RF: fix render for macOS

applications/fslpy/fslpy.ipynb

 %% Cell type:markdown id: tags:
 
 # `fslpy`
 
 
 > *Note*: This practical assumes that you have FSL 6.0.4 or newer installed.
 
 
 [`fslpy`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/) 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](#the-image-class-and-other-data-types)
   * [Creating images](#creating-images)
   * [Working with image data](#working-with-image-data)
   * [Loading other file types](#loading-other-file-types)
   * [NIfTI coordinate systems](#nifti-coordinate-systems)
   * [Transformations and resampling](#transformations-and-resampling)
 * [FSL wrapper functions](#fsl-wrapper-functions)
   * [In-memory images](#in-memory-images)
   * [Loading outputs into Python](#loading-outputs-into-python)
   * [The `fslmaths` wrapper](#the-fslmaths-wrapper)
 * [The `FileTree`](#the-filetree)
   * [Describing your data](#describing-your-data)
   * [Using the `FileTree`](#using-the-filetree)
   * [Building a processing pipeline with `FileTree`](#building-a-processing-pipeline-with-filetree)
   * [The `FileTreeQuery`](#the-filetreequery)
 * [Calling shell commands](#calling-shell-commands)
   * [The `runfsl` function](#the-runfsl-function)
   * [Submitting to the cluster](#submitting-to-the-cluster)
   * [Redirecting output](#redirecting-output)
 * [FSL atlases](#fsl-atlases)
   * [Querying atlases](#querying-atlases)
   * [Loading atlas images](#loading-atlas-images)
   * [Working with atlases](#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:
 
 %% Cell type:code id: tags:
 
 ``` 
 %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)
 ```
 
 %% Cell type:markdown id: tags:
 
 And a little function that we can use to generate a simple orthographic plot:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 And another function which uses FSLeyes for more complex plots:
 
 %% Cell type:code id: tags:
 
 ``` 
 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:
+    except (ImportError, AttributeError):
         # 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')
 ```
 
 %% Cell type:markdown id: tags:
 
 <a class="anchor" id="the-image-class-and-other-data-types"></a>
 ## The `Image` class, and other data types
 
 
 The
 [`fsl.data.image`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.image.html#fsl.data.image.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](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.image.html#fsl.data.image.Image)
 > to explore all of the options.
 
 
 Some simple image processing routines are also provided - these are covered
 [below](#image-processing).
 
 
 <a class="anchor" id="creating-images"></a>
 ### Creating images
 
 
 It's easy to create an `Image` - you can create one from a file name:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 You can create an `Image` from an existing `nibabel` image:
 
 %% Cell type:code id: tags:
 
 ``` 
 # 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)
 print(std1mm)
 ```
 
 %% Cell type:markdown id: tags:
 
 Or you can create an `Image` from a `numpy` array:
 
 %% Cell type:code id: tags:
 
 ``` 
 data = np.zeros((182, 218, 182))
 img = Image(data, xform=np.eye(4))
 print(img)
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 img = Image(data, header=std1mm.header)
 ```
 
 %% Cell type:markdown id: tags:
 
 You can save an image to file via the `save` method:
 
 %% Cell type:code id: tags:
 
 ``` 
 img.save('empty')
 !ls
 ```
 
 %% Cell type:markdown id: tags:
 
 `Image` objects have all of the attributes you might expect:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 and a number of useful methods:
 
 %% Cell type:code id: tags:
 
 ``` 
 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'))
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 print(std2mm)
 print(std2mm.nibImage)
 ```
 
 %% Cell type:markdown id: tags:
 
 <a class="anchor" id="working-with-image-data"></a>
 ### Working with image data
 
 
 You can get the image data as a `numpy` array via the `data` attribute:
 
 %% Cell type:code id: tags:
 
 ``` 
 data = std2mm.data
 print(data.min(), data.max())
 ortho(data, (45, 54, 45))
 ```
 
