introduction_to_jupyter_notebooks.ipynb

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   "source": [
    "Jupyter notebooks are extremely cool ways to demo your code or work interactively. They are separated into cells, just like Mathematica.\n",
    "\n",
    "To...\n",
    "* **Close a notebook**: File > \"Close and halt\". Do NOT close the tab! Python will still run in the background.\n",
    "* **Edit text**: Double-click a cell. Shift+Enter when done.\n",
    "* **Evaluate code in a cell**: Shif+Enter.\n",
    "* **Create a new cell above or below**: ESC+a, ESC+b\n",
    "* **See all the keyboard shortcuts**: ESC+h\n",
    "\n",
    "See the Jupyter cheatsheet for many more commands!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cell types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can control the type of cell in Cell -> Cell Type, and choose whether your cell is\n",
    "- text\n",
    "- code  \n",
    "\n",
    "Text cells can be formatted following Markdown conventions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 0\n",
    "\n",
    "0.1: Open the keyboard shortcuts (see bullet list above). What are the two different keyboard input modes?\n",
    "\n",
    "0.2: Create a new cell below this one, and change it from a code cell to a text cell. Write a cool message to yourselves, using some markdown formatting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some code and cool things"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 'Hello, world!'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the **output of a cell** in the last line is automatically printed if it's a variable or unassigned output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 4\n",
    "2 * x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a bunch of **\"magic\" commands** that can really up your game."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%lsmagic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, let's test how long a silly function takes..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def silly_function():\n",
    "    np.random.randint(0, high=100, size=int(1e6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%timeit\n",
    "silly_function()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Auto-complete suggestion** Place the cursor at the end of `np.ar` below and hit *TAB* on the keyboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.ar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you should get at list of all the function in numpy starting with `ar`\n",
    "\n",
    "**Get function documentation** Place the cursor inside the brackets and hit and hold *SHIFT* and then hit *TAB*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you should get a window showing the first part of the documentation string for the function. Press the \"plus\" to extend the view or the \"arrow\" to make it a full width window. The documentation string can also be written to an output cell as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(np.arange)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define $x$ and $y$ below such that $x$ is a numpy array from 1 to 9 (inclusive) and $y=1/x$. Then, run your cell and the next cell and look at the resulting plot. Does it make sense?\n",
    "\n",
    "__HINT__: Use the documentation \"arange\" from above to define $x$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# x = ???\n",
    "# y = ???"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6, 3))\n",
    "ax.scatter(x, y)\n",
    "ax.set(title='Hello, world!', xlabel='x', ylabel='y = 1/x');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# When you're done\n",
    "\n",
    "1. Close the notebook by hitting File > \"Close and halt\".\n",
    "2. In the \"Home page\" tab, click \"Quit\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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