def QA_workflow(c, prep_c, name='QA'):
""" Workflow that generates a Quality Assurance json
"""
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
from nipype.interfaces.freesurfer import ApplyVolTransform
from nipype.interfaces import freesurfer as fs
from nipype.interfaces.io import FreeSurferSource
from scripts.u0a14c5b5899911e1bca80023dfa375f2.QA_utils import (tsnr_roi,
art_output)
from ..utils.reportsink.io import JSONSink
# Define Workflow
workflow = pe.Workflow(name=name)
datagrabber = c.datagrabber.create_dataflow()
infosource = datagrabber.get_node('subject_id_iterable')
#workflow.connect(infosource, 'subject_id', inputspec, 'subject_id')
art_info = pe.MapNode(
util.Function(input_names=['art_file', 'intensity_file', 'stats_file'],
output_names=['table', 'out', 'intensity_plot'],
function=art_output),
name='art_output',
iterfield=["art_file", "intensity_file", "stats_file"])
#fssource = pe.Node(interface = FreeSurferSource(),name='fssource')
#fssource.inputs.subjects_dir = c.surf_dir
roidevplot = tsnr_roi(plot=False,
name='tsnr_stddev_roi',
roi=['all'],
onsets=False)
roidevplot.inputs.inputspec.TR = c.TR
roisnrplot = tsnr_roi(plot=False,
name='SNR_roi',
roi=['all'],
onsets=False)
roisnrplot.inputs.inputspec.TR = c.TR
#workflow.connect(fssource, ('aparc_aseg', pickaparc), roisnrplot, 'inputspec.aparc_aseg')
#workflow.connect(fssource, ('aparc_aseg', pickaparc), roidevplot, 'inputspec.aparc_aseg')
workflow.connect(infosource, 'subject_id', roidevplot, 'inputspec.subject')
workflow.connect(infosource, 'subject_id', roisnrplot, 'inputspec.subject')
workflow.connect(infosource, ("subject_id", myfssource, c.surf_dir),
roisnrplot, "inputspec.aparc_aseg")
workflow.connect(infosource, ("subject_id", myfssource, c.surf_dir),
roidevplot, "inputspec.aparc_aseg")
tablecombine = pe.MapNode(util.Function(input_names=['roidev', 'roisnr'],
output_names=['combined_table'],
function=combine_table),
name='combinetable',
iterfield=['roidev', 'roisnr'])
#workflow.connect(infosource,'subject_id',fssource,'subject_id')
#workflow.connect(inputspec,'sd',fssource,'subjects_dir')
#fssource.inputs.subjects_dir = c.surf_dir
roisnrplot.inputs.inputspec.sd = c.surf_dir
roidevplot.inputs.inputspec.sd = c.surf_dir
workflow.connect(datagrabber, 'datagrabber.art_intensity', art_info,
'intensity_file')
workflow.connect(datagrabber, 'datagrabber.art_stats', art_info,
'stats_file')
workflow.connect(datagrabber, 'datagrabber.outlier_files', art_info,
'art_file')
workflow.connect(datagrabber, 'datagrabber.reg_file', roidevplot,
'inputspec.reg_file')
workflow.connect(datagrabber, 'datagrabber.tsnr_stddev', roidevplot,
'inputspec.tsnr_file')
#workflow.connect(fssource,('aparc_aseg',pickaparc),roidevplot,'inputspec.aparc_aseg')
workflow.connect(roidevplot, 'outputspec.roi_table', tablecombine,
'roidev')
workflow.connect(datagrabber, 'datagrabber.reg_file', roisnrplot,
'inputspec.reg_file')
workflow.connect(datagrabber, 'datagrabber.tsnr', roisnrplot,
'inputspec.tsnr_file')
workflow.connect(roisnrplot, 'outputspec.roi_table', tablecombine,
'roisnr')
js = pe.Node(interface=JSONSink(), name="jsoner")
workflow.connect(tablecombine, "combined_table", js, "SNR_table")
workflow.connect(art_info, "table", js, "art")
workflow.connect(art_info, "out", js, "outliers")
workflow.connect(datagrabber, ("datagrabber.mincost", getmincost), js,
"mincost")
js.inputs.norm_thresh = prep_c.norm_thresh
js.inputs.z_thresh = prep_c.z_thresh
return workflow
def QA_workflow(QAc, c=foo, name='QA'):
