def scan_allsyst(template_settings, steps, hypo_param_selections, outdir,
minimizer_settings, metric, debug_mode):
"""Scan (separately) all systematics (i.e., non-fixed params).
Parameters
----------
template_settings
steps
hypo_param_selections
outdir
minimizer_settings
metric
debug_mode
Returns
-------
restults : dict
Keys are param names, values are the scan results
"""
outdir = expanduser(expandvars(outdir))
mkdir(outdir, warn=False)
hypo_maker = DistributionMaker(template_settings)
hypo_maker.select_params(hypo_param_selections)
data_dist = hypo_maker.get_outputs(return_sum=True)
minimizer_settings = from_file(minimizer_settings)
analysis = Analysis()
results = OrderedDict() # pylint: disable=redefined-outer-name
for param in hypo_maker.params:
if param.is_fixed:
continue
logging.info('Scanning %s', param.name)
nominal_value = param.value
outfile = join(
outdir,
'{:s}_{:d}_steps_{:s}_scan.json'.format(param.name, steps, metric))
if isfile(outfile):
raise IOError(
'`outfile` "{}" exists, not overwriting.'.format(outfile))
results[param.name] = analysis.scan(
data_dist=data_dist,
hypo_maker=hypo_maker,
hypo_param_selections=hypo_param_selections,
metric=metric,
param_names=param.name,
steps=steps,
only_points=None,
outer=True,
profile=False,
minimizer_settings=minimizer_settings,
outfile=outfile,
debug_mode=debug_mode)
to_file(results[param.name], outfile)
param.value = nominal_value
logging.info('Done scanning param "%s"', param.name)
logging.info('Done.')
return results
def profile_scan(data_settings, template_settings, param_names, steps,
only_points, no_outer, data_param_selections,
hypo_param_selections, profile, outfile, minimizer_settings,
metric, debug_mode):
"""Perform a profile scan.
Parameters
----------
data_settings
template_settings
param_names
steps
only_points
no_outer
data_param_selections
hypo_param_selections
profile
outfile
minimizer_settings
metric
debug_mode
Returns
-------
results
analysis
"""
outfile = expanduser(expandvars(outfile))
if isfile(outfile):
raise IOError('`outfile` "{}" already exists!'.format(outfile))
minimizer_settings = from_file(minimizer_settings)
hypo_maker = DistributionMaker(template_settings)
if data_settings is None:
if (data_param_selections is None
or data_param_selections == hypo_param_selections):
data_maker = hypo_maker
else:
data_maker = deepcopy(hypo_maker)
data_maker.select_params(data_param_selections)
else:
data_maker = DistributionMaker(data_settings)
data_maker.select_params(data_param_selections)
data_dist = data_maker.get_outputs(return_sum=True)
analysis = Analysis()
results = analysis.scan(data_dist=data_dist,
hypo_maker=hypo_maker,
hypo_param_selections=hypo_param_selections,
metric=metric,
param_names=param_names,
steps=steps,
only_points=only_points,
outer=not no_outer,
profile=profile,
minimizer_settings=minimizer_settings,
outfile=outfile,
debug_mode=debug_mode)
to_file(results, outfile)
logging.info("Done.")
return results, analysis
def compare(outdir,
ref,
ref_label,
test,
test_label,
asymm_max=None,
asymm_min=None,
combine=None,
diff_max=None,
diff_min=None,
fract_diff_max=None,
fract_diff_min=None,
json=False,
pdf=False,
png=False,
ref_abs=False,
ref_param_selections=None,
sum=None,
test_abs=False,
test_param_selections=None):
"""Compare two entities. The result each entity specification is
formatted into a MapSet and stored to disk, so that e.g. re-running
a DistributionMaker is unnecessary to reproduce the results.
