def test_fooof_checks():
"""Test various checks, errors and edge cases in FOOOF."""
xs, ys = gen_power_spectrum([3, 50], [50, 2], [10, 0.5, 2])
tfm = FOOOF(verbose=False)
# Check dimension error
with raises(ValueError):
tfm.fit(xs, np.reshape(ys, [1, len(ys)]))
# Check shape mismatch error
with raises(ValueError):
tfm.fit(xs[:-1], ys)
# Check wrong data type error
with raises(ValueError):
tfm.fit(list(xs), list(ys))
# Check trim_spectrum range
tfm.fit(xs, ys, [3, 40])
# Check freq of 0 issue
xs, ys = gen_power_spectrum([3, 50], [50, 2], [10, 0.5, 2])
tfm.fit(xs, ys)
assert tfm.freqs[0] != 0
# Check fit, and string report model error (no data / model fit)
tfm = FOOOF(verbose=False)
with raises(ValueError):
tfm.fit()
def test_fooof_fit_failure():
"""Test FOOOF fit failures."""
## Induce a runtime error, and check it runs through
tfm = FOOOF(verbose=False)
tfm._maxfev = 5
tfm.fit(*gen_power_spectrum([3, 50], [50, 2], [10, 0.5, 2, 20, 0.3, 4]))
# Check after failing out of fit, all results are reset
for result in OBJ_DESC['results']:
assert np.all(np.isnan(getattr(tfm, result)))
## Monkey patch to check errors in general
# This mimics the main fit-failure, without requiring bad data / waiting for it to fail.
tfm = FOOOF(verbose=False)
def raise_runtime_error(*args, **kwargs):
raise FitError('Test-MonkeyPatch')
tfm._fit_peaks = raise_runtime_error
# Run a FOOOF fit - this should raise an error, but continue in try/except
tfm.fit(*gen_power_spectrum([3, 50], [50, 2], [10, 0.5, 2, 20, 0.3, 4]))
# Check after failing out of fit, all results are reset
for result in OBJ_DESC['results']:
assert np.all(np.isnan(getattr(tfm, result)))
def test_fooof_checks():
"""Test various checks, errors and edge cases in FOOOF.
This tests all the input checking done in `_prepare_data`.
"""
xs, ys = gen_power_spectrum([3, 50], [50, 2], [10, 0.5, 2])
tfm = FOOOF(verbose=False)
## Check checks & errors done in `_prepare_data`
# Check wrong data type error
with raises(DataError):
tfm.fit(list(xs), list(ys))
# Check dimension error
with raises(DataError):
tfm.fit(xs, np.reshape(ys, [1, len(ys)]))
# Check shape mismatch error
with raises(InconsistentDataError):
tfm.fit(xs[:-1], ys)
# Check complex inputs error
with raises(DataError):
tfm.fit(xs, ys.astype('complex'))
# Check trim_spectrum range
tfm.fit(xs, ys, [3, 40])
# Check freq of 0 issue
xs, ys = gen_power_spectrum([3, 50], [50, 2], [10, 0.5, 2])
tfm.fit(xs, ys)
assert tfm.freqs[0] != 0
# Check error if there is a post-logging inf or nan
with raises(DataError): # Double log (1) -> -inf
tfm.fit(np.array([1, 2, 3]), np.log10(np.array([1, 2, 3])))
with raises(DataError): # Log (-1) -> NaN
tfm.fit(np.array([1, 2, 3]), np.array([-1, 2, 3]))
## Check errors & errors done in `fit`
# Check fit, and string report model error (no data / model fit)
tfm = FOOOF(verbose=False)
with raises(NoDataError):
tfm.fit()
def test_fooof_report(skip_if_no_mpl):
"""Check that running the top level model method runs."""
tfm = FOOOF(verbose=False)
tfm.report(*gen_power_spectrum([3, 50], [50, 2], [10, 0.5, 2, 20, 0.3, 4]))
assert tfm
def test_fooof_debug():
"""Test FOOOF in debug mode, including with fit failures."""
tfm = FOOOF(verbose=False)
tfm._maxfev = 5
tfm.set_debug_mode(True)
assert tfm._debug is True
with raises(FitError):
tfm.fit(*gen_power_spectrum([3, 50], [50, 2], [10, 0.5, 2, 20, 0.3, 4]))
def test_fooof_fit_knee():
"""Test FOOOF fit, with a knee."""
ap_params = [50, 2, 1]
gaussian_params = [10, 0.5, 2, 20, 0.3, 4]
xs, ys = gen_power_spectrum([3, 50], ap_params, gaussian_params)
tfm = FOOOF(aperiodic_mode='knee', verbose=False)
tfm.fit(xs, ys)
# Note: currently, this test has no accuracy checking at all
assert True
def test_fooof_fit_nk_noise():
"""Test FOOOF fit on noisy data, to make sure nothing breaks."""
ap_params = [50, 2]
gauss_params = [10, 0.5, 2, 20, 0.3, 4]
nlv = 1.0
xs, ys = gen_power_spectrum([3, 50], ap_params, gauss_params, nlv)
tfm = FOOOF(max_n_peaks=8, verbose=False)
tfm.fit(xs, ys)
# No accuracy checking here - just checking that it ran
assert tfm.has_model
def test_fooof_fit_knee():
"""Test FOOOF fit, with a knee."""
ap_params = [50, 10, 1]
gauss_params = [10, 0.3, 2, 20, 0.1, 4, 60, 0.3, 1]
nlv = 0.0025
xs, ys = gen_power_spectrum([1, 150], ap_params, gauss_params, nlv)
tfm = FOOOF(aperiodic_mode='knee', verbose=False)
tfm.fit(xs, ys)
# Check model results - aperiodic parameters
assert np.allclose(ap_params, tfm.aperiodic_params_, [1, 2, 0.2])
# Check model results - gaussian parameters
for ii, gauss in enumerate(group_three(gauss_params)):
assert np.allclose(gauss, tfm.gaussian_params_[ii], [2.0, 0.5, 1.0])
def test_fooof_fit_nk():
"""Test FOOOF fit, no knee."""
ap_params = [50, 2]
gauss_params = [10, 0.5, 2, 20, 0.3, 4]
nlv = 0.0025
xs, ys = gen_power_spectrum([3, 50], ap_params, gauss_params, nlv)
tfm = FOOOF(verbose=False)
tfm.fit(xs, ys)
# Check model results - aperiodic parameters
assert np.allclose(ap_params, tfm.aperiodic_params_, [0.5, 0.1])
# Check model results - gaussian parameters
for ii, gauss in enumerate(group_three(gauss_params)):
assert np.allclose(gauss, tfm.gaussian_params_[ii], [2.0, 0.5, 1.0])
def test_fooof_fit_failure():
"""Test that fit handles a failure."""
# Use a new FOOOF, that is monkey-patched to raise an error
# This mimicks the main fit-failure, without requiring bad data / waiting for it to fail.
tfm = FOOOF(verbose=False)
def raise_runtime_error(*args, **kwargs):
raise RuntimeError('Test-MonkeyPatch')
tfm._fit_peaks = raise_runtime_error
# Run a FOOOF fit - this should raise an error, but continue in try/except
tfm.fit(*gen_power_spectrum([3, 50], [50, 2], [10, 0.5, 2, 20, 0.3, 4]))
# Check after failing out of fit, all results are reset
for result in get_description()['results']:
cur_res = getattr(tfm, result)
assert cur_res is None or np.all(np.isnan(cur_res))