[MNT] - Minor updates & fixes

specparam/plts/event.py

@@ -78,7 +78,7 @@ def plot_event_model(event, **plot_kwargs):
             color=PARAM_COLORS['presence'], ax=next(axes))
         next(axes).axis('off')
 
-    # 03: goodness of fit
+    # 03: metrics
     for ind, glabel in enumerate(event.results.metrics.labels):
         plot_param_over_time_yshade(\
             None, event.results.event_time_results[glabel],

specparam/plts/group.py

@@ -53,7 +53,7 @@ def plot_group_model(group, **plot_kwargs):
 
     # Goodness of fit plot
     ax1 = plt.subplot(gs[0, 1])
-    plot_group_goodness(group, ax1, **scatter_kwargs, custom_styler=None)
+    plot_group_metrics(group, ax1, **scatter_kwargs, custom_styler=None)
 
     # Center frequencies plot
     ax2 = plt.subplot(gs[1, :])
@@ -79,17 +79,17 @@ def plot_group_aperiodic(group, ax=None, **plot_kwargs):
     if group.modes.aperiodic.name == 'knee':
         plot_scatter_2(group.results.get_params('aperiodic', 'exponent'), 'Exponent',
                        group.results.get_params('aperiodic', 'knee'), 'Knee',
-                       'Aperiodic Fit', ax=ax)
+                       'Aperiodic Parameters', ax=ax)
     else:
         plot_scatter_1(group.results.get_params('aperiodic', 'exponent'), 'Exponent',
-                       'Aperiodic Fit', ax=ax)
+                       'Aperiodic Parameters', ax=ax)
 
 
 @savefig
 @style_plot
 @check_dependency(plt, 'matplotlib')
-def plot_group_goodness(group, ax=None, **plot_kwargs):
-    """Plot goodness of fit results, in a scatter plot.
+def plot_group_metrics(group, ax=None, **plot_kwargs):
+    """Plot metrics results, in a scatter plot.
 
     Parameters
     ----------
@@ -101,17 +101,26 @@ def plot_group_goodness(group, ax=None, **plot_kwargs):
         Additional plot related keyword arguments, with styling options managed by ``style_plot``.
     """
 
-    # Get indices of metrics to plot
-    err_ind = find_first_ind(group.results.metrics.labels, 'error')
-    err_label = group.results.metrics.labels[err_ind]
-    gof_ind = find_first_ind(group.results.metrics.labels, 'gof')
-    gof_label = group.results.metrics.labels[gof_ind]
+    if len(group.results.metrics) == 0:
+        ax.set(xticks=[], yticks=[])
 
-    plot_scatter_2(group.results.get_metrics(err_label),
-                   group.results.metrics.flabels[err_ind],
-                   group.results.get_metrics(gof_label),
-                   group.results.metrics.flabels[gof_ind],
-                   'Fit Quality', ax=ax)
+    if len(group.results.metrics) == 1:
+        plot_scatter_1(group.results.get_metrics(group.results.metrics.labels[0]),
+                       group.results.metrics.flabels[0],
+                       'Metrics', ax=ax)
+
+    elif len(group.results.metrics) >= 2:
+        ind1 = 0
+        ind2 = 1
+        if 'error' in group.results.metrics.categories:
+            ind1 = find_first_ind(group.results.metrics.labels, 'error')
+        if 'gof' in group.results.metrics.categories:
+            ind2 = find_first_ind(group.results.metrics.labels, 'gof')
+        plot_scatter_2(group.results.get_metrics(group.results.metrics.labels[ind1]),
+                       group.results.metrics.flabels[ind1],
+                       group.results.get_metrics(group.results.metrics.labels[ind2]),
+                       group.results.metrics.flabels[ind2],
+                       'Metrics', ax=ax)
 
 
 @savefig
@@ -130,5 +139,5 @@ def plot_group_peak_frequencies(group, ax=None, **plot_kwargs):
         Additional plot related keyword arguments, with styling options managed by ``style_plot``.
     """
 
