data_path = '/home/arokem/data/osmosis/'
for subject in ['FP']:#, 'HT']:
subject_path = os.path.join(data_path, subject)
wm_mask_file = os.path.join(subject_path, '%s_wm_mask.nii.gz'%subject)
wm_nifti = ni.load(wm_mask_file)
wm_data = wm_nifti.get_data()
cc_file = os.path.join(data_path,subject,'%s_cc.nii.gz'%subject)
cc_nifti = ni.load(cc_file)
cc_data = cc_nifti.get_data()
idx = np.where(np.logical_and(cc_data, wm_data))
for b in [1000, 2000, 4000]:
data_1, data_2 = oio.get_dwi_data(b, subject=subject)
TM1 = dti.TensorModel(*data_1, mask=wm_data)
TM2 = dti.TensorModel(*data_2, mask=wm_data)
for TM in [TM1, TM2]:
AD = TM.axial_diffusivity[idx]
RD = TM.radial_diffusivity[idx]
MD = TM.mean_diffusivity[idx]
print('For: %s and b=%s: AD=%1.4f, RD=%1.4f, MD=%1.4f'%(
subject, b, np.median(AD), np.median(RD), np.median(MD)))
idx_AD = np.argmin(np.abs(AD - np.median(AD)))
idx_RD = np.argmin(np.abs(RD - np.median(RD)))
idx_MD = np.argmin(np.abs(MD - np.median(MD)))
print('For: %s and b=%s: AD idx=%s, RD idx =%s, MD idx=%s'%(
subject, b, idx_AD, idx_RD, idx_MD))
data_path = '/hsgs/u/arokem/tmp/'
ssh = sge.SSH(hostname='proclus.stanford.edu', username='******', port=22)
batch_sge = []
for subject in ['FP']: #,'HT']
subject_path = os.path.join(oio.data_path, subject)
wm_mask_file = os.path.join(subject_path, '%s_wm_mask.nii.gz' % subject)
wm_nifti = ni.load(wm_mask_file)
wm_data = wm_nifti.get_data()
n_wm_vox = np.sum(wm_data)
wm_file = "%s_wm_mask.nii.gz" % subject
for b in [1000, 2000, 4000]:
ad_rd = oio.get_ad_rd(subject, b)
for data_i, data in enumerate(oio.get_dwi_data(b, subject)):
file_stem = (data_path + '%s/' % subject +
data[0].split('/')[-1].split('.')[0])
for l1_ratio in l1_ratios:
for alpha in alphas:
for i in range(int(n_wm_vox / 10000) + 2):
params_file = "%s_SSD_l1ratio%s_alpha%s_%03d.nii.gz" % (
file_stem, l1_ratio, alpha, i)
params_dict = dict(data_path=data_path,
i=i,
data_i=data_i,
subject=subject,
b=b,
l1_ratio=l1_ratio,
alpha=alpha,
# Freely assume that the bvals are the same
new_bvals = np.hstack([0, SD_1.bvals[:,SD_1.b_idx]])
#
TM1 = dti.TensorModel(pred1, new_bvecs1, new_bvals,
mask=wm_mask, params_file="temp")
TM2 = dti.TensorModel(pred2, new_bvecs2, new_bvals, mask=wm_mask,
params_file="temp")
pdd_rel = oza.pdd_reliability(TM1, TM2)
return pdd_rel
if __name__=="__main__":
oio.data_path = data_path
data = oio.get_dwi_data(b, subject)
ad_rd = oio.get_ad_rd(subject, b)
subject_path = os.path.join(data_path, subject)
wm_mask_file = os.path.join(subject_path, wm_file)
wm_nifti = ni.load(wm_mask_file)
wm_data = wm_nifti.get_data()
wm_idx = np.where(wm_data==1)
# Preserve memory by getting rid of this data:
del wm_data
# Now set the mask:
mask = np.zeros(wm_nifti.shape)
mask[wm_idx[0], wm_idx[1], wm_idx[2]] = 1
solver_params = dict(alpha=alpha,
