def test_bh():
Q = 1.0
p_vals = p.T
useless_bh = bh_procedure(p_vals, Q)
# Since the FDR is 100%, the bh_procedure should return the exact same thing as the original data.
#assert_almost_equal(data[...,7], useless_bh[...,7])
#assert_almost_equal(np.ravel(pval), useless_bh)
Q_real = .25
real_bh = bh_procedure(p_vals, Q_real)
assert (not (np.all(np.ravel(p_vals) != real_bh)))
def test_bh():
Q = 1.0
p_vals = p.T
useless_bh = bh_procedure(p_vals, Q)
# Since the FDR is 100%, the bh_procedure should return the exact same thing as the original data.
#assert_almost_equal(data[...,7], useless_bh[...,7])
#assert_almost_equal(np.ravel(pval), useless_bh)
Q_real = .25
real_bh = bh_procedure(p_vals, Q_real)
assert(not (np.all(np.ravel(p_vals) != real_bh)))
def small_q_bh():
Q = .005
p_vals = p.T
small_q_bh = bh_procedure(p_vals, Q)
# Since the Q value is so small, there should be no significant tests found
# so the original p_vals (shape is 1-d, (len, 1)) should be returned rather
# than an np.array object of shape (len,)
assert_equals(small_q_bh.shape, p_vals.shape)
# It should return p_vals if it finds no signficant tests
assert_equals(small_q_bh.all, p_vals.all)
def small_q_bh():
Q = .005
p_vals = p.T
small_q_bh = bh_procedure(p_vals, Q)
# Since the Q value is so small, there should be no significant tests found
# so the original p_vals (shape is 1-d, (len, 1)) should be returned rather
# than an np.array object of shape (len,)
assert_equals(small_q_bh.shape, p_vals.shape)
# It should return p_vals if it finds no signficant tests
assert_equals(small_q_bh.all, p_vals.all)
def test_bh():
img = nib.load(pathtoclassdata + "ds114_sub009_t2r1.nii")
data = img.get_data()[..., 4:]
# Read in the convolutions.
convolved = np.loadtxt(pathtoclassdata + "ds114_sub009_t2r1_conv.txt")[4:]
# Create design matrix.
beta,t,df,p = t_stat(data, convolved,[1,1])
beta2, t2,df2,p2 = t_stat(data, convolved,[0,1])
Q = 1.0
pval = p.T
useless_bh = bh_procedure(pval, Q)
# Since the FDR is 100%, the bh_procedure should return the exact same thing as the original data.
#assert_almost_equal(data[...,7], useless_bh[...,7])
#assert_almost_equal(np.ravel(pval), useless_bh)
Q_real = .25
real_bh = bh_procedure(pval, Q_real)
#assert_not_equals(data[...,7], real_bh[...,7])
assert(not (np.all(np.ravel(pval) != real_bh)))
def test_bh():
img = nib.load(pathtoclassdata + "ds114_sub009_t2r1.nii")
data = img.get_data()[..., 4:]
# Read in the convolutions.
convolved = np.loadtxt(pathtoclassdata + "ds114_sub009_t2r1_conv.txt")[4:]
# Create design matrix.
beta, t, df, p = t_stat(data, convolved, [1, 1])
beta2, t2, df2, p2 = t_stat(data, convolved, [0, 1])
Q = 1.0
pval = p.T
useless_bh = bh_procedure(pval, Q)
