def qap_functional_spatial_workflow(workflow, resource_pool, config):
# resource pool should have:
# mean_functional
# functional_brain_mask
import os
import sys
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
import nipype.algorithms.misc as nam
import nipype.interfaces.utility as niu
import nipype.algorithms.misc as nam
from qap_workflows_utils import qap_functional_spatial
from qap.viz.interfaces import PlotMosaic
from workflow_utils import check_input_resources
if "mean_functional" not in resource_pool.keys():
from functional_preproc import mean_functional_workflow
workflow, resource_pool = mean_functional_workflow(workflow, resource_pool, config)
if "functional_brain_mask" not in resource_pool.keys():
from functional_preproc import functional_brain_mask_workflow
workflow, resource_pool = functional_brain_mask_workflow(workflow, resource_pool, config)
spatial_epi = pe.Node(
niu.Function(
input_names=[
"mean_epi",
"func_brain_mask",
"direction",
"subject_id",
"session_id",
"scan_id",
"site_name",
],
output_names=["qc"],
function=qap_functional_spatial,
),
name="qap_functional_spatial",
)
# Subject infos
if "ghost_direction" not in config.keys():
config["ghost_direction"] = "y"
spatial_epi.inputs.direction = config["ghost_direction"]
spatial_epi.inputs.subject_id = config["subject_id"]
spatial_epi.inputs.session_id = config["session_id"]
spatial_epi.inputs.scan_id = config["scan_id"]
if "site_name" in config.keys():
spatial_epi.inputs.site_name = config["site_name"]
if len(resource_pool["mean_functional"]) == 2:
node, out_file = resource_pool["mean_functional"]
workflow.connect(node, out_file, spatial_epi, "mean_epi")
else:
spatial_epi.inputs.mean_epi = resource_pool["mean_functional"]
if len(resource_pool["functional_brain_mask"]) == 2:
node, out_file = resource_pool["functional_brain_mask"]
workflow.connect(node, out_file, spatial_epi, "func_brain_mask")
else:
spatial_epi.inputs.func_brain_mask = resource_pool["functional_brain_mask"]
if config.get("write_report", False):
plot = pe.Node(PlotMosaic(), name="plot_mosaic")
plot.inputs.subject = config["subject_id"]
metadata = [config["session_id"], config["scan_id"]]
if "site_name" in config.keys():
metadata.append(config["site_name"])
plot.inputs.metadata = metadata
plot.inputs.title = "Mean EPI"
if len(resource_pool["mean_functional"]) == 2:
node, out_file = resource_pool["mean_functional"]
workflow.connect(node, out_file, plot, "in_file")
else:
plot.inputs.in_file = resource_pool["mean_functional"]
# Enable this if we want masks
# if len(resource_pool['functional_brain_mask']) == 2:
# node, out_file = resource_pool['functional_brain_mask']
# workflow.connect(node, out_file, plot, 'in_mask')
# else:
# plot.inputs.in_mask = resource_pool['functional_brain_mask']
resource_pool["qap_mosaic"] = (plot, "out_file")
out_csv = op.join(config["output_directory"], "qap_functional_spatial.csv")
spatial_epi_to_csv = pe.Node(nam.AddCSVRow(in_file=out_csv), name="qap_functional_spatial_to_csv")
workflow.connect(spatial_epi, "qc", spatial_epi_to_csv, "_outputs")
resource_pool["qap_functional_spatial"] = (spatial_epi_to_csv, "csv_file")
return workflow, resource_pool
def qap_functional_spatial_workflow(workflow, resource_pool, config):
# resource pool should have:
# mean_functional
# functional_brain_mask
import os
import sys
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
from qap_workflows_utils import qap_functional_spatial, write_to_csv
from workflow_utils import check_input_resources
if "mean_functional" not in resource_pool.keys():
from functional_preproc import mean_functional_workflow
workflow, resource_pool = mean_functional_workflow(workflow, resource_pool, config)
if "functional_brain_mask" not in resource_pool.keys():
from functional_preproc import functional_brain_mask_workflow
workflow, resource_pool = functional_brain_mask_workflow(workflow, resource_pool, config)
spatial_epi = pe.Node(
util.Function(
input_names=[
"mean_epi",
"func_brain_mask",
"direction",
"subject_id",
"session_id",
"scan_id",
"site_name",
],
output_names=["qc"],
function=qap_functional_spatial,
),
name="qap_functional_spatial",
)
spatial_epi_to_csv = pe.Node(
util.Function(input_names=["sub_qap_dict"], output_names=["outfile"], function=write_to_csv),
