diff --git a/advanced_topics/08_fslpy.ipynb b/advanced_topics/08_fslpy.ipynb
index 15761daf766d2f362fbb6f4aee31e3759d15deb3..f7d8ee05e52f8d3440e8c7dc8befd875c30a7d08 100644
--- a/advanced_topics/08_fslpy.ipynb
+++ b/advanced_topics/08_fslpy.ipynb
@@ -7,9 +7,13 @@
"# `fslpy`\n",
"\n",
"\n",
+ "**Important:** Portions of this practical require `fslpy` 2.9.0, due to be\n",
+ "released with FSL 6.0.4, in Spring 2020.\n",
+ "\n",
+ "\n",
"[`fslpy`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/) is a\n",
"Python library which is built into FSL, and contains a range of functionality\n",
- "for working with neuroimaging data from Python.\n",
+ "for working with FSL and with neuroimaging data from Python.\n",
"\n",
"\n",
"This practical highlights some of the most useful features provided by\n",
@@ -79,16 +83,17 @@
"metadata": {},
"outputs": [],
"source": [
- "def ortho(data, voxel, fig=None, **kwargs):\n",
+ "def ortho(data, voxel, fig=None, cursor=False, **kwargs):\n",
" \"\"\"Simple orthographic plot of a 3D array using matplotlib.\n",
"\n",
- " :arg data: 3D numpy array\n",
- " :arg voxel: XYZ coordinates for each slice\n",
- " :arg fig: Existing figure and axes for overlay plotting\n",
+ " :arg data: 3D numpy array\n",
+ " :arg voxel: XYZ coordinates for each slice\n",
+ " :arg fig: Existing figure and axes for overlay plotting\n",
+ " :arg cursor: Show a cursor at the `voxel`\n",
"\n",
" All other arguments are passed through to the `imshow` function.\n",
"\n",
- " :returns: The figure and axes (which can be passed back in as the\n",
+ " :returns: The figure and orthogaxes (which can be passed back in as the\n",
" `fig` argument to plot overlays).\n",
" \"\"\"\n",
"\n",
@@ -112,6 +117,15 @@
" yax.imshow(yslice, **kwargs)\n",
" zax.imshow(zslice, **kwargs)\n",
"\n",
+ " if cursor:\n",
+ " cargs = {'color' : (0, 1, 0), 'linewidth' : 1}\n",
+ " xax.axvline( y, **cargs)\n",
+ " xax.axhline(data.shape[2] - z, **cargs)\n",
+ " yax.axvline( x, **cargs)\n",
+ " yax.axhline(data.shape[2] - z, **cargs)\n",
+ " zax.axvline( x, **cargs)\n",
+ " zax.axhline(data.shape[1] - y, **cargs)\n",
+ "\n",
" for ax in (xax, yax, zax):\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n",
@@ -708,7 +722,7 @@
"std_tstat1[std_tstat1 < 3] = 0\n",
"\n",
"fig = ortho(std2mm.data, mnivoxels, cmap=plt.cm.gray)\n",
- "fig = ortho(std_tstat1, mnivoxels, cmap=plt.cm.inferno, fig=fig)"
+ "fig = ortho(std_tstat1, mnivoxels, cmap=plt.cm.inferno, fig=fig, cursor=True)"
]
},
{
@@ -903,8 +917,11 @@
"aligned = aligned.out.data\n",
"aligned[aligned < 1] = 0\n",
"\n",
- "fig = ortho(std2mm .data, (45, 54, 45), cmap=plt.cm.gray)\n",
- "fig = ortho(aligned.data, (45, 54, 45), cmap=plt.cm.inferno, fig=fig)"
+ "peakvox = np.abs(aligned).argmax()\n",
+ "peakvox = np.unravel_index(peakvox, aligned.shape)\n",
+ "\n",
+ "fig = ortho(std2mm .data, peakvox, cmap=plt.cm.gray)\n",
+ "fig = ortho(aligned.data, peakvox, cmap=plt.cm.inferno, fig=fig, cursor=True)"
]
},
{
@@ -923,7 +940,7 @@
"outputs": [],
"source": [
"img = Image('bighead_cropped')\n",
- "betted = bet(img, LOAD, f=0.3, m=True)\n",
+ "betted = bet(img, LOAD, f=0.3, mask=True)\n",
"\n",
"fig = ortho(img .data, (80, 112, 85), cmap=plt.cm.gray)\n",
"fig = ortho(betted.output .data, (80, 112, 85), cmap=plt.cm.inferno, fig=fig)\n",
@@ -946,8 +963,9 @@
"which is hopefully a little easier to use from within Python.\n",
"\n",
"\n",
- "You can apply an `fslmaths` operation by specifying the input file, *chaining*\n",
- "method calls together, and finally calling the `run()` method. For example:"
+ "You can apply an `fslmaths` operation by specifying the input image,\n",
+ "*chaining* method calls together, and finally calling the `run()` method. For\n",
+ "example:"
]
},
{
@@ -1109,6 +1127,9 @@
"source": [
"from fsl.utils.filetree import FileTree\n",
