intro.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Introduction to Jupyter Notebooks</h1>\n",
    "<p>We are going to review core functionalities of Jupyter notebooks.</p>\n",
    "<p><i>~ Expert users may skip this introductory tutorial! ~</i></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Execute Python code</h2>\n",
    "<p>The folowing cell will execute a simple python command.<br>\n",
    "In order to execute the cell:\n",
    "<nl>\n",
    "  <li>Click the cell</li>\n",
    "  <li>Press SHIFT+ENTER</li>\n",
    "</nl>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Hello World!!!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>The output generated by the Python command is reported right after the cell</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Execute BASH code</h2>\n",
    "<p>If instead of Python you want to use BASH: the cell must begin with the header line <tt><b>%%bash</b></tt>. In this way the whole cell is interpreted as BASH.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "echo \"Hello World!!!\"\n",
    "echo \"Any other line is again BASH!\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Execute both Python and BASH</h2>\n",
    "<p>In order to mix Python and BASH, start each BASH line with the character \"<tt><b>!</b></tt>\", and leave Python as it is. <br>\n",
    "Comments start with \"<tt><b>#</b></tt>\".</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bash (this is a comment)\n",
    "!echo \"I love this school and this is BASH!\"\n",
    "# python\n",
    "print(\"I love this school and this is Python!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Where does the Jupyter notebook run?</h2>\n",
    "<p>Jupyter Notebooks is simply invoking commands that are executed in your local directory.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# where are we? show the local directory\n",
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list content of the local directory \n",
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Download a file from the web, give it a look and delete it</h2>\n",
    "<p>Let's download the file <br>\n",
    "<tt><b>Si_ONCV_PBE-1.1.upf</b></tt><br>\n",
    "from the URL <tt>http://www.quantum-simulation.org/potentials/sg15_oncv/upf/Si_ONCV_PBE-1.1.upf<tt></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download a file\n",
    "!wget -N http://www.quantum-simulation.org/potentials/sg15_oncv/upf/Si_ONCV_PBE-1.1.upf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Let's give a quick look at the file <tt><b>Si_ONCV_PBE-1.1.upf</b></tt><p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# give a look\n",
    "!cat Si_ONCV_PBE-1.1.upf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Let's remove the file <tt><b>Si_ONCV_PBE-1.1.upf</b></tt><p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove the file \n",
    "!rm Si_ONCV_PBE-1.1.upf\n",
    "\n",
    "# list content of the local directory \n",
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Write/Read a text file</h2>\n",
    "<p>We review how I/O operations with text files work in Python.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the file hello.dat in write mode\n",
    "with open(\"hello.dat\", \"w\") as file :\n",
    "    file.write(\"Hello!!\") # write"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list content of the local directory \n",
    "!ls \n",
    "\n",
    "# give a quick look at the file \n",
    "!cat hello.dat "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the file hello.dat in read mode \n",
    "with open(\"hello.dat\", \"r\") as file :\n",
    "    data = file.read() # read \n",
    "\n",
    "# data holds the content read from the file\n",
    "print(data) # show "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove the file \n",
    "!rm hello.dat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Convert Python data structures to/from a JSON file</h2>\n",
    "<p>We show how Python data structures can be easily dumped in/loaded from a JSON file.</p>\n",
    "<p>Let's create a Python data structure.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a dictionary contains keys and values\n",
    "data={}\n",
    "data[\"project\"]=\"liquid_project\"\n",
    "\n",
    "# it can be nested \n",
    "data[\"material\"]={}\n",
    "data[\"material\"][\"name\"]=\"water\"\n",
    "data[\"material\"][\"formula\"]=\"H2O\"\n",
    "data[\"material\"][\"density\"]=1\n",
    "\n",
    "# lists contain information which can be iterated\n",
    "data[\"material\"][\"atomic_species\"]=[\"O\",\"H\"]\n",
    "\n",
    "# show the data \n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Write the Python data in the JSON file <tt><b>data.json</b></tt>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "# write data to JSON file \n",
    "with open('data.json', 'w') as file:\n",
    "    json.dump(data, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Give a quick look at the file <tt><b>data.json</b></tt></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# give a quick look at the file \n",
    "!cat data.json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Read the Python data from the JSON file <tt><b>data.json</b></tt>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "# read data from JSON file \n",
    "with open('data.json',\"r\") as file:\n",
    "    d = json.load(file)\n",
    "\n",
    "# show the data \n",
    "print(d) \n",
    "# pretty print the data\n",
    "print(json.dumps(data, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove the file \n",
    "!rm data.json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Generate data and create a simple plot</h2>\n",
    "<p>We are going to generate the (X,Y) data points for the functions: $Y=X$, and $Y=X^2$, then plot the data points.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# generate 6 points equally spaced from 0 to 3.\n",
    "x = np.linspace(0,3,6) \n",
    "\n",
    "# generate y points for each x (implicit iteration)\n",
    "y1 = x\n",
    "y2 = x*x\n",
    "\n",
    "# show data\n",
    "print(\"x  : \", x)\n",
    "print(\"y1 : \", y1)\n",
    "print(\"y2 : \", y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot \n",
    "fig, ax = plt.subplots(1, 1)\n",
    "ax.plot(x, y1, 'bo-', label=\"$y=x$\" )\n",
    "ax.plot(x, y2, 'ro-', label=\"$y=x^2$\" )\n",
    "\n",
    "# set labels \n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "\n",
    "# set legend\n",
    "plt.legend()\n",
    "\n",
    "# set title \n",
    "plt.title(\"Functions\")\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<h3>Hi 5! You are no longer a toddler!</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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