GoodVibes module

class goodvibes.GoodVibes.Logger(filein, append, csv)

Bases: object

Enables output to terminal and to text file.

Writes GV output to .dat or .csv files.

csv

decides if comma separated value file is written.

Type:bool

log

file to write GV output to.

Type:file object

thermodata

decides if string passed to logger is thermochemical data, needing to be separated by commas

Type:bool

fatal(message)

finalize()

write(message, thermodata=False)

goodvibes.GoodVibes.add_time(tm, cpu)

Calculate elapsed time.

goodvibes.GoodVibes.all_same(items)

Returns bool for checking if all items in a list are the same.

goodvibes.GoodVibes.check_dup(files, thermo_data)

Check for duplicate species from among all files based on energy, rotational constants and frequencies

Energy cutoff = 1 microHartree RMS Rotational Constant cutoff = 1kHz RMS Freq cutoff = 10 wavenumbers

goodvibes.GoodVibes.check_files(log, files, thermo_data, options, STARS, l_o_t, solvation_model, orientation, grid)

Perform checks for consistency in calculation output files for computational projects

Check for consistency in: Gaussian version, solvation state/gas phase, level of theory/basis set, charge and multiplicity, standard concentration, potential linear molecule errors, transition state verification, empirical dispersion models

goodvibes.GoodVibes.get_boltz(files, thermo_data, clustering, clusters, temperature, dup_list)

Obtain Boltzmann factors, Boltzmann sums, and weighted free energy values.

Used for selectivity and boltzmann requested options.

Parameters: files (list): list of files to find Boltzmann factors for. thermo_data (dict): dict of calc_bbe objects with thermodynamic data to use for Boltzmann averaging. clustering (bool): flag for file clustering clusters (list): definitions for the requested clusters temperature (float): temperature to compute Boltzmann populations at dup_list (list): list of potential duplicates

Returns:boltz_facs, weighted_free_energy, boltz_sum dict: dictionary of files with corresponding Boltzmann factors. dict: dictionary of files with corresponding weighted Gibbs free energy. float: Boltzmann sum computed from Boltzmann factors and Gibbs free energy.

goodvibes.GoodVibes.get_selectivity(pattern, files, boltz_facs, boltz_sum, temperature, log, dup_list)

Calculate selectivity as enantioselectivity/diastereomeric ratio.

Parameters: pattern (str): pattern to recognize for selectivity calculation, i.e. “R”:”S”. files (str): files to use for selectivity calculation. boltz_facs (dict): dictionary of Boltzmann factors for each file used in the calculation. boltz_sum (float) temperature (float)

Returns: float: enantiomeric/diasteriomeric ratio. str: pattern used to identify ratio. float: Gibbs free energy barrier. bool: flag for failed selectivity calculation. str: preferred enantiomer/diastereomer configuration.

goodvibes.GoodVibes.main()

goodvibes.GoodVibes.print_check_fails(log, check_attribute, file, attribute, option2=False)

Function for printing checks to the terminal

goodvibes.media

goodvibes.vib_scale_factors

class goodvibes.vib_scale_factors.ScalingData(level_basis, zpe_fac, zpe_ref, zpe_meth, harm_fac, harm_ref, harm_meth, fund_fac, fund_ref, fund_meth)

Bases: tuple

fund_fac

Alias for field number 7

fund_meth

Alias for field number 9

fund_ref

Alias for field number 8

harm_fac

Alias for field number 4

harm_meth

Alias for field number 6

harm_ref

Alias for field number 5

level_basis

Alias for field number 0

zpe_fac

Alias for field number 1

zpe_meth

Alias for field number 3

zpe_ref

Alias for field number 2

Module contents