nipype.interfaces.fsl.model module

The fsl module provides classes for interfacing with the FSL command line tools. This was written to work with FSL version 4.1.4.

Cluster

Link to code

Bases: FSLCommand

Wrapped executable: cluster.

Uses FSL cluster to perform clustering on statistical output

Examples

>>> cl = Cluster()
>>> cl.inputs.threshold = 2.3
>>> cl.inputs.in_file = 'zstat1.nii.gz'
>>> cl.inputs.out_localmax_txt_file = 'stats.txt'
>>> cl.inputs.use_mm = True
>>> cl.cmdline
'cluster --in=zstat1.nii.gz --olmax=stats.txt --thresh=2.3000000000 --mm'
in_file : a pathlike object or string representing an existing file
Input volume. Maps to a command-line argument: --in=%s.
threshold : a float
Threshold for input volume. Maps to a command-line argument: --thresh=%.10f.
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
connectivity : an integer (int or long)
The connectivity of voxels (default 26). Maps to a command-line argument: --connectivity=%d.
cope_file : a pathlike object or string representing a file
Cope volume. Maps to a command-line argument: --cope=%s.
dlh : a float
Smoothness estimate = sqrt(det(Lambda)). Maps to a command-line argument: --dlh=%.10f.
environ : a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’
Environment variables. (Nipype default value: {})
find_min : a boolean
Find minima instead of maxima. Maps to a command-line argument: --min. (Nipype default value: False)
fractional : a boolean
Interprets the threshold as a fraction of the robust range. Maps to a command-line argument: --fractional. (Nipype default value: False)
minclustersize : a boolean
Prints out minimum significant cluster size. Maps to a command-line argument: --minclustersize. (Nipype default value: False)
no_table : a boolean
Suppresses printing of the table info. Maps to a command-line argument: --no_table. (Nipype default value: False)
num_maxima : an integer (int or long)
No of local maxima to report. Maps to a command-line argument: --num=%d.
out_index_file : a boolean or a pathlike object or string representing a file
Output of cluster index (in size order). Maps to a command-line argument: --oindex=%s.
out_localmax_txt_file : a boolean or a pathlike object or string representing a file
Local maxima text file. Maps to a command-line argument: --olmax=%s.
out_localmax_vol_file : a boolean or a pathlike object or string representing a file
Output of local maxima volume. Maps to a command-line argument: --olmaxim=%s.
out_max_file : a boolean or a pathlike object or string representing a file
Filename for output of max image. Maps to a command-line argument: --omax=%s.
out_mean_file : a boolean or a pathlike object or string representing a file
Filename for output of mean image. Maps to a command-line argument: --omean=%s.
out_pval_file : a boolean or a pathlike object or string representing a file
Filename for image output of log pvals. Maps to a command-line argument: --opvals=%s.
out_size_file : a boolean or a pathlike object or string representing a file
Filename for output of size image. Maps to a command-line argument: --osize=%s.
out_threshold_file : a boolean or a pathlike object or string representing a file
Thresholded image. Maps to a command-line argument: --othresh=%s.
output_type : ‘NIFTI’ or ‘NIFTI_PAIR’ or ‘NIFTI_GZ’ or ‘NIFTI_PAIR_GZ’
FSL output type.
peak_distance : a float
Minimum distance between local maxima/minima, in mm (default 0). Maps to a command-line argument: --peakdist=%.10f.
pthreshold : a float
P-threshold for clusters. Maps to a command-line argument: --pthresh=%.10f. Requires inputs: dlh, volume.
std_space_file : a pathlike object or string representing a file
Filename for standard-space volume. Maps to a command-line argument: --stdvol=%s.
use_mm : a boolean
Use mm, not voxel, coordinates. Maps to a command-line argument: --mm. (Nipype default value: False)
volume : an integer (int or long)
Number of voxels in the mask. Maps to a command-line argument: --volume=%d.
warpfield_file : a pathlike object or string representing a file
File contining warpfield. Maps to a command-line argument: --warpvol=%s.
xfm_file : a pathlike object or string representing a file
Filename for Linear: input->standard-space transform. Non-linear: input->highres transform. Maps to a command-line argument: --xfm=%s.
index_file : a pathlike object or string representing a file
Output of cluster index (in size order).
localmax_txt_file : a pathlike object or string representing a file
Local maxima text file.
localmax_vol_file : a pathlike object or string representing a file
Output of local maxima volume.
max_file : a pathlike object or string representing a file
Filename for output of max image.
mean_file : a pathlike object or string representing a file
Filename for output of mean image.
pval_file : a pathlike object or string representing a file
Filename for image output of log pvals.
size_file : a pathlike object or string representing a file
Filename for output of size image.
threshold_file : a pathlike object or string representing a file
Thresholded image.
Cluster.filemap = {'out_index_file': 'index', 'out_localmax_txt_file': 'localmax.txt', 'out_localmax_vol_file': 'localmax', 'out_max_file': 'max', 'out_mean_file': 'mean', 'out_pval_file': 'pval', 'out_size_file': 'size', 'out_threshold_file': 'threshold'}

ContrastMgr

Link to code

Bases: FSLCommand

Wrapped executable: contrast_mgr.

