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series_mod.py
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series_mod.py
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# -*- coding: utf-8 -*-
# coding: utf-8
#import os
#import sys
#import re
#import commands
import nipype.pipeline.engine as pe
#import nipype.interfaces.fsl as fsl
import nipype.interfaces.utility as util
from CPAC.series_mod.utils import compute_ROI_corr
from CPAC.series_mod.utils import compute_ROI_pcorr
from CPAC.series_mod.utils import compute_MI
from CPAC.series_mod.utils import compute_TE
#from CPAC.series_mod.utils import compute_ApEn
def create_ROI_corr():
"""
Simple Network Correlation Matrix calculation for ROIs in the mask file
Parameters
----------
None
Returns
-------
corr : workflow
Correlation Workflow
Notes
-----
`Source <https://github.com/FCP-INDI/C-PAC/blob/master/CPAC/series_mod/series_mod.py>`_
Workflow Inputs: ::
inputspec.in_file : string (existing nifti file)
Input EPI 4D Volume
inputspec.mask_file : string (existing nifti file)
Input Whole Brain Mask of EPI 4D Volume
Workflow Outputs: ::
outputspec.corr_matrix : ROI_number * ROI_number array
Corr Workflow Procedure:
1. Generate Correlation Matrix from the input EPI 4D volume and EPI mask.
Workflow Graph:
Detailed Workflow Graph:
References
----------
Examples
--------
>>> from CPAC import series_mod
>>> wf = series_mod.create_ROI_corr()
>>> wf.inputs.inputspec.in_file = '/home/data/Project/subject/func/in_file.nii.gz'
>>> wf.inputs.inputspec.mask_file = '/home/data/Project/subject/func/mask.nii.gz'
>>> wf.run()
in_file = ('/home/asier/git/C-PAC/CPAC/series_mod/Standard-clean_func_preproc.nii.gz')
mask_file = ('/home/asier/git/C-PAC/CPAC/series_mod/AAL_Contract_90_3MM.nii.gz')
from CPAC import series_mod
wf = series_mod.create_ROI_corr()
wf.inputs.inputspec.in_file = in_file
wf.inputs.inputspec.mask_file = mask_file
wf.base_dir = '/home/asier/git/C-PAC/CPAC/series_mod'
wf.run()
"""
ROI_corr = pe.Workflow(name='ROI_corr')
inputNode = pe.Node(util.IdentityInterface(fields=[
'in_file',
'mask_file'
]),
name='inputspec')
outputNode = pe.Node(util.IdentityInterface(fields=[
'corr_mat']),
name='outputspec')
corr_matNode = pe.Node(util.Function(input_names=['in_file', 'mask_file'],
output_names=['corr_mat'],
function=compute_ROI_corr),
name='corr_calc')
ROI_corr.connect(inputNode, 'in_file',
corr_matNode, 'in_file')
ROI_corr.connect(inputNode, 'mask_file',
corr_matNode, 'mask_file')
ROI_corr.connect(corr_matNode, 'corr_mat',
outputNode, 'corr_mat')
return ROI_corr
def create_ROI_pcorr():
"""
Simple Network Partial Correlation Matrix calculation for ROIs in the mask file
Parameters
----------
None
Returns
-------
pcorr : workflow
Partial Correlation Workflow
Notes
-----
`Source <https://github.com/FCP-INDI/C-PAC/blob/master/CPAC/series_mod/series_mod.py>`_
Workflow Inputs: ::
inputspec.in_file : string (existing nifti file)
Input EPI 4D Volume
inputspec.mask_file : string (existing nifti file)
Input Whole Brain Mask of EPI 4D Volume
Workflow Outputs: ::
outputspec.pcorr_matrix : ROI_number * ROI_number array
Corr Workflow Procedure:
