if l != 0:
DesignMat[curr_pos:curr_pos + l, k] = np.arange(1, l + 1) #Off
state_X[curr_pos:curr_pos + l] = 0
contrasts[k] = -1
curr_pos = curr_pos + l
k = k + 1
l = len(curr[(curr['periods'] == n) & (curr['State'] == 'transi')])
if l != 0:
DesignMat[curr_pos:curr_pos + l, k] = np.arange(1, l + 1) #on
state_X[curr_pos:curr_pos + l] = 1
contrasts[k] = 1
curr_pos = curr_pos + l
k = k + 1
muols = MUOLS(Y=X, X=DesignMat)
muols.fit()
tvals, pvals, dfs = muols.t_test(contrasts, pval=True, two_tailed=True)
T[t, :] = tvals[0]
t = t + 1
print t
#Store variables
#############################################################################
np.save(os.path.join(BASE_PATH, 'toward_on', 'univariate', 'T.npy'), T)
#Retreive variables
#############################################################################
T = np.load(os.path.join(BASE_PATH, 'toward_on', 'univariate', 'T.npy'))
#Second Level analysis
INPUT = os.path.join(BASE_PATH, "results", "Freesurfer", "asd_vs_controls")
OUTPUT = os.path.join(INPUT, "univariate_analysis")
X = np.load(os.path.join(INPUT, "X.npy"))
y = np.load(os.path.join(INPUT, "y.npy"))
Z = X[:, :3]
Y = X[:, 3:]
assert np.sum(mask_arr) == Y.shape[1]
DesignMat = np.zeros((Z.shape[0], Z.shape[1] + 1)) # y, intercept, age, sex
DesignMat[:, 0] = (y.ravel() - y.ravel().mean()) # y
DesignMat[:, 1] = 1 # intercept
DesignMat[:, 2] = Z[:, 1] # age
DesignMat[:, 3] = Z[:, 2] # sex
muols = MUOLS(Y=Y, X=DesignMat)
muols.fit()
tvals, pvals, dfs = muols.t_test(contrasts=[1, 0, 0, 0], pval=True)
np.savez(os.path.join(OUTPUT, "pvals", "pvals.npz"), pvals[0])
np.savez(os.path.join(OUTPUT, "tvals", "tvals.npz"), tvals[0])
np.savez(os.path.join(OUTPUT, "log10pvals", "log10pvals.npz"),
-np.log10(pvals[0]))
create_texture.create_texture_file(OUTPUT=os.path.join(OUTPUT, "pvals"),
TEMPLATE_PATH=TEMPLATE_PATH,
MASK_PATH=MASK_PATH,
beta_path=os.path.join(
OUTPUT, "pvals", "pvals.npz"),
penalty_start=3,
threshold=False)
X = np.vstack(images)
X = X[:, mask_bool.ravel()]
X = nilearn.signal.clean(X,
detrend=True,
standardize=True,
confounds=None,
low_pass=None,
high_pass=None,
t_r=3)
#mean_X=(X[lab==1].mean(axis=0) + X[0:20,:].mean(axis=0) ) / float(2)
#X=X-mean_X
X = X - X.mean(axis=0)
muols = MUOLS(Y=X, X=DesignMat)
muols.fit()
contrasts = np.identity((8))
tvals, pvals, dfs = muols.t_test(contrasts,
pval=True,
two_tailed=True)
for m in range(0, 8):
T[t, :] = tvals[m]
betas[t, :] = muols.coef[m, :]
subject[t] = subject_num
t = t + 1
print t
BASE_PATH = "/neurospin/brainomics/2016_classif_hallu_fmri"
y = np.zeros((128))
np.save(os.path.join(BASE_PATH, 'results', 'svm_with_HC', 'T_IMA.npy'), T_IMA)
X = np.hstack([np.random.randn(n, px-1), np.ones((n, 1))]) # X with intercept
f = tempfile.mktemp(dir='/volatile')
Y = np.random.randn(n, py_info + py_noize)
# Causal model: add X on the first py_info variable
Y[:, :py_info] += np.dot(X, beta)
np.save(f, Y)
del Y
contrasts = np.identity(X.shape[1])
## Read data Y as a memory map
##############################
Y_memmap = np.load(f + '.npy', mmap_mode='r')
