Update for new MNE syntax

hypyp/analyses.py

@@ -90,8 +90,8 @@ def pow(epochs: mne.Epochs, fmin: float, fmax: float, n_fft: int, n_per_seg: int
     # computing power spectral density on epochs signal
     # average in the 1-second window around event (mean, but can choose 'median')
     kwargs = dict(fmin=fmin, fmax=fmax, n_fft=n_fft, n_per_seg=n_per_seg,
-                  tmin=None, tmax=None, method='welch', picks='all', proj=False,
-                  n_jobs=1)
+                  tmin=None, tmax=None, method='welch', picks='all', exclude=[],
+                  proj=False, remove_dc=True, n_jobs=1)
     spectrum = EpochsSpectrum(epochs, **kwargs)
     psds = spectrum.get_data()
     freq_list = spectrum.freqs
@@ -804,8 +804,8 @@ def xwt(sig1: mne.Epochs, sig2: mne.Epochs,
     sfreq = sig1.info['sfreq']
     assert sig1.info['sfreq'] == sig2.info['sfreq'], "Sig1 et sig2 should have the same sfreq value."
 
-    n_epochs1, n_chans1, n_samples1 = sig1.get_data().shape
-    n_epochs2, n_chans2, n_samples2 = sig2.get_data().shape
+    n_epochs1, n_chans1, n_samples1 = sig1.get_data(copy=False).shape
+    n_epochs2, n_chans2, n_samples2 = sig2.get_data(copy=False).shape
 
     assert n_epochs1 == n_epochs2, "n_epochs1 and n_epochs2 should have the same number of epochs."
     assert n_chans1 == n_chans2, "n_chans1 and n_chans2 should have the same number of channels."

hypyp/utils.py

@@ -150,8 +150,8 @@ def merge(epoch_S1: mne.Epochs, epoch_S2: mne.Epochs) -> mne.Epochs:
 
     # picking data per epoch
     for l in range(0, len(epoch_S1)):
-        data_S1 = epoch_S1[l].get_data()
-        data_S2 = epoch_S2[l].get_data()
+        data_S1 = epoch_S1[l].get_data(copy=True)
+        data_S2 = epoch_S2[l].get_data(copy=True)
 
         data_S1 = np.squeeze(data_S1, axis=0)
         data_S2 = np.squeeze(data_S2, axis=0)
@@ -338,7 +338,7 @@ def generate_random_epoch(epoch: mne.Epochs, mu: float=0, sigma: float=2.0)-> mn
 
     # Get epochs as a 3D NumPy array of shape (n_epochs, n_channels, n_times)
     # Get the arrays’ shape
-    data_shape = epoch.get_data().shape
+    data_shape = epoch.get_data(copy=False).shape
     i, j, k = data_shape
 
     # Generate a numpy.array with same shape from the normal distribution

tests/test_stats.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 # coding=utf-8
 
+import pytest
 import random
 import numpy as np
 import scipy
@@ -139,7 +140,7 @@ def test_behav_corr(epochs):
                                      p_thresh=0.05,
                                      multiple_corr=False,
                                      verbose=False)
-    assert corr_tuple.r in [-1, 1]
+    assert pytest.approx(corr_tuple.r) in [-1, 1]
 
     # test for connectivity values data
     # generate artificial group of 2 subjects repeated
@@ -310,9 +311,9 @@ def test_utils(epochs):
     liste = ep_hyper.info['ch_names']
     ch_index1 = liste.index(ch_name + '_S1')
     ch_index2 = liste.index(ch_name + '_S2')
-    ep_hyper_data = ep_hyper.get_data()
-    epo1_data = epochs.epo1.get_data()
-    epo2_data = epochs.epo2.get_data()
+    ep_hyper_data = ep_hyper.get_data(copy=True)
+    epo1_data = epochs.epo1.get_data(copy=True)
+    epo2_data = epochs.epo2.get_data(copy=True)
     for i in range(0, len(ep_hyper_data[ne][ch_index1])):
         assert ep_hyper_data[ne][ch_index1][i] == epo1_data[ne][nch][i]
         assert ep_hyper_data[ne][ch_index2][i] == epo2_data[ne][nch][i]