tesliper 0.8.0

a package for batch processing of spectra-related Gaussian output files

Tesliper

Tesliper: Theoretical Spectroscopist Little Helper is a program for batch processing of Gaussian output files. It is focused on calculation of vibrational, electronic, and scattering spectra from Gaussian-calculated quantum properties of molecule conformers. Please note, that this project is still under developenemt, thus it may be prone to errors. Tesliper is written in Python 3.6 and makes use of some additional third party packages (see below or requirements.txt). It may be used as a package or as a stand-alone application with dedicated GUI.

• Tesliper
• Getting Started
  • Requirements
  • Installing to your Python distribution
  • A standalone application
• Documentation
  • Using in Python scripts
    • Basics
    • Filtering conformers
    • Available data genres
  • Using a graphical interface
• License
• Contributing to Tesliper
  • Bugs and suggestions
  • Participating in code

Getting Started

You can use Tesliper from python or as standalone application with dedicated graphical user inteface. See below for details.

Requirements

Python 3.6+
numpy
openpyxl
matplotlib (needed only for gui)

This package is written in Python 3.6. and will not work with any previous release.

Installing to your Python distribution

Tesliper is available on PyPI, you can install it to your python distribution by simply running:

> python -m pip install tesliper

or

> python -m pip install tesliper[gui]

if you would like to be able to use a graphical interface.

A standalone application

This option is currently available only for Windows users. To get your copy of Tesliper, simply download an .exe file from latest relase. This file is a standalone application, no installation is required.

Documentation

Full documentation is on its way! For now please refer to the short tutorial below.

tesliper is designed to work with multiple conformers of a compound, represented by a number of files obtained from Gaussian quantum-chemical computations software. It allows to easily exclude conformers that are not suitable for further analysis: erroneous, not optimized, of higher energy or lower contribution than a user-given threshold. It also implements an RMSD sieve, enabling one to filter out similar structures. Finally, it lets you calculate theoretical IR, VCD, UV, ECD, Raman, and ROA spectra for all conformers or as a population-weighted average and export obtained spectral data in one of supported file formats: .txt, .csv, or .xlsx.

There are some conventions that are important to note:

• tesliper stores multiple data entries of various types for each conformer. To prevent confusion with Python's data type and with data itself, tesliper refers to specific kinds of data as "genres". Genres in code are represented by specific strings, used as identifiers. To learn about data genres known to tesliper, see Available data genres section, which lists them.
• tesliper identifies conformers using a stem of an extracted file (i.e. its filename without extension). When files with identical names are extracted in course of subsequent Tesliper.extract calls or in recursive extraction using Tesliper.extract(recursive=True), they are treated as data for one conformer. This enables to join data from subsequent calculations steps, e.g. geometry optimization, vibrational spectra simulation, and electronic spectra simulation. Please note that if specific data genre is available from more than one calculation job, only recently extracted values will be stored.
• tesliper was designed to deal with multiple conformers of single molecule and may not work properly when used to process data concerning different molecules (i.e. having different number of atoms, different number of degrees of freedom, etc.). If you want to use it for such purpose anyway, you may set Tesliper.conformers.allow_data_inconsistency to True. tesliper will then stop complaining and try to do its best.

Using in Python scripts

Basics

Tesliper class is the main access point to tesliper's functionality. It allows you to extract data from specified files, provides a proxy to the trimming functionality, gives access to data in form of specialized arrays, enables you to calculate and average desired spectra, and provides an easy way to export data.

Most basic use might look like this:

>>> from tesliper import Tesliper
>>> tslr = Tesliper()
>>> tslr.extract()
>>> tslr.calculate_spectra()
>>> tslr.average_spectra()
>>> tslr.export_averaged()

This extracts data from files in the current working directory, calculates available spectra using standard parameters, averages them using available energy values, and exports to current working directory in .txt format.

You can customize this process by specifying call parameters for used methods and modifying Tesliper's configuration attributes:

• to change source directory or location of exported files instantiate Tesliper object with input_dir and output_dir parameters specified, respectively. You can also set appropriate attributes on the instance directly.
• To extract only selected files in input_dir use wanted_files init parameter. It should be given an iterable of filenames you want to parse. Again, you can also directly set an identically named attribute.
• To change parameters used for calculation of spectra, modify appropriate entries of parameters attribute.
• Use other export methods to export more data and specify fmt parameter in method's call to export to other file formats.

