blackfish 0.3.6

ORCA quantum chemistry data parser

Blackfish 🐋

Installation

pip install blackfish

Quick Start

from blackfish import OrcaParser

# Load the ORCA output file
parser = OrcaParser("my_calculation.out")

# Data can then be parsed as polars DataFrames
ir_spectrum = parser.get_ir_spectrum()
nacme = parser.get_nacme()
tddft_roots = parser.get_tddft_roots()
tddft_soc_spectrum = parser.get_tddft_soc_spectrum()
soc_states = parser.get_soc_states()
socme = parser.get_socme()

# Energy values are stored in an Energies object.
# If a value can't be parsed, it returns None
energies = parser.get_energies()
energies.fspe                 # Hartree
energies.fspe(unit="kj/mol")  # kJ/mol
energies.gibbs_free_energy
energies.gibbs_minus_electronic_energy
energies.entropy
energies.enthalpy
energies.zero_point_energy

# or simply convert energies
energy_in_kjmol = OrcaParser.convert_energy(energy_in_kcalmol, from_unit="kcal/mol", to_unit="kj/mol")

# Available energy units
# - hartree
# - ev
# - 1/cm
# - kj/mol
# - kcal/mol

Examples

IR Spectrum Analysis

# Get IR spectrum data
ir_spectrum = orca.get_ir_spectrum()

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

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

Electronic State Analysis

# Get excited state data
tddft_roots = orca.get_tddft_roots()

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

SOC States Analysis

# Get SOC states
soc_states = orca.get_soc_states()

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

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

# Summarize spin components per state
spin_summary = soc_states.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.get_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)

TDDFT SOC Spectrum Analysis

# Get absorption spectrum
tddft_soc_spectrum = orca.get_tddft_soc_spectrum()

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

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

# Summarize by spin multiplicity
by_mult = tddft_soc_spectrum.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

License

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