About

A pure-python¹ 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, structure analysis, interpolation) are handled by rgpycrumbs, which is a required dependency. chemparseplot parses output files, delegates heavy computation to rgpycrumbs, and produces publication-quality plots.

Installation

pip install chemparseplot

Suite config (shared with rgpycrumbs — no ~/.config/chemparseplot):

# ~/.config/rgpkgs/config.toml  or  ./rgpkgs.toml
# [pins] lock = "uv.lock"
# [pins.packages] …

Feature extras (plot, neb, …) are transitional install convenience. Target is the same on-demand uv / ensure_import design as the rgpycrumbs hub; prefer bare install + suite pins for new work.

# transitional:
# With plotting support
pip install "chemparseplot[plot]"
# Everything
pip install "chemparseplot[all]"

For development:

git clone https://github.com/HaoZeke/chemparseplot
cd chemparseplot
uv sync --all-extras

See the installation guide and quickstart for details.

Ecosystem Overview

chemparseplot is part of the rgpycrumbs suite of interlinked libraries.

Features

Parsing computational chemistry output files into structured data
Plotting with scientific color maps (camera-ready)
Unit preserving throughout via pint
Computation delegated to rgpycrumbs for surface fitting, interpolation, and structure analysis
NEB stitching (stitch_neb_segments) for continuous multi-segment bands (v1.8+)
Metadata-native eOn CON frames and typed parser results for NEB and single-ended tools
Unit-aware plot helpers (convert_neb_values, shared strip rendering) used by rgpycrumbs CLIs

Supported Engines

ORCA (5.x)
- Geometry scan (OPT) energy profiles
- Nudged elastic band (NEB) path visualization
eOn
- Saddle search parsing (Dimer, GPRD, LBFGS methods)
- NEB path energy profiles with landscape projections
Sella
- Saddle point optimization result parsing
Trajectory formats
- HDF5 trajectories (ChemGP output with pre-computed forces)
- Generic ASE-readable formats (extxyz, .traj) for NEB analysis

Documentation

Full documentation is at https://chemparseplot.rgoswami.me. This includes:

A quickstart guide
Tutorials for common workflows
API reference

Contributing

Contributions are welcome. See CONTRIBUTING.md for development setup and guidelines, and our Code of Conduct.

For bug reports or questions, open an issue on GitHub.

License

MIT. However, this is an academic resource, so please cite as much as possible via:

The Zenodo DOI for general use.
The wailord paper for ORCA usage

Acknowledgments

This project builds on work supported by the University of Iceland and the Icelandic Research Fund. chemparseplot relies on rgpycrumbs for computational modules.

Footnotes

¹ To distinguish it from my other thin-python wrapper projects

Metadata

Release files for chemparseplot 1.11.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for chemparseplot 1.11.0
File	Size	Uploaded
chemparseplot-1.11.0.tar.gz	155.2 kB	Aug 10, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for chemparseplot 1.11.0
File	Interpreter	ABI	Platform	Reset
chemparseplot-1.11.0-py3-none-any.whl 200.8 kB Aug 10, 2026	Python 3	none	any	Details

Total release size: 356.0 kB

Release files / chemparseplot-1.11.0.tar.gz

Download URL	chemparseplot-1.11.0.tar.gz
Size	155.2 kB
Tags	Source
SHA-256 checksum How to use checksums	`d14de2098cc8d0032ce5043ee3b8af67e4391d87a2b222012ad3730bde4fac7d`
BLAKE2b-256 checksum How to use checksums	`38b05c5650a35f503546cab161099499205702887e4e48fb7721d17a3c214570`
Upload date	Aug 10, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/HaoZeke/chemparseplot
Publishing commit: github.com/HaoZeke/chemparseplot/tree/439da3ccb61f395d59d463ddee71cfd2743adcd8

Workflow

Publishing configuration: github.com/HaoZeke/chemparseplot/blob/439da3ccb61f395d59d463ddee71cfd2743adcd8/.github/workflows/release.yml
Publishing logs: github.com/HaoZeke/chemparseplot/actions/runs/31377120647/attempts/1

Artifact

Subject: chemparseplot-1.11.0.tar.gz
SHA-256 checksum: d14de2098cc8d0032ce5043ee3b8af67e4391d87a2b222012ad3730bde4fac7d Verifying attestations

Release files / chemparseplot-1.11.0-py3-none-any.whl

Download URL	chemparseplot-1.11.0-py3-none-any.whl
Size	200.8 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`583a03d43fb95be499e325b774a85a490c2ade7e686fae630db75308f3b7b5c5`
BLAKE2b-256 checksum How to use checksums	`84b4d15567e78f1027a64d4469b19cabf86d9be642933169d38f0cbdaf3b4953`
Upload date	Aug 10, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/HaoZeke/chemparseplot
Publishing commit: github.com/HaoZeke/chemparseplot/tree/439da3ccb61f395d59d463ddee71cfd2743adcd8

Workflow

Publishing configuration: github.com/HaoZeke/chemparseplot/blob/439da3ccb61f395d59d463ddee71cfd2743adcd8/.github/workflows/release.yml
Publishing logs: github.com/HaoZeke/chemparseplot/actions/runs/31377120647/attempts/1

Artifact

Subject: chemparseplot-1.11.0-py3-none-any.whl
SHA-256 checksum: 583a03d43fb95be499e325b774a85a490c2ade7e686fae630db75308f3b7b5c5 Verifying attestations