"\n",
+ "# Create a FileTree, giving\n",
+ "# it our tree specification,\n",
+ "# and the path to our data.\n",
"tree = FileTree.read('mydata.tree', 'mydata')"
]
},
@@ -1118,7 +1139,9 @@
"source": [
"We can list all of the T1 images via the `FileTree.get_all` method. The\n",
"`glob_vars='all'` option tells the `FileTree` to fill in the `T1w` template\n",
- "with all possible combinations of variables:"
+ "with all possible combinations of variables. The `FileTree.extract_variables`\n",
+ "method accepts a file path, and gives you back the variable values contained\n",
+ "within:"
]
},
{
@@ -1184,9 +1207,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now we can use the `FileTree` to generate the relevant file names for us.\n",
- "Here we'll use the `FileTree.get_all_trees` method to create a sub-tree for\n",
- "each subject and each session:"
+ "Now we can use the `FileTree` to generate the relevant file names for us,\n",
+ "which we can then pass on to BET. Here we'll use the `FileTree.get_all_trees`\n",
+ "method to create a sub-tree for each subject and each session:"
]
},
{
@@ -1205,7 +1228,7 @@
"print('Done!')\n",
"\n",
"example = tree.update(subject='A', session='1')\n",
- "render('{} {} -ot mask -ol -w 2 -mc 0 1 0'.format(\n",
+ "render('{} {} -ot mask -o -w 2 -mc 0 1 0'.format(\n",
" example.get('T1w'),\n",
" example.get('T1w_brain_mask')))\n"
]
@@ -1256,11 +1279,11 @@
"metadata": {},
"outputs": [],
"source": [
- "print('All T1w images for subject A')\n",
+ "print('All files for subject A')\n",
"for template in query.templates:\n",
- " print(' ', template)\n",
- " for file in query.query(template, subject='A'):\n",
- " print(file)"
+ " print(' {} files:'.format(template))\n",
+ " for match in query.query(template, subject='A'):\n",
+ " print(' ', match.filename)"
]
},
{
@@ -1414,12 +1437,12 @@
"outputs": [],
"source": [
"from fsl.utils.run import runfsl\n",
- "runfsl('bet 08_fslpy/bighead_cropped bighead_cropped_brain')\n",
+ "runfsl('bet bighead_cropped bighead_cropped_brain')\n",
"runfsl('fslroi bighead_cropped_brain bighead_slices 0 -1 0 -1 90 3')\n",
"runfsl('fast -o bighead_fast bighead_slices')\n",
"\n",
"render('-vl 80 112 91 -xh -yh '\n",
- " '08_fslpy/bighead_cropped '\n",
+ " 'bighead_cropped '\n",
" 'bighead_slices.nii.gz -cm brain_colours_1hot -b 30 '\n",
" 'bighead_fast_seg.nii.gz -ot label -o')"
]
@@ -1445,8 +1468,9 @@
"\n",
"\n",
"The semantics of the `run` and `runfsl` functions are slightly different when\n",
- "you use the `submit` option - when you submit a job, the `run`/`runfsl` will\n",
- "return immediately, and will return a string containing the job ID:"
+ "you use the `submit` option - when you submit a job, the `run`/`runfsl`\n",
+ "functions will return immediately, and will return a string containing the job\n",
+ "ID:"
]
},
{
@@ -1790,7 +1814,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Calling `get` on a :meth:`ProbabilisticAtlas` will return a probability image:"
+ "Calling `get` on a `ProbabilisticAtlas` will return a probability image:"
]
},
{
@@ -1802,7 +1826,7 @@
"stddir = op.expandvars('${FSLDIR}/data/standard/')\n",
"std2mm = Image(op.join(stddir, 'MNI152_T1_2mm'))\n",
"\n",
- "frontal = probatlas.get(name='Frontal Pole')\n",
+ "frontal = probatlas.get(name='Frontal Pole').data\n",
"frontal = np.ma.masked_where(frontal < 1, frontal)\n",
"\n",
"fig = ortho(std2mm.data, (45, 54, 45), cmap=plt.cm.gray)\n",
diff --git a/advanced_topics/08_fslpy.md b/advanced_topics/08_fslpy.md
index 437a3d3620fe87e5da1f45b8732ab43dad0b9c17..b59a49a53ceb2a4f56b6df637bcc53e86f4eb698 100644
--- a/advanced_topics/08_fslpy.md
+++ b/advanced_topics/08_fslpy.md
@@ -1,9 +1,13 @@
# `fslpy`
+**Important:** Portions of this practical require `fslpy` 2.9.0, due to be
+released with FSL 6.0.4, in Spring 2020.
