This notebook will perform *python* MIGP dimension reduction, run group ICA, and then plot the group ICs.
*[Run python `MIGP`](#run-python-migp)
*[Run `melodic`](#run-python-melodic)
*[Plot group ICs](#plot-python-group-ics)
Firstly we will import the necessary packages for this notebook:
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``` python
importglob
importrandom
importnibabelasnb
importnumpyasnp
fromscipy.sparse.linalgimportsvds,eigs
importmatplotlib.pyplotasplt
fromnilearnimportplotting
fromnilearnimportimage
importos.pathasop
```
%% Cell type:markdown id: tags:
It will be necessary to know the location where the data was stored so that we can load the brainmask.
> **Note**: [`expanduser`](https://docs.python.org/3.7/library/os.path.html#os.path.expanduser) will expand the `~` to the be users home directory
%% Cell type:code id: tags:
``` python
data_dir=op.expanduser('~/nilearn_data')
```
%% Cell type:markdown id: tags:
<aclass="anchor"id="run-python-migp"></a>
### Run python `MIGP`
Firstly we need to set the MIGP parameters:
> **Note:**
> 1. [`glob.glob`](https://docs.python.org/3/library/glob.html) will create a list of filenames that match the glob/wildcard pattern
> 2. [`nb.load`](https://nipy.org/nibabel/gettingstarted.html) from the [`nibabel`](https://nipy.org/nibabel/index.html) package will load the image into [`nibabel.Nifti1Image`](https://nipy.org/nibabel/reference/nibabel.nifti1.html) object. This will not load the actual data though.
> 3. We use a list comprehension to loop through all the filenames and load them with [`nibabel`](https://nipy.org/nibabel/index.html)
# set user parameters (equivalent to melodic defaults)
GO='pyMIGP.nii.gz'# output filename
dPCA_int=299# internal number of components - typically 2-4 times number of timepoints in each run (if you have enough RAM for that)
dPCA_out=299# number of eigenvectors to output - should be less than dPCAint and more than the final ICA dimensionality
sep_vn=False# switch on separate variance nomalisation for each input dataset
```
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> **Note:**
> 1. [`random.shuffle`](https://docs.python.org/3.7/library/random.html#random.shuffle) will shuffle a list, in this instance it shuffles the list of [`nibabel.Nifti1Image`](https://nipy.org/nibabel/reference/nibabel.nifti1.html) objects
> 2. [`ravel`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ravel.html) will unfold a n-d array into vector. Similar to the `:` operator in Matlab
> 3. [`reshape`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html) works similarly to reshape in Matlab, but be careful becase the default order is different from Matlab.
> 4. [`.T`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.T.html) does a transpose in `numpy`
> 5. The final element of an array is indexed with `-1` in `numpy`, as opposed to `end` in Matlab
> 6. [`svds`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.svds.html) and [`eigs`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.eigs.html?highlight=eigs#scipy.sparse.linalg.eigs) come from the [`scipy.sparse.linalg`](https://docs.scipy.org/doc/scipy/reference/sparse.linalg.html) package
> 7. [`svds`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.svds.html) and [`eigs`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.eigs.html?highlight=eigs#scipy.sparse.linalg.eigs) are very similar to their Matlab counterparts, but be careful because Matlab `svds` returns $U$, $S$, and $V$, whereas python `svds` returns $U$, $S$, and $V^T$
> 8. We index into the output of `eigs(W@W.T, dPCA_int)[1]` to only return the 2nd output (index 1)
Generate a command line string and run group ```melodic``` on the Matlab MIGP dimension reduced data with a dimension of 10 components. We disable MIGP because it was already run separately in Matlab.
> **Note**:
> 1. Here we use python [f-strings](https://www.python.org/dev/peps/pep-0498/), formally known as literal string interpolation, which allow for easy formatting
> 2. [`op.join`](https://docs.python.org/3.7/library/os.path.html#os.path.join) will join path strings using the platform-specific directory separator
> 1. Here we use the `!` operator to execute the command in the shell
> 2. The `{}` will expand the contained python variable in the shell
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``` python
# run melodic
!{melodic_cmd}
```
%% Cell type:markdown id: tags:
<aclass="anchor"id="plot-python-group-ics"></a>
### Plot group ICs
Now we can load and plot the group ICs generated by ```melodic```.
This function will be used to plot ICs:
> **NOTE:**
> 1. Here we use [`plot_stat_map`](https://nilearn.github.io/modules/generated/nilearn.plotting.plot_stat_map.html) from the [`nilearn`](https://nilearn.github.io/index.html) package to plot the orthographic images
> 2. [`subplots`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html) from `matplotlib.pyplot` creates a figure and multiple subplots
> 3. [`find_xyz_cut_coords`](https://nilearn.github.io/modules/generated/nilearn.plotting.find_xyz_cut_coords.html) from the [`nilearn`](https://nilearn.github.io/index.html) package will find the image coordinates of the center of the largest activation connected component
> 4. [`zip`](https://docs.python.org/3.3/library/functions.html#zip) takes iterables and aggregates them in a tuple. Here it is used to iterate through two lists simultaneously
> 5. [`iter_img`](https://nilearn.github.io/modules/generated/nilearn.image.iter_img.html) from the [`nilearn`](https://nilearn.github.io/index.html) package creates an iterator from an image that steps through each volume/time-point of the image