 %% Cell type:markdown id: tags:
 
 > 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 slc = std2mm[:, :, 45]
 std2mm[0:10, :, :] *= 2
 ```
 
 %% Cell type:markdown id: tags:
 
 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 pass`loadData=False` when creating
    the `Image` to achieve this).
  - The `Image` object will keep track of modifications to the data (only whether
    or not the image has been modified) - this can be queried via the `saveState`
    attribute.
 
 
 <a class="anchor" id="loading-other-file-types"></a>
 ### Loading other file types
 
 
 The
 [`fsl.data`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.html#module-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`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.bitmap.html)
   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`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.dicom.html)
   class uses `dcm2niix` to load NIfTI images contained within a DICOM
   directory<sup>*</sup>.
 * The
   [`fsl.data.mghimage.MGHImage`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.mghimage.html)
   class can be used to load `.mgh`/`.mgz` images (they are converted into
   NIfTI images).
 * The
   [`fsl.data.dtifit`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.dtifit.html)
   module contains functions for loading and working with the output of the
   FSL `dtifit` tool.
 * The
   [`fsl.data.featanalysis`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.featanalysis.html),
   [`fsl.data.featimage`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.featimage.html),
   and
   [`fsl.data.featdesign`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.featdesign.html)
   modules contain classes and functions for loading data from FEAT
   directories.
 * Similarly, the
   [`fsl.data.melodicanalysis`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.melodicanalysis.html)
   and
   [`fsl.data.melodicimage`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.melodicimage.html)
   modules contain classes and functions for loading data from MELODIC
   directories.
 * The
   [`fsl.data.gifti`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.gifti.html),
   [`fsl.data.freesurfer`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.freesurfer.html),
   and
   [`fsl.data.vtk`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.vtk.html)
   modules contain functionality form loading surface data from GIfTI,
   freesurfer, and ASCII VTK files respectively.
 
 
 > <sup>*</sup>You must make sure that
 > [`dcm2niix`](https://github.com/rordenlab/dcm2niix/) is installed on your
 > system in order to use this class.
 
 
 <a class="anchor" id="nifti-coordinate-systems"></a>
 ### 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!
 
 %% Cell type:code id: tags:
 
 ``` 
 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]
 ```
 
 %% Cell type:markdown id: tags:
 
 The code above is a bit fiddly, so instead of figuring it out, you can just
 use the
 [`affine.transform`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.transform.affine.html#fsl.transform.affine.transform)
 function:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 > 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 featdir = '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])
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 func2std = np.loadtxt(op.join(featdir, 'reg', 'example_func2standard.mat'))
 ```
 
 %% Cell type:markdown id: tags:
 
 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):
 
 %% Cell type:code id: tags:
 
 ``` 
 func = Image(op.join(featdir, 'reg', 'example_func'))
 std  = Image(op.expandvars(op.join('$FSLDIR', 'data', 'standard', 'MNI152_T1_2mm')))
 ```
 
 %% Cell type:markdown id: tags:
 
 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
 
 %% Cell type:code id: tags:
 
 ``` 
 vox2fsl = func.getAffine('voxel', 'fsl')
 fsl2mni = std .getAffine('fsl',   'world')
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 > In the next section we will use the
 > [`fsl.transform.flirt.fromFlirt`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.transform.flirt.html#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:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 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.
 
 
 <a class="anchor" id="transformations-and-resampling"></a>
 ### 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`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.transform.flirt.html)
 and
 [`fsl.utils.image.resample`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.utils.image.resample.html)
 modules.
 
 
 Let's make sure we've got our source and reference images loaded:
 
 %% Cell type:code id: tags:
 
