BigDFT.Logfiles module

This module is useful to process a logfile of BigDFT run, in yaml format. It also provides some tools to extract typical informations about the run, like the energy, the eigenvalues and so on.

get_logs(files)

Return a list of loaded logfiles from files, which is a list of paths leading to logfiles.

Args:

Parameters:

files – List of filenames indicating the logfiles

Returns:

List of Logfile instances associated to filename

document_quantities(doc, to_extract)

Extract information from the runs.

Warning

This routine was designed for the previous parse_log.py script and it is here only for backward compatibility purposes.

perform_operations(variables, ops, debug=False)

Perform operations given by ‘ops’. ‘variables’ is a dictionary of variables i.e. key=value.

Warning

This routine was designed for the previous parse_log.py script and it is here only for backward compatibility purposes.

process_logfiles(files, instructions, debug=False)

Process the logfiles in files with the dictionary ‘instructions’.

Warning

This routine was designed for the previous parse_log.py script and it is here only for backward compatibility purposes.

find_iterations(log)

Identify the different block of the iterations of the wavefunctions optimization.

Todo

Should be generalized and checked for mixing calculation and O(N) logfiles

Parameters:

log (dictionary) – logfile load

Returns:

wavefunction residue per iterations, per each subspace diagonalization

Return type:

numpy array of rank two

build_tuple(inp)

recursively build a tuple from a dict, for hashing

Parameters:

inp (dict) – input dictionary to be “tuple-ised”

Returns:

tuple

plot_wfn_convergence(wfn_it, gnrm_cv, label=None, ax=None)

Plot the convergence of the wavefunction coming from the find_iterations function. Cumulates the plot in matplotlib.pyplot module

Parameters:

• wfn_it – list coming from find_iterations()

• gnrm_cv – convergence criterion for the residue of the wfn_it list

• label – label for the given plot

class Logfile(*args, **kwargs)

Import a Logfile from a filename in yaml format, a list of filenames, an archive (compressed tar file), a dictionary or a list of dictionaries.

Parameters:

• *args – sequence of logfiles to be parsed. If it is longer than one item, the logfiles are considered as belonging to the same run.

• **kwargs –

  describes how the data can be read. Keywords can be:

  • archive: name of the archive from which retrieve the logfiles.

  • member: name of the logfile within the archive. If absent, all the

    files of the archive will be considered as args.

  • label: the label of the logfile instance

  • dictionary: parsed logfile given as a dictionary,

    serialization of the yaml logfile

Example

>>> l = Logfile('one.yaml','two.yaml')
>>> l = Logfile(archive='calc.tgz')
>>> l = Logfile(archive='calc.tgz',member='one.yaml')
>>> l = Logfile(dictionary=dict1)
>>> l = Logfile(dictionary=[dict1, dict2])

Todo

Document the automatically generated attributes, perhaps via an inner function in futile python module

get_dos(**kwargs)

Get the density of states from the logfile.

Fill a py:class:~BigDFT.DoS.DoS class object with the information which is stored in this logfile.

Parameters:

**kwargs – Keyword Arguments of the py:class:~BigDFT.DoS.DoS class.

Returns:

class instance. Filled with bandarrays and

fermi_level.

Return type:

BigDFT.DoS.DoS

get_brillouin_zone()

Return an instance of the BrillouinZone class, useful for band structure. :returns: Brillouin Zone of the logfile :rtype: BigDFT.BZ.BrillouinZone

SCF_convergence(ax=None)

Plot the wavefunction convergence. :Example:

>>> tt=Logfile('log-with-wfn-optimization.yaml',label='a label')
>>> tt.wfn_plot()

geopt_plot(ax=None)

For a set of logfiles construct the convergence plot if available. Plot the Maximum value of the forces against the difference between the minimum value of the energy and the energy of the iteration. Also an errorbar is given indicating the noise on the forces for a given point. Show the plot as per plt.show() with matplotlib.pyplots as plt

Example:

>>> tt=Logfile('log-with-geometry-optimization.yaml')
>>> tt.geopt_plot()

to_json(outfile)

Convert the logfile stream into a JSON file.

Parameters:

outfile (str) – Path of the JSON output file.

get_is_linear()

Returns True if this calculation was done in the linear scaling mode.

Todo: there should be a more intentional way to do this.

Returns:

True if linear scaling calculation mode.

Return type:

(logical)

get_intermediate_energies()

Gathers all the intermediary energy values and puts them in a list.

For the case of mixing, this is the FKS values. For linear scaling, it is the energies computed during kernel optimization.

Returns:

list of intermediary energy values.

Return type:

(list)

check_convergence()

Check if a calculation has converged.

Returns:

True if converged.

Return type:

(logical)

check_convergence_cdft(cdft=False, cdft_thresh=0.01)

Check if a calculation has converged when doing CDFT.

Parameters:

• cdft (logical) – if CDFT is used a separate check is activated.

• cdft_thresh (float) – the threshold for the deviation of Tr(KW)

• 1.0. (from) –

Returns:

True if converged.

Return type:

(logical)

class Energies(filename, units='AU', disp=None, strict=True)

Find the energy terms from a BigDFT logfile. May also accept malformed logfiles that are issued, for instance, from a badly terminated run that had I/O error.

Parameters:

• filename (str) – path of the logfile

• units (str) – may be ‘AU’ or ‘kcal/mol’

• disp (float) – dispersion energy (will be added to the total energy)

• strict (bool) – assume a well-behaved logfile

property to_dict

Generate dictionary of log file attributes

Returns:

log attributes

Return type:

dict