Use FSL contrast_mgr command to evaluate contrasts

In interface mode this file assumes that all the required inputs are in the same location. This has deprecated for FSL versions 5.0.7+ as the necessary corrections file is no longer generated by FILMGLS.

corrections : a pathlike object or string representing an existing file
Statistical corrections used within FILM modelling.
dof_file : a pathlike object or string representing an existing file
Degrees of freedom.
param_estimates : a list of items which are a pathlike object or string representing an existing file
Parameter estimates for each column of the design matrix.
sigmasquareds : a pathlike object or string representing an existing file
Summary of residuals, See Woolrich, et. al., 2001.
tcon_file : a pathlike object or string representing an existing file
Contrast file containing T-contrasts. Maps to a command-line argument: %s (position: -1).
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
contrast_num : a long integer >= 1
Contrast number to start labeling copes from. Maps to a command-line argument: -cope.
environ : a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’
Environment variables. (Nipype default value: {})
fcon_file : a pathlike object or string representing an existing file
Contrast file containing F-contrasts. Maps to a command-line argument: -f %s.
output_type : ‘NIFTI’ or ‘NIFTI_PAIR’ or ‘NIFTI_GZ’ or ‘NIFTI_PAIR_GZ’
FSL output type.
suffix : a unicode string
Suffix to put on the end of the cope filename before the contrast number, default is nothing. Maps to a command-line argument: -suffix %s.
copes : a list of items which are a pathlike object or string representing an existing file
Contrast estimates for each contrast.
fstats : a list of items which are a pathlike object or string representing an existing file
F-stat file for each contrast.
neffs : a list of items which are a pathlike object or string representing an existing file
Neff file ?? for each contrast.
tstats : a list of items which are a pathlike object or string representing an existing file
T-stat file for each contrast.
varcopes : a list of items which are a pathlike object or string representing an existing file
Variance estimates for each contrast.
zfstats : a list of items which are a pathlike object or string representing an existing file
Z-stat file for each F contrast.
zstats : a list of items which are a pathlike object or string representing an existing file
Z-stat file for each contrast.

DualRegression

Link to code

Bases: FSLCommand

Wrapped executable: dual_regression.

Wrapper Script for Dual Regression Workflow

Examples

>>> dual_regression = DualRegression()
>>> dual_regression.inputs.in_files = ["functional.nii", "functional2.nii", "functional3.nii"]
>>> dual_regression.inputs.group_IC_maps_4D = "allFA.nii"
>>> dual_regression.inputs.des_norm = False
>>> dual_regression.inputs.one_sample_group_mean = True
>>> dual_regression.inputs.n_perm = 10
>>> dual_regression.inputs.out_dir = "my_output_directory"
>>> dual_regression.cmdline
'dual_regression allFA.nii 0 -1 10 my_output_directory functional.nii functional2.nii functional3.nii'
>>> dual_regression.run() # doctest: +SKIP
group_IC_maps_4D : a pathlike object or string representing an existing file
4D image containing spatial IC maps (melodic_IC) from the whole-group ICA analysis. Maps to a command-line argument: %s (position: 1).
in_files : a list of items which are a pathlike object or string representing an existing file
List all subjects’ preprocessed, standard-space 4D datasets. Maps to a command-line argument: %s (position: -1).
n_perm : an integer (int or long)
Number of permutations for randomise; set to 1 for just raw tstat output, set to 0 to not run randomise at all. Maps to a command-line argument: %i (position: 5).
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
con_file : a pathlike object or string representing an existing file
Design contrasts for final cross-subject modelling with randomise. Maps to a command-line argument: %s (position: 4).
des_norm : a boolean
Whether to variance-normalise the timecourses used as the stage-2 regressors; True is default and recommended. Maps to a command-line argument: %i (position: 2). (Nipype default value: True)
design_file : a pathlike object or string representing an existing file
Design matrix for final cross-subject modelling with randomise. Maps to a command-line argument: %s (position: 3).
environ : a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’
Environment variables. (Nipype default value: {})
one_sample_group_mean : a boolean
Perform 1-sample group-mean test instead of generic permutation test. Maps to a command-line argument: -1 (position: 3).
out_dir : a pathlike object or string representing a directory
This directory will be created to hold all output and logfiles. Maps to a command-line argument: %s (position: 6). (Nipype default value: output)
output_type : ‘NIFTI’ or ‘NIFTI_PAIR’ or ‘NIFTI_GZ’ or ‘NIFTI_PAIR_GZ’
FSL output type.

out_dir : a pathlike object or string representing an existing directory

FEAT

Link to code

Bases: FSLCommand

Wrapped executable: feat.

Uses FSL feat to calculate first level stats

fsf_file : a pathlike object or string representing an existing file
File specifying the feat design spec file. Maps to a command-line argument: %s (position: 0).
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
environ : a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’
Environment variables. (Nipype default value: {})
output_type : ‘NIFTI’ or ‘NIFTI_PAIR’ or ‘NIFTI_GZ’ or ‘NIFTI_PAIR_GZ’
FSL output type.

feat_dir : a pathlike object or string representing an existing directory

FEATModel

Link to code

Bases: FSLCommand

Wrapped executable: feat_model.

Uses FSL feat_model to generate design.mat files

ev_files : a list of items which are a pathlike object or string representing an existing file
Event spec files generated by level1design. Maps to a command-line argument: %s (position: 1).
fsf_file : a pathlike object or string representing an existing file
File specifying the feat design spec file. Maps to a command-line argument: %s (position: 0).
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
environ : a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’
Environment variables. (Nipype default value: {})
output_type : ‘NIFTI’ or ‘NIFTI_PAIR’ or ‘NIFTI_GZ’ or ‘NIFTI_PAIR_GZ’
FSL output type.
con_file : a pathlike object or string representing an existing file
Contrast file containing contrast vectors.
design_cov : a pathlike object or string representing an existing file
Graphical representation of design covariance.
design_file : a pathlike object or string representing an existing file
Mat file containing ascii matrix for design.
design_image : a pathlike object or string representing an existing file
Graphical representation of design matrix.
fcon_file : a pathlike object or string representing a file
Contrast file containing contrast vectors.

