AutoGAMESS[1] was used for workflow automation.

[1] Ferrari, Brian. "AutoGAMESS: A Python package for automation of GAMESS (US) Raman calculations." Journal of Open Source Software 4.41 (2019): 1612.

This function creates a new directory tree for a GAMESS project, also makes
  a couple of text files for use with other functions.

  Parameters
  ----------
  maindir: string
      A directory string (including the final `/`) that points to the
      directory that the project tree will be spawned in.
  csvfile: string
      A directory string (including the final `.csv`) that points to the
      text file containing project information. Read module documentation
      for csv file format.
  initial_coords_dict: dictionary [Optional]
      This should be a dictionary with the key being the specie and the
      value being a list that of its initial coordinates.
  title: string [Optional]
      A directory string (including the final `/`) that will be used as
      the head of project directory tree.
  make_inputs: boolean True/False [Optional]
      if True then new_project will call input_builder at the end.

  Notes 1
  ----------
  If the molecules you wish to build are not already defined in the
  general autogamess coordinate dictionary, then initial_coords_dict
  must be passed.

  To see the autogamess coordinate dictionary simply print out
  >>> ag.dictionaries.molecule_dictionary

  Returns
  ----------
  This function returns nothing

  Notes 2
  ----------
  The format of the spawned directory tree is as follows:

                                  maindir
                                     |
                                   title
                      -------------------------------------
                      |        |        |        |        |
                    Codes    Inps     Logs  Batch_Files  Spreadsheets
                      |        |        |                     |
                ---------    Block    -----------      1 file per specie
                |       |             |    |    |
          Text_Files  Scripts      Fail   Pass  Sorted
                                    |      |        |
                                 -------  Block   1 directory per specie
                                 |     |
                           Unsolved   Solved


  Sections in directory tree labeled 'Block' are directory trees with the
  following format:
                    1 directory per run type
                                |
                    1 directory per specie

  Examples
  ----------
  >>> import autogamess as ag
  >>>
  >>> csvfile = './input.csv'
  >>> maindir = './'
  >>> title   = 'Project Title/'
  >>>
  >>> ag.new_project(maindir, csvfile, title=title)
  >>>

This function builds optimization input files.

Parameters
----------
inputfile: string
    This should be a full directory string that points to the input
    csv file.
save_dir: string
    this should be a full directory string that points to the directory
    you wish to save the inputs in.
initial_coords_dict: dictionary [Optional]
    This should be a dictionary with the key being the specie and the
    value being a list of the symmetry group and symmetry unique atom
    coordinates. Examples can be seen in the AutoGAMESS GitHub repository
    as well as by prinint out the default dictionary, see Notes section.
proj_title: string [Optional]
    This should be a string ending with `\n`

Notes 1
----------
If the molecules you wish to build are not already defined in the
general autogamess coordinate dictionary, then initial_coords_dict
must be passed.

To see the autogamess coordinate dictionary simply print out
>>> ag.dictionaries.molecule_dictionary

Returns
----------
This function returns nothing

Notes 2
----------
This function uses the EMSL Basis Set Exchange module to import
external basis sets[1]. This function also uses the Periodic_Elements
package by VaasuDevanS [2].

[1] https://github.com/MolSSI-BSE/basis_set_exchange
[2] https://github.com/VaasuDevanS/Periodic_Elements


Examples
----------
>>> import autogamess as ag
>>>
>>> csvfile = './input.csv'
>>> savedir = './'
>>> title   = 'Project\n'
>>>
>>> ag.input_builder(csvfile, savedir, proj_title=title)
>>>

This function writes a hessian calculation input file using a previously
run optimization input file and the log file generated by the calculation.

Parameters
----------
optfile: string
    This should be a string that points to the input file of an
    already run optimization file. (FULL DIRECTORY STRING REQUIRED)
logfile: string
    This should be a string that points to the log file of an
    already run optimization file. (FULL DIRECTORY STRING REQUIRED)

Returns
-------
This function returns nothing if it terminates successfully, otherwise
it returns ValueError.

Example
-------
>>> import autogamess as ag
>>>
>>> logfile = './Optimization_Log_Folder/IBv6_NH3_CCSD-T_CC6_opt.log'
>>> optfile = './IBv6_NH3_CCSD-T_CC6_opt.inp'
>>>
>>> ag.opt2hes(optfile, logfile)
>>>

This function writes a raman calculation input file using a previously
run hessian input file and the dat file generated by the calculation.

Parameters
----------
hesfile: string
    This should be a string that points to the input file of an
    already run hessian file. (FULL DIRECTORY STRING REQUIRED)
datfile: string
    This should be a string that points to the DAT file of an
    already run hessian file. (FULL DIRECTORY STRING REQUIRED)

Returns
-------
This function returns nothing if it terminates successfully, otherwise
it returns ValueError.

Example
-------
>>> import autogamess as ag
>>>
>>> datfile = '../restart/IBv6_NH3_CCSD-T_CC6_hes.dat'
>>> hesfile = './IBv6_NH3_CCSD-T_CC6_hes.inp'
>>>
>>> ag.hes2raman(hesfile, datfile)
>>>

This function sorts all the loose log files in the 'Logs' directory.

Parameters
----------
projdir: string
    A directory string (including the final `/`) that points to the
    project head directory.
logsdir: string
    A directory string (including the final `/`) that points to the
    directory containing the log files.

Returns
----------
This function returns nothing

Notes
----------
For this function to work properly the project directory tree must be
in the exact format that the 'new_project' function spawned it in.

Examples
----------
>>>import autogamess as ag
>>>
>>>projdir = './Example/'
>>>logsdir = './logs/'
>>>
>>>ag.sort_logs(projdir, logsdir)
>>>

This function fills in the spreadsheets initially generated by new_project
with data collected from the outfiles of calculations.

