Correct typo

day_1/hands_on_1.ipynb

@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Hand-on session 1: Basics\n",
     "\n",
@@ -16,18 +17,18 @@
     "5. Create a new nix-file with the name `file_2.nix` using the ``nixio.FileMode.ReadOnly`` mode. What happens.\n",
     "\n",
     "### Your solution"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "source": [],
+   "metadata": {},
    "outputs": [],
-   "metadata": {}
+   "source": []
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Exercise 2: Storing regularly sampled data\n",
     "\n",
@@ -39,12 +40,13 @@
     "    2. What is the sampling rate (or sampling interval)?\n",
     "3. You may want to plot it to get an impression of the data.\n",
     "    1. What might be the correct labeling of the plot?\n"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import os\n",
     "import scipy.io as spio\n",
@@ -59,12 +61,11 @@
     "    data = spio.loadmat(os.path.join(\"resources\", \"intra_data.mat\"))\n",
     "\n",
     "    return data[\"time\"][0], data[\"voltage\"][0]"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Now we want to store the data in a nix file\n",
     "\n",
@@ -75,18 +76,18 @@
     "5. Close the file to ensure proper saving (best practice).\n",
     "\n",
     "### Your solution"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "source": [],
+   "metadata": {},
    "outputs": [],
-   "metadata": {}
+   "source": []
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Exercise 3: reading data from the file and creating a fully labeled plot\n",
     "\n",
@@ -98,12 +99,13 @@
     "5. Get a time axis of the appropriate length \n",
     "6. Plot the data\n",
     "7. label the x- and y-axis (you may want to use the function ``get_label`` below)"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def get_label(obj):\n",
     "    \"\"\" Returns a string that can be used as an axis label. Constructs the label from DataArray or Dimension objects.\n",
@@ -121,26 +123,25 @@
     "        label = obj.label\n",
     "\n",
     "    return label\n"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Your solution"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "source": [],
+   "metadata": {},
    "outputs": [],
-   "metadata": {}
+   "source": []
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Exercise 4: Irregularly sampled data.\n",
     "\n",
@@ -152,31 +153,32 @@
     "5. If you have time, reopen in ``ReadOnly`` mode and create a fully labeled plot that contains the membrane voltage and the respective spike times.\n",
     "\n",
     "### Your solution"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "source": [],
+   "metadata": {},
    "outputs": [],
-   "metadata": {}
+   "source": []
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Exercise 5: Storing multidimensional data\n",
     "\n",
-    "1. Load the data and find out the dimensionality of the data.  (One dimension represents time, the other the local field potentials measured in paralell recording channels).\n",
+    "1. Load the data and find out the dimensionality of the data.  (One dimension represents time, the other the local field potentials measured in parallel recording channels).\n",
     "2. Store it in a nix file. Don't forget to add the dimension descriptors.\n",
     "\n",
     "3. Create a labeled plot of the data."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import os\n",
     "\n",
@@ -190,61 +192,59 @@
     "    data = spio.loadmat(os.path.join(\"resources\", \"lfp_fake_data.mat\"))\n",
     "\n",
     "    return data[\"time\"][0], data[\"lfp\"]"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Your solution"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "source": [],
+   "metadata": {},
    "outputs": [],
-   "metadata": {}
+   "source": []
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Bonus: Did you bring your own data? Try to store it in NIX..."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "source": [],
+   "metadata": {},
    "outputs": [],
-   "metadata": {}
+   "source": []
   }
  ],
  "metadata": {
-  "orig_nbformat": 4,
+  "interpreter": {
+   "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python",
-   "version": "3.9.5",
-   "mimetype": "text/x-python",
    "codemirror_mode": {
     "name": "ipython",
     "version": 3
    },
-   "pygments_lexer": "ipython3",
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
    "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.9.5 64-bit"
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-  "interpreter": {
-   "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
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-}
+}