+
+
[`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.
+for working with FSL and with neuroimaging data from Python.
This practical highlights some of the most useful features provided by
@@ -60,16 +64,17 @@ And a little function that we can use to generate a simple orthographic plot:
```
-def ortho(data, voxel, fig=None, **kwargs):
+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 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 axes (which can be passed back in as the
+ :returns: The figure and orthogaxes (which can be passed back in as the
`fig` argument to plot overlays).
"""
@@ -93,6 +98,15 @@ def ortho(data, voxel, fig=None, **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([])
@@ -535,7 +549,7 @@ 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)
+fig = ortho(std_tstat1, mnivoxels, cmap=plt.cm.inferno, fig=fig, cursor=True)
```
@@ -690,8 +704,11 @@ aligned = flirt(tstat1, std2mm, applyxfm=True, init=func2std, out=LOAD)
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)
+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)
```
@@ -702,7 +719,7 @@ prefix will be available in the object that is returned:
```
img = Image('bighead_cropped')
-betted = bet(img, LOAD, f=0.3, m=True)
+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)
@@ -722,8 +739,9 @@ 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:
+You can apply an `fslmaths` operation by specifying the input image,
+*chaining* method calls together, and finally calling the `run()` method. For
+example:
```
@@ -848,12 +866,17 @@ Now that we have a `.tree` file which describe our data, we can create a
```
from fsl.utils.filetree import FileTree
+# Create a FileTree, giving
+# it our tree specification,
+# and the path to our data.
tree = FileTree.read('mydata.tree', 'mydata')
```
We can list all of the T1 images via the `FileTree.get_all` method. The
`glob_vars='all'` option tells the `FileTree` to fill in the `T1w` template
-with all possible combinations of variables:
+with all possible combinations of variables. The `FileTree.extract_variables`
+method accepts a file path, and gives you back the variable values contained
+within:
```
@@ -895,9 +918,9 @@ sub_{subject}
```
-Now we can use the `FileTree` to generate the relevant file names for us.
-Here we'll use the `FileTree.get_all_trees` method to create a sub-tree for
-each subject and each session:
+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:
```
@@ -911,7 +934,7 @@ for subtree in tree.get_all_trees('T1w', glob_vars='all'):
print('Done!')
example = tree.update(subject='A', session='1')
-render('{} {} -ot mask -ol -w 2 -mc 0 1 0'.format(
+render('{} {} -ot mask -o -w 2 -mc 0 1 0'.format(
example.get('T1w'),
example.get('T1w_brain_mask')))
@@ -947,11 +970,11 @@ which match a set of variable values:
```
-print('All T1w images for subject A')
+print('All files for subject A')
for template in query.templates:
- print(' ', template)
- for file in query.query(template, subject='A'):
- print(file)
+ print(' {} files:'.format(template))
+ for match in query.query(template, subject='A'):
+ print(' ', match.filename)
```
@@ -1057,12 +1080,12 @@ same usage as the `run` function:
```
from fsl.utils.run import runfsl
-runfsl('bet 08_fslpy/bighead_cropped bighead_cropped_brain')
+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 '
- '08_fslpy/bighead_cropped '
+ 'bighead_cropped '
'bighead_slices.nii.gz -cm brain_colours_1hot -b 30 '
'bighead_fast_seg.nii.gz -ot label -o')
```
@@ -1085,8 +1108,9 @@ command.
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` will
-return immediately, and will return a string containing the job ID:
+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:
```
@@ -1318,14 +1342,14 @@ fig = ortho(frontal, (45, 54, 45), cmap=plt.cm.winter, fig=fig)
```
-Calling `get` on a :meth:`ProbabilisticAtlas` will return a probability image:
+Calling `get` on a `ProbabilisticAtlas` will return a probability image:
```
stddir = op.expandvars('${FSLDIR}/data/standard/')
std2mm = Image(op.join(stddir, 'MNI152_T1_2mm'))
-frontal = probatlas.get(name='Frontal Pole')
+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)