08_fslpy.md 31.1 KB
Newer Older
1
2
# `fslpy`

3

Paul McCarthy's avatar
Paul McCarthy committed
4
5
6
[`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.
7

8
9
10
11
12
13
14
15
16
17
18
19

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)
Paul McCarthy's avatar
Paul McCarthy committed
20
21
22
  * [Creating images](#creating-images)
  * [Working with image data](#working-with-image-data)
  * [Loading other file types](#loading-other-file-types)
Paul McCarthy's avatar
Paul McCarthy committed
23
24
  * [NIfTI coordinate systems](#nifti-coordinate-systems)
  * [Image processing](#image-processing)
25
* [FSL wrapper functions](#fsl-wrapper-functions)
Paul McCarthy's avatar
Paul McCarthy committed
26
27
28
  * [In-memory images](#in-memory-images)
  * [Loading outputs into Python](#loading-outputs-into-python)
  * [The `fslmaths` wrapper](#the-fslmaths-wrapper)
Paul McCarthy's avatar
Paul McCarthy committed
29
30
31
32
* [The `filetree`](#the-filetree)
* [Calling shell commands](#calling-shell-commands)
  * [`runfsl` and `submit`](#runfsl-and-submit)
  * [Redirecting output](#redirecting-output)
Paul McCarthy's avatar
Paul McCarthy committed
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
* [FSL atlases](#fsl-atlases)
  * [Querying atlases](#querying-atlases)
  * [Loading atlas images](#loading-atlas-images)
  * [Working with atlases](#working-with-atlases)


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")
```


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


```
def ortho(data, voxel, fig=None, **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

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

    :returns:   The figure and axes (which can be passed back in as the
                `fig` argument to plot overlays).
    """
Paul McCarthy's avatar
Paul McCarthy committed
70
71
72
73

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

Paul McCarthy's avatar
Paul McCarthy committed
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
    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)

    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:


```
import shlex
import IPython.display as display
from fsleyes.render import main

def render(cmdline):
    prefix = '-of screenshot.png -hl -c 2 '
    main(shlex.split(prefix + cmdline))
    return display.Image('screenshot.png')
```
113
114
115
116
117
118


<a class="anchor" id="the-image-class-and-other-data-types"></a>
## The `Image` class, and other data types


Paul McCarthy's avatar
Paul McCarthy committed
119
120
121
122
123
124
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:
125
126
127
128
129
130

- 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

Paul McCarthy's avatar
Paul McCarthy committed
131

132
133
134
135
Some simple image processing routines are also provided - these are covered
[below](#image-processing).


Paul McCarthy's avatar
Paul McCarthy committed
136
<a class="anchor" id="creating-images"></a>
137
138
### Creating images

Paul McCarthy's avatar
Paul McCarthy committed
139

140
141
It's easy to create an `Image` - you can create one from a file name:

Paul McCarthy's avatar
Paul McCarthy committed
142

143
144
```
from fsl.data.image import Image
Paul McCarthy's avatar
Paul McCarthy committed
145

146
147
148
149
150
151
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'))
Paul McCarthy's avatar
Paul McCarthy committed
152
print(img)
153
154
```

Paul McCarthy's avatar
Paul McCarthy committed
155
156
157

You can create an `Image` from an existing `nibabel` image:

158
159
160
161
162
163

```
# 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)
Paul McCarthy's avatar
Paul McCarthy committed
164
165
```

166
167
168

Or you can create an `Image` from a `numpy` array:

Paul McCarthy's avatar
Paul McCarthy committed
169

170
171
172
173
174
```
data = np.zeros((100, 100, 100))
img = Image(data, xform=np.eye(4))
```

Paul McCarthy's avatar
Paul McCarthy committed
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
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'))
Paul McCarthy's avatar
Paul McCarthy committed
206
mask2mm = Image(op.join(stddir, 'MNI152_T1_2mm_brain_mask'))
Paul McCarthy's avatar
Paul McCarthy committed
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233

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
Paul McCarthy's avatar
Paul McCarthy committed
234
print(data.min(), data.max())
Paul McCarthy's avatar
Paul McCarthy committed
235
ortho(data, (45, 54, 45))
Paul McCarthy's avatar
Paul McCarthy committed
236
237
```

Paul McCarthy's avatar
Paul McCarthy committed
238
239
240

> 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.
Paul McCarthy's avatar
Paul McCarthy committed
241
242
243
244
245
246
247


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


```
slc = std2mm[:, :, 45]
Paul McCarthy's avatar
Paul McCarthy committed
248
std2mm[0:10, :, :] *= 2
Paul McCarthy's avatar
Paul McCarthy committed
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
```


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:
270

Paul McCarthy's avatar
Paul McCarthy committed
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
* 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.
307
308


Paul McCarthy's avatar
Paul McCarthy committed
309
310
311
> <sup>*</sup>You must make sure that `dcm2niix` is installed on your system
> in order to use this class.

312

Paul McCarthy's avatar
Paul McCarthy committed
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
<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!