-    plot_hist(group.results.get_params('peak', 0)[:, 0], 'Center Frequency',
-              'Peaks - Center Frequencies', x_lims=group.data.freq_range, ax=ax)
+    plot_hist(group.results.get_params('peak', 'cf')[:, 0], 'Center Frequency',
+              'Peak Parameters - Center Frequencies', x_lims=group.data.freq_range, ax=ax)

specparam/plts/time.py

@@ -66,7 +66,7 @@ def plot_time_model(time, **plot_kwargs):
             colors=[PARAM_COLORS[plabel] for plabel in time.modes.periodic.params.labels],
             title='Periodic Parameters - ' + blabel, ax=next(axes))
 
-    # 03: goodness of fit
+    # 03: metrics
     err_ind = find_first_ind(time.results.metrics.labels, 'error')
     gof_ind = find_first_ind(time.results.metrics.labels, 'gof')
     plot_params_over_time(None, \

specparam/reports/save.py

@@ -3,7 +3,7 @@
 from specparam.io.utils import create_file_path
 from specparam.modutils.dependencies import safe_import, check_dependency
 from specparam.plts.templates import plot_text
-from specparam.plts.group import (plot_group_aperiodic, plot_group_goodness,
+from specparam.plts.group import (plot_group_aperiodic, plot_group_metrics,
                                   plot_group_peak_frequencies)
 from specparam.reports.strings import (gen_settings_str, gen_model_results_str,
                                        gen_group_results_str, gen_time_results_str,
@@ -99,7 +99,7 @@ def save_group_report(group, file_name, file_path=None, add_settings=True):
 
     # Goodness of fit plot
     ax2 = plt.subplot(grid[1, 1])
-    plot_group_goodness(group, ax2, custom_styler=None)
+    plot_group_metrics(group, ax2, custom_styler=None)
 
     # Peak center frequencies plot
     ax3 = plt.subplot(grid[2, :])

specparam/reports/strings.py

@@ -323,7 +323,6 @@ def gen_methods_report_str(concise=False):
     return output
 
 
-# TODO: UPDATE
 def gen_methods_text_str(model=None):
     """Generate a string representation of a template methods report.
 
@@ -334,32 +333,43 @@ def gen_methods_text_str(model=None):
         If None, the text is returned as a template, without values.
     """
 
-    template = (
+    if model:
+        settings_names = list(model.algorithm.settings.values.keys())
+        settings_values = list(model.algorithm.settings.values.values())
+    else:
+        settings_names = []
+        settings_values = []
+
+    template = [
         "The periodic & aperiodic spectral parameterization algorithm (version {}) "
         "was used to parameterize neural power spectra. "
         "The model was fit with {} aperiodic mode and {} periodic mode. "
         "Settings for the algorithm were set as: "
-        "peak width limits : {}; "
-        "max number of peaks : {}; "
-        "minimum peak height : {}; "
-        "peak threshold : {}; ."
+    ]
+
+    if settings_names:
+        settings_strs = [el + ' : {}, ' for el in settings_names]
+        settings_strs[-1] = settings_strs[-1][:-2] + '. '
+        template.extend(settings_strs)
+    else:
+        template.extend('XX. ')
+
+    template.extend([
         "Power spectra were parameterized across the frequency range "
         "{} to {} Hz."
-    )
+    ])
 
-    if model:
-        freq_range = model.data.freq_range if model.data.has_data else ('XX', 'XX')
+    if model and model.data.has_data:
+        freq_range = model.data.freq_range
     else:
         freq_range = ('XX', 'XX')
 
