from design_matrix import * from t import * from correlation import * import numpy as np import matplotlib.pyplot as plt Betas_vol = {'Beta_vols'+str(x): design_matrix('sub001', x, TR = 2.5)[2] for x in range(1,13)} #1 House vs face hf = t_map(Betas_vol,1,8) plt.imshow(hf[:,:,45]) plt.colorbar() plt.title('House vs Face') plt.savefig( "house_vs_face.png", dpi = 100) plt.close() #2 House vs every other category regressor for i in range(2,9): plt.subplot(4,2,i-1) plt.tight_layout() fix = t_map(Betas_vol,1,i) plt.imshow(fix[:,:,45]) plt.clim(-3,6) plt.title(str(i)) plt.colorbar() plt.savefig( "house_everything.png", dpi = 100) plt.close() ######3 cat vs scram
from design_matrix import * from t import * from correlation import * import numpy as np import matplotlib.pyplot as plt Betas_vol = { 'Beta_vols' + str(x): design_matrix('sub001', x, TR=2.5)[2] for x in range(1, 13) } #1 House vs face hf = t_map(Betas_vol, 1, 8) plt.imshow(hf[:, :, 45]) plt.colorbar() plt.title('House vs Face') plt.savefig('../../images/house_vs_face.png', dpi=100) plt.close() #2 House vs every other category regressor for i in range(2, 9): plt.subplot(4, 2, i - 1) plt.tight_layout() fix = t_map(Betas_vol, 1, i) plt.imshow(fix[:, :, 45]) plt.clim(-3, 6) plt.title(str(i)) plt.colorbar() plt.savefig("../../images/house_everything.png", dpi=100) plt.close()
plt.imshow(mean_data[:,:,45],cmap='gray',alpha=0.5,interpolation='nearest') #mean_data2[~mask2]=np.nan #plt.imshow(mean_data2[:,:,45],cmap='gray',alpha=0.5,interpolation='nearest') #mean_data3[~mask3]=np.nan #plt.imshow(mean_data3[:,:,45],cmap='gray',alpha=0.5,interpolation='nearest') #Design Matrix plt.imshow(X[:,0:9], aspect = 0.1, interpolation = 'nearest', cmap = 'gray') plt.colorbar() plt.savefig('desing_matrix.png') plt.close() X, Y, betas_vols, mask, U, Y_demeaned, mean_data, projection_vols = design_matrix(subject, run) plt.imshow(X, aspect = 0.1, interpolation = 'nearest', cmap = 'gray') plt.colorbar() plt.savefig('desing_matrix_dt_pca.png') plt.close() #Betas Values betas_vols[~mask]=np.nan plt.imshow(betas_vols[:,:,45,0], interpolation ='nearest') plt.savefig('betas_vols_house.png') plt.close() plt.imshow(betas_vols[:,:,45,7], interpolation ='nearest') plt.savefig('betas_vols_face.png') plt.close()
plt.imshow(mean_data[:,:,45],cmap='gray',alpha=0.5,interpolation='nearest') #mean_data2[~mask2]=np.nan #plt.imshow(mean_data2[:,:,45],cmap='gray',alpha=0.5,interpolation='nearest') #mean_data3[~mask3]=np.nan #plt.imshow(mean_data3[:,:,45],cmap='gray',alpha=0.5,interpolation='nearest') #Design Matrix plt.imshow(X[:,0:9], aspect = 0.1, interpolation = 'nearest', cmap = 'gray') plt.colorbar() plt.savefig('../../images/desing_matrix.png') plt.close() X, Y, betas_vols, mask, U, Y_demeaned, mean_data, projection_vols = design_matrix(subject, run) plt.imshow(X, aspect = 0.1, interpolation = 'nearest', cmap = 'gray') plt.colorbar() plt.savefig('../../images/desing_matrix_dt_pca.png') plt.close() #Betas Values betas_vols[~mask]=np.nan plt.imshow(betas_vols[:,:,45,0], interpolation ='nearest') plt.savefig('../../images/betas_vols_house.png') plt.close() plt.imshow(betas_vols[:,:,45,7], interpolation ='nearest') plt.savefig('../../images/betas_vols_face.png') plt.close()
from design_matrix import * from t import * from corrleation import * import numpy as np import matplotlib.pyplot as plt Betas_vol = {'Beta_vols'+str(x): design_matrix('sub001', x, TR = 2.5)[2] for x in range(1,13)} ##### house for i in range(2,9): plt.subplot(4,2,i-1) fixed_house = t_map(Betas_vol,1,i) plt.imshow(fixed_house[:,:,45]) plt.clim(-3,6) plt.colorbar() plt.savefig( "house_everything.png", dpi = 100) ###### cat for i in np.concatenate((range(1,3), range(4,9))): plt.subplot(4,2,i-1) fixed_house = t_map(Betas_vol,3,i) plt.imshow(fixed_house[:,:,45]) plt.clim(-5,3) plt.colorbar() plt.savefig( "cat_everything.png", dpi = 100) for i in range(1, 9):