08_fslpy.md

fslpy

Important: Portions of this practical require fslpy 2.9.0, due to be released with FSL 6.0.4, in Spring 2020.

fslpy is a Python library which is built into FSL, and contains a range of functionality for working with FSL and with neuroimaging data from Python.

This practical highlights some of the most useful features provided by fslpy. You may find fslpy useful if you are writing Python code to perform analyses and image processing in conjunction with FSL.

• The Image class, and other data types
  • Creating images
  • Working with image data
  • Loading other file types
  • NIfTI coordinate systems
  • Transformations and resampling
• FSL wrapper functions
  • In-memory images
  • Loading outputs into Python
  • The fslmaths wrapper
• The FileTree
  • Describing your data
  • Using the FileTree
  • Building a processing pipeline with FileTree
  • The FileTreeQuery
• Calling shell commands
  • The runfsl function
  • Submitting to the cluster
  • Redirecting output
• FSL atlases
  • Querying atlases
  • Loading atlas images
  • Working with atlases

Note: fslpy is distinct from fslpython - fslpython is the Python environment that is baked into FSL. fslpy is a Python library which is installed into the fslpython environment.

Let's start with some standard imports and environment set-up:

%matplotlib inline
import matplotlib.pyplot as plt
import os
import os.path as op
import nibabel as nib
import numpy as np
import warnings
warnings.filterwarnings("ignore")
np.set_printoptions(suppress=True, precision=4)

And a little function that we can use to generate a simple orthographic plot:

def ortho(data, voxel, fig=None, cursor=False, **kwargs):
    """Simple orthographic plot of a 3D array using matplotlib.

    :arg data:   3D numpy array
    :arg voxel:  XYZ coordinates for each slice
    :arg fig:    Existing figure and axes for overlay plotting
    :arg cursor: Show a cursor at the `voxel`

    All other arguments are passed through to the `imshow` function.

    :returns:   The figure and orthogaxes (which can be passed back in as the
                `fig` argument to plot overlays).
    """

    voxel = [int(round(v)) for v in voxel]

    data            = np.asanyarray(data, dtype=np.float)
    data[data <= 0] = np.nan

    x, y, z = voxel
    xslice  = np.flipud(data[x, :, :].T)
    yslice  = np.flipud(data[:, y, :].T)
    zslice  = np.flipud(data[:, :, z].T)

    if fig is None:
        fig = plt.figure()
        xax = fig.add_subplot(1, 3, 1)
        yax = fig.add_subplot(1, 3, 2)
        zax = fig.add_subplot(1, 3, 3)
    else:
        fig, xax, yax, zax = fig

    xax.imshow(xslice, **kwargs)
    yax.imshow(yslice, **kwargs)
    zax.imshow(zslice, **kwargs)

    if cursor:
        cargs = {'color' : (0, 1, 0), 'linewidth' : 1}
        xax.axvline(                y, **cargs)
        xax.axhline(data.shape[2] - z, **cargs)
        yax.axvline(                x, **cargs)
        yax.axhline(data.shape[2] - z, **cargs)
        zax.axvline(                x, **cargs)
        zax.axhline(data.shape[1] - y, **cargs)

    for ax in (xax, yax, zax):
        ax.set_xticks([])
        ax.set_yticks([])
    fig.tight_layout(pad=0)

    return (fig, xax, yax, zax)

And another function which uses FSLeyes for more complex plots:

def render(cmdline):

    import shlex
    import IPython.display as display

    prefix = '-of screenshot.png -hl -c 2 '

    try:
        from fsleyes.render import main
        main(shlex.split(prefix + cmdline))

    except ImportError:
        # fall-back for macOS - we have to run
        # FSLeyes render in a separate process
        from fsl.utils.run import runfsl
        prefix = 'render ' + prefix
        runfsl(prefix + cmdline, env={})

    return display.Image('screenshot.png')

The Image class, and other data types

The fsl.data.image module provides the Image class, which sits on top of nibabel and contains some handy functionality if you need to work with coordinate transformations, or do some FSL-specific processing. The Image class provides features such as:

• Support for NIFTI1, NIFTI2, and ANALYZE image files
• Access to affine transformations between the voxel, FSL and world coordinate systems
• Ability to load metadata from BIDS sidecar files

The Image class behaves differently to the nibabel.Nifti1Image. For example, when you create an Image object, the default behaviour is to load the image data into memory. This is configurable however; take a look at the documentation to explore all of the options.

Some simple image processing routines are also provided - these are covered below.

