverse=True) # still present largest-first + else: + enriched.sort(key=lambda t: t[0], reverse=True) + chosen = enriched[:count] + + tiles = [] + for val, esd, oid in chosen: + b64 = img_b64(objects_dir / f"{oid}.jpg") + if b64: + tiles.append({"img": b64, + "esd": f"{esd:.0f} µm" if esd is not None else "—", + "id": oid}) + return tiles + + +# --------------------------------------------------------------------------- # +# Report assembly +# --------------------------------------------------------------------------- # +def load_hardware(): + try: + return json.loads(HARDWARE_JSON.read_text()) + except OSError: + return {} + + +def _load_acquisition(acquisition_path: str): + """Read one acquisition and compute every stat the report can draw on. + + Returns a dict of raw results + a ``common`` block of display-ready fields + shared by every render path. Callers decide which sections / charts / gallery + to build from it, so the heavy TSV + image work happens exactly once.""" + img_dir = Path(acquisition_path.rstrip("/")) + objects_dir = Path(img_to(str(img_dir), "objects")) + clean_dir = Path(img_to(str(img_dir), "clean")) + + metadata = json.loads((img_dir / "metadata.json").read_text()) + tsv = find_tsv(objects_dir) + if tsv is None: + raise FileNotFoundError(f"No ecotaxa_*.tsv in {objects_dir}") + headers, rows = read_tsv(tsv) + if not rows: + raise ValueError("TSV has no object rows") + + sharp = compute_sharpness(rows) + conc = compute_concentration(rows) + homo = compute_homogeneity(rows) + spatial = compute_spatial(rows) + stuck = compute_stuck(rows) + esds = esd_values(rows) + + def pct(vals, p): + return statistics.quantiles(vals, n=100)[p - 1] if len(vals) > 1 else (vals[0] if vals else 0) + + hardware = load_hardware() + instrument = f"PlanktoScope {hardware.get('hat_type', '')} v{hardware.get('hat_version', '')}".strip() + stats = { + "total": len(rows), + "esd_min": min(esds) if esds else 0, + "esd_p10": pct(esds, 10), + "esd_median": statistics.median(esds) if esds else 0, + "esd_p90": pct(esds, 90), + "esd_max": max(esds) if esds else 0, + "flowingPct": 100 - stuck["stuckPct"], + "stuckPct": stuck["stuckPct"], + "perFrame": conc["perFrame"], + } + + # Aligned ESD / stuck arrays for the histogram (drop rows with no ESD). + esd_pairs = [(fnum(r.get("object_equivalent_diameter")), stuck["stuck_flags"][i]) + for i, r in enumerate(rows)] + esd_pairs = [(e, s) for e, s in esd_pairs if e is not None] + + common = { + "meta": metadata, + "serial": metadata.get("acq_instrument_serial_number", "—"), + "instrument": instrument, + "instrument_short": instrument.replace("PlanktoScope ", "").strip() or instrument, + "brand_logo": img_b64(HERE / "logo.png"), + "position": fmt_position(metadata.get("object_lat"), metadata.get("object_lon")), + "lat_disp": fmt_coord(metadata.get("object_lat")), + "lon_disp": fmt_coord(metadata.get("object_lon")), + "sampled": fmt_sampled(metadata.get("object_date"), metadata.get("object_time")), + "stats": stats, + "sharp": sharp, "conc": conc, "homo": homo, "spatial": spatial, "stuck": stuck, + "flat_field": img_b64(clean_dir / "flat_color.jpg"), + } + name = "_".join(filter(None, [ + metadata.get("sample_project"), metadata.get("sample_id"), + metadata.get("acq_id")])) or img_dir.name + + return { + "img_dir": img_dir, "objects_dir": objects_dir, "clean_dir": clean_dir, + "metadata": metadata, "rows": rows, + "sharp": sharp, "conc": conc, "homo": homo, "spatial": spatial, + "stuck": stuck, "stats": stats, + "esd_arr": [e for e, _ in esd_pairs], + "esd_stuck": [s for _, s in esd_pairs], + "thirds": (spatial["leftPct"], spatial["centerPct"], spatial["rightPct"]), + "common": common, "name": name, + } + + +def normalize_sections(raw): + """Coerce the dialog's section toggles. Absent dict = every section on.""" + if not raw: + return {k: True for k in SECTION_KEYS} + return {k: bool(raw.get(k, False)) for k in SECTION_KEYS} + + +def normalize_gallery(raw): + """Coerce the dialog's 'Objects images' options.""" + raw = raw or {} + mode = "random" if str(raw.get("mode", "top")).lower() == "random" else "top" + try: + count = int(raw.get("count", GALLERY_COUNT)) + except (TypeError, ValueError): + count = GALLERY_COUNT + count = max(1, min(200, count)) + crit = str(raw.get("criteria", "equivalent_diameter")).lower() + if crit not in CRITERIA_COLS: + crit = "equivalent_diameter" + return {"mode": mode, "count": count, "criteria": crit} + + +def build_qc_rows(sharp, stuck, homo, sections): + """QC rows for the toggled focus / clogging / stability panels only. + + Shaped like the calibration rows (key/title/value/measure/state/track) so + both feed the same table and the same cover conformance panel.""" + b, s, cv = sharp["blurryPct"], stuck["stuckPct"], homo["cv"] + spec = [ + ("focus", b, f"{b:.1f}%", f"{b:.1f}% of objects blurry", 30), + ("clogging", s, f"{s:.1f}%", f"{s:.1f}% of objects stuck", 10), + ("stability", cv, f"{cv:.0f}%", f"CV {cv:.0f}% across frames", 100), + ] + rows = [] + for key, num, value, measure, ceiling in spec: + if not sections.get(key): + continue + ok = num < ceiling + rows.append({ + "key": key, "title": TOLERANCE[key]["title"], "num": num, + "value": value, "measure": measure, "pass": ok, + "unit": TOLERANCE[key]["unit"], "limit": TOLERANCE[key]["limit"], + "state": "good" if ok else "alert", + "label": "Pass" if ok else "Fail", + "track": _track(key, num), + }) + return rows + + +def _fmt_range(values, dec: int) -> str: + """A readout spanning one or many acquisitions, at the quantity's precision.""" + lo, hi = min(values), max(values) + if abs(hi - lo) < (0.05 if dec else 0.5): + return f"{lo:.