def test_time_shift_4():
# this isn't a good check, but increases coverage
cond_all=2*np.arange(1,101)
from time_shift import make_shift_matrix,time_correct
delta_y=2*(np.arange(34))/34
shifted=make_shift_matrix(cond_all,delta_y)
from event_related_fMRI_functions import hrf_single, np_convolve_30_cuts
def make_convolve_lambda(hrf_function,TR,num_TRs):
convolve_lambda=lambda x: np_convolve_30_cuts(x,np.ones(x.shape[0]),hrf_function,TR,np.linspace(0,(num_TRs-1)*TR,num_TRs),15)[0]
return convolve_lambda
convolve_lambda=make_convolve_lambda(hrf_single,2,239)
hrf_matrix=time_correct(convolve_lambda,shifted,239)
assert(hrf_matrix[0,0]==0)
def test_time_shift_4():
# this isn't a good check, but increases coverage
cond_all=2*np.arange(1,101)
from time_shift import make_shift_matrix,time_correct
delta_y=2*(np.arange(34))/34
shifted=make_shift_matrix(cond_all,delta_y)
from event_related_fMRI_functions import hrf_single, np_convolve_30_cuts
def make_convolve_lambda(hrf_function,TR,num_TRs):
convolve_lambda=lambda x: np_convolve_30_cuts(x,np.ones(x.shape[0]),hrf_function,TR,np.linspace(0,(num_TRs-1)*TR,num_TRs),15)[0]
return convolve_lambda
convolve_lambda=make_convolve_lambda(hrf_single,2,239)
hrf_matrix=time_correct(convolve_lambda,shifted,239)
assert(hrf_matrix[0,0]==0)
# creating the .txt file for the events2neural function
cond_all = np.row_stack((cond1, cond2, cond3))
cond_all = sorted(cond_all, key=lambda x: x[0])
cond_all = np.array(cond_all)[:, 0]
delta_y = 2 * (np.arange(34)) / 34
shifted = make_shift_matrix(cond_all, delta_y)
def make_convolve_lambda(hrf_function, TR, num_TRs):
convolve_lambda = lambda x: np_convolve_30_cuts(
x, np.ones(x.shape[0]), hrf_function, TR,
np.linspace(0, (num_TRs - 1) * TR, num_TRs), 15)[0]
return convolve_lambda
convolve_lambda = make_convolve_lambda(hrf_single, TR, num_TR)
hrf_matrix = time_correct(convolve_lambda, shifted, num_TR)
np.savetxt("../data/hrf/" + i + "_hrf.txt", hrf_matrix)
sys.stdout.write("-")
sys.stdout.flush()
sys.stdout.write("\n")
# if we want to save all the hrf matrices:
# 1 loops, best of 3: 1min 4s per loop
#########################################
#Create convovled HRF for each condition#
#########################################
def make_convolve_lambda(hrf_function, TR, num_TRs):
convolve_lambda = lambda x: np_convolve_30_cuts(
x, np.ones(x.shape[0]), hrf_function, TR,
np.linspace(0, (num_TRs - 1) * TR, num_TRs), 15)
return convolve_lambda
convolve_lambda = make_convolve_lambda(hrf_single, TR, num_TR)
hrf_matrix_all = time_correct(convolve_lambda, shifted_all, num_TR)
hrf_matrix_1 = time_correct(convolve_lambda, shifted_1, num_TR)
hrf_matrix_2 = time_correct(convolve_lambda, shifted_2, num_TR)
hrf_matrix_3 = time_correct(convolve_lambda, shifted_3, num_TR)
n_vols = data.shape[-1]
##########################################
# Create PCA features for our regression #
##########################################
mask = nib.load(path_to_data + i + '/anatomy/inplane001_brain_mask.nii.gz')
mask_data = mask.get_data()
mask_data = make_mask(np.ones(data.shape[:-1]), mask_data, fit=True)
mask_data = mask_data != 0
mask_data = mask_data.astype(int)
cond_all=np.array(cond_all)[:,0]
delta_y=2*(np.arange(34))/34
shifted=make_shift_matrix(cond_all,delta_y)
def make_convolve_lambda(hrf_function,TR,num_TRs):
convolve_lambda=lambda x: np_convolve_30_cuts(x,np.ones(x.shape[0]),hrf_function,TR,np.linspace(0,(num_TRs-1)*TR,num_TRs),15)[0]
return convolve_lambda
convolve_lambda=make_convolve_lambda(hrf_single,TR,num_TR)
hrf_matrix=time_correct(convolve_lambda,shifted,num_TR)
np.savetxt("../data/hrf/"+i+"_hrf.txt",hrf_matrix)
sys.stdout.write("-")
sys.stdout.flush()
sys.stdout.write("\n")
# if we want to save all the hrf matrices:
# 1 loops, best of 3: 1min 4s per loop
delta_y=2*(np.arange(34))/34
shifted_all=make_shift_matrix(cond_all,delta_y)
shifted_1= make_shift_matrix(cond1[:,0],delta_y)
shifted_2= make_shift_matrix(cond2[:,0],delta_y)
shifted_3= make_shift_matrix(cond3[:,0],delta_y)
def make_convolve_lambda(hrf_function,TR,num_TRs):
convolve_lambda=lambda x: np_convolve_30_cuts(x,np.ones(x.shape[0]),hrf_function,TR,np.linspace(0,(num_TRs-1)*TR,num_TRs),15)
return convolve_lambda
convolve_lambda=make_convolve_lambda(hrf_single,TR,num_TR)
hrf_matrix_all=time_correct(convolve_lambda,shifted_all,num_TR)
hrf_matrix_1=time_correct(convolve_lambda,shifted_1,num_TR)
hrf_matrix_2=time_correct(convolve_lambda,shifted_2,num_TR)
hrf_matrix_3=time_correct(convolve_lambda,shifted_3,num_TR)
n_vols = data.shape[-1]
mask = nib.load(path_to_data+i+'/anatomy/inplane001_brain_mask.nii.gz')
mask_data = mask.get_data()
mask_data = make_mask(np.ones(data.shape[:-1]), mask_data, fit=True)
mask_data = mask_data!=0
mask_data = mask_data.astype(int)
###PCA SHIT###