>>> tslr = Tesliper(input_dir="./myjob/optimization/", output_dir="./myjob/output/")
>>> tslr.wanted_files = ["one", "two", "three"]  # only files with this names
>>> tslr.extract()  # use tslr.input_dir as source
>>> tslr.extract(path="./myjob/vcd_sim/")  # use other input_dir
>>> tslr.conformers.trim_not_optimized()  # trimming out unwanted conformers
>>> tslr.parameters["vcd"].update({"start": 500, "stop": 2500, "width": 2})
>>> tslr.calculate_spectra(genres=["vcd"])  # we want only VCD spectrum
>>> tslr.average_spectra()
>>> tslr.export_averaged(mode="w")  # overwrite previously exported files
>>> tslr.export_activities(fmt="csv")  # save activities for analysis elsewhere
>>> tslr.output_dir = "./myjob/ecd_sim/"
>>> tslr.export_job_file(  # prepare files for next step of calculations
...     route="# td=(singlets,nstates=80) B3LYP/Def2TZVP"
... )

When modifying Tesliper.parameters be cerfull to not delete any of the parameters. If you need to revert to standard parameters values, you can find them in Tesliper.standard_parameters.

>>> tslr.parameters["ir"] = {
...     "start": 500, "stop": 2500, "width": 2
... }  # this will cause problems!
>>> tslr.parameters = tslr.standard_parameters  # revert to default values

Trimming functionality, used in previous example in tslr.conformers.trim_not_optimized(), allows you to filter out conformers that shouldn't be used in further processing and analysis. You can trim off conformers that were not optimized, contain imaginary frequencies, or have other unwanted qualities. Conformers with similar geometry may be discarded using an RMSD sieve. For more information about trimming, please refer to Filtering conformers section.

For more exploratory analysis, Tesliper provides an easy way to access desired data as an instance of specialized DataArray class. Those objects implement a number of convenience methods for dealing with specific data genres. To get data in this form use array = tslr["genre"] were "genre" is string with the name of desired data genre. You may find a list of available genres in the section Available data genres.

>>> energies = tslr["gib"]
>>> energies.values
array([-304.17061762, -304.17232455, -304.17186735])
>>> energies.populations
array([0.0921304 , 0.56174031, 0.3461293 ])
>>> energies.full_name
'Thermal Free Energy'

Please note, that if some conformers do not provide values for a specific data genre, it will be ignored when retriving data for DataArray instantiation, regardles if it were trimmed off or not.

>>> tslr = Tesliper()
>>> tslr.conformers.update([
>>> ...     ('one', {'gib': -304.17061762}),
>>> ...     ('two', {'gib': -304.17232455}),
>>> ...     ('three', {'gib': -304.17186735}),
>>> ...     ('four', {})
>>> ... ])
>>> tslr.conformers.kept
[True, True, True, True]
>>> energies = tslr["gib"]
>>> energies.filenames
array(['one', 'two', 'three'], dtype='<U5')

Filtering conformers

tesliper offers means to easily and quickly filter conformers with unwanted properties. It calls such process a 'trimming'. A number of trimming methods are available via Tesliper.conformers attribute, that stores data extracted so far. A brief reference for these methods may be find below.

• trim_imaginary_frequencies(): mark all conformers with imaginary frequencies as "not kept".
• trim_not_optimized(): mark all conformers that failed structure optimization as "not kept".
• trim_non_normal_termination(): mark all conformers, which calculation job did not terminate normally, as "not kept".
• trim_inconsistent_sizes(): mark as "not kept" all conformers that contain any iterable data genre, that is of different length, than in case of majority of conformers.
• trim_incomplete(): mark incomplete conformers as "not kept".
• trim_non_matching_stoichiometry(wanted: str): mark all conformers with stoichiometry other than wanted as "not kept". If not given, wanted evaluates to the most common stoichiometry.
• trim_to_range(genre: str, minimum: float, maximum: float, attribute: str): marks as "not kept" all conformers, which numeric value of data of specified genre is outside of the range specified by minimum and maximum values. attribute is optional, it lets you specify if something other than original values should be considered (e.g. "populations" for energies genre).
• trim_rmsd(threshold: float, window_size: float, geometry_genre: str, energy_genre: str, ignore_hydrogen: bool): marks as "not kept" all conformers, that are not identical, according to provided RMSD threshold and energy difference. Conformers, which energy difference (dE) is higher than given window_size are always treated as different, while those with dE smaller than window_size and RMSD value smaller than given threshold are considered identical. From two identical conformers, the one with lower energy is "kept", and the other is discarded (marked as "not kept"). Used geometry genre and energies genre might be specified, otherwise default values of "geometry" and "scf" are assumed. ignore_hydrogen specifies if hydrogen atoms should be ignored when calculating RMSD values, it defaults to True.

You may also use Tesliper.comformers.kept to manipulate which conformers are processed ("kept"), and which are not. This is a list of booleans, that may be modified in a few ways, as described below.