{dec}f}" + return f"{lo:.{dec}f}\u2013{hi:.{dec}f}" + + +def build_conformance(acqs, sections): + """Cover panel: one track per evaluated check, one tick per acquisition. + + With several acquisitions the ticks share a track, so the spread across a + project reads at a glance — tight clusters mean a stable instrument.""" + rows = [] + for key in TOLERANCE_ORDER: + if not sections.get(key): + continue + ticks, values, singles = [], [], [] + for i, a in enumerate(acqs, 1): + src = next((c for c in a["calibration"] + a["qc"] if c.get("key") == key), None) + if not src or not src.get("track"): + continue + ticks.append({"pos": src["track"]["pos"], "state": src["state"], "idx": i}) + values.append(src["num"]) + singles.append(src["value"]) + if not ticks: + continue + # Alternate the tick labels above/below the track where acquisitions + # cluster, so their numbers stay readable. + ticks.sort(key=lambda t: t["pos"]) + prev, low = -99.0, False + for t in ticks: + low = (not low) if (t["pos"] - prev) < 6.0 else False + t["low"] = low + prev = t["pos"] + + s = TOLERANCE[key] + rows.append({ + "key": key, "title": s["title"], "limit": s["limit"], "unit": s["unit"], + "lo_label": s["lo_label"], "hi_label": s["hi_label"], + "good_start": _track(key, s["good"][0])["pos"], + "good_end": _track(key, s["good"][1])["pos"], + "ticks": ticks, "numbered": len(ticks) > 1 and len(ticks) <= 6, + "state": ("alert" if any(t["state"] == "alert" for t in ticks) + else "review" if any(t["state"] == "review" for t in ticks) else "good"), + "readout": _fmt_range(values, s["dec"]) if len(values) > 1 else singles[0], + }) + return rows + + +def build_qc_table(sharp, stuck, conc, homo): + """Legacy full QC table (green-pass / red-fail) used by the single-acq path.""" + return [ + {"label": "Focus quality", + "value": f"{sharp['blurryPct']:.1f}% blurry", + "limit": "< 30% blurry", "pass": sharp["blurryPct"] < 30}, + {"label": "Flowcell clogging (stuck)", + "value": f"{stuck['stuckPct']:.1f}% stuck", + "limit": "< 10% stuck", "pass": stuck["stuckPct"] < 10}, + {"label": "Concentration", + "value": f"{conc['perFrame']:.2f} obj/frame", + "limit": "1–50 obj/frame", "pass": 1 <= conc["perFrame"] <= 50}, + {"label": "Spatial homogeneity", + "value": f"CV {homo['cv']:.0f}%", + "limit": "CV < 100%", "pass": homo["cv"] < 100}, + ] + + +def build_context(acquisition_path: str): + """Legacy single-acquisition context (full + one-pager). Unchanged output. + + Returns ``(full_ctx, panel_ctx, name)``. Still used by the backward-compatible + ``acquisition_path`` MQTT payload""" + L = _load_acquisition(acquisition_path) + sharp, conc, homo, spatial, stuck = L["sharp"], L["conc"], L["homo"], L["spatial"], L["stuck"] + qc = build_qc_table(sharp, stuck, conc, homo) + base = { + "generated": datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M UTC"), + **L["common"], + "qc": qc, + "qc_pass": all(row["pass"] for row in qc), + "gallery": gallery_tiles(L["rows"], L["objects_dir"]), + "gallery_count": GALLERY_COUNT, + } + full_ctx = { + **base, + "chart_esd": chart_esd(L["esd_arr"], L["esd_stuck"], L["stats"]["esd_median"]), + "chart_sharpness": chart_sharpness(sharp["values"], sharp["median"], BLURRY_LAPLACIAN_MAX), + "chart_heatmap": chart_heatmap(spatial["points"], L["thirds"]), + } + panel_ctx = { + **base, + "chart_esd": chart_esd(L["esd_arr"], L["esd_stuck"], L["stats"]["esd_median"], panel=True), + "chart_sharpness": chart_sharpness(sharp["values"], sharp["median"], BLURRY_LAPLACIAN_MAX, panel=True), + "chart_heatmap": chart_heatmap(spatial["points"], L["thirds"], panel=True), + "chart_concentration": chart_concentration(homo["perFrameCounts"], conc["perFrame"]), + } + return full_ctx, panel_ctx, L["name"] + + +def build_acq_context(acquisition_path: str, sections: dict, gallery_opts: dict): + """Per-acquisition context for the combined 'Report export' template. Only + the toggled sections' heavy work (calibration, charts) is done.""" + L = _load_acquisition(acquisition_path) + sharp, homo, spatial, stuck = L["sharp"], L["homo"], L["spatial"], L["stuck"] + + calibration = compute_calibration(L["clean_dir"] / "flat_color.jpg", sections) + qc_rows = build_qc_rows(sharp, stuck, homo, sections) + + cal_alert = any(r["state"] == "alert" for r in calibration) + qc_fail = any(not r["pass"] for r in qc_rows) + acq_pass = not cal_alert and not qc_fail + issues = sum(r["state"] == "alert" for r in calibration) + sum(not r["pass"] for r in qc_rows) + evaluated = len(calibration) + len(qc_rows) + + show_calibration = any(sections.get(k) for k in ("lightness", "saturation", "cleanness", "alignment")) + show_qc = bool(qc_rows) + + crit_col, crit_label = CRITERIA_COLS[gallery_opts["criteria"]] + gallery = gallery_tiles(L["rows"], L["objects_dir"], gallery_opts["criteria"], + gallery_opts["mode"], gallery_opts["count"]) + gallery_cols, gallery_tile_mm = gallery_layout(len(gallery)) + + ctx = { + **L["common"], + "sections": sections, + "calibration": calibration, + "show_calibration": show_calibration, + "qc": qc_rows, + "show_qc": show_qc, + "acq_pass": acq_pass, + "issues": issues, + "evaluated": evaluated, + "gallery": gallery, + "gallery_opts": gallery_opts, + "gallery_criteria_label": crit_label, + "gallery_cols": gallery_cols, + "gallery_tile_mm": gallery_tile_mm, + # size distribution is always shown; focus/spatial charts are gated + "chart_esd": chart_esd(L["esd_arr"], L["esd_stuck"], L["stats"]["esd_median"]), + "chart_sharpness": (chart_sharpness(sharp["values"], sharp["median"], BLURRY_LAPLACIAN_MAX, duo=True) + if sections.get("focus") else None), + "chart_heatmap": (chart_heatmap(spatial["points"], L["thirds"]) + if sections.get("spatial") else None), + "name": L["name"], + } + return ctx + + +def build_report(acquisition_path: str) -> dict: + """Render both PDFs (full + one-pager) and return their paths. (Legacy.)""" + full_ctx, panel_ctx, name = build_context(acquisition_path) + + env = Environment(loader=FileSystemLoader(str(HERE)), + autoescape=select_autoescape(["html"])) + REPORTS_DIR.mkdir(parents=True, exist_ok=True) + + out = REPORTS_DIR / f"{name}_report.pdf" + HTML(string=env.get_template("template.html").render(**full_ctx), + base_url=str(HERE)).write_pdf(str(out)) + + onepage = REPORTS_DIR / f"{name}_report_onepage.pdf" + HTML(string=env.get_template("template_onepage.html").render(**panel_ctx), + base_url=str(HERE)).write_pdf(str(onepage)) + + return {"full": out, "onepage": onepage} + + +def build_combined_report(acquisition_paths, sections=None, gallery_opts=None) -> dict: + """Render ONE full PDF covering all selected acquisitions with the chosen + sections + gallery options. Returns {'full': Path}.""" + sections = normalize_sections(sections) + gallery_opts = normalize_gallery(gallery_opts) + + acqs = [] + errors = [] + for p in acquisition_paths: + try: + acqs.append(build_acq_context(p, sections, gallery_opts)) + except Exception as exc: # noqa: BLE001 — skip a bad acq, keep the rest + traceback.print_exc() + errors.append(f"{Path(p).name}: {exc}") + if not acqs: + raise ValueError("No acquisition could be loaded: " + "; ".join(errors) if errors + else "No acquisitions selected") + + generated = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M UTC") + doc = { + "generated": generated, + "sections": sections, + "gallery_opts": gallery_opts, + "acqs": acqs, + "multi": len(acqs) > 1, + "n_acq": len(acqs), + # cover / running fields taken from the first acquisition + "brand_logo": acqs[0]["brand_logo"], + "serial": acqs[0]["serial"], + "instrument_short": acqs[0]["instrument_short"], + "cover_project": acqs[0]["meta"].get("sample_project") or "—", + "total_objects": sum(a["stats"]["total"] for a in acqs), + "all_pass": all(a["acq_pass"] for a in acqs), + "total_issues": sum(a["issues"] for a in acqs), + "conformance": build_conformance(acqs, sections), + "errors": errors, + } + + env = Environment(loader=FileSystemLoader(str(HERE)), + autoescape=select_autoescape(["html"])) + REPORTS_DIR.mkdir(parents=True, exist_ok=True) + + if len(acqs) == 1: + stem = acqs[0]["name"] + else: + proj = re.sub(r"[^A-Za-z0-9]+", "-", str(acqs[0]["meta"].get("sample_project") or "report")).strip("-") + stem = f"{proj}_{len(acqs)}acq" + out = REPORTS_DIR / f"{stem}_report.pdf" + HTML(string=env.get_template("template_combined.html").render(**doc), + base_url=str(HERE)).write_pdf(str(out)) + return {"full": out} + + +# --------------------------------------------------------------------------- # +# MQTT (asyncio / aiomqtt) +# --------------------------------------------------------------------------- # +async def publish(client: "aiomqtt.Client", payload: dict) -> None: + await client.publish(TOPIC_OUT, json.dumps(payload)) + print(f"[reporter] -> {TOPIC_OUT}: {payload}", flush=True) + + +async def _handle_generate(client: "aiomqtt.Client", data: dict) -> None: + """Dispatch a generate request. Two payload shapes are accepted: + + NEW {action, acquisition_paths:[...], sections:{...}, gallery:{...}} + -> one combined full PDF (the "Report export" dialog). + LEGACY {action, acquisition_path:"..."