Merge pull request #182 from fooof-tools/nans

[ENH] Add `check_data` mode to deal with running with/without nan values

Author: TomDonoghue

fooof/objs/fit.py

@@ -39,9 +39,15 @@
     The maximum number of calls to the curve fitting function.
 _error_metric : str
     The error metric to use for post-hoc measures of model fit error.
+
+Run Modes
+---------
 _debug : bool
     Whether the object is set in debug mode.
     This should be controlled by using the `set_debug_mode` method.
+_check_data : bool
+    Whether to check added data for NaN or Inf values, and fail out if present.
+    This should be controlled by using the `set_check_data_mode` method.
 
 Code Notes
 ----------
@@ -184,12 +190,16 @@ def __init__(self, peak_width_limits=(0.5, 12.0), max_n_peaks=np.inf, min_peak_h
         # The maximum number of calls to the curve fitting function
         self._maxfev = 5000
         # The error metric to calculate, post model fitting. See `_calc_error` for options
-        #   Note: this is used to check error post-hoc, not an objective function for fitting models
+        #   Note: this is for checking error post fitting, not an objective function for fitting
         self._error_metric = 'MAE'
-        # Set whether in debug mode, in which an error is raised if a model fit fails
+
+        ## 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
+        self._check_data = True
 
-        # Set internal settings, based on inputs, & initialize data & results attributes
+        # Set internal settings, based on inputs, and initialize data & results attributes
         self._reset_internal_settings()
         self._reset_data_results(True, True, True)
 
@@ -312,7 +322,7 @@ def add_data(self, freqs, power_spectrum, freq_range=None, clear_results=True):
                                  clear_results=self.has_model and clear_results)
 
         self.freqs, self.power_spectrum, self.freq_range, self.freq_res = \
-            self._prepare_data(freqs, power_spectrum, freq_range, 1, self.verbose)
+            self._prepare_data(freqs, power_spectrum, freq_range, 1)
 
 
     def add_settings(self, fooof_settings):
@@ -432,6 +442,14 @@ def fit(self, freqs=None, power_spectrum=None, freq_range=None):
         # In rare cases, the model fails to fit, and so uses try / except
         try:
 
+            # If not set to fail on NaN or Inf data at add time, check data here
+            #   This serves as a catch all for curve_fits which will fail given NaN or Inf
+            #   Because FitError's are by default caught, this allows fitting to continue
+            if not self._check_data:
+                if np.any(np.isinf(self.power_spectrum)) or np.any(np.isnan(self.power_spectrum)):
+                    raise FitError("Model fitting was skipped because there are NaN or Inf "
+                                   "values in the data, which preclude model fitting.")
+
             # Fit the aperiodic component
             self.aperiodic_params_ = self._robust_ap_fit(self.freqs, self.power_spectrum)
             self._ap_fit = gen_aperiodic(self.freqs, self.aperiodic_params_)
@@ -675,7 +693,7 @@ def copy(self):
 
 
     def set_debug_mode(self, debug):
-        """Set whether debug mode, wherein an error is raised if fitting is unsuccessful.
+        """Set debug mode, which controls if an error is raised if model fitting is unsuccessful.
 
         Parameters
         ----------
@@ -686,6 +704,18 @@ def set_debug_mode(self, debug):
         self._debug = debug
 
 
+    def set_check_data_mode(self, check_data):
+        """Set check data mode, which controls if an error is raised if NaN or Inf data are added.
+
+        Parameters
+        ----------
+        check_data : bool
+            Whether to run in check data mode.
+        """
+
+        self._check_data = check_data
+
+
     def _check_width_limits(self):
         """Check and warn about peak width limits / frequency resolution interaction."""
 
@@ -795,7 +825,8 @@ def _robust_ap_fit(self, freqs, power_spectrum):
             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.")
+            raise FitError("Model fitting failed due to sub-sampling "
+                           "in the robust aperiodic fit.")
 
         return aperiodic_params
 
@@ -1110,8 +1141,7 @@ def _calc_error(self, metric=None):
             raise ValueError(msg)
 
 
-    @staticmethod
-    def _prepare_data(freqs, power_spectrum, freq_range, spectra_dim=1, verbose=True):
+    def _prepare_data(self, freqs, power_spectrum, freq_range, spectra_dim=1):
         """Prepare input data for adding to current object.
 
