fsl.data.featimage
¶
This module provides the FEATImage
class, a subclass of
Image
designed to encapsulate data from a FEAT analysis.
This module also provides the modelFit()
function.
-
class
fsl.data.featimage.
FEATImage
(path, **kwargs)¶ Bases:
fsl.data.image.Image
An
Image
which contains the input data from a FEAT analysis.The
FEATImage
class makes use of the functions defined in thefeatanalysis
module.An example of using the
FEATImage
class:import fsl.data.featimage as featimage # You can pass in the name of the # .feat directory, or the filtered_func_data # file contained within that directory. img = featimage.FEATImage('myanalysis.feat/filtered_func_data.nii.gz') # Query information about the FEAT analysis print(img.numEVs()) print(img.contrastNames()) print(img.numPoints()) # Get the model fit residuals res4d = img.getResiduals() # Get the full model fit for voxel # [23, 30, 42] (in this example, we # have 4 EVs - the first argument # is a contrast vector). img.fit([1, 1, 1, 1], [23, 30, 42], fullModel=True)
-
getFEATDir
()¶ Returns the FEAT directory this image is contained in.
-
getAnalysisName
()¶ Returns the FEAT analysis name, which is the FEAT directory name, minus the
.feat
/.gfeat
suffix.
-
isFirstLevelAnalysis
()¶ Returns
True
if the FEAT analysis described bysettings
is a first level analysis,False
otherwise.
-
getTopLevelAnalysisDir
()¶ Returns the path to the higher level analysis directory of which this FEAT analysis is a part, or
None
if this analysis is not part of another analysis.
-
getReportFile
()¶ Returns the path to the FEAT report - see
featanalysis.getReportFile()
.
-
hasStats
()¶ Returns
True
if the analysis for thisFEATImage
contains a statistical analysis.
-
getDesign
(voxel=None)¶ Returns the analysis design matrix as a
numpy
array with shape \(numPoints\times numEVs\). SeeFEATFSFDesign.getDesign()
.
-
numPoints
()¶ Returns the number of points (e.g. time points, number of subjects, etc) in the analysis.
-
numEVs
()¶ Returns the number of explanatory variables (EVs) in the analysis.
-
evNames
()¶ Returns a list containing the name of each EV in the analysis.
-
numContrasts
()¶ Returns the number of contrasts in the analysis.
-
contrastNames
()¶ Returns a list containing the name of each contrast in the analysis.
-
contrasts
()¶ Returns a list containing the analysis contrast vectors.
-
thresholds
()¶ Returns the statistical thresholds used in the analysis.
-
clusterResults
(contrast)¶ Returns the clusters found in the analysis.
See :func:.featanalysis.loadClusterResults`
-
getPE
(ev)¶ Returns the PE image for the given EV (0-indexed).
-
getResiduals
()¶ Returns the residuals of the full model fit.
-
getCOPE
(con)¶ Returns the COPE image for the given contrast (0-indexed).
-
getZStats
(con)¶ Returns the Z statistic image for the given contrast (0-indexed).
-
getClusterMask
(con)¶ Returns the cluster mask image for the given contrast (0-indexed).
-
fit
(contrast, xyz)¶ Calculates the model fit for the given contrast vector at the given voxel. See the
modelFit()
function.Parameters: - contrast – The contrast vector (pass all 1s for a full model fit).
- xyz – Coordinates of the voxel to calculate the model fit for.
-
-
fsl.data.featimage.
modelFit
(data, design, contrast, pes, firstLevel=True)¶ Calculates the model fit to the given data for the given contrast vector.
Parameters: - data – The input data
- design – The design matrix
- contrast – The contrast vector (pass all 1s for a full model fit)
- pes – Parameter estimates for each EV in the design matrix
- firstLevel – If
True
(the default), the mean of the input data is added to the result.
Returns: The best fit of the model to the data.