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)
def times_time_shift_2():
# time_shift_cond
assert(np.all(np.arange(5)-1==time_shift_cond(np.arange(5),1) ))
# make_shift_matrix
assert(np.all(make_shift_matrix(np.arange(5),np.arange(2))==
np.array([0,1,2,3,4,
-1,0,1,2,3]).reshape((2,-1)).T))
def times_time_shift_2():
# time_shift_cond
assert (np.all(np.arange(5) - 1 == time_shift_cond(np.arange(5), 1)))
# make_shift_matrix
assert (np.all(
make_shift_matrix(np.arange(5), np.arange(2)) == np.array(
[0, 1, 2, 3, 4, -1, 0, 1, 2, 3]).reshape((2, -1)).T))
cond3 = np.loadtxt(path_to_data + i +
"/model/model001/onsets/task001_run001/cond003.txt")
TR = 2
tr_times = np.arange(0, 30, TR)
hrf_at_trs = np.array([hrf_single(x) for x in tr_times])
# 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("-")
cond3 = np.loadtxt(path_to_data + i +
"/model/model001/onsets/task001_run001/cond003.txt")
TR = 2
tr_times = np.arange(0, 30, TR)
hrf_at_trs = np.array([hrf_single(x) for x in tr_times])
# 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_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)
#########################################
#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
def test_time_shift_4():
happy2=make_shift_matrix(np.arange(100),np.array([1,3,5]))
assert(np.all(happy2[:,0]== -1+np.arange(100)))
assert(np.all(happy2[:,2]== -5+np.arange(100)))
cond3=np.loadtxt(path_to_data+ i+ "/model/model001/onsets/task001_run001/cond003.txt")
TR = 2
tr_times = np.arange(0, 30, TR)
hrf_at_trs = np.array([hrf_single(x) for x in tr_times])
# 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()
cond2=np.loadtxt(path_to_data+ i+ "/model/model001/onsets/task001_run001/cond002.txt")
cond3=np.loadtxt(path_to_data+ i+ "/model/model001/onsets/task001_run001/cond003.txt")
TR = 2
tr_times = np.arange(0, 30, TR)
hrf_at_trs = np.array([hrf_single(x) for x in tr_times])
# 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_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)
def test_time_shift_4():
happy2 = make_shift_matrix(np.arange(100), np.array([1, 3, 5]))
assert (np.all(happy2[:, 0] == -1 + np.arange(100)))
assert (np.all(happy2[:, 2] == -5 + np.arange(100)))