# Since the FDR is 100%, the bh_procedure should return the exact same thing as the original data.
# assert_almost_equal(data[...,7], useless_bh[...,7])
# assert_almost_equal(np.ravel(pval), useless_bh)
Q_real = 0.25
real_bh = bh_procedure(pval, Q_real)
# assert_not_equals(data[...,7], real_bh[...,7])
assert not (np.all(np.ravel(pval) != real_bh))
mask_data = mask.get_data()
rachels_ones = np.ones((64, 64, 34))
fitted_mask = make_mask(rachels_ones, mask_data, fit=True)
fitted_mask[fitted_mask > 0] = 1
#####################################
# 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,
def no_q_test():
Q = 0
p_vals = p.T
no_bh = bh_procedure(p_vals, Q)
np.testing.assert_array_equal(no_bh, p_vals)
B, t, df, p = t_stat(data, my_hrf, np.array([0, 1]))
#########################
# Benjamini-Hochberg #
#########################
print(
"# ==== BEGIN Visualization of Masked data over original brain data ==== #"
)
p_vals = p.T # shape of p_vals is (139264, 1)
print("# ==== No Mask, bh_procedure ==== #")
# a fairly large false discovery rate
Q = .4
significant_pvals = bh_procedure(p_vals, Q)
# Reshape significant_pvals to shape of data
reshaped_sig_p = np.reshape(significant_pvals, data.shape[:-1])
slice_reshaped_sig_p = reshaped_sig_p[..., 7]
original_slice = data[..., 7]
plt.imshow(slice_reshaped_sig_p)
plt.colorbar()
plt.title('Significant p-values (No mask)')
plt.savefig(location_of_images + "NOMASK_significant_p_slice.png")
plt.close()
print("# ==== END No Mask, bh_procedure ==== #")
print("# ==== BEGIN varying the Q value = .005 (FDR) ==== #")
Q = .005
plt.savefig(location_of_images+"rachels_ones.png")
plt.close()
#data_1d = np.ravel(data)
# subset into p-values array
#smaller_p = p_vals[ == 1]
"""
print("# ==== BEGIN Visualization of Masked data over original brain data ==== #")
p_vals = p.T # shape of p_vals is (139264, 1)
print("# ==== No Mask, bh_procedure ==== #")
# a fairly large false discovery rate
Q = .4
significant_pvals = bh_procedure(p_vals, Q)
#print(significant_pvals)
# Reshape significant_pvals to shape of data
reshaped_sig_p = np.reshape(significant_pvals, data.shape[:-1])
slice_reshaped_sig_p = reshaped_sig_p[...,7]
original_slice = data[...,7]
plt.imshow(slice_reshaped_sig_p)
plt.colorbar()
plt.title('Significant p-values (No mask)')
plt.savefig(location_of_images+"NOMASK_significant_p_slice.png")
plt.close()
print("# ==== END No Mask, bh_procedure ==== #")
#significant_pvals_old = bh_procedure(p_vals, fdr)
###############################
# Benjamini Hochberg Analysis #
###############################
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
def no_q_test():
Q = 0
p_vals = p.T
no_bh = bh_procedure(p_vals, Q)
np.testing.assert_array_equal(no_bh, p_vals)
plt.savefig(location_of_images+"rachels_ones.png")
plt.close()
#data_1d = np.ravel(data)
# subset into p-values array
#smaller_p = p_vals[ == 1]
"""
print("# ==== BEGIN Visualization of Masked data over original brain data ==== #")
p_vals = p.T
print("# ==== No Mask, bh_procedure ==== #")
# a fairly large false discovery rate
Q = .4
significant_pvals = bh_procedure(p_vals, Q)
#print(significant_pvals)
# Reshape significant_pvals to shape of data
reshaped_sig_p = np.reshape(significant_pvals, data.shape[:-1])
slice_reshaped_sig_p = reshaped_sig_p[...,7]
original_slice = data[...,7]
plt.imshow(slice_reshaped_sig_p)
plt.colorbar()
plt.title('Significant p-values (No mask)')
plt.savefig(location_of_images+"NOMASK_significant_p_slice.png")
plt.close()
print("# ==== END No Mask, bh_procedure ==== #")
#significant_pvals_old = bh_procedure(p_vals, fdr)
#data_1d = np.ravel(data)
# subset into p-values array
#smaller_p = p_vals[ == 1]
"""
print(
"# ==== BEGIN Visualization of Masked data over original brain data ==== #"
)
p_vals = p.T
print("# ==== No Mask, bh_procedure ==== #")
# a fairly large false discovery rate
Q = .4
significant_pvals = bh_procedure(p_vals, Q)
#print(significant_pvals)
# Reshape significant_pvals to shape of data
reshaped_sig_p = np.reshape(significant_pvals, data.shape[:-1])
slice_reshaped_sig_p = reshaped_sig_p[..., 7]
original_slice = data[..., 7]
plt.imshow(slice_reshaped_sig_p)
plt.colorbar()
plt.title('Significant p-values (No mask)')
plt.savefig(location_of_images + "NOMASK_significant_p_slice.png")
plt.close()
print("# ==== END No Mask, bh_procedure ==== #")
#significant_pvals_old = bh_procedure(p_vals, fdr)