""" Workflow that generates a Quality Assurance Report
Parameters
----------
name : name of workflow
Inputs
------
inputspec.subject_id : Subject id
inputspec.config_params : configuration parameters to print in PDF (in the form of a 2D List)
inputspec.in_file : original functional run
inputspec.art_file : art outlier file
inputspec.reg_file : bbregister file
inputspec.tsnr_detrended : detrended image
inputspec.tsnr : signal-to-noise ratio image
inputspec.tsnr_mean : mean image
inputspec.tsnr_stddev : standard deviation image
inputspec.ADnorm : norm components file from art
inputspec.TR : repetition time of acquisition
inputspec.sd : freesurfer subjects directory
"""
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
from nipype.interfaces.freesurfer import ApplyVolTransform
from nipype.interfaces import freesurfer as fs
from nipype.interfaces.io import FreeSurferSource
from scripts.u0a14c5b5899911e1bca80023dfa375f2.QA_utils import (
plot_ADnorm, tsdiffana, tsnr_roi, combine_table, art_output,
plot_motion, plot_ribbon, plot_anat, overlay_new, overlay_dB,
spectrum_ts_table)
from ..utils.reportsink.io import ReportSink
# Define Workflow
workflow = pe.Workflow(name=name)
inputspec = pe.Node(interface=util.IdentityInterface(fields=[
'subject_id', 'config_params', 'in_file', 'art_file', 'motion_plots',
'reg_file', 'tsnr', 'tsnr_detrended', 'tsnr_stddev', 'ADnorm', 'TR',
'sd'
]),
name='inputspec')
infosource = pe.Node(util.IdentityInterface(fields=['subject_id']),
name='subject_names')
if QAc.test_mode:
infosource.iterables = ('subject_id', [QAc.subjects[0]])
else:
infosource.iterables = ('subject_id', QAc.subjects)
datagrabber = preproc_datagrabber(c)
datagrabber.inputs.node_type = c.motion_correct_node
orig_datagrabber = get_dataflow(c)
workflow.connect(infosource, 'subject_id', datagrabber, 'subject_id')
workflow.connect(infosource, 'subject_id', orig_datagrabber, 'subject_id')
workflow.connect(orig_datagrabber, 'func', inputspec, 'in_file')
workflow.connect(infosource, 'subject_id', inputspec, 'subject_id')
workflow.connect(datagrabber, ('outlier_files', sort), inputspec,
'art_file')
workflow.connect(datagrabber, ('reg_file', sort), inputspec, 'reg_file')
workflow.connect(datagrabber, ('tsnr', sort), inputspec, 'tsnr')
workflow.connect(datagrabber, ('tsnr_stddev', sort), inputspec,
'tsnr_stddev')
workflow.connect(datagrabber, ('tsnr_detrended', sort), inputspec,
'tsnr_detrended')
workflow.connect(datagrabber, ('art_norm', sort), inputspec, 'ADnorm')
inputspec.inputs.TR = c.TR
inputspec.inputs.sd = c.surf_dir
# Define Nodes
plot_m = pe.MapNode(util.Function(input_names=['motion_parameters'],
output_names=['fname_t', 'fname_r'],
function=plot_motion),
name="motion_plots",
iterfield=['motion_parameters'])
workflow.connect(datagrabber, ('motion_parameters', sort), plot_m,
'motion_parameters')
#workflow.connect(plot_m, 'fname',inputspec,'motion_plots')
tsdiff = pe.MapNode(util.Function(input_names=['img'],
output_names=['out_file'],
function=tsdiffana),
name='tsdiffana',
iterfield=["img"])
art_info = pe.MapNode(
util.Function(input_names=['art_file', 'intensity_file', 'stats_file'],
output_names=['table', 'out', 'intensity_plot'],
function=art_output),
name='art_output',
iterfield=["art_file", "intensity_file", "stats_file"])
fssource = pe.Node(interface=FreeSurferSource(), name='fssource')
plotribbon = pe.Node(util.Function(input_names=['Brain'],
output_names=['images'],
function=plot_ribbon),
name="plot_ribbon")
