def test_Cluster_outputs():
output_map = dict(index_file=dict(),
localmax_txt_file=dict(),
localmax_vol_file=dict(),
max_file=dict(),
mean_file=dict(),
pval_file=dict(),
size_file=dict(),
threshold_file=dict(),
)
outputs = Cluster.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_Cluster_outputs():
output_map = dict(
index_file=dict(),
localmax_txt_file=dict(),
localmax_vol_file=dict(),
max_file=dict(),
mean_file=dict(),
pval_file=dict(),
size_file=dict(),
threshold_file=dict(),
)
outputs = Cluster.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
tcon=group_con),
name='highermodel')
## Cluster results
# make binary masks of sig clusters
binarize = Node(Binarize(min=0.95, max=1.0),
name='binarize',
iterfield='in_file')
# mask T-map before clustering
mask_tmaps = Node(ApplyMask(), name='mask_tmaps')
# clusterize and extract cluster stats/peaks
clusterize = Node(Cluster(threshold=2.3,
out_index_file='outindex.nii',
out_localmax_txt_file='localmax.txt'),
name='clusterize')
# make pictures if time
# In[ ]:
sbc2_workflow2 = Workflow(name='sbc2_workflow2')
sbc2_workflow2.connect([(infosource2, datagrabber, [('roi', 'roi')]),
(datagrabber, merge, [('roi', 'in_files')]),
(merge, highermodel, [('merged_file', 'in_file')]),
(highermodel, datasink, [('t_corrected_p_files',
'rand_corrp_files_withNC')]),
(highermodel, datasink,
[('tstat_files', 'rand_tstat_files_withNC')])])
def test_Cluster_inputs():
input_map = dict(args=dict(argstr='%s',
),
connectivity=dict(argstr='--connectivity=%d',
),
cope_file=dict(argstr='--cope=%s',
),
dlh=dict(argstr='--dlh=%.10f',
),
environ=dict(nohash=True,
usedefault=True,
),
find_min=dict(),
fractional=dict(),
ignore_exception=dict(nohash=True,
usedefault=True,
),
in_file=dict(argstr='--in=%s',
mandatory=True,
),
minclustersize=dict(argstr='--minclustersize',
),
no_table=dict(),
num_maxima=dict(argstr='--num=%d',
),
out_index_file=dict(argstr='--oindex=%s',
hash_files=False,
),
out_localmax_txt_file=dict(argstr='--olmax=%s',
hash_files=False,
),
out_localmax_vol_file=dict(argstr='--olmaxim=%s',
hash_files=False,
),
out_max_file=dict(argstr='--omax=%s',
hash_files=False,
),
out_mean_file=dict(argstr='--omean=%s',
hash_files=False,
),
out_pval_file=dict(argstr='--opvals=%s',
hash_files=False,
),
out_size_file=dict(argstr='--osize=%s',
hash_files=False,
),
out_threshold_file=dict(argstr='--othresh=%s',
hash_files=False,
),
output_type=dict(),
peak_distance=dict(argstr='--peakdist=%.10f',
),
pthreshold=dict(argstr='--pthresh=%.10f',
requires=['dlh', 'volume'],
),
std_space_file=dict(argstr='--stdvol=%s',
),
terminal_output=dict(nohash=True,
),
threshold=dict(argstr='--thresh=%.10f',
mandatory=True,
),
use_mm=dict(),
volume=dict(argstr='--volume=%d',
),
warpfield_file=dict(argstr='--warpvol=%s',
),
xfm_file=dict(argstr='--xfm=%s',
),
)
inputs = Cluster.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def create_subject_ffx_wf(
sub_id, bet_fracthr, spatial_fwhm, susan_brightthresh, hp_vols,
lp_vols, remove_hemi, film_thresh, film_model_autocorr, use_derivs, tr,
tcon_subtractive, cluster_threshold, cluster_thresh_frac, cluster_p,
dilate_clusters_voxel, cond_ids, dsdir, work_basedir):
# todo: new mapnode inputs: cluster_threshold, cluster_p
"""
Make a workflow including preprocessing, first level, and second level GLM analysis for a given subject.
