def get_group(self, inds):
"""Get a FOOOFGroup object with the specified sub-selection of model fits.
Parameters
----------
inds : array_like of int or array_like of bool
Indices to extract from the object.
If a boolean mask, True indicates indices to select.
Returns
-------
fg : FOOOFGroup
The requested selection of results data loaded into a new FOOOFGroup object.
"""
# Check and convert indices encoding to list of int
inds = check_inds(inds)
# Initialize a new FOOOFGroup object, with same settings as current FOOOFGroup
fg = FOOOFGroup(*self.get_settings(), verbose=self.verbose)
# Add data for specified power spectra, if available
# The power spectra are inverted back to linear, as they are re-logged when added to FOOOF
if self.has_data:
fg.add_data(self.freqs, np.power(10, self.power_spectra[inds, :]))
# If no power spectrum data available, copy over data information & regenerate freqs
else:
fg.add_meta_data(self.get_meta_data())
# Add results for specified power spectra
fg.group_results = [self.group_results[ind] for ind in inds]
return fg
def drop(self, inds):
"""Drop one or more model fit results from the object.
Parameters
----------
inds : int or array_like of int or array_like of bool
Indices to drop model fit results for.
If a boolean mask, True indicates indices to drop.
Notes
-----
This method sets the model fits as null, and preserves the shape of the model fits.
"""
for ind in check_inds(inds):
fm = self.get_fooof(ind)
fm._reset_data_results(clear_results=True)
self.group_results[ind] = fm.get_results()