} + -> the original full + one-pager pair (per-row button). + + PDF generation is CPU-bound (matplotlib + WeasyPrint), so it runs in a + worker thread via ``asyncio.to_thread`` to keep the MQTT client responsive. + Messages are awaited one at a time, which serialises generation — matplotlib's + pyplot state is not safe to drive from concurrent threads.""" + paths = data.get("acquisition_paths") + if isinstance(paths, list) and paths: + label = f"{len(paths)} acquisition(s)" + await publish(client, {"status": "generating", "count": len(paths), + "acquisition_paths": paths}) + try: + res = await asyncio.to_thread( + build_combined_report, paths, data.get("sections"), data.get("gallery")) + full = res["full"] + await publish(client, {"status": "done", "acquisition_paths": paths, + "path": str(full), "filename": full.name, + "url": f"/api/files/reports/{full.name}", + "filename_full": full.name, + "url_full": f"/api/files/reports/{full.name}"}) + print(f"[reporter] wrote {full} ({label})", flush=True) + except Exception as exc: # noqa: BLE001 — report any failure to the UI + traceback.print_exc() + await publish(client, {"status": "error", "acquisition_paths": paths, + "error": str(exc)}) + return + + acq = data.get("acquisition_path") + if not acq: + return + print(f"[reporter] generating report for {acq}", flush=True) + await publish(client, {"status": "generating", "acquisition_path": acq}) + try: + res = await asyncio.to_thread(build_report, acq) + full, onep = res["full"], res["onepage"] + await publish(client, {"status": "done", "acquisition_path": acq, + # legacy fields (= full report) kept for compatibility + "path": str(full), "filename": full.name, + "url": f"/api/files/reports/{full.name}", + # both formats, so the dashboard can offer a choice + "filename_full": full.name, + "url_full": f"/api/files/reports/{full.name}", + "filename_onepage": onep.name, + "url_onepage": f"/api/files/reports/{onep.name}"}) + print(f"[reporter] wrote {full} and {onep}", flush=True) + except Exception as exc: # noqa: BLE001 — report any failure to the UI + traceback.print_exc() + await publish(client, {"status": "error", "acquisition_path": acq, "error": str(exc)}) + + +async def _handle_message(client: "aiomqtt.Client", message: "aiomqtt.Message") -> None: + try: + data = json.loads(message.payload.decode()) + except (ValueError, UnicodeDecodeError): + print(f"[reporter] bad payload: {message.payload!r}", flush=True) + return + if not isinstance(data, dict) or data.get("action") != "generate": + return + await _handle_generate(client, data) + + +async def start() -> None: + REPORTS_DIR.mkdir(parents=True, exist_ok=True) + async with aiomqtt.Client(hostname=MQTT_HOST, port=MQTT_PORT) as client: + await client.subscribe(TOPIC_IN) + print(f"[reporter] connected {MQTT_HOST}:{MQTT_PORT}, subscribed {TOPIC_IN}", flush=True) + async for message in client.messages: + await _handle_message(client, message) + + +def main() -> None: + if "--once" in sys.argv: # local test: legacy single-acq (full + one-pager) + res = build_report(sys.argv[sys.argv.index("--once") + 1]) + print(f"wrote {res['full']} and {res['onepage']}") + return + if "--payload" in sys.argv: # local test: exact dialog payload (JSON), no MQTT + data = json.loads(sys.argv[sys.argv.index("--payload") + 1]) + res = build_combined_report(data.get("acquisition_paths") or [data["acquisition_path"]], + data.get("sections"), data.get("gallery")) + print(f"wrote {res['full']}") + return + asyncio.run(start()) + + +if __name__ == "__main__": + main() diff --git a/reporter/planktoscope-org.reporter.service b/reporter/planktoscope-org.reporter.service new file mode 100644 index 000000000..05abb3910 --- /dev/null +++ b/reporter/planktoscope-org.reporter.service @@ -0,0 +1,18 @@ +[Unit] +Description=Run the PlanktoScope PDF report generator +Wants=mosquitto.service +After=mosquitto.service +After=nodered.service + +[Service] +Type=simple +Environment=HOME=/home/pi +Environment=REPORTER_OUTPUT=/home/pi/data/reports +WorkingDirectory=/opt/PlanktoScope/reporter +ExecStart=/usr/local/bin/uv run main.py +User=pi +Group=pi +Restart=on-failure + +[Install] +WantedBy=multi-user.target diff --git a/reporter/pyproject.toml b/reporter/pyproject.toml new file mode 100644 index 000000000..0f1b39dba --- /dev/null +++ b/reporter/pyproject.toml @@ -0,0 +1,50 @@ +[project] +name = "planktoscope-reporter" +version = "0.0.1" +description = "Server-side PDF acquisition report generator for the PlanktoScope" +requires-python = ">=3.13.5,<4.0" +readme = "README.md" +license = "GPL-3.0-or-later" +keywords = ["planktoscope"] +classifiers = [ + "Intended Audience :: Science/Research", + "Natural Language :: English", + "Topic :: Scientific/Engineering", +] +dependencies = [ + "aiomqtt>=2.4.0,<3", + "numpy>=2.3.3,<3", + "pillow>=11.0,<12", + "matplotlib>=3.9,<4", + "jinja2>=3.1,<4", + "weasyprint>=61,<70", +] + +[project.urls] +Homepage = "https://www.planktoscope.org" +Repository = "https://github.com/fairscope/PlanktoScope/tree/main/reporter" +Documentation = "https://docs.planktoscope.community/" + +[dependency-groups] +dev = [ + "poethepoet>=0.37.0,<0.38", + "mypy>=1.18.2,<2", + "ruff>=0.13.3,<0.14", +] + +[tool.uv] +package = false + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.ruff] +line-length = 100 + +[tool.poe.tasks] +fmt = "ruff format" +lint-ruff = "ruff check" +lint = ["lint-ruff"] +fmt-check = "ruff format --check" +check = ["fmt-check", "lint"] diff --git a/reporter/template.html b/reporter/template.html new file mode 100644 index 000000000..b6f4f8124 --- /dev/null +++ b/reporter/template.html @@ -0,0 +1,401 @@ + + + + +PlanktoScope Acquisition Report — {{ meta.acq_id }} (full) + + + + + +
+
+
+ PlanktoScope +
PlanktoScopeFairScope · Quantitative Imaging
+
+
+
Acquisition Report
+
{{ serial }} · {{ instrument_short }}
+
+
+ +
+
Quantitative plankton imaging · Quality-control readout
+

