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update string gen with new helpers
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@TomDonoghue
TomDonoghue committed Mar 26, 2024
1 parent 6e641a4 commit 626479f
Showing 1 changed file with 19 additions and 36 deletions.
55 changes: 19 additions & 36 deletions specparam/core/strings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@

from specparam.core.errors import NoModelError
from specparam.data.utils import get_periodic_labels
from specparam.utils.data import compute_presence
from specparam.utils.data import compute_arr_desc, compute_presence
from specparam.version import __version__ as MODULE_VERSION

###################################################################################################
Expand Down Expand Up @@ -384,10 +384,10 @@ def gen_group_results_str(group, concise=False):
'',
'Aperiodic Fit Values:',
*[el for el in [' Knees - Min: {:6.2f}, Max: {:6.2f}, Mean: {:5.2f}'
.format(np.nanmin(kns), np.nanmax(kns), np.nanmean(kns)),
.format(*compute_arr_desc(kns)),
] if group.aperiodic_mode == 'knee'],
'Exponents - Min: {:6.3f}, Max: {:6.3f}, Mean: {:5.3f}'
.format(np.nanmin(exps), np.nanmax(exps), np.nanmean(exps)),
.format(*compute_arr_desc(exps)),
'',

# Peak Parameters
Expand All @@ -398,9 +398,9 @@ def gen_group_results_str(group, concise=False):
# Goodness if fit
'Goodness of fit metrics:',
' R2s - Min: {:6.3f}, Max: {:6.3f}, Mean: {:5.3f}'
.format(np.nanmin(r2s), np.nanmax(r2s), np.nanmean(r2s)),
.format(*compute_arr_desc(r2s)),
'Errors - Min: {:6.3f}, Max: {:6.3f}, Mean: {:5.3f}'
.format(np.nanmin(errors), np.nanmax(errors), np.nanmean(errors)),
.format(*compute_arr_desc(errors)),
'',

# Footer
Expand Down Expand Up @@ -469,14 +469,11 @@ def gen_time_results_str(time_model, concise=False):
'',
'Aperiodic Fit Values:',
*[el for el in [' Knees - Min: {:6.2f}, Max: {:6.2f}, Mean: {:6.2f}'
.format(np.nanmin(time_model.time_results['knee'] if has_knee else 0),
np.nanmax(time_model.time_results['knee'] if has_knee else 0),
np.nanmean(time_model.time_results['knee'] if has_knee else 0)),
.format(*compute_arr_desc(time_model.time_results['knee']) \
if has_knee else [0, 0, 0]),
] if has_knee],
'Exponents - Min: {:6.3f}, Max: {:6.3f}, Mean: {:5.3f}'
.format(np.nanmin(time_model.time_results['exponent']),
np.nanmax(time_model.time_results['exponent']),
np.nanmean(time_model.time_results['exponent'])),
.format(*compute_arr_desc(time_model.time_results['exponent'])),
'',

# Periodic parameters
Expand All @@ -486,21 +483,16 @@ def gen_time_results_str(time_model, concise=False):
np.nanmean(time_model.time_results[pe_labels['cf'][ind]]),
np.nanmean(time_model.time_results[pe_labels['pw'][ind]]),
np.nanmean(time_model.time_results[pe_labels['bw'][ind]]),
100 * sum(~np.isnan(time_model.time_results[pe_labels['cf'][ind]])) \
/ len(time_model.time_results[pe_labels['cf'][ind]])) \
compute_presence(time_model.time_results[pe_labels['cf'][ind]]))
for ind, label in enumerate(band_labels)],
'',

# Goodness if fit
'Goodness of fit (mean values across windows):',
' R2s - Min: {:6.3f}, Max: {:6.3f}, Mean: {:5.3f}'
.format(np.nanmin(time_model.time_results['r_squared']),
np.nanmax(time_model.time_results['r_squared']),
np.nanmean(time_model.time_results['r_squared'])),
.format(*compute_arr_desc(time_model.time_results['r_squared'])),
'Errors - Min: {:6.3f}, Max: {:6.3f}, Mean: {:5.3f}'
.format(np.nanmin(time_model.time_results['error']),
np.nanmax(time_model.time_results['error']),
np.nanmean(time_model.time_results['error'])),
.format(*compute_arr_desc(time_model.time_results['error'])),
'',

# Footer
Expand Down Expand Up @@ -567,17 +559,11 @@ def gen_event_results_str(event_model, concise=False):
'',
'Aperiodic params (values across events):',
*[el for el in [' Knees - Min: {:6.2f}, Max: {:6.2f}, Mean: {:6.2f}'
.format(np.nanmin(np.mean(event_model.event_time_results['knee'], 1) \
if has_knee else 0),
np.nanmax(np.mean(event_model.event_time_results['knee'], 1) \
if has_knee else 0),
np.nanmean(np.mean(event_model.event_time_results['knee'], 1) \
if has_knee else 0)),
.format(*compute_arr_desc(np.mean(event_model.event_time_results['knee'], 1) \
if has_knee else [0, 0, 0])),
] if has_knee],
'Exponents - Min: {:6.3f}, Max: {:6.3f}, Mean: {:5.3f}'
.format(np.nanmin(np.mean(event_model.event_time_results['exponent'], 1)),
np.nanmax(np.mean(event_model.event_time_results['exponent'], 1)),
np.nanmean(np.mean(event_model.event_time_results['exponent'], 1))),
.format(*compute_arr_desc(np.mean(event_model.event_time_results['exponent'], 1))),
'',

# Periodic parameters
Expand All @@ -587,21 +573,18 @@ def gen_event_results_str(event_model, concise=False):
np.nanmean(event_model.event_time_results[pe_labels['cf'][ind]]),
np.nanmean(event_model.event_time_results[pe_labels['pw'][ind]]),
np.nanmean(event_model.event_time_results[pe_labels['bw'][ind]]),
100 * sum(sum(~np.isnan(event_model.event_time_results[pe_labels['cf'][ind]]))) \
/ event_model.event_time_results[pe_labels['cf'][ind]].size)
compute_presence(event_model.event_time_results[pe_labels['cf'][ind]],
average=True, output='percent'))
for ind, label in enumerate(band_labels)],
'',

# Goodness if fit
'Goodness of fit (values across events):',
' R2s - Min: {:6.3f}, Max: {:6.3f}, Mean: {:5.3f}'
.format(np.nanmin(np.mean(event_model.event_time_results['r_squared'], 1)),
np.nanmax(np.mean(event_model.event_time_results['r_squared'], 1)),
np.nanmean(np.mean(event_model.event_time_results['r_squared'], 1))),
.format(*compute_arr_desc(np.mean(event_model.event_time_results['r_squared'], 1))),

'Errors - Min: {:6.3f}, Max: {:6.3f}, Mean: {:5.3f}'
.format(np.nanmin(np.mean(event_model.event_time_results['error'], 1)),
np.nanmax(np.mean(event_model.event_time_results['error'], 1)),
np.nanmean(np.mean(event_model.event_time_results['error'], 1))),
.format(*compute_arr_desc(np.mean(event_model.event_time_results['error'], 1))),
'',

# Footer
Expand Down

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