Remove desktop app — skill-only figure extraction - #1
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…igitizing Adds the bridge from a professional-literature PDF to the figures inside it, so the skill can extract data from charts in papers, not just standalone images: - pdf.py: PdfDocument (pypdfium2 render, bitmap-figure detection, high-res crop, caption harvesting to label figures) + scan_figures() → crops every figure to PNG and writes a small manifest.json. Vector-drawn charts (no bitmap) surface under vector_pages with a full-page render to crop visually. pypdfium2 is optional and lazy-loaded, so the package still imports with only cv2+numpy. - export.py: export_figures() gathers several figures' extracted CSVs into one multi-sheet Excel workbook + a provenance sheet (CSV stays the primary output). - CLI: pdf-figures, pdf-page, xlsx subcommands; self-test reports the optional dep. - tests: test_pdf / test_export (51 pass); sample PDF + scatter assets. Scope is deliberately figures-only: no table extraction, no text mining. The Python API is purely additive and backward-compatible. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
… calibration Shrinks the manual work in digitizing a figure: the engine finds the geometry, Claude supplies only the semantics it reads by vision. - detect_ticks(): locate tick-mark pixel positions along each axis (spine-aware — ticks are the runs protruding beyond the axis line; longest-stroke group on a bare border, which also drops log/minor ticks). Reports spacing_cv as a confidence signal; returns None when a chart has no tick marks so the caller falls back to the grid overlay. Validated on synthetic linear (6/6 within 0.9px) and log-y (4/4 decades) charts. - suggest_series(): dominant colors + connected-component geometry to ready-to-use HSV targets tagged markers/line/region (fill-ratio + size + span heuristics), with near-identical k-means clusters merged so each real series appears once. - draw_geometry_overlay() + a "geometry" CLI command: one call emits the geometry JSON and an annotated PNG for Claude to eyeball before calibrating. - self-test now also asserts tick recovery. detect_ticks is best-effort by contract: precise on charts with real ticks, silent (None) otherwise — never silently wrong. 56 tests pass. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The desktop app demanded too much manual operation for what Claude Code already does natively (read a figure, reason, converse). FigDataX is now a single agentic skill: the engine measures geometry, Claude supplies the semantics. The app source stays fully recoverable at tag app-v0.2.0 (git checkout app-v0.2.0 -- app/). - Remove app/ (PySide6 GUI, py2app packaging, AI provider layer). - CI collapses to one job: skill pytest + self-test; trigger on skill-v* tags. - .gitignore drops the app-specific venv/build/dmg entries. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
… image The user crops/screenshots figures from papers themselves (single or multiple images); the skill's job is precise extraction from those images. Removes pdf.py, the pdf-figures/pdf-page CLI commands, the pypdfium2 dependency, and their tests. Excel export (xlsx command, export_figures) stays — it gathers per-figure CSVs into one workbook regardless of where the images came from. Also fixes a detect_ticks UnboundLocalError on spine-style axes that CI caught (stroke_len now computed in both regimes). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Removes app/ (recoverable at tag app-v0.2.0), collapses CI to a single skill test+self-test job, trims .gitignore. Part of the pivot to a lean agentic figure-extraction skill.
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