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[MNT] - Add check_freqs to check for uneven frequency samples #269

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Merged
TomDonoghue merged 1 commit into from
Jun 29, 2023
Merged

[MNT] - Add check_freqs to check for uneven frequency samples #269

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13 changes: 12 additions & 1 deletion fooof/objs/fit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -197,7 +197,10 @@ def __init__(self, peak_width_limits=(0.5, 12.0), max_n_peaks=np.inf, min_peak_h
## RUN MODES
# Set default debug mode - controls if an error is raised if model fitting is unsuccessful
self._debug = False
# Set default check data mode - controls if an error is raised if NaN / Inf data are added
# Set default data checking modes - controls which checks get run on input data
# check_freqs: check the frequency values, and raises an error for uneven spacing
self._check_freqs = True
# check_data: checks the power values and raises an error for any NaN / Inf values
self._check_data = True

# Set internal settings, based on inputs, and initialize data & results attributes
Expand Down Expand Up @@ -1221,6 +1224,14 @@ def _prepare_data(self, freqs, power_spectrum, freq_range, spectra_dim=1):
# Log power values
power_spectrum = np.log10(power_spectrum)

## Data checks - run checks on inputs based on check modes

if self._check_freqs:
# Check if the frequency data is unevenly spaced, and raise an error if so
freq_diffs = np.diff(freqs)
if not np.all(np.isclose(freq_diffs, freq_res)):
raise DataError("The input frequency values are not evenly spaced. "
"The model expects equidistant frequency values in linear space.")
if self._check_data:
# Check if there are any infs / nans, and raise an error if so
if np.any(np.isinf(power_spectrum)) or np.any(np.isnan(power_spectrum)):
Expand Down
6 changes: 5 additions & 1 deletion fooof/tests/objs/test_fit.py
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Expand Up @@ -161,7 +161,11 @@ def test_fooof_checks():
tfm.fit(xs, ys)
assert tfm.freqs[0] != 0

# Check error if there is a post-logging inf or nan
# Check error for `check_freqs` - for if there is non-even frequency values
with raises(DataError):
tfm.fit(np.array([1, 2, 4]), np.array([1, 2, 3]))

# Check error for `check_data` - for 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
Expand Down