bug fix mic

mne_connectivity/spectral/epochs.py

@@ -407,7 +407,7 @@ def __init__(self, n_signals, n_cons, n_freqs, n_times, n_jobs=1):
         if self.name == 'MIC':
             self.patterns = np.empty((2, self.n_cons), dtype=object)
 
-    def compute_con(self, indices, ranks, n_epochs):
+    def compute_con(self, indices, ranks, n_epochs=1):
         """Compute multivariate imag. part of coherency between signals."""
         assert self.name in ['MIC', 'MIM'], (
             'the class name is not recognised, please contact the '
@@ -439,11 +439,7 @@ def compute_con(self, indices, ranks, n_epochs):
                 self._compute_mic(
                     E, C, n_seeds, n_times, U_bar_aa, U_bar_bb, con_i)
             else:
-                self._compute_mim(E, con_i)
-
-            # Eq. 15 for MIM (same principle for MIC)
-            if all(np.unique(seed_idcs) == np.unique(target_idcs)):
-                self.con_scores[con_i] *= 0.5
+                self._compute_mim(E, seed_idcs, target_idcs, con_i)
 
             con_i += 1
 
@@ -550,12 +546,16 @@ def _compute_mic(self, E, C, n_seeds, n_times, U_bar_aa, U_bar_bb, con_i):
                 beta, axis=3))[..., 0]
         ) / np.linalg.norm(alpha, axis=2) * np.linalg.norm(beta, axis=2)).T
 
-    def _compute_mim(self, E, con_i):
+    def _compute_mim(self, E, seed_idcs, target_idcs, con_i):
         """Compute MIM (a.k.a. GIM if seeds == targets)."""
         # Eq. 14
         self.con_scores[con_i] = np.matmul(
             E, E.transpose(0, 1, 3, 2)).trace(axis1=2, axis2=3).T
 
+        # Eq. 15
+        if all(np.unique(seed_idcs) == np.unique(target_idcs)):
+            self.con_scores[con_i] *= 0.5
+
     def reshape_results(self):
         """Remove time dimension from results, if necessary."""
         if self.n_times == 0:
@@ -845,7 +845,7 @@ def __init__(self, n_signals, n_cons, n_freqs, n_times, n_lags, n_jobs=1):
                 'frequency resolution (%i)' % (n_lags, self.freq_res, ))
         self.n_lags = n_lags
 
-    def compute_con(self, indices, ranks, n_epochs):
+    def compute_con(self, indices, ranks, n_epochs=1):
         """Compute multivariate state-space Granger causality."""
         assert self.name in ['GC', 'GC time-reversed'], (
             'the class name is not recognised, please contact the '
@@ -1181,7 +1181,7 @@ class _GCEst(_GCEstBase):
     name = "GC"
 
 
-class _TRGCEst(_GCEstBase):
+class _GCTREst(_GCEstBase):
     """time-reversed[seeds -> targets] state-space GC estimator."""
 
     name = "GC time-reversed"
@@ -1415,7 +1415,7 @@ def _get_and_verify_data_sizes(data, sfreq, n_signals=None, n_times=None,
                    'pli': _PLIEst, 'pli2_unbiased': _PLIUnbiasedEst,
                    'dpli': _DPLIEst, 'wpli': _WPLIEst,
                    'wpli2_debiased': _WPLIDebiasedEst, 'mic': _MICEst,
-                   'mim': _MIMEst, 'gc': _GCEst, 'gc_tr': _TRGCEst}
+                   'mim': _MIMEst, 'gc': _GCEst, 'gc_tr': _GCTREst}
 
 
 def _check_estimators(method):
@@ -2063,9 +2063,10 @@ def _check_rank_input(rank, data, sfreq, indices):
                 con_data = EpochsArray(
                     data_arr[:, con_idcs], con_info, verbose=False)
 
-                rank[group_i][con_i] = sum(
-                    compute_rank(con_data, tol=1e-10, tol_kind='relative',
-                                 verbose=False).values())
+                s = np.linalg.svd(con_data.get_data(), compute_uv=False)
+                rank[group_i][con_i] = np.min(
+                    [np.count_nonzero(epoch >= epoch[0] * 1e-10)
+                     for epoch in s])
 
         logger.info('Estimated data ranks:')
         con_i = 1