-    methods_str = template.format(MODULE_VERSION,
-                                  model.modes.aperiodic.name if model else 'XX',
-                                  model.modes.periodic.name if model else 'XX',
-                                  model.algorithm.settings.peak_width_limits if model else 'XX',
-                                  model.algorithm.settings.max_n_peaks if model else 'XX',
-                                  model.algorithm.settings.min_peak_height if model else 'XX',
-                                  model.algorithm.settings.peak_threshold if model else 'XX',
-                                  *freq_range)
+    methods_str = ''.join(template).format(\
+        MODULE_VERSION,
+        model.modes.aperiodic.name if model else 'XX',
+        model.modes.periodic.name if model else 'XX',
+        *settings_values,
+        *freq_range)
 
     return methods_str
 
@@ -401,21 +411,18 @@ def gen_model_results_str(model, concise=False):
         _report_str_model(model),
         '',
 
-        # Aperiodic parameters
         'Aperiodic Parameters (\'{}\' mode)'.format(model.modes.aperiodic.name),
         '(' + ', '.join(model.modes.aperiodic.params.labels) + ')',
         ', '.join(['{:2.4f}'] * \
             len(model.results.params.aperiodic.params)).format(*model.results.params.aperiodic.params),
         '',
 
-        # Peak parameters
         'Peak Parameters (\'{}\' mode) {} peaks found'.format(\
             model.modes.periodic.name, model.results.n_peaks),
         *[peak_str.format(*op) for op in model.results.params.periodic.params],
         '',
 
-        # Metrics
-        'Model fit quality metrics:',
+        'Model metrics:',
         *['{:>18s} is {:1.4f} {:8s}'.format('{:s} ({:s})'.format(*key.split('_')), res, ' ') \
             for key, res in model.results.metrics.results.items()],
         '',
@@ -460,28 +467,31 @@ def gen_group_results_str(group, concise=False):
         _report_str_model(group),
         '',
 
-        # Aperiodic parameters
         'Aperiodic Parameters (\'{}\' mode)'.format(group.modes.aperiodic.name),
         *[el for el in [\
             '{:8s} - Min: {:6.2f}, Max: {:6.2f}, Mean: {:5.2f}'.format(label, \
                 *compute_arr_desc(group.results.get_params('aperiodic', label))) \
                     for label in group.modes.aperiodic.params.labels]],
         '',
 
-        # Peak Parameters
         'Peak Parameters (\'{}\' mode) {} total peaks found'.format(\
             group.modes.periodic.name, sum(group.results.n_peaks)),
         '',
+    ]
 
-        # Metrics
-        'Model fit quality metrics:',
-        *['{:>18s} -  Min: {:6.3f}, Max: {:6.3f}, Mean: {:5.3f}'.format(\
-            '{:s} ({:s})'.format(*label.split('_')),
-            *compute_arr_desc(group.results.get_metrics(label))) \
-                for label in group.results.metrics.labels],
-        '',
+    if len(group.results.metrics) > 0:
+        str_lst.extend([
+            'Model metrics:',
+            *['{:>18s} -  Min: {:6.3f}, Max: {:6.3f}, Mean: {:5.3f}'.format(\
+                '{:s} ({:s})'.format(*label.split('_')),
+                *compute_arr_desc(group.results.get_metrics(label))) \
+                    for label in group.results.metrics.labels],
+            '',
+            ])
+
+    str_lst.extend([
         DIVIDER,
-    ]
+    ])
 
     output = _format(str_lst, concise)
 
@@ -525,15 +535,13 @@ def gen_time_results_str(time, concise=False):
         _report_str_model(time),
         '',
 
-        # Aperiodic parameters
         'Aperiodic Parameters (\'{}\' mode)'.format(time.modes.aperiodic.name),
         *[el for el in [\
             '{:8s} - Min: {:6.2f}, Max: {:6.2f}, Mean: {:5.2f}'.format(label, \
                 *compute_arr_desc(time.results.time_results[label])) \
                     for label in time.modes.aperiodic.params.labels]],
         '',
 