workflow.connect(fssource, 'ribbon', plotribbon, 'Brain')
plotanat = pe.Node(util.Function(input_names=['brain'],
output_names=['images'],
function=plot_anat),
name="plot_anat")
plotmask = plotanat.clone('plot_mask')
workflow.connect(datagrabber, 'mask', plotmask, 'brain')
roidevplot = tsnr_roi(plot=False,
name='tsnr_stddev_roi',
roi=['all'],
onsets=False)
roidevplot.inputs.inputspec.TR = c.TR
roisnrplot = tsnr_roi(plot=False,
name='SNR_roi',
roi=['all'],
onsets=False)
roisnrplot.inputs.inputspec.TR = c.TR
workflow.connect(fssource, ('aparc_aseg', pickfirst), roisnrplot,
'inputspec.aparc_aseg')
workflow.connect(fssource, ('aparc_aseg', pickfirst), roidevplot,
'inputspec.aparc_aseg')
workflow.connect(infosource, 'subject_id', roidevplot, 'inputspec.subject')
workflow.connect(infosource, 'subject_id', roisnrplot, 'inputspec.subject')
tablecombine = pe.MapNode(util.Function(
input_names=['roidev', 'roisnr', 'imagetable'],
output_names=['imagetable'],
function=combine_table),
name='combinetable',
iterfield=['roidev', 'roisnr', 'imagetable'])
adnormplot = pe.MapNode(util.Function(
input_names=['ADnorm', 'TR', 'norm_thresh', 'out'],
output_names=['plot'],
function=plot_ADnorm),
name='ADnormplot',
iterfield=['ADnorm', 'out'])
adnormplot.inputs.norm_thresh = c.norm_thresh
workflow.connect(art_info, 'out', adnormplot, 'out')
convert = pe.Node(interface=fs.MRIConvert(), name='converter')
voltransform = pe.MapNode(interface=ApplyVolTransform(),
name='register',
iterfield=['source_file'])
overlaynew = pe.MapNode(util.Function(
input_names=['stat_image', 'background_image', 'threshold', "dB"],
output_names=['fnames'],
function=overlay_dB),
name='overlay_new',
iterfield=['stat_image'])
overlaynew.inputs.dB = False
overlaynew.inputs.threshold = 20
overlaymask = pe.MapNode(util.Function(
input_names=['stat_image', 'background_image', 'threshold'],
output_names=['fnames'],
function=overlay_new),
name='overlay_mask',
iterfield=['stat_image'])
overlaymask.inputs.threshold = 0.5
workflow.connect(convert, 'out_file', overlaymask, 'background_image')
overlaymask2 = overlaymask.clone('acompcor_image')
workflow.connect(convert, 'out_file', overlaymask2, 'background_image')
workflow.connect(datagrabber, 'tcompcor', overlaymask, 'stat_image')
workflow.connect(datagrabber, 'acompcor', overlaymask2, 'stat_image')
workflow.connect(datagrabber, ('mean_image', sort), plotanat, 'brain')
ts_and_spectra = spectrum_ts_table()
timeseries_segstats = tsnr_roi(plot=False,
name='timeseries_roi',
roi=['all'],
onsets=False)
workflow.connect(inputspec, 'tsnr_detrended', timeseries_segstats,
'inputspec.tsnr_file')
workflow.connect(inputspec, 'reg_file', timeseries_segstats,
'inputspec.reg_file')
workflow.connect(infosource, 'subject_id', timeseries_segstats,
'inputspec.subject')
workflow.connect(fssource, ('aparc_aseg', pickfirst), timeseries_segstats,
'inputspec.aparc_aseg')
timeseries_segstats.inputs.inputspec.TR = c.TR
ts_and_spectra.inputs.inputspec.tr = c.TR
workflow.connect(timeseries_segstats, 'outputspec.roi_file',
ts_and_spectra, 'inputspec.stats_file')
write_rep = pe.Node(interface=ReportSink(orderfields=[
'Introduction', 'in_file', 'config_params', 'Art_Detect',
'Global_Intensity', 'Mean_Functional', 'Ribbon', 'Mask',
'motion_plot_translations', 'motion_plot_rotations', 'tsdiffana',
'ADnorm', 'A_CompCor', 'T_CompCor', 'TSNR_Images', 'tsnr_roi_table'
]),
name='report_sink')
write_rep.inputs.Introduction = "Quality Assurance Report for fMRI preprocessing."