This pipeline includes:
- skull stripping
- spatial smoothing
- removing the irrelevant hemisphere
- temporal band pass filter
- 1st level GLM
- averaging f-contrasts from 1st level GLM
- clustering run-wise f-tests, dilating clusters, and returning binary roi mask
"""
from nipype.algorithms.modelgen import SpecifyModel
from nipype.interfaces.fsl import BET, SUSAN, ImageMaths
from nipype.interfaces.fsl.model import SmoothEstimate, Cluster
from nipype.interfaces.fsl.maths import TemporalFilter, MathsCommand
from nipype.interfaces.utility import Function
from nipype.pipeline.engine import Workflow, Node, MapNode
from nipype.workflows.fmri.fsl import create_modelfit_workflow
from nipype.interfaces.fsl.maths import MultiImageMaths
from nipype.interfaces.utility import IdentityInterface
import sys
from os.path import join as pjoin
import os
sys.path.insert(
0, "/data/project/somato/raw/code/roi_glm/custom_node_functions.py")
# TODO: don't hardcode this
import custom_node_functions
# set up sub-workflow
sub_wf = Workflow(name='subject_%s_wf' % sub_id)
# set up sub-working-directory
subwf_wd = pjoin(work_basedir, 'subject_ffx_wfs',
'subject_%s_ffx_workdir' % sub_id)
if not os.path.exists(subwf_wd):
os.makedirs(subwf_wd)
sub_wf.base_dir = subwf_wd
# Grab bold files for all four runs of one subject.
# in the order [d1_d5, d5_d1, blocked_design1, blocked_design2]
grab_boldfiles = Node(Function(
function=custom_node_functions.grab_boldfiles_subject,
input_names=['sub_id', 'cond_ids', 'ds_dir'],
output_names=['boldfiles']),
name='grab_boldfiles')
grab_boldfiles.inputs.sub_id = sub_id
grab_boldfiles.inputs.cond_ids = cond_ids
grab_boldfiles.inputs.ds_dir = dsdir
getonsets = Node(Function(
function=custom_node_functions.grab_blocked_design_onsets_subject,
input_names=['sub_id', 'prepped_ds_dir'],
output_names=['blocked_design_onsets_dicts']),
name='getonsets')
getonsets.inputs.sub_id = sub_id
getonsets.inputs.prepped_ds_dir = dsdir
# pass bold files through preprocessing pipeline
bet = MapNode(BET(frac=bet_fracthr, functional=True, mask=True),
iterfield=['in_file'],
name='bet')
pick_mask = Node(Function(function=custom_node_functions.pick_first_mask,
input_names=['mask_files'],
output_names=['first_mask']),
name='pick_mask')
# SUSAN smoothing node
susan = MapNode(SUSAN(fwhm=spatial_fwhm,
brightness_threshold=susan_brightthresh),
iterfield=['in_file'],
name='susan')
# bandpass filter node
bpf = MapNode(TemporalFilter(highpass_sigma=hp_vols / 2.3548,
lowpass_sigma=lp_vols / 2.3548),
iterfield=['in_file'],
name='bpf')
# cut away hemisphere node
if remove_hemi == 'r':
roi_args = '-roi 96 -1 0 -1 0 -1 0 -1'
elif remove_hemi == 'l':
roi_args = '-roi 0 96 0 -1 0 -1 0 -1'
else:
raise IOError('did not recognite value of remove_hemi %s' %
remove_hemi)
cut_hemi_func = MapNode(MathsCommand(),
iterfield=['in_file'],
name='cut_hemi_func')
cut_hemi_func.inputs.args = roi_args
cut_hemi_mask = MapNode(MathsCommand(),
iterfield=['in_file'],
name='cut_hemi_mask')
cut_hemi_mask.inputs.args = roi_args
# Make Design and Contrasts for that subject
# subject_info ist a list of two "Bunches", each for one run, containing conditions, onsets, durations
designgen = Node(Function(
input_names=['subtractive_contrast', 'blocked_design_onsets_dicts'],
output_names=['subject_info', 'contrasts'],
function=custom_node_functions.make_bunch_and_contrasts),
name='designgen')
designgen.inputs.subtractive_contrasts = tcon_subtractive
# create 'session_info' for modelfit
modelspec = MapNode(SpecifyModel(input_units='secs'),
name='modelspec',
iterfield=['functional_runs', 'subject_info'])
modelspec.inputs.high_pass_filter_cutoff = hp_vols * tr
modelspec.inputs.time_repetition = tr
flatten_session_infos = Node(Function(