Parameters
----------
outdir : string
Store output plots to this directory
ref : string or array of strings
Pipeline settings config file that generates reference output,
or a stored map or map set. Multiple pipelines, maps, or map sets are
supported
ref_abs : bool
Use the absolute value of the reference plot for comparisons
ref_label : string
Label for reference
ref_param-selections : string
Param selections to apply to ref pipeline config(s). Not
applicable if ref specifies stored map or map sets
test : string or array of strings
Pipeline settings config file that generates test output, or a
stored map or map set. Multiple pipelines, maps, or map sets are
supported
test_abs : bool
Use the absolute value of the test plot for comparisons
test_label : string
Label for test
test_param_selections : None or string
Param selections to apply to test pipeline config(s). Not
applicable if test specifies stored map or map sets
combine : None or string or array of strings
Combine by wildcard string, where string globbing (a la command
line) uses asterisk for any number of wildcard characters. Use
single quotes such that asterisks do not get expanded by the
shell. Multiple combine strings supported
sum : None or int
Sum over (and hence remove) the specified axis or axes. I.e.,
project the map onto remaining (unspecified) axis or axes
json : bool
Save output maps in compressed json (json.bz2) format
pdf : bool
Save plots in PDF format. If neither this nor png is
specified, no plots are produced
png : bool
Save plots in PNG format. If neither this nor pdf is specfied,
no plots are produced
diff_min : None or float
Difference plot vmin; if you specify only one of diff_min or
diff_max, symmetric limits are automatically used (min = -max)
diff_max : None or float
Difference plot max; if you specify only one of diff_min or
diff_max, symmetric limits are automatically used (min = -max)
fract_diff_min : None or float
Fractional difference plot vmin; if you specify only one of
fract_diff_min or fract_diff_max, symmetric limits are
automatically used (min = -max)
fract_diff_max : None or float
Fractional difference plot max; if you specify only one of
fract_diff_min or fract_diff_max, symmetric limits are
automatically used (min = -max)
asymm_min : None or float
Asymmetry plot vmin; if you specify only one of asymm_min or
asymm_max, symmetric limits are automatically used (min = -max)
asymm_max : None or float
Fractional difference plot max; if you specify only one of
asymm_min or asymm_max, symmetric limits are automatically used
(min = -max)
Returns
-------
summary_stats : dict
Dictionary containing a summary for each h Map processed
diff : MapSet
MapSet of the difference
- (Test - Ref)
fract_diff : MapSet
MapSet of the fractional difference
- (Test - Ref) / Ref
asymm : MapSet
MapSet of the asymmetric fraction difference or pull
- (Test - Ref) / sqrt(Ref)
"""
ref_plot_label = ref_label
if ref_abs and not ref_label.startswith('abs'):
ref_plot_label = 'abs(%s)' % ref_plot_label
test_plot_label = test_label
if test_abs and not test_label.startswith('abs'):
test_plot_label = 'abs(%s)' % test_plot_label
plot_formats = []
if pdf:
plot_formats.append('pdf')
if png:
plot_formats.append('png')
diff_symm = True
if diff_min is not None and diff_max is None:
diff_max = -diff_min
diff_symm = False
if diff_max is not None and diff_min is None:
diff_min = -diff_max
diff_symm = False
fract_diff_symm = True
if fract_diff_min is not None and fract_diff_max is None:
fract_diff_max = -fract_diff_min
fract_diff_symm = False
if fract_diff_max is not None and fract_diff_min is None:
fract_diff_min = -fract_diff_max
fract_diff_symm = False
asymm_symm = True
if asymm_max is not None and asymm_min is None:
asymm_min = -asymm_max
asymm_symm = False
if asymm_min is not None and asymm_max is None:
asymm_max = -asymm_min
asymm_symm = False