         Parameters
@@ -1125,8 +1155,6 @@ def _prepare_data(freqs, power_spectrum, freq_range, spectra_dim=1, verbose=True
             Frequency range to restrict power spectrum to. If None, keeps the entire range.
         spectra_dim : int, optional, default: 1
             Dimensionality that the power spectra should have.
-        verbose : bool, optional
-            Whether to be verbose in printing out warnings.
 
         Returns
         -------
@@ -1181,7 +1209,7 @@ def _prepare_data(freqs, power_spectrum, freq_range, spectra_dim=1, verbose=True
         #   Aperiodic fit gets an inf if freq of 0 is included, which leads to an error
         if freqs[0] == 0.0:
             freqs, power_spectrum = trim_spectrum(freqs, power_spectrum, [freqs[1], freqs.max()])
-            if verbose:
+            if self.verbose:
                 print("\nFOOOF WARNING: Skipping frequency == 0, "
                       "as this causes a problem with fitting.")
 
@@ -1192,12 +1220,13 @@ def _prepare_data(freqs, power_spectrum, freq_range, spectra_dim=1, verbose=True
         # Log power values
         power_spectrum = np.log10(power_spectrum)
 
-        # 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.")
+        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.")
 
         return freqs, power_spectrum, freq_range, freq_res
 

fooof/objs/group.py

@@ -222,7 +222,7 @@ def add_data(self, freqs, power_spectra, freq_range=None):
             self._reset_group_results()
 
         self.freqs, self.power_spectra, self.freq_range, self.freq_res = \
-            self._prepare_data(freqs, power_spectra, freq_range, 2, self.verbose)
+            self._prepare_data(freqs, power_spectra, freq_range, 2)
 
 
     def report(self, freqs=None, power_spectra=None, freq_range=None, n_jobs=1, progress=None):
@@ -476,8 +476,9 @@ def get_fooof(self, ind, regenerate=True):
             The FOOOFResults data loaded into a FOOOF object.
         """
 
-        # Initialize a FOOOF object, with same settings as current FOOOFGroup
+        # Initialize a FOOOF object, with same settings & check data mode as current FOOOFGroup
         fm = FOOOF(*self.get_settings(), verbose=self.verbose)
+        fm.set_check_data_mode(self._check_data)
 
         # Add data for specified single power spectrum, if available
         #   The power spectrum is inverted back to linear, as it is re-logged when added to FOOOF

fooof/objs/utils.py

@@ -178,6 +178,9 @@ def combine_fooofs(fooofs):
     if len(fg) == temp_power_spectra.shape[0]:
         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]))
+
     # Add data information information
     fg.add_meta_data(fooofs[0].get_meta_data())
 

fooof/tests/objs/test_fit.py

@@ -12,7 +12,7 @@
 from fooof.core.items import OBJ_DESC
 from fooof.core.errors import FitError
 from fooof.core.utils import group_three
-from fooof.sim import gen_power_spectrum
+from fooof.sim import gen_freqs, gen_power_spectrum
 from fooof.data import FOOOFSettings, FOOOFMetaData, FOOOFResults
 from fooof.core.errors import DataError, NoDataError, InconsistentDataError
 
@@ -396,7 +396,7 @@ def raise_runtime_error(*args, **kwargs):
         assert np.all(np.isnan(getattr(tfm, result)))
 
 def test_fooof_debug():
-    """Test FOOOF fit failure in debug mode."""
+    """Test FOOOF in debug mode, including with fit failures."""
 
     tfm = FOOOF(verbose=False)
     tfm._maxfev = 5
@@ -406,3 +406,22 @@ def test_fooof_debug():
 
     with raises(FitError):
         tfm.fit(*gen_power_spectrum([3, 50], [50, 2], [10, 0.5, 2, 20, 0.3, 4]))
+
+def test_fooof_check_data():
+    """Test FOOOF in with check data mode turned off, including with NaN data."""
+
+    tfm = FOOOF(verbose=False)
+
+    tfm.set_check_data_mode(False)
+    assert tfm._check_data is False
+
+    # Add data, with check data turned off
+    #   In check data mode, adding data with NaN should run
+    freqs = gen_freqs([3, 50], 0.5)
+    powers = np.ones_like(freqs) * np.nan
+    tfm.add_data(freqs, powers)
+    assert tfm.has_data
+
+    # Model fitting should execute, but return a null model fit, given the NaNs, without failing
+    tfm.fit()
+    assert not tfm.has_model