write_rep.inputs.base_directory = os.path.join(QAc.sink_dir)
write_rep.inputs.report_name = "Preprocessing_Report"
write_rep.inputs.json_sink = QAc.json_sink
workflow.connect(infosource, 'subject_id', write_rep, 'container')
workflow.connect(plotanat, 'images', write_rep, "Mean_Functional")
write_rep.inputs.table_as_para = False
# Define Inputs
convert.inputs.out_type = 'niigz'
convert.inputs.in_type = 'mgz'
# Define Connections
workflow.connect(inputspec, 'TR', adnormplot, 'TR')
workflow.connect(inputspec, 'subject_id', fssource, 'subject_id')
workflow.connect(inputspec, 'sd', fssource, 'subjects_dir')
workflow.connect(inputspec, 'in_file', write_rep, 'in_file')
workflow.connect(datagrabber, 'art_intensity', art_info, 'intensity_file')
workflow.connect(datagrabber, 'art_stats', art_info, 'stats_file')
workflow.connect(inputspec, 'art_file', art_info, 'art_file')
workflow.connect(art_info, ('table', to1table), write_rep, 'Art_Detect')
workflow.connect(ts_and_spectra, 'outputspec.imagetable', tablecombine,
'imagetable')
workflow.connect(art_info, 'intensity_plot', write_rep, 'Global_Intensity')
workflow.connect(plot_m, 'fname_t', write_rep, 'motion_plot_translations')
workflow.connect(plot_m, 'fname_r', write_rep, 'motion_plot_rotations')
workflow.connect(inputspec, 'in_file', tsdiff, 'img')
workflow.connect(tsdiff, "out_file", write_rep, "tsdiffana")
workflow.connect(inputspec, ('config_params', totable), write_rep,
'config_params')
workflow.connect(inputspec, 'reg_file', roidevplot, 'inputspec.reg_file')
workflow.connect(inputspec, 'tsnr_stddev', roidevplot,
'inputspec.tsnr_file')
workflow.connect(roidevplot, 'outputspec.roi_table', tablecombine,
'roidev')
workflow.connect(inputspec, 'reg_file', roisnrplot, 'inputspec.reg_file')
workflow.connect(inputspec, 'tsnr', roisnrplot, 'inputspec.tsnr_file')
workflow.connect(roisnrplot, 'outputspec.roi_table', tablecombine,
'roisnr')
workflow.connect(tablecombine, ('imagetable', to1table), write_rep,
'tsnr_roi_table')
workflow.connect(inputspec, 'ADnorm', adnormplot, 'ADnorm')
workflow.connect(adnormplot, 'plot', write_rep, 'ADnorm')
workflow.connect(fssource, 'orig', convert, 'in_file')
workflow.connect(convert, 'out_file', voltransform, 'target_file')
workflow.connect(inputspec, 'reg_file', voltransform, 'reg_file')
workflow.connect(inputspec, 'tsnr', voltransform, 'source_file')
workflow.connect(plotribbon, 'images', write_rep, 'Ribbon')
workflow.connect(voltransform, 'transformed_file', overlaynew,
'stat_image')
workflow.connect(convert, 'out_file', overlaynew, 'background_image')
workflow.connect(overlaynew, 'fnames', write_rep, 'TSNR_Images')
workflow.connect(overlaymask, 'fnames', write_rep, 'T_CompCor')
workflow.connect(overlaymask2, 'fnames', write_rep, 'A_CompCor')
workflow.connect(plotmask, 'images', write_rep, 'Mask')
workflow.write_graph()
return workflow
def QA_workflow(QAc,c=foo, name='QA'):
""" Workflow that generates a Quality Assurance Report
Parameters
----------
name : name of workflow
Inputs
------
inputspec.subject_id :
inputspec.config_params :
inputspec.in_file :
inputspec.art_file :
inputspec.motion_plots :
inputspec.reg_file :
inputspec.tsnr_detrended :
