From ee1184f3ad6517a37836dd0ef63da2c522ea0b51 Mon Sep 17 00:00:00 2001
From: Sean Fitzgibbon <seanf@fmrib.ox.ac.uk>
Date: Sat, 21 Mar 2020 17:30:49 +0000
Subject: [PATCH] updated docs
---
talks/matlab_vs_python/migp/MIGP.ipynb | 34 ++++++------
talks/matlab_vs_python/migp/matlab_MIGP.ipynb | 23 +++-----
talks/matlab_vs_python/migp/python_MIGP.ipynb | 53 ++++++++++++-------
3 files changed, 58 insertions(+), 52 deletions(-)
diff --git a/talks/matlab_vs_python/migp/MIGP.ipynb b/talks/matlab_vs_python/migp/MIGP.ipynb
index 5dd73e8..1a0baf9 100644
--- a/talks/matlab_vs_python/migp/MIGP.ipynb
+++ b/talks/matlab_vs_python/migp/MIGP.ipynb
@@ -58,13 +58,13 @@
"<a class=\"anchor\" id=\"download-the-data\"></a>\n",
"## Fetch the data\n",
"\n",
- "This data is a derivative from the COBRE sample found in the International Neuroimaging Data-sharing Initiative (http://fcon_1000.projects.nitrc.org/indi/retro/cobre.html), originally released under Creative Commons - Attribution Non-Commercial.\n",
+ "This data is a derivative from the [COBRE](http://fcon_1000.projects.nitrc.org/indi/retro/cobre.html) sample found in the International Neuroimaging Data-sharing Initiative, originally released under Creative Commons - Attribution Non-Commercial.\n",
"\n",
"It comprises 10 preprocessed resting-state fMRI selected from 72 patients diagnosed with schizophrenia and 74 healthy controls (6mm isotropic, TR=2s, 150 volumes).\n",
"\n",
"Create a directory in the users home directory to store the downloaded data:\n",
"\n",
- "> **NOTE:** `expanduser` will expand the `~` to the be users home directory:"
+ "> **NOTE:** [`expanduser`](https://docs.python.org/3.7/library/os.path.html#os.path.expanduser) will expand the `~` to the be users home directory:"
]
},
{
@@ -85,7 +85,7 @@
"source": [
"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"
+ "> **Note:** We use a method from [`nilearn`](https://nilearn.github.io/index.html) called [`fetch_cobre`](https://nilearn.github.io/modules/generated/nilearn.datasets.fetch_cobre.html) to download the fMRI data"
]
},
{
@@ -107,9 +107,9 @@
"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",
+ "> 1. We use [`clean_img`](https://nilearn.github.io/modules/generated/nilearn.image.clean_img.html) from the [`nilearn`](https://nilearn.github.io/index.html) package to regress confounds from the data\n",
+ "> 2. We use [`smooth_img`](https://nilearn.github.io/modules/generated/nilearn.image.smooth_img.html) from the [`nilearn`](https://nilearn.github.io/index.html) package to spatially smooth the data\n",
+ "> 3. [`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 four lists simultaneously\n",
"> 4. We use list comprehension to loop through all the filenames and append suffixes\n"
]
},
@@ -138,10 +138,10 @@
"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"
+ "> 1. We use [`load_mni152_brain_mask`](https://nilearn.github.io/modules/generated/nilearn.datasets.load_mni152_brain_mask.html) from the [`nilearn`](https://nilearn.github.io/index.html) package to load the MNI152 mask\n",
+ "> 2. We use [`resample_to_img`](https://nilearn.github.io/modules/generated/nilearn.image.resample_to_img.html) 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`](https://nilearn.github.io/modules/generated/nilearn.image.math_img.html) from the [`nilearn`](https://nilearn.github.io/index.html) package to binarize the resample mask\n",
+ "> 4. The mask is plotted using [`plot_anat`](https://nilearn.github.io/modules/generated/nilearn.plotting.plot_anat.html) from the [`nilearn`](https://nilearn.github.io/index.html) package"
]
},
{
@@ -175,8 +175,8 @@
"\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`"
+ "> 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\n",
+ "> 3. [`','.join(smooth)`](https://docs.python.org/3/library/stdtypes.html#str.join) will create a comma seprated string of all the items in the list `smooth`"
]
},
{
@@ -221,11 +221,11 @@
"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"
+ "> 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\n",
+ "> 2. [`subplots`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html) from `matplotlib.pyplot` creates a figure and multiple subplots\n",
+ "> 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\n",
+ "> 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\n",
+ "> 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"
]
},
{
diff --git a/talks/matlab_vs_python/migp/matlab_MIGP.ipynb b/talks/matlab_vs_python/migp/matlab_MIGP.ipynb
index ca0089a..b017170 100644
--- a/talks/matlab_vs_python/migp/matlab_MIGP.ipynb
+++ b/talks/matlab_vs_python/migp/matlab_MIGP.ipynb
@@ -34,7 +34,7 @@
"source": [
"It will be necessary to know the location where the data was stored so that we can load the brainmask:\n",
"\n",
- "> **Note**: `expanduser` will expand the `~` to the be users home directory"
+ "> **Note**: [`expanduser`](https://docs.python.org/3.7/library/os.path.html#os.path.expanduser) will expand the `~` to the be users home directory"