input_names=['nested_list'],
output_names=['flat_list'],
function=custom_node_functions.flatten_nested_list),
name='flatten_session_infos')
# Fist-level workflow
modelfit = create_modelfit_workflow(f_contrasts=True)
modelfit.inputs.inputspec.interscan_interval = tr
modelfit.inputs.inputspec.film_threshold = film_thresh
modelfit.inputs.inputspec.model_serial_correlations = film_model_autocorr
modelfit.inputs.inputspec.bases = {'dgamma': {'derivs': use_derivs}}
# node that reshapes list of copes returned from modelfit
cope_sorter = Node(Function(input_names=['copes', 'varcopes', 'contrasts'],
output_names=['copes', 'varcopes', 'n_runs'],
function=custom_node_functions.sort_copes),
name='cope_sorter')
# average zfstats from both runs
split_zfstats = Node(Function(
function=custom_node_functions.split_zfstats_runs,
input_names=['zfstats_list'],
output_names=['zfstat_run1', 'zfstat_run2']),
name='split_zfstats')
average_zfstats = Node(MultiImageMaths(op_string='-add %s -div 2'),
name='mean_images')
# estimate smoothness of 1st lvl zf-files
smoothest = MapNode(SmoothEstimate(),
name='smoothest',
iterfield=['mask_file', 'zstat_file'])
cluster = MapNode(Cluster(),
name='cluster',
iterfield=['in_file', 'volume', 'dlh'])
cluster.inputs.threshold = cluster_threshold
cluster.inputs.pthreshold = cluster_p
cluster.inputs.fractional = cluster_thresh_frac
cluster.inputs.no_table = True
cluster.inputs.out_threshold_file = True
cluster.inputs.out_pval_file = True
cluster.inputs.out_localmax_vol_file = True
cluster.inputs.out_max_file = True
cluster.inputs.out_size_file = True
# dilate clusters
dilate = MapNode(MathsCommand(args='-kernel sphere %i -dilD' %
dilate_clusters_voxel),
iterfield=['in_file'],
name='dilate')
# binarize the result to a mask
binarize_roi = MapNode(ImageMaths(op_string='-nan -thr 0.001 -bin'),
iterfield=['in_file'],
name='binarize_roi')
# connect preprocessing
sub_wf.connect(grab_boldfiles, 'boldfiles', bet, 'in_file')
sub_wf.connect(bet, 'out_file', susan, 'in_file')
sub_wf.connect(susan, 'smoothed_file', bpf, 'in_file')
sub_wf.connect(bpf, 'out_file', cut_hemi_func, 'in_file')
sub_wf.connect(bet, 'mask_file', cut_hemi_mask, 'in_file')
# connect to 1st level model
sub_wf.connect(cut_hemi_func, 'out_file', modelspec, 'functional_runs')
sub_wf.connect(getonsets, 'blocked_design_onsets_dicts', designgen,
'blocked_design_onsets_dicts')
sub_wf.connect(designgen, 'subject_info', modelspec, 'subject_info')
sub_wf.connect(modelspec, 'session_info', flatten_session_infos,
'nested_list')
sub_wf.connect(flatten_session_infos, 'flat_list', modelfit,
'inputspec.session_info')
sub_wf.connect(designgen, 'contrasts', modelfit, 'inputspec.contrasts')
sub_wf.connect(cut_hemi_func, 'out_file', modelfit,
'inputspec.functional_data')
# connect to cluster thresholding
sub_wf.connect(cut_hemi_mask, 'out_file', smoothest, 'mask_file')
sub_wf.connect(modelfit.get_node('modelestimate'), 'zfstats', smoothest,
'zstat_file')
sub_wf.connect(modelfit.get_node('modelestimate'), 'zfstats', cluster,
'in_file')
sub_wf.connect(smoothest, 'dlh', cluster, 'dlh')
sub_wf.connect(smoothest, 'volume', cluster, 'volume')
sub_wf.connect(cluster, 'threshold_file', dilate, 'in_file')
sub_wf.connect(dilate, 'out_file', binarize_roi, 'in_file')
# connect to averaging f-contrasts
sub_wf.connect(modelfit.get_node('modelestimate'), 'zfstats',
split_zfstats, 'zfstats_list')
sub_wf.connect(split_zfstats, 'zfstat_run1', average_zfstats, 'in_file')
sub_wf.connect(split_zfstats, 'zfstat_run2', average_zfstats,
'operand_files')
# redirect to outputspec
# TODO: redirekt outputspec to datasink in meta-wf
outputspec = Node(IdentityInterface(fields=[
'threshold_file', 'index_file', 'pval_file', 'localmax_txt_file'