inputspec.tsnr :
inputspec.tsnr_mean :
inputspec.tsnr_stddev :
inputspec.ADnorm :
inputspec.TR :
inputspec.sd : freesurfer subjects directory
"""
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
from nipype.interfaces.freesurfer import ApplyVolTransform
from nipype.interfaces import freesurfer as fs
from nipype.interfaces.io import FreeSurferSource
from scripts.u0a14c5b5899911e1bca80023dfa375f2.QA_utils import (plot_ADnorm,
tsdiffana,
tsnr_roi,
combine_table,
art_output,
plot_motion,
plot_ribbon,
plot_anat,
overlay_new,
overlay_dB,
spectrum_ts_table)
from ..utils.reportsink.io import ReportSink
# Define Workflow
workflow =pe.Workflow(name=name)
inputspec = pe.Node(interface=util.IdentityInterface(fields=['subject_id',
'config_params',
'in_file',
'art_file',
'motion_plots',
'reg_file',
'tsnr',
'tsnr_detrended',
'tsnr_stddev',
'ADnorm',
'TR',
'sd']),
name='inputspec')
infosource = pe.Node(util.IdentityInterface(fields=['subject_id']),
name='subject_names')
if QAc.test_mode:
infosource.iterables = ('subject_id', [QAc.subjects[0]])
else:
infosource.iterables = ('subject_id', QAc.subjects)
datagrabber = preproc_datagrabber(c)
datagrabber.inputs.node_type = c.motion_correct_node
orig_datagrabber = get_dataflow(c)
workflow.connect(infosource, 'subject_id',
datagrabber, 'subject_id')
workflow.connect(infosource, 'subject_id', orig_datagrabber, 'subject_id')
workflow.connect(orig_datagrabber, 'func', inputspec, 'in_file')
workflow.connect(infosource, 'subject_id', inputspec, 'subject_id')
workflow.connect(datagrabber, ('outlier_files',sort), inputspec, 'art_file')
workflow.connect(datagrabber, ('reg_file', sort), inputspec, 'reg_file')
workflow.connect(datagrabber, ('tsnr',sort), inputspec, 'tsnr')
workflow.connect(datagrabber, ('tsnr_stddev',sort), inputspec, 'tsnr_stddev')
workflow.connect(datagrabber, ('tsnr_detrended',sort), inputspec, 'tsnr_detrended')
workflow.connect(datagrabber, ('art_norm',sort), inputspec, 'ADnorm')
inputspec.inputs.TR = c.TR
inputspec.inputs.sd = c.surf_dir
# Define Nodes
plot_m = pe.MapNode(util.Function(input_names=['motion_parameters'],
output_names=['fname_t','fname_r'],
function=plot_motion),
name="motion_plots",
iterfield=['motion_parameters'])
workflow.connect(datagrabber,('motion_parameters', sort),plot_m,'motion_parameters')
#workflow.connect(plot_m, 'fname',inputspec,'motion_plots')
tsdiff = pe.MapNode(util.Function(input_names = ['img'],
output_names = ['out_file'],
function=tsdiffana),
name='tsdiffana', iterfield=["img"])
art_info = pe.MapNode(util.Function(input_names = ['art_file','intensity_file','stats_file'],
output_names = ['table','out','intensity_plot'],
function=art_output),
name='art_output', iterfield=["art_file","intensity_file","stats_file"])
fssource = pe.Node(interface = FreeSurferSource(),name='fssource')
plotribbon = pe.Node(util.Function(input_names=['Brain'],
output_names=['images'],
function=plot_ribbon),
name="plot_ribbon")
workflow.connect(fssource, 'ribbon', plotribbon, 'Brain')
plotanat = pe.Node(util.Function(input_names=['brain'],
output_names=['images'],
function=plot_anat),
name="plot_anat")
plotmask = plotanat.clone('plot_mask')
workflow.connect(datagrabber,'mask', plotmask,'brain')
