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):
