def test_fg_fail():
"""Test FOOOFGroup fit, in a way that some fits will fail.
Also checks that model failures don't cause errors.
"""
# Create some noisy spectra that will be hard to fit
fs, ps = gen_group_power_spectra(10, [3, 6], [1, 1], [10, 1, 1], nlvs=10)
# Use a fg with the max iterations set so low that it will fail to converge
ntfg = FOOOFGroup()
ntfg._maxfev = 5
# Fit models, where some will fail, to see if it completes cleanly
ntfg.fit(fs, ps)
# Check that results are all
for res in ntfg.get_results():
assert res
# Test that get_params works with failed model fits
outs1 = ntfg.get_params('aperiodic_params')
outs2 = ntfg.get_params('aperiodic_params', 'exponent')
outs3 = ntfg.get_params('peak_params')
outs4 = ntfg.get_params('peak_params', 0)
outs5 = ntfg.get_params('gaussian_params', 2)
# Test shortcut labels
outs6 = ntfg.get_params('aperiodic')
outs6 = ntfg.get_params('peak', 'CF')
# Test the property attributes related to null model fits
# This checks that they do the right thing when there are null fits (failed fits)
assert ntfg.n_null_ > 0
assert ntfg.null_inds_
def test_fg_drop():
"""Test function to drop results from FOOOFGroup."""
n_spectra = 3
xs, ys = gen_group_power_spectra(n_spectra, *default_group_params())
tfg = FOOOFGroup(verbose=False)
# Test dropping one ind
tfg.fit(xs, ys)
tfg.drop(0)
dropped_fres = tfg.group_results[0]
for field in dropped_fres._fields:
assert np.all(np.isnan(getattr(dropped_fres, field)))
# Test dropping multiple inds
tfg.fit(xs, ys)
drop_inds = [0, 2]
tfg.drop(drop_inds)
for drop_ind in drop_inds:
dropped_fres = tfg.group_results[drop_ind]
for field in dropped_fres._fields:
assert np.all(np.isnan(getattr(dropped_fres, field)))
# Test that a FOOOFGroup that has had inds dropped still works with `get_params`
cfs = tfg.get_params('peak_params', 1)
exps = tfg.get_params('aperiodic_params', 'exponent')
assert np.all(np.isnan(exps[drop_inds]))
assert np.all(np.invert(np.isnan(np.delete(exps, drop_inds))))
def test_fg_report(skip_if_no_mpl):
"""Check that running the top level model method runs."""
n_spectra = 2
xs, ys = gen_group_power_spectra(n_spectra, *default_group_params())
tfg = FOOOFGroup(verbose=False)
tfg.report(xs, ys)
assert tfg
def test_fg_fit_nk_noise():
"""Test FOOOFGroup fit, no knee, on noisy data, to make sure nothing breaks."""
n_spectra = 5
xs, ys = gen_group_power_spectra(n_spectra, *default_group_params(), nlvs=1.0)
tfg = FOOOFGroup(max_n_peaks=8, verbose=False)
tfg.fit(xs, ys)
# No accuracy checking here - just checking that it ran
assert tfg.has_model
def test_fit_fooof_3d(tfg):
n_spectra = 2
xs, ys = gen_group_power_spectra(n_spectra, *default_group_params())
ys = np.stack([ys, ys], axis=0)
tfg = FOOOFGroup()
fgs = fit_fooof_3d(tfg, xs, ys)
assert len(fgs) == 2
for fg in fgs:
assert fg
def test_fg_fit_par():
"""Test FOOOFGroup fit, running in parallel."""
n_spectra = 2
xs, ys = gen_group_power_spectra(n_spectra, *default_group_params())
tfg = FOOOFGroup(verbose=False)
tfg.fit(xs, ys, n_jobs=2)
out = tfg.get_results()
assert out
assert len(out) == n_spectra
assert isinstance(out[0], FOOOFResults)
assert np.all(out[1].aperiodic_params)
def test_fg_fit_nk():
"""Test FOOOFGroup fit, no knee."""
n_spectra = 2
xs, ys = gen_group_power_spectra(n_spectra, *default_group_params(), nlvs=0)
tfg = FOOOFGroup(verbose=False)
tfg.fit(xs, ys)
out = tfg.get_results()
assert out
assert len(out) == n_spectra
assert isinstance(out[0], FOOOFResults)
assert np.all(out[1].aperiodic_params)
def test_fg_fit_knee():
"""Test FOOOFGroup fit, with a knee."""
n_spectra = 2
ap_params = [50, 2, 1]
gaussian_params = [10, 0.5, 2, 20, 0.3, 4]
xs, ys = gen_group_power_spectra(n_spectra, [1, 150], ap_params, gaussian_params, nlvs=0)
tfg = FOOOFGroup(aperiodic_mode='knee', verbose=False)
tfg.fit(xs, ys)
# No accuracy checking here - just checking that it ran
assert tfg.has_model
def test_fit_fooof_3d(tfg):
n_groups = 2
n_spectra = 3
xs, ys = gen_group_power_spectra(n_spectra, *default_group_params())
ys = np.stack([ys] * n_groups, axis=0)
spectra_shape = np.shape(ys)
tfg = FOOOFGroup()
fgs = fit_fooof_3d(tfg, xs, ys)
assert len(fgs) == n_groups == spectra_shape[0]
for fg in fgs:
assert fg
assert len(fg) == n_spectra
assert fg.power_spectra.shape == spectra_shape[1:]