FEATRegister

Link to code

Bases: BaseInterface

Register feat directories to a specific standard

feat_dirs : a list of items which are a pathlike object or string representing an existing directory
Lower level feat dirs.
reg_image : a pathlike object or string representing an existing file
Image to register to (will be treated as standard).
reg_dof : an integer (int or long)
Registration degrees of freedom. (Nipype default value: 12)
fsf_file : a pathlike object or string representing an existing file
FSL feat specification file.

FILMGLS

Link to code

Bases: FSLCommand

Wrapped executable: film_gls.

Use FSL film_gls command to fit a design matrix to voxel timeseries

Examples

Initialize with no options, assigning them when calling run:

>>> from nipype.interfaces import fsl
>>> fgls = fsl.FILMGLS()
>>> res = fgls.run('in_file', 'design_file', 'thresh', rn='stats') #doctest: +SKIP

Assign options through the inputs attribute:

>>> fgls = fsl.FILMGLS()
>>> fgls.inputs.in_file = 'functional.nii'
>>> fgls.inputs.design_file = 'design.mat'
>>> fgls.inputs.threshold = 10
>>> fgls.inputs.results_dir = 'stats'
>>> res = fgls.run() #doctest: +SKIP

Specify options when creating an instance:

>>> fgls = fsl.FILMGLS(in_file='functional.nii', design_file='design.mat', threshold=10, results_dir='stats')
>>> res = fgls.run() #doctest: +SKIP
in_file : a pathlike object or string representing an existing file
Input data file. Maps to a command-line argument: %s (position: -3).
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
autocorr_estimate_only : a boolean
Perform autocorrelation estimatation only. Maps to a command-line argument: -ac. Mutually exclusive with inputs: autocorr_estimate_only, fit_armodel, tukey_window, multitaper_product, use_pava, autocorr_noestimate.
autocorr_noestimate : a boolean
Do not estimate autocorrs. Maps to a command-line argument: -noest. Mutually exclusive with inputs: autocorr_estimate_only, fit_armodel, tukey_window, multitaper_product, use_pava, autocorr_noestimate.
brightness_threshold : a long integer >= 0
Susan brightness threshold, otherwise it is estimated. Maps to a command-line argument: -epith %d.
design_file : a pathlike object or string representing an existing file
Design matrix file. Maps to a command-line argument: %s (position: -2).
environ : a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’
Environment variables. (Nipype default value: {})
fit_armodel : a boolean
Fits autoregressive model - default is to use tukey with M=sqrt(numvols). Maps to a command-line argument: -ar. Mutually exclusive with inputs: autocorr_estimate_only, fit_armodel, tukey_window, multitaper_product, use_pava, autocorr_noestimate.
full_data : a boolean
Output full data. Maps to a command-line argument: -v.
mask_size : an integer (int or long)
Susan mask size. Maps to a command-line argument: -ms %d.
multitaper_product : an integer (int or long)
Multitapering with slepian tapers and num is the time-bandwidth product. Maps to a command-line argument: -mt %d. Mutually exclusive with inputs: autocorr_estimate_only, fit_armodel, tukey_window, multitaper_product, use_pava, autocorr_noestimate.
output_pwdata : a boolean
Output prewhitened data and average design matrix. Maps to a command-line argument: -output_pwdata.
output_type : ‘NIFTI’ or ‘NIFTI_PAIR’ or ‘NIFTI_GZ’ or ‘NIFTI_PAIR_GZ’
FSL output type.
results_dir : a pathlike object or string representing a directory
Directory to store results in. Maps to a command-line argument: -rn %s. (Nipype default value: results)
smooth_autocorr : a boolean
Smooth auto corr estimates. Maps to a command-line argument: -sa.
threshold : a floating point number >= 0.0
Threshold. Maps to a command-line argument: %f (position: -1). (Nipype default value: 1000.0)
tukey_window : an integer (int or long)
Tukey window size to estimate autocorr. Maps to a command-line argument: -tukey %d. Mutually exclusive with inputs: autocorr_estimate_only, fit_armodel, tukey_window, multitaper_product, use_pava, autocorr_noestimate.
use_pava : a boolean
Estimates autocorr using PAVA. Maps to a command-line argument: -pava.
corrections : a pathlike object or string representing an existing file
Statistical corrections used within FILM modeling.
dof_file : a pathlike object or string representing an existing file
Degrees of freedom.
logfile : a pathlike object or string representing an existing file
FILM run logfile.
param_estimates : a list of items which are a pathlike object or string representing an existing file
Parameter estimates for each column of the design matrix.
residual4d : a pathlike object or string representing an existing file
Model fit residual mean-squared error for each time point.
results_dir : a pathlike object or string representing an existing directory
Directory storing model estimation output.
sigmasquareds : a pathlike object or string representing an existing file
Summary of residuals, See Woolrich, et. al., 2001.
thresholdac : a pathlike object or string representing an existing file
The FILM autocorrelation parameters.

FLAMEO

Link to code

Bases: FSLCommand

Wrapped executable: flameo.