{{ meta.acq_id }}

+

Automated quality-control and size-distribution report for acquisition {{ meta.acq_id }}, project {{ meta.sample_project }}, sample {{ meta.sample_id }}. {{ stats.total }} objects were segmented from {{ meta.acq_nb_frame }} frames across a {{ meta.acq_imaged_volume }} mL imaged volume.

+
+
+
+
Quality control · Determination
+
{{ 'Passed' if qc_pass else 'Review required' }}
+
{{ qc|length }} metrics evaluated · {{ qc|rejectattr('pass')|list|length }} outside acceptable range
+
+
+
{{ stats.total }}
+
Objects segmented
+
+
+
+ +
+
Project
{{ meta.sample_project or "—" }}
+
Sample ID
{{ meta.sample_id or "—" }}
+
Acquisition ID
{{ meta.acq_id or "—" }}
+
Operator
{{ meta.sample_operator or "—" }}
+
Gear
{{ meta.sample_gear or "—" }}
+
Serial
{{ serial }}
+
Position
{{ position }}
+
Sampled (UTC)
{{ sampled }}
+
Instrument
{{ instrument_short }}
+
Median ESD
{{ "%.1f"|format(stats.esd_median) }} µm
+
Imaged volume
{{ meta.acq_imaged_volume }} mL
+
Generated (UTC)
{{ generated.split(' ')[0] }}
+
+ +
+ Generated {{ generated }} · WeasyPrint + Cover · 01 / 05 +
+
+ + +
+
+
PlanktoScope · Acquisition Report
+
{{ meta.acq_id }} · {{ serial }} · 02 / 05
+
+ +
+
01

Sample & acquisition metadata

+
EcoTaxa identity fields
+
+ +
+ + + + + + + + + + + +
Sample
Project{{ meta.sample_project or "—" }}
Sample ID{{ meta.sample_id or "—" }}
Operator{{ meta.sample_operator or "—" }}
Gear{{ meta.sample_gear or "—" }}
Latitude{{ lat_disp }}
Longitude{{ lon_disp }}
Date / time (UTC){{ sampled }}
Pumped volume{{ meta.acq_pumped_volume }} mL
Imaged volume{{ meta.acq_imaged_volume }} mL
+ + + + + + + + + + + +
Acquisition
Acquisition ID{{ meta.acq_id or "—" }}
Instrument{{ instrument_short }}
Serial{{ serial }}
Frames captured{{ meta.acq_nb_frame }}
Pixel size{{ meta.process_pixel_size }} µm/px
Flowcell thickness{{ meta.acq_flowcell_thickness }} µm
Camera ISO{{ meta.acq_camera_iso }}
Shutter speed{{ meta.acq_camera_shutter_speed }} ms
Minimum ESD{{ meta.process_min_ESD }} µm
+
+ +
+

Comment

+

{{ meta.sample_comment or "No sample comment recorded." }} Processed {{ (meta.process_datetime or meta.acq_local_datetime or "—")|replace("T", " ") }} with the PlanktoScope segmentation pipeline; objects below the {{ meta.process_min_ESD }} µm minimum ESD were excluded.

+
+ +
+

Method & QC thresholds

+

ESD denotes the object equivalent spherical diameter in µm and is reported as measured. Acceptance limits — focus < 30% blurry; flowcell clogging < 10% stuck; concentration 1–50 objects per frame; spatial homogeneity CV < 100%.

+
+ +
+ {{ instrument_short }} · {{ serial }} + §01 Metadata · 02 / 05 +
+
+ + +
+
+
PlanktoScope · Acquisition Report
+
{{ meta.acq_id }} · {{ serial }} · 03 / 05
+
+ +
+
02

Quality control summary

+
Dashboard thresholds · n = {{ stats.total }}
+
+ + + + + {% for row in qc %} + + + + + + + {% endfor %} + +
MetricMeasuredAcceptable rangeDetermination
{{ row.label }}{{ row.value }}{{ row.limit }}{{ 'PASS' if row.pass else 'FAIL' }}
+ + {% if flat_field %} +
+
Flat fieldFig. 1 · Illumination
+ flat field +
Fig. 1 Median of the first frames, used for illumination correction across the field of view before segmentation.
+
+ {% endif %} + + {% if chart_sharpness %} +
+
Sharpness distributionFig. 2 · Laplacian var.
+ sharpness distribution +
Fig. 2 Object sharpness (object_blur_laplacian). Median {{ "%.3f"|format(sharp.median) }}; {{ "%.1f"|format(sharp.blurryPct) }}% of objects fall below the blurry cutoff (dashed).
+
+ {% endif %} + +
+ {{ instrument_short }} · {{ serial }} + §02 Quality control · 03 / 05 +
+
+ + +
+
+
PlanktoScope · Acquisition Report
+
{{ meta.acq_id }} · {{ serial }} · 04 / 05
+
+ +
+
03

Size distribution

+
Equivalent spherical diameter · {{ stats.total }} objects
+
+ +
+
Median ESD
{{ "%.1f"|format(stats.esd_median) }}
µm · P50
+
Interdecile
{{ stats.esd_p10|round|int }}–{{ stats.esd_p90|round|int }}
µm · P10–P90
+
Full range
{{ stats.esd_min|round|int }}–{{ stats.esd_max|round|int }}
µm · min–max
+
Objects / frame
{{ "%.1f"|format(stats.perFrame) }}
1–50
+
Total
{{ stats.total }}
segmented
+
+ + {% if chart_esd %} +
+
Size distributionFig. 3 · ESD µm · log
+ ESD histogram +
Fig. 3 Log-binned equivalent spherical diameter. Flowing objects {{ "%.1f"|format(stats.flowingPct) }}%; stuck objects {{ "%.1f"|format(stats.stuckPct) }}%; dashed line marks the median.
+
+ {% endif %} + +
+ {% if chart_heatmap %} +
+
Spatial densityFig. 4 · Flowcell field
+ spatial heatmap +
Fig. 4 Object distribution across the flowcell (camera orientation).
+
+ {% endif %} +
+
Spatial summary
+ + + + + + +
Total objects{{ stats.total }}
Flowing{{ "%.1f"|format(stats.flowingPct) }}%
Stuck{{ "%.1f"|format(stats.stuckPct) }}%
Left / Ctr / Right{{ "%.0f"|format(spatial.leftPct) }} / {{ "%.0f"|format(spatial.centerPct) }} / {{ "%.0f"|format(spatial.rightPct) }}%
Homogeneity (CV){{ "%.0f"|format(homo.cv) }}%
+
+
+ +
+ {{ instrument_short }} · {{ serial }} + §03 Size distribution · 04 / 05 +
+
+ + +
+
+
PlanktoScope · Object Gallery
+
{{ meta.acq_id }} · {{ serial }} · 05 / 05
+
+ +
+
+
§ 04 · Largest objects by diameter
+