Creating images

It's easy to create an Image - you can create one from a file name:

from fsl.data.image import Image

stddir = op.expandvars('${FSLDIR}/data/standard/')

# load a FSL image - the file
# suffix is optional, just like
# in real FSL-land!
std1mm = Image(op.join(stddir, 'MNI152_T1_1mm'))
print(std1mm)

You can create an Image from an existing nibabel image:

# load a nibabel image, and
# convert it into an FSL image
nibimg = nib.load(op.join(stddir, 'MNI152_T1_1mm.nii.gz'))
std1mm = Image(nibimg)

Or you can create an Image from a numpy array:

data = np.zeros((182, 218, 182))
img = Image(data, xform=np.eye(4))

If you have generated some data from another Image (or from a nibabel.Nifti1Image) you can use the header option to set the header information on the new image:

img = Image(data, header=std1mm.header)

You can save an image to file via the save method:

img.save('empty')
!ls

Image objects have all of the attributes you might expect:

stddir = op.expandvars('${FSLDIR}/data/standard/')
std1mm = Image(op.join(stddir, 'MNI152_T1_1mm'))

print('name:         ', std1mm.name)
print('file:         ', std1mm.dataSource)
print('NIfTI version:', std1mm.niftiVersion)
print('ndim:         ', std1mm.ndim)
print('shape:        ', std1mm.shape)
print('dtype:        ', std1mm.dtype)
print('nvals:        ', std1mm.nvals)
print('pixdim:       ', std1mm.pixdim)

and a number of useful methods:

std2mm  = Image(op.join(stddir, 'MNI152_T1_2mm'))
mask2mm = Image(op.join(stddir, 'MNI152_T1_2mm_brain_mask'))

print(std1mm.sameSpace(std2mm))
print(std2mm.sameSpace(mask2mm))
print(std2mm.getAffine('voxel', 'world'))

An Image object is a high-level wrapper around a nibabel image object - you can always work directly with the nibabel object via the nibImage attribute:

print(std2mm)
print(std2mm.nibImage)

Working with image data

You can get the image data as a numpy array via the data attribute:

data = std2mm.data
print(data.min(), data.max())
ortho(data, (45, 54, 45))

Note that Image.data will give you the data in its underlying type, unlike the nibabel.get_fdata method, which up-casts image data to floating-point.

You can also read and write data directly via the Image object:

slc = std2mm[:, :, 45]
std2mm[0:10, :, :] *= 2

Doing so has some advantages that may or may not be useful, depending on your use-case:

• The image data will be kept on disk - only the parts that you access will be loaded into RAM (you will also need to passloadData=False when creating the Image to achieve this).
• The Image object will keep track of modifications to the data - this can be queried via the saveState attribute.

Loading other file types

The fsl.data package has a number of other classes for working with different types of FSL and neuroimaging data. Most of these are higher-level wrappers around the corresponding nibabel types:

• The fsl.data.bitmap.Bitmap class can be used to load a bitmap image (e.g. jpg, png, etc) and convert it to a NIfTI image.
• The fsl.data.dicom.DicomImage class uses dcm2niix to load NIfTI images contained within a DICOM directory^*.
• The fsl.data.mghimage.MGHImage class can be used too load .mgh/.mgz images (they are converted into NIfTI images).
• The fsl.data.dtifit module contains functions for loading and working with the output of the FSL dtifit tool.
• The fsl.data.featanalysis, fsl.data.featimage, and fsl.data.featdesign modules contain classes and functions for loading data from FEAT directories.
• Similarly, the fsl.data.melodicanalysis and fsl.data.melodicimage modules contain classes and functions for loading data from MELODIC directories.
• The fsl.data.gifti, fsl.data.freesurfer, and fsl.data.vtk modules contain functionality form loading surface data from GIfTI, freesurfer, and ASCII VTK files respectively.

^*You must make sure that dcm2niix is installed on your system in order to use this class.

NIfTI coordinate systems

The Image.getAffine method gives you access to affine transformations which can be used to convert coordinates between the different coordinate systems associated with a NIfTI image. Have some MNI coordinates you'd like to convert to voxels? Easy!

stddir = op.expandvars('${FSLDIR}/data/standard/')
std2mm = Image(op.join(stddir, 'MNI152_T1_2mm'))

mnicoords = np.array([[0,   0,  0],
                      [0, -18, 18]])

world2vox = std2mm.getAffine('world', 'voxel')
vox2world = std2mm.getAffine('voxel', 'world')

# Apply the world->voxel
# affine to the coordinates
voxcoords = (np.dot(world2vox[:3, :3], mnicoords.T)).T + world2vox[:3, 3]

The code above is a bit fiddly, so instead of figuring it out, you can just use the affine.transform function:

from fsl.transform.affine import transform

voxcoords = transform(mnicoords, world2vox)

# just to double check, let's transform
# those voxel coordinates back into world
# coordinates
backtomni = transform(voxcoords, vox2world)

for m, v, b in zip(mnicoords, voxcoords, backtomni):
    print(m, '->', v, '->', b)

The Image.getAffine method can give you transformation matrices between any of these coordinate systems:

• 'voxel': Image data voxel coordinates
• 'world': mm coordinates, defined by the sform/qform of an image
• 'fsl': The FSL coordinate system, used internally by many FSL tools (e.g. FLIRT)

Oh, that example was too easy I hear you say? Try this one on for size. Let's say we have run FEAT on some task fMRI data, and want to get the MNI coordinates of the voxel with peak activation.

This is what people used to use Featquery for, back in the un-enlightened days.

