Faster, more reliable first-time setup: drop unused TF/StarDist, pin deps; restore folder-table sorting#2
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Nothing in the pipeline imports stardist or tensorflow, and they were the main cause of slow (~5-10 min, ~1 GB) and failure-prone first-time setup. Mac launcher no longer forces Python 3.11 (a TensorFlow constraint) and now accepts any Python 3.10+; Windows launchers auto-detect 3.12/3.13. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Re-implements the column sorting (click header to toggle asc/desc with arrow indicator) that existed in Arda's local copy, adapted to the new XSS-safe textContent row rendering. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Unpinned installs meant each computer resolved different library versions - a major source of machine-to-machine differences. Versions pinned from the tested working environment (Python 3.12.5, macOS). Launchers now target Python 3.10-3.12 to match the pinned numpy. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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What changed
Three setup/reliability improvements, one commit each:
Remove unused
tensorflow+stardistdependencies — nothing in the current pipeline (K-means + distance-transform watershed) imports them (as your own comment in requirements.txt noted). They were the main reason first-time setup took ~5-10 min / ~1 GB and failed outright on Python versions without TensorFlow wheels. The Mac launcher's TensorFlow install/verify machinery and the Python 3.11 pin (a TensorFlow constraint) are removed with them; docs updated to match.Restore the sortable batch ledger on the folder-analysis page — click a column header to sort asc/desc with an arrow indicator. This existed in Arda's local copy but predated the fork; re-implemented on top of the XSS-safe
textContentrow rendering rather than copied verbatim.Pin exact dependency versions (Python 3.10-3.12) — unpinned installs meant each machine resolved different library versions, a repeated source of "works on my computer" issues (e.g.
cv2.RotatedRectneeds newer OpenCV builds). Pinned from a tested working env (Python 3.12.5, macOS). Launchers now target 3.10-3.12 to match the pinned numpy 1.26.Why
First-time setup on new lab computers (both macOS and Windows) was slow and occasionally failed; these were the root causes we could identify.
Notes for review
.dm3-calibrated image ran in ~3 s with linked-metadata calibration (schema 6).🤖 Generated with Claude Code