# univariate analysis: fit by blocks of 2**26 elements
muols = MUOLS(Y=Y_memmap, X=X)
time1 = time.time()
muols.fit(block=True, max_elements=2 ** 26)
time2 = time.time()
tvals, pvals, dfs = muols.t_test(contrasts=contrasts,
pval=True,
two_tailed=True)
print time2 - time1
del muols
# univariate analysis: fit by blocks of 2**27 elements
muols = MUOLS(Y=Y_memmap, X=X)
time3 = time.time()
muols.fit(block=True, max_elements=2 ** 27)
time4 = time.time()
tvals, pvals, dfs = muols.t_test(contrasts=contrasts,
"""
fsl5.0-randomise -i /neurospin/ciclops/projects/BIPLi7/Clinicaldata/Analysis/Processing_February_2019/Reconstruct_gridding/Processing_quantif/TPI_Analysisoutputs_smallmask/ALLcs.nii.gz -m /neurospin/ciclops/projects/BIPLi7/Clinicaldata/Analysis/Processing_February_2019/Reconstruct_gridding/Processing_quantif/TPI_Analysisoutputs_smallmask/mask.nii.gz -o /neurospin/ciclops/projects/BIPLi7/Clinicaldata/Analysis/Processing_February_2019/Reconstruct_gridding/Processing_quantif/TPI_Analysisoutputs_smallmask/li~1+age+sex -d /neurospin/ciclops/projects/BIPLi7/Clinicaldata/Analysis/Processing_February_2019/Reconstruct_gridding/Processing_quantif/TPI_Analysisoutputs_smallmask/li~1+age+sex_design.mat -t /neurospin/ciclops/projects/BIPLi7/Clinicaldata/Analysis/Processing_February_2019/Reconstruct_gridding/Processing_quantif/TPI_Analysisoutputs_smallmask/li~1+age+sex_contrast.mat -T -n 2000 -C 3
fsl5.0-randomise -i /neurospin/ciclops/projects/BIPLi7/Clinicaldata/Analysis/Processing_February_2019/Reconstruct_gridding/Processing_quantif/TPI_Analysisoutputs_fsl-thres3.5/ALLcs.nii.gz -m /neurospin/ciclops/projects/BIPLi7/Clinicaldata/Analysis/Processing_February_2019/Reconstruct_gridding/Processing_quantif/TPI_Analysisoutputs_fsl-thres3.5/mask.nii.gz -o /neurospin/ciclops/projects/BIPLi7/Clinicaldata/Analysis/Processing_February_2019/Reconstruct_gridding/Processing_quantif/TPI_Analysisoutputs_fsl-thres3.5/li~1+age+sex -d /neurospin/ciclops/projects/BIPLi7/Clinicaldata/Analysis/Processing_February_2019/Reconstruct_gridding/Processing_quantif/TPI_Analysisoutputs_fsl-thres3.5/li~1+age+sex_design.mat -t /neurospin/ciclops/projects/BIPLi7/Clinicaldata/Analysis/Processing_February_2019/Reconstruct_gridding/Processing_quantif/TPI_Analysisoutputs_fsl-thres3.5/li~1+age+sex_contrast.mat -T -n 2000 -C 3.5
fsl5.0-randomise -i /neurospin/ciclops/projects/BIPLi7/Clinicaldata/Analysis/Processing_February_2019/Reconstruct_gridding/Processing_quantif/TPI_Analysisoutputs_fsl-thres4/ALLcs.nii.gz -m /neurospin/ciclops/projects/BIPLi7/Clinicaldata/Analysis/Processing_February_2019/Reconstruct_gridding/Processing_quantif/TPI_Analysisoutputs_fsl-thres4/mask.nii.gz -o /neurospin/ciclops/projects/BIPLi7/Clinicaldata/Analysis/Processing_February_2019/Reconstruct_gridding/Processing_quantif/TPI_Analysisoutputs_fsl-thres4/li~1+age+sex -d /neurospin/ciclops/projects/BIPLi7/Clinicaldata/Analysis/Processing_February_2019/Reconstruct_gridding/Processing_quantif/TPI_Analysisoutputs_fsl-thres4/li~1+age+sex_design.mat -t /neurospin/ciclops/projects/BIPLi7/Clinicaldata/Analysis/Processing_February_2019/Reconstruct_gridding/Processing_quantif/TPI_Analysisoutputs_fsl-thres4/li~1+age+sex_contrast.mat -T -n 2000 -C 4
