diff --git a/talks/matlab_vs_python/migp/fetch_data.ipynb b/talks/matlab_vs_python/migp/fetch_data.ipynb
index 67e9b76f1f0f3a53e560508a3e98ad6f83baff75..6778e404ee079e8b4f132865f8ff52a2b7307a33 100644
--- a/talks/matlab_vs_python/migp/fetch_data.ipynb
+++ b/talks/matlab_vs_python/migp/fetch_data.ipynb
@@ -37,32 +37,6 @@
"import matplotlib.pyplot as plt"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This function will be used to plot ICs later:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def map_plot(d):\n",
- "\n",
- " N = d.shape[-1]\n",
- "\n",
- " fig, ax = plt.subplots(int(np.ceil((N/2))),2, figsize=(12, N))\n",
- "\n",
- " for img, ax0 in zip(image.iter_img(d), ax.ravel()):\n",
- " coord = plotting.find_xyz_cut_coords(img, activation_threshold=2.3)\n",
- " plotting.plot_stat_map(img, cut_coords=coord, vmax=10, axes=ax0)\n",
- " \n",
- " return fig"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -72,7 +46,7 @@
"\n",
"Create a directory in the users home directory to store the downloaded data:\n",
"\n",
- "`expanduser` will expand the `~` to the be users home directory:"
+ "> **NOTE:** `expanduser` will expand the `~` to the be users home directory:"
]
},
{
@@ -91,7 +65,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Download the data (if not already downloaded). We use a method from [`nilearn`](https://nilearn.github.io/index.html) called `fetch_cobre` to download the fMRI data:"
+ "Download the data (if not already downloaded):\n",
+ "\n",
+ "> **Note:** We use a method from [`nilearn`](https://nilearn.github.io/index.html) called `fetch_cobre` to download the fMRI data"
]
},
{
@@ -110,7 +86,13 @@
"<a class=\"anchor\" id=\"clean-the-data\"></a>\n",
"## Clean the data\n",
"\n",
- "We use methods from [`nilearn`](https://nilearn.github.io/index.html) to regress confounds from the data (```clean_img```) and to spatially smooth the data with a gaussian filter of 10mm FWHM (```smooth_img```):"
+ "Regress confounds from the data and to spatially smooth the data with a gaussian filter of 10mm FWHM.\n",
+ "\n",
+ "> **Note:**\n",
+ "> 1. We use `clean_img` from the [`nilearn`](https://nilearn.github.io/index.html) package to regress confounds from the data\n",
+ "> 2. We use `smooth_img` from the [`nilearn`](https://nilearn.github.io/index.html) package to spatially smooth the data\n",
+ "> 3. `zip` takes iterables and aggregates them in a tuple. Here it is used to iterate through four lists simultaneously\n",
+ "> 4. We use list comprehension to loop through all the filenames and append suffixes\n"
]
},
{
@@ -135,7 +117,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "To run ```melodic``` we will need a brain mask in MNI152 space at the same resolution as the fMRI. Here we use [`nilearn`](https://nilearn.github.io/index.html) methods to load the MNI152 mask (```load_mni152_brain_mask```), resample to the resolution of the fMRI (```resample_to_img```), and binarize (```math_img```):"
+ "To run ```melodic``` we will need a brain mask in MNI152 space at the same resolution as the fMRI. \n",
+ "\n",
+ "> **Note:**\n",
+ "> 1. We use `load_mni152_brain_mask` from the [`nilearn`](https://nilearn.github.io/index.html) package to load the MNI152 mask\n",
+ "> 2. We use `resample_to_img` from the [`nilearn`](https://nilearn.github.io/index.html) package to resample the mask to the resolution of the fMRI \n",
+ "> 3. We use `math_img` from the [`nilearn`](https://nilearn.github.io/index.html) package to binarize the resample mask\n",
+ "> 4. The mask is plotted using `plot_anat` from the [`nilearn`](https://nilearn.github.io/index.html) package"
]
},
{
@@ -165,7 +153,12 @@
"<a class=\"anchor\" id=\"run-melodic\"></a>\n",
"### Run ```melodic```\n",
"\n",
- "Generate a command line string and run group ```melodic``` on the smoothed fMRI with a dimension of 10 components:"
+ "Generate a command line string and run group ```melodic``` on the smoothed fMRI with a dimension of 10 components:\n",
+ "\n",
+ "> **Note**: \n",
+ "> 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\n",
+ "> 2. `op.join` will join path strings using the platform-specific directory separator\n",
+ "> 3. `','.join(smooth)` will create a comma seprated string of all the items in the list `smooth`"
]
},
{
@@ -179,6 +172,15 @@
"print(melodic_cmd)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> **Note:** \n",
+ "> 1. Here we use the `!` operator to execute the command in the shell\n",
+ "> 2. The `{}` will expand the contained python variable in the shell"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -198,7 +200,14 @@
"\n",
"Now we can load and plot the group ICs generated by ```melodic```.\n",
"\n",
- "This function will be used to plot ICs:"
+ "This function will be used to plot ICs:\n",
+ "\n",
+ "> **NOTE:**\n",
+ "> 1. Here we use `plot_stat_map` from the `nilearn` package to plot the orthographic images\n",
+ "> 2. `subplots` from `matplotlib.pyplot` creates a figure and multiple subplots\n",
+ "> 3. `find_xyz_cut_coords` from the `nilearn` package will find the image coordinates of the center of the largest activation connected component\n",
+ "> 4. `zip` takes iterables and aggregates them in a tuple. Here it is used to iterate through two lists simultaneously\n",
+ "> 5. `iter_img` from the `nilearn` package creates an iterator from an image that steps through each volume/time-point of the image"
]
},
{