Object Gallery

+
+
Plate I
{{ gallery|length }} objects · descending ESD
+
+ +
+ {% for o in gallery %} +
+ object {{ loop.index }} +
{{ '%02d'|format(loop.index) }}{{ o.esd }}
+
+ {% endfor %} +
+ +

Plate I  The {{ gallery|length }} largest segmented objects by equivalent spherical diameter, in descending order. Index above each thumbnail; ESD at right.

+ +
+ Generated {{ generated }} · WeasyPrint + §04 Object gallery · 05 / 05 +
+
+ + + diff --git a/reporter/template_combined.html b/reporter/template_combined.html new file mode 100644 index 000000000..5b683f35f --- /dev/null +++ b/reporter/template_combined.html @@ -0,0 +1,734 @@ + + + + +PlanktoScope Acquisition Report{% if multi %} — {{ n_acq }} acquisitions{% else %} — {{ acqs[0].meta.acq_id }}{% endif %} + + + + +{# ---------- shared macro: one tolerance track ---------- #} +{% macro track(t, state, ticks=None, numbered=False) -%} +
+
+ {% if t.good_start > 0.5 %}
{% endif %} + {% if t.good_end < 99.5 %}
{% endif %} + {% if ticks %} + {% for k in ticks %} +
{% if numbered %}{{ k.idx }}{% endif %}
+ {% endfor %} + {% else %} +
+ {% endif %} +
+{%- endmacro %} + +{# ============================== COVER ============================== #} +
+
+
+ +
PlanktoScopeFairScope · Quantitative imaging
+
+
+
Acquisition report
+
{{ serial }} · {{ instrument_short }}
+
+
+ +
+
Quantitative plankton imaging · Conformance certificate
+ {% if multi %} +

{{ cover_project }}

+

Combined quality-control and size-distribution report for {{ n_acq }} acquisitions of project {{ cover_project }}, totalling {{ total_objects }} segmented objects. Each acquisition is reported on its own pages against the same acceptance limits.

+ {% else %} +

{{ acqs[0].meta.acq_id }}

+

Quality-control and size-distribution report for acquisition {{ acqs[0].meta.acq_id }}, sample {{ acqs[0].meta.sample_id }} of project {{ acqs[0].meta.sample_project }}. {{ acqs[0].stats.total }} objects were segmented from {{ acqs[0].meta.acq_nb_frame }} frames.

+ {% endif %} + +
+
+
+
Quality control · Determination
+
{{ 'Within limits' if all_pass else 'Review required' }}
+
{{ n_acq }} acquisition{{ 's' if multi else '' }} evaluated · {{ total_issues }} measurement{{ '' if total_issues == 1 else 's' }} outside limits
+
+
+
Objects segmented
+
{{ total_objects }}
+
+
+
+ +
+ + {% if conformance %} +
+
+ Conformance + {{ conformance|length }} check{{ '' if conformance|length == 1 else 's' }}{% if multi %} · {{ n_acq }} acquisitions per track{% endif %} +
+ + {% for c in conformance %} +
+
{{ c.title }}
+
{{ c.readout }}{{ c.unit }}
+
+
+ {% if c.good_start > 0.5 %}
{% endif %} + {% if c.good_end < 99.5 %}
{% endif %} + {% for k in c.ticks %} +
{% if c.numbered %}{{ k.idx }}{% endif %}
+ {% endfor %} +
+
{{ c.limit }}
+
{{ 'Within' if c.state == 'good' else ('Review' if c.state == 'review' else 'Outside') }}
+
+ {% endfor %} + +
+ Acceptable rangeMeasured value + Each track is scaled to its own check +
+
+ {% endif %} + +
+ + {% if multi %} + + + + + {% for a in acqs %} + + + + + + + + + {% endfor %} + +
Acquisitions in this report
#AcquisitionSampleObjectsMedian ESDDetermination
{{ '%02d'|format(loop.index) }}{{ a.meta.acq_id }}{{ a.meta.sample_id or '—' }}{{ a.stats.total }}{{ "%.0f"|format(a.stats.esd_median) }} µm{{ 'Within' if a.acq_pass else 'Review' }}
+ {% else %} +
+
Project
{{ cover_project }}
+
Sample
{{ acqs[0].meta.sample_id or '—' }}
+
Acquisition
{{ acqs[0].meta.acq_id or '—' }}
+
Serial
{{ serial }}
+
Instrument
{{ instrument_short }}
+
Generated (UTC)
{{ generated.split(' ')[0] }}
+
+ {% endif %} + + {% if errors %} +
Skipped {{ errors|join('; ') }}
+ {% endif %} +
+ +{# ========================= PER-ACQUISITION ========================= #} +{% for a in acqs %} +{% set ai = loop.index %} +
+ +
+
+ {{ a.meta.acq_id }} + {{ a.meta.sample_project }} · {{ a.meta.sample_id }} · {{ a.stats.total }} objects +
+
+ {{ 'Within limits' if a.acq_pass else 'Review' }} + {% if multi %}Acq {{ ai }}/{{ n_acq }} · {% endif %}{{ a.evaluated }} checks · {{ a.issues }} flagged +
+
+ + {% if sections.metadata %} +
+
+ 01 +