Use FSL flameo command to perform higher level model fits

Examples

Initialize FLAMEO with no options, assigning them when calling run:

>>> from nipype.interfaces import fsl
>>> flameo = fsl.FLAMEO()
>>> flameo.inputs.cope_file = 'cope.nii.gz'
>>> flameo.inputs.var_cope_file = 'varcope.nii.gz'
>>> flameo.inputs.cov_split_file = 'cov_split.mat'
>>> flameo.inputs.design_file = 'design.mat'
>>> flameo.inputs.t_con_file = 'design.con'
>>> flameo.inputs.mask_file = 'mask.nii'
>>> flameo.inputs.run_mode = 'fe'
>>> flameo.cmdline
'flameo --copefile=cope.nii.gz --covsplitfile=cov_split.mat --designfile=design.mat --ld=stats --maskfile=mask.nii --runmode=fe --tcontrastsfile=design.con --varcopefile=varcope.nii.gz'
cope_file : a pathlike object or string representing an existing file
Cope regressor data file. Maps to a command-line argument: --copefile=%s.
cov_split_file : a pathlike object or string representing an existing file
Ascii matrix specifying the groups the covariance is split into. Maps to a command-line argument: --covsplitfile=%s.
design_file : a pathlike object or string representing an existing file
Design matrix file. Maps to a command-line argument: --designfile=%s.
mask_file : a pathlike object or string representing an existing file
Mask file. Maps to a command-line argument: --maskfile=%s.
run_mode : ‘fe’ or ‘ols’ or ‘flame1’ or ‘flame12’
Inference to perform. Maps to a command-line argument: --runmode=%s.
t_con_file : a pathlike object or string representing an existing file
Ascii matrix specifying t-contrasts. Maps to a command-line argument: --tcontrastsfile=%s.
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
burnin : an integer (int or long)
Number of jumps at start of mcmc to be discarded. Maps to a command-line argument: --burnin=%d.
dof_var_cope_file : a pathlike object or string representing an existing file
Dof data file for varcope data. Maps to a command-line argument: --dofvarcopefile=%s.
environ : a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’
Environment variables. (Nipype default value: {})
f_con_file : a pathlike object or string representing an existing file
Ascii matrix specifying f-contrasts. Maps to a command-line argument: --fcontrastsfile=%s.
fix_mean : a boolean
Fix mean for tfit. Maps to a command-line argument: --fixmean.
infer_outliers : a boolean
Infer outliers - not for fe. Maps to a command-line argument: --inferoutliers.
log_dir : a pathlike object or string representing a directory
Maps to a command-line argument: --ld=%s. (Nipype default value: stats)
n_jumps : an integer (int or long)
Number of jumps made by mcmc. Maps to a command-line argument: --njumps=%d.
no_pe_outputs : a boolean
Do not output pe files. Maps to a command-line argument: --nopeoutput.
outlier_iter : an integer (int or long)
Number of max iterations to use when inferring outliers. Default is 12. Maps to a command-line argument: --ioni=%d.
output_type : ‘NIFTI’ or ‘NIFTI_PAIR’ or ‘NIFTI_GZ’ or ‘NIFTI_PAIR_GZ’
FSL output type.
sample_every : an integer (int or long)
Number of jumps for each sample. Maps to a command-line argument: --sampleevery=%d.
sigma_dofs : an integer (int or long)
Sigma (in mm) to use for Gaussian smoothing the DOFs in FLAME 2. Default is 1mm, -1 indicates no smoothing. Maps to a command-line argument: --sigma_dofs=%d.
var_cope_file : a pathlike object or string representing an existing file
Varcope weightings data file. Maps to a command-line argument: --varcopefile=%s.
copes : a list of items which are a pathlike object or string representing an existing file
Contrast estimates for each contrast.
fstats : a list of items which are a pathlike object or string representing an existing file
F-stat file for each contrast.
mrefvars : a list of items which are a pathlike object or string representing an existing file
Mean random effect variances for each contrast.
pes : a list of items which are a pathlike object or string representing an existing file
Parameter estimates for each column of the design matrix for each voxel.
res4d : a list of items which are a pathlike object or string representing an existing file
Model fit residual mean-squared error for each time point.
stats_dir : a pathlike object or string representing a directory
Directory storing model estimation output.
tdof : a list of items which are a pathlike object or string representing an existing file
Temporal dof file for each contrast.
tstats : a list of items which are a pathlike object or string representing an existing file
T-stat file for each contrast.
var_copes : a list of items which are a pathlike object or string representing an existing file
Variance estimates for each contrast.
weights : a list of items which are a pathlike object or string representing an existing file
Weights file for each contrast.
zfstats : a list of items which are a pathlike object or string representing an existing file
Z stat file for each f contrast.
zstats : a list of items which are a pathlike object or string representing an existing file
Z-stat file for each contrast.
FLAMEO.references_ = [{'entry': None, 'tags': ['method']}, {'entry': None, 'tags': ['method']}]

GLM

Link to code

Bases: FSLCommand

Wrapped executable: fsl_glm.