Let's start by identifying the voxel with the biggest t-statistic:

featdir = op.join('08_fslpy', 'fmri.feat')

tstat1 = Image(op.join(featdir, 'stats', 'tstat1')).data

# Recall from the numpy practical that
# argmax gives us a 1D index into a
# flattened view of the array. We can
# use the unravel_index function to
# convert it into a 3D index.
peakvox = np.abs(tstat1).argmax()
peakvox = np.unravel_index(peakvox, tstat1.shape)
print('Peak voxel coordinates for tstat1:', peakvox, tstat1[peakvox])

Now that we've got the voxel coordinates in functional space, we need to transform them into MNI space. FEAT provides a transformation which goes directly from functional to standard space, in the reg directory:

func2std = np.loadtxt(op.join(featdir, 'reg', 'example_func2standard.mat'))

But ... wait a minute ... this is a FLIRT matrix! We can't just plug voxel coordinates into a FLIRT matrix and expect to get sensible results, because FLIRT works in an internal FSL coordinate system, which is not quite 'voxel', and not quite 'world'. So we need to do a little more work. Let's start by loading our functional image, and the MNI152 template (the source and reference images of our FLIRT matrix):

func = Image(op.join(featdir, 'reg', 'example_func'))
std  = Image(op.expandvars(op.join('$FSLDIR', 'data', 'standard', 'MNI152_T1_2mm')))

Now we can use them to get affines which convert between all of the different coordinate systems - we're going to combine them into a single uber-affine, which transforms our functional-space voxels into MNI world coordinates via:

1. functional voxels -> FLIRT source space
2. FLIRT source space -> FLIRT reference space
3. FLIRT referece space -> MNI world coordinates

vox2fsl = func.getAffine('voxel', 'fsl')
fsl2mni = std .getAffine('fsl',   'world')

Combining two affines into one is just a simple dot-product. There is a concat() function which does this for us, for any number of affines:

from fsl.transform.affine import concat

# To combine affines together, we
# have to list them in reverse -
# linear algebra is *weird*.
funcvox2mni = concat(fsl2mni, func2std, vox2fsl)
print(funcvox2mni)

In the next section we will use the fsl.transform.flirt.fromFlirt function, which does all of the above for us.

So we've now got some voxel coordinates from our functional data, and an affine to transform into MNI world coordinates. The rest is easy:

mnicoords = transform(peakvox, funcvox2mni)
mnivoxels = transform(mnicoords, std.getAffine('world', 'voxel'))
mnivoxels = [int(round(v)) for v in mnivoxels]
print('Peak activation (MNI coordinates):', mnicoords)
print('Peak activation (MNI voxels):     ', mnivoxels)

Note that in the above example we are only applying a linear transformation into MNI space - in reality you would also want to apply your non-linear structural-to-standard transformation too. This is covered in the next section.

Transformations and resampling

Now, it's all well and good to look at t-statistic values and voxel coordinates and so on and so forth, but let's spice things up a bit and look at some images. Let's display our peak activation location in MNI space. To do this, we're going to resample our functional image into MNI space, so we can overlay it on the MNI template. This can be done using some handy functions from the fsl.transform.flirt and fsl.utils.image.resample modules.

Let's make sure we've got our source and reference images loaded:

featdir = op.join(op.join('08_fslpy', 'fmri.feat'))
tstat1  = Image(op.join(featdir, 'stats', 'tstat1'))
std     = Image(op.expandvars(op.join('$FSLDIR', 'data', 'standard', 'MNI152_T1_2mm')))

Now we'll load the example_func2standard FLIRT matrix, and adjust it so that it transforms from functional world coordinates into standard world coordinates - this is what is expected by the resampleToReference function, used below:

from fsl.transform.flirt import fromFlirt

func2std = np.loadtxt(op.join(featdir, 'reg', 'example_func2standard.mat'))
func2std = fromFlirt(func2std, tstat1, std, 'world', 'world')

Now we can use resampleToReference to resample our functional data into MNI152 space. This function returns a numpy array containing the resampled data, and an adjusted voxel-to-world affine transformation. But in this case, we know that the data will be aligned to MNI152, so we can ignore the affine:

from fsl.utils.image.resample import resampleToReference

std_tstat1 = resampleToReference(tstat1, std, func2std)[0]
std_tstat1 = Image(std_tstat1, header=std.header)

Now that we have our t-statistic image in MNI152 space, we can plot it in standard space using matplotlib:

stddir = op.expandvars('${FSLDIR}/data/standard/')
std2mm = Image(op.join(stddir, 'MNI152_T1_2mm'))

std_tstat1                 = std_tstat1.data
std_tstat1[std_tstat1 < 3] = 0

fig = ortho(std2mm.data, mnivoxels, cmap=plt.cm.gray)
fig = ortho(std_tstat1,  mnivoxels, cmap=plt.cm.inferno, fig=fig, cursor=True)

In the example above, we resampled some data from functional space into standard space using a linear transformation. But we all know that this is not how things work in the real world - linear transformations are for kids. The real world is full of lions and tigers and bears and warp fields.

The fsl.transform.fnirt and fsl.transform.nonlinear modules contain classes and functions for working with FNIRT-style warp fields (modules for working with lions, tigers, and bears are still under development).

