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nipype.interfaces.fsl.model

Cluster

ContrastMgr

FEAT

FEATModel

FEATRegister

Register feat directories to a specific standard

Inputs:

[Mandatory]
feat_dirs : (a directory name)
        Lower level feat dirs
reg_image : (a file name)
        image to register to (will be treated as standard)

[Optional]
ignore_exception : (a boolean)
        Print an error message instead of throwing an exception in case the interface fails to run
reg_dof : (an integer)
        registration degrees of freedom

Outputs:

fsf_file : (an existing file name)
        FSL feat specification file

FILMGLS

FLAMEO

L2Model

Generate subject specific second level model

Examples

>>> from nipype.interfaces.fsl import L2Model
>>> model = L2Model(num_copes=3) # 3 sessions

Inputs:

[Mandatory]
num_copes : (an integer)
        number of copes to be combined

[Optional]
ignore_exception : (a boolean)
        Print an error message instead of throwing an exception in case the interface fails to run

Outputs:

design_con : (an existing file name)
        design contrast file
design_grp : (an existing file name)
        design group file
design_mat : (an existing file name)
        design matrix file

Level1Design

Generate FEAT specific files

Examples

>>> level1design = Level1Design()
>>> level1design.inputs.interscan_interval = 2.5
>>> level1design.inputs.bases = {'dgamma':{'derivs': False}}
>>> level1design.inputs.session_info = 'session_info.npz'
>>> level1design.run() 

Inputs:

[Mandatory]
bases : (a dictionary with keys which are 'dgamma' and with values which are a dictionary with keys which are 'derivs' and with values which are a boolean or a dictionary with keys which are 'gamma' and with values which are a dictionary with keys which are 'derivs' and with values which are a boolean or a dictionary with keys which are 'none' and with values which are None)
        name of basis function and options e.g., {'dgamma': {'derivs': True}}
interscan_interval : (a float)
        Interscan  interval (in secs)
model_serial_correlations : (a boolean)
        Option to model serial correlations using an autoregressive estimator (order 1). Setting this option is only useful in the context of the fsf file. If you set this to False, you need to repeat this option for FILMGLS by setting autocorr_noestimate to True
session_info    Session specific information generated by ``modelgen.SpecifyModel``

[Optional]
contrasts : (a list of items which are a tuple of the form: (a string, 'T', a list of items which are a string, a list of items which are a float) or a tuple of the form: (a string, 'T', a list of items which are a string, a list of items which are a float, a list of items which are a float) or a tuple of the form: (a string, 'F', a list of items which are a tuple of the form: (a string, 'T', a list of items which are a string, a list of items which are a float) or a tuple of the form: (a string, 'T', a list of items which are a string, a list of items which are a float, a list of items which are a float)))
        List of contrasts with each contrast being a list of the form - [('name', 'stat', [condition list], [weight list], [session list])]. if session list is None or not provided, all sessions are used. For F contrasts, the condition list should contain previously defined T-contrasts.
ignore_exception : (a boolean)
        Print an error message instead of throwing an exception in case the interface fails to run

Outputs:

ev_files : (a list of items which are a list of items which are an existing file name)
        condition information files
fsf_files : (an existing file name)
        FSL feat specification files

MELODIC

MultipleRegressDesign

Generate multiple regression design

Note

FSL does not demean columns for higher level analysis.

Please see FSL documentation for more details on model specification for higher level analysis.

Examples

>>> from nipype.interfaces.fsl import L2Model
>>> model = MultipleRegressDesign()
>>> model.inputs.contrasts = [['group mean','T',['reg1'],[1]]]
>>> model.inputs.regressors = dict(reg1=[1,1,1],reg2=[2.,-4,3])
>>> model.run() 

Inputs:

[Mandatory]
contrasts : (a list of items which are a tuple of the form: (a string, 'T', a list of items which are a string, a list of items which are a float) or a tuple of the form: (a string, 'F', a list of items which are a tuple of the form: (a string, 'T', a list of items which are a string, a list of items which are a float)))
        List of contrasts with each contrast being a list of the form - [('name', 'stat', [condition list], [weight list])]. if session list is None or not provided, all sessions are used. For F contrasts, the condition list should contain previously defined T-contrasts without any weight list.
regressors : (a dictionary with keys which are a string and with values which are a list of items which are a float)
        dictionary containing named lists of regressors

[Optional]
groups : (a list of items which are an integer)
        list of group identifiers (defaults to single group)
ignore_exception : (a boolean)
        Print an error message instead of throwing an exception in case the interface fails to run

Outputs:

design_con : (an existing file name)
        design t-contrast file
design_fts : (an existing file name)
        design f-contrast file
design_grp : (an existing file name)
        design group file
design_mat : (an existing file name)
        design matrix file

Randomise

SMM

SmoothEstimate