FSL GLM:

Example

>>> import nipype.interfaces.fsl as fsl
>>> glm = fsl.GLM(in_file='functional.nii', design='maps.nii', output_type='NIFTI')
>>> glm.cmdline
'fsl_glm -i functional.nii -d maps.nii -o functional_glm.nii'
design : a pathlike object or string representing an existing file
File name of the GLM design matrix (text time courses for temporal regression or an image file for spatial regression). Maps to a command-line argument: -d %s (position: 2).
in_file : a pathlike object or string representing an existing file
Input file name (text matrix or 3D/4D image file). Maps to a command-line argument: -i %s (position: 1).
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
contrasts : a pathlike object or string representing an existing file
Matrix of t-statics contrasts. Maps to a command-line argument: -c %s.
dat_norm : a boolean
Switch on normalization of the data time series to unit std deviation. Maps to a command-line argument: --dat_norm.
demean : a boolean
Switch on demeaining of design and data. Maps to a command-line argument: --demean.
des_norm : a boolean
Switch on normalization of the design matrix columns to unit std deviation. Maps to a command-line argument: --des_norm.
dof : an integer (int or long)
Set degrees of freedom explicitly. Maps to a command-line argument: --dof=%d.
environ : a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’
Environment variables. (Nipype default value: {})
mask : a pathlike object or string representing an existing file
Mask image file name if input is image. Maps to a command-line argument: -m %s.
out_cope : a pathlike object or string representing a file
Output file name for COPE (either as txt or image. Maps to a command-line argument: --out_cope=%s.
out_data_name : a pathlike object or string representing a file
Output file name for pre-processed data. Maps to a command-line argument: --out_data=%s.
out_f_name : a pathlike object or string representing a file
Output file name for F-value of full model fit. Maps to a command-line argument: --out_f=%s.
out_file : a pathlike object or string representing a file
Filename for GLM parameter estimates (GLM betas). Maps to a command-line argument: -o %s (position: 3).
out_p_name : a pathlike object or string representing a file
Output file name for p-values of Z-stats (either as text file or image). Maps to a command-line argument: --out_p=%s.
out_pf_name : a pathlike object or string representing a file
Output file name for p-value for full model fit. Maps to a command-line argument: --out_pf=%s.
out_res_name : a pathlike object or string representing a file
Output file name for residuals. Maps to a command-line argument: --out_res=%s.
out_sigsq_name : a pathlike object or string representing a file
Output file name for residual noise variance sigma-square. Maps to a command-line argument: --out_sigsq=%s.
out_t_name : a pathlike object or string representing a file
Output file name for t-stats (either as txt or image. Maps to a command-line argument: --out_t=%s.
out_varcb_name : a pathlike object or string representing a file
Output file name for variance of COPEs. Maps to a command-line argument: --out_varcb=%s.
out_vnscales_name : a pathlike object or string representing a file
Output file name for scaling factors for variance normalisation. Maps to a command-line argument: --out_vnscales=%s.
out_z_name : a pathlike object or string representing a file
Output file name for Z-stats (either as txt or image. Maps to a command-line argument: --out_z=%s.
output_type : ‘NIFTI’ or ‘NIFTI_PAIR’ or ‘NIFTI_GZ’ or ‘NIFTI_PAIR_GZ’
FSL output type.
var_norm : a boolean
Perform MELODIC variance-normalisation on data. Maps to a command-line argument: --vn.
out_cope : a list of items which are a pathlike object or string representing an existing file
Output file name for COPEs (either as text file or image).
out_data : a list of items which are a pathlike object or string representing an existing file
Output file for preprocessed data.
out_f : a list of items which are a pathlike object or string representing an existing file
Output file name for F-value of full model fit.
out_file : a pathlike object or string representing an existing file
File name of GLM parameters (if generated).
out_p : a list of items which are a pathlike object or string representing an existing file
Output file name for p-values of Z-stats (either as text file or image).
out_pf : a list of items which are a pathlike object or string representing an existing file
Output file name for p-value for full model fit.
out_res : a list of items which are a pathlike object or string representing an existing file
Output file name for residuals.
out_sigsq : a list of items which are a pathlike object or string representing an existing file
Output file name for residual noise variance sigma-square.
out_t : a list of items which are a pathlike object or string representing an existing file
Output file name for t-stats (either as text file or image).
out_varcb : a list of items which are a pathlike object or string representing an existing file
Output file name for variance of COPEs.
out_vnscales : a list of items which are a pathlike object or string representing an existing file
Output file name for scaling factors for variance normalisation.
out_z : a list of items which are a pathlike object or string representing an existing file
Output file name for COPEs (either as text file or image).

L2Model

Link to code

Bases: BaseInterface

Generate subject specific second level model

Examples

>>> from nipype.interfaces.fsl import L2Model
>>> model = L2Model(num_copes=3) # 3 sessions
num_copes : a long integer >= 1
Number of copes to be combined.
design_con : a pathlike object or string representing an existing file
Design contrast file.
design_grp : a pathlike object or string representing an existing file
Design group file.
design_mat : a pathlike object or string representing an existing file
Design matrix file.

Level1Design

Link to code

Bases: BaseInterface

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() # doctest: +SKIP
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’ or ‘gammasigma’ or ‘gammadelay’ and with values which are any value or a dictionary with keys which are ‘custom’ and with values which are a dictionary with keys which are ‘bfcustompath’ and with values which are a unicode string or a dictionary with keys which are ‘none’ and with values which are a dictionary with keys which are any value and with values which are any value 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 : any value
Session specific information generated by modelgen.SpecifyModel.
contrasts : a list of items which are a tuple of the form: (a unicode string, ‘T’, a list of items which are a unicode string, a list of items which are a float) or a tuple of the form: (a unicode string, ‘T’, a list of items which are a unicode string, a list of items which are a float, a list of items which are a float) or a tuple of the form: (a unicode string, ‘F’, a list of items which are a tuple of the form: (a unicode string, ‘T’, a list of items which are a unicode string, a list of items which are a float) or a tuple of the form: (a unicode string, ‘T’, a list of items which are a unicode 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.
orthogonalization : a dictionary with keys which are an integer (int or long) and with values which are a dictionary with keys which are an integer (int or long) and with values which are a boolean or an integer (int or long)
Which regressors to make orthogonal e.g., {1: {0:0,1:0,2:0}, 2: {0:1,1:1,2:0}} to make the second regressor in a 2-regressor model orthogonal to the first. (Nipype default value: {})
ev_files : a list of items which are a list of items which are a pathlike object or string representing an existing file
Condition information files.
fsf_files : a list of items which are a pathlike object or string representing an existing file
FSL feat specification files.

