blackfish 0.2.5

Various ORCA 6 tools

Blackfish 🐋

Blackfish is a Python library for parsing and visualizing ORCA quantum chemistry output files. It provides an easy-to-use interface for extracting spectral data and generating publication-quality plots using Altair.

Features

• Parse ORCA output files for:
  • IR spectra
  • Spin-orbit coupling (SOC) states
  • SOC absorption spectra
  • Non-adiabatic coupling matrix elements (NACME)
  • Electronic excited states
• Generate interactive visualizations:
  • IR spectra plots
  • SOC absorption spectra with Gaussian broadening
  • Peak detection and labeling
• Interactive UI components using Marimo for spectrum analysis

Installation

pip install blackfish

Quick Start

from blackfish import ORCA

# Load ORCA output file
orca = ORCA("my_calculation.out")

# Access different types of data as Polars DataFrames
ir_data = orca.ir_spectrum
soc_data = orca.soc_states
nacme_data = orca.nacme
roots_data = orca.roots
absorption_data = orca.soc_absorption_spectrum

# Get SOC absorption spectrum with Gaussian broadening
soc_chart = orca.soc_absorption_spectrum_chart(
    fwhm=2000,       # Gaussian broadening FWHM
    peaks=True,      # Show peak markers
    peak_threshold=0.3  # Minimum peak threshold
)

Examples

IR Spectrum Analysis

# Get IR spectrum data
ir_data = orca.ir_spectrum

# View strongest vibrational modes
strongest_modes = ir_data.filter(pl.col("rel_intensity") > 0.5)
print(strongest_modes)

# Get frequencies above 3000 cm⁻¹
high_freq = ir_data.filter(pl.col("frequency_cm") > 3000)
print(high_freq)

Electronic State Analysis

# Get excited state data
roots = orca.roots

# Group transitions by multiplicity
by_mult = roots.group_by("mult").agg([
    pl.count(),
    pl.mean("energy_cm").alias("avg_energy")
])

SOC States Analysis

# Get SOC states
soc = orca.soc_states

# Get contributions to specific SOC state
state1 = soc.filter(pl.col("state") == 1)

# Find states with large spin mixing
mixed_states = soc.filter(pl.col("weight") > 0.2)

# Summarize spin components per state
spin_summary = soc.group_by("state").agg([
    pl.n_unique("spin").alias("n_spin_components"),
    pl.max("weight").alias("max_contribution")
])

NACME Analysis

# Get non-adiabatic coupling elements
nacme = orca.nacme

# Find atoms with strong coupling
strong_coupling = nacme.filter(pl.col("magnitude") > 0.1)

# Get coupling vectors for specific atoms
h_atoms = nacme.filter(pl.col("symbol") == "H")

# Sort atoms by coupling magnitude
sorted_coupling = nacme.sort("magnitude", descending=True)

SOC Absorption Spectrum Analysis

# Get absorption spectrum
abs_spec = orca.soc_absorption_spectrum

# Find intense transitions
intense = abs_spec.filter(pl.col("rel_intensity") > 0.5)

# Get visible region transitions
visible = abs_spec.filter(
    (pl.col("wavelength_nm") >= 380) &
    (pl.col("wavelength_nm") <= 700)
)

# Summarize by spin multiplicity
by_mult = abs_spec.group_by("mult").agg([
    pl.count(),
    pl.mean("energy_ev").alias("avg_energy"),
    pl.max("osc_strength").alias("max_intensity")
])

Data Structure

Blackfish uses Polars DataFrames for efficient data handling. Common DataFrame schemas include:

IR Spectrum

• mode: Vibrational mode number
• frequency_cm: Frequency in cm⁻¹
• intensity: IR intensity
• rel_intensity: Normalized intensity
• tx/ty/tz: Transition dipole components

Excited States (Roots)

• root: State number
• mult: Spin multiplicity
• donor: Donor orbital
• acceptor: Acceptor orbital
• weight: Configuration weight
• energy_cm: Energy in cm⁻¹

SOC States

• state: SOC state number
• spin: Spin component
• root: Contributing root state
• weight: State contribution weight
• energy_cm: Energy in cm⁻¹

NACME

• id: Atom index
• symbol: Atomic symbol
• x/y/z: Coupling vector components
• magnitude: Total coupling magnitude

SOC Absorption Spectrum

• state: Final state number
• mult: State multiplicity
• energy_ev: Transition energy in eV
• energy_cm: Energy in cm⁻¹
• wavelength_nm: Wavelength in nm
• osc_strength: Oscillator strength
• rel_intensity: Normalized intensity

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the Apache2.0 License - see the LICENSE file for details.