def task_correlators():
for dirname in directories:
for meson in ['pion', 'kaon']:
corr_tsv_files = []
for corr_xml in glob.glob(os.path.join(dirname, 'corr', 'corr.config-*.{}.xml.gz'.format(meson))):
corr_tsv = names.correlator_tsv(corr_xml, meson)
corr_tsv_files.append(corr_tsv)
yield make_single_transform(dirname,
correlators.io_extract_pion_corr,
corr_xml,
corr_tsv)
if len(corr_tsv_files) > 0:
path_pion_mass = names.tsv_extract(dirname, meson + '_mass')
yield {
'actions': [(correlators.analysis.io_extract_mass, [corr_tsv_files, path_pion_mass])],
'name': path_pion_mass,
'file_dep': corr_tsv_files,
'targets': [path_pion_mass],
}
path_effective_mass = names.tsv_extract(dirname, meson + '_effective_mass')
yield {
'actions': [(correlators.analysis.io_effective_mass, [corr_tsv_files, path_effective_mass])],
'name': path_effective_mass,
'file_dep': corr_tsv_files,
'targets': [path_effective_mass],
}
def task_running_mean():
for dirname in directories:
for window in [100, 10]:
path_in = names.tsv_extract(dirname, 'AcceptP')
path_out = names.tsv_extract(dirname, 'AcceptP-running_mean_{}'.format(window))
yield make_single_transform(dirname,
transforms.io_running_mean,
path_in,
path_out,
window=window)
def task_wflow():
for dirname in directories:
files_t0 = []
files_w0 = []
for xml_file in glob.glob(os.path.join(dirname, 'wflow', 'wflow.config-*.out.xml')):
file_e = names.wflow_xml_shard_name(xml_file, 'e')
file_t2e = names.wflow_xml_shard_name(xml_file, 't2e')
file_w = names.wflow_xml_shard_name(xml_file, 'w')
file_t0 = names.wflow_xml_shard_name(xml_file, 't0')
file_w0 = names.wflow_xml_shard_name(xml_file, 'w0')
files_t0.append(file_t0)
files_w0.append(file_w0)
yield make_single_transform(dirname,
wflow.io_convert_xml_to_tsv,
xml_file,
file_e)
yield make_single_transform(dirname,
wflow.io_compute_t2_e,
file_e,
file_t2e)
yield make_single_transform(dirname,
wflow.io_compute_w,
file_e,
file_w)
yield make_single_transform(dirname,
wflow.io_compute_intersection,
file_t2e,
file_t0)
yield make_single_transform(dirname,
wflow.io_compute_intersection,
file_w,
file_w0)
for name, files in [('t0', files_t0), ('w0', files_w0)]:
path_out = names.wflow_tsv(dirname, name)
yield {
'actions': [(wflow.merge_intersections, [files, path_out])],
'name': path_out,
'file_dep': files,
'targets': [path_out],
}
yield make_single_transform(dirname,
wflow.io_w0_to_a,
names.tsv_extract(dirname, 'w0'),
names.tsv_extract(dirname, 'a_mev_from_w0'))
def task_make_plot():
tasks = []
for dirname in directories:
tasks.append(plot_generic(dirname, 'w_plaq', 'Update Number', 'Plaquette (cold is 1.0)', 'Plaquette'))
#tasks.append(plot_generic(dirname, 'w_plaq-vs-md_time', 'MD Time', 'Plaquette (cold is 1.0)', 'Plaquette'))
tasks.append(plot_generic(dirname, 'deltaH', 'Update Number', r'$\Delta H$', 'MD Energy'))
#tasks.append(plot_generic(dirname, 'deltaH-vs-md_time', 'MD Time', r'$\Delta H$', 'MD Energy', transform_delta_h_md_time))
tasks.append(plot_generic(dirname, 'minutes_for_trajectory', 'Update Number', r'Minutes', 'Time for Trajectory'))
tasks.append(plot_generic(dirname, 'n_steps', 'Update Number', r'Step Count (coarsest time scale)', 'Integration Steps'))
#tasks.append(plot_generic(dirname, 'n_steps-vs-md_time', 'MD Time', r'Step Count (coarsest time scale)', 'Integration Steps'))
tasks.append(plot_generic(dirname, 'md_time', 'Update Number', r'MD Time', 'MD Distance'))
tasks.append(plot_generic(dirname, 'tau0', 'Update Number', r'MD Step Size', 'MD Step Size'))
tasks.append(plot_generic(dirname, 'DeltaDeltaH', 'Update Number', r'$\Delta \Delta H$', 'Reversibility'))
tasks.append(plot_generic(dirname, 'DeltaDeltaH_over_DeltaH', 'Update Number', r'$\Delta \Delta H / \Delta H$', 'Reversibility'))
tasks.append(plot_generic(dirname, 'exp_deltaH', 'Update Number', r'$\exp(-\Delta H)$', 'MD EnergeReversibility'))
tasks.append(plot_generic(dirname, 't0', 'Update Number', r'$t_0$', 'Wilson Flow Scale Setting'))
tasks.append(plot_generic(dirname, 'w0', 'Update Number', r'$w_0$', 'Wilson Flow Scale Setting'))
tasks.append(plot_generic(dirname, 'AcceptP', 'Update Number', r'Accepted', 'Acceptance Rate'))
tasks.append(plot_generic(dirname, 'AcceptP-running_mean_100', 'Update Number', r'Running mean (100 step window)', 'Acceptance Rate'))
tasks.append(plot_generic(dirname, 'AcceptP-running_mean_10', 'Update Number', r'Running mean (10 step window)', 'Acceptance Rate'))
path_out = names.tsv_extract(dirname, 'AcceptP-running_mean_100')
for task in tasks:
for subtask in task:
yield subtask
def plot_generic(dirname, name, *args, **kwargs):
path_in = names.tsv_extract(dirname, name)
path_out = names.plot(dirname, name)
yield {
'actions':
[(visualizers.plot_generic, [path_in, path_out] + list(args), kwargs)],
'name':
path_out,
'file_dep': [path_in],
'targets': [path_out],
}
path_out = names.plot(dirname, name + '_autoy')
kwargs = dict(kwargs)
kwargs['use_auto_ylim'] = True
yield {
'actions':
[(visualizers.plot_generic, [path_in, path_out] + list(args), kwargs)],
'name':
path_out,
'file_dep': [path_in],
'targets': [path_out],
}