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Add a first-reconstruction tutorial and agent skills - #569
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Interactive CLI walkthrough for a first reconstruction: environment check, OME-Zarr conversion, config authoring, napari viewing, and parameter sweeps. Includes a QPI 2D-from-defocus worked example using the zenodo recOrder_session sample. Un-ignores .claude/skills/ so the skill ships with the repo.
talonchandler
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July 16, 2026 18:57
Two-call skill: `wo compute-tf` on a config + input position, then `wo view` on the resulting transfer function (napari, backgrounded).
talonchandler
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For 2D reconstructions, the transfer function is stored as a singular system (U, S, Vh). `wo view` now reconstructs H = U @ diag(S) @ Vh at each lateral frequency and displays the resulting transfer function(s) — absorption and phase for 2D phase, the optical TF for 2D fluorescence — instead of the raw singular vectors. 3D display is unchanged.
talonchandler
marked this pull request as draft
July 31, 2026 22:06
Relocate skill content from .claude/skills/ to .skills/ as the single canonical, harness-agnostic source of truth. Add symlinks so existing discovery paths still resolve to it: - .claude/skills -> ../.skills (Claude Code project skill scan path) - .agents/skills -> ../.skills (Codex CLI/ChatGPT skill scan path) Update .gitignore: .claude/* still ignores session state, but !.claude/skills un-ignores the symlink so it's tracked. No content changes; checksums of all 8 skill files are unchanged.
…torial - Rewrite waveorder-tutorial SKILL.md around the staged onboarding workflow: suitability, data/metadata identification, ROI selection, draft reconstructions, manual sweeps, auto-tuning, and scaling - Add scripts/crop_roi.py for ROI cropping and extend scripts/sweep.py with two-parameter grid sweeps shown on two napari sliders - Add iohub convert route and ROI cropping to conversion reference; add optimization block, RL/RLGC, and expanded symptom table to config reference - Add human-readable tutorial at docs/guide/first-reconstruction.md and link it with the skill at the top of the README - Remove all em dashes; move imports to module top in tutorial scripts
…asets Two reconstructions of the same channel previously produced identically named layers, so users could not tell them apart in the layer list.
Gradient methods are local (travel ~lr per iteration); grid_search uses lr as the grid step centered on init. Add the coarse-to-fine recipe to the skill and config reference.
talonchandler
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talonchandler
marked this pull request as ready for review
September 2, 2026 21:32
srivarra
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Sep 3, 2026
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This was super cool!
Just a few small suggestions.
Start drafts with a small regularization_strength, sweep forward model parameters for the fewest artifacts (least ringing, sharpest result), then increase regularization until acceptable as the final step.
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Prefixing with waveorder- makes it easier to filter waveorder-specific skills, per review.
The tutorial scripts are now self-contained: uv run resolves their dependencies without a prebuilt environment. Docs and docstrings invoke them with uv run.
--y-pixel-size and --x-pixel-size override the isotropic --yx-pixel-size shorthand.
run_in_background is specific to one agent runtime. State the requirement instead (non-blocking, no interactive stdin, confirm the process stays up) with a POSIX fallback.
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The confocal pinhole is modeled only for 3D fluorescence reconstructions (2D raises NotImplementedError); its diameter is given in demagnified image space in micrometers. Consolidate this with the RL/RLGC restriction in the config reference, skill, and guide.
srivarra
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Oct 14, 2026
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Looks good! Just a couple of nitpicks.
The script does not read OME-XML metadata, so omitted flags produced a default scale with no warning; the doc also claimed metadata was read.
--values null previously stayed the string 'null' and crashed e.g. a confocal_pinhole_diameter sweep. Numeric casts stay first because PyYAML parses dotless floats like 1e-4 as strings.
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@srivarra, I think this is ready. Feel free to modify and/or merge. Thanks! |
srivarra
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Oct 16, 2026
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Adds an onboarding path for first-time waveorder users: a written tutorial, an agentic skill that walks the same workflow interactively, and a small skill for inspecting transfer functions.
docs/guide/first-reconstruction.md: a six-stage guide from raw data to a tuned reconstruction: is waveorder right for you, what data and metadata do you have, draft reconstructions, manual sweeps, automatic tuning, and scaling up. Linked at the top of the README..skills/waveorder-tutorial: the agentic companion to the guide. The agent asks the stage questions, converts data to OME-Zarr, writes draft configs, runs sweeps, and shows everything in napari. Includes reference docs and scripts (example data fetch, TIFF/array converter, ROI cropper, and a 1D/2D sweep helper)..skills/waveorder-visualize-otf: computes a transfer function from a config and opens it in napari.How to trial: check out this branch, open Claude Code in the repo root, and run
/waveorder-tutorial. With no data of your own it fetches a small sample and reconstructs 2D phase from a defocus stack; bring your own TIFF/zarr/array for a complete test.