Updated docs

talks/matlab_vs_python/migp/demo_MIGP_NIFTI.m

@@ -20,7 +20,6 @@ masksize=size(mask);
 mask=reshape(mask,prod(masksize),1);
 
 demean = @(x) x - repmat(mean(x,1),[size(x,1), 1, 1]);
-ss_svds = @(x,n) svds(x, n);
 
 for i = 1:Nsub
 
@@ -37,7 +36,7 @@ for i = 1:Nsub
     % var-norm separately for each subject
     if sep_vn==true
         fprintf('\tNormalising by voxel-wise variance\r');
-        [uu,ss,vv]=ss_svds(grot,30);
+        [uu,ss,vv]=svds(grot,30);
         vv(abs(vv)<2.3*std(vv(:)))=0;
         stddevs=max(std(grot-uu*ss*vv'),0.001);
         grot=grot./repmat(stddevs,size(grot,1),1);

talks/matlab_vs_python/migp/matlab_MIGP.ipynb

@@ -32,9 +32,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It will be necessary to know the location where the data was stored so that we can load the brainmask. \n",
+    "It will be necessary to know the location where the data was stored so that we can load the brainmask:\n",
     "\n",
-    "`expanduser` will expand the `~` to the be users home directory:"
+    "> **Note**: `expanduser` will expand the `~` to the be users home directory"
    ]
   },
   {
@@ -53,7 +53,11 @@
     "<a class=\"anchor\" id=\"run-matlab-melodic\"></a>\n",
     "### Run ```melodic```\n",
     "\n",
-    "Generate a command line string and run group ```melodic``` on the Matlab 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` will join path strings using the platform-specific directory separator"
    ]
   },
   {
@@ -67,6 +71,15 @@
     "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,
@@ -86,7 +99,14 @@
     "\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"
    ]
   },
   {

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",
-    "`expanduser` will expand the `~` to the be users home directory:"
+    "> **Note:** `expanduser` will expand the `~` to the be users home directory:"
    ]
   },
   {
@@ -57,7 +57,12 @@
     "<a class=\"anchor\" id=\"run-python-migp\"></a>\n",
     "### Run python `MIGP`\n",
     "\n",
-    "Firstly we need to set the MIGP parameters:"
+    "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`"
    ]
   },
   {
@@ -77,6 +82,21 @@
     "sep_vn = False        # switch on separate variance nomalisation for each input dataset"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "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",
+    "> 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",
+    "> 8. We index into the output of `eigs(W@W.T, dPCA_int)[1]` to only return the 2nd output (index 1)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,