intro_to_jupyter_notebook.ipynb

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    "# Introduction to Jupyter Notebook\n",
    "\n",
    "*Alexander Fu*\n",
    "\n",
    "*2018-05-14*\n",
    "\n",
    "\n",
    "\n",
    "This is a minimal working example of jupyter notebook. It's used to demostrate some of it's very basic functions.\n",
    "\n",
    "**Jupyter notebook** is a literature programming tool which is a combination of a Read–Eval–Print Loop (REPL, for example a shell / python interpreter) and a authoring interface for documentation purpose.\n",
    "\n",
    "## Installation\n",
    "\n",
    "1. Install Anaconda. https://docs.anaconda.com/anaconda/install/\n",
    "2. Install jupyter notebook. If you use Windows, you might want to install using `Anaconda Navigator` https://docs.anaconda.com/anaconda/navigator/. If you use UNIX, you can do it using `conda install jupyter notebook`. After that, you can start jupyter notebook using command line `jupyter notebook` in a terminal. A browser window will be opened showing the current directory, and you can create new `.ipynb` files using it.\n",
    "3. (Optionally) install editors to get a better authoring experience.\n",
    "    1. `nteract` https://nteract.io/desktop , which provide \"double click to open notebook\" and \"latex live preview\". It's more suitable for document editing.\n",
    "    2. Text editors like Atom (through `hydrogen` plugin) or Emacs (through EIN plugins or org-mode). Those will provide better integration with Git and better data analysis / coding experience.\n",
    "\n",
    "\n",
    "## Introduction to format\n",
    "\n",
    "The basic unit in a jupyter notebook file is \"cell\", which is a chunk of text/code. The two most commonly used types of cell are \"markdown cell\" and \"code cell\". \n",
    "\n",
    "If you haven't heard of markdown, it's basically a minimal markup syntax, used for easily enter markups like **bold**, *italic*, ~~strike out~~ and  $inline\\ formul\\alpha$ or display-style \n",
    "$$\\mathbb{E}\\mathrm{quations}$$\n",
    "\n",
    "Code cell is easy to understand: they are short, executable code snippets. A code cell can be \"executed\" by press `Shift-return`, which is not much different from you copy-paste the code into a shell/interpreter and press return to run.\n",
    "\n",
    "An example:\n",
    "\n"
   ]
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   "execution_count": 2,
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load numpy library\n",
    "import numpy as np\n",
    "# load matplotlib library for scientific plotting\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# generate a 2D gaussian random variable, represented using (x,y)\n",
    "x, y = np.random.multivariate_normal([0, 0], [[2, 4], [4, 10]], 5000).T\n",
    "plt.plot(x, y, '.')\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "            {\"margin\": {\"b\": 0, \"l\": 0, \"r\": 0, \"t\": 0}},\n",
       "            {\"showLink\": true, \"linkText\": \"Export to plot.ly\"}\n",
       "        ).then(function () {return Plotly.addFrames('8dabbb03-4194-4643-b23d-212185efb3e1',{});}).then(function(){Plotly.animate('8dabbb03-4194-4643-b23d-212185efb3e1');})\n",
       "        });</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# copied from https://plot.ly/python/3d-scatter-plots/\n",
    "# a 3D scatter plot example\n",
    "\n",
    "# load interactive ploting lib `plotly`\n",
    "from plotly import __version__\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "# This can be comment out as session is shared between cells\n",
    "import numpy as np\n",
    "\n",
    "# generate 3D gaussian\n",
    "x, y, z = np.random.multivariate_normal(np.array([0,0,0]),\n",
    "                                        np.array([[1,0,0],\n",
    "                                                  [0,1,0],\n",
    "                                                  [0,0,1]]), 400).transpose()\n",
    "\n",
    "# plot\n",
    "trace1 = go.Scatter3d(\n",
    "    x=x,\n",
    "    y=y,\n",
    "    z=z,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        size=4,\n",
    "        color=np.sqrt(x*x+y*y+z*z),                # set color to an array/list of desired values\n",
    "        colorscale='Viridis',   # choose a colorscale\n",
    "        opacity=0.8\n",
    "    )\n",
    ")\n",
    "\n",
    "data = [trace1]\n",
    "layout = go.Layout(\n",
    "    margin=dict(\n",
    "        l=0,\n",
    "        r=0,\n",
    "        b=0,\n",
    "        t=0\n",
    "    )\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "iplot(fig, filename='3d-scatter-colorscale')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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