outdir = os.path.expanduser(os.path.expandvars(outdir))
mkdir(outdir)
# Get the reference distribution(s) into the form of a test MapSet
p_ref = None
ref_source = None
if isinstance(ref, Map):
p_ref = MapSet(ref)
ref_source = MAP_SOURCE_STR
elif isinstance(ref, MapSet):
p_ref = ref
ref_source = MAPSET_SOURCE_STR
elif isinstance(ref, Pipeline):
if ref_param_selections is not None:
ref.select_params(ref_param_selections)
p_ref = ref.get_outputs()
ref_source = PIPELINE_SOURCE_STR
elif isinstance(ref, DistributionMaker):
if ref_param_selections is not None:
ref.select_params(ref_param_selections)
p_ref = ref.get_outputs()
ref_source = DISTRIBUTIONMAKER_SOURCE_STR
else:
if len(ref) == 1:
try:
ref_pipeline = Pipeline(config=ref[0])
except:
pass
else:
ref_source = PIPELINE_SOURCE_STR
if ref_param_selections is not None:
ref_pipeline.select_params(ref_param_selections)
p_ref = ref_pipeline.get_outputs()
else:
try:
ref_dmaker = DistributionMaker(pipelines=ref)
except:
pass
else:
ref_source = DISTRIBUTIONMAKER_SOURCE_STR
if ref_param_selections is not None:
ref_dmaker.select_params(ref_param_selections)
p_ref = ref_dmaker.get_outputs()
if p_ref is None:
try:
p_ref = [Map.from_json(f) for f in ref]
except:
pass
else:
ref_source = MAP_SOURCE_STR
p_ref = MapSet(p_ref)
if p_ref is None:
assert ref_param_selections is None
assert len(ref) == 1, 'Can only handle one MapSet'
try:
p_ref = MapSet.from_json(ref[0])
except:
pass
else:
ref_source = MAPSET_SOURCE_STR
if p_ref is None:
raise ValueError(
'Could not instantiate the reference Pipeline, DistributionMaker,'
' Map, or MapSet from ref value(s) %s' % ref)
ref = p_ref
logging.info('Reference map(s) derived from a ' + ref_source)
# Get the test distribution(s) into the form of a test MapSet
p_test = None
test_source = None
if isinstance(test, Map):
p_test = MapSet(test)
test_source = MAP_SOURCE_STR
elif isinstance(test, MapSet):
p_test = test
test_source = MAPSET_SOURCE_STR
elif isinstance(test, Pipeline):
if test_param_selections is not None:
test.select_params(test_param_selections)
p_test = test.get_outputs()
test_source = PIPELINE_SOURCE_STR
elif isinstance(test, DistributionMaker):
if test_param_selections is not None:
test.select_params(test_param_selections)
p_test = test.get_outputs()
test_source = DISTRIBUTIONMAKER_SOURCE_STR
else:
if len(test) == 1:
try:
test_pipeline = Pipeline(config=test[0])
except:
pass
else:
test_source = PIPELINE_SOURCE_STR
if test_param_selections is not None:
test_pipeline.select_params(test_param_selections)
p_test = test_pipeline.get_outputs()
else:
try:
test_dmaker = DistributionMaker(pipelines=test)
except:
pass
else:
test_source = DISTRIBUTIONMAKER_SOURCE_STR
if test_param_selections is not None:
test_dmaker.select_params(test_param_selections)
p_test = test_dmaker.get_outputs()
if p_test is None:
try:
p_test = [Map.from_json(f) for f in test]
except:
pass
else:
test_source = MAP_SOURCE_STR
p_test = MapSet(p_test)
if p_test is None:
assert test_param_selections is None
assert len(test) == 1, 'Can only handle one MapSet'
try:
p_test = MapSet.from_json(test[0])
except:
pass
else:
test_source = MAPSET_SOURCE_STR
if p_test is None:
raise ValueError(
'Could not instantiate the test Pipeline, DistributionMaker, Map,'
' or MapSet from test value(s) %s' % test)
test = p_test
logging.info('Test map(s) derived from a ' + test_source)
if combine is not None:
ref = ref.combine_wildcard(combine)
test = test.combine_wildcard(combine)
if isinstance(ref, Map):
ref = MapSet([ref])
if isinstance(test, Map):
test = MapSet([test])
if sum is not None:
ref = ref.sum(sum)
test = test.sum(sum)
# Set the MapSet names according to args passed by user
ref.name = ref_label
test.name = test_label
# Save to disk the maps being plotted (excluding optional aboslute value
# operations)
if json:
refmaps_path = os.path.join(outdir, 'maps__%s.json.bz2' % ref_label)
to_file(ref, refmaps_path)
