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
#-------# # third # testconv_3 = convolution_specialized(all_tr_times,neural_prediction, hrf_single,all_tr_times) #--------# # fourth # on_off = np.zeros(174) real_times,on_off[:-1] = np.linspace(0,432.5,173+1),neural_prediction hrf_function,TR,record_cuts= hrf_single, 2.5 ,np.linspace(0,432.5,173+1) # testconv_4_1 = np_convolve_30_cuts(real_times,on_off,hrf_function,TR,record_cuts,cuts=1) testconv_4_15 = np_convolve_30_cuts(real_times,on_off,hrf_function,TR,record_cuts,cuts=15) testconv_4_30 = np_convolve_30_cuts(real_times,on_off,hrf_function,TR,record_cuts,cuts=30) #-------# # fifth # testconv_5 = fast_convolution(all_tr_times,neural_prediction,fast_hrf,all_tr_times) #######################
def test_convolution():
#################
# i. Can the user-created functions match np.convolve in np.convolve territory
TR = 2.5
tr_times = np.arange(0, 30, TR)
hrf_at_trs = np.array([hrf_single(x) for x in tr_times])
n_vols = 173
neural_prediction = events2neural(location_to_class_data+'ds114_sub009_t2r1_cond.txt',TR,n_vols)
all_tr_times = np.arange(173) * TR
##################
# a. np.convolve #
##################
testconv_np = np.convolve(neural_prediction, hrf_at_trs) # hrf_at_trs sample data
N = len(neural_prediction) # N == n_vols == 173
M = len(hrf_at_trs) # M == 12
testconv_np=testconv_np[:N]
#####################
# b. user functions #
#####################
#--------#
# second #
testconv_2 = convolution(all_tr_times,neural_prediction,hrf_single)
#-------#
# third #
testconv_3 = convolution_specialized(all_tr_times,neural_prediction,
hrf_single,all_tr_times)
#--------#
# fourth #
on_off = np.zeros(174)
real_times,on_off[:-1] = np.linspace(0,432.5,173+1),neural_prediction
hrf_function,TR,record_cuts= hrf_single, 2.5 ,np.linspace(0,432.5,173+1)
#
testconv_4_1 = np_convolve_30_cuts(real_times,on_off,hrf_function,TR,record_cuts,cuts=1)
testconv_4_15 = np_convolve_30_cuts(real_times,on_off,hrf_function,TR,record_cuts,cuts=15)
testconv_4_30 = np_convolve_30_cuts(real_times,on_off,hrf_function,TR,record_cuts,cuts=30)
#-------#
# fifth #
testconv_5 = fast_convolution(all_tr_times,neural_prediction,fast_hrf,all_tr_times)
additional_runs=[testconv_np,testconv_2,testconv_3,testconv_4_1,testconv_4_15,testconv_4_30,testconv_5]
names=["testconv_np","testconv_2","testconv_3","testconv_4_1","testconv_4_15","testconv_4_30","testconv_5"]
print("Max difference between model and testconv_np:")
for i,my_convolved in enumerate(additional_runs):
if my_convolved.shape[0]==testconv_np.shape[0]:
print(names[i],max(abs(testconv_np-my_convolved)))
else:
print(names[i],max(abs(testconv_np-my_convolved[:-1])))
# Actual asserts
for i,my_convolved in enumerate(additional_runs):
if my_convolved.shape[0]==testconv_np.shape[0]:
assert (max(abs(testconv_np-my_convolved) < .0001))
else:
assert (max(abs(testconv_np-my_convolved[:-1]) < .0001))
#-------#
# third #
testconv_3 = convolution_specialized(all_tr_times, neural_prediction,
hrf_single, all_tr_times)
#--------#
# fourth #
on_off = np.zeros(174)
real_times, on_off[:-1] = np.linspace(0, 432.5, 173 + 1), neural_prediction
hrf_function, TR, record_cuts = hrf_single, 2.5, np.linspace(0, 432.5, 173 + 1)
#
testconv_4_1 = np_convolve_30_cuts(real_times,
on_off,
hrf_function,
TR,
record_cuts,
cuts=1)
testconv_4_15 = np_convolve_30_cuts(real_times,
on_off,
hrf_function,
TR,
record_cuts,
cuts=15)
testconv_4_30 = np_convolve_30_cuts(real_times,
on_off,
hrf_function,
TR,
record_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