Sample & acquisition metadata

+
+
EcoTaxa identity fields
+
+ +
+ + + + + + + + + + + +
Sample
Project{{ a.meta.sample_project or "—" }}
Sample ID{{ a.meta.sample_id or "—" }}
Operator{{ a.meta.sample_operator or "—" }}
Gear{{ a.meta.sample_gear or "—" }}
Latitude{{ a.lat_disp }}
Longitude{{ a.lon_disp }}
Date / time (UTC){{ a.sampled }}
Pumped volume{{ a.meta.acq_pumped_volume }} mL
Imaged volume{{ a.meta.acq_imaged_volume }} mL
+ + + + + + + + + + + +
Acquisition
Acquisition ID{{ a.meta.acq_id or "—" }}
Instrument{{ a.instrument_short }}
Serial{{ a.serial }}
Frames captured{{ a.meta.acq_nb_frame }}
Pixel size{{ a.meta.process_pixel_size }} µm/px
Flowcell thickness{{ a.meta.acq_flowcell_thickness }} µm
Camera ISO{{ a.meta.acq_camera_iso }}
Shutter speed{{ a.meta.acq_camera_shutter_speed }} ms
Minimum ESD{{ a.meta.process_min_ESD }} µm
+
+ +
+

Comment

+

{{ a.meta.sample_comment or "No sample comment recorded." }} Processed {{ (a.meta.process_datetime or a.meta.acq_local_datetime or "—")|replace("T", " ") }} with the PlanktoScope segmentation pipeline; objects below the {{ a.meta.process_min_ESD }} µm minimum ESD were excluded.

+
+ {% endif %} + + {% if a.show_calibration %} +
+
+ 02 +

Optics & calibration

+
+
Flat-field diagnostics
+
+ + + + + {% for c in a.calibration %} + + + + + + + + {% endfor %} + +
CheckMeasuredLimitDetermination
{{ c.title }} + {{ c.value }} + {{ c.unit }} + {% if c.track %}{{ track(c.track, c.state) }}{% endif %}{{ c.track.limit if c.track else '—' }}{{ c.label }}
+ {% endif %} + + {% if a.show_qc %} +
+
+ 03 +

Quality control

+
+
Dashboard thresholds · n = {{ a.stats.total }}
+
+ + + + + {% for row in a.qc %} + + + + + + + + {% endfor %} + +
MetricMeasuredLimitDetermination
{{ row.title }} + {{ row.value }} + {{ row.unit }} + {{ track(row.track, row.state) }}{{ row.track.limit }}{{ 'Pass' if row.pass else 'Fail' }}
+ {% endif %} + + {% set duo_both = a.flat_field and a.sections.focus and a.chart_sharpness %} + {% if a.flat_field or (a.sections.focus and a.chart_sharpness) %} +
+ {% if a.flat_field %} +
+
Flat fieldFig. 1 · Illumination
+ +
Fig. 1 Median flat field used for illumination correction. Mean luminance, chroma, debris spots and edge uniformity are all measured from this image.
+
+ {% endif %} + {% if a.sections.focus and a.chart_sharpness %} +
+
SharpnessFig. 2 · Focus measure
+ +
Fig. 2 Focus measure per object. Median {{ "%.3f"|format(a.sharp.median) }}; {{ "%.1f"|format(a.sharp.blurryPct) }}% of objects fall below the blurry cutoff (dashed).
+
+ {% endif %} +
+ {% endif %} + +
+
+ 04 +

Size distribution

+
+
Equivalent spherical diameter · {{ a.stats.total }} objects
+
+ +
+
Median ESD
{{ "%.1f"|format(a.stats.esd_median) }}
µm · P50
+
Interdecile
{{ a.stats.esd_p10|round|int }}–{{ a.stats.esd_p90|round|int }}
µm · P10–P90
+
Full range
{{ a.stats.esd_min|round|int }}–{{ a.stats.esd_max|round|int }}
µm · min–max
+
Objects / frame
{{ "%.1f"|format(a.stats.perFrame) }}
1–50
+
Total
{{ a.stats.total }}
segmented
+
+ + {% if a.chart_esd %} +
+
Size distributionFig. 3 · ESD µm · log
+ +
Fig. 3 Log-binned equivalent spherical diameter. Flowing objects {{ "%.1f"|format(a.stats.flowingPct) }}%; stuck objects {{ "%.1f"|format(a.stats.stuckPct) }}%; dashed line marks the median.
+
+ {% endif %} + + {% if sections.spatial %} +
+ {% if a.chart_heatmap %} +
+
Spatial densityFig. 4 · Flowcell field
+ +
Fig. 4 Object distribution across the flowcell, in camera orientation.
+
+ {% endif %} +
+
Spatial summary
+ + + + + + + + +
Total objects{{ a.stats.total }}
Flowing{{ "%.1f"|format(a.stats.flowingPct) }}%
Stuck{{ "%.1f"|format(a.stats.stuckPct) }}%
Left / Ctr / Right{{ "%.0f"|format(a.spatial.leftPct) }} / {{ "%.0f"|format(a.spatial.centerPct) }} / {{ "%.0f"|format(a.spatial.rightPct) }}%
Frames{{ a.homo.nFrames }}
Objects / frame{{ "%.1f"|format(a.stats.perFrame) }}
Flow stability (CV){{ "%.0f"|format(a.homo.cv) }}%
+
+
+ {% endif %} + + {% if a.gallery %} +
+
+
+ 05 +

Object gallery

+
+
{{ a.gallery|length }} objects · {{ 'random selection' if a.gallery_opts.mode == 'random' else 'largest first' }} by {{ a.gallery_criteria_label|lower }}
+
+ +
+ {% for o in a.gallery %} +
+ +
{{ '%02d'|format(loop.index) }}{{ o.esd }}
+
+ {% endfor %} +
+ +

Objects{{ a.gallery|length }} objects from {{ a.meta.acq_id }}, {% if a.gallery_opts.mode == 'random' %}drawn at random{% else %}ranked by {{ a.gallery_criteria_label|lower }}, largest first{% endif %}. Index at left of each tile, equivalent spherical diameter at right.