MELODIC

Link to code

Bases: FSLCommand

Wrapped executable: melodic.

Multivariate Exploratory Linear Optimised Decomposition into Independent Components

Examples

>>> melodic_setup = MELODIC()
>>> melodic_setup.inputs.approach = 'tica'
>>> melodic_setup.inputs.in_files = ['functional.nii', 'functional2.nii', 'functional3.nii']
>>> melodic_setup.inputs.no_bet = True
>>> melodic_setup.inputs.bg_threshold = 10
>>> melodic_setup.inputs.tr_sec = 1.5
>>> melodic_setup.inputs.mm_thresh = 0.5
>>> melodic_setup.inputs.out_stats = True
>>> melodic_setup.inputs.t_des = 'timeDesign.mat'
>>> melodic_setup.inputs.t_con = 'timeDesign.con'
>>> melodic_setup.inputs.s_des = 'subjectDesign.mat'
>>> melodic_setup.inputs.s_con = 'subjectDesign.con'
>>> melodic_setup.inputs.out_dir = 'groupICA.out'
>>> melodic_setup.cmdline
'melodic -i functional.nii,functional2.nii,functional3.nii -a tica --bgthreshold=10.000000 --mmthresh=0.500000 --nobet -o groupICA.out --Ostats --Scon=subjectDesign.con --Sdes=subjectDesign.mat --Tcon=timeDesign.con --Tdes=timeDesign.mat --tr=1.500000'
>>> melodic_setup.run() # doctest: +SKIP
in_files : a list of items which are a pathlike object or string representing an existing file
Input file names (either single file name or a list). Maps to a command-line argument: -i %s (position: 0).
ICs : a pathlike object or string representing an existing file
Filename of the IC components file for mixture modelling. Maps to a command-line argument: --ICs=%s.
approach : a unicode string
Approach for decomposition, 2D: defl, symm (default), 3D: tica (default), concat. Maps to a command-line argument: -a %s.
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
bg_image : a pathlike object or string representing an existing file
Specify background image for report (default: mean image). Maps to a command-line argument: --bgimage=%s.
bg_threshold : a float
Brain/non-brain threshold used to mask non-brain voxels, as a percentage (only if –nobet selected). Maps to a command-line argument: --bgthreshold=%f.
cov_weight : a float
Voxel-wise weights for the covariance matrix (e.g. segmentation information). Maps to a command-line argument: --covarweight=%f.
dim : an integer (int or long)
Dimensionality reduction into #num dimensions (default: automatic estimation). Maps to a command-line argument: -d %d.
dim_est : a unicode string
Use specific dim. estimation technique: lap, bic, mdl, aic, mean (default: lap). Maps to a command-line argument: --dimest=%s.
environ : a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’
Environment variables. (Nipype default value: {})
epsilon : a float
Minimum error change. Maps to a command-line argument: --eps=%f.
epsilonS : a float
Minimum error change for rank-1 approximation in TICA. Maps to a command-line argument: --epsS=%f.
log_power : a boolean
Calculate log of power for frequency spectrum. Maps to a command-line argument: --logPower.
mask : a pathlike object or string representing an existing file
File name of mask for thresholding. Maps to a command-line argument: -m %s.
max_restart : an integer (int or long)
Maximum number of restarts. Maps to a command-line argument: --maxrestart=%d.
maxit : an integer (int or long)
Maximum number of iterations before restart. Maps to a command-line argument: --maxit=%d.
migp : a boolean
Switch on MIGP data reduction. Maps to a command-line argument: --migp.
migpN : an integer (int or long)
Number of internal Eigenmaps. Maps to a command-line argument: --migpN %d.
migp_factor : an integer (int or long)
Internal Factor of mem-threshold relative to number of Eigenmaps (default: 2). Maps to a command-line argument: --migp_factor %d.
migp_shuffle : a boolean
Randomise MIGP file order (default: TRUE). Maps to a command-line argument: --migp_shuffle.
mix : a pathlike object or string representing an existing file
Mixing matrix for mixture modelling / filtering. Maps to a command-line argument: --mix=%s.
mm_thresh : a float
Threshold for Mixture Model based inference. Maps to a command-line argument: --mmthresh=%f.
no_bet : a boolean
Switch off BET. Maps to a command-line argument: --nobet.
no_mask : a boolean
Switch off masking. Maps to a command-line argument: --nomask.
no_mm : a boolean
Switch off mixture modelling on IC maps. Maps to a command-line argument: --no_mm.
non_linearity : a unicode string
Nonlinearity: gauss, tanh, pow3, pow4. Maps to a command-line argument: --nl=%s.
num_ICs : an integer (int or long)
Number of IC’s to extract (for deflation approach). Maps to a command-line argument: -n %d.
out_all : a boolean
Output everything. Maps to a command-line argument: --Oall.
out_dir : a pathlike object or string representing a directory
Output directory name. Maps to a command-line argument: -o %s.
out_mean : a boolean
Output mean volume. Maps to a command-line argument: --Omean.
out_orig : a boolean
Output the original ICs. Maps to a command-line argument: --Oorig.
out_pca : a boolean
Output PCA results. Maps to a command-line argument: --Opca.
out_stats : a boolean
Output thresholded maps and probability maps. Maps to a command-line argument: --Ostats.
out_unmix : a boolean
Output unmixing matrix. Maps to a command-line argument: --Ounmix.
out_white : a boolean
Output whitening/dewhitening matrices. Maps to a command-line argument: --Owhite.
output_type : ‘NIFTI’ or ‘NIFTI_PAIR’ or ‘NIFTI_GZ’ or ‘NIFTI_PAIR_GZ’
FSL output type.
pbsc : a boolean
Switch off conversion to percent BOLD signal change. Maps to a command-line argument: --pbsc.
rem_cmp : a list of items which are an integer (int or long)
Component numbers to remove. Maps to a command-line argument: -f %d.
remove_deriv : a boolean
Removes every second entry in paradigm file (EV derivatives). Maps to a command-line argument: --remove_deriv.
report : a boolean
Generate Melodic web report. Maps to a command-line argument: --report.
report_maps : a unicode string
Control string for spatial map images (see slicer). Maps to a command-line argument: --report_maps=%s.
s_con : a pathlike object or string representing an existing file
T-contrast matrix across subject-domain. Maps to a command-line argument: --Scon=%s.
s_des : a pathlike object or string representing an existing file
Design matrix across subject-domain. Maps to a command-line argument: --Sdes=%s.
sep_vn : a boolean
Switch off joined variance normalization. Maps to a command-line argument: --sep_vn.
sep_whiten : a boolean
Switch on separate whitening. Maps to a command-line argument: --sep_whiten.
smode : a pathlike object or string representing an existing file
Matrix of session modes for report generation. Maps to a command-line argument: --smode=%s.
t_con : a pathlike object or string representing an existing file
T-contrast matrix across time-domain. Maps to a command-line argument: --Tcon=%s.
t_des : a pathlike object or string representing an existing file
Design matrix across time-domain. Maps to a command-line argument: --Tdes=%s.
tr_sec : a float
TR in seconds. Maps to a command-line argument: --tr=%f.
update_mask : a boolean
Switch off mask updating. Maps to a command-line argument: --update_mask.
var_norm : a boolean
Switch off variance normalization. Maps to a command-line argument: --vn.