Let's imagine that we have defined an ROI in MNI152 space, and we want to project it into the space of our functional data. We can do this by combining the nonlinear structural to standard registration produced by FNIRT with the linear functional to structural registration generated by FLIRT. First of all, we'll load images from each of the functional, structural, and standard spaces:

featdir = op.join('08_fslpy', 'fmri.feat')
func    = Image(op.join(featdir, 'reg', 'example_func'))
struc   = Image(op.join(featdir, 'reg', 'highres'))
std     = Image(op.expandvars(op.join('$FSLDIR', 'data', 'standard', 'MNI152_T1_2mm')))

Now, let's say we have obtained our seed location in MNI152 coordinates. Let's convert them to MNI152 voxels just to double check:

seedmni    = [-48, -74, -9]
seedmnivox = transform(seedmni, std.getAffine('world', 'voxel'))
ortho(std.data, seedmnivox, cursor=True)

Now we'll load the FNIRT warp field, which encodes a nonlinear transformation from structural space to standard space. FNIRT warp fields are often stored as coefficient fields to reduce the file size, but in order to use it, we must convert the coefficient field into a deformation (a.k.a. displacement) field. This takes a few seconds:

from fsl.transform.fnirt     import readFnirt
from fsl.transform.nonlinear import coefficientFieldToDeformationField

struc2std = readFnirt(op.join(featdir, 'reg', 'highres2standard_warp'), struc, std)
struc2std = coefficientFieldToDeformationField(struc2std)

We'll also load our FLIRT functional to structural transformation, adjust it so that it transforms between voxel coordinate systems instead of the FSL coordinate system, and invert so it can transform from structural voxels to functional voxels:

from fsl.transform.affine import invert
func2struc = np.loadtxt(op.join(featdir, 'reg', 'example_func2highres.mat'))
func2struc = fromFlirt(func2struc, func, struc, 'voxel', 'voxel')
struc2func = invert(func2struc)

Now we can transform our seed coordinates from MNI152 space into functional space in two stages. First, we'll use our deformation field to transform from MNI152 space into structural space:

seedstruc = struc2std.transform(seedmni, 'world', 'voxel')
seedfunc  = transform(seedstruc, struc2func)

print('Seed location in MNI coordinates:  ', seedmni)
print('Seed location in functional voxels:', seedfunc)
ortho(func.data, seedfunc, cursor=True)

FNIRT warp fields kind of work backwards - we can use them to transform reference coordinates into source coordinates, but would need to invert the warp field using invwarp if we wanted to transform from source coordinates into referemce coordinates.

Of course, we can also use our deformation field to resample an image from structural space into MNI152 space. The applyDeformation function takes an Image and a DeformationField, and returns a numpy array containing the resampled data.

from fsl.transform.nonlinear import applyDeformation

strucmni = applyDeformation(struc, struc2std)

# remove low-valued voxels,
# just for visualisation below
strucmni[strucmni < 1] = 0

fig = ortho(std.data, [45, 54, 45], cmap=plt.cm.gray)
fig = ortho(strucmni, [45, 54, 45], fig=fig)

The premat option to applyDeformation can be used to specify our linear functional to structural transformation, and hence resample a functional image into MNI152 space:

tstatmni = applyDeformation(tstat1, struc2std, premat=func2struc)
tstatmni[tstatmni < 3] = 0

fig = ortho(std.data, [45, 54, 45], cmap=plt.cm.gray)
fig = ortho(tstatmni, [45, 54, 45], fig=fig)

There are a few other useful functions tucked away in the fsl.utils.image and fsl.transform packages, with more to be added in the future.

FSL wrapper functions

The fsl.wrappers package is the home of "wrapper" functions for a range of FSL tools. You can use them to call an FSL tool from Python code, without having to worry about constructing a command-line, or saving/loading input/output images.

The fsl.wrappers functions also allow you to submit jobs to be run on the cluster - this is described below.

You can use the FSL wrapper functions with file names, similar to calling the corresponding tool via the command-line:

from fsl.wrappers import robustfov

robustfov('08_fslpy/bighead', 'bighead_cropped')

render('08_fslpy/bighead bighead_cropped -cm blue')

The fsl.wrappers functions strive to provide an interface which is as close as possible to the command-line tool - most functions use positional arguments for required options, and keyword arguments for all other options, with argument names equivalent to command line option names. For example, the usage for the command-line bet tool is as follows:

Usage:    bet <input> <output> [options]

Main bet2 options:
  -o          generate brain surface outline overlaid onto original image
  -m          generate binary brain mask
  -s          generate approximate skull image
  -n          don't generate segmented brain image output
  -f <f>      fractional intensity threshold (0->1); default=0.5; smaller values give larger brain outline estimates
  -g <g>      vertical gradient in fractional intensity threshold (-1->1); default=0; positive values give larger brain outline at bottom, smaller at top
  -r <r>      head radius (mm not voxels); initial surface sphere is set to half of this
  -c <x y z>  centre-of-gravity (voxels not mm) of initial mesh surface.
...

