def test_hypothesis1():
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])
assert_almost_equal(beta,beta2)
assert t.all() == t2.all()
assert beta.shape[1] == np.prod(data.shape[0:-1])
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))
def test_hypothesis2():
# example from http://www.jarrodmillman.com/rcsds/lectures/glm_intro.html
# it should be pointed out that hypothesis just looks at simple linear regression
psychopathy = [11.416, 4.514, 12.204, 14.835,
8.416, 6.563, 17.343, 13.02,
15.19 , 11.902, 22.721, 22.324]
clammy = [0.389, 0.2 , 0.241, 0.463,
4.585, 1.097, 1.642, 4.972,
7.957, 5.585, 5.527, 6.964]
Y = np.asarray(psychopathy)
B, t, df, p = t_stat(Y, clammy, [0, 1])
assert np.round(t,6)==np.array([[ 1.914389]])
assert np.round(p,6)==np.array([[ 0.042295]])
def test_hypothesis_3():
# new multiple-regression
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.
X = np.ones((convolved.shape[0], 2))
X[:, 1] = convolved
beta, t, df, p = t_stat(data, convolved, [0, 1])
beta2, t2, df2, p2 = t_stat_mult_regression_single(data, X)
beta3, t3, df3, p3 = t_stat_mult_regression(data, X)
assert_array_equal(t, t2)
assert_array_equal(t, np.atleast_2d(t3[1, :]))
def test_hypothesis2():
# example from http://www.jarrodmillman.com/rcsds/lectures/glm_intro.html
# it should be pointed out that hypothesis just looks at simple linear
# regression
psychopathy = [11.416, 4.514, 12.204, 14.835,
8.416, 6.563, 17.343, 13.02,
15.19 , 11.902, 22.721, 22.324]
clammy = [0.389, 0.2 , 0.241, 0.463,
4.585, 1.097, 1.642, 4.972,
7.957, 5.585, 5.527, 6.964]
Y = np.asarray(psychopathy)
B, t, df, p = t_stat(Y, clammy, [0, 1])
assert np.round(t,6)==np.array([[ 1.914389]])
assert np.round(p,6)==np.array([[ 0.042295]])
def test_hypothesis_3():
# new multiple-regression
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.
X=np.ones((convolved.shape[0],2))
X[:,1]=convolved
beta,t,df,p = t_stat(data, convolved,[0,1])
beta2, t2,df2,p2 = t_stat_mult_regression_single(data, X)
beta3, t3,df3,p3 = t_stat_mult_regression(data, X)
assert_array_equal(t,t2)
assert_array_equal(t,np.atleast_2d(t3[1,:]))
hrf_at_trs = np.array([hrf_single(x) for x in tr_times])
n_vols=data.shape[-1]
# 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])
np.savetxt(pathtodata+ i+ "/model/model001/onsets/task001_run001/cond_all.txt",cond_all)
neural_prediction = events2neural(pathtodata+ i+ "/model/model001/onsets/task001_run001/cond_all.txt",TR,n_vols)
convolved = np.convolve(neural_prediction, hrf_at_trs) # hrf_at_trs sample
N = len(neural_prediction) # N == n_vols == 173
M = len(hrf_at_trs) # M == 12
np_hrf=convolved[:N]
B,t,df,p = t_stat(data, np_hrf, np.array([0,1]))
#Simple mask function
mask = nib.load(pathtodata+i+'/anatomy/inplane001_brain_mask.nii.gz')
mask_data = mask.get_data()
t_mean[...,int(i[-1])] = make_mask(np.reshape(t,(64,64,34)), mask_data, fit=True)
final = present_3d(np.mean(t_mean,axis=3))
#######################
# Plot the results #
#######################
# Path to the subject 009 fMRI data used in class.
pathtoclassdata = "data/ds114/"
# Add path to functions to the system path.
sys.path.append(os.path.join(os.path.dirname(__file__), "../functions/"))
# Load our benjamini-hochberg function
from benjamini_hochberg import bh_procedure
from hypothesis import t_stat
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])
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)
# 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])
np.savetxt(condition_location + "cond_all.txt", cond_all)
neural_prediction = events2neural(condition_location + "cond_all.txt", TR,
n_vols)
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
np_hrf = convolved[:N]
#=================================================
""" Run hypothesis testing script"""
B_my, t_my, df, p_my = t_stat(data, my_hrf, np.array([0, 1]))
print("'my' convolution single regression (t,p):")
print(t_my, p_my)
print("means of (t,p) for 'my' convolution: (" + str(np.mean(t_my)) +
str(np.mean(p_my)) + ")")
B_np, t_np, df, p_np = t_stat(data, np_hrf, np.array([0, 1]))
print("np convolution single regression (t,p):")
print(t_np, p_np)
print("means of (t,p) for np convolution: (" + str(np.mean(t_np)) +
str(np.mean(p_np)) + ")")
B, t, df, p = t_stat(data, my_hrf, np.array([0, 1]))
img = nib.load(pathtodata + "BOLD/task001_run001/bold.nii.gz")
data = img.get_data()
data = data[..., 6:] # Knock off the first 6 observations.