testmaps_path = os.path.join(outdir, 'maps__%s.json.bz2' % test_label)
to_file(test, testmaps_path)
if set(test.names) != set(ref.names):
raise ValueError('Test map names %s do not match ref map names %s.' %
(sorted(test.names), sorted(ref.names)))
# Aliases to save keystrokes
def masked(x):
return np.ma.masked_invalid(x.nominal_values)
def zero_to_nan(map):
newmap = deepcopy(map)
mask = np.isclose(newmap.nominal_values, 0, rtol=0, atol=EPSILON)
newmap.hist[mask] = np.nan
return newmap
reordered_test = []
new_ref = []
diff_maps = []
fract_diff_maps = []
asymm_maps = []
summary_stats = {}
for ref_map in ref:
test_map = test[ref_map.name].reorder_dimensions(ref_map.binning)
if ref_abs:
ref_map = abs(ref_map)
if test_abs:
test_map = abs(test_map)
diff_map = test_map - ref_map
fract_diff_map = (test_map - ref_map) / zero_to_nan(ref_map)
asymm_map = (test_map - ref_map) / zero_to_nan(ref_map**0.5)
abs_fract_diff_map = np.abs(fract_diff_map)
new_ref.append(ref_map)
reordered_test.append(test_map)
diff_maps.append(diff_map)
fract_diff_maps.append(fract_diff_map)
asymm_maps.append(asymm_map)
min_ref = np.min(masked(ref_map))
max_ref = np.max(masked(ref_map))
min_test = np.min(masked(test_map))
max_test = np.max(masked(test_map))
total_ref = np.sum(masked(ref_map))
total_test = np.sum(masked(test_map))
mean_ref = np.mean(masked(ref_map))
mean_test = np.mean(masked(test_map))
max_abs_fract_diff = np.max(masked(abs_fract_diff_map))
mean_abs_fract_diff = np.mean(masked(abs_fract_diff_map))
median_abs_fract_diff = np.median(masked(abs_fract_diff_map))
mean_fract_diff = np.mean(masked(fract_diff_map))
min_fract_diff = np.min(masked(fract_diff_map))
max_fract_diff = np.max(masked(fract_diff_map))
std_fract_diff = np.std(masked(fract_diff_map))
mean_diff = np.mean(masked(diff_map))
min_diff = np.min(masked(diff_map))
max_diff = np.max(masked(diff_map))
std_diff = np.std(masked(diff_map))
median_diff = np.nanmedian(masked(diff_map))
mad_diff = np.nanmedian(masked(np.abs(diff_map)))
median_fract_diff = np.nanmedian(masked(fract_diff_map))
mad_fract_diff = np.nanmedian(masked(np.abs(fract_diff_map)))
min_asymm = np.min(masked(fract_diff_map))
max_asymm = np.max(masked(fract_diff_map))
total_asymm = np.sqrt(np.sum(masked(asymm_map)**2))
summary_stats[test_map.name] = OrderedDict([
('min_ref', min_ref),
('max_ref', max_ref),
('total_ref', total_ref),
('mean_ref', mean_ref),
('min_test', min_test),
('max_test', max_test),
('total_test', total_test),
('mean_test', mean_test),
('max_abs_fract_diff', max_abs_fract_diff),
('mean_abs_fract_diff', mean_abs_fract_diff),
('median_abs_fract_diff', median_abs_fract_diff),
('min_fract_diff', min_fract_diff),
('max_fract_diff', max_fract_diff),
('mean_fract_diff', mean_fract_diff),
('std_fract_diff', std_fract_diff),
('median_fract_diff', median_fract_diff),
('mad_fract_diff', mad_fract_diff),
('min_diff', min_diff),
('max_diff', max_diff),
('mean_diff', mean_diff),
('std_diff', std_diff),
('median_diff', median_diff),
('mad_diff', mad_diff),
('min_asymm', min_asymm),
('max_asymm', max_asymm),
('total_asymm', total_asymm),
])
logging.info('Map %s...', ref_map.name)
logging.info(' Ref map(s):')
logging.info(' min :' + ('%.2f' % min_ref).rjust(12))
logging.info(' max :' + ('%.2f' % max_ref).rjust(12))
logging.info(' total :' + ('%.2f' % total_ref).rjust(12))
logging.info(' mean :' + ('%.2f' % mean_ref).rjust(12))
logging.info(' Test map(s):')
logging.info(' min :' + ('%.2f' % min_test).rjust(12))
logging.info(' max :' + ('%.2f' % max_test).rjust(12))
logging.info(' total :' + ('%.2f' % total_test).rjust(12))
logging.info(' mean :' + ('%.2f' % mean_test).rjust(12))
logging.info(' Absolute fract. diff., abs((Test - Ref) / Ref):')
logging.info(' max : %.4e', max_abs_fract_diff)
logging.info(' mean : %.4e', mean_abs_fract_diff)
logging.info(' median: %.4e', median_abs_fract_diff)