out_dir : a pathlike object or string representing an existing directory report_dir : a pathlike object or string representing an existing directory

MultipleRegressDesign

Link to code

Bases: BaseInterface

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 MultipleRegressDesign
>>> model = MultipleRegressDesign()
>>> model.inputs.contrasts = [['group mean', 'T',['reg1'],[1]]]
>>> model.inputs.regressors = dict(reg1=[1, 1, 1], reg2=[2.,-4, 3])
>>> model.run() # doctest: +SKIP
contrasts : a list of items which are a tuple of the form: (a unicode string, ‘T’, a list of items which are a unicode string, a list of items which are a float) or a tuple of the form: (a unicode string, ‘F’, a list of items which are a tuple of the form: (a unicode string, ‘T’, a list of items which are a unicode 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 unicode string and with values which are a list of items which are a float
Dictionary containing named lists of regressors.
groups : a list of items which are an integer (int or long)
List of group identifiers (defaults to single group).
design_con : a pathlike object or string representing an existing file
Design t-contrast file.
design_fts : a pathlike object or string representing an existing file
Design f-contrast file.
design_grp : a pathlike object or string representing an existing file
Design group file.
design_mat : a pathlike object or string representing an existing file
Design matrix file.

Randomise

Link to code

Bases: FSLCommand

Wrapped executable: randomise.

FSL Randomise: feeds the 4D projected FA data into GLM modelling and thresholding in order to find voxels which correlate with your model