roidevplot = tsnr_roi(plot=False,name='tsnr_stddev_roi',roi=['all'],onsets=False)
roidevplot.inputs.inputspec.TR = c.TR
roisnrplot = tsnr_roi(plot=False,name='SNR_roi',roi=['all'],onsets=False)
roisnrplot.inputs.inputspec.TR = c.TR
workflow.connect(fssource, ('aparc_aseg', pickfirst), roisnrplot, 'inputspec.aparc_aseg')
workflow.connect(fssource, ('aparc_aseg', pickfirst), roidevplot, 'inputspec.aparc_aseg')
workflow.connect(infosource, 'subject_id', roidevplot, 'inputspec.subject')
workflow.connect(infosource, 'subject_id', roisnrplot, 'inputspec.subject')
tablecombine = pe.MapNode(util.Function(input_names = ['roidev',
'roisnr',
'imagetable'],
output_names = ['imagetable'],
function = combine_table),
name='combinetable', iterfield=['roidev','roisnr','imagetable'])
adnormplot = pe.MapNode(util.Function(input_names = ['ADnorm','TR','norm_thresh','out'],
output_names = ['plot'],
function=plot_ADnorm),
name='ADnormplot', iterfield=['ADnorm','out'])
adnormplot.inputs.norm_thresh = c.norm_thresh
workflow.connect(art_info,'out',adnormplot,'out')
convert = pe.Node(interface=fs.MRIConvert(),name='converter')
voltransform = pe.MapNode(interface=ApplyVolTransform(),name='register',iterfield=['source_file'])
overlaynew = pe.MapNode(util.Function(input_names=['stat_image','background_image','threshold',"dB"],
output_names=['fnames'], function=overlay_dB),
name='overlay_new', iterfield=['stat_image'])
overlaynew.inputs.dB = False
overlaynew.inputs.threshold = 20
overlaymask = pe.MapNode(util.Function(input_names=['stat_image','background_image','threshold'],
output_names=['fnames'], function=overlay_new),
name='overlay_mask',iterfield=['stat_image'])
overlaymask.inputs.threshold = 0.5
workflow.connect(convert,'out_file', overlaymask,'background_image')
overlaymask2 = overlaymask.clone('acompcor_image')
workflow.connect(convert,'out_file', overlaymask2,'background_image')
workflow.connect(datagrabber,'tcompcor',overlaymask,'stat_image')
workflow.connect(datagrabber,'acompcor',overlaymask2,'stat_image')
workflow.connect(datagrabber, ('mean_image', sort), plotanat, 'brain')
ts_and_spectra = spectrum_ts_table()
timeseries_segstats = tsnr_roi(plot=False,name='timeseries_roi',roi=['all'],onsets=False)
workflow.connect(inputspec,'tsnr_detrended', timeseries_segstats,'inputspec.tsnr_file')
workflow.connect(inputspec,'reg_file', timeseries_segstats,'inputspec.reg_file')
workflow.connect(infosource, 'subject_id', timeseries_segstats, 'inputspec.subject')
workflow.connect(fssource, ('aparc_aseg', pickfirst), timeseries_segstats, 'inputspec.aparc_aseg')
timeseries_segstats.inputs.inputspec.TR = c.TR
ts_and_spectra.inputs.inputspec.tr = c.TR
workflow.connect(timeseries_segstats,'outputspec.roi_file',ts_and_spectra, 'inputspec.stats_file')
write_rep = pe.Node(interface=ReportSink(orderfields=['Introduction',
'in_file',
'config_params',
'Art_Detect',
'Global_Intensity',
'Mean_Functional',
'Ribbon',
'Mask',
'motion_plot_translations',
'motion_plot_rotations',
'tsdiffana',
'ADnorm',
'A_CompCor',
'T_CompCor',
'TSNR_Images',
'tsnr_roi_table']),
name='report_sink')
write_rep.inputs.Introduction = "Quality Assurance Report for fMRI preprocessing."