Parameters
----------
projdir: string [Optional]
    This should be a full directory string pointing to the project
    directory initially created by new_project.
sorteddir: string [Optional]
    This should be a full directory string pointing to the sorted log
    files directory.
sheetsdir: string [Optional]
    This should be a full directory string pointing to the spreadsheets
    directory.

Notes
----------
If projdir is not passed to fill_spreadsheets function then both other
parameters MUST be passed to it. Similarly if projdir is passed the
other two parameters MUST be left blank.

Once `fill_spreadsheets` has parsed the data file, it will move the log file
into the `Pass` directory implying that the calculation successfully terminated, or it will move it to `Fail` directory if termination unsuccessful.

Returns
-------
This function returns nothing.

Example
-------
>>> import autogamess as ag
>>>
>>> projdir = './Your Project Title/'
>>>
>>> ag.fill_spreadsheets(projdir)
>>>

>>> import autogamess as ag
>>>
>>> sorteddir = './project/Logs/Sorted/'
>>> sheetsdir = './project/Spreadsheets/'
>>>
>>> ag.fill_spreadsheets(sorteddir=sorteddir, sheetsdir=sheetsdir)
>>>

This function collects data from GAMESS(us) log files.

Parameters
----------
filename: string
    This should be a string that points to the log file of any
    GAMESS(us) calculation. (FULL DIRECTORY STRING REQUIRED)

Returns
-------
data: object
    This is an object with all the data collected from the log file.
    Below is a list of the attributes associated with `data` based on
    each log file type.

    all   files: `cpu`, `time`
    opt   files: `bond_lengths`, `bond_angles`
    hes   files: `vib_freq`, `ir_inten`
    raman files: `raman`
    vscf  files: `vscf_freq`, vscf_ir

Notes
-------
This function is primarily intended for interal use by AutoGAMESS.

Example
-------
>>> import autogamess as ag
>>>
>>> filename = './AGv0-0-6_NH3_CCSD-T_CC6_opt.log'
>>>
>>> ag.get_data(filename)
>>>

This function make vibrational frequency vs. IR/Raman intensity line plots.

Parameters
----------
file: string
    This should be a string that points to the log file of a hessian or
    Raman GAMESS(us) calculation. (FULL DIRECTORY STRING REQUIRED)
savedir: string
    This should be a string that points to the directory in which you
    would like to save the png of the plot.(FULL DIRECTORY STRING REQUIRED)
cmap: list [Optional]
    This should be a list of Matplotlib allowed color choices. Each symmetry
    will be plotted with a different color in the list.
method: string [Optional]
    This should be string giving the method for line broadening, options are
    `Gaussian`, `Lorentzian`, None(defualt).
sig: integer or float [Optional]
    This should be a numerical value to be used as the FWHM for the line
    broadening method chosen. Default: 300 wavenumbers
flag: list [Optional]
    This should be a list of integers, in particular 1,2 and 3. This list
    tells the function what to plot and what to omit from the plot.
    Please see the Notes section for more details.
reverse_x: boolean True/False [Optional]
    if True then x-axis will be in reverse (ie: 300---150----0).

Notes
-------
The `flag` parameter is used as follows:

[Default] `flag=[]`    ---> All lines are plotted
          `flag=[1]`   ---> Vertical lines are not plotted
          `flag=[2]`   ---> Spectral line not plotted
          `flag=[3]`   ---> Gaussian/Lorentzian lines not plotted
          `flag=[1,2]` --->V Vertical lines and Spectral line not plotted

List combination follow the same format, all possible list combinations
are allowed.

Returns
-------
This function returns nothing.

Example
-------
>>> import autogamess as ag
>>>
>>> file    = './AGv0-0-6_NH3_CCSD-T_CC6_hes.log'
>>> savedir = './'
>>>
>>> ag.make_plot(file, savedir)
>>>
>>> cmap = ['b', 'r', 'k', 'c']
>>> ag.make_plot(file, savedir, cmap=cmap)
>>>
>>> method = 'Lorentzian'
>>> sig    = 450
>>>
>>> ag.make_plot(file, savedir, cmap=cmap, method=method, sig=sig)
>>>

This function generates scaling factors and scaled frequencies.

Parameters
----------
projdir: string
    This should be a full directory string pointing to the project
    directory initlly created by new_project.
expt_dict: dictionary
    This should be a python dictionary with the experimental frequency
    values for all species that the user wants to generate scaling factors
    for in it. Format is explained in Notes section.
species: list
    This should be a list of all species the user would like scaling factors
    generated for. Any molecule in the list must have experimental data in
    the `expt_dict` associated with it.
method: string [Optional]
    This should be string giving the method for scaling factor calculation,
    options are `scott`(defualt).

Notes
-------
`expt_dict` format should be as follows:

        {`specie`: [`nu_1`, `nu_2`, ... , `nu_N`]}

 where `specie` must be written the same way as the Excel spreadsheet file
 for that molecule is written. Each frequency, `nu`, should be given in
 the same order as they appear (left to right) in the spreadsheet.

 `species` list format can be in any order but must adhere to the rule
 that any element in `species` is a key for `expt_dict`

 Once execution of this function is completed the `Hessian` worksheet
 will be updated to have a coulmn giving `Scaling Factor/RMS`, as well
 as the scaled frequencies will appear in parathesis next to the predicted
 frequencies.

Returns
-------
This function returns nothing.

Example
-------
>>> import autogamess as ag
>>>
>>> projdir   = './Your Project Title/'
>>> expt_dict = {'H2O': [1595, 3657, 3756]}
>>> species   = ['H2O']
>>>
>>> ag.generate_scaling_factors(projdir, expt_dict, species)
>>>