chemparseplot: Chemical Parsers and Plotters#

chemparseplot logo
Parse

Structured extractors for ORCA, eOn, Sella, and ChemGP outputs.

Quickstart
Project

RMSD reaction-valley coordinates and unit-aware energy scales.

Features
Plot

Publication-ready profiles, landscapes, and structure strips.

Tutorial Introduction

About#

A pure-python parsing and plotting library for computational chemistry outputs. chemparseplot extracts structured data from quantum chemistry codes (ORCA, eOn, Sella, ChemGP) and produces publication-quality, unit-aware visualizations with scientific color maps.

Computational tasks (surface fitting, interpolation, structure analysis) are delegated to rgpycrumbs, a required dependency for landscape GPs. Input files for the same engines are written by pychum. The header Ecosystem menu jumps between the three Shibuya sites.

Suite stack#

How parsers, plot helpers, and the CLI suite relate:

        flowchart TB
  subgraph engines["Engines"]
    EON[eOn]
    ORCA[ORCA]
    SELLA[Sella]
  end
  subgraph cpp["chemparseplot"]
    PARSE[parse.*]
    PLOT[plot.neb / plot.optimization]
    SFC[SurfaceFitConfig]
  end
  subgraph rgp["rgpycrumbs"]
    SURF[surfaces GP]
    CLI[eon plt-neb / plt-min]
    TOML["plot.toml --config"]
  end
  subgraph pch["pychum"]
    INP[ORCA / NWChem inputs]
  end
  INP --> engines
  engines --> PARSE
  PARSE --> PLOT
  PLOT --> SURF
  SFC --> PLOT
  TOML --> CLI
  CLI --> PLOT
  CLI --> SFC

Tip

Dense landscape fits: keep auto_thin off by default; opt in via SurfaceFitConfig or rgpycrumbs plot TOML keys auto_thin / max_surface_points.

Documentation structure#

This documentation follows the Diataxis framework.

Getting Started

Guides

How-to

Explanation

Reference guides

Reference

Development

License#

MIT. Cite via the Zenodo DOI and the wailord paper for ORCA usage.

#+begin_export rst Indices and tables ================

:ref:`genindex`#

:ref:`modindex`#