So to use the bet() wrapper function, pass <input> and <output> as positional arguments, and pass the additional options as keyword arguments:

from fsl.wrappers import bet

bet('bighead_cropped', 'bighead_cropped_brain', f=0.3, m=True, s=True)

render('bighead_cropped             -b 40 '
       'bighead_cropped_brain       -cm hot '
       'bighead_cropped_brain_skull -ot mask -mc 0.4 0.4 1 '
       'bighead_cropped_brain_mask  -ot mask -mc 0   1   0 -o -w 5')

Some FSL commands accept arguments which cannot be used as Python identifiers - for example, the -2D option to flirt cannot be used as an identifier in Python, because it begins with a number. In situations like this, an alias is used. So to set the -2D option to flirt, you can do this:

# "twod" applies the -2D flag
flirt('source.nii.gz', 'ref.nii.gz', omat='src2ref.mat', twod=True)

Some of the fsl.wrappers functions also support aliases which may make your code more readable. For example, when calling bet, you can use either m=True or mask=True to apply the -m command line flag.

In-memory images

It can be quite awkward to combine image processing with FSL tools and image processing in Python. The fsl.wrappers package tries to make this a little easier for you - if you are working with image data in Python, you can pass Image or nibabel objects directly into fsl.wrappers functions - they will be automatically saved to temporary files and passed to the underlying FSL command:

cropped = Image('bighead_cropped')

bet(cropped, 'bighead_cropped_brain')

betted = Image('bighead_cropped_brain')

fig = ortho(cropped.data, (80, 112, 85), cmap=plt.cm.gray)
fig = ortho(betted .data, (80, 112, 85), cmap=plt.cm.inferno, fig=fig)

Loading outputs into Python

By using the special fsl.wrappers.LOAD symbol, you can also have any output files produced by the tool automatically loaded into memory for you:

from fsl.wrappers import LOAD

cropped = Image('bighead_cropped')

# The loaded result is called "output",
# because that is the name of the
# argument in the bet wrapper function.
betted  = bet(cropped, LOAD).output

fig = ortho(cropped.data, (80, 112, 85), cmap=plt.cm.gray)
fig = ortho(betted .data, (80, 112, 85), cmap=plt.cm.inferno, fig=fig)

You can use the LOAD symbol for any output argument - any output files which are loaded will be available through the return value of the wrapper function:

from fsl.wrappers import flirt

std2mm   = Image(op.expandvars(op.join('$FSLDIR', 'data', 'standard', 'MNI152_T1_2mm')))
tstat1   = Image(op.join('08_fslpy', 'fmri.feat', 'stats', 'tstat1'))
func2std = np.loadtxt(op.join('08_fslpy', 'fmri.feat', 'reg', 'example_func2standard.mat'))

aligned = flirt(tstat1, std2mm, applyxfm=True, init=func2std, out=LOAD)

# Here the resampled tstat image
# is called "out", because that
# is the name of the flirt argument.
aligned = aligned.out.data
aligned[aligned < 1] = 0

peakvox = np.abs(aligned).argmax()
peakvox = np.unravel_index(peakvox, aligned.shape)

fig = ortho(std2mm .data, peakvox, cmap=plt.cm.gray)
fig = ortho(aligned.data, peakvox, cmap=plt.cm.inferno, fig=fig, cursor=True)

For tools like bet and fast, which expect an output prefix or basename, you can just set the prefix to LOAD - all output files with that prefix will be available in the object that is returned:

img    = Image('bighead_cropped')
betted = bet(img, LOAD, f=0.3, mask=True)

fig = ortho(img               .data, (80, 112, 85), cmap=plt.cm.gray)
fig = ortho(betted.output     .data, (80, 112, 85), cmap=plt.cm.inferno, fig=fig)
fig = ortho(betted.output_mask.data, (80, 112, 85), cmap=plt.cm.summer,  fig=fig, alpha=0.5)

The fslmaths wrapper

Most of the fsl.wrappers functions aim to provide an interface which is as close as possible to the underlying FSL tool. Ideally, if you read the command-line help for a tool, you should be able to figure out how to use the corresponding wrapper function. The wrapper for the fslmaths command is a little different, however. It provides more of an object-oriented interface, which is hopefully a little easier to use from within Python.

You can apply an fslmaths operation by specifying the input image, chaining method calls together, and finally calling the run() method. For example:

from fsl.wrappers import fslmaths
fslmaths('bighead_cropped')            \
  .mas(  'bighead_cropped_brain_mask') \
  .run(  'bighead_cropped_brain')

render('bighead_cropped bighead_cropped_brain -cm hot')

Of course, you can also use the fslmaths wrapper with in-memory images:

wholehead   = Image('bighead_cropped')
brainmask   = Image('bighead_cropped_brain_mask')

eroded      = fslmaths(brainmask).ero().ero().run()
erodedbrain = fslmaths(wholehead).mas(eroded).run()

fig = ortho(wholehead  .data, (80, 112, 85), cmap=plt.cm.gray)
fig = ortho(brainmask  .data, (80, 112, 85), cmap=plt.cm.summer,  fig=fig)
fig = ortho(erodedbrain.data, (80, 112, 85), cmap=plt.cm.inferno, fig=fig)

The FileTree

The fsl.utils.filetree library provides functionality which allows you to work with structured data directories, such as HCP or BIDS datasets. You can use filetree for both reading and for creating datasets.

