Merge pull request #122 from coganlab/indexing

Indexing

envs/requirements.txt

@@ -1,6 +1,6 @@
 bids>=0.0
 mne
-mne-bids
+mne-bids<=0.14
 numpy
 matplotlib
 scipy

ieeg/viz/ensemble.py

@@ -37,8 +37,13 @@ def figure_compare(raw: list[Raw], labels: list[str], avg: bool = True,
     for title, data in zip(labels, raw):
         title: str
         data: Raw
-        psd = data.compute_psd(n_jobs=n_jobs, **kwargs,
-                               n_fft=int(data.info['sfreq']))
+        kwargs['method'] = kwargs.get('method', 'welch')
+        if kwargs['method'] == 'multitaper':
+            kwargs['adaptive'] = kwargs.get('adaptive', True)
+            kwargs['bandwidth'] = kwargs.get('bandwidth', 10)
+        elif kwargs['method'] == 'welch':
+            kwargs['n_fft'] = kwargs.get('n_fft', int(data.info['sfreq']))
+        psd = data.compute_psd(n_jobs=n_jobs, **kwargs)
         fig = psd.plot(average=avg, spatial_colors=avg)
         fig.subplots_adjust(top=0.85)
         fig.suptitle('{}filtered'.format(title), size='xx-large',

ieeg/viz/mri.py

@@ -382,7 +382,7 @@ def plot_on_average(sigs: Signal | str | mne.Info | list[Signal | str, ...],
 
         if rm_wm:
             these_picks = pick_no_wm(these_picks, gen_labels(
-                new, subj, subj_dir, new.ch_names))
+                new, subj, subj_dir, picks=new.ch_names))
 
         if len(these_picks) == 0:
             continue
@@ -764,8 +764,8 @@ def gen_labels(info: mne.Info, sub: str = None, subj_dir: str = None,
             new_labels[p] = label[0]
             continue
         while not ((not any(w in label[i] for w in bad_words)) and
-                   label[i + 1] > 0.05):
-            if (i + 2) <= len(label.T):  # end of labels
+                   float(label[i + 1]) > 0.05):
+            if (i + 2) >= len(label.T):  # end of labels
                 i = 0
                 break
             elif label[i + 2].isspace():  # empty label
@@ -791,10 +791,14 @@ def gen_labels(info: mne.Info, sub: str = None, subj_dir: str = None,
                      overwrite=True)
     mne.set_log_level("INFO")
     TASK = "SentenceRep"
-    sub_num = 22
+    sub_num = 29
     layout = get_data(TASK, root=LAB_root)
-    subj_dir = op.join(LAB_root, "ECoG_Recon_Full")
+    subj_dir = op.join(LAB_root, "..", "ECoG_Recon")
     sub_pad = "D" + str(sub_num).zfill(4)
+    info = subject_to_info(f"D{sub_num}", subj_dir)
+    labels = gen_labels(info, sub=f"D{sub_num}", subj_dir=subj_dir,
+                        atlas=".BN_atlas")
+
     # sub = "D{}".format(sub_num)
 
     # filt = raw_from_layout(layout.derivatives['clean'], subject=sub_pad,
@@ -804,7 +808,7 @@ def gen_labels(info: mne.Info, sub: str = None, subj_dir: str = None,
     # sample_path = mne.datasets.sample.data_path()
     # subjects_dir = sample_path / "subjects"
 
-    brain = plot_subj("D5")
+    # brain = plot_subj("D5")
     # fig = plot_on_average(["D24", "D81"], rm_wm=False, hemi='both',
     #                       transparency=0.4,
     #                       picks=list(range(28)) + list(range(52, 176)),

setup.py

@@ -79,7 +79,7 @@ def get_file_list(path, ext):
 
 setup(
     name='ieeg',
-    version='0.5.1',
+    version='0.5.2',
     packages=find_packages(
         where='.',
         include=['ieeg*'],