 ``` 
 featdir =  'fmri.feat'
 tstat1  = Image(op.join(featdir, 'stats', 'tstat1'))
 std     = Image(op.expandvars(op.join('$FSLDIR', 'data', 'standard', 'MNI152_T1_2mm')))
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 from fsl.transform.flirt import fromFlirt
 
 func2std = np.loadtxt(op.join(featdir, 'reg', 'example_func2standard.mat'))
 func2std = fromFlirt(func2std, tstat1, std, 'world', 'world')
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 from fsl.utils.image.resample import resampleToReference
 
 std_tstat1 = resampleToReference(tstat1, std, func2std)[0]
 std_tstat1 = Image(std_tstat1, header=std.header)
 ```
 
 %% Cell type:markdown id: tags:
 
 Now that we have our t-statistic image in MNI152 space, we can plot it in
 standard space using `matplotlib`:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 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`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.transform.fnirt.html#fsl.transform.fnirt.fromFnirt)
 and
 [`fsl.transform.nonlinear`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.transform.nonlinear.html)
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 featdir = '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')))
 ```
 
 %% Cell type:markdown id: tags:
 
 Now, let's say we have obtained our seed location in MNI152 coordinates. Let's
 convert them to MNI152 voxels just to double check:
 
 %% Cell type:code id: tags:
 
 ``` 
 seedmni    = [-48, -74, -9]
 seedmnivox = transform(seedmni, std.getAffine('world', 'voxel'))
 ortho(std.data, seedmnivox, cursor=True)
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 > 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.
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 There are a few other useful functions tucked away in the
 [`fsl.utils.image`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.utils.image.html)
 and
 [`fsl.transform`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.transform.html)
 packages, with more to be added in the future.
 
 
 <a class="anchor" id="fsl-wrapper-functions"></a>
 ## FSL wrapper functions
 
 
 The
 [fsl.wrappers](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.wrappers.html)
 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](#submitting-to-the-cluster).
 
 
 You can use the FSL wrapper functions with file names, similar to calling the
 corresponding tool via the command-line:
 
 %% Cell type:code id: tags:
 
 ``` 
 from fsl.wrappers import robustfov
 
 robustfov('bighead', 'bighead_cropped')
 
 render('bighead bighead_cropped -cm blue')
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 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')
 ```
 
 %% Cell type:markdown id: tags:
 
 > 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.
 
 
 <a class="anchor" id="in-memory-images"></a>
 ### 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 <a class="anchor" id="loading-outputs-into-python"></a>
 ### 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 from fsl.wrappers import flirt
 
 std2mm   = Image(op.expandvars(op.join('$FSLDIR', 'data', 'standard', 'MNI152_T1_2mm')))
 tstat1   = Image(op.join('fmri.feat', 'stats', 'tstat1'))
 func2std = np.loadtxt(op.join('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)
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 <a class="anchor" id="the-fslmaths-wrapper"></a>
 ### 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 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')
 ```
 
 %% Cell type:markdown id: tags:
 
 Of course, you can also use the `fslmaths` wrapper with in-memory images:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 <a class="anchor" id="the-filetree"></a>
 ## The `FileTree`
 
 
 The
 [`fsl.utils.filetree`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.utils.filetree.html)
 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](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.utils.filetree.html)
 to get a feel for how powerful it can be.
 
 
 <a class="anchor" id="describing-your-data"></a>
 ### 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):
 
 %% Cell type:code id: tags:
 
 ``` 
 %%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
 ```
 
 %% Cell type:markdown id: tags:
 
 To use `filetree` with this dataset, we must first describe its structure - we
 do this by creating a `.tree` file:
 
 %% Cell type:code id: tags:
 
 ``` 
 %%writefile mydata.tree
 sub_{subject}
   T2w.nii.gz
   ses_{session}
     T1w.nii.gz
 ```
 
 %% Cell type:markdown id: tags:
 
 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`).
 