+
+ {% endif %} + +
+{% endfor %} + + + diff --git a/reporter/template_onepage.html b/reporter/template_onepage.html new file mode 100644 index 000000000..7214e02e6 --- /dev/null +++ b/reporter/template_onepage.html @@ -0,0 +1,304 @@ + + + + +PlanktoScope Acquisition Report — {{ meta.acq_id }} + + + + + +
+ +
+
+ PlanktoScope +
PlanktoScopeFairScope Imaging
+
+
+
Acquisition Report
+
{{ meta.acq_id }} · {{ serial }} · {{ meta.object_date }}
+
+
+ +
+
+
Quality control & size-distribution readout
+

{{ meta.acq_id }}

+
+
+
{{ 'Passed' if qc_pass else 'Review required' }}
+
{{ qc|rejectattr('pass')|list|length }} of {{ qc|length }} metrics outside range
+
+
+ + +
+
Project
{{ meta.sample_project or "—" }}
+
Sample ID
{{ meta.sample_id or "—" }}
+
Operator
{{ meta.sample_operator or "—" }}
+
Gear
{{ meta.sample_gear or "—" }}
+
Position
{{ position }}
+
Sampled
{{ sampled }}
+ +
Acquisition ID
{{ meta.acq_id or "—" }}
+
Instrument
{{ instrument_short }}
+
Serial
{{ serial }}
+
Frames
{{ meta.acq_nb_frame }}
+
Imaged volume
{{ meta.acq_imaged_volume }} mL
+
Pumped volume
{{ meta.acq_pumped_volume }} mL
+ +
Pixel size
{{ meta.process_pixel_size }} µm/px
+
Flowcell
{{ meta.acq_flowcell_thickness }} µm
+
Camera ISO
{{ meta.acq_camera_iso }}
+
Shutter
{{ meta.acq_camera_shutter_speed }} ms
+
Min ESD
{{ meta.process_min_ESD }} µm
+
Total objects
{{ stats.total }}
+
+ + +
+
01

Quality control

+
Dashboard thresholds
+
+ + + + {% for row in qc %} + + + + + + + {% endfor %} + +
MetricMeasuredAcceptableResult
{{ row.label }}{{ row.value|replace(' obj/frame', ' obj/fr') }}{{ row.limit|replace(' blurry', '')|replace(' stuck', '')|replace(' obj/frame', '')|replace('CV ', '') }}{{ 'PASS' if row.pass else 'FAIL' }}
+ + +
+
02

Size & composition

+
Equivalent spherical diameter · n = {{ stats.total }}
+
+
+
+ + + + + +
Median ESD{{ "%.1f"|format(stats.esd_median) }} µmP50
Interdecile range{{ "%.1f"|format(stats.esd_p10) }}–{{ "%.1f"|format(stats.esd_p90) }} µmP10–P90
Full range{{ "%.1f"|format(stats.esd_min) }}–{{ "%.1f"|format(stats.esd_max) }} µmmin–max
Objects per frame{{ "%.2f"|format(stats.perFrame) }}1–50
+
+
+ + + + + +
Flowing objects{{ "%.1f"|format(stats.flowingPct) }}%{{ (stats.flowingPct / 100 * stats.total)|round|int }} obj
Stuck objects{{ "%.1f"|format(stats.stuckPct) }}%{{ (stats.stuckPct / 100 * stats.total)|round|int }} obj
Left / Centre / Right{{ "%.0f"|format(spatial.leftPct) }} / {{ "%.0f"|format(spatial.centerPct) }} / {{ "%.0f"|format(spatial.rightPct) }}%of field
Homogeneity (CV){{ "%.0f"|format(homo.cv) }}%< 100%
+
+
+ + +
+ {% if chart_esd %} +
+
Size distributionESD µm · log
+ ESD distribution +
Median {{ stats.esd_median|round|int }} µm. Flowing vs. stuck; dashed median.
+
+ {% endif %} + {% if chart_sharpness %} +
+
SharpnessLaplacian var.
+ Sharpness +
{{ "%.1f"|format(sharp.blurryPct) }}% blurry. Below the dashed focus cutoff.
+
+ {% endif %} + {% if chart_heatmap %} +
+
Spatial densityFlowcell
+ Spatial heatmap +
CV {{ "%.0f"|format(homo.cv) }}%. Distribution across the field.
+
+ {% endif %} + {% if chart_concentration %} +
+
ConcentrationBy frame · n = {{ meta.acq_nb_frame }}
+ Concentration by frame +
{{ "%.2f"|format(stats.perFrame) }} obj/frame. Mean across the run.
+
+ {% endif %} +
+ +
+ Generated {{ generated }} · WeasyPrint + {{ instrument_short }} · {{ serial }} +
+
+ + +{% if gallery %} +
+
+
+ PlanktoScope +
PlanktoScopeFairScope Imaging
+
+
+
Object Gallery
+
{{ meta.acq_id }} · {{ serial }} · {{ meta.object_date }}
+
+
+ +
+
+
Largest objects by diameter
+

Object Gallery

+
+
Plate I
{{ gallery|length }} objects · descending ESD
+
+ +
+ {% for o in gallery %} +
+ object {{ loop.index }} +
{{ '%02d'|format(loop.index) }}{{ o.esd }}
+
+ {% endfor %} +
+ +

Plate I  The {{ gallery|length }} largest segmented objects by equivalent spherical diameter, descending. Index above each thumbnail; ESD at right.

+ +
+ Generated {{ generated }} · WeasyPrint + {{ instrument_short }} · {{ serial }} +
+
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