#Load convolution files
cond1 = np.loadtxt(condition_location + "cond001.txt")
cond2 = np.loadtxt(condition_location + "cond002.txt")
cond3 = np.loadtxt(condition_location + "cond003.txt")
#Convolution and t-values
all_stimuli = np.array(
sorted(list(cond2[:, 0]) + list(cond3[:, 0]) + list(cond1[:, 0])))
my_hrf = convolution_specialized(all_stimuli, np.ones(len(all_stimuli)),
hrf_single, np.linspace(0, 239 * 2 - 2, 239))
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)
# 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])
np.savetxt(condition_location+"cond_all.txt",cond_all)
neural_prediction = events2neural(condition_location+"cond_all.txt",TR,n_vols)
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
np_hrf=convolved[:N]
#=================================================
""" Run hypothesis testing script"""
B_my,t_my,df,p_my = t_stat(data, my_hrf, np.array([0,1]))
print("'my' convolution single regression (t,p):")
print(t_my,p_my)
print("means of (t,p) for 'my' convolution: (" +str(np.mean(t_my))+str(np.mean(p_my)) +")")
B_np,t_np,df,p_np = t_stat(data, np_hrf, np.array([0,1]))
print("np convolution single regression (t,p):")
print(t_np,p_np)
print("means of (t,p) for np convolution: (" +str(np.mean(t_np))+str(np.mean(p_np)) +")")
B,t,df,p = t_stat(data, my_hrf, np.array([0,1]))
t_final2 = np.zeros((data_smooth.shape[:-1]))
t_final3 = np.zeros((data_smooth.shape[:-1]))
#Run per slice in order to correct for time
for j in range(data_smooth.shape[2]):
data_smooth_slice = data_smooth[:,:,j,:]
data_rough_slice = data_rough[:,:,j,:]
#Create design matrix
X = np.ones((n_vols,7))
X[:,1] = convolve[:,j]
X[:,2]=np.linspace(-1,1,num=X.shape[0]) #drift
X[:,3:]=fourier_creation(X.shape[0],2)[:,1:]
beta1,t1,df1,p1 = t_stat_mult_regression(data_rough_slice, X)
beta2, t2, df2, p2 = t_stat(data_smooth_slice,convolve[:,j], c=[0,1] )
beta3, t3, df3, p3 = t_stat(data_rough_slice,convolve[:,j], c=[0,1] )
t1 = t1[1,:]
t2 = t2.T
t3= t3.T
t_final1[:,:,j] = t1.reshape(data_rough_slice.shape[:-1])
t_final2[:,:,j] = t2.reshape(data_smooth_slice.shape[:-1])
t_final3[:,:,j] = t3.reshape(data_rough_slice.shape[:-1])
np.save("../data/t_stat/"+i+"_tstat_rough_full.npy", t_final1)
# Path to the subject 009 fMRI data used in class.
pathtoclassdata = "data/ds114/"
# Add path to functions to the system path.
sys.path.append(os.path.join(os.path.dirname(__file__), "../functions/"))
# Load our benjamini-hochberg function
from benjamini_hochberg import bh_procedure
from hypothesis import t_stat
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])
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)
t_final = np.zeros((data.shape[:-1]))
#p_final = np.zeros((data.shape[:-1]))
t_final2 = np.zeros((data.shape[:-1]))
for j in range(data.shape[2]):
data_slice = data[:,:,j,:]
X = np.ones((n_vols,6))
X[:,1] = convolve[:,j]
X[:,2]=np.linspace(-1,1,num=X.shape[0]) #drift
X[:,3:]=fourier_creation(X.shape[0],3)[:,1:]
beta,t,df,p = t_stat_mult_regression(data_slice, X)
beta2, t2, df2, p2 = t_stat(data_slice,convolve[:,j], c=[0,1] )
t = t[1,:]
#p = p[1,:]
t2 = t2.T
MRSS, fitted, residuals = glm_diagnostics(beta, X, data_slice)
t_final[:,:,j] = t.reshape(data_slice.shape[:-1])
#p_final[:,:,j] = p.reshape(data_slice.shape[:-1])
#t_final2[:,:,j] = t2.reshape(data_slice.shape[:-1])
#residual_final[:,:,j,:] = residuals.reshape(data_slice.shape)
np.save("../data/glm/t_stat/"+i+"_tstat.npy", t_final)