English ｜ 中文
Three Orange Data Mining widgets that turn assay plate photos into tables and fit dose-response curves — without writing code. The image core reuses the coffee-ring-analyzer algorithm, so results match its Streamlit / mobile / CLI versions. Sibling package to orange-spectra.
| Widget | What it does |
|---|---|
| Microplate Reader | Read a microtitre-plate photo (6–384 wells) into a per-well table: mean intensity, or thresholded count / intensity, with well labels (A1…H12). |
| Coffee-Ring Reader | Quantify a spotted-assay (coffee-ring) plate: per-cell area/intensity over a grid, blank-column (col 0) normalization and replicate averaging → a concentration / ratio table. |
| Dose-Response Fit | Fit a 3- or 4-parameter logistic curve to any concentration + signal table and report EC50, LOD and R², with the fitted curve overlaid on the points. |
The outputs are ordinary Orange Tables, so they plug straight into Data
Table, scatter plots, PCA, or the classification widgets — e.g. the
Dose-Response Fit widget accepts either reader's output, or any colorimetric
assay you already have in a spreadsheet.
Microplate Reader ─wells──┐
├──► Dose-Response Fit ──► EC50 / LOD / curve
Coffee-Ring Reader ─cells─┘ (also: PCA · PLS-DA · Data Table)
Works on Windows, macOS (Apple Silicon & Intel), and Linux — pure Python (numpy / scipy / matplotlib / Pillow / Orange3).
A. Desktop App (from orangedatamining.com):
Options ▸ Add-ons… ▸ Add more…, type orange-assay, tick it, OK,
restart.
B. pip Orange:
pip install orange-assay
python -m Orange.canvasAfter restarting Orange, an Assay category with the three widgets appears in the toolbox. Each widget has an ℹ How to use box, and F1 opens its online help page. Full tutorial: https://tai-shengyeh.github.io/spectraview/assay.html (English).
Traditional-Chinese Windows note: if the Add-ons dialog crashes with
UnicodeDecodeError: 'cp950' codec…, install from a command prompt instead:"…\Programs\Orange\python.exe" -m pip install orange-assay, then restart Orange (or set thePYTHONUTF8=1environment variable).
- Microplate Reader → choose a plate photo → pick the format (e.g. 96) → Wells output → Data Table.
- Or Coffee-Ring Reader → choose a spotted-plate photo → set rows × columns (column 0 = blank) → enter the concentrations → Cells output.
- Feed either output to Dose-Response Fit → pick the concentration and signal columns → read EC50 / LOD / R² and the fitted curve.
python tests/test_core.py # pure logic
QT_QPA_PLATFORM=offscreen python tests/test_widgets.py # widget smoke testsMIT.
orange-assay — Orange Data Mining 盤面影像與劑量反應 widgets
English ｜ 中文
三個 Orange Data Mining widgets，把檢測盤照片 變成資料表並擬合劑量反應曲線——全程拖拉、免寫程式。影像核心重用 coffee-ring-analyzer 的演算法，結果與其 Streamlit／手機／CLI 版本一致。與 orange-spectra 為姊妹套件。
| Widget | 功能 |
|---|---|
| Microplate Reader | 讀微孔盤照片（6–384 孔）成每孔一列的表：平均亮度，或閾值面積／強度，附孔位標籤（A1…H12）。 |
| Coffee-Ring Reader | 量化點樣（咖啡環）盤：格點逐格取面積／強度、除以同列 blank（第 0 欄）、平均重複列 → 濃度／比值 表。 |
| Dose-Response Fit | 對任何 濃度＋訊號 表擬合 3 或 4 參數邏輯曲線，回報 EC50、LOD、R²，並把擬合曲線疊在資料點上。 |
輸出都是一般的 Orange Table，可直接接 Data Table、散佈圖、PCA 或分類 widget；
Dose-Response Fit 接受兩個 reader 的輸出，也吃你已在試算表裡的比色法數據。
Microplate Reader ─wells──┐
├──► Dose-Response Fit ──► EC50 / LOD / 曲線
Coffee-Ring Reader ─cells─┘ （也可接 PCA · PLS-DA · Data Table）
可在 Windows、macOS（Apple 晶片與 Intel）、Linux 執行——純 Python （numpy / scipy / matplotlib / Pillow / Orange3）。
A. 桌面版 App：Options ▸ Add-ons… ▸ Add more… 輸入 orange-assay →
打勾 → OK → 重啟。
B. pip 版 Orange：
pip install orange-assay
python -m Orange.canvas重啟後工具箱會出現 Assay 分類（3 個 widgets）。每個 widget 都有 「ℹ 說明」盒子，按 F1 會開線上說明頁。教學網頁：https://tai-shengyeh.github.io/spectraview/assay.html（English）。
繁體中文 Windows 注意： 若 Add-ons 對話框出現
UnicodeDecodeError: 'cp950' codec…崩潰，改用命令列安裝："…\Programs\Orange\python.exe" -m pip install orange-assay，再重開 Orange （或設環境變數PYTHONUTF8=1）。
- Microplate Reader → 選盤面照片 → 選規格（如 96）→ Wells 輸出 → Data Table。
- 或 Coffee-Ring Reader → 選點樣盤照片 → 設列×欄（第 0 欄＝blank）→ 填濃度 → Cells 輸出。
- 任一輸出接 Dose-Response Fit → 選濃度與訊號欄 → 讀 EC50／LOD／R² 與曲線。
python tests/test_core.py # 純邏輯
QT_QPA_PLATFORM=offscreen python tests/test_widgets.py # widget 煙霧測試MIT。