import matplotlib.pyplot as plt # ============================================================================= # Parameters wf = 1 scid = 'nsc' wave_type = 'abp' trial = 2 pwd = '/home/ov/data/stacked/selected/trial' + str(trial) + '/' # ============================================================================= # Load waves: waves = np.load(pwd + 'wf{}_{}.npy'.format(wf, scid)) plt.plot(waves[1000]) plt.show() # ============================================================================= # Fit-Transforming PCA on acquired waves: fitted = utils.pca_projections(waves, 3, svd_solver='arpack') print('Fitted PCA: {}'.format(fitted.shape)) xs = fitted[:, 0] ys = fitted[:, 1] zs = fitted[:, 2] utils.plot_3d_mod(xs, ys, zs, title='wf{} {}'.format(wf, scid), s=5, edge='navy', xlabel='PC1', ylabel='PC2', zlabel='PC3', add_info='PAT{} ({})'.format(wf, scid))
pwd_mat1 = pwd + 'nfpat' + str( patient_nr) + '.icp.' + scid + 'seg.waves.mat' pwd_mat2 = pwd + 'nfpat' + str( patient_nr) + '.testicp.' + scid + 'seg.waves.mat' concatenated_waves = scipy.io.loadmat(pwd_mat1)['waves_mat'] concatenated_waves = np.concatenate( (concatenated_waves, scipy.io.loadmat(pwd_mat2)['waves_mat']), axis=0) # Normalization: from sklearn.preprocessing import normalize concatenated_waves = normalize(concatenated_waves, norm='max') print(concatenated_waves.shape) # Fit-Transforming PCA on acquired waves: fitted = utils.pca_projections(concatenated_waves, 3, svd_solver='arpack') print(fitted.shape) xs = fitted[:, 0] ys = fitted[:, 1] zs = fitted[:, 2] utils.plot_3d(xs, ys, zs, title='NFPAT{} {} PCA projection'.format(patient_nr, scid), xlabel='PC1', ylabel='PC2', zlabel='PC3', s=1)
# ============================================================================= # Parameters patient_nr = 10 scid = 'scp' wave_type = 'abp' pwd = '/home/ov/data/stacked/' # ============================================================================= # Load waves: #stacked_waves = np.load(pwd_stacked_out) stacked_waves = utils.load_waves_by_pat_scid([patient_nr], scid, pwd) waves = utils.acces_waves_by_type(None, stacked_waves, wave_type) print('Stacked size: {}'.format(stacked_waves.shape)) print('Unstacked {} waves: {}'.format(wave_type, waves.shape)) # ============================================================================= # Fit-Transforming PCA on acquired waves: fitted = utils.pca_projections(waves, 3, svd_solver='arpack') print('Fitted PCA: {}'.format(fitted.shape)) xs = fitted[:, 0] ys = fitted[:, 1] zs = fitted[:, 2] """ utils.plot_3d_mod(xs, ys, zs, title='', s=1, edge = 'navy', xlabel = 'PC1', ylabel = 'PC2', zlabel = 'PC3', add_info = 'PAT{} ({})'.format(patient_nr, scid)) if input('Satisfied? (y|N)\n') != 'y': raise Exception('Not satisified :(')
import matplotlib # ============================================================================= # Parameters: scp_mean = 3 nsc_mean = 3 scid = 'scp+nsc' pwd = '/home/ov/data/stacked/selected/trial2/' # ============================================================================= # Load clusters: clusters_nsc = np.load(pwd + 'nsc_clusters_hdbscan.npy', allow_pickle=True) clusters_scp = np.load(pwd + 'scp_clusters_hdbscan.npy', allow_pickle=True) print(clusters_scp[scp_mean].shape) print(clusters_nsc[nsc_mean].shape) fitted_scp = utils.pca_projections(clusters_scp[scp_mean], 3, svd_solver='arpack') fitted_nsc = utils.pca_projections(clusters_nsc[nsc_mean], 3, svd_solver='arpack') xs_list = [] ys_list = [] zs_list = [] xs_list.append(fitted_scp[:, 0]) xs_list.append(fitted_nsc[:, 0]) ys_list.append(fitted_scp[:, 1]) ys_list.append(fitted_nsc[:, 1]) zs_list.append(fitted_scp[:, 2]) zs_list.append(fitted_nsc[:, 2])