]
},
{
@@ -57,7 +57,7 @@
"\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"
+ "> 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"
]
},
{
@@ -102,11 +102,11 @@
"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"
+ "> 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\n",
+ "> 2. [`subplots`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html) from `matplotlib.pyplot` creates a figure and multiple subplots\n",
+ "> 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\n",
+ "> 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\n",
+ "> 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"
]
},
{
@@ -144,13 +144,6 @@
"ics = nb.load('matmigp.gica/melodic_IC.nii.gz')\n",
"fig = map_plot(ics)"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
@@ -169,7 +162,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.4"
+ "version": "3.7.6"
}
},
"nbformat": 4,
diff --git a/talks/matlab_vs_python/migp/python_MIGP.ipynb b/talks/matlab_vs_python/migp/python_MIGP.ipynb
index 7c3188f..50993ca 100644
--- a/talks/matlab_vs_python/migp/python_MIGP.ipynb
+++ b/talks/matlab_vs_python/migp/python_MIGP.ipynb
@@ -38,7 +38,7 @@
"source": [
"It will be necessary to know the location where the data was stored so that we can load the brainmask. \n",
"\n",
- "> **Note:** `expanduser` will expand the `~` to the be users home directory:"
+ "> **Note**: [`expanduser`](https://docs.python.org/3.7/library/os.path.html#os.path.expanduser) will expand the `~` to the be users home directory"
]
},
{
@@ -60,9 +60,9 @@
"Firstly we need to set the MIGP parameters:\n",
"\n",
"> **Note:**\n",
- "> 1. `glob.glob` will create a list of filenames that match the glob/wildcard pattern\n",
- "> 2. `nb.load` from the `nibabel` package will load the image into `nibabel.Nifti1Image` object. This will not load the actual data though.\n",
- "> 3. We use a list comprehension to loop through all the filenames and load them with `nibabel`"
+ "> 1. [`glob.glob`](https://docs.python.org/3/library/glob.html) will create a list of filenames that match the glob/wildcard pattern\n",
+ "> 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.\n",
+ "> 3. We use a list comprehension to loop through all the filenames and load them with [`nibabel`](https://nipy.org/nibabel/index.html)"
]
},
{
@@ -87,13 +87,13 @@
"metadata": {},
"source": [
"> **Note:**\n",
- "> 1. `random.shuffle` will shuffle a list, in this instance it shuffles the list of `nibabel.Nifti1Image` objects\n",
- "> 2. `ravel` will unfold a n-d array into vector. Similar to the `:` operator in Matlab\n",
- "> 3. `reshape` works similarly to reshape in Matlab, but be careful becase the default order is different from Matlab.\n",
- "> 4. `.T` does a transpose in `numpy`\n",
+ "> 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\n",
+ "> 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\n",
+ "> 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.\n",
+ "> 4. [`.T`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.T.html) does a transpose in `numpy`\n",
"> 5. The final element of an array is indexed with `-1` in `numpy`, as opposed to `end` in Matlab\n",
- "> 6. `svds` and `eigs` come from the `scipy.sparse.linalg` package\n",
- "> 7. `svds` and `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 $V^T$\n",
+ "> 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\n",
+ "> 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 $V^T$\n",
"> 8. We index into the output of `eigs(W@W.T, dPCA_int)[1]` to only return the 2nd output (index 1)"
]
},
@@ -163,7 +163,11 @@
"<a class=\"anchor\" id=\"run-python-melodic\"></a>\n",
"### Run ```melodic```\n",
"\n",
- "Generate a command line string and run group ```melodic``` on the Python MIGP dimension reduced data with a dimension of 10 components:"
+ "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.\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`](https://docs.python.org/3.7/library/os.path.html#os.path.join) will join path strings using the platform-specific directory separator"
]
},
{
@@ -177,6 +181,15 @@
"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,
@@ -196,7 +209,14 @@
"\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`](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\n",
+ "> 2. [`subplots`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html) from `matplotlib.pyplot` creates a figure and multiple subplots\n",
+ "> 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\n",
+ "> 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\n",
+ "> 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"
]
},
{
@@ -234,13 +254,6 @@
"ics = nb.load('pymigp.gica/melodic_IC.nii.gz')\n",
"fig = map_plot(ics)"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
@@ -259,7 +272,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.4"
+ "version": "3.7.6"
}
},
"nbformat": 4,
--
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