def test_4(): # checks that neighbor_smoothing_binary works zeros = np.zeros((5, 5, 5)) rachel = np.zeros((5, 5, 5)) rachel[2, 2:4, 2] = 1 rachel_n = neighbor_smoothing_binary(rachel, 3) assert_array_almost_equal(rachel_n, zeros)
#####################################
# Run bh_procedure for each subject #
#####################################
p_3d = np.load("../data/p-values/" + name + "_pvalue.npy")
p_1d = np.ravel(p_3d)
mask = fitted_mask
mask_1d = np.ravel(mask)
p_bh = p_1d[mask_1d == 1]
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,
toolbar_width = len(q1)
sys.stdout.write("Benjamini Hochberg: ")
sys.stdout.write("[%s]" % (" " * toolbar_width))
sys.stdout.flush()
sys.stdout.write("\b" *
(toolbar_width + 1)) # return to start of line, after '['
bh = [] # values a*6 + b - 1
count_a = 0
for a, b in itertools.product(range(len(q1)), range(5)):
bh_first = bh_procedure(p_bh, q1[a])
bh_3d = masking_reshape_end(bh_first, mask, off_value=.5)
bh_3d[bh_3d < .5] = 0
bh_3d_1_good = 1 - bh_3d
first = neighbor_smoothing_binary(bh_3d_1_good, neighbors1[b])
bh.append(first)
if count_a == a and b == 4:
sys.stdout.write("-")
sys.stdout.flush()
count_a += 1
sys.stdout.write("\n")
#------------------#
# Image comparison #
#------------------#
present_bh = np.ones((len(q1) * 64, 5 * 64))
sys.stdout.flush()
sys.stdout.write("\b" * (toolbar_width+1)) # return to start of line, after '['
bh=[] # values a*6 + b - 1
count_a=0
for a,b in itertools.product(range(len(q1)),range(5)):
bh_first = bh_procedure(p_bh,q1[a])
bh_3d = masking_reshape_end(bh_first,mask,off_value=.5)
bh_3d[bh_3d<.5]=0
bh_3d_1_good = 1-bh_3d
first = neighbor_smoothing_binary(bh_3d_1_good,neighbors1[b])
bh.append(first)
if count_a==a and b==4:
sys.stdout.write("-")
sys.stdout.flush()
count_a+=1
sys.stdout.write("\n")
#------------------#
# Image comparison #
#####################################
# Run bh_procedure for each subject #
#####################################
p_3d = np.load("../data/p-values/" + name + "_pvalue.npy")
p_1d = np.ravel(p_3d)
mask = fitted_mask
mask_1d = np.ravel(mask)
p_bh = p_1d[mask_1d == 1]
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
######################################