write_rep.inputs.base_directory = os.path.join(QAc.sink_dir)
write_rep.inputs.report_name = "Preprocessing_Report"
write_rep.inputs.json_sink = QAc.json_sink
workflow.connect(infosource,'subject_id',write_rep,'container')
workflow.connect(plotanat, 'images', write_rep, "Mean_Functional")
write_rep.inputs.table_as_para=False
# Define Inputs
convert.inputs.out_type = 'niigz'
convert.inputs.in_type = 'mgz'
# Define Connections
workflow.connect(inputspec,'TR',adnormplot,'TR')
workflow.connect(inputspec,'subject_id',fssource,'subject_id')
workflow.connect(inputspec,'sd',fssource,'subjects_dir')
workflow.connect(inputspec,'in_file',write_rep,'in_file')
workflow.connect(datagrabber,'art_intensity',art_info,'intensity_file')
workflow.connect(datagrabber,'art_stats',art_info,'stats_file')
workflow.connect(inputspec,'art_file',art_info,'art_file')
workflow.connect(art_info,('table',to1table), write_rep,'Art_Detect')
workflow.connect(ts_and_spectra,'outputspec.imagetable',tablecombine, 'imagetable')
workflow.connect(art_info,'intensity_plot',write_rep,'Global_Intensity')
workflow.connect(plot_m, 'fname_t',write_rep,'motion_plot_translations')
workflow.connect(plot_m, 'fname_r',write_rep,'motion_plot_rotations')
workflow.connect(inputspec,'in_file',tsdiff,'img')
workflow.connect(tsdiff,"out_file",write_rep,"tsdiffana")
workflow.connect(inputspec,('config_params',totable), write_rep,'config_params')
workflow.connect(inputspec,'reg_file',roidevplot,'inputspec.reg_file')
workflow.connect(inputspec,'tsnr_stddev',roidevplot,'inputspec.tsnr_file')
workflow.connect(roidevplot,'outputspec.roi_table',tablecombine,'roidev')
workflow.connect(inputspec,'reg_file',roisnrplot,'inputspec.reg_file')
workflow.connect(inputspec,'tsnr',roisnrplot,'inputspec.tsnr_file')
workflow.connect(roisnrplot,'outputspec.roi_table',tablecombine,'roisnr')
workflow.connect(tablecombine, ('imagetable',to1table), write_rep, 'tsnr_roi_table')
workflow.connect(inputspec,'ADnorm',adnormplot,'ADnorm')
workflow.connect(adnormplot,'plot',write_rep,'ADnorm')
workflow.connect(fssource,'orig',convert,'in_file')
workflow.connect(convert,'out_file',voltransform,'target_file')
workflow.connect(inputspec,'reg_file',voltransform,'reg_file')
workflow.connect(inputspec,'tsnr',voltransform, 'source_file')
workflow.connect(plotribbon, 'images', write_rep, 'Ribbon')
workflow.connect(voltransform,'transformed_file', overlaynew,'stat_image')
workflow.connect(convert,'out_file', overlaynew,'background_image')
workflow.connect(overlaynew, 'fnames', write_rep, 'TSNR_Images')
workflow.connect(overlaymask, 'fnames', write_rep, 'T_CompCor')
workflow.connect(overlaymask2, 'fnames', write_rep, 'A_CompCor')
workflow.connect(plotmask,'images',write_rep,'Mask')
workflow.write_graph()
return workflow
def QA_workflow(c, prep_c,name='QA'):
""" Workflow that generates a Quality Assurance json
"""
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
from nipype.interfaces.freesurfer import ApplyVolTransform
from nipype.interfaces import freesurfer as fs
from nipype.interfaces.io import FreeSurferSource
