##################
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,
################## 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)
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))
##################
# a. np.convolve #
##################
convolved = 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
convolved = convolved[:N]
################################
# b. convolution (my function) #
################################
my_convolved = convolution(np.linspace(0, 432.5, 173), neural_prediction,
hrf_single)
#######################
# c. Plot Comparision #
#######################
plt.plot(np.linspace(0, 432.5, 173), convolved, label="np.convolved")
plt.plot(np.linspace(0, 432.5, 173),
my_convolved,
label="my convolution function")
plt.title("Examining if 'convolution' can do the same thing as 'np.convolve' ")
plt.xlabel("Time")
plt.ylabel("Predicted Hemoglobin response")
plt.legend(loc='lower right', shadow=True, fontsize="smaller")
plt.savefig(location_of_images + 'test_comparision.png')
plt.close()
##################
# a. np.convolve #
##################
convolved = 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
convolved=convolved[:N]
################################
# b. convolution (my function) #
################################
my_convolved=convolution(np.linspace(0,432.5,173),neural_prediction,hrf_single)
#######################
# c. Plot Comparision #
#######################
plt.plot(np.linspace(0,432.5,173),convolved,label="np.convolved")
plt.plot(np.linspace(0,432.5,173),my_convolved,label="my convolution function")
plt.title("Examining if 'convolution' can do the same thing as 'np.convolve' ")
plt.xlabel("Time")
plt.ylabel("Predicted Hemoglobin response")
plt.legend(loc='lower right', shadow=True,fontsize="smaller")
plt.savefig(location_of_images+'test_comparision.png')
plt.close()
#######################
# user convolution #
#######################
# note: np.linspace(0,239*2-2,239) ==all_tr_times
cond_all = np.array(
sorted(list(cond2[:, 0]) + list(cond3[:, 0]) +
list(cond1[:, 0]))) # could also just x_s_array
#--------#
# second #
#--------#
conv_2 = convolution(cond_all, np.ones(len(cond_all)), hrf_single)
scaled_2 = (conv_2 - np.mean(conv_2)) / (2 * np.std(conv_2)) + .4
#-------#
# third #
#-------#
conv_3 = convolution_specialized(cond_all, np.ones(len(cond_all)), hrf_single,
np.linspace(0, 239 * 2 - 2, 239))
scaled_3 = (conv_3 - np.mean(conv_3)) / (2 * np.std(conv_3)) + .4
#--------#
# fourth #
#--------#
real_times, on_off = cond_all, np.ones(len(cond_all))
####################### # user convolution # ####################### # note: np.linspace(0,239*2-2,239) ==all_tr_times cond_all=np.array(sorted(list(cond2[:,0])+list(cond3[:,0])+list(cond1[:,0]))) # could also just x_s_array #--------# # second # #--------# conv_2 = convolution(cond_all,np.ones(len(cond_all)),hrf_single) scaled_2=(conv_2-np.mean(conv_2))/(2*np.std(conv_2)) +.4 #-------# # third # #-------# conv_3 = convolution_specialized(cond_all,np.ones(len(cond_all)),hrf_single,np.linspace(0,239*2-2,239)) scaled_3=(conv_3-np.mean(conv_3))/(2*np.std(conv_3)) +.4 #--------# # fourth # #--------#