This practical gives a very brief introduction to the filetree library - refer to the full documentation to get a feel for how powerful it can be.

Describing your data

To introduce filetree, we'll begin with a small example. Imagine that we have a dataset which looks like this:

mydata
├── sub_A
│   ├── ses_1
│   │   └── T1w.nii.gz
│   ├── ses_2
│   │   └── T1w.nii.gz
│   └── T2w.nii.gz
├── sub_B
│   ├── ses_1
│   │   └── T1w.nii.gz
│   ├── ses_2
│   │   └── T1w.nii.gz
│   └── T2w.nii.gz
└── sub_C
    ├── ses_1
    │   └── T1w.nii.gz
    ├── ses_2
    │   └── T1w.nii.gz
    └── T2w.nii.gz

(Run the code cell below to create a dummy data set with the above structure):

%%bash
for sub in A B C; do
  subdir=mydata/sub_$sub/
  mkdir -p $subdir
  cp $FSLDIR/data/standard/MNI152_T1_2mm.nii.gz $subdir/T2w.nii.gz
  for ses in 1 2; do
    sesdir=$subdir/ses_$ses/
    mkdir $sesdir
    cp $FSLDIR/data/standard/MNI152_T1_2mm.nii.gz $sesdir/T1w.nii.gz
  done
done

To use filetree with this dataset, we must first describe its structure - we do this by creating a .tree file:

%%writefile mydata.tree
sub_{subject}
  T2w.nii.gz
  ses_{session}
    T1w.nii.gz

A .tree file is simply a description of the structure of your data directory - it describes the file types (also known as templates) which are present in the dataset (T1w and T2w), and the variables which are implicitly present in the structure of the dataset (subject and session).

Using the FileTree

Now that we have a .tree file which describe our data, we can create a FileTree to work with it:

from fsl.utils.filetree import FileTree

# Create a FileTree, giving
# it our tree specification,
# and the path to our data.
tree = FileTree.read('mydata.tree', 'mydata')

We can list all of the T1 images via the FileTree.get_all method. The glob_vars='all' option tells the FileTree to fill in the T1w template with all possible combinations of variables. The FileTree.extract_variables method accepts a file path, and gives you back the variable values contained within:

for t1file in tree.get_all('T1w', glob_vars='all'):
    fvars = tree.extract_variables('T1w', t1file)
    print(t1file, fvars)

The FileTree.update method allows you to "fill in" variable values; it returns a new FileTree object which can be used on a selection of the data set:

treeA = tree.update(subject='A')
for t1file in treeA.get_all('T1w', glob_vars='all'):
    fvars = treeA.extract_variables('T1w', t1file)
    print(t1file, fvars)

Building a processing pipeline with FileTree

Let's say we want to run BET on all of our T1 images. Let's start by modifying our .tree definition to include the BET outputs:

%%writefile mydata.tree
sub_{subject}
  T2w.nii.gz
  ses_{session}
    T1w.nii.gz
    T1w_brain.nii.gz
    T1w_brain_mask.nii.gz

Now we can use the FileTree to generate the relevant file names for us, which we can then pass on to BET. Here we'll use the FileTree.get_all_trees method to create a sub-tree for each subject and each session:

from fsl.wrappers import bet
tree = FileTree.read('mydata.tree', 'mydata')
for subtree in tree.get_all_trees('T1w', glob_vars='all'):
    t1file  = subtree.get('T1w')
    t1brain = subtree.get('T1w_brain')
    print('Running BET: {} -> {} ...'.format(t1file, t1brain))
    bet(t1file, t1brain, mask=True)
print('Done!')

example = tree.update(subject='A', session='1')
render('{} {} -ot mask -o -w 2 -mc 0 1 0'.format(
    example.get('T1w'),
    example.get('T1w_brain_mask')))

The FileTreeQuery

The filetree module contains another class called the FileTreeQuery, which provides an interface that is more convenient if you are reading data from large datasets with many different file types and variables.

When you create a FileTreeQuery, it scans the entire data directory and identifies all of the values that are present for each variable defined in the .tree file:

from fsl.utils.filetree import FileTreeQuery
tree = FileTree.read('mydata.tree', 'mydata')
query = FileTreeQuery(tree)
print('T1w variables:', query.variables('T1w'))
print('T2w variables:', query.variables('T2w'))

The FileTreeQuery.query method will return the paths to all existing files which match a set of variable values:

print('All files for subject A')
for template in query.templates:
    print('  {} files:'.format(template))
    for match in query.query(template, subject='A'):
        print('   ', match.filename)

Calling shell commands

The fsl.utils.run module provides the run and runfsl functions, which are wrappers around the built-in subprocess library.