Firstly, it is the most straightforward to just assign a new list of boolean values to the kept attribute. This list should have the same number of elements as the number of conformers contained. A ValueError is raised if it doesn't.

>>> c = tslr.conformers  # {one={}, two={}, tree={}}
>>> c.kept
[True, True, True]
>>> c.kept = [False, True, False]
>>> c.kept
[False, True, False]
>>> c.kept = [False, True, False, True]
Traceback (most recent call last):
...
ValueError: Must provide boolean value for each known conformer.
4 values provided, 3 excepted.

Secondly, list of filenames of conformers intended to be kept may be given. Only these conformers will be kept. If given filename is not in the underlying Conformers' dictionary, KeyError is raised.

>>> c.kept = ['one']
>>> c.kept
[True, False, False]
>>>  c.kept = ['two', 'other']
Traceback (most recent call last):
...
KeyError: Unknown conformers: other.

Thirdly, list of integers representing conformers indices may br given. Only conformers with specified indices will be kept. If one of given integers cant be translated to conformer's index, IndexError is raised. Indexing with negative values is not supported currently.

>>> c.kept = [1, 2]
>>> c.kept
[False, True, True]
>>> c.kept = [2, 3]
Traceback (most recent call last):
...
IndexError: Indexes out of bounds: 3.

Fourthly, assigning True or False to this attribute will mark all conformers as kept or not kept respectively.

>>> c.kept = False
>>> c.kept
[False, False, False]
>>> c.kept = True
>>> c.kept
[True, True, True]

Lastly, list of kept values may be modified by setting its elements to True or False. It is advised against, however, as mistake such as m.kept[:2] = [True, False, False] will break some functionality by forcibly changing size of kept list.

Available data genres

Data genres, their availability from specific types of calculation, and their brief description are as follows:

• freq: list of floats, available from freq job -- harmonic vibrational frequencies (cm^-1)
• mass: list of floats, available from freq job -- reduced masses (AMU)
• frc: list of floats, available from freq job -- force constants (mDyne/A)
• iri: list of floats, available from freq job -- IR intensities (KM/mole)
• dip: list of floats, available from freq=VCD job -- dipole strengths (10**-40 esu2-cm2)
• rot: list of floats, available from freq=VCD job -- rotational strengths (10**-44 esu2-cm2)
• emang: list of floats, available from freq=VCD job -- E-M angle = Angle between electric and magnetic dipole transition moments (deg)
• depolarp: list of floats, available from freq=Raman job -- depolarization ratios for plane incident light
• depolaru: list of floats, available from freq=Raman job -- depolarization ratios for unpolarized incident light
• ramanactiv: list of floats, available from freq=Raman job -- Raman scattering activities (A**4/AMU)
• ramact: list of floats, available from freq=ROA job -- Raman scattering activities (A**4/AMU)
• depp: list of floats, available from freq=ROA job -- depolarization ratios for plane incident light
• depu: list of floats, available from freq=ROA job -- depolarization ratios for unpolarized incident light
• alpha2: list of floats, available from freq=ROA job -- Raman invariants Alpha2 = alpha2 (A4/AMU)
• beta2: list of floats, available from freq=ROA job -- Raman invariants Beta2 = beta(alpha)2 (A4/AMU)
• alphag: list of floats, available from freq=ROA job -- ROA invariants AlphaG = alphaG'(104 A5/AMU)
• gamma2: list of floats, available from freq=ROA job -- ROA invariants Gamma2 = beta(G')2 (104 A**5/AMU)
• delta2: list of floats, available from freq=ROA job -- ROA invariants Delta2 = beta(A)2, (104 A**5/AMU)
• raman1: list of floats, available from freq=ROA job -- Far-From-Resonance Raman intensities =ICPu/SCPu(180) (K)
• roa1: list of floats, available from freq=ROA job -- ROA intensities =ICPu/SCPu(180) (10**4 K)
• cid1: list of floats, available from freq=ROA job -- CID=(ROA/Raman)*10**4 =ICPu/SCPu(180)
• raman2: list of floats, available from freq=ROA job -- Far-From-Resonance Raman intensities =ICPd/SCPd(90) (K)
• roa2: list of floats, available from freq=ROA job -- ROA intensities =ICPd/SCPd(90) (10**4 K)
• cid2: list of floats, available from freq=ROA job -- CID=(ROA/Raman)*10**4 =ICPd/SCPd(90)
• raman3: list of floats, available from freq=ROA job -- Far-From-Resonance Raman intensities =DCPI(180) (K)
• roa3: list of floats, available from freq=ROA job -- ROA intensities =DCPI(180) (10**4 K)
• cid3: list of floats, available from freq=ROA job -- CID=(ROA/Raman)*10**4 =DCPI(180)
• rc180: list of floats, available from freq=ROA job -- RC180 = degree of circularity
• wavelen: list of floats, available from td job -- excitation energies (nm)
• ex_en: list of floats, available from td job -- excitation energies (eV)
• eemang: list of floats, available from td job -- E-M angle = Angle between electric and magnetic dipole transition moments (deg)
• vdip: list of floats, available from td job -- dipole strengths (velocity)
• ldip: list of floats, available from td job -- dipole strengths (length)
• vrot: list of floats, available from td job -- rotatory strengths (velocity) in cgs (10**-40 erg-esu-cm/Gauss)
• lrot: list of floats, available from td job -- rotatory strengths (length) in cgs (10**-40 erg-esu-cm/Gauss)
• vosc: list of floats, available from td job -- oscillator strengths
• losc: list of floats, available from td job -- oscillator strengths
• transitions: list of tuples of tuples of (int, int, float), available from td job -- transitions (first to second) and their coefficients (third)
• scf: float, always available -- SCF energy
• zpe: float, available from freq job -- Sum of electronic and zero-point Energies (Hartree/Particle)
• ten: float, available from freq job -- Sum of electronic and thermal Energies (Hartree/Particle)
• ent: float, available from freq job -- Sum of electronic and thermal Enthalpies (Hartree/Particle)
• gib: float, available from freq job -- Sum of electronic and thermal Free Energies (Hartree/Particle)
• zpecorr: float, available from freq job -- Zero-point correction (Hartree/Particle)
• tencorr: float, available from freq job -- Thermal correction to Energy (Hartree/Particle)
• entcorr: float, available from freq job -- Thermal correction to Enthalpy (Hartree/Particle)
• gibcorr: float, available from freq job -- Thermal correction to Gibbs Free Energy (Hartree/Particle)
• command: str, always available -- command used for calculations
• normal_termination: bool, always available -- true if Gaussian job seem to exit normally, false otherwise
• optimization_completed: bool, available from opt job -- true if structure optimization was performed successfully
• version: str, always available -- version of Gaussian software used
• charge: int, always available -- molecule's charge
• multiplicity: int, always available -- molecule's spin multiplicity
• input_atoms: list of str, always available -- input atoms as a list of atoms' symbols
• input_geom: list of tuples of floats, always available -- input geometry as X, Y, Z coordinates of atoms
• stoichiometry: str, always available -- molecule's stoichiometry
• molecule_atoms: list of ints, always available -- molecule's atoms as atomic numbers
• geometry: list of tuples of floats, always available -- molecule's geometry (last one found in file) as X, Y, Z coordinates of atoms

