wailord.io.orca module

An ad-hoc treatment of orca output files

This should implement a grammar, but currently consists of a number of utility structures and functions to parse data from the orca output format

Example

See the tests for more

$ poetry run

Some more details.

Todo

• Make grammar

• Make classes

• Test the experiments more

• Pass a list of experiments to ignore

• Test the ordering of variables

• Make classes for the order

• Handle split jobs

• Propagate exceptions instead of passing the buck with warnings

• Setup proper logging

• Scrape NIST Web book for spectra, properties https://webbook.nist.gov/cgi/cbook.cgi?ID=C71432&Mask=800#Electronic-Spec

• Document more things (e.g. SP -> Single point calculation a.k.a. energy calculation)

• Return interesting things

wailord.io.orca.calc_htst(product, reactant, transition_state, temperature)

Calculates the HTST rate constant.

This is calculated as:

\[k^{HTST} =\]

rac{∏_{i=1 … N}𝜈ᵢ}{∏_{j=1 … N-1}𝜈ⱼ^‡}e^{ rac{-𝛥E}{k_BT}}

Where * \(𝜈ᵢ\) are the final/initial state vibrational frequencies and

\(𝜈ⱼ^‡\) are the vibrational frequencies at the saddle point

• \(𝛥E\) is the difference between the energy of the state and the saddle point \(𝛥E=V_{saddle}-V_{other}\)

It is important to note that the \(k_f\) and \(k_b\) are relative measures and the rate constant is the ratio of the two. In some cases it might be necessary to weigh the results by the magnitude of ‘c’

Args:

product(orcaVis): The product visitor class reactant(orcaVis): The reactant visitor class transition_state(orcaVis): The transition state visitor class temperature(float): The temperature

Returns:

kout(rateconst) : Returns a named tuple of forward and backward rates

wailord.io.orca.genEBASet(rootdir, deci=3, latex=False, full=False, order_basis=['STO-3G', '3-21G', '6-31G', '6-311G', '6-311G*', '6-311G**', '6-311++G**', '6-311++G(2d,2p)', '6-311++G(2df,2pd)', '6-311++G(3df,3pd)'], order_theory=['HF', 'UHF', 'QCISD', 'CCSD', 'QCISD(T)', 'CCSD(T)'])

Takes in a Path object, and typically returns bond angles and energies. Optionally returns a TeX table or a full dataset with the filenames and geometries. Depreciate this eventually.

wailord.io.orca.getBA(dat, x, y, z, indi=[0, 1, 2])

Takes in a data frame of xyz coordinates and uses it to generate the plane angle, indices are used such that the first is the relative center

wailord.io.orca.getBL(dat, x, y, z, indi=[0, 1])

Takes in a data frame of xyz coordinates and uses it to calculate the bond length

wailord.io.orca.getRunInfo(runf)

Determines the runtime parameters from the output path

The implementation uses an ordered dictionary to ensure that the path fragments are matched to the correct keys.

Note

This will only work with wailord experiments at the moment

Parameters

run (Path) – Runtime output path

Returns

A simple unordered dictionary of paramters

Return type

runinf (dict)

wailord.io.orca.get_e(orcaoutdat, basis, system)

This takes in an orcaout data frame and spits out the energy

class wailord.io.orca.inpcart(atype, x, y, z)

Bases: tuple

atype

Alias for field number 0

x

Alias for field number 1

y

Alias for field number 2

z

Alias for field number 3

class wailord.io.orca.orcaExp(expfolder, deci=3, order_basis=['STO-3G', '3-21G', '6-31G', '6-311G', '6-311G*', '6-311G**', '6-311++G**', '6-311++G(2d,2p)', '6-311++G(2df,2pd)', '6-311++G(3df,3pd)'], order_theory=['HF', 'UHF', 'QCISD', 'CCSD', 'QCISD(T)', 'CCSD(T)'])

Bases: object

The class meant to handle experiments generated with wailord.

The general concept is that this is meant to work with the setup wailord generates. Remember to use df.round() for pretty printing!

get_energy_surface(etype=['Actual Energy', 'SCF Energy'])

Populates an energy surface dataframe

This essentially walks over the generated set of files, and fills out calls to the base orcaVis class.

