def prep_cwas_workflow(c, subject_infos):
print('Preparing CWAS workflow')
p_id, s_ids, scan_ids, s_paths = (list(tup) for tup in zip(*subject_infos))
print('Subjects', s_ids)
wf = pe.Workflow(name='cwas_workflow')
wf.base_dir = c.workingDirectory
from CPAC.cwas import create_cwas
import numpy as np
regressor = np.loadtxt(c.cwasRegressorFile)
cw = create_cwas()
cw.inputs.inputspec.roi = c.cwasROIFile
cw.inputs.inputspec.subjects = s_paths
cw.inputs.inputspec.regressor = regressor
cw.inputs.inputspec.cols = c.cwasRegressorCols
cw.inputs.inputspec.f_samples = c.cwasFSamples
cw.inputs.inputspec.strata = c.cwasRegressorStrata # will stay None?
cw.inputs.inputspec.parallel_nodes = c.cwasParallelNodes
ds = pe.Node(nio.DataSink(), name='cwas_sink')
out_dir = os.path.dirname(s_paths[0]).replace(s_ids[0], 'cwas_results')
ds.inputs.base_directory = out_dir
ds.inputs.container = ''
wf.connect(cw, 'outputspec.F_map',
ds, 'F_map')
wf.connect(cw, 'outputspec.p_map',
ds, 'p_map')
wf.run(plugin='MultiProc',
plugin_args={'n_procs': c.numCoresPerSubject})
def prep_cwas_workflow(c, subject_infos):
print 'Preparing CWAS workflow'
p_id, s_ids, scan_ids, s_paths = (list(tup) for tup in zip(*subject_infos))
print 'Subjects', s_ids
wf = pe.Workflow(name='cwas_workflow')
wf.base_dir = c.workingDirectory
from CPAC.cwas import create_cwas
import numpy as np
regressor = np.loadtxt(c.cwasRegressorFile)
cw = create_cwas()
cw.inputs.inputspec.roi = c.cwasROIFile
cw.inputs.inputspec.subjects = s_paths
cw.inputs.inputspec.regressor = regressor
cw.inputs.inputspec.cols = c.cwasRegressorCols
cw.inputs.inputspec.f_samples = c.cwasFSamples
cw.inputs.inputspec.strata = c.cwasRegressorStrata # will stay None?
cw.inputs.inputspec.parallel_nodes = c.cwasParallelNodes
ds = pe.Node(nio.DataSink(), name='cwas_sink')
out_dir = os.path.dirname(s_paths[0]).replace(s_ids[0], 'cwas_results')
ds.inputs.base_directory = out_dir
ds.inputs.container = ''
wf.connect(cw, 'outputspec.F_map', ds, 'F_map')
wf.connect(cw, 'outputspec.p_map', ds, 'p_map')
wf.run(plugin='MultiProc', plugin_args={'n_procs': c.numCoresPerSubject})
def prep_cwas_workflow(c, subject_infos):
from CPAC.cwas import create_cwas
import numpy as np
try:
import mkl
mkl.set_num_threads(c.cwasThreads)
except ImportError:
pass
print 'Preparing CWAS workflow'
p_id, s_ids, scan_ids, s_paths = (list(tup) for tup in zip(*subject_infos))
print 'Subjects', s_ids
# Read in list of subject functionals
lines = open(c.cwasFuncFiles).readlines()
spaths = [ l.strip().strip('"') for l in lines ]
# Read in design/regressor file
regressor = np.loadtxt(c.cwasRegressorFile)
wf = pe.Workflow(name='cwas_workflow')
wf.base_dir = c.workingDirectory
cw = create_cwas()
cw.inputs.inputspec.roi = c.cwasROIFile
cw.inputs.inputspec.subjects = spaths
cw.inputs.inputspec.regressor = regressor
cw.inputs.inputspec.cols = c.cwasRegressorCols
cw.inputs.inputspec.f_samples = c.cwasFSamples
cw.inputs.inputspec.strata = c.cwasRegressorStrata # will stay None?