]),
name='outputspec')
sub_wf.connect(cluster, 'threshold_file', outputspec, 'threshold_file')
sub_wf.connect(cluster, 'index_file', outputspec, 'index_file')
sub_wf.connect(cluster, 'pval_file', outputspec, 'pval_file')
sub_wf.connect(cluster, 'localmax_txt_file', outputspec,
'localmax_txt_file')
sub_wf.connect(binarize_roi, 'out_file', outputspec, 'roi')
# run subject-lvl workflow
# sub_wf.write_graph(graph2use='colored', dotfilename='./subwf_graph.dot')
# sub_wf.run(plugin='MultiProc', plugin_args={'n_procs': 6})
# sub_wf.run(plugin='CondorDAGMan')
# sub_wf.run()
return sub_wf
'cope' + str(Contrast) + '.feat',
StatsFolder2)
print('Now working on ' + StatsPath)
#cd to output directory to store FSL cluster output
cwd = os.getcwd()
os.chdir(OutputPath)
if LoadResults != 1:
#read smoothness file parse for DLH value and volume
smoothdata = np.genfromtxt(
os.path.join(StatsPath, 'smoothness'))
dlh = smoothdata[0, 1]
vol = int(smoothdata[1, 1])
cl = Cluster()
cl.inputs.threshold = zthresh
cl.inputs.in_file = os.path.join(cwd, StatsPath,
'zstat1.nii.gz')
cl.inputs.dlh = dlh
cl.inputs.volume = vol
cl.inputs.pthreshold = 1000
cl.inputs.terminal_output = 'file'
c = cl.run()
clusterdata = np.genfromtxt(os.path.join(cwd, OutputPath,
'stdout.nipype'),
skip_header=1)
#observed cluster sizes
emp_c = clusterdata[:, 1]
#RFT FWE corrected p-values
fwe_p = clusterdata[:, 2]
def test_Cluster_inputs():
input_map = dict(
args=dict(argstr='%s', ),
connectivity=dict(argstr='--connectivity=%d', ),
cope_file=dict(argstr='--cope=%s', ),
dlh=dict(argstr='--dlh=%.10f', ),
environ=dict(
nohash=True,
usedefault=True,
),
find_min=dict(),
fractional=dict(),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
in_file=dict(
argstr='--in=%s',
mandatory=True,
),
minclustersize=dict(argstr='--minclustersize', ),
no_table=dict(),
num_maxima=dict(argstr='--num=%d', ),
out_index_file=dict(
argstr='--oindex=%s',
hash_files=False,
),
out_localmax_txt_file=dict(
argstr='--olmax=%s',
hash_files=False,
),
out_localmax_vol_file=dict(
argstr='--olmaxim=%s',
hash_files=False,
),
out_max_file=dict(
argstr='--omax=%s',
hash_files=False,
),
out_mean_file=dict(
argstr='--omean=%s',
hash_files=False,
),
out_pval_file=dict(
argstr='--opvals=%s',
hash_files=False,
),
out_size_file=dict(
argstr='--osize=%s',
hash_files=False,
),
out_threshold_file=dict(
argstr='--othresh=%s',
hash_files=False,
),
output_type=dict(),
peak_distance=dict(argstr='--peakdist=%.10f', ),
pthreshold=dict(
argstr='--pthresh=%.10f',
requires=['dlh', 'volume'],
),
std_space_file=dict(argstr='--stdvol=%s', ),
terminal_output=dict(nohash=True, ),
threshold=dict(
argstr='--thresh=%.10f',
mandatory=True,
),
use_mm=dict(),
volume=dict(argstr='--volume=%d', ),
warpfield_file=dict(argstr='--warpvol=%s', ),
xfm_file=dict(argstr='--xfm=%s', ),
)
inputs = Cluster.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
#print(OutputPath)
StatsPath = os.path.join(Exp,Task,StatsFolder1,'cope'+str(Contrast)+'.feat',StatsFolder2)
print('Now working on '+StatsPath)
#cd to output directory to store FSL cluster output
cwd = os.getcwd()
os.chdir(OutputPath)
if LoadResults!=1:
#read smoothness file parse for DLH value and volume
smoothdata = np.genfromtxt(os.path.join(StatsPath,'smoothness'))
dlh = smoothdata[0,1]
vol = int(smoothdata[1,1])
cl = Cluster()
cl.inputs.threshold = zthresh
cl.inputs.in_file = os.path.join(cwd,StatsPath,'zstat1.nii.gz')
cl.inputs.dlh = dlh
cl.inputs.volume = vol
cl.inputs.pthreshold = 1000
cl.inputs.terminal_output = 'file'
c = cl.run()
clusterdata = np.genfromtxt(os.path.join(cwd,OutputPath,'stdout.nipype'),skip_header=1)
#observed cluster sizes
emp_c = clusterdata[:,1]
#RFT FWE corrected p-values
fwe_p = clusterdata[:,2]
os.chdir(cwd)