Parameters

• etype (list of str, optional) – This is passed to the base

• class call (OrcaVis) –

Returns

Returns a data frame of etype energies

Return type

pd.DataFrame

get_final_sp_energy()

Returns a datframe of only the final single point energies

Proxies calls to the base orcaVis class over a series of generated files

Parameters

None –

Returns

Returns a data frame of final energies

Return type

pd.DataFrame

get_ir_spec()

Returns a datframe of the “ir spectrum”

Proxies calls to the base orcaVis class over a series of generated files

Parameters

None –

Returns

Returns a data frame of frequencies

Return type

pd.DataFrame

get_population(poptype=['Mulliken', 'Loewdin'], /)

Populates a population dataframe

This essentially walks over the generated set of files, and fills out calls to the base orcaVis class.

Parameters

• poptype (list of str, optional) – This is passed to the base

• class call (OrcaVis) –

Returns

Returns a data frame of etype energies

Return type

pd.DataFrame

get_vib_freq()

Returns a datframe of the vibrational modes

Proxies calls to the base orcaVis class over a series of generated files

Parameters

None –

Returns

Returns a data frame of frequencies

Return type

pd.DataFrame

get_vpt2_transitions()

Returns a datframe of the fundamental transitions table

Proxies calls to the base orcaVis class over a series of generated files

Parameters

None –

Returns

Returns a data frame of frequencies

Return type

pd.DataFrame

handle_exp(efol)

Populates the internal file variables from the path

Alert:

This is not meant to be called by the user!!!!

Parameters

efol (Path) – Output path

inpconf

Populated by handle_exp

orclist

Populated by handle_exp

visit_coord_block(node, visited_children)

Makes a dict of the section (as key) and the key/value pairs.

visit_meta(node, visited_children)

Returns the overall output.

class wailord.io.orca.orcaVis(ofile)

Bases: object

The class meant to handle ORCA output files.

Todo

• Add a grammar and recursive descent later

final_sp_e()

get_evals(ofile)

get_final_e(dat=False)

ir_spec()

Grabs the non-ZPE corrected IR Spectra and the dipole derivatives for intensities

mult_energy_surface(etype=['Actual Energy', 'MDCI', 'MDCI w/o Triples', 'SCF Energy'], npoints=None)

Multiple Energy surface dataframe generator

This is a helper function to obtain a dataframe which contains multiple energy surfaces. The implementation leverages the reduce function from functools to merge a list of dataframes generated from the single_energy_surface calls.

Parameters

• etype (str,optional) – The type of calculated energy surface to return. Defaults to [“Actual Energy”, “MDCI”, “MDCI w/o Triples”, “SCF Energy”] but can be any valid subset of the same.

• npoints (int,optional) – The number of points over which a scan has taken place. Defaults to the number of evaluations calculated in the output file.

Returns

Returns a data frame of bond_length and energies

Return type

pd.DataFrame

mult_population_analysis(poptype=['Mulliken', 'Loewdin'], /)

Multiple population analysis dataframe generator

This is a helper function to obtain a dataframe which contains multiple population analysis outputs. The implementation is similar to the energy surface helper.

Parameters

poptype (str,optional) – The type of calculated energy surface to return.

Returns

Returns a data frame of population analysis types

Return type

pd.DataFrame

single_energy_surface(etype='Actual Energy', npoints=None)

Single energy surface dataframe generator

For say, QCISD(T), this is essentially the same as a QCISD calculation.

Note

MDCI types are meant to work with single reference correlation methods

Parameters

• etype (str,optional) – The type of calculated energy surface to

• Defaults to 'Actual Energy' and can be any of `["Actual Energy" (return.) –

• "MDCI" –

• w/o Triples" ("MDCI) –

• Energy"]` ("SCF) –

• npoints (int,optional) – The number of points over which a scan has taken place. Defaults to the number of evaluations calculated in the output file.

Returns

Returns a data frame of energy surfaces

Return type

pd.DataFrame

single_population_analysis(poptype='Mulliken', /)

Single population analysis dataframe generator

Parameters

• poptype (str,optional) – The type of population analysis to

• Defaults to 'Mulliken'. (return.) –

Returns

Returns a data frame of the population analysis

Return type

pd.DataFrame

vib_freq()

Get the vibrational frequencies, and fails if there are more than one imaginary frequency

vpt2_transitions()

Grabs the fundamental transition analysis from a VPT2 calculation

class wailord.io.orca.orcaout(final_energy, fGeom, basis, filename, system, spin, theory)

Bases: tuple

basis

Alias for field number 2

fGeom

Alias for field number 1

filename

Alias for field number 3

final_energy

Alias for field number 0

spin

Alias for field number 5

system

Alias for field number 4

theory

Alias for field number 6

wailord.io.orca.parseOut(filename, plotter=False)

Handles orca outputs with regex for energy and coordinates