tesliper also uses ir, vcd, uv, ecd, raman, and roa when referring to calculated spectra.

Using a graphical interface

If you are using tesliper as a standalone application, simply double click on the Tesliper.exe file to start the application. To invoke it from the command line, run python -m tesliper.gui. GUI consists of three panels and a number of controls. The panels are: "Extracted data", "Energies list", and "Spectra view". First two offer a list of conformers read so far using "Chose files" and "Chose folder" buttons on the left. The last enables to preview calculated spectra.

• "Extracted data" panel shows an identifier of each conformer (a file name) and an overview of data extracted. Little checkboxes on the left of each conformer may be clicked to mark this conformer as "kept" or "not kept".
• "Energies list" offers the same set of conformers and checkboxes, but with energies values listed for each conformer. The view may be changed using "Show" dropdown box in "Filter kept conformers" section of controls, to present difference in energy between conformers or their percentage contribution in population.
• "Spectra view" tab shows calculated spectra. It may be controlled using "Calculate spectra" section. After choosing a spectra genre to calculate you may control if it is simulated using lorentzian or gaussian fitting function, change peak width, spectra bounds, etc. You may view spectra for one conformer, all of them stacked, or averaged. You may also load an experimental spectrum (.txt format) for comparison.

Once done with extracting files and tweaking parameters, export selected data to desired format or save the session for later using buttons in "Session control" section.

License

This project is licensed with BSD 2-Clause license. See LICENSE.txt file for details.

Contributing to Tesliper

Contributions are welcome! tesliper is a growing project and definitely has room for improvements.

Bugs and suggestions

Bug reports are of great value, if you encounter a problem please let me know by submitting a new issue. If you have a suggestion how tesliper can be improved, please let me know as well!

Participating in code

If you'd like to contribute to tesliper's codebase, that's even better! If there is a specific bug that you know how to fix or a feature you'd like to develop, please let me know via issues. To get your change introduced to the codebase, please make a Pull Request to the fixes branch for bug fixes or to the dev branch for new features.

If at a loss, do not hesitate to reach to me directly! :)