Frequently Asked Questions #

Common questions about chemparseplot with answers and solutions.

Installation #

How do I install chemparseplot?#

# Basic installation
pip install chemparseplot

# With plotting dependencies
pip install "chemparseplot[plot]"

# With all optional dependencies
pip install "chemparseplot[all]"

See: Installation Guide

How do I install with conda/pixi?#

# Using pixi
pixi add chemparseplot

# Using conda (via conda-forge)
conda install -c conda-forge chemparseplot

I get “ModuleNotFoundError: No module named ‘chemparseplot’”#

Ensure you installed in the correct Python environment:

# Check Python path
python -c "import sys; print(sys.executable)"

# Install in correct environment
/path/to/python -m pip install chemparseplot

Usage #

How do I parse ORCA NEB output?#

from pathlib import Path
from chemparseplot.parse.orca.neb import parse_orca_neb

data = parse_orca_neb("job", working_dir=Path("calculation"))
print(f"Energies: {data['energies']}")
print(f"Barrier: {data['barrier_forward']:.2f} eV")

See: ORCA NEB Tutorial

How do I create an energy profile plot?#

from chemparseplot.plot.neb import plot_orca_neb_energy_profile

plot_orca_neb_energy_profile(data, "profile.pdf")

See: ORCA NEB Tutorial

How do I parse eOn NEB output?#

from chemparseplot.parse.eon.neb import aggregate_neb_landscape_data

data = aggregate_neb_landscape_data(dat_paths, con_paths, y_col=2)

See: Parse eOn NEB Calculations

Errors #

“ImportError: No module named ‘opi’”#

OPI (ORCA Python Interface) is required for ORCA 6.1+ parsing:

pip install orca-pi

Or use legacy parser for ORCA < 6.1:

from chemparseplot.parse.orca.neb import parse_orca_neb_fallback

data = parse_orca_neb_fallback("job", Path("calc"))

“RMSD coordinates required for landscape plot”#

Landscape plots require geometry output. Ensure your calculation includes:

%output
  Print[P_Molden] true
  Print[MOs] true
end

“Contour levels must be increasing”#

Energy data may have numerical issues. Check for:

Duplicate energy values
NaN or Inf values
Very small energy differences

Fix by filtering or smoothing data.

Performance #

How can I speed up batch plotting?#

Use parallel processing:

rgpycrumbs chemgp batch -c config.toml -j 4

The -j 4 flag uses 4 parallel workers.

Why is landscape plotting slow?#

Surface fitting is computationally expensive. Options:

Use fewer points (downsample)
Opt-in auto_thin / SurfaceFitConfig (default off) to cap fit size
Use simpler interpolation method (RBF instead of GP)
Use Nystrom approximation for large datasets

How do I thin dense minimization movies without changing defaults?#

Use SurfaceFitConfig (TOML key names match rgpycrumbs plot config):

from chemparseplot.plot.neb import SurfaceFitConfig

cfg = SurfaceFitConfig.from_mapping({"auto_thin": True, "max_surface_points": 64})

Default auto_thin is False so existing scripts keep full-cloud fits.

Compatibility #

Which ORCA versions are supported?#

ORCA 6.1+: Full support via OPI
ORCA < 6.1: Limited support via .interp file parsing

Which Python versions are supported?#

Python 3.10, 3.11, 3.12

Does chemparseplot work on Windows?#

Yes, but some features may have limited support:

Plotting works on all platforms
OPI requires ORCA 6.1+ (Windows available)
Some parallel features work best on Linux/macOS

Development #

How do I contribute?#

See: Contributing Guidelines

How do I report a bug?#

Create an issue on GitHub: GitHub Issues

Include:

chemparseplot version
Python version
ORCA/eOn version
Minimal reproducible example
Error message

How do I request a feature?#

Create an issue on GitHub with:

Feature description
Use case
Example input/output
Priority (nice-to-have vs critical)