 
 <a class="anchor" id="using-the-filetree"></a>
 ### Using the `FileTree`
 
 
 Now that we have a `.tree` file which describe our data, we can create a
 `FileTree` to work with it:
 
 %% Cell type:code id: tags:
 
 ``` 
 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')
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 for t1file in tree.get_all('T1w', glob_vars='all'):
     fvars = tree.extract_variables('T1w', t1file)
     print(t1file, fvars)
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 treeA = tree.update(subject='A')
 for t1file in treeA.get_all('T1w', glob_vars='all'):
     fvars = treeA.extract_variables('T1w', t1file)
     print(t1file, fvars)
 ```
 
 %% Cell type:markdown id: tags:
 
 <a class="anchor" id="building-a-processing-pipeline-with-filetree"></a>
 ### 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 %%writefile mydata.tree
 sub_{subject}
   T2w.nii.gz
   ses_{session}
     T1w.nii.gz
     T1w_brain.nii.gz
     T1w_brain_mask.nii.gz
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 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')))
 ```
 
 %% Cell type:markdown id: tags:
 
 <a class="anchor" id="the-filetreequery"></a>
 ### The `FileTreeQuery`
 
 
 The `filetree` module contains another class called the
 [`FileTreeQuery`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.utils.filetree.query.html),
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 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'))
 ```
 
 %% Cell type:markdown id: tags:
 
 The `FileTreeQuery.query` method will return the paths to all existing files
 which match a set of variable values:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 <a class="anchor" id="calling-shell-commands"></a>
 ## Calling shell commands
 
 
 The
 [`fsl.utils.run`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.utils.run.html)
 module provides the `run` and `runfsl` functions, which are wrappers around
 the built-in [`subprocess`
 library](https://docs.python.org/3/library/subprocess.html).
 
 
 The default behaviour of `run` is to return the standard output of the
 command:
 
 %% Cell type:code id: tags:
 
 ``` 
 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 fslpy.md').strip())
 print('Words in this notebook:', run(['wc', '-w', 'fslpy.md']).strip())
 ```
 
 %% Cell type:markdown id: tags:
 
 But you can control what `run` returns, depending on your needs. Let's create
 a little script to demonstrate the options:
 
 %% Cell type:code id: tags:
 
 ``` 
 %%writefile mycmd
 #!/usr/bin/env bash
 exitcode=$1
 
 echo "Standard output!"
 echo "Standard error :(" >&2
 
 exit $exitcode
 ```
 
 %% Cell type:markdown id: tags:
 
 And let's not forget to make it executable:
 
 %% Cell type:code id: tags:
 
 ``` 
 !chmod a+x mycmd
 ```
 
 %% Cell type:markdown id: tags:
 
 You can use the `stdout`, `stderr` and `exitcode` arguments to control the
 return value:
 
 %% Cell type:code id: tags:
 
 ``` 
 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))
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 run("./mycmd 99")
 ```
 
 %% Cell type:markdown id: tags:
 
 <a class="anchor" id="the-runfsl-function"></a>
 ### 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 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')
 ```
 
 %% Cell type:markdown id: tags:
 
 <a class="anchor" id="submitting-to-the-cluster"></a>
 ### 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`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.utils.fslsub.html)
 > 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 jobid  = run('ls', submit=True)
 print('Job ID:', jobid)
 ```
 
 %% Cell type:markdown id: tags:
 
 Once the job finishes, we should be able to read the usual `.o` and `.e`
 files:
 
 %% Cell type:code id: tags:
 
 ``` 
 stdout = f'ls.o{jobid}'
 print('Job output')
 print(open(stdout).read())
 ```
 
 %% Cell type:markdown id: tags:
 
 All of the `fsl.wrappers` functions also accept the `submit` argument:
 
 %% Cell type:code id: tags:
 
 ``` 
 jobid = bet('bighead', 'bighead_brain', submit=True)
 print('Job ID:', jobid)
 ```
 
 %% Cell type:markdown id: tags:
 
 > 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 `hold` function to wait until a job
 has completed:
 