1. Generate Partial Correlation Matrix from the input EPI 4D volume and EPI mask.
Workflow Graph:
Detailed Workflow Graph:
References
----------
Examples
--------
>>> from CPAC import series_mod
>>> wf = series_mod.create_ROI_corr()
>>> wf.inputs.inputspec.in_file = '/home/data/Project/subject/func/in_file.nii.gz'
>>> wf.inputs.inputspec.mask_file = '/home/data/Project/subject/func/mask.nii.gz'
>>> wf.run()
in_file = ('/home/asier/git/C-PAC/CPAC/series_mod/Standard-clean_func_preproc.nii.gz')
mask_file = ('/home/asier/git/C-PAC/CPAC/series_mod/AAL_Contract_90_3MM.nii.gz')
from CPAC import series_mod
wf = series_mod.create_ROI_corr()
wf.inputs.inputspec.in_file = in_file
wf.inputs.inputspec.mask_file = mask_file
wf.base_dir = '/home/asier/git/C-PAC/CPAC/series_mod'
wf.run()
"""
ROI_pcorr = pe.Workflow(name='ROI_pcorr')
inputNode = pe.Node(util.IdentityInterface(fields=[
'in_file',
'mask_file'
]),
name='inputspec')
outputNode = pe.Node(util.IdentityInterface(fields=[
'pcorr_mat']),
name='outputspec')
pcorr_matNode = pe.Node(util.Function(input_names=['in_file', 'mask_file'],
output_names=['pcorr_mat'],
function=compute_ROI_pcorr),
name='pcorr_calc')
ROI_pcorr.connect(inputNode, 'in_file',
pcorr_matNode, 'in_file')
ROI_pcorr.connect(inputNode, 'mask_file',
pcorr_matNode, 'mask_file')
ROI_pcorr.connect(pcorr_matNode, 'pcorr_mat',
outputNode, 'pcorr_mat')
return ROI_pcorr
def create_MI():
"""
Simple Network Correlation Matrix calculation for ROIs in the mask file
Parameters
----------
None
Returns
-------
corr : workflow
Correlation Workflow
Notes
-----
`Source <https://github.com/FCP-INDI/C-PAC/blob/master/CPAC/series_mod/series_mod.py>`_
Workflow Inputs: ::
inputspec.in_file : string (existing nifti file)
Input EPI 4D Volume
inputspec.bins : integer
How many states do you want to be the data transformed to
Workflow Outputs: ::
outputspec.MI_matrix : [ROI_number * ROI_number] array
Corr Workflow Procedure:
1. Generate MI Matrix from the input EPI 4D volume and EPI mask.
Workflow Graph:
Detailed Workflow Graph:
References
----------
Examples
--------
>>> from CPAC import series_mod
>>> wf = series_mod.create_MI()
>>> wf.inputs.inputspec.in_file = '/home/data/Project/subject/func/in_file.nii.gz'
>>> wf.inputs.inputspec.mask_file = '/home/data/Project/subject/func/mask.nii.gz'
>>> wf.run()
in_file = ('/home/asier/git/C-PAC/CPAC/series_mod/Standard-clean_func_preproc.nii.gz')
mask_file = ('/home/asier/git/C-PAC/CPAC/series_mod/AAL_Contract_90_3MM.nii.gz')
from CPAC import series_mod
wf = series_mod.create_MI()
wf.inputs.inputspec.in_file = in_file
wf.inputs.inputspec.mask_file = mask_file
wf.base_dir = '/home/asier/git/C-PAC/CPAC/series_mod'
wf.run()
"""
MI = pe.Workflow(name='MI_comp')
inputNode = pe.Node(util.IdentityInterface(fields=[
'in_file',
'mask_file'
]),
name='inputspec')
outputNode = pe.Node(util.IdentityInterface(fields=[
'MI_mat']),
name='outputspec')
MI_mat = pe.Node(util.Function(input_names=['in_file', 'mask_file'],
output_names=['MI_mat'],
function=compute_MI),
name='MI_mat')
MI.connect(inputNode, 'in_file',
MI_mat, 'in_file')
MI.connect(inputNode, 'mask_file',
MI_mat, 'mask_file')
MI.connect(MI_mat, 'MI_mat',
outputNode, 'MI_mat')
return MI
def create_TE():
"""
Simple Network Correlation Matrix calculation for ROIs in the mask file
Parameters
----------
None
Returns
-------
corr : workflow
Correlation Workflow
Notes
-----
`Source <https://github.com/FCP-INDI/C-PAC/blob/master/CPAC/series_mod/series_mod.py>`_
Workflow Inputs: ::
inputspec.in_file : string (existing nifti file)
Input EPI 4D Volume
inputspec.bins : integer
How many states do you want to be the data transformed to
Workflow Outputs: ::
outputspec.TE_matrix : [ROI_number * ROI_number] array
Corr Workflow Procedure:
1. Generate TE Matrix from the input EPI 4D volume and EPI mask.
Workflow Graph:
Detailed Workflow Graph:
References
----------
Examples
--------
>>> from CPAC import series_mod
>>> wf = series_mod.create_TE()
>>> wf.inputs.inputspec.in_file = '/home/data/Project/subject/func/in_file.nii.gz'
>>> wf.inputs.inputspec.mask_file = '/home/data/Project/subject/func/mask.nii.gz'
>>> wf.run()
in_file = ('/home/asier/git/C-PAC/CPAC/series_mod/Standard-clean_func_preproc.nii.gz')
mask_file = ('/home/asier/git/C-PAC/CPAC/series_mod/AAL_Contract_90_3MM.nii.gz')
wf = series_mod.create_TE()
wf.inputs.inputspec.in_file = in_file
wf.inputs.inputspec.mask_file = mask_file
wf.base_dir = '/home/asier/git/C-PAC/CPAC/series_mod'
wf.run()
"""
TE = pe.Workflow(name='TE_comp')
inputNode = pe.Node(util.IdentityInterface(fields=[
'in_file',
'mask_file'
]),
name='inputspec')
outputNode = pe.Node(util.IdentityInterface(fields=[
'TE_mat']),
name='outputspec')
TE_mat = pe.Node(util.Function(input_names=['in_file', 'mask_file'],
output_names=['MI_mat'],
function=compute_TE),
name='MI_mat')
TE.connect(inputNode, 'in_file',
TE_mat, 'in_file')
TE.connect(inputNode, 'mask_file',
TE_mat, 'mask_file')
TE.connect(TE_mat, 'TE_mat',
outputNode, 'TE_mat')
return TE
#def create_ApEn():
#
# """
# ApEn calculation for fMRI file
#
# Parameters
# ----------
#
# None
#
# Returns
# -------
# corr : workflow
# Correlation Workflow
#
# Notes
# -----
#
# `Source <https://github.com/FCP-INDI/C-PAC/blob/master/CPAC/series_mod/series_mod.py>`_
#
# Workflow Inputs: ::
#
# inputspec.in_file : string (existing nifti file)
# Input EPI 4D Volume
#
# inputspec.m_param : parameter m, time series size
#
# inputspec.r_param : parameter r - factor?¿
#
# Workflow Outputs: ::
#
# outputspec.result_vector : ApEn value
#
# References
# ----------
#
# Examples
# --------
# >>> from CPAC import series_mod
# >>> wf = series_mod.create_ApEn()
# >>> wf.inputs.inputspec.in_file = '/home/data/Project/subject/func/in_file.nii.gz'
# >>> wf.inputs.inputspec.m_param = 30
# >>> wf.inputs.inputspec.m_param = 4
# >>> wf.run()
#
# """
#
#
#
# ApEn = pe.Workflow(name='ApEn')
# inputNode = pe.Node(util.IdentityInterface(fields=[
# 'in_file',
# 'm_param',
# 'r_param'
# ]),
# name='inputspec')
#
#
# outputNode = pe.Node(util.IdentityInterface(fields=[
# 'result_vector']),
# name='outputspec')
#
#
#
# ApEn_calc_Node = pe.Node(util.Function(input_names=['in_file', 'm_param','r_param'],
# output_names=['result_vector'],
# function=compute_ApEn),
# name='ApEn_calc')
#
#
# ApEn.connect(inputNode, 'in_file',
# ApEn_calc_Node, 'in_file')
# ApEn.connect(inputNode, 'm_param',
# ApEn_calc_Node, 'm_param')
# ApEn.connect(inputNode, 'r_param',
# ApEn_calc_Node, 'r_param')
#
# ApEn.connect(ApEn_calc_Node, 'result_vector',
# outputNode, 'result_vector')
#
#
#
# return ApEn