diff --git a/getting_started/05_nifti.ipynb b/getting_started/05_nifti.ipynb
index 85b1c32fcd6cd5937234c6ac0cbd6dde5a93b057..9d8551e0970ce313c6801143e06d5b4859db3b2d 100644
--- a/getting_started/05_nifti.ipynb
+++ b/getting_started/05_nifti.ipynb
@@ -12,10 +12,10 @@
"\n",
"\n",
"Building upon `nibabel`, the\n",
- "[`fsl.data`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.html#module-fsl.data)\n",
- "package contains a number of FSL-specific classes and functions which you may\n",
- "find useful. This is covered in a different practical\n",
- "(`advanced_topics/08_fslpy.ipynb`).\n",
+ "[`fslpy`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/) library\n",
+ "contains a number of FSL-specific classes and functions which you may find\n",
+ "useful. But let's start with `nibabel` - `fslpy` is introduced in a different\n",
+ "practical (`advanced_topics/08_fslpy.ipynb`).\n",
"\n",
"\n",
"## Contents\n",
@@ -50,8 +50,8 @@
"# display header object\n",
"imhdr = imobj.header\n",
"\n",
- "# extract data (as an numpy array)\n",
- "imdat = imobj.get_data().astype(float)\n",
+ "# extract data (as a numpy array)\n",
+ "imdat = imobj.get_fdata()\n",
"print(imdat.shape)"
]
},
@@ -59,26 +59,28 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "> Make sure you use the full filename, including the .nii.gz extension.\n",
- "\n",
+ "> Make sure you use the full filename, including the `.nii.gz` extension.\n",
+ "> `fslpy` provides FSL-like automatic file suffix detection though.\n",
"\n",
- "> We use the expandvars() function above to insert the FSLDIR\n",
+ "> We use the `expandvars()` function above to insert the FSLDIR\n",
"> environmental variable into our string. This function is\n",
"> discussed more fully in the file management practical.\n",
"\n",
- "Reading the data off the disk is not done until `get_data()` is called.\n",
+ "Reading the data off the disk is not done until `get_fdata()` is called.\n",
"\n",
"> Pitfall:\n",
">\n",
- "> The option `mmap=False`is necessary as turns off memory mapping,\n",
- "> which otherwise would be invoked for uncompressed NIfTI files but not for\n",
- "> compressed files. Since some functionality behaves differently on memory\n",
- "> mapped objects, it is advisable to turn this off.\n",
+ "> The option `mmap=False` disables memory mapping, which would otherwise be\n",
+ "> invoked for uncompressed NIfTI files but not for compressed files. Since\n",
+ "> some functionality behaves differently on memory mapped objects, it is\n",
+ "> advisable to turn this off unless you specifically want it.\n",
"\n",
"Once the data is read into a numpy array then it is easily manipulated.\n",
"\n",
- "> We recommend converting it to float at the start to avoid problems with\n",
- "> integer arithmetic and overflow, though this is not compulsory.\n",
+ "> The `get_fdata` method will return floating point data, regardless of the\n",
+ "> underlying image data type. If you want the image data in the type that it\n",
+ "> is stored (e.g. integer ROI labels), then use\n",
+ "> `imdat = np.asanyarray(imobj.dataobj)` instead.\n",
"\n",
"---\n",
"\n",
@@ -155,6 +157,7 @@
"<a class=\"anchor\" id=\"writing-images\"></a>\n",
"## Writing images\n",
"\n",
+ "\n",
"If you have created a modified image by making or modifying a numpy array then\n",
"you need to put this into a NIfTI image object in order to save it to a file.\n",
"The easiest way to do this is to copy all the header info from an existing\n",
@@ -190,8 +193,9 @@
"> creating an entirely separate image, like a simulation.\n",
"\n",
"If the voxel size of the image is different, then extra modifications will be\n",
- "required. Take a look at the `fslpy` practical for more advanced image\n",
- "manipulation options (`advanced_topics/08_fslpy.ipynb`).\n",
+ "required. Take a look at the `fslpy` practical for some extra image\n",
+ "manipulation options, including cropping and resampling\n",
+ "(`advanced_topics/08_fslpy.ipynb`).\n",
"\n",
"---\n",
"\n",
@@ -202,7 +206,10 @@
"\n",
"Write some code to read in a 4D fMRI image (you can find one\n",
"[here](http://www.fmrib.ox.ac.uk/~mark/files/av.nii.gz) if you don't have one\n",
- "handy), calculate the tSNR and then save the 3D result."