-        # Peak Parameters
         'Peak Parameters (\'{}\' mode) - mean values across windows'.format(\
             time.modes.periodic.name),
         *[peak_str.format(*[band_label] + \
@@ -543,8 +551,7 @@ def gen_time_results_str(time, concise=False):
             for band_label in time.results.bands.labels],
         '',
 
-        # Metrics
-        'Model fit quality metrics (values across windows):',
+        'Model metrics (values across windows):',
         *['{:>18s} -  Min: {:6.3f}, Max: {:6.3f}, Mean: {:5.3f}'.format(\
             '{:s} ({:s})'.format(*key.split('_')),
             *compute_arr_desc(time.results.time_results[key])) \
@@ -597,15 +604,13 @@ def gen_event_results_str(event, concise=False):
         _report_str_model(event),
         '',
 
-        # Aperiodic parameters
         'Aperiodic Parameters (\'{}\' mode)'.format(event.modes.aperiodic.name),
         *[el for el in [\
             '{:8s} - Min: {:6.2f}, Max: {:6.2f}, Mean: {:5.2f}'.format(label, \
                 *compute_arr_desc(np.mean(event.results.event_time_results[label]))) \
                     for label in event.modes.aperiodic.params.labels]],
         '',
 
-        # Peak Parameters
         'Peak Parameters (\'{}\' mode) - mean values across windows'.format(\
             event.modes.periodic.name),
         *[peak_str.format(*[band_label] + \
@@ -616,8 +621,7 @@ def gen_event_results_str(event, concise=False):
             for band_label in event.results.bands.labels],
         '',
 
-        # Metrics
-        'Model fit quality metrics (values across events):',
+        'Model metrics (values across events):',
         *['{:>18s} -  Min: {:6.3f}, Max: {:6.3f}, Mean: {:5.3f}'.format(\
             '{:s} ({:s})'.format(*key.split('_')),
             *compute_arr_desc(np.mean(event.results.event_time_results[key], 1))) \

specparam/results/results.py

@@ -160,7 +160,7 @@ def add_results(self, results):
 
 
     def get_results(self):
-        """Return model fit parameters and goodness of fit metrics.
+        """Return model fit parameters and metrics.
 
         Returns
         -------

specparam/tests/models/test_model.py

@@ -110,15 +110,15 @@ def test_fit_knee():
         assert np.allclose(gauss, tfm.results.params.periodic.get_params('fit')[ii], [2.0, 0.5, 1.0])
 
 def test_fit_default_metrics():
-    """Test goodness of fit & error metrics, post model fitting."""
+    """Test computing metrics, post model fitting."""
 
     tfm = SpectralModel(verbose=False)
 
     # Hack fake data with known properties: total error magnitude 2
     tfm.data.power_spectrum = np.array([1, 2, 3, 4, 5])
     tfm.results.model.modeled_spectrum = np.array([1, 2, 5, 4, 5])
 
-    # Check default goodness of fit and error measures
+    # Check default metrics
     tfm.results.metrics.compute_metrics(tfm.data, tfm.results)
     assert np.isclose(tfm.results.metrics.results['error_mae'], 0.4)
     assert np.isclose(tfm.results.metrics.results['gof_rsquared'], 0.75757575)

specparam/tests/plts/test_group.py

@@ -31,10 +31,10 @@ def test_plot_group_aperiodic(tfg, skip_if_no_mpl):
                          file_name='test_plot_group_aperiodic.png')
 
 @plot_test
-def test_plot_group_goodness(tfg, skip_if_no_mpl):
+def test_plot_group_metrics(tfg, skip_if_no_mpl):
 
-    plot_group_goodness(tfg, file_path=TEST_PLOTS_PATH,
-                        file_name='test_plot_group_goodness.png')
+    plot_group_metrics(tfg, file_path=TEST_PLOTS_PATH,
+                       file_name='test_plot_group_metrics.png')
 
 @plot_test
 def test_plot_group_peak_frequencies(tfg, skip_if_no_mpl):