Example

>>> import nipype.interfaces.fsl as fsl
>>> rand = fsl.Randomise(in_file='allFA.nii', mask = 'mask.nii', tcon='design.con', design_mat='design.mat')
>>> rand.cmdline
'randomise -i allFA.nii -o "randomise" -d design.mat -t design.con -m mask.nii'
in_file : a pathlike object or string representing an existing file
4D input file. Maps to a command-line argument: -i %s (position: 0).
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
base_name : a unicode string
The rootname that all generated files will have. Maps to a command-line argument: -o "%s" (position: 1). (Nipype default value: randomise)
c_thresh : a float
Carry out cluster-based thresholding. Maps to a command-line argument: -c %.1f.
cm_thresh : a float
Carry out cluster-mass-based thresholding. Maps to a command-line argument: -C %.1f.
demean : a boolean
Demean data temporally before model fitting. Maps to a command-line argument: -D.
design_mat : a pathlike object or string representing an existing file
Design matrix file. Maps to a command-line argument: -d %s (position: 2).
environ : a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’
Environment variables. (Nipype default value: {})
f_c_thresh : a float
Carry out f cluster thresholding. Maps to a command-line argument: -F %.2f.
f_cm_thresh : a float
Carry out f cluster-mass thresholding. Maps to a command-line argument: -S %.2f.
f_only : a boolean
Calculate f-statistics only. Maps to a command-line argument: --f_only.
fcon : a pathlike object or string representing an existing file
F contrasts file. Maps to a command-line argument: -f %s.
mask : a pathlike object or string representing an existing file
Mask image. Maps to a command-line argument: -m %s.
num_perm : an integer (int or long)
Number of permutations (default 5000, set to 0 for exhaustive). Maps to a command-line argument: -n %d.
one_sample_group_mean : a boolean
Perform 1-sample group-mean test instead of generic permutation test. Maps to a command-line argument: -1.
output_type : ‘NIFTI’ or ‘NIFTI_PAIR’ or ‘NIFTI_GZ’ or ‘NIFTI_PAIR_GZ’
FSL output type.
p_vec_n_dist_files : a boolean
Output permutation vector and null distribution text files. Maps to a command-line argument: -P.
raw_stats_imgs : a boolean
Output raw ( unpermuted ) statistic images. Maps to a command-line argument: -R.
seed : an integer (int or long)
Specific integer seed for random number generator. Maps to a command-line argument: --seed=%d.
show_info_parallel_mode : a boolean
Print out information required for parallel mode and exit. Maps to a command-line argument: -Q.
show_total_perms : a boolean
Print out how many unique permutations would be generated and exit. Maps to a command-line argument: -q.
tcon : a pathlike object or string representing an existing file
T contrasts file. Maps to a command-line argument: -t %s (position: 3).
tfce : a boolean
Carry out Threshold-Free Cluster Enhancement. Maps to a command-line argument: -T.
tfce2D : a boolean
Carry out Threshold-Free Cluster Enhancement with 2D optimisation. Maps to a command-line argument: --T2.
tfce_C : a float
TFCE connectivity (6 or 26; default=6). Maps to a command-line argument: --tfce_C=%.2f.
tfce_E : a float
TFCE extent parameter (default=0.5). Maps to a command-line argument: --tfce_E=%.2f.
tfce_H : a float
TFCE height parameter (default=2). Maps to a command-line argument: --tfce_H=%.2f.
var_smooth : an integer (int or long)
Use variance smoothing (std is in mm). Maps to a command-line argument: -v %d.
vox_p_values : a boolean
Output voxelwise (corrected and uncorrected) p-value images. Maps to a command-line argument: -x.
x_block_labels : a pathlike object or string representing an existing file
Exchangeability block labels file. Maps to a command-line argument: -e %s.
f_corrected_p_files : a list of items which are a pathlike object or string representing an existing file
F contrast FWE (Family-wise error) corrected p values files.
f_p_files : a list of items which are a pathlike object or string representing an existing file
F contrast uncorrected p values files.
fstat_files : a list of items which are a pathlike object or string representing an existing file
F contrast raw statistic.
t_corrected_p_files : a list of items which are a pathlike object or string representing an existing file
T contrast FWE (Family-wise error) corrected p values files.
t_p_files : a list of items which are a pathlike object or string representing an existing file
F contrast uncorrected p values files.
tstat_files : a list of items which are a pathlike object or string representing an existing file
T contrast raw statistic.

SMM

Link to code

Bases: FSLCommand

Wrapped executable: mm --ld=logdir.

Spatial Mixture Modelling. For more detail on the spatial mixture modelling see Mixture Models with Adaptive Spatial Regularisation for Segmentation with an Application to FMRI Data; Woolrich, M., Behrens, T., Beckmann, C., and Smith, S.; IEEE Trans. Medical Imaging, 24(1):1-11, 2005.

mask : a pathlike object or string representing an existing file
Mask file. Maps to a command-line argument: --mask="%s" (position: 1).
spatial_data_file : a pathlike object or string representing an existing file
Statistics spatial map. Maps to a command-line argument: --sdf="%s" (position: 0).
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
environ : a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’
Environment variables. (Nipype default value: {})
no_deactivation_class : a boolean
Enforces no deactivation class. Maps to a command-line argument: --zfstatmode (position: 2).
output_type : ‘NIFTI’ or ‘NIFTI_PAIR’ or ‘NIFTI_GZ’ or ‘NIFTI_PAIR_GZ’
FSL output type.

activation_p_map : a pathlike object or string representing an existing file deactivation_p_map : a pathlike object or string representing an existing file null_p_map : a pathlike object or string representing an existing file

SmoothEstimate

Link to code

Bases: FSLCommand

Wrapped executable: smoothest.

Estimates the smoothness of an image

Examples

>>> est = SmoothEstimate()
>>> est.inputs.zstat_file = 'zstat1.nii.gz'
>>> est.inputs.mask_file = 'mask.nii'
>>> est.cmdline
'smoothest --mask=mask.nii --zstat=zstat1.nii.gz'
dof : an integer (int or long)
Number of degrees of freedom. Maps to a command-line argument: --dof=%d. Mutually exclusive with inputs: zstat_file.
mask_file : a pathlike object or string representing an existing file
Brain mask volume. Maps to a command-line argument: --mask=%s.
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
environ : a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’
Environment variables. (Nipype default value: {})
output_type : ‘NIFTI’ or ‘NIFTI_PAIR’ or ‘NIFTI_GZ’ or ‘NIFTI_PAIR_GZ’
FSL output type.
residual_fit_file : a pathlike object or string representing an existing file
Residual-fit image file. Maps to a command-line argument: --res=%s. Requires inputs: dof.
zstat_file : a pathlike object or string representing an existing file
Zstat image file. Maps to a command-line argument: --zstat=%s. Mutually exclusive with inputs: dof.
dlh : a float
Smoothness estimate sqrt(det(Lambda)).
resels : a float
Number of resels.
volume : an integer (int or long)
Number of voxels in mask.
SmoothEstimate.aggregate_outputs(runtime=None, needed_outputs=None)

Collate expected outputs and apply output traits validation.

nipype.interfaces.fsl.model.load_template(name)

Load a template from the model_templates directory

Parameters:name (str) – The name of the file to load
Returns:template
Return type:string.Template