minor changes

code/analysis/scpt_pca.py

@@ -213,7 +213,7 @@
 Y = avg_snr
 rho = np.zeros((X.shape[1], 1))
 for i in range(X.shape[1]):
-    tmpcorr = scipy.stats.pearsonr(X[:, i], Y)  # or pearsonr
+    tmpcorr = scipy.stats.pearsonr(X[:, i], Y)
     rho[i, 0] = tmpcorr[0]
 
 surf_path = (gitrepo_dir + 'data/surfaces/')
@@ -344,7 +344,7 @@
 rho = np.zeros((X.shape[1], Y.shape[1]))
 for i in range(X.shape[1]):
     for j in range(Y.shape[1]):
-        tmpcorr = scipy.stats.pearsonr(X[:, i], Y[:, j])  # or pearsonr
+        tmpcorr = scipy.stats.pearsonr(X[:, i], Y[:, j])
         rho[i, j] = tmpcorr[0]
 
 surf_path = (gitrepo_dir + 'data/surfaces/')
@@ -590,7 +590,7 @@
 rho = np.zeros((X.shape[1], Y.shape[1]))
 for i in range(X.shape[1]):
     for j in range(Y.shape[1]):
-        tmpcorr = scipy.stats.pearsonr(X[:, i], Y[:, j])  # or pearsonr
+        tmpcorr = scipy.stats.pearsonr(X[:, i], Y[:, j])
         rho[i, j] = tmpcorr[0]
 
 # save to csv

code/analysis/scpt_pls.py

@@ -199,7 +199,6 @@ def pls_cv_distance_dependent_par(X, Y, coords, trainpct=0.75, lv=0,
 pyls.save_results(outpath + 'pls/plsresults_schaefer100.hdf5', results)
 
 train, test = pls_cv_distance_dependent_par(X, Y, coords=coor)
-lv = 0  # latent variable
 np.save(outpath + 'pls/pls_train_schaefer100.npy', train)
 np.save(outpath + 'pls/pls_test_schaefer100.npy', test)
 
@@ -214,10 +213,10 @@ def pls_cv_distance_dependent_par(X, Y, coords, trainpct=0.75, lv=0,
 
 # visualize pls results
 # covariance explained
+lv = 0  # latent variable
 cv = results['singvals']**2 / np.sum(results['singvals']**2)
 null_singvals = results['permres']['permsingvals']
 cv_spins = null_singvals**2 / sum(null_singvals**2)
-p = (1+sum(null_singvals[lv, :] > results['singvals'][lv]))/(1+nspinsall)
 
 myplot = sns.scatterplot(np.arange(len(cv[:10])), cv[:10]*100,
                          facecolors='darkslategrey', s=70)

code/preprocessing/scpt_runBrainstorm.m

@@ -12,10 +12,12 @@
 addpath(genpath('/usr/local/brainstorm3/'));
 
 % Run Brainstorm
-hcp_meg_process_connectivity(hcp_dir, subjList, badChannels, reports_dir) 
+fcn_hcp_meg_process_rest(hcp_dir, subjList, badChannels, reports_dir) 
 
 %% hctsa on MEG: parcellate
-% addpath(genpath('/home/gshafiei/data1/Projects/HCP_Reinder/matlabcode'));
+% requires cifti-matlab
+% (https://github.com/Washington-University/cifti-matlab)
+% addpath(genpath('/home/gshafiei/data1/Projects/packages/cifti-matlab'));
 hcp_dir = '/path/to/megdata/and/results/';
 datapath = strcat(hcp_dir, 'brainstormResults/vertexTimeSeries');
 tspath = strcat(hcp_dir, 'parcellated/HCP_MEG_TimeSeries/Schaefer100/');