logging.info(' Fractional difference, (Test - Ref) / Ref:')
logging.info(' min : %.4e', min_fract_diff)
logging.info(' max : %.4e', max_fract_diff)
logging.info(' mean : %.4e +/- %.4e', mean_fract_diff,
std_fract_diff)
logging.info(' median: %.4e +/- %.4e', median_fract_diff,
mad_fract_diff)
logging.info(' Difference, Test - Ref:')
logging.info(' min : %.4e', min_diff)
logging.info(' max : %.4e', max_diff)
logging.info(' mean : %.4e +/- %.4e', mean_diff, std_diff)
logging.info(' median: %.4e +/- %.4e', median_diff, mad_diff)
logging.info(' Asymmetry, (Test - Ref) / sqrt(Ref)')
logging.info(' min : %.4e', min_asymm)
logging.info(' max : %.4e', max_asymm)
logging.info(' total : %.4e (sum in quadrature)', total_asymm)
logging.info('')
ref = MapSet(new_ref)
test = MapSet(reordered_test)
diff = MapSet(diff_maps)
fract_diff = MapSet(fract_diff_maps)
asymm = MapSet(asymm_maps)
if json:
diff.to_json(
os.path.join(
outdir,
'diff__%s__%s.json.bz2' % (test_plot_label, ref_plot_label)))
fract_diff.to_json(
os.path.join(
outdir, 'fract_diff__%s___%s.json.bz2' %
(test_plot_label, ref_plot_label)))
asymm.to_json(
os.path.join(
outdir,
'asymm__%s___%s.json.bz2' % (test_plot_label, ref_plot_label)))
to_file(
summary_stats,
os.path.join(
outdir,
'stats__%s__%s.json.bz2' % (test_plot_label, ref_plot_label)))
for plot_format in plot_formats:
# Plot the raw distributions
plotter = Plotter(stamp='',
outdir=outdir,
fmt=plot_format,
log=False,
annotate=False,
symmetric=False,
ratio=False)
plotter.plot_2d_array(ref, fname='distr__%s' % ref_plot_label)
plotter.plot_2d_array(test, fname='distr__%s' % test_plot_label)
# Plot the difference (test - ref)
plotter = Plotter(stamp='',
outdir=outdir,
fmt=plot_format,
log=False,
annotate=False,
symmetric=diff_symm,
ratio=False)
plotter.label = '%s - %s' % (test_plot_label, ref_plot_label)
plotter.plot_2d_array(
test - ref,
fname='diff__%s__%s' % (test_plot_label, ref_plot_label),
#vmin=diff_min, vmax=diff_max
)
# Plot the fractional difference (test - ref)/ref
plotter = Plotter(stamp='',
outdir=outdir,
fmt=plot_format,
log=False,
annotate=False,
symmetric=fract_diff_symm,
ratio=True)
plotter.label = ('(%s-%s)/%s' %
(test_plot_label, ref_plot_label, ref_plot_label))
plotter.plot_2d_array(
(test - ref) / MapSet([zero_to_nan(r) for r in ref]),
fname='fract_diff__%s__%s' % (test_plot_label, ref_plot_label),
#vmin=fract_diff_min, vmax=fract_diff_max
)
# Plot the asymmetry (test - ref)/sqrt(ref)
plotter = Plotter(stamp='',
outdir=outdir,
fmt=plot_format,
log=False,
annotate=False,
symmetric=asymm_symm,
ratio=True)
plotter.label = (r'$(%s - %s)/\sqrt{%s}$' %
(test_plot_label, ref_plot_label, ref_plot_label))
plotter.plot_2d_array(
(test - ref) / MapSet([zero_to_nan(r**0.5) for r in ref]),
fname='asymm__%s__%s' % (test_plot_label, ref_plot_label),
#vmin=asymm_min, vmax=asymm_max
)
return summary_stats, diff, fract_diff, asymm
def main():
args = parse_args()
init_args_d = vars(args)
# NOTE: Removing extraneous args that won't get passed to instantiate the
# HypoTesting object via dictionary's `pop()` method.
set_verbosity(init_args_d.pop('v'))
detector = init_args_d.pop('detector')
selection = init_args_d.pop('selection')
# Normalize and convert `*_pipeline` filenames; store to `*_maker`
# (which is argument naming convention that HypoTesting init accepts).
filenames = init_args_d.pop('pipeline')
if filenames is not None:
filenames = sorted([normcheckpath(fname) for fname in filenames])
ps_str = init_args_d['param_selections']
if ps_str is None:
ps_list = None
else:
ps_list = [x.strip().lower() for x in ps_str.split(',')]
data_maker = DistributionMaker(filenames)
data_maker.select_params(ps_list)
for data_pipeline in data_maker.pipelines:
# Need a special case where PID is a separate stage
if 'pid' in data_pipeline.stage_names:
raise ValueError("Special case for separate PID stage currently "
"not implemented.")
