JarvisPLOT 1.3.11

Jarvis-PLOT: YAML-driven plotting engine

Jarvis-PLOT

Jarvis-PLOT is a lightweight, Python/Matplotlib-based plotting framework developed for Jarvis-HEP,
but it can also be used as a standalone scientific plotting tool.

It provides a simple command-line interface (CLI) to generate publication-quality figures from YAML configuration files, with most layout and style decisions handled by predefined profiles and style cards.

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Installation

pip install Jarvis-PLOT

The PyPI distribution name is now Jarvis-PLOT. The Python import package and entrypoint remain unchanged:

import jarvisplot

jplot path/to/config.yaml

If you have an older environment that still uses the historical package name, replace:

pip uninstall jarvisplot
pip install Jarvis-PLOT

Command-Line Usage

Display help information:

jplot -h

Run Jarvis-PLOT with one or more YAML configuration files:

jplot path/to/config.yaml

Rebuild local cache for the current project workdir:

jplot path/to/config.yaml --rebuild-cache

Project Workdir and Cache

• You can set project.workdir in YAML.
• If output.dir is omitted, Jarvis-PLOT defaults to <workdir>/plots/.
• Data cache is stored in <workdir>/.cache/.
• Profiling pipelines are prebuilt once and reused from cache when source fingerprint and profile settings are unchanged.
• Profiling uses a fast two-stage grid reduction (pregrid + render bin) for large datasets.

Example: SUSYRun2 Ternary Plots

jplot ./bin/SUSYRun2_EWMSSM.yaml
jplot ./bin/SUSYRun2_GEWMSSM.yaml

Note: The data file paths inside the YAML files must be updated to match your local setup.

Example: Dynesty Runplot

Jarvis-PLOT includes a reusable dynesty runplot format. With a dataset named dynesty, the figure can use the built-in card without writing axes or layer details:

DataSet:
- name: dynesty
  path: path/to/dynesty_result.csv
  type: csv

Figures:
- name: dynesty_logL_vs_logX
  enable: true
  style:
  - a4paper_2x1
  - dynesty_runplot

See docs/specs/DYNESTY_RUNPLOT.md for the default axes, KDE, scatter overlay, and evidence summary behavior.

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Notes

• Figures are saved automatically to the output paths defined in the YAML configuration.
• Common output formats include PNG and PDF (backend-dependent).
• Saved figures include file metadata such as Creator: Jarvis-PLOT, powered by Jarvis-HEP and Jarvis-PLOT version: X.Y.Z; the PNG Description also includes both fields for macOS Finder compatibility.
• Jarvis-PLOT works in headless environments (SSH, batch jobs) without any GUI backend.

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Requirements

Python

• Python ≥ 3.10 (tested on 3.10–3.13)

Required Packages

• numpy
• pandas
• matplotlib
• pyyaml
• jsonschema
• scipy — numerical utilities
• h5py — required for loading HDF5 data files
• shapely
• scipy
• sympy

Github Page

https://github.com/Pengxuan-Zhu-Phys/Jarvis-PLOT

Documentation

https://pengxuan-zhu-phys.github.io/Jarvis-Docs/

Repository Docs

Tracked project docs live in docs/.

• docs/README.md - repo doc index
• docs/context/JARVIS_PLOT_CONTEXT.md - primary Codex-facing boundary doc
• docs/context/CODE_MAP_JARVIS_PLOT.md - concrete code owner map
• docs/context/JARVIS_PLOT_FRAMEWORK_LOGIC.md - runtime execution contract
• docs/roadmap/IMPLEMENTATION_ROADMAP.md - active backlog and future work list
• docs/dev/DEVELOPER_RULES.md - current pipeline and cache rules
• docs/dev/MEMORY_OPTIMIZATION_GUIDE.md - narrow-table memory notes

Read the context docs and roadmap before changing parsing, transforms, rendering, or layout-related behavior.

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License

MIT License