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%% Cell type:markdown id: tags:
# `fslpy`
[`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 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.
> **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.
* [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)
* [FSL atlases](#fsl-atlases)
* [The `filetree`](#the-filetree)
* [Image processing](#image-processing)
* [FSL wrapper functions](#fsl-wrapper-functions)
* [NIfTI coordinate systems](#nifti-coordinate-systems)
<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
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:
```
import os.path as op
from fsl.data.image import Image
stddir = op.expandvars('${FSLDIR}/data/standard/')
# load a FSL image - the file
# suffix is optional, just like
# in real FSL-land!
img = Image(op.join(stddir, 'MNI152_T1_1mm'))
print(img)
```
%% Cell type:markdown id: tags:
You can create an `Image` from an existing `nibabel` image:
%% Cell type:code id: tags:
```
import nibabel as nib
# load a nibabel image, and
# convert it into an FSL image
nibimg = nib.load(op.join(stddir, 'MNI152_T1_1mm.nii.gz'))
img = Image(nibimg)
```
%% Cell type:markdown id: tags:
Or you can create an `Image` from a `numpy` array:
%% Cell type:code id: tags:
```
import numpy as np
data = np.zeros((100, 100, 100))
img = Image(data, xform=np.eye(4))
```
%% Cell type:markdown id: tags:
You can save an image to file via the `save` method:
%% Cell type:code id: tags:
```
img.save('empty.nii.gz')
```
%% Cell type:markdown id: tags:
`Image` objects have all of the attributes you might expect:
%% Cell type:code id: tags:
```
stddir = op.expandvars('${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_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())
```
%% Cell type:markdown id: tags:
> Note that this 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, :, :] = 0
```
%% 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 - 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.mghimahe.MGHImage`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.mghimage.html)
class can be used too 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 VTK files respectively.
> <sup>*</sup>You must make sure that `dcm2niix` is installed on your system
> in order to use this class.
<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:
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:
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:
`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))
```
%% Cell type:markdown id: tags:
<a class="anchor" id="the-filetree"></a>
## The `filetree`
<a class="anchor" id="nifti-coordinate-systems"></a>
## NIfTI coordinate systems
<a class="anchor" id="image-processing"></a>
## Image processing
<a class="anchor" id="fsl-wrapper-functions"></a>
## FSL wrapper functions
<a class="anchor" id="nifti-coordinate-systems"></a>
## NIfTI coordinate systems
The `getAffine` method gives you access to affine transformations which can be
used to convert coordinates between the different coordinate systems
associated with an image. Have some MNI coordinates you'd like to convert to
voxels? Easy!
%% Cell type:code id: tags:
```
mnicoords = np.array([[0, 0, 0],
[0, -18, 18]])
world2vox = img.getAffine('world', 'voxel')
vox2world = img.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 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)
```
%% 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 = op.join(op.join('05_nifti', 'fmri.feat'))
# The Image.data attribute returns a
# numpy array containing, well, the
# image data.
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)
```
%% Cell type:markdown id: tags:
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)
print('Peak activation (MNI coordinates):', mnicoords)
```
%% 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. But this is left as an exercise
> for the reader ;).
<a class="anchor" id="image-processing"></a>
## Image processing
Now, it's all well and good to look at t-statistric 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
%% Cell type:code id: tags:
```
from IPython.display import Image as Screenshot
!fsleyes render -of screenshot.png -std
```
%% Cell type:markdown id: tags:
### (Advanced) Transform coordinates with nonlinear warpfields
have to use your own dataset
advanced_topics/08_fslpy.md
+ 298
− 10
View file @ a7a82e8a
# `fslpy`
# THIS IS A WORK IN PROGRESS - DO NOT READ
[`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 neuroimaging data from Python.
`fslpy` is a Python library which is built into FSL, and contains a range of
functionality for working with neuroimaging data in an FSL context.
This practical highlights some of the most useful features provided by
`fslpy`. You may find `fslpy` useful if you are writing Python code to
......@@ -17,68 +17,356 @@ 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)
* [FSL atlases](#fsl-atlases)
* [The `filetree`](#the-filetree)
* [NIfTI coordinate systems](#nifti-coordinate-systems)
* [Image processing](#image-processing)
* [FSL wrapper functions](#fsl-wrapper-functions)
* [NIfTI coordinate systems](#nifti-coordinate-systems)
<a class="anchor" id="the-image-class-and-other-data-types"></a>
## 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:
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
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:
```
import os.path as op
from fsl.data.image import Image
stddir = op.expandvars('${FSLDIR}/data/standard/')
# load a FSL image - the file
# suffix is optional, just like
# in real FSL-land!
img = Image(op.join(stddir, 'MNI152_T1_1mm'))
print(img)
```
You can crearte an `Image` from an existing `nibabel` image:
You can create an `Image` from an existing `nibabel` image:
```
import nibabel as nib
# load a nibabel image, and
# convert it into an FSL image
nibimg = nib.load(op.join(stddir, 'MNI152_T1_1mm.nii.gz'))
img = Image(nibimg)
``
```
Or you can create an `Image` from a `numpy` array:
```
import numpy as np
data = np.zeros((100, 100, 100))
img = Image(data, xform=np.eye(4))
```
You can save an image to file via the `save` method:
```
img.save('empty.nii.gz')
```
`Image` objects have all of the attributes you might expect:
```
stddir = op.expandvars('${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_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)
```
<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:
```
data = std2mm.data
print(data.min, data.max())
```
> Note that this 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, :, :] = 0
```
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 - 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.mghimahe.MGHImage`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.mghimage.html)
class can be used too 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 VTK files respectively.
> <sup>*</sup>You must make sure that `dcm2niix` is installed on your system
> in order to use this class.
<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:
```
import fsl.data.atlases as atlases
atlases.rescanAtlases()
```
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))
```
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)
```
`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))
```
<a class="anchor" id="the-filetree"></a>
## The `filetree`
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