cmd_file = os.path.join(cmd_file_path, '%s.py'%name)
print("Generating: %s"%cmd_file)
sge.py_cmd(ssh,
code,
file_name=cmd_file,
python=python_path)
cmd_file = os.path.join(cmd_file_path, '%s.py'%name)
batch_sge.append(sge.qsub_cmd(
'%s %s'%(bashcmd, cmd_file), name, mem_usage=mem))
# Analyses done:
# Reliability, isotropic model accuracy, diffusion model accuracy,
# fitted model parameters
ssh = sge.SSH(hostname=hostname,username=username, port=port)
batch_sge = []
# For aggregating later:
emd_file_names = []
other_file_names = []
# Start qsub generation:
subj_file_nums = []
for sid_idx, sid in enumerate(sid_list):
data_path = os.path.join(hcp_path, "%s/T1w/Diffusion"%sid)
wm_data_file = nib.load(os.path.join(data_path,"wm_mask_no_vent.nii.gz"))
wm_vox_num = np.sum(np.round(wm_data_file.get_data()).astype(int))
# For dividing up data
This is a wrapper for creating sge commands for parallel computation of model
parameters for SFM models from many subjects.
"""
import os
import nibabel as ni
import numpy as np
import osmosis.io as oio
from osmosis.parallel import sge
import osmosis.parallel.kfold_xval_precision_template as mb_template
reload(mb_template)
template = sge.getsourcelines(mb_template)[0]
ssh = sge.SSH(hostname='proclus.stanford.edu', username='******', port=22)
batch_sge = []
for i in np.arange(65):
params_dict = dict(i=i)
code = sge.add_params(template, params_dict)
name = 'kfold_xval_sph_cc_m%s' % (i)
cmd_file = '/home/klchan13/pycmd/%s.py' % name
print("Generating: %s" % cmd_file)
sge.py_cmd(ssh,
code,
file_name=cmd_file,
python='/home/klchan13/anaconda/bin/python')
cmd_file = '/home/klchan13/pycmd/%s.py' % name
import os
import nibabel as ni
import numpy as np
import osmosis.io as oio
from osmosis.parallel import sge
import osmosis.parallel.ssd_template as ssd_template
reload(ssd_template)
template = sge.getsourcelines(ssd_template)[0]
alphas = [0.0001, 0.0005, 0.001, 0.0025, 0.005, 0.0075, 0.01, 0.025, 0.05]
l1_ratios = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
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])