The default behaviour of run is to return the standard output of the command:

from fsl.utils.run import run

# You can pass the command
# and its arguments as a single
# string, or as a sequence
print('Lines in this notebook:', run('wc -l 08_fslpy.md').strip())
print('Words in this notebook:', run(['wc', '-w', '08_fslpy.md']).strip())

But you can control what run returns, depending on your needs. Let's create a little script to demonstrate the options:

%%writefile mycmd
#!/usr/bin/env bash
exitcode=$1

echo "Standard output!"
echo "Standard error :(" >&2

exit $exitcode

And let's not forget to make it executable:

!chmod a+x mycmd

You can use the stdout, stderr and exitcode arguments to control the return value:

print('run("./mycmd 0"):                                          ',
       run("./mycmd 0").strip())
print('run("./mycmd 0", stdout=False):                            ',
       run("./mycmd 0", stdout=False))
print('run("./mycmd 0",                            exitcode=True):',
       run("./mycmd 0",                            exitcode=True))
print('run("./mycmd 0", stdout=False,              exitcode=True):',
       run("./mycmd 0", stdout=False,              exitcode=True))
print('run("./mycmd 0",               stderr=True):               ',
       run("./mycmd 0",               stderr=True))
print('run("./mycmd 0", stdout=False, stderr=True):               ',
       run("./mycmd 0", stdout=False, stderr=True).strip())
print('run("./mycmd 0",               stderr=True, exitcode=True):',
       run("./mycmd 0",               stderr=True, exitcode=True))

print('run("./mycmd 1",                            exitcode=True):',
       run("./mycmd 1",                            exitcode=True))
print('run("./mycmd 1", stdout=False,              exitcode=True):',
       run("./mycmd 1", stdout=False,              exitcode=True))

So if only one of stdout, stderr, or exitcode is True, run will only return the corresponding value. Otherwise run will return a tuple which contains the requested outputs.

If you run a command which returns a non-0 exit code, the default behaviour (if you don't set exitcode=True) is for a RuntimeError to be raised:

run("./mycmd 99")

The runfsl function

The runfsl function is a wrapper around run which simply makes sure that the command you are calling is inside the $FSLDIR/bin/ directory. It has the same usage as the run function:

from fsl.utils.run import runfsl
runfsl('bet bighead_cropped bighead_cropped_brain')
runfsl('fslroi bighead_cropped_brain bighead_slices 0 -1 0 -1 90 3')
runfsl('fast -o bighead_fast bighead_slices')

render('-vl 80 112 91 -xh -yh '
       'bighead_cropped '
       'bighead_slices.nii.gz -cm brain_colours_1hot -b 30 '
       'bighead_fast_seg.nii.gz -ot label -o')

Submitting to the cluster

Both the run and runfsl accept an argument called submit, which allows you to submit jobs to be executed on the cluster via the FSL fsl_sub command.

Cluster submission is handled by the fsl.utils.fslsub module - it contains lower level functions for managing and querying jobs that have been submitted to the cluster. The functions defined in this module can be used directly if you have more complicated requirements.

The semantics of the run and runfsl functions are slightly different when you use the submit option - when you submit a job, the run/runfsl functions will return immediately, and will return a string containing the job ID:

jobid  = run('ls', submit=True)
print('Job ID:', jobid)

Once the job finishes, we should be able to read the usual .o and .e files:

stdout = f'ls.o{jobid}'
print('Job output')
print(open(stdout).read())

All of the fsl.wrappers functions also accept the submit argument:

jobid = bet('08_fslpy/bighead', 'bighead_brain', submit=True)
print('Job ID:', jobid)

But an error will occur if you try to pass in-memory images, or LOAD any outputs when you call a wrapper function with submit=True.

After submitting a job, you can use the wait function to wait until a job has completed:

from fsl.utils.run import wait
jobid = bet('08_fslpy/bighead', 'bighead_brain', submit=True)
print('Job ID:', jobid)
wait(jobid)
print('Done!')
render('08_fslpy/bighead bighead_brain -cm hot')

When you use submit=True, you can also specify cluster submission options - you can include any arguments that are accepted by the fslsub.submit function

jobs = []
jobs.append(runfsl('robustfov -i 08_fslpy/bighead -r bighead_cropped',    submit=True, queue='short.q'))
jobs.append(runfsl('bet bighead_cropped bighead_brain',                   submit=True, queue='short.q', wait_for=jobs[-1]))
jobs.append(runfsl('fslroi bighead_brain bighead_slices 0 -1 111 3 0 -1', submit=True, queue='short.q', wait_for=jobs[-1]))
jobs.append(runfsl('fast -o bighead_fast bighead_slices',                 submit=True, queue='short.q', wait_for=jobs[-1]))
print('Waiting for', jobs, '...')
wait(jobs)

render('-vl 80 112 91 -xh -zh -hc '
       'bighead_brain '
       'bighead_slices.nii.gz -cm brain_colours_1hot -b 30 '
       'bighead_fast_seg.nii.gz -ot label -o')

Redirecting output

The log option, accepted by both run and fslrun, allows for more fine-grained control over what is done with the standard output and error streams.

