Give to AI Formulas from Charts
Plotparse is a deterministic software tool that extracts charts from PDF files and recovers the analytical formulas of their curves without using neural networks. The system processes both vector graphics and raster images to calibrate axes and fit data to one of eleven mathematical models. It prioritizes model parsimony and reproducibility, ensuring that every output value can be traced back to specific geometric features of the document.
- ▪The tool uses a deterministic pipeline that avoids neural networks to ensure every output number is traceable to specific geometric features.
- ▪It supports both vector PDF graphics for high accuracy and raster scans using morphological opening and Tesseract for label recognition.
- ▪Model selection relies on parsimony and AICc criteria rather than maximum R-squared to prevent overfitting with high-degree polynomials.
- ▪The system detects axis scales, including logarithmic ones, to correctly interpret linear trends that may represent exponential relationships.
- ▪Curve labels are assigned using legend swatches or proximity rules to ensure accurate one-to-one matching between text and data series.
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Story provenance
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/BorisYamp/plotparse |
| Publication time | Sun, 13 Sep 2026 09:52:22 +0000 |
| Retrieval time | 2026-09-13T10:11:50.705Z |
| Last seen | 2026-09-13T10:11:50.705Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | RBPgOx1Re4-8 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
plotparse Finds charts in PDF files and recovers the analytical formula of every curve on them. No neural networks anywhere: the whole pipeline is deterministic, reproducible and explainable — every number in the output can be traced back to a specific geometric feature of the page. $ analyze_pdf paper.pdf Page 1 — source: vector PDF graphics Chart detected, confidence 0.96. X axis: "X", linear scale, range 0…10, 6 ticks, calibration R² 1.0000 Y axis: "Y", linear scale, range 0…50, 6 ticks, calibration R² 1.0000 Series 1 "linear A" (line, blue, 200 points), X ∈ [0; 10], Y ∈ [0.9868; 20.99] FORMULA: y = 2·x + 0.9868 model "linear", R² = 1.00000, RMSE = 5.774e-13, 2 params Series 2 "quad B" (line, red, 200 points), X ∈ [0; 10], Y ∈ [-0.01318; 49.99] FORMULA: y = 0.5·x^2 + 0.0002635·x -…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.