else:
return_sum = True
baseline_maps = data_maker.get_outputs(return_sum=return_sum)
det_sel = []
if detector.strip() != '':
det_sel.append(detector.strip())
if selection.strip() != '':
det_sel.append(selection.strip())
det_sel_label = ' '.join(det_sel)
det_sel_plot_label = det_sel_label
if det_sel_plot_label != '':
det_sel_plot_label += ', '
det_sel_file_label = det_sel_label
if det_sel_file_label != '':
det_sel_file_label += '_'
det_sel_file_label = det_sel_file_label.replace(' ', '_')
for data_param in data_maker.params.free:
# Calculate a shifted value based on the prior if possible
if hasattr(data_param, 'prior') and (data_param.prior is not None):
# Gaussian priors are easy - just do 1 sigma
if data_param.prior.kind == 'gaussian':
data_param.value = \
data_param.value + data_param.prior.stddev
shift_label = r"$1\sigma$"
# Else do 10%, or +/- 1 if the baseline is zero
else:
if data_param.value != 0.0:
data_param.value = 1.1 * data_param.value
shift_label = r"10%"
else:
data_param.value = 1.0
shift_label = r"1"
# For no prior also do 10%, or +/- 1 if the baseline is zero
else:
if data_param.value != 0.0:
data_param.value = 1.1 * data_param.value
shift_label = r"10%"
else:
data_param.value = 1.0
shift_label = r"1"
up_maps = data_maker.get_outputs(return_sum=return_sum)
data_maker.params.reset_free()
if hasattr(data_param, 'prior') and (data_param.prior is not None):
if data_param.prior.kind == 'gaussian':
data_param.value = \
data_param.value - data_param.prior.stddev
else:
if data_param.value != 0.0:
data_param.value = 0.9 * data_param.value
else:
data_param.value = -1.0
else:
if data_param.value != 0.0:
data_param.value = 0.9 * data_param.value
else:
data_param.value = -1.0
down_maps = data_maker.get_outputs(return_sum=return_sum)
data_maker.params.reset_free()
baseline_map = baseline_maps['total']
baseline_map.set_errors(error_hist=None)
up_map = up_maps['total']
up_map.set_errors(error_hist=None)
down_map = down_maps['total']
down_map.set_errors(error_hist=None)
pid_names = baseline_map.binning['pid'].bin_names
if pid_names is None:
logging.warn('There are no names given for the PID bins, thus '
'they will just be numbered in both the the plot '
'save names and titles.')
pid_names = [
x for x in range(0, baseline_map.binning['pid'].num_bins)
]
gridspec_kw = dict(left=0.04, right=0.966, wspace=0.32)
fig, axes = plt.subplots(nrows=2,
ncols=len(pid_names),
gridspec_kw=gridspec_kw,
sharex=False,
sharey=False,
figsize=(7 * len(pid_names), 14))
for i, pid_name in enumerate(pid_names):
baseline = baseline_map.split(dim='pid', bin=pid_name)
up_to_plot = up_map.split(dim='pid', bin=pid_name)
up_to_plot = (up_to_plot - baseline) / baseline * 100.0
down_to_plot = down_map.split(dim='pid', bin=pid_name)
down_to_plot = (down_to_plot - baseline) / baseline * 100.0
if isinstance(pid_name, int):
pid_name = 'PID Bin %i' % (pid_name)
else:
pid_name += ' Channel'
up_to_plot.plot(fig=fig,
ax=axes[0][i],
title="%s " % (pid_name) + "\n" + " %s + %s" %
(tex_axis_label(data_param.name), shift_label),
titlesize=30,
cmap=plt.cm.seismic,
clabel='% Change from Baseline',
clabelsize=30,
xlabelsize=24,
ylabelsize=24,
symm=True)
down_to_plot.plot(fig=fig,
ax=axes[1][i],
title="%s " % (pid_name) + "\n" + " %s - %s" %
(tex_axis_label(data_param.name), shift_label),
titlesize=30,
cmap=plt.cm.seismic,
clabel='% Change from Baseline',
clabelsize=30,
xlabelsize=24,
ylabelsize=24,
symm=True)
fig.subplots_adjust(hspace=0.4)
savename = det_sel_file_label
if savename != '' and savename[-1] != '_':
savename += '_'
savename += '%s_variation.png' % (data_param.name)
mkdir(args.logdir, warn=False)
fig.savefig(os.path.join(args.logdir, savename), bbox_inches='tight')
plt.close(fig.number)