+ "handy), calculate the tSNR and then save the 3D result.\n",
+ "\n",
+ "> The tSNR of a time series signal is simply its mean divided by its standard\n",
+ "> deviation."
]
},
{
diff --git a/getting_started/05_nifti.md b/getting_started/05_nifti.md
index f20063e3b7d66ec3eb0d628ee5751a7836d692c0..67139c538b3f79da2dd24057f8e82acaae4cebe7 100644
--- a/getting_started/05_nifti.md
+++ b/getting_started/05_nifti.md
@@ -6,10 +6,10 @@ MINC, MGH). `nibabel` is included within the FSL python environment.
Building upon `nibabel`, the
-[`fsl.data`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/fsl.data.html#module-fsl.data)
-package contains a number of FSL-specific classes and functions which you may
-find useful. This is covered in a different practical
-(`advanced_topics/08_fslpy.ipynb`).
+[`fslpy`](https://users.fmrib.ox.ac.uk/~paulmc/fsleyes/fslpy/latest/) library
+contains a number of FSL-specific classes and functions which you may find
+useful. But let's start with `nibabel` - `fslpy` is introduced in a different
+practical (`advanced_topics/08_fslpy.ipynb`).
## Contents
@@ -38,31 +38,33 @@ imobj = nib.load(filename, mmap=False)
# display header object
imhdr = imobj.header
-# extract data (as an numpy array)
-imdat = imobj.get_data().astype(float)
+# extract data (as a numpy array)
+imdat = imobj.get_fdata()
print(imdat.shape)
```
-> Make sure you use the full filename, including the .nii.gz extension.
-
+> Make sure you use the full filename, including the `.nii.gz` extension.
+> `fslpy` provides FSL-like automatic file suffix detection though.
-> We use the expandvars() function above to insert the FSLDIR
+> We use the `expandvars()` function above to insert the FSLDIR
> environmental variable into our string. This function is
> discussed more fully in the file management practical.
-Reading the data off the disk is not done until `get_data()` is called.
+Reading the data off the disk is not done until `get_fdata()` is called.
> Pitfall:
>
-> The option `mmap=False`is necessary as turns off memory mapping,
-> which otherwise would be invoked for uncompressed NIfTI files but not for
-> compressed files. Since some functionality behaves differently on memory
-> mapped objects, it is advisable to turn this off.
+> The option `mmap=False` disables memory mapping, which would otherwise be
+> invoked for uncompressed NIfTI files but not for compressed files. Since
+> some functionality behaves differently on memory mapped objects, it is
+> advisable to turn this off unless you specifically want it.
Once the data is read into a numpy array then it is easily manipulated.
-> We recommend converting it to float at the start to avoid problems with
-> integer arithmetic and overflow, though this is not compulsory.
+> The `get_fdata` method will return floating point data, regardless of the
+> underlying image data type. If you want the image data in the type that it
+> is stored (e.g. integer ROI labels), then use
+> `imdat = np.asanyarray(imobj.dataobj)` instead.
---
@@ -109,6 +111,7 @@ print(affine, code)
<a class="anchor" id="writing-images"></a>
## Writing images
+
If you have created a modified image by making or modifying a numpy array then
you need to put this into a NIfTI image object in order to save it to a file.
The easiest way to do this is to copy all the header info from an existing
@@ -134,8 +137,9 @@ where `newdata` is the numpy array (the above is a random example only) and
> creating an entirely separate image, like a simulation.
If the voxel size of the image is different, then extra modifications will be
-required. Take a look at the `fslpy` practical for more advanced image
-manipulation options (`advanced_topics/08_fslpy.ipynb`).
+required. Take a look at the `fslpy` practical for some extra image
+manipulation options, including cropping and resampling
+(`advanced_topics/08_fslpy.ipynb`).
---
@@ -148,6 +152,9 @@ Write some code to read in a 4D fMRI image (you can find one
[here](http://www.fmrib.ox.ac.uk/~mark/files/av.nii.gz) if you don't have one
handy), calculate the tSNR and then save the 3D result.
+> The tSNR of a time series signal is simply its mean divided by its standard
+> deviation.
+
```
# Calculate tSNR
```