from __future__ import print_function

%%bash
for f in data/T1-a data/T1-b
  do
  bet $f ${f}_brain -m
  fslmaths $f -s 2 ${f}_smooth
  fslmaths ${f}_smooth -mas ${f}_brain_mask ${f}_smooth_mask
done

ls data/*_smooth_mask.nii.gz

from nipype.interfaces import fsl
from nipype import Workflow, Node

skullstrip = Node(fsl.BET(mask=True), name="skullstrip")
smooth = Node(fsl.IsotropicSmooth(fwhm=4), name="smooth")
mask = Node(fsl.ApplyMask(), name="mask")

wf = Workflow(name="smoothflow")
wf.base_dir = "."
wf.connect(skullstrip, "mask_file", mask, "mask_file")
wf.connect(smooth, "out_file", mask, "in_file")

from nipype import IdentityInterface

inputs = Node(IdentityInterface(fields=["mri_file"]), name="inputs")
wf.connect(inputs, "mri_file", skullstrip, "in_file")
wf.connect(inputs, "mri_file", smooth, "in_file")

from nipype import config, logging
config.set('logging', 'workflow_level', 'CRITICAL')
config.set('logging', 'interface_level', 'CRITICAL')
logging.update_logging(config)

from os.path import abspath
files = ["data/T1-a.nii.gz", "data/T1-b.nii.gz"]
files = map(abspath, files)

for f in files:
    inputs.inputs.mri_file = f
    wf.run()

inputs.iterables = ("mri_file", files)

wf.run(plugin="MultiProc", plugin_args={"n_proc": 2})

ls smoothflow/_mri_file*/smooth/*.nii.gz

from nipype import Function
def square_func(x):
    return x ** 2
square = Function(["x"], ["f_x"], square_func)

print(square.run(x=2).outputs.f_x)

from nipype import MapNode
square_node = MapNode(square, name="square", iterfield=["x"])

square_node.inputs.x = range(4)
print(square_node.run().outputs.f_x)

def power_func(x, y):
    return x ** y

power = Function(["x", "y"], ["f_xy"], power_func)
power_node = MapNode(power, name="power", iterfield=["x", "y"])
power_node.inputs.x = range(4)
power_node.inputs.y = range(4)
print(power_node.run().outputs.f_xy)

power_node = MapNode(power, name="power", iterfield=["x"])
power_node.inputs.x = range(4)
power_node.inputs.y = 3
print(power_node.run().outputs.f_xy)

!make clean