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[MNT] - Minor updates from linting #270

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Merged
merged 1 commit into from
Jun 30, 2023
Merged

[MNT] - Minor updates from linting #270

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59 changes: 33 additions & 26 deletions fooof/objs/fit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -252,7 +252,7 @@ def _reset_internal_settings(self):

# Bandwidth limits are given in 2-sided peak bandwidth
# Convert to gaussian std parameter limits
self._gauss_std_limits = tuple([bwl / 2 for bwl in self.peak_width_limits])
self._gauss_std_limits = tuple(bwl / 2 for bwl in self.peak_width_limits)

# Otherwise, assume settings are unknown (have been cleared) and set to None
else:
Expand Down Expand Up @@ -378,7 +378,8 @@ def add_results(self, fooof_result):
self._check_loaded_results(fooof_result._asdict())


def report(self, freqs=None, power_spectrum=None, freq_range=None, plt_log=False, **plot_kwargs):
def report(self, freqs=None, power_spectrum=None, freq_range=None,
plt_log=False, **plot_kwargs):
"""Run model fit, and display a report, which includes a plot, and printed results.

Parameters
Expand Down Expand Up @@ -791,9 +792,10 @@ def _simple_ap_fit(self, freqs, power_spectrum):
aperiodic_params, _ = curve_fit(get_ap_func(self.aperiodic_mode),
freqs, power_spectrum, p0=guess,
maxfev=self._maxfev, bounds=ap_bounds)
except RuntimeError:
raise FitError("Model fitting failed due to not finding parameters in "
"the simple aperiodic component fit.")
except RuntimeError as excp:
error_msg = ("Model fitting failed due to not finding parameters in "
"the simple aperiodic component fit.")
raise FitError(error_msg) from excp

return aperiodic_params

Expand Down Expand Up @@ -847,12 +849,14 @@ def _robust_ap_fit(self, freqs, power_spectrum):
aperiodic_params, _ = curve_fit(get_ap_func(self.aperiodic_mode),
freqs_ignore, spectrum_ignore, p0=popt,
maxfev=self._maxfev, bounds=ap_bounds)
except RuntimeError:
raise FitError("Model fitting failed due to not finding "
"parameters in the robust aperiodic fit.")
except TypeError:
raise FitError("Model fitting failed due to sub-sampling "
"in the robust aperiodic fit.")
except RuntimeError as excp:
error_msg = ("Model fitting failed due to not finding "
"parameters in the robust aperiodic fit.")
raise FitError(error_msg) from excp
except TypeError as excp:
error_msg = ("Model fitting failed due to sub-sampling "
"in the robust aperiodic fit.")
raise FitError(error_msg) from excp

return aperiodic_params

Expand Down Expand Up @@ -981,8 +985,8 @@ def _fit_peak_guess(self, guess):

# Unpacks the embedded lists into flat tuples
# This is what the fit function requires as input
gaus_param_bounds = (tuple([item for sublist in lo_bound for item in sublist]),
tuple([item for sublist in hi_bound for item in sublist]))
gaus_param_bounds = (tuple(item for sublist in lo_bound for item in sublist),
tuple(item for sublist in hi_bound for item in sublist))

# Flatten guess, for use with curve fit
guess = np.ndarray.flatten(guess)
Expand All @@ -991,13 +995,15 @@ def _fit_peak_guess(self, guess):
try:
gaussian_params, _ = curve_fit(gaussian_function, self.freqs, self._spectrum_flat,
p0=guess, maxfev=self._maxfev, bounds=gaus_param_bounds)
except RuntimeError:
raise FitError("Model fitting failed due to not finding "
"parameters in the peak component fit.")
except LinAlgError:
raise FitError("Model fitting failed due to a LinAlgError during peak fitting. "
"This can happen with settings that are too liberal, leading, "
"to a large number of guess peaks that cannot be fit together.")
except RuntimeError as excp:
error_msg = ("Model fitting failed due to not finding "
"parameters in the peak component fit.")
raise FitError(error_msg) from excp
except LinAlgError as excp:
error_msg = ("Model fitting failed due to a LinAlgError during peak fitting. "
"This can happen with settings that are too liberal, leading, "
"to a large number of guess peaks that cannot be fit together.")
raise FitError(error_msg) from excp

# Re-organize params into 2d matrix
gaussian_params = np.array(group_three(gaussian_params))
Expand Down Expand Up @@ -1164,8 +1170,8 @@ def _calc_error(self, metric=None):
self.error_ = np.sqrt(((self.power_spectrum - self.fooofed_spectrum_) ** 2).mean())

else:
msg = "Error metric '{}' not understood or not implemented.".format(metric)
raise ValueError(msg)
error_msg = "Error metric '{}' not understood or not implemented.".format(metric)
raise ValueError(error_msg)


def _prepare_data(self, freqs, power_spectrum, freq_range, spectra_dim=1):
Expand Down Expand Up @@ -1258,10 +1264,11 @@ def _prepare_data(self, freqs, power_spectrum, freq_range, spectra_dim=1):
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)):
raise DataError("The input power spectra data, after logging, contains NaNs or Infs. "
"This will cause the fitting to fail. "
"One reason this can happen is if inputs are already logged. "
"Inputs data should be in linear spacing, not log.")
error_msg = ("The input power spectra data, after logging, contains NaNs or Infs. "
"This will cause the fitting to fail. "
"One reason this can happen is if inputs are already logged. "
"Inputs data should be in linear spacing, not log.")
raise DataError(error_msg)

return freqs, power_spectrum, freq_range, freq_res

Expand Down
2 changes: 1 addition & 1 deletion fooof/objs/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -179,7 +179,7 @@ def combine_fooofs(fooofs):
fg.power_spectra = temp_power_spectra

# Set the check data mode, as True if any of the inputs have it on, False otherwise
fg.set_check_data_mode(any([getattr(f_obj, '_check_data') for f_obj in fooofs]))
fg.set_check_data_mode(any(getattr(f_obj, '_check_data') for f_obj in fooofs))

# Add data information information
fg.add_meta_data(fooofs[0].get_meta_data())
Expand Down