def test_convolution_specialized(): stimuli=np.array([0,5,15]) on_off1=np.array([0,1,0]) on_off2=np.array([1,0,1]) x=np.linspace(0,45,91) # 0, .5, 1, 1.5, 2, ... 45 HRF1=convolution_specialized(stimuli,on_off1,hrf_single,x) y1=np.array([hrf_single(x_i-5) for x_i in x]) # what it should be doing HRF2=convolution_specialized(stimuli,on_off2,hrf_single,x) y2=np.array([hrf_single(x_i)+hrf_single(x_i-15) for x_i in x]) #what it should be doing assert all(HRF1 == y1) assert all(HRF2 == y2)
def test_time_shift():
# Intialize values for class data.
TR = 2.5
tr_times = np.arange(0, 30, TR)
hrf_at_trs = np.array([hrf_single(x) for x in tr_times])
# Load class data.
n_vols = 173
neural_prediction = events2neural(pathtoclassdata+'ds114_sub009_t2r1_cond.txt', TR, n_vols)
# Get np.convolve time course.
convolved = np.convolve(neural_prediction, hrf_at_trs)
N = len(neural_prediction) # N == n_vols == 173
# Compare shifted time courses by hand and using function.
actual_shifted = convolved[5:(5+N)]
exp_convolved2, exp_shifted = time_shift(convolved, neural_prediction, 5)
assert_almost_equal(actual_shifted, exp_shifted)
def test_time_shift():
# Intialize values for class data.
TR = 2.5
tr_times = np.arange(0, 30, TR)
hrf_at_trs = np.array([hrf_single(x) for x in tr_times])
# Load class data.
n_vols = 173
neural_prediction = events2neural(pathtoclassdata+'ds114_sub009_t2r1_cond.txt', TR, n_vols)
# Get np.convolve time course.
convolved = np.convolve(neural_prediction, hrf_at_trs)
N = len(neural_prediction) # N == n_vols == 173
# Compare shifted time courses by hand and using function.
actual_shifted = convolved[5:(5+N)]
exp_convolved2, exp_shifted = time_shift(convolved, neural_prediction, 5)
assert_almost_equal(actual_shifted, exp_shifted)
# for convolution_specialized cond1=np.loadtxt(condition_location+"cond001.txt") cond2=np.loadtxt(condition_location+"cond002.txt") cond3=np.loadtxt(condition_location+"cond003.txt") ######################## # X Matrix Creation # ######################## ################### # np.convolve # ################### TR = 2 tr_times = np.arange(0, 30, TR) hrf_at_trs = np.array([hrf_single(x) for x in tr_times]) n_vols = data.shape[-1] # time slices X_np = np.ones((n_vols,4)) cond_string = ["cond001.txt","cond002.txt","cond003.txt"] for i,name in enumerate(cond_string): nueral_prediction = events2neural(condition_location+name,TR,n_vols) hrf_long = np.convolve(nueral_prediction, hrf_at_trs) X_np[:,i+1] = hrf_long[:-(len(hrf_at_trs)-1)] all_tr_times = np.arange(n_vols) * TR ############################### # convolution_specialized #
behav = pd.read_table(path_to_data + i + behav_suffix, sep=" ")
num_TR = float(behav["NumTRs"])
# Suppose that TR=2. We know this is not a good assumption.
# Also need to look into the hrf function.
cond1 = np.loadtxt(path_to_data + i +
"/model/model001/onsets/task001_run001/cond001.txt")
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 = 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]
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))
from event_related_fMRI_functions import hrf_single,convolution_specialized
one_zeros = np.zeros(40)
one_zeros[4] = 1
one_zeros[16:20]=1
plt.scatter(np.arange(40),one_zeros)
plt.xlim(-1,40)
plt.title("Stimulus pattern")
plt.savefig(location_of_created_images+"on_off_pattern.png")
plt.close()
plt.plot(np.linspace(0,30,200),np.array([hrf_single(x) for x in np.linspace(0,30,200)]))
plt.title("Single HRF, started at t=0")
plt.savefig(location_of_created_images+"hrf_pattern.png")
plt.close()
convolved=convolution_specialized(np.arange(40),one_zeros,hrf_single,np.linspace(0,60,300))
plt.plot(np.linspace(0,60,300),convolved)
plt.title("Convolution")
plt.savefig(location_of_created_images+"initial_convolved.png")
plt.close()
sys.path.append(functions)
from event_related_fMRI_functions import hrf_single, convolution_specialized
one_zeros = np.zeros(40)
one_zeros[4] = 1
one_zeros[16:20] = 1
plt.scatter(np.arange(40), one_zeros)
plt.xlim(-1, 40)
plt.title("Stimulus pattern")
plt.savefig(location_of_created_images + "on_off_pattern.png")
plt.close()
plt.plot(np.linspace(0, 30, 200),
np.array([hrf_single(x) for x in np.linspace(0, 30, 200)]))
plt.title("Single HRF, started at t=0")
plt.savefig(location_of_created_images + "hrf_pattern.png")
plt.close()
convolved = convolution_specialized(np.arange(40), one_zeros, hrf_single,
np.linspace(0, 60, 300))
plt.plot(np.linspace(0, 60, 300), convolved)
plt.title("Convolution")
plt.savefig(location_of_created_images + "initial_convolved.png")
plt.close()
colors = [
"#CCCCFF", "#C4C3D0", "#92A1CF", "#2A52BE", "#003399", "#120A8F",
"#000080", "#002366"
]