```
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 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:


```
Paul McCarthy's avatar
Paul McCarthy committed
375
featdir = op.join('08_fslpy', 'fmri.feat')
Paul McCarthy's avatar
Paul McCarthy committed
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512

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)
```


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. But this is left as [an exercise
> for the
> reader](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.transform.fnirt.html).


<a class="anchor" id="image-processing"></a>
### Image processing


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.utils.image.resample`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.utils.image.resample.html)
module:


```
from fsl.transform.flirt import fromFlirt
from fsl.utils.image.resample import resampleToReference

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')))

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

# All of the functions in the resample module
# return a numpy array containing the resampled
# data, and an adjusted voxel-to-world affine
# transformation. But when using the
# resampleToReference function, the affine will
# be the same as the MNI152 2mm affine, so we
# can ignore it.
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'))

Paul McCarthy's avatar
Paul McCarthy committed
513
514
std_tstat1                 = std_tstat1.data
std_tstat1[std_tstat1 < 3] = 0
Paul McCarthy's avatar
Paul McCarthy committed
515

Paul McCarthy's avatar
Paul McCarthy committed
516
517
fig = ortho(std2mm.data, mnivoxels, cmap=plt.cm.gray)
fig = ortho(std_tstat1,  mnivoxels, cmap=plt.cm.inferno, fig=fig)
Paul McCarthy's avatar
Paul McCarthy committed
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
```


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)
package, with more to be added in the future. The [`fsl.transform`]() package
also contains a wealth of functionality for working with linear (FLIRT) and
non-linear (FNIRT) transformations.


<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.


Paul McCarthy's avatar
Paul McCarthy committed
539
540
541
You can use the FSL wrapper functions with file names, similar to calling the
corresponding tool via the command-line:

Paul McCarthy's avatar
Paul McCarthy committed
542
543
544

```
from fsl.wrappers import bet, robustfov, LOAD
Paul McCarthy's avatar
Paul McCarthy committed
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585

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

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


The `fsl.wrapper` 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:


```
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')
Paul McCarthy's avatar
Paul McCarthy committed
586
587
```

Paul McCarthy's avatar
Paul McCarthy committed
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610

> 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)
> ```


<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.wrapper` 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.wrapper` functions - they will
be automatically saved to temporary files and passed to the underlying FSL
command:

Paul McCarthy's avatar
Paul McCarthy committed
611
612
613
614

```
cropped = Image('bighead_cropped')

Paul McCarthy's avatar
Paul McCarthy committed
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
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)
```


<a class="anchor" id="loading-outputs-into-python"></a>
### Loading outputs into Python


By using the special `fsl.wrappers.LOAD` symbol, you can have any output
files produced by the tool automatically loaded in too:


```
cropped = Image('bighead_cropped')
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 returned in a dictionary, with the argument name used as
the key:


```
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)

print(aligned)

aligned = aligned['out'].data
aligned[aligned < 1] = 0

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


For tools like `bet`, 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 returned dictionary:


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

print(betted)

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)
```


<a class="anchor" id="the-fslmaths-wrapper"></a>
### The `fslmaths` wrapper


*Most* of the `fsl.wrapper` 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 file, *chaining*
method calls together, and finally calling the `run()` method. For example:


```
Paul McCarthy's avatar
Paul McCarthy committed
699
from fsl.wrappers import fslmaths
Paul McCarthy's avatar
Paul McCarthy committed
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
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)
Paul McCarthy's avatar
Paul McCarthy committed
721
722
723
```


Paul McCarthy's avatar
Paul McCarthy committed
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
<a class="anchor" id="the-filetree"></a>
## The `filetree`



<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:


```
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")
```


<a class="anchor" id="runfsl-and-submit"></a>
### `runfsl` and `submit`


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('fslroi 08_fslpy/bighead_cropped bighead_slices 0 -1 0 -1 90 5')
runfsl('fast -o bighead_fast bighead_slices')
```



<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):


```
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
```


886
887
888
<a class="anchor" id="fsl-atlases"></a>
## FSL atlases

Paul McCarthy's avatar
Paul McCarthy committed
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905

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()
```


Paul McCarthy's avatar
Paul McCarthy committed
906
907
908
909
<a class="anchor" id="querying-atlases"></a>
### Querying atlases


Paul McCarthy's avatar
Paul McCarthy committed
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
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))
```


Paul McCarthy's avatar
Paul McCarthy committed
957
958
959
960
<a class="anchor" id="loading-atlas-images"></a>
### Loading atlas images


Paul McCarthy's avatar
Paul McCarthy committed
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
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)
```


Paul McCarthy's avatar
Paul McCarthy committed
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
<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:


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


Calling `get` on a :meth:`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')
frontal = np.ma.masked_where(frontal < 1, frontal)

fig = ortho(std2mm,  (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


Paul McCarthy's avatar
Paul McCarthy committed
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
`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))
```