"""
###############################################################################
"""
~/git/scripts/brainomics/image_clusters_analysis_nilearn.py li~1+age+sex_tstat1.nii.gz -o li~1+age+sex_tstat1 --thresh_neg_low 0 --thresh_neg_high 0 --thresh_pos_low 3 --thresh_size 10
"""
###############################################################################
## OLDIES
muols = MUOLS(Y=X,X=DesignMat)
muols.fit()
print('launching the t_test')
tvals, pvals_ttest, df1 = muols.t_test(contrasts=[1, 0, 0], pval=True)
#%time tvals, pvals_ttest, df1 = muols.t_test(contrasts=[1, 0, 0], pval=True)
print('t_test done')
print('launching maxT test')
#tvals2, pvals_maxT, df2 = muols.t_test_maxT(contrasts=np.array([1, 0, 0]), nperms=1000, two_tailed=False)
#print('maxT test done')
tvals, pvals_maxT, df3 = muols.t_test_maxT(contrasts=np.array([1, 0, 0]), nperms=1000, two_tailed=True)
#♣tvals3, minP, df3 = muols.t_test_minP(contrasts=np.array([1, 0, 0]), nperms=5, two_tailed=True)
mhist, bins, patches= plt.hist([pvals_ttest[0,:],pvals_maxT[0,:]],
color=['blue','red'],
label=['pvals_ttest','pvals_maxT'])
mycoefs=np.zeros(mask_arr.shape)
#assert np.sum(mask_arr) == Y.shape[1]
print "ok read"
#############################################################################
## BASIC MULM
DesignMat = np.zeros((Z.shape[0], Z.shape[1] + 1))
DesignMat[:, 0] = (y.ravel() - y.ravel().mean()) # y
DesignMat[:, 1] = 1 # intercept
DesignMat[:, 2] = Z[:, 1] # age
DesignMat[:, 3] = Z[:, 2] # gender
contrasts = np.array([[1, 0, 0, 0]])
from mulm import MUOLS
muols = MUOLS(Y=X, X=DesignMat)
muols.fit(block=True)
print "ok fit"
tvals, pvals, dfs = muols.t_test(contrasts=contrasts,
pval=True,
two_tailed=True)
print "ok t_test"
del muols
arr = np.zeros(mask_arr.shape)
arr[mask_arr] = pvals[0]
out_im = nib.Nifti1Image(arr, affine=mask_ima.get_affine())
out_im.to_filename(os.path.join(OUTPUT_UNIVARIATE, "pval_adrs.nii.gz"))
p_log10 = -np.log10(pvals)
arr = np.zeros(mask_arr.shape)
arr[mask_arr] = p_log10[0]
f = tempfile.mktemp()
Y = np.random.randn(n, py_info + py_noize)
# Causal model: add X on the first py_info variable
Y[:, :py_info] += np.dot(X, beta)
print("Save matrix to disk")
np.save(f, Y)
del Y
contrasts = np.identity(X.shape[1])
## Read data Y as a memory map
##############################
Y_memmap = np.load(f + '.npy', mmap_mode='r')
# univariate analysis: fit by blocks of 2**27 elements
muols = MUOLS(Y=Y_memmap, X=X)
time3 = time.time()
muols.fit(block=True, max_elements=2**27)
time4 = time.time()
tvals, pvals, dfs = muols.t_test(contrasts=contrasts,
pval=True,
two_tailed=True)
print("Mmap by block(max_elements=2 ** 27): ", time4 - time3)
del muols
# univariate analysis: fit in one go
muols = MUOLS(Y=Y_memmap, X=X)
time5 = time.time()
muols.fit(block=False)
time6 = time.time()
tvals, pvals, dfs = muols.t_test(contrasts=contrasts,