You can use 'tee' to redirect the standard output and error streams of the command to the standard output and error streams of the calling command (your python script):

print('Teeing:')
_ = run('./mycmd 0', log={'tee' : True})

Or you can use 'stdout' and 'stderr' to redirect the standard output and error streams of the command to files:

with open('stdout.log', 'wt') as o, \
     open('stderr.log', 'wt') as e:
     run('./mycmd 0', log={'stdout' : o, 'stderr' : e})
print('\nRedirected stdout:')
!cat stdout.log
print('\nRedirected stderr:')
!cat stderr.log

Finally, you can use 'cmd' to log the command itself to a file (useful for pipeline logging):

with open('commands.log', 'wt') as cmdlog:
     run('./mycmd 0',         log={'cmd' : cmdlog})
     run('wc -l 08_fslpy.md', log={'cmd' : cmdlog})

print('\nCommand log:')
!cat commands.log

FSL atlases

The fsl.data.atlases module provides access to all of the atlas images that are stored in the $FSLDIR/data/atlases/ directory of a standard FSL installation. It can be used to load and query probabilistic and label-based atlases.

The atlases module needs to be initialised using the rescanAtlases function:

import fsl.data.atlases as atlases
atlases.rescanAtlases()

Querying atlases

You can list all of the available atlases using listAtlases:

for desc in atlases.listAtlases():
    print(desc)

listAtlases returns a list of AtlasDescription objects, each of which contains descriptive information about one atlas. You can retrieve the AtlasDescription for a specific atlas via the getAtlasDescription function:

desc = atlases.getAtlasDescription('harvardoxford-cortical')
print(desc.name)
print(desc.atlasID)
print(desc.specPath)
print(desc.atlasType)

Each AtlasDescription maintains a list of AtlasLabel objects, each of which represents one region that is defined in the atlas. You can access all of the AtlasLabel objects via the labels attribute:

for lbl in desc.labels[:5]:
    print(lbl)

Or you can retrieve a specific label using the find method:

# search by region name
print(desc.find(name='Occipital Pole'))

# or by label value
print(desc.find(value=48))

Loading atlas images

The loadAtlas function can be used to load the atlas image:

# For probabilistic atlases, you
# can ask for the 3D ROI image
# by setting loadSummary=True.
# You can also request a
# resolution - by default the
# highest resolution version
# will be loaded.
lblatlas = atlases.loadAtlas('harvardoxford-cortical',
                             loadSummary=True,
                             resolution=2)

# By default you will get the 4D
# probabilistic atlas image (for
# atlases for which this is
# available).
probatlas = atlases.loadAtlas('harvardoxford-cortical',
                              resolution=2)

print(lblatlas)
print(probatlas)

Working with atlases

Both LabelAtlas and ProbabilisticAtlas objects have a method called get, which can be used to extract ROI images for a specific region:

stddir = op.expandvars('${FSLDIR}/data/standard/')
std2mm = Image(op.join(stddir, 'MNI152_T1_2mm'))

frontal = lblatlas.get(name='Frontal Pole').data
frontal = np.ma.masked_where(frontal < 1, frontal)

fig = ortho(std2mm.data, (45, 54, 45), cmap=plt.cm.gray)
fig = ortho(frontal,     (45, 54, 45), cmap=plt.cm.winter, fig=fig)

Calling get on a ProbabilisticAtlas will return a probability image:

stddir = op.expandvars('${FSLDIR}/data/standard/')
std2mm = Image(op.join(stddir, 'MNI152_T1_2mm'))

frontal = probatlas.get(name='Frontal Pole').data
frontal = np.ma.masked_where(frontal < 1, frontal)

fig = ortho(std2mm.data, (45, 54, 45), cmap=plt.cm.gray)
fig = ortho(frontal,     (45, 54, 45), cmap=plt.cm.inferno, fig=fig)

The get method can be used to retrieve an image for a region by:

• an AtlasLabel object
• The region index
• The region value
• The region name

LabelAtlas objects have a method called label, which can be used to interrogate the atlas at specific locations:

# The label method accepts 3D
# voxel or world coordinates
val = lblatlas.label((25, 52, 43), voxel=True)
lbl = lblatlas.find(value=val)
print('Region at voxel [25, 52, 43]: {} [{}]'.format(val, lbl.name))


# or a 3D weighted or binary mask
mask = np.zeros(lblatlas.shape)
mask[30:60, 30:60, 30:60] = 1
mask = Image(mask, header=lblatlas.header)

lbls, props = lblatlas.label(mask)
print('Labels in mask:')
for lbl, prop in zip(lbls, props):
    lblname = lblatlas.find(value=lbl).name
    print('  {} [{}]: {:0.2f}%'.format(lbl, lblname, prop))

ProbabilisticAtlas objects have an analogous method called values:

vals = probatlas.values((25, 52, 43), voxel=True)
print('Regions at voxel [25, 52, 43]:')
for idx, val in enumerate(vals):
    if val > 0:
        lbl = probatlas.find(index=idx)
        print('  {} [{}]: {:0.2f}%'.format(lbl.value, lbl.name, val))

print('Average proportions of regions within mask:')
vals = probatlas.values(mask)
for idx, val in enumerate(vals):
    if val > 0:
        lbl = probatlas.find(index=idx)
        print('  {} [{}]: {:0.2f}%'.format(lbl.value, lbl.name, val))