from scripts.u0a14c5b5899911e1bca80023dfa375f2.QA_utils import (tsnr_roi,
art_output)
from ..utils.reportsink.io import JSONSink
# Define Workflow
workflow =pe.Workflow(name=name)
datagrabber = c.datagrabber.create_dataflow()
infosource = datagrabber.get_node('subject_id_iterable')
#workflow.connect(infosource, 'subject_id', inputspec, 'subject_id')
art_info = pe.MapNode(util.Function(input_names = ['art_file','intensity_file','stats_file'],
output_names = ['table','out','intensity_plot'],
function=art_output),
name='art_output', iterfield=["art_file","intensity_file","stats_file"])
#fssource = pe.Node(interface = FreeSurferSource(),name='fssource')
#fssource.inputs.subjects_dir = c.surf_dir
roidevplot = tsnr_roi(plot=False,name='tsnr_stddev_roi',roi=['all'],onsets=False)
roidevplot.inputs.inputspec.TR = c.TR
roisnrplot = tsnr_roi(plot=False,name='SNR_roi',roi=['all'],onsets=False)
roisnrplot.inputs.inputspec.TR = c.TR
#workflow.connect(fssource, ('aparc_aseg', pickaparc), roisnrplot, 'inputspec.aparc_aseg')
#workflow.connect(fssource, ('aparc_aseg', pickaparc), roidevplot, 'inputspec.aparc_aseg')
workflow.connect(infosource, 'subject_id', roidevplot, 'inputspec.subject')
workflow.connect(infosource, 'subject_id', roisnrplot, 'inputspec.subject')
workflow.connect(infosource,("subject_id", myfssource, c.surf_dir),roisnrplot,"inputspec.aparc_aseg")
workflow.connect(infosource,("subject_id", myfssource, c.surf_dir),roidevplot,"inputspec.aparc_aseg")
tablecombine = pe.MapNode(util.Function(input_names = ['roidev',
'roisnr'],
output_names = ['combined_table'],
function = combine_table),
name='combinetable', iterfield=['roidev','roisnr'])
#workflow.connect(infosource,'subject_id',fssource,'subject_id')
#workflow.connect(inputspec,'sd',fssource,'subjects_dir')
#fssource.inputs.subjects_dir = c.surf_dir
roisnrplot.inputs.inputspec.sd = c.surf_dir
roidevplot.inputs.inputspec.sd = c.surf_dir
workflow.connect(datagrabber,'datagrabber.art_intensity',art_info,'intensity_file')
workflow.connect(datagrabber,'datagrabber.art_stats',art_info,'stats_file')
workflow.connect(datagrabber,'datagrabber.outlier_files',art_info,'art_file')
workflow.connect(datagrabber,'datagrabber.reg_file',roidevplot,'inputspec.reg_file')
workflow.connect(datagrabber,'datagrabber.tsnr_stddev',roidevplot,'inputspec.tsnr_file')
#workflow.connect(fssource,('aparc_aseg',pickaparc),roidevplot,'inputspec.aparc_aseg')
workflow.connect(roidevplot,'outputspec.roi_table',tablecombine,'roidev')
workflow.connect(datagrabber,'datagrabber.reg_file',roisnrplot,'inputspec.reg_file')
workflow.connect(datagrabber,'datagrabber.tsnr',roisnrplot,'inputspec.tsnr_file')
workflow.connect(roisnrplot,'outputspec.roi_table',tablecombine,'roisnr')
js = pe.Node(interface=JSONSink(),name="jsoner")
workflow.connect(tablecombine,"combined_table",js,"SNR_table")
workflow.connect(art_info,"table",js,"art")
workflow.connect(art_info,"out",js,"outliers")
workflow.connect(datagrabber,("datagrabber.mincost",getmincost),js,"mincost")
js.inputs.norm_thresh = prep_c.norm_thresh
js.inputs.z_thresh = prep_c.z_thresh
return workflow
