Merge pull request #269 from fooof-tools/checkf

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

Author: TomDonoghue

fooof/objs/fit.py

@@ -198,7 +198,10 @@ 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
@@ -1241,6 +1244,14 @@ 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)):

fooof/tests/objs/test_fit.py

@@ -164,7 +164,11 @@ 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