 %% Cell type:code id: tags:
 
 ``` 
 from fsl.utils.run import hold
 jobid = bet('bighead', 'bighead_brain', submit=True)
 print('Job ID:', jobid)
 hold(jobid)
 print('Done!')
 render('bighead bighead_brain -cm hot')
 ```
 
 %% Cell type:markdown id: tags:
 
 When you use `submit=True`, you can also specify cluster submission options -
 you can include any arguments that are accepted by the
 [`fslsub.submit`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.utils.fslsub.html#fsl.utils.fslsub.submit)
 function
 
 %% Cell type:code id: tags:
 
 ``` 
 jobs = []
 jobs.append(runfsl('robustfov -i 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, '...')
 hold(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')
 ```
 
 %% Cell type:markdown id: tags:
 
 <a class="anchor" id="redirecting-output"></a>
 ### 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):
 
 %% Cell type:code id: tags:
 
 ``` 
 print('Teeing:')
 _ = run('./mycmd 0', log={'tee' : True})
 ```
 
 %% Cell type:markdown id: tags:
 
 Or you can use `'stdout'` and `'stderr'` to redirect the standard output and
 error streams of the command to files:
 
 %% Cell type:code id: tags:
 
 ``` 
 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
 ```
 
 %% Cell type:markdown id: tags:
 
 Finally, you can use `'cmd'` to log the command itself to a file (useful for
 pipeline logging):
 
 %% Cell type:code id: tags:
 
 ``` 
 with open('commands.log', 'wt') as cmdlog:
      run('./mycmd 0',      log={'cmd' : cmdlog})
      run('wc -l fslpy.md', log={'cmd' : cmdlog})
 
 print('\nCommand log:')
 !cat commands.log
 ```
 
 %% Cell type:markdown id: tags:
 
 <a class="anchor" id="fsl-atlases"></a>
 ## FSL atlases
 
 
 The
 [`fsl.data.atlases`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.atlases.html)
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 import fsl.data.atlases as atlases
 atlases.rescanAtlases()
 ```
 
 %% Cell type:markdown id: tags:
 
 <a class="anchor" id="querying-atlases"></a>
 ### Querying atlases
 
 
 You can list all of the available atlases using `listAtlases`:
 
 %% Cell type:code id: tags:
 
 ``` 
 for desc in atlases.listAtlases():
     print(desc)
 ```
 
 %% Cell type:markdown id: tags:
 
 `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:
 
 %% Cell type:code id: tags:
 
 ``` 
 desc = atlases.getAtlasDescription('harvardoxford-cortical')
 print(desc.name)
 print(desc.atlasID)
 print(desc.specPath)
 print(desc.atlasType)
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 for lbl in desc.labels[:5]:
     print(lbl)
 ```
 
 %% Cell type:markdown id: tags:
 
 Or you can retrieve a specific label using the `find` method:
 
 %% Cell type:code id: tags:
 
 ``` 
 # search by region name
 print(desc.find(name='Occipital Pole'))
 
 # or by label value
 print(desc.find(value=48))
 ```
 
 %% Cell type:markdown id: tags:
 
 <a class="anchor" id="loading-atlas-images"></a>
 ### Loading atlas images
 
 
 The `loadAtlas` function can be used to load the atlas image:
 
 %% Cell type:code id: tags:
 
 ``` 
 # 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 <a class="anchor" id="working-with-atlases"></a>
 ### 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 Calling `get` on a `ProbabilisticAtlas` will return a probability image:
 
 %% Cell type:code id: tags:
 
 ``` 
 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)
 ```
 
 %% Cell type:markdown id: tags:
 
 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:
 
 %% Cell type:code id: tags:
 
 ``` 
 # 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))
 ```
 
 %% Cell type:markdown id: tags:
 
 `ProbabilisticAtlas` objects have an analogous method called `values`:
 
 %% Cell type:code id: tags:
 
 ``` 
 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))
 ```