name="qap_functional_spatial_to_csv",
)
if len(resource_pool["mean_functional"]) == 2:
node, out_file = resource_pool["mean_functional"]
workflow.connect(node, out_file, spatial_epi, "mean_epi")
else:
spatial_epi.inputs.mean_epi = resource_pool["mean_functional"]
if len(resource_pool["functional_brain_mask"]) == 2:
node, out_file = resource_pool["functional_brain_mask"]
workflow.connect(node, out_file, spatial_epi, "func_brain_mask")
else:
spatial_epi.inputs.func_brain_mask = resource_pool["functional_brain_mask"]
# Subject infos
if "ghost_direction" not in config.keys():
config["ghost_direction"] = "y"
spatial_epi.inputs.direction = config["ghost_direction"]
spatial_epi.inputs.subject_id = config["subject_id"]
spatial_epi.inputs.session_id = config["session_id"]
spatial_epi.inputs.scan_id = config["scan_id"]
if "site_name" in config.keys():
spatial_epi.inputs.site_name = config["site_name"]
workflow.connect(spatial_epi, "qc", spatial_epi_to_csv, "sub_qap_dict")
resource_pool["qap_functional_spatial"] = (spatial_epi_to_csv, "outfile")
return workflow, resource_pool
def qap_functional_spatial_workflow(workflow, resource_pool, config):
# resource pool should have:
# mean_functional
# functional_brain_mask
import os
import sys
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
import nipype.algorithms.misc as nam
import nipype.interfaces.utility as niu
import nipype.algorithms.misc as nam
from qap_workflows_utils import qap_functional_spatial
from qap.viz.interfaces import PlotMosaic
from workflow_utils import check_input_resources
if 'mean_functional' not in resource_pool.keys():
from functional_preproc import mean_functional_workflow
workflow, resource_pool = \
mean_functional_workflow(workflow, resource_pool, config)
if 'functional_brain_mask' not in resource_pool.keys():
from functional_preproc import functional_brain_mask_workflow
workflow, resource_pool = \
functional_brain_mask_workflow(workflow, resource_pool, config)
spatial_epi = pe.Node(niu.Function(input_names=[
'mean_epi', 'func_brain_mask', 'direction', 'subject_id', 'session_id',
'scan_id', 'site_name'
],
output_names=['qc'],
function=qap_functional_spatial),
name='qap_functional_spatial')
# Subject infos
if 'ghost_direction' not in config.keys():
config['ghost_direction'] = 'y'
spatial_epi.inputs.direction = config['ghost_direction']
spatial_epi.inputs.subject_id = config['subject_id']
spatial_epi.inputs.session_id = config['session_id']
spatial_epi.inputs.scan_id = config['scan_id']
if 'site_name' in config.keys():
spatial_epi.inputs.site_name = config['site_name']
if len(resource_pool['mean_functional']) == 2:
node, out_file = resource_pool['mean_functional']
workflow.connect(node, out_file, spatial_epi, 'mean_epi')
else:
spatial_epi.inputs.mean_epi = resource_pool['mean_functional']
if len(resource_pool['functional_brain_mask']) == 2:
node, out_file = resource_pool['functional_brain_mask']
workflow.connect(node, out_file, spatial_epi, 'func_brain_mask')
else:
spatial_epi.inputs.func_brain_mask = \
resource_pool['functional_brain_mask']
if config.get('write_report', False):
plot = pe.Node(PlotMosaic(), name='plot_mosaic')
plot.inputs.subject = config['subject_id']
metadata = [config['session_id'], config['scan_id']]
if 'site_name' in config.keys():
metadata.append(config['site_name'])
plot.inputs.metadata = metadata
plot.inputs.title = 'Mean EPI'
if len(resource_pool['mean_functional']) == 2:
node, out_file = resource_pool['mean_functional']
workflow.connect(node, out_file, plot, 'in_file')
else:
plot.inputs.in_file = resource_pool['mean_functional']
# Enable this if we want masks
# if len(resource_pool['functional_brain_mask']) == 2:
# node, out_file = resource_pool['functional_brain_mask']
# workflow.connect(node, out_file, plot, 'in_mask')
# else:
# plot.inputs.in_mask = resource_pool['functional_brain_mask']
resource_pool['qap_mosaic'] = (plot, 'out_file')
out_csv = op.join(config['output_directory'], 'qap_functional_spatial.csv')
spatial_epi_to_csv = pe.Node(nam.AddCSVRow(in_file=out_csv),
name='qap_functional_spatial_to_csv')
workflow.connect(spatial_epi, 'qc', spatial_epi_to_csv, '_outputs')
resource_pool['qap_functional_spatial'] = (spatial_epi_to_csv, 'csv_file')
return workflow, resource_pool
