pymatgen.io.abinit.abitimer module

This module provides objects for extracting timing data from the ABINIT output files It also provides tools to analye and to visualize the parallel efficiency.

class AbinitTimer(sections, info, cpu_time, wall_time)

Bases: object

Container class storing the timing results.

cpuwall_histogram(**kwargs)

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
tight_layout	True to call fig.tight_layout (default: False)

get_dataframe(sort_key=u'wall_time', **kwargs)

Return a pandas DataFrame with entries sorted according to sort_key.

get_section(section_name)

get_values(keys)

Return a list of values associated to a particular list of keys.

names_and_values(key, minval=None, minfract=None, sorted=True)

Select the entries whose value[key] is >= minval or whose fraction[key] is >= minfract Return the names of the sections and the corresponding values.

ncpus

Total number of CPUs employed.

order_sections(key, reverse=True)

Sort sections according to the value of key.

pie(**kwargs)

Plot pie chart for this timer.

Parameters:

• key – Keyword used to extract data from the timer.
• minfract – Don’t show sections whose relative weight is less that minfract.
• ax – matplotlib Axes or None if a new figure should be created.

Returns:

matplotlib figure

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
tight_layout	True to call fig.tight_layout (default: False)

scatter_hist(**kwargs)

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
tight_layout	True to call fig.tight_layout (default: False)

sum_sections(keys)

to_csv(fileobj=<open file '<stdout>', mode 'w'>)

Write data on file fileobj using CSV format.

to_table(sort_key=u'wall_time', stop=None)

Return a table (list of lists) with timer data

totable(sort_key=u'wall_time', stop=None)

Return a table (list of lists) with timer data

class AbinitTimerParser

Bases: _abcoll.Iterable

Responsible for parsing a list of output files, extracting the timing results and analyzing the results. Assume the Abinit output files have been produced with timopt -1.

Example

parser = AbinitTimerParser() parser.parse(list_of_files)

To analyze all *.abo files withing top, use:

parser, paths, okfiles = AbinitTimerParser.walk(top=”.”, ext=”.abo”)

BEGIN_TAG = u'-<BEGIN_TIMER'

END_TAG = u'-<END_TIMER>'

Error

alias of AbinitTimerParserError

filenames

List of files that have been parsed successfully.

get_sections(section_name)

Return the list of sections stored in self.timers() given section_name A fake section is returned if the timer does not have section_name.

parse(filenames)

Read and parse a filename or a list of filenames. Files that cannot be opened are ignored. A single filename may also be given.

Return: list of successfully read files.

pefficiency()

Analyze the parallel efficiency.

Returns:ParallelEfficiency object.

plot_all(show=True, **kwargs)

Call all plot methods provided by the parser.

plot_efficiency(**kwargs)

Plot the parallel efficiency

Parameters:

• key – Parallel efficiency is computed using the wall_time.
• what – Specifies what to plot: good for sections with good parallel efficiency. bad for sections with bad efficiency. Options can be concatenated with +.
• nmax – Maximum number of entries in plot
• ax – matplotlib Axes or None if a new figure should be created.

kwargs	Meaning
linewidth	matplotlib linewidth. Default: 2.0
markersize	matplotlib markersize. Default: 10

Returns:matplotlib figure

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
tight_layout	True to call fig.tight_layout (default: False)

plot_pie(**kwargs)

Plot pie charts of the different timers.

Parameters:

• key – Keyword used to extract data from timers.
• minfract – Don’t show sections whose relative weight is less that minfract.

Returns:

matplotlib figure

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
tight_layout	True to call fig.tight_layout (default: False)

plot_stacked_hist(**kwargs)

Plot stacked histogram of the different timers.

Parameters:

• key – Keyword used to extract data from the timers. Only the first nmax sections with largest value are show.
• mmax – Maximum nuber of sections to show. Other entries are grouped together in the others section.
• ax – matplotlib Axes or None if a new figure should be created.

Returns:

matplotlib figure

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
tight_layout	True to call fig.tight_layout (default: False)

section_names(ordkey=u'wall_time')

Return the names of sections ordered by ordkey. For the time being, the values are taken from the first timer.

summarize(**kwargs)

Return pandas DataFrame with the most important results stored in the timers.

timers(filename=None, mpi_rank=u'0')

Return the list of timers associated to the given filename and MPI rank mpi_rank.

classmethod walk(top=u'.', ext=u'.abo')

Scan directory tree starting from top, look for files with extension ext and parse timing data.

Return: (parser, paths, okfiles)

where parser is the new object, paths is the list of files found and okfiles is the list of files that have been parsed successfully. (okfiles == paths) if all files have been parsed.

exception AbinitTimerParserError

Bases: exceptions.Exception

Errors raised by AbinitTimerParser

class AbinitTimerSection(name, cpu_time, cpu_fract, wall_time, wall_fract, ncalls, gflops)

Bases: object

Record with the timing results associated to a section of code.

FIELDS = (u'name', u'wall_time', u'wall_fract', u'cpu_time', u'cpu_fract', u'ncalls', u'gflops')

NUMERIC_FIELDS = [u'wall_time', u'wall_fract', u'cpu_time', u'cpu_fract', u'ncalls', u'gflops']

STR_FIELDS = [u'name']

classmethod fake()

to_csvline(with_header=False)

Return a string with data in CSV format

to_dict()

to_tuple()

class ParallelEfficiency(filenames, ref_idx, *args, **kwargs)

Bases: dict

bad_sections(key=u'wall_time', criterion=u'mean', nmax=5)

good_sections(key=u'wall_time', criterion=u'mean', nmax=5)

totable(stop=None, reverse=True)

alternate(*iterables)

[a[0], b[0], … , a[1], b[1], …, a[n], b[n] …] >>> alternate([1,4], [2,5], [3,6]) [1, 2, 3, 4, 5, 6]