[BUG] Allow Epochs.compute_tfr() for the multitaper method and comp…

…lex/phase outputs (mne-tools#12842)

.github/workflows/tests.yml

@@ -131,4 +131,4 @@ jobs:
       - uses: codecov/codecov-action@v4
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
-        if: success()
+        if: always()

doc/changes/devel/12842.bugfix.rst

@@ -0,0 +1 @@
+Fix bug where :meth:`mne.Epochs.compute_tfr` could not be used with the multitaper method and complex or phase outputs, by `Thomas Binns`_.

mne/decoding/receptive_field.py

@@ -117,7 +117,7 @@ def __init__(
     ):
         self.tmin = tmin
         self.tmax = tmax
-        self.sfreq = float(sfreq)
+        self.sfreq = sfreq
         self.feature_names = feature_names
         self.estimator = 0.0 if estimator is None else estimator
         self.fit_intercept = fit_intercept
@@ -154,7 +154,7 @@ def _delay_and_reshape(self, X, y=None):
                 X,
                 self.tmin,
                 self.tmax,
-                self.sfreq,
+                self.sfreq_,
                 fill_mean=self.fit_intercept_,
             )
             X = _reshape_for_est(X)
@@ -182,12 +182,13 @@ def fit(self, X, y):
             raise ValueError(
                 f"scoring must be one of {sorted(_SCORERS.keys())}, got {self.scoring} "
             )
+        self.sfreq_ = float(self.sfreq)
         X, y, _, self._y_dim = self._check_dimensions(X, y)
 
         if self.tmin > self.tmax:
             raise ValueError(f"tmin ({self.tmin}) must be at most tmax ({self.tmax})")
         # Initialize delays
-        self.delays_ = _times_to_delays(self.tmin, self.tmax, self.sfreq)
+        self.delays_ = _times_to_delays(self.tmin, self.tmax, self.sfreq_)
 
         # Define the slice that we should use in the middle
         self.valid_samples_ = _delays_to_slice(self.delays_)
@@ -200,7 +201,7 @@ def fit(self, X, y):
             estimator = TimeDelayingRidge(
                 self.tmin,
                 self.tmax,
-                self.sfreq,
+                self.sfreq_,
                 alpha=self.estimator,
                 fit_intercept=self.fit_intercept_,
                 n_jobs=self.n_jobs,

mne/decoding/tests/test_search_light.py

@@ -358,6 +358,7 @@ def test_sklearn_compliance(estimator, check):
         "check_transformer_data_not_an_array",
         "check_n_features_in",
         "check_fit2d_predict1d",
+        "check_do_not_raise_errors_in_init_or_set_params",
     )
     if any(ignore in str(check) for ignore in ignores):
         return

mne/decoding/time_delaying_ridge.py

@@ -287,24 +287,22 @@ def __init__(
         n_jobs=None,
         edge_correction=True,
     ):
-        if tmin > tmax:
-            raise ValueError(f"tmin must be <= tmax, got {tmin} and {tmax}")
-        self.tmin = float(tmin)
-        self.tmax = float(tmax)
-        self.sfreq = float(sfreq)
-        self.alpha = float(alpha)
+        self.tmin = tmin
+        self.tmax = tmax
+        self.sfreq = sfreq
+        self.alpha = alpha
         self.reg_type = reg_type
         self.fit_intercept = fit_intercept
         self.edge_correction = edge_correction
         self.n_jobs = n_jobs
 
     @property
     def _smin(self):
-        return int(round(self.tmin * self.sfreq))
+        return int(round(self.tmin_ * self.sfreq_))
 
     @property
     def _smax(self):
-        return int(round(self.tmax * self.sfreq)) + 1
+        return int(round(self.tmax_ * self.sfreq_)) + 1
 
     def fit(self, X, y):
         """Estimate the coefficients of the linear model.
@@ -323,6 +321,12 @@ def fit(self, X, y):
         """
         _validate_type(X, "array-like", "X")
         _validate_type(y, "array-like", "y")
+        self.tmin_ = float(self.tmin)
+        self.tmax_ = float(self.tmax)
+        self.sfreq_ = float(self.sfreq)
+        self.alpha_ = float(self.alpha)
+        if self.tmin_ > self.tmax_:
+            raise ValueError(f"tmin must be <= tmax, got {self.tmin_} and {self.tmax_}")
         X = np.asarray(X, dtype=float)
         y = np.asarray(y, dtype=float)
         if X.ndim == 3:
@@ -349,7 +353,7 @@ def fit(self, X, y):
             self.edge_correction,
         )
         self.coef_ = _fit_corrs(
-            self.cov_, x_y_, n_ch_x, self.reg_type, self.alpha, n_ch_x
+            self.cov_, x_y_, n_ch_x, self.reg_type, self.alpha_, n_ch_x
         )
         # This is the sklearn formula from LinearModel (will be 0. for no fit)
         if self.fit_intercept:

mne/time_frequency/tests/test_tfr.py

@@ -1530,6 +1530,13 @@ def test_epochs_compute_tfr_stockwell(epochs, freqs, return_itc):
     assert tfr.comment == "1"
 
 
+@pytest.mark.parametrize("output", ("complex", "phase"))
+def test_epochs_compute_tfr_multitaper_complex_phase(epochs, output):
+    """Test Epochs.compute_tfr(output="complex"/"phase")."""
+    tfr = epochs.compute_tfr("multitaper", freqs_linspace, output=output)
+    assert len(tfr.shape) == 5
+
+
 @pytest.mark.parametrize("copy", (False, True))
 def test_epochstfr_iter_evoked(epochs_tfr, copy):
     """Test EpochsTFR.iter_evoked()."""

mne/time_frequency/tfr.py

@@ -1533,7 +1533,8 @@ def _compute_tfr(self, data, n_jobs, verbose):
         ]
         # deal with the "taper" dimension
         if self._needs_taper_dim:
-            expected_shape.insert(1, self._data.shape[1])
+            tapers_dim = 1 if _get_instance_type_string(self) != "Epochs" else 2
+            expected_shape.insert(1, self._data.shape[tapers_dim])
         self._shape = tuple(expected_shape)
 
     @verbose