cw.inputs.inputspec.parallel_nodes = c.cwasParallelNodes
cw.inputs.inputspec.memory_limit = c.cwasMemory
cw.inputs.inputspec.dtype = c.cwasDtype
ds = pe.Node(nio.DataSink(), name='cwas_sink')
out_dir = os.path.dirname(s_paths[0]).replace(s_ids[0], 'cwas_results')
ds.inputs.base_directory = out_dir
ds.inputs.container = ''
wf.connect(cw, 'outputspec.F_map',
ds, 'F_map')
wf.connect(cw, 'outputspec.p_map',
ds, 'p_map')
wf.run(plugin='MultiProc',
plugin_args={'n_procs': c.numCoresPerSubject})
def test_cwas():
from CPAC.cwas import create_cwas
import numpy as np
import os, glob
import time
g_string = '/home/data/Projects/ADHD200/Manuel_sink/*/func/scan_id_rest_1/scan_id_anat_1/csf_threshold_0.4/gm_threshold_0.2/wm_threshold_0.66/nc_5/selector_0.2.3.7/bandpass_freqs_0.009.0.1/rest_residual_filtered_4mm.nii.gz'
roi_file = '/usr/share/fsl/4.1/data/standard/MNI152_T1_4mm_brain_mask.nii.gz'
subjects_list = glob.glob(g_string)
c = create_cwas()
c.inputs.inputspec.roi = roi_file
c.inputs.inputspec.subjects = subjects_list[:5]
c.inputs.inputspec.regressor = np.arange(len(subjects_list[:5]))[:,np.newaxis]
c.inputs.inputspec.f_samples = 5000
c.inputs.inputspec.parallel_nodes = 10
c.base_dir = '/home/data/Projects/cwas_tests/results_fs%i_pn%i' % (c.inputs.inputspec.f_samples,
c.inputs.inputspec.parallel_nodes)
start = time.clock()
c.run(plugin='MultiProc', plugin_args={'n_procs' : 10})
end = time.clock()
print "%.2gs" % (end-start)
def run_cwas(self):
import os
os.chdir("%s/C-PAC" % self.base)
from CPAC.cwas import create_cwas
import numpy as np
import time
from os import path as op
###
# Paths and Other Inputs
# - ideally this section can be changed with the rest staying the same
# however, we aren't there yet
###
# File with initial grey matter mask
roi_file = op.join(self.base, "rois/grey_matter_4mm.nii.gz")
# File with list of subject functionals
sfile = op.join(self.base, "configs", "%s_funcpaths_4mm.txt" % self.name)
# File with regressors
rfile = op.join(self.base, "configs", "%s_regressors.txt" % self.name)
# Column(s) of interest in regressors
cols = [1]
# Number of permutations
nperms = 1000
###
# Setup
###
# Read in list of subject functionals
subjects_list = [l.strip().strip('"') for l in open(sfile).readlines()]
# Read in design/regressor file
regressors = np.loadtxt(rfile)
# Setup the inputs
c = create_cwas()
c.inputs.inputspec.roi = roi_file
c.inputs.inputspec.subjects = subjects_list
c.inputs.inputspec.cols = cols
c.inputs.inputspec.regressor = regressors
c.inputs.inputspec.f_samples = nperms
c.inputs.inputspec.parallel_nodes = 4
# c.base_dir = op.join(obase, 'results_fs%i_pn%i' % \
# (c.inputs.inputspec.f_samples, c.inputs.inputspec.parallel_nodes))
c.base_dir = op.join(self.base, "results_%s.py" % self.name)
# export MKL_NUM_THREADS=X # in command line
# import mkl
# mkl.set_num_threads(X)
# try:
# import mkl
# mkl.set_num_threads(nthreads)
# except ImportError:
# pass
# Run it!
start = time.clock()
c.run(plugin="MultiProc", plugin_args={"n_procs": 4})
end = time.clock()
print "time: %.2gs" % (end - start)
def cwas_workflow(c):
from CPAC.cwas import create_cwas
from string import Template
import numpy as np
import time
if isinstance(c.cwas, dict):
c.cwas = Configuration(c.cwas)
try:
import mkl
mkl.set_num_threads(c.cwas.threads)
except ImportError:
pass
# Auto-complete base
fields = ["prior_mask_file", "file_with_functional_paths", "regressor_file"]
for field in fields:
s = Template(getattr(c.cwas, field))
c.cwas.update(field, s.substitute(base=c.cwas.base))
# Read in list of subject functionals
lines = open(c.cwas.file_with_functional_paths).readlines()
spaths = [ l.strip().strip('"') for l in lines ]
# Read in design/regressor file
regressor = np.loadtxt(c.cwas.regressor_file)
# Load workflow
wf = pe.Workflow(name='cwas_workflow')
wf.base_dir = c.workingDirectory
# Setup CWAS set of commands
cw = create_cwas()
cw.inputs.inputspec.roi = c.cwas.prior_mask_file
cw.inputs.inputspec.subjects = spaths
cw.inputs.inputspec.regressor = regressor
cw.inputs.inputspec.cols = c.cwas.regressors_of_interest
cw.inputs.inputspec.f_samples = c.cwas.n_permutations
cw.inputs.inputspec.strata = c.cwas.strata
cw.inputs.inputspec.parallel_nodes = c.cwas.parallel_nodes
cw.inputs.inputspec.memory_limit = c.cwas.memory_limit
cw.inputs.inputspec.dtype = c.cwas.dtype
# Output directory
ds = pe.Node(nio.DataSink(), name='cwas_sink')
ds.inputs.base_directory = os.path.join(c.outputDirectory, "cwas_results")
ds.inputs.container = ''
# Link F-stats and P-values
wf.connect(cw, 'outputspec.F_map',
ds, 'F_map')
wf.connect(cw, 'outputspec.p_map',
ds, 'p_map')
# Run CWAS
start = time.time()
wf.run(plugin='MultiProc',
plugin_args={'n_procs': c.numCoresPerSubject})
end = time.time()
# Return time it took
print 'It took', end-start, 'seconds.'
return (end-start)
