def test_2(): x = np.arange(100) x2 = 50 - x mask = np.zeros((5,5,4)) mask[:, 1:4, 1:3] = 1 x3 = x.reshape((5, 5, 4)) output3 = t_grouping_neighbor(x3, mask, 50, neighbors = 1, prop = False, abs_on = False, binary = True , off_value = 0,masked_value = .5) assert(np.sum(output3[0]) == 50) mask[:, 1:4, 1:3] = 1 x4 = x2.reshape((5, 5, 4)) output4 = t_grouping_neighbor(x4, mask, .5, neighbors = 2, prop = True, abs_on = True, binary = True , off_value = 0, masked_value = .5) assert(np.sum(output4[0]) == 50) # Test for when neighbors != None and binary == False output5 = t_grouping_neighbor(x4, mask, .5, neighbors = 2, prop = True, abs_on = True, binary = False, off_value = 0, masked_value = .5) assert(output5 == False) # Test for when binary == False output6 = t_grouping_neighbor(x4, mask, .5, neighbors = None, prop = True, abs_on = True, binary = False, off_value = 0, masked_value = .5) assert(np.sum(output6[0]) == 66)
def test_2():
x = np.arange(100)
x2 = 50 - x
mask = np.zeros((5, 5, 4))
mask[:, 1:4, 1:3] = 1
x3 = x.reshape((5, 5, 4))
output3 = t_grouping_neighbor(x3,
mask,
50,
neighbors=1,
prop=False,
abs_on=False,
binary=True,
off_value=0,
masked_value=.5)
assert (np.sum(output3[0]) == 50)
mask[:, 1:4, 1:3] = 1
x4 = x2.reshape((5, 5, 4))
output4 = t_grouping_neighbor(x4,
mask,
.5,
neighbors=2,
prop=True,
abs_on=True,
binary=True,
off_value=0,
masked_value=.5)
assert (np.sum(output4[0]) == 50)
# Test for when neighbors != None and binary == False
output5 = t_grouping_neighbor(x4,
mask,
.5,
neighbors=2,
prop=True,
abs_on=True,
binary=False,
off_value=0,
masked_value=.5)
assert (output5 == False)
# Test for when binary == False
output6 = t_grouping_neighbor(x4,
mask,
.5,
neighbors=None,
prop=True,
abs_on=True,
binary=False,
off_value=0,
masked_value=.5)
assert (np.sum(output6[0]) == 66)
bh_final = neighbor_smoothing_binary(bh_3d_1_good, neighbors)
bh_mean[..., i] = bh_3d_1_good
#####################################
# Run t_grouping for each subject #
#####################################
t_3d = np.load("../data/t_stat/" + name + "_tstat.npy")
#mask = fitted_mask
t_group = t_grouping_neighbor(t_3d,
mask,
prop_t,
neighbors=neighbors,
prop=True,
abs_on=True,
binary=True,
off_value=0,
masked_value=.5)[0]
t_mean[..., i] = t_group
######################################
# Run beta grouping for each subject #
######################################
beta_3d = np.load("../data/betas/" + name + "_beta.npy")
beta_group = t_grouping_neighbor(beta_3d,
mask,
prop_beta,
neighbors=neighbors,
########## Final Output ##########
#####################################
#Create final output.
total=np.zeros((3*64,3*64))
ward = label
t_final = data_new
prop_t = 0.15
rachels_ones = np.ones((64, 64, 34))
fitted_mask = make_mask(rachels_ones, mask_data, fit = True)
fitted_mask[fitted_mask > 0] = 1
neighbors=1
t_cluster = t_grouping_neighbor(t_stat, fitted_mask, prop_t, neighbors = neighbors, prop = True, abs_on = True, binary = True, off_value = 0, masked_value = .5)[0]
t_cluster = t_cluster[...,19:22]
t_final_abs_max = np.max(abs(t_final))
for i in range(ward.shape[-1]):
total[(i*64):((i+1)*64),0:64]=ward[...,i]*1/5 - 2/5 # centering around 0
for i in range(t_final.shape[-1]):
total[(i*64):((i+1)*64),64:128]=t_final[...,i]*1/t_final_abs_max # centering around 0
for i in range(t_cluster.shape[-1]):
total[(i*64):((i+1)*64),128:192]=t_cluster[...,i] -1/2 # centering around 0
##############
toolbar_width=len(prod2)
sys.stdout.write("T - Analysis: ")
sys.stdout.write("[%s]" % (" " * toolbar_width))
sys.stdout.flush()
sys.stdout.write("\b" * (toolbar_width+1)) # return to start of line, after '['
t_val=[] #values c*5 + d - 1
count_c=0
for c,d in itertools.product(range(5),range(5)):
second=t_grouping_neighbor(t_3d, mask, prod2[c], neighbors= neighbors2[d],
prop=True,abs_on=True, binary=True ,off_value=0,masked_value=.5)[0]
t_val.append(second)
if count_c==c and d==4:
sys.stdout.write("-")
sys.stdout.flush()
count_c+=1
sys.stdout.write("\n")
#------------------#
# Image comparison #
#------------------#
present_t = np.ones((5*64,5*64))
total = np.zeros((3 * 64, 3 * 64))
ward = label
t_final = data_new
prop_t = 0.15
rachels_ones = np.ones((64, 64, 34))
fitted_mask = make_mask(rachels_ones, mask_data, fit=True)
fitted_mask[fitted_mask > 0] = 1
neighbors = 1
t_cluster = t_grouping_neighbor(t_stat,
fitted_mask,
prop_t,
neighbors=neighbors,
prop=True,
abs_on=True,
binary=True,
off_value=0,
masked_value=.5)[0]
t_cluster = t_cluster[..., 19:22]
t_final_abs_max = np.max(abs(t_final))
for i in range(ward.shape[-1]):
total[(i * 64):((i + 1) * 64),
0:64] = ward[..., i] * 1 / 5 - 2 / 5 # centering around 0
for i in range(t_final.shape[-1]):
total[(i * 64):((i + 1) * 64),
64:128] = t_final[..., i] * 1 / t_final_abs_max # centering around 0
mask = make_mask(inner_ones, mask, True)
mask[mask > 0] = 1
t_vals = t
t_vals_3d = t_vals.reshape(data.shape[:-1])
pro = [.25, .1, .1, .05, .025]
folks = [1, 1, 5, 5, 10]
plt.close()
for i in np.arange(5):
start, cutoff = t_grouping_neighbor(t_vals_3d,
mask,
pro[i],
prop=True,
neighbors=folks[i],
abs_on=True)
plt.imshow(present_3d(2 * start - 1),
interpolation='nearest',
cmap="seismic")
plt.title("T statistics " + str(pro[i]) + " proportion \n (cutoff=" +
str(cutoff) + ") , neighbors: " + str(folks[i]))
plt.colorbar()
plt.savefig(location_of_images + str(pro[i]) + "_" + str(folks[i]) +
"_t.png")
plt.close()
##################
# Beta #
##################
mask= make_mask(inner_ones,mask,True)
mask[mask>0]=1
t_vals=t
t_vals_3d=t_vals.reshape(data.shape[:-1])
pro=[.25,.1,.1,.05,.025]
folks=[1,1,5,5,10]
plt.close()
for i in np.arange(5):
start,cutoff=t_grouping_neighbor(t_vals_3d,mask,pro[i],prop=True,neighbors= folks[i],abs_on=True)
plt.imshow(present_3d(2*start-1),interpolation='nearest',cmap="seismic")
plt.title("T statistics " +str(pro[i])+" proportion \n (cutoff=" + str(cutoff)+") , neighbors: " + str(folks[i]))
plt.colorbar()
plt.savefig(location_of_images+str(pro[i])+"_" + str(folks[i])+"_t.png")
plt.close()
##################
# Beta #
##################
b1 = B[1]
#cutoff = .6
b1_vals_3d=b1.reshape(data.shape[:-1])
pro=[.25,.1,.1,.05,.025]
folks=[1,1,5,5,10]
toolbar_width = len(prod2)
sys.stdout.write("T - Analysis: ")
sys.stdout.write("[%s]" % (" " * toolbar_width))
sys.stdout.flush()
sys.stdout.write("\b" *
(toolbar_width + 1)) # return to start of line, after '['
t_val = [] #values c*5 + d - 1
count_c = 0
for c, d in itertools.product(range(5), range(5)):
second = t_grouping_neighbor(t_3d,
mask,
prod2[c],
neighbors=neighbors2[d],
prop=True,
abs_on=True,
binary=True,
off_value=0,
masked_value=.5)[0]
t_val.append(second)
if count_c == c and d == 4:
sys.stdout.write("-")
sys.stdout.flush()
count_c += 1
sys.stdout.write("\n")
#------------------#
# Image comparison #
#------------------#
bh_first = bh_procedure(p_bh, q)
bh_3d = masking_reshape_end(bh_first, mask, off_value = .5)
bh_3d[bh_3d < .5] = 0
bh_3d_1_good = 1 - bh_3d
bh_final = neighbor_smoothing_binary(bh_3d_1_good, neighbors)
bh_mean[..., i] = bh_3d_1_good
#####################################
# Run t_grouping for each subject #
#####################################
t_3d = np.load("../data/t_stat/" + name + "_tstat.npy")
#mask = fitted_mask
t_group = t_grouping_neighbor(t_3d, mask, prop_t, neighbors = neighbors,
prop = True, abs_on = True, binary = True, off_value = 0, masked_value = .5)[0]
t_mean[..., i] = t_group
######################################
# Run beta grouping for each subject #
######################################
beta_3d = np.load("../data/betas/" + name + "_beta.npy")
beta_group = t_grouping_neighbor(beta_3d, mask, prop_beta, neighbors = neighbors,
prop = True, abs_on = True, binary = True, off_value = 0, masked_value = .5)[0]
beta_mean[..., i] = beta_group
sys.stdout.write("-")
sys.stdout.flush()