data_path = '/hsgs/u/arokem/tmp/'
ssh = sge.SSH(hostname='proclus.stanford.edu',username='******', port=22)
batch_sge = []
for subject in ['FP']: #,'HT']
subject_path = os.path.join(oio.data_path, subject)
wm_mask_file = os.path.join(subject_path, '%s_wm_mask.nii.gz'%subject)
wm_nifti = ni.load(wm_mask_file)
wm_data = wm_nifti.get_data()
n_wm_vox = np.sum(wm_data)
wm_file = "%s_wm_mask.nii.gz"%subject
for b in [1000, 2000, 4000]:
ad_rd = oio.get_ad_rd(subject, b)
for data_i, data in enumerate(oio.get_dwi_data(b, subject)):
file_stem = (data_path + '%s/'%subject +
data[0].split('/')[-1].split('.')[0])
for l1_ratio in l1_ratios:
for alpha in alphas:
for i in range(int(n_wm_vox/10000)+2):
params_file="%s_SSD_l1ratio%s_alpha%s_%03d.nii.gz"%(
file_stem,
l1_ratio,
alpha,
i)
params_dict = dict(
data_path=data_path,
i=i,
data_i=data_i,
# Lines above this one are auto-generated by the wrapper to provide as params:
# data_path, b, subject, data_i, alpha, rho, wm_file, i, ad, rd, params_file
import osmosis.model.sparse_deconvolution as ssd
import osmosis.io as oio
import nibabel as ni
import os
import numpy as np
if __name__ == "__main__":
oio.data_path = data_path
data = oio.get_dwi_data(b, subject)[data_i]
solver_params = dict(alpha=alpha,
l1_ratio=l1_ratio,
fit_intercept=False,
positive=True)
subject_path = os.path.join(data_path, subject)
wm_mask_file = os.path.join(subject_path, wm_file)
wm_nifti = ni.load(wm_mask_file)
wm_data = wm_nifti.get_data()
wm_idx = np.where(wm_data == 1)
low = i * 10000
# Make sure not to go over the edge of the mask:
high = np.min([(i + 1) * 10000, int(np.sum(wm_data))])
# Preserve memory by getting rid of this data:
del wm_data
# Now set the mask:
mask = np.zeros(wm_nifti.shape)
data_path = oio.data_path
rrmse_dti = {}
rrmse_ssd = {}
for subject in ['FP', 'HT']:
subject_path = os.path.join(data_path, subject)
wm_mask_file = os.path.join(subject_path, '%s_wm_mask.nii.gz'%subject)
wm_nifti = ni.load(wm_mask_file).get_data()
wm_mask = np.zeros(wm_nifti.shape)
wm_mask[np.where(wm_nifti==1)] = 1
wm_idx = np.where(wm_nifti>0)
rrmse_dti[subject] = {}
rrmse_ssd[subject] = {}
for b in [1000, 2000, 4000]:
data_1, data_2 = oio.get_dwi_data(b, subject=subject)
TM1 = dti.TensorModel(*data_1, mask=wm_mask)
TM2 = dti.TensorModel(*data_2, mask=wm_mask)
rrmse_dti[subject][b] = ozm.cross_predict(TM1, TM2)
print subject
print b
rmse_mask = rrmse_dti[subject][b][wm_idx]
print "DTI: %s voxels above 1"%(len(np.where(rmse_mask>1)[0])/float(len(rmse_mask)))
print "Median rRMSE: %s"%np.median(rmse_mask)
ad_rd = oio.get_ad_rd(subject, b)
SD1 = ssd.SparseDeconvolutionModel(*data_1, mask=wm_mask,
axial_diffusivity=ad_rd[0]['AD'],
radial_diffusivity=ad_rd[0]['RD'])
SD2 = ssd.SparseDeconvolutionModel(*data_2, mask=wm_mask,
axial_diffusivity=ad_rd[1]['AD'],
radial_diffusivity=ad_rd[1]['RD'])
