typos and add stats

README.md

@@ -14,13 +14,15 @@ This course is specifically aimed at MSc and PhD students with basic knowledge o
 * SEMINAR 1 (04.09) EEG analysis, Machine Learning in EEG
 * SEMINAR 2 (04.09) MRI data analysis, sources, databases, tools 
   * Before seminar, please, do the following:
-    1) Download data from link https://drive.google.com/drive/folders/1P0ZhS1EoDY6fncnJb7foNFPjY5uoN6r0?usp=sharing
-    2) Clone repository to your local machine
-    3) Run docker locally and ensure it working with command `docker run hello-world`
-    4) In terminal: `cd to NEUROML/seminar2`
-    5) Type command `docker build -t neuroml/seminar2:0.0.1` and wait for successfull build
-    6) Run `docker run --rm -it -v /directory/to/downloaded/data:/workspace/data -p 8080:8080 neuroml/seminar2:0.0.1`
-    7) Open browser (preferebly Chrome) -> localhost:8080
+    1) Install Docker https://docs.docker.com/get-docker/
+    2) Download `data` folder from link https://drive.google.com/drive/folders/12FAfkKwGVsz0nYjkodvXMLtqtoWlEtIq?usp=sharing
+    3) Clone repository to your local machine
+    4) Run docker locally and ensure it working with command `docker run hello-world`
+    5) In terminal: `cd NEUROML/seminar2`
+    6) Type command `docker build -t neuroml/seminar2:0.0.1` and wait for successfull build
+    7) Run `docker run --rm -it -v /directory/to/downloaded/data:/workspace/data -p 8080:8080 neuroml/seminar2:0.0.1`
+    * `/directory/to/downloaded/data` from 2)
+    8) Open browser (preferebly Chrome) -> localhost:8080
   * Before the seminar you are to install FreeSurfer https://surfer.nmr.mgh.harvard.edu/
 * SEMINAR 3 (04.09) Machine Learning for structural MRI data analysis
   * Before the seminar you are to get an account and granted access here https://db.humanconnectome.org/data/projects/HCP_1200

seminar2/seminar2_1_mri.ipynb

@@ -448,12 +448,10 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "Assume that you recieved new dicom from hospital and you need to convert to convinient NIFTI, let's review the tool distinctive from simple on above:"
+    "Assume that you recieved new dicom from hospital and you need to convert to convinient NIFTI, let's review the tool distinctive from simple one represented above:"
    ]
   },
   {

seminar2/seminar2_3_freesurfer.ipynb

@@ -968,7 +968,7 @@
    "metadata": {},
    "source": [
     "___\n",
-    "Output directory if freesurfer"
+    "Output directory of freesurfer recon-all command"
    ]
   },
   {
@@ -1265,6 +1265,15 @@
     "### Freesurfer Output Visualization"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Add text about freesurfer surafce representation and reconstraction from volume"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1298,6 +1307,15 @@
     "my_cmap = matplotlib.colors.LinearSegmentedColormap('my_colormap',cdict,64,1.9)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "More about resticted and unrestricted data"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1400,6 +1418,15 @@
     "                       darkness=.5)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Try to find can find broka and wernicke zone on the next images"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1426,7 +1453,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.DataFrame()"
+    "!cat /workspace/data/fs_preproc/100206/stats/aseg.stats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "seg_data_path = '/workspace/data/fs_preproc/100206/stats/aseg.stats'\n",
+    "\n",
+    "ColHeader = next(filter(lambda line: line.startswith('# ColHeaders'), open(seg_data_path).readlines()))\n",
+    "names = ColHeader.split()[2:]\n",
+    "data = pd.read_csv(seg_data_path, names=names, comment='#', delim_whitespace